From bc2234af696810f3cf02a1019ae6b22d2c4bad26 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 16:31:50 +0200 Subject: [PATCH 01/17] Update cvae.py --- tutorials/quality-control/cvae.py | 37 +++++++++++++++++-------------- 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/tutorials/quality-control/cvae.py b/tutorials/quality-control/cvae.py index 17ac821..8aab501 100644 --- a/tutorials/quality-control/cvae.py +++ b/tutorials/quality-control/cvae.py @@ -7,11 +7,12 @@ import torch import torch.nn as nn + class ConditionalVariationalAutoEncoder(Application): """Conditional Variational Autoencoder (CVAE) Application. This application implements a conditional variational autoencoder (CVAE), - which extends the standard VAE by conditioning both the encoder and + which extends the standard VAE by conditioning both the encoder and decoder on additional information (condition vector c). The encoder maps the input and condition into a latent distribution @@ -189,7 +190,9 @@ def __init__( optimizer=None, **kwargs, ): - self.encoder = encoder or self._get_default_encoder(input_size, condition_dim, channels) + self.encoder = encoder or self._get_default_encoder( + input_size, condition_dim, channels + ) self.fc_mu = nn.Linear( channels[-1], latent_dim, @@ -265,9 +268,9 @@ def _get_default_encoder(self, input_size, condition_dim, channels): """ decoder = MultiLayerPerceptron( - in_features = input_size + condition_dim, - hidden_features = channels, - out_features = channels[-1], + in_features=input_size + condition_dim, + hidden_features=channels, + out_features=channels[-1], ) return decoder @@ -318,10 +321,10 @@ def _get_default_decoder(self, input_size, channels): """ encoder = MultiLayerPerceptron( - in_features = channels[0], - hidden_features = channels, - out_features = input_size, - ) + in_features=channels[0], + hidden_features=channels, + out_features=input_size, + ) return encoder def encode(self, x, c): @@ -361,7 +364,7 @@ def encode(self, x, c): if len(c.shape) == 1: c = c.unsqueeze(1) - + x = torch.cat([x, c], dim=1) x = self.encoder(x) mu = self.fc_mu(x) @@ -441,12 +444,12 @@ def decode(self, z, c): if len(c.shape) == 1: c = c.unsqueeze(1) - + z = torch.cat([z, c], dim=1) x = self.fc_dec(z) x = self.decoder(x) return x - + def train_preprocess(self, batch): """Preprocesses a batch of data for training. @@ -487,7 +490,7 @@ def train_preprocess(self, batch): val_preprocess = train_preprocess test_preprocess = train_preprocess - + def training_step(self, batch, batch_idx): """Performs a single training step. @@ -576,7 +579,7 @@ def test_step(self, batch, batch_idx): tensor(5440.9023, grad_fn=) """ - x, y, c = self.test_preprocess(batch) + x, y, c = self.test_preprocess(batch) y_hat, mu, log_var, z = self(x, c) rec_loss, KLD = self.compute_loss(y_hat, y, mu, log_var) tot_loss = rec_loss + self.beta * KLD @@ -591,7 +594,7 @@ def test_step(self, batch, batch_idx): logger=True, ) return tot_loss - + def validation_step(self, batch, batch_idx): """Performs a single validation step. @@ -643,7 +646,7 @@ def validation_step(self, batch, batch_idx): logger=True, ) return tot_loss - + def compute_loss(self, y_hat, y, mu, log_var): """Computes reconstruction and KL divergence losses. @@ -725,4 +728,4 @@ def forward(self, x, c): mu, log_var = self.encode(x, c) z = self.reparameterize(mu, log_var) y_hat = self.decode(z, c) - return y_hat, mu, log_var, z \ No newline at end of file + return y_hat, mu, log_var, z From 553f825c00002ab6fe7ae555244513b9ef337550 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 16:32:03 +0200 Subject: [PATCH 02/17] Update QC.ipynb --- tutorials/quality-control/QC.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/quality-control/QC.ipynb b/tutorials/quality-control/QC.ipynb index 9697e0e..5ea7108 100644 --- a/tutorials/quality-control/QC.ipynb +++ b/tutorials/quality-control/QC.ipynb @@ -15,14 +15,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "6258e05a-ca7f-479b-a891-e7785f981f05", "metadata": {}, "outputs": [], "source": [ "# Uncomment codes in this cell if using Colab/Kaggle.\n", "\n", - "# !pip install deeplay ipympl \n", + "# !pip install deeplay ipympl\n", "# from google.colab import output\n", "# output.enable_custom_widget_manager()" ] From f4114200af14c95c4154830fccc15101d4485a4a Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 17:35:51 +0200 Subject: [PATCH 03/17] Update cvae.py --- tutorials/quality-control/cvae.py | 329 +++++++++++++++++++----------- 1 file changed, 213 insertions(+), 116 deletions(-) diff --git a/tutorials/quality-control/cvae.py b/tutorials/quality-control/cvae.py index 8aab501..7731cfa 100644 --- a/tutorials/quality-control/cvae.py +++ b/tutorials/quality-control/cvae.py @@ -1,11 +1,14 @@ -from typing import Optional, Callable +"""Conditional Variational Autoencoder (CVAE) Application.""" + +from __future__ import annotations + +from typing import Any, Callable from deeplay.components import MultiLayerPerceptron from deeplay.applications import Application from deeplay.external import Optimizer, Adam - import torch -import torch.nn as nn +from torch import nn class ConditionalVariationalAutoEncoder(Application): @@ -13,31 +16,31 @@ class ConditionalVariationalAutoEncoder(Application): This application implements a conditional variational autoencoder (CVAE), which extends the standard VAE by conditioning both the encoder and - decoder on additional information (condition vector c). + decoder on additional information (condition vector `c`). The encoder maps the input and condition into a latent distribution - parameterized by mean (mu) and log-variance (log_var). A latent vector z - is sampled using the reparameterization trick. The decoder reconstructs - the input from z and the same condition. + parameterized by mean (`mu`) and log-variance (`log_var`). A latent vector + `z` is sampled using the reparameterization trick. The decoder reconstructs + the input from `z` and the same condition. The default structure is as follows: Encoder: - 1. Concatenate input x and condition c - 2. MLP(input_size + condition_dim → hidden layers → channels[-1]) - 3. Linear → mu - 4. Linear → log_var + 1. Concatenate input `x` and condition `c` + 2. MLP(input_size + condition_dim -> hidden layers -> channels[-1]) + 3. Linear -> `mu` + 4. Linear -> `log_var` Latent sampling: - z = mu + std * eps (reparameterization trick) + `z = mu + std * eps` (reparameterization trick) Decoder: - 1. Concatenate z and condition c - 2. Linear(latent_dim + condition_dim → channels[-1]) - 3. MLP(channels[-1] → hidden layers → input_size) + 1. Concatenate `z` and condition `c` + 2. Linear(latent_dim + condition_dim -> channels[-1]) + 3. MLP(channels[-1] → hidden layers -> input_size) Loss: - total_loss = reconstruction_loss + beta * KL_divergence + `total_loss = reconstruction_loss + beta * KL_divergence` Parameters ---------- @@ -47,25 +50,26 @@ class ConditionalVariationalAutoEncoder(Application): Dimensionality of the conditioning vector. channels: list[int] Hidden layer sizes for encoder/decoder MLP. - encoder: nn.Module, optional - Custom encoder module. If None, a default MLP is used. - decoder: nn.Module, optional - Custom decoder module. If None, a default MLP is used. + encoder: nn.Module | None, optional + Custom encoder module. If `None` (default), defaults to an MLP. + decoder: nn.Module | None, optional + Custom decoder module. If `None` (default), defaults to an MLP. reconstruction_loss: Callable, optional - Loss function for reconstruction. (Default: BCELoss with sum reduction) - latent_dim: int - Dimensionality of latent space. - beta: float - Weight for KL divergence term (β-VAE). - optimizer: Optimizer, optional - Optimizer used for training. (Default: Adam) + Loss function for reconstruction. + Defaults to BCELoss with sum reduction. + latent_dim: int, optional + Dimensionality of latent space. Defaults to `2`. + beta: float, optional + Weight for KL divergence term (β-VAE). Defaults to `1.0`. + optimizer: Optimizer | None, optional + Optimizer used for training. If `None` (default), defaults to Adam. Attributes ---------- encoder: nn.Module - Encoder network mapping (x, c) → latent representation. + Encoder network mapping (x, c) -> latent representation. decoder: nn.Module - Decoder network mapping (z, c) → reconstructed input. + Decoder network mapping (z, c) -> reconstructed input. fc_mu: nn.Linear Linear layer producing mean of latent distribution. fc_var: nn.Linear @@ -181,15 +185,17 @@ def __init__( self, input_size: int, condition_dim: int, - channels: list, - encoder: Optional[nn.Module] = None, - decoder: Optional[nn.Module] = None, - reconstruction_loss: Optional[Callable] = nn.BCELoss(reduction="sum"), - latent_dim=int, - beta=1, - optimizer=None, - **kwargs, - ): + channels: list[int], + encoder: nn.Module | None = None, + decoder: nn.Module | None = None, + reconstruction_loss: Callable = nn.BCELoss(reduction="sum"), + latent_dim: int = 2, # TODO: Check that 2 is good default. + beta: float = 1.0, + optimizer: Optimizer | None = None, + **kwargs: Any, + ) -> None: + # TODO: Add docstring also for init. + self.encoder = encoder or self._get_default_encoder( input_size, condition_dim, channels ) @@ -205,8 +211,17 @@ def __init__( latent_dim + condition_dim, channels[-1], ) - self.decoder = decoder or self._get_default_decoder(input_size, channels[::-1]) - self.reconstruction_loss = reconstruction_loss or nn.BCELoss(reduction="sum") + + if decoder is not None: + self.decoder = decoder + else: + self._get_default_decoder(input_size, channels[::-1]) + + if reconstruction_loss is not None: + self.reconstruction_loss = reconstruction_loss + else: + nn.BCELoss(reduction="sum") + self.latent_dim = latent_dim self.beta = beta @@ -218,20 +233,25 @@ def __init__( def params(self): return self.parameters() - def _get_default_encoder(self, input_size, condition_dim, channels): - """Creates the default encoder network. + def _get_default_encoder( + self: ConditionalVariationalAutoEncoder, + input_size: int, + condition_dim: int, + channels: list[int], + ) -> nn.Module: + """Create the default encoder network. This method constructs a default encoder using a multilayer perceptron (MLP). The encoder takes the concatenation of input data and condition - vector as input and maps it to a latent feature representation. + vector as input and maps it onto a latent feature representation. Parameters ---------- - input_size : int + input_size: int Dimensionality of the input data. - condition_dim : int + condition_dim: int Dimensionality of the conditioning vector. - channels : list[int] + channels: list[int] Hidden layer sizes for the encoder. Returns @@ -252,15 +272,15 @@ def _get_default_encoder(self, input_size, condition_dim, channels): MultiLayerPerceptron( (blocks): LayerList( (0): LinearBlock( - (layer): Layer[Linear](in_features=794, out_features=256, bias=True) + (layer): Layer[Linear](in_features=794, out_features=256, ...) (activation): Layer[ReLU]() ) (1): LinearBlock( - (layer): Layer[Linear](in_features=256, out_features=128, bias=True) + (layer): Layer[Linear](in_features=256, out_features=128, ...) (activation): Layer[ReLU]() ) (2): LinearBlock( - (layer): Layer[Linear](in_features=128, out_features=128, bias=True) + (layer): Layer[Linear](in_features=128, out_features=128, ...) (activation): Layer[Identity]() ) ) @@ -274,17 +294,22 @@ def _get_default_encoder(self, input_size, condition_dim, channels): ) return decoder - def _get_default_decoder(self, input_size, channels): - """Creates the default decoder network. + def _get_default_decoder( + self: ConditionalVariationalAutoEncoder, + input_size: int, + channels: list[int], + ) -> nn.Module: + """Create the default decoder network. This method constructs a default decoder using a multilayer perceptron - (MLP). The decoder maps latent features back to the original input space. + (MLP). The decoder maps latent features back to the original input + space. Parameters ---------- - input_size : int + input_size: int Dimensionality of the reconstructed output. - channels : list[int] + channels: list[int] Hidden layer sizes for the decoder. Returns @@ -303,21 +328,22 @@ def _get_default_decoder(self, input_size, channels): >>> decoder = cvae._get_default_decoder(784, [128, 256]) >>> decoder MultiLayerPerceptron( - (blocks): LayerList( - (0): LinearBlock( - (layer): Layer[Linear](in_features=128, out_features=128, bias=True) - (activation): Layer[ReLU]() - ) - (1): LinearBlock( - (layer): Layer[Linear](in_features=128, out_features=256, bias=True) - (activation): Layer[ReLU]() - ) - (2): LinearBlock( - (layer): Layer[Linear](in_features=256, out_features=784, bias=True) - (activation): Layer[Identity]() + (blocks): LayerList( + (0): LinearBlock( + (layer): Layer[Linear](in_features=128, out_features=128, ...) + (activation): Layer[ReLU]() + ) + (1): LinearBlock( + (layer): Layer[Linear](in_features=128, out_features=256, ...) + (activation): Layer[ReLU]() + ) + (2): LinearBlock( + (layer): Layer[Linear](in_features=256, out_features=784, ...) + (activation): Layer[Identity]() + ) ) ) - ) + """ encoder = MultiLayerPerceptron( @@ -325,9 +351,14 @@ def _get_default_decoder(self, input_size, channels): hidden_features=channels, out_features=input_size, ) + return encoder - def encode(self, x, c): + def encode( + self: ConditionalVariationalAutoEncoder, + x: torch.Tensor, + c: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor]: """Encodes input data into latent distribution parameters. This method concatenates the input data with the condition vector and @@ -336,17 +367,17 @@ def encode(self, x, c): Parameters ---------- - x : torch.Tensor + x: torch.Tensor Input data of shape (batch_size, input_size). - c : torch.Tensor + c: torch.Tensor Conditioning vector of shape (batch_size, condition_dim). Returns ------- - mu : torch.Tensor - Mean of the latent distribution. - log_var : torch.Tensor - Log-variance of the latent distribution. + (torch.Tensor, torch.Tensor) + A tuple, `(mu, log_var)`, with two PyTorch tensors: + - `mu` is the mean of the latent distribution. + - `log_var` is the og-variance of the latent distribution. Examples -------- @@ -360,6 +391,7 @@ def encode(self, x, c): >>> mu, log_var = cvae.encode(x, c) >>> mu.shape, log_var.shape (torch.Size([10, 20]), torch.Size([10, 20])) + """ if len(c.shape) == 1: @@ -372,24 +404,28 @@ def encode(self, x, c): return mu, log_var - def reparameterize(self, mu, log_var): - """Samples a latent vector using the reparameterization trick. + def reparameterize( + self: ConditionalVariationalAutoEncoder, + mu: torch.Tensor, + log_var: torch.Tensor, + ) -> torch.Tensor: + """Sample a latent vector using the reparameterization trick. - This method generates a latent variable z by sampling from a normal + This method generates a latent variable `z` by sampling from a normal distribution defined by the given mean and log-variance. It enables backpropagation through stochastic sampling. Parameters ---------- - mu : torch.Tensor + mu: torch.Tensor Mean of the latent distribution. - log_var : torch.Tensor + log_var: torch.Tensor Log-variance of the latent distribution. Returns ------- torch.Tensor - Sampled latent vector z. + Sampled latent vector `z`. Examples -------- @@ -403,14 +439,22 @@ def reparameterize(self, mu, log_var): >>> z = cvae.reparameterize(mu, log_var) >>> z.shaoe >>> torch.Size([10, 20]) + """ std = torch.exp(0.5 * log_var) eps = torch.randn_like(std) - return eps * std + mu - def decode(self, z, c): - """Decodes latent variables into reconstructed input. + z = eps * std + mu + + return z + + def decode( + self: ConditionalVariationalAutoEncoder, + z: torch.Tensor, + c: torch.Tensor, + ) -> torch.Tensor: + """Decode latent variables into reconstructed input. This method concatenates the latent vector with the condition vector, transforms it through a linear layer, and passes it through the decoder @@ -418,9 +462,9 @@ def decode(self, z, c): Parameters ---------- - z : torch.Tensor + z: torch.Tensor Latent vector of shape (batch_size, latent_dim). - c : torch.Tensor + c: torch.Tensor Conditioning vector of shape (batch_size, condition_dim). Returns @@ -440,6 +484,7 @@ def decode(self, z, c): >>> x_hat = cvae.decode(z, c) >>> x_hat.shape torch.Size([10, 784]) + """ if len(c.shape) == 1: @@ -448,22 +493,27 @@ def decode(self, z, c): z = torch.cat([z, c], dim=1) x = self.fc_dec(z) x = self.decoder(x) + return x - def train_preprocess(self, batch): + def train_preprocess( + self: ConditionalVariationalAutoEncoder, + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor], + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Preprocesses a batch of data for training. - This method prepares the input, target, and condition tensors by ensuring - they are in the correct format (e.g., channel-first if required). + This method prepares the input, target, and condition tensors by + ensuring they are in the correct format (e.g., channel-first if + required). Parameters ---------- - batch : tuple - A tuple containing (x, y, c). + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor] + A tuple containing `(x, y, c)`. Returns ------- - tuple + tuple[torch.Tensor, torch.Tensor, torch.Tensor] Preprocessed (x, y, c). Examples @@ -480,28 +530,35 @@ def train_preprocess(self, batch): >>> x_p, y_p, c_p = cvae.train_preprocess(batch) >>> x_p.shape, y_p.shape, c_p.shape (torch.Size([10, 784]), torch.Size([10, 784]), torch.Size([10, 10])) + """ x, y, c = batch + x = self._maybe_to_channel_first(x) y = self._maybe_to_channel_first(y) c = self._maybe_to_channel_first(c) + return x, y, c val_preprocess = train_preprocess test_preprocess = train_preprocess - def training_step(self, batch, batch_idx): - """Performs a single training step. + def training_step( + self: ConditionalVariationalAutoEncoder, + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor], + batch_idx: int, + ) -> torch.Tensor: + """Perform a single training step. This method processes a batch of data, computes the forward pass, calculates reconstruction and KL divergence losses, and logs them. Parameters ---------- - batch : tuple + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor] A batch of training data (x, y, c). - batch_idx : int + batch_idx: int Index of the current batch. Returns @@ -525,12 +582,16 @@ def training_step(self, batch, batch_idx): >>> loss_train = cvae.training_step(batch, _) >>> loss_train tensor(5442.5708, grad_fn=) + """ x, y, c = self.train_preprocess(batch) + y_hat, mu, log_var, z = self(x, c) + rec_loss, KLD = self.compute_loss(y_hat, y, mu, log_var) tot_loss = rec_loss + self.beta * KLD + loss = {"rec_loss": rec_loss, "KL": KLD, "total_loss": tot_loss} for name, v in loss.items(): self.log( @@ -541,9 +602,14 @@ def training_step(self, batch, batch_idx): prog_bar=True, logger=True, ) + return tot_loss - def test_step(self, batch, batch_idx): + def test_step( + self: ConditionalVariationalAutoEncoder, + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor], + batch_idx: int, + ) -> torch.Tensor: """Performs a single test step. This method evaluates the model on a test batch and logs the @@ -551,9 +617,9 @@ def test_step(self, batch, batch_idx): Parameters ---------- - batch : tuple + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor] A batch of test data (x, y, c). - batch_idx : int + batch_idx: int Index of the current batch. Returns @@ -577,12 +643,16 @@ def test_step(self, batch, batch_idx): >>> loss_test = cvae.test_step(batch, _) >>> loss_test tensor(5440.9023, grad_fn=) + """ x, y, c = self.test_preprocess(batch) + y_hat, mu, log_var, z = self(x, c) + rec_loss, KLD = self.compute_loss(y_hat, y, mu, log_var) tot_loss = rec_loss + self.beta * KLD + loss = {"rec_loss": rec_loss, "KL": KLD, "total_loss": tot_loss} for name, v in loss.items(): self.log( @@ -593,9 +663,15 @@ def test_step(self, batch, batch_idx): prog_bar=True, logger=True, ) + return tot_loss - def validation_step(self, batch, batch_idx): + # TODO: why not val_step? for consistency with val_preprocess? + def validation_step( + self: ConditionalVariationalAutoEncoder, + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor], + batch_idx: int, + ) -> torch.Tensor: """Performs a single validation step. This method evaluates the model on a validation batch and logs the @@ -603,9 +679,9 @@ def validation_step(self, batch, batch_idx): Parameters ---------- - batch : tuple + batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor] A batch of validation data (x, y, c). - batch_idx : int + batch_idx: int Index of the current batch. Returns @@ -629,12 +705,15 @@ def validation_step(self, batch, batch_idx): >>> loss_val = cvae.validation_step(batch, _) >>> loss_val tensor(5437.9136, grad_fn=) + """ x, y, c = self.val_preprocess(batch) + y_hat, mu, log_var, z = self(x, c) rec_loss, KLD = self.compute_loss(y_hat, y, mu, log_var) tot_loss = rec_loss + self.beta * KLD + loss = {"rec_loss": rec_loss, "KL": KLD, "total_loss": tot_loss} for name, v in loss.items(): self.log( @@ -645,9 +724,16 @@ def validation_step(self, batch, batch_idx): prog_bar=True, logger=True, ) + return tot_loss - def compute_loss(self, y_hat, y, mu, log_var): + def compute_loss( + self: ConditionalVariationalAutoEncoder, + y_hat: torch.Tensor, + y: torch.Tensor, + mu: torch.Tensor, + log_var: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor]: """Computes reconstruction and KL divergence losses. This method calculates the reconstruction loss between the predicted @@ -656,18 +742,18 @@ def compute_loss(self, y_hat, y, mu, log_var): Parameters ---------- - y_hat : torch.Tensor + y_hat: torch.Tensor Reconstructed output. - y : torch.Tensor + y: torch.Tensor Ground truth target. - mu : torch.Tensor + mu: torch.Tensor Mean of latent distribution. - log_var : torch.Tensor + log_var: torch.Tensor Log-variance of latent distribution. Returns ------- - tuple + tuple[torch.Tensor, torch.Tensor] Reconstruction loss and KL divergence. Examples @@ -684,29 +770,36 @@ def compute_loss(self, y_hat, y, mu, log_var): >>> mu, log_var = torch.randn(10, 20), torch.randn(10, 20) >>> cvae.compute_loss(y_hat, y, mu, log_var) (tensor(7845.0801), tensor(177.7961)) + """ rec_loss = self.reconstruction_loss(y_hat, y) + KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp()) + return rec_loss, KLD - def forward(self, x, c): - """Defines the forward pass of the CVAE. + def forward( + self: ConditionalVariationalAutoEncoder, + x: torch.Tensor, + c: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Define the forward pass of the CVAE. This method encodes the input and condition into a latent distribution, samples a latent vector, and decodes it to reconstruct the input. Parameters ---------- - x : torch.Tensor + x: torch.Tensor Input data. - c : torch.Tensor + c: torch.Tensor Conditioning vector. Returns ------- - tuple - (y_hat, mu, log_var, z) + tuple[torch.Tensor, torch.Tensor, torch.Tensor] + A tuple of PyTorch tensors, `(y_hat, mu, log_var, z)`. Examples -------- @@ -720,12 +813,16 @@ def forward(self, x, c): >>> y_hat, mu, log_var, z = cvae(x, c) >>> y_hat.shape, mu.shape, log_var.shape, z.shape (torch.Size([10, 784]), - torch.Size([10, 20]), - torch.Size([10, 20]), - torch.Size([10, 20])) + torch.Size([10, 20]), + torch.Size([10, 20]), + torch.Size([10, 20])) + """ mu, log_var = self.encode(x, c) + z = self.reparameterize(mu, log_var) + y_hat = self.decode(z, c) + return y_hat, mu, log_var, z From bb2173c0582694e334726f64f61785d6c37698b9 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 18:10:52 +0200 Subject: [PATCH 04/17] Update QC.ipynb --- tutorials/quality-control/QC.ipynb | 98 +++++++++++++++--------------- 1 file changed, 50 insertions(+), 48 deletions(-) diff --git a/tutorials/quality-control/QC.ipynb b/tutorials/quality-control/QC.ipynb index 5ea7108..9cef2f5 100644 --- a/tutorials/quality-control/QC.ipynb +++ b/tutorials/quality-control/QC.ipynb @@ -5,12 +5,12 @@ "id": "76696fae-05c2-4ce9-b282-31fe2110993b", "metadata": {}, "source": [ - "# Quality Control of AFM Force Spectroscopy Data with Conditional Variational Autoencoders\n", + "# Quality Control of AFM Force Spectroscopy Curves with Conditional Variational Autoencoders\n", "\n", "\n", "\"Open\n", "\n", - "This notebook provides you with a complete code example that first trains a conditional variational autoencoder on AFM force spectroscopy curves in a self-supervised way; then constructs a latent space where the AFM curves are placed; and finally uses the latent space to check the quality of the AFM curves.\n" + "This notebook provides you with a complete code example that first trains a conditional variational autoencoder (cVAE) on AFM force spectroscopy curves in a self-supervised way; then, it constructs a latent space where the AFM curves are placed; and finally it uses the latent space to check the quality of the AFM curves.\n" ] }, { @@ -52,16 +52,16 @@ "\n", "This dataset contains AFM force spectroscopy collected using a culture of 3T3 cells (a widely used, immortalized mouse fibroblast cell line derived from Swiss albino mouse embryos).\n", "\n", - "This dataset contains\n", - "- `approach` is a folder with the approach curves, as files `0.npy`, `1.npy`, etc.\n", - "- `retraction` is a folder with the retraction curves, paired with the approach curves.\n", - "- `label.npy` is the label file for the approach curves: `0` is for good curves, `1` is for bad curves, and `-1` is for curves of unknown quality.\n", - "- `contact_point.npy` contains the contact points for each curve." + "This dataset contains:\n", + "- `approach`: a folder with the approach curves, as files `0.npy`, `1.npy`, etc.\n", + "- `retraction`: a folder with the retraction curves, paired with the approach curves.\n", + "- `label.npy`: the label file for the approach curves: `0` is for good curves, `1` is for bad curves, and `-1` is for curves of unknown quality.\n", + "- `contact_point.npy`: contains the contact points for each curve." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "0dca1629-7f9b-449b-8ec5-31eef5211d15", "metadata": {}, "outputs": [], @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "ebb91fd0", "metadata": {}, "outputs": [], @@ -109,10 +109,12 @@ "retract_curves = [np.load(f) for f in retract_files]\n", "\n", "# Load the labels\n", - "labels = np.load(Path.cwd() / \"3t3_cell_dataset\" / \"label.npy\")\n", + "label_file = Path.cwd() / \"3t3_cell_dataset\" / \"label.npy\"\n", + "labels = np.load(label_file)\n", "\n", "# Load the contact points\n", - "contactpoints = np.load(Path.cwd() / \"3t3_cell_dataset\" / \"contact_point.npy\")" + "contactpoint_file = Path.cwd() / \"3t3_cell_dataset\" / \"contact_point.npy\"\n", + "contactpoints = np.load(contactpoint_file)" ] }, { @@ -120,12 +122,12 @@ "id": "16017ef3-00b0-4b10-a3ec-0e5b3e445792", "metadata": {}, "source": [ - "... print the number of approach/retraction curves and labels ..." + "... print the number of approach/retraction curves, labels, and contact points ..." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "856df450-2de3-4dbb-a50b-529a4b8e1b8a", "metadata": {}, "outputs": [ @@ -161,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "13df8c66-5f50-4c13-8de7-36ad4d6c501e", "metadata": {}, "outputs": [ @@ -206,7 +208,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -306,12 +308,12 @@ "id": "f2acb9ef-5e35-4503-b8c4-4994e7aaaa5a", "metadata": {}, "source": [ - "For the approach curves, resample each approach curve to adjust its length align with the shortest approach curve ..." + "For the approach curves, resample each approach curve to adjust its length to align with the shortest approach curve ..." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "47f795e6", "metadata": {}, "outputs": [], @@ -320,17 +322,15 @@ "resampled_contactpoints = []\n", "for approach_curve, contactpoint in zip(approach_curves, contactpoints):\n", " # Resample each approach curve to adjust its length to the shortest one\n", - " steps = np.round( # TODO: Would interpolation be better than downsampling? Please, try and let me know.\n", - " np.linspace(0, approach_curve.shape[0] - 1, min_approach_curve_length) # TODO: Try also to downsample the approach curve to, for example, 100 points and see if this improves the result.\n", + " steps = np.round(\n", + " np.linspace(0, approach_curve.shape[0] - 1, min_approach_curve_length)\n", " ).astype(int)\n", " resampled_approach_curve = approach_curve[steps][:, 1]\n", " resampled_approach_curves.append(resampled_approach_curve)\n", "\n", " # Determine the contact point for the resampled approach curve\n", " resampled_approach_position = approach_curve[steps][:, 0]\n", - " resampled_contactpoint = (\n", - " np.abs(resampled_approach_position - contactpoint).argmin()\n", - " )\n", + " resampled_contactpoint = np.abs(resampled_approach_position - contactpoint).argmin()\n", " resampled_contactpoints.append(resampled_contactpoint)" ] }, @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "d12a2c50-462e-4d8a-b35c-6e5d5dfdef55", "metadata": {}, "outputs": [], @@ -362,9 +362,7 @@ "norm_contact_points = []\n", "\n", "for resampled_contactpoint in resampled_contactpoints:\n", - " norm_contact_points.append(\n", - " resampled_contactpoint / min_approach_curve_length\n", - " )" + " norm_contact_points.append(resampled_contactpoint / min_approach_curve_length)" ] }, { @@ -432,13 +430,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "efe7d626-8e0e-44b7-8224-ea950b84cc49", "metadata": {}, "outputs": [], "source": [ "import torch\n", "\n", + "\n", "class AFMDataset(torch.utils.data.Dataset):\n", " \"\"\"Dataset for AFM Force Spectroscopy.\n", "\n", @@ -501,7 +500,7 @@ " contactpoints : array-like\n", " Corresponding contact points.\n", " \"\"\"\n", - " \n", + "\n", " self.curves = np.array(curves)\n", " self.contactpoints = np.array(contactpoints)\n", "\n", @@ -513,7 +512,7 @@ " int\n", " Number of curves in the dataset.\n", " \"\"\"\n", - " \n", + "\n", " return self.curves.shape[0]\n", "\n", " def __getitem__(self, idx):\n", @@ -533,7 +532,7 @@ " list\n", " [curve, target, condition], where each element is a torch.Tensor.\n", " \"\"\"\n", - " \n", + "\n", " curve = torch.tensor(self.curves[idx]).float()\n", " contactpoint = torch.tensor(self.contactpoints[idx]).float()\n", " return [curve, curve, contactpoint]" @@ -549,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c3fc6142-8d6a-41f0-a60d-55f2d7e688f9", "metadata": {}, "outputs": [], @@ -559,9 +558,12 @@ "\n", "# Randomly select the train and test data\n", "(\n", - " curves_train, curves_test,\n", - " labels_train, labels_test,\n", - " contactpoints_train, contactpoints_test,\n", + " curves_train,\n", + " curves_test,\n", + " labels_train,\n", + " labels_test,\n", + " contactpoints_train,\n", + " contactpoints_test,\n", ") = train_test_split(\n", " norm_approach_curves,\n", " labels,\n", @@ -978,7 +980,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "974c3c17-57a1-4dc8-b18b-a5e4332cbbf9", "metadata": {}, "outputs": [ @@ -1001,7 +1003,7 @@ "with torch.no_grad():\n", " for indx, batch in enumerate(test_dataloader):\n", " x, y_list, c = batch\n", - " y_hat_list, _, _, _ = best_cvae(x,c)\n", + " y_hat_list, _, _, _ = best_cvae(x, c)\n", "\n", "\n", "# Plot the comparison between the reconstruction and the originals\n", @@ -1010,11 +1012,11 @@ "for curve_idx, ax in enumerate(axs.ravel()):\n", " data_apporach_plot_raw = y_list[curve_idx].numpy()[::-1]\n", " data_apporach_plot_reconstruct = y_hat_list[curve_idx].numpy()[::-1]\n", - " \n", + "\n", " ax.plot(data_apporach_plot_raw, label=\"Raw Data\")\n", - " \n", + "\n", " ax.plot(data_apporach_plot_reconstruct, label=\"Reconstruction Data\")\n", - " \n", + "\n", " ax.set_xlabel(\"Step\", fontsize=12)\n", " if curve_idx == 0:\n", " ax.set_ylabel(\"Normolized Force\", fontsize=12)\n", @@ -1042,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "a4b7622c-7d2c-43e8-9bfa-f8a39aaf6aa5", "metadata": {}, "outputs": [], @@ -1051,14 +1053,14 @@ "all_dataloader = dl.DataLoader(\n", " AFMDataset(curves=norm_approach_curves, contactpoints=norm_contact_points),\n", " batch_size=len(norm_approach_curves),\n", - " shuffle=False\n", + " shuffle=False,\n", ")\n", "\n", "# collect the decoding curves and encoding latent space in the full dataset\n", "with torch.no_grad():\n", " for indx, batch in enumerate(all_dataloader):\n", " x_list, _, c = batch\n", - " _, mu_list, log_var_list, _ = best_cvae(x_list,c)\n", + " _, mu_list, log_var_list, _ = best_cvae(x_list, c)\n", "\n", "# Transfer them into Numpy array\n", "x_list = x_list.numpy()\n", @@ -1076,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "cb7dcbcb-afe7-43eb-a9e8-2fd7ca6dee2d", "metadata": {}, "outputs": [ @@ -1094,7 +1096,7 @@ "source": [ "# Plot the latent space with labels\n", "plt.figure(figsize=(6, 4))\n", - "plt.scatter(log_var_list[:,0],log_var_list[:,1], c=labels)\n", + "plt.scatter(log_var_list[:, 0], log_var_list[:, 1], c=labels)\n", "plt.xlabel(\"log_var_0\", fontsize=12)\n", "plt.ylabel(\"log_var_1\", fontsize=12)\n", "plt.colorbar()\n", @@ -1292,9 +1294,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "py_env_book", "language": "python", - "name": "conda-base-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1306,7 +1308,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.9" + "version": "3.10.15" } }, "nbformat": 4, From b6bef7f226b09eef73656ce6ae505ba4f4f5d038 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 20:07:57 +0200 Subject: [PATCH 05/17] Update cvae.py --- tutorials/quality-control/cvae.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/tutorials/quality-control/cvae.py b/tutorials/quality-control/cvae.py index 7731cfa..b2c6de0 100644 --- a/tutorials/quality-control/cvae.py +++ b/tutorials/quality-control/cvae.py @@ -196,9 +196,15 @@ def __init__( ) -> None: # TODO: Add docstring also for init. - self.encoder = encoder or self._get_default_encoder( - input_size, condition_dim, channels - ) + if encoder is not None: + self.encoder = encoder + else: + self.encoder = self._get_default_encoder( + input_size, + condition_dim, + channels, + ) + self.fc_mu = nn.Linear( channels[-1], latent_dim, @@ -215,12 +221,15 @@ def __init__( if decoder is not None: self.decoder = decoder else: - self._get_default_decoder(input_size, channels[::-1]) + self.decoder = self._get_default_decoder( + input_size, + channels[::-1], + ) if reconstruction_loss is not None: self.reconstruction_loss = reconstruction_loss else: - nn.BCELoss(reduction="sum") + self.reconstruction_loss = nn.BCELoss(reduction="sum") self.latent_dim = latent_dim self.beta = beta From 379ed117613ece693ac8e4f488e9ec252930ab8a Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 20:08:00 +0200 Subject: [PATCH 06/17] Update QC.ipynb --- tutorials/quality-control/QC.ipynb | 2177 +++++++++++++++++++++++----- 1 file changed, 1805 insertions(+), 372 deletions(-) diff --git a/tutorials/quality-control/QC.ipynb b/tutorials/quality-control/QC.ipynb index 9cef2f5..2f3e3a1 100644 --- a/tutorials/quality-control/QC.ipynb +++ b/tutorials/quality-control/QC.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "6258e05a-ca7f-479b-a891-e7785f981f05", "metadata": {}, "outputs": [], @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "0dca1629-7f9b-449b-8ec5-31eef5211d15", "metadata": {}, "outputs": [], @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "aa587672", "metadata": {}, "outputs": [ @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "e81d235b", "metadata": {}, "outputs": [ @@ -313,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "47f795e6", "metadata": {}, "outputs": [], @@ -330,7 +330,9 @@ "\n", " # Determine the contact point for the resampled approach curve\n", " resampled_approach_position = approach_curve[steps][:, 0]\n", - " resampled_contactpoint = np.abs(resampled_approach_position - contactpoint).argmin()\n", + " resampled_contactpoint = np.abs(\n", + " resampled_approach_position - contactpoint\n", + " ).argmin()\n", " resampled_contactpoints.append(resampled_contactpoint)" ] }, @@ -344,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "d12a2c50-462e-4d8a-b35c-6e5d5dfdef55", "metadata": {}, "outputs": [], @@ -362,7 +364,9 @@ "norm_contact_points = []\n", "\n", "for resampled_contactpoint in resampled_contactpoints:\n", - " norm_contact_points.append(resampled_contactpoint / min_approach_curve_length)" + " norm_contact_points.append(\n", + " resampled_contactpoint / min_approach_curve_length\n", + " )" ] }, { @@ -375,13 +379,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "7baf1475", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -430,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "efe7d626-8e0e-44b7-8224-ea950b84cc49", "metadata": {}, "outputs": [], @@ -451,21 +455,21 @@ "\n", " Parameters\n", " ----------\n", - " curves : array-like\n", + " curves: array-like\n", " Collection of AFM curves. Shape: (num_samples, curve_length).\n", - " contactpoints : array-like\n", + " contactpoints: array-like\n", " Corresponding contact points for each curve. Shape: (num_samples, ...).\n", "\n", " Attributes\n", " ----------\n", - " curves : np.ndarray\n", + " curves: np.ndarray\n", " Stored AFM curves.\n", - " contactpoints : np.ndarray\n", + " contactpoints: np.ndarray\n", " Stored contact point values.\n", "\n", " Input\n", " -----\n", - " idx : int\n", + " idx: int\n", " Index of the sample to retrieve.\n", "\n", " Output\n", @@ -488,17 +492,19 @@ " (torch.Size([100]), torch.Size([100]), torch.Size([]))\n", " >>> len(dataset)\n", " 10\n", + "\n", " \"\"\"\n", "\n", " def __init__(self, curves, contactpoints):\n", - " \"\"\"Initializes the AFM dataset.\n", + " \"\"\"Initialize the AFM dataset.\n", "\n", " Parameters\n", " ----------\n", - " curves : array-like\n", + " curves: array-like\n", " Collection of AFM curves.\n", - " contactpoints : array-like\n", + " contactpoints: array-like\n", " Corresponding contact points.\n", + "\n", " \"\"\"\n", "\n", " self.curves = np.array(curves)\n", @@ -511,6 +517,7 @@ " -------\n", " int\n", " Number of curves in the dataset.\n", + "\n", " \"\"\"\n", "\n", " return self.curves.shape[0]\n", @@ -524,13 +531,14 @@ "\n", " Parameters\n", " ----------\n", - " idx : int\n", + " idx: int\n", " Index of the sample.\n", "\n", " Returns\n", " -------\n", " list\n", " [curve, target, condition], where each element is a torch.Tensor.\n", + "\n", " \"\"\"\n", "\n", " curve = torch.tensor(self.curves[idx]).float()\n", @@ -575,7 +583,7 @@ "# Instantiate the dataloader for train data\n", "train_dataloader = dl.DataLoader(\n", " AFMDataset(curves=curves_train, contactpoints=contactpoints_train),\n", - " batch_size=len(curves_train),\n", + " batch_size=len(curves_train), # TODO: why is the batch size equal to the whole dataset? I understand it in the test case, but not for the training.\n", ")\n", "\n", "# Instantiate the dataloader for test data\n", @@ -603,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "f8d791df", "metadata": {}, "outputs": [ @@ -720,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "c11ec507-1683-4a91-beb2-138a03377a6c", "metadata": {}, "outputs": [ @@ -730,7 +738,7 @@ "text": [ "GPU available: True (mps), used: True\n", "TPU available: False, using: 0 TPU cores\n", - "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n" + "HPU available: False, using: 0 HPUs\n" ] } ], @@ -749,7 +757,7 @@ "csv_logger = CSVLogger(\"logs\")\n", "\n", "# Instantiate the trainer\n", - "vae_trainer = dl.Trainer(\n", + "cvae_trainer = dl.Trainer(\n", " max_epochs=10_000,\n", " accelerator=\"auto\",\n", " callbacks=[checkpoint_callback],\n", @@ -772,159 +780,79 @@ "metadata": { "scrolled": true }, - "outputs": [], - "source": [ - "vae_trainer.fit(cvae, train_dataloader, test_dataloader)" - ] - }, - { - "cell_type": "markdown", - "id": "4e2873af-aae9-4893-b00b-28e2ac5d798b", - "metadata": {}, - "source": [ - "... and load the best model from the callback function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cbb1ef9-5f05-4c0e-b1ea-17a7ee65298e", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"The best model is saved at {checkpoint_callback.best_model_path}\")\n", - "\n", - "best_cvae = ConditionalVariationalAutoEncoder.load_from_checkpoint(\n", - " checkpoint_callback.best_model_path\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "4bd27db2-cb4b-4cca-a511-0665e8e5a94d", - "metadata": {}, - "source": [ - "You can also load a pretrained model by uncommenting the code below." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "f77d6742-1965-49d2-880a-71cf35e6b9c4", - "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Loading the model at /Users/iluvatar/Desktop/Bruker_QC/personalrepo/ASAP/tutorial/QC/models/epoch=6886-step=6887.ckpt\n" + "\n", + " | Name | Type | Params | Mode \n", + "---------------------------------------------------------------------\n", + "0 | encoder | MultiLayerPerceptron | 974 K | train\n", + "1 | fc_mu | Linear | 130 | train\n", + "2 | fc_var | Linear | 130 | train\n", + "3 | fc_dec | Linear | 256 | train\n", + "4 | decoder | MultiLayerPerceptron | 975 K | train\n", + "5 | reconstruction_loss | MSELoss | 0 | train\n", + "6 | train_metrics | MetricCollection | 0 | train\n", + "7 | val_metrics | MetricCollection | 0 | train\n", + "8 | test_metrics | MetricCollection | 0 | train\n", + "9 | optimizer | Adam | 0 | train\n", + "---------------------------------------------------------------------\n", + "2.0 M Trainable params\n", + "0 Non-trainable params\n", + "2.0 M Total params\n", + "7.805 Total estimated model params size (MB)\n", + "42 Modules in train mode\n", + "0 Modules in eval mode\n" ] - } - ], - "source": [ - "# Uncomment if you want to load a pretrained model\n", - "\n", - "pretraiend_model_path = Path.cwd() / \"models\" / \"epoch=6886-step=6887.ckpt\"\n", - "\n", - "print(f\"Loading the model at {pretraiend_model_path}\")\n", - "\n", - "cvae.load_state_dict(\n", - " torch.load(pretraiend_model_path, map_location=\"cpu\")[\"state_dict\"]\n", - ")\n", - "best_cvae = cvae" - ] - }, - { - "cell_type": "markdown", - "id": "e06fc9c9-beab-4145-838e-b0ce1e1a8ddd", - "metadata": {}, - "source": [ - "### 2.5. Evaluate the Trained VAE" - ] - }, - { - "cell_type": "markdown", - "id": "1ff1c8b8-57a3-4268-bcd1-0a8dbcaf3ffb", - "metadata": {}, - "source": [ - "Evaluate the trained VAE using both the train dataset ..." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "8f44e391-85b2-45b7-82cd-68a984176903", - "metadata": {}, - "outputs": [ + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40692234edff4a55b5daae843797effd", + "model_id": "bd50a7e0c2a64905a86ba410c0312c82", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Testing: | …" + "Sanity Checking: | | 0/? [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃ Test metric ┃ DataLoader 0 ┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ test_KL_epoch │ 271.2106018066406 │\n", - "│ test_rec_loss_epoch │ 2246.874755859375 │\n", - "│ test_total_loss_epoch │ 2518.085693359375 │\n", - "└───────────────────────────┴───────────────────────────┘\n", - "\n" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "7ec8f778bdcb40ad90fa01f5541bff91", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│\u001b[36m \u001b[0m\u001b[36m test_KL_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 271.2106018066406 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test_rec_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2246.874755859375 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test_total_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2518.085693359375 \u001b[0m\u001b[35m \u001b[0m│\n", - "└───────────────────────────┴───────────────────────────┘\n" + "Training: | | 0/? [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃ Test metric ┃ DataLoader 0 ┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ test_KL_epoch │ 56.41820526123047 │\n", - "│ test_rec_loss_epoch │ 681.2374877929688 │\n", - "│ test_total_loss_epoch │ 737.6557006835938 │\n", - "└───────────────────────────┴───────────────────────────┘\n", - "\n" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "a1b9f11e16d4457583b54ffef388006a", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│\u001b[36m \u001b[0m\u001b[36m test_KL_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 56.41820526123047 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test_rec_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 681.2374877929688 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test_total_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 737.6557006835938 \u001b[0m\u001b[35m \u001b[0m│\n", - "└───────────────────────────┴───────────────────────────┘\n" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃ Test metric ┃ DataLoader 0 ┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ test_KL_epoch │ 271.2106628417969 │\n", + "│ test_rec_loss_epoch │ 2195.51904296875 │\n", + "│ test_total_loss_epoch │ 2466.729736328125 │\n", + "└───────────────────────────┴───────────────────────────┘\n", + "\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│\u001b[36m \u001b[0m\u001b[36m test_KL_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 271.2106628417969 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test_rec_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2195.51904296875 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test_total_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2466.729736328125 \u001b[0m\u001b[35m \u001b[0m│\n", + "└───────────────────────────┴───────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluate performance of trained cVAE on train dataset\n", + "\n", + "_ = cvae_trainer.test(best_cvae, train_dataloader)" + ] + }, + { + "cell_type": "markdown", + "id": "afe57e06", + "metadata": {}, + "source": [ + "... and on the test dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ea0ece5-70f1-4a4f-b4fa-7543c2dfd2c8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "45145ffd6ed9435db78adbeadc98f532", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: | | 0/? [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃ Test metric ┃ DataLoader 0 ┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ test_KL_epoch │ 56.418212890625 │\n", + "│ test_rec_loss_epoch │ 679.7703247070312 │\n", + "│ test_total_loss_epoch │ 736.1885375976562 │\n", + "└───────────────────────────┴───────────────────────────┘\n", + "\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│\u001b[36m \u001b[0m\u001b[36m test_KL_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 56.418212890625 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test_rec_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 679.7703247070312 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test_total_loss_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 736.1885375976562 \u001b[0m\u001b[35m \u001b[0m│\n", + "└───────────────────────────┴───────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluate performance of trained cVAE on test dataset\n", + "\n", + "_ = cvae_trainer.test(best_cvae, test_dataloader)" + ] + }, + { + "cell_type": "markdown", + "id": "da654f88", + "metadata": {}, + "source": [ + "### 2.6. Reconstruct and Denoise the Approach Curves" + ] + }, + { + "cell_type": "markdown", + "id": "01a5c2d8-45ed-4a56-930e-88415a355105", + "metadata": {}, + "source": [ + "The trained cVAE can used to reconstruct and denoise the approach curves." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "974c3c17-57a1-4dc8-b18b-a5e4332cbbf9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Put the model under evaluation mode\n", + "best_cvae.eval()\n", + "\n", + "# Collect the decoding curves and encoding latent space in the test dataset\n", + "with torch.no_grad():\n", + " # Note that the test dataset has a single batch with all the curves\n", + " for batch in test_dataloader:\n", + " x, y_list, c = batch\n", + " y_hat_list, _, _, _ = best_cvae(x, c)\n", + "\n", + "\n", + "# Plot the comparison between the reconstruction and the originals\n", + "fig, axs = plt.subplots(1, 4, figsize=((12, 3)))\n", + "\n", + "for curve_idx, ax in enumerate(axs.ravel()):\n", + " data_apporach_plot_raw = y_list[curve_idx].numpy()[::-1]\n", + " data_apporach_plot_reconstruct = y_hat_list[curve_idx].numpy()[::-1]\n", + "\n", + " ax.plot(data_apporach_plot_raw, label=\"Raw Data\")\n", + "\n", + " ax.plot(data_apporach_plot_reconstruct, label=\"Reconstruction Data\")\n", + "\n", + " ax.set_xlabel(\"Step\", fontsize=12)\n", + " if curve_idx == 0:\n", + " ax.set_ylabel(\"Normolized Force\", fontsize=12)\n", + " ax.legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1bf35d60-428a-4de8-a884-45900f6f1d70", + "metadata": {}, + "source": [ + "### 2.7. Visualize the Latent Space " + ] + }, + { + "cell_type": "markdown", + "id": "5b44c224-09bc-4f56-bd93-525197e62e2b", + "metadata": {}, + "source": [ + "Collect the decoding curves and latent space vectors for all curves ..." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a4b7622c-7d2c-43e8-9bfa-f8a39aaf6aa5", + "metadata": {}, + "outputs": [], + "source": [ + "# Construct the dataset\n", + "all_dataloader = dl.DataLoader(\n", + " AFMDataset(curves=norm_approach_curves, contactpoints=norm_contact_points),\n", + " batch_size=len(norm_approach_curves),\n", + " shuffle=False,\n", + ")\n", + "\n", + "# Collect the decoded curves and corresponding latent space vectors\n", + "with torch.no_grad():\n", + " for batch in all_dataloader:\n", + " x_list, _, c = batch\n", + " _, mu_list, log_var_list, _ = best_cvae(x_list, c)\n", + "\n", + "# Cast the curves and latent space vectors into Numpy arrays\n", + "x_list = x_list.numpy()\n", + "mu_list = mu_list.numpy()\n", + "log_var_list = log_var_list.numpy()" + ] + }, + { + "cell_type": "markdown", + "id": "10cb61ee-7c9e-42da-9937-f02db077c000", + "metadata": {}, + "source": [ + "... and check which curves are good or bad in the latent space with the provided label ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb7dcbcb-afe7-43eb-a9e8-2fd7ca6dee2d", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1094,12 +2589,14 @@ } ], "source": [ - "# Plot the latent space with labels\n", + "# Plot the latent space with labels for good and bad curves\n", "plt.figure(figsize=(6, 4))\n", + "\n", "plt.scatter(log_var_list[:, 0], log_var_list[:, 1], c=labels)\n", "plt.xlabel(\"log_var_0\", fontsize=12)\n", "plt.ylabel(\"log_var_1\", fontsize=12)\n", "plt.colorbar()\n", + "\n", "plt.show()" ] }, @@ -1113,79 +2610,20 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 27, "id": "0f54bf3c-e2cc-4253-ad16-419f1adf97f8", "metadata": {}, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7e346ba6edd846bd845c6687793a693c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FigureWidget({\n", - " 'data': [{'marker': {'color': [red, red, red, ..., orange, orange, orange],\n", - " 'size': 10},\n", - " 'mode': 'markers',\n", - " 'name': 'points',\n", - " 'type': 'scatter',\n", - " 'uid': 'a7a06958-507d-4b3a-8873-49df067fc5c9',\n", - " 'x': {'bdata': ('LqrcOhFN7Ds3OxS7G0UCvKKlObxORA' ... 'sOwRg8H81Lu4249r2zPhC+wfryvQ=='),\n", - " 'dtype': 'f4'},\n", - " 'xaxis': 'x',\n", - " 'y': {'bdata': ('cboyPXNVzT2r2Tu+nW0pvuKTcL8i+W' ... '66pki+0iB+vhLnlsCEj5jAihCWwA=='),\n", - " 'dtype': 'f4'},\n", - " 'yaxis': 'y'},\n", - " {'line': {'color': 'red'},\n", - " 'mode': 'lines',\n", - " 'name': 'curve',\n", - " 'type': 'scatter',\n", - " 'uid': 'dbc63828-4d37-4852-a553-8b93fe086bc4',\n", - " 'x': [],\n", - " 'xaxis': 'x2',\n", - " 'y': [],\n", - " 'yaxis': 'y2'},\n", - " {'marker': {'color': 'black', 'line': {'width': 2}, 'size': 10, 'symbol': 'x'},\n", - " 'mode': 'markers',\n", - " 'name': 'selected',\n", - " 'type': 'scatter',\n", - " 'uid': '23d172a5-07ce-4743-af49-53a87c0cb2a7',\n", - " 'x': [],\n", - " 'xaxis': 'x',\n", - " 'y': [],\n", - " 'yaxis': 'y'}],\n", - " 'layout': {'annotations': [{'font': {'size': 16},\n", - " 'showarrow': False,\n", - " 'text': 'Embedding Space',\n", - " 'x': 0.225,\n", - " 'xanchor': 'center',\n", - " 'xref': 'paper',\n", - " 'y': 1.0,\n", - " 'yanchor': 'bottom',\n", - " 'yref': 'paper'},\n", - " {'font': {'size': 16},\n", - " 'showarrow': False,\n", - " 'text': 'Corresponding Curve',\n", - " 'x': 0.775,\n", - " 'xanchor': 'center',\n", - " 'xref': 'paper',\n", - " 'y': 1.0,\n", - " 'yanchor': 'bottom',\n", - " 'yref': 'paper'}],\n", - " 'height': 400,\n", - " 'template': '...',\n", - " 'width': 900,\n", - " 'xaxis': {'anchor': 'y', 'domain': [0.0, 0.45]},\n", - " 'xaxis2': {'anchor': 'y2', 'domain': [0.55, 1.0]},\n", - " 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0]},\n", - " 'yaxis2': {'anchor': 'x2', 'domain': [0.0, 1.0]}}\n", - "})" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'plotly'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_objects\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgo\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msubplots\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m make_subplots\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'plotly'" + ] } ], "source": [ @@ -1193,7 +2631,7 @@ "from plotly.subplots import make_subplots\n", "from IPython.display import display\n", "\n", - "# Extract 2D latent variables (e.g., log-variance dimensions for visualization)\n", + "# Extract 2D latent variables (log-variance dimensions for visualization)\n", "x = log_var_list[:, 0]\n", "y = log_var_list[:, 1]\n", "\n", @@ -1207,9 +2645,11 @@ "colors = [\"red\", \"green\", \"orange\"]\n", "marker_colors = [colors[c] for c in labels]\n", "\n", - "# Create subplot layout: left = embedding space, right = curve visualization\n", + "# Create subplot layout: left=embedding space, right=curve visualization\n", "fig = make_subplots(\n", - " rows=1, cols=2, subplot_titles=(\"Embedding Space\", \"Corresponding Curve\")\n", + " rows=1,\n", + " cols=2,\n", + " subplot_titles=(\"Embedding Space\", \"Corresponding Curve\"),\n", ")\n", "\n", "# Scatter plot of latent space points\n", @@ -1248,20 +2688,21 @@ "\n", "\n", "def update_curve(trace, points, selector):\n", - " \"\"\"\n", - " Callback function triggered when a point in the embedding space is clicked.\n", + " \"\"\"Callback function triggered when a point in embedding space is clicked.\n", "\n", " Parameters\n", " ----------\n", - " trace : plotly trace\n", + " trace: plotly trace\n", " The scatter plot trace that received the click.\n", - " points : plotly.callbacks.Points\n", + " points: plotly.callbacks.Points\n", " Information about the clicked points.\n", - " selector : plotly.callbacks.InputDeviceState\n", + " selector: plotly.callbacks.InputDeviceState\n", " Additional selection info (not used here).\n", + "\n", " \"\"\"\n", + "\n", " if points.point_inds:\n", - " idx = points.point_inds[0] # index of selected point\n", + " idx = points.point_inds[0] # Index of selected point\n", " selected_color = marker_colors[idx]\n", "\n", " with fig_widget.batch_update():\n", @@ -1282,14 +2723,6 @@ "# Display interactive widget\n", "display(fig_widget)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ab4bea3-370b-4481-8943-e0589c2b891b", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 21e0340310a3dba1d23a310d757276f1db8ab498 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 20:10:36 +0200 Subject: [PATCH 07/17] u --- ...ulation_of_DNA_chains_imaged_via_AFM.ipynb | 2807 ----------------- .../DNA.ipynb} | 0 .../{ => dna-imaging}/psd_noise_model.npz | Bin 3 files changed, 2807 deletions(-) delete mode 100644 tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb rename tutorials/{Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb => dna-imaging/DNA.ipynb} (100%) rename tutorials/{ => dna-imaging}/psd_noise_model.npz (100%) diff --git a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb b/tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb deleted file mode 100644 index b0a9769..0000000 --- a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb +++ /dev/null @@ -1,2807 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cell_md_title", - "metadata": { - "id": "cell_md_title" - }, - "source": [ - "# Simulation of 'DNA chains on a substrate imaged via AFM'\n", - "**This notebook provides a complete example of generating a dataset of simulated DNA chains relxed on a substrate imaged via AFM.**\n", - "\n", - "- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n", - "- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n", - "- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)\n", - "\n", - "Outputs are written to `OUT_DIR/` as `.npy` plus `manifest.csv` (which documents the user inputs used to create the images and masks).\n", - "\n", - ">\n", - "---\n", - "**Cell execution order:** run cells 1 → 12 in sequence. \n", - "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/Prakhar-Dutta/ASAP/blob/e45f2c6830e4c44078ab3183df7dfcbdf1f6fcc1/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_01", - "metadata": { - "id": "cell_md_01" - }, - "source": [ - "# 1. **Imports and global settings**\n", - "## 1.1. Imports\n", - "\n", - "First we import all the libraries that will be necessary for simulation of the images. If you plan to use molecular dynamics for the generation of the DNA chains, then please import the molecular dynamics libraries as well." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "cell_code_01", - "metadata": { - "id": "cell_code_01" - }, - "outputs": [], - "source": [ - "# Install dependencies (uncomment in a fresh environment / Colab)\n", - "# !pip cache purge\n", - "# Chain generation mode\n", - "USE_MD = False # True → OpenMM Langevin dynamics\n", - " # False → fast 2-D persistent-walk (no OpenMM needed)\n", - "# Molecular dynamics\n", - "!pip install -q scipy matplotlib pillow\n", - "if USE_MD:\n", - " !pip install openmm[cuda13] #change the cuda version depending on the CUDA version available on your system.\n", - " import openmm\n", - " import openmm.unit as unit\n", - "\n", - "import os\n", - "from pathlib import Path\n", - "import re\n", - "import csv\n", - "import traceback\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import animation\n", - "from IPython.display import HTML, display\n", - "\n", - "\n", - "\n", - "\n", - "# Image processing\n", - "from scipy.ndimage import (\n", - " gaussian_filter, grey_dilation, distance_transform_edt,\n", - " binary_dilation, median_filter, affine_transform\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "#1.2 Defining the variables\n", - "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." - ], - "metadata": { - "id": "1oW9llw-imr1" - }, - "id": "1oW9llw-imr1" - }, - { - "cell_type": "code", - "source": [ - "# ─────────────────────────────────────────────\n", - "# Global settings (these are the variables that need to be changed to change the appearance of the resulting images)\n", - "# ─────────────────────────────────────────────\n", - "\n", - "# Dataset\n", - "OUT_DIR = \"dna_dataset_1000_4lengths\" # Name of the output directory that you would like to store your images and masks in\n", - "N_SAMPLES = 1000 # Number of samples that will be generated\n", - "BASE_SEED = 0 # Seed for reproducibility\n", - "\n", - "# Image resolution (keep fixed for U-Net)\n", - "NX = 512\n", - "NY = 512\n", - "\n", - "# Ring + MD\n", - "N_BEADS = 90 # default bead count for vizualization\n", - "BEAD_COUNTS = [70, 80, 90, 100] # four chain lengths (1 bead corresponds to 10 base pairs on DNA approximately)\n", - "BOND_LENGTH = 1.0 # distance between beads\n", - "PERSISTENCE_BONDS = 23.0 # used to determine the range of angles which the chain is allowed to turn\n", - "BASE_Z = 5.0 # Height above the substrate that the chain starts at before relaxing\n", - "\n", - "ANGLE_STIFNESS_MULT = 0.6 # If USE_MD is False, then angle stiffness multiplier control chain propogation through space\n", - "\n", - "# MD recording\n", - "N_FRAMES = 200\n", - "STEPS_PER_FRAME = 200 # Between every frame local energy minimization happens for the given number of steps\n", - "\n", - "# AFM rendering\n", - "AFM_KW = dict(\n", - " nx=NX, ny=NY,\n", - " dna_diameter_nm=2.0, # mapping to physical values\n", - " tip_radius_nm=0.01, # Radius of tip (modelled as hemishpere for convolution)\n", - " max_height_nm=6.0, # Maximum height for the colorbar\n", - " target_nm_per_px=0.08, # Matching pixels to size of chain\n", - " max_radius_px=96, # following parameters are used for improving the visualization of the chain\n", - " radius_shrink_px=2.0,\n", - " final_blur_sigma_px=0.20,\n", - " apply_edge_taper=True,\n", - " taper_sigma_nm=0.45,\n", - " taper_floor=0.10,\n", - " add_center_ridge=True,\n", - " ridge_sigma_nm=0.25,\n", - " ridge_amp_nm=0.25,\n", - " grain_nm=0.0,\n", - " grain_sigma_px=0.6,\n", - " enable_crossing_boost=True, # To boost the height of the crossings\n", - " min_separation_beads=12, # Distance to other chain segments to not boost indiscriminately\n", - " boost_window_beads=2, # How many beads to boost the height for\n", - " guaranteed_offset_nm=1.0, # How much to boost\n", - " boost_method=\"additive\", # add to the height of beads being boosted\n", - " boost_profile=\"gaussian\", # Height increase profile\n", - " boost_sigma_beads=None,\n", - " far_clip_nm=2.5, # Clip bead heights for beads that are not part of a crossing\n", - " far_clip_window_beads=3, # How many beads to clip the height for\n", - " return_crossing_info=True, # To ensure that mask information is passed to other functions. Change to False if masks are not needed\n", - " return_masks=True,\n", - ")\n", - "\n", - "# DNA mask\n", - "DNA_MASK_DILATE_PX = 3 # Dilation of mask for better training\n", - "\n", - "# Crossing mask\n", - "CROSS_MIN_SEP_BEADS = 12 # Where to stop for the crossing calculation\n", - "CROSS_SIGMA_CENTER_PX = 5.0 # How many pixels make us the center of a crossing\n", - "CROSS_SIGMA_PERP_PX = 1.8 # Crossing coloring parameter (how many pixels to modulate for the crossing)\n", - "CROSS_CHAIN_EXTENT = 5.0 # For the chain weighting of the mask\n", - "CROSS_CENTER_WEIGHT = 1.7 # For the chain weighting of the mask (chain-weighted guassian)\n", - "CROSS_CHAIN_WEIGHT = 0.9\n", - "CROSS_CLIP_TO_DNA_MASK = True" - ], - "metadata": { - "id": "sSqadS02hzPe" - }, - "id": "sSqadS02hzPe", - "execution_count": 33, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#1.3 Noise\n", - "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", - "\n", - "\n", - "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", - "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", - "\n" - ], - "metadata": { - "id": "-e3QvQzGlKYY" - }, - "id": "-e3QvQzGlKYY" - }, - { - "cell_type": "code", - "source": [ - "#Noise\n", - "TARGET_NOISE_RMS_NM = 0.19 # This variable controls the height of the noise in nanometers and this is added to the final image\n", - "\n", - "\n", - "# Blank SPM noise\n", - "USE_BLANK_SPM_NOISE = True\n", - "BLANK_SPM_PATH = \"/content/20240411_blank_water.0_00000.spm\" # optional; if missing/invalid, PSD fallback noise is used\n", - "\n", - "\n", - "# PSD-model fallback noise (used when no blank .spm file is available)\n", - "USE_PSD_NOISE_FALLBACK = True\n", - "MODEL_PATH = \"/content/psd_noise_model.npz\"\n", - "PSD_NOISE_METHOD = \"mean_full2d\" # or \"lognormal_full2d\" / \"empirical_radial\"\n", - "PSD_NOISE_STD_SCALE = 2.0\n", - "\n", - "#Checking if output directory will be created\n", - "os.makedirs(OUT_DIR, exist_ok=True)\n", - "for _sub in [\"images\", \"dna_masks\", \"cross_masks\", \"meta\"]:\n", - " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", - "\n", - "print(\"Output folder:\", os.path.abspath(OUT_DIR))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "47h81smulGxd", - "outputId": "ba1026d2-8f61-4ce2-f7fe-9b6aae526059" - }, - "id": "47h81smulGxd", - "execution_count": 34, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Output folder: /content/dna_dataset_1000_4lengths\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_02", - "metadata": { - "id": "cell_md_02" - }, - "source": [ - "## 2. Chain Creation (3-D Persistent Random Walk)\n", - "Now we start with creating the DNA chain.\n", - "\n", - "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", - "\n", - "A 3-D plot is shown at the end of the cell so you can inspect the initial geometry immediately." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "cell_code_02", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 607 - }, - "id": "cell_code_02", - "outputId": "3e2e58d9-df87-4b6e-d23e-829c3c60e822" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "def make_tangled_ring_initial(\n", - " seed,\n", - " n_beads=N_BEADS,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " base_z=BASE_Z,\n", - "):\n", - " \"\"\"\n", - " Generates a closed (ring) polymer with a persistent random-walk tangent.\n", - " Each step rotates the current direction by a small random angle around a\n", - " perpendicular axis. Ring closure is enforced by linearly distributing\n", - " the end-to-end gap across all beads. The chain is then lifted to base_z\n", - " so it can relax down to the z=0 substrate during MD.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - "\n", - " steps = np.zeros((n_beads, 3), dtype=np.float32)\n", - " vec = np.array([1.0, 0.0, 0.0], dtype=np.float32) #Initialization of vector for chain propogation\n", - "\n", - " for k in range(n_beads):\n", - " dtheta = rng.normal(scale=np.sqrt(1.0 / persistence_bonds))\n", - " axis = rng.normal(size=3).astype(np.float32) #choose an axis at random for chain propogation in 3D\n", - " axis -= axis.dot(vec) * vec # remove component along vec\n", - " axis_norm = np.linalg.norm(axis)\n", - " if axis_norm < 1e-8:\n", - " axis = np.array([0.0, 0.0, 1.0], dtype=np.float32)\n", - " axis_norm = 1.0\n", - " axis /= axis_norm\n", - " vec = vec * np.cos(dtheta) + np.cross(axis, vec) * np.sin(dtheta) # Calculating vector for chain propogation\n", - " vec /= np.linalg.norm(vec)\n", - " steps[k] = bond_length * vec\n", - "\n", - " coords = np.cumsum(steps, axis=0) # Coordinates for open chain\n", - "\n", - " # Enforce ring closure\n", - " closure_error = coords[-1] - coords[0]\n", - " t = np.linspace(0, 1, n_beads, dtype=np.float32).reshape(-1, 1) # Based on the distance between first and last bead a closure error is propogated to all bead positions to form a closed chain\n", - " coords -= t * closure_error\n", - " coords -= coords.mean(axis=0) # This method works better than directing the chain to go away and then come back to original position\n", - "\n", - " # Lift above substrate\n", - " coords[:, 2] += float(base_z) # Changing z values to lift the chain off the substrate\n", - " return coords.astype(np.float32)\n", - "\n", - "\n", - "# ── Quick visualisation ──────────────────────────────────────────────────────\n", - "_demo_coords = make_tangled_ring_initial(seed=43)\n", - "_cc = np.vstack([_demo_coords, _demo_coords[0]]) # close the ring for plotting\n", - "\n", - "fig = plt.figure(figsize=(7, 6))\n", - "ax = fig.add_subplot(111, projection=\"3d\")\n", - "ax.plot(_cc[:, 0], _cc[:, 1], _cc[:, 2], \"-o\", markersize=3, linewidth=1.2)\n", - "ax.scatter(_cc[0, 0], _cc[0, 1], _cc[0, 2], color=\"red\", s=60, zorder=5, label=\"bead 0\")\n", - "\n", - "# Draw substrate plane\n", - "_xs = np.linspace(_cc[:, 0].min(), _cc[:, 0].max(), 2)\n", - "_ys = np.linspace(_cc[:, 1].min(), _cc[:, 1].max(), 2)\n", - "_X, _Y = np.meshgrid(_xs, _ys)\n", - "ax.plot_surface(_X, _Y, np.zeros_like(_X), alpha=0.15, color=\"cyan\")\n", - "\n", - "ax.set_xlabel(\"x (sim units)\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z\")\n", - "ax.set_title(f\"Initial ring — seed 42, {len(_demo_coords)} beads\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_03", - "metadata": { - "id": "cell_md_03" - }, - "source": [ - "## 3. Molecular Dynamics Relaxation\n", - "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", - "\n", - "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", - "Here we have added:\n", - "\n", - "1. Harmonic bonds\n", - "2. Bending-angle stiffness\n", - "3. A surface-attraction to cause chain relaxation\n", - "4. A hard-wall force to keep chain above substrate\n", - "5. A short-range WCA repulsion to ensure chain does not take non physical configurations\n", - "\n", - "The function then records `n_frames` snapshots which can be used for visualization." - ] - }, - { - "cell_type": "markdown", - "source": [ - "We start with testing the OpenMM installation (can be tricky at times)" - ], - "metadata": { - "id": "wFFhsVZhqU8a" - }, - "id": "wFFhsVZhqU8a" - }, - { - "cell_type": "code", - "source": [ - "## Test the OpenMM installation before running the following cell to ensure that GPU acceleration will work\n", - "import openmm.testInstallation\n", - "\n", - "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", - "try:\n", - " openmm.testInstallation.main()\n", - "except Exception as e:\n", - " print(f\"Test failed: {e}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3NjBSwfAe4Mj", - "outputId": "05dd0ee7-e21b-4b54-c39b-cfacc339576f" - }, - "id": "3NjBSwfAe4Mj", - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "OpenMM Version: 8.5\n", - "Git Revision: b55e60882dffcddb2532cceda4201c0fdc9ec2d5\n", - "\n", - "There are 4 Platforms available:\n", - "\n", - "1 Reference - Successfully computed forces\n", - "2 CPU - Successfully computed forces\n", - "3 CUDA - Error computing forces with CUDA platform\n", - "4 OpenCL - Successfully computed forces\n", - "\n", - "CUDA platform error: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "\n", - "Median difference in forces between platforms:\n", - "\n", - "Reference vs. CPU: 6.29228e-06\n", - "Reference vs. OpenCL: 6.74321e-06\n", - "CPU vs. OpenCL: 7.92217e-07\n", - "\n", - "All differences are within tolerance.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "And then once we are sure that OpenMM is correctly installed we run the MD functions" - ], - "metadata": { - "id": "9XbRNS3TqbKH" - }, - "id": "9XbRNS3TqbKH" - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "cell_code_03", - "metadata": { - "id": "cell_code_03" - }, - "outputs": [], - "source": [ - "def run_md_relaxation(coords0, seed,\n", - " n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME):\n", - " \"\"\"\n", - " Takes initial bead coordinates and runs Langevin dynamics (pure OpenMM).\n", - "\n", - " Forces applied:\n", - " - Harmonic bonds + angle stiffness along the ring\n", - " - Surface attraction (linear in z) + hard wall at z = 0\n", - " - Short-range WCA repulsion between non-bonded beads\n", - "\n", - " Returns a list of (n_beads, 3) coordinate arrays (n_frames + 1 entries;\n", - " frame 0 is the pre-minimisation geometry).\n", - " \"\"\"\n", - " coords0 = np.asarray(coords0, dtype=np.float32)\n", - " n = int(coords0.shape[0])\n", - "\n", - " extent = (coords0.max(axis=0) - coords0.min(axis=0)).max() # how big the bounding box for the molecular dynamics needs to be\n", - " box = float(extent + 10.0)\n", - "\n", - " # ── Reduced-unit constants (matches polychrom temperature=1 convention) ──\n", - " # All lengths in nm, energies in kJ/mol.\n", - " BOLTZ_kJmol = 0.00831445 # kJ / (mol · K)\n", - " temperature = 1.0 # K (reduced; sets energy scale kT ≈ 0.0083 kJ/mol)\n", - " kT = BOLTZ_kJmol * temperature # kJ/mol\n", - " conlen = 1.0 # nm\n", - " mass_amu = 100.0 # Da per bead\n", - " collision_rate = 0.6 # ps⁻¹\n", - " error_tol = 0.005\n", - "\n", - " # ── Build system ─────────────────────────────────────────────────────────\n", - " system = openmm.System()\n", - " system.setDefaultPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - " for _ in range(n):\n", - " system.addParticle(mass_amu)\n", - "\n", - " # Remove COM drift — applied every 50 steps\n", - " system.addForce(openmm.CMMotionRemover(50)) # To ensure CPU and GPU calculation are consistent (since GPU minimizations calculations can suffer from drift)\n", - "\n", - " # ── 1. Harmonic bonds ────────────────────────────────────────────────────\n", - "\n", - " # OpenMM HarmonicBondForce: E = (k/2)·(r − r0)² → k = kT / wiggle²\n", - " bond_L = 1.0 # nm\n", - " wiggle = 0.1 # nm (bondWiggleDistance)\n", - " k_bond = kT / (wiggle ** 2) # kJ/mol/nm²\n", - "\n", - " bond_force = openmm.HarmonicBondForce()\n", - " bond_force.setUsesPeriodicBoundaryConditions(True)\n", - " for i in range(n):\n", - " bond_force.addBond(i, (i + 1) % n, bond_L, k_bond)\n", - " system.addForce(bond_force)\n", - "\n", - " # ── 2. Bending (angle) stiffness ─────────────────────────────────────────\n", - " # E = kT · k_angle · (1 − cos(θ − π)) — equilibrium at θ = π (straight)\n", - " k_angle = 12.0\n", - " angle_force = openmm.CustomAngleForce(\n", - " \"kT * kangle * (1 - cos(theta - 3.141592653589793))\"\n", - " )\n", - " angle_force.addGlobalParameter(\"kT\", kT)\n", - " angle_force.addGlobalParameter(\"kangle\", k_angle)\n", - " for i in range(n):\n", - " angle_force.addAngle((i - 1) % n, i, (i + 1) % n, [])\n", - " system.addForce(angle_force)\n", - "\n", - " # ── 3. Surface: linear attraction (z > 0) + harmonic hard wall (z < 0) ──\n", - " wall_expr = (\n", - " \"kT * (\"\n", - " \" F_pull * z * step(z) + \"\n", - " \" 0.5 * wallK * (z^2) * step(-z)\"\n", - " \")\"\n", - " )\n", - " wall_force = openmm.CustomExternalForce(wall_expr)\n", - " wall_force.addGlobalParameter(\"kT\", kT)\n", - " wall_force.addGlobalParameter(\"F_pull\", 9.0)\n", - " wall_force.addGlobalParameter(\"wallK\", 100.0)\n", - " for i in range(n):\n", - " wall_force.addParticle(i, [])\n", - " system.addForce(wall_force)\n", - "\n", - " # ── 4. WCA repulsion (repulsion between non-bonded pairs only) ──────────────────────────────\n", - " rep_sigma = 1.4 * conlen # nm\n", - " rep_cutoff = 2.3 * conlen # nm\n", - " rep_expr = (\n", - " \"4*eps*((sig/r)^12 - (sig/r)^6 + 0.25) * step(cutoff - r); \"\n", - " \"cutoff = 2^(1.0/6.0) * sig\"\n", - " )\n", - " rep_force = openmm.CustomNonbondedForce(rep_expr)\n", - " rep_force.setNonbondedMethod(openmm.CustomNonbondedForce.CutoffPeriodic)\n", - " rep_force.setCutoffDistance(rep_cutoff)\n", - " rep_force.addGlobalParameter(\"eps\", 1.7 * kT)\n", - " rep_force.addGlobalParameter(\"sig\", rep_sigma)\n", - " for i in range(n):\n", - " rep_force.addParticle([])\n", - " for i in range(n): # exclude bonded neighbours\n", - " rep_force.addExclusion(i, (i + 1) % n)\n", - " system.addForce(rep_force)\n", - "\n", - " # ── Integrator ────────────────────────────────────────────────────────────\n", - " integrator = openmm.VariableLangevinIntegrator(temperature, collision_rate, error_tol)\n", - " integrator.setRandomNumberSeed(int(seed))\n", - "\n", - " # ── Platform selection: OpenCL → CPU fallback ─────────────────────────────\n", - " context = None\n", - " for platform_name in (\"OpenCL\", \"CPU\"): # CUDA did not work on google colab, so using OpenCL here\n", - " try:\n", - " platform = openmm.Platform.getPlatformByName(platform_name)\n", - " context = openmm.Context(system, integrator, platform)\n", - " print(f\"Using {platform_name} platform.\")\n", - " break\n", - " except openmm.OpenMMException as e:\n", - " print(f\" {platform_name} unavailable: {e}\")\n", - " except Exception as e:\n", - " print(f\" {platform_name} failed unexpectedly: {e}\")\n", - "\n", - " if context is None:\n", - " raise RuntimeError(\"No OpenMM platform could be initialised.\")\n", - "\n", - " # ── Set initial positions and box ─────────────────────────────────────────\n", - " positions = [\n", - " openmm.Vec3(float(coords0[i, 0]), float(coords0[i, 1]), float(coords0[i, 2]))\n", - " for i in range(n)\n", - " ]\n", - " context.setPositions(positions)\n", - " context.setPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - "\n", - " # Capture the true initial geometry before any minimisation (just in case the chain reaches substrate even before the 1st frame)\n", - " frames = []\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " # Cap iterations so that extra compute is not used once chain relaxation is complete\n", - " openmm.LocalEnergyMinimizer.minimize(context, tolerance=10, maxIterations=200)\n", - "\n", - " # ── Record frames ─────────────────────────────────────────────────────────\n", - " for _ in range(int(n_frames)):\n", - " integrator.step(int(steps_per_frame))\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " return frames # n_frames + 1 entries (frame 0 = initial geometry)" - ] - }, - { - "cell_type": "markdown", - "source": [ - "##4. Non-MD chain generation\n", - "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." - ], - "metadata": { - "id": "Bx7_OzWRr-l3" - }, - "id": "Bx7_OzWRr-l3" - }, - { - "cell_type": "code", - "source": [ - "def make_ring_2d_persistent_initial(\n", - " seed,\n", - " n_beads,\n", - " bond_length=BOND_LENGTH, # From Cell 1\n", - " persistence_bonds=PERSISTENCE_BONDS, # From Cell 1\n", - " angle_stiffness_mult=ANGLE_STIFNESS_MULT, # New parameter (only in Non-MD, defined in Cell 1)\n", - " z_std=0.08, # Small Gaussian noise for Z\n", - " base_z=0.2, # Low altitude for \"deposited\" look\n", - " angle_limit=np.pi/2 # Constraint to keep walk exploring\n", - "):\n", - " \"\"\"\n", - " Generates a 2D persistent ring and returns it as a list of frames [ (N,3) ].\n", - " Replaces both the initial generation and the MD relaxation cells.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - " n = int(n_beads)\n", - "\n", - " # 1. Angular exploration\n", - " sigma_dtheta = np.sqrt(2.0 / max(persistence_bonds, 1.0)) / max(angle_stiffness_mult, 1e-6) #Which angles are permissible for placements of the next bead\n", - "\n", - " theta0 = rng.uniform(0.0, 2.0 * np.pi)\n", - " # Clipping the normal distribution implements the \"angle constraint\"\n", - " dtheta = rng.normal(0.0, sigma_dtheta, size=n).astype(np.float64)\n", - " dtheta = np.clip(dtheta, -angle_limit, angle_limit)\n", - " thetas = theta0 + np.cumsum(dtheta)\n", - "\n", - " # 2. XY Coordinates\n", - " bonds = np.stack([np.cos(thetas), np.sin(thetas)], axis=1) * float(bond_length)\n", - " xy = np.zeros((n, 2), dtype=np.float64)\n", - " xy[1:] = np.cumsum(bonds[:-1], axis=0)\n", - "\n", - " # 3. Seamless Closure Correction\n", - " # Ensures the last bead connects back to the first with bond_length consistency\n", - " end_error = (xy[-1] + bonds[-1]) - xy[0]\n", - " t = (np.arange(n, dtype=np.float64) / n)[:, None]\n", - " xy = xy - t * end_error\n", - " xy -= xy.mean(axis=0) # Center at origin\n", - "\n", - " # 4. Assembly with Gaussian Z-noise\n", - " coords = np.zeros((n, 3), dtype=np.float32)\n", - " coords[:, 0] = xy[:, 0].astype(np.float32)\n", - " coords[:, 1] = xy[:, 1].astype(np.float32)\n", - " # Assign random small z values to simulate a deposited state\n", - " coords[:, 2] = rng.normal(loc=base_z, scale=z_std, size=n).astype(np.float32)\n", - "\n", - " # Return as a list containing one frame to mimic run_md_relaxation output\n", - " return [coords]" - ], - "metadata": { - "id": "yXs0I0akr948" - }, - "id": "yXs0I0akr948", - "execution_count": 36, - "outputs": [] - }, - { - "cell_type": "markdown", - "id": "cell_md_04", - "metadata": { - "id": "cell_md_04" - }, - "source": [ - "## 5. MD Animation\n", - "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", - "\n", - "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", - "Running this cell will automatically generate and display the animation for a single deterministic chain (seed 0, default bead count)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "cell_code_04", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 228 - }, - "id": "cell_code_04", - "outputId": "21c1cd4a-654f-4fc1-d966-057f8bc2acbc" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generating initial ring and running MD (seed=0)…\n" - ] - }, - { - "output_type": "error", - "ename": "NameError", - "evalue": "name 'make_tangled_ring_initial' is not defined", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_10425/24671148.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# ── Run animation automatically (deterministic seed) ─────────────────────────\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Generating initial ring and running MD (seed=0)…\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0m_anim_coords0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_tangled_ring_initial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m _anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n\u001b[1;32m 61\u001b[0m \u001b[0mn_frames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_FRAMES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'make_tangled_ring_initial' is not defined" - ] - } - ], - "source": [ - "def show_md_animation(frames, interval_ms=150):\n", - " \"\"\"\n", - " Renders an in-notebook HTML5 animation of MD frames.\n", - " Pure visualisation — does not mutate any arrays.\n", - " \"\"\"\n", - " frames = [np.asarray(f, dtype=np.float32) for f in frames]\n", - "\n", - " # Use only the first frame for x/y limits — avoids COM drift blowing the scale\n", - " first = frames[0]\n", - " xy_pad = 5.0\n", - " x_min, x_max = first[:, 0].min() - xy_pad, first[:, 0].max() + xy_pad\n", - " y_min, y_max = first[:, 1].min() - xy_pad, first[:, 1].max() + xy_pad\n", - "\n", - " # Clamp z: floor at -1 (just below substrate), ceiling from initial max + padding\n", - " z_min = -1.0\n", - " z_max = float(first[:, 2].max()) + 3.0\n", - "\n", - " x_plane = np.linspace(x_min, x_max, 2)\n", - " y_plane = np.linspace(y_min, y_max, 2)\n", - " X_plane, Y_plane = np.meshgrid(x_plane, y_plane)\n", - " Z_plane = np.zeros_like(X_plane)\n", - "\n", - " fig = plt.figure(figsize=(6, 6))\n", - " ax = fig.add_subplot(111, projection=\"3d\")\n", - "\n", - " def _set_axes():\n", - " ax.set_xlim(x_min, x_max)\n", - " ax.set_ylim(y_min, y_max)\n", - " ax.set_zlim(z_min, z_max)\n", - " ax.set_xlabel(\"x\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z (nm)\")\n", - "\n", - " def init():\n", - " _set_axes()\n", - " return []\n", - "\n", - " def update(i):\n", - " ax.cla()\n", - " c = frames[i]\n", - " # Clip beads to visible z range for plotting so outliers don't distort\n", - " visible = (c[:, 2] >= z_min) & (c[:, 2] <= z_max)\n", - " cc = np.vstack([c, c[0]])\n", - " ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-\", linewidth=1)\n", - " ax.scatter(c[visible, 0], c[visible, 1], c[visible, 2], s=5)\n", - " ax.plot_surface(X_plane, Y_plane, Z_plane, alpha=0.2, color=\"cyan\")\n", - " _set_axes()\n", - " ax.set_title(f\"Frame {i}/{len(frames)-1} | z ∈ [{c[:,2].min():.1f}, {c[:,2].max():.1f}] nm\")\n", - " return []\n", - "\n", - " ani = animation.FuncAnimation(\n", - " fig, update, frames=len(frames), init_func=init,\n", - " interval=int(interval_ms), blit=False\n", - " )\n", - " plt.close(fig)\n", - " display(HTML(ani.to_jshtml()))\n", - "\n", - "\n", - "# ── Run animation automatically (deterministic seed) ─────────────────────────\n", - "print(\"Generating initial ring and running MD (seed=0)…\")\n", - "_anim_coords0 = make_tangled_ring_initial(seed=0)\n", - "_anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - "print(f\"MD done — {len(_anim_frames)} frames. Rendering animation…\")\n", - "show_md_animation(_anim_frames)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_05", - "metadata": { - "id": "cell_md_05" - }, - "source": [ - "## 6. Height based AFM Rendering\n", - "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, now we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", - "\n", - "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "cell_code_05", - "metadata": { - "id": "cell_code_05" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Low-level geometry helpers\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def _seg_intersect_2d(p1, p2, q1, q2, eps=1e-12):\n", - " \"\"\"\n", - " Strict 2-D segment intersection.\n", - " Returns (hit, pt, t, u) where pt = p1 + t*(p2-p1) = q1 + u*(q2-q1).\n", - " Returns (False, None, None, None) for parallel / collinear segments.\n", - " \"\"\"\n", - " p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n", - " q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n", - " r = p2 - p1; s = q2 - q1\n", - " rxs = r[0]*s[1] - r[1]*s[0]\n", - " if abs(rxs) < eps:\n", - " return False, None, None, None\n", - " qp = q1 - p1\n", - " t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n", - " u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n", - " if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n", - " return True, p1 + t*r, t, u\n", - " return False, None, None, None\n", - "\n", - "\n", - "def _circ_sep(n, i, j):\n", - " \"\"\"Circular distance between bead indices on a ring of n beads.\"\"\"\n", - " d = abs(int(i) - int(j))\n", - " return min(d, n - d)\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Structuring-element / grid helpers\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def disk_footprint(r_px: float):\n", - " \"\"\"Boolean circular footprint of radius r_px pixels.\"\"\"\n", - " if r_px < 0.5:\n", - " return np.ones((1, 1), dtype=bool)\n", - " r = int(np.ceil(r_px))\n", - " y, x = np.ogrid[-r:r+1, -r:r+1]\n", - " return (x*x + y*y) <= r*r\n", - "\n", - "\n", - "def upsample_nn(a, out_shape):\n", - " \"\"\"Nearest-neighbour upsample array a to out_shape.\"\"\"\n", - " ny_out, nx_out = out_shape\n", - " ny_in, nx_in = a.shape\n", - " if (ny_out, nx_out) == (ny_in, nx_in):\n", - " return a\n", - " sy = max(ny_out // ny_in, 1)\n", - " sx = max(nx_out // nx_in, 1)\n", - " a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n", - " return a_up[:ny_out, :nx_out]\n", - "\n", - "\n", - "def choose_effective_grid(xmin, xmax, ymin, ymax, nx, ny,\n", - " target_nm_per_px=0.10):\n", - " \"\"\"\n", - " If the physical pixel size is already coarser than target_nm_per_px,\n", - " use the full grid directly. Otherwise return a reduced grid that\n", - " will be upsampled after rendering.\n", - " \"\"\"\n", - " px = (xmax - xmin) / max(nx - 1, 1)\n", - " py = (ymax - ymin) / max(ny - 1, 1)\n", - " p = 0.5 * (px + py)\n", - " if p >= target_nm_per_px:\n", - " return nx, ny, p\n", - " factor = int(np.ceil(target_nm_per_px / max(p, 1e-12)))\n", - " nx_eff = max(128, nx // factor)\n", - " ny_eff = max(128, ny // factor)\n", - " px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n", - " return nx_eff, ny_eff, px_eff\n", - "\n", - "\n", - "def disk_cap_structure(radius_nm: float, p_nm_per_px: float,\n", - " max_radius_px=96):\n", - " \"\"\"\n", - " Hemispherical-cap structuring element representing the DNA cross-section.\n", - " Each pixel inside the disk gets a height equal to the spherical-cap height\n", - " at that radial distance.\n", - " \"\"\"\n", - " r_px = radius_nm / max(p_nm_per_px, 1e-12)\n", - " R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n", - " y, x = np.ogrid[-R:R+1, -R:R+1]\n", - " d2 = x*x + y*y\n", - " inside = d2 <= (r_px * r_px)\n", - " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", - " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", - " structure[inside] = np.sqrt(\n", - " np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n", - " ).astype(np.float32)\n", - " return inside.astype(bool), structure\n", - "\n", - "\n", - "def disk_sphere_structure(tip_radius_nm: float, p_nm_per_px: float,\n", - " max_radius_px=128):\n", - " \"\"\"\n", - " Spherical-cap structuring element representing the AFM tip geometry.\n", - " Used to convolve the surface with the tip shape.\n", - " \"\"\"\n", - " Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n", - " R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n", - " y, x = np.ogrid[-R:R+1, -R:R+1]\n", - " d2 = x*x + y*y\n", - " inside = d2 <= (Rpx * Rpx)\n", - " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", - " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", - " structure[inside] = (\n", - " np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n", - " - tip_radius_nm\n", - " ).astype(np.float32)\n", - " return inside.astype(bool), structure\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Crossing boost\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def _taper_weight(idx_off, w, profile=\"gaussian\", sigma=None):\n", - " \"\"\"\n", - " Smooth weight in [0, 1] used to fade the crossing height boost along\n", - " the bead-index window of half-width w.\n", - "\n", - " profile:\n", - " 'gaussian' — smooth dome (recommended)\n", - " 'hemisphere' — half-circle taper\n", - " 'linear' — simple linear ramp\n", - " \"\"\"\n", - " if w <= 0:\n", - " return 1.0\n", - " a = abs(idx_off)\n", - " if profile == \"linear\":\n", - " return 1.0 - (a / (w + 1.0))\n", - " if profile == \"hemisphere\":\n", - " x = a / (w + 1.0)\n", - " return float(np.sqrt(max(0.0, 1.0 - x*x)))\n", - " # gaussian (default)\n", - " if sigma is None:\n", - " sigma = max(1e-6, 0.5 * (w + 0.5))\n", - " g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n", - " g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n", - " if g_end < 0.999:\n", - " g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n", - " return g\n", - "\n", - "\n", - "def _collect_intersections(xy_nm, min_separation_beads=12,\n", - " merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Brute-force all segment pairs of a closed ring polyline to find crossings.\n", - " Skips adjacent/shared-vertex pairs and contour-nearby pairs.\n", - " Clusters nearby raw hits via _merge_crossing_clusters.\n", - "\n", - " Returns list of dicts:\n", - " {pt_nm, seg_i, seg_j, t, u, n_hits}\n", - " \"\"\"\n", - " xy = np.asarray(xy_nm, dtype=float)\n", - " n = int(xy.shape[0])\n", - " raw = []\n", - " for i in range(n):\n", - " i2 = (i + 1) % n\n", - " p1, p2 = xy[i], xy[i2]\n", - " for j in range(i + 1, n):\n", - " j2 = (j + 1) % n\n", - " if i == j or i2 == j or j2 == i:\n", - " continue\n", - " if _circ_sep(n, i, j) < int(min_separation_beads):\n", - " continue\n", - " q1, q2 = xy[j], xy[j2]\n", - " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", - " if hit:\n", - " raw.append(dict(pt=np.array(pt, float),\n", - " seg_i=int(i), seg_j=int(j),\n", - " t=float(t), u=float(u)))\n", - "\n", - " clusters = _merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n", - " return clusters\n", - "\n", - "\n", - "def _merge_crossing_clusters(raw_list, merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Greedy proximity-based clustering of raw crossing dicts.\n", - " Each dict must have key 'pt' (2-D float array).\n", - " Returns averaged clusters with representative metadata.\n", - " \"\"\"\n", - " clusters = []\n", - " for r in raw_list:\n", - " pt = r[\"pt\"].astype(float)\n", - " placed = False\n", - " for c in clusters:\n", - " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", - " c[\"pt_sum\"] += pt\n", - " c[\"n\"] += 1\n", - " c[\"items\"].append(r)\n", - " placed = True\n", - " break\n", - " if not placed:\n", - " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n", - "\n", - " out = []\n", - " for c in clusters:\n", - " pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n", - " rep = c[\"items\"][0]\n", - " out.append(dict(\n", - " pt_nm = pt,\n", - " seg_i = int(rep[\"seg_i\"]),\n", - " seg_j = int(rep[\"seg_j\"]),\n", - " t = float(rep[\"t\"]),\n", - " u = float(rep[\"u\"]),\n", - " n_hits = int(c[\"n\"]),\n", - " ))\n", - " return out\n", - "\n", - "\n", - "def boost_crossings_intersections(\n", - " x_nm, y_nm, zc_nm,\n", - " crossings=None,\n", - " min_separation_beads=12,\n", - " boost_window_beads=2,\n", - " guaranteed_offset_nm=1.0,\n", - " boost_method=\"additive\",\n", - " boost_profile=\"gaussian\",\n", - " boost_sigma_beads=None,\n", - " far_clip_nm=2.5,\n", - " far_clip_window_beads=12,\n", - " merge_radius_nm=0.5,\n", - "):\n", - " \"\"\"\n", - " Boost z-height at strand crossings so they are visually distinct in\n", - " the AFM image.\n", - "\n", - " For each crossing:\n", - " - Determines which strand is on top (higher z)\n", - " - Raises top-strand beads in a tapered window to at least\n", - " (bottom_max + guaranteed_offset_nm)\n", - " - Optionally clips non-crossing beads to far_clip_nm\n", - "\n", - " Returns (modified_z, crossing_info_list).\n", - " \"\"\"\n", - " x = np.asarray(x_nm, dtype=np.float32)\n", - " y = np.asarray(y_nm, dtype=np.float32)\n", - " z = np.asarray(zc_nm, dtype=np.float32).copy()\n", - " n = int(z.shape[0])\n", - " if n < 5:\n", - " return z.astype(np.float32), []\n", - "\n", - " if crossings is None:\n", - " xy = np.stack([x, y], axis=1)\n", - " crossings = _collect_intersections(\n", - " xy,\n", - " min_separation_beads=int(min_separation_beads),\n", - " merge_radius_nm=float(merge_radius_nm),\n", - " )\n", - "\n", - " def get_segment_indices(center, w):\n", - " return np.array([(center + k) % n for k in range(-w, w+1)],\n", - " dtype=np.int32)\n", - "\n", - " crossing_info = []\n", - " crossing_centers = []\n", - " w = int(boost_window_beads)\n", - "\n", - " for c in crossings:\n", - " i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n", - " i2 = (i + 1) % n; j2 = (j + 1) % n\n", - " ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n", - "\n", - " bead_i = i if ti < 0.5 else i2\n", - " bead_j = j if uj < 0.5 else j2\n", - " zi = float((1.0 - ti) * z[i] + ti * z[i2])\n", - " zj = float((1.0 - uj) * z[j] + uj * z[j2])\n", - "\n", - " top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j, bead_i)\n", - " top_seg = get_segment_indices(top_center, w)\n", - " bottom_seg = get_segment_indices(bottom_center, w)\n", - "\n", - " bottom_local_max = float(z[bottom_seg].max())\n", - " top_local_max = float(z[top_seg].max())\n", - "\n", - " if boost_method == \"absolute\":\n", - " target_height = bottom_local_max + float(guaranteed_offset_nm)\n", - " else:\n", - " target_height = max(top_local_max, bottom_local_max) + float(guaranteed_offset_nm)\n", - "\n", - " for off in range(-w, w+1):\n", - " k = (top_center + off) % n\n", - " wt = float(_taper_weight(off, w,\n", - " profile=boost_profile,\n", - " sigma=boost_sigma_beads))\n", - " z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n", - "\n", - " crossing_centers.extend([int(top_center), int(bottom_center)])\n", - " crossing_info.append(dict(\n", - " pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n", - " seg_i = int(i),\n", - " seg_j = int(j),\n", - " top_center = int(top_center),\n", - " bottom_center= int(bottom_center),\n", - " ))\n", - "\n", - " # Clip beads far from any crossing\n", - " if crossing_centers and far_clip_nm is not None:\n", - " centers = np.array(crossing_centers, dtype=np.int32)\n", - " r = int(far_clip_window_beads)\n", - " for k in range(n):\n", - " d = int(np.min(np.minimum(np.abs(k - centers),\n", - " n - np.abs(k - centers))))\n", - " if d > r:\n", - " z[k] = min(z[k], float(far_clip_nm))\n", - "\n", - " return z.astype(np.float32), crossing_info\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Main AFM renderer\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def create_z_based_afm(\n", - " coords,\n", - " nx=800, ny=800,\n", - " dna_diameter_nm=2.0,\n", - " tip_radius_nm=0.01,\n", - " max_height_nm=6.0,\n", - " target_nm_per_px=0.08,\n", - " max_radius_px=96,\n", - " radius_shrink_px=2.0,\n", - " final_blur_sigma_px=0.20,\n", - " apply_edge_taper=True,\n", - " taper_sigma_nm=0.45,\n", - " taper_floor=0.10,\n", - " add_center_ridge=True,\n", - " ridge_sigma_nm=0.25,\n", - " ridge_amp_nm=0.25,\n", - " grain_nm=0.0,\n", - " grain_sigma_px=0.6,\n", - " grain_seed=1,\n", - " enable_crossing_boost=True,\n", - " min_separation_beads=12,\n", - " boost_window_beads=2,\n", - " guaranteed_offset_nm=1.0,\n", - " boost_method=\"additive\",\n", - " boost_profile=\"gaussian\",\n", - " boost_sigma_beads=None,\n", - " crossings_precomputed=None,\n", - " far_clip_nm=2.5,\n", - " far_clip_window_beads=12,\n", - " return_crossing_info=False,\n", - " return_masks=True,\n", - " extent=None,\n", - "):\n", - " \"\"\"\n", - " Master AFM renderer. Pipeline:\n", - "\n", - " 1. Project 3-D bead coords onto a 2-D effective grid\n", - " 2. Optionally boost crossing heights (boost_crossings_intersections)\n", - " 3. Rasterise closed polyline with z-interpolation\n", - " 4. Apply disk_cap_structure (DNA cylindrical cross-section)\n", - " 5. Apply disk_sphere_structure (tip-sample convolution)\n", - " 6. Clip, blur, edge-taper, center-ridge\n", - " 7. Optional synthetic grain noise\n", - " 8. Upsample to requested (nx, ny)\n", - "\n", - " Returns (afm_image, debug_dict) when return_masks=True.\n", - " \"\"\"\n", - " x, y, z = coords.T\n", - " if extent is None:\n", - " xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n", - " ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n", - " else:\n", - " xmin, xmax, ymin, ymax = extent\n", - "\n", - " nx_eff, ny_eff, p_eff = choose_effective_grid(\n", - " xmin, xmax, ymin, ymax, nx, ny, target_nm_per_px)\n", - "\n", - " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n", - " 0, nx_eff - 1)\n", - " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n", - " 0, ny_eff - 1)\n", - "\n", - " zc = (z - z.min()).astype(np.float32)\n", - "\n", - " # ── Crossing boost ───────────────────────────────────────────────────────\n", - " crossing_info = []\n", - " if enable_crossing_boost:\n", - " zc, crossing_info = boost_crossings_intersections(\n", - " x.astype(np.float32), y.astype(np.float32), zc,\n", - " crossings=crossings_precomputed,\n", - " min_separation_beads=int(min_separation_beads),\n", - " boost_window_beads=int(boost_window_beads),\n", - " guaranteed_offset_nm=float(guaranteed_offset_nm),\n", - " boost_method=str(boost_method),\n", - " boost_profile=str(boost_profile),\n", - " boost_sigma_beads=boost_sigma_beads,\n", - " far_clip_nm=far_clip_nm,\n", - " far_clip_window_beads=int(far_clip_window_beads),\n", - " )\n", - "\n", - " # ── Rasterise polyline ───────────────────────────────────────────────────\n", - " z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n", - " line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n", - " npts = len(ix)\n", - " for k in range(npts):\n", - " k2 = (k + 1) % npts\n", - " x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n", - " x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n", - " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", - " if n_seg <= 1:\n", - " z_line[y0, x0] = max(z_line[y0, x0], z0)\n", - " line_mask[y0, x0] = True\n", - " continue\n", - " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", - " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", - " zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n", - " if xs.size > 1:\n", - " keep = np.ones(xs.shape[0], dtype=bool)\n", - " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", - " xs, ys, zs = xs[keep], ys[keep], zs[keep]\n", - " z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n", - " line_mask[ys, xs] = True\n", - "\n", - " # ── DNA cap dilation ─────────────────────────────────────────────────────\n", - " r_eff = max(0.5*float(dna_diameter_nm) - float(radius_shrink_px)*p_eff, 0.2)\n", - " fp_dna, cap = disk_cap_structure(r_eff, p_eff, max_radius_px=max_radius_px)\n", - " surface = grey_dilation(z_line, footprint=fp_dna,\n", - " structure=cap).astype(np.float32)\n", - " dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna) > 0\n", - " surface[~dna_region] = 0.0\n", - "\n", - " # ── Tip convolution ──────────────────────────────────────────────────────\n", - " r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n", - " if r_tip_px >= 0.5:\n", - " fp_tip, tip_struct = disk_sphere_structure(\n", - " float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n", - " surface = grey_dilation(surface, footprint=fp_tip,\n", - " structure=tip_struct).astype(np.float32)\n", - "\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - " if final_blur_sigma_px > 0:\n", - " surface = gaussian_filter(surface,\n", - " sigma=float(final_blur_sigma_px)).astype(np.float32)\n", - "\n", - " # ── Edge taper + center ridge ────────────────────────────────────────────\n", - " if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n", - " dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n", - " dist_nm = dist_px * np.float32(p_eff)\n", - " if apply_edge_taper:\n", - " sig = max(float(taper_sigma_nm), 1e-6)\n", - " factor = taper_floor + (1.0 - taper_floor) * np.exp(\n", - " -(dist_nm**2) / (2.0 * sig**2))\n", - " surface[dna_region] *= factor[dna_region]\n", - " if add_center_ridge and ridge_amp_nm > 0:\n", - " rs = max(float(ridge_sigma_nm), 1e-6)\n", - " ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n", - " surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - "\n", - " # ── Synthetic grain ──────────────────────────────────────────────────────\n", - " if grain_nm and grain_nm > 0:\n", - " rng = np.random.default_rng(int(grain_seed))\n", - " n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n", - " if grain_sigma_px and grain_sigma_px > 0:\n", - " n = gaussian_filter(n, sigma=float(grain_sigma_px)).astype(np.float32)\n", - " std = float(n[dna_region].std()) if np.any(dna_region) else float(n.std())\n", - " if std > 1e-9:\n", - " n /= np.float32(std)\n", - " surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - "\n", - " # ── Upsample ─────────────────────────────────────────────────────────────\n", - " if (nx_eff, ny_eff) != (nx, ny):\n", - " surface_up = upsample_nn(surface, (ny, nx)).astype(np.float32)\n", - " line_mask_up = upsample_nn(line_mask.astype(np.uint8), (ny, nx)) > 0\n", - " dna_region_up = upsample_nn(dna_region.astype(np.uint8), (ny, nx)) > 0\n", - " else:\n", - " surface_up = surface\n", - " line_mask_up = line_mask\n", - " dna_region_up = dna_region\n", - "\n", - " if return_masks:\n", - " debug = {\n", - " \"line_mask\": line_mask_up,\n", - " \"dna_region\": dna_region_up,\n", - " \"extent\": (xmin, xmax, ymin, ymax),\n", - " \"p_nm_per_px\": float((xmax - xmin) / max(nx - 1, 1)),\n", - " }\n", - " if return_crossing_info:\n", - " debug[\"crossings\"] = crossing_info\n", - " return surface_up, debug\n", - "\n", - " return surface_up, (xmin, xmax, ymin, ymax)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_06", - "metadata": { - "id": "cell_md_06" - }, - "source": [ - "## 7. Noise Functions\n", - "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", - "\n", - "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", - "2. If no blank `.spm` file is supplied, the notebook falls back to a PSD-based synthetic noise model.\n", - "\n", - "Note: Topostats is an AFM imaging software package developed at the University of Sheffield which is used for filtering of AFM data. We desribe functions that perform operations similar to Topostats to clean the noise data from the blank files before adding it to the AFM_img.\n", - "\n", - "### Mathematics of PSD-Matched Noise Synthesis\n", - "\n", - "All three methods in the code utilize **Frequency-Domain Synthesis**. The core principle is that the height field $z(x, y)$ can be generated from a target Power Spectral Density (PSD) using the inverse Fourier Transform.\n", - "\n", - "#### 1. General Synthesis Workflow\n", - "For an output image of shape $(N_y, N_x)$:\n", - "1. **Define a PSD**: Let $S(f_x, f_y)$ be the target power spectrum.\n", - "2. **Generate Complex Spectrum**: A complex field $Z(f_x, f_y)$ is created in the frequency domain:\n", - " $$Z(f_x, f_y) = \\sqrt{S(f_x, f_y)} \\cdot e^{i \\phi(f_x, f_y)}$$\n", - " where $\\phi$ is a random phase drawn from a uniform distribution $U(0, 2\\pi)$.\n", - "3. **Inverse Transform**: The spatial noise $z(x, y)$ is the real part of the Inverse Fast Fourier Transform (IFFT) of $Z$.\n", - "4. **RMS Scaling**: The image is normalized such that its standard deviation matches the target Root Mean Square (RMS) roughness." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "cell_code_06", - "metadata": { - "id": "cell_code_06" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Parsing the .spm file for height data\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def robust_range(a):\n", - " \"\"\"Return (p1, p99, p99-p1) of finite values in array a.\"\"\"\n", - " a = np.asarray(a, dtype=np.float32)\n", - " p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n", - " return float(p1), float(p99), float(p99 - p1)\n", - "\n", - "\n", - "def looks_like_afm_height(img):\n", - " \"\"\"True if the image has a finite, positive, sane dynamic range.\"\"\"\n", - " p1, p99, rng = robust_range(img)\n", - " return np.isfinite(rng) and 0 < rng <= 1e9\n", - "\n", - "\n", - "def maybe_to_nm(img):\n", - " \"\"\"Auto-convert from metres to nm if values look metre-scale.\"\"\"\n", - " p1, p99, rng = robust_range(img)\n", - " mx = max(abs(p1), abs(p99))\n", - " if mx < 1e-3:\n", - " return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n", - " return img.astype(np.float32), \"as-is (nm or counts)\"\n", - "\n", - "\n", - "def parse_blocks(filename, max_blocks=128):\n", - " \"\"\"\n", - " Read a Bruker .spm file and parse binary data-block metadata.\n", - " Returns (raw_bytes, header_string, block_list).\n", - " \"\"\"\n", - " raw = open(filename, \"rb\").read()\n", - " i_end = raw.find(b\"\\x1a\")\n", - " if i_end == -1:\n", - " i_end = min(len(raw), 400_000)\n", - " header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n", - "\n", - " matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n", - " if not matches:\n", - " raise RuntimeError(\"No 'Data offset' found in header.\")\n", - "\n", - " blocks = []\n", - " for k, pos in enumerate(matches[:max_blocks]):\n", - " win = header[max(0, pos-2500): min(len(header), pos+2500)]\n", - "\n", - " def grab_int(key):\n", - " m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n", - " return int(m.group(1)) if m else None\n", - "\n", - " def grab_float(key):\n", - " m = re.search(\n", - " rf\"{re.escape(key)}\\s*:\\s*([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\",\n", - " win)\n", - " return float(m.group(1)) if m else None\n", - "\n", - " name_candidates = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n", - " name = name_candidates[-1].strip() if name_candidates else None\n", - "\n", - " off = grab_int(\"Data offset\")\n", - " length = grab_int(\"Data length\")\n", - " bpp = grab_int(\"Bytes/pixel\")\n", - " nx_b = grab_int(\"Samps/line\")\n", - " ny_b = grab_int(\"Number of lines\")\n", - " zscale = grab_float(\"Z scale\")\n", - " zoff = grab_float(\"Z offset\")\n", - "\n", - " if None in (off, length, bpp, nx_b, ny_b):\n", - " continue\n", - " blocks.append(dict(name=name or f\"block_{k}\",\n", - " off=off, length=length, bpp=bpp,\n", - " nx=nx_b, ny=ny_b,\n", - " zscale=zscale, zoff=zoff))\n", - "\n", - " uniq, seen = [], set()\n", - " for b in blocks:\n", - " if b[\"off\"] not in seen:\n", - " uniq.append(b); seen.add(b[\"off\"])\n", - " if not uniq:\n", - " raise RuntimeError(\"Found 'Data offset' strings but no parseable blocks.\")\n", - " return raw, header, uniq\n", - "\n", - "\n", - "def read_block_candidates(raw, b):\n", - " \"\"\"\n", - " Decode one binary data block, trying all plausible dtypes.\n", - " Applies z-scale / z-offset if present.\n", - " Returns list of (dtype_str, image_array).\n", - " \"\"\"\n", - " off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n", - " nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n", - " blob = raw[off:off+length]\n", - " if len(blob) < length:\n", - " raise RuntimeError(\"Truncated block.\")\n", - "\n", - " n = nx_b * ny_b\n", - " cands = []\n", - " dtype_map = {2: [\"i2\", \"u2\"],\n", - " 4: [\"i4\", \"f4\"],\n", - " 1: [\"u1\"]}\n", - " for dt in dtype_map.get(bpp, []):\n", - " arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n", - " if arr.size != n:\n", - " continue\n", - " cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n", - "\n", - " out = []\n", - " for dt, img in cands:\n", - " z = img\n", - " if b.get(\"zscale\") is not None:\n", - " z = z * np.float32(b[\"zscale\"])\n", - " if b.get(\"zoff\") is not None:\n", - " z = z + np.float32(b[\"zoff\"])\n", - " out.append((dt, z))\n", - " return out\n", - "\n", - "\n", - "def choose_best_height_image(raw, blocks, verbose=True):\n", - " \"\"\"\n", - " Iterate all blocks and dtype candidates, pick the one that:\n", - " - passes looks_like_afm_height\n", - " - has the highest log-variance score\n", - " \"\"\"\n", - " best, best_score = None, -np.inf\n", - " for b in blocks:\n", - " try:\n", - " for dt, img in read_block_candidates(raw, b):\n", - " if not looks_like_afm_height(img):\n", - " continue\n", - " p1, p99, rng = robust_range(img)\n", - " var = float(np.var(img))\n", - " score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n", - " if score > best_score:\n", - " best_score = score\n", - " best = (b, dt, img, (p1, p99, rng, var))\n", - " except Exception:\n", - " continue\n", - "\n", - " if best is None:\n", - " raise RuntimeError(\"Could not find a plausible AFM height image decode.\")\n", - " b, dt, img, stats = best\n", - " if verbose:\n", - " p1, p99, rng, var = stats\n", - " print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n", - " f\"bpp={b['bpp']} decode={dt}\")\n", - " print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, range={rng:.3g}, var={var:.3g}\")\n", - " return b, img\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# TopoStats-like background / noise extraction\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def plane_remove(z, mask=None):\n", - " \"\"\"Fit and subtract a tilted plane from z (optionally background-only).\"\"\"\n", - " ny, nx = z.shape\n", - " Y, X = np.mgrid[0:ny, 0:nx]\n", - " A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n", - " b = z.ravel()\n", - " if mask is not None:\n", - " m = mask.ravel().astype(bool)\n", - " A, b = A[m], b[m]\n", - " C, *_ = np.linalg.lstsq(A, b, rcond=None)\n", - " plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n", - " return (z - plane).astype(np.float32)\n", - "\n", - "\n", - "def line_flatten_median(z, mask=None):\n", - " \"\"\"Subtract per-row median (background pixels only if mask given).\"\"\"\n", - " out = z.astype(np.float32, copy=True)\n", - " for i in range(out.shape[0]):\n", - " if mask is None or not np.any(mask[i]):\n", - " med = np.median(out[i])\n", - " else:\n", - " med = np.median(out[i, mask[i]])\n", - " out[i] -= np.float32(med)\n", - " return out\n", - "\n", - "\n", - "def feature_mask(z, smooth_sigma_px=3.0, thresh_sigma=3.0, dilate_px=4):\n", - " \"\"\"\n", - " Detect features (DNA, particles) as pixels deviating by more than\n", - " thresh_sigma MADs from the smooth-residual background.\n", - " \"\"\"\n", - " z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", - " resid = (z - z_s).astype(np.float32)\n", - " med = float(np.median(resid))\n", - " mad = float(np.median(np.abs(resid - med)))\n", - " sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n", - " feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n", - " if dilate_px and dilate_px > 0:\n", - " r = int(dilate_px)\n", - " yy, xx = np.ogrid[-r:r+1, -r:r+1]\n", - " fp = (xx*xx + yy*yy) <= r*r\n", - " feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n", - " return feat\n", - "\n", - "\n", - "def inpaint_simple(z, mask, smooth_sigma_px=8.0):\n", - " \"\"\"Replace masked pixels with a heavily blurred background estimate.\"\"\"\n", - " bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", - " out = z.copy()\n", - " out[mask] = bg[mask]\n", - " return out\n", - "\n", - "\n", - "def bandpass_noise(z, low_sigma_px=1.0, high_sigma_px=30.0):\n", - " \"\"\"\n", - " Bandpass filter: subtract large-scale trend from small-scale smoothed\n", - " image to isolate mid-frequency noise texture.\n", - " \"\"\"\n", - " low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n", - " high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n", - " return (low - high).astype(np.float32)\n", - "\n", - "\n", - "def extract_topostats_like_noise(\n", - " z_in,\n", - " median_size=3,\n", - " bg_quantile=40,\n", - " feature_sigma_px=3.0,\n", - " feature_thresh_sigma=3.0,\n", - " feature_dilate_px=4,\n", - " inpaint_sigma_px=10.0,\n", - " band_low_sigma_px=0.8,\n", - " band_high_sigma_px=35.0,\n", - " target_rms_nm=None,\n", - "):\n", - " \"\"\"\n", - " Full noise-extraction pipeline:\n", - " plane-flatten → line-flatten → detect features → inpaint → bandpass.\n", - " Optionally rescale to target RMS amplitude.\n", - " Returns (rms_nm, noise_texture, z_flattened, feature_mask).\n", - " \"\"\"\n", - " z = z_in.astype(np.float32)\n", - " if median_size and median_size > 1:\n", - " z = median_filter(z, size=int(median_size)).astype(np.float32)\n", - "\n", - " q = np.percentile(z, float(bg_quantile))\n", - " bg0 = z <= q\n", - "\n", - " z_flat = plane_remove(z, mask=bg0)\n", - " z_flat = line_flatten_median(z_flat, mask=bg0)\n", - "\n", - " feat = feature_mask(z_flat,\n", - " smooth_sigma_px=feature_sigma_px,\n", - " thresh_sigma=feature_thresh_sigma,\n", - " dilate_px=feature_dilate_px)\n", - " z_bg = inpaint_simple(z_flat, feat, smooth_sigma_px=inpaint_sigma_px)\n", - " tex = bandpass_noise(z_bg,\n", - " low_sigma_px=band_low_sigma_px,\n", - " high_sigma_px=band_high_sigma_px)\n", - "\n", - " bg = ~feat\n", - " rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n", - " if target_rms_nm is not None:\n", - " target = float(target_rms_nm)\n", - " if rms > 1e-12:\n", - " tex *= target / rms\n", - " rms = target\n", - "\n", - " return rms, tex.astype(np.float32), z_flat.astype(np.float32), feat.astype(bool)\n", - "\n", - "\n", - "def tile_or_crop(tex, out_shape, seed=0):\n", - " \"\"\"\n", - " Tile tex to at least out_shape, then take a random (deterministic) crop.\n", - " \"\"\"\n", - " ty, tx = tex.shape\n", - " oy, ox = out_shape\n", - " if (ty, tx) == (oy, ox):\n", - " return tex\n", - " tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n", - " rng = np.random.default_rng(seed)\n", - " y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n", - " x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n", - " return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n", - "\n", - "\n", - "def load_blank_spm_noise(blank_path, target_rms_nm=0.06, verbose=True):\n", - " \"\"\"\n", - " High-level loader: parse SPM file → select best image → convert to nm\n", - " → extract background noise texture.\n", - " Returns (rms_nm, noise_texture, diagnostics_dict).\n", - " \"\"\"\n", - " raw, header, blocks = parse_blocks(blank_path)\n", - " b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n", - " img_nm, unit_note = maybe_to_nm(img0)\n", - " if verbose:\n", - " print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n", - "\n", - " rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n", - " img_nm,\n", - " median_size=3,\n", - " bg_quantile=45,\n", - " feature_sigma_px=3.0,\n", - " feature_thresh_sigma=3.0,\n", - " feature_dilate_px=6,\n", - " inpaint_sigma_px=10.0,\n", - " band_low_sigma_px=0.7,\n", - " band_high_sigma_px=40.0,\n", - " target_rms_nm=target_rms_nm,\n", - " )\n", - " diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n", - " \"height_nm\": img_nm, \"unit_note\": unit_note}\n", - " if verbose:\n", - " print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n", - " f\"tex shape: {noise_tex.shape}\")\n", - " return rms_nm, noise_tex, diag\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# PSD-based fallback noise synthesis\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "TARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\n", - "EPS = 1e-12 #Safety buffer to ensure no division by 0 or square roots producing NaN values\n", - "\n", - "\n", - "def load_model(path=MODEL_PATH):\n", - " \"\"\"Load the PSD noise model from a .npz file.\"\"\"\n", - " data = np.load(path)\n", - " return {\n", - " \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n", - " \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n", - " \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n", - " \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n", - " }\n", - "\n", - "\n", - "def generate_noise(\n", - " seed,\n", - " target_shape=TARGET_SHAPE,\n", - " target_rms_nm=TARGET_NOISE_RMS_NM,\n", - " method=\"mean_full2d\",\n", - " std_scale=1.0,\n", - " model=None,\n", - " model_path=MODEL_PATH,\n", - "):\n", - " \"\"\"\n", - " Generate a single random AFM-like background noise image from the PSD model.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed : int\n", - " RNG seed for reproducibility.\n", - " target_shape : (rows, cols)\n", - " Output image shape. The PSD is resized automatically if it differs\n", - " from the shape used when building the model.\n", - " target_rms_nm : float or None\n", - " RMS amplitude in nm. None -> draw from the blank-file ensemble.\n", - " method : str\n", - " 'mean_full2d' -- mean log-PSD; randomness from phase only (recommended).\n", - " 'lognormal_full2d' -- draw a random PSD from the fitted log-normal\n", - " distribution (more sample-to-sample variation).\n", - " 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n", - " std_scale : float\n", - " Multiplier on log_psd_std for lognormal method (>1 -> more variation).\n", - " model : dict or None\n", - " Pre-loaded model dict. If None, loaded from model_path on each call.\n", - " model_path : path-like\n", - " Path to the .npz file (used only when model is None).\n", - "\n", - " Returns\n", - " -------\n", - " z : np.ndarray, shape target_shape, dtype float32\n", - " Height noise field in nm.\n", - " \"\"\"\n", - " if model is None:\n", - " model = load_model(model_path)\n", - "\n", - " rng = np.random.default_rng(int(seed))\n", - " out_shape = tuple(target_shape)\n", - "\n", - " if method == \"mean_full2d\":\n", - " psd_sample = np.exp(model[\"log_psd_mean\"])\n", - "\n", - " elif method == \"lognormal_full2d\":\n", - " noise = rng.standard_normal(model[\"log_psd_mean\"].shape).astype(np.float32)\n", - " psd_sample = np.exp(model[\"log_psd_mean\"] + std_scale * model[\"log_psd_std\"] * noise)\n", - "\n", - " elif method == \"empirical_radial\":\n", - " radial_psd = np.exp(model[\"log_psd_mean\"].mean(axis=1))\n", - " radial_freq_for_interp = np.abs(np.fft.fftfreq(model[\"log_psd_mean\"].shape[0], d=1.0))\n", - "\n", - " fy = np.fft.fftfreq(out_shape[0], d=1.0)\n", - " fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n", - " fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n", - " kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n", - "\n", - " psd_sample = np.interp(\n", - " kr.ravel(),\n", - " radial_freq_for_interp,\n", - " radial_psd,\n", - " left=float(radial_psd[0]),\n", - " right=float(radial_psd[-1]),\n", - " ).reshape(kr.shape).astype(np.float32)\n", - "\n", - " else:\n", - " raise ValueError(\n", - " f\"Unknown method {method!r}. \"\n", - " \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n", - " )\n", - "\n", - " psd_ny, psd_nx = psd_sample.shape\n", - " out_ny, out_nx = out_shape\n", - " rfft_nx = out_nx // 2 + 1\n", - "\n", - " if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n", - " from scipy.ndimage import zoom\n", - " psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx), order=1)\n", - "\n", - " psd_sample = psd_sample.astype(np.float32)\n", - "\n", - " phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n", - " spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n", - "\n", - " z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n", - " z -= np.float32(np.mean(z))\n", - "\n", - " target = float(rng.choice(model[\"rms_values_nm\"])) if target_rms_nm is None else float(target_rms_nm)\n", - " cur = float(np.std(z))\n", - " if cur > EPS:\n", - " z *= target / cur\n", - "\n", - " return z\n", - "\n", - "\n", - "def sample_noise_image(noise_source, noise_asset, seed, out_shape, target_rms_nm):\n", - " \"\"\"Return a per-sample noise image from either blank.spm or PSD fallback.\"\"\"\n", - " if not USE_BLANK_SPM_NOISE or noise_asset is None:\n", - " return None\n", - "\n", - " if noise_source == \"blank_spm\":\n", - " noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n", - " return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n", - "\n", - " if noise_source == \"psd_model\":\n", - " return generate_noise(\n", - " seed=seed,\n", - " target_shape=out_shape,\n", - " target_rms_nm=target_rms_nm,\n", - " method=PSD_NOISE_METHOD,\n", - " std_scale=PSD_NOISE_STD_SCALE,\n", - " model=noise_asset,\n", - " ).astype(np.float32)\n", - "\n", - " return None\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_07", - "metadata": { - "id": "cell_md_07" - }, - "source": [ - "##8. DNA Ground-Truth Mask\n", - "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", - "\n", - "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", - "\n", - "We also dilate the mask by a value of **DNA_MASK_DILATE_PX** to ensure that the mask has enough width for optimal training." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "cell_code_07", - "metadata": { - "id": "cell_code_07" - }, - "outputs": [], - "source": [ - "def nm_to_px(coords_xy_nm, extent, nx, ny):\n", - " \"\"\"Map (x, y) in nm to integer pixel indices (ix, iy).\"\"\"\n", - " x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n", - " xmin, xmax, ymin, ymax = extent\n", - " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n", - " 0, nx - 1)\n", - " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n", - " 0, ny - 1)\n", - " return ix, iy\n", - "\n", - "\n", - "def rasterize_closed_polyline_mask(coords, extent, nx, ny):\n", - " \"\"\"\n", - " Return a 1-px-wide binary raster of the closed bead polyline.\n", - " Each consecutive bead pair is drawn by dense linear interpolation;\n", - " duplicate pixels are de-duplicated.\n", - " \"\"\"\n", - " coords = np.asarray(coords, dtype=np.float32)\n", - " ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n", - " out = np.zeros((ny, nx), dtype=bool)\n", - " npts = len(ix)\n", - " for k in range(npts):\n", - " k2 = (k + 1) % npts\n", - " x0, y0 = int(ix[k]), int(iy[k])\n", - " x1, y1 = int(ix[k2]), int(iy[k2])\n", - " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", - " if n_seg <= 1:\n", - " out[y0, x0] = True\n", - " continue\n", - " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", - " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", - " if xs.size > 1:\n", - " keep = np.ones(xs.shape[0], dtype=bool)\n", - " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", - " xs, ys = xs[keep], ys[keep]\n", - " out[ys, xs] = True\n", - " return out\n", - "\n", - "\n", - "def make_dna_mask_from_beads(coords, extent, nx, ny, dilate_px=3):\n", - " \"\"\"\n", - " Rasterise the polyline and dilate by dilate_px pixels.\n", - " Returns a uint8 mask (0/1).\n", - " \"\"\"\n", - " line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n", - " if dilate_px and dilate_px > 0:\n", - " line = binary_dilation(line, structure=disk_footprint(float(dilate_px)))\n", - " return line.astype(np.uint8)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_08", - "metadata": { - "id": "cell_md_08" - }, - "source": [ - "## 9. Crossing Detection & Crossing Mask\n", - "\n", - "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", - "\n", - "We have set the masks for the crossings as chain weighted gaussians so that the machine learning models can have more information about the crossings and thus have better predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "cell_code_08", - "metadata": { - "id": "cell_code_08" - }, - "outputs": [], - "source": [ - "def _canon_axis(u):\n", - " \"\"\"\n", - " Normalise a 2-D vector and canonicalise its sign so that +v and -v\n", - " are treated as the same axis direction.\n", - " Returns None if the vector is degenerate.\n", - " \"\"\"\n", - " u = np.asarray(u, dtype=float)\n", - " nrm = float(np.linalg.norm(u))\n", - " if nrm < 1e-12:\n", - " return None\n", - " u = u / nrm\n", - " if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n", - " u = -u\n", - " return u\n", - "\n", - "\n", - "def find_polyline_crossings(coords_xy, min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Detect all strand crossings of a closed ring polyline in XY.\n", - "\n", - " Skips adjacent / shared-vertex segment pairs and pairs whose circular\n", - " contour separation is less than min_separation_beads.\n", - " Nearby raw hits are clustered via _merge_crossing_clusters.\n", - "\n", - " Returns list of dicts:\n", - " {\n", - " pt_nm : (2,) float32 — crossing position in nm\n", - " axes_nm : list of unit vectors — chain directions at the crossing\n", - " seg_i : int — first segment index\n", - " seg_j : int — second segment index\n", - " t : float — parametric position along segment i\n", - " u : float — parametric position along segment j\n", - " n_hits : int — raw intersection count merged into this cluster\n", - " }\n", - " \"\"\"\n", - " xy = np.asarray(coords_xy, dtype=float)\n", - " n = int(xy.shape[0])\n", - "\n", - " raw = []\n", - " for i in range(n):\n", - " i2 = (i + 1) % n\n", - " p1, p2 = xy[i], xy[i2]\n", - " for j in range(i + 1, n):\n", - " j2 = (j + 1) % n\n", - " if i == j or i2 == j or j2 == i:\n", - " continue\n", - " if _circ_sep(n, i, j) < int(min_separation_beads):\n", - " continue\n", - " q1, q2 = xy[j], xy[j2]\n", - " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", - " if not hit:\n", - " continue\n", - " ua = _canon_axis(p2 - p1)\n", - " ub = _canon_axis(q2 - q1)\n", - " if ua is None or ub is None:\n", - " continue\n", - " raw.append(dict(pt=np.array(pt, float),\n", - " axes=[ua, ub],\n", - " seg_i=int(i), seg_j=int(j),\n", - " t=float(t), u=float(u)))\n", - "\n", - " # Cluster nearby raw intersections\n", - " clusters = []\n", - " for r in raw:\n", - " pt = r[\"pt\"]\n", - " placed = False\n", - " for c in clusters:\n", - " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", - " c[\"pt_sum\"] += pt\n", - " c[\"n\"] += 1\n", - " c[\"axes\"].extend(r[\"axes\"])\n", - " c[\"items\"].append(r)\n", - " placed = True\n", - " break\n", - " if not placed:\n", - " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n", - " \"axes\": list(r[\"axes\"]), \"items\": [r]})\n", - "\n", - " out = []\n", - " for c in clusters:\n", - " pt_mean = c[\"pt_sum\"] / c[\"n\"]\n", - " axes = []\n", - " for uvec in c[\"axes\"]:\n", - " uvec = _canon_axis(uvec)\n", - " if uvec is None:\n", - " continue\n", - " if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n", - " continue\n", - " axes.append(uvec)\n", - " rep = c[\"items\"][0]\n", - " out.append(dict(\n", - " pt_nm = np.asarray(pt_mean, dtype=np.float32),\n", - " axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n", - " if axes else [np.array([1.0, 0.0], np.float32)],\n", - " seg_i = int(rep[\"seg_i\"]),\n", - " seg_j = int(rep[\"seg_j\"]),\n", - " t = float(rep[\"t\"]),\n", - " u = float(rep[\"u\"]),\n", - " n_hits = int(c[\"n\"]),\n", - " ))\n", - " return out\n", - "\n", - "\n", - "def _add_crossing_patch(mask, pt_px, axes_px,\n", - " sigma_center=2.0,\n", - " chain_extent=5.0,\n", - " sigma_perp=1.8,\n", - " center_weight=1.0,\n", - " chain_weight=0.8):\n", - " \"\"\"\n", - " Paint one crossing onto mask as:\n", - " center_weight * isotropic_Gaussian\n", - " + chain_weight * max(anisotropic Gaussians aligned to chain axes)\n", - "\n", - " Uses np.maximum so multiple crossings compose correctly.\n", - " \"\"\"\n", - " H, W = mask.shape\n", - " cx, cy = float(pt_px[0]), float(pt_px[1])\n", - " sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n", - " R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n", - " x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n", - " y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n", - "\n", - " xs = np.arange(x0, x1+1, dtype=float)\n", - " ys = np.arange(y0, y1+1, dtype=float)\n", - " X, Y = np.meshgrid(xs, ys)\n", - " dx = X - cx; dy = Y - cy\n", - "\n", - " gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n", - "\n", - " g_chain = np.zeros_like(gc)\n", - " for v in axes_px:\n", - " vx, vy = float(v[0]), float(v[1])\n", - " u_par = dx*vx + dy*vy\n", - " u_perp = -dx*vy + dy*vx\n", - " g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n", - " + u_perp**2 / sigma_perp**2))\n", - " g_chain = np.maximum(g_chain, g)\n", - "\n", - " patch = center_weight * gc + chain_weight * g_chain\n", - " mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n", - "\n", - "\n", - "def make_crossing_mask(coords, extent, nx, ny,\n", - " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", - " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", - " chain_extent=CROSS_CHAIN_EXTENT,\n", - " center_weight=CROSS_CENTER_WEIGHT,\n", - " chain_weight=CROSS_CHAIN_WEIGHT,\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " crossings_precomputed=None,\n", - " merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n", - "\n", - " Uses find_polyline_crossings unless crossings_precomputed is supplied\n", - " (recommended: reuse the same crossing list that was used for the\n", - " height boost so the mask and the AFM image are perfectly aligned).\n", - " \"\"\"\n", - " coords = np.asarray(coords, dtype=np.float32)\n", - " xy_nm = coords[:, :2].astype(float)\n", - "\n", - " if crossings_precomputed is None:\n", - " crossings = find_polyline_crossings(\n", - " xy_nm,\n", - " min_separation_beads=min_separation_beads,\n", - " merge_radius_nm=float(merge_radius_nm),\n", - " )\n", - " else:\n", - " crossings = crossings_precomputed\n", - "\n", - " xmin, xmax, ymin, ymax = extent\n", - " sx = (nx - 1) / (xmax - xmin)\n", - " sy = (ny - 1) / (ymax - ymin)\n", - "\n", - " mask = np.zeros((ny, nx), dtype=np.float32)\n", - "\n", - " for c in crossings:\n", - " x_nm, y_nm = c[\"pt_nm\"]\n", - " px = (x_nm - xmin) * sx\n", - " py = (y_nm - ymin) * sy\n", - "\n", - " axes_px = []\n", - " for v in c[\"axes_nm\"]:\n", - " vx_px = v[0] * sx; vy_px = v[1] * sy\n", - " nrm = float(np.hypot(vx_px, vy_px))\n", - " if nrm < 1e-12:\n", - " continue\n", - " axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n", - " if not axes_px:\n", - " axes_px = [np.array([1.0, 0.0], dtype=float)]\n", - "\n", - " _add_crossing_patch(\n", - " mask,\n", - " pt_px=(px, py),\n", - " axes_px=axes_px,\n", - " sigma_center=float(sigma_center_px),\n", - " chain_extent=float(chain_extent),\n", - " sigma_perp=float(sigma_perp_px),\n", - " center_weight=float(center_weight),\n", - " chain_weight=float(chain_weight),\n", - " )\n", - "\n", - " m = float(mask.max())\n", - " if m > 1e-12:\n", - " mask /= m\n", - " return mask.astype(np.float32), crossings" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_09", - "metadata": { - "id": "cell_md_09" - }, - "source": [ - "## 10. Decoy Chain Functions\n", - "\n", - "real AFM images often contain small chain segments that are not part of the main chain and thus constitute the noise in that sample, but since those segments are broken off DNA, they cannot be neatly integrated into the noise model.\n", - "Therefore, we offer the possibility of addition of small DNA chains to every every sample in the dataset where ``add_decoy = True`` which would then contain a small 2–4 bead \"decoy\" fragment placed in a corner of the image.\n", - "\n", - "The decoy appears in the AFM image but is **excluded** from both\n", - "ground-truth masks, training the model to ignore isolated unconnected fragments." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "cell_code_09", - "metadata": { - "id": "cell_code_09" - }, - "outputs": [], - "source": [ - "def make_decoy_chain(seed, n_beads_range=(2, 4), bond_length=BOND_LENGTH):\n", - " \"\"\"\n", - " Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n", - " Z heights are set to mimic surface-deposited DNA.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - " n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n", - " coords = np.zeros((n_b, 3), dtype=np.float32)\n", - " angle = rng.uniform(0, 2 * np.pi)\n", - " dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n", - "\n", - " for i in range(1, n_b):\n", - " da = rng.normal(0, 0.3)\n", - " c, s = np.cos(da), np.sin(da)\n", - " R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n", - " dirn = R @ dirn\n", - " dirn = dirn / np.linalg.norm(dirn)\n", - " coords[i] = coords[i-1] + bond_length * dirn\n", - "\n", - " coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n", - " return coords\n", - "\n", - "\n", - "def place_decoy_in_corner(decoy_coords, global_extent,\n", - " corner_margin_nm=2.0, seed=None):\n", - " \"\"\"\n", - " Translate decoy_coords so its centroid lands in a randomly chosen\n", - " corner of global_extent = (xmin, xmax, ymin, ymax).\n", - " \"\"\"\n", - " if seed is None:\n", - " seed = int(np.sum(decoy_coords))\n", - " rng = np.random.default_rng(int(seed))\n", - " xmin, xmax, ymin, ymax = global_extent\n", - " corner = rng.integers(0, 4)\n", - " targets = [\n", - " (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n", - " (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n", - " (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n", - " (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n", - " ]\n", - " tx, ty = targets[corner]\n", - " centroid = decoy_coords.mean(axis=0)\n", - " offset = np.array([tx, ty, 0.0]) - centroid\n", - " return decoy_coords + offset" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_10", - "metadata": { - "id": "cell_md_10" - }, - "source": [ - "## 11. Chain Generation Pipeline\n", - "\n", - "Now, we are finally ready to generate on sample in our dataset. The following functions are used for building the final image and masks:\n", - "- `generate_chain_with_beads` with `n_beads` defaulting to `N_BEADS`\n", - "- `generate_one_sample_multilength` generates the image and mask (this is what iterates to produce the dataset)\n", - "- `random_transform_noise_texture` applies a per-sample random affine transform to the noise texture for variety (in case of blank .spm files)\n", - "- Noise injection uses `blank.spm` when available and the PSD model fallback otherwise\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "cell_code_10", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cell_code_10", - "outputId": "f21a85c7-5c3e-432a-8c70-76f58292c879" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "No blank .spm file found; trying PSD fallback noise model.\n", - "Loaded PSD fallback noise model from: /content/psd_noise_model.npz\n" - ] - } - ], - "source": [ - "def random_transform_noise_texture(tex, seed):\n", - " \"\"\"\n", - " Apply a deterministic random affine transform (rotation, anisotropic\n", - " scaling, translation, optional flips, amplitude jitter) to the blank.spm\n", - " noise texture. Ensures every sample gets a unique-looking noise pattern\n", - " even though only one blank SPM scan was acquired.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - " tex = np.asarray(tex, dtype=np.float32)\n", - "\n", - " theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n", - " base_scale = float(rng.uniform(0.85, 1.15))\n", - " anis = float(rng.uniform(0.85, 1.15))\n", - " sx, sy = base_scale * anis, base_scale / anis\n", - "\n", - " c, s = float(np.cos(theta)), float(np.sin(theta))\n", - " A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n", - " M = np.linalg.inv(A).astype(np.float32)\n", - "\n", - " h, w = tex.shape\n", - " center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n", - " t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n", - " float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n", - " offset = center - M @ (center + t)\n", - "\n", - " out = affine_transform(tex, matrix=M, offset=offset,\n", - " output_shape=tex.shape, order=1,\n", - " mode=\"wrap\", prefilter=False).astype(np.float32)\n", - "\n", - " if bool(rng.integers(0, 2)):\n", - " out = out[::-1, :]\n", - " if bool(rng.integers(0, 2)):\n", - " out = out[:, ::-1]\n", - " out *= float(rng.uniform(0.90, 1.10))\n", - " return out.astype(np.float32)\n", - "\n", - "\n", - "def generate_chain_with_beads(seed, n_beads=N_BEADS, add_decoy=False):\n", - " \"\"\"\n", - " Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n", - " and optionally generate a corner-placed decoy fragment.\n", - "\n", - " Returns dict:\n", - " seed, n_beads, coords (N,3), crossings (list), decoy_coords or None\n", - " \"\"\"\n", - " seed = int(seed)\n", - " n_beads = int(n_beads)\n", - "\n", - " if USE_MD:\n", - " coords0 = make_tangled_ring_initial(\n", - " seed,\n", - " n_beads=n_beads,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " base_z=BASE_Z,\n", - " )\n", - " frames = run_md_relaxation(coords0, seed=seed,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - " else:\n", - " frames = make_ring_2d_persistent_initial(\n", - " seed,\n", - " n_beads=n_beads,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n", - " )\n", - "\n", - " last_coords = frames[-1].astype(np.float32)\n", - "\n", - " crossings = find_polyline_crossings(\n", - " last_coords[:, :2],\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " merge_radius_nm=0.5,\n", - " )\n", - "\n", - " decoy_coords = None\n", - " if add_decoy:\n", - " xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n", - " ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n", - " decoy_raw = make_decoy_chain(seed + 9999)\n", - " decoy_coords = place_decoy_in_corner(\n", - " decoy_raw, (xmin, xmax, ymin, ymax),\n", - " corner_margin_nm=15.0, seed=seed)\n", - "\n", - " return dict(seed=seed, n_beads=n_beads,\n", - " coords=last_coords, crossings=crossings,\n", - " decoy_coords=decoy_coords)\n", - "\n", - "\n", - "def render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None):\n", - " \"\"\"\n", - " Render AFM image + DNA mask + crossing mask for a chain dict.\n", - "\n", - " Decoys are rendered into the AFM image via np.maximum compositing\n", - " but are deliberately excluded from both ground-truth masks.\n", - " \"\"\"\n", - " seed = int(chain[\"seed\"])\n", - " coords = chain[\"coords\"]\n", - " crossings = chain[\"crossings\"]\n", - " decoy_coords = chain.get(\"decoy_coords\", None)\n", - "\n", - " padding = 10.0\n", - " xmin = coords[:, 0].min() - padding\n", - " xmax = coords[:, 0].max() + padding\n", - " ymin = coords[:, 1].min() - padding\n", - " ymax = coords[:, 1].max() + padding\n", - " global_extent = (xmin, xmax, ymin, ymax)\n", - "\n", - " afm_img, dbg = create_z_based_afm(\n", - " coords, grain_seed=seed,\n", - " crossings_precomputed=crossings,\n", - " extent=global_extent,\n", - " **AFM_KW\n", - " )\n", - "\n", - " if decoy_coords is not None:\n", - " decoy_coords = place_decoy_in_corner(\n", - " decoy_coords, global_extent,\n", - " corner_margin_nm=8.0, seed=seed)\n", - " decoy_kw = {**AFM_KW,\n", - " \"apply_edge_taper\": False,\n", - " \"enable_crossing_boost\": False,\n", - " \"add_center_ridge\": False}\n", - " decoy_afm, _ = create_z_based_afm(\n", - " decoy_coords, extent=global_extent, **decoy_kw)\n", - " afm_img = np.maximum(afm_img, decoy_afm)\n", - "\n", - " actual_extent = dbg[\"extent\"]\n", - "\n", - " noise_img = sample_noise_image(\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " seed=seed,\n", - " out_shape=afm_img.shape,\n", - " target_rms_nm=TARGET_NOISE_RMS_NM,\n", - " )\n", - " if noise_img is not None:\n", - " afm_img = np.clip((afm_img + noise_img).astype(np.float32),\n", - " 0, AFM_KW[\"max_height_nm\"])\n", - "\n", - " dna_mask = make_dna_mask_from_beads(\n", - " coords, actual_extent, NX, NY, dilate_px=DNA_MASK_DILATE_PX)\n", - "\n", - " cross_mask, crossings_out = make_crossing_mask(\n", - " coords, actual_extent, NX, NY,\n", - " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", - " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", - " chain_extent=CROSS_CHAIN_EXTENT,\n", - " center_weight=CROSS_CENTER_WEIGHT,\n", - " chain_weight=CROSS_CHAIN_WEIGHT,\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " crossings_precomputed=crossings,\n", - " merge_radius_nm=0.5,\n", - " )\n", - "\n", - " if CROSS_CLIP_TO_DNA_MASK:\n", - " cross_mask = cross_mask * dna_mask.astype(np.float32)\n", - "\n", - " return dict(\n", - " seed=seed,\n", - " afm_img=afm_img.astype(np.float32),\n", - " dna_mask=dna_mask.astype(np.uint8),\n", - " cross_mask=cross_mask.astype(np.float32),\n", - " extent=np.array(actual_extent, dtype=np.float32),\n", - " n_crossings=int(len(crossings_out)),\n", - " decoy_coords=decoy_coords,\n", - " )\n", - "\n", - "\n", - "def generate_one_sample_multilength(seed, n_beads, noise_source=\"none\",\n", - " noise_asset=None, add_decoy=False):\n", - " \"\"\"\n", - " Generate one complete sample at a specified bead count.\n", - " Uses blank.spm-derived noise when available and otherwise falls back\n", - " to PSD-based synthetic noise generation.\n", - " \"\"\"\n", - " chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n", - "\n", - " s = render_chain_and_masks(\n", - " chain,\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " )\n", - " s[\"n_beads\"] = int(n_beads)\n", - " return s\n", - "\n", - "\n", - "# ── Load noise asset once; prefer blank.spm, fall back to PSD model ───────────\n", - "noise_source = \"none\"\n", - "noise_asset = None\n", - "noise_diag = {\"source\": \"none\"}\n", - "\n", - "if USE_BLANK_SPM_NOISE:\n", - " blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n", - " if blank_path and os.path.isfile(blank_path):\n", - " try:\n", - " noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n", - " blank_path, target_rms_nm=TARGET_NOISE_RMS_NM, verbose=True)\n", - " noise_source = \"blank_spm\"\n", - " print(f\"Loaded blank .spm noise texture RMS ≈ {noise_rms_nm:.3f} nm\")\n", - " except Exception as e:\n", - " print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n", - " else:\n", - " print(\"No blank .spm file found; trying PSD fallback noise model.\")\n", - "\n", - " if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n", - " try:\n", - " noise_asset = load_model(MODEL_PATH)\n", - " noise_source = \"psd_model\"\n", - " noise_diag = {\n", - " \"source\": \"psd_model\",\n", - " \"model_path\": str(MODEL_PATH),\n", - " \"method\": PSD_NOISE_METHOD,\n", - " \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n", - " }\n", - " print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n", - " except Exception as e:\n", - " print(\"PSD fallback noise model failed; proceeding without noise. Error:\", e)\n", - " noise_asset = None\n", - "else:\n", - " print(\"Noise injection disabled.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_11", - "metadata": { - "id": "cell_md_11" - }, - "source": [ - "## 12. Sanity Check\n", - "\n", - "It is always a good idea to check if the pipeline is working correctly and producing the inteded resutls and so we have added a sanity check that can be performed before starting the generation of the entire dataset.\n", - "Here we render one sample per bead count for the bead counts provided in the global variables and by default we have set `add_decoy=True` here.\n", - "\n", - "This check displays the AFM image, DNA mask, and crossing mask side-by-side.\n", - "We also print the extent / decoy-placement diagnostics for verification and adjustment." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "cell_code_11", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "cell_code_11", - "outputId": "9b890f12-a0d1-4eea-de35-ad3b13fb6e05" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "==================== SAMPLE 1 (Seed: 0, 70 beads) ====================\n", - "Image window (nm): X=[-20.6, 19.4], Y=[-18.8, 16.6]\n", - "Decoy XY bounds (nm): X=[11.4, 11.5], Y=[-11.3, -10.3]\n", - "Decoy Z range (nm): -0.41 – 0.41\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 2 (Seed: 1, 80 beads) ====================\n", - "Image window (nm): X=[-18.0, 21.3], Y=[-19.3, 21.2]\n", - "Decoy XY bounds (nm): X=[13.0, 13.7], Y=[12.8, 13.6]\n", - "Decoy Z range (nm): -0.03 – 0.03\n", - "Decoy inside extent.\n", - " Decoy height ≈ 0\n", - "\n", - "==================== SAMPLE 3 (Seed: 2, 90 beads) ====================\n", - "Image window (nm): X=[-20.0, 16.0], Y=[-18.0, 20.6]\n", - "Decoy XY bounds (nm): X=[7.8, 8.3], Y=[-11.0, -9.1]\n", - "Decoy Z range (nm): -0.26 – 0.15\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 4 (Seed: 3, 100 beads) ====================\n", - "Image window (nm): X=[-22.7, 27.1], Y=[-16.9, 19.5]\n", - "Decoy XY bounds (nm): X=[18.8, 19.4], Y=[-10.3, -7.4]\n", - "Decoy Z range (nm): -0.39 – 0.44\n", - "Decoy inside extent.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "bead_counts = list(BEAD_COUNTS)\n", - "seeds = [int(BASE_SEED + k) for k in range(len(bead_counts))]\n", - "\n", - "samples = [\n", - " generate_one_sample_multilength(\n", - " seeds[i], n_beads=bead_counts[i],\n", - " noise_source=noise_source, noise_asset=noise_asset, add_decoy=True\n", - " )\n", - " for i in range(len(bead_counts))\n", - "]\n", - "\n", - "fig, axes = plt.subplots(len(samples), 3, figsize=(16, 5 * len(samples)))\n", - "\n", - "for r, s in enumerate(samples):\n", - " extent = s[\"extent\"]\n", - " img = s[\"afm_img\"]\n", - "\n", - " print(f\"\\n{'='*20} SAMPLE {r+1} (Seed: {s['seed']}, \"\n", - " f\"{s['n_beads']} beads) {'='*20}\")\n", - " print(f\"Image window (nm): X=[{extent[0]:.1f}, {extent[1]:.1f}], \"\n", - " f\"Y=[{extent[2]:.1f}, {extent[3]:.1f}]\")\n", - "\n", - " if s.get(\"decoy_coords\") is not None:\n", - " d = s[\"decoy_coords\"]\n", - " print(f\"Decoy XY bounds (nm): \"\n", - " f\"X=[{d[:,0].min():.1f}, {d[:,0].max():.1f}], \"\n", - " f\"Y=[{d[:,1].min():.1f}, {d[:,1].max():.1f}]\")\n", - " print(f\"Decoy Z range (nm): {d[:,2].min():.2f} – {d[:,2].max():.2f}\")\n", - " oob = (d[:,0].min() < extent[0] or d[:,0].max() > extent[1] or\n", - " d[:,1].min() < extent[2] or d[:,1].max() > extent[3])\n", - " print(\" Decoy out-of-bounds!\" if oob else \"Decoy inside extent.\")\n", - " if d[:,2].max() < 0.1:\n", - " print(\" Decoy height ≈ 0\")\n", - "\n", - " ax1, ax2, ax3 = axes[r]\n", - " ext_list = list(extent)\n", - "\n", - " ax1.imshow(img, cmap=\"afmhot\", origin=\"lower\", extent=ext_list)\n", - " ax1.set_title(f\"AFM image\\n(seed {s['seed']}, {s['n_beads']} beads)\")\n", - " ax1.axis(\"off\")\n", - "\n", - " ax2.imshow(s[\"dna_mask\"], cmap=\"gray\", origin=\"lower\", extent=ext_list)\n", - " ax2.set_title(\"DNA mask (decoy excluded)\")\n", - " ax2.axis(\"off\")\n", - "\n", - " ax3.imshow(s[\"cross_mask\"], cmap=\"magma\", origin=\"lower\", extent=ext_list)\n", - " ax3.set_title(f\"Crossing mask (n={s['n_crossings']})\")\n", - " ax3.axis(\"off\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "source": [ - "##13. Benchmarking\n", - "\n", - "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." - ], - "metadata": { - "id": "3YoKkN3e5dIo" - }, - "id": "3YoKkN3e5dIo" - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "WLAYkyIQlhuX", - "metadata": { - "id": "WLAYkyIQlhuX", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "2efe41b8-37bd-4d1b-a01e-d50eed42cac6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "==============================================================\n", - " SYSTEM PROFILE\n", - "==============================================================\n", - " OS : Linux 6.6.113+\n", - " CPU (logical) : 2 cores\n", - " RAM available : 11.5 GB\n", - " Mode : Non-MD (2D Walk)\n", - "\n", - "==============================================================\n", - " RUNNING BENCHMARK\n", - "==============================================================\n", - " Rep 1/3: 0.05s\n", - " Rep 2/3: 0.06s\n", - " Rep 3/3: 0.06s\n", - "==============================================================\n", - " Median Wall Time: 0.06 s\n", - " Peak RAM (RSS) : 611.8 MB\n", - " Projected Total : 0.1 hours for 4000 samples\n", - "==============================================================\n" - ] - } - ], - "source": [ - "import time\n", - "import tracemalloc\n", - "import os\n", - "import platform\n", - "import psutil\n", - "import threading\n", - "import statistics\n", - "\n", - "# ── Helper: peak RSS tracker ──────\n", - "class _PeakMemTracker:\n", - " def __init__(self):\n", - " self._proc = psutil.Process(os.getpid())\n", - " self.peak_mb = 0.0\n", - " self._stop = threading.Event()\n", - "\n", - " def _run(self):\n", - " while not self._stop.is_set():\n", - " try:\n", - " rss = self._proc.memory_info().rss / 1e6\n", - " if rss > self.peak_mb:\n", - " self.peak_mb = rss\n", - " except Exception: pass\n", - " self._stop.wait(0.05)\n", - "\n", - " def start(self):\n", - " self._t = threading.Thread(target=self._run, daemon=True)\n", - " self._t.start()\n", - "\n", - " def stop(self):\n", - " self._stop.set(); self._t.join()\n", - "\n", - "# ── System Info ──\n", - "cpu_logical = psutil.cpu_count(logical=True)\n", - "if USE_MD:\n", - " platforms = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]\n", - " active_platform = \"OpenCL\" if \"OpenCL\" in platforms else \"CPU\"\n", - "\n", - "print(\"=\" * 62)\n", - "print(\" SYSTEM PROFILE\")\n", - "print(\"=\" * 62)\n", - "print(f\" OS : {platform.system()} {platform.release()}\")\n", - "print(f\" CPU (logical) : {cpu_logical} cores\")\n", - "print(f\" RAM available : {psutil.virtual_memory().available / 1e9:.1f} GB\")\n", - "if USE_MD:\n", - " print(f\" OpenMM Platforms: {platforms}\")\n", - "print(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\n", - "print()\n", - "\n", - "BENCH_SEED = int(BASE_SEED)\n", - "BENCH_N_BEADS = int(BEAD_COUNTS[0])\n", - "BENCH_REPS = 3\n", - "\n", - "def _bench_stages(seed, n_beads):\n", - " t = {}\n", - " t0 = time.perf_counter()\n", - "\n", - " if USE_MD:\n", - " coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n", - " t[\"1_chain_init\"] = time.perf_counter() - t0\n", - "\n", - " t0 = time.perf_counter()\n", - " frames = run_md_relaxation(coords0, seed=seed, n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME)\n", - " t[\"2_md_relax\"] = time.perf_counter() - t0\n", - " else:\n", - " # Skip initial 3D step and run 2D persistent walk\n", - " t[\"1_chain_init\"] = 0.0\n", - " t0 = time.perf_counter()\n", - " frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n", - " t[\"2_md_relax\"] = time.perf_counter() - t0\n", - "\n", - " last_coords = frames[-1]\n", - " t0 = time.perf_counter()\n", - " crossings = find_polyline_crossings(last_coords[:, :2])\n", - " t[\"3_crossing_detect\"] = time.perf_counter() - t0\n", - "\n", - " t0 = time.perf_counter()\n", - " chain = dict(seed=seed, n_beads=n_beads, coords=last_coords, crossings=crossings)\n", - " render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None)\n", - " t[\"4_afm_render_masks\"] = time.perf_counter() - t0\n", - " return t\n", - "\n", - "print(\"=\" * 62)\n", - "print(\" RUNNING BENCHMARK\")\n", - "print(\"=\" * 62)\n", - "\n", - "wall_times = []\n", - "mem_tracker = _PeakMemTracker()\n", - "mem_tracker.start()\n", - "\n", - "for rep in range(BENCH_REPS):\n", - " t0 = time.perf_counter()\n", - " _ = _bench_stages(BENCH_SEED + rep, BENCH_N_BEADS)\n", - " elapsed = time.perf_counter() - t0\n", - " wall_times.append(elapsed)\n", - " print(f\" Rep {rep+1}/{BENCH_REPS}: {elapsed:.2f}s\")\n", - "\n", - "mem_tracker.stop()\n", - "med_wall = statistics.median(wall_times)\n", - "total_samples = int(N_SAMPLES) * len(BEAD_COUNTS)\n", - "est_hours = (med_wall * total_samples) / 3600\n", - "\n", - "print(\"=\" * 62)\n", - "print(f\" Median Wall Time: {med_wall:.2f} s\")\n", - "print(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\n", - "print(f\" Projected Total : {est_hours:.1f} hours for {total_samples} samples\")\n", - "print(\"=\" * 62)\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_12", - "metadata": { - "id": "cell_md_12" - }, - "source": [ - "## 14.Dataset Generation\n", - "\n", - "Finally, we are ready to generate the full dataset. The amount of samples and the lenghts of the chains is defined in the global variables and will be referenced here.\n", - "This notebook generates the full dataset (default: 1000 samples × 4 chain lengths).\n", - "\n", - "\n", - "Progress is printed every 10 samples and a `manifest.csv` tracks all file paths." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cell_code_12", - "metadata": { - "id": "cell_code_12" - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "manifest_path = os.path.join(OUT_DIR, \"manifest.csv\")\n", - "print(f\"Starting dataset generation: {N_SAMPLES} samples…\")\n", - "\n", - "# Define the header including global parameters\n", - "header = [\n", - " \"index\", \"seed\", \"n_beads\", \"bond_length\", \"persistence_bonds\",\n", - " \"image_npy\", \"dna_mask_npy\", \"cross_mask_npy\", \"meta_npz\",\n", - " \"n_crossings\", \"has_decoy\", \"afm_params_json\"\n", - "]\n", - "\n", - "with open(manifest_path, \"w\", newline=\"\") as f:\n", - " w = csv.writer(f)\n", - " w.writerow(header)\n", - "\n", - " for idx in range(int(N_SAMPLES)):\n", - " # Seed override for a known-problematic sample\n", - " if idx == 832:\n", - " seed = int(BASE_SEED + idx + 9997)\n", - " print(f\"Special override for index 832: seed = {seed}\")\n", - " else:\n", - " seed = int(BASE_SEED + idx)\n", - "\n", - " n_beads = int(BEAD_COUNTS[idx % len(BEAD_COUNTS)])\n", - " add_decoy = (idx % 5 == 0)\n", - "\n", - " s = generate_one_sample_multilength(\n", - " seed, n_beads=n_beads,\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " add_decoy=add_decoy,\n", - " )\n", - "\n", - " img_path = os.path.join(\"images\", f\"img_{idx:04d}.npy\")\n", - " dna_path = os.path.join(\"dna_masks\", f\"dna_{idx:04d}.npy\")\n", - " cross_path = os.path.join(\"cross_masks\", f\"cross_{idx:04d}.npy\")\n", - " meta_path = os.path.join(\"meta\", f\"meta_{idx:04d}.npz\")\n", - "\n", - " np.save(os.path.join(OUT_DIR, img_path), s[\"afm_img\"])\n", - " np.save(os.path.join(OUT_DIR, dna_path), s[\"dna_mask\"])\n", - " np.save(os.path.join(OUT_DIR, cross_path), s[\"cross_mask\"])\n", - "\n", - " # Save detailed metadata\n", - " np.savez_compressed(\n", - " os.path.join(OUT_DIR, meta_path),\n", - " seed = s[\"seed\"],\n", - " n_beads = int(s[\"n_beads\"]),\n", - " extent = s[\"extent\"],\n", - " n_crossings= s[\"n_crossings\"],\n", - " has_decoy = add_decoy,\n", - " bond_length = BOND_LENGTH,\n", - " persistence = PERSISTENCE_BONDS\n", - " )\n", - "\n", - " # Write to manifest including JSON-serialized AFM parameters\n", - " w.writerow([\n", - " idx, seed, n_beads, BOND_LENGTH, PERSISTENCE_BONDS,\n", - " img_path, dna_path, cross_path, meta_path,\n", - " s[\"n_crossings\"], add_decoy, json.dumps(AFM_KW)\n", - " ])\n", - "\n", - " if (idx + 1) % 10 == 0:\n", - " print(f\"Progress: {idx+1}/{N_SAMPLES}\")\n", - "\n", - "print(\"\\nDone. Dataset written to:\", OUT_DIR)\n", - "print(\"Manifest:\", manifest_path)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb b/tutorials/dna-imaging/DNA.ipynb similarity index 100% rename from tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb rename to tutorials/dna-imaging/DNA.ipynb diff --git a/tutorials/psd_noise_model.npz b/tutorials/dna-imaging/psd_noise_model.npz similarity index 100% rename from tutorials/psd_noise_model.npz rename to tutorials/dna-imaging/psd_noise_model.npz From 211e28740aabdbea06bf4ca6f002bf6bd542cb47 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 20:35:01 +0200 Subject: [PATCH 08/17] u --- tutorials/dna-imaging/{DNA.ipynb => DNA_simulation.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tutorials/dna-imaging/{DNA.ipynb => DNA_simulation.ipynb} (100%) diff --git a/tutorials/dna-imaging/DNA.ipynb b/tutorials/dna-imaging/DNA_simulation.ipynb similarity index 100% rename from tutorials/dna-imaging/DNA.ipynb rename to tutorials/dna-imaging/DNA_simulation.ipynb From a8802fb859b1fc208d2769751cf7865a15473cd5 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 20:39:22 +0200 Subject: [PATCH 09/17] Update DNA_simulation.ipynb --- tutorials/dna-imaging/DNA_simulation.ipynb | 1928 ++++++++++++++++++-- 1 file changed, 1826 insertions(+), 102 deletions(-) diff --git a/tutorials/dna-imaging/DNA_simulation.ipynb b/tutorials/dna-imaging/DNA_simulation.ipynb index ce79655..e8b07a9 100644 --- a/tutorials/dna-imaging/DNA_simulation.ipynb +++ b/tutorials/dna-imaging/DNA_simulation.ipynb @@ -7,7 +7,23 @@ "id": "cell_md_title" }, "source": [ - "# Simulation of 'DNA chains on a substrate imaged via AFM'\n**This notebook provides a complete example of generating a dataset of simulated DNA chains relxed on a substrate imaged via AFM.**\n\n- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)\n\nOutputs are written to `OUT_DIR/` as `.npy` plus `manifest.csv` (which documents the user inputs used to create the images and masks).\n\n>\n---\n**Cell execution order:** run cells 1 → 12 in sequence. \nCells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Prakhar-Dutta/ASAP/blob/1b5cfc5173e1bed4e59575e6dd4724cb20682620/tutorial/Not_final_version_DNA_notebook%20(1).ipynb)" + "# Simulation of the AFM-Imaging of DNA Chains Deposited on a Substrate\n", + "\n", + "\n", + "\"Open # TODO: Add correct link to ASAP notebook.\n", + "\n", + "This notebook provides you with a complete example to generate a simulated dataset of the AFM imaging of DNA chains deposited on a substrate.\n", + "\n", + "- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n", + "- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n", + "- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)\n", + "\n", + "Outputs are written to `OUT_DIR/` as `.npy` plus `manifest.csv` (which documents the user inputs used to create the images and masks).\n", + "\n", + ">\n", + "---\n", + "**Cell execution order:** run cells 1 → 12 in sequence. \n", + "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset." ] }, { @@ -28,16 +44,16 @@ "execution_count": 1, "id": "cell_code_01", "metadata": { - "id": "cell_code_01", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "cell_code_01", "outputId": "8e1cfb67-fb2f-4019-e828-00f03c01d4e8" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Collecting openmm[cuda13]\n", " Downloading openmm-8.5.0-cp312-cp312-manylinux_2_34_x86_64.whl.metadata (1.4 kB)\n", @@ -121,17 +137,23 @@ }, { "cell_type": "markdown", - "source": [ - "#1.2 Defining the variables\n", - "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." - ], + "id": "1oW9llw-imr1", "metadata": { "id": "1oW9llw-imr1" }, - "id": "1oW9llw-imr1" + "source": [ + "#1.2 Defining the variables\n", + "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." + ] }, { "cell_type": "code", + "execution_count": 50, + "id": "sSqadS02hzPe", + "metadata": { + "id": "sSqadS02hzPe" + }, + "outputs": [], "source": [ "# ─────────────────────────────────────────────\n", "# Global settings (these are the variables that need to be changed to change the appearance of the resulting images)\n", @@ -205,16 +227,14 @@ "CROSS_CENTER_WEIGHT = 1.7 # For the chain weighting of the mask (chain-weighted guassian)\n", "CROSS_CHAIN_WEIGHT = 0.9\n", "CROSS_CLIP_TO_DNA_MASK = True" - ], - "metadata": { - "id": "sSqadS02hzPe" - }, - "id": "sSqadS02hzPe", - "execution_count": 50, - "outputs": [] + ] }, { "cell_type": "markdown", + "id": "-e3QvQzGlKYY", + "metadata": { + "id": "-e3QvQzGlKYY" + }, "source": [ "#1.3 Noise\n", "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", @@ -223,14 +243,28 @@ "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", "\n" - ], - "metadata": { - "id": "-e3QvQzGlKYY" - }, - "id": "-e3QvQzGlKYY" + ] }, { "cell_type": "code", + "execution_count": 26, + "id": "47h81smulGxd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "47h81smulGxd", + "outputId": "4a3be80f-5c9c-4b70-fbb9-582470dff3c8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output folder: /content/dna_dataset_1000_4lengths\n" + ] + } + ], "source": [ "#Noise\n", "TARGET_NOISE_RMS_NM = 0.19 # This variable controls the height of the noise in nanometers and this is added to the final image\n", @@ -253,24 +287,6 @@ " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", "\n", "print(\"Output folder:\", os.path.abspath(OUT_DIR))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "47h81smulGxd", - "outputId": "4a3be80f-5c9c-4b70-fbb9-582470dff3c8" - }, - "id": "47h81smulGxd", - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Output folder: /content/dna_dataset_1000_4lengths\n" - ] - } ] }, { @@ -302,14 +318,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -406,26 +422,18 @@ }, { "cell_type": "markdown", - "source": [ - "We start with testing the OpenMM installation (can be tricky at times)" - ], + "id": "wFFhsVZhqU8a", "metadata": { "id": "wFFhsVZhqU8a" }, - "id": "wFFhsVZhqU8a" + "source": [ + "We start with testing the OpenMM installation (can be tricky at times)" + ] }, { "cell_type": "code", - "source": [ - "## Test the OpenMM installation before running the following cell to ensure that GPU acceleration will work\n", - "import openmm.testInstallation\n", - "\n", - "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", - "try:\n", - " openmm.testInstallation.main()\n", - "except Exception as e:\n", - " print(f\"Test failed: {e}\")" - ], + "execution_count": 5, + "id": "3NjBSwfAe4Mj", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -433,12 +441,10 @@ "id": "3NjBSwfAe4Mj", "outputId": "091b8222-4bf3-4cd3-bb30-f68eb55c2670" }, - "id": "3NjBSwfAe4Mj", - "execution_count": 5, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\n", "OpenMM Version: 8.5\n", @@ -462,17 +468,27 @@ "All differences are within tolerance.\n" ] } + ], + "source": [ + "## Test the OpenMM installation before running the following cell to ensure that GPU acceleration will work\n", + "import openmm.testInstallation\n", + "\n", + "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", + "try:\n", + " openmm.testInstallation.main()\n", + "except Exception as e:\n", + " print(f\"Test failed: {e}\")" ] }, { "cell_type": "markdown", - "source": [ - "And then once we are sure that OpenMM is correctly installed we run the MD functions" - ], + "id": "9XbRNS3TqbKH", "metadata": { "id": "9XbRNS3TqbKH" }, - "id": "9XbRNS3TqbKH" + "source": [ + "And then once we are sure that OpenMM is correctly installed we run the MD functions" + ] }, { "cell_type": "code", @@ -636,17 +652,23 @@ }, { "cell_type": "markdown", - "source": [ - "##4. Non-MD chain generation\n", - "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." - ], + "id": "Bx7_OzWRr-l3", "metadata": { "id": "Bx7_OzWRr-l3" }, - "id": "Bx7_OzWRr-l3" + "source": [ + "##4. Non-MD chain generation\n", + "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." + ] }, { "cell_type": "code", + "execution_count": null, + "id": "yXs0I0akr948", + "metadata": { + "id": "yXs0I0akr948" + }, + "outputs": [], "source": [ "def make_ring_2d_persistent_initial(\n", " seed,\n", @@ -790,13 +812,7 @@ "\n", " # Return as a list containing one frame to mimic run_md_relaxation output\n", " return [coords]" - ], - "metadata": { - "id": "yXs0I0akr948" - }, - "id": "yXs0I0akr948", - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", @@ -826,16 +842,16 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Generating initial ring and running MD (seed=0)…\n" ] }, { - "output_type": "error", "ename": "NameError", "evalue": "name 'make_tangled_ring_initial' is not defined", + "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", @@ -934,7 +950,527 @@ }, "outputs": [], "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n# Low-level geometry helpers\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef _seg_intersect_2d(p1, p2, q1, q2, eps=1e-12):\n \"\"\"\n Strict 2-D segment intersection.\n Returns (hit, pt, t, u) where pt = p1 + t*(p2-p1) = q1 + u*(q2-q1).\n Returns (False, None, None, None) for parallel / collinear segments.\n \"\"\"\n p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n r = p2 - p1; s = q2 - q1\n rxs = r[0]*s[1] - r[1]*s[0]\n if abs(rxs) < eps:\n return False, None, None, None\n qp = q1 - p1\n t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n return True, p1 + t*r, t, u\n return False, None, None, None\n\n\ndef _circ_sep(n, i, j):\n \"\"\"Circular distance between bead indices on a ring of n beads.\"\"\"\n d = abs(int(i) - int(j)) #Helper function to find out which beads will later form crossings and which beads are just neighbours\n return min(d, n - d)\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# Structuring-element / grid helpers\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef disk_footprint(r_px: float):\n \"\"\"Boolean circular footprint of radius r_px pixels.\"\"\"\n if r_px < 0.5:\n return np.ones((1, 1), dtype=bool)\n r = int(np.ceil(r_px)) #Used to convert individual beads into DNA like chain (dilates each bead to a disk like structure)\n y, x = np.ogrid[-r:r+1, -r:r+1]\n return (x*x + y*y) <= r*r\n\n\ndef upsample_nn(a, out_shape):\n \"\"\"\n Nearest-neighbour upsample array a to out_shape using integer repeat.\n Truncates to exactly out_shape after repeating.\n\n Returns an ndarray of the same dtype as a with shape out_shape.\n \"\"\"\n ny_out, nx_out = out_shape\n ny_in, nx_in = a.shape\n if (ny_out, nx_out) == (ny_in, nx_in):\n return a\n sy = max(ny_out // ny_in, 1)\n sx = max(nx_out // nx_in, 1)\n a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n return a_up[:ny_out, :nx_out]\n\n\n#internal render grid now comes directly from extent and user nm_per_px (changed from earlier logic after Tom's comments on how chains should sit in the frame and what users should be able to choose)\ndef compute_internal_render_grid(extent, target_nm_per_px, min_size=16):\n \"\"\"\n Build the internal render grid from physical extent and user nm_per_px.\n Ensures pixel count is at least min_size along each axis.\n\n Returns (nx, ny, actual_nm_per_px) as (int, int, float).\n \"\"\"\n xmin, xmax, ymin, ymax = extent\n width_nm = float(xmax - xmin)\n height_nm = float(ymax - ymin)\n p_nm_per_px = max(float(target_nm_per_px), 1e-6)\n nx = max(int(min_size), int(np.ceil(width_nm / p_nm_per_px)) + 1)\n ny = max(int(min_size), int(np.ceil(height_nm / p_nm_per_px)) + 1)\n return nx, ny, p_nm_per_px\n\n\ndef resize_image_to_shape(a, out_shape, order=1):\n \"\"\"\n Resize 2D array a to out_shape using scipy.ndimage.zoom.\n Falls back to edge-padding if zoom undershoots the target shape.\n\n Returns a float32 array of exactly out_shape.\n \"\"\"\n a = np.asarray(a)\n out_shape = tuple(int(v) for v in out_shape)\n if a.shape == out_shape:\n return a.copy()\n zoom_factors = (out_shape[0] / a.shape[0], out_shape[1] / a.shape[1])\n out = zoom(a, zoom_factors, order=order, mode=\"nearest\", prefilter=(order > 1))\n out = out[:out_shape[0], :out_shape[1]]\n if out.shape != out_shape:\n pad_y = max(0, out_shape[0] - out.shape[0])\n pad_x = max(0, out_shape[1] - out.shape[1])\n out = np.pad(out, ((0, pad_y), (0, pad_x)), mode=\"edge\")\n out = out[:out_shape[0], :out_shape[1]]\n return out\n\n\ndef disk_cap_structure(radius_nm: float, p_nm_per_px: float,\n max_radius_px=96):\n \"\"\"\n Hemispherical-cap structuring element representing the DNA cross-section.\n Each pixel inside the disk gets a height equal to the spherical-cap height\n at that radial distance.\n\n Returns (footprint_bool, structure_float32) where footprint_bool is the\n circular boolean mask and structure_float32 holds cap heights in nm\n (pixels outside the cap are set to -1e9).\n \"\"\"\n r_px = radius_nm / max(p_nm_per_px, 1e-12)\n R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n y, x = np.ogrid[-R:R+1, -R:R+1]\n d2 = x*x + y*y\n inside = d2 <= (r_px * r_px)\n d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n structure[inside] = np.sqrt(\n np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n ).astype(np.float32)\n return inside.astype(bool), structure\n\n\ndef AFM_tip(tip_radius_nm: float, p_nm_per_px: float,\n max_radius_px=128):\n \"\"\"\n Spherical-cap structuring element representing the AFM tip geometry.\n Used to convolve the surface with the tip shape.\n The structure values are offset so that the tip apex sits at zero height.\n\n Returns (footprint_bool, structure_float32) where footprint_bool is the\n circular boolean mask and structure_float32 holds tip heights in nm\n (pixels outside the tip are set to -1e9).\n \"\"\"\n Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n y, x = np.ogrid[-R:R+1, -R:R+1]\n d2 = x*x + y*y\n inside = d2 <= (Rpx * Rpx)\n d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n structure[inside] = (\n np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n - tip_radius_nm\n ).astype(np.float32)\n return inside.astype(bool), structure\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# Crossing boost\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef _taper_weight(idx_off, w, profile=\"gaussian\", sigma=None):\n \"\"\"\n Smooth weight in [0, 1] used to fade the crossing height boost along\n the bead-index window of half-width w.\n\n profile:\n 'gaussian' — smooth dome (recommended)\n 'hemisphere' — half-circle taper\n 'linear' — simple linear ramp\n\n Returns a float in [0, 1], where 1 is the peak weight (idx_off == 0)\n and the weight decays toward 0 at the window boundary.\n \"\"\"\n if w <= 0:\n return 1.0\n a = abs(idx_off)\n if profile == \"linear\":\n return 1.0 - (a / (w + 1.0))\n if profile == \"hemisphere\":\n x = a / (w + 1.0)\n return float(np.sqrt(max(0.0, 1.0 - x*x)))\n # gaussian (default)\n if sigma is None:\n sigma = max(1e-6, 0.5 * (w + 0.5))\n g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n if g_end < 0.999:\n g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n return g\n\n\ndef _collect_intersections(xy_nm, min_separation_beads=12,\n merge_radius_nm=0.5):\n \"\"\"\n Brute-force all segment pairs of a closed ring polyline to find crossings.\n Skips adjacent/shared-vertex pairs and contour-nearby pairs.\n Clusters nearby raw hits via _merge_crossing_clusters.\n\n Returns list of dicts:\n {pt_nm, seg_i, seg_j, t, u, n_hits}\n \"\"\"\n xy = np.asarray(xy_nm, dtype=float)\n n = int(xy.shape[0])\n raw = []\n for i in range(n):\n i2 = (i + 1) % n\n p1, p2 = xy[i], xy[i2]\n for j in range(i + 1, n):\n j2 = (j + 1) % n\n if i == j or i2 == j or j2 == i:\n continue\n if _circ_sep(n, i, j) < int(min_separation_beads):\n continue\n q1, q2 = xy[j], xy[j2]\n hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n if hit:\n raw.append(dict(pt=np.array(pt, float),\n seg_i=int(i), seg_j=int(j),\n t=float(t), u=float(u)))\n\n clusters = _merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n return clusters\n\n\ndef _merge_crossing_clusters(raw_list, merge_radius_nm=0.5):\n \"\"\"\n Greedy proximity-based clustering of raw crossing dicts.\n Each dict must have key 'pt' (2-D float array).\n Returns averaged clusters with representative metadata.\n \"\"\"\n clusters = []\n for r in raw_list:\n pt = r[\"pt\"].astype(float)\n placed = False\n for c in clusters:\n if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n c[\"pt_sum\"] += pt\n c[\"n\"] += 1\n c[\"items\"].append(r)\n placed = True\n break\n if not placed:\n clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n\n out = []\n for c in clusters:\n pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n rep = c[\"items\"][0]\n out.append(dict(\n pt_nm = pt,\n seg_i = int(rep[\"seg_i\"]),\n seg_j = int(rep[\"seg_j\"]),\n t = float(rep[\"t\"]),\n u = float(rep[\"u\"]),\n n_hits = int(c[\"n\"]),\n ))\n return out\n\n\ndef boost_crossings_intersections(\n x_nm, y_nm, zc_nm,\n crossings=None,\n min_separation_beads=12,\n boost_window_beads=2,\n guaranteed_offset_nm=1.0,\n boost_method=\"additive\",\n boost_profile=\"gaussian\",\n boost_sigma_beads=None,\n far_clip_nm=2.5,\n far_clip_window_beads=12,\n merge_radius_nm=0.5,\n):\n \"\"\"\n Boost z-height at strand crossings so they are visually distinct in\n the AFM image.\n\n For each crossing:\n - Determines which strand is on top (higher z)\n - Raises top-strand beads in a tapered window to at least\n (bottom_max + guaranteed_offset_nm)\n - Optionally clips non-crossing beads to far_clip_nm\n\n Returns (modified_z, crossing_info_list).\n \"\"\"\n x = np.asarray(x_nm, dtype=np.float32)\n y = np.asarray(y_nm, dtype=np.float32)\n z = np.asarray(zc_nm, dtype=np.float32).copy()\n n = int(z.shape[0])\n if n < 5:\n return z.astype(np.float32), []\n\n if crossings is None:\n xy = np.stack([x, y], axis=1)\n crossings = _collect_intersections(\n xy,\n min_separation_beads=int(min_separation_beads),\n merge_radius_nm=float(merge_radius_nm),\n )\n\n def get_segment_indices(center, w):\n return np.array([(center + k) % n for k in range(-w, w+1)],\n dtype=np.int32)\n\n crossing_info = []\n crossing_centers = []\n w = int(boost_window_beads)\n\n for c in crossings:\n i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n i2 = (i + 1) % n; j2 = (j + 1) % n\n ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n\n bead_i = i if ti < 0.5 else i2\n bead_j = j if uj < 0.5 else j2\n zi = float((1.0 - ti) * z[i] + ti * z[i2])\n zj = float((1.0 - uj) * z[j] + uj * z[j2])\n\n top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j, bead_i)\n top_seg = get_segment_indices(top_center, w)\n bottom_seg = get_segment_indices(bottom_center, w)\n\n bottom_local_max = float(z[bottom_seg].max())\n top_local_max = float(z[top_seg].max())\n\n if boost_method == \"absolute\":\n target_height = bottom_local_max + float(guaranteed_offset_nm)\n else:\n target_height = max(top_local_max, bottom_local_max) + float(guaranteed_offset_nm)\n\n for off in range(-w, w+1):\n k = (top_center + off) % n\n wt = float(_taper_weight(off, w,\n profile=boost_profile,\n sigma=boost_sigma_beads))\n z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n\n crossing_centers.extend([int(top_center), int(bottom_center)])\n crossing_info.append(dict(\n pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n seg_i = int(i),\n seg_j = int(j),\n top_center = int(top_center),\n bottom_center= int(bottom_center),\n ))\n\n # Clip beads far from any crossing\n if crossing_centers and far_clip_nm is not None:\n centers = np.array(crossing_centers, dtype=np.int32)\n r = int(far_clip_window_beads)\n for k in range(n):\n d = int(np.min(np.minimum(np.abs(k - centers),\n n - np.abs(k - centers))))\n if d > r:\n z[k] = min(z[k], float(far_clip_nm))\n\n return z.astype(np.float32), crossing_info\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# Main AFM renderer\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef create_z_based_afm(\n coords,\n nx=800, ny=800,\n dna_diameter_nm=2.0,\n tip_radius_nm=0.01,\n max_height_nm=6.0,\n target_nm_per_px=0.08,\n max_radius_px=96,\n radius_shrink_px=2.0,\n final_blur_sigma_px=0.20,\n apply_edge_taper=True,\n taper_sigma_nm=0.45,\n taper_floor=0.10,\n add_center_ridge=True,\n ridge_sigma_nm=0.25,\n ridge_amp_nm=0.25,\n grain_nm=0.0,\n grain_sigma_px=0.6,\n grain_seed=1,\n enable_crossing_boost=True,\n min_separation_beads=12,\n boost_window_beads=2,\n guaranteed_offset_nm=1.0,\n boost_method=\"additive\",\n boost_profile=\"gaussian\",\n boost_sigma_beads=None,\n crossings_precomputed=None,\n far_clip_nm=2.5,\n far_clip_window_beads=12,\n return_crossing_info=False,\n return_masks=True,\n extent=None,\n):\n \"\"\"\n Master AFM renderer. Pipeline:\n\n 1. Project 3-D bead coords onto a 2-D effective grid\n 2. Optionally boost crossing heights (boost_crossings_intersections)\n 3. Rasterise closed polyline with z-interpolation\n 4. Apply disk_cap_structure (DNA cylindrical cross-section)\n 5. Apply AFM_tip (tip-sample convolution)\n 6. Clip, blur, edge-taper, center-ridge\n 7. Optional synthetic grain noise\n 8. Upsample to requested (nx, ny)\n\n Returns (afm_image, debug_dict) when return_masks=True.\n \"\"\"\n x, y, z = coords.T\n if extent is None:\n xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n else:\n xmin, xmax, ymin, ymax = extent\n\n # CHANGED: render directly on the physical grid selected upstream\n nx_eff, ny_eff = int(nx), int(ny)\n px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n py_eff = (ymax - ymin) / max(ny_eff - 1, 1)\n p_eff = 0.5 * (px_eff + py_eff)\n\n ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n 0, nx_eff - 1)\n iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n 0, ny_eff - 1)\n\n zc = (z - z.min()).astype(np.float32)\n\n # ── Crossing boost ───────────────────────────────────────────────────────\n crossing_info = []\n if enable_crossing_boost:\n zc, crossing_info = boost_crossings_intersections(\n x.astype(np.float32), y.astype(np.float32), zc,\n crossings=crossings_precomputed,\n min_separation_beads=int(min_separation_beads),\n boost_window_beads=int(boost_window_beads),\n guaranteed_offset_nm=float(guaranteed_offset_nm),\n boost_method=str(boost_method),\n boost_profile=str(boost_profile),\n boost_sigma_beads=boost_sigma_beads,\n far_clip_nm=far_clip_nm,\n far_clip_window_beads=int(far_clip_window_beads),\n )\n\n # ── Rasterise polyline ───────────────────────────────────────────────────\n z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n npts = len(ix)\n for k in range(npts):\n k2 = (k + 1) % npts\n x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n if n_seg <= 1:\n z_line[y0, x0] = max(z_line[y0, x0], z0)\n line_mask[y0, x0] = True\n continue\n xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n if xs.size > 1:\n keep = np.ones(xs.shape[0], dtype=bool)\n keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n xs, ys, zs = xs[keep], ys[keep], zs[keep]\n z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n line_mask[ys, xs] = True\n\n # ── DNA cap dilation ─────────────────────────────────────────────────────\n # CHANGED: keep the DNA diameter in physical units; radius_shrink_px is disabled upstream\n r_eff = max(0.5 * float(dna_diameter_nm), 0.2)\n fp_dna, cap = disk_cap_structure(r_eff, p_eff, max_radius_px=max_radius_px)\n surface = grey_dilation(z_line, footprint=fp_dna,\n structure=cap).astype(np.float32)\n dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna) > 0\n surface[~dna_region] = 0.0\n\n # ── Tip convolution ──────────────────────────────────────────────────────\n r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n if r_tip_px >= 0.5:\n fp_tip, tip_struct = AFM_tip(\n float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n surface = grey_dilation(surface, footprint=fp_tip,\n structure=tip_struct).astype(np.float32)\n\n np.clip(surface, 0, max_height_nm, out=surface)\n if final_blur_sigma_px > 0:\n surface = gaussian_filter(surface,\n sigma=float(final_blur_sigma_px)).astype(np.float32)\n\n # ── Edge taper + center ridge ────────────────────────────────────────────\n if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n dist_nm = dist_px * np.float32(p_eff)\n if apply_edge_taper:\n sig = max(float(taper_sigma_nm), 1e-6)\n factor = taper_floor + (1.0 - taper_floor) * np.exp(\n -(dist_nm**2) / (2.0 * sig**2))\n surface[dna_region] *= factor[dna_region]\n if add_center_ridge and ridge_amp_nm > 0:\n rs = max(float(ridge_sigma_nm), 1e-6)\n ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n np.clip(surface, 0, max_height_nm, out=surface)\n\n # ── Synthetic grain ──────────────────────────────────────────────────────\n if grain_nm and grain_nm > 0:\n rng = np.random.default_rng(int(grain_seed))\n n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n if grain_sigma_px and grain_sigma_px > 0:\n n = gaussian_filter(n, sigma=float(grain_sigma_px)).astype(np.float32)\n std = float(n[dna_region].std()) if np.any(dna_region) else float(n.std())\n if std > 1e-9:\n n /= np.float32(std)\n surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n np.clip(surface, 0, max_height_nm, out=surface)\n\n if return_masks:\n debug = {\n \"line_mask\": line_mask,\n \"dna_region\": dna_region,\n \"extent\": (xmin, xmax, ymin, ymax),\n \"p_nm_per_px\": float(p_eff),\n }\n if return_crossing_info:\n debug[\"crossings\"] = crossing_info\n return surface, debug\n\n return surface, (xmin, xmax, ymin, ymax)" + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Low-level geometry helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def _seg_intersect_2d(p1, p2, q1, q2, eps=1e-12):\n", + " \"\"\"\n", + " Strict 2-D segment intersection.\n", + " Returns (hit, pt, t, u) where pt = p1 + t*(p2-p1) = q1 + u*(q2-q1).\n", + " Returns (False, None, None, None) for parallel / collinear segments.\n", + " \"\"\"\n", + " p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n", + " q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n", + " r = p2 - p1; s = q2 - q1\n", + " rxs = r[0]*s[1] - r[1]*s[0]\n", + " if abs(rxs) < eps:\n", + " return False, None, None, None\n", + " qp = q1 - p1\n", + " t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n", + " u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n", + " if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n", + " return True, p1 + t*r, t, u\n", + " return False, None, None, None\n", + "\n", + "\n", + "def _circ_sep(n, i, j):\n", + " \"\"\"Circular distance between bead indices on a ring of n beads.\"\"\"\n", + " d = abs(int(i) - int(j)) #Helper function to find out which beads will later form crossings and which beads are just neighbours\n", + " return min(d, n - d)\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Structuring-element / grid helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def disk_footprint(r_px: float):\n", + " \"\"\"Boolean circular footprint of radius r_px pixels.\"\"\"\n", + " if r_px < 0.5:\n", + " return np.ones((1, 1), dtype=bool)\n", + " r = int(np.ceil(r_px)) #Used to convert individual beads into DNA like chain (dilates each bead to a disk like structure)\n", + " y, x = np.ogrid[-r:r+1, -r:r+1]\n", + " return (x*x + y*y) <= r*r\n", + "\n", + "\n", + "def upsample_nn(a, out_shape):\n", + " \"\"\"\n", + " Nearest-neighbour upsample array a to out_shape using integer repeat.\n", + " Truncates to exactly out_shape after repeating.\n", + "\n", + " Returns an ndarray of the same dtype as a with shape out_shape.\n", + " \"\"\"\n", + " ny_out, nx_out = out_shape\n", + " ny_in, nx_in = a.shape\n", + " if (ny_out, nx_out) == (ny_in, nx_in):\n", + " return a\n", + " sy = max(ny_out // ny_in, 1)\n", + " sx = max(nx_out // nx_in, 1)\n", + " a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n", + " return a_up[:ny_out, :nx_out]\n", + "\n", + "\n", + "#internal render grid now comes directly from extent and user nm_per_px (changed from earlier logic after Tom's comments on how chains should sit in the frame and what users should be able to choose)\n", + "def compute_internal_render_grid(extent, target_nm_per_px, min_size=16):\n", + " \"\"\"\n", + " Build the internal render grid from physical extent and user nm_per_px.\n", + " Ensures pixel count is at least min_size along each axis.\n", + "\n", + " Returns (nx, ny, actual_nm_per_px) as (int, int, float).\n", + " \"\"\"\n", + " xmin, xmax, ymin, ymax = extent\n", + " width_nm = float(xmax - xmin)\n", + " height_nm = float(ymax - ymin)\n", + " p_nm_per_px = max(float(target_nm_per_px), 1e-6)\n", + " nx = max(int(min_size), int(np.ceil(width_nm / p_nm_per_px)) + 1)\n", + " ny = max(int(min_size), int(np.ceil(height_nm / p_nm_per_px)) + 1)\n", + " return nx, ny, p_nm_per_px\n", + "\n", + "\n", + "def resize_image_to_shape(a, out_shape, order=1):\n", + " \"\"\"\n", + " Resize 2D array a to out_shape using scipy.ndimage.zoom.\n", + " Falls back to edge-padding if zoom undershoots the target shape.\n", + "\n", + " Returns a float32 array of exactly out_shape.\n", + " \"\"\"\n", + " a = np.asarray(a)\n", + " out_shape = tuple(int(v) for v in out_shape)\n", + " if a.shape == out_shape:\n", + " return a.copy()\n", + " zoom_factors = (out_shape[0] / a.shape[0], out_shape[1] / a.shape[1])\n", + " out = zoom(a, zoom_factors, order=order, mode=\"nearest\", prefilter=(order > 1))\n", + " out = out[:out_shape[0], :out_shape[1]]\n", + " if out.shape != out_shape:\n", + " pad_y = max(0, out_shape[0] - out.shape[0])\n", + " pad_x = max(0, out_shape[1] - out.shape[1])\n", + " out = np.pad(out, ((0, pad_y), (0, pad_x)), mode=\"edge\")\n", + " out = out[:out_shape[0], :out_shape[1]]\n", + " return out\n", + "\n", + "\n", + "def disk_cap_structure(radius_nm: float, p_nm_per_px: float,\n", + " max_radius_px=96):\n", + " \"\"\"\n", + " Hemispherical-cap structuring element representing the DNA cross-section.\n", + " Each pixel inside the disk gets a height equal to the spherical-cap height\n", + " at that radial distance.\n", + "\n", + " Returns (footprint_bool, structure_float32) where footprint_bool is the\n", + " circular boolean mask and structure_float32 holds cap heights in nm\n", + " (pixels outside the cap are set to -1e9).\n", + " \"\"\"\n", + " r_px = radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (r_px * r_px)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = np.sqrt(\n", + " np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "def AFM_tip(tip_radius_nm: float, p_nm_per_px: float,\n", + " max_radius_px=128):\n", + " \"\"\"\n", + " Spherical-cap structuring element representing the AFM tip geometry.\n", + " Used to convolve the surface with the tip shape.\n", + " The structure values are offset so that the tip apex sits at zero height.\n", + "\n", + " Returns (footprint_bool, structure_float32) where footprint_bool is the\n", + " circular boolean mask and structure_float32 holds tip heights in nm\n", + " (pixels outside the tip are set to -1e9).\n", + " \"\"\"\n", + " Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (Rpx * Rpx)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = (\n", + " np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n", + " - tip_radius_nm\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Crossing boost\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def _taper_weight(idx_off, w, profile=\"gaussian\", sigma=None):\n", + " \"\"\"\n", + " Smooth weight in [0, 1] used to fade the crossing height boost along\n", + " the bead-index window of half-width w.\n", + "\n", + " profile:\n", + " 'gaussian' — smooth dome (recommended)\n", + " 'hemisphere' — half-circle taper\n", + " 'linear' — simple linear ramp\n", + "\n", + " Returns a float in [0, 1], where 1 is the peak weight (idx_off == 0)\n", + " and the weight decays toward 0 at the window boundary.\n", + " \"\"\"\n", + " if w <= 0:\n", + " return 1.0\n", + " a = abs(idx_off)\n", + " if profile == \"linear\":\n", + " return 1.0 - (a / (w + 1.0))\n", + " if profile == \"hemisphere\":\n", + " x = a / (w + 1.0)\n", + " return float(np.sqrt(max(0.0, 1.0 - x*x)))\n", + " # gaussian (default)\n", + " if sigma is None:\n", + " sigma = max(1e-6, 0.5 * (w + 0.5))\n", + " g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n", + " g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n", + " if g_end < 0.999:\n", + " g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n", + " return g\n", + "\n", + "\n", + "def _collect_intersections(xy_nm, min_separation_beads=12,\n", + " merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Brute-force all segment pairs of a closed ring polyline to find crossings.\n", + " Skips adjacent/shared-vertex pairs and contour-nearby pairs.\n", + " Clusters nearby raw hits via _merge_crossing_clusters.\n", + "\n", + " Returns list of dicts:\n", + " {pt_nm, seg_i, seg_j, t, u, n_hits}\n", + " \"\"\"\n", + " xy = np.asarray(xy_nm, dtype=float)\n", + " n = int(xy.shape[0])\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if hit:\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " clusters = _merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n", + " return clusters\n", + "\n", + "\n", + "def _merge_crossing_clusters(raw_list, merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Greedy proximity-based clustering of raw crossing dicts.\n", + " Each dict must have key 'pt' (2-D float array).\n", + " Returns averaged clusters with representative metadata.\n", + " \"\"\"\n", + " clusters = []\n", + " for r in raw_list:\n", + " pt = r[\"pt\"].astype(float)\n", + " placed = False\n", + " for c in clusters:\n", + " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = pt,\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def boost_crossings_intersections(\n", + " x_nm, y_nm, zc_nm,\n", + " crossings=None,\n", + " min_separation_beads=12,\n", + " boost_window_beads=2,\n", + " guaranteed_offset_nm=1.0,\n", + " boost_method=\"additive\",\n", + " boost_profile=\"gaussian\",\n", + " boost_sigma_beads=None,\n", + " far_clip_nm=2.5,\n", + " far_clip_window_beads=12,\n", + " merge_radius_nm=0.5,\n", + "):\n", + " \"\"\"\n", + " Boost z-height at strand crossings so they are visually distinct in\n", + " the AFM image.\n", + "\n", + " For each crossing:\n", + " - Determines which strand is on top (higher z)\n", + " - Raises top-strand beads in a tapered window to at least\n", + " (bottom_max + guaranteed_offset_nm)\n", + " - Optionally clips non-crossing beads to far_clip_nm\n", + "\n", + " Returns (modified_z, crossing_info_list).\n", + " \"\"\"\n", + " x = np.asarray(x_nm, dtype=np.float32)\n", + " y = np.asarray(y_nm, dtype=np.float32)\n", + " z = np.asarray(zc_nm, dtype=np.float32).copy()\n", + " n = int(z.shape[0])\n", + " if n < 5:\n", + " return z.astype(np.float32), []\n", + "\n", + " if crossings is None:\n", + " xy = np.stack([x, y], axis=1)\n", + " crossings = _collect_intersections(\n", + " xy,\n", + " min_separation_beads=int(min_separation_beads),\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + "\n", + " def get_segment_indices(center, w):\n", + " return np.array([(center + k) % n for k in range(-w, w+1)],\n", + " dtype=np.int32)\n", + "\n", + " crossing_info = []\n", + " crossing_centers = []\n", + " w = int(boost_window_beads)\n", + "\n", + " for c in crossings:\n", + " i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n", + " i2 = (i + 1) % n; j2 = (j + 1) % n\n", + " ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n", + "\n", + " bead_i = i if ti < 0.5 else i2\n", + " bead_j = j if uj < 0.5 else j2\n", + " zi = float((1.0 - ti) * z[i] + ti * z[i2])\n", + " zj = float((1.0 - uj) * z[j] + uj * z[j2])\n", + "\n", + " top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j, bead_i)\n", + " top_seg = get_segment_indices(top_center, w)\n", + " bottom_seg = get_segment_indices(bottom_center, w)\n", + "\n", + " bottom_local_max = float(z[bottom_seg].max())\n", + " top_local_max = float(z[top_seg].max())\n", + "\n", + " if boost_method == \"absolute\":\n", + " target_height = bottom_local_max + float(guaranteed_offset_nm)\n", + " else:\n", + " target_height = max(top_local_max, bottom_local_max) + float(guaranteed_offset_nm)\n", + "\n", + " for off in range(-w, w+1):\n", + " k = (top_center + off) % n\n", + " wt = float(_taper_weight(off, w,\n", + " profile=boost_profile,\n", + " sigma=boost_sigma_beads))\n", + " z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n", + "\n", + " crossing_centers.extend([int(top_center), int(bottom_center)])\n", + " crossing_info.append(dict(\n", + " pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n", + " seg_i = int(i),\n", + " seg_j = int(j),\n", + " top_center = int(top_center),\n", + " bottom_center= int(bottom_center),\n", + " ))\n", + "\n", + " # Clip beads far from any crossing\n", + " if crossing_centers and far_clip_nm is not None:\n", + " centers = np.array(crossing_centers, dtype=np.int32)\n", + " r = int(far_clip_window_beads)\n", + " for k in range(n):\n", + " d = int(np.min(np.minimum(np.abs(k - centers),\n", + " n - np.abs(k - centers))))\n", + " if d > r:\n", + " z[k] = min(z[k], float(far_clip_nm))\n", + "\n", + " return z.astype(np.float32), crossing_info\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Main AFM renderer\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def create_z_based_afm(\n", + " coords,\n", + " nx=800, ny=800,\n", + " dna_diameter_nm=2.0,\n", + " tip_radius_nm=0.01,\n", + " max_height_nm=6.0,\n", + " target_nm_per_px=0.08,\n", + " max_radius_px=96,\n", + " radius_shrink_px=2.0,\n", + " final_blur_sigma_px=0.20,\n", + " apply_edge_taper=True,\n", + " taper_sigma_nm=0.45,\n", + " taper_floor=0.10,\n", + " add_center_ridge=True,\n", + " ridge_sigma_nm=0.25,\n", + " ridge_amp_nm=0.25,\n", + " grain_nm=0.0,\n", + " grain_sigma_px=0.6,\n", + " grain_seed=1,\n", + " enable_crossing_boost=True,\n", + " min_separation_beads=12,\n", + " boost_window_beads=2,\n", + " guaranteed_offset_nm=1.0,\n", + " boost_method=\"additive\",\n", + " boost_profile=\"gaussian\",\n", + " boost_sigma_beads=None,\n", + " crossings_precomputed=None,\n", + " far_clip_nm=2.5,\n", + " far_clip_window_beads=12,\n", + " return_crossing_info=False,\n", + " return_masks=True,\n", + " extent=None,\n", + "):\n", + " \"\"\"\n", + " Master AFM renderer. Pipeline:\n", + "\n", + " 1. Project 3-D bead coords onto a 2-D effective grid\n", + " 2. Optionally boost crossing heights (boost_crossings_intersections)\n", + " 3. Rasterise closed polyline with z-interpolation\n", + " 4. Apply disk_cap_structure (DNA cylindrical cross-section)\n", + " 5. Apply AFM_tip (tip-sample convolution)\n", + " 6. Clip, blur, edge-taper, center-ridge\n", + " 7. Optional synthetic grain noise\n", + " 8. Upsample to requested (nx, ny)\n", + "\n", + " Returns (afm_image, debug_dict) when return_masks=True.\n", + " \"\"\"\n", + " x, y, z = coords.T\n", + " if extent is None:\n", + " xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n", + " ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n", + " else:\n", + " xmin, xmax, ymin, ymax = extent\n", + "\n", + " # CHANGED: render directly on the physical grid selected upstream\n", + " nx_eff, ny_eff = int(nx), int(ny)\n", + " px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n", + " py_eff = (ymax - ymin) / max(ny_eff - 1, 1)\n", + " p_eff = 0.5 * (px_eff + py_eff)\n", + "\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n", + " 0, nx_eff - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n", + " 0, ny_eff - 1)\n", + "\n", + " zc = (z - z.min()).astype(np.float32)\n", + "\n", + " # ── Crossing boost ───────────────────────────────────────────────────────\n", + " crossing_info = []\n", + " if enable_crossing_boost:\n", + " zc, crossing_info = boost_crossings_intersections(\n", + " x.astype(np.float32), y.astype(np.float32), zc,\n", + " crossings=crossings_precomputed,\n", + " min_separation_beads=int(min_separation_beads),\n", + " boost_window_beads=int(boost_window_beads),\n", + " guaranteed_offset_nm=float(guaranteed_offset_nm),\n", + " boost_method=str(boost_method),\n", + " boost_profile=str(boost_profile),\n", + " boost_sigma_beads=boost_sigma_beads,\n", + " far_clip_nm=far_clip_nm,\n", + " far_clip_window_beads=int(far_clip_window_beads),\n", + " )\n", + "\n", + " # ── Rasterise polyline ───────────────────────────────────────────────────\n", + " z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n", + " line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n", + " x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " z_line[y0, x0] = max(z_line[y0, x0], z0)\n", + " line_mask[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys, zs = xs[keep], ys[keep], zs[keep]\n", + " z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n", + " line_mask[ys, xs] = True\n", + "\n", + " # ── DNA cap dilation ─────────────────────────────────────────────────────\n", + " # CHANGED: keep the DNA diameter in physical units; radius_shrink_px is disabled upstream\n", + " r_eff = max(0.5 * float(dna_diameter_nm), 0.2)\n", + " fp_dna, cap = disk_cap_structure(r_eff, p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(z_line, footprint=fp_dna,\n", + " structure=cap).astype(np.float32)\n", + " dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna) > 0\n", + " surface[~dna_region] = 0.0\n", + "\n", + " # ── Tip convolution ──────────────────────────────────────────────────────\n", + " r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n", + " if r_tip_px >= 0.5:\n", + " fp_tip, tip_struct = AFM_tip(\n", + " float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(surface, footprint=fp_tip,\n", + " structure=tip_struct).astype(np.float32)\n", + "\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + " if final_blur_sigma_px > 0:\n", + " surface = gaussian_filter(surface,\n", + " sigma=float(final_blur_sigma_px)).astype(np.float32)\n", + "\n", + " # ── Edge taper + center ridge ────────────────────────────────────────────\n", + " if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n", + " dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n", + " dist_nm = dist_px * np.float32(p_eff)\n", + " if apply_edge_taper:\n", + " sig = max(float(taper_sigma_nm), 1e-6)\n", + " factor = taper_floor + (1.0 - taper_floor) * np.exp(\n", + " -(dist_nm**2) / (2.0 * sig**2))\n", + " surface[dna_region] *= factor[dna_region]\n", + " if add_center_ridge and ridge_amp_nm > 0:\n", + " rs = max(float(ridge_sigma_nm), 1e-6)\n", + " ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n", + " surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " # ── Synthetic grain ──────────────────────────────────────────────────────\n", + " if grain_nm and grain_nm > 0:\n", + " rng = np.random.default_rng(int(grain_seed))\n", + " n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n", + " if grain_sigma_px and grain_sigma_px > 0:\n", + " n = gaussian_filter(n, sigma=float(grain_sigma_px)).astype(np.float32)\n", + " std = float(n[dna_region].std()) if np.any(dna_region) else float(n.std())\n", + " if std > 1e-9:\n", + " n /= np.float32(std)\n", + " surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " if return_masks:\n", + " debug = {\n", + " \"line_mask\": line_mask,\n", + " \"dna_region\": dna_region,\n", + " \"extent\": (xmin, xmax, ymin, ymax),\n", + " \"p_nm_per_px\": float(p_eff),\n", + " }\n", + " if return_crossing_info:\n", + " debug[\"crossings\"] = crossing_info\n", + " return surface, debug\n", + "\n", + " return surface, (xmin, xmax, ymin, ymax)" ] }, { @@ -975,7 +1511,519 @@ }, "outputs": [], "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n# Parsing the .spm file for height data\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef robust_range(a):\n \"\"\"\n Compute the robust dynamic range of an array using the 1st and 99th percentiles.\n Only finite values are included in the percentile calculation.\n\n Returns (p1, p99, range) as (float, float, float).\n \"\"\"\n a = np.asarray(a, dtype=np.float32)\n p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n return float(p1), float(p99), float(p99 - p1)\n\n\ndef looks_like_afm_height(img):\n \"\"\"\n Heuristic check for whether img looks like a valid AFM height image.\n Rejects arrays with non-finite, zero, or implausibly large dynamic ranges.\n\n Returns True if the image passes all sanity checks, False otherwise.\n \"\"\"\n p1, p99, rng = robust_range(img)\n return np.isfinite(rng) and 0 < rng <= 1e9\n\n\ndef maybe_to_nm(img):\n \"\"\"\n Auto-convert img from metres to nanometres if its values appear metre-scale\n (i.e., the 99th-percentile absolute value is below 1e-3).\n\n Returns (img_nm, unit_note) where img_nm is float32 in nm and\n unit_note is a short string describing the conversion applied.\n \"\"\"\n p1, p99, rng = robust_range(img)\n mx = max(abs(p1), abs(p99))\n if mx < 1e-3:\n return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n return img.astype(np.float32), \"as-is (nm or counts)\"\n\n\ndef parse_blocks(filename, max_blocks=128):\n \"\"\"\n Read a Bruker .spm file and parse binary data-block metadata.\n Returns (raw_bytes, header_string, block_list).\n \"\"\"\n raw = open(filename, \"rb\").read()\n i_end = raw.find(b\"\\x1a\")\n if i_end == -1:\n i_end = min(len(raw), 400_000)\n header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n\n matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n if not matches:\n raise RuntimeError(\"No 'Data offset' found in header.\")\n\n blocks = []\n for k, pos in enumerate(matches[:max_blocks]):\n win = header[max(0, pos-2500): min(len(header), pos+2500)]\n\n def grab_int(key):\n m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n return int(m.group(1)) if m else None\n\n def grab_float(key):\n m = re.search(\n rf\"{re.escape(key)}\\s*:\\s*([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\",\n win)\n return float(m.group(1)) if m else None\n\n name_candidates = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n name = name_candidates[-1].strip() if name_candidates else None\n\n off = grab_int(\"Data offset\")\n length = grab_int(\"Data length\")\n bpp = grab_int(\"Bytes/pixel\")\n nx_b = grab_int(\"Samps/line\")\n ny_b = grab_int(\"Number of lines\")\n zscale = grab_float(\"Z scale\")\n zoff = grab_float(\"Z offset\")\n\n if None in (off, length, bpp, nx_b, ny_b):\n continue\n blocks.append(dict(name=name or f\"block_{k}\",\n off=off, length=length, bpp=bpp,\n nx=nx_b, ny=ny_b,\n zscale=zscale, zoff=zoff))\n\n uniq, seen = [], set()\n for b in blocks:\n if b[\"off\"] not in seen:\n uniq.append(b); seen.add(b[\"off\"])\n if not uniq:\n raise RuntimeError(\"Found 'Data offset' strings but no parseable blocks.\")\n return raw, header, uniq\n\n\ndef read_block_candidates(raw, b):\n \"\"\"\n Decode one binary data block, trying all plausible dtypes.\n Applies z-scale / z-offset if present.\n Returns list of (dtype_str, image_array).\n \"\"\"\n off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n blob = raw[off:off+length]\n if len(blob) < length:\n raise RuntimeError(\"Truncated block.\")\n\n n = nx_b * ny_b\n cands = []\n dtype_map = {2: [\"i2\", \"u2\"],\n 4: [\"i4\", \"f4\"],\n 1: [\"u1\"]}\n for dt in dtype_map.get(bpp, []):\n arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n if arr.size != n:\n continue\n cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n\n out = []\n for dt, img in cands:\n z = img\n if b.get(\"zscale\") is not None:\n z = z * np.float32(b[\"zscale\"])\n if b.get(\"zoff\") is not None:\n z = z + np.float32(b[\"zoff\"])\n out.append((dt, z))\n return out\n\n\ndef choose_best_height_image(raw, blocks, verbose=True):\n \"\"\"\n Iterate all blocks and dtype candidates, pick the one that:\n - passes looks_like_afm_height\n - has the highest log-variance score\n\n Returns (best_block_dict, best_image_float32).\n Raises RuntimeError if no plausible height image is found.\n \"\"\"\n best, best_score = None, -np.inf\n for b in blocks:\n try:\n for dt, img in read_block_candidates(raw, b):\n if not looks_like_afm_height(img):\n continue\n p1, p99, rng = robust_range(img)\n var = float(np.var(img))\n score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n if score > best_score:\n best_score = score\n best = (b, dt, img, (p1, p99, rng, var))\n except Exception:\n continue\n\n if best is None:\n raise RuntimeError(\"Could not find a plausible AFM height image decode.\")\n b, dt, img, stats = best\n if verbose:\n p1, p99, rng, var = stats\n print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n f\"bpp={b['bpp']} decode={dt}\")\n print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, range={rng:.3g}, var={var:.3g}\")\n return b, img\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# TopoStats-like background / noise extraction\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef plane_remove(z, mask=None):\n \"\"\"\n Fit and subtract a tilted plane from z using least-squares.\n If mask is provided, only background (mask == True) pixels are used for fitting.\n\n Returns the plane-subtracted image as float32.\n \"\"\"\n ny, nx = z.shape\n Y, X = np.mgrid[0:ny, 0:nx]\n A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n b = z.ravel()\n if mask is not None:\n m = mask.ravel().astype(bool)\n A, b = A[m], b[m]\n C, *_ = np.linalg.lstsq(A, b, rcond=None)\n plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n return (z - plane).astype(np.float32)\n\n\ndef line_flatten_median(z, mask=None):\n \"\"\"\n Subtract the per-row median from z to remove line-by-line offset artefacts.\n If mask is provided, the median is computed over background (mask == True)\n pixels only; rows with no unmasked pixels fall back to the full-row median.\n\n Returns the line-flattened image as float32.\n \"\"\"\n out = z.astype(np.float32, copy=True)\n for i in range(out.shape[0]):\n if mask is None or not np.any(mask[i]):\n med = np.median(out[i])\n else:\n med = np.median(out[i, mask[i]])\n out[i] -= np.float32(med)\n return out\n\n\ndef feature_mask(z, smooth_sigma_px=3.0, thresh_sigma=3.0, dilate_px=4):\n \"\"\"\n Detect features (DNA, particles) as pixels deviating by more than\n thresh_sigma MADs from the smooth-residual background.\n The detected region is optionally dilated by dilate_px pixels.\n\n Returns a boolean array of the same shape as z, True where features are detected.\n \"\"\"\n z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n resid = (z - z_s).astype(np.float32)\n med = float(np.median(resid))\n mad = float(np.median(np.abs(resid - med)))\n sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n if dilate_px and dilate_px > 0:\n r = int(dilate_px)\n yy, xx = np.ogrid[-r:r+1, -r:r+1]\n fp = (xx*xx + yy*yy) <= r*r\n feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n return feat\n\n\ndef inpaint_simple(z, mask, smooth_sigma_px=8.0):\n \"\"\"\n Replace masked pixels (e.g., DNA features) with a heavily blurred background\n estimate so that subsequent noise extraction is not biased by foreground features.\n\n Returns the inpainted image as float32.\n \"\"\"\n bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n out = z.copy()\n out[mask] = bg[mask]\n return out\n\n\ndef bandpass_noise(z, low_sigma_px=1.0, high_sigma_px=30.0):\n \"\"\"\n Bandpass filter: subtract large-scale trend from small-scale smoothed\n image to isolate mid-frequency noise texture.\n\n Returns the bandpass-filtered image as float32.\n \"\"\"\n low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n return (low - high).astype(np.float32)\n\n\ndef extract_topostats_like_noise(\n z_in,\n median_size=3,\n bg_quantile=40,\n feature_sigma_px=3.0,\n feature_thresh_sigma=3.0,\n feature_dilate_px=4,\n inpaint_sigma_px=10.0,\n band_low_sigma_px=0.8,\n band_high_sigma_px=35.0,\n target_rms_nm=None,\n):\n \"\"\"\n Full noise-extraction pipeline:\n plane-flatten → line-flatten → detect features → inpaint → bandpass.\n Optionally rescale to target RMS amplitude.\n\n Returns (rms_nm, noise_texture, z_flattened, feature_mask) where:\n rms_nm : float — RMS amplitude of the extracted noise in nm\n noise_texture : float32 ndarray — the bandpass noise field\n z_flattened : float32 ndarray — plane + line flattened height map\n feature_mask : bool ndarray — True where features (DNA) were detected\n \"\"\"\n z = z_in.astype(np.float32)\n if median_size and median_size > 1:\n z = median_filter(z, size=int(median_size)).astype(np.float32)\n\n q = np.percentile(z, float(bg_quantile))\n bg0 = z <= q\n\n z_flat = plane_remove(z, mask=bg0)\n z_flat = line_flatten_median(z_flat, mask=bg0)\n\n feat = feature_mask(z_flat,\n smooth_sigma_px=feature_sigma_px,\n thresh_sigma=feature_thresh_sigma,\n dilate_px=feature_dilate_px)\n z_bg = inpaint_simple(z_flat, feat, smooth_sigma_px=inpaint_sigma_px)\n tex = bandpass_noise(z_bg,\n low_sigma_px=band_low_sigma_px,\n high_sigma_px=band_high_sigma_px)\n\n bg = ~feat\n rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n if target_rms_nm is not None:\n target = float(target_rms_nm)\n if rms > 1e-12:\n tex *= target / rms\n rms = target\n\n return rms, tex.astype(np.float32), z_flat.astype(np.float32), feat.astype(bool)\n\n\ndef tile_or_crop(tex, out_shape, seed=0):\n \"\"\"\n Tile tex to at least out_shape, then take a random (deterministic) crop.\n The crop position is seeded for reproducibility.\n\n Returns a float32 array of exactly out_shape.\n \"\"\"\n ty, tx = tex.shape\n oy, ox = out_shape\n if (ty, tx) == (oy, ox):\n return tex\n tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n rng = np.random.default_rng(seed)\n y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n\n\ndef load_blank_spm_noise(blank_path, target_rms_nm=0.06, verbose=True):\n \"\"\"\n High-level loader: parse SPM file → select best image → convert to nm\n → extract background noise texture.\n\n Returns (rms_nm, noise_texture, diagnostics_dict) where:\n rms_nm : float — RMS amplitude of the background noise in nm\n noise_texture : float32 ndarray — the extracted noise texture\n diagnostics_dict: dict with keys flattened, feature_mask, height_nm, unit_note\n \"\"\"\n raw, header, blocks = parse_blocks(blank_path)\n b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n img_nm, unit_note = maybe_to_nm(img0)\n if verbose:\n print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n\n rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n img_nm,\n median_size=3,\n bg_quantile=45,\n feature_sigma_px=3.0,\n feature_thresh_sigma=3.0,\n feature_dilate_px=6,\n inpaint_sigma_px=10.0,\n band_low_sigma_px=0.7,\n band_high_sigma_px=40.0,\n target_rms_nm=target_rms_nm,\n )\n diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n \"height_nm\": img_nm, \"unit_note\": unit_note}\n if verbose:\n print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n f\"tex shape: {noise_tex.shape}\")\n return rms_nm, noise_tex, diag\n\n# ─────────────────────────────────────────────────────────────────────────────\n# PSD-based fallback noise synthesis\n# ─────────────────────────────────────────────────────────────────────────────\n\nTARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\nEPS = 1e-12 #Safety buffer to ensure no division by 0 or square roots producing NaN values\n\n\ndef load_model(path=MODEL_PATH):\n \"\"\"\n Load the PSD noise model from a .npz file.\n\n Returns a dict with keys: log_psd_mean, log_psd_std, radial_freq,\n rms_values_nm — all as float32 arrays.\n \"\"\"\n data = np.load(path)\n return {\n \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n }\n\n\ndef generate_noise(\n seed,\n target_shape=TARGET_SHAPE,\n target_rms_nm=TARGET_NOISE_RMS_NM,\n method=\"mean_full2d\",\n std_scale=1.0,\n model=None,\n model_path=MODEL_PATH,\n):\n \"\"\"\n Generate a single random AFM-like background noise image from the PSD model.\n\n Parameters\n ----------\n seed : int\n RNG seed for reproducibility.\n target_shape : (rows, cols)\n Output image shape. The PSD is resized automatically if it differs\n from the shape used when building the model.\n target_rms_nm : float or None\n RMS amplitude in nm. None -> draw from the blank-file ensemble.\n method : str\n 'mean_full2d' -- mean log-PSD; randomness from phase only (recommended).\n 'lognormal_full2d' -- draw a random PSD from the fitted log-normal\n distribution (more sample-to-sample variation).\n 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n std_scale : float\n Multiplier on log_psd_std for lognormal method (>1 -> more variation).\n model : dict or None\n Pre-loaded model dict. If None, loaded from model_path on each call.\n model_path : path-like\n Path to the .npz file (used only when model is None).\n\n Returns\n -------\n z : np.ndarray, shape target_shape, dtype float32\n Height noise field in nm.\n \"\"\"\n if model is None:\n model = load_model(model_path)\n\n rng = np.random.default_rng(int(seed))\n out_shape = tuple(target_shape)\n\n if method == \"mean_full2d\":\n psd_sample = np.exp(model[\"log_psd_mean\"])\n\n elif method == \"lognormal_full2d\":\n noise = rng.standard_normal(model[\"log_psd_mean\"].shape).astype(np.float32)\n psd_sample = np.exp(model[\"log_psd_mean\"] + std_scale * model[\"log_psd_std\"] * noise)\n\n elif method == \"empirical_radial\":\n radial_psd = np.exp(model[\"log_psd_mean\"].mean(axis=1))\n radial_freq_for_interp = np.abs(np.fft.fftfreq(model[\"log_psd_mean\"].shape[0], d=1.0))\n\n fy = np.fft.fftfreq(out_shape[0], d=1.0)\n fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n\n psd_sample = np.interp(\n kr.ravel(),\n radial_freq_for_interp,\n radial_psd,\n left=float(radial_psd[0]),\n right=float(radial_psd[-1]),\n ).reshape(kr.shape).astype(np.float32)\n\n else:\n raise ValueError(\n f\"Unknown method {method!r}. \"\n \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n )\n\n psd_ny, psd_nx = psd_sample.shape\n out_ny, out_nx = out_shape\n rfft_nx = out_nx // 2 + 1\n\n if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n from scipy.ndimage import zoom\n psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx), order=1)\n\n psd_sample = psd_sample.astype(np.float32)\n\n phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n\n z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n z -= np.float32(np.mean(z))\n\n target = float(rng.choice(model[\"rms_values_nm\"])) if target_rms_nm is None else float(target_rms_nm)\n cur = float(np.std(z))\n if cur > EPS:\n z *= target / cur\n\n return z\n\n\ndef sample_noise_image(noise_source, noise_asset, seed, out_shape, target_rms_nm):\n \"\"\"\n Dispatch noise generation to the appropriate backend based on noise_source.\n If noise is disabled or no asset is available, returns None.\n\n noise_source : 'blank_spm' — apply a random affine transform to the\n preloaded blank SPM texture and crop to out_shape.\n 'psd_model' — synthesise noise from the PSD model.\n anything else — return None (no noise).\n\n Returns a float32 ndarray of shape out_shape, or None.\n \"\"\"\n if not USE_BLANK_SPM_NOISE or noise_asset is None:\n return None\n\n if noise_source == \"blank_spm\":\n noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n\n if noise_source == \"psd_model\":\n return generate_noise(\n seed=seed,\n target_shape=out_shape,\n target_rms_nm=target_rms_nm,\n method=PSD_NOISE_METHOD,\n std_scale=PSD_NOISE_STD_SCALE,\n model=noise_asset,\n ).astype(np.float32)\n\n return None\n" + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Parsing the .spm file for height data\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def robust_range(a):\n", + " \"\"\"\n", + " Compute the robust dynamic range of an array using the 1st and 99th percentiles.\n", + " Only finite values are included in the percentile calculation.\n", + "\n", + " Returns (p1, p99, range) as (float, float, float).\n", + " \"\"\"\n", + " a = np.asarray(a, dtype=np.float32)\n", + " p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n", + " return float(p1), float(p99), float(p99 - p1)\n", + "\n", + "\n", + "def looks_like_afm_height(img):\n", + " \"\"\"\n", + " Heuristic check for whether img looks like a valid AFM height image.\n", + " Rejects arrays with non-finite, zero, or implausibly large dynamic ranges.\n", + "\n", + " Returns True if the image passes all sanity checks, False otherwise.\n", + " \"\"\"\n", + " p1, p99, rng = robust_range(img)\n", + " return np.isfinite(rng) and 0 < rng <= 1e9\n", + "\n", + "\n", + "def maybe_to_nm(img):\n", + " \"\"\"\n", + " Auto-convert img from metres to nanometres if its values appear metre-scale\n", + " (i.e., the 99th-percentile absolute value is below 1e-3).\n", + "\n", + " Returns (img_nm, unit_note) where img_nm is float32 in nm and\n", + " unit_note is a short string describing the conversion applied.\n", + " \"\"\"\n", + " p1, p99, rng = robust_range(img)\n", + " mx = max(abs(p1), abs(p99))\n", + " if mx < 1e-3:\n", + " return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n", + " return img.astype(np.float32), \"as-is (nm or counts)\"\n", + "\n", + "\n", + "def parse_blocks(filename, max_blocks=128):\n", + " \"\"\"\n", + " Read a Bruker .spm file and parse binary data-block metadata.\n", + " Returns (raw_bytes, header_string, block_list).\n", + " \"\"\"\n", + " raw = open(filename, \"rb\").read()\n", + " i_end = raw.find(b\"\\x1a\")\n", + " if i_end == -1:\n", + " i_end = min(len(raw), 400_000)\n", + " header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n", + "\n", + " matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n", + " if not matches:\n", + " raise RuntimeError(\"No 'Data offset' found in header.\")\n", + "\n", + " blocks = []\n", + " for k, pos in enumerate(matches[:max_blocks]):\n", + " win = header[max(0, pos-2500): min(len(header), pos+2500)]\n", + "\n", + " def grab_int(key):\n", + " m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n", + " return int(m.group(1)) if m else None\n", + "\n", + " def grab_float(key):\n", + " m = re.search(\n", + " rf\"{re.escape(key)}\\s*:\\s*([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\",\n", + " win)\n", + " return float(m.group(1)) if m else None\n", + "\n", + " name_candidates = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n", + " name = name_candidates[-1].strip() if name_candidates else None\n", + "\n", + " off = grab_int(\"Data offset\")\n", + " length = grab_int(\"Data length\")\n", + " bpp = grab_int(\"Bytes/pixel\")\n", + " nx_b = grab_int(\"Samps/line\")\n", + " ny_b = grab_int(\"Number of lines\")\n", + " zscale = grab_float(\"Z scale\")\n", + " zoff = grab_float(\"Z offset\")\n", + "\n", + " if None in (off, length, bpp, nx_b, ny_b):\n", + " continue\n", + " blocks.append(dict(name=name or f\"block_{k}\",\n", + " off=off, length=length, bpp=bpp,\n", + " nx=nx_b, ny=ny_b,\n", + " zscale=zscale, zoff=zoff))\n", + "\n", + " uniq, seen = [], set()\n", + " for b in blocks:\n", + " if b[\"off\"] not in seen:\n", + " uniq.append(b); seen.add(b[\"off\"])\n", + " if not uniq:\n", + " raise RuntimeError(\"Found 'Data offset' strings but no parseable blocks.\")\n", + " return raw, header, uniq\n", + "\n", + "\n", + "def read_block_candidates(raw, b):\n", + " \"\"\"\n", + " Decode one binary data block, trying all plausible dtypes.\n", + " Applies z-scale / z-offset if present.\n", + " Returns list of (dtype_str, image_array).\n", + " \"\"\"\n", + " off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n", + " nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n", + " blob = raw[off:off+length]\n", + " if len(blob) < length:\n", + " raise RuntimeError(\"Truncated block.\")\n", + "\n", + " n = nx_b * ny_b\n", + " cands = []\n", + " dtype_map = {2: [\"i2\", \"u2\"],\n", + " 4: [\"i4\", \"f4\"],\n", + " 1: [\"u1\"]}\n", + " for dt in dtype_map.get(bpp, []):\n", + " arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n", + " if arr.size != n:\n", + " continue\n", + " cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n", + "\n", + " out = []\n", + " for dt, img in cands:\n", + " z = img\n", + " if b.get(\"zscale\") is not None:\n", + " z = z * np.float32(b[\"zscale\"])\n", + " if b.get(\"zoff\") is not None:\n", + " z = z + np.float32(b[\"zoff\"])\n", + " out.append((dt, z))\n", + " return out\n", + "\n", + "\n", + "def choose_best_height_image(raw, blocks, verbose=True):\n", + " \"\"\"\n", + " Iterate all blocks and dtype candidates, pick the one that:\n", + " - passes looks_like_afm_height\n", + " - has the highest log-variance score\n", + "\n", + " Returns (best_block_dict, best_image_float32).\n", + " Raises RuntimeError if no plausible height image is found.\n", + " \"\"\"\n", + " best, best_score = None, -np.inf\n", + " for b in blocks:\n", + " try:\n", + " for dt, img in read_block_candidates(raw, b):\n", + " if not looks_like_afm_height(img):\n", + " continue\n", + " p1, p99, rng = robust_range(img)\n", + " var = float(np.var(img))\n", + " score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n", + " if score > best_score:\n", + " best_score = score\n", + " best = (b, dt, img, (p1, p99, rng, var))\n", + " except Exception:\n", + " continue\n", + "\n", + " if best is None:\n", + " raise RuntimeError(\"Could not find a plausible AFM height image decode.\")\n", + " b, dt, img, stats = best\n", + " if verbose:\n", + " p1, p99, rng, var = stats\n", + " print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n", + " f\"bpp={b['bpp']} decode={dt}\")\n", + " print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, range={rng:.3g}, var={var:.3g}\")\n", + " return b, img\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# TopoStats-like background / noise extraction\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def plane_remove(z, mask=None):\n", + " \"\"\"\n", + " Fit and subtract a tilted plane from z using least-squares.\n", + " If mask is provided, only background (mask == True) pixels are used for fitting.\n", + "\n", + " Returns the plane-subtracted image as float32.\n", + " \"\"\"\n", + " ny, nx = z.shape\n", + " Y, X = np.mgrid[0:ny, 0:nx]\n", + " A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n", + " b = z.ravel()\n", + " if mask is not None:\n", + " m = mask.ravel().astype(bool)\n", + " A, b = A[m], b[m]\n", + " C, *_ = np.linalg.lstsq(A, b, rcond=None)\n", + " plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n", + " return (z - plane).astype(np.float32)\n", + "\n", + "\n", + "def line_flatten_median(z, mask=None):\n", + " \"\"\"\n", + " Subtract the per-row median from z to remove line-by-line offset artefacts.\n", + " If mask is provided, the median is computed over background (mask == True)\n", + " pixels only; rows with no unmasked pixels fall back to the full-row median.\n", + "\n", + " Returns the line-flattened image as float32.\n", + " \"\"\"\n", + " out = z.astype(np.float32, copy=True)\n", + " for i in range(out.shape[0]):\n", + " if mask is None or not np.any(mask[i]):\n", + " med = np.median(out[i])\n", + " else:\n", + " med = np.median(out[i, mask[i]])\n", + " out[i] -= np.float32(med)\n", + " return out\n", + "\n", + "\n", + "def feature_mask(z, smooth_sigma_px=3.0, thresh_sigma=3.0, dilate_px=4):\n", + " \"\"\"\n", + " Detect features (DNA, particles) as pixels deviating by more than\n", + " thresh_sigma MADs from the smooth-residual background.\n", + " The detected region is optionally dilated by dilate_px pixels.\n", + "\n", + " Returns a boolean array of the same shape as z, True where features are detected.\n", + " \"\"\"\n", + " z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " resid = (z - z_s).astype(np.float32)\n", + " med = float(np.median(resid))\n", + " mad = float(np.median(np.abs(resid - med)))\n", + " sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n", + " feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n", + " if dilate_px and dilate_px > 0:\n", + " r = int(dilate_px)\n", + " yy, xx = np.ogrid[-r:r+1, -r:r+1]\n", + " fp = (xx*xx + yy*yy) <= r*r\n", + " feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n", + " return feat\n", + "\n", + "\n", + "def inpaint_simple(z, mask, smooth_sigma_px=8.0):\n", + " \"\"\"\n", + " Replace masked pixels (e.g., DNA features) with a heavily blurred background\n", + " estimate so that subsequent noise extraction is not biased by foreground features.\n", + "\n", + " Returns the inpainted image as float32.\n", + " \"\"\"\n", + " bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " out = z.copy()\n", + " out[mask] = bg[mask]\n", + " return out\n", + "\n", + "\n", + "def bandpass_noise(z, low_sigma_px=1.0, high_sigma_px=30.0):\n", + " \"\"\"\n", + " Bandpass filter: subtract large-scale trend from small-scale smoothed\n", + " image to isolate mid-frequency noise texture.\n", + "\n", + " Returns the bandpass-filtered image as float32.\n", + " \"\"\"\n", + " low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n", + " high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n", + " return (low - high).astype(np.float32)\n", + "\n", + "\n", + "def extract_topostats_like_noise(\n", + " z_in,\n", + " median_size=3,\n", + " bg_quantile=40,\n", + " feature_sigma_px=3.0,\n", + " feature_thresh_sigma=3.0,\n", + " feature_dilate_px=4,\n", + " inpaint_sigma_px=10.0,\n", + " band_low_sigma_px=0.8,\n", + " band_high_sigma_px=35.0,\n", + " target_rms_nm=None,\n", + "):\n", + " \"\"\"\n", + " Full noise-extraction pipeline:\n", + " plane-flatten → line-flatten → detect features → inpaint → bandpass.\n", + " Optionally rescale to target RMS amplitude.\n", + "\n", + " Returns (rms_nm, noise_texture, z_flattened, feature_mask) where:\n", + " rms_nm : float — RMS amplitude of the extracted noise in nm\n", + " noise_texture : float32 ndarray — the bandpass noise field\n", + " z_flattened : float32 ndarray — plane + line flattened height map\n", + " feature_mask : bool ndarray — True where features (DNA) were detected\n", + " \"\"\"\n", + " z = z_in.astype(np.float32)\n", + " if median_size and median_size > 1:\n", + " z = median_filter(z, size=int(median_size)).astype(np.float32)\n", + "\n", + " q = np.percentile(z, float(bg_quantile))\n", + " bg0 = z <= q\n", + "\n", + " z_flat = plane_remove(z, mask=bg0)\n", + " z_flat = line_flatten_median(z_flat, mask=bg0)\n", + "\n", + " feat = feature_mask(z_flat,\n", + " smooth_sigma_px=feature_sigma_px,\n", + " thresh_sigma=feature_thresh_sigma,\n", + " dilate_px=feature_dilate_px)\n", + " z_bg = inpaint_simple(z_flat, feat, smooth_sigma_px=inpaint_sigma_px)\n", + " tex = bandpass_noise(z_bg,\n", + " low_sigma_px=band_low_sigma_px,\n", + " high_sigma_px=band_high_sigma_px)\n", + "\n", + " bg = ~feat\n", + " rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n", + " if target_rms_nm is not None:\n", + " target = float(target_rms_nm)\n", + " if rms > 1e-12:\n", + " tex *= target / rms\n", + " rms = target\n", + "\n", + " return rms, tex.astype(np.float32), z_flat.astype(np.float32), feat.astype(bool)\n", + "\n", + "\n", + "def tile_or_crop(tex, out_shape, seed=0):\n", + " \"\"\"\n", + " Tile tex to at least out_shape, then take a random (deterministic) crop.\n", + " The crop position is seeded for reproducibility.\n", + "\n", + " Returns a float32 array of exactly out_shape.\n", + " \"\"\"\n", + " ty, tx = tex.shape\n", + " oy, ox = out_shape\n", + " if (ty, tx) == (oy, ox):\n", + " return tex\n", + " tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n", + " rng = np.random.default_rng(seed)\n", + " y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n", + " x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n", + " return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n", + "\n", + "\n", + "def load_blank_spm_noise(blank_path, target_rms_nm=0.06, verbose=True):\n", + " \"\"\"\n", + " High-level loader: parse SPM file → select best image → convert to nm\n", + " → extract background noise texture.\n", + "\n", + " Returns (rms_nm, noise_texture, diagnostics_dict) where:\n", + " rms_nm : float — RMS amplitude of the background noise in nm\n", + " noise_texture : float32 ndarray — the extracted noise texture\n", + " diagnostics_dict: dict with keys flattened, feature_mask, height_nm, unit_note\n", + " \"\"\"\n", + " raw, header, blocks = parse_blocks(blank_path)\n", + " b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n", + " img_nm, unit_note = maybe_to_nm(img0)\n", + " if verbose:\n", + " print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n", + "\n", + " rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n", + " img_nm,\n", + " median_size=3,\n", + " bg_quantile=45,\n", + " feature_sigma_px=3.0,\n", + " feature_thresh_sigma=3.0,\n", + " feature_dilate_px=6,\n", + " inpaint_sigma_px=10.0,\n", + " band_low_sigma_px=0.7,\n", + " band_high_sigma_px=40.0,\n", + " target_rms_nm=target_rms_nm,\n", + " )\n", + " diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n", + " \"height_nm\": img_nm, \"unit_note\": unit_note}\n", + " if verbose:\n", + " print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n", + " f\"tex shape: {noise_tex.shape}\")\n", + " return rms_nm, noise_tex, diag\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# PSD-based fallback noise synthesis\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "TARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\n", + "EPS = 1e-12 #Safety buffer to ensure no division by 0 or square roots producing NaN values\n", + "\n", + "\n", + "def load_model(path=MODEL_PATH):\n", + " \"\"\"\n", + " Load the PSD noise model from a .npz file.\n", + "\n", + " Returns a dict with keys: log_psd_mean, log_psd_std, radial_freq,\n", + " rms_values_nm — all as float32 arrays.\n", + " \"\"\"\n", + " data = np.load(path)\n", + " return {\n", + " \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n", + " \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n", + " \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n", + " \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n", + " }\n", + "\n", + "\n", + "def generate_noise(\n", + " seed,\n", + " target_shape=TARGET_SHAPE,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " method=\"mean_full2d\",\n", + " std_scale=1.0,\n", + " model=None,\n", + " model_path=MODEL_PATH,\n", + "):\n", + " \"\"\"\n", + " Generate a single random AFM-like background noise image from the PSD model.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " RNG seed for reproducibility.\n", + " target_shape : (rows, cols)\n", + " Output image shape. The PSD is resized automatically if it differs\n", + " from the shape used when building the model.\n", + " target_rms_nm : float or None\n", + " RMS amplitude in nm. None -> draw from the blank-file ensemble.\n", + " method : str\n", + " 'mean_full2d' -- mean log-PSD; randomness from phase only (recommended).\n", + " 'lognormal_full2d' -- draw a random PSD from the fitted log-normal\n", + " distribution (more sample-to-sample variation).\n", + " 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n", + " std_scale : float\n", + " Multiplier on log_psd_std for lognormal method (>1 -> more variation).\n", + " model : dict or None\n", + " Pre-loaded model dict. If None, loaded from model_path on each call.\n", + " model_path : path-like\n", + " Path to the .npz file (used only when model is None).\n", + "\n", + " Returns\n", + " -------\n", + " z : np.ndarray, shape target_shape, dtype float32\n", + " Height noise field in nm.\n", + " \"\"\"\n", + " if model is None:\n", + " model = load_model(model_path)\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " out_shape = tuple(target_shape)\n", + "\n", + " if method == \"mean_full2d\":\n", + " psd_sample = np.exp(model[\"log_psd_mean\"])\n", + "\n", + " elif method == \"lognormal_full2d\":\n", + " noise = rng.standard_normal(model[\"log_psd_mean\"].shape).astype(np.float32)\n", + " psd_sample = np.exp(model[\"log_psd_mean\"] + std_scale * model[\"log_psd_std\"] * noise)\n", + "\n", + " elif method == \"empirical_radial\":\n", + " radial_psd = np.exp(model[\"log_psd_mean\"].mean(axis=1))\n", + " radial_freq_for_interp = np.abs(np.fft.fftfreq(model[\"log_psd_mean\"].shape[0], d=1.0))\n", + "\n", + " fy = np.fft.fftfreq(out_shape[0], d=1.0)\n", + " fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n", + " fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n", + " kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n", + "\n", + " psd_sample = np.interp(\n", + " kr.ravel(),\n", + " radial_freq_for_interp,\n", + " radial_psd,\n", + " left=float(radial_psd[0]),\n", + " right=float(radial_psd[-1]),\n", + " ).reshape(kr.shape).astype(np.float32)\n", + "\n", + " else:\n", + " raise ValueError(\n", + " f\"Unknown method {method!r}. \"\n", + " \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n", + " )\n", + "\n", + " psd_ny, psd_nx = psd_sample.shape\n", + " out_ny, out_nx = out_shape\n", + " rfft_nx = out_nx // 2 + 1\n", + "\n", + " if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n", + " from scipy.ndimage import zoom\n", + " psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx), order=1)\n", + "\n", + " psd_sample = psd_sample.astype(np.float32)\n", + "\n", + " phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n", + " spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n", + "\n", + " z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n", + " z -= np.float32(np.mean(z))\n", + "\n", + " target = float(rng.choice(model[\"rms_values_nm\"])) if target_rms_nm is None else float(target_rms_nm)\n", + " cur = float(np.std(z))\n", + " if cur > EPS:\n", + " z *= target / cur\n", + "\n", + " return z\n", + "\n", + "\n", + "def sample_noise_image(noise_source, noise_asset, seed, out_shape, target_rms_nm):\n", + " \"\"\"\n", + " Dispatch noise generation to the appropriate backend based on noise_source.\n", + " If noise is disabled or no asset is available, returns None.\n", + "\n", + " noise_source : 'blank_spm' — apply a random affine transform to the\n", + " preloaded blank SPM texture and crop to out_shape.\n", + " 'psd_model' — synthesise noise from the PSD model.\n", + " anything else — return None (no noise).\n", + "\n", + " Returns a float32 ndarray of shape out_shape, or None.\n", + " \"\"\"\n", + " if not USE_BLANK_SPM_NOISE or noise_asset is None:\n", + " return None\n", + "\n", + " if noise_source == \"blank_spm\":\n", + " noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n", + " return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n", + "\n", + " if noise_source == \"psd_model\":\n", + " return generate_noise(\n", + " seed=seed,\n", + " target_shape=out_shape,\n", + " target_rms_nm=target_rms_nm,\n", + " method=PSD_NOISE_METHOD,\n", + " std_scale=PSD_NOISE_STD_SCALE,\n", + " model=noise_asset,\n", + " ).astype(np.float32)\n", + "\n", + " return None\n" ] }, { @@ -1002,7 +2050,63 @@ }, "outputs": [], "source": [ - "def nm_to_px(coords_xy_nm, extent, nx, ny):\n \"\"\"\n Map (x, y) coordinates in nm to integer pixel indices within a grid.\n Clamps results to [0, nx-1] and [0, ny-1] to prevent out-of-bounds access.\n\n Returns (ix, iy) as int32 arrays of the same length as coords_xy_nm.\n \"\"\"\n x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n xmin, xmax, ymin, ymax = extent\n ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n 0, nx - 1)\n iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n 0, ny - 1)\n return ix, iy\n\n\ndef rasterize_closed_polyline_mask(coords, extent, nx, ny):\n \"\"\"\n Return a 1-px-wide binary raster of the closed bead polyline.\n Each consecutive bead pair is drawn by dense linear interpolation;\n duplicate pixels are de-duplicated.\n\n Returns a bool array of shape (ny, nx) with True on the polyline.\n \"\"\"\n coords = np.asarray(coords, dtype=np.float32)\n ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n out = np.zeros((ny, nx), dtype=bool)\n npts = len(ix)\n for k in range(npts):\n k2 = (k + 1) % npts\n x0, y0 = int(ix[k]), int(iy[k])\n x1, y1 = int(ix[k2]), int(iy[k2])\n n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n if n_seg <= 1:\n out[y0, x0] = True\n continue\n xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n if xs.size > 1:\n keep = np.ones(xs.shape[0], dtype=bool)\n keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n xs, ys = xs[keep], ys[keep]\n out[ys, xs] = True\n return out\n\n\ndef make_dna_mask_from_beads(coords, extent, nx, ny, dilate_px=3):\n \"\"\"\n Rasterise the polyline and dilate by dilate_px pixels using a\n circular disk footprint to give the mask physical width.\n\n Returns a uint8 array of shape (ny, nx) with values 0 or 1.\n \"\"\"\n line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n if dilate_px and dilate_px > 0:\n line = binary_dilation(line, structure=disk_footprint(float(dilate_px)))\n return line.astype(np.uint8)" + "def nm_to_px(coords_xy_nm, extent, nx, ny):\n", + " \"\"\"\n", + " Map (x, y) coordinates in nm to integer pixel indices within a grid.\n", + " Clamps results to [0, nx-1] and [0, ny-1] to prevent out-of-bounds access.\n", + "\n", + " Returns (ix, iy) as int32 arrays of the same length as coords_xy_nm.\n", + " \"\"\"\n", + " x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n", + " xmin, xmax, ymin, ymax = extent\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n", + " 0, nx - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n", + " 0, ny - 1)\n", + " return ix, iy\n", + "\n", + "\n", + "def rasterize_closed_polyline_mask(coords, extent, nx, ny):\n", + " \"\"\"\n", + " Return a 1-px-wide binary raster of the closed bead polyline.\n", + " Each consecutive bead pair is drawn by dense linear interpolation;\n", + " duplicate pixels are de-duplicated.\n", + "\n", + " Returns a bool array of shape (ny, nx) with True on the polyline.\n", + " \"\"\"\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n", + " out = np.zeros((ny, nx), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0 = int(ix[k]), int(iy[k])\n", + " x1, y1 = int(ix[k2]), int(iy[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " out[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys = xs[keep], ys[keep]\n", + " out[ys, xs] = True\n", + " return out\n", + "\n", + "\n", + "def make_dna_mask_from_beads(coords, extent, nx, ny, dilate_px=3):\n", + " \"\"\"\n", + " Rasterise the polyline and dilate by dilate_px pixels using a\n", + " circular disk footprint to give the mask physical width.\n", + "\n", + " Returns a uint8 array of shape (ny, nx) with values 0 or 1.\n", + " \"\"\"\n", + " line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n", + " if dilate_px and dilate_px > 0:\n", + " line = binary_dilation(line, structure=disk_footprint(float(dilate_px)))\n", + " return line.astype(np.uint8)" ] }, { @@ -1028,7 +2132,219 @@ }, "outputs": [], "source": [ - "def _canon_axis(u):\n \"\"\"\n Normalise a 2-D vector and canonicalise its sign so that +v and -v\n are treated as the same axis direction.\n Returns None if the vector is degenerate.\n \"\"\"\n u = np.asarray(u, dtype=float)\n nrm = float(np.linalg.norm(u))\n if nrm < 1e-12:\n return None\n u = u / nrm\n if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n u = -u\n return u\n\n\ndef find_polyline_crossings(coords_xy, min_separation_beads=CROSS_MIN_SEP_BEADS,\n merge_radius_nm=0.5):\n \"\"\"\n Detect all strand crossings of a closed ring polyline in XY.\n\n Skips adjacent / shared-vertex segment pairs and pairs whose circular\n contour separation is less than min_separation_beads.\n Nearby raw hits are clustered via _merge_crossing_clusters.\n\n Returns list of dicts:\n {\n pt_nm : (2,) float32 — crossing position in nm\n axes_nm : list of unit vectors — chain directions at the crossing\n seg_i : int — first segment index\n seg_j : int — second segment index\n t : float — parametric position along segment i\n u : float — parametric position along segment j\n n_hits : int — raw intersection count merged into this cluster\n }\n \"\"\"\n xy = np.asarray(coords_xy, dtype=float)\n n = int(xy.shape[0])\n\n raw = []\n for i in range(n):\n i2 = (i + 1) % n\n p1, p2 = xy[i], xy[i2]\n for j in range(i + 1, n):\n j2 = (j + 1) % n\n if i == j or i2 == j or j2 == i:\n continue\n if _circ_sep(n, i, j) < int(min_separation_beads):\n continue\n q1, q2 = xy[j], xy[j2]\n hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n if not hit:\n continue\n ua = _canon_axis(p2 - p1)\n ub = _canon_axis(q2 - q1)\n if ua is None or ub is None:\n continue\n raw.append(dict(pt=np.array(pt, float),\n axes=[ua, ub],\n seg_i=int(i), seg_j=int(j),\n t=float(t), u=float(u)))\n\n # Cluster nearby raw intersections\n clusters = []\n for r in raw:\n pt = r[\"pt\"]\n placed = False\n for c in clusters:\n if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n c[\"pt_sum\"] += pt\n c[\"n\"] += 1\n c[\"axes\"].extend(r[\"axes\"])\n c[\"items\"].append(r)\n placed = True\n break\n if not placed:\n clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n \"axes\": list(r[\"axes\"]), \"items\": [r]})\n\n out = []\n for c in clusters:\n pt_mean = c[\"pt_sum\"] / c[\"n\"]\n axes = []\n for uvec in c[\"axes\"]:\n uvec = _canon_axis(uvec)\n if uvec is None:\n continue\n if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n continue\n axes.append(uvec)\n rep = c[\"items\"][0]\n out.append(dict(\n pt_nm = np.asarray(pt_mean, dtype=np.float32),\n axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n if axes else [np.array([1.0, 0.0], np.float32)],\n seg_i = int(rep[\"seg_i\"]),\n seg_j = int(rep[\"seg_j\"]),\n t = float(rep[\"t\"]),\n u = float(rep[\"u\"]),\n n_hits = int(c[\"n\"]),\n ))\n return out\n\n\ndef _add_crossing_patch(mask, pt_px, axes_px,\n sigma_center=2.0,\n chain_extent=5.0,\n sigma_perp=1.8,\n center_weight=1.0,\n chain_weight=0.8):\n \"\"\"\n Paint one crossing onto mask as:\n center_weight * isotropic_Gaussian\n + chain_weight * max(anisotropic Gaussians aligned to chain axes)\n\n Uses np.maximum so multiple crossings compose correctly.\n Modifies mask in-place; returns None.\n \"\"\"\n H, W = mask.shape\n cx, cy = float(pt_px[0]), float(pt_px[1])\n sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n\n xs = np.arange(x0, x1+1, dtype=float)\n ys = np.arange(y0, y1+1, dtype=float)\n X, Y = np.meshgrid(xs, ys)\n dx = X - cx; dy = Y - cy\n\n gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n\n g_chain = np.zeros_like(gc)\n for v in axes_px:\n vx, vy = float(v[0]), float(v[1])\n u_par = dx*vx + dy*vy\n u_perp = -dx*vy + dy*vx\n g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n + u_perp**2 / sigma_perp**2))\n g_chain = np.maximum(g_chain, g)\n\n patch = center_weight * gc + chain_weight * g_chain\n mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n\n\ndef make_crossing_mask(coords, extent, nx, ny,\n sigma_center_px=CROSS_SIGMA_CENTER_PX,\n sigma_perp_px=CROSS_SIGMA_PERP_PX,\n chain_extent=CROSS_CHAIN_EXTENT,\n center_weight=CROSS_CENTER_WEIGHT,\n chain_weight=CROSS_CHAIN_WEIGHT,\n min_separation_beads=CROSS_MIN_SEP_BEADS,\n crossings_precomputed=None,\n merge_radius_nm=0.5):\n \"\"\"\n Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n\n Uses find_polyline_crossings unless crossings_precomputed is supplied\n (recommended: reuse the same crossing list that was used for the\n height boost so the mask and the AFM image are perfectly aligned).\n\n Returns (cross_mask, crossings) where:\n cross_mask : float32 ndarray of shape (ny, nx), values in [0, 1]\n crossings : list of crossing dicts as returned by find_polyline_crossings\n \"\"\"\n coords = np.asarray(coords, dtype=np.float32)\n xy_nm = coords[:, :2].astype(float)\n\n if crossings_precomputed is None:\n crossings = find_polyline_crossings(\n xy_nm,\n min_separation_beads=min_separation_beads,\n merge_radius_nm=float(merge_radius_nm),\n )\n else:\n crossings = crossings_precomputed\n\n xmin, xmax, ymin, ymax = extent\n sx = (nx - 1) / (xmax - xmin)\n sy = (ny - 1) / (ymax - ymin)\n\n mask = np.zeros((ny, nx), dtype=np.float32)\n\n for c in crossings:\n x_nm, y_nm = c[\"pt_nm\"]\n px = (x_nm - xmin) * sx\n py = (y_nm - ymin) * sy\n\n axes_px = []\n for v in c[\"axes_nm\"]:\n vx_px = v[0] * sx; vy_px = v[1] * sy\n nrm = float(np.hypot(vx_px, vy_px))\n if nrm < 1e-12:\n continue\n axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n if not axes_px:\n axes_px = [np.array([1.0, 0.0], dtype=float)]\n\n _add_crossing_patch(\n mask,\n pt_px=(px, py),\n axes_px=axes_px,\n sigma_center=float(sigma_center_px),\n chain_extent=float(chain_extent),\n sigma_perp=float(sigma_perp_px),\n center_weight=float(center_weight),\n chain_weight=float(chain_weight),\n )\n\n m = float(mask.max())\n if m > 1e-12:\n mask /= m\n return mask.astype(np.float32), crossings" + "def _canon_axis(u):\n", + " \"\"\"\n", + " Normalise a 2-D vector and canonicalise its sign so that +v and -v\n", + " are treated as the same axis direction.\n", + " Returns None if the vector is degenerate.\n", + " \"\"\"\n", + " u = np.asarray(u, dtype=float)\n", + " nrm = float(np.linalg.norm(u))\n", + " if nrm < 1e-12:\n", + " return None\n", + " u = u / nrm\n", + " if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n", + " u = -u\n", + " return u\n", + "\n", + "\n", + "def find_polyline_crossings(coords_xy, min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Detect all strand crossings of a closed ring polyline in XY.\n", + "\n", + " Skips adjacent / shared-vertex segment pairs and pairs whose circular\n", + " contour separation is less than min_separation_beads.\n", + " Nearby raw hits are clustered via _merge_crossing_clusters.\n", + "\n", + " Returns list of dicts:\n", + " {\n", + " pt_nm : (2,) float32 — crossing position in nm\n", + " axes_nm : list of unit vectors — chain directions at the crossing\n", + " seg_i : int — first segment index\n", + " seg_j : int — second segment index\n", + " t : float — parametric position along segment i\n", + " u : float — parametric position along segment j\n", + " n_hits : int — raw intersection count merged into this cluster\n", + " }\n", + " \"\"\"\n", + " xy = np.asarray(coords_xy, dtype=float)\n", + " n = int(xy.shape[0])\n", + "\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if not hit:\n", + " continue\n", + " ua = _canon_axis(p2 - p1)\n", + " ub = _canon_axis(q2 - q1)\n", + " if ua is None or ub is None:\n", + " continue\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " axes=[ua, ub],\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " # Cluster nearby raw intersections\n", + " clusters = []\n", + " for r in raw:\n", + " pt = r[\"pt\"]\n", + " placed = False\n", + " for c in clusters:\n", + " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"axes\"].extend(r[\"axes\"])\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n", + " \"axes\": list(r[\"axes\"]), \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt_mean = c[\"pt_sum\"] / c[\"n\"]\n", + " axes = []\n", + " for uvec in c[\"axes\"]:\n", + " uvec = _canon_axis(uvec)\n", + " if uvec is None:\n", + " continue\n", + " if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n", + " continue\n", + " axes.append(uvec)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = np.asarray(pt_mean, dtype=np.float32),\n", + " axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n", + " if axes else [np.array([1.0, 0.0], np.float32)],\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def _add_crossing_patch(mask, pt_px, axes_px,\n", + " sigma_center=2.0,\n", + " chain_extent=5.0,\n", + " sigma_perp=1.8,\n", + " center_weight=1.0,\n", + " chain_weight=0.8):\n", + " \"\"\"\n", + " Paint one crossing onto mask as:\n", + " center_weight * isotropic_Gaussian\n", + " + chain_weight * max(anisotropic Gaussians aligned to chain axes)\n", + "\n", + " Uses np.maximum so multiple crossings compose correctly.\n", + " Modifies mask in-place; returns None.\n", + " \"\"\"\n", + " H, W = mask.shape\n", + " cx, cy = float(pt_px[0]), float(pt_px[1])\n", + " sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n", + " R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n", + " x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n", + " y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n", + "\n", + " xs = np.arange(x0, x1+1, dtype=float)\n", + " ys = np.arange(y0, y1+1, dtype=float)\n", + " X, Y = np.meshgrid(xs, ys)\n", + " dx = X - cx; dy = Y - cy\n", + "\n", + " gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n", + "\n", + " g_chain = np.zeros_like(gc)\n", + " for v in axes_px:\n", + " vx, vy = float(v[0]), float(v[1])\n", + " u_par = dx*vx + dy*vy\n", + " u_perp = -dx*vy + dy*vx\n", + " g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n", + " + u_perp**2 / sigma_perp**2))\n", + " g_chain = np.maximum(g_chain, g)\n", + "\n", + " patch = center_weight * gc + chain_weight * g_chain\n", + " mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n", + "\n", + "\n", + "def make_crossing_mask(coords, extent, nx, ny,\n", + " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", + " chain_extent=CROSS_CHAIN_EXTENT,\n", + " center_weight=CROSS_CENTER_WEIGHT,\n", + " chain_weight=CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed=None,\n", + " merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n", + "\n", + " Uses find_polyline_crossings unless crossings_precomputed is supplied\n", + " (recommended: reuse the same crossing list that was used for the\n", + " height boost so the mask and the AFM image are perfectly aligned).\n", + "\n", + " Returns (cross_mask, crossings) where:\n", + " cross_mask : float32 ndarray of shape (ny, nx), values in [0, 1]\n", + " crossings : list of crossing dicts as returned by find_polyline_crossings\n", + " \"\"\"\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " xy_nm = coords[:, :2].astype(float)\n", + "\n", + " if crossings_precomputed is None:\n", + " crossings = find_polyline_crossings(\n", + " xy_nm,\n", + " min_separation_beads=min_separation_beads,\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + " else:\n", + " crossings = crossings_precomputed\n", + "\n", + " xmin, xmax, ymin, ymax = extent\n", + " sx = (nx - 1) / (xmax - xmin)\n", + " sy = (ny - 1) / (ymax - ymin)\n", + "\n", + " mask = np.zeros((ny, nx), dtype=np.float32)\n", + "\n", + " for c in crossings:\n", + " x_nm, y_nm = c[\"pt_nm\"]\n", + " px = (x_nm - xmin) * sx\n", + " py = (y_nm - ymin) * sy\n", + "\n", + " axes_px = []\n", + " for v in c[\"axes_nm\"]:\n", + " vx_px = v[0] * sx; vy_px = v[1] * sy\n", + " nrm = float(np.hypot(vx_px, vy_px))\n", + " if nrm < 1e-12:\n", + " continue\n", + " axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n", + " if not axes_px:\n", + " axes_px = [np.array([1.0, 0.0], dtype=float)]\n", + "\n", + " _add_crossing_patch(\n", + " mask,\n", + " pt_px=(px, py),\n", + " axes_px=axes_px,\n", + " sigma_center=float(sigma_center_px),\n", + " chain_extent=float(chain_extent),\n", + " sigma_perp=float(sigma_perp_px),\n", + " center_weight=float(center_weight),\n", + " chain_weight=float(chain_weight),\n", + " )\n", + "\n", + " m = float(mask.max())\n", + " if m > 1e-12:\n", + " mask /= m\n", + " return mask.astype(np.float32), crossings" ] }, { @@ -1056,7 +2372,56 @@ }, "outputs": [], "source": [ - "def make_decoy_chain(seed, n_beads_range=(2, 4), bond_length=BOND_LENGTH):\n \"\"\"\n Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n Z heights are set to mimic surface-deposited DNA. The chain propagates via\n small random angular deflections from an initial random direction.\n\n Returns a float32 array of shape (n_beads, 3) with (x, y, z) coordinates in nm.\n \"\"\"\n rng = np.random.default_rng(int(seed))\n n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n coords = np.zeros((n_b, 3), dtype=np.float32)\n angle = rng.uniform(0, 2 * np.pi)\n dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n\n for i in range(1, n_b):\n da = rng.normal(0, 0.3)\n c, s = np.cos(da), np.sin(da)\n R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n dirn = R @ dirn\n dirn = dirn / np.linalg.norm(dirn)\n coords[i] = coords[i-1] + bond_length * dirn\n\n coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n return coords\n\n\ndef place_decoy_in_corner(decoy_coords, global_extent,\n corner_margin_nm=2.0, seed=None):\n \"\"\"\n Translate decoy_coords so its centroid lands in a randomly chosen\n corner of global_extent = (xmin, xmax, ymin, ymax).\n The corner is chosen deterministically from seed.\n\n Returns a float32 array of the same shape as decoy_coords with updated positions.\n \"\"\"\n if seed is None:\n seed = int(np.sum(decoy_coords))\n rng = np.random.default_rng(int(seed))\n xmin, xmax, ymin, ymax = global_extent\n corner = rng.integers(0, 4)\n targets = [\n (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n ]\n tx, ty = targets[corner]\n centroid = decoy_coords.mean(axis=0)\n offset = np.array([tx, ty, 0.0]) - centroid\n return decoy_coords + offset" + "def make_decoy_chain(seed, n_beads_range=(2, 4), bond_length=BOND_LENGTH):\n", + " \"\"\"\n", + " Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n", + " Z heights are set to mimic surface-deposited DNA. The chain propagates via\n", + " small random angular deflections from an initial random direction.\n", + "\n", + " Returns a float32 array of shape (n_beads, 3) with (x, y, z) coordinates in nm.\n", + " \"\"\"\n", + " rng = np.random.default_rng(int(seed))\n", + " n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n", + " coords = np.zeros((n_b, 3), dtype=np.float32)\n", + " angle = rng.uniform(0, 2 * np.pi)\n", + " dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n", + "\n", + " for i in range(1, n_b):\n", + " da = rng.normal(0, 0.3)\n", + " c, s = np.cos(da), np.sin(da)\n", + " R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n", + " dirn = R @ dirn\n", + " dirn = dirn / np.linalg.norm(dirn)\n", + " coords[i] = coords[i-1] + bond_length * dirn\n", + "\n", + " coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n", + " return coords\n", + "\n", + "\n", + "def place_decoy_in_corner(decoy_coords, global_extent,\n", + " corner_margin_nm=2.0, seed=None):\n", + " \"\"\"\n", + " Translate decoy_coords so its centroid lands in a randomly chosen\n", + " corner of global_extent = (xmin, xmax, ymin, ymax).\n", + " The corner is chosen deterministically from seed.\n", + "\n", + " Returns a float32 array of the same shape as decoy_coords with updated positions.\n", + " \"\"\"\n", + " if seed is None:\n", + " seed = int(np.sum(decoy_coords))\n", + " rng = np.random.default_rng(int(seed))\n", + " xmin, xmax, ymin, ymax = global_extent\n", + " corner = rng.integers(0, 4)\n", + " targets = [\n", + " (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n", + " (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n", + " (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n", + " (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n", + " ]\n", + " tx, ty = targets[corner]\n", + " centroid = decoy_coords.mean(axis=0)\n", + " offset = np.array([tx, ty, 0.0]) - centroid\n", + " return decoy_coords + offset" ] }, { @@ -1088,8 +2453,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "No blank .spm file found; trying PSD fallback noise model.\n", "Loaded PSD fallback noise model from: /content/psd_noise_model.npz\n" @@ -1097,7 +2462,253 @@ } ], "source": [ - "def random_transform_noise_texture(tex, seed):\n \"\"\"\n Apply a deterministic random affine transform (rotation, anisotropic\n scaling, translation, optional flips, amplitude jitter) to the blank.spm\n noise texture. Ensures every sample gets a unique-looking noise pattern\n even though only one blank SPM scan was acquired.\n\n Returns the transformed texture as a float32 array of the same shape as tex.\n \"\"\"\n rng = np.random.default_rng(int(seed))\n tex = np.asarray(tex, dtype=np.float32)\n\n theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n base_scale = float(rng.uniform(0.85, 1.15))\n anis = float(rng.uniform(0.85, 1.15))\n sx, sy = base_scale * anis, base_scale / anis\n\n c, s = float(np.cos(theta)), float(np.sin(theta))\n A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n M = np.linalg.inv(A).astype(np.float32)\n\n h, w = tex.shape\n center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n offset = center - M @ (center + t)\n\n out = affine_transform(tex, matrix=M, offset=offset,\n output_shape=tex.shape, order=1,\n mode=\"wrap\", prefilter=False).astype(np.float32)\n\n if bool(rng.integers(0, 2)):\n out = out[::-1, :]\n if bool(rng.integers(0, 2)):\n out = out[:, ::-1]\n out *= float(rng.uniform(0.90, 1.10))\n return out.astype(np.float32)\n\n\ndef generate_chain_with_beads(seed, n_beads=N_BEADS, add_decoy=False):\n \"\"\"\n Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n and optionally generate a corner-placed decoy fragment.\n\n Returns dict:\n seed, n_beads, coords (N,3), crossings (list), decoy_coords or None\n \"\"\"\n seed = int(seed)\n n_beads = int(n_beads)\n\n if USE_MD:\n coords0 = make_tangled_ring_initial(\n seed,\n n_beads=n_beads,\n bond_length=BOND_LENGTH,\n persistence_bonds=PERSISTENCE_BONDS,\n base_z=BASE_Z,\n )\n frames = run_md_relaxation(coords0, seed=seed,\n n_frames=N_FRAMES,\n steps_per_frame=STEPS_PER_FRAME)\n else:\n frames = make_ring_2d_persistent_initial(\n seed,\n n_beads=n_beads,\n bond_length=BOND_LENGTH,\n persistence_bonds=PERSISTENCE_BONDS,\n angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n )\n\n last_coords = frames[-1].astype(np.float32)\n\n crossings = find_polyline_crossings(\n last_coords[:, :2],\n min_separation_beads=CROSS_MIN_SEP_BEADS,\n merge_radius_nm=0.5,\n )\n\n decoy_coords = None\n if add_decoy:\n xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n decoy_raw = make_decoy_chain(seed + 9999)\n decoy_coords = place_decoy_in_corner(\n decoy_raw, (xmin, xmax, ymin, ymax),\n corner_margin_nm=15.0, seed=seed)\n\n return dict(seed=seed, n_beads=n_beads,\n coords=last_coords, crossings=crossings,\n decoy_coords=decoy_coords)\n\n\ndef render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None):\n \"\"\"\n Render AFM image + DNA mask + crossing mask for a chain dict.\n\n Decoys are rendered into the AFM image via np.maximum compositing\n but are deliberately excluded from both ground-truth masks.\n\n Returns a dict with keys:\n afm_img : float32 ndarray (NY, NX) — the rendered AFM height image\n dna_mask : uint8 ndarray (NY, NX) — binary DNA segmentation mask (0/1)\n cross_mask : float32 ndarray (NY, NX) — soft crossing probability mask [0, 1]\n extent : float32 array (4,) — (xmin, xmax, ymin, ymax) in nm\n n_crossings: int — number of detected crossings\n decoy_coords: float32 ndarray or None — placed decoy bead coordinates\n seed : int — the seed used for this sample\n \"\"\"\n seed = int(chain[\"seed\"])\n coords = chain[\"coords\"]\n crossings = chain[\"crossings\"]\n decoy_coords = chain.get(\"decoy_coords\", None)\n\n padding = 10.0\n xmin = coords[:, 0].min() - padding\n xmax = coords[:, 0].max() + padding\n ymin = coords[:, 1].min() - padding\n ymax = coords[:, 1].max() + padding\n global_extent = (xmin, xmax, ymin, ymax)\n\n # CHANGED: internal render grid is determined from extent and nm_per_px\n nx_phys, ny_phys, _ = compute_internal_render_grid(\n global_extent, AFM_KW[\"target_nm_per_px\"]\n )\n afm_kw_phys = {**AFM_KW, \"nx\": nx_phys, \"ny\": ny_phys}\n\n afm_img_hr, dbg = create_z_based_afm(\n coords, grain_seed=seed,\n crossings_precomputed=crossings,\n extent=global_extent,\n **afm_kw_phys\n )\n\n if decoy_coords is not None:\n decoy_coords = place_decoy_in_corner(\n decoy_coords, global_extent,\n corner_margin_nm=8.0, seed=seed)\n decoy_kw = {**afm_kw_phys,\n \"apply_edge_taper\": False,\n \"enable_crossing_boost\": False,\n \"add_center_ridge\": False}\n decoy_afm_hr, _ = create_z_based_afm(\n decoy_coords, extent=global_extent, **decoy_kw)\n afm_img_hr = np.maximum(afm_img_hr, decoy_afm_hr)\n\n actual_extent = dbg[\"extent\"]\n\n noise_img = sample_noise_image(\n noise_source=noise_source,\n noise_asset=noise_asset,\n seed=seed,\n out_shape=afm_img_hr.shape,\n target_rms_nm=TARGET_NOISE_RMS_NM,\n )\n if noise_img is not None:\n afm_img_hr = np.clip((afm_img_hr + noise_img).astype(np.float32),\n 0, AFM_KW[\"max_height_nm\"])\n\n dna_mask_hr = make_dna_mask_from_beads(\n coords, actual_extent, nx_phys, ny_phys, dilate_px=DNA_MASK_DILATE_PX)\n\n cross_mask_hr, crossings_out = make_crossing_mask(\n coords, actual_extent, nx_phys, ny_phys,\n sigma_center_px=CROSS_SIGMA_CENTER_PX,\n sigma_perp_px=CROSS_SIGMA_PERP_PX,\n chain_extent=CROSS_CHAIN_EXTENT,\n center_weight=CROSS_CENTER_WEIGHT,\n chain_weight=CROSS_CHAIN_WEIGHT,\n min_separation_beads=CROSS_MIN_SEP_BEADS,\n crossings_precomputed=crossings,\n merge_radius_nm=0.5,\n )\n\n if CROSS_CLIP_TO_DNA_MASK:\n cross_mask_hr = cross_mask_hr * dna_mask_hr.astype(np.float32)\n\n # CHANGED: downsample AFM image and masks to final NX x NY network resolution\n afm_img = resize_image_to_shape(afm_img_hr.astype(np.float32), (NY, NX), order=1).astype(np.float32)\n dna_mask = (resize_image_to_shape(dna_mask_hr.astype(np.float32), (NY, NX), order=0) > 0.5).astype(np.uint8)\n cross_mask = resize_image_to_shape(cross_mask_hr.astype(np.float32), (NY, NX), order=1).astype(np.float32)\n\n return dict(\n seed=seed,\n afm_img=afm_img.astype(np.float32),\n dna_mask=dna_mask.astype(np.uint8),\n cross_mask=cross_mask.astype(np.float32),\n extent=np.array(actual_extent, dtype=np.float32),\n n_crossings=int(len(crossings_out)),\n decoy_coords=decoy_coords,\n )\n\n\ndef generate_one_sample_multilength(seed, n_beads, noise_source=\"none\",\n noise_asset=None, add_decoy=False):\n \"\"\"\n Generate one complete sample at a specified bead count.\n Uses blank.spm-derived noise when available and otherwise falls back\n to PSD-based synthetic noise generation.\n\n Returns the dict produced by render_chain_and_masks, augmented with\n n_beads (int) for downstream use.\n \"\"\"\n chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n\n s = render_chain_and_masks(\n chain,\n noise_source=noise_source,\n noise_asset=noise_asset,\n )\n s[\"n_beads\"] = int(n_beads)\n return s\n\n\n# ── Load noise asset once; prefer blank.spm, fall back to PSD model ───────────\nnoise_source = \"none\"\nnoise_asset = None\nnoise_diag = {\"source\": \"none\"}\n\nif USE_BLANK_SPM_NOISE:\n blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n if blank_path and os.path.isfile(blank_path):\n try:\n noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n blank_path, target_rms_nm=TARGET_NOISE_RMS_NM, verbose=True)\n noise_source = \"blank_spm\"\n print(f\"Loaded blank .spm noise texture RMS ≈ {noise_rms_nm:.3f} nm\")\n except Exception as e:\n print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n else:\n print(\"No blank .spm file found; trying PSD fallback noise model.\")\n\n if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n try:\n noise_asset = load_model(MODEL_PATH)\n noise_source = \"psd_model\"\n noise_diag = {\n \"source\": \"psd_model\",\n \"model_path\": str(MODEL_PATH),\n \"method\": PSD_NOISE_METHOD,\n \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n }\n print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n except Exception as e:\n print(\"PSD fallback noise model failed; proceeding without noise. Error:\", e)\n noise_asset = None\nelse:\n print(\"Noise injection disabled.\")\n" + "def random_transform_noise_texture(tex, seed):\n", + " \"\"\"\n", + " Apply a deterministic random affine transform (rotation, anisotropic\n", + " scaling, translation, optional flips, amplitude jitter) to the blank.spm\n", + " noise texture. Ensures every sample gets a unique-looking noise pattern\n", + " even though only one blank SPM scan was acquired.\n", + "\n", + " Returns the transformed texture as a float32 array of the same shape as tex.\n", + " \"\"\"\n", + " rng = np.random.default_rng(int(seed))\n", + " tex = np.asarray(tex, dtype=np.float32)\n", + "\n", + " theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n", + " base_scale = float(rng.uniform(0.85, 1.15))\n", + " anis = float(rng.uniform(0.85, 1.15))\n", + " sx, sy = base_scale * anis, base_scale / anis\n", + "\n", + " c, s = float(np.cos(theta)), float(np.sin(theta))\n", + " A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n", + " M = np.linalg.inv(A).astype(np.float32)\n", + "\n", + " h, w = tex.shape\n", + " center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n", + " t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n", + " float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n", + " offset = center - M @ (center + t)\n", + "\n", + " out = affine_transform(tex, matrix=M, offset=offset,\n", + " output_shape=tex.shape, order=1,\n", + " mode=\"wrap\", prefilter=False).astype(np.float32)\n", + "\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[::-1, :]\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[:, ::-1]\n", + " out *= float(rng.uniform(0.90, 1.10))\n", + " return out.astype(np.float32)\n", + "\n", + "\n", + "def generate_chain_with_beads(seed, n_beads=N_BEADS, add_decoy=False):\n", + " \"\"\"\n", + " Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n", + " and optionally generate a corner-placed decoy fragment.\n", + "\n", + " Returns dict:\n", + " seed, n_beads, coords (N,3), crossings (list), decoy_coords or None\n", + " \"\"\"\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " base_z=BASE_Z,\n", + " )\n", + " frames = run_md_relaxation(coords0, seed=seed,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + " else:\n", + " frames = make_ring_2d_persistent_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n", + " )\n", + "\n", + " last_coords = frames[-1].astype(np.float32)\n", + "\n", + " crossings = find_polyline_crossings(\n", + " last_coords[:, :2],\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " decoy_coords = None\n", + " if add_decoy:\n", + " xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n", + " ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n", + " decoy_raw = make_decoy_chain(seed + 9999)\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_raw, (xmin, xmax, ymin, ymax),\n", + " corner_margin_nm=15.0, seed=seed)\n", + "\n", + " return dict(seed=seed, n_beads=n_beads,\n", + " coords=last_coords, crossings=crossings,\n", + " decoy_coords=decoy_coords)\n", + "\n", + "\n", + "def render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None):\n", + " \"\"\"\n", + " Render AFM image + DNA mask + crossing mask for a chain dict.\n", + "\n", + " Decoys are rendered into the AFM image via np.maximum compositing\n", + " but are deliberately excluded from both ground-truth masks.\n", + "\n", + " Returns a dict with keys:\n", + " afm_img : float32 ndarray (NY, NX) — the rendered AFM height image\n", + " dna_mask : uint8 ndarray (NY, NX) — binary DNA segmentation mask (0/1)\n", + " cross_mask : float32 ndarray (NY, NX) — soft crossing probability mask [0, 1]\n", + " extent : float32 array (4,) — (xmin, xmax, ymin, ymax) in nm\n", + " n_crossings: int — number of detected crossings\n", + " decoy_coords: float32 ndarray or None — placed decoy bead coordinates\n", + " seed : int — the seed used for this sample\n", + " \"\"\"\n", + " seed = int(chain[\"seed\"])\n", + " coords = chain[\"coords\"]\n", + " crossings = chain[\"crossings\"]\n", + " decoy_coords = chain.get(\"decoy_coords\", None)\n", + "\n", + " padding = 10.0\n", + " xmin = coords[:, 0].min() - padding\n", + " xmax = coords[:, 0].max() + padding\n", + " ymin = coords[:, 1].min() - padding\n", + " ymax = coords[:, 1].max() + padding\n", + " global_extent = (xmin, xmax, ymin, ymax)\n", + "\n", + " # CHANGED: internal render grid is determined from extent and nm_per_px\n", + " nx_phys, ny_phys, _ = compute_internal_render_grid(\n", + " global_extent, AFM_KW[\"target_nm_per_px\"]\n", + " )\n", + " afm_kw_phys = {**AFM_KW, \"nx\": nx_phys, \"ny\": ny_phys}\n", + "\n", + " afm_img_hr, dbg = create_z_based_afm(\n", + " coords, grain_seed=seed,\n", + " crossings_precomputed=crossings,\n", + " extent=global_extent,\n", + " **afm_kw_phys\n", + " )\n", + "\n", + " if decoy_coords is not None:\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_coords, global_extent,\n", + " corner_margin_nm=8.0, seed=seed)\n", + " decoy_kw = {**afm_kw_phys,\n", + " \"apply_edge_taper\": False,\n", + " \"enable_crossing_boost\": False,\n", + " \"add_center_ridge\": False}\n", + " decoy_afm_hr, _ = create_z_based_afm(\n", + " decoy_coords, extent=global_extent, **decoy_kw)\n", + " afm_img_hr = np.maximum(afm_img_hr, decoy_afm_hr)\n", + "\n", + " actual_extent = dbg[\"extent\"]\n", + "\n", + " noise_img = sample_noise_image(\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " seed=seed,\n", + " out_shape=afm_img_hr.shape,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " )\n", + " if noise_img is not None:\n", + " afm_img_hr = np.clip((afm_img_hr + noise_img).astype(np.float32),\n", + " 0, AFM_KW[\"max_height_nm\"])\n", + "\n", + " dna_mask_hr = make_dna_mask_from_beads(\n", + " coords, actual_extent, nx_phys, ny_phys, dilate_px=DNA_MASK_DILATE_PX)\n", + "\n", + " cross_mask_hr, crossings_out = make_crossing_mask(\n", + " coords, actual_extent, nx_phys, ny_phys,\n", + " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", + " chain_extent=CROSS_CHAIN_EXTENT,\n", + " center_weight=CROSS_CENTER_WEIGHT,\n", + " chain_weight=CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed=crossings,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " if CROSS_CLIP_TO_DNA_MASK:\n", + " cross_mask_hr = cross_mask_hr * dna_mask_hr.astype(np.float32)\n", + "\n", + " # CHANGED: downsample AFM image and masks to final NX x NY network resolution\n", + " afm_img = resize_image_to_shape(afm_img_hr.astype(np.float32), (NY, NX), order=1).astype(np.float32)\n", + " dna_mask = (resize_image_to_shape(dna_mask_hr.astype(np.float32), (NY, NX), order=0) > 0.5).astype(np.uint8)\n", + " cross_mask = resize_image_to_shape(cross_mask_hr.astype(np.float32), (NY, NX), order=1).astype(np.float32)\n", + "\n", + " return dict(\n", + " seed=seed,\n", + " afm_img=afm_img.astype(np.float32),\n", + " dna_mask=dna_mask.astype(np.uint8),\n", + " cross_mask=cross_mask.astype(np.float32),\n", + " extent=np.array(actual_extent, dtype=np.float32),\n", + " n_crossings=int(len(crossings_out)),\n", + " decoy_coords=decoy_coords,\n", + " )\n", + "\n", + "\n", + "def generate_one_sample_multilength(seed, n_beads, noise_source=\"none\",\n", + " noise_asset=None, add_decoy=False):\n", + " \"\"\"\n", + " Generate one complete sample at a specified bead count.\n", + " Uses blank.spm-derived noise when available and otherwise falls back\n", + " to PSD-based synthetic noise generation.\n", + "\n", + " Returns the dict produced by render_chain_and_masks, augmented with\n", + " n_beads (int) for downstream use.\n", + " \"\"\"\n", + " chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n", + "\n", + " s = render_chain_and_masks(\n", + " chain,\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " )\n", + " s[\"n_beads\"] = int(n_beads)\n", + " return s\n", + "\n", + "\n", + "# ── Load noise asset once; prefer blank.spm, fall back to PSD model ───────────\n", + "noise_source = \"none\"\n", + "noise_asset = None\n", + "noise_diag = {\"source\": \"none\"}\n", + "\n", + "if USE_BLANK_SPM_NOISE:\n", + " blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n", + " if blank_path and os.path.isfile(blank_path):\n", + " try:\n", + " noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n", + " blank_path, target_rms_nm=TARGET_NOISE_RMS_NM, verbose=True)\n", + " noise_source = \"blank_spm\"\n", + " print(f\"Loaded blank .spm noise texture RMS ≈ {noise_rms_nm:.3f} nm\")\n", + " except Exception as e:\n", + " print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n", + " else:\n", + " print(\"No blank .spm file found; trying PSD fallback noise model.\")\n", + "\n", + " if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n", + " try:\n", + " noise_asset = load_model(MODEL_PATH)\n", + " noise_source = \"psd_model\"\n", + " noise_diag = {\n", + " \"source\": \"psd_model\",\n", + " \"model_path\": str(MODEL_PATH),\n", + " \"method\": PSD_NOISE_METHOD,\n", + " \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n", + " }\n", + " print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n", + " except Exception as e:\n", + " print(\"PSD fallback noise model failed; proceeding without noise. Error:\", e)\n", + " noise_asset = None\n", + "else:\n", + " print(\"Noise injection disabled.\")\n" ] }, { @@ -1130,8 +2741,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", "Using OpenCL platform.\n", @@ -1169,14 +2780,14 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1235,31 +2846,31 @@ }, { "cell_type": "markdown", + "id": "3YoKkN3e5dIo", + "metadata": { + "id": "3YoKkN3e5dIo" + }, "source": [ "##13. Benchmarking\n", "\n", "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." - ], - "metadata": { - "id": "3YoKkN3e5dIo" - }, - "id": "3YoKkN3e5dIo" + ] }, { "cell_type": "code", "execution_count": null, "id": "WLAYkyIQlhuX", "metadata": { - "id": "WLAYkyIQlhuX", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "WLAYkyIQlhuX", "outputId": "2efe41b8-37bd-4d1b-a01e-d50eed42cac6" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "==============================================================\n", " SYSTEM PROFILE\n", @@ -1284,7 +2895,120 @@ } ], "source": [ - "import time\nimport tracemalloc\nimport os\nimport platform\nimport psutil\nimport threading\nimport statistics\n\n# ── Helper: peak RSS tracker ──────\nclass _PeakMemTracker:\n def __init__(self):\n self._proc = psutil.Process(os.getpid())\n self.peak_mb = 0.0\n self._stop = threading.Event()\n\n def _run(self):\n while not self._stop.is_set():\n try:\n rss = self._proc.memory_info().rss / 1e6\n if rss > self.peak_mb:\n self.peak_mb = rss\n except Exception: pass\n self._stop.wait(0.05)\n\n def start(self):\n self._t = threading.Thread(target=self._run, daemon=True)\n self._t.start()\n\n def stop(self):\n self._stop.set(); self._t.join()\n\n# ── System Info ──\ncpu_logical = psutil.cpu_count(logical=True)\nif USE_MD:\n platforms = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]\n active_platform = \"OpenCL\" if \"OpenCL\" in platforms else \"CPU\"\n\nprint(\"=\" * 62)\nprint(\" SYSTEM PROFILE\")\nprint(\"=\" * 62)\nprint(f\" OS : {platform.system()} {platform.release()}\")\nprint(f\" CPU (logical) : {cpu_logical} cores\")\nprint(f\" RAM available : {psutil.virtual_memory().available / 1e9:.1f} GB\")\nif USE_MD:\n print(f\" OpenMM Platforms: {platforms}\")\nprint(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\nprint()\n\nBENCH_SEED = int(BASE_SEED)\nBENCH_N_BEADS = int(BEAD_COUNTS[0])\nBENCH_REPS = 3\n\ndef _bench_stages(seed, n_beads):\n \"\"\"\n Run one full sample-generation pipeline and record wall-clock time\n for each stage: chain initialisation, MD relaxation (or 2-D walk),\n crossing detection, and AFM render + mask creation.\n\n Returns a dict mapping stage labels to elapsed times in seconds:\n {'1_chain_init', '2_md_relax', '3_crossing_detect', '4_afm_render_masks'}\n \"\"\"\n t = {}\n t0 = time.perf_counter()\n\n if USE_MD:\n coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n t[\"1_chain_init\"] = time.perf_counter() - t0\n\n t0 = time.perf_counter()\n frames = run_md_relaxation(coords0, seed=seed, n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME)\n t[\"2_md_relax\"] = time.perf_counter() - t0\n else:\n # Skip initial 3D step and run 2D persistent walk\n t[\"1_chain_init\"] = 0.0\n t0 = time.perf_counter()\n frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n t[\"2_md_relax\"] = time.perf_counter() - t0\n\n last_coords = frames[-1]\n t0 = time.perf_counter()\n crossings = find_polyline_crossings(last_coords[:, :2])\n t[\"3_crossing_detect\"] = time.perf_counter() - t0\n\n t0 = time.perf_counter()\n chain = dict(seed=seed, n_beads=n_beads, coords=last_coords, crossings=crossings)\n render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None)\n t[\"4_afm_render_masks\"] = time.perf_counter() - t0\n return t\n\nprint(\"=\" * 62)\nprint(\" RUNNING BENCHMARK\")\nprint(\"=\" * 62)\n\nwall_times = []\nmem_tracker = _PeakMemTracker()\nmem_tracker.start()\n\nfor rep in range(BENCH_REPS):\n t0 = time.perf_counter()\n _ = _bench_stages(BENCH_SEED + rep, BENCH_N_BEADS)\n elapsed = time.perf_counter() - t0\n wall_times.append(elapsed)\n print(f\" Rep {rep+1}/{BENCH_REPS}: {elapsed:.2f}s\")\n\nmem_tracker.stop()\nmed_wall = statistics.median(wall_times)\ntotal_samples = int(N_SAMPLES) * len(BEAD_COUNTS)\nest_hours = (med_wall * total_samples) / 3600\n\nprint(\"=\" * 62)\nprint(f\" Median Wall Time: {med_wall:.2f} s\")\nprint(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\nprint(f\" Projected Total : {est_hours:.1f} hours for {total_samples} samples\")\nprint(\"=\" * 62)\n" + "import time\n", + "import tracemalloc\n", + "import os\n", + "import platform\n", + "import psutil\n", + "import threading\n", + "import statistics\n", + "\n", + "# ── Helper: peak RSS tracker ──────\n", + "class _PeakMemTracker:\n", + " def __init__(self):\n", + " self._proc = psutil.Process(os.getpid())\n", + " self.peak_mb = 0.0\n", + " self._stop = threading.Event()\n", + "\n", + " def _run(self):\n", + " while not self._stop.is_set():\n", + " try:\n", + " rss = self._proc.memory_info().rss / 1e6\n", + " if rss > self.peak_mb:\n", + " self.peak_mb = rss\n", + " except Exception: pass\n", + " self._stop.wait(0.05)\n", + "\n", + " def start(self):\n", + " self._t = threading.Thread(target=self._run, daemon=True)\n", + " self._t.start()\n", + "\n", + " def stop(self):\n", + " self._stop.set(); self._t.join()\n", + "\n", + "# ── System Info ──\n", + "cpu_logical = psutil.cpu_count(logical=True)\n", + "if USE_MD:\n", + " platforms = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]\n", + " active_platform = \"OpenCL\" if \"OpenCL\" in platforms else \"CPU\"\n", + "\n", + "print(\"=\" * 62)\n", + "print(\" SYSTEM PROFILE\")\n", + "print(\"=\" * 62)\n", + "print(f\" OS : {platform.system()} {platform.release()}\")\n", + "print(f\" CPU (logical) : {cpu_logical} cores\")\n", + "print(f\" RAM available : {psutil.virtual_memory().available / 1e9:.1f} GB\")\n", + "if USE_MD:\n", + " print(f\" OpenMM Platforms: {platforms}\")\n", + "print(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\n", + "print()\n", + "\n", + "BENCH_SEED = int(BASE_SEED)\n", + "BENCH_N_BEADS = int(BEAD_COUNTS[0])\n", + "BENCH_REPS = 3\n", + "\n", + "def _bench_stages(seed, n_beads):\n", + " \"\"\"\n", + " Run one full sample-generation pipeline and record wall-clock time\n", + " for each stage: chain initialisation, MD relaxation (or 2-D walk),\n", + " crossing detection, and AFM render + mask creation.\n", + "\n", + " Returns a dict mapping stage labels to elapsed times in seconds:\n", + " {'1_chain_init', '2_md_relax', '3_crossing_detect', '4_afm_render_masks'}\n", + " \"\"\"\n", + " t = {}\n", + " t0 = time.perf_counter()\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n", + " t[\"1_chain_init\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " frames = run_md_relaxation(coords0, seed=seed, n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME)\n", + " t[\"2_md_relax\"] = time.perf_counter() - t0\n", + " else:\n", + " # Skip initial 3D step and run 2D persistent walk\n", + " t[\"1_chain_init\"] = 0.0\n", + " t0 = time.perf_counter()\n", + " frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n", + " t[\"2_md_relax\"] = time.perf_counter() - t0\n", + "\n", + " last_coords = frames[-1]\n", + " t0 = time.perf_counter()\n", + " crossings = find_polyline_crossings(last_coords[:, :2])\n", + " t[\"3_crossing_detect\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " chain = dict(seed=seed, n_beads=n_beads, coords=last_coords, crossings=crossings)\n", + " render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None)\n", + " t[\"4_afm_render_masks\"] = time.perf_counter() - t0\n", + " return t\n", + "\n", + "print(\"=\" * 62)\n", + "print(\" RUNNING BENCHMARK\")\n", + "print(\"=\" * 62)\n", + "\n", + "wall_times = []\n", + "mem_tracker = _PeakMemTracker()\n", + "mem_tracker.start()\n", + "\n", + "for rep in range(BENCH_REPS):\n", + " t0 = time.perf_counter()\n", + " _ = _bench_stages(BENCH_SEED + rep, BENCH_N_BEADS)\n", + " elapsed = time.perf_counter() - t0\n", + " wall_times.append(elapsed)\n", + " print(f\" Rep {rep+1}/{BENCH_REPS}: {elapsed:.2f}s\")\n", + "\n", + "mem_tracker.stop()\n", + "med_wall = statistics.median(wall_times)\n", + "total_samples = int(N_SAMPLES) * len(BEAD_COUNTS)\n", + "est_hours = (med_wall * total_samples) / 3600\n", + "\n", + "print(\"=\" * 62)\n", + "print(f\" Median Wall Time: {med_wall:.2f} s\")\n", + "print(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\n", + "print(f\" Projected Total : {est_hours:.1f} hours for {total_samples} samples\")\n", + "print(\"=\" * 62)\n" ] }, { @@ -1423,9 +3147,10 @@ } ], "metadata": { + "accelerator": "GPU", "colab": { - "provenance": [], - "gpuType": "T4" + "gpuType": "T4", + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -1434,9 +3159,8 @@ "language_info": { "name": "python", "version": "3.10.0" - }, - "accelerator": "GPU" + } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From 132a2ae25ebb3936c771c3e4c95eebf5ace0ae20 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 20:41:27 +0200 Subject: [PATCH 10/17] Update QC.ipynb --- tutorials/quality-control/QC.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/tutorials/quality-control/QC.ipynb b/tutorials/quality-control/QC.ipynb index 2f3e3a1..71b54f2 100644 --- a/tutorials/quality-control/QC.ipynb +++ b/tutorials/quality-control/QC.ipynb @@ -9,6 +9,7 @@ "\n", "\n", "\"Open\n", + "#TODO: Add correct link to ASAP notebook.\n", "\n", "This notebook provides you with a complete code example that first trains a conditional variational autoencoder (cVAE) on AFM force spectroscopy curves in a self-supervised way; then, it constructs a latent space where the AFM curves are placed; and finally it uses the latent space to check the quality of the AFM curves.\n" ] From 8c556ac786d4e44209711e9b79c6adc1d19f6534 Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sat, 11 Apr 2026 20:47:58 +0200 Subject: [PATCH 11/17] Update DNA_simulation.ipynb --- tutorials/dna-imaging/DNA_simulation.ipynb | 164 ++++++++++++--------- 1 file changed, 94 insertions(+), 70 deletions(-) diff --git a/tutorials/dna-imaging/DNA_simulation.ipynb b/tutorials/dna-imaging/DNA_simulation.ipynb index e8b07a9..a9882a2 100644 --- a/tutorials/dna-imaging/DNA_simulation.ipynb +++ b/tutorials/dna-imaging/DNA_simulation.ipynb @@ -10,20 +10,45 @@ "# Simulation of the AFM-Imaging of DNA Chains Deposited on a Substrate\n", "\n", "\n", - "\"Open # TODO: Add correct link to ASAP notebook.\n", + "\"Open\n", + "#TODO: Add correct link to ASAP notebook.\n", "\n", "This notebook provides you with a complete example to generate a simulated dataset of the AFM imaging of DNA chains deposited on a substrate.\n", "\n", - "- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n", - "- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n", - "- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)\n", + "#TODO: Explain better how the output data look like by modifying the subsequent paragraph\n", "\n", "Outputs are written to `OUT_DIR/` as `.npy` plus `manifest.csv` (which documents the user inputs used to create the images and masks).\n", + "- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n", + "- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n", + "- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e537ab21", + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment codes in this cell if using Colab/Kaggle.\n", "\n", - ">\n", - "---\n", - "**Cell execution order:** run cells 1 → 12 in sequence. \n", - "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset." + "#TODO: Add here all imports taht might be needed in Colab/Kaggle." + ] + }, + { + "cell_type": "markdown", + "id": "4794bc11", + "metadata": {}, + "source": [ + "#TODO: General, ensure all lines are up to 79 chars including " + ] + }, + { + "cell_type": "markdown", + "id": "dfc299d6", + "metadata": {}, + "source": [ + "# 1. Imports and Global Settings" ] }, { @@ -33,15 +58,14 @@ "id": "cell_md_01" }, "source": [ - "# 1. **Imports and global settings**\n", - "## 1.1. Imports\n", + "### 1.1. Imports\n", "\n", - "First we import all the libraries that will be necessary for simulation of the images. If you plan to use molecular dynamics for the generation of the DNA chains, then please import the molecular dynamics libraries as well." + "First you need to import all the necessary libraries for the simulation of the images." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "cell_code_01", "metadata": { "colab": { @@ -55,53 +79,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting openmm[cuda13]\n", - " Downloading openmm-8.5.0-cp312-cp312-manylinux_2_34_x86_64.whl.metadata (1.4 kB)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from openmm[cuda13]) (2.0.2)\n", - "Collecting OpenMM-CUDA-13==8.5.0 (from openmm[cuda13])\n", - " Downloading openmm_cuda_13-8.5.0-py3-none-manylinux_2_34_x86_64.whl.metadata (420 bytes)\n", - "Collecting nvidia-cuda-runtime<14,>=13 (from OpenMM-CUDA-13==8.5.0->openmm[cuda13])\n", - " Downloading nvidia_cuda_runtime-13.2.51-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB)\n", - 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"\u001b[?25hInstalling collected packages: openmm, nvidia-nvvm, nvidia-nvjitlink, nvidia-cuda-runtime, nvidia-cuda-nvrtc, nvidia-cuda-cupti, nvidia-cuda-crt, nvidia-cufft, nvidia-cuda-nvcc, OpenMM-CUDA-13\n", - "Successfully installed OpenMM-CUDA-13-8.5.0 nvidia-cuda-crt-13.2.51 nvidia-cuda-cupti-13.2.23 nvidia-cuda-nvcc-13.2.51 nvidia-cuda-nvrtc-13.2.51 nvidia-cuda-runtime-13.2.51 nvidia-cufft-12.2.0.37 nvidia-nvjitlink-13.2.51 nvidia-nvvm-13.2.51 openmm-8.5.0\n" + "^C\n", + "zsh:1: no matches found: openmm[cuda13]\n" + ] + }, + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'openmm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 12\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m USE_MD:\n\u001b[1;32m 11\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip3 install openmm[cuda13] #change the cuda version depending on the CUDA version available on your system.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mopenmm\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mopenmm\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01munit\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01munit\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'openmm'" ] } ], "source": [ + "#TODO: Polish the imports (see also next cell). Not sure why the need the purge.\n", + "\n", "# Install dependencies (uncomment in a fresh environment / Colab)\n", "# !pip cache purge\n", "# Chain generation mode\n", @@ -135,6 +131,24 @@ "\n" ] }, + { + "cell_type": "markdown", + "id": "95376f61", + "metadata": {}, + "source": [ + "If you plan to use molecular dynamics for the generation of the DNA chains, then please import the molecular dynamics libraries as well." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e856d40a", + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: move here the imports for the MD removing them from above" + ] + }, { "cell_type": "markdown", "id": "1oW9llw-imr1", @@ -142,13 +156,14 @@ "id": "1oW9llw-imr1" }, "source": [ - "#1.2 Defining the variables\n", + "### 1.2. Defining the Variables\n", + "\n", "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "sSqadS02hzPe", "metadata": { "id": "sSqadS02hzPe" @@ -247,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "47h81smulGxd", "metadata": { "colab": { @@ -306,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "cell_code_02", "metadata": { "colab": { @@ -432,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "3NjBSwfAe4Mj", "metadata": { "colab": { @@ -492,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "cell_code_03", "metadata": { "id": "cell_code_03" @@ -943,7 +958,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "cell_code_05", "metadata": { "id": "cell_code_05" @@ -1504,7 +1519,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "cell_code_06", "metadata": { "id": "cell_code_06" @@ -2043,7 +2058,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "cell_code_07", "metadata": { "id": "cell_code_07" @@ -2125,7 +2140,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "cell_code_08", "metadata": { "id": "cell_code_08" @@ -2365,7 +2380,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "cell_code_09", "metadata": { "id": "cell_code_09" @@ -2442,7 +2457,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "cell_code_10", "metadata": { "colab": { @@ -2729,7 +2744,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "cell_code_11", "metadata": { "colab": { @@ -3153,12 +3168,21 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "py_env_book", + "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.10.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" } }, "nbformat": 4, From a33f8e9c69defaf9fe3cf78fecab06ebb4e185aa Mon Sep 17 00:00:00 2001 From: Giovanni Volpe Date: Sun, 12 Apr 2026 08:16:09 +0300 Subject: [PATCH 12/17] Update DNA_simulation.ipynb --- tutorials/dna-imaging/DNA_simulation.ipynb | 74 ++++++++++++---------- 1 file changed, 42 insertions(+), 32 deletions(-) diff --git a/tutorials/dna-imaging/DNA_simulation.ipynb b/tutorials/dna-imaging/DNA_simulation.ipynb index a9882a2..6c317c3 100644 --- a/tutorials/dna-imaging/DNA_simulation.ipynb +++ b/tutorials/dna-imaging/DNA_simulation.ipynb @@ -40,7 +40,9 @@ "id": "4794bc11", "metadata": {}, "source": [ - "#TODO: General, ensure all lines are up to 79 chars including " + "#TODO: General, ensure all lines are up to 79 chars including the comments.\n", + "\n", + "#TODO: Comment functions in the style of the QC notebook (DeepTrack2 style)" ] }, { @@ -65,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "cell_code_01", "metadata": { "colab": { @@ -98,35 +100,23 @@ "source": [ "#TODO: Polish the imports (see also next cell). Not sure why the need the purge.\n", "\n", - "# Install dependencies (uncomment in a fresh environment / Colab)\n", - "# !pip cache purge\n", - "# Chain generation mode\n", - "USE_MD = True # True → OpenMM Langevin dynamics\n", - " # False → fast 2-D persistent-walk (no OpenMM needed)\n", - "# Molecular dynamics\n", - "!pip3 install -q scipy matplotlib pillow\n", - "if USE_MD:\n", - " !pip3 install openmm[cuda13] #change the cuda version depending on the CUDA version available on your system.\n", - " import openmm\n", - " import openmm.unit as unit\n", - "\n", "import os\n", - "from pathlib import Path\n", "import re\n", - "import csv\n", - "import traceback\n", + "from pathlib import Path\n", + "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import animation\n", "from IPython.display import HTML, display\n", "\n", - "\n", - "\n", - "\n", - "# Image processing\n", "from scipy.ndimage import (\n", - " gaussian_filter, grey_dilation, distance_transform_edt,\n", - " binary_dilation, median_filter, affine_transform, zoom\n", + " affine_transform,\n", + " binary_dilation,\n", + " distance_transform_edt,\n", + " gaussian_filter,\n", + " grey_dilation,\n", + " median_filter,\n", + " zoom,\n", ")\n", "\n" ] @@ -141,12 +131,24 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e856d40a", "metadata": {}, "outputs": [], "source": [ - "#TODO: move here the imports for the MD removing them from above" + "#TODO: move here the imports for the MD removing them from above\n", + "\n", + "USE_MD = True # True -> OpenMM Langevin dynamics\n", + " # False -> fast 2-D persistent-walk (no OpenMM needed\n", + "\n", + "# Install dependencies (uncomment in a fresh environment / Colab)\n", + "# !pip cache purge\n", + "# Molecular dynamics\n", + "!pip3 install -q scipy matplotlib pillow\n", + "if USE_MD:\n", + " !pip3 install openmm[cuda13] #change the cuda version depending on the CUDA version available on your system.\n", + " import openmm\n", + " import openmm.unit as unit" ] }, { @@ -251,7 +253,8 @@ "id": "-e3QvQzGlKYY" }, "source": [ - "#1.3 Noise\n", + "### 1.3. Noise\n", + "\n", "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", "\n", "\n", @@ -312,6 +315,7 @@ }, "source": [ "## 2. Chain Creation (3-D Persistent Random Walk)\n", + "\n", "Now we start with creating the DNA chain.\n", "\n", "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", @@ -421,6 +425,7 @@ }, "source": [ "## 3. Molecular Dynamics Relaxation\n", + "\n", "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", "\n", "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", @@ -672,7 +677,8 @@ "id": "Bx7_OzWRr-l3" }, "source": [ - "##4. Non-MD chain generation\n", + "## 4. Non-MD Chain Generation\n", + "\n", "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." ] }, @@ -836,7 +842,8 @@ "id": "cell_md_04" }, "source": [ - "## 5. MD Animation\n", + "## 5. Animation\n", + "\n", "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", "\n", "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", @@ -950,7 +957,8 @@ "id": "cell_md_05" }, "source": [ - "## 6. Height based AFM Rendering\n", + "## 6. Height-Based AFM Rendering\n", + "\n", "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", "\n", "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." @@ -1496,6 +1504,7 @@ }, "source": [ "## 7. Noise Functions\n", + "\n", "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", "\n", "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", @@ -2048,7 +2057,8 @@ "id": "cell_md_07" }, "source": [ - "##8. DNA Ground-Truth Mask\n", + "## 8. DNA Ground-Truth Mask\n", + "\n", "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", "\n", "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", @@ -2131,7 +2141,7 @@ "id": "cell_md_08" }, "source": [ - "## 9. Crossing Detection & Crossing Mask\n", + "## 9. Crossing Detection and Mask\n", "\n", "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", "\n", @@ -2866,7 +2876,7 @@ "id": "3YoKkN3e5dIo" }, "source": [ - "##13. Benchmarking\n", + "## 13. Benchmarking\n", "\n", "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." ] From 46f8be60a64139f76809da5994c1ccb2acb24765 Mon Sep 17 00:00:00 2001 From: Prakhar-Dutta Date: Fri, 24 Apr 2026 20:16:35 +0200 Subject: [PATCH 13/17] This is the file with the PEP8 style code and other fixes Changed the code to have the required style and other fixes to function descriptions. --- ...ged_via_AFM_draft_for_Giovanni_check.ipynb | 5146 +++++++++++++++++ 1 file changed, 5146 insertions(+) create mode 100644 tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb diff --git a/tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb b/tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb new file mode 100644 index 0000000..bffb928 --- /dev/null +++ b/tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb @@ -0,0 +1,5146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell_md_title", + "metadata": { + "id": "cell_md_title" + }, + "source": [ + "# Simulation of 'DNA chains on a substrate imaged via AFM'\n", + "**This notebook is intended to create simulations of DNA chain relaxation on a substrate imaged via AFM. By simulating physical relaxation and electrostatic attraction between a DNA chain and the substrate it allows users to generate high fidelity data.**\n", + "\n", + "This notebook generates a complete dataset stored within user defined directory `OUT_DIR`, primarily consisting of Synthetic AFM Images saved as `.npy` raw heightmap data (incorporating realistic noise from `.spm` blanks or PSD models) and optional `.png` files for quick visual inspection.\n", + "\n", + " To support machine learning workflows, the pipeline produces DNA Segmentation Masks in `.npy` format for pixel-wise background separation, alongside specialized Crossing Detection Masks that use Gaussian-weighted targets to highlight molecular intersections. Comprehensive Metadata for every sample, including bead counts and mechanical parameters, is saved in `.csv` or `.json` files, all of which are indexed in a central **manifest.csv** that links each generated image to its corresponding ground-truth masks and input configurations.\n", + ">\n", + "---\n", + "**Cell execution order:** run cells 1 → 12 in sequence. \n", + "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github.com/Prakhar-Dutta/ASAP/blob/1b5cfc5173e1bed4e59575e6dd4724cb20682620/tutorial/Not_final_version_DNA_notebook%20(1).ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "glVt-o8bCqSu", + "metadata": { + "id": "glVt-o8bCqSu" + }, + "outputs": [], + "source": [ + "# Uncomment codes in this cell if using Colab/Kaggle.\n", + "import os\n", + "from pathlib import Path\n", + "import re\n", + "import csv\n", + "import traceback\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import animation\n", + "from IPython.display import HTML, display\n", + "from __future__ import annotations\n", + "from typing import Any\n", + "# Image processing\n", + "from scipy.ndimage import (\n", + " gaussian_filter, grey_dilation, distance_transform_edt,\n", + " binary_dilation, median_filter, affine_transform, zoom\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2nb1FTtFC_q0", + "metadata": { + "id": "2nb1FTtFC_q0" + }, + "source": [ + "# 1. **With or without molecular dynamics?**\n", + "## 1.1. Imports\n", + "\n", + "This notebook allows two possible ways of building the simulated DNA chains; with or without the use of **coarse-grained molecular dynamics**. Molecular dynamics based simulations are computationally expensive, but provide closeness to real AFM imaging. For users who would like to use molecular dynamics, they would need to import all the libraries that will be necessary for simulation of the images." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cell_code_01", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cell_code_01", + "outputId": "58e34d6d-77ae-4b49-de3a-d8be68431282" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting openmm[cuda12]\n", + " Downloading openmm-8.5.1-cp312-cp312-manylinux_2_34_x86_64.whl.metadata (1.4 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from openmm[cuda12]) (2.0.2)\n", + "Collecting OpenMM-CUDA-12==8.5.1 (from openmm[cuda12])\n", + " Downloading openmm_cuda_12-8.5.1-py3-none-manylinux_2_34_x86_64.whl.metadata (405 bytes)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.90)\n", + "Requirement already satisfied: nvidia-cuda-nvcc-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.5.82)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.93)\n", + "Requirement already satisfied: nvidia-cuda-cupti-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.90)\n", + "Requirement already satisfied: nvidia-cufft-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (11.3.3.83)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.12/dist-packages (from nvidia-cufft-cu12->OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.93)\n", + "Downloading openmm_cuda_12-8.5.1-py3-none-manylinux_2_34_x86_64.whl (2.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading openmm-8.5.1-cp312-cp312-manylinux_2_34_x86_64.whl (14.4 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.4/14.4 MB\u001b[0m \u001b[31m80.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: openmm, OpenMM-CUDA-12\n", + "Successfully installed OpenMM-CUDA-12-8.5.1 openmm-8.5.1\n" + ] + } + ], + "source": [ + "# Install dependencies (uncomment in a fresh environment / Colab)\n", + "# Chain generation mode\n", + "USE_MD = True # True → OpenMM Langevin dynamics\n", + " # False → fast 2-D persistent-walk (no OpenMM needed)\n", + "# Molecular dynamics\n", + "!pip3 install -q scipy matplotlib pillow\n", + "if USE_MD:\n", + " !pip3 install openmm[cuda12]\n", + "# change the cuda version\n", + "# depending on the CUDA version available on your system.\n", + "# Ensure that CUDA can be accessed by OpenMM when running locally.\n", + " import openmm\n", + " import openmm.unit as unit\n" + ] + }, + { + "cell_type": "markdown", + "id": "1oW9llw-imr1", + "metadata": { + "id": "1oW9llw-imr1" + }, + "source": [ + "# 1.2 Defining the variables\n", + "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "sSqadS02hzPe", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sSqadS02hzPe", + "outputId": "b345b36e-0d30-4a4a-c7db-29e09caf319a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Output folder: /content/dna_dataset_100_4lengths_MD\n" + ] + } + ], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Global settings\n", + "#(these are the variables that need to be changed\n", + "# to change the appearance of the resulting images)\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "# Dataset\n", + "OUT_DIR = \"dna_dataset_100_4lengths_MD\"\n", + "# Name of the output directory that you would like to store your data in\n", + "N_SAMPLES = 100\n", + "# Number of samples that will be generated\n", + "BASE_SEED = 1\n", + "# Seed for reproducibility\n", + "\n", + "# Image resolution\n", + "NX = 512\n", + "NY = 512\n", + "\n", + "# Ring + MD\n", + "N_BEADS = 90\n", + "# default bead count for vizualization via chain creation cell\n", + "BEAD_COUNTS = [70, 80, 90, 100]\n", + "# four chain lengths that will be created in the dataset\n", + "#(1 bead corresponds to 10 base pairs on DNA approximately)\n", + "BOND_LENGTH = 1.0\n", + "# distance between beads\n", + "PERSISTENCE_BONDS = 23.0\n", + "# used to determine the range of angles which the chain is allowed to turn\n", + "K_ANGLE = 12.0\n", + "# used to determine the angular stiffness during relaxation\n", + "# (higher means more stiffness)\n", + "BASE_Z = 5.0\n", + "# Height above the substrate that the chain starts at before relaxing\n", + "\n", + "ANGLE_STIFNESS_MULT = 0.4\n", + "# If USE_MD is False, then angle stiffness multiplier controls\n", + "# chain propogation through space\n", + "\n", + "# MD recording\n", + "N_FRAMES = 200\n", + "STEPS_PER_FRAME = 200\n", + "# Between every frame local energy minimization happens for\n", + "#the given number of steps\n", + "\n", + "# AFM rendering\n", + "\n", + "tip_radius = 1\n", + "# Please select the tip radius here (Ideal range is 1nm-5nm)\n", + "nm_per_px = 2\n", + "# Internal AFM render sampling in nm / pixel before downsampling\n", + "\n", + "AFM_KW = dict(\n", + " nx=NX, ny=NY,\n", + " dna_diameter_nm=2.0,\n", + " # mapping to physical values\n", + " tip_radius_nm=0.01*tip_radius,\n", + " # Scaling factor is applied for internal control to match\n", + " max_height_nm=6.0,\n", + " # Maximum height for the colorbar\n", + " target_nm_per_px=0.04*nm_per_px,\n", + " # Scaling factor (0.04) is applied for internal control\n", + " max_radius_px=96,\n", + " # upper limit for the size of the chain in pixels\n", + " radius_shrink_px=0.0,\n", + " # Set to 0 so that rendering can match physical values\n", + " final_blur_sigma_px=0.20,\n", + " # When changing tip radius and nm_per_px to observe the expected\n", + " apply_edge_taper=True,\n", + " # physical effect, remove the scaling factors (0.04) for\n", + " # tip_radius_nm and target_nm_per_px\n", + " taper_sigma_nm=0.45,\n", + " # and scale all other parameters accordingly.\n", + " taper_floor=0.10,\n", + " add_center_ridge=True,\n", + " ridge_sigma_nm=0.25,\n", + " # This controls width of center ridge\n", + " ridge_amp_nm=0.25,\n", + " # This controls height of center ridge\n", + " grain_nm=0.0,\n", + " # This adds more noise to the chain\n", + " grain_sigma_px=0.6,\n", + " enable_crossing_boost=True,\n", + " # To boost the height of the crossings\n", + " min_separation_beads=12,\n", + " # Distance to other chain segments to not boost indiscriminately\n", + " boost_window_beads=2,\n", + " # How many beads to boost the height for\n", + " guaranteed_offset_nm=1.0,\n", + " # How much to boost\n", + " boost_method=\"additive\",\n", + " # add to the height of beads being boosted\n", + " boost_profile=\"gaussian\",\n", + " # Height increase profile\n", + " boost_sigma_beads=None,\n", + " far_clip_nm=2.5,\n", + " # Clip bead heights for beads that are not part of a crossing\n", + " far_clip_window_beads=3,\n", + " # How many beads to clip the height for\n", + " return_crossing_info=True,\n", + " # To ensure that mask information is passed to other functions.\n", + " # Change to False if masks are not needed\n", + " return_masks=True,\n", + ")\n", + "\n", + "\n", + "#-------------------------------------\n", + "# DNA mask\n", + "DNA_MASK_DILATE_PX = 3\n", + "# Dilation of mask for better training\n", + "#-------------------------------------\n", + "\n", + "#---------------------------\n", + "# Crossing mask\n", + "CROSS_MIN_SEP_BEADS = 12\n", + "# Where to stop for the crossing calculation\n", + "CROSS_SIGMA_CENTER_PX = 5.0\n", + "# How many pixels make up the center of a crossing\n", + "CROSS_SIGMA_PERP_PX = 1.8\n", + "# Crossing coloring parameter (how many pixels to modulate for the crossing)\n", + "CROSS_CHAIN_EXTENT = 5.0\n", + "# For the chain weighting of the mask\n", + "CROSS_CENTER_WEIGHT = 1.7\n", + "# For the chain weighting of the mask (chain-weighted guassian)\n", + "CROSS_CHAIN_WEIGHT = 0.9\n", + "CROSS_CLIP_TO_DNA_MASK = True\n", + "#---------------------------\n", + "\n", + "#Checking if output directory will be created\n", + "os.makedirs(OUT_DIR, exist_ok=True)\n", + "for _sub in [\"images\", \"dna_masks\", \"cross_masks\", \"meta\"]:\n", + " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", + "\n", + "print(\"Output folder:\", os.path.abspath(OUT_DIR))" + ] + }, + { + "cell_type": "markdown", + "id": "-e3QvQzGlKYY", + "metadata": { + "id": "-e3QvQzGlKYY" + }, + "source": [ + "# 1.3 Noise\n", + "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", + "\n", + "\n", + "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The blank file is a scan of the substrate **without** the sample of interest. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", + "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "47h81smulGxd", + "metadata": { + "id": "47h81smulGxd" + }, + "outputs": [], + "source": [ + "#Noise\n", + "TARGET_NOISE_RMS_NM = 0.27\n", + "# This variable controls the height of the noise\n", + "# in nanometers and this is added to the final image\n", + "\n", + "\n", + "# Blank SPM noise\n", + "USE_BLANK_SPM_NOISE = True\n", + "USE_PLANE_REMOVE = True\n", + "# This flag is used to control plane tilt removal from the noise image\n", + "USE_LINE_FLATTEN = True\n", + "# This flag is used to control line flattening (median filtering)\n", + "# from the noise image\n", + "USE_BANDPASS_FILTER = True\n", + "# This flag is used to control bandpass filtering on the noise image\n", + "BLANK_SPM_PATH = \"/content/20240411_blank_water.0_00000.spm\"\n", + "# optional; if missing/invalid, PSD fallback noise is used\n", + "# Change path when running locally. If using Colab, upload the file\n", + "\n", + "\n", + "# PSD-model fallback noise (used when blank .spm file is not available)\n", + "USE_PSD_NOISE_FALLBACK = True\n", + "MODEL_PATH = \"/content/psd_noise_model.npz\"\n", + "# Change path when running locally. If using Colab, upload the noise file\n", + "PSD_NOISE_METHOD = \"mean_full2d\"\n", + "# or \"lognormal_full2d\" / \"empirical_radial\"\n", + "PSD_NOISE_STD_SCALE = 2.0\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_02", + "metadata": { + "id": "cell_md_02" + }, + "source": [ + "## 2. Chain Creation (3-D Persistent Random Walk)\n", + "Now we start with creating the DNA chain. This is the toppology of the chain before relaxation is performed using molecular dynamnics.\n", + "\n", + "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", + "\n", + "A 3-D plot is shown at the end of the cell so you can inspect the initial geometry immediately." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cell_code_02", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "id": "cell_code_02", + "outputId": "827cae1a-7620-4319-f912-46054640e7cc" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "def make_tangled_ring_initial(\n", + " seed: int,\n", + " n_beads: int = N_BEADS,\n", + " bond_length: float = BOND_LENGTH,\n", + " persistence_bonds: float = PERSISTENCE_BONDS,\n", + " base_z: float = BASE_Z,\n", + ")-> np.ndarray:\n", + " \"\"\"Generates a closed (ring) polymer with a persistent random-walk tangent.\n", + "\n", + " Each step rotates the current direction by a small random angle around a\n", + " perpendicular axis. Ring closure is enforced by linearly distributing\n", + " the end-to-end gap across all beads. The chain is then lifted to base_z\n", + " so it can relax down to the z=0 substrate during MD.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " random seed for reproducibility\n", + " n_beads : int, optional\n", + " number of beads in the polymer ring. Defaults to N_BEADS.\n", + " bond_length : float, optional\n", + " the distance between consecutive beads. Defaults to BOND_LENGTH.\n", + " persistence_bonds : float, optional\n", + " the number of beads used to enforce persistence length in the chain.\n", + " Defaults to PERSISTENCE_BONDS.\n", + " base_z : float\n", + " the height to lift the chain above the substrate. Defaults to BASE_Z.\n", + "\n", + " Returns\n", + " -------\n", + " Returns a list of [coords] with shape (N, 3).\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + "\n", + " steps = np.zeros((n_beads, 3), dtype=np.float32)\n", + " #Initialization of vector for chain propogation\n", + " vec = np.array([1.0, 0.0, 0.0], dtype=np.float32)\n", + "\n", + " for k in range(n_beads):\n", + " dtheta = rng.normal(scale=np.sqrt(1.0 / persistence_bonds))\n", + " #choose an axis at random for chain propogation in 3D\n", + " axis = rng.normal(size=3).astype(np.float32)\n", + " # remove component along vec\n", + " axis -= axis.dot(vec) * vec\n", + " axis_norm = np.linalg.norm(axis)\n", + " # failsafe\n", + " if axis_norm < 1e-8:\n", + " axis = np.array([0.0, 0.0, 1.0], dtype=np.float32)\n", + " axis_norm = 1.0\n", + " axis /= axis_norm\n", + " # Calculating vector for chain propogation\n", + " vec = vec * np.cos(dtheta) + np.cross(axis, vec) * np.sin(dtheta)\n", + " vec /= np.linalg.norm(vec)\n", + " steps[k] = bond_length * vec\n", + "\n", + " coords = np.cumsum(steps, axis=0) # Coordinates for open chain\n", + "\n", + " # Enforce ring closure\n", + " closure_error = coords[-1] - coords[0]\n", + " # Based on the distance between first and last bead a\n", + " # closure error is propogated to all bead positions to form a closed chain\n", + " t = np.linspace(0, 1, n_beads, dtype=np.float32).reshape(-1, 1)\n", + " coords -= t * closure_error\n", + " coords -= coords.mean(axis=0)\n", + "\n", + " # Lift above substrate\n", + " # (z values are changed to lift the chain off the substrate)\n", + " coords[:, 2] += float(base_z)\n", + " return coords.astype(np.float32)\n", + "\n", + "\n", + "# ── Quick visualisation ──────────────────────────────────────────────────────\n", + "demo_coords = make_tangled_ring_initial(seed=43)\n", + "cc = np.vstack([demo_coords, demo_coords[0]]) # close the ring for plotting\n", + "\n", + "fig = plt.figure(figsize=(7, 6))\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-o\", markersize=3, linewidth=1.2)\n", + "ax.scatter(cc[0, 0], cc[0, 1], cc[0, 2], color=\"red\",\n", + " s=60, zorder=5, label=\"bead 0\")\n", + "\n", + "# Draw substrate plane\n", + "xs = np.linspace(cc[:, 0].min(), cc[:, 0].max(), 2)\n", + "ys = np.linspace(cc[:, 1].min(), cc[:, 1].max(), 2)\n", + "X, Y = np.meshgrid(xs, ys)\n", + "ax.plot_surface(X, Y, np.zeros_like(X), alpha=0.15, color=\"cyan\")\n", + "\n", + "ax.set_xlabel(\"x (sim units)\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z\")\n", + "ax.set_title(f\"Initial ring — seed 43, {len(demo_coords)} beads\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_03", + "metadata": { + "id": "cell_md_03" + }, + "source": [ + "## 3. Molecular Dynamics Relaxation\n", + "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", + "\n", + "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", + "Here we have added:\n", + "\n", + "1. Harmonic bonds\n", + "2. Bending-angle stiffness\n", + "3. A surface-attraction to cause chain relaxation\n", + "4. A hard-wall force to keep chain above substrate\n", + "5. A short-range WCA repulsion to ensure chain does not take non physical configurations\n", + "\n", + "The function then records `n_frames` snapshots which can be used for visualization." + ] + }, + { + "cell_type": "markdown", + "id": "wFFhsVZhqU8a", + "metadata": { + "id": "wFFhsVZhqU8a" + }, + "source": [ + "We start with testing the OpenMM installation (can be tricky at times)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3NjBSwfAe4Mj", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3NjBSwfAe4Mj", + "outputId": "8a5a7999-63ee-4a5e-a3c5-da9d17a0745f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "OpenMM Version: 8.5.1\n", + "Git Revision: f7fa0c27c1f8d943c339d67b3bf22f026d0bd8b5\n", + "\n", + "There are 4 Platforms available:\n", + "\n", + "1 Reference - Successfully computed forces\n", + "2 CPU - Successfully computed forces\n", + "3 CUDA - Successfully computed forces\n", + "4 OpenCL - Successfully computed forces\n", + "\n", + "Median difference in forces between platforms:\n", + "\n", + "Reference vs. CPU: 6.28625e-06\n", + "Reference vs. CUDA: 6.7605e-06\n", + "CPU vs. CUDA: 7.74371e-07\n", + "Reference vs. OpenCL: 6.74321e-06\n", + "CPU vs. OpenCL: 7.94239e-07\n", + "CUDA vs. OpenCL: 1.74909e-07\n", + "\n", + "All differences are within tolerance.\n" + ] + } + ], + "source": [ + "# Test the OpenMM installation before running the following cell\n", + "# to ensure that GPU acceleration will work\n", + "import openmm.testInstallation\n", + "\n", + "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", + "try:\n", + " openmm.testInstallation.main()\n", + "except Exception as e:\n", + " print(f\"Test failed: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9XbRNS3TqbKH", + "metadata": { + "id": "9XbRNS3TqbKH" + }, + "source": [ + "And then once we are sure that OpenMM is correctly installed, we run the MD functions" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cell_code_03", + "metadata": { + "id": "cell_code_03" + }, + "outputs": [], + "source": [ + "def run_md_relaxation(\n", + " coords0: np.ndarray,\n", + " seed: int,\n", + " n_frames: int = N_FRAMES,\n", + " steps_per_frame: int = STEPS_PER_FRAME\n", + ")-> list[np.ndarray]:\n", + " \"\"\"\n", + " Takes initial bead coordinates and runs Langevin dynamics using OpenMM\n", + "\n", + " This function uses the coordinates generated using\n", + " the `make_tangled_ring_initial` function to\n", + " run a molecular dynamics simulation.\n", + " There are certain forces that are defined\n", + " and applied to the bead which are then resolved using OpenMM.\n", + " The forces applied are:\n", + " - Harmonic bonds + angle stiffness along the ring\n", + " - Surface attraction (linear in z) + hard wall at z = 0\n", + " - Short-range Weeks–Chandler–Andersen (WCA)\n", + " repulsion between non-bonded beads\n", + " Drift removal is also performed using OpenMM's CMMotionRemover.\n", + " The variable langevin integrator from OpenMM is used and the forces are\n", + " resolved for 'steps_per_frame' steps for every frame in n_frames.\n", + "\n", + " Parameters\n", + " ----------\n", + " initial_coordinates : np.ndarray\n", + " The starting [x, y, z] coordinates of the polymer beads.\n", + " seed : int\n", + " Random seed for the Langevin integrator.\n", + " num_frames : int, optional\n", + " Number of frames to record during the simulation. Defaults to N_FRAMES.\n", + " steps_per_frame : int, optional\n", + " Number of integration steps to perform between recorded frames.\n", + " Defaults to STEPS_PER_FRAME.\n", + "\n", + " Returns\n", + " -------\n", + " A list of coordinate arrays of shape (num_beads, 3). The list contains\n", + " num_frames + 1 entries, where frame 0 is the pre-minimization geometry.\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If no OpenMM platform (CUDA, OpenCL, or CPU) could be initialised.\n", + "\n", + " Examples\n", + " --------\n", + " >>> frames = run_md_relaxation(_anim_coords0, seed=1, n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + " >>> print({len(frames), frames[0].shape})\n", + " {11, (100, 3)}\n", + "\n", + " \"\"\"\n", + "\n", + " coords0 = np.asarray(coords0, dtype=np.float32)\n", + " n = int(coords0.shape[0])\n", + "\n", + " # how big the bounding box for the molecular dynamics needs to be\n", + " extent = (coords0.max(axis=0) - coords0.min(axis=0)).max()\n", + " box = float(extent + 10.0)\n", + "\n", + "\n", + " # All lengths in nm, energies in kJ/mol.\n", + " BOLTZ_kJmol = 0.00831445 # kJ / (mol · K)\n", + " temperature = 1.0 # K (sets energy scale kT ≈ 0.0083 kJ/mol)\n", + " kT = BOLTZ_kJmol * temperature # kJ/mol\n", + " conlen = 1.0 # nm\n", + " mass_amu = 100.0 # Da per bead\n", + " collision_rate = 0.6 # ps⁻¹\n", + " error_tol = 0.005\n", + "\n", + " # ── Build system ─────────────────────────────────────────────────────────\n", + " system = openmm.System()\n", + " system.setDefaultPeriodicBoxVectors(\n", + " openmm.Vec3(box, 0, 0),\n", + " openmm.Vec3(0, box, 0),\n", + " openmm.Vec3(0, 0, box),\n", + " )\n", + " for _ in range(n):\n", + " system.addParticle(mass_amu)\n", + "\n", + " # Remove COM drift — applied every 50 steps\n", + " system.addForce(openmm.CMMotionRemover(10))\n", + " # To ensure CPU and GPU calculation are consistent\n", + " # (since GPU minimizations calculations can suffer from drift)\n", + "\n", + " # ── 1. Harmonic bonds ────────────────────────────────────────────────────\n", + "\n", + " # OpenMM HarmonicBondForce: E = (k/2)·(r − r0)² → k = kT / wiggle²\n", + " bond_L = BOND_LENGTH # nm\n", + " wiggle = 0.1 # nm (bondWiggleDistance)\n", + " k_bond = kT / (wiggle ** 2) # kJ/mol/nm²\n", + "\n", + " bond_force = openmm.HarmonicBondForce()\n", + " bond_force.setUsesPeriodicBoundaryConditions(True)\n", + " for i in range(n):\n", + " bond_force.addBond(i, (i + 1) % n, bond_L, k_bond)\n", + " system.addForce(bond_force)\n", + "\n", + " # ── 2. Bending (angle) stiffness ─────────────────────────────────────────\n", + " # E = kT · k_angle · (1 − cos(θ − π)) — equilibrium at θ = π (straight)\n", + " k_angle = K_ANGLE\n", + " angle_force = openmm.CustomAngleForce(\n", + " \"kT * kangle * (1 - cos(theta - 3.141592653589793))\"\n", + " )\n", + " angle_force.addGlobalParameter(\"kT\", kT)\n", + " angle_force.addGlobalParameter(\"kangle\", k_angle)\n", + " for i in range(n):\n", + " angle_force.addAngle((i - 1) % n, i, (i + 1) % n, [])\n", + " system.addForce(angle_force)\n", + "\n", + " # ── 3. Surface: linear attraction (z > 0) + harmonic hard wall (z < 0) ───\n", + " wall_expr = (\n", + " \"kT * (\"\n", + " \" F_pull * z * step(z) + \"\n", + " \" 0.5 * wallK * (z^2) * step(-z)\"\n", + " \")\"\n", + " )\n", + " wall_force = openmm.CustomExternalForce(wall_expr)\n", + " wall_force.addGlobalParameter(\"kT\", kT)\n", + " wall_force.addGlobalParameter(\"F_pull\", 9.0)\n", + " wall_force.addGlobalParameter(\"wallK\", 100.0)\n", + " for i in range(n):\n", + " wall_force.addParticle(i, [])\n", + " system.addForce(wall_force)\n", + "\n", + " # ── 4. WCA repulsion (repulsion between non-bonded pairs only) ───────────\n", + " rep_sigma = 1.6 * conlen # nm\n", + " rep_cutoff = 2.3 * conlen # nm\n", + " rep_expr = (\n", + " \"4*eps * ( (sig / (r + shift))^12 - (sig / (r + shift))^6 + 0.25 )\"\n", + " \"* step(cutoff - r);\"\n", + " \"cutoff = 2^(1.0/6.0) * sig\"\n", + " )\n", + " rep_force = openmm.CustomNonbondedForce(rep_expr)\n", + " rep_force.setNonbondedMethod(openmm.CustomNonbondedForce.CutoffPeriodic)\n", + " rep_force.setCutoffDistance(rep_cutoff)\n", + " rep_force.addGlobalParameter(\"eps\", 1.7 * kT)\n", + " rep_force.addGlobalParameter(\"sig\", rep_sigma)\n", + " rep_force.addGlobalParameter(\"shift\", 1e-8)\n", + " for i in range(n):\n", + " rep_force.addParticle([])\n", + " # exclude bonded neighbours\n", + " for i in range(n):\n", + " rep_force.addExclusion(i, (i + 1) % n)\n", + " system.addForce(rep_force)\n", + "\n", + " # ── Integrator ───────────────────────────────────────────────────────────\n", + " integrator = openmm.VariableLangevinIntegrator(temperature,\n", + " collision_rate, error_tol)\n", + " integrator.setRandomNumberSeed(int(seed))\n", + "\n", + " # ── Platform selection: OpenCL → CPU fallback ────────────────────────────\n", + " context = None\n", + " # OpenCL enables Mac users to be able to use GPU acceleration as well\n", + " for platform_name in (\"CUDA\",\"OpenCL\",\"CPU\"):\n", + " try:\n", + " platform = openmm.Platform.getPlatformByName(platform_name)\n", + " context = openmm.Context(system, integrator, platform)\n", + " print(f\"Using {platform_name} platform.\")\n", + " break\n", + " except openmm.OpenMMException as e:\n", + " print(f\" {platform_name} unavailable: {e}\")\n", + " except Exception as e:\n", + " print(f\" {platform_name} failed unexpectedly: {e}\")\n", + "\n", + " if context is None:\n", + " raise RuntimeError(\"No OpenMM platform could be initialised.\")\n", + "\n", + " # ── Set initial positions and box ────────────────────────────────────────\n", + " positions = [\n", + " openmm.Vec3(float(coords0[i, 0]),\n", + " float(coords0[i, 1]), float(coords0[i, 2]))\n", + " for i in range(n)\n", + " ]\n", + " context.setPositions(positions)\n", + " context.setPeriodicBoxVectors(\n", + " openmm.Vec3(box, 0, 0),\n", + " openmm.Vec3(0, box, 0),\n", + " openmm.Vec3(0, 0, box),\n", + " )\n", + "\n", + " # Capture the true initial geometry before any minimisation\n", + " # (just in case the chain reaches substrate even before the 1st frame)\n", + " frames = []\n", + " state = context.getState(getPositions=True)\n", + " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", + " frames.append(pos.copy())\n", + "\n", + " # Cap iterations\n", + " openmm.LocalEnergyMinimizer.minimize(context,\n", + " tolerance=50, maxIterations=200)\n", + "\n", + " # ── Record frames ────────────────────────────────────────────────────────\n", + " for _ in range(int(n_frames)):\n", + " integrator.step(int(steps_per_frame))\n", + " state = context.getState(getPositions=True)\n", + " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", + " frames.append(pos.copy())\n", + "\n", + " return frames # n_frames + 1 entries (frame 0 = initial geometry)" + ] + }, + { + "cell_type": "markdown", + "id": "Bx7_OzWRr-l3", + "metadata": { + "id": "Bx7_OzWRr-l3" + }, + "source": [ + "## 4. Non-MD chain generation\n", + "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yXs0I0akr948", + "metadata": { + "id": "yXs0I0akr948" + }, + "outputs": [], + "source": [ + "def make_ring_2d_persistent_initial(\n", + " seed: int,\n", + " n_beads: int,\n", + " bond_length: float = BOND_LENGTH, # From Cell 1\n", + " persistence_bonds: float = PERSISTENCE_BONDS, # From Cell 1\n", + " angle_stiffness_mult: float = ANGLE_STIFNESS_MULT, # From Cell 1\n", + " # Small Gaussian noise for Z\n", + " z_std: float = 0.08,\n", + " # Low altitude for \"deposited\" look\n", + " base_z: float = 0.2,\n", + " # Acts as a soft per-step cap\n", + " angle_limit: float = np.pi/2\n", + ") -> list[np.ndarray]:\n", + " \"\"\"\n", + " Generate a closed 2D persistent ring\n", + " and return it as [coords] with shape (N, 3).\n", + "\n", + " The function builds a periodic sequence of turning angles, smooths the\n", + " sequence to suppress sharp corners, and enforces total turning to ensure\n", + " the tangent field is compatible with a closed loop. The resulting curve is\n", + " resampled by arc length to maintain uniform bead spacing.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " Random seed for reproducibility.\n", + " num_beads : int\n", + " Number of beads in the polymer ring.\n", + " bond_length : float, optional\n", + " The desired distance between consecutive beads.\n", + " Defaults to BOND_LENGTH.\n", + " persistence_bonds : float, optional\n", + " Stiffness parameter; larger values result in a smoother ring with\n", + " longer directional memory. Defaults to PERSISTENCE_BONDS.\n", + " angle_stiffness_multiplier : float, optional\n", + " Multiplier for angular fluctuations; larger values result in\n", + " smaller fluctuations. Defaults to ANGLE_STIFNESS_MULT.\n", + " z_standard_deviation : float, optional\n", + " Standard deviation for the Gaussian noise applied to the z-axis.\n", + " Defaults to 0.08.\n", + " base_z : float, optional\n", + " The baseline altitude for the deposited coordinates. Defaults to 0.2.\n", + " angle_limit : float, optional\n", + " A soft cap on local turning increments (radians). Defaults to np.pi/2.\n", + "\n", + " Returns\n", + " -------\n", + " coords : np.ndarray\n", + " Array of shape (N, 3) containing the [x, y, z] coordinates\n", + "\n", + " Raises\n", + " ------\n", + " ValueError\n", + " If num_beads is less than 6 or if the generated ring has zero\n", + " contour length.\n", + "\n", + " Examples\n", + " --------\n", + " >>> coords = make_ring_2d_persistent_initial(seed=42)\n", + " >>> print(coords.shape)\n", + " (90, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " n = int(n_beads)\n", + " if n < 6:\n", + " raise ValueError(\"n_beads must be at least\"\n", + " \"6 for a stable closed persistent ring.\")\n", + "\n", + " bond_length = float(bond_length)\n", + "\n", + " # ------------------------------------------------------------------\n", + " # 1) Build a periodic turning-angle process on the ring\n", + " # ------------------------------------------------------------------\n", + " # Keep the same qualitative scaling as the old code:\n", + " # larger persistence_bonds / angle_stiffness_mult => less angular noise\n", + " sigma_dtheta = (\n", + " np.sqrt(2.0 / max(float(persistence_bonds), 1.0))\n", + " / max(float(angle_stiffness_mult), 1e-6)\n", + " )\n", + "\n", + " # Raw local turning increments\n", + " dtheta = rng.normal(0.0, sigma_dtheta, size=n).astype(np.float64)\n", + " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", + "\n", + " # Circular smoothing of turning increments to suppress sharp local corners.\n", + " # Stronger persistence / stiffness => broader smoothing.\n", + " smooth_width = int(np.clip(np.round(max(float(persistence_bonds), 1.0)\n", + " / 6.0), 1, 8))\n", + " if smooth_width > 0:\n", + " offsets = np.arange(-smooth_width, smooth_width + 1, dtype=np.float64)\n", + " kernel_sigma = max(smooth_width / 2.0, 1e-6)\n", + " kernel = np.exp(-0.5 * (offsets / kernel_sigma) ** 2)\n", + " kernel /= kernel.sum()\n", + "\n", + " dtheta_smooth = np.zeros_like(dtheta)\n", + " for shift, w in zip(offsets.astype(int), kernel):\n", + " dtheta_smooth += w * np.roll(dtheta, shift)\n", + " dtheta = dtheta_smooth\n", + "\n", + " # Enforce exact total turning of 2*pi for a closed planar ring.\n", + " # This avoids the nonphysical post hoc closure drift used before.\n", + " total_turn = dtheta.sum()\n", + " dtheta += (2.0 * np.pi - total_turn) / n\n", + "\n", + " # Re-clip gently after correction, then re-enforce total turning once more.\n", + " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", + " dtheta += (2.0 * np.pi - dtheta.sum()) / n\n", + "\n", + " # ------------------------------------------------------------------\n", + " # 2) Build periodic tangent angles and integrate to a dense closed path\n", + " # ------------------------------------------------------------------\n", + " theta0 = rng.uniform(0.0, 2.0 * np.pi)\n", + " thetas = theta0 + np.cumsum(dtheta)\n", + "\n", + " # Use a dense piecewise-linear curve first; later resample uniformly.\n", + " tangents = np.stack([np.cos(thetas), np.sin(thetas)], axis=1)\n", + "\n", + " # Integrate unit-speed steps to get a provisional loop\n", + " xy = np.zeros((n, 2), dtype=np.float64)\n", + " xy[1:] = np.cumsum(tangents[:-1] * bond_length, axis=0)\n", + " end_error = (xy[-1] + tangents[-1] * bond_length) - xy[0]\n", + "\n", + " # Distribute closure correction through the bond vectors,\n", + " # which is less distorting than shifting coordinates linearly.\n", + " bond_corr = end_error / n\n", + " bonds = tangents * bond_length - bond_corr[None, :]\n", + "\n", + " # Renormalize bond lengths back toward bond_length while keeping closure.\n", + " bond_norms = np.linalg.norm(bonds, axis=1)\n", + " good = bond_norms > 1e-12\n", + " bonds[good] *= (bond_length / bond_norms[good])[:, None]\n", + "\n", + " # Re-enforce closure after renormalization\n", + " bonds -= bonds.sum(axis=0, keepdims=True) / n\n", + "\n", + " # Reconstruct coordinates from corrected bonds\n", + " xy = np.zeros((n, 2), dtype=np.float64)\n", + " xy[1:] = np.cumsum(bonds[:-1], axis=0)\n", + "\n", + " # ------------------------------------------------------------------\n", + " # 3) Arc-length resample so bead spacing stays uniform and physical\n", + " # ------------------------------------------------------------------\n", + " closed_xy = np.vstack([xy, xy[0]])\n", + " seg = np.linalg.norm(np.diff(closed_xy, axis=0), axis=1)\n", + " s = np.concatenate([[0.0], np.cumsum(seg)])\n", + " total_len = s[-1]\n", + "\n", + " if total_len <= 1e-12:\n", + " raise ValueError(\"Degenerate ring generated;\"\n", + " \"total contour length is zero.\")\n", + "\n", + " target_s = np.linspace(0.0, total_len, n + 1)[:-1]\n", + "\n", + " x_resamp = np.interp(target_s, s, closed_xy[:, 0])\n", + " y_resamp = np.interp(target_s, s, closed_xy[:, 1])\n", + " xy = np.stack([x_resamp, y_resamp], axis=1)\n", + "\n", + " # Final contour-length normalization\n", + " # so mean bond length matches bond_length\n", + " final_bonds = np.roll(xy, -1, axis=0) - xy\n", + " mean_bond = np.mean(np.linalg.norm(final_bonds, axis=1))\n", + " if mean_bond > 1e-12:\n", + " xy *= (bond_length / mean_bond)\n", + "\n", + " # Center at origin\n", + " xy -= xy.mean(axis=0, keepdims=True)\n", + "\n", + " # ------------------------------------------------------------------\n", + " # 4) Assembly with preserved Gaussian z-noise logic\n", + " # ------------------------------------------------------------------\n", + " coords = np.zeros((n, 3), dtype=np.float32)\n", + " coords[:, 0] = xy[:, 0].astype(np.float32)\n", + " coords[:, 1] = xy[:, 1].astype(np.float32)\n", + " coords[:, 2] = rng.normal(loc=base_z,\n", + " scale=z_std, size=n).astype(np.float32)\n", + "\n", + " # Return as a list containing one frame to mimic run_md_relaxation output\n", + " return [coords]" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_04", + "metadata": { + "id": "cell_md_04" + }, + "source": [ + "## 5. MD Animation\n", + "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", + "\n", + "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", + "Running this cell will automatically generate and display the animation for a single deterministic chain (seed 0, default bead count)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cell_code_04", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 373 + }, + "id": "cell_code_04", + "outputId": "0286465d-0134-4014-f750-7942d7920a3a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generating initial ring and running MD (seed=0)…\n", + "Using CUDA platform.\n", + "MD done — 201 frames. Rendering animation…\n" + ] + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_5960/277962667.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 92\u001b[0m steps_per_frame=STEPS_PER_FRAME)\n\u001b[1;32m 93\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"MD done — {len(anim_frames)} frames. Rendering animation…\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0mshow_md_animation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manim_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/tmp/ipykernel_5960/277962667.py\u001b[0m in \u001b[0;36mshow_md_animation\u001b[0;34m(frames, interval_ms)\u001b[0m\n\u001b[1;32m 82\u001b[0m )\n\u001b[1;32m 83\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m 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here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2183\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2184\u001b[0;31m result = print_method(\n\u001b[0m\u001b[1;32m 2185\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2186\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m\u001b[0;34m(*args, 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rest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 72\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;32mif\u001b[0m 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\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshorthand_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 111\u001b[0m \"\"\"\n\u001b[1;32m 112\u001b[0m \u001b[0mParameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def show_md_animation(\n", + " frames: list[np.ndarray],\n", + " interval_ms: int = 150\n", + ") -> None:\n", + " \"\"\"\n", + " Renders an in-notebook HTML5 animation of MD frames.\n", + "\n", + " This function is used to visualize the molecular dynamics simulation\n", + " generated using the `run_md_relaxation` function.\n", + " A plane has been added for visualization purposes\n", + " and the range of absolute z values is also displayed.\n", + " This function is purely for visualisation and does not mutate any arrays.\n", + "\n", + " Parameters\n", + " ----------\n", + " frames : list\n", + " A list of coordinate arrays of shape (num_beads, 3).\n", + " interval_ms : int, optional\n", + " The time interval between frames in milliseconds. Defaults to 150.\n", + "\n", + " Returns\n", + " -------\n", + " None\n", + " The function calls `IPython.display.display` to render the\n", + " animation object directly in the notebook.\n", + "\n", + " Examples\n", + " --------\n", + " >>> show_md_animation(frames)\n", + "\n", + " \"\"\"\n", + "\n", + " frames = [np.asarray(f, dtype=np.float32) for f in frames]\n", + "\n", + " # Use only the first frame for x/y limits\n", + " # — avoids COM drift blowing the scale\n", + " first = frames[0]\n", + " xy_pad = 5.0\n", + " x_min, x_max = first[:, 0].min() - xy_pad, first[:, 0].max() + xy_pad\n", + " y_min, y_max = first[:, 1].min() - xy_pad, first[:, 1].max() + xy_pad\n", + "\n", + " # Clamp z: floor at -1 (just below substrate),\n", + " # ceiling from initial max + padding\n", + " z_min = -1.0\n", + " z_max = float(first[:, 2].max()) + 3.0\n", + "\n", + " x_plane = np.linspace(x_min, x_max, 2)\n", + " y_plane = np.linspace(y_min, y_max, 2)\n", + " X_plane, Y_plane = np.meshgrid(x_plane, y_plane)\n", + " Z_plane = np.zeros_like(X_plane)\n", + "\n", + " fig = plt.figure(figsize=(6, 6))\n", + " ax = fig.add_subplot(111, projection=\"3d\")\n", + "\n", + " def _set_axes():\n", + " ax.set_xlim(x_min, x_max)\n", + " ax.set_ylim(y_min, y_max)\n", + " ax.set_zlim(z_min, z_max)\n", + " ax.set_xlabel(\"x\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z (nm)\")\n", + "\n", + " def init():\n", + " _set_axes()\n", + " return []\n", + "\n", + " def update(i):\n", + " ax.cla()\n", + " c = frames[i]\n", + " # Clip beads to visible z range for plotting so outliers don't distort\n", + " visible = (c[:, 2] >= z_min) & (c[:, 2] <= z_max)\n", + " cc = np.vstack([c, c[0]])\n", + " ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-\", linewidth=1)\n", + " ax.scatter(c[visible, 0], c[visible, 1], c[visible, 2], s=5)\n", + " ax.plot_surface(X_plane, Y_plane, Z_plane, alpha=0.2, color=\"cyan\")\n", + " _set_axes()\n", + " ax.set_title(f\"Frame {i}/{len(frames)-1} | \"\n", + " f\"z ∈ [{c[:,2].min():.1f}, {c[:,2].max():.1f}] nm\")\n", + " return []\n", + "\n", + " ani = animation.FuncAnimation(\n", + " fig, update, frames=len(frames), init_func=init,\n", + " interval=int(interval_ms), blit=False\n", + " )\n", + " plt.close(fig)\n", + " display(HTML(ani.to_jshtml()))\n", + "\n", + "\n", + "# ── Run animation automatically (deterministic seed) ─────────────────────────\n", + "print(\"Generating initial ring and running MD (seed=0)…\")\n", + "anim_coords0 = make_tangled_ring_initial(seed=1)\n", + "anim_frames = run_md_relaxation(anim_coords0, seed=1,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + "print(f\"MD done — {len(anim_frames)} frames. Rendering animation…\")\n", + "show_md_animation(anim_frames)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_05", + "metadata": { + "id": "cell_md_05" + }, + "source": [ + "## 6. Height based AFM Rendering\n", + "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", + "\n", + "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cell_code_05", + "metadata": { + "id": "cell_code_05" + }, + "outputs": [], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Low-level geometry helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def _seg_intersect_2d(\n", + " p1: np.ndarray,\n", + " p2: np.ndarray,\n", + " q1: np.ndarray,\n", + " q2: np.ndarray,\n", + " eps: float = 1e-12\n", + ")-> tuple[bool, np.ndarray | None, float | None, float | None]:\n", + " \"\"\"Calculates the strict 2-D intersection of 2 line segments.\n", + "\n", + " This function is one of the functions used for the creation of the\n", + " chain from the bead coordinates.\n", + " The intersection point is defined by the parameters t and u such that:\n", + " point = p1 + t * (p2 - p1) = q1 + u * (q2 - q1)\n", + "\n", + " Parameters\n", + " ----------\n", + " p1, p2 : np.ndarray\n", + " Start and end points of the first line segment.\n", + " q1, q2 : np.ndarray\n", + " Start and end points of the second line segment.\n", + " eps : float, optional\n", + " A small positive value for numerical stability. Defaults to 1e-12.\n", + "\n", + " Returns\n", + " -------\n", + " is_intersecting : bool\n", + " True if the segments intersect, False otherwise.\n", + " intersection_point : np.ndarray or None\n", + " The [x, y] coordinates of the intersection point, or None if no\n", + " intersection exists.\n", + " parameter_t : float or None\n", + " The interpolation parameter along the first segment, or None.\n", + " parameter_u : float or None\n", + " The interpolation parameter along the second segment, or None.\n", + "\n", + " Examples\n", + " --------\n", + " >>> hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " >>> print(hit, pt, t, u)\n", + " False None None None\n", + "\n", + " \"\"\"\n", + "\n", + " p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n", + " q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n", + " r = p2 - p1; s = q2 - q1\n", + " rxs = r[0]*s[1] - r[1]*s[0]\n", + " if abs(rxs) < eps:\n", + " return False, None, None, None\n", + " qp = q1 - p1\n", + " t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n", + " u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n", + " if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n", + " return True, p1 + t*r, t, u\n", + " return False, None, None, None\n", + "\n", + "\n", + "def _circ_sep(\n", + " n: int,\n", + " i: int,\n", + " j: int\n", + ") -> int:\n", + " \"\"\"Circular distance between bead indices on a ring of n beads.\n", + "\n", + " This helper function determines the minimal separation between two beads\n", + " along the perimeter of the ring, accounting for the periodic boundary.\n", + " It is used to distinguish between beads that form physical crossings\n", + " and those that are simply sequential neighbors. This function is used\n", + " downstream for crossing calculations\n", + "\n", + " Parameters\n", + " ----------\n", + " n : int\n", + " Number of beads in the ring.\n", + " i, j : int\n", + " Indices of the two beads for which to calculate the separation.\n", + "\n", + " Returns\n", + " -------\n", + " int\n", + " The minimal distance between the two beads.\n", + "\n", + " Examples\n", + " --------\n", + " >>> d = _circ_sep(n, i, j)\n", + " >>> d.type\n", + " int\n", + "\n", + " \"\"\"\n", + "\n", + " d = abs(int(i) - int(j))\n", + " return min(d, n - d)\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Structuring-element / grid helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def disk_footprint(\n", + " r_px: float\n", + ") -> np.ndarray:\n", + " \"\"\"Boolean circular footprint of radius r_px pixels.\n", + "\n", + " This function creates a 2D boolean mask representing a disk of a\n", + " specified radius. It is used to convert discrete bead coordinates\n", + " into a continuous chain structure by dilating each bead coordinate\n", + " into a disk-like structure. This is what builds the DNA chain radius.\n", + "\n", + " Parameters\n", + " ----------\n", + " r_px : float\n", + " Radius of the disk in pixels.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " A boolean array with True values inside the disk and false outside.\n", + "\n", + " Examples\n", + " --------\n", + " >>> footprint = disk_footprint(r_px)\n", + " >>> print(footprint.shape, footprint.dtype)\n", + " (5, 5) bool\n", + "\n", + " \"\"\"\n", + "\n", + " if r_px < 0.5:\n", + " return np.ones((1, 1), dtype=bool)\n", + " r = int(np.ceil(r_px))\n", + " y, x = np.ogrid[-r:r+1, -r:r+1]\n", + " return (x*x + y*y) <= r*r\n", + "\n", + "\n", + "def upsample_nn(\n", + " a: np.ndarray,\n", + " out_shape: tuple[int, int]\n", + " ) -> np.ndarray:\n", + " \"\"\"Nearest-neighbour upsample array a to out_shape.\n", + "\n", + " This function increases the resolution of an input array (such as an AFM\n", + " height map or a boolean mask) by repeating elements.\n", + " If the target dimensions are not exact multiples of the input dimensions,\n", + " the resulting array is clipped to the specified target shape.\n", + "\n", + " Parameters\n", + " ----------\n", + " a : np.ndarray\n", + " Input array to be upsampled.\n", + " out_shape : tuple\n", + " Target shape of the upsampled array.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " Upsampled array of shape out_shape\n", + "\n", + " Examples\n", + " --------\n", + " >>> upsampled_array = upsample_nn(input_array, target_shape)\n", + " >>> print(upsampled_array.shape)\n", + " (target_shape[0], target_shape[1])\n", + "\n", + " \"\"\"\n", + "\n", + " ny_out, nx_out = out_shape\n", + " ny_in, nx_in = a.shape\n", + " if (ny_out, nx_out) == (ny_in, nx_in):\n", + " return a\n", + " sy = max(ny_out // ny_in, 1)\n", + " sx = max(nx_out // nx_in, 1)\n", + " a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n", + " return a_up[:ny_out, :nx_out]\n", + "\n", + "\n", + "#internal render grid now comes directly from extent and user nm_per_px\n", + "\n", + "def compute_internal_render_grid(\n", + " extent: tuple[float, float, float, float],\n", + " target_nm_per_px: float,\n", + " min_size: int = 16\n", + ") -> tuple[int, int, float]:\n", + " \"\"\"Build the internal render grid from physical extent and user nm_per_px.\n", + "\n", + " This function determines the size of the\n", + " grid for the resolution that the user desires.\n", + " It is based on values chosen for target_nm_per_px and nm_per_pix.\n", + " It also handles potential zero-division or negative resolution values.\n", + "\n", + " Parameters\n", + " ----------\n", + " extent : tuple\n", + " boundaries of the simulation space\n", + " target_nm_per_px : float\n", + " desired resolution multiplied by a scaling factor in nm/pixel\n", + " min_size : int, optional\n", + " minimum size of the grid in pixels. Default 16\n", + "\n", + " Returns\n", + " -------\n", + " tuple\n", + " size of the grid\n", + "\n", + " Examples\n", + " --------\n", + " >>> nx, ny, p_nm_per_px = compute_internal_render_grid(extent,\n", + " target_nm_per_px)\n", + " >>> print(nx, ny, p_nm_per_px)\n", + " 256 256 0.5\n", + "\n", + " \"\"\"\n", + "\n", + " xmin, xmax, ymin, ymax = extent\n", + " width_nm = float(xmax - xmin)\n", + " height_nm = float(ymax - ymin)\n", + " p_nm_per_px = max(float(target_nm_per_px), 1e-6)\n", + " nx = max(int(min_size), int(np.ceil(width_nm / p_nm_per_px)) + 1)\n", + " ny = max(int(min_size), int(np.ceil(height_nm / p_nm_per_px)) + 1)\n", + " return nx, ny, p_nm_per_px\n", + "\n", + "\n", + "def resize_image_to_shape(\n", + " a: np.ndarray,\n", + " out_shape: tuple[int, int],\n", + " order: int =1\n", + " ) -> np.ndarray:\n", + " \"\"\"Resize 2D array a to out_shape using scipy.ndimage.zoom.\n", + "\n", + " This function utilizes `scipy.ndimage.zoom` to scale the input array.\n", + " It includes safety checks for edge-case padding and cropping to ensure\n", + " the output array matches the requested dimensions exactly.\n", + "\n", + " Parameters\n", + " ----------\n", + " a : np.ndarray\n", + " Input array to be resized.\n", + " out_shape : tuple\n", + " Target shape of the resized\n", + " order : int, optional\n", + " Order of the interpolation. Defaults to 1.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " Resized array of shape out_shape\n", + "\n", + " Examples\n", + " --------\n", + " >>> resized_array = resize_image_to_shape(input_array, target_shape)\n", + " >>> print(resized_array.shape)\n", + " (target_shape[0], target_shape[1])\n", + "\n", + " \"\"\"\n", + "\n", + " a = np.asarray(a)\n", + " out_shape = tuple(int(v) for v in out_shape)\n", + " if a.shape == out_shape:\n", + " return a.copy()\n", + " zoom_factors = (out_shape[0] / a.shape[0], out_shape[1] / a.shape[1])\n", + " out = zoom(a, zoom_factors, order=order, mode=\"nearest\",\n", + " prefilter=(order > 1))\n", + " out = out[:out_shape[0], :out_shape[1]]\n", + " if out.shape != out_shape:\n", + " pad_y = max(0, out_shape[0] - out.shape[0])\n", + " pad_x = max(0, out_shape[1] - out.shape[1])\n", + " out = np.pad(out, ((0, pad_y), (0, pad_x)), mode=\"edge\")\n", + " out = out[:out_shape[0], :out_shape[1]]\n", + " return out\n", + "\n", + "\n", + "def dna_shape(\n", + " radius_nm: float,\n", + " p_nm_per_px: float,\n", + " max_radius_px: int = 96\n", + ")-> np.ndarray:\n", + " \"\"\"Hemispherical-cap structuring element\n", + " representing the DNA cross-section.\n", + "\n", + " This function generates a 2D height map representing the physical profile\n", + " of a DNA strand. Each pixel within the radius of the DNA is assigned a\n", + " height value based on the spherical-cap formula, representing the vertical\n", + " profile encountered during an AFM scan.\n", + "\n", + " Parameters\n", + " ----------\n", + " radius_nm : float\n", + " Radius of the disk in nanometers.\n", + " p_nm_per_px : float\n", + " Physical pixel size in nanometers.\n", + " max_radius_px : int, optional\n", + " Maximum radius in pixels. Defaults to 96.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " Height map of shape (2*R+1, 2*R+1)\n", + "\n", + " Example\n", + " -------\n", + " >>> height_map = dna_shape(radius_nm, p_nm_per_px)\n", + "\n", + " \"\"\"\n", + "\n", + " r_px = radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (r_px * r_px)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = np.sqrt(\n", + " np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "def AFM_tip(\n", + " tip_radius_nm: float,\n", + " p_nm_per_px: float,\n", + " max_radius_px: int = 128\n", + " ) -> tuple[np.ndarray, np.ndarray]:\n", + " \"\"\"Spherical-cap structuring element representing the AFM tip geometry.\n", + " Used to convolve the surface with the tip shape.\n", + "\n", + " This function calculates the relative vertical profile of a spherical AFM\n", + " tip. The resulting height map is used to perform a morphological dilation\n", + " on the sample surface, simulating the physical interaction (convolution)\n", + " between the tip and the sample. The height is normalized so that the\n", + " tip apex is at 0.0.\n", + "\n", + " Parameters\n", + " ----------\n", + " tip_radius_nanometers : float\n", + " The physical radius of the AFM tip in nanometers.\n", + " nanometers_per_pixel : float\n", + " The spatial resolution of the simulation grid in nanometers per pixel.\n", + " maximum_radius_pixels : int, optional\n", + " A safety limit for the pixel radius of the structuring element to\n", + " prevent excessive memory consumption. Defaults to 128.\n", + "\n", + " Returns\n", + " -------\n", + " occupancy_mask : np.ndarray\n", + " A 2D boolean array representing the circular footprint of the tip.\n", + " height_structure_element : np.ndarray\n", + " A 2D float32 array containing the vertical profile offsets of the\n", + " spherical cap. Pixels outside the tip footprint are assigned a\n", + " large negative value (-1e9).\n", + "\n", + " Example\n", + " -------\n", + " >>> occupancy_mask, height_structure_element = AFM_tip(tip_radius_nm,\n", + " p_nm_per_px)\n", + "\n", + " \"\"\"\n", + "\n", + " Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (Rpx * Rpx)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = (\n", + " np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n", + " - tip_radius_nm\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Crossing boost\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def calculate_crossing_taper_weight(\n", + " idx_off: int,\n", + " w: int,\n", + " profile: str = \"gaussian\",\n", + " sigma: float | None = None\n", + " ) -> float:\n", + " \"\"\"Smooth weight in [0, 1] used to fade the crossing height boost along\n", + " the bead-index window of half-width w.\n", + "\n", + " This function generates a weighting factor between 0 and 1 used to\n", + " gradually decrease the height offset applied to DNA segments near a\n", + " detected crossing. It ensures that the transition between the boosted\n", + " crossing region and the standard polymer chain is smooth.\n", + " There are 3 types of profiles possible to be applied\n", + " for smoothing a crossing, guassian, hemisphere, and linear.\n", + "\n", + " Parameters\n", + " ----------\n", + " idx_off : int\n", + " The distance in bead indices from the center of the crossing.\n", + " w : int\n", + " The number of beads on either side of the center over which the\n", + " weight should taper to zero.\n", + " profile : str, optional\n", + " The mathematical shape of the taper. Options are:\n", + " - 'gaussian': A smooth bell-shaped decay (recommended).\n", + " - 'hemisphere': A circular arc taper.\n", + " - 'linear': A simple linear ramp.\n", + " Defaults to 'gaussian'.\n", + " sigma : float, optional\n", + " The standard deviation for the Gaussian profile. If None, it is\n", + " automatically determined based on the window width.\n", + "\n", + " Returns\n", + " -------\n", + " float\n", + " A weighting factor in the range [0.0, 1.0].\n", + "\n", + " Examples\n", + " --------\n", + " >>> wt = calculate_crossing_taper_weight(idx_off, w)\n", + "\n", + " \"\"\"\n", + " if w <= 0:\n", + " return 1.0\n", + " a = abs(idx_off)\n", + " if profile == \"linear\":\n", + " return 1.0 - (a / (w + 1.0))\n", + " if profile == \"hemisphere\":\n", + " x = a / (w + 1.0)\n", + " return float(np.sqrt(max(0.0, 1.0 - x*x)))\n", + " # gaussian (default)\n", + " if sigma is None:\n", + " sigma = max(1e-6, 0.5 * (w + 0.5))\n", + " g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n", + " g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n", + " if g_end < 0.999:\n", + " g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n", + " return g\n", + "\n", + "\n", + "def collect_intersections(\n", + " xy_nm: np.ndarray,\n", + " min_separation_beads: int = 12,\n", + " merge_radius_nm: float = 0.5\n", + " )-> list[dict]:\n", + " \"\"\"Identify all self-intersections in a closed ring polyline.\n", + "\n", + " This function performs a brute-force search across all segment pairs of\n", + " the polyline to find crossings. It ignores adjacent segments, shared\n", + " vertices, and segments that are close to each other along the contour\n", + " of the ring. Detected intersections are subsequently clustered to\n", + " handle multi-segment vertex hits.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " xy_nm : np.ndarray\n", + " Array of shape (n, 2)\n", + " min_separation_beads : int, optional\n", + " Minimum separation between segments in beads. Defaults to 12.\n", + " merge_radius_nm : float, optional\n", + " Maximum separation between segments in nanometers. Defaults to 0.5.\n", + "\n", + " Returns\n", + " -------\n", + " list of dicts:\n", + " {pt_nm, seg_i, seg_j, t, u, n_hits}\n", + " list[dict]\n", + " A list of dictionaries, where each dictionary represents a unique\n", + " crossing and contains:\n", + " - \"pt_nm\": The [x, y] coordinates of the intersection.\n", + " - \"seg_i\": The index of the first segment involved.\n", + " - \"seg_j\": The index of the second segment involved.\n", + " - \"t\": Interpolation parameter along segment A.\n", + " - \"u\": Interpolation parameter along segment B.\n", + " - \"n_hits\": The number of raw intersections merged into this cluster.\n", + "\n", + " Examples\n", + " --------\n", + " >>> crossings = collect_intersections(xy_nm,\n", + " min_separation_beads=int(min_separation_beads),\n", + " merge_radius_nm=float(merge_radius_nm)\n", + " >>> crossings.type\n", + " list\n", + "\n", + " \"\"\"\n", + "\n", + " xy = np.asarray(xy_nm, dtype=float)\n", + " n = int(xy.shape[0])\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if hit:\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " clusters = merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n", + " return clusters\n", + "\n", + "\n", + "def merge_crossing_clusters(\n", + " raw_list: list[dict],\n", + " merge_radius_nm: float = 0.5\n", + " ) -> list[dict]:\n", + " \"\"\"Group nearby raw intersection points into single averaged clusters.\n", + "\n", + " This function uses a greedy proximity-based algorithm to cluster\n", + " intersections that occur close to each other (e.g., where multiple\n", + " segments meet at a single vertex). It calculates the centroid for each\n", + " cluster and retains metadata from the first intersection encountered in\n", + " each group.\n", + "\n", + " Parameters\n", + " ----------\n", + " raw_list : list[dict]\n", + " A list of dictionaries containing raw intersection data. Each\n", + " dictionary must include the key \"point\" (a 2D numpy array).\n", + " merge_radius_nm : float, optional\n", + " The maximum Euclidean distance allowed between an intersection point\n", + " and a cluster's current centroid for it to be merged. Defaults to 0.5.\n", + "\n", + " Returns\n", + " -------\n", + " list[dict]\n", + "\n", + " Examples\n", + " --------\n", + " clusters = merge_crossing_clusters(raw_list,\n", + " merge_radius_nm=float(merge_radius_nm))\n", + " >>> clusters.type\n", + " list\n", + "\n", + " \"\"\"\n", + "\n", + " clusters = []\n", + " for r in raw_list:\n", + " pt = r[\"pt\"].astype(float)\n", + " placed = False\n", + " for c in clusters:\n", + " if np.linalg.norm(pt -\n", + " c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = pt,\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def boost_crossings_intersections(\n", + " x_nm: np.ndarray,\n", + " y_nm: np.ndarray,\n", + " zc_nm: np.ndarray,\n", + " crossings: list[dict] | None = None,\n", + " min_separation_beads: int = 12,\n", + " boost_window_beads: int = 2,\n", + " guaranteed_offset_nm: float = 1.0,\n", + " boost_method: str = \"additive\",\n", + " boost_profile: str = \"gaussian\",\n", + " boost_sigma_beads: float | None = None,\n", + " far_clip_nm: float | None =2.5,\n", + " far_clip_window_beads: int = 12,\n", + " merge_radius_nm: float = 0.5,\n", + ") -> tuple[np.ndarray, list[dict]]:\n", + " \"\"\"Boost z-height at strand crossings so they are visually distinct in\n", + " the AFM image.\n", + "\n", + " This function identifies self-intersections in a 2D projection and\n", + " physically separates the strands in 3D. For each crossing, it determines\n", + " which segment is 'on top' and raises its vertical profile using a tapered\n", + " window to ensure a realistic and visually distinct crossover point.\n", + " For each crossing it:\n", + " - Determines which strand is on top (higher z)\n", + " - Raises top-strand beads in a tapered window to at least\n", + " (bottom_max + guaranteed_offset_nm)\n", + " - Optionally clips non-crossing beads to far_clip_nm\n", + "\n", + " Parameters\n", + " ----------\n", + " x_nm : np.ndarray\n", + " The x-coordinates of the polymer chain.\n", + " y_nm : np.ndarray\n", + " The y-coordinates of the polymer chain.\n", + " zc_nm : np.ndarray\n", + " The initial z-coordinates of the polymer chain.\n", + " crossings : list[dict]\n", + " Pre-computed crossing data. If None, crossings are detected\n", + " automatically using `collect_polyline_intersections`.\n", + " min_separation_beads : int, optional\n", + " Minimum separation between segments in beads. Defaults to 12.\n", + " boost_window_beads : int, optional\n", + " The number of beads to boost. Defaults to 2.\n", + " guaranteed_offset_nm : float, optional\n", + " The minimum boost height in nanometers. Defaults to 1.0.\n", + " boost_method : str\n", + " The logic for height calculation\n", + " boost_profile : str\n", + " The shape of the tapering profile\n", + " boost_sigma_beads : float\n", + " The standard deviation for gaussian tapering\n", + " far_clip_nm : float\n", + " If applied caps the height of beads far from any crossing to this value.\n", + " far_clip_window_beads : int\n", + " The number of beads to clip.\n", + " merge_radius_nm : float\n", + " The maximum Euclidean distance for nearby raw intersections.\n", + "\n", + " Returns\n", + " -------\n", + " z : np.ndarray\n", + " The updated z-coordinates with crossings boosted.\n", + " crossing_info : list[dict]\n", + " Information regarding the identified and modified crossings.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z, crossing_info = boost_crossings_intersections(x, y, z)\n", + " >>> print(z.shape, type(crossing_info))\n", + " (N,3) \n", + "\n", + " \"\"\"\n", + "\n", + " x = np.asarray(x_nm, dtype=np.float32)\n", + " y = np.asarray(y_nm, dtype=np.float32)\n", + " z = np.asarray(zc_nm, dtype=np.float32).copy()\n", + " n = int(z.shape[0])\n", + " if n < 5:\n", + " return z.astype(np.float32), []\n", + "\n", + " if crossings is None:\n", + " xy = np.stack([x, y], axis=1)\n", + " crossings = collect_intersections(\n", + " xy,\n", + " min_separation_beads=int(min_separation_beads),\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + "\n", + " def get_segment_indices(\n", + " center: int,\n", + " w: int\n", + " ) -> np.ndarray:\n", + " \"\"\"\n", + " Calculate indices in a window around a center bead,\n", + " handling periodicity.\n", + "\n", + " Parameters\n", + " ----------\n", + " center : int\n", + " The index of the center bead.\n", + " w : int\n", + " The number of beads on either side of the center\n", + " to include.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + "\n", + " \"\"\"\n", + "\n", + " return np.array([(center + k) % n for k in range(-w, w+1)],\n", + " dtype=np.int32)\n", + "\n", + " crossing_info = []\n", + " crossing_centers = []\n", + " w = int(boost_window_beads)\n", + "\n", + " for c in crossings:\n", + " i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n", + " i2 = (i + 1) % n; j2 = (j + 1) % n\n", + " ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n", + "\n", + " bead_i = i if ti < 0.5 else i2\n", + " bead_j = j if uj < 0.5 else j2\n", + " zi = float((1.0 - ti) * z[i] + ti * z[i2])\n", + " zj = float((1.0 - uj) * z[j] + uj * z[j2])\n", + "\n", + " top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j,\n", + " bead_i)\n", + " top_seg = get_segment_indices(top_center, w)\n", + " bottom_seg = get_segment_indices(bottom_center, w)\n", + "\n", + " bl_max = float(z[bottom_seg].max())\n", + " tl_max = float(z[top_seg].max())\n", + "\n", + " if boost_method == \"absolute\":\n", + " target_height = bl_max + float(guaranteed_offset_nm)\n", + " else:\n", + " target_height = max(tl_max, bl_max) + float(guaranteed_offset_nm)\n", + "\n", + " for off in range(-w, w+1):\n", + " k = (top_center + off) % n\n", + " wt = float(calculate_crossing_taper_weight(off, w,\n", + " profile=boost_profile,\n", + " sigma=boost_sigma_beads))\n", + " z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n", + "\n", + " crossing_centers.extend([int(top_center), int(bottom_center)])\n", + " crossing_info.append(dict(\n", + " pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n", + " seg_i = int(i),\n", + " seg_j = int(j),\n", + " top_center = int(top_center),\n", + " bottom_center= int(bottom_center),\n", + " ))\n", + "\n", + " # Clip beads far from any crossing\n", + " if crossing_centers and far_clip_nm is not None:\n", + " centers = np.array(crossing_centers, dtype=np.int32)\n", + " r = int(far_clip_window_beads)\n", + " for k in range(n):\n", + " d = int(np.min(np.minimum(np.abs(k - centers),\n", + " n - np.abs(k - centers))))\n", + " if d > r:\n", + " z[k] = min(z[k], float(far_clip_nm))\n", + "\n", + " return z.astype(np.float32), crossing_info\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Main AFM renderer\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def create_z_based_afm(\n", + " coords: np.ndarray,\n", + " nx: int = 800,\n", + " ny: int = 800,\n", + " dna_diameter_nm: float = 2.0,\n", + " tip_radius_nm: float = 0.01,\n", + " max_height_nm: float = 6.0,\n", + " target_nm_per_px: float = 0.08,\n", + " max_radius_px: int = 96,\n", + " radius_shrink_px: float = 2.0,\n", + " final_blur_sigma_px: float = 0.20,\n", + " apply_edge_taper: bool = True,\n", + " taper_sigma_nm: float = 0.45,\n", + " taper_floor: float = 0.10,\n", + " add_center_ridge: bool = True,\n", + " ridge_sigma_nm: float = 0.25,\n", + " ridge_amp_nm: float = 0.25,\n", + " grain_nm: float = 0.0,\n", + " grain_sigma_px: float = 0.6,\n", + " grain_seed: int = 1,\n", + " enable_crossing_boost: bool = True,\n", + " min_separation_beads: int = 12,\n", + " boost_window_beads: int = 2,\n", + " guaranteed_offset_nm: float = 1.0,\n", + " boost_method: str = \"additive\",\n", + " boost_profile: str = \"gaussian\",\n", + " boost_sigma_beads: float | None = None,\n", + " crossings_precomputed: list[dict] | None = None,\n", + " far_clip_nm: float | None = 2.5,\n", + " far_clip_window_beads: int = 12,\n", + " return_crossing_info: bool = False,\n", + " return_masks: bool = True,\n", + " extent: tuple[float, float, float, float] | None = None,\n", + ") -> tuple[np.ndarray, dict | tuple]:\n", + " \"\"\"Master AFM renderer for simulating images from 3D coordinates.\n", + "\n", + " This pipeline projects 3D bead coordinates onto a 2D grid, manages\n", + " molecular crossings, rasterizes the polymer backbone, applies\n", + " morphological dilations for DNA shape and AFM tip convolution, and\n", + " adds realistic optical and physical artifacts (taper, ridge, noise). It\n", + " follows the following steps:\n", + " 1. Project 3-D bead coords onto a 2-D effective grid\n", + " 2. Optionally boost crossing heights (boost_crossings_intersections)\n", + " 3. Rasterise closed polyline with z-interpolation\n", + " 4. Apply dna_shape (DNA cylindrical cross-section)\n", + " 5. Apply AFM_tip (tip-sample convolution)\n", + " 6. Clip, blur, edge-taper, center-ridge\n", + " 7. Optional synthetic grain noise\n", + " 8. Upsample to requested (nx, ny)\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (n, 3) containing x, y, z coordinates.\n", + " nx : int\n", + " Number of pixels in the x-direction.\n", + " ny : int\n", + " Number of pixels in the y-direction.\n", + " dna_diameter_nm : float\n", + " DNA diameter in nanometers.\n", + " tip_radius_nm : float\n", + " Tip radius in nanometers.\n", + " max_height_nm : float\n", + " Maximum height in nanometers.\n", + " target_nm_per_px : float\n", + " Target number of nanometers per pixel.\n", + " max_radius_px : int\n", + " Maximum radius in pixels.\n", + " radius_shrink_px : float\n", + " Radius shrinking factor.\n", + " final_blur_sigma_px : float\n", + " Final blur sigma in pixels.\n", + " apply_edge_taper : bool\n", + " Apply edge taper.\n", + " taper_sigma_nm : float\n", + " Taper sigma in nanometers.\n", + " taper_floor : float\n", + " Taper floor.\n", + " add_center_ridge : bool\n", + " Add center ridge.\n", + " ridge_sigma_nm : float\n", + " Ridge sigma in nanometers.\n", + " ridge_amp_nm : float\n", + " Ridge amplitude in nanometers.\n", + " grain_nm : float\n", + " Grain size in nanometers.\n", + " grain_sigma_px : float\n", + " Grain sigma in pixels.\n", + " grain_seed : int\n", + " Grain seed.\n", + " enable_crossing_boost : bool\n", + " Enable crossing boost.\n", + " min_separation_beads : int\n", + " Minimum separation between segments in beads.\n", + " boost_window_beads : int\n", + " The number of beads to boost.\n", + " guaranteed_offset_nm : float\n", + " The minimum boost height in nanometers.\n", + " boost_method : str\n", + " The logic for height calculation\n", + " boost_profile : str\n", + " The shape of the tapering profile\n", + " boost_sigma_beads : float\n", + " The standard deviation for gaussian tapering\n", + " crossings_precomputed : list[dict]\n", + " Pre-computed crossing data.\n", + " far_clip_nm : float\n", + " If applied caps the height of beads\n", + " far from any crossing to this value.\n", + " far_clip_window_beads : int\n", + " The number of beads to clip.\n", + " return_crossing_info : bool\n", + " Return crossing info.\n", + " return_masks : bool\n", + " Return masks.\n", + " extent : tuple[float, float, float, float]\n", + " Extent of the output image.\n", + "\n", + " Returns\n", + " -------\n", + " afm_image : np.ndarray\n", + " The simulated 2D AFM height map.\n", + " debug_dict : dict or tuple\n", + " A dictionary containing masks and metadata (if return_masks=True),\n", + " or a tuple containing the physical (xmin, xmax, ymin, ymax) extent.\n", + "\n", + " Examples\n", + " --------\n", + " >>> afm_image, debug_dict = create_z_based_afm(coords, nx=800, ny=800,\n", + " return_masks=True)\n", + " >>> print(afm_image.shape, type(debug_dict))\n", + " (800, 800) \n", + "\n", + " \"\"\"\n", + "\n", + " x, y, z = coords.T\n", + " if extent is None:\n", + " xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n", + " ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n", + " else:\n", + " xmin, xmax, ymin, ymax = extent\n", + "\n", + " # CHANGED: render directly on the physical grid selected upstream\n", + " nx_eff, ny_eff = int(nx), int(ny)\n", + " px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n", + " py_eff = (ymax - ymin) / max(ny_eff - 1, 1)\n", + " p_eff = 0.5 * (px_eff + py_eff)\n", + "\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n", + " 0, nx_eff - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n", + " 0, ny_eff - 1)\n", + "\n", + " zc = (z - z.min()).astype(np.float32)\n", + "\n", + " # ── Crossing boost ───────────────────────────────────────────────────────\n", + " crossing_info = []\n", + " if enable_crossing_boost:\n", + " zc, crossing_info = boost_crossings_intersections(\n", + " x.astype(np.float32), y.astype(np.float32), zc,\n", + " crossings=crossings_precomputed,\n", + " min_separation_beads=int(min_separation_beads),\n", + " boost_window_beads=int(boost_window_beads),\n", + " guaranteed_offset_nm=float(guaranteed_offset_nm),\n", + " boost_method=str(boost_method),\n", + " boost_profile=str(boost_profile),\n", + " boost_sigma_beads=boost_sigma_beads,\n", + " far_clip_nm=far_clip_nm,\n", + " far_clip_window_beads=int(far_clip_window_beads),\n", + " )\n", + "\n", + " # ── Rasterise polyline ───────────────────────────────────────────────────\n", + " z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n", + " line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n", + " x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " z_line[y0, x0] = max(z_line[y0, x0], z0)\n", + " line_mask[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys, zs = xs[keep], ys[keep], zs[keep]\n", + " z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n", + " line_mask[ys, xs] = True\n", + "\n", + " # ── DNA cap dilation ─────────────────────────────────────────────────────\n", + "\n", + " r_eff = max(0.5 * float(dna_diameter_nm), 0.2)\n", + " fp_dna, cap = dna_shape(r_eff, p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(z_line, footprint=fp_dna,\n", + " structure=cap).astype(np.float32)\n", + " dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna)>0\n", + " surface[~dna_region] = 0.0\n", + "\n", + " # ── Tip convolution ──────────────────────────────────────────────────────\n", + " r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n", + " if r_tip_px >= 0.5:\n", + " fp_tip, tip_struct = AFM_tip(\n", + " float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(surface, footprint=fp_tip,\n", + " structure=tip_struct).astype(np.float32)\n", + "\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + " if final_blur_sigma_px > 0:\n", + " fbsp=float(final_blur_sigma_px)\n", + " surface = gaussian_filter(surface,\n", + " sigma=fbsp).astype(np.float32)\n", + "\n", + " # ── Edge taper + center ridge ────────────────────────────────────────────\n", + " if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n", + " dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n", + " dist_nm = dist_px * np.float32(p_eff)\n", + " if apply_edge_taper:\n", + " sig = max(float(taper_sigma_nm), 1e-6)\n", + " factor = taper_floor + (1.0 - taper_floor) * np.exp(\n", + " -(dist_nm**2) / (2.0 * sig**2))\n", + " surface[dna_region] *= factor[dna_region]\n", + " if add_center_ridge and ridge_amp_nm > 0:\n", + " rs = max(float(ridge_sigma_nm), 1e-6)\n", + " ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n", + " surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " # ── Synthetic grain ──────────────────────────────────────────────────────\n", + " if grain_nm and grain_nm > 0:\n", + " rng = np.random.default_rng(int(grain_seed))\n", + " n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n", + " if grain_sigma_px and grain_sigma_px > 0:\n", + " n = gaussian_filter(n,\n", + " sigma=float(grain_sigma_px)).astype(np.float32)\n", + " v=n[dna_region].std()\n", + " std = float(v) if np.any(dna_region) else float(n.std())\n", + " if std > 1e-9:\n", + " n /= np.float32(std)\n", + " surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " if return_masks:\n", + " debug = {\n", + " \"line_mask\": line_mask,\n", + " \"dna_region\": dna_region,\n", + " \"extent\": (xmin, xmax, ymin, ymax),\n", + " \"p_nm_per_px\": float(p_eff),\n", + " }\n", + " if return_crossing_info:\n", + " debug[\"crossings\"] = crossing_info\n", + " return surface, debug\n", + "\n", + " return surface, (xmin, xmax, ymin, ymax)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_06", + "metadata": { + "id": "cell_md_06" + }, + "source": [ + "## 7. Noise Functions\n", + "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", + "\n", + "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", + "2. If no blank `.spm` file is supplied, the notebook falls back to a PSD-based synthetic noise model.\n", + "\n", + "Note: Topostats is an AFM imaging software package developed at the University of Sheffield which is used for filtering of AFM data. We desribe functions that perform operations similar to Topostats to clean the noise data from the blank files before adding it to the AFM_img.\n", + "\n", + "### Mathematics of PSD-Matched Noise Synthesis\n", + "\n", + "All three methods in the code utilize **Frequency-Domain Synthesis**. The core principle is that the height field $z(x, y)$ can be generated from a target Power Spectral Density (PSD) using the inverse Fourier Transform.\n", + "\n", + "#### 1. General Synthesis Workflow\n", + "For an output image of shape $(N_y, N_x)$:\n", + "1. **Define a PSD**: Let $S(f_x, f_y)$ be the target power spectrum.\n", + "2. **Generate Complex Spectrum**: A complex field $Z(f_x, f_y)$ is created in the frequency domain:\n", + " $$Z(f_x, f_y) = \\sqrt{S(f_x, f_y)} \\cdot e^{i \\phi(f_x, f_y)}$$\n", + " where $\\phi$ is a random phase drawn from a uniform distribution $U(0, 2\\pi)$.\n", + "3. **Inverse Transform**: The spatial noise $z(x, y)$ is the real part of the Inverse Fast Fourier Transform (IFFT) of $Z$.\n", + "4. **RMS Scaling**: The image is normalized such that its standard deviation matches the target Root Mean Square (RMS) roughness." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "cell_code_06", + "metadata": { + "id": "cell_code_06" + }, + "outputs": [], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Parsing the .spm file for height data\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def robust_range(a:np.ndarray | list) -> tuple[float, float, float]:\n", + " \"\"\"Calculate the 1st and 99th percentiles of finite values in an array.\n", + "\n", + " This function provides a robust measure of the data range by ignoring\n", + " extreme outliers (the top and bottom 1%) as well as non-finite values\n", + " (NaNs and infinities).\n", + "\n", + " Parameters\n", + " ----------\n", + " a : np.ndarray | list\n", + " The input data array to be evaluated.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[float, float, float]\n", + " A tuple containing:\n", + " - percentile_1: The 1st percentile of the finite data.\n", + " - percentile_99: The 99th percentile of the finite data.\n", + " - robust_spread: The difference between the 99th and 1st percentiles.\n", + "\n", + " Examples\n", + " --------\n", + " >>> a,b,c = robust_range(img)\n", + " >>> print(a,b,c)\n", + " 262 1023 761\n", + "\n", + " \"\"\"\n", + "\n", + " a = np.asarray(a, dtype=np.float32)\n", + " p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n", + " return float(p1), float(p99), float(p99 - p1)\n", + "\n", + "\n", + "def looks_like_afm_height(img: np.ndarray) -> bool:\n", + " \"\"\"Determine if an image array represents a valid AFM height map.\n", + "\n", + " This function checks if the dynamic range of the image (excluding\n", + " extreme outliers) is finite, strictly positive, and within a physically\n", + " sane upper bound.\n", + "\n", + " Parameters\n", + " ----------\n", + " img : np.ndarray\n", + " The 2D array representing the simulated or real AFM height map.\n", + "\n", + " Returns\n", + " -------\n", + " bool\n", + " True if the image has a valid, finite, and positive dynamic range;\n", + " False otherwise (e.g., if the array is flat, infinite, or all NaNs).\n", + "\n", + " Examples\n", + " --------\n", + " >>> Flag = looks_like_afm_height(img)\n", + " >>> print(Flag)\n", + " True\n", + "\n", + " \"\"\"\n", + "\n", + " p1, p99, rng = robust_range(img)\n", + " return np.isfinite(rng) and 0 < rng <= 1e9\n", + "\n", + "\n", + "def maybe_to_nm(img: np.ndarray) -> tuple[np.ndarray, str]:\n", + " \"\"\"Auto-convert from metres to nm if values look metre-scale.\n", + "\n", + " This function is used to convert the height map from metres to nm if\n", + " the measured height is in metres.\n", + "\n", + " Parameters\n", + " ----------\n", + " img : np.ndarray\n", + " The 2D array representing the AFM height map.\n", + "\n", + " Returns\n", + " -------\n", + " A tuple consisting of:\n", + " - np.ndarray\n", + " The image after the converion of the height map if needed.\n", + " - str\n", + " either as is or meters to nanometers depending on passed values.\n", + "\n", + " Examples\n", + " --------\n", + " >>> img = maybe_to_nm(img)\n", + " >>> print(img.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " p1, p99, rng = robust_range(img)\n", + " mx = max(abs(p1), abs(p99))\n", + " if mx < 1e-3:\n", + " return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n", + " return img.astype(np.float32), \"as-is (nm or counts)\"\n", + "\n", + "\n", + "def parse_blocks(\n", + " filename: str | Path,\n", + " max_blocks: int = 128\n", + ") -> tuple[bytes, str, list[dict]]:\n", + " \"\"\"Read a .spm file and parse binary data-block metadata.\n", + "\n", + " This function extracts the ASCII header from a Scanning Probe\n", + " Microscopy (SPM) file, locates the metadata blocks for the image data,\n", + " and parses the structural parameters required to read the raw binary data.\n", + "\n", + " Parameters\n", + " ----------\n", + " filename: str | Path\n", + " The path to the .spm file to be parsed.\n", + " max_blocks: int\n", + " The maximum number of blocks to parse.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[bytes, str, list[dict]]\n", + " A tuple containing:\n", + " - raw: The raw binary data from the .spm file.\n", + " - header: The ASCII header from the .spm file.\n", + " - uniq: A list of dictionaries containing the parsed block metadata.\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If no 'Data offset' strings are found in the header.\n", + "\n", + " Examples\n", + " --------\n", + " >>> raw, header, blocks = parse_blocks(filename)\n", + " >>> print(type(raw), type(header), type(blocks))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " raw = open(filename, \"rb\").read()\n", + " i_end = raw.find(b\"\\x1a\")\n", + " if i_end == -1:\n", + " i_end = min(len(raw), 400_000)\n", + " header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n", + "\n", + " matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n", + " if not matches:\n", + " raise RuntimeError(\"No 'Data offset' found in header.\")\n", + "\n", + " blocks = []\n", + " for k, pos in enumerate(matches[:max_blocks]):\n", + " win = header[max(0, pos-2500): min(len(header), pos+2500)]\n", + "\n", + " def grab_int(key):\n", + " m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n", + " return int(m.group(1)) if m else None\n", + "\n", + " def grab_float(key):\n", + " pattern = (\n", + " rf\"{re.escape(key)}\\s*:\\s*\"\n", + " r\"([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\"\n", + " )\n", + " m = re.search(pattern, win)\n", + " return float(m.group(1)) if m else None\n", + "\n", + " name_candidate = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n", + " name = name_candidate[-1].strip() if name_candidate else None\n", + "\n", + " off = grab_int(\"Data offset\")\n", + " length = grab_int(\"Data length\")\n", + " bpp = grab_int(\"Bytes/pixel\")\n", + " nx_b = grab_int(\"Samps/line\")\n", + " ny_b = grab_int(\"Number of lines\")\n", + " zscale = grab_float(\"Z scale\")\n", + " zoff = grab_float(\"Z offset\")\n", + "\n", + " if None in (off, length, bpp, nx_b, ny_b):\n", + " continue\n", + " blocks.append(dict(name=name or f\"block_{k}\",\n", + " off=off, length=length, bpp=bpp,\n", + " nx=nx_b, ny=ny_b,\n", + " zscale=zscale, zoff=zoff))\n", + "\n", + " uniq, seen = [], set()\n", + " for b in blocks:\n", + " if b[\"off\"] not in seen:\n", + " uniq.append(b); seen.add(b[\"off\"])\n", + " if not uniq:\n", + " raise RuntimeError(\"Found 'Data offset' strings\"\n", + " \" but no parseable blocks.\")\n", + " return raw, header, uniq\n", + "\n", + "\n", + "def read_block_candidates(\n", + " raw: bytes,\n", + " b: dict\n", + ") -> list[tuple[str, np.ndarray]]:\n", + " \"\"\"Decode a binary data block using all plausible NumPy data types.\n", + "\n", + " This function attempts to parse the\n", + " binary blob using all logical dtypes for the given bytes-per-pixel and\n", + " applies physical Z-scaling to the results.\n", + "\n", + " Parameters\n", + " ----------\n", + " raw : bytes\n", + " The raw binary data from the .spm file.\n", + " b : dict\n", + " The parsed block metadata.\n", + "\n", + " Returns\n", + " -------\n", + " list[tuple[str, np.ndarray]]\n", + " A list of candidate tuples. Each tuple contains:\n", + " - dtype_str: The string representation of the data type.\n", + " - image_array: The decoded image array.\n", + "\n", + " Raises\n", + " ------\n", + " Runtime error\n", + " - Truncated block.\n", + "\n", + " Examples\n", + " --------\n", + " >>> candidates = read_block_candidates(raw, b)\n", + " >>> print(type(candidates))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n", + " nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n", + " blob = raw[off:off+length]\n", + " if len(blob) < length:\n", + " raise RuntimeError(\"Truncated block.\")\n", + "\n", + " n = nx_b * ny_b\n", + " cands = []\n", + " dtype_map = {2: [\"i2\", \"u2\"],\n", + " 4: [\"i4\", \"f4\"],\n", + " 1: [\"u1\"]}\n", + " for dt in dtype_map.get(bpp, []):\n", + " arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n", + " if arr.size != n:\n", + " continue\n", + " cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n", + "\n", + " out = []\n", + " for dt, img in cands:\n", + " z = img\n", + " if b.get(\"zscale\") is not None:\n", + " z = z * np.float32(b[\"zscale\"])\n", + " if b.get(\"zoff\") is not None:\n", + " z = z + np.float32(b[\"zoff\"])\n", + " out.append((dt, z))\n", + " return out\n", + "\n", + "\n", + "def choose_best_height_image(\n", + " raw: bytes,\n", + " blocks: list[dict],\n", + " verbose: bool = True\n", + ")-> tuple[dict, np.ndarray]:\n", + " \"\"\"Evaluate all data blocks and dtypes\n", + " to find the most plausible AFM height map.\n", + "\n", + " This function iterate all blocks and dtype candidates\n", + " and then picks the one that:\n", + " - passes looks_like_afm_height\n", + " - has the highest log-variance score\n", + " (which is done to favor candidates with more topographical detail)\n", + "\n", + " Parameters\n", + " ----------\n", + " raw : bytes\n", + " The raw binary data from the .spm file.\n", + " blocks : list[dict]\n", + " The parsed block metadata.\n", + " verbose : bool\n", + " Whether to print diagnostics.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[dict, np.ndarray]\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If no suitable block is found.\n", + "\n", + " Examples\n", + " --------\n", + " >>> b, img = choose_best_height_image(raw, blocks)\n", + " >>> print(type(b), type(img))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " best, best_score = None, -np.inf\n", + " for b in blocks:\n", + " try:\n", + " for dt, img in read_block_candidates(raw, b):\n", + " if not looks_like_afm_height(img):\n", + " continue\n", + " p1, p99, rng = robust_range(img)\n", + " var = float(np.var(img))\n", + " score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n", + " if score > best_score:\n", + " best_score = score\n", + " best = (b, dt, img, (p1, p99, rng, var))\n", + " except Exception:\n", + " continue\n", + "\n", + " if best is None:\n", + " raise RuntimeError(\"Could not find a\"\n", + " \"plausible AFM height image decode.\")\n", + " b, dt, img, stats = best\n", + " if verbose:\n", + " p1, p99, rng, var = stats\n", + " print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n", + " f\"bpp={b['bpp']} decode={dt}\")\n", + " print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, \"\n", + " f\"range={rng:.3g}, var={var:.3g}\")\n", + " return b, img\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# TopoStats-like background / noise extraction\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def plane_remove(\n", + " z: np.ndarray,\n", + " mask: np.ndarray | None = None,\n", + ")-> np.ndarray:\n", + " \"\"\"Fit and subtract a first-order tilted plane from a 2D height map.\n", + "\n", + " This function performs background leveling by fitting a plane defined\n", + " by the equation z = ax + by + c to the image. If a mask is provided,\n", + " the fit is calculated only using the pixels where the mask is True\n", + " (typically representing the flat substrate).\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height map.\n", + " mask : np.ndarray | None, optional\n", + " A boolean mask of the same shape as z. If provided, the fit is\n", + " calculated only using the pixels where the mask is True.\n", + " This is useful for excluding features like DNA from the plane fit.\n", + " Defaults to None.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The flattened height map.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z_flat = plane_remove(z)\n", + " >>> print(z_flat.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " ny, nx = z.shape\n", + " Y, X = np.mgrid[0:ny, 0:nx]\n", + " A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n", + " b = z.ravel()\n", + " if mask is not None:\n", + " m = mask.ravel().astype(bool)\n", + " A, b = A[m], b[m]\n", + " C, *_ = np.linalg.lstsq(A, b, rcond=None)\n", + " plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n", + " return (z - plane).astype(np.float32)\n", + "\n", + "\n", + "def line_flatten_median(\n", + " z: np.ndarray,\n", + " mask: np.ndarray | None = None\n", + ")-> np.ndarray:\n", + " \"\"\"Subtract per-row median (background pixels only if mask given).\n", + "\n", + " AFM images often exhibit horizontal stripes due to low-frequency noise or\n", + " z-axis drift during scanning. This function levels each scan line\n", + " independently by shifting its median value to zero.\n", + "\n", + " Parameters\n", + " ----------\n", + " z: np.ndarray\n", + " The 2D height map\n", + " mask: np.ndarray | None, optional\n", + " A boolean mask of the same shape as z. If provided,\n", + " the median is calculated only using the pixels where the mask is True.\n", + " This prevents features (like DNA) from biasing the\n", + " flattening of the background. Defaults to None.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The flattened height map.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z_flat = line_flatten_median(z)\n", + " >>> print(z_flat.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " out = z.astype(np.float32, copy=True)\n", + " for i in range(out.shape[0]):\n", + " if mask is None or not np.any(mask[i]):\n", + " med = np.median(out[i])\n", + " else:\n", + " med = np.median(out[i, mask[i]])\n", + " out[i] -= np.float32(med)\n", + " return out\n", + "\n", + "\n", + "def feature_mask(\n", + " z: np.ndarray,\n", + " smooth_sigma_px: float = 3.0,\n", + " thresh_sigma: float = 3.0,\n", + " dilate_px: int = 4\n", + ")-> np.ndarray:\n", + " \"\"\"Detects topographical features as pixels deviating by more than\n", + " thresh_sigma MADs from the smooth-residual background.\n", + "\n", + " This function identifies pixels that deviate significantly from a smoothed\n", + " local background. It uses the Median Absolute Deviation (MAD) to estimate\n", + " the noise floor, making it robust against the features it is trying to\n", + " detect.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height map.\n", + " smooth_sigma_px : float, optional\n", + " The smoothing width in pixels. Defaults to 3.0.\n", + " thresh_sigma : float, optional\n", + " The number of standard deviations above the median to consider a pixel\n", + " as a feature. Defaults to 3.0.\n", + " dilate_px : int, optional\n", + " The radius of curcular footprint to dilate the feature mask.\n", + " Defaults to 4.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " A boolean mask where true indicates a detected feature\n", + "\n", + " Examples\n", + " --------\n", + " >>> feat = feature_mask(z)\n", + " >>> print(feat.shape, feat.dtype)\n", + " (800, 800) bool\n", + "\n", + " \"\"\"\n", + "\n", + " z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " resid = (z - z_s).astype(np.float32)\n", + " med = float(np.median(resid))\n", + " mad = float(np.median(np.abs(resid - med)))\n", + " sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n", + " feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n", + " if dilate_px and dilate_px > 0:\n", + " r = int(dilate_px)\n", + " yy, xx = np.ogrid[-r:r+1, -r:r+1]\n", + " fp = (xx*xx + yy*yy) <= r*r\n", + " feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n", + " return feat\n", + "\n", + "\n", + "def inpaint_simple(\n", + " z: np.ndarray,\n", + " mask: np.ndarray,\n", + " smooth_sigma_px: float = 8.0,\n", + ") -> np.ndarray:\n", + " \"\"\"Replace masked regions with a smooth estimate of the local background.\n", + "\n", + " This function calculates a heavily blurred version of the\n", + " original image to serve as a background proxy and patches the masked\n", + " areas with this estimate.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height map.\n", + " mask : np.ndarray\n", + " A boolean mask where True indicates pixels to be inpainted (features)\n", + " smooth_sigma_px : float, optional\n", + " The sigma value for the gaussian filter.\n", + " Higher value resutls in smoother inpainted images. Defaults to 8.0.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The inpainted height\n", + "\n", + " Examples\n", + " --------\n", + " >>> z_bg = inpaint_simple(z, feat)\n", + " >>> print(z_bg.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " out = z.copy()\n", + " out[mask] = bg[mask]\n", + " return out\n", + "\n", + "\n", + "def bandpass_noise(\n", + " z: np.ndarray,\n", + " low_sigma_px: float = 1.0,\n", + " high_sigma_px: float = 30.0,\n", + ") -> np.ndarray:\n", + " \"\"\"Performs bandpass filtering on the noise image by\n", + " isolating mid-frequency noise using difference of guassians filter.\n", + "\n", + " This function performs a bandpass filter by calculating the difference\n", + " between two Gaussian-blurred versions of the same image. It effectively\n", + " removes:\n", + " 1. High-frequency noise: Suppressed by the `low_sigma` blur.\n", + " 2. Low-frequency trends: (like tilt or bowing) Removed by subtracting\n", + " the `high_sigma` blur.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height\n", + " low_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the high frequency cutoff.\n", + " Defaults to 1.0.\n", + " high_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the low frequency cutoff.\n", + " Defaults to 30.0.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The bandpassed height map.\n", + "\n", + " Examples\n", + " --------\n", + " >>> tex = bandpass_noise(z)\n", + " >>> print(tex.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n", + " high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n", + " return (low - high).astype(np.float32)\n", + "\n", + "\n", + "def extract_topostats_like_noise(\n", + " z_in: np.ndarray,\n", + " median_size: int = 3,\n", + " bg_quantile: float = 40.0,\n", + " feature_sigma_px: float = 3.0,\n", + " feature_thresh_sigma: float = 3.0,\n", + " feature_dilate_px: int = 4,\n", + " inpaint_sigma_px: float = 10.0,\n", + " band_low_sigma_px: float = 0.8,\n", + " band_high_sigma_px: float = 35.0,\n", + " target_rms_nm: float | None = None,\n", + " apply_plane_remove: bool = True,\n", + " apply_line_flatten: bool = True,\n", + " apply_bandpass: bool = True,\n", + ") -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"Execute the full noise-extraction pipeline on an AFM height map.\n", + "\n", + " This function isolates the pure substrate noise texture by leveling\n", + " the image (plane flatten and line flatten),\n", + " masking out topographical features (detect features), inpainting those\n", + " regions, and applying a bandpass filter.\n", + " The resulting image can be rescaled to a target RMS value.\n", + " The resulting texture can be used to augment synthetic datasets.\n", + "\n", + " Parameters\n", + " ----------\n", + " z_in : np.ndarray\n", + " The 2D height map.\n", + " median_size : int, optional\n", + " The size of the median filter to apply. Defaults to 3.\n", + " bg_quantile : float, optional\n", + " The quantile to used to generate a rough initial background guess\n", + " for the preliminary plane and line leveling. Defaults to 40.0.\n", + " feature_sigma_px : float, optional\n", + " The gaussian sigma for background estimation for the feature mask.\n", + " Defaults to 3.0.\n", + " feature_thresh_sigma : float, optional\n", + " The number of standard deviations above the median\n", + " to consider a pixel as a feature. Defaults to 3.0.\n", + " feature_dilate_px : int, optional\n", + " The radius of curcular footprint to dilate the feature mask.\n", + " Defaults to 4.\n", + " inpaint_sigma_px : float, optional\n", + " The sigma value for the gaussian filter.\n", + " Higher value resutls in smoother inpainted image. Defaults to 10.0.\n", + " band_low_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the high frequency cutoff.\n", + " Defaults to 0.8.\n", + " band_high_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the low frequency cutoff.\n", + " Defaults to 35.0.\n", + " target_rms_nm: float | None, optional\n", + " If provided, rescales the extracted noise texture\n", + " to match this target RMS roughness. Defaults to None.\n", + " apply_plane_remove : bool, optional\n", + " Whether to apply the plane remove step. Defaults to True.\n", + " apply_line_flatten : bool, optional\n", + " Whether to apply the line flatten step. Defaults to True.\n", + " apply_bandpass : bool, optional\n", + " Whether to apply the bandpass step. Defaults to True.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[float, np.ndarray, np.ndarray, np.ndarray]\n", + " A tuple containing:\n", + " - rms_nm: The RMS roughness of the extracted noise texture.\n", + " - noise_texture: The extracted, zero mean noise texture.\n", + " - z_flat: The flattened height map.\n", + " - feature_mask: A boolean mask where true indicates a detected feature.\n", + "\n", + " Examples\n", + " --------\n", + " >>> rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(z)\n", + " >>> print(rms_nm)\n", + " 0.06\n", + " >>> print(noise_tex.shape)\n", + " (800, 800)\n", + " >>> print(z_flat.shape)\n", + " (800, 800)\n", + " >>> print(feat.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " z = z_in.astype(np.float32)\n", + "\n", + " if median_size and median_size > 1:\n", + " z = median_filter(z, size=int(median_size)).astype(np.float32)\n", + "\n", + " q = np.percentile(z, float(bg_quantile))\n", + " bg0 = z <= q\n", + "\n", + " z_flat = z.copy()\n", + "\n", + " if apply_plane_remove:\n", + " z_flat = plane_remove(z_flat, mask=bg0)\n", + "\n", + " if apply_line_flatten:\n", + " z_flat = line_flatten_median(z_flat, mask=bg0)\n", + "\n", + " feat = feature_mask(\n", + " z_flat,\n", + " smooth_sigma_px=feature_sigma_px,\n", + " thresh_sigma=feature_thresh_sigma,\n", + " dilate_px=feature_dilate_px,\n", + " )\n", + "\n", + " z_bg = inpaint_simple(\n", + " z_flat,\n", + " feat,\n", + " smooth_sigma_px=inpaint_sigma_px,\n", + " )\n", + "\n", + " if apply_bandpass:\n", + " tex = bandpass_noise(\n", + " z_bg,\n", + " low_sigma_px=band_low_sigma_px,\n", + " high_sigma_px=band_high_sigma_px,\n", + " )\n", + " else:\n", + " tex = z_bg.astype(np.float32)\n", + "\n", + " bg = ~feat\n", + " rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n", + "\n", + " if target_rms_nm is not None:\n", + " target = float(target_rms_nm)\n", + " if rms > 1e-12:\n", + " tex *= target / rms\n", + " rms = target\n", + "\n", + " return (\n", + " rms,\n", + " tex.astype(np.float32),\n", + " z_flat.astype(np.float32),\n", + " feat.astype(bool),\n", + " )\n", + "\n", + "\n", + "def tile_or_crop(\n", + " tex: np.ndarray,\n", + " out_shape: tuple[int, int],\n", + " seed: int | None = 0,\n", + ")-> np.ndarray:\n", + " \"\"\"Tile a texture array and extract a deterministic random crop.\n", + "\n", + " This function ensures that an extracted background noise texture can be\n", + " resized to fit any target simulated image size. If the texture is smaller\n", + " than the target, it is tiled (repeated) to cover the area. A random crop\n", + " is then taken to prevent obvious repeating patterns at the origin.\n", + "\n", + " Parameters\n", + " ----------\n", + " tex : np.ndarray\n", + " The 2D height map\n", + " out_shape : tuple[int, int]\n", + " The target output shape.\n", + " seed : int | None, optional\n", + " The random seed. Defaults to 0.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The resized and cropped noise texture.\n", + "\n", + " Examples\n", + " --------\n", + " >>> tex = tile_or_crop(tex, (ny, nx))\n", + " >>> print(tex.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " ty, tx = tex.shape\n", + " oy, ox = out_shape\n", + " if (ty, tx) == (oy, ox):\n", + " return tex\n", + " tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n", + " rng = np.random.default_rng(seed)\n", + " y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n", + " x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n", + " return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n", + "\n", + "\n", + "def load_blank_spm_noise(\n", + " blank_path: str | Path,\n", + " target_rms_nm: float = 0.06,\n", + " verbose: bool = True,\n", + " apply_plane_remove: bool = USE_PLANE_REMOVE,\n", + " apply_line_flatten: bool = USE_LINE_FLATTEN,\n", + " apply_bandpass: bool = USE_BANDPASS_FILTER,\n", + ") -> tuple[float, np.ndarray, dict]:\n", + " \"\"\"Load an SPM file and extract a calibrated background noise texture.\n", + "\n", + " This high-level function handles the full lifecycle of noise extraction:\n", + " 1. Parsing the Bruker .spm binary and metadata.\n", + " 2. Heuristically selecting the best height channel.\n", + " 3. Calibrating raw digital units to physical nanometers.\n", + " 4. Leveling, masking, and bandpass-filtering the substrate noise.\n", + "\n", + " Parameters\n", + " ----------\n", + " blank_path : str | Path\n", + " The path to the blank .spm file.\n", + " target_rms_nm : float, optional\n", + " The target RMS roughness of the extracted noise texture.\n", + " Defaults to 0.06.\n", + " verbose : bool, optional\n", + " Whether to print progress messages and diagnostic statistics.\n", + " Defaults to True.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[float, np.ndarray, dict]\n", + " A tuple containing:\n", + " - rms_nm: The RMS roughness of the extracted noise texture.\n", + " - noise_texture: The extracted, zero mean noise texture.\n", + " - diagnostics_dict: A dictionary containing diagnostic information.\n", + "\n", + " Examples\n", + " --------\n", + " >>> rms_nm, noise_tex, diag = load_blank_spm_noise(blank_path)\n", + " >>> print(rms_nm)\n", + " 0.06\n", + " >>> print(noise_tex.shape)\n", + " (800, 800)\n", + " >>> print(diag['flattened'].shape)\n", + " (800, 800)\n", + " >>> print(diag['feature_mask'].shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " raw, header, blocks = parse_blocks(blank_path)\n", + " b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n", + " img_nm, unit_note = maybe_to_nm(img0)\n", + " if verbose:\n", + " print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n", + "\n", + " rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n", + " img_nm,\n", + " median_size=3,\n", + " bg_quantile=45,\n", + " feature_sigma_px=3.0,\n", + " feature_thresh_sigma=3.0,\n", + " feature_dilate_px=6,\n", + " inpaint_sigma_px=10.0,\n", + " band_low_sigma_px=0.7,\n", + " band_high_sigma_px=40.0,\n", + " target_rms_nm=target_rms_nm,\n", + " )\n", + " diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n", + " \"height_nm\": img_nm, \"unit_note\": unit_note}\n", + " if verbose:\n", + " print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n", + " f\"tex shape: {noise_tex.shape}\")\n", + " return rms_nm, noise_tex, diag\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# PSD-based fallback noise synthesis\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "TARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\n", + "#Safety buffer to ensure no division by 0 or square roots producing NaN values\n", + "EPS = 1e-12\n", + "\n", + "\n", + "def load_model(\n", + " path: str | Path = MODEL_PATH\n", + " ) -> dict[str, np.ndarray]:\n", + " \"\"\"\n", + " Load the Power Spectral Density (PSD) noise model\n", + " from a compressed archive.\n", + "\n", + " The model typically contains the statistical distribution of substrate\n", + " roughness in the frequency domain, allowing for the generation of\n", + " synthetic noise that matches the 'color' and texture of the substrate.\n", + "\n", + " Parameters\n", + " ----------\n", + " path : str | Path, optional\n", + " The path to the compressed archive containing the PSD model.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, np.ndarray]\n", + " A dictionary containing the PSD model components namely:\n", + " - \"log_psd_mean\": The mean of the log-transformed radial PSD.\n", + " - \"log_psd_std\": The standard deviation of the\n", + " log-transformed radial PSD.\n", + " - \"radial_freq\": The spatial frequency coordinates.\n", + " - \"rms_values_nm\": RMS values associated with the training set.\n", + "\n", + " Examples\n", + " --------\n", + " >>> model = load_model()\n", + " >>> print(type(model))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " data = np.load(path)\n", + " return {\n", + " \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n", + " \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n", + " \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n", + " \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n", + " }\n", + "\n", + "\n", + "def generate_noise(\n", + " seed: int,\n", + " target_shape: tuple[int, int] = TARGET_SHAPE,\n", + " target_rms_nm: float | None = TARGET_NOISE_RMS_NM,\n", + " method: str = \"mean_full2d\",\n", + " std_scale: float = 1.0,\n", + " model: dict | None = None,\n", + " model_path: str | Path | None = MODEL_PATH,\n", + ") -> np.ndarray:\n", + " \"\"\"\n", + " Generate a single random AFM-like background noise image\n", + " from the PSD model.\n", + "\n", + " This function performs Fourier synthesis to create textures that match\n", + " the spatial frequency characteristics of the PSD model provided.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " RNG seed for reproducibility.\n", + " target_shape : tuple[int, int]\n", + " Output image shape (rows, cols).\n", + " The PSD is resized automatically if it differs\n", + " from the shape used when building the model. Defaults to TARGET_SHAPE.\n", + " target_rms_nm : float | None\n", + " RMS amplitude in nm.\n", + " If None a value is randomly taken from the blank-file ensemble.\n", + " Defaults to TARGET_NOISE_RMS_NM.\n", + " method : str\n", + " 'mean_full2d' -- mean log-PSD;\n", + " variance comes only from random phase (recommended).\n", + " 'lognormal_full2d' -- Samples a new PSD\n", + " from the log-normal distribution. (more sample to sample variation)\n", + " 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n", + " std_scale : float\n", + " Multiplier for the PSD on log_psd_std\n", + " for lognormal method (>1 -> more variation).\n", + " model : dict | None\n", + " Pre-loaded model dict. If None, loaded from model_path on each call.\n", + " model_path : str | Path | None\n", + " Path to the .npz file (used only when model is None).\n", + "\n", + " Returns\n", + " -------\n", + " z: np.ndarray\n", + "\n", + " Raises\n", + " ------\n", + " ValueError\n", + " If an unknown method is provided.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z = generate_noise(seed=42)\n", + " >>> print(z.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " if model is None:\n", + " model = load_model(model_path)\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " out_shape = tuple(target_shape)\n", + "\n", + " if method == \"mean_full2d\":\n", + " psd_sample = np.exp(model[\"log_psd_mean\"])\n", + "\n", + " elif method == \"lognormal_full2d\":\n", + " mu= model[\"log_psd_mean\"]\n", + " noise = rng.standard_normal(mu.shape).astype(np.float32)\n", + " psd_sample = np.exp(mu + std_scale * mu * noise)\n", + "\n", + " elif method == \"empirical_radial\":\n", + " mu = model[\"log_psd_mean\"]\n", + " radial_psd = np.exp(mu.mean(axis=1))\n", + " radial_freq_for_interp = np.abs(np.fft.fftfreq(mu.shape[0], d=1.0))\n", + "\n", + " fy = np.fft.fftfreq(out_shape[0], d=1.0)\n", + " fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n", + " fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n", + " kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n", + "\n", + " psd_sample = np.interp(\n", + " kr.ravel(),\n", + " radial_freq_for_interp,\n", + " radial_psd,\n", + " left=float(radial_psd[0]),\n", + " right=float(radial_psd[-1]),\n", + " ).reshape(kr.shape).astype(np.float32)\n", + "\n", + " else:\n", + " raise ValueError(\n", + " f\"Unknown method {method!r}. \"\n", + " \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n", + " )\n", + "\n", + " psd_ny, psd_nx = psd_sample.shape\n", + " out_ny, out_nx = out_shape\n", + " rfft_nx = out_nx // 2 + 1\n", + "\n", + " if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n", + " from scipy.ndimage import zoom\n", + " psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx),\n", + " order=1)\n", + "\n", + " psd_sample = psd_sample.astype(np.float32)\n", + "\n", + " phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n", + " spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n", + "\n", + " z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n", + " z -= np.float32(np.mean(z))\n", + " rmsv = model[\"rms_values_nm\"]\n", + " trmsf = float(target_rms_nm)\n", + " target = float(rng.choice(rmsv)) if target_rms_nm is None else trmsf\n", + " cur = float(np.std(z))\n", + " if cur > EPS:\n", + " z *= target / cur\n", + "\n", + " return z\n", + "\n", + "def random_transform_noise_texture(\n", + " tex: np.ndarray,\n", + " seed: int\n", + ") -> np.ndarray:\n", + " \"\"\"Apply a deterministic random affine transform to a noise texture.\n", + "\n", + " This function generates a reproducible random transformation of an input\n", + " texture using the provided seed. The transformation includes rotation,\n", + " anisotropic scaling, translation, optional vertical and horizontal flips,\n", + " and a final amplitude jitter.\n", + "\n", + " It is intended to produce visually distinct noise realizations from a\n", + " single acquired blank SPM texture, while preserving the overall texture\n", + " statistics.\n", + "\n", + " Parameters\n", + " ----------\n", + " tex: np.ndarray\n", + " Input noise texture of shape (H, W).\n", + " seed: int\n", + " Random seed controlling the random transformation.\n", + "\n", + " Returns\n", + " -------\n", + " out: np.ndarray\n", + " Transformed noise texture of shape (H, W).\n", + "\n", + " Examples\n", + " --------\n", + " >>> transformed = random_transform_noise_texture(tex, seed=42)\n", + " transformed\n", + " >>> transformed.shape == tex.shape\n", + " True\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " tex = np.asarray(tex, dtype=np.float32)\n", + "\n", + " theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n", + " base_scale = float(rng.uniform(0.85, 1.15))\n", + " anis = float(rng.uniform(0.85, 1.15))\n", + " sx, sy = base_scale * anis, base_scale / anis\n", + "\n", + " c, s = float(np.cos(theta)), float(np.sin(theta))\n", + " A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n", + " M = np.linalg.inv(A).astype(np.float32)\n", + "\n", + " h, w = tex.shape\n", + " center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n", + " t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n", + " float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n", + " offset = center - M @ (center + t)\n", + "\n", + " out = affine_transform(tex, matrix=M, offset=offset,\n", + " output_shape=tex.shape, order=1,\n", + " mode=\"wrap\", prefilter=False).astype(np.float32)\n", + "\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[::-1, :]\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[:, ::-1]\n", + " out *= float(rng.uniform(0.90, 1.10))\n", + " return out.astype(np.float32)\n", + "\n", + "\n", + "def sample_noise_image(\n", + " noise_source: str,\n", + " noise_asset: dict | np.ndarray | None,\n", + " seed: int,\n", + " out_shape: tuple[int, int],\n", + " target_rms_nm: float | None,\n", + " ) -> np.ndarray | None:\n", + " \"\"\"Return a per-sample noise image from either blank.spm or PSD fallback.\n", + "\n", + " Parameters\n", + " ----------\n", + " noise_source : str\n", + " 'blank_spm' or 'psd_model'\n", + " noise_asset : dict | np.ndarray | None\n", + " If 'blank_spm', this is the 2D texture array.\n", + " If 'psd_model', this is the dictionary containing PSD statistics.\n", + " seed : int\n", + " RNG seed for reproducibility.\n", + " out_shape : tuple[int, int]\n", + " The (rows, cols) of the desired height map.\n", + " target_rms_nm : float | None\n", + " If provided, rescales the extracted noise texture\n", + " to match this target RMS roughness.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray | None\n", + " The per-sample noise image or None if disabled.\n", + "\n", + " Examples\n", + " --------\n", + " >>> noise_tex = sample_noise_image(noise_source, noise_asset,\n", + " seed, out_shape, target_rms_nm)\n", + " >>> print(noise_tex.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " if noise_asset is None:\n", + " return None\n", + "\n", + " if noise_source == \"blank_spm\":\n", + " noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n", + " return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n", + "\n", + " if noise_source == \"psd_model\":\n", + " return generate_noise(\n", + " seed=seed,\n", + " target_shape=out_shape,\n", + " target_rms_nm=target_rms_nm,\n", + " method=PSD_NOISE_METHOD,\n", + " std_scale=PSD_NOISE_STD_SCALE,\n", + " model=noise_asset,\n", + " ).astype(np.float32)\n", + "\n", + " return None\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_07", + "metadata": { + "id": "cell_md_07" + }, + "source": [ + "## 8. DNA Ground-Truth Mask\n", + "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", + "\n", + "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", + "\n", + "We also dilate the mask by a value of **DNA_MASK_DILATE_PX** to ensure that the mask has enough width for optimal training." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "cell_code_07", + "metadata": { + "id": "cell_code_07" + }, + "outputs": [], + "source": [ + "def nm_to_px(\n", + " coords_xy_nm: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int\n", + ") -> tuple[np.ndarray, np.ndarray]:\n", + " \"\"\"Map (x, y) coordinates in nanometres to integer pixel indices (ix, iy).\n", + "\n", + " Linearly maps each coordinate from the physical extent to the discrete\n", + " pixel grid, then clips to valid index bounds.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords_xy_nm : np.ndarray\n", + " The (x, y) coordinates in nanometres.\n", + " extent : tuple[float, float, float, float]\n", + " The physical extent (xmin, xmax, ymin, ymax) in nanometres.¨\n", + " nx : int\n", + " The number of pixels in the x-direction.\n", + " ny : int\n", + " The number of pixels in the y-direction.\n", + "\n", + " Returns\n", + " -------\n", + " ix, iy : tuple[np.ndarray, np.ndarray]\n", + " The integer pixel indices which are:\n", + " - ix: The x-coordinate indices.\n", + " - iy: The y-coordinate indices.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ix, iy = nm_to_px(coords_xy_nm, extent, nx, ny)\n", + " >>> print(ix.dtype, iy.dtype)\n", + " int32 int32\n", + "\n", + " \"\"\"\n", + "\n", + " x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n", + " xmin, xmax, ymin, ymax = extent\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n", + " 0, nx - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n", + " 0, ny - 1)\n", + " return ix, iy\n", + "\n", + "\n", + "def rasterize_closed_polyline_mask(\n", + " coords: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int\n", + ") -> np.ndarray:\n", + " \"\"\"Rasterize a closed bead polyline into a 1-pixel-wide binary mask.\n", + "\n", + " Each consecutive bead pair (including the wrap-around from the last\n", + " bead back to the first) is drawn onto the grid using dense linear\n", + " interpolation along the segment. Duplicate pixels that would arise\n", + " at segment junctions are de-duplicated before writing.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (N, 3) or (N, 2) containing bead positions in\n", + " nanometres. Only the first two columns (x, y) are used.\n", + " extent : tuple[float, float, float, float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometres.\n", + " nx : int\n", + " The number of pixels in the x-axis.\n", + " ny : int\n", + " The number of pixels in the y-axis.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " A binary mask of shape (ny, nx),\n", + " with True at every pixel where the polyline is present\n", + " and False everywhere else.\n", + "\n", + " Examples\n", + " --------\n", + " >>> mask = rasterize_closed_polyline_mask(coords, extent, nx=512, ny=512)\n", + " >>> print(mask.shape, mask.dtype)\n", + " (512, 512) bool\n", + "\n", + " \"\"\"\n", + "\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n", + " out = np.zeros((ny, nx), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0 = int(ix[k]), int(iy[k])\n", + " x1, y1 = int(ix[k2]), int(iy[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " out[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys = xs[keep], ys[keep]\n", + " out[ys, xs] = True\n", + " return out\n", + "\n", + "\n", + "def make_dna_mask_from_beads(\n", + " coords: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int,\n", + " dilate_px: int = 3\n", + " ) -> np.ndarray:\n", + " \"\"\"Build a binary DNA segmentation mask from bead coordinates.\n", + "\n", + "\n", + " Rasterizes the closed bead polyline into a 1-pixel-wide line mask\n", + " and optionally dilates it by a disk of radius ``dilate_px`` pixels\n", + " to produce a mask wide enough for robust segmentation training.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (N, 3) or (N,2) containing bead positions in nanometres.\n", + " Only the first two columns (x, y) are used.\n", + " extent : tuple[float, float, float, float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometres.\n", + " nx : int\n", + " The number of pixels in the x-axis.\n", + " ny : int\n", + " The number of pixels in the y-axis.\n", + " dilate_px : int, optional\n", + " Radius in pixels of the disk structuring element used to dilate\n", + " the rasterized line. Set to 0 to skip dilation. Defaults to 3.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " unit8 binary mask of shape (ny, nx)\n", + " with values 0 (background) or 1 (DNA).\n", + "\n", + " Examples\n", + " --------\n", + " >>> mask = make_dna_mask_from_beads(coords, extent, nx=512, ny=512,\n", + " dilate_px=DNA_MASK_DILATE_PX)\n", + " >>> print(mask.shape)\n", + " (512, 512)\n", + "\n", + " \"\"\"\n", + "\n", + " line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n", + " if dilate_px and dilate_px > 0:\n", + " fdp = float(dilate_px)\n", + " line = binary_dilation(line, structure=disk_footprint(fdp))\n", + " return line.astype(np.uint8)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_08", + "metadata": { + "id": "cell_md_08" + }, + "source": [ + "## 9. Crossing Detection & Crossing Mask\n", + "\n", + "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", + "\n", + "We have set the masks for the crossings as chain weighted gaussians so that the machine learning models can have more information about the crossings and thus have better predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "cell_code_08", + "metadata": { + "id": "cell_code_08" + }, + "outputs": [], + "source": [ + "def canon_axis(\n", + " u: np.ndarray) -> np.ndarray | None:\n", + " \"\"\"Normalize a 2-D vector and canonicalise its sign.\n", + "\n", + " Ensures that +v and -v are treated as the same axis direction by\n", + " flipping the sign so the first nonzero component is always positive.\n", + " This makes axis comparisons order-independent.\n", + "\n", + " Parameters\n", + " ----------\n", + " u: np.ndarray\n", + " A 2-D vector to normalize\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray | None\n", + " Unit vector with canonicalized sign, or\n", + " None if u is zero or if input has a norm below 1e-12.\n", + "\n", + " Examples\n", + " --------\n", + " >>> u = np.array([1.0, 2.0])\n", + " >>> v = canon_axis(u)\n", + " >>> print(v)\n", + " [0.4472136 0.8944272]\n", + "\n", + " \"\"\"\n", + "\n", + " u = np.asarray(u, dtype=float)\n", + " nrm = float(np.linalg.norm(u))\n", + " if nrm < 1e-12:\n", + " return None\n", + " u = u / nrm\n", + " if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n", + " u = -u\n", + " return u\n", + "\n", + "\n", + "def find_polyline_crossings(\n", + " coords_xy: np.ndarray,\n", + " min_separation_beads: int = CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm: float = 0.5\n", + ") -> list[dict]:\n", + " \"\"\"Detect all strand crossings of a closed ring polyline in XY.\n", + "\n", + " Performs a brute-force search over all segment pairs, skipping\n", + " adjacent segments, shared-vertex pairs, and pairs whose circular\n", + " contour separation is less than ``minimum_separation_beads``. Raw\n", + " intersection hits that land within ``merge_radius_nm`` of each other\n", + " are clustered into a single crossing, and duplicate chain-axis\n", + " directions (within ~18°) are deduplicated per cluster.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords_xy : np.ndarray\n", + " Array of shape (N,2) containing the [x,y] bead positions in nanometers\n", + " min_separation_beads: int, optional\n", + " Minimum circular contour separation (in beads)\n", + " required between 2 segments\n", + " before their intersection is considered. Defaults to CROSS_MIN_SEP_BEADS.\n", + " merge_radius_nm: float, optional\n", + " Maximum distance in nanometers\n", + " between 2 raw intersections that are clustered\n", + " into a single crossing. Defaults to 0.5 nm.\n", + "\n", + " Returns\n", + " -------\n", + " list[dict]\n", + " Each dict represents one crossing and contains:\n", + " - ``pt_nm`` : np.ndarray, shape (2,) — position in nanometres.\n", + " - ``axes_nm`` : list of np.ndarray — deduplicated unit chain\n", + " direction vectors at the crossing.\n", + " - ``seg_i`` : int — index of the first segment.\n", + " - ``seg_j`` : int — index of the second segment.\n", + " - ``t`` : float — parametric position along segment i.\n", + " - ``u`` : float — parametric position along segment j.\n", + " - ``n_hits`` : int — number of raw hits merged into this cluster.\n", + "\n", + " Examples\n", + " --------\n", + " >>> crossings = find_polyline_crossings(coords[:,:2])\n", + " >>> print(type(crossings))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " xy = np.asarray(coords_xy, dtype=float)\n", + " n = int(xy.shape[0])\n", + "\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if not hit:\n", + " continue\n", + " ua = canon_axis(p2 - p1)\n", + " ub = canon_axis(q2 - q1)\n", + " if ua is None or ub is None:\n", + " continue\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " axes=[ua, ub],\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " # Cluster nearby raw intersections\n", + " clusters = []\n", + " for r in raw:\n", + " pt = r[\"pt\"]\n", + " placed = False\n", + " for c in clusters:\n", + " fmc=float(merge_radius_nm)\n", + " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= fmc:\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"axes\"].extend(r[\"axes\"])\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n", + " \"axes\": list(r[\"axes\"]), \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt_mean = c[\"pt_sum\"] / c[\"n\"]\n", + " axes = []\n", + " for uvec in c[\"axes\"]:\n", + " uvec = canon_axis(uvec)\n", + " if uvec is None:\n", + " continue\n", + " if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n", + " continue\n", + " axes.append(uvec)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = np.asarray(pt_mean, dtype=np.float32),\n", + " axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n", + " if axes else [np.array([1.0, 0.0], np.float32)],\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def add_crossing_patch(\n", + " mask: np.ndarray,\n", + " pt_px: tuple[float, float],\n", + " axes_px: list[np.ndarray],\n", + " sigma_center: float = 2.0,\n", + " chain_extent: float = 5.0,\n", + " sigma_perp: float = 1.8,\n", + " center_weight: float = 1.0,\n", + " chain_weight: float = 0.8\n", + ") -> None:\n", + " \"\"\"Paint one crossing onto a float mask using a chain-weighted Gaussian.\n", + "\n", + " The patch is the sum of two components, composed with np.maximum so\n", + " that multiple crossings accumulate correctly without overwriting:\n", + "\n", + " - An isotropic Gaussian centred on the crossing point, scaled by\n", + " ``center_weight``.\n", + " - The per-axis maximum of anisotropic Gaussians aligned to each chain\n", + " direction (elongated along the chain, narrow across it), scaled by\n", + " ``chain_weight``.\n", + "\n", + " Parameters\n", + " ----------\n", + " mask: np.ndarray\n", + " Float32 array of shape (H,W) to paint onto.\n", + " pt_px: tuple[float, float]\n", + " (x,y) crossing point in pixel coordinates.\n", + " axes_px: list[np.ndarray]\n", + " Unit vectors giving chain directions at the crossing,\n", + " in pixel coordinates\n", + " sigma_center: float, optional\n", + " Parameter for standard deviation in pixels of the\n", + " isotropic center gaussian. Defaults to 2.0.\n", + " chain_extent: float, optional\n", + " Multiplier applied to \"sigma_center\" to set the\n", + " parallel standard deviation of the anisotropic chain gaussians.\n", + " Defaults to 5.0.\n", + " sigma_perp: float, optional\n", + " Parameter for standard deviation in pixels across each chain direction.\n", + " Defaults to 1.8\n", + " center_weight: float, optional\n", + " Amplitude of the isotropic center gaussian. Defaults to 1.0.\n", + " chain_weight: float, optional\n", + " Amplitude of the anisotropic chain gaussians. Defaults to 0.8.\n", + "\n", + " \"\"\"\n", + "\n", + " H, W = mask.shape\n", + " cx, cy = float(pt_px[0]), float(pt_px[1])\n", + " sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n", + " R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n", + " x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n", + " y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n", + "\n", + " xs = np.arange(x0, x1+1, dtype=float)\n", + " ys = np.arange(y0, y1+1, dtype=float)\n", + " X, Y = np.meshgrid(xs, ys)\n", + " dx = X - cx; dy = Y - cy\n", + "\n", + " gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n", + "\n", + " g_chain = np.zeros_like(gc)\n", + " for v in axes_px:\n", + " vx, vy = float(v[0]), float(v[1])\n", + " u_par = dx*vx + dy*vy\n", + " u_perp = -dx*vy + dy*vx\n", + " g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n", + " + u_perp**2 / sigma_perp**2))\n", + " g_chain = np.maximum(g_chain, g)\n", + "\n", + " patch = center_weight * gc + chain_weight * g_chain\n", + " mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n", + "\n", + "\n", + "def make_crossing_mask(\n", + " coords: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int,\n", + " sigma_center_px: float = CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px: float = CROSS_SIGMA_PERP_PX,\n", + " chain_extent: float = CROSS_CHAIN_EXTENT,\n", + " center_weight: float = CROSS_CENTER_WEIGHT,\n", + " chain_weight: float = CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads: int = CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed: list[dict] | None = None,\n", + " merge_radius_nm: float = 0.5\n", + ") -> tuple[np.ndarray, list[dict]]:\n", + " \"\"\"Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n", + "\n", + " For each detected (or precomputed) crossing, paints a chain-weighted\n", + " Gaussian blob onto a float32 canvas using ``add_crossing_patch``.\n", + " The final mask is normalised to [0, 1].\n", + "\n", + " Passing ``crossings_precomputed`` is strongly recommended in the\n", + " dataset generation pipeline: reusing the same crossing list that was\n", + " passed to the AFM renderer guarantees that the mask and the image are\n", + " perfectly spatially aligned.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (N,3) or (N,2) containing bead positions in nanometers.\n", + " Only the first two columns (x, y) are used.\n", + " Extent: tuple[float, float, float, float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometers.\n", + " nx: int\n", + " Number of pixels along the x-axis\n", + " ny: int\n", + " Number of pixels along the y-axis\n", + " sigma_center_px: float, optional\n", + " Parameter for standard deviation in pixels of the\n", + " isotropic center gaussian. Defaults to CROSS_SIGMA_CENTER_PX.\n", + " sigma_perp_px: float, optional\n", + " Parameter for standard deviation in pixels across the\n", + " chain direction at each crossing. Defaults to CROSS_SIGMA_PERP_PX.\n", + " chain_extent: float, optional\n", + " Multiplier on \"sigma_center_px\" that controls how far the\n", + " chain aligned gaussians extend along the strand.\n", + " Defaults to CROSS_CHAIN_EXTENT.\n", + " center_weight: float, optional\n", + " Amplitude of the isotropic center gaussian.\n", + " Defaults to CROSS_CENTER_WEIGHT.\n", + " chain_weight: float, optional\n", + " Amplitude of the anisotropic chain gaussians.\n", + " Defaults to CROSS_CHAIN_WEIGHT.\n", + " min_separation_beads: int, optional\n", + " Minimum circular contour separation (in beads)\n", + " required between 2 segments before their intersection is considered.\n", + " Defaults to CROSS_MIN_SEP_BEADS.\n", + " crossings_precomputed: list[dict] | None, optional\n", + " precomputed crossing list from \"find_polyline_crossings\"\n", + " or the AFM renderer. If None, crossings are detected automatically.\n", + " Defaults to None.\n", + " merge_radius_nm: float, optional\n", + " Maximum distance in nanometers between 2 raw\n", + " intersections that are clustered into a single crossing.\n", + " Defaults to 0.5 nm.\n", + "\n", + " Returns\n", + " -------\n", + " crossing_mask: np.ndarray\n", + " Float32 array of shape (ny, nx) with values in [0, 1].\n", + " crossings: list[dict]\n", + " Each dict represents one crossing\n", + "\n", + " Examples\n", + " --------\n", + " >>> crossing_mask, crossings = make_crossing_mask(coords,\n", + " extent, nx=512, ny=512, crossings_precomputed=precomputed_crossings)\n", + " >>> print(crossing_mask.shape, len(crossings))\n", + " (512, 512) 4\n", + "\n", + " \"\"\"\n", + "\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " xy_nm = coords[:, :2].astype(float)\n", + "\n", + " if crossings_precomputed is None:\n", + " crossings = find_polyline_crossings(\n", + " xy_nm,\n", + " min_separation_beads=min_separation_beads,\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + " else:\n", + " crossings = crossings_precomputed\n", + "\n", + " xmin, xmax, ymin, ymax = extent\n", + " sx = (nx - 1) / (xmax - xmin)\n", + " sy = (ny - 1) / (ymax - ymin)\n", + "\n", + " mask = np.zeros((ny, nx), dtype=np.float32)\n", + "\n", + " for c in crossings:\n", + " x_nm, y_nm = c[\"pt_nm\"]\n", + " px = (x_nm - xmin) * sx\n", + " py = (y_nm - ymin) * sy\n", + "\n", + " axes_px = []\n", + " for v in c[\"axes_nm\"]:\n", + " vx_px = v[0] * sx; vy_px = v[1] * sy\n", + " nrm = float(np.hypot(vx_px, vy_px))\n", + " if nrm < 1e-12:\n", + " continue\n", + " axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n", + " if not axes_px:\n", + " axes_px = [np.array([1.0, 0.0], dtype=float)]\n", + "\n", + " add_crossing_patch(\n", + " mask,\n", + " pt_px=(px, py),\n", + " axes_px=axes_px,\n", + " sigma_center=float(sigma_center_px),\n", + " chain_extent=float(chain_extent),\n", + " sigma_perp=float(sigma_perp_px),\n", + " center_weight=float(center_weight),\n", + " chain_weight=float(chain_weight),\n", + " )\n", + "\n", + " m = float(mask.max())\n", + " if m > 1e-12:\n", + " mask /= m\n", + " return mask.astype(np.float32), crossings" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_09", + "metadata": { + "id": "cell_md_09" + }, + "source": [ + "## 10. Decoy Chain Functions\n", + "\n", + "real AFM images often contain small chain segments that are not part of the main chain and thus constitute the noise in that sample, but since those segments are broken off DNA, they cannot be neatly integrated into the noise model.\n", + "Therefore, we offer the possibility of addition of small DNA chains to every every sample in the dataset where ``add_decoy = True`` which would then contain a small 2–4 bead \"decoy\" fragment placed in a corner of the image.\n", + "\n", + "The decoy appears in the AFM image but is **excluded** from both\n", + "ground-truth masks, training the model to ignore isolated unconnected fragments." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "cell_code_09", + "metadata": { + "id": "cell_code_09" + }, + "outputs": [], + "source": [ + "def make_decoy_chain(\n", + " seed: int,\n", + " n_beads_range: tuple[int, int] = (2, 4),\n", + " bond_length: float = BOND_LENGTH\n", + ") -> np.ndarray:\n", + " \"\"\"Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n", + " Z heights are set to mimic surface-deposited DNA.\n", + "\n", + " This function is used to generate a small decoy chain segment\n", + " with the number of beads given by ``n_beads_range``.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed: int\n", + " Random seed for number of beads within range, ``n_beads_range``.\n", + " n_beads_range: tuple[int, int], optional\n", + " Range of number of beads to generate the decoy. Defaults to (2,4)\n", + " bond_length: float, optional\n", + " Bond length in nanometers. Defaults to BOND_LENGTH.\n", + "\n", + " Returns\n", + " -------\n", + " coords: np.ndarray\n", + " Array of shape (N,3) containing bead positions in nanometers.\n", + "\n", + " Examples\n", + " --------\n", + " >>> coords = make_decoy_chain(seed=42)\n", + " >>> print(coords.shape)\n", + " (3, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n", + " coords = np.zeros((n_b, 3), dtype=np.float32)\n", + " angle = rng.uniform(0, 2 * np.pi)\n", + " dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n", + "\n", + " for i in range(1, n_b):\n", + " da = rng.normal(0, 0.3)\n", + " c, s = np.cos(da), np.sin(da)\n", + " R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n", + " dirn = R @ dirn\n", + " dirn = dirn / np.linalg.norm(dirn)\n", + " coords[i] = coords[i-1] + bond_length * dirn\n", + "\n", + " coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n", + " return coords\n", + "\n", + "\n", + "def place_decoy_in_corner(\n", + " decoy_coords: np.ndarray,\n", + " global_extent: tuple[float, float, float, float],\n", + " corner_margin_nm: float = 2.0,\n", + " seed: int | None = None,\n", + ") -> np.ndarray:\n", + " \"\"\"Translate decoy_coords so its centroid lands in a randomly chosen\n", + " corner of global_extent = (xmin, xmax, ymin, ymax).\n", + "\n", + " This function takes the coordinates of the decoy and places it at a\n", + " distance of corner_margin_nm away from one of the corners of the image.\n", + "\n", + " Parameters\n", + " ----------\n", + " decoy_coords: np.ndarray\n", + " Array of shape (N,3) containing bead positions in nanometers.\n", + " global_extent: tuple[float float float float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometers.\n", + " corner_margin_nm: float, optional\n", + " Distance in nanometers to place the decoy inside the frame.\n", + " Defaults to 2.0.\n", + " seed: int | None, optional\n", + " Random seed for corner selection. Defaults to None.\n", + "\n", + " Returns\n", + " -------\n", + " decoy_coords: np.ndarray\n", + "\n", + " Examples\n", + " --------\n", + " >>> decoy_coords = place_decoy_in_corner(decoy_coords, global_extent)\n", + " >>> print(decoy_coords.shape)\n", + " (3, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " if seed is None:\n", + " seed = int(np.sum(decoy_coords))\n", + " rng = np.random.default_rng(int(seed))\n", + " xmin, xmax, ymin, ymax = global_extent\n", + " corner = rng.integers(0, 4)\n", + " targets = [\n", + " (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n", + " (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n", + " (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n", + " (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n", + " ]\n", + " tx, ty = targets[corner]\n", + " centroid = decoy_coords.mean(axis=0)\n", + " offset = np.array([tx, ty, 0.0]) - centroid\n", + " return decoy_coords + offset" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_10", + "metadata": { + "id": "cell_md_10" + }, + "source": [ + "## 11. Chain Generation Pipeline\n", + "\n", + "Now, we are finally ready to generate on sample in our dataset. The following functions are used for building the final image and masks:\n", + "- `generate_chain_with_beads` with `n_beads` defaulting to `N_BEADS`\n", + "- `generate_one_sample_multilength` generates the image and mask (this is what iterates to produce the dataset)\n", + "- `random_transform_noise_texture` applies a per-sample random affine transform to the noise texture for variety (in case of blank .spm files)\n", + "- Noise injection uses `blank.spm` when available and the PSD model fallback otherwise\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "cell_code_10", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cell_code_10", + "outputId": "11d6a172-8ae8-4db4-a093-63e2a8f379a2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "No blank .spm file found; trying PSD fallback noise model.\n", + "Loaded PSD fallback noise model from: /content/psd_noise_model.npz\n" + ] + } + ], + "source": [ + "def generate_chain_with_beads(\n", + " seed: int,\n", + " n_beads: int = N_BEADS,\n", + " add_decoy: bool = False\n", + ") -> dict[str, Any]:\n", + " \"\"\"Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n", + " and optionally generate a corner-placed decoy fragment.\n", + "\n", + " This function creates an initial ring polymer with a specified number of\n", + " beads, optionally relaxes it using molecular dynamics, and extracts the\n", + " final bead coordinates. Self-crossings are then detected from the final\n", + " 2D projection of the chain.\n", + "\n", + " Optionally, a decoy chain fragment can also be generated and placed near\n", + " one corner of the main chain bounding box.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed: int\n", + " Random seed for initial ring creation.\n", + " n_beads: int\n", + " Number of beads in the initial ring. Defaults to N_BEADS.\n", + " add_decoy: bool\n", + " Whether to add a corner-placed decoy fragment. Defaults to False.\n", + "\n", + " Returns\n", + " -------\n", + " A dict containing the following keys:\n", + " - ``\"seed\"`` : int\n", + " The normalized integer seed.\n", + " - ``\"n_beads\"`` : int\n", + " Number of beads in the generated chain.\n", + " - ``\"coords\"`` : np.ndarray\n", + " Final bead coordinates with shape ``(N, 3)`` and dtype\n", + " ``np.float32``.\n", + " - ``\"crossings\"`` : list\n", + " Detected self-crossings in the 2D projected chain.\n", + " - ``\"decoy_coords\"`` : np.ndarray or None\n", + " Coordinates of the optional decoy fragment, or ``None`` if no\n", + " decoy was added.\n", + "\n", + " Examples\n", + " --------\n", + " >>> chain = generate_chain_with_beads(seed=42, n_beads=10)\n", + " >>> chain[\"seed\"]\n", + " 42\n", + " >>> chain[\"n_beads\"]\n", + " 10\n", + " >>> chain[\"coords\"].shape\n", + " (10, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " base_z=BASE_Z,\n", + " )\n", + " frames = run_md_relaxation(coords0, seed=seed,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + " else:\n", + " frames = make_ring_2d_persistent_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n", + " )\n", + "\n", + " last_coords = frames[-1].astype(np.float32)\n", + "\n", + " crossings = find_polyline_crossings(\n", + " last_coords[:, :2],\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " decoy_coords = None\n", + " if add_decoy:\n", + " xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n", + " ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n", + " decoy_raw = make_decoy_chain(seed + 9999)\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_raw, (xmin, xmax, ymin, ymax),\n", + " corner_margin_nm=15.0, seed=seed)\n", + "\n", + " return dict(seed=seed, n_beads=n_beads,\n", + " coords=last_coords, crossings=crossings,\n", + " decoy_coords=decoy_coords)\n", + "\n", + "\n", + "def render_chain_and_masks(\n", + " chain: dict,\n", + " noise_source: str = \"none\",\n", + " noise_asset: dict | np.ndarray | None = None,\n", + ") -> dict[str, Any]:\n", + " \"\"\"Render AFM image + DNA mask + crossing mask for a chain dict.\n", + "\n", + " This function renders a high-resolution AFM image from a chain sample,\n", + " optionally composites a decoy fragment into the image, adds background\n", + " noise, and generates corresponding DNA and crossing masks.\n", + "\n", + " The AFM image is first rendered on an internal physical grid determined\n", + " from the chain extent and the target pixel size. The image and masks are\n", + " then resized to the final network resolution.\n", + "\n", + " Decoy fragments, when present, are composited into the AFM image using\n", + " maximum-intensity blending, but are deliberately excluded from both the\n", + " DNA mask and crossing mask.\n", + "\n", + " Parameters\n", + " ----------\n", + " chain: dict\n", + " A dict containing the following keys:\n", + " -``\"seed\"``\n", + " -``\"coords\"``\n", + " -``\"crossings\"``.\n", + " The optional key\n", + " ``\"decoy_coords\"`` may also be present\n", + " noise_source: str\n", + " Noise generation source passed to `sample_noise_image`.\n", + " noise_asset: dict | np.ndarray | None\n", + " Noise asset passed to `sample_noise_image`\n", + "\n", + " Returns\n", + " -------\n", + " A dict containing the following keys:\n", + " - ``\"seed\"`` : int\n", + " Random seed associated with the sample.\n", + " - ``\"afm_img\"`` : np.ndarray\n", + " Final AFM image with shape ``(NY, NX)`` and dtype\n", + " ``np.float32``.\n", + " - ``\"dna_mask\"`` : np.ndarray\n", + " Final binary DNA mask with shape ``(NY, NX)`` and dtype\n", + " ``np.uint8``.\n", + " - ``\"cross_mask\"`` : np.ndarray\n", + " Final crossing mask with shape ``(NY, NX)`` and dtype\n", + " ``np.float32``.\n", + " - ``\"extent\"`` : np.ndarray\n", + " Physical image extent as ``(xmin, xmax, ymin, ymax)`` with dtype\n", + " ``np.float32``.\n", + " - ``\"n_crossings\"`` : int\n", + " Number of crossing regions included in the crossing mask.\n", + " - ``\"decoy_coords\"`` : np.ndarray or None\n", + " Final decoy coordinates, or ``None`` if no decoy was used.\n", + "\n", + " Examples\n", + " --------\n", + " >>> sample = generate_chain_with_beads(seed=1)\n", + " >>> rendered = render_chain_and_masks(sample)\n", + " >>> rendered[\"afm_img\"].shape\n", + " (NY, NX)\n", + " >>> rendered[\"dna_mask\"].dtype\n", + " dtype('uint8')\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(chain[\"seed\"])\n", + " coords = chain[\"coords\"]\n", + " crossings = chain[\"crossings\"]\n", + " decoy_coords = chain.get(\"decoy_coords\", None)\n", + "\n", + " padding = 10.0\n", + " xmin = coords[:, 0].min() - padding\n", + " xmax = coords[:, 0].max() + padding\n", + " ymin = coords[:, 1].min() - padding\n", + " ymax = coords[:, 1].max() + padding\n", + " global_extent = (xmin, xmax, ymin, ymax)\n", + "\n", + " # internal render grid is determined from extent and nm_per_px\n", + " nx_phys, ny_phys, _ = compute_internal_render_grid(\n", + " global_extent, AFM_KW[\"target_nm_per_px\"]\n", + " )\n", + " afm_kw_phys = {**AFM_KW, \"nx\": nx_phys, \"ny\": ny_phys}\n", + "\n", + " afm_img_hr, dbg = create_z_based_afm(\n", + " coords, grain_seed=seed,\n", + " crossings_precomputed=crossings,\n", + " extent=global_extent,\n", + " **afm_kw_phys\n", + " )\n", + "\n", + " if decoy_coords is not None:\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_coords, global_extent,\n", + " corner_margin_nm=8.0, seed=seed)\n", + " decoy_kw = {**afm_kw_phys,\n", + " \"apply_edge_taper\": False,\n", + " \"enable_crossing_boost\": False,\n", + " \"add_center_ridge\": False}\n", + " decoy_afm_hr, _ = create_z_based_afm(\n", + " decoy_coords, extent=global_extent, **decoy_kw)\n", + " afm_img_hr = np.maximum(afm_img_hr, decoy_afm_hr)\n", + "\n", + " actual_extent = dbg[\"extent\"]\n", + "\n", + " noise_img = sample_noise_image(\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " seed=seed,\n", + " out_shape=afm_img_hr.shape,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " )\n", + " if noise_img is not None:\n", + " afm_img_hr = np.clip((afm_img_hr + noise_img).astype(np.float32),\n", + " 0, AFM_KW[\"max_height_nm\"])\n", + "\n", + " dna_mask_hr = make_dna_mask_from_beads(\n", + " coords, actual_extent, nx_phys, ny_phys, dilate_px=DNA_MASK_DILATE_PX)\n", + "\n", + " cross_mask_hr, crossings_out = make_crossing_mask(\n", + " coords, actual_extent, nx_phys, ny_phys,\n", + " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", + " chain_extent=CROSS_CHAIN_EXTENT,\n", + " center_weight=CROSS_CENTER_WEIGHT,\n", + " chain_weight=CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed=crossings,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " if CROSS_CLIP_TO_DNA_MASK:\n", + " cross_mask_hr = cross_mask_hr * dna_mask_hr.astype(np.float32)\n", + "\n", + " #Logic for maintaining user defined resolution\n", + " afm_img = resize_image_to_shape(afm_img_hr.astype(np.float32),\n", + " (NY, NX), order=1).astype(np.float32)\n", + " dna_mask = (resize_image_to_shape(dna_mask_hr.astype(np.float32),\n", + " (NY, NX), order=0)>0.5).astype(np.uint8)\n", + " cross_mask = resize_image_to_shape(cross_mask_hr.astype(np.float32),\n", + " (NY, NX), order=1).astype(np.float32)\n", + "\n", + " return dict(\n", + " seed=seed,\n", + " afm_img=afm_img.astype(np.float32),\n", + " dna_mask=dna_mask.astype(np.uint8),\n", + " cross_mask=cross_mask.astype(np.float32),\n", + " extent=np.array(actual_extent, dtype=np.float32),\n", + " n_crossings=int(len(crossings_out)),\n", + " decoy_coords=decoy_coords,\n", + " )\n", + "\n", + "\n", + "def generate_one_sample_multilength(\n", + " seed: int,\n", + " n_beads: int,\n", + " noise_source: str = \"none\",\n", + " noise_asset: dict | np.ndarray | None = None,\n", + " add_decoy: bool = False\n", + ") -> dict[str, Any]:\n", + " \"\"\"Generate one complete sample at a specified bead count.\n", + "\n", + " This function generates a single chain realization with the requested\n", + " number of beads, renders the corresponding AFM image and supervision\n", + " masks, and appends the bead count to the returned sample dictionary.\n", + "\n", + " Noise can optionally be injected during rendering using either a blank\n", + " SPM-derived texture or a PSD-based synthetic noise model, depending on\n", + " the provided noise configuration.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed: int\n", + " Random seed for initial ring creation.\n", + " n_beads: int\n", + " Number of beads in the initial ring.\n", + " noise_source: str, optional\n", + " Noise generation source passed to `render_chain_and_masks`.\n", + " Defaults to \"none\".\n", + " noise_asset: dict | np.ndarray | None, optional\n", + " Noise asset passed to `render_chain_and_masks`. Defaults to `None`.\n", + " add_decoy: bool, optional\n", + " Whether to add a corner-placed decoy fragment. Defaults to `False`.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, Any]\n", + " Dictionary containing the rendered sample, including AFM image,\n", + " DNA mask, crossing mask, physical extent, crossing count, optional\n", + " decoy coordinates, and the bead count under ``\"n_beads\"``.\n", + "\n", + " Notes\n", + " -----\n", + " This function prints which noise model is being used for the noise\n", + " injection and if no noise model has been provided then\n", + " it prints a statement that reflects that noise injection has been disabled.\n", + "\n", + " Examples\n", + " --------\n", + " >>> sample = generate_one_sample_multilength(seed=1, n_beads=300)\n", + " >>> sample[\"n_beads\"]\n", + " 300\n", + "\n", + " >>> sample[\"afm_img\"].shape\n", + " (NY, NX)\n", + "\n", + " \"\"\"\n", + "\n", + " chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n", + "\n", + " s = render_chain_and_masks(\n", + " chain,\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " )\n", + " s[\"n_beads\"] = int(n_beads)\n", + " return s\n", + "\n", + "\n", + "# ── Load noise asset once; prefer blank.spm, fall back to PSD model ──────────\n", + "noise_source = \"none\"\n", + "noise_asset = None\n", + "noise_diag = {\"source\": \"none\"}\n", + "\n", + "if USE_BLANK_SPM_NOISE:\n", + " blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n", + " if blank_path and os.path.isfile(blank_path):\n", + " try:\n", + " noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n", + " blank_path,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " verbose=True,\n", + " )\n", + " noise_source = \"blank_spm\"\n", + " print(f\"Loaded blank .spm noise texture\"\n", + " f\" RMS ≈ {noise_rms_nm:.3f} nm\")\n", + " except Exception as e:\n", + " print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n", + " else:\n", + " print(\"No blank .spm file found; trying PSD fallback noise model.\")\n", + "\n", + "if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n", + " try:\n", + " noise_asset = load_model(MODEL_PATH)\n", + " noise_source = \"psd_model\"\n", + " noise_diag = {\n", + " \"source\": \"psd_model\",\n", + " \"model_path\": str(MODEL_PATH),\n", + " \"method\": PSD_NOISE_METHOD,\n", + " \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n", + " }\n", + " print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n", + " except Exception as e:\n", + " print(\"PSD fallback noise model failed;\"\n", + " \"proceeding without noise. Error:\", e)\n", + " noise_asset = None\n", + "\n", + "if noise_source == \"none\":\n", + " print(\"Noise injection disabled.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_11", + "metadata": { + "id": "cell_md_11" + }, + "source": [ + "## 12. Sanity Check\n", + "\n", + "It is always a good idea to check if the pipeline is working correctly and producing the inteded resutls and so we have added a sanity check that can be performed before starting the generation of the entire dataset.\n", + "Here we render one sample per bead count for the bead counts provided in the global variables and by default we have set `add_decoy=True` here.\n", + "\n", + "This check displays the AFM image, DNA mask, and crossing mask side-by-side.\n", + "We also print the extent / decoy-placement diagnostics for verification and adjustment." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "cell_code_11", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "cell_code_11", + "outputId": "6de2628c-9de0-49c3-c21d-f970a504a895" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using CUDA platform.\n", + "Using CUDA platform.\n", + "Using CUDA platform.\n", + "Using CUDA platform.\n", + "\n", + "==================== SAMPLE 1 (Seed: 1, 70 beads) ====================\n", + "Image window (nm): X=[-16.7, 16.5], Y=[-16.4, 16.1]\n", + "Decoy XY bounds (nm): X=[8.2, 8.8], Y=[7.7, 8.5]\n", + "Decoy Z range (nm): -0.03 – 0.03\n", + "Decoy inside extent.\n", + " Decoy height ≈ 0\n", + "\n", + "==================== SAMPLE 2 (Seed: 2, 80 beads) ====================\n", + "Image window (nm): X=[-20.8, 19.6], Y=[-20.7, 20.9]\n", + "Decoy XY bounds (nm): X=[11.4, 11.9], Y=[-13.6, -11.7]\n", + "Decoy Z range (nm): -0.26 – 0.15\n", + "Decoy inside extent.\n", + "\n", + "==================== SAMPLE 3 (Seed: 3, 90 beads) ====================\n", + "Image window (nm): X=[-27.8, 25.9], Y=[-14.4, 14.1]\n", + "Decoy XY bounds (nm): X=[17.6, 18.2], Y=[-7.8, -4.9]\n", + "Decoy Z range (nm): -0.39 – 0.44\n", + "Decoy inside extent.\n", + "\n", + "==================== SAMPLE 4 (Seed: 4, 100 beads) ====================\n", + "Image window (nm): X=[-20.9, 20.9], Y=[-24.3, 21.5]\n", + "Decoy XY bounds (nm): X=[-13.0, -12.7], Y=[-16.8, -15.8]\n", + "Decoy Z range (nm): -0.24 – 0.24\n", + "Decoy inside extent.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "bead_counts = list(BEAD_COUNTS)\n", + "seeds = [int(BASE_SEED + k) for k in range(len(bead_counts))]\n", + "\n", + "samples = [\n", + " generate_one_sample_multilength(\n", + " seeds[i], n_beads=bead_counts[i],\n", + " noise_source=noise_source, noise_asset=noise_asset, add_decoy=True\n", + " )\n", + " for i in range(len(bead_counts))\n", + "]\n", + "\n", + "fig, axes = plt.subplots(len(samples), 3, figsize=(16, 5 * len(samples)))\n", + "\n", + "for r, s in enumerate(samples):\n", + " extent = s[\"extent\"]\n", + " img = s[\"afm_img\"]\n", + "\n", + " print(f\"\\n{'='*20} SAMPLE {r+1} (Seed: {s['seed']}, \"\n", + " f\"{s['n_beads']} beads) {'='*20}\")\n", + " print(f\"Image window (nm): X=[{extent[0]:.1f}, {extent[1]:.1f}], \"\n", + " f\"Y=[{extent[2]:.1f}, {extent[3]:.1f}]\")\n", + "\n", + " if s.get(\"decoy_coords\") is not None:\n", + " d = s[\"decoy_coords\"]\n", + " print(f\"Decoy XY bounds (nm): \"\n", + " f\"X=[{d[:,0].min():.1f}, {d[:,0].max():.1f}], \"\n", + " f\"Y=[{d[:,1].min():.1f}, {d[:,1].max():.1f}]\")\n", + " print(f\"Decoy Z range (nm): {d[:,2].min():.2f} – {d[:,2].max():.2f}\")\n", + " oob = (d[:,0].min() < extent[0] or d[:,0].max() > extent[1] or\n", + " d[:,1].min() < extent[2] or d[:,1].max() > extent[3])\n", + " print(\" Decoy out-of-bounds!\" if oob else \"Decoy inside extent.\")\n", + " if d[:,2].max() < 0.1:\n", + " print(\" Decoy height ≈ 0\")\n", + "\n", + " ax1, ax2, ax3 = axes[r]\n", + " ext_list = list(extent)\n", + "\n", + " ax1.imshow(img, cmap=\"afmhot\", origin=\"lower\", extent=ext_list)\n", + " ax1.set_title(f\"AFM image\\n(seed {s['seed']}, {s['n_beads']} beads)\")\n", + " ax1.axis(\"off\")\n", + "\n", + " ax2.imshow(s[\"dna_mask\"], cmap=\"gray\", origin=\"lower\", extent=ext_list)\n", + " ax2.set_title(\"DNA mask (decoy excluded)\")\n", + " ax2.axis(\"off\")\n", + "\n", + " ax3.imshow(s[\"cross_mask\"], cmap=\"magma\", origin=\"lower\", extent=ext_list)\n", + " ax3.set_title(f\"Crossing mask (n={s['n_crossings']})\")\n", + " ax3.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3YoKkN3e5dIo", + "metadata": { + "id": "3YoKkN3e5dIo" + }, + "source": [ + "## 13. Benchmarking\n", + "\n", + "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "WLAYkyIQlhuX", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WLAYkyIQlhuX", + "outputId": "992f00b2-2e7e-4360-905a-b8835067cda8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==============================================================\n", + " SYSTEM PROFILE\n", + "==============================================================\n", + " OS : Linux 6.6.113+\n", + " CPU (logical) : 2 cores\n", + " RAM available : 11.6 GB\n", + " OpenMM Platforms: ['Reference', 'CPU', 'CUDA', 'OpenCL']\n", + " Active Platform : CUDA\n", + " Mode : MD (OpenMM)\n", + "\n", + "==============================================================\n", + " RUNNING BENCHMARK\n", + "==============================================================\n", + "Using CUDA platform.\n", + " Rep 1/3: 2.39s\n", + "Using CUDA platform.\n", + " Rep 2/3: 2.73s\n", + "Using CUDA platform.\n", + " Rep 3/3: 2.31s\n", + "==============================================================\n", + " Median Wall Time: 2.39 s\n", + " Mean Wall Time : 2.48 s\n", + " Peak RAM (RSS) : 641.1 MB\n", + " Projected Total : 0.3 hours for 400 samples\n", + "==============================================================\n" + ] + } + ], + "source": [ + "import time\n", + "import tracemalloc\n", + "import os\n", + "import platform\n", + "import psutil\n", + "import threading\n", + "import statistics\n", + "\n", + "class _PeakMemTracker:\n", + " \"\"\"Track peak resident memory usage in a background thread.\n", + "\n", + " This helper periodically samples the current process RSS and stores the\n", + " largest value observed during the tracked interval.\n", + "\n", + " Attributes\n", + " ----------\n", + " peak_mb : float\n", + " Peak resident set size observed during tracking, in megabytes.\n", + " \"\"\"\n", + "\n", + " def __init__(self, poll_interval_s: float = 0.05) -> None:\n", + " \"\"\"Initialize the memory tracker.\n", + "\n", + " Parameters\n", + " ----------\n", + " poll_interval_s : float, optional\n", + " Sampling interval in seconds, by default ``0.05``.\n", + " \"\"\"\n", + " self._process = psutil.Process(os.getpid())\n", + " self._poll_interval_s = float(poll_interval_s)\n", + " self._stop_event = threading.Event()\n", + " self._thread: threading.Thread | None = None\n", + " self.peak_mb = 0.0\n", + "\n", + " def _run(self) -> None:\n", + " \"\"\"Continuously sample RSS until tracking is stopped.\"\"\"\n", + " while not self._stop_event.is_set():\n", + " try:\n", + " rss_mb = self._process.memory_info().rss / 1e6\n", + " if rss_mb > self.peak_mb:\n", + " self.peak_mb = rss_mb\n", + " except Exception:\n", + " pass\n", + "\n", + " self._stop_event.wait(self._poll_interval_s)\n", + "\n", + " def start(self) -> None:\n", + " \"\"\"Start background memory tracking.\"\"\"\n", + " self._stop_event.clear()\n", + " self._thread = threading.Thread(target=self._run, daemon=True)\n", + " self._thread.start()\n", + "\n", + " def stop(self) -> None:\n", + " \"\"\"Stop background memory tracking.\"\"\"\n", + " self._stop_event.set()\n", + " if self._thread is not None:\n", + " self._thread.join()\n", + "\n", + "\n", + "def _bench_stages(\n", + " seed: int,\n", + " n_beads: int\n", + ") -> dict[str, float]:\n", + " \"\"\"Benchmark the major stages of one sample-generation pipeline run.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " Random seed for the benchmarked sample.\n", + " n_beads : int\n", + " Number of beads in the benchmarked chain.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, float]\n", + " Stage timings in seconds. Keys are:\n", + " ``\"1_chain_init\"``, ``\"2_md_relax\"``, ``\"3_crossing_detect\"``,\n", + " and ``\"4_afm_render_masks\"``.\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + "\n", + " timings: dict[str, float] = {}\n", + " t0 = time.perf_counter()\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n", + " timings[\"1_chain_init\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " frames = run_md_relaxation(\n", + " coords0,\n", + " seed=seed,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME,\n", + " )\n", + " timings[\"2_md_relax\"] = time.perf_counter() - t0\n", + " else:\n", + " timings[\"1_chain_init\"] = 0.0\n", + "\n", + " t0 = time.perf_counter()\n", + " frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n", + " timings[\"2_md_relax\"] = time.perf_counter() - t0\n", + "\n", + " last_coords = frames[-1]\n", + "\n", + " t0 = time.perf_counter()\n", + " crossings = find_polyline_crossings(last_coords[:, :2])\n", + " timings[\"3_crossing_detect\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " chain = {\n", + " \"seed\": seed,\n", + " \"n_beads\": n_beads,\n", + " \"coords\": last_coords,\n", + " \"crossings\": crossings,\n", + " }\n", + " render_chain_and_masks(\n", + " chain,\n", + " noise_source=\"none\",\n", + " noise_asset=None,\n", + " )\n", + " timings[\"4_afm_render_masks\"] = time.perf_counter() - t0\n", + "\n", + " return timings\n", + "\n", + "\n", + "def benchmark_pipeline(\n", + " seed: int,\n", + " n_beads: int,\n", + " n_reps: int = 3,\n", + " total_samples: int | None = None,\n", + " print_report: bool = True,\n", + ") -> dict[str, Any]:\n", + " \"\"\"Benchmark the sample-generation pipeline and report timing statistics.\n", + "\n", + " This function prints a system profile, runs repeated benchmark passes for\n", + " one representative sample configuration, tracks peak RSS memory usage, and\n", + " estimates the total runtime for the full dataset.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " Base random seed for the benchmark runs.\n", + " n_beads : int\n", + " Number of beads used in each benchmarked sample.\n", + " n_reps : int, optional\n", + " Number of benchmark repetitions, by default ``3``.\n", + " total_samples : int or None, optional\n", + " Total number of samples to use when projecting full runtime. If\n", + " ``None``, the projection is omitted.\n", + " print_report : bool, optional\n", + " Whether to print the system and benchmark summary, by default ``True``.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, Any]\n", + " Dictionary containing benchmark results, including wall times, stage\n", + " timings, median runtime, peak RSS memory, and projected total runtime.\n", + "\n", + " Notes\n", + " -----\n", + " When ``USE_MD`` is enabled, the function also reports available OpenMM\n", + " platforms and uses the same MD settings as the rest of the notebook.\n", + "\n", + " Examples\n", + " --------\n", + " >>> results = benchmark_pipeline(seed=0, n_beads=300, n_reps=3)\n", + " >>> results[\"median_wall_time_s\"] > 0\n", + " True\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + " n_reps = int(n_reps)\n", + "\n", + " cpu_logical = psutil.cpu_count(logical=True)\n", + " available_ram_gb = psutil.virtual_memory().available / 1e9\n", + "\n", + " platforms = None\n", + " active_platform = None\n", + " if USE_MD:\n", + " platforms = [\n", + " openmm.Platform.getPlatform(i).getName()\n", + " for i in range(openmm.Platform.getNumPlatforms())\n", + " ]\n", + " active_platform = (\n", + " \"CUDA\" if \"CUDA\" in platforms else\n", + " \"OpenCL\" if \"OpenCL\" in platforms else\n", + " \"CPU\"\n", + " )\n", + "\n", + " if print_report:\n", + " print(\"=\" * 62)\n", + " print(\" SYSTEM PROFILE\")\n", + " print(\"=\" * 62)\n", + " print(f\" OS : {platform.system()} {platform.release()}\")\n", + " print(f\" CPU (logical) : {cpu_logical} cores\")\n", + " print(f\" RAM available : {available_ram_gb:.1f} GB\")\n", + " if USE_MD:\n", + " print(f\" OpenMM Platforms: {platforms}\")\n", + " print(f\" Active Platform : {active_platform}\")\n", + " print(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\n", + " print()\n", + "\n", + " print(\"=\" * 62)\n", + " print(\" RUNNING BENCHMARK\")\n", + " print(\"=\" * 62)\n", + "\n", + " wall_times: list[float] = []\n", + " stage_timings: list[dict[str, float]] = []\n", + "\n", + " mem_tracker = _PeakMemTracker()\n", + " mem_tracker.start()\n", + "\n", + " for rep in range(n_reps):\n", + " rep_seed = seed + rep\n", + " t0 = time.perf_counter()\n", + " rep_stage_timings = _bench_stages(rep_seed, n_beads)\n", + " elapsed = time.perf_counter() - t0\n", + "\n", + " wall_times.append(elapsed)\n", + " stage_timings.append(rep_stage_timings)\n", + "\n", + " if print_report:\n", + " print(f\" Rep {rep + 1}/{n_reps}: {elapsed:.2f}s\")\n", + "\n", + " mem_tracker.stop()\n", + "\n", + " median_wall_time_s = statistics.median(wall_times)\n", + " mean_wall_time_s = statistics.mean(wall_times)\n", + "\n", + " stage_keys = stage_timings[0].keys() if stage_timings else []\n", + " median_stage_times_s = {\n", + " key: statistics.median(t[key] for t in stage_timings)\n", + " for key in stage_keys\n", + " }\n", + " ts = total_samples\n", + " projected_total_hours = None\n", + " if ts is not None:\n", + " projected_total_hours = (median_wall_time_s * int(ts)) / 3600\n", + "\n", + " if print_report:\n", + " print(\"=\" * 62)\n", + " print(f\" Median Wall Time: {median_wall_time_s:.2f} s\")\n", + " print(f\" Mean Wall Time : {mean_wall_time_s:.2f} s\")\n", + " print(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\n", + " if projected_total_hours is not None:\n", + " print(\n", + " f\" Projected Total : {projected_total_hours:.1f} hours \"\n", + " f\"for {int(ts)} samples\"\n", + " )\n", + " print(\"=\" * 62)\n", + "\n", + " return {\n", + " \"seed\": seed,\n", + " \"n_beads\": n_beads,\n", + " \"n_reps\": n_reps,\n", + " \"mode\": \"md\" if USE_MD else \"non_md\",\n", + " \"cpu_logical\": cpu_logical,\n", + " \"available_ram_gb\": available_ram_gb,\n", + " \"openmm_platforms\": platforms,\n", + " \"active_platform\": active_platform,\n", + " \"wall_times_s\": wall_times,\n", + " \"stage_timings_s\": stage_timings,\n", + " \"median_wall_time_s\": median_wall_time_s,\n", + " \"mean_wall_time_s\": mean_wall_time_s,\n", + " \"median_stage_times_s\": median_stage_times_s,\n", + " \"peak_rss_mb\": mem_tracker.peak_mb,\n", + " \"total_samples\": int(ts) if ts is not None else None,\n", + " \"projected_total_hours\": projected_total_hours,\n", + " }\n", + "\n", + "\n", + "BENCH_SEED = int(BASE_SEED)\n", + "BENCH_N_BEADS = int(BEAD_COUNTS[0])\n", + "BENCH_REPS = 3\n", + "\n", + "benchmark_results = benchmark_pipeline(\n", + " seed=BENCH_SEED,\n", + " n_beads=BENCH_N_BEADS,\n", + " n_reps=BENCH_REPS,\n", + " total_samples=int(N_SAMPLES) * len(BEAD_COUNTS),\n", + " print_report=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_12", + "metadata": { + "id": "cell_md_12" + }, + "source": [ + "## 14.Dataset Generation\n", + "\n", + "Finally, we are ready to generate the full dataset. The amount of samples and the lenghts of the chains is defined in the global variables and will be referenced here.\n", + "This notebook generates the full dataset (default: 1000 samples × 4 chain lengths). There is added functionality to preview the images for a gives set or range of indices.\n", + "\n", + "\n", + "Progress is printed every 10 samples and a `manifest.csv` tracks all file paths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_12", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "cell_code_12", + "outputId": "71d9aef3-5908-4e56-b371-b8b1815dd09b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting dataset generation: 100 samples…\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + "Special override for index 832: seed = 10005\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + "Progress: 10/100\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + "Progress: 20/100\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n", + " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "Using OpenCL platform.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOpenMMException\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_406/2280809092.py\u001b[0m in \u001b[0;36mrun_md_relaxation\u001b[0;34m(coords0, seed, n_frames, steps_per_frame)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[0mplatform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopenmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlatform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetPlatformByName\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplatform_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopenmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mintegrator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplatform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 157\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Using {platform_name} platform.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/openmm/openmm.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 6216\u001b[0m \"\"\"\n\u001b[0;32m-> 6217\u001b[0;31m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mContext_swiginit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_Context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mOpenMMException\u001b[0m: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_406/3678955248.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0madd_decoy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m s = generate_one_sample_multilength(\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mn_beads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_beads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_406/1540153343.py\u001b[0m in \u001b[0;36mgenerate_one_sample_multilength\u001b[0;34m(seed, n_beads, noise_source, noise_asset, add_decoy)\u001b[0m\n\u001b[1;32m 371\u001b[0m \"\"\"\n\u001b[1;32m 372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m \u001b[0mchain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_chain_with_beads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_beads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_decoy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0madd_decoy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 375\u001b[0m s = render_chain_and_masks(\n", + "\u001b[0;32m/tmp/ipykernel_406/1540153343.py\u001b[0m in \u001b[0;36mgenerate_chain_with_beads\u001b[0;34m(seed, n_beads, add_decoy)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0mbase_z\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBASE_Z\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m )\n\u001b[0;32m--> 131\u001b[0;31m frames = run_md_relaxation(coords0, seed=seed,\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0mn_frames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_FRAMES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m steps_per_frame=STEPS_PER_FRAME)\n", + "\u001b[0;32m/tmp/ipykernel_406/2280809092.py\u001b[0m in \u001b[0;36mrun_md_relaxation\u001b[0;34m(coords0, seed, n_frames, steps_per_frame)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mopenmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpenMMException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\" {platform_name} unavailable: {e}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mexcept\u001b[0m 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"# set(range(4, 41)) -> export indices 4 through 40 inclusive\n", + "# {0, 3, 10, 25} -> export only specific indices\n", + "# set() -> export none\n", + "PREVIEW_PNG_INDICES = set(range(4, 8))\n", + "\n", + "# Separate folder for preview PNGs\n", + "PREVIEW_PNG_DIR = os.path.join(OUT_DIR, \"preview_pngs\")\n", + "if EXPORT_PREVIEW_PNGS:\n", + " os.makedirs(PREVIEW_PNG_DIR, exist_ok=True)\n", + "\n", + "# Define the header including global parameters\n", + "header = [\n", + " \"index\", \"seed\", \"n_beads\", \"bond_length\", \"persistence_bonds\",\n", + " \"image_npy\", \"dna_mask_npy\", \"cross_mask_npy\", \"meta_npz\",\n", + " \"n_crossings\", \"has_decoy\", \"afm_params_json\"\n", + "]\n", + "\n", + "with open(manifest_path, \"w\", newline=\"\") as f:\n", + " w = csv.writer(f)\n", + " w.writerow(header)\n", + "\n", + " for idx in range(int(N_SAMPLES)):\n", + " # Seed override for a known-problematic sample\n", + " if idx == 7:\n", + " seed = int(BASE_SEED + idx + 9997)\n", + " print(f\"Special override for index 832: seed = {seed}\")\n", + " else:\n", + " seed = int(BASE_SEED + idx)\n", + "\n", + " n_beads = int(BEAD_COUNTS[idx % len(BEAD_COUNTS)])\n", + " add_decoy = (idx % 5 == 0)\n", + "\n", + " s = generate_one_sample_multilength(\n", + " seed,\n", + " n_beads=n_beads,\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " add_decoy=add_decoy,\n", + " )\n", + "\n", + " img_path = os.path.join(\"images\", f\"img_{idx:04d}.npy\")\n", + " dna_path = os.path.join(\"dna_masks\", f\"dna_{idx:04d}.npy\")\n", + " cross_path = os.path.join(\"cross_masks\", f\"cross_{idx:04d}.npy\")\n", + " meta_path = os.path.join(\"meta\", f\"meta_{idx:04d}.npz\")\n", + "\n", + " np.save(os.path.join(OUT_DIR, img_path), s[\"afm_img\"])\n", + " np.save(os.path.join(OUT_DIR, dna_path), s[\"dna_mask\"])\n", + " np.save(os.path.join(OUT_DIR, cross_path), s[\"cross_mask\"])\n", + "\n", + " # Save detailed metadata\n", + " np.savez_compressed(\n", + " os.path.join(OUT_DIR, meta_path),\n", + " seed = s[\"seed\"],\n", + " n_beads = int(s[\"n_beads\"]),\n", + " extent = s[\"extent\"],\n", + " n_crossings = s[\"n_crossings\"],\n", + " has_decoy = add_decoy,\n", + " bond_length = BOND_LENGTH,\n", + " persistence = PERSISTENCE_BONDS\n", + " )\n", + "\n", + " # ------------------------------------------------------------\n", + " # Optional PNG export for selected indices\n", + " # ------------------------------------------------------------\n", + " if EXPORT_PREVIEW_PNGS and idx in PREVIEW_PNG_INDICES:\n", + " png_path = os.path.join(PREVIEW_PNG_DIR, f\"img_{idx:04d}.png\")\n", + "\n", + " fig, ax = plt.subplots(figsize=(6, 6), dpi=150)\n", + " ax.imshow(s[\"afm_img\"], cmap=\"gray\", origin=\"lower\")\n", + " ax.set_title(f\"Sample {idx} | beads={n_beads} |\"\n", + " f\" crossings={s['n_crossings']}\")\n", + " ax.axis(\"off\")\n", + " fig.tight_layout(pad=0)\n", + " fig.savefig(png_path, bbox_inches=\"tight\", pad_inches=0)\n", + " plt.close(fig)\n", + "\n", + " # Write to manifest including JSON-serialized AFM parameters\n", + " w.writerow([\n", + " idx, seed, n_beads, BOND_LENGTH, PERSISTENCE_BONDS,\n", + " img_path, dna_path, cross_path, meta_path,\n", + " s[\"n_crossings\"], add_decoy, json.dumps(AFM_KW)\n", + " ])\n", + "\n", + " if (idx + 1) % 10 == 0:\n", + " print(f\"Progress: {idx+1}/{N_SAMPLES}\")\n", + "\n", + "print(\"\\nDone. Dataset written to:\", OUT_DIR)\n", + "print(\"Manifest:\", manifest_path)\n", + "\n", + "if EXPORT_PREVIEW_PNGS:\n", + " print(\"Preview PNG directory:\", PREVIEW_PNG_DIR)\n", + " print(\"PNG preview indices:\", sorted(PREVIEW_PNG_INDICES))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "openmm-cuda", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From dff29ddd13d34fd0982e9a7efffce358f950307b Mon Sep 17 00:00:00 2001 From: Prakhar-Dutta Date: Thu, 30 Apr 2026 15:08:56 +0200 Subject: [PATCH 14/17] Delete tutorial directory --- ...ged_via_AFM_draft_for_Giovanni_check.ipynb | 5146 ----------------- 1 file changed, 5146 deletions(-) delete mode 100644 tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb diff --git a/tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb b/tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb deleted file mode 100644 index bffb928..0000000 --- a/tutorial/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni_check.ipynb +++ /dev/null @@ -1,5146 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cell_md_title", - "metadata": { - "id": "cell_md_title" - }, - "source": [ - "# Simulation of 'DNA chains on a substrate imaged via AFM'\n", - "**This notebook is intended to create simulations of DNA chain relaxation on a substrate imaged via AFM. By simulating physical relaxation and electrostatic attraction between a DNA chain and the substrate it allows users to generate high fidelity data.**\n", - "\n", - "This notebook generates a complete dataset stored within user defined directory `OUT_DIR`, primarily consisting of Synthetic AFM Images saved as `.npy` raw heightmap data (incorporating realistic noise from `.spm` blanks or PSD models) and optional `.png` files for quick visual inspection.\n", - "\n", - " To support machine learning workflows, the pipeline produces DNA Segmentation Masks in `.npy` format for pixel-wise background separation, alongside specialized Crossing Detection Masks that use Gaussian-weighted targets to highlight molecular intersections. Comprehensive Metadata for every sample, including bead counts and mechanical parameters, is saved in `.csv` or `.json` files, all of which are indexed in a central **manifest.csv** that links each generated image to its corresponding ground-truth masks and input configurations.\n", - ">\n", - "---\n", - "**Cell execution order:** run cells 1 → 12 in sequence. \n", - "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github.com/Prakhar-Dutta/ASAP/blob/1b5cfc5173e1bed4e59575e6dd4724cb20682620/tutorial/Not_final_version_DNA_notebook%20(1).ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "glVt-o8bCqSu", - "metadata": { - "id": "glVt-o8bCqSu" - }, - "outputs": [], - "source": [ - "# Uncomment codes in this cell if using Colab/Kaggle.\n", - "import os\n", - "from pathlib import Path\n", - "import re\n", - "import csv\n", - "import traceback\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import animation\n", - "from IPython.display import HTML, display\n", - "from __future__ import annotations\n", - "from typing import Any\n", - "# Image processing\n", - "from scipy.ndimage import (\n", - " gaussian_filter, grey_dilation, distance_transform_edt,\n", - " binary_dilation, median_filter, affine_transform, zoom\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "2nb1FTtFC_q0", - "metadata": { - "id": "2nb1FTtFC_q0" - }, - "source": [ - "# 1. **With or without molecular dynamics?**\n", - "## 1.1. Imports\n", - "\n", - "This notebook allows two possible ways of building the simulated DNA chains; with or without the use of **coarse-grained molecular dynamics**. Molecular dynamics based simulations are computationally expensive, but provide closeness to real AFM imaging. For users who would like to use molecular dynamics, they would need to import all the libraries that will be necessary for simulation of the images." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cell_code_01", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cell_code_01", - "outputId": "58e34d6d-77ae-4b49-de3a-d8be68431282" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting openmm[cuda12]\n", - " Downloading openmm-8.5.1-cp312-cp312-manylinux_2_34_x86_64.whl.metadata (1.4 kB)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from openmm[cuda12]) (2.0.2)\n", - "Collecting OpenMM-CUDA-12==8.5.1 (from openmm[cuda12])\n", - " Downloading openmm_cuda_12-8.5.1-py3-none-manylinux_2_34_x86_64.whl.metadata (405 bytes)\n", - "Requirement already satisfied: nvidia-cuda-runtime-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.90)\n", - "Requirement already satisfied: nvidia-cuda-nvcc-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.5.82)\n", - "Requirement already satisfied: nvidia-cuda-nvrtc-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.93)\n", - "Requirement already satisfied: nvidia-cuda-cupti-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.90)\n", - "Requirement already satisfied: nvidia-cufft-cu12 in /usr/local/lib/python3.12/dist-packages (from OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (11.3.3.83)\n", - "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.12/dist-packages (from nvidia-cufft-cu12->OpenMM-CUDA-12==8.5.1->openmm[cuda12]) (12.8.93)\n", - "Downloading openmm_cuda_12-8.5.1-py3-none-manylinux_2_34_x86_64.whl (2.3 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading openmm-8.5.1-cp312-cp312-manylinux_2_34_x86_64.whl (14.4 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.4/14.4 MB\u001b[0m \u001b[31m80.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: openmm, OpenMM-CUDA-12\n", - "Successfully installed OpenMM-CUDA-12-8.5.1 openmm-8.5.1\n" - ] - } - ], - "source": [ - "# Install dependencies (uncomment in a fresh environment / Colab)\n", - "# Chain generation mode\n", - "USE_MD = True # True → OpenMM Langevin dynamics\n", - " # False → fast 2-D persistent-walk (no OpenMM needed)\n", - "# Molecular dynamics\n", - "!pip3 install -q scipy matplotlib pillow\n", - "if USE_MD:\n", - " !pip3 install openmm[cuda12]\n", - "# change the cuda version\n", - "# depending on the CUDA version available on your system.\n", - "# Ensure that CUDA can be accessed by OpenMM when running locally.\n", - " import openmm\n", - " import openmm.unit as unit\n" - ] - }, - { - "cell_type": "markdown", - "id": "1oW9llw-imr1", - "metadata": { - "id": "1oW9llw-imr1" - }, - "source": [ - "# 1.2 Defining the variables\n", - "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "sSqadS02hzPe", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sSqadS02hzPe", - "outputId": "b345b36e-0d30-4a4a-c7db-29e09caf319a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Output folder: /content/dna_dataset_100_4lengths_MD\n" - ] - } - ], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Global settings\n", - "#(these are the variables that need to be changed\n", - "# to change the appearance of the resulting images)\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "# Dataset\n", - "OUT_DIR = \"dna_dataset_100_4lengths_MD\"\n", - "# Name of the output directory that you would like to store your data in\n", - "N_SAMPLES = 100\n", - "# Number of samples that will be generated\n", - "BASE_SEED = 1\n", - "# Seed for reproducibility\n", - "\n", - "# Image resolution\n", - "NX = 512\n", - "NY = 512\n", - "\n", - "# Ring + MD\n", - "N_BEADS = 90\n", - "# default bead count for vizualization via chain creation cell\n", - "BEAD_COUNTS = [70, 80, 90, 100]\n", - "# four chain lengths that will be created in the dataset\n", - "#(1 bead corresponds to 10 base pairs on DNA approximately)\n", - "BOND_LENGTH = 1.0\n", - "# distance between beads\n", - "PERSISTENCE_BONDS = 23.0\n", - "# used to determine the range of angles which the chain is allowed to turn\n", - "K_ANGLE = 12.0\n", - "# used to determine the angular stiffness during relaxation\n", - "# (higher means more stiffness)\n", - "BASE_Z = 5.0\n", - "# Height above the substrate that the chain starts at before relaxing\n", - "\n", - "ANGLE_STIFNESS_MULT = 0.4\n", - "# If USE_MD is False, then angle stiffness multiplier controls\n", - "# chain propogation through space\n", - "\n", - "# MD recording\n", - "N_FRAMES = 200\n", - "STEPS_PER_FRAME = 200\n", - "# Between every frame local energy minimization happens for\n", - "#the given number of steps\n", - "\n", - "# AFM rendering\n", - "\n", - "tip_radius = 1\n", - "# Please select the tip radius here (Ideal range is 1nm-5nm)\n", - "nm_per_px = 2\n", - "# Internal AFM render sampling in nm / pixel before downsampling\n", - "\n", - "AFM_KW = dict(\n", - " nx=NX, ny=NY,\n", - " dna_diameter_nm=2.0,\n", - " # mapping to physical values\n", - " tip_radius_nm=0.01*tip_radius,\n", - " # Scaling factor is applied for internal control to match\n", - " max_height_nm=6.0,\n", - " # Maximum height for the colorbar\n", - " target_nm_per_px=0.04*nm_per_px,\n", - " # Scaling factor (0.04) is applied for internal control\n", - " max_radius_px=96,\n", - " # upper limit for the size of the chain in pixels\n", - " radius_shrink_px=0.0,\n", - " # Set to 0 so that rendering can match physical values\n", - " final_blur_sigma_px=0.20,\n", - " # When changing tip radius and nm_per_px to observe the expected\n", - " apply_edge_taper=True,\n", - " # physical effect, remove the scaling factors (0.04) for\n", - " # tip_radius_nm and target_nm_per_px\n", - " taper_sigma_nm=0.45,\n", - " # and scale all other parameters accordingly.\n", - " taper_floor=0.10,\n", - " add_center_ridge=True,\n", - " ridge_sigma_nm=0.25,\n", - " # This controls width of center ridge\n", - " ridge_amp_nm=0.25,\n", - " # This controls height of center ridge\n", - " grain_nm=0.0,\n", - " # This adds more noise to the chain\n", - " grain_sigma_px=0.6,\n", - " enable_crossing_boost=True,\n", - " # To boost the height of the crossings\n", - " min_separation_beads=12,\n", - " # Distance to other chain segments to not boost indiscriminately\n", - " boost_window_beads=2,\n", - " # How many beads to boost the height for\n", - " guaranteed_offset_nm=1.0,\n", - " # How much to boost\n", - " boost_method=\"additive\",\n", - " # add to the height of beads being boosted\n", - " boost_profile=\"gaussian\",\n", - " # Height increase profile\n", - " boost_sigma_beads=None,\n", - " far_clip_nm=2.5,\n", - " # Clip bead heights for beads that are not part of a crossing\n", - " far_clip_window_beads=3,\n", - " # How many beads to clip the height for\n", - " return_crossing_info=True,\n", - " # To ensure that mask information is passed to other functions.\n", - " # Change to False if masks are not needed\n", - " return_masks=True,\n", - ")\n", - "\n", - "\n", - "#-------------------------------------\n", - "# DNA mask\n", - "DNA_MASK_DILATE_PX = 3\n", - "# Dilation of mask for better training\n", - "#-------------------------------------\n", - "\n", - "#---------------------------\n", - "# Crossing mask\n", - "CROSS_MIN_SEP_BEADS = 12\n", - "# Where to stop for the crossing calculation\n", - "CROSS_SIGMA_CENTER_PX = 5.0\n", - "# How many pixels make up the center of a crossing\n", - "CROSS_SIGMA_PERP_PX = 1.8\n", - "# Crossing coloring parameter (how many pixels to modulate for the crossing)\n", - "CROSS_CHAIN_EXTENT = 5.0\n", - "# For the chain weighting of the mask\n", - "CROSS_CENTER_WEIGHT = 1.7\n", - "# For the chain weighting of the mask (chain-weighted guassian)\n", - "CROSS_CHAIN_WEIGHT = 0.9\n", - "CROSS_CLIP_TO_DNA_MASK = True\n", - "#---------------------------\n", - "\n", - "#Checking if output directory will be created\n", - "os.makedirs(OUT_DIR, exist_ok=True)\n", - "for _sub in [\"images\", \"dna_masks\", \"cross_masks\", \"meta\"]:\n", - " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", - "\n", - "print(\"Output folder:\", os.path.abspath(OUT_DIR))" - ] - }, - { - "cell_type": "markdown", - "id": "-e3QvQzGlKYY", - "metadata": { - "id": "-e3QvQzGlKYY" - }, - "source": [ - "# 1.3 Noise\n", - "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", - "\n", - "\n", - "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The blank file is a scan of the substrate **without** the sample of interest. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", - "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "47h81smulGxd", - "metadata": { - "id": "47h81smulGxd" - }, - "outputs": [], - "source": [ - "#Noise\n", - "TARGET_NOISE_RMS_NM = 0.27\n", - "# This variable controls the height of the noise\n", - "# in nanometers and this is added to the final image\n", - "\n", - "\n", - "# Blank SPM noise\n", - "USE_BLANK_SPM_NOISE = True\n", - "USE_PLANE_REMOVE = True\n", - "# This flag is used to control plane tilt removal from the noise image\n", - "USE_LINE_FLATTEN = True\n", - "# This flag is used to control line flattening (median filtering)\n", - "# from the noise image\n", - "USE_BANDPASS_FILTER = True\n", - "# This flag is used to control bandpass filtering on the noise image\n", - "BLANK_SPM_PATH = \"/content/20240411_blank_water.0_00000.spm\"\n", - "# optional; if missing/invalid, PSD fallback noise is used\n", - "# Change path when running locally. If using Colab, upload the file\n", - "\n", - "\n", - "# PSD-model fallback noise (used when blank .spm file is not available)\n", - "USE_PSD_NOISE_FALLBACK = True\n", - "MODEL_PATH = \"/content/psd_noise_model.npz\"\n", - "# Change path when running locally. If using Colab, upload the noise file\n", - "PSD_NOISE_METHOD = \"mean_full2d\"\n", - "# or \"lognormal_full2d\" / \"empirical_radial\"\n", - "PSD_NOISE_STD_SCALE = 2.0\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_02", - "metadata": { - "id": "cell_md_02" - }, - "source": [ - "## 2. Chain Creation (3-D Persistent Random Walk)\n", - "Now we start with creating the DNA chain. This is the toppology of the chain before relaxation is performed using molecular dynamnics.\n", - "\n", - "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", - "\n", - "A 3-D plot is shown at the end of the cell so you can inspect the initial geometry immediately." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cell_code_02", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 607 - }, - "id": "cell_code_02", - "outputId": "827cae1a-7620-4319-f912-46054640e7cc" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "def make_tangled_ring_initial(\n", - " seed: int,\n", - " n_beads: int = N_BEADS,\n", - " bond_length: float = BOND_LENGTH,\n", - " persistence_bonds: float = PERSISTENCE_BONDS,\n", - " base_z: float = BASE_Z,\n", - ")-> np.ndarray:\n", - " \"\"\"Generates a closed (ring) polymer with a persistent random-walk tangent.\n", - "\n", - " Each step rotates the current direction by a small random angle around a\n", - " perpendicular axis. Ring closure is enforced by linearly distributing\n", - " the end-to-end gap across all beads. The chain is then lifted to base_z\n", - " so it can relax down to the z=0 substrate during MD.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed : int\n", - " random seed for reproducibility\n", - " n_beads : int, optional\n", - " number of beads in the polymer ring. Defaults to N_BEADS.\n", - " bond_length : float, optional\n", - " the distance between consecutive beads. Defaults to BOND_LENGTH.\n", - " persistence_bonds : float, optional\n", - " the number of beads used to enforce persistence length in the chain.\n", - " Defaults to PERSISTENCE_BONDS.\n", - " base_z : float\n", - " the height to lift the chain above the substrate. Defaults to BASE_Z.\n", - "\n", - " Returns\n", - " -------\n", - " Returns a list of [coords] with shape (N, 3).\n", - "\n", - " \"\"\"\n", - "\n", - " rng = np.random.default_rng(int(seed))\n", - "\n", - " steps = np.zeros((n_beads, 3), dtype=np.float32)\n", - " #Initialization of vector for chain propogation\n", - " vec = np.array([1.0, 0.0, 0.0], dtype=np.float32)\n", - "\n", - " for k in range(n_beads):\n", - " dtheta = rng.normal(scale=np.sqrt(1.0 / persistence_bonds))\n", - " #choose an axis at random for chain propogation in 3D\n", - " axis = rng.normal(size=3).astype(np.float32)\n", - " # remove component along vec\n", - " axis -= axis.dot(vec) * vec\n", - " axis_norm = np.linalg.norm(axis)\n", - " # failsafe\n", - " if axis_norm < 1e-8:\n", - " axis = np.array([0.0, 0.0, 1.0], dtype=np.float32)\n", - " axis_norm = 1.0\n", - " axis /= axis_norm\n", - " # Calculating vector for chain propogation\n", - " vec = vec * np.cos(dtheta) + np.cross(axis, vec) * np.sin(dtheta)\n", - " vec /= np.linalg.norm(vec)\n", - " steps[k] = bond_length * vec\n", - "\n", - " coords = np.cumsum(steps, axis=0) # Coordinates for open chain\n", - "\n", - " # Enforce ring closure\n", - " closure_error = coords[-1] - coords[0]\n", - " # Based on the distance between first and last bead a\n", - " # closure error is propogated to all bead positions to form a closed chain\n", - " t = np.linspace(0, 1, n_beads, dtype=np.float32).reshape(-1, 1)\n", - " coords -= t * closure_error\n", - " coords -= coords.mean(axis=0)\n", - "\n", - " # Lift above substrate\n", - " # (z values are changed to lift the chain off the substrate)\n", - " coords[:, 2] += float(base_z)\n", - " return coords.astype(np.float32)\n", - "\n", - "\n", - "# ── Quick visualisation ──────────────────────────────────────────────────────\n", - "demo_coords = make_tangled_ring_initial(seed=43)\n", - "cc = np.vstack([demo_coords, demo_coords[0]]) # close the ring for plotting\n", - "\n", - "fig = plt.figure(figsize=(7, 6))\n", - "ax = fig.add_subplot(111, projection=\"3d\")\n", - "ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-o\", markersize=3, linewidth=1.2)\n", - "ax.scatter(cc[0, 0], cc[0, 1], cc[0, 2], color=\"red\",\n", - " s=60, zorder=5, label=\"bead 0\")\n", - "\n", - "# Draw substrate plane\n", - "xs = np.linspace(cc[:, 0].min(), cc[:, 0].max(), 2)\n", - "ys = np.linspace(cc[:, 1].min(), cc[:, 1].max(), 2)\n", - "X, Y = np.meshgrid(xs, ys)\n", - "ax.plot_surface(X, Y, np.zeros_like(X), alpha=0.15, color=\"cyan\")\n", - "\n", - "ax.set_xlabel(\"x (sim units)\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z\")\n", - "ax.set_title(f\"Initial ring — seed 43, {len(demo_coords)} beads\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_03", - "metadata": { - "id": "cell_md_03" - }, - "source": [ - "## 3. Molecular Dynamics Relaxation\n", - "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", - "\n", - "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", - "Here we have added:\n", - "\n", - "1. Harmonic bonds\n", - "2. Bending-angle stiffness\n", - "3. A surface-attraction to cause chain relaxation\n", - "4. A hard-wall force to keep chain above substrate\n", - "5. A short-range WCA repulsion to ensure chain does not take non physical configurations\n", - "\n", - "The function then records `n_frames` snapshots which can be used for visualization." - ] - }, - { - "cell_type": "markdown", - "id": "wFFhsVZhqU8a", - "metadata": { - "id": "wFFhsVZhqU8a" - }, - "source": [ - "We start with testing the OpenMM installation (can be tricky at times)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3NjBSwfAe4Mj", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3NjBSwfAe4Mj", - "outputId": "8a5a7999-63ee-4a5e-a3c5-da9d17a0745f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "OpenMM Version: 8.5.1\n", - "Git Revision: f7fa0c27c1f8d943c339d67b3bf22f026d0bd8b5\n", - "\n", - "There are 4 Platforms available:\n", - "\n", - "1 Reference - Successfully computed forces\n", - "2 CPU - Successfully computed forces\n", - "3 CUDA - Successfully computed forces\n", - "4 OpenCL - Successfully computed forces\n", - "\n", - "Median difference in forces between platforms:\n", - "\n", - "Reference vs. CPU: 6.28625e-06\n", - "Reference vs. CUDA: 6.7605e-06\n", - "CPU vs. CUDA: 7.74371e-07\n", - "Reference vs. OpenCL: 6.74321e-06\n", - "CPU vs. OpenCL: 7.94239e-07\n", - "CUDA vs. OpenCL: 1.74909e-07\n", - "\n", - "All differences are within tolerance.\n" - ] - } - ], - "source": [ - "# Test the OpenMM installation before running the following cell\n", - "# to ensure that GPU acceleration will work\n", - "import openmm.testInstallation\n", - "\n", - "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", - "try:\n", - " openmm.testInstallation.main()\n", - "except Exception as e:\n", - " print(f\"Test failed: {e}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9XbRNS3TqbKH", - "metadata": { - "id": "9XbRNS3TqbKH" - }, - "source": [ - "And then once we are sure that OpenMM is correctly installed, we run the MD functions" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cell_code_03", - "metadata": { - "id": "cell_code_03" - }, - "outputs": [], - "source": [ - "def run_md_relaxation(\n", - " coords0: np.ndarray,\n", - " seed: int,\n", - " n_frames: int = N_FRAMES,\n", - " steps_per_frame: int = STEPS_PER_FRAME\n", - ")-> list[np.ndarray]:\n", - " \"\"\"\n", - " Takes initial bead coordinates and runs Langevin dynamics using OpenMM\n", - "\n", - " This function uses the coordinates generated using\n", - " the `make_tangled_ring_initial` function to\n", - " run a molecular dynamics simulation.\n", - " There are certain forces that are defined\n", - " and applied to the bead which are then resolved using OpenMM.\n", - " The forces applied are:\n", - " - Harmonic bonds + angle stiffness along the ring\n", - " - Surface attraction (linear in z) + hard wall at z = 0\n", - " - Short-range Weeks–Chandler–Andersen (WCA)\n", - " repulsion between non-bonded beads\n", - " Drift removal is also performed using OpenMM's CMMotionRemover.\n", - " The variable langevin integrator from OpenMM is used and the forces are\n", - " resolved for 'steps_per_frame' steps for every frame in n_frames.\n", - "\n", - " Parameters\n", - " ----------\n", - " initial_coordinates : np.ndarray\n", - " The starting [x, y, z] coordinates of the polymer beads.\n", - " seed : int\n", - " Random seed for the Langevin integrator.\n", - " num_frames : int, optional\n", - " Number of frames to record during the simulation. Defaults to N_FRAMES.\n", - " steps_per_frame : int, optional\n", - " Number of integration steps to perform between recorded frames.\n", - " Defaults to STEPS_PER_FRAME.\n", - "\n", - " Returns\n", - " -------\n", - " A list of coordinate arrays of shape (num_beads, 3). The list contains\n", - " num_frames + 1 entries, where frame 0 is the pre-minimization geometry.\n", - "\n", - " Raises\n", - " ------\n", - " RuntimeError\n", - " If no OpenMM platform (CUDA, OpenCL, or CPU) could be initialised.\n", - "\n", - " Examples\n", - " --------\n", - " >>> frames = run_md_relaxation(_anim_coords0, seed=1, n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - " >>> print({len(frames), frames[0].shape})\n", - " {11, (100, 3)}\n", - "\n", - " \"\"\"\n", - "\n", - " coords0 = np.asarray(coords0, dtype=np.float32)\n", - " n = int(coords0.shape[0])\n", - "\n", - " # how big the bounding box for the molecular dynamics needs to be\n", - " extent = (coords0.max(axis=0) - coords0.min(axis=0)).max()\n", - " box = float(extent + 10.0)\n", - "\n", - "\n", - " # All lengths in nm, energies in kJ/mol.\n", - " BOLTZ_kJmol = 0.00831445 # kJ / (mol · K)\n", - " temperature = 1.0 # K (sets energy scale kT ≈ 0.0083 kJ/mol)\n", - " kT = BOLTZ_kJmol * temperature # kJ/mol\n", - " conlen = 1.0 # nm\n", - " mass_amu = 100.0 # Da per bead\n", - " collision_rate = 0.6 # ps⁻¹\n", - " error_tol = 0.005\n", - "\n", - " # ── Build system ─────────────────────────────────────────────────────────\n", - " system = openmm.System()\n", - " system.setDefaultPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - " for _ in range(n):\n", - " system.addParticle(mass_amu)\n", - "\n", - " # Remove COM drift — applied every 50 steps\n", - " system.addForce(openmm.CMMotionRemover(10))\n", - " # To ensure CPU and GPU calculation are consistent\n", - " # (since GPU minimizations calculations can suffer from drift)\n", - "\n", - " # ── 1. Harmonic bonds ────────────────────────────────────────────────────\n", - "\n", - " # OpenMM HarmonicBondForce: E = (k/2)·(r − r0)² → k = kT / wiggle²\n", - " bond_L = BOND_LENGTH # nm\n", - " wiggle = 0.1 # nm (bondWiggleDistance)\n", - " k_bond = kT / (wiggle ** 2) # kJ/mol/nm²\n", - "\n", - " bond_force = openmm.HarmonicBondForce()\n", - " bond_force.setUsesPeriodicBoundaryConditions(True)\n", - " for i in range(n):\n", - " bond_force.addBond(i, (i + 1) % n, bond_L, k_bond)\n", - " system.addForce(bond_force)\n", - "\n", - " # ── 2. Bending (angle) stiffness ─────────────────────────────────────────\n", - " # E = kT · k_angle · (1 − cos(θ − π)) — equilibrium at θ = π (straight)\n", - " k_angle = K_ANGLE\n", - " angle_force = openmm.CustomAngleForce(\n", - " \"kT * kangle * (1 - cos(theta - 3.141592653589793))\"\n", - " )\n", - " angle_force.addGlobalParameter(\"kT\", kT)\n", - " angle_force.addGlobalParameter(\"kangle\", k_angle)\n", - " for i in range(n):\n", - " angle_force.addAngle((i - 1) % n, i, (i + 1) % n, [])\n", - " system.addForce(angle_force)\n", - "\n", - " # ── 3. Surface: linear attraction (z > 0) + harmonic hard wall (z < 0) ───\n", - " wall_expr = (\n", - " \"kT * (\"\n", - " \" F_pull * z * step(z) + \"\n", - " \" 0.5 * wallK * (z^2) * step(-z)\"\n", - " \")\"\n", - " )\n", - " wall_force = openmm.CustomExternalForce(wall_expr)\n", - " wall_force.addGlobalParameter(\"kT\", kT)\n", - " wall_force.addGlobalParameter(\"F_pull\", 9.0)\n", - " wall_force.addGlobalParameter(\"wallK\", 100.0)\n", - " for i in range(n):\n", - " wall_force.addParticle(i, [])\n", - " system.addForce(wall_force)\n", - "\n", - " # ── 4. WCA repulsion (repulsion between non-bonded pairs only) ───────────\n", - " rep_sigma = 1.6 * conlen # nm\n", - " rep_cutoff = 2.3 * conlen # nm\n", - " rep_expr = (\n", - " \"4*eps * ( (sig / (r + shift))^12 - (sig / (r + shift))^6 + 0.25 )\"\n", - " \"* step(cutoff - r);\"\n", - " \"cutoff = 2^(1.0/6.0) * sig\"\n", - " )\n", - " rep_force = openmm.CustomNonbondedForce(rep_expr)\n", - " rep_force.setNonbondedMethod(openmm.CustomNonbondedForce.CutoffPeriodic)\n", - " rep_force.setCutoffDistance(rep_cutoff)\n", - " rep_force.addGlobalParameter(\"eps\", 1.7 * kT)\n", - " rep_force.addGlobalParameter(\"sig\", rep_sigma)\n", - " rep_force.addGlobalParameter(\"shift\", 1e-8)\n", - " for i in range(n):\n", - " rep_force.addParticle([])\n", - " # exclude bonded neighbours\n", - " for i in range(n):\n", - " rep_force.addExclusion(i, (i + 1) % n)\n", - " system.addForce(rep_force)\n", - "\n", - " # ── Integrator ───────────────────────────────────────────────────────────\n", - " integrator = openmm.VariableLangevinIntegrator(temperature,\n", - " collision_rate, error_tol)\n", - " integrator.setRandomNumberSeed(int(seed))\n", - "\n", - " # ── Platform selection: OpenCL → CPU fallback ────────────────────────────\n", - " context = None\n", - " # OpenCL enables Mac users to be able to use GPU acceleration as well\n", - " for platform_name in (\"CUDA\",\"OpenCL\",\"CPU\"):\n", - " try:\n", - " platform = openmm.Platform.getPlatformByName(platform_name)\n", - " context = openmm.Context(system, integrator, platform)\n", - " print(f\"Using {platform_name} platform.\")\n", - " break\n", - " except openmm.OpenMMException as e:\n", - " print(f\" {platform_name} unavailable: {e}\")\n", - " except Exception as e:\n", - " print(f\" {platform_name} failed unexpectedly: {e}\")\n", - "\n", - " if context is None:\n", - " raise RuntimeError(\"No OpenMM platform could be initialised.\")\n", - "\n", - " # ── Set initial positions and box ────────────────────────────────────────\n", - " positions = [\n", - " openmm.Vec3(float(coords0[i, 0]),\n", - " float(coords0[i, 1]), float(coords0[i, 2]))\n", - " for i in range(n)\n", - " ]\n", - " context.setPositions(positions)\n", - " context.setPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - "\n", - " # Capture the true initial geometry before any minimisation\n", - " # (just in case the chain reaches substrate even before the 1st frame)\n", - " frames = []\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " # Cap iterations\n", - " openmm.LocalEnergyMinimizer.minimize(context,\n", - " tolerance=50, maxIterations=200)\n", - "\n", - " # ── Record frames ────────────────────────────────────────────────────────\n", - " for _ in range(int(n_frames)):\n", - " integrator.step(int(steps_per_frame))\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " return frames # n_frames + 1 entries (frame 0 = initial geometry)" - ] - }, - { - "cell_type": "markdown", - "id": "Bx7_OzWRr-l3", - "metadata": { - "id": "Bx7_OzWRr-l3" - }, - "source": [ - "## 4. Non-MD chain generation\n", - "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "yXs0I0akr948", - "metadata": { - "id": "yXs0I0akr948" - }, - "outputs": [], - "source": [ - "def make_ring_2d_persistent_initial(\n", - " seed: int,\n", - " n_beads: int,\n", - " bond_length: float = BOND_LENGTH, # From Cell 1\n", - " persistence_bonds: float = PERSISTENCE_BONDS, # From Cell 1\n", - " angle_stiffness_mult: float = ANGLE_STIFNESS_MULT, # From Cell 1\n", - " # Small Gaussian noise for Z\n", - " z_std: float = 0.08,\n", - " # Low altitude for \"deposited\" look\n", - " base_z: float = 0.2,\n", - " # Acts as a soft per-step cap\n", - " angle_limit: float = np.pi/2\n", - ") -> list[np.ndarray]:\n", - " \"\"\"\n", - " Generate a closed 2D persistent ring\n", - " and return it as [coords] with shape (N, 3).\n", - "\n", - " The function builds a periodic sequence of turning angles, smooths the\n", - " sequence to suppress sharp corners, and enforces total turning to ensure\n", - " the tangent field is compatible with a closed loop. The resulting curve is\n", - " resampled by arc length to maintain uniform bead spacing.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed : int\n", - " Random seed for reproducibility.\n", - " num_beads : int\n", - " Number of beads in the polymer ring.\n", - " bond_length : float, optional\n", - " The desired distance between consecutive beads.\n", - " Defaults to BOND_LENGTH.\n", - " persistence_bonds : float, optional\n", - " Stiffness parameter; larger values result in a smoother ring with\n", - " longer directional memory. Defaults to PERSISTENCE_BONDS.\n", - " angle_stiffness_multiplier : float, optional\n", - " Multiplier for angular fluctuations; larger values result in\n", - " smaller fluctuations. Defaults to ANGLE_STIFNESS_MULT.\n", - " z_standard_deviation : float, optional\n", - " Standard deviation for the Gaussian noise applied to the z-axis.\n", - " Defaults to 0.08.\n", - " base_z : float, optional\n", - " The baseline altitude for the deposited coordinates. Defaults to 0.2.\n", - " angle_limit : float, optional\n", - " A soft cap on local turning increments (radians). Defaults to np.pi/2.\n", - "\n", - " Returns\n", - " -------\n", - " coords : np.ndarray\n", - " Array of shape (N, 3) containing the [x, y, z] coordinates\n", - "\n", - " Raises\n", - " ------\n", - " ValueError\n", - " If num_beads is less than 6 or if the generated ring has zero\n", - " contour length.\n", - "\n", - " Examples\n", - " --------\n", - " >>> coords = make_ring_2d_persistent_initial(seed=42)\n", - " >>> print(coords.shape)\n", - " (90, 3)\n", - "\n", - " \"\"\"\n", - "\n", - " rng = np.random.default_rng(int(seed))\n", - " n = int(n_beads)\n", - " if n < 6:\n", - " raise ValueError(\"n_beads must be at least\"\n", - " \"6 for a stable closed persistent ring.\")\n", - "\n", - " bond_length = float(bond_length)\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 1) Build a periodic turning-angle process on the ring\n", - " # ------------------------------------------------------------------\n", - " # Keep the same qualitative scaling as the old code:\n", - " # larger persistence_bonds / angle_stiffness_mult => less angular noise\n", - " sigma_dtheta = (\n", - " np.sqrt(2.0 / max(float(persistence_bonds), 1.0))\n", - " / max(float(angle_stiffness_mult), 1e-6)\n", - " )\n", - "\n", - " # Raw local turning increments\n", - " dtheta = rng.normal(0.0, sigma_dtheta, size=n).astype(np.float64)\n", - " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", - "\n", - " # Circular smoothing of turning increments to suppress sharp local corners.\n", - " # Stronger persistence / stiffness => broader smoothing.\n", - " smooth_width = int(np.clip(np.round(max(float(persistence_bonds), 1.0)\n", - " / 6.0), 1, 8))\n", - " if smooth_width > 0:\n", - " offsets = np.arange(-smooth_width, smooth_width + 1, dtype=np.float64)\n", - " kernel_sigma = max(smooth_width / 2.0, 1e-6)\n", - " kernel = np.exp(-0.5 * (offsets / kernel_sigma) ** 2)\n", - " kernel /= kernel.sum()\n", - "\n", - " dtheta_smooth = np.zeros_like(dtheta)\n", - " for shift, w in zip(offsets.astype(int), kernel):\n", - " dtheta_smooth += w * np.roll(dtheta, shift)\n", - " dtheta = dtheta_smooth\n", - "\n", - " # Enforce exact total turning of 2*pi for a closed planar ring.\n", - " # This avoids the nonphysical post hoc closure drift used before.\n", - " total_turn = dtheta.sum()\n", - " dtheta += (2.0 * np.pi - total_turn) / n\n", - "\n", - " # Re-clip gently after correction, then re-enforce total turning once more.\n", - " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", - " dtheta += (2.0 * np.pi - dtheta.sum()) / n\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 2) Build periodic tangent angles and integrate to a dense closed path\n", - " # ------------------------------------------------------------------\n", - " theta0 = rng.uniform(0.0, 2.0 * np.pi)\n", - " thetas = theta0 + np.cumsum(dtheta)\n", - "\n", - " # Use a dense piecewise-linear curve first; later resample uniformly.\n", - " tangents = np.stack([np.cos(thetas), np.sin(thetas)], axis=1)\n", - "\n", - " # Integrate unit-speed steps to get a provisional loop\n", - " xy = np.zeros((n, 2), dtype=np.float64)\n", - " xy[1:] = np.cumsum(tangents[:-1] * bond_length, axis=0)\n", - " end_error = (xy[-1] + tangents[-1] * bond_length) - xy[0]\n", - "\n", - " # Distribute closure correction through the bond vectors,\n", - " # which is less distorting than shifting coordinates linearly.\n", - " bond_corr = end_error / n\n", - " bonds = tangents * bond_length - bond_corr[None, :]\n", - "\n", - " # Renormalize bond lengths back toward bond_length while keeping closure.\n", - " bond_norms = np.linalg.norm(bonds, axis=1)\n", - " good = bond_norms > 1e-12\n", - " bonds[good] *= (bond_length / bond_norms[good])[:, None]\n", - "\n", - " # Re-enforce closure after renormalization\n", - " bonds -= bonds.sum(axis=0, keepdims=True) / n\n", - "\n", - " # Reconstruct coordinates from corrected bonds\n", - " xy = np.zeros((n, 2), dtype=np.float64)\n", - " xy[1:] = np.cumsum(bonds[:-1], axis=0)\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 3) Arc-length resample so bead spacing stays uniform and physical\n", - " # ------------------------------------------------------------------\n", - " closed_xy = np.vstack([xy, xy[0]])\n", - " seg = np.linalg.norm(np.diff(closed_xy, axis=0), axis=1)\n", - " s = np.concatenate([[0.0], np.cumsum(seg)])\n", - " total_len = s[-1]\n", - "\n", - " if total_len <= 1e-12:\n", - " raise ValueError(\"Degenerate ring generated;\"\n", - " \"total contour length is zero.\")\n", - "\n", - " target_s = np.linspace(0.0, total_len, n + 1)[:-1]\n", - "\n", - " x_resamp = np.interp(target_s, s, closed_xy[:, 0])\n", - " y_resamp = np.interp(target_s, s, closed_xy[:, 1])\n", - " xy = np.stack([x_resamp, y_resamp], axis=1)\n", - "\n", - " # Final contour-length normalization\n", - " # so mean bond length matches bond_length\n", - " final_bonds = np.roll(xy, -1, axis=0) - xy\n", - " mean_bond = np.mean(np.linalg.norm(final_bonds, axis=1))\n", - " if mean_bond > 1e-12:\n", - " xy *= (bond_length / mean_bond)\n", - "\n", - " # Center at origin\n", - " xy -= xy.mean(axis=0, keepdims=True)\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 4) Assembly with preserved Gaussian z-noise logic\n", - " # ------------------------------------------------------------------\n", - " coords = np.zeros((n, 3), dtype=np.float32)\n", - " coords[:, 0] = xy[:, 0].astype(np.float32)\n", - " coords[:, 1] = xy[:, 1].astype(np.float32)\n", - " coords[:, 2] = rng.normal(loc=base_z,\n", - " scale=z_std, size=n).astype(np.float32)\n", - "\n", - " # Return as a list containing one frame to mimic run_md_relaxation output\n", - " return [coords]" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_04", - "metadata": { - "id": "cell_md_04" - }, - "source": [ - "## 5. MD Animation\n", - "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", - "\n", - "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", - "Running this cell will automatically generate and display the animation for a single deterministic chain (seed 0, default bead count)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cell_code_04", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 373 - }, - "id": "cell_code_04", - "outputId": "0286465d-0134-4014-f750-7942d7920a3a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generating initial ring and running MD (seed=0)…\n", - "Using CUDA platform.\n", - "MD done — 201 frames. Rendering animation…\n" - ] - }, - { - "output_type": "error", - "ename": "KeyboardInterrupt", - "evalue": "", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_5960/277962667.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 92\u001b[0m steps_per_frame=STEPS_PER_FRAME)\n\u001b[1;32m 93\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"MD done — {len(anim_frames)} frames. 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"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 1460\u001b[0m \u001b[0;31m# if we have got this far, then there was no shortcut possible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1461\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhas_inverse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1462\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1463\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1464\u001b[0m raise ValueError('It is not possible to compute transA - transB '\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 1345\u001b[0m \u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1346\u001b[0m \"\"\"\n\u001b[0;32m-> 1347\u001b[0;31m return (composite_transform_factory(self, other)\n\u001b[0m\u001b[1;32m 1348\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTransform\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1349\u001b[0m NotImplemented)\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mcomposite_transform_factory\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 2520\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAffine2D\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAffine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2521\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mCompositeAffine2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2522\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mCompositeGenericTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, a, b, **kwargs)\u001b[0m\n\u001b[1;32m 2354\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2356\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2357\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2358\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, shorthand_name)\u001b[0m\n\u001b[1;32m 108\u001b[0m \"\"\"\n\u001b[1;32m 109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshorthand_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 111\u001b[0m \"\"\"\n\u001b[1;32m 112\u001b[0m \u001b[0mParameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "def show_md_animation(\n", - " frames: list[np.ndarray],\n", - " interval_ms: int = 150\n", - ") -> None:\n", - " \"\"\"\n", - " Renders an in-notebook HTML5 animation of MD frames.\n", - "\n", - " This function is used to visualize the molecular dynamics simulation\n", - " generated using the `run_md_relaxation` function.\n", - " A plane has been added for visualization purposes\n", - " and the range of absolute z values is also displayed.\n", - " This function is purely for visualisation and does not mutate any arrays.\n", - "\n", - " Parameters\n", - " ----------\n", - " frames : list\n", - " A list of coordinate arrays of shape (num_beads, 3).\n", - " interval_ms : int, optional\n", - " The time interval between frames in milliseconds. Defaults to 150.\n", - "\n", - " Returns\n", - " -------\n", - " None\n", - " The function calls `IPython.display.display` to render the\n", - " animation object directly in the notebook.\n", - "\n", - " Examples\n", - " --------\n", - " >>> show_md_animation(frames)\n", - "\n", - " \"\"\"\n", - "\n", - " frames = [np.asarray(f, dtype=np.float32) for f in frames]\n", - "\n", - " # Use only the first frame for x/y limits\n", - " # — avoids COM drift blowing the scale\n", - " first = frames[0]\n", - " xy_pad = 5.0\n", - " x_min, x_max = first[:, 0].min() - xy_pad, first[:, 0].max() + xy_pad\n", - " y_min, y_max = first[:, 1].min() - xy_pad, first[:, 1].max() + xy_pad\n", - "\n", - " # Clamp z: floor at -1 (just below substrate),\n", - " # ceiling from initial max + padding\n", - " z_min = -1.0\n", - " z_max = float(first[:, 2].max()) + 3.0\n", - "\n", - " x_plane = np.linspace(x_min, x_max, 2)\n", - " y_plane = np.linspace(y_min, y_max, 2)\n", - " X_plane, Y_plane = np.meshgrid(x_plane, y_plane)\n", - " Z_plane = np.zeros_like(X_plane)\n", - "\n", - " fig = plt.figure(figsize=(6, 6))\n", - " ax = fig.add_subplot(111, projection=\"3d\")\n", - "\n", - " def _set_axes():\n", - " ax.set_xlim(x_min, x_max)\n", - " ax.set_ylim(y_min, y_max)\n", - " ax.set_zlim(z_min, z_max)\n", - " ax.set_xlabel(\"x\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z (nm)\")\n", - "\n", - " def init():\n", - " _set_axes()\n", - " return []\n", - "\n", - " def update(i):\n", - " ax.cla()\n", - " c = frames[i]\n", - " # Clip beads to visible z range for plotting so outliers don't distort\n", - " visible = (c[:, 2] >= z_min) & (c[:, 2] <= z_max)\n", - " cc = np.vstack([c, c[0]])\n", - " ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-\", linewidth=1)\n", - " ax.scatter(c[visible, 0], c[visible, 1], c[visible, 2], s=5)\n", - " ax.plot_surface(X_plane, Y_plane, Z_plane, alpha=0.2, color=\"cyan\")\n", - " _set_axes()\n", - " ax.set_title(f\"Frame {i}/{len(frames)-1} | \"\n", - " f\"z ∈ [{c[:,2].min():.1f}, {c[:,2].max():.1f}] nm\")\n", - " return []\n", - "\n", - " ani = animation.FuncAnimation(\n", - " fig, update, frames=len(frames), init_func=init,\n", - " interval=int(interval_ms), blit=False\n", - " )\n", - " plt.close(fig)\n", - " display(HTML(ani.to_jshtml()))\n", - "\n", - "\n", - "# ── Run animation automatically (deterministic seed) ─────────────────────────\n", - "print(\"Generating initial ring and running MD (seed=0)…\")\n", - "anim_coords0 = make_tangled_ring_initial(seed=1)\n", - "anim_frames = run_md_relaxation(anim_coords0, seed=1,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - "print(f\"MD done — {len(anim_frames)} frames. Rendering animation…\")\n", - "show_md_animation(anim_frames)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_05", - "metadata": { - "id": "cell_md_05" - }, - "source": [ - "## 6. Height based AFM Rendering\n", - "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", - "\n", - "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cell_code_05", - "metadata": { - "id": "cell_code_05" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Low-level geometry helpers\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def _seg_intersect_2d(\n", - " p1: np.ndarray,\n", - " p2: np.ndarray,\n", - " q1: np.ndarray,\n", - " q2: np.ndarray,\n", - " eps: float = 1e-12\n", - ")-> tuple[bool, np.ndarray | None, float | None, float | None]:\n", - " \"\"\"Calculates the strict 2-D intersection of 2 line segments.\n", - "\n", - " This function is one of the functions used for the creation of the\n", - " chain from the bead coordinates.\n", - " The intersection point is defined by the parameters t and u such that:\n", - " point = p1 + t * (p2 - p1) = q1 + u * (q2 - q1)\n", - "\n", - " Parameters\n", - " ----------\n", - " p1, p2 : np.ndarray\n", - " Start and end points of the first line segment.\n", - " q1, q2 : np.ndarray\n", - " Start and end points of the second line segment.\n", - " eps : float, optional\n", - " A small positive value for numerical stability. Defaults to 1e-12.\n", - "\n", - " Returns\n", - " -------\n", - " is_intersecting : bool\n", - " True if the segments intersect, False otherwise.\n", - " intersection_point : np.ndarray or None\n", - " The [x, y] coordinates of the intersection point, or None if no\n", - " intersection exists.\n", - " parameter_t : float or None\n", - " The interpolation parameter along the first segment, or None.\n", - " parameter_u : float or None\n", - " The interpolation parameter along the second segment, or None.\n", - "\n", - " Examples\n", - " --------\n", - " >>> hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", - " >>> print(hit, pt, t, u)\n", - " False None None None\n", - "\n", - " \"\"\"\n", - "\n", - " p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n", - " q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n", - " r = p2 - p1; s = q2 - q1\n", - " rxs = r[0]*s[1] - r[1]*s[0]\n", - " if abs(rxs) < eps:\n", - " return False, None, None, None\n", - " qp = q1 - p1\n", - " t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n", - " u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n", - " if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n", - " return True, p1 + t*r, t, u\n", - " return False, None, None, None\n", - "\n", - "\n", - "def _circ_sep(\n", - " n: int,\n", - " i: int,\n", - " j: int\n", - ") -> int:\n", - " \"\"\"Circular distance between bead indices on a ring of n beads.\n", - "\n", - " This helper function determines the minimal separation between two beads\n", - " along the perimeter of the ring, accounting for the periodic boundary.\n", - " It is used to distinguish between beads that form physical crossings\n", - " and those that are simply sequential neighbors. This function is used\n", - " downstream for crossing calculations\n", - "\n", - " Parameters\n", - " ----------\n", - " n : int\n", - " Number of beads in the ring.\n", - " i, j : int\n", - " Indices of the two beads for which to calculate the separation.\n", - "\n", - " Returns\n", - " -------\n", - " int\n", - " The minimal distance between the two beads.\n", - "\n", - " Examples\n", - " --------\n", - " >>> d = _circ_sep(n, i, j)\n", - " >>> d.type\n", - " int\n", - "\n", - " \"\"\"\n", - "\n", - " d = abs(int(i) - int(j))\n", - " return min(d, n - d)\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Structuring-element / grid helpers\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def disk_footprint(\n", - " r_px: float\n", - ") -> np.ndarray:\n", - " \"\"\"Boolean circular footprint of radius r_px pixels.\n", - "\n", - " This function creates a 2D boolean mask representing a disk of a\n", - " specified radius. It is used to convert discrete bead coordinates\n", - " into a continuous chain structure by dilating each bead coordinate\n", - " into a disk-like structure. This is what builds the DNA chain radius.\n", - "\n", - " Parameters\n", - " ----------\n", - " r_px : float\n", - " Radius of the disk in pixels.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " A boolean array with True values inside the disk and false outside.\n", - "\n", - " Examples\n", - " --------\n", - " >>> footprint = disk_footprint(r_px)\n", - " >>> print(footprint.shape, footprint.dtype)\n", - " (5, 5) bool\n", - "\n", - " \"\"\"\n", - "\n", - " if r_px < 0.5:\n", - " return np.ones((1, 1), dtype=bool)\n", - " r = int(np.ceil(r_px))\n", - " y, x = np.ogrid[-r:r+1, -r:r+1]\n", - " return (x*x + y*y) <= r*r\n", - "\n", - "\n", - "def upsample_nn(\n", - " a: np.ndarray,\n", - " out_shape: tuple[int, int]\n", - " ) -> np.ndarray:\n", - " \"\"\"Nearest-neighbour upsample array a to out_shape.\n", - "\n", - " This function increases the resolution of an input array (such as an AFM\n", - " height map or a boolean mask) by repeating elements.\n", - " If the target dimensions are not exact multiples of the input dimensions,\n", - " the resulting array is clipped to the specified target shape.\n", - "\n", - " Parameters\n", - " ----------\n", - " a : np.ndarray\n", - " Input array to be upsampled.\n", - " out_shape : tuple\n", - " Target shape of the upsampled array.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " Upsampled array of shape out_shape\n", - "\n", - " Examples\n", - " --------\n", - " >>> upsampled_array = upsample_nn(input_array, target_shape)\n", - " >>> print(upsampled_array.shape)\n", - " (target_shape[0], target_shape[1])\n", - "\n", - " \"\"\"\n", - "\n", - " ny_out, nx_out = out_shape\n", - " ny_in, nx_in = a.shape\n", - " if (ny_out, nx_out) == (ny_in, nx_in):\n", - " return a\n", - " sy = max(ny_out // ny_in, 1)\n", - " sx = max(nx_out // nx_in, 1)\n", - " a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n", - " return a_up[:ny_out, :nx_out]\n", - "\n", - "\n", - "#internal render grid now comes directly from extent and user nm_per_px\n", - "\n", - "def compute_internal_render_grid(\n", - " extent: tuple[float, float, float, float],\n", - " target_nm_per_px: float,\n", - " min_size: int = 16\n", - ") -> tuple[int, int, float]:\n", - " \"\"\"Build the internal render grid from physical extent and user nm_per_px.\n", - "\n", - " This function determines the size of the\n", - " grid for the resolution that the user desires.\n", - " It is based on values chosen for target_nm_per_px and nm_per_pix.\n", - " It also handles potential zero-division or negative resolution values.\n", - "\n", - " Parameters\n", - " ----------\n", - " extent : tuple\n", - " boundaries of the simulation space\n", - " target_nm_per_px : float\n", - " desired resolution multiplied by a scaling factor in nm/pixel\n", - " min_size : int, optional\n", - " minimum size of the grid in pixels. Default 16\n", - "\n", - " Returns\n", - " -------\n", - " tuple\n", - " size of the grid\n", - "\n", - " Examples\n", - " --------\n", - " >>> nx, ny, p_nm_per_px = compute_internal_render_grid(extent,\n", - " target_nm_per_px)\n", - " >>> print(nx, ny, p_nm_per_px)\n", - " 256 256 0.5\n", - "\n", - " \"\"\"\n", - "\n", - " xmin, xmax, ymin, ymax = extent\n", - " width_nm = float(xmax - xmin)\n", - " height_nm = float(ymax - ymin)\n", - " p_nm_per_px = max(float(target_nm_per_px), 1e-6)\n", - " nx = max(int(min_size), int(np.ceil(width_nm / p_nm_per_px)) + 1)\n", - " ny = max(int(min_size), int(np.ceil(height_nm / p_nm_per_px)) + 1)\n", - " return nx, ny, p_nm_per_px\n", - "\n", - "\n", - "def resize_image_to_shape(\n", - " a: np.ndarray,\n", - " out_shape: tuple[int, int],\n", - " order: int =1\n", - " ) -> np.ndarray:\n", - " \"\"\"Resize 2D array a to out_shape using scipy.ndimage.zoom.\n", - "\n", - " This function utilizes `scipy.ndimage.zoom` to scale the input array.\n", - " It includes safety checks for edge-case padding and cropping to ensure\n", - " the output array matches the requested dimensions exactly.\n", - "\n", - " Parameters\n", - " ----------\n", - " a : np.ndarray\n", - " Input array to be resized.\n", - " out_shape : tuple\n", - " Target shape of the resized\n", - " order : int, optional\n", - " Order of the interpolation. Defaults to 1.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " Resized array of shape out_shape\n", - "\n", - " Examples\n", - " --------\n", - " >>> resized_array = resize_image_to_shape(input_array, target_shape)\n", - " >>> print(resized_array.shape)\n", - " (target_shape[0], target_shape[1])\n", - "\n", - " \"\"\"\n", - "\n", - " a = np.asarray(a)\n", - " out_shape = tuple(int(v) for v in out_shape)\n", - " if a.shape == out_shape:\n", - " return a.copy()\n", - " zoom_factors = (out_shape[0] / a.shape[0], out_shape[1] / a.shape[1])\n", - " out = zoom(a, zoom_factors, order=order, mode=\"nearest\",\n", - " prefilter=(order > 1))\n", - " out = out[:out_shape[0], :out_shape[1]]\n", - " if out.shape != out_shape:\n", - " pad_y = max(0, out_shape[0] - out.shape[0])\n", - " pad_x = max(0, out_shape[1] - out.shape[1])\n", - " out = np.pad(out, ((0, pad_y), (0, pad_x)), mode=\"edge\")\n", - " out = out[:out_shape[0], :out_shape[1]]\n", - " return out\n", - "\n", - "\n", - "def dna_shape(\n", - " radius_nm: float,\n", - " p_nm_per_px: float,\n", - " max_radius_px: int = 96\n", - ")-> np.ndarray:\n", - " \"\"\"Hemispherical-cap structuring element\n", - " representing the DNA cross-section.\n", - "\n", - " This function generates a 2D height map representing the physical profile\n", - " of a DNA strand. Each pixel within the radius of the DNA is assigned a\n", - " height value based on the spherical-cap formula, representing the vertical\n", - " profile encountered during an AFM scan.\n", - "\n", - " Parameters\n", - " ----------\n", - " radius_nm : float\n", - " Radius of the disk in nanometers.\n", - " p_nm_per_px : float\n", - " Physical pixel size in nanometers.\n", - " max_radius_px : int, optional\n", - " Maximum radius in pixels. Defaults to 96.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " Height map of shape (2*R+1, 2*R+1)\n", - "\n", - " Example\n", - " -------\n", - " >>> height_map = dna_shape(radius_nm, p_nm_per_px)\n", - "\n", - " \"\"\"\n", - "\n", - " r_px = radius_nm / max(p_nm_per_px, 1e-12)\n", - " R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n", - " y, x = np.ogrid[-R:R+1, -R:R+1]\n", - " d2 = x*x + y*y\n", - " inside = d2 <= (r_px * r_px)\n", - " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", - " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", - " structure[inside] = np.sqrt(\n", - " np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n", - " ).astype(np.float32)\n", - " return inside.astype(bool), structure\n", - "\n", - "\n", - "def AFM_tip(\n", - " tip_radius_nm: float,\n", - " p_nm_per_px: float,\n", - " max_radius_px: int = 128\n", - " ) -> tuple[np.ndarray, np.ndarray]:\n", - " \"\"\"Spherical-cap structuring element representing the AFM tip geometry.\n", - " Used to convolve the surface with the tip shape.\n", - "\n", - " This function calculates the relative vertical profile of a spherical AFM\n", - " tip. The resulting height map is used to perform a morphological dilation\n", - " on the sample surface, simulating the physical interaction (convolution)\n", - " between the tip and the sample. The height is normalized so that the\n", - " tip apex is at 0.0.\n", - "\n", - " Parameters\n", - " ----------\n", - " tip_radius_nanometers : float\n", - " The physical radius of the AFM tip in nanometers.\n", - " nanometers_per_pixel : float\n", - " The spatial resolution of the simulation grid in nanometers per pixel.\n", - " maximum_radius_pixels : int, optional\n", - " A safety limit for the pixel radius of the structuring element to\n", - " prevent excessive memory consumption. Defaults to 128.\n", - "\n", - " Returns\n", - " -------\n", - " occupancy_mask : np.ndarray\n", - " A 2D boolean array representing the circular footprint of the tip.\n", - " height_structure_element : np.ndarray\n", - " A 2D float32 array containing the vertical profile offsets of the\n", - " spherical cap. Pixels outside the tip footprint are assigned a\n", - " large negative value (-1e9).\n", - "\n", - " Example\n", - " -------\n", - " >>> occupancy_mask, height_structure_element = AFM_tip(tip_radius_nm,\n", - " p_nm_per_px)\n", - "\n", - " \"\"\"\n", - "\n", - " Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n", - " R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n", - " y, x = np.ogrid[-R:R+1, -R:R+1]\n", - " d2 = x*x + y*y\n", - " inside = d2 <= (Rpx * Rpx)\n", - " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", - " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", - " structure[inside] = (\n", - " np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n", - " - tip_radius_nm\n", - " ).astype(np.float32)\n", - " return inside.astype(bool), structure\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Crossing boost\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def calculate_crossing_taper_weight(\n", - " idx_off: int,\n", - " w: int,\n", - " profile: str = \"gaussian\",\n", - " sigma: float | None = None\n", - " ) -> float:\n", - " \"\"\"Smooth weight in [0, 1] used to fade the crossing height boost along\n", - " the bead-index window of half-width w.\n", - "\n", - " This function generates a weighting factor between 0 and 1 used to\n", - " gradually decrease the height offset applied to DNA segments near a\n", - " detected crossing. It ensures that the transition between the boosted\n", - " crossing region and the standard polymer chain is smooth.\n", - " There are 3 types of profiles possible to be applied\n", - " for smoothing a crossing, guassian, hemisphere, and linear.\n", - "\n", - " Parameters\n", - " ----------\n", - " idx_off : int\n", - " The distance in bead indices from the center of the crossing.\n", - " w : int\n", - " The number of beads on either side of the center over which the\n", - " weight should taper to zero.\n", - " profile : str, optional\n", - " The mathematical shape of the taper. Options are:\n", - " - 'gaussian': A smooth bell-shaped decay (recommended).\n", - " - 'hemisphere': A circular arc taper.\n", - " - 'linear': A simple linear ramp.\n", - " Defaults to 'gaussian'.\n", - " sigma : float, optional\n", - " The standard deviation for the Gaussian profile. If None, it is\n", - " automatically determined based on the window width.\n", - "\n", - " Returns\n", - " -------\n", - " float\n", - " A weighting factor in the range [0.0, 1.0].\n", - "\n", - " Examples\n", - " --------\n", - " >>> wt = calculate_crossing_taper_weight(idx_off, w)\n", - "\n", - " \"\"\"\n", - " if w <= 0:\n", - " return 1.0\n", - " a = abs(idx_off)\n", - " if profile == \"linear\":\n", - " return 1.0 - (a / (w + 1.0))\n", - " if profile == \"hemisphere\":\n", - " x = a / (w + 1.0)\n", - " return float(np.sqrt(max(0.0, 1.0 - x*x)))\n", - " # gaussian (default)\n", - " if sigma is None:\n", - " sigma = max(1e-6, 0.5 * (w + 0.5))\n", - " g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n", - " g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n", - " if g_end < 0.999:\n", - " g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n", - " return g\n", - "\n", - "\n", - "def collect_intersections(\n", - " xy_nm: np.ndarray,\n", - " min_separation_beads: int = 12,\n", - " merge_radius_nm: float = 0.5\n", - " )-> list[dict]:\n", - " \"\"\"Identify all self-intersections in a closed ring polyline.\n", - "\n", - " This function performs a brute-force search across all segment pairs of\n", - " the polyline to find crossings. It ignores adjacent segments, shared\n", - " vertices, and segments that are close to each other along the contour\n", - " of the ring. Detected intersections are subsequently clustered to\n", - " handle multi-segment vertex hits.\n", - "\n", - " Parameters\n", - " ----------\n", - "\n", - " xy_nm : np.ndarray\n", - " Array of shape (n, 2)\n", - " min_separation_beads : int, optional\n", - " Minimum separation between segments in beads. Defaults to 12.\n", - " merge_radius_nm : float, optional\n", - " Maximum separation between segments in nanometers. Defaults to 0.5.\n", - "\n", - " Returns\n", - " -------\n", - " list of dicts:\n", - " {pt_nm, seg_i, seg_j, t, u, n_hits}\n", - " list[dict]\n", - " A list of dictionaries, where each dictionary represents a unique\n", - " crossing and contains:\n", - " - \"pt_nm\": The [x, y] coordinates of the intersection.\n", - " - \"seg_i\": The index of the first segment involved.\n", - " - \"seg_j\": The index of the second segment involved.\n", - " - \"t\": Interpolation parameter along segment A.\n", - " - \"u\": Interpolation parameter along segment B.\n", - " - \"n_hits\": The number of raw intersections merged into this cluster.\n", - "\n", - " Examples\n", - " --------\n", - " >>> crossings = collect_intersections(xy_nm,\n", - " min_separation_beads=int(min_separation_beads),\n", - " merge_radius_nm=float(merge_radius_nm)\n", - " >>> crossings.type\n", - " list\n", - "\n", - " \"\"\"\n", - "\n", - " xy = np.asarray(xy_nm, dtype=float)\n", - " n = int(xy.shape[0])\n", - " raw = []\n", - " for i in range(n):\n", - " i2 = (i + 1) % n\n", - " p1, p2 = xy[i], xy[i2]\n", - " for j in range(i + 1, n):\n", - " j2 = (j + 1) % n\n", - " if i == j or i2 == j or j2 == i:\n", - " continue\n", - " if _circ_sep(n, i, j) < int(min_separation_beads):\n", - " continue\n", - " q1, q2 = xy[j], xy[j2]\n", - " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", - " if hit:\n", - " raw.append(dict(pt=np.array(pt, float),\n", - " seg_i=int(i), seg_j=int(j),\n", - " t=float(t), u=float(u)))\n", - "\n", - " clusters = merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n", - " return clusters\n", - "\n", - "\n", - "def merge_crossing_clusters(\n", - " raw_list: list[dict],\n", - " merge_radius_nm: float = 0.5\n", - " ) -> list[dict]:\n", - " \"\"\"Group nearby raw intersection points into single averaged clusters.\n", - "\n", - " This function uses a greedy proximity-based algorithm to cluster\n", - " intersections that occur close to each other (e.g., where multiple\n", - " segments meet at a single vertex). It calculates the centroid for each\n", - " cluster and retains metadata from the first intersection encountered in\n", - " each group.\n", - "\n", - " Parameters\n", - " ----------\n", - " raw_list : list[dict]\n", - " A list of dictionaries containing raw intersection data. Each\n", - " dictionary must include the key \"point\" (a 2D numpy array).\n", - " merge_radius_nm : float, optional\n", - " The maximum Euclidean distance allowed between an intersection point\n", - " and a cluster's current centroid for it to be merged. Defaults to 0.5.\n", - "\n", - " Returns\n", - " -------\n", - " list[dict]\n", - "\n", - " Examples\n", - " --------\n", - " clusters = merge_crossing_clusters(raw_list,\n", - " merge_radius_nm=float(merge_radius_nm))\n", - " >>> clusters.type\n", - " list\n", - "\n", - " \"\"\"\n", - "\n", - " clusters = []\n", - " for r in raw_list:\n", - " pt = r[\"pt\"].astype(float)\n", - " placed = False\n", - " for c in clusters:\n", - " if np.linalg.norm(pt -\n", - " c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", - " c[\"pt_sum\"] += pt\n", - " c[\"n\"] += 1\n", - " c[\"items\"].append(r)\n", - " placed = True\n", - " break\n", - " if not placed:\n", - " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n", - "\n", - " out = []\n", - " for c in clusters:\n", - " pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n", - " rep = c[\"items\"][0]\n", - " out.append(dict(\n", - " pt_nm = pt,\n", - " seg_i = int(rep[\"seg_i\"]),\n", - " seg_j = int(rep[\"seg_j\"]),\n", - " t = float(rep[\"t\"]),\n", - " u = float(rep[\"u\"]),\n", - " n_hits = int(c[\"n\"]),\n", - " ))\n", - " return out\n", - "\n", - "\n", - "def boost_crossings_intersections(\n", - " x_nm: np.ndarray,\n", - " y_nm: np.ndarray,\n", - " zc_nm: np.ndarray,\n", - " crossings: list[dict] | None = None,\n", - " min_separation_beads: int = 12,\n", - " boost_window_beads: int = 2,\n", - " guaranteed_offset_nm: float = 1.0,\n", - " boost_method: str = \"additive\",\n", - " boost_profile: str = \"gaussian\",\n", - " boost_sigma_beads: float | None = None,\n", - " far_clip_nm: float | None =2.5,\n", - " far_clip_window_beads: int = 12,\n", - " merge_radius_nm: float = 0.5,\n", - ") -> tuple[np.ndarray, list[dict]]:\n", - " \"\"\"Boost z-height at strand crossings so they are visually distinct in\n", - " the AFM image.\n", - "\n", - " This function identifies self-intersections in a 2D projection and\n", - " physically separates the strands in 3D. For each crossing, it determines\n", - " which segment is 'on top' and raises its vertical profile using a tapered\n", - " window to ensure a realistic and visually distinct crossover point.\n", - " For each crossing it:\n", - " - Determines which strand is on top (higher z)\n", - " - Raises top-strand beads in a tapered window to at least\n", - " (bottom_max + guaranteed_offset_nm)\n", - " - Optionally clips non-crossing beads to far_clip_nm\n", - "\n", - " Parameters\n", - " ----------\n", - " x_nm : np.ndarray\n", - " The x-coordinates of the polymer chain.\n", - " y_nm : np.ndarray\n", - " The y-coordinates of the polymer chain.\n", - " zc_nm : np.ndarray\n", - " The initial z-coordinates of the polymer chain.\n", - " crossings : list[dict]\n", - " Pre-computed crossing data. If None, crossings are detected\n", - " automatically using `collect_polyline_intersections`.\n", - " min_separation_beads : int, optional\n", - " Minimum separation between segments in beads. Defaults to 12.\n", - " boost_window_beads : int, optional\n", - " The number of beads to boost. Defaults to 2.\n", - " guaranteed_offset_nm : float, optional\n", - " The minimum boost height in nanometers. Defaults to 1.0.\n", - " boost_method : str\n", - " The logic for height calculation\n", - " boost_profile : str\n", - " The shape of the tapering profile\n", - " boost_sigma_beads : float\n", - " The standard deviation for gaussian tapering\n", - " far_clip_nm : float\n", - " If applied caps the height of beads far from any crossing to this value.\n", - " far_clip_window_beads : int\n", - " The number of beads to clip.\n", - " merge_radius_nm : float\n", - " The maximum Euclidean distance for nearby raw intersections.\n", - "\n", - " Returns\n", - " -------\n", - " z : np.ndarray\n", - " The updated z-coordinates with crossings boosted.\n", - " crossing_info : list[dict]\n", - " Information regarding the identified and modified crossings.\n", - "\n", - " Examples\n", - " --------\n", - " >>> z, crossing_info = boost_crossings_intersections(x, y, z)\n", - " >>> print(z.shape, type(crossing_info))\n", - " (N,3) \n", - "\n", - " \"\"\"\n", - "\n", - " x = np.asarray(x_nm, dtype=np.float32)\n", - " y = np.asarray(y_nm, dtype=np.float32)\n", - " z = np.asarray(zc_nm, dtype=np.float32).copy()\n", - " n = int(z.shape[0])\n", - " if n < 5:\n", - " return z.astype(np.float32), []\n", - "\n", - " if crossings is None:\n", - " xy = np.stack([x, y], axis=1)\n", - " crossings = collect_intersections(\n", - " xy,\n", - " min_separation_beads=int(min_separation_beads),\n", - " merge_radius_nm=float(merge_radius_nm),\n", - " )\n", - "\n", - " def get_segment_indices(\n", - " center: int,\n", - " w: int\n", - " ) -> np.ndarray:\n", - " \"\"\"\n", - " Calculate indices in a window around a center bead,\n", - " handling periodicity.\n", - "\n", - " Parameters\n", - " ----------\n", - " center : int\n", - " The index of the center bead.\n", - " w : int\n", - " The number of beads on either side of the center\n", - " to include.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - "\n", - " \"\"\"\n", - "\n", - " return np.array([(center + k) % n for k in range(-w, w+1)],\n", - " dtype=np.int32)\n", - "\n", - " crossing_info = []\n", - " crossing_centers = []\n", - " w = int(boost_window_beads)\n", - "\n", - " for c in crossings:\n", - " i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n", - " i2 = (i + 1) % n; j2 = (j + 1) % n\n", - " ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n", - "\n", - " bead_i = i if ti < 0.5 else i2\n", - " bead_j = j if uj < 0.5 else j2\n", - " zi = float((1.0 - ti) * z[i] + ti * z[i2])\n", - " zj = float((1.0 - uj) * z[j] + uj * z[j2])\n", - "\n", - " top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j,\n", - " bead_i)\n", - " top_seg = get_segment_indices(top_center, w)\n", - " bottom_seg = get_segment_indices(bottom_center, w)\n", - "\n", - " bl_max = float(z[bottom_seg].max())\n", - " tl_max = float(z[top_seg].max())\n", - "\n", - " if boost_method == \"absolute\":\n", - " target_height = bl_max + float(guaranteed_offset_nm)\n", - " else:\n", - " target_height = max(tl_max, bl_max) + float(guaranteed_offset_nm)\n", - "\n", - " for off in range(-w, w+1):\n", - " k = (top_center + off) % n\n", - " wt = float(calculate_crossing_taper_weight(off, w,\n", - " profile=boost_profile,\n", - " sigma=boost_sigma_beads))\n", - " z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n", - "\n", - " crossing_centers.extend([int(top_center), int(bottom_center)])\n", - " crossing_info.append(dict(\n", - " pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n", - " seg_i = int(i),\n", - " seg_j = int(j),\n", - " top_center = int(top_center),\n", - " bottom_center= int(bottom_center),\n", - " ))\n", - "\n", - " # Clip beads far from any crossing\n", - " if crossing_centers and far_clip_nm is not None:\n", - " centers = np.array(crossing_centers, dtype=np.int32)\n", - " r = int(far_clip_window_beads)\n", - " for k in range(n):\n", - " d = int(np.min(np.minimum(np.abs(k - centers),\n", - " n - np.abs(k - centers))))\n", - " if d > r:\n", - " z[k] = min(z[k], float(far_clip_nm))\n", - "\n", - " return z.astype(np.float32), crossing_info\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Main AFM renderer\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def create_z_based_afm(\n", - " coords: np.ndarray,\n", - " nx: int = 800,\n", - " ny: int = 800,\n", - " dna_diameter_nm: float = 2.0,\n", - " tip_radius_nm: float = 0.01,\n", - " max_height_nm: float = 6.0,\n", - " target_nm_per_px: float = 0.08,\n", - " max_radius_px: int = 96,\n", - " radius_shrink_px: float = 2.0,\n", - " final_blur_sigma_px: float = 0.20,\n", - " apply_edge_taper: bool = True,\n", - " taper_sigma_nm: float = 0.45,\n", - " taper_floor: float = 0.10,\n", - " add_center_ridge: bool = True,\n", - " ridge_sigma_nm: float = 0.25,\n", - " ridge_amp_nm: float = 0.25,\n", - " grain_nm: float = 0.0,\n", - " grain_sigma_px: float = 0.6,\n", - " grain_seed: int = 1,\n", - " enable_crossing_boost: bool = True,\n", - " min_separation_beads: int = 12,\n", - " boost_window_beads: int = 2,\n", - " guaranteed_offset_nm: float = 1.0,\n", - " boost_method: str = \"additive\",\n", - " boost_profile: str = \"gaussian\",\n", - " boost_sigma_beads: float | None = None,\n", - " crossings_precomputed: list[dict] | None = None,\n", - " far_clip_nm: float | None = 2.5,\n", - " far_clip_window_beads: int = 12,\n", - " return_crossing_info: bool = False,\n", - " return_masks: bool = True,\n", - " extent: tuple[float, float, float, float] | None = None,\n", - ") -> tuple[np.ndarray, dict | tuple]:\n", - " \"\"\"Master AFM renderer for simulating images from 3D coordinates.\n", - "\n", - " This pipeline projects 3D bead coordinates onto a 2D grid, manages\n", - " molecular crossings, rasterizes the polymer backbone, applies\n", - " morphological dilations for DNA shape and AFM tip convolution, and\n", - " adds realistic optical and physical artifacts (taper, ridge, noise). It\n", - " follows the following steps:\n", - " 1. Project 3-D bead coords onto a 2-D effective grid\n", - " 2. Optionally boost crossing heights (boost_crossings_intersections)\n", - " 3. Rasterise closed polyline with z-interpolation\n", - " 4. Apply dna_shape (DNA cylindrical cross-section)\n", - " 5. Apply AFM_tip (tip-sample convolution)\n", - " 6. Clip, blur, edge-taper, center-ridge\n", - " 7. Optional synthetic grain noise\n", - " 8. Upsample to requested (nx, ny)\n", - "\n", - " Parameters\n", - " ----------\n", - " coords : np.ndarray\n", - " Array of shape (n, 3) containing x, y, z coordinates.\n", - " nx : int\n", - " Number of pixels in the x-direction.\n", - " ny : int\n", - " Number of pixels in the y-direction.\n", - " dna_diameter_nm : float\n", - " DNA diameter in nanometers.\n", - " tip_radius_nm : float\n", - " Tip radius in nanometers.\n", - " max_height_nm : float\n", - " Maximum height in nanometers.\n", - " target_nm_per_px : float\n", - " Target number of nanometers per pixel.\n", - " max_radius_px : int\n", - " Maximum radius in pixels.\n", - " radius_shrink_px : float\n", - " Radius shrinking factor.\n", - " final_blur_sigma_px : float\n", - " Final blur sigma in pixels.\n", - " apply_edge_taper : bool\n", - " Apply edge taper.\n", - " taper_sigma_nm : float\n", - " Taper sigma in nanometers.\n", - " taper_floor : float\n", - " Taper floor.\n", - " add_center_ridge : bool\n", - " Add center ridge.\n", - " ridge_sigma_nm : float\n", - " Ridge sigma in nanometers.\n", - " ridge_amp_nm : float\n", - " Ridge amplitude in nanometers.\n", - " grain_nm : float\n", - " Grain size in nanometers.\n", - " grain_sigma_px : float\n", - " Grain sigma in pixels.\n", - " grain_seed : int\n", - " Grain seed.\n", - " enable_crossing_boost : bool\n", - " Enable crossing boost.\n", - " min_separation_beads : int\n", - " Minimum separation between segments in beads.\n", - " boost_window_beads : int\n", - " The number of beads to boost.\n", - " guaranteed_offset_nm : float\n", - " The minimum boost height in nanometers.\n", - " boost_method : str\n", - " The logic for height calculation\n", - " boost_profile : str\n", - " The shape of the tapering profile\n", - " boost_sigma_beads : float\n", - " The standard deviation for gaussian tapering\n", - " crossings_precomputed : list[dict]\n", - " Pre-computed crossing data.\n", - " far_clip_nm : float\n", - " If applied caps the height of beads\n", - " far from any crossing to this value.\n", - " far_clip_window_beads : int\n", - " The number of beads to clip.\n", - " return_crossing_info : bool\n", - " Return crossing info.\n", - " return_masks : bool\n", - " Return masks.\n", - " extent : tuple[float, float, float, float]\n", - " Extent of the output image.\n", - "\n", - " Returns\n", - " -------\n", - " afm_image : np.ndarray\n", - " The simulated 2D AFM height map.\n", - " debug_dict : dict or tuple\n", - " A dictionary containing masks and metadata (if return_masks=True),\n", - " or a tuple containing the physical (xmin, xmax, ymin, ymax) extent.\n", - "\n", - " Examples\n", - " --------\n", - " >>> afm_image, debug_dict = create_z_based_afm(coords, nx=800, ny=800,\n", - " return_masks=True)\n", - " >>> print(afm_image.shape, type(debug_dict))\n", - " (800, 800) \n", - "\n", - " \"\"\"\n", - "\n", - " x, y, z = coords.T\n", - " if extent is None:\n", - " xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n", - " ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n", - " else:\n", - " xmin, xmax, ymin, ymax = extent\n", - "\n", - " # CHANGED: render directly on the physical grid selected upstream\n", - " nx_eff, ny_eff = int(nx), int(ny)\n", - " px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n", - " py_eff = (ymax - ymin) / max(ny_eff - 1, 1)\n", - " p_eff = 0.5 * (px_eff + py_eff)\n", - "\n", - " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n", - " 0, nx_eff - 1)\n", - " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n", - " 0, ny_eff - 1)\n", - "\n", - " zc = (z - z.min()).astype(np.float32)\n", - "\n", - " # ── Crossing boost ───────────────────────────────────────────────────────\n", - " crossing_info = []\n", - " if enable_crossing_boost:\n", - " zc, crossing_info = boost_crossings_intersections(\n", - " x.astype(np.float32), y.astype(np.float32), zc,\n", - " crossings=crossings_precomputed,\n", - " min_separation_beads=int(min_separation_beads),\n", - " boost_window_beads=int(boost_window_beads),\n", - " guaranteed_offset_nm=float(guaranteed_offset_nm),\n", - " boost_method=str(boost_method),\n", - " boost_profile=str(boost_profile),\n", - " boost_sigma_beads=boost_sigma_beads,\n", - " far_clip_nm=far_clip_nm,\n", - " far_clip_window_beads=int(far_clip_window_beads),\n", - " )\n", - "\n", - " # ── Rasterise polyline ───────────────────────────────────────────────────\n", - " z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n", - " line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n", - " npts = len(ix)\n", - " for k in range(npts):\n", - " k2 = (k + 1) % npts\n", - " x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n", - " x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n", - " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", - " if n_seg <= 1:\n", - " z_line[y0, x0] = max(z_line[y0, x0], z0)\n", - " line_mask[y0, x0] = True\n", - " continue\n", - " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", - " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", - " zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n", - " if xs.size > 1:\n", - " keep = np.ones(xs.shape[0], dtype=bool)\n", - " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", - " xs, ys, zs = xs[keep], ys[keep], zs[keep]\n", - " z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n", - " line_mask[ys, xs] = True\n", - "\n", - " # ── DNA cap dilation ─────────────────────────────────────────────────────\n", - "\n", - " r_eff = max(0.5 * float(dna_diameter_nm), 0.2)\n", - " fp_dna, cap = dna_shape(r_eff, p_eff, max_radius_px=max_radius_px)\n", - " surface = grey_dilation(z_line, footprint=fp_dna,\n", - " structure=cap).astype(np.float32)\n", - " dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna)>0\n", - " surface[~dna_region] = 0.0\n", - "\n", - " # ── Tip convolution ──────────────────────────────────────────────────────\n", - " r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n", - " if r_tip_px >= 0.5:\n", - " fp_tip, tip_struct = AFM_tip(\n", - " float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n", - " surface = grey_dilation(surface, footprint=fp_tip,\n", - " structure=tip_struct).astype(np.float32)\n", - "\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - " if final_blur_sigma_px > 0:\n", - " fbsp=float(final_blur_sigma_px)\n", - " surface = gaussian_filter(surface,\n", - " sigma=fbsp).astype(np.float32)\n", - "\n", - " # ── Edge taper + center ridge ────────────────────────────────────────────\n", - " if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n", - " dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n", - " dist_nm = dist_px * np.float32(p_eff)\n", - " if apply_edge_taper:\n", - " sig = max(float(taper_sigma_nm), 1e-6)\n", - " factor = taper_floor + (1.0 - taper_floor) * np.exp(\n", - " -(dist_nm**2) / (2.0 * sig**2))\n", - " surface[dna_region] *= factor[dna_region]\n", - " if add_center_ridge and ridge_amp_nm > 0:\n", - " rs = max(float(ridge_sigma_nm), 1e-6)\n", - " ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n", - " surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - "\n", - " # ── Synthetic grain ──────────────────────────────────────────────────────\n", - " if grain_nm and grain_nm > 0:\n", - " rng = np.random.default_rng(int(grain_seed))\n", - " n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n", - " if grain_sigma_px and grain_sigma_px > 0:\n", - " n = gaussian_filter(n,\n", - " sigma=float(grain_sigma_px)).astype(np.float32)\n", - " v=n[dna_region].std()\n", - " std = float(v) if np.any(dna_region) else float(n.std())\n", - " if std > 1e-9:\n", - " n /= np.float32(std)\n", - " surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - "\n", - " if return_masks:\n", - " debug = {\n", - " \"line_mask\": line_mask,\n", - " \"dna_region\": dna_region,\n", - " \"extent\": (xmin, xmax, ymin, ymax),\n", - " \"p_nm_per_px\": float(p_eff),\n", - " }\n", - " if return_crossing_info:\n", - " debug[\"crossings\"] = crossing_info\n", - " return surface, debug\n", - "\n", - " return surface, (xmin, xmax, ymin, ymax)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_06", - "metadata": { - "id": "cell_md_06" - }, - "source": [ - "## 7. Noise Functions\n", - "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", - "\n", - "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", - "2. If no blank `.spm` file is supplied, the notebook falls back to a PSD-based synthetic noise model.\n", - "\n", - "Note: Topostats is an AFM imaging software package developed at the University of Sheffield which is used for filtering of AFM data. We desribe functions that perform operations similar to Topostats to clean the noise data from the blank files before adding it to the AFM_img.\n", - "\n", - "### Mathematics of PSD-Matched Noise Synthesis\n", - "\n", - "All three methods in the code utilize **Frequency-Domain Synthesis**. The core principle is that the height field $z(x, y)$ can be generated from a target Power Spectral Density (PSD) using the inverse Fourier Transform.\n", - "\n", - "#### 1. General Synthesis Workflow\n", - "For an output image of shape $(N_y, N_x)$:\n", - "1. **Define a PSD**: Let $S(f_x, f_y)$ be the target power spectrum.\n", - "2. **Generate Complex Spectrum**: A complex field $Z(f_x, f_y)$ is created in the frequency domain:\n", - " $$Z(f_x, f_y) = \\sqrt{S(f_x, f_y)} \\cdot e^{i \\phi(f_x, f_y)}$$\n", - " where $\\phi$ is a random phase drawn from a uniform distribution $U(0, 2\\pi)$.\n", - "3. **Inverse Transform**: The spatial noise $z(x, y)$ is the real part of the Inverse Fast Fourier Transform (IFFT) of $Z$.\n", - "4. **RMS Scaling**: The image is normalized such that its standard deviation matches the target Root Mean Square (RMS) roughness." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "cell_code_06", - "metadata": { - "id": "cell_code_06" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Parsing the .spm file for height data\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def robust_range(a:np.ndarray | list) -> tuple[float, float, float]:\n", - " \"\"\"Calculate the 1st and 99th percentiles of finite values in an array.\n", - "\n", - " This function provides a robust measure of the data range by ignoring\n", - " extreme outliers (the top and bottom 1%) as well as non-finite values\n", - " (NaNs and infinities).\n", - "\n", - " Parameters\n", - " ----------\n", - " a : np.ndarray | list\n", - " The input data array to be evaluated.\n", - "\n", - " Returns\n", - " -------\n", - " tuple[float, float, float]\n", - " A tuple containing:\n", - " - percentile_1: The 1st percentile of the finite data.\n", - " - percentile_99: The 99th percentile of the finite data.\n", - " - robust_spread: The difference between the 99th and 1st percentiles.\n", - "\n", - " Examples\n", - " --------\n", - " >>> a,b,c = robust_range(img)\n", - " >>> print(a,b,c)\n", - " 262 1023 761\n", - "\n", - " \"\"\"\n", - "\n", - " a = np.asarray(a, dtype=np.float32)\n", - " p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n", - " return float(p1), float(p99), float(p99 - p1)\n", - "\n", - "\n", - "def looks_like_afm_height(img: np.ndarray) -> bool:\n", - " \"\"\"Determine if an image array represents a valid AFM height map.\n", - "\n", - " This function checks if the dynamic range of the image (excluding\n", - " extreme outliers) is finite, strictly positive, and within a physically\n", - " sane upper bound.\n", - "\n", - " Parameters\n", - " ----------\n", - " img : np.ndarray\n", - " The 2D array representing the simulated or real AFM height map.\n", - "\n", - " Returns\n", - " -------\n", - " bool\n", - " True if the image has a valid, finite, and positive dynamic range;\n", - " False otherwise (e.g., if the array is flat, infinite, or all NaNs).\n", - "\n", - " Examples\n", - " --------\n", - " >>> Flag = looks_like_afm_height(img)\n", - " >>> print(Flag)\n", - " True\n", - "\n", - " \"\"\"\n", - "\n", - " p1, p99, rng = robust_range(img)\n", - " return np.isfinite(rng) and 0 < rng <= 1e9\n", - "\n", - "\n", - "def maybe_to_nm(img: np.ndarray) -> tuple[np.ndarray, str]:\n", - " \"\"\"Auto-convert from metres to nm if values look metre-scale.\n", - "\n", - " This function is used to convert the height map from metres to nm if\n", - " the measured height is in metres.\n", - "\n", - " Parameters\n", - " ----------\n", - " img : np.ndarray\n", - " The 2D array representing the AFM height map.\n", - "\n", - " Returns\n", - " -------\n", - " A tuple consisting of:\n", - " - np.ndarray\n", - " The image after the converion of the height map if needed.\n", - " - str\n", - " either as is or meters to nanometers depending on passed values.\n", - "\n", - " Examples\n", - " --------\n", - " >>> img = maybe_to_nm(img)\n", - " >>> print(img.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " p1, p99, rng = robust_range(img)\n", - " mx = max(abs(p1), abs(p99))\n", - " if mx < 1e-3:\n", - " return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n", - " return img.astype(np.float32), \"as-is (nm or counts)\"\n", - "\n", - "\n", - "def parse_blocks(\n", - " filename: str | Path,\n", - " max_blocks: int = 128\n", - ") -> tuple[bytes, str, list[dict]]:\n", - " \"\"\"Read a .spm file and parse binary data-block metadata.\n", - "\n", - " This function extracts the ASCII header from a Scanning Probe\n", - " Microscopy (SPM) file, locates the metadata blocks for the image data,\n", - " and parses the structural parameters required to read the raw binary data.\n", - "\n", - " Parameters\n", - " ----------\n", - " filename: str | Path\n", - " The path to the .spm file to be parsed.\n", - " max_blocks: int\n", - " The maximum number of blocks to parse.\n", - "\n", - " Returns\n", - " -------\n", - " tuple[bytes, str, list[dict]]\n", - " A tuple containing:\n", - " - raw: The raw binary data from the .spm file.\n", - " - header: The ASCII header from the .spm file.\n", - " - uniq: A list of dictionaries containing the parsed block metadata.\n", - "\n", - " Raises\n", - " ------\n", - " RuntimeError\n", - " If no 'Data offset' strings are found in the header.\n", - "\n", - " Examples\n", - " --------\n", - " >>> raw, header, blocks = parse_blocks(filename)\n", - " >>> print(type(raw), type(header), type(blocks))\n", - " \n", - "\n", - " \"\"\"\n", - "\n", - " raw = open(filename, \"rb\").read()\n", - " i_end = raw.find(b\"\\x1a\")\n", - " if i_end == -1:\n", - " i_end = min(len(raw), 400_000)\n", - " header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n", - "\n", - " matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n", - " if not matches:\n", - " raise RuntimeError(\"No 'Data offset' found in header.\")\n", - "\n", - " blocks = []\n", - " for k, pos in enumerate(matches[:max_blocks]):\n", - " win = header[max(0, pos-2500): min(len(header), pos+2500)]\n", - "\n", - " def grab_int(key):\n", - " m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n", - " return int(m.group(1)) if m else None\n", - "\n", - " def grab_float(key):\n", - " pattern = (\n", - " rf\"{re.escape(key)}\\s*:\\s*\"\n", - " r\"([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\"\n", - " )\n", - " m = re.search(pattern, win)\n", - " return float(m.group(1)) if m else None\n", - "\n", - " name_candidate = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n", - " name = name_candidate[-1].strip() if name_candidate else None\n", - "\n", - " off = grab_int(\"Data offset\")\n", - " length = grab_int(\"Data length\")\n", - " bpp = grab_int(\"Bytes/pixel\")\n", - " nx_b = grab_int(\"Samps/line\")\n", - " ny_b = grab_int(\"Number of lines\")\n", - " zscale = grab_float(\"Z scale\")\n", - " zoff = grab_float(\"Z offset\")\n", - "\n", - " if None in (off, length, bpp, nx_b, ny_b):\n", - " continue\n", - " blocks.append(dict(name=name or f\"block_{k}\",\n", - " off=off, length=length, bpp=bpp,\n", - " nx=nx_b, ny=ny_b,\n", - " zscale=zscale, zoff=zoff))\n", - "\n", - " uniq, seen = [], set()\n", - " for b in blocks:\n", - " if b[\"off\"] not in seen:\n", - " uniq.append(b); seen.add(b[\"off\"])\n", - " if not uniq:\n", - " raise RuntimeError(\"Found 'Data offset' strings\"\n", - " \" but no parseable blocks.\")\n", - " return raw, header, uniq\n", - "\n", - "\n", - "def read_block_candidates(\n", - " raw: bytes,\n", - " b: dict\n", - ") -> list[tuple[str, np.ndarray]]:\n", - " \"\"\"Decode a binary data block using all plausible NumPy data types.\n", - "\n", - " This function attempts to parse the\n", - " binary blob using all logical dtypes for the given bytes-per-pixel and\n", - " applies physical Z-scaling to the results.\n", - "\n", - " Parameters\n", - " ----------\n", - " raw : bytes\n", - " The raw binary data from the .spm file.\n", - " b : dict\n", - " The parsed block metadata.\n", - "\n", - " Returns\n", - " -------\n", - " list[tuple[str, np.ndarray]]\n", - " A list of candidate tuples. Each tuple contains:\n", - " - dtype_str: The string representation of the data type.\n", - " - image_array: The decoded image array.\n", - "\n", - " Raises\n", - " ------\n", - " Runtime error\n", - " - Truncated block.\n", - "\n", - " Examples\n", - " --------\n", - " >>> candidates = read_block_candidates(raw, b)\n", - " >>> print(type(candidates))\n", - " \n", - "\n", - " \"\"\"\n", - "\n", - " off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n", - " nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n", - " blob = raw[off:off+length]\n", - " if len(blob) < length:\n", - " raise RuntimeError(\"Truncated block.\")\n", - "\n", - " n = nx_b * ny_b\n", - " cands = []\n", - " dtype_map = {2: [\"i2\", \"u2\"],\n", - " 4: [\"i4\", \"f4\"],\n", - " 1: [\"u1\"]}\n", - " for dt in dtype_map.get(bpp, []):\n", - " arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n", - " if arr.size != n:\n", - " continue\n", - " cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n", - "\n", - " out = []\n", - " for dt, img in cands:\n", - " z = img\n", - " if b.get(\"zscale\") is not None:\n", - " z = z * np.float32(b[\"zscale\"])\n", - " if b.get(\"zoff\") is not None:\n", - " z = z + np.float32(b[\"zoff\"])\n", - " out.append((dt, z))\n", - " return out\n", - "\n", - "\n", - "def choose_best_height_image(\n", - " raw: bytes,\n", - " blocks: list[dict],\n", - " verbose: bool = True\n", - ")-> tuple[dict, np.ndarray]:\n", - " \"\"\"Evaluate all data blocks and dtypes\n", - " to find the most plausible AFM height map.\n", - "\n", - " This function iterate all blocks and dtype candidates\n", - " and then picks the one that:\n", - " - passes looks_like_afm_height\n", - " - has the highest log-variance score\n", - " (which is done to favor candidates with more topographical detail)\n", - "\n", - " Parameters\n", - " ----------\n", - " raw : bytes\n", - " The raw binary data from the .spm file.\n", - " blocks : list[dict]\n", - " The parsed block metadata.\n", - " verbose : bool\n", - " Whether to print diagnostics.\n", - "\n", - " Returns\n", - " -------\n", - " tuple[dict, np.ndarray]\n", - "\n", - " Raises\n", - " ------\n", - " RuntimeError\n", - " If no suitable block is found.\n", - "\n", - " Examples\n", - " --------\n", - " >>> b, img = choose_best_height_image(raw, blocks)\n", - " >>> print(type(b), type(img))\n", - " \n", - "\n", - " \"\"\"\n", - "\n", - " best, best_score = None, -np.inf\n", - " for b in blocks:\n", - " try:\n", - " for dt, img in read_block_candidates(raw, b):\n", - " if not looks_like_afm_height(img):\n", - " continue\n", - " p1, p99, rng = robust_range(img)\n", - " var = float(np.var(img))\n", - " score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n", - " if score > best_score:\n", - " best_score = score\n", - " best = (b, dt, img, (p1, p99, rng, var))\n", - " except Exception:\n", - " continue\n", - "\n", - " if best is None:\n", - " raise RuntimeError(\"Could not find a\"\n", - " \"plausible AFM height image decode.\")\n", - " b, dt, img, stats = best\n", - " if verbose:\n", - " p1, p99, rng, var = stats\n", - " print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n", - " f\"bpp={b['bpp']} decode={dt}\")\n", - " print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, \"\n", - " f\"range={rng:.3g}, var={var:.3g}\")\n", - " return b, img\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# TopoStats-like background / noise extraction\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def plane_remove(\n", - " z: np.ndarray,\n", - " mask: np.ndarray | None = None,\n", - ")-> np.ndarray:\n", - " \"\"\"Fit and subtract a first-order tilted plane from a 2D height map.\n", - "\n", - " This function performs background leveling by fitting a plane defined\n", - " by the equation z = ax + by + c to the image. If a mask is provided,\n", - " the fit is calculated only using the pixels where the mask is True\n", - " (typically representing the flat substrate).\n", - "\n", - " Parameters\n", - " ----------\n", - " z : np.ndarray\n", - " The 2D height map.\n", - " mask : np.ndarray | None, optional\n", - " A boolean mask of the same shape as z. If provided, the fit is\n", - " calculated only using the pixels where the mask is True.\n", - " This is useful for excluding features like DNA from the plane fit.\n", - " Defaults to None.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " The flattened height map.\n", - "\n", - " Examples\n", - " --------\n", - " >>> z_flat = plane_remove(z)\n", - " >>> print(z_flat.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " ny, nx = z.shape\n", - " Y, X = np.mgrid[0:ny, 0:nx]\n", - " A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n", - " b = z.ravel()\n", - " if mask is not None:\n", - " m = mask.ravel().astype(bool)\n", - " A, b = A[m], b[m]\n", - " C, *_ = np.linalg.lstsq(A, b, rcond=None)\n", - " plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n", - " return (z - plane).astype(np.float32)\n", - "\n", - "\n", - "def line_flatten_median(\n", - " z: np.ndarray,\n", - " mask: np.ndarray | None = None\n", - ")-> np.ndarray:\n", - " \"\"\"Subtract per-row median (background pixels only if mask given).\n", - "\n", - " AFM images often exhibit horizontal stripes due to low-frequency noise or\n", - " z-axis drift during scanning. This function levels each scan line\n", - " independently by shifting its median value to zero.\n", - "\n", - " Parameters\n", - " ----------\n", - " z: np.ndarray\n", - " The 2D height map\n", - " mask: np.ndarray | None, optional\n", - " A boolean mask of the same shape as z. If provided,\n", - " the median is calculated only using the pixels where the mask is True.\n", - " This prevents features (like DNA) from biasing the\n", - " flattening of the background. Defaults to None.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " The flattened height map.\n", - "\n", - " Examples\n", - " --------\n", - " >>> z_flat = line_flatten_median(z)\n", - " >>> print(z_flat.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " out = z.astype(np.float32, copy=True)\n", - " for i in range(out.shape[0]):\n", - " if mask is None or not np.any(mask[i]):\n", - " med = np.median(out[i])\n", - " else:\n", - " med = np.median(out[i, mask[i]])\n", - " out[i] -= np.float32(med)\n", - " return out\n", - "\n", - "\n", - "def feature_mask(\n", - " z: np.ndarray,\n", - " smooth_sigma_px: float = 3.0,\n", - " thresh_sigma: float = 3.0,\n", - " dilate_px: int = 4\n", - ")-> np.ndarray:\n", - " \"\"\"Detects topographical features as pixels deviating by more than\n", - " thresh_sigma MADs from the smooth-residual background.\n", - "\n", - " This function identifies pixels that deviate significantly from a smoothed\n", - " local background. It uses the Median Absolute Deviation (MAD) to estimate\n", - " the noise floor, making it robust against the features it is trying to\n", - " detect.\n", - "\n", - " Parameters\n", - " ----------\n", - " z : np.ndarray\n", - " The 2D height map.\n", - " smooth_sigma_px : float, optional\n", - " The smoothing width in pixels. Defaults to 3.0.\n", - " thresh_sigma : float, optional\n", - " The number of standard deviations above the median to consider a pixel\n", - " as a feature. Defaults to 3.0.\n", - " dilate_px : int, optional\n", - " The radius of curcular footprint to dilate the feature mask.\n", - " Defaults to 4.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " A boolean mask where true indicates a detected feature\n", - "\n", - " Examples\n", - " --------\n", - " >>> feat = feature_mask(z)\n", - " >>> print(feat.shape, feat.dtype)\n", - " (800, 800) bool\n", - "\n", - " \"\"\"\n", - "\n", - " z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", - " resid = (z - z_s).astype(np.float32)\n", - " med = float(np.median(resid))\n", - " mad = float(np.median(np.abs(resid - med)))\n", - " sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n", - " feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n", - " if dilate_px and dilate_px > 0:\n", - " r = int(dilate_px)\n", - " yy, xx = np.ogrid[-r:r+1, -r:r+1]\n", - " fp = (xx*xx + yy*yy) <= r*r\n", - " feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n", - " return feat\n", - "\n", - "\n", - "def inpaint_simple(\n", - " z: np.ndarray,\n", - " mask: np.ndarray,\n", - " smooth_sigma_px: float = 8.0,\n", - ") -> np.ndarray:\n", - " \"\"\"Replace masked regions with a smooth estimate of the local background.\n", - "\n", - " This function calculates a heavily blurred version of the\n", - " original image to serve as a background proxy and patches the masked\n", - " areas with this estimate.\n", - "\n", - " Parameters\n", - " ----------\n", - " z : np.ndarray\n", - " The 2D height map.\n", - " mask : np.ndarray\n", - " A boolean mask where True indicates pixels to be inpainted (features)\n", - " smooth_sigma_px : float, optional\n", - " The sigma value for the gaussian filter.\n", - " Higher value resutls in smoother inpainted images. Defaults to 8.0.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " The inpainted height\n", - "\n", - " Examples\n", - " --------\n", - " >>> z_bg = inpaint_simple(z, feat)\n", - " >>> print(z_bg.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", - " out = z.copy()\n", - " out[mask] = bg[mask]\n", - " return out\n", - "\n", - "\n", - "def bandpass_noise(\n", - " z: np.ndarray,\n", - " low_sigma_px: float = 1.0,\n", - " high_sigma_px: float = 30.0,\n", - ") -> np.ndarray:\n", - " \"\"\"Performs bandpass filtering on the noise image by\n", - " isolating mid-frequency noise using difference of guassians filter.\n", - "\n", - " This function performs a bandpass filter by calculating the difference\n", - " between two Gaussian-blurred versions of the same image. It effectively\n", - " removes:\n", - " 1. High-frequency noise: Suppressed by the `low_sigma` blur.\n", - " 2. Low-frequency trends: (like tilt or bowing) Removed by subtracting\n", - " the `high_sigma` blur.\n", - "\n", - " Parameters\n", - " ----------\n", - " z : np.ndarray\n", - " The 2D height\n", - " low_sigma_px : float, optional\n", - " The sigma value for gaussian filter defining the high frequency cutoff.\n", - " Defaults to 1.0.\n", - " high_sigma_px : float, optional\n", - " The sigma value for gaussian filter defining the low frequency cutoff.\n", - " Defaults to 30.0.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " The bandpassed height map.\n", - "\n", - " Examples\n", - " --------\n", - " >>> tex = bandpass_noise(z)\n", - " >>> print(tex.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n", - " high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n", - " return (low - high).astype(np.float32)\n", - "\n", - "\n", - "def extract_topostats_like_noise(\n", - " z_in: np.ndarray,\n", - " median_size: int = 3,\n", - " bg_quantile: float = 40.0,\n", - " feature_sigma_px: float = 3.0,\n", - " feature_thresh_sigma: float = 3.0,\n", - " feature_dilate_px: int = 4,\n", - " inpaint_sigma_px: float = 10.0,\n", - " band_low_sigma_px: float = 0.8,\n", - " band_high_sigma_px: float = 35.0,\n", - " target_rms_nm: float | None = None,\n", - " apply_plane_remove: bool = True,\n", - " apply_line_flatten: bool = True,\n", - " apply_bandpass: bool = True,\n", - ") -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:\n", - " \"\"\"Execute the full noise-extraction pipeline on an AFM height map.\n", - "\n", - " This function isolates the pure substrate noise texture by leveling\n", - " the image (plane flatten and line flatten),\n", - " masking out topographical features (detect features), inpainting those\n", - " regions, and applying a bandpass filter.\n", - " The resulting image can be rescaled to a target RMS value.\n", - " The resulting texture can be used to augment synthetic datasets.\n", - "\n", - " Parameters\n", - " ----------\n", - " z_in : np.ndarray\n", - " The 2D height map.\n", - " median_size : int, optional\n", - " The size of the median filter to apply. Defaults to 3.\n", - " bg_quantile : float, optional\n", - " The quantile to used to generate a rough initial background guess\n", - " for the preliminary plane and line leveling. Defaults to 40.0.\n", - " feature_sigma_px : float, optional\n", - " The gaussian sigma for background estimation for the feature mask.\n", - " Defaults to 3.0.\n", - " feature_thresh_sigma : float, optional\n", - " The number of standard deviations above the median\n", - " to consider a pixel as a feature. Defaults to 3.0.\n", - " feature_dilate_px : int, optional\n", - " The radius of curcular footprint to dilate the feature mask.\n", - " Defaults to 4.\n", - " inpaint_sigma_px : float, optional\n", - " The sigma value for the gaussian filter.\n", - " Higher value resutls in smoother inpainted image. Defaults to 10.0.\n", - " band_low_sigma_px : float, optional\n", - " The sigma value for gaussian filter defining the high frequency cutoff.\n", - " Defaults to 0.8.\n", - " band_high_sigma_px : float, optional\n", - " The sigma value for gaussian filter defining the low frequency cutoff.\n", - " Defaults to 35.0.\n", - " target_rms_nm: float | None, optional\n", - " If provided, rescales the extracted noise texture\n", - " to match this target RMS roughness. Defaults to None.\n", - " apply_plane_remove : bool, optional\n", - " Whether to apply the plane remove step. Defaults to True.\n", - " apply_line_flatten : bool, optional\n", - " Whether to apply the line flatten step. Defaults to True.\n", - " apply_bandpass : bool, optional\n", - " Whether to apply the bandpass step. Defaults to True.\n", - "\n", - " Returns\n", - " -------\n", - " tuple[float, np.ndarray, np.ndarray, np.ndarray]\n", - " A tuple containing:\n", - " - rms_nm: The RMS roughness of the extracted noise texture.\n", - " - noise_texture: The extracted, zero mean noise texture.\n", - " - z_flat: The flattened height map.\n", - " - feature_mask: A boolean mask where true indicates a detected feature.\n", - "\n", - " Examples\n", - " --------\n", - " >>> rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(z)\n", - " >>> print(rms_nm)\n", - " 0.06\n", - " >>> print(noise_tex.shape)\n", - " (800, 800)\n", - " >>> print(z_flat.shape)\n", - " (800, 800)\n", - " >>> print(feat.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " z = z_in.astype(np.float32)\n", - "\n", - " if median_size and median_size > 1:\n", - " z = median_filter(z, size=int(median_size)).astype(np.float32)\n", - "\n", - " q = np.percentile(z, float(bg_quantile))\n", - " bg0 = z <= q\n", - "\n", - " z_flat = z.copy()\n", - "\n", - " if apply_plane_remove:\n", - " z_flat = plane_remove(z_flat, mask=bg0)\n", - "\n", - " if apply_line_flatten:\n", - " z_flat = line_flatten_median(z_flat, mask=bg0)\n", - "\n", - " feat = feature_mask(\n", - " z_flat,\n", - " smooth_sigma_px=feature_sigma_px,\n", - " thresh_sigma=feature_thresh_sigma,\n", - " dilate_px=feature_dilate_px,\n", - " )\n", - "\n", - " z_bg = inpaint_simple(\n", - " z_flat,\n", - " feat,\n", - " smooth_sigma_px=inpaint_sigma_px,\n", - " )\n", - "\n", - " if apply_bandpass:\n", - " tex = bandpass_noise(\n", - " z_bg,\n", - " low_sigma_px=band_low_sigma_px,\n", - " high_sigma_px=band_high_sigma_px,\n", - " )\n", - " else:\n", - " tex = z_bg.astype(np.float32)\n", - "\n", - " bg = ~feat\n", - " rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n", - "\n", - " if target_rms_nm is not None:\n", - " target = float(target_rms_nm)\n", - " if rms > 1e-12:\n", - " tex *= target / rms\n", - " rms = target\n", - "\n", - " return (\n", - " rms,\n", - " tex.astype(np.float32),\n", - " z_flat.astype(np.float32),\n", - " feat.astype(bool),\n", - " )\n", - "\n", - "\n", - "def tile_or_crop(\n", - " tex: np.ndarray,\n", - " out_shape: tuple[int, int],\n", - " seed: int | None = 0,\n", - ")-> np.ndarray:\n", - " \"\"\"Tile a texture array and extract a deterministic random crop.\n", - "\n", - " This function ensures that an extracted background noise texture can be\n", - " resized to fit any target simulated image size. If the texture is smaller\n", - " than the target, it is tiled (repeated) to cover the area. A random crop\n", - " is then taken to prevent obvious repeating patterns at the origin.\n", - "\n", - " Parameters\n", - " ----------\n", - " tex : np.ndarray\n", - " The 2D height map\n", - " out_shape : tuple[int, int]\n", - " The target output shape.\n", - " seed : int | None, optional\n", - " The random seed. Defaults to 0.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " The resized and cropped noise texture.\n", - "\n", - " Examples\n", - " --------\n", - " >>> tex = tile_or_crop(tex, (ny, nx))\n", - " >>> print(tex.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " ty, tx = tex.shape\n", - " oy, ox = out_shape\n", - " if (ty, tx) == (oy, ox):\n", - " return tex\n", - " tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n", - " rng = np.random.default_rng(seed)\n", - " y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n", - " x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n", - " return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n", - "\n", - "\n", - "def load_blank_spm_noise(\n", - " blank_path: str | Path,\n", - " target_rms_nm: float = 0.06,\n", - " verbose: bool = True,\n", - " apply_plane_remove: bool = USE_PLANE_REMOVE,\n", - " apply_line_flatten: bool = USE_LINE_FLATTEN,\n", - " apply_bandpass: bool = USE_BANDPASS_FILTER,\n", - ") -> tuple[float, np.ndarray, dict]:\n", - " \"\"\"Load an SPM file and extract a calibrated background noise texture.\n", - "\n", - " This high-level function handles the full lifecycle of noise extraction:\n", - " 1. Parsing the Bruker .spm binary and metadata.\n", - " 2. Heuristically selecting the best height channel.\n", - " 3. Calibrating raw digital units to physical nanometers.\n", - " 4. Leveling, masking, and bandpass-filtering the substrate noise.\n", - "\n", - " Parameters\n", - " ----------\n", - " blank_path : str | Path\n", - " The path to the blank .spm file.\n", - " target_rms_nm : float, optional\n", - " The target RMS roughness of the extracted noise texture.\n", - " Defaults to 0.06.\n", - " verbose : bool, optional\n", - " Whether to print progress messages and diagnostic statistics.\n", - " Defaults to True.\n", - "\n", - " Returns\n", - " -------\n", - " tuple[float, np.ndarray, dict]\n", - " A tuple containing:\n", - " - rms_nm: The RMS roughness of the extracted noise texture.\n", - " - noise_texture: The extracted, zero mean noise texture.\n", - " - diagnostics_dict: A dictionary containing diagnostic information.\n", - "\n", - " Examples\n", - " --------\n", - " >>> rms_nm, noise_tex, diag = load_blank_spm_noise(blank_path)\n", - " >>> print(rms_nm)\n", - " 0.06\n", - " >>> print(noise_tex.shape)\n", - " (800, 800)\n", - " >>> print(diag['flattened'].shape)\n", - " (800, 800)\n", - " >>> print(diag['feature_mask'].shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " raw, header, blocks = parse_blocks(blank_path)\n", - " b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n", - " img_nm, unit_note = maybe_to_nm(img0)\n", - " if verbose:\n", - " print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n", - "\n", - " rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n", - " img_nm,\n", - " median_size=3,\n", - " bg_quantile=45,\n", - " feature_sigma_px=3.0,\n", - " feature_thresh_sigma=3.0,\n", - " feature_dilate_px=6,\n", - " inpaint_sigma_px=10.0,\n", - " band_low_sigma_px=0.7,\n", - " band_high_sigma_px=40.0,\n", - " target_rms_nm=target_rms_nm,\n", - " )\n", - " diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n", - " \"height_nm\": img_nm, \"unit_note\": unit_note}\n", - " if verbose:\n", - " print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n", - " f\"tex shape: {noise_tex.shape}\")\n", - " return rms_nm, noise_tex, diag\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# PSD-based fallback noise synthesis\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "TARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\n", - "#Safety buffer to ensure no division by 0 or square roots producing NaN values\n", - "EPS = 1e-12\n", - "\n", - "\n", - "def load_model(\n", - " path: str | Path = MODEL_PATH\n", - " ) -> dict[str, np.ndarray]:\n", - " \"\"\"\n", - " Load the Power Spectral Density (PSD) noise model\n", - " from a compressed archive.\n", - "\n", - " The model typically contains the statistical distribution of substrate\n", - " roughness in the frequency domain, allowing for the generation of\n", - " synthetic noise that matches the 'color' and texture of the substrate.\n", - "\n", - " Parameters\n", - " ----------\n", - " path : str | Path, optional\n", - " The path to the compressed archive containing the PSD model.\n", - "\n", - " Returns\n", - " -------\n", - " dict[str, np.ndarray]\n", - " A dictionary containing the PSD model components namely:\n", - " - \"log_psd_mean\": The mean of the log-transformed radial PSD.\n", - " - \"log_psd_std\": The standard deviation of the\n", - " log-transformed radial PSD.\n", - " - \"radial_freq\": The spatial frequency coordinates.\n", - " - \"rms_values_nm\": RMS values associated with the training set.\n", - "\n", - " Examples\n", - " --------\n", - " >>> model = load_model()\n", - " >>> print(type(model))\n", - " \n", - "\n", - " \"\"\"\n", - "\n", - " data = np.load(path)\n", - " return {\n", - " \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n", - " \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n", - " \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n", - " \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n", - " }\n", - "\n", - "\n", - "def generate_noise(\n", - " seed: int,\n", - " target_shape: tuple[int, int] = TARGET_SHAPE,\n", - " target_rms_nm: float | None = TARGET_NOISE_RMS_NM,\n", - " method: str = \"mean_full2d\",\n", - " std_scale: float = 1.0,\n", - " model: dict | None = None,\n", - " model_path: str | Path | None = MODEL_PATH,\n", - ") -> np.ndarray:\n", - " \"\"\"\n", - " Generate a single random AFM-like background noise image\n", - " from the PSD model.\n", - "\n", - " This function performs Fourier synthesis to create textures that match\n", - " the spatial frequency characteristics of the PSD model provided.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed : int\n", - " RNG seed for reproducibility.\n", - " target_shape : tuple[int, int]\n", - " Output image shape (rows, cols).\n", - " The PSD is resized automatically if it differs\n", - " from the shape used when building the model. Defaults to TARGET_SHAPE.\n", - " target_rms_nm : float | None\n", - " RMS amplitude in nm.\n", - " If None a value is randomly taken from the blank-file ensemble.\n", - " Defaults to TARGET_NOISE_RMS_NM.\n", - " method : str\n", - " 'mean_full2d' -- mean log-PSD;\n", - " variance comes only from random phase (recommended).\n", - " 'lognormal_full2d' -- Samples a new PSD\n", - " from the log-normal distribution. (more sample to sample variation)\n", - " 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n", - " std_scale : float\n", - " Multiplier for the PSD on log_psd_std\n", - " for lognormal method (>1 -> more variation).\n", - " model : dict | None\n", - " Pre-loaded model dict. If None, loaded from model_path on each call.\n", - " model_path : str | Path | None\n", - " Path to the .npz file (used only when model is None).\n", - "\n", - " Returns\n", - " -------\n", - " z: np.ndarray\n", - "\n", - " Raises\n", - " ------\n", - " ValueError\n", - " If an unknown method is provided.\n", - "\n", - " Examples\n", - " --------\n", - " >>> z = generate_noise(seed=42)\n", - " >>> print(z.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " if model is None:\n", - " model = load_model(model_path)\n", - "\n", - " rng = np.random.default_rng(int(seed))\n", - " out_shape = tuple(target_shape)\n", - "\n", - " if method == \"mean_full2d\":\n", - " psd_sample = np.exp(model[\"log_psd_mean\"])\n", - "\n", - " elif method == \"lognormal_full2d\":\n", - " mu= model[\"log_psd_mean\"]\n", - " noise = rng.standard_normal(mu.shape).astype(np.float32)\n", - " psd_sample = np.exp(mu + std_scale * mu * noise)\n", - "\n", - " elif method == \"empirical_radial\":\n", - " mu = model[\"log_psd_mean\"]\n", - " radial_psd = np.exp(mu.mean(axis=1))\n", - " radial_freq_for_interp = np.abs(np.fft.fftfreq(mu.shape[0], d=1.0))\n", - "\n", - " fy = np.fft.fftfreq(out_shape[0], d=1.0)\n", - " fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n", - " fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n", - " kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n", - "\n", - " psd_sample = np.interp(\n", - " kr.ravel(),\n", - " radial_freq_for_interp,\n", - " radial_psd,\n", - " left=float(radial_psd[0]),\n", - " right=float(radial_psd[-1]),\n", - " ).reshape(kr.shape).astype(np.float32)\n", - "\n", - " else:\n", - " raise ValueError(\n", - " f\"Unknown method {method!r}. \"\n", - " \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n", - " )\n", - "\n", - " psd_ny, psd_nx = psd_sample.shape\n", - " out_ny, out_nx = out_shape\n", - " rfft_nx = out_nx // 2 + 1\n", - "\n", - " if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n", - " from scipy.ndimage import zoom\n", - " psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx),\n", - " order=1)\n", - "\n", - " psd_sample = psd_sample.astype(np.float32)\n", - "\n", - " phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n", - " spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n", - "\n", - " z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n", - " z -= np.float32(np.mean(z))\n", - " rmsv = model[\"rms_values_nm\"]\n", - " trmsf = float(target_rms_nm)\n", - " target = float(rng.choice(rmsv)) if target_rms_nm is None else trmsf\n", - " cur = float(np.std(z))\n", - " if cur > EPS:\n", - " z *= target / cur\n", - "\n", - " return z\n", - "\n", - "def random_transform_noise_texture(\n", - " tex: np.ndarray,\n", - " seed: int\n", - ") -> np.ndarray:\n", - " \"\"\"Apply a deterministic random affine transform to a noise texture.\n", - "\n", - " This function generates a reproducible random transformation of an input\n", - " texture using the provided seed. The transformation includes rotation,\n", - " anisotropic scaling, translation, optional vertical and horizontal flips,\n", - " and a final amplitude jitter.\n", - "\n", - " It is intended to produce visually distinct noise realizations from a\n", - " single acquired blank SPM texture, while preserving the overall texture\n", - " statistics.\n", - "\n", - " Parameters\n", - " ----------\n", - " tex: np.ndarray\n", - " Input noise texture of shape (H, W).\n", - " seed: int\n", - " Random seed controlling the random transformation.\n", - "\n", - " Returns\n", - " -------\n", - " out: np.ndarray\n", - " Transformed noise texture of shape (H, W).\n", - "\n", - " Examples\n", - " --------\n", - " >>> transformed = random_transform_noise_texture(tex, seed=42)\n", - " transformed\n", - " >>> transformed.shape == tex.shape\n", - " True\n", - "\n", - " \"\"\"\n", - "\n", - " rng = np.random.default_rng(int(seed))\n", - " tex = np.asarray(tex, dtype=np.float32)\n", - "\n", - " theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n", - " base_scale = float(rng.uniform(0.85, 1.15))\n", - " anis = float(rng.uniform(0.85, 1.15))\n", - " sx, sy = base_scale * anis, base_scale / anis\n", - "\n", - " c, s = float(np.cos(theta)), float(np.sin(theta))\n", - " A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n", - " M = np.linalg.inv(A).astype(np.float32)\n", - "\n", - " h, w = tex.shape\n", - " center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n", - " t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n", - " float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n", - " offset = center - M @ (center + t)\n", - "\n", - " out = affine_transform(tex, matrix=M, offset=offset,\n", - " output_shape=tex.shape, order=1,\n", - " mode=\"wrap\", prefilter=False).astype(np.float32)\n", - "\n", - " if bool(rng.integers(0, 2)):\n", - " out = out[::-1, :]\n", - " if bool(rng.integers(0, 2)):\n", - " out = out[:, ::-1]\n", - " out *= float(rng.uniform(0.90, 1.10))\n", - " return out.astype(np.float32)\n", - "\n", - "\n", - "def sample_noise_image(\n", - " noise_source: str,\n", - " noise_asset: dict | np.ndarray | None,\n", - " seed: int,\n", - " out_shape: tuple[int, int],\n", - " target_rms_nm: float | None,\n", - " ) -> np.ndarray | None:\n", - " \"\"\"Return a per-sample noise image from either blank.spm or PSD fallback.\n", - "\n", - " Parameters\n", - " ----------\n", - " noise_source : str\n", - " 'blank_spm' or 'psd_model'\n", - " noise_asset : dict | np.ndarray | None\n", - " If 'blank_spm', this is the 2D texture array.\n", - " If 'psd_model', this is the dictionary containing PSD statistics.\n", - " seed : int\n", - " RNG seed for reproducibility.\n", - " out_shape : tuple[int, int]\n", - " The (rows, cols) of the desired height map.\n", - " target_rms_nm : float | None\n", - " If provided, rescales the extracted noise texture\n", - " to match this target RMS roughness.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray | None\n", - " The per-sample noise image or None if disabled.\n", - "\n", - " Examples\n", - " --------\n", - " >>> noise_tex = sample_noise_image(noise_source, noise_asset,\n", - " seed, out_shape, target_rms_nm)\n", - " >>> print(noise_tex.shape)\n", - " (800, 800)\n", - "\n", - " \"\"\"\n", - "\n", - " if noise_asset is None:\n", - " return None\n", - "\n", - " if noise_source == \"blank_spm\":\n", - " noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n", - " return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n", - "\n", - " if noise_source == \"psd_model\":\n", - " return generate_noise(\n", - " seed=seed,\n", - " target_shape=out_shape,\n", - " target_rms_nm=target_rms_nm,\n", - " method=PSD_NOISE_METHOD,\n", - " std_scale=PSD_NOISE_STD_SCALE,\n", - " model=noise_asset,\n", - " ).astype(np.float32)\n", - "\n", - " return None\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_07", - "metadata": { - "id": "cell_md_07" - }, - "source": [ - "## 8. DNA Ground-Truth Mask\n", - "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", - "\n", - "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", - "\n", - "We also dilate the mask by a value of **DNA_MASK_DILATE_PX** to ensure that the mask has enough width for optimal training." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "cell_code_07", - "metadata": { - "id": "cell_code_07" - }, - "outputs": [], - "source": [ - "def nm_to_px(\n", - " coords_xy_nm: np.ndarray,\n", - " extent: tuple[float, float, float, float],\n", - " nx: int,\n", - " ny: int\n", - ") -> tuple[np.ndarray, np.ndarray]:\n", - " \"\"\"Map (x, y) coordinates in nanometres to integer pixel indices (ix, iy).\n", - "\n", - " Linearly maps each coordinate from the physical extent to the discrete\n", - " pixel grid, then clips to valid index bounds.\n", - "\n", - " Parameters\n", - " ----------\n", - " coords_xy_nm : np.ndarray\n", - " The (x, y) coordinates in nanometres.\n", - " extent : tuple[float, float, float, float]\n", - " The physical extent (xmin, xmax, ymin, ymax) in nanometres.¨\n", - " nx : int\n", - " The number of pixels in the x-direction.\n", - " ny : int\n", - " The number of pixels in the y-direction.\n", - "\n", - " Returns\n", - " -------\n", - " ix, iy : tuple[np.ndarray, np.ndarray]\n", - " The integer pixel indices which are:\n", - " - ix: The x-coordinate indices.\n", - " - iy: The y-coordinate indices.\n", - "\n", - " Examples\n", - " --------\n", - " >>> ix, iy = nm_to_px(coords_xy_nm, extent, nx, ny)\n", - " >>> print(ix.dtype, iy.dtype)\n", - " int32 int32\n", - "\n", - " \"\"\"\n", - "\n", - " x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n", - " xmin, xmax, ymin, ymax = extent\n", - " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n", - " 0, nx - 1)\n", - " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n", - " 0, ny - 1)\n", - " return ix, iy\n", - "\n", - "\n", - "def rasterize_closed_polyline_mask(\n", - " coords: np.ndarray,\n", - " extent: tuple[float, float, float, float],\n", - " nx: int,\n", - " ny: int\n", - ") -> np.ndarray:\n", - " \"\"\"Rasterize a closed bead polyline into a 1-pixel-wide binary mask.\n", - "\n", - " Each consecutive bead pair (including the wrap-around from the last\n", - " bead back to the first) is drawn onto the grid using dense linear\n", - " interpolation along the segment. Duplicate pixels that would arise\n", - " at segment junctions are de-duplicated before writing.\n", - "\n", - " Parameters\n", - " ----------\n", - " coords : np.ndarray\n", - " Array of shape (N, 3) or (N, 2) containing bead positions in\n", - " nanometres. Only the first two columns (x, y) are used.\n", - " extent : tuple[float, float, float, float]\n", - " The physical bounding box (xmin, xmax, ymin, ymax) in nanometres.\n", - " nx : int\n", - " The number of pixels in the x-axis.\n", - " ny : int\n", - " The number of pixels in the y-axis.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " A binary mask of shape (ny, nx),\n", - " with True at every pixel where the polyline is present\n", - " and False everywhere else.\n", - "\n", - " Examples\n", - " --------\n", - " >>> mask = rasterize_closed_polyline_mask(coords, extent, nx=512, ny=512)\n", - " >>> print(mask.shape, mask.dtype)\n", - " (512, 512) bool\n", - "\n", - " \"\"\"\n", - "\n", - " coords = np.asarray(coords, dtype=np.float32)\n", - " ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n", - " out = np.zeros((ny, nx), dtype=bool)\n", - " npts = len(ix)\n", - " for k in range(npts):\n", - " k2 = (k + 1) % npts\n", - " x0, y0 = int(ix[k]), int(iy[k])\n", - " x1, y1 = int(ix[k2]), int(iy[k2])\n", - " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", - " if n_seg <= 1:\n", - " out[y0, x0] = True\n", - " continue\n", - " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", - " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", - " if xs.size > 1:\n", - " keep = np.ones(xs.shape[0], dtype=bool)\n", - " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", - " xs, ys = xs[keep], ys[keep]\n", - " out[ys, xs] = True\n", - " return out\n", - "\n", - "\n", - "def make_dna_mask_from_beads(\n", - " coords: np.ndarray,\n", - " extent: tuple[float, float, float, float],\n", - " nx: int,\n", - " ny: int,\n", - " dilate_px: int = 3\n", - " ) -> np.ndarray:\n", - " \"\"\"Build a binary DNA segmentation mask from bead coordinates.\n", - "\n", - "\n", - " Rasterizes the closed bead polyline into a 1-pixel-wide line mask\n", - " and optionally dilates it by a disk of radius ``dilate_px`` pixels\n", - " to produce a mask wide enough for robust segmentation training.\n", - "\n", - " Parameters\n", - " ----------\n", - " coords : np.ndarray\n", - " Array of shape (N, 3) or (N,2) containing bead positions in nanometres.\n", - " Only the first two columns (x, y) are used.\n", - " extent : tuple[float, float, float, float]\n", - " The physical bounding box (xmin, xmax, ymin, ymax) in nanometres.\n", - " nx : int\n", - " The number of pixels in the x-axis.\n", - " ny : int\n", - " The number of pixels in the y-axis.\n", - " dilate_px : int, optional\n", - " Radius in pixels of the disk structuring element used to dilate\n", - " the rasterized line. Set to 0 to skip dilation. Defaults to 3.\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " unit8 binary mask of shape (ny, nx)\n", - " with values 0 (background) or 1 (DNA).\n", - "\n", - " Examples\n", - " --------\n", - " >>> mask = make_dna_mask_from_beads(coords, extent, nx=512, ny=512,\n", - " dilate_px=DNA_MASK_DILATE_PX)\n", - " >>> print(mask.shape)\n", - " (512, 512)\n", - "\n", - " \"\"\"\n", - "\n", - " line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n", - " if dilate_px and dilate_px > 0:\n", - " fdp = float(dilate_px)\n", - " line = binary_dilation(line, structure=disk_footprint(fdp))\n", - " return line.astype(np.uint8)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_08", - "metadata": { - "id": "cell_md_08" - }, - "source": [ - "## 9. Crossing Detection & Crossing Mask\n", - "\n", - "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", - "\n", - "We have set the masks for the crossings as chain weighted gaussians so that the machine learning models can have more information about the crossings and thus have better predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "cell_code_08", - "metadata": { - "id": "cell_code_08" - }, - "outputs": [], - "source": [ - "def canon_axis(\n", - " u: np.ndarray) -> np.ndarray | None:\n", - " \"\"\"Normalize a 2-D vector and canonicalise its sign.\n", - "\n", - " Ensures that +v and -v are treated as the same axis direction by\n", - " flipping the sign so the first nonzero component is always positive.\n", - " This makes axis comparisons order-independent.\n", - "\n", - " Parameters\n", - " ----------\n", - " u: np.ndarray\n", - " A 2-D vector to normalize\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray | None\n", - " Unit vector with canonicalized sign, or\n", - " None if u is zero or if input has a norm below 1e-12.\n", - "\n", - " Examples\n", - " --------\n", - " >>> u = np.array([1.0, 2.0])\n", - " >>> v = canon_axis(u)\n", - " >>> print(v)\n", - " [0.4472136 0.8944272]\n", - "\n", - " \"\"\"\n", - "\n", - " u = np.asarray(u, dtype=float)\n", - " nrm = float(np.linalg.norm(u))\n", - " if nrm < 1e-12:\n", - " return None\n", - " u = u / nrm\n", - " if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n", - " u = -u\n", - " return u\n", - "\n", - "\n", - "def find_polyline_crossings(\n", - " coords_xy: np.ndarray,\n", - " min_separation_beads: int = CROSS_MIN_SEP_BEADS,\n", - " merge_radius_nm: float = 0.5\n", - ") -> list[dict]:\n", - " \"\"\"Detect all strand crossings of a closed ring polyline in XY.\n", - "\n", - " Performs a brute-force search over all segment pairs, skipping\n", - " adjacent segments, shared-vertex pairs, and pairs whose circular\n", - " contour separation is less than ``minimum_separation_beads``. Raw\n", - " intersection hits that land within ``merge_radius_nm`` of each other\n", - " are clustered into a single crossing, and duplicate chain-axis\n", - " directions (within ~18°) are deduplicated per cluster.\n", - "\n", - " Parameters\n", - " ----------\n", - " coords_xy : np.ndarray\n", - " Array of shape (N,2) containing the [x,y] bead positions in nanometers\n", - " min_separation_beads: int, optional\n", - " Minimum circular contour separation (in beads)\n", - " required between 2 segments\n", - " before their intersection is considered. Defaults to CROSS_MIN_SEP_BEADS.\n", - " merge_radius_nm: float, optional\n", - " Maximum distance in nanometers\n", - " between 2 raw intersections that are clustered\n", - " into a single crossing. Defaults to 0.5 nm.\n", - "\n", - " Returns\n", - " -------\n", - " list[dict]\n", - " Each dict represents one crossing and contains:\n", - " - ``pt_nm`` : np.ndarray, shape (2,) — position in nanometres.\n", - " - ``axes_nm`` : list of np.ndarray — deduplicated unit chain\n", - " direction vectors at the crossing.\n", - " - ``seg_i`` : int — index of the first segment.\n", - " - ``seg_j`` : int — index of the second segment.\n", - " - ``t`` : float — parametric position along segment i.\n", - " - ``u`` : float — parametric position along segment j.\n", - " - ``n_hits`` : int — number of raw hits merged into this cluster.\n", - "\n", - " Examples\n", - " --------\n", - " >>> crossings = find_polyline_crossings(coords[:,:2])\n", - " >>> print(type(crossings))\n", - " \n", - "\n", - " \"\"\"\n", - "\n", - " xy = np.asarray(coords_xy, dtype=float)\n", - " n = int(xy.shape[0])\n", - "\n", - " raw = []\n", - " for i in range(n):\n", - " i2 = (i + 1) % n\n", - " p1, p2 = xy[i], xy[i2]\n", - " for j in range(i + 1, n):\n", - " j2 = (j + 1) % n\n", - " if i == j or i2 == j or j2 == i:\n", - " continue\n", - " if _circ_sep(n, i, j) < int(min_separation_beads):\n", - " continue\n", - " q1, q2 = xy[j], xy[j2]\n", - " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", - " if not hit:\n", - " continue\n", - " ua = canon_axis(p2 - p1)\n", - " ub = canon_axis(q2 - q1)\n", - " if ua is None or ub is None:\n", - " continue\n", - " raw.append(dict(pt=np.array(pt, float),\n", - " axes=[ua, ub],\n", - " seg_i=int(i), seg_j=int(j),\n", - " t=float(t), u=float(u)))\n", - "\n", - " # Cluster nearby raw intersections\n", - " clusters = []\n", - " for r in raw:\n", - " pt = r[\"pt\"]\n", - " placed = False\n", - " for c in clusters:\n", - " fmc=float(merge_radius_nm)\n", - " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= fmc:\n", - " c[\"pt_sum\"] += pt\n", - " c[\"n\"] += 1\n", - " c[\"axes\"].extend(r[\"axes\"])\n", - " c[\"items\"].append(r)\n", - " placed = True\n", - " break\n", - " if not placed:\n", - " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n", - " \"axes\": list(r[\"axes\"]), \"items\": [r]})\n", - "\n", - " out = []\n", - " for c in clusters:\n", - " pt_mean = c[\"pt_sum\"] / c[\"n\"]\n", - " axes = []\n", - " for uvec in c[\"axes\"]:\n", - " uvec = canon_axis(uvec)\n", - " if uvec is None:\n", - " continue\n", - " if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n", - " continue\n", - " axes.append(uvec)\n", - " rep = c[\"items\"][0]\n", - " out.append(dict(\n", - " pt_nm = np.asarray(pt_mean, dtype=np.float32),\n", - " axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n", - " if axes else [np.array([1.0, 0.0], np.float32)],\n", - " seg_i = int(rep[\"seg_i\"]),\n", - " seg_j = int(rep[\"seg_j\"]),\n", - " t = float(rep[\"t\"]),\n", - " u = float(rep[\"u\"]),\n", - " n_hits = int(c[\"n\"]),\n", - " ))\n", - " return out\n", - "\n", - "\n", - "def add_crossing_patch(\n", - " mask: np.ndarray,\n", - " pt_px: tuple[float, float],\n", - " axes_px: list[np.ndarray],\n", - " sigma_center: float = 2.0,\n", - " chain_extent: float = 5.0,\n", - " sigma_perp: float = 1.8,\n", - " center_weight: float = 1.0,\n", - " chain_weight: float = 0.8\n", - ") -> None:\n", - " \"\"\"Paint one crossing onto a float mask using a chain-weighted Gaussian.\n", - "\n", - " The patch is the sum of two components, composed with np.maximum so\n", - " that multiple crossings accumulate correctly without overwriting:\n", - "\n", - " - An isotropic Gaussian centred on the crossing point, scaled by\n", - " ``center_weight``.\n", - " - The per-axis maximum of anisotropic Gaussians aligned to each chain\n", - " direction (elongated along the chain, narrow across it), scaled by\n", - " ``chain_weight``.\n", - "\n", - " Parameters\n", - " ----------\n", - " mask: np.ndarray\n", - " Float32 array of shape (H,W) to paint onto.\n", - " pt_px: tuple[float, float]\n", - " (x,y) crossing point in pixel coordinates.\n", - " axes_px: list[np.ndarray]\n", - " Unit vectors giving chain directions at the crossing,\n", - " in pixel coordinates\n", - " sigma_center: float, optional\n", - " Parameter for standard deviation in pixels of the\n", - " isotropic center gaussian. Defaults to 2.0.\n", - " chain_extent: float, optional\n", - " Multiplier applied to \"sigma_center\" to set the\n", - " parallel standard deviation of the anisotropic chain gaussians.\n", - " Defaults to 5.0.\n", - " sigma_perp: float, optional\n", - " Parameter for standard deviation in pixels across each chain direction.\n", - " Defaults to 1.8\n", - " center_weight: float, optional\n", - " Amplitude of the isotropic center gaussian. Defaults to 1.0.\n", - " chain_weight: float, optional\n", - " Amplitude of the anisotropic chain gaussians. Defaults to 0.8.\n", - "\n", - " \"\"\"\n", - "\n", - " H, W = mask.shape\n", - " cx, cy = float(pt_px[0]), float(pt_px[1])\n", - " sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n", - " R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n", - " x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n", - " y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n", - "\n", - " xs = np.arange(x0, x1+1, dtype=float)\n", - " ys = np.arange(y0, y1+1, dtype=float)\n", - " X, Y = np.meshgrid(xs, ys)\n", - " dx = X - cx; dy = Y - cy\n", - "\n", - " gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n", - "\n", - " g_chain = np.zeros_like(gc)\n", - " for v in axes_px:\n", - " vx, vy = float(v[0]), float(v[1])\n", - " u_par = dx*vx + dy*vy\n", - " u_perp = -dx*vy + dy*vx\n", - " g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n", - " + u_perp**2 / sigma_perp**2))\n", - " g_chain = np.maximum(g_chain, g)\n", - "\n", - " patch = center_weight * gc + chain_weight * g_chain\n", - " mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n", - "\n", - "\n", - "def make_crossing_mask(\n", - " coords: np.ndarray,\n", - " extent: tuple[float, float, float, float],\n", - " nx: int,\n", - " ny: int,\n", - " sigma_center_px: float = CROSS_SIGMA_CENTER_PX,\n", - " sigma_perp_px: float = CROSS_SIGMA_PERP_PX,\n", - " chain_extent: float = CROSS_CHAIN_EXTENT,\n", - " center_weight: float = CROSS_CENTER_WEIGHT,\n", - " chain_weight: float = CROSS_CHAIN_WEIGHT,\n", - " min_separation_beads: int = CROSS_MIN_SEP_BEADS,\n", - " crossings_precomputed: list[dict] | None = None,\n", - " merge_radius_nm: float = 0.5\n", - ") -> tuple[np.ndarray, list[dict]]:\n", - " \"\"\"Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n", - "\n", - " For each detected (or precomputed) crossing, paints a chain-weighted\n", - " Gaussian blob onto a float32 canvas using ``add_crossing_patch``.\n", - " The final mask is normalised to [0, 1].\n", - "\n", - " Passing ``crossings_precomputed`` is strongly recommended in the\n", - " dataset generation pipeline: reusing the same crossing list that was\n", - " passed to the AFM renderer guarantees that the mask and the image are\n", - " perfectly spatially aligned.\n", - "\n", - " Parameters\n", - " ----------\n", - " coords : np.ndarray\n", - " Array of shape (N,3) or (N,2) containing bead positions in nanometers.\n", - " Only the first two columns (x, y) are used.\n", - " Extent: tuple[float, float, float, float]\n", - " The physical bounding box (xmin, xmax, ymin, ymax) in nanometers.\n", - " nx: int\n", - " Number of pixels along the x-axis\n", - " ny: int\n", - " Number of pixels along the y-axis\n", - " sigma_center_px: float, optional\n", - " Parameter for standard deviation in pixels of the\n", - " isotropic center gaussian. Defaults to CROSS_SIGMA_CENTER_PX.\n", - " sigma_perp_px: float, optional\n", - " Parameter for standard deviation in pixels across the\n", - " chain direction at each crossing. Defaults to CROSS_SIGMA_PERP_PX.\n", - " chain_extent: float, optional\n", - " Multiplier on \"sigma_center_px\" that controls how far the\n", - " chain aligned gaussians extend along the strand.\n", - " Defaults to CROSS_CHAIN_EXTENT.\n", - " center_weight: float, optional\n", - " Amplitude of the isotropic center gaussian.\n", - " Defaults to CROSS_CENTER_WEIGHT.\n", - " chain_weight: float, optional\n", - " Amplitude of the anisotropic chain gaussians.\n", - " Defaults to CROSS_CHAIN_WEIGHT.\n", - " min_separation_beads: int, optional\n", - " Minimum circular contour separation (in beads)\n", - " required between 2 segments before their intersection is considered.\n", - " Defaults to CROSS_MIN_SEP_BEADS.\n", - " crossings_precomputed: list[dict] | None, optional\n", - " precomputed crossing list from \"find_polyline_crossings\"\n", - " or the AFM renderer. If None, crossings are detected automatically.\n", - " Defaults to None.\n", - " merge_radius_nm: float, optional\n", - " Maximum distance in nanometers between 2 raw\n", - " intersections that are clustered into a single crossing.\n", - " Defaults to 0.5 nm.\n", - "\n", - " Returns\n", - " -------\n", - " crossing_mask: np.ndarray\n", - " Float32 array of shape (ny, nx) with values in [0, 1].\n", - " crossings: list[dict]\n", - " Each dict represents one crossing\n", - "\n", - " Examples\n", - " --------\n", - " >>> crossing_mask, crossings = make_crossing_mask(coords,\n", - " extent, nx=512, ny=512, crossings_precomputed=precomputed_crossings)\n", - " >>> print(crossing_mask.shape, len(crossings))\n", - " (512, 512) 4\n", - "\n", - " \"\"\"\n", - "\n", - " coords = np.asarray(coords, dtype=np.float32)\n", - " xy_nm = coords[:, :2].astype(float)\n", - "\n", - " if crossings_precomputed is None:\n", - " crossings = find_polyline_crossings(\n", - " xy_nm,\n", - " min_separation_beads=min_separation_beads,\n", - " merge_radius_nm=float(merge_radius_nm),\n", - " )\n", - " else:\n", - " crossings = crossings_precomputed\n", - "\n", - " xmin, xmax, ymin, ymax = extent\n", - " sx = (nx - 1) / (xmax - xmin)\n", - " sy = (ny - 1) / (ymax - ymin)\n", - "\n", - " mask = np.zeros((ny, nx), dtype=np.float32)\n", - "\n", - " for c in crossings:\n", - " x_nm, y_nm = c[\"pt_nm\"]\n", - " px = (x_nm - xmin) * sx\n", - " py = (y_nm - ymin) * sy\n", - "\n", - " axes_px = []\n", - " for v in c[\"axes_nm\"]:\n", - " vx_px = v[0] * sx; vy_px = v[1] * sy\n", - " nrm = float(np.hypot(vx_px, vy_px))\n", - " if nrm < 1e-12:\n", - " continue\n", - " axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n", - " if not axes_px:\n", - " axes_px = [np.array([1.0, 0.0], dtype=float)]\n", - "\n", - " add_crossing_patch(\n", - " mask,\n", - " pt_px=(px, py),\n", - " axes_px=axes_px,\n", - " sigma_center=float(sigma_center_px),\n", - " chain_extent=float(chain_extent),\n", - " sigma_perp=float(sigma_perp_px),\n", - " center_weight=float(center_weight),\n", - " chain_weight=float(chain_weight),\n", - " )\n", - "\n", - " m = float(mask.max())\n", - " if m > 1e-12:\n", - " mask /= m\n", - " return mask.astype(np.float32), crossings" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_09", - "metadata": { - "id": "cell_md_09" - }, - "source": [ - "## 10. Decoy Chain Functions\n", - "\n", - "real AFM images often contain small chain segments that are not part of the main chain and thus constitute the noise in that sample, but since those segments are broken off DNA, they cannot be neatly integrated into the noise model.\n", - "Therefore, we offer the possibility of addition of small DNA chains to every every sample in the dataset where ``add_decoy = True`` which would then contain a small 2–4 bead \"decoy\" fragment placed in a corner of the image.\n", - "\n", - "The decoy appears in the AFM image but is **excluded** from both\n", - "ground-truth masks, training the model to ignore isolated unconnected fragments." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "cell_code_09", - "metadata": { - "id": "cell_code_09" - }, - "outputs": [], - "source": [ - "def make_decoy_chain(\n", - " seed: int,\n", - " n_beads_range: tuple[int, int] = (2, 4),\n", - " bond_length: float = BOND_LENGTH\n", - ") -> np.ndarray:\n", - " \"\"\"Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n", - " Z heights are set to mimic surface-deposited DNA.\n", - "\n", - " This function is used to generate a small decoy chain segment\n", - " with the number of beads given by ``n_beads_range``.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed: int\n", - " Random seed for number of beads within range, ``n_beads_range``.\n", - " n_beads_range: tuple[int, int], optional\n", - " Range of number of beads to generate the decoy. Defaults to (2,4)\n", - " bond_length: float, optional\n", - " Bond length in nanometers. Defaults to BOND_LENGTH.\n", - "\n", - " Returns\n", - " -------\n", - " coords: np.ndarray\n", - " Array of shape (N,3) containing bead positions in nanometers.\n", - "\n", - " Examples\n", - " --------\n", - " >>> coords = make_decoy_chain(seed=42)\n", - " >>> print(coords.shape)\n", - " (3, 3)\n", - "\n", - " \"\"\"\n", - "\n", - " rng = np.random.default_rng(int(seed))\n", - " n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n", - " coords = np.zeros((n_b, 3), dtype=np.float32)\n", - " angle = rng.uniform(0, 2 * np.pi)\n", - " dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n", - "\n", - " for i in range(1, n_b):\n", - " da = rng.normal(0, 0.3)\n", - " c, s = np.cos(da), np.sin(da)\n", - " R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n", - " dirn = R @ dirn\n", - " dirn = dirn / np.linalg.norm(dirn)\n", - " coords[i] = coords[i-1] + bond_length * dirn\n", - "\n", - " coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n", - " return coords\n", - "\n", - "\n", - "def place_decoy_in_corner(\n", - " decoy_coords: np.ndarray,\n", - " global_extent: tuple[float, float, float, float],\n", - " corner_margin_nm: float = 2.0,\n", - " seed: int | None = None,\n", - ") -> np.ndarray:\n", - " \"\"\"Translate decoy_coords so its centroid lands in a randomly chosen\n", - " corner of global_extent = (xmin, xmax, ymin, ymax).\n", - "\n", - " This function takes the coordinates of the decoy and places it at a\n", - " distance of corner_margin_nm away from one of the corners of the image.\n", - "\n", - " Parameters\n", - " ----------\n", - " decoy_coords: np.ndarray\n", - " Array of shape (N,3) containing bead positions in nanometers.\n", - " global_extent: tuple[float float float float]\n", - " The physical bounding box (xmin, xmax, ymin, ymax) in nanometers.\n", - " corner_margin_nm: float, optional\n", - " Distance in nanometers to place the decoy inside the frame.\n", - " Defaults to 2.0.\n", - " seed: int | None, optional\n", - " Random seed for corner selection. Defaults to None.\n", - "\n", - " Returns\n", - " -------\n", - " decoy_coords: np.ndarray\n", - "\n", - " Examples\n", - " --------\n", - " >>> decoy_coords = place_decoy_in_corner(decoy_coords, global_extent)\n", - " >>> print(decoy_coords.shape)\n", - " (3, 3)\n", - "\n", - " \"\"\"\n", - "\n", - " if seed is None:\n", - " seed = int(np.sum(decoy_coords))\n", - " rng = np.random.default_rng(int(seed))\n", - " xmin, xmax, ymin, ymax = global_extent\n", - " corner = rng.integers(0, 4)\n", - " targets = [\n", - " (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n", - " (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n", - " (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n", - " (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n", - " ]\n", - " tx, ty = targets[corner]\n", - " centroid = decoy_coords.mean(axis=0)\n", - " offset = np.array([tx, ty, 0.0]) - centroid\n", - " return decoy_coords + offset" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_10", - "metadata": { - "id": "cell_md_10" - }, - "source": [ - "## 11. Chain Generation Pipeline\n", - "\n", - "Now, we are finally ready to generate on sample in our dataset. The following functions are used for building the final image and masks:\n", - "- `generate_chain_with_beads` with `n_beads` defaulting to `N_BEADS`\n", - "- `generate_one_sample_multilength` generates the image and mask (this is what iterates to produce the dataset)\n", - "- `random_transform_noise_texture` applies a per-sample random affine transform to the noise texture for variety (in case of blank .spm files)\n", - "- Noise injection uses `blank.spm` when available and the PSD model fallback otherwise\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "cell_code_10", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cell_code_10", - "outputId": "11d6a172-8ae8-4db4-a093-63e2a8f379a2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "No blank .spm file found; trying PSD fallback noise model.\n", - "Loaded PSD fallback noise model from: /content/psd_noise_model.npz\n" - ] - } - ], - "source": [ - "def generate_chain_with_beads(\n", - " seed: int,\n", - " n_beads: int = N_BEADS,\n", - " add_decoy: bool = False\n", - ") -> dict[str, Any]:\n", - " \"\"\"Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n", - " and optionally generate a corner-placed decoy fragment.\n", - "\n", - " This function creates an initial ring polymer with a specified number of\n", - " beads, optionally relaxes it using molecular dynamics, and extracts the\n", - " final bead coordinates. Self-crossings are then detected from the final\n", - " 2D projection of the chain.\n", - "\n", - " Optionally, a decoy chain fragment can also be generated and placed near\n", - " one corner of the main chain bounding box.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed: int\n", - " Random seed for initial ring creation.\n", - " n_beads: int\n", - " Number of beads in the initial ring. Defaults to N_BEADS.\n", - " add_decoy: bool\n", - " Whether to add a corner-placed decoy fragment. Defaults to False.\n", - "\n", - " Returns\n", - " -------\n", - " A dict containing the following keys:\n", - " - ``\"seed\"`` : int\n", - " The normalized integer seed.\n", - " - ``\"n_beads\"`` : int\n", - " Number of beads in the generated chain.\n", - " - ``\"coords\"`` : np.ndarray\n", - " Final bead coordinates with shape ``(N, 3)`` and dtype\n", - " ``np.float32``.\n", - " - ``\"crossings\"`` : list\n", - " Detected self-crossings in the 2D projected chain.\n", - " - ``\"decoy_coords\"`` : np.ndarray or None\n", - " Coordinates of the optional decoy fragment, or ``None`` if no\n", - " decoy was added.\n", - "\n", - " Examples\n", - " --------\n", - " >>> chain = generate_chain_with_beads(seed=42, n_beads=10)\n", - " >>> chain[\"seed\"]\n", - " 42\n", - " >>> chain[\"n_beads\"]\n", - " 10\n", - " >>> chain[\"coords\"].shape\n", - " (10, 3)\n", - "\n", - " \"\"\"\n", - "\n", - " seed = int(seed)\n", - " n_beads = int(n_beads)\n", - "\n", - " if USE_MD:\n", - " coords0 = make_tangled_ring_initial(\n", - " seed,\n", - " n_beads=n_beads,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " base_z=BASE_Z,\n", - " )\n", - " frames = run_md_relaxation(coords0, seed=seed,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - " else:\n", - " frames = make_ring_2d_persistent_initial(\n", - " seed,\n", - " n_beads=n_beads,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n", - " )\n", - "\n", - " last_coords = frames[-1].astype(np.float32)\n", - "\n", - " crossings = find_polyline_crossings(\n", - " last_coords[:, :2],\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " merge_radius_nm=0.5,\n", - " )\n", - "\n", - " decoy_coords = None\n", - " if add_decoy:\n", - " xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n", - " ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n", - " decoy_raw = make_decoy_chain(seed + 9999)\n", - " decoy_coords = place_decoy_in_corner(\n", - " decoy_raw, (xmin, xmax, ymin, ymax),\n", - " corner_margin_nm=15.0, seed=seed)\n", - "\n", - " return dict(seed=seed, n_beads=n_beads,\n", - " coords=last_coords, crossings=crossings,\n", - " decoy_coords=decoy_coords)\n", - "\n", - "\n", - "def render_chain_and_masks(\n", - " chain: dict,\n", - " noise_source: str = \"none\",\n", - " noise_asset: dict | np.ndarray | None = None,\n", - ") -> dict[str, Any]:\n", - " \"\"\"Render AFM image + DNA mask + crossing mask for a chain dict.\n", - "\n", - " This function renders a high-resolution AFM image from a chain sample,\n", - " optionally composites a decoy fragment into the image, adds background\n", - " noise, and generates corresponding DNA and crossing masks.\n", - "\n", - " The AFM image is first rendered on an internal physical grid determined\n", - " from the chain extent and the target pixel size. The image and masks are\n", - " then resized to the final network resolution.\n", - "\n", - " Decoy fragments, when present, are composited into the AFM image using\n", - " maximum-intensity blending, but are deliberately excluded from both the\n", - " DNA mask and crossing mask.\n", - "\n", - " Parameters\n", - " ----------\n", - " chain: dict\n", - " A dict containing the following keys:\n", - " -``\"seed\"``\n", - " -``\"coords\"``\n", - " -``\"crossings\"``.\n", - " The optional key\n", - " ``\"decoy_coords\"`` may also be present\n", - " noise_source: str\n", - " Noise generation source passed to `sample_noise_image`.\n", - " noise_asset: dict | np.ndarray | None\n", - " Noise asset passed to `sample_noise_image`\n", - "\n", - " Returns\n", - " -------\n", - " A dict containing the following keys:\n", - " - ``\"seed\"`` : int\n", - " Random seed associated with the sample.\n", - " - ``\"afm_img\"`` : np.ndarray\n", - " Final AFM image with shape ``(NY, NX)`` and dtype\n", - " ``np.float32``.\n", - " - ``\"dna_mask\"`` : np.ndarray\n", - " Final binary DNA mask with shape ``(NY, NX)`` and dtype\n", - " ``np.uint8``.\n", - " - ``\"cross_mask\"`` : np.ndarray\n", - " Final crossing mask with shape ``(NY, NX)`` and dtype\n", - " ``np.float32``.\n", - " - ``\"extent\"`` : np.ndarray\n", - " Physical image extent as ``(xmin, xmax, ymin, ymax)`` with dtype\n", - " ``np.float32``.\n", - " - ``\"n_crossings\"`` : int\n", - " Number of crossing regions included in the crossing mask.\n", - " - ``\"decoy_coords\"`` : np.ndarray or None\n", - " Final decoy coordinates, or ``None`` if no decoy was used.\n", - "\n", - " Examples\n", - " --------\n", - " >>> sample = generate_chain_with_beads(seed=1)\n", - " >>> rendered = render_chain_and_masks(sample)\n", - " >>> rendered[\"afm_img\"].shape\n", - " (NY, NX)\n", - " >>> rendered[\"dna_mask\"].dtype\n", - " dtype('uint8')\n", - "\n", - " \"\"\"\n", - "\n", - " seed = int(chain[\"seed\"])\n", - " coords = chain[\"coords\"]\n", - " crossings = chain[\"crossings\"]\n", - " decoy_coords = chain.get(\"decoy_coords\", None)\n", - "\n", - " padding = 10.0\n", - " xmin = coords[:, 0].min() - padding\n", - " xmax = coords[:, 0].max() + padding\n", - " ymin = coords[:, 1].min() - padding\n", - " ymax = coords[:, 1].max() + padding\n", - " global_extent = (xmin, xmax, ymin, ymax)\n", - "\n", - " # internal render grid is determined from extent and nm_per_px\n", - " nx_phys, ny_phys, _ = compute_internal_render_grid(\n", - " global_extent, AFM_KW[\"target_nm_per_px\"]\n", - " )\n", - " afm_kw_phys = {**AFM_KW, \"nx\": nx_phys, \"ny\": ny_phys}\n", - "\n", - " afm_img_hr, dbg = create_z_based_afm(\n", - " coords, grain_seed=seed,\n", - " crossings_precomputed=crossings,\n", - " extent=global_extent,\n", - " **afm_kw_phys\n", - " )\n", - "\n", - " if decoy_coords is not None:\n", - " decoy_coords = place_decoy_in_corner(\n", - " decoy_coords, global_extent,\n", - " corner_margin_nm=8.0, seed=seed)\n", - " decoy_kw = {**afm_kw_phys,\n", - " \"apply_edge_taper\": False,\n", - " \"enable_crossing_boost\": False,\n", - " \"add_center_ridge\": False}\n", - " decoy_afm_hr, _ = create_z_based_afm(\n", - " decoy_coords, extent=global_extent, **decoy_kw)\n", - " afm_img_hr = np.maximum(afm_img_hr, decoy_afm_hr)\n", - "\n", - " actual_extent = dbg[\"extent\"]\n", - "\n", - " noise_img = sample_noise_image(\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " seed=seed,\n", - " out_shape=afm_img_hr.shape,\n", - " target_rms_nm=TARGET_NOISE_RMS_NM,\n", - " )\n", - " if noise_img is not None:\n", - " afm_img_hr = np.clip((afm_img_hr + noise_img).astype(np.float32),\n", - " 0, AFM_KW[\"max_height_nm\"])\n", - "\n", - " dna_mask_hr = make_dna_mask_from_beads(\n", - " coords, actual_extent, nx_phys, ny_phys, dilate_px=DNA_MASK_DILATE_PX)\n", - "\n", - " cross_mask_hr, crossings_out = make_crossing_mask(\n", - " coords, actual_extent, nx_phys, ny_phys,\n", - " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", - " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", - " chain_extent=CROSS_CHAIN_EXTENT,\n", - " center_weight=CROSS_CENTER_WEIGHT,\n", - " chain_weight=CROSS_CHAIN_WEIGHT,\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " crossings_precomputed=crossings,\n", - " merge_radius_nm=0.5,\n", - " )\n", - "\n", - " if CROSS_CLIP_TO_DNA_MASK:\n", - " cross_mask_hr = cross_mask_hr * dna_mask_hr.astype(np.float32)\n", - "\n", - " #Logic for maintaining user defined resolution\n", - " afm_img = resize_image_to_shape(afm_img_hr.astype(np.float32),\n", - " (NY, NX), order=1).astype(np.float32)\n", - " dna_mask = (resize_image_to_shape(dna_mask_hr.astype(np.float32),\n", - " (NY, NX), order=0)>0.5).astype(np.uint8)\n", - " cross_mask = resize_image_to_shape(cross_mask_hr.astype(np.float32),\n", - " (NY, NX), order=1).astype(np.float32)\n", - "\n", - " return dict(\n", - " seed=seed,\n", - " afm_img=afm_img.astype(np.float32),\n", - " dna_mask=dna_mask.astype(np.uint8),\n", - " cross_mask=cross_mask.astype(np.float32),\n", - " extent=np.array(actual_extent, dtype=np.float32),\n", - " n_crossings=int(len(crossings_out)),\n", - " decoy_coords=decoy_coords,\n", - " )\n", - "\n", - "\n", - "def generate_one_sample_multilength(\n", - " seed: int,\n", - " n_beads: int,\n", - " noise_source: str = \"none\",\n", - " noise_asset: dict | np.ndarray | None = None,\n", - " add_decoy: bool = False\n", - ") -> dict[str, Any]:\n", - " \"\"\"Generate one complete sample at a specified bead count.\n", - "\n", - " This function generates a single chain realization with the requested\n", - " number of beads, renders the corresponding AFM image and supervision\n", - " masks, and appends the bead count to the returned sample dictionary.\n", - "\n", - " Noise can optionally be injected during rendering using either a blank\n", - " SPM-derived texture or a PSD-based synthetic noise model, depending on\n", - " the provided noise configuration.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed: int\n", - " Random seed for initial ring creation.\n", - " n_beads: int\n", - " Number of beads in the initial ring.\n", - " noise_source: str, optional\n", - " Noise generation source passed to `render_chain_and_masks`.\n", - " Defaults to \"none\".\n", - " noise_asset: dict | np.ndarray | None, optional\n", - " Noise asset passed to `render_chain_and_masks`. Defaults to `None`.\n", - " add_decoy: bool, optional\n", - " Whether to add a corner-placed decoy fragment. Defaults to `False`.\n", - "\n", - " Returns\n", - " -------\n", - " dict[str, Any]\n", - " Dictionary containing the rendered sample, including AFM image,\n", - " DNA mask, crossing mask, physical extent, crossing count, optional\n", - " decoy coordinates, and the bead count under ``\"n_beads\"``.\n", - "\n", - " Notes\n", - " -----\n", - " This function prints which noise model is being used for the noise\n", - " injection and if no noise model has been provided then\n", - " it prints a statement that reflects that noise injection has been disabled.\n", - "\n", - " Examples\n", - " --------\n", - " >>> sample = generate_one_sample_multilength(seed=1, n_beads=300)\n", - " >>> sample[\"n_beads\"]\n", - " 300\n", - "\n", - " >>> sample[\"afm_img\"].shape\n", - " (NY, NX)\n", - "\n", - " \"\"\"\n", - "\n", - " chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n", - "\n", - " s = render_chain_and_masks(\n", - " chain,\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " )\n", - " s[\"n_beads\"] = int(n_beads)\n", - " return s\n", - "\n", - "\n", - "# ── Load noise asset once; prefer blank.spm, fall back to PSD model ──────────\n", - "noise_source = \"none\"\n", - "noise_asset = None\n", - "noise_diag = {\"source\": \"none\"}\n", - "\n", - "if USE_BLANK_SPM_NOISE:\n", - " blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n", - " if blank_path and os.path.isfile(blank_path):\n", - " try:\n", - " noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n", - " blank_path,\n", - " target_rms_nm=TARGET_NOISE_RMS_NM,\n", - " verbose=True,\n", - " )\n", - " noise_source = \"blank_spm\"\n", - " print(f\"Loaded blank .spm noise texture\"\n", - " f\" RMS ≈ {noise_rms_nm:.3f} nm\")\n", - " except Exception as e:\n", - " print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n", - " else:\n", - " print(\"No blank .spm file found; trying PSD fallback noise model.\")\n", - "\n", - "if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n", - " try:\n", - " noise_asset = load_model(MODEL_PATH)\n", - " noise_source = \"psd_model\"\n", - " noise_diag = {\n", - " \"source\": \"psd_model\",\n", - " \"model_path\": str(MODEL_PATH),\n", - " \"method\": PSD_NOISE_METHOD,\n", - " \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n", - " }\n", - " print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n", - " except Exception as e:\n", - " print(\"PSD fallback noise model failed;\"\n", - " \"proceeding without noise. Error:\", e)\n", - " noise_asset = None\n", - "\n", - "if noise_source == \"none\":\n", - " print(\"Noise injection disabled.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_11", - "metadata": { - "id": "cell_md_11" - }, - "source": [ - "## 12. Sanity Check\n", - "\n", - "It is always a good idea to check if the pipeline is working correctly and producing the inteded resutls and so we have added a sanity check that can be performed before starting the generation of the entire dataset.\n", - "Here we render one sample per bead count for the bead counts provided in the global variables and by default we have set `add_decoy=True` here.\n", - "\n", - "This check displays the AFM image, DNA mask, and crossing mask side-by-side.\n", - "We also print the extent / decoy-placement diagnostics for verification and adjustment." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "cell_code_11", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "cell_code_11", - "outputId": "6de2628c-9de0-49c3-c21d-f970a504a895" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Using CUDA platform.\n", - "Using CUDA platform.\n", - "Using CUDA platform.\n", - "Using CUDA platform.\n", - "\n", - "==================== SAMPLE 1 (Seed: 1, 70 beads) ====================\n", - "Image window (nm): X=[-16.7, 16.5], Y=[-16.4, 16.1]\n", - "Decoy XY bounds (nm): X=[8.2, 8.8], Y=[7.7, 8.5]\n", - "Decoy Z range (nm): -0.03 – 0.03\n", - "Decoy inside extent.\n", - " Decoy height ≈ 0\n", - "\n", - "==================== SAMPLE 2 (Seed: 2, 80 beads) ====================\n", - "Image window (nm): X=[-20.8, 19.6], Y=[-20.7, 20.9]\n", - "Decoy XY bounds (nm): X=[11.4, 11.9], Y=[-13.6, -11.7]\n", - "Decoy Z range (nm): -0.26 – 0.15\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 3 (Seed: 3, 90 beads) ====================\n", - "Image window (nm): X=[-27.8, 25.9], Y=[-14.4, 14.1]\n", - "Decoy XY bounds (nm): X=[17.6, 18.2], Y=[-7.8, -4.9]\n", - "Decoy Z range (nm): -0.39 – 0.44\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 4 (Seed: 4, 100 beads) ====================\n", - "Image window (nm): X=[-20.9, 20.9], Y=[-24.3, 21.5]\n", - "Decoy XY bounds (nm): X=[-13.0, -12.7], Y=[-16.8, -15.8]\n", - "Decoy Z range (nm): -0.24 – 0.24\n", - "Decoy inside extent.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "bead_counts = list(BEAD_COUNTS)\n", - "seeds = [int(BASE_SEED + k) for k in range(len(bead_counts))]\n", - "\n", - "samples = [\n", - " generate_one_sample_multilength(\n", - " seeds[i], n_beads=bead_counts[i],\n", - " noise_source=noise_source, noise_asset=noise_asset, add_decoy=True\n", - " )\n", - " for i in range(len(bead_counts))\n", - "]\n", - "\n", - "fig, axes = plt.subplots(len(samples), 3, figsize=(16, 5 * len(samples)))\n", - "\n", - "for r, s in enumerate(samples):\n", - " extent = s[\"extent\"]\n", - " img = s[\"afm_img\"]\n", - "\n", - " print(f\"\\n{'='*20} SAMPLE {r+1} (Seed: {s['seed']}, \"\n", - " f\"{s['n_beads']} beads) {'='*20}\")\n", - " print(f\"Image window (nm): X=[{extent[0]:.1f}, {extent[1]:.1f}], \"\n", - " f\"Y=[{extent[2]:.1f}, {extent[3]:.1f}]\")\n", - "\n", - " if s.get(\"decoy_coords\") is not None:\n", - " d = s[\"decoy_coords\"]\n", - " print(f\"Decoy XY bounds (nm): \"\n", - " f\"X=[{d[:,0].min():.1f}, {d[:,0].max():.1f}], \"\n", - " f\"Y=[{d[:,1].min():.1f}, {d[:,1].max():.1f}]\")\n", - " print(f\"Decoy Z range (nm): {d[:,2].min():.2f} – {d[:,2].max():.2f}\")\n", - " oob = (d[:,0].min() < extent[0] or d[:,0].max() > extent[1] or\n", - " d[:,1].min() < extent[2] or d[:,1].max() > extent[3])\n", - " print(\" Decoy out-of-bounds!\" if oob else \"Decoy inside extent.\")\n", - " if d[:,2].max() < 0.1:\n", - " print(\" Decoy height ≈ 0\")\n", - "\n", - " ax1, ax2, ax3 = axes[r]\n", - " ext_list = list(extent)\n", - "\n", - " ax1.imshow(img, cmap=\"afmhot\", origin=\"lower\", extent=ext_list)\n", - " ax1.set_title(f\"AFM image\\n(seed {s['seed']}, {s['n_beads']} beads)\")\n", - " ax1.axis(\"off\")\n", - "\n", - " ax2.imshow(s[\"dna_mask\"], cmap=\"gray\", origin=\"lower\", extent=ext_list)\n", - " ax2.set_title(\"DNA mask (decoy excluded)\")\n", - " ax2.axis(\"off\")\n", - "\n", - " ax3.imshow(s[\"cross_mask\"], cmap=\"magma\", origin=\"lower\", extent=ext_list)\n", - " ax3.set_title(f\"Crossing mask (n={s['n_crossings']})\")\n", - " ax3.axis(\"off\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3YoKkN3e5dIo", - "metadata": { - "id": "3YoKkN3e5dIo" - }, - "source": [ - "## 13. Benchmarking\n", - "\n", - "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "WLAYkyIQlhuX", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WLAYkyIQlhuX", - "outputId": "992f00b2-2e7e-4360-905a-b8835067cda8" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "==============================================================\n", - " SYSTEM PROFILE\n", - "==============================================================\n", - " OS : Linux 6.6.113+\n", - " CPU (logical) : 2 cores\n", - " RAM available : 11.6 GB\n", - " OpenMM Platforms: ['Reference', 'CPU', 'CUDA', 'OpenCL']\n", - " Active Platform : CUDA\n", - " Mode : MD (OpenMM)\n", - "\n", - "==============================================================\n", - " RUNNING BENCHMARK\n", - "==============================================================\n", - "Using CUDA platform.\n", - " Rep 1/3: 2.39s\n", - "Using CUDA platform.\n", - " Rep 2/3: 2.73s\n", - "Using CUDA platform.\n", - " Rep 3/3: 2.31s\n", - "==============================================================\n", - " Median Wall Time: 2.39 s\n", - " Mean Wall Time : 2.48 s\n", - " Peak RAM (RSS) : 641.1 MB\n", - " Projected Total : 0.3 hours for 400 samples\n", - "==============================================================\n" - ] - } - ], - "source": [ - "import time\n", - "import tracemalloc\n", - "import os\n", - "import platform\n", - "import psutil\n", - "import threading\n", - "import statistics\n", - "\n", - "class _PeakMemTracker:\n", - " \"\"\"Track peak resident memory usage in a background thread.\n", - "\n", - " This helper periodically samples the current process RSS and stores the\n", - " largest value observed during the tracked interval.\n", - "\n", - " Attributes\n", - " ----------\n", - " peak_mb : float\n", - " Peak resident set size observed during tracking, in megabytes.\n", - " \"\"\"\n", - "\n", - " def __init__(self, poll_interval_s: float = 0.05) -> None:\n", - " \"\"\"Initialize the memory tracker.\n", - "\n", - " Parameters\n", - " ----------\n", - " poll_interval_s : float, optional\n", - " Sampling interval in seconds, by default ``0.05``.\n", - " \"\"\"\n", - " self._process = psutil.Process(os.getpid())\n", - " self._poll_interval_s = float(poll_interval_s)\n", - " self._stop_event = threading.Event()\n", - " self._thread: threading.Thread | None = None\n", - " self.peak_mb = 0.0\n", - "\n", - " def _run(self) -> None:\n", - " \"\"\"Continuously sample RSS until tracking is stopped.\"\"\"\n", - " while not self._stop_event.is_set():\n", - " try:\n", - " rss_mb = self._process.memory_info().rss / 1e6\n", - " if rss_mb > self.peak_mb:\n", - " self.peak_mb = rss_mb\n", - " except Exception:\n", - " pass\n", - "\n", - " self._stop_event.wait(self._poll_interval_s)\n", - "\n", - " def start(self) -> None:\n", - " \"\"\"Start background memory tracking.\"\"\"\n", - " self._stop_event.clear()\n", - " self._thread = threading.Thread(target=self._run, daemon=True)\n", - " self._thread.start()\n", - "\n", - " def stop(self) -> None:\n", - " \"\"\"Stop background memory tracking.\"\"\"\n", - " self._stop_event.set()\n", - " if self._thread is not None:\n", - " self._thread.join()\n", - "\n", - "\n", - "def _bench_stages(\n", - " seed: int,\n", - " n_beads: int\n", - ") -> dict[str, float]:\n", - " \"\"\"Benchmark the major stages of one sample-generation pipeline run.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed : int\n", - " Random seed for the benchmarked sample.\n", - " n_beads : int\n", - " Number of beads in the benchmarked chain.\n", - "\n", - " Returns\n", - " -------\n", - " dict[str, float]\n", - " Stage timings in seconds. Keys are:\n", - " ``\"1_chain_init\"``, ``\"2_md_relax\"``, ``\"3_crossing_detect\"``,\n", - " and ``\"4_afm_render_masks\"``.\n", - "\n", - " \"\"\"\n", - "\n", - " seed = int(seed)\n", - " n_beads = int(n_beads)\n", - "\n", - " timings: dict[str, float] = {}\n", - " t0 = time.perf_counter()\n", - "\n", - " if USE_MD:\n", - " coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n", - " timings[\"1_chain_init\"] = time.perf_counter() - t0\n", - "\n", - " t0 = time.perf_counter()\n", - " frames = run_md_relaxation(\n", - " coords0,\n", - " seed=seed,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME,\n", - " )\n", - " timings[\"2_md_relax\"] = time.perf_counter() - t0\n", - " else:\n", - " timings[\"1_chain_init\"] = 0.0\n", - "\n", - " t0 = time.perf_counter()\n", - " frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n", - " timings[\"2_md_relax\"] = time.perf_counter() - t0\n", - "\n", - " last_coords = frames[-1]\n", - "\n", - " t0 = time.perf_counter()\n", - " crossings = find_polyline_crossings(last_coords[:, :2])\n", - " timings[\"3_crossing_detect\"] = time.perf_counter() - t0\n", - "\n", - " t0 = time.perf_counter()\n", - " chain = {\n", - " \"seed\": seed,\n", - " \"n_beads\": n_beads,\n", - " \"coords\": last_coords,\n", - " \"crossings\": crossings,\n", - " }\n", - " render_chain_and_masks(\n", - " chain,\n", - " noise_source=\"none\",\n", - " noise_asset=None,\n", - " )\n", - " timings[\"4_afm_render_masks\"] = time.perf_counter() - t0\n", - "\n", - " return timings\n", - "\n", - "\n", - "def benchmark_pipeline(\n", - " seed: int,\n", - " n_beads: int,\n", - " n_reps: int = 3,\n", - " total_samples: int | None = None,\n", - " print_report: bool = True,\n", - ") -> dict[str, Any]:\n", - " \"\"\"Benchmark the sample-generation pipeline and report timing statistics.\n", - "\n", - " This function prints a system profile, runs repeated benchmark passes for\n", - " one representative sample configuration, tracks peak RSS memory usage, and\n", - " estimates the total runtime for the full dataset.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed : int\n", - " Base random seed for the benchmark runs.\n", - " n_beads : int\n", - " Number of beads used in each benchmarked sample.\n", - " n_reps : int, optional\n", - " Number of benchmark repetitions, by default ``3``.\n", - " total_samples : int or None, optional\n", - " Total number of samples to use when projecting full runtime. If\n", - " ``None``, the projection is omitted.\n", - " print_report : bool, optional\n", - " Whether to print the system and benchmark summary, by default ``True``.\n", - "\n", - " Returns\n", - " -------\n", - " dict[str, Any]\n", - " Dictionary containing benchmark results, including wall times, stage\n", - " timings, median runtime, peak RSS memory, and projected total runtime.\n", - "\n", - " Notes\n", - " -----\n", - " When ``USE_MD`` is enabled, the function also reports available OpenMM\n", - " platforms and uses the same MD settings as the rest of the notebook.\n", - "\n", - " Examples\n", - " --------\n", - " >>> results = benchmark_pipeline(seed=0, n_beads=300, n_reps=3)\n", - " >>> results[\"median_wall_time_s\"] > 0\n", - " True\n", - "\n", - " \"\"\"\n", - "\n", - " seed = int(seed)\n", - " n_beads = int(n_beads)\n", - " n_reps = int(n_reps)\n", - "\n", - " cpu_logical = psutil.cpu_count(logical=True)\n", - " available_ram_gb = psutil.virtual_memory().available / 1e9\n", - "\n", - " platforms = None\n", - " active_platform = None\n", - " if USE_MD:\n", - " platforms = [\n", - " openmm.Platform.getPlatform(i).getName()\n", - " for i in range(openmm.Platform.getNumPlatforms())\n", - " ]\n", - " active_platform = (\n", - " \"CUDA\" if \"CUDA\" in platforms else\n", - " \"OpenCL\" if \"OpenCL\" in platforms else\n", - " \"CPU\"\n", - " )\n", - "\n", - " if print_report:\n", - " print(\"=\" * 62)\n", - " print(\" SYSTEM PROFILE\")\n", - " print(\"=\" * 62)\n", - " print(f\" OS : {platform.system()} {platform.release()}\")\n", - " print(f\" CPU (logical) : {cpu_logical} cores\")\n", - " print(f\" RAM available : {available_ram_gb:.1f} GB\")\n", - " if USE_MD:\n", - " print(f\" OpenMM Platforms: {platforms}\")\n", - " print(f\" Active Platform : {active_platform}\")\n", - " print(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\n", - " print()\n", - "\n", - " print(\"=\" * 62)\n", - " print(\" RUNNING BENCHMARK\")\n", - " print(\"=\" * 62)\n", - "\n", - " wall_times: list[float] = []\n", - " stage_timings: list[dict[str, float]] = []\n", - "\n", - " mem_tracker = _PeakMemTracker()\n", - " mem_tracker.start()\n", - "\n", - " for rep in range(n_reps):\n", - " rep_seed = seed + rep\n", - " t0 = time.perf_counter()\n", - " rep_stage_timings = _bench_stages(rep_seed, n_beads)\n", - " elapsed = time.perf_counter() - t0\n", - "\n", - " wall_times.append(elapsed)\n", - " stage_timings.append(rep_stage_timings)\n", - "\n", - " if print_report:\n", - " print(f\" Rep {rep + 1}/{n_reps}: {elapsed:.2f}s\")\n", - "\n", - " mem_tracker.stop()\n", - "\n", - " median_wall_time_s = statistics.median(wall_times)\n", - " mean_wall_time_s = statistics.mean(wall_times)\n", - "\n", - " stage_keys = stage_timings[0].keys() if stage_timings else []\n", - " median_stage_times_s = {\n", - " key: statistics.median(t[key] for t in stage_timings)\n", - " for key in stage_keys\n", - " }\n", - " ts = total_samples\n", - " projected_total_hours = None\n", - " if ts is not None:\n", - " projected_total_hours = (median_wall_time_s * int(ts)) / 3600\n", - "\n", - " if print_report:\n", - " print(\"=\" * 62)\n", - " print(f\" Median Wall Time: {median_wall_time_s:.2f} s\")\n", - " print(f\" Mean Wall Time : {mean_wall_time_s:.2f} s\")\n", - " print(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\n", - " if projected_total_hours is not None:\n", - " print(\n", - " f\" Projected Total : {projected_total_hours:.1f} hours \"\n", - " f\"for {int(ts)} samples\"\n", - " )\n", - " print(\"=\" * 62)\n", - "\n", - " return {\n", - " \"seed\": seed,\n", - " \"n_beads\": n_beads,\n", - " \"n_reps\": n_reps,\n", - " \"mode\": \"md\" if USE_MD else \"non_md\",\n", - " \"cpu_logical\": cpu_logical,\n", - " \"available_ram_gb\": available_ram_gb,\n", - " \"openmm_platforms\": platforms,\n", - " \"active_platform\": active_platform,\n", - " \"wall_times_s\": wall_times,\n", - " \"stage_timings_s\": stage_timings,\n", - " \"median_wall_time_s\": median_wall_time_s,\n", - " \"mean_wall_time_s\": mean_wall_time_s,\n", - " \"median_stage_times_s\": median_stage_times_s,\n", - " \"peak_rss_mb\": mem_tracker.peak_mb,\n", - " \"total_samples\": int(ts) if ts is not None else None,\n", - " \"projected_total_hours\": projected_total_hours,\n", - " }\n", - "\n", - "\n", - "BENCH_SEED = int(BASE_SEED)\n", - "BENCH_N_BEADS = int(BEAD_COUNTS[0])\n", - "BENCH_REPS = 3\n", - "\n", - "benchmark_results = benchmark_pipeline(\n", - " seed=BENCH_SEED,\n", - " n_beads=BENCH_N_BEADS,\n", - " n_reps=BENCH_REPS,\n", - " total_samples=int(N_SAMPLES) * len(BEAD_COUNTS),\n", - " print_report=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_12", - "metadata": { - "id": "cell_md_12" - }, - "source": [ - "## 14.Dataset Generation\n", - "\n", - "Finally, we are ready to generate the full dataset. The amount of samples and the lenghts of the chains is defined in the global variables and will be referenced here.\n", - "This notebook generates the full dataset (default: 1000 samples × 4 chain lengths). There is added functionality to preview the images for a gives set or range of indices.\n", - "\n", - "\n", - "Progress is printed every 10 samples and a `manifest.csv` tracks all file paths." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cell_code_12", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "cell_code_12", - "outputId": "71d9aef3-5908-4e56-b371-b8b1815dd09b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting dataset generation: 100 samples…\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - "Special override for index 832: seed = 10005\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - "Progress: 10/100\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - "Progress: 20/100\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOpenMMException\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_406/2280809092.py\u001b[0m in \u001b[0;36mrun_md_relaxation\u001b[0;34m(coords0, seed, n_frames, steps_per_frame)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[0mplatform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopenmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlatform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetPlatformByName\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplatform_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopenmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mintegrator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplatform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 157\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Using {platform_name} platform.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/openmm/openmm.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 6216\u001b[0m \"\"\"\n\u001b[0;32m-> 6217\u001b[0;31m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mContext_swiginit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_Context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mOpenMMException\u001b[0m: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_406/3678955248.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0madd_decoy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m s = generate_one_sample_multilength(\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mn_beads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_beads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/tmp/ipykernel_406/1540153343.py\u001b[0m in \u001b[0;36mgenerate_one_sample_multilength\u001b[0;34m(seed, n_beads, noise_source, noise_asset, add_decoy)\u001b[0m\n\u001b[1;32m 371\u001b[0m \"\"\"\n\u001b[1;32m 372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m \u001b[0mchain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_chain_with_beads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_beads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_decoy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0madd_decoy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 375\u001b[0m s = render_chain_and_masks(\n", - "\u001b[0;32m/tmp/ipykernel_406/1540153343.py\u001b[0m in \u001b[0;36mgenerate_chain_with_beads\u001b[0;34m(seed, n_beads, add_decoy)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0mbase_z\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBASE_Z\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m )\n\u001b[0;32m--> 131\u001b[0;31m frames = run_md_relaxation(coords0, seed=seed,\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0mn_frames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_FRAMES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m steps_per_frame=STEPS_PER_FRAME)\n", - "\u001b[0;32m/tmp/ipykernel_406/2280809092.py\u001b[0m in \u001b[0;36mrun_md_relaxation\u001b[0;34m(coords0, seed, n_frames, steps_per_frame)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mopenmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpenMMException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\" {platform_name} unavailable: {e}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\" {platform_name} failed unexpectedly: {e}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "import os\n", - "import csv\n", - "import json\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "manifest_path = os.path.join(OUT_DIR, \"manifest.csv\")\n", - "print(f\"Starting dataset generation: {N_SAMPLES} samples…\")\n", - "\n", - "# ============================================================\n", - "# Optional preview PNG export settings\n", - "# ============================================================\n", - "EXPORT_PREVIEW_PNGS = True\n", - "\n", - "# Choose indices to export as PNG previews.\n", - "# Examples:\n", - "# set(range(4, 41)) -> export indices 4 through 40 inclusive\n", - "# {0, 3, 10, 25} -> export only specific indices\n", - "# set() -> export none\n", - "PREVIEW_PNG_INDICES = set(range(4, 8))\n", - "\n", - "# Separate folder for preview PNGs\n", - "PREVIEW_PNG_DIR = os.path.join(OUT_DIR, \"preview_pngs\")\n", - "if EXPORT_PREVIEW_PNGS:\n", - " os.makedirs(PREVIEW_PNG_DIR, exist_ok=True)\n", - "\n", - "# Define the header including global parameters\n", - "header = [\n", - " \"index\", \"seed\", \"n_beads\", \"bond_length\", \"persistence_bonds\",\n", - " \"image_npy\", \"dna_mask_npy\", \"cross_mask_npy\", \"meta_npz\",\n", - " \"n_crossings\", \"has_decoy\", \"afm_params_json\"\n", - "]\n", - "\n", - "with open(manifest_path, \"w\", newline=\"\") as f:\n", - " w = csv.writer(f)\n", - " w.writerow(header)\n", - "\n", - " for idx in range(int(N_SAMPLES)):\n", - " # Seed override for a known-problematic sample\n", - " if idx == 7:\n", - " seed = int(BASE_SEED + idx + 9997)\n", - " print(f\"Special override for index 832: seed = {seed}\")\n", - " else:\n", - " seed = int(BASE_SEED + idx)\n", - "\n", - " n_beads = int(BEAD_COUNTS[idx % len(BEAD_COUNTS)])\n", - " add_decoy = (idx % 5 == 0)\n", - "\n", - " s = generate_one_sample_multilength(\n", - " seed,\n", - " n_beads=n_beads,\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " add_decoy=add_decoy,\n", - " )\n", - "\n", - " img_path = os.path.join(\"images\", f\"img_{idx:04d}.npy\")\n", - " dna_path = os.path.join(\"dna_masks\", f\"dna_{idx:04d}.npy\")\n", - " cross_path = os.path.join(\"cross_masks\", f\"cross_{idx:04d}.npy\")\n", - " meta_path = os.path.join(\"meta\", f\"meta_{idx:04d}.npz\")\n", - "\n", - " np.save(os.path.join(OUT_DIR, img_path), s[\"afm_img\"])\n", - " np.save(os.path.join(OUT_DIR, dna_path), s[\"dna_mask\"])\n", - " np.save(os.path.join(OUT_DIR, cross_path), s[\"cross_mask\"])\n", - "\n", - " # Save detailed metadata\n", - " np.savez_compressed(\n", - " os.path.join(OUT_DIR, meta_path),\n", - " seed = s[\"seed\"],\n", - " n_beads = int(s[\"n_beads\"]),\n", - " extent = s[\"extent\"],\n", - " n_crossings = s[\"n_crossings\"],\n", - " has_decoy = add_decoy,\n", - " bond_length = BOND_LENGTH,\n", - " persistence = PERSISTENCE_BONDS\n", - " )\n", - "\n", - " # ------------------------------------------------------------\n", - " # Optional PNG export for selected indices\n", - " # ------------------------------------------------------------\n", - " if EXPORT_PREVIEW_PNGS and idx in PREVIEW_PNG_INDICES:\n", - " png_path = os.path.join(PREVIEW_PNG_DIR, f\"img_{idx:04d}.png\")\n", - "\n", - " fig, ax = plt.subplots(figsize=(6, 6), dpi=150)\n", - " ax.imshow(s[\"afm_img\"], cmap=\"gray\", origin=\"lower\")\n", - " ax.set_title(f\"Sample {idx} | beads={n_beads} |\"\n", - " f\" crossings={s['n_crossings']}\")\n", - " ax.axis(\"off\")\n", - " fig.tight_layout(pad=0)\n", - " fig.savefig(png_path, bbox_inches=\"tight\", pad_inches=0)\n", - " plt.close(fig)\n", - "\n", - " # Write to manifest including JSON-serialized AFM parameters\n", - " w.writerow([\n", - " idx, seed, n_beads, BOND_LENGTH, PERSISTENCE_BONDS,\n", - " img_path, dna_path, cross_path, meta_path,\n", - " s[\"n_crossings\"], add_decoy, json.dumps(AFM_KW)\n", - " ])\n", - "\n", - " if (idx + 1) % 10 == 0:\n", - " print(f\"Progress: {idx+1}/{N_SAMPLES}\")\n", - "\n", - "print(\"\\nDone. Dataset written to:\", OUT_DIR)\n", - "print(\"Manifest:\", manifest_path)\n", - "\n", - "if EXPORT_PREVIEW_PNGS:\n", - " print(\"Preview PNG directory:\", PREVIEW_PNG_DIR)\n", - " print(\"PNG preview indices:\", sorted(PREVIEW_PNG_INDICES))" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "openmm-cuda", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.20" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file From 98b61486221c81cd3a102b7f7004ebb90c84468e Mon Sep 17 00:00:00 2001 From: Prakhar-Dutta Date: Thu, 30 Apr 2026 15:09:20 +0200 Subject: [PATCH 15/17] Delete tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb --- ...maged_via_AFM_draft_for_Giovanni (1).ipynb | 1442 ----------------- 1 file changed, 1442 deletions(-) delete mode 100644 tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb diff --git a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb b/tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb deleted file mode 100644 index ce79655..0000000 --- a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_for_Giovanni (1).ipynb +++ /dev/null @@ -1,1442 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cell_md_title", - "metadata": { - "id": "cell_md_title" - }, - "source": [ - "# Simulation of 'DNA chains on a substrate imaged via AFM'\n**This notebook provides a complete example of generating a dataset of simulated DNA chains relxed on a substrate imaged via AFM.**\n\n- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)\n\nOutputs are written to `OUT_DIR/` as `.npy` plus `manifest.csv` (which documents the user inputs used to create the images and masks).\n\n>\n---\n**Cell execution order:** run cells 1 → 12 in sequence. \nCells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Prakhar-Dutta/ASAP/blob/1b5cfc5173e1bed4e59575e6dd4724cb20682620/tutorial/Not_final_version_DNA_notebook%20(1).ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_01", - "metadata": { - "id": "cell_md_01" - }, - "source": [ - "# 1. **Imports and global settings**\n", - "## 1.1. Imports\n", - "\n", - "First we import all the libraries that will be necessary for simulation of the images. If you plan to use molecular dynamics for the generation of the DNA chains, then please import the molecular dynamics libraries as well." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "cell_code_01", - "metadata": { - "id": "cell_code_01", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8e1cfb67-fb2f-4019-e828-00f03c01d4e8" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting openmm[cuda13]\n", - " Downloading openmm-8.5.0-cp312-cp312-manylinux_2_34_x86_64.whl.metadata (1.4 kB)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from openmm[cuda13]) (2.0.2)\n", - "Collecting OpenMM-CUDA-13==8.5.0 (from openmm[cuda13])\n", - " Downloading openmm_cuda_13-8.5.0-py3-none-manylinux_2_34_x86_64.whl.metadata (420 bytes)\n", - "Collecting nvidia-cuda-runtime<14,>=13 (from OpenMM-CUDA-13==8.5.0->openmm[cuda13])\n", - " Downloading nvidia_cuda_runtime-13.2.51-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB)\n", - 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"\u001b[?25hInstalling collected packages: openmm, nvidia-nvvm, nvidia-nvjitlink, nvidia-cuda-runtime, nvidia-cuda-nvrtc, nvidia-cuda-cupti, nvidia-cuda-crt, nvidia-cufft, nvidia-cuda-nvcc, OpenMM-CUDA-13\n", - "Successfully installed OpenMM-CUDA-13-8.5.0 nvidia-cuda-crt-13.2.51 nvidia-cuda-cupti-13.2.23 nvidia-cuda-nvcc-13.2.51 nvidia-cuda-nvrtc-13.2.51 nvidia-cuda-runtime-13.2.51 nvidia-cufft-12.2.0.37 nvidia-nvjitlink-13.2.51 nvidia-nvvm-13.2.51 openmm-8.5.0\n" - ] - } - ], - "source": [ - "# Install dependencies (uncomment in a fresh environment / Colab)\n", - "# !pip cache purge\n", - "# Chain generation mode\n", - "USE_MD = True # True → OpenMM Langevin dynamics\n", - " # False → fast 2-D persistent-walk (no OpenMM needed)\n", - "# Molecular dynamics\n", - "!pip3 install -q scipy matplotlib pillow\n", - "if USE_MD:\n", - " !pip3 install openmm[cuda13] #change the cuda version depending on the CUDA version available on your system.\n", - " import openmm\n", - " import openmm.unit as unit\n", - "\n", - "import os\n", - "from pathlib import Path\n", - "import re\n", - "import csv\n", - "import traceback\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import animation\n", - "from IPython.display import HTML, display\n", - "\n", - "\n", - "\n", - "\n", - "# Image processing\n", - "from scipy.ndimage import (\n", - " gaussian_filter, grey_dilation, distance_transform_edt,\n", - " binary_dilation, median_filter, affine_transform, zoom\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "#1.2 Defining the variables\n", - "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." - ], - "metadata": { - "id": "1oW9llw-imr1" - }, - "id": "1oW9llw-imr1" - }, - { - "cell_type": "code", - "source": [ - "# ─────────────────────────────────────────────\n", - "# Global settings (these are the variables that need to be changed to change the appearance of the resulting images)\n", - "# ─────────────────────────────────────────────\n", - "\n", - "# Dataset\n", - "OUT_DIR = \"dna_dataset_1000_4lengths\" # Name of the output directory that you would like to store your images and masks in\n", - "N_SAMPLES = 1000 # Number of samples that will be generated\n", - "BASE_SEED = 0 # Seed for reproducibility\n", - "\n", - "# Image resolution (keep fixed for U-Net)\n", - "NX = 512\n", - "NY = 512\n", - "\n", - "# Ring + MD\n", - "N_BEADS = 90 # default bead count for vizualization\n", - "BEAD_COUNTS = [70, 80, 90, 100] # four chain lengths (1 bead corresponds to 10 base pairs on DNA approximately)\n", - "BOND_LENGTH = 1.0 # distance between beads\n", - "PERSISTENCE_BONDS = 23.0 # used to determine the range of angles which the chain is allowed to turn\n", - "K_ANGLE = 12.0 # used to determine the angular stiffness during relaxation (higher means more stiffness)\n", - "BASE_Z = 5.0 # Height above the substrate that the chain starts at before relaxing\n", - "\n", - "ANGLE_STIFNESS_MULT = 0.4 # If USE_MD is False, then angle stiffness multiplier controls chain propogation through space\n", - "\n", - "# MD recording\n", - "N_FRAMES = 200\n", - "STEPS_PER_FRAME = 200 # Between every frame local energy minimization happens for the given number of steps\n", - "\n", - "# AFM rendering\n", - "\n", - "tip_radius = 1 # Please select the tip radius here (Ideal range is 1nm-5nm)\n", - "nm_per_px = 2 # Internal AFM render sampling in nm / pixel before downsampling\n", - "\n", - "AFM_KW = dict(\n", - " nx=NX, ny=NY,\n", - " dna_diameter_nm=2.0, # mapping to physical values\n", - " tip_radius_nm=0.01*tip_radius, # Scaling factor is applied for internal control to match\n", - " max_height_nm=6.0, # Maximum height for the colorbar\n", - " target_nm_per_px=0.04*nm_per_px, # Scaling factor is applied for internal controls to match\n", - " max_radius_px=96, # following parameters are used for improving the visualization of the chain\n", - " radius_shrink_px=0.0, # Set to 0 so that rendering can match physical values\n", - " final_blur_sigma_px=0.20, # When changing tip radius and nm_per_px to observe the expected\n", - " apply_edge_taper=True, # physical effect, remove the scaling factors for tip_radius_nm and target_nm_per_px\n", - " taper_sigma_nm=0.45, # and scale all other parameters accordingly.\n", - " taper_floor=0.10,\n", - " add_center_ridge=True,\n", - " ridge_sigma_nm=0.25, # This controls width of center ridge\n", - " ridge_amp_nm=0.25, # This controls height of center ridge\n", - " grain_nm=0.0, # This adds more noise to the chain\n", - " grain_sigma_px=0.6, enable_crossing_boost=False, # To boost the height of the crossings\n", - " min_separation_beads=12, # Distance to other chain segments to not boost indiscriminately\n", - " boost_window_beads=2, # How many beads to boost the height for\n", - " guaranteed_offset_nm=1.0, # How much to boost\n", - " boost_method=\"additive\", # add to the height of beads being boosted\n", - " boost_profile=\"gaussian\", # Height increase profile\n", - " boost_sigma_beads=None,\n", - " far_clip_nm=2.5, # Clip bead heights for beads that are not part of a crossing\n", - " far_clip_window_beads=3, # How many beads to clip the height for\n", - " return_crossing_info=True, # To ensure that mask information is passed to other functions. Change to False if masks are not needed\n", - " return_masks=True,\n", - ")\n", - "\n", - "# DNA mask\n", - "DNA_MASK_DILATE_PX = 3 # Dilation of mask for better training\n", - "\n", - "# Crossing mask\n", - "CROSS_MIN_SEP_BEADS = 12 # Where to stop for the crossing calculation\n", - "CROSS_SIGMA_CENTER_PX = 5.0 # How many pixels make us the center of a crossing\n", - "CROSS_SIGMA_PERP_PX = 1.8 # Crossing coloring parameter (how many pixels to modulate for the crossing)\n", - "CROSS_CHAIN_EXTENT = 5.0 # For the chain weighting of the mask\n", - "CROSS_CENTER_WEIGHT = 1.7 # For the chain weighting of the mask (chain-weighted guassian)\n", - "CROSS_CHAIN_WEIGHT = 0.9\n", - "CROSS_CLIP_TO_DNA_MASK = True" - ], - "metadata": { - "id": "sSqadS02hzPe" - }, - "id": "sSqadS02hzPe", - "execution_count": 50, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#1.3 Noise\n", - "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", - "\n", - "\n", - "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", - "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", - "\n" - ], - "metadata": { - "id": "-e3QvQzGlKYY" - }, - "id": "-e3QvQzGlKYY" - }, - { - "cell_type": "code", - "source": [ - "#Noise\n", - "TARGET_NOISE_RMS_NM = 0.19 # This variable controls the height of the noise in nanometers and this is added to the final image\n", - "\n", - "\n", - "# Blank SPM noise\n", - "USE_BLANK_SPM_NOISE = True\n", - "BLANK_SPM_PATH = \"/content/20240411_blank_water.0_00000.spm\" # optional; if missing/invalid, PSD fallback noise is used\n", - "\n", - "\n", - "# PSD-model fallback noise (used when no blank .spm file is available)\n", - "USE_PSD_NOISE_FALLBACK = True\n", - "MODEL_PATH = \"/content/psd_noise_model.npz\"\n", - "PSD_NOISE_METHOD = \"mean_full2d\" # or \"lognormal_full2d\" / \"empirical_radial\"\n", - "PSD_NOISE_STD_SCALE = 2.0\n", - "\n", - "#Checking if output directory will be created\n", - "os.makedirs(OUT_DIR, exist_ok=True)\n", - "for _sub in [\"images\", \"dna_masks\", \"cross_masks\", \"meta\"]:\n", - " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", - "\n", - "print(\"Output folder:\", os.path.abspath(OUT_DIR))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "47h81smulGxd", - "outputId": "4a3be80f-5c9c-4b70-fbb9-582470dff3c8" - }, - "id": "47h81smulGxd", - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Output folder: /content/dna_dataset_1000_4lengths\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_02", - "metadata": { - "id": "cell_md_02" - }, - "source": [ - "## 2. Chain Creation (3-D Persistent Random Walk)\n", - "Now we start with creating the DNA chain.\n", - "\n", - "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", - "\n", - "A 3-D plot is shown at the end of the cell so you can inspect the initial geometry immediately." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cell_code_02", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 607 - }, - "id": "cell_code_02", - "outputId": "38649466-c300-46ec-a88d-46b1908c1bbd" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "def make_tangled_ring_initial(\n", - " seed,\n", - " n_beads=N_BEADS,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " base_z=BASE_Z,\n", - "):\n", - " \"\"\"\n", - " Generates a closed (ring) polymer with a persistent random-walk tangent.\n", - " Each step rotates the current direction by a small random angle around a\n", - " perpendicular axis. Ring closure is enforced by linearly distributing\n", - " the end-to-end gap across all beads. The chain is then lifted to base_z\n", - " so it can relax down to the z=0 substrate during MD.\n", - "\n", - " Returns a list of [coords] with shape (N, 3).\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - "\n", - " steps = np.zeros((n_beads, 3), dtype=np.float32)\n", - " vec = np.array([1.0, 0.0, 0.0], dtype=np.float32) #Initialization of vector for chain propogation\n", - "\n", - " for k in range(n_beads):\n", - " dtheta = rng.normal(scale=np.sqrt(1.0 / persistence_bonds))\n", - " axis = rng.normal(size=3).astype(np.float32) #choose an axis at random for chain propogation in 3D\n", - " axis -= axis.dot(vec) * vec # remove component along vec\n", - " axis_norm = np.linalg.norm(axis)\n", - " if axis_norm < 1e-8:\n", - " axis = np.array([0.0, 0.0, 1.0], dtype=np.float32)\n", - " axis_norm = 1.0\n", - " axis /= axis_norm\n", - " vec = vec * np.cos(dtheta) + np.cross(axis, vec) * np.sin(dtheta) # Calculating vector for chain propogation\n", - " vec /= np.linalg.norm(vec)\n", - " steps[k] = bond_length * vec\n", - "\n", - " coords = np.cumsum(steps, axis=0) # Coordinates for open chain\n", - "\n", - " # Enforce ring closure\n", - " closure_error = coords[-1] - coords[0]\n", - " t = np.linspace(0, 1, n_beads, dtype=np.float32).reshape(-1, 1) # Based on the distance between first and last bead a closure error is propogated to all bead positions to form a closed chain\n", - " coords -= t * closure_error\n", - " coords -= coords.mean(axis=0) # This method works better than directing the chain to go away and then come back to original position\n", - "\n", - " # Lift above substrate\n", - " coords[:, 2] += float(base_z) # Changing z values to lift the chain off the substrate\n", - " return coords.astype(np.float32)\n", - "\n", - "\n", - "# ── Quick visualisation ──────────────────────────────────────────────────────\n", - "_demo_coords = make_tangled_ring_initial(seed=43)\n", - "_cc = np.vstack([_demo_coords, _demo_coords[0]]) # close the ring for plotting\n", - "\n", - "fig = plt.figure(figsize=(7, 6))\n", - "ax = fig.add_subplot(111, projection=\"3d\")\n", - "ax.plot(_cc[:, 0], _cc[:, 1], _cc[:, 2], \"-o\", markersize=3, linewidth=1.2)\n", - "ax.scatter(_cc[0, 0], _cc[0, 1], _cc[0, 2], color=\"red\", s=60, zorder=5, label=\"bead 0\")\n", - "\n", - "# Draw substrate plane\n", - "_xs = np.linspace(_cc[:, 0].min(), _cc[:, 0].max(), 2)\n", - "_ys = np.linspace(_cc[:, 1].min(), _cc[:, 1].max(), 2)\n", - "_X, _Y = np.meshgrid(_xs, _ys)\n", - "ax.plot_surface(_X, _Y, np.zeros_like(_X), alpha=0.15, color=\"cyan\")\n", - "\n", - "ax.set_xlabel(\"x (sim units)\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z\")\n", - "ax.set_title(f\"Initial ring — seed 42, {len(_demo_coords)} beads\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_03", - "metadata": { - "id": "cell_md_03" - }, - "source": [ - "## 3. Molecular Dynamics Relaxation\n", - "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", - "\n", - "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", - "Here we have added:\n", - "\n", - "1. Harmonic bonds\n", - "2. Bending-angle stiffness\n", - "3. A surface-attraction to cause chain relaxation\n", - "4. A hard-wall force to keep chain above substrate\n", - "5. A short-range WCA repulsion to ensure chain does not take non physical configurations\n", - "\n", - "The function then records `n_frames` snapshots which can be used for visualization." - ] - }, - { - "cell_type": "markdown", - "source": [ - "We start with testing the OpenMM installation (can be tricky at times)" - ], - "metadata": { - "id": "wFFhsVZhqU8a" - }, - "id": "wFFhsVZhqU8a" - }, - { - "cell_type": "code", - "source": [ - "## Test the OpenMM installation before running the following cell to ensure that GPU acceleration will work\n", - "import openmm.testInstallation\n", - "\n", - "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", - "try:\n", - " openmm.testInstallation.main()\n", - "except Exception as e:\n", - " print(f\"Test failed: {e}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3NjBSwfAe4Mj", - "outputId": "091b8222-4bf3-4cd3-bb30-f68eb55c2670" - }, - "id": "3NjBSwfAe4Mj", - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "OpenMM Version: 8.5\n", - "Git Revision: b55e60882dffcddb2532cceda4201c0fdc9ec2d5\n", - "\n", - "There are 4 Platforms available:\n", - "\n", - "1 Reference - Successfully computed forces\n", - "2 CPU - Successfully computed forces\n", - "3 CUDA - Error computing forces with CUDA platform\n", - "4 OpenCL - Successfully computed forces\n", - "\n", - "CUDA platform error: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "\n", - "Median difference in forces between platforms:\n", - "\n", - "Reference vs. CPU: 6.29541e-06\n", - "Reference vs. OpenCL: 6.74321e-06\n", - "CPU vs. OpenCL: 7.88764e-07\n", - "\n", - "All differences are within tolerance.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "And then once we are sure that OpenMM is correctly installed we run the MD functions" - ], - "metadata": { - "id": "9XbRNS3TqbKH" - }, - "id": "9XbRNS3TqbKH" - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "cell_code_03", - "metadata": { - "id": "cell_code_03" - }, - "outputs": [], - "source": [ - "def run_md_relaxation(coords0, seed,\n", - " n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME):\n", - " \"\"\"\n", - " Takes initial bead coordinates and runs Langevin dynamics (pure OpenMM).\n", - "\n", - " Forces applied:\n", - " - Harmonic bonds + angle stiffness along the ring\n", - " - Surface attraction (linear in z) + hard wall at z = 0\n", - " - Short-range WCA repulsion between non-bonded beads\n", - "\n", - " Returns a list of (n_beads, 3) coordinate arrays (n_frames + 1 entries;\n", - " frame 0 is the pre-minimisation geometry).\n", - " \"\"\"\n", - " coords0 = np.asarray(coords0, dtype=np.float32)\n", - " n = int(coords0.shape[0])\n", - "\n", - " extent = (coords0.max(axis=0) - coords0.min(axis=0)).max() # how big the bounding box for the molecular dynamics needs to be\n", - " box = float(extent + 10.0)\n", - "\n", - " # ── Reduced-unit constants (matches polychrom temperature=1 convention) ──\n", - " # All lengths in nm, energies in kJ/mol.\n", - " BOLTZ_kJmol = 0.00831445 # kJ / (mol · K)\n", - " temperature = 1.0 # K (reduced; sets energy scale kT ≈ 0.0083 kJ/mol)\n", - " kT = BOLTZ_kJmol * temperature # kJ/mol\n", - " conlen = 1.0 # nm\n", - " mass_amu = 100.0 # Da per bead\n", - " collision_rate = 0.6 # ps⁻¹\n", - " error_tol = 0.005\n", - "\n", - " # ── Build system ─────────────────────────────────────────────────────────\n", - " system = openmm.System()\n", - " system.setDefaultPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - " for _ in range(n):\n", - " system.addParticle(mass_amu)\n", - "\n", - " # Remove COM drift — applied every 50 steps\n", - " system.addForce(openmm.CMMotionRemover(50)) # To ensure CPU and GPU calculation are consistent (since GPU minimizations calculations can suffer from drift)\n", - "\n", - " # ── 1. Harmonic bonds ────────────────────────────────────────────────────\n", - "\n", - " # OpenMM HarmonicBondForce: E = (k/2)·(r − r0)² → k = kT / wiggle²\n", - " bond_L = BOND_LENGTH # nm\n", - " wiggle = 0.1 # nm (bondWiggleDistance)\n", - " k_bond = kT / (wiggle ** 2) # kJ/mol/nm²\n", - "\n", - " bond_force = openmm.HarmonicBondForce()\n", - " bond_force.setUsesPeriodicBoundaryConditions(True)\n", - " for i in range(n):\n", - " bond_force.addBond(i, (i + 1) % n, bond_L, k_bond)\n", - " system.addForce(bond_force)\n", - "\n", - " # ── 2. Bending (angle) stiffness ─────────────────────────────────────────\n", - " # E = kT · k_angle · (1 − cos(θ − π)) — equilibrium at θ = π (straight)\n", - " k_angle = K_ANGLE\n", - " angle_force = openmm.CustomAngleForce(\n", - " \"kT * kangle * (1 - cos(theta - 3.141592653589793))\"\n", - " )\n", - " angle_force.addGlobalParameter(\"kT\", kT)\n", - " angle_force.addGlobalParameter(\"kangle\", k_angle)\n", - " for i in range(n):\n", - " angle_force.addAngle((i - 1) % n, i, (i + 1) % n, [])\n", - " system.addForce(angle_force)\n", - "\n", - " # ── 3. Surface: linear attraction (z > 0) + harmonic hard wall (z < 0) ──\n", - " wall_expr = (\n", - " \"kT * (\"\n", - " \" F_pull * z * step(z) + \"\n", - " \" 0.5 * wallK * (z^2) * step(-z)\"\n", - " \")\"\n", - " )\n", - " wall_force = openmm.CustomExternalForce(wall_expr)\n", - " wall_force.addGlobalParameter(\"kT\", kT)\n", - " wall_force.addGlobalParameter(\"F_pull\", 9.0)\n", - " wall_force.addGlobalParameter(\"wallK\", 100.0)\n", - " for i in range(n):\n", - " wall_force.addParticle(i, [])\n", - " system.addForce(wall_force)\n", - "\n", - " # ── 4. WCA repulsion (repulsion between non-bonded pairs only) ──────────────────────────────\n", - " rep_sigma = 1.4 * conlen # nm\n", - " rep_cutoff = 2.3 * conlen # nm\n", - " rep_expr = (\n", - " \"4*eps*((sig/r)^12 - (sig/r)^6 + 0.25) * step(cutoff - r); \"\n", - " \"cutoff = 2^(1.0/6.0) * sig\"\n", - " )\n", - " rep_force = openmm.CustomNonbondedForce(rep_expr)\n", - " rep_force.setNonbondedMethod(openmm.CustomNonbondedForce.CutoffPeriodic)\n", - " rep_force.setCutoffDistance(rep_cutoff)\n", - " rep_force.addGlobalParameter(\"eps\", 1.7 * kT)\n", - " rep_force.addGlobalParameter(\"sig\", rep_sigma)\n", - " for i in range(n):\n", - " rep_force.addParticle([])\n", - " for i in range(n): # exclude bonded neighbours\n", - " rep_force.addExclusion(i, (i + 1) % n)\n", - " system.addForce(rep_force)\n", - "\n", - " # ── Integrator ────────────────────────────────────────────────────────────\n", - " integrator = openmm.VariableLangevinIntegrator(temperature, collision_rate, error_tol)\n", - " integrator.setRandomNumberSeed(int(seed))\n", - "\n", - " # ── Platform selection: OpenCL → CPU fallback ─────────────────────────────\n", - " context = None\n", - " for platform_name in (\"CUDA\",\"OpenCL\",\"CPU\"): # OpenCL enables Mac users to be able to use GPU acceleration as well\n", - " try:\n", - " platform = openmm.Platform.getPlatformByName(platform_name)\n", - " context = openmm.Context(system, integrator, platform)\n", - " print(f\"Using {platform_name} platform.\")\n", - " break\n", - " except openmm.OpenMMException as e:\n", - " print(f\" {platform_name} unavailable: {e}\")\n", - " except Exception as e:\n", - " print(f\" {platform_name} failed unexpectedly: {e}\")\n", - "\n", - " if context is None:\n", - " raise RuntimeError(\"No OpenMM platform could be initialised.\")\n", - "\n", - " # ── Set initial positions and box ─────────────────────────────────────────\n", - " positions = [\n", - " openmm.Vec3(float(coords0[i, 0]), float(coords0[i, 1]), float(coords0[i, 2]))\n", - " for i in range(n)\n", - " ]\n", - " context.setPositions(positions)\n", - " context.setPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - "\n", - " # Capture the true initial geometry before any minimisation (just in case the chain reaches substrate even before the 1st frame)\n", - " frames = []\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " # Cap iterations\n", - " openmm.LocalEnergyMinimizer.minimize(context, tolerance=10, maxIterations=200)\n", - "\n", - " # ── Record frames ─────────────────────────────────────────────────────────\n", - " for _ in range(int(n_frames)):\n", - " integrator.step(int(steps_per_frame))\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " return frames # n_frames + 1 entries (frame 0 = initial geometry)" - ] - }, - { - "cell_type": "markdown", - "source": [ - "##4. Non-MD chain generation\n", - "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." - ], - "metadata": { - "id": "Bx7_OzWRr-l3" - }, - "id": "Bx7_OzWRr-l3" - }, - { - "cell_type": "code", - "source": [ - "def make_ring_2d_persistent_initial(\n", - " seed,\n", - " n_beads,\n", - " bond_length=BOND_LENGTH, # From Cell 1\n", - " persistence_bonds=PERSISTENCE_BONDS, # From Cell 1\n", - " angle_stiffness_mult=ANGLE_STIFNESS_MULT, # From Cell 1\n", - " z_std=0.08, # Small Gaussian noise for Z\n", - " base_z=0.2, # Low altitude for \"deposited\" look\n", - " angle_limit=np.pi/2 # Acts as a soft per-step cap\n", - "):\n", - " \"\"\"\n", - " Generate a closed 2D persistent ring and return it as [coords] with shape (N, 3).\n", - "\n", - " Key ideas:\n", - " - Build a periodic sequence of turning angles (curvature increments).\n", - " - Smooth that sequence on the ring to suppress sharp local corners.\n", - " - Enforce total turning = 2*pi so the tangent field is compatible with a closed loop.\n", - " - Integrate the tangent field to produce a smooth closed curve.\n", - " - Resample by arc length to keep bead spacing close to bond_length.\n", - " - Preserve the original Gaussian z-noise deposited-state logic.\n", - "\n", - " Parameter roles:\n", - " - persistence_bonds:\n", - " Larger values -> smoother / stiffer ring with longer directional memory.\n", - " Smaller values -> rougher / more flexible ring.\n", - " - angle_stiffness_mult:\n", - " Larger values -> smaller angular fluctuations -> smoother chain.\n", - " Smaller values -> larger angular fluctuations -> more tortuous chain.\n", - " - angle_limit:\n", - " Used as a soft cap on local turning increments.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - " n = int(n_beads)\n", - " if n < 6:\n", - " raise ValueError(\"n_beads must be at least 6 for a stable closed persistent ring.\")\n", - "\n", - " bond_length = float(bond_length)\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 1) Build a periodic turning-angle process on the ring\n", - " # ------------------------------------------------------------------\n", - " # Keep the same qualitative scaling as the old code:\n", - " # larger persistence_bonds / angle_stiffness_mult => less angular noise\n", - " sigma_dtheta = (\n", - " np.sqrt(2.0 / max(float(persistence_bonds), 1.0))\n", - " / max(float(angle_stiffness_mult), 1e-6)\n", - " )\n", - "\n", - " # Raw local turning increments\n", - " dtheta = rng.normal(0.0, sigma_dtheta, size=n).astype(np.float64)\n", - " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", - "\n", - " # Circular smoothing of turning increments to suppress sharp local corners.\n", - " # Stronger persistence / stiffness => broader smoothing.\n", - " smooth_width = int(np.clip(np.round(max(float(persistence_bonds), 1.0) / 6.0), 1, 8))\n", - " if smooth_width > 0:\n", - " offsets = np.arange(-smooth_width, smooth_width + 1, dtype=np.float64)\n", - " kernel_sigma = max(smooth_width / 2.0, 1e-6)\n", - " kernel = np.exp(-0.5 * (offsets / kernel_sigma) ** 2)\n", - " kernel /= kernel.sum()\n", - "\n", - " dtheta_smooth = np.zeros_like(dtheta)\n", - " for shift, w in zip(offsets.astype(int), kernel):\n", - " dtheta_smooth += w * np.roll(dtheta, shift)\n", - " dtheta = dtheta_smooth\n", - "\n", - " # Enforce exact total turning of 2*pi for a closed planar ring.\n", - " # This avoids the nonphysical post hoc closure drift used before.\n", - " total_turn = dtheta.sum()\n", - " dtheta += (2.0 * np.pi - total_turn) / n\n", - "\n", - " # Re-clip gently after correction, then re-enforce total turning once more.\n", - " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", - " dtheta += (2.0 * np.pi - dtheta.sum()) / n\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 2) Build periodic tangent angles and integrate to a dense closed path\n", - " # ------------------------------------------------------------------\n", - " theta0 = rng.uniform(0.0, 2.0 * np.pi)\n", - " thetas = theta0 + np.cumsum(dtheta)\n", - "\n", - " # Use a dense piecewise-linear curve first; later resample uniformly.\n", - " tangents = np.stack([np.cos(thetas), np.sin(thetas)], axis=1)\n", - "\n", - " # Integrate unit-speed steps to get a provisional loop\n", - " xy = np.zeros((n, 2), dtype=np.float64)\n", - " xy[1:] = np.cumsum(tangents[:-1] * bond_length, axis=0)\n", - " end_error = (xy[-1] + tangents[-1] * bond_length) - xy[0]\n", - "\n", - " # Distribute closure correction through the bond vectors,\n", - " # which is less distorting than shifting coordinates linearly.\n", - " bond_corr = end_error / n\n", - " bonds = tangents * bond_length - bond_corr[None, :]\n", - "\n", - " # Renormalize bond lengths back toward bond_length while keeping closure.\n", - " bond_norms = np.linalg.norm(bonds, axis=1)\n", - " good = bond_norms > 1e-12\n", - " bonds[good] *= (bond_length / bond_norms[good])[:, None]\n", - "\n", - " # Re-enforce closure after renormalization\n", - " bonds -= bonds.sum(axis=0, keepdims=True) / n\n", - "\n", - " # Reconstruct coordinates from corrected bonds\n", - " xy = np.zeros((n, 2), dtype=np.float64)\n", - " xy[1:] = np.cumsum(bonds[:-1], axis=0)\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 3) Arc-length resample so bead spacing stays uniform and physical\n", - " # ------------------------------------------------------------------\n", - " closed_xy = np.vstack([xy, xy[0]])\n", - " seg = np.linalg.norm(np.diff(closed_xy, axis=0), axis=1)\n", - " s = np.concatenate([[0.0], np.cumsum(seg)])\n", - " total_len = s[-1]\n", - "\n", - " if total_len <= 1e-12:\n", - " raise ValueError(\"Degenerate ring generated; total contour length is zero.\")\n", - "\n", - " target_s = np.linspace(0.0, total_len, n + 1)[:-1]\n", - "\n", - " x_resamp = np.interp(target_s, s, closed_xy[:, 0])\n", - " y_resamp = np.interp(target_s, s, closed_xy[:, 1])\n", - " xy = np.stack([x_resamp, y_resamp], axis=1)\n", - "\n", - " # Final contour-length normalization so mean bond length matches bond_length\n", - " final_bonds = np.roll(xy, -1, axis=0) - xy\n", - " mean_bond = np.mean(np.linalg.norm(final_bonds, axis=1))\n", - " if mean_bond > 1e-12:\n", - " xy *= (bond_length / mean_bond)\n", - "\n", - " # Center at origin\n", - " xy -= xy.mean(axis=0, keepdims=True)\n", - "\n", - " # ------------------------------------------------------------------\n", - " # 4) Assembly with preserved Gaussian z-noise logic\n", - " # ------------------------------------------------------------------\n", - " coords = np.zeros((n, 3), dtype=np.float32)\n", - " coords[:, 0] = xy[:, 0].astype(np.float32)\n", - " coords[:, 1] = xy[:, 1].astype(np.float32)\n", - " coords[:, 2] = rng.normal(loc=base_z, scale=z_std, size=n).astype(np.float32)\n", - "\n", - " # Return as a list containing one frame to mimic run_md_relaxation output\n", - " return [coords]" - ], - "metadata": { - "id": "yXs0I0akr948" - }, - "id": "yXs0I0akr948", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "id": "cell_md_04", - "metadata": { - "id": "cell_md_04" - }, - "source": [ - "## 5. MD Animation\n", - "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", - "\n", - "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", - "Running this cell will automatically generate and display the animation for a single deterministic chain (seed 0, default bead count)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cell_code_04", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 228 - }, - "id": "cell_code_04", - "outputId": "db68e636-09bb-4ac9-af95-187f12ebf7d6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generating initial ring and running MD (seed=0)…\n" - ] - }, - { - "output_type": "error", - "ename": "NameError", - "evalue": "name 'make_tangled_ring_initial' is not defined", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_7956/24671148.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# ── Run animation automatically (deterministic seed) ─────────────────────────\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Generating initial ring and running MD (seed=0)…\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0m_anim_coords0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_tangled_ring_initial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m _anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n\u001b[1;32m 61\u001b[0m \u001b[0mn_frames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_FRAMES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'make_tangled_ring_initial' is not defined" - ] - } - ], - "source": [ - "def show_md_animation(frames, interval_ms=150):\n", - " \"\"\"\n", - " Renders an in-notebook HTML5 animation of MD frames.\n", - " Pure visualisation — does not mutate any arrays.\n", - "\n", - " \"\"\"\n", - " frames = [np.asarray(f, dtype=np.float32) for f in frames]\n", - "\n", - " # Use only the first frame for x/y limits — avoids COM drift blowing the scale\n", - " first = frames[0]\n", - " xy_pad = 5.0\n", - " x_min, x_max = first[:, 0].min() - xy_pad, first[:, 0].max() + xy_pad\n", - " y_min, y_max = first[:, 1].min() - xy_pad, first[:, 1].max() + xy_pad\n", - "\n", - " # Clamp z: floor at -1 (just below substrate), ceiling from initial max + padding\n", - " z_min = -1.0\n", - " z_max = float(first[:, 2].max()) + 3.0\n", - "\n", - " x_plane = np.linspace(x_min, x_max, 2)\n", - " y_plane = np.linspace(y_min, y_max, 2)\n", - " X_plane, Y_plane = np.meshgrid(x_plane, y_plane)\n", - " Z_plane = np.zeros_like(X_plane)\n", - "\n", - " fig = plt.figure(figsize=(6, 6))\n", - " ax = fig.add_subplot(111, projection=\"3d\")\n", - "\n", - " def _set_axes():\n", - " ax.set_xlim(x_min, x_max)\n", - " ax.set_ylim(y_min, y_max)\n", - " ax.set_zlim(z_min, z_max)\n", - " ax.set_xlabel(\"x\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z (nm)\")\n", - "\n", - " def init():\n", - " _set_axes()\n", - " return []\n", - "\n", - " def update(i):\n", - " ax.cla()\n", - " c = frames[i]\n", - " # Clip beads to visible z range for plotting so outliers don't distort\n", - " visible = (c[:, 2] >= z_min) & (c[:, 2] <= z_max)\n", - " cc = np.vstack([c, c[0]])\n", - " ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-\", linewidth=1)\n", - " ax.scatter(c[visible, 0], c[visible, 1], c[visible, 2], s=5)\n", - " ax.plot_surface(X_plane, Y_plane, Z_plane, alpha=0.2, color=\"cyan\")\n", - " _set_axes()\n", - " ax.set_title(f\"Frame {i}/{len(frames)-1} | z ∈ [{c[:,2].min():.1f}, {c[:,2].max():.1f}] nm\")\n", - " return []\n", - "\n", - " ani = animation.FuncAnimation(\n", - " fig, update, frames=len(frames), init_func=init,\n", - " interval=int(interval_ms), blit=False\n", - " )\n", - " plt.close(fig)\n", - " display(HTML(ani.to_jshtml()))\n", - "\n", - "\n", - "# ── Run animation automatically (deterministic seed) ─────────────────────────\n", - "print(\"Generating initial ring and running MD (seed=0)…\")\n", - "_anim_coords0 = make_tangled_ring_initial(seed=0)\n", - "_anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - "print(f\"MD done — {len(_anim_frames)} frames. Rendering animation…\")\n", - "show_md_animation(_anim_frames)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_05", - "metadata": { - "id": "cell_md_05" - }, - "source": [ - "## 6. Height based AFM Rendering\n", - "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", - "\n", - "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "cell_code_05", - "metadata": { - "id": "cell_code_05" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n# Low-level geometry helpers\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef _seg_intersect_2d(p1, p2, q1, q2, eps=1e-12):\n \"\"\"\n Strict 2-D segment intersection.\n Returns (hit, pt, t, u) where pt = p1 + t*(p2-p1) = q1 + u*(q2-q1).\n Returns (False, None, None, None) for parallel / collinear segments.\n \"\"\"\n p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n r = p2 - p1; s = q2 - q1\n rxs = r[0]*s[1] - r[1]*s[0]\n if abs(rxs) < eps:\n return False, None, None, None\n qp = q1 - p1\n t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n return True, p1 + t*r, t, u\n return False, None, None, None\n\n\ndef _circ_sep(n, i, j):\n \"\"\"Circular distance between bead indices on a ring of n beads.\"\"\"\n d = abs(int(i) - int(j)) #Helper function to find out which beads will later form crossings and which beads are just neighbours\n return min(d, n - d)\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# Structuring-element / grid helpers\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef disk_footprint(r_px: float):\n \"\"\"Boolean circular footprint of radius r_px pixels.\"\"\"\n if r_px < 0.5:\n return np.ones((1, 1), dtype=bool)\n r = int(np.ceil(r_px)) #Used to convert individual beads into DNA like chain (dilates each bead to a disk like structure)\n y, x = np.ogrid[-r:r+1, -r:r+1]\n return (x*x + y*y) <= r*r\n\n\ndef upsample_nn(a, out_shape):\n \"\"\"\n Nearest-neighbour upsample array a to out_shape using integer repeat.\n Truncates to exactly out_shape after repeating.\n\n Returns an ndarray of the same dtype as a with shape out_shape.\n \"\"\"\n ny_out, nx_out = out_shape\n ny_in, nx_in = a.shape\n if (ny_out, nx_out) == (ny_in, nx_in):\n return a\n sy = max(ny_out // ny_in, 1)\n sx = max(nx_out // nx_in, 1)\n a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n return a_up[:ny_out, :nx_out]\n\n\n#internal render grid now comes directly from extent and user nm_per_px (changed from earlier logic after Tom's comments on how chains should sit in the frame and what users should be able to choose)\ndef compute_internal_render_grid(extent, target_nm_per_px, min_size=16):\n \"\"\"\n Build the internal render grid from physical extent and user nm_per_px.\n Ensures pixel count is at least min_size along each axis.\n\n Returns (nx, ny, actual_nm_per_px) as (int, int, float).\n \"\"\"\n xmin, xmax, ymin, ymax = extent\n width_nm = float(xmax - xmin)\n height_nm = float(ymax - ymin)\n p_nm_per_px = max(float(target_nm_per_px), 1e-6)\n nx = max(int(min_size), int(np.ceil(width_nm / p_nm_per_px)) + 1)\n ny = max(int(min_size), int(np.ceil(height_nm / p_nm_per_px)) + 1)\n return nx, ny, p_nm_per_px\n\n\ndef resize_image_to_shape(a, out_shape, order=1):\n \"\"\"\n Resize 2D array a to out_shape using scipy.ndimage.zoom.\n Falls back to edge-padding if zoom undershoots the target shape.\n\n Returns a float32 array of exactly out_shape.\n \"\"\"\n a = np.asarray(a)\n out_shape = tuple(int(v) for v in out_shape)\n if a.shape == out_shape:\n return a.copy()\n zoom_factors = (out_shape[0] / a.shape[0], out_shape[1] / a.shape[1])\n out = zoom(a, zoom_factors, order=order, mode=\"nearest\", prefilter=(order > 1))\n out = out[:out_shape[0], :out_shape[1]]\n if out.shape != out_shape:\n pad_y = max(0, out_shape[0] - out.shape[0])\n pad_x = max(0, out_shape[1] - out.shape[1])\n out = np.pad(out, ((0, pad_y), (0, pad_x)), mode=\"edge\")\n out = out[:out_shape[0], :out_shape[1]]\n return out\n\n\ndef disk_cap_structure(radius_nm: float, p_nm_per_px: float,\n max_radius_px=96):\n \"\"\"\n Hemispherical-cap structuring element representing the DNA cross-section.\n Each pixel inside the disk gets a height equal to the spherical-cap height\n at that radial distance.\n\n Returns (footprint_bool, structure_float32) where footprint_bool is the\n circular boolean mask and structure_float32 holds cap heights in nm\n (pixels outside the cap are set to -1e9).\n \"\"\"\n r_px = radius_nm / max(p_nm_per_px, 1e-12)\n R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n y, x = np.ogrid[-R:R+1, -R:R+1]\n d2 = x*x + y*y\n inside = d2 <= (r_px * r_px)\n d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n structure[inside] = np.sqrt(\n np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n ).astype(np.float32)\n return inside.astype(bool), structure\n\n\ndef AFM_tip(tip_radius_nm: float, p_nm_per_px: float,\n max_radius_px=128):\n \"\"\"\n Spherical-cap structuring element representing the AFM tip geometry.\n Used to convolve the surface with the tip shape.\n The structure values are offset so that the tip apex sits at zero height.\n\n Returns (footprint_bool, structure_float32) where footprint_bool is the\n circular boolean mask and structure_float32 holds tip heights in nm\n (pixels outside the tip are set to -1e9).\n \"\"\"\n Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n y, x = np.ogrid[-R:R+1, -R:R+1]\n d2 = x*x + y*y\n inside = d2 <= (Rpx * Rpx)\n d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n structure[inside] = (\n np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n - tip_radius_nm\n ).astype(np.float32)\n return inside.astype(bool), structure\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# Crossing boost\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef _taper_weight(idx_off, w, profile=\"gaussian\", sigma=None):\n \"\"\"\n Smooth weight in [0, 1] used to fade the crossing height boost along\n the bead-index window of half-width w.\n\n profile:\n 'gaussian' — smooth dome (recommended)\n 'hemisphere' — half-circle taper\n 'linear' — simple linear ramp\n\n Returns a float in [0, 1], where 1 is the peak weight (idx_off == 0)\n and the weight decays toward 0 at the window boundary.\n \"\"\"\n if w <= 0:\n return 1.0\n a = abs(idx_off)\n if profile == \"linear\":\n return 1.0 - (a / (w + 1.0))\n if profile == \"hemisphere\":\n x = a / (w + 1.0)\n return float(np.sqrt(max(0.0, 1.0 - x*x)))\n # gaussian (default)\n if sigma is None:\n sigma = max(1e-6, 0.5 * (w + 0.5))\n g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n if g_end < 0.999:\n g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n return g\n\n\ndef _collect_intersections(xy_nm, min_separation_beads=12,\n merge_radius_nm=0.5):\n \"\"\"\n Brute-force all segment pairs of a closed ring polyline to find crossings.\n Skips adjacent/shared-vertex pairs and contour-nearby pairs.\n Clusters nearby raw hits via _merge_crossing_clusters.\n\n Returns list of dicts:\n {pt_nm, seg_i, seg_j, t, u, n_hits}\n \"\"\"\n xy = np.asarray(xy_nm, dtype=float)\n n = int(xy.shape[0])\n raw = []\n for i in range(n):\n i2 = (i + 1) % n\n p1, p2 = xy[i], xy[i2]\n for j in range(i + 1, n):\n j2 = (j + 1) % n\n if i == j or i2 == j or j2 == i:\n continue\n if _circ_sep(n, i, j) < int(min_separation_beads):\n continue\n q1, q2 = xy[j], xy[j2]\n hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n if hit:\n raw.append(dict(pt=np.array(pt, float),\n seg_i=int(i), seg_j=int(j),\n t=float(t), u=float(u)))\n\n clusters = _merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n return clusters\n\n\ndef _merge_crossing_clusters(raw_list, merge_radius_nm=0.5):\n \"\"\"\n Greedy proximity-based clustering of raw crossing dicts.\n Each dict must have key 'pt' (2-D float array).\n Returns averaged clusters with representative metadata.\n \"\"\"\n clusters = []\n for r in raw_list:\n pt = r[\"pt\"].astype(float)\n placed = False\n for c in clusters:\n if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n c[\"pt_sum\"] += pt\n c[\"n\"] += 1\n c[\"items\"].append(r)\n placed = True\n break\n if not placed:\n clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n\n out = []\n for c in clusters:\n pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n rep = c[\"items\"][0]\n out.append(dict(\n pt_nm = pt,\n seg_i = int(rep[\"seg_i\"]),\n seg_j = int(rep[\"seg_j\"]),\n t = float(rep[\"t\"]),\n u = float(rep[\"u\"]),\n n_hits = int(c[\"n\"]),\n ))\n return out\n\n\ndef boost_crossings_intersections(\n x_nm, y_nm, zc_nm,\n crossings=None,\n min_separation_beads=12,\n boost_window_beads=2,\n guaranteed_offset_nm=1.0,\n boost_method=\"additive\",\n boost_profile=\"gaussian\",\n boost_sigma_beads=None,\n far_clip_nm=2.5,\n far_clip_window_beads=12,\n merge_radius_nm=0.5,\n):\n \"\"\"\n Boost z-height at strand crossings so they are visually distinct in\n the AFM image.\n\n For each crossing:\n - Determines which strand is on top (higher z)\n - Raises top-strand beads in a tapered window to at least\n (bottom_max + guaranteed_offset_nm)\n - Optionally clips non-crossing beads to far_clip_nm\n\n Returns (modified_z, crossing_info_list).\n \"\"\"\n x = np.asarray(x_nm, dtype=np.float32)\n y = np.asarray(y_nm, dtype=np.float32)\n z = np.asarray(zc_nm, dtype=np.float32).copy()\n n = int(z.shape[0])\n if n < 5:\n return z.astype(np.float32), []\n\n if crossings is None:\n xy = np.stack([x, y], axis=1)\n crossings = _collect_intersections(\n xy,\n min_separation_beads=int(min_separation_beads),\n merge_radius_nm=float(merge_radius_nm),\n )\n\n def get_segment_indices(center, w):\n return np.array([(center + k) % n for k in range(-w, w+1)],\n dtype=np.int32)\n\n crossing_info = []\n crossing_centers = []\n w = int(boost_window_beads)\n\n for c in crossings:\n i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n i2 = (i + 1) % n; j2 = (j + 1) % n\n ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n\n bead_i = i if ti < 0.5 else i2\n bead_j = j if uj < 0.5 else j2\n zi = float((1.0 - ti) * z[i] + ti * z[i2])\n zj = float((1.0 - uj) * z[j] + uj * z[j2])\n\n top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j, bead_i)\n top_seg = get_segment_indices(top_center, w)\n bottom_seg = get_segment_indices(bottom_center, w)\n\n bottom_local_max = float(z[bottom_seg].max())\n top_local_max = float(z[top_seg].max())\n\n if boost_method == \"absolute\":\n target_height = bottom_local_max + float(guaranteed_offset_nm)\n else:\n target_height = max(top_local_max, bottom_local_max) + float(guaranteed_offset_nm)\n\n for off in range(-w, w+1):\n k = (top_center + off) % n\n wt = float(_taper_weight(off, w,\n profile=boost_profile,\n sigma=boost_sigma_beads))\n z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n\n crossing_centers.extend([int(top_center), int(bottom_center)])\n crossing_info.append(dict(\n pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n seg_i = int(i),\n seg_j = int(j),\n top_center = int(top_center),\n bottom_center= int(bottom_center),\n ))\n\n # Clip beads far from any crossing\n if crossing_centers and far_clip_nm is not None:\n centers = np.array(crossing_centers, dtype=np.int32)\n r = int(far_clip_window_beads)\n for k in range(n):\n d = int(np.min(np.minimum(np.abs(k - centers),\n n - np.abs(k - centers))))\n if d > r:\n z[k] = min(z[k], float(far_clip_nm))\n\n return z.astype(np.float32), crossing_info\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# Main AFM renderer\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef create_z_based_afm(\n coords,\n nx=800, ny=800,\n dna_diameter_nm=2.0,\n tip_radius_nm=0.01,\n max_height_nm=6.0,\n target_nm_per_px=0.08,\n max_radius_px=96,\n radius_shrink_px=2.0,\n final_blur_sigma_px=0.20,\n apply_edge_taper=True,\n taper_sigma_nm=0.45,\n taper_floor=0.10,\n add_center_ridge=True,\n ridge_sigma_nm=0.25,\n ridge_amp_nm=0.25,\n grain_nm=0.0,\n grain_sigma_px=0.6,\n grain_seed=1,\n enable_crossing_boost=True,\n min_separation_beads=12,\n boost_window_beads=2,\n guaranteed_offset_nm=1.0,\n boost_method=\"additive\",\n boost_profile=\"gaussian\",\n boost_sigma_beads=None,\n crossings_precomputed=None,\n far_clip_nm=2.5,\n far_clip_window_beads=12,\n return_crossing_info=False,\n return_masks=True,\n extent=None,\n):\n \"\"\"\n Master AFM renderer. Pipeline:\n\n 1. Project 3-D bead coords onto a 2-D effective grid\n 2. Optionally boost crossing heights (boost_crossings_intersections)\n 3. Rasterise closed polyline with z-interpolation\n 4. Apply disk_cap_structure (DNA cylindrical cross-section)\n 5. Apply AFM_tip (tip-sample convolution)\n 6. Clip, blur, edge-taper, center-ridge\n 7. Optional synthetic grain noise\n 8. Upsample to requested (nx, ny)\n\n Returns (afm_image, debug_dict) when return_masks=True.\n \"\"\"\n x, y, z = coords.T\n if extent is None:\n xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n else:\n xmin, xmax, ymin, ymax = extent\n\n # CHANGED: render directly on the physical grid selected upstream\n nx_eff, ny_eff = int(nx), int(ny)\n px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n py_eff = (ymax - ymin) / max(ny_eff - 1, 1)\n p_eff = 0.5 * (px_eff + py_eff)\n\n ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n 0, nx_eff - 1)\n iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n 0, ny_eff - 1)\n\n zc = (z - z.min()).astype(np.float32)\n\n # ── Crossing boost ───────────────────────────────────────────────────────\n crossing_info = []\n if enable_crossing_boost:\n zc, crossing_info = boost_crossings_intersections(\n x.astype(np.float32), y.astype(np.float32), zc,\n crossings=crossings_precomputed,\n min_separation_beads=int(min_separation_beads),\n boost_window_beads=int(boost_window_beads),\n guaranteed_offset_nm=float(guaranteed_offset_nm),\n boost_method=str(boost_method),\n boost_profile=str(boost_profile),\n boost_sigma_beads=boost_sigma_beads,\n far_clip_nm=far_clip_nm,\n far_clip_window_beads=int(far_clip_window_beads),\n )\n\n # ── Rasterise polyline ───────────────────────────────────────────────────\n z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n npts = len(ix)\n for k in range(npts):\n k2 = (k + 1) % npts\n x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n if n_seg <= 1:\n z_line[y0, x0] = max(z_line[y0, x0], z0)\n line_mask[y0, x0] = True\n continue\n xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n if xs.size > 1:\n keep = np.ones(xs.shape[0], dtype=bool)\n keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n xs, ys, zs = xs[keep], ys[keep], zs[keep]\n z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n line_mask[ys, xs] = True\n\n # ── DNA cap dilation ─────────────────────────────────────────────────────\n # CHANGED: keep the DNA diameter in physical units; radius_shrink_px is disabled upstream\n r_eff = max(0.5 * float(dna_diameter_nm), 0.2)\n fp_dna, cap = disk_cap_structure(r_eff, p_eff, max_radius_px=max_radius_px)\n surface = grey_dilation(z_line, footprint=fp_dna,\n structure=cap).astype(np.float32)\n dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna) > 0\n surface[~dna_region] = 0.0\n\n # ── Tip convolution ──────────────────────────────────────────────────────\n r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n if r_tip_px >= 0.5:\n fp_tip, tip_struct = AFM_tip(\n float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n surface = grey_dilation(surface, footprint=fp_tip,\n structure=tip_struct).astype(np.float32)\n\n np.clip(surface, 0, max_height_nm, out=surface)\n if final_blur_sigma_px > 0:\n surface = gaussian_filter(surface,\n sigma=float(final_blur_sigma_px)).astype(np.float32)\n\n # ── Edge taper + center ridge ────────────────────────────────────────────\n if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n dist_nm = dist_px * np.float32(p_eff)\n if apply_edge_taper:\n sig = max(float(taper_sigma_nm), 1e-6)\n factor = taper_floor + (1.0 - taper_floor) * np.exp(\n -(dist_nm**2) / (2.0 * sig**2))\n surface[dna_region] *= factor[dna_region]\n if add_center_ridge and ridge_amp_nm > 0:\n rs = max(float(ridge_sigma_nm), 1e-6)\n ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n np.clip(surface, 0, max_height_nm, out=surface)\n\n # ── Synthetic grain ──────────────────────────────────────────────────────\n if grain_nm and grain_nm > 0:\n rng = np.random.default_rng(int(grain_seed))\n n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n if grain_sigma_px and grain_sigma_px > 0:\n n = gaussian_filter(n, sigma=float(grain_sigma_px)).astype(np.float32)\n std = float(n[dna_region].std()) if np.any(dna_region) else float(n.std())\n if std > 1e-9:\n n /= np.float32(std)\n surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n np.clip(surface, 0, max_height_nm, out=surface)\n\n if return_masks:\n debug = {\n \"line_mask\": line_mask,\n \"dna_region\": dna_region,\n \"extent\": (xmin, xmax, ymin, ymax),\n \"p_nm_per_px\": float(p_eff),\n }\n if return_crossing_info:\n debug[\"crossings\"] = crossing_info\n return surface, debug\n\n return surface, (xmin, xmax, ymin, ymax)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_06", - "metadata": { - "id": "cell_md_06" - }, - "source": [ - "## 7. Noise Functions\n", - "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", - "\n", - "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", - "2. If no blank `.spm` file is supplied, the notebook falls back to a PSD-based synthetic noise model.\n", - "\n", - "Note: Topostats is an AFM imaging software package developed at the University of Sheffield which is used for filtering of AFM data. We desribe functions that perform operations similar to Topostats to clean the noise data from the blank files before adding it to the AFM_img.\n", - "\n", - "### Mathematics of PSD-Matched Noise Synthesis\n", - "\n", - "All three methods in the code utilize **Frequency-Domain Synthesis**. The core principle is that the height field $z(x, y)$ can be generated from a target Power Spectral Density (PSD) using the inverse Fourier Transform.\n", - "\n", - "#### 1. General Synthesis Workflow\n", - "For an output image of shape $(N_y, N_x)$:\n", - "1. **Define a PSD**: Let $S(f_x, f_y)$ be the target power spectrum.\n", - "2. **Generate Complex Spectrum**: A complex field $Z(f_x, f_y)$ is created in the frequency domain:\n", - " $$Z(f_x, f_y) = \\sqrt{S(f_x, f_y)} \\cdot e^{i \\phi(f_x, f_y)}$$\n", - " where $\\phi$ is a random phase drawn from a uniform distribution $U(0, 2\\pi)$.\n", - "3. **Inverse Transform**: The spatial noise $z(x, y)$ is the real part of the Inverse Fast Fourier Transform (IFFT) of $Z$.\n", - "4. **RMS Scaling**: The image is normalized such that its standard deviation matches the target Root Mean Square (RMS) roughness." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "cell_code_06", - "metadata": { - "id": "cell_code_06" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n# Parsing the .spm file for height data\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef robust_range(a):\n \"\"\"\n Compute the robust dynamic range of an array using the 1st and 99th percentiles.\n Only finite values are included in the percentile calculation.\n\n Returns (p1, p99, range) as (float, float, float).\n \"\"\"\n a = np.asarray(a, dtype=np.float32)\n p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n return float(p1), float(p99), float(p99 - p1)\n\n\ndef looks_like_afm_height(img):\n \"\"\"\n Heuristic check for whether img looks like a valid AFM height image.\n Rejects arrays with non-finite, zero, or implausibly large dynamic ranges.\n\n Returns True if the image passes all sanity checks, False otherwise.\n \"\"\"\n p1, p99, rng = robust_range(img)\n return np.isfinite(rng) and 0 < rng <= 1e9\n\n\ndef maybe_to_nm(img):\n \"\"\"\n Auto-convert img from metres to nanometres if its values appear metre-scale\n (i.e., the 99th-percentile absolute value is below 1e-3).\n\n Returns (img_nm, unit_note) where img_nm is float32 in nm and\n unit_note is a short string describing the conversion applied.\n \"\"\"\n p1, p99, rng = robust_range(img)\n mx = max(abs(p1), abs(p99))\n if mx < 1e-3:\n return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n return img.astype(np.float32), \"as-is (nm or counts)\"\n\n\ndef parse_blocks(filename, max_blocks=128):\n \"\"\"\n Read a Bruker .spm file and parse binary data-block metadata.\n Returns (raw_bytes, header_string, block_list).\n \"\"\"\n raw = open(filename, \"rb\").read()\n i_end = raw.find(b\"\\x1a\")\n if i_end == -1:\n i_end = min(len(raw), 400_000)\n header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n\n matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n if not matches:\n raise RuntimeError(\"No 'Data offset' found in header.\")\n\n blocks = []\n for k, pos in enumerate(matches[:max_blocks]):\n win = header[max(0, pos-2500): min(len(header), pos+2500)]\n\n def grab_int(key):\n m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n return int(m.group(1)) if m else None\n\n def grab_float(key):\n m = re.search(\n rf\"{re.escape(key)}\\s*:\\s*([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\",\n win)\n return float(m.group(1)) if m else None\n\n name_candidates = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n name = name_candidates[-1].strip() if name_candidates else None\n\n off = grab_int(\"Data offset\")\n length = grab_int(\"Data length\")\n bpp = grab_int(\"Bytes/pixel\")\n nx_b = grab_int(\"Samps/line\")\n ny_b = grab_int(\"Number of lines\")\n zscale = grab_float(\"Z scale\")\n zoff = grab_float(\"Z offset\")\n\n if None in (off, length, bpp, nx_b, ny_b):\n continue\n blocks.append(dict(name=name or f\"block_{k}\",\n off=off, length=length, bpp=bpp,\n nx=nx_b, ny=ny_b,\n zscale=zscale, zoff=zoff))\n\n uniq, seen = [], set()\n for b in blocks:\n if b[\"off\"] not in seen:\n uniq.append(b); seen.add(b[\"off\"])\n if not uniq:\n raise RuntimeError(\"Found 'Data offset' strings but no parseable blocks.\")\n return raw, header, uniq\n\n\ndef read_block_candidates(raw, b):\n \"\"\"\n Decode one binary data block, trying all plausible dtypes.\n Applies z-scale / z-offset if present.\n Returns list of (dtype_str, image_array).\n \"\"\"\n off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n blob = raw[off:off+length]\n if len(blob) < length:\n raise RuntimeError(\"Truncated block.\")\n\n n = nx_b * ny_b\n cands = []\n dtype_map = {2: [\"i2\", \"u2\"],\n 4: [\"i4\", \"f4\"],\n 1: [\"u1\"]}\n for dt in dtype_map.get(bpp, []):\n arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n if arr.size != n:\n continue\n cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n\n out = []\n for dt, img in cands:\n z = img\n if b.get(\"zscale\") is not None:\n z = z * np.float32(b[\"zscale\"])\n if b.get(\"zoff\") is not None:\n z = z + np.float32(b[\"zoff\"])\n out.append((dt, z))\n return out\n\n\ndef choose_best_height_image(raw, blocks, verbose=True):\n \"\"\"\n Iterate all blocks and dtype candidates, pick the one that:\n - passes looks_like_afm_height\n - has the highest log-variance score\n\n Returns (best_block_dict, best_image_float32).\n Raises RuntimeError if no plausible height image is found.\n \"\"\"\n best, best_score = None, -np.inf\n for b in blocks:\n try:\n for dt, img in read_block_candidates(raw, b):\n if not looks_like_afm_height(img):\n continue\n p1, p99, rng = robust_range(img)\n var = float(np.var(img))\n score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n if score > best_score:\n best_score = score\n best = (b, dt, img, (p1, p99, rng, var))\n except Exception:\n continue\n\n if best is None:\n raise RuntimeError(\"Could not find a plausible AFM height image decode.\")\n b, dt, img, stats = best\n if verbose:\n p1, p99, rng, var = stats\n print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n f\"bpp={b['bpp']} decode={dt}\")\n print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, range={rng:.3g}, var={var:.3g}\")\n return b, img\n\n\n# ─────────────────────────────────────────────────────────────────────────────\n# TopoStats-like background / noise extraction\n# ─────────────────────────────────────────────────────────────────────────────\n\ndef plane_remove(z, mask=None):\n \"\"\"\n Fit and subtract a tilted plane from z using least-squares.\n If mask is provided, only background (mask == True) pixels are used for fitting.\n\n Returns the plane-subtracted image as float32.\n \"\"\"\n ny, nx = z.shape\n Y, X = np.mgrid[0:ny, 0:nx]\n A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n b = z.ravel()\n if mask is not None:\n m = mask.ravel().astype(bool)\n A, b = A[m], b[m]\n C, *_ = np.linalg.lstsq(A, b, rcond=None)\n plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n return (z - plane).astype(np.float32)\n\n\ndef line_flatten_median(z, mask=None):\n \"\"\"\n Subtract the per-row median from z to remove line-by-line offset artefacts.\n If mask is provided, the median is computed over background (mask == True)\n pixels only; rows with no unmasked pixels fall back to the full-row median.\n\n Returns the line-flattened image as float32.\n \"\"\"\n out = z.astype(np.float32, copy=True)\n for i in range(out.shape[0]):\n if mask is None or not np.any(mask[i]):\n med = np.median(out[i])\n else:\n med = np.median(out[i, mask[i]])\n out[i] -= np.float32(med)\n return out\n\n\ndef feature_mask(z, smooth_sigma_px=3.0, thresh_sigma=3.0, dilate_px=4):\n \"\"\"\n Detect features (DNA, particles) as pixels deviating by more than\n thresh_sigma MADs from the smooth-residual background.\n The detected region is optionally dilated by dilate_px pixels.\n\n Returns a boolean array of the same shape as z, True where features are detected.\n \"\"\"\n z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n resid = (z - z_s).astype(np.float32)\n med = float(np.median(resid))\n mad = float(np.median(np.abs(resid - med)))\n sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n if dilate_px and dilate_px > 0:\n r = int(dilate_px)\n yy, xx = np.ogrid[-r:r+1, -r:r+1]\n fp = (xx*xx + yy*yy) <= r*r\n feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n return feat\n\n\ndef inpaint_simple(z, mask, smooth_sigma_px=8.0):\n \"\"\"\n Replace masked pixels (e.g., DNA features) with a heavily blurred background\n estimate so that subsequent noise extraction is not biased by foreground features.\n\n Returns the inpainted image as float32.\n \"\"\"\n bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n out = z.copy()\n out[mask] = bg[mask]\n return out\n\n\ndef bandpass_noise(z, low_sigma_px=1.0, high_sigma_px=30.0):\n \"\"\"\n Bandpass filter: subtract large-scale trend from small-scale smoothed\n image to isolate mid-frequency noise texture.\n\n Returns the bandpass-filtered image as float32.\n \"\"\"\n low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n return (low - high).astype(np.float32)\n\n\ndef extract_topostats_like_noise(\n z_in,\n median_size=3,\n bg_quantile=40,\n feature_sigma_px=3.0,\n feature_thresh_sigma=3.0,\n feature_dilate_px=4,\n inpaint_sigma_px=10.0,\n band_low_sigma_px=0.8,\n band_high_sigma_px=35.0,\n target_rms_nm=None,\n):\n \"\"\"\n Full noise-extraction pipeline:\n plane-flatten → line-flatten → detect features → inpaint → bandpass.\n Optionally rescale to target RMS amplitude.\n\n Returns (rms_nm, noise_texture, z_flattened, feature_mask) where:\n rms_nm : float — RMS amplitude of the extracted noise in nm\n noise_texture : float32 ndarray — the bandpass noise field\n z_flattened : float32 ndarray — plane + line flattened height map\n feature_mask : bool ndarray — True where features (DNA) were detected\n \"\"\"\n z = z_in.astype(np.float32)\n if median_size and median_size > 1:\n z = median_filter(z, size=int(median_size)).astype(np.float32)\n\n q = np.percentile(z, float(bg_quantile))\n bg0 = z <= q\n\n z_flat = plane_remove(z, mask=bg0)\n z_flat = line_flatten_median(z_flat, mask=bg0)\n\n feat = feature_mask(z_flat,\n smooth_sigma_px=feature_sigma_px,\n thresh_sigma=feature_thresh_sigma,\n dilate_px=feature_dilate_px)\n z_bg = inpaint_simple(z_flat, feat, smooth_sigma_px=inpaint_sigma_px)\n tex = bandpass_noise(z_bg,\n low_sigma_px=band_low_sigma_px,\n high_sigma_px=band_high_sigma_px)\n\n bg = ~feat\n rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n if target_rms_nm is not None:\n target = float(target_rms_nm)\n if rms > 1e-12:\n tex *= target / rms\n rms = target\n\n return rms, tex.astype(np.float32), z_flat.astype(np.float32), feat.astype(bool)\n\n\ndef tile_or_crop(tex, out_shape, seed=0):\n \"\"\"\n Tile tex to at least out_shape, then take a random (deterministic) crop.\n The crop position is seeded for reproducibility.\n\n Returns a float32 array of exactly out_shape.\n \"\"\"\n ty, tx = tex.shape\n oy, ox = out_shape\n if (ty, tx) == (oy, ox):\n return tex\n tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n rng = np.random.default_rng(seed)\n y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n\n\ndef load_blank_spm_noise(blank_path, target_rms_nm=0.06, verbose=True):\n \"\"\"\n High-level loader: parse SPM file → select best image → convert to nm\n → extract background noise texture.\n\n Returns (rms_nm, noise_texture, diagnostics_dict) where:\n rms_nm : float — RMS amplitude of the background noise in nm\n noise_texture : float32 ndarray — the extracted noise texture\n diagnostics_dict: dict with keys flattened, feature_mask, height_nm, unit_note\n \"\"\"\n raw, header, blocks = parse_blocks(blank_path)\n b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n img_nm, unit_note = maybe_to_nm(img0)\n if verbose:\n print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n\n rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n img_nm,\n median_size=3,\n bg_quantile=45,\n feature_sigma_px=3.0,\n feature_thresh_sigma=3.0,\n feature_dilate_px=6,\n inpaint_sigma_px=10.0,\n band_low_sigma_px=0.7,\n band_high_sigma_px=40.0,\n target_rms_nm=target_rms_nm,\n )\n diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n \"height_nm\": img_nm, \"unit_note\": unit_note}\n if verbose:\n print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n f\"tex shape: {noise_tex.shape}\")\n return rms_nm, noise_tex, diag\n\n# ─────────────────────────────────────────────────────────────────────────────\n# PSD-based fallback noise synthesis\n# ─────────────────────────────────────────────────────────────────────────────\n\nTARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\nEPS = 1e-12 #Safety buffer to ensure no division by 0 or square roots producing NaN values\n\n\ndef load_model(path=MODEL_PATH):\n \"\"\"\n Load the PSD noise model from a .npz file.\n\n Returns a dict with keys: log_psd_mean, log_psd_std, radial_freq,\n rms_values_nm — all as float32 arrays.\n \"\"\"\n data = np.load(path)\n return {\n \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n }\n\n\ndef generate_noise(\n seed,\n target_shape=TARGET_SHAPE,\n target_rms_nm=TARGET_NOISE_RMS_NM,\n method=\"mean_full2d\",\n std_scale=1.0,\n model=None,\n model_path=MODEL_PATH,\n):\n \"\"\"\n Generate a single random AFM-like background noise image from the PSD model.\n\n Parameters\n ----------\n seed : int\n RNG seed for reproducibility.\n target_shape : (rows, cols)\n Output image shape. The PSD is resized automatically if it differs\n from the shape used when building the model.\n target_rms_nm : float or None\n RMS amplitude in nm. None -> draw from the blank-file ensemble.\n method : str\n 'mean_full2d' -- mean log-PSD; randomness from phase only (recommended).\n 'lognormal_full2d' -- draw a random PSD from the fitted log-normal\n distribution (more sample-to-sample variation).\n 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n std_scale : float\n Multiplier on log_psd_std for lognormal method (>1 -> more variation).\n model : dict or None\n Pre-loaded model dict. If None, loaded from model_path on each call.\n model_path : path-like\n Path to the .npz file (used only when model is None).\n\n Returns\n -------\n z : np.ndarray, shape target_shape, dtype float32\n Height noise field in nm.\n \"\"\"\n if model is None:\n model = load_model(model_path)\n\n rng = np.random.default_rng(int(seed))\n out_shape = tuple(target_shape)\n\n if method == \"mean_full2d\":\n psd_sample = np.exp(model[\"log_psd_mean\"])\n\n elif method == \"lognormal_full2d\":\n noise = rng.standard_normal(model[\"log_psd_mean\"].shape).astype(np.float32)\n psd_sample = np.exp(model[\"log_psd_mean\"] + std_scale * model[\"log_psd_std\"] * noise)\n\n elif method == \"empirical_radial\":\n radial_psd = np.exp(model[\"log_psd_mean\"].mean(axis=1))\n radial_freq_for_interp = np.abs(np.fft.fftfreq(model[\"log_psd_mean\"].shape[0], d=1.0))\n\n fy = np.fft.fftfreq(out_shape[0], d=1.0)\n fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n\n psd_sample = np.interp(\n kr.ravel(),\n radial_freq_for_interp,\n radial_psd,\n left=float(radial_psd[0]),\n right=float(radial_psd[-1]),\n ).reshape(kr.shape).astype(np.float32)\n\n else:\n raise ValueError(\n f\"Unknown method {method!r}. \"\n \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n )\n\n psd_ny, psd_nx = psd_sample.shape\n out_ny, out_nx = out_shape\n rfft_nx = out_nx // 2 + 1\n\n if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n from scipy.ndimage import zoom\n psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx), order=1)\n\n psd_sample = psd_sample.astype(np.float32)\n\n phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n\n z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n z -= np.float32(np.mean(z))\n\n target = float(rng.choice(model[\"rms_values_nm\"])) if target_rms_nm is None else float(target_rms_nm)\n cur = float(np.std(z))\n if cur > EPS:\n z *= target / cur\n\n return z\n\n\ndef sample_noise_image(noise_source, noise_asset, seed, out_shape, target_rms_nm):\n \"\"\"\n Dispatch noise generation to the appropriate backend based on noise_source.\n If noise is disabled or no asset is available, returns None.\n\n noise_source : 'blank_spm' — apply a random affine transform to the\n preloaded blank SPM texture and crop to out_shape.\n 'psd_model' — synthesise noise from the PSD model.\n anything else — return None (no noise).\n\n Returns a float32 ndarray of shape out_shape, or None.\n \"\"\"\n if not USE_BLANK_SPM_NOISE or noise_asset is None:\n return None\n\n if noise_source == \"blank_spm\":\n noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n\n if noise_source == \"psd_model\":\n return generate_noise(\n seed=seed,\n target_shape=out_shape,\n target_rms_nm=target_rms_nm,\n method=PSD_NOISE_METHOD,\n std_scale=PSD_NOISE_STD_SCALE,\n model=noise_asset,\n ).astype(np.float32)\n\n return None\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_07", - "metadata": { - "id": "cell_md_07" - }, - "source": [ - "##8. DNA Ground-Truth Mask\n", - "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", - "\n", - "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", - "\n", - "We also dilate the mask by a value of **DNA_MASK_DILATE_PX** to ensure that the mask has enough width for optimal training." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "cell_code_07", - "metadata": { - "id": "cell_code_07" - }, - "outputs": [], - "source": [ - "def nm_to_px(coords_xy_nm, extent, nx, ny):\n \"\"\"\n Map (x, y) coordinates in nm to integer pixel indices within a grid.\n Clamps results to [0, nx-1] and [0, ny-1] to prevent out-of-bounds access.\n\n Returns (ix, iy) as int32 arrays of the same length as coords_xy_nm.\n \"\"\"\n x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n xmin, xmax, ymin, ymax = extent\n ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n 0, nx - 1)\n iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n 0, ny - 1)\n return ix, iy\n\n\ndef rasterize_closed_polyline_mask(coords, extent, nx, ny):\n \"\"\"\n Return a 1-px-wide binary raster of the closed bead polyline.\n Each consecutive bead pair is drawn by dense linear interpolation;\n duplicate pixels are de-duplicated.\n\n Returns a bool array of shape (ny, nx) with True on the polyline.\n \"\"\"\n coords = np.asarray(coords, dtype=np.float32)\n ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n out = np.zeros((ny, nx), dtype=bool)\n npts = len(ix)\n for k in range(npts):\n k2 = (k + 1) % npts\n x0, y0 = int(ix[k]), int(iy[k])\n x1, y1 = int(ix[k2]), int(iy[k2])\n n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n if n_seg <= 1:\n out[y0, x0] = True\n continue\n xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n if xs.size > 1:\n keep = np.ones(xs.shape[0], dtype=bool)\n keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n xs, ys = xs[keep], ys[keep]\n out[ys, xs] = True\n return out\n\n\ndef make_dna_mask_from_beads(coords, extent, nx, ny, dilate_px=3):\n \"\"\"\n Rasterise the polyline and dilate by dilate_px pixels using a\n circular disk footprint to give the mask physical width.\n\n Returns a uint8 array of shape (ny, nx) with values 0 or 1.\n \"\"\"\n line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n if dilate_px and dilate_px > 0:\n line = binary_dilation(line, structure=disk_footprint(float(dilate_px)))\n return line.astype(np.uint8)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_08", - "metadata": { - "id": "cell_md_08" - }, - "source": [ - "## 9. Crossing Detection & Crossing Mask\n", - "\n", - "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", - "\n", - "We have set the masks for the crossings as chain weighted gaussians so that the machine learning models can have more information about the crossings and thus have better predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "cell_code_08", - "metadata": { - "id": "cell_code_08" - }, - "outputs": [], - "source": [ - "def _canon_axis(u):\n \"\"\"\n Normalise a 2-D vector and canonicalise its sign so that +v and -v\n are treated as the same axis direction.\n Returns None if the vector is degenerate.\n \"\"\"\n u = np.asarray(u, dtype=float)\n nrm = float(np.linalg.norm(u))\n if nrm < 1e-12:\n return None\n u = u / nrm\n if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n u = -u\n return u\n\n\ndef find_polyline_crossings(coords_xy, min_separation_beads=CROSS_MIN_SEP_BEADS,\n merge_radius_nm=0.5):\n \"\"\"\n Detect all strand crossings of a closed ring polyline in XY.\n\n Skips adjacent / shared-vertex segment pairs and pairs whose circular\n contour separation is less than min_separation_beads.\n Nearby raw hits are clustered via _merge_crossing_clusters.\n\n Returns list of dicts:\n {\n pt_nm : (2,) float32 — crossing position in nm\n axes_nm : list of unit vectors — chain directions at the crossing\n seg_i : int — first segment index\n seg_j : int — second segment index\n t : float — parametric position along segment i\n u : float — parametric position along segment j\n n_hits : int — raw intersection count merged into this cluster\n }\n \"\"\"\n xy = np.asarray(coords_xy, dtype=float)\n n = int(xy.shape[0])\n\n raw = []\n for i in range(n):\n i2 = (i + 1) % n\n p1, p2 = xy[i], xy[i2]\n for j in range(i + 1, n):\n j2 = (j + 1) % n\n if i == j or i2 == j or j2 == i:\n continue\n if _circ_sep(n, i, j) < int(min_separation_beads):\n continue\n q1, q2 = xy[j], xy[j2]\n hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n if not hit:\n continue\n ua = _canon_axis(p2 - p1)\n ub = _canon_axis(q2 - q1)\n if ua is None or ub is None:\n continue\n raw.append(dict(pt=np.array(pt, float),\n axes=[ua, ub],\n seg_i=int(i), seg_j=int(j),\n t=float(t), u=float(u)))\n\n # Cluster nearby raw intersections\n clusters = []\n for r in raw:\n pt = r[\"pt\"]\n placed = False\n for c in clusters:\n if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n c[\"pt_sum\"] += pt\n c[\"n\"] += 1\n c[\"axes\"].extend(r[\"axes\"])\n c[\"items\"].append(r)\n placed = True\n break\n if not placed:\n clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n \"axes\": list(r[\"axes\"]), \"items\": [r]})\n\n out = []\n for c in clusters:\n pt_mean = c[\"pt_sum\"] / c[\"n\"]\n axes = []\n for uvec in c[\"axes\"]:\n uvec = _canon_axis(uvec)\n if uvec is None:\n continue\n if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n continue\n axes.append(uvec)\n rep = c[\"items\"][0]\n out.append(dict(\n pt_nm = np.asarray(pt_mean, dtype=np.float32),\n axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n if axes else [np.array([1.0, 0.0], np.float32)],\n seg_i = int(rep[\"seg_i\"]),\n seg_j = int(rep[\"seg_j\"]),\n t = float(rep[\"t\"]),\n u = float(rep[\"u\"]),\n n_hits = int(c[\"n\"]),\n ))\n return out\n\n\ndef _add_crossing_patch(mask, pt_px, axes_px,\n sigma_center=2.0,\n chain_extent=5.0,\n sigma_perp=1.8,\n center_weight=1.0,\n chain_weight=0.8):\n \"\"\"\n Paint one crossing onto mask as:\n center_weight * isotropic_Gaussian\n + chain_weight * max(anisotropic Gaussians aligned to chain axes)\n\n Uses np.maximum so multiple crossings compose correctly.\n Modifies mask in-place; returns None.\n \"\"\"\n H, W = mask.shape\n cx, cy = float(pt_px[0]), float(pt_px[1])\n sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n\n xs = np.arange(x0, x1+1, dtype=float)\n ys = np.arange(y0, y1+1, dtype=float)\n X, Y = np.meshgrid(xs, ys)\n dx = X - cx; dy = Y - cy\n\n gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n\n g_chain = np.zeros_like(gc)\n for v in axes_px:\n vx, vy = float(v[0]), float(v[1])\n u_par = dx*vx + dy*vy\n u_perp = -dx*vy + dy*vx\n g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n + u_perp**2 / sigma_perp**2))\n g_chain = np.maximum(g_chain, g)\n\n patch = center_weight * gc + chain_weight * g_chain\n mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n\n\ndef make_crossing_mask(coords, extent, nx, ny,\n sigma_center_px=CROSS_SIGMA_CENTER_PX,\n sigma_perp_px=CROSS_SIGMA_PERP_PX,\n chain_extent=CROSS_CHAIN_EXTENT,\n center_weight=CROSS_CENTER_WEIGHT,\n chain_weight=CROSS_CHAIN_WEIGHT,\n min_separation_beads=CROSS_MIN_SEP_BEADS,\n crossings_precomputed=None,\n merge_radius_nm=0.5):\n \"\"\"\n Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n\n Uses find_polyline_crossings unless crossings_precomputed is supplied\n (recommended: reuse the same crossing list that was used for the\n height boost so the mask and the AFM image are perfectly aligned).\n\n Returns (cross_mask, crossings) where:\n cross_mask : float32 ndarray of shape (ny, nx), values in [0, 1]\n crossings : list of crossing dicts as returned by find_polyline_crossings\n \"\"\"\n coords = np.asarray(coords, dtype=np.float32)\n xy_nm = coords[:, :2].astype(float)\n\n if crossings_precomputed is None:\n crossings = find_polyline_crossings(\n xy_nm,\n min_separation_beads=min_separation_beads,\n merge_radius_nm=float(merge_radius_nm),\n )\n else:\n crossings = crossings_precomputed\n\n xmin, xmax, ymin, ymax = extent\n sx = (nx - 1) / (xmax - xmin)\n sy = (ny - 1) / (ymax - ymin)\n\n mask = np.zeros((ny, nx), dtype=np.float32)\n\n for c in crossings:\n x_nm, y_nm = c[\"pt_nm\"]\n px = (x_nm - xmin) * sx\n py = (y_nm - ymin) * sy\n\n axes_px = []\n for v in c[\"axes_nm\"]:\n vx_px = v[0] * sx; vy_px = v[1] * sy\n nrm = float(np.hypot(vx_px, vy_px))\n if nrm < 1e-12:\n continue\n axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n if not axes_px:\n axes_px = [np.array([1.0, 0.0], dtype=float)]\n\n _add_crossing_patch(\n mask,\n pt_px=(px, py),\n axes_px=axes_px,\n sigma_center=float(sigma_center_px),\n chain_extent=float(chain_extent),\n sigma_perp=float(sigma_perp_px),\n center_weight=float(center_weight),\n chain_weight=float(chain_weight),\n )\n\n m = float(mask.max())\n if m > 1e-12:\n mask /= m\n return mask.astype(np.float32), crossings" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_09", - "metadata": { - "id": "cell_md_09" - }, - "source": [ - "## 10. Decoy Chain Functions\n", - "\n", - "real AFM images often contain small chain segments that are not part of the main chain and thus constitute the noise in that sample, but since those segments are broken off DNA, they cannot be neatly integrated into the noise model.\n", - "Therefore, we offer the possibility of addition of small DNA chains to every every sample in the dataset where ``add_decoy = True`` which would then contain a small 2–4 bead \"decoy\" fragment placed in a corner of the image.\n", - "\n", - "The decoy appears in the AFM image but is **excluded** from both\n", - "ground-truth masks, training the model to ignore isolated unconnected fragments." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "cell_code_09", - "metadata": { - "id": "cell_code_09" - }, - "outputs": [], - "source": [ - "def make_decoy_chain(seed, n_beads_range=(2, 4), bond_length=BOND_LENGTH):\n \"\"\"\n Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n Z heights are set to mimic surface-deposited DNA. The chain propagates via\n small random angular deflections from an initial random direction.\n\n Returns a float32 array of shape (n_beads, 3) with (x, y, z) coordinates in nm.\n \"\"\"\n rng = np.random.default_rng(int(seed))\n n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n coords = np.zeros((n_b, 3), dtype=np.float32)\n angle = rng.uniform(0, 2 * np.pi)\n dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n\n for i in range(1, n_b):\n da = rng.normal(0, 0.3)\n c, s = np.cos(da), np.sin(da)\n R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n dirn = R @ dirn\n dirn = dirn / np.linalg.norm(dirn)\n coords[i] = coords[i-1] + bond_length * dirn\n\n coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n return coords\n\n\ndef place_decoy_in_corner(decoy_coords, global_extent,\n corner_margin_nm=2.0, seed=None):\n \"\"\"\n Translate decoy_coords so its centroid lands in a randomly chosen\n corner of global_extent = (xmin, xmax, ymin, ymax).\n The corner is chosen deterministically from seed.\n\n Returns a float32 array of the same shape as decoy_coords with updated positions.\n \"\"\"\n if seed is None:\n seed = int(np.sum(decoy_coords))\n rng = np.random.default_rng(int(seed))\n xmin, xmax, ymin, ymax = global_extent\n corner = rng.integers(0, 4)\n targets = [\n (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n ]\n tx, ty = targets[corner]\n centroid = decoy_coords.mean(axis=0)\n offset = np.array([tx, ty, 0.0]) - centroid\n return decoy_coords + offset" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_10", - "metadata": { - "id": "cell_md_10" - }, - "source": [ - "## 11. Chain Generation Pipeline\n", - "\n", - "Now, we are finally ready to generate on sample in our dataset. The following functions are used for building the final image and masks:\n", - "- `generate_chain_with_beads` with `n_beads` defaulting to `N_BEADS`\n", - "- `generate_one_sample_multilength` generates the image and mask (this is what iterates to produce the dataset)\n", - "- `random_transform_noise_texture` applies a per-sample random affine transform to the noise texture for variety (in case of blank .spm files)\n", - "- Noise injection uses `blank.spm` when available and the PSD model fallback otherwise\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "cell_code_10", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cell_code_10", - "outputId": "35683590-39ac-4cc1-e2c5-d04cd3cd4ea4" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "No blank .spm file found; trying PSD fallback noise model.\n", - "Loaded PSD fallback noise model from: /content/psd_noise_model.npz\n" - ] - } - ], - "source": [ - "def random_transform_noise_texture(tex, seed):\n \"\"\"\n Apply a deterministic random affine transform (rotation, anisotropic\n scaling, translation, optional flips, amplitude jitter) to the blank.spm\n noise texture. Ensures every sample gets a unique-looking noise pattern\n even though only one blank SPM scan was acquired.\n\n Returns the transformed texture as a float32 array of the same shape as tex.\n \"\"\"\n rng = np.random.default_rng(int(seed))\n tex = np.asarray(tex, dtype=np.float32)\n\n theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n base_scale = float(rng.uniform(0.85, 1.15))\n anis = float(rng.uniform(0.85, 1.15))\n sx, sy = base_scale * anis, base_scale / anis\n\n c, s = float(np.cos(theta)), float(np.sin(theta))\n A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n M = np.linalg.inv(A).astype(np.float32)\n\n h, w = tex.shape\n center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n offset = center - M @ (center + t)\n\n out = affine_transform(tex, matrix=M, offset=offset,\n output_shape=tex.shape, order=1,\n mode=\"wrap\", prefilter=False).astype(np.float32)\n\n if bool(rng.integers(0, 2)):\n out = out[::-1, :]\n if bool(rng.integers(0, 2)):\n out = out[:, ::-1]\n out *= float(rng.uniform(0.90, 1.10))\n return out.astype(np.float32)\n\n\ndef generate_chain_with_beads(seed, n_beads=N_BEADS, add_decoy=False):\n \"\"\"\n Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n and optionally generate a corner-placed decoy fragment.\n\n Returns dict:\n seed, n_beads, coords (N,3), crossings (list), decoy_coords or None\n \"\"\"\n seed = int(seed)\n n_beads = int(n_beads)\n\n if USE_MD:\n coords0 = make_tangled_ring_initial(\n seed,\n n_beads=n_beads,\n bond_length=BOND_LENGTH,\n persistence_bonds=PERSISTENCE_BONDS,\n base_z=BASE_Z,\n )\n frames = run_md_relaxation(coords0, seed=seed,\n n_frames=N_FRAMES,\n steps_per_frame=STEPS_PER_FRAME)\n else:\n frames = make_ring_2d_persistent_initial(\n seed,\n n_beads=n_beads,\n bond_length=BOND_LENGTH,\n persistence_bonds=PERSISTENCE_BONDS,\n angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n )\n\n last_coords = frames[-1].astype(np.float32)\n\n crossings = find_polyline_crossings(\n last_coords[:, :2],\n min_separation_beads=CROSS_MIN_SEP_BEADS,\n merge_radius_nm=0.5,\n )\n\n decoy_coords = None\n if add_decoy:\n xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n decoy_raw = make_decoy_chain(seed + 9999)\n decoy_coords = place_decoy_in_corner(\n decoy_raw, (xmin, xmax, ymin, ymax),\n corner_margin_nm=15.0, seed=seed)\n\n return dict(seed=seed, n_beads=n_beads,\n coords=last_coords, crossings=crossings,\n decoy_coords=decoy_coords)\n\n\ndef render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None):\n \"\"\"\n Render AFM image + DNA mask + crossing mask for a chain dict.\n\n Decoys are rendered into the AFM image via np.maximum compositing\n but are deliberately excluded from both ground-truth masks.\n\n Returns a dict with keys:\n afm_img : float32 ndarray (NY, NX) — the rendered AFM height image\n dna_mask : uint8 ndarray (NY, NX) — binary DNA segmentation mask (0/1)\n cross_mask : float32 ndarray (NY, NX) — soft crossing probability mask [0, 1]\n extent : float32 array (4,) — (xmin, xmax, ymin, ymax) in nm\n n_crossings: int — number of detected crossings\n decoy_coords: float32 ndarray or None — placed decoy bead coordinates\n seed : int — the seed used for this sample\n \"\"\"\n seed = int(chain[\"seed\"])\n coords = chain[\"coords\"]\n crossings = chain[\"crossings\"]\n decoy_coords = chain.get(\"decoy_coords\", None)\n\n padding = 10.0\n xmin = coords[:, 0].min() - padding\n xmax = coords[:, 0].max() + padding\n ymin = coords[:, 1].min() - padding\n ymax = coords[:, 1].max() + padding\n global_extent = (xmin, xmax, ymin, ymax)\n\n # CHANGED: internal render grid is determined from extent and nm_per_px\n nx_phys, ny_phys, _ = compute_internal_render_grid(\n global_extent, AFM_KW[\"target_nm_per_px\"]\n )\n afm_kw_phys = {**AFM_KW, \"nx\": nx_phys, \"ny\": ny_phys}\n\n afm_img_hr, dbg = create_z_based_afm(\n coords, grain_seed=seed,\n crossings_precomputed=crossings,\n extent=global_extent,\n **afm_kw_phys\n )\n\n if decoy_coords is not None:\n decoy_coords = place_decoy_in_corner(\n decoy_coords, global_extent,\n corner_margin_nm=8.0, seed=seed)\n decoy_kw = {**afm_kw_phys,\n \"apply_edge_taper\": False,\n \"enable_crossing_boost\": False,\n \"add_center_ridge\": False}\n decoy_afm_hr, _ = create_z_based_afm(\n decoy_coords, extent=global_extent, **decoy_kw)\n afm_img_hr = np.maximum(afm_img_hr, decoy_afm_hr)\n\n actual_extent = dbg[\"extent\"]\n\n noise_img = sample_noise_image(\n noise_source=noise_source,\n noise_asset=noise_asset,\n seed=seed,\n out_shape=afm_img_hr.shape,\n target_rms_nm=TARGET_NOISE_RMS_NM,\n )\n if noise_img is not None:\n afm_img_hr = np.clip((afm_img_hr + noise_img).astype(np.float32),\n 0, AFM_KW[\"max_height_nm\"])\n\n dna_mask_hr = make_dna_mask_from_beads(\n coords, actual_extent, nx_phys, ny_phys, dilate_px=DNA_MASK_DILATE_PX)\n\n cross_mask_hr, crossings_out = make_crossing_mask(\n coords, actual_extent, nx_phys, ny_phys,\n sigma_center_px=CROSS_SIGMA_CENTER_PX,\n sigma_perp_px=CROSS_SIGMA_PERP_PX,\n chain_extent=CROSS_CHAIN_EXTENT,\n center_weight=CROSS_CENTER_WEIGHT,\n chain_weight=CROSS_CHAIN_WEIGHT,\n min_separation_beads=CROSS_MIN_SEP_BEADS,\n crossings_precomputed=crossings,\n merge_radius_nm=0.5,\n )\n\n if CROSS_CLIP_TO_DNA_MASK:\n cross_mask_hr = cross_mask_hr * dna_mask_hr.astype(np.float32)\n\n # CHANGED: downsample AFM image and masks to final NX x NY network resolution\n afm_img = resize_image_to_shape(afm_img_hr.astype(np.float32), (NY, NX), order=1).astype(np.float32)\n dna_mask = (resize_image_to_shape(dna_mask_hr.astype(np.float32), (NY, NX), order=0) > 0.5).astype(np.uint8)\n cross_mask = resize_image_to_shape(cross_mask_hr.astype(np.float32), (NY, NX), order=1).astype(np.float32)\n\n return dict(\n seed=seed,\n afm_img=afm_img.astype(np.float32),\n dna_mask=dna_mask.astype(np.uint8),\n cross_mask=cross_mask.astype(np.float32),\n extent=np.array(actual_extent, dtype=np.float32),\n n_crossings=int(len(crossings_out)),\n decoy_coords=decoy_coords,\n )\n\n\ndef generate_one_sample_multilength(seed, n_beads, noise_source=\"none\",\n noise_asset=None, add_decoy=False):\n \"\"\"\n Generate one complete sample at a specified bead count.\n Uses blank.spm-derived noise when available and otherwise falls back\n to PSD-based synthetic noise generation.\n\n Returns the dict produced by render_chain_and_masks, augmented with\n n_beads (int) for downstream use.\n \"\"\"\n chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n\n s = render_chain_and_masks(\n chain,\n noise_source=noise_source,\n noise_asset=noise_asset,\n )\n s[\"n_beads\"] = int(n_beads)\n return s\n\n\n# ── Load noise asset once; prefer blank.spm, fall back to PSD model ───────────\nnoise_source = \"none\"\nnoise_asset = None\nnoise_diag = {\"source\": \"none\"}\n\nif USE_BLANK_SPM_NOISE:\n blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n if blank_path and os.path.isfile(blank_path):\n try:\n noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n blank_path, target_rms_nm=TARGET_NOISE_RMS_NM, verbose=True)\n noise_source = \"blank_spm\"\n print(f\"Loaded blank .spm noise texture RMS ≈ {noise_rms_nm:.3f} nm\")\n except Exception as e:\n print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n else:\n print(\"No blank .spm file found; trying PSD fallback noise model.\")\n\n if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n try:\n noise_asset = load_model(MODEL_PATH)\n noise_source = \"psd_model\"\n noise_diag = {\n \"source\": \"psd_model\",\n \"model_path\": str(MODEL_PATH),\n \"method\": PSD_NOISE_METHOD,\n \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n }\n print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n except Exception as e:\n print(\"PSD fallback noise model failed; proceeding without noise. Error:\", e)\n noise_asset = None\nelse:\n print(\"Noise injection disabled.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_11", - "metadata": { - "id": "cell_md_11" - }, - "source": [ - "## 12. Sanity Check\n", - "\n", - "It is always a good idea to check if the pipeline is working correctly and producing the inteded resutls and so we have added a sanity check that can be performed before starting the generation of the entire dataset.\n", - "Here we render one sample per bead count for the bead counts provided in the global variables and by default we have set `add_decoy=True` here.\n", - "\n", - "This check displays the AFM image, DNA mask, and crossing mask side-by-side.\n", - "We also print the extent / decoy-placement diagnostics for verification and adjustment." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "cell_code_11", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "cell_code_11", - "outputId": "0faaec91-945f-4177-f9c2-23e4d63dd9ee" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - " CUDA unavailable: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "Using OpenCL platform.\n", - "\n", - "==================== SAMPLE 1 (Seed: 0, 70 beads) ====================\n", - "Image window (nm): X=[-19.8, 19.0], Y=[-17.7, 18.8]\n", - "Decoy XY bounds (nm): X=[11.0, 11.0], Y=[-10.2, -9.2]\n", - "Decoy Z range (nm): -0.41 – 0.41\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 2 (Seed: 1, 80 beads) ====================\n", - "Image window (nm): X=[-16.3, 15.3], Y=[-17.8, 17.3]\n", - "Decoy XY bounds (nm): X=[7.0, 7.7], Y=[8.9, 9.6]\n", - "Decoy Z range (nm): -0.03 – 0.03\n", - "Decoy inside extent.\n", - " Decoy height ≈ 0\n", - "\n", - "==================== SAMPLE 3 (Seed: 2, 90 beads) ====================\n", - "Image window (nm): X=[-22.3, 20.0], Y=[-18.9, 21.4]\n", - "Decoy XY bounds (nm): X=[11.8, 12.3], Y=[-11.8, -9.9]\n", - "Decoy Z range (nm): -0.26 – 0.15\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 4 (Seed: 3, 100 beads) ====================\n", - "Image window (nm): X=[-30.1, 29.6], Y=[-17.2, 15.1]\n", - "Decoy XY bounds (nm): X=[21.3, 21.9], Y=[-10.6, -7.7]\n", - "Decoy Z range (nm): -0.39 – 0.44\n", - "Decoy inside extent.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "bead_counts = list(BEAD_COUNTS)\n", - "seeds = [int(BASE_SEED + k) for k in range(len(bead_counts))]\n", - "\n", - "samples = [\n", - " generate_one_sample_multilength(\n", - " seeds[i], n_beads=bead_counts[i],\n", - " noise_source=noise_source, noise_asset=noise_asset, add_decoy=True\n", - " )\n", - " for i in range(len(bead_counts))\n", - "]\n", - "\n", - "fig, axes = plt.subplots(len(samples), 3, figsize=(16, 5 * len(samples)))\n", - "\n", - "for r, s in enumerate(samples):\n", - " extent = s[\"extent\"]\n", - " img = s[\"afm_img\"]\n", - "\n", - " print(f\"\\n{'='*20} SAMPLE {r+1} (Seed: {s['seed']}, \"\n", - " f\"{s['n_beads']} beads) {'='*20}\")\n", - " print(f\"Image window (nm): X=[{extent[0]:.1f}, {extent[1]:.1f}], \"\n", - " f\"Y=[{extent[2]:.1f}, {extent[3]:.1f}]\")\n", - "\n", - " if s.get(\"decoy_coords\") is not None:\n", - " d = s[\"decoy_coords\"]\n", - " print(f\"Decoy XY bounds (nm): \"\n", - " f\"X=[{d[:,0].min():.1f}, {d[:,0].max():.1f}], \"\n", - " f\"Y=[{d[:,1].min():.1f}, {d[:,1].max():.1f}]\")\n", - " print(f\"Decoy Z range (nm): {d[:,2].min():.2f} – {d[:,2].max():.2f}\")\n", - " oob = (d[:,0].min() < extent[0] or d[:,0].max() > extent[1] or\n", - " d[:,1].min() < extent[2] or d[:,1].max() > extent[3])\n", - " print(\" Decoy out-of-bounds!\" if oob else \"Decoy inside extent.\")\n", - " if d[:,2].max() < 0.1:\n", - " print(\" Decoy height ≈ 0\")\n", - "\n", - " ax1, ax2, ax3 = axes[r]\n", - " ext_list = list(extent)\n", - "\n", - " ax1.imshow(img, cmap=\"afmhot\", origin=\"lower\", extent=ext_list)\n", - " ax1.set_title(f\"AFM image\\n(seed {s['seed']}, {s['n_beads']} beads)\")\n", - " ax1.axis(\"off\")\n", - "\n", - " ax2.imshow(s[\"dna_mask\"], cmap=\"gray\", origin=\"lower\", extent=ext_list)\n", - " ax2.set_title(\"DNA mask (decoy excluded)\")\n", - " ax2.axis(\"off\")\n", - "\n", - " ax3.imshow(s[\"cross_mask\"], cmap=\"magma\", origin=\"lower\", extent=ext_list)\n", - " ax3.set_title(f\"Crossing mask (n={s['n_crossings']})\")\n", - " ax3.axis(\"off\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "source": [ - "##13. Benchmarking\n", - "\n", - "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." - ], - "metadata": { - "id": "3YoKkN3e5dIo" - }, - "id": "3YoKkN3e5dIo" - }, - { - "cell_type": "code", - "execution_count": null, - "id": "WLAYkyIQlhuX", - "metadata": { - "id": "WLAYkyIQlhuX", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "2efe41b8-37bd-4d1b-a01e-d50eed42cac6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "==============================================================\n", - " SYSTEM PROFILE\n", - "==============================================================\n", - " OS : Linux 6.6.113+\n", - " CPU (logical) : 2 cores\n", - " RAM available : 11.5 GB\n", - " Mode : Non-MD (2D Walk)\n", - "\n", - "==============================================================\n", - " RUNNING BENCHMARK\n", - "==============================================================\n", - " Rep 1/3: 0.05s\n", - " Rep 2/3: 0.06s\n", - " Rep 3/3: 0.06s\n", - "==============================================================\n", - " Median Wall Time: 0.06 s\n", - " Peak RAM (RSS) : 611.8 MB\n", - " Projected Total : 0.1 hours for 4000 samples\n", - "==============================================================\n" - ] - } - ], - "source": [ - "import time\nimport tracemalloc\nimport os\nimport platform\nimport psutil\nimport threading\nimport statistics\n\n# ── Helper: peak RSS tracker ──────\nclass _PeakMemTracker:\n def __init__(self):\n self._proc = psutil.Process(os.getpid())\n self.peak_mb = 0.0\n self._stop = threading.Event()\n\n def _run(self):\n while not self._stop.is_set():\n try:\n rss = self._proc.memory_info().rss / 1e6\n if rss > self.peak_mb:\n self.peak_mb = rss\n except Exception: pass\n self._stop.wait(0.05)\n\n def start(self):\n self._t = threading.Thread(target=self._run, daemon=True)\n self._t.start()\n\n def stop(self):\n self._stop.set(); self._t.join()\n\n# ── System Info ──\ncpu_logical = psutil.cpu_count(logical=True)\nif USE_MD:\n platforms = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]\n active_platform = \"OpenCL\" if \"OpenCL\" in platforms else \"CPU\"\n\nprint(\"=\" * 62)\nprint(\" SYSTEM PROFILE\")\nprint(\"=\" * 62)\nprint(f\" OS : {platform.system()} {platform.release()}\")\nprint(f\" CPU (logical) : {cpu_logical} cores\")\nprint(f\" RAM available : {psutil.virtual_memory().available / 1e9:.1f} GB\")\nif USE_MD:\n print(f\" OpenMM Platforms: {platforms}\")\nprint(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\nprint()\n\nBENCH_SEED = int(BASE_SEED)\nBENCH_N_BEADS = int(BEAD_COUNTS[0])\nBENCH_REPS = 3\n\ndef _bench_stages(seed, n_beads):\n \"\"\"\n Run one full sample-generation pipeline and record wall-clock time\n for each stage: chain initialisation, MD relaxation (or 2-D walk),\n crossing detection, and AFM render + mask creation.\n\n Returns a dict mapping stage labels to elapsed times in seconds:\n {'1_chain_init', '2_md_relax', '3_crossing_detect', '4_afm_render_masks'}\n \"\"\"\n t = {}\n t0 = time.perf_counter()\n\n if USE_MD:\n coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n t[\"1_chain_init\"] = time.perf_counter() - t0\n\n t0 = time.perf_counter()\n frames = run_md_relaxation(coords0, seed=seed, n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME)\n t[\"2_md_relax\"] = time.perf_counter() - t0\n else:\n # Skip initial 3D step and run 2D persistent walk\n t[\"1_chain_init\"] = 0.0\n t0 = time.perf_counter()\n frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n t[\"2_md_relax\"] = time.perf_counter() - t0\n\n last_coords = frames[-1]\n t0 = time.perf_counter()\n crossings = find_polyline_crossings(last_coords[:, :2])\n t[\"3_crossing_detect\"] = time.perf_counter() - t0\n\n t0 = time.perf_counter()\n chain = dict(seed=seed, n_beads=n_beads, coords=last_coords, crossings=crossings)\n render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None)\n t[\"4_afm_render_masks\"] = time.perf_counter() - t0\n return t\n\nprint(\"=\" * 62)\nprint(\" RUNNING BENCHMARK\")\nprint(\"=\" * 62)\n\nwall_times = []\nmem_tracker = _PeakMemTracker()\nmem_tracker.start()\n\nfor rep in range(BENCH_REPS):\n t0 = time.perf_counter()\n _ = _bench_stages(BENCH_SEED + rep, BENCH_N_BEADS)\n elapsed = time.perf_counter() - t0\n wall_times.append(elapsed)\n print(f\" Rep {rep+1}/{BENCH_REPS}: {elapsed:.2f}s\")\n\nmem_tracker.stop()\nmed_wall = statistics.median(wall_times)\ntotal_samples = int(N_SAMPLES) * len(BEAD_COUNTS)\nest_hours = (med_wall * total_samples) / 3600\n\nprint(\"=\" * 62)\nprint(f\" Median Wall Time: {med_wall:.2f} s\")\nprint(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\nprint(f\" Projected Total : {est_hours:.1f} hours for {total_samples} samples\")\nprint(\"=\" * 62)\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_12", - "metadata": { - "id": "cell_md_12" - }, - "source": [ - "## 14.Dataset Generation\n", - "\n", - "Finally, we are ready to generate the full dataset. The amount of samples and the lenghts of the chains is defined in the global variables and will be referenced here.\n", - "This notebook generates the full dataset (default: 1000 samples × 4 chain lengths). There is added functionality to preview the images for a gives set or range of indices.\n", - "\n", - "\n", - "Progress is printed every 10 samples and a `manifest.csv` tracks all file paths." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cell_code_12", - "metadata": { - "id": "cell_code_12" - }, - "outputs": [], - "source": [ - "import os\n", - "import csv\n", - "import json\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "manifest_path = os.path.join(OUT_DIR, \"manifest.csv\")\n", - "print(f\"Starting dataset generation: {N_SAMPLES} samples…\")\n", - "\n", - "# ============================================================\n", - "# Optional preview PNG export settings\n", - "# ============================================================\n", - "EXPORT_PREVIEW_PNGS = False\n", - "\n", - "# Choose indices to export as PNG previews.\n", - "# Examples:\n", - "# set(range(4, 41)) -> export indices 4 through 40 inclusive\n", - "# {0, 3, 10, 25} -> export only specific indices\n", - "# set() -> export none\n", - "PREVIEW_PNG_INDICES = set(range(4, 41))\n", - "\n", - "# Separate folder for preview PNGs\n", - "PREVIEW_PNG_DIR = os.path.join(OUT_DIR, \"preview_pngs\")\n", - "if EXPORT_PREVIEW_PNGS:\n", - " os.makedirs(PREVIEW_PNG_DIR, exist_ok=True)\n", - "\n", - "# Define the header including global parameters\n", - "header = [\n", - " \"index\", \"seed\", \"n_beads\", \"bond_length\", \"persistence_bonds\",\n", - " \"image_npy\", \"dna_mask_npy\", \"cross_mask_npy\", \"meta_npz\",\n", - " \"n_crossings\", \"has_decoy\", \"afm_params_json\"\n", - "]\n", - "\n", - "with open(manifest_path, \"w\", newline=\"\") as f:\n", - " w = csv.writer(f)\n", - " w.writerow(header)\n", - "\n", - " for idx in range(int(N_SAMPLES)):\n", - " # Seed override for a known-problematic sample\n", - " if idx == 832:\n", - " seed = int(BASE_SEED + idx + 9997)\n", - " print(f\"Special override for index 832: seed = {seed}\")\n", - " else:\n", - " seed = int(BASE_SEED + idx)\n", - "\n", - " n_beads = int(BEAD_COUNTS[idx % len(BEAD_COUNTS)])\n", - " add_decoy = (idx % 5 == 0)\n", - "\n", - " s = generate_one_sample_multilength(\n", - " seed,\n", - " n_beads=n_beads,\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " add_decoy=add_decoy,\n", - " )\n", - "\n", - " img_path = os.path.join(\"images\", f\"img_{idx:04d}.npy\")\n", - " dna_path = os.path.join(\"dna_masks\", f\"dna_{idx:04d}.npy\")\n", - " cross_path = os.path.join(\"cross_masks\", f\"cross_{idx:04d}.npy\")\n", - " meta_path = os.path.join(\"meta\", f\"meta_{idx:04d}.npz\")\n", - "\n", - " np.save(os.path.join(OUT_DIR, img_path), s[\"afm_img\"])\n", - " np.save(os.path.join(OUT_DIR, dna_path), s[\"dna_mask\"])\n", - " np.save(os.path.join(OUT_DIR, cross_path), s[\"cross_mask\"])\n", - "\n", - " # Save detailed metadata\n", - " np.savez_compressed(\n", - " os.path.join(OUT_DIR, meta_path),\n", - " seed = s[\"seed\"],\n", - " n_beads = int(s[\"n_beads\"]),\n", - " extent = s[\"extent\"],\n", - " n_crossings = s[\"n_crossings\"],\n", - " has_decoy = add_decoy,\n", - " bond_length = BOND_LENGTH,\n", - " persistence = PERSISTENCE_BONDS\n", - " )\n", - "\n", - " # ------------------------------------------------------------\n", - " # Optional PNG export for selected indices\n", - " # ------------------------------------------------------------\n", - " if EXPORT_PREVIEW_PNGS and idx in PREVIEW_PNG_INDICES:\n", - " png_path = os.path.join(PREVIEW_PNG_DIR, f\"img_{idx:04d}.png\")\n", - "\n", - " fig, ax = plt.subplots(figsize=(6, 6), dpi=150)\n", - " ax.imshow(s[\"afm_img\"], cmap=\"gray\", origin=\"lower\")\n", - " ax.set_title(f\"Sample {idx} | beads={n_beads} | crossings={s['n_crossings']}\")\n", - " ax.axis(\"off\")\n", - " fig.tight_layout(pad=0)\n", - " fig.savefig(png_path, bbox_inches=\"tight\", pad_inches=0)\n", - " plt.close(fig)\n", - "\n", - " # Write to manifest including JSON-serialized AFM parameters\n", - " w.writerow([\n", - " idx, seed, n_beads, BOND_LENGTH, PERSISTENCE_BONDS,\n", - " img_path, dna_path, cross_path, meta_path,\n", - " s[\"n_crossings\"], add_decoy, json.dumps(AFM_KW)\n", - " ])\n", - "\n", - " if (idx + 1) % 10 == 0:\n", - " print(f\"Progress: {idx+1}/{N_SAMPLES}\")\n", - "\n", - "print(\"\\nDone. Dataset written to:\", OUT_DIR)\n", - "print(\"Manifest:\", manifest_path)\n", - "\n", - "if EXPORT_PREVIEW_PNGS:\n", - " print(\"Preview PNG directory:\", PREVIEW_PNG_DIR)\n", - " print(\"PNG preview indices:\", sorted(PREVIEW_PNG_INDICES))" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - }, - "accelerator": "GPU" - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file From e4949b21928b164a446aa4d35e4150b92abdb4ef Mon Sep 17 00:00:00 2001 From: Prakhar-Dutta Date: Thu, 30 Apr 2026 15:09:33 +0200 Subject: [PATCH 16/17] Delete tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb --- ...ulation_of_DNA_chains_imaged_via_AFM.ipynb | 2807 ----------------- 1 file changed, 2807 deletions(-) delete mode 100644 tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb diff --git a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb b/tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb deleted file mode 100644 index b0a9769..0000000 --- a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM.ipynb +++ /dev/null @@ -1,2807 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cell_md_title", - "metadata": { - "id": "cell_md_title" - }, - "source": [ - "# Simulation of 'DNA chains on a substrate imaged via AFM'\n", - "**This notebook provides a complete example of generating a dataset of simulated DNA chains relxed on a substrate imaged via AFM.**\n", - "\n", - "- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n", - "- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n", - "- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)\n", - "\n", - "Outputs are written to `OUT_DIR/` as `.npy` plus `manifest.csv` (which documents the user inputs used to create the images and masks).\n", - "\n", - ">\n", - "---\n", - "**Cell execution order:** run cells 1 → 12 in sequence. \n", - "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/Prakhar-Dutta/ASAP/blob/e45f2c6830e4c44078ab3183df7dfcbdf1f6fcc1/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_01", - "metadata": { - "id": "cell_md_01" - }, - "source": [ - "# 1. **Imports and global settings**\n", - "## 1.1. Imports\n", - "\n", - "First we import all the libraries that will be necessary for simulation of the images. If you plan to use molecular dynamics for the generation of the DNA chains, then please import the molecular dynamics libraries as well." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "cell_code_01", - "metadata": { - "id": "cell_code_01" - }, - "outputs": [], - "source": [ - "# Install dependencies (uncomment in a fresh environment / Colab)\n", - "# !pip cache purge\n", - "# Chain generation mode\n", - "USE_MD = False # True → OpenMM Langevin dynamics\n", - " # False → fast 2-D persistent-walk (no OpenMM needed)\n", - "# Molecular dynamics\n", - "!pip install -q scipy matplotlib pillow\n", - "if USE_MD:\n", - " !pip install openmm[cuda13] #change the cuda version depending on the CUDA version available on your system.\n", - " import openmm\n", - " import openmm.unit as unit\n", - "\n", - "import os\n", - "from pathlib import Path\n", - "import re\n", - "import csv\n", - "import traceback\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import animation\n", - "from IPython.display import HTML, display\n", - "\n", - "\n", - "\n", - "\n", - "# Image processing\n", - "from scipy.ndimage import (\n", - " gaussian_filter, grey_dilation, distance_transform_edt,\n", - " binary_dilation, median_filter, affine_transform\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "#1.2 Defining the variables\n", - "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." - ], - "metadata": { - "id": "1oW9llw-imr1" - }, - "id": "1oW9llw-imr1" - }, - { - "cell_type": "code", - "source": [ - "# ─────────────────────────────────────────────\n", - "# Global settings (these are the variables that need to be changed to change the appearance of the resulting images)\n", - "# ─────────────────────────────────────────────\n", - "\n", - "# Dataset\n", - "OUT_DIR = \"dna_dataset_1000_4lengths\" # Name of the output directory that you would like to store your images and masks in\n", - "N_SAMPLES = 1000 # Number of samples that will be generated\n", - "BASE_SEED = 0 # Seed for reproducibility\n", - "\n", - "# Image resolution (keep fixed for U-Net)\n", - "NX = 512\n", - "NY = 512\n", - "\n", - "# Ring + MD\n", - "N_BEADS = 90 # default bead count for vizualization\n", - "BEAD_COUNTS = [70, 80, 90, 100] # four chain lengths (1 bead corresponds to 10 base pairs on DNA approximately)\n", - "BOND_LENGTH = 1.0 # distance between beads\n", - "PERSISTENCE_BONDS = 23.0 # used to determine the range of angles which the chain is allowed to turn\n", - "BASE_Z = 5.0 # Height above the substrate that the chain starts at before relaxing\n", - "\n", - "ANGLE_STIFNESS_MULT = 0.6 # If USE_MD is False, then angle stiffness multiplier control chain propogation through space\n", - "\n", - "# MD recording\n", - "N_FRAMES = 200\n", - "STEPS_PER_FRAME = 200 # Between every frame local energy minimization happens for the given number of steps\n", - "\n", - "# AFM rendering\n", - "AFM_KW = dict(\n", - " nx=NX, ny=NY,\n", - " dna_diameter_nm=2.0, # mapping to physical values\n", - " tip_radius_nm=0.01, # Radius of tip (modelled as hemishpere for convolution)\n", - " max_height_nm=6.0, # Maximum height for the colorbar\n", - " target_nm_per_px=0.08, # Matching pixels to size of chain\n", - " max_radius_px=96, # following parameters are used for improving the visualization of the chain\n", - " radius_shrink_px=2.0,\n", - " final_blur_sigma_px=0.20,\n", - " apply_edge_taper=True,\n", - " taper_sigma_nm=0.45,\n", - " taper_floor=0.10,\n", - " add_center_ridge=True,\n", - " ridge_sigma_nm=0.25,\n", - " ridge_amp_nm=0.25,\n", - " grain_nm=0.0,\n", - " grain_sigma_px=0.6,\n", - " enable_crossing_boost=True, # To boost the height of the crossings\n", - " min_separation_beads=12, # Distance to other chain segments to not boost indiscriminately\n", - " boost_window_beads=2, # How many beads to boost the height for\n", - " guaranteed_offset_nm=1.0, # How much to boost\n", - " boost_method=\"additive\", # add to the height of beads being boosted\n", - " boost_profile=\"gaussian\", # Height increase profile\n", - " boost_sigma_beads=None,\n", - " far_clip_nm=2.5, # Clip bead heights for beads that are not part of a crossing\n", - " far_clip_window_beads=3, # How many beads to clip the height for\n", - " return_crossing_info=True, # To ensure that mask information is passed to other functions. Change to False if masks are not needed\n", - " return_masks=True,\n", - ")\n", - "\n", - "# DNA mask\n", - "DNA_MASK_DILATE_PX = 3 # Dilation of mask for better training\n", - "\n", - "# Crossing mask\n", - "CROSS_MIN_SEP_BEADS = 12 # Where to stop for the crossing calculation\n", - "CROSS_SIGMA_CENTER_PX = 5.0 # How many pixels make us the center of a crossing\n", - "CROSS_SIGMA_PERP_PX = 1.8 # Crossing coloring parameter (how many pixels to modulate for the crossing)\n", - "CROSS_CHAIN_EXTENT = 5.0 # For the chain weighting of the mask\n", - "CROSS_CENTER_WEIGHT = 1.7 # For the chain weighting of the mask (chain-weighted guassian)\n", - "CROSS_CHAIN_WEIGHT = 0.9\n", - "CROSS_CLIP_TO_DNA_MASK = True" - ], - "metadata": { - "id": "sSqadS02hzPe" - }, - "id": "sSqadS02hzPe", - "execution_count": 33, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#1.3 Noise\n", - "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", - "\n", - "\n", - "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", - "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", - "\n" - ], - "metadata": { - "id": "-e3QvQzGlKYY" - }, - "id": "-e3QvQzGlKYY" - }, - { - "cell_type": "code", - "source": [ - "#Noise\n", - "TARGET_NOISE_RMS_NM = 0.19 # This variable controls the height of the noise in nanometers and this is added to the final image\n", - "\n", - "\n", - "# Blank SPM noise\n", - "USE_BLANK_SPM_NOISE = True\n", - "BLANK_SPM_PATH = \"/content/20240411_blank_water.0_00000.spm\" # optional; if missing/invalid, PSD fallback noise is used\n", - "\n", - "\n", - "# PSD-model fallback noise (used when no blank .spm file is available)\n", - "USE_PSD_NOISE_FALLBACK = True\n", - "MODEL_PATH = \"/content/psd_noise_model.npz\"\n", - "PSD_NOISE_METHOD = \"mean_full2d\" # or \"lognormal_full2d\" / \"empirical_radial\"\n", - "PSD_NOISE_STD_SCALE = 2.0\n", - "\n", - "#Checking if output directory will be created\n", - "os.makedirs(OUT_DIR, exist_ok=True)\n", - "for _sub in [\"images\", \"dna_masks\", \"cross_masks\", \"meta\"]:\n", - " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", - "\n", - "print(\"Output folder:\", os.path.abspath(OUT_DIR))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "47h81smulGxd", - "outputId": "ba1026d2-8f61-4ce2-f7fe-9b6aae526059" - }, - "id": "47h81smulGxd", - "execution_count": 34, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Output folder: /content/dna_dataset_1000_4lengths\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_02", - "metadata": { - "id": "cell_md_02" - }, - "source": [ - "## 2. Chain Creation (3-D Persistent Random Walk)\n", - "Now we start with creating the DNA chain.\n", - "\n", - "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", - "\n", - "A 3-D plot is shown at the end of the cell so you can inspect the initial geometry immediately." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "cell_code_02", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 607 - }, - "id": "cell_code_02", - "outputId": "3e2e58d9-df87-4b6e-d23e-829c3c60e822" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "def make_tangled_ring_initial(\n", - " seed,\n", - " n_beads=N_BEADS,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " base_z=BASE_Z,\n", - "):\n", - " \"\"\"\n", - " Generates a closed (ring) polymer with a persistent random-walk tangent.\n", - " Each step rotates the current direction by a small random angle around a\n", - " perpendicular axis. Ring closure is enforced by linearly distributing\n", - " the end-to-end gap across all beads. The chain is then lifted to base_z\n", - " so it can relax down to the z=0 substrate during MD.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - "\n", - " steps = np.zeros((n_beads, 3), dtype=np.float32)\n", - " vec = np.array([1.0, 0.0, 0.0], dtype=np.float32) #Initialization of vector for chain propogation\n", - "\n", - " for k in range(n_beads):\n", - " dtheta = rng.normal(scale=np.sqrt(1.0 / persistence_bonds))\n", - " axis = rng.normal(size=3).astype(np.float32) #choose an axis at random for chain propogation in 3D\n", - " axis -= axis.dot(vec) * vec # remove component along vec\n", - " axis_norm = np.linalg.norm(axis)\n", - " if axis_norm < 1e-8:\n", - " axis = np.array([0.0, 0.0, 1.0], dtype=np.float32)\n", - " axis_norm = 1.0\n", - " axis /= axis_norm\n", - " vec = vec * np.cos(dtheta) + np.cross(axis, vec) * np.sin(dtheta) # Calculating vector for chain propogation\n", - " vec /= np.linalg.norm(vec)\n", - " steps[k] = bond_length * vec\n", - "\n", - " coords = np.cumsum(steps, axis=0) # Coordinates for open chain\n", - "\n", - " # Enforce ring closure\n", - " closure_error = coords[-1] - coords[0]\n", - " t = np.linspace(0, 1, n_beads, dtype=np.float32).reshape(-1, 1) # Based on the distance between first and last bead a closure error is propogated to all bead positions to form a closed chain\n", - " coords -= t * closure_error\n", - " coords -= coords.mean(axis=0) # This method works better than directing the chain to go away and then come back to original position\n", - "\n", - " # Lift above substrate\n", - " coords[:, 2] += float(base_z) # Changing z values to lift the chain off the substrate\n", - " return coords.astype(np.float32)\n", - "\n", - "\n", - "# ── Quick visualisation ──────────────────────────────────────────────────────\n", - "_demo_coords = make_tangled_ring_initial(seed=43)\n", - "_cc = np.vstack([_demo_coords, _demo_coords[0]]) # close the ring for plotting\n", - "\n", - "fig = plt.figure(figsize=(7, 6))\n", - "ax = fig.add_subplot(111, projection=\"3d\")\n", - "ax.plot(_cc[:, 0], _cc[:, 1], _cc[:, 2], \"-o\", markersize=3, linewidth=1.2)\n", - "ax.scatter(_cc[0, 0], _cc[0, 1], _cc[0, 2], color=\"red\", s=60, zorder=5, label=\"bead 0\")\n", - "\n", - "# Draw substrate plane\n", - "_xs = np.linspace(_cc[:, 0].min(), _cc[:, 0].max(), 2)\n", - "_ys = np.linspace(_cc[:, 1].min(), _cc[:, 1].max(), 2)\n", - "_X, _Y = np.meshgrid(_xs, _ys)\n", - "ax.plot_surface(_X, _Y, np.zeros_like(_X), alpha=0.15, color=\"cyan\")\n", - "\n", - "ax.set_xlabel(\"x (sim units)\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z\")\n", - "ax.set_title(f\"Initial ring — seed 42, {len(_demo_coords)} beads\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_03", - "metadata": { - "id": "cell_md_03" - }, - "source": [ - "## 3. Molecular Dynamics Relaxation\n", - "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", - "\n", - "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", - "Here we have added:\n", - "\n", - "1. Harmonic bonds\n", - "2. Bending-angle stiffness\n", - "3. A surface-attraction to cause chain relaxation\n", - "4. A hard-wall force to keep chain above substrate\n", - "5. A short-range WCA repulsion to ensure chain does not take non physical configurations\n", - "\n", - "The function then records `n_frames` snapshots which can be used for visualization." - ] - }, - { - "cell_type": "markdown", - "source": [ - "We start with testing the OpenMM installation (can be tricky at times)" - ], - "metadata": { - "id": "wFFhsVZhqU8a" - }, - "id": "wFFhsVZhqU8a" - }, - { - "cell_type": "code", - "source": [ - "## Test the OpenMM installation before running the following cell to ensure that GPU acceleration will work\n", - "import openmm.testInstallation\n", - "\n", - "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", - "try:\n", - " openmm.testInstallation.main()\n", - "except Exception as e:\n", - " print(f\"Test failed: {e}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3NjBSwfAe4Mj", - "outputId": "05dd0ee7-e21b-4b54-c39b-cfacc339576f" - }, - "id": "3NjBSwfAe4Mj", - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "OpenMM Version: 8.5\n", - "Git Revision: b55e60882dffcddb2532cceda4201c0fdc9ec2d5\n", - "\n", - "There are 4 Platforms available:\n", - "\n", - "1 Reference - Successfully computed forces\n", - "2 CPU - Successfully computed forces\n", - "3 CUDA - Error computing forces with CUDA platform\n", - "4 OpenCL - Successfully computed forces\n", - "\n", - "CUDA platform error: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", - "\n", - "Median difference in forces between platforms:\n", - "\n", - "Reference vs. CPU: 6.29228e-06\n", - "Reference vs. OpenCL: 6.74321e-06\n", - "CPU vs. OpenCL: 7.92217e-07\n", - "\n", - "All differences are within tolerance.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "And then once we are sure that OpenMM is correctly installed we run the MD functions" - ], - "metadata": { - "id": "9XbRNS3TqbKH" - }, - "id": "9XbRNS3TqbKH" - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "cell_code_03", - "metadata": { - "id": "cell_code_03" - }, - "outputs": [], - "source": [ - "def run_md_relaxation(coords0, seed,\n", - " n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME):\n", - " \"\"\"\n", - " Takes initial bead coordinates and runs Langevin dynamics (pure OpenMM).\n", - "\n", - " Forces applied:\n", - " - Harmonic bonds + angle stiffness along the ring\n", - " - Surface attraction (linear in z) + hard wall at z = 0\n", - " - Short-range WCA repulsion between non-bonded beads\n", - "\n", - " Returns a list of (n_beads, 3) coordinate arrays (n_frames + 1 entries;\n", - " frame 0 is the pre-minimisation geometry).\n", - " \"\"\"\n", - " coords0 = np.asarray(coords0, dtype=np.float32)\n", - " n = int(coords0.shape[0])\n", - "\n", - " extent = (coords0.max(axis=0) - coords0.min(axis=0)).max() # how big the bounding box for the molecular dynamics needs to be\n", - " box = float(extent + 10.0)\n", - "\n", - " # ── Reduced-unit constants (matches polychrom temperature=1 convention) ──\n", - " # All lengths in nm, energies in kJ/mol.\n", - " BOLTZ_kJmol = 0.00831445 # kJ / (mol · K)\n", - " temperature = 1.0 # K (reduced; sets energy scale kT ≈ 0.0083 kJ/mol)\n", - " kT = BOLTZ_kJmol * temperature # kJ/mol\n", - " conlen = 1.0 # nm\n", - " mass_amu = 100.0 # Da per bead\n", - " collision_rate = 0.6 # ps⁻¹\n", - " error_tol = 0.005\n", - "\n", - " # ── Build system ─────────────────────────────────────────────────────────\n", - " system = openmm.System()\n", - " system.setDefaultPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - " for _ in range(n):\n", - " system.addParticle(mass_amu)\n", - "\n", - " # Remove COM drift — applied every 50 steps\n", - " system.addForce(openmm.CMMotionRemover(50)) # To ensure CPU and GPU calculation are consistent (since GPU minimizations calculations can suffer from drift)\n", - "\n", - " # ── 1. Harmonic bonds ────────────────────────────────────────────────────\n", - "\n", - " # OpenMM HarmonicBondForce: E = (k/2)·(r − r0)² → k = kT / wiggle²\n", - " bond_L = 1.0 # nm\n", - " wiggle = 0.1 # nm (bondWiggleDistance)\n", - " k_bond = kT / (wiggle ** 2) # kJ/mol/nm²\n", - "\n", - " bond_force = openmm.HarmonicBondForce()\n", - " bond_force.setUsesPeriodicBoundaryConditions(True)\n", - " for i in range(n):\n", - " bond_force.addBond(i, (i + 1) % n, bond_L, k_bond)\n", - " system.addForce(bond_force)\n", - "\n", - " # ── 2. Bending (angle) stiffness ─────────────────────────────────────────\n", - " # E = kT · k_angle · (1 − cos(θ − π)) — equilibrium at θ = π (straight)\n", - " k_angle = 12.0\n", - " angle_force = openmm.CustomAngleForce(\n", - " \"kT * kangle * (1 - cos(theta - 3.141592653589793))\"\n", - " )\n", - " angle_force.addGlobalParameter(\"kT\", kT)\n", - " angle_force.addGlobalParameter(\"kangle\", k_angle)\n", - " for i in range(n):\n", - " angle_force.addAngle((i - 1) % n, i, (i + 1) % n, [])\n", - " system.addForce(angle_force)\n", - "\n", - " # ── 3. Surface: linear attraction (z > 0) + harmonic hard wall (z < 0) ──\n", - " wall_expr = (\n", - " \"kT * (\"\n", - " \" F_pull * z * step(z) + \"\n", - " \" 0.5 * wallK * (z^2) * step(-z)\"\n", - " \")\"\n", - " )\n", - " wall_force = openmm.CustomExternalForce(wall_expr)\n", - " wall_force.addGlobalParameter(\"kT\", kT)\n", - " wall_force.addGlobalParameter(\"F_pull\", 9.0)\n", - " wall_force.addGlobalParameter(\"wallK\", 100.0)\n", - " for i in range(n):\n", - " wall_force.addParticle(i, [])\n", - " system.addForce(wall_force)\n", - "\n", - " # ── 4. WCA repulsion (repulsion between non-bonded pairs only) ──────────────────────────────\n", - " rep_sigma = 1.4 * conlen # nm\n", - " rep_cutoff = 2.3 * conlen # nm\n", - " rep_expr = (\n", - " \"4*eps*((sig/r)^12 - (sig/r)^6 + 0.25) * step(cutoff - r); \"\n", - " \"cutoff = 2^(1.0/6.0) * sig\"\n", - " )\n", - " rep_force = openmm.CustomNonbondedForce(rep_expr)\n", - " rep_force.setNonbondedMethod(openmm.CustomNonbondedForce.CutoffPeriodic)\n", - " rep_force.setCutoffDistance(rep_cutoff)\n", - " rep_force.addGlobalParameter(\"eps\", 1.7 * kT)\n", - " rep_force.addGlobalParameter(\"sig\", rep_sigma)\n", - " for i in range(n):\n", - " rep_force.addParticle([])\n", - " for i in range(n): # exclude bonded neighbours\n", - " rep_force.addExclusion(i, (i + 1) % n)\n", - " system.addForce(rep_force)\n", - "\n", - " # ── Integrator ────────────────────────────────────────────────────────────\n", - " integrator = openmm.VariableLangevinIntegrator(temperature, collision_rate, error_tol)\n", - " integrator.setRandomNumberSeed(int(seed))\n", - "\n", - " # ── Platform selection: OpenCL → CPU fallback ─────────────────────────────\n", - " context = None\n", - " for platform_name in (\"OpenCL\", \"CPU\"): # CUDA did not work on google colab, so using OpenCL here\n", - " try:\n", - " platform = openmm.Platform.getPlatformByName(platform_name)\n", - " context = openmm.Context(system, integrator, platform)\n", - " print(f\"Using {platform_name} platform.\")\n", - " break\n", - " except openmm.OpenMMException as e:\n", - " print(f\" {platform_name} unavailable: {e}\")\n", - " except Exception as e:\n", - " print(f\" {platform_name} failed unexpectedly: {e}\")\n", - "\n", - " if context is None:\n", - " raise RuntimeError(\"No OpenMM platform could be initialised.\")\n", - "\n", - " # ── Set initial positions and box ─────────────────────────────────────────\n", - " positions = [\n", - " openmm.Vec3(float(coords0[i, 0]), float(coords0[i, 1]), float(coords0[i, 2]))\n", - " for i in range(n)\n", - " ]\n", - " context.setPositions(positions)\n", - " context.setPeriodicBoxVectors(\n", - " openmm.Vec3(box, 0, 0),\n", - " openmm.Vec3(0, box, 0),\n", - " openmm.Vec3(0, 0, box),\n", - " )\n", - "\n", - " # Capture the true initial geometry before any minimisation (just in case the chain reaches substrate even before the 1st frame)\n", - " frames = []\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " # Cap iterations so that extra compute is not used once chain relaxation is complete\n", - " openmm.LocalEnergyMinimizer.minimize(context, tolerance=10, maxIterations=200)\n", - "\n", - " # ── Record frames ─────────────────────────────────────────────────────────\n", - " for _ in range(int(n_frames)):\n", - " integrator.step(int(steps_per_frame))\n", - " state = context.getState(getPositions=True)\n", - " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", - " frames.append(pos.copy())\n", - "\n", - " return frames # n_frames + 1 entries (frame 0 = initial geometry)" - ] - }, - { - "cell_type": "markdown", - "source": [ - "##4. Non-MD chain generation\n", - "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." - ], - "metadata": { - "id": "Bx7_OzWRr-l3" - }, - "id": "Bx7_OzWRr-l3" - }, - { - "cell_type": "code", - "source": [ - "def make_ring_2d_persistent_initial(\n", - " seed,\n", - " n_beads,\n", - " bond_length=BOND_LENGTH, # From Cell 1\n", - " persistence_bonds=PERSISTENCE_BONDS, # From Cell 1\n", - " angle_stiffness_mult=ANGLE_STIFNESS_MULT, # New parameter (only in Non-MD, defined in Cell 1)\n", - " z_std=0.08, # Small Gaussian noise for Z\n", - " base_z=0.2, # Low altitude for \"deposited\" look\n", - " angle_limit=np.pi/2 # Constraint to keep walk exploring\n", - "):\n", - " \"\"\"\n", - " Generates a 2D persistent ring and returns it as a list of frames [ (N,3) ].\n", - " Replaces both the initial generation and the MD relaxation cells.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - " n = int(n_beads)\n", - "\n", - " # 1. Angular exploration\n", - " sigma_dtheta = np.sqrt(2.0 / max(persistence_bonds, 1.0)) / max(angle_stiffness_mult, 1e-6) #Which angles are permissible for placements of the next bead\n", - "\n", - " theta0 = rng.uniform(0.0, 2.0 * np.pi)\n", - " # Clipping the normal distribution implements the \"angle constraint\"\n", - " dtheta = rng.normal(0.0, sigma_dtheta, size=n).astype(np.float64)\n", - " dtheta = np.clip(dtheta, -angle_limit, angle_limit)\n", - " thetas = theta0 + np.cumsum(dtheta)\n", - "\n", - " # 2. XY Coordinates\n", - " bonds = np.stack([np.cos(thetas), np.sin(thetas)], axis=1) * float(bond_length)\n", - " xy = np.zeros((n, 2), dtype=np.float64)\n", - " xy[1:] = np.cumsum(bonds[:-1], axis=0)\n", - "\n", - " # 3. Seamless Closure Correction\n", - " # Ensures the last bead connects back to the first with bond_length consistency\n", - " end_error = (xy[-1] + bonds[-1]) - xy[0]\n", - " t = (np.arange(n, dtype=np.float64) / n)[:, None]\n", - " xy = xy - t * end_error\n", - " xy -= xy.mean(axis=0) # Center at origin\n", - "\n", - " # 4. Assembly with Gaussian Z-noise\n", - " coords = np.zeros((n, 3), dtype=np.float32)\n", - " coords[:, 0] = xy[:, 0].astype(np.float32)\n", - " coords[:, 1] = xy[:, 1].astype(np.float32)\n", - " # Assign random small z values to simulate a deposited state\n", - " coords[:, 2] = rng.normal(loc=base_z, scale=z_std, size=n).astype(np.float32)\n", - "\n", - " # Return as a list containing one frame to mimic run_md_relaxation output\n", - " return [coords]" - ], - "metadata": { - "id": "yXs0I0akr948" - }, - "id": "yXs0I0akr948", - "execution_count": 36, - "outputs": [] - }, - { - "cell_type": "markdown", - "id": "cell_md_04", - "metadata": { - "id": "cell_md_04" - }, - "source": [ - "## 5. MD Animation\n", - "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", - "\n", - "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", - "Running this cell will automatically generate and display the animation for a single deterministic chain (seed 0, default bead count)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "cell_code_04", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 228 - }, - "id": "cell_code_04", - "outputId": "21c1cd4a-654f-4fc1-d966-057f8bc2acbc" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Generating initial ring and running MD (seed=0)…\n" - ] - }, - { - "output_type": "error", - "ename": "NameError", - "evalue": "name 'make_tangled_ring_initial' is not defined", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_10425/24671148.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# ── Run animation automatically (deterministic seed) ─────────────────────────\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Generating initial ring and running MD (seed=0)…\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0m_anim_coords0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_tangled_ring_initial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m _anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n\u001b[1;32m 61\u001b[0m \u001b[0mn_frames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_FRAMES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'make_tangled_ring_initial' is not defined" - ] - } - ], - "source": [ - "def show_md_animation(frames, interval_ms=150):\n", - " \"\"\"\n", - " Renders an in-notebook HTML5 animation of MD frames.\n", - " Pure visualisation — does not mutate any arrays.\n", - " \"\"\"\n", - " frames = [np.asarray(f, dtype=np.float32) for f in frames]\n", - "\n", - " # Use only the first frame for x/y limits — avoids COM drift blowing the scale\n", - " first = frames[0]\n", - " xy_pad = 5.0\n", - " x_min, x_max = first[:, 0].min() - xy_pad, first[:, 0].max() + xy_pad\n", - " y_min, y_max = first[:, 1].min() - xy_pad, first[:, 1].max() + xy_pad\n", - "\n", - " # Clamp z: floor at -1 (just below substrate), ceiling from initial max + padding\n", - " z_min = -1.0\n", - " z_max = float(first[:, 2].max()) + 3.0\n", - "\n", - " x_plane = np.linspace(x_min, x_max, 2)\n", - " y_plane = np.linspace(y_min, y_max, 2)\n", - " X_plane, Y_plane = np.meshgrid(x_plane, y_plane)\n", - " Z_plane = np.zeros_like(X_plane)\n", - "\n", - " fig = plt.figure(figsize=(6, 6))\n", - " ax = fig.add_subplot(111, projection=\"3d\")\n", - "\n", - " def _set_axes():\n", - " ax.set_xlim(x_min, x_max)\n", - " ax.set_ylim(y_min, y_max)\n", - " ax.set_zlim(z_min, z_max)\n", - " ax.set_xlabel(\"x\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z (nm)\")\n", - "\n", - " def init():\n", - " _set_axes()\n", - " return []\n", - "\n", - " def update(i):\n", - " ax.cla()\n", - " c = frames[i]\n", - " # Clip beads to visible z range for plotting so outliers don't distort\n", - " visible = (c[:, 2] >= z_min) & (c[:, 2] <= z_max)\n", - " cc = np.vstack([c, c[0]])\n", - " ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-\", linewidth=1)\n", - " ax.scatter(c[visible, 0], c[visible, 1], c[visible, 2], s=5)\n", - " ax.plot_surface(X_plane, Y_plane, Z_plane, alpha=0.2, color=\"cyan\")\n", - " _set_axes()\n", - " ax.set_title(f\"Frame {i}/{len(frames)-1} | z ∈ [{c[:,2].min():.1f}, {c[:,2].max():.1f}] nm\")\n", - " return []\n", - "\n", - " ani = animation.FuncAnimation(\n", - " fig, update, frames=len(frames), init_func=init,\n", - " interval=int(interval_ms), blit=False\n", - " )\n", - " plt.close(fig)\n", - " display(HTML(ani.to_jshtml()))\n", - "\n", - "\n", - "# ── Run animation automatically (deterministic seed) ─────────────────────────\n", - "print(\"Generating initial ring and running MD (seed=0)…\")\n", - "_anim_coords0 = make_tangled_ring_initial(seed=0)\n", - "_anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - "print(f\"MD done — {len(_anim_frames)} frames. Rendering animation…\")\n", - "show_md_animation(_anim_frames)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_05", - "metadata": { - "id": "cell_md_05" - }, - "source": [ - "## 6. Height based AFM Rendering\n", - "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, now we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", - "\n", - "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "cell_code_05", - "metadata": { - "id": "cell_code_05" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Low-level geometry helpers\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def _seg_intersect_2d(p1, p2, q1, q2, eps=1e-12):\n", - " \"\"\"\n", - " Strict 2-D segment intersection.\n", - " Returns (hit, pt, t, u) where pt = p1 + t*(p2-p1) = q1 + u*(q2-q1).\n", - " Returns (False, None, None, None) for parallel / collinear segments.\n", - " \"\"\"\n", - " p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n", - " q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n", - " r = p2 - p1; s = q2 - q1\n", - " rxs = r[0]*s[1] - r[1]*s[0]\n", - " if abs(rxs) < eps:\n", - " return False, None, None, None\n", - " qp = q1 - p1\n", - " t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n", - " u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n", - " if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n", - " return True, p1 + t*r, t, u\n", - " return False, None, None, None\n", - "\n", - "\n", - "def _circ_sep(n, i, j):\n", - " \"\"\"Circular distance between bead indices on a ring of n beads.\"\"\"\n", - " d = abs(int(i) - int(j))\n", - " return min(d, n - d)\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Structuring-element / grid helpers\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def disk_footprint(r_px: float):\n", - " \"\"\"Boolean circular footprint of radius r_px pixels.\"\"\"\n", - " if r_px < 0.5:\n", - " return np.ones((1, 1), dtype=bool)\n", - " r = int(np.ceil(r_px))\n", - " y, x = np.ogrid[-r:r+1, -r:r+1]\n", - " return (x*x + y*y) <= r*r\n", - "\n", - "\n", - "def upsample_nn(a, out_shape):\n", - " \"\"\"Nearest-neighbour upsample array a to out_shape.\"\"\"\n", - " ny_out, nx_out = out_shape\n", - " ny_in, nx_in = a.shape\n", - " if (ny_out, nx_out) == (ny_in, nx_in):\n", - " return a\n", - " sy = max(ny_out // ny_in, 1)\n", - " sx = max(nx_out // nx_in, 1)\n", - " a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n", - " return a_up[:ny_out, :nx_out]\n", - "\n", - "\n", - "def choose_effective_grid(xmin, xmax, ymin, ymax, nx, ny,\n", - " target_nm_per_px=0.10):\n", - " \"\"\"\n", - " If the physical pixel size is already coarser than target_nm_per_px,\n", - " use the full grid directly. Otherwise return a reduced grid that\n", - " will be upsampled after rendering.\n", - " \"\"\"\n", - " px = (xmax - xmin) / max(nx - 1, 1)\n", - " py = (ymax - ymin) / max(ny - 1, 1)\n", - " p = 0.5 * (px + py)\n", - " if p >= target_nm_per_px:\n", - " return nx, ny, p\n", - " factor = int(np.ceil(target_nm_per_px / max(p, 1e-12)))\n", - " nx_eff = max(128, nx // factor)\n", - " ny_eff = max(128, ny // factor)\n", - " px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n", - " return nx_eff, ny_eff, px_eff\n", - "\n", - "\n", - "def disk_cap_structure(radius_nm: float, p_nm_per_px: float,\n", - " max_radius_px=96):\n", - " \"\"\"\n", - " Hemispherical-cap structuring element representing the DNA cross-section.\n", - " Each pixel inside the disk gets a height equal to the spherical-cap height\n", - " at that radial distance.\n", - " \"\"\"\n", - " r_px = radius_nm / max(p_nm_per_px, 1e-12)\n", - " R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n", - " y, x = np.ogrid[-R:R+1, -R:R+1]\n", - " d2 = x*x + y*y\n", - " inside = d2 <= (r_px * r_px)\n", - " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", - " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", - " structure[inside] = np.sqrt(\n", - " np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n", - " ).astype(np.float32)\n", - " return inside.astype(bool), structure\n", - "\n", - "\n", - "def disk_sphere_structure(tip_radius_nm: float, p_nm_per_px: float,\n", - " max_radius_px=128):\n", - " \"\"\"\n", - " Spherical-cap structuring element representing the AFM tip geometry.\n", - " Used to convolve the surface with the tip shape.\n", - " \"\"\"\n", - " Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n", - " R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n", - " y, x = np.ogrid[-R:R+1, -R:R+1]\n", - " d2 = x*x + y*y\n", - " inside = d2 <= (Rpx * Rpx)\n", - " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", - " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", - " structure[inside] = (\n", - " np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n", - " - tip_radius_nm\n", - " ).astype(np.float32)\n", - " return inside.astype(bool), structure\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Crossing boost\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def _taper_weight(idx_off, w, profile=\"gaussian\", sigma=None):\n", - " \"\"\"\n", - " Smooth weight in [0, 1] used to fade the crossing height boost along\n", - " the bead-index window of half-width w.\n", - "\n", - " profile:\n", - " 'gaussian' — smooth dome (recommended)\n", - " 'hemisphere' — half-circle taper\n", - " 'linear' — simple linear ramp\n", - " \"\"\"\n", - " if w <= 0:\n", - " return 1.0\n", - " a = abs(idx_off)\n", - " if profile == \"linear\":\n", - " return 1.0 - (a / (w + 1.0))\n", - " if profile == \"hemisphere\":\n", - " x = a / (w + 1.0)\n", - " return float(np.sqrt(max(0.0, 1.0 - x*x)))\n", - " # gaussian (default)\n", - " if sigma is None:\n", - " sigma = max(1e-6, 0.5 * (w + 0.5))\n", - " g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n", - " g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n", - " if g_end < 0.999:\n", - " g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n", - " return g\n", - "\n", - "\n", - "def _collect_intersections(xy_nm, min_separation_beads=12,\n", - " merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Brute-force all segment pairs of a closed ring polyline to find crossings.\n", - " Skips adjacent/shared-vertex pairs and contour-nearby pairs.\n", - " Clusters nearby raw hits via _merge_crossing_clusters.\n", - "\n", - " Returns list of dicts:\n", - " {pt_nm, seg_i, seg_j, t, u, n_hits}\n", - " \"\"\"\n", - " xy = np.asarray(xy_nm, dtype=float)\n", - " n = int(xy.shape[0])\n", - " raw = []\n", - " for i in range(n):\n", - " i2 = (i + 1) % n\n", - " p1, p2 = xy[i], xy[i2]\n", - " for j in range(i + 1, n):\n", - " j2 = (j + 1) % n\n", - " if i == j or i2 == j or j2 == i:\n", - " continue\n", - " if _circ_sep(n, i, j) < int(min_separation_beads):\n", - " continue\n", - " q1, q2 = xy[j], xy[j2]\n", - " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", - " if hit:\n", - " raw.append(dict(pt=np.array(pt, float),\n", - " seg_i=int(i), seg_j=int(j),\n", - " t=float(t), u=float(u)))\n", - "\n", - " clusters = _merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n", - " return clusters\n", - "\n", - "\n", - "def _merge_crossing_clusters(raw_list, merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Greedy proximity-based clustering of raw crossing dicts.\n", - " Each dict must have key 'pt' (2-D float array).\n", - " Returns averaged clusters with representative metadata.\n", - " \"\"\"\n", - " clusters = []\n", - " for r in raw_list:\n", - " pt = r[\"pt\"].astype(float)\n", - " placed = False\n", - " for c in clusters:\n", - " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", - " c[\"pt_sum\"] += pt\n", - " c[\"n\"] += 1\n", - " c[\"items\"].append(r)\n", - " placed = True\n", - " break\n", - " if not placed:\n", - " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n", - "\n", - " out = []\n", - " for c in clusters:\n", - " pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n", - " rep = c[\"items\"][0]\n", - " out.append(dict(\n", - " pt_nm = pt,\n", - " seg_i = int(rep[\"seg_i\"]),\n", - " seg_j = int(rep[\"seg_j\"]),\n", - " t = float(rep[\"t\"]),\n", - " u = float(rep[\"u\"]),\n", - " n_hits = int(c[\"n\"]),\n", - " ))\n", - " return out\n", - "\n", - "\n", - "def boost_crossings_intersections(\n", - " x_nm, y_nm, zc_nm,\n", - " crossings=None,\n", - " min_separation_beads=12,\n", - " boost_window_beads=2,\n", - " guaranteed_offset_nm=1.0,\n", - " boost_method=\"additive\",\n", - " boost_profile=\"gaussian\",\n", - " boost_sigma_beads=None,\n", - " far_clip_nm=2.5,\n", - " far_clip_window_beads=12,\n", - " merge_radius_nm=0.5,\n", - "):\n", - " \"\"\"\n", - " Boost z-height at strand crossings so they are visually distinct in\n", - " the AFM image.\n", - "\n", - " For each crossing:\n", - " - Determines which strand is on top (higher z)\n", - " - Raises top-strand beads in a tapered window to at least\n", - " (bottom_max + guaranteed_offset_nm)\n", - " - Optionally clips non-crossing beads to far_clip_nm\n", - "\n", - " Returns (modified_z, crossing_info_list).\n", - " \"\"\"\n", - " x = np.asarray(x_nm, dtype=np.float32)\n", - " y = np.asarray(y_nm, dtype=np.float32)\n", - " z = np.asarray(zc_nm, dtype=np.float32).copy()\n", - " n = int(z.shape[0])\n", - " if n < 5:\n", - " return z.astype(np.float32), []\n", - "\n", - " if crossings is None:\n", - " xy = np.stack([x, y], axis=1)\n", - " crossings = _collect_intersections(\n", - " xy,\n", - " min_separation_beads=int(min_separation_beads),\n", - " merge_radius_nm=float(merge_radius_nm),\n", - " )\n", - "\n", - " def get_segment_indices(center, w):\n", - " return np.array([(center + k) % n for k in range(-w, w+1)],\n", - " dtype=np.int32)\n", - "\n", - " crossing_info = []\n", - " crossing_centers = []\n", - " w = int(boost_window_beads)\n", - "\n", - " for c in crossings:\n", - " i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n", - " i2 = (i + 1) % n; j2 = (j + 1) % n\n", - " ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n", - "\n", - " bead_i = i if ti < 0.5 else i2\n", - " bead_j = j if uj < 0.5 else j2\n", - " zi = float((1.0 - ti) * z[i] + ti * z[i2])\n", - " zj = float((1.0 - uj) * z[j] + uj * z[j2])\n", - "\n", - " top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j, bead_i)\n", - " top_seg = get_segment_indices(top_center, w)\n", - " bottom_seg = get_segment_indices(bottom_center, w)\n", - "\n", - " bottom_local_max = float(z[bottom_seg].max())\n", - " top_local_max = float(z[top_seg].max())\n", - "\n", - " if boost_method == \"absolute\":\n", - " target_height = bottom_local_max + float(guaranteed_offset_nm)\n", - " else:\n", - " target_height = max(top_local_max, bottom_local_max) + float(guaranteed_offset_nm)\n", - "\n", - " for off in range(-w, w+1):\n", - " k = (top_center + off) % n\n", - " wt = float(_taper_weight(off, w,\n", - " profile=boost_profile,\n", - " sigma=boost_sigma_beads))\n", - " z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n", - "\n", - " crossing_centers.extend([int(top_center), int(bottom_center)])\n", - " crossing_info.append(dict(\n", - " pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n", - " seg_i = int(i),\n", - " seg_j = int(j),\n", - " top_center = int(top_center),\n", - " bottom_center= int(bottom_center),\n", - " ))\n", - "\n", - " # Clip beads far from any crossing\n", - " if crossing_centers and far_clip_nm is not None:\n", - " centers = np.array(crossing_centers, dtype=np.int32)\n", - " r = int(far_clip_window_beads)\n", - " for k in range(n):\n", - " d = int(np.min(np.minimum(np.abs(k - centers),\n", - " n - np.abs(k - centers))))\n", - " if d > r:\n", - " z[k] = min(z[k], float(far_clip_nm))\n", - "\n", - " return z.astype(np.float32), crossing_info\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Main AFM renderer\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def create_z_based_afm(\n", - " coords,\n", - " nx=800, ny=800,\n", - " dna_diameter_nm=2.0,\n", - " tip_radius_nm=0.01,\n", - " max_height_nm=6.0,\n", - " target_nm_per_px=0.08,\n", - " max_radius_px=96,\n", - " radius_shrink_px=2.0,\n", - " final_blur_sigma_px=0.20,\n", - " apply_edge_taper=True,\n", - " taper_sigma_nm=0.45,\n", - " taper_floor=0.10,\n", - " add_center_ridge=True,\n", - " ridge_sigma_nm=0.25,\n", - " ridge_amp_nm=0.25,\n", - " grain_nm=0.0,\n", - " grain_sigma_px=0.6,\n", - " grain_seed=1,\n", - " enable_crossing_boost=True,\n", - " min_separation_beads=12,\n", - " boost_window_beads=2,\n", - " guaranteed_offset_nm=1.0,\n", - " boost_method=\"additive\",\n", - " boost_profile=\"gaussian\",\n", - " boost_sigma_beads=None,\n", - " crossings_precomputed=None,\n", - " far_clip_nm=2.5,\n", - " far_clip_window_beads=12,\n", - " return_crossing_info=False,\n", - " return_masks=True,\n", - " extent=None,\n", - "):\n", - " \"\"\"\n", - " Master AFM renderer. Pipeline:\n", - "\n", - " 1. Project 3-D bead coords onto a 2-D effective grid\n", - " 2. Optionally boost crossing heights (boost_crossings_intersections)\n", - " 3. Rasterise closed polyline with z-interpolation\n", - " 4. Apply disk_cap_structure (DNA cylindrical cross-section)\n", - " 5. Apply disk_sphere_structure (tip-sample convolution)\n", - " 6. Clip, blur, edge-taper, center-ridge\n", - " 7. Optional synthetic grain noise\n", - " 8. Upsample to requested (nx, ny)\n", - "\n", - " Returns (afm_image, debug_dict) when return_masks=True.\n", - " \"\"\"\n", - " x, y, z = coords.T\n", - " if extent is None:\n", - " xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n", - " ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n", - " else:\n", - " xmin, xmax, ymin, ymax = extent\n", - "\n", - " nx_eff, ny_eff, p_eff = choose_effective_grid(\n", - " xmin, xmax, ymin, ymax, nx, ny, target_nm_per_px)\n", - "\n", - " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n", - " 0, nx_eff - 1)\n", - " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n", - " 0, ny_eff - 1)\n", - "\n", - " zc = (z - z.min()).astype(np.float32)\n", - "\n", - " # ── Crossing boost ───────────────────────────────────────────────────────\n", - " crossing_info = []\n", - " if enable_crossing_boost:\n", - " zc, crossing_info = boost_crossings_intersections(\n", - " x.astype(np.float32), y.astype(np.float32), zc,\n", - " crossings=crossings_precomputed,\n", - " min_separation_beads=int(min_separation_beads),\n", - " boost_window_beads=int(boost_window_beads),\n", - " guaranteed_offset_nm=float(guaranteed_offset_nm),\n", - " boost_method=str(boost_method),\n", - " boost_profile=str(boost_profile),\n", - " boost_sigma_beads=boost_sigma_beads,\n", - " far_clip_nm=far_clip_nm,\n", - " far_clip_window_beads=int(far_clip_window_beads),\n", - " )\n", - "\n", - " # ── Rasterise polyline ───────────────────────────────────────────────────\n", - " z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n", - " line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n", - " npts = len(ix)\n", - " for k in range(npts):\n", - " k2 = (k + 1) % npts\n", - " x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n", - " x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n", - " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", - " if n_seg <= 1:\n", - " z_line[y0, x0] = max(z_line[y0, x0], z0)\n", - " line_mask[y0, x0] = True\n", - " continue\n", - " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", - " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", - " zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n", - " if xs.size > 1:\n", - " keep = np.ones(xs.shape[0], dtype=bool)\n", - " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", - " xs, ys, zs = xs[keep], ys[keep], zs[keep]\n", - " z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n", - " line_mask[ys, xs] = True\n", - "\n", - " # ── DNA cap dilation ─────────────────────────────────────────────────────\n", - " r_eff = max(0.5*float(dna_diameter_nm) - float(radius_shrink_px)*p_eff, 0.2)\n", - " fp_dna, cap = disk_cap_structure(r_eff, p_eff, max_radius_px=max_radius_px)\n", - " surface = grey_dilation(z_line, footprint=fp_dna,\n", - " structure=cap).astype(np.float32)\n", - " dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna) > 0\n", - " surface[~dna_region] = 0.0\n", - "\n", - " # ── Tip convolution ──────────────────────────────────────────────────────\n", - " r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n", - " if r_tip_px >= 0.5:\n", - " fp_tip, tip_struct = disk_sphere_structure(\n", - " float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n", - " surface = grey_dilation(surface, footprint=fp_tip,\n", - " structure=tip_struct).astype(np.float32)\n", - "\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - " if final_blur_sigma_px > 0:\n", - " surface = gaussian_filter(surface,\n", - " sigma=float(final_blur_sigma_px)).astype(np.float32)\n", - "\n", - " # ── Edge taper + center ridge ────────────────────────────────────────────\n", - " if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n", - " dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n", - " dist_nm = dist_px * np.float32(p_eff)\n", - " if apply_edge_taper:\n", - " sig = max(float(taper_sigma_nm), 1e-6)\n", - " factor = taper_floor + (1.0 - taper_floor) * np.exp(\n", - " -(dist_nm**2) / (2.0 * sig**2))\n", - " surface[dna_region] *= factor[dna_region]\n", - " if add_center_ridge and ridge_amp_nm > 0:\n", - " rs = max(float(ridge_sigma_nm), 1e-6)\n", - " ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n", - " surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - "\n", - " # ── Synthetic grain ──────────────────────────────────────────────────────\n", - " if grain_nm and grain_nm > 0:\n", - " rng = np.random.default_rng(int(grain_seed))\n", - " n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n", - " if grain_sigma_px and grain_sigma_px > 0:\n", - " n = gaussian_filter(n, sigma=float(grain_sigma_px)).astype(np.float32)\n", - " std = float(n[dna_region].std()) if np.any(dna_region) else float(n.std())\n", - " if std > 1e-9:\n", - " n /= np.float32(std)\n", - " surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n", - " np.clip(surface, 0, max_height_nm, out=surface)\n", - "\n", - " # ── Upsample ─────────────────────────────────────────────────────────────\n", - " if (nx_eff, ny_eff) != (nx, ny):\n", - " surface_up = upsample_nn(surface, (ny, nx)).astype(np.float32)\n", - " line_mask_up = upsample_nn(line_mask.astype(np.uint8), (ny, nx)) > 0\n", - " dna_region_up = upsample_nn(dna_region.astype(np.uint8), (ny, nx)) > 0\n", - " else:\n", - " surface_up = surface\n", - " line_mask_up = line_mask\n", - " dna_region_up = dna_region\n", - "\n", - " if return_masks:\n", - " debug = {\n", - " \"line_mask\": line_mask_up,\n", - " \"dna_region\": dna_region_up,\n", - " \"extent\": (xmin, xmax, ymin, ymax),\n", - " \"p_nm_per_px\": float((xmax - xmin) / max(nx - 1, 1)),\n", - " }\n", - " if return_crossing_info:\n", - " debug[\"crossings\"] = crossing_info\n", - " return surface_up, debug\n", - "\n", - " return surface_up, (xmin, xmax, ymin, ymax)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_06", - "metadata": { - "id": "cell_md_06" - }, - "source": [ - "## 7. Noise Functions\n", - "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", - "\n", - "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", - "2. If no blank `.spm` file is supplied, the notebook falls back to a PSD-based synthetic noise model.\n", - "\n", - "Note: Topostats is an AFM imaging software package developed at the University of Sheffield which is used for filtering of AFM data. We desribe functions that perform operations similar to Topostats to clean the noise data from the blank files before adding it to the AFM_img.\n", - "\n", - "### Mathematics of PSD-Matched Noise Synthesis\n", - "\n", - "All three methods in the code utilize **Frequency-Domain Synthesis**. The core principle is that the height field $z(x, y)$ can be generated from a target Power Spectral Density (PSD) using the inverse Fourier Transform.\n", - "\n", - "#### 1. General Synthesis Workflow\n", - "For an output image of shape $(N_y, N_x)$:\n", - "1. **Define a PSD**: Let $S(f_x, f_y)$ be the target power spectrum.\n", - "2. **Generate Complex Spectrum**: A complex field $Z(f_x, f_y)$ is created in the frequency domain:\n", - " $$Z(f_x, f_y) = \\sqrt{S(f_x, f_y)} \\cdot e^{i \\phi(f_x, f_y)}$$\n", - " where $\\phi$ is a random phase drawn from a uniform distribution $U(0, 2\\pi)$.\n", - "3. **Inverse Transform**: The spatial noise $z(x, y)$ is the real part of the Inverse Fast Fourier Transform (IFFT) of $Z$.\n", - "4. **RMS Scaling**: The image is normalized such that its standard deviation matches the target Root Mean Square (RMS) roughness." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "cell_code_06", - "metadata": { - "id": "cell_code_06" - }, - "outputs": [], - "source": [ - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# Parsing the .spm file for height data\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def robust_range(a):\n", - " \"\"\"Return (p1, p99, p99-p1) of finite values in array a.\"\"\"\n", - " a = np.asarray(a, dtype=np.float32)\n", - " p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n", - " return float(p1), float(p99), float(p99 - p1)\n", - "\n", - "\n", - "def looks_like_afm_height(img):\n", - " \"\"\"True if the image has a finite, positive, sane dynamic range.\"\"\"\n", - " p1, p99, rng = robust_range(img)\n", - " return np.isfinite(rng) and 0 < rng <= 1e9\n", - "\n", - "\n", - "def maybe_to_nm(img):\n", - " \"\"\"Auto-convert from metres to nm if values look metre-scale.\"\"\"\n", - " p1, p99, rng = robust_range(img)\n", - " mx = max(abs(p1), abs(p99))\n", - " if mx < 1e-3:\n", - " return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n", - " return img.astype(np.float32), \"as-is (nm or counts)\"\n", - "\n", - "\n", - "def parse_blocks(filename, max_blocks=128):\n", - " \"\"\"\n", - " Read a Bruker .spm file and parse binary data-block metadata.\n", - " Returns (raw_bytes, header_string, block_list).\n", - " \"\"\"\n", - " raw = open(filename, \"rb\").read()\n", - " i_end = raw.find(b\"\\x1a\")\n", - " if i_end == -1:\n", - " i_end = min(len(raw), 400_000)\n", - " header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n", - "\n", - " matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n", - " if not matches:\n", - " raise RuntimeError(\"No 'Data offset' found in header.\")\n", - "\n", - " blocks = []\n", - " for k, pos in enumerate(matches[:max_blocks]):\n", - " win = header[max(0, pos-2500): min(len(header), pos+2500)]\n", - "\n", - " def grab_int(key):\n", - " m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n", - " return int(m.group(1)) if m else None\n", - "\n", - " def grab_float(key):\n", - " m = re.search(\n", - " rf\"{re.escape(key)}\\s*:\\s*([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\",\n", - " win)\n", - " return float(m.group(1)) if m else None\n", - "\n", - " name_candidates = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n", - " name = name_candidates[-1].strip() if name_candidates else None\n", - "\n", - " off = grab_int(\"Data offset\")\n", - " length = grab_int(\"Data length\")\n", - " bpp = grab_int(\"Bytes/pixel\")\n", - " nx_b = grab_int(\"Samps/line\")\n", - " ny_b = grab_int(\"Number of lines\")\n", - " zscale = grab_float(\"Z scale\")\n", - " zoff = grab_float(\"Z offset\")\n", - "\n", - " if None in (off, length, bpp, nx_b, ny_b):\n", - " continue\n", - " blocks.append(dict(name=name or f\"block_{k}\",\n", - " off=off, length=length, bpp=bpp,\n", - " nx=nx_b, ny=ny_b,\n", - " zscale=zscale, zoff=zoff))\n", - "\n", - " uniq, seen = [], set()\n", - " for b in blocks:\n", - " if b[\"off\"] not in seen:\n", - " uniq.append(b); seen.add(b[\"off\"])\n", - " if not uniq:\n", - " raise RuntimeError(\"Found 'Data offset' strings but no parseable blocks.\")\n", - " return raw, header, uniq\n", - "\n", - "\n", - "def read_block_candidates(raw, b):\n", - " \"\"\"\n", - " Decode one binary data block, trying all plausible dtypes.\n", - " Applies z-scale / z-offset if present.\n", - " Returns list of (dtype_str, image_array).\n", - " \"\"\"\n", - " off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n", - " nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n", - " blob = raw[off:off+length]\n", - " if len(blob) < length:\n", - " raise RuntimeError(\"Truncated block.\")\n", - "\n", - " n = nx_b * ny_b\n", - " cands = []\n", - " dtype_map = {2: [\"i2\", \"u2\"],\n", - " 4: [\"i4\", \"f4\"],\n", - " 1: [\"u1\"]}\n", - " for dt in dtype_map.get(bpp, []):\n", - " arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n", - " if arr.size != n:\n", - " continue\n", - " cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n", - "\n", - " out = []\n", - " for dt, img in cands:\n", - " z = img\n", - " if b.get(\"zscale\") is not None:\n", - " z = z * np.float32(b[\"zscale\"])\n", - " if b.get(\"zoff\") is not None:\n", - " z = z + np.float32(b[\"zoff\"])\n", - " out.append((dt, z))\n", - " return out\n", - "\n", - "\n", - "def choose_best_height_image(raw, blocks, verbose=True):\n", - " \"\"\"\n", - " Iterate all blocks and dtype candidates, pick the one that:\n", - " - passes looks_like_afm_height\n", - " - has the highest log-variance score\n", - " \"\"\"\n", - " best, best_score = None, -np.inf\n", - " for b in blocks:\n", - " try:\n", - " for dt, img in read_block_candidates(raw, b):\n", - " if not looks_like_afm_height(img):\n", - " continue\n", - " p1, p99, rng = robust_range(img)\n", - " var = float(np.var(img))\n", - " score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n", - " if score > best_score:\n", - " best_score = score\n", - " best = (b, dt, img, (p1, p99, rng, var))\n", - " except Exception:\n", - " continue\n", - "\n", - " if best is None:\n", - " raise RuntimeError(\"Could not find a plausible AFM height image decode.\")\n", - " b, dt, img, stats = best\n", - " if verbose:\n", - " p1, p99, rng, var = stats\n", - " print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n", - " f\"bpp={b['bpp']} decode={dt}\")\n", - " print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, range={rng:.3g}, var={var:.3g}\")\n", - " return b, img\n", - "\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# TopoStats-like background / noise extraction\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "def plane_remove(z, mask=None):\n", - " \"\"\"Fit and subtract a tilted plane from z (optionally background-only).\"\"\"\n", - " ny, nx = z.shape\n", - " Y, X = np.mgrid[0:ny, 0:nx]\n", - " A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n", - " b = z.ravel()\n", - " if mask is not None:\n", - " m = mask.ravel().astype(bool)\n", - " A, b = A[m], b[m]\n", - " C, *_ = np.linalg.lstsq(A, b, rcond=None)\n", - " plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n", - " return (z - plane).astype(np.float32)\n", - "\n", - "\n", - "def line_flatten_median(z, mask=None):\n", - " \"\"\"Subtract per-row median (background pixels only if mask given).\"\"\"\n", - " out = z.astype(np.float32, copy=True)\n", - " for i in range(out.shape[0]):\n", - " if mask is None or not np.any(mask[i]):\n", - " med = np.median(out[i])\n", - " else:\n", - " med = np.median(out[i, mask[i]])\n", - " out[i] -= np.float32(med)\n", - " return out\n", - "\n", - "\n", - "def feature_mask(z, smooth_sigma_px=3.0, thresh_sigma=3.0, dilate_px=4):\n", - " \"\"\"\n", - " Detect features (DNA, particles) as pixels deviating by more than\n", - " thresh_sigma MADs from the smooth-residual background.\n", - " \"\"\"\n", - " z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", - " resid = (z - z_s).astype(np.float32)\n", - " med = float(np.median(resid))\n", - " mad = float(np.median(np.abs(resid - med)))\n", - " sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n", - " feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n", - " if dilate_px and dilate_px > 0:\n", - " r = int(dilate_px)\n", - " yy, xx = np.ogrid[-r:r+1, -r:r+1]\n", - " fp = (xx*xx + yy*yy) <= r*r\n", - " feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n", - " return feat\n", - "\n", - "\n", - "def inpaint_simple(z, mask, smooth_sigma_px=8.0):\n", - " \"\"\"Replace masked pixels with a heavily blurred background estimate.\"\"\"\n", - " bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", - " out = z.copy()\n", - " out[mask] = bg[mask]\n", - " return out\n", - "\n", - "\n", - "def bandpass_noise(z, low_sigma_px=1.0, high_sigma_px=30.0):\n", - " \"\"\"\n", - " Bandpass filter: subtract large-scale trend from small-scale smoothed\n", - " image to isolate mid-frequency noise texture.\n", - " \"\"\"\n", - " low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n", - " high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n", - " return (low - high).astype(np.float32)\n", - "\n", - "\n", - "def extract_topostats_like_noise(\n", - " z_in,\n", - " median_size=3,\n", - " bg_quantile=40,\n", - " feature_sigma_px=3.0,\n", - " feature_thresh_sigma=3.0,\n", - " feature_dilate_px=4,\n", - " inpaint_sigma_px=10.0,\n", - " band_low_sigma_px=0.8,\n", - " band_high_sigma_px=35.0,\n", - " target_rms_nm=None,\n", - "):\n", - " \"\"\"\n", - " Full noise-extraction pipeline:\n", - " plane-flatten → line-flatten → detect features → inpaint → bandpass.\n", - " Optionally rescale to target RMS amplitude.\n", - " Returns (rms_nm, noise_texture, z_flattened, feature_mask).\n", - " \"\"\"\n", - " z = z_in.astype(np.float32)\n", - " if median_size and median_size > 1:\n", - " z = median_filter(z, size=int(median_size)).astype(np.float32)\n", - "\n", - " q = np.percentile(z, float(bg_quantile))\n", - " bg0 = z <= q\n", - "\n", - " z_flat = plane_remove(z, mask=bg0)\n", - " z_flat = line_flatten_median(z_flat, mask=bg0)\n", - "\n", - " feat = feature_mask(z_flat,\n", - " smooth_sigma_px=feature_sigma_px,\n", - " thresh_sigma=feature_thresh_sigma,\n", - " dilate_px=feature_dilate_px)\n", - " z_bg = inpaint_simple(z_flat, feat, smooth_sigma_px=inpaint_sigma_px)\n", - " tex = bandpass_noise(z_bg,\n", - " low_sigma_px=band_low_sigma_px,\n", - " high_sigma_px=band_high_sigma_px)\n", - "\n", - " bg = ~feat\n", - " rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n", - " if target_rms_nm is not None:\n", - " target = float(target_rms_nm)\n", - " if rms > 1e-12:\n", - " tex *= target / rms\n", - " rms = target\n", - "\n", - " return rms, tex.astype(np.float32), z_flat.astype(np.float32), feat.astype(bool)\n", - "\n", - "\n", - "def tile_or_crop(tex, out_shape, seed=0):\n", - " \"\"\"\n", - " Tile tex to at least out_shape, then take a random (deterministic) crop.\n", - " \"\"\"\n", - " ty, tx = tex.shape\n", - " oy, ox = out_shape\n", - " if (ty, tx) == (oy, ox):\n", - " return tex\n", - " tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n", - " rng = np.random.default_rng(seed)\n", - " y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n", - " x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n", - " return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n", - "\n", - "\n", - "def load_blank_spm_noise(blank_path, target_rms_nm=0.06, verbose=True):\n", - " \"\"\"\n", - " High-level loader: parse SPM file → select best image → convert to nm\n", - " → extract background noise texture.\n", - " Returns (rms_nm, noise_texture, diagnostics_dict).\n", - " \"\"\"\n", - " raw, header, blocks = parse_blocks(blank_path)\n", - " b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n", - " img_nm, unit_note = maybe_to_nm(img0)\n", - " if verbose:\n", - " print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n", - "\n", - " rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n", - " img_nm,\n", - " median_size=3,\n", - " bg_quantile=45,\n", - " feature_sigma_px=3.0,\n", - " feature_thresh_sigma=3.0,\n", - " feature_dilate_px=6,\n", - " inpaint_sigma_px=10.0,\n", - " band_low_sigma_px=0.7,\n", - " band_high_sigma_px=40.0,\n", - " target_rms_nm=target_rms_nm,\n", - " )\n", - " diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n", - " \"height_nm\": img_nm, \"unit_note\": unit_note}\n", - " if verbose:\n", - " print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n", - " f\"tex shape: {noise_tex.shape}\")\n", - " return rms_nm, noise_tex, diag\n", - "\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "# PSD-based fallback noise synthesis\n", - "# ─────────────────────────────────────────────────────────────────────────────\n", - "\n", - "TARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\n", - "EPS = 1e-12 #Safety buffer to ensure no division by 0 or square roots producing NaN values\n", - "\n", - "\n", - "def load_model(path=MODEL_PATH):\n", - " \"\"\"Load the PSD noise model from a .npz file.\"\"\"\n", - " data = np.load(path)\n", - " return {\n", - " \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n", - " \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n", - " \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n", - " \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n", - " }\n", - "\n", - "\n", - "def generate_noise(\n", - " seed,\n", - " target_shape=TARGET_SHAPE,\n", - " target_rms_nm=TARGET_NOISE_RMS_NM,\n", - " method=\"mean_full2d\",\n", - " std_scale=1.0,\n", - " model=None,\n", - " model_path=MODEL_PATH,\n", - "):\n", - " \"\"\"\n", - " Generate a single random AFM-like background noise image from the PSD model.\n", - "\n", - " Parameters\n", - " ----------\n", - " seed : int\n", - " RNG seed for reproducibility.\n", - " target_shape : (rows, cols)\n", - " Output image shape. The PSD is resized automatically if it differs\n", - " from the shape used when building the model.\n", - " target_rms_nm : float or None\n", - " RMS amplitude in nm. None -> draw from the blank-file ensemble.\n", - " method : str\n", - " 'mean_full2d' -- mean log-PSD; randomness from phase only (recommended).\n", - " 'lognormal_full2d' -- draw a random PSD from the fitted log-normal\n", - " distribution (more sample-to-sample variation).\n", - " 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n", - " std_scale : float\n", - " Multiplier on log_psd_std for lognormal method (>1 -> more variation).\n", - " model : dict or None\n", - " Pre-loaded model dict. If None, loaded from model_path on each call.\n", - " model_path : path-like\n", - " Path to the .npz file (used only when model is None).\n", - "\n", - " Returns\n", - " -------\n", - " z : np.ndarray, shape target_shape, dtype float32\n", - " Height noise field in nm.\n", - " \"\"\"\n", - " if model is None:\n", - " model = load_model(model_path)\n", - "\n", - " rng = np.random.default_rng(int(seed))\n", - " out_shape = tuple(target_shape)\n", - "\n", - " if method == \"mean_full2d\":\n", - " psd_sample = np.exp(model[\"log_psd_mean\"])\n", - "\n", - " elif method == \"lognormal_full2d\":\n", - " noise = rng.standard_normal(model[\"log_psd_mean\"].shape).astype(np.float32)\n", - " psd_sample = np.exp(model[\"log_psd_mean\"] + std_scale * model[\"log_psd_std\"] * noise)\n", - "\n", - " elif method == \"empirical_radial\":\n", - " radial_psd = np.exp(model[\"log_psd_mean\"].mean(axis=1))\n", - " radial_freq_for_interp = np.abs(np.fft.fftfreq(model[\"log_psd_mean\"].shape[0], d=1.0))\n", - "\n", - " fy = np.fft.fftfreq(out_shape[0], d=1.0)\n", - " fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n", - " fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n", - " kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n", - "\n", - " psd_sample = np.interp(\n", - " kr.ravel(),\n", - " radial_freq_for_interp,\n", - " radial_psd,\n", - " left=float(radial_psd[0]),\n", - " right=float(radial_psd[-1]),\n", - " ).reshape(kr.shape).astype(np.float32)\n", - "\n", - " else:\n", - " raise ValueError(\n", - " f\"Unknown method {method!r}. \"\n", - " \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n", - " )\n", - "\n", - " psd_ny, psd_nx = psd_sample.shape\n", - " out_ny, out_nx = out_shape\n", - " rfft_nx = out_nx // 2 + 1\n", - "\n", - " if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n", - " from scipy.ndimage import zoom\n", - " psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx), order=1)\n", - "\n", - " psd_sample = psd_sample.astype(np.float32)\n", - "\n", - " phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n", - " spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n", - "\n", - " z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n", - " z -= np.float32(np.mean(z))\n", - "\n", - " target = float(rng.choice(model[\"rms_values_nm\"])) if target_rms_nm is None else float(target_rms_nm)\n", - " cur = float(np.std(z))\n", - " if cur > EPS:\n", - " z *= target / cur\n", - "\n", - " return z\n", - "\n", - "\n", - "def sample_noise_image(noise_source, noise_asset, seed, out_shape, target_rms_nm):\n", - " \"\"\"Return a per-sample noise image from either blank.spm or PSD fallback.\"\"\"\n", - " if not USE_BLANK_SPM_NOISE or noise_asset is None:\n", - " return None\n", - "\n", - " if noise_source == \"blank_spm\":\n", - " noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n", - " return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n", - "\n", - " if noise_source == \"psd_model\":\n", - " return generate_noise(\n", - " seed=seed,\n", - " target_shape=out_shape,\n", - " target_rms_nm=target_rms_nm,\n", - " method=PSD_NOISE_METHOD,\n", - " std_scale=PSD_NOISE_STD_SCALE,\n", - " model=noise_asset,\n", - " ).astype(np.float32)\n", - "\n", - " return None\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_07", - "metadata": { - "id": "cell_md_07" - }, - "source": [ - "##8. DNA Ground-Truth Mask\n", - "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", - "\n", - "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", - "\n", - "We also dilate the mask by a value of **DNA_MASK_DILATE_PX** to ensure that the mask has enough width for optimal training." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "cell_code_07", - "metadata": { - "id": "cell_code_07" - }, - "outputs": [], - "source": [ - "def nm_to_px(coords_xy_nm, extent, nx, ny):\n", - " \"\"\"Map (x, y) in nm to integer pixel indices (ix, iy).\"\"\"\n", - " x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n", - " xmin, xmax, ymin, ymax = extent\n", - " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n", - " 0, nx - 1)\n", - " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n", - " 0, ny - 1)\n", - " return ix, iy\n", - "\n", - "\n", - "def rasterize_closed_polyline_mask(coords, extent, nx, ny):\n", - " \"\"\"\n", - " Return a 1-px-wide binary raster of the closed bead polyline.\n", - " Each consecutive bead pair is drawn by dense linear interpolation;\n", - " duplicate pixels are de-duplicated.\n", - " \"\"\"\n", - " coords = np.asarray(coords, dtype=np.float32)\n", - " ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n", - " out = np.zeros((ny, nx), dtype=bool)\n", - " npts = len(ix)\n", - " for k in range(npts):\n", - " k2 = (k + 1) % npts\n", - " x0, y0 = int(ix[k]), int(iy[k])\n", - " x1, y1 = int(ix[k2]), int(iy[k2])\n", - " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", - " if n_seg <= 1:\n", - " out[y0, x0] = True\n", - " continue\n", - " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", - " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", - " if xs.size > 1:\n", - " keep = np.ones(xs.shape[0], dtype=bool)\n", - " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", - " xs, ys = xs[keep], ys[keep]\n", - " out[ys, xs] = True\n", - " return out\n", - "\n", - "\n", - "def make_dna_mask_from_beads(coords, extent, nx, ny, dilate_px=3):\n", - " \"\"\"\n", - " Rasterise the polyline and dilate by dilate_px pixels.\n", - " Returns a uint8 mask (0/1).\n", - " \"\"\"\n", - " line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n", - " if dilate_px and dilate_px > 0:\n", - " line = binary_dilation(line, structure=disk_footprint(float(dilate_px)))\n", - " return line.astype(np.uint8)" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_08", - "metadata": { - "id": "cell_md_08" - }, - "source": [ - "## 9. Crossing Detection & Crossing Mask\n", - "\n", - "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", - "\n", - "We have set the masks for the crossings as chain weighted gaussians so that the machine learning models can have more information about the crossings and thus have better predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "cell_code_08", - "metadata": { - "id": "cell_code_08" - }, - "outputs": [], - "source": [ - "def _canon_axis(u):\n", - " \"\"\"\n", - " Normalise a 2-D vector and canonicalise its sign so that +v and -v\n", - " are treated as the same axis direction.\n", - " Returns None if the vector is degenerate.\n", - " \"\"\"\n", - " u = np.asarray(u, dtype=float)\n", - " nrm = float(np.linalg.norm(u))\n", - " if nrm < 1e-12:\n", - " return None\n", - " u = u / nrm\n", - " if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n", - " u = -u\n", - " return u\n", - "\n", - "\n", - "def find_polyline_crossings(coords_xy, min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Detect all strand crossings of a closed ring polyline in XY.\n", - "\n", - " Skips adjacent / shared-vertex segment pairs and pairs whose circular\n", - " contour separation is less than min_separation_beads.\n", - " Nearby raw hits are clustered via _merge_crossing_clusters.\n", - "\n", - " Returns list of dicts:\n", - " {\n", - " pt_nm : (2,) float32 — crossing position in nm\n", - " axes_nm : list of unit vectors — chain directions at the crossing\n", - " seg_i : int — first segment index\n", - " seg_j : int — second segment index\n", - " t : float — parametric position along segment i\n", - " u : float — parametric position along segment j\n", - " n_hits : int — raw intersection count merged into this cluster\n", - " }\n", - " \"\"\"\n", - " xy = np.asarray(coords_xy, dtype=float)\n", - " n = int(xy.shape[0])\n", - "\n", - " raw = []\n", - " for i in range(n):\n", - " i2 = (i + 1) % n\n", - " p1, p2 = xy[i], xy[i2]\n", - " for j in range(i + 1, n):\n", - " j2 = (j + 1) % n\n", - " if i == j or i2 == j or j2 == i:\n", - " continue\n", - " if _circ_sep(n, i, j) < int(min_separation_beads):\n", - " continue\n", - " q1, q2 = xy[j], xy[j2]\n", - " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", - " if not hit:\n", - " continue\n", - " ua = _canon_axis(p2 - p1)\n", - " ub = _canon_axis(q2 - q1)\n", - " if ua is None or ub is None:\n", - " continue\n", - " raw.append(dict(pt=np.array(pt, float),\n", - " axes=[ua, ub],\n", - " seg_i=int(i), seg_j=int(j),\n", - " t=float(t), u=float(u)))\n", - "\n", - " # Cluster nearby raw intersections\n", - " clusters = []\n", - " for r in raw:\n", - " pt = r[\"pt\"]\n", - " placed = False\n", - " for c in clusters:\n", - " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", - " c[\"pt_sum\"] += pt\n", - " c[\"n\"] += 1\n", - " c[\"axes\"].extend(r[\"axes\"])\n", - " c[\"items\"].append(r)\n", - " placed = True\n", - " break\n", - " if not placed:\n", - " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n", - " \"axes\": list(r[\"axes\"]), \"items\": [r]})\n", - "\n", - " out = []\n", - " for c in clusters:\n", - " pt_mean = c[\"pt_sum\"] / c[\"n\"]\n", - " axes = []\n", - " for uvec in c[\"axes\"]:\n", - " uvec = _canon_axis(uvec)\n", - " if uvec is None:\n", - " continue\n", - " if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n", - " continue\n", - " axes.append(uvec)\n", - " rep = c[\"items\"][0]\n", - " out.append(dict(\n", - " pt_nm = np.asarray(pt_mean, dtype=np.float32),\n", - " axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n", - " if axes else [np.array([1.0, 0.0], np.float32)],\n", - " seg_i = int(rep[\"seg_i\"]),\n", - " seg_j = int(rep[\"seg_j\"]),\n", - " t = float(rep[\"t\"]),\n", - " u = float(rep[\"u\"]),\n", - " n_hits = int(c[\"n\"]),\n", - " ))\n", - " return out\n", - "\n", - "\n", - "def _add_crossing_patch(mask, pt_px, axes_px,\n", - " sigma_center=2.0,\n", - " chain_extent=5.0,\n", - " sigma_perp=1.8,\n", - " center_weight=1.0,\n", - " chain_weight=0.8):\n", - " \"\"\"\n", - " Paint one crossing onto mask as:\n", - " center_weight * isotropic_Gaussian\n", - " + chain_weight * max(anisotropic Gaussians aligned to chain axes)\n", - "\n", - " Uses np.maximum so multiple crossings compose correctly.\n", - " \"\"\"\n", - " H, W = mask.shape\n", - " cx, cy = float(pt_px[0]), float(pt_px[1])\n", - " sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n", - " R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n", - " x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n", - " y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n", - "\n", - " xs = np.arange(x0, x1+1, dtype=float)\n", - " ys = np.arange(y0, y1+1, dtype=float)\n", - " X, Y = np.meshgrid(xs, ys)\n", - " dx = X - cx; dy = Y - cy\n", - "\n", - " gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n", - "\n", - " g_chain = np.zeros_like(gc)\n", - " for v in axes_px:\n", - " vx, vy = float(v[0]), float(v[1])\n", - " u_par = dx*vx + dy*vy\n", - " u_perp = -dx*vy + dy*vx\n", - " g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n", - " + u_perp**2 / sigma_perp**2))\n", - " g_chain = np.maximum(g_chain, g)\n", - "\n", - " patch = center_weight * gc + chain_weight * g_chain\n", - " mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n", - "\n", - "\n", - "def make_crossing_mask(coords, extent, nx, ny,\n", - " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", - " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", - " chain_extent=CROSS_CHAIN_EXTENT,\n", - " center_weight=CROSS_CENTER_WEIGHT,\n", - " chain_weight=CROSS_CHAIN_WEIGHT,\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " crossings_precomputed=None,\n", - " merge_radius_nm=0.5):\n", - " \"\"\"\n", - " Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n", - "\n", - " Uses find_polyline_crossings unless crossings_precomputed is supplied\n", - " (recommended: reuse the same crossing list that was used for the\n", - " height boost so the mask and the AFM image are perfectly aligned).\n", - " \"\"\"\n", - " coords = np.asarray(coords, dtype=np.float32)\n", - " xy_nm = coords[:, :2].astype(float)\n", - "\n", - " if crossings_precomputed is None:\n", - " crossings = find_polyline_crossings(\n", - " xy_nm,\n", - " min_separation_beads=min_separation_beads,\n", - " merge_radius_nm=float(merge_radius_nm),\n", - " )\n", - " else:\n", - " crossings = crossings_precomputed\n", - "\n", - " xmin, xmax, ymin, ymax = extent\n", - " sx = (nx - 1) / (xmax - xmin)\n", - " sy = (ny - 1) / (ymax - ymin)\n", - "\n", - " mask = np.zeros((ny, nx), dtype=np.float32)\n", - "\n", - " for c in crossings:\n", - " x_nm, y_nm = c[\"pt_nm\"]\n", - " px = (x_nm - xmin) * sx\n", - " py = (y_nm - ymin) * sy\n", - "\n", - " axes_px = []\n", - " for v in c[\"axes_nm\"]:\n", - " vx_px = v[0] * sx; vy_px = v[1] * sy\n", - " nrm = float(np.hypot(vx_px, vy_px))\n", - " if nrm < 1e-12:\n", - " continue\n", - " axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n", - " if not axes_px:\n", - " axes_px = [np.array([1.0, 0.0], dtype=float)]\n", - "\n", - " _add_crossing_patch(\n", - " mask,\n", - " pt_px=(px, py),\n", - " axes_px=axes_px,\n", - " sigma_center=float(sigma_center_px),\n", - " chain_extent=float(chain_extent),\n", - " sigma_perp=float(sigma_perp_px),\n", - " center_weight=float(center_weight),\n", - " chain_weight=float(chain_weight),\n", - " )\n", - "\n", - " m = float(mask.max())\n", - " if m > 1e-12:\n", - " mask /= m\n", - " return mask.astype(np.float32), crossings" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_09", - "metadata": { - "id": "cell_md_09" - }, - "source": [ - "## 10. Decoy Chain Functions\n", - "\n", - "real AFM images often contain small chain segments that are not part of the main chain and thus constitute the noise in that sample, but since those segments are broken off DNA, they cannot be neatly integrated into the noise model.\n", - "Therefore, we offer the possibility of addition of small DNA chains to every every sample in the dataset where ``add_decoy = True`` which would then contain a small 2–4 bead \"decoy\" fragment placed in a corner of the image.\n", - "\n", - "The decoy appears in the AFM image but is **excluded** from both\n", - "ground-truth masks, training the model to ignore isolated unconnected fragments." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "cell_code_09", - "metadata": { - "id": "cell_code_09" - }, - "outputs": [], - "source": [ - "def make_decoy_chain(seed, n_beads_range=(2, 4), bond_length=BOND_LENGTH):\n", - " \"\"\"\n", - " Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n", - " Z heights are set to mimic surface-deposited DNA.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - " n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n", - " coords = np.zeros((n_b, 3), dtype=np.float32)\n", - " angle = rng.uniform(0, 2 * np.pi)\n", - " dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n", - "\n", - " for i in range(1, n_b):\n", - " da = rng.normal(0, 0.3)\n", - " c, s = np.cos(da), np.sin(da)\n", - " R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n", - " dirn = R @ dirn\n", - " dirn = dirn / np.linalg.norm(dirn)\n", - " coords[i] = coords[i-1] + bond_length * dirn\n", - "\n", - " coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n", - " return coords\n", - "\n", - "\n", - "def place_decoy_in_corner(decoy_coords, global_extent,\n", - " corner_margin_nm=2.0, seed=None):\n", - " \"\"\"\n", - " Translate decoy_coords so its centroid lands in a randomly chosen\n", - " corner of global_extent = (xmin, xmax, ymin, ymax).\n", - " \"\"\"\n", - " if seed is None:\n", - " seed = int(np.sum(decoy_coords))\n", - " rng = np.random.default_rng(int(seed))\n", - " xmin, xmax, ymin, ymax = global_extent\n", - " corner = rng.integers(0, 4)\n", - " targets = [\n", - " (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n", - " (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n", - " (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n", - " (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n", - " ]\n", - " tx, ty = targets[corner]\n", - " centroid = decoy_coords.mean(axis=0)\n", - " offset = np.array([tx, ty, 0.0]) - centroid\n", - " return decoy_coords + offset" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_10", - "metadata": { - "id": "cell_md_10" - }, - "source": [ - "## 11. Chain Generation Pipeline\n", - "\n", - "Now, we are finally ready to generate on sample in our dataset. The following functions are used for building the final image and masks:\n", - "- `generate_chain_with_beads` with `n_beads` defaulting to `N_BEADS`\n", - "- `generate_one_sample_multilength` generates the image and mask (this is what iterates to produce the dataset)\n", - "- `random_transform_noise_texture` applies a per-sample random affine transform to the noise texture for variety (in case of blank .spm files)\n", - "- Noise injection uses `blank.spm` when available and the PSD model fallback otherwise\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "cell_code_10", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cell_code_10", - "outputId": "f21a85c7-5c3e-432a-8c70-76f58292c879" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "No blank .spm file found; trying PSD fallback noise model.\n", - "Loaded PSD fallback noise model from: /content/psd_noise_model.npz\n" - ] - } - ], - "source": [ - "def random_transform_noise_texture(tex, seed):\n", - " \"\"\"\n", - " Apply a deterministic random affine transform (rotation, anisotropic\n", - " scaling, translation, optional flips, amplitude jitter) to the blank.spm\n", - " noise texture. Ensures every sample gets a unique-looking noise pattern\n", - " even though only one blank SPM scan was acquired.\n", - " \"\"\"\n", - " rng = np.random.default_rng(int(seed))\n", - " tex = np.asarray(tex, dtype=np.float32)\n", - "\n", - " theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n", - " base_scale = float(rng.uniform(0.85, 1.15))\n", - " anis = float(rng.uniform(0.85, 1.15))\n", - " sx, sy = base_scale * anis, base_scale / anis\n", - "\n", - " c, s = float(np.cos(theta)), float(np.sin(theta))\n", - " A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n", - " M = np.linalg.inv(A).astype(np.float32)\n", - "\n", - " h, w = tex.shape\n", - " center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n", - " t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n", - " float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n", - " offset = center - M @ (center + t)\n", - "\n", - " out = affine_transform(tex, matrix=M, offset=offset,\n", - " output_shape=tex.shape, order=1,\n", - " mode=\"wrap\", prefilter=False).astype(np.float32)\n", - "\n", - " if bool(rng.integers(0, 2)):\n", - " out = out[::-1, :]\n", - " if bool(rng.integers(0, 2)):\n", - " out = out[:, ::-1]\n", - " out *= float(rng.uniform(0.90, 1.10))\n", - " return out.astype(np.float32)\n", - "\n", - "\n", - "def generate_chain_with_beads(seed, n_beads=N_BEADS, add_decoy=False):\n", - " \"\"\"\n", - " Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n", - " and optionally generate a corner-placed decoy fragment.\n", - "\n", - " Returns dict:\n", - " seed, n_beads, coords (N,3), crossings (list), decoy_coords or None\n", - " \"\"\"\n", - " seed = int(seed)\n", - " n_beads = int(n_beads)\n", - "\n", - " if USE_MD:\n", - " coords0 = make_tangled_ring_initial(\n", - " seed,\n", - " n_beads=n_beads,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " base_z=BASE_Z,\n", - " )\n", - " frames = run_md_relaxation(coords0, seed=seed,\n", - " n_frames=N_FRAMES,\n", - " steps_per_frame=STEPS_PER_FRAME)\n", - " else:\n", - " frames = make_ring_2d_persistent_initial(\n", - " seed,\n", - " n_beads=n_beads,\n", - " bond_length=BOND_LENGTH,\n", - " persistence_bonds=PERSISTENCE_BONDS,\n", - " angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n", - " )\n", - "\n", - " last_coords = frames[-1].astype(np.float32)\n", - "\n", - " crossings = find_polyline_crossings(\n", - " last_coords[:, :2],\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " merge_radius_nm=0.5,\n", - " )\n", - "\n", - " decoy_coords = None\n", - " if add_decoy:\n", - " xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n", - " ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n", - " decoy_raw = make_decoy_chain(seed + 9999)\n", - " decoy_coords = place_decoy_in_corner(\n", - " decoy_raw, (xmin, xmax, ymin, ymax),\n", - " corner_margin_nm=15.0, seed=seed)\n", - "\n", - " return dict(seed=seed, n_beads=n_beads,\n", - " coords=last_coords, crossings=crossings,\n", - " decoy_coords=decoy_coords)\n", - "\n", - "\n", - "def render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None):\n", - " \"\"\"\n", - " Render AFM image + DNA mask + crossing mask for a chain dict.\n", - "\n", - " Decoys are rendered into the AFM image via np.maximum compositing\n", - " but are deliberately excluded from both ground-truth masks.\n", - " \"\"\"\n", - " seed = int(chain[\"seed\"])\n", - " coords = chain[\"coords\"]\n", - " crossings = chain[\"crossings\"]\n", - " decoy_coords = chain.get(\"decoy_coords\", None)\n", - "\n", - " padding = 10.0\n", - " xmin = coords[:, 0].min() - padding\n", - " xmax = coords[:, 0].max() + padding\n", - " ymin = coords[:, 1].min() - padding\n", - " ymax = coords[:, 1].max() + padding\n", - " global_extent = (xmin, xmax, ymin, ymax)\n", - "\n", - " afm_img, dbg = create_z_based_afm(\n", - " coords, grain_seed=seed,\n", - " crossings_precomputed=crossings,\n", - " extent=global_extent,\n", - " **AFM_KW\n", - " )\n", - "\n", - " if decoy_coords is not None:\n", - " decoy_coords = place_decoy_in_corner(\n", - " decoy_coords, global_extent,\n", - " corner_margin_nm=8.0, seed=seed)\n", - " decoy_kw = {**AFM_KW,\n", - " \"apply_edge_taper\": False,\n", - " \"enable_crossing_boost\": False,\n", - " \"add_center_ridge\": False}\n", - " decoy_afm, _ = create_z_based_afm(\n", - " decoy_coords, extent=global_extent, **decoy_kw)\n", - " afm_img = np.maximum(afm_img, decoy_afm)\n", - "\n", - " actual_extent = dbg[\"extent\"]\n", - "\n", - " noise_img = sample_noise_image(\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " seed=seed,\n", - " out_shape=afm_img.shape,\n", - " target_rms_nm=TARGET_NOISE_RMS_NM,\n", - " )\n", - " if noise_img is not None:\n", - " afm_img = np.clip((afm_img + noise_img).astype(np.float32),\n", - " 0, AFM_KW[\"max_height_nm\"])\n", - "\n", - " dna_mask = make_dna_mask_from_beads(\n", - " coords, actual_extent, NX, NY, dilate_px=DNA_MASK_DILATE_PX)\n", - "\n", - " cross_mask, crossings_out = make_crossing_mask(\n", - " coords, actual_extent, NX, NY,\n", - " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", - " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", - " chain_extent=CROSS_CHAIN_EXTENT,\n", - " center_weight=CROSS_CENTER_WEIGHT,\n", - " chain_weight=CROSS_CHAIN_WEIGHT,\n", - " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", - " crossings_precomputed=crossings,\n", - " merge_radius_nm=0.5,\n", - " )\n", - "\n", - " if CROSS_CLIP_TO_DNA_MASK:\n", - " cross_mask = cross_mask * dna_mask.astype(np.float32)\n", - "\n", - " return dict(\n", - " seed=seed,\n", - " afm_img=afm_img.astype(np.float32),\n", - " dna_mask=dna_mask.astype(np.uint8),\n", - " cross_mask=cross_mask.astype(np.float32),\n", - " extent=np.array(actual_extent, dtype=np.float32),\n", - " n_crossings=int(len(crossings_out)),\n", - " decoy_coords=decoy_coords,\n", - " )\n", - "\n", - "\n", - "def generate_one_sample_multilength(seed, n_beads, noise_source=\"none\",\n", - " noise_asset=None, add_decoy=False):\n", - " \"\"\"\n", - " Generate one complete sample at a specified bead count.\n", - " Uses blank.spm-derived noise when available and otherwise falls back\n", - " to PSD-based synthetic noise generation.\n", - " \"\"\"\n", - " chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n", - "\n", - " s = render_chain_and_masks(\n", - " chain,\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " )\n", - " s[\"n_beads\"] = int(n_beads)\n", - " return s\n", - "\n", - "\n", - "# ── Load noise asset once; prefer blank.spm, fall back to PSD model ───────────\n", - "noise_source = \"none\"\n", - "noise_asset = None\n", - "noise_diag = {\"source\": \"none\"}\n", - "\n", - "if USE_BLANK_SPM_NOISE:\n", - " blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n", - " if blank_path and os.path.isfile(blank_path):\n", - " try:\n", - " noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n", - " blank_path, target_rms_nm=TARGET_NOISE_RMS_NM, verbose=True)\n", - " noise_source = \"blank_spm\"\n", - " print(f\"Loaded blank .spm noise texture RMS ≈ {noise_rms_nm:.3f} nm\")\n", - " except Exception as e:\n", - " print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n", - " else:\n", - " print(\"No blank .spm file found; trying PSD fallback noise model.\")\n", - "\n", - " if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n", - " try:\n", - " noise_asset = load_model(MODEL_PATH)\n", - " noise_source = \"psd_model\"\n", - " noise_diag = {\n", - " \"source\": \"psd_model\",\n", - " \"model_path\": str(MODEL_PATH),\n", - " \"method\": PSD_NOISE_METHOD,\n", - " \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n", - " }\n", - " print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n", - " except Exception as e:\n", - " print(\"PSD fallback noise model failed; proceeding without noise. Error:\", e)\n", - " noise_asset = None\n", - "else:\n", - " print(\"Noise injection disabled.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_11", - "metadata": { - "id": "cell_md_11" - }, - "source": [ - "## 12. Sanity Check\n", - "\n", - "It is always a good idea to check if the pipeline is working correctly and producing the inteded resutls and so we have added a sanity check that can be performed before starting the generation of the entire dataset.\n", - "Here we render one sample per bead count for the bead counts provided in the global variables and by default we have set `add_decoy=True` here.\n", - "\n", - "This check displays the AFM image, DNA mask, and crossing mask side-by-side.\n", - "We also print the extent / decoy-placement diagnostics for verification and adjustment." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "cell_code_11", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "cell_code_11", - "outputId": "9b890f12-a0d1-4eea-de35-ad3b13fb6e05" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "==================== SAMPLE 1 (Seed: 0, 70 beads) ====================\n", - "Image window (nm): X=[-20.6, 19.4], Y=[-18.8, 16.6]\n", - "Decoy XY bounds (nm): X=[11.4, 11.5], Y=[-11.3, -10.3]\n", - "Decoy Z range (nm): -0.41 – 0.41\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 2 (Seed: 1, 80 beads) ====================\n", - "Image window (nm): X=[-18.0, 21.3], Y=[-19.3, 21.2]\n", - "Decoy XY bounds (nm): X=[13.0, 13.7], Y=[12.8, 13.6]\n", - "Decoy Z range (nm): -0.03 – 0.03\n", - "Decoy inside extent.\n", - " Decoy height ≈ 0\n", - "\n", - "==================== SAMPLE 3 (Seed: 2, 90 beads) ====================\n", - "Image window (nm): X=[-20.0, 16.0], Y=[-18.0, 20.6]\n", - "Decoy XY bounds (nm): X=[7.8, 8.3], Y=[-11.0, -9.1]\n", - "Decoy Z range (nm): -0.26 – 0.15\n", - "Decoy inside extent.\n", - "\n", - "==================== SAMPLE 4 (Seed: 3, 100 beads) ====================\n", - "Image window (nm): X=[-22.7, 27.1], Y=[-16.9, 19.5]\n", - "Decoy XY bounds (nm): X=[18.8, 19.4], Y=[-10.3, -7.4]\n", - "Decoy Z range (nm): -0.39 – 0.44\n", - "Decoy inside extent.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "bead_counts = list(BEAD_COUNTS)\n", - "seeds = [int(BASE_SEED + k) for k in range(len(bead_counts))]\n", - "\n", - "samples = [\n", - " generate_one_sample_multilength(\n", - " seeds[i], n_beads=bead_counts[i],\n", - " noise_source=noise_source, noise_asset=noise_asset, add_decoy=True\n", - " )\n", - " for i in range(len(bead_counts))\n", - "]\n", - "\n", - "fig, axes = plt.subplots(len(samples), 3, figsize=(16, 5 * len(samples)))\n", - "\n", - "for r, s in enumerate(samples):\n", - " extent = s[\"extent\"]\n", - " img = s[\"afm_img\"]\n", - "\n", - " print(f\"\\n{'='*20} SAMPLE {r+1} (Seed: {s['seed']}, \"\n", - " f\"{s['n_beads']} beads) {'='*20}\")\n", - " print(f\"Image window (nm): X=[{extent[0]:.1f}, {extent[1]:.1f}], \"\n", - " f\"Y=[{extent[2]:.1f}, {extent[3]:.1f}]\")\n", - "\n", - " if s.get(\"decoy_coords\") is not None:\n", - " d = s[\"decoy_coords\"]\n", - " print(f\"Decoy XY bounds (nm): \"\n", - " f\"X=[{d[:,0].min():.1f}, {d[:,0].max():.1f}], \"\n", - " f\"Y=[{d[:,1].min():.1f}, {d[:,1].max():.1f}]\")\n", - " print(f\"Decoy Z range (nm): {d[:,2].min():.2f} – {d[:,2].max():.2f}\")\n", - " oob = (d[:,0].min() < extent[0] or d[:,0].max() > extent[1] or\n", - " d[:,1].min() < extent[2] or d[:,1].max() > extent[3])\n", - " print(\" Decoy out-of-bounds!\" if oob else \"Decoy inside extent.\")\n", - " if d[:,2].max() < 0.1:\n", - " print(\" Decoy height ≈ 0\")\n", - "\n", - " ax1, ax2, ax3 = axes[r]\n", - " ext_list = list(extent)\n", - "\n", - " ax1.imshow(img, cmap=\"afmhot\", origin=\"lower\", extent=ext_list)\n", - " ax1.set_title(f\"AFM image\\n(seed {s['seed']}, {s['n_beads']} beads)\")\n", - " ax1.axis(\"off\")\n", - "\n", - " ax2.imshow(s[\"dna_mask\"], cmap=\"gray\", origin=\"lower\", extent=ext_list)\n", - " ax2.set_title(\"DNA mask (decoy excluded)\")\n", - " ax2.axis(\"off\")\n", - "\n", - " ax3.imshow(s[\"cross_mask\"], cmap=\"magma\", origin=\"lower\", extent=ext_list)\n", - " ax3.set_title(f\"Crossing mask (n={s['n_crossings']})\")\n", - " ax3.axis(\"off\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "source": [ - "##13. Benchmarking\n", - "\n", - "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." - ], - "metadata": { - "id": "3YoKkN3e5dIo" - }, - "id": "3YoKkN3e5dIo" - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "WLAYkyIQlhuX", - "metadata": { - "id": "WLAYkyIQlhuX", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "2efe41b8-37bd-4d1b-a01e-d50eed42cac6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "==============================================================\n", - " SYSTEM PROFILE\n", - "==============================================================\n", - " OS : Linux 6.6.113+\n", - " CPU (logical) : 2 cores\n", - " RAM available : 11.5 GB\n", - " Mode : Non-MD (2D Walk)\n", - "\n", - "==============================================================\n", - " RUNNING BENCHMARK\n", - "==============================================================\n", - " Rep 1/3: 0.05s\n", - " Rep 2/3: 0.06s\n", - " Rep 3/3: 0.06s\n", - "==============================================================\n", - " Median Wall Time: 0.06 s\n", - " Peak RAM (RSS) : 611.8 MB\n", - " Projected Total : 0.1 hours for 4000 samples\n", - "==============================================================\n" - ] - } - ], - "source": [ - "import time\n", - "import tracemalloc\n", - "import os\n", - "import platform\n", - "import psutil\n", - "import threading\n", - "import statistics\n", - "\n", - "# ── Helper: peak RSS tracker ──────\n", - "class _PeakMemTracker:\n", - " def __init__(self):\n", - " self._proc = psutil.Process(os.getpid())\n", - " self.peak_mb = 0.0\n", - " self._stop = threading.Event()\n", - "\n", - " def _run(self):\n", - " while not self._stop.is_set():\n", - " try:\n", - " rss = self._proc.memory_info().rss / 1e6\n", - " if rss > self.peak_mb:\n", - " self.peak_mb = rss\n", - " except Exception: pass\n", - " self._stop.wait(0.05)\n", - "\n", - " def start(self):\n", - " self._t = threading.Thread(target=self._run, daemon=True)\n", - " self._t.start()\n", - "\n", - " def stop(self):\n", - " self._stop.set(); self._t.join()\n", - "\n", - "# ── System Info ──\n", - "cpu_logical = psutil.cpu_count(logical=True)\n", - "if USE_MD:\n", - " platforms = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]\n", - " active_platform = \"OpenCL\" if \"OpenCL\" in platforms else \"CPU\"\n", - "\n", - "print(\"=\" * 62)\n", - "print(\" SYSTEM PROFILE\")\n", - "print(\"=\" * 62)\n", - "print(f\" OS : {platform.system()} {platform.release()}\")\n", - "print(f\" CPU (logical) : {cpu_logical} cores\")\n", - "print(f\" RAM available : {psutil.virtual_memory().available / 1e9:.1f} GB\")\n", - "if USE_MD:\n", - " print(f\" OpenMM Platforms: {platforms}\")\n", - "print(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\n", - "print()\n", - "\n", - "BENCH_SEED = int(BASE_SEED)\n", - "BENCH_N_BEADS = int(BEAD_COUNTS[0])\n", - "BENCH_REPS = 3\n", - "\n", - "def _bench_stages(seed, n_beads):\n", - " t = {}\n", - " t0 = time.perf_counter()\n", - "\n", - " if USE_MD:\n", - " coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n", - " t[\"1_chain_init\"] = time.perf_counter() - t0\n", - "\n", - " t0 = time.perf_counter()\n", - " frames = run_md_relaxation(coords0, seed=seed, n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME)\n", - " t[\"2_md_relax\"] = time.perf_counter() - t0\n", - " else:\n", - " # Skip initial 3D step and run 2D persistent walk\n", - " t[\"1_chain_init\"] = 0.0\n", - " t0 = time.perf_counter()\n", - " frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n", - " t[\"2_md_relax\"] = time.perf_counter() - t0\n", - "\n", - " last_coords = frames[-1]\n", - " t0 = time.perf_counter()\n", - " crossings = find_polyline_crossings(last_coords[:, :2])\n", - " t[\"3_crossing_detect\"] = time.perf_counter() - t0\n", - "\n", - " t0 = time.perf_counter()\n", - " chain = dict(seed=seed, n_beads=n_beads, coords=last_coords, crossings=crossings)\n", - " render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None)\n", - " t[\"4_afm_render_masks\"] = time.perf_counter() - t0\n", - " return t\n", - "\n", - "print(\"=\" * 62)\n", - "print(\" RUNNING BENCHMARK\")\n", - "print(\"=\" * 62)\n", - "\n", - "wall_times = []\n", - "mem_tracker = _PeakMemTracker()\n", - "mem_tracker.start()\n", - "\n", - "for rep in range(BENCH_REPS):\n", - " t0 = time.perf_counter()\n", - " _ = _bench_stages(BENCH_SEED + rep, BENCH_N_BEADS)\n", - " elapsed = time.perf_counter() - t0\n", - " wall_times.append(elapsed)\n", - " print(f\" Rep {rep+1}/{BENCH_REPS}: {elapsed:.2f}s\")\n", - "\n", - "mem_tracker.stop()\n", - "med_wall = statistics.median(wall_times)\n", - "total_samples = int(N_SAMPLES) * len(BEAD_COUNTS)\n", - "est_hours = (med_wall * total_samples) / 3600\n", - "\n", - "print(\"=\" * 62)\n", - "print(f\" Median Wall Time: {med_wall:.2f} s\")\n", - "print(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\n", - "print(f\" Projected Total : {est_hours:.1f} hours for {total_samples} samples\")\n", - "print(\"=\" * 62)\n" - ] - }, - { - "cell_type": "markdown", - "id": "cell_md_12", - "metadata": { - "id": "cell_md_12" - }, - "source": [ - "## 14.Dataset Generation\n", - "\n", - "Finally, we are ready to generate the full dataset. The amount of samples and the lenghts of the chains is defined in the global variables and will be referenced here.\n", - "This notebook generates the full dataset (default: 1000 samples × 4 chain lengths).\n", - "\n", - "\n", - "Progress is printed every 10 samples and a `manifest.csv` tracks all file paths." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cell_code_12", - "metadata": { - "id": "cell_code_12" - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "manifest_path = os.path.join(OUT_DIR, \"manifest.csv\")\n", - "print(f\"Starting dataset generation: {N_SAMPLES} samples…\")\n", - "\n", - "# Define the header including global parameters\n", - "header = [\n", - " \"index\", \"seed\", \"n_beads\", \"bond_length\", \"persistence_bonds\",\n", - " \"image_npy\", \"dna_mask_npy\", \"cross_mask_npy\", \"meta_npz\",\n", - " \"n_crossings\", \"has_decoy\", \"afm_params_json\"\n", - "]\n", - "\n", - "with open(manifest_path, \"w\", newline=\"\") as f:\n", - " w = csv.writer(f)\n", - " w.writerow(header)\n", - "\n", - " for idx in range(int(N_SAMPLES)):\n", - " # Seed override for a known-problematic sample\n", - " if idx == 832:\n", - " seed = int(BASE_SEED + idx + 9997)\n", - " print(f\"Special override for index 832: seed = {seed}\")\n", - " else:\n", - " seed = int(BASE_SEED + idx)\n", - "\n", - " n_beads = int(BEAD_COUNTS[idx % len(BEAD_COUNTS)])\n", - " add_decoy = (idx % 5 == 0)\n", - "\n", - " s = generate_one_sample_multilength(\n", - " seed, n_beads=n_beads,\n", - " noise_source=noise_source,\n", - " noise_asset=noise_asset,\n", - " add_decoy=add_decoy,\n", - " )\n", - "\n", - " img_path = os.path.join(\"images\", f\"img_{idx:04d}.npy\")\n", - " dna_path = os.path.join(\"dna_masks\", f\"dna_{idx:04d}.npy\")\n", - " cross_path = os.path.join(\"cross_masks\", f\"cross_{idx:04d}.npy\")\n", - " meta_path = os.path.join(\"meta\", f\"meta_{idx:04d}.npz\")\n", - "\n", - " np.save(os.path.join(OUT_DIR, img_path), s[\"afm_img\"])\n", - " np.save(os.path.join(OUT_DIR, dna_path), s[\"dna_mask\"])\n", - " np.save(os.path.join(OUT_DIR, cross_path), s[\"cross_mask\"])\n", - "\n", - " # Save detailed metadata\n", - " np.savez_compressed(\n", - " os.path.join(OUT_DIR, meta_path),\n", - " seed = s[\"seed\"],\n", - " n_beads = int(s[\"n_beads\"]),\n", - " extent = s[\"extent\"],\n", - " n_crossings= s[\"n_crossings\"],\n", - " has_decoy = add_decoy,\n", - " bond_length = BOND_LENGTH,\n", - " persistence = PERSISTENCE_BONDS\n", - " )\n", - "\n", - " # Write to manifest including JSON-serialized AFM parameters\n", - " w.writerow([\n", - " idx, seed, n_beads, BOND_LENGTH, PERSISTENCE_BONDS,\n", - " img_path, dna_path, cross_path, meta_path,\n", - " s[\"n_crossings\"], add_decoy, json.dumps(AFM_KW)\n", - " ])\n", - "\n", - " if (idx + 1) % 10 == 0:\n", - " print(f\"Progress: {idx+1}/{N_SAMPLES}\")\n", - "\n", - "print(\"\\nDone. Dataset written to:\", OUT_DIR)\n", - "print(\"Manifest:\", manifest_path)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 14d62c5c069b888029c1b13e8eb0dd4e948f8dfc Mon Sep 17 00:00:00 2001 From: Prakhar-Dutta Date: Thu, 30 Apr 2026 15:10:55 +0200 Subject: [PATCH 17/17] Latest notebook for DNA Sim Notebook for DNA Sim after comments of meeting on 29.4.2026 have been rectified. --- tutorials/20240411_blank_water.0_00000.spm | 38838 ++++++++++++++++ ...ged_via_AFM_draft_correct_formatting.ipynb | 5135 ++ 2 files changed, 43973 insertions(+) create mode 100644 tutorials/20240411_blank_water.0_00000.spm create mode 100644 tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_correct_formatting.ipynb diff --git a/tutorials/20240411_blank_water.0_00000.spm b/tutorials/20240411_blank_water.0_00000.spm new file mode 100644 index 0000000..7c99f2f --- /dev/null +++ b/tutorials/20240411_blank_water.0_00000.spm @@ -0,0 +1,38838 @@ +\*File list +\Version: 0x10000106 +\Date: 09:46:44 AM Thu Apr 11 2024 +\Start context: OL2BIG +\Data length: 80960 +\User Rating: 0 +\Text: +\History: +\Navigator note: +\Engage X Pos: -19783.4 um +\Engage Y Pos: -42151.3 um +\*Equipment list +\Description: Dimension Icon +\Torsion: No +\Sensor: None +\Vision: uEye UI148xLE-C #1 +\EC Pot: None +\Scanner file: 2067D_1B57E.scn +\SECM: No +\*Scanner list +\Scanner type: Dim 4000 +\Head Type: Morpheus +\Serial Number: 2067D +\Z serial: 1B57E +\Part Number: 931-002-301 +\Z Part Number: 840-002-379 +\Piezo size: G +\File name: 2067D_1B57E.scn +\Retracted offset der: 4.55 +\Extended offset der: -6.6563 +\Allow rotation: Allow +\Piezo cal: 440 +\X sensitivity: 63.0636 +\X derate: 0.07031 +\X mag: 1.2 +\X mag1: 0.85 +\X arg: 3.4 +\Fast arg derate: 0 +\Rounding Minimum: 0.1 +\Orthogonality: 0 +\Y sensitivity: 63.0636 +\Y derate: 0.07031 +\Y mag: 0.9 +\Y mag1: 1.18 +\Y arg: 3.7 +\Slow arg derate: 0.005 +\X slow sensitivity: 60.0724 +\X slow derate: 0.07029 +\Y fast sensitivity: 60.0724 +\Y fast derate: 0.07029 +\X slow-fast coupling: 0.12171 +\X slow-fast coupling derating: -0 +\Y slow-fast coupling: 0.147242 +\Y slow-fast coupling derating: -0 +\Fast cal freq: 2.50401 +\Slow cal freq: 4.89064 +\Xs-Yf coupling: 0.00170358 +\Xs-Yf coupling derating: -1.23204e-05 +\Ys-Xf coupling: -0.0144994 +\Ys-Xf coupling derating: -3.29532e-05 +\X offset sens: 94 +\Y offset sens: 91 +\Max scan size: 40 +\Z min: -220 +\Z max: 220 +\Z engage position: 0 +\Z to X coupling: 0 +\Z to Y coupling: 5 +\StrainXY Scanner: Yes +\OptoXY x sensitivity: 190.5 +\OptoXY y sensitivity: 203.4 +\OptoXY orthogonality: 0 +\OptoXY x sens fitted: 110 +\OptoXY y sens fitted: 120 +\OptoXY orthog fitted: 0 +\OptoXY scan angle cor: 0 +\OptoXY pgain/igain: 0.5 +\Optoxy FitVersion: V1 +\OptoXY xsenscal_x: 0 +\OptoXY xsenscal_y: 0 +\OptoXY xsenscal_xy: 0 +\OptoXY xsenscal_xx: 0 +\OptoXY xsenscal_yy: 0 +\OptoXY xsenscal_xxy: 0 +\OptoXY xsenscal_xyy: 0 +\OptoXY xsenscal_xxx: 0 +\OptoXY xsenscal_yyy: 0 +\OptoXY xsenscal_xxyy: 0 +\OptoXY xsenscal_xxxy: 0 +\OptoXY xsenscal_xyyy: 0 +\OptoXY xsenscal_xxxx: 0 +\OptoXY xsenscal_yyyy: 0 +\OptoXY ysenscal_x: 0 +\OptoXY ysenscal_y: 0 +\OptoXY ysenscal_xy: 0 +\OptoXY ysenscal_xx: 0 +\OptoXY ysenscal_yy: 0 +\OptoXY ysenscal_xxy: 0 +\OptoXY ysenscal_xyy: 0 +\OptoXY ysenscal_xxx: 0 +\OptoXY ysenscal_yyy: 0 +\OptoXY ysenscal_xxyy: 0 +\OptoXY ysenscal_xxxy: 0 +\OptoXY ysenscal_xyyy: 0 +\OptoXY ysenscal_xxxx: 0 +\OptoXY ysenscal_yyyy: 0 +\OptoXY orthocal_x: 0 +\OptoXY orthocal_y: 0 +\OptoXY orthocal_xy: 0 +\OptoXY orthocal_xx: 0 +\OptoXY orthocal_yy: 0 +\ClosedLoop x igain: 60 +\ClosedLoop y igain: 60 +\Z range sens: 9.39656 +\StrainZ Scanner: Yes +\Z sensor sens: 468.9 +\Z sensor gain factor: 1.6 +\Z sensor offset gain: 3.35 +\Full Range Sensor Gain: 1.4 +\Z sensor LinOC: -0.0613397 +\Z sensor LinC1: 1 +\Z sensor LinC2: 0.338864 +\Z sensor LinC3: -0.577851 +\Z sensor LinC4: -0.43127 +\Z sensor Linearization: Yes +\Adaptive XY Feedback Scanner: Yes +\Adaptive ScanSizeExpRatio: 0.5 +\Adaptive Minimal Scan Size: 10 +\Adaptive Minimal Scan Rate: 0.2 +\Adaptive x RefPolarity: -1 +\Adaptive y RefPolarity: -1 +\Adaptive x PlantGain: 2 +\Adaptive y PlantGain: 2 +\Adaptive x SensorNoise: 320 +\Adaptive y SensorNoise: 320 +\Adaptive x CutOffFreq: 400 +\Adaptive y CutOffFreq: 400 +\Adaptive x Feedback BW: 10 +\Adaptive y Feedback BW: 10 +\Adaptive X DC Gain: 0.6 +\Adaptive Y DC Gain: 0.6 +\Illumination Scale Factor: 0.5 +\Illumination Range: 1023 +\X center offset: -32 +\Y center offset: -28 +\Adaptive Max scan size: 38 +\OLXY Scanner: No +\Max tip velocity: 4500 +\XY digital CL xIgain: 40 +\XY digital CL yIgain: 40 +\XY digital CL pgain/igain: 0.5 +\XY digital CL cutoff frequency: 1200 +\@Sens. Zsens: V 5.929148 nm/V +\@Sens. CurrentSens: V -10.00000 nA/V +\@Sens. SECPMSens: V 100.0000 mV/V +\@Sens. Xsensor: V 3410.089 nm/V +\@Sens. Ysensor: V 3641.009 nm/V +\*Ciao scan list +\Parameter select: Main +\Operating mode: Image +\Tip Serial Number: TC_BoxC_Tip3 +\Tip Scanned Distance: 1282.76 +\Tip Frame Count: 1 +\Scan Size: 1000 nm +\Slow Axis Size: 1000 nm +\X Position: 0 +\Y Position: 0 +\X Offset: 0 nm +\Y Offset: 0 nm +\Rotate Ang.: 0 +\Stage X: 140369 +\Stage Y: 161098 +\Stage Z: -12698.1 +\Stage Optics: -1215.23 +\X round: 0.1 +\Tip Offset X: 0 +\Tip Offset Y: 0 +\NanoTrack X Offset Set: 0 +\NanoTrack Y Offset Set: 0 +\NanoTrack X Offset Actual: 0 +\NanoTrack Y Offset Actual: 0 +\Samps/line: 512 +\Lines: 512 +\Y disable: Enabled +\Aspect Ratio: 1:1 +\Link Slow Axis Size: Yes +\bidirectional scan: Disabled +\Rectilinear Scan: Disabled +\Calibrate Scan Line Shift: Off +\Scan line shift: 0 +\QNM scan line shift: 2.048 +\Line offset factor: 0 +\Line average: No +\Scan Rate: 3.99503 +\Dark Lift Delay: 300 +\Tip Velocity: 8.87784 +\Zoom in at Constant: Scan Rate +\XY Move Tip Pos: Surface +\Minimum scan rate: 0.1 +\Lift rate: 4 +\X drift: 0 +\Y drift: 0 +\Step XY size: 300 +\Cycles: 10 +\Step XY period: 0.005 +\Step size: 3 +\Units: Metric +\Setpoint Units: Volts +\Clear Buffer for new Scan Area: No +\Color table: 2 +\Scope dualtrace: Dual +\Auto X Sep: 0 +\Auto Y Sep: 0 +\Auto pattern: Linear +\Auto number: 2 +\Capture direction: Down +\Capture prelines: 50 +\Engage Setpoint: 0.85 +\Min int. gain: 8 +\Parameter Update Retract: Disabled +\Parameter Update FB: On +\Capture mode : Continuous +\Captured Automatically: 0 +\MIROView Scan Direction: 0 +\Gain Normalization: None +\Gain Factor: 1 +\Initial Setpoint: 0.850851 +\Potential Feedback: Off +\Potential Feedback Inter: Off +\Frequency Feedback: Off +\Frequency Feedback Inter: Off +\Potential Type: Off +\Potential Type Inter: Off +\Freq. Control: User-defined +\Freq. Control Inter: User-defined +\Drive feedback: Disabled +\Drive time: 0 +\Drive setpoint: 1 +\Drive gain: 80 +\Start size: 13199.8 nm +\Pass 2 size: 10000 nm +\Pass 3 size: 5000 nm +\Pass 4 size: 2000 nm +\Feature scan angle: 0 +\Feature Scan rate: 1 +\Feature step size: 0.138889 +\Feature setpoint: 0.8 +\Feature threshold: 2000 +\Pass delay: 5 +\Z offset: 0 +\Z tolerance: 1135.97 +\Engage X offset: 0 nm +\Engage Y offset: 0 nm +\Engage step size: 0.138889 +\Engage Z sens.: 12.5 +\Engage Z avg.: 10 +\Engage Z delay: 0.5 +\Height engage: Disabled +\Extended Current sens: 1 pA/V +\PF Extended Current sens: 20 pA/V LBW +\Bias limit: 12 V +\AFM Current Sensitivity: 1 nA/V +\SECPM sens: 100 mV/V +\Current range: 200 nA +\Generic amp sens: 1 V/A +\Z Range: 3.2219 +\Strip chart : Disabled +\Strip Chart Rate: 500 +\Strip Chart Size: 100 +\XY Closed Loop: Off +\OptoXY Do Linear: Off +\Z Closed Loop: Off +\Analog2 High Voltage: 0 +\Analog22 High Voltage: 0 +\Output 1 Data Type: Off +\Output 2 Data Type: Off +\Meter display ctrl.: Hidden +\Meter Display Mode: Vertical/Horiz +\Stop motor: Disabled +\Display level: 127 +\XY Feedback Control: Digital +\Show Thermal Tune Button: Both +\Show HSDC Button: Both +\Show Point & Shoot Button: Both +\Show Potential Controls: Neither +\Show Frame Rate Control: Hidden +\Show Repulsion Time Indicator: Hidden +\Enable Optical Image Dewarping: Off +\Z Sensor Correction: Disabled +\XY Drift Correction: Disabled +\Torsional Freq: 950 +\Torsional Q: 1000 +\Sample Rate: 10 +\Sample Points: 256 +\Cycles to Average: 32 +\Harmonics To Correct: 1 +\Max Harmonics: 20 +\Force Data Points: 64 +\Calculation Period: 60 +\Interaction Time: 0.24 +\Invert Torsional: Yes +\Invert Vertical: Yes +\Unload Fit Region: 0.7 +\Lift Height: 6 +\Sync Distance: 25.8984 +\Sync Distance New: 25.8984 +\Sync Distance QNM: 103.45 +\Peak Force Engage Amplitude: 6 +\Peak Force Engage Setpoint: 0.04 +\Top Fit Region: 0.1 +\Peak Force Amplitude: 10 +\QNM Calibration: DefaultByZ +\Adhesion Algorithm: Threshold Crossing +\Adhesion Fit %: 0.1 +\Cursor A: 0 +\Cursor B: 2 +\Live Cell Background Removal: Off +\Dynamic Sync Distance: Off +\Force Filter: Off +\Signal/Noise Threshold: 2 +\Cutoff Modulus: 0.3 +\Auto Sync Distance: Engage Only +\Auto Sync Distance QNM: Off +\Fluid Cell: No +\Medium: Fluid +\Deformation Fit Region: 0.85 +\Tip Radius: 5 +\Cantilever Angle: 8 +\Sample Poisson's Ratio: 0 +\Tip Half Angle: 0 +\Include Adhesion Force: Yes +\Min Force Fit Boundary: 20 +\Max Force Fit Boundary: 80 +\Modulus Fit Model: Hertzian (Spherical) +\Phase Unwrap Threshold: 100 +\Phase Unwrap Window: 50 +\Phase Unwrap Mode: Auto +\ScanAsyst Setup: Never +\ScanAsyst Auto Control: Off +\ScanAsyst Auto Gain: Off +\ScanAsyst Auto Setpoint: Off +\ScanAsyst Auto Scan Rate: Off +\ScanAsyst Auto Z Limit: Off +\ScanAsyst Noise Threshold: 0.5 +\ScanAsyst AutoConfig Frames: 0 +\Scan Target Threshold: 0.001 +\Scan Acceptable Threshold: 0.2 +\Scan Max Optimizing Time: 60 +\Skip Offset Optimization: No +\Perfusion Cell Temperature: +\FalseDefl Scan DitheringBits: 0 +\Scan Single Frame Number: 1 +\Idle Depolarize Warning Time: 7200 +\Idle Depolarize Withdraw Time: 18000 +\Idle Depolarize Threshold: 0.05 +\Auto Adjust Rounding: Yes +\Peak Current Average Region: 0.0001 +\I2C Test: Off +\Peak Force Freq Method: Pre-Set +\PFT Freq: 8 kHz +\Peak Force Frequency: 2 +\Peak Force Freq Divider: 64 +\Tip Drive Noise Threshold: 0.2 +\Tip Drive PFT Amplitude: 100 +\SyncDistQNM Filter Factor: 1 +\Use Freq Based QNM Calibration: PFT Ampl Sens Only +\Sync Distance QNM 8K: 0 +\Sync Distance QNM 4K: 0 +\Sync Distance QNM 2K: 0 +\Sync Distance QNM 1K: 0 +\Sync Distance QNM 0.5K: 0 +\Sync Distance QNM 0.25K : 0 +\Sync Distance QNM 0.125K: 0 +\PFT Amplitude Sens 8K: 0 +\PFT Amplitude Sens 4K: 0 +\PFT Amplitude Sens 2K: 0 +\PFT Amplitude Sens 1K: 0 +\PFT Amplitude Sens 0.5K: 0 +\PFT Amplitude Sens 0.25K: 0 +\PFT Amplitude Sens 0.125K: 0 +\Auto PFT Ampl. Sens: Off +\Apply DDS3 Sens Factor: Yes +\PFT Ampl. Sens Factor: 0.01 +\SyncDistance Display: Percentage +\ScanAsyst Min. Setpoint: 0.02 +\ScanAsyst Max. Setpoint: 3 +\ScanAsyst Gain Factor: 0 +\ScanAsyst Gain Threshold: 500 +\PeakForce Capture: Allow +\Reduced Z Delay: 3 +\Z Auto Center Boundary: 0 +\EOL Capture Enable: Off +\EOL Capture Trigger Position: End of Trace +\EOL Capture Time Interval: 1.01171 +\EOL Caputre Ramp Option: None +\EOL Capture Ramp Delay: 0.8 +\EOL Capture Camera Source: Internal +\External captured frame path: E: +\EOL Capture File Reading Timeout: 8 +\EOL Capture Output: Show +\EOL Send Stop Trigger: On +\NanoTrack Path Simulation: Off +\PseudoRT Path Simulation: Off +\NanoTrack Max Row Shift: 10 +\NanoTrack Max Col Shift: 10 +\NanoTrack Channel: 1 +\NanoTrack Min Correlation: 0.8 +\Scan Park Lift Height: 500 +\Adaptive Interleave: Never +\Z Slew Rate Limit: 0 +\Touch Calibration Sweep Range: 400 +\Touch Calibration Force Setpoint: 0.2 +\Auto Amplitude Setpoint: Off +\Free Amplitude: 500 +\Setpoint/Amplitude Ratio: 0.9 +\Amplitude Lift Height: 100 +\Fast Tapping: Off +\FF Phase Shift: 0 +\FF Use Min. Trace Retrace: On +\FF Block Converge Data: Off +\Converge Lines: 0 +\FF Use Z Sensor Data: Off +\FF Zin2Zout Scale: 1 +\FF Defl2Z Scale: 0 +\LP CutOff: 200 +\Defl HP CutOff: 10 +\FF Use Line Tracking: On +\AmplitudeCutRatio: 0.4 +\DeflectionCutRatioOfRMS: 5 +\BadPointsLimitPercentage: 0.1 +\AbsMDiffTraceRetrace2AbsMTrace: 0.5 +\TraceRetraceDiffLimit: 1.5 +\Max Data Points: 256 +\Curves to Average: None +\IR Emission: Off +\IR Trigger: Off +\IR Trigger Mode: Continuous +\IR Trigger Pulse Len: 0.053336 +\IR Trigger Frequency: 0 +\IR Trigger Echo Count: 0 +\IR Trigger Skip Slots: 0 +\IR Group Triggers: 0 +\IR Trigger is EOF: Off +\IR Trigger is EOL: Off +\IR Trigger PFT Marker: PFT Generated Marker +\TM DDS1 Route: Tapping Piezo Only +\Ramp Segments Max: Max 30 +\Actuator Sensitivity: 1 +\QNM Mode: Off +\XY Spiral Skipped: No +\Baseline Avg %: 0.0664 +\PFC Has FV Lines: Yes +\XY Sample Hold Ctrl: Enabled +\@InterleaveList: S [InterleaveOffMode] "Disabled" +\@LinearizeList: S [LinearizeOffMode] "modeLinearizeOff" +\@FPGA Z Feedback: S [] "On" +\@MicroscopeList: S [SoftHarmoniX] "PeakForce QNM" +\@LPDeflection: S [LPFilterOn] "Enabled" +\@LP TM Deflection: S [LPFilterOn] "Enabled" +\@LP Current: S [LPFilterOn] "Enabled" +\@LP Vertical Deflection: S [LPFilterOn] "Enabled" +\@Low Pass Frequency: S [LPFilterOn] "Enabled" +\@LP Friction: S [] "Disabled" +\@LP TM Friction: S [] "Enabled" +\@PicoAnglerPollOnOff: S [PicoAnglerPollOff] "Disabled" +\@ThermalTuneMaxFreq: S [ThermalTuneData6M] "5 - 2000 KHz" +\@TipBiasCntrlList: S [TipBiasCntrlGround] "Ground" +\@Sample Bias Control: S [] "Ground" +\@HSADCMuxOnOff: S [HSADCMuxAny] "modeHSADCMuxAny" +\@HsdcChanADataType: S [HsdcDataTypeOff] "Off" +\@HsdcChanBDataType: S [HsdcDataTypeOff] "Off" +\@FastScan List: S [] "" +\@FastZFeedback List: S [DigitalZFastScan] "Digital" +\@TR Mode: S [] "Disabled" +\@VerticalGainControlList: S [] "Disabled" +\@SCM Feedback: S [] "" +\@DarkLiftList: S [] "" +\@2:SPMFeedbackList: S [SPMFb] "Peak Force" +\@3:SPMFeedbackList: S [SPMFb] "Peak Force" +\@4:DDS1RoutingTappingMode: S [] "Null" +\@5:DDS1RoutingTappingMode: S [] "Null" +\@4:DDS1RoutingFastScanAsystMode: S [] "" +\@5:DDS1RoutingFastScanAsystMode: S [] "" +\@2:Drive1Routing: S [] "Null" +\@3:Drive1Routing: S [] "Null" +\@2:Drive1Monitor: S [] "Not Available" +\@3:Drive1Monitor: S [] "Not Available" +\@2:MainLockInEnableContactMode: S [LockIn1Disabled] "Disabled" +\@3:MainLockInEnableContactMode: S [LockIn1Disabled] "Disabled" +\@4:MainLockInEnableFastScanAsystMode: S [] "" +\@5:MainLockInEnableFastScanAsystMode: S [] "" +\@4:MainLockInEnableNonContactMode: S [] "" +\@5:MainLockInEnableNonContactMode: S [] "" +\@4:LockIn1SourceTappingMode: S [] "Disabled" +\@5:LockIn1SourceTappingMode: S [] "Vertical" +\@4:LockIn1SourceFastScanAsystMode: S [] "Disabled" +\@5:LockIn1SourceFastScanAsystMode: S [] "" +\@2:MainLockInSource: S [] "Disabled" +\@3:MainLockInSource: S [] "Disabled" +\@4:Zmod: S [] "" +\@5:Zmod: S [] "" +\@2:LateralGainControl: S [LateralGainDisabled] "Disabled" +\@3:LateralGainControl: S [LateralGainDisabled] "Disabled" +\@2:InputFbList: S [InputFbOff] "Off" +\@3:InputFbList: S [InputFbOff] "Off" +\@4:TR Mode: S [] "Disabled" +\@5:TR Mode: S [] "Disabled" +\@4:TRCouplingCheck: S [] "" +\@5:TRCouplingCheck: S [] "" +\@2:LockIn2DDS2Enable: S [LockIn2Disabled] "Disabled" +\@3:LockIn2DDS2Enable: S [LockIn2Disabled] "Disabled" +\@4:DDS2HarmonicList: S [] "1" +\@5:DDS2HarmonicList: S [] "1" +\@2:LockIn2Source: S [] "Disabled" +\@3:LockIn2Source: S [] "Disabled" +\@2:Drive2Routing: S [] "Null" +\@3:Drive2Routing: S [] "Null" +\@2:Drive2Monitor: S [] "Not Available" +\@3:Drive2Monitor: S [] "Not Available" +\@4:PRLockInTypeList: S [] "" +\@5:PRLockInTypeList: S [] "" +\@4:PiezoACBiasCtrlList: S [] "" +\@5:PiezoACBiasCtrlList: S [] "" +\@4:VerticalGainControlList: S [] "Enabled" +\@5:VerticalGainControlList: S [] "Enabled" +\@4:SCM Feedback: S [] "" +\@5:SCM Feedback: S [] "" +\@4:TUNAList: S [TUNAOffMode] "Disabled" +\@5:TUNAList: S [TUNAOffMode] "Disabled" +\@2:Drive3RoutingSoftHarmoniX: S [DDS3ToZOff] "Z PFT" +\@3:Drive3RoutingSoftHarmoniX: S [DDS3ToZOff] "Z Offset" +\@4:Drive3RoutingSoftHarmoniXRamp: S [] "" +\@5:Drive3RoutingSoftHarmoniXRamp: S [DDS3ToZOff] "Z Offset" +\@4:Drive3Routing: S [] "" +\@5:Drive3Routing: S [] "" +\@2:Drive3Monitor: S [] "Not Available" +\@3:Drive3Monitor: S [] "Not Available" +\@2:LockIn3EnableList: S [LockIn3Enabled] "Enabled" +\@3:LockIn3EnableList: S [LockIn3Disabled] "Disabled" +\@2:LockIn3SourceSoftHarmoniX: S [] "Vertical" +\@3:LockIn3SourceSoftHarmoniX: S [] "Disabled" +\@4:LockIn3SourceNonSoftHarmoniX: S [] "Vertical" +\@5:LockIn3SourceNonSoftHarmoniX: S [] "Vertical" +\@2:HsdcChanCDataType: S [DeflectionError] "Deflection Error" +\@3:HsdcChanCDataType: S [HsdcDataTypeOff] "Off" +\@2:HsdcChanDDataType: S [HsdcDataTypeHeight] "Height" +\@3:HsdcChanDDataType: S [HsdcDataTypeOff] "Off" +\@2:HsdcTriggerChan: S [HsdcDataTypeHeight] "Height" +\@3:HsdcTriggerChan: S [HsdcDataTypeHeight] "Height" +\@Sens. DeflSens: V 1.000000 +\@Sens. AmplSens: V 15.46000 nm/V +\@Sens. DeflectionIn1BSens: V 15.46000 nm/V +\@Sens. FVModulusSens: V 1.000000 Pa/Arb +\@Sens. FVLogModulusSens: V 1.000000 log(Pa)/log(Arb) +\@Sens. FVForceSens: V 1.000000 nN/Arb +\@Sens. FVStiffnessSens: V 1.000000 N/m*Arb +\@Sens. FVSweepF0Sens: V 1.000000 Hz/Arb +\@Sens. FVSweepQSens: V 1.000000 1/Arb +\@Sens. FVSweepAmpSens: V 1.000000 nm/Arb +\@Sens. FVSweepPhaseSens: V 1.000000 /Arb +\@Sens. FVSweepFitRSqSens: V 1.000000 1/Arb +\@Sens. FVCRNormContactStiffSens: V 1.000000 1/Arb +\@Sens. FVCRDampingCoeffSens: V 1.000000 1/Arb +\@Sens. FVCRRadiusSens: V 1.000000 nm/Arb +\@Sens. FVCRStorageModSens: V 1.000000 Pa/Arb +\@Sens. FVCRLossModSens: V 1.000000 Pa/Arb +\@Sens. FVCRLossTanSens: V 1.000000 1/Arb +\@Sens. ECBiasSens: V 1000.000 mV/V +\@Sens. AFMSetFastScanSens: V 1.000000 V/V +\@Sens. LSPRDataSensitivity: V 1.000000 +\@Sens. LSPRAmplitudeSensitivity: V 1.000000 +\@Sens. HSPRDataSensitivity: V 1.000000 +\@Sens. HSPRAmplitudeSensitivity: V 1.000000 +\@Sens. tunaSens: V 10.00000 pA/V +\@Sens. biassens: V 1.000000 V/V +\@Sens. testsens: V 1.000000 V/V +\@Sens. ZsensSens: V 214.7583 nm/V +\@Sens. ZPicoSens: V 1000.000 nm/V +\@Sens. SecmEtipSens: V 1.000000 V/V +\@Sens. SecmItipSens: V 1.000000 nA/V +\@Sens. SecmISens: V 100.0000 uA/V +\@Sens. Amplitude Error: V 15.46000 nm/V +\@Sens. Phase: V 1.000000 +\@Sens. Phase Error: V 1.000000 +\@Sens. Friction: V 1.000000 +\@Sens. TM Deflection: V 15.46000 nm/V +\@Sens. TM Friction: V 1.000000 +\@Sens. Inphase: V 1.000000 +\@Sens. Quadrature: V 1.000000 +\@Sens. Amplitude1: V 1.000000 +\@Sens. Raw Deflection: V 1.000000 +\@Sens. Raw Deflection Gain: V 1.000000 +\@Sens. Counter 1: V 1.000000 +\@Sens. Counter 2: V 1.000000 +\@Sens. Freq. 1: V 1.000000 +\@Sens. Freq. 2: V 1.000000 +\@Sens. Front Panel Output 1: V 1.000000 +\@Sens. Fast Z Analog: V 1.000000 +\@Sens. Fast Z: V 1.000000 +\@Sens. Fast RMS: V 1.000000 +\@Sens. Fast Deflection Analog: V 1.000000 +\@Sens. Fast Deflection Error: V 1.000000 +\@Sens. Frequency: V 1.000000 +\@Sens. Potential: V 1.000000 +\@Sens. Potential (HV): V 1.000000 +\@Sens. dCdZ: V 1.000000 +\@Sens. TR Amplitude: V 1.000000 +\@Sens. TR Amplitude Error: V 1.000000 +\@Sens. TR Phase: V 1.000000 +\@Sens. Amplitude2: V 1.000000 +\@Sens. HS PR Phase: V 1.000000 +\@Sens. MFM Amplitude: V 1.000000 +\@Sens. MFM Phase: V 1.000000 +\@Sens. dC/dV Amplitude: V 1.000000 +\@Sens. dC/dV Amplitude Error: V 1.000000 +\@Sens. dC/dV I: V 1.000000 +\@Sens. dC/dV Q: V 1.000000 +\@Sens. dC/dV Phase: V 1.000000 +\@Sens. Feedback Bias: V 1.000000 +\@Sens. Log(Resistance): V 1.000000 +\@Sens. Input1: V 1.000000 +\@Sens. Input2: V 1.000000 +\@Sens. Input3: V 1.000000 +\@Sens. DriveAmplitude3Sens: V 135.9160 nm/V +\@Sens. ForceDeflSens: V 1.000000 +\@Sens. ForceSens: V 0.006000000 V/Arb +\@Sens. DissipationSens: V 1.000000 +\@Sens. DeformationSens: V 1.000000 +\@Sens. StiffnessSens: V 1.000000 +\@Sens. LogStiffnessSens: V 1.000000 +\@Sens. SneddonModulusSens: V 1.000000 +\@Sens. LogSneddonModulusSens: V 1.000000 +\@Offset PotentialInputOffset: V [Sens. Potential] (0.0003051758 V/LSB) 0 V +\@Offset SecmInputOffsetEtip: V [Sens. SecmEtipSens] (0.00000000572205 V/LSB) -0 V +\@Offset SecmInputOffsetItip: V [Sens. SecmItipSens] (0.00000000572205 V/LSB) 0 V +\@Offset SecmInputOffsetI: V [Sens. SecmISens] (0.00000000572205 V/LSB) -0 V +\@Sample period: V (0.1000000 us/LSB) 10.00000 us +\@Z center: V [Sens. Zsens] (0.006713867 V/LSB) 41.76697 V +\@1:ZLimit: V [Sens. Zsens] (0.006713867 V/LSB) 342.8812 V +\@1:ZLimitRamp: V [Sens. Zsens] (0.006713867 V/LSB) 352.1152 V +\@1:AmplitudeLimit1: V (0.0000009313226 mV/LSB) 4000.000 mV +\@1:TRAmplitudeLimit: V (0.0000009313226 mV/LSB) 2500.000 mV +\@1:SCMAmplitudeLimit: V (0.0000009313226 mV/LSB) 4000.000 mV +\@2:CurrentLimit: V (0.00000000572205 V/LSB) 24.57600 V +\@2:AmplitudeLimit1LockIn1and2: V (0.0000009313226 mV/LSB) 4000.000 mV +\@2:AmplitudeLimit1LockIn1: V (0.0000009313226 mV/LSB) 4000.000 mV +\@2:AmplitudeLimit1LockIn2: V (0.0000009313226 mV/LSB) 4000.000 mV +\@2:DeflectionLimitNDma: V (0.00000000572205 V/LSB) 24.57600 V +\@2:DeflectionLimitLockIn3LSADC1: V (0.00000000572205 V/LSB) 4.096000 V +\@2:DeflectionLimit: V (0.00000000572205 V/LSB) 24.57600 V +\@2:TMDeflectionLimitLockIn3LSADC1: V (0.00000000572205 V/LSB) 24.57600 V +\@2:TMDeflectionLimit: V (0.00000000572205 V/LSB) 24.57600 V +\@2:HSADC2GainLockIn2and1: V (0.0000009313226 mV/LSB) 4000.000 mV +\@2:HSADC2GainLockIn2: V (0.0000009313226 mV/LSB) 4000.000 mV +\@2:HSADC2GainLockIn1: V (0.0000009313226 mV/LSB) 4000.000 mV +\@2:LSADC2Gain: V (0.00000000572205 V/LSB) 24.57600 V +\@2:LSADC9Gain: V (0.00000000572205 V/LSB) 24.57600 V +\@Lift Start Height: V [Sens. Zsens] (0.005231952 V/LSB) 0 V +\@Lift Scan Height: V [Sens. Zsens] (0.005231952 V/LSB) 4.506534 V +\@Drive height: V [Sens. Zsens] (0.005231952 V/LSB) 0 V +\@2:SoftHarmoniXSetpoint: V [Sens. ForceDeflSens] (0.00006250000 V/LSB) 0.01856250 V +\@3:SoftHarmoniXSetpoint: V [Sens. ForceDeflSens] (0.00006250000 V/LSB) 0 V +\@2:AFMSetDeflection: V [Sens. DeflSens] (0.0003662109 V/LSB) -10.00012 V +\@3:AFMSetDeflection: V [Sens. DeflSens] (0.0003662109 V/LSB) 0.5000000 V +\@2:TMSetAmplitude: V [Sens. AmplSens] (0.06103516 mV/LSB) 250.0000 mV +\@3:TMSetAmplitude: V [Sens. AmplSens] (0.06103516 mV/LSB) 0 mV +\@2:TMSetDeflection: V [Sens. DeflSens] (0.0003662109 V/LSB) 0 V +\@3:TMSetDeflection: V [Sens. DeflSens] (0.0003662109 V/LSB) 0 V +\@2:TMSetFastScan: V (0.0003662109 V/LSB) 0 V +\@3:TMSetFastScan: V (0.0003662109 V/LSB) 0 V +\@2:AFMSetFastScanAnalog: V [Sens. AFMSetFastScanSens] (0.0003662109 V/LSB) 0 V +\@3:AFMSetFastScanAnalog: V [Sens. AFMSetFastScanSens] (0.0003662109 V/LSB) 0 V +\@2:AFMSetFastScanDigital: V [Sens. AFMSetFastScanSens] (0.0003662109 V/LSB) 0 V +\@3:AFMSetFastScanDigital: V [Sens. AFMSetFastScanSens] (0.0003662109 V/LSB) 0 V +\@2:STMSetCurrent: V [Sens. CurrentSens] (0.0003750000 V/LSB) 0.1000000 V +\@3:STMSetCurrent: V [Sens. CurrentSens] (0.0003750000 V/LSB) 10.00000 V +\@2:TRSetAmplitude: V (0.01525879 mV/LSB) 270.0000 mV +\@3:TRSetAmplitude: V (0.01525879 mV/LSB) 270.0000 mV +\@2:SetTRVertDefl: V (0.0003662109 V/LSB) 0 V +\@3:SetTRVertDefl: V (0.0003662109 V/LSB) 0 V +\@2:SECPMSetPotential: V [Sens. SECPMSens] (0.0003750000 V/LSB) 2.000000 V +\@3:SECPMSetPotential: V [Sens. SECPMSens] (0.0003750000 V/LSB) 2.000000 V +\@2:TMSetPhase: V (0.005493164 /LSB) 0 +\@3:TMSetPhase: V (0.005493164 /LSB) 0 +\@2:DigitalSetPoint: V [Sens. DeflectionIn1BSens] (0.0003750000 V/LSB) 0.2500000 V +\@3:DigitalSetPoint: V [Sens. DeflectionIn1BSens] (0.0003750000 V/LSB) 0.2500000 V +\@2:TRSetPhase: V (0.005493164 /LSB) 0 +\@3:TRSetPhase: V (0.005493164 /LSB) 0 +\@2:NanoTrenchSetPoint: V (0.00006250000 V/LSB) 0 V +\@3:NanoTrenchSetPoint: V (0.00006250000 V/LSB) 0 V +\@2:AFMFbIgain2Factor: V (1.000000 1/LSB) 32.00000 +\@3:AFMFbIgain2Factor: V (1.000000 1/LSB) 32.00000 +\@2:AFMFbPgain2: V (0.3125000 1/LSB) 0 +\@3:AFMFbPgain2: V (0.3125000 1/LSB) 0 +\@2:Sample Bias: V (0.3051758 mV/LSB) 0 mV +\@3:Sample Bias: V (0.3051758 mV/LSB) 0 mV +\@2:Tip Bias: V (0.0003051758 V/LSB) 0 V +\@3:Tip Bias: V (0.0003051758 V/LSB) 0 V +\@2:Analog2: V (0.0003051758 V/LSB) 0 V +\@3:Analog2: V (0.0003051758 V/LSB) 0 V +\@2:CantFrequency: V (0.00001164153 kHz/LSB) 412.0000 kHz +\@3:CantFrequency: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:CantFrequencyContactMode: V (0.00001164153 kHz/LSB) 105.0000 kHz +\@3:CantFrequencyContactMode: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:CantDrive: V (0.3051758 mV/LSB) 37.84180 mV +\@3:CantDrive: V (0.3051758 mV/LSB) 0 mV +\@2:CantDriveContactMode: V (0.3051758 mV/LSB) 0 mV +\@3:CantDriveContactMode: V (0.3051758 mV/LSB) 0 mV +\@2:CantDriveNulled: V (0.3051758 mV/LSB) 0 mV +\@3:CantDriveNulled: V (0.3051758 mV/LSB) 0 mV +\@2:DriveDCOffsetTappingMode: V (0.0003051758 V/LSB) 0 V +\@3:DriveDCOffsetTappingMode: V (0.0003051758 V/LSB) 0 V +\@2:DriveDCOffsetNonTapping: V (0.0003051758 V/LSB) 0 V +\@3:DriveDCOffsetNonTapping: V (0.0003051758 V/LSB) 0 V +\@2:DriveDCOffsetFastScanAsyst: V (0.0003051758 V/LSB) 0 V +\@3:DriveDCOffsetFastScanAsyst: V (0.0003051758 V/LSB) 0 V +\@2:ReferenceFrequencyTappingMode: V (0.00001164153 kHz/LSB) 412.0000 kHz +\@3:ReferenceFrequencyTappingMode: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:ReferenceFrequency1: V (0.00001164153 kHz/LSB) 105.0000 kHz +\@3:ReferenceFrequency1: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:CantPhase: V (0.005493164 /LSB) 167.3990 +\@3:CantPhase: V (0.005493164 /LSB) 0 +\@2:CantPhaseContactMode: V (0.005493164 /LSB) 0 +\@3:CantPhaseContactMode: V (0.005493164 /LSB) 0 +\@2:LockInBW1: V (0.00001000000 kHz/LSB) 25.91627 kHz +\@3:LockInBW1: V (0.00001000000 kHz/LSB) 0.5000000 kHz +\@2:LockInBW1ContactMode: V (0.00001000000 kHz/LSB) 0.2120000 kHz +\@3:LockInBW1ContactMode: V (0.00001000000 kHz/LSB) 0.5000000 kHz +\@2:TRCantileverFrequency: V (0.00001164153 kHz/LSB) 1000.000 kHz +\@3:TRCantileverFrequency: V (0.00001164153 kHz/LSB) 1000.000 kHz +\@2:ReferenceFrequencyTRMode: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@3:ReferenceFrequencyTRMode: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:TROnAnalog3: V (0.0003051758 V/LSB) 10.00000 V +\@3:TROnAnalog3: V (0.0003051758 V/LSB) 10.00000 V +\@2:ReferenceFrequency2: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@3:ReferenceFrequency2: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:ReferenceFrequency2Harmonic: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@3:ReferenceFrequency2Harmonic: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:DrivePhase2: V (0.005493164 /LSB) 0 +\@3:DrivePhase2: V (0.005493164 /LSB) 0 +\@2:LockIn2BW: V (0.00001000000 kHz/LSB) 0.5000000 kHz +\@3:LockIn2BW: V (0.00001000000 kHz/LSB) 0.5000000 kHz +\@2:LockIn2BWHarmonic: V (0.00001000000 kHz/LSB) 0.2180000 kHz +\@3:LockIn2BWHarmonic: V (0.00001000000 kHz/LSB) 0.2130000 kHz +\@2:DriveFrequency2: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@3:DriveFrequency2: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:DriveAmplitude2: V (0.3051758 mV/LSB) 0 mV +\@3:DriveAmplitude2: V (0.3051758 mV/LSB) 0 mV +\@2:Drive2 DC Offset: V (0.0003051758 V/LSB) 0 V +\@3:Drive2 DC Offset: V (0.0003051758 V/LSB) 0 V +\@2:PRLowSpeedLockinBW: V (0.00001000000 kHz/LSB) 0.3000000 kHz +\@3:PRLowSpeedLockinBW: V (0.00001000000 kHz/LSB) 0.3000000 kHz +\@2:PRLowSpeedDriveAmplitude: V (0.6103516 mV/LSB) 2000.000 mV +\@3:PRLowSpeedDriveAmplitude: V (0.6103516 mV/LSB) 2000.000 mV +\@2:PRLowSpeedDriveFrequency: V (0.00001164153 kHz/LSB) 15.00000 kHz +\@3:PRLowSpeedDriveFrequency: V (0.00001164153 kHz/LSB) 15.00000 kHz +\@2:PRLowSpeedDrivePhase: V (0.005493164 /LSB) 0 +\@3:PRLowSpeedDrivePhase: V (0.005493164 /LSB) 0 +\@2:PRHighSpeedLockinBW: V (0.00001000000 kHz/LSB) 0.7500000 kHz +\@3:PRHighSpeedLockinBW: V (0.00001000000 kHz/LSB) 0.7500000 kHz +\@2:PRHighSpeedDriveAmplitude: V (0.3051758 mV/LSB) 2000.000 mV +\@3:PRHighSpeedDriveAmplitude: V (0.3051758 mV/LSB) 2000.000 mV +\@2:PRHighSpeedDriveFrequency: V (0.00001164153 kHz/LSB) 60.00000 kHz +\@3:PRHighSpeedDriveFrequency: V (0.00001164153 kHz/LSB) 60.00000 kHz +\@2:PRHighSpeedDrivePhase: V (0.005493164 /LSB) 0 +\@3:PRHighSpeedDrivePhase: V (0.005493164 /LSB) 0 +\@2:MFMLockinBW: V (0.00001000000 kHz/LSB) 0.2110000 kHz +\@3:MFMLockinBW: V (0.00001000000 kHz/LSB) 0.2110000 kHz +\@2:MFMDriveAmplitude: V (0.3051758 mV/LSB) 0 mV +\@3:MFMDriveAmplitude: V (0.3051758 mV/LSB) 0 mV +\@2:MFMDriveFrequency: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@3:MFMDriveFrequency: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:MFMDrivePhase: V (0.005493164 /LSB) 0 +\@3:MFMDrivePhase: V (0.005493164 /LSB) 0 +\@2:MFMDriveDCOffset: V (0.0003051758 V/LSB) 0 V +\@3:MFMDriveDCOffset: V (0.0003051758 V/LSB) 0 V +\@2:ACBiasAmpl: V (0.3051758 mV/LSB) 0 mV +\@3:ACBiasAmpl: V (0.3051758 mV/LSB) 0 mV +\@2:BiasFrequency: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@3:BiasFrequency: V (0.00001164153 kHz/LSB) 0.5000000 kHz +\@2:SCMLockInBW: V (0.00001000000 kHz/LSB) 0.5000000 kHz +\@3:SCMLockInBW: V (0.00001000000 kHz/LSB) 0.5000000 kHz +\@2:SCMLockInPhase: V (0.005493164 /LSB) 0 +\@3:SCMLockInPhase: V (0.005493164 /LSB) 0 +\@2:DCBias: V (0.0003051758 V/LSB) 0 V +\@3:DCBias: V (0.0003051758 V/LSB) 0 V +\@2:Capacitance Sensor Frequency: V (0.03204346 MHz/LSB) 880.0000 MHz +\@3:Capacitance Sensor Frequency: V (0.03204346 MHz/LSB) 880.0000 MHz +\@2:SCMIgain: V (0.001525879 1/LSB) 10.00000 +\@3:SCMIgain: V (0.001525879 1/LSB) 10.00000 +\@2:SCMPgain: V (0.001525879 1/LSB) 10.00000 +\@3:SCMPgain: V (0.001525879 1/LSB) 10.00000 +\@2:SCMFeedbackSetpoint: V (0.06103516 mV/LSB) 200.0000 mV +\@3:SCMFeedbackSetpoint: V (0.06103516 mV/LSB) 200.0000 mV +\@2:SCMFeedbackUpperLimit: V (0.0003051758 V/LSB) 6.000000 V +\@3:SCMFeedbackUpperLimit: V (0.0003051758 V/LSB) 6.000000 V +\@2:DC Test Bias: V [Sens. testsens] (0.0003051758 V/LSB) 0 V +\@3:DC Test Bias: V [Sens. testsens] (0.0003051758 V/LSB) 0 V +\@2:DC Sample Bias: V [Sens. biassens] (0.0003051758 V/LSB) 0 V +\@3:DC Sample Bias: V [Sens. biassens] (0.0003051758 V/LSB) 0 V +\@2:SampleBiasWithPiezo: V [Sens. biassens] (0.0003051758 V/LSB) 0 V +\@3:SampleBiasWithPiezo: V [Sens. biassens] (0.0003051758 V/LSB) 0 V +\@2:Bias Upper Limit: V (0.0003051758 V/LSB) 0 V +\@3:Bias Upper Limit: V (0.0003051758 V/LSB) 0 V +\@2:Bias Lower Limit: V (0.0003051758 V/LSB) 0 V +\@3:Bias Lower Limit: V (0.0003051758 V/LSB) 0 V +\@2:Feedback Igain: V (0.01525879 1/LSB) 0 +\@3:Feedback Igain: V (0.01525879 1/LSB) 0 +\@2:TunaFeedbackSetpoint: V [Sens. tunaSens] (0.0003750000 V/LSB) 0 V +\@3:TunaFeedbackSetpoint: V [Sens. tunaSens] (0.0003750000 V/LSB) 0 V +\@2:DriveFrequency3SoftHarmoniX: V (0.00001164153 kHz/LSB) 7.812500 kHz +\@3:DriveFrequency3SoftHarmoniX: V (0.00001164153 kHz/LSB) 0.005000000 kHz +\@2:DriveFrequency3NonSoftHarmoniX: V (0.00001164153 kHz/LSB) 0.005000000 kHz +\@3:DriveFrequency3NonSoftHarmoniX: V (0.00001164153 kHz/LSB) 0.005000000 kHz +\@2:DriveAmplitude3SoftHarmoniX: V [Sens. DriveAmplitude3Sens] (0.3051758 mV/LSB) 73.57486 mV +\@3:DriveAmplitude3SoftHarmoniX: V [Sens. DriveAmplitude3Sens] (0.3051758 mV/LSB) 0 mV +\@2:DriveAmplitude3NonSoftHarmoniX: V (0.3051758 mV/LSB) 0 mV +\@3:DriveAmplitude3NonSoftHarmoniX: V (0.3051758 mV/LSB) 0 mV +\@2:DriveAmplitude3NonSoftHarmoniX2X: V (0.6103516 mV/LSB) 0 mV +\@3:DriveAmplitude3NonSoftHarmoniX2X: V (0.6103516 mV/LSB) 0 mV +\@2:Drive3 DC Offset: V (0.0003051758 V/LSB) 0 V +\@3:Drive3 DC Offset: V (0.0003051758 V/LSB) 0 V +\@2:ReferenceFrequency3SoftHarmoniX: V (0.00001164153 kHz/LSB) 7.812500 kHz +\@3:ReferenceFrequency3SoftHarmoniX: V (0.00001164153 kHz/LSB) 0.005000000 kHz +\@2:ReferenceFrequency3NonSoftHarmoniX: V (0.00001164153 kHz/LSB) 0.005000000 kHz +\@3:ReferenceFrequency3NonSoftHarmoniX: V (0.00001164153 kHz/LSB) 0.005000000 kHz +\@2:LockIn3BWSoftHarmoniX: V (0.00001000000 kHz/LSB) 0.8242300 kHz +\@3:LockIn3BWSoftHarmoniX: V (0.00001000000 kHz/LSB) 0.005000000 kHz +\@2:LockIn3BWNonSoftHarmoniX: V (0.00001000000 kHz/LSB) 0.005000000 kHz +\@3:LockIn3BWNonSoftHarmoniX: V (0.00001000000 kHz/LSB) 0.005000000 kHz +\@2:Analog3: V (0.0003051758 V/LSB) 0 V +\@3:Analog3: V (0.0003051758 V/LSB) 0 V +\@2:Analog4: V (0.0003051758 V/LSB) 0 V +\@3:Analog4: V (0.0003051758 V/LSB) 0 V +\@2:TR Drive Amplitude: V (0.3051758 mV/LSB) 0 mV +\@3:TR Drive Amplitude: V (0.3051758 mV/LSB) 0 mV +\@2:TR Lock-In Phase: V (0.005493164 /LSB) 0 +\@3:TR Lock-In Phase: V (0.005493164 /LSB) 0 +\@2:DrivePhase3NonSoftHarmoniX: V (0.005493164 /LSB) 0 +\@3:DrivePhase3NonSoftHarmoniX: V (0.005493164 /LSB) 0 +\@2:SSRMSampleBias: V (0.0003051758 V/LSB) 0 V +\@3:SSRMSampleBias: V (0.0003051758 V/LSB) 0 V +\@2:TMFbIgain: V (0.01250000 1/LSB) 0.5000000 +\@3:TMFbIgain: V (0.01250000 1/LSB) 0.5000000 +\@2:TMFbPgain: V (0.01250000 1/LSB) 5.000000 +\@3:TMFbPgain: V (0.01250000 1/LSB) 5.000000 +\@2:AFMFbIgain: V (0.01250000 1/LSB) 10.00000 +\@3:AFMFbIgain: V (0.01250000 1/LSB) 2.000000 +\@2:AFMFbPgain: V (0.01250000 1/LSB) 20.00000 +\@3:AFMFbPgain: V (0.01250000 1/LSB) 5.000000 +\@2:SoftHarmoniXFbIgain: V (0.01250000 1/LSB) 4.500000 +\@3:SoftHarmoniXFbIgain: V (0.01250000 1/LSB) 10.00000 +\@2:SoftHarmoniXFbPgain: V (0.01250000 1/LSB) 0 +\@3:SoftHarmoniXFbPgain: V (0.01250000 1/LSB) 0 +\@2:STMFbIgain: V (0.3125000 1/LSB) 1.000000 +\@3:STMFbIgain: V (0.3125000 1/LSB) 1.000000 +\@2:STMFbPgain: V (0.3125000 1/LSB) 2.000000 +\@3:STMFbPgain: V (0.3125000 1/LSB) 0 +\@2:STMESFbIgain: V (0.3125000 1/LSB) 10.00000 +\@3:STMESFbIgain: V (0.3125000 1/LSB) 10.00000 +\@2:STMESFbPgain: V (0.3125000 1/LSB) 0 +\@3:STMESFbPgain: V (0.3125000 1/LSB) 0 +\@2:DriveAmplitude4SoftHarmoniX: V (0.3051758 mV/LSB) 10000.00 mV +\@3:DriveAmplitude4SoftHarmoniX: V (0.3051758 mV/LSB) 0 mV +\@2:BaselineAvgPtsSoftHarmoniX: V (1.000000 1/LSB) 85.00000 +\@3:BaselineAvgPtsSoftHarmoniX: V (1.000000 1/LSB) 32.00000 +\@2:DeflLowPassFrequency: V (0.02000000 kHz/LSB) 2.500000 kHz +\@3:DeflLowPassFrequency: V (0.02000000 kHz/LSB) 2.500000 kHz +\@2:DeflLowPassFrequencySoftHarmoniX: V (0.02000000 kHz/LSB) 20.00000 kHz +\@3:DeflLowPassFrequencySoftHarmoniX: V (0.02000000 kHz/LSB) 40.00000 kHz +\@2:FricLowPassFrequency: V (0.001000000 kHz/LSB) 2.539063 kHz +\@3:FricLowPassFrequency: V (0.001000000 kHz/LSB) 2.000000 kHz +\@2:TMDeflLowPassFrequency: V (0.02000000 kHz/LSB) 2.500000 kHz +\@3:TMDeflLowPassFrequency: V (0.02000000 kHz/LSB) 2.000000 kHz +\@2:CurrentLowPassFrequency: V (0.02000000 kHz/LSB) 10.00000 kHz +\@3:CurrentLowPassFrequency: V (0.02000000 kHz/LSB) 10.00000 kHz +\@2:OtherModesLowPassFrequency: V (0.02000000 kHz/LSB) 2.000000 kHz +\@3:OtherModesLowPassFrequency: V (0.02000000 kHz/LSB) 2.000000 kHz +\@2:TMFricLowPassFrequency: V (0.001000000 kHz/LSB) 2.500000 kHz +\@3:TMFricLowPassFrequency: V (0.001000000 kHz/LSB) 10.00000 kHz +\@2:FastZTMFbIgain: V (0.03125000 1/LSB) 1.000000 +\@3:FastZTMFbIgain: V (0.03125000 1/LSB) 1.000000 +\@2:FastZTMFbPgain: V (0.03125000 1/LSB) 1.000000 +\@3:FastZTMFbPgain: V (0.03125000 1/LSB) 0 +\@2:InputIgain: V (0.001525879 1/LSB) 10.00000 +\@3:InputIgain: V (0.001525879 1/LSB) 10.00000 +\@2:InputPgain: V (0.001525879 1/LSB) 10.00000 +\@3:InputPgain: V (0.001525879 1/LSB) 10.00000 +\@2:TRFbIgain: V (0.3125000 1/LSB) 0.5000000 +\@3:TRFbIgain: V (0.3125000 1/LSB) 0.5000000 +\@2:TRFbPgain: V (0.3125000 1/LSB) 5.000000 +\@3:TRFbPgain: V (0.3125000 1/LSB) 5.000000 +\@2:VertDeflLowPassFrequency: V (0.02000000 kHz/LSB) 10.00000 kHz +\@3:VertDeflLowPassFrequency: V (0.02000000 kHz/LSB) 10.00000 kHz +\@2:TRLockInBW1: V (0.00001000000 kHz/LSB) 150.0000 kHz +\@3:TRLockInBW1: V (0.00001000000 kHz/LSB) 150.0000 kHz +\@2:DrivePhase3SoftHarmoniX: V (0.005493164 /LSB) 0 +\@3:DrivePhase3SoftHarmoniX: V (0.005493164 /LSB) 0 +\@2:Output 1 Input Data: S [ImageOff] "Off" +\@2:Output 2 Input Data: S [ImageOff] "Off" +\@2:Output 1 Output Data: S [Analog3] "Analog3" +\@2:Output 2 Output Data: S [Analog3] "Analog3" +\@LogStiffnessOffset: V [Sens. LogStiffnessSens] (0.0004882813 log(Arb)/LSB) 0 log(Arb) +\@LogSneddonModulusOffset: V [Sens. LogSneddonModulusSens] (0.0004882813 log(Arb)/LSB) 0 log(Arb) +\*Fast Scan list +\Phase Shift: 508 +\Fast Integral Gain: 20 +\Fast Proportional Gain: 20 +\Fast Interleave Igain: 0 +\Fast Interleave Pgain: 0 +\Contact Gain: 123 +\Q-Control Gain: 0 +\QC Interleave Gain: 0 +\QC Lift Gain: 0 +\Z Sensor Preamp Gain: 2.18338 +\Z Sensor Preamp Offset: -0.0699633 +\High Pass Filter: 1 +\Low Pass Filter: 1 +\All Pass Filter: 1 +\Interleave Mode: Disabled +\Lift Mode: Disabled +\QC Interleave Mode: Disabled +\QC Lift Mode: Disabled +\Tapping Engage Fast I Gain: 10 +\Tapping Engage Fast P Gain: 10 +\Contact Engage Fast I Gain: 5 +\Contact Engage Fast P Gain: 5 +\Tapping Minimum Scanning I Gain: 0.1 +\Tapping Minimum Scanning P Gain: 0.1 +\Contact Minimum Scanning I Gain: 0.1 +\Contact Minimum Scanning P Gain: 0.1 +\Tapping Maximum Scan Rate: 3 +\Contact Maximum Scan Rate: 4 +\Determine Contact Gain: Enabled +\Check for active tip: Enabled +\*Ciao image list +\Data offset: 80960 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: Line +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [ZSensor] "Height Sensor" +\@Z magnify: C [2:Z scale] 0.001515754 +\@2:Z scale: V [Sens. ZsensSens] (0.00000000572205 V/LSB) 24.57600 V +\@2:Z offset: V [Sens. ZsensSens] (0.00000000572205 V/LSB) 0 V +\*Ciao image list +\Data offset: 1129536 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: None +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [PeakForceError] "Peak Force Error" +\@Z magnify: C [2:Z scale] 0.009277344 +\@2:Z scale: V [Sens. ForceDeflSens] (0.00000000095367 V/LSB) 4.096000 V +\@2:Z offset: V [Sens. ForceDeflSens] (0.00000000095367 V/LSB) 0.0006838942 V +\*Ciao image list +\Data offset: 2178112 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: None +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [Stiffness] "DMTModulus" +\@Z magnify: C [2:Z scale] 0.00003906250 +\@2:Z scale: V [Sens. StiffnessSens] (0.0000009536743 Arb/LSB) 4096.000 Arb +\@2:Z offset: V [Sens. StiffnessSens] (0.0000009536743 Arb/LSB) 0.03955364 Arb +\*Ciao image list +\Data offset: 3226688 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: None +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [LogStiffness] "LogDMTModulus" +\@Z magnify: C [2:Z scale] 0.1437500 +\@2:Z scale: V [Sens. LogStiffnessSens] (0.00000000745058 log(Arb)/LSB) 32.00000 log(Arb) +\@2:Z offset: V [Sens. LogStiffnessSens] (0.00000000745058 log(Arb)/LSB) -2.267206 log(Arb) +\*Ciao image list +\Data offset: 4275264 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: None +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [Adhesion] "Adhesion" +\@Z magnify: C [2:Z scale] 0.001139323 +\@2:Z scale: V [Sens. ForceSens] (0.0000009536743 Arb/LSB) 4096.000 Arb +\@2:Z offset: V [Sens. ForceSens] (0.0000009536743 Arb/LSB) 0.9261093 Arb +\*Ciao image list +\Data offset: 5323840 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: None +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [Indentation] "Indentation" +\@Z magnify: C [2:Z scale] 0.01250000 +\@2:Z scale: V [Sens. DeformationSens] (0.00000000093132 V/LSB) 4.000000 V +\@2:Z offset: V [Sens. DeformationSens] (0.00000000093132 V/LSB) 0.01733094 V +\*Ciao image list +\Data offset: 6372416 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: None +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [Dissipation] "Dissipation" +\@Z magnify: C [2:Z scale] 0.00002319336 +\@2:Z scale: V [Sens. DissipationSens] (0.0000009536743 Arb/LSB) 4096.000 Arb +\@2:Z offset: V [Sens. DissipationSens] (0.0000009536743 Arb/LSB) -0.00002765656 Arb +\*Ciao image list +\Data offset: 7420992 +\Data length: 1048576 +\Bytes/pixel: 4 +\Start context: OL +\Data type: AFM +\Data Type Description: +\Note: +\Plane fit: 0 0 0 5 +\Frame direction: Down +\Capture start line: 0 +\Color Table Index: 12 +\Relative frame time: 1586.77 +\Invalid Data Flag: None +\Invalid Data Fill: None +\Samps/line: 512 +\Number of lines: 512 +\Aspect Ratio: 1:1 +\Scan Size: 1 1 ~m +\Scan Line: Main +\Line Direction: Retrace +\Highpass: 0 +\Lowpass: 0 +\Realtime Planefit: Bow +\Offline Planefit: None +\Valid data start X: 0 +\Valid data start Y: 0 +\Valid data len X: 512 +\Valid data len Y: 512 +\Tip x width correction factor: 1 +\Tip y width correction factor: 1 +\Tip x width correction factor sigma: 1 +\Tip y width correction factor sigma: 1 +\@2:Image Data: S [Height] "Height" +\@Z magnify: C [2:Z scale] 0.003560941 +\@2:Z scale: V [Sens. Zsens] (0.00000007983325 V/LSB) 342.8812 V +\@2:Z offset: V [Sens. Zsens] (0.00000007983325 V/LSB) 0 V +\*File list end +Nߎ߬߳YMgw-ez}tTLo߆p݉Q-"= +$ߧ ߤP>:ߺG߭!hZNxߜ]DWҏ͉UcdJrV~\]!j`" _% eQ P + Ͼ |-oi/A+%<+[1I?](k]hUM1VG$h2Q ü )u! B!W'#]#l\$(O&!%&:(Y,)T|)fp(M[&(*qR,,j:. +ய,/ނ.1T1?10`N3%4.1P4}6.4B3o68vL66$9A9 +{:;0E9ˡ==7A& Bz?ிB@#C+EYBCEB*GAEGEH[nJIHiIG&IDISKGIMUNkP)Q7$OvNRPR?QSP:QUE~Wf +U\VWNU6XXPVmY\X[ Y_[\ZajaCF^adeadibdzed׳e֪eHg2f3ieil஍lnkCmhk$o'p^mosu$u2r;o|s6}tprsqxxwЧvCyw*Mxyѿy +wQx}cz15>zl ~~Sd1=fHlQc +Ն|و)LE8jUV[lG0lw--I4e˗0V}AHYฐࢺ3W20,dL:R}rŀaȢ͚ e{ؔ +6_n)2Q=~Hq-<ۉýk2_d8ڮtɱ늰-رM!༰:G.ੋࣂUԸ۸;௃k$=Խ׽>?mBP1+nC\jduo [.!/\z1Iow"|ݕbdlVk*~2A%c4PYqظA r=7|8T+ +N2m-:9E>V9p!֊*|Sjp- DgFW{0}Q>`aT9PBgf୻c r'q7ᇜ,Z14J*% ? + a~ +[ \ \zQ d-?Kss;;USjң<:*}ጃNdu/x^bj߻Eeߨߙߢ߈?M9 :߄/Tî8.T1v߻MxߣI߷>* 7jqB௃0.ͥO]S +&R G ~@ W + ! 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""""""j*j*j*j*j*j*  ɉɉɉɉɉɉX +X +X +X +X +X +))))))PP PP PP PP PP PP ddddddH H H H H H H F%F%F%F%F%F%F%{;{;{;{;{;{;ˊˊˊˊˊˊ`_`_`_`_`_`_????? xxxxxxxxxxxxg'g'g'g'g'g'}}}}}}}}}}}}XXXXXXXI I I I I aaaaaan/ n/ n/ n/ n/ n/ 8x8x8x8x8x8xkkkkkjjjjjjj Z#Z#Z#Z#Z#Z#((((((111111FFFFFF̌ ̌ ̌ ̌ ̌ ttttttV V V V V + + + + + + +[[[[[[G G G G G wwwwwwwwwwwwWWWWWW AAAAAAM M M M M M      \\\\\\zzzzz      44444MNMNMNMNMNTTTTTT------""""""JJJJJJd#d#d#d#d#d#d#X X X X X X kkkkkk878787878787DDDDDDMMMMMMd d d d d / / / / / / llllll[[[[[[))))))% % % % % %      iiiiii0 0 0 0 0 0 [[[[[[^ ^ ^ ^ ^ ^ ``````W W W W W W WWWWWW,l,l + + + + +yyyyyyyV V V V V V m m m m m m N +N +N +N +N +N +N +‚ ‚ ‚ ‚ ‚ ‚ ++++++OOOOOO     T T T T T T  + + + + + +JJJJJJppppp]$]$]$]$]$]$]$[[[[[$$$$$$      7x +7x +7x +7x +7x +7x +7x +888888\\\\\\\\\\\\ K K K K K K____________f f f f f f f >>>>>PPPPPPXXXXXX111111111111N---EEEU U U U U ++++++YYYYYY=}'=}'=}'=}'=}'=}'bbbbbbbAAAAAA !a !a !a !a !a !a SSSSSS      &&&&&&EEEEEEEEEEEE//////eeeeee{{{{{{{{{{{{k+ +yyyyyyaaaaaaa_ _ _ _ _ _ GGGGG&&&&&& K K K K K^^^^^^)))))w w ppppppWWWWWWnnnnnn((((((JJJJJ*)*)*)*)*)*)*){ +{ +{ +{ +{ +{ +      ՕՕՕՕՕ,,,,,,%%%%%%3 3 3 3 3 3 ) ) ) ) ) ) NNNNNNXXXXXX]]]]]]~ ~ ~ ~ ~ ~ i i i i i i ` ` ` ` ` ` 666666 LLLLLL%%%%%%''''''PPPPPPPPPP!"!"!"!"!"!"!"BBBBBBy8y8y8y8y8y8o/o/o/o/o/o/q0q0q0q0q0q0LLLLLLi)i)i)i)i)i)WWWWWWLLLLLL:::::888888WWWWWW```````U +U +4u4u4u4u4u4umm     ` ` ` `" " " " " " }}}}}}}ccccc       >>>>> VUVUVUVUVUz +z +z +z +z +z +z +ґґґґґ;;X X X X X X       ?????pppppp++++++#####66666666666666\\\\\lllllld$d$d$d$d$d$HHHHHHxxxxxxjjjjjj% % % % % % % ZZZZZZ!a!a!a!a!a!aHHHHHHH{;{;{;{;{;{; 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[[[[[[[HHHHjjjjj + + + + + +S S S S S S FFFFFFmm mm mm mm mm mm n n n n n n XXXXXX EEEEEE[[[[[[uuuuuu\\\\\\\`"`"`"`"`"     q +q +q +q +q +q +q +]` ` ` ` ` ` ` l-l-o o ͌ ͌ ͌ ͌ ͌ ~= +~= +~= +~= +~= +~= +Ԕ +Ԕ +Ԕ +Ԕ +Ԕ + LLLLLLLLLLLL++++++      iiiiii6vsssss      iiiiii\ \ \ \ \ \ //////     111111L88888``````]]]]]]YYYYYYY +Y +Y +Y +Y +Y + *******.n.n.n.n.n.n.n ******MMMMMM MMMMM|{|{|{|{|{|{M M M M M M ~ddddddbbbbb2 2 2 2 2 2 2 IIIIIII# # # # # # ||||||rrrrrra! a! a! a! a! a!       ` ` ` ` ` ` 44444dd dd dd dd dd dd (((((( ZZZZZZZZZZZZYYYYYYY::::::oooooow7w7w7w7w7w7;;;;;ffffff + + + + + +i)i)i)i)i)i)      ;;;;;;uuuuuuCCCCCC``````FFFFF##wyx.....      ::::::::::::yyyyyyKKKKKKKKKKYYYYYYYYYYYYJJJJJJ]] ]] ]] ]] ]] ]] ]] ]] ]] ]] ]] ]] ]] ]] _____DD333333oooooCCCCCC""""""jjjjjj + + + + + +O O O O O O ggggggs3s3s3s3s3s3{{{{{{------wwwwwwwwwwww||||||!a!a!a!a!a!aooooooeeeeeeeeeeee      QQQQQQKKKKKKJJJJJJ3 3 3 3 3 3  + + + + + +kkkkkk + + + + + +p p p p p p 666666 + + + + + +\DDDDDDp0 p0 p0 p0 p0 p0 qqqqqqqqqqqq{{{{{{{55555 (((((((((((((( +K +K +K +K +K +KVVVVVV""""""ll ll ll ll ll ll /////// + + + + + +)!)!)!)!)!)!        3s3s3s3s3s3s=====yyyyyyJJJJJJjj +jj +jj +jj +jj +""""""k,k,k,k,k,k,bcbcbcbcbcbc   1 DDDDDDҒҒҒҒҒo/o/o/o/o/o/9: 9: 9: 9: 9: 9: ~~~~~g'g'g'g'g'g'g'w7w7111111I I I I I I ]]]]]]B B B B B eeeeeeZZZZZZy9 y9 y9 y9 y9 y9 kkkkkk,,,,,,,,,, + + + + + +||||||||||||||UUUUU YYYYY)))))))))))) + + + + +aaaaaa[ +[ +[ +[ +[ +[ +vvvvv------------uuuuuuux8$x8$x8$x8$x8$ + + + + + + ************DDDDDWWWWWWOOOOOWWWWWWGGGGGGxxxxxxxxxxxxxx)i)i)i)i)i)igg gg gg gg gg gg gg ======      i +i +i +i +i +i +i +[ +[ +vvvvv222222V +V +V +V +V +V +V +PPPPPP333333 + + + + + + +AA AA AA AA AA AA dddddd      :;:;:;:;:;:;3s3s3s3s3s3sF F F F F F F M M M M MLLLLLLLiiiiii]]]]]]       ] +] +) ) ) .......>>>>>>E E E E E ffffffHIHIHIHIHIHIwwwwwI I I I I I 0$0$0$0$0$0$f f f f f f ??????~~~~~??????###### '''''>>>>>>>>>>>>MMMMMMMMMMzzzzzzzzzzzzVVVVVVgfgfgfgfgfgf ΎΎΎΎΎΎ + + + + + +############777777> > > > > >~ >~ >~ >~ >~ >~ >~ -m -m -m -m -m WWWWWWW\ \ \ \ \ \ L L L L L L(h(h(h(h(heeeeeeeeeeeeeeUUUUUU]]]]]]JJJJJJu5"u5"u5"u5"u5"l l l l l l X + + + + + + + + + + + +~~~~~~JJJJJJ[[[[[[ BBBBBBBBBB||||||\ \ \ \ \ \       JJJJJJ + + + + + +55555,,,,,QQQQQQ"""""")i)i)i)i)i)i)im m m m m m 666666 666666+++++++}}}}}BBBBBB  pp.... 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RRRRRR!!!!!!*****ggggg      !b!b!b!b!b!b!bIIIIII888888u5u5u5u5u5u5/////       jjjjjjjjjjjj(h(h(h(h(h(hXXXXXXKKKKKK1111111-- jijijijijiji            o/o/o/o/o/o/o/;;;;;;;TTTTTT!!x7x7x7x7x7x7BBBBBBYXYX777777ښښښښښgfgfgfgfgfgfa!a!a!a!a!a!a!EEE֖ +֖ +֖ +֖ +֖ +VVVVPPPPP))))))m-m-m-m-m-m-SSSSSSwwwwwwwwwwww666666ssssssssssssDDDDDDDDDDDDOOOOO             X X X X X X &&&&&&11111llllll      PPPPPPFFFFFF>>>>>>ښښښښښ((((((ʊʊʊʊʊʊʊJ + J + J + J + J + J + """"""""""u5u5u5u5u5u5u5NNNNNNLJ LJ LJ LJ LJ LJ P P P P P        -l-l-l-l-l-l%%%%%000000YYYYYY|||||||uuuuuuQ Q Q Q Q Q ,,zzzzzzzzzzzz[[[[[[bbbbbbbbbbbbbbyyyyyyyyyySSSSSSlllllleeeeeeiiiiiillllllj*UT      GGGGGG + + + + + +_(_(_(_(_(_(_(yyyyyy__2%&%&%&%&%&%&;}}}*i*i*i*i*i*i''''']]]]]]ddddddWWWWWWWWWWWWxxxxxx1100000OOOOOOj j j j j ` ` ` ` ` `> $$$$$$RRRRRM!M!M!M!M!M!^ ^ ^ ^ ^ ^ ^ ~~~~~~~~~~~~H +H +H +H +H +H +H + + + + + + +w7w7w7w7w7}}}}}}}}}}}}CC CC CC CC CC CC + + + + + +xxxxxx++++++^^^^^ϏϏϏϏϏϏhhhhhGGGGGG{{{{{{DDDDDgggggg'('('('('('(UUUUUUu u u u u u yyyyyyhhhhhhy9 y9 y9 y9 y9 y9 D D D D D D mmmmm       ======&&&&&&E E E E E f&f&f&f&f&f&ffffffffffffffxxxxxxx1212<= <= <= <= <= <= :::::::::: 0o0o0o0o0o0ovvvvvv/0/0/0/0/0/0^^^^^^z z z z z z z RRRRRf!f!f!f!f!f!!a !a B??????A +K SSSSSS{:{:{:{:{:{:mmmmmmb +b +b +b +b +b +X X X X X X 8 +8 +8 +8 +8 +8 +PPPPPP666666#c#c#c#c#c#c??????w w w w w w xxxxxxxWWWWWWllllllqqqqqq}=}=}=}=}=}=QQQQQQG G G G G G חחחחחח + + + + + + + + + + + +J + J + J + J + J + ssssssssssss222222222222EE +EE +זזזזזEEEEEELLLLLL$$$$$$bbbbbbb      ^^^^^^i i i i i i      GFGFGFGFGFGF      KKKKKKJ J J J J J 333333bbbbbb1 1 1 1 1 1  UUUUUULLLLLLLl +l +l +l +l +l +AAAAAAAAAAAATTTTTT6w 6w 6w 6w 6w 6w YYYYYYYYYYYY]]]]]]]]]]]]]]      m-m-m-m-m-m->=>=>=>=>=>=      u +u +u +u +u +u +>>>>>>tttttttZZZZZZ%e%eEE 555555KK]]]]]]OOOOYYYYYYddddddPPPPPP + + + + + +R R R R R R ''''''OOOOOO)s s s s s s  + + + + + +k +k +k +k +k +k +rrrrrr||||||o o o o o uuuuuu 222222222222xxxxxxݜ"HHHHHHH}}}}}KKKKKK\\\\\\@@@@@@zzzzzz%%%%%%m,m,m,m,m,n-n-,l^^^^^^+k +k +k +k +k +k kkkkkkllll ՕՕՕՕՕՕZZZZZZ3 3 3 3 3 :: +:: +:: +:: +:: +:: +> > > > > > ))))))))))))OO OO OO OO OO OO OO u5u5u5u5u5u5,l ,l ,l ,l ,l ,l 00000       00000      k+ k+ k+ k+ k+ k+ 888888:```````````` ======` ` ` ` ` ` ` o o o o o o HHHHHHxxxxxx? ? ? ? ? ? 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S +/75"!D#%խ&t)%3/y&ՙW/>Y7չ8^S/!#3mv@6[Aj?y@ռw8*KAPHЮCjU \ No newline at end of file diff --git a/tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_correct_formatting.ipynb b/tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_correct_formatting.ipynb new file mode 100644 index 0000000..e55ce1b --- /dev/null +++ b/tutorials/Simulation_of_DNA_chains_imaged_via_AFM_draft_correct_formatting.ipynb @@ -0,0 +1,5135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell_md_title", + "metadata": { + "id": "cell_md_title" + }, + "source": [ + "# Simulation of 'DNA chains on a substrate imaged via AFM'\n", + "**This notebook is intended to create simulations of DNA chain relaxation on a substrate imaged via AFM. By simulating physical relaxation and electrostatic attraction between a DNA chain and the substrate it allows users to generate high fidelity data.**\n", + "\n", + "This notebook generates a complete dataset stored within user defined directory `OUT_DIR`, primarily consisting of Synthetic AFM Images saved as `.npy` raw heightmap data (incorporating realistic noise from `.spm` blanks or PSD models) and optional `.png` files for quick visual inspection.\n", + "\n", + " To support machine learning workflows, the pipeline produces DNA Segmentation Masks in `.npy` format for pixel-wise background separation, alongside specialized Crossing Detection Masks that use Gaussian-weighted targets to highlight molecular intersections. Comprehensive Metadata for every sample, including bead counts and mechanical parameters, is saved in `.csv` or `.json` files, all of which are indexed in a central **manifest.csv** that links each generated image to its corresponding ground-truth masks and input configurations.\n", + ">\n", + "---\n", + "**Cell execution order:** run cells 1 → 12 in sequence. \n", + "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github.com/Prakhar-Dutta/ASAP/blob/1b5cfc5173e1bed4e59575e6dd4724cb20682620/tutorial/Not_final_version_DNA_notebook%20(1).ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "glVt-o8bCqSu", + "metadata": { + "id": "glVt-o8bCqSu" + }, + "outputs": [], + "source": [ + "# Uncomment codes in this cell if using Colab/Kaggle.\n", + "import os\n", + "from pathlib import Path\n", + "import re\n", + "import csv\n", + "import traceback\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import animation\n", + "from IPython.display import HTML, display\n", + "from __future__ import annotations\n", + "from typing import Any\n", + "# Image processing\n", + "from scipy.ndimage import (\n", + " gaussian_filter, grey_dilation, distance_transform_edt,\n", + " binary_dilation, median_filter, affine_transform, zoom\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2nb1FTtFC_q0", + "metadata": { + "id": "2nb1FTtFC_q0" + }, + "source": [ + "# 1. **With or without molecular dynamics?**\n", + "## 1.1. Imports\n", + "\n", + "This notebook allows two possible ways of building the simulated DNA chains; with or without the use of **coarse-grained molecular dynamics**. Molecular dynamics based simulations are computationally expensive, but provide closeness to real AFM imaging. For users who would like to use molecular dynamics, they would need to import all the libraries that will be necessary for simulation of the images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_01", + "metadata": { + "id": "cell_code_01" + }, + "outputs": [], + "source": [ + "# Install dependencies (uncomment in a fresh environment / Colab)\n", + "# Chain generation mode\n", + "USE_MD = False # True → OpenMM Langevin dynamics\n", + " # False → fast 2-D persistent-walk (no OpenMM needed)\n", + "# Molecular dynamics\n", + "!pip3 install -q scipy matplotlib pillow\n", + "if USE_MD:\n", + " !pip3 install openmm[cuda12]\n", + "# change the cuda version\n", + "# depending on the CUDA version available on your system.\n", + "# Ensure that CUDA can be accessed by OpenMM when running locally.\n", + " import openmm\n", + " import openmm.unit as unit\n" + ] + }, + { + "cell_type": "markdown", + "id": "1oW9llw-imr1", + "metadata": { + "id": "1oW9llw-imr1" + }, + "source": [ + "# 1.2 Defining the variables\n", + "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sSqadS02hzPe", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sSqadS02hzPe", + "outputId": "bd5bd9fd-6adb-4862-c328-cebac6fe5fc6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Output folder: /content/dna_dataset_100_4lengths_MD\n" + ] + } + ], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Global settings\n", + "#(these are the variables that need to be changed\n", + "# to change the appearance of the resulting images)\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "# Dataset\n", + "OUT_DIR = \"dna_dataset_100_4lengths_MD\"\n", + "# Name of the output directory that you would like to store your data in\n", + "N_SAMPLES = 1000\n", + "# Number of samples that will be generated\n", + "BASE_SEED = 1\n", + "# Seed for reproducibility\n", + "\n", + "# Image resolution\n", + "NX = 512\n", + "NY = 512\n", + "\n", + "# Ring + MD\n", + "N_BEADS = 90\n", + "# default bead count for vizualization via chain creation cell\n", + "BEAD_COUNTS = [70, 80, 90, 100]\n", + "# four chain lengths that will be created in the dataset\n", + "#(1 bead corresponds to 10 base pairs on DNA approximately)\n", + "BOND_LENGTH = 1.0\n", + "# distance between beads\n", + "PERSISTENCE_BONDS = 23.0\n", + "# used to determine the range of angles which the chain is allowed to turn\n", + "K_ANGLE = 12.0\n", + "# used to determine the angular stiffness during relaxation\n", + "# (higher means more stiffness)\n", + "BASE_Z = 5.0\n", + "# Height above the substrate that the chain starts at before relaxing\n", + "\n", + "ANGLE_STIFNESS_MULT = 0.4\n", + "# If USE_MD is False, then angle stiffness multiplier controls\n", + "# chain propogation through space\n", + "\n", + "# MD recording\n", + "N_FRAMES = 200\n", + "STEPS_PER_FRAME = 200\n", + "# Between every frame local energy minimization happens for\n", + "#the given number of steps\n", + "\n", + "# AFM rendering\n", + "\n", + "tip_radius = 1\n", + "# Please select the tip radius here (Ideal range is 1nm-5nm)\n", + "nm_per_px = 2\n", + "# Internal AFM render sampling in nm / pixel before downsampling\n", + "\n", + "AFM_KW = dict(\n", + " nx=NX, ny=NY,\n", + " dna_diameter_nm=2.0,\n", + " # mapping to physical values\n", + " tip_radius_nm=0.01*tip_radius,\n", + " # Scaling factor is applied for internal control to match\n", + " max_height_nm=6.0,\n", + " # Maximum height for the colorbar\n", + " target_nm_per_px=0.04*nm_per_px,\n", + " # Scaling factor (0.04) is applied for internal control\n", + " max_radius_px=96,\n", + " # upper limit for the size of the chain in pixels\n", + " radius_shrink_px=0.0,\n", + " # Set to 0 so that rendering can match physical values\n", + " final_blur_sigma_px=0.20,\n", + " # When changing tip radius and nm_per_px to observe the expected\n", + " apply_edge_taper=True,\n", + " # physical effect, remove the scaling factors (0.04) for\n", + " # tip_radius_nm and target_nm_per_px\n", + " taper_sigma_nm=0.45,\n", + " # and scale all other parameters accordingly.\n", + " taper_floor=0.10,\n", + " add_center_ridge=True,\n", + " ridge_sigma_nm=0.25,\n", + " # This controls width of center ridge\n", + " ridge_amp_nm=0.25,\n", + " # This controls height of center ridge\n", + " grain_nm=0.0,\n", + " # This adds more noise to the chain\n", + " grain_sigma_px=0.6,\n", + " enable_crossing_boost=True,\n", + " # To boost the height of the crossings\n", + " min_separation_beads=12,\n", + " # Distance to other chain segments to not boost indiscriminately\n", + " boost_window_beads=2,\n", + " # How many beads to boost the height for\n", + " guaranteed_offset_nm=1.0,\n", + " # How much to boost\n", + " boost_method=\"additive\",\n", + " # add to the height of beads being boosted\n", + " boost_profile=\"gaussian\",\n", + " # Height increase profile\n", + " boost_sigma_beads=None,\n", + " far_clip_nm=2.5,\n", + " # Clip bead heights for beads that are not part of a crossing\n", + " far_clip_window_beads=3,\n", + " # How many beads to clip the height for\n", + " return_crossing_info=True,\n", + " # To ensure that mask information is passed to other functions.\n", + " # Change to False if masks are not needed\n", + " return_masks=True,\n", + ")\n", + "\n", + "\n", + "#-------------------------------------\n", + "# DNA mask\n", + "DNA_MASK_DILATE_PX = 3\n", + "# Dilation of mask for better training\n", + "#-------------------------------------\n", + "\n", + "#---------------------------\n", + "# Crossing mask\n", + "CROSS_MIN_SEP_BEADS = 12\n", + "# Where to stop for the crossing calculation\n", + "CROSS_SIGMA_CENTER_PX = 5.0\n", + "# How many pixels make up the center of a crossing\n", + "CROSS_SIGMA_PERP_PX = 1.8\n", + "# Crossing coloring parameter (how many pixels to modulate for the crossing)\n", + "CROSS_CHAIN_EXTENT = 5.0\n", + "# For the chain weighting of the mask\n", + "CROSS_CENTER_WEIGHT = 1.7\n", + "# For the chain weighting of the mask (chain-weighted guassian)\n", + "CROSS_CHAIN_WEIGHT = 0.9\n", + "CROSS_CLIP_TO_DNA_MASK = True\n", + "#---------------------------\n", + "\n", + "#Checking if output directory will be created\n", + "os.makedirs(OUT_DIR, exist_ok=True)\n", + "for _sub in [\"images\", \"dna_masks\", \"cross_masks\", \"meta\"]:\n", + " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", + "\n", + "print(\"Output folder:\", os.path.abspath(OUT_DIR))" + ] + }, + { + "cell_type": "markdown", + "id": "-e3QvQzGlKYY", + "metadata": { + "id": "-e3QvQzGlKYY" + }, + "source": [ + "# 1.3 Noise\n", + "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", + "\n", + "\n", + "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The blank file is a scan of the substrate **without** the sample of interest. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", + "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47h81smulGxd", + "metadata": { + "id": "47h81smulGxd" + }, + "outputs": [], + "source": [ + "#Noise\n", + "TARGET_NOISE_RMS_NM = 0.27\n", + "# This variable controls the height of the noise\n", + "# in nanometers and this is added to the final image\n", + "\n", + "\n", + "# Blank SPM noise\n", + "USE_BLANK_SPM_NOISE = True\n", + "USE_PLANE_REMOVE = True\n", + "# This flag is used to control plane tilt removal from the noise image\n", + "USE_LINE_FLATTEN = True\n", + "# This flag is used to control line flattening (median filtering)\n", + "# from the noise image\n", + "USE_BANDPASS_FILTER = True\n", + "# This flag is used to control bandpass filtering on the noise image\n", + "BLANK_SPM_PATH = \"/content/20240411_blank_water.0_00000.spm\"\n", + "# optional; if missing/invalid, PSD fallback noise is used\n", + "# Change path when running locally. If using Colab, upload the file\n", + "\n", + "\n", + "# PSD-model fallback noise (used when blank .spm file is not available)\n", + "USE_PSD_NOISE_FALLBACK = True\n", + "MODEL_PATH = \"/content/psd_noise_model.npz\"\n", + "# Change path when running locally. If using Colab, upload the noise file\n", + "PSD_NOISE_METHOD = \"mean_full2d\"\n", + "# or \"lognormal_full2d\" / \"empirical_radial\"\n", + "PSD_NOISE_STD_SCALE = 2.0\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_02", + "metadata": { + "id": "cell_md_02" + }, + "source": [ + "## 2. Chain Creation (3-D Persistent Random Walk)\n", + "Now we start with creating the DNA chain. This is the toppology of the chain before relaxation is performed using molecular dynamnics.\n", + "\n", + "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", + "\n", + "A 3-D plot is shown at the end of the cell so you can inspect the initial geometry immediately." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_02", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "id": "cell_code_02", + "outputId": "fb1a86eb-685d-4da4-8ddb-53d8d943a9a9" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "

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\n" + }, + "metadata": {} + } + ], + "source": [ + "def make_tangled_ring_initial(\n", + " seed: int,\n", + " n_beads: int = N_BEADS,\n", + " bond_length: float = BOND_LENGTH,\n", + " persistence_bonds: float = PERSISTENCE_BONDS,\n", + " base_z: float = BASE_Z,\n", + ")-> np.ndarray:\n", + " \"\"\"Generates a closed (ring) polymer with a persistent random-walk tangent.\n", + "\n", + " Each step rotates the current direction by a small random angle around a\n", + " perpendicular axis. Ring closure is enforced by linearly distributing\n", + " the end-to-end gap across all beads. The chain is then lifted to base_z\n", + " so it can relax down to the z=0 substrate during MD.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " random seed for reproducibility\n", + " n_beads : int, optional\n", + " number of beads in the polymer ring. Defaults to N_BEADS.\n", + " bond_length : float, optional\n", + " the distance between consecutive beads. Defaults to BOND_LENGTH.\n", + " persistence_bonds : float, optional\n", + " the number of beads used to enforce persistence length in the chain.\n", + " Defaults to PERSISTENCE_BONDS.\n", + " base_z : float\n", + " the height to lift the chain above the substrate. Defaults to BASE_Z.\n", + "\n", + " Returns\n", + " -------\n", + " Returns a list of [coords] with shape (N, 3).\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + "\n", + " steps = np.zeros((n_beads, 3), dtype=np.float32)\n", + " #Initialization of vector for chain propogation\n", + " vec = np.array([1.0, 0.0, 0.0], dtype=np.float32)\n", + "\n", + " for k in range(n_beads):\n", + " dtheta = rng.normal(scale=np.sqrt(1.0 / persistence_bonds))\n", + " #choose an axis at random for chain propogation in 3D\n", + " axis = rng.normal(size=3).astype(np.float32)\n", + " # remove component along vec\n", + " axis -= axis.dot(vec) * vec\n", + " axis_norm = np.linalg.norm(axis)\n", + " # failsafe\n", + " if axis_norm < 1e-8:\n", + " axis = np.array([0.0, 0.0, 1.0], dtype=np.float32)\n", + " axis_norm = 1.0\n", + " axis /= axis_norm\n", + " # Calculating vector for chain propogation\n", + " vec = vec * np.cos(dtheta) + np.cross(axis, vec) * np.sin(dtheta)\n", + " vec /= np.linalg.norm(vec)\n", + " steps[k] = bond_length * vec\n", + "\n", + " coords = np.cumsum(steps, axis=0) # Coordinates for open chain\n", + "\n", + " # Enforce ring closure\n", + " closure_error = coords[-1] - coords[0]\n", + " # Based on the distance between first and last bead a\n", + " # closure error is propogated to all bead positions to form a closed chain\n", + " t = np.linspace(0, 1, n_beads, dtype=np.float32).reshape(-1, 1)\n", + " coords -= t * closure_error\n", + " coords -= coords.mean(axis=0)\n", + "\n", + " # Lift above substrate\n", + " # (z values are changed to lift the chain off the substrate)\n", + " coords[:, 2] += float(base_z)\n", + " return coords.astype(np.float32)\n", + "\n", + "\n", + "# ── Quick visualisation ──────────────────────────────────────────────────────\n", + "demo_coords = make_tangled_ring_initial(seed=43)\n", + "cc = np.vstack([demo_coords, demo_coords[0]]) # close the ring for plotting\n", + "\n", + "fig = plt.figure(figsize=(7, 6))\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-o\", markersize=3, linewidth=1.2)\n", + "ax.scatter(cc[0, 0], cc[0, 1], cc[0, 2], color=\"red\",\n", + " s=60, zorder=5, label=\"bead 0\")\n", + "\n", + "# Draw substrate plane\n", + "xs = np.linspace(cc[:, 0].min(), cc[:, 0].max(), 2)\n", + "ys = np.linspace(cc[:, 1].min(), cc[:, 1].max(), 2)\n", + "X, Y = np.meshgrid(xs, ys)\n", + "ax.plot_surface(X, Y, np.zeros_like(X), alpha=0.15, color=\"cyan\")\n", + "\n", + "ax.set_xlabel(\"x (sim units)\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z\")\n", + "ax.set_title(f\"Initial ring — seed 43, {len(demo_coords)} beads\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_03", + "metadata": { + "id": "cell_md_03" + }, + "source": [ + "## 3. Molecular Dynamics Relaxation\n", + "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", + "\n", + "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", + "Here we have added:\n", + "\n", + "1. Harmonic bonds\n", + "2. Bending-angle stiffness\n", + "3. A surface-attraction to cause chain relaxation\n", + "4. A hard-wall force to keep chain above substrate\n", + "5. A short-range WCA repulsion to ensure chain does not take non physical configurations\n", + "\n", + "The function then records `n_frames` snapshots which can be used for visualization." + ] + }, + { + "cell_type": "markdown", + "id": "wFFhsVZhqU8a", + "metadata": { + "id": "wFFhsVZhqU8a" + }, + "source": [ + "We start with testing the OpenMM installation (can be tricky at times)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3NjBSwfAe4Mj", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3NjBSwfAe4Mj", + "outputId": "8a5a7999-63ee-4a5e-a3c5-da9d17a0745f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "OpenMM Version: 8.5.1\n", + "Git Revision: f7fa0c27c1f8d943c339d67b3bf22f026d0bd8b5\n", + "\n", + "There are 4 Platforms available:\n", + "\n", + "1 Reference - Successfully computed forces\n", + "2 CPU - Successfully computed forces\n", + "3 CUDA - Successfully computed forces\n", + "4 OpenCL - Successfully computed forces\n", + "\n", + "Median difference in forces between platforms:\n", + "\n", + "Reference vs. CPU: 6.28625e-06\n", + "Reference vs. CUDA: 6.7605e-06\n", + "CPU vs. CUDA: 7.74371e-07\n", + "Reference vs. OpenCL: 6.74321e-06\n", + "CPU vs. OpenCL: 7.94239e-07\n", + "CUDA vs. OpenCL: 1.74909e-07\n", + "\n", + "All differences are within tolerance.\n" + ] + } + ], + "source": [ + "# Test the OpenMM installation before running the following cell\n", + "# to ensure that GPU acceleration will work\n", + "import openmm.testInstallation\n", + "\n", + "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", + "try:\n", + " openmm.testInstallation.main()\n", + "except Exception as e:\n", + " print(f\"Test failed: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9XbRNS3TqbKH", + "metadata": { + "id": "9XbRNS3TqbKH" + }, + "source": [ + "And then once we are sure that OpenMM is correctly installed, we run the MD functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_03", + "metadata": { + "id": "cell_code_03" + }, + "outputs": [], + "source": [ + "def run_md_relaxation(\n", + " coords0: np.ndarray,\n", + " seed: int,\n", + " n_frames: int = N_FRAMES,\n", + " steps_per_frame: int = STEPS_PER_FRAME,\n", + ")-> list[np.ndarray]:\n", + " \"\"\"\n", + " Takes initial bead coordinates and runs Langevin dynamics using OpenMM\n", + "\n", + " This function uses the coordinates generated using the\n", + " `make_tangled_ring_initial` function to run a molecular dynamics\n", + " simulation. There are certain forces that are defined and applied to the\n", + " bead which are then resolved using OpenMM.\n", + " The forces applied are:\n", + " - Harmonic bonds + angle stiffness along the ring\n", + " - Surface attraction (linear in z) + hard wall at z = 0\n", + " - Short-range Weeks–Chandler–Andersen (WCA)\n", + " repulsion between non-bonded beads\n", + " Drift removal is also performed using OpenMM's CMMotionRemover.\n", + " The variable langevin integrator from OpenMM is used and the forces are\n", + " resolved for 'steps_per_frame' steps for every frame in n_frames.\n", + "\n", + " Parameters\n", + " ----------\n", + " initial_coordinates : np.ndarray\n", + " The starting [x, y, z] coordinates of the polymer beads.\n", + " seed : int\n", + " Random seed for the Langevin integrator.\n", + " num_frames : int, optional\n", + " Number of frames to record during the simulation. Defaults to N_FRAMES.\n", + " steps_per_frame : int, optional\n", + " Number of integration steps to perform between recorded frames.\n", + " Defaults to STEPS_PER_FRAME.\n", + "\n", + " Returns\n", + " -------\n", + " A list of coordinate arrays of shape (num_beads, 3). The list contains\n", + " num_frames + 1 entries, where frame 0 is the pre-minimization geometry.\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If no OpenMM platform (CUDA, OpenCL, or CPU) could be initialised.\n", + "\n", + " Examples\n", + " --------\n", + " >>> frames = run_md_relaxation(_anim_coords0, seed=1, n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + " >>> print({len(frames), frames[0].shape})\n", + " {11, (100, 3)}\n", + "\n", + " \"\"\"\n", + "\n", + " coords0 = np.asarray(coords0, dtype=np.float32)\n", + " n = int(coords0.shape[0])\n", + "\n", + " # how big the bounding box for the molecular dynamics needs to be\n", + " extent = (coords0.max(axis=0) - coords0.min(axis=0)).max()\n", + " box = float(extent + 10.0)\n", + "\n", + "\n", + " # All lengths in nm, energies in kJ/mol.\n", + " BOLTZ_kJmol = 0.00831445 # kJ / (mol · K)\n", + " temperature = 1.0 # K (sets energy scale kT ≈ 0.0083 kJ/mol)\n", + " kT = BOLTZ_kJmol * temperature # kJ/mol\n", + " conlen = 1.0 # nm\n", + " mass_amu = 100.0 # Da per bead\n", + " collision_rate = 0.6 # ps⁻¹\n", + " error_tol = 0.005\n", + "\n", + " # ── Build system ─────────────────────────────────────────────────────────\n", + " system = openmm.System()\n", + " system.setDefaultPeriodicBoxVectors(\n", + " openmm.Vec3(box, 0, 0),\n", + " openmm.Vec3(0, box, 0),\n", + " openmm.Vec3(0, 0, box),\n", + " )\n", + " for _ in range(n):\n", + " system.addParticle(mass_amu)\n", + "\n", + " # Remove COM drift — applied every 50 steps\n", + " system.addForce(openmm.CMMotionRemover(10))\n", + " # To ensure CPU and GPU calculation are consistent\n", + " # (since GPU minimizations calculations can suffer from drift)\n", + "\n", + " # ── 1. Harmonic bonds ────────────────────────────────────────────────────\n", + "\n", + " # OpenMM HarmonicBondForce: E = (k/2)·(r − r0)² → k = kT / wiggle²\n", + " bond_L = BOND_LENGTH # nm\n", + " wiggle = 0.1 # nm (bondWiggleDistance)\n", + " k_bond = kT / (wiggle ** 2) # kJ/mol/nm²\n", + "\n", + " bond_force = openmm.HarmonicBondForce()\n", + " bond_force.setUsesPeriodicBoundaryConditions(True)\n", + " for i in range(n):\n", + " bond_force.addBond(i, (i + 1) % n, bond_L, k_bond)\n", + " system.addForce(bond_force)\n", + "\n", + " # ── 2. Bending (angle) stiffness ─────────────────────────────────────────\n", + " # E = kT · k_angle · (1 − cos(θ − π)) — equilibrium at θ = π (straight)\n", + " k_angle = K_ANGLE\n", + " angle_force = openmm.CustomAngleForce(\n", + " \"kT * kangle * (1 - cos(theta - 3.141592653589793))\"\n", + " )\n", + " angle_force.addGlobalParameter(\"kT\", kT)\n", + " angle_force.addGlobalParameter(\"kangle\", k_angle)\n", + " for i in range(n):\n", + " angle_force.addAngle((i - 1) % n, i, (i + 1) % n, [])\n", + " system.addForce(angle_force)\n", + "\n", + " # ── 3. Surface: linear attraction (z > 0) + harmonic hard wall (z < 0) ───\n", + " wall_expr = (\n", + " \"kT * (\"\n", + " \" F_pull * z * step(z) + \"\n", + " \" 0.5 * wallK * (z^2) * step(-z)\"\n", + " \")\"\n", + " )\n", + " wall_force = openmm.CustomExternalForce(wall_expr)\n", + " wall_force.addGlobalParameter(\"kT\", kT)\n", + " wall_force.addGlobalParameter(\"F_pull\", 9.0)\n", + " wall_force.addGlobalParameter(\"wallK\", 100.0)\n", + " for i in range(n):\n", + " wall_force.addParticle(i, [])\n", + " system.addForce(wall_force)\n", + "\n", + " # ── 4. WCA repulsion (repulsion between non-bonded pairs only) ───────────\n", + " rep_sigma = 1.6 * conlen # nm\n", + " rep_cutoff = 2.3 * conlen # nm\n", + " rep_expr = (\n", + " \"4*eps * ( (sig / (r + shift))^12 - (sig / (r + shift))^6 + 0.25 )\"\n", + " \"* step(cutoff - r);\"\n", + " \"cutoff = 2^(1.0/6.0) * sig\"\n", + " )\n", + " rep_force = openmm.CustomNonbondedForce(rep_expr)\n", + " rep_force.setNonbondedMethod(openmm.CustomNonbondedForce.CutoffPeriodic)\n", + " rep_force.setCutoffDistance(rep_cutoff)\n", + " rep_force.addGlobalParameter(\"eps\", 1.7 * kT)\n", + " rep_force.addGlobalParameter(\"sig\", rep_sigma)\n", + " rep_force.addGlobalParameter(\"shift\", 1e-8)\n", + " for i in range(n):\n", + " rep_force.addParticle([])\n", + " # exclude bonded neighbours\n", + " for i in range(n):\n", + " rep_force.addExclusion(i, (i + 1) % n)\n", + " system.addForce(rep_force)\n", + "\n", + " # ── Integrator ───────────────────────────────────────────────────────────\n", + " integrator = openmm.VariableLangevinIntegrator(temperature,\n", + " collision_rate, error_tol)\n", + " integrator.setRandomNumberSeed(int(seed))\n", + "\n", + " # ── Platform selection: OpenCL → CPU fallback ────────────────────────────\n", + " context = None\n", + " # OpenCL enables Mac users to be able to use GPU acceleration as well\n", + " for platform_name in (\"CUDA\",\"OpenCL\",\"CPU\"):\n", + " try:\n", + " platform = openmm.Platform.getPlatformByName(platform_name)\n", + " context = openmm.Context(system, integrator, platform)\n", + " print(f\"Using {platform_name} platform.\")\n", + " break\n", + " except openmm.OpenMMException as e:\n", + " print(f\" {platform_name} unavailable: {e}\")\n", + " except Exception as e:\n", + " print(f\" {platform_name} failed unexpectedly: {e}\")\n", + "\n", + " if context is None:\n", + " raise RuntimeError(\"No OpenMM platform could be initialised.\")\n", + "\n", + " # ── Set initial positions and box ────────────────────────────────────────\n", + " positions = [\n", + " openmm.Vec3(float(coords0[i, 0]),\n", + " float(coords0[i, 1]), float(coords0[i, 2]))\n", + " for i in range(n)\n", + " ]\n", + " context.setPositions(positions)\n", + " context.setPeriodicBoxVectors(\n", + " openmm.Vec3(box, 0, 0),\n", + " openmm.Vec3(0, box, 0),\n", + " openmm.Vec3(0, 0, box),\n", + " )\n", + "\n", + " # Capture the true initial geometry before any minimisation\n", + " # (just in case the chain reaches substrate even before the 1st frame)\n", + " frames = []\n", + " state = context.getState(getPositions=True)\n", + " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", + " frames.append(pos.copy())\n", + "\n", + " # Cap iterations\n", + " openmm.LocalEnergyMinimizer.minimize(context,\n", + " tolerance=50, maxIterations=200)\n", + "\n", + " # ── Record frames ────────────────────────────────────────────────────────\n", + " for _ in range(int(n_frames)):\n", + " integrator.step(int(steps_per_frame))\n", + " state = context.getState(getPositions=True)\n", + " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", + " frames.append(pos.copy())\n", + "\n", + " return frames # n_frames + 1 entries (frame 0 = initial geometry)" + ] + }, + { + "cell_type": "markdown", + "id": "Bx7_OzWRr-l3", + "metadata": { + "id": "Bx7_OzWRr-l3" + }, + "source": [ + "## 4. Non-MD chain generation\n", + "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yXs0I0akr948", + "metadata": { + "id": "yXs0I0akr948" + }, + "outputs": [], + "source": [ + "def make_ring_2d_persistent_initial(\n", + " seed: int,\n", + " n_beads: int,\n", + " bond_length: float = BOND_LENGTH, # From Cell 1\n", + " persistence_bonds: float = PERSISTENCE_BONDS, # From Cell 1\n", + " angle_stiffness_mult: float = ANGLE_STIFNESS_MULT, # From Cell 1\n", + " # Small Gaussian noise for Z\n", + " z_std: float = 0.08,\n", + " # Low altitude for \"deposited\" look\n", + " base_z: float = 0.2,\n", + " # Acts as a soft per-step cap\n", + " angle_limit: float = np.pi/2,\n", + ") -> list[np.ndarray]:\n", + " \"\"\"\n", + " Generate a closed 2D persistent ring and return it as [coords].\n", + "\n", + " The function builds a periodic sequence of turning angles, smooths the\n", + " sequence to suppress sharp corners, and enforces total turning to ensure\n", + " the tangent field is compatible with a closed loop. The resulting curve is\n", + " resampled by arc length to maintain uniform bead spacing.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " Random seed for reproducibility.\n", + " num_beads : int\n", + " Number of beads in the polymer ring.\n", + " bond_length : float, optional\n", + " The desired distance between consecutive beads.\n", + " Defaults to BOND_LENGTH.\n", + " persistence_bonds : float, optional\n", + " Stiffness parameter; larger values result in a smoother ring with\n", + " longer directional memory. Defaults to PERSISTENCE_BONDS.\n", + " angle_stiffness_multiplier : float, optional\n", + " Multiplier for angular fluctuations; larger values result in\n", + " smaller fluctuations. Defaults to ANGLE_STIFNESS_MULT.\n", + " z_standard_deviation : float, optional\n", + " Standard deviation for the Gaussian noise applied to the z-axis.\n", + " Defaults to 0.08.\n", + " base_z : float, optional\n", + " The baseline altitude for the deposited coordinates. Defaults to 0.2.\n", + " angle_limit : float, optional\n", + " A soft cap on local turning increments (radians). Defaults to np.pi/2.\n", + "\n", + " Returns\n", + " -------\n", + " coords : np.ndarray\n", + " Array of shape (N, 3) containing the [x, y, z] coordinates\n", + "\n", + " Raises\n", + " ------\n", + " ValueError\n", + " If num_beads is less than 6 or if the generated ring has zero\n", + " contour length.\n", + "\n", + " Examples\n", + " --------\n", + " >>> coords = make_ring_2d_persistent_initial(seed=42)\n", + " >>> print(coords.shape)\n", + " (90, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " n = int(n_beads)\n", + " if n < 6:\n", + " raise ValueError(\"n_beads must be at least\"\n", + " \"6 for a stable closed persistent ring.\")\n", + "\n", + " bond_length = float(bond_length)\n", + "\n", + " # ------------------------------------------------------------------\n", + " # 1) Build a periodic turning-angle process on the ring\n", + " # ------------------------------------------------------------------\n", + "\n", + " # larger persistence_bonds / angle_stiffness_mult => less angular noise\n", + " sigma_dtheta = (\n", + " np.sqrt(2.0 / max(float(persistence_bonds), 1.0))\n", + " / max(float(angle_stiffness_mult), 1e-6)\n", + " )\n", + "\n", + " # Raw local turning increments\n", + " dtheta = rng.normal(0.0, sigma_dtheta, size=n).astype(np.float64)\n", + " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", + "\n", + " # Circular smoothing of turning increments to suppress sharp local corners.\n", + " # Stronger persistence / stiffness => broader smoothing.\n", + " smooth_width = int(np.clip(np.round(max(float(persistence_bonds), 1.0)\n", + " / 6.0), 1, 8))\n", + " if smooth_width > 0:\n", + " offsets = np.arange(-smooth_width, smooth_width + 1, dtype=np.float64)\n", + " kernel_sigma = max(smooth_width / 2.0, 1e-6)\n", + " kernel = np.exp(-0.5 * (offsets / kernel_sigma) ** 2)\n", + " kernel /= kernel.sum()\n", + "\n", + " dtheta_smooth = np.zeros_like(dtheta)\n", + " for shift, w in zip(offsets.astype(int), kernel):\n", + " dtheta_smooth += w * np.roll(dtheta, shift)\n", + " dtheta = dtheta_smooth\n", + "\n", + " # Enforce exact total turning of 2*pi for a closed planar ring.\n", + " # This avoids the nonphysical post hoc closure drift used before.\n", + " total_turn = dtheta.sum()\n", + " dtheta += (2.0 * np.pi - total_turn) / n\n", + "\n", + " # Re-clip gently after correction, then re-enforce total turning once more.\n", + " dtheta = np.clip(dtheta, -float(angle_limit), float(angle_limit))\n", + " dtheta += (2.0 * np.pi - dtheta.sum()) / n\n", + "\n", + " # ---------------------------------------------------------------------\n", + " # 2) Build periodic tangent angles and integrate to a dense closed path\n", + " # ---------------------------------------------------------------------\n", + " theta0 = rng.uniform(0.0, 2.0 * np.pi)\n", + " thetas = theta0 + np.cumsum(dtheta)\n", + "\n", + " # Use a dense piecewise-linear curve first; later resample uniformly.\n", + " tangents = np.stack([np.cos(thetas), np.sin(thetas)], axis=1)\n", + "\n", + " # Integrate unit-speed steps to get a provisional loop\n", + " xy = np.zeros((n, 2), dtype=np.float64)\n", + " xy[1:] = np.cumsum(tangents[:-1] * bond_length, axis=0)\n", + " end_error = (xy[-1] + tangents[-1] * bond_length) - xy[0]\n", + "\n", + " # Distribute closure correction through the bond vectors\n", + " bond_corr = end_error / n\n", + " bonds = tangents * bond_length - bond_corr[None, :]\n", + "\n", + " # Renormalize bond lengths back toward bond_length while keeping closure.\n", + " bond_norms = np.linalg.norm(bonds, axis=1)\n", + " good = bond_norms > 1e-12\n", + " bonds[good] *= (bond_length / bond_norms[good])[:, None]\n", + "\n", + " # Re-enforce closure after renormalization\n", + " bonds -= bonds.sum(axis=0, keepdims=True) / n\n", + "\n", + " # Reconstruct coordinates from corrected bonds\n", + " xy = np.zeros((n, 2), dtype=np.float64)\n", + " xy[1:] = np.cumsum(bonds[:-1], axis=0)\n", + "\n", + " # -----------------------------------------------------------------\n", + " # 3) Arc-length resample so bead spacing stays uniform and physical\n", + " # -----------------------------------------------------------------\n", + " closed_xy = np.vstack([xy, xy[0]])\n", + " seg = np.linalg.norm(np.diff(closed_xy, axis=0), axis=1)\n", + " s = np.concatenate([[0.0], np.cumsum(seg)])\n", + " total_len = s[-1]\n", + "\n", + " if total_len <= 1e-12:\n", + " raise ValueError(\"Degenerate ring generated;\"\n", + " \"total contour length is zero.\")\n", + "\n", + " target_s = np.linspace(0.0, total_len, n + 1)[:-1]\n", + "\n", + " x_resamp = np.interp(target_s, s, closed_xy[:, 0])\n", + " y_resamp = np.interp(target_s, s, closed_xy[:, 1])\n", + " xy = np.stack([x_resamp, y_resamp], axis=1)\n", + "\n", + " # Final contour-length normalization\n", + " # so mean bond length matches bond_length\n", + " final_bonds = np.roll(xy, -1, axis=0) - xy\n", + " mean_bond = np.mean(np.linalg.norm(final_bonds, axis=1))\n", + " if mean_bond > 1e-12:\n", + " xy *= (bond_length / mean_bond)\n", + "\n", + " # Center at origin\n", + " xy -= xy.mean(axis=0, keepdims=True)\n", + "\n", + " # ------------------------------------------------------------------\n", + " # 4) Assembly with preserved Gaussian z-noise logic\n", + " # ------------------------------------------------------------------\n", + " coords = np.zeros((n, 3), dtype=np.float32)\n", + " coords[:, 0] = xy[:, 0].astype(np.float32)\n", + " coords[:, 1] = xy[:, 1].astype(np.float32)\n", + " coords[:, 2] = rng.normal(loc=base_z,\n", + " scale=z_std, size=n).astype(np.float32)\n", + "\n", + " # Return as a list containing one frame to mimic run_md_relaxation output\n", + " return [coords]" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_04", + "metadata": { + "id": "cell_md_04" + }, + "source": [ + "## 5. MD Animation\n", + "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", + "\n", + "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", + "Running this cell will automatically generate and display the animation for a single deterministic chain (seed 0, default bead count)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_04", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 373 + }, + "id": "cell_code_04", + "outputId": "0286465d-0134-4014-f750-7942d7920a3a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generating initial ring and running MD (seed=0)…\n", + "Using CUDA platform.\n", + "MD done — 201 frames. Rendering animation…\n" + ] + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_5960/277962667.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 92\u001b[0m steps_per_frame=STEPS_PER_FRAME)\n\u001b[1;32m 93\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"MD done — {len(anim_frames)} frames. 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rest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 72\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;32mif\u001b[0m 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deltas_per_point, pos)\u001b[0m\n\u001b[1;32m 436\u001b[0m def _draw_ticks(self, renderer, edgep1, centers, deltas, highs,\n\u001b[1;32m 437\u001b[0m deltas_per_point, pos):\n\u001b[0;32m--> 438\u001b[0;31m \u001b[0mticks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 439\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axinfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"i\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/matplotlib/axis.py\u001b[0m in 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\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshorthand_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 111\u001b[0m \"\"\"\n\u001b[1;32m 112\u001b[0m \u001b[0mParameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def show_md_animation(\n", + " frames: list[np.ndarray],\n", + " interval_ms: int = 150,\n", + ") -> None:\n", + " \"\"\"\n", + " Renders an in-notebook HTML5 animation of MD frames.\n", + "\n", + " This function is used to visualize the molecular dynamics simulation\n", + " generated using the `run_md_relaxation` function.\n", + " A plane has been added for visualization purposes and the range of\n", + " absolute z values is also displayed.\n", + " This function is purely for visualisation and does not mutate any arrays.\n", + "\n", + " Parameters\n", + " ----------\n", + " frames : list\n", + " A list of coordinate arrays of shape (num_beads, 3).\n", + " interval_ms : int, optional\n", + " The time interval between frames in milliseconds. Defaults to 150.\n", + "\n", + " Returns\n", + " -------\n", + " None\n", + " The function calls `IPython.display.display` to render the\n", + " animation object directly in the notebook.\n", + "\n", + " Examples\n", + " --------\n", + " >>> show_md_animation(frames)\n", + "\n", + " \"\"\"\n", + "\n", + " frames = [np.asarray(f, dtype=np.float32) for f in frames]\n", + "\n", + " # Use only the first frame for x/y limits\n", + " # — avoids COM drift blowing the scale\n", + " first = frames[0]\n", + " xy_pad = 5.0\n", + " x_min, x_max = first[:, 0].min() - xy_pad, first[:, 0].max() + xy_pad\n", + " y_min, y_max = first[:, 1].min() - xy_pad, first[:, 1].max() + xy_pad\n", + "\n", + " # Clamp z: floor at -1 (just below substrate),\n", + " # ceiling from initial max + padding\n", + " z_min = -1.0\n", + " z_max = float(first[:, 2].max()) + 3.0\n", + "\n", + " x_plane = np.linspace(x_min, x_max, 2)\n", + " y_plane = np.linspace(y_min, y_max, 2)\n", + " X_plane, Y_plane = np.meshgrid(x_plane, y_plane)\n", + " Z_plane = np.zeros_like(X_plane)\n", + "\n", + " fig = plt.figure(figsize=(6, 6))\n", + " ax = fig.add_subplot(111, projection=\"3d\")\n", + "\n", + " def _set_axes():\n", + " ax.set_xlim(x_min, x_max)\n", + " ax.set_ylim(y_min, y_max)\n", + " ax.set_zlim(z_min, z_max)\n", + " ax.set_xlabel(\"x\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z (nm)\")\n", + "\n", + " def init():\n", + " _set_axes()\n", + " return []\n", + "\n", + " def update(i):\n", + " ax.cla()\n", + " c = frames[i]\n", + " # Clip beads to visible z range for plotting so outliers don't distort\n", + " visible = (c[:, 2] >= z_min) & (c[:, 2] <= z_max)\n", + " cc = np.vstack([c, c[0]])\n", + " ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-\", linewidth=1)\n", + " ax.scatter(c[visible, 0], c[visible, 1], c[visible, 2], s=5)\n", + " ax.plot_surface(X_plane, Y_plane, Z_plane, alpha=0.2, color=\"cyan\")\n", + " _set_axes()\n", + " ax.set_title(f\"Frame {i}/{len(frames)-1} | \"\n", + " f\"z ∈ [{c[:,2].min():.1f}, {c[:,2].max():.1f}] nm\")\n", + " return []\n", + "\n", + " ani = animation.FuncAnimation(\n", + " fig, update, frames=len(frames), init_func=init,\n", + " interval=int(interval_ms), blit=False\n", + " )\n", + " plt.close(fig)\n", + " display(HTML(ani.to_jshtml()))\n", + "\n", + "\n", + "# ── Run animation automatically (deterministic seed) ─────────────────────────\n", + "print(\"Generating initial ring and running MD (seed=0)…\")\n", + "anim_coords0 = make_tangled_ring_initial(seed=1)\n", + "anim_frames = run_md_relaxation(anim_coords0, seed=1,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + "print(f\"MD done — {len(anim_frames)} frames. Rendering animation…\")\n", + "show_md_animation(anim_frames)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_05", + "metadata": { + "id": "cell_md_05" + }, + "source": [ + "## 6. Height based AFM Rendering\n", + "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", + "\n", + "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_05", + "metadata": { + "id": "cell_code_05" + }, + "outputs": [], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Low-level geometry helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def _seg_intersect_2d(\n", + " p1: np.ndarray,\n", + " p2: np.ndarray,\n", + " q1: np.ndarray,\n", + " q2: np.ndarray,\n", + " eps: float = 1e-12,\n", + ")-> tuple[bool, np.ndarray | None, float | None, float | None]:\n", + " \"\"\"Calculates the strict 2-D intersection of 2 line segments.\n", + "\n", + " This function is one of the functions used for the creation of the\n", + " chain from the bead coordinates.\n", + " The intersection point is defined by the parameters t and u such that:\n", + " point = p1 + t * (p2 - p1) = q1 + u * (q2 - q1)\n", + "\n", + " Parameters\n", + " ----------\n", + " p1, p2 : np.ndarray\n", + " Start and end points of the first line segment.\n", + " q1, q2 : np.ndarray\n", + " Start and end points of the second line segment.\n", + " eps : float, optional\n", + " A small positive value for numerical stability. Defaults to 1e-12.\n", + "\n", + " Returns\n", + " -------\n", + " is_intersecting : bool\n", + " True if the segments intersect, False otherwise.\n", + " intersection_point : np.ndarray or None\n", + " The [x, y] coordinates of the intersection point, or None if no\n", + " intersection exists.\n", + " parameter_t : float or None\n", + " The interpolation parameter along the first segment, or None.\n", + " parameter_u : float or None\n", + " The interpolation parameter along the second segment, or None.\n", + "\n", + " Examples\n", + " --------\n", + " >>> hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " >>> print(hit, pt, t, u)\n", + " False None None None\n", + "\n", + " \"\"\"\n", + "\n", + " p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n", + " q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n", + " r = p2 - p1; s = q2 - q1\n", + " rxs = r[0]*s[1] - r[1]*s[0]\n", + " if abs(rxs) < eps:\n", + " return False, None, None, None\n", + " qp = q1 - p1\n", + " t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n", + " u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n", + " if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n", + " return True, p1 + t*r, t, u\n", + " return False, None, None, None\n", + "\n", + "\n", + "def _circ_sep(\n", + " n: int,\n", + " i: int,\n", + " j: int,\n", + ") -> int:\n", + " \"\"\"Circular distance between bead indices on a ring of n beads.\n", + "\n", + " This helper function determines the minimal separation between two beads\n", + " along the perimeter of the ring, accounting for the periodic boundary.\n", + " It is used to distinguish between beads that form physical crossings\n", + " and those that are simply sequential neighbors. This function is used\n", + " downstream for crossing calculations\n", + "\n", + " Parameters\n", + " ----------\n", + " n : int\n", + " Number of beads in the ring.\n", + " i, j : int\n", + " Indices of the two beads for which to calculate the separation.\n", + "\n", + " Returns\n", + " -------\n", + " int\n", + " The minimal distance between the two beads.\n", + "\n", + " Examples\n", + " --------\n", + " >>> d = _circ_sep(n, i, j)\n", + " >>> d.type\n", + " int\n", + "\n", + " \"\"\"\n", + "\n", + " d = abs(int(i) - int(j))\n", + " return min(d, n - d)\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Structuring-element / grid helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def disk_footprint(\n", + " r_px: float,\n", + ") -> np.ndarray:\n", + " \"\"\"Boolean circular footprint of radius r_px pixels.\n", + "\n", + " This function creates a 2D boolean mask representing a disk of a\n", + " specified radius. It is used to convert discrete bead coordinates into a\n", + " continuous chain structure by dilating each bead coordinate into a\n", + " disk-like structure. This is what builds the DNA chain radius.\n", + "\n", + " Parameters\n", + " ----------\n", + " r_px : float\n", + " Radius of the disk in pixels.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " A boolean array with True values inside the disk and false outside.\n", + "\n", + " Examples\n", + " --------\n", + " >>> footprint = disk_footprint(r_px)\n", + " >>> print(footprint.shape, footprint.dtype)\n", + " (5, 5) bool\n", + "\n", + " \"\"\"\n", + "\n", + " if r_px < 0.5:\n", + " return np.ones((1, 1), dtype=bool)\n", + " r = int(np.ceil(r_px))\n", + " y, x = np.ogrid[-r:r+1, -r:r+1]\n", + " return (x*x + y*y) <= r*r\n", + "\n", + "\n", + "def upsample_nn(\n", + " a: np.ndarray,\n", + " out_shape: tuple[int, int],\n", + ") -> np.ndarray:\n", + " \"\"\"Nearest-neighbour upsample array a to out_shape.\n", + "\n", + " This function increases the resolution of an input array (such as an AFM\n", + " height map or a boolean mask) by repeating elements. If the target\n", + " dimensions are not exact multiples of the input dimensions, the resulting\n", + " array is clipped to the specified target shape.\n", + "\n", + " Parameters\n", + " ----------\n", + " a : np.ndarray\n", + " Input array to be upsampled.\n", + " out_shape : tuple\n", + " Target shape of the upsampled array.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " Upsampled array of shape out_shape\n", + "\n", + " Examples\n", + " --------\n", + " >>> upsampled_array = upsample_nn(input_array, target_shape)\n", + " >>> print(upsampled_array.shape)\n", + " (target_shape[0], target_shape[1])\n", + "\n", + " \"\"\"\n", + "\n", + " ny_out, nx_out = out_shape\n", + " ny_in, nx_in = a.shape\n", + " if (ny_out, nx_out) == (ny_in, nx_in):\n", + " return a\n", + " sy = max(ny_out // ny_in, 1)\n", + " sx = max(nx_out // nx_in, 1)\n", + " a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n", + " return a_up[:ny_out, :nx_out]\n", + "\n", + "\n", + "#internal render grid comes directly from extent and user nm_per_px\n", + "\n", + "def compute_internal_render_grid(\n", + " extent: tuple[float, float, float, float],\n", + " target_nm_per_px: float,\n", + " min_size: int = 16,\n", + ") -> tuple[int, int, float]:\n", + " \"\"\"Build the internal render grid from physical extent and user nm_per_px.\n", + "\n", + " This function determines the size of the grid for the resolution that the\n", + " user desires. It is based on values chosen for target_nm_per_px and\n", + " nm_per_pix. It also handles potential zero-division or negative resolution\n", + " values.\n", + "\n", + " Parameters\n", + " ----------\n", + " extent : tuple\n", + " boundaries of the simulation space\n", + " target_nm_per_px : float\n", + " desired resolution multiplied by a scaling factor in nm/pixel\n", + " min_size : int, optional\n", + " minimum size of the grid in pixels. Default 16\n", + "\n", + " Returns\n", + " -------\n", + " tuple\n", + " size of the grid\n", + "\n", + " Examples\n", + " --------\n", + " >>> nx, ny, p_nm_per_px = compute_internal_render_grid(extent,\n", + " target_nm_per_px)\n", + " >>> print(nx, ny, p_nm_per_px)\n", + " 256 256 0.5\n", + "\n", + " \"\"\"\n", + "\n", + " xmin, xmax, ymin, ymax = extent\n", + " width_nm = float(xmax - xmin)\n", + " height_nm = float(ymax - ymin)\n", + " p_nm_per_px = max(float(target_nm_per_px), 1e-6)\n", + " nx = max(int(min_size), int(np.ceil(width_nm / p_nm_per_px)) + 1)\n", + " ny = max(int(min_size), int(np.ceil(height_nm / p_nm_per_px)) + 1)\n", + " return nx, ny, p_nm_per_px\n", + "\n", + "\n", + "def resize_image_to_shape(\n", + " a: np.ndarray,\n", + " out_shape: tuple[int, int],\n", + " order: int =1,\n", + ") -> np.ndarray:\n", + " \"\"\"Resize 2D array a to out_shape using scipy.ndimage.zoom.\n", + "\n", + " This function utilizes `scipy.ndimage.zoom` to scale the input array. It\n", + " includes safety checks for edge-case padding and cropping to ensure the\n", + " output array matches the requested dimensions exactly.\n", + "\n", + " Parameters\n", + " ----------\n", + " a : np.ndarray\n", + " Input array to be resized.\n", + " out_shape : tuple\n", + " Target shape of the resized\n", + " order : int, optional\n", + " Order of the interpolation. Defaults to 1.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " Resized array of shape out_shape\n", + "\n", + " Examples\n", + " --------\n", + " >>> resized_array = resize_image_to_shape(input_array, target_shape)\n", + " >>> print(resized_array.shape)\n", + " (target_shape[0], target_shape[1])\n", + "\n", + " \"\"\"\n", + "\n", + " a = np.asarray(a)\n", + " out_shape = tuple(int(v) for v in out_shape)\n", + " if a.shape == out_shape:\n", + " return a.copy()\n", + " zoom_factors = (out_shape[0] / a.shape[0], out_shape[1] / a.shape[1])\n", + " out = zoom(a, zoom_factors, order=order, mode=\"nearest\",\n", + " prefilter=(order > 1))\n", + " out = out[:out_shape[0], :out_shape[1]]\n", + " if out.shape != out_shape:\n", + " pad_y = max(0, out_shape[0] - out.shape[0])\n", + " pad_x = max(0, out_shape[1] - out.shape[1])\n", + " out = np.pad(out, ((0, pad_y), (0, pad_x)), mode=\"edge\")\n", + " out = out[:out_shape[0], :out_shape[1]]\n", + " return out\n", + "\n", + "\n", + "def dna_shape(\n", + " radius_nm: float,\n", + " p_nm_per_px: float,\n", + " max_radius_px: int = 96,\n", + ")-> np.ndarray:\n", + " \"\"\"Hemispherical structuring element representing the DNA cross-section.\n", + "\n", + " This function generates a 2D height map representing the physical profile\n", + " of a DNA strand. Each pixel within the radius of the DNA is assigned a\n", + " height value based on the spherical-cap formula, representing the vertical\n", + " profile encountered during an AFM scan.\n", + "\n", + " Parameters\n", + " ----------\n", + " radius_nm : float\n", + " Radius of the disk in nanometers.\n", + " p_nm_per_px : float\n", + " Physical pixel size in nanometers.\n", + " max_radius_px : int, optional\n", + " Maximum radius in pixels. Defaults to 96.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " Height map of shape (2*R+1, 2*R+1)\n", + "\n", + " Example\n", + " -------\n", + " >>> height_map = dna_shape(radius_nm, p_nm_per_px)\n", + " >>> print(height_map.shape, height_map.dtype)\n", + " (5, 5) float32\n", + "\n", + " \"\"\"\n", + "\n", + " r_px = radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (r_px * r_px)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = np.sqrt(\n", + " np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "def AFM_tip(\n", + " tip_radius_nm: float,\n", + " p_nm_per_px: float,\n", + " max_radius_px: int = 128,\n", + ") -> tuple[np.ndarray, np.ndarray]:\n", + " \"\"\"Spherical-cap structuring element representing the AFM tip geometry.\n", + "\n", + "\n", + " This function calculates the relative vertical profile of a spherical AFM\n", + " tip. The resulting height map is used to perform a morphological dilation\n", + " on the sample surface, simulating the physical interaction (convolution)\n", + " between the tip and the sample. The height is normalized so that the\n", + " tip apex is at 0.0.\n", + "\n", + " Parameters\n", + " ----------\n", + " tip_radius_nanometers : float\n", + " The physical radius of the AFM tip in nanometers.\n", + " nanometers_per_pixel : float\n", + " The spatial resolution of the simulation grid in nanometers per pixel.\n", + " maximum_radius_pixels : int, optional\n", + " A safety limit for the pixel radius of the structuring element to\n", + " prevent excessive memory consumption. Defaults to 128.\n", + "\n", + " Returns\n", + " -------\n", + " occupancy_mask : np.ndarray\n", + " A 2D boolean array representing the circular footprint of the tip.\n", + " height_structure_element : np.ndarray\n", + " A 2D float32 array containing the vertical profile offsets of the\n", + " spherical cap. Pixels outside the tip footprint are assigned a\n", + " large negative value (-1e9).\n", + "\n", + " Example\n", + " -------\n", + " >>> occupancy_mask, height_structure_element = AFM_tip(tip_radius_nm,\n", + " p_nm_per_px)\n", + " >>> print(occupancy_mask.shape, occupancy_mask.dtype)\n", + " (5, 5) bool\n", + "\n", + " \"\"\"\n", + "\n", + " Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (Rpx * Rpx)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = (\n", + " np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n", + " - tip_radius_nm\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Crossing boost\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def calculate_crossing_taper_weight(\n", + " idx_off: int,\n", + " w: int,\n", + " profile: str = \"gaussian\",\n", + " sigma: float | None = None,\n", + ") -> float:\n", + " \"\"\"Smooth weight in [0, 1] used to fade the crossing height boost.\n", + "\n", + " This function generates a weighting factor between 0 and 1 used to\n", + " gradually decrease the height offset applied to DNA segments near a\n", + " detected crossing. It ensures that the transition between the boosted\n", + " crossing region and the standard polymer chain is smooth.\n", + " There are 3 types of profiles possible to be applied\n", + " for smoothing a crossing, guassian, hemisphere, and linear.\n", + "\n", + " Parameters\n", + " ----------\n", + " idx_off : int\n", + " The distance in bead indices from the center of the crossing.\n", + " w : int\n", + " The number of beads on either side of the center over which the weight\n", + " should taper to zero.\n", + " profile : str, optional\n", + " The mathematical shape of the taper. Options are:\n", + " - 'gaussian': A smooth bell-shaped decay (recommended).\n", + " - 'hemisphere': A circular arc taper.\n", + " - 'linear': A simple linear ramp.\n", + " Defaults to 'gaussian'.\n", + " sigma : float, optional\n", + " The standard deviation for the Gaussian profile. If None, it is\n", + " automatically determined based on the window width.\n", + "\n", + " Returns\n", + " -------\n", + " float\n", + " A weighting factor in the range [0.0, 1.0].\n", + "\n", + " Examples\n", + " --------\n", + " >>> wt = calculate_crossing_taper_weight(idx_off, w)\n", + " >>> print(wt.dtype)\n", + " float32\n", + "\n", + " \"\"\"\n", + "\n", + " if w <= 0:\n", + " return 1.0\n", + " a = abs(idx_off)\n", + " if profile == \"linear\":\n", + " return 1.0 - (a / (w + 1.0))\n", + " if profile == \"hemisphere\":\n", + " x = a / (w + 1.0)\n", + " return float(np.sqrt(max(0.0, 1.0 - x*x)))\n", + " # gaussian (default)\n", + " if sigma is None:\n", + " sigma = max(1e-6, 0.5 * (w + 0.5))\n", + " g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n", + " g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n", + " if g_end < 0.999:\n", + " g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n", + " return g\n", + "\n", + "\n", + "def collect_intersections(\n", + " xy_nm: np.ndarray,\n", + " min_separation_beads: int = 12,\n", + " merge_radius_nm: float = 0.5,\n", + ")-> list[dict]:\n", + " \"\"\"Identify all self-intersections in a closed ring polyline.\n", + "\n", + " This function performs a brute-force search across all segment pairs of\n", + " the polyline to find crossings. It ignores adjacent segments, shared\n", + " vertices, and segments that are close to each other along the contour of\n", + " the ring. Detected intersections are subsequently clustered to handle\n", + " multi-segment vertex hits.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " xy_nm : np.ndarray\n", + " Array of shape (n, 2)\n", + " min_separation_beads : int, optional\n", + " Minimum separation between segments in beads. Defaults to 12.\n", + " merge_radius_nm : float, optional\n", + " Maximum separation between segments in nanometers. Defaults to 0.5.\n", + "\n", + " Returns\n", + " -------\n", + " list of dicts:\n", + " {pt_nm, seg_i, seg_j, t, u, n_hits}\n", + " list[dict]\n", + " A list of dictionaries, where each dictionary represents a unique\n", + " crossing and contains:\n", + " - \"pt_nm\": The [x, y] coordinates of the intersection.\n", + " - \"seg_i\": The index of the first segment involved.\n", + " - \"seg_j\": The index of the second segment involved.\n", + " - \"t\": Interpolation parameter along segment A.\n", + " - \"u\": Interpolation parameter along segment B.\n", + " - \"n_hits\": The number of raw intersections merged into this cluster.\n", + "\n", + " Examples\n", + " --------\n", + " >>> clusters = collect_intersections(xy_nm,\n", + " min_separation_beads=int(min_separation_beads),\n", + " merge_radius_nm=float(merge_radius_nm)\n", + " >>> print(clusters.type)\n", + " list[dicts]\n", + "\n", + " \"\"\"\n", + "\n", + " xy = np.asarray(xy_nm, dtype=float)\n", + " n = int(xy.shape[0])\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if hit:\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " clusters = merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n", + " return clusters\n", + "\n", + "\n", + "def merge_crossing_clusters(\n", + " raw_list: list[dict],\n", + " merge_radius_nm: float = 0.5,\n", + ") -> list[dict]:\n", + " \"\"\"Group nearby raw intersection points into single averaged clusters.\n", + "\n", + " This function uses a greedy proximity-based algorithm to cluster\n", + " intersections that occur close to each other (e.g., where multiple\n", + " segments meet at a single vertex). It calculates the centroid for each\n", + " cluster and retains metadata from the first intersection encountered in\n", + " each group.\n", + "\n", + " Parameters\n", + " ----------\n", + " raw_list : list[dict]\n", + " A list of dictionaries containing raw intersection data. Each\n", + " dictionary must include the key \"point\" (a 2D numpy array).\n", + " merge_radius_nm : float, optional\n", + " The maximum Euclidean distance allowed between an intersection point\n", + " and a cluster's current centroid for it to be merged. Defaults to 0.5.\n", + "\n", + " Returns\n", + " -------\n", + " list[dict]\n", + "\n", + " Examples\n", + " --------\n", + " >>> clusters = merge_crossing_clusters(raw_list,\n", + " merge_radius_nm=float(merge_radius_nm))\n", + " >>> print(clusters.type)\n", + " list[dict]\n", + "\n", + " \"\"\"\n", + "\n", + " clusters = []\n", + " for r in raw_list:\n", + " pt = r[\"pt\"].astype(float)\n", + " placed = False\n", + " for c in clusters:\n", + " if np.linalg.norm(pt -\n", + " c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = pt,\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def boost_crossings_intersections(\n", + " x_nm: np.ndarray,\n", + " y_nm: np.ndarray,\n", + " zc_nm: np.ndarray,\n", + " crossings: list[dict] | None = None,\n", + " min_separation_beads: int = 12,\n", + " boost_window_beads: int = 2,\n", + " guaranteed_offset_nm: float = 1.0,\n", + " boost_method: str = \"additive\",\n", + " boost_profile: str = \"gaussian\",\n", + " boost_sigma_beads: float | None = None,\n", + " far_clip_nm: float | None =2.5,\n", + " far_clip_window_beads: int = 12,\n", + " merge_radius_nm: float = 0.5,\n", + ") -> tuple[np.ndarray, list[dict]]:\n", + " \"\"\"Boost z-height at strand crossings so they are visually distinct.\n", + "\n", + " This function identifies self-intersections in a 2D projection and\n", + " physically separates the strands in 3D. For each crossing, it determines\n", + " which segment is 'on top' and raises its vertical profile using a tapered\n", + " window to ensure a realistic and visually distinct crossover point.\n", + " For each crossing it:\n", + " - Determines which strand is on top (higher z)\n", + " - Raises top-strand beads in a tapered window to at least\n", + " (bottom_max + guaranteed_offset_nm)\n", + " - Optionally clips non-crossing beads to far_clip_nm\n", + "\n", + " Parameters\n", + " ----------\n", + " x_nm : np.ndarray\n", + " The x-coordinates of the polymer chain.\n", + " y_nm : np.ndarray\n", + " The y-coordinates of the polymer chain.\n", + " zc_nm : np.ndarray\n", + " The initial z-coordinates of the polymer chain.\n", + " crossings : list[dict]\n", + " Pre-computed crossing data. If None, crossings are detected\n", + " automatically using `collect_polyline_intersections`.\n", + " min_separation_beads : int, optional\n", + " Minimum separation between segments in beads. Defaults to 12.\n", + " boost_window_beads : int, optional\n", + " The number of beads to boost. Defaults to 2.\n", + " guaranteed_offset_nm : float, optional\n", + " The minimum boost height in nanometers. Defaults to 1.0.\n", + " boost_method : str\n", + " The logic for height calculation\n", + " boost_profile : str\n", + " The shape of the tapering profile\n", + " boost_sigma_beads : float\n", + " The standard deviation for gaussian tapering\n", + " far_clip_nm : float\n", + " If applied caps the height of beads far from any crossing to this value.\n", + " far_clip_window_beads : int\n", + " The number of beads to clip.\n", + " merge_radius_nm : float\n", + " The maximum Euclidean distance for nearby raw intersections.\n", + "\n", + " Returns\n", + " -------\n", + " z : np.ndarray\n", + " The updated z-coordinates with crossings boosted.\n", + " crossing_info : list[dict]\n", + " Information regarding the identified and modified crossings.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z, crossing_info = boost_crossings_intersections(x, y, z)\n", + " >>> print(z.shape, type(crossing_info))\n", + " (N,3) \n", + "\n", + " \"\"\"\n", + "\n", + " x = np.asarray(x_nm, dtype=np.float32)\n", + " y = np.asarray(y_nm, dtype=np.float32)\n", + " z = np.asarray(zc_nm, dtype=np.float32).copy()\n", + " n = int(z.shape[0])\n", + " if n < 5:\n", + " return z.astype(np.float32), []\n", + "\n", + " if crossings is None:\n", + " xy = np.stack([x, y], axis=1)\n", + " crossings = collect_intersections(\n", + " xy,\n", + " min_separation_beads=int(min_separation_beads),\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + "\n", + " def get_segment_indices(\n", + " center: int,\n", + " w: int,\n", + " ) -> np.ndarray:\n", + " \"\"\"\n", + " Calculate indices in a window around a center bead,\n", + " handling periodicity.\n", + "\n", + " Parameters\n", + " ----------\n", + " center : int\n", + " The index of the center bead.\n", + " w : int\n", + " The number of beads on either side of the center\n", + " to include.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + "\n", + " \"\"\"\n", + "\n", + " return np.array([(center + k) % n for k in range(-w, w+1)],\n", + " dtype=np.int32)\n", + "\n", + " crossing_info = []\n", + " crossing_centers = []\n", + " w = int(boost_window_beads)\n", + "\n", + " for c in crossings:\n", + " i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n", + " i2 = (i + 1) % n; j2 = (j + 1) % n\n", + " ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n", + "\n", + " bead_i = i if ti < 0.5 else i2\n", + " bead_j = j if uj < 0.5 else j2\n", + " zi = float((1.0 - ti) * z[i] + ti * z[i2])\n", + " zj = float((1.0 - uj) * z[j] + uj * z[j2])\n", + "\n", + " top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j,\n", + " bead_i)\n", + " top_seg = get_segment_indices(top_center, w)\n", + " bottom_seg = get_segment_indices(bottom_center, w)\n", + "\n", + " bl_max = float(z[bottom_seg].max())\n", + " tl_max = float(z[top_seg].max())\n", + "\n", + " if boost_method == \"absolute\":\n", + " target_height = bl_max + float(guaranteed_offset_nm)\n", + " else:\n", + " target_height = max(tl_max, bl_max) + float(guaranteed_offset_nm)\n", + "\n", + " for off in range(-w, w+1):\n", + " k = (top_center + off) % n\n", + " wt = float(calculate_crossing_taper_weight(off, w,\n", + " profile=boost_profile,\n", + " sigma=boost_sigma_beads))\n", + " z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n", + "\n", + " crossing_centers.extend([int(top_center), int(bottom_center)])\n", + " crossing_info.append(dict(\n", + " pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n", + " seg_i = int(i),\n", + " seg_j = int(j),\n", + " top_center = int(top_center),\n", + " bottom_center= int(bottom_center),\n", + " ))\n", + "\n", + " # Clip beads far from any crossing\n", + " if crossing_centers and far_clip_nm is not None:\n", + " centers = np.array(crossing_centers, dtype=np.int32)\n", + " r = int(far_clip_window_beads)\n", + " for k in range(n):\n", + " d = int(np.min(np.minimum(np.abs(k - centers),\n", + " n - np.abs(k - centers))))\n", + " if d > r:\n", + " z[k] = min(z[k], float(far_clip_nm))\n", + "\n", + " return z.astype(np.float32), crossing_info\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Main AFM renderer\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def create_z_based_afm(\n", + " coords: np.ndarray,\n", + " nx: int = 800,\n", + " ny: int = 800,\n", + " dna_diameter_nm: float = 2.0,\n", + " tip_radius_nm: float = 0.01,\n", + " max_height_nm: float = 6.0,\n", + " target_nm_per_px: float = 0.08,\n", + " max_radius_px: int = 96,\n", + " radius_shrink_px: float = 2.0,\n", + " final_blur_sigma_px: float = 0.20,\n", + " apply_edge_taper: bool = True,\n", + " taper_sigma_nm: float = 0.45,\n", + " taper_floor: float = 0.10,\n", + " add_center_ridge: bool = True,\n", + " ridge_sigma_nm: float = 0.25,\n", + " ridge_amp_nm: float = 0.25,\n", + " grain_nm: float = 0.0,\n", + " grain_sigma_px: float = 0.6,\n", + " grain_seed: int = 1,\n", + " enable_crossing_boost: bool = True,\n", + " min_separation_beads: int = 12,\n", + " boost_window_beads: int = 2,\n", + " guaranteed_offset_nm: float = 1.0,\n", + " boost_method: str = \"additive\",\n", + " boost_profile: str = \"gaussian\",\n", + " boost_sigma_beads: float | None = None,\n", + " crossings_precomputed: list[dict] | None = None,\n", + " far_clip_nm: float | None = 2.5,\n", + " far_clip_window_beads: int = 12,\n", + " return_crossing_info: bool = False,\n", + " return_masks: bool = True,\n", + " extent: tuple[float, float, float, float] | None = None,\n", + ") -> tuple[np.ndarray, dict | tuple]:\n", + " \"\"\"Master AFM renderer for simulating images from 3D coordinates.\n", + "\n", + " This pipeline projects 3D bead coordinates onto a 2D grid, manages\n", + " crossings, rasterizes the polymer backbone, applies morphological\n", + " dilations for DNA shape and AFM tip convolution, and adds realistic optical\n", + " and physical artifacts (taper, ridge, noise).\n", + " It follows the following steps:\n", + " 1. Project 3-D bead coords onto a 2-D effective grid\n", + " 2. Rasterise closed polyline with z-interpolation\n", + " 3. Optionally boost crossing heights (boost_crossings_intersections)\n", + " 4. Apply dna_shape (DNA cylindrical cross-section)\n", + " 5. Apply AFM_tip (tip-sample convolution)\n", + " 6. Clip, blur, edge-taper, center-ridge\n", + " 7. Optional synthetic grain noise\n", + " 8. Upsample to requested (nx, ny)\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (n, 3) containing x, y, z coordinates.\n", + " nx : int\n", + " Number of pixels in the x-direction.\n", + " ny : int\n", + " Number of pixels in the y-direction.\n", + " dna_diameter_nm : float\n", + " DNA diameter in nanometers.\n", + " tip_radius_nm : float\n", + " Tip radius in nanometers.\n", + " max_height_nm : float\n", + " Maximum height in nanometers.\n", + " target_nm_per_px : float\n", + " Target number of nanometers per pixel.\n", + " max_radius_px : int\n", + " Maximum radius in pixels.\n", + " radius_shrink_px : float\n", + " Radius shrinking factor.\n", + " final_blur_sigma_px : float\n", + " Final blur sigma in pixels.\n", + " apply_edge_taper : bool\n", + " Apply edge taper.\n", + " taper_sigma_nm : float\n", + " Taper sigma in nanometers.\n", + " taper_floor : float\n", + " Taper floor.\n", + " add_center_ridge : bool\n", + " Add center ridge.\n", + " ridge_sigma_nm : float\n", + " Ridge sigma in nanometers.\n", + " ridge_amp_nm : float\n", + " Ridge amplitude in nanometers.\n", + " grain_nm : float\n", + " Grain size in nanometers.\n", + " grain_sigma_px : float\n", + " Grain sigma in pixels.\n", + " grain_seed : int\n", + " Grain seed.\n", + " enable_crossing_boost : bool\n", + " Enable crossing boost.\n", + " min_separation_beads : int\n", + " Minimum separation between segments in beads.\n", + " boost_window_beads : int\n", + " The number of beads to boost.\n", + " guaranteed_offset_nm : float\n", + " The minimum boost height in nanometers.\n", + " boost_method : str\n", + " The logic for height calculation\n", + " boost_profile : str\n", + " The shape of the tapering profile\n", + " boost_sigma_beads : float\n", + " The standard deviation for gaussian tapering\n", + " crossings_precomputed : list[dict]\n", + " Pre-computed crossing data.\n", + " far_clip_nm : float\n", + " If applied caps the height of beads\n", + " far from any crossing to this value.\n", + " far_clip_window_beads : int\n", + " The number of beads to clip.\n", + " return_crossing_info : bool\n", + " Return crossing info.\n", + " return_masks : bool\n", + " Return masks.\n", + " extent : tuple[float, float, float, float]\n", + " Extent of the output image.\n", + "\n", + " Returns\n", + " -------\n", + " afm_image : np.ndarray\n", + " The simulated 2D AFM height map.\n", + " debug_dict : dict or tuple\n", + " A dictionary containing masks and metadata (if return_masks=True),\n", + " or a tuple containing the physical (xmin, xmax, ymin, ymax) extent.\n", + "\n", + " Examples\n", + " --------\n", + " >>> afm_image, debug_dict = create_z_based_afm(coords, nx=800, ny=800,\n", + " return_masks=True)\n", + " >>> print(afm_image.shape, type(debug_dict))\n", + " (800, 800) \n", + "\n", + " \"\"\"\n", + "\n", + " x, y, z = coords.T\n", + " if extent is None:\n", + " xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n", + " ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n", + " else:\n", + " xmin, xmax, ymin, ymax = extent\n", + "\n", + " nx_eff, ny_eff = int(nx), int(ny)\n", + " px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n", + " py_eff = (ymax - ymin) / max(ny_eff - 1, 1)\n", + " p_eff = 0.5 * (px_eff + py_eff)\n", + "\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n", + " 0, nx_eff - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n", + " 0, ny_eff - 1)\n", + "\n", + " zc = (z - z.min()).astype(np.float32)\n", + "\n", + " # ── Crossing boost ───────────────────────────────────────────────────────\n", + " crossing_info = []\n", + " if enable_crossing_boost:\n", + " zc, crossing_info = boost_crossings_intersections(\n", + " x.astype(np.float32), y.astype(np.float32), zc,\n", + " crossings=crossings_precomputed,\n", + " min_separation_beads=int(min_separation_beads),\n", + " boost_window_beads=int(boost_window_beads),\n", + " guaranteed_offset_nm=float(guaranteed_offset_nm),\n", + " boost_method=str(boost_method),\n", + " boost_profile=str(boost_profile),\n", + " boost_sigma_beads=boost_sigma_beads,\n", + " far_clip_nm=far_clip_nm,\n", + " far_clip_window_beads=int(far_clip_window_beads),\n", + " )\n", + "\n", + " # ── Rasterise polyline ───────────────────────────────────────────────────\n", + " z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n", + " line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n", + " x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " z_line[y0, x0] = max(z_line[y0, x0], z0)\n", + " line_mask[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys, zs = xs[keep], ys[keep], zs[keep]\n", + " z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n", + " line_mask[ys, xs] = True\n", + "\n", + " # ── DNA cap dilation ─────────────────────────────────────────────────────\n", + "\n", + " r_eff = max(0.5 * float(dna_diameter_nm), 0.2)\n", + " fp_dna, cap = dna_shape(r_eff, p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(z_line, footprint=fp_dna,\n", + " structure=cap).astype(np.float32)\n", + " dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna)>0\n", + " surface[~dna_region] = 0.0\n", + "\n", + " # ── Tip convolution ──────────────────────────────────────────────────────\n", + " r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n", + " if r_tip_px >= 0.5:\n", + " fp_tip, tip_struct = AFM_tip(\n", + " float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(surface, footprint=fp_tip,\n", + " structure=tip_struct).astype(np.float32)\n", + "\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + " if final_blur_sigma_px > 0:\n", + " fbsp=float(final_blur_sigma_px)\n", + " surface = gaussian_filter(surface,\n", + " sigma=fbsp).astype(np.float32)\n", + "\n", + " # ── Edge taper + center ridge ────────────────────────────────────────────\n", + " if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n", + " dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n", + " dist_nm = dist_px * np.float32(p_eff)\n", + " if apply_edge_taper:\n", + " sig = max(float(taper_sigma_nm), 1e-6)\n", + " factor = taper_floor + (1.0 - taper_floor) * np.exp(\n", + " -(dist_nm**2) / (2.0 * sig**2))\n", + " surface[dna_region] *= factor[dna_region]\n", + " if add_center_ridge and ridge_amp_nm > 0:\n", + " rs = max(float(ridge_sigma_nm), 1e-6)\n", + " ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n", + " surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " # ── Synthetic grain ──────────────────────────────────────────────────────\n", + " if grain_nm and grain_nm > 0:\n", + " rng = np.random.default_rng(int(grain_seed))\n", + " n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n", + " if grain_sigma_px and grain_sigma_px > 0:\n", + " n = gaussian_filter(n,\n", + " sigma=float(grain_sigma_px)).astype(np.float32)\n", + " v=n[dna_region].std()\n", + " std = float(v) if np.any(dna_region) else float(n.std())\n", + " if std > 1e-9:\n", + " n /= np.float32(std)\n", + " surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " if return_masks:\n", + " debug = {\n", + " \"line_mask\": line_mask,\n", + " \"dna_region\": dna_region,\n", + " \"extent\": (xmin, xmax, ymin, ymax),\n", + " \"p_nm_per_px\": float(p_eff),\n", + " }\n", + " if return_crossing_info:\n", + " debug[\"crossings\"] = crossing_info\n", + " return surface, debug\n", + "\n", + " return surface, (xmin, xmax, ymin, ymax)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_06", + "metadata": { + "id": "cell_md_06" + }, + "source": [ + "## 7. Noise Functions\n", + "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", + "\n", + "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", + "2. If no blank `.spm` file is supplied, the notebook falls back to a PSD-based synthetic noise model.\n", + "\n", + "Note: Topostats is an AFM imaging software package developed at the University of Sheffield which is used for filtering of AFM data. We desribe functions that perform operations similar to Topostats to clean the noise data from the blank files before adding it to the AFM_img.\n", + "\n", + "### Mathematics of PSD-Matched Noise Synthesis\n", + "\n", + "All three methods in the code utilize **Frequency-Domain Synthesis**. The core principle is that the height field $z(x, y)$ can be generated from a target Power Spectral Density (PSD) using the inverse Fourier Transform.\n", + "\n", + "#### 1. General Synthesis Workflow\n", + "For an output image of shape $(N_y, N_x)$:\n", + "1. **Define a PSD**: Let $S(f_x, f_y)$ be the target power spectrum.\n", + "2. **Generate Complex Spectrum**: A complex field $Z(f_x, f_y)$ is created in the frequency domain:\n", + " $$Z(f_x, f_y) = \\sqrt{S(f_x, f_y)} \\cdot e^{i \\phi(f_x, f_y)}$$\n", + " where $\\phi$ is a random phase drawn from a uniform distribution $U(0, 2\\pi)$.\n", + "3. **Inverse Transform**: The spatial noise $z(x, y)$ is the real part of the Inverse Fast Fourier Transform (IFFT) of $Z$.\n", + "4. **RMS Scaling**: The image is normalized such that its standard deviation matches the target Root Mean Square (RMS) roughness." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_06", + "metadata": { + "id": "cell_code_06" + }, + "outputs": [], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Parsing the .spm file for height data\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def robust_range(a:np.ndarray | list) -> tuple[float, float, float]:\n", + " \"\"\"Calculate the 1st and 99th percentiles of finite values in an array.\n", + "\n", + " This function provides a robust measure of the data range by ignoring\n", + " extreme outliers (the top and bottom 1%) as well as non-finite values\n", + " (NaNs and infinities).\n", + "\n", + " Parameters\n", + " ----------\n", + " a : np.ndarray | list\n", + " The input data array to be evaluated.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[float, float, float]\n", + " A tuple containing:\n", + " - percentile_1: The 1st percentile of the finite data.\n", + " - percentile_99: The 99th percentile of the finite data.\n", + " - robust_spread: The difference between the 99th and 1st percentiles.\n", + "\n", + " Examples\n", + " --------\n", + " >>> a,b,c = robust_range(img)\n", + " >>> print(a,b,c)\n", + " 262 1023 761\n", + "\n", + " \"\"\"\n", + "\n", + " a = np.asarray(a, dtype=np.float32)\n", + " p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n", + " return float(p1), float(p99), float(p99 - p1)\n", + "\n", + "\n", + "def looks_like_afm_height(img: np.ndarray) -> bool:\n", + " \"\"\"Determine if an image array represents a valid AFM height map.\n", + "\n", + " This function checks if the dynamic range of the image (excluding\n", + " extreme outliers) is finite, strictly positive, and within a physically\n", + " sane upper bound.\n", + "\n", + " Parameters\n", + " ----------\n", + " img : np.ndarray\n", + " The 2D array representing the simulated or real AFM height map.\n", + "\n", + " Returns\n", + " -------\n", + " bool\n", + " True if the image has a valid, finite, and positive dynamic range;\n", + " False otherwise (e.g., if the array is flat, infinite, or all NaNs).\n", + "\n", + " Examples\n", + " --------\n", + " >>> Flag = looks_like_afm_height(img)\n", + " >>> print(Flag)\n", + " True\n", + "\n", + " \"\"\"\n", + "\n", + " p1, p99, rng = robust_range(img)\n", + " return np.isfinite(rng) and 0 < rng <= 1e9\n", + "\n", + "\n", + "def maybe_to_nm(img: np.ndarray) -> tuple[np.ndarray, str]:\n", + " \"\"\"Auto-convert from metres to nm if values look metre-scale.\n", + "\n", + " This function is used to convert the height map from metres to nm if the\n", + " measured height is in metres.\n", + "\n", + " Parameters\n", + " ----------\n", + " img : np.ndarray\n", + " The 2D array representing the AFM height map.\n", + "\n", + " Returns\n", + " -------\n", + " A tuple consisting of:\n", + " - np.ndarray\n", + " The image after the converion of the height map if needed.\n", + " - str\n", + " either as is or meters to nanometers depending on passed values.\n", + "\n", + " Examples\n", + " --------\n", + " >>> img = maybe_to_nm(img)\n", + " >>> print(img.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " p1, p99, rng = robust_range(img)\n", + " mx = max(abs(p1), abs(p99))\n", + " if mx < 1e-3:\n", + " return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n", + " return img.astype(np.float32), \"as-is (nm or counts)\"\n", + "\n", + "\n", + "def parse_blocks(\n", + " filename: str | Path,\n", + " max_blocks: int = 128,\n", + ") -> tuple[bytes, str, list[dict]]:\n", + " \"\"\"Read a .spm file and parse binary data-block metadata.\n", + "\n", + " This function extracts the ASCII header from a Scanning Probe Microscopy\n", + " (SPM) file, locates the metadata blocks for the image data, and parses the\n", + " structural parameters required to read the raw binary data.\n", + "\n", + " Parameters\n", + " ----------\n", + " filename: str | Path\n", + " The path to the .spm file to be parsed.\n", + " max_blocks: int\n", + " The maximum number of blocks to parse.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[bytes, str, list[dict]]\n", + " A tuple containing:\n", + " - raw: The raw binary data from the .spm file.\n", + " - header: The ASCII header from the .spm file.\n", + " - uniq: A list of dictionaries containing the parsed block metadata.\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If no 'Data offset' strings are found in the header.\n", + "\n", + " Examples\n", + " --------\n", + " >>> raw, header, blocks = parse_blocks(filename)\n", + " >>> print(type(raw), type(header), type(blocks))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " raw = open(filename, \"rb\").read()\n", + " i_end = raw.find(b\"\\x1a\")\n", + " if i_end == -1:\n", + " i_end = min(len(raw), 400_000)\n", + " header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n", + "\n", + " matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n", + " if not matches:\n", + " raise RuntimeError(\"No 'Data offset' found in header.\")\n", + "\n", + " blocks = []\n", + " for k, pos in enumerate(matches[:max_blocks]):\n", + " win = header[max(0, pos-2500): min(len(header), pos+2500)]\n", + "\n", + " def grab_int(key):\n", + " m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n", + " return int(m.group(1)) if m else None\n", + "\n", + " def grab_float(key):\n", + " pattern = (\n", + " rf\"{re.escape(key)}\\s*:\\s*\"\n", + " r\"([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\"\n", + " )\n", + " m = re.search(pattern, win)\n", + " return float(m.group(1)) if m else None\n", + "\n", + " name_candidate = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n", + " name = name_candidate[-1].strip() if name_candidate else None\n", + "\n", + " off = grab_int(\"Data offset\")\n", + " length = grab_int(\"Data length\")\n", + " bpp = grab_int(\"Bytes/pixel\")\n", + " nx_b = grab_int(\"Samps/line\")\n", + " ny_b = grab_int(\"Number of lines\")\n", + " zscale = grab_float(\"Z scale\")\n", + " zoff = grab_float(\"Z offset\")\n", + "\n", + " if None in (off, length, bpp, nx_b, ny_b):\n", + " continue\n", + " blocks.append(dict(name=name or f\"block_{k}\",\n", + " off=off, length=length, bpp=bpp,\n", + " nx=nx_b, ny=ny_b,\n", + " zscale=zscale, zoff=zoff))\n", + "\n", + " uniq, seen = [], set()\n", + " for b in blocks:\n", + " if b[\"off\"] not in seen:\n", + " uniq.append(b); seen.add(b[\"off\"])\n", + " if not uniq:\n", + " raise RuntimeError(\"Found 'Data offset' strings\"\n", + " \" but no parseable blocks.\")\n", + " return raw, header, uniq\n", + "\n", + "\n", + "def read_block_candidates(\n", + " raw: bytes,\n", + " b: dict,\n", + ") -> list[tuple[str, np.ndarray]]:\n", + " \"\"\"Decode a binary data block using all plausible NumPy data types.\n", + "\n", + " This function attempts to parse the binary blob using all logical dtypes\n", + " for the given bytes-per-pixel and applies physical Z-scaling to the\n", + " results.\n", + "\n", + " Parameters\n", + " ----------\n", + " raw : bytes\n", + " The raw binary data from the .spm file.\n", + " b : dict\n", + " The parsed block metadata.\n", + "\n", + " Returns\n", + " -------\n", + " list[tuple[str, np.ndarray]]\n", + " A list of candidate tuples. Each tuple contains:\n", + " - dtype_str: The string representation of the data type.\n", + " - image_array: The decoded image array.\n", + "\n", + " Raises\n", + " ------\n", + " Runtime error\n", + " - Truncated block.\n", + "\n", + " Examples\n", + " --------\n", + " >>> candidates = read_block_candidates(raw, b)\n", + " >>> print(type(candidates))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n", + " nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n", + " blob = raw[off:off+length]\n", + " if len(blob) < length:\n", + " raise RuntimeError(\"Truncated block.\")\n", + "\n", + " n = nx_b * ny_b\n", + " cands = []\n", + " dtype_map = {2: [\"i2\", \"u2\"],\n", + " 4: [\"i4\", \"f4\"],\n", + " 1: [\"u1\"]}\n", + " for dt in dtype_map.get(bpp, []):\n", + " arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n", + " if arr.size != n:\n", + " continue\n", + " cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n", + "\n", + " out = []\n", + " for dt, img in cands:\n", + " z = img\n", + " if b.get(\"zscale\") is not None:\n", + " z = z * np.float32(b[\"zscale\"])\n", + " if b.get(\"zoff\") is not None:\n", + " z = z + np.float32(b[\"zoff\"])\n", + " out.append((dt, z))\n", + " return out\n", + "\n", + "\n", + "def choose_best_height_image(\n", + " raw: bytes,\n", + " blocks: list[dict],\n", + " verbose: bool = True,\n", + ")-> tuple[dict, np.ndarray]:\n", + " \"\"\"Evaluate all data blocks to find the most plausible AFM height map.\n", + "\n", + " This function iterate all blocks and dtype candidates and then picks the\n", + " one that:\n", + " - passes looks_like_afm_height\n", + " - has the highest log-variance score\n", + " (which is done to favor candidates with more topographical detail)\n", + "\n", + " Parameters\n", + " ----------\n", + " raw : bytes\n", + " The raw binary data from the .spm file.\n", + " blocks : list[dict]\n", + " The parsed block metadata.\n", + " verbose : bool\n", + " Whether to print diagnostics.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[dict, np.ndarray]\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If no suitable block is found.\n", + "\n", + " Examples\n", + " --------\n", + " >>> b, img = choose_best_height_image(raw, blocks)\n", + " >>> print(type(b), type(img))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " best, best_score = None, -np.inf\n", + " for b in blocks:\n", + " try:\n", + " for dt, img in read_block_candidates(raw, b):\n", + " if not looks_like_afm_height(img):\n", + " continue\n", + " p1, p99, rng = robust_range(img)\n", + " var = float(np.var(img))\n", + " score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n", + " if score > best_score:\n", + " best_score = score\n", + " best = (b, dt, img, (p1, p99, rng, var))\n", + " except Exception:\n", + " continue\n", + "\n", + " if best is None:\n", + " raise RuntimeError(\"Could not find a\"\n", + " \"plausible AFM height image decode.\")\n", + " b, dt, img, stats = best\n", + " if verbose:\n", + " p1, p99, rng, var = stats\n", + " print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n", + " f\"bpp={b['bpp']} decode={dt}\")\n", + " print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, \"\n", + " f\"range={rng:.3g}, var={var:.3g}\")\n", + " return b, img\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# TopoStats-like background / noise extraction\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def plane_remove(\n", + " z: np.ndarray,\n", + " mask: np.ndarray | None = None,\n", + ")-> np.ndarray:\n", + " \"\"\"Fit and subtract a first-order tilted plane from a 2D height map.\n", + "\n", + " This function performs background leveling by fitting a plane defined by\n", + " the equation z = ax + by + c to the image. If a mask is provided, the fit\n", + " is calculated only using the pixels where the mask is True (typically\n", + " representing the flat substrate).\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height map.\n", + " mask : np.ndarray | None, optional\n", + " A boolean mask of the same shape as z. If provided, the fit is\n", + " calculated only using the pixels where the mask is True.\n", + " This is useful for excluding features like DNA from the plane fit.\n", + " Defaults to None.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The flattened height map.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z_flat = plane_remove(z)\n", + " >>> print(z_flat.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " ny, nx = z.shape\n", + " Y, X = np.mgrid[0:ny, 0:nx]\n", + " A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n", + " b = z.ravel()\n", + " if mask is not None:\n", + " m = mask.ravel().astype(bool)\n", + " A, b = A[m], b[m]\n", + " C, *_ = np.linalg.lstsq(A, b, rcond=None)\n", + " plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n", + " return (z - plane).astype(np.float32)\n", + "\n", + "\n", + "def line_flatten_median(\n", + " z: np.ndarray,\n", + " mask: np.ndarray | None = None,\n", + ")-> np.ndarray:\n", + " \"\"\"Subtract per-row median (background pixels only if mask given).\n", + "\n", + " AFM images often exhibit horizontal stripes due to low-frequency noise or\n", + " z-axis drift during scanning. This function levels each scan line\n", + " independently by shifting its median value to zero.\n", + "\n", + " Parameters\n", + " ----------\n", + " z: np.ndarray\n", + " The 2D height map\n", + " mask: np.ndarray | None, optional\n", + " A boolean mask of the same shape as z. If provided,\n", + " the median is calculated only using the pixels where the mask is True.\n", + " This prevents features (like DNA) from biasing the\n", + " flattening of the background. Defaults to None.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The flattened height map.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z_flat = line_flatten_median(z)\n", + " >>> print(z_flat.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " out = z.astype(np.float32, copy=True)\n", + " for i in range(out.shape[0]):\n", + " if mask is None or not np.any(mask[i]):\n", + " med = np.median(out[i])\n", + " else:\n", + " med = np.median(out[i, mask[i]])\n", + " out[i] -= np.float32(med)\n", + " return out\n", + "\n", + "\n", + "def feature_mask(\n", + " z: np.ndarray,\n", + " smooth_sigma_px: float = 3.0,\n", + " thresh_sigma: float = 3.0,\n", + " dilate_px: int = 4,\n", + ")-> np.ndarray:\n", + " \"\"\"Detects topographical features as pixels deviating by a threshold.\n", + "\n", + " This function identifies pixels that deviate significantly from a smoothed\n", + " local background. It uses the Median Absolute Deviation (MAD) to estimate\n", + " the noise floor, making it robust against the features it is trying to\n", + " detect.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height map.\n", + " smooth_sigma_px : float, optional\n", + " The smoothing width in pixels. Defaults to 3.0.\n", + " thresh_sigma : float, optional\n", + " The number of standard deviations above the median to consider a pixel\n", + " as a feature. Defaults to 3.0.\n", + " dilate_px : int, optional\n", + " The radius of curcular footprint to dilate the feature mask.\n", + " Defaults to 4.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " A boolean mask where true indicates a detected feature\n", + "\n", + " Examples\n", + " --------\n", + " >>> feat = feature_mask(z)\n", + " >>> print(feat.shape, feat.dtype)\n", + " (800, 800) bool\n", + "\n", + " \"\"\"\n", + "\n", + " z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " resid = (z - z_s).astype(np.float32)\n", + " med = float(np.median(resid))\n", + " mad = float(np.median(np.abs(resid - med)))\n", + " sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n", + " feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n", + " if dilate_px and dilate_px > 0:\n", + " r = int(dilate_px)\n", + " yy, xx = np.ogrid[-r:r+1, -r:r+1]\n", + " fp = (xx*xx + yy*yy) <= r*r\n", + " feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n", + " return feat\n", + "\n", + "\n", + "def inpaint_simple(\n", + " z: np.ndarray,\n", + " mask: np.ndarray,\n", + " smooth_sigma_px: float = 8.0,\n", + ") -> np.ndarray:\n", + " \"\"\"Replace masked regions with a smooth estimate of the local background.\n", + "\n", + " This function calculates a heavily blurred version of the original image to\n", + " serve as a background proxy and patches the masked areas with this\n", + " estimate.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height map.\n", + " mask : np.ndarray\n", + " A boolean mask where True indicates pixels to be inpainted (features)\n", + " smooth_sigma_px : float, optional\n", + " The sigma value for the gaussian filter.\n", + " Higher value resutls in smoother inpainted images. Defaults to 8.0.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The inpainted height\n", + "\n", + " Examples\n", + " --------\n", + " >>> z_bg = inpaint_simple(z, feat)\n", + " >>> print(z_bg.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " out = z.copy()\n", + " out[mask] = bg[mask]\n", + " return out\n", + "\n", + "\n", + "def bandpass_noise(\n", + " z: np.ndarray,\n", + " low_sigma_px: float = 1.0,\n", + " high_sigma_px: float = 30.0,\n", + ") -> np.ndarray:\n", + " \"\"\"Performs bandpass filtering on the noise image.\n", + "\n", + " This function performs a bandpass filter by calculating the difference\n", + " between two Gaussian-blurred versions of the same image. It effectively\n", + " removes:\n", + " 1. High-frequency noise: Suppressed by the `low_sigma` blur.\n", + " 2. Low-frequency trends: (like tilt or bowing) Removed by subtracting\n", + " the `high_sigma` blur.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : np.ndarray\n", + " The 2D height\n", + " low_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the high frequency cutoff.\n", + " Defaults to 1.0.\n", + " high_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the low frequency cutoff.\n", + " Defaults to 30.0.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The bandpassed height map.\n", + "\n", + " Examples\n", + " --------\n", + " >>> tex = bandpass_noise(z)\n", + " >>> print(tex.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n", + " high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n", + " return (low - high).astype(np.float32)\n", + "\n", + "\n", + "def extract_topostats_like_noise(\n", + " z_in: np.ndarray,\n", + " median_size: int = 3,\n", + " bg_quantile: float = 40.0,\n", + " feature_sigma_px: float = 3.0,\n", + " feature_thresh_sigma: float = 3.0,\n", + " feature_dilate_px: int = 4,\n", + " inpaint_sigma_px: float = 10.0,\n", + " band_low_sigma_px: float = 0.8,\n", + " band_high_sigma_px: float = 35.0,\n", + " target_rms_nm: float | None = None,\n", + " apply_plane_remove: bool = True,\n", + " apply_line_flatten: bool = True,\n", + " apply_bandpass: bool = True,\n", + ") -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"Execute the full noise-extraction pipeline on an AFM height map.\n", + "\n", + " This function isolates the pure substrate noise texture by leveling the\n", + " image (plane flatten and line flatten), masking out topographical features\n", + " (detect features), inpainting those regions, and applying a bandpass\n", + " filter.\n", + " The resulting image can be rescaled to a target RMS value.\n", + " The resulting texture can be used to augment synthetic datasets.\n", + "\n", + " Parameters\n", + " ----------\n", + " z_in : np.ndarray\n", + " The 2D height map.\n", + " median_size : int, optional\n", + " The size of the median filter to apply. Defaults to 3.\n", + " bg_quantile : float, optional\n", + " The quantile to used to generate a rough initial background guess for the\n", + " preliminary plane and line leveling. Defaults to 40.0.\n", + " feature_sigma_px : float, optional\n", + " The gaussian sigma for background estimation for the feature mask.\n", + " Defaults to 3.0.\n", + " feature_thresh_sigma : float, optional\n", + " The number of standard deviations above the median to consider a pixel as\n", + " a feature. Defaults to 3.0.\n", + " feature_dilate_px : int, optional\n", + " The radius of curcular footprint to dilate the feature mask. Defaults to\n", + " 4.\n", + " inpaint_sigma_px : float, optional\n", + " The sigma value for the gaussian filter. Higher value resutls in smoother\n", + " inpainted image. Defaults to 10.0.\n", + " band_low_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the high frequency cutoff.\n", + " Defaults to 0.8.\n", + " band_high_sigma_px : float, optional\n", + " The sigma value for gaussian filter defining the low frequency cutoff.\n", + " Defaults to 35.0.\n", + " target_rms_nm: float | None, optional\n", + " If provided, rescales the extracted noise textureto match this target RMS\n", + " roughness. Defaults to None.\n", + " apply_plane_remove : bool, optional\n", + " Whether to apply the plane remove step. Defaults to True.\n", + " apply_line_flatten : bool, optional\n", + " Whether to apply the line flatten step. Defaults to True.\n", + " apply_bandpass : bool, optional\n", + " Whether to apply the bandpass step. Defaults to True.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[float, np.ndarray, np.ndarray, np.ndarray]\n", + " A tuple containing:\n", + " - rms_nm: The RMS roughness of the extracted noise texture.\n", + " - noise_texture: The extracted, zero mean noise texture.\n", + " - z_flat: The flattened height map.\n", + " - feature_mask: A boolean mask where true indicates a detected feature.\n", + "\n", + " Examples\n", + " --------\n", + " >>> rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(z)\n", + " >>> print(rms_nm)\n", + " 0.06\n", + " >>> print(noise_tex.shape)\n", + " (800, 800)\n", + " >>> print(z_flat.shape)\n", + " (800, 800)\n", + " >>> print(feat.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " z = z_in.astype(np.float32)\n", + "\n", + " if median_size and median_size > 1:\n", + " z = median_filter(z, size=int(median_size)).astype(np.float32)\n", + "\n", + " q = np.percentile(z, float(bg_quantile))\n", + " bg0 = z <= q\n", + "\n", + " z_flat = z.copy()\n", + "\n", + " if apply_plane_remove:\n", + " z_flat = plane_remove(z_flat, mask=bg0)\n", + "\n", + " if apply_line_flatten:\n", + " z_flat = line_flatten_median(z_flat, mask=bg0)\n", + "\n", + " feat = feature_mask(\n", + " z_flat,\n", + " smooth_sigma_px=feature_sigma_px,\n", + " thresh_sigma=feature_thresh_sigma,\n", + " dilate_px=feature_dilate_px,\n", + " )\n", + "\n", + " z_bg = inpaint_simple(\n", + " z_flat,\n", + " feat,\n", + " smooth_sigma_px=inpaint_sigma_px,\n", + " )\n", + "\n", + " if apply_bandpass:\n", + " tex = bandpass_noise(\n", + " z_bg,\n", + " low_sigma_px=band_low_sigma_px,\n", + " high_sigma_px=band_high_sigma_px,\n", + " )\n", + " else:\n", + " tex = z_bg.astype(np.float32)\n", + "\n", + " bg = ~feat\n", + " rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n", + "\n", + " if target_rms_nm is not None:\n", + " target = float(target_rms_nm)\n", + " if rms > 1e-12:\n", + " tex *= target / rms\n", + " rms = target\n", + "\n", + " return (\n", + " rms,\n", + " tex.astype(np.float32),\n", + " z_flat.astype(np.float32),\n", + " feat.astype(bool),\n", + " )\n", + "\n", + "\n", + "def tile_or_crop(\n", + " tex: np.ndarray,\n", + " out_shape: tuple[int, int],\n", + " seed: int | None = 0,\n", + ")-> np.ndarray:\n", + " \"\"\"Tile a texture array and extract a deterministic random crop.\n", + "\n", + " This function ensures that an extracted background noise texture can be\n", + " resized to fit any target simulated image size. If the texture is smaller\n", + " than the target, it is tiled (repeated) to cover the area. A random crop is\n", + " then taken to prevent obvious repeating patterns at the origin.\n", + "\n", + " Parameters\n", + " ----------\n", + " tex : np.ndarray\n", + " The 2D height map\n", + " out_shape : tuple[int, int]\n", + " The target output shape.\n", + " seed : int | None, optional\n", + " The random seed. Defaults to 0.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " The resized and cropped noise texture.\n", + "\n", + " Examples\n", + " --------\n", + " >>> tex = tile_or_crop(tex, (ny, nx))\n", + " >>> print(tex.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " ty, tx = tex.shape\n", + " oy, ox = out_shape\n", + " if (ty, tx) == (oy, ox):\n", + " return tex\n", + " tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n", + " rng = np.random.default_rng(seed)\n", + " y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n", + " x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n", + " return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n", + "\n", + "\n", + "def load_blank_spm_noise(\n", + " blank_path: str | Path,\n", + " target_rms_nm: float = 0.06,\n", + " verbose: bool = True,\n", + " apply_plane_remove: bool = USE_PLANE_REMOVE,\n", + " apply_line_flatten: bool = USE_LINE_FLATTEN,\n", + " apply_bandpass: bool = USE_BANDPASS_FILTER,\n", + ") -> tuple[float, np.ndarray, dict]:\n", + " \"\"\"Load an SPM file and extract a calibrated background noise texture.\n", + "\n", + " This high-level function handles the full lifecycle of noise extraction:\n", + " 1. Parsing the Bruker .spm binary and metadata.\n", + " 2. Heuristically selecting the best height channel.\n", + " 3. Calibrating raw digital units to physical nanometers.\n", + " 4. Leveling, masking, and bandpass-filtering the substrate noise.\n", + "\n", + " Parameters\n", + " ----------\n", + " blank_path : str | Path\n", + " The path to the blank .spm file.\n", + " target_rms_nm : float, optional\n", + " The target RMS roughness of the extracted noise texture.\n", + " Defaults to 0.06.\n", + " verbose : bool, optional\n", + " Whether to print progress messages and diagnostic statistics.\n", + " Defaults to True.\n", + "\n", + " Returns\n", + " -------\n", + " tuple[float, np.ndarray, dict]\n", + " A tuple containing:\n", + " - rms_nm: The RMS roughness of the extracted noise texture.\n", + " - noise_texture: The extracted, zero mean noise texture.\n", + " - diagnostics_dict: A dictionary containing diagnostic information.\n", + "\n", + " Examples\n", + " --------\n", + " >>> rms_nm, noise_tex, diag = load_blank_spm_noise(blank_path)\n", + " >>> print(rms_nm)\n", + " 0.06\n", + " >>> print(noise_tex.shape)\n", + " (800, 800)\n", + " >>> print(diag['flattened'].shape)\n", + " (800, 800)\n", + " >>> print(diag['feature_mask'].shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " raw, header, blocks = parse_blocks(blank_path)\n", + " b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n", + " img_nm, unit_note = maybe_to_nm(img0)\n", + " if verbose:\n", + " print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n", + "\n", + " rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n", + " img_nm,\n", + " median_size=3,\n", + " bg_quantile=45,\n", + " feature_sigma_px=3.0,\n", + " feature_thresh_sigma=3.0,\n", + " feature_dilate_px=6,\n", + " inpaint_sigma_px=10.0,\n", + " band_low_sigma_px=0.7,\n", + " band_high_sigma_px=40.0,\n", + " target_rms_nm=target_rms_nm,\n", + " )\n", + " diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n", + " \"height_nm\": img_nm, \"unit_note\": unit_note}\n", + " if verbose:\n", + " print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n", + " f\"tex shape: {noise_tex.shape}\")\n", + " return rms_nm, noise_tex, diag\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# PSD-based fallback noise synthesis\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "TARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\n", + "#Safety buffer to ensure no division by 0 or square roots producing NaN values\n", + "EPS = 1e-12\n", + "\n", + "\n", + "def load_model(\n", + " path: str | Path = MODEL_PATH,\n", + ") -> dict[str, np.ndarray]:\n", + " \"\"\"\n", + " Load the Power Spectral Density (PSD) noise model\n", + " from a compressed archive.\n", + "\n", + " The model typically contains the statistical distribution of substrate\n", + " roughness in the frequency domain, allowing for the generation of\n", + " synthetic noise that matches the 'color' and texture of the substrate.\n", + "\n", + " Parameters\n", + " ----------\n", + " path : str | Path, optional\n", + " The path to the compressed archive containing the PSD model.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, np.ndarray]\n", + " A dictionary containing the PSD model components namely:\n", + " - \"log_psd_mean\": The mean of the log-transformed radial PSD.\n", + " - \"log_psd_std\": The standard deviation of the\n", + " log-transformed radial PSD.\n", + " - \"radial_freq\": The spatial frequency coordinates.\n", + " - \"rms_values_nm\": RMS values associated with the training set.\n", + "\n", + " Examples\n", + " --------\n", + " >>> model = load_model()\n", + " >>> print(type(model))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " data = np.load(path)\n", + " return {\n", + " \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n", + " \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n", + " \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n", + " \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n", + " }\n", + "\n", + "\n", + "def generate_noise(\n", + " seed: int,\n", + " target_shape: tuple[int, int] = TARGET_SHAPE,\n", + " target_rms_nm: float | None = TARGET_NOISE_RMS_NM,\n", + " method: str = \"mean_full2d\",\n", + " std_scale: float = 1.0,\n", + " model: dict | None = None,\n", + " model_path: str | Path | None = MODEL_PATH,\n", + ") -> np.ndarray:\n", + " \"\"\"\n", + " Generate a single random AFM-like background noise image from the PSD.\n", + "\n", + " This function performs Fourier synthesis to create textures that match\n", + " the spatial frequency characteristics of the PSD model provided.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " RNG seed for reproducibility.\n", + " target_shape : tuple[int, int]\n", + " Output image shape (rows, cols).\n", + " The PSD is resized automatically if it differs\n", + " from the shape used when building the model. Defaults to TARGET_SHAPE.\n", + " target_rms_nm : float | None\n", + " RMS amplitude in nm.\n", + " If None a value is randomly taken from the blank-file ensemble.\n", + " Defaults to TARGET_NOISE_RMS_NM.\n", + " method : str\n", + " 'mean_full2d' -- mean log-PSD;\n", + " variance comes only from random phase (recommended).\n", + " 'lognormal_full2d' -- Samples a new PSD\n", + " from the log-normal distribution. (more sample to sample variation)\n", + " 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n", + " std_scale : float\n", + " Multiplier for the PSD on log_psd_std\n", + " for lognormal method (>1 -> more variation).\n", + " model : dict | None\n", + " Pre-loaded model dict. If None, loaded from model_path on each call.\n", + " model_path : str | Path | None\n", + " Path to the .npz file (used only when model is None).\n", + "\n", + " Returns\n", + " -------\n", + " z: np.ndarray\n", + "\n", + " Raises\n", + " ------\n", + " ValueError\n", + " If an unknown method is provided.\n", + "\n", + " Examples\n", + " --------\n", + " >>> z = generate_noise(seed=42)\n", + " >>> print(z.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " if model is None:\n", + " model = load_model(model_path)\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " out_shape = tuple(target_shape)\n", + "\n", + " if method == \"mean_full2d\":\n", + " psd_sample = np.exp(model[\"log_psd_mean\"])\n", + "\n", + " elif method == \"lognormal_full2d\":\n", + " mu= model[\"log_psd_mean\"]\n", + " noise = rng.standard_normal(mu.shape).astype(np.float32)\n", + " psd_sample = np.exp(mu + std_scale * mu * noise)\n", + "\n", + " elif method == \"empirical_radial\":\n", + " mu = model[\"log_psd_mean\"]\n", + " radial_psd = np.exp(mu.mean(axis=1))\n", + " radial_freq_for_interp = np.abs(np.fft.fftfreq(mu.shape[0], d=1.0))\n", + "\n", + " fy = np.fft.fftfreq(out_shape[0], d=1.0)\n", + " fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n", + " fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n", + " kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n", + "\n", + " psd_sample = np.interp(\n", + " kr.ravel(),\n", + " radial_freq_for_interp,\n", + " radial_psd,\n", + " left=float(radial_psd[0]),\n", + " right=float(radial_psd[-1]),\n", + " ).reshape(kr.shape).astype(np.float32)\n", + "\n", + " else:\n", + " raise ValueError(\n", + " f\"Unknown method {method!r}. \"\n", + " \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n", + " )\n", + "\n", + " psd_ny, psd_nx = psd_sample.shape\n", + " out_ny, out_nx = out_shape\n", + " rfft_nx = out_nx // 2 + 1\n", + "\n", + " if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n", + " from scipy.ndimage import zoom\n", + " psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx),\n", + " order=1)\n", + "\n", + " psd_sample = psd_sample.astype(np.float32)\n", + "\n", + " phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n", + " spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n", + "\n", + " z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n", + " z -= np.float32(np.mean(z))\n", + " rmsv = model[\"rms_values_nm\"]\n", + " trmsf = float(target_rms_nm)\n", + " target = float(rng.choice(rmsv)) if target_rms_nm is None else trmsf\n", + " cur = float(np.std(z))\n", + " if cur > EPS:\n", + " z *= target / cur\n", + "\n", + " return z\n", + "\n", + "def random_transform_noise_texture(\n", + " tex: np.ndarray,\n", + " seed: int,\n", + ") -> np.ndarray:\n", + " \"\"\"Apply a deterministic random affine transform to a noise texture.\n", + "\n", + " This function generates a reproducible random transformation of an input\n", + " texture using the provided seed. The transformation includes rotation,\n", + " anisotropic scaling, translation, optional vertical and horizontal flips,\n", + " and a final amplitude jitter.\n", + " It is intended to produce visually distinct noise realizations from a\n", + " single acquired blank SPM texture, while preserving the overall texture\n", + " statistics.\n", + "\n", + " Parameters\n", + " ----------\n", + " tex: np.ndarray\n", + " Input noise texture of shape (H, W).\n", + " seed: int\n", + " Random seed controlling the random transformation.\n", + "\n", + " Returns\n", + " -------\n", + " out: np.ndarray\n", + " Transformed noise texture of shape (H, W).\n", + "\n", + " Examples\n", + " --------\n", + " >>> transformed = random_transform_noise_texture(tex, seed=42)\n", + " transformed\n", + " >>> transformed.shape == tex.shape\n", + " True\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " tex = np.asarray(tex, dtype=np.float32)\n", + "\n", + " theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n", + " base_scale = float(rng.uniform(0.85, 1.15))\n", + " anis = float(rng.uniform(0.85, 1.15))\n", + " sx, sy = base_scale * anis, base_scale / anis\n", + "\n", + " c, s = float(np.cos(theta)), float(np.sin(theta))\n", + " A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n", + " M = np.linalg.inv(A).astype(np.float32)\n", + "\n", + " h, w = tex.shape\n", + " center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n", + " t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n", + " float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n", + " offset = center - M @ (center + t)\n", + "\n", + " out = affine_transform(tex, matrix=M, offset=offset,\n", + " output_shape=tex.shape, order=1,\n", + " mode=\"wrap\", prefilter=False).astype(np.float32)\n", + "\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[::-1, :]\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[:, ::-1]\n", + " out *= float(rng.uniform(0.90, 1.10))\n", + " return out.astype(np.float32)\n", + "\n", + "\n", + "def sample_noise_image(\n", + " noise_source: str,\n", + " noise_asset: dict | np.ndarray | None,\n", + " seed: int,\n", + " out_shape: tuple[int, int],\n", + " target_rms_nm: float | None,\n", + ") -> np.ndarray | None:\n", + " \"\"\"Return a per-sample noise image from either blank.spm or PSD fallback.\n", + "\n", + " Parameters\n", + " ----------\n", + " noise_source : str\n", + " 'blank_spm' or 'psd_model'\n", + " noise_asset : dict | np.ndarray | None\n", + " If 'blank_spm', this is the 2D texture array.\n", + " If 'psd_model', this is the dictionary containing PSD statistics.\n", + " seed : int\n", + " RNG seed for reproducibility.\n", + " out_shape : tuple[int, int]\n", + " The (rows, cols) of the desired height map.\n", + " target_rms_nm : float | None\n", + " If provided, rescales the extracted noise texture\n", + " to match this target RMS roughness.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray | None\n", + " The per-sample noise image or None if disabled.\n", + "\n", + " Examples\n", + " --------\n", + " >>> noise_tex = sample_noise_image(noise_source, noise_asset,\n", + " seed, out_shape, target_rms_nm)\n", + " >>> print(noise_tex.shape)\n", + " (800, 800)\n", + "\n", + " \"\"\"\n", + "\n", + " if noise_asset is None:\n", + " return None\n", + "\n", + " if noise_source == \"blank_spm\":\n", + " noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n", + " return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n", + "\n", + " if noise_source == \"psd_model\":\n", + " return generate_noise(\n", + " seed=seed,\n", + " target_shape=out_shape,\n", + " target_rms_nm=target_rms_nm,\n", + " method=PSD_NOISE_METHOD,\n", + " std_scale=PSD_NOISE_STD_SCALE,\n", + " model=noise_asset,\n", + " ).astype(np.float32)\n", + "\n", + " return None\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_07", + "metadata": { + "id": "cell_md_07" + }, + "source": [ + "## 8. DNA Ground-Truth Mask\n", + "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", + "\n", + "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", + "\n", + "We also dilate the mask by a value of **DNA_MASK_DILATE_PX** to ensure that the mask has enough width for optimal training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_07", + "metadata": { + "id": "cell_code_07" + }, + "outputs": [], + "source": [ + "def nm_to_px(\n", + " coords_xy_nm: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int,\n", + ") -> tuple[np.ndarray, np.ndarray]:\n", + " \"\"\"Map (x, y) coordinates in nanometres to integer pixel indices (ix, iy).\n", + "\n", + " Linearly maps each coordinate from the physical extent to the discrete\n", + " pixel grid, then clips to valid index bounds.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords_xy_nm : np.ndarray\n", + " The (x, y) coordinates in nanometres.\n", + " extent : tuple[float, float, float, float]\n", + " The physical extent (xmin, xmax, ymin, ymax) in nanometres.¨\n", + " nx : int\n", + " The number of pixels in the x-direction.\n", + " ny : int\n", + " The number of pixels in the y-direction.\n", + "\n", + " Returns\n", + " -------\n", + " ix, iy : tuple[np.ndarray, np.ndarray]\n", + " The integer pixel indices which are:\n", + " - ix: The x-coordinate indices.\n", + " - iy: The y-coordinate indices.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ix, iy = nm_to_px(coords_xy_nm, extent, nx, ny)\n", + " >>> print(ix.dtype, iy.dtype)\n", + " int32 int32\n", + "\n", + " \"\"\"\n", + "\n", + " x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n", + " xmin, xmax, ymin, ymax = extent\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n", + " 0, nx - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n", + " 0, ny - 1)\n", + " return ix, iy\n", + "\n", + "\n", + "def rasterize_closed_polyline_mask(\n", + " coords: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int,\n", + ") -> np.ndarray:\n", + " \"\"\"Rasterize a closed bead polyline into a 1-pixel-wide binary mask.\n", + "\n", + " Each consecutive bead pair (including the wrap-around from the last bead\n", + " back to the first) is drawn onto the grid using dense linear interpolation\n", + " along the segment. Duplicate pixels that would arise at segment junctions\n", + " are de-duplicated before writing.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (N, 3) or (N, 2) containing bead positions in\n", + " nanometres. Only the first two columns (x, y) are used.\n", + " extent : tuple[float, float, float, float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometres.\n", + " nx : int\n", + " The number of pixels in the x-axis.\n", + " ny : int\n", + " The number of pixels in the y-axis.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " A binary mask of shape (ny, nx),\n", + " with True at every pixel where the polyline is present\n", + " and False everywhere else.\n", + "\n", + " Examples\n", + " --------\n", + " >>> mask = rasterize_closed_polyline_mask(coords, extent, nx=512, ny=512)\n", + " >>> print(mask.shape, mask.dtype)\n", + " (512, 512) bool\n", + "\n", + " \"\"\"\n", + "\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n", + " out = np.zeros((ny, nx), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0 = int(ix[k]), int(iy[k])\n", + " x1, y1 = int(ix[k2]), int(iy[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " out[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys = xs[keep], ys[keep]\n", + " out[ys, xs] = True\n", + " return out\n", + "\n", + "\n", + "def make_dna_mask_from_beads(\n", + " coords: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int,\n", + " dilate_px: int = 3,\n", + ") -> np.ndarray:\n", + " \"\"\"Build a binary DNA segmentation mask from bead coordinates.\n", + "\n", + " Rasterizes the closed bead polyline into a 1-pixel-wide line mask and\n", + " optionally dilates it by a disk of radius ``dilate_px`` pixels to produce a\n", + " mask wide enough for robust segmentation training.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (N, 3) or (N,2) containing bead positions in nanometres.\n", + " Only the first two columns (x, y) are used.\n", + " extent : tuple[float, float, float, float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometres.\n", + " nx : int\n", + " The number of pixels in the x-axis.\n", + " ny : int\n", + " The number of pixels in the y-axis.\n", + " dilate_px : int, optional\n", + " Radius in pixels of the disk structuring element used to dilate\n", + " the rasterized line. Set to 0 to skip dilation. Defaults to 3.\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " unit8 binary mask of shape (ny, nx)\n", + " with values 0 (background) or 1 (DNA).\n", + "\n", + " Examples\n", + " --------\n", + " >>> mask = make_dna_mask_from_beads(coords, extent, nx=512, ny=512,\n", + " dilate_px=DNA_MASK_DILATE_PX)\n", + " >>> print(mask.shape)\n", + " (512, 512)\n", + "\n", + " \"\"\"\n", + "\n", + " line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n", + " if dilate_px and dilate_px > 0:\n", + " fdp = float(dilate_px)\n", + " line = binary_dilation(line, structure=disk_footprint(fdp))\n", + " return line.astype(np.uint8)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_08", + "metadata": { + "id": "cell_md_08" + }, + "source": [ + "## 9. Crossing Detection & Crossing Mask\n", + "\n", + "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", + "\n", + "We have set the masks for the crossings as chain weighted gaussians so that the machine learning models can have more information about the crossings and thus have better predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_08", + "metadata": { + "id": "cell_code_08" + }, + "outputs": [], + "source": [ + "def canon_axis(\n", + " u: np.ndarray) -> np.ndarray | None:\n", + " \"\"\"Normalize a 2-D vector and canonicalise its sign.\n", + "\n", + " Ensures that +v and -v are treated as the same axis direction by flipping\n", + " the sign so the first nonzero component is always positive. This makes axis\n", + " comparisons order-independent.\n", + "\n", + " Parameters\n", + " ----------\n", + " u: np.ndarray\n", + " A 2-D vector to normalize\n", + "\n", + " Returns\n", + " -------\n", + " np.ndarray | None\n", + " Unit vector with canonicalized sign, or None if u is zero or if input has\n", + " a norm below 1e-12.\n", + "\n", + " Examples\n", + " --------\n", + " >>> u = np.array([1.0, 2.0])\n", + " >>> v = canon_axis(u)\n", + " >>> print(v)\n", + " [0.4472136 0.8944272]\n", + "\n", + " \"\"\"\n", + "\n", + " u = np.asarray(u, dtype=float)\n", + " nrm = float(np.linalg.norm(u))\n", + " if nrm < 1e-12:\n", + " return None\n", + " u = u / nrm\n", + " if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n", + " u = -u\n", + " return u\n", + "\n", + "\n", + "def find_polyline_crossings(\n", + " coords_xy: np.ndarray,\n", + " min_separation_beads: int = CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm: float = 0.5,\n", + ") -> list[dict]:\n", + " \"\"\"Detect all strand crossings of a closed ring polyline in XY.\n", + "\n", + " Performs a brute-force search over all segment pairs, skipping adjacent\n", + " segments, shared-vertex pairs, and pairs whose circular contour separation\n", + " is less than ``minimum_separation_beads``. Raw intersection hits that land\n", + " within ``merge_radius_nm`` of each other are clustered into a single\n", + " crossing, and duplicate chain-axis directions (within ~18°) are\n", + " deduplicated per cluster.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords_xy : np.ndarray\n", + " Array of shape (N,2) containing the [x,y] bead positions in nanometers\n", + " min_separation_beads: int, optional\n", + " Minimum circular contour separation (in beads) required between 2\n", + " segments before their intersection is considered. Defaults to\n", + " CROSS_MIN_SEP_BEADS.\n", + " merge_radius_nm: float, optional\n", + " Maximum distance in nanometers between 2 raw intersections that are\n", + " clustered into a single crossing. Defaults to 0.5 nm.\n", + "\n", + " Returns\n", + " -------\n", + " list[dict]\n", + " Each dict represents one crossing and contains:\n", + " - ``pt_nm`` : np.ndarray, shape (2,) — position in nanometres.\n", + " - ``axes_nm`` : list of np.ndarray — deduplicated unit chain\n", + " direction vectors at the crossing.\n", + " - ``seg_i`` : int — index of the first segment.\n", + " - ``seg_j`` : int — index of the second segment.\n", + " - ``t`` : float — parametric position along segment i.\n", + " - ``u`` : float — parametric position along segment j.\n", + " - ``n_hits`` : int — number of raw hits merged into this cluster.\n", + "\n", + " Examples\n", + " --------\n", + " >>> crossings = find_polyline_crossings(coords[:,:2])\n", + " >>> print(type(crossings))\n", + " \n", + "\n", + " \"\"\"\n", + "\n", + " xy = np.asarray(coords_xy, dtype=float)\n", + " n = int(xy.shape[0])\n", + "\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if not hit:\n", + " continue\n", + " ua = canon_axis(p2 - p1)\n", + " ub = canon_axis(q2 - q1)\n", + " if ua is None or ub is None:\n", + " continue\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " axes=[ua, ub],\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " # Cluster nearby raw intersections\n", + " clusters = []\n", + " for r in raw:\n", + " pt = r[\"pt\"]\n", + " placed = False\n", + " for c in clusters:\n", + " fmc=float(merge_radius_nm)\n", + " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= fmc:\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"axes\"].extend(r[\"axes\"])\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n", + " \"axes\": list(r[\"axes\"]), \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt_mean = c[\"pt_sum\"] / c[\"n\"]\n", + " axes = []\n", + " for uvec in c[\"axes\"]:\n", + " uvec = canon_axis(uvec)\n", + " if uvec is None:\n", + " continue\n", + " if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n", + " continue\n", + " axes.append(uvec)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = np.asarray(pt_mean, dtype=np.float32),\n", + " axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n", + " if axes else [np.array([1.0, 0.0], np.float32)],\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def add_crossing_patch(\n", + " mask: np.ndarray,\n", + " pt_px: tuple[float, float],\n", + " axes_px: list[np.ndarray],\n", + " sigma_center: float = 2.0,\n", + " chain_extent: float = 5.0,\n", + " sigma_perp: float = 1.8,\n", + " center_weight: float = 1.0,\n", + " chain_weight: float = 0.8,\n", + ") -> None:\n", + " \"\"\"Paint one crossing onto a float mask using a chain-weighted Gaussian.\n", + "\n", + " The patch is the sum of two components, composed with np.maximum so that\n", + " multiple crossings accumulate correctly without overwriting:\n", + "\n", + " - An isotropic Gaussian centred on the crossing point, scaled by\n", + " ``center_weight``.\n", + " - The per-axis maximum of anisotropic Gaussians aligned to each chain\n", + " direction (elongated along the chain, narrow across it), scaled by\n", + " ``chain_weight``.\n", + "\n", + " Parameters\n", + " ----------\n", + " mask: np.ndarray\n", + " Float32 array of shape (H,W) to paint onto.\n", + " pt_px: tuple[float, float]\n", + " (x,y) crossing point in pixel coordinates.\n", + " axes_px: list[np.ndarray]\n", + " Unit vectors giving chain directions at the crossing,\n", + " in pixel coordinates\n", + " sigma_center: float, optional\n", + " Parameter for standard deviation in pixels of the\n", + " isotropic center gaussian. Defaults to 2.0.\n", + " chain_extent: float, optional\n", + " Multiplier applied to \"sigma_center\" to set the\n", + " parallel standard deviation of the anisotropic chain gaussians.\n", + " Defaults to 5.0.\n", + " sigma_perp: float, optional\n", + " Parameter for standard deviation in pixels across each chain direction.\n", + " Defaults to 1.8\n", + " center_weight: float, optional\n", + " Amplitude of the isotropic center gaussian. Defaults to 1.0.\n", + " chain_weight: float, optional\n", + " Amplitude of the anisotropic chain gaussians. Defaults to 0.8.\n", + "\n", + " \"\"\"\n", + "\n", + " H, W = mask.shape\n", + " cx, cy = float(pt_px[0]), float(pt_px[1])\n", + " sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n", + " R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n", + " x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n", + " y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n", + "\n", + " xs = np.arange(x0, x1+1, dtype=float)\n", + " ys = np.arange(y0, y1+1, dtype=float)\n", + " X, Y = np.meshgrid(xs, ys)\n", + " dx = X - cx; dy = Y - cy\n", + "\n", + " gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n", + "\n", + " g_chain = np.zeros_like(gc)\n", + " for v in axes_px:\n", + " vx, vy = float(v[0]), float(v[1])\n", + " u_par = dx*vx + dy*vy\n", + " u_perp = -dx*vy + dy*vx\n", + " g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n", + " + u_perp**2 / sigma_perp**2))\n", + " g_chain = np.maximum(g_chain, g)\n", + "\n", + " patch = center_weight * gc + chain_weight * g_chain\n", + " mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n", + "\n", + "\n", + "def make_crossing_mask(\n", + " coords: np.ndarray,\n", + " extent: tuple[float, float, float, float],\n", + " nx: int,\n", + " ny: int,\n", + " sigma_center_px: float = CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px: float = CROSS_SIGMA_PERP_PX,\n", + " chain_extent: float = CROSS_CHAIN_EXTENT,\n", + " center_weight: float = CROSS_CENTER_WEIGHT,\n", + " chain_weight: float = CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads: int = CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed: list[dict] | None = None,\n", + " merge_radius_nm: float = 0.5,\n", + ") -> tuple[np.ndarray, list[dict]]:\n", + " \"\"\"Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n", + "\n", + " For each detected (or precomputed) crossing, paints a chain-weighted\n", + " Gaussian blob onto a float32 canvas using ``add_crossing_patch``.The final\n", + " mask is normalised to [0, 1].\n", + "\n", + " Passing ``crossings_precomputed`` is strongly recommended in the dataset\n", + " generation pipeline: reusing the same crossing list that was passed to the\n", + " AFM renderer guarantees that the mask and the image are perfectl\n", + " spatially aligned.\n", + "\n", + " Parameters\n", + " ----------\n", + " coords : np.ndarray\n", + " Array of shape (N,3) or (N,2) containing bead positions in nanometers.\n", + " Only the first two columns (x, y) are used.\n", + " Extent: tuple[float, float, float, float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometers.\n", + " nx: int\n", + " Number of pixels along the x-axis\n", + " ny: int\n", + " Number of pixels along the y-axis\n", + " sigma_center_px: float, optional\n", + " Parameter for standard deviation in pixels of the\n", + " isotropic center gaussian. Defaults to CROSS_SIGMA_CENTER_PX.\n", + " sigma_perp_px: float, optional\n", + " Parameter for standard deviation in pixels across the\n", + " chain direction at each crossing. Defaults to CROSS_SIGMA_PERP_PX.\n", + " chain_extent: float, optional\n", + " Multiplier on \"sigma_center_px\" that controls how far the\n", + " chain aligned gaussians extend along the strand.\n", + " Defaults to CROSS_CHAIN_EXTENT.\n", + " center_weight: float, optional\n", + " Amplitude of the isotropic center gaussian.\n", + " Defaults to CROSS_CENTER_WEIGHT.\n", + " chain_weight: float, optional\n", + " Amplitude of the anisotropic chain gaussians.\n", + " Defaults to CROSS_CHAIN_WEIGHT.\n", + " min_separation_beads: int, optional\n", + " Minimum circular contour separation (in beads)\n", + " required between 2 segments before their intersection is considered.\n", + " Defaults to CROSS_MIN_SEP_BEADS.\n", + " crossings_precomputed: list[dict] | None, optional\n", + " precomputed crossing list from \"find_polyline_crossings\"\n", + " or the AFM renderer. If None, crossings are detected automatically.\n", + " Defaults to None.\n", + " merge_radius_nm: float, optional\n", + " Maximum distance in nanometers between 2 raw\n", + " intersections that are clustered into a single crossing.\n", + " Defaults to 0.5 nm.\n", + "\n", + " Returns\n", + " -------\n", + " crossing_mask: np.ndarray\n", + " Float32 array of shape (ny, nx) with values in [0, 1].\n", + " crossings: list[dict]\n", + " Each dict represents one crossing\n", + "\n", + " Examples\n", + " --------\n", + " >>> crossing_mask, crossings = make_crossing_mask(coords,\n", + " extent, nx=512, ny=512, crossings_precomputed=precomputed_crossings)\n", + " >>> print(crossing_mask.shape, len(crossings))\n", + " (512, 512) 4\n", + "\n", + " \"\"\"\n", + "\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " xy_nm = coords[:, :2].astype(float)\n", + "\n", + " if crossings_precomputed is None:\n", + " crossings = find_polyline_crossings(\n", + " xy_nm,\n", + " min_separation_beads=min_separation_beads,\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + " else:\n", + " crossings = crossings_precomputed\n", + "\n", + " xmin, xmax, ymin, ymax = extent\n", + " sx = (nx - 1) / (xmax - xmin)\n", + " sy = (ny - 1) / (ymax - ymin)\n", + "\n", + " mask = np.zeros((ny, nx), dtype=np.float32)\n", + "\n", + " for c in crossings:\n", + " x_nm, y_nm = c[\"pt_nm\"]\n", + " px = (x_nm - xmin) * sx\n", + " py = (y_nm - ymin) * sy\n", + "\n", + " axes_px = []\n", + " for v in c[\"axes_nm\"]:\n", + " vx_px = v[0] * sx; vy_px = v[1] * sy\n", + " nrm = float(np.hypot(vx_px, vy_px))\n", + " if nrm < 1e-12:\n", + " continue\n", + " axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n", + " if not axes_px:\n", + " axes_px = [np.array([1.0, 0.0], dtype=float)]\n", + "\n", + " add_crossing_patch(\n", + " mask,\n", + " pt_px=(px, py),\n", + " axes_px=axes_px,\n", + " sigma_center=float(sigma_center_px),\n", + " chain_extent=float(chain_extent),\n", + " sigma_perp=float(sigma_perp_px),\n", + " center_weight=float(center_weight),\n", + " chain_weight=float(chain_weight),\n", + " )\n", + "\n", + " m = float(mask.max())\n", + " if m > 1e-12:\n", + " mask /= m\n", + " return mask.astype(np.float32), crossings" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_09", + "metadata": { + "id": "cell_md_09" + }, + "source": [ + "## 10. Decoy Chain Functions\n", + "\n", + "real AFM images often contain small chain segments that are not part of the main chain and thus constitute the noise in that sample, but since those segments are broken off DNA, they cannot be neatly integrated into the noise model.\n", + "Therefore, we offer the possibility of addition of small DNA chains to every every sample in the dataset where ``add_decoy = True`` which would then contain a small 2–4 bead \"decoy\" fragment placed in a corner of the image.\n", + "\n", + "The decoy appears in the AFM image but is **excluded** from both\n", + "ground-truth masks, training the model to ignore isolated unconnected fragments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_09", + "metadata": { + "id": "cell_code_09" + }, + "outputs": [], + "source": [ + "def make_decoy_chain(\n", + " seed: int,\n", + " n_beads_range: tuple[int, int] = (2, 4),\n", + " bond_length: float = BOND_LENGTH,\n", + ") -> np.ndarray:\n", + " \"\"\"Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n", + "\n", + " This function is used to generate a small decoy chain segment with the\n", + " number of beads given by ``n_beads_range``.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed: int\n", + " Random seed for number of beads within range, ``n_beads_range``.\n", + " n_beads_range: tuple[int, int], optional\n", + " Range of number of beads to generate the decoy. Defaults to (2,4)\n", + " bond_length: float, optional\n", + " Bond length in nanometers. Defaults to BOND_LENGTH.\n", + "\n", + " Returns\n", + " -------\n", + " coords: np.ndarray\n", + " Array of shape (N,3) containing bead positions in nanometers.\n", + "\n", + " Examples\n", + " --------\n", + " >>> coords = make_decoy_chain(seed=42)\n", + " >>> print(coords.shape)\n", + " (3, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n", + " coords = np.zeros((n_b, 3), dtype=np.float32)\n", + " angle = rng.uniform(0, 2 * np.pi)\n", + " dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n", + "\n", + " for i in range(1, n_b):\n", + " da = rng.normal(0, 0.3)\n", + " c, s = np.cos(da), np.sin(da)\n", + " R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n", + " dirn = R @ dirn\n", + " dirn = dirn / np.linalg.norm(dirn)\n", + " coords[i] = coords[i-1] + bond_length * dirn\n", + "\n", + " coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n", + " return coords\n", + "\n", + "\n", + "def place_decoy_in_corner(\n", + " decoy_coords: np.ndarray,\n", + " global_extent: tuple[float, float, float, float],\n", + " corner_margin_nm: float = 2.0,\n", + " seed: int | None = None,\n", + ") -> np.ndarray:\n", + " \"\"\"Translate decoy_coords so its centroid lands in a random corner.\n", + "\n", + " This function takes the coordinates of the decoy and places it at a\n", + " distance of corner_margin_nm away from one of the corners of the image.\n", + "\n", + " Parameters\n", + " ----------\n", + " decoy_coords: np.ndarray\n", + " Array of shape (N,3) containing bead positions in nanometers.\n", + " global_extent: tuple[float float float float]\n", + " The physical bounding box (xmin, xmax, ymin, ymax) in nanometers.\n", + " corner_margin_nm: float, optional\n", + " Distance in nanometers to place the decoy inside the frame.\n", + " Defaults to 2.0.\n", + " seed: int | None, optional\n", + " Random seed for corner selection. Defaults to None.\n", + "\n", + " Returns\n", + " -------\n", + " decoy_coords: np.ndarray\n", + "\n", + " Examples\n", + " --------\n", + " >>> decoy_coords = place_decoy_in_corner(decoy_coords, global_extent)\n", + " >>> print(decoy_coords.shape)\n", + " (3, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " if seed is None:\n", + " seed = int(np.sum(decoy_coords))\n", + " rng = np.random.default_rng(int(seed))\n", + " xmin, xmax, ymin, ymax = global_extent\n", + " corner = rng.integers(0, 4)\n", + " targets = [\n", + " (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n", + " (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n", + " (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n", + " (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n", + " ]\n", + " tx, ty = targets[corner]\n", + " centroid = decoy_coords.mean(axis=0)\n", + " offset = np.array([tx, ty, 0.0]) - centroid\n", + " return decoy_coords + offset" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_10", + "metadata": { + "id": "cell_md_10" + }, + "source": [ + "## 11. Chain Generation Pipeline\n", + "\n", + "Now, we are finally ready to generate on sample in our dataset. The following functions are used for building the final image and masks:\n", + "- `generate_chain_with_beads` with `n_beads` defaulting to `N_BEADS`\n", + "- `generate_one_sample_multilength` generates the image and mask (this is what iterates to produce the dataset)\n", + "- `random_transform_noise_texture` applies a per-sample random affine transform to the noise texture for variety (in case of blank .spm files)\n", + "- Noise injection uses `blank.spm` when available and the PSD model fallback otherwise\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_10", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cell_code_10", + "outputId": "315c98f4-59d2-4f4a-fe10-b812a25e6b21" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Chosen block: 'Z offset: V [Sens. DeformationSens] (0.00000000093132 V/LSB) 0.01733094 V' 512x512 bpp=4 decode= dict[str, Any]:\n", + " \"\"\"Build a tangled ring with n_beads and run MD relaxation if needed.\n", + "\n", + " This function creates an initial ring polymer with a specified number of\n", + " beads, optionally relaxes it using molecular dynamics, and extracts the\n", + " final bead coordinates. Self-crossings are then detected from the final\n", + " 2D projection of the chain.\n", + " Optionally, a decoy chain fragment can also be generated and placed near\n", + " one corner of the main chain bounding box.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed: int\n", + " Random seed for initial ring creation.\n", + " n_beads: int\n", + " Number of beads in the initial ring. Defaults to N_BEADS.\n", + " add_decoy: bool\n", + " Whether to add a corner-placed decoy fragment. Defaults to False.\n", + "\n", + " Returns\n", + " -------\n", + " A dict containing the following keys:\n", + " - ``\"seed\"`` : int\n", + " The normalized integer seed.\n", + " - ``\"n_beads\"`` : int\n", + " Number of beads in the generated chain.\n", + " - ``\"coords\"`` : np.ndarray\n", + " Final bead coordinates with shape ``(N, 3)`` and dtype\n", + " ``np.float32``.\n", + " - ``\"crossings\"`` : list\n", + " Detected self-crossings in the 2D projected chain.\n", + " - ``\"decoy_coords\"`` : np.ndarray or None\n", + " Coordinates of the optional decoy fragment, or ``None`` if no\n", + " decoy was added.\n", + "\n", + " Examples\n", + " --------\n", + " >>> chain = generate_chain_with_beads(seed=42, n_beads=10)\n", + " >>> chain[\"seed\"]\n", + " 42\n", + " >>> chain[\"n_beads\"]\n", + " 10\n", + " >>> chain[\"coords\"].shape\n", + " (10, 3)\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " base_z=BASE_Z,\n", + " )\n", + " frames = run_md_relaxation(coords0, seed=seed,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + " else:\n", + " frames = make_ring_2d_persistent_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n", + " )\n", + "\n", + " last_coords = frames[-1].astype(np.float32)\n", + "\n", + " crossings = find_polyline_crossings(\n", + " last_coords[:, :2],\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " decoy_coords = None\n", + " if add_decoy:\n", + " xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n", + " ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n", + " decoy_raw = make_decoy_chain(seed + 9999)\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_raw, (xmin, xmax, ymin, ymax),\n", + " corner_margin_nm=15.0, seed=seed)\n", + "\n", + " return dict(seed=seed, n_beads=n_beads,\n", + " coords=last_coords, crossings=crossings,\n", + " decoy_coords=decoy_coords)\n", + "\n", + "\n", + "def render_chain_and_masks(\n", + " chain: dict,\n", + " noise_source: str = \"none\",\n", + " noise_asset: dict | np.ndarray | None = None,\n", + ") -> dict[str, Any]:\n", + " \"\"\"Render AFM image + DNA mask + crossing mask for a chain dict.\n", + "\n", + " This function renders a high-resolution AFM image from a chain sample,\n", + " optionally composites a decoy fragment into the image, adds background\n", + " noise, and generates corresponding DNA and crossing masks.\n", + " The AFM image is first rendered on an internal physical grid determined\n", + " from the chain extent and the target pixel size. The image and masks are\n", + " then resized to the final network resolution.\n", + " Decoy fragments, when present, are composited into the AFM image using\n", + " maximum-intensity blending, but are deliberately excluded from both the\n", + " DNA mask and crossing mask.\n", + "\n", + " Parameters\n", + " ----------\n", + " chain: dict\n", + " A dict containing the following keys:\n", + " -``\"seed\"``\n", + " -``\"coords\"``\n", + " -``\"crossings\"``.\n", + " The optional key\n", + " ``\"decoy_coords\"`` may also be present\n", + " noise_source: str\n", + " Noise generation source passed to `sample_noise_image`.\n", + " noise_asset: dict | np.ndarray | None\n", + " Noise asset passed to `sample_noise_image`\n", + "\n", + " Returns\n", + " -------\n", + " A dict containing the following keys:\n", + " - ``\"seed\"`` : int\n", + " Random seed associated with the sample.\n", + " - ``\"afm_img\"`` : np.ndarray\n", + " Final AFM image with shape ``(NY, NX)`` and dtype\n", + " ``np.float32``.\n", + " - ``\"dna_mask\"`` : np.ndarray\n", + " Final binary DNA mask with shape ``(NY, NX)`` and dtype\n", + " ``np.uint8``.\n", + " - ``\"cross_mask\"`` : np.ndarray\n", + " Final crossing mask with shape ``(NY, NX)`` and dtype\n", + " ``np.float32``.\n", + " - ``\"extent\"`` : np.ndarray\n", + " Physical image extent as ``(xmin, xmax, ymin, ymax)`` with dtype\n", + " ``np.float32``.\n", + " - ``\"n_crossings\"`` : int\n", + " Number of crossing regions included in the crossing mask.\n", + " - ``\"decoy_coords\"`` : np.ndarray or None\n", + " Final decoy coordinates, or ``None`` if no decoy was used.\n", + "\n", + " Examples\n", + " --------\n", + " >>> sample = generate_chain_with_beads(seed=1)\n", + " >>> rendered = render_chain_and_masks(sample)\n", + " >>> rendered[\"afm_img\"].shape\n", + " (NY, NX)\n", + " >>> rendered[\"dna_mask\"].dtype\n", + " dtype('uint8')\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(chain[\"seed\"])\n", + " coords = chain[\"coords\"]\n", + " crossings = chain[\"crossings\"]\n", + " decoy_coords = chain.get(\"decoy_coords\", None)\n", + "\n", + " padding = 10.0\n", + " xmin = coords[:, 0].min() - padding\n", + " xmax = coords[:, 0].max() + padding\n", + " ymin = coords[:, 1].min() - padding\n", + " ymax = coords[:, 1].max() + padding\n", + " global_extent = (xmin, xmax, ymin, ymax)\n", + "\n", + " # internal render grid is determined from extent and nm_per_px\n", + " nx_phys, ny_phys, _ = compute_internal_render_grid(\n", + " global_extent, AFM_KW[\"target_nm_per_px\"]\n", + " )\n", + " afm_kw_phys = {**AFM_KW, \"nx\": nx_phys, \"ny\": ny_phys}\n", + "\n", + " afm_img_hr, dbg = create_z_based_afm(\n", + " coords, grain_seed=seed,\n", + " crossings_precomputed=crossings,\n", + " extent=global_extent,\n", + " **afm_kw_phys\n", + " )\n", + "\n", + " if decoy_coords is not None:\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_coords, global_extent,\n", + " corner_margin_nm=8.0, seed=seed)\n", + " decoy_kw = {**afm_kw_phys,\n", + " \"apply_edge_taper\": False,\n", + " \"enable_crossing_boost\": False,\n", + " \"add_center_ridge\": False}\n", + " decoy_afm_hr, _ = create_z_based_afm(\n", + " decoy_coords, extent=global_extent, **decoy_kw)\n", + " afm_img_hr = np.maximum(afm_img_hr, decoy_afm_hr)\n", + "\n", + " actual_extent = dbg[\"extent\"]\n", + "\n", + " noise_img = sample_noise_image(\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " seed=seed,\n", + " out_shape=afm_img_hr.shape,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " )\n", + " if noise_img is not None:\n", + " afm_img_hr = np.clip((afm_img_hr + noise_img).astype(np.float32),\n", + " 0, AFM_KW[\"max_height_nm\"])\n", + "\n", + " dna_mask_hr = make_dna_mask_from_beads(\n", + " coords, actual_extent, nx_phys, ny_phys, dilate_px=DNA_MASK_DILATE_PX)\n", + "\n", + " cross_mask_hr, crossings_out = make_crossing_mask(\n", + " coords, actual_extent, nx_phys, ny_phys,\n", + " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", + " chain_extent=CROSS_CHAIN_EXTENT,\n", + " center_weight=CROSS_CENTER_WEIGHT,\n", + " chain_weight=CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed=crossings,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " if CROSS_CLIP_TO_DNA_MASK:\n", + " cross_mask_hr = cross_mask_hr * dna_mask_hr.astype(np.float32)\n", + "\n", + " #Logic for maintaining user defined resolution\n", + " afm_img = resize_image_to_shape(afm_img_hr.astype(np.float32),\n", + " (NY, NX), order=1).astype(np.float32)\n", + " dna_mask = (resize_image_to_shape(dna_mask_hr.astype(np.float32),\n", + " (NY, NX), order=0)>0.5).astype(np.uint8)\n", + " cross_mask = resize_image_to_shape(cross_mask_hr.astype(np.float32),\n", + " (NY, NX), order=1).astype(np.float32)\n", + "\n", + " return dict(\n", + " seed=seed,\n", + " afm_img=afm_img.astype(np.float32),\n", + " dna_mask=dna_mask.astype(np.uint8),\n", + " cross_mask=cross_mask.astype(np.float32),\n", + " extent=np.array(actual_extent, dtype=np.float32),\n", + " n_crossings=int(len(crossings_out)),\n", + " decoy_coords=decoy_coords,\n", + " )\n", + "\n", + "\n", + "def generate_one_sample_multilength(\n", + " seed: int,\n", + " n_beads: int,\n", + " noise_source: str = \"none\",\n", + " noise_asset: dict | np.ndarray | None = None,\n", + " add_decoy: bool = False,\n", + ") -> dict[str, Any]:\n", + " \"\"\"Generate one complete sample at a specified bead count.\n", + "\n", + " This function generates a single chain realization with the requested\n", + " number of beads, renders the corresponding AFM image and supervision masks,\n", + " and appends the bead count to the returned sample dictionary.\n", + "\n", + " Noise can optionally be injected during rendering using either a blank\n", + " SPM-derived texture or a PSD-based synthetic noise model, depending on\n", + " the provided noise configuration.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed: int\n", + " Random seed for initial ring creation.\n", + " n_beads: int\n", + " Number of beads in the initial ring.\n", + " noise_source: str, optional\n", + " Noise generation source passed to `render_chain_and_masks`.\n", + " Defaults to \"none\".\n", + " noise_asset: dict | np.ndarray | None, optional\n", + " Noise asset passed to `render_chain_and_masks`. Defaults to `None`.\n", + " add_decoy: bool, optional\n", + " Whether to add a corner-placed decoy fragment. Defaults to `False`.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, Any]\n", + " Dictionary containing the rendered sample, including AFM image,\n", + " DNA mask, crossing mask, physical extent, crossing count, optional\n", + " decoy coordinates, and the bead count under ``\"n_beads\"``.\n", + "\n", + " Notes\n", + " -----\n", + " This function prints which noise model is being used for the noise\n", + " injection and if no noise model has been provided then\n", + " it prints a statement that reflects that noise injection has been disabled.\n", + "\n", + " Examples\n", + " --------\n", + " >>> sample = generate_one_sample_multilength(seed=1, n_beads=300)\n", + " >>> sample[\"n_beads\"]\n", + " 300\n", + "\n", + " >>> sample[\"afm_img\"].shape\n", + " (NY, NX)\n", + "\n", + " \"\"\"\n", + "\n", + " chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n", + "\n", + " s = render_chain_and_masks(\n", + " chain,\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " )\n", + " s[\"n_beads\"] = int(n_beads)\n", + " return s\n", + "\n", + "\n", + "# ── Load noise asset once; prefer blank.spm, fall back to PSD model ──────────\n", + "noise_source = \"none\"\n", + "noise_asset = None\n", + "noise_diag = {\"source\": \"none\"}\n", + "\n", + "if USE_BLANK_SPM_NOISE:\n", + " blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n", + " if blank_path and os.path.isfile(blank_path):\n", + " try:\n", + " noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n", + " blank_path,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " verbose=True,\n", + " )\n", + " noise_source = \"blank_spm\"\n", + " print(f\"Loaded blank .spm noise texture\"\n", + " f\" RMS ≈ {noise_rms_nm:.3f} nm\")\n", + " except Exception as e:\n", + " print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n", + " else:\n", + " print(\"No blank .spm file found; trying PSD fallback noise model.\")\n", + "\n", + "if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n", + " try:\n", + " noise_asset = load_model(MODEL_PATH)\n", + " noise_source = \"psd_model\"\n", + " noise_diag = {\n", + " \"source\": \"psd_model\",\n", + " \"model_path\": str(MODEL_PATH),\n", + " \"method\": PSD_NOISE_METHOD,\n", + " \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n", + " }\n", + " print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n", + " except Exception as e:\n", + " print(\"PSD fallback noise model failed;\"\n", + " \"proceeding without noise. Error:\", e)\n", + " noise_asset = None\n", + "\n", + "if noise_source == \"none\":\n", + " print(\"Noise injection disabled.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_11", + "metadata": { + "id": "cell_md_11" + }, + "source": [ + "## 12. Sanity Check\n", + "\n", + "It is always a good idea to check if the pipeline is working correctly and producing the inteded resutls and so we have added a sanity check that can be performed before starting the generation of the entire dataset.\n", + "Here we render one sample per bead count for the bead counts provided in the global variables and by default we have set `add_decoy=True` here.\n", + "\n", + "This check displays the AFM image, DNA mask, and crossing mask side-by-side.\n", + "We also print the extent / decoy-placement diagnostics for verification and adjustment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_11", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "cell_code_11", + "outputId": "64d10490-da39-480e-c877-91bf8c6b4f66" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "==================== SAMPLE 1 (Seed: 1, 70 beads) ====================\n", + "Image window (nm): X=[-23.3, 21.1], Y=[-17.7, 17.8]\n", + "Decoy XY bounds (nm): X=[12.8, 13.4], Y=[9.4, 10.2]\n", + "Decoy Z range (nm): -0.03 – 0.03\n", + "Decoy inside extent.\n", + " Decoy height ≈ 0\n", + "\n", + "==================== SAMPLE 2 (Seed: 2, 80 beads) ====================\n", + "Image window (nm): X=[-20.5, 19.6], Y=[-24.6, 23.0]\n", + "Decoy XY bounds (nm): X=[11.4, 11.9], Y=[-17.6, -15.7]\n", + "Decoy Z range (nm): -0.26 – 0.15\n", + "Decoy inside extent.\n", + "\n", + "==================== SAMPLE 3 (Seed: 3, 90 beads) ====================\n", + "Image window (nm): X=[-20.6, 16.8], Y=[-19.6, 21.7]\n", + "Decoy XY bounds (nm): X=[8.5, 9.1], Y=[-13.1, -10.1]\n", + "Decoy Z range (nm): -0.39 – 0.44\n", + "Decoy inside extent.\n", + "\n", + "==================== SAMPLE 4 (Seed: 4, 100 beads) ====================\n", + "Image window (nm): X=[-23.0, 23.8], Y=[-22.8, 23.7]\n", + "Decoy XY bounds (nm): X=[-15.2, -14.9], Y=[-15.3, -14.3]\n", + "Decoy Z range (nm): -0.24 – 0.24\n", + "Decoy inside extent.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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y2k1RxihHHHEEF110Edddd51G8yiKovgsXbqUo446ilmzZvHYY48xffr0/s9OO+003nzzTR588MHI7+dyOXp6eja4HmEL3Mrw0dvby5IlS/jKV76ywfd92WWX0d7ezjPPPMNmm20GwM4778x+++3HLbfckjdx5bDDDuOMM87g7rvv5otf/OIGb6uiDCcbd84dRRkkS5cu5YUXXuATn/jEgM9+/etfs+OOO1JbW0tdXR0f/vCHWbx4cd46TU1NnH322Wy66aaUl5czb948rrzyygG1OXK5HNdccw3z58+noqKCqVOncsopp9DY2Ji3nud5XHrppcycOZOqqir23ntvXn755djHs+mmm454Cq3u7m7uueceFi5cyMyZM/uX33///fT29nLqqaf2L0skEnz1q19l+fLl/P3vf4+1/bfffpsDDjiA6upqZsyYwSWXXILneXnrxO3P+++/n0WLFjFjxgzKy8uZO3cu3/3ud8lmswP2e+ONNzJ37lwqKyvZeeedeeqpp0Lb95Of/IT58+dTVVXF+PHj2Wmnnbjjjjvy1tlxxx2ZMGEC999/f6xjVhRFUeKzzz778O1vf5tly5Zx++23D/j8wgsvZPXq1Vx//fWxtvfuu+/y1FNPcdRRR3HUUUexdOlS/va3v8X6rqS8ev311znmmGOor69n8uTJfPvb38bzPN577z0OPfRQ6urqmDZtGj/4wQ/yvt/T08OFF17IjjvuSH19PdXV1ey55548/vjjA/YV5+8Sl8bGRnbeeWdmzpzJa6+9FrleV1cXDz/8cOjfQ93d3ZxzzjlMnjyZ2tpaDjnkEJYvXx66nRUrVvDFL36RqVOnUl5ezvz58/nlL38Zur+LL76YLbfckoqKCqZPn85nP/tZ3nrrrf512tvb+drXvtb/N9aHPvQhvv/97+f9TbBw4UI++tGPhrblQx/6EAcccEDkMQsPPfQQe+65J9XV1dTW1rJo0aK8v70ee+wxkskkF154Yd737rjjDhKJRN44i3NcLieccAKzZ88esFzGls1InIvly5fz6U9/murqaqZMmcI555wTWRdsv/32o729nUceeSTyeBRFUT5oXHXVVbS1tXHTTTflGRrCvHnzOOuss/rfJxIJTj/9dJYsWcL8+fMpLy/n4YcfBuC5557jU5/6FHV1ddTU1LDvvvvy//7f/8vbXm9vL9/5znfYYostqKioYOLEieyxxx559+ZVq1Zx4oknMnPmTMrLy5k+fTqHHnoo77zzTv86bk0Nmbh411138b3vfY+ZM2dSUVHBvvvuy5tvvjnguH76058yZ86cvP+f49bpkD64++672WabbaisrOTjH/84L774IgA33HAD8+bNo6Kigr322iuv3QBPPfUUn/vc59hss80oLy9n00035ZxzzhmQ5itOP4Txq1/9inQ6zbnnnlv0WFz+8pe/sG7dugF/U5Xav4Phnnvu4aCDDuo3NAA+8YlPsOWWW3LXXXflrTtlyhQ+8pGPqGah/EegkRrKBxIRLXbYYYe85Y888gif//zn2XfffbnyyisBk5vwr3/9a/8fJB0dHSxcuJAVK1ZwyimnsNlmm/G3v/2Nb37zm6xcuZJrrrmmf3unnHIKt9xyCyeeeCJnnnkmS5cu5dprr+W5557jr3/9K5lMBjBCzKWXXsqBBx7IgQceyLPPPsv+++9PT0/PBuiNePz+97+nqamJL3zhC3nLn3vuOaqrq9l6663zlu+88879n++xxx4Ft53NZvnkJz/JrrvuylVXXcXDDz/MRRddRF9fH5dcckn/enH785ZbbqGmpob/+q//oqamhscee4wLL7yQlpYWrr766v7t3XTTTZxyyinstttunH322bz99tsccsghTJgwgU033bR/vZ///OeceeaZHH744Zx11ll0dXXxwgsv8I9//IOjjz4671h22GEH/vrXv5bQs4qiKEpcjj32WC644AL++Mc/cvLJJ+d9tueee7LPPvtw1VVX8dWvfrVotMadd95JdXU1Bx10EJWVlcydO5clS5aw2267xW7PkUceydZbb80VV1zBgw8+yKWXXsqECRO44YYb2GeffbjyyitZsmQJX//61/nYxz7GggULAFPX6xe/+AWf//znOfnkk2ltbeWmm27igAMO4J///CfbbbcdEO/vEpd169ax33770dDQwJNPPsncuXMj2//MM8/Q09Mz4O8hMFGXt99+O0cffTS77bYbjz32GIsWLRqw3urVq9l11137hYrJkyfz0EMPcdJJJ9HS0sLZZ58NmN/6gw46iD/96U8cddRRnHXWWbS2tvLII4/w0ksvMXfuXDzP45BDDuHxxx/npJNOYrvttuMPf/gD5557LitWrOBHP/oRYMbBySefzEsvvZQXofqvf/2L119/nf/+7/8ueN5uu+02jj/+eA444ACuvPJKOjo6uP7669ljjz147rnnmD17Nvvssw+nnnoql19+OZ/+9KfZYYcdWLlyJWeccQaf+MQn+mdixjmuoTLc56Kzs5N9992Xd999lzPPPJMZM2Zw22238dhjj4XuX4Snv/71r3zmM58Z8vEoiqL8J/C///u/zJkzp6S/Gx577DHuuusuTj/9dCZNmsTs2bN5+eWX2XPPPamrq+O8884jk8lwww03sNdee/Hkk0+yyy67AMb0vvzyy/nSl77EzjvvTEtLC08//TTPPvtsf83Lww47jJdffpkzzjiD2bNns2bNGh555BHefffdUCPd5oorriCZTPL1r3+d5uZmrrrqKr7whS/wj3/8o3+d66+/ntNPP50999yTc845h3feeYdPf/rTjB8/Pm/iYyGeeuopHnjgAU477TQALr/8cg466CDOO+88rrvuOk499VQaGxu56qqr+OIXv5j323T33XfT0dHBV7/6VSZOnMg///lPfvKTn7B8+XLuvvvu/vUG0w833ngjX/nKV7jgggsGFbn7t7/9jUQiwfbbbx/6eZz+7ejoCK0P55JKpRg/fjxgJjOsWbMmL8JZ2Hnnnfn9738/YPmOO+7IfffdF/PIFGUM4ynKB5D//u//9gCvtbU1b/lZZ53l1dXVeX19fZHf/e53v+tVV1d7r7/+et7yb3zjG14qlfLeffddz/M876mnnvIAb8mSJXnrPfzww3nL16xZ45WVlXmLFi3ycrlc/3oXXHCBB3jHH398Scc2f/58b+HChSV9Jw6HHXaYV15e7jU2NuYtX7RokTdnzpwB67e3t3uA941vfKPgdo8//ngP8M4444z+Zblczlu0aJFXVlbmrV271vO8+P3peZ7X0dExYD+nnHKKV1VV5XV1dXme53k9PT3elClTvO22287r7u7uX+/GG2/0gLw+PPTQQ7358+cXPA7hy1/+sldZWRlrXUVRFCWfm2++2QO8f/3rX5Hr1NfXe9tvv33/+4suusgDvLVr13pPPvmkB3g//OEP+z+fNWuWt2jRogHb+fCHP+x94Qtf6H9/wQUXeJMmTfJ6e3uLtlP2+eUvf7l/WV9fnzdz5kwvkUh4V1xxRf/yxsZGr7KyMu/3vK+vL++3R9abOnWq98UvfrF/WZy/S+w+W7lypTd//nxvzpw53jvvvFP0OH7xi194gPfiiy/mLX/++ec9wDv11FPzlh999NEe4F100UX9y0466SRv+vTp3rp16/LWPeqoo7z6+vr+3+Rf/vKXA86NIH//3HfffR7gXXrppXmfH3744V4ikfDefPNNz/M8r6mpyauoqPDOP//8vPXOPPNMr7q62mtra4s85tbWVm/cuHHeySefnLd81apVXn19fd7y9vZ2b968ed78+fO9rq4ub9GiRV5dXZ23bNmy/nXiHJfneQP67fjjj/dmzZo14DsytoSROBfXXHONB3h33XXXgGMFvMcff3xAu7bcckvvU5/61IDliqIoH0Sam5s9wDv00ENjfwfwksmk9/LLL+ct//SnP+2VlZV5b731Vv+y999/36utrfUWLFjQv+yjH/1o6N8zQmNjowd4V199dcF2LFy4MO9/3ccff9wDvK233jrvb5PFixfn/Y3Q3d3tTZw40fvYxz6W97fSLbfcMuD/5ygAr7y83Fu6dGn/shtuuMEDvGnTpnktLS39y7/5zW96QN66Yf/nX3755V4ikej/bY7bD/bfh4sXL/YSiYT33e9+t+gxRHHMMcd4EydOHLA8bv96XvA3QLGH/ffDv/71Lw/wbr311gH7Pvfccz2gXwMRLrvsMg/wVq9ePejjVZSxgKafUj6QrF+/nnQ6PSAv8Lhx44qG1999993sueeejB8/nnXr1vU/PvGJT5DNZvnzn//cv159fT377bdf3no77rgjNTU1/SkmHn30UXp6egYUl5bZdGOBlpYWHnzwQQ488EDGjRuX91lnZyfl5eUDviO5Qd1Q0ChOP/30/tcyw7Cnp4dHH30UiN+fQN7s3NbWVtatW8eee+5JR0cHr776KmCKnq9Zs4avfOUreXlFTzjhBOrr6/PaNm7cOJYvX86//vWvoscxfvx4Ojs7Y82wUBRFUUqnpqaG1tbW0M8WLFjA3nvvzVVXXVXw9+eFF17gxRdfzKsR9fnPf55169bxhz/8IXZbvvSlL/W/TqVS7LTTTniex0knndS/fNy4cXzoQx/i7bffzltXfntyuRwNDQ309fWx00478eyzz+Z9N27an+XLl7Nw4UJ6e3v585//zKxZs4p+Z/369QD9s/0EmdV35pln5i13/zbxPI977rmHgw8+GM/z8n6fDzjgAJqbm/uP55577mHSpEmcccYZA9ohf//8/ve/J5VKDdjv1772NTzP46GHHgKgvr6eQw89lDvvvLM/LVU2m+U3v/lNf0qlKB555BGampr6z7c8UqkUu+yyS97fE1VVVdxyyy288sorLFiwgAcffJAf/ehHeekd4hzXUBiJc/H73/+e6dOnc/jhh+cda6Fi8fJ3r6IoimL+PwZKLrS8cOFCttlmm/732WyWP/7xj3z6059mzpw5/cunT5/O0UcfzV/+8pf+fY0bN46XX36ZN954I3TblZWVlJWV8cQTTwxIzxyHE088Me//4j333BOg/++Xp59+mvXr13PyySeTTgdJX77whS8M+DuiEPvuu29etIREohx22GF5/SnL7b+f7P/z29vbWbduHbvtthue5/Hcc8/1r1NKP1x11VWcddZZXHnllUUjPQuxfv36gv1QrH8BjjvuOB555JGijyVLlvR/R/7eLUWTkXbq77qysaPppxTF4tRTT+Wuu+7iU5/6FJtssgn7778/RxxxBJ/85Cf713njjTd44YUXmDx5cug21qxZ079ec3MzU6ZMKbjesmXLANhiiy3yPp88eXJJfxyMJPfccw9dXV0DUk+B+aMhLAdzV1dX/+fFSCaTeX/EAWy55ZYA/Xkv4/YnwMsvv8x///d/89hjj/X/ESg0NzcD0f2eyWQGtOX888/n0UcfZeedd2bevHnsv//+HH300ey+++4D2iHiynAIGYqiKMpA2traIn8LwKRnWLhwIT/72c8455xzQte5/fbbqa6uZs6cOf35jCsqKpg9ezZLliwJTe0Thi1ugxHbKyoqmDRp0oDlYiAIv/rVr/jBD37Aq6++Sm9vb//yzTffvP91nL9LhGOPPZZ0Os0rr7zCtGnTYrVf8JwaVsuWLSOZTA5InfShD30o7/3atWtpamrixhtv5MYbbwzdtvw+v/XWW3zoQx/KE0Jcli1bxowZMwaIRJLiUn67wfzj/5vf/IannnqKBQsW8Oijj7J69WqOPfbYgscqYtA+++wT+nldXV3e+913352vfvWr/PSnP+WAAw4YUFQzznENhZE4F8uWLWPevHkD/lZxt2njeZ7+baMoiuIjvxVRkyyisH/jwdy7Ozo6Qu+/W2+9Nblcjvfee4/58+dzySWXcOihh7Lllluy7bbb8slPfpJjjz2Wj3zkI4ARta+88kq+9rWvMXXqVHbddVcOOuggjjvuuFh/F7h/04gWIcaA/AbPmzcvb710Ol00tVWh/ciEQjv9s73cNibeffddLrzwQh544IEBhoX8n19KPzz55JM8+OCDnH/++YOqo+Hi/j1lU6x/AebMmTNAiyiG6C2laDKqWSj/KaipoXwgmThxIn19fbS2tub94zxlyhSef/55/vCHP/DQQw/x0EMPcfPNN3Pcccfxq1/9CjAzKvfbbz/OO++80G2LGJ/L5ZgyZUqei24TZYqMRZYsWUJ9fT0HHXTQgM+mT5/O448/PuCf3ZUrVwIwY8aMYWlD3P5sampi4cKF1NXVcckllzB37lwqKip49tlnOf/88wcUc4/D1ltvzWuvvcbvfvc7Hn74Ye655x6uu+46LrzwQr7zne/krdvY2EhVVVUsM0dRFEUpjeXLl9Pc3DzgH2qbBQsWsNdee3HVVVf11z2w8TyPO++8k/b29rzZksKaNWtoa2sbEM0ZRiqVirVM9ivcfvvtnHDCCXz605/m3HPPZcqUKaRSKS6//PK84tJx/i4RPvvZz3LrrbeyePFiLr/88qJtB/P3EJjfrri5sG3kN/WYY47h+OOPD11HxJbh5oADDmDq1KncfvvtLFiwgNtvv51p06aFFj23kTbfdtttoSKPa050d3fzxBNPAMbA6OjooKqqasjtjxISstnsoLY30ueisbFxwEQQRVGUDyp1dXXMmDGDl156qaTvDeV/xAULFvDWW29x//3388c//pFf/OIX/OhHP+JnP/tZf+To2WefzcEHH8x9993HH/7wB7797W9z+eWX89hjj0XWehDi/P0yHETtp9j+s9lsf82w888/n6222orq6mpWrFjBCSeckPd/ftx+mD9/Pk1NTdx2222ccsopA0ynUpg4cWLByJA4/dvW1kZbW1vRfaVSqX79Q4rUi/5is3LlSiZMmDAgikPa6U7CUZSNDTU1lA8kW221FQBLly4d8A9eWVkZBx98MAcffDC5XI5TTz2VG264gW9/+9vMmzePuXPn0tbWVvSf5rlz5/Loo4+y++67F/zjRdJDvPHGG3mu/Nq1awcVNjrcrFy5kscff5wTTjghNKRxu+224xe/+AWvvPJKnjgkBa+k2Gkhcrkcb7/9dr8hBPD6668D9M/6iNufTzzxBOvXr+fee+/tL8gK5lzb2P1uz9bs7e1l6dKlfPSjH81bv7q6miOPPJIjjzySnp4ePvvZz/K9732Pb37zm/1hnbIft2i6oiiKMjzcdtttgBG0C3HxxRez1157ccMNNwz47Mknn2T58uVccsklA+7XjY2NfPnLX+a+++7jmGOOGb6GO/z2t79lzpw53HvvvXni9kUXXTRg3WJ/lwhnnHEG8+bN48ILL6S+vp5vfOMbRdth/z304Q9/uH/5rFmzyOVy/VEIwmuvvZb3/cmTJ1NbW0s2m431d9E//vEPent7yWQyoevMmjWLRx99dMCkE0kdaafUSqVSHH300dxyyy1ceeWV3HfffZx88smRooHdDjCGUbE2gzknr7zyCt///vc5//zz+cY3vsGPf/zjko4rjPHjx9PU1DRguR2NAiNzLmbNmsVLL700YEKKu02hr6+P9957j0MOOaTYYSmKonxgOOigg7jxxhv5+9//zsc//vFBbWPy5MlUVVWF3n9fffVVkslkXgTDhAkTOPHEEznxxBNpa2tjwYIFXHzxxXnpMOfOncvXvvY1vva1r/HGG2+w3Xbb8YMf/IDbb799UG0U5Df4zTffZO+99+5f3tfXxzvvvDNikxiEF198kddff51f/epXHHfccf3Lo1J0xumHSZMm8dvf/pY99tiDfffdl7/85S+DnpS51VZbsWTJEpqbmweks47L97///QGTJsOYNWtWf0aLTTbZhMmTJ/P0008PWO+f//xnqB6zdOlSJk2atFFNtFWUMLSmhvKBRP7ocG/8bmqIZDLZ/+Ms4XxHHHEEf//730Nzbjc1NdHX19e/Xjab5bvf/e6A9fr6+vr/kf3EJz5BJpPhJz/5SZ5Lf8011wzu4IaZX//61+RyudDUUwCHHnoomUyG6667rn+Z53n87Gc/Y5NNNmG33XaLtZ9rr7027/vXXnstmUyGfffdF4jfnyJm2H3Z09OT1z6AnXbaicmTJ/Ozn/2Mnp6e/uW33HLLAJHBHRdlZWVss802eJ6XlzIE4Nlnn419zIqiKEp8HnvsMb773e+y+eabR/4mCQsXLmSvvfbiyiuv7A+9FyT11Lnnnsvhhx+e9zj55JPZYostIqMCh4uw36p//OMf/P3vf89bL87fJTbf/va3+frXv843v/lNrr/++qLt2HHHHSkrKxvw99CnPvUpgDzxHgb+bZJKpTjssMO45557Qmerrl27tv/1YYcdxrp16/J+7wXphwMPPJBsNjtgnR/96EckEon+dgnHHnssjY2NnHLKKbS1tcUyog444ADq6uq47LLLBvyGu23+xz/+wfe//33OPvtsvva1r3Huuedy7bXX8uSTT5Z0XGHMnTuX5uZmXnjhhf5lK1eu5H/+53/y1huJc3HggQfy/vvv89vf/rZ/WUdHR2Taqn//+990dXXp3zeKoigW5513HtXV1XzpS19i9erVAz5/6623WLx4ccFtpFIp9t9/f+6///5+kRpg9erV3HHHHeyxxx79qa7cvwlqamqYN29e/98DHR0dA/7mmTt3LrW1taF/M5TKTjvtxMSJE/n5z3/er3mAyeqwISZjhv3t5HnegD4utR9mzpzJo48+SmdnJ/vtt9+Afo7Lxz/+cTzP45lnnhnU92FwNTXA/C3yu9/9jvfee69/2Z/+9Cdef/11Pve5zw3YzzPPPDNoI05RxhIaqaF8IJkzZw7bbrstjz76aF5u5C996Us0NDSwzz77MHPmTJYtW8ZPfvITtttuu/7ZnOeeey4PPPAABx10ECeccAI77rgj7e3tvPjii/z2t7/lnXfeYdKkSSxcuJBTTjmFyy+/nOeff57999+fTCbDG2+8wd13383ixYs5/PDDmTx5Ml//+te5/PLLOeiggzjwwAN57rnneOihh2KHA/75z3/uL1C+du1a2tvbufTSSwETpmpHLCQSCRYuXNifSqEYS5YsYcaMGey1116hn8+cOZOzzz6bq6++mt7eXj72sY9x33338dRTT7FkyZKiMybB5DF/+OGHOf7449lll1146KGHePDBB7ngggv6Zw/E7c/ddtuN8ePHc/zxx3PmmWeSSCS47bbbBggLmUyGSy+9lFNOOYV99tmHI488kqVLl3LzzTcPyGO5//77M23aNHbffXemTp3KK6+8wrXXXsuiRYvyZpI+88wzNDQ0cOihh8bqW0VRFCWchx56iFdffZW+vj5Wr17NY489xiOPPMKsWbN44IEH8iLkorjooovyZhKCMQLuuece9ttvv8htHHLIISxevJg1a9YUrN0xFA466CDuvfdePvOZz7Bo0SKWLl3Kz372M7bZZpu8tANx/i5xufrqq2lubua0006jtra2oNBfUVHB/vvvz6OPPsoll1zSv3y77bbj85//PNdddx3Nzc3stttu/OlPf+qvP2JzxRVX8Pjjj7PLLrtw8skns80229DQ0MCzzz7Lo48+SkNDA2D+Ub/11lv5r//6L/75z3+y55570t7ezqOPPsqpp57KoYceysEHH8zee+/Nt771Ld555x0++tGP8sc//pH777+fs88+e0Bdie23355tt92Wu+++m6233poddtihaN/X1dVx/fXXc+yxx7LDDjtw1FFHMXnyZN59910efPBBdt99d6699lq6uro4/vjj2WKLLfje974HwHe+8x3+93//lxNPPJEXX3yR6urqWMcVxlFHHcX555/PZz7zGc4880w6Ojq4/vrr2XLLLfOKxY/EuTj55JO59tprOe6443jmmWeYPn06t912W2RarUceeYSqqir222+/ov2rKIryQWHu3LnccccdHHnkkWy99dYcd9xxbLvttvT09PC3v/2Nu+++mxNOOKHodi699FIeeeQR9thjD0499VTS6TQ33HAD3d3dXHXVVf3rbbPNNuy1117suOOOTJgwgaeffprf/va3nH766YDJdLDvvvtyxBFHsM0225BOp/mf//kfVq9ezVFHHTXk4y0rK+Piiy/mjDPOYJ999uGII47gnXfe4ZZbbmHu3LkjXp9hq622Yu7cuXz9619nxYoV1NXVcc899wwwVAbTD/PmzeOPf/wje+21FwcccACPPfbYgBpbxdhjjz2YOHEijz76aGTdrmIMpqYGwAUXXMDdd9/N3nvvzVlnnUVbWxtXX301H/7whznxxBPz1l2zZg0vvPACp5122qDaqChjCk9RPqD88Ic/9GpqaryOjo7+Zb/97W+9/fff35syZYpXVlbmbbbZZt4pp5zirVy5Mu+7ra2t3je/+U1v3rx5XllZmTdp0iRvt912877//e97PT09eeveeOON3o477uhVVlZ6tbW13oc//GHvvPPO895///3+dbLZrPed73zHmz59uldZWenttdde3ksvveTNmjXLO/7444sey0UXXeQBoY+LLroor92Ad9RRR8Xqo1dffdUDvP/6r/8quF42m/Uuu+wyb9asWV5ZWZk3f/587/bbb4+1j+OPP96rrq723nrrLW///ff3qqqqvKlTp3oXXXSRl81mB6wfpz//+te/ervuuqtXWVnpzZgxwzvvvPO8P/zhDx7gPf7443nbu+6667zNN9/cKy8v93baaSfvz3/+s7dw4UJv4cKF/evccMMN3oIFC7yJEyd65eXl3ty5c71zzz3Xa25uztvW+eef72222WZeLpeLdeyKoihKPjfffHPeb1hZWZk3bdo0b7/99vMWL17stbS0DPiO/AauXbt2wGcLFy70AG/RokWe53nePffc4wHeTTfdFNmGJ554wgO8xYsXR64TtU/5TQtrx/z58/vf53K5/t/N8vJyb/vtt/d+97vfeccff7w3a9as/vXi/F0iffavf/2rf1k2m/U+//nPe+l02rvvvvsij8PzPO/ee+/1EomE9+677+Yt7+zs9M4880xv4sSJXnV1tXfwwQd777333oC/LTzP81avXu2ddtpp3qabbuplMhlv2rRp3r777uvdeOONeet1dHR43/rWt7zNN9+8f73DDz/ce+utt/rXaW1t9c455xxvxowZXiaT8bbYYgvv6quvjvxtveqqqzzAu+yyywoep8vjjz/uHXDAAV59fb1XUVHhzZ071zvhhBO8p59+2vM8zzvnnHO8VCrl/eMf/8j73tNPP+2l02nvq1/9aknHFdZvf/zjH71tt93WKysr8z70oQ95t99+e//YshmJc7Fs2TLvkEMO8aqqqrxJkyZ5Z511lvfwww+H/q20yy67eMccc0wp3asoivKB4fXXX/dOPvlkb/bs2V5ZWZlXW1vr7b777t5PfvITr6urq389wDvttNNCt/Hss896BxxwgFdTU+NVVVV5e++9t/e3v/0tb51LL73U23nnnb1x48Z5lZWV3lZbbeV973vf69cf1q1b55122mneVltt5VVXV3v19fXeLrvs4t11111523H/13388cc9wLv77rvz1lu6dKkHeDfffHPe8h//+Mf9f7/svPPO3l//+ldvxx139D75yU8W7auwPpD9XH311XnLw9r173//2/vEJz7h1dTUeJMmTfJOPvlk7//+7//y2hm3H2bNmtX/96Hwj3/8w6utrfUWLFiQpxPF5cwzz/TmzZtX9Djs43b7d7C89NJL/XrKuHHjvC984QveqlWrBqx3/fXXe1VVVaF/UyvKxkbC84a56o+ibCQ0NzczZ84crrrqKk466aTRbs4G4fe//z0HHXQQ//d//5eXO1sZOt3d3cyePZtvfOMbnHXWWaPdHEVRFEWJRTabZZtttuGII44ITfE41lm8eDHnnHMO77zzDpttttloN+c/jueff54ddtiBZ599NladNEVRFOWDRS6XY/LkyXz2s5/l5z//+Wg3Z1R5++232WqrrXjooYf602iPNbbffnv22msvfvSjH412UxRlyGhNDeUDS319Peeddx5XX301uVxutJuzQXj88cc56qij1NAYAW6++WYymQxf+cpXRrspiqIoihKbVCrFJZdcwk9/+tO81FcbA57ncdNNN7Fw4UI1NEaIK664gsMPP1wNDUVRFIWurq4BaZ1vvfVWGhoaItNVf5CYM2cOJ510EldcccVoNyWUhx9+mDfeeINvfvObo90URRkWNFJDURRFURRFUZSNhvb2dh544AEef/xxfv7zn3P//fdzyCGHjHazFEVRFOU/mieeeIJzzjmHz33uc0ycOJFnn32Wm266ia233ppnnnmGsrKy0W6ioigfILRQuKIoiqIoiqIoGw1r167l6KOPZty4cVxwwQVqaCiKoijKBmD27Nlsuumm/PjHP6ahoYEJEyZw3HHHccUVV6ihoSjKBkcjNRRFURRFURRFURRFURRFURRF2SjQmhqKoiiKoiiKoiiKoiiKoiiKomwUqKmhKIqiKIqiKIqiKIqiKIqiKMpGgZoaiqIoiqIoiqIoiqIoiqIoiqJsFMQuFJ5IJEayHQCkAM96hFFN4MR4QMJ/n/AfOK/lvWd9R8g5r+395qz1Pee17NfdXhRJIANUYTq8D+gE2p022KQx/WEfT9yH0O0/+mK2s1SqgOlAPdAMrMIck00Scyw5vx2lEtXPSaAMqPMfXcB6TL/G2WY1MM7f7jpMP8VtTxIo9x9dQA/BeUyEPBc6T0ln/WTI90YC18203+eAXv+5k+DY7OvMfh12XFFtt8e7e527nyWASmASMBlzPXRgxlqr34ZqYKr/eSfwFmYcZK1tSVs85zlqn9IXXf423bE3CdgC2MTfv30s7cB7wJt+O6Ou71Kwx0ahfo8aK2lgIrBdArbOQE8C3uiFN3OmjR6mb+V+0wY0Ysa17D/l7EeON0d+X0e1vwKoxVyzcgzu8bn3Wem7XMjrsGVekYd9T5d7wCaYe9g4/32F38YO4F3gFcz9IWu1LwlMwIyDNOacN/nfce8j5cAUzPjs8NeV/kpgfheymDHbwcgjfS/9FUYG0w8Q9JVcF42DLMG1If5+UBRFURRlbDKUEp6JRGy5QlEURVGU/zA8r7CSHPuvhHKMwNHH8Ah1LlUY4SeLEX66/H25gnwvRlzxCARzyBcqIRBtXCHOxV4Wto4rlkUJcPY27PaIGJS2PkthxFoxOGQbvQTCvyuOl4Lss97fVy9GMOtgYH+KYFnmt6cHI8zF+dOzC1gBvO+03SVJIJjGNTbEOJA+6iI451UYUXEyRpCswQiPrwMr/bYUQsTnbn977vrSfyIApgiMmZy/TMTZQiZc1JhzP3e/bwvMsp7NYMOrogwX93UKYxRlgRaMsN9HuOES9Yg6btlGjvx95kI+ywDjgU0xIusKzHnuxZzzMn95ub9ePUYcbiUYL+4x2s/SHvtekSY411HH0IAx8Gr89x6mr3KY6yeJEfC7rHYMBffeJkQZGu77JKafpqRhq1lQWwubvAuV6+E1z5gYXRiDQ64L1+xx70ulHJOH6ZdOgvMaNlZcA9ndj3tOil1f9v7d1x7mfvi+364JmPFT7T9PBj6MGVevYu4rHQQG5nrMtSHnPuwekMEYH/OBLf31Gwjuld3+6zaCayzhPxczigZD2j+uDMHvgYxZ+3xnCQwtuXfH7WtFURRFURRFURRFUZQNQWxTYxxG4GimtBmltvBbiAyBkJrGCJZdGMHHFnh6rNciSKas1yKMykxz/M9tIQ3rswT5M35tRMS2jQARut3jiTq+MoyBUY0RX6UtMiM4SyBwrcHM+BUBUbZZqrEhwqFEMSQx/bgOI/iKiC9iaRmBQFuKmJbDnKNi7SgnMAlEBCyGiGki7pf5360GZmDMjIl+u0XstGdTF0NMpLA2l2OMk5T18AhE6izBcdvjTLYrz0nnddizLYjbwnrK+r6IivYyaWtY+6M+c7/rCt/CVIyRACby4T3yr/mktb77HBaxIdjnxu4buz+wXucwQvByTB+9T9Dv6wnMpyr//RSCqAARhsNE8UJGgByb3EvE5CjzHynM+Jjgr7MWI0j3kn8td4bsf0MQFbHRA6zrg2WrobYZVnWYPmsgMIHESC7EYI8nS2BqyBgvtK04vxnF1rHvn2GGdI7AxGnAnFe5b26Cub8kgWn+cwuByZcl/7q1kXvqJIyZMQeYlIL6HEzwgqg26XuJhqrx29BBYJjEQfq02GSDtLWPMoJIMzE2hBxBxIncw917j6IoiqIoiqIoiqIoymgS29SQWe1xhRYR7yXCQ9L0RNFKkOZDZrAWm7Eq4osI37Zw3mu1VQQZO6ojClv8smfS2/sshSyBaSDikIjmkrpE0txIqiiZqe6maQlrY9RySXmVsbZX7m8/Ze1HUg21EszQHQ4h1p7h20s80c0mSxCVI6mmJN1Qhb+tZkyanA6M8L2OwaW4cpHx0kdgfMhYK6V/7GgEEcntZzeNDgSz2uX8Q9AHck3ZAmOUiREmbidDXoelodoU+Fg1dPdAZ6/pV0nrZW83ytCw2+C2yRWXw14LPZjro4GBZpj9Ws5LB+Y6qyI82kDGt1wbdjSMrCPXhed/XoWZuT/Jf1RjrttujCkgKddkXEg6ph6KRwwNhkJGZ9QyiUx604MGX5lvxhgy7QTXjERriTkz3IaMbDdL4XubYBvIUZFxpURqhK0rgr6MtQTmntKAidKoIbjeqgjMRTE2wn6jMv73JmOMkTTQljXrNgNLMenJ1hNExXkEKZ/iGsu2aSwReW76P5tuzLVcTnDNyH02irD7kaIoiqIoiqIoiqIoymgT29RoKnHDIsJGiacuOUykggiDYdsj4rOs82wjqZ5kpn9cxASRCIYO8usLxKUPEyUhKYzsGffd/me2KBqHqMgNd7mYCp0YMU3SnIhhNByipSt22TPeRZCUfbkCfjHsKBl59GJMIDE5xASzUyQlGHz6Fo98g2mouIaY3bZCKY5sEd8+lgyBySORA25kRNhryD9PEoFgGyxCOZDzoKM3P+2QLaiHmSmFlskxueu4Zoa8F7E1zpgR07QV0ye2mWQL2n3kC7T2ccj4cs+JRM1UAzMx0SDtmAiWFZh7ln3tRt2/hgMxxApFboUZS/KdNsx9oAlzP6skEO0l6kDSsdnp6oYL+54g58hGrnfbhLVrO2St12HRUVHvbTPNfnaRMSCCfyuBiSrf6fU/s80Me3t2FFMXZny0EdxX1mBSWTWH7F/6vBTsNHnFfsw9f7/2OQjbXtjyuBF2iqIoiqIoiqIoiqIoG4JBV95yZ2ynCdIMiUhfbOaoS5hoYqeEcUXyOG2cjplx24VJ6eHmDxc851GGSbk1DZMfvxEjRq2ncMqlMCRnvZtyyD4Guz+lL0XcDRPwosRi+3MxluQk9xCkGymEGxEgKXXs74mI5qZKcmdU20V8S0EExLBZxJ0EM5QT1nrSD3adhMEQR0iXMS/7siODXMJmlrvnXs6TpDGKwo4csY2yqNRSbnSGnDdJTSazttsITIQ3gaUd5r1EwrjbDdsXIa/tcekKpnYUi212uPU2iiHHlMUI0WJeuOdQzplbsyQsEsC+TmX2fi/gJWC9B29jojTse0GUWD4chEWyuCZTlMGUwdzDxhHUtpDIGzGP5J5qm2Yyk38ox2Sb2xI55kZfSFsl2kZqS5RhxmbG/9y9l4SZGe5nbmRH2PfCEHMjSzD+Zf+F+kTuW52Y8bGO/CihUk0L+9ymMX0h94lOgrRhcYkyMuxoqxTxCsAriqIoiqIoiqIoiqKMFoMyNVKY1BrjCWpDlPuPLPAKxgAYbuxZx4VEZCGNEfIqyU/xJAKrLeaIMC2iTwWBqTHef54AvEZQGLsUCs14FgOlimAGfhojNLcQ5LqPMi/cWfR2GhwptiwzhYuJammMAFrrt6fG32aD/+gmEL5ErJQ22UXVRQC0RfVCs6RLQQQ+SW0mM6eFkRDjbEFc6qTIDG6JEpDojjjmhlCJGVfj/NfNwGp/e2EzzmXMioEI+YJkWKSG/SjHRBtsCWw2ESrLoL0b1jRDa9Ycw3OYout22is3uiGu4WC3vdBntvgsx1NGYPKE9Z0I5XJeJCoqTMi2z59roOB8x10mRbTXAB0evIxJISR1B7DWH03CjA+5Vqsw9wG5dqoxfdWEuS+4kQZDnZlvp0sT8882psUsknElxoVdQ6kPc7+SGkRiRLnGU9j5dhnsuemjtGgVOTYxHOK0LYpxBCnP5De2DNNnKzFjsDPqy4PAvo/YdW0URVEURVEURVEURVHGGoMyNcowERAfwoguZf7yXszM1H8PS9PCEUMiQ3Fjow94hyClSpezvi3euOKTzIRtwgg7FRhxaSpGRG/BCG5DFTIrMH25ib99EVpFSOsiMBLCDAF3trNtKsjMYA8z09wtuh6GPRvYrosiJonbh/Y+7b4U7Fn3SWf5YJHZ0CJgD2eKnDCSGMNBjIwqgtoKfZhxksb0dRsDxe4425exNQVjJK3CnL8yTESFmEyScqoXUxOhmWBmuy3WQ74BITUBNgU+Xg87bAUzN4HycujuhqZGaGmDtg5oe9mYGrYxFZY+rpixIWPBNeKwlrvXo/3aNivCIlhy1nqEfB7WXnufhQwN97kF0yd9mH4v9RwPN2ERW2EmVhozbmXMyn2sBhNt0sDA63WoQrZtZrjROvJsL5M6Q2I8uYaxFOW2z1+hh7svrNcbynga6r4SGLNzK0zaM6nnkSVIITbchoOd3kyidorVtVIURVEURVEURVEURRkNBmVqdGOiFaoJ6hhIqqnVGKFsJBAxTGYfSzobycMetn5bke2FCTaSfiXnf9+eES4pjzz/vT2bNYURn+v9zyQFVw9GhAoT36VIdxNGrGomKEIr3xWBqZCIbPeNbFfyt/cSFF8Pw55VLTOBJX2PFFaWNFgpok0N6Qu7Le7MfvuzwYp+YmqMRCHmMOwxJmNDRNg+67PBCMKSZz9DUFi4yn9OYEzDTVIwuRIqKyGdgmwWVq6D/+eZcSOFl+1Z9nbqMTGmpgA7Z+Dj28P3n4LeZ8zn04FZQFUlZHNmPAqFajgUOn/2d8LWk/MfNQ5sw1FSocn1YBMWERRFlOBdTCSXcyzX5WiLvGGGho3cH8sx96MpwAzMzH+5r2UwRk0DA/sU8muuyDiS2htSc8KOzBJS1vekrW46MTfSx8Pc97oJN4vsbbnnjJjPox1FUyoe5re0liB6T8ZgA6aey0gaa3KOygju/SNtHiuKoiiKoiiKoiiKosRlUKZGDjNbuQOTjgkC8U8Er5HATa8iIm6YmGyLcSIK9ZCfIihMnMxgZsZuTiAwVyehLgmpBEzMwoRcIHD2YKIg+jAmz3jMLOgcRjRswYjEXRjTxxXXsgSFez2CPPY2UhQ7SpxzRUK7UG1HSN/Y3xOBXoRjSc8j51NmSMNA40SWQb44Lftz62wkCBcoS0Fmc4/UGItCCkxDUMDYw5y71ZQm/LnjthtjPiUxAnS1v70J+NEb46GuBtK+i9bdYxqQ6wyiGGS78lyNif7ZNgnTyqGnG9bk4P964S9PwBMEhuQ2mDFc0Wrastxpa5QBYc+aTxI+Ftxr1hXiC40DGWtipsl2bTPLTW9WiEJidyFTYziiF4YTOyWY27eSGq0GY65OwaTOm5iCyiQkE5DLQToLLZ4xE6RuhN1/kvIrQWB2ShokuTdI2rVOAmPJTu8VZn6GfZYjqA0RZU7bxlXYObO3ZSMmrFubaDSQyBk7lZvUMwmjHXgDY2CIoSRm93CmnYpCzpVEpCmKoiiKoiiKoiiKoowVBl0oPIcRtFqLrThMuIJY2GswYlEFxpAYjxGGJ2DEuFZMhInUqWgnmO0qM/CnAdsC88qhNgVJD1IpM0M+kTSz6bdJgZcAz4PuLDR0wfouWOOZQuLNBAaFFOW1xWGXsLztIhCW+ccjBorMjnbrKcjrDEGapHKC9D0wsKB2hb+uFAd2oyrC8vOHCYvybKcvESHYTTvlRnGUamzYM7835OxriXhIEBQz7mFwhePLMWaZFOiWtDJrMWN0E3/5Mkwqt4p1kFqXX/+lG2NEdBFEatjCbxIjak9OQHkGVvbA85gi4E3kn7d1BIZWh79dyC8AL/sNiw4IMztsY0zak6T0c2YL0TK+bcHdHY+FcAVw17wIWzbWZvmLqSfiuFwPYM5XDcYIk5pHE4Dxaagqh2QS+rLQ1RuYz1JfQwxpwTZ9swT3KIkcsCPm0gyMxnD7Oep6l/VzIZ9hfSZj3CbsvNn3LjFsE5h7fZRpsqFIYa5JqZkkESotBb7TQenFxQeDfe8Ii3xyDTRFURRFURRFURRFUZTRpCRTI0zU2FCCX5iRIc8iqoERjbbGzD6fXAGVaUj0QV8vtGVNOp82jKAnpobkna/DT9VSA7UV0N4GbX1mZnwWSCSgNgkTaqGiDLJ9kOuF8j7o8kyO+mXkRxGI6GiLj8VI+O2ROgoeRviSWbqugCwClESl1BAUBRbzQtKZvIcRzlMERd7tFENhKWIKzbS3hUoR1uW1jTubf7CCsS2UR1EoqmWwiJEgs9PFuJF6FtK2OEi0kPRVNUaELsec3ybyo3cg3HCyHyJEyrOHGd/LstDdYsbm2wR1SAQPk/6qhSDCqJv8yAhbsA4Tq+32pDHjSvLxy7UG4fePKKL6UsatW5A9LJIkbDtRM/vdZ3k92rP7XaSf7QgqWS7XfwWBaN4BJLLQ0QU9HrR60OwZA2015rx7BOmNwqLXJDpGzGAxCuyUbFEROGFRXRB+H5F6GmHnzE6x5u7DvkbEDK4iSOfWiymqLabwaJHF9KGkBRNTWUxSGHhON4SpFjVJwL6X2MXcFUVRFEVRFEVRFEVRRpvYpoYI4Ha+9C4K16wYLgoZGkIVRpTdHPgosMV4qKmGzk5Y3w4dXjDTuAprVvMkmDbVrJtJQ8KDpmZYugz+3Q2rPTNzXdI4VQO16yGT8GtNeEbkfhcjDpdS56FQmpZ2ghoCaYKaGK4oZ5slfQRiMphZ2psCM6pM6qx3uuC5rBH4xMxxxXFCXtvPbtulDbaIaUdpyCMsFVWKYBZ2oZnacZHZ2RLZIinChrI9QYTdBGb8ZJzPeog/E9wWyysxBlQSIzK3Wm2204HJfsKuA8i/JqSv2zGpaxKYcb8lJipjKfnGkNRO6SI4f/axFUPOZRnGVJyKMQh7MWm11jEwvdFgSWH6LIs5PhH3o4yssIginHWjojPGUoQGBGNQhH+pg5OyPmvFnNvVBJEKlR6ksub8tmCuC4nUyFrbqiAw3ArtX0RuWeaaFcXMpqiIDbmmwlLLiYFoXw+yHL89lZjxV48xNDbD3ANXAu8UOK4NhdwneglSIsp1A0G0oF3PREzOkRyLhX5f7c/U1FAURVEURVEURVEUZawQ29SoxpgAIqLZBcJHsmApxBddZJZ5D6aYcs5/iGguj34BPgllSRhXCxP80IbWVmhugTe74EXP1BdoJBCaZV9Jzwhm5RiR0F6nGAmC/uzECJFhYridbkrEx2J0EMwGzmDSm1T3QMqD1mxQG0IK/7qzrAsZHIWOxzZXoqI0RKzLEBSvzmDGUANGvCs1lZNNGmMQjMOMzxbM8Uux9TCSzoMCzzCwf2T8ZQjMAVle7HxJn3RizEGJxhHkGpP92vuzX4fNgPcw10ITwQx+u9B9WFtgYPHnMAFakKiMWv9Rh5kZPx3TH2v9/YvoHradUqM3yjGitUQhiMguBl3Yd6KeixkbYxHXuLJTgkltjC4Gjmd7/bAIFLknJAjMVLcPJAqiUMqvOP0Wtt2waA8XqSmRsrZj33NqCOrQVGLuq+sxhvPqiG3K8XhsONPDw9zvmgmuNzGVasiPtOnCnI+wekwj0a5ikwcURVEURVEURVEURVHGArFNjU5M6qYagvz/DYy8EGSLK3Y6DFdslZoTbfi1PjrBy5kUUZ5nPpM2y6zxTA4mroWlPTCuEvpy0NINy5vhVc/MaG8nfOa6CLXdBIVyCyG550XIHoeZ8Z8L2YeNGDVhaVkgSAsi27ZTD4lQ2O6fpNUEKWdcUyMqSqOYuWELwEnntQh2YmbUYwTvKUmoy0B1GaQT0NwNr3Sb+hGrI44ziiSBOVKGEdcljY1dgyFsnIaJqQnrM/vZPf6wlExiHIhobBtRYUJyDjNeJdop7LilSDbOOmGCo5gXUvNAUk31YcZoB8FMcRc71U1YNINbCLwCYyBNw9QAmUQgxuYw5/E1TKRIG/lpiqJSEhHxuTzsgtZZjOArr2UWv72NYuM1zMQYy4aGjRgbYX1lp7tLEESzuNezO35ss8K9ZuRacq+VQn1VrC/tKC6JBCwnMHPlnu6axWHmcQ5joHVjTAz7Guz22yGRBpK6LIUxl6sJDMANlZ7KNVGkpkk9xhxMYe7Taxma0VtqmyRSKxnyvDFcF4qiKIqiKIqiKIqifDCIbWpI6qYpGIF0HcNjaMjsX9skKESYCCrLejGzX5f5b8b56lUnRhx6GzNzt4NAGCv3YFwTVDQFQmkHQfqnKCFHCqWL+FdI8ElhhKoa/3hlxnEjhQ0NCGbouuukMaaIzJAvJxCg+qznZkyf2iK3m8IlTAAuZm7Ygr5EwkBgLojJIqKliGI9QKcHiSx0d0MyYSJI4hhDYcgMc4lAkIiZbvIjdKKwhXp5HybsuevJuvZyechYdusz9BJeC6IQItZLH9ppusQok89FFJb9Sz90YM59odRorqmRs5a7KankvFZiZsVvihFjE/7+3scYGm8QmBAiikf1pd3/dp/Y6clE+G231rXXc9scZqLAwPMStt//JMLMHft82/dQGb8SKZCzlrkmnttnYX0Yp08TmLE7HmNuyHjrxJjQLQSp9wpdy1nMOHejGpIEaamS/vakLo5tdIw2Ocz1OwnTLqnhM1Tk/Mk5DKstEjZhwL0PasSGoiiKoiiKoiiKoihjhdimhqT9qMeI6e8OcccZfzv2zPIOwk2NuGKjiFqvY4TVKi8QcVowApk7y7cbI3DF2Z89s1+Enji1IES0q/Lft/uPTvILRkcdk2xD6glUY9L8TMQYJXKMndZ2RfgVI6UKEx0ihWkld75dz8I2LnCWyWs5dtvIsLfRR1AHREQ0SVe2DmOyJDxIWCqiiKeDqauRxZw/u7ZLKduIk3IlTPiNWt+d+W4bENJ39hgPE/ZtpE5Fpf++B3P+7HNa7X8mUUNdmP7uIzBS3CLLEC7uy3HZr93jzvn7WI+5pmb47ZJz/BZBUXJBxrFdkycMuw/6rIdNsfPrGjNhKZM2xuiMuNjXqrwPS+8UNfZsAyrnfG5vE+e1++y+jmqn3D+kzlGlv+8u/3UtZszLfa2Q2Ry2T7lWJvrbKycwDGS7cevhjCRyniQ1YeMQtyd1RiQKJoW5ThsJP9awPrXvb/9J14iiKIqiKIqiKIqiKBs3JZka3QTic1gx11KQ2cBVBAVqO4t8xxWRbbFVhElJQdVQQluKRVnY0SR2PneZDd9L4ZoXEjHRizlmKcpsi7VRgqPnf2cCJtXPRP91rf95G7AKUwy3jaAfxVjwMMJ3LUYsS/j77cCIWxIt0uUfgwhgErXiztCVvrCFUhGeZYxIupcNSZQg52JHjYQJ+cXMDHs/Ual83HXcWeMiOIvQWkWQoss2juzvyzjsxIwlCGpYSPqc1Zhz2Wodn+xbIjns9vX52y/HiJ9igkSdO1u07sEYGFLjost/vIspyuwahRCkQbJFd3ds2WZYF4XvM2nMuBYzrVC6Ittg+k81NOSc2wZOWKSKEGXO2de2F+NByGv72X3tIqZzFpMCqgIzpsWAqMeYLDLuJNItLhIVl8Nca3K/CzPMRhO5f0qbhlqrSgzlcsxxe5j7fqFC8IVMDI3UUBRFURRFURRFURRlrBDb1AAjBr2PEaDWDXHHIoJmrfeFTI2wtBiuyOIK1UNBTJcyoovY2nUVeimc3keO1zZFhLCZ63aql2pMLYo5GHGqF5NOaz3mPKwjv8i0tFXScSX8z+VYIDA2JLLDNjUqCYrT2mmlKgjqd1T525IoCTs9zGiKxDLL3E6BJYKrfJbCCIZ2SqZCBkYhY8MmTMy1PxOzQmafb52Aj9XDpEp/XGShqxPa2qEnZ87JMuAl81H/8XT67e7AmHdJgtRMrlkmuOK2mGVVmLE1HngPM6ZKOX/rgGcxRlvCb08rQW0Nty0SNSJtSRGcE6mJMg4z1nqANRjTLuzamoS5JiRVTyfm/rSe8PQ6tkiP83pjJiyaJi5R49kVt4uNe9fkgPh9K7VSOvz3co3UYyIWwIypFgYXUeER3ONSFE9jNZp0Ya6pBvw0ikMgi+kzMQYTBCkIowg75xqloSiKoiiKoiiKoijKWKMkU2M5RmSUVEfDQRvhs6vDCJslbwuUhaIlSsFO12EbDq4YaguzQiFjA/KNhzgCpAjWqwhm6PcSmCR2fnS3qLVE1IgwHIdOgkgLOd4qjIBcS2AWeBihUYT00RYJkxgxfDNgMuYcepi2idAu4p47RsJmt9s55r2Q15DfR2Epu9x1ZL1KYBawQxJ2mQm1tdDdA+vWw4vN8JxnhM0uzMz1ddZ3C4myEulgv7fb4SKzuGsx4nEvpdc26caMrXUE6eQq/YeMP0mzJtjbr8Ccr3K/DZMxJkuN3+blwP8jfPxW+PusJCiAXqi+zX9S/QzbwLVrIdifYy1zIy/c9cK2H/be/e5QzIwocgTRFA3kR4MNZdsS0TNW8TDH20lg9AwH3QSRi8V+n2wTQ+51YgiO9j1eURRFURRFURRFURRFKMnU6GT4zAxhMClAwkTpoQguZRghdRPMrO81GAOhJ2RfNnZqoDRGXJXUTB3O9yWCQSIvwiI23HRI9ndb/EeYIJuMWG5vqxTCZrnLeZL6Hd2YPipW6HxDIKmcZgHbYqIGxHR5B1iBMc/scRYmcLrFwe1IHPe17NfeXqHX8pzEiPG1QCoHK1ZBYg20tMHSLvhbDpYSnsKrmCgr14Fd+yIKOY4OzIzwdzDC52DPZR+mjyXiQmolFLu+5/oP+U455vqQSI9qzHUJZtzJNSc1PFb663Zhrlu5Rgqxsc88LxSVIWaHmBgZgtRzHuYcSQqiQpFFhaJaXDPD/axUpK1hacNKSTP1n0CxiL/BIOZuXMJMXttEUxRFURRFURRFURRFGW1KMjVGkrhCY1hajLhiS8Z/dkWjPszM8PkERZBd4U9wZ61mgA8TiIZiMEi6IEnltAIjwLYzcAZ9WDSA+5m9fhhh9RyGi25MuqsmgigNKQg+FkTHBEHaLIlMacXM7n8XE0VQaIzYM92j0q6E1dOI29fu+ZT+fMGDf68zQnOL3+415I/PhPO6WJSBO1YKRQNJWySSYjhm2Nspp+JsT0wySQ+UwVyLUv+jm6CmgoitUsfGrr8htVyixmNYurr/BGQ8uEXR5d4zBZOia2oSWjx40zMpuqKKr4c9FzMzhhL5ksacb0mNNNyCvjI43PvfaKcVVBRFURRFURRFURRFsRk1U0NmZdsziiW6IS6liCxpf39h3xWTohMzS17SPJWH7MMW8XLki68StSG1E6oxhZyTGMG2hYEz16PEdPezOBTLjz9YPIIZxGNxlrtHUE9B0kw1EKSwiWN6xTEzCh17WH0X97WYXe2Y+hUrCcwhKdLtttVOgybRPWJChB1XXCPBbpMIlgnMdZKN2HZcShkfb2POm+yvEhM1VYe5/hKYPsgQXFdS9FxMDakjEnUzs1Mv/acg581NL1WO6bs6TMTSrARsUQO1ZfBeG6zszp+F7z5HRV+44znqs8EchxRyH8vYNY42pnGUIbh2UgS1lIph/w6poaEoiqIoiqIoiqIoylhj1EwNW7yUmdbvU9jUEBNEhKU+4kUKiNhXSVDTwK5pIft/ASOKi6CTGrClgXn5E5hogC6C1EIitlZhxPUUJlrATilUaBZ9mIAUJSoW+s5IMBbFLQ8j1K0gSBs1lBnfhepp4Ly3DQ/72W6b3WeSkqnT+rwYaYJIlEqCIudtmHFXaCzYr10xWxAzowxTl6QDE5WzIWj1H3Y7JNpDIjTsdsqzXZS+i6A2D8765f52JBqkl7E5hgeDa0BI1MMcTG2ZqeUwtRbG10BHB3Tm8g2xQkZF2DL3/XCI3WLyjdWaDfZvR5qgnlEvYyNKrRApjKk+mcCsXEtgooaRxlxzcq4/iCnAFEVRFEVRFEVRFEUZ+4yaqWELWJKPPyrvt5gEWxLMzG4lKKpaCDEaxBBJWssT1iOLidAIm5kfNkPXTn0lYin+s0RrtBAUeZZIB9swsesO2DU3ogTEnLNcZq7bs4hl/ah6D/+pSDTJYHBracj27JnKrqAeJeomGdj3Mtbt2i92UXeIFnRTGONvKqY2ggiOnUAjph6GfQ3Y7XONlpyznv06iblGJvj7bIpoz0gi53A95jizGKPFToEj6eFEVJbjbcSYPTZVmHodmwKrMUXHw9bb2BHxWWr41ACTkzClAuqqIJuDte2wvDcoQB82dks1NoaLDWVmSO0jMcTj7LcCmIExBsowfSyp4tYztu+trmnVgblXhB13EnPNTcQcbyfGbGrEHK+iKIqiKIqiKIqiKMpYYoOaGjITO+U/VmOMBBFQwqgGJmEEXQhqALRR2NDIYETaDPlmRlQRVMg3MXCWFZoNL+lwXBNCcv+73y/zjytBkCJJ6lOUUvRc+jNNvkEStt5YFt/GEu6YIOR9KaKovY0ygmiLSoK0MB0Y8TCs6HoOY2jM99dvwox7GdO9BKJjAiNau8K0fGYTNh6GmnbK3pedTmgwM727MccqpkUWc500h2wvTSDKyjG47ysI0u/8p9KH6bMVQG0OKjqhogKSSejIBvfMsLEbZlzYxp19D5N0aJB/jxvLZDCRgZMw99rlFDfu5P7qYfpOjM9u/1nG11iLZEgR/O7Z0VzthI//FMa0Ged/r5PgN7aNsX9uFUVRFEVRFEVRFEX54BHb1HBT8AyGSsws0HH+6yRGaPl3ge90YOoPiHAZljrFRupzSC0CmzBDo5DJgbXcfR82kznMAAmL8KjEzIjvxkSbdDA4YUzMlCrMsYrhszGIjGOVqCgdQQT2Uvo3gUmBNAuTGmiTaqjLQDoB2Sw0dMLrvfA8Zva3jaRlm4S5BhoJakhIPY6E/zpKtHRTZNnHZY9Zz99eKXVtwpDIASGOoSLrVWIiU6QOjdQa6SG/5ohEOkFgDsly2X4fpm5JIyaya7DX2VhGioLLeZQUQzVARS8k2iGZgrVZ0wfSh27h9ChTA2u7Ml7sOkj4+++hNEN2Q+NhxpNEqsStK9FKkB4rg7nXZghSfXUTbcgXwzZNwvp9sEwBpmGMvAzm3LxHkObNJYuJPFlHkP5NzED9HVEURVEURVEURVEUZSwS29TIkC92DAaZ4d5FkC6lWMSFLbYWQ4QnW+SUbbjmhf2dMOEmTNyOU5jZFcBx3icJUl01ET7zPC524WQ7DdVgtiNi8AdVyHLTNbnGlDwPRnSswqRO2zEBW0+BWTOgvBw8Dzo6oaEBqldAs2fGuZseaD3G8EgR5MOXGeKScshOcxV1fB756bHssZLFXIerGP5oBo/82hhuW6UdIhRLzRs7NZuI8Xb9j7Di0m6UQZf/6KTwebMNn0IM5vpy2zdY0piItWqCe5xEo9lpuTKY+8qbHqxoM2Nknf/oJLjfxDFrc9ZDPpO0Xz0EhorUbIB4vw8ZjNFXixnXkk4tjtkwGPoIDMNi14qLrCtRKZICTmq/gOmDCus7YePE/W2QaCK7j+1ovcGOFRkLso0OTDRXoWOWff4nRzIpiqIoiqIoiqIoivKfQ0nppwYrmgudBFEXIlqWOus9ChHWXDMjbJa6vI+z3zAhO+w9EcsEu65CG0Zg62DwIpIYGX3+diQVUR/5op08J6zv2edRCprX+N9r9ts3VmdcjxT2uLFnsQ/V6EkC4zERSpMSUJ6G1nZY1widndDeDq1d0OqZCKYZmBnVkuamGyNQrnG2W2p7CtVYkfcyNocb20SxoysEGZMSfVGOEe4lAqCJIIIk5X8uUViuEO8in9vXu3zXrm8i+y6WVizMEIpLsQiwQvuswER3zcbMwq8hMHY8zL21xXp0YqJUPMx9oYMg8qXQfsMMvKj6G9JXErmQJogeKnb/qMGkVJvrH8dq4GXgbYYurMtYklSH0kcJgqifwSKGjtx/y/zlKYKUghBuuEF+NIud2muov602azERgCmGPhFBURRFURRFURRFURRlLBLb1AirNzEYRiIHedJ52GKl3WaZHWunnQoT66KOsZB5Ecf0kDaIyFhqP7jFzUWEFRHQFSBtISuFEUalaHqGQCCW2d+tDC2dymghIqakHJN+KTY7Pw5DjVzxMH26DGjJQWpFUB+iy//MrqdiC/BDFSTFIICB6WSGMxpHDEW7bo0tnss5scevrJt0XqcxY7PSei/fkfRadsos97VtSOWsZVLzRgyMXoI0bbaBlbK26R5j1LHHvSfmnNeJkOVR+7DNHKnPUoO5nkWsr8H0XSNBmiX7XmCf/0KRBKWmQ5Koi3KC8d6BMciiDAr7mpV70ES/3U0E/SP1IeR+JQZOVLF32wAa52+7F1NnRI6l1Ho4NtIWGe/uPdkey2EGeoL83yX398b+/Roscq57S/hOHJPfTeeoKIqiKIqiKIqiKIoyWpRkagjDIbzEpYqgmHKY6GILSraZ4WKnn5LXrrHhEW50YH3uClWFUrjYy9w2S+7yuOKyzDx2xciolFju8nJMMdiJGANDRF6JlOnARAO0sHHVHUhixNzxBMeVwAifqzEC72AYDgNPttOIMYxE8C2EPUaHQgLTHxKB49anKBVJ+SRph0QwLcMI2uMwYyyBOcY1BMadK/4Wei3HL2m2RMCf6H8m0U1h5oWNe43LdiWawBa27RRCaeu1TSHzIo6xIfeWqNeFzAOJoOnFjKUqTJ+Px5gBFQRi/2pMNFwTA1N8SdRCoba6kS/F7lEpTA0UiR5JYs7/av85ytRow0RmvE9Q90GOMUFglIz3j7ec4J61EhPREYacCzEOJRVTU8h6g4l2sscS5P8eyHvXxHDXLWaYDVfERlwkykTqMYVRbNwoiqIoiqIoiqIoiqJsSEpOP7WhkdzxUosiDBHfXDPDFveT1vtEyLMrRrnbsdPORAl9cWtulDr7XwwNW/ws9VyI+DwRIxJmMcLjKswMeKk7sDEZGmD6pBJj2MhMdRGBWzFiZrG+jopCcoVJmeVf6kxvO5qmGHG2m8Acs6Rn6iaIMpBIh3KM2J3CnFNJkzOUejiuqJvECNrbAvPTMHm8WWflWvh/wDsEqdGwvhsVpWGnUmr1H0mMcJ8h33ywU0jJDH7bIEiFfCbrS2RM2DFC8RnpUSZGoRofCed9lIlRaKxKOrJGjLBvRzCA6Z8oI0EMTGlnWMoj19DAWifKUC7H3E/EpO0lqBdUyMDrwdx/VhdZR2ou2eOjWCSZREENN3Zf2L85bh/GiWp0+9OeKFDs9264kSgg26xUFEVRFEVRFEVRFEUZy2zQmhqDQXL8FxJ4XAEpTkRFoW3J99zlnvPeFvuiBEr7IcWYSxXF7ZnuhcSyqGO0TRGp1dCCmdG9mo3PyLDJYo4nizFt6giE/OmYiIHlwLsR3y82pm0zS9LllNJfEjGRxIixw9HXk4DNMIJ2J8Y4EAFfhN9ujCkgn4mRVmj/GYwx1MlAUTjrt98VuNMYQ2m76bDFPFP4/JG1QbqrsEiMsMgMWZ712yw1NKTf3BockB/xEGZSulFXIsJXYoT3QsJ3IuK1TZiJEbaubWjI+7BoKpz1CmHXXImLff7dNoVhp7ySou2yXNJCVfhteIugmLtEBEkhbDuiRlJ+FSNLUB9kpCglWkMMTSHsPmxH/pX6uyP7cKNrSo0mGQwSAaRFwhVFURRFURRFURRF2VgoydSA/HRPG0JwKSYChxka9vJipoNbdyMubuSGu095zhLMgI2qJyLiXyVGlK/zlzcRpGSJ09e2aJrBCPBSBHwCJpVLBUE9ARGxRewV8VL2JyaMO3vXNgJGuwBtCjOTfxbwoTRMKIOyMkgkIJuFxg54uc8I2GsZKHIXEqEhf/x4xCuCbJPB9H0Npg3tBMKvvIYgDVgxpM1ybqsw51XowhQJbsKYGnFMFBkv4zHG0FqiBX+7jTmMAbESWLkONtkE+nrN95sZWFS5mKFhR8SUkV+3AOszO/rBjdCISh8nqaUmYcyuTowQv46BfZR0vudux10etszGjdaIqvtRKFpsOBEDLKrddsou2ySSz6S/JKJGxrN7XbgmU6ki/2CIEykRt5/FvHHrZsg2XMM6zERz91uMwf4eDQWPeIbGhvi9VxRFURRFURRFURRFiUPJhcLl9YYyNUrBTQcSVRhcREt3uRyPW+S1EFGmiQhdUgMgTFyWXOb1GHG6GtgU2LwcKjOwqhNezpqIChEP4yCicQVmFv00fx8Z8nPN12GE7DqMMSD58Dv8RzMm0sEWOdMEBcdzmJnUUfVORpokRqTeIQk718LMSVBZCUn/pPX1QU0TtK80x9GNOSb5btj2bNxjEuHZFS5FIA4r6i39VeM/6glERJnZnsO0r7XAsco5lbRKjf52xDBJ+ttb5x9n3FRi9jH3EBTjjksHpgj6s52QegVSSZPSTOp2RBVTdg0Ouy1iQEQRZQoUek5irq/pwFZAeRrG98HzmD7rIf9cCoWMrzDDI0rIdg0Yu11hr+NcT5OBqQlzXXteYKB2YK5L91xKUW5bqJcxJdFcfRgjVSJA5D6Wsx5u0fkoNoSJ4VLMYApbJ6qNdnQc1rr2Q/owba0v/RtWhyJuermx9hs7VtqhKIqiKIqiKIqiKIoCJZoabi72sSa62CKVbVbYAmfYTG5bJA0TsWR5IYHK3rfsSwRAW1wWYVFm8G+OEeZlpv5mSdh6KoyvhxUrobUB1ufyZ/UXOnZ530dQnFpei/DdgTE2pF6B5y/r9ddrJshl342JIJngP5dhRPpyfxsJRi8XewaYAXykHGZNhfJyaGuD9nbo7oPOHDR1G5G9l/BaCcVEUDfaxzU0KjFGRRmmz5rJz40vxY1l/9IGSd+Tw5zbQqnDJJKnjCAFlucvm4gRt5OY6IwGwqNJwiILbKQdfZRWSDyLMVheA7LN5vvLCMaG7C/pPIh4bxNmMnnk18mQ1+55smtrJDBjdhNgHjC7FsrLoLwJvKxpu5h3bg0O24gQ3HtBVDSHfa907ynuOvZ9KcqMSmIMrEmYMffZD8GCj9RRU53G6+2jr72D1Wv7+Pc78Pc18HSvOS4PM2Yq/Yd9Hci+5fME5tpuJrim7bE0Vu73cSlmXBTCNffcbdm/DbYhl8KMtwwDjZFSolbG0m/sho4eURRFURRFURRFURRFKcSQ0k+NJcEFwsUjm2LtdT8PE7GKiWS2kGuLXWCEw7mYVEmTgRkVMLXWiGBrm6Cx14hhPV3QkoS2TujJDZwVbRsnQs56tlOiNGLE6gr/O5J6SgyeMoIoA5mB7UYcSJqqcmsfXQTGx2jU5EgS1EfI9sLq1dDcA2/0wPKsEWXb/Ta2Y47ZFevDjKxiY8heLv1XQxDxInUuJB1NOYFZlLb2KSl7JCKmM2Q/aX+7bvRANTAOmIIR6qeOM6m2ulrN8UrqqKgIA8E9Tql5UOr57MYYR2KMScoyOxqgUAoq954S1lZ7WbFr3CWNMQFmApMT4OWguQU6sub8VWPMvW6Ca0MIS0UVZoS5r8MiuMLWkee0/3Cvp4T12ThgV+CwnWD7nSrZZJMyaio96O6gp6OP5oYsmRx0NcD6Rni/14x5DzMOJaWXfb7lftGHGTee1Q9hZulIYAv/Q4nuKDRe7Osu6ryFRdsVem8vt8+ZpF2za9zY646F38zBsLG2W1EURVEURVEURVGU/zxKNjU2NCJ0xknbMRhDwxUXXREsbB9xDB0RIysJZurPAnYEtq2DqROgqgrSaejthfIUVDZAWw+8usaI4O9jCly7qaeKzZh1RTRJKROWMiZqVr5t3ohBYAt0SfLrb2wIkgQ1QiRqoQ14vs80cg3wNkFqpkIUqitQ6Ny6grUUXU8ysJCyiLVStNs1nvoIipy7+0hj0oZN8D9v8vcjZlS9v25lGsbVQ3eP2UkrgThdbJyEibtx0gq525Aoj25rWSHjIuk8hz3i7DfqenaR6CIPWOvBinZzba0lSJ/WQ37arnKCm2OZ/+gmuF7CojNsA0RMA3cdW+x2DZ4EQW2dPv99NUEKuS1SsNPm8JFZML2sk9y6Tpa3QHMztLbCmnXw/hpY2wCtXcaomexvS9pmmxmuqdFJEKnj1voYbjE+QVAXphrT3/5l3D/O4yKREWLa2mM4LComjJG+h7n35CjccQSlX5ODwU3BGIaaGoqiKIqiKIqiKIqijBUGbWrYwstIFYtOY2ajVwHrMYJtVL5/29Cwa2l4zrIwogwN28DA+cxeHnX8kmu93F9nPfBvINkGvQmY2AWV5WZD2ZzZXg9GlF+LEV4bMGKqKziFvQ4TnWSWtxgrUUKwRD1k/PWldkMrxjgoJSVR2Paj2lcK1cA2mLoIfRgToxGTPkiMA0m5FYewWdtRs/HDtikRK71+O6SvRSCUSAwxf+JcJ+WYGflTMVEYEzBi8zsEgrsUPS8DJvWB1wAdfbCCQBAebF+HifFRuNeAiOeuYG+vG/cRtT/Zl7S12LULZmys91+vwIyRtQQRPFmMKF6JiYzJYO45Zf4xVfjv3yQogO62ya6n4BaXDjsGaaN8N00Q8VNFcA2KqTE5DVMmQE0FrFoFa9dCb49Jt9baBmta4JUWeKvPjAFJoyU1M6TfwsaFG7Uh7QkzNIbL4KjEjO9ZBH3eBSzH9HNDjG2kMMbNVEwati6C1HliVNnHPFz3IZuwtGQZzJhx0zV65I9Vl5FoXxzqMX3YTlBfRg0MRVEURVEURVEURVHGMiWbGiLiuJEDgxVBwrYnVGFy4G8KrATewoiTfQQiqpvv3a2lYQtLcdoYNavXFqdcodKeBW0Lfvb6Hkb4bgVW52BVM8xuhokZqE4ZgS6bG1hg1hUUcV4XmgEss35F4AwzhGTW/2YYYSuNEYGlqHZ3yPHGRdIz2abKUCI7ejBpjroI6oS0EMwuL3Xbrjhrz5gPm9UdtUz6tSfi87gkMOLsNpgxP47A0JPizd0EY74BeBpItRoRt9F/SPqbUvc/mPUFEcVlBn5YtIZdP8P+jJDlMPD6k2cpqG3XiLHNmDABvh0T7eJhxo8reNdhroFpmH6v87cvEVMr/O9JnQTBNrCkLo57jwi7ftx7RBXGrNsEI4hLCjgxWFIJ6O6GNWtg7TqTbqy720R2reuF17PGMF3rf09SlMm5kH7IWc9hD/f8exGPodKLuXZXYs6LLGsiSJ9WiBTG8J4PbJU099DWPhOJswrzaCS4V9j3vsH+XonZA8FYK7c+t++3YiSF9VdUH4a1y/59HGy/yxiLMiunYiIIu4DXMedEDFS3JpSiKIqiKIqiKIqiKMpYoKRC4RAunIctj0M5RlDPEsyktunGCC3VmILaaYxo14URXWS2tYstGIa1t1AakiiKzSKnwGd2H/ViIgwqMMdQ3wvje40AmSZfcHWjRARbvPVClmG9FuMnx0BRqxpTdHhz/1GDMTLex5yPVRhRdzCiVpJg9ruYGl0MTSTrxszkfp98kRaGR2j1yM+H7xJnzMRthxhN7jnJYMbCeEy/rcOk1HoHI9JK8WZpq5xf28Rz2xynTTLeBiNau2NLCptHERUxJdtqibFPaaOYW/ayuN+1acb0cTNm9n+Vv16nv0yixMpCvi/HniWI0oDCBpfUrsH/jlz7HQTnQraVTkB5BpJJ6OmFnm5o7YGGPpOe7l3MddFEMH7FQLTrOriRF4N9DAe9mPv3egYauIUihRIEUR7bANuWwYxxkEhAcyukO/KL3UukmW04ucQ1vRPkR7HBwIicQqZo2H4L7VPMu6Eg0SwZzDgOi7hrxbR1pv9cS2CiriMwAxVFURRFURRFURRFUcYKsU2NMOFnqOkyKjGiejdGsHUFnG6MoFuFScNjCzQ9hIvjYYZGWMRFXMJmXhfajtsnIk5KzngwYlUTxpCRAsDjMSaAFOyVAtKSCiROtEaYSBZmZuC3aSbwEcwM9XFp6PKgOWtE5TX+/gul1pJUVTAwCkP6X4oOJ/xjG6o4FmWKhM3qL0TUOXW3NZKERfxkCETpFoxg/Q4mKsNNYeUR1BMQsbWTIJrDvgYKjftC4ycOYWMuYb3uZGBEhvuM/50sZtxtaBG1DyOwN2PuSxLhYEcwFLqfyLUg40rOhz3TXsZur/Vazme7v+8VGMF+JiYSYXwKpo6H+npIpqClGVa2wat9sAx4z/+eHYmQIqg5E3YMUVEabsRGIRNkOIi6N0UhxzUF2BqYl4SqNLR0Qns3rOsx5s5agjFnR73Z2Pdpu1aQPMu5s+9ZkvrN7lv7unJN6EIROmHtcX9bh+O3NkUQsWPXhLFZC7wMzMEcb6W1fzUzFEVRFEVRFEVRFEUZiwyppoadEqPUWaUJjNA/hWAm/7sMTJHUALyAiSoAIwh2Y2aS9hKfMJGpkNAbJnoXSyljf+aaGpUE/ZXAiEtS/6AJIyxlMMJZJ4FpI6Kb3Wb3OczwKIaHMYvqgXQS2nPQlDMzc9cRFBYvRFSqIKzjwPl8OEUyMQFqMOMjQ5DCJqy2hqRGqsKcD9sMyGL63C4YLSZNmFE11GMJM2ZqMQKkHIeIkCm/zXb6KTmWeozhV4W5dt7FGISueBnH0BgMMqbLCFIzSdvkGDqHsP0NhR2dI+ZRoYiSsO9L+iFJGVXjb9M2KlsIauTY9Pmf9WLGQRqYWA6Ta2HceCjLQHMbvNUEz/SaVFPrCB9HUthcDA3bjCiUaspmpCM1SsVOXdaNMV17c5DoMONL+rbZ/1yMil4K1wOy719yfHaUjNyDBek71ywJMzNKMTYKGRjDEQ05GTMm3yKIbBL6MGmn1mLGnRjUHWwc166iKIqiKIqiKIqiKB88SjI1wqIEYHBpMpIYIXouRsTrwAiyLjmMsdFAabnQ3YiBUgwNF1u8t2df2yKfmwbJ7hMRwOxZ230E4m85QUqTLoyhYJs7GYJ0RXb7hyI05jC5058FMjkjBLb7j1aKG0bS1rgMRRhzxUNJbVWHqYGweQKml0N5Cpp64KVeY4TZporMWN7S/04dUJWCsgR4OWPqyEz9tcBqguihUqNABouMVZkdXoaJ4Eli+no9JvWWzEKvwoydGn9dScVWitElwu1gkG3XYaIL6qxjKPPbvA4jNndhxlhYXZfRJo0xucRMEiHbPfelbK8K0x8pAhND6jsUOifdGFOuAWjshWwbrOuGTg+WdcILWXOfbC+wnR7MPQTM+JBzUkrERtTno4XsX0yKFgIzXKJg7Da6Ka2KYf9e2NEtcY9Zfk+KmaCFoqLiRHGU8hsovy8zgC0w4+a9iHU7/Ucp21cURVEURVEURVEURRktBl1TY6jihwj8PRix9i2KC56DMTSiKJSSxF0mjzKMEVPlv+8iEL36MKKQXbw8TIyS9xIhUO5vU2Z0hxWbxvpulGA9mHOxFiOeeuSnwhlrSIHeNMEs+EnALGBWEjargQl15jhWN8HaXrNeN0ZYnumvPw2Yk4LacvCy0NkTGDntBDOTZVa9nS5MGMqs6Yz/8Mg3XIRm4A3/800JiraLCSaCuDwk572IkT3k10BxhVN7VrqYc2X+djsGcTz2cUnUixh4Uk9FZoq3YcT6JsaOsZHBjI86gvHSwsDZ+TbufS9sPYkkk8ifDut1sWOX4uerge4cZLugs8uMDTHdim1DzqcYgLb4H2VoFIrMGO4IDUnXVUEQVSLmSzPhqQhtsoTXUrJTmoVFaQlhxxJ175eUYmKcpJx140RlxImIiWKwhoasn8CMh2ZMujI3SiOsPYqiKIqiKIqiKIqiKGOdkmpqFPusFNGl0n+0EEQHDBdhqaOKrR/23n4uwwjjm/nPWYxwWIER6FowtQ/WEF7TwEXEX0l9JOvXYkQoEe0qCfLwh6WMGYrgOJSi3RsKEUArMebPBEwqlWnAJgkYV2b6sKEZ2rpgXa8R+Wv9x+b+oxrTf2uzsKLDjDcpgit1SyRdUjv5UShybsLGSSl972EuuChRWopkv44ZW5UEs9K7/Tb1EoydLEY877C+b89YDxN205ixXEl+rZesv4/B0Aos9bcraYLknEkEUg+lpYsbaRKY9k3BmC6dmPMupmTYuY2qM2C/78McpxiepV5j0l8tBOe2jfx0aHGQSCqpuWKL72FmRqFIjeE0NZIYE2kGpu+rCAyxLLAKeA1zbQ4X9rXrRlJEmRH2vd+NAAlLu+eaJlERgvJ52Pti0VWlnoMM5r7Xi/ldWs/YMRQVRVEURVEURVEURVGGwqDTT9nYAk4c4aUcI2xVYATRDoz4Epa/286lLm2IIxK6wlPUa5zXUc8ZTPqiacD4BHR75n1NwtSkaMgGQqQdrRHWLjnGnL++CNFlwERgKoEgKpEspQiaLkny+3FjMDME+xyJUF6FEWvbPDOLvbcriLaQSIvJmP4c579fiUm/IrVYJCpG0oDJbHa7kHMUpYx1mz5//8VmZmcxZovM9rcFbVcodQ2MQvUSJDIj5W+vkyCyY7CGg13nQArCy3iWm4udOmgszQQXY9LD9EMz5jhc4VooJja7qZDiINEUUuulDjNma/3lEj3S4D93Otu2o25cJPqrm6BeiLSzUAoqN0XScEZweZgxLQadfS+SWg7S98M1VsLu81G4qQXdqJ0sQfSGrFfMFA/DjaBy7ynFokmK9U0Sk5auxm9zK0GkWJh5aUeg2FE9iqIoiqIoiqIoiqIoY5FBFwp3c5dnyE/TUUw078GkGZHiuDkCEU5IYMyOGv991v+8ieER2qJm34eJUSIMvQus84K8+Vlf+WoiSFkUJgaFzc4VwVGKQZcRRHlIeqE2wgsLx0WKTEtESY4g1VIpxoaIr+4s55FOWSWz/O0Z45JmSVLWyLhI+svGYcaMtLUBk3rlXaILaIvQV0i4jiNWFiOqv1KY81OOOVeSQiiuwVJo24JEY9jnbijCZQpjMonIK9e/jOuoyI/hFKwHi4cR1lcTjCExXSRlUynbkod7/ct5hUAgl+3LZxWYMTvRf5Z0dDmMiZEhMODkepDtVGAiTcTok0gRu/C9iPN2jZAwMyMs7dZwnyfPP6YVGLNWxnyZ3wYp+D2c+y0UjTGYa9k1JOznUrfjFXhvI/deO6VUoTRpcn23Etw/2yPamcHU7pExJ/eesBR5iqIoiqIoiqIoiqIoY4GSTY2wlCwiJqcJhKlCQmwPfiFca1lYUdYURrAbT5CSRcTrQpEQyRiv7fXDZma773swqVFEiKvGzKqWugetGGPDFoJcwUqOU957znKJNLA/H2pEhRy35JkvluIkahsSISECti18bShjoxfTR+sxfS8FskXcnYRJaZPGzLoX80bMM1vctIu2x+kLqRch4vBwiq5SEHwS5jrqxaTfkaifsFn4gp0SqZhZ4BE/IqPYtiQtmMzwjivquiLwaJobUebrYK6TKJOoGmNWJAhq7tRhzrfcMyswkRl1BDVOWjD3lBbMWJa6KVIc3vPXlciOSgLDoIN8I1TEbzmntvHsFtjeEIhhK6aXfW+OY5QWG5s5Bkb3RRkb9jbd91JAXkwv2U6h61H2F5aOym6Lu36xa02utzL/taTJK9QGiRy07xFhfVuJufek/fVbiX89K4qiKIqiKIqiKIqijAZDNjXE0JC86H3EK0YaJ7d3JUa4LicwDYql7xGiUo6EzWq3Z8EWWl9mP4MRDxsxAmITQbSJGB5lRdqXxAjCkvZIZt4OR1oo28AQgbTF+rxUIbkSk85JRFcwx7+OYBbwUJE0Onb6IhcR5Xsx40BS9qQxfS6Fn/uA9zHGWYf1kDQ/IqJK1EehPk9gxnYdQZqioUTOyDblOYNJabY5pl5IN6aAuxxvMUE2TlRQqUjhZom4iDoXUrzcTm8W59qOm9ZpQyNpnFwDEMKF9LCZ9mHINdKLOd9Sm8eOJpJ7UDvm/L/jP0tKPhnrNQQ3bfmupF6zrw/7GrL3YdeHEZF7QxoaYZRq3saJpnLXixsdZBsAGYypPsF/3YW517eSb07b6aOitlmo3WG460rqPYlocSPOwojze5IkMMWkllMjWntDURRFURRFURRFUZSxzZALhdtmgIicbhqpQjsXA0AaIrPxezBCjqSbkpn3cWa0Fnot+5BUM2UY0dreBoQbG/JahFtJDyWGhhRhlnQzblSGvS0p0lxNfqqYsIK0dp2HQulJyv2H1EwolA6rGHafSe0P/PZCfv2QwWKn5pGZ0QmMeFhMtHNne6/HjJPVBDUppFizPYNeDI0KAiFPZraH7TONmWk/zd9eGiNqdpE/ezsu7ixvqfsxDSNav0WQmkfaY4/JsIiM4TQFUhjRfTymT1YSPSNczr0b7RQlkrvmIQxMiTSa1GHGY6f/yGL6I8XAyCr3vSDXYYqgHkaLs45c63UE17dc422YPm8h6I9azBgZ578Ww6nVX98di/as/EL3iwxBgW65h40EaYL0hJIqbiRqNrgREaVEG7jnVdrZDiwn6C+79kSUER7HhAwzyKLWLSXtlIt9r7NTRHZgjk2M9XaCKA01NBRFURRFURRFURRFGesMuqaGIHn6xSSQnNzFKAfmAtsAU1JQmYJsH6zIwfPA2xhhWgTBuEJOmJnhppeyZ7DKjGa7kC7kC0o4r21xuZJA6BrnH5cUYHYLI9tGhjwkwsWNUBABOElQYLqd/EK6UkS8ncBQEYOoVFFPEAFSBE9JadPo70dMjQ6MmDoUYVLGi/SnmDqDEdUkf7yYHIXMnASmnyZjZmGDGWurMMdq1yKowJgNkm6sniAFViNB+q1S0hTZ50UiaVZh+vst/7U9w1rGp2toFBKsByNQpzD9sQlGPG/y2ybFtOPiRjrIdSImpS1q2+uGRUhsSJrIn6kPgZgton9U30tqoDKCGfVy/fYSRF2AiSBqJjhewRbUIRCjJxPU2qj1l9l1cez7RiHzxTX3ygiiRRqJHwVXCknyU3C1ExRktxkJgwMK3wPt6ynKpBJyBEXkbRM+KlIj7L07dnLO+7A2yHt7/MVNgZfCpOObj4kMqsuYZU298BLwHOY+956/fikF7hVFURRFURRFURRFUUaTIUVqyCz43iLrhVGDEVpmALU5871WL5jZLMJeXJHFzssur8PqaUC+ICW1EtwZt3EMAYkukILJdQSRJq0MTP0hs75rCNI51ZEfrSIzacWw6PW3XUsQ1SBtXwv8C1MEW2pOSH7+wRga0k922q8EgYEhESW2iTUU4qZRiYsYbDZuZISIu1lMf0/BCK59BNEerRjhuQvTFxMJijdL+q0WBp9WRgR8MMe/hmDWdAMDx41tdBWLVJJx31VCm+zvQ1B82r4OC63v9rGkEpPXsr0EwXUh15xdAL6YWTPShInafQQz5Avdi6ow113af9Ri7m1TMGPy7xjxWMy2QsZdxv/+ePINkh4CY8ROMRVmaIQZG+5ruQ7EkIkbYVcKOcxYbPTfdxGd0qwUEpj7U4bgupf+sOtzhEXS2NuwDZ84iKkgqcqiIkMKGSSlRlpJ1Ejc9YUMMBXzOzsFSOWg2zPmVSPB73YpdXYURVEURVEURVEURVHGAoOK1MhZz4MVp3owNRnSQLkHvVnz/i2CmgJxsaMwRNR1c9Xb68mM6rD1hbBUJu4ye/8eZgayFPYNE4P7CHLki0Beg5mFXZEGz++HDn87r2NEdjGAJhOki+nApKlpI1/UtNNgdVFa+imZESyRHzUY02USQZqmBoIohuFEjCEpNi8pwkoVWV1x0U5vZEfq9BDU2qjDCO/TMEJyB8bgaCGYId1EEE3SRn56q8Eg7ZIC8R1W+6PWt7HHvBhzSfKNMTHtJC2ZiONdhM+Wl1Rvnf62xChz2xSVfsfud7mmZCyKCC/ROOWY8dVFUEDbFXtHkjCBNsq4dfspDFeoF9PLwxxj3KimNOZ6m4EZkx2Y/mknMIUqnX26RgUMFOvDTCO5j2QIooFGIvWQpHYrNdpHajVJlJKdmk9qF6UxfW2bgfY1DwPvBzaDiQyyU4zJ/cb9LZT2yv1Xrs049Vo8ZxuDvcdIFNh7GBMjlzX3rmXAu5R23O71rSiKoiiKoiiKoiiKMpqUZGoUmyVeSDxyaceILVLwOYsRWtfF+G5Yeh1XcHFFraiHbM/+TlgakahZ5CJQSXqoKPHSFdQ7CQToTsDrC9JWtWFm779LIDC/S5Aqps/fRjsDi1bLcdlmTamRBN0YcVsEcSnC7YqLw0klJu1RivxUW72UNrM7LO2MHRkh78VUA2NgiLGB/1mXv2+ZWS4CvywTIX4oInwps7VhYHF7SY0khoVEDaUwkRFlBGnOJDqgG9O3nYSL9XJ8Mray5I+hKCPDvf7lvZg2di2ANOZ8jyffOBrp1DeSCqmKoPaNpI5rx9RjGayoL8aF0IY5tjcJ+jTqviB1DTIERemlTa3Wd+W6bidIa2WPwbBoDHc5zjoSeSXX9UgZS6VuswwzPiZixrdb90NMAjHJwswKGP7jSRJE2lWSP8blngVBdIW0T+4/Em1X7PyE4f4+FTNk+jATBCT9G5jr/n3iRcjZv7NqaCiKoiiKoiiKoiiKMpYYUk0NEWrSGKGwivzi3lEFhsGIJY2YmcIpglREhdLdCCKY2oKOLcjJ7OVUyHfCTA3IF4pgoCheiBxGLAoTLiUCQYq0ynOr/531BIW4m8kX0+2Zx03+oxh9/nZkRv5QI2m6MKKinB8pPj7clGHEXDF2Ohi8IOlGa4Qts8foSszYLbc+s/tNBGsZn8NZ5NgWtcXkgkBsLvS9SoL0RBnM9ZgiSEEmxkY3RrBfRVDYvpCo2Y2Z+S6poqIMC/d12Hu7MLS9Tpagfon0pdxPRsLcSGKE6Bn+83gCcyOHKc7+DGbMx7kHFcOO0CjUJpnxL/V2MpixLxFYbh0MMTXt1EkQLnBHmRnyLJEEpRSelnaIIQSB4TCc5kEW03dyH5DoLXc8dWP6KuqeNNwGTRoTWSeRc+UEKeTWEkS5Jcg3Re3+sX+rCpnlghiYYjDLfUp+MwpdL3J/kx966ddihKXtUxRFURRFURRFURRFGSsMuVC4zJgtxwipKYKZxMWQgsyl4oqOrlDnpsFxIzHs7w3HDFQ7zYiQwIimEzBivYinIsyJ4WBHRnQQHoEiERJxxFYpKj4c9GDE7Q2RFqiNYFZ7WD+UiptbX57tZVJToIOgcLO9vl33oVAkwVD7R4yvCoKoix6MESE1FGQc2GmdpJC0LS7nCAyOBKZfV2IMjWbi5c+XGgVhs7Td2eJRz/LaThNkGzWuSSR9KNEIQzn/UiPCrpdTRlA/ZSp+LZ8ySCehLweVPaY9b2L6K07KKRc7xZDcE6KOI4kRxysJzEKpbyEpiuxIMlsEd42juJEZ9mtplxjKccev1Aupsb7bhYlKGUwdlyh6MaZ3O2acpwnS40nEhtzb+xjZKDKhDJOibhOMKVZNcF7qCO4fUi8pkwYvCW09xjR7F3NNF2pjWKo3ufbLCCYPyJgp9vuVI0htVwpRkS+KoiiKoiiKoiiKoihjgZJNDTd3u8zgbyEQYCVdz2ghKW7KCU8tFZVmyt2Gu07YfiAQ2oQkRtQaT34hcBGYOgkMCjE4oqIq5DjA9KttntjHMpjc8HEZ7u2KyCxpx0TgjhozkmZJRM247RHhMyydmIiEaYLoBbf/C5lIUlBdZmp3Flk/CjG1+jACbh2wOUbwnozpG0lNlPLb2YwRfCUNllswuhxzPdb766zDRF3EjbDp849PIkDs79nXjnuN2MaEmBNVfjuSBLPKJTLAvmbscyQpoURsF7NHDK8o7NnsVQQ3N4lgSRFEnpQBFWWQyUAuBxP6YHYuMCI6CKIAxAyzI2PEvLBTS9kRBW5aOLuNdRhxfApBbRw5j3bqOLtPXYpFZbjPtiki/T8BmO63eRlBMe9CSISN1O6RcSn3ODF2xCwcjDkkyLXRQ2D0uNeoGH2EfDYS9GKiMsQETWDGZw3G7JmSgLm1MGmCMTV6+2DVOuhsM6ZGK4HxKEYNIc8472Xs9xCMY0kPpyiKoiiKoiiKoiiK8kFjUKaGK7zIbN0ua50NgYhKbiqPMsyM7CRBOifXnIgboRF1LCI8SuoVG0lHJSJ0liA3vMyytyMwMv56knLGTatlp6Hy/OMbhykoPNn//vuYWcByvCJEj6TZMRTs449CRPHxGDG5DZOuqxSh1I3GEYG6HCMq12FESqmnEJaazLMeGQIBU2arS/H0KIFRznmyQNvluxlMFMEsArNC2tAGLMUIqk3Wd9w+lIgIWUdMk7h0+O2VFGhuf8g+C6VxswV6Efvd+g+uYO9Ga4QZKDZidkktCikqnSI/4kFMzjTmnNUB5WWQ8kNc+vqgzTPjYA1GeLbTguUIUg5NJEiz14sZj6sx50bSJWUJHwtTgJn+Nmr876/FGFAiVguuGSGv7c9cCkVr2J/JcdVijA2J6JEUelHIuRajIYPpy3rMNSrRQxn/8/WYCKHBRArYFIpSi+rruIh5JxFPvQTGg4ukAZTxIebYJEw/TAU2rYBNpkF1NbR3wPJV8Jc2eAFjGvVa2/IIznmhsS5j2DZ+N9Q93b0WFUVRFEVRFEVRFEVRxgKxTY0UgTAaJbxsCES0TGBmB0u9CinKCkFNARGB3CLRYQItDBQNRYi2ZwLnyK+BIKlzJG96j9WWJowA1owR9uqtbZaTL8Jm/WeZBS21Buwc6NKuFEZEmwtslYYpldCSg/c7YVkO3sEIaCKK2ylbIN6MZhF17f2GrYO1TlxxUWZg2wJfGNJPEtFRqjgv+3LPtwiKIrRnMOMlS3BeJDLELhTexcAZ71L43TW2ZAa3jJ8a/ztrCe8nmcE/2W9Lmb++Xdx7BfAWxkQp1g891jqlzmDvJqinUUZ+oeawqA37GGR/9rpyjdhRNlECfdJa3zZIJLpA+j+JEdJnEfSZ1KNoIpjVLtECYoDI9lr6jOic9aC9z1wzL2LOj6xvp4GSiARZ1uP3UYP/nS6C+hQuYmpNwk975W+jneA6d6OuBDednr1Nm6hUU4WWtWGiB+S19JH0tfsdiVCR8S5RKjJW5boKqwu0IZH7cwJzTqKuFUkRKBF1ZZhzkmBg/SI7Csc2PyXiqAqYXQ2bbgLjJxijrLEZ/t0G/4cxvsLOkdRUkesjLMInKopjpLBN2HLMsVWgxoaiKIqiKIqiKIqiKGOH2KaGzJQfDmyRULBz67vr2stqCMS1SoxQOBEj+MgsadluE0GKDhF2C9X6sFNr2bVCJKJAoi5keZrAYJDPZRa9pKSR2ekN5KcSqsSYMiIW1RGIb7Z4brdL6MakFHofmOhBub8TSemU9p9llq2YLSLEd1JcFC8niESwiwnbho+IcRAIcnaEiN3vLnFqJ0gEUCuBWFqKsGfPMhYxWgTyDkz/SV9JofAKjPmUwQic68kXnqVmSTeB8dER0q40Qd59/H00FTheaYekaRIToc3/XjMmiqCJ+ELxYNPxSDSKXcC6HTOGmwlMBVuMl3UhMC88gmLgENxs5Dtx2ueK63L9ShtnAvOACZWQ8GBNlxHqe/y2rvfbDkGtnx4gnYPenGlfCyaiYD3hRpCI3DmMOI2/XgdB6rRC41LSM3kYs1HuU62YcWSP00Lbsfu7ULRGHBE8ixlb7QRmrR1d4zkPOV+uWdZuHYNrzEhRb7mXSrvdAugQfzwUQwp5j/P324A5r1FGj6R1kvogUXVQwsahHanXB3hZaG6BpmZo6YJ3OoxZ1hKy/wwmSmYa5regG2OONRKMp7BonVKQe7RcM3IPt+sNyfbt53K/TRJ5I+nSKgfRBkVRFEVRFEVRFEVRlJEgtqkhgqsII0OhGiOS1Frb7CQQx+xG2aKMREDITOEyjODyIaAmY2Zfd3rBumv8760mKHwcNnM/DNvYkFm0Iu7Ls6S9GYcRWHMEs/Nlprst8kkaFjnGMv97Inxn/G3YRZqj2tYMvAKszUJ1p2lTJ0Zk7PT3ITUfpB6ApBQKS4Fki5JlGGG/3t+X1GOQaA8xdar9NtvHKEKYlzBiXrsXRFmIwSRppSoJ0r3YYqm0pwIj/I33v9OOEf1aKFxbQ2Zq27PrIZg9LgKunB9JHSU1KkSIbWegMSbflZRmUW1I+/03GXPeVzNwtraN9IOYURkCUb6ZcCF4pKjFtLuX4FylrHZAvrFiRzNBftSKGHv4z3IfiZNCCQZGL9jv2zFmRBXQ2WX2uwpTH0IMINtwaPCfmwiihVoJ6lkUapNcN7b5ENdc6sO/FshPdSTXlIzDsGOPEuPdKCn3czfaw13PPi8eA0X0qONw9yVGRyHKCSIhJOJJojiktg3kR28NhoS/r1rMvUnGmx1l6CJRWE3WuoVScInxbl8HrZgIqo4u6F5tUpm1EBQ6D4viqgFmA1v5bZYIoZZYR1qcNCbd2STyf1ckErDMWldqzsjxiQku0ThpYA6wSdUwNU5RFEVRFEVRFEVRFGWIxDY1bNHPFdtKEVrTGMF0FsYMkBROIjLboqEIbzIrer3/aCOYMV+HEYdmVMMm1ZBIQiIBvb2wZh1U9QbFkkWEdAVBe6a0HX0g6WpEbGolENPFKJiMiRSZ4LeplUBQDxMlbTFMxC6JsJDjjTuDXaINZHvuzGcpPm0X7w0rSC7HImmXKjFGQhWm31rIz4svhZsnEZgllcCkpEnBMnOCySmfy0FHB6xoMGlY3iaINJC+ltoW6zGisxhDtf7yqQSpxNZbxyY1AOTcSaSAmEVlBOm9JF2QKxja0SIymz+JESOl/+x+jUoL45Lw2yx1BloxxaALpcGRmeItBObFOooLxiPBGn/fYmpU+21qjFjfI+h7Sc0T1TdRNTrsbdmvoyITspix9CLG3Mt64WNbkDHSgzk2SetVSnokMdokHZrUwImDmFK9BNekIPcL1+yxI9c85/MwczZsnMYxj9z7eti5cY2SUrDrLbnYkT5iGA92P2JUrcXcS+KmrLMj2OLuB4LrtBlzbfzb/rAIkvJuAub8NzG0Pnapw9SAmYa5x7pGrj2WpN+7McexmiDaUH4P1/rbUxRFURRFURRFURRFGQvENjVkxj8MFM+KCTEyk1tSNY0nqIMhERCtGEHXLootIqDULWglSJcCQURDBTBhPEyZApk0ZHPQ2gqtHdDXbAR5OzWVpHySR4ZACJfZutJmMVVEVExb35kETMcI73UEBWbFmIgSwe10LpJiKEPp6ZWkIDL+9lyB1i6i2xPRJhFqZSa1CF7yXdtkqcYYDdOBmQmYlIaqFFSkoDoNU8bBjOlQWQktLbC2Gdb0wrtZkxJoNcH5k/6sIDBcqjBC3wwCwyRFILat8LdhGyyyDTEwhByB6SGzybut70QJ6zLuYGAqn1LOTQ9GCMRvc5R4LqZBGjMOWgjMlw0RlRGGK+6GCdJifqUJzlsFQYolEZJF/C+Wui7MzHCXu+vL+CxGivyxVGgmfjHEAEwQRB8NhpzzWgqeyz3B/ty+X0B8Q8h9LjaGbfMozOAYifEo9/7hQn4rbFNyJCj1viD3HDFJ2zH3hxWYey8EqQ4Hg23IS/HySoIUfn3+cxPB+JK2y2+DGG5i3kNgvmeAutpBNk5RFEVRFEVRFEVRFGWYiW1qVGNmpNrpXIqJOSKw2GmQKjACyzp/HTErpHZAoVQq7qxo2T5AaxukUpBOQy4LjU3wTosp0rrSarekJxFBVtIlVWPE2TRBHQARxUToz5CfemkGRuCv8r9jpxsJEyVdwVDS2vQRzNQvRYSzc9YXOxdR4puIw1IbQMyM9QR9IcLtdGDPJGyegeoKqK6CqirIZMyjogKyffDa2/Dianijy2ynCSPUS+oTOw2YbTyI0SARCkm/XbbY74rYUh9DtuNiz/SPk2JnOIRbmb3dTtC/haI6ooqNj1UqMObWNIwgK8WEq/2HjP8spg9eB14gX0gtJq7Lc1QkRJx7jzyn/fU7GVofi1EGQYTZcOARpLuzU6VFpRtzI7JsAyhqfXnY96HhNi3EDJbzFsdwGm5s43mk9y/GrH1/gcD0FpNBxqBEzkFQF6kaUxdGzMEygvtk3PMhBqOYIlJLo4UgqrHdfy2GY5QBFjaGxPTvjBvKoiiKoiiKoiiKoiiKMsLENjVEoClFbJG6ArUEqV/sVCSSVqqTwaXeEBGtGXh/PfR0G6GdBLS3Q6tnZsPaRkzCb9N0gugEqW1RTlCrYjVBFImbAz7nP7dhtr8OIxqtI6g/IfsSXAFU2iN1HuT4S61ZIqZGnDohUWmBbLHNLe5tr98BLMtBZw9U98H4dqj1d94H5BLQmYP3+uCNnKlvICl+3ELjEC5yy5iQfu6N+K5NKbOlw75r912xCIG4eMQT0MWU2RgQsbbOf0zERClJOrIqTNROezZIDyY1cAQxtWzTAaKjmgbbTvtc21FHQ0XS3sHwRS5ImiQxTsXccPsqypALE6ijXkfdh2S7rjhvXx9p62HXqnGj2CC4r4lRGteEGg5jJRNzn0NFanhUE0S3Sc2hMmudMn89iYqQyDh5lBHU6JHzHnUeXaTmTb3/LPfPRoLi9l0EkYqlGnFyjB3A+8MZUqMoiqIoiqIoiqIoijIEYpsaqzDCtwiDYQKxiwgokpqmk0DoFMG6g8GLT1mMCfEu0JaD9haY0AWpJLR1mX3ZBc6TBOLrOIwQVEYgQvVhIgveJqjvELZPSWXUQSCgiYDnEdRzEAoJtpKWK0EgAI4Urhjqmi7ubG633asw/VLmQUXW9KOIdX3Wd6RoeQ8DRVNCtou1XIrTjrQgKRQyg+JGJNnbksLHMk5sgVgiaoZrhv9IEWWSSQ2UPsx1J+nKJOopAfRkYTlBke4OgjohgkQmhUUYxYk4gnwBGGuZHbngju9CtT6I8XncNrrbsdOdyWtJh2WneBPTNE3QN/Z16Kb/k33FaWuhCDh33bD7hJhZ0zCpjar9toqA3kJgYInRKtvcUAXuZX9uLZzhRiIvKjCpFKcSRJXJNS8GgtRd2jwJk8ugIgnVSZPKqbcXlq8zkUxvE9Qvkvbbv7GFjsXDmPK1/vfSBL+xUgh9sBFKEuG0HOjcUCdRURRFURRFURRFURSlCLFNjbXOe3eGexgi9ktO+0qCWdPdGOGzjcHnuO/FFMpuw5gUa4HqniDffaO/ngiuYh40kZ/zX4TGPoxoH2Vo2BTKBS8zrKMiA2xtqIwg7VW70zY7OiasPbb4XIrwbr+Oeh9mrsis3y4GFt22tyHRL+7yuG0ci9pZsTZJarUazPmUWim9mPNYhRGFu4H3YmwvChHCRxKZdR+GCK5iWKwh36yR6y5uG+OaGFHv3WusWDolWd81Q2TMSsHkwRhP5QQ1biTCSGbl1/rPE1JQWwblfsTTes/cb5oIxoqkJLPNRTcVV6F7b6E+KMXkkKiHMowBPBvYKgMzyiDjQUcPvN9nRO+1mHuCRGbJMcQt/D2cUS9xo0MGQwJzjY/DGDs1/rNEJnUQpA7L+J9PAeZXw+zNodJ3u/v6YMVK8/v1PMY0GIzxkCOYGCC1oqoJ+kHSIQ52TIMxW6QAu6IoiqIoiqIoiqIoylggtqlhEyfVkdBFUEBbIhgkuqEcM9O1kcEZGx5GSOvAiIKrCATWPoIZqiJwSUqjRkx6KcldviFxhTYRgqWAsT0r244ysQt2C0NN2RInnc1gkZQ0w7U9CPrEY/AFmkeCNEa8nIQRO8sJopD6CHLeV2AKAw+FDWH42HUCwvYv15cYNzI2S22bnVrJXT5YikViCBLlIWNqPMZ06MVEa7UU2ZZLhuDci/nXizEzJvifTQfml8O0cdDTC6saTao2GRvtBFFgXshDjqHQubFrKsh9r9C5CTMzIBjTEzDjejowpxw2HQfpJKxsgddyJsLgfcw9VYTzuKmTBkshgzTKAB7OfVdhUq9V+/uSgt9SH0n6NIX5jUkCNV3QuhySSejKQmsfLG2D5zC/W64JWMpvrKREKyO410gdmQxmDPb46wymbzopbGQriqIoiqIoiqIoiqJsaEo2NdzZ/cVEkl5gGUHqmlp/pzIrVQTFwRobksYmS37RaZlRLsKlpJiqIkh9NRK4+fwhfHa0CF9SfNg1LWxzRmZqu+lIhst8EIoVHIYg7Yqk7UoTFMXtJT/tlL2dQqJqsX0KZRiBtYvSTY1CQn0hirVLjLlJmBnZFQTpmexaDlI3ZihRGjDwekuRX2R+uMZEIUPDfV+qoREWSTCU2fylCMBh+5P7QQ5zPmsofYxJNJjMkBdToxdj6q7CCN/vd0H1WrOzbDa4V8k9SiIdJEJCaq5I+6Ttdootu2aNpNirtLZV6HyGLZOaHhWY1EpzgU0yMKESkh40t8LyDnjVg3cwZkyxujdDQSJpJN2djPdWgvomGwoxxdv9dngE57vQust6obzBLJfzJekY3T5zf2PjsBpjxiUw5pzU15CUZrX+ftpC9lcMiXIcixF0iqIoiqIoiqIoiqJ8MIltaoigXqowbM+ql/z6Mks6jRFgxJQoFRHVRYSzC9q6+fal1oH9Xal7IYKwpH0q5fhkhreIj3Ksdj+5QrAr6kbVurBFzDAxOYzBCFZhoprbf0IK029VGKGsHiMCZzDi3RrMzGU76iTMuHBF5WJtlzz2HkHKsChDTdooKX8ymBQqTQQFeQvtR9LuJDGCZWeB9T3MuMr5++j19yOmhtvGQmZaCtOXm2FMEjD9+XLE+knM8YmhJJEh0i45nqgaKVHENTRsYT3utkuJLnLXCfvOYO5HNjnMOW4kKOIcN2WSPY6y/jbE4HGLkicwovOKHCT8QSH3oAxB+rsezHmVIuxihEkKIDFpPevZHmdZzD1WxGtZL07tGHktBkULQW0UPOjtMY+2HujyTHtdM3a4Cn7Ltsox18ImmHutpFjqwUSILMVcaxtScJfaIaWs3xBjvah0hXH3IW3qJDB+kpiIEql9VCr2b2ypRouiKIqiKIqiKIqiKMpIUbKpMVTxSGapyusmAkEvbjtEYElZr90CwWkGRj5AvmBpH7zMjhZRLyzdUxgy01pEXhGTCs0qtwViaXeaoF/cmbph4q4tKg9HuhdbHI4Sr8QYKvcfYm5UEdQxcQ0m19Ao9rDbkyEQfaVQseSLr8cI/h1OG9MYAXSHJGxebnL/r+2C/yMQYSEYh5ICyT4PVZi0OwmMUN1L9PjswcySXkcQtSAzpEshjYn0mAts6h+rCNKvED4W5bqxx6tbJFtqRUiKs1IoNK7scRxWpwKC6AM7JVCYiRd3n8XWHwpZjBHQ7r8PS/Um9xuJhqggqGlQLJWd3FfccdFBvvGUI6hBJCZHN/n3I7voeZhZZe/HvTcSsn5YH0p0UQvmGqjvg44+s912jEjvGpdufZ64RpqME7lvyHtJ6TYdY/BIlJ+M+xpMX7xJcN6GC7n/SFskIkaiX4a6bft3wo6OKWYAx8G+3sTkShPfrINgrNtjXlEURVEURVEURVEUZawQ29QIE+yGI3d5nDRQduolW2AJS9NhL0s5n4lQJO22xVhbtBFxzY7eiGqTpJeRtFeFojLsNrhEFf2OEiDjpIqKgytKRpkbcrwegZgpn0v6mQ5MX6QIolekpon9sNsdlQO/HJgJzMEIm5VARQoyfiPW98ELwEvki4xpjAA6PwOzJ0FLG7R3GTOkniAlkBQzFjHVFvEkfU8P8QyK4ajvkcSYKdXm8PoL/IroHYabcs0dB/ZM/lKF2EKiaqHx687aDzNDwtjQtW3CKNSGcsyYrsCco6mYMbIaEy1QKJqnEGHnJYcxDSQtVitBSjfIv8fFSf1l3/PiINuTFE/v+c8QFJ1u8dso9++w82q3M+qeliSo3VFLYMJV+svrMf1eSRDRIsZPnf+9SuKZGm60gTu+7d+NMn/bZdbyLozB08HQ7rmSTguC+0vYORpMurwwJBqpWJuSzmvXzNBIDUVRFEVRFEVRFEVRxgqDKhQOpc34tBlMQWHBFlVcE8L93J2xXsq2bbEbBgqPKeuRCPk8alZ02KxmVwR2UzGFzdwNez8cuBEgMLAfxRDoIpixLTOn+/zXU4AZ/usu/9GGicrpIIiQiBoLSYxQOR4jHk9LwPgyqK2B8nLIedDQCp1tJtVVG8E5q/G/U5GDxjZ4rRnewqxnz3qXWhjj/NciIPZi0tms8p9dAdOeVS5RGcMhyEux37UE0SddwEoKGxKF9i19XMoMbSHM0Ci0jTAjTAyVYvvZkKmDBoMdNVTpP2ox40YiUYb7GDow4zXM9JN9xamhEnZNu9sJWybXQiPB9WVHhtjXb5x7bKHorwpMuq2JmD61o++kH6SGjx290ooxleLUYpL92BGH9rHY0RJSv8NNT9jL8JxrKSQvxkax81AM15CIc92F7c/uA3f79rOiKIqiKIqiKIqiKMpoM6KmRhojUoERcFIYkcpNGTRYosR3V4SJSo3ivneFWRG4XOMhbB9CmEgbNVu90HrurPeRNjSi2mEjgp60q48gz7+I++UYQXAuJsJChMn3gdeA5dZ3oshhDJN3MDPCp3gwuxs2y8GEakgmwcuZtDS7YcRGSQdUC9QloNODtxpN2ql1BPUq6jECdY3/KLP22YcRG1f537GRwsnjCerAtGHqJAy1WHHab/c4TBRADcEYKZSWrZjYaJ9HSaHTQ7yxE8fMiGt4yOdxhfUNSRozBiRKx742xcBLE6SDasCc82b/e00MrvhyHAqd+7j7K5R6Kur+Yy+PE+VjR3nFOcd2H/dhzIkVBAapRCB1ERilFQQmg3zWQzxjx0bOsRgBEsUg9y95iKFjH39UVFkp2IZ5qRE0UdurtLabwRxLM6X1S1jEo/t6tK9VRVEURVEURVEURVEUYVCmRlxxw65h0IkRXIaSj9wVzVxzIUyIcV/L+zBTw17XFuoKFXAtJgyGmR6uieJ+t9jrDSkuufu2xUs7x7+9Xi9GdEwkoDoF5TnozRkxeC1BKptC9PjrrgPexaTA2aIXpjYZMbnH34/UH8hhDJB1QI8H7X1GlGwmKL5dQxBhI4JmF2ZsSlqdBn87gqSKqcNEoIz3vytmhpv2RgrrxoniEEFyJrANft2AhKkDkvSPpQVjsoSd86ixaX8+GOwxGvZZ2OtijNVZ3pOBWQRjQ2aqd2D6v5HACGoliDRax/ClBxoppD5MGjO+xQSIivwYyrHYplWUQB51z+ukePquodbMkBSDEzHXcsZf1oQxXONEewwFifYZ77chibk3SQF4m1LOh0f+JIEKBvcb4d5L3PdaU0NRFEVRFEVRFEVRlLHEkGpqlEKOwdUeKCa2FDM6bNw0Qm4EhrvfQjPUi4lG7uxo+31YXw7G4NiQhLUvbGZ2L0aEfwXIejCjz4h5EBQYd/u+2H47/W3mMGZCOfmplTow4mQrgYlmF+Ft97/Tau1X2tBJkJIKjPBbiTEyMgT1NWSf7/v7s8VuoQyYhhGSJT1OB0EdD5tKTGTGdGA2pjh4hb/eGoxhsppoQ6PU4r0iZofNmg87t/ZjJBlNY6AME7lTTVAnQlI+dWLOXxv50QBj4VoshIxZiSqa5i9vIIgqkQiI4T7HbqH6KKN5JPrNNVrdz8ZhjMOt/VR2qQR09MK7WdNfDQSRWiMReZPCRGNNx5ijEBgbYjYNB4MxZ+xz5NYdsd+rsaEoiqIoiqIoiqIoylhh0OmnNjSuoBJWwDROpEahCA1buLSjKVwxLmy9YuJslIjvioru+1zE69Eiqk8EiZhYiRFVawgiKaowxXfbMGJeKccjtS6kSHAfQXSFpKKJ6v82TF0NMTJsQ0baILPaMxihu8JvexVG4G7GGBlinIS1PeMfq9SRiTJ+Ev52J/n7aQHe8I+nxTOztyUKJCxVW1SanzjY49VdhrV8Q4210TA1khgjQwp/N2LSHzWSb4Zt6KiooZLCjKtaglRrtQRjuQZjlK0nMGqG+/jce6MrkruRcPZ3bOqA7ZIwPw25nHn05sz1/h7wl5DvhF0TZZhIqM2BbZOwWS1UV5q6PC3+RZYiMBUaMGnvmjDX4HBdB5JqSiLFxAAaC9E+hSYGKIqiKIqiKIqiKIqijEXGtKnhiisyO92eOerOIg2L1BAx2xWyXVEtSvC1X9uFvN31bSPEzpfuFnF117UZ6zPBBbvt0q8ZjIgotS0SGMNB8tKLmF+NEetLidzJYkTGbut9WCqdMGTmfSGk3RDUV6ggqJvQhDEf3IgLG6nH4fntlMLCLhI9shIjMktf9hEUYbdnbqeth4yrrLWtsO3br93xmXBeu2O+1CiksOVxcNsy0sg5rcOYa+MITI1ehq/o+2gg/Sj1KHoI0hvVYo7dHpMbwrCxhXy3rZAf2eERRDPMT8F+9bDDXJi/FXge9PZCWzs0N8PSdTD9DViWhWe9IGrLjXZIYIycOZj0c+kcrGuGtS2mHzo8c08pIzBgWzD3qPcxae+aGZ5+kuu6ieDe1Yg5V2NhzBWLeFSTQ1EURVEURVEURVGUscSYNjUgOo1JmOBiryuinV1zwU7BI8KSbT64wk2hNElh0SG2oZElSGmUJOjohLOuTdR7MW9sMXCsGR4ZjEg8mSClTwVGSJXCvm0YUU/qEpRKnKLFg6UPYzR0Yc5VD8FM87UUNzSkfc0FPnfHTM7fV4og3VXG37eYN7JMiln3YPqxlfy0WS5haabcz6IMjVLGViGxM0oMda+3QqmDhotyjJExCSNg1/oPKRrvYc7Fuoh22MaLmKuFCnlvaMQU68Bcb7ahaxemljpHpZ5n+x5USsHsYuNDKAd2rIYDJ8E20+Bj25poCjCmBgko74N0GiZXwKJN4fW1UNFmjMS3GXg+xOR5H3Ntp/D7wDJCEv6+2zDjIEcQrVWFuc6GMzXUcsz5kN+H0R5DYakdk85797WiKIqiKIqiKIqiKMpoM2ZNDVdQcVOZhH3uCv9gBKtxGBFJ8uV3E9Q6kNnxkF9EGgoLci5haaQEEdBSIesV+74cZ8paJtuzvzPaqXLSGFFwIkYQTGH6twljCDRiBP8OBkYijAUkmkPGUAdG0EwQFBIfClJPJI0xfCYTpJ9KYYRUSUvT5O/b89ed4D+n/LY0YqI81mD61BWp3RQ/btq0qEikuBQyTMLMPhe5vtx2xUWuhVJMkHrMbP1JmPOQwfS9nI8pBIXii2231H1vSNw6QVJ3ZihUEqRkg6DeSCeFz12UqeXeq9LAzhPh3J1gh818E8Mb+N1sFjo6oKkRmpqhqht2x0Q7lQGvkn8vT2DMDEk9Z487+74qzymC+xiY8TCc91SP4D4yFkwCN+LRjYoMi4hUFEVRFEVRFEVRFEUZC4xJUyMs5ZT9WdL53P2OIGmAusg3PboxM3N7yRfYegkiO9zZ61EpclyBzhbL7MiQMHHZ3U7YNkVAdb/jpuKyo0M2tLnhYQyjJr9N6wlmjcujh9GflRwH6fse/zFUEpiIgJkYc63Mf1QRjEVJjdXnv16PGZ8AM4CtgNn1UFMD2Rw0t8M7LaYOx7sEUSRi0sl+3bHgRmUMB24KMoi/DztSJK7JJWnO5NqOSxYzDtf472VGfg4jfLcQpAYKI+r6HOuEReKUMgYqMcWtJ2Gu7S7/udt/2P0VFU1n7zusHbtOhQs/BttMD6Iz3O/lstDdBS3NxtBoazMpqSpTML8W5pbBn9fAi1b7WgmKoocRlUatDGNsjMS9tBJj/EobWxi9FFRRUY6FoiAVRVEURVEURVEURVHGAoM2NWwBRGaYSzqnoeCKK1GzScPqaYiZIPUHJFLgPeuzHEboipp5L0aEHRlhGwc4r+1n12DoZWB/ROWXt3G3HZZWRWYWywx/SS0Tllt+Q9CLiSBo8d+LGTNWZ7RvSNKYc9OKEdBFEO5lYF0QeS/nsAUTSVDeCtMTkPLdshpMtEcvxjTp8bffTP7Ydvt/JITJqMiPYvuKK7AnCFIoweDSkDVijCLbXJTXdmq6oVCJOReSam00x36UCF3q+c8QRDz0EpiUXRHbiyOC2+d99zq4YEvYalr+Cp5140wmoLwCxo2HGb2QKYPGBmNsjBsPW2wBc7ZM8/neBL95qJd73jVpnoqZqFG/VTIuhuP3zEWMoB6MmVHMOLF/Y8WoHI5xFWZmFHpvf0dRFEVRFEVRFEVRFGW0GZSpkcCIqnUEKVzEQGhi6KJLlLFRSHCR3PHVVrt6MLNumwlm3ovZUIg+iqcHkdQu9nvIL1obJla5M5vdYypWZ0OWZRlYp0MMndGquaEmRj4SVZDECMFNGHG9kIDuCoftwCtAdw7GNwVpstox41oiOjx/mZsSyE6vIwaYzF4frnPlXifueI5jbhSqX+NZj8GmU5K0cyOFnOtyhifCZ6htKdTndvqlYmOgCxPdIuvaBdXD0pnZ+yi2rAqYVw+zZxjjwo7SyBvDSaiugZmz02y5fTndnTnWvN/L2hV9jJuQYObWtZRvOZuO3jQfXvo8D7ybG3TdHgh+J9zaL3HuqRJBFxVh14eJxLJrOkVtt5ygPlGZv14r5h4ylNRY9iQAt2aG1tNQFEVRFEVRFEVRFGVjYFCmhog2vQTFVsUIqCMwEPpCvhM3B3sps0dFUKwFpmJmsCcxdQck7Yybairpf8eeAY6/njszXEyElPVdSZlj1zNw01DFEcPcCI+waJCo74k5kyUQHcdSWhw7RZa8FsNnJGZBjzXETJAUaD1EG2q26SCRN1n/ew2YMWyLpdKHcv3BQMOsGiOKSi0PiVRahxFHowpiy+z83oh1wpDZ7a6gHpYezo2UgGAmetQYlvvLWMUjP83aWDb3SulHO6VZoXtTXGPD/mx+FRw0DaqqI6LgcsboyPq57DLVSSpqyqiYlGFmXS/TpjSTTCdJ12Qg20dVRYoP7zCenZ9dz6vrBn9/kag3MGZCBfnXW1SkUCWmCH2l/3k7xnTsIr8tXQO/OgC5Z0oqPInwyBJMJJBoLyHOb6wd8Rg3ClI+U2NDURRFURRFURRFUZSxQkmmhogbks9eBJUcRvCowoioYISYdv+1zFZvIXq2tC2aJCNeh80eTWKEp1pM/YFNMWLuOv+xOmSfGUykyQRMnYMKfztSF8IWJWVmeDf5ERzSF1FRGbaID4EIZqcTgXzxs1QRTvYr7Rmt1FM2GYyoV4np1zLrkcL0caP/6GBsi79DJUcgSBYSG5OYvqoiMNpkVrykS2uP+G4UScx1UEtw/UH+tWCbCjbjMOevASPKFhuXMuZkHMqYlH24RAngxUTZsW6C9VE8iiRsRryYwhv62hUTqpj5UKzfC0VsCLZQnsOMy63rYPPpkE5b6aY8y8zIQk83tLdDRyck3+6hsqqH2voUtXUJyHpks1m8FU1ULG2jfmKaKRVZDtsZ3vo9PEo8AyGKDKaWSD3B70Af5lpsZeC1VANs7n8ni7l+VgCrKC1KyB4Tks5PzMtyfz9lBHWLxNSuIojgKrbtQg9CXsv2FUVRFEVRFEVRFEVRxgKxTQ13BrY7W1WMjlaMCDQLI1yV+Z834qfRKbL9YjNG7dcSUVEHbALMxsyUbQfeB5aF7C/ht28GJqKjFiMUSeRDPYGYLCKWHFcjJr2PiEhCobQ5dmooEW5lJrzMSpfZ9/Id+7kYg035lMCcm0oCs0GMqsGKx2WY87A1MA2oTAQifQojXLZi8t2/jYmksQu2/6ch0RbFEAOjF9NXEgEks7wLUU4wtrrJN9HWYoy9pL+eFNjuovCYsUXVOOclKkKpUBqkKCE8TkqkjRkpEi8mnxg5IpQP1diwjWf7mYhlxUyNuMh9zD2fIohX+A8w99CPVMPhs2HqNGNqJPyG9PZBT68xM7q7Td2MhkZTILyr29xD0uksZf4Pi5eDXC5LZWWWadO6mTIFZk6G0z8BXU/Cn3sHnw4sg/mNGI85V3bkVC9mu/b9ugNzHiVSo5Pi15rbV/ZzVESImOKT/LZVEdzDXyfa1BiMmSHnrxKYEvM4FEVRFEVRFEVRFEVRRpqSTI1CSM76XoLIidmYWaUdmBmnhWpZuGkx4oovZZiZ5WJQJAjSX6UZKNpl/PXH+a+lxoaINzXki0pdGHEqh8mF7qYScdNUQbhIKO1wZ3O7aT1sAXIkyWAMiOmYPmzDGEFrGZwImMD06ZbA/BRMKoeMP7o8z4846IXeHqjMmSgZMAJhA8Nb78AdqxuDYSICaR/5KauiKMf09yYYYbMZeIN8QdM2OOwUZe615PZPI2asD6Z2RVxTQ3BFdzfCSbZjp+Qai0j0l5h4aUx72zBmhfSlmJp29IzcE4Y6Tl3zwibKcBhuXLPKTi8mpkYPkPCgPAW19UnKa8tIlyXxctDe3EPn+300NkJrG3R2mCiNvj5jfHgedHWZ6A3PM8u7u425sXYdzNwEZs6ELWfAtw6E7JPwZFPpRpkY5hIpBeZ3oo0gfZt7vtqBF/zjFKMy7niNc61AUFMnjUm1OAOY6H/3ff+zQvuwIyDtZzeCyF6exvy2qqmhKIqiKIqiKIqiKMpYYdCFwgvNwJai4Q0YAasFMys/KoWOK4LGNTaEXowI3EOQomNzgroaUjy5nECcqSK/aLhddFsMiKy/3Q6MsBUlUIWZGVEiohTzFsFsNNLO4O+7DVMEWGYZN1O8iLpNynpUYaIz6vn/7P15sCTZWd6PfzJr3+/e6/T0bNJoByEBhjEShA2ybALbAoED2fiHhW1sRAS2+CJAICFjgwETRpjALDZ8QfwwAbax2WwUYDBIaLOsdSSNRjPd0+vda98r8/vHm2+fU9lZdevevr3M6DwRNVW3Kivz5Dkns6af57zPIwRjfwT9CQwDaE2gFcj+r2PGRKsSjhP2HLEzPJ4NUOJbhbkk+IiY8TBwFpnLPvAM8PQB+4/blWE923P4qJUzcSFiHklrH98WWpIyNTJINdYEuZfcynjmMavbe8g96lYsihRFRGA6gYxJDRnPp5EKtW2kX3sY4Uph5zXcKmYJF96cbW4H7DFUGyW1MguAwQjGEyislUg/cA6WlyEMKF2+Rr97ievXxvS60OtDEEAqBb4v38+kxZpqPJbPJhPoDmFSh3IJNiL2/cEVeOtjkH0f/M+dw7VfK5WuWK/1d2be/AuQe+lhjxUfn4MEqhHye7qPqfZRUXrecSBZ2LCF9SRhQ38LHRwcHBwcHBwcHBwcHBwcHO4FHNl+yn4/jj6SZZFGCL4hxnaly82kpRJdascCs1eN2oQ1GHLHzmeYRNtoULKuon4Es6p1D1lxq6tpdX9DjNgxiPbdix623Yjd9iQk9Yvmf9SQvugjRH9Sn9xujJHz3+NmC6yDoIG4at2lY1tDxnAzhOsjMzZqg9RGzlUrX1Q8Ok67ITsDQPerFTv3usChZOVBYsAaMpc3kLndm/OdtPWYlfkQ/24OY5Gkfddgvq2NVizFszSSSHWYHqd4lkb8WtBV78dRzZBGqroeQa7nzyL3Kp2PY6bnZ7w9s6qw8pig9wIiPNnnlUcIcq1mO4x4uCjsKokw9rcX2+5O32/iVTaTiVRtkUpBpQInTkAqRWo0IlvZJJ0eM5lIFcZoFGVuRI2eBPLdwVA+742jCsFAhI9qTTSSIIDnp+D/eTnk/hz++yHUBv2N0d+Hg8SMo2JWZVPI9O9dHGNk4cC16G+1UFxChI6ke2p8PsT3nyR2+hgrLc33cHBwcHBwcHBwcHBwcHBwcLjbOJL91EHiRogRB5TwTiMiw3WERIyvRNdsiKRVo0kkjAogegIDhDzvIZkNdYwgoWTPGUxmxg6ysnWAWTWt5JW9OnaIEEj6nIRZ/vVx2J9phsWQ6dXpd2IVteIoeRx5JIz9YaKqDEz7R8jYNpB+H0TPKmYkBaofJ+JzMIWIL9Xosx4yL+ZV3dwLmNdHWmHTREh0XQW/zbRQBMbqSHNtbGLZJu1hev6eRKzjiogY6AOXkGoQu7LJvgZ9DPE5sPZpP8ezHOxztdsdxxix+zkO8Uvtgx5ExI0ckv+jx+4iRHE3Om5gPbSqSvtORU59qM1eExH88tHzfdH2+yQLo8eNeD/dThHDFphtqynNoJgn3gyG0N3uULl4EYZDyGSYXLpOb6/HYChixqBvsjQIRagYR2LHYGzuKWkglxZ9ZOVEhupDNZhMqDbbLK2OeVsVcr8d8lvjg/sijVwDyxgxsI+Mbzt6HMf9Y5agAbMtCe3XKo6lkN/W08h8voJU4MVFyKTf2LiwEV84oNe3isIODg4ODg4ODg4ODg4ODg4O9wIOZT81a9V1/PM0QoauIoSyChqV6G8QQSFODOlKbCVMdZ8qbKhfvXrS5xHCNochDbeAC4igYZMwavtSxhDDSlwqlLBMRfvPYVaJq0gTh5JE8VXmScSZiiZaHaGkqY17Pf8hj4yrVgloFYZadDURAlrFIa2OuB2EmIesii9jVsrr6upB9PcJTNZKFyHqN3n2rjr2kPPbRuaRCkhd5Jw1y0ERIHPXztNIsrSBacJUty0gfTtG+nMUvWdX6SiBfQ24iMyHJKuaJCFDH2qLNbL+tnGY+XOQMNhBxNUcImycw1ihdZC8l2tI36oApg8VIWHapqsefbcRbadEs+Yw6BjME29s+BhxAOv4BwW829vfiXtJFjnPNEbAyCN9O8ZUoin0vFbLYhM17o7Y/cQ1Up+SmoNGA65fh3ZLRI1RlMMTgoSJI+eZy0I+J5UZng/pqOBjZRUq63n8Rx6CYhGv3abY6fKS++v8Q67w7t8y135SX/rI/e1+jDCVjY65iwgGV7j1+8csQWPeb6xdPaEVQcuIYF+KHuXo7wLwJDdbYelvrP7OgREtdL7Zv7M5pA/S0T4dHBwcHBwcHBwcHBwcHBwc7gUsLGr4sb9n2WLoTpVsBmMPpdYrurI7LhLYRH8aQ1SrcFFBSMhytP9c9D39zhhD0OxifOyV3FMCXsnaavR5PMR8HDtfJRSToKvfF6leUTLpKAHM9woGiGjRRvroCrKCv4MhzOKWUjaZe5yrxjOIuPJQ9JxDxmIfIa2VkNYchjtF9N4OqKin4mAbIdJbyNzVVdhxC5uA6fGYZflkYzPadxG53jLI9TtCrskSYoG1ChRz8vB9ODOElS58BhNaPIs4VhJV96eCiYpiej0uWk2k+7KruFQQ1T5QMriIqf4oYAQNzU1QcaWJEUftea33LrvvhtZDhbwCpnqjw81C6yx4mAo3JdRVnOpwc5Wb3ZdZjF3QrFwW+3tJ1W/aF4u0VUVorehJIdeh2iFVkOvQJs3TwANl2FiXCoyrV6DRhMlYqjBGIyncwINsNnrkRMTIZCGdFtcqEBurMATfg3wBVpYhmw4jNWSM5/lia5UKeWAZvm4Dfn1r9rzS6qZW1IdFROTKY36fbhVJ+RXx1/H34lVO2s967fSj9vYx/T3rx13FsUm0D3vMiohQsozMX70+7kSFkYODg4ODg4ODg4ODg4ODg8OiOFKlhv16lrChAeEthGRuYCoeWsy377CJMq3uWEdsXF50Fk6sQD4Dgx7s7sP1HeiNZJ+nEeucPcQv/wJC9uhq/lz0+ky0bQt4PNrWXl2uK9DVy/8gxFedH3dWxL2CPrKKPY8QXnsI0WpbDtkrxm3ERY1b7acJMn4NDKHdQsSrrait8WOpbcuzBdqXRUREyGMqNbQCwBYq4t9V6Lkr0a/VFRPrc31WgrSBiID2GGWi41aRKpjVJVhfk1XyD4/hgW1YvwgfRISNDtMVBj5m9XcZuaYfyUCtDNf24ZNIuLZ+7yBiXve5FLVHV5WrcIp1fBWG9JGKzkWFArUYqiNzvI4ROJWM1/vCrHBxtbZrMS0QHEZQ0+3STAsbeo3Z45ZF+rEYnTsYUnuWGGuPQQFjM1aw2q8B6vb8SLpOR8g8CRA7Os1hUSGoiNzDVWzWXJdP7MG7Pgx/9axYSg36UnHheVJ1QU7+rlZFqDhxAqrLHulCFj/tEQ7GtOtj9vYlMNxDxI58Hnr7XQqf/DSh5xOMAoLRhHF/TB744jPw/9+a/fszQa6tXat/K9FzNzrXWwnMnlWdEf9tjf+uJtlRjaI2bWJ+Z3vROegiglnQe4FmIVUQ8eYU8LwlOH8SqkUIxtBqwTPXoDtr0js4ODg4ODg4ODg4ODg4ODjcYRw5KHzWaxDSag+zarmHWU2u5JhaOynhp6gi5G0FWYWqZO5J4L4KnD8BXgq6Pdjag0u7cCkKpc4ixFwG42VfwOQE6OrTHFDNQjkP7S5kI9XiMiaDY8LNpHESfIQMqiBk5xiTeTCPkNWVsSnMCmCtZLmXESLE3gVM8LtdqZJkpaKwcxz09axA5kUwQeZZCxlzfW9WIPazEbriWldjt6JnDTC2t1PMymdRgUSrPdS6al5WTHwOTxDB6DPR69aOvF8oQrEIJ9bgBT0Ybsn1qMKG7qeAiImnkeqas3m476SEP2/uG5GhEzuXefAw15OS8zof7OtKq74ymLnXxFS9NKLHEJNVcAK5b+i1qvZfewiZnHS96j3vVjCOjqWCbil6X3MdtBJDs2P0Rq72XWp1Zwt7IPfSR4HnAaspEYczKUj7MB7BlQF8MjRijlZs6P07jjA6juYOlZnOcLGFxD7mNyHow+QiPDiC8+fENiqXFSEjDGA0lsqNTBZWV2BpGdLpkPFkyKAFg37IaATFAuRyUCxBvuCRy3ukMz4MBowGE4YDqf4IAshm4EvPwz94Gv59Pbnf9XwUmhNjV/wcFfPujUnb2Nsl3UtbyH04jckvsn9jVbyKWwDmkEqrGuZ6qSK/kWdS8MAqrFZhMIL9Jly9Cp8d3Wxl5eDg4ODg4ODg4ODg4ODg4HC3cKhKDcUs6ymbsFYCC6aJIF0hqt7/GQw5m0KIzg1k5bWuUlbP9r0WvOf/wvsD8e4fMG1ptQ48gBA2WYSMrFj7raYglxGCqzeGC01ZlaxWRdpOtaGx2x0npHyEFHoh8AIf1nxpw+4YPgZ8AiFKk4hAtYpRSxM9h2cLEa+iVQOzgjgePJsE20rFt57h6NUaSrzrXNOV61plo2Hsz0brFNu2q8PN80k/j2NW/8PNlSv29ov2URd4AiFUz08g2JQV9pWyhD83e0Y8KWKuJbV9U1u5AXCxDxcuyOr4TyGB5IuMlz3PfEyQeCM6bgpzH1LhQrfV4yv5P8QE29tCkeYLqPCo1+kAU0lyu6DjXECElRMYglrFLa0o0GwgOzx6aL1nI0O0Ij8Hy3kZt0kkIvRGMAilj4qYHwc99yZG9I1Dq6Y6TM9Luxorfo1/ZAj/owPfmYXlJam00O8EAQQTk5nRaono0uuHN/I2cnlYXobljTSV+1dgfR0yGbzxiLDbJbu3T6beYDiQFnvAmRAe3EAupgWhIvetIkmgmFWxMUvMsP9WizRto12NZdtP2TZ0YKqaVjEilF6XvQl86mn43OfkN0zzkewsGQcHBwcHBwcHBwcHBwcHB4e7jUNVauhzEtESf0+JlHmkjH7PJsTbGEuUYrSfbvQYAlcD+L/cvGpU/b9PAmse5CI2eBgK0dlG/NezoZB4W4jNjYa+ql1HPGfAJnyU4FQ7oJOI1dVKWjzfQyCMjPOT/NHtvtGVz7rC+SDS7KjVDLcLSlJqqPs8OxWY3W7bEulWV0JnMKuPtdJmFgn7bIESqgfZFy0iZIDMtTrSNyqi+QnbHdQm9e7vEK1mb0K/B40+fHwgVU9qh6OCnYZJb0Wf6T2ixzQ5Hz8vnR/zqsP0GupE+7YFiwHThGzb2u88C7RR1K4967x120UzJ44KW5BRKyQVgNUqS7Nt2phz1OwNraDS0GfNy9DqqqcHcHlgzmVs7TsHnI2Oqf3eRypTrmCqaOKw5+oi6AGX+rDbgTNpETAUvg9hKhqjQKotJoGIMIWCPPJ5EdIKtQzexjqcPiNfbDTw+n0m44BO26PfDQlDyeYYj6IMDu4sSa8i7qxKjHnWcUmfKyYcLCj7se+rNaTajtnVR9vAfiCWjFdwQoaDg4ODg4ODg4ODg4ODg8O9iUNnasxaZWqvuoebyZV5fyvhM8b42I8RIs3HVGT0ENJFBY0UIjrkkYyMLwTOeGZl8QARNC4iRFwaKAZC1NQR4rXObMJYKzzux1R8aLu1fZ8BPjmEyVD200dI1Y61zySCXysLFvHaV9scFUOUNDwOwkktieKreRf9ruYNzBM07O2Tqnbs10cVIPQ8NABaV98fR/WLXiSLhlYfN2bNEVsQir8X/9y2oImH1atd0GGJXj1OG7jag0kfrgXwXqavKx2bbHTcBkbMs0UCGzqvlFRPmlPxuaTWYzcsjkgWLBY9x+OwkboVdJF71C7m+tdz0yoSvedolYme9yT6zmr0KCFihY71Fes7ej9JR9vp6n0lw7VSSMWi48QnWvArn4B/7MHznjf9me9ZY+xBygc/K3ZT6bSIFLkcpLxQgjVGIxiNGF28wv6nN9m6NGBnG7rRD0apJKLI7u7duY7jwsQsoWLW7+g8UST+d5IoonOjg/mNHSOivVqVDZFx3sYsSshiQsQdHBwcHBwcHBwcHBwcHBwc7gUcyX4KZq8kPagyY95ntmd9HSEUdbWyVjYE1vYnEDFjA8nPOJGC3YmEfm+HJtti3/qeknRK/B2Ul6GVGjkMcapBzc8QVYBE29vWPkoIxUkm23opXhWShAzGikuFFD0XrV65FSEggyGrdH+LQqtWDqrSSIL2QxB7fSvCxhgZb+2fo+zHtx5pRDArIWPcQProTlR+aD8clkSeZ21z0PZHQR2xqcmG0A/Fzm2L5D7qYwLK5/WhZkTEV5gfpd/1vgLTRPazoXpH7YWa1nvabr1uPabzNRR6bWsAtG7bx2SH6L1Ev6eh2Comqw3fODrGDrOrNGwcRihtA7/XgtFn4J8X4P775tgweSJKeJHA4XkQhDDsDMltbeN5PpNun/2PX+bDHwp45iK02iZIPJcXIWR7f4GTuA3QOZ0kjiYJFpB8PR/1N9YWqQIiMTLapo65t9ntqyG/rWvRs4ODg4ODg4ODg4ODg4ODg8O9gCOLGrB4NcaiJEw88HaACeuOI42E3T6MEHF5oDsRkuYiQsBpVYMKJYfFCFnR3EMIPyWaR4hQEg/hnQWboI73iZJM9ntgiP4VRLTRDIIJpiJhm5uD1pOObe/Tfl/JR803WZTg9jDh1bYt0KzxTqrCsKsCjsMKRit9DgtdiawB0nlMjssqIij5SO7K55Axv51ZCna7Ft1uVkWQx/TcSzOds2CTz3D4MQiRKoLdgza0tk+yl1JhTNupeRf2NgchPr/s6iEw1Q22kPhsEDbi10wmepSROarXfzwDSG37LiH3Cb0Hav5MENufjwnF1uqP+D4XhV5Peg8/CAPg93eh+3H4viw8dGL+9iGSuTGZiJ3UqB+QaXZIlVp4AfhpD9+Dbk9EjWF0Evmh2FaFd2HgNbRbr0Edg1liBgnvxSuw9L153036LGu9r1kyzYTtQISMlyL3wsKMbRwcHBwcHBwcHBwcHBwcHBzuNI4kasQJ8FmrRxd5L+kzm3NSH/44D+VhVhJDFNydhRek4FQAjZF4g/cQOyIVJ7T6Y1EMgGvWMZQ8XYQAjpNPcdgrb9WeRwn+MUI2FjArbHPR97QioXdAO7TKwLbwstukx2lhrHvmIS6EJFVozBpfu8IGpon1uNgCd45wziIB8ytIX9uZKUvRcw+TN3AnbGvm5bHYiBP3s/Zhb2OHB9t9nxQqfTuh15Lm1XiYOTirjw8iee0MHyXt7dd2JZBnfedeh4fMw3VgGekzJcfbGKFGBTdbpNI8oiTko/3ZFR/70fZH6RebuFcBZdE5NQH+fAt+/EPwz14Bj566eZspa7VQgsTHAANIdccUe3381RVqD63xyP4mO7sibPSj7JBeAK0BPHWEc7tVqBg+S9xOQpJgGa+8SIKd3wHzRRC9JmYJyyqYHdRWBwcHBwcHBwcHBwcHBwcHhzuJQ4kas1aEx3Ergoa9f11JrCtbbfjICuQKQvJVgHIJ1gtwZgz7++Lvvx99VkXIujYSeFu39qlVBxoEruHCcZL/dhGgthWVeuVr9YQGXWsQei7aRv+2hZ0kaOaIrsZW8tq2zFIvfhtx8kzFDH0dzzqYN776eZIwRcL7dxIjxIpnyHRgtoaOLyN9s4cRhW4nkgKDlfC3YVfY2NvNszRLIjnh1gPaF0F8zqh4lLHaNI/ATUJ8e3v84q+TnpMqiO40VLCcl9fiIzZ7L0JWzGvYewMjsOo97DBjWUDmeB65DjRr4Sj3uRQioGYxdliH3c8AeM82ZP4c/uFL4GWPTn9uC1Ig1RphKI9+Z0Jmr0EmmOAPxpRKcPoU9Hrg7cBwJOf21EgyX+40Jph8lriYDUZ4i4vOiwqc9n6Bm35P511TKkbFKw/TiHh+HZkjLlPDwcHBwcHBwcHBwcHBwcHhXsGR7aeSVtp7CZ8T2+6wqz3tCgUl35UcbSGiRS3aZtyAdEvIuatjqc7YRQi2CXKyNeAs4hEeRu/pQ/3+NRdgkRDvRbDIeWslgE26BhgLl1HUvjxCRnrR+11mixoa+pphWrywRRSFTbB5CQ8/9jex50XGNU4gx62A7HwNO2/jdkK95dvcPJd3ECJPrXkOkzdyFMwSApPIdxU6tI+0qmfE0azWbgd03oDJKMH6G6aDqu3vzdrfYXCY+82dFjYyyD0rF73uIvetJGuzInK/Wom2bSAi2x4ibrSYtjNaFF1kjmsll1oRHRV2aPlRBeAW8L9b4H8E3jiEl76EG4MYH8sw+s94AuM2NJsjQupkMiGlAjz6Alhehs0t6LTh6Tp88pmj2dQdJ/S+5yP33Fn5GvHXSfuJ4yjXjmZHaTWJ/samMNU7NWRxgIODg4ODg4ODg4ODg4ODg8O9gIVFjTjJnES6xAlrXX1sWxXNqvaYtWras75vryLWXIldhEy7CngBDAMh+vYxFk26Tx+p2CgjJOEaQhIqGdfChOge1U8+jlmkVBJxFa+YsKs3lKzuIuR1iBFBZq02D5Dzsu22bIEC63lW5UWScJG0Cjjejln7DTGVNzqetsjiIyu+C4jQMMs65zgRxp71dbzvbieSqpXs/o6HXcftwjzMXJ41H243ca/VI0HUDiVGYXrFub72mLYlSxI+k4Seo4iji+BuCBtLCFk8wogTaYzYkUbuV+vI9bCP3Ot2kWsjbjl1mOonFSDmVfcsigmLhYgvggbwv7rAp+H/l4KXPh/SCb9UN841hMkYOh3o90OWlmDjdIalWpH1+4c80hzQ2AtpfiLk6WfufnWOogicRMa8ixGv4wsG4r8T9v1KxWoNIYfk6yMuiMbf8xHBXG3N9L0A+R29zOzcDQcHBwcHBwcHBwcHBwcHB4c7jYVFjTTTK9VnVWrY1hm2XU+c3LYxi5i396ukjU369xAyaCv6W1d9jxBiJk7MBxjblkb0PRVHhggpd7tyE2aJP8TeC2J/e5jzUfIri6kqSVmvF6mYsd+PixsHCRmLVBLY26QR4k7JMhWnesg4qN2NEnNKzq0gtk9XuTOixt3GLMuppM+1nyex97S6JT728Tk3a3X3rQSGg8zJCtNVLUnzJel10jazRI74NvH3NSMgLpgE1nN8vh6XDdWs+1sStDLLQ4SNJYywqWKQihoggsYzyDWxz81iBtxMUs8bc33/XiD3k9AE/lcbUo/D3/fhRY9AJuoMrdAIEUHD9yQAvFoDL5UiX0mTXSlBsUiqHJDN9ti73uJPPj2+YY81Dx7T99jDZIMsCg8R2M8jv5N7iJ2i3g9nVWvYc1Ur+TQsPk/y9aLfg5vnefw3wxZHwCwc2OPm+5SDg4ODg4ODg4ODg4ODg4PD3cLCooauqk8iTOLEqb1KP4lYTdpH0r6TyHI7/HfMbIIqjZCsWYww0MCQRq3ocSdwENGUBJu4wno9RghNJbF124z1nmZfqLAUX/07S5w4jKCR1Fas9qqgsY5UxGggdAYhCRuYKpoBQtZeir4/jM7xdts93WuYVa0Rv25sEUItyYrI6v5B7Ps2Zl2/Scc6DHzM+MatfWYdL07Ax4WKw8xDm5xPytKI30dmZWz4sX0tAm1PChEm0sxuY7ytReA08BBwKg+FNLR60JqY+5SHiH97yGr5bW7Odolfu/G26bnfqwLGLDSBd7cg+Bi8cQwveBT8FIQBjMfyGI1F2CgVYf0ElJZ80pUCZLMwGRMOhjx1scMP/+GYP9k9WKDwMBUUFeQ+dA25Xx037PtgIzqWbcFn52zMEjniY39QFdMic8C+hiaIsPxsmzsODg4ODg4ODg4ODg4ODg7PbSwsavQxVjJwMAGqWRgHVSjMWnUdJwGTSEElKJNWlmsIbiH6vIOQM3faT/2g85t33kmwqxpsVBDxYAmTQTJAyLJ9TFXEvMoMYp8dhCSiVP/W9uWRcSgjtlJlIOXBODQrzVvIOF5HBJgtxOs/KV/guYo4eR//zN5G53uAzO8lTGh8HPZ8Sppr8fl2WPJS2zRExjFpbs763jwk2eWoeGcLB2rZZh/3IGJ3ESzSDxuIaKpCYo6Db6jaVjDXbBnwhzAcS/i15o9o5cYA2ERWzCeF1cdJ76Oez72IFvA/O9B/HL59Ag8+KGJGqyU5Gf0BTCZSxbF0DU6fHnHfI32yxQIhHp9+Zsw/e9eQ9+3JHD0oM0SrNGqIsNFHqmRmiRo63ofNXwqRarXHMZlPei3ZgoZdeRQfY83CyMS2iYuX9utZVUlJ1Rsqrvh8ft2LHRwcHBwcHBwcHBwcHBwc7n0sLGossorb3iZESNYxJoQ7w81k5SIki/13/DlJ1PAw3v4thNBvc2cClJUUs8kmm4QekRzqe1RSWZFFLJvWESEBhJBT6ycfI2zMavdRieD4eWi1hdpjTZDVz1WEgM9HX9DV6GNE8FjBBLQrqacTNE7MwfEFud9uxMWIWdskPcffi5+7rqKuYkLlFxEq5olmi8KeM2qDE2/7IvuIv9aKpABjr5ZGrqcacq4a4K65J22m7eOU2J1XJRW//yRVydh9XsZUf6loq1VROldTJF9LOvbjqK3NqK3Z6LNygGQCRe9rddUAk2VwItZPTUwOx+2oxIjPxbtV7dED/qQDj/9fePkF+PsPQz4LpTIUy1K50e9BswmeB+lSQP1qn1//9X3e24EPT6azleZBfzMuY4LbZ1X0eYiYqDkU2j96LRxk46a/jwqtWNK5aFdqwM3z1b7X6vyL20cl3TcP+t2JLyDQe7iDg4ODg4ODg4ODg4ODg4PDvYKFRY0kkcG2lwpj72kGhL1yNMTYQSnZokSOEpmKeHVH0rPa8MB0gHg+Os4QIbPa3BlSJoVZfb0aPbRaJcRYmaidiW0LdatkYRdZ+au+6kqu5hAydoAh2nRV8CJYpF1xslN96HVl9HbUDq3UUCJXiUAlifPRY4gINKeQ/tRjeEifqTXVNmLLswh0lb891+4EQath0CFSfZIEW6CL4yAf+wAZ+yFmzOP7nkfsz/t7Ucz63iKiAtxMvKrFmn6mFRC2vVoGIZTVhmvAtI2Vvco9ScCIr4a3t89H+12N3rfFWQ2xVxI53iadY0nQe4BawvWZrsjwkPtCC3O9ah9oeLhNfBeQ60QrsQbceh6Q3lOzGNFGhZtB1LY7IQ7HEQDdEC7swf/7AXjpffDG14CXTjMawpUrY564BP/nMnz8j4ZcZ8hl5Jo7bC6P5kg0MeedBBUV45lGtyL+JIn+s2CLx5Oo3RmmxTaYnjP22C3yG6vXSgYnbDg4ODg4ODg4ODg4ODg4ONw7WFjUgGmCVMlh+xnrtU0a2ytYC5jVrQFCWmcQMm872i7NNJmTRPzbIoYdQp6K9p/FCCtJZEyc0DkOaLXEOcQn/+FlWK+Kvft4DPUOfGIPPjyWtjW4dRJS0UdyKTqIcKDElgoMtqgBBxPR+vqgfooTeHHBYBQ9Osj5KvFuf0+JYSXp0kjFyRcCGx4MQ7MSvx49YNoO7SBkon1mkb7axxDntwseImisIe2fRVLGiUz7OQ575bb9/RHGuiapHfHtb3XeHySKzRM04t9X4tR+3/6+fjbGVBv1MBkiKlwmZU3osy1wJAkaGgwdIrZSOcw9pBNtl0eEjpK1X73/qBCgAl3SavnQanchem8JsTlaTkNjAtuhWE21ou1LSGWKZpakrX2p2JLFhE03ODr5rAJSFXNfVlFaSfs7ERatAmTWer2GhGpXkXP9+Cb8xG9D4I1pj+ByGy6NpN/2MULEUe+vKhQcJCwsare2COzKDPuZhPfChGPrfSCHzBW1IcwglU19TCZLfI7OqtjQa1ArpRwcHBwcHBwcHBwcHBwcHBzuBSwsasS9vZOgn80ielRwWMWs/HwQIe6eQioYJkyLFLNWjya9jn9HyZgShoC0V1SPEYJ8Frmt31FbGbWFmQW1vUkBq2l48AycOiV+770+bG3BXhMujkWAOKrdUxJ0FfgQWWUcJ6y0bXGiGObbgM07nv1a/7bHXokznRNxu5WD9h8iq7MvA1cQsrKJrI7W/S0KHXddGX8nqjQ8DDE7z/7GXg0/S3TSua0EOkzPRbtKQb9/mLE8Sn8cdv7aIkdc8LD/titqbEudAFPp0EH6QUU0u+opqV1Jx7LtolYxlR9Fqw0aVl9GxI7zCGHcx8xzHRslk29UcEQf+NHBwxBSGShVIF+CQh4qBVipSM7Mp5+E7ia0J2Y+lKL2aBVT1joHtbLLRH2ibbZtuA4DrZYqRc/a3z2mhdHbDa2SOYn0aQ4Rf2rIuW0C20N477Zp1+1q2524T9hIqh7yEz6D2Rk6aURcL0Xb1pC+bCC/dy2mKyYP8xvr4ODg4ODg4ODg4ODg4ODgcC/g0JUaME2wYr2nSCLTbM953UaJwCLGWkVXBWu1xizyPIw99D3biqOEVE0sIyuPy548Kh5kQrgawPuBiySLMGmE6MxZ5zzPtmiAkO8TYGUMp3eFyATodGF7B/aHJmvidniVq3gxD9pPNqll2+8c9F37dTwbRPehtktLCPnaRAhSu2oniZTLYoKv64j480mEyIwf/zAYYeyf1P5nUdirpQ9z/BCT5aIWOLqS3xbMihgyuof0yywrKl11rRUEtpXYIiLD3SImbdHMXm2ucwiMGKDXBtzcD3bFxTj2XpJwYd+zQkygN5h8DlvoLGPuR3p9BkDZhxM+bKxI5dVgCMMB9PswnsBoAmkfSkUoFCCTgZQPXtQwz4NcFk6fhhd9QZrqySI+AQyHMB7TbgakUlBvQ7E5bXmk10oWERvUEkvzRHoYAfFs9N4uxpbsMEKiBr53MRUxA+6s9VAKGYeTyD1cz60JPAN8LnqdJPwp7tY8tysHdY4eti22kGHDj22TdP4q3PrWNlnkfjpgulIuntnhRA0HBwcHBwcHBwcHBwcHB4dnCw4laigymOoHJbuy0es+0+S4vVrdwxBmtk93GriOEGg24sTOLEHDft/OjKggJN/zgBUPykXI58H3oTOCVgOqobQvfmww1kmHwRgRNv4P4F2DjWtRxQFCcO8jhOOY463UOAri/WdbAM0isJKqMuzv67guIVU4p5A+3EOISLULaiD9oeSzzqmzwPMRwnmIEPe6Wt/us6MQhRqqfdB3510UswLeFfFqi4b1WQ6xwCog56th02q/pivsVXRKapc9Z2yxJQmzKhcOOv+DKlniAsKsY8dJ5ll9p+KOvV/7u0nHShIt4p/DtFhXQUSBdaYFlCwiZNSAE1koeuBPYDSW+ZoDVmuwtg5LNbmHBCGMRiJq9Lqwvy/ixsoKLC3JNvk8lCse2WKGVMajUPTYOOmTf945vFyO8Pp1Bld6tOoB+/silKyWwR+Zc+qPoDGWayWNtC3rwTiQ+dKyziWNqdyoRuffwlx7B1VvBExXPNyqtZId8J5mOmtHKwqymGtBt68AZzB5OireNJF75yD2/SzTYpiKM3o93Snob51WEN1K9ohWDOnvqv5OpjCVKfbc1nukF322iemTDmI71UR+f7Sqyefme8osQeO4bBIdHBwcHBwcHBwcHBwcHBwcjgOHFjWyCCl4P0KY9DCVB3WEPBlh/LfjFRxKxhA96yrjMcYj316ZHM9NsMmV+ApS+7NRtO89ZGXvMyH4XfB6UUZDKMTPJZIFjaNCCaY28BngiWj/toXLAJP1caeskA7CrBW5s0QE22fffk9JthayqroebTOwHj1M1QYYu5TTwMNIHskEEYdGGMLZ/k7cbkYJvKRV6TZRbp9PksBhe82Hse2TqgFC67MMhkSMH18rM2xLn4n1/TZGdJkFXXVdRPo36XM9nv0cFxhmfcc+x1krwe3nee20j61zZRYxaoduL3ItJK0sj4+rjeVo/xuYMVKCOIvMrY00nKrAUhXabdhpSR/rfBqMoNsRQXQ8hlQKPB9qNahWoFCUz+4/Byunc+SXC+RWSpRPlkkRQKMhCkguKy3sdqHZpFWf0GzCYADpNKyuyCaTiYgk3S4ELcgFkMtAMStWVf0BeENzzfnIvFARoghsZEUAuT6CC4Fcj1vMDr6G48uISCEi5VL0qERtVIHCj96rYn4rVORT+6secg+4goh+fYxQodUcS5jqFa0e9DE5KHaO0O2GVmZMuLV+TCH98iByjnq/UAvEbUwfaOWFPf/V5k/7pI/8Dtrv2VVOammm308SNWCxa9PBwcHB4dbwnd/5nZw+fXrh7X/+53+ep5566ja2yMHBwcHBwcHBweHexKFFDSXPqpi8ijxCytYxRE6caLQRWM9KyKWi/Qyt7fUz27aG2Ot4BoF+FmDyMj6rx4o+HFnHPg4CT73vbQFmAFxAyCjb799uu3rgL2oPo2TsYfMkFoVN0Cd9Ft9u3n70vPcwlQdKlMdFkyLioV9Dzq+FsZq5jiEo7XbY88LOmUgixpPIeNun3t7uoGqBJEsjMAS5riDXfAIV5saIaLEbbW/3h6KC9EM6Ouctpsd5Eu3DY/o6ibdRYYsUs8Y16RpNGt+k6oz4dvGwb93mIEs0rSDQPptVMTSvYmMWVpgO/tbx0FX+Goy9lILlGqRTYivVGUnfF4FKHvI5sZAKQxEvMlnJyigVoViCc+dgdSNF+aUPkHn4fryVFSiX5EtPPEG4tclgp0WmmCG1sgLFAt5SjVpnRDYzppWDMIBsRkSVwUCcqfI5sbsilOdUSkSVMIT0GPzA3EdKQNWHbArKBTi5KgLJXhPO7cLjQ7Fyu8LxCrlJUKHKFpHWo0fZk/PMeBBOoDc2eTl6T1Z7tS1EeNbgdPt6CzBzZ2K91nuBbc12pxDPvTgKVNypIqJGAZnHKaQv1NLKrlZSIRKmhUkwQodar9mWZLOu+7jo6wQNBwcHh9uDX/qlX+LBBx+88ffLX/5yyuXywt9/zWteQ6PRmHrvu7/7u/nABz5wbG10cHBwcHBwcHBwuBdxaFFjjBBQn4j+9pGV0ENERFD/9UXJpDjRncEQQ0lZBrOIlqT31OoEbiZajxN5hCyyV+km+dBrFUcBY7vSQwSheSJFASH8S9H3lRyfFz59WCQFwduvk/raJrqTSLxFg8EnCGk5QkhMDzk3tatSUk7FKLVmWcaIQlr9YhN7zHmtz0kXwLy5GyfR7T6aIHNhCRmvJWTsQPI8Pk3yKnkPIS+/CPiClLTp6Ql8EOkPm4DW3A0VcpK895POI6l6IwlJgoZt66PbJFVyxPs+LkbOg84TJcJtstZ+tvdP7H37vSXkmlxBxkCvUX2oqJGPzq0/hL09qbpYWRWrqWoVllc9SkWPlB8yHoUQCRmZjBw3l4WVNY/SiQr++fvwHn0Uzp6FUknKLba3pUJjOCIMQggCUSz8FGSyZJaLZLJdMtkxuaxYVw2GMB6JvVWrJVUZel6TQOyu0mnJ8Rj1wQ+l/5Y8WClALi8CSDYD6QwUc1DLwOpQrpkG5l6lFUQ6viOOLz9jFB2rEfX3LjJHKh5kJtCdyG9JHak+2I2+o+PVxcz/pPuLVmt5mOo+vY/p+dzJLJAM01UPR7WfUvHy0xjxNofcU7YxuTs2ku7fYMQN+5qx73nx61qfk35nnbDh4ODgcOv4wAc+wCOPPHLj70qlQioVr0tfHC972ctueu+P/uiPGI9v/j/wN7zhDfze7/3ekY/l4ODg4ODg4ODgcC/hSKKGklOK6xhyWUkkXTE/i3SNk6v6PbtBB4kXB5EuakeiVihZhAhrMR2Ube8vaQX/QVBSPmn1vI0l4ARCTuWj9/aRldN1kgk4zQA4gRD5AUISdjl6gG8aIcnUDsxe0RsnpGcJG7rNopZBsxAiRH1S3oXasFSj9wcYAeAk0ic+Qnw+jRB+SRUFkDwP51UuzEN8m/hc1mDeKlJ9McbMuaR9VYEXAH85Dy97RHjvpc9Bry/2ZZeZFjZsm695ooZ9jHjIsLY3SQRSkluvnQzTxLdm5+j8i4//YUlQJaH1OPrdWYJG0vH0HMsY2yPNLlHxohQ9ij7kU5BPSyVEKg3BRCou7jsrmsT6fTlWz5VIra9GfmptRo0O42FAJueT8kN6+z3C4ZjCqRr+/eckBTybhUZdHoMh7OxAp4OXy1FYCkSNaLVlcAEvm4N0mmJ+SHF5TDAJmAQQBh7jcUivHTAZB4QBBIEIK60WVGtQLMLSfrS7oQSTTwJotmEQwLV6dH0NYWdi7OCySGZFPuovrbprA9cwxPmtCAJamdS19rMTHX8/EHGlhVy7jdi2OvcmGLECbr5f25/ZIlvI7alkOwgqlGkFo97PJhjLvUUQYCofFVrRFu+Pw0B/83yM+HLQb2gw47WDg4ODw2J49atfTbFYvPH3qVOnWFpauq3HnFXp8aVf+qV86EMfYnNz87Ye38HBwcHBwcHBweFO4NCiRtLK6/jqc5t4jK8Sje8rTljathlJ28dfz6rcyGCyGFQUyCMk2hMIkVaIthkjJNJRLVnmedQrslEbzkXH1NXFIVKRkER6K0lbjrbX3IUWJpD9KFCv+aSxPAwhvegq/MPsRyt07MBgzULQVdllpC9XPBhEq9QvMU1sxq2kbNgrmu1geb0YZgkFB52rikT7mDD0UvT+Pjev3FZB4xzwkAcbZVllH0xkoX+xP3+1uS1uaPtsUUrFArVcskUMtfiJe//7yHWRs/aXRuZskWnbn050XgdlgRyEFSRQ3kOIb81Osedi0ip0u81FRMgoR68z0SOHXPd6L6jlJTejXIJ8Qaou8gURBMplOHkSNk6lyNy/jvf858OpU/Jhq0Wm0yUTTKRMYzCg+NknpbxjZRlWV2XQhgNJDe/3pVJjOBC/qlpVDgZSgjEaiTVVKiWDXpS7gg/4vgfZHNlMhiIhDEdSujGZEAxGDBs9NrY6nLo+YWsLtrahvi9VHc0O7IzlPtcaydi0MRUTE2ANyUQ6l4JaCDkpQGE3lP7zEWGjw2whbhbsMYt/d4wI4G1kjmlFggqrev0mVRjFr8n4diquxqt5kn4zbhdCZM6tI6KrBp3XEdH1CouLLfHtVPxVJFUr2aK8vk66n8+6juLXnP37HO9vBwcHB4f5+K7v+i6Wl5f5p//0n7KysnK3mwPAW9/6Vp7//Ofz9re/nccff/xuN8fBwcHBwcHBwcHhlnBoUWMRzFtVPY8kmUWSx9+Ph4XHP08jZPFJhBA/gawM1hWzOYR82kAIvgZmFe3tWo06RvIldGW7bZWiwbnx/lIrnglSHaME8pjkFfKLQsn3o8Ku0DjO/lJC8AwyXjWE8C5Fn48wfvA94KlQyNungKsYq7Gk8Fys9+x5Z+c92NkcSbC/N6vvdWzUPktJxqTxyiAZGsuIhVC9AZvXheseDuTzunVecWguh9o1abv0OBmk7+yQ5pG13RAjjtnXo/azXcXRQK6jMqaqohRtFw9tPww84MXAy5D+el+0zxHJ94f4d/OIIJJn2tZNqzP0/QqwWhFbqfXIWqqQh1IZKhUpsCgWIVsrkCrm8NIpCAMRJCoVUT6KHei0pTSi0yUMArxsVrYZ9OWz4YiwXof9PanUCAMJwRhFPRSGMOgTjoTy93JZvFoVsnnwotmSy8LGBjzwAFSWoN2CZh06HfxWm3x9n1xtk0JxH88fMxxCvyfzpzORObOHPKsQqgHbZeQaOwWshTAKYT+Use5iBCJ7Ttn9TcL7s8YnCeOoXXWShQf773kketK1bN8/4xV3d0LcGCLXfQERKtcxtni3cr9Ogi1i2KJfMON1/Lvz9pt0nIO+5+Dg4OAA73rXu9jY2ADgscceo1AoHPCNO49v+IZv4PnPfz6bm5v8y3/5L/nTP/3Tu90kBwcHBwcHBwcHhyPhtogacDABkuT9fZh9xW2SbCiRNUZWfm8hJGkTk0kwiT4bMG1/dLsQRMfbtf62CaekjIIhRsjoc7C91Z3CUSo0PIRoLiKikoeMg12hUkDI1ucBZ3yo5SCTgt4ItgbSf02EqG0hxO0W0wT8rGPHoRUMmoWifatCzSIrvu2VzEmCh1aW6DYZpqs1VFjQrJDuCHb3hSMPJiLoVJDzngXdB9wsjNl/a76JbQ9nr363xcZZ/XkFeBipiFChaQUZ020OL274iPB4ChEhhxgLKq1imoU0Um2Qi46v1j+2mKHVJeUMLC/ByooUXpw+JbkZhZJHrpjCr5TxchJC4ZXKIir4KfFy2t2BZkOqLro9UMFidxdaHUJCGA7x2i2C0CNotGhsD9ndntBtQxBCKhXeyLdIpyHliX6RTkG1NqaQzeGXylCMyA/fl+P3BxDsQ6sJjYZUgOzvQ7uN12rhTSZkMlCrwXgiX8nuQK4NSxNoh9CLHt3Q5FWsI/OqH8iYXUdEhj4iXqkl1KyxjFfOHJX0nre9vf95lUjx6zRJyIy/nlWpcKtQ26gnkPuU5oLsMNt+7jiOaVdkJJ2vvp6FpPtZkjjk7KccHBwcBMvLy3ie3Fn/9E//lBMnTgCwsrJyS/kYAPV6nclk8V+MarVKJpM51DFe+tKXAvAlX/IlDAYD3vzmN/O7v/u7tNtthsNZS2kcHBwcHBwcHBwc7i14YRguxO/o/7wf+gDRs2/9HSeg5q0ATiLQM5hcCiXHtdpCCWk/2k73oTZD9r6U2D2uVbQZ61ggRGvc3uewmLWa+dkEHyHCTyFVCVmEMG1gCNUAGdMTSPXMMkKc+whBeBURhLTaYBS9tn3qbdJSCf2k1eb56BgPeHA+K2/uDOFKIDZWHZJXeCf9bb8/6wqxrXHi4ekZhNC/DziNrKSvEbXLhw/04f3Ruatl2KJIYUKxdf4H1mcwPfe1jfOuh3zU1jOYHIZtpsW3ReABDwLPB84jY70PfBLJdWgmtCNE5o4XtaEWnZ9WZ6ggon26moG1JXGHWlkRIWN9DU6cSZE/UYNKRSotSkWxgPI8yEnGBWHUU54Pk7EIDP2+lNB0e9BsEDTbeKHkZHjBhP3tMRcuwOYmdDtSoJFOQ6EIlbK4U2mIt+9DJg0rp7IUnn8O/3mPwPoJGA3ER8r3ojTyUI7dbMLWlogsPbG2Go8CBv2QyQTGY4/hMKTbDuh2odsOaLWg05Gv1uvQ6kB3AMNIzFAxrQNsIuS7ProLjJ+Oya0gLgYeVA2inx321yguvtzu+2kaUy1xu8PK7f6wKzfiVRyzfl/tZ4XmgpSi1xr6vr/Y/y7c3MYj/v+Dg4ODw72Gxx57jP/23/4by8vLwK3f3973vvfRbrdv/P3t3/7tPPnkkwt//yd+4iduCgv/wi/8QlZXVxfeh/5T8Ed/9Ef5oz/6Ix5//HGuXbu28PcdHA7CgnRDIjzvtq3BdHBwcHBwcLjHEYbzl07fdlEDkn3R40RLEpmVRD6lEML7Rcgqbw9ZWfxZhAw9anj2rULJcp9p0tpeHX8vwcPYNB2WLD8MNBvjNEKEV6JjdhEi9RomJF3ttuyHh6zgV8I8tB7xTIn4SuKk1ds+slL9i1Pw6nV49Jx8dvEK/J+r8L5Q2tTj+MhP2+fergJRwUuDezVk+BTwGuC+JdhswScn4sl/HRE3DrNi2r7G7HOxg79taBv1c7XOsVFErr0MQoiroGiLJvPgI7ZbX4xU5dQiNbA5luv4EnKeWvkxwvST5qssIYSrZmjkMSJmAThZhvNnJLt7bQ2WalCupchX02Q3lvFOnhQPqnRaVIZ02lqmHkplxmQsAkK/LxZQnY68r0zxYCjKBRB0uly5MOLTn5aYjUwkZhQjQaNSkSDyQkEOl8p4pGtl0o8+jPeil8CZ50F+BXrX4OpTsLsnFRr9gbRjOBKlpB/JRp4vIoxHVNnhS9sGQwgmBL0Bg3qPxt6EnW3Y3ILr1+DyVbjeFTEqDVRTkMtINcf1kQh7V5FroL3geB4Vi4oTccHQT3gvSVSMV3nEBbJns1AcR9JvrM90n8yqZon3g49cWw8hgmMZU4Hyf52o4eDg8HmOa9eucfLkyWPb30te8hI+8YlPHNv+AP7gD/6A17zmNUf+/rd8y7fwK7/yK8fYIofPdzhRw8HBwcHBweEoOEjUONb/S5hl62GTuArb/sb+3kH/y6M2RqvIqv4BImpo4OwiAoKPkKBZzIr/WxUebBugeObH3YYGJivpqyHRSmDfLjsWouNuYCyGshgxos90ULBd8G5XW9hCRhxx73wbs+ZUDjidgYdPw5mzEESdsNeGCw0RWWZZKc2ryJgFtYjxkAsuhxDvQ4zNj9pSpTFiQr8PlTS8KIBToRD+H0SsbJKglQoqrKmFU1K/aZvi8KL2qfWVWnzZ++giVlQqiC1a6aSZJVUkR+NRYDUnWdnqdpDG5GB4CJmqVQUl5Nw1rNxHCNcSInIUU+IctbYM5++Xx/o6lFZzpEs5/HIRr1QUv6bVNQnvVpuIMBS7qfFY8jEGAxES2i0pdWg0GNU7TEYBfsojk0/h+R6j3phhP2B/Z8LFi7C1KYUclbIIGGEgu+l0JVaj25VikGoVsueqeA88AGcfgfx9cmb+toSI7+7AM5dEyNBE82IRlpZEzJhMokcgf4OEiafS0O/jewN8LyQIZLNgEp3ORASjdjTGK2U4tSKdfboOKw0oTUzuzq1k7xyERSyRZgkfcUuqeds8lxAPRFck/caqiOvHtlnkWtVqyBPI9diLHg4ODg6fz3jXu951o0LjqPhH/+gfcfny5Rt/X7hw4RZbdTN+8Ad/kJ/+6Z+eeu8HfuAH+NIv/dKFvv/P/tk/4/Wvfz0/9VM/xbvf/e5jb5+Dg4ODg4ODg4PDceBYRY3DEOM20bvovtUap42sXN9HCOinWHwFu1Z6nESI0wayMrnDrRP792JFRg6pkDiDEMAThKjW1fBHDXheFBqeO8KITgGGcJ9lY2PbeMHNq4zt7extDhrDACHGn+zDXzwOl7egVpbF8JPAVK/Y+7YDs5PaGkdSW+PzPYuMRxYRCVKIUHcOyYooA3t9I3aUo8/XMX2nFmsqBCxh8iiamGqHg9oZP6dUdLwNRIDQzBl7PA5LducxAsQaUrkDsDOApif5D03ketbAaiVwVTTpRA+Q69bDVGysFST4u1CAExuwuiqVEdkcZPM+rK/graxCuWwSwrO5KMAkEFupQV/UlW5X/Jr6A8JWi3B3j9bemP29kF5PsitKpRHZbOQKtQkXnoGr12C7I+HbBQ8qOchnpU2FvOgNXuQqVamEPLx3jbPpj5ILwTu7K23Zuk7w9AWGH3uCJz7UpF4POXmyzZmHCxTOn8TL5iSooz+QypHROPI5CiAICPsDJp0eg/aYel2C569vwvYWXN2GqwO4jFwDXaASea2Nx7A9gIuBVATdrgwIG7OCrJnxXtL7RxEu7gWh+ag4TOWMbpskXtrQ+7AtlnSRah2QefA08Mwhju3g4ODwXEC1WsXzPN773veyvLzMxsbGwpkZzWaTIAj4mq/5Gp55xtxBt7e3D5WZcRR88IMfvOm99773veTzed75znfyN//m35ybw/Gyl72Ml73sZfyX//JfbmczHRwcHBwcHBwcHG4Jd6WeU6st8ghhYgcTJ9ll2GTLBCE/P4WQrwMO9n+3UUCI2glCjLa4dwK4bwcKCBm+hPSXEsNtbr+gQXSMXaR/i5jMgxCp1NC/D/rnXXx8ksjNRcYwRESdPwLe04dTl0VEOIHMg8tI/2gFgtpFqV2XfexZVRtxoUWhFSpqO1WK9qlB18Vou2eQbIldRBDKIKvqU5gKijywnIJaGmopqPkQjmG7L1UUlxGBIAlaMTKr7Rp4XIjatI5YhWlmzWGhIslq1O4UIqpdRK7BeijXcIgJ/i5E55nBCD9a3TPBCDtlYCUDqyviIAVS6NBoSBB3KuNTWPPxT5+Bc+dEVdC8jMkE+j0RBvp9aNTFbqrTIdjdY9zuMWwN2NuVwomdHWh3pLhjqSbixNYWfOJJeLoD25EwM4rOYbULJ7pwYgC1ilRoaFVKqwXtVkB993Oc++QlCiekaiRod9l9psOH/m/AXzwBuz04UQp50fkuz7vvKU5uSMFGEEhhhu+LWJLJRI5YA3GgGvSh3hDB5dp12NqF60OZ+z2MnVt9DGFLBOJGNO7xcbZFOjurxhYe09GYZTEC1CL35XnX7K1WX9hkvf33Qcd9rkDFTh9jI2f3wawKyQCZI08j9xL9zXVwcHD4fMFf+kt/iXQ6ze/8zu9QrVaBxWz03v/+9zMYyLKPb/qmb7qncinq9ToAr3/963nHO97Bq171Kl7wghewvr4+8zvPe97zOHnyJNevX79DrXRwcHBwcHBwcHBYHHckU+PGPhCicg0hZ9PIatCrmBXpSpjFqzhs8iW+snRRgkrDT0HIu3uxsuK4kcKQjSDn3GPa6ulOwsPkZegq/KPkjiSJX3A0r/w0QrhXESK2jRE2bKh90qLtsv9Oel+tonTOq0XYCLOS3t6+iFQ4PIxYNz0cVSQUCkLgb27Bhy/CRxBCcjPhHPR8Z6mZ2ncZZN6UonZ0MMRokrCRQgjUHMY+q2F9rrkhHoZcjYcoe0gA+AuBExnwR3Jv2MEIGdvI+CwDrwReWYPnn4P7zkK5Al4uS7aap3yyQvFUhfK5NbyT98PZB6Fahv3L0NgXEaPbE/Wj1xNxY2+fSbPNsDOkszegvgetNtT3JeJibw86bQg8SGdh5MHlFjzehyuhjNkg6odTSF7Ii6rw4FlxjcrlRFMZjURw6PVElNCcDaICjGtb8IEd+PhYhKk0UjVzDjhfgo0y5FKQ8iUPI58Xd6pMFA2STkn/jkcicHQ6Uniy24adPuyOoBFKP3YR8WwLc0/Ulf36Wh+e9bDHLBO17yHgbEYqWZ4cwUcnIqIcNZcj6VhHrcw4TJ6Gh7GHuxPC73EjiwiRKxjLtsuIMGHbddm/tXafzPuNPaontsvUcHBweDbgTW96E/l8nre//e0Ui8WDvwD87M/+LK2WGIP+yI/8yA3x4NmAb/7mb+alL30pX/u1X8sLXvCCxG1+67d+i3/xL/4FH/vYx+5w6xyeS3CZGg4ODg4ODg5HwT0RFK5YB74AIWUraeiN4aPAe0gmtW0/cEUQez4ISkQXiDzkEVJ/C1OlcVTUkJXiWsDtYcjc47CzOk58PqxQjpOeh8lpKSLCRgpD9CZdOklzchaStou/lxTsG2AyMeIr5muIoPFi4KGiVACkUtDtw+e68OE+PImQ4UlzWzM7Zl3NNoGpFR1aWaKfJV17WhG0jpD6TaQd/RnHiaOGCBp/pQhf+iDUSvD0RXjvdfg0RuAZRO26H/jqFXjsi+D5L8lQOr9O7tQK3vKyBFZsbMDKGShugL8KXg7oQO/TcOki7O1GpQxbsLfHuDug2xjS2J3cEAH29iRSo9WSvxt9aI2F/G8jc+QKYtekwfJZhOB/EfCSrAguJ0+KcKFZ3s0mXL8Oe/swGMHEi0QIoDeRwO5PhlLN0o3GoBL170bUVyoMFjyopKCchkoGVpZgaVkyPapVqC1BNiNiSaMu4szuHuzUYacFOyMRljeR+5ZWzMB0jkJc0NBnzSe6H/jiDLwsEto26/C/r8N7hiJMHVVAtol3mCbb49d6kjVd0rVvz++kbfScRhix/dkCH/hC5P6wFoXPXAzhvcg4JMGmCpLEDRtO1HBwcHiu4ld+5Vf4xm/8RrLZ7MEbA2984xvZ39/n93//9+n3F/2/nXsTr3zlK/mJn/gJvuIrviLx8w9/+MO85S1vcfkaDkeGEzUcHBwcHBwcjoI7GhQ+D0Xg+QjhcrImRGynA5sDIZA6M75nk1WB9d4iWENW34OQrvr3EFnBuomIG0cNw11Gsjm0fWmMdUuPe6sS5LksZijiJFzcZkVXlecxEz/AVPAUo23mEZlxn/qkfo3PVy/2nt2ueKi8tlXzMuwqFq2GGCFzd9iFTE/m8zOhyZlpz2iX3W77eHHE7d7i5+mT3M9axTHkcFZVPvDVwMuX4Eu/AB44D4Wo8uDxLRgEcl4phNzfIBJ2HoaXv9In94XPwzt1Wtj0Sln8mPI5SQ73BhDuInLDCEZRZsb2NuzuEV67xs6VIc1GSKsNu7siYrSa0GhCuw3dIbQDEWr2MVk+O9GjH51DCRE51U5sMob9OvSH4Hswmsj+rndhcwydQMbSzpnpItUoWjkRYCp6lGbZs/otH0JlDNUxnOhDNg8bWVjfgFMnYelElnQpB0HAqN2nvjPh2lW4cgVq16G6C+W+3Mu2ovPRTBO1hdOfkCRRQ8e9jQgk7SHcV4SHSlIp4l2DxzEVG7Z1VRcRluddb0kiRpKN1LyqPhtaIZWLHnqvtn8DAg5nabgoVCQsIL93t+P34T7gFcBDZSjkRMzqt+V37zrzr8nDVLM4ODg4PJfw8Y9/nBe+8IX4/mLLVl7xilfwkY985LZnY9wpfPCDH+Rv/+2/zS/8wi/wt/7W37rp85e//OU88MADd6FlDg4ODg4ODg4ODrNxx0SNElIlMQb220LwjCdCAj6C2GOEGA/wHrNzHw4iW5T8VU/+EUK0tqz9DaLXhyFubOKsEP29hREwdFV7i6NbrjhMI8lyJousVo9/NkbmjBKUNgGrhGIVk0sxiLbXTJcGMldmCVK6n1T00G1mjXVSdYxHMtGq28U/08oKzd4oR8fbRoj1UZTjcIGDiVj9p/osS6x5mFfZAcYySzNy+ixeBbWEiIPVolQvlMtw331SbPH8Fbi8Y2yt1oHzaXheLcrRKKTF72g8knKKZlMCJzwkDLxYFJ8mLxq9rS24eoXw6lUm23Ua2wMuX4Jr16Rwo9GEZhs6PehOzHn0kPmxh/R3G2M3peOm9mFdpMqiH8DHdoTA7kd9NMBUiNlVP2qv1be2xfqsF+0fjHDkIfe5KjL3VxD7qXIZajWoLnlkqzm8cgVSPn6pz0q6SRgOCUKpHElnIL0N2S74oZyHzu1UtN+4bZHCFrPqSGVOcU/2s7YMxTS8uAgrPdgNja2bPsYsNgcDpu2vbCRVZ+h34u/pa82vqUbnqOOp43McUAG1gLl+c8jvYIjMj97Mbx8NPnA2OlazC92BBMB7wBlMv9s2dw1kHOwFA07McHBw+HzBF33RF5FKpTh//vxCgsaHP/xhRqMRFy5ceM4IGord3V0+9rGP8Zf/8l9mbW3tps8feOABNjY22Nraugutc3BwcHBwcHBwcLgZd0TU8BAiuoPYteQnQsBokOkrkJXXeGKn0g7gc8jK8w4mrBkWI1zyCGm1x7SFTxZDDA5Jzk2YBQ0sVoJKCbErLEZO3W77p7gv+u2AnQGhNkR2kLbiqG2Ik+5K5OeYzlnJIqKEnbui9j0qVtj70dcqdi0hZOMYs1rczvZIEinyiKCQZTrovB8dzyah7WoMPXaSkDGrn5KI2kl0rB2kUsAmlIfMFjRs4lxfz7LwOQziwoiubu9x+ByCMfB+4MJVWL0Kz78Mr6hDrQoPPghjDxoB5LMSir6xDKdOyyPsD8XLqdsVnyXPgyCU50pFwirCUEokBn2CvTq97Rb93Q57u1KwcekZeOo6bLehOTar6CP3HgJkjDvRs5Ls8fmnlRYDZJyewggiSdUI6dj39VhJGCLkuwpw4+j7KqbodZFCMjv6PRgOQnKTEM/3IJPBwyO7NGZ5HBCGY1K+6EG+B/4OhB2YRA3oRvvVuaUCxCxhYYgIbZ+aSGHMQx2oFqCch/s9WB7ApbFscznqy+PO9gljj1mYIGM0wFzLtvCpVmu3Ah+5X2xgLApz0fvbt7jvWdDKk2tAM4BMYCpuHkaEw4kH+cgG7SrwSeR+EocTNhwcHJ7r+PZv/3Z+8id/knw+f+C2P//zP0+v1+Otb30r7Xb7DrTu7uDtb387n/3sZ/m+7/s+XvjCF0599pa3vIUHH3yQH/7hH+bjH//4XWqhg4ODg4ODg4ODg8EdEzXGCHmcwtio5BBLqDOeuMZk0tDrw9M9ETWSVtsuQrZowOswtn0fQ14dppLCR8j1StR2DVruM5/AVSI+y3QFivrvHycOI2jMI9RnQTNJVpFxGyFkWBPpAw3QHjO/X5Rgj2dF5BChKMe0NZSStUrYakZKPBtCif8CyWNr949azoww83GAITPj/ZNGhJCzCEGp7dNV99tIvoKu2rfP0d6XbdsUt3OKt9V+rYT4QQRwXEjJI5UNJeR8m9FzkLD9PCwifigpf1D7kvILAkQcbCJiwHgXCo/Do8+H06fFjqpYhFwlg09AMJpQrXlkShlSfgi9rvjZhVFVxmgszH6vJ3ZTvR79/R6N3TH7WyN2tmFnV2I1NltwqQOfjrIlmkh/V5Gw72LURhW7UhhbsEK0rdIbaqG3aA7DYcUfu/rFw1SHqYWah9w/6/tQKkG1BoX+mOxoJF5euRz4Pnk/xXq6SSYzIhWJGkEo3dbryXF03tWi4za4uVojLmT2kcq1Ugi1vlxXhYI4gQ0CGIzFNu0gG6Qk6PWbJJ7a82qR+6DaZY2QsVWhCoz4oHkaR71P6/1Iw9a96Dg95L55XBUhNjxEgGpE+9d7ZQ0RNB6IxiIErtdlLOysnEUEIQcHB4dnO375l3+ZXC7H6173OjKZzNxtv+3bvo1Wq8Vv//ZvMxjcjjv3vYdf+7Vf48knn+RHf/RHefWrXz312etf/3oefPBBvv/7v58//MM/vDsNdHBwcHBwcHBwcIhwaFFjESI0iRTRFct6QCXOGtEXchET2OoLMWbbYhw2HPwgj/bDkjZq92IT31rpMetYBYQUPQ+cRkjxEfAZ4CMYkvC4sMi+MgjJXUDsYrSPZ0FXLheRc3kY8WyvRJ+rXYv2xQ6y+vdatP9Z4oJt41TE2A+dQIh4Jf62EdEgjLYrYiplMlHblDDUSgvbSiV+LvH3ywiBqd/Tqotx1IYWQsw+BLwE44Wv1jJlhER8ErOKnwWOHxcf4gKPjUXmvBe1TUWhHDLODwJn0pLt8AQy9zYx194szLLHOqgN8z7LI7kNDaTPPKT/lqL3y9FD7cFyWRE6z52D6pkKqUoR0im8Tpf+Zh3fD0l7oVhNDQaEhQLeZALBhLDdIdyv0+sEtFshezsB29vwzDNwbVNyfHYmsBPAdih9otkYWnnUi9qlNnMqULaR+1MHmfdqhZbBiJazqn1m9dmsKq6kMYrvd4i5N3WARgfKDbGfarWg2uiTzqfxMxnIliBThEyGTCbNaqaB7/eZBJK90OtCZwT96KagYk0eY6k3S9TQ9g4Qge9yCNkhnM5FgnUWHvJgryv71YyNRaGimQqZSeLjYaDCpg37uu5jKriOuv8mMiaXYu/PukfF2zIPSeetNmVdzJwYIaLmCMgMpDJnNJT5vo0RYmft08HBweG5gFwuh+d5fPCDHzwwO6Pf7/NlX/ZlNJtNnn76aYLg889Q9v3vfz9PPPEEjz32GOn09D8VX/GKV/Cud72Lf/7P/zm/+qu/epda6ODg4ODg4ODg4HAIUSP+v/8HeZvbYoSPkCwaRKskuNq4XAK+oA/VvhAyeyQHFN9N9DCBwDB/ZXoGqWg4j+SFnChAJiOEY24ybad1J5HBVFvkMZkSNpS8Va/+ZYQgP4OIM5UUeCnojSUzQDMo9hFyeBMh8+J9o6uG1TpKK1f073WkaifF9D47VntrmAoNu3pDRY0hxm7soOoCtavSoGZ7ZbVmGwyjPnjUg+dloDEUwlZX9J+K2tBgdhXFYWynkrafR6oqCfsIImCsIf2U86Dmw/qKrJRvtWCwb85pk2kxziap7YyHRaFtVrsm/Vvt5c4iQksWEa60gqqAjP0yQryWgDUfzp+CR54HDz4Ey/dX4f778Wo1UWc2t+hfbNDthKQzI/I5KJQC0pkudLqMewOau2OuXoHrm7C/B9e24PIuPB1KRYhWGKkQNUEEs1WMLV4FGXswQsY2hjDW6og1xGIoj/TvtWibpHuDb51zyTqG5qS0kHmvc6SBiIRJ9nZpjJ1eFiPEtIeSCVKvS3VLJgMn012K6RReJiOBG/kc5PP42SyVYIfVbo9GQ/oq34TC2OR6jDHCoW23NevaGiH37zSQmUBlBKtZWFmB5WU4sQMv3YcnxvBx5N5/2IqVpOPb19VRxQ4Vq1U8yWLuEUeB3mMWOb9Z9FpcALVhi/06NnsYAUqt0DzEynE3gPva8tmW9Vm8zQ4ODg7PFbzsZS/D933+/M//nFwuh+/7eF7yL9hHP/pRJpMJX/VVX0Wj0bjDLb338I//8T8mnU7zrd/6rTd9tr6+zote9CLW19fZ3r5dpooODg4ODg4ODg4O83FkUSOOJK91OyNB7afAeJornkKIllq0zW70fCv2H0dBPnY8zVhQhCxG9qo101NIxUKuB6OeEKnbGJuaoyCeaXEY9BGStIEQtXGyVMWMEmbF/DIyLvnoO82JBChvY1asq1VLPMbeMY8AAQAASURBVNzdrqhQ0lrJQn0/jckEUMsUFRq0AqFgtSuPCeDNA2kvml+hqbTQFeWzhDgdxxHT1jMqaqh1GUglwSlfVpvX92QMNRxe+2+Tg62hZs3jwwgcNtQrX0WBDaTaJRvlJHhp2O5CvwWDoRCcOnfiRK1WzWiYsY6F9kcS9DtqUxS32gIRwVaiRwEZvwxGTGtb+1IR7cQSnD8vllMn7s/jnToF62uwvBKFRfQprebwM0PSuRSZfJp0MYuXSUO9Tn17wuZ12JQ8cK5eg8t9ETNU0BgxPSe1sqWIsSYKkfvQJkb8iN+PJohI28UQ4lrpY49TOjrvKsbCLsN0lVLZ6u8AmctXMMH38Ws9FbW9ZvXvJGpLqwu7exICnkpDNhuQzvXIFQviS6VWVGFAttKmWOpTLITk8pBNT1+f9vU6YlrUsCsmbPSR+0MGqA5gqRkFkqegVoGNIWw3oRAeLtMl6ZqG6eq7W6neADnHJuZayVvv3y74seeDtlPoudpCu2brBJj7KMg8BhmXMSJ+qA2Ws5xycHB4LuIf/IN/wM/+7M8eaDGleNWrXuXEDAthGPInf/InfNmXfRmPPvroTZ9/z/d8D1evXuWd73znXWidg4ODg4ODg4ODwxFEjUVJqFTCezYxp4GsnrVtHQmRHXJ4wv44oGHgOYTkUeL1KIRPFzmXaxjyKS6SzEKSbzxIP9mWSUriL7qqPkD6uElyGK6u/M9iqmtUANH2q41YExGedNW9tll9/nMIaauB6nZlhYoZGet4KlboKvllzDzQHAs/2q8GwVczUCpKJkB/INZlwzAi6H3JirYX5N0QNUIYBzAIjRWTikRjTIiw9ncWCVAOMYJKgAk71hXPRcxYx8ltxXGRh1oJNUbGSEnkIIBeAN2RGWsloJsIkRnPmrErK7IIWa4B0XF7Gn2t4+sh86CTcG5FpEpjg4h8L8FKFfCh0ZfqiR6G4C+npJCgUoVyBVnav7oibxSLMByIddKJFTLLQ8jmpBQhCOSzMGQ4gOEQgonkRPSH03NYg+fL1jlptZCGcW9hKjLsDJI4lETuWX9rPxYx81qttbJR/2u1Whppm4p/JUzVkd4bk+63WvWUR6pLNOdGBZnMCPx9uS7SaSjkoVIZklke4IehBKqnUiISFfJki2mKpRHlEhTzkO9JRZlen7XoPOvR8b1YW5L6ZRD14xNjCPZhqSPXZHcCl4bwmfBwVRrxShE9zkHVWIdFiMle0kqyo1pQLQr7t3LWmCchaTu7UkozfELrGD3kN6nN7T8vBwcHh7uJf/fv/t3CgoZDMn71V3+Vr/iKr0gUNRwcHBwcHBwcHBzuNhYWNZJsR45qXRFfGa55DReYJlHvNHRFtpJARxVWlNTPMO0Br0JEnFS2oaui9Tt2YK0tFnjRtnUOR07ZtmBxKCk6wJCtfux7k9jDPg+1G1LbKiVFbSFDiXP9TO1z8ohYoOSukrYDa79K0ulq9lwG1lahUJQ8gN1dGE2EmM1kIkEjquSwxY0wlOqFXg8GI+HEQYSL4cQIFUo2TybQaMp7VaQCoYOsdm5ixBi1yepiVvjbfXvc0JDqLTnNG7ZKTYwwM2Q6dyQpT0NFJx2DImYstErAtusKom1OIP3RQqqSmrF9ao7GOrBRgbP3SRVGpQzDEWxuQqsDjQa0muKKtLwM2awQ8oQh+NGoD/pR8PcIPJ/Q8+V1u82o2WXYC+h3Aup12K/Dzg5s70huxiZSdaHCS9pqo65obyAB1nWMnZiS5koIz6qOivdnDpkjmnGidlOaN9GIxkErXUrR98rRWI0xAm/cyk3Fsw1kvp2J9q/2eBpM7Q8h1YB8AZaWpPvCECnjyWSkdCI/hnKZ4nKT1ZUR+/uwtw/1NmQnZl7b9yS7HSS8pyIlSH9/Erg4kWo1D2MrN4tUT6q08DEVL3a1Fdbr47CfsqH3mYPyZ24VHjcLGbf6G2tXcKgAtgo8gMzrizhBw8HB4bkJzX/44Ac/SDabPWBrGI1GfPmXfzmdTodWq3Xg9g4ODg4ODg4ODg4O9w6OtVJjHpmUVBWgFiprGMuSu2mDoYHge7ewDyW3T0fPNYTY7CHk80WEPE1aoazk/Y3qAITcHGNWP29jbGEO8mu3VzYvalcVtxw6aOWwfpZGCGzNdYhbTGWYFjJUzCgABR/yaSjlIZ+HMIBmF7Z6sB+aCgk9bw/J9SgW4eRJWN+QuIVGQ1bpZyLbnakqDa3aCEW8GPSh3ZYKDxU9RiPhzTsdGI3N9yYT6I/M2JYQ4WIVGVdddZ9FSOjdqK9ViNEA39sxt7VSY4TYFWkWgq7QjhOkNvGbxVggqQiXjb7fxQS56xxQ0WuIsQU7AZyLPr+CEbrKmKDtgi9ixenTcP5+WD+dgVKJMJ2G4ZDGlTZPfCqg1YZKRXJAQiCsN+DyFbxWS5SObo9wZ4fJXoPWdp9OB/b3RcwaDqT4oNeH69fhwlX4bCDX2yWMWAfmOtP5pKHfcVuufPTQPtT7w0G2eAPkGt/F5NhorkgRuSfo9VHB5GmoAKX5Q5eYtojzon2sIuH1Z6KxG2CqZfSaTwGFgczzIIjmfsqX0o10Wqo1/ApMJvjdHqX6iOXdIdUKFLOQ7U9fc/H7f/yeoFVUajfmIRUBV5F7mF77alOWs14r+Q7G/k3nrv25zt8k0SOp8ug4cLt/j7zYAxa73ya1K0zYTnNclpCxvMbRMkwcHJ6LKJfLrK6ucvHixbvdFIdbxAtf+EI8z+MDH/gAmUyGdDo9MzsD4PHHHycIAh577DFnOXUArl69yt7eHisrKzd9dvLkSVZWVtjbu5V/OTk4ODg4ODg4ODgcDUeq1Jj1z4R5BJBNDGK9bgKfiV7fzv8lTnFnVqeqZYkS+0reNxFyT0Nck2D78nsIQR23v7GrN2ZByepstJ9RtN9FCNnDwBY0VpAV+SWmz1vJW/u9PFBIQT4lQkalLNUWlQpUq8K9Nppw+Rps1WWFeSkrwkcqJYv3iwU4cxYeeZ7H8lqKcCIr9cfjSMDwY4S+NXEnY+h2RdQYjaJiAKKF/x2pGgijTgpDGAyg2ZLA7c4QumPoBjJOagE2RsZKhSbta47Q3z6HqxJSISMuKiaJjB5GaKohYfZqxaRt3kFI9QxmPmvFTQ05Px1r/c4JpOJKrzMfEbhOpuHUBmyckLDoYgkh1HNZ/HIJJhPKvSHr633wZFzTKRiPYdTskZ5cxa/v46UzMBww3GlJ+Pd12N2BZhNabRnfUlHOYzKGVigiy+eQ+4rdnwMWy7UZYwj9RUVB/V7d+ruNzPkVjDWVVqqp8KXXp4qVTaarBFJRf64hBHUJ6WfN6NEqMx8zHyahCBpBIMJcOJrAaCh/pNOSrTEe45VLZIoZ8vkh+YIIi9mOBH2nrTYXmM5CCpkWz7QyR0UNFc5UrFFBUysutJpALZ8058EW5WzYglxShouNo1ZrqBVaKmpTn+O3QtRr0A74tm28DsrWmHfO+rltGdcHnkEEjTpO1HD4/MO5c+f4qq/6qpvef8lLXsJXf/VX82/+zb9ZeF87Ozv87u/+7nE2z+EW8C3f8i14nscv/MIv3KjSmIdf/dVfZTwe8+3f/u0MBoMDt3eAt73tbZw8eZJ/+A//4U2ffe/3fi+bm5v81E/91F1omYODg4ODg4ODw+c7DlWpsYjXdxKZZP9t+3yrvcetBGcfBndC2NDzVxFDLZvaGNuZWaSUHVTdxlj9HAZZhFRcQgg63ZfmLxyG7Ju1elj3oWRnNXqUmbYvKmCEjBxRRUYWijnEv78gGQq1JckvLpdkRX8uL0LCAztSfZFKiYVOsezhp3y8aMF5oZqhfLKMn88S9geU+9E/UHV1nhf9x1qt5xESTALGnSGj3ogwCG+s5hsNQ1pNETZuEIShrP7f34dmQ6o4Wi0JY+4NoTMy1RpdDOGawogcagOV1O+e1VcqHnQw4ohaCh0E3cYmRMOEv+1ttBJoDyPQjDCVC1olsM60jQ2YG8c4ancNE96uVR9LBTizAefOwclTYhUG0N/r4tV7+Nl90qmQzt6YdFpEj0xGNA8PzcYY4rWHeCmfYBywuytj0WqK8NXtiCi1vAS1mohZfgoubwmpr3N/HnSex7ezM3AOQ5CrrZ5WOVSi9zWjp4vJbRhihM5J1Jdpbiafi4gAtYzcR57EXM8jjJio1/wYGIVir9brQqcLy90habXwCkNIRxdSLk+mlKVU6VKthlSrUGrLHNd91pi25yM6rgp6fvR3H6mO0f6z85FyiPh1FglH12yaBlKV0sJYgsWJedtiyhZDkjDPYm8etDJpOdq/CkvHDb0fqo1e/Ld10d9ZmF2hEq8ucnZTDp8veMUrXsG3fdu3Tb13/vx5vvqrv3rmd37pl35p4f1vbW3x27/921PvXbp0iR/+4R8+VDsdbg0/+7M/i+/7vPGNb8T3D5KCBd/xHd/Bz/3czzEeO2nXwcHBwcHBwcHB4bmAQ1VqLEK2zIO90tZ+Pgh2JsBR4CEk/wQh0G6XpYiHEIAgBJ0SmCpWHGal91H+yeVhQrbVNkhXSGs+hAa0jw44RlzQiI9VBunTNaZDkQtRGypEIocP+QwUIiGjVJLV+rUqlMry9/KyiBvZvE+2mscvFwnxWB+OCcYBXiZNqlbGz2WEjJ0E4lGVzkgQg+fjjYZ4QSCs9tRJ3PyP3VQwIdXrkRtGtRS+T+gBgyGVZodgMIoqOzzCScCwN2F/T6oC2m3JbGi35HWjCd0+9AZSvVFCBJ41TNVNHbEj6mJITJv8r0Z9WYneU3FkiKxcfyp6b5F5qyvY45VR9ljqexp0rqSnTSKDjGslOhetutE221ZiBeS8S0AtB9US5HJSgXP6DJw5DadOQ6HgQRgynkAqFeJ7E7xcnvKpPKUTiKIxHBK0OnQ6Yp00iGzBJmOpwtndlSqNVgvq+0LW56K8cDwRQra3xbZsXhiyzlO1QtOV83ptKHGuRLqKPDqm8+5FRUT8KTJtwTaJnnW8VfTQ6hg1wNDKDfsYI6R6ZheZEy1MmLhaDG0g176KCX1gMJaqF98Hv5CLktgrsLQsnZbJwGBAqtWiWu+xst9ndxfKe1DoQTs0VSW+1b5J1A92JZlWW8Sh8ymNsS/rYay36pgMGu13ncfMeB1/1n7QsbIDs+chfl9T+0HdD0xnHOn9VNvYXuAYYARgbastMMZ/D+f9LtqWckmv4+8dBrag5OBwr8PzvBtk9mtf+1re8Y53ALC8vMz9999/2467sbFx04r1Xq/H6173upu2vXDhAl//9V9PGIYEwVGkVgcbqVSKD3zgA/i+z8te9rK59lLADfHiy77syxgOh3zsYx8jDN0d7ijQOZwkIPm+j+d5rm8dHBwcHBwcHBzuOA5tPzUL82xvkkgWJXd0ZbHtr55DVgYrKegjhNcW0x7zi0ItOG43YaPWUE1MFcqdXCGrYbraDiX50phKADBkXDzoG6a92OPvEe2rghCzGoCcw+QPlKL3l31YKsJSTcSLYgEqVeFTS0V5Xa545AseuWoOr1qGTBavUIBSCS/l4wfRumzPF0+qIBTWejKW0IxMJkqU9kTcsFvrWxShH5u9QSAN0SBq38fDg0GfdKdj3vd8wsmETKdDsV7nRGdIuxnQ2A/pdKVyY39frKxabake6PakeuNUGK2UR+bDWSK7IU8IdyW404j4s+HBiQxUc1KZEoaw15aQZR8hfbVyRy2vsMYvfn0mrVuMvzdrBbePCaNewpD+KlzpQ0WOsge1lIz1yorw5eWy2BitrcLKKpSWs6JqeT7ZTFqUrXIZL58nXalIQAoh7O4SfvJx6vUGraZkm/R7IihpxczWFjTbEjbeBYI2PBOx6R2gHpp7RZbpjAYfqWS6D8m90fmrVm06XkOrT4Nov+3oeZf5Nj5qJVVBrjvNhJhEx1uP+raG3N+awGejx/6MMekj1QyQfN3q8apMV3wFSFcv3Vcl85JH4YUvhvX7IbciWzUuiWrUbJKr7lKp9CmXJNck14LMyIgaOgf0HqNVAErVJVlG2RhFfddC7JD0/q6faX96mBB1/Vv3bb+vf6uoU4n+riO2XEnjc9B6XrX20+NlmM450t8jMLZfB/2meMicW0XmVxuZm0nVGbN+Z3X+2r+tSVV38ff0N8HDVPbodVBD+ixvfa7imqNfHe51vOENb+Dnf/7nASG7M5nMAd+4fSgUCnzBF3zBTe+/9KUvpd1u89nPfpaXvvSld75hzzE0m00KhcKBYoZidXWVwWDgbKaOAd/xHd9BKpXijW98402f/et//a+ZTCa8853vvAstc3BwcHBwcHBw+HzGwqKGvQI0vnLfXkk7b6VoPCtAySElXTQ0fA1TbZBBiKDLCFk1z75pHu6UxdU8q6GjQq1/7JXjtjWOQm2vetb2Sj5qhUAOQ3T5sX3Ex9V+raRmBSFlleRTgrsYPcpALQ2rVdjYgPU1qNaEs66UoVLzyBV8suUs6XJBltkX8lCuQCYtYkU2Ix5CYWge4xEM+5EnUVSREQTRhAqjZHC4EZ4RhJhkcM985nlRhUf+hqAh1RyhCCO5vKgKURu8KIDDq9XIDgYsN5vUmh0G3Qnt+oh6XUSNZlMI915X7Ks6HdFe+iPojOH+EKppOcSnm0LuelGfrXvwyAY8/LBYKAUT2dcTTwpZr6vDs5jMBZ1jWgGkn8XHbxZxGkTzJJ43UcJUjSxhxAt9qHBVAvI+FNNQK4mYsbwMq2sy5pUqpLM+xWqKXDGNVyzKJMhFJTtLy1CtRMKULyx6rwfjMcPOiGvXYGdHqmHabahHFTGjgUwFtcvaR0jYXigCRw9zDdoErhLyGYRcPhk95zHXilq0gREF9FltlvQx7/pWK7IVq9905b+2qwCUU5DyYDyBcijXjlpTJZHKB1Vv2de6ChshMufSG8t4Dz0Ip54P6fsRSrsJk2uRHVVAKu2TzUG+IENVyEBuZEK91zHzLI/0+zPIHJol3iZRTyr8djHzS+9VceI+vi+7wsGuAKkgQlEKk+Fhf29R+0T7O3o8287qRl4Ji1tTeRghyM7asT+PI8k2zt5+XsVG0nY6xzVofoXp+3iI3JNU1HNwuBfx0EMP8cpXvhKQ1ff5fP4ut2g+fN8nn8+zurrKN33TNwHQbrddJschoX2Xz+cXFjR+4zd+g26366ymjgnj8ZjJJPmXPpPJkEql7nCLHBwcHBwcHBwcHA4hasC0Jcss2MSpvaJ23neUtK0C9yMrqDOY1btK0nkICXave4Qfp6DhISSUruxW0u4KZvW+QonqWdUsmahtuRmf28e0X/sIwa3VM2WmV+zre1UfqnlYqsDGuuQonDoJyyseuVKKTDFDupzHy+eF3C6VRDxIpSGblYRoTyyKCCYiTIShWE1NAlEJRkNhgUcjqcAIQ9lOvwtGvNAz8K33M1lhbO3qDV/P1JMQZd8TcSOfF+EkF4kuwyFetUqq16XY71PYr1Nrjxh2R7T2JzQaUqnRbIgt0mQs1QWtpmgx4wlsdWXcWogwUAZqPpw5AydOiJ6yvw9Xr8PjLfgo8DQy35esvtbx1muyhFltHdinlTCuWsnTRohZJYHTCNl/EiNgpDFWWdnoOBUfakWolqUra0siamysy3NlI092uYSXzQqjns9H4xSIWJWRoHBR4Vqot1fY7rD7VINnPtXlE5+A63uw14f2CNoTOa8zwAOn5Libe9Dal77cxWTWqHgQIteCLf6ECHG7b/XdACHY25gcBRUu7O+q6HGQaKkZIzXknqb3shZwNfr+55Agbg+TY6JZJke9f9hihooxw5HMxdF+G65cgVwVlvqQq0C/ARc/DU8/DfU64XCA75nhyWegGF1iFeQ+1Izaq1ZFWuFiQwVQfda2KRaxXLKrM+zXSfCi8+1i7KPs+6JWmtjfX7SPtcpPrahssUgrcOLioX5P50yIsd1SQUS3SfruIm1KOp69r7i4AcYC6xSSz1LBVKbob22IqW46SLxzcLhT+PIv/3Je//rX8+IXvzgx8Ptex+nTp/n1X/91AOr1Or/yK78CwN7eHj/0Qz90N5t2z8IOnn7Tm960sJjxXd/1XUwmE37mZ37GWX45ODg4ODg4ODg4PMdxaPupgyxGFEmkyjxMEFJPifoBYoWhq5ZTiIVPGiH0txdq9bMfaWRV7TmEJNVg3W0OT4aNmF4Jbv9zz4s97PeWEKK7iMkg0ByFG9UZGVitwWpkP7S2CidPwurJDPn1En6pKMJFviAkdyolQkQ6Y8SJcUT3qRowHJhKjUlA2O9JiMUwohjDUDx8Q/BSvtXoGa9TabE8Gg6jSoyoUiOVgmxOhBJNJs/mIsElJX5Zk0AElXxethsM8MplcoMB2W6P4n6TtW6fQWdCvSF5EOMJdKIMjsEgsk6qm35XYWE7gAsXosqEDlzpwxM9eDyQPA0N4y5GY6EkrlbflBDRaxDNjVHCWIIhWkcIwVoAXojMr1q0r9WoTa3ocxU1dOyrRRGtVtekMqNYlBD3tVVYWk+TXq3hrSzhra7JeKdSImyMRqL4BIEIWkEoKd+7e7C7Q7i1RWtnyIXPwEc/Ch+9BpcmIlaoSLdMFDJ9Uo6JBxfrEgi+h7l36Hna0Pmu1SzjaL8TRGjYxIgZtwpdma/Evh5vD7lub5cRxjZmrDT7o90XLaP8nj02Lr2f2sZHWDpdJLtexUulYG8fWk3CwZBuY8TujuSWtNqi7xV9qGWlkAZgswubfZkjWr1i3+uzyP1AcyhmQccjfr+Jw7O29xKeVXRqInO2y7QllFa5HfZeqbCvGRtpjD1bIXrkMGPdQnJ09jBCmD234tdl/P15VFx8wYD9vXm/zSrobWNstHYwdolaQfQ85D5yndsTlO7gsCi+4Ru+gTe/+c1sbGxw/vz5I+8n1P9XiOF973sfv/7rvz5FoC+CRYOpk7C0tMR3fud3AjAYDHjta18LwObmJl/3dV831ebPJ6ho8b73vQ+AV77ylQsJGSpcPPbYY4zHYz70oQ993vXdnYJeR0njsqjo5ODg4ODg4ODg4HCcOJL91LxtZr2eZSmiCBAiqA9cQMgXJZLKGEuqfW4fKXgvYoQQ25cwliu6CvsoBGycnEuCvQp5BTiBqc5QAk9DpJfSkp2xsgxr69Fq/VUhnStrObIbS3jLS1AoCsGd8kVc8H2pwBiPopOaSHjCYADjMWGrTdjpEWq5+yRgPBjTbga028KN2+5UKd84T/kWU6qvPUSnqC61SZfreBlpg5fy8fI5KJYIez3C3X3wPfzRUPyzqlURXsLI4qqQErI+mxWBYzLG63ZJVcr4gyGZbpdyu004nhBOAjrNCe1WSL8vBQmeB0vXhNOfhFAfwf4IPr4Hwz0TBK0r4jei8dbw8SyGvFWP/AmmGmCACYbXbvCZJlaViK4CDwOPZKXqot0W8vpKQz5f9yDrSQ5ItSoCRrUi47u2JkJGqeJRrvhk1qqwVBMxo1wWH618XsY4RE68FNmHjUZysG4HdnYJ9vbp14fs7kTZJC3Ynci830TEh2J0HvvA9S3ZbmsHvFDmYxCd+yJUxhIi0uUQAtyuCrPhR/M+x3TVwR6mAiQJY0z2RgdTXdXACFFxUjuJnD4sLaNja1drjELYq8MTnwnZvNbj5Kke959vsHF2l2y1IGUZ2SzBYEynE7K3L7recChTvFwSK7FCXgLHg03o9OW8PKYzNlR4UzspPQ/7vPQ9rZ44SNDQfvBnPOv+tEIpwFSk6dyfVdUQf28ebOvEDPAAptpBw+W1kmeMsanSNsC0iDPPPireV7N+O+PvxYWO+Puax3MdETM0TF7PaTU6nwwifCzyW+HgcDtQqVR405vexNve9jay2ezBX7DQ6XTY3p5e9vJHf/RHN4QEG5PJhNFoxH/8j/9x4f2/5CUv4T/9p/809V42m+X06dOHaidALpfji7/4iwEh51utFgBbW1t85Vd+5VQ7L1++fOj9Pxugoe6f/OQnASgWiwsR5BcuXAAkt2QymdDtdm9bGx0EP/RDP8TS0tINOzAb3/3d383e3h6//Mu/fOcb5uDg4ODg4ODg8HmLQ4kat7LNIiSdEpO2tz1Me/83uXP5GPcK1B7kdsIeHyUClxHfda3IuGEzhVhNLRVgNarKWFmB1VVYP+GxtJ4RwrQqRDfFoggCEFVkjEWVmEykpKEvZQ1Bp0vQ7jIZjBm0R7SbIYNB1LZAiNZmS+ydJpGvSxA1WkUN+6FihudJwUY+D8vLAcVi94a+ks1BZTlNutYl7A3o1/vgeRSzu5KwnErJlz0gnZ5+7fsQpMVSKZ/HG48lF2K4ghcGMJ5QbbWo9LqE44AT7QknT48IQo9s3qffGfPkZ+GJJ2C/A9t9GIxN34PJm1Hf+z2MVZIdNp5CyP01YCUjAdvboRE/lLTXKgId22XAD2Uocjk5rWz02bkVKVLJZGF5SbLVi0VY34DlVZ/qWhaqVbxS5EFVrZiNstlozKMKnMHACFetFuzXCff3Ge61aGyP2d2DzU3Y3pK2q6imfaBhzR6wsw07IeyOJGdnH2MnNQseRhytRP2plS2a0xMnj1UM0jakMFVKA2Zb4I0RUngfY5lXjY5fivartk2av6EVJmDswbpzjrEofCSqplIRO7hz533WH6qQvf+kXLSZLEzGpPb3qWY2uS9oUCqOGUQ+WOOxjMdwKDqU7xtyPxed1wjpQ52LSfkYcXEjqUIjTu7bnx0E/a6Ok92X9r5mzZFZYpKHqWrQ8y5E7+8j46xCJBi7KRWVbFEjfl6LilazFgvM+nzWAgTdTsXPcew9zdLwmJ+T4uBwu/DII4/w0pe+lNe+9rV867d+68Lfu3jxIh/84AcBeM973sO//bf/9lDHPQwh/v73v58HHnhg6r3z58/z4z/+4zdtW6lU+Jqv+ZqF9uv7PqVSCYAHHnjgBmkPYlllBzQPh0N+53d+Z+E234t43eteB8Bv/uZvHmqV/3/+z/+ZMAz5xm/8RmcvdYfx9re/PVHQAPixH/sxJ2g4ODg4ODg4ODjccSwsaiRZFc3b5jD7jBM1HoaYUusPFTMmHP44DodDiLE6KkevSwgZvAys5iQIfHlFVuzfCARfSZFfLZFeW5Ll3fmcMOV+Kgr6Hom11GAgVkTjEWG3x6TdYzQI6HfGdBoB3a4EbTeawoFHDlPiYNQRTnw4iuZgJFz4Kmr401Ub0Sb4vkRj7OxEGeGevFepwLn7J6znBvhpn0I1LZUZlSjEejiU9g5HwvB6CGlfKsuB/JSwxumUCDXZnPRgiJxzpYw3nuCFAaV+n9LJpgglfoqw3aFWbXLqJOzswuXLcGkT+uOooMWP2pkSYrQ/hq0O1MZm1b/65JeBjSw8tAEPnYF6Az78NHxkICuzJ8g1VUQEi1PAWiayFopiL4pRbvt4JDrE2bMiZmQywn8vLcmpF1fypFcqeCsrIlzlspKPUioJSZ7NyACMhsKITyYyoM2mlAHs7TLea9La7rO7C9vbMi57u7C3B/WutFWDqSfR+Z2I2j+aSAVCGyH+u9ycH0J0vlq1ouHWKeR+UkeI2x2mKyji14HmIOj3CtHrCtOh4iqMYL2npPAQEQDUrqgYjZvajzWQ6psepppmyK3d5+x7dDYav/vPwcaDJbIPnYXzD8CJDZnHkwns75Ffv8qZE5c4cW2LwX6XdjNgb1ds00YjsVLbjuZTA1MVlOfmTAcv1pZ42/TZFh+SvmOLTbNoL/szFVTsh32cuGhCwt/28dLIvS9y37oxTwJkDrWBLZIFAPt3zBaM438ntSfpN3bR6p151T5Jx9bf1A6mcsNF6zrcDfyNv/E3+Mmf/MlDf+/P/uzP+Lt/9+/ehhYthgsXLvAN3/ANN73/6KOP8qlPfeqW97+0tMRv/dZv3fh7d3eXtbW1W97v3cRhxQzF61//eidmODg4ODg4ODg4ODgAt1CpsYgdxqL7SlqdG1/JGs+AuNcwiyy715G0YliJyizGUqaMCBobeTi5ASdPSK7C6gqsnfCpbBTxlyp4lYpYD5WKkcUUwpL3eoTNpmRl9HsErS7D3oRBL6DVCGm35aNmQ6yFel3zPIkGPgigP4DuCEZBJGJg2cx4kPEgnxVuHUSHCCOrKjzRH1Jp44q0vAyjcchkMiBX8PC9kFzZI6fVBd0xNBqE+w26TaH6SitZWF6RCoVKRYQMD1EfctGBQ6RaoVDkhsgxHEC1FnV6iFfqUMtmKJ0YcP9em6Wa2P30B6IHlcqiExSL0u7hSEj/azvQaIvwkPKEtC5mYaUGZ85K4PjVq6Ij7F0ylRp5xE7s4RTcd0KyMTJR7EUuK1ko+bz0T7sFp0/DqVNiSVWpQGa1ip/P4pVLwpKXylApm6oMzSkZjWXM+31Z3j8Uu6lwfw92d2nvDNjfCdjeMWLGzg40WtAYQyfqrhVMJYCHkK87SNZGF7Gp6yEEu+Zu2HN4NdpHMfpbcx5GiK3VDiImaBXUPNLcDmBfRbI98tHfDcQqK94GhVagDTHzVTM9mtH3tWJEQ6iPch/RMPeU/YjEu3RaxjZdKYg6tbwkY1gow3gIgQhPYXGX8dhjeyvkmYtSOdNsyXy43oFPDuEixupL78txO6gkJJH0896bV61hixV2FUXSZ/FKhri9k32MOOmfR+57FUw4OkxXE7YwFYYpazvdl207pd+P/223IV7dEj/vRXBQ1VJSZZLOcweHu4Fv+qZv4ju+4zvmbmNnJfz+7/8+P/IjPwJwk93UvYKLFy/y2GOPce7cOX7t137txvu3mj9QrVb5sz/7MwBardaNXI57Hdrmw0LH/VWvepUTNBwcHBwcHBwcHBwcbuBIlRrzcJh/qiURWM9WPNvan2T9orY4Ggqtf5eJsjWKcGpDVvCfPQMbJz0qKxnSK1W89TUh7Av5qDrDNypEu0O4u8dkc4dhs0+vF9JuGtGi2RTitN8X8rTdEYGjN4R+aFZBqy2Prp73QnMOajtT8iAbZVGDNGEwgtZACOQ206u4yw24fA1Onwopl0Ih95dGPPTwdVZWNxmPobEfsrcX0mrJ6W2cGLGy1qOwWsRbXxcFIpUW9WGckgOnUpYFU9TL2YwIAR6i1BQKeJUymcGAVHWP9cYmjYZ0WbksgsJ9zyvgbaxHJRRjwr064/0mnTaMhiFBaEhrLwwZj0L29+HSM1LRsurLKnPfk77ZqMDzHpHqmmJBvosn319e9iiWPUqlgEYDHnzYZ/lMEa9UgmwGT3MyNGwhn5cvZiwxI5hEPmFN6HZhb0+yUbo9hrtNdndCdndga0sEmp1dqSqpD4ytlgbZKyHdw1RkjIHLGEs2rYiIX39V4CGkukMzMSaYAORtTECyfR3Mg86ZLFLpcipq41NI2Hi8WkOh++4zLV5ouLme761iFblWtUIlB+TTMnUyafDT4KWlSgg8Ufy6LZko1zfhqadpfOwij7+/xUc+Cp/bgUYQiWKh9PtlTBVJkhBw0H1wlqAQJ9pnVQPa+0kSx+P7OMjuad5vkI+x1Moi11EaGbseMm4lxNJsP/pOFRFCBkglh2avzBPsk96bh6P+xjo43GsolUp813d9F29+85vJZrMUCoWZ23Y6HX7xF3+RH/zBHwRgNBrR682Sku8N9Ho93vOe9/AXf/EX/N7v/R4A586d43/+z/8JQCqV4sSJE4febyaT4bHHHgOE8K/X6zc+a7fbvPKVr5zaPgxDrl+/fsSzOBxOnTp143W8SqVWqy28n6tXr954/aIXvYggCGg2m3O+4eDg4ODg4ODg4ODw+YZjETWOQprYpNVBK5PVxiOFCcK9F+Fh/Ndv1T7mTkDHIIXJUdiIXo8xgsYSsGEJGvfdB6fvS1E+U8NbXY2yFKpSVpBOS1nEaBgJGi2C7R26F7bYvDhgZ1cKFnp9aDUlgqLdkUd/KFUY3cCsbI+s/QHjVR8PdfatcyCEyiQSRALY60N7JKTjFuJ/r7ZNGaASwlYfPve0lR2Sho9+JiRbCBkHUUzGAFIhrFfgwQfhVDvgxKBDNQS/FlltDfrGK6tYkP7wU9LJ2ZyIAR6RR1YAqbIIIYMhXhCytLHHemNEqx3pBCmP1MaaKEiFvAghG2uken1yQwlUv3EVTSaE7Tatp3eoPy36wuqKBLinUlJtsbQkuSenzvqUqinSWV9EDTz8FKQqRcJqjUeWNhkOQnL3n4SNE1G7PVFG0hmx2oostAgCKQnxIpK81RJrsb09glab0V6Lzv6QXk9sjHZ2RMzY3YX9OjQ6stK9g4gWfcyq9xAh/rejz/dZ/NrXsO42RvQaR/tWm6c4sbwIAqS916LvD4DPRu/NuodlMeHVPUx1g4oyx3WfsKuW0oioUcxJlU2tBrlaQapstJpoNBZfqVYbdncJr1+ndbXF1ibstKExkfPSQOl9ZIyOilkihN12SB6LWdULs6oOFm1PXNCw96G2YnWkD7QyZ4AIYlrloxU4FaCG9HsL8+NqV2fEj2G/f5CIcxgkVcAcBB+Zp7OEQgeH48RDDz3EW9/6Vv7+3//7B2578eJFfvzHf5yf+Zmfuf0Nuw2wCflPfOITnDlzBoCNjQ3+w3/4DwDk83n+yl/5K4fet+d5U2JBrVabEgQA+v0+X//1X3/U5h+qLbea9fG7v/u7AHzt137tcTTJwcHBwcHBwcHBweE5jIVFDYs+vYFFVpjOWnG7KMmiREsheq0rtu8FaCWDCi7aViXd71VRw2OaAM0D54DnI6JGiBDCISJonCrA6XU4cwbuOwun7ktTum8Z7/RJYck1SyHlC6k/GEar9PcJd7bpXdnn0mcHfPZJsbPp92E4ge5QgrEHI+gFQiAqgarZAnaYLUif2nkFej4qagSI2xH7QjzuR/sbYUhuMJUoigkmy6A3hs9dETJTyeoishL+XFP0mp1t2N4Oub/VYf3+kNxSnrA3YDyY4PkemWokaqQiAaBSFWFCgz8yGVk+73mQyeAt1cg/fJbzhR16+30m/Sg6utOGrc2oQiIn+yxXREzwIm8hEEFlZ5fy3j61pQlBZK+Vi75Sq0FxOUd+KU9uuSgqRyYj39V9ZHN41SpUq+SGQ/HmqlZE1MjmRN2ZjOW4QQCDjpDivZ48j0Zi1VVvMGj0ae0O2d8XEaMX5YPvRhkN9S40x0bM6GHEhiHGVmoHGccAubZKyE0rjL43mDHHm0gFxRayel6/04/mgVpOJWVxzIOdpXAx2l+X2fezNEJ0ryD3MK1A8aJ2DRHh5rhsf+xrOwVkImuxfAHSpZzMnXJkGRZMxMNsNIThgGA4glCyZ6pZqHVhHJprUB/zbLqSKhD0tW/9nZSnNI+4t489q+Ii/n7cWimcse2sz9RiqoexipolQmkVmc6tPuY3YJEqFi/hdVz4n4ekPJik1/FjhJjfrwJyX9Rr5F5dPODw7MerXvUq3va2t/GVX/mVc7d73/vex+/8zu/w4Q9/mP/xP/7HHWrdncPW1tYN8r5SqfCmN70JEHup7/me7zm24+Tz+Rtiwb2Kt771rYRhyL/6V//qbjfFIQFf93Vfd1MFkOL3fu/3eM973nOHW+Tg4ODg4ODg4OBwyEyNJAJqFtmSZDNiv2+vsJ0FtTjRFbMwm8S80/CRlblnEM91NU1oIWRn6y616yD41gOEzFqKHroKXa1WSsBGAU6fEEHj7H1w6lya8tkl/FMnYX0DlmrCnIeI7VC3J0T8/j7B1eu0ntnnmacnfPaz8LmLcLUNe1Emgq5w1hBmXaU/iP7W+aErz21CVMlCPQ/dRleUg8wbHxEkdCW17huEwCswnXGgVQJKritJqVZI3Qlc2oLtPdjahp3dkPs2O6yudSEMmUyEL67W+mSLDfyUR7aQElUhnwfPx8ukJWA7k4EgNMLGiQ3yxQK5dgf6A4JuT+yr+n0RQvIF6e9iMToDTxo/GkWd2iWcBJRK0tgwlEiPEycgf+4E3nJN9leIBJdsTNTwPGG0U75UX/heFOg+kTDp8UTaMhpKe3qS6B62WrL9cEi/NaaxO6ZRh909qO9DvS6VON0ONDtGzIhXZ2h10wCpqGlFY1jFBNfXovk5QKolrlrjaSPErKbfJvl+pST7YVbBa6WQfZwkpJG5VUYqoNai9/YRQcS2z7qdK+JVQ/N98HwvSqCPhLYgMP5vrRbj7ojhUN72PdM/YcJjEeh2PqYv9BrVPrC3O0gwmfU8S8xIEkBmCRiz3ltUmM4h90ut0mshcztJGIiuupnzKD5H50Hnb1I2R/z1LOh5qq3fvVwN6fDsxxve8Abe/va389BDDyV+rvkJv/3bv81b3vIWnnjiiTvZvLuGVqt1g9DPZrP84R/+4Y3P4kHhwC3nctxt6Dhrdcof//Ef383mOByA1772tbz85S9P/Ozd7343H/jAB+5wixwcHBwcHBwcHBwOIWrEMY/cituK2M9J+0n6TO1idFX+8GjNvG1QsvwcEhpcRMi6pzg8UanEfh4h//IYq5o2pkpGyavDkG32MZT4VzucNLKC/CRCFmcwuRQrGTi5bMKiNzZg7USK0pka3plTImjUIkEDT1bw98R2KNzdJdzeYf9Cg099MuSTn4KLW3B9LET0NrIKX0WNDIbMHjC9wjmFEIa5qL0+0r9tpklpW6yJr1bXsVplukJD96VCh/2+F227HG1TicZlAnQCyVZmT7LE9/ehVg3JZIQrLhahUg3I5wLSGSiXR1RrAzJZ8H2PdCWPV2tBKo2n1lSlkjDJnodXq8JKilR/AGEgNk+lkqSzn7tfBBHPE/Go0YTeLmG9Tnj1Ovt7IqykLb0iDCDs96Dh4aXTsnS/kJd8Dz+BNg0CEUo0lLXVjLJBMOHfQUDY7RI2mvSaIxr1kH5fCnTqdanOaNQlL6PZEluxfmgqrfrWQ6s0dGX8EnDamrf5aPx03mrFls6DDub+kERSJ4ke9lyBxYUN/d4s0lerykrRQ69pFQw3EWFD5/hx2k/FcUOECGVIwyB6oY/QE4u4Zotwb5/Obp96XaZVfwjDcJrkntfOWfe7CaaKagPpi21MVc5hziX+etYzVlvtipCDBAz7vYN+3/IYkU2fa8jcuIiIcknVNz7ym1GLtW0fmRtx0X5Wn9si3VHtq6JklRtj7ELCHW433vzmN/NDP/RDFG8I8zfj537u5/j+7/9++v0+3e69Upd7ZzEcDqdIfs/zWFtbu/H38vIyTz755N1o2rFBz2dvb+8ut8TBwcHBwcHBwcHB4dmKhUUNDdpdBLPsQeZ5pdvHySKkkRIuPaZJwHsBurK1iVjk+IgtzdMIYb8oUalZFmsIOVZGzn0U7a9BZCOECamtIwRYa8HjKBGrwolmR2wA9wGrHmQ9+bycglNrcP5+OPeAx4nTGcq1FOlCCr9agbV1WF+DWlUEjSAQorvTgXqdyZVr9J7ZZvvykCefhI99Gj65K2KGrpzfxYga2j4wFlBJ7dcgcBV2BhjBwhYj7Nf2e5no2Fpx4UX7LEf9AWZ+6hz0MJUcWUw1SSraRzCGXgO6fSHx02l5FIvyyGalGKNagXIlJJ2CdDpkeaVLpdzF98FPi8iRKhbA9/B8X+yBctmIeA4NM61WT4WCvLe3BxcvEl65yujaNvtXh3Q7UlCh5zIYyGalQZN8oUmhmpX9g6gd8Qk0HstYNptiKRYEhJ125OkF4XDEuN5hMhgx6Ech33URMDpdmQqtlgg97Z4EtHeYtpayKzO0WuMaxpKpiFnNrsH1ah+l45NHRKqAabsynUP70TG0mse+98TFL8WiVRsq/qlA6GOEkwJCWJcxApoXned1xA7ruKrN7HtnkqDnEU0fW90IkeqgYBLZiE0kgL4/oNee0GyJCNUeTPdrExGQZhH9cVFA79W2oFRExMEO0gcTu50k2xvGxQx7/0lVF1rFNbG2SbK9sqd90ut5onQGuXeeRcS3JWSsPUww+CzBxkeE5IeR/gDpi2cw/bIIFvmNjfeffmb/FuSQ8dXKucB6ODgcJx566CFe8pKXzBU0Lly4wEc+8hFHdMcQhuFUn4xGoxth45lMhq/6qq+6W007FLTN4MQMBwcHBwcHBwcHB4dbx8KihhLqh0XcCmSexYi+n0WIn9Xota7kbiJk/r2wdi9A2vMEQkiBkEJtDFm3aLWGjxDrtehZQ4VziNhRiB6p6BhXMLkDB6121moFFQCURFxFiLX7s7BSFDJ+PBad4sxpePAhOPtIjsLpFfHhT6fE/mh5CapRhUYYij1RlAA92d6lfqHOhSeGfO5z8LnL8HhTgpR3MKSZrtSfME0M92acgwpI8fOySWg/9pn9nq7Wryd8txsd30YGEZpUDNFKEq3qUNFDq126A8gMIO1JsUW+AbmM9GkmLRpCoSDFDrks1HahVJQiiWw2pLbUo1joiQtVziNXa5HOZyAMCUPwcxmx9up1YWcXMhnCQZ/J9R26V/bp77Rpt+XjXE4yyqUqRIYok4FMKUuqkodVycwgDCUgehK7qsdj2NuHrS1C3yecBAxbA/rNEWEoHzebUqwxGIiAUa/Lex1xzaI3ElLczstQQeNGbkn0fA3ZrhEdvo8R8kJMoLsKW2tI9YwKBkvW2CiRPUaul3r0XhEhcFUYsUnceGbLQVAxLBMdezXadyfal9pOZTE31xFyX7CFvFuBZvloqPMQEQv03qGPNJKpocU4YYiWbES2Z6GEzwcTwiAkCGR8RyMYBtMr+FWYmgcdsxHTFVdgBKeadQ4+078JtkBhVyLYn2vAut5L9P6mn6uoZVfL2VVb9j7nVWwcBBXdtpD7v1bU6X1mljgRIPdvtatSgXYHk7dyWEEh3uak6o14FUsWmTPr0bPO0Q4iOu8foR0ODrNwUIbGe9/7Xt797nfzF3/xF1PEt0MyWq0Wr3nNawDJy3jLW95y47NcLjf1991CGIa84x3vmHrv7W9/+91pjIODg4ODg4ODg4PDcxKHCgo/CuGimEcW2fucIORZGjiFZFYo4X0N+BTw5C204zgxQgggJTqr0UOJWyXf5olBQ8SOZRQ9ZxGyS0WMFEJ6daLte4iw0zxgvwol+vT1ACGFl4GNDJxdg7VV4TZ3diT3YHsHVrdhdX1EbnmAX61K0HW1Is/5SNDo9YTRvn6dwaXr1C93uHY54OIFePoyPNmEC8BlDEmm4pgSeMuIHcsIqXJZlPSdtdKe2Ptx8SMuqKkFkt1fam+kYtMqhlTXihFdqZ+x3vdDSIWQHUJmGAU1+5BvCrmcimIxyrsSr5Hy5blaFQcq34dSKaRW7ZPNmlaVaz6FVgc/nSKYhHQ7IbtbE3Y3xzSbkhEehuJQ9dDDsH6+RGp9Ba9UhFwOv5ATZaVaE8VjNBJx5Pr1aZspgMmE8Po19q8OGI2FA2+3YX+PG6R3J4phGA5lvjSa0OpDfwKDUMZwgAny1qoMFRFGyBxuI/N5hCGl25gw9zg8pNqhhLEDU7HOJrCzGIFwgrGCykV/NxHiWQUXJcDjlkNxst1uRwqTqXMaQ+ZrcLjuV6sV9DwPI6DMglZ3rUbH28X0Ryb6PAcUfSjmoVyCYgnS2ShcIwgjTzIigSMgDEJ9STCxCoRij1lirb6nIkv8OtZqjwkmf8iuCLDzO+L7tR86r3TMkq5tO5zbbluIETaS9r8oJlEbLiEh73qvX2QfATKH60yLOseVYzFrEUH8HAPkGllGxO0SZi49CXwaGS8Hh1vFG97wBn7wB3+QRx55JPHz3/zN3+QHfuAH+MxnPnOHW/bcQL/fnxILUqkU//t//++pbXK5HP/1v/7X296WMAz563/9r9/4+w/+4A9u+zEdHBwcHBwcHBwcHD5/sbCocZy2T0nElf26g6xcbSGEUR5jmaGhsyoWLCJuJBHaSStzF/XVT7JE0VBq7VDNwVgEfYTk3Yr+1pXHSVUIuhI9TtzNg00eauC2El0SICyEvL72vChLOA1eOiXh0dWKkOL5WIXG5ibjq5vUn2lx+TJsbYrd0V7P2EzZ1ib2CmrNGVDroUXnmM4DxSwx46DnWYIIGKFDg8dTCAFYw4yz/Wzb/2SQ6oAU4AeQDiA1irz4e5Dfl8IX34+iLaLMbj8VaQ8VqfIIkcqP1dWAUnmI78NwALu7cPEZuHZdhsH3xOrq1Em47xykq0X88/fB6TOys3xeNkilYTAgfOop+MxnGF/eJAimZ1EQwM5WwJXLYiUVBNDri3Y1Ghq3sSjLnGEAvdBU39jVGPrQzJIxMieUCNY5qZUvGp49CyFGhNrDiImaQ6NjoaS+VkvkonFbwYizRN/RihFd4a8VBjoXkgQNu/JnK9r3uTQU0rA7gKdCaZ9WbQWYlfzHgUnUB53o9VJ0ngNM1VMRqBZhaUkepVqaVK0sCkc6LQOJJ8+TgGAiWSy241kSZt1v7SqNWWM4Qu4HTes7mpmTxdz39DO7OkPve32m+9Em61OY6pWkNuh80f0dRdAg2v/16PVRxHWtVrud0CoWuw9tjJHfnFNIn2rlk95b89H72u8ODodFPp/ne77ne3jzm99MWS0PY/jpn/5pvu/7vo92e5aU7XBYTCaTRDHh5MmTd+T4m5ubd+Q4DncOhUKBXC6X+Fm322UwOC5TTQcHBwcHBwcHB4fDYWFR4zB2Sotg3qpfECL5cYQM3EDIKrUKOonxLa+TvLJbCawiQkZnmbaE6WA8zO2KinmrkO1VsHGSaIIQtopFvNnjx7DJ/tsBtZ1aQ/qiP4KdPTlwuQyrq3D2rJDj9z+UonJuGe/kSSnlqFSN5VSnK0v3r28yuXKNxqUWVy7DpWdgcwv26hIMDbICeCU6tvro26ua+yxOEMbzAuz3Z22f9Jz0OmkfKlZpm7eQapoUJnRcvenBkPMFjF9+F5N9kgKyIWRCSAXRdweQa0RWVGlZWZ/Pi+gRhCJ27O5FMRqhhDhfvApP12XuB8i1kevBTksqbrrdbc43RuTv3yFVLYn/VDoNmSxhr8fkqYtsf+w62zuyUN9GEEgRx+Z1GebJWOI0Oj0Jjw5DQy6rAKDChRLOY+v9LnJ9bjK9It2e73o9qUgUz0tIglbUqNWSVs/krIdtudbFtFttoHykskorANqYwPj4XIwLomo/N4zGtz6GlbHs+9NIddIQUzkRYu4/Sh5rBcth76taIVaPtS9ESOpadNxKAZZqsLwMueUCXrks13A6LRNlKP5hYbNJvzmk3RbBajSWj1WQsUUAG/Z9UQWNQULfxb9j70ftv+w8HIUeU+/Pet+Ii756r89jqn3iULExh7Gw0syVo+B23KNvx2/sPAyBzyHXxDlMtUaIyYXRee7c7x0OgwceeIB3vOMdfPM3fzOel/wL/cwzz/Dxj3/cCRp3CE5scDgq3vnOd/L3/t7fS/zse7/3e/n3//7f3+EWOTg4ODg4ODg4OAgWFjUyHI8fvI15pIv67WtlxjJCutyHIbFGiJ/5U9H2+r6SmzVEECkzvZpefcw1q6OFkJFxK5p4G+NEeJzou1sh5krwHUS02St2Ndy42ZP8h1IJVpbh1Gk4dV+ayrll/DMnYX0danaFRjeq0NhifHWTxuU2ly6FXLoMl6/Afl2I8SWLoeshRKOS4PF+WrTaxmM69NcmQf3Ydsx5tsPC40Rq/JhgxtjOX9C56SMiEZhqDg2wHmIqVVTUULsq/W4ues8PxLIqP4RsS6ovAPJpqZLI5kSAaHXh6b6QkQ2kL3OIiHI9gO3PwuVdePHTdZ73SJ2102lK1QzpYgZ8n35zxOZTHZ54QgKh4whDqbTZrkNnKMKKLVyAEXnsCgebeFYidIQIVtoXcRI8/jre93EqzJ4nKUQsK2KqE9QOTAUOFVG0QkCrCLQtecS2KWW10RY2kyyM7NXvmjWhtlNL0Xs6LnE7JG1jEbmXqQ3WUe6rKjSocGOvps8CtTysrsDGBiytp8msVMTnLJ8XBW08hkYDLl+m/eR1Lj054NIlqQLqDKav1Vn9YYt+9uMwUIFnlvWULXBlMRZ/BUzWyggjHuo4x6HiiG+9vhcsDMH8bvkcTeSah4P2VUfGuoSpcDqJ2JupwLgHfIyDM1UcHABe/epX87a3vY1Xv/rVM7d573vfyzve8Q6Xn+HgcI/jr/21v8bLXvayRHHy3e9+Nx/60IfuQqscHBwcHBwcHBwcBAuLGsdJtEBygKn9mRLOGqi6xPQqbl1tq6RqO3pPCUOtzMhhLK20UkAfehy1C/KsfSphF6+4sNtre6LPOpfbBV11rOfrYUjSWXYhSn4qYTkC+iEMx2I5VanAxkmfyn1LpM6egvUNqEVEaEiUoSGWU5Or12lcanLlmYDLl+HaNdhtQG8MOV9CswllXPaith20inseksSK+OtZ74VMf9f+XCsEiG0Tt2wJYw+b7FWbFg2l3kHmqtqEaf5GDkNeKpGZtt7TVf3pUOzAQESO3BByHXk/CA2RrfZOPQyp2yQSJVoSmfGSF485e9+YVKpHuwXXN6Wi5tp1GERselzY2R/CbmDGS89NyXf9Wysy1KJG7eI0N0ArIGwECa8XqcqwBSgPESTK0SOFsbfS1fw6frboYl/bSqRXMde9tt0WMOJtgOm2qhXUJlLFo2Hyen/Rv22LpDRyzc4T1BaB5nqkkDmQQgS2U8CpGpw+DafPepTP1PBVnCxGCfWdNuzs0Htmm+sX+lx6RubL/r6EvPcxom8TM7YKe17MqnCbBxUzVITWvojvR8e9iPwGPAScRcYtA4Qe1EO4iIjb+3OOqWN10Hw7ClRQ8zAWbIvCFqXvxu9sBhGJatGzLSaNkXtSC1et4bAYXvOa18wVNAD+1//6X07QcHB4FuB1r3sdr3zlKxM/++///b/z3ve+9w63yMHBwcHBwcHBwcFgYVFjntf9YaFklpJ+9r4zCFFZRVa93w+cx9jEdBHSeB9jgaP5BWOE7FQho42pwGhGf9srYXOYVb9qI5TFELU20TkLSoDfKWEjgxCXZ4GTvqyqLYfQDOEJ4BNI38TJexVuKpjqgAxQSEGlBMsromFUTldF0DhxUkjQXFQicEPQ2CK4eo3W5SZXL4VcugRXr8DWHjRGUb8FMjZPI226zq3Nn1n2Ugd9Ft9GiVSbtNPPlJTMRH9r1kLS2MarDJQ4V2sjNXnwMKJcATPflMC1CV07eNy24slHj2IglQkrVVhPw0odroZC6NajYysx2gIuj6B8FU6ckCqPq9fg44/D5QG0JlFlCEZQsdHEWCvZK+V1DIPYQ3MudjDCgX6m/RQXpGZVbdiYJSTYFRB+dDytiNFqmiQLK7uvdSyWou/vM59UnmVPBkbwsSt5ZmGCjI8KPreSV1CIHhPknIrIHNkowPoqrK1BaS1PanVZqjQyaQmG7/fF0+zadQa7HQYDydDJZKRCSMe6ixDZV6Jnu/rkVu5zOs/tfB+Ybfmltm4VZLwq0d8BMA6NlZSKSfNwO6ozNIOiEv3d5HDjqlVCxwm7ajFemaIiUQ35rT2FhIWvRd/RHKF9ZK6OkOtl+Zjb6PDcw/d///fzT/7JP5n5+WAw4Kd+6qf4sR/7sTvYKgcHh+NGv99nNDrOfxk6ODg4ODg4ODg4HB4LixrHiRxmhfUQY03kIQTwo9Fjw4OiB7ksjAOoD4UgfxK4ipAtSgJrMKrt99/GEK5J/+vdZdrjXveRxRCzqWhbJUhtmxTP+g6x924X1oG/DPylDTi7AZ4P17bg4zviKGNnPNhWTUuIFVcter0CrKXgzCo8cB4efABOnC+QObMOGyfEiD+fEy+pbl+WcG9tMrl6jealFpefCXnmEly5Ctd2YWcoBFgf6fctZPX0Nrcu9CxSoREmvDdrX7aQphkYSpiWo8+uYIh93T+YsbUJeyW1lVC1iVW1KAKZa6etdmaj40HyqvUMZpV/Hsj6kpFQqcLJE/BIB/b24ZmWZCz0mBYI9gL49AX49GV47yZ8ECGnM4iN2+novOPkcp3pkGuFvSJf8zJURBlEbSxgxEBtR1xU0utUqyvmCYJJK/dVfEhhSH24uTJDkUb6eT063zxm5X8GEWMWRTjj9az2J0FtyW7Frk6JfrXSm2CstwghiEK/CULJztjfg2YDxhMYDqHXhXabbCakWhWdYziMPppAqw/5UMa4abVVg9jtMVzkvLXiwp7nNpKuX3u/auv1JHJt6jWs154KcboftSDT+XA7ESD9pNfHvRCZqlU8OeT8tZorQObJWeDFwP0elD3IZ6OKxgHshSJIX0Subf2NvZOViA7PTtRqNSqVSuJnly5d4h3veAe/+Iu/eIdb5eDgcNx485vfzM/93M/d7WY4ODg4ODg4ODh8nuOuiBo+QijWECJoB7iEWVGqhGWQElJldwitQMJ3ryGEeQdpvFpkBJiTUf9/9bu3Vxjb5K0KGT0MeZtGSDsSto8LGiHTZE/8veMmgZTITHswmMBWC/Z68MQ+fGosQo/ahYAhx3WV7TLS5+pLv1IWz/2TJ2HjVIr8iRre6oqkhufzkPLFo6jVhO1tJlc3aVxqcfWSWE5dvQqbuzI+Dcw4XuXwq5VnIckayn4m4W9bfLKflaS3LYh0BfgKsmJ5BbPqXjMFbEsjmLZAs1fp64ptPZbuA0ylzD6GzM8g14GeZxkh6e1KjkLUxjaSmTHahaWOVNhoe2qe2FWpZVATGY894JO7Ing8jVxLRNs8iZCcp6LjKlEdYFZox6+ZfasvB9ExwFQ+lBFRcoCxK7KFtWzUB2nk2txnseoGG/aY2pZYlaiv9Pz1elbhYw14gZ6vH1lWBdJHti1V/DjxY8+r2lkEto3WrUDD6LV/K4hoUwIIonD3DnT3B+Ry26QaDbwQwskEJiJRhGFIKu2Rz8nlXixKxVYqDZUWrPbB70NqIn2kq/srHkxCESwvY+b0LKi9kd5v430WF4aTMEbGtYOZp/Z1qL8BWuWk93G9dm933pH+jtxLKCJzIoX03WXkmrfvPykfej40R9CZmDHdRK7vABk77WMHh6PAZWg4ODz78Ff/6l/lxS9+8U3v//Ef/zEf+chH7nyDHBwcHBwcHBwcHGK4K6JGGB14HWOJtIeprLgSbZcdG+Kyi7GYUSJzwnSVhxKmugo4iyHSbOsNhZKjajOkxI0dKKvbJQkaSec1z6bmqNDj+8j5fyqErV1gV8iqfUxmBUwTydovNYTw1FXq1SwsVWF1VbLAC2tFvJVl47ufSslS79EQ2h3Y3aVztcH1yxMuXxE7o61d2B9I/6mdj9p3xQkwzZSw7ZW0HzXnw6520LbHXy9SjTFL1FBSOh4ar7ZkXYTU85nOQQhjj3imhJ5rAamAKGLCo8cYYr0YHUNt0ToIeajt0/GxLbFORvu5HO0z34UT3ch2LDqpAOlbDU/eRFZZ72MqleL2NgNEHAwRwn8lamc/6oN6bHvPei/pGlK7NiVMtU+KmCyRMnACmSdbwKej/jgs9Jrdi76v+Tn6ma5SVyu6QnR+mj8xRGyLukgfdTHVDvEclSQsss083Golly28TqK/l5H76SpQyZk88EE/ZFDvk+3L3WE4COl2xU2u0xE3qmAiVV7Fklz6J09AfyCh4eUrcD5Sr2o5qObkBJp9uDSSMX0amTP9hLbaFmw6bmPrM/v5oL6ME+t21VRccApj732+Qc+7gsyJHjLHLyNzZhexBrw2keIdrXDU/BQ7/0irPBwc5uENb3gDX/u1X3vT+7/xG7/BO97xDh5//PG70CoHB4ej4u/8nb/Dl3zJl9z0/m/91m/xnve85y60yMHBwcHBwcHBwWEad0XUGCCkigoabQxZNUEIz17UOA2itVfYwzQhpkSwBrXaFiSL2kHF7YRsgcKL/R0ny23Ms0E6ympuz3oo6XwZseGC6QwDu6rEzmRYQkhFJXjLwFIJVlZgdQWqq2kyq1V5o1KBbAbCQDxp2m1oNRk1utR3J1y/LoHCe7vQG8hxVtOwsSSE6KNNeLJhrJDqobS1ibGf8TEkfzo6h32EGLVtXBYRMBSzhAxdvW33j72NVvSoIKQWPrno73h+RDwg2xbLyhgLK60WUnsrJfY7yNxXmy573rWi9xRaEaGe+1r5UEZI+lpo/PEfAM77Un2whcyRvrX/JOK4i4xNA6mA8pGxmBXqniTuaR+ppVIXY/emwdVVTKj9MnLNd7g1SxsVNNVCzraJSyNE7gombydERJQeMAymRZg+Zt7ZQe427DmjsKtBVGTQcZ91XsdBsmsehc5BFS1PAiezsFKBShlKRbkmszkIJyHdHtTrsHkdLl2W63g4gmoFTp+CM2dhZRnSaSnQyufFuSobiK45HsL2CFoh7AcyT0DGVysAkioiVJg+asVEUlXMrKqZuPCsgtudRAZj/eQh81RzVO4UNGdmK/pb57m2oYmIUTnMb6zed+NjFCLz+zgqjByem0in03zRF30Rjz766NT7o9GIj3zkI07QcHB4luHHfuzH+Pqv//qb3v+BH/gBfu3Xfu0utMjBwcHBwcHBwcHhZtwVUWPM/8fenwfbtpXl/fhnrr7da3dnn/623A4QNCiiMSUCsQylUkHLBkmUBCoakbJLSQwGgfgFNRI0phPFGIGUxqZCIWCDP8EIKiCNF7iX25/+nN2vvp+/P9753jHW2HOutfY++zT3Mp6qVWvtuWYz5phjjHXO87zv8wqx28MQqXaUbx8hgdReyoZriWMT/nYRaLW1mdfuxRYEtFOUtHbFDPtznMCR9DnpunFCjRKr7muEELMu8WrvY/9dwNgaPZmlUYClRTiyKsXBS0cqBCsrUFuEQlHCthsNqDeg2YBmk36rT7MpGkevB/kCnChLHfGlZTh+XGqf3L4Fz2lEZF4LvngRPt6WKH8l9lIIEaq2T5pNEmCsxWwi8qDiht2/SZk2ai+lJL6KMRoNrxkdbiaPS5RqrQAlMFvIOM5gBKVqdL0Wpqh1XPvtz5sx+/QQEUizXmrIsz0yNgWT1e6qhjz/TNSucxi7tRCTybEdcx0XceNN26nChl2/RqO/M9bnZrT/enRvV0M4KwHrZvdkMZHpKk5tAWcwdQXiYAuhen57rLikuQogRaSfiwhZfIn5rYi0vSq82ZZK9n0q1JIsjxHflpEMoVtzcNsxOHVK5uOxEwELx4qkS3nCdodhs8fGRsjjT8BDZ+BcS/ruRE9qtZSKsLoWkM/DxnrIaAjdPmz14ExkTaTCm4pAIM9W70Pbrn2p65Rmv+0XcX0f9737TN3sjYPAzuhS4XjW+XJE2TJMCgYtrr0FlosmYgeo2WC2iDpCxqrOD/u+dL1zfxO9qOGRhLe97W287nWv27P9TW96Ez//8z9/A1rk4eFxNVhbW4utj7OxsUG9Xo85wsPDw8PDw8PDw+P644aIGmAKvyaRRHZhWoivoeDal+g2l3yahxS3xQ8VRxRulL97nGYDxN3LLGsVV3SJEyYCaz/7+6T99XMeIVvVAqiA1BNYrIgQsbwM1ZUcmdWahGhXK5Kl0WjC5ctw8aIIHKMRQSB2NumMRIAvLEhSR6Ui79mMiBghUl+82YJLu/BIT8jkTSYtkEZRu9Q6SIUEJUJt8th9zjamCRbu30nPzy6Ea9tGjaL2ZDHR8XoO185phKlFYRcxtsnvfPTeZ7Ko90FgZyYMECI9E7W9hwgpJYy1VAZjIaMWM62YNsT1EUwnNO2oeFeUWUcEBfv4IZM2RIcJ7Q9dW1Rc2CT+fonaphk62Wibrj/uPWlWhmaAqMhQQfqhy6S12iwUEBJca6mMkOen4pBmBqnopmJVBSNaHQGOB3B0UTIubrkFjp9KUzlWIb1cg3yBoNUi19hgPOqxuwObHRkHFWB5AdaOyjxO59L0xwEXLw35/MMhn78Ijw/EEnCHeJsu7Se7LostNMP+hEkX+gzccRaXsWGLlaH1vl9kkb5VW7YGMpZnFQHXNULF5zA6voAZU3ofdhbitUCIzPlWzHfaRl2j4n5f3c9X8ww9nt4IgoBUau+vRBiGhOH1zFHy8PC4Wrz4xS/mnnvuudHN8PDw8PDw8PDw8JiJGyZqQDzZpPUH4mxDXPJFo/4VcVHOceSMe303GluJMN1mF/5OIsbjMjZwjotrg9u2aaLGrJd7vGZplDBZGtU81CKnqdUjUFyNfKhqNTHVz2TEZL/VYrS+RdjpMibFoDUkCCCXg1Qael3RO7pd2N6GTluEjF4PtvvwaA/u78PjCHnqFhJuY4jSDCbS384iiBNwXMRlwiRl1MQ9GzDktF7PzhrS+gxalLlv3Y8rfCVF5yuZeZiFhG3LsSZS+PtxzPgvYOpKaG0JtXurI32vBd0VGYwdmC04JI01nH3i5tONKJ4cIuTzFkbccJ+XIoUIQBnrWBW0XHFMi6EvIWIR0XU0C2UHYy02D42XxViJLSHPCUz2jL4amILmKgLq812Kjl8pii65sgJrR6B6rExqbSVSKjIQBBSquxSLPTJZSAVGgOgNYHcXzp6Fy5eHbG3BJx6Gv70CTwxN3ZGkou66TUVJO7stbr6lYra5WTAuAmefaZgno2Iaskjtl6NI/2pmVlzWlIseMg5U3MpH5ziJEUbT0Tm3kDm4cxVtnYUkoV1rnEzr01TCZw8PG9/3fd/Ht3zLt+zZ/ru/+7u8733vuwEt8vDwuBr883/+z3nBC16wZ/u73vUuPvzhD9+AFnl4eHh4eHh4eHjE44aKGi4CJDq2ipChHaZbdtjEWRizr0usuaR23Gc7etYuaB2XAWCfM47QdUWOJLJO3+M+p5z3eUWNPBIBrvUM8kA5BbVylKWxBKWlPOnViAldqEmKRRCICX+pTKpUYNBs09gecuECPPIoPHwGzjdgZxzVIAgiq6aISdwNhWD/EkIoJz0/LdBtRxG7z2M/IoYN91m5ZGlcFogS+ipchJh6JLXoO80uSCLIrwWSIv5t4U4zH+z9OpgIca3x4NYBqSEEuRLzAXL/bgbFLEHDbatmHNxI6P3OygYZI2PQFTbBZA1phkQNuAPJjBhghKEtjBCyn/vWNUsLNDejz7ruqdCn9Srscau1WkrAQgBLVVhdhZVVKC3lSFUrUKlKYYzhCMZjCEMKBVhagqVt2GiINdH2Djz2OVhIwziA9SE8NIQL4d4aN9Mi+JUgjyPA4wSLpPV4mnAShzhhZD/QTCwVQ2qIaJUBNpB1TMWqWRghfdbBZGkVgTuBNWQ+pqLvL0Tvdk2p64Eicn8qIuv6MO966+GhSKfTPP/5z+eZz3zmxPbhcMinPvUp7r///hvUMg8Pj4PgF37hF3jZy162Z/twOOTjH/84Dz300A1olYeHh4eHh4eHh0c8bipRI0QIHo0sHzNZ7FiRRLLECQiziPI4Ekztg7TwuIs40tw9j0sCBkzasbiFz+Pa6AoaccIGzru+skgf5qL3MlAtSELG8pJkaZSOVGBxSTYuLUO5BuEYgjR0uwTb23TPNzh7tsfn/h4+cRY+PxCib4iJFleroyHwBPAIpnbCLLj96N7jtONsEjRlfdbj7eyMJHEgFfXRIobkt4UtLSa9G73q7LWeupaYl6R19xsi9Um2MfZaw+h9CRG7lNjcwtQxSeqjuPHmXtfuexUDngpIaqd9b5rNM0Ksvr6EETMOep9D5PnsMjl+7eLx+h4gZLRmamjmzQKwVIaV5ei1EpBfqciczuclrWrch16X1HjE6io876sDlpZC8l+Av9+AzTGcH8ClgRDc60jheBUc9XkmjQFitrkC0bRMi6TMqqRzxuEgYkaAsWirIs9jB7nvh5gsfL5fkU7vtY/0ZQaZjzoXU0wvKH8t0Y5eJczvkIoqcUK8FzU8kvCLv/iLvPa1r92z/d//+3/PL/7iL96AFnl4eBwUp0+f5r777qNWq01sP3fuHD/+4z/O7/3e792glnl4eHh4eHh4eHjE41BEDZfwisuasDErIreP2LLkos+zSEObvJ5Giidts8+vhYKV1B7HHGcTb2MmfdHjrFU0srqMidTVCOwu8b7mSeJMEtnkChrLmGK1BaCUgoWSaBhLy5KlkapVpULwyhos3A6ZY9Lq/Dr0B4zSD7K5PuJLD8ODF4X43EXIuAym/oCS/x2EEJzXficOSX3t1lZxI4iTrpckZugxai91EjiOsT7rI+Tu40g0e4P4AuE3M8bIs7JFGB0fStDHkehqWaXzWMWOuAh8e/7Yfa373kz9pSIfSJ+42S0wKSjqWqCWSjuYIu+HVfhZ+3jWuTTrRgXEUvR5IQ1LCyJSrh6B4nKBYKEqVb9zWRiNoNeHXp90MGLxSIalU3mW1vpUKgPKfwd/fwmujOWetpHxrqKkK2TMQ3YnZbTF/a3b7DXc3q59b6+xhykE6LnBCH9ae+WwrtMGHmXyd1LFKV3/Z8EVluyi8tOOifs+jazVaksYZzfn4jD7w+PpgRe84AU8+9nP3lNL4xOf+ASf+cxnfC0ND4+nGN761rfyrd/6rXu2/+zP/iz/5//8nxvQIg8PDw8PDw8PD4/p2JeoYRcnVgJK6zaUo+9HCClUJ76oqisiuCggxHIh2reDIZPjBAtti55TCdu46yaRcDZZpDYyer64ttqFgF1iadr+ZSRKXiNkd5Do3RaTxHCSWOFmaCTdl0ZxK4lbBMp5qC6IhrG0BPnlEiwuSkR3+QhkbkFo/R4EAxil6G53Of/EkEfOwZmOWLDYViV23/es7zLEk2Ru9HZSP9mYN1I4LmNAj48rxG7bMQ0xVkNaHLyIRMGnEeIxjgA/bGjb7ELp9viKyzg5SDZMHWO5ZI997ec1ZJx2kGfeZNKCyD2vXXPmZs3QyAF3IVH5Q8Q66jyTgk/KerkiotZMaXFjxC27X1XgqAKLUUmclRVYXE6RXarCwgLkC5BKQb8PnQ6jepNufUgYpMiHQ3LpMbUFKBfkvLuIoLGDWY/i+gPi1yGc72FvpolLjCeJGXq/eWQejhFhQG3SpmE/5HuIqb0yiK4Zlxl4tVCR1EYbeY5ue+2+1nZkML+xKjaqZVnbaa/7uxgHLTafj/7Wuje2uOOuM7ZtnYcHwCte8Qr+8T/+x3u2/97v/R4f/OAHb0CLPDw8PDw8PDw8PDw8PL6cMLeokUMIwTRCBHWig1eBUwgJGiIE6AWEbInLskiKHAUjiPQQ8ibDJKEcFyXsRqxq9H1SZHAcaa5R2Upq20Wt49q73whtO9I4i0gHiwiZBmJP0mEv4bcf2w/dP43YKBWJal0AhQAqRSkQvrgI5cUs6cUFUTeWl6FYQ2iuDNCGsAu9Jv1mn3YLukNpv9YVUMKshyHIwYyLdLRvUiaO/Zp2ry6xbJ8jKZoYzHjQsaD9ksLUzgiR7Is2IiyB2App4ehbkewNFTguMWnTdNhQGyx99TAEq47RuGwVO9J8XriZAZq9Ydut2deMy5xJylxyMS1T5nqiCNyCCDZ1hMDXOikwuQZA/PiyM1Z0rdDns9971H6d99kNkPGq9mE5JEtjeQGOrEo9jeJyiaBWhVIJ8jkYDqHXI2w2aWz0OPNoyObmiFRKrnrmDPy/c/CpscwBFQ5UmIzLzIgTV6fNRf3eXX9tETcu00fn3TKTYplmthzWmLIFk/08j8OAe60Msr6qlZ/WM6kiY/co8txbiAWg1mOxzzMtY02DDnoYsbKAqRM0sva1heB5skI8vryQSqUIgr0zfzweMx57+cvD46mGt73tbbz0pS/ds/2Nb3wjf/AHf3ADWuTh4eHh4eHh4eExG3OLGkeA2xCiqYGQgiFCzlcQEmQLIVs22RtBOq0BOUz0qRbHPWjdArvIt5Jk9rsNjVgtRfvbxM61gBZ0LgCni3B8CKOBqX+ghWjdNk8j8ePIZe1TJarzGeE5FxbEeqqwWiZYXISFqhQTzmR5Mh4/rEPnMmyu09tu0+mIgw0Ywj0btfUyYtHUZlJQSLLsUswr1uxH1NHz20SzkvXl6D2NyV5R8aqDjOdGdLySxhXgvjycLMK9XTjfF/L3QfbWnzgssi+DqbviFqmfRihrdhHEj19XmHD30awVFYAuI3NYSV6bRHazXuxzuGMW5/ONRBc4i9xXA5lvKl6qoKGYNu7sTJq09dkV6mahHJ1rXhsrJfO1aHgeqGktjVVYXM2QWV2IamkUIEhJgfBmi856gyceGfKJv4P7L8DWUPrjQggPIOu2/XxzTI4zdxzGZbK4cMdG6Hy2sxTsPlSRQwn7JjIvUpjsvT577QHh6sbZjS5sD0a8WEKe8RbGJqqGETvXo1edSQE+CZr1AmYcDTFWW3G4GfrD4+bF29/+dn7oh35oz/af/umf5u1vf/sNaJGHh8dBcfz4cZ71rGextLS057tz586xvb19A1rl4eHh4eHh4eHhMRtzixppDPk1wHj17yDFoccIWRhnOTUNAYdr+aFErHqxu5HXSvpnEPLoJLCCEEQbSPsrCJlk1xOwz++SZxoFC5NZAgptg3rWbwGDEI4uwX27MOjJcVcwYs40kpiYz7pfCZOlkSUSIjJQyEOhCMUiZMoFqFahWIJeFzYfhfFZsasZtuHiE3Q/9yDrjza4tAnbfSEXu5gshy2E/N7GPHPN0LD7KXD+jsN+BY+kffR6WkhZ7WuKmCwEFQuSRLcxMhbOAo/24WQGlkpwLIAjHdmuNUP2Q2LPwhjpY4i3+4kbx7qv1olQcWLsHOeezyaFsY7VeaNjP8BkTGmBY23ryPocJ8DZ0fWHaUml83fI/vq+hxR/1raojZG9AO5HRNP9lYSflq3iIov0K5i+nheZ6NgSUC2JSLm6KnZyQXVBJnguB4M+dNoMt3a5+Hifz30e/uYifHYgmWENZD2aZeekSMrOSMqyUsSJW7PWAhW4G8g6kyGy0MOMRR3nA2Q8z3sfNyt0rjYxY0KztVSEA2OBtt/5pHPew+Nq8fznP59nPvOZpNPpPd+NRiOfqeHh8RTD29/+9thaGh4eHh4eHh4eHh43O+YWNTYQD/pZxEgZEQmKCDl8ccb+dqRpCYlWVZuMXUz2wizYUcRKdmmEr77U0qQSva8gokY5OkcHETpSGL/yHiZS3a2lobDtlgbW/nZ0shJUPaJshy7UA3GISUfb9lIEBknihvt3DVNkXa2nygUol+WVLWUJcjlIp2E4gEuX4fIl2NqCMIR+n93Ht/nSp5p88rMjPnsJHh4bIlRJ7Fb0t00m2pHXcbAJUDdyWy1vMsRnQcSJOknn1owDrYPQsfbTa2mh9jhydYgU9m2EcKQFx1pyzvNMChrXCnFF5+2/7ch5rHe7uLV7LhuaqWLPLb2fLDJ+7LooOn903KugYEfZx73HCR5XixxS6ySpZs80dEm2VIK9Y8vdbmcT6PjS7+cVcArIHC1jsg/Utk3thsAIkm72ziKybpWAYh7KJSiVA9KVIlTKkMvDeAStNuGly6w/tMP9nx3x2bPwSF8i/BtRm09F19hlsq5EXG2buP6Kq3GkcAWM0Pk8DTrGBpjfhyFGoExb39sisi2i3gzZQftBiAga82Rf5JBxsID0wwWmZzba4zGL/MaqqNZGAhPmwX5FP4+nJ77/+78/tpaGh4fHUw//4l/8C573vOfFfvfbv/3bfOxjH7vOLfLw8PDw8PDw8PCYH3OLGm7NBxdKtNwK3IOIA1eAv0MyOWbZSS0ghXxXonP1ERL5MYR4SYJr12MXWbb30YLZC9FLbYi0rsI4anMRIW8GGFI3rkiq/q3XsiPHleRziXv1b38MscH5bEdIpgFCaPWI7+M4MimO5Fey9MlaGkA5B5WKWE/VFiC3UIBqBQoFCAIYDaHdhvV1wmaT9s6QR7445m/+Dj5xEb40lueww96ocretLrGY1Oa4e8oi/vkLCPms9ihaU2XkHBfXTzbpPiv7Z9pYDpHndBappaER/j3kWV1rUSMpG8f+Oy5SPo6ET1mf3W02MojF3CKmkL1aNQ0xRLc9F6Y9Zzdzw/7uajBkvoLRszBPv9rvcYR5UoZKEtKIkKC1Eexx1Eb6W6PwVxBxSS3kdD7r+lVJS2JVKg2pVEBAAONQvOI6bcLHH+fC/Zt87tNjPvcIPNYR8SITXX8ZuAN51p9H5osWr3bFCnc8ThOG7P3mfdY6rvTdFlbscavrQAdTr0nXiadDbPgskS6FCBLHkd/KE8h6dD9iI1afcXwBOI0I+ZVo2zZiqbcx5biAyUwtjy9fBEEQW0sDpJ5GGD7V5EQPjy9PBEHAW9/6Vl7zmtewvLy85/vxeMyHP/xhHnjggRvQOg8PDw8PDw8PD4/5MLeoYRN5BUzErBJaVYRkuQO4NYBSEZY7kAtl34eJFydSSPHeO5GaHVqueoQQrFngDEJkKYllF+zVaGb7fEow2iRMOmq3W4QZROBYRojEMiLG6HXsDBDtA5uA075x7YiS3kEIuY3opcWrXQI4jjAlYZtur0T3mMYIG8W8iBlLS7C4HJCrFUXlKBYlqpsAaovQaEC3R7c7YGsbLjbg7FiI/Q1MsXaFK9zY/WDvMwtB1N4KQtadwohKu0gUshbFte87iShXG6XDoFbGyFhxyUZXKDgslJF5NEDI7T7J5Lt+jhM33H2T2mk/vyJCdp5EbMXOYezkdIzaWQhx4qFey7Z3UthE+dWIQiNEADwMEjspS2OebXFz0v6c1DdtJCsiwIxpXUt17h4HbkfmhGZ/aQH7hYzUyCkUZI3NZiDIpiGdkrSv+i6sb7DxyC4PPTDm4YdlLu9iMnO0Lk6IqVuhIkGcoDHvy4W7hrlrw9h513219o1aTWndoxGyFjQT+vepDHtuBci9lzDWcvpbdxQJHLgdWMpJPxT7MoY+T7w4ob/PtyG/s0uYGhtrSB+rKGL/xmp7VFjzoobHO97xDl796lfHfvfv/t2/45d/+Zevc4s8PDwOgne/+91813d9F5lM/H8Df/AHf5B3v/vd17lVHh4eHh4eHh4eHvvDvmpqqJ97EUPAaVRtFSFLlpD6Dek0lItwsi1kySYmm0FJsCxwDHgWcEdGCLtcAOkAwjH0x7A2hPWxEFktJGvgMsZKJq5wsU0w2lZU/ei4OsbmYxzdxzISxbqKiQS36w5MEyumbcP5zoUWRrejk21hZt6o8LT1ejK6O4BCToqEV6uQqxUJaguRD1VWiNBsRr48dpwgnaHcv8DSYpNCTtrWjfpJycYMxr6rj8kwGTJbxEgSIpS81OuVMLY8YEi2abY4es5pgofuO4uccwUq+95VMDlsf3olt9cwfapCkmtL5o7zWfej+9nksYsBko1TQohRJd7tOe5eyyWltfh6kqiUijnuIDjocUkk/DQRIwlJ9lq6Pe7+tW7OJpNjK4upVbOIjP0KsJqCTMpkB1XysFCFTEZei0uwdCRF7vgKHD0m9lPdHgz6tOtDGnXo9GAYTq4rbUQk3kbm2yUmLdq0iLeNgwgbs7Kh4sRZMAR+Fsk+W0PG5TYizjwdBQ37pUKO1gLS7XlkfCwBlQByUQGjI0O4cyxjSy0C9bzp6JhnAPcEsJaFQkp+YwNgPIYjI7hlaMaDWk3WkbmsoruHRzabJZvNxn43GAwYDp/q1W08PL48kMvlEgUNgF6vx2jkKzF5eHh4eHh4eHjc3Jhb1MgjRFs1+mxH9CrhohG1xQJksmKPkssBXch0Jep+iInEXQBOZ+DOGqzWIJ+TY8IQGk0p87A4hszYeH/XEWIryQYpjiQbIgRxP7rhBuItb59jGxFLnhFdw84Isc89T0bGtOyFJNhZITbhaZ/H3WajhvS9ku9ZIJ+V4uCFokR2pwvRhnxBWNHRGPp9saBKpSCTIZVOkS9ALWfqc2gdkGWEUDsefe4gz/RxpO9sm6JpJJgdzR9i/PGbSGaIWoWNEAJYI8xnndcWH1KY5659Z9dWietL9xm6xHMOI5IpiX8YGQM6l5oIoThA+tzNFFIkZRi4f88ad1q7RiP2zyDEaIipbxJ3Trs/bSJ/RHyGhj4XPV5Jc1uMmlcEPCjc/rLf7f6ME86S+tSuo2H3Q2B9dqF2STZUvEsh417XovwYjhZhqSg2U9UqLC5CEMgaeeI4rHzFCbLf8Hw4fgdk8tDfhUqZ6qXPsHpum6Nb0BlB2IVCKGLVJvKcOxgRSqHPUEWWJCT1p/aR3adxa7KLuCwwFb5UfNaC4dq2LPI7Ys9FFRz1mJsZ9tzWOaIWiSUmxcQAuVctEl+M1vVUSt4zXRg1xUKuE+2fQ9bvNeCOKpyoQrkiS38A9PqwsQ7DjhxXRtagITIGO0zW6Hm6iUke+8N3fud38vVf//Wx3/3f//t/+Yu/+Ivr2yAPD48D4Qd+4Ad4znOek/j9e9/7Xj75yU9exxZ5eHh4eHh4eHh4HAxzixpZhHCpRn/bJJKeKA/k05COgv9LJRE17gzg+SH0xsKjhyEEEQuaISLc0xEhNYJWE85egc/1hDSvYzIGOkzWnnAjz92/lVAbIQS3RkVn2Uvm7SIWHnnneJhuNTVO2Ge/sAnSaVkacShiio3rsyjmJCmjUhYyK1uIQrzVE3s0hFYLrqwTXrpE69w2Fx/rcuECjHtix3UrEumbj66xiFhElTDZMxpVrFZF80b12lkP+mwVaWsfuw/mIUiVKMwymRGQJAQQ870rVinhXIra2kDGpM6Dg8SnpqLz1ZCxuY1EzcPkWBoxvfi6/dklkuPGi4o0+ei6LYyw0UXIzUrMcUlzTuGSyBolrs9Xs3nizjOvGHNQYjUpo8v+rGMmFbVZx5475qbNzaT95oHaDfUQ4SEDjDuwOoLja3D6FJw6JUR2vw9H76pS+IdfDc/8Dsg9G0hDuA6Lz2AxXeS5xY+zurLNscfgzHk4fwUe78qzuMhkoXgbeu9z/zg4iMt4SeoH9/nb866PrMkdjGCbwmSK3RK9FqPv+si8PA88ggjXN7OwYYs3avFWQH5j9fdJf2N1H82UzGZE2KhUJBjgljQ8J4D2KHp+I/mNDUJIjSSrI5eDIAoa6HahXodPN+DxscnS6GLsGX2MroeNl7zkJTz3uc+N/e4jH/kIn/rUp65zizw8PA6Cb/u2b+Puu+/esz0MQ9785jfzX/7Lf2F9ff0GtMzDw8PDw8PDw8Njf5ibt1LCL4cQTW2EHINIzECigBnBwibclYKVFVhdhXxeSLh2B+ptycLodqU+dbsFnS70hkKkdICdEM6E8Gh0TrsIt17PLiQbR5y5hKJG7VeRugErmLoWNrmnxI7aa9nndQWNpIyNq4lotX3t48hSPb8STnZEPZiI3zQR8VWIiK9qjqBaEZUjkxHfkdEIej3Y3mL7oQ2+9Lkejz8BOzvQawtZeBx55m2EJDyLFJbVtmqBYY3AjyM0XajQoORxHNztmtGRI5kotQUVt3C7/R4njMTt60blF5H+KCJZQyps9KzP84obGWQcfiUSmX0J+DuEzHahBK8ttiVlE9hR8kkZEGlk/K8h/XkeebaaHeL6588znpWU7Ud/ZxHxK4/0TQOToZOUoZEkWtlzy/5uHsI6jYg0MGmN5T5nrVlRiz7vYOr4JI1pd15OeyazUEayoMoYK7ZdoDuGVheOZ+H4CTh1T4lsMQNhSHDbrQQn7oGsViQKhcXOLZBarJFfLpEv7RIEYwZ9aA5NttsgvhmAybqZJS7NEgnde59HcLOP0Tmvgqda9YXIczoFPK8Md9wq2QrdLly8Ap9fN2Jdi/n6/3rDthu0hQ0VG3VN0YwTzfzZRdbgeh1ODcSObHVVlnWARgvaPdipQ2cAvY78zrbakpnRC2WdagBXxvAFJCvIDhQAI/wnCY8eX15YXFykrIPMwc7ODs1m8zq3yMPD4yBYWVmhWCzGfvfqV7+a//W//pe3kfPw8PDw8PDw8HjKYF/BuOrD3mbS1idE7Eya0QkrIWxegfU2LC9BawSXd+Bs2/i3q9VGBlM/YIgpXLtLMiGlketJGQ027G0ZjEd7IbrWQ0g2iNpu2Od0axBMEzTcbQeBXlPhRufruTXrJMTYr8TddyqQqO50GoJsRkJ1c1nIpCVLoxMxXt0umWDEQg2OHZXMmXwORuuw2xGi/TLGmsgWWtxMCDv6fRq09ofej/udWiPFiRtx1je2AOWKYPa+7md727Rnp0XDB0jdlSISMd5ExrPaWtli3zQo2T5A5tTOHMdNy1KZ1eca9a0EaQERrQJE6BthRJlpz1C3u/U0QAQSHcMF5PlqbRrYO55dwcD9295OwnewVxhR5DF1ctoYcUUzgnTM2HVj1GJs1jy2x7792Z4f0+pr2NAaEhP2QtHnHJANZLoO+hCGAanlRVGMT56CYoAYwNVhuAvNL8Fjf0f3U5/h/CfWuf9TY/7+IXioKSLxGfYKuXGYtZYliTtx+8UJvmPne/u7uGOU3NexpPNmfQClLUhnYTCEVh+KKTgxljl1GXne15qi2W9Wjn2cbTPVw6wFXSb7aSf6/gIyPs53YPNBOLkN4wxsteHsJpzD1B4pIONqhKl91MfYOe4SLw7aa8V+xDmPpyd+/ud/nle+8pWx3/3sz/4s73znO69zizw8PPaL5z//+fzyL/8yL3jBC/Z898lPfpKHH37YCxoeHh4eHh4eHh5PKcwtaoQY8s8mQZTM62KitFWQeKwpPt9Xom196/gAIRurCLnSx5D1uk8SiaK1G2xhwyWVXKJN/1bLn6NAKQcnhvDYWLIPLmKK8mqkshux7mZm2NsPg/SxszD0/nDuJWDvvSedKwzl9eSHJ1M9RpIiU29At0elEnLnHSJCnT0LnbaJklaffbcY+CwBY1r7NKtDa1/of6OyyJgoI2TkLobstwk2+7PWaEljxuiI5NoZcW2ctZ9GiTcxtjZ1ZJzruFUicB70gccQglLrBvRi9tOIaa0zEkT7zYqkt6GksAphfWSst6PvVZjRftNsAVu8csd7KubdFgVA+kZJVDDPW89hF6TWv+1r6HdxGVPE7K/v4+i7JSQb5pbob62lo4W6s5i6MK1oewcjjGl73TFsX0vv315fbEEjsD4nIY+MeRUySpj6RWXglgW4/RSsnUiRO7IAa0fhyBEJ0+83oH0/9AZw7hFaf/cF1j91hie+0OLhR0IeuQSP9kT2OBPdYz++GU/Cvu8kTBMy7O/ttdkWKOPEDPvYuLFtE+0jRNT4Yh8euWTGsmaVnUd+d9okZ4PZOKgoofWkNEspbg7HHWPfvz0v1NrOFXlAxqRa/mltqIsjyF40tVI0QEDvuYT8zg4w83HI7DVEBSRboPPw8PDweOrip3/6p2MFjfe+9738h//wH/jiF794A1rl4eHh4eHh4eHhcXDsK1NDiSmb5LM/K0HSR6KBLyHEixK+LjRaeBsTXT8vseRGdSeRbHrOIUJCX0SIr1XgSEWyNrLbsBEKKeQW8tWi0/b57CyDWQLMQRFHaim5ZNs8uZZPE8eHkcvUGMJxCONR9IpaPBxCt8ug0aG1NaLRgPV1OHMWnrgE57rybPQZ2iQ3MdcNnH3sIrf2cfb3ej/an0reF6PvNGLZPacLJRftAuFJSBK89HPcvY0QwvsSMrY18llFMJ0b8/rQh9H5OlP2yQLHEKuoBYTw3sHMK3tOTSOZXTuhMcaGSP92BTt3zLvnjtuWwYgaSpyOE/Z1SeS4aPCkCPFpkeM6RjKYjJ8TwNoihCnYbsF2z6wJZxBCWAnfhnUutQKKu4Z9fVf8iRvvSVBbM62Jk0eI6AVgGVirwh23SC2N6pESqaVFUR5rC5J51WrB1jbhuXPs/M0DfOZPN/i7L4x5bAc2+2Llp2vxNrMFDTD9Fgf3+eh92uuxZk6pGKf1LlQ4c8UM+33aNh1fWtspjTw7JfoLiAjUQkQNN9Mvg6lbYo8PFdcaSB/NK0zazyqNzKdZooY9JlSk0f7S/lTRXvd311v792wnus8O8WtPDxF2Wkg/7fc31ra+8vDw8PB4+uF973ufFzQ8PDw8PDw8PDyekphb1AgSXhBPpmpx4CRBA4RkaTIf0RZ3fjsyOg5ja1/b2mo7er+yLWTSlVBIsCZ7iaEhpkCtEnhFxMYqjRCG18JNehqJHGAenB35PnbedbtsjNSN0Vi8bECyNYYDOs0R58/B44+LoHF+Cy4NzL1tYwjwadHZ7nhwszqS9rPtV2AyIyi07jUuohuMdZIek2RjpMdMI6XtCGr3uD6T43k/mRkHQR4R3W5BCO4sUgB5w2q3izhRJk4Mmtb2uHo19vldcQpkLhQwEetuoWH7mccR4y7itiftG9dOzR7bju5nuSaFldcGUnthMID1Lbg43JuFolCRKongt9sTl9WS9NlGHiNY2a88UEhBrQpHVsVtKlvOii9cLi8ecakU7OwQnj1H57Nf4v4/3+HPPw2f2IEnMDUZVDxzxVoVbHVsq62fEv6u8DfLjkjvP4eIDkvRvWUQ8cHOhJsmbJCwTQWgWnR+LR6ei7apqKlz1D5XFsnauT1ql9ZMykT77QL3k2zHZCNAxBPNNMxhxkod6ee4c+i67Y4Vt76Gmx3kZgopVDy0MzNc9DFi/X7XqllrqYeHh4fHzY9arcZv/MZv8E/+yT/Z893u7i7d7ixDSg8PDw8PDw8PD4+bE4cmatjEh5JfWYSEaiWc8yBihsImrOKIFyXhXIuNEUKw/S3wyXB25KoSX5oBECcaHDaxPS0jISlSOu77iUyNkZWpMYruIPo8GobiRFWHnQY0BkKCap0TO5I/7lpxpFdc++PIuThidICxnHLFHN3mPn89Jq6d9nVSzt8u7GwGW+iwr+sSstcKLcRCR0nvLKYmRFIB6yQxIIuJnh9jIrvjhBy7pok73uyofPs72w5O63fY32Fti4vsx9nH3p60bdbxfYRkvgIUtyCdge4QekNodOFsCH8f7RP3LHX9sKPmbcT1h01Ua1uSamuoFZ4KGWXkWWuWUm8sDnGdriRkVHY7FLa2CcplETRGQzhzhtbnHuWBT7b43N/DI7uSfXIOs+6qSEF0P7noOuXo8xCx5WowSbDD5LiYlnmi62AWERjuSsPtOVjKwziAs01ID6TIdZPJuWqvL0lzTAl2FSz096Xk3J/Wt3GfVQkppX4rIn50MTaJHYT4P8Ps2htBdH9aqyUbtVHrX4SYDC79fdPfEK2RkraOaTO5ztgCr72GxtkgaoZHkWTRQvvsanA91joPDw8Pj2uDd7/73Xzrt37rnu2f+cxneN3rXsdf/uVf3oBWeXh4eHh4eHh4eFw95hY17PoENmkURzgrualRtErcHAZs4ieumLRNes665jxiRA4T1TtA7mU3eu0H2l5tc1wxa3tfmB0pb2+LJRojUWM8gvDJTI2RvALk7/EIwtCqvRFPNMbBJnHdqPSke3Jhk+qw91quiBLGHGN/r9ea9ezjyHz9bFuhaeS5ktfXm9xTEU4J4zqGEHb7JqmvwJCfJ5HocrWGSkfvu0hRZSW2Z4lPMFk3YoSpa2D3k/2c7PoT9hh3xRE3Wj9ujEw7XjGM7utvgU83TEZPD0NqT7P/UoEtxAibcWPP7gdX0JiWrZFDsgZqiMXYcWA1B6UshGPIBJKY0e/D9g7kLnapDS9SqDdJV84w6g9pnt3m4c/3uP9+eHwL6qG0dREh71XQGmEsyzQLY2i1yRZv7XnhjqMkQUPFrAJwBLi7DHefhKUlyGTgli1YOgO5umQb7bD32cVBz6vZCD0Mma/PRzPptObEFnvFiT6SvaLfqXVTm/lF6SxSi+mZyDzqRddrY+ppZDFiUQZ5BkVM8XcVYHpIxtVZTHaHm6XhBg5of9hjUNcnnceHAW2DK1R6eHh4eDy18IpXvIK777479ruf+7mf84KGh4eHh4eHh4fHUxr7ztSI8/q294FJktslvGdBCz7bxKpdcFeJN7UZaTKdzFG7j8GUfaZBo4L1PEP2RqLPg3mj+90+1M/2d8Rst8/7pCARGmHjyUyNJ0WN4MlMjfEolCSOcC/J77bfvl5cpHycoDEP4o6Lu+9pRH7c+Is7Ju45KHHnChcqcmS5vshHrz5CfK5jIszjosndfgidv9OIhdWd0atSglQgY2OnI9d4ECkoDXvrdcyDOOIzri3u327mhi1+uOdy2zSNbB8hxPU2yWN6FjTSfYCxbNLr2rZUs9rmtlMLmS8jBPmdObjrKJw8DgsL0b4hpFOQzUImDe0WjMcDsltbjEbb7O6GnD8HDz8CD23AE0MRaTTbQ7MFWgiZr0KWFolWkVULb2tx+KS5qGuhCz2mgLGcCvvQasNCDRaixJKlbRE19HzzvtTKUD/Xo3ZrVoa2bYCpIeGu9x1kjKvgNK1IdhwyiOj0XGT+DIFHEaGkgynQDdKPVeAU8mwrTP5+6JpiC3iukBEnatjjyM0C2s+6q+NYn73C/q1T4WvM7N9YDw8PD4+bE0mixlve8hYvaHh4eHh4eHh4eDzlsW9RI+6zux8YskYjg9UqxCZyNGJWsYAQQOrNrxGoQyYjptVzfoAQdloEVW1NwBSOVisXjabdL5mlhV81EvYg3uRgiKxZ9ib2/nEkfRxZb/e1S94+KWqMx4SjMcGT2RnAUOyownEYFRPfm7UQJ3LECVVuu5Qgc4WHWUja1z2PHa2ftJ89TpX41Pcky5s4uDZN1xIq2uUwEfaaWTBtsrrPxH02mqVxC3B8QUoygNSX6HUkW+AWZG4pAT5trO33uc6CigR55D610HhcRlPc2HPbppg23/R6OjeniZWaFWALN/qM4toWJ0ja7cogfb6KWCLdcwzuuhNOnJQaGsVyQK8XUt+VZ5QKIJWWWiCNBmxvhZw7Bw+dhUebcAEZI5qRY9tN2UXbYXIds8d2nKDhzgv3s31cKuqjS0ibz52Fo5dhqSy2Xw81TK2PEfHzL2k+qvCi7W9F96v3qM9uTPJznDYWtP0FpP/sNcS2oruA2JntIDVCGsSvCfq7dBEzRuw1KESeUZe9/Zj0POwxZWcD6XsGEVPcOkv62wemyLpaYdljWC209LezYN1LG5OJ5eHh4eFx8+O9730vL3nJS/Zsf9WrXsV73/te+v2rNSf08PDw8PDw8PDwuLG4qpoaMEm6xJE7asdxBCHw1EpEi/jaUaB2hGgeIV8q0XcthJjLACcQG5B8CgYhbIbwGEKYqT++2nIoSVyOzmmLGmpHM0ukUII36R4PAiUdZ1kaJYkbcfvpu77GoQgVakH15AenpsZ4FIqgEUYiSML53DZMI27ddk5r+zRy3L3WfhAw6bevRXVtEtEmGeNgZw1Nu4fDQBYhEpV0tslZtddxiwgrkohnhdqfDYDtNqR7oml1hzK3xkjWQDU693nnXO54iHu+87Rl2thVkaGAqUswL+IEmGnz2iZuVWxMGpt2f9tzY9r57faknH0DxB4qhVhgXQIWdiB4HJptqO/C4pLMyeEQ0mlI5yVro9WD7S04fwGeOAePt4ytUhFZ53T8aCabWpbFwe2zuO/sftGsAFvk08+aRdREyP9gDOkuZLtyXB2z3ttrh7u+uC8V+WxLLxVrtE32PN6P6Gz/1ijZryK6zkH9vWohFm2aFTJtHRgiv2/bCdcMMEXUXbsnex+YfDZxQrKeaxHJDtEsxg7S35fYW99D3ytWOzoYkUXt0KpRQxpjyXTx8PDw8HhqYHFxkXw+v2f77u6uFzQ8PDw8PDw8PDyeFphb1HDJljhiVUlijbrWaNQVpEhrDSGDGkik6yUmSZomxlIkHx2n5OYWQsxkERLmJLCQhsEYWqPJtiip1Y2OyWEIvyyGROojtj5NphNUzPH9fpBBBJ40Qnp12EsExwkE04h1JfPcDIRxGJXSGGMyNUZRzPNoRDgymRrj8V7SzCUf7e/i2ue2yS3MHUeezkPeJR3j9oO9rxYTzkV/20TkyNonZJIk1fPkkedUQqKUtzGZO9cCSmpqWwJr+zxIEgxCZKxfic5Zilh8La6eRgjdEqbwsms1FDfeXCLa/hxHiLtjyD1Gi0BrVkFcVlXSPHSj1yHeDkuj8fOYgumagRE6+5Ux/aHWTCoSqH2T+2zc+RknaOSQ/gZZAzeA83U4WYdT5+GWRTh1HJZXYHkJqgtQqQSMRiHtDnQ6sLMNm13YCWU91XoOSsQPontzhWN33U4SDJPEybh7tfdXEUDrD/Vj9s9j6tTo9qTxApOkf9yYuxoEyHioRe3SouFu5sdB7Qvda+mapOK9whUr3d/YuOdk90sR+Y09Hu3bxBRCt9vewYij6Wi/fvR5BxlL+jt+DKikREwLxwcTlj08PDw8rj+++7u/mzvuuONGN8PDw8PDw8PDw8PjmuJAmRqQTDSrT3sBE/VdRYQIrZehRYVdItEmwNoI4ZLBEGVK7ChZVxmIcLEevVox59So3gXE772EsbfqYsSNwyartS8yUXvU7iVAyNI1TPS9RlXDJJmIs809/7So+RDJuggjWymxoAolO2OsmRpjwnH45KZxKMeoqFSKzqOF0t1o9mmZGfa2acSce6/2tmlkq3tsEslpR1Xr2NAMDptsVpJPz6U1KG5Dxu9FjFXaYQpcNtQmyH6udh+4cy+pxoHC7pMuQqDXMRY7O8hcKiDjcQnpl6Z1bBzJbGOc8NmN7k86Rv9WaziYFHO0FkGSiKVQItaGrh16jIqbKihsYyLTXRSBuxBRS23sAkRcPY8RuPSacaKjOyf0Ho5EbchGbdTaFwMgCCGfh6VluOMOWDxVIb9UIpNLMd7apV7vMBhCuwOtoayTQwxJ3sesJ13iM9FmreM2koQyO1MjDnp9W1gKmBSV9Vxx48zuP/v7uLUO57tZY8WF2lltMz2rJQn6m5YE/d1T4c2uZeIGCbjPxf09CKzj8ph6HXlkvVKxrogRK+y2jZy/24iIAyY7BWQNaAFrIwhH0jfn8fDw8PB4KuAHfuAHuOeee250Mzw8PDw8PDw8PDyuKfYlatifXbJFiXCNjF9CbKI0wtm22SggZMwyJirWhUZuuwnSIUIs7lrtmGbhFGKi0ZUEqkTt6iGk7jpGNDkMBMj934aQxUPgXNTuAGOppZ7wSnLZ/TBvW2ziz87UGCMihQoV4QQ7GE4whSHms/qyLwTi498F0mMhuHaYzHZwCcQkgt0mQW3rmaTI4zii077fJMSRmSoaxZHPdkFq+9ralgFyz2oloxHv82ZO7BdKOLo2NPtBHOGr40EJcB2fm5jI7HOYjIQie63app1b3+f5bLcHTCT8NCMEN6rdRVIbi8hao0R6DpOtpTZ1SfNM14ZWdOwy0meL0XkvIlZEKqTGCRlJokYZWQNWo3MupGCtCreegDtuhTufEVB7xhqZ208RHDtKkM9DvU76sUfJXTrPaNSnO4B+NI21NoIKdVpraDO6B/va7riKEw+nrT2uaGOfy4aKulqAvIz0m9Zv0Cwh1/5tlmDhPrMkAWlejJE5YAt5+8U0QUOvYQsZSWKG+0xcEVhFPs10KSPZFCrSaQFwXWMHmAysJNF+hBkjNlrAg8BD1j0cpJaUh4eHh8f1xXve8x5e+MIXxn73L//lv+SDH/zg9W2Qh4eHh4eHh4eHxzXC3KIGxEeSBgjBsozUuahhIpuLGHFCCRElukKEcDwWfa9kTxshVGYV1J5FJNlQgraJiXTViNYSQlSqzdVhEDdphLC8ExF29P7Vb34neim5VUHEA7UGcUlR990WEFxCzxZ5QoulS00wZ0F08eDJ71Jp8e7P56CQF9ubWk08/bMXYXdb2rZrtdHNwLAFCxtxRK9rbWQXiVaSOy7rZhpcoc0VX+K+T8oiGSPPSsnADteP1HOFk2nk8SwS1u0DtQW6gilyrGNmiIzDKkKYxmU+udfbj5ih96BrQlIdCxs6J6cJPbaQ57ZXxVQdY5oh0WB6RP4QIXSVSNYi61kmCyqrXdisLA21oTqFzPcyYhV0Rw1OHIMTJ+D0KTh5S5rcrcfgtlsJjh6DYgEGQwjH0OuTYky1AkdXpZ5Q0IZcaGyGBpjstZZzT3FrtyLuOdhiZNLa757Hvl8Vb4rRu2YsqO2XFvuOGwdxY8e+j6T9D4KDihn7OT+Y8eK+FEn9qr8TVeT3cgmTcaj2aDqfbKiQsoj0fYjJULTXtiR4IcPDw8PjqYVnPvOZ3HnnnRQKhT3fffGLX+Shhx6i2+3GHOnh4eHh4eHh4eHx1MO+RA2YJIaVdFGycAUhr3oY73klZNSKSY/JIVkMyxif8RRCdp0HzhAfQToLei3b5kMjhHNMCidK5FYRYrKOIYdmFfCehhBDMGoGyAIi+hQwXvNKpo2s4+xzqEjgvsNeAWFCzHBewZOsZABBSlQMh2lORd7pQQDdPlzehq229MXFjumbuDoH88CNPHY/g4mm14wFtQWzMylUlFKy2r5/21pFn3+SEDCPQKBe+q7QdL0x7R7i+hX2jhX9LkTGX5LtEtF3WeJFvqSI+lnChgqJWvB53syokMn6Ivr87ftNKvbeR8RD3VdrT2SQLIZZZTJtW6ddRCioRtvb0bmSBA1tG0zW1dCMCiX3+wOpkdHrSuH2J48fDqHbEbu4dgfqdRiNKBZhZRW6vWgaX4R+11hOqTBl92+cMDENrgAZ9/0sQUQRIH2k403rmdhZJUrGu8JtXF/a54XJWiHT9r+WcEUIdyzO6vd5Mr+0bsYS8psJphi4XsMuAq6i2yLS52AyOYbI+H8YmR8eHh4eHk8PvOMd7+Brv/ZrY797/etfz1/+5V9e5xZ5eHh4eHh4eHh4XDvsW9RQ2KTTACH8tE7GLoaMLiARu7boEWCieFV4UH/5YbRfFngiOreSQkpk2wVclfRUEldJwxyGRC0hAsqJ6HxPRO1VglNfak3Tsdp/EIwQq6nHrGsoCdpBBAK1ArIj5ZMI5LhtNkmmgodNBqr9VDiech/jEb3OmPoubG/BlW0424L1Eex0pY0dpBbDZUyfHJTYT4pmV3FJo8w1kr/DXuJZx4FmArkkqBKkSu7ZhLItDLmk/DSS1u3r6wGXvHfbN61NbqaA3c9ghJ8M8YJah0lrrmlIqqnhjmUt0F1FnnOd/dWxsYWSeaPHbcEQRNRoY4j2WWT8MkIKlxFR8jhCKu8CXyKeUI8bO7qthKxHWrulC2x3YXhFxI3RGIJgxIlgg2K3R+rCRQjHjFsdBo0ug2aPdn3EOCraPBhAb2zWPy0wv4P0bVImhZt1MU3gs+eXvW/cfbqkvrZHRclM1AdLyNps72uLcfY57c/unI3b7rbpsMQNXXdU5FKR3BZXbQHHtiWzxfyk/p5mN6d9Ukd+U85gak6NML9dJaReSw1jJbfM3t9YkIyPEvAZjKWgHQgwdl5uLQ4PDw8PDw8PDw8PDw8PDw+PG4l9ixpxpIvWHljHFNC1SacUImDchli4LCGEtJLO2pCcdUwfIWHaGJsO9WPvIQSlFtfVCGUlzsCQS0ruZRDCZxshhy5Ex+YRwlK9921bmaux3qgj4kkXIZxCTOFeFQvmjVRP+mz3sZJOdl0NQvMOEASBhHcHgVQG7/XY2Rjw2OPwwGPwxR14NJT6CptRO21iy752XMaF/X1qjnc95wBTdDkp2tk+dz/ar4QZE0pSu3UpdPy57XLvJe5a9rGwtw+uFey54IpYLinqtt9tV1w/2iS1Pgf7HCouzbM4uH0Sl6UBJvOmhTzraVkSti2Ptm+WHd08sEWvWcgCdyBCaBF5HqvAQgbSQ5P1pYgTkdzPK5io+SbSz5tjyHdh9RLsNKHRgM3NHmtHe5TLQZSwETIaQi4PC1U4dRJGIzhzFhojY+PUj86rwqA7VlzhIY0h5jUrKW6sJAl+cfNeoc/btfjS515mMiPOHjdu1oV7btg7Z5ME08NAgBHnVSxYwNhAaV0KfanY0LeOj3vtB1rMXAt4NzBrns7jNCK83Y2MVR239lxSUc3OmNqNPqu4HyDPpoWxRIx7lh4eHh4eNw/e/e538w3f8A2x37361a/mT//0T69zizw8PDw8PDw8PDyuLQ5sP+VCiUe7KLPuP0ZEj8tI5PMChqxsY7IxCtb2ClKTQi1wNOpXCTwlRjV6VKOflRy3MUJqCCwgBM6OdV43CtXOnrgahFFbt6Nzqu+5aw0z77mm7a9kvpv1oUXCwaqpQSRqEMJgSKcxZn0dLjbhYihijxZAnnXNOHFDX3HWWSnrPbS26zPQ80StfHJwuhH3SuKpiKX7qnCk59wPeRhHiOq73e5r4TFvX09FtiqGVEyqVaLtdj8niUFJ+7gEsD6POAJ2mkjifrb7SkneFIYkdaEZXVonQJ/xAMkWasQccy3RjK6r9/8oEAxl+2Xk2dhik0umu5+zyDoVYrK18ggBXQtFZ+z3odmUUhrhOCQMob4L7Tbk85DLwepaiupCSCoI6YemHtAQyRDrsjcbw4b+rXVs8hgrLVv0ibsf+xzzrA8uBshz1Ou49TSmtXlWRkZS3x8WtI9VVFVhoIf0u1r06W+hvU7FCUw25hVk4gQGXfOHiHXjKmYegfm9DDGCqbbtNJK1oYK4CjQ70TFa8wTms8ny8PDw8LgxOHr0KMViMfa7K1eu0OkcxNTXw8PDw8PDw8PD4+bFvkSNuKh83Q7GNz8uolOJ7DZCAGlEcR1j1aFRsGUkCjaLKfKthJISLzahDaaGRVy0sZKQdlaHRkzno+voeTR69TCsNrQwuLZXMzUOI+rchS1mhCSTirJzCMMR426fYX/EaCjWN9p/+6mbMe9+ScSjChJaSwOMVYrWWbEzUPQ4jTgOne1Z5Pn1MaStm50Rdw9JgkYS0XhYhKkShbatjRavV5JZiVFXMNB+SYpin9bGeUhpW4Byv3O3xVlPJQkccdfNIRHmpzBZXDonNSpd7e2uNTSifQMRQ7Wve0xaZqkNmr2IJhHrVUwtCRXz+pg1rwgslGFpCVZXoFaDfCGaqgPodqX2xu4upIIxly/BRgOaY7OmKameJOa5Qodmyuk4c+f9tDFizwlbTAUzf5X0d4VArftxrUjywxY0QkwWhmIXGR+6BtmFum0hYz/XsAViiBcbwayNcb+zWsh+FyPW15E5pMEDGeT3bwUZd3lMfRP7WeoYCZh8vh4eHh4eNxde9rKXcerUqdjv3v/+9/PYY49d5xZ5eHh4eHh4eHh4XHscWqaGYlpE+S7wCHA22qa2Q7aIkEGyNE4g3uABJrtBo1Q7TBLdA6YLEUH0XROTEZKL2mQXVw0wkfGHARVxNKL7WpJCtujzJNEYmuSMyYaFMOgz6g1IEVIoQjELudH+bVFgPgIxjngHQwAqOahEWtr6Tvd3CWMVu7D2UwsVO7vBjZDWPnIzMey2JhGj80RTz4uU8662L42oPRpxX8LUolH0mCw474ocbvaEi2mih02uThMR5qmnMa3OhotFhGANkPvTzC8VPg878j4OZcQm71jUFq310kRs2TYwY25We+zva9H5NKMsiwgdiwi5XMtCuSgZGoXolc6IoBECqZTZ3unAxgbs9GSM6NzfYbKWBsTPATBzDowI6ApkcXPWzdrRY3TsgRFIkgRwOzNrP9lUerx7T9PG+bWCimwu5hVqVDC0P9s2cLMyU5IwQH5fNzBrnf5+6jNS0ekYUmtKhWA746fD5DjX31gPDw8Pj5sPP/IjP8K9994b+91//+//nfvvv/86t8jDw8PDw8PDw8Pj2mNuUSOJJHYJpSTfbTva1SWjbAwxBbXVD1ytokJMVoWSYnZx6SSMEFIU9t6wnldtWEaIZdRhYlbU/GFA+6GL9F0XGIZiQYUkZjAejEiFIRSLUCiSGQw52RvR7TVotmH7HGyMDDlqR17D7Cj5WZH/ml2jBdzVikifLxhblCDmBcmkoe6jZLF6xMfV0NDzuJZSVytYaN2XAJPxkoQkslm987WWTDl6HYnOrYSjRvdrX9nkvxKY9n2Ezmsa3Mj7JCTV09iPkKHoA5/HWOPYWVkD5L7mPZeOIaJ3teLRQt114CKTa1UBIXhvB54NrGQgHENnLPtfxgiUSmarndS0dtjjTQU3zcYpRddcy8CxJcnSqFQhmzWCZL4AlZHYUvX7krExHMJ4NEmK22M4qQ02Qoz1k0b0zyMOuJkDOs7sotk651yLKdeOSc/nChX23y6mZV25woD+fT3FjlmwM6D07/1mh00r2j1AggfqMccp9Df2DFI7SftJMzVUVNa2qjDiC4V7eHh43HzIZDKkUvH/Oh4MBozHPs/Ow8PDw8PDw8Pj6Ym5RY1pNiT2+zSSb9a57O9bGCHCRoBErVcxEab1hH0VY0zkqVsEXKNWtTDyQcnTm4E8azNZRL08hFMD8eofjyAcjSGbgSNHYHGR4OhRCuUyp4cPsrGxy7kdOLcrokYGQ0amkf6Z1seKpD4YYwrsHkOi00MkovhydH4brlXOtG02NOtm2rOIK449KzPDRdy2AmKbtoSxVquz997i2u0KfUrqp5Cxu4vJdmogwtCtwElE3BggVklKVM5jITaNxJ53DiQJGAeZC1cOcIwLWzzQz9XoVUaEhCLyXLRPtcDys5E+rQUwGIpN3hUMSdzFiC4uCa+I+7yMEVTyUVsWgGNpOL4CR1ZF0FhehrU1WFxJk8sHZDKQyqQotYaMx2MuXoQnHoetbbh4Gba6phbGDsYaK0nYsIVBzYRSkSXHZMbbPM/PzgpSQlyFRX0G7vWT5m2ckOES/LOyF8Ipf+s2Jew1iyHOrvBawBYzpon6ce1256n7G5Z0vVnQDJ04lJD5UozOlUPmg4eHh4fHzYUPfvCDvPCFL4z97uUvfzkf/OAHr2+DPDw8PDw8PDw8PK4TrlrUcJHHWEVdC7IoRIqbriHEThOJula7mmlI+l6tfMAQem4NgzgUEfuYFEIqXi+LnCRonZI0ck8LwG19ydbI5SBdzEGhKKHg6Qxk0gThmF5nTLsFo6GQWccRYl5JrwEiPswjakA8Wa6k5BghyMrR9w32Ri0nIS5bI8niZhphb19n1ud5SVRFmkmf+g6mSK8irv024ao1JTaRPh8i/a91HYaYbJQ8IqakonfNYnJFm/3cg52lEZeVFXf89chGmga7/2zrshSTwqfWedD6PyHSbzWk37vAZijzeRt51yh1XT+yTNYNcdvhCmU6HrR2TBkR9Y4vw223wKnTsHY0oLyYpbRaInNkCXJ56Peg2STV34FwTLsNFy7C+ctwuQfryLo1sN6nZTqEmDoglehVwmQA7WDm+bxCnsLOHuhjMrFcMSMultSda3FZG/OO4zhxQ9/LwNHoBdJ/F5B1c17oONMfTtsCLw72vJg2l+KuEyeQqX2U2ideKzG9htiwlZA1Z5vJ7A8PDw8Pj5sD6XSaIIj/17PP0vDw8PDw8PDw8Hg6Y9+Fwu33OGihb9un3bau0kjVqyFkAoQs7iOEVGef53EjZW37FiU7q9H3XSZtcOz/HmQR8ieFkIrtfbbjsKF9qqTbk1kko8iGajyGThuurMNuHba3GT5+ju1zLXZ2gKGQrQWkT5sYMlhtlea9v7ho4z4mi2AbI0ppjRSb/NRjZtnRENOuaZHQ7vfuvge1ngIh/zYxEfNqt5Z0Plfc0KyCNWQO5ZHMC7dIMchYO4sRNgZIJPUl5HnZGSvz3I9LoM6a6yocpDFZJTcCrphhZ0+piKZ1e2Ayk0zt5kJkPD6CjPO6dYxdpybp+rYI5Gar2G1TUSMHFDNQqUiGxokTsHz7AuljR2BxCapVSKekKnivT68H9QbUd2F3B3b70kbNPtM2ulkQ9vqmYz0PrCJjbBEZZxmM3Zk9H5MyCdzMqSTxcl4ECZ/tv93ter9xYsisa+SQ57zL/oqVB5hsrDLS77uYotwuVDyz1xn7d0bFc70H/Rumz1kVPjOYTJ0he5+LPoeDzs0BInS1EfH5Rv62edxY/NZv/RZ33313bDT4937v9/LFL36RP/mTP7n+DfPw8PDw8PDw8PDw8PD4ssW+amrAdPsQfU9hIneVCCpg6gAUovNdQUi0/ZIuT0TntTNC5j2HkrFxpF0GESnWEBuaCsaiZAf4EhLdq9dqIERowF7B43ojjRBtx4BTwDMCuGMRTh+VoO9OB/pbDfKFSwSjkWxsNAgbTXKZMcsrcGsXKtuw1YbN8SQhvBh93r2KNqpIdBmJCFco2WsPRtt3fhZZmRQB7W63X/Y+06K89yN2aO0WzfpJ2l8JWZvgTGMI0yMI+dxFhIpGwrWuIOS2nksFuBwyzwJMpodLnk5rn7YxCSrAVKLzNti/sHhYsK2miHl3n6GuSa7Y0UIyvtQ6KUtyVkFSdootIujfi0g/qZihmTXlkryKRSiVILVQgZXIhyqdkgIaITAeMeiHdDvQakOrD+1wUnDZwow5d8yrIKvEeg4ZY4tIFL4tfKlA5Ao1irh5aNe4sMl0vf60uetiP/u6tTXi5qebSdQGziNreR55/j3mRxrpt1uR34km5rfBhb1+6fVtkcP9bIseSSKPfS86hnUMaDCBivIqVu0yWdx+XmxG92fXPPLxvl+++NjHPsaDDz4YK2p87dd+Lc961rO8qOHhcZPhB3/wB/noRz96o5vh4eHh4eHh4eHhcc1waPZTSYRSGbGxOI6QLUr0tBGy7wrGnkctS/oIyajb3XMf1OZpWmR5FqlPcCsiDKwhpJ8SoRsI0bONIULHJHuSX2+kkDbfAdxdgJNLsFCTEhr1XbichVxuyEqpRbZUhiCAADKlHEeOphmPR1SrcP4cpM5BpyGklooNSvYeBkImrcCU4LNJQJcQdDM2XMLUJTDjSE7NtnHJ2zjsV9CwMW9mhE2Caz+PkbE2RgQLFTtgbx+B6Uc9Rw0Zx4vReR7CWJLF+fkfZB5pBoIWuw4wEePzwH6WdnvmbYttLRVHAMe9259D4jOvdFsJeRbuGIsTNOJeWO9p56VFwmtlWFqG1VXILZYJimoLl4ZUAL0e7O4w3t6lUx/Saokw2RtJGzVDQ6P03QwRV2Cw26TrltqUjZAxoiS2vW8c3MyIuH5225J0LNbf+51nSfsnZWKpHVzXOnY/Y07tq25DfrseY28NJjsrxhaH4kRF3Z5mbz8lIW68ZZA5r+3Sa6ulWBERHkeYtXyEPG87u9DuC7Ud9NkZHh4eHk9NPPHEEzSb+zFY9PDw8PDw8PDw8Hhq4cCiRhwJY29TAk+thjRSVMk4JUxGGI/wRYylRgETCavkXZzFy36gNQfiolarGDFD7bOGGDIyh2RAdKL7aSWcZxZsIvYw/dC3kLaPgcYALu1CvQXlAhxZg6MZyFeypFeX4PgxsbhptQiGIwqtDrV2h3Yb6lUoFiDfgsx4r7XPtYCOg7jtcSRokmiRRJja59JI6CRCPO55zLuN6LzZ6KV91sdkUSShhxlTGoWt2U72JHXFHjszQYULrRHTtq6p/WvbL10N2tZ7h/nmpIpJ6ag9KYTg16yUOrPnQxDzmrWPvT3uHcx6lccUA7cxTdCI2+YS5jrmMkAuA/mCZGhUKpCulaFWk4mXTolN3DiEwYBua8TurrjFtZrQiUQNHRvz1BJSpJB+vogItBrprwXC7fd5hTlXMIoTK5jyvb1d+0zXens+T5u3cc8zrp323/tZd1UsPI7Uc6oh436D+DoT9lqjz/xJK0CMKKWZM26WmmKW6KpjFmTtOBf9rb+Z+tL7L2DqqIyj9x2MTaS+x41fD4+//uu/5oUvfCH33HPPnu+++qu/mmc+85l84QtfuAEt8/D48sU3f/M3c+zYsT3b/+zP/owLFy7cgBZ5eHh4eHh4eHh4XD8cyH7KjYR1SSWNBgUhWLaRjAw9xn7X4yqILYraVCkZ1GFvNOzVIElIGCAE1XkMsZZCCKwVjG3L3QgR9Bgmon4/CBABJY8QTlpIet4o9yRo+88B9REst0XkOJ2ChQU4fmeJ6vNuI/Xs++D4HZCrQP0y9Pv0H7tMpy2FwoMAMhnIpiA7NgR9JmqzkmaHCSUA1SZHX0oEaiQz7M2ggPjnGUcqqzhlH5u0v3use42kbVWE/FxGxvIIIZDPYXzplZy1i9GPMDUSkohhJXdtayWXrFX7qzB6tzOJ7LolScLPvBhH11JBY14SXAVK7Qc99jDmd1KmgEuEgxlviiryzEqYTLE4SznbWmqel328ti8ViHaRzUA2B0E+B4W8KB1BCgZ96Hah06XfHdPpSimcdhd6YyM8jDC1NSDeksnNGNDofLt/XGFmXiSJRnF/TxM87HmshPw87bBFDhX15p2r8yKLyc44joyTAfI7cYnZ67YKR3F/h8iztDOP7HVP2+xmZ2lfFqOXZtrsYOaUfbweo7Z0mhGpL/2NtddfDw8X//N//k+e//znx4oar3jFK/jUpz7lRQ0Pj+uMf/tv/y333Xffnu3/8T/+Rz73uc/dgBZ5eHh4eHh4eHh4XD9clf1UnIWHEkxFa1tcVoOS5Bo5XsJEraYQQriEsXs6DJsnt9iyjSZSq+McJmJVRY2jCJkVYIo2axtDTPR5CrlPLbQbhxFy38ejY+oIGbXL1d9jF5MVk47aHAZQKUPllmUyz7oP7vwqKNwOQRGyJch9jlZjyNY2tFpyk8UClLJQHJqiutnofC3iPeSvFkNE3FGCU0UtFcd0XMEkCTotOtsV0AKMxRPMVwA67rxJyCJFmO9C7GByyFg4jzxbO4tJa0GEmMwKjVC3+yAOru++3S6NvE8ihuNEyYOSmEkZNvMcp+8NjGi533ZMy8yZdowSy66IpXUJMpg+1O/sfd2MsaT3xLEVPYQggFQKglQAqbT8kQpgOIROh7DRoLU7pNmEdge648kofM3WSBLglCC3M9Ts+7paTOt3bUdSFkWcIKdWWvOOA3v82XNLzz2e8j7vNdT+TetAbSNr9ePMvw5OsykMnP1S7A0gcIU1FXJyGDvFHnt/YzU7Q9fQnPW3ZiWVovsZsL/6Ih4eLoJgPyuxh4eHh4eHh4eHh4eHh8fV4dBqatik8yxSD8Rq6jiT9QLy0auIKeSaxljpzNOOaZh2rEa5u+gixcHz8KSV0AghiI4hRZ1LUZszCOn1CJLFkUT4DjCWJjWkLy5F1+nu435caKQ/CNHVAjYbcP4C3PL4NuVz50kdOQapAqRKUL8COzuMO33GI0hnoFyG6gKUt6HQMdkZaev9MGELYy7xp1ZOKqy4RaGn4aDR57MwTQTIAAsIMZ619lWRr8pkZLkNHVduRopLDitmCS7T2mi3y7abOSy4ZPU07FcYsbMf4jIw3L5yBRwtqO4SwLsYElhfmkHlZl8Qsy3uZbfXfoWIdmEY/1SkcATyORXAeMSwP6bdhmZTXp2hIa81s8e1NXOFvAyyxixHx12J7nW/YtS0rIy4z7MyONwxouPgagU2N+PBfd9vRtAQWZcjvZchJpPnakRozSzRNdUVXpLWGbeQu53t5SKPWCbWMP2dZ/J3Nh29q7Dq4XFQhKHP8fHw8PDw8PDw8PDw8PC4ftiXqOESLTaBV8BEOXcxtkquBY6igkS1VzBEXyb6+0gajhRhFMJjLSGQNJr0aiLLDwotMGujh5DU9yL3kY32u4xE8G4zXdRQaxstuLyM9JtGYR8EdYRUV4GgBWwP4fHzsPjJFqnU37Ny/grZE0dILVSg04WtbSq1NEcHIe12SKcN/b6IG/kmZIdGTNBI9hZXJ764NihDTG0GG2MMiZvFCGBu0es4uESfS/bGCW/ziALTxp7aAV1GMn/0OWg2QoP4KPG4iPtp14oTOubJWshgCnuD9KG277CgNnJF5F63otf1QFLmjr1do9rtaHgldpeQuay2YQ3r+CTbKWZ8N00wSgVEqRqIqGG/CJ7UOcIQRiMYhJM1hhpM1k2x22oXrM4g4mshutcu+yOwbZI9YFKUSPqc9DfsHbeaOXO1GSQqbLhtUqHgIPZKuva32VuQPQ62iDYNKta6mTUu7PZq1k0haksXGQP6e+sig/yurGAEHT3HEnCyDMUMXO7AuC+/W9OyGT08PvnJT/LQQw9x11137fnuq77qq7j33nt54IEHbkDLPDw8PDw8PDw8PDw8PL7csC9RwyUNlTzTCPWTCNHfBB4kWdAgOo8S1RpdW0RImFsW4dRJIfOyD8O5DlzARLPfLBgyWQhWhRmN0k9CB7mfHQwRpwLQ1Qo2to3LiIi0bsO5c1DIt+hsPMqxE09QvmMNTp6EZzyD8m23UWi1Ge606G/UWXh0h9GwS6MNnR0hU21rnTYHFzXSSGbKKWTMaPHidaSAcTPmGLuI7QiTFWN7z7uZDXHiW9xnPb9LUBOzHzHXsdGP7kMLzesY0PPvxBxjI85b383cwPpOP9ukc5JVDZhxqXMtE7XpMOdUDbgDOBH9/SjwBQ5XOIlDXD8Rs03XquWojbWUELvVDKxmoDiCK23YCSf7eFomhvuys1/UUmy65ZGTpRGpGa7OMS0DRq/lFtkeIWNyiJDoDeaviZOUYTFNxLDfbTI+bj20xYzDqKliFxNXAVTboDZNKizu55x9ps9bG7P20/alMPNO57ndb3YGht5XBckOXImOPQecQX5Pkq6bRrIyVEDJIeP/eB7uPA2VKqxegq2zUieqweFYPXo8PfGud72L5z3vebGixitf+Uo++9nPelHDw8PDw8PDw8PDw8PD47pgX4XCXQIrg5BHR5E6ArelYKEM9Rakx/AlxFbJJUnUC7yHIbHVgqMIdHvQH0ChAAsVONoRIkaLmV4NspiCwJpdsYFkguxHUFBPcrc4bwZDGCd5lA+ja9qkqRKfVwsl/pUwHACdPtQbsL4B5Qqs3lWDr/wquOcFkD8GjEmPt0m3LpG/cIbS5+4nGD/BcNinfB6WtyWadwt53lpY9iBRvWr/08JYqShpOG0whhjbnQEmYjltfa/nt/vTLR48jZxOgpsZ4Yoc+l7GZCloFgoYy6c2Ms6SouRtOxnXisb+DJPz0W6bimx2u1LW/loPRs9Rn3LfB8E2QrT2MfNgLbruYdVyULjPMEj4jLWtiDynu4CvAJaLUcYEkA6gO4RUaOzCVMRTwQAm56k93tz2gKwBtrXYnjluZWY8KWw8+ZoUNuz7HiPzQOsNxZ47gtr37SfLbZZg4YobCrVVcueyrkkKe32aRwhQmyadH3r/I2e/AjLeNDuhHx1XRvrgEWSM7gdXKzRr23TepTHjxM6gcMeuLdCoWPgMJItxMIKlvuxzhr2ioY51tSHsYvonA3QHkqhXrkChKBlKR5A1XgVxDw8PDw8PDw8PDw8PDw8Pj5sVc4saMEm6ZBCi6CjwTOBZVThxTISIZhNyT0BtDJ9ForXtSOkMsImQS3ZtAbUlOtGEux6AxQAaoQgjLUxEr5K280YcK/JRW+9BsgVSCNH1IPA54mtqTDuXEkQ28azWUrNgk6SHiTryXIoIodcjsk/pQa8LmQxk1pbg+N2w8FUQrACbMOpBYwiNBuFunWGnTxhCIQ/LFbG/Gffl3tYw1kJDTL0LfZYdhGSLI7FDTFF27TsVYubpN7WkIrquRoGr6KEvJZFVfLJrgcwSNuYhMe1o+BxmXA+Rca3CRRBdvwbchzyfzehdi8rb960EpwuXkJ5FULu1T2wyeIw8gz6HLzS0gbNIdkANmWcqLh3WteIyJeIEDXebkttHkDH010C3JYRvIdpexYiwKmoMkLUhbny6IoaLFmZOJs73VMoIGwFPqhipaHCHYxgNoR9K2wZIm7U2yH7WES0SrWNMz+eOrXlEDX3XbIACsu5oJowKCRvInOgx+Vzs+0jqw1R0ngVEiFaRUgUOex6qoFyL9h0i43wcta/BjSmGHbC3T+2i4LboaEP7dRm4E3huCm47BdUqDAdQuwiVhvTP5zD3pmtOFxFx7PUkg6xXS2O45wk4eUYyIs8i41TryGg2yWGJ7R5PH2xvb9NoNKhWqze6KR4eHh4eHh4eHh4eHh5fxth3oXCbzNIitEeAxRrk88LPZXNwbAX6G9ALDRGtUby70SuOLAmA88CXQsiFJlK6jiFFMwhp02F/Vhl2FG8JU2x3Vn2GOHSRorsXovZlo/PtYDIY9hMZfVjQPtFoW42GHgzl1WlD79I2xYuPkFq9S/x2hufh/P3wwN8z+tIjXPniFo8/DmfOwsUN2B2ZZ6b3ehTpQy16rnYq+jzUgx72ZjhotsZBoYJaFWOppNkjLfbaSSkBarfhIGJG3LFgxLYBct9bGFFBbWMyCMG/gKkNo31XZ3Ic7yeaPu6YWWP5MDOD4qBksi0G7FeAnAU7OwUMORw4n21ytoWsQ3VkrLYxJG4WuB04jrGDs0VBrYGTBLfP9XlozZIa0i8Tz0m1DHjScopUyrwHAeloHzcTROf3vGMlHbVhFZNF1o/6IM5yyBU14rbp3xkkQ+k0si6sBnAkB9UstMZwpgvnxrI2qmWfPqcOso5ukSzOqG1SESNSVjE1nGybJi3kvYOpxdSJvmswPbvMFkhniVXzQtcddw10hbck0VJrLq0BKwtQKkE6LULEUg1u68i6rllgeswQ6dck27fzwNkQytFFdV6oMJJmsnbH9f4d87h58YY3vIFarcZrX/vaG90UDw8PDw8PDw8PDw8Pjy9jHKhQuG3tUwbKacikJZo/jNigYgFWCnB7R/bpYYjc80hkqE3+67mVRJlGPinBuN/Iby3kPcRE1TeRWg77jeAdIvfRQEi2wNquNSduFBGkz8eurTEYQasFZ8/B+ONb3Dn4f6yEGdLPeg60tuCRBwgfepjO+S1a9TGjkfCqo8DY59gkcT66P7U2UYJURRUlsbU4tdqN2VY5B+mfFMZbfhEhO0MMiekSo7btS1x2hl0rxCZt7bbZn+PEgCSBQCPK08g4GSHk7TqGkHYzdlwrGru+Btbn0NknCXFkqr5nuLb++fbzPmy4lk92JoraHxUwWUQdjJixG3M+fX6aWdDEFKm3C4vHQbOU7HVM1yh9xtoXQ2AcWn2yp3BGSljrbJagUKBY6bCwMGapBrtdCAemXsQmk7UZpiGPzJlTUTsus1d41H6c13pK703XhxwyNxcCqGShVoOlFBR2oNyE9VDWCrvGSA8jTO6yVzxS4WMbYzu3gAg0+nzteaRiVT063yammHrSM1TRpIoRfFSUc/sA5h/PrpAaJ3zZIkoqZluIEXFy0aI7GsF4DNksLCzA8S34KoygC3LvaWS90XPq9XU8bkavONhihhc0PObFS1/6Uj7xiU/wkY985EY3xcPDw8PDw8PDw8PDw+Npjquyn1pAiLK1VSGwCgX5cjwSbq7QhEpHCCMwpM0p4G6EOFKrjBxG8HgQIbGSyJSDWje1kGKo65gIX42uPwhx00cI0JsNKmioqNED2kPYrcOgD1ub0Gpt85WVv2WltQHjMcHZc9BokGbMQhVOn5LnuXoF1jdhYxuuRHU16hh7JbVb2sUU81YEmCjjIpMF0esY0swunDvruaYwdk9qdZOOtnWYLGKuY7QcXadrnV8JcL2mtkMzLOxtbt2UeZCK7l0jyrPIeNfMnmlwa2lopgHOZ3sfm2iOI5/jSGow4t61wrUkRO0IfV3Icki/LyEZZLXo+y/NaIvaqW1iimnP03YlxO1+dsnzHjJHSsh47AxhqJ2eTkM6Ja9sViZdJv1kWtVSp8+tnSajUUguBxtbsN2AwkjWsXXmt7trIetVN7rXJiZ7ROs86D3FvetnWyjTNeYCsgY8ApwYw4kWnOrBkYwIqoRmjqvwqZZzI+Q3oBz9PbS228LJGJlHWk9H62xo+7PIc1/B2LyFGAuqOOgadRz5LQsQwSeuRoWL/WZUufvb8zvu3FpPYw04VoGVFSiXIJWW39hsFno9KAVwNDTrQBrpr9OI2KuZSCqGbQOfR2wdk+a+/jZ6eMThHe94B8ePH+c7vuM7JrZ/0zd9Ex/60Ie8qOHh4eHh4eHh4eHh4eFxzbEvUcNGDrEyOZ2HY8ckYjSTEbKl3xcrqpUVKJYkqhQk8h8km6PVgc2GkO0BkI+4vVsHQkx9BiHKDhNqQTJCCHElk7LR302e+lGpY4S4zGPqanSRe0t3oN2FfPTUC39+gXsvXmTpZIl0rQprRygsDsh3uhzpdjldH3Dl0pizZ+CxxyG8CMPupLVVCSEj6+wlDtMIiXsUIdTGGDse2z5MiVU7ghviMyfSCNm2ickCUZKzjBCaajeTxhSGVnFCo+6HGMHHLTZst1+ttZRUnVdM04jyhegcPYSAniVoKJSwDzBZMraAYSPJ5iyOlLah9QmeDr75Ss4uAvcCd6XgtjwsVmFjUyzUzpKcmRJiBErN7tF+m9Y/WktFx2U7Zl8dr5qFkA/hSD0SNvJ5UYSXV2BlDSorkMrDiV1YWSFTKnEy9QABDdE/MrK+5tqG2J8nK6yP2BFtY8a/LY7Z78T8Pc3STAnwHUTYeBwptr4ygNuizBLN5NLMLlsA0gw9FSw1+0r7PY8Q+8eR51uMttk1ebLASg6Or0AmCxc24ZGWuabWS4q7x2J0Tb2eW9TeHguuZZRuc5EkgtjZG9MyN3QMVoHbAjh5DJaXRMgIkbEzHsNCFbIZ6FsKRColv7H9gQhgu125r1wKsmkIx3BkJL+x98e008NjFh555BEeeOABWq0W5XJ54ruf/dmfJZ/P8/a3v51Waz+Vyjw8PDw8PDw8PDw8PDw85se+7af0cx8hytf7kLkI2U0YhtAdQq8PwQiO5KFSkGLTBEK0DAbQaMAjO/Bx4Ex0vsIIlkYiaKwj5Nu1gJJWYCKstR7CU13QUIwwFl5ZTAaDkoCljHCpjEMI0qROnYCv/BqClTVobBNcPgfr65SvrLOW2aDf69NsSj2OwQjGg8lMh3z0UsFA7alqiMhgF/RWkl5tfXrERwvHRYfrtgEm+rqLCBsNxEbMtmDRY7MYMlSFHhU17H1T0fcZDNmqfRYyP/Gfis6tdjpFTG2Y/SJuTMYRorZ90Ky22edQUte2WHoqW86sAs8F7jsCS0uQy0ImBXdfho1QMsHsjB0bOm6Xo/Nohs86sk7F1Z3QNUTFw2ljpBGdJwMsbMLdl+CuUZrikSNwy91QPI3Q2CPIFyXlodsh1WiwuPs4te0unIFLI3gIibTXNumzVLszhY5bXed0f7ed00QL145KxT6tI6TWWrqG6jWvIGKOLQ7pGNU1wx2Pbn0jzXhSgVDr04DJstKC6fU+7FyWBp4fSF9vROdTUVVhf1bRqRO1o221Ia7eUpIgEfe3K4bEwRUz9Fi1MtwO4fFNWO/Kb2x/BN2+iBPVFNTyYvcYRCllwxF0OnBpGz49hL+PzpMZQ3UsAtEAOJfQHg+PefCGN7yBM2fO8PrXv57bb7/9ye3FYpE3v/nNDIdD3vrWt97AFnp4eHh4eHh4eHh4eHg8nbHvQuEjjAjwELAbQv6KKQauUa5l4BnAVxRgNQ+5nHiBd7pwpQ2PIhG9ajOVQkiwNCYLIAlK3sHBoszVikXrfGik8NMFWrA8jymSrcXVFxACrFyGYhEy5RzBkSNw4h5YvA1WrkAxK7Y445BCu8NKfUC7HUp0cAjhFoyHxiamiolCbyEEYi16paNtSnSqGNbCFGCeZpnkbtfPmnXTss4bJ0yFyHPewtT2GGCIUBsZJoueq/Ch50nKiLCRRcZ+jclaK12uIi2KycyNaVHgcXY27jFuX6rtlpLOdk2UpxpaSIZXqQGtHuQCWXMWQrgHEbcuIfPDHXtad+J0tF8aGaMqDrqihhLg9t8u7DGlAlIqOu7yBrSvNKg2mqQGfSgMIIiqeYRtUWOyOUil6fdhewsub8LlvqmnUYqu4z5TbYuOizjro3nttVyRIYusI8vIXFdRQOt02P3hrqsZTJ2TuOvEjVOdwzvI74XOSz1/N3oFQHEMqbF5blrTRcc5TD5zXZe0LhDR/eSs7/U9LjtjmgAYt47FwT2vtmmACLUfA/5+G8JtI571kX48CTw7DadLUM7LOfp92G7D40P5nb0Y9VOAiDznMPM8CbbApBZgHh4ufu3Xfo377ruPH/3RH93z3Ute8hI+9rGPeSsqDw8PDw8PDw8PDw8Pj2uCfYkaSiIpwbGOkMX2Prb3/3lgswt3dGE5FRGDY1OwdyU6ZgcjTswiUytExDyG2N5m/6TLjfAMDxDCu4ApaDvAiDRKPtp9fFD0kX5RMnA3em2FsFWH3T50uxCkO9x15Amqtz4C1QqkhpLGUS7DwgLp1WUWe33CUYtxCKOxZNt0dqATmkyLRQw5p0SjFlp2CXnbM9/ODoiLao6zosL6zj7HNAyQcackXVIGhC1iuFkcWn8i6fgsMj6XkDGqfa8CymEU5dZrp2Le4/aNsxBKIlm1PsFTWeBTEvixNtzdlroaWeRZriEiQAURUHcx2TpK+nYRUeQKZhxvYbK7XMwSBrTuSxER/5ajdqwh5Ht9c8jKo2fJLd4PRzbEXyocSV2NdEqKJvS6tOojtrag2RFhUYty6xyzbcnsNiVZJCWJGnECo32MZlnovVSi7zSr5WLUJhcB0veL0fH9aL+4ttrX1nV6B3kODYy9nCvcFJB+sQVv3ceuReMKPXbWmR43Tx2d8Yx9XIFxmrChsNd/vfdH2Ct66HM6D2yM4N4GrLVknBFCIxSBr4RkHelvwTx1izTLrhS1v4uIVnHP1cMjCS960Yv48Ic/7EUNDw8PDw8PDw8PDw8Pj2uCfQWPK3mmmJYlMUZI5M8gZNTxsZBgWYS0KQJ3IuTeg9E+89jenAJuj47fRSxGmjw1IknLiN//MYSYO4sQRTVgNYBySuxFLozhCeLrVMyLEdI/dYxdjApCKyPYakH3DKTSIatHLlK9835YKEr6Rq8r5u2LiwBkghRLXGQcNul2oVGH3Q40u4YoU4IvRKKB20xmOswD+/lPyy5I2h+mj59p40ujku1x5IoCbg0Km2DUCHTNNGog43KbwxEzXIytd9sixyVR3Rcxn13MsrC6mdFHMjE2MPUuqsh9ZpA5oKIaGJu2BkbAsDHPmqTzS22o7DHfR8jlDELo3wkcy8hUWyiLQDg6c54wGErG1EI1SqHKSpGirU3GO3U6zRHdrtRR0HotqajdLkFvtzep7XEZTTA5HuK2aT/mMNZsGWRtHyJ9uc7edauA1NY5hqn7o8JmHOwshxEyl9rE1ywJMP3v3psrlNoZTa7lUxCz3e0H+9ya0TUNtpg7K1sD9mYpzhIZO8AXkXX+5Fj6WAX/AOnvBSQ7w64pM21MLyBZTSvR/heR/veihkcc3vCGNzAajfjX//pfUywWJ7776Z/+aVKpFL/0S79Ep5MkDXt4eBwU9XqdwWBANpud2L6wsEA2m2UweKrm3Xp4eHh4eHh4eHjMxr5EjXnsSlz0EHJdbVcyGAuTNYR0WUYIlyFCAl8imSwKERJxiBA52zw1rHIChNQ8idjbtJE+yCFE511rsLoC7Q58+gL8eQ++xNUTSUqKZZEMgjvTcLoAtxbgruNw130ZFu9ahmoV+j1hTfsDSKUlWyMVQADp0Yhqe8TKdoftbWg2ZbfxyEQ6jxHSuIE8m6ttd1IUt/6tUeMlTFSxbUejxb1nCUMBpu6HG6ked02sfe129qPXDoYgvR5imxKYZQyxPsbUV7Cj1e3PxHwGkzV0vYuHBwj5bS9KKkDsVxzTPtHaD1r3QQtSV6Lvd5Dxqn02Txu1f9S2qxy9ekhEu91WvW4amfMdpC5NJgP5gkyvQbNP/vIVAkIpQFQuS4bGhQuMH3+Cyw9sc+HcmJ1dqVmk/TSI7qMVnXtaBlKcxdGsTA3YS+irzeBu9DnHZH2MEvL87FofOk+LmGfhZhzEwRUCVEyxBcUxkyKLbZlkixBx90TMvtOOHTvHJLXZPa9b48Q9R8D0c86CZizuIuOii/mtrSJCxxHk91aLpqugHgf7/tuIAOXpaI8ktFotfvInfxKAn/iJn5j4rlwu8+Y3v5kgCHjLW95yI5rn4fG0xste9jL+/M//nG/6pm+a2P47v/M7vPSlL+VDH/rQDWqZh4eHh4eHh4eHx7XHvkQNLQyrxI1aZCQhjUR9VjER/H2EcFkDbgVOl4XcGwYwGMODW/BhJAMjDip+gBCIV5PNcD0RIsTTWUxB4SZCQl0KYNyGx0bQ7MMjw4NZaiUhjViQPAd4wRLccxvcciucvj3Dwm3LpE4eh3wO6nUIAvGYSkd+/uUyjEYEjSb5xW1WVro0GyH9HgzHMNoRYUOLf+u9Xg1JF4e4yPIUQiafQsSiNCY7YhdTELqPkHNJGRN2EfMkxNk42bCzN24E9NoqaqQREa3EZB0cuzB7ynnH+lu3xUXFBzHbrxYFJDJ8FWPPplkO68jciSOjXaSRzKfTSLT5WkqG8250Lu2DrnXuLeYTTdJIf6rwoq8ysjYqYWxntOkYrWHqyzRCWBpJjaHhMLqPdKRyFIsy73p96HQIW236nRGjkUzJdAoyI1OXoorcm4oaMDn/4jKZ4oh+rO+nbVPbvD7SbymMsBFE96diUhUjHmktHVsYccdQksChwoDaS9mChmYqZZgUD5KyLdzv4xCXEWZvtzNIFFlkbKjAlcMU+m5hanvYFlj2/dtiY8bZPu13QIWkpagNdUz2XBWpa3VLBhajCusj4HIL/rQjWZRx2AUejs7bRH5nnwqBAx43Fn/6p3/K13/91/N1X/d1E9uDIOAbv/Eb+chHPsJHP/rRG9Q6D48vLwTBPIaHHh4eHh4eHh4eHk9tzC1qKJmpxJFab9jRsiWESFGST6OkxwgxcglTnHsHIWLWhrBclMjl/gBOALdE+7di2tFGhAGILwx9M6MJPIYU1NXi1iHwxRCyDQgbhnDVguuHgTxCLo6Ay03IRopRrjAmTNXJrHfIFp8gVyuSqlXFdmppGWo1KRiey0G1KvU1On2GwxajsXj7j0Yw2JX6GrtWmzNcWyJMidMFjE//EBMZr9HsuehdrbCUsFeLqF2mix2wlyzG+dsmJOOIyusBFc2UbC0gNm23IHNwgNSJOIOxegNzj7aoozZbmuXhWu7Y+9ok7UGxhsz7Y5hI801kDiiBPuv82t5l4D7g2cDRrLR3eyxriQobO4jw1UHm5LzzLMQIFikk80DJe7XWuxVTv+ZKdP5idF8hRkxpdaDdgm4v6s9qWeZbviAXS6egUCBdLVGp9ahWRhRLkE9NZiQk0RZxgsG0LA53m3tuew5oBpJ+32FSBAuQ34LjyDMdIRZGO5hC4q4A6IoQcW3RrI95BJr9jMd5jnUFDTDzvYCIaCeQrIgVYCEX3XdfMu6uYMSGgfVy26/j2M7eUFHSrmdSiT7nMIXoO0hhcL2OZs0cG8NqRkomjUPZ4S7E4nA75l4HyG+11hB6KgQNeNx4/PEf/zEvfvGL94gaAC9+8Ys5ffo0b37zm3nPe95zA1rn4eHh4eHh4eHh4eHh8XTDvjI1XJsMOzI2hxB7dyDkjkbU7iIESZtJS6JLwF8B6z1Yu2D23Yi+m1aH4FrUKDgo4mx6bKJYSSi1TRkg5JNaJNkFfuHaEOFaNPwJpA7GE114dBMeenzMsdUu1WqXpUU4dnyHo7duU7qjR5DLScHwQl7qaywswHBIZjxmeXyZ8bjNaCSOVa0+bLdlPBSRaPt8dM12YqsODhU0KhjLpRYyvs4j5KkWfi4imRwr0TF6vIpGbqZPnPWUTbi6+9rigD7vHCai/XrWerHbNkLuUefKk9ZH0d+B83K36RhWqzec7XrvbkbHfsZvFlkzjiPC1BgRXNYR4a9Nct2FuOtoBsAAWUt6A1Mn4yzyrLU2Q3efbdXzd62/1ZLnJFG2UAGyaWj34OwQ/g6JeFchQIW1ftS2Xh8G/ShbYxQS9PuwtRXV1EjL/CsWyeZ3yeVHZDOQDibtr1JR36lAY4sPbj/FbXO/m7YtTtDTz3EiShNTcF0zp3S9s89lF7sfO+/utdy2JAkvSfcUh9B6zQsVH3QdqkXv2ei7whAqZciPoDkyfdBkstB5UmaK/a4/0inkt+QWRDyrYsSMjej8tkjRAz4PtMbwpU0obMk6uT2W+TXNUkpFdw+P/eAtb3kLpVKJH/7hH97z3d13381/+2//jTvuuINf+IVfoNfzVVo8PK4l3vOe9/Bd3/VdfPjDH77RTfHw8PDw8PDw8PC4JtiXqKFwSZcQIUh2EEKyiilWquRxEF1MsztGCLHSAHJjE8U8T1T2zQS7rRmEPD+KkFxFJon3bYR82sFE7rrnuBYYRtcNEMK3jRBtGxuwsgFrBThxVIoWFwpd8tVtMqWS1NWoLUTiRgEWFwnGI3LjESvjK/R6XRp12N6B7Q4MQrn3LELePYRkpqg1jj57m6zcz70riViMXpoR1MN4v7v+793onrWmixLkdu0NzTRySf44G6042xp7PuSQCGnNuLlRBew7wANIZkYKExmu89CO9HfvW2tFYG1Tktm2qgqt79IYwnreyG7d9wqyFug5G0zaKc0DHUt1hMh9PDq/1uRQQWeWZd5+METm8jlkfIVdEbWawAWEZNZofM1uGBCJGkCnC60WNBpQ2W2SK23DaCiFNlJacDcgCAJSgdQOz2Um61gMkTWmgBDWrrg0TdxgxrakfWbNjyC6vy3keYSYiH9XREs550l6t88/K+PEXmvsNtnjW8feiMk5Ou+apPNIn0MbEeN3kPm/NYZiQ8bCJWQs1JnMDLOtplyRKO4+VYTdQJ65zkcVycfIeji0jq0j68BjIaRCM/4OKwvQw8NGo9Gg2Wwmfl+tVnnTm95EPp/nDW94w3VsmYfHlx+Wl5fJ5/M3uhkeHh4eHh4eHh4e1wwHKhQeZ9ehwsYmEqmvHudqEXQSIVzUVkpJlTbXJpr/esEmnhYR65tnEIkaacgH4iTTH8LGWMjHLEJCafT89cAQ6fcy5qGPou1hF3LbkoyxeAUK5Ra1zGXSYQjhGBZqRtioLRIMBuRbbRa3+iyvjFndFTudTFNItkIAzdAUElaf9yZCqmWRsdFlPvsfJf8KmJoGeYw9ikY/bzIZSQ/Shjp7azK4UDuduIjwJIsfGzoHRhj7lxspzmnm0661TS3iVNRwa2rYWRh29LyKkVjf2+9JmCVuDBAS2I70V7L5oH0Xd98u0kzWxLCfsT6/eWOI+4ioUUeyMtKYrAmtr6EZGjrftMZLow07u7C9DSv1LtmFDkEuL6kbo7HMvVSKXD6gWJLyNtUKVLpSMFzJ6QFi5deL2uGS9PZ70jYX07I04vZzMya03kXf+S5OOJwFNyto2nEhk8S9Zk+pqJnBFCzvIutCnPgYJw65gocKG5r51cP85m1F7dhGxri2KUnkdPso7jMYYWMHETaKmFova1H7VDhXIamPz7rwuH74yEc+wjd+4zfyghe8IPZ77/Xv4eHh4eHh4eHh4eHhcRg4UKaGjTA6yQKSpXALcDewUhEHlU4Pdrsmk+M84q++hRB7dlSpi6So3JsRSp6pBQnAMIzI7RH0QmN9o0SfRgxfr3vUegJ2FH5a292GK+tQKEI6MyJgh4UwjISNUPz+c5EdValMsLBAeaXFsUaHdluizRsdyQDphvK8s4i10BqG0NVo+REmyt22JXOh/aREvFp4Kemo0e9af2Ea2TkNSsRqHQk3ajpuf5uM13clLvebhXJQBEg/K+FrF2x3of1uiw1uxkbK2abXsO/J3tfO0nDfU0zvByWgrxbzziG1R1tEbK+OYuqxaJu3gQeRDKN5o9n7iKC2Rbwo08SIcVqovA00O5Kp0e5AvxtSGo4IxlH18NFICg2VSuSqORZqfZaWQpoNyfDo1aE3lnNlkXVnjcmC3G6fXM2YnLZGJ2Vr2EJhkjho20zZx7vn0zbMokO1wHwn+pxC+kdfVaTOxwIifD2BPDv9HbLHuCtoqFBjCzl9JjPAdF4MEcGkw3xCRhzs+9V6GouI1dntwKkCFLIyVOptudZpJDNEa+fUozYmCYxPpd9Yj6cGPvjBD3LmzBne9KY38R3f8R2x+3zHd3wHX/jCF3jve997nVvn4eHh4eHh4eHh4eHh8XTB3KJGUkSubisjgsY/SMHJI2ILPxoJYVcIpQj44lgiik8ihNJFhHzpWOdT8kuJWhU+rhfislDmQYiQY19ArHRqQHFsLJ80OruNKVrc28f5DwOjqI1gBI0sQr5tDiGzJc43hDAejzkd7lAjJKXFwjMZCFJQLMDSErlej9XhBr1+j80NeHQDzo3kGs3oemWEQKwhpG4Gkz0RRO2ZlkWh1jVaI8Im2l3S8WqhxGVcRHjcuI8TCa63rUsN8ddfiv7eROxmXLFALaWmWfnoPiog6bvep2Yc2BkOdkaH++7aUV3LsT6LnA0QMvg+4AhmXBYzcmAYQmds7O/229Zpx9iZGra41x9Drwe9LnQ6UG13CUpdgnxbBMRcDgp5gmqV2kqXY50B/V5kW9WFdtfYq6nFWtZqy/Uo8Bx33ylrm20v5Ypn9ngJpxxn1x1KyhbE2j+D9EMGY1c3RtZd/V0pRdvKCPmvVlT2NRRD4oUJu/C3zhcVtDU7QkU+F9oHce1P2pZDhLh7gXuWoFaVL/t9WVu7PVgaifBxBPmNWUeE4y32WnKpoN3j+mUMenx54POf/zyvfvWreeKJJ3jta19LLpeb+P7ee+/lv/7X/8rp06f5+Z//+RvUSg+Ppwe+53u+hz/4gz/gH/7Df7jnu9/8zd/kVa96FX/8x3/MaHSjDFE9PDw8PDw8PDw8rg3mFjXiSCQlnFoYu5XUGM5fFsJGCcIKQiJppHQGIV6OIlZNA4wnuNp3jBGC9ktIRP+1xDQ7Hd02D0HYRQgkzRhQUngXsQRpYvz0bf/76wkVX5TwU4I6DU+GnI8jkjeVCknnW1SruwSVioga+TzkC1BbIBiPKYxHrHY2Wbs4oHoB6Mp9aiZOGiHY7sAUEFcyMYv0g9pQKeEW1y/zWObEQclDtaDRKG4lfUNnP/d6cWS5Zhjc6P8e5hExI4spSj6Itmv9jBTynJXktcUKMGSubi9jLJjKSGT7EBHq1NpLiWOYjG63X3amhk1WH7aw4c7dpPOngROI8JpGal58HrFx0ja2kMj9Noc/N0OMqGFnFzU7sLMDFy8BQZfV3jq5MCTI5yXVrViC1RXy4ZjV0SadzoB6HRp16AzF1k4J9CEi3KSROX4j1hec68YJGvYzc23HdKzYglicJVzcc9bfl1L0UoGnjQjoG5gi9Fr3pkGyuDxPFoMKVgXkd+5I9FktqYYY8VhfQ4wtlW3FOO33R+37tDZMYxsK2+b6Wr9J1/QlZP6uIqJnh0n7K21LG3gEyUzy8DhM7Ozs8BM/8RO02+3Y+hm1Wo377ruP06dPc/bs2RvQQg+PpweuXLnCZz7zGZ797GdTq9UmvltbW+P9738/3/7t38773//+G9RCDw8PDw8PDw8Pj2uDuUUN9dV3bWo0kruPEEafRYhQMJ7mq4jdS4W9BZnLCOlj23coIbTLfPUMrgYuyeZG5NvE2yykEUKritzvEYSAuogQabYtzI20/AiR/u1gBA21HEr1Ib0N+RyUK7C4OKC4sEM2l5M9aguQzYm4Ua0QtGtkK01K5QGLeTgaiO1WBhkPLSQr4zKGtNSaBWOkr25BnnUbQ/Yehge8ku9K4Gs9jwKmxkjf2tfun6SMHZucvtHoAWcRSzeFksIakZ5xPscV+1YBpIipVaJZG7b9lx1ra4t9Nvlsi0Ej57urqZURB3u+2uR53HwdIfNQM6fOYeoOaK2RETImrsXc7CFjrYvpy00g2wCegG4XBn0oFEYsro3E9i2VFmGDBYJ+n2KtweLigNVVyezo9qDTgE5oaoDUkHXnjJz2hkfg22vdk2sMZkzo55S1v404K6qx8zeYuhJHMPZSGaRf1pH1RW2htqO/Z43HeceBZj0sILZQi8icU7GwCFSykM9ANg2jFFzuwiN9eDRqjwYA2OuKbQWn87QOPBQdo9cYI899JXqPVuonX1rM3L4nPacvGO5xrfHxj3+cv/mbv+Frv/Zr93z3/d///Zw6dYo3vvGN/NVf/dUNaJ2Hx9MDr33ta7nnnnt4yUtesue7IAj49m//dr7whS/w6KOP3oDWeXh4eHh4eHh4eFwbzC1qrCBEimYgaBSsEkxKTmnhVRsdJHK/hBAuFYTMzmGIH404VaJlF/EGv1akXFzU8Kx9Z5FcSrjZRas1+rvBjYucjkODvfUUnrQk6UF5FxY2pDBxNtdmObhClhDGI4keDxAmtt9jPBgRBLBYhVsakGtBfixjZAdTMHcXU1OgjyECa8iY2CZ+/BwUNmGPdX859hKIijj7Kffvm0HQULikpAoQblaGTSi7dTP0cwYRllRwUus3JX8jpyZgkqgOnfMrmQymr7QewWEgbs7aRc5d0VD3W8cU0lYBDcwYUXFDiysf1nMOkHVAM2ha0fW3gM0x7O5KxkWlCiebsNDuke71xb8vl4NcFoolUrUFltb69HoDul1oNKHRhWZfzqnPDWSMx9ke3UjYmRgKdzypOOUKi7bArBZeKrwRfS4gYvJq9NnOQnLPdZhzOMQUaVcrK/1NywInA1grwpE1KJegVIZ2G249B0ubkiWh9Tc0c0TFCD2Pjm+1LtuytulcrSMCShF5/jVkPGsbx5j5DDJm1pF118PjWuEDH/gAZ86c4Y1vfCPf+Z3fuef7F7/4xZw4cYKf+7mf4z3vec8NaKGHx9MDP/dzP8fa2hrPec5z9nz3mte8hnvvvZcf//Ef55Of/OQNaJ2Hh4eHh4eHh4fH4WNfNTWUEAkwUdu2j7lCMzSUSNLI+3p03BqSoaEEnxbO7WCIFyXBexw+4uxMpm1X+x7NMJjmna92SkoUqS3MjczMiMMYERyUAA4w99gCdlpw+Qpks0AQMhq1WO4Nye3WCXJ5ETd6PYbNDt2dPpk0HD0q/GtlHUrbUOkL+baDkHVqUaaEcoAh5ZWM62Gygg4ritgmTLXQeFx/wF7S1bXAicviOAxkMQTtQWutxGUdBc737v5qzZXFjAkVElVoTDrehRLLeYwoYJOoScfofc9TNDxurrrbbLLXFqR0/up39vGDqB1VhAiuYwjmq0EmOqcS1H3MHCgj/VMGml1ot2C3Dqu7bTLNJsFCVTKiUmkol2F1hdxoxFJvi3p9yPYO1OvQHMLuWOaXktQNDifb6VpAx53CXoMUaWcfW3xTUUKFNj1uwGQGRhfph23EeipuPZlHrJ4HQ+AKRrzS37E88FzgrjScPAkLVahURA9OBZJpE/Qlc+gyxprMzpLS/lErOQ0gsNelPiaTI4uIxbaVnP07q4LODsYm0MPjWuL+++/nM5/5DC972cvIZrN7vr/vvvv46q/+an7/93+f4XDIcOhziDw89ou/+Iu/4OzZszzrWc8ind4b1vCP/tE/4tSpU17U8PDw8PDw8PDweNpgblHjjPW5h/EKh73kqQslEEfWfiMMCddGCCi14Bha17hWSCJ83ftII+TQcYQwOs908lXbf63/S57U3/sh6EKEBFRyUC1bAqTWQL0Jm1uQSkGrBaXzPYrFHpmMbMtmhXNNp+HI0YClFWg1Q9auwJFLcGIdLu3A432xSVLizS5Ird7u2u4SUmulgbGHul70hl0LYhrcGhG24HEQglT7Q2vJYJ1TCdqBde4kItbOvIi7RhyezM5BsmWySL2JdabP6bhoejBjf5qFmF3PpYTJYNiYckySODNN5Egz+Wzc89mWR2qhV0Qi3IuY2hQHHX8BMqdui865E52zE73XkLF+LIBqHvoDqO9Cc2dEbrdJplKXyVUqScZGtUrQ61GuN1lZGdJsQL8HgyG0m7AZyjqqGVE3E1LI2NJi5poNo+PaJulV0EhZn3OIYJZBnoeS82lM9oLWqVBhYMB0UVmzisbW62owQn4jbKSByyEMxlAsQKEghb0vX4FHL8K5vvymPI6IIraIpqKIPe/td/uzrlt2MXAVdjaQMdHBFDfX7JKbVfjyePrh//v//j/W19d54xvfyIkTJ/Z8/7rXvY4f+ZEf4X//7//N61//es6fPx9zFg8Pj2n4tm/7Nj70oQ/xzd/8zbHf33777dRqNXZ3d69zyzw8PDw8PDw8PDwOH3OLGja0JkOBSSubWcdo1LtawDQxEcVKKtnEcJbJaP7DwLTo9bh7SCNk71Ek4noXEw18I5BDiGAl+NReSPtzl/31V4gQhErorqVgJQvFDCyUxS4lkxFR48oVGA6FmFtehmPHYHE1Q2WtRGaxAtkcw2aXo5ca3Hq5w8blMQ89DMFjkO4KiauZGBpxrIRkz3ppYd0SJqpdC8lfS7hWOEmwI8bBtGs/Hvz2vmPk/tR2q4T0lWasqKXZGCMGqbXZYUSZpxB7ua8swK01+LPLQoTqd4o4YcCupaHrQodke580MpfWMIWNA0zU+DS4czXOnihJYNW2usfb0e4jTL9qpP1Bs3IC5DlqnQOdqyFS86GI1F44CixmIZeCbgd2dmFjEwrlJpVsmlQ6DZksFPJSy6ZUIr20wHJnyGDQZzyG4Qi656Delmj/bW6u7LAs0gfLyBoaIHN6G1mzNKtHyfYg2qa1IAqIsHwMU7h+F2PZtIP8pgww4y4X7VuLzhUn9NgZIpr9cNgYI8/kr+vQ/AzcWpVx8OBl+GhDamqoVaGLHpOZTHHjPQ4qZmgdo53ob11L7JeK2LZo6uFxLRCGIb/2a7/GuXPneOMb38jzn//8ie9TKfm1eeUrX8nx48f53d/9XT7/+c/7WhseHvtAGIb80R/9Effddx+nT5/e8/3b3/52Hn/8cf7wD//wBrTOw8PDw8PDw8PD43BxIFFjv7CJxgFCsuwySbLEefVrAw/Tj38/UJutJeAkQq6tImTaAGP1olZKKSZJ0cNGGiEGb8VYeKltUQd4BHgwat9+oDUGUsBSDo7WoFKWAPFyRYLEW01o1CWaPIweUqWWpnrnGul77xKFI18g02qxuL7OwuYWRy5sUlvboFbr84xN2O1AuyMFkUdD6PahPRJis430Wwfp8270nseIGx2uvVVKaL3sjAw308HeDw63CHafyawlJXw1it0ufn61CJBxdAfwdXfBLbfAF/8oeV9bwIDJQs86T5MEjQAhmW9FisNXMUJIg2QxblqEul3Pw93HhW1LZZ/HfoYdJHJeMwkO+kzzyFwtIZkvDYztWgWTGXMaODmAxSF0BuI0lctBNjMiV2hQqFbEryibgXRKCjKsrFIYhxwdbxGGA4biBEfzImxH9TXOc22s+w4COytGyXW16dNMCvc3QOdAG1njQASgHJMiRA9ZP+zaSwXgGcBzU7Kene3Cx5D12oZmQB0UaeT3oBj9PUKe845zjXXg/xfCFy/Dsy/DYgAPh/AJZtcPShp/07IjQ0yf2LaRcZlkdrbSzZbd4/H0xAc+8AHOnTvHz/zMz8TW2ACps/HiF7+YL37xi7z1rW/lt3/7t69zKz08nrr4lV/5Fb7whS/wS7/0S7H1NV772tdy5swZPvWpT92A1nl4eHh4eHh4eHgcHg4salwNqTrN29wmGWfZWh0ErpWHe023LWp9UgXuWILnFiEMoNWGz+/CQ2Nj4aRkUsjhRdG7be9hLJzsYsDz1C9IwhiJkn8EqPUg3IRqU4LD02kYDGC3C42BEIyrbbGdOnlqJD5U1QVYXRUFZDiEE8dJ9fsU19e5be1Bjp96lMaVDhvrYmdV34V6Q+oB1BvQ6EBzbCyn7OwMFTdyTNbdaB6sC+fuDyVN7Yh+F/NmdiQdl4QhQozqOLQJX5vQnXUeN7I7rh0B8kyrSNmGIGXEnGlzMu58mt2QhBySuXAUOIKIKWMkK2SazZMdle6uB3HrkBvFHpdhEmflpaLMYZG7XURc0Gw0vU4LI1BdAs6FcEsIp3rAOuRzUK3A0vKA7E6dVD5PEARQLIrisVCFcEx+NOLIcIfBYMggEhuDyxD05NxXEGL7Rkfh95Hnq2YXtt1U3Lyyn51mMW0i69MmkxlBKo64x5eAk1lZs9Ob8Mk5fZaS7Mri9itE19H6UiqKxWVibWOyUwqhyZ6Yhlm/sdOyDO2+TbKqs7NTDvt31sNjGj73uc/xgz/4gzz++OP82I/9WKz/P0itjXe84x381E/9FL//+7/Pm9/8ZkajJNncw8ND8Wd/9mc88cQTPPvZz34yC0rxohe9iD/8wz/kX/2rf8WHPvQhwvBG/gvBw8PDw8PDw8PD4+C4IaLGNLhe/QHSSCUaD+uf3i6pGUfggiFqRwifWKtEHvYtIySoN7z+rbUR1L/8sDBGiMFHEFJ/FSHWVETZ5OCEbB+xQmmF8MQQVodQbJnixrvRNXPA6i5cfgB6fUgXrnA6/3nS/T6sLItFTiYtYkehQPrEGkVGFJcvs7hSZ21nxO42bFuvnd1I4JgibqgVTdH6W+sgaETyYcK2VUoS1+zI58O+ftK4Cad8Nw1u+/RvreXRB85fEJFpHRnH9uIwSyBRTBM1CkiWxr0BHFuS+dTvAdtGsOrOOH9ce9xt7nfTItr1+8N+fio+TnuOY2RObSL3XgDKXajuwOKGJGjkcjtUCAnCEJYWoWjV1xiNKY3HHBvvEjCiWIBSEfLnodKCi6ERVDTrQefWvBZ1cdk5B4FmG01DCTgFnIg+p5F2byDij65BtqgXJ9j0gAeAYQ++5qJYU00Te1VggsnC3PqKO1Yt/zad7WpZFjffusDFhO+T2pW07sw7xu3vpwmVanN1mBlnHh7TsLm5yU/91E/x4IMP8pa3vIVjx47F7re8vMzy8jL33Xcf99xzDz/+4z/OhQsXrnNrPTyeevin//Sf8qu/+qt83/d9H9VqdeK706dP8773vY9/82/+De9617uo192KUB4eHh4eHh4eHh43Pw4samiRVhtxJMw82RZ2VHzI3kh0u3bEtAh9u9bBNBIrjtRJijxX4rwJrO+Kb/1OFx4ZiNXTJUzRWpvgntWGg0KJ0HR0zTymMLNaeh0ESrqdR0jPNSR6Xws6a1R0Dons3+rD+Svw2IN9isVzrDRaZNaWJdy/UJSquAQwGhGUS7CySj6XZ6XWYmGpy9LOkJ3tkO1t2NqG7S0RN3YbUO5AczQpbmhktm1LpQXlM8gzOOyC4qHzOYnYv1lJwDhi1SanM0i2xCLyTDc3xWJsG+lLrWmTJIi4mFVbR+3m0hnJAAqQAsrdqB216Pg4kSpwXjrf9Lmk2WtFZcOdi3Hz3ya0rxbTxCclkatIIfEqplh6HdisQ/my1LIJAjgZ1qmG1npbLEI2C5UKwXBIZTQik2lSqY4oliSLauUSbLWgNYTeGNpDuBDCY8iaZQsbgdUmzUrS9b2IKc6t1lHav4dpC5hC7LqejVhHVVLSh42xiK3N6DWI2qR9FjIp1mh9pjrwhWibUjlxY1nJfL1/ff62LZNbb8PO3uo628fWsUl9M+/40ufgjulZv6f2WhUnZLhjn+gaheizivQeHtca4/GYX//1X2dxcZFf/MVfnLpvKpXiu7/7u2k0GrzmNa+5Ti308HjqYjQa8UM/9EOcOnWKb/3Wb93zfSaT4T/9p//ExYsX+Z3f+Z0b0EIPDw8PDw8PDw+Pq8OBRQ21abHJMIVL/ro2GPYxcZHwcVY7s6JbCwh5lcXYE03zlE86l71do3FbSITtOPJDugw8hLz3uDbihQs7mjhA+n4XU2y7y2RmiEsCw14yMo7gCxCScB0hl0tITZEakhlSAZYDWMtDNQ/NJlx4vA+DKyxu7pBbKBAsVGGhJsJGkJJXpQL5HKlqhXytTXaxRWWpzdLOgMUt2F6E7R3J3NjdgZ0G1LvQGMqzjLOlGmCyYtT2RWuMwOE9m2uVjXG9YYsamgFVQJ77LjBqQTaQ8XQUU5A8rh+V7FUbNCVyhyQLj22EoF4cwPamNGJzBOeQ8abriRZIV+g6oJ91TXFFxGmLmRuBb58LTMZKIboHtXIbcbj2TVnMPEshYk4l+qxFr3MDyFyxGxpyggYLQDoMYXEM+cKTVlRBAMVCnmy5QTrTJZcNWVySudRsSg2bRhPyDalh00f6Si2xdO3UWh9dJDuiG/WLvnQt0X7uYebh1UKv0UHGwlYI3VBs8c4hVlqtqP+WgOcAaxmx6hqM4XIPvhjK+Opgfp8uYu63jIzBsXNNW8BwxcvA2tfdliR0ugLJQaGZKLqu2dd2rzmK2abzU+G2Nc7Gbppgcq2yMz08/uRP/oTd3V1e8pKX8F3f9V1T9/2Gb/gG/sf/+B987GMf47d+67euUws9PJ66+NVf/VVOnz7Nc5/73NjvX/Oa1/CiF72I3/iN3+Bv//Zvr3PrPDw8PDw8PDw8PA6OIJzTTDUI4ukOrXVg+4qPnM9D628QgiaK49/jn67vdraDFpNNIonSwDGk6G4eIWjPI9Yg85LaSXY2Wm/gCELspxDS7SJC6F3rIuZ2TY8KUfQyQhzbVidxkcRuBLbd37atVoCpW6ER730MgXgCOI5ElZeidizkxdq/VoOVFVg7AssrsLgIlZU82ZUFgnIZCgURN7K5KMR+JEU6uj1otRg3mwx3mrR3hmxuRaLGbmRLtRNZU7Vh18rcaCPEZT/6ex0hPcOorcei7x5DxsJBn4+SngetVXK9oSRmJuGzvlQgy2Keuz5rjYJfQObPowjBreNE53Mq2nchemUQQv4C8iySsArciWSHtJGsgS3MXNK1Iy5Tw0ZciYQMhnxPgvscVTDNIdZHdyPk9zYydnaRe6rHtGm/yCLzxxZNqoiAlI/av4jUHVkF1nJwYg1OnYLTp+HkqYDq8QqptRWC2qIUvQmBYTSfdnYYbW7R3uxS3w3Z2ZG5tLsDFy/CA1fggZ7cj4q+aSQr607gliIsVaDTg4eb8JmxzCE7c0bvQbM3ekyuz/uBLWpnkXWlhskY6GBqUej5i8DXZOGVJ+Du20XbGQ7h0hX420fho02x52tb56xisrl2ovPqeLaFrf22P84Gyj7HQfokDgFGsFchwhYjdB3Xse1m2dhtc196LhXvptlPlYHmAb3Xk/794OFhY3V1lZ/8yZ/kJ37iJ0ilUntqAdjY2trizJkz/NEf/RFvfOMbCcOQ8fip8Evt4XH9cdttt/Hrv/7rvOhFL0pcj5944gl+5Ed+hPe///2+zobHoeNqxlQQHDgG08PDw8PDw+MpjjCc7slzYFFDCdMFhPDPYexT6kzWmXCRxxRYtQn5uHclzmbZKtWAWxBCcISIGme5uihi22u8iJA6ZSaFhCZCyl4Lu44sQrTegRCPGYRgfgC5P5vYdYUMV8xwhY1gynFqnTNGSNbT0XsaEzm8DBwPYLUIS0vyWlyUshprR2FpOSBfzZGqVQmWl8SWKpOVehsEMBpKcZJuh7DVhmaT0U6DdmNIfTt8MmvjSXFjB3aaUB9CIxSCsocIHetRf+xGfVZDnheI6KGChyv+TINGdqcQwr0z53E3EklCRpq9ooad/aBR2yo23oVYAG0AX0L6V+dsF1M35gjwFWkpxry5Bb81hscT2pZF1opTyNgZIILGBvIc90ss22KpQm3qXLh2PLY4GiL9swR8PfAVeQjHYi93GZnblxCBZ1rmVxK0n/PIHMog41SFggLSj7ZgWELG7xIibJxcgxMnRNw4eRJqJ8qkjywTLNbEiiqVEma/04VGXebTYEC/3mPrcp/HHg35+8/B56/AE2Mh9vsY8ruGrC9HU3CkAqMxPNiCT0d2VU0mn0smuh+twaBZEfOS+KmYd3ut1ec4YG/9jwLwDSV47T1w913iwtXrwcYGfOEh+PR5GbPb0fEFRDRaS8E4gLMjGaNbTK6f7vgLYv6Oy8xwt9v3b4t0B4FmxWQxdUbGyNpXxwi7cb896Wh/zcRyf2ddUUPPM+v5HZSU8KKGx7xIp9NkMhle8YpX8La3vY21tbWp+49GIwaDAR/4wAd4/etfT6PR4NKlS9eptR4eTx1kMhn+83/+z7zyla+kUqnE7jMYDHj961/PO9/5ThqNxnVuocfTGV7U8PDw8PDw8DgIZokaB/pXQhaJJD6NkC0L0fYWYhXSw0QBK8GqRFUWU/TZjoJXUqyPIWE0krwc/a2kmda5sEkp9VBXKxWNXr0a2OT+CCF0W1F7lHgsRPexQXz0+NWggog0GkE/K2NgmpAxS+BwLVd0YPQRAlCJTCW+MkAxhHQHxqEQi+02dLsSNF6vhywt9VjsjcmPxwTdHpRLkrmRzUnBgHQGslmCYhHKJTILVRbabcorLZYbfRrbQ3a2I1uqLXnf3RUrndZArHR2R9I3aUwEeheTSWDXK1ARBIxgltSftr3aCBMlra8Bh/e81QbKHdMHhfvfBjc6W6FZCvpZic1PA5/E2AulkQLfJ5AxWUhBKQsrWbh9FZaXJVkgtbu3LUrUq+CoBd63iY/wn5f6VIsq+1h7XOs9ueeMI6F1bl8BPt+TufwYk+LsQZ6LiqHV6KUEc43JGh5FTN/0outq/YqwLw0bj+U1GsGpcYul0Yj0aCRWbzqfCnkorhFERUvyjQYrlbNsb++Sy4WUUnB8LGu3vebqs78whot1acMmRhTQ+9fxo+uvWjIpOT7rv6txa467/ug5NEtK26jZNCtAbQBXrsB4JH2y24TzO/DIDjyBPD+1oKoAt2bhq1bkXKlL8v12dB17Dmgb3XkSOO/TtseJGwdBCiMCnkKemdrBXUHusxtdL4/JvtLfWK07pLZV+nvYZzJDT9czFYHt3zu1NfTxuh7XE6PRiNFoxG/+5m9y7tw5XvWqV/GMZzyDr/mar4ndP51Ok06nefnLX87LX/5yPv7xj/Mrv/IrnD17lr/6q7+6zq338Lh5MRwO+aEf+iHW1tZ4+ctfHrtPNpvll37pl3jGM57BRz/6Uf72b/+WRx999Dq31MPDw8PDw8PDw2M+zJ2pkY4iLQOEqFwBTmIIF83U2EAIlwZRUWCMRZWKGZnoPJqF0UWih3cwZFSAEFJLTFqSaPTuFURAidNs1EJplgBgRxrPC72fhahdOSRyVj3fD5MAUrJzCSGntF7BFtK/KtrEReDP+nuWqGH/rQTbCoaYVeuhClEGSwaqZbGjqi1AdUFsqY6uweJyikItR2ahDNUKlEpSTDyblYrRhBFjOxZ1pNeFVptxvUG/3qGz3WdnRwqK7+7Abh06HWg0YGsXNjpwOZTI+l1McV2tiTLAZA0pOavF1fVvrcvhQgU5jaDXMdOIrnUYz7uARMoPMWT/QQQ512bKzdgg5nMcmsiYVnI3QLI3vha4PSWWSKtHoFqFbEaErC89BH+0LVHwSoiOovtaQUj9IDr3JpPzHeYjxG0oua6iiBK67tiFeMsp+7MSvEejdu5giqVfzfPVbIsFZN0DY7WkVm96L2qdtY2sIwUko2Uleq1l4fhKlLFxEk6egqW1HNlakaBcgnJFJt7SEiwuQSqQ9IWHHuLyZy/xxc8OOHcO2h2Zav0BNLvy6kR1NnrIc9ei3A1EYLHt7my7ov1a+8VlaOj39tqkIl8O+W1QK6kTyDi8NYDlDPTSsD2CiwPJ1LoUtVUzf9RS7AU5eP4KNAfwsQ34DLJWqECb9IzdzIy4sZV0jK4p7jzOR/c2ito5tI4JnP1qiJXeLdG7Cg+7yP26dWhUZFcxQ9sxQOZZAxnbWnAdpI8WkTFWwqyZXeR35gwyHp+8P5+p4XED8OxnP5tv+qZv4uu+7uv43u/93rmOeeihh3jb297Gu971rmvcOg+PpxZe+tKXcuedd/KqV72Kr/qqr5q675/92Z/xMz/zM/z1X//1dWqdx9MVPlPDw8PDw8PD4yA4tEwNJUmUmmgjxBAIGXcEIVOOIMSKeuTbhWYz1rsSy22E5NzAFIFWUUOLPpejc44xXu5KCsbd3jyEcA4hjVQomDcSW8moVnT9PEIWHWYxYUUHqd1x2drm+qkrbLEiTtCYJWzAdNJOiz9XMc9D7XiGiJPUqCHlMjodeQ0HWqR4TK3WpVrrU1xokl4oQ6UiNTfyeXnlcpAPoFSUcPSFPqmlRQqtFvl6k2q9ydpOh/ou1CNRY2cH1jdgeQuONOAWq7B4BxlPalOldQzUN16juJVUHGIEESUC+0z6zWtktEa2H9bzVmJVzQDaGJL5arKNXNLZtqEJYr5XuIR1iIzxy0B1DEfHYjNWXYBuRzJoshl4fhruHsncaCAEvY47JcivRNttAWlWHQxivneJ8qRoet0261mplRwcvEaEXiuLsdvSLKsU8aKAjrUW0ie6FqlYqpkKowGE65KdoGVpGvU+pXKf8kKT8nKd9LGeZD7l86JcbO9Ap0ulPObOZ8DRo9DrQ78n2U4qELZaUkej2YGd4eQ6nY/aX8TUolAxWufVNExbZ2atPTlMEfUqQu6fitp0fiAWYWeQ344mZu4GTApKvT6cuQKboQgfamnljnEXcUKaa08VNy7jsqK0L1ajdrUQ0UD71F6fdd9edG/aN8eRtbeGjC8VQnV//b3Udw0c6CNihtYjalvHjTB2fZXoGF33tNaOh8eNxv3338/9999Pp9Phe77ne+YSye666y5e+tKX8pu/+ZvA1RFqHh5PJ3zgAx8A4Bu+4Rv4yq/8yqnz6SUveQm/8zu/w9/8zd/4OeTh4eHh4eHh4XHTYe5MjVIQ7CGjtLjwPUiB3QIm08LNgNAoeSX/1f6kjkR3a5StDY2Qr2IKpCrBrATNQVFEBJgQIZda03ffAxU0UlE7bmR5SiUhkzI0DkPUUJJOCcYV5Nlo5HEBY8lVzsBCFDherUKlIiU1yhX5XKlApZqiUM2QqpRIrSyRWlqULI58XmoEgLC3/YEw580mYasF9SZhu82gNWB7c8TGhlhT1Ruy225d6jtstmA3NPUL1NpJMzbUfsommu1sjp3odT2gmS95zNw4SC2PpOedVEvDfu42CduKru1iCYkYfy7wtUdgeRH6fbiyCRtNmQcaDb6DsTDSOVtnbzaTZj7NioKP+07vS7OYpkXeuwS2jmUVXPVcKoIdVNAoIRkWVUzkfCa6fhfTH3aRbY2kbzEpYj1ZVyN6rSJr1tEKrC5CpSyOUwsLUsfm9OmAhduXSR1ZkTnU6UKrGdXYGBKORgwHId1OSH03FFFjV7I32m3Y2YaNTZk7OyMRClqYrA0V2+wMuy5mbmnb51lf7CwhVyBQQeho9KpF/VlLQTkFWyP4ZAifI76AexYRAG5D1ikVwi8hFnVbGPHcRpxQPO1vd5t+tq0SbWSQWjU5TCF6HbNuH6SsY4qI/duzkOdvC67uuFbxFcw4U3H8LCYDyYaOszyT1lOa3TFxDZ+p4XEDkclk+N7v/V7e/OY3U6lUWF1dnbr/cDik1+vx4Q9/mNe97nUAdDodrly5cj2a6+FxUyOXy/HLv/zL/LN/9s8ol8uJ+/V6PYbDIW9605v43d/9Xba2tny9DY99w2dqeHh4eHh4eBwEh1YovGzZT9kETA14ZvTKMhkFq2TIECFHLiJEpx0d30dsNK53/E+AESUOGhE/TwT4tYaKSxnr7zhye5od1SxRw448Visn9bdfwdiDqfVJCSEfy3lJvCjkpUZ4Oi2OU/kc5KLvVlbh1O1Zlu9eJXX8KFSqUCxI5kYqBUEAw5Gw572usK/9ATSbjDe36G62qG8O2NoSgnZrS8jZegOaLdjpwe7ACAQ9TPS7bUs1sr5rIOO0flVP5uqg41MFgXmPmfa842zHbOh87RAvaug11hARcxWZv1cwhYuVnFfye542q6XZvKKGTfxqzQ47ywH2zku7vkaAsThaRMbvGCF8LzFbLE1qSyU6XwUjlmiGm4oamoFTx9gAzbLJU2F3kajodfS5AJRScKQGp0/CM+6EO+9NUzlWlSyobCZSyawrjEbQ6TBqdejWB3RaI3o9aDZE0LhyGTY3YacOjSjzqR4agUOFmJ71We0DXUu8aetL3PjTbTmEZL8FuB0RifJpKcHTHcMDA/hLZI7GIYeIGscwdSjUtk/Hpi1+zRIz3O1J7VaoNZr2uo6DEmLZ2ET6S+tVuDZwKedzBumHr0REDZ2fdtZeGG1bR2wZ7SyzESKg7Fe4j4MXNTxuFrzwhS/kh3/4h7n11lsT623E4ZOf/CRve9vbuHTpkq+54eEB/OiP/ig/9mM/xi233DLX/u985zv54z/+YwA++9nP8vDDD1/L5nk8TeBFDQ8PDw8PD4+D4JqIGvquhORxxBZEi8pqUeah9XkHIVzU4kVJPy3iPLbOqYKHet1rJLVN0Nr2LVjb0xhP8BstOFxraCFiO1J9hCHR1IZkWtR+HMHtEnT6rsSsZj/kEeLwNCbSVzM3StHnXEq0iScj4wND+uUycGwZnvVM+IqvzlG9dVlUjoUFUTyKJVFAUlGLRyMYDaX2RqspRTZ2thldWmf3QpsLF8VSZzSCICWZG5cviwtPqw+tnhQY74aGgNeI8zaGLKxjSEB3DLnFqadB7Z4OOg7tOhTzZgLZNTP0uradkL2fC72Gij72s3dFP7UkGmGEzIOiwKQFlUt+u7DbpfVIQsSqZ5dJAcMliNXSSLONTiNZAVvAw8CDTBc18oigsICMcbXoKUVt2ULqHdSjeypgisyrQKUR8POKVYoqkhV3G2JDpILHyQrcfhruvANuvRVqK2kytTKpcgkKBchlRRFIpyEMxbuq34NWi7DfZzwY06932V4fSubTtli71Xcl86nehkYPmiNjX6RWgSrUtDD9bwsb9vusdUb/1uyExeie1WZQM0TWmbTkc6GZTyVM5pX2/X5rKMW1e55tKjYXkWelWWzpqO3bGAHOnYv2OXStXkXG6nLUfhVg9WU/g3XreC0cHmDmtF7PrUnjip+65tvzqedFDY+bDM985jP5lm/5Fv7BP/gHfN/3fd/cxz322GP8wR/8AQ8++CDvfOc7r2ELPTxufrz0pS/l3nvv5RWveAXPe97z5j7uz//8z/n0pz/N+973Pj760Y9ewxZ6PNXhRQ0PDw8PDw+Pg+CaZ2ooeWMXvdXoUDtS1I1IVrHiCEK8ZDB1MjSSuYeQQTUMYa7X6WKsUfpMRsXCZH2OpyMymPoWavtjE9Hqh2+TqkkWVBBP0insTI0OkwWecwjRdhwhl8vRNi1Wq8WqNQJbRadhtM+REtx9NzznOXD8liyFtSosLRHUalJ1vFyGohYVz0jmxngE3R502rC1RXj+PIOzl9m+1KPbEQ0kk5Xkjp0dqevR6wtJu7EBVzZgvS7EotZ5uBB9zlptHFn9qONKbav0e60dcdgIENKdqF3z1nyJg4pcdjZP3LPWuWILN6G17WqEi1ntUwHIrgmgcNuqY1HH0B3R+xNINpi7zhCdv4D06QmEJFY7pxRizXM/8CWSs1RAiOmTCMF8Gri1CMdXoZQX27P/twMfH8uYCjFFwW1iucP+BQ299p3RdXWeLQInS3DLcTh+AlZXolrhywGlWoZ0VYqIB/9/9v482patLO+Af7X6frenb27fCyIBwS6xSSQ6RtSET4wRo6IOEkWHiQk6JCIiRIgmY0QTNdiRRNGhRIPGZPjhl4iigIDgpbnc9px7T7/71fdV3x9vvWfOXbtqrbX32XufhvmMse5au1Y1s2bNqnXu88zneUtFcT9lMnKDjMcwGoUP7BFBo8l4o06/3qPZCKjXxfmkEVVbdXE/NTtScFsjqbSovD6PV5Dng4oUsz5f7DFni9Ue5l7brUAYJef3C3HijP23iutF5LdrHhHBPET0WsEUXtf1o+2O7tuOGLSdIPZvrQo/dh9lkLFSwwibuXB9dQulMIXCdbyCjFEtHK+xkutO1HC4RXHkyBEefPBBvuZrvoa3vOUtwGzjbnNzk8985jN87GMf41/8i39xwK10cLi18eCDD3LkyBF+7Md+jK/7uq8DZruPnnvuOd70pjfx27/92wfdRIfbFE7UcHBwcHBwcNgLDlzUsGOkphFHaWRmcQVDnlQxJKZmzytR3MMUMFXhIx++95FZwbqekmhK/ETrJeymNsHtACU0PaQf4ohYLbKubo4ktwYkE442NH5KiUwtBF8Ij6NxVCqk6EtnC+cxBG8aKKRhrioFjE+fgePH4MhRqCzlSS0t4C0uwPwC1KpSkKNQgHRKbBgeMuO83oDVVYLLlwjWN8TFkc2K2yOdJhgMxe3hB3SfX+Gpx3t89rPw/EVY6cms5ksIqd1CxqPWcCH8vBCen4odKtANMTPz+5ioGJ2Vv8HOOjG7gZKL0aLGCvt4ntUmhU0M6/WHnSSqwo+867a6rz47EXX62CTyXv73JYUZJ3HjU8duDjOui8jzR+slJB03jdwz8xi3Rh65dy4jYyHOnWPDC493Ang58NV3w6MPyPLPPQn/30vw52OJsbKF1qJ1Tn12V4dHiewSUpPhFOYeqiLizNEKLC3AwjwsLsLCYihuLEBpPk+6VsarlKFUkuy3bOjcSKUknqrfh26XoNOFVptRs023Prxee2NryxI3GnLbbXRhM6xbo8R3O+zHTXbWiog+Y5SIVyeFLaTd6i676LhMEjb0uaji2QriprCv/zSRRIWdWSPdSsi40Oew7RLR38ceEt/VxAjkOcxvbC48ZgO5lu2wvZtO1HC4xZHNZikWi7zqVa/il37pl1hcXJxpu9FoRLvd5iMf+Qjf+Z3fCcBgMGB9PSnozsHhzkWxWCSbzfL2t7+d173udZRKpanbdLtd3v72t/Nrv/ZrNBoN2u39CD10uFPgRA0HBwcHBweHveBA46cUWih2GnRGv84ItQkWrWuh0VEdhMCJxtLkELImup4WV1YC0CZob1dRIxqfYy8vIH2hETBJBGkeIXC1YPEsTo0gsiyaP69xJ+PIfnQWsNYWUMdGxvpsR1QV01AuiGYxPy/JU0tL8lpcTlM8Uia9NC/M7PwclMoiWOisczw5cK8vxTTW16HbDTuoILU4cllYXIJCgfHnnmL1zz/HZz8LV6/AZh1WW3CpD5fHQtylrHMEmeGsdUO0kC7WeuMUeHmZAE8gdQiujIRcv4Y4B/brf+uUKNV4HSUuVdCw628oCWpH1IAZA5Bcd8Jebt/nttsqhRC2y5gi5+po6QPnEfJ2FtguEtutERU19NxVDD2K3Psb4bG2wmPrdnH3RDry0vV6zFZXR9uzCHwF8P95DB65F65ehQ9+Cj7QgyfYHoOlokY+/FuLOWuR6FmOqSKOFgy3Ces5ZJzW0jBXkltlcUGG/eIiLC3C3IJHcT5Paq6CV6nIvZTPy/2UzRpxYziU2jXdHrRaBM0m/XqPdn1EY8unviVxbmtrsLIC1zZhbbRd2OhghA19RsS5bTSKay+ulUl9dZiCSJzwZr/nEVH0GHKtmpiYLvs3c5pbQ6O3pglhWSSebA7jjFNRxC5UP8TUSNHtVIRSYSOFEeD02E0najjcRnj1q1/Ne9/73j1v/+d//uf87b/9t/exRQ4Otx9++Zd/me/5nu/Z1TY/+qM/yjvf+c4DapHD7Qgnajg4ODg4ODjsBdNEjV3/K8GefatQl8U48jcYB4du02P7bFObsFGST2ft6kxeO/5IyU2dlW4XZNX93chM8STYAsBeioqDtHvWKBSdsa5zo/SYtiChYtKk/Wmtjby1bNLMYLv/NfJkhHGCZDAkq93H6thRwraJkN7zGEGjH37Wv7tj6HWgN5D63+0ONJvQbECzMWap0WSu0SO/1CTVmIdySeKoanOm5kYxVEVyWRE6Om2J1RmOIOjLsnwOigXSi/Ms3zvHizJN7rrLp92SmecrK7CyBa2B1A9Yb8sM9BHGIWTXqsgDpYwIGdUKnD0rBPJwCJ97GnqXhGBPY4SnuDGj+5t1xn4RIUi1D1XQUMJSnTN2jJQKG1prRQlLMLUz9PrpNnbb7Otrj5kSQp4+wHYCdYi4FDaZLGrYAts8pj6H1uKJjk8bWlNgE0Og23E+k2b6q3C6F3jIeWuR8QESOfXkED51BT7Uh2fDNulzSmELQnp/zUrA673VR+4rFbUKmCi0HlLzotGEelecFFtbxmGxVA9YXOpRaw3JL/RJ1/py75TLUK1BQZU5wA9gNIReH6/dptBqUWi3qNU7HNnqUl8fUauJJpJOQWoDUkMzvmzn3UbYvihpr2LzNOeBPQ6nOWjUYTDLc3G/YP8uRduj162DOB5U3C2Ef0f34yX8rb+fdsSjCpx6z9lRViO2O8RUuNN+0d9Mu9D4yPpsi+l21N6t7p5xcIjiM5/5DG9605t49NFHd1VvQ3HXXXfxtre97frfFy5c4L/8l/+yn010cLjl8fu///ucO3eOb/zGb+SLv/iLZ9rma7/2a6lWq7z//e/nAx/4wAG30MHBwcHBwcHB4fMVMzs1Sp43kQhXMjyFzNQ/Fn5/HkOwKAkThboObBLKJrF862/9PGD/hYs4qCtC4ztAZrgqSWkLLknba55/ESEfu0wmVpXAUgJMySfbFaHErpJUSdAZ9RXM7Nskl4ZeQ3V/KDGpefkaH1YIt4/GFUX7Q4WZGkbc0PgpFTaUnC16UM5AtSyxOUuLxrmxuORRnsuQr2Tx5mswN483J++UihKpEwTizEinhJTt96SQBkjMjueFxcU3CDY28Zsdxv0h406fzmafrS0p0bG5CZevwNV16PSg05Xa5Pnw4o9GkElJkXPPg0oV7r4Ljp+Qie6feBz+70UpOL2GjP0+28eI9r8KVlpsWb+L5uIrsmFf6qxqHQM2IWm/dNyogKAikxZQbmGi21Qk7FrXTmGTqzrWSsCjwIsRUcPHCAyfQiK5kmpTZBFxphL+re0dWa8oomNV/25i8v4PGlpPQ6N5asDdKfA9ODeWCLMGs4kmtitm1meYhxG29H7We0jfS4T3uyfOjYWyuDbUAbW0LPdTdT5NrpKHxXm85SNi7ygURRz0UhD4Unej14PBUESOThcaDcYr62xdbHLxQsCFF+DSZbi2DusDcSDozP4Wcg9ssrNAt7rnWsRfuzRGpFCivsvke+MI4qCpI+NwUm2Ug4I9NvUZm0Wu1zJyLzYQsadvbRO3fdxvrJ57HjnXCuII0xouerwoVIizn9vR54X9+2rfj7ou7H2mpXNqONxMLC8v8+IXvxiAV77yldeFit2Oy3q9zsc+9jEAnnrqKb7v+75vfxvq4HAL46GHHuLUqVP84A/+IN/wDd8ATL+Hnn32Wc6fP89//a//lf/+3//7YTTT4RaFc2o4ODg4ODg47AX7Fj9VDEWNuBnUUSKngBAuJYTcvDpl35rpHSVw7M/ROKnDmolbQSJfdDa6Fx67iZB3Oou1jSGEFRmE+DyKIduuhu9Jl8UmbW13QBBZZsdC2UVjwcT56DYl4uOnonEpuq9G+LLbqKJKBVN0VpfrttHrpMtB+m4BIdW1fSra6EtJ2UoO5msSSbUY1gbQshqLix7lhRyppXm8+TmpnVGrSUTVyVMwH6bXjzrQ3JKp692O1NoYDaV6eKsNvS4MRwS9HtTrBJ0uQX/AqNNnc81na0s0kPUNaIeFFlotmQHfasNmD+oBjD2opuFMWYo0P/08fGgMzyBujTEmLqnCdlFJ3U0av6biRB0ZT3HkeHQ29zQoEa91bNTZoWNYxTk/PKYWDo6LPYsS00eBx8Jr1kYy+tcQgjXadrvdWqRbo6q0yH0UcbPfo23RczgMLABnkeumgpC6mDRGyb4mcTP47e+iz7hZUUaeJ0XMfZRl531UQq77QhYWKnIvLS+bYuJHj0H1RAVveTm80ebFuZFKQ8oLG+aLKJhOi8jRasGVqwQXLlB/fosLzwdcuACXLokQuNE3z0YV6zrIuNjAxATqtd9kZ3RhJuzr45haDyNkfGntmyhUtKuG69aJrwFzI7Cfk7Osa/8mau2XDEY8nKWmRtJ3+kyZR57V55kubpcwdXqiYy8qhEZFDYUTNRxud2SzWarVKl/xFV/Bu971LnK5HPPz87vez2g0ol6vX//7ySef5Ju+6Zuuf7e5ublPLXZwuLVQKpUoFAq86U1v4rWvfS2VSmVqzY1Op0O32+UXfuEXeMc73kGn0zmk1jrcKnCihoODg4ODg8NesK+iBkzOD4+SOGmlP9oyAAEAAElEQVSE0LJJKHvWt/6dw8xah+2kUZILQAmYWWp57BUZhFg7hhBSCo1Z0mgOjftYC9ujUShngQcR8neMZO0/g5m5bCOOyIoui6uHEX1pzQEVNcDUIpi0bzuCRGOAhhi3hS6zizjHzfSNziiOLktjInx0tvFcuOx6rQ2g5EEtL4XEa1WoVCQlZ2FBZp4vLHmUF/Nkjy/ByZNw9z1w171QWJbWDerQXIVmC/yx2ChG4zCaaiAErc5E73TE1TEaiQBSrxMMRwxbA5pbI3odn24XNjckHqvVkroCg4H0kQcs1WBuDs5fgA9dhk8HQuLmkJocywjpGoT9qhn1WijZzrrvIkSlEuca/WLPpLbHhF5fjSTTHP00MnbvDz+vAy8g4zSOANX9xokaUdeUrlcIP0eLPStyGKeQ1iHJhe/9yHaTyNw4qFPFFhPtdu4nCsizwBZjWkifqriZBNsJFfewjQqBk+Ah946KFnm2i4S2UGg7N+aA+TwszklB8SNH4L774cSZDLmlKt7SEhw7KlaOWg0qZYl3y+XCgefLfdNowMo1uHQJ//mL1J/f5OLzPi9cgMuXYGUdNvtm7PbD9y2kGPta2Af6v6fqCtNzyyHPhHuAu4BjaaiEuX3XRvBXAfwNyULWboSHWZAJ+6+M9HOD7fVSJiE6nm2XYhCz3iRhI7pc7/sscg812P6bqsewfwP0uUvM99HfWf1ef2OdU8PhTsVLX/pS/u2//bcsLS3xspe9bF/2ef78eV7/+tcD0G63+Yu/+It92a+Dw62I17/+9fzDf/gPeeihh7j77runrv+bv/mbvOUtb+GZZ545+MY53DJwooaDg4ODg4PDXrBvokbBEjVmmVWqn5W41Rm6SrarIGALIFHaI07EAOOAKCAk7UEIG0oaVcKX7UywSVitk6ERTYOwffPAGUTYqCLE55PA84gAEq2RQczfcYJF9HN0mb7SMetifa8kq66rZHsPISTVKZDGFMlWkSNjbb8bUcOumQBCYB7DOAlUMNEonSJQTkEpC6W81K+Ymw9FjUU4chSOnS2QP3MMThwXprZcAc+TguH9HqQzkEkLKesHUhA55cnfY1+EjNFIMqZGocjR78m63Q40WwTDIcNWn15jwLDv448DvEyG7GIFMmn87oCg06WxMeLiBXjiKXhqA9Y6kPOgmoFSCtIB9EfQGRsifIAhfzUCSsl+/U7XVVeAEroaa1PBxHg1EALWjvmqhNteQ4S1afNHdYxEHwx67W13xzScRe6Davi3nkcDiQjqYIjUOBfRJNjPBX2O6D05xMQg6Yx9bb/eGzrmkx6Rdo2IGiJoVMP9DBFy+xpyv9j3MzGf7WVRASaOXE6CLWL5yDW3I6iymPtIxQ0VQGpALQULRTh1DO69D06fgvnlNOVjFTJHw5tqKbRyzM9DtQr5Kng5EQEHLdhahSuX4cIFRucvsHVOhI2LFyS6bX0Dmn3oBvIcaSDuHXXthcan68/JJUzMVBF53iwCSx7Mh3XMhwFc68PjY3g83If2p11oXoXB/RC1vLAtjyDXvgk8HZ7DrHVZpjkuouskbRO3rS1e2L+n+nzWvtA+isZSRcef7a5TEWQeEaM0QtKJGg53Ku655x6+4zu+g7vvvpvv+I7v2Lf9rq2t8fM///Osr6/zn//zf963/To43Gr42q/9Wn7iJ36CL/3SL5267p/92Z/xkz/5k/zf//t/D6FlDrcCnKjh4ODg4ODgsBfsm6iRs+Kn4oi7ae+wnUzXaAu74KmuG81eh+3kuJKMBeAi+5udnkOInDwyAzwuBsieqa5ih5KjHkIiL1ptDMJ9rSJEaFSESSK+UjHvkwSOqLARFUWiLzDErZLfBWRW9TmE/M4gxF4KIYnTmPoi00SNKHlrRy0pqsgM+LnwpaRs9FVAinNXcjKRfK4mk8pPnvRYOJohlctQrGYo1zJktYp3sSj2iVwYupJKy7J0KHJ4yOcA4+bwwxE39mHQl3oC45FkTnW7BKMhDId4+TwcPSoiSaOBv7bB1vk67baYPrY2YW0Nen3hgn1fdtNuQ70p4sZwCN2BjLG+1T8aa6TFoTVqTUUPJTCVeNRxpqT2fHiNNJ5Jx88qImo8yewFmu170QuPoQLVtH0UgYeRuhsn0pDNiG60NZKC2s+yfdZ7dGzGPUNAztd2kvgYt5e+B4iLoon0n/aTzrj3ELJ9DRnv6ohR14eOc1scKobba52bDUTU0Bo5k4hrRWAtm+RomgR1OaljZGS1Vdtn163Rl8YzLQAnKnDmOBw7LpFUC4sexWqGYi1HcSFPZr4qX9xzD9zzCJTvkR4M2jBegfY1uHIBnn6G8TPPsXluixeeD7h0EVZWJKat3YXmUK7DZeRZfTXs71HY/iPAaUydFxWMo44zFVtXw2s2Cs9pDvO8HiPPqE22F46/EZQwwnQzPI/GHvcVdT+lZvwOto+luO/AjC373tUaJPobq2JlkrBm/7ZVkP69hlwzcKKGw52PxcVFXvGKVwDw2GOP8TM/8zP7st92u329YPLVq1f57u/+7n3Zr4PDrYSHHnqIe++9l+/8zu/kNa95zcR1n332Wd72trfx7ne/+3Aa53BT4UQNBwcHBwcHh71gmqixq38l2ATIbikKnTmqcRbaLCWvojNPo+/2ZyXGA/bHpaGkuWa5n0DI48sI6bqbzH6dERsgRJDGr2i80KQZ3UkOjTjHhr1e9DNsjygKrHf9TtuiESZZTOyL1ikIwmXHEaLwQng+0UEzq1PDJt2UyG6H+22G/aRRORrL1MWQm5kRZEeQ7UJuBfLPQSkXMFcZUiwMWV6E48dgfhGqVY+5pTSlpRJePgepFF6xIE4OLRyez0EuL5+DQJbncuHU5hQUQklqPJaK4OMR3mgscVVBIG6OVhO6Pbx8lvkjGeZO5vDm5whIMd7Yor3SZnMT+n0RMRoNKUY+HMjfrRY0WqKdDAbQHkEnMOdvx/PA9vvHt67fOOzDPEbUyGahGhpUclmY70v/jsM+b5OMuJnueu/NilS4fgGJPioVpR+6LRPhFhVN9F0Jenu8K46wfda5XadBBTowpHkZIc5PVCXOLJuRvr60Ak/52+/zprXtEFMrR49RRMjekrXc/t+0pGejLo+77+37M/oMjEOAuSe0D1vhthoRpyKH/awohf2RBhZHUve73YZCHgb9gO5zQ3r9IZl0m1J5k8Xli5x40UWqX9XFe2wOSkfAK0ImC7UCeGkYDEj3eswPR/hj6b1sFkol2KpDpg5Bzwhz5bCv1xFxYB1Tx0VJeBWIUpHldgRhGhEaziDPqBRyrXyMU2E/0EXEtzTJRexnRVS80zpF076zn93RdeOgDrCRta4WCldEx2z0NUKe+RscXs0aB4dbARsbG/yf//N/ANjc3NxWGyOTyVCtVpM2nYhyuczXf/3XAxJPFVfHw/d9Go29yqYODjcfTz75JE8++SQvf/nL2dzcpFgsUigUYte97777eOihhw65hQ4ODg4ODg4ODncSZhY14ki2KNkS/Tu6ve3QUGickzYkbh/27NFcuI09O1qhM7Btomia6KFOhHnMbPcOQuasszsSS4WbFmbGt86UVZJq2kz0ODdF0nrRZXGEqvadTZJpe5QADLBieDzIBkI+ziPxUMcw0SZ2zRAtPK77TxI1VEBRwjLqxBkgBFoXiXnJsT0iSMn9LmFR6UCinFK+iBzVTlhY+DKcuAjzVZibCzh+fMSx4w2KBSFay7UUqVIBL5PGy6RJlwtQKEocVSotRZLzeYmrKoQbpdJC4uZDgWM0lnoDwxE06tBsiHOjVsO7Z05sJAuLkE6TunaN+epF5hpNxmMYDnza9TFbG4EIBF2o16VOx2AohH+jDu2OJGL1B9AcSYyPkrt+IP2gbg4vvHYa56R9OwByQ0N6V/swX4TTA1gZy/pKjtv3SQZTT+VG0UYEg6cQHajQkf1eA65g7hElpe1ItSIy/qKxdHaEFAnLddyouKguhaWqlI3I5UIHTR9aWzKWljD3SICMs2fYXtCZ8O86pgaQ9q8tIsZh0vJJAmQcfOQZ5RGpd4D0eRsj0uozyA/7Ywjcm4IzR+Huu2BxSWLd+gOpE3PpoghtXipgcWHMg5vXeHD0Yebw4eEtqVmTyUJqKOpQrQbHjpEe9JkfB6TSbYrF4Ho5jlQK/E0Y90KhJQXZNFzx4dxYxoHWJZnFpaL9pNe6jxGPN8PPce46fWZpvxD2S5vJY12fjftJ7Ced46TrPm1M2PvW31g/sjzav3H70/sItkcFOjh8PuLDH/4wi4uL1/9+5JFH+MVf/EUAKpUKf+tv/a097feuu+5iY2Njx/LV1VW++Zu/ecfy4XDIhz70oT0dy8HhZuCtb30rb33rW/nu7/5ufvzHf5y77rordr0zZ85wzz33cO7cuUNuoYODg4ODg4ODw52AmeOnMjPU1Ij7m8jfNqmtBEohXMeejYv1rjOwa4iL4ngKWj6cx8Q5edY+1GWgM1YnIY2JpVFRoo2QbLslspJmYkc/T9rGbtck58Ys8VJxTo6oCDSHIfoWEXK3g8xOvhyul0FI3jqGSM9i8vpTGOFCxRIVTuxi6pNy7j2E9DyOFAhexBQ5zniwFUgm/0q4P43R0eK3dh5/xYNaCY4tS7xOqSgaRa1m0qdyeShXU2QKWby0RyafJl2ryLT1QkFiq7I5IW4LBSFyPU9eIKrD5gasrEpM1dISzC8IQ5zLw2gIq6uwti7RVpkM9PsEG5sMO0MCP2DUG9HaGNJqSzRVpwsbGyJujIYiBGzVZfeEZpLRCNoD6AxE3AmAfiCRPC8gwobWgBkgxKSHqVMwRkSG1fAansDUiCgj98LzyL2l0KixSdfPdlzZD5QiMqYq4f5TmFn6Wmj8mHUN7ZowGbaLnfqeZbugZh9f19exp+NvDrirBseOyGXt98U1s9WEQQDj0HgzGkt/XgI+jMQl7aUodNznSYiL3NuPuhBRHAdelYVX3Q/33SPDNZOVMjJXrsDFS9Ivnie1a87eBQ8+nOLIFxwj/dADcP99cOw05EvQa8Hmmozza1fh6jXGq+t0VlpcuzTi0iW4egWurcB6XWrJ1Ipw+gj4HlzcgBe6cH4k4sYqxiXmYZ6BdiyVLi9gosRUzLFrFUWRQVwqxzFRVw2MS2emH8EDwKSxshdhW/vL/h1V91IG83xWAc92fWSQ+/UIUufI8+BSIM/cXrjupoufcnAA4NSpU7zhDW+4/vfRo0d53etedyDHarVavO1tb7v+d7fb5ed+7ucO5FgODvuNv/f3/h5vfvOb+fIv//LY7z/wgQ/wlre8hT/90z893IY5HCpc/JSDg4ODg4PDXrBvNTXSM4oaSct0eYrt8ShKUGphXxUh7KibKiYa6UU5eOCEzLT+m2vwQiBk2BYyU7fB7sUILTKcxsxan5VQtImlpIgZeyZ2EimlIoxd9Hg3wsUkISPu2CmEFDyGuCO0MHcW6fc1hNh9HiGh4/pDCesicu20ULo9411J5VkGWSZszwMI+VgMlw3C9qyFx9A4oZNhe3X2fA9DehY9WMjC0TzMl6BWDWseF8JyGwWoVMSYkc6ISaNcS5MrpinUclCbky/LJYmeymVFrCjkwUuJ6lCvS1Xk8RiOH4f5OWGICUSBGAxlPe2Nbg+2tsIiG2OCTodgq8G4P8If+4xbPbY2AgYDMYI0m7CxHtY396QuxyA8bLslZT9A1t1sw/kefAZxGW2xnSDW+goB4iTS6KpTGCfDaURQ+jTw8XD5UeS+y4ZjYQW5x9qY2g5a26DKdqeExpvpdUyFbbsQXit1AVUx7hwVNWwiNjqO9bmRiiwvALmMKZnSH4qo0w33U/LkMo48GPmQCWSbYkEu13AI3aHEf10C/hoRiuwaMDZSYdvbmBnt+ozTz7OKGvYs+mgdof1EHngIeEUB7i9BLSf9NR5Bty/PVXwR/xYX4cRxETZO3Z2l8tApeMkXwQOPQakGgzY01mVAbm1JrtrmJv7VazTOb3DhvM+FsHj46ho0etL/p5ahWgtvny240oPnkNca290nsF0wtYUNHRs+piZNElKImDGHjMchMi4a7G9Npr1iWk2NuL/jnHl6T6hAoXV2NKJOoxv1d6YTLi8gv7EngEeBx45CKQdPX4VnRuKuWgc+60QNB4dY1Go1vvIrvxKAs2fP8vM///MHdqzBYHA9IqvVavHa1772wI7l4LAfeOihh/iJn/gJvvVbvzX2+6effprPfvazvO997+PXf/3XD7l1DocBJ2o4ODg4ODg47AX7VlMjSqpcPwDJsUdxYoeSSzVM1I3GC3UwM0LtGf/DcL0ysDqE4hXwAigGQshrFv5eM8+VFLNnec+CaWLOJKEj+llnwmtcjibQRuOadF0v8j4L7GuigoP2fxbpfzBOFyWOk/bfR0hqFaW0bkiAIR1VtFK3xqS2jjARSuvhPlqYQtrqEjmCkJPr4d995Ppr7FcPIamLAzgygJMtOLUBC0WTLlUsCXGby0pMTrUGlfKYYmnMwsKAcrVDvpgiPV+FSkXqcVQqUiwgFVKH3R502sIKp9OilqglZK4GmZwUz6jXpXJ4phXW6ZAGeu0W3tw8KQIYjggaDQpHWgS+TzAc0d/qUa+b/vF96IW6SLtl+nI0FENI+hx0+sZ5k8OIGmNMVJKKd2NkhrzOfB+F1+sI8DXAsRTcNwePPiDE/+VVePY8/E1XCH8twH0cqW1Qsfat0T46HjqI8LSBEMmj8O8aEjNVxtSAUOFCX9GHVB4o5KS7FakUVEqhqSYjQlC/B82WFKz2x2E03EDGit5nY2TAeEB3JO3sh+dxMuwXdbxofY0BJo7NC/ejbhd1mcD2e1/J97iYquh9vJd7e1b0kYLd6R5c6Bn3jDqpNPZrcSjjLZUSnS6TGeLlVigVnsAbjWD5iNxIvg/plKiCqRRk0qSGAwpbXSrVNsWSuecCoD6EwTXIrsBaAJd9U7uhgxF0bdeX9oktlumza1Zo3ZEOZoxG66HcTKjIkPQ7G62jYQtfXmQ97cMa5r7W31D9jVXhX5/JGj+YQoTMK+tQycrCBUx9GQcHh3g0Gg3+4A/+AJDaG7//+78PwL333ssf/dEfkUqlKJfL+3KsXC7HN37jNwJSh+Pv/J2/c/27VqvFy1/+8tjtWq3WvhzfwWG3ePLJJ3n961/PuXPn+OEf/mHy+fy27x944AEeeOABLly4wHve8x76/d38wjs4ODg4ODg4OHy+YmZRoxKubBPTSpTaM/EnEXHRbHolmXSfdiFtG/3w9WkkDuPEQIjXIob8ymEIHM1n3y30HDuYvP9pmCUOJGmOatRFoQLAAnJuA0zmuy0eTSJE7XVswiv6HiDneBUjDqgoNLDeJ/WlZvtHocSuzrzXZVoXwhZwbKjQsomZ/W4XldfzUgJWCwwrYazCWBfjCFLnTXsMXhNohOKIJw6NbAZKeVioiiGjUoH5eSiXfUoln6XFTUqVLdLlPJm5Cp5mWRVLojB0ugSDAeNWH4oFMkcW4IEH4cgJSJfB35K6G6trcoaFohQoxxM1pTYny0cjvLkaXvg/ckG/T2lzk1KvLzYNgLFP0OnS2hrR65neGw8D1tZEpFm4CNfacHUIWyOJ+Ul7MPQlZmmIzIa/Er7rNR8iTowe8GXAl5yAM2fg5ElYXpJTrVWF6Ex9WlwibURcuj8DZ+ckumlzS67LCOOy0WigS4iDRFuuDht1eSgZq66SKlAuhDqQJ9FbQSBmmWJJujGVDu+dnFw7dd+MRxLftbkF3fBmHo6kXkSzI5Fm2ay8MhnZd7YVlooIXRw5TD2ZXjgu1xHhTYWZBuae0kg0D+OS0XGvJLyKIdF7M+keOwhhowE8jVyLOeQ+1XbVCO85H4ZNuc5BAIEP/rjD8cFzlDfrpE4cl8i1alUuhJeS8ZwvQL5AppihUDBpbvkcpLsw8KHuS39eQOqWDDH1P5KEhsB63QhsoeRWgsb56TNLfwcDtj8Do+cfNz50bI0wIqL+Tuu+o9v44Tq9cJsnxnBivD3Kzs2TdHCYDaPRiEuXLgFw6dIlarUaZ86c4bd/+7cBKBQKvPSlL92XY6VSKU6fPn397yAIYouN93o9vuZrvmbbMt/3+chHPrIv7XBwmIZms8mb3vQmnn76aX7yJ3+Ss2fP7ljn+7//+xkOh/zLf/kvb0ILHRwcHBwcHBwcbjfMzFPMY4hHJZK1oHaL7bFRcdDl6qrQmaOTIkOiUMJ6A3FoaGySOgOqmFmn02ppxKGMkDh5DKGehDj3RZx4EScwxK2v/WoXyY7G7tjYKwlqfzfCXDsVCpRU3C15qC4cO0LIJm81EsUWX+xj6N+6DzuKK2ftQ8feFmZGfRG59oSfK8BRDx7Jw9l5qIa5S80WNFrQGMBqAM0hpIew2IXjTVgsicGiviVEbLksNS7K5YBqpcfcfI9cOUO+ViS9NA+ZNL7v07nSYGWlge/D8slLzLfaoXWgLGLGs89Cuy2qQ7EotodiSRj1dDivP5eTeCtfroA3GAhDb9u1xz5ep0Ot0aA2CmlOzyMYDJhb77C4MODhh6XUx7VrYhDRQ/TDaKFmF85twqcDuR4tq9+ziPPibBEefQSOnxAhYziCCxfh/HnY2IJe6JCaC1+nKrC0DOtrQooq4a/RYxoB1bWu0RjzDNm0rqNGWeWQgtJHj8ork5Xz6vWEN19YkO7JZMQokMnK9Zqfg1wxhT/yabfEwaIJYKmUREx12vK3lkjxxyJ81OvQaovDo9MTV0dvJOfRw8x877Jd7NNxqTFeZUTc0GLuw3C82hFKtptD92F/VqHOi6y3H7BdNFvIvVNiuyicQtwuQRuCa6Go4cNoPOB4f4Vat0e604EjR+SmKRTCRgaQyZAu5ihVU5QrPsWCCE35DGQH0n8a/6R1MIqY2i2zvO40ZJDrMIeJQlSHldagURcjbO8De+zoM7yHPNe1r2ftM436qyPi3VHM776Dg8PeceHCBb7sy74MEPfGs88+eyDHSYp6KxaL/OVf/uW2Zf1+n4K6Rx0cDgnvfve7OXnyJG9/+9t3fOd5nosrdHBwcHBwcHBwmBkzixp9xB1xFBE40gghdRmZ9b0ZrhMlrZWo0rgLLd6919my6jDYxGT120WFb4R86SFkalSAiCJObIiLooq+xzktouuPEaJZCSzbGRMlPon5Ltr2KPllf6dOG5ss2wvsmiRxx7GPz4Tvov2TZuf52ISvxqF0kD7TYtQV4GQYnXT2jPCt/Z7oBgGwuiHjZyU8xgjIjiHTEZK82xOiO5+Hcjj7f25OCP5SecTSUpP5Tp9cOcugOeDSJbh0SfSHRn3AXaPnWKrXCQoF/K0Gg60O2UyAl0kz8lOk8xkyJ45KleZ0RjKwymVxcSjTng+FD3VpBIT5U12p4Hz9wooAUpzfoHC0jT8OGLX6dNdatENtZawOhZZEV5XPAVdkrOt8zhxCXj6YhS/9QrjnHjlvEGHn4gV49gI0fbl/vbDvesDlFmwMYaMrzh91aug17YSvaByZilYDzJgvYoj/yhgWl+CRR6R7XnhBnBfZDJw4IcJGLi+iRjoD6UKW3HwJr1TEG4+pNtosLHbwQwUlnQqfRQPjQFAHhwognY4IQO2WiBzdLvQH0B1AZSDtb1njRqERXiVMnZFyeJ5NhJjWGfI6pu2aIHE4iBgqWyzV2fsqMMyznVD3AC8UNvxA+mw8hvFojD+qMzcakxmPZVzOzYmC5qWgWMSrVslVWpRKXfJ5cdJkU6ZvdDyUMMXso6LqnSxiRKG/i2nkd3YJIzJdCV8ryDMvWnNFx9LQ+k4nC+z1uT5EhA0VGjVizcHB4cZx7do1vuVbvmXH8qWlJX7hF37h0NqRyWSuu0dAHCaf7/U5fvVXf5Uf+IEfoNOJ8yE77Bd+//d/ny/4gi9IrLHh4ODg4ODg4ODgMAtmFjXWEZLlCDJjuYoQL1WEvDuPzPrVbG51OWj0imK/SCp1BKiYocdUF8heoAWWIdlBMomsj/49aZZ1EpmpEVzTCKQ48WKSmBG33K6zsFeoA0OJUIW9zyDmPSpUJAk+04hfMK4AdfHUgOIYsmvw/BYUPCilRKRo9+S880hh3AymroCHiADdHqx2ZDwV1mAxJ0WTKxWpG76xAQtrA/L5AYO+1EcmENJ8bQ2arSGlJ68S4OGPAgoFKcUxCiOR8gVYPtYkX0iRyXpUlnN4Z8/iHVkWIaOQD50bubATwh7wfcnxqc2ZZQD9AV6pjDcakvJ90p0O+Y0N5noD8H2CVpvm+oDiZlgQvSznc/oCtDpyztUKLC3A2ZPw2GOwvMx1d8vGpoghvVDQUDJc77f1EQQjU3tCXRgZtgtzWoy+ZF3zACHSK5j6GVoAupAVl8aZ+7Kkl+dZWF5nY80XR8wyZBZrePmwuEYqhZfLSj2TcgXGY7Jbm2SW28LGD8MiEeqM0fHoBzAa4TdaLC6N6XZh0Id6Q4q09wcibDSb0GhAtQnFEbQCQ8D3rT4BE+WjjrGo4KvnrqJOEuw+2iuS3GF2MXNtew+5nkcxRHkKETZoawRVKGyMfTyvyUI2hZfPyzUol+UmyOUgnyOdy5APDUj5vGh3qZ6JQJpDitBfQ+5dO2LJT3jdqQLHAPntySDiUhH5rVUHot4fV5HrpPef7fqxn7n7EbGl118nDezGVeng4JCMdrvN7/zO7+xYnk6n+eM//uMdyxcXF/mzP/uzbcuKxeINtyOdTm8TV4Ig4JWvfOX1v8fjMV/4hV+4bZter3fDxz1MTHKi/NVf/RWlUmnbsrvvvpuv+qqvwvdne4p+9Vd/NSsrK9f/7vf7N1QQ+fMFTzzxBN/3fd/HhQsXeOMb37jtu+/8zu9kY2ODn/qpn7pJrXNwcHBwcHBwcLhdMLOoMQBeQAiXkxiiYwGZdbsavvsIQaUESDe6oxuEFg+eR8hRLWatGeLTilFPQoBpd7Roq024TYqEmuRGSFpuz8a2Cc9JjoY4JLkyktaPK0K+WyjptdvZ5HEiRpxDJupMiVtP11FBaBUR4f56DIWxFLH+8gycLkv9jLkejAMTwQPyeWUM9XX5fBkhoytjONOFY5ehkpfkqNU1ESm0TnIQSPyR54kjQmf3j/yAlAe1ihE12m0pQ1Auj0mnx2SzsLw84J57n6B6doHcPafg9Bkh3yXvJ+wAzxQijyKdFiLZl4wgr9uBchnP92EkBcjn5uvM9QcwHDLqj6lvjHnxik+3K5Pr86GOkvJgfsEjvTwP4xGVTIe52pil5VCU6YcFmsObbgj0feiPJappHJg4KxU1mggp64fvCx7kw5vLR3SFuTSUU3L8XAqWq/DIw/DYS3Jk7jmNNz9HbnGRY42GWC1yeVhaCgn1tBTXyKTlRApFYd+LBbyxL+u3OzAYQKkoFyuVkv4BGA5IbW2xuNSSC9fv09wccWQ5kNiunjhEGg0xytzVEidHuwPdPrTG0A1MAWYVd7S2yxFM/Y0AEdE6bK+Vo0h6PsyK6D2V9HfUbaZOiVbMdteP34VgLby+KcjnA3LFFpXCutwYhQKksnIRs1mylSy1OY9aLaAUpq2lPBFJ8hjXn4+Q9X22ixdRQeNOxwgReArAcaSeSxbpI7umiwpm+ls3itnXjSCN+Y1VMSXLnSsoOTjcKhiPxzz33HM7lj/33HNU1DoJzM3N8f73vx8Qt0VUeNgrPM/jvvvuu/53EATbiosHQcArXvGK2G3/+q//el/acCP4oi/6oh3RRR/84AfJ6QSRCFKpVGzU0T333DPzMaPX6x/8g3/AtWvXrv/96U9/msFgL4G4dz62trbY2NjYsXx+fp6lpaWb0CIHBwcHBwcHB4fbDbuq/ekjhPElhPjV/PxrwDlMce048kOJEbuwszoFZiVlPIRomcNETyn5r7O9VViZ5tZQcUQjq+yZyRnMjHIfmUG7Fp5vXJvszzYRGBcfY4sV0Wiq6D6TctOjmMWVEfd93N+7gfZZ9FiTyK9J7bP7a9q+4o6nRKhdWLeIODI2RpBeNY4OkPGnYzYVftaiuOo4UDdCNpA6C7kebLUgJ+YAgkD0hkJOZqKPx1K3Y20g90gGqG1CJQepQDSKbkfMAz1fJrYvVuHy5YD77t/gsWwgBSpSKbEHbG2FByhI7YJB5H/OM2mT7ROKGnhlsYOEooZXKIi7I/w+2++xtLHJ4qke47H0XooAfzRm2B5SOFqDY0dh7JMpNrireY1eT4qGDwfStHJF9AEvJSLO6gpcuQLNMKoohTQ7CKQvO+GFnc+JYFGrSdM9L4yaqkC5liKd9sgXUiwcz3H8bA7v+FG8uTk5UCqFV8jLTtMZsZcUCiJopPWVCi9MRkSgACmqUa3JezZn3BqBb/Kocjk5zmgEvR618iblpT7jgc+gM2Rhy6fTERdHuw1b9dC9URexo9UVUacfChwdTByV1krQugg69rRWggprOv7tmC77XtA6RNHnZZxQEff9tM/63kWe89H9ekCmD8WmRHNtbcFSa0S518cbDiXnLJO5XuDEq1TJlNoU8n1TlD0N6ZERQtOYKK+oI+PzLYIK5Dw3kd9Ujd9rI9FTF8L3pP7IsL0mk4r0tgAyC0qIC7OMiE/6XE1P2sjBweFAYTsHNjc3ednLXgaIY+Nnf/Znr3+XzWb53u/93n05pud5pNPb7/yPf/zjO9YLgoDv//7v37H8F3/xF/elHVH8s3/2z2LFiJ/7uZ8jk9nV/9bcMKL987//9//e9veb3/xm1tbWAHjPe95DvV4/tLbdznjxi1/Ml3/5l/PBD37wZjfFwcHBwcHBwcHhFsau/vUfIDNqBwjp1sMUJJ0kImQQwaGCIeqUfG5hZjDPcvwNhGRWAmceOIU4RhYRMuZ5ZKZ9nFjiIfFEdyEzhbVALZg4mCxCSOYQEvE8hii3CciooAE7SUVboNBYHnU3RM/NjpuJ7s+3vo/uf1YhI277rLX/3cS72ISkvd9Ztp/FdTGpzZOgbg3dRgvTb4RtzWJEMA0d0HNRMWuIENFtTDRPAyF7s0BuJMSs3e5CF4op0RY6I8mj3wj32UWKTmeArCeiRiNcJw0stmGtLtFUR47VOTp/niCbI1hZoXGtR7maIrs8D51FIYwV6bTkYWkMhe/L99lMeJN58l6tQWlsRI/BAK8gLoZUEIhI4Pukux2yna5U21Zrie9TOdrgwaDLUP6kUIDcXJHsXAkvk2bc7LJ1sckLzwvRHQSiK3gpY55Ip0WHqFRg7miO3JF50kvzpJYWSJWKpItZ0tWSkOPttrgqut2wsneH66x4Ph+KGGF1cHVopEIxw/NEOfI8OWgQgJeTg49zVo2SAILws5cTlWU0ElWq24VcjvRgSHo4JNdqUah1GfdH+IMxnXZAoyFixuZmKG40pW5LtyvF2Jtd6Pjy/OiH42kBETtUNFNRNVoEugCcDtfPY8TgFvL83QrHpB371g/3bT+fbMzq4ACu10GxhQ19LnYDcet0uvLqtmHY7pNrt0XpCqPASGcgmyFdSFMosr1YePhg7oT3Rg9Th0WPD3d+5FQSmsCngOcwIqxGnCU5VjxkrCxgxou6ODrIGJt1rnA7XFd/Y4vAWWB596fi4OBwwOh2u9sEhUwmsyOqCiCXy/Grv/qrB9IGz/N21AIJgoAv+ZIvOZDjfdu3fRup1O1R5eetb33r9c+vetWraDQa/MiP/AhXrly5ia269fFVX/VVvOY1r3GihoODg4ODg4ODw0TsekrTCCG7YHdkkwoYdk73mN3HRWnEFOE+ysAJhATMIYRfH5nRGidqpBDi50EkRiuPIX6aGEKnghE8SgjRmCe+yHmSoAFGpLCLBCuRb+faR7fdjUgwiyNj0no5DHmo7Uxjih0X2O6iUEGqi+kPu71J7o/oOrZgERUv7HXiHBz2Onb8lM74VgyBi+Hn0/pKwVJZ+PD+UPjzINwomxHSViNermKIaBVF7IL06vLIBZAZgzcWseIiImrA9rGUCcAbGTK3AGwEcLUD689CEPjc89zzpNMeg+6Y8QhOnoJTZ7sUtraENFbkcngL8yJAKKpVE0UFQvxn0kbk8D3w8tvXUbGjV4K5oUQ7ZbOy/1QKlpeoVvrgB3K+aU+ElLy4JjIeLB7tks2OJM7KE147k4FS2SO1MIdXKkImTSqXxVtewrv3XrjrIcjcC6n5MJcICFowvgTXnoLPfFpUA38s9RqqVWlvygstHr5kQwW+UVG0Mno6Y6mEoSNDz1f/DsIaG9lQ6lLrTS50c4zG4u7I58lU+mRGQxiNyLe7zLW7tJs+i4sSOdZoyjhqhQXGG3Vod6HXDyOqfHn+tZBnTDccR3nEsbEajodcOEYfRZ5rBYyTrB6OpUvheMyH648QJ5nWWNAxGUV0WZxLQ6EOgQYm5k/FmN5IxJtWSxwrc/UB2WZLnC6lEmRSIjrl8+TKOWrVDuWKCBvZtBF09TdB+0PbbYu7s7o0bEHmdoD+BqoAoaIFyPOrHr5g9vMfI32p/avv0WfiNPgYMa4Tbj8H3DdpIwcHh1sCo9GI3/iN39ixvFgsxhYhz+fzB9IOz/P49m//9gPZ9+2Kb/zGbwTg3/27f8f6+jrD4dDV33BwcHBwcHBwcHC4AcwsaiwRJrUg5P9uoGSLxv6osBEw++zROBSR7PEFoJwJa/6OhShLKmrqI0TNJYzIoue0Gb4HiANkGSGdNhFiR4kom2yzhYkoyW67LKKEvV27Y9b/pUlaL84xMs2xEV2uZKLOzF1AHDD35eB0Tmog+Ehs0tYQnh7AU4GIR0pI7qad0XWiQkecQyPar7rMjlmJu+49ZNbzOnJ9c8B8IIWo02khWkMDA+2+rN/FxL70EAJZZ6tH662o0wNEVHsBiY9ph98VkPGk4pG2O4UQxg3kRrw6hEtPwuKTPjUPlotw+rQYCPo9n0q1jU5O9JCJ8ZXFuhTL9iCdD0WOTEYI+1xerBFpK5oqnZFi4x4hiR9mYHmeuD5A1tF9ZDKQzlyvP2H6PpAbrteDbI7U4gK1hYAahBFE4XEqFby5eSG7czlRjeYX4PR9wL3g1WA8hHETxi3wWxA0od+HwVAEjdFYClj4vsRqpUMnAEh0VLdn7CHXRQ2rdL0tYtjvHmI7KZbk/MdjK76KUJ1JGwFnPIbRiFS5TGowpNbpUOt06LdHdDsBw0FAq2ncG622If83N2GtDamxGS8agZcNX53w/Ug4ZlRYU6FRx6S6htQpNcQ4PaLjMsk5lvR3dFknPGYBI2p0RnJuzYa8eq0R1W4PL6zZQsoLFa0SXqlAtpimkB9LSlrGFIPPIqLpMsbZZJPvsz4Xo0LIrQr7upeR52wR6QcV1EGu56TfsDioEGE7JvWZpO61vSCLOBqPInVvHBwcbk90u13K5fK2Zblcjg9/+MPX//Y8jxe96EWH3bRbCp/5zGd4+OGHd8RK7Sc+8YlPAPDqV7+ac+fOAfD000/fdkXY9wOrq6tcuXKFEydO7PhuaWmJkydPcvny5ZvQMgcHBwcHBwcHh9sBM4saOmNfCZLOLg5iz6TX993GCkXhIeRfCbgCvDAytS8ukhyHpRFWWsRXiTAlBhVXMZFZhN9rm1WkiDoIou+6btws2bjaGvuBuHiquHXilqeQ8z0NPJaDFy3Co/fAmTMyy9oPhKB95hkYPAOXuyIU2Meddk2jgkTc3/YyO19f25iyvtPaLNNmI2ttlCcRTrvTgkrLHFcFK70hBogwcQ0ZK7lwnSpCRNpt1m2HyFi8jBlLHkIMFpH7RwUgPZbGCo3D9w4iop0O4EhKtIBmS3j7dJiyBPK5WoX5uQHZ3AAPmJuHwnoLL5PG8yBbypKqVU3RcYKwGEYoXqhzwfNEgMiG0VaBb6wrOWu5b10JPxAhoViAShkC8DIZOVYmrG+RL4iosrgApXLoGsmJ2EIWeuegfgU2r8LGpsROdbvSrk5bTnzQl/a1W/hbHl42g6fCAx4MBgzbPcbDAC90cARjHy/l4aU8Al/aGwT6HznvIBDHSbaSxysV8LzwaVAqC/MeBCJiaL0Oze/OhmJNKcArl2E0pNDpUhiNoN9nrjVgoT6k3QrodiQ5a2sLViuQvQJeXdw8mXCM2LUlBuGy1XAMaG0gdZzpNlsIgR0nrk2LnYpi2vfqYKthanr0gO5QLlWnK5dtrtmn0GyGTo3MdYHJy2TJFNKUSmNKRem6XAryvgh9RxDxNA98EuMM2C32StofBjQaKo88A8rIcySFeXalkGdEHiMyb+7yOLZYDvvTJzVkQkMdePxW7mQHB4epGI+338TdbndbkfF0Os273vWu6397nsd3fdd3HVr7Dhv/7b/9N0aj7b84b3jDG3jnO9+5QwBKwj/+x/+Yku2WnQFa9+N973vf9WVvfvObuXjxIu973/tii2ffqfi1X/s1lpeXeec737nju3/yT/4JGxsb/MAP/MBNaJmDg4ODg4ODg8PtgJlFjZD/vT7L1M6A3wv2Y0btACme+gxCuvSYHmelM4S7TCbPNH4ljYlfAiGK7Nn2UWLeFkqmzRxWYWMSqbiX2cf2PoMpy73Id3kkluulR+BlL4IzpyX5J52WifP9PjT7cG0oAlIT04/TxJm4c7FdItH1VLCwnS7Rc9hNRE2AkLJXwn1osXmNMJvLQNqD9tD0RQ4hFxvhNk1MHRLdZwYhZ5WU1n0rSVlB4lu0mL2S2KlwfRXgNI6mBpwtwqOPwPw8vPCCEOOjkTn5XFZEDeXg02kxQFQqAzEbpGF+vkuh3LlO7mdyHqW5LREitPFg3BPqzlCnRjYjwoRW/AbzDrI8lxOnQyYtB02lxF3h+5YQkA0LeodFpFMZ6K3D+XPw1JOwviEDS4UErW0xHDLsjxj0YdAdMxyGfHnGu77v8cCn3RJyXVOp/MD6rAlbQajVBLJsPJZmVSp9soUmmYxHLu+Rr9RJZVL444BgHJDJp8gUs+KGyeXDgiKh0KPF2CtVuS6DIflOm3ynQ7XdZ9wfMWoP2Fjz0YSPAMg2oD4yTjG9B1LIWF9HhAu7zgQYVwdsjz+LPkMmOTSiyyfdN/a9NULG6SB89UK3Rr0uLpSF+oBCuwWDefBL0sJUCopFcpUC1dqAcljXPZeBwiAk+EOT0PpY7pduQlsm4UZ+hw4DJUKnA8bxpRMDtjDPUH0WFcNtWkyuVXUYCJDn/ComDsvBweHOxHg85ru/+7uv/+15Hn/5l3+57W9b9Lgd8M//+T/fIVwo3v3ud8d+94M/+IMz7/9P/uRPtgkg73jHO1haWtp1O7X2xu/93u+xvr7O2972Nl544YVd78fBwcHBwcHBwcHh8wkzixpNhITRWenzCOFiF6c9TATILHrNEo8jtjyEIK5gCu5q4eZrTJ8RnEWIqFy4zZD4SCR72W4INtv1YZOUcetNWpbkxoj7Ps6lYbtOrosJwXYeezyGXhdWV+D8OlwYGceLHb+UhKiYYjtYUpiiybqO1k7ZT8JSZ0MXkTGjNQgywLEMnFmGZg+ubAmRVw/XsWOxOuy8Hh6mmK8SlCpuVZHxp3+rO0PJ7DwmbmgufJ1Kwb3HRbCo1+HaClytw8A6cM6DypqQxOmU8Oy1GhSkdAXZHMzNQakoFc19XwwVc/NDsrntPqtK1SPfbIli4IdxVNkcXrUKpWIoVCRciXQmrEERnlHgQ69P0O0a5eD8OREAclmo1fAWFmV/a2uiRpSK4ubI50M1sS2WoGaTVG+DzVXRPdotOVw6Ze66UUiu90KDxw77VCCChu+L6aPbk4SkIBDXQKEI+VxAqRSECVljcW4NpR9rc7C4CNW5FKW5LF6tAuUyXrkiVd9z2VBpScv5ViswGJDpD8gMh+TbLfKldbzUSASNLFQ25HzWO5D1jViRxoz5Pjufq+oW0zEUFVOZ8LdimoAa93cDI9BpBFXXl9oz3Y70/bDn4w+GpIZD6bxsKPwU8qQLmevJY3Z82hBo+rLPRrjfG7nfsxgBWtu5H+L5jSCH+Q3Khcu0MHoLecboeafDzypy1cLlWufpZqCOXBt9Jjs4OHz+IAgCfuVXfmXbso985COx63784x/fsSyTmfl/MXaFJJHiFa94xY7vPvWpTx1o3Yrf+q3f2vb3Bz/4QXK53PW//+AP/oBTp04B4oTxvEm/wvCP/tE/AuArv/Ir6XQ6/NN/+k95/PHH97nVtxbe/e53c+LECX7oh35ox3ff8i3fwqVLl3jHO95x+A1zcHBwcHBwcHC45THz/3FovjsIuaVma511qphGsO8n4v63RqN+cgjBtYzUhygibe0C5xDyaBo0EkZjVw6C1FESTyO54sjIIOFz0rK4GKxZXBRDxJXwGWB4Gda68MVbcN9dwluvr8GzL8DVhqxfYnu/zHqdVdDQ9VWUyoXtPAhBQ2Og8pjrqrOjM4hI074qM6efRcaJH35XwbiAtE1D63OAIR1V9FNiVWfd63fHMaKGve4IQ8KmA4n2+eznYKMOV7tCLNqOmEwgET65oeynAJTWhUvOpKGQh0pYxsJLyTaViryyOXOlPGBpOaBSaV+vQZ4KY69K83W8Qh4vlSJQdSCCIBS/AssS4Xe6tJsBrZYaNmT0ZbJw5GSG3GMPSqHwchlOnxL1ploRUSOdloIUV67AxiapIGD0wiYXLsDKivRP3tJQxiMp69Hphk4WTDO1tYEvLqPmEFrhwiJQSUG5GNZ5CBOnrm/rS1LX4iIsL8P8vM/ycp+lpT65hR70+yL6UA7tF6FrJVWAoi9WkfEYmk3SmQxHvVXS6YFEhq3JqRdWoNiQdmldFR8RC/Ve0LoZOh51vNhujLi/ifm849rFfB8nbOizX6UkjSAMkLGVDk0612PBVLkYjWAwxB+MGI+kHs9gBPWh1DTaQAj7HnKfNLixe36McUpNc+wdBrQ+UR9xFPqRl9YBUuhzD+QcSpjIMdXq9LsoDvM31sHB4fMXSQR7XFzT3/zN3xxIG77oi74IP2aixWBws+Rfg8997nPb/n7ggQeu/xvoD//wDzl79iz33HPP1ALtDzzwAAAf+9jH+NZv/Vb+6I/+6I6tufHt3/7tvOENb4j97r3vfS8/8zM/c8gtcnBwcHBwcHBwuF2wp2lUA4T8tjO8J8UoeRxeREiAKcaqRb2V9E0j7d5kurskjbS5xXYyz46Xso8J20nI6PfjmO3i2m7X2rAdFNFjzYJJYobtzNB9jpBr20WcCk9twhOfgJc8D+U8XN6ETzdEFGpg6qpE2xh3/Og5RNdXok+LJitxuhtSzXZA2ONRCU4/PLcxpqZFFiFW68iYUfeRRsGUEVGjjREd1LERFXRSiGiiIo2HEc9sh4r2SwER3DTS7ShwIgOlDKxvwrm+tEvbbPeZPcbV6ZLzIdMLC5C3Ib/JdaEi7UG5AJViWEMbIfEzWTFMlCvWuimJtpqbG1AoDPDCeuKK6+cSFlfvtOVdy2z0etBqSmTWcATjcYDvi9DykpcM+YJ7uzA/Jwf3xzAaysp48ne7HdbYaBH0B9Tr8Nw5eHZd+mEuC4UwKswPRLDojcx1th1H9tjWGe9aoH3gQ7cd9qG3feykPWl7rwvXrokYdOI4HD8BR460qS30KCz3SNW6IsZUq+CVRFXKhcpSWLzcCyAXBBzN16nO96hUpGh2oQDldVhfh2wHcoGMRz8cDxrFlArHUSNsv16KOGEj+jkO2j+2AynpXvUwtYX0/tL7Jp+Ra5rPQzqbwkulQgUtFDUGQ2i3GDT7UhemA+0+rAfwPBIDp+e0HyJmUv2imwGNLUxjxt1un911jECjSHLg6DYODg4ONwNxgsIjjzxyE1pya2E4NNL1q171KgCeeOIJHn744Zm2z2azvPe97+WVr3xlokvmdkc6nU509YzH4x21YBwcHBwcHBwcHBwUexI17JnpsyJODDgIjDBRWR4yS74NnEGifcAUbI3GnahTQt91lrJNkmv0i41xzLY2VGhRktUm32xSMYpJ301CnHMjiHyOkr72NhrN1ARWB/Cxq9J+LRrcYzspmoRp7hAbWhQ5SqhGo7MUGUzxbl2eR2ZG+5hZz30MoajikkZm6UzqATJG9LqNw/0PkXEChkzWGfRaWwBrG/uaRoUt7fe2tawXtqWIqadRRKJ9rgzEMdImfsypeKbjLWOtpzFbuQC8oRl/xQHkm0Lga3/m0uLwqGQlLcjzRNyo1eSVy20vqQHGnTEcQaMu+kN3GLYpdIX4PtRbMBhLlNkggGoG7n8Amdbf60uB8OeeZbDRYjTw8X2xSviDIcFgBL5PrzHg0iW4tCG1czpAbQiloXl46Xgl7EPtKxXp0tZ6Kjbp2ND1M4GJQPPCvut3YK0DjQBYgyMrcOo8HD8GZ8+OOXtXg/nlLqn5ihQa932pLxIE4KelI/N5mKtBOkW6XKFS2SCT2SSXHVOtikGlkIf8GpQ7co1ygcT7dTCE/xBZ1kKeaXYh+kniBpHlOs70GR51WcUJlFmMgKbjLJ+SdpfLIojlSmmpOWIXVQ986A8YtAbUt0S3avbkHDrhOfTYPeF/O0CfQXo/ahiJfX3G1isO0d/YSYJG0nPSwcHBweHWwlvf+lbm5+f58R//cU6cODHTNv/6X/9rrl27xs/+7M9y7ty5A26hg4ODg4ODg4ODw+2Bgwm8DRF1CiTN6N9v2DEePUzB7xJSvyCPxJ1cRCJQtJ1KFCuScurjCOYkAt/uA9s5ouSqHVUUFRn2u6/s/flM3r+2bYi4NjxrG40ciyt0niTO2J9t0cg+1ggjEEF81I4ShYtIQfM8QpB2MaR+G1Nk2Y6hifapuiXs66dujpbVTt02hdTIKFhttWG7MJJmwtv3gNZO8MLjvQCshgWkLyNjdBieZxS2SyM6dtVxouEGOgs+jZD3QWDibzI+5IdyXkUPMl5YB2BdUqEyGescLAVwNIZGB66OYWVkCpyXwv0U0yJ6aJ+MEDfF5ga0n3iecr1O80qLC8/0WL3m0wmLlaTTElM0HIpG0OnAhYtwKYCrCKGfsc7Xt85P6xccCd+1X/3IdQRD0NvuA+1Twr7LBPL8uAps+ZBtwUILTq3CF62CPw64hwFz2R6pTENsHfkO5HN4+byw/cViaGXIhAXVM+RTKY6Vm1TmehSLAbmc9PXGOhQ2odCBii/juo2Mx0HYtyVM8foe010bChW6loET4f4uIWNMx2CS28rep461TMrUJCmXIF/NG4Um5cnF830C32c8CsS904JWd3vk0qRaQrczAuT65IFjiPMmWjuljTxbN9jpxJpl/7A74djBwcHB4eZD63B84AMfoFgsAvCe97yH++67j1QqFVt349WvfjUAX/3VX823fuu38vjjj8dGcN2OSKozEgTBgdZDcXBwcHBwcHBwuP0xs6iRZ3sdgVkQjVG6WRghMScNJO6nhskq15cdXQSTScJp5xL9J3g02kXJciVTVSCw6xzv5z/jo8ef5RpWgdNIJJJmu2sc1zWE6G0zGxkX5xwBQ2hGRREVEGzRx95GSe1x2IZryLVVQlrjmiZBBY2l8FxByF2dOR4Xe5VCiGZ733HCHTHfx7llFANMYXIVOrQNAeLYUOi1W8A4j3QmuPZnDiMOZZH7thGem+b46+dseP7LQDkwjoVSHwp9M06jNVBaCCl+NVx2CuNiGQfQH5m2gXFAPPssDIcDqrUVul14/nk4vwabgWlTAyHt60A3MJFoKlQFbCfD7XtqCRmz96fBC+BTPqxgZr3bxeltp0tU5ND+HCLupM1wH6tAYwjFi7AwJ+Ujjvc75PIdCKBQ9MhVcmQWa3hHj0AwL2JGLnc998vL50jX2lQrG2RydUrFEXPz4owpl6G6BVt1qPclUiyPeU5lkL+XEDJ8i+2OsySXhhakfxA4k4F1X4p0a60WHdO2KAfG2aECmopJWow+l5X3dD4jeVq5vOxhMIRuj3F3QE9KkNAfQH8s99Bm+LIFjjsNAdLvDwCPZmApI5FdYx82enDJN/fQGtIvtiAft7+46xvFYU0ecHBwcHDYOz772c9e//yFX/iFeJ7H//gf/4Ov/uqvTqy58fDDD/OhD30I3/f5ju/4Dv7X//pft3WtjR/+4R/mrW99a+x3v/RLv8QP//APH3KLHBwcHBwcHBwcbifMLGrMIaRaY8I6SeKFvUwJ0sOEOiGuIjOT/fDvTtiebGTdSVEus4gzcbPzo3FTtvNgliinvSLqTLDbMMu2dt0ILbAdJfWTjhc9rn4/bVa53UfRGcnaly2E6O4j1zG6/iRojZUT4Stj7QsMcTu21teZ/HrDxF2zaa4kW1iyoe4QLQZti1pxIpcWk96wlh/DEP1a10Nrh2g9BhVLlEDVcz1lrae1Otpsj3IaY0SfPiI4rEeOrY4C2+Fiv+eROh9Hj8KRoxJV5Y/hii81FtbC42rskvaxiljz4bmreyEI2zqPuBA85DlVDTth6EO7J8S/1qfQ+zCDjAFbYLTj4/TcA2u/ASLwLYT98OTT4q5YWhQ+P5WCcjlgaanPmbvWqA1HpMY+zI2E7M/n5JWVWCovnaaUyZCvNKjM9SmXfKpV2NyE9TVYW4dSC1oDaAfmvPuIGJDDiBsaCzdi+7hUEVOv3xpQH8n7NaufVbTQa639Poe4osrhuRfCVzErxeTzBcjkUngaO5UKX6MhtFr06z22tqDekGvRQe7dNrduEeqow2qvyCB992gavuQBiS1Lp6HZhPMvwOiqEfJ0LPeQaxknyk6bHJD0fHFwcHBwuLWhwsTXf/3X8853vpPv/d7vZWFhIXbdQkGCUX/3d3+Xn/iJn+AXf/EXWV1dPbS27icymQy5XC72u9FotK0miYODg4ODg4ODg0MUM4saa0wmS6LEWMCNk0KzQGfzR4vE2nEpWkNDiVubAIyS/lGyfZLAkYQowa4zy4PIOtE+0xnTh9Fn2oYkNBGHgDo0AkyBYK1hEBU3kvYXFQCmkXNxM5Kj29h59PYgttsRF7GlpH0RIYavIf0eFRU0wkndDvYsfm1XkvCC9XdcX0cFjzjxIyqM+da6tnNCX1ci57jEznGl4p1dw2CECBTaHyo+9NgexTbACClKqoMQ/GnM/RQVM/RcVEhaRFKZThyHThcqVWBdtt9CSO80cBa4F1gqQDEH2bATnm/CZ4HnwuNlwzYUkes3QgrZr3SkjefDNus8Ru3HPCJ+aNvUjWQ7N1TkyGJin4rhZx/YGsB4Xeqa5/OgtSzn56DRCLh/UGepPyS9OIc3PydWjEJYqT1fEDUmnyddqVArb5AvtZlfHLK5EVCpSD/Vt2BzCzabkBuaa6M1XTRmbDk8zw5mxr9e42G4rIFE7g3Ca6iiVxYpBD6PiBeZ8Dw1Tixr9UsOKKSk4HytBgvzUJzPQaUswk3KCufzxwx7Y9rtMHqqb0TSafF3B4Ek0VOdKNoXOg7amPpBilnanMb03xLhs2QAzZaMkVZTiqbrvd1Drl2d2Z7/9n1pPxcOEplDOIaDg4ODA/zIj/wIjz/+OEePHuWHfuiHOHv2bOK6P/mTP8kXfMEX8GM/9mM888wzh9hKBwcHBwcHBwcHh5uPmUWNae6KaKSQXRvhoMgQDyGPypjaGVFhI4uQqVkMIa+Fd+NcANHzmOYqmAab+I66NPQ4+vdhEX1xZLtdbBoMYWoXq9XYIptwt4WbSUhycEQdCbP2r71d3CDWGeo6S90+vubZq8NDSVbYHj0UBulscx7YdUR2696x2xa3jS1+2evEjSHYfh3sce9jRMjo/QA7Z4F3MVFiGrlkR7L5hEXjEeHBHjNFDPEbV3Mm6tS4GMATT0jNDAJYW9suQo0RseEu4OV5uPuUlKUY+zLDvdmCdGC26SPxUilMXRoVLECIYptIV6iQpf1SwYg+qfBvjafKhPvOIC6DOiKk9IHxUJKWMm0phj4IYKUBjRZsbfmcudzi2KkuS6frpI8vQ7WKVyjISRVDq0OxAIUC+VqLbKNFZa5OqTRifk6KsFdWobwOWw1otqEzNoW2o+LGAFNIfBheVxWg+hjhR/tC3RJlRES6JwXLRSgVRUzKZ2AwgI0taPTCvspJ04tFicvKV3KiThVDu8p4DGOfYDQmGPuMRkLq90em/s1hzr2079c08nug9VOKmDo5WvMCpA+fCz8niaxJSIf7PBru99oYPvEC5C7BMIDeGFojEZnWMPfVtMi86PnYcYUHiRzi2Jm1fQ4ODg4ON4bf/M3fBOBP/uRP+O3f/m0effTRxHW/+Zu/mUcffZR6vc4P/dAP8dGPfvSwmnlg+J3f+R3+w3/4Dze7GQ4ODg4ODg4ODrc49r1QuE0cxkUT7VYUmAadUW0TsHosO3alhSEAo7NvYTvxFefWSIoZSsIspDQYIeMwHBqw89zsGfSLCIHVRci2dbaLGgq7j3fT5knCxjRRI84FYRPpthCg73YBcCVv1d2RRKrqLOjodY9eTyb8HddeRfQck87ZJi2j68aNo+g56GxzkHOPu46KIUKq6r2kBam1b3tI/YMmO+/vEUJUa38mCRr63gXqbXjycSGQ2wE8jThN9B5tIC6hYR+eOieEewA0AzgXSFvsWisqZtpCqsZHTXI/2edSj3xnO1VU2NAZ/QWMm0drlni+uZf7Y2ivwXpdYobuOjPm3vtanDjZpryQJXviKN6Z01JYm4zEURWKMD9Par5FtlLieG2Lha0ui2sjqlWYq0G9LjFOjYaIO60hdAITC5YLz7cYvqsrTR1I+tJnoIoiNeAe4GVz8EX3ioumUIBsyPCvrMAzz0JuTQrHz8/D4qLET2Vz4GUzUlwjFxZE7/eh02Hc6dPtBPS6Uk9jMDZisjoiog67/YZ9H6sIt4iQ9ClkbC0h493D1OlRR9JenBAqCnaQZ2gTeHoYCmBsf/UwDo1Zz2WSmHEQvyHqXHJwcHBwOFx86lOf4tlnn6VWq3HkyJHEWhuPPfYYAPfffz+PP/44/X4/dr3bBVeuXOH8+fM3uxkODg4ODg4ODg63OPZF1LDJX5uItWNz9O/9JF0ChAjVXPIoOWbPwtdonWgMUNIr7vvodnGwz9+OXfLZSUr7mJiY6Lr7iaT2K/FfBc4gtRXGSPHaDQzpOKmGxl4wi4gxy3dJbgd733up4WIT83HuCCJ/R9uRIhlRkSL6ne18iRN8Zul/HWsatQWG+I4jT9WJoegg5K+2Q2f8x0HrNNjnnNQfOt4uIm6LFGa82f/7rYXNnwXyYaFsLX6udUHs8aHXN21t32R7DZjdjtsmppaHujTySL9kMM4wmzDX2CaQ51JrCPUV6PZEhLh8OeDUyQH3pLconDwujgYvFdbYkIxsigW8YhEWFinUtzhSXadY7rC44LO1BVtbEkdVr8ur0YL2ADq+qXeizo0C4jixHWAq9iqZvoWQ/Pem4b6j8MD9cOSIOGnaHei0JSbM96FaFUHj2FFYXhZho1jN4uXzEqnleeEDzId+j0GzT6MBjSa0etALHTYVxMXQCft5Up2mvSJJXNNzVwGjjlyrsPcZhH9fQ/py0v2ahADjELKTwtUlqELbbp9L0eeA3R77mbjfwkYPEWcOQ3R3cHBwcNiOb/iGbwDgHe94B9/7vd/L4uJi4rrvec97eMtb3sInPvEJPvzhD7OysnJYzXRwcHBwcHBwcHA4dNyQqDEpkiOJnN9vYkRJ10nfazviooOSsBdBA7YLJ9E+UFItSl4fFpII+DySp19FCOYLSKTPpNnDu3VpxG2fRNpP6t+k9eOcHIo0ZhxMareS05OOZe87zo2zG9gkZPTesZF0nKTzUOJWo6S0wHmG2WaE6yz+adCC7QeFIaYexCShyG6Pvts1JXYzTnVWeg4TSbWKmbWvZHSA9FEGU5x7DiHrKxjiOo1ERq2uSARTqQRnhz50wqIKubBiRzoj1ohcFkplURUqFdKFInNzdcr1NgubHVpbIzY2YHNDoqm2NmGrLsdo98W5odFTNoFu90OAiC/Z8P2uNLz4BNx/D8zNS9zUhYtw/ryIKIOB6C5HjsDx43DyBCwfT1NaLJCZr4SFNYqQSYtI46UggPEooNeTU+0OTLtARAS9RvtNxE8SpUfImFX3CoiAq9/b9YJqmOjCABFfZh3vPqa+yY0g7tnmY2ILbUEcJj/fbgSuVKuDg4PDzcWP/uiP8ulPf5pTp07x+te/nnvuuSd2vbe85S2ARDi9+c1v5sknnzzEVs6Or/zKr+Tv//2/f7Ob4eDg4ODg4ODgcBvjhp0aSbPlo8TKQZEt0xDXvqgYM8mpMWmbKOIIpiRSPO7zQSCJ8LedGmXgeAZOpaA5gpS/3VkCeyOIJyFuP3H9Etd2e3ncNbL3r8vUdRJ3fNvZMElQiNuvLZQRWWavlyTaJI0X+5zi7qUo4tbz2V7UezfXLofM4NcCwRpbtL6LfUxrXxR2TReNJZp27kl/z3quUadFGAh1fbntblGRQF0Odv0grcPRwhRprwJHi/DwWeH802mJdfJSHkGjiTcYSOzU2Bd3Q7kkfxez4AdQyEtxi+VlMs0GlXqd4maDufUmS+sj1tdhPRQ4trZC50YbOgOp3aCOjThRww/bVwJefAq+9GVw970pCgVYu+aztgrPXIDNNiwU4f6zcO+9cOokzB3Jkj8+T2ppkesVzUslOcHhEEZDgtEIf+QzHEC3C92+HLcQHlPdM/1dXKvdXM+kl16vsbUsGjGl1+4UcAJTq+Qi8AzJrqWDQNIz4SAjuxwcHBwcbk38xm/8BgB//Md/zLvf/W5e/OIXA+B5O/+v5DWveQ0PP/wwGxsb/NiP/Rgf+tCHDrWtSfj2b/92Xve613H27FnuvffeHd+/973v5T/9p/90E1rm4ODg4ODg4OBwu2FmUcMuCKyIErz28rhlNxtxZHjS7PpJYkjS33s5x4MWNfQYsD0GaAcCKQp8AjgbzqpusHPG+36KG9NcGru5NvZ3Sc6GSbnwSY6P6D707+jx4o6dJIpNWifODRLFLMKHjd0SoFlkHLwUiSUre7KPegBPIOTuVWYjd1UsUPeDh3FfRNscLeatsM8v7jwnfad1Quzrq26MLMbFYrtZBkgskhbjjrpbkupAbCEkfQERCs8Cj2WE70+lJMbp2jWYe6bNqX6X/FIFajW8fk+Y/1Ho+Uh5kMtBpiIVuYdDyX1aapOu1yksbZHf2KC21mJhTZwb6t7YqkOrKcfq9WEw2tn+IOyDYg7uuxde+Qo4/aJ50gs1aLbwV+t0u2P6Q+mvYh7mF+DUaY+j91bwlhbxFhdgYcGoNRBmVrVgYwN/s057a8j6Olyqw9NjcX+pW0PFtoMQNOwaS3HfRz/b34MZGx4m8kzHwkE8qzOI0BMVIRWz/s7erIkDDg4ODg6Hi09+8pN8xVd8BZlMhve97318xVd8Rex6Knr88R//McPhkDe84Q383u/93qHX3CiXy5TLZb7v+76PN77xjRSLxdj1Op0OTzzxBM8888yhts/BwcHBwcHBweH2xMyixiRidBYyerfkvRJLaUyczl4ImyQCeda4oEnrTNpHHFk/iZDdDWzyTj/r7Ovo7PYoeReduawRQi+MYdwJix4jJG9SnYTdtlkJRvuYvvWdvV97PSLfRaF9oN8nkX+TEDc+kkS5JNeIfh89F3u96HZJbZnUzr18txdooXGAYhpqZZn532+KG0FrYUyDZ+3LFpQmPUui38WJXRAvrkaRA44Ax8LPWjhbC5LrvaLihodxYgzC9Wc5T7vtbYQAr4fvXhOe/YT0QQo4lhXh4cEtn9OnGlQWO2RLWTLlAqnFplQBL5dECcmHhbdTKVlWLosIMjePNz9HcWGTwuIGc+sdFtbHbK4HbG5KDYtOG1ptqdkdRCvOI7ssleDsGSjPZxn7KUabXdpXOly5MKbThVoOjlThzCk4cwaqp+dI3X0aFhckIqtYkMrhvi8KSrsD6xuMr67QuNTi6lW4ugKXmhJpt4UZCxoRtl+OA7tuhi1YaQyXPYbi6m3YCJAxsIJcRw8ZC3Vmi2XbLexYs7jxlvQ7k/Rc2a0jS+9Njd5ycHBwcLj10WxKNbQPfehDtNttXv7yl7O0tBS7brVaBaTmxk/91E/xF3/xF3zyk5/k2rVrB97O+++/n7e85S1827d928T1zp07x0//9E/zy7/8ywfeJgcHBwcHBwcHhzsDXhAEM3Egcdbmbd8zmUzWaJ9ZZuemkJmrpXC7MeIa2CuhFBUAoq8okZ6yPk+b3ZuEIOHlRz7bdQDsz0lIIzPBNRM/H75SSE78GkKs2lEqceeu56M1NeYwRYWbCAHZZfu1smNaZiXOtL15zOxnJTO11kUfE0NjC1l2X8URdkpgRl0Q0fUmiUpR2P0TXT9JbFEiUl0BUUfIJNEkqb27GT9xMU03Ar3/jgHHkbHWBjaR+gMt5F6cRkirqBHt+ySnw6T97GVGehY5h5PIGGwj9THqmJoKHub+0Wupr9Eu2hiHFOLY0NcycA/wYB7uOyX1KZaXYHFJdIJsJSciQTZLtpgllU/jZbN4C/Nw9iwsLYsrotcTR0SjEVYO32S0tkV/o019Y0yzCa2WODYGg3C8edaYCyTdCiCfk8SrbFaWt1qwuSmmi3xe6micvjvDkXsqFO85AadPQ60m7QRxl/R7suHaOuPLV2i+sMXF58ecOwdPnoO/3oDPhv2u90gHGU/6jPEwws9u+z7ueZ7DiBrDmHVsMThOkFdhV5+zKlg1EeJ/FNlGt9urSKPjb9K4nuU3VovYz3J/5JG6IRox10bu7YkC6mz/XNiBaf9+cHBwcHC4Mbz//e/n7/7dvzvz+t/8zd/Me9/73gNskeCtb30rP/7jPz51vX//7/89/+pf/asDb4/DzcFe//0A4Hk3nJbt4ODg4ODgcJsiCCazQzP/K6HEZCLTJp/j6IvoDN1JKCIkewFDttzojN6kiCD7O/v7aZ+TjqFOhOi+40jr3UJJ0mNIQeI5hJSqhN9fBD6FEIX2jN8kQUPbojPT15Brozn8UWFEr99u2q4uGx8hB2sYwjEfHmsFuIbJuVexQ2sYJMHDCDuTXBSTHBjR5Rm2iy86yzsqjOgyeyZ4OtIW20Viv0c/R9sSt16Q8PkgoEWOX0AKx3sYonnW+9Ameffr3t3tOY+QMd3FCKodtkeqgSG9D6JPR8i9peR4FvBHUv8CYNAXAWEwgMzagNFoQCotpTRyOSiWPApn+qSXlmD5iNTZyOWkOEepBJUqVKtkajUyC5vkl5ostXr0GkNazYDhMBQ0rIF/3VjRvp4WxWgoekkqDXM1OHIUjp3KUD1RJXf6COljR6QgeKUqxcz9QMSMdvu6uBKsrNC9vMXVi2MuXoDLl2GjIWOngBEYBuE1GVh9nkeeaXlE/NhgdmHDvqfV7aCOGxWussQ7z+KeiXZ77gYWwvb3kGfseeR51Y1sr26t3d6fGn/mYeqgxEH3G3WE6TIVYgjbNu2Yc0jtkBSmrw7yueLg4ODgcHD40R/9Ud71rnfx0pe+dKb1/82/+Td8z/d8Dz/90z/NBz7wgX1ty+te9zpe85rXAOLUmIbf/d3f5V3vete+tsHBwcHBwcHBweHOx8xOjZd4HucQx8RuoGSL5tXHZdRvaxBCtOgM0j5CcN1I+mvUgRGd1Yv1niQARImvScKGvutn24GRNOM++n0c0sjM+UeBh4EjeaiUIJOC9SZ8YAAfR2YTaxt1ZnSSqGFDBacomZhCSL0sRvTYbXRMFik8fRQRYYoIAdcBnkfIQh0XSU4JGzqm8ojQk0IIQRVjottO+xukr4rAfNi2LsaVYIsbStSP2DmWbeeM3da49se1IU68iBsn0c+zkpEqUE0ijO2C3dOEpThEnTNJfT0Nu4l+uhVRQMa8OjUWEUHyFLBQEH2iXILFRZifE8dEIQ+1OSmhUS5BoeSRvvsM3sMPw7FjonZkMmKrGI1gMDTqRL0umVPdDkGjSdDuSBFyj1DV8K5v128P6Xf960aLwRDSGY9CAcoVj8LJRVJnTuEtLkKtKgXBM1kgPG4vdGesrxOsrBCsrdO+1ubSxYAXnocXXoCLq3BlIALAGvIcX0NEi6hjrwrciwgIa4hw0Gb6uLafa5PuAxWU4uLtohFUKYTwfwj40hzce7foOakUXFmFjz0HfxXAs5jaMNH7kxnaDjJGToavPiIiqsC7G6jonEPu12m/l3mkoH0ubG89fE2Dc2o4ODg43LqYm5sjl8vxy7/8y3z5l3851WqVXC43cZtGo0GvJ174N77xjfzhH/7hno79xje+ke/6ru8CoFKpUCqVpm7zcz/3c7ztbW+j0+nQbs9Sqc3hdoVzajg4ODg4ODjsBfvm1DiNkB6aNT8L2aRxGAEmYmgaAoRMVjJ1P4rJRmfZ224NHyGE9D06S96eeWt/P2nWP+wkc5Nm3Md9nwSN4WoAbU8I+E4f+kO4MhJxoGOtPyl2ishnPT/dxm5HCiEd55DroXUDoo6QSRhh4mYKiPMnE+5vK7KfaWKGEnJnPVjOQN6D9ggu+eL66LN7d4AKXOq2qCHiRhvpb63F0CeZ5I/G2syCJPdGnNsnuny355hHrmMJuY+3iI90yyEkvBZJtl070xAljG1BMYuJlStginNr3JN9b2gNgzY7nRW3C3rIfZJF7tUusI6cz3oPyj0o12FjC+ZrsLQER4+IfgCQKaRIV0t4uSz0ulDfEktFviB5Uem0DP5MWpwbhSLM92HQx2u18HrhE1dFDS8UNcY+hcGAQuCLNSMITJGNkmRReYWiiBn5ghQuT4UylLozwtirYHWN/sU1Nq70WF2Bq9fg4kW4vAYrAxEy2sj1tOOb4vrqGub+KxNfNNuG/eya5o5IEjWTnuFD5L6vB9K9S4tQrkgXrazDk1tGJFExxYu8T0MOETMeAU5gxJ5W+Jrl/k5hYrum9ZeNEXJtwNSXcXBwcHC4vVEPbaDf9E3fBMDb3/52XvGKV/DiF7+YI0eOxG5Tq9Wo1WoA/Pqv//oNHX9WAfv8+fM8++yzfOpTn2J1dfWGjung4ODg4ODg4PD5i5lFjT5SdNdDyGmNq1BCMoocQgzXMITVrDO+NaZkPxElhr0J77q+HSUV/TztOFGibdLnaPsmoYlETF0KoBhyln2EBGuw3e2gRH1U1LDPQQk4JefiSHkl+ZSMjMYqzYIAE62i9Rlsx8o0aLTKPHAf8LICvOQkLC/I9xeuwl9cgo8hZF10n/a5Jx1Po7K2wvXnkToI1bC92kdJ4ziPuFA0/maWcRJdljQukkSO3YgNeWQm/NmwnXXgsrXvITK+VLhJKlw86Rhx7qYM0ocnEXF0ESH5Nc7MFo6UYB2HbVxD+v5WKGA8i4MoijrG7XMME+XVRQSPDtDpQieMoBqPJI6q14N+32e+36bMCunhSGKe5uagWpVC3fkcZHMiSGSzUK2IKDEYSlHx8TgsnhGIKJFKocU1PC2qkQrPykvJ/vJ5WQ8P0ilZDtKwXk+KgW9uwtWrjFfW6K02WLs85OJFuHYVVtfg2gZc68u1ayDX13Y7xUFj6LrIb8ZgwrqKWa+Diuv2cy0a4RR9PnSBK8ATIyhegK2OpH61u/BCS65pNOJvN23SdqWRc19D+qiE3Cdau0b7LK7vsoj4Mxee3wVmv0+SfrcdHBwcHO4cvOlNbwLgta99LY888givfvWreeihhxLXPwxX3Qc/+EHe+ta38v73v//Aj+Xg4ODg4ODg4HBnY2ZR40mEcNGaCCpqdNg5+1ajgUoI4ZLkajhszEI4aVu9KZ9nQVxh7aTYKX23HQNJLpUO0v+TZiqr6yIOUUHDXh53ndQhokV9NXZpL7PnA3YSah5C0OnMfa1rAaageAFxZ9yXghfV4MX3wX33SVxPuyNEcO2S7MM+97g2xok29rlqsdwRQhqqkDGLEyMg/noo1O0TJVHjZpLHCWSzzEpPalcPuY5pRNgoYYjTUfjdU+FLI8xmuc5xQob9ruTrEeAMoagRXuCxD+2xHOMyImD4mLGgrpIspkDzzXJt3Mh430D6ooWM4zFmXA2RRKdx3aRJNZtQb8ByPWCp2aS22aWwuEl6YU6ykGpzoYhREiEimxXBIpWGfErqXgxDIWI8FsGiWJQIqW3qZsr627sueoijYwyDnlFamk3YqsP6OsPLK9QvtVi95nPtKly+Ii6NtRZsjOU6qqDRRYSyaY4AP9xG77/9cA/ob5EdL6jHsp8R9jMezHPgqQDWW5BtmevVxkQizvJ8ScIQce7os3CMcTMtI/eMPmu3MM9fhT4X9Td2lkg3BwcHB4fPP/zGb/wGAP/zf/5P/uN//I98yZd8yaG34bd+67f4rd/6LZ566imefPLJQz++g4ODg4ODg4PDnYeZRY2LyOzqKqaQsyKOkNVZ3wOEHNoP54UWG9dZ/3vBtIiSqFMj7rMizvmgsMWLOII6KX/dXncSOabbKfGbwUSDacHtuNekdnuY+Bdth5Jqe6mtMCvySK2QZbbX21BCsuJBzYOlHJw6BmdOSiHjIBAy9cIlePKCkOIacXYjIpqKOE2MyKIujknRa0Pr+NOuW9w9E10+SdQAc72UnFV3RRIZPEDI2OcwxKle4wFC1irxvNsizUkuDXUKqbBRBoppyGVgNIbBWPr6MvA00ucad7YctjOPKS5dxwiqtwNUpAyQvq8jfXuU7QLfGBj50GtBqwPNlugHW1tSyHthYcTCYoP5xRbFxQ1S8zW8hQWJiKpWRdxIpcVdkcmKStKoizLiebC4ALl8aFkIxQu9SteZfB+GA1FWfF9EjXYbOh2paN5oEKytM1hvsHGlz+WLcOUKrKxIJNNKR85RxQw7pk5j86b9Dmif7Ceiz+6453rceBog7pHLSLuj924W83sU5zibJm5oJF/T2j6DTBzIWtsniZgqsKxi7o8bhcZZ6f5vl/vMwcHBwWE6PvrRj/Lcc8/x2GOPUSwWyWaz0ze6QfT7ffr9Po8//vie63U4ODg4ODg4ODg4xGHmQuFJluRJxI0SevtBUmnx1mp4PK1voLO3dwubdGUXn6N/284KJZh19rVdJyBJ3NjrzHvFIjL7vYhEmKxiyC1tm93mOGHDPpcc2wu1rwNXia+9sF+oIJFS9yGz+TWaKAtUc7C8BHPz4srI5iAbEuJX1+HTl+HTHSmwqzE/dpyYIklEiPusopyes7o/YHI0mhbq3Q9Ex0SUUNVrVQlfKeT8lUSeJPplELfAItLHSiT3MMT7LOMxSciIftZInUeR2gFFTMTPVeAc8AJCICtyiNBVZXtdHi00fVAC235DRZ1U5PMict3mMUJeAVNvpgxUMzBXlsSp+TlYWICFRdEnFhahsFAkM1/Fm6+FokZKXBteiqDZxF/bhF6XVKWEd/KEFO3QGhmK6wIHImR0e1J0vD8IRY0WQb2B3+4ybnZorvVZW4XVVbhyVV6rddgcCUFfx4zBHkaI2mRyTSW70HUeGe/dKdvM2v/2/QvJjqIo9DkQdZh4yHUKS59sE5OjovV+uIomRVvpeJo1xm8ayphnfx/zLLku1rpC4Q4ODg53BH7qp36Kr/qqr+LBBx9MrLexV5w/f55Lly4B8Cu/8iu8+93v3tf9O9x+cIXCHRwcHBwcHPaCaYXCb1jUOCzkkNnNJxBCSaN0NhDifa8zSmchZaNuBn2pq0FfhXB5PWybnQs/TdTYK5aQOgU5hDjcQEhF3WdcTQ07Qsk+t5y1v5PhsnNIHFG0j/czAiiN1Hq4N3zNYxwSlSws12CuAuMA2gPYGsCVLjzTlVi0NYR0U1JUi1DrOWrUTxyi/a+EpLoyou2MzszW/drClr2vG0Vcn6cQEvxo+JoPv1tFxt4W0ifT7gklkNVJMGD2osHRsRR3v+j4KiCui5MIaarXRB0aG+ycZZ5GBI1y+Fn3uxVudzvMIFfC2bM+239nkX5Zxogaek2KmKLqlSzUSjBXhfl5EfgWFkTcmFvwyFdzBOkM4zGMgxSjYcCw0aXfGVPIw+LJPPlTy3gL8+LWsIUMG0EoajTqBJ0u/nDMqDOks9mj3fRpt2FzQ+pmrK/D2gastmFtbGKjVFjsh68G0wUNdZuVkbG8iIhWlxDRa68Cljq97PFjfzfpb9jppNF7XGvTlDCxbVqLZr/E6puBFHJeJ5AJBGNM/aNrhHGITtRwcHBwuKPw2te+lpe85CUAfN3XfR2PPvronvbzp3/6p3z84x8H4I/+6I/4f//v/+1XEx3uADhRw8HBwcHBwWEvuGNEjTJwKnwtYWamXwKeQciXG0WU2Iqry5BBSK0MQjwuIkScugvaYZtWMKKGTcAmfd4rMhjiV2OEbLFiWvyUQknkE+GrjBB1LyDnY0ekJMWt3Ai88JgnkILKWlOhCFQ9yKSgHsCKL+T9OsatY++jEO5HBSaNV0oiGaOxT7aoEXWnaK2PqJhhF2efhBslOXX/BcTV8gXA0RQMfVNsu45cr6eZPfJt0kzwpPWnCX+2kKSRNnm2C0128fm4Y+gYsNvY5fYocGz3kV3jJfpZx/g8poB6lu1iaREoeVBJQ7Uo4kZtTgSOSkUMGs2GqS0zHkk0Wz4Pi0tw5gwcPZmhNJclnU2ZZ3nKCy+6XPnADxj1x3TqQzrNMcOBaBz1LajXZf+NhsRhbbZhawQbgYw5rZ2hgsZW+FJnkzomPOSa29cwFZ5nBbn/T4ctegGJPeyw+6L1dv9OctrF/W0v03Gbjayn43IYtq+Nec5EowdvF2SQZ+8ZxDFXQK7VOvBE+O5EDQcHB4c7Fy9/+cs5e/bsnrZ9/PHHefrpp/e5RQ53Cpyo4eDg4ODg4LAXTBM1bpt/JfQws89TCOEyxpB+XW5cJIjWK0iqT1BBIjqKGKJWSbcmQi6rW8J2RURJrpT13V7bPkLI/STxIkolJS3TiJ9VRJCxI4l6ke0OgqjTSLFzyOx9mwjPBpKEowXS49wEOoNaidMRxoFgwz73qFMFzNiqYs4fjHMkQAhMLWZsixo6k1v3nXSeScuTxoCKKVmMiLMMPFqEo0uwWYfNpolqusjua9ho36UxsUFxmORcsmt72AS0znSfJUrIrhHiY87DJ/l+vFVh9wmRz9qHWpB6gIgAS8gseSWU+8j16ATQGYUFxNtQ2YRyWfax1YGVAQwDuVeKQDULRxYgnZaa4b3uiGJxRDqz06QRBOY1HIpA0mhArw/9PrSa0GhKeY3WABpjaATyrLMjz/TerCPuGxWsVHithOet9499bXvIPaC1JED2rw4JrHXt52r0/tXouRQ7n1deZB1d5ke+13VSGIEpi6k108fU2NB6Qxo9dTtDa3zoc3MJc01UbHNwcHBwuHPx0Y9+lI9+9KM3uxkODg4ODg4ODg4OM+G2ETXGCOHSQoh3ncWtBbI1V3y/kDTLdoyZmayxJnYsyQAh+WyCO84p4CFEUS38rEJI3DnoeSqBPmSn4yNKmAaRvydB255CiFV1Zeg5xG2/nznuNrQguc7sVgJR+9CGRstoO7WPVCBQ8lLbb/eRXgubkM8ikTJHkHoO+dT2gu4+0PWNE0JJV6x1bCFl1pnaUVHAhl0QPosZX9eAa12YH0G1LMR1oyXk9qyuJQ9xB2gslLb9AiKMJG1jOw008qscvneQGd1x2+m6ep3sWgQa3XWQwtlhIU74SYKOwQHG1bCOEMoVTKF0jXPqAG0fml3IdOVaXwuX28+TSgB+AJ0uXL0Ga+vi6EicMB+uPxxCpw3NNvR6UlqjM4CWJSqqKKFChr5U0O1jIqN0vPgYUWuMGQP2M8YL17kWflaHRybcxh57cVFREC8cRePl9Ls4QUQxjnyvorkKGLYTKW6s3q7jt4OMxU2MI7GAnH/c88nBwcHBwcHBwcHBwcHBwcHhZuC2ETVge+6/LSZMqplwEFDiPQolgCqYGcddhACMtk9nwC5hSLu42fFphGA/gZBMW4iToY4h3uIIvgncJVjfq0tDSemh1V7dTzqyj6host/IIK6DBeQcV9jpPFCyU2Nh7CLMcbCFCyVM++GycnisKkLyn03DffNQKIZEsLWTbh/O1aHchc+yfca2vsfV6ZgErZFhR2aBjLF2uN8BJl7MD/vkM0D3KhTDk2774maa1aWRDY+ZwghHw7ANRzBFz3U2vo9xJumYmUfG8AkkHunCWKJqdKa+3idaV2M+PNdMeG6XESI8zj1k92N0tv6tjiTH1CSnUICMSRU4tOaG1tZQN00XQzKrwKAiYDF8D8birBj0w36MDMIdMWyhqDHyoT+E9hB6gRGeVNBQ8aIZtkEjxHSdaJxYEK6jzzcVc5eR8aMuCM/ah45fHZ/qVhlG9jurABYXmafPNDtiMCpQ6ru6ReLcdru9128HqPtPx1WG288l5eDg4ODg4ODg4ODg4ODgcGdjV6JGNErpZkJJqFspX1/dBFrs157VGwctdq6xQnF9qpn7Kjr0MDFBs557HHkaLXBdDV8BQlgqua2CgR+zj4OA1lLQ9igRakOJtrgCwHH70/c80o8ZTGHjUXiceUTQWASO5+Cee8J6BWmu11X2PJnFnnsB+hdEvGpiiN51RGzaC/mn19mu2dFFSOu4WeEtxC2yCmR84+LRQs2zYISIIFvWvn2k+PiXAveVYDSCJwZyrD5yznmEZD8O3J+D08uwmJeIsKcvQtWHZxG3h8bZ2PFWYCJtSmF7o64fW3yzhY1oDFoQWf9mI0nAsBE9N9sxpN+rM2OA9Nsi0vcaedfH1NFRMTUfbj8A6mNxW3jAONhOyEfJefvvcbi91sZQoUvb00bG+Wa43BYMksa97lcFjRNITZhljENsGO5zBXNfBhhRLOrWiutb7Uf7PU7QgO1Okkm/afr8jhubk8SMvQq/t9JvLBhB1cHBwcHBwcHBwcHBwcHBweFWwsyihochhPskZ+5/vkNnNNuZ+UnkVA+JMoLkKKchcAUhy9OYwrS2UySOzLPfdZ0oAWmTkG0MKXozY0aUyGywPd4GjOARFTOmEZ36nd2/StYpWdpBxvcyMsv9bAOyWfBSEPgw9mEwgM0tuLIq/WUTkEr87raWBcgYUZEler3G1t82OohocCly3rZgZSMpikfJahs+4r54cA58H1LXRHzIII6WEkKiL1bh5BEpWD0cwfMvQMs3IoaS7dqmOmb2dz5c3mGnMDXJaQTbZ9ffqsJG3GfYfm9GEbdMhY0u8Q4HW3TsIePlevyYtUPtM60BES1mbT8bNAZLnTa6zQC5fhq/txuoI+ok8BBwFyImqjirhcaLkTbY0X3R07L7MfoeFQGTxsas4sEOZ8uUv/eKPHLPqEvrVhnTDg4ODg4ODg4ODg4ODg4ODrcSvCCIBpPEI+951yOQGsiMWhdHcWvAm/ACI2BMIrzLyGzwLEJaNjDZ92m2k4RRUeQwiDd1WlQwZKcuT1mv6PnbBLhCBY0+cn45a181ZBb5Szwo54XUH4fMai+QWeoXgfPA88THkCW1/7AJSg+5nlqTZYwpYB+3bgnpixRhHyDihvbj4hxUyjAYwVYbNtvGRdJE+uQypnC0uo+iIkVUxLDH0SSiOsldEOc+uBnQc4obh8R81nf7/tT7Kzqm1dmiEWpVTNSUXl+tS6LQ/kyH66bZHvEUJ2ho3JIKGSpqaOzfCBM9FUXcNcgg4+ooImScwIgZKmD2MRFnGm3WR55BW0iUWYvtAnHUjUHCuy3u2sKe3d+T7k3t+zihNyq03Oh4zCDP4DlMbZGo4HizMeM/F3bASyzm4uDg4ODg4HCnY6//fgDwvNsqLdvBwcHBwcFhHxEEk1nXmf+VoLOFC5gZ2JqVHkeUa+HmHEJc9bh5hGMhfNd6BHcq4vrXrvOQhBRCpN2FkJ8rwFVM9BHEE3+HSdQHmJnimm8fFz0VR2Da4o66KVKIgLGMCCVagN1DSNtPBJDpGVK2HO6jYb1mjf/SWgdagP0w4CHXVB0WHmb8xxHSJeARhHR+Cvg0cn4PIP2UBhpNGDWh4Yt7SGudeOF7EyOa2LPpk2LLotcujvCHZCHOn/B+szApFkk/29Di09HHdHRslcNXERl7l9hev0djqeLId73+WeT6tJCxYIuctuipol8P4+qIig76O6BCisafqbNERbJTwAMenMnCQgq6Q3h+LO1vYxwhw7D9x5AoOL3f7aLjNqLxUrBTZNB3rf+iRdS1DomKNNFrksbEwAVMd67FiRd7eS6OkHuzErYhh7kmSX2QxYiyXW6tOEYHBwcHBwcHBwcHBwcHBweHg8Kupj5sIYSTEmkahVTHkCl5TA2IBYT8agAXuDnChubRe9y57pIoiWy/zzJbOItcqwVM5JNm4EfraUSPc5jXU0UJrQMSLWAeJY/tWdpKVOaQc9PZ60ocghFLtCixChpKcKaR8VzFCGWzQGuA7GauciE8rhYK3y20CL1dtH4c7ncNuW/1ntX+bIafG+H3z4XLCmEbAl/6RolxFUeiJLAi7nxTMd9NEj6UVI6bmZ+KfB99v1ki6iTxL3rfzFoXSJ0SJYxw00SuA0wekxr3lcLEudnHjHMWqKNjHLOOihBpzPNe7yUl4LOIO+ORNDxQgkpRaq5casLaGJ5Efk9sl04WcUKVw/1rIfJpomxUXI1eexUGhhhRzy5iH+2recyzsI5xtej3ccewl8WJLLNCXTQVRODR617H3G8pjNA6h1z7EeIca3Bn/sY5ODg4ODg4ODg4ODg4ODg42NiVqDGHkC1KBGucTQ5TTHQOOA0cQYiuAWbW/zqzFzHeL6hTRAmtOx17IdQ00iZASNIW24m8KGYVNHS/OUwh4zFmRv9eroeOO7vQOZjInjgCGYyIkbe2V2JYiVuN3BmHbZ4Lt8lgImj0fTf922d3Re095P6pYOKc4maU67okfKfnmELOQ8UDJXRtx0Y/PNYVzD26hZCk0UioKKGrbVORaZp4k+TQSPp7t5gWKXRQiIqL0fFI5PMsLiqF1pkB47ixSflN4oXM6PHUkaGOiwzmnoq2RdsfN24DttfbCBCiXR0b1++ZMWx14FoXmgFcG8s4a7OzALU6PdbCvz1MvJZ+nzQOJ30eYpwm2u6kPk8josZ8eC7qJlHEPbP2c5wVEZFCXSUa/VVEhA2QZ+nx8FUN19M6JwEyTm6WoOfg4ODg4ODg4ODg4ODg4OBwGJhZ1FhAxIoqQvZoIVONvFACbIgUg70XIZKeQgi3vRRQ3g9ohMnNOv7tgCFCiqmrRknT3RJjSkLmMQW9swjZuYSMoQFSe+ECN5YXr+RkBkPc2995Me8pazu7GLJGT2l0js4aD5D4nAo7I8yUxJ+lpoYSq7PCQ/pqMdx2DbmXdiMIDpFM/i2EEJ1D+qqD3Ld2u9UBE70eSnbPimh/z7I+U9aNu45xx5r0fjOg7oBZvlfBTd1vcTFCASLAqVMpen/6kXX1XV/29zlkTJfCY2u0YJ+d12TauA0w903dalsGGWcbQHYs+9eXippJ+wuQZ8h8+NJIuCbx4rQtYiT1wyz3qZ5zF+PYmnUc7cc4KyPPG61tpG1Q91wqsu69SD+9gPRzl88P4d7BwcHBwcHBwcHBwcHBwcFhZlFjCSFaepiM9XXiBYtVJJv/EvBJhOyyCbPDhNZPULLNYSfGCCmmM8cnEYBJzgAVNNQNYRcC7iEEp8bf2LE2N4q4CJg44ntotUFnmPvWu4pfihFmhnTRWqbbxtXz2A008kn3a59HBiGdF5A+LSHEZZyokXRPBUg/azzcFkZ0jM6Q3y00zgt2OmTioqSS+ikaQRX3nR19ZtfN0M+phPdbocbGrKJQDnm+lpDrtUJ83RMdp2mSa3dE36OumhQyrk4gQl07PJ6KezZ24y6KCgcjxPVzje0OHnUfTPot8DBuhKPIfemzvcB59PyS3DCzIotxk8F2cdOOrpqEG4mdSiETBvKYZ9EAeS6vsX08eIiocR8i9HwK+a3dz+eqg4ODg4ODg4ODg4ODg4ODw62MmUWNFYRg0Vm/SrzEET1Ph+udQ0itmwWN3clh6n84YSMekwjmWbZT8lhnY9tRNylk/KQxcSo3eh1sYl1hzzKPzti3I6ts8lGJVi16bJOCQ0S403VUGLmKiHp7JRALyGxsPYa6MLRdWYTcrCBEZwZDbu4FSgjvFzKYYs5a9yBOvIguSxI4JglE0foY9t+3kjPjRqCimxLpGZJF2GhdkUn7jCP81aVRRMbcKjLGZ3Ey7AX6HNgN1P2xgbSrhYhytjAed36z9Esc1MWlQqweJ2C7+GjH1tltnfTc2U17Gsg10e00Ii7OtbOKiBnqfDvsWEcHBwcHBwcHBwcHBwcHBweHmwkvCIKZeBfPm53qVjHhRuIw9qsQtU0Q7pa4uxF3x6TeuhWIVyXOC0h++xLSV5sImaiOnCQS0Y/5fFjQmep6bTWWJfo5SpYnzW7XGfVKLKvzKI3Matf6MCD9somQrHGz6Wdp+xLS71uYaDS7Rsg8Mgt7PlxWR4oA30yB0EYWOIOpf6DugTTbI3LAXAdF3DWIrhPFJAeCHa3kR76PLr9VYY9lHYtRItuLvCCeOE+KpFKoKySHjCu7psxekEZcAxVMTFQr4dh72bfW6FDXjd77cZFTN/IMShLkMpgoPRUabME2imjU17Q26fns9rdJnXEBe3de7eU3dsZ/Luw81i7+/eDg4ODg4OBwZ2Gv/34A8LxdlQB1cHBwcHBwuIMQBJPZkgMRNeKgpGcBIdXmESIsjcxQvYaQxT4mCkQLOd9oXM5e4CGkv0YOzbrNbmoJ3Axxw0NI+tOYOhHqUughJPUmO+PCbBIvKmgcpqiRinlpv9uf40jgONjnpMJX2/peo3BKCIk4xrhR9jK7PQscQ4SMVQwBqu1UAlWPpUTqGBFWFsL1tF7BQc2wn4Y8JpZHoUXUVdiY5X6Irp+E6Cx8n+2z5pO+h70R7Ha8VtLY1nWijoE4gvxGYLuhJsVO2X8nHdNDrhNsj3LaKzJIvZZFZHyOkbGp9ZZ6zF4MfRpsUSNOyDoopJA+y7CzoLrtPkoS35Kg0XIqBt3ovayuEi12vow4vXLIb+ga8szR+hx67C4iRM0CJ2o4ODg4ODg47BZO1HBwcHBwcHDYC6aJGvvyrwQ7niMKJU9OIUT6MnAkC/MZIYvHPpzrw+cwRWuzCBGj9RduBnQG7Cz/BLPJxknEYzSmJBqtc9DQ3PZjCAnZQ9wC6sroI2SkikgZttcmwFoOphbEfrlqpiGJFktqQ3QWezQKSeFH1rHrMniYfPtoDM1eMEb6266jkUJmu5cwBYI12mkLM1aKSI0BrQVyM2nCuCLvI8yMdh3b9qx6n+3XKoUZS1GHRxRJUT72tYoTC6eNSZ2Nr2Ndl2kUWVINEnURJI2v6DG0LXu9R+KI86TxPOkYNzKzPw5jTGxSBXF9aRHyLvI8aWGKb8fBFrbsZ6ReUxURJokHBwUdG1prI3rfJ7mQZnkuapydnuMk2GMybn85RKy+K3w/kobFDFRT4AWw2pPfWA+5Xvq7rKKpg4ODg4ODg4ODg4ODg4ODw+2EGxY1dKauhxCwUYIkhxAtXwg8VIKFIhRChmg8hnYbsv3tUSOaJd4nnjzdLZS4tEmyWUjpaevMImbYiBJ3ul102Y0iKfKngIgaPlLEdx0huPSa6ex2dSdowe8BRuCx+9ImrA9DmInr4yh5GEcsTiI/o21Xh06ACAxZZAz22B8y2McIFXrcNCYKzC62rkJKJ2zHHKYWgsbg3EpQgjTqkrEFDUWWnXFVSU4H2H6udlRXVNCA3Y3FHELEFzDkeoCQzfa9Mak9uzneXoTMpPVvhtMrigAh51X88RHhuohxgI2Qe8gm7uPcVro/2P6sVviR9Q76uZPBjA2P7c+A6LMmCntMJgly2nezoIQInx12uipSiNj5GPBFOThahmIe0mnwx9DrQ71nrgfheagLcVaXhoODg4ODg4ODg4ODg4ODg8OtghsWNTRSSmOkovARgrYKzBegVIJ+D7bq0PKhMZbCy1tIBEeD7fFG+0FaFRGirYAQOFozYq+IEzIm1Q2IEmBJBN6NINqmpAgmFYsGCEEWR6ppjEkeIzTFzQq/GaRqUt8mtWUSUZ4041udQuXwcx3prxupPWAjSpSPkPHfwMRTzSFj9ihyzQpI9FQKuMR2p8dBI4e5x1WE8JE+0XghG7Pct3YBZr1+SbFNSYJFXPRQMOG7KDTuqxS+NNpIyetJTi0VanbjlrHv0VtBkNhvqCDtY/oyHbOeOnTirn9UhIyKYfZ3B9mHGtNl19LQ2jf2WI17LkbH8360UyPw4kS2AOnPMjCfhlpZHJCNBmx15Xf2GiJiNzHPM932VhNHHRwcHBwcHBwcHBwcHBwcHKbhhkWNPnA5/BxH3gwR0eI5gA0obUIvEBJXo446CLk4hxC4+xk5lUNir+5DyJvz0owbRpKIEUd2KZSI2y0xP2tbJgkauv8RIvJUSHataERNO9wumiGvhLpNOB50BFVSf0ZdGnF9OWmmtP05en10xn6L2WdU7wV2BMwAuIBk4t8P3JWGYhFyYSGEq5uSi9/ncMjIMvAQ8EAKFjzj0mmN5V56AiFMd3vdk5wO02Kkkt7jhNBpbQowhd9bGKK9i5C/+zGW91O4vB0wxIxNfY7YtTvU6aWiB9ZyG/b6N+LE2SvURVHHOEnsOi1223T9JHfGfjwb15HfraTn2BrwPFDrwsIleWY3A2l/DxMxWMXUO3FwcHBwcHBwcHBwcHBwcHC4XbErUcNj+0xtmD5jNgBWECLlCaAQrqxRJRohozUd9ptsySAzXDsIKXSVG4u0mhQ5lZRxH93e3jYaqTINWrBWi1pHCeFpTg1dL4sQXBr1Ze9HSTwl2m2yP85Bc9CChh5Dj5NUWDoqbMwykz4u1kiLgasLYT8i0HYDjYTxgXIFSgVpx2ZXYsOucTikZA74YuDLcnD3MSgVZflgAKurkG1LOwaICKC1PmaBimLRazlJfIoKFnEiRtzySdDoJEWGvdcYiN5n0fE3y/PhdoY+wzeRcZBBxOsO5lprIetZHS76mxPtr8PovxE7i94nPfeSnkVRcWOWSEM7pnBWka6F1My4CJQDE/tlj2V10rSntMHBwcHBwcHBwcHBwcHBwcHhVsfMokYGmd1fCP/WIrodptcaGCEzRhvsJPkyGGHjICJ1usC58Bg6Y/WgyfdJmHRs7Ye97D9KqCZBiUfNss+xPe/ejoaJknZxbYsj9va7fye5X+IQ57iYtr7CFuui0TeHhQAhJz8CXKpDri73mIpyqxysc0Sh0XGVNBTCwgLDIfQHMBzLOFkETiC1B/R5sMX02KdU5G8b9rWLcxHZy6PvNxqns5frnXTv7aV+xu0MfUao+04FohSmPg3sdDvoNYvrQ1vUuJl9aQsLdlunOU50mX2eSdDi3RqFplFePaQ/p517O1wv2gb9jQVzfRwcHBwcHBwcHBwcHBwcHBxuZ3hBEMzEEx33POYwBXWVbFlDZubeisSdXah2vwjqqFPDfo9+1r/jInNs14ON3RCyca6RWV5pRMzwMESYHlfrSdjFe6OunFlf+4E4h0b0Pe562ARi3MzwpOtiv/yYz4cJJSPt84lG4Bw07gbuAWohIz0OIBeANzbRZRpt4yPC5bPhe5Jrw0PGmEZZwc7rEzczf5qgEX3fC4qY+gnTYIszkwS36P1wp9Yw0LGaZrtYnUJizHLIWIlGpyU5bmD7Nb0ZwoZ9faOihDrekgQtXccuQK/1jKJII8LgHDJ5IINxVjQRMXOvDqL9xIz/XNgBz5tVknZwcHBwcHC407DXfz8AeN4Np2U7ODg4ODg43KYIgslMyMz/StBC23Y0ho8QVTm2Fx69FVBDiixnkJntW+xfoedoBEmcK8D+O9on++lomFXUsBFgXDEBJs5KSbpocd9ohEo0biUufkWXx7VXCb5ZXCmTBKTo8e36IDb5b5+POlHi+iQp0uggYAszSaLJrUBiPo/U+LBZ/mL4OoLU/TiCIbBr4ect5Hq0EULWhn3tbYEvDkmC017EtGh0XtI6BWZzjel5pKasN+n89hsqSg453Bn5KmjofaX3ooocen9Oc/DY3x/0PTgNcUJonBgajajKImMoY708RNDZYvt10f6pIa6nCsbRkkXG4SDcnx0bdav8zjo4ODg4ODg4ODg4ODg4ODjcDMwsauTZTt5ppMg8xg2hZLnWI7hZxEsGWEJmmXeQma43Anu2vB1tFCX69TNMJ2lvFNFZ4rOIGjZJp8Suzqi2Cboo+WsLGXEk9KRILH3PImMoy/a4sR6mvoq9XVSsiX62ESCEqZKmk8QB3V6Jxji3hv0e/bwfUKJT9ztmZ5/eCG5UNEtj7nft2wHm2neRGedV5DyKyPVTx88cEqETIIJGVNQAuSZ2LE4c4mbv34g7yC74nIQhxsU0S/+psBEVyqLHsKPlDlLkyCGuCI2U2y8hdxJsQSPOPTXCxCIlOZ5utoARh6hDDOTeiJ6D3r8FZOzPAQuYGKkUMq422Oka03uthly3qACbRu6vJUTw0N/YAXIf3grCp4ODg4ODg4ODg4ODg4ODg8NhY2ZRQ4sAB2wnscoI2aJE3QCJntnAFDveDfaD8PMRwucKMjN2gxsj95Sw1BgrmC5aJDkkdntucbPLp8VOxSHpO50pnLTNJIeGjSSRw0NIuwXE7VPB1DdpI9dmM/xb2zJpX9HPSionFU3XdaYRqZNmiO9n5I0Su2p8mMWxMss+FRlM3ZukqJtpKCEkahZpn876H2NcGmXECbUQHr+JFDC37/kx8iyIK/SsZG/cd3F1NG5U0ND2TAvBGYZtyrP9Wk2KH7OFTkVcPY0kUXA/oQW61UFy0KLGJPE0eo/GIU4kvtHrfKOwzyUqHNvL9Ly0z6tIVNsx5L7IYX4TW+E2Q+S+sl1kdr0R20Gnv7M55NmpY0pr12wi99de7vHDdA85ODg4ODg4ODg4ODg4ODg47DdmFjVW2EkeZzCxGTqzW4nPAlLsuLVjT5MbUw33v7WL7aLww/ausz2SaK8YkjzDO47QtL+bhFndAHZkk/49SciY5taIWz+uDbtxoETXsQnAHOLoOYtEFY0RMWMdId4bmBnLaaS/h5F9JfWvLbbp8Srh8Yrh/voIAdjEkL02kTqNTN0vaB2JHvtHNmcwzgjtwwxyzqvsPYJIyXx1amSQa6fXUON1fORefR44H/5dCV9V5BrUkOdAD+mDEkLi9q3lccT3pGuzl/oZs1xPJZMXw/arANcJ35vsnB2fJGBG75kkd9d+wnaDTBNwbhR7cYjBzmfKrYY4cSbps94bc8Ap4DTy7FHhQp9v+uohgrveW+rG0H1qHQ0V4DyMyFjEOLxKyD2YRu7zWe8DD7k3i+F2TthwcHBwcHBwcHBwcHBwcHC4HTGzqHEtfLcJxRRCSg4Q0qWAiT9RAncVIXO6MxxDI4qCcNs48jAOcQThfhZUnpUM3ct+p0Fz6aeJE9NmSydtE10nbka9tiNO9NBtkj4rOT5Aru+yB2kPMr4s72KiVEBIQCX1FDqjP+q80Gus7Uoj4/A0MlNanQabyDjV8aTRR/b5JokY+0X6ZTDCg8bHJMFD2j5NkCggROoxa9/qKFpDRKO9YICITrD9Phoj97bGLeks9FXgHNLP6bBdR4BXenB0GbJZ6HShHzK54xG0+lKr43PAZcz1n1Vs0rYdBPpIfz6GjKWCB8NAnoFPAE8j4oY97uyIIvseSBI2ZhEJ94IhUA/3e5DRRNMEjOhzJoh5T7rGxLwfFpLEi7hlHjLWSxgRDGQs63OthdxL7fDzGvJ7qM+tSWKtHqeOjMmjmN/WAsbhkUeExVmckRlr+1y43yji2nSrClAODg4ODg4ODg4ODg4ODg6fn5hZ1IibWa45+pcRUkXJHZ2JXUYImixCsE4TNoYI+ZNBiJocprBzBhNNZBM3WqdB26L1GZRMt+ob7wp2sVed2T8I9xcl3eNwkGRckpgxSbSYRdDQdbReSgmZ0RsgfauvUcK+kki6Maa2SQe5jusIqX0FueZja90oMafCjkLdA0nOkhampsAQIRG32E6U2gWe44SMWV00s0LrCsD0MRnMsI6H3F/zmPvNJox1nb2gFL5UvBgjZOwKQrBmw33rPalxOJr/PwccB04tw/HjkM1AfwDtFrQH0BnIPmt9WT/qQprFPXOQ95eebwsYpyBVgKwPmT6kA1OMOyo42rDbb5/TQbs19F6127BfiHueTHq2RD9Hzz9OxCBm2WEJG5OipuIEjZT1rj+kWpPFjpXKY5wZbXb3m6TPgrVw2xIintTC/eaR31y9J+tT9mfXvtHfV31lMcKofW30t9AJGw4ODg4ODg4ODg4ODg4ODrcKZhY1YDupY9eWGCKEdRtDempUhhYOnzbrPIuQoVUMaVNBSJw8hmi8hJmJnAqPozNOPYyQERdnMytyCFF0BFO8VSOMNtlOwt8sRIlEmzC0a57ErRNHwtr7zSJ9ry4AH5mNfwXph264nj1DPSliBwyRdgmZ8R6Es96vIoLDbvtS2x8lO7WGgz3rX2esj9hORkbbHSVVD4I8300U1KTjat97yH2nJKmPcUqkkWtYZ/f9m8Pci2eQ8fBR4AWkb9vhehqdk8fMHK+F22aBa3VY74aixwjWe4acVUfOOqYIedy5xxHcBy1qgIhgfwV8zodsR851hPR3tP5L9F7ScWaLcXHOhejn/cJB9E0cqT/NzRBdHnddk0QOIuseJKEedy6zCBv6bgvfHuHvlifPuRxy//V22RbY3h9jRGTrYn5jC8i9ByYibRL0mVDFRPRVkN84fWbUkUkKa8h9abtRDts14+Dg4ODg4ODg4ODg4ODg4JCEmUUNJbtBCByf7SStuiM08qSJIXomFdlVzCFRL0cRIvVsBRarUMhA4MPKOnyqJ8R4H0MYaht6mNn5N0K+eBj3h4dxDmiEkjo1bgamiRFM+H7adzZsMUDrP+i56/mnmVzUO0pgdhGyTHPc9XpBvAPHhh4rSjrajg09nu2isWdT69iNttNuq72fg4o2mhW2AKOw+zSF9OkVtgs0WpdGHTZ7IYNVCKkghY+P1qDTkH1vhd932R5NVULI0TmEBB0Azw2gNTDbrFj7tgU2+xz9mM/R98MgV31MEXvYKYjp+LMJaCJ/J4kXByFqHBTpH0fi63tUsJj0fLLXixMQiVmW5N7ZL8QJGXGfk763x+4A+c1TETwbyDOuh4z/dUSUUCE4yYWi3+s60XiocfjqI+KgjsVZ4hbziEh9HDgJ3J2DYwtQzst+2i14ckPaOsSIdxqjtVfXo4ODg4ODg4ODg4ODg4ODg8N+Y2ZRQ0krjdWA7SJGFLutaaEzTUfIbO/lMsxX5bjNFmz0ZXZ/HSNqaNHyWUST3aCPEPBXrGVKqh022R1HEtpkanRW8aSZ40TWi/tbBYw2IkBohJOKOzaxrM4A3U90n9H9ttkOdZUobBFM92O7QbzIuva7HQVE5HP0mNHPtpARJU/TmDgZFXqGTB8HKsRMukei0AgYdUDoa4BxNij0OkXhISRqzmrvbqGCUwkhapdGIjSmEFHxebZHyWnElxduM0TEAI3NUUK3HdOeFEK2zkruH/Zs8egYscdrHLFvfweztXcvwkacGyJpnb30WdwzJOl9Uh/YbpVZHDhRHJTrJPo+TbyI+16fCRuI6yyF/Ea1EGFvC7kvmsj9orV8tC6NTgywnVQqlOgzxyPZ5aGi7qzQCLwOInouF+BIDVIp6A2g2ZH2b7A9ylGf/c6p4eDg4ODg4ODg4ODg4ODgcKvAC4JgJq6i5nkcR2aBa6yUEpZKzOwH4V9ACvTegxAvA4QcuowQRzrT+6BJluiM7Bt1gOwVsxJu075TpCJ/R4lGnQWsglHSOav7wc5gj9tf0jLdR9x+Jy1LIlCT2mgjLrpIx1H0+irZvgCcRYQ2HxnvKwjxl1QjJosULC8jpOYGs5GPZeT+ymIis5SEPMyxlw/bcgJ4EXIuDSR+6lOYIuJxqLC9ro6HOZdm+FI3jYpiOoaiTow4sekwRcVovJo9Mz9OKIxrm31O9thLcijMgrhjRz/vJ5IEjeiyacePOlvi+iK6bJrosVtMEj53K2iocLgW2UcZeV7oOE8Sa3Q8D8J1c8i9No/89oHc++eZvZbTrLgfeDg83ggRYlaAi8gzTicO7Bijs/1zYQc876BGp4ODg4ODg8Otjr3++wHA83aVlu3g4ODg4OBwByEIJk/VnlnUeMzzOIqQrnmE1Bkhs69biNhwBSFEbpR8ymEKCKtTQN0Ch0HuljG1OjKYCJHd1ETYL+xG1FBEa2rM4mAAQ7LZApX2fxxha7sJVGRIEi0mEdLTCNHo93uNVYojTnXGdHS2dAWpqbKMRLZoprwtsl0Mt9OC2dpOrWsxxtSamRW6j6R4Gns9LVit98lujjMJXwi8MgeLBVjIQHos9TGeAP4acWtMgof0SQnpxyLSH9pP2tY6pmgxbB8fUTEgbp2DRA5DTqcxTrI+2x1Jk67VJFL+Rs4j6X6Zdm/vlVbWbXdz3LjjJQmecQ4vvSen3QezYBbxYtp3UYeO1rQAU39Cvytgfr+iz8NJsVM15JmzgKlRA0bYrCPuuWvsjKXaLdKY3ziNV1ThZNLYdKKGg4ODg4ODw27hRA0HBwcHBweHvWCaqDHzvxLmgQeBU2UoFyCThlTI+gx9aHTgr1rwIYT0vRFo/Qa4Oe4ILXwMQh5pLNbNgE34xZF/TFim28c5M5L2YRfR1topSuZGoeLH2NpfGhlUObaLXz12FoSehriZ4dHorbj1o+TpLI4OFYIyCNm3iNSHyCGODB2PI+Q8UgjpnUJIyCpm5vYWIvA12D15PUt704jYchaZaZ0Kj/UphPgkXJbBFDH2kWswS3tOAa88CUuLkMtBswlbddn3JJeGfQ4aSaVRNyXkGXIE6TetBzAm2fGi+zpsKGm9BNyLjIU24lS5hHHOTLtWSST+bundaa6MWVwSuz2mCklRAcd2eiXdl5NipKbdm+rYuVHnX1KfzSJg2Odhi7vaVo2HG4efC9Z3+uzT5/Ukl4sdmZdDBMC7gHuzsFCFTEp+Zz0PxgG0B/DsJvw58Bw35trQwuOTrpmDg4ODg4ODg4ODg4ODg4PDrYqZRY0BQkwercLcHORzkM4IKTIaQaMBKy04x97I3CgOmmSZVJy6hRCtAZMjRPaCLCa+SyOeZoFNLqoTIMf2iJ8+8cVcp0U+EfO9LXCowBPn2NDoJvvvMlKD4WTY1jXE2bCBOWdvwrsef9qscFvoiTuHpNni9nZK0qaR8V1AxJh8+L3OktaYmDYyNjSiS90SxxARxA//1iz9pPZpu7Ix7dSZ00nIhMfT+BgfIdufw4gaReQ6aI0OHcezzPAeAJ0ulHryvrYu1+8SkwWIKFTcGCJ90UKEFe2nFibmZtp+DhsFZAz/7Tm496TU9fn4FfjESPpCCydHI7PiHEtxDiGYPHbjBIxJ7oK4z9OQ1K+2Y8teJ84BFoc4EVU/T+oXMIWwb9SZsdv36Ge9B7Ud9hi1r7E6tNQxpSLiLMKrHWWmjrEssJCHY/NQLkM6JTUvxj50uzBuw7mBODa2pvTDLHBihoODg4ODg4ODg4ODg4ODw+2ImUWNOvAk0LwKR9dhqQbZsEq070OzLYRvLnwlFTe92chYrwHxkVIHFTPlIcR5CTNzvsNkAtveVt+LCJm9HO5rjBBcq+yshRFHeMZ9F+fo8JF+qiAkb4OdpHbcNh5wFHjEas9q5NjRc4q+T3OjJJGGdlvsdbVP4hwvHnJ+1bD9DYz7RLfTGK6o2yGFuDLmMbUkjmMIyisIca+E50mkb+YykE/BYAgrgdxf/fA4PSYLgyMk//5vwnYPENFgM7KOFvxWR0SS20jPfw5T7Pj/dw2q12T7NUQwmRY7NQkBRiy8Fi7TSLmU9Uq67odJvqaRMX8GeMnD8OD90OtC9dNQPQ+fHMDTiEhnF7WHeOI+Ot5mdVxNIt3j1tktps3St90HdrxanGgxSchI2q9+toWDWZxKSdiLOyP6ri8VV+IQYNxaKhraxe6nPceibQQTyfY8ELTgWBuOzkExFzo1xvKsWAt/nFREOcwaMw4ODg4ODg4ODg4ODg4ODg63CmYWNdrA55CipbUhLK6bzO80QlBuIsSlErhRMvKwc/GToDPID7sdAUIS2xFNWiR21hgbP9xHA+OK8JDro/FOkyJukr6LczTo8TzMDGQw11ddJ0oC9sM29BCSfi1s10WEAFahS10g05wYNlIxyyZhGskbFTi0VkYP49KZBT5ybhcxNSRGCMl5NFynHy4/m4K/cxzun4eFGgyH8Nzz8LE1+Axwle2zw5MwDtdVcSBu/T6z5+7PI4WDl8NtngX+FOMoUvFtPxAXN6VRQxrfpeMjzhV0GCgiMVmn87AwB/nQtjM/D7UcZAc72zMtXmka0Q+Txb2o8JMU3bRbJIkOeg2KyLioIPeICpsqkO1WzJh2DXd7jScJB9E6GJOEIfu7qPssDlpTo4bc6+qU02dw9BhJzy/7ed1FIs6uAeUAlrdMwXDdVwsRiMeYQvb2c+xGhSEHBwcHBwcHBwcHBwcHBweH2wEzixo6k7SDkL+XMMSXxiApAaozWGE7yZKKvN8M4kVjn24WtLaEklGzRq3Y5Gg3fK2yk+i0ia647aOziUn4246gUQEoQGbzLyGuhHy4rI0IGOvh9l2kqPQL4bZtthdYVoIvHXOeswguccui5xUdX9FZ6XYRX62Tsdcx6SPOiTJCAM+F76cQt0rOgyM1eOQkvPJlcPQYdFrwqc/Aak8EihVEHFFRYxbBbT/unxTiUFlA+qiFuD5mqZ0xDSrazYKkc7HFjcNwbGSQvjgLHJuTWL1nn4GNTfjEBfiLtoi7mxiXSRyi18+LLLPPKc4BERU0tNaLktz6HNN7NO45MsmJYT8Pove+3hsecp8/hAh0PUS8u4SIlkNrH5Punai4M239WREnqESFFTsyiwmf7b8nicLp8FVGhK8TiLixiTz/9JrY+7HHSHTfdlt8zLO9gTwT7Hg8jQFsY5xg9rPCD9fzrWVO3HBwcHBwcHBwcHBwcHBwcLgTMbOoobCjeECIlSJCBCqZ0ma7kDGJoP18JF1s0ukg9hUnaESJtEkROLaoobUjAmSwVBGi8why7buIyNXAODF8hBhvJrR50jX32UkUz+osiSMLo8eziWIVlZSI3CtU2HkWIXvvBh4ETnlQrUCtCstH4PQpqNWgHQoa/+cj8MGBiD9bGEHjMO8Jjdt6Kjx+PTyX/UCF2Vwe6gSySeU4gv4w+mWE9MMa8Ffr8MmtMIJrDM+NhdDvYQTEaJuS3ApxjjV7uZ6fXWfBJryzGPGpgBm3dr0SHcdRgn+SgyOun9OIYJlDotTOAqezcszc0ESdDcJ2KZEed92S3CyT1psGrfOTxcT4JdUSUkeDnluSuBF9z2NcdDbUVdTCCMhp5Dmo42KSMBI9VtKz2Y6LS2FcYHlMdKLWyYlOFvDYLtp+Pv7GOjg4ODg4ODg4ODg4ODg43NmYWdSIzvq1yeEKEmGjEVRRp8BuSDWHG4PWgrBnCE+LpUlyPegMcBUaNFtfo5qU3Ftje6SQHS9lCyRRJBF/k2ZKx7V30rIoyagz3rX+xRhxvGxiCoLvBUG4/WWE9O0DqQAKvhT69ZDZ/usb8PxV+Kvz8FfABYQMnRbnE3dus6yTZuc+o7P62xhieFrszm4wCo+dI75f7TizpDGY9PdBYhURdz45Bm8sbdd4oaggofdGEkm/mzbHEe7aR7bAmEHGrodcM3XKdax1ksZxkmNLP+eQZ/kZ4O4s3FuCe4/A4iK0O9C7AE/UDfFfCduj4tVeBOzd9FUGOAbcFba1jdxDKxOOrddoN24NkPPT511UnBkibqa6tX4RuQ7RfUYRdYRErwmRv1XEmQvb1IusE7f/KNzvrIODg4ODg4ODg4ODg4ODw52EXTk1lIzReA0QsqQVfh6Gn22Hhv3Zi3mHm19j41aDPfN5t1DSWmOpZt1G31WQ0JoZOhs6hyHtNJ5I608okaozyisI6aqz7weYItFKmk9zi0TbN6tIFoV9HJ3xfBfwiAdHc/z/2XvvMEmq6v//1d2Tw+bMwi7sEhcUBAEJuwQBJaogOYqIkvkqoCBBRKKBFQVBEQQWJH5ARRAQVAw/kSSCIGlZWNi8M7OTQ3f9/jh1pm7fqeruSRvgvJ6nn+6urq66detW9cz7fc85dGThrR4phP0B+cW241ABGSKh26UHETpfDV+3t8KEDqhaIf3Y1AkLcvAmknZKC48nHVPcGNDzA8kFjSuRWf0jnXW1YHgDUWSNzggvVtelv6TJry8Qd6NJEn/1/hAX5bU6iiOXmqLOb6M71nwR3I+60AgrP0VSodREnci1V4lci2oojETG5RKidF9J40m35a7jXh/1wMbAjqNg6+kweTLU1UEqBStWwAYrYFaTiOytRNe3GmJ+v8Udl9sv7nMp1AEbAVPD9yuQe5FGL8QREJlBbkRQkpmhaISHRkT4uFFy7jgoNUImzsCMOycBcq1qSrhVxJugcdv1PzMMwzAMwzAMwzAMw/gw0O/0Uz4anbGSvqmQ3DQYcc85Zx0zNiL6a2hoEW+Nqig2U1gJnIeeOxX+64kKN2v0B4gx0YLMyu4iXyCtQArnjkRyzlcR1d1YhQiQTeQbAbp/t81xAp8vACZ9FrcO4XFVIvVANgCmpqGuDFZmI/Gy2BisRHLojwu3vxxJHeWfK02/9W6430VZSLVLfzUj/dBIVGfEFa+LzWZ33ydFwFSGx7gRYmxom/Q7jYiB80H42t92PSK8t5JvdsSlDYpDz6UbNeR/5q9faLvuvWJtE2bddGmlRhpUhd/rJt5MSsU8QPqzM3yuQs6tRmhoOqQkcTwO31SoQMb2ptXw8Q1hk42holLSpS1bBgsWwsIGaXNN+D29B7gmjb+PUu9Hbrv9NkJUq6kHiabpQO4nbURRSe73/Kgav7i27i8uQsLdpxohGaJ7rX5Hz6NvIruRGH5KvVLMHPf8aUrHdqIaKnFGaKEJBH5UiGEYhmEYhmEYhmEYxrpMyaZGnCiiQlYb0czRcgqnPfLTASVt+6NMMbHLFS41XYxGULQ7n5VKlihHuwqHum039U5X+KzCqu4nQxTVQdiGLkR4rQu3o+upIOibX7654b/P0VeA9NO4+KggGIRtq0UKHo8CGrPwehssCOAdxJQrVNRa06yNR4TfViTlTSETohUxDpYTiaqdRMXiSzVr/OsjqSZLhkiYnoGYGulw/xpNVU1kgMXVIQCpCTIZMWUaiArbdzrbSUJNSpdCM9eTDB33vCal51kb8MdkXPt8Yd7vozgTI87UUAG9M3zuQsbSKuS8+GmS4vorKb1RBWL4TQXWq4cxoyCbg/cXwmtvwstL4a1uWJqT+4ymTFORvZPC14J/zRYi6XON/nk33K9GiGgtGv93R9vm7ruUNFTudaf3wwrErB2H3EfUQGpDTMp28qNUfNOhFAMjaT01j9QMVnPF/75NHDAMwzAMwzAMwzAM46NCv2pqJM3U1vQevqjkGxiFDA19bSTjC3AZopRQEAlw/RWvVHBXsbwREelqkQGi6XhcMV1TubjnTAVGbaPOcnZFdJ2ZHneuk4TtYq/jjidw2uOmz2oAXkGE/veC0oti6z4bESGzAUn3U4gOonRW2ndqBunM7mLXQ6nGXwUSnTEBEaenIMf7HvACYsCkkMLP3chM96RjHwfMRM7dB0Tpbvx9VyDGldvXxaJs4ogTdZPuG1C4H1YHKaRvdEz1EBm7xSJt1Dx0Z8/HRa7EPau4vYJI3FfDUU3HrLNuoXbovjOIWbc+MCMNM6phvZGQC2DBAnjuLXh6JbwY7tffbgaJ2tDxXOzeE3fdJo3zuL7Rgtx+Ci33tX5Pi3jrPtVUddfxv+ei7dA+dWvE6DFXhcsaEWMpziiMu18l3beS0HbEmTf2O2sYhmEYhmEYhmEYxkeNfkVq+LORdSa8zgbVQtKQP8M+7T3rcn82KfRfkFeRKUO+aKWzh5Nmo6+LuEKfilZt4QPvs2JCsotbo0HPp848riSa+VtoFribesZdrjO4k4S0YgJ1KcaG7tM13nrIn7nehdSwWFxgX4XIItEcK4ut6KBRTEnbK6dvIe9S8PurjDBtEGJoaITNEmA+sJAogkevhw4KkwnXXYoYQK5hATImasP9phFRt8nZfiHTKYlCY2FtMDU0NdsIJEXX+LAty4FFiPlTDL8Git43/es27lmFei1S7dZzcIuVFzM03Pfp8Hg2BLaohsnjoLYOVjXBf9+DP66E50mO0HGF8lLPiX8fSTIm4j73I7z8tpQShZG0bcVNJ6b3lR7E1FlFFIFWRRQ540awaeSanw7M3V4S/j3NbYe2q8x57a47nIZGOUOQq9IwDMMwDMMwDMMwDGOIKFmn8POT+6lQVCzR2bHQV6zyBZe4XPlxgqWu6xdrTSMpQcYT1W9wiyEvYs2YGjoD2k19Mly4M5b7K47r9zJI/+n2NLWM4htaceJo3OelCJ1J4nepURpxIqCmzCq2bxUH3VnzsHrOm5IU0VBMnNT3uo1OxIDoRI5Lz2ND+L4OOc9q9sQVPlaqENE+hRgVcWm5NKJnFflFy4vNxI9b5pslvuEZl0ZnIMZGiqjIdoooeshNp+ZSHq6vKfXc4ut6v+kOX2divj8YfOG9LGz3GMSE0Puc9okWkl5FdB78ayPu2uxCTJlXgIZ2mLIEJjVKpMbLbfAGycYcyHhqof/nwjc24ki6pyUtK2RcFHqvz/441b7V869jRVN+uea8a1hUIcaiknTP9I/FvyY0OkMNZo0U8VOYFTNJ4q6puPOVSfisGonwqi2wH8MwDMMwDMMwDMMwjNVJvyI13Nc9iLjjmgYqJFc6Gy6UFqPU2aSFxPEKRGisCfeZJcoxX2w2+nCQIkoHVUg4Hgxx4pv7utRoDX89Fby6kbbHzQb2RdEksTRu1njcOYwT6dP9fO0fp5vmKYWIcmORMaL58XUmdRsiBrt1AgZCf2dC68znJJHRv07ilrkmTDPwNnKsamp0IYJoCjH+xhKJ3ytIvj46iFLpFIqcUtFVx0ohcdr9jr8sbrZ/0ncGE61RQZSeSwtOtyPmTAdRSikdI/Xho4JIyG5Don1035r+qZWB1SxQg1j7z78e1XSsQYTljcJjcO+vGkXwZng8rqkRZ2xAdI1o6qyVwBs5qO+AkR2yXS1oX2pKqYHgRyG4FDMv4pYlGRilRGoUaoNGw6jwn0b6zr/Hq0FcFvOZbq/QPdF9n0XOTaf3uUag6T0sbvv63J+6Gkm/symkDtHIAt81DMMwDMMwDMMwDMNYnQzK1IiLgtCZpRXhw03lMZj0GHFijKb3yRGl+uhCZqc3JHxH0dnP2j43z/1g0L7R18NBnCAcJ8gliYX+OorW1dDZyUmpkeIMjf6aGf5nrlitM9D913FpyuJmWGtqpGpEiJsKzEzDmDRUpaAyDZkMdGXh3U54HUnRtJjo3MWJ6kmo4KnrqlCt21FxvIxofOp6Pc66bo2NuGNLunbUwFhJvnjp1j9JAxPD1zVExdzjrpE3kAiNxRQunu7iC6LFDDX/u4XW94XggRga+j2N1qgJX9chY0VTrY0kisKoDl9nkaiv98PnTvr222CudR3jWgBaz6HO+C9HROUNEGOjPgWpILpeXQNPzT5dFhdh59Z3cU0ONTiK1Ypx91WGjO1RRH3VhBginUlfjiHOqI1b5i6Pi7bw74fFlvnfj2uX+z39nQmIDAW9hlPIuNJ+cM9L0jEVIkd8H+r1rrWC3PYV+50ttr84NFKk2G+JYRiGYRiGYRiGYRjG6mJApoaSJC6queCLLv73/NmkhSIy4ihDojQ0v3kWmTHdQWlREiki4VBT0ej+s+F2BiJWDmTG9mBIEuWKiWlJgpfWJ9Hvxh1PMUNjoCKvb4b4gr4vUPrHVYaI1OORYtmTgSnlMHkk1NRAEEBHB7T3QLYzEo+rkXGk41W32YUIvUnjyS34HWfSqeg7ARGntS6HO9Z0fRWJ40wMvOP0RX437Ztf/6IHmXHfjEQp1IWPDqJZ33qOA6Q4+BJKS+EV156kqI1SzAt/e34KKl0voPjMc59OJNpCz3MlUd+pGaDRGxp9URYuW4b0n97XMkSpqVJIXw70fkG4D7dWQyViQo0m6s86PYZAxmRH+L4tPC5NP6Wmi3sdumbgQO5PrulSjoj3o8I2rpeB8WVQloWVPfBa2Ca3pk0x4gwMf//a/kJmRrHoDPe3qBSRPslc0e+nkOutm8gMBjkHWsdG+yzpnhW3P+03TWPoExBd51pTym+fv37SZ8UYhdzDagbwXcMwDMMwDMMwDMMwjOGgXzU1XHFIhZqkmdwquqixUeEsd3PiF0vHkUQayS+/ASJip8K2NFNaKhg3okLTEunyABEMlzLwdETDRVL/FBOrkj73t+fO9sZ79s2LuG0MxtCI26ZrZrivszHL1dCYDMwA1g/fZwJoaYfuHmjqgtc74O2cjBUVH3vITzeks90bEEG71Tsuf7Z34C3XC0tTXC1GxqTuM+549ZFk5Pj7cD9XQyRp3DcA/0Nm+lci5kYlIoJrGjm9ZtXgGwgpotRamt7INW1K3UahcR533yiFgKhwfR0i1jYgUQVq4rjisNYWqQ7fq8HQTpQSSsdKC/nRawNpX3f4vTHAesC0FIwpg2wO2rKy35XAu0jUSCtRrQX3MdSkkH4Yg/RbZfh+MrBBBsZVS+RTQ7uMcx3jg7kPJN1rlLh0XYWulSSzQ5/jIhwg/3qMQ691Nwqmi8gw1G1ohIdrbCQdl0slct6T1tcoEa3/ot+PizosVE8jiVqkiPwohr5ujGEYhmEYhmEYhmEYxkDpd6SGKxZpeqJCIomm4agL120jEv4GKk6CpImZFL6ej4iTmn+8VEFNj0FnbauY3x1uayjE+eHAn4GtQtZAZ8gX20+xh7/uUOMfrztTWoXCcmSMjUPSTY1FzuPbwOIeSPXAmBQ0BPAqkkbINQAqkDE1AhGzOxFx1jXzfFPDJ242t6aQ0WLUpaZzStpH0uduLZS4c5BFhPtOxAjcEOmrxUSmTQtSNLq1H210ySD9NxI5J02IYaBtdWerK76YXCoDNTfKEXNiGjChApZ2SRTLO8jxu1QQnfsK5Gapxdb1nGqb1cDVqB81y/rTtk7EtBiFGCY1gRhy6SAakyuAt5DzNFw1e+IoR87raOReWYWcy4YcrGqHpkCMwneQe3F/Uk8pGpEDxe8nmnoLIkNPjQO3JpAa6sVMjSSjQwm85W7b3GgtNfLcc+OOVd909O8Z/ucZoqLwSaiRMhIZexpplDR5oNRrphoxrmqR+8SKwqsbhmEYhmEYhmEYhmGsNgacfqqH0tKLlCEz5mciYst7iOjlzyh2Z5z7wo6bvkPFo0pENP0gfM7GfK8YOiO9y9l/DhE3V7B6RcOB4ka+uMt8Eau/29TtdhPNtncjA1LeuqvLAPKNFJ0pXY0I6rVhm5ci4vzbSL0MgLJAzmlcTQStz9KIiKCaqqgJmXVdrMCuH13ho2moKpBrIO5zvRiTZonHiZHuOsUKAgfI8SwI1xmFGINVyDXZRd/UVf1hFFER61akP919Zxn8bO84c6nU9qYQ42sSsF4V1FVBczekEjbQRWT6QFSHIqkAtI7FwRxjBhGnJwDjR0BtLaTTEmUUNEB3V1QMfLDEmXBx17KOmyxiulTjpDwKoCMrnzcy+AgNtz3FtqOfu8Xq/fuDmnx+PQ3fxIC+hoq7Hz8CxCfjfO7W2NCoJchPn1Zo2+7nXZR2rscBsxAz7B36Rhm6/ZlkkmiBc+2fSuR+8BZy3vtjyBqGYRiGYRiGYRiGYQwn/TI1fEEnbtapn+Iig6QtKSMqTuyKK67QkyTIpp2HFrTVfOUDMTNctGaCilBayDkp/Y5rrGi7VQzT2dou7iz/oay1kZRSpdDM91JMDvcca3s1r/vqNjCKoW3R8ZAKn1cgYqKmEOsocVtaR0GFRM2VX0xUjBOH/W1rlEZceiBXDHdrIRQzNJL27Y45jWLRaydFlHrqDWSsNyL9ptdnJSJaa3tLTVmziui6dFMxKTqLXdP1JPVb0r76W0MjiW6gsQsaO+HtQEye9gLrFkrF5RqumfB1qYavTwY5NxOB6hR0dUJrJ6zshuacnKdWRLjWfSRF5ijaLr1n6fsqokg7raGTIzKjesi/t2SRyKUWonsgRPe1wd6HffqbJkm/A1Gb9XyUkVxLI+2sq4K+9qumuCvWjpz3Wg0BjQDUa9+tW6PtjMO9NuLuPRqR4u53FGKaajq5Qm2P+53VMaL77wm300T0m2sYhmEYhmEYhmEYhrG20O9IDRWAapCZ8a5oooVzmxExWVOBvE8kiLlFl+PSe0DfGbcaLVCqORAn8msUhrsfV8xTEbKTvjNSVcRTMVBT0bjmRk94fO5M5ZSzXpahm8VcRpRWRWuV6Cx7v2988dhPQZKUliSNRD1ovZGG8LG2GBouPUSFk31zrNTaECqA6gx0rU+QJNbGRWckRWyoqaHmin/RFRIwi1HIUClDxM7JyHlsJDqHK5Dx2kjfyJVxSKRAU7iemzKuEMUMAIjEUbcQvS/yFhtjriDbn/EYIMf8NtCUk7YuYeBRWWmiuhp6f9HrvL/byyCpnSYikRBtAbR3ygz5d5Bzodf7Bsg5WRE+kmbQ6zU8ItxmOVFEUx2RuaT3MTUEG4mKoZeH6zQjEStJ5s9QUaoxEjdW9D6tRkYZ+SZ0XISG/p6591KtjRF3jyyEH8lWHj70HqLRJO76btv946tEzpMaFBD9BmkqMj0fy8L2axq5pCiNOOMT8u9P2ra18V5vGIZhGIZhGIZhGIYB/SwUDlGe8vXCRx35QrgKhW7anqWIKOYWoPYpJG6WKrCUOw/dl86Q1dmnWWfdaqIO0Jnl/gxrFbxUJFPR25+p3EU0s909DlfcHKq0LGWI4KWCZSXS143hc+A9dN/FZsO762aIim7Xhe+bGXgB6eFGhciB4vZNNyIMatRGHEmmBjGv3e9o38ZtT9f3i/0G3jpum5Nme6soOgapMVKOHNdyRAx1a3CosKv7G4PUnHgPEUuHcpa2Gp1x9V+KXedJs877c101A68TzXbvT3FtFcwzzkONATVD+xPZ4qL3ox7EBNb6J2ospZG0WfXhfjWiolDx9TLk/jARuZZVJB8VLtc+ABkHbYhgPpLIuC4L27SMqAD26hS71RyoIqp7pNeIGkjaJo120vuzb2YkmRq+EeqOi7T3vSQjPu5e20F+PZ00fe/DruGuaNvHIKkbx5FvsOQQc/K/yFgJkHGihe4HakgMZaSNYRiGYRiGYRiGYRjGcFKyqaGzTXX2q4p5Kv50ImJlJyK4uGKbfq4zmpUk4WUggowWKR6NiIM681hrK/jCpc6yBhHzVJD011PhrJPomOJmYSeZBEMtAAZE6UXSiMhZgwhf44lm1XeGr9vpW0DanVHst9EV9dqRc9mCzBhfWw2NoUBnKjchY6GZ4imEkoyLpIgDf3a0vx3IN9E6iEyVUgwNXzDtQc6bfreR6LhcVMxV0bUGEVQXMnTRRT7u/vw+iYt8gfzxO5j9DqQ2QAq5v4xF7i9ae6eNKEpoMO3qQATqFeTXYOghKtK9KnxoaqBVJB+LO9O/DjmnKohrvRg1mLqQa30lYqZ0h8c5LjxWPQ9VYVuGs7ZCOVF0WCrc/wRgejpMY5iLzuFyZIwuJarfkmRk+CYGzvoahacpEtuR6yRLfvRWHHHXsvarH+Wl92zdv/6eaBowNWbdwucZZzsajafnT81Bvbd3IOco7npKaq9hGIZhGIZhGIZhGMa6SMmmhkY5qFjTjhQQ1Tz5LpqGRjeus22hdCOjv+KL5nzXGbu5sI2tCeu7URuueBxHznteG+hGBNBmxHjYDJhOJLiuQgTKxYj4pxErOiPZF/sUnXXeQ5RGbF2hv7P2XfR4tZ5AKemDNHWPvk5KPZX03aTlGhGk11ZSYeqkZQGRGdcMfEAklMZ9x22vPncQCbvDgRYNL3QcbsodbadGQqwJ3CLzPci9pVj0kt77KsOHRnyliCKCtKaPGg7dRNemRkyMDt83Epm0ej3HESD3vyXh+nruM2E7NMVaR/hQMb+HfGFcx2Fb2Nbh7PsUYlxshhyvHveUKhhVD+3dsLINVnVFhq1r8FLCs/86hRzjWGBKGbSkYX5XZHTr+Nc+8fGNNo3cS4oc800Ot03uta4m+xIik8Q1+/S3GKJzq7+xftsKvTYMwzAMwzAMwzAMw1gXKdnU0MgMiGaNaiqkCvLTfajgprNbXXHWNwiScuMPRHjpQmYbl0KWaBb72mRW+OgM4iRxuwsRvqoQEXAC+fnkO8iv9aGmhptKR9+reLa6+0PbOlDBWlP3pJH+GEhNA+hfKiIlKfWUGw3jrhv3fT9SZhX5hYvj8CMdSmln3Lpx0USa+mh5+F5nzet3VdQfbMSERm/FpaJSNKJK09w1kl+XpxR0xruKwQMZYwFROjLtd3dmveIaMZVIJNVYYAowOQV1acjloDMQM/JNYD4i0LtF3d3+ziIGZkA0W9+9lpP6Qo2XLuf4fVFdH+65bCU6/256voFeV3FoJIJrilci0WbrIZEpFUh/NPbAu03wXleUylBNGI1WKJRuqlikRiUwJQNzRkNdPby0EJ7rgkVEpnihceP+Lmof96eftA2+EapGexX5hd31nLuRH3rv9u+hcdFNwxFBaBiGYRiGYRiGYRiGsTop2dSoIhJCVIQZgeRnV2HSzc/u1gZw0xhBX0HFFYVWJ8M1C30o0VnehYRczam+nChdiltI2C8Aq4JYjvzC5/rZcKaX8dG6Dzkkb39/SCNmxihEPO5BxtnqbL9vFiSlTCp1W+45LnOWQ7zwH7dM68qoKeIWry5F3FxOlIarmqj2jKap0vQ3g621occbVxfCPeYKZJwEMeuWcs9ISh1U7Ds6811NXD12bYNbi8EVz7WY8wSkJs0kfdRCkIMP2iWCahn5KYPce6ai0SlqIrnCeSkRRW7dlIDSBW23poSe+zhjZDC4BkM50lczgfXTUFcm5k9jD7zdA/9DaryoOavHUIGYXsUMjSRTA6R/VuVgcQtsUA5TqiHXBRsj43xp+Ggivl6K26/9rdFSj4wTjcLRIt86YaAOMcU0ysffnx53N3Lv05RkAX3PtxkbhmEYhmEYhmEYhmF8WCjZ1NCUJSpwVSE518cS1a9Q8byayOhQMc4V6+JSdvizXQ1BIycKiZeViCA4BTknep5UCEtKy6QpTOqIChBXh88t9BXBIX6m/2DQ2ef9nfmvYrdGaLQiY1BFwdWNH6kxmG0o7nH4YndczQldpwIxisaG6zQj5lYr0TgqJGqmiYwEFY11HGmdjjhh221jqcJ3kqHhGqQabeBGiPVnX/01ucoQo0zrSbQQzYTX9rkiuSuoqwlQRmS41YWfrWqFxYEI9O8TGU06luNSBMUJ03o/KDWyaaAmRBkSMTGWqLB4GyLwD7bGji/+6/0qBbTkoLNLPm8M99VGfp0JyI9WgdJNDZcA+d1aHMBz7bCyI7x2ymC9KmjthBHdsg9Nq6iGsPar3kdLMXy0TzUd2Wikf1uI0l1p9JwWep+MjA+tk5QlujbdiQPN5N9/Cv3OGoZhGIZhGIZhGIZhrMuUbGqMBiYigkwGEW90hv8SZBZtc7juOGe9LDJ7VHPRFxLr4maUftTRqIokypHzMi18rkX6uYNIHCsktKuoOhIRYNXQWEF+IWI31ZHO2ibcj9ahcEnKKe/TFe5rIGiu/45w/yp8ryn6G6XRn3Hu109wxXQVhGvC9bqRfnEFTzXHCu1TP6tD0gBp1IGmRFIBWCM23Lao2amfuQJv4K2ruOPTbYN7H9D6PZpCSSOR9DhVZI4z4AaKGm06hnUfLnERGv6xuAW5VwLtAbyNpDXSItR6A9ZzldRna0KQrgamAlukYP0yqMnAqh54uQeeJ0pPNhR0IbVfWomMSu1XjRjR+5sK/KuQsaHRPqXU04h7r0Xf3weCAIIUTK+A2hroyUJVd7RfHY/ueCg1pVkKMTAmIJMCNOJRv18bfqaRGXodjQjb9x5iKAXItT6GKMpnFXLNu8ab++yOq7U53aJhGIZhGIZhGIZhGEYplGxqbAx8og7GVkF7OyxuFaGuAVgAvEskbKvwo+K3zkL1TQ33WV+boZGM9qcKfmlECBuJ9L0W9e5AjIKFFC/uq+dkJGKMVCLC4ihEKHPztrt53FUY7Ar356ZC6Qq/2+B8NtTozHkVPNekWKfHrecGolQ0hdrU337xrxWd4T8SEUOnhMvfCx+rnHX9ayvJQMwRpZZz13W/nwn3q2ly1Gxw60n4+3AjWfpzjNomiCIgNAJJzbtuROTWotGDRY3YOJLSGfnGRhYxedX8BbmulhKlnMo433PTCeVinqHvufLNpaFGjcLyFIyuhLpKyLRBbU8/fjhKRPvc7Xc1y7Ru0yiiqBf3t8Q1k1LOMvdZP0saf5qirQOoroSNN5ZUYY2tct5akPOp9Yn6ix5LiiiqDKJaSBXA+sDGGdhgBKRzsKIZFuVk/cVI/RX9XjVyftQYcwu9Q3z6qbjfX8MwDMMwDMMwDMMwjHWRkrWpaUhO+PIyWLRKUqi8jRgbjeTP1F9FVD/DndXvi5T+s7uOkU85MoN3DCLqaiqSXPj8brheB9EM5lIjFzSdmM4abkIMie5wudbbiBNVxyCz+seloTwNXT3y/UXIzOcl5Ed8DDU6m39N4aZpqiaq/dBGdB6Ga0zrjO0JyIz6SUg/tyLnr5nI7ImLhAqcz10ayL9WNTWYirJdyLjQKB/oK/a7+/AjhdzXuSKv3RQ/Wk9BC8NrzYEeZNw3kGxGDCWugeE+3M80qqXL+SxHJEJrGiU/jVhcf7m45pAbGTMcdCBFzLM5WNQKFW3QFIiJvXKY9umixqWaVl3ImNZx6EcuFUo1lWRouNeFjqOeFJRloKVD0lHpvWw5/Tc03PuqFvjWKKpqJO3fesh1XA2sVw6jamHZCng7gNeQ2iuNRIYGYTuXkD8O3DGUNFnAJg4YhmEYhmEYhmEYhvFhoGRT4/8D3l8mAtObuahoa5yg7KciKiMqupuUFsNqaSSTRmYpT0UE7AARcDuJ6khAJMppfvc4UogQPBqJ8lCBdRlR4eJmRHhTAdkVbCsQoU4LmFcBE8tg/VpIpWFVC5R3R2aDK0h+2NAoCRUnR4fPXYgIqqmHhosccv7VQOlExkArIpJqrRutWeKmhnK34dNIlErOXScdbldNShXl43CFZV+0J2GZvvZnmKvxknP26aah0vtNXH2OYtQj509NvELEpZhycQ0ON/pCU4fp9eNuT3HNjGKmhvaFRnbA8Bgbasy8BbwbHozW8lhd92oV/9XEgvzzHRelgfM+IL6f9bW7vAcxLv7eDm+/JGZOQ04MnCaiyJkkg85Hz7cWMh+FjDc1QfVemkN+T1uAdzqg6gN4PyeRGUk1gtTwcamOaZeZGYZhGIZhGIZhGIZhfBgp2dR4GfhfLirWq0KLn3Yn5zx8fIElTnj5MFCNiFXtRLPLB4OmXhkTvl6FiGxNiPjsphyJ63cVY1WEn4bMDi4Pt7UYmY2stTHc+gSuIKgRHTmiwuIB0NEDTe1hkd9uOWYVwEeF2+0iMluG6zynw2Py0/oMVVoin26k/9VI0v21IkJoxzDs00f3VRe+zyDnpoaoQHicyeWeX/czvY4L9Zdb36NYOil/f4o/i95PseTfK7T4+QQkNVZAZJa50UmloNcDRDPn+zM+4qI0XIHdT0WVFD2gx5cUpVHI3ND1V4cZXKyuz3CiaZs0vZkaW4G3jvvaf+8/J0UxaDRQM7CkJ6qjoffzcUT3xm6iqKgW4g0xjdiqQgzPUUSmoNYKWY6YyYvDbWWAVE4+c6NCtGaNbleLxSvaJ+7xxf3Oflh+Yw3DMAzDMAzDMAzD+GhTsqnRSb7IomaGzlLXwqYdiDijM/3d9FPKh3kWqfZJHXJMXQz+2DQqQgVzNTNai2zfFVd1ZnsaEX+XIudLhW81Rwq11TW0MmGbUmF7ursioU5rXZQhYh6IsNdClPt9qCM30ohhs374nEXGYgsiHA5HuhwV1jUXfzPSJ1qoe3XU+Mgh/b8C6fNK56HFiN2oqEJ59bVGiwr9il7rer7jakoktc1dx++PJFPTvy+kkXvMlsDmGUgHMD8nY7gnbKtbJFn3qWK0Rke0EaXk0tRZajyVeq78NEdJ/VHo8yRBXinV1PDXX5dxTUjfdNBoLz8qw6+XEffsbsd/7T7rfauLqHC3Rl/VkZ8qLEv0G9eCRDYtI97Y6EEMNy1GruaD7k/vHXHmt3vvHoGM5xTR/UWjOPwUjx/WyQKGYRiGYRiGYRiGYRhKv+u96oxkFckrEUFF05Lkwo3qjH4oLM4lFcFdV9EaFzmGJkoDImG6nagIu/Z1sZncbgqcrrBtK8kXwfyaC67A6D+r4KcRAaOIxHM31ZTOaK5EZjjXh/tegcxKLpQiayBoe8YhImQTIjxqSqbhQo9Bc/+3esuHgkLCN0h/L0HGRR1i6pQTzbDXWd3FzMMcfWflp5BzqMJpkkgf10Ydt76IHye6Fqqzo+bYVGC9auhslzHkiuBqvOj3yoHJSGRHDTIGFhNFI0Hx/vDRfWhkmm9kxNXX8Gtt4Dy7bY9rx4flnlgMN8rFj0BQ3BRohSIxlKQxqZ+50Q5qZnQQjY0uosi2Uci9pcz5bAlyrWsUWKFzpbVVGr02JN2/XTMjQ2TM6f1X21hBdB+Pi0ABS+9oGIZhGIZhGIZhGMaHk36ZGprf3BWhdMapOwvcFaAgX0zxRZYPU5QGRNEZPZSemqcYzUSCbBmRIaDiarFZ5n6KFX9GsZ+bHvqeE/99FVK4fGIKKlPQFUBDIMLdKqKUTDojXmcYJ81KLoU4kVzpQcya18L1WsPH6sz/zxDvS8+HOyPdTZvknvd2omiRZqQv1GTxTZ1C152bMssVVuPqVZSS2icJV3T1X7v3EDUSapHj+aAVVgVijrUQCcZu3QoVf1ucbXQhRtdA0yilEcNoBJGJqxFJcaJ2nOkBhe8Jcf2XK+FRKm471pb7rm9QqHGm51MLq5fyfYjMDDXhKpx19JpxTYwuotpQvlG4FDnnn0jBlEqoqpTPGlslPdVi4APyx2sS/TlXWm/DT5HmmpPusbrERWesDefZMAzDMAzDMAzDMAxjKOmXqaFCi4qGccRFXiQJ5B/G2aMqpmnalKEgh4jUrYippIVmRxOloYo7H66AWQhX5PZFRn+Wvc7c1xnMZSloDGBRIMWxFyPisQrNgfM9zVlfTFhWMdJNW6ZtcWdT+8fQQiRkfxjwhXA/AkDNRCUgSsUE+UXeS73OckRCcFz6pFLaGpc+Ke7Zfx03e13HTzvwNrAwkDGwAjHP2ojEaZcsMpt+SZE2x6U88kkDE4GZSOSHmmaNznM7+cYKCc9x0U+r4x6oNW5qiVLQJRWhTvq+WzdJa/RUEhlfauiqcVnsOnejW1LOs/4oxdUrgfyxGNefuo0JwPQyqMpAUzcsz8n5aiZK31ToHq0mQmUaqmshUwZdPTLuFiL3uqGuNVIePiiwbT+qzl/mR2msLQaWYRiGYRiGYRiGYRjGUFGyqaGGhlskGPqKK4XWcWceu8s/DKgopzOBhxrXdChHZhBXhs9aE0Pra6iAOYKoboaKroW2n4QvaOcQcfA9oC0nAt/7iJnhiqRqTqSJInrihDqNAKoJj6k2fHQRzcbvJD910IeddJFnyE+3FPddd8z0J8VRFXJj6PDWK5TiJ0mkL8XMcKO8kqJw1CgDGUeNRAbXQO8jpYq9I4ANgU2BsSmp6dGGiNstiLi9DLnO3MgkiL8P+gy3uZFCrqcJSHq2SuT6XURyLQgXjdhxxfYKYDywETAlBfVlsqOGLCzMyr3hA5KLt/uGhvtIx7yG/L70I4f88VkJrF8Ou02A6ip44z14swsanOPoRsZV0n0xh9Tj+UsWRqyITNWVwLsM3X1ef4RdQydpLPjXnIs7jj6skZCGYRiGYRiGYRiGYRjQD1OjPGG5L7C44r67jkZ3DIcordERfkqewWwPShOEyhCBryZsQxsiGA5HkegA6T/twzJEcC1HTIFViNCmURxjiATIFvqmLtEUOnHpc+JEcF3WicyAX4wYKk30rZFRhuShH4H0ZydRVInfN9XILPgNw++UI2LoAqIi30Ndg2NtJi5CIy5qQmfL14ePOqQve5DUOQ3EF5Iv1o/VRGnO1IRyTQu/nf52dXlSnQw/XU+GwveFDmS8uftfHUXYIUqbthAZuyMCudZzRCnWepD+KqNvxJNvKMVFtMQZPkNJBmnzSKL7hZ7fVuT6LYQatW5dFi2W3RNuc2IaKspgXAAjs2KMdSPGhntu4wyLJEOjlPRdbl+rmd4r7Oegq1M+6+mWce0a7poOL8l4CZC+edH5jnv8PknGYYbodyJA+s0dD2Xk18aIOzbXnCy2v6H6HXTx0xMahmEYhmEYhmEYhmGsSUo2NUpNP+O+j0tDMxyoGAaDF3MyiBlQRmQgaNFvTamkpJGZz5Od76xCBNgGRLwaSjG+i0iI60JEympELBtDVBBZC7WrCVKOCI/uzF1NBZUjfra7imh+Chg1t/TYuogEL3+Gfg9i8qTC9eIEdp1FPgVYPzwene2uNQx0Xx8VksTcOFOjBpgGbAxMz0BdHSxtgmeQvvfTMrn7SBqXmjpIRWxNN+TPhvcFfHe5+7n/cFNMVSC1WSqRcb0ooc2ry8Rw0eNqQ4Tv95FrKyyt0CtslyMmYpzoHPdaCYiuoR6kj8sS1vW/199aGiDHsCzcVwdyXXWWuA2/JkQXYjj+D+gOYFInVHZBRyBGwLJwHTWbtR39MTTizAzXwHDv+a55pPfr5Vl4pwmmjYQN14Mp3bBwJfy3W0zedpKvgTSRKau/BXrfzSBjoMp5rkFS8bUH+aZFefh5HTAiJdtdGMDLyLjSwvNx57zQ+Im7fofb8OtXrkrDMAzDMAzDMAzDMIxhZMhMDVhzM+mHamZqGWIObIQYBirkaQ7/D5BoA/c41VBYDxHBskSi3gdIGpYWhq5vAkRYa0REsSoi8a2a/Jm/mvJJi0i7M61zzue+GaHvNX2UGicQ5ZlXs0ejBbRd+t0sUWSGfi+ODJEBo7nu1QwJELGwJjyGj0rqKcivn5FkbKSQftX6CA1ZWN4kES7vIv3ZTf4s8FLGYbOzfRVc+zN+i5ka7hhTY3BauOxVRCxvItmQWZ24QjlEZqCLFhGvJDIM4/rLX6bjXItAZxAR3K1dFFcYvL/idRYxWzUFnZq1Se0slW4kWmsVcvzlQb7pqtEdpUZk+CnWfJPIJykVk0aTNQALu2FEG0wdCeVl0BzI9bGAwveUCqRm0PrIb4Fbw6YcuSfVA/VpGF0BU8bCmDESHdLcDF1d0NMDqRRUVkJ1tTxyOXjzXShbBW8R1b8pFT+6J27ygB8pORTkWDuuR8MwDMMwDMMwDMMwDBikqeGmpIkTl3wxVNNEDUeB8KHYXhoR2KsRo6KSSBBeTv5sXZDjWIYIem4KoBpk5rZGRyyg/+JVMbKIKNdONJNc0wZVhA8VMxuJojL6Q4Acfy3SFz2ISeHWMtBznCSQFtunRnS4QnoW6eNmoloha2Km/pog7b1OJ7xWepAx2I6IpK3IWHXNtwARYkuNnNKZ9X5+f9eIiEsH5Kb+KcXYgCi1kRoD64X7zSGi9Jo67674Dn0jTFzKnXUK9avbV2ouqNkI+UZDJYUF/f6gxldSmqXB4JqXasS446y/0RhJy+Jw96Pv3XG1Avg38FYbVL4VXSuLKH4/VnNaz8MYpCZJDTI+q4GRZVBTK2bGhhvC1PWgugaCHGRzYmrkcmJspNMQBNDSDB3dsP4quU41SkePI+73012e9taJ6wP/PAwVa2rSgmEYhmEYhmEYhmEYhk/JpkYhscUXoFxB011HBS2/WPjaIlhrAeJ3w+dKZJbueERwHUVkbqhopGlpahFxs55odnstItJqWh238PJQ0onUnVDhW1Pk5BDxbqApsFQMTSHHlpSGi5j3hXAjAFQQbiMSdHvC/TYRFYReW8bIcOLOWE9775OEYIjOiaYz6iBeWE4yH+PI0Delm0ZEldHXfHFT6LjnKq6uhnv9dxDVXRiJiOMtxKcqW1349zM3vZrb9/peDb+A+Pthof3EmULDYfoON3pOS62ZAfnHX+g1Ma+hr9nmLtci4IsYWESK/ha0IvVpxiBpBkcRmRodORjVDqmVUFUp9TvKK6CmBkaPgvp6idIIgO4u6OiA7h5YsSKKtPPrgCSNl7h+SLqe3egq9/d1XRtThmEYhmEYhmEYhmEYSaSCIChJ6xiVSpbnklKt+GlSfGHTf702oHUjVFiuAyYiURdNSL2MViKhTHOsjwwftUiUxihE+AKZybwUKTaspshw09+UQcXw62bECYmltKkC6VNNOaVRBG6dAjU1mhk+I6gYKaL6BlpfYrjRPi72HCf+uteapgjDWU+Fcj+lkftdF9ekKCYK63plyLn0x4n7yHnP+n014yCKWFhTaHt8gd59uOtlvOVxqZUUt180UiOFmKEgho6mbEpKP+Wfv7WBJDOjlMgMYt77/RYn5MehfdZJlG4rbnv+d4qhafb0eMqQ34QxwFRgShnUpOXzMeUwcyrMnAmjRkN3N6xcAcuWQ2MzPP42/COQ3xIorY6KtjPuvhv3O+tfb0MVuVHinwt9SBX4+8EwDMMwjA83A/37ASCVsqpehmEYhvFRJQgKK+jDVlPDFTbj0mgMteg+VOgsXxXYNO+9Rj5oEWUVibQDNSKiGzE3RgETUlCTkvWac5KG6nkksmO4jY2h7tuBCKiuGKwpuUYTznJGDJ6VRMaQ7kcjAoZDtI0bx25ERAViuGgNEU25NRype+LakCQGxwnlcbO3XQPEFTbjZnQXugb9Wd6F0JQ3amq459BdJ87Q0M9Wl3FUjLg+9s0k6BtN436/2PYhuneMRgqlT0aug/eIrom18f4YhxupE2cIJZka+t0kdF0dM1rHJ0NkBCSlYqokqgVURWRAqJmaRe7VDcj9eAWFx7rWVnJpRoyJFUBLj5i1ZcCKLmh/A5Y2QVk5NLfB4hUSNdIJvIacY4160jYXIs7M8SM1kn5f19bfWsMwDMMwDMMwDMMwjIHS7/RThURhnRGuaMHSYqbGcBQ2HQhxwlsWEbM0BVJSO3sQwaobEdOmAFVlUFMO2QByXTA+K+LlMj78Ra/TRMKi+wCZjd6I9IObKmk421JOfgSOiq/6uRZp1sgMzXWvRtVw47bHX+ZGBrjP7vqFzIkkQ6PQ93SdUmrgZIgMK4hMIE3J5Edmre0Ca5z4niTKx0VjuBTr30rE8CtH7h9re9/4+JEqxSI0Su0/N9WXbqsbuS7Lyd++HxUEUQo+fVQjxkYtkamhBrXW7inW/25b3N+xTNi29vB1J7AiBz2Lpb1aDL4VGf8t5Kdx802HQr+xepyFMEPDMAzDMAzDMAzDMIwPOyWbGhq94M7IjaMUEWVtMTFckgQ3xZ95HkeACFsfIEJlVzdUd0dpZlax+tJPrWnc2dBpQqEvfK8ppobDLPBn2Gv++9HITO00IjRmyT+/lYjgSdjOhcj5KjVaYbBtdo0KXyCGeJHY34biGohJ5kWx6zSFzHKvQc5dS4F1a4FJSB/nEIFYCzHnEDF3FcNraJQj59qN8unvefONJVfEdpclred+7n7m46YLakdm+zcg/dxGZAat7aih0R9TA+LHr4877vXcViL94xoefmolfdY+7ELGblv4XBtuSyPyOsPt1BLdr5IoJ0pBpftMhftYSn7KNr3n673GHfs6Lt2aNTlne249m6R+ibvG3eM3DMMwDMMwDMMwDMP4MFOyqdFOJCxlCqzX35mha9NM0iTRrZgA59MKvIGYGxmiGgFaxHl1zPxf06ig2I30hy4bznPtC6hViJGxHjA9fFYBshEZ022IsFmGRNfUIaZGOyI0rw4KGRVxYrAfsaGUmnqmVFOxDKn1UEmyqZFC+mxM+NyJmBoaqaMz7FMF9qn3k4Gmn0qF7axHri+tp9DZz20obnSMfhYnMsdF1rivk+4bapR1IWaPRghotJCK7v2ZkV9sPd3vUKHiu2tolGpqEPPeJW68uoZCB/kpAP1IDX10O8tbketZ2+WmO9OUacVwTVo3ZVqbdzyahq0Q7rnTaDAttF5JFMVRjLgxYJEahmEYhmEYhmEYhmF82OlX+qkeooLOpazfXzNgTZEktg20/SqitRZb8SPAcBsZkC/+6esyJMpgErB5CjaugtpqaGiDtg4R6TuRFFiNiAGSQdKD9UcMH0riIgN0eVJkRiFRs5jgWQi32HKc2ZBB+rcemAaMQ4TcLCIeL0HuF2WIeO9TFm6jiij90nKiCI/+oBFS5eE+1UzrL37fx72PWxbXnqTIGYjqM7SE7XTFbRXXMwze1FATuiLcXgfJ57PYfvx7pNa1cFO5xZkaEH99JuFHF7nmBEQRGH5kRq7As752zZCB4Nd9UeO0msiYUwPbbWPc9eu2W6PXdB/9iTAyQ8MwDMMwDMMwDMMwjI8iJZsaunIxob9Q7Y1C669pCs2SjxOZCs00Hky6IhUE14aiyesCae/ZTUvTgURlLAigpgNy3fB+jxRjXoGYGS1Efb0CiTZIMzBxfaC4Ka5cgdiPFoh71tdxQmbcteUvK1SLowsxJ+KoAmYAU4GR4X414qAGEXm7iDc0KsLv1BDVO6hC7i9vJ+yvGO0MvJh7odRIhSJo3Pdxz+7nKnJrjR41NJLaU0qbC907q4HxwITwdTdyLSwhSuFUCm5ERlzkRSFDIy5Kw31dSuok7TO3yHzOW981CNxIjVzC66EiA6wPbIikr+pBDL0PkELgbqSIkmRuuNtMO5/111gPEl4bhmEYhmEYhmEYhmF8mOiXqaEkGReu2FRIrFrb6mlAsmDn5jt313XTg/hpTwZTM8Rm15ZOnJjsiqYdwGIkHdK7AfT0iHHRQrzA2c3qSznl4gqv7rjxjY1S0tG4JI0lP23PQNC+GkFUt2AVIpivpLjB0ENUQHkVYm40e23XaIXVUVy8kJGhQn0SWlsC4g0lrSHT5TyS6uq4onahe0Ghz6qRCKX1EPOoDDGcNDVYZ4H9u9t3X1eE23HHqG9g+OZGIUPD34eP+xvimxVx6yaloPIfruk52DGVQ8au3lPaEaN0FVHUjbs/97jcNrjXvDvW4n5H40yLJJPHf20YhmEYhmEYhmEYhvFhoV+mhivsFRKFkkSjJLFlKE0OdxZwf3HTxPizfF3cmcsDER0LsTYaPmsjSSaUK5yqkNxJZFYMxmxShlMo1FoLGhWVFBFUKMLCF3h9ehj8OOsCFiHGUTki6HYQ9Xmx72ptAogMDFdoTyNCutYxWB3ibLFZ8UnGRzliJGhR9E7yheZu56F1M8qJajPo+ShDjnkwxmYKiYAZRZTWS9vht7vQNtwINT2nbkop36yI+yzJ2Ci2b/93Qk3iQv3if8e9Btx7uRrSWkdDC3kPhBxRfZ4UUdopHc9+BFVcW9UIysasp/soRlyfua/N2DAMwzAMwzAMwzAM48NGyaaGOxsZigslccJqKYLrYBmomZElX8grZFa4qViSGIy5YhTHndEc9+zOch7oWHNnoGeQsaHi41CcVzcSQ/elArcrgLu4x+XPAC9lpnacSeejYngHfaMk3Jn6XcgsdRVl+xNRodec4kcOaB/40Suri/6k/alAIlY0/VAzfQtZp4GxSPTE6PB9N2KCrCRKg5byvtdfNJ1XmigiRM2WRgrX1IgzILQdPeF39Vrw62kUSjtVSoSGayinEpYVu6b9cZ/z3rsGQ5qoILuO2zIkjZTWZtH6HWpYdMTsU+ujuLh9k4S7nQzRufe/464XZ+qUct3bb5BhGIZhGIZhGIZhGB82SjY1yildqC80U3RtFltKaY+mh/FnJeecZ3d28dp2jB8G/CLESSKqK1qqGeXOoNaHL8iniQpYVxAJ0M3e9gZjllQAE5EC264IvTR8dFJY4Cz2utB1Viw9mtbJeA8Rw93IDjclEETFk4d6nGuEw+okTlBP+txFDRgtrN5F1B8ZxMyYBmxaIQXr61LQ0gHLumBhkJ+eajB9qSZYD7AMKbyuY1RThLVT+Ny7wrqm/3IjnwpFaxSK1PDXidtvnGDvGyzudRoXoRBnOvjraD/H7VOvzUryi413UFpUh2u2+sfhjx/dt1uryk9jptv0j82PXPELpBf63TUMwzAMwzAMwzAMw1jXKdnUcGePF0qL466vJAkv/ut1Ad/UcNOa6LObLmUw9TU+Cug4KtUgKFVEdaMs1GTya1TEGRsgF8VIohQ+WWQGfjtR2qQ4YbVQ+91rpTzc/mRgI2BMuKwFKZSdA5roO270ONz3ccfiz/T3Z6mrcJo0LschBdPnI0K9K7S74/3DFI3kGxrQV4COE6RTSB81kl8rQ9NnrQ9sk4JPjIfNpsLYMdDVCfPnQ9NiqfOiplFcP/anb3Pkmyq6zThDwKfY9eTezzIx67rb8Z/98ZKEb5i5xqWbqqu/4y1O4I+rKdKD1MNoJ4raUMPBTxOVhFsTxSUXs9w3VVxD3L9/+cTdM/370EBNV8MwDMMwDMMwDMMwjLWdkk0NnTWtYovOYE8ScCA+NYZvcKwOdNa+FrrtYWB51MuJRKs44VPxBdC4GcEfdVLIbOh6ZCy1IEJ+Kd8r1dhwU6YlFXrWMZqJeZ0lKuzcTlRg2Z+NHTdj3EeFSk1h1YpEQmSRiI2qcFkT0i+jiKIk4mZoJx0HxIuavsCpwnRcurUGYAmSFmkgOf3XNeIMKt+09Je579WUyBGl7qoCNgbmjILtpsOUSVBdDW1t0N0tzw25yCxL6uf+CNMq1hcrAu5TyBR0r7G4IuGF2lJKhEYcen9VIyZDVGtE65XEmTRDER2XIyqq3oMYGxo1VMpvht5v/GsiziBzfws11ZqumyJKPRfXz0nGZSFD0zAMwzAMwzAMwzAM48NCvwqFQ346kwpEvNOityoqdSCiUNxM8qT0GIoKOrVIWh7CfWme+v6gwrkaMJBfxDeJunD/eiw6U92vgaDHkYp5dj/zxXdYvampMkjO/8mI6Po+kuJooAVyhwI99kqkzsD4cNkSYCGSOidJnC1kbLjLShVV/fOXQ0yWVeTXdXDHrmuY6FguJCb76W5aETF7GfnpZ/Q8jQy/0+Lso1BkRJJ5mGS2xAn5ynyK17JIimJYlwXUJPOiUBSHK0zr/UbrWlQAnatgZQN0dcOqZnhvKbR2wfIcLEBqkmjtkkLRGv7yoepn3wRMMjR0n+61EBCNXdc8TBLw/WswKfLFfV2G/Mbo/VgNALewtt6Th9oo7yQy8+O2XY6cY7df/HtRHL6h4UaJQXS+O5Hjrwv3o4a6m+7MTZ3n/8YWu34rkPtMGrl3tdC3PohhGIZhGIZhGIZhGMbaSL9NDSVAxJZp4bM781vzubeSn7rGLZ6bRD1SSHcMUUHdZcAbQFs/2ziaSCBW8WkZ8E6BbVUiM+c1HZDOzPfxj0HFJne2sL7WWg5ViNiZQQTtFvLz7w8XrghWzuo1VArRhUQmaGHdaqRfOog3CNRUijMt/GLhKe+9/xr6zqR2RWyN0IjrJ01NU0m+oNpC4THqb8sVaZUK5Pj1PLnfTRJKi83WjksFF7dd/awUYVP7348ccaM/1iX8aBjIF+j9Zf57tx9VJF4M/DMHry2Qa74DMWe1fkYb+aaptqOQaZX0fiDEmRdxpoYr2uu+1VjQ+74WD9ftqODvRy74RkZcOjXdbwb5bRmPmLGNRGNTr/m4e1mSKTMQksZyNbAZ8lvRBixCfluSrp244yzlHGaQFGYjiMz5ADFcl4fPatSrwVHs/l6J/DaOAiaF75uR38UPSmiTYRiGYRiGYRiGYRjGmmbApkYFMAWYgQhOKsa5dTfKEcFFi6wWEzvdmc5diEjUhaTB6exn+9JIBMBMRBDSAroZJEqhnWThRwW7uDbHiXJxx+WaHOWICD4GEegyiAimwvlwEyDC20Kiotdrg/CcI0qx1BQu07oVcfiz5H3R0jcy0gnr4S3Xseuuk1SwOY2YZBOQcZ8K19VaG4XGVRKuANuNzN7PkB/t5NYVgHzh0hfCk9LQJO27v6ig7IrS7n7ilq0L+P3lvnfFfYiO300ZlHOeA2RsdyDmhm7TTSeWJEKvDmMjzryIi6TQ44m7X/jpqNzvlyOGRJWzPM6YK2TWqRE8AjEROsi/pvW7xcwZtxbGUEQUpcL21CPGQBViaK6kr3Hq7tMlLrIi7ntjkN/YkUTnQtNiabRYK3Kv0EkExagIv58iSjHXhKRCMwzDMAzDMAzDMAzDWBcYsKnhikUqRHciIr0K1SoqZSlNbAkQYWWVsywuPUcpVIePMqKZ92qwJLWlEhGr68I2uDP1XbHMF+LcZf7s/3S43TpEAJsQHksDkfmzOkRfzRW/uogTL+PQ4sYdBdYptP04IbPQ58VQUb6cvmli9BxXEKU1y4ZtV+Ouv8eQ9h4B0h+uQZMJ21NOlJbGTcHjF1bWZa6AWyxCqj8kpRhy3/vtcZevC6hgr9EHeh9xTTAl6biS7ntxUTS+uB0nduuyoT6fSpw5oCJ6sfuv3x69Z/uGRlJ0VJzZkEPM2OXI2G9Gxr1rVuTo2+64KDrdnlvwe6DGrprEi5xtdCBjpJy+hqh/bO75K1bfSev7aKoprffhRvno9pPMYJ9WxOB2j6fU32jDMAzDMAzDMAzDMIy1gQGbGh3A24jQlCIyDboQccU1D5LEt7goh4EUuo1D07+oQKyCUCMyKzVO+NoIiezQegdxaU0KiXP+s4qQ3eE2GxGjRUU1NTU+TJRiIJQyC73U/SS9jmuHb0jFCaqat16jMPT8qfGSRQypLqJCvjru+xt1o2m3ID+tlo4d/SyDjJtaotRFKm66uDVfVOx1l+kxDkYMLyNKKeTjzpz38dNUJUU36Tpri8DagxxzJZHAD/n9674OnOe4MRgXXZNLeB33XgXowVAoSsN/303fYvXF0OtnJGL8dYcPP7JCiTPB1IhuRcwDiK4X9/uFipUrOe91qf2XQq5xTTOn9201eRqR41LjayyR4dHprau/BXHLkgiQY/932A534oDeA9xzE3eO4kye1W1wG4ZhGIZhGIZhGIZhDDUDNjVAohk0qqI83FgXfUWjNCJuaU5wfZ8hKsg81LOOO4F3+7H+CGBq2K7l5Is+SREASW12Z76qgNSMiGBlSB72Cc52GxkaI2dNkiRmu5+7rwcirqtwrBSql5H0eZIZ4qI1MyqIzp07k1kjMwaDCqFJ48o1WnrCdmTC1+3Ei5JxaYLcaINS8c9lisiAc2tKuLP53WMInO+oAeLPSPfNmwoknU9F+HkrUf2JNY1rVATee7zXLn4aLn+b/nPca31fasREf0mKaHJruvT3OnVrzvQQ1chJk2/klRLdo+MdJM1TJflmkvu9QmZNf48jg0TWTUZSQNWEbW9D0iIuJTJdapDaFBsB9eXQEkBLDjpy8tvWEB6DRldoDYxSyCK1LiC6TtRo9Y31yrCNajxq5Eg3YuSbkWEYhmEYhmEYhmEYxoeFQZkaIEJLHSLq1JFfLFnT5tQhpoEKXeXh97qQmajPUVrqjHJE+EwTRVMMFVmkSOoKotRTxUgyNlSA9D/rRGpqfLwCqjOwuBP+k4O3EGNjbZmd3l+KGRqFvlNKGh9d36+Vocv1u0mzt/vbtiyRAKmzojNEgqKbBggi8VDTxJQqnibNMnfNAk3P1UoUYdQfcVLbWkhg96kgujH40SOKuz33tZ4HTeHlbsddRylDxOpaxOirDtu7gsggXZNpqzLIPWdk2JYWIlFazR3tA3eZRoclRS75Job77H8OyXUtBkqSoabnrhoZB9XIuOuPideNGMMric5fOVGUmjum3GffSCtD+n0SMj4yiNHVSpTqrZRxXSwiIo4yxNCYhhTVrgq30xTuX6lHCnlvmoaPjYdxYyDIQXc3dHXDym5Y3A5LW+C9rHy/MewbN81iMaqQSJDxyG+LGq1qXlQhfVVHdO60YH0j8CqlFQHPkG/otrBmrz/DMAzDMAzDMAzDMIw4Bm1qVCGi08aIKAkiIKkIXA3UVkJlmVNDIoC2NkkP9R7FBacyZDbsCETY6UFygieZGjpb1Z0RrKk6XNz0Tx3AknD7dYho1kJhoyFO7E0yNHT9CqAsC+kclAfSf6WkUBlKXIPAT2/T3+0oAX1FSV1e6jYKrZ9kTMTNNC/1u3Hr+Pnl3doK7uxwnTWvovcoZIy2hw831z/I+NLC0X5NCt2va/Loa003BQOfae0XScZ577bFTRHk3hj8YylmPumzphDSQupx6ehSRLPxlxGZAu30v0bJUJMiMmzriWqZtCPH4hdvL5TOKSlaw0075T5D3+8Uig7rD3HjwF2mxzUCEdHrEPO5jb6ooK7RQG6NJRe3j9zi4cWu20rEVBiP3DsbkSgJjTzQ8xBXWyMuWqNUcsj12hDuozI8phXA+4ghMQLYPAWfrIUZk2G9yVBbB+k0pFLh2M5CRzs0NsEHK2FVO6xohv+2wTPhsRQjjUSLbISYLGquEr6uAmoroKoMylKQCTukoxNW9JRWQDxFVPx8DGJCLQv7wEwNwzAMwzAMwzAMwzDWNko2NQoJTyoCVyECVEVGhJ2yDJSVRSJPT4/MXl3VLYbG60iUQjHBpRwRjesQAXQRYjjEMRZYj2hWsIr3K5CZqs1EM3z99DmtwDgkPZTWBdGC5356FD9nvs4G7qawSfMm0JyNok2WIQJZf6I04mZ/u/vMOMsCokgC1+RR8binhDYntcE3dfx+TZqd7vdhKaJZ3EzuuHVKpdh+3VRD7roqoGp6Hk2p40YaaF0YzaHfgczMdmsylGrAuO+1tkY1+QJynNjso+PLrWNTjhgxaqy5aYLcc9SfKA9dL0DGVReFx5YeQzv5M9fXpJDq1jJJI/eMtvDRRGS2ZJx1tc+0v9QE880+n6DAA6K+020Vuk9MQATpViRSIs70LSb4u2ZUG5FpUUE0zvQ7Vcj9chwi+hPucylyX3NNZE3Dp9dANVFNmkKmrqZOAvkNqAqPEcTgKDa+IP9c6b2wzHlN2LZ2p83l4bLFyO+N1opqI+rXGmBKGtYfCyPqxUTI5qC6Wh5VlVBWDqlRMHYcTJ4sERyNjTDyVWho7psWKu6eqdesRmaMBarD31f/dzabFSOlrR2WBpK66n+IOVOIFHIPGxXub2X4naFOd2YYhmEYhmEYhmEYhjEUlGxqqFDup0rpRAyDDCL6jALKsxKNUN4diec5RBhahZgLy8PvJUVbuMJqZbj9HCIwNpAselYjxkpt+D5FlNu/O9xuN/ninQo3HYhQNooozYyKvK5o6YqBmr9cZ5f7IlB5+NAZ6e8jQpl+XwvHloJrFugxlTnHoYKn5lZXYb2cqIaJ5mVPEeWE709qG3+GtY6LYmmC/NQ6vkHhR7zEfd+fAe+3KWn7PgOZue1/3zU7OpAxWYaMPRUh25Bx1B4+6/iLa19SmzTiKY2M6ToiwVVT0PQHv2gy4fZ0LHVQeDz4kSRJkQhq8vVHFB2uyIRSSZGfMkuv6XYik8bvP388ap/ode8aHz7uvbRYKirdR1Kf1AKbIBFzLYhh/A4yRnLe912SDNJOorRTep2PcI6rEkl3NAG5X2aQsaipmdQA8o9Xo47UVPA/d9upvx1tRNE8I4juYe49zj8m96Fmhkb81RClxKoJn9uRvtJ7uBYFbwiPKa6GRQPw7yx0LYDKBbJ+TRo2mQAbTxdDo60N2rqgs0cMjzKgowNWtMk2te6IbhP6nvsA+d18N3zdCFTloCwX/f5AZKaqsbQobGNTTB/p/nRsas0rwv5eyeDrBhmGYRiGYRiGYRiGYQwX/YrUUJFJUeFSBduV5BcCH4XMrIXI1FiKmBlJNTRUEKwjmv2rqU1UKCskdDYi0R8q0KTDNmixZchPg+Mfj85w1lnrEAmTaed1ikgkqw3XXRZ+L3C+Vx0eh6Yz6fI+ryTKX15IAHYFSRXjNH86RMJ5u7Neb7ovovQ/VUTCeGP4nVKEZ3f/2v4yIsNJa08UEmp1O/7rjPOAyCBxU3npo5RUXcVMi0JieTFDRNdxX3cSiZ8LifpKU0dpkexS+tk1S3Q72scq4mqu+8GmhtHaIV3IGKwL99FJslHk3wPcz/zXKiZnyReZ9dwWansFMkZ1hvzqoBq5PlSg7yxh/0kmRA/5118p0RpJ2yxm+GUQg6GG/ALXjUR1YSA5SiPJ2NBjV/G7nijCogaJGBhLdH9V8R/y07X5bXWjxlx8w1TRa0wNYDUCtF/jItAgf8y6prTeq0Dul+XkR1fpttXciauvpObCi8Dbzn5H5aBlGdSUw6pWeKkJ3s5K36QQ07MS+R1cSBR94W7Xfe1OHliGXPdvEV1b9UTnXdvchEwcaCd53GgtrFrnvV5vzQnHbBiGYRiGYRiGYRiGsbbQr5oaSfneVbRtc5anEdFH83MTft5IskhYgczErQ8frrioERrF6gq0hvtxhTE1OFTYLyQu99B3hrE707kSEfYyROm2RiEzaVfEfK+dyLDw9621BqCwaOkeixolY4nSvqgQpdvT8+GKYq7gp7OWm4giNZL2V6g9bjoXN/LErZ9QiBRybkYi/TgufK1RJEuRmiulpE7pD8X6Ok5kLjSTXsXPNvILR7s1W5LaUMg8gPzaJ+7MaY3A8WeQ6zZVVHZFXx0b7vrdRML1aEQgXUZ0DfjisrbJN6riIjh0NrwWAq8LX2ttAk275tYqCcK21yFjYxWrz9RQIwXyhe3+4po3+ih0TcWZRO5zKfvLEBWTVyPZjTCLI85s8ce1tr8y3O4opJ8qicafRnw1IOdLozX0XKs5rDWWNMoizgijwGu9zuLua8XqakB+qjP/utHXGl3kvi9EF2KE634yQFsOGhphUTO8gTxWhv2gprIanmr4Jd1zXBMwS75xnCKKTKxxjnElySnptFZKffhc5WxXz2ErpUcPGoZhGIZhGIZhGIZhrAkGVCi80KxiJYcIXG3k54NPEolqgY8jwraKTzrLvZXS02HEzWoerEDjbq8ybKumdEqF7WskP0pDv1dslnexGbH+LGqdUatpVHqIDB+3sLnfB9ovnUT1Hfx89HHCa5L4jrONTqKZ1P2JHMgg53srYItyWH8UjB4pn61sgP+GLpEaBq5AD31ne7tCZNpbHtd+VzSM+4yEz+I+dwVsvGWQL3THzYyPa1ehfeo40CgZf18aBTSSKG1QB2IUrYw5LjXs6pDr1jct/CidQu99UVojryYSmZxqvOm225F0OUuIjLeB1HoZDE3kH8dAo2DU0IL8FFWFtlfINIPIkIzbRg9yTkHuQynkXtBA/v3Hv75LMQP9MaX3YzWX9QdEI4j8NHha1NpNARVnlPnLkq4RN3Ir663j137xo4MC8s0qtz8D71EKaipoxEMNoSkbwIIWORc5xDjQQuP6u1bZj/24+PeXNuS61jSEbr/4lCMpyqaF67vncRUyZvxr3zAMwzAMwzAMwzAMY21kyEyNJEo1FDoR0Udnc2s9CxXKFvezjcOFzjxWUUqLyzaHn5cRzaodLKWkh9Fc8C3k93WSKK7GRlz7dFa3b3QkvXZTtgxECCtDRO4NMrB+LYyohmw3rOqABa1SE6AREePGEM1A1335aZ206LmmP3PT0rj40QR4r+PeK3FmEUQmg55/3zAqJt767Ytbxxd71dyoID+qQAVbNTa0qHnSeQcRR18mSmMDUUow1yDy+yzu2Pz3KmjrDP8RiPirxadbkMiNNFHBZmUorqNSKSRoa9SNXh9x7VLTzY2ugfxxWMhIKHZfdQV6HxW39T7pivalmBlJx61RP+OALVIi4i8IJL2RRof5RpAfJaF950b4FOoHf4z767rmqRul4S+Lw79XxfVnnOmShF4jGq3WDiwAlgVR9KJGI6nxoM9xDOQ+qvebYnQjY6QcifQbQXQPLUMiTgzDMAzDMAzDMAzDMNYFBmRqQLIgO1B6kOK2tchM0mpErKlAZng3IznI/WiI1YnOjNXUIVorQ9N3qACos7MHMvvXJ27meDsyM1vTqLjpQuJm1bvbckVH/3MV31So01m//rl22xJnaLjibxJlRGlQ2rLwRiPQKAL3B4ihsRLp46mICK4F2dV8UQFcBdy42em+uJlkdBTDPWb32PVZDQZtm29E+EKq+1ncvuIMgjizwxeRXUMiTWS4qfGVdP2o2eW20S3IHGewuft2x4FfS0ENjRRynWgKtQ7gVUSIX4kYWMVqy6xuypB70kikzQHSj0mRY2ou+X2TZPjEUShawxfw3e9kKc0EKmXcue/Lket0aiWMDqCrM6rH40dL+BSKyijWF8Wu0VIiYIp9v1Ab4q5dXVfPhVtLRK9/t9aQe4/SH1s1d9xxsbp4j6gWU53TjixSh6Wd5MLihmEYhmEYhmEYhmEYawsDNjWGQ4zRehYrnfcggsvo8DPNqT4QkmY59wcVf7vpW6DVn53sit4DmXEeZ4ZowfFO+p68uIgE11xxl8WRQsTnKkR46yZfZPYNkUKRBr7IruJodfgYAUwKn5cieec13/1K5FxrAeRJyExxd6ZzGunTDqedK8NlGjWjbfaPOyn6ob+4/aARK/4scd8MiDM3ID9llt8eP5qkmEGiM8d1fGithVKuHTedkJp1PUT9V0aUWimp3/z2uZEa+uhGjKsXkfOl/be2kELG3jhgPFFkSTdSc0Rr5RSLrnA/ixuDcee6kPhfyNgYCKVE3eh11tItEVNai8Fdv9TrqJChUex+Usgg9qNDBmOI+OcmbntqYATkp9zS86OmohpNSUbl6iaHXG9NREXktU9rkfPbTmmpHg3DMAzDMAzDMAzDMNYUAzY1hhqdDdyNpO9YhQgrOqtb0+cMVvh0Z5gnzcBVYUrNChfXvHBfQ1+xyn32Z68PhoCowDPkpz+JI659Wo9BDYIgfK9Gjab98rcTN4s8TiB0RdcKZFbweGB9JPVJdbjflcDbyDnXKAJ9VCBC2+jwO5Xh9lT81zY2IONF88JrihU3dZLbN+5rt8Bw0rEq7rHrwy8q3E6U7sk3fpJMFb9Nfnt8M8P/fty5cFOJxRUI90kj/bweck4Whsv92f8akRLXX3jruZEbtcj5HxEuewv4NzLjf3WmmCqFDDAB2BJpc4CcVz12SE6rV0gk918nrV8MNU3TRHUrIBKjkwqPJ5llcbjf0cLur2ZFEO9w9pN0vHHpn3T/brRVsTa6ywpFU+jn/TE2/O2UGjkSZ9i6abhyyPjQ+2qcUeTfS1YHaeQ+XIYYyauIDGBNU9fK2mUuGoZhGIZhGIZhGIZhxDFoU2Mooh9AZv52I8WCNWWHip2ak30oxE+dRauCrzuLVmehu3nxXdFJ0xzpzPNC4tfqEqogP92NT5zRooWbJxAVbtYitqsQk6CV+LoQSSJm3DOIWDYRSSk2BRHOy5FzvAyYjwjord421QjSaAFNA6TjoAOZbfwG8F/y6zAoFc7rJLE0zuByZ1urQOkfaxI6cztOsPWLl+u+XBMtyXzx2x3XFvd9d7ieOxs7iTJkLHwK2KYc/twdmRru/txrx8ePWFDjTQX4aqAiLe15NwfPE0VklUIKuUfoDUsjdIaDkcAnkDHbg0SULCQy+TRSKqlP3esgqbaDO/bivl/s3GsU03TEiEqH7XwduYb97+h586/RJPPOF+CbESNqSbi8hb4ms2smuJFH7n785X5b4oi7LkqNkEmiv99Pig4p1I9J5tKaQg3sBsTUaCMa0zpGNDLHMAzDMAzDMAzDMAxjbWZApoYbdVBIwHcFLn2flDqlNXzECedDVXRb9+8+Mt7ncWlHCkVj5Lz37jpuFIA/a3cwx+EWInZnbfvt8Nuj361FjIapiMkQEImUrcjs+Q5vX3FtjzMx4tqrAvcqREjrRgTtDxBxLa7Irc6OXw6MCh8qzHaG23ov3EacoeEWEE8SlrV97j71XKtplSSc+8fsR+LE9UnS53oOdTtxbUraf5KQqsZPsTRFIxBRfBNgkxSMHwdVi6LPNQpI2+UWzI47Fm1DF9J3amjMALaeCJkMjFwEb2dlnJUyK7w6bOM0oC4FbYGI9+8WObaB0kNY7Bm5HpYg47U/M9hdI7SUiIFSzCz3Mx2jmjIojZh8lUTXnEZeaVRUB/lmsdvOYqghoQaZXl+F7v1xgn9StEjSmE/6PK59/vtSDEj/dSnHU4hi65XSpqSoniT8e4/eS/zfzW7kmtNt+8efFH1kGIZhGIZhGIZhGIaxtjEk6aeShJw48TNJCBvulBcBIs5WhW3SPOjF0pT4n/vr+rP942b/6+uhyIWfQSIQtC8hv0C1iyuK6XMZ0UlvCh9dREW2G4jSQMXN7Ha3XcpxdCDRN41EtTp6SE6V46LH1I4Iy83hPjvD11rbwMU93sB7+GaVv2+3/1QQ9M9X3Gt/n0kUmtmvNSXc8xP3Xfe4/M/c51JrVEwDNkdMrroySGeizzSqR1ObxV0rcenZ3JRVGumSBerqoKoSxiyOTCoV/fWYfROpFtgY2BHYsA66OmB+j0QNDBerkALmkH+dDQT/nBea3V/oPuSOX40Ya0AinVrDz5YgbQ+Q/hyNREfVEF0vboq3OLM2qd1a/LqaKHpK21JK2/11goTl7rH6x+yv418D/qMYSdtIWhfyzWnX8E0nrNef/UN+X/jRW4UMF5dCacGGwwQ0DMMwDMMwDMMwDMNY3fTb1HDFsCRxTokTA9ekqOJGTZQyA9YladZsIUMnTrAqJlAVwhX43BRZSbOcXWFNjzuDiMwrwocW2daHW/A2rq3FZjTH0YOIqi0x20lC25tFZsm3E4ngXUjERytRQdsMUpNlJCK+qpiuz1rwWmttQL5A6ZsgEInHvvA8kD4oBd1XbdjOHvLPie4zbv/+8lKE+DIkDZnWq+nsgZ6eqDB4BfnppkoRRXXcZJx1GxGTID0fMmlY1COCvH5ejgjvWed9FSKgqzAP8F4bvJkTIX9RCW0ZDEOZgqeUPitkbsSJ/lnkPL2PmBVp8uvg6PkrQ66RBuQa1JRypdyH/POtkVZ6DerMfjeiK2k77v70/hV3T3U/c4m7nyYZrUnHFCf299cIcfvONXTc1+573+zw9x33WrdTyjLoO1bNuDAMwzAMwzAMwzAM48NOKgiCkjSQilSqj1hSSr2F1YE7i7yYGKkz4f0Zxmp0xL0vlg4o53zmCspuseikRxIZYBIicK9EBEmNLIkzNPwoBK0P0kV+5IVrSGn7tK6Iv/84oX91nmMtZK7HrG3WaIBcuE4d0lebpWCzCqjPQFMPvNslfdeKiLrLiAR3VzhNOjeuqREUeO0vGwyjgK2RAtXLkfRaK4nOoTvW4p7VxCnUjnLEMKgFNghfVziPl5Hi7ZUMzADU4uSd4XtNg1QXvu9EDK4u53Mtrp4N2zMZ6QtNfZVDooreDb+7Osahf0/Q8TBQ3HuL++xGJcT1tWtMqiGn0S9xBpammBuDnM/G8JGUSq1YaqdScFP66XEkvY97+OtAfv/4gn4u5tm9/rLI8XbTN1WWa3gOZhy55yvutd5n3Lb5x5Fk1BQyktfU72wayJb250IfUqn+3kUMwzAMw/iwUKLcEEsqNSSJJQzDMAzDWAcJgsKJsgf1V4JfWyMupc9wkyISTNsoLHi6Aq0r2LuziJOERXd/cdvVGbyB90zMMnc/vrmh0RQTgK2Qmer/Q4oAazFtf/s4+9dtaERCJ6WnIlKqEEE7S36kwOoW09w0Rorbp2mknaMRcX5WFXxsGtTXw8oGGPEuNHTJmPgg3JbONHe35RI3O9pdPpR9oMJnxnk9CdgS2HAErOyGN9vhTSQyoY3IKNC2+MZMnEHlU4FcM5WI2dOIGAxaHL6D/GulUBRBnMlXjkTNNBPVcGgOH3H451kL1mv0RjdRdE4aMT3UOBmuosYZYCyR4RMg/bOS/HPQH+LuA0pSX/vvNQ2U3nM0lZtbuDwdtrnS+bzQz8BQjGnXINWxo/eyQsXl3ciNQvfLuHa6xiLOvnRsdJBfzLyMKJVad8z2+kvS9wOiaKNyIoPPNRv934Ak47zQeFidlCP3WcMwDMMwDMMwDMMwjLWBkk2NpHQl/vLVJbykkcZXITOStVaGivBue0DErpHhI0tUO8Kd+V7qXNJix5gkXMZ9331dgQhHE4DpwPqImFtGJPrFFWh3jRgVCVXMK1a3wieNGEQjETG0kdKE8tWBznzWY1fzJocI5gu7IP0BVJRBexe05aK0VKOQ2etawBriBcNi43moxncmbNPEsH0qgq4HbDgSJoyHsVkYsQLqV8kYeJdI4Ne2QBTh4B9TEuXIWEsRid2dSCREJ1HdFn97hVLiuNfZCGBDxAR4H4k4UUG3lPZliUyN6nCZRh/UEM3gb0DqSAz0fKjwDvFpxsqdfeo6bgRToe0WixyLG19xhpFLFrkmu8N21RLdB9X8SSPjqJbI/BkZPrcXafdgcaMkIDr+cqKIKzeySb8TFymSdI+MG0NqCFYjx96JjB+3KLpGC40iSmnXQv9JOkdx/apjR40eNZ7U/HHHRtJvTylmx3Ch14f+LpmpYRiGYRiGYRiGYRjG2kK/IjWSZo+64qr/+XBQiaTnGYsIqFqDoAsROlfRtwh4TfidOkRk1RoSPr7AGCc49vf4ignjZUSpgDYHNqyAEeXQ3gEvZaMCv37/JgmiOlO5m/5Hzqi4W4UIgW7dijVFoXQ1IDP4FwKNWXh2VWTo1CH1GMYhY0KF9WIpfgoZdKUaWoVIIWN3FrAZUV0LFRDbW+GDbijLQLZLxvhYRMDX8wqRqVNG4Zn4Ps1ExaXTiClYiQi+DUSGkU+ciYb3Oo2YNBuH21yERBk1ItddC6VHV+js9h6imfVVSH+MCrezpMRt+aSIRHCdua+GAeE+FxPVq4CojkWh86vjs7+pqpKMDX8dTaVUj4wbbbu2USM51JTSCC43KmB14UcO+dFp/rG6ERtubY24NrvL1fAaj4xlvbfr70BZ+NnUcD017twUacVIIf2qBpem/3JNUpcsUQRUNXKdlhOZT+3kjxE1rnVfcVE8rgE3FKnuClGOmGHjwucRyLViGIZhGIZhGIZhGIaxNlByTQ3DMAzDMAzDMAzDMAzDMAzDMIw1SdyEbMMwDMMwDMMwDMMwDMMwDMMwjLUOMzUMwzAMwzAMwzAMwzAMwzAMw1gnMFPDMAzDMAzDMAzDMAzDMAzDMIx1AjM1DMMwDMMwDMMwDMMwDMMwDMNYJzBTwzAMwzAMwzAMwzAMwzAMwzCMdQIzNQzDMAzDMAzDMAzDMAzDMAzDWCcwU8MwDMMwDMMwDMMwDMMwDMMwjHUCMzUMwzAMwzAMwzAMwzAMwzAMw1gnMFPDMAzDMAzDMAzDMAzDMAzDMIx1AjM1DMMwDMMwDMMwDMMwDMMwDMNYJzBTwzAMwzAMwzAMwzAMwzAMwzCMdQIzNQzDMAzDMAzDMAzDMAzDMAzDWCcwU8MwDMMwDMMwDMMwDMMwDMMwjHUCMzUMwzAMwzAMwzAMwzAMwzAMw1gnMFPDMAzDMAzDMAzDMAzDMAzDMIx1AjM1DMMwDMMwDMMwDMMwDMMwDMNYJzBTwzAMwzAMwzAMwzAMwzAMwzCMdQIzNQzDMAzDMAzDMAzDMAzDMAzDWCcwU8MwDMMwDMMwDMMwDMMwDMMwjHUCMzUMwzAMwzAMwzAMwzAMwzAMw1gnMFPDMAzDMAzDMAzDMAzDMAzDMIx1AjM1DMMwDMMwDMMwDMMwDMMwDMNYJzBTwzAMwzAMwzAMwzAMwzAMwzCMdQIzNQzDMAzDMAzDMAzDMAzDMAzDWCcwU8MwDMMwDMMwDMMwDMMwDMMwjHUCMzUMwzAMwzAMwzAMwzAMwzAMw1gnMFPDMAzDMAzDMAzDMAzDMAzDMIx1AjM1DMMwDMMwDMMwDMMwDMMwDMNYJzBTwzAMwzAMwzAMwzAMwzAMwzCMdQIzNQzDMAzDMAzDMAzDMAzDMAzDWCcwU8MwjFimT5/O8ccfv6abYRiGYRiGYRiGYRiGYRiG0YuZGoaxhrn++utJpVLssMMOieukUqnYx6RJk3rXueSSS0ilUqTTad57770+21i1ahXV1dWkUilOO+20YTkWwzAMwzAMwzAMwzAMwzCM4aRsTTfAMD7qzJs3j+nTp/PMM8/w5ptvMnPmzNj19tprL4499ti8ZdXV1X3Wq6ys5K677uLcc8/NW/7AAw/0q13/+9//SKfN9zQMwzAMwzAMwzAMwzAMY+3BFEvDWIPMnz+fv//97/zwhz9k/PjxzJs3L3HdTTbZhKOPPjrvcfDBB/dZb9999+Wuu+7qs/zOO+9kv/32K7ltlZWVlJeXl7y+YRiGYRiGYRiGYRiGYRjGcGOmhmGsQebNm8fo0aPZb7/9OOSQQwqaGqVy5JFH8uKLL/Laa6/1Llu8eDFPPvkkRx55ZMnb8Wtq3HrrraRSKf76179yxhlnMH78eEaNGsXJJ59MV1cXjY2NHHvssYwePZrRo0dz7rnnEgRB3ja///3vs9NOOzF27Fiqq6vZdtttue+++/rsu729nTPOOINx48ZRX1/PgQceyPvvv08qleKSSy7JW/f999/nS1/6EhMnTqSyspJZs2bxy1/+suTjNAzDMAzDMAzDMAzDMAxj3cFMDcNYg8ybN48vfOELVFRUcMQRR/DGG2/wr3/9K3bdjo4Oli9fnvfo7Ozss97s2bOZOnUqd955Z++yu+++m7q6un5FaiRx+umn88Ybb/Cd73yHAw88kJtuuokLL7yQAw44gGw2y+WXX84uu+zCNddcw+2335733blz57LNNttw6aWXcvnll1NWVsYXv/hFHn744bz1jj/+eK677jr23XdfrrrqKqqrq2PbvmTJEnbccUeeeOIJTjvtNObOncvMmTM58cQTufbaawd9rIZhGIZhGIZhGIZhGIZhrF2YqWEYa4jnnnuO1157jcMPPxyAXXbZhalTpyZGa9x8882MHz8+7xGXZiqVSnH44YfnfabmSWVl5aDbPXHiRH7/+99zyimncNttt/GpT32Ka665hi233JJ58+bxta99jQcffJCpU6f2iZh4/fXX+elPf8qpp57K2WefzV//+le23HJLfvjDH/au8/zzz3PPPfdw1llncdttt3HKKadw9913s8022/RpywUXXEA2m+WFF17gwgsv5Ktf/SoPPfQQhx9+OJdccgnt7e2DPl7DMAzDMAzDMAzDMAzDMNYezNQwjDXEvHnzmDhxIrvvvjsgZsRhhx3Gr3/9a7LZbJ/1DzroIB5//PG8xz777BO77SOPPJI333yTf/3rX73P/Uk9VYgTTzyRVCrV+36HHXYgCAJOPPHE3mWZTIbtttuOt99+O++7bmHzhoYGmpqa2HXXXXn++ed7lz/66KMAnHLKKXnfPf300/PeB0HA/fffzwEHHEAQBHkRLPvssw9NTU152zUMwzAMwzAMwzAMwzAMY92nbE03wDA+imSzWX7961+z++67M3/+/N7lO+ywAz/4wQ/44x//yN577533nalTp/LpT3+6pO1vs802bLbZZtx5552MGjWKSZMmscceewxJ2zfYYIO89yNHjgRg/fXX77O8oaEhb9nvfvc7lqTtAwABAABJREFULrvsMl588cW81FmuSbJgwQLS6TQbbrhh3ndnzpyZ937ZsmU0NjZy0003cdNNN8W2denSpSUelWEYhmEYhmEYhmEYhmEY6wJmahjGGuDJJ59k0aJF/PrXv+bXv/51n8/nzZvXx9ToL0ceeSQ33HAD9fX1HHbYYaTTQxOYlclkSl7uFgp/+umnOfDAA5k9ezbXX389kydPpry8nFtuuSWv/kep5HI5AI4++miOO+642HU+9rGP9Xu7hmEYhmEYhmEYhmEYhmGsvZipYRhrgHnz5jFhwgR++tOf9vnsgQce4P/+7//42c9+lpeuqb8ceeSRXHTRRSxatKhPwe41wf33309VVRV/+MMf8mp73HLLLXnrTZs2jVwux/z589l44417l7/55pt5640fP576+nqy2WzJESyGYRiGYRiGYRiGYRiGYazbmKlhGKuZ9vZ2HnjgAb74xS9yyCGH9Pl8ypQp3HXXXfzmN7/hsMMOG/B+ZsyYwbXXXkt7ezvbb7/9YJo8JGQyGVKpVF69kHfeeYcHH3wwb7199tmHCy64gOuvv54f/ehHvcuvu+66Pts7+OCDufPOO3n55ZfZcsst8z5ftmwZ48ePH/oDMQzDMAzDMAzDMAzDMAxjjWGmhmGsZn7zm9/Q3NzMgQceGPv5jjvuyPjx45k3b96gTA2AM888c1DfH0r2228/fvjDH/KZz3yGI488kqVLl/LTn/6UmTNn8tJLL/Wut+2223LwwQdz7bXXsmLFCnbccUf+/Oc/8/rrrwP59TeuvPJKnnrqKXbYYQdOOukktthiC1auXMnzzz/PE088wcqVK1f7cRqGYRiGYRiGYRiGYRiGMXwMTZJ9wzBKZt68eVRVVbHXXnvFfp5Op9lvv/149NFHWbFixWpu3fCxxx57cPPNN7N48WLOOuss7rrrLq666io+//nP91n3tttu49RTT+Xhhx/mvPPOo6uri7vvvhuAqqqq3vUmTpzIM888wwknnMADDzzAaaedxty5c1m5ciVXXXXVajs2wzAMwzAMwzAMwzAMwzBWD6nAreRrGIaxlvLiiy+yzTbbcMcdd3DUUUet6eYYhmEYhmEYhmEYhmEYhrEGsEgNwzDWOtrb2/ssu/baa0mn08yePXsNtMgwDMMwDMMwDMMwDMMwjLUBq6lhGMZax9VXX81zzz3H7rvvTllZGY888giPPPIIX/nKV1h//fXXdPMMwzAMwzAMwzAMwzAMw1hDWPopwzDWOh5//HG+853v8N///peWlhY22GADjjnmGC644ALKysyLNQzDMAzDMAzDMAzDMIyPKmZqGIZhGIZhGIZhGIZhGIZhGIaxTmA1NQzDMAzDMAzDMAzDMAzDMAzDWCcwU8MwDMMwDMMwDMMwDMMwDMMwjHUCMzWMjzRXX301m222Gblcbk03JZbp06dz/PHHr+lmDCvHH388dXV1a7oZeey2227stttu/frOz372MzbYYAM6OzuHp1GGYRiGEcOtt95KKpXi2WefHfA27rnnHsaMGUNLS0vRdVOpFJdccsmA9/VRZTj6bai3+ac//YlUKsWf/vSn3mWHH344hx566JDtwzAMw1izDOR/3Q8j06dPZ//99x/y7Z5yyinstddeQ77doeDRRx+lrq6OZcuWremmGMaQYKaG8ZFl1apVXHXVVZx33nmk0+vupbBixQquueYaZs+ezfjx4xk1ahQ77rgjd99996C3fc8997DjjjsyatQoxo4dy5w5c3j44Yf7rJfL5bj66qvZcMMNqaqq4mMf+xh33XXXoPe/LnH88cfT1dXFjTfeuKabYhiGsc6iAr0+qqqqmDJlCvvssw8//vGPaW5u7vOdSy65hFQqxcSJE2lra+vzeaF/WhsbG6mqqiKVSvHqq68O+fGsC2SzWS6++GJOP/30tW6SgbHmOe+887j//vv597//vaabYhiGsVby1ltvcfLJJ7PRRhtRVVXFiBEj2HnnnZk7dy7t7e1runnGamT+/Pn84he/4Pzzz1/t+3711Vf5zGc+Q11dHWPGjOGYY47pY1585jOfYebMmVxxxRWrvX2GMRysu0quYQySX/7yl/T09HDEEUes6aYMin/84x9ccMEFjBkzhm9/+9t873vfo6amhsMPP5yLL754wNu97rrrOOywwxg3bhxXXnklF154IU1NTey///488MADeetecMEFnHfeeey1115cd911bLDBBhx55JH8+te/HuzhrTNUVVVx3HHH8cMf/pAgCNZ0cwzDMNZpLr30Um6//XZuuOEGTj/9dADOOussttpqK1566aXY7yxdupQbbrihX/u59957SaVSTJo0iXnz5g263esiv/3tb/nf//7HV77ylTXdFGMtZJtttmG77bbjBz/4wZpuimEYxlrHww8/zFZbbcU999zDAQccwHXXXccVV1zBBhtswDnnnMOZZ565ppvYh8cee4zHHntsTTfjQ8ncuXPZcMMN2X333VfrfhcuXMjs2bN58803ufzyy/nGN77Bww8/zF577UVXV1feuieffDI33nhj7EQhw1jXMFPD+Mhyyy23cOCBB1JVVbWmmzIoZs2axRtvvMGDDz7ImWeeyamnnsof//hH9thjD6666ipaW1sHtN3rrruOT37yk/z2t7/lq1/9KmeddRZ/+ctfqKur41e/+lXveu+//z4/+MEPOPXUU7nppps46aST+O1vf8uuu+7KOeecQzabHapDXes59NBDWbBgAU899dSabophGMY6zWc/+1mOPvpoTjjhBL71rW/xhz/8gSeeeIKlS5dy4IEHxs583Hrrrbnmmmv6NSvyjjvuYN999+WII47gzjvvHMpDWGe45ZZb2HnnnVlvvfXWdFOMtZRDDz2UBx54oKT0ZIZhGB8V5s+fz+GHH860adP473//y9y5cznppJM49dRTueuuu/jvf//LrFmzEr+fy+Xo6OhYjS0WKioqqKioWO37/bDT3d3NvHnz1kjKxssvv5zW1laefPJJzjjjDM4//3zuuece/v3vf3PrrbfmrXvwwQfT2dnJvffeu9rbaRhDjZkaxkeS+fPn89JLL/HpT3+6z2e//vWv2Xbbbamvr2fEiBFstdVWzJ07N2+dxsZGzjrrLNZff30qKyuZOXMmV111VZ/aHLlcjmuvvZZZs2ZRVVXFxIkTOfnkk2loaMhbLwgCLrvsMqZOnUpNTQ277747r7zySknHsuGGGzJt2rS8ZalUis997nN0dnby9ttvl7Qdn1WrVjFhwgRSqVTvshEjRlBXV0d1dXXvsoceeoju7m5OOeWUvP1/7WtfY+HChfzjH/8oaX9vv/02++yzD7W1tUyZMoVLL720T8RDqf350EMPsd9++zFlyhQqKyuZMWMG3/3ud2MNlptuuokZM2ZQXV3N9ttvz9NPPx3bvuuuu45Zs2ZRU1PD6NGj2W677foIYNtuuy1jxozhoYceKumYDcMwjNLZY489uPDCC1mwYAF33HFHn88vuugilixZUnK0xrvvvsvTTz/N4YcfzuGHH878+fP5+9//XtJ3NeXV66+/ztFHH83IkSMZP348F154IUEQ8N5773HQQQcxYsQIJk2a1GeWe1dXFxdddBHbbrstI0eOpLa2ll133TXWFC/l7xKfhoYGtt9+e6ZOncr//ve/xPU6Ojp49NFHY/8e6uzs5Oyzz2b8+PHU19dz4IEHsnDhwtjtvP/++3zpS19i4sSJVFZWMmvWLH75y1/G7u+SSy5hk002oaqqismTJ/OFL3yBt956q3ed1tZWvv71r/f+jbXpppvy/e9/P+9vgjlz5vDxj388ti2bbrop++yzT+IxK4888gi77rortbW11NfXs99+++X97fXkk0+STqe56KKL8r535513kkql8sZZKcflc/zxxzN9+vQ+y3VsuQzHuVi4cCGf+9znqK2tZcKECZx99tmJdcH22msvWltbefzxxxOPxzAM46PG1VdfTUtLCzfffDOTJ0/u8/nMmTPzIjVSqRSnnXYa8+bNY9asWVRWVvLoo48C8MILL/DZz3629//tPffck//v//v/8rbX3d3Nd77zHTbeeGOqqqoYO3Ysu+yyS969efHixZxwwglMnTqVyspKJk+ezEEHHcQ777zTu45fU0PrKd1zzz1873vfY+rUqVRVVbHnnnvy5ptv9jmun/70p2y00UZ5/z+XWqdD++Dee+9liy22oLq6mk996lP85z//AeDGG29k5syZVFVVsdtuu+W1G+Dpp5/mi1/8IhtssAGVlZWsv/76nH322X0mtJTSD3H86le/oqysjHPOOafosfj89a9/Zfny5X3+pupv/w6E+++/n/33358NNtigd9mnP/1pNtlkE+655568dSdMmMDHPvYx0yyMDwVla7oBhrEmUNHiE5/4RN7yxx9/nCOOOII999yTq666CpDchH/72996/yBpa2tjzpw5vP/++5x88slssMEG/P3vf+db3/oWixYt4tprr+3d3sknn8ytt97KCSecwBlnnMH8+fP5yU9+wgsvvMDf/vY3ysvLARFiLrvsMvbdd1/23Xdfnn/+efbee+8+oYL9YfHixQCMGzduQN/fbbfduO+++7juuus44IAD6Ojo4LrrrqOpqSnvj7MXXniB2tpaNt9887zvb7/99r2f77LLLgX3lc1m+cxnPsOOO+7I1VdfzaOPPsrFF19MT08Pl156ae96pfbnrbfeSl1dHf/v//0/6urqePLJJ7noootYtWoV11xzTe/2br75Zk4++WR22mknzjrrLN5++20OPPBAxowZw/rrr9+73s9//nPOOOMMDjnkEM4880w6Ojp46aWX+Oc//8mRRx6Zdyyf+MQn+Nvf/tbP3jYMwzBK4ZhjjuH888/nscce46STTsr7bNddd2WPPfbg6quv5mtf+1qeAR/HXXfdRW1tLfvvvz/V1dXMmDGDefPmsdNOO5XcnsMOO4zNN9+cK6+8kocffpjLLruMMWPGcOONN/ZGTM6bN49vfOMbfPKTn2T27NmATBz4xS9+wRFHHMFJJ51Ec3MzN998M/vssw/PPPMMW2+9NVDa3yU+y5cvZ6+99mLlypX8+c9/ZsaMGYntf+655+jq6urz9xDAl7/8Ze644w6OPPJIdtppJ5588kn222+/PustWbKEHXfcsVeoGD9+PI888ggnnngiq1at4qyzzgLkt37//ffnj3/8I4cffjhnnnkmzc3NPP7447z88svMmDGDIAg48MADeeqppzjxxBPZeuut+cMf/sA555zD+++/z49+9CNAxsFJJ53Eyy+/zJZbbtnbln/961+8/vrrfPvb3y543m6//XaOO+449tlnH6666ira2tq44YYb2GWXXXjhhReYPn06e+yxB6eccgpXXHEFn/vc5/jEJz7BokWLOP300/n0pz/NV7/61ZKPa7AM9blob29nzz335N133+WMM85gypQp3H777Tz55JOx+1fh6W9/+xuf//znB308hmEYHwZ++9vfstFGG/Xr74Ynn3ySe+65h9NOO41x48Yxffp0XnnlFXbddVdGjBjBueeeS3l5OTfeeCO77bYbf/7zn9lhhx0AMb2vuOIKvvzlL7P99tuzatUqnn32WZ5//vnewtQHH3wwr7zyCqeffjrTp09n6dKlPP7447z77ruxRrrLlVdeSTqd5hvf+AZNTU1cffXVHHXUUfzzn//sXeeGG27gtNNOY9ddd+Xss8/mnXfe4XOf+xyjR49m6tSpJfXB008/zW9+8xtOPfVUAK644gr2339/zj33XK6//npOOeUUGhoauPrqq/nSl76U99t077330tbWxte+9jXGjh3LM888w3XXXcfChQvzIg8G0g833XQTX/3qVzn//PO57LLLSjoWl7///e+kUim22Wab2M9L6d+2trbY+nA+mUyG0aNHAzKZYenSpWy33XZ91tt+++35/e9/32f5tttuy4MPPljikRnGWkxgGB9Bvv3tbwdA0NzcnLf8zDPPDEaMGBH09PQkfve73/1uUFtbG7z++ut5y7/5zW8GmUwmePfdd4MgCIKnn346AIJ58+blrffoo4/mLV+6dGlQUVER7LfffkEul+td7/zzzw+A4Ljjjuv38a1YsSKYMGFCsOuuu/b7u8qSJUuCPffcMwB6H+PGjQv+/ve/56233377BRtttFGf77e2tgZA8M1vfrPgfo477rgACE4//fTeZblcLthvv/2CioqKYNmyZUEQlN6fQRAEbW1tffZz8sknBzU1NUFHR0cQBEHQ1dUVTJgwIdh6662Dzs7O3vVuuummAAjmzJnTu+yggw4KZs2aVfA4lK985StBdXV1SesahmEY+dxyyy0BEPzrX/9KXGfkyJHBNtts0/v+4osvDoBg2bJlwZ///OcACH74wx/2fj5t2rRgv/3267OdrbbaKjjqqKN6359//vnBuHHjgu7u7qLt1H1+5Stf6V3W09MTTJ06NUilUsGVV17Zu7yhoSGorq7O+z3v6enJ++3R9SZOnBh86Utf6l1Wyt8lbp8tWrQomDVrVrDRRhsF77zzTtHj+MUvfhEAwX/+85+85S+++GIABKecckre8iOPPDIAgosvvrh32YknnhhMnjw5WL58ed66hx9+eDBy5Mje3+Rf/vKXfc6Non//PPjggwEQXHbZZXmfH3LIIUEqlQrefPPNIAiCoLGxMaiqqgrOO++8vPXOOOOMoLa2NmhpaUk85ubm5mDUqFHBSSedlLd88eLFwciRI/OWt7a2BjNnzgxmzZoVdHR0BPvtt18wYsSIYMGCBb3rlHJcQRD06bfjjjsumDZtWp/v6NhShuNcXHvttQEQ3HPPPX2OFQieeuqpPu3aZJNNgs9+9rN9lhuGYXwUaWpqCoDgoIMOKvk7QJBOp4NXXnklb/nnPve5oKKiInjrrbd6l33wwQdBfX19MHv27N5lH//4x2P/nlEaGhoCILjmmmsKtmPOnDl5/+s+9dRTARBsvvnmeX+bzJ07N+9vhM7OzmDs2LHBJz/5yby/lW699dY+/z8nAQSVlZXB/Pnze5fdeOONARBMmjQpWLVqVe/yb33rWwGQt27c//lXXHFFkEqlen+bS+0H9+/DuXPnBqlUKvjud79b9BiSOProo4OxY8f2WV5q/wZB9DdAsYf798O//vWvAAhuu+22Pvs+55xzAqBXA1Euv/zyAAiWLFky4OM1jLUBSz9lfCRZsWIFZWVl1NXV5S0fNWpU0fD6e++9l1133ZXRo0ezfPny3senP/1pstksf/nLX3rXGzlyJHvttVfeettuuy11dXW9KSaeeOIJurq6OP300/PSDehsuv6Sy+U46qijaGxs5LrrrhvQNgBqamrYdNNNOe6447j33nv55S9/2ZtOwQ2TbG9vp7Kyss/3tVZJqbnNTzvttN7XOsOwq6uLJ554Aii9P4G82bnNzc0sX76cXXfdlba2Nl577TUAnn32WZYuXcpXv/rVvJyixx9/PCNHjsxr26hRo1i4cCH/+te/ih7H6NGjaW9vL2mGhWEYhtF/6urqEosbzp49m913352rr7664O/PSy+9xH/+8x+OOOKI3mVHHHEEy5cv5w9/+EPJbfnyl7/c+zqTybDddtsRBAEnnnhi7/JRo0ax6aab5qWDzGQyvb89uVyOlStX0tPTw3bbbcfzzz+f991S0/4sXLiQOXPm0N3dzV/+8pc+qSnjWLFiBUDvbD9FZ/WdccYZecv9v02CIOD+++/ngAMOIAiCvN/nffbZh6ampt7juf/++xk3blxv4XcX/fvn97//PZlMps9+v/71rxMEAY888ggAI0eO5KCDDuKuu+7qTUuVzWa5++67e1MqJfH444/T2NjYe771kclk2GGHHfL+nqipqeHWW2/l1VdfZfbs2Tz88MP86Ec/ykvvUMpxDYbhOBe///3vmTx5MoccckjesRYqFq9/9xqGYRgScQlQX1/fr+/NmTOHLbbYovd9Npvlscce43Of+xwbbbRR7/LJkydz5JFH8te//rV3X6NGjeKVV17hjTfeiN12dXU1FRUV/OlPf+qTnrkUTjjhhLz/i3fddVeA3r9fnn32WVasWMFJJ51EWVmU9OWoo47q83dEIfbcc8+8aAmNRDn44IPz+lOXu38/uf/nt7a2snz5cnbaaSeCIOCFF17oXac//XD11Vdz5plnctVVVxWN9CzEihUrCvZDsf4FOPbYY3n88ceLPubNm9f7Hf17tz+ajLbTfteNdR1LP2UYDqeccgr33HMPn/3sZ1lvvfXYe++9OfTQQ/nMZz7Tu84bb7zBSy+9xPjx42O3sXTp0t71mpqamDBhQsH1FixYAMDGG2+c9/n48eP79ceBcvrpp/Poo49y2223JeabLoUvfvGLlJWV8dvf/rZ32UEHHcTGG2/MBRdcwN133w3IHw1xOZi16Fmx9B8A6XQ67484gE022QSgN+9lqf0J8Morr/Dtb3+bJ598svePQKWpqQlI7vfy8vI+bTnvvPN44okn2H777Zk5cyZ77703Rx55JDvvvHOfdqi4MhRChmEYhtGXlpaWxN8CkPQMc+bM4Wc/+xlnn3127Dp33HEHtbW1bLTRRr1GfVVVFdOnT2fevHmxqX3icMVtELG9qqqqT+rHkSNH9hoIyq9+9St+8IMf8Nprr9Hd3d27fMMNN+x9XcrfJcoxxxxDWVkZr776KpMmTSqp/Urg1bBasGAB6XS6T+qkTTfdNO/9smXLaGxs5KabbuKmm26K3bb+Pr/11ltsuummeUKIz4IFC5gyZUofkUhTXOpvN8g//nfffTdPP/00s2fP5oknnmDJkiUcc8wxBY9VxaA99tgj9vMRI0bkvd9555352te+xk9/+lP22WcfvvSlL+V9XspxDYbhOBcLFixg5syZff5W8bfpEgSB/W1jGIYRor8VSZMsknB/40Hu3W1tbbH3380335xcLsd7773HrFmzuPTSSznooIPYZJNN2HLLLfnMZz7DMcccw8c+9jFARO2rrrqKr3/960ycOJEdd9yR/fffn2OPPbakvwv8v2lUi1BjQH+DZ86cmbdeWVlZ0dRWhfajEwrd9M/ucteYePfdd7nooov4zW9+08ew0P/z+9MPf/7zn3n44Yc577zzBlRHw8f/e8qlWP8CbLTRRn20iGKo3tIfTcY0C+PDgpkaxkeSsWPH0tPTQ3Nzc94/zhMmTODFF1/kD3/4A4888giPPPIIt9xyC8ceeyy/+tWvAJlRuddee3HuuefGblvF+Fwux4QJE/JcdJckU2QwfOc73+H666/nyiuvLPpPfSHefvttHn300T7/FI8ZM4Zddtklr2bE5MmTeeqpp/r8s7to0SIApkyZMuB2uJTan42NjcyZM4cRI0Zw6aWXMmPGDKqqqnj++ec577zz+hRzL4XNN9+c//3vf/zud7/j0Ucf5f777+f666/noosu4jvf+U7eug0NDdTU1JRk5hiGYRj9Y+HChTQ1NfX5h9pl9uzZ7Lbbblx99dW9dQ9cgiDgrrvuorW1NW+2pLJ06VJaWlr6RHPGkclkSlqm+1XuuOMOjj/+eD73uc9xzjnnMGHCBDKZDFdccUVecelS/i5RvvCFL3Dbbbcxd+5crrjiiqJtB/l7COS3q9Rc2C76m3r00Udz3HHHxa6jYstQs88++zBx4kTuuOMOZs+ezR133MGkSZNii567aJtvv/32WJHHNyc6Ozv505/+BIiB0dbWRk1NzaDbnyQkZLPZAW1vuM9FQ0NDn4kghmEYH1VGjBjBlClTePnll/v1vcH8jzh79mzeeustHnroIR577DF+8Ytf8KMf/Yif/exnvZGjZ511FgcccAAPPvggf/jDH7jwwgu54oorePLJJxNrPSil/P0yFCTtp9j+s9lsb82w8847j80224za2lref/99jj/++Lz/80vth1mzZtHY2Mjtt9/OySef3Md06g9jx44tGBlSSv+2tLTQ0tJSdF+ZTKZX/9Ai9aq/uCxatIgxY8b0ieLQdg60/qphrC2YqWF8JNlss80AmD9/fp9/8CoqKjjggAM44IADyOVynHLKKdx4441ceOGFzJw5kxkzZtDS0lL0n+YZM2bwxBNPsPPOOxf840XTQ7zxxht5rvyyZcv6FTb605/+lEsuuYSzzjqL8847r+TvxbFkyRIg/h/r7u5uenp6et9vvfXW/OIXv+DVV1/NE4e04JUWOy1ELpfj7bff7jWEAF5//XWA3lkfpfbnn/70J1asWMEDDzzQW5AV5Fy7uP3uztbs7u5m/vz5faJcamtrOeywwzjssMPo6uriC1/4At/73vf41re+1RvWqfvxi6YbhmEYQ8Ptt98OiKBdiEsuuYTddtuNG2+8sc9nf/7zn1m4cCGXXnppn/t1Q0MDX/nKV3jwwQc5+uijh67hHvfddx8bbbQRDzzwQJ64ffHFF/dZt9jfJcrpp5/OzJkzueiiixg5ciTf/OY3i7bD/Xtoq6226l0+bdo0crlcbxSC8r///S/v++PHj6e+vp5sNlvS30X//Oc/6e7upry8PHadadOm8cQTT/SZdKKpI92UWplMhiOPPJJbb72Vq666igcffJCTTjopUTRw2wFiGBVrM8g5efXVV/n+97/Peeedxze/+U1+/OMf9+u44hg9ejSNjY19lrvRKDA852LatGm8/PLLfSak+NtUenp6eO+99zjwwAOLHZZhGMZHhv3335+bbrqJf/zjH3zqU58a0DbGjx9PTU1N7P33tddeI51O50UwjBkzhhNOOIETTjiBlpYWZs+ezSWXXJKXDnPGjBl8/etf5+tf/zpvvPEGW2+9NT/4wQ+44447BtRGRX+D33zzTXbffffe5T09PbzzzjvDNolB+c9//sPrr7/Or371K4499tje5UkpOkvph3HjxnHfffexyy67sOeee/LXv/51wJMyN9tsM+bNm0dTU1OfdNal8v3vf7/PpMk4pk2b1pvRYr311mP8+PE8++yzfdZ75plnYvWY+fPnM27cuGGZaGsYqxOrqWF8JNE/Ovwbv58aIp1O9/44azjfoYceyj/+8Y/YnNuNjY29gv+hhx5KNpvlu9/9bp/1enp6ev+R/fSnP015eTnXXXddnkt/7bXXlnw8d999N2eccQZHHXUUP/zhD0v+XhIzZ84knU5z991357Vp4cKFPP3003mzGw466CDKy8u5/vrre5cFQcDPfvYz1ltvPXbaaaeS9vmTn/wk7/s/+clPKC8vZ8899wRK708VM9x2d3V15bUPYLvttmP8+PH87Gc/o6urq3f5rbfe2kdk8MdFRUUFW2yxBUEQ5KUMAXj++edLPmbDMAyjdJ588km++93vsuGGG3LUUUcVXHfOnDnstttuXHXVVb2h94qmnjrnnHM45JBD8h4nnXQSG2+8cWJU4FAR91v1z3/+k3/84x9565Xyd4nLhRdeyDe+8Q2+9a1vccMNNxRtx7bbbktFRUWfv4c++9nPAuSJ99D3b5NMJsPBBx/M/fffHztbddmyZb2vDz74YJYvX573e69oP+y7775ks9k+6/zoRz8ilUr1tks55phjaGho4OSTT6alpaUkI2qfffZhxIgRXH755X1+w/02//Of/+T73/8+Z511Fl//+tc555xz+MlPfsKf//znfh1XHDNmzKCpqYmXXnqpd9miRYv4v//7v7z1huNc7LvvvnzwwQfcd999vcva2toS01b997//paOjw/6+MQzDcDj33HOpra3ly1/+cu+kQJe33nqLuXPnFtxGJpNh77335qGHHuoVqUEmGd55553ssssuvamu/L8J6urqmDlzZu/fA21tbX3+5pkxYwb19fWxfzP0l+22246xY8fy85//PG+S47x58wZUw6O/xP3tFARBnz7ubz9MnTqVJ554gvb2dvbaa68+/Vwqn/rUpwiCgOeee25A34eB1dQA+Vvkd7/7He+9917vsj/+8Y+8/vrrfPGLX+yzn+eee27ARpxhrE1YpIbxkWSjjTZiyy235IknnsjLjfzlL3+ZlStXssceezB16lQWLFjAddddx9Zbb907m/Occ87hN7/5Dfvvvz/HH3882267La2trfznP//hvvvu45133mHcuHHMmTOHk08+mSuuuIIXX3yRvffem/Lyct544w3uvfde5s6dyyGHHML48eP5xje+wRVXXMH+++/PvvvuywsvvMAjjzxSUjjgM888w7HHHsvYsWPZc889+/zA7bTTTnkRIKlUijlz5vSmUohj/PjxfOlLX+IXv/gFe+65J1/4whdobm7m+uuvp729nW9961u9606dOpWzzjqLa665hu7ubj75yU/y4IMP8vTTTzNv3ryiMyZB8pg/+uijHHfcceywww488sgjPPzww5x//vm9swdK7c+ddtqJ0aNHc9xxx3HGGWeQSqW4/fbb+wgL5eXlXHbZZZx88snsscceHHbYYcyfP59bbrmlTx7Lvffem0mTJrHzzjszceJEXn31VX7yk5+w33775c0kfe6551i5ciUHHXRQ0WM2DMMwknnkkUd47bXX6OnpYcmSJTz55JM8/vjjTJs2jd/85jd5EXJJXHzxxXkzCUGMgPvvv5+99torcRsHHnggc+fOZenSpQVrdwyG/fffnwceeIDPf/7z7LfffsyfP5+f/exnbLHFFnlpB0r5u8TnmmuuoampiVNPPZX6+vqCQn9VVRV77703TzzxBJdeemnv8q233pojjjiC66+/nqamJnbaaSf++Mc/9tYfcbnyyit56qmn2GGHHTjppJPYYostWLlyJc8//zxPPPEEK1euBOQf9dtuu43/9//+H8888wy77rorra2tPPHEE5xyyikcdNBBHHDAAey+++5ccMEFvPPOO3z84x/nscce46GHHuKss87qU1dim222Ycstt+Tee+9l88035xOf+ETRvh8xYgQ33HADxxxzDJ/4xCc4/PDDGT9+PO+++y4PP/wwO++8Mz/5yU/o6OjguOOOY+ONN+Z73/seIGk+f/vb33LCCSfwn//8h9ra2pKOK47DDz+c8847j89//vOcccYZtLW1ccMNN7DJJpvkFYsfjnNx0kkn8ZOf/IRjjz2W5557jsmTJ3P77bcnptV6/PHHqampYa+99irav4ZhGB8VZsyYwZ133slhhx3G5ptvzrHHHsuWW25JV1cXf//737n33ns5/vjji27nsssu4/HHH2eXXXbhlFNOoaysjBtvvJHOzk6uvvrq3vW22GILdtttN7bddlvGjBnDs88+y3333cdpp50GSKaDPffck0MPPZQtttiCsrIy/u///o8lS5Zw+OGHD/p4KyoquOSSSzj99NPZY489OPTQQ3nnnXe49dZbmTFjxrDXZ9hss82YMWMG3/jGN3j//fcZMWIE999/fx9DZSD9MHPmTB577DF222039tlnH5588sk+NbaKscsuuzB27FieeOKJxLpdxRhITQ2A888/n3vvvZfdd9+dM888k5aWFq655hq22morTjjhhLx1ly5dyksvvcSpp546oDYaxlpFYBgfUX74wx8GdXV1QVtbW++y++67L9h7772DCRMmBBUVFcEGG2wQnHzyycGiRYvyvtvc3Bx861vfCmbOnBlUVFQE48aNC3baaafg+9//ftDV1ZW37k033RRsu+22QXV1dVBfXx9stdVWwbnnnht88MEHvetks9ngO9/5TjB58uSguro62G233YKXX345mDZtWnDccccVPI5bbrklABIft9xyS167geDwww8v2j/d3d3BddddF2y99dZBXV1dUFdXF+y+++7Bk08+2WfdbDYbXH755cG0adOCioqKYNasWcEdd9xRdB9BEATHHXdcUFtbG7z11lvB3nvvHdTU1AQTJ04MLr744iCbzfZZv5T+/Nvf/hbsuOOOQXV1dTBlypTg3HPPDf7whz8EQPDUU0/lbe/6668PNtxww6CysjLYbrvtgr/85S/BnDlzgjlz5vSuc+ONNwazZ88Oxo4dG1RWVgYzZswIzjnnnKCpqSlvW+edd16wwQYbBLlcrqRjNwzDMPLxf9MqKiqCSZMmBXvttVcwd+7cYNWqVX2+c/HFFwdAsGzZsj6fzZkzJwCC/fbbLwiCILj//vsDILj55psT2/CnP/0pAIK5c+cmrpO0T/1Ni2vHrFmzet/ncrne383Kyspgm222CX73u98Fxx13XDBt2rTe9Ur5u0T77F//+lfvsmw2GxxxxBFBWVlZ8OCDDyYeRxAEwQMPPBCkUqng3XffzVve3t4enHHGGcHYsWOD2tra4IADDgjee++9AAguvvjivHWXLFkSnHrqqcH6668flJeXB5MmTQr23HPP4Kabbspbr62tLbjggguCDTfcsHe9Qw45JHjrrbd612lubg7OPvvsYMqUKUF5eXmw8cYbB9dcc03ib+vVV18dAMHll19e8Dh9nnrqqWCfffYJRo4cGVRVVQUzZswIjj/++ODZZ58NgiAIzj777CCTyQT//Oc/87737LPPBmVlZcHXvva1fh1XXL899thjwZZbbhlUVFQEm266aXDHHXf0ji2X4TgXCxYsCA488MCgpqYmGDduXHDmmWcGjz76aOzfSjvssENw9NFH96d7DcMwPjK8/vrrwUknnRRMnz49qKioCOrr64Odd945uO6664KOjo7e9YDg1FNPjd3G888/H+yzzz5BXV1dUFNTE+y+++7B3//+97x1LrvssmD77bcPRo0aFVRXVwebbbZZ8L3vfa9Xf1i+fHlw6qmnBptttllQW1sbjBw5Mthhhx2Ce+65J287/v+6Tz31VAAE9957b9568+fP76MnBEEQ/PjHP+79+2X77bcP/va3vwXbbrtt8JnPfKZoX8X1ge7nmmuuyVse167//ve/wac//emgrq4uGDduXHDSSScF//73v/PaWWo/TJs2rffvQ+Wf//xnUF9fH8yePTtPJyqVM844I5g5c2bR43CP2+/fgfLyyy/36imjRo0KjjrqqGDx4sV91rvhhhuCmpqa2L+pDWNdIxUEQ1z1xzDWEZqamthoo424+uqrOfHEE9d0c1YLv//979l///3597//nZc72xg8nZ2dTJ8+nW9+85uceeaZa7o5hmEYhlES2WyWLbbYgkMPPTQ2xePazty5czn77LN555132GCDDdZ0cz50vPjii3ziE5/g+eefL6lOmmEYhvHRIpfLMX78eL7whS/w85//fE03Z43y9ttvs9lmm/HII4/0ptFe29hmm23Ybbfd+NGPfrSmm2IYg8ZqahgfWUaOHMm5557LNddcQy6XW9PNWS089dRTHH744WZoDAO33HIL5eXlfPWrX13TTTEMwzCMkslkMlx66aX89Kc/zUt9tS4QBAE333wzc+bMMUNjmLjyyis55JBDzNAwDMMw6Ojo6JPW+bbbbmPlypXstttua6ZRaxEbbbQRJ554IldeeeWabkosjz76KG+88UZeOnHDWJexSA3DMAzDMAzDMNYZWltb+c1vfsNTTz3Fz3/+cx566CEOPPDANd0swzAMw/hQ86c//Ymzzz6bL37xi4wdO5bnn3+em2++mc0335znnnuOioqKNd1EwzA+QlihcMMwDMMwDMMw1hmWLVvGkUceyahRozj//PPN0DAMwzCM1cD06dNZf/31+fGPf8zKlSsZM2YMxx57LFdeeaUZGoZhrHYsUsMwDMMwDMMwDMMwDMMwDMMwjHUCq6lhGIZhGIZhGIZhGIZhGIZhGMY6gZkahmEYhmEYhmEYhmEYhmEYhmGsE5ipYRiGYRiGYRiGYRiGYRiGYRjGOoEVCh8kqVSKFDABqAMqgOXAMmedDOAWLskNwX7TwCRgOtAMvAu0hstTznPcA+cZp21B+OgKHxVANmzv+sAU4A1ghXc81eH+yoGPheu/ELanGDXADKAjXL8m/P7ycJsjgJVASwnbikP7wu0P9zOQY+km+bykkb7wv5+LeZ0NH6W2zW+P+z5Lfj9rO+qRMZUCRof7XgS0h5+XJ+xPz6/b3iDmuQc5/3679JHztlUKKafNaWdZKmY9t42B98jRv/2623Vfx+1XKXfWqQqXpYGZwDZAJ/Ba+Fg5wPYMlHLgU8DmwIvAe0Al8EHYrlKoR67lFHItNyLj3yeN9PdI4JNALXKNNgDvh9/1v5dG7hWdwNLw+2n6P14y4fdqgYnh/kcCTyD3IPfac6/jocJKTRmrg1TKvxMZhmEY6zL294OxOkilTMIxDMP4MBEEPWu6Cess9os4BFQgHZlChOWGcLmK6a5grGJpoT95i33u042I0CqmqxhZahiOb2qkEKOiHlgVbr8y3GY2pn09zvP88LM4gTWNGD8BIo7mwm23htuvQUTk9vCzznDdCkTc1OX9QY/HP9aU81muwHZTyLktJMS7pMNjqECMmnbne1XIMbn7ctvnC+9qhmmb1ZjJIeenGum7lYixRfiZttE9//45TloW14465Dx1M3Dh2DdUXCHaPRfuur6R0V9h3N9/3Pu467ErfF+GnLMRRON/AdCGmJatg2jPQEkh1+AC4K2wDROQ6yONXINxBoWSRkzE2RkYVQ5vdsHTOTFHfHSctgP/Dl93hu97SD52HfdqdNaF79tKOcAQvc/oM8i9SO8Jin89mpRgGIZhGIZhGIZhGIbx4cdMjUFSjkRM1BB1ZjUi7GXIn7lfyoziSsRM6EKE6kLrZolMARX7VARWY0PF7YB8g8UVc1PkC8aZsB1VSIREOTA2PK66sG1tzvquiLqQSHj3xeJaYJNw2duI+dMdPk8K91ceHo8eWwYYF35/OdBUoD98UkRmiRooej588T/JSCojMjV8MyMTPruCfCWwMRJB8w4SsdIFjAE2RCJqljrf02fXgCgUVQMytjqIoirc/ncNEN8E6M97HT9lyAx5NyJhoOaC2+fuwzdZ3H24Y3qo8PeTRDcixtcj/dGA9EM3Et1QamTEUNINvBK+XhU+tyAmWnX4utA1Ug9sAEysgrH10NYAVZ2FjdQu8iPPCqFRVgFy3Y1Arl/tt0KGi08P0v9LgeeQNjaRbzAqcWPIMAzDMAzDMAzDMAzD+HBipsYgqUZE7EpEWKxAxLjFiPCvM5pLMTRUwJ+CpHZpJ18EVJHeF1N76JtOyE37ot91ReW4GfsuOUQ41xRHtUSGS2vYtrjv+eaIplBKh98fF75uJIpoUQFUj037rAIRRUeG2+mPoUG4vfWBqchAX4UYDcuJZv+XhfuGvv2aQkyWMvKjbPzjxVk+CvhYBjapgfoW6AqkHzcM29FDZGqUii/eqinTFbOu9neG/HHhfr8UY6Mc6fsJyPG7kTWDjdbwTbZCqdCIOYbVRYCcu+awDS3IGEmTb+rFoeO9myiSaaja1OgtayCKiii2r1GIebiqAypS0JXtG/0wWNTErQc2CvcZkG+MpJF7Z0DhCI4A6X+N/FCjzUXva3FGainodg3DMAzDMAzDMAzDMIx1AzM1BolGaowPX3chaWEC8gXgUma3jwq3NZp4Ab8ciXSoQGooVCMCZR1yIt2UMK7I5xoccdEaittGrauRQwTF18P2FUo746L1H1SAr0GMkR76GgN6DKORNDjt4TpTEcOoDXiJvn0yBZiM9Hej95kbhTEd2LgOVnXBA11iKrjHUBG+7/KWp8Nj1j7Q7Squ0ZFDjIRq5KC7OuWYNw3X1dn+pYqn7jnyhX+3fXHba0XGnl7ccamXCi3TCJDRSPuXI+cO+lczJAk3kgj6pskabETIUJJDTIMm8tO7FeuDFHL+WxlaUyMOTZ9Wyn5agWeA17IwohWyQWQuDhUTkLHTgly79Yg55Bq0GcQ003RWcedZ62q4dVzi0qr597T+jJkMcv8pZlIZhmEYhmEYhmEYhmEYaw9magwSFbW1QHNb+GghXnCOE90qEFFfxcB6osLULpqGR4t2NyOCXpmzrpuyJ+0s03279Qzcz/z9uLPkW8PHCmT2uR8B4Kc6SiNmQy0iZI5ExP4uYAkicDY538uSPxte006NDY/1DSR9jUsKMYDWQ4okr/KOM0UUFbEQ2KQHqtN9i29r4e0aRGB1ox8y4fIeotnscedEa6f0IJEgbwMbd0lh6apwnaVhG993vlsV7qPYTHntW7/WRSGTpD586Cx3V4TXPkgytXScLUDOU3fYxiyRcF6BGHlaQ6a/grA7Dv0aI+7z2oBbc6XUsr5ZZJyvTREAWvOnBTEPtZblULaxEhn3E4A/Af9BUq41evspQ+51cYaKGhn+c1L9DL2n6nWoY7UUNM2dYRiGYRiGYRiGYRiGse5gpsYgaQPeJCoEvQBJPRU3a7qGqGaEfl6ORCNUI6bBCkSYiyuK3Q28Gn53B0S0XhS2IS7NkJtuKku+OAiRIaIz85MKNeuyTue1f1xZomiGEcDmYX8sD49Nc+0vJYoA0ULY7YjZMSJ8Xx1+Vk9ymqUgPPZVREWy3RoUKnR2IKbHyp4oekbRAuSVznt3X9o21xxSM0BnxgdIvYya8Ni0La+Fz9PCY2lF0u+0OPvaHjGz/h0ev247KUrD7Xete6J1B/zPJof7XoGYLCuJio+7NUX8YvKuGNxFlKrL7ROQcTsR6f9VDCwaYW00MIqh6dCWImOrEO1FPl/d6DgaimibQripz7qRMeSTIxo77vnXMeneq/z7lruuRnBkkPMyATHiFlF6Wq0sUequtcmEMgzDMAzDMAzDMAzDMOIxU2OQtAEvIuJcLVEB7Di0RoQWvNXZxRXh97WItBbEjhMedQY44XdbCqzrisZxaagqkNnSHUT57uMGhGtw+DUV0kTRCJ3hPrT2RooowqMrfFZjxBUrNRqkHBH5x4TbCJDIBrc/3eiEReQXDfYjGfTRCbzZI/vQFFYV4T60Pojbr0o3YlBVhP2iZgzhczY8zk2QceBGYXQhURtBuE6AnFc1TcYixsMSonoVLq6h4QuzGhk0NdzeAvLNGO3HsWG7PyAaH37ERzFjIZfwWUDhsTfUDFXdAx13paSPijvuOiQlmR85FMfqFMg13VUXyZEHal4Od7sawodboyaF3B/V8MgSb/76URlqcOAsC7xnkHvQVCRKpBNJSfcGcm0VogypdzMeuT4Xs/aZUYZhGIZhGIZhGIZhGEY+ZmoMAe2ImTGCvjOPXTQllS8qapRDlvwi3EnbaUaE6jQixLUkrEfMNtzojXrEQFhBlO5KReykh29qlCOCoi+kajqmpYgA7KenUtzZ4+1EomcLkZmhx6c1K9zj1Vna7vbciI1uJP3U8vB1G1HEgqZVKjabWyNKuskvRpxCBO6ZwCv0FUPbiAq+d5CfpmkcMrP838iYcdMxJQnqus9y5LxNR0yF9+kbYdKMGCbuedGxFxf5Ucp7F601MZDUU2uSeqLrtJsobVw78dFRPhqV4heVX9OkkfGk6dvicKOthoseIpPNHZNVwLZIdM9iJOJshfN5JdH9B+IjNnA+c1+XERW1H4Fcd3XIvaKTwlFE1cCsFHyqHpZn4dlW+F+pB2sYhmEYhmEYhmEYhmGsEczUGCJUEC0kivYgoqgrWmsaFleoLyastgHzyZ/tXKxArltnQ8XxSkT4XoGIgOORiI1m4k2VuO1rNIZbZLsrPKZO8kXWuHRH7rY7w3U1PZKi6WEqyDcw0s77lPecRmauZ5B8/o3e/jQqo1CflSPGwSTEOHgvbFc90neNiJBcn4KqQATVdLhc+7iNaNa6e0yVRLVQqrzP/bonZeF2u5C+qEDE4fHEG1qdyPhYikRrqHAflz5soIZEhuLjfSgZqv3ojP5WpF/WQ8b+W0j9h7ZwvULGZEuBz9cUATI+hrsoeTG0Po5PJdLXm2WgIhvVa1HjYxRyjt1Ucv417S5zU7RVI1FPdcj9bCES9daJ3AOS6hvhLF+Vg85ArpNMwrqGYRiGYRiGYRiGYRjG2oGZGkNIFhHRWimc3sYV5CCKzujPfpoQYT6LzFDuJEqtVAgV8XrC/a5EhMWxiNhbjUSdtHrrx1GBiMQaZeLWa2gnv0B1IUNDiRNkta+09odfyFu3HZd6qoy+Roi/3SRSSJHy7ZB2ryQyF7SuwhhEqM0GYnRshAioagpNQfqgMWb7Kv5qZE8tUQ0CN81VDVIbYzQi1GrUy7JwvQ/omzYrQM5fJyL01ob9MJSz9LW4fTPxNU8GSjVRNM5QblfRvq1B+nQqpY3NpOiWtQU952tr0euAMKooJwZhGbAZMo6XIWaCpnjrIt7Q0O3EmRsghsZbRHV7NMWci2/+dgDPBfDfFnmtD8MwDMMwDMMwDMMwDGPtxUyNISKFmAvrI+LzYqI6AHFCqKZM0XRLKuiXIp4GiCjeQ1SnI6mOR6FtaKFwjQBoRgTwKvJno/tRG2oY1Ibf6ySqreFGA6ih4dakKNQevxCwuz9dRwuMJwnR7jbaiFJ5aWSBXw9kRPhaDaE6RGANgBlImqj54edV4TpdzvulRIaUmg1ZxPDYnKhguM+74TYWE0WijCeq46HjoQIxNWrC5WVExombLiwO7Z9sgXUGgp7rob55ZJD+H0Fk7vgUi0gqRgtyjkYi5p329XsUNhYrkeNuK7DOmsQ1BdZGcojp0BTAm0S1LLLI+ehExlQ1cj6SzrF/j9D71jvhd7R+D0Sp5eIK3bvvl5Jf46eYwWUYhmEYhmEYhmEYhmGsWczUGELqkYgHFZ9ryC8K7lKGpFzxC+aWIthmiWaclyMiXH9naI9ARPQyRGBvRITdSeTXFkhKQ6XFljvoa3h0IUKjiuk6YzpH4WgMX7CMK2Kt9TD0dVzRYCXrrO/WxdC+yiBpa6qQPPoBUh+jAplNnkNqcSwkMm4gMqHaiCI4RhIVPIcovVRSOqAV5NcUaCGKBHFF2E5EDO4J21RqtIWmtloebnMoix9XE6XWGo4i4WpCqelWjhx/d7jfUop8JxUD70HGZxrp/zco7ZobaFonP4pIKVQzZyCsDWmT/ELe/jjWe1YL0tb3kHOgtYS0Dk9A/nWt245D0+k1O/tzzeS468U1LvT7rqFhpoZhGIZhGIZhGIZhGMbajZkaQ4zWkehBBH8//YlShhggKcRQUPFNhcFCaZ+CcPs5RBDsb6qeTNgujXggbGsbIg62htsucz6Pq3PhGgRqtGTDxwoiAbosfFQRiZb6rGmr4o4x7rkKMY+yREWqS0klpc8Vzv41H/9UpP+bEaNHBdilYb+4plTgPTRlVDuR2RGEbXsFmYVeClp42z1WwnYsCttaqrCeRkTuLoa+MHQZ0meaMm0oTQ2tG7ICGYuuCabm2IjwdSP5tVrKkHFUjlxXZUgkhn8dZZDxU0/xm58bIdRfUyPlPXxc0zDp+9VEkVDF6CCqR7OmGIGYhJ1EEUx6nBpZpn2/HEkVpf2qEVWuoeGSFMnl3y/1ntZDcl8EzrqumeG+NwzDMAzDMAzDMAzDMNZezNQYQlqQqAetqdBJsiCZRYQ9jR5QShHpddsqtnbQv0LKWpy8ytv3KiJhPo2kAqpERD41OxQ1VVzcbS111lNBsxypM1GDRD9opIIWLYd8IdLtC529PxYRTj9w9ufPrPYjOHSZUkZkvjSF2xuLpNNaj0iQfSvsA9/ISDI23ONvJ0qJo/svxazy0fQ8SZEHLiru1yB9PRz1FbReitZzGUp0xr5rIOSIDLtKJCXYesBzSFowkHE8Htg4AxuMgPUy8N4K+CDomy6qEkkpVk1UQD3JENRzH1eMPYm095xkaujYTCq2ruaRRpbo2NB2t5Df/1nWrKGhhuEGRPeXbqL7RA653qeE6zSSHz2k9XLUVNBrJSlqolCqOjUpCqVmU9NW96eGhkVqGIZhGIZhGIZhGIZhrP2YqTGEaLHuJJG0DBFVe8J1F1DcxHCFebeWgUYHuLUrSiVAxN7F3nKN+tD9VhLV14gTyKvCdVbFtME3ajrDdeoRUboJMXVAhOWp4edLiERcv18qkDRPDYgpouaLtjeOOGNDhcscYmBoPYIeJN+/nkM1jqq87bhmhmtu+Pj1O6rIN4ZKJUmwrg7b0Imch1HhPuqQ/vcNgqHATbE11CQJ/IoeywSkyLeaGhXARGCPOth+C+n33zwDQcygrUDGYJoo4mdxzH7VICp0bbniuR+ZUSxSw49I0P2nkPEIcl7d+hLVwMfDY3gBMRpBzLjhMJn6Qxox06qQ+5JGg1UQRd2sQKKO9P7lo+nq3GiJpHtjsXumpi6rQSJIGon6S78fOOtapIZhGIZhGIZhGIZhGMa6g5kaQ0SaKIVTEiqotpI/+7rQ+q4oqKmSqhFhfzEDTy8UEM2Udut/BETpp9oQQ6Mtpq0pRDyvoe+s8SRUQG4jPy2TitITgf8SFW12hWZtY0BkfOCso7nxC6Wt0ddq2KiwuYxIwF4ePrsGkh8x4kdpxO03Q36faGH1pALY/SWDjIUKpE/Lw+1niFIxjUDGR3+ieNZmNBXXKGSMavH3bkQw/187THgXenLwak/8tVGO1EFpDrdXS34dEz3fWoA6iTKk/gxExd5VGHeNjCRTA/LNEB1PdYhhs4T86y6DmIHrO/sDOf+TwuOPq90zVBQr0K7jroUo4iuFXENZovRsS4jMuDj8dHNutEYxs8E3gMuRPpuC1Mzxz2dc2ikrFG4YhmEYhmEYhmEYhrH2Y6bGIBmJiIkq3BUSkLVGgEYAFEPz6es2KxEhewwizFYgBkDHQBruMB4R/xYjM5pVYG0p0M6AKI9/qdEAQbgPLXwNkVDZjojVm4TbXUB+PYUaROztpm86IDUXihkarvHhCpdd9J05XpHQ/qT0U+5M+wpEuNVi6WnEYBiFpD76gMEL0AHSH58I37chNTw+QAR/TU2mwv9A0GgFPQ9acL0UQ244yCHHp5FDKkarWP50F6xcKO17Nuh73NWIAbAofGhkgXvuxoXbayB/XLtRPinkGtwGGbcNRH3uiuNQ2NRQ3EiBEWGbdFwpNcg12oqkRVNDMkM03lYxPOelDDFje8i/9tyohm7kXrQyfK+Fu/174ioi0zSOuGvZNxSL9al+Xovc2yqJjL4sUb0SXeYaURapYRiGYRiGYRiGYRiGsfZjpsYgmYgI1FBazYPyIuu4xKUdUoFcoxZWMjhTI40ItNVEkRmK5ppXcXdkuL6mm1oVLi8Lt9GOiJmatqqNSHjWuh/+8VSGxwPwPhI1oQWoXeGzA4mi0HoOLr7Y6YqerpHhiteu6KzbcN/723f340dp+KaGn8+/Gjlf1cC24XH+lcGlccohY2mriTB6FLz9PrzaEtUycAs0D5QKJHJAhWoVgrVGylDjFnmGeGG7K2yP3rjUnNLUZasC+f4q+jIaMZaWEtV6cAV2jc7pJD96w6+TofUuNP1aLfmmRrF6Gjifu+OpO2x3E33HuI7fFvJNpS5kPHUxcPOqEOWIEbR5uP2XwjZqpNY4olR6XfSNQvLbVEqUkl47eu9QdCz7xpGPfncUEtnSFbazGzFt3XpBOubUhNHtG4ZhGIZhGIZhGIZhGGsvZmoMkgXee539qwWyXaqRegCF6m4UohF4HhFQJ4XPg43SqEPa+wF9heBqRLBtQkyLWcgxvUJUCByiNC8rEdFVZ8QTfj9A+mkl+TPJdWb6CERY/AB4m/+fvT+NmS0767vh36656p7HM3af7tOj3TYY4yYTT3DygJJHOIkyKkIhQkSRhRkSiJQPkRAokQgfLAKyiJI8SDgDJEoUyAQR78PDyxuMGQw2bvc8ndOnz3zPU9017/fDtS6vVeveu6ru4XQfO9dPKt1Vu/aw9lprV5/+/9d1XWKGxH3XQkTrUSvR4zoFWlxbjQ1GvA/TAI0iL0ojPs9esK2F1ABpAh8vwxMpfKF3clNDRdkacH0DXluHL/WlHkgruodxtQlGkXJUQM+rH3IWxOMX/o37WP+GwrbWhBkgcyscozK+hobOiWl8urgivn6Mpn0quu9ik6Lg9rmHzO+QJDguPHbU/YbmWFxHQ2kCL7rvwhR3obn4INDUcJdcm24g82AW+AjwZAFuDOTZ3j7D62qkURUfJaIRcVqvoxjsG9PFR/MUkbG+hIyxph5bROZFGxl3TTdnGIZhGIZhGIZhGIZhPNyYhnNKwrz9VeBxRPB7G8lzHwpuc0j6qDVG197IY4CIh6/g6wKc1tQoI/nms+oPaB2IGvAs8AyyKrwY7VdEIhH6yD3O46M1pt32KYYLjhfdvgvuOiro6mr3LCYV08N8/CFJxrbwu0nTzug5QlFa0TRkabQNJE3RddcBo8Zf+zcvymIeEWRfBf6o59P8aN+G/RcaASr0TxplEa+81znyIE2NeBz0czH6PKoAN9F3GnVxF3lmNKpCI1o0HVHZfd/Gp94KUxKFacu6iKmh0UvVoJ15kRpavFqNlTDNUtzuOFJJU2JNgl53VPRQuO+o8ewjvzFax2PPtWUGeDyBR2fhYBdWB8OF6c9qjkwDV5HfzO2gTW3EcMkqKt5x+9wEfhdYQvq95l67yPOyj/xujYv8MAzDMAzDMAzDMAzDMB4uzNQ4IzQf/wVEHG0gIlyYSmcFeAyJSLjP8VL46OrkLiJuTipwjmrvNCLmbuTso6l0tEh3ERETQwNEoy16iMB7leEi3nv41C7n8CvRy0h/qJDcxZsamhLmtMKoHq959OO0U7HBkWemxKJpGr2P25rX7h7w2+59lhml/azF5OO6G2VEyJ1H5sIWPvolr8hxeM8l99JaKCfhNPU04rbF/R/XQIlF5qx0VPHx8fvQ4NLUTsoWMtfK7q/OQb1WIfgbCt863pr6rYI8+2qUxFE7+r6Kjw45INt80iiT0wjsDWScNYJDzcNdhqOldC71ORpFpWh6q32kb7bd/tvAGynUD6Gdyr1UGE7FdlxKSB/pbwSICXGI/EbF5mHHXTMcoy7eDOwAryO/O48j46xjdIgvOr+MjMv7USfGMAzDMAzDMAzDMAzDOD5mapwBWlR6HhHfdHVzvOJ8gIiBugr8uKbGDCKIZqWnGdW2rNocNaQod1x0O6SMiIw95H42kBXbYZTBFGKMXMen8OkgQuqsO+aaO9eT7ny7eONjC1nxrmJ5Eb9qPhSYJ7nfEiJO7nO0b+O0SVkRAVnXyFrBfZoUTOsjvpsFriB9sRVdo4TMrxmkb0H6qYLc67g+0vuoIXPhpKZGWCekjE/3lHW98Lrxdo3IGRU5E15P7y88VxzpENaoCI+N3yuaykifxdCsU5E+vo/Y2Dhk2EzKMsb0HF18XZgBYj7A0WLucaHs8HqTzLuwz4uI0foUkp5sBz9fZpEIrDaS1i5rTgyQuah1NLSfN4A/BHbacr5beKPupCnPKoj5so839LRGT1a6Po3YUMNXC6yHz4JGJqlZ1MWnF0vd8Zt44zPLTDMMwzAMwzAMwzAMwzAeLszUOAMaiFC5hqzALuLz9lcQoUwLGZfxkQoamTAJo1Lu5KFFxVX4VlNFr69mRB7TSIHg+8h96ermUGCcQgRFrXehBsoqkmqr7fYPhcdtvLC+w7BJon2m+4Ur3/PQFd51pDDwW4yvMxAKxLEImxXBkSVWx+fIa6eK1uPuo4MvZhxGchSQftaxDAuuV5GxHHBU+M0yE1oZ+52EGcRkeZvh/tLrjBL5db+UyYT6OIIjL4pjEB0zybm7eJMw3HcaqQtTRMbjMLhOVgqsLPMljixpIfN9gIzjFDIWPXx9F/2rKdnC84Rpz2LCdhy49q+6+1NjM/y90b81/G/DKPT3LEyPtY3U+dAC5inZ6aAmRQulh6atppmawv9O6DX0mdLfnTb+90Ij0TS9FMi9aq2N8Le5gzem4nRyhmEYhmEYhmEYhmEYxsOHmRpnQNn9VWFPRTHNtV9ABM09xCA4SXqZHj7CYVKxsODadhlZ3b+L1OPYRURCXbWcRREprPvxC3BnG144FEMjrBdRQlK3PIrc5z5yX01EMAxXumtNgDJynhYiOMZpmFTY1FQyscCo5kjYDl2FrTnzj9u3CT69Vouj9UXC2gT6N476GFeXYBL2kToAneiaalqo8NpD7l9rB8SmSV5khM6duD2jIiZGtbUdnCuuszLK1BjVd4OcY+LohaxzhZ8nMZt0v6zvF5HohiJi3Nxl2GSIzZuQcWmzysAHkSiJVxBDUE0DvX+tFxGK9uE4xW1Oou+qiMG3iaS7u4n8/oRjr6mltphs/OOxUTMjPGdoOB03hVwfMWTCtpSQmhhdxDQGGRM198L5HaZVKyKm1CoSVaKp19QQCX+Dw2fIojQMwzAMwzAMwzAMwzAefszUOCUryKr1HfzqajgqRtYQMbOFiNLHST2FO89xi4v3EMF0BklrpCvENX1MXB8jpAF8qAFXLkKjDtfeFjOiz9F6EhcQEfgesnr7ALjhrr+O7wfN269CaN6187bXkJokZSQaQ/tD6yWoYKlFm/ME1QJi8rTxqWhm3D2v5Vw/XjWfZWyA7xMVSY9rFqgoG67410LgKRKN0sLXR1ChuRi8jyMbwjbDsDgeRqFM2tYiIjSD9J/WkDnAC9x5ERrKOKNhFMc1Ko4bOVBFajA8jgj+WdEZeenLslIYqdGpEUvPAn9hClZnYP+uPCOD4LgwdZTOIR3X8HyxGRSygzyvml4pa2y7wDv4Z0ANtIrblnVMbBZlmUpZKcHGEc7RAn4eLSI1Mc4h/beJN9DCeRtGs2ikh/5WaR/s4+uohL/V+jyEz5BhGIZhGIZhGIZhGIbx8GKmxil5BF+XIl7FnOCFNc3tniCi4TlEeNzm5OlOEmQANR98VoojFUBV7NP2qECYRx+41oTffgmSokSYbDMcWaEpaNp4cVz74B5inITiZwcR5yeNXIjpIcLjNNkrqncQsX8Gubc846iB1PcAeAmfWmic2RQL5XGtg3DbFDLGa0gfTSqol5D50QrOq69wTrXw82YQvQ8F8CzU/AkFaJ1Ho9LvJIipMoMI/yVEdO7j0xhpiqM8YTjNeZ+3T973o1bUx6Zb3f09mODcIP0/g4j+N5FoBk3ZlmdqwLAwH7ZFhfU2Mif+ZAU+eBnubGUXjNdzxHMqvkaesYBr++2c7xQdK73OrHtNErkR97FGQWh6vW7QPkUjLPR7JYzqUVOj6F57wO8hz+ys+7uLNzRjI0lT/w3whdzvM/xclzg6D8PnyzAMwzAMwzAMwzAMw3i4MVPjlOwjwpuKanFufDUb1FQAXwy7744/aY2DEiL0lRBRuYUIkuH5ykjtg+OsVteCui8Cd1pyzE18vvsQrQGRICvOw33CiIcUX7/gpGjapSrZxkiKrE6fQ/ojrwh6Fek3NZlAxmeS+h2QLWaH24qI+HoJn5Zr0sLch1E71AwrIP25z3Ch9ZgscTbLBFAhWPsxFHpH1cHQOihat6HttmvKrDwRPhTP9XlQE6UYtHsUWWOTJ0KH/Rdee9Lx3UXm9Zv4dGrj2hX2eziv1MxLkJRISwXY3YN3t7yBMEl0Svg3NK7C1FNZ7cojrkGy416jiM2ABElB9zjSZ/cZnpvh+NSQZ+IA+a0Io5L0tzN+30N+Xw+Q37ICR1PwpdFf7Y9t9wqJfwfVBIyjPQzDMAzDMAzDMAzDMIyHFzM1TomKs3niaihCqojbBN7FF609DRUkBdajiKnxRXyKlmlkxfkcIgTukZ/aKW5zHxFcu4ggq8ZNzDbwQvC+hS8WHjJgOLrgpOzia5hk0UYMhfPICvvDjH328avYtf/juhl55K3OD6khufznkHuu4s2ccUJzh2zDIkXufZ/xxb7jyJG4rXkmh447HC2OHu6vBt0ePqVSJzgu3j9OSaQGEkhfTbt7yjOhsogjBbLuU+kE29VAyYtwwLXlTXwtnBrDBbD1umEbtEh14vZv4MfsAOmvkvv7Tgtu35b5ucNwNNc4gyPPnNJ+H3VfZ0VYqHsa+CgS+fTbDKcgC9uobSshz6ZGUsS/nVkpvvSeusF5YkJDQ9NIxSTIM1lHxqUf7BcaGxatYRiGYRiGYRiGYRiG8XBjpsYpaSDC+aiUPTC8UryPX41/GgGyj0/nNIcYGLqifBb4CLCACHhvIKuo89LdhKgA33LnXSG//sYhYtCoIJ7ga1rEhs1ZrIBWgyUvhVUbEYqnkP5oZ1y3DVzDp8QKowfGCcpZqYfiz3Wk3xfd+zbS/1qn4CRjniKCsb6f9Jgwaig2ABJk/i4h8+UWfmV7WJgcjhocOt5qzOm4lzjaN3Gbw/HQejN6vZP2TXituL1hFFV8TBg5NI3cSxsxver4tErhMfH1esjYdhFBfQFJS7eHpGDTawyQZ/DL+KLYOj/jWjyTRpTEhtFxCOfFpGiqKb3uFGIyvoo8Uy2y+x589EQP+Z0qu1doLsBRUyE8PisKKXyvn7OMjRLyezaP9H0LP19Dc8NMDcMwDMMwDMMwDMMwjIcbMzVOSVhYNktkDLfFgvJxUuLkXXsXEZP3EKFQxbkGIuCliKB+l6NpW8ahpsk8XpzPa4cyBzyBrMJ+h7MxMkKa+GiQLLpICipdXZ+3ajs0dzR6RqNu8sgyNbIE2CpesE2By0j/HzJc3Pi4436aeaJtC8+TIMbLR5D+2mE4XU8YAUDO+3AlfTj38yIn4ns4ROqvnNTsySJsQ57RUUTmdReJSJpDUiitISZdKG5npVyKn9t4XPscjaZRYyrL0IxNjfBz1r2FbYm/n6QvS0g0UZvJamiEhOnRDpFIrfv4qKi8eaDRGWvucx/5XakzbI7pcfGzplFMWncma16l0fGxqbKORATt402SenCeImLwGYZhGIZhGIZhGIZhGA8vZmqckg3yBcgsQqGtyOlqTOCufYiIiloQWYs5ryNC9RqTpZ3KYh8RnZtj9isiwrCmllnn+IbGJCvOVTAeRQ9pdx0RrsOaFnn9Haap0iLFKuqHK+nzTI00OLeOsdYO0NX6ccSEHvdekXUtXb0etl/JSkGVZ+bgjq9yVGwOr6sRE/3g+9OmYMtilAGlz94U8Jj7fAHpi99DzKhi0D6i93FaJJD7BrmvbXyh95K7jt53wtF+jlNZxdEa8fvwHnVb3vs8GsBV/G/EpM9qPJ7b7vi8flK0tkhMm2EzdhT6H6u4/0f1V/g5QX7HDoLv1PjsIpE6jwNPT9AWwzAMwzAMwzAMwzAM4/3DTI1T0iNbRMsiFOJ6I/Y7DpoOZgsRKHuIKLuHCHWjikpPQhcR5cdRR0TSR5EIjaxaFqMoIVEDPeRezqJvtOh2CRFxq/jaDXnnLyIC9zlEaC0jfXiDo/0YR92oCaJm1U2kLzTVTRw1cBwz7KyI52kXn/4nbF8Jn5onL1pD99OC67p6PsvwCIXuIsN1JM4SvX5YdJqM9zXgIlILopFApQC3+n7ehgbUpG2sBPuGJuIk9S4GOe+zjsmK3ojNjnFtPu5vg6YJ65BtykwhpsAmPpJjCp+eL++Z09+vHsNmZRzFpvMmyzAbRH/zIl2yrl9w7eu5Yy4jhrBhGIZhGIZhGIZhGIbx8GKmxinJEyAnMTayvivjBbZJqCJiYoKIcyqga4HqDpPVKwhF9uOignETSbN0wFHxexxxBMNZUEf6RsXmqnvfJDvao+iOSRFRuocv8py18j8kXD1+iBhM9xGRt8DRWhMEn08r7GedJ+z/UeO6hRTFBhGjta+0lkRWtEN4H3V8LRW9x3H3M+32bTMcRaPnPsk8jMenhDxLYa0U/a6CCNd1910hhb0BvIWMnYra+uyMY9wzP474GpOapCFhBFhetIZGn2hauTfIjtDJYg54Cqk1cjPYXkP6eRkxMXbxtUUuIpFbN5HnKOs+1AgLDYkwlVch57i8GiTHNaLC++8AX2F8JJhhGIZhGIZhGIZhGIbx/mKmxinJEyDzxLUCsnpZc7qHaCqgPbILesdpV9QEARESQzMkXlE9SkAvIGmaWoxPM5WFCvnvIAJmyvGFwT4isOe18bgkwAeB52uw34HXBxJxktXvSor0233ElNDzJBy9n9ig6Af7rCFpebQIdMKwsRRHeJyWPINMDYr2iOu0XHu7SN/oavi4QHheGio1bCZpk6J9oXVHzrqeRgExLFaQ8VRjSusxXEDSTtWROXEfOExlvyW8MZhVZF7JMjImNR/C/eNjwmLY4wy+eD7F1wjNggQx9cK6MceJpqoDl5D5cdudp4ZEZ+ncWcP/boURK3lRGgXXJo3SyDJf1dhQcyM0P7KiM44b/aM1d8LfzVePcbxhGIZhGIZhGIZhGIbx3mOmxikZtco6L3VMnhC5hJgaGmlBtF98TOr220LE4SbDRkZcByKPBiL07iNplrTNmut+kpXcvQn3G3eOsyIBrpTg+RW4eR/ebotRscfoFEBhOp68VEYx8Thr0WOQPlSjIE6po9d4EPTJLkgdo2l9wrbEUQ+FaDvR/kTXSaJtYWqhIt4wUPPrJKme4nuII0pSfE0VkB+6G8gzch64gpgZLyM1Y/pI4ewlxJDSwvFZhsuoSIpxfR0TH9uP9g3nzagoobBNReT3YAqZz7vuPlqcrH9BxrCJRGTMI78554EnkIiPLYaN2AHS37cQ8yTrulPI790OPh2ZHhvOudA4C8dk1Os49xU/AyeNWDMMwzAMwzAMwzAMwzDeG8zUOCV5IlqW8BmuKA7z7IOsel4FZhHhXUVVGC18a4SGCpuLSHqfmwwXY86jhNTB+ACy0vo+fgW3rqI/qdkwTrAfJwBXkPs7iciYAut92NiF3b7c26gojazrjzODVGwOV7/HqCEy6hwFpK9PWsw9j0nGTdOGaQopGDZxRtWmUPLSHWnkSzvYR4twn8bEyLqH+G8PEdnriGHXQIT3PbdPEZnnd5EUYeBrO+zgjRgV2+PnNXx/VtE2o84dGhs6Jvpd3bX3EJmLVeS35CLyPOu8P2laL5B+exFfl6aAGEBT+MiWmHBbbOYVkTRfOk5x28KojQEyh0rRtjhC4yT3F0aChH1rGIZhGIZhGIZhGIZhPLyYqXFKRqXOyRM9s9LLdBFxVVPy5OWSV0KRUMU9FRrPMWxOxO2Jt2mqqirDaYZOKtbqvamAPcBHDYT3HNcjCSkiQnSTk4n9KfBiCnO7kKZ+tfpxyDM1slbXZ/VVmexxigXsImJmbTHaiNCaHwkiJJ92Rbne2xbZtTPimhpZURph9EkYMRAbGlnH5Zl+J7mHuG0DJOJCU6u18c+Dzsd9hufWBtIXYcowRU2fJDjHSckyhSZB+6eBGAL77tULtu0hz3HZvc9L/TRpO/XVRQwgNQ8qyFzsMBxVlvcczCF9qLU1lt22dxhOeRemOyvhI3v6+GLk7WBbaGycFWZqGIZhGIZhGIZhGIZhPNyYqXFK4jzu4apugm3h3xAVAvuIqdHFr1yO0/nkkSACowqNCUcjB/KEzT4iVmo74tXVxxH4QhFUaxecQ1a+X8Ovttai5JryJey/OGWW7nMSDoHrqfTpLpOLu6PMjDTaZ1xB9FGGl756iPicF+1RRIRqFa533f5nhYr4RbKNjNjsCMmK0AC5l060Xc+vHMfIUJFbo2LyjglNhwP3Xuf0rvt7B+nz+wynG9PnUN/HnFVkyaTn0AieMH0XwALwTcgz9Zr7vovMEZ0nfeQ+tzh+fRvIjtTRvk8Qo2gZn3ZO50/8e1hAfgPOIenfisjv1CxiOsW/N/r8D9x9LCLpwbSA+xPu+3W3ffsE95ZH3lw2DMMwDMMwDMMwDMMwHi7M1DglBXyR2wKykriEL04Mk6esiYsT55kg8XfTwDcjKax2kRXnHSbnALiOX/Fccq8qkxcOD1dYq7C5ADyN5NwPxcoCw+K2iuohutK/Sz5q4jRz9ksR8X+TyYoij0uzpOOs31eQyJgKUj8grBsRmlJZUQxEf/PapxErFXwx9dOsvs8iNmdGGVnaL1n3ofTxK+lD1NQITYk4eiXvvgrI/C4gz9XBiH21nQOkX8Pi1Skihq/jI5TeSwr4+THObHgaiU7YRQwMbWsHP7+0PzRlnRohobk4ygTKI+tZ0HYnyLM9jaR1C2tehPVRwjR7SgUZx0N8VEweWth92t1DA0mrVXfHrR/znvKIo3wsUsMwDMMwDMMwDMMwDOPhxkyNUzKFiMwthkXuFsNCe54AHIu5WSJfGB2QJU5OA08ig/lHSGRE0bVNhf1Rombq2pogouEKXpjW1eujzIW4BoPeh772g+NThoXDMLIljnLpkS96JsAjwDPAu8BLGft2EeEzNgxi00HvITx3lrBZwYu2bde+FEmjowbQNLIqfZvh6JA88X7SKIUOvvbFWZEXdRF+Dlfp9/B9EO8X3lc890H6XGtpxEwSsaHRLFm1LcK2xoS1QrSdGl0QR5IoasydJfo8TrnzbzB6LAtITYxFZA7fwpsa68D/Qvq5g++Tojvnjvusz/VxDY1RNVXUMOkAr7u2dPHPVIhGjG3iU5GBGFJdjqaVi9upxtijyNg33PW2kLRVZxmtlJdmzTAMwzAMwzAMwzAMw3j4MFPjlKhAN0BEuJv4XPOjIjTyxN1R5EVu1BBRPSxurIXH+0wu/pWBDwHnkfvYQkTVNrJS/CDYNxYBC8H2ovu7C7yKpMFRaog4qREpWaK/btc+DO8tpIikuKogaXg6ePG4iV/BHt/jDD66Q00WGBYzYyG8iqTbmUfG+YY7xx3EwNBV8vOuz7Rmw7j0U+MIReqTomZCKDpnpc/KEnPLSKqgFiKsayRASDzPixz9YVGjQCNc1KjRmhuT9MVJi11ntTOrFk4VmRt1ZO43T3k9pYDMncvI/N9hfJTBAPgKMr/bDM/9PvJsaY0VraFRQ+b7Hqc3v7IiNcLPm/gaMP1gOwybsAOkHyeN+AqpInPvHNJ/68BbSHTIKJP1OOSlVTMMwzAMwzAMwzAMwzAeXszUOCWxaH6ACHgljkYlwNECxMpJ0wmpSKyFkBNEDCy6z3Gh5vjY8NoJvri3fl5x519H7ivcV0XxMPVUeL+biAAZmiGzSOqaDde+PFNDhdIqImr2EfOgiIi3m4jx8keISKyr2GuIAHqH7NRCFSSaYlT6H+1TNQO0EPNFJCpjCxH4td3N4P0hEjmSZ2hkGV0PAh2DGSSi5RBZ3R4WvB5l5ijLSBqkd92rjTet4ggNnddh32VFpoTpuNRcyDJejkM4L/VzkrM9nsP6eRoxHur4SI5xadwKyByF4dRxIWUkgms+uv441pA5n3B0LhcQw6OBN9SqyLw76ygTJTQAwyLdSc4+p5nfNeRZm0L6L0V+R/Y4u5RheZFK4+rkGIZhGIZhGIZhGIZhGO8vZmo8AFQIz0qVo3nudZ/jiJx519oFXkDEP40o2EJqB+QJnHV8RIOaCz1kJXQTMTG2ERMiKy+/rrrPSj0V3rdGbmgx4SnEKNGUXUl0Pn1PsP8SEv1QwQmdCfxmKsbCGwyvqG8hhkZYgDhEIyjC8QlRgV2jCdQUKiECso5dFiq8qggdp9Yi4/2DQOtPpIiB9GFk/JqIUK7tyoq0CdHIiD3ERNJoJJC5Vkb6o4vMob7bpueG7OgINZN0TswjhkKT8XUWYmJRfdQ+Ya0abUfYvioyv9TYGzdORWReruCjtLJMkAFivGm/bDC5MK99BMP3qXVv9FkduPaPMjEnRZ/FcenJJiEvZd44Csgz2EWeqYF7r8/VWWH1NAzDMAzDMAzDMAzDML72MFPjARGuUA9FySIigpYR0yAsZJzHKGEwRUTSFiKwPoqP3BglnC7jV6RrzYk+UjB8GxESDxGRdhExTrKuHQrksamhbS/hxdkGIrhr3YVydKwaCErV7X8FeGIKHrsM+/vw1m3YTI+ujtc8/nl91kGE8xI+4gCOmg5hn+Daew+/Gj4vykONmlDY13adRoydc22ftOC5RtDoPVaReRHvM07EXUPGXq+rxbf7iNl0CYng2MaPXZxGLHwf/gVvwpUQ4f+kfZRlboSFqrMMjXgMG8izCTLnxpkrOp/n8REVWXQRo20Db5SNSw8VGxmx4J4C9925NRXTuKiS45IX7TKKvEiZ45Ig/fQ2ModnkN+ks6wpk3ddwzAMwzAMwzAMwzAM4+HGTI0HRCjohcJ/A3gCSQnUQkyEF8kWq2eQlfbbyGr5PIFQi+5qlEFWEd64bVrMPEwppefawAu66/hV+rjzx9EZcQoqGBZhNT3PIpLK6BAvUIbFt8N0XQV8dEEXic6Yn4JKGbZa0Ex9u2tB+/X6FYZXd+t3YSRBVpRILMLr9/tIFEvJfR7Vvw+Cy8i9XGOyegJaNPo2kqJrHm/G6HjlRWmE87bjrqf1QlLEYLkCfDCBqdQbGmEUBmQbGlkF2vcQQ2MSwyaP2JzKEtXDqKGsAtopMj80Ndk4Qb6HPB9l8lNPKWGUSx6TFKsP2zrumqch7L9JjYq4r0HmSonh35BJ0CLoOte1BslZ3++o9FmGYRiGYRiGYRiGYRjGw4mZGmdIuKq6ighzoTA+g4j6zyUwl4pgr6u3Y7SWRJXsKIkQFZ+biHB9wGhBVtMj7ZMtkMfCoX4OoxtiUTyJXuDvq46I8o8gK+HvIemGDvH1O8J7CfP27yAr0pspHDTh5Xfg83sSHTBguL5DlhhZ5WgdhbyUX1kid9gHOp55fRsaOlmcdOV6ghg3M8iK/3GmhkZo9JE2v+aO12iZ0NSI25UlXGsExiPA48ClIkwNYLoM631o9n2/aFqvMFJpEH0OaSNjfJYmUZYgDzLnLiIC+42c49SwmtQwOADexBdCPy7hvM2aw9qXsekWHn/cOTXJMaHJmGVWhL9ruj3et4A87w3kN+w4kSRxJM2DMG9GPe+GYRiGYRiGYRiGYRjGw4uZGmfIFD51UYejItkS8CFgrgB7fVmhnpfKSNP8NBk2R/LQVeNtJlvJv8X4IuKxmJlVhyE2M2KDoYREmzyOFDbWdFMzDKcEiq8bCrmbiPB9b1+iJd5C+iRvZfuotE+heB8bHYNovyzxd1R/xZ9jAThr2zjUILuJjNfB6N0BHxERpl5qIkaH1ouIxyn8nNVfGvFyoQBXp+HaHrzZEUH/dnRMbGLEkS8hcb+fBWHED4iZMYeYMh+rwLU+3O/7Au9KC5lnB0zWzyEnuYesqKYsYyOez3ByAb6CREwdIkbDJOZGeH2dV/ofDu2nMAWdRoxppNkkv0fvN+FvzoOKfjEMwzAMwzAMwzAMwzDOBjM1zggVn1UAzFp5XkaEs5f68A4i9m2SXftCTQqYXBTsIWbFOAaMj/6IyTMtYpMj3qY1B2r4yIsN5J6yVtOnwbl0ewURR5tIuq4dhoXrODJkVOqe0MyIaz4UgvehuJ/3Puyb8H1WH2WJx5OsmC8gkS6HjE5BlnXuuttfC6mrqRFHBoXtyBN0+/iUTbuH8AcDeBUZi7imiX4eFaERX/usVsmH1y4iZsafKMFzc7BQlnRZ7ELt8KipsY3UEMlKTXWWjDMCyfic1Ud5bSy6V1ZkRAmJnthl/HzKMvY0Eqfm/urviKY1KyHPaxmJillBoovyKDFZqq8HRWy+WbSGYRiGYRiGYRiGYRjGw4+ZGmeEisdVJCJjj6MGQxt4A0l9cw8R83RVfdb51BgZlc6oihecz4qsiII42iBvdbn+1XtL8Dn1d/E1OzrB/qME7wQRSGtIGqptfBqlca884ogMNTTCaIK8SIu8dobHjFp5P4iOGSeial2QScTW0FxK8bUvbiP9r/VQYvJSTsWphNrAjQHc6EhKq3h+awRIGIGTF6ERXqc3Zp9RxGOiAnkdiRS4Avxfj8ITF2FrG37vTfijTnb9Dn3e5pHUb5qGSqOLzqIQdzhGWXOliDwvBXy0V5x+KjzXcWkhhmqPySISsiI1tNZNWMtGjbce0u8FZN4euO0FxOxI8P28iBSav4XMpffT2IiNOcMwDMMwDMMwDMMwDOPhxUyNM+QQEccuIOLeNkdXOV9HxHkVLGMzYNJ0UOBTvBQQoyAr4uOk5K0Kj2tgZIn3moKr5f5uuO9qiGAcmhJ5KYlCsXsfeNl9l9VvcbTIOFNDr6FGhhYlD8+hnycVkfPMjBKSlqyM9IemG5skSkPb2UP6Totc511fTQttf8ddexVfD+WA4dRo8V99H69cdwEOfBERpbeie9D7Dk26SVa+nzbVT9ZYJ8gz+AHgqQSunIP+AL5wDf5TC+6SHUmVIuPUQFJWVdz2J4BZ4G3k2T1NW8P+UoNJ/+pYzyDG6CZijqoxFEdu5BHXowg5SZRW2N4SYlZoIe8Eeabnkb5p4w2jNnIPmipu2m1XA/Yp4JuA38VHXy26Nm7z3qWB0qgei9QwDMMwDMMwDMMwDMP42sBMjTNG0/7EqW1KiAjYdn/DFEejBP4k2i+uXdBDBMUiPvojPHYRERWPWyOAjPYsILUxDty1YoMBhiMBNNpEBeQSIniWg+/zxPQuXvw8AP4QSWcTC51Zhsaogt2hmaH76LYssyHFR53o/gWOivmxqaFmyRyyGn0KEX3V+BqVzipGRdcGMq9iY0PbpAXCNWJCTRQVixvIqng9Po5Y0b/xS/fbx6csKkT7553vQZKVrklNtz4yz6bKsLUDaxvwmwe+wLwyhRgg64jYfwsZIzV/iki0xzcgZsP/YtgQ0T6fpHh7Cf/Mhn2I+6y/C/eR35CGa4vWvokjYfKivM6K0Jyq41PIbeLnRRUxK/TZKeDnVwf/O6imWj/4vAjMF6AS3ETNnWfnAdzPKMLf46xoJsMwDMMwDMMwDMMwDOPhwUyNM0YjNA4ZFnazUkTFhkBetEZe+pkSIjbW3ecqXkQsuM8tsleljyJPkF4BPl6CSgleaMMbqYiPbXz0xRQiVt5hOMVPEREssyZcHBHQ56hwv41cq4qI1aVg/6w+HCVMqoYaRp3Ehoa+j00nfWXVpdBUWUVkHNr4Veo1fMRGNzrnODrIvWsapJjQzAjNjQFiQvTw87KNN2XClFuxcRbfb5ZxwYTtz6Lq2pQXUTCOOEJH+0WF/w0kquRaB158WZ6RGxxtfx1YRvpiHzE9brlzqDHSxEcmxMdrqqhxKbQ0aqeMPBc6B7KOGwDX3Pez+HlzHjEw7yEpxfTY5ohr63w4ThRXGK2knxvImIFEsBzgn/kOfo4p8TOtadS0D9WAbA+kb3WfTXyKq/eSrIglwzAMwzAMwzAMwzAM4+HETI1TornklR4ijMaRFZr2JVxpnUUcRRATnzNM5RSfp8tkYmYRL1i2yRcU7wHvJvCRKfjIABY6Ulj5FrLSvY2I94vIvYbpnRr4HPxZYn4YnZGXPidFBFBtc2j05KWGCs0HbYsWM64iQrZGsWT1Yz/Y/zDYj+i9RgnMAY8ifXIT6Ye33TY1mtTwOk7ufhWw434LozTiV4pf8Z6XRiu+56y0VGHB77MiwZtu4fNQYHz0QRiZo5/D7SD9peL/FmIIZNXRmEYiMLR2Rlxrogu8iKSs0gL3IQN88fVRQryOh84nbf84Ab+MmIlFxHx5xLW5iZ+3LY5GaIGP7qi6fUeNn6ZfCqMz4u/LQXv2EZNMn58w+ig2NHDt28f/HuncvMtwVFsc4fZekmfcGYZhGIZhGIZhGIZhGA8XZmqckjmOiqUqfFYZrl+QZTBkidsFhoXJMGKg6v5qUd4914a43sJxVmZXENG3g4iMeawB/28X1jfgA4kcs4ykhbqLrC6/jwiffbxoqyvUs8yMUISOxdks4giHPHMgL/qgjgjDlwvQGsArSB92Ge5zFWnVPNH0OWGbk+h9GSkw/SFkhb3WGLiPLyLfju5h0mgNyO6bMOVWHLERXiM2d0Li/hwEf7Xw9lmvYE8Ro6uIT5FVQgR7TbkUkxWdkTUWIVoXQ022Oj4yoIakU7vM0fo3IRvulUUVMaz28SnZQnRc1MgIIzb0+7x5P4UYCAvIs/cqEm2S4COBwtRo8XU1OmzSKBL9zdDoEx0DTY1Vd38P3f1qu7NSlsWE5k0BMZJ2kRpD4bP3MGDRGoZhGIZhGIZhGIZhGA83ZmqcklEri7Wo7jiRLC+9VPhZBdEGPoUN+HoLWSmbJkVXgfeQtFGj2nkXuadKKgWU68ATUyLsVppwz6WkUrEzbDtki5cqwI9aIV3BmyRqLuSlSYqNAn1fQATiZ0vw7BLstuH+NryFjGMJEboHeDMqTNGkNRHisQrrOSwDFwpQT+DNvkRpgF/RXkWKQO/jIzYmiU7IIozKyBL7w++0nfE95UUYDHgwERqKml4ziDiuRmADmVNbeMFe+yYuAj9uXun97rjzdaL9FhFTo4QI7Cd5fqrAM8gz+UXgHXz0VGgyFRkeE4LPWZFJBaQWyxVk7q8j5ts2PuIjfMZC9Pvw8zhCc2QeGYN7iLExQOZrzd2bpp4aNTdGmRzngKfdefbdNk2XNglnPR/zfnsNwzAMwzAMwzAMwzCMhxMzNU7J3ojvxq2QVkJxPN4Wf9ZUL0oZET33Mo6ZlCYi+H6jO/8b5AuMmsP/DrJ6vAT8iS5cqMNUImaHpqop4cXX0IxQVNQ9h6xKf8VdP07xo+J3ggjgsZERR1jE0RwE+wyAZh/KZbgwBfPbfnvZtWMXLwqHqcDC88ZmQeKO2UDGYiGFxxCRW8+VIhEcjyGpqdQ4OU1diTjl1KhohvjYcVEiD8rQAHk22sjcDaN1dhFj6DIyDw7wIroSRp3kpTLT7YOM4/W4MjKf3nWvk5qCdcQsW0TSXalhE9Y4iSNqwnZU8MXAlQoSgaWRJVqLIjSawrl42jmkpkIdMSuXkHF4E/889PD1M0LidFPjojaWEIPkJmJq6HMQp0Ubxajfp+OgRuQu2enJDMMwDMMwDMMwDMMwjIcPMzVOySgRrYOPohiX/iUW6LOiDVJ8oedS8H0bEeVOKkCrQHgO+GZEBL45Yv8CIt7rSu5uBz7ShWupbF9CVq9XEYMkvJ94Zb2K61VkhfgUUo8ipMzRtD7g+yRcya/3E4v5ut9toJlC+xbUEkl/03PHTrtXBzFuBtHxWTUcwr99pN9eHMgq+1bwnRZKH7j7C00orbcQjl8DLyJnEZsYSvi56/bRmg9xFEuWGTBgODriQVFCUitdcte9467Zcq95pL/U9AgLU8PR+RMbWLHIHpMi8/fXkbE4qaC9h8zxxxCBXgvOh+nAYlMja8zI+KyRDPfw5ml8/+Gcj+8zQfowrK2TNVe0kP2zwAcQkb+DPAtq+rQYX+8i6/cqi3eRguB7iMkXmiTj0okpce2hsrte+MzFbcqiAXwYMVTj3x3DMAzDMAzDMAzDMAzj4cRMjQdIynCNgKw0J0VklXcXSZMTp1SKKSGi4yV3zuvulVWDYFK0wHUPSXMTCulZbe4haXA0p/4rSNoprR0xjYiVVXzanLxolAFy3F183n41gkBE7dDAiduSJV5rQXDty7g/14HPuQ1NvKmiKaTm3Dn2gnbkpTmKTagOMo41/HgWkTRHC67f1vB1BLSd4M2vBlKnZI1sUyNOKxW3K3Xn33fb55AxgaOr7MP6GfF8O2larBiNRugG51sC/gRwARHv9V51bqmBceiOK3I0/Vd4fhieC+Ff3Se+PzVQTkMXH82wgZ974SuscZI1dmVk/vWReQMy917Hp82KTYlwXuvfeGwb7ryh8RebBdqWGmJqnnOf94NjtAZIXHcmi1ERGsoOvoB9XDQ9bOM4gyN8xrUmSBwJE7cpnhPzyD23kN+gk0a8GIZhGIZhGIZhGIZhGO8dZmo8YA4YTj8UU0YKVx8ynKt+FFVEDFZD4P4p26hFsNeQSIbdjH1C0TEWg0OREkQs7CJGi64UJ/peUTFWxepDfCqcuMB4lrAZpxqKozV0H/2rrwO3rY8vJq1GQBkRhLW2xihhP09Q7yKGSdndT4r0kfabCrKhqaGowZI1D+LV/nkRG1qYOXH3ctVtu4uYBuH+kwjRp6GMmCqaamgKeBJYLUG/D7vRhXV+aR+UkfRjuhI/L/Ii/hwaaeNSbZ2GbXwkhZpYWSnB8mqClPD31wjO1UWexwLSZ/pcxOOcBN+FUUtl/O9JIdgeoobTnNt/DZm3e8h4bePNAr0/PW6SlFOj+l3Tbj3u/t7Fp+GKjQ0yPofRbfrsh78XWZEjKcNG5QzyG3XRHWumhmEYhmEYhmEYhmEYxsOPmRpnhK4UjokF/RhNezSp4NpHVm+/jTcBTouull5HVpuH9zEqJVYWan7cwEeo6CQblXZHxf0BIuqHK9xjQyNLwFbCKI0sw2AQHBNGJ2h0g65Gj8XPrNQ+4fUHSL/1EUG47rZpgfPt4Pp6P+E9h2JzGxnjOPomFMbj1f6hOaTppnQsBsB5JFIkwQvVKsAn+CgRjYjokJ/6KmzPJPN2gI+2WERSNZ0Drvdkvm2OOI8abtq+UWNPxj4PysiIietoxObFqPRT4TZNFRX2bdh/sSmhhMenyPwr4dPVwbBZoKihMoeYGffxz+02YiKpcTKuL7MKy2ukSDiXC0iERNVtfxb5Dfw88ruhv5lagycvUiM0i4rB3/j3K+xHPU7nf9/db5WjNX8MwzAMwzAMwzAMwzCMhxMzNc6ABFhFBMBtjpeyZ8a9tplslXCKRFK8hhejT0rVXXseSRelgvhp0CiIW8HnLAFeyUolFEY35KUSComNjH7wN6+N417hNbNWiIfvVXTW6Ih38cKwCqXhCnv9G5ou4YpzNUayIlxiYVzFZjVkwhXrC+79FvAW8A3u3Ptu3ymkroWmHUuRtFAzSB2HtSM959H7mmT+9dw1p5Ei1E8hhsmL+L6Kz6N1Y/ReR6VXy4oYiLfD+NRJpyGMmlGjKGussiI1wmijFB+p00T6roiYY9rnaXScbgsLfqvJGj5/Wc9QHYlSmEcMhTtuvwo+SinLUNBrqjGQlb5MmWI49dg08EF3raZr3xzDc1jvTfsSjvabUsGbJFvIXIvbCL52yJK77rb7exfpr3EGtGEYhmEYhmEYhmEYhvFwYKbGGVBEhOApRHw9GL37V5kGHkUEvZscT2w9izQpmpZogaM1JJQ8QXMUfXyR9KzUUaPSSWWJteH2kKzvwloVeavaYThiY5ShEV4rjljpky3oHrh2NKI2ZPVBKFCrIaH9p4LyNCLctsiOBtIoDR07jWqYQ+bkK4ipoQXeQ3F83x17GLRFV62PWp2fZdxlmQbhvRXdvdTwKY4Ocq6Rl1YrvOes9ELh53j/45oak+4fiu+hYZUVsREekxW1pIaUGjpNZDyqwb7xPA3fh/NN508SfA7RKLHz7nqHyHxI3fYyR+vr5I3tqHlSxs9fkL7qIubsPvDb7vpaLL6Aj/DQ+8oyNPQ5n0ZqDKkRo9ElYRv0uivANyNz/gZigtxCooXGRSYZhmEYhmEYhmEYhmEYDwdmapwBulIeJjcByogQ9xgiPE/hRbkYXWEcFgzO2qfM8QqG1xAzpoAUO9ZV81lRBUSfxwm9ecKrniPrvKNE6vjcIVqz4RARJzXtU5jWK+t8g+hvlqERpuyJhfEu+W3sIA+XptKC4T4NDZms1fD6/Rzwre79C4gIGwq2mmIqbkcJWYWv598CXg3aUsAX5dbokB5Sw2EDX9sgj/i7KpJaKkFSGOk8nXF/NU1aBy9m5xlzKlZnXSfLuMibN+H7LENpHJPsE6eWiguBj/o9COdWeD5tXw2Zx5OYOlkiflxXJjQ8NBpjGR+5oNEgWlumgk9NF19T217Cz50swuOVHeBL+HRpGh2iTCPzZhefPis0KcO+riOmzCPufPeROR0bPUUkmuMq8HgJKglc7sIXgOsM1wQyDMMwDMMwDMMwDMMwHm7M1DgDVGTTfPiTUEKMjDpHRbuYMlIYfI/8ouBaR2FSGsDT7q/W0tAC4Xmr4+GoWBuv/tYaCGHkQfg+j1CE1ALdKdmFf2OxuYCIoCVEvNfjNCXYLkfz/et1stoQkrWaXvcdJ3p38amE4nOE9xSmztL0WwPECCgj9Sda7v70pSZWGZ9GR6kiKYUu4udLDy9ca7s1oiOsHTJJLY0stOD6NL6A/QAxZZYRs2kfmWs918ZVRExuZpwvJC8CI54P+hzqfcXjm2c05I1jgjyfVeTZ60XfZRkZcYqp+DqTmp4a/aHPUta5wu2FYFsY9REaXqGZo/NtAfkNuIeMQ/h9D7l/OPq7lmfGhZSQ+aBppcLjwjkW39+e+15NwxnkGVhAnuVN5LdzGkmbdxn5fdzBP29xZEcdMTSeK0A1gWZXzt0mv/i8YRiGYRiGYRiGYRiG8XBipsYZUEcE3TaT19PoIyLiASK6acqiLLpuXy3+m2WcxELhOGrIyuW3kdRXmhpmlBibtaI8ib4PxfrwRfQ3PF+4n97HISJankP6dYfsiITUtf0+MgZz7vg6PtVRi6NpjkKhVY2TSVbVh/efhYrgOq46Zh2GzQPwonBYiDlFhOxGcMyOe20g/VJBVtdfcff4kvte728WMaweR0TgUHjXNmhf5hkAx6WHpPGpMlyXZYCMwRQyhw/xYro+N2rYhBFPkC00Z5lJsdHRz9hHiY2NMIIhnJ81vFg+B3zZ3d8gOi40NmJDIzbB8tI3wej5l7c9LyolfJYS5PlR00kNsQN8zZcWcA2poRL+tmhavQSZR2o0xvel9U/i36WG27bn2rLktt3J2De+r/D3qIDUYvk/avBOF369L9tnkXRSs/jf38PguPice8BbA+gOxMTpIH/1WVUTyDAMwzAMwzAMwzAMw3i4MVPjDCjg89GDiIHjal50gHfcfnOIuDaqsPUhPgoiSxA8SZHbJpJXfhcRo2tkp1Qat7o8TAsTmxqD4LtwHzgqRmt0Qtvtcx74KCImv5zTNpB+U1NoChFON5Fi6lrnRFPkNPACuoq0Wu9hnBCeZryPReppRLxdc++vuOu+jTckSsH+MFwwXFfoLyLjsQf8Ib4GxQBfp2DZ3V/c9mLQD4quvA/bH0avnIWY2wuuoe25i68x00fmWQuJ2FBjaQYxEJpICrTYeJkk/VK4bdSq+yxzKiv6YxopaH7JtVmLaIf9pAXpQ0NjXB2QrLaExmDYLhj97MX3mmUkKtqXRby51cLXANrG/4aEz7MaH7FpErdb+yHsnwRf8L6IpIhaQsy5SSPaUsSwuwFsJnBYhGbfmywa2ZQgqdN28PMn7NM28nt7032vBnQv2E/ropixYRiGYRiGYRiGYRiG8XBjpsYZoAWP+0yeXgZE5H4j2pYl+NcR0XeAiMF5aCHmoju3FpvOW/GuwqDmm9dC0qFBEpsQ8f2FwmYofOp1Q5FXV46HIn6IFsjW42YRcf8eR8XarONVgC2695qzfwkRzvfdOafd9+v4GiJ5gnNMKOiGkQ9KATETlpBohFVkfmwjIvI02XUGFDVYlpEi8q8jBk0XMURAzJ5n3D57+CLfyi5SJ6CLrzGiEQxhmqJRBZ5PQ3hOjTQpIve9jzf99FXBR+hom0IDq0g2eabGqHZlGQdZInYDeSYSfJRQltEQGxnxK6/NcVs0ukVF9viHOT5fbKqFpljcVo0egmFBP0WE/kOGi3iHkUsH0TnzzDy9h7BgfZvh35IK0q8NxqccCxkgxub/cyjt6SCG35Zr3y3kWdtkOIVW3F9aJybPyAvH0jAMwzAMwzAMwzAMw3h4MVPjlKjgFxebBl/IVgt8a4TAqPQ48fYqIm4/ioh2ZXyKlZgpZHV5A1kh33TX3XXvVdhsI+Lyq3gjoIwXmcPV1/G9hu3MiuhQ8VOPLyJ9EEZG9MkWacGnatJz7eNF+3ERBSrY6jVAxM/ngcUSvNaTCIpZd3693xZHV5hrm2IBN77fEI2GUDOmjvSrivTgV6jH5pXeb9fdr4qzavA8CnxzFeYb0NuRFDp3kQLvW1E7mkhKqrfxUSoq1vZ571eih+NxyNF+ayHCdJjeKOyX45oXecRmRJ54XUDSTi0i5tDbDNcjaeCfeY18yDtvgjx3YVRAHNlUQ9IopUhEQgsxruI0TFl/tS9CU1EjJnSfYrBNozWq+Gixu+46WqclvIe4bkZeBEkBH6W0iY/ECbnjzn8eMfnyojWy0kANkGd3H99ne+61hRifan7FUSSx4TrKHDVDwzAMwzAMwzAMwzAM4+HHTI1TkhVZocwjwuh9vHmgovKkoqyuYn8bEe/awXdFJHXVFLKavIYvmN10xy3ii0TX3KuJCJo77jya1kprOORFKSijUv/Ex9UREfMQER7DguGjoiMqiIj5R0j0SLi6fFTfhfvh3q8W4Jk5WN+Et1Pps1l8Kq+47VnpiOKIFThq+oCvDzGNNzRawffav/FK9/CcmrZnB1/kfDWBb1uBi+fhhdfg83vwBfJT+bTdq4qsYtdUQsdZIX+WZAnKIdovYZ+GRuFpIko0VVcX6c8woiprnleR52YPSWF2E4m4WULGpII3xLLMER1PHZcaPooB/H0V8dELavypAbTsjrnD8PwJrzGKrLkcCv0aGXKITw9WiY4b9z68ThGZZ1eR36AW3ijRY9TomGW4P0LKyPMTFu/WfjpATI1GdF8D953W9tBi4WFUkjLJPDJjwzAMwzAMwzAMwzAM4+HGTI0zIM/Y0FX3HYbNiOPQxNfeiCM8ysBF4DFkxf4asgK6g+SXBxFxu4iQv4oM+Aoi2Gp++UHQvlGRCZC/4jkramHg2r+NT9GlomWWcDjAC8VFxAS5z7AIHV83r41KC9hM4eAAiqmYDZrGqctw5MK4VdvxvSbuXuJIHb2PCiKs7uHTYlU5Ol/CKBUVY7UouEatHKTQbkO7A+tNGe91xtduWUBSl+0jK/IftGCbN3+yxP88sfm4IvQoisgz8hHgLaQf4iiAuAaORitsIH3cQ8ZSo6T0+DjiIqu9aihm1dnRFFt91y41tOrInFl2L23DuDoUWSbhqM9hfY2syKms4/U3KIxQ6bn3U+5v+FsSmlR9/G9BGD1Uxj+LWfNT08mpUdtGjKI4LZmmMEvdObVGRswg+JtGLzj9nDMMwzAMwzAMwzAMwzAeLGZqnJJRJsAeIsSNE55jVpACxfeRldp5hkjHXUNrERwiRkUovGq6lmlEUNfizPtBu3uIIRLXusiLJtDvsvaN+2IvulYxaFucGiYUbsNjwpXleo1J0yi1gBdS6LREUG0iqY5SRLRWETQkbFe8Ta+vn9WsUGNDU1vtA4sJXCnBu11ZXR4KwbGIqnVNppB2xiv0XwV+dQ2eWYMXGS6oHVLG1w/pIA94CRkHjfrII2tl+6RMGjmgZM2lsxaWVVTXVEvxs6hjqD+C/eDvGjIX1VTawxtYkB2lo+cM57r2aRgRpOg87gbH6msNGX+NatB2xemgss4ZRiqN62c1W7Qtefel5+0F72vuvZoz+8hvlppt4e9JGOGhz7iaeGVkjPTcfYZ/JzQSQz93kYgZPU85OFa/VyNKDUxlEP3N6hOL1DAMwzAMwzAMwzAMw3i4MVPjATOJoaGrs3uI+fBNwDngy4hImLdCe4CIrbfwaXU07UtI6vabdefaZtg0IOMaeYbGScRmFTR15XTKsJkB2eJsiAqgKkaOW7WuDBBDYM2938w4d157Q0KBNsvcKCMi7yW3/TZiZCymIrKWyRZ4W+5etDD4EvAKElVRR/qsiYi6/wsxrdSw0JXuoYitaYBSt88+YpDcZ7wJFH6v7Z2kn7OMmpg8Ayx+f5ar5BOk/deQqB8VvkODSt+HRd87SC0NHdewAHYcfRCi7Q/neLg9fq+EKdl0TMNaOFkie9Y5wu2xWB+ObRocM8qojNGaGwRtUpNlgJgZHY7WAQnfZxkHfeTZqeLrABUYbbKFRlCRo3NVP8fRGln9El6jgDw/hmEYhmEYhmEYhmEYxsOLmRqnZFy6pknRouLPAh8twkHfn38U2wyvYg4F0pAeYmxoSqxJhH29/mnvT4XHLPE7id7HRY5137A4+nHbo4KrXksF0+MUbA9F4bBOQPi9nn/fXa+fwkZPoirC7/WvFgbXeit15IHU8fkAUrD6JcQkGSAr1Adu32m3/yYyB1JEUL4RtHfXnW9SE0iZZNxDQ6DE6BRrWcJ+PMZnaWiE19WV/ToHY0NDozXC1f5tfK2ZQrAf0fuw/VlpxWJTIbxPnRNqCui8CsX8ME1T3I6QMIJEnyFNgxbOvbhNauToOTVKIo+4DQV8mjXwvzGQ3U9wtP2JOyY0drL2z5sjZXxdIZ3nGkGVZZbGfRBHtsxmXMMwDMMwDMMwDMMwDMN4eDBT4wGhQv4kYrKKl1W8oNjlaKqZLDSPfPg5S/gbIAbIKCE/q13hyvbws66ADoXgPLKEzbzPKiKrINnJ2e+4aNsLSHqvGSSC4WDUQdHxunK/F20L+2eAmBqa7mmLYcEUhkXaKiIKdxHjYgdfUFnnxCp+xf4iEs2hq9C33T00g/OrqAzD9VKOg6YUyiM2NOpBm7P2Df+Gxs6DMjTGRTck0SsU98sMi/dhdEaWYZh3L3lRE7GpF86f8G+W0ZHXBt1nCol60Dmkqei0TojuG6aSqiHPQ1j/Q59Dne9h4e74d0H31eNTZE7H5kh8nwR/i8g80t+nrH7M+m3T384G/tlLGY4KC9sctz88v14jqzC7YRiGYRiGYRiGYRiG8fBgpsYpiYU3pYYU515jshRUmlv/XeDFvi+EW2LYtBjVBj1PHsddrR+eP17tXEeExCZHU1mFhKu181ZqZwmYYaqps6QIXEXGZovJTQ2QMbmMCMYaMRGuqj9E0hy1kb7WQukVjorXShXpyx3gNWS89dyv4YvEa5/PAs+5a7yLj8SYxFjSgsyTGAjjDI1Q9NdoiCTj+/CYvPcPwtDIimjQ8dIXHJ2fmm6qGHw3KlIjQcZFa0IcIOPRZrjuRBgZED9PceSDmpmF6LisSCZtt5oLZWRuawSQ1oyJ07epCTILPA6cR56Hm/h0aQnyjGvNHf0dynqe1STRuZogBksfX/MnNjL0fQkx6xruOvp7kpVSK4uuu0ZY3ybLKIvraajxEv9mjjOSDcMwDMMwDMMwDMMwjPcXMzXOgCzxvYpEBHSZrI6DCuCHSFqhb0BSD73rtj9shPnqJxXJs1ZG561mVyH0rCkg/dvl+CuyK4ipkSBjosK1omJ2bNLEInXYlgbwGGKUvMKwwBrXR2kjorO+X0dqZeg1R1FH6rRsIqbJaQhFfjUHevh0TbpP3mr7Hl70LiEi9kkNt5g4AkPbExYBjw0NTaGk7S8G2/OiNHQuV4APJfANNdjtwJt9eBmJoAmLlKu5kCe4x2mo4qiKsH5KlmGjptqWe11EjAJNTafnCedrEYn6+QbgyTrcOZR5ogbGjruOmnKjIo7U2NLv5oAPIfPt7eC6saGhptAFpJZF4q6t6ajCtufRdfdcIv+3JWxnaG7UGI4w0nRuhmEYhmEYhmEYhmEYxsOLmRqnJE9s6yCi8ySCs5Ii4u4GIkbqqvFxPIjV7uNoIULiJFEouipc32d9r3/HCZghBUQ8LTCZcQRe6F5n2CyapA+biPEwynCJt4cCdrzCfuDaUkMEXa3zkdeWAZKi6h6+HoK+xlFETIS9CfYdxzQidIe1OsalZYLhe0oQwb/G2Zp2cZRGaMCEhb7j9FIaMRBHZuRFnITplwYp7DpD4GMNWG9KhE0J6XM1wcL6DVnzR68VppuKozNCQ0bR8zXxps0KPu2UprIjOkafhdkCNGqwf+iLfXcRY2bG3UMF+S3TPtRzhOg1qsATSNq0d9151LyIo15KwDxiFi7g69GokRKavaOeT30mQ+MwzzANj9E21Fw76njj0DAMwzAMwzAMwzAMw3g4MVPjlOQJbU0mF2tLiHioouQa8BVE5KvgU9vERX1T990yfuX+JNeqIKbEaSIhUncOFUZVNA77oxzsGx8bC6N5K9hHoVEXDXft7QnbravZ9VpVRNA8RET/UWlu7h6jfZqWKKwTEK+w30QKgXfxBoEK7GG9Eq0zoiZGlkg7Cl3NfhYGwgzS72sMr8DPih7IQ+sfNBmfXm1S4vRGoQFQwqdUynrFxbjHFQfX9z2kMPshEh2x0JWUYAPEDLiIzMtdfERAnmmlhkT4XFZd20MzJG5LuL2C/B5o/44y4PRZeH0Ar23B59x91N15NIXaoju3poXKaztIH+v1vwK8GRxziC8qrvcwjUQQNZCoo3tun8eQedYCXnfXzkN/g2JjLcvUiOkgfdxAUnBpDRLDMAzDMAzDMAzDMAzj4cVMjfcZFfbm8KmGeshq4Q2GV0braucGPn3SeeBRZAX/JuMFuXngktt/Y4L9Y1Rc11QzuuK+hBgr7WC/aUQ01G3h6vbY2DiuSA8ioB4iguekxbC7SHqgw2DbDLJS++CY1x9FARmXAsNRIfF9dxBhOYzoiWsJaJ+fhhYy5mfBNDLmMVmmDQzfbzj+KpqfBXGERmxGxK9R3+WdQ9+HYnkHb+60gT/qwjX3WQ3EKWT8wqiWEtkpt+L+qyDzcxeftiuL1F1jEfkt2Ufmc2w0xtcaIKbLHv735xBfGLyHLzyv+4eEkRF1JEJjFqkHsxHtq0W4w+e/6u7t9xBDo4nUvHkUMUe2kaiXPDRlWF5U0CS/K53gnrWfDcMwDMMwDMMwDMMwjIcXMzUeAEWOJ9LrIMSpUeIV7GVEtFxFDAyNGphCcuPfZLy438EL7HkC6SjiexoggmAJLzCqkDpq1XMJvyr9JPUzZpAV3hp1MSkDhlMwlRHheIfjFQ0fh0YHaERMVk0BGK6zELczPFd8TPh3Us7CsEmQCKI6srL+MNge/o0NjPCz7hOnzdKaCJOk04rbNO417phwW1a0Rvg8h5/byPN0gJgbOwwbWDr+cRvyTI343ivupRE6eRExOse0rsYe46MO1AxoIuN5HjEitP4Hrv13Ga5vERJu0/owSc69he0FMTQ0CuS6a7NG03Tc532Gjd2QQrC/tiXrOuPmfYr0wS0ezvpFhmEYhmEYhmEYhmEYxjBmapySEl7EryDCXgNZYayFpLNEtVAYPWB4RXQeei4VDbvIyuJdRAScxKQIxc6TrJLX1dvxNs3bH0YZtPB1BcKIjRqymlzrKawdsw0JvrDwJCm3RqH1FcIix2dBH1lhrvesInneNbJqUjyMlBEBHHx6rKy0TPHKef3cJ3/eNdw5jyMsT2poTGJ2aBojFcrriOjeRUzEMJpG53wTmedtfNopZRcpkl1nmFHREzFFRPyP+zNGn71dfHHwPHMovP+yO/8HgKeBP0AiTfTYLhJBUc85X9ge7acuw5FQSgUxIlPXznNIMfEu8FZwjjUk5ZTW8Wgjc6PNsOESFqbPSmkXvx/FADGk9HfcMAzDMAzDMAzDMAzDeHgxU+OUlPFRB+eQgreazkRNitjYqCKRFU3E/AjTvYxCowyK7rg+Ig6+il81Pg4VYx8EWaJnGUl5tY0XOlNEJF1CVmIf19RI8au4R+XanwQ1iE6SckZTbGkarE50Hm2b5vvPqocA4yN6wpX+oagepkJ6rygi47WHRARMIfO/i08nNMDPsdj0iA2NaSTNUAeZP8cZz7MwNOLxKLl7SJFooI+VoTiAF/uSvktrgPSRZ1ajqbIiTNTwyErVpWM6bvxUxFdDbJSpoX/HmZVhmrOue9WQsVxEinuH96LzOixknkVY1yfrt6AGPIf069uIUTHH0TRV+4iRUgzuZ8od30L6NUHMB91n1H1Oynv9LBmGYRiGYRiGYRiGYRgnw0yNU1II/taRwsBr+LRGWuBbxbkyIuQtMizGTSKoqZivufJxfzdPdQcPlgJeJIZhgV9Xxcf7T5K2S1NvnUaIrCCpvA7JNoRmkHZmpaUqIJEiT7vPL+NrEkB2OiYy3iujjCZNL5QXzZEnNM8gJtttZBX6WdBBTDSN0nga6cc33N+PIvdyDUlPpdENmpItbGsSfY7HXWuJ9KLtk5gVat5pGrBRNUnCdmkqNC08P9eFDwJPIqmV+u5er5Hd5yV3rvCZz4paqCKGTizoxxwiY6fGaGwSTZriLkb7ZxP5TWkj0U+7iNlQZrjGTAX5zdog3zxtI2NeI9toSN31esh4lPDp8NQM1PGKTSKd+w18JEXYJ2r8jCrEPilnVbjeMAzDMAzDMAzDMAzDeDCYqXFKQhH1EF+v4aL728GLygkiNJ/Hp645LiepOZBFFW+4PEhUPO27az6C9IMWOt/CC7Xz+KgOFXJHnfc01BCx+hHgBY5GixQRk0rHKRTiZ4AVxDBYcsfGRa+zxOc8TnIvKtxmmSZKA5mHh4hYfRYr0eO0RloMO3XX+8gUnJ+Gl9bgVway6l8NvzLSr2FUkgrhei4VuzVSAuSZimuSjIq60Dotis69uKB0aEiGx/bwgvs1ZJw/NA1XHoOZGfi9l+CFXWnXO0hUgUZkXHV/byHzQs2qrOdMi4iH80tRY0BNHZ1fsWB/2jFVI+dt5Heqj0TOzCH3rpEzM4jpsc/oiLBdsqPOEsQUWcCPZRsxSdT8AumTcnBMOMfVDC0xHJmSRPvGKc/GUXPtOnDtNwzDMAzDMAzDMAzDMB5uzNQ4JeHq+V1E5LwIPAtcKkNrAL/S9/naVRRe5/ipk8L0PqddTVx1bc9aRT5KKD8ubSSqooMIpZfc5y13nW13nTLwODCLiMUPKhVMggjJFxBD4h7ZkS5FxGDRlEgqTpcQcfYycj93EQE7S+jNuoc4EkFX9R8HFW/HCbh7wEv4uXcWxMKx1ozYwxkBBTi3KuNbvOcjTDRVUAFvWuhLV+mH0Tta36LHsHEB/pkLt4fb4ntNEaFd61Potio+omo2aF+CRFHgttUKUKnB7g7cvQdb+yL8P1WEb6pAqwRvHsJaT577qQI0BnKeNjK+OwxH42gUypS7x3D+1IEn8FFYLfwcic2yPNRI6jBZujlNQ6XtmnZ9su+uVwv6ZxQDhiOWFH3uSsi9JnjD9138/ZeDa2TN2djo0DZ1GH4u9D40xVYYcaJF15VV4E8hxcp/Z8z9GYZhGIZhGIZhGIZhGO8/ZmqcERqRcAhcTOCpOpxbgBv70Ity/zTdfscVmqvAFUSwe5vJamjkERY9VgFZU7eosNjn9OZJH2+c6GTbQgwgTdGjUREX8AWXw74pMyx+n4Yqsip7xl3nTbJXZ6sorO0O66NoAegOPnVPnjGRt6peRVetydIiOzKlxtHonFDYzarPoTQ5WTTQOMJr3MOvoN8Cfn8Peu/Aetv3WcXt02ZYYI5X18f3rwWvw/RU+jeMrogjNUbVnZhyrx33dxnp4wa+No4+AyqYNwfwzgZMlWC764pcF+Dqebh4EVaW4fNfhs/dhtUyPLEESweQ7Il5WXHXu+/+FhEhfR4xDXoMF8CeRkwz3PcbHJ1fo6KANDXavDt2nKmlETI9/JzfQMZKn9k+EnmSZYKOo4AvAn8Pb2b1kT7ZDfYLC3/HBlp4DxpRowaI1jkp4E20Kfd+M2i3fh/2Zwvp5zgVnmEYhmEYhmEYhmEYhvFwYqbGGaKFg+cTmCrDnT1469CLippaR8W3kzCNiLA3OJ2pEYrLKgKGRYkXkRRL1zi7egwalaDXTpFoiS5+1fwGR42UGUSUDE2NEiczOXTV/j5yn2HdgBgdz1AA1VoL24wvyBxeM2bGvfr4mhE6nnreAiJ+H3A0BVMcpTHueg+SMJ3QHwLXdmXbNr5QfAXpc903qxZEVmq1rHobaoTl1dTQ1fipe68CeBl4Comketdtm0eeJ017FJ6jgjc6+ik0uzIWZeDcPFx5FC5fhoUFWLoG9dvy3D/1JDzShs0vQqUvz1IPMcJexc8/jWiI53FYkLuV8X0YqZBl4lQQs6bMZHO0jDcyy4gRo0adnnuD4QLwMUn0Xo2DJfeaRsyFNaQ/wt+bGfw9axRPXiH10BA5ZNhsDK+tvy3L7l7U1Ghx9HlZBz6Pj84xDMMwDMMwDMMwDMMwHm7M1DglmodfBb99YHsAL+7D7/ZlJfJ+sG8JEdqWEZFz+xjX6iLpjjStzWkYRO+17oWK6zPAR5CV/nmmhq5oV9F4HB1E2FQhUk2eBBGLf4fsleC6ml2Zdq9tslPdjELz+HcRQTvPXEpdm5r4CJbwu9MYBxWk6PQskn6n6bY1kNXl9/DjW2XY5ImjM0al6Hmv0T5rBZ81AkjF+bDv8qJYYtEejhb6HuCjKcI+qeEjD1Ik3VkDScNVcZ+vuH03kXk0g0QoaESJmhr1IlRKUCpDuQhJApUdqFXh6hV45FF5f+cu7O3IM/3sFXjuwwn9fsrNu8C7MF9wz9tAxvIOMnf1OdB6MrPIuFcQ07KDRL9kEaaIi8e76u5Hn69xaASNpqRrc9RYHPd7o0aCjof+fQSJGrmJ3HcRSa1VdNvaiPlQQZ4DTbWl45BFWA8lNm3U8NDIrhpiouwG9xQ/GwPkN2GX9+/ZMQzDMAzDMAzDMAzDMCbHTI1TEkc79IAvAc2+5GgPxXg1NT6QwOUy/HoHvshREU1XK8er1vuIMAiTRwlMItJp6ixFTY5NRKTOo4Ksetd2jcvdv48ImR3XrhZi+qiRkpfCKRb1ZxCx8pDjmxog93eIiKnnEGMhFm31/lU0js2PrHRJk6JpxBpIBEYHEegvuO2vIYbXAFlF3sGnYYqF3rjGgLbt/UD7SMdRI3O2g/cwutB1Xs2IMBJD+34KEcyLyHjdQp6dS+5VQGpctJB+1GLhZWT+NJG5tADMJVCvQNnlIKpVYXYWGg2oVKBYhF4Xqrfl/flz8Mhl2N6BG+9Arw1Pn4fn/2SF1W97EgpF/lznK9T+F6QDaLVgags+WIbX9sXAu+P6ZB7408BTZbjele23EJH9guu/vagfskT9MJIlrI8x7jdATVnwRcmPixp/JaRvNfJjGunnm8g4lJB538QbNhpZs+72qSCmjBqeWVTcubtkP4caKbThrreIGMKj0LRdWTV2DMMwDMMwDMMwDMMwjIcHMzXOkIr7e42j6ZJABLgOsFKEy7Mws35UcKwjKYcKiCi3i1+lruL2OFQILLk2hYWGJ+UuEqExKvVUAZ+3fo18U0MFyg7ehCjhzYXjmgNNd8wsIp5unuAcbaTdV5Fxejf6XouxFzkaJRDm+z9uv+KuW0MMlTVEzNV+mHLf6XW2ouO08LZ+r5+1KHJotjwoc0OjL2LC+hdh9MRx2pN1vH6uIsXZ60hUxCo+zdIWMqZNfDF3FcXLSNRRFTExQIyEZWApgXNlmJ+TV2MKSiWoVmBxUYyNchn6fdjY8O2p16FcgakpuHABZmZhaRGu/J9PUvjzf5G0WubKdIO/cvFL7G92ufZ2yvoaPPMsvPEm8EV4zXXiInC1DFcWobgOg74I8TMJPFmEL/bh/5PKfUwxnJJK50TYf4eIiQDDKb/G0WPYkIrHLyROfxZGjkwDzyH9vYZEHulz33AvTXGlhcP38HWGwn3zDC6tp1HDF5Mf4Ov0gNz7PcSo0OdkVF+U8WmyDMMwDOM0VCoVSqXT/W9WmqYcHp6kmpVhGIZhGIZhfP1jpsYpiVMRhZEbBaSDlxAheBcR6qYLYgC0g33DQr2LiMDWQyIlGm6fXfJXUWcVjdZzlzm++H7A6CiNBBEUi4w2TcqIEK33H9byyGorjBYeU8RoOUDqIzwJfJnjC5Ha/gV8Ln0drzA6Iys9lY65roLPGpO8tFa6iv66O3YbWU3eQuZDggjXc4hAHxZ0H0R/dbvWrVDTSMXphOObPSFqCBTw41ZBaq10XLuz6j1kvR93nTiNlKaFCqMS1My4BDxdhkZVLjI1LaZDZRNuDaQv9JnRyJ4FoF6ARkGOWy7BoymcX5Goi1oN5uYlMqNUgko1YXa+wNRChaRUpLPboljqUW/AVAOmZyT6otsRU6NQgKVzRYrPPgdz/ycJM/Cn+sxMTTFz7x5TC2+yda/D5WenaEw3uXd3wIc2oexueDCArS2J8JoHninDh58Wg2PtGtCWe1pBoptiAymkR7YZGRoPo8ZC/+p7NQPi37pCxr6pG6fLrh2fRwwnnYd15HdgGx8hdp/hQul6Dz2OGoqKppjSmiehwRfWqdl3n9UIHUVWRJZhGIZhjCNJEp566imSxP8X+dOf/jSf+MQnTnXeu3fv8vGPfxyAN998k37/JEtpDMMwDMMwDOPrEzM1TkmWUBYK3ueAb0SE1Rtu/60OvLIhwrYWxAVvENxm2ATQdE15/ysTC5BhO1QUV4F62m0bZVhMgp77lmtzVpSGpvrR1FLhKvDQ/NH2LSFi/i2ya2uE9N25VznZ6mpNcROm+QprfIT9qO3NKlqtUQtZYm8WBURwvY6sTtcoDRX2Z4GPV+FPVeDVA/j8wAvASixKlxGxH0S83UfGV4Xik0RsVBHTR++t4a5xwW2/766xn3P8qGuWkf7Xfi0h467pobSmhX4Gb84tA1crcOUi9PqQpnBuVUyNtV1odFzh7wIUEjmuUYJGRQyLuTmo1WF2RoyJhQWJxui03ferFUozdZJyGapVmJmBbpcy9zl3bo/ViyVqc1UKpYT+3iHFYp9GA8pVKK/MkJSriC1xFaa+CZ7rw/J1Zvt9ZtfXYHqalW7Cn/vOQw52erRasLcH9+7CrVuw1YVGCo9egA9+EA4O4Nw9eKIt87yKzIc2wwXlx0XDFJH5Dv43Jd43TPGlFIK/+gxrmqnQyEiDY9tIpFcLMVfU+EqQ37Tfdt9pm7N+izTyaFRdjaznUZ9dnVtaL2QSc6+b0xbDMAzDyGJ+fp5v/dZvpV6v84u/+IunjsyIOX/+PK+++iqDwYDv/u7vZnd3l9/5nd9hIwwdNQzDMAzDMIz/TTFT45So8JZVG0AjMHQF/aL77gXgy6nPkw9eWG/h06+oENfMuQYMi5Bxup5QjC8iougziOB4Ped8x6HpXlmExoBGpGif6N94tfk88CFEBH1ngvb1EENAiyKPW78W9okWkm4iYqYaMCWOCrtKKJ5qHYIeRw2NQsb+8XlaSGqeHYYjQ2rAU+dgZQXOXYOdTZkvuxnn0eLiev8alaBFr7cYLto9KSXEjKsiqdR6yNz9eAk+vAA3tuC3ckKG4n7TugrhD80UYt6AT/OFuxct9K2p08LxqAHnEriwBAuLsL0t9SrKFegewHQZSgWYqkh6qGJRjI2paViYF9NicVH+zswl1OaqVGoFSrUSzb0+xUqR0uPnSVaWoT+AjXXSvT36uwewf0h9oQ4XL5BcuACDAaUbN6jdvseg3aNUK1Fo1CFRKb0BxedhqQLtPQnnmJ+Hbpdarcbliy0GO3t0m10ON1vMXetSLEGrC2v70OlI2qtHH4Fv24XC78NaR8ZDa1KEvz3aR1pfRMe7hswHjQKaQeb5GsPPbjhvsyIw9HOB4Wcbhk0N/V6NNU07hxvPeffduLodYZRRnrERGjnxK2u/cbQ4WiDdMAzDMGI+8pGP8B3f8R088sgj/OAP/uADv16hUOAXfuEXAPiX//Jf8tZbb/Gbv/mbfOELX3jg1zYMwzAMwzCMhxUzNU5JlogWinybiCithcRVbN5jWIwMyYv+CInNjCxzIxTxQYTN+wwL6WdZdyFsh4qkamDo93k1Ewb4NFYlxrctxUeJ5BkrYbtUbAWfnmYDETE7eDFd9xtlamjfhQWWNZKAaJ9wNXm4sl3Tj3WCbRq5kRSg14PDnswXPW9MHYlw6CEitdYZqOIjLML7mGSsNf3VLMP1CcrA+Spcugj7h1DeH55nJXyqsVAgTxBTo8yw8K6fe/hi6RqpolEaYcHpxG1bLcPctBgTzaZEaKQD6bPz5yFJJBpjztXCSAqSLmp5GepTCdW5GuWZGsVamWR+TpyPapUZ7ayZGag3oNuFXo/WO/fYWe8xPQ1Tq4skly7B0pLcXLfL4P4WO2s9GoMBjVabpLnnRmPLtbgA7ZZEfdRrMEhheQl6fQqHTartDpX79yG9zeFhn60tOGxBpyVNmJmFy5fh4PfhFcTcGjAcjRCOwwy+SHYReNpte9EdOwNcxJtJGnlRDM6R9XsyScSE7qtmbtWNpT6ffXyERjwXw9RzVWQ+hZEhecRGY1abjsNp0rUZhmEYX998z/d8Dx//+Md55pln+ON//I+/L2345Cc/CcAf/MEf8Lu/+7vvialiGIZhGIZhGA8jZmqcknH1Hw7xBXBVHIzrShxXgMuKyMgSIGPRs49EaWiu+lBwPyvCldyhkRC3JTQ39PM28BrD6ZZGRWDsIKu+NZ9+2IbQbAhNjVB03UYEdRVSk+CYLFQs1RXyGk2gqbCS6PtY7A3Rleql4Pst4EvAwm1YvQe/0YIvkm/atNw99PBCcWhkxf02iYlVRupPVJF5qrVdpoGGO8HcDJzfl217bt95RDBv4M2h8JzhthI+tZQaMZvufRVJQzaFmDY113GdVO6nkohRMT0lXsHde7C7B6urEgxRqUjB76VlqYtBoUChUqK0OEthZoqkXhPjolCQQhqFguSwGqTikHS70Nl2DS3R6RVYW3fmSLkiearW1sSk2N9j43abd96C6ekB51s7LF29TdJ6B2or0oO7X4a1u3B4KNerVaFYkuvOz0O3Q1IoML21y4XtHba3od2Gg33YWIerVxPK5ZTtrqSl23DjEkcIKdqHNTcWT+ELaafBcWX8b0AcmTEuYiMkThGl+84g9T+2EfOk6a69FZy7jE8NpdFFTdf+OL3UKGITo+7Or/U0DMMwDOM0/Ot//a955plnePzxx1ldXX2/mwPAxz72Ma5cucIP/dAPkaZn+S95wzAMwzAMw/jawEyNUzLpquC8/O95+2WhEQxhbYqQvGiNEF3Br8K/RhykiKhYQ4TGcTUtssiKHIkFz6xjNF//HvAm2auv8whFyyIipA6QiAc9VqMdwnstMpzeJjQ9JhFw9Ry14DyhkRKOcV4KKt1X6SJ1V36vI8L+iwynKItRcyysS6KmSnj+cfUWCkiERBm57mUkiuQQEZhB+vB+E8rXoNv30RQFRDyfwqc6WnLn6+BTEKmIjntfd9tb7jpqelSA8wmc00gLJBPU5i7s9KBQkkCHx69CYwru3IV+Dy5egEuXoFKDYqNGYXWZpF6HYkFMhOlpmJkWM6FccZNhIIU5ul1oNaHVIm02JQRkkEK7TXO3x/37YqIwGJDeuSPH1GrsvX6HL3+xz+uvy+mX3u7zfOFVLiz/Gjx7C3Z34JUX4d4dKJVI5uZkx3oDGnVxYIpFmJ2leG6Zc80WrVabXhfeuQHrG9BJS8zM9VmpD1hoynzIq/ugac3AG00LiGGkz8k28CrDURo6B/TvJNEaWfNXt2n00RzyPG4xbMxp9M6cG/stZC5X3dw4SS0Y3b+E1F2ZAt5mfBRXFqMiQwzDMIyvbQqF4aUr//gf/2P+7t/9uyOPWVhYoFzOi5uFNE0ZDIb/xfqpT32KX/7lXz55Q4ELFy7wxS9+EZB2h4XIAZaWlrh79y4//MM/zC/+4i+e6lqGYRiGYRiG8bWGmRqnpIyIgyrEaSqXrBQrIcdNCwQiUoKIgCqw55kYo9LF6HfaTv3fuyXgWeB1JLXTcRhlXmR9VjE+Lh4er6w+jrBZAS4h/bPutpUQYXcaudcdREANV6XnibhhG8J+UxMh7LvQXIhfSfQ5Pn9ID0kRdg+/qj2PvPbqZ712aGyosVBF0hAl+KLTbbfvAiJ+q9lwgNQ4uT2Awp6I5O+47WXgceBRJL3RLPAts3B5FW7ehTddmqoaUHKNLAHTRaiWpE7E5kDG7XwJHi3Bs5fFoGgdShFtEsn4tL8v5sWHvrHI6jesMnV3h/X1Jq1DWFwuMHV1GaanSaoVqQBer0OhKMZBqQjVmguV6kKnC92OXKDZJN3dJd3aYbB/SHOnQ7cjZsraXandsb4O567dpX0oPVmvwwt/CL/xAlxvwsVNqN2Aa9f2ef7F/8nVq/+TVkvSiC0swOyiRIsk83OS9mpuXnJkVaswM01y/jzlwYBHe+/SbvfY2YG9Xdjd6EIKM1VYbso4aU2MLLNKx7GEmE1VJGJB69poujWdC2E0VRzNFG4v4o0nnS9xijVtSzvYZ5tsYzJ1bZtx++67+TMumiie5+EzpfVbLjH8vE7KLGIamalhGIbxtU+SJKysrAxtKxaLvPjii8zNzX11W5ZZMCndbpfNzU0ODg74wAc+QK/nK0bFJsdJWFtbo1KpkCQJL7zwAktLSywsLFCpVL7a9tXVVf7tv/23fPnLX+all1469TUNwzAMwzAM42sFMzVOyRSyGliFwqrbtouI52eF5qhX8ySLSYwNjVaI00HpX02pNEmqorzrj/p+VNtPGzzfRdLzaLqvEiJUNtz3h/jV6bGhMUmqHe27uM15/RxHnGiqHRiOXIjZiPbNI8uMic2uAtIPGlmyhPTHNHABX5vkOj6V1YeAVXec1nDQCJ++2/82YlRcBD6AFBZvAvMl+MBVOHdeAhFab8tJZ+tQq0vh7kJRhP75OdjYhIW7UC/BR7+xxOq5hDJdikW+aizUGgWKpYRBUmT5YoXZDz0K588zPXeTp+6+zd5mj6nlupgZqyuSXqpYEjMjSeRvsSDVt3s9SSG1swPNQ9KdHXobO3T3WmyviZmwvQ1NN4na7sHu9+GdawN2tiVr1GELfusr8NtNMXkKrg9ub8P/77fgy1+WEhpzc1KcfGGhx+LiJrPzW1RX56iszFG4fEm+rFRgbo5k0KfS6XBp/za3b6e8+CL82q9Jdqy7W/L8TyNm0hbZvwUpPj1VDzHx7pL9WxTPn6xXGZ/KqoqvRROfL0wXN488d+8gBl3Wvj2GjZcDN7eKwT5ZjIp60giQeeAOPrXZJFEfeuz+mP0MwzCMh5Pz58/z6KOPfvXzzMwMv/Zrv0axmPevrZNz7949rl+/zssvv8z3fu/3nvn5Q9Qcee655wD45//8n/PJT35yKOKkUCjwq7/6q/zpP/2neeeddx5oewzDMAzDMAzjYcFMjVOi4qGKZnUk5cohZ2tqDBDBTVe9h6J3nqiuf8OUSWFEQfx9312jxnAUxVkxzow5LTX8ynTw6Yz0XsJomixDIK+WRlZ7syIudJvWjFBjpYyItuvRvnrteLW5PpRaK0P3C8e7ga83EvarmiVaZHsGEWv1uKJ7TSEi/NU5aBVhI4XPb4lZUXffLQLTCdxIpU81PdmSa8tiApeqsFDwxcwb9a/W2ObJJyWiYWNdAhOWl1xKqQQWlhLOP96ASpmN9QFTc2WWnztHgQH7X7nG2s02y+cKLFxuUJyegmpFTnJuFeYXxKhotbn42E26yym1C3OSWmpmBi5chGoD1u7A5qbrWGdqbO+QtloMtrZpbx/S2W2zfm/A7h5sbIihsb8H+64Ied8durUHxQSmanJ/X34NfvWuFO+uu7GtuNdBCzZb0CjB9CZM35NmlUowNZVy7tw2Vx7bYaXVpnilI87HYACDAUm1QqVRZn6+w1QDtrck3VfRXaeJpG3aCeZT/FxpFEXX7beXsc+oKB+l5ObBOWQubiLzIDTbwvPq/Fp1bf1/EaMs67nSNFEaHdHER5PEUU4x4fbw+j0kguX33Tl77hodvDkXkiDjpb/T9h9DwzCMrw0+9rGP8cEPfnBo25/7c3+O7/qu73qg1/385z/PG2+8wW/8xm/wb/7Nv3mg18rj7/29v8cjjzzCJz7xiaHtjz76KP/3//1/873f+73cvHnzfWmbYRiGYRiGYbyXmI5zSvYYFv/b+BzxZ0mKL8o8ixctW3ijQtMMhQJ5LDrGERHhPh2kpkOLo5w0kmJcuqXTnDtExdRHECEX/CpzrQvRwxseWaaPokZEj+FxzBKGdXt4jkWkLkURSeW0VII3e/CbwTniiI+wD9TYqDBsZOgK9xqS8qmAXwlfwpsVFfdXaxQUg2vovdWRqIonHoflZalLUfh9+PWmCMOLCTwxBx+ahmvbcOcAFguwOgszqRTuvjQPS3PiL2gH1GqwtCj+w9JywqOPFbj2Vp+pKTh/sUCpKismK9Nlao9dgPk5ZitVCX+Yn4eNDabu3oN2m9pSneLqihga1RpcvCh5qIoFKbzd6ZAOUooVl2IqTeXVaMDqM1Jx+6WXJMVUUiDtdmltHtDa7XGw02VjXb7a2JBi47u7sHsAh11oduV50DRKyQbMJPAnnpPokXe34Jobm3NubDaCcasC9R5U96F+ANVN8S1mK2JU9Hsptfp95tI+ycK8hH4c7JO2OxT6XRYW4MpjElSyuwudG3DQld8AjUDSaI0YjbDQCKUsczI2GuKoIzUVusg19/G/a1nPamgMagqquuuTXsb+GtGmx5Qy7iU2DOM0VzEpYh5qzRGNBKm7bbGpUQbOI6netLaLYRiG8XCRJAk//uM/Tr1e/+q2P/Nn/gwf+9jH3rM2/LN/9s+4ffs2v/Zrv8ZXvvKV9+y6WXS7Xb7/+7+f6elpPv7xjw999x3f8R381E/9FN/93d9Nu93OPoFhGIZhGIZhfJ1gpsYpiQW7Q0TQy4tyUFFZU8SEaBqkvGM1X34KXHX7ryFi4LT7vImsSobhaAD9HKZfGuCjTLRGhKZoivPljzMd4voR4fXJ+C6L05gbBUTs7+GLU19CTA4V+m8jQnSHo/2i1y4g6WuWEcNgneGCyrp/3G7cNWaAbwA+WIF0AAtTsDgDnVvDB2o9Dk3tNIPMCS1srHUM4qQJZcQouezaqPu3kbEHEYxr+MiBMj7VVjn4rlaQqInFRWnryjRcacJKAhfm4JFHYWUZHjuEvQPxDVaXJXIhKcDCvJgYYeeUyzAzX6QyU6U0NwWVCo3lHYqVIpXFaZJS0YWwFGGqAaWyhHCcOycFuLc26R+0KZagVCvLRff25DUzLTUoCgX5fO8u23fbdLspC4P7pGlCLSmQ1BuwdAkY0Ly1xfbbm5AkDHop29sp21uwty9mxt4u7OxBswetPhwM/DNWQgyyZtB/3S7cvgVvOa1gCRHNtVZFwfVx2Y1DBaimUOnJ+fptqG/D/fswe2NAkmwwc9Ak6fXot9oUSwUqjRIXL/Yol1Kah3DzJtxbh+qOL3gfdfuR+ai/I5rWKf4+NvPCCC7d3kXSON1nuC5LfC7wxoT+JhWRiJK81FclpF+3kd+rcb8PoWk7aQSZRm4UyDZWBkj/aPu2GJ/yzTAMwzg5P/uzP8uVK1eOdUySJHz7t3/7V+tInDX/6l/9K/7rf/2vI/f53Oc+x+7u7sh93ktu3LjB933f9/FN3/RN/MIv/MJQTZC/+lf/Krdu3eKHf/iH38cWGoZhGIZhGMaDx0yNM0aNgyy0kO08Ik428aYE+JX0o0Q7FfVW8OK0CohNxJTQFDFZKWZ67vgqIublXWuUiJ9HVn7/hGyzIyvP/WlMjT7Sl1tIP6wAH0ZWYteASgKF1Ec2xBEsYZTEHFJv4g6y0lwFX/2+iq9TEVJBakw8UYbLC7LKvl4X4T9NJIgg5IJr9x3EpAJ4DV+wO0u01jRE54En6+ILVCpwdwO+2JH702ieaXfvmooqAcoFaNSgXIKay5HVbMLBPhRb8M11+MCTcPGcBE4sL0t5ijSVehiN2RLFehlKJQqlAkkhEWdE+zKBZKpBMtWAShVKJRpzLraoVhND4qu9juSp2t6BcgU21tn73Re5d/2QQgLnKm2mzhfp7B6yd3OH+Y1tCgtzcp5Skd79LXZ3Bty5A+2X2xzsw+Ur7/KBfpHazDTpnTvcu9bk9RdSkiRlMJAoie1tCfTYOoBbA1hP/fjtI8/QY8BqArupjEcDuYW3rsH1PnyhL3OjgTcCcfNi0fV9BR+5oSZHPwW2pZ8KRRj0BzzaP2BusUhxZgrOnaNYKNDY2uLS1C5b97tsbkK17CNugsCYIxE/ihacV7MlTpkWP9/hc6qGm55f5388H8PpXHL3rGbFPfxvTYwaP5uI6dEm2+ANfyOKiIkH0u9ZESrx8ZqirUn274rWB0mDz4ZhGMbZ8Oyzzx4xC65cuUK1Ws054uxJo394DQYD/tgf+2NDBsXa2hrb29vvWZvOildffZU33niDc+fO8VM/9VNfNTYKhQKf/OQnefPNN/nZn/3Z97mVhmEYhmEYhvHgMFPjPSSs8TCHiG47DJsaZXxR6SxU1NRivbvAo+6899znMBoDhsXIMjLoWSJlEb8aOqzhAJOZDaPy3+cdH5seJzU2BkifDILPKiTXEliahYU+HO7DlxhONRMKpzOIKF1FUtJkjUPHfa8r4mt4M2oDeLcL89tQK0O5J0WnD9Oj93YZibpYAL4JiZzoDaRod5bIi9tXi3K3u3CuJt7B4rSkODrAp706V4B6VYyPakWE9GIJZmcl4KFcgtVVKdrdbsG5Fbh8GT74nARFlCsJpaU5cU2UWo1kasrlnAooBDMuQQpIaF4qPb7Xk9xNymBAur8HN26Q3LlNd3OP6y8dcvOmREQcHBzy9MIW29f3+N3fHtDvN5mba3L5kYQrj0K7ndJuw40b8PI1uN+Fy28NaDSu8WS9DFvb3Lt+yPXrYtD0unDtBqx1pH+3gHeBm8h8mUYiB7bc+C6n8jy13eebwKsdeB0fvYAbi7Bo9677bgkR4jU6q+rmCCmkLqym35PaHU+UYPbJy/Dxj0O/T/KF36fU6VDdkaLplYrM40rqz3ceSReXJ8b3kDmsJkVoMOQZkHEqtPhzfEyYui5FnoUD11eHZEc+POf2e8ftm2VwEh3bQyKuFvAG5qjfCf2tHRfZcdq0d4ZhGP87UyqVKJelqtZP/uRP8nf+zt/56neFQmEoZdSDptvt0u36f7Xt7OzwzDPP0O8P/1eg2WzGh37N0u/3+cxnPsNHP/pRvvu7v/ur2+v1OouLi+9jywzDMAzDMAzjwWOmxhmg4ramcIrR1f0LiBi5ixgaDURE15XeamaME+Huu5de+xB4Esl9H6dWCo2G2Mio4cX9Ct7IiPPjh+mplDIiAmvUgqbdylpBHQqkWeRtPy6hQHkfqWHxIeBKCqslWFmBxWuw3xfxWiMYUnw9innEcCogERFZaXc0TZfWq+gj/dcE3kRWoG+14ak2LBzATB0uJfCBFN5ABFo1l8qI8L1YgLQM5fbRlfQq0M4gQnYZlxZpAFNTMOVyj81vyj6rwDecg8sXoduTEhONhngMxYLU2VZfYna+QGMqISn0mZmFJ5+rUb84L+mhkgJJoy4OSEi5LO7IoJ+jCqdSlHswcJPH7dTpkO7vk6qxMRgw2N2n32xT6XZJW33SJGF7J+XmTbj+Dnz5hfts7MC1+9LPyxV48m7K5ro0YW8P1rfg3Y4I/Dd2YPBrKR+//xrFBF59Fe6sy0C12nCz4+s87CFztuvGbA2fFu6O6/evuM8XEIPgOr7mTBVvfoXp13ru7z330qLsWkNC51CyJV1UKMDCYp+ZaoVkcRUO2zA1RXLpIrMrHa5yhzu3W9zdgGrbR2oUouuGqaPU9Kjh0yxpLRV9TrOMjficsbmZM9q0kSiXZcTM2RlxXA3p51E1NLJ+A/VZ2ULGa1RkRd/tZxiGYZwtc3NzrKysAPBDP/RD/MAP/MBXvwvTID1IsqIrPvvZz/ITP/ET78n1Hyb6/T69nsUaGoZhGIZhGP/7YabGGTAHXEHE0m386mNlCngCEZw3EFNjz30XCtgqDh6HFKkVoauiu/gV5KGxEb4PRcsGkqqoigiFmvpowFGhVCkhJso3IgLrXXffd5EaFGFx6zANVtgnWVr4SVJejUIjV24Bl9tQ2YcbA19IuIUXqM8jES/ziCDaQcyBu8hYxSu+y0iqqR7SbxrVoWl/fs9teyaFYhfO1+Bbm3JfdxFxeRtfuHxnAJW2bK/gx6iGzJsl97fkrlMFlqbh3HmJqqiU4fwt2G/BuaLUu7h4UQIm5uelzkWhlJAA9ekC1ZmKqOm1GlSrnGOdpR40HluBudmhlFLAcO6sgwNoZ8niAZ0uDPqk/QH9gxaDfkra6dLabtFsyukHAzh0CyYvVNvUz89z+Wqbt99qs9mE5ABubEo0xX13tZUObL8NdzdhtgGVolyq6fbZA95Zhy/8PzKmakC9fdOL+y03Th3Xl1qrJhThO+5cLYYNSzUU9HOHfHFde+cAiUpYiHqsnEJtHxZ3JVJm0O5RbO5KbqwUuHqVZHGRldmXufzal3jlbRi0/blHRVINgn003VMNed63GTY1QtMxfHazfj9G3aeaIgXyI81AzKN2sM8kEliKjGMNqZOzidyH1cAwDMN4sJTLZb7927/9q4bFJz7xCb7v+77vPbt+mqb8+q//Op2Ot8J/7ud+jv/yX/7Le9aGh50XXniBVqtFrVb76rann36aubk5dnZ23seWGYZhGIZhGMaDw0yNM+Ay8H8ALyLipYqmSgFfrFqLQR8iomkRn5bmpEK+FsTVa2n0REhcQyJ8P4uvyaGRIircFoJj9f0UYgBcwK/Q1oK7XXyKrLzr5zEqZ7/el0ZGtBh/ztTd0y1gtw+lHbiZHhVcEyTqZA4RTdW8mEfMqB1krNbxxxYR02MXMSnileUHiOiqJlUCzCTwWCrC8h6+aPkcIsCfc+fcQETbErIyfR45RtNcdYCVIlx9BK5cEUNjMIDHV2H+AC4uSSTGzg489UzChat1yosz4nCkSN6pel2qfacp9HvUl6ekYnitNmxYDFIputFpf9XhSjtdWluH9DOW0yeJi1boDUhKJdrNPrtrHXo9iRrZ3pbi3INULt3rSVN6vSaFt9v80Ut9PvemjNMyMtfUZLvvxmK7D7c2YGoDlstQTKWv9XuNZnrcjWs8Tw6R+dPGGxfx3LvpxmHP9X2KPCcdxETUWhUHDD8jWVETIPNDx1Tr4LRwxckPYGMTLl27w/Rbb0okzGFTcoZdukzyxOOsnH+BtNSniczp0BgN0XZ0XTsL7jpqvmh0UNltC02B8D6yooVGoREqatr2EBNO21TCm0p7wfbw+CwDNeQeYnBdRJ6XLvLbcxoDtITV0jAMwwh5/vnn+bZv+7avfp6amuLHfuzHziQK47Of/Sxra2vjdwxI05Qf//Ef5/DwcPzO/5vy0z/90/yDf/APuHz58le3/a2/9bf4zGc+w+///u+/jy0zDMMwDMMwjAeHmRpngKYxyksd1cGvBNdoCN1+UkMjXEUdCpMDRKRTUyGv4LRSRITfS4hovx/tHwqNRfzq73ddu2cQ4XkZX5ugiAjBfXx6K13BHRLm0s8TNfV7becFt+1tfMRFvG9MBSil0E58DYxY0G25+z9wbVfx9zJiNryDpLNRU0NXv+uKePDFuLWf1pHxvpLCbhs2XD2EWUTY3cHXPXgDmQsVRIxfRqI2iu68U/ji0wXg8hw8/SRceqJGp9lja6vHcx+Q+hgrzyxwuHHIvestFs9XKF+5JIU1EjcCxYILlUhJtzZpv3MPBgOqjz1O0jokvXdP0kchxkN7v0tzt8egn0KS0O8O2Nz0URZpKqZKkjivJIHNTdje6bKzIzUjqhUpp7G/B3sHUjsEpHB2oQSvvwWtXp/f3oSXUhmjOeDKEqz2oLAj6aV2kOdnHZlfq12pgXIHMTZ0fPbx9WU2gjHTsdbx77mx0rHTgtf7wXHgDb7QEAD/7OY9M+HngWvjjBvrNtBMYXcfNtZh861tGq++TuH8KuztS9qvao1kaZm5i9M8dn6Hm3twcyD3tkV+qrq+u3+9PvjoiAX32orOEZ4rNjUGjP4dAXnWNxDTZRb4IDKHQebxPWSedxg2PbPOG9cA0fO/hoxfCXkm8urejCPB/3bdOcHxhmEYX2988pOf5Fu+5Vv48Ic/zPPPP3/q8924cYMf//EfH9r2y7/8y1+TRbkNwzAMwzAMw3j4MFPjDFgD/gCJ1GhydOVvz736+HRD4FPjHAfNpa/vVURXcTbcTyMu4KhwqCZCF4kY2EBWt2uB7VAojFefHyLi5DVEUP7TSESDrtZOkRQx64jR0cMLwnq+kDB/fygAh/tpdEYBiVx492jXADKha/h+rgBXgflEtk3j8/KrKRIaEIVgn0cRYT3haJqhtmuDjkcDMUDqbr8bSH9eAx7pwUuIMXIJn66si9Ro6LrvVoEPIKvQS0UoF+G+K2o9C8yXYHpK0k1dvgSPXS1QffIRylvbnNtbp1pPmHlilcLVK/TeuM6g2aLaKMLsjNzkzo4I5YsLUpBid4fu3U3efa1JqZzw2OMbsLPN1rVtdndSyTiVSqHzjQ0ptA0pvR7s7oruPhhAOoD9A9jvwVxVgj029uBuU+qXTCcwXRTzozWQWiA6T/quL/u7Mh4vIyLzJWC2Dk9eFVNk7RXg0Ndq0Doud11/HzI8ZztuDDSyRedXbKyFhl0VMbAW3dhq4fm+mx8l5PnWeaXH5xlxoWGnn1vIfWrURwdotyUYZm8XBvfWKDCAnW149BGYWYb2AbWlBo8/ssNbN+DlQ993sSmY1Q5lgBgdK0j/VvDGT0xoOMbkRW30kTFI3TUuI2nT6vhUb4m7fx2LMJ1X+FtVxUd+hewjz8pSzj1OSsG1sYHMobNId2cYhvG1xg//8A/z1/7aXwMkXdHy8vKJz/XpT3+aX/qlX/rq54ODA1544YVTt9GYjB/8wR/kl3/5l4e2/fRP/zTf+q3fymBgyRoNwzAMwzCMrz/M1DglBSTNyy1GpzEJc9yfhDi/vYqC4fs2IuhfxguxeSldQhFvHxGLw9RKWRMjrMfRc/urMVBHRPlLiJh/k+FivgV8TYFdjvZVLM5mCbL7iDlQJL/2iB6r9UKeAj5agKV50fTb7l5jM6kVvG/jozW0X/eiNvfdfc64a5Xd+wVEqL6FF+xVrN90254FvqEATwzgV5C+2nfn+2NFmC1JtMNBR9pRBuarsLIMS4uwuCSmxvQjCySXLlKcmWGl1yOZniJ5/DFYWKC0vsH84hr0e7C7R5oOSN+9CeUKSakExSLNF97ixd/d584tqNVSpmeuk/ZTrr2dcueu79B2R/qu5XJ+DQZSdHu/I5EWA2A/FbF5bx+mEqilvoD6QQobA2+iacTAAF+XYgcRlzfdthlgsSbFzJtN6LoHp4PMnzQ4R5YgrQZC/KyENV5g2CBM3Ti0kflRxKc72wn20ToVKUfnanz+0NDQv1p7RU2NVl9Moc1N2Hhrh8qtXRrzFaozc1CZg26LJE2pVqFegeqhjywpBNedBO3/JjIv8yI9RjHqWiliCmmx8H2krSVkbOMIKY3YiH+j1AQNzVrtc005pcbtSRgg86OOzDU1WuK0eYZhGF+PzMzM8PLLL7OwsMDU1NSxjk3TlNTV2fr0pz/NT/3UTwGwu7trKaLeR1566aUj2z74wQ++Z8XbDcMwDMMwDOO9xkyNU3IFEdnWR+yj4uNJBLgk55UVrVFDBMVz+FRG6wyL8Vkmh4q3BUR07AefY8E4bIN+dx8RmncQg+d+xr2qwZAgxsTOiLZopEhYzLiCX93eDLaX8MWztUCxCpRPudcSUkaijV8xHgurM4iQrWnCNI1Wxb3yjKGKO38Nn75IV6Brf2n6qA3E7AB4rgjnyzDTHh7DYhHm5qRwdLsDiyWJzFhcgNVzcOG8FAdfOF+ldH5Zjmy1KMzOwPlzsLQMSUKysMDMMxeh0yHd2iJttTncbtPvtWn03mRno8ev/88Of/SK3Ei5APfWBsxMw7u34P4GVErSb4M+HHS98aOFtZv4aIE2YkrcRgpgz7sxn2LYqNIi7L3gXD03ZvvuuynXZ6023LgJN9bgjbaPyjhODYTQeAhNQH31GS4Evo2v4RGeQ9samhOxmRGbliWyxfqwpschYvrsbMPaGjQaKTOzKY8slKBah6QGhRL93oC9fWh3hyMiqu4aTSYjRZ7PbcSoO6kpMIolpB9edNc4RJ6TQ46muBrVzrAvq4hh2kLmQTvY7ySkSLRWC/9bp8+5rWc1DOPrlVKpxMLCAv/9v/93Ll26NJHgPRgMWF/3/8r9+Z//ef7RP/pHwLDBYRiGYRiGYRiG8V5ipsYpUSF91P/SaYqVcftBfoHeUeaGvq8gKVXmEJF9DxHRO3jxPytNjW5TYS9MNxXuG75XIfgQeAGfpicWLnXfeffSiAdNSZV1/wtu3y3ElGm5bd/szv8yvhZABV8Muork7ddi5WowkMLmLtxrD9ddUCpIn5Xd9Qbu/PfctqL7fp2jESIlxBBRAX8ZqCbwTir7byMi8nRwzDbwljMJ9vD9XUSyQ83MQKEgGaLmZqV+98oqrKzAyrkCMxemSRYWXDXwbbh/X8InBqvihqyvk96/z2Bvn0GzTWe3xf5uys6uFKWuvNvk5i347bfhxdT10wCuvS2m2A4ikk/1oNLzESdanF2NiDbeYOi7e2nijamUYWNKj+3gnwWN+lHxW4+5C3ylCbeb8v6mO2cYUROTNbcVvXb43ISpzpQs8zHLwIjfZ0Vh5dWh2Ebms9aEaAGtjphYvZ7UHynOuDCVtEu6t8v6m9t86SvwqusPNQWrHL/Q9f74XU6MmjZqdGq/daL9slKBhcTptfT50mdXn/HToEXXK/jom3HtMgzD+FpkdXWVK1eu8Nxzz/HzP//zI/e9c+cO777rk3xub2/z5//8nzfzwjAMwzAMwzCMhwozNU6JmgajKOMLQ49aBRwaFOG28O8oc2MWiRy5gEQFqGCqqaK03kScOicUbcNrZxka4X6JO/dmsF/W/anhoimktCZCKMbqRFSx8hmkX19BDIYrSN2O+0iRcDVoBogwqeaDtl+LmdeAlRTu7cCXkTGI0SLkagS1EEH2Rdfei4hZoSaR3mMPn5aohqSyeaQIC1NS/PntgbT9ywynu2oCn0OE7fsMR3zM1mF6Wmp5l0pw8ZIEYCyuFJhZrlJemJb8U1MN2WFvn0HzkP5hh+LtOyT7e3RvrtFZ32F/p8/urhTn3t52dS/2pQbGrQ14tSU1P6qIYKwFuDvIHGkGfRxGzqiB1cULz2p81Nz5tM7LPkeNjRbDkQWakikUwNeRsZ9y/a4pm2L0uai6a+fViNDrhH/haKqovOczfEay3mdJPVmmRpg2aRD8HSDjkiSQFNxxu1tw80sc/vaX+NIXOry54w0NTa1VGXG/MPysnxUlslPAFRCDT9NqjbpmXpsKwff6u6ZFwfX3I2scT4P+fuvvr2EYxtcTf/kv/2X+4l/8i3zP93zPyP1+67d+i+vXr/Orv/qr/If/8B/em8YZhmEYhmEYhmGcEDM1Tkm8cjzOoV9ExO4dRODOE001BUpsauSZGDBsdJSRtFPP1aDQgy/2fF0HbY8aG6VgW5axAUeF24SjYmLe35gBYny86t4vIUL1RnAfVXzhdDV/ZpCUM48idUIa+BRRYZt6+JRIYRSGGgbnkfRGdzgapaHtVlOogo9I2EeKwGvkS53hsdH2htvedV9MJ174vcXRvt1BBHiQ/RaR6JTWoRThLpdheRkefxyWH6lTWZ4lmZuDeg2qNXE9CvLqtgds3O6S3LtNuVJge73Pzo7Ut9jehsMm7O5JlEazA+0Ubnalrbvu/uZcu9QMq+ALu6v4rv2sqZY0MiNMLaWGxoHrmyZHTb9JRHats3GIGBxJxnnUjBq4/rvk+v82440N/T6rcHiesJ1nbIwi3ic0hdTQSEGKsidQKMqGdHcPXn6Jzh++wDufu8PLb8BhHx5PYJD6fl9B5nUWBWTO9hkd4XJcSsjciI2LReBJZJ68zdG6NTHx70vWdbS+Tx95FuPIpjCa47QUsCgNwzC+fvjwhz/Md33Xd/GpT32K2dnZ3P0+/elPs76+zn/7b/+NV1555T1soWEYhmEYhmEYxskxU+OMiQW2EiK8amomFdDbDK8Q1iiD46af0u+qyMr/xTJcG0jdiiZ+pXMo1mWZF7GxEYu2dUT41kLb3Yx98kgRQ2cHMQ7mGBad1ZjQbQdIlISmlXoEEdnvAW/hV/mHfZDVDjUm7iOibl5EjQrmPbJNKjVCso7XehAtxLi504fFfZ9GCUav/lfh+bz7u74H5QQuX5aAjMXVItXlWakSPj0tKYlaLXEnGg2oVun0Cty9C+12ylSjz+3bUnS62YSdXTjswEEPmn2fRuoGYmBoKrAmfmwPXFtqQTtVVA7vJUw/BTLPKvgIlgNOLjhrPZMsM0PRYs8l13/fUoKLPfj/4g2zceSNTZa4fVYRD3oeNYhUmK+U4dw5WFlNKCV9WFujvdFme73PoCem11Pn4MMFWL4Pd11qsHdGXCcuzH0W6G9XHPFyHvgQMpe06HseasSG0TGxmUvw/S5+voUG5AHDNXZOg5kahmF8vTAzM8PP/dzP8S3f8i25+3zhC1/gn/yTf8Jv/MZvcHBw8B62zjAMwzAMwzAM4/SYqXHGxHn9q8AqIsxraqVDZPX+O8jK4zLDYtq492pUaHTDABGgK8DuAbyb+giCK+51iIjSTYZT9ISmgLY7rhlQQtIvfRQRi19ATIrjCIkqjO9wtEBxynAqmx5iXlSR6JNH3XFvIrUVemQLoCk+7ZFyiKQx0uLP8fjoiu8yIuTP4CNq1DRRE0UjXdSACgVjLYDdREwUvY8Y7Us1UAqIybOEqyVSE//ikctw/jxUi33S9Q1IBySVitznzVuws0OyskxaLLF1u8mtm7KCv1aDd27A/XVZ2X8w8GnIDl17p13fdvEFv7VPNty2uLaAGk+j0FRHGqVxGqFZjZFRqZV03mjNlssz0NuD6nGLTGTwoItFh4bGAKAA8wuw+mgN2i0OdnpMDQak3QHlCjxyAS70YHZGUlR9Wx9evw+/nx5NA6VodJYyqubIccgyd1J8QfClAiwPjkYohTQQE6SLmB9a9yeM2gif6wH+OV0BHnffvcHp55qiz/tZpuoyDMN4P/h3/+7f8fzzz+d+/zf/5t/kc5/7HLdu3XoPW2UYhmEYhmEYhnF2mKlxBoQinK4sVmFM6w4sAE+4hfb3dqGe+nRJWqSW6DxZ74uIIL2CCOEdfLqkDnBrIOZFggj0zyCpm/qImXENKVSsQmiY+ikvWkMF3rY7R4uTi74q5o5DU+YcuGuWEcFd2xRGsIRkpbHZRfoNfG2P0NTRmhgfRMTx38Hn7u/gTYqOO/68a891vFEQo0XXe9H3TwH/F2KcXEeE+xngMeBCTVJOPfIIPPMszJ+rMuinbNzpkN5eY3V7l3SQcu21Dgf7cPmR2xRL8PorKW++LZmpGnW4vwG3u9J3uqpe/04BjxRgqQzX22IeafF2LcQdp2ialC7eiIiNo6rrjzb5InzIAGn/JGiEyGEL1nvD9TrGkSVia1qjB0loaGjh9JVlKMzPcvuPWnQ7cKk4oFKBpUW48ih0OpKarNWCekXmWGwQ5qEpxjaYvF8nIazZcReZTx8a+No9eW3T1GF15Fk74GikhI6Njo8agt+IPIMaOXSWBlReKizDMIyvBWq1Gj/6oz/Kd37nd5Ik/tes1+vR7YrN/elPf5r/9J/+E4PBg7bvDcMwDMMwDMMwHhxmapySuA6GCnMalaF1FfaAUhGqVZgrw4WO5P6/G+wbr1COjY0CfoXzVfe+585/HREHu0hqlifd61FEqD6HmCD3OJp+SqMY1NgYRN/3XDv33X2My5V/VgwQEfYrSPFzXFt7+NREsQAZiqia4iY2QMKCwFpofB+fCioUVrV+hwq0Kd4E6pG/qjtrNfs0ErFTdu1fQYqQrxTgsTlYWYGpKTh/MWHhiXloTJFs7dDrdbh3D3Z323S78NJLsLYBK2+llMvwypvw9i4sl2C+AhsdEXx3XBvDmgRzwKAE1aK8L+FTCTUQsyU0fI4bjZMVJKGr61cRQ+0mkxkbx7nuPeB3DofTk50ENfk0HdmDIE49dQjsdKXuSXO9yfaWmJ/dPsws1blY7dHpdrl9S+qibO7CYOAjk0aJ8Pqb8RjS/69z9qYG+DRsX8LNsTHtauFr52g0SWiqZh2b4tPIDfA1XUK0DkxW7ZxRhPVyLFLDMIyvRa5cucIrr7xCrVYbMjS63S7/9J/+U37sx37sfWydYRiGYRiGYRjG2WKmxikJTY0wfUkRERNnEeFyH9hri3i53ZXPWkciXtUOw4K9Cn01xJhYRYTQbWSV/yEi5laQSINZ4MOIYP4GUkj424pQS2F/MCxalxERN4nakgb7aAqmPLH4QaZs0QiTWbzYqYWKQzNG9w0NGU1Xk9c+7dcOUoz6NUTUbwXfayomFf97SFodGC2cxql/tD0HiAGlkTQXSnB5Ga5ehQsXoN6ApUfqJDMzpJ0u3YMOzQPY3YGdHVmlf/c+vLwOO/d8Sq8WsNeD6Z7cwzp+Vf6M65s3EfG22oFLHYnaOO/apgW+G27fhuvLUWnGtP/HoUXGG+61ydmbGreQKKQ9jidox/cWp916EJSR+RQ+460+fPlFaB7sUyjA6jlIymWSS5ep9Hqc27nGndtw7T58uSnP+riIkgT5PVhFTKUqZ/+cxsbPBjL3xhlCmnZqHZ/+TOeSRn8oYVq8NvLbeRt41b0nOG7avdcC9ZPeb5GzjfgwDMN4L7ly5Qq/9Eu/RL1eH9qepin/+T//ZzM0DMMwDMMwDMP4usNMjVMS1rkIRcqi23aINy8GLRGgNVpij+E0UFmFwvVcIEJgExHF7yFC9uOISKri3utINMCKu+5NxNS405fB1poUDXy0Qyl49d01+njhvYyvMxFTxqctelCiYAUR4HWVdjhp43oaml4Khlexx30ai51bSHH1sIB7uI8WKU7dvieh4M59CCwC50pw5Rw89hg88SRceKxKdaFBMtWAbo/B5jabd9rcvw/378PhodTNqNegXIFXOiLma+HkAmJ07SHzYx2fRkvvJ0Xmj47XFL5OCMh8Ouf2e82dL0sYLiMm2wHjx72P9NltZD7puJyVwK4RIoccT8gmakcZEcXPOqVRfD199jRtlhZlbx3C1rakoSoVob5Qg8eukN64yb17cO1deKfli6B38NEOWdeZQVLPLbjPGwybAA+CAfIb1GF8PZTdjGMTjkZ5hONZQObcNkcL1+u+dWQsN5k82iaOUDMMw/haYWlpiZ/5mZ/hox/96JHvfv3Xf53v+77vex9aZRiGYRiGYRiG8WAxU+OUFIP3sbGhxbH7iMi8hYiKmuqohC/EnWVm6DlVpNtG0kBpHQ0QoXjRHdsCbiDpX5bdNe8iYu+77hx77rh51/YtxMSoAE8jq7lfcdsbiMitAmQs+lXdtQ+D9pw1RXeNGtJvh/g+D9ujK+zreIFyVBqlONXMALmHUAQtIH3W4/jpbGIK7h60GPm5IlxZhatPwOOPw8Un6lQvrcD8HDQPaV+7zeatQ+7chrV1uHkL9g+llsLSIqzWoNSROYG71yn3fsO9stKElZBjdty9NVxf9JDxfAR4DonwyRPM4fjCfwt4B5lnGnFzVqaGjnvzmG0CeVbKri36HN05wXmOg85VjXTpAY0SLM5JTZXFJWhMQTLowf019t64yxf+EL70DrQGYlLsIc9DnnFQR6K1PlKAJIU3U/kN2HkA96PPiJpmN094Hn1u834LE+Q5XMfXKgrRlFSH+KLjk6LRRHodwzCMrxU+/elP85f+0l86sv0LX/gCP/ADP8D29vZ73yjDMAzDMAzDMIwHjJkapyRMgZQXbaGrj7WodA0vpKb4FElhgfHw+AF+dXsYSaDXfxQZyNcQYXoVMS2uI0Kvmh3ng/Me4KMyVEh8BFnx/w5iaswBTyAib5bQV3f75xXLDtuotTCOyzSSRkvTUO0j/af9kZWuS6+pkztrFbZu0/6uuFcolFbw9TNOiwr5daRfV6el+PMTT8D5x2pULq/C8hJUKtBqMej22d+Dtgs9SBJodWGnB+sd6Az8/fUYrp/RJduEUTFda68Ukb7UYtWa0qiMpDPbxxdRj1fe69w5jnDc48HUqdAUYsc11kpIOrcFfF9cZzjN2qQptkJGpYXSmh2aQq2PK/adQKUKM9OwMA8LC1AoFBi88RZ3Xj3g9n046MsYhTUo8q6/ADyZwNVp2DmETnfyouLHRZ+R055bn8k4/VRYA0d/z6aQ+4znk5rIWvumhzdDsurchMc96LRjhmEYZ82P/diP8V3f9V1Htu/s7PC3//bf5vXXX38fWmW8H3zmM585su3v//2/T7//IP7LbxiGYRiGYRjvP2ZqnAEq1mrdi7yVxiBC2xxS+HoDbxiEgltYLFcL6e7j0y/pdWbceS4ggtybiNi3gK9fUEWEwDW3/4w7zy5HhcM5d16lj5gfYeqpBL/afgmJCDlgtGCqpsZx0VQ9DSTiRK+hAqT2W1gIPCwyHArHXfwYqYkT1hApuv3r+NRb2scaUXMaUV6vUwcWSrCyJHUTVi9XqFxeIVlehnodej3SQUqxklAsQqcN+wfQbMmYbaWw2xUjKUy/o8aGmjN5NUQ06kTfh+mIesgcegefnkojZfYYThd0ErH/QdFFok+OG01TQsZYV/XvIM9kL/h+Bnl+jlMDJKytE6PppiqIYZfgDaNSEYolKRJeLEHa7bC/1WVzXcytijuH1o7JipYpIIbmIvLcpAM4GMi9nWUdk/iaZxF1E9bMAf+cFpHfMU2D12V8ursy/vdMa8rsIb8jWf2gz7xhGMbXCn/jb/wN/uE//IdUKpWvbkvTlJ/4iZ/gM5/5DPfu3XsfW2e81zz33HNHtr388svvQ0sMwzAMwzAM473BTI1TUuDoiuEsAV8FuyIitj2CL76dIKJdeFwoinYYXoleRqIXVtz7W0gaKoLt24hhoimIuoig96i75kHUvil3rkO8OLwO/D6+LgiIIHseEdTb+BRNowTB06zO19RcBwxHqChxzQz9q+meZvCpuVTMrCACZ7iyv4WMzbw7pufOcRVvGG26dsR9p0WGY2FXC29rgeOGO/diA1ZX4dx5qC1PkcwvwFQDen3S3R3S+2vsr7VY34A7d+HWLbjXlutv4SNWstJDhSvVs8jqw5A4yqOCRP7UkD4LTaCTRt+clipi5LWRFGsaaXJcNPJJ08JpXQ6liDwXHY5nCIzqk0LwmsJHOJSLMD0NszNQrSckBTjY7LK3C7Oz8PyH4UsvwRv70paua9MOw2M9wJuMrRS2D+FaXyK1zsLU0AgzvUeNSgkjzE5jcMTGRhkZ72l8Gjw1NRRNP9bBz90CMnf1N0CjSfLalnL6FHOGYRjvFRcuXOAXf/EXKRZ9EtQ0Tfkf/+N/8KM/+qOk6VkleDS+FpiamqJQGP6/j2azSa/3fvwrzTAMwzAMwzDeG8zUOCVF/EriME1KjG6vImJm1X0Oi+PqfrEwqFECiqaYmkOEyn38KnoV0K8DLzIs/g2QItA7HBXme0j6ql183n1dvazCbuquqQLhOjKBNPXRg0ALdEN+0e8sKkgkSQNZfb+HtPs80ncHwAuIUQC+/kkVMYW0H9VM0QLlsXiqBdZjwbgBfAPwaAJ3U+mjZSRdV6MhqYamZwpSQGGqAUkBOoewtcXezR2uX4cb78CNm3DnUO5hG1+TZY3sguV9smtpnJQZZNX/nDvvLjIeWr9jl6MROqPS/GRRwNf2aOJ/lNrB95py7AAZ20bw/UnnXhup/9DOaa+mtTqrxA1qXqpRp4ZoA5irwvIKXHyqwfTTF0mTAgcvvMXObp/pafjmb4aNDXjzLSkW32O4DkRIC5kvbwKv96VGzmbGfseljpiie3gTNUXmRYo3BTc5ubGh5wNfTH3fnVsjW2JmgQ8iz8ebyJj1kLl5i+E+GmVqGIZhfK3w9NNPHxGxX3rpJf76X//rZmj8b8hnPvMZLl68OLTtJ3/yJ/niF7/4PrXIMAzDMAzDMB48Zmqckji6IixAHf5vpYp+NUQQDlc7q2lRCT4n0bEheh2NktBaCD1EqD9AhNpmxnElsle1bwGfd+9VxFXDRsVXTfNzgAinfWQF+IP632dNCbSF3OMUk62mLiFC/DI+xdI6Pp3VFSQCowv8AV7U1hXnWvuiA7wO3Ef6YJZh40oLbMfCcgF4DPizrmBF1aUOmklgvgylErTb0OsnUC7Lq9eDg3362/vcvwc3b4qhcfdABOoddx8HiKERpp46y6LbOuY9d/4y3hh73P3VQtB916b42uH8nSRFlUYwqYEQ308D6c8BvtbHW8j4nSYFlhaHH/X9DieLAsmihKSG09RnXVzR+AI8/Qg8+WyJqT/1EfimbybZ3oLXb7F2/4DpGSgUZIr0Uz9PNZ1aFlqw+y5na3LFqabC/lfTT8dPa7GcdIwGyG9Yx523xNEoODVdzyHz5F23/yFne9+GYRgPE//iX/wLkmTY5v2RH/kR2u0HlWjQMAzDMAzDMAzj4cJMjVOSVysiXlGs9QmqiNj2ArKKOA2+VzE4NEaUMM+8Fs3WguO6YltXlr+NF9xDUkQQzhP74hXpfcRQSIPvYrEwS7AsjPjuuPQRU6GKL0quK8OVMPWN1sJYQaILmvg6J31E9KzhI2XC8esi6Yy6yKpwTeqgUTil6JhRRkIFmK3C9oEcv5zA6izMz8GFC3DpItTmatCoS5RGtw07OxysH7K2DutrsL4vq96zDI0w/Y9G0hy3UHZMgkSxXED6ad1d93V3vRnEECri66yENUyUIt7UK3DU4Ivp46ODIDv6YA+5f60HMqlsc9raH3q9s0DF/i4yB1vu77kaPP00zD82R/LY47ByibTTpdcZsLkJ+/tiaHQ6/lnXPstrmxoAZ1n3pI38ZsXzrIg3F0rR9tOghmERMTWyaoioUatz4mGp82IYhvGg+M7v/E5WVlaGtv3Wb/0Wb7zxxvvUIsMwDMMwDMMwjPceMzVOSVY6lDT6TiMqyu79DmI8hCmgdOW2rpQPxUpdya4CcQER9jUlkhoaGumh5kZMC1nhHkdwKJofP+QkqX00D/5BxvmOi0Zr1PEpiNSgCKMmtL9UyN1x380hq/x3kYiHJvAGIsrvcDQ9l5o+WiS6jBgcel+aaiqsb6KmgvartvmFPUgGbp8yLC/DuXPwxFW4+ESN2lOXSKbd2Q8O6G7tsbnWZ30NNrZhp+/F/CZi7qxztE81amccavho0fSYAmJoPOeuqWm71JTQuhO6Ej424ULS4DUItp2EJr4w9HFF6zInj+g4y5RqGiWk6ecWEJOuAlSr0JhKKFWKMOjD4S6s32fzbof7a3BwAPfuwcaurw3Tx8/BMJWVRthMI6mg8gpjH4cKvg93o++qiNE1j8ytMM2VzpG8c8Zp9bLQZ3gWXyQ8ROtsaOo8W6NsGMbXMx/96Ef5mZ/5GZaWlr667caNG3zqU5/i+vXr71/DjPeNb/3Wb+XP/tk/O7Tt1Vdf5T/+x//4PrXIMAzDMAzDMN4bzNQ4JbGgqyvX0+B9CRH/NF98XmFjFSTBC/bxKncVkVVcLiBi8wBvamj9iwQR5tv44sJaWDmLKvmGx3Gp4FNUnQZd2V7E169QoVoLLmsOfZB7UyNgFb96ewZfIL2F1ATIE1S12PYAn/ZG21GP7vER4JI739v46IX7wAsDSYF1CSgVoVaTAuGXrlaoPnGJZHkJSmU42IftbVrrTdbXYX0dtpoyri33uodEaMSirkbtTEIJac8+kkYsK62ZpjLT+w/puvvS/kjINzXCY0elUpuEk5gZSvhMjaKUcZ0S0h8JowX6SSggRkMdEegXkfous0VYmoVeN6VzZ4Py5z9P8vLLDN69xd1bfQ4OYGcXdvdhOxWTYh0frQHeCNW26ryNU0VNgv5W6bOrpl1W7RSN7HkeuFKGu134cvB9Xn8VEVNHTbJRlIHLrk1x7Z4Kcn/byDN/mnliGIbxtcC5c+d44oknhrbt7u7y4osvvk8tMt5vLl++zJUrV4a2bW5u8tprr71PLTIMwzAMwzCM9wYzNU6JioixsBt/VvG5R3ZqKCUWIUOTRM+pxaB1ZfIuIkKquK/tWQaeRgTx64wWeLUYc5za6SS08cbKWdHFR1Bo5EpYUwKG8/er4bONX2WukQoDJqvNER6nguk2PlrmHPAMIlZnmUUaTVIDalVYXJBIjerKLMn8PNQb0OuSHrbo7+yxfn/AWhClobVROviol6zUZJP287R7HSL9V0aEdTVN1CQKi8WHhEXsQzMjK2VanHbqtNEaJ2VSU62EjwQC6RONsBpXe2MSEny0VhWJ2pgvweXzsLoC3Q6s3+1TXL9BofguO9spzQMxwkhhY08iZ+4iqcHC6BONXgoLnqsJdtzIhSrwLBJ5sYtEXjQ5Oh+KiFn3GGJQlAfS16Pqj+jc0YiSSZ7BOjJn2wz/ZmrRdTUbzzKqxjAM42FkamqKf//v//3QtjRN+cQnPvE+tch4GCiXy0Of0zSl0zmramCGYRiGYRiG8fBipsYpiQtvh0WOVfzV9FC6+rmCCKaTCK7x+XRbC8lvr1EfWauUVRDcxQuJeRTGfD8OvScVf89avNb7VFNIjQa9TjnYR6+/77br51CM1/7QWhl5hk88RnEB6+Xg+mH6rjK+/xcSWFqS18JqmWRpyanVSKGErU0O7++xtg7374uArWmwWog4ve/OG5oKx+njKlJnROeOFpvex0cF6Sr9PMG5yHDKr6xIjdDc0NRTYcTAWRY1P0u6SETCPD6Vm6Z6Om2UBvgIo6o7dxmolqDRgKlpKavS70OSwP5+SqcDs7OwtAj318RY2EbGKzYMNQ1dOBaarkvHVds/7j404usD7nq/hsw/Rc837e5hH0n91O7LfndyrrGKGGh33fk2xrRDaSIFz2MjWKPeTlu3wzAM42uF8+fP02g0hrbdv3+f/f39nCOMr3eWl5f57Gc/O7St1+vxF/7CX3h/GmQYhmEYhmEY7yFmapySeUSki6M1QtGx4l777u8cYjTsclTgzaoTkbVPEV84OQtNJaSrzMcJyT0mT2MUU0AE4Qq+NshZEaffyrqPsHZJSB+/ij2s8aD7ajHlBnL/TSYXr7W+wCGywv88kpppDW86NHCmxhSsrMDKKpQXpqU4eKUsy/N39xhs7bB+P5W6CZu+MHiYVmsWb0R03GuSftbogARZdd/BGyR9117tHxXe55AolH38yvo4OqPAUVMjK4ok3F+fkQdhahQQw0CjBRru/aSRCppyLA3Ooa/jGhr67Gkapx7yOzHjtuu8mJmG+Xl5VStQm0qozdUo7PSYIuHcowntTpv1TTnPNjLfsq4X11VJg89hwfZxhds1lZXWcAl/E7QYuBYE30ZMiiJHo7MqDBuFMxwtJD4JXcQoyYpu62VsOymaps/WthqG8bDy2c9+9siq/B/5kR9hY2NSm9gwDMMwDMMwDOPrBzM1TkkdEQBTRLQLUxUN8JEZdWAJETNXkNXHBwxHC8CwUJwl6Ov2cVEe00jR510kFc1ZGg2KptTRFf5ZtRVOS3j/uup/1D5Z36XR+zBqI0XEzJO0fRd4GamPMIuYAet4I0CLm89NS4qh+ZUyxcU5STuVpnDYIt3cZP/uAffuIamnDqQg8hZeJF4CPujOeYiIvPcQUXnUPNAaGg1kZfwG2X2l29TkwF2rgU/BpHVbYHiOZtXUyIrcGFV/Iw81QiahgRhL68hcn8NHWkxKB6nNoHVMNMLquJRcW1YR0f8eviaMRmvUCzA7A3NzEpExM5sw//gC5WeeYGZzC9ot2rttSi+sUS3DpTrcOvS1M/S+NHIiNu/ilGCxqRenA9O/RXfvLwPvcLT/EuT3TqN58kyF0JzV67XwRtlxOIvfLo3GCtF0aykyXnvIeBmGYTxsPP/881y8eHFo2yuvvGJ1E/4356/8lb9Ckgz/y+pXfuVX6HYfxL/6DcMwDMMwDOPhwkyNU7KFmBNlxEjoISvidZVyD+nkFeBRRGwtIYWllSIiinfxAl7eaurQLNE0SuF3uPM/DTwH/AYi1h1XSNSV36NEc41Q0OLbp81tX0D6oYQYBrpqOjQzYtE2K1KA4LgwOiAWdqeQ/tthOJXVpDSB1915nsKL1j1kLszi0g2VRbyuL9RhZhaKRej1YX+f7voO63f73LkD9zfgXh9uIPND+3MReDSBy1VY70Ay8N9lRfsoGnVRc/c47v4OkVRCN/H9Fq6QDyMC4uiAvAiMMEKDCdoQMs7QCFNx1ZFoCL3PcfUdsjjEF5Y/bbqsKvJsdJHnQ6MUNGqrWoKKq3SdptCYLVG+eE5ylLVa9NY2uPPyLvfuwf4BdAcynjXXRjUb1BAd9QzEBukgaEsdX4+j7z7vIubQdsZ9af9oH+X1T1yDZB15XrIiTSbhNBE+ReQ5aCJzQn/Tptz2HaRf9054fsMwjAfJlStX+Nmf/VmuXr361W3b29t86lOf4g//8A/fx5YZ7zff//3ff8TU+Lmf+zna7eNW1DIMwzAMwzCMrz3M1Dglu4ggpgK5mhyhAKeFdkv4gt5reHFtwHCR30mIi2Snwfsa8EgCiwn0BicTA2cRMftgxD4VpEjwHpOJ5uOoIubAIrJS/F23PRZkY+E2L01X1qr02CzKioiZBBWTO8i9vwJcBv4kXtD+xvNQSWCqDqUKFColKLlH7uCAdGOT/bVD7t2Dd9+FO01ZHf8WYoypcNwCdlPo9WB9ILVUmkjKHE2bldV+XU1fZXKDQIVfrQ8SivuheA7DQnocGRCfW/vstLUpQkLTTeeq/m/8JpMXCVfiCCiNrjiuNNBHxqyCGJk9xCzQlFQNoFGDeh2KJamjUSwXZW7s7sLWFvu3dnn37T4bG7C1D3d60o4ZhsX3cHziewFvOoXPjZpAFxGztYfMYY1O2UAMjXCsYpMzjCaLr5nFPjI+J0nlNY88T1v4380ak6efShCTsYr03R4+ldY8MlduIHPfMAzjYWNlZYXnn39+aFuz2eQ3f/M3358GGYZhGIZhGIZhPASYqXFKBohImSIr3MP6FRpJ0UZSBmnqFa2LUMabEVoAOyYU7PX7LGEwFhQ3U3g3nUz0m0XE0nWGV4DHq61rHC3GXcKv9j4LU2MeWT2taa3CVF55tQO0X+J7LXHUyAjf7wbbjkMBn9bpXURk1ZQ8f6IBz12B5UV49hkol+C116HbTUgrZWnrYAC9LoO9fba3UnZ24dYu3E4l/c1+dC97wIvARk9Mj+vu3lbdS02lONlA191jmNosz3Ag2qfHUVPg/8/enwdZll3l3fDv3DnnuTJrHnrWjISEZEQYWxIYeBE2/AFhjBhtGQlhS4ADHEGA7VfCBCCMLCYjjJjEEDL4Q4TAsl5kMCCk1oRarR5r6Bpznu88nO+PdZb2vjvPvZlZlVVZ3b1+ETfuzTPus88+p7ueZ6+10lKk9TtWaB6BjJU2Nzdj3z++b0DEiGB+DVcv5CBqLegY3K+p0UHE98eR+zOBG8N+EfYYKa9y9FhEcW4cSkVxrtod2i0pFl6tQqUt16ep7CrBubR2jE/YV34tjSzyLE8n7fPH2ybdtWXCaBwf/9nrF7Wh62/mOZsAzgCngQuIsUHSdthbar0MrtC6P2aqiIFjZoZhGHcz4Ux8gDi+1f/jMp6L2LgwDMMwDMMwnk+YqXFAdBBxzc9NP4UIaouIEVCjWyAs0C309fqniB43h4hyHVwO/bT9KsCngUfoH2kBIm6eA+4BViN4NJb9NR2NTztYVkVEfV8EVSOhjYjCIzhDokzvlECa0mgBMYCWENMgi4iRadEau9Vo8IXcXsbGzdBJ2qRCs1IAJoZhbgaOzsHRU3lyY0NslTdoNWLa2zVytRpUq8Rra2zMV7l0CS5ehPmWM5WKONFV27uI9PNCsk0TmWE+jBPM06jiDLQwnVivqAt/md9POu4ItkmbtR+mBVNxfCRpz35NDf9YaQK6pm47qH/OazTDbmJ9L6pItNF5XH2NLcQ0aAJHWlJT49hxKJ07BqdOQS4Lm5vElQqNaod6I/E4YvfsaYRQzM66GL1M0bR7WkXGsN6PDt1GohLWxuj3vB20lFJA+q2ApGNbxr3/Kuz9vmhkXMhG8jEMw7hbyWazfPjDH96x/Gu+5msOoTXG3cRP/dRP8eIXv7hr2S/90i+ljhfDMAzDMAzDeC5ipsYtkhZJAa4weA6XV9/fJy0FUnjM8Dx+OqB+aWc6uBnru5EBZoFXT0AhC0eX4XNIrYg0QiH5hvd3DplV3USiVsaBexHxdJXdZ0SXkdntWo+kgDMm0s4fEvZFk27jp1eaqjQyiICvM+FDGuw0aDrAWh3WNmBiHJq1DtmhNoODsjLTaRGvrxNvbrF1Y5svfD7mY5+E9Rocz8NcBooNyMTSF/M4IVvP6acs28ClOuuVaqmG9L3u41+fT5pBlKU74qGXcaJjP4wOCMd1EXkmyuxe5Lxf23yDIxT2D5I8N2+U6LjVKAo1EhrAWWB6DCYnYWiySDQyDMUCEEGtTn29xtoabG5ApS77aNHyLJKeTVNGafvC/t4tiqaNi3qoIs/mFGKSqXHl105Ju7dpvw+SGpIWqo6Yr36Uhb5L9ptiLI2DTotmGIZxUJw5c4Zisdi17OrVq5TLu01ZMZ7rZLPZHVE8nU7HojUMwzAMwzCM5w1matwi/j8dBnCz4Yu4vO1HkRn2NXYKf35Nh14CYRYnQvuidihMh/vp8dOMD91OReupEchkYXy5WzyPgm37/VNpFHgxIpbeSNo9iwivl+ifxsdPa6X0SiHUL8VNeMywwPhe0HZPI7PrFxHht5/w2UGiVv50Ay59AV61BK1Wm3vu2YIYikWIWi3Y2KA8X+bpJ+FTX4AnajJb/lWnpc5C5Wmo1kT030DMKTUwNM2Zz25pllrJNWgR8zQDJG0chXUwNNLCF83D32kGkn+OAUQ01yiltOchrR29hPWbjaLYC+H17ociMnY0zdYAMqa0SPhsDmanYGIS8sNFyOelsEYMENNpdajXZRxUm64Oj9bqSDyyLx3fj5QJzYZe/dNGzK5l5PnU440GfRAe2z9H+K7yz5VN+qHi/X0zBkQVeQa0vkkeGdN+ary08avt2u3+5ZBrXseMDcMw7j5+5md+hpGRkR3LnnnmmUNqkWEYhmEYhmEYxt2BmRq3iAp1WUTILOLE2hrSwUVkprGmdPLFv7SIjV7GRJqI2/HOkRY9kCY8+8eLEWHz8hpEsYh7Wni6iIj7mnt+N9FvMPmsJ9vWEINjnf3XJdBr60VaH/nGTy+hcy/kgeNIlMkmIkQvIxEGYZ0RJZOs/yKw0YbOdRjOSzunp2FiQhveodWG1VW4siZmxTgwPATZDIxHMo5GETNFI260iPPNiOyF5Hg55N5qLZE0MTxEz5ehdy0T/TtsW2jWqZCu1zSKXNduY6NXmrHbZWhAd2TFfoiQ6zqDGDg6ftToHANmSzA6CsNjGXJjwzA4JKZGqwmtFp12h3YH2i1odcR0UPOqlHxv4dKBpUUhpRmZBNs2cJEeEa5AfCm59vDdtN9+0H1yiLlbZv+1TmJcgfFez3SYIkv3C685baxobZoaVlvDMIy7i1e+8pU89NBDXcuefPJJPv7xjx9Si4y7hXPnzvHa1762a9ny8jJ/+qd/ekgtMgzDMAzDMIw7j5kat4gKgUVEHJvE1T4oI6LhKCJcq0jaobcAvNsyJcKlthpIzrGKE2HzSbv89DlpKWnaSN7/P9kSQ6KGq3cwDNyHpEFaY3dTYx2p5bGRHLeMq+txO2dBqyir4r1GI2wi/bFf4buJ5PAv4AypkeSY9ZTj5ZDUPRs4obgBbGzCwgIMD0OukCEqFoijiNJgnYnJDjODcGQDpvLS/nodWm0xUNYQ4drnZopfFxBz5gjdhbR9+tXTSBuTuj6ttkm4n37r/dA+0nsFvU2NXmaGf56DSH2korgfSRDjomT6oW3Uvohx5qUWcm8h166Gx2AWsjnI5iKpJJ/Py9pqlc7GFuXNFtvbUKlBI3bjaRt5r2yxcyykPeP93icxO99DGeQdMER3HRv/2GlpwNL6vuW1McZFg9wMGqWikTNFugvZq7njmx56P/zf/vktOYdhGHc7L3/5y3nwwQe7lj399NM8/PDDh9Qi427hzJkzvOY1r+latrq6ykc+8pFDapFhGIZhGIZh3HnM1LhF6rjUOppqqo5ENqgYN4yIhePJujJifGQQwbyGE9zSojj8bz8iQcW6BiJ01r1jaJHjptfWXjPyN4HPI4MhhytoXkLMkb2yDTyFE0u1mPXtIryeHDCBRDqAmDFX2H9R6g5iKkTIfat6nzQxtIREtIwi9/YkYm4VshDHkM1CrpiFQoEoE1EcL3Hf/VVWV2OqnwRasLYG9QYstaSOwHX2HyWQxhjSHwPI/dBIk93qVITFx/We+kJxWnqmOPjtf9rB+jL9ozTS6jiE6Y8OSpxOO06a+RiihtcmLoXaFnIPZxGzaxI4m4VmDPVO8tKNZWx0XWW7TadSY2NdxsNWWfpoE4naWUYKXreSvbT4u9LP0PCXp63TiIhNdt6nfvumRY+FtNl7FIT/fgMXIRRGmmm9G9+cyeEiV/y0c5ngOxzH63S/Jw3DMAzDMAzDMAzDMIy7GzM1bhFNN6WiXQkRNjcRYbyZrJtGBDcVlzVPfBERy5u44tRpqanCmdgq5oFLOxUaIzpjXJf5Qp9/XD2WP/s7k1zDVUTs3Gv6mYMo3LtftG1NpC81/c8LgL9Cio/vdZZ4nsSQQPp0FSe+px0jixgp48BLgNEITg/DxCjMzMDxYzA1DZl8VlIMFYpkRkcZPTvAS9tP8cTTDZ5ahMevSN89Gcu4OQhDQ+sYXED6aI3u8YD3W8dMOA6U0KDwoxN0fYhfxDv8+BFBvQgNDP/7IGfa9xobOXoXilfq3jZ+21aAx4B7gJkMPHQGJqfg4kXodBJDI9U5iGm3odmEWkPu2XnEJKkF52jR/QIPTaZeJkcvc7OBi9AYQZ6FgeQat3BmR/i5lVRvPvqO8guUZ7x14Zj1x6r2Rxg15G8bfpPss8zNRUEZhmEYhmEYhmEYhmEYh4OZGreICmd1XEqUdSTNjs5C19nPOvtdZ61rihY/RZIvMJPyHf5OW6b1LMJ0OgWcALubyN9BhMwng/bcCTLAMVyh7L2eu4kIlI8iRsRZxGzQotR7QYV9TdulKad69Zdu0wGmMvCyU3DffZJVaHQMzjw4QPH0EWi2oNOGXJa4WoV6g1ymQzYn4+NykmboWtL2g6CNq2ni38Nehob+rcXBQ1RE1+3TRPS09FP+bPpOsH4v9Gr3Xo/R63oOgg7p0SZ14Gnk3s4AL8lAqSS+FnhtT3E3clkYKEEmB4sNMTTqyDguIc+F9qWaKX76rPCefulU3vo0Wsh4GQBeAdybg2wHrnTgM8izpfunGT238o7Q6AvfzFBDwzc2fHJ0mxEx8i7V1HwaZabpqfwUYf74DMfGfuuHGIZhHDTZbJapqamuZZ1Oh6WlpUNqkWEYhmEYhmEYxt2FmRq3iKYtUSFsBbhItzDdoTs/vy841tk5KzkU3djld792+RSS/XSG/F7E5VuJGNAUOW32F8ExiNTyWEFMlX6GRBR86kjaqQ0kykT710890w+dua3FjfeSgoikjcsdqDWlRMLAAIyORZSOTcDp07C1DYuLxNtl2msbZAZKkC/QjlqUkbRCVURUPshZ4/41az/4M9jDVE6+uBwKvmrKZb1j+9++yZGWuiqM9NitX/12p/3ey33JI8bWKvuPIrrVlESaxuzxDpyZhziC7TIUckj6KSC8iigTMTICx47DzA0YuiEp7ZrIWA5ToGmtib1GsmjdDNhZs4XkPCXgvkH4R6dgeQmiFXgCea7SIjP2cz96tcl/B0Ypy9LQOhqhKREn66aT3xVcij81IdPwx75hGMZhcurUKd75znd2LatUKvzLf/kvD6lFhmEYhmEYhmEYdxdmatwivgDdwZkaTZxIFkZG+OJfBhFe02awp4nwNysg+jPs71TURQFJY1NFhMW9nDdCzIQxXHTJXk0N/dsXMlXE9OuQ7DZrP0bu3xa795dvplwDnliC4wtwdA7abSCTSVyOEhDTWl6nvNFm6MUzDM0UKBaf+NLxm9y+9F1p6XtC8dbvQ782QZiWTGe95+ge26GREf7WMZ5B6syUkEiGfvc3zcyAvY/hFvJ8lTi4CJj9UEcih6pb8KIKjLXhzHCSgirFMcvks0xOSSHxS8/AKzbgVSPw9Br8n4aYXyG+WZp2n/0+LCC1PmqI8deLKIJs4izoPVfBP0d36rBbjdDo9+mVfso3yELywP3AC5HxuYzU+rnap636HjZDwzAMwzAMwzAMwzAM4+7HTI0DooMItC8AjgCXcEJZv9nBsDczQ8W2m0mjM4DUicghQuZezY0o2beAiJj9ijqnkZbaZS/7tBABuogTvtNQkbWX8OmLvSrS++mVeqXj0eV7MRgGkzaqebXahMUFGBqEwaGYTq1BJo5FpSaiUo5ZW4PCVg2osVF2BeXL7D1N1s0Qit6tpN1J4MCX8KMpoHs2vBbD1uO1SB9H/ca0mlWjyJja7ZrD9oS/+43jGInSuFNGXhplpK5LuQ0PAVMtqZkRpThLcSdmexuuXIG1VZgswplj0KxAvrH7tWrfpEVslJDC5TOI0N9LwG8AX6hCfAVydXlntBGzcRgZL8/s+er7o+8IP9WUGii6Pq2d+k7VsTSI9HMTMVIfROqZxMi7bwsxHdP6L4O8a7I91huGYRiGYRiGYRiGYRh3F2ZqHBCaamYUMTUus3s9Bt1P8+L3EoL9HPP7ncmfRcTIEZxoF9PbKPAZQ2Z1N5EZ4vs1NZqIIBoaCMNJW9Z77LeFiK7HcHnx0whndfvL4mC70MAI60Eo/j3YLfVUhIihRaSPp5F7PzQk9RMKBUknRIwU2QBazZhKBS5/eonFhQ4LK9JPLaR/bzXlUVoboTudD7hxNYjcC03P0+sY2l8qJOP97lcoOu2YMfKsbLG/VFuhuaHLduN2GkV7xX+GGy1od6AwkIViUf6I5Uo6rTbLy3DxAlyZh/UYVp6ES1UxG7SA/W7mhn9ev6C7GoUDuEibkDrw6Q48uiXvjVHkfTGC1KnZRKIeDiqqyI+mGkJMiDEkZd8iLurNR43KfNK+sWQ7NTXGEJNDaxb1GtsZpE8LXlvM2DAMwzAMwzAMwzAMw7i7MVPjAPFztvvC2BhuJv5+Z7X7f9+M2KYz6+uIgD2JCJu7zZDP4sTFq3Snm9krWsjXb3cWOJ6sW+/TZhU4S/Suv6AicZqx4X/rMfv9Dd3Gh4r1u/W9RuMUkvaOl2BkFEZHYXImSzQzDSPDUK1BJkOxFFEaiLl8ucWjX4SNlhNc93t/i7gomjRzIIf0d4v0+gQ5RBAGVxjd7wMdz7363zd/+uGn/lL2W2clPO+zTXgu4QyCyRJMTsLA9CAMDkGrBfU6NJrEjRaNOtTqUG3DYgxPl8VUrNBtgPZDx69GPHSS/a8my4rIs71M+v2r4VJU6TFGkXdYWio5HV9qour++yFO9n0IuBdJ5fdXwHVkrIa1Q/K41H3rONO1mbRT330LSJRGeJ15pB807ZQfSWQYhnGY/MEf/MGOZd/xHd9Bs3nQUx+MZxvFYpH3v//9XcviOOZbv/VbD6dBhmEYhmEYhnFImKlxgGwBXwAu4AS0HJLffRz4HCLURaQLuv1ENRXhC+xPEFaTpYUIlEO4dC39ajhoOq0s+59RDyIWjiOzrf1aBip67iaEa5RL2L4czujQdWkRGz5hcevY+5tguf9bP70iNkqIMJxDrncAGChK+YzJKRg/NwWzczA0LAU28nmKpQyZTIdKRYpG6yzyvRYkV4pIep0RpF7Aaso2OgNd+9/vHzUatuguEp5mYvT6vVduJmXa7WQvpsBBo8ZXEZgchePHITM6mhSuiKFaIV5bpbJeZ2MDKmVoxPIMbOPqw+znOZwE5hBRfynZv4KMlePJp07/2hr+OTeTY/WLfCgCr0Ei1R5Dxtlsct61XdqrURWDwNFRONKB+TKUYzdOe7VPo51ADI5LwKlk/Q3kvRsSIc9wWGz8bhuvhmE8/8s5XRMAAQAASURBVJiZmdmxbGkpraqS8XwjiiKmp6d3LLfxYRiGYRiGYTzfMFPjANkGPs5OwXQUeBkiHlZx6VR6peZJQwX2AiLC1XC1DXbDn11dSo6h0RC9TI0YmcW90qdN/WjSbSYobURg7WfKtBBRch0RUfX8WeCrI3hRBJ/pwCO4dE1pdTXiYB3sTIUVpqYKIxV0/7ToADUzVKweAkaHZRb++GyRzJEpGBiATCS5qIaHIZdlfa3F2ips1uW+1HHCbC+0H7XfBhHBWmewp9GhO0oj7aNCsN9XvcblQRoBWq8li5tVfzP0ujf9OIyZ+C3k2a8hteNzOfhSSMbwMDRbUC6zuhyztAxrG/I+aeAE+/1EtqiZ+gBitK7jonE2kFRpE8lH0zSBKwLfq4/UWM3RbbDoPaiSmHvJ8jySkq2WtGG3vq8g751qG07MwBsKMLIKfxfsH5pwGrXVQZ6nFeAk8lz6xex9mt6+/nX0S6dmGIZhGIZhGIZhGIZhHD5mahwQGUQgXKW7qHcDKap7GmdEpM223osw69cUyNJbgO61L7i0MH5Oeuhf8Plm0EiPUIhtIbO4d2OdnemphoH7czBbhOJ2tzifJkSGaahUsNTc+1oc2DdCNCImZqc5EqLpnTSNTSkrtTQGhyA/UpIcVPm8VIWuN6DZlJoaVdjagq2OiMAN5J6s9TnXseR8N3A1FRaSNqalBvOFXq2lEtbVSOursB7Dzd5/HZ+9xlUBSTEUAU9yc+nNlLBOyF6LiO8XvSbffNL+VYOo1/OoET9NxL+oVKA5v0Ku0yGTy0KrRdxo0WxAvQaVZvf7osze6uCA9O0pXE2aEWR8arubiGGiaej8PupnnKihMZl8X2dnnZ0YeBRnVBWQ4uRpkRJpbAF/D5TKkM3B2AAMRZCL3fH9c+m3jnetUbQJDGXgVBGuVOF8cG3+8+G/P56Nqc0Mw3hu8YpXvIKhoaGuZV/84hdZXl4+pBYZhmEYhmEYhmHcfZipcUCoSaCzz8GJkMuIUDePE3lDIXYvdBChU2dT30qaFBW55xBxc4neNS5ullDw3M0k2I0JoNOCxzquUHGvYuH+ecLz5XFidCbYxi8g3q8gdRaJwPHTCpXyEphRLEA2n4VMFqIIGk3Y2CBeW2NztcXmJmxXoRqLaK3pwXoRIQL1dNK+VWS/p5J1zWBbjd7Qa0mLYvG3969vL2bObmQRIXsGGfuLKcdS06tXxFIvwsgMNaHC+x8Hy/eb3iuNUPAeQsbkQHL8Ndwz5F9TBldQHqDagI112FptMZpdJzNQglyeTjum1ZISGy1PxE8T2vuNzQIyNueBK8hY8bdvI/elgkRt7DUCpI28f/LJtReDY2tfP+MdcwwpLr61x3N0kPHyCSDagAersBa7yC+f0ODwn9sSMD0IZ2fg4mX4dNu9jzLIs6Qp+MzEMAzjbuJf/at/tSP91B/90R/xxBNPHFKLDMMwDMMwDMMw7j7M1Dgg/MiE0KjYRsTFFiL4+tvs1dBQbqXAcoiKz+OIULnN/mtn7AUVGQcQUbRyk8fZBD4Rw3bbpcXSWda9RG3/b7ztwrRYIeH+oZieQR6eCBF3h4HhgtQEHxzJEA0UpV5Cuw2dDlTKtNa3WVvpsLoKGzWXXkhn4vcTV3OICDuFi7bx049pW7W2RwMX0ZGjf5qqvURn7Ef4LSD1Gl6MmE+b7IzEaCIz/SP2nkYN3DXoOO1VS0XvV4TcGy0eHdKrToPip2PyjcQISat0Ilm2jYsAAlfUG+SezCbbjwOFrAyJbB6yuYxE8cQxnVaHdgc6McTxzjGbdr29inyfx0WPRHQX7Y5xKen2YyipCVhF7mkTdz/8seWnytL+m2f3MTSMmCVriMn6OcQAWqW7SHoammIqQoyUM8BYDlptaHa6z51DTMJJpP6Rld01DMMwDMMwDMMwDMN4dmGmxi0ygEsBpMJtKNK2k3XDiLh4UKbEfinSLXBqOpwYSVFT4PaYGgPIzOgBZCZ7jZuLMllDxOMCO82M3aIQ0tal5edPQ00Qv6B3ExFqQWaiz+WlQHixCKWhLNHQoNTRaLegXodajWalSbkMW9uw2hRhWMXlzT7nB5npvuZtr/UA/PRb2s4q3WmFIlx0SkjaOXulp9qrsZFHojTmknargTSVHGcdV0Nkv7Pk/egc6I5C8b/1dx4RsLfYaWqEacvCyJ4Mrqh9aDqpabKRHLuKe45KybLVpL1qSJWQlEilotTUKA5kyYyNQj4H9QZxu0OnI4ZHiH+PC7iUYv77RI02rW2h15IW6bHfNEuDiFEwjqRAW8aNQW2f3/dar2cb+BQSfdGPDHAOSZv1NGLKrAIXkb5UwzjNnNMIucHk7/uBlwLVLfjENjwad5tqWeRZ8o2nW0m1ZhiGcVC8/vWv51u+5Vu6lj366KO8973vPaQWGYZhGIZhGIZh3J2YqXGLDOOKAPdCRUZNQaOC825i+kEzgMxi1roMdZwwnw/asVu7RhBRs9ZnG0VTNOVx4vrNmBox0td6jH6pp3oZGn6f7zf1kZ5Hox80Fc89g3BmVsToKAOZbCQ/iCRSY2uLeHmFymqNzU3YrEiUwkVE7N2tSHgMfBF4jO4Z6b7QremmWuxM+9Xx1ofHTTtXeM37HZ96Lp1xr+L3FK7IvRau75dGqR+73Xcli5gJ2gbf6BlLlof9peMzgzwzI8h92vL2LyTXsY2LWoiQ2f+TyX7NZJ3/kv1ShEUMMREU8jAwCO0tOp2YThs6bYnUSLvmEmLSjCLP8BVcyquwT8I0allv+X6fv0HEcOjgTDX/2GnGYjbZ9jy7R0NEiBF2CveOLCPvTY0g03brOdSQGEn2G0DG3JGkvZfa8DDynKlZq++hLVxdET/lnGEYxmEyOjrK1NRU17JKpcLCwsIhtcgwDMMwDMMwDOPuxEyNW0QL0+5lOxWvw9nBd8rY2PbaoqwlbVLhV+nXniLwMkQYfJTdBcsGIryWcOlw9kKY8klJE7NJWbZbn/p978/SD3P1h7+LiCAOSRqbAcgXYHQETp2EwZEc5JIcQ602NJq0tqosL8PKMqyXRey/hAjjRXYfQ2kRNL6Z4dfQCMeTmkr+dYTX7o+JWx2P28BngL9JfuuxbiBCt5+2yG8P9D9vaGKFInramOjQHVUQ1l2IEQE9LdJAow2Gk22vIfdrEolCWUfMDo3MipHoAhXoH0JE+WZyjAFgIJIojUwEEUmeqQiIIjptJP1UJ70fouQYo0nbNL1YeL/CPvHX3Wwtng4SlbKGiyoKjbVeEVPg+rpB7/eF1rkZQWqVlJF3jG+A+Wm3NI1fIzn+BDLWx3E1Tq7h7o9G7qiZacXBDcMwDMMwDMMwDMMwnp2YqXGLrLA3EbjDzkLc4Uz1mxGTVejbi1CpAqC/n59fP01YT2uTpn9KE9pzuDRbGsWhxbDriICfw0UcpBEl22STc/jn8QXIfsJ2WmqpNOE87XeYssf/O4NEHBxJrvMIsL0FrXE4dTbL8ReOEM1NwtCwCNa1GnG1SrvaoFaFcgW2Wm72eZ6bS0fmFzqPvL91mfa5buubCDrbXb97ReioqJ82+78fLbrTDem+Va8dYYH2iP794Ivn+neYgopgnZo1Wn/Fj1SIcc9jWlouPXYZEccfwKWxmkvWbaW0uYMYGzngKFJLYx2XHmk4D+PjMDoGuWLSolYbmi067SRSo4fKroW6V5FnSet4aMopfzyE41r7RE2l/RobFWAhOa9vyvqGRlp9jZBSsm1aHRVNRzaQtHPd207HNl7bdXxuIdEgdSRdmN8e//74x9D2Q+/n3jAM406Sy+V44Qtf2LUsjmM+//nPH1KLDMMwDMMwDMMw7l7M1DgAbsaICH/f7Oz4AURcX6d/wWUV+lq4Wcq90jL5pBkQLeAZREwMjY0cIvqv40wN3T+Lixpo92ivGhqDyfZ1XDogv2CzT6+27xaJ0cu4oMd6EFNmKGnbePK71YSBATj+0inyX/ta2Xp5RVaUy7CyzPpineVl2NiQa2ohAu8RpB+XUvqiH73ST2WR/tXImw7d9TTUmPG/fQHYN4p8022/49M3HHqZEb6hsdux0yIBdosO8M8T44yycPz0E+FjXI2S8WRZFrlnG+xMXaXr9XoGkfuQT85fKMhYGRyE3GABsjloNOhUa9QrHWp1GTat2LXTj6xpIJEnfoRYtk8faD/o9Y/hIiD2cz9byPOs6Zsa7Oz/0NzoRSlpezvZNp8sV2NmLAODne4xlKPbvA2jTxaT6xpKtj8BTEcwHLt7lA326/XOMAzDOAy+/du/nZ/8yZ/sWvbhD3+Yt73tbYfTIOOu5Bu+4RvI5br/+faxj32M7e3tHnsYhmEYhmEYxnMTMzXuMFlE6OwgYps/W/5mjI1BRBgv09vU0ILHDVz6p1AcVkExnDGvRgLeti0krUubne1tIHU6whnsmuZHzx+aIaEIPoiYNRlklrh+mnTPtg6jXdJEyv0YGrt9tM6CFsIeAAo5mX0/fO8s3PcyKC/D4hK0WtCJ2Z4v87nPwOefghvrch11pHj6K5B6GSvcXH0PnX2vs/TV2MghBgzecv86fHG43/jz1+0WsZEWPdHPeEhLJdQPXzBPMzfC9vrt9M/ti9t7iZaqIWN6DUm9VESMjjV2jvMMYhyMe8f3a8BkMhBF8pEfEDcaNNYrrK3GrK7CdgXqsYx1TVlXxRlWalb1M3NyyTYasaOGTh44jpgAqynXmkYWeX9o6iaNLlJTwjfVdjOatI9KyXHnkGd9M7m2fCTPU8Z7mamhkYYfkbON9NMSEEdQimAslmUNbzttmxqmodlhxoZhGIfB2972NjKZblv4fe97H9Vqv6ptxvONt7zlLeTz+a5lv/3bv83GxsYhtcgwDMMwDMMwDgczNQ6QiO5C4GnkEYGwhptB7Auw+zU2qogg3i9KQw0LjXYIxUe/Df4+MenmhBbtTaODGCy91vUS7vXaVZzPI6l7phDx+FJyXN2/l5Ghx1EBVlMEqbC8V2OjE/z2i23XETF2NgObHRFQsxnIZCKgCZUylLfh3vtgoET+0g2uL1X4/KpcQxsRYCcRQ+oGTijfKxqBEZoZobis6LXrdaVFYITifmh29NrW/3u3SIq0cabX0w8/dVC/c6Vdhx4/fM7S2pO2rIOkXmoh90j7wU/v5Y+VMiLab3r7aNnXrmPHSCH5WoPt9TZr67C5CZWGK5Kt5sk6zvjpZcgo+vyoCajPfwaJLpkGXgBcQMZev74fQ8b6cHJdq8lx9R2gz5ZvmvjpodLoIAbGDPDaCCYzcK2dFHXPQi4jz/5JxEzyn8V+6LtpA1iPoRw788V/Z+WSzxyuoL1/DMMwjDvN93zP93Dfffd1LfvQhz7EX//1Xx9SiwzDMAzDMAzDMO5uzNQ4QDS9Sx0RNNPS3GjBYBW3ewnFexXXVPzsJ0zGiNDnnydtNrVGIihtb3l4vFslbcZ/FhH66976Jm62uS/qpqWRySAz5GeQPt4CHsFFiIwl29eSbXXWu0aNhKltwuvsJMccSX5vdkTknS1KI9b+9jGmblwnKkih8OjlL4fhEYonZ7nvniX+74X4S4WStVjyRtLmQfZvavhmjW9s6Mz5XsaDpkPSmiV63WljKIzU8FOR+ZETkG4y7GZq6DF7jSmNfBhExnocbB8et5dpErbTX9crCsBvU4PuehI+2pfVZJ8aYlrVSQT25BzHkPIZ7bYUAyfuQKNJfXWbZ56Bq1dk+eQYPLEkY6ucHKOJGCU5dtaYCd8hLXbWyMkh430beApnrIbovVLjYQ44jfTfdtIW30DVCKw28swN4Z43PZ5+x8HvQeBEHo5NwvCyHGN2AtotOFGHjVjauEb6+OhldMTAVeDp5NsvTN5Iri2HvGu2cSaUmlOGYRh3mnvuuYfR0dGuZdeuXWN5efmQWmQYhmEYhmEYhnF3Y6bGAaKRBnPAKHAdJ+5pgekpxNjYQgS1UEhOEwF3Y69FwsPzpBVY9kVSrWVxM0LfMCL2btItrqYZKUoOmEDSx1QRMfg8Eomis/SzdIvSvlCaRSIfXogIskvIPZhP2nIUETUvJec5i8xUX6bbwAmPq/0Abib+RZzYPjUOM9OQaTfZuLhCNgsDxyfJdzrEjz/OM//f03zqkZiVpH1jEVRiZ5LcYH+Ght9OP/VPmApI24/3W2ftDyV9UE76R4VpTSmUo3s8+OcM2xDWdEiL+ICdYzuMeAjJIAbVGVykxHCyj5pRvaJM0givpV+URppZEBaeBhlnx5L2zONMoiausLdGZdWA7ZoE8tSqEFfrRJmI8mab5WVYXoaBQTh5HP5qA+Yb3efUtGI5XASGPqf+Mws73wnDiHCv9UH81Hf++PaFfR0nA/AlMy7tXqlBGONMg/A5x/tbDbkCsNmA9rwce3IAxkahUoXCJgy1ZRu/fbsxiDzX15F3RzNlGx03mgZrkJ0RMIZhGHeKbDZLsVjsWtbpdCztlGEYhmEYhmEYRh/M1DgAtAZECxGpx5EUL5uIMA9OKFtDZmBv9ThWL7H1oEkTHf114d/7EfoKiHmjhb4rwTnDc6tg2UJy/TcQw2AUMSZUmNQMwv6+WicERIDVmeoqtI4kx2ghAn4WETKnkHu0muwbCppRst0AIsBu4Wao15N2DgAvHIT7TsOpsxlGT45DqSi1NEolqNaoXrjB5z9Z5eEFMU+ORnB0FF6wAX+PCOE3SJ81r+SSvsgjM+U10kRTbPUyEfzi4Pq3RpkMIil+yogAHKYMS4v26DUG1NzR/t9Cxn2aoOwbG5pSSqMb9JxqdOQQMb6K9J1GNxXoTqPV6/p9kT80Y3q1q9ff4FK4+cL+GPAgEhGwEOzfxtXQKSTtX2lDuSIRGZ1Wh0yjSacT04mh0YCNMlxbhnLDpVPy211C7uEyLhWWpiIbQPpMjZQw0sR/jsM0ZH6/K5ombQu5lxV2mguF5JPDRW2EdU9CM6mFq73zf5J9TgNfmWxTa8BCW8ZlA2fShYaKf3x/WdhvaeizXKI7ZaAZGoZh3EmiKOLbv/3b+aEf+qGu5R/72Md4xzvecUitMgzDMAzDMAzDuPsxU+MAKCEC8RZiZGzhhDtFBb9asm4KEQjX6BYKfSH5ZqMk+qFicprweBDn0hRQJxARdyk4TyhC6/V2kH5ZQ0RGFYNHESE/3Ffb2/E+DURYLiMib4yI6zoz+woiwA4k6xeTY4dpp0jOfzb5rCEGhAqs2q77C/CyU3DyFAxPFWF6GoaGoNmAWo346adZe/Q6T1+RVDoF4EYMKxvSL5dxYnE/Ckh9gSmkDsIVnCHgmwRpqZdU4NWIimNIJFEjOW6F9PuuERta0LlDdwozv59OIql8JpN+uQZ8jm5Dz48SUTOmgESLtJE+HkL6toJE5xSS45VxhZ61zoSO41Jw/b0iNvxxo6hB0asQdXitGiURGl9bycdfruNZx8owyb3uSHqlfAEyWSATUSxGFIsxnQ5c3xTDooPc73LSL1pPpukd2xf5J5BnroH0f9j2LVx6rF7RLdqn+jw1kbG2kWyzSve7Su9lWu2Mfv2pdTcWEUNPI4RONyG3CvNb8MVknUaKlHHjQNundTz0XasRPFqHI61N2gY1PmeQ/jZTwzCMwyCfz/NLv/RLXcviOOZ7v/d7D6lFhmEYhmEYhmEYzw7M1DgAVNwrJH8vIQJaOPtei02PAv8QEQv/GhEbs7gZzx3cjOf9GBsqQoeGSrhNWEh6t3z1+xH6xoF7EIH1gnfONNJmcuvfVUTULJM+217R9DF6zdfoLuDspyjy62fMJ8v9FGDajiFEhL4/gpcX4IkmPN4Rs2Uyub6HsvCq0/DiF8PciRzR2CgUC5DLQrVNvLJK/fw1lh+vcaUq93MY+V5GZvarSLzbPVbRt5i0LZv83WBn8ewQjR7S3zrjPUr6rVedCE319ABwOgerbfg/sdvWv29Rck3XEHNPBfFWsE0REZHHkP6v4FJdDSAifiv5W2scqGGQVp8mS2/8uh+9+kajfDRyJRS1fbNPoz7ySb+ogVNDhP8Vr13gDJciTsTPAPlIDI2cvnkzWQaGs0xOtBgehsFNGGu74tYgvxeS9m7QvU4ZQSKPFkmPkPHvW4guyyHPbz7ZfgN5/vQ9FvZNPzOoX3SXmpbrdNeW+b8t+PSaPJNLyXbnkHFRQSI3FpP2qOk2hPTFMq5WzQ3cs69o1JUazjFipJXoLkRupoZhGHeSb/3WbyWfz+++oWEYhmEYhmEYhtGFmRoHQB0R4Sq4Gb9paBqhOeBsTmbvf7YtAn4eEXuLiJCoM7H3mvopg8yYP4cIe5cRca8ZbJMmRO43vVS/NpxAhHDN+x/W0/B/h1EGfmqcTaRftDB1WqoZZQARmq/Sv/9B+rdAt1Cu/TKerBtMjtmJYbkt32eS5acjuHcC7p2Dl74IztxfoHh8kujIjKScanegXqe9XWXteo3yNkSR6/dGcm41NPbS7+2kL7QOi1/cW7Nwpx1HTa5NXKTQDW97/aRl7Y6QcfrVQ/AV98LiNqydh88k6+5BhPYtpL5IMzn+taQPN9kpvI8Cp5C+vZFso1E4ReT50XGv97ySHDuTbKNith+p4qdQKuAKZatRmGZWqCCu/anF6fWY4T6aIksjXMaS/rmGS8/kt0ULc6tortcwmoOpSRgahGa9Qy7XJFvIcuRIi6PHYLsMnTXYjruLv4MzOxV/eSa55nV6PwO6nR8J5vdJDjFGjibHegZXnF1Rw6nlHSc0BHzDK80Y1evyz99CamCoGRslbRlM2hPhokI0Gi5O1mvf63nCVGp6r4dxJl8HMUk2cMZeP5PXMAzjoPn6r/96fvqnf5pCodC1/H3vex9LS0s99jIMwzAMwzAMwzDATI0DoYGI1LuJYSquTQMjeSh1YDBR/VVQHEVmHVdwQtteTIccIrJOIaLdKN2isRYZDgtIh8KtpnRqB+sz7BR6e11jARFX19g5azw8b69aBr6A6++T9jvPzpn2EdIHDZxoX0LqH0SIAdJGBHY1k3RWfS75bgHzLbmmlwJzRTg9C+fOwpkzcOxcicLJI0RTU1Lhud2G7W06K6tsLddZWIT1dYg6ch4VxbVw9F5pISbABnJPNfXOIN33xReQtW80GkGjTNrIOFtHjC/fIPHJI3UOXnGPpNeaqcG31GDtmvTdMcQQ0Dbp+TQ9VNo4qSAz7TNJH/iRFio294qqGMQZH357/SiKgaRdm8jYi3HpqfxxHSMC9xQSMaLCdmiSqAmQw/WjzvBfTLabpzsqSqM5ppL26vnzSJTK0RGYTvyvWhVKNMiXsuRyMDoiZke0LkZaP/znQFNbXUQiRvSZUyOt6W3bYWcf63jRKB5NE+ZHWKkRkUu2aeIMo9AE0n7rdQlqgPmGp39vtE1byLvwCNKPet4KYlwUk2vXeiy9GEzareYTuH7R1G9ZJNqljUubZhiGcbs4e/Ysv/qrv8rRo0d3rHv44YepVHZLTGk8H/nO7/xOXvnKV3Yt+/M//3P+5E/+5JBaZBiGYRiGYRiHh5kaB8ReZvfqjOIRYDAH5VZ3tMIgItKt49ISKSooaj79EJ3FvAQ8jQivWi8g4316pePxUzC16Y5i8Nf3Q/PkbyGz+FVgDVNdhTO8fcIZ9f7ytGMMIJEhHWSmt5oSalas40yNCeBlOPFcDZg83WaGRnMUcDO8p0fg6BycPQP33AtTJwfJnjxGND0NAwOJobFFvLTM9vUNLl+CCxfg2lWIq3LsVtIfm8H178UsqtBde8M3EfR6MzjRVg0DNcaauCLST+EMln7kkBIhcQydNgxErli7nqOf4aaz+rOIAVIFnsDVpQjTkoWmlH+tWjMlra+0DTnETFDDqJ38VsOqjov+GUi20Ror4fjS840Ax3EREHrMZdKNNP+6tSbIIDIWH5qBl75YxtDA1ABkMkTZDPF2hXIZtrehWoN6pztipV99CI1YWUP6WI2CLGJiFb11em1p6DO6haTTqiNGrR5rHLlfG7hx40c3hMcOzRN/nZoTiv9u8qkCjyXnO4rcg6u496Pu26D7uv0IqCzSBy2cIajGU9Y7jo6DdXZGehiGYRwUURSRy+X4yEc+wokTJ7rWNZtN/vRP/5Tf/M3fPKTWGXc7R48eZWRk5Et/x3HM8vIyq6urffYyDMMwDMMwjOcmZmrcQSpIh9+bh4lxWFl2Yp4Wya7jaiD4NTUGEHFV60aosaGzwmcQAX4VEe+0qHUvQ6OXsZBmMuwnHUsdlx7GnyGuhDU0+h073M//gJv9/oIMVGO4Fov4OoCInPOIQOmL3po66AjSh1oLpeh9BoBSBINZGB+G8XGYmYa5OTh9LsPU2VE4robGILSasLlJvLhE5eoqz1yEp8/DF56E5boI3HlcoXiN6tmroZGGpjRqen+3ECG4kVzrGCLcjuL6fBBXbLkfX0pXtQTXF2FxGT69IYK31lvQlEXhDHtF0z8N4AwBjQxJE7FD8ytsT4udY9UfT9uI6L3gXV8FZ06oQaA1cLSv8I7jRxt0cOZPne4+a3vHCSMaRpB7PoRET81lJQLj2FE4dgzG5kpE01NEhQJUq7S3q9TrUKlAuSJtruHSt63Qm05yHfqe8Pt0kySNWrBP2njTlF0dpB81LZf2jZpTmyn76v30jx+ap2HfavSY1lApsnM8kLTjSVzaND9tXAcXzaE1YGYRY0L7LK3/0syWGhLpYnOjDcO4XWQyGd761rfyC7/wCzvWbWxs8FVf9VU88sgjh9Ay49nAS17yEt71rnd1LVteXuZNb3rTIbXIMAzDMAzDMA4XMzXuIB1EBJ0egYUN+PuqiGglRGjT1Dx5pDbGECLQLiEC9QlExFOBL4vUenhZcoz5ZNsG6SYApAuHOjNdU8D0mmG9FxrAJURIDNME6XevNoR/p0VtFHAC9CjwigycPQorW3DvphgGbdysbC2ureL/M8nfg7ioDC3oPJiBgSwMFWB0DMbGpP7BxCRMT0dMzuUYOj5BdOokTE1JDqFWCypV4uVlqtdWuHqpzcWLcPEZeLIuYvgZXBTFdW7e0NBZ+X4KHU1B5Yv7Osu/jovg8NM0afbuerJtWuRPjNzH//G0mG3ruNRZHaQIvBoUvUwxFcrDwvUaDZRWQD5tmUbV6LWlRXPEiLh/gZ2pvVqICZNHohe0H7eRvgzvg1+8vZYcM63+iT4rGoWjhskQyXgChiMYGxUTc2YaxicgP1SQsZPNwvY21XKH7bKYGtWGtL+BS3kVCu3hdbdS1mn6tt1qzPioiaJmg3+d28l1hREXWvPDf77TUtV1gt8aZdJBxmgu2N43YjW9n55TqSMmlhYyH0Hubz9jwk/nF7bJDA3DMG4nr3vd6/iFX/gFoqj7/4I2Njb4gR/4ATM0jF0Jx45hGIZhGIZhPJ8xU+MOosLrWhmeqUtqlTJOyPVTB80C9yKzkK/jxGhf1MsjRscpXHHwLZzY2CvVlKLFd5s4ARXcrPYK+zc1KogYrrPHfdIiNHodPy3FUClpWxOXDmi4CIMlWNyUv6/jIjDO4aIF1CwaRMTZgeR3KQtDORgowOCg1DUYGRXPYnICJqYzjEwXGZgcIDMzBVPTMJ3U0Gi1YGuLeGmRyqVFrp1v8ORT8NQluLAtUQ15JCpkAzF6tlP6ZS9EiGA7hgi8VZzYn/W20RnvOgZC4Vb/zif90EDGGHQLyZmkL5dwESZ6bI2K2IsxU8OJzopuHxpo2sZw3KpI7ovRoXCu2+sxs3QL1x2k/84hY/Rycg2+yO2PyVC8T0Nrm4zhUlhpMfTh5HcmlmEyNAQTEzA6lYexcSgWoNWGTodGPWZrE7a2oNJ20SFtnCnUj5uNrlIjIaxtkfbcVpH779dB6RVVkza+O8G37qPmT1jbJS3Sg+D8/jtKn+8G8qztBf9+97rHhmEYt0Iul+M7v/M7yWaz/ORP/uQOUbrdbvOf//N/5nd+53cOqYXGs4Vv+IZv2LHsT//0T4njm4n3NQzDMAzDMIxnP2Zq3GG2gUfqkkJqnZ3RDFr/YBMRNycRMb+SbK+RCCBCnqY22kZE8xq7p3Ty61HMJOe7ggiLWcQogfRZ73pev6iwT5v+OelvNt1ShKSWGkaE9hi55rWGpOy5VIVHELF6ABGvh5PfGplRSr4HszBUhJESDA7A8DAMj4ipMTkh6abGprIMTg+SHx8iMz0lbsfomHwXitDpwOYmLCyw/eQ8X/i7Kl98Gh5bhPOJoXEV6d9xpI7FZVxBau0rnxLyQJZT+kgNi1Fc2imd8a7ivRZ31sgfv7/jYFtwZgU408yvkaE1QPxxmXbcWyFt/zSRPhS81bwI66xoFAa4yCPtZ62jUUXMvwYufVRoaPgmShp6TK2doREaL8jCTBGWqmJoFDMyxsbHJUojO1SCgRIUCtDYpl2pUSnHEqVRg1rsIjW0eLpvavhpu3r13V7uSQYZmxHyPDX7b/6liJQSzlDzDQa/z9LakRadpLVSNui+T+E9DaO7ct56v3363tRxqmno0qJxYKfhZxiGcVCcOHGCH/mRHyGXy/HmN7+ZbDabut0v//Iv87M/+7N3uHXGs40oinj729++Y/nP//zPH0JrDMMwDMMwDOPuwEyNO8w28Cgi5KkBEQqBDUQQbyKzwIvJ+pXk44t/ai6sI2mp1ITwhdq03PYZRPQ7lvwu42Y9t3ACd0gJKbg9krRF60PcbtqIwLuNE/3bwGIbPrsMn2lJn8WIiTCatHE4ae9EBkYGYXgAxkZgcAjGxxJDY1j8ioEBGBrPkx8fIjs6RDQ1KVPsR8ekKEKxCPk8tDtQLsPiIvVL13nyc2X+18fhM2V4oiORDxVcYeo6ztAgaV8DF+0A0tdqvmgxax+tdTBFukmhURm9Zrj7M+R1H63BAfIi6FUDAVw00Z2aD+gbP71m8PtjPG28qwGgfVlHismr8J0J9gv7qB9aq+M08hyVgWngBdOwUYH1WMbfQEnSmE1MwPhkhmh8TFJPEUG7Q6vWYmsLNjagXJP7oWmgtJZHSDFpt9Yp0Tb3M2FCssi7Je/1x2740VzhWAnfYWnRHn4/+/VY0tJr9UqZl+mxPEauo4IzAAdxpkYdd435YD+wSA3DMA6Gr//6r+ftb387o6OjvOpVr+q77Qc/+EF+4id+glZrt3g8wzAMwzAMwzAMI8RMjTtAmOalhst132tW+ipSyyCHiHMqnPoCYAuZZX0NKXK76R0vFBH9FD54v4dwBXbnEeFvIzlf+M/sHJJaZw43Q32vguhB4JsAJL/ngZWmGBptnFEzjRgb48DcIBw7ApNTYmBMTjivYnQUsgN5MmMjRMUCUalENDoqwvPoiKSZyucglwciqFbF0FhZof3MFa4+usHDD8d8cgv+HqmL4vfbFnLfOojIPYiko7qSci3hrPwQjTBQoT8tCsM/HsHfccq6LN3RDbuJ4v3OcVDkcKmxqsE6PyLAN3H0t98u39zQZ8o3Ekdw9T7CPtxt5r7epyPIGFtMvpsNuLQtqbtOJccZHpaMU4WhAtHAgET6tJrE1QrNcoNyWaI0Ki33bmgmbd32rkXbpwXKtb5Jv8iNXsS4Qum7CfoaBbHOzmgKPZZvqmhBeNhZEF7fb/osrLI3ouA7DX0u1LTQ1F1+raAjyPvrCt3v0jtl1hmG8dzhHe94B29729u6lg0PDzM9Pd1zH00V9KpXvYrz58+ztrZ2W9toPDd4z3vew+TkZNeyn/qpn+Lxxx8/pBYZhmEYhmEYxuFjpsYBksUVM/bRXPTbiBGgtQlUmA5nWKswqFEblZRtSNadT9ZvBOf1xU7/b1+0XUdE8hnEANAi1BphEIqdhWS74eRYw4j4vB5st58Z4/shTDdzD2LGLCbrNNXUCGJqTEcwNQQnT8LZszA7C6NjEcNjWXLDA5IXaGSEqFCQKfX5xLgoFCQqI5eDdtIL5Qq0W1CrEa+uwtIy608v88UvxHxuWVJ1LbPz3uu9HAKOkxSPRuqOhGgx7RIiWIf9r4WRs3SnX/LHEjgTK0zT4wv3GgmgaYR2u2d3UvQtIvdwqc82vpCeVjtGryeLiNgqoKtJMomYEMvsNDX2eq1axFsNoTywtAFLsdyDJrBRk8ZMTGXIT4/IOMtmoAlxvUl5q8P6mtTTqLadodHEFUX3Bf04OWeJ9GiIvdJCzFA1LPaCn3JLi6OH54+D46UZJhnkPTKARHuF+KZUeH/T7nW4r9YFaQXnzyHP4Engae7smDYM49nP0NAQmUyG3/u93+N1r3sduVyOXG7v/xu9vb3NH/zBH/DWt76Vej0tDs8wdvKmN72J7//+7+9KYdZqtZifn6fZvFPTigzDMAzDMAzj7sNMjQMkTJmi4rPODu8gkQ2TiMjq178IZ4mnzbQPiZFojjZiOITrwoLhocmxihgSYZHeXoV2NQUSuBQ5aVmi74RYmEEMjdnkb78w9BhwNAcnj8DcUTh+DM7eEzF+coRofFxSSQ0Pi4lRLEE22bsTi4nRakEtkb9rdamfUa0Qlyt0KjVaS2uU15ssL8HaOiw2nTgO6emf/AiBflEAGSRdVhFYwPW34hsRWe9Y+ltF8SIiGof3XL9byccvNN4PX1TeS/2Bm4keUPK4lGu74Rsb/t+KRhc9ADwD/GWyjRad38IZHfsxNGLEdDmPmCbHkGew1pFj35uB+5LyGZ0ONFsZifoZHoZWm7i8Tbtcl1oaVdiuSURPDWdqXGfnc6vGRpXuSJXd2p3xttP7t9+EJ1lkbJ7BpbvzjRcdV7vRQfqu0Gf70NDYi7ERpkvT1HzKCGJqxEg/6zujn0liGIYxOjrKiRMn+NjHPsaRI0f2tW+n0+Hxxx8njmPe+MY3cuHChdvUSuO5SC6X4+TJkztqsvzlX/4l73nPew6pVYZhGIZhGIZxd2CmxgHii2iDSP2DFboLZzeQlEla1FlFQdj/THFwERchYf76XrUWyoi5obP7/WtRYVkF+RqStqWAiKpZ0kXJWxG000gTbQeQPj6Vg/tLsNqE5bqYPDMRnD4KDzwAR4/C9NEcoyfGiE6elJRSMaI2F4sQJ0ZGswn1uijM1QpUKsTtNp1yjVa1SbtSp7YhIvTaGjTqUlh8dBQKOWh6HZFP+mYEMSUqyLhQA0pNo7TrKiKmzCBigIWmBjhhWmuqqIHlp/3RWeoq3Ma4saYRQH7Ehl8kPC1NWRglE3vnTrvPA7jZ/NUe2/Qihxh/q6TXlPDJ4FIZaZSRH3mh9WaeQVK0KXWkr4eQ+3EzY3UdeBy5R0PAVBaKMUx14J4JeMXLoNOGbA7a9RZxo0U0MgrLyzSemWf50jYLC2KMlRuu7Q2caZhGr1Ri/cyNQcSQKCPj8GauN0ratoiMzTCSxDdMdqPOzns7hhhCZW+djvE0cyes49HP3Mog7+MsEqESbqPn2IvBZxjG84dischP/MRP8I53vGNf+62vr/Oxj32Mer3Od3zHd1jdDOOmmJ2d5T/9p//UtazdbvPxj3/8kFpkGIZhGIZhGHcPZmocIJr2BKRjewmTdSTCIEIEVb9A8UESCtS+6NdBBLzLiMhZ9M4fISmpZhGB8RlcNEiEmBu1PucdSI63ycEU4PWjWvJJO6aRPh4fgvvOSGDFlQXY7MDsGDz4IDzwUIaxuQFyRyaJjszAkVloNeH6DUkBNDEp9TKqVUkvtb1NvL5Oc6tKfaNBvdKiXo3Z3IJKGdbXYbssvxtNKSy+sCKeyBBOlG4h4uwJZEa65u7fRvq9SneUjo8vxmvx7l7b+X2bloKqhbvnbZy5EB5Tx21Y/yAkLNgdCto+OWQMRciM/jJ7R8+TT2lrSB44ikQP5BHD8DzODGohRtx15D7osdYRcb5Cd32S/aCGYj451vEYxguQacLUOExPiaGRzcDIeJZobBjGx2F+nvmLVZ74Iqwsw/yijFstLt9EDJ29RhFomq1xZFz16ms1I2/2PaOpr+q4exQaDRm6x4lGY+xmduSBlyHGy8PImImQcZRPPlobI2x/L8PWT6uWQ0yTG8j4CLeNcCaXJYUxDAPgda97Ha9//et5+9vfvud9PvOZz/DhD3+Y69ev88u//Mu3sXXG84Hv+I7v2LHst37rt/gP/+E/HEJrDMMwDMMwDOPuwkyNA8Sfh1dL/vZnHOv3NJIS5xrps8S1psA2/c2DvZI2qzuLiM4ZRPDNJufVme8vRWbLawnCIk44vs7OAs5KhAj6g4i4ehCmRgdXPHoyaZ/WRGg0kwLAR2B8AkaGxbs4eqbI8LFRoukpmJiAU6dgchLOX6B15Tqt7RrFySGiiXGo1emsrVPbbLC10mBzPWZjU+oc1OtSF3xtHa6sQ7kFxTY0O2JgbLWhGksKIpA0VJo+qEz3mGgm/Vbv0y/bSL2NEtLXA6QL0b55AS5qwl+vxgjeNmnisi/op8109+sUKFl6C+4aCVJK2q8pnvY6i38TeAzpp70I8NpPk0ifa8SRtq+JSw2m/dRB0nvpde0lhVMaMS76Yb0DxRYUspLZLJuVOi7F8UHyR8aJTpyEUontG5ucf6LN+fNiki1WZH993rdx0TzhfdHfYVungXOIiXaRneOrmRxz8yauMbzesJ6LHyXhRwZptFI/o0UZTa5Bo0H0ngwky0dwZo9GPqW1Da8tBWRsNJK21JH+CftW0aglm09tGM9f3vjGN/Jt3/ZtAHzZl30ZDz74YM9t//AP/5A//uM/7lp2/vx5Hn744dvaRuP5wXd913fx7//9vyeK3P8BfOhDH+JHf/RHLfLHMAzDMAzDMDBT45YpkR6RocKcCmeT3rrjiFjXawbzMeBe4CkkSuKgIzhIzlvBCcAd4MsQEbCFGAZVZCZ6G5nlfB8iDm4hImwvgVxTLh1U+cIaInrOIf04mnwfiWAgJzULxsZgagpmZiNKR0bITk8STU5IDYORUZg+ApOzxE+dZ+vqBos3OkwfqTE+vU61HLO80GZzU2bOb2yKkbG9Ba22HP/GOny6Ldd1OmmXRlzUcJER2n+aqssfF5oeqt/91PtSTa5zhN5Cqx+toUKuGhea3ky3yyBjVQV9ranhH0tJMytCQySs5RBGbHRw9T/2amYoLdLTbqWh6byuIIbSeWR8hunUtH1+eqEmB/NsqVGyCGy24GxRMpsVSzB0ZIjc6RMwNwuzR2BtjeXHl3nyKbi8DOUObMRyDVvIuNlkf9ECEWJonEz201o+Pg1c/Zw0isjYrabsm5beKTy/fmeA4eSTQczNCLmfN+jd31ngKi71VB75j1MJefecBo4k23wRV0S+QHeEkRZoj5B32CDOLKriUsD5NUZ031Zy/tvxvjUM4+4miiI++tGP8sADD3D8+PG+2/7gD/4gf/M3f8P169eZn5+/Qy00nm+84hWvYGRkpGvZY489xuLi4iG1yDAMwzAMwzDuLszUuEUmEBEynIkc40T9HCJQlxGxrpTs0yuX/zhiIERITY6tg250ct7N4O+ziEj4KZywvoJc41nEjNEZ+L1m/YObFX9QaPHsseQzhczenh2Co3Nw7BicOAlTx0vkpsaIJiel4MXwkEyXr9dhbRUGByATsbrU4QuPwrlqTKPeYnkZLj8jZsbWJmxtQ60G1ba7hwuxzOyPEWE0k1xnBSfG+oXTNVWPj6bPSUvTE6LjJ8/uIqtfN0VTVmmhZF+MVpE/i0uPVk85Rhx8675qirS87WGnoaFGxtXketU8mEVMmv2mo+pHC0k5teYdW68xTN+UlqLoVlERfwzX36ODUstlbAyygwUZi7k88do6tU/8PY/87Rbnl2GhLf2whasfUmNnjZy0aA0fNfn0me5lgPWb16kmwm6G224pynTcaX2MItI/ZeT56RWhtAk8mazvJPtpqrtl5Jl/UR6OZqFSk7EVIQbwUNJ+kmu8nOyTR1LATSV/V9iZgo9drtkwjOc+b3rTm3j3u9/NxMQEmUwmdZt2u81TTz3FV37lV7K1tUWzeVDTNgxjJ1/zNV/D933f93Ute/TRRy3tlGEYhmEYhmF4mKlxi5xFBLOL9I5MaCFiax2J0HgGMQvC2ehDiKkwCkxE8BKgFkvExjq9Uz4dFE0kJdajiPA3kSzTtE8xIibebKHhm0VNodGkTdPA3DCcPpV8zsLsqRLMzBCNj0OxIPl/6nVod6BaIV5cJH78CcpfuMjf/z185lG4cg3mjoiBsbIqqYDqSTRGA1cjo4ZLFTaF3Icmri4DuGgIrQHQz9gJU/f0osJOYyQNjdhQQyUTrPPPq4wigvA2IiZr/Yo0wVqvLZ981+kuEt1LdNdZ83600kOI0PxZXBqgW0X72q8Lo21WQkOjV8TBbuM6j5gzVcSIKCA1PU4gfTqUgzOzcObeHGNf+SDR2bNiprXacPUq85+b54mLcLUlZgzI8/Q0LrLrODK2/Ocs7OMCzuibQO7/E8jzud/IGNhbZExamjIfXaZF67VORRG5R3lclESW7qglTdXnFwWPcJE4XwDiJnxZW/p7EDGD1DRbwkV1VHEF6o8i74sLyDM8nLTlSczMMAxDUky9//3v70rx49Nut5mfn+ftb387H/zgB4lje3MYt5coipibm6NUKn1pWRzHLCwsUKns5f8KDcMwDMMwDOP5gZkat8gppBPXcQJ3Globo4LMWA7Ty2iNgg4uB30eEelGkr9vt6mxnHx8oVEjDlrIbOpriJgYCtq3AxWlh5LPMBLFcmQQTp2Ee++Bk6cjpk4NEx2ZTiIzMtBqSfHvep3O1jbtjTKN9Qqbax3OX4CFRajEcH4Vbqy6VEnV5LuBCKWt5PcmktZoI/m7jIjATdzsfL+OwwBO2O11Tf1MjZvpV00rlUn59rfxhecCMn6XkGvUl0GaAaD9ogWbw+38aA2tp6BpkDRCQOuilBBBXwt3DyBj/mbnvWp0gP9P/TBlUlpNil6RHP36voWYGWpAaD2PAWA6C7PTcN99MPWqe4le///A+BFob8G1S3CpRasV00RMzevJ/pu4vsghgv0U8Pd0m2O+oTAOvA6YysDTHXgEea/c7rnDmtpM20HwWyNCqsh7K4Pc2w26x04YSaTp0sKoIv9etYF6x42vBtKHI7h30hLOoKkjJk8Dea/VkBRWA4gBZNKkYTy/ueeee/jQhz7U09C4cOECX/jCF/imb/qmO9wy4/nMq1/9an7913+9a1kcx3zrt37rIbXIMAzDMAzDMO5OzNS4RSYQwWwMETv3Urqvxs6Zzm1EjItIZhHHkiNf63XspyRgBhGs91poWbmEiI+aWqiW7D+JiKwVxLxR4XQvIvDNkkeE2xwy2/qItqMAR6bh5Ek4fS7D+KlRMrMzYmgQwfY2VMp0tqvUN6pU1uqsrcasLMPqKiwuSURGhEv9o/UlfDOjnVx/Hbkv28nveUSw1fQ42g/+/SzghPq0+xYFH7//crjUVvvFNxvU0AgjNVREbifXkcVdcyGlPXosTQukRk4h2Ca8Nt02vH49xzAu5dE4EpVws4J8jKth4y9LMzTCdvrXmmH3wvZ+WjFNyTUADGVgelIMjdMPlsi/8AEYOwfkoL0p43Jpia31mGY7KTKftNkvBN9CzNHJHm1RgX8MOJODKCfpmLRA/UGg91YLbKs5pe3p97zru2oJeUbGkf5axkVpKH4qq/CZIOV3BnkOl3CmsNbAqCPX70ec1JEIjxFcofoR5J4ZhvH85vTp07zvfe/bUT/jc5/7HJ/85CcB+OAHP8j//t//+zCaZzxPefnLX85v//Zvk8t1//PsL/7iL6jVaj32MgzDMAzDMIznJ2Zq3CJaKHoIZ2zsJvJHiLBWS9lWazY8mvw9hoiC+/mnzADwAlzaqr3iF7aOk3MWEENjBDFb1tld+L0VVLycxOXLn0FS/hwpwMlZOHkKjh+HsWODZOeOwPi47Ly9TWdller8BltrUitjbRWWV2B1BdY3Yb0KG00RP7dwEQgt77OFCKRaCFwFfSUsmpwWpaB1R7TWRoQrahzur2SS64WbL2KthpSeJzyGGg5VZBZ7Dlffwm+TH4mz2/WHhCmT9NhFYCYPhQguNlxh6hX2Vxhbjxe22TcpssGy8F757dtvtIZSQMbpNDA9ADNH4NhxGLtnGo7MQG0RMhEsXiV+4klWvrjAjesxnaarDaOifA1nZK0i5mK/5ywCGh1YbIoZeat1bHyTZxBnSg0hhmIRl4LNrwvj7+tHVdSRyJHTyW81btLuQ7+xpLSRvppHrtePytHnN7yeQnIdGgWjNUfAojQM4/lMqVTiv/yX/8JXf/VXdy2/du0aP/ADP8Df/M3fHE7DjOc1p06d4r3vfS/33HNP1/JPfOITvPWtb2V7ey+JIg3DMAzDMAzj+YOZGrfIEiK4nUaEykcRU2I3QXIAEeJ6za5uI8LbFmIk7CVPvoqKJeBFSTvWk3U5do/2CIVRFRqXkOLh13FiYlo6n5vFP5bWpZhCahSMI6bGXAFOzsE95+DsOZg9O0h2dkaqMZdKUKkQb21SubHBlQstbszD0hKsr8PaOmw0YLst/almhZ9uqo6I61o8O0xfNICIpFlc8e5eqY06uIiLKDneCBLVs40Iq2n3vZRs10q2u1nzyI/YSLsvGoVSDtpLj+3DqAa/hkevKAiCdUNIxM3MIDSb0Gi4vm/g7nu/MRqeK+x73zAKowDC7f37l1Z3ZC/jWeuSHIlgahxmZ2FmLkduZhLKZZj/OGRzdC5f4fr/9zh/+b/rPH0NyjW5z0cQkyBCxp6i5lo/rgMf70gExBrynMTIPdXIir0aHWo45ZDnexs3/geBLwNOFeDvGvIe0Joq2s+5ZP+hZP/V5LidZP8q6cZRr799/PRnl5PPIu75KfTYr5icW9NaKWX2VqfGMIznLj/3cz/HG9/4xq5lnU6H7/me7zFDwzgUMpkMv/Ebv8FrXvOaruVLS0t87/d+L08++eQhtcwwDMMwDMMw7l7M1LhFHkcEeM0dr2ZEP3E0h5tJHhIhYtxc8rnEzdXS0HoYWWQmeZ39RW1oW9rI7OgbdBf27VcweL/n0O8I6Zs5xCAaRMTfsQjmJuHMGbjvfjhydojs8Tmi2SNiaDRbUhR8c4uVhRaXr8Dly7CyApsN2OyIUKuFt7UAeB03+ztMX6SiuH5r9IUK7zpbPc3c8etZFHGptI4nyy/g0gWpKDyQXOsAYnrciknk1yDQa/HbqPUgMkm7dFa+fw0+meBvHaOakihtLGiND5B+eyHwqmGYmYYnr8l4qnv7FZB+SEvZ5RsUIWkGiv7dLxogNDf8c2m0i98PxWSdPosFJHroFHB0EEZHYXhYooeYmSZeWIDPfgaqNTZXWnzqb1r85dOw1HHiv96HXvVXel1vjIznh5H7cC5pi0Z5rSPCf8Pbp9/x8kib9PrC/h8B5gZgvOHGgo4xrbExhUsjtpasK+CiPoq4aI0wYsOPCurVRq2ZoinhwEU2temO9NH0aDl2Go8xu0cF9Xo3G4bx7Off/tt/y/d8z/eQyXT/l+0Hf/AH+ehHP3pIrTKe72QyGV7xilfsWP4t3/ItPProoyl7GIZhGIZhGIZhpsYtUkIEu3Vc8WPYmfbGF34HcIJbyFHg64DZCFZjeHofbdFzNoDHkBnTWZyIul9U3PVTGkG3AHurERr60foE44hAq8XBR4CJIhw5IimnZs8OkDlzkmh6GgYGoNOBaoV4aYny4jYrKxKhsbgIC02JzKgg5oWmCqsmHxVf1bwJZ+/rt28C+GmNQqE8TZzVbdqIcTCOzPCfT447hAjjY7g6Fy1urV/BGQqa8qqFCMw6FurJtUwgfdMvMkCvX8kCZ5D0QmukGwZZnAF0DvgnY/Bl90nUzPmW7KvGjz4LGv2B1x7t87S0UT46TnWshmZZv0gBHz/aQ881gOunBtKHp4GXAPcOwpmTMD4GzQa0ai1ym1vE8wvMf3Gd5SUJ2ri8KIaG1nbQaKEaYvCEbUi7vnC5GgdDSBqs8eR4WtTeN9n6XbNGWnRStltGzJPchqu3E9LEmQ3+u6GGmLJbuDRbftvTjIy0ZVqLYzz5vo6YOh26I1zUHCvgat74/abXplEmvZ6xvUTFGYbx7OMrvuIr+Jmf+Zkd9QqazSbXrl2j07Gn37jz5PN5/vAP/5DR0dGu5bVajWvXrh1SqwzDMAzDMAzj7sdMjVvkQURkW6S7yHY4A1x/+yJuiD9jfTmGRxAxsMDeUslEiDEwiZgsKlYvc3OmRq/aEf0Ewb0SCo4azXBv8q0i/DAwNQlHZmF6JiIzPUU0My1T49sd2N4iXlyiem2FG5fbXL8mBcHXW5I2SwuBbyJ9sIn0i4qseXrPzNbr7+DSJGnhZE0v5ZtVvYTjJiJcbyBRM/O4yIhisiyLjKEVXPqfm0EjDRSth5FDUiUNAM946xZxJoqfgsqPyEmrV1DD9W3OW677aARAIbm+I6Oy/AuL8MmW7KuRLNng2Nq/anqEpMlOaj75BlwvcyD8DsV237zT+i755HpHkNRur8rD6WE4egzOnoViEfI5yMQtmJ+n8cwCa2ti4mxtwXbFRSJp6q8y3TVqMsF3+Nu/Br+v/TZHdN8/7ct+kS75pA0VdkaMtJBaO8PI8+Qbe2p2Rbi0bn7kTh0xZfX5Cce0/47U/fS+6ZjKJPtuIs/FNP1TTs0kv/3Io7C/9By9omOs3oZhPDc5ffr0DkPj/PnzfOADH+B//s//eTiNMp7X5PN5fvzHf5x/+k//adfyra0t/vW//tdcuHDhcBpmGIZhGIZhGM8CzNS4RerAE3Tnofdn+6cJZA1EdEsTZ9eA/4uYEwtI1EIeySXfq/6G0kFE6ylEZFSRd6tHO9LI4gTiXkJoP0FwN9JMEn8meRMRe4cQkXIgJ/7F1CSMHBkgmpqEoSHIF6CxDZubtG4sM/9Mk0uX4Oo1WN6EjdhFacRIkeNV5H7l6E7RlJZeKe2awdVBUTPEv9eK/7uDK5Ssou+mt76BiMURIh6HM9p3wz93mrHSSJaNIjUcNGpF27blXZ8vWOsYaLLzXg/gogw63vZ+m5Qccr2fX4TLm/CpTTHZ1LwIax740QV63F6mg9/OMDWa3tfdohTC9vr9WUAiaEaQfhxBIlS+ahBefg5mj8DomJR1yWQgn4dcMQfNJu1Wh2wWogg2N6FSkeNpv1WQflj2rilipwjfzyxTA0vfJVuISbWES/Xk39MQ/x43knbptReT73rS1me8Y+aTdWpq6H9EtnFRENp2f6z5+M9NG3f/dFwMJB89RjVpZxN5N2rdkJDB5JrXg/Pob+0nTR9nBoZhPD/4B//gH/D+97+/a9nnP/95vvZrv5b5+fnDaZTxvOalL30pX/EVX8GP//iPdy1vNpv8zM/8DB/4wAcOqWWGYRiGYRiG8ezATI1bZBsRyzUVjArmYcoepY0r0BwKtHq8q4io30HE6Awiyu9masSIMLlB94zpvQp3GcQQaeCMkDSBtVeCBo1c2Mv5QgG3gxN6R5GZ4XkglxWxuFSC7EARBgehUJC0U5UqbGywuVTj6jW4fAVurMByS4T0BnI/1nBCL7g6ANqOsCaERsyAi1LIB231RfcQv9/VWNL+CYXUStK2YbpF5b3iH08FYo0IyOKKTjeRcbpBek0BNT5yiCBcp/cYLgbr/VRC4Yz4NjJ2/3cd8nVn2gzgTI0Qf3yFUSNp16yCuEZ9hIWp/fb4+6eZGXqP88BJpL5LE3keJoFXFOA1D8KZszA6AlEG4g4MjuYYPjNNNDsOm5t0WjH1Gmysw+ISrDeknXVcCrRlumuzpJkakB6toW3WCIt1JBroKi692ABiAGi0TtgnamaEURQRrhaMjvcNdo4Ffd7VuPPTtOm50kwvvGVhJJAf5aNjt+7t38ZFSYWmRhN57ofY+ZyF/VpEnrn1lOMYhvHc48d+7McYGBjoWvYrv/IrZmgYh8KDDz7Ir/3ar/HKV75yx7r3v//9vPOd7zyEVhmGYRiGYRjGswszNW6ReboF0xFEHFaBTUXcQUSoW6M7778vAistRGgcZv9Fa2uIiKyC535QMbNJuiCoZJBr6QTn8GdW96p70Eu81RnUVcRYOUIyiz+SDxHEUfIjyiSmRoXW6iYrSx2Wl2B5BVaaIlSqqdErrU4oausyxa/tEG6r6XxiukX38Fi6nxYYzyHjo4mYV3rdVaRmwywy017Nqzzp6Z98ehlMueSYLcTMKCP1DXQmfTjTfRB4DSJef57e9zCL9GuZbsE6HMe+qKz1FlTsLiB94psiePv70SJx8Nvv1453DK3z0QEuem3w2+d/+4TnzyN1QF6C9OMiLk3Y0TyMjEAhMdpyOYhjGD89Ru51r4V8nviv/ortrQ7LK3DtOlzflDFZQe51GTGy1NDwozR842UvxkYb6d8FpAC9RgENIanxSojRocXnI28/v46M1qAIo35KyJjpeMvT7on+nRZFs180SmSNnQacFpJPe7e1k320NkuaIat9PYSkqmohY94wDMMw7gTDw8P86q/+aqqhAfDf//t/t/ouhmEYhmEYhrEHzNS4RdbpTp1UB84idSEuIqmpqohorGlsNCVLFHwrbbrzw+9nBr9Ga9wMbcSk0ZndaUJqHjiB1BVYR4oIbyLXMIYMKO2HkLQ0Ov4yNUkquFoNAwXI5SWNTxR3oNOWT7sD9TqtWotKBba3odyQfbeRPldjw5+J7gvHIbpNC+lDv2aG3x+dYJ3u22JnCifoTrHURMaBngNcap1ZxNhRU6OAm62+H1SonkzOVULu6zbd4q6mGmsl2xxF7u8xxBBZQQyR8Nh6fxS9bv30MrRC8ds3KPztlA7pJoQeQyNTwBWS1nRaoaHh7xdG2Wj6KhXDTwGvjaRmxkZZIjGawPEMzE5K5NDQMEweLRCVikSNOtkjSVq05WXK1zeYvx6zuABL67DSFuG8gtzzZeTvtJRTvhCflhYtvJYmrmC7vleywHHgH+YgV4C/qsg1atRGE/d86Tj366nouXqZGb2MobTIMH+5v5//LIX49zVE2+TX9VDUrAvP6a/PI+9WjeZIeycZhmEYxu2iWCzy2te+NnXdf/tv/41HHnnkDrfIMAzDMAzDMJ6dmKlxAKiQDyKkryGpa44hKWEqyGzvLSSKo01/0V9ns2sO/gpOgNtveqIQTSuz3WO9n+IqFA9jxLh4BfB1D8CNdVhYgEeTbRrIdaWlyeonHuqse50lr2lzSlkYGoSRYRgeiciUCrJ1vQH1OjTqdJodWk1otdzM/XbSlvnkeHW6xe9QGNY2aP/qcXwzIhR1O963HqeFmyEeHl+F1mpyfXmcqaHitL+9/tbImf3O2YuRe9zEmWKh0Kt9kUvacz1p//1I/y8Cn6Lb2IjYeX/9FFua9sqPjPCvS/us5f2Nt32YJqrfdYdGyTYubZof4RRed/gMqcgPYuZ8ZQZefARWNmGpI/udzMK5WTh3Do6fgNkTeXInjxKNDEOtDgODxFeu0PjCk1x8ZJsLF+DKdViquSL1arht4+pGaHol6I4uCA0O/5p9NJqnkfz2a6NkO5BP2j8HvDI550LShjJivG6wM2qmlzFAyja9TIywnzVlVCbpZ00N1wi2q+DGvH8fs8k1TiEm8SZiEOlzpNFQpOyr15FHIuBIzl3BMIznOlEUEUXdb7I4jonjW/0/KsPYH9lslj/6oz/aMR7b7Taf/vSnefvb306lYv9lMgzDMAzDMIy9YKbGLVJlp/D6NDJTWmcF420zhit4myYI+jPpVfBsIDOLC4gQeSs54EeRGe1Ps1N07IWfskYLCleqMJqFezNwueMEfRVIex0j/K0C9AiuAPE4SU2NjJTPKA1AvpQlGhiAfA7aLfnkC7Q6EZUqtJJcOirSZ3ERG77ovhf8ItN+LYIwciATfGuu/14pk7QNOmPfp5O01xd4a7gUZPs1NapI4fFwrOhM9tCk2QYeS843g9yLOeAfAJ9FonI0ZZR/j3W86t8qJocpn0L8FGxF3NjWQtADyTX4tRrC6AsV7zXK4hmcgRW+2HpFj+Rw0TBZ4Csi+KqTMDEOF9bgPEnaqTG49x44dw/MnS6SOzZDNDEhAzSKiJeX6Fx8houfWuezn4VrN+BGVdJMacH6MmIcNZI+PpusexK515qOqkC3odHLEAyfqQJurJWBpzowV5N3x0gEX3EOHrgP5ufh+g04vwxrbWdw+mM1zVDq977wx3fadvmkfWrQTiIRXwtIH/smr5o0YeqoGJdWbSr5veLtp6aQjkG/lo1Gf+j4WGVnLRHDMJ6bvOUtb+Hrv/7ru5b9yZ/8Cb/yK79ySC0yno/8s3/2z/jgBz+YarJ98zd/Mx/60IfMaDMMwzAMwzCMfWCmxi2yyU7xr4IIyuN0i8rZZFkNETN1drgvJmZxRbJvIOJbhBP9m7iC1/tFc+Tnd9vQ294X7vX8nwcal0WY3EjaqylsNlOOAztF6VAE9XPcD+IJu5Ev3saJOtlOdhpkq5Ll8jOwsSEZqbLJcY5FMB+LYNorxY1eo98+FdDVCPGLe/tpk8IoDd/48M8RGhwNdtYsATENQlNDjY6bzaycZn6FpoO2rYL0VR5Jv1RB6kq8PAvFNnwx2VYjDTrI/R7EGV16rLSUT72iJDRlUCE5bg0xNNRQ6pfyCLrTDakxsFt6MZLzDSPieBMxFgaBB4fg1EkZd9ODMFuH2RwcPwonT8LRM0Xyx47A5CQUi9BqEm9u0bqxzOKVBheehmvzcC0xNDZwtWL0ewip//FSJNLgBjtTzPVKQdUL3wTRiKBryP0sAdUYvrgIxSGJgJqYgIkKlLa6zTvfuEuLsumXYqqfFFNC+ruOPONbSD8cQ96VaZFr/rn03FoLpIa8G31z0I+00neXf136t0aUGYbx/CBNRDaMO0mxWOR3fud3yGR2/ld9fn6e1dVVMzQMwzAMwzAMY5+YqXGLhIJzDpl5rhEVmqdf0yJteOvSBOAIEXWbiChaQ8TWUdxMbC2AvV80IkJNg37CnkYqaJv8GfnXEUGxmLSliis0nJZ6CnYKo36/tZL9csh1hmljYiDuxOJatFtSObyQh6FBmrkSC4tlVsvSpiYwG8FADqaacLnHdaaJ7qHZAq4guu7jR27419RLLglnr7fpFn/VNGnjinj729+OUpGhWKzn1KLoOnN/HLg3D2NtiTDqIP07mHwfSba5wE5TI83YUMLlm8i9ayPit45N3/wJhXP91hRhvumUdk6fKGn3KyKYjOGRZL9hYCoPA4Nw6Rl4ak365EgJZudgagZyMxMwNSnhQ40G8fq6GBpXG1y8BPMLsFkXk2odEe/LSKqjRVy0gaZF07R0ei3+Czl8L+xVkouQ5/KZ5Lya9ml+C77weTgSwWAerjZkmwru+dV+0/Np9EjYl2EU0m5SjJ8aiuS4eWQsDSNjbrdIEDVrVpCxseXt06tGh//uyiDjK6wTYxiGYRi3kze84Q3kcjv/ybW5uckP/dAP8dd//deH0CrDMAzDMAzDeHZjpsYt4guNJUQsnURmIWsufRXaGki6lTLd4lso8C8mv+uIEKepp1T8v9m5XCWkGHUhaeONPeyTlk+/g1yDCpFN0nPT+1EO4ax7X3zWOhrjiHh+DTF0ZmOIEzVTTI0WNJP4lnIFKmXyrRpjY3BpSyINSkh6mmpTDJJzSH+u0R19EV6biqbtlGX+36G4vBex2b9uTZGjtTJCEV4NsDDVld9/t0qakaapswaTNpQRA67akHNPA3MZeKIjYnIRuVcZdhpZ4fHTzq/nVaG5nnymkPGpBpVu5++nv/sJ6qG5EkYJFZEUcWXElGmRpPrqQKMO22VZNpeH+87C2XMRY0cHiUZHJOVUpwNbW7Tnl1i8VOb8ebh0CS4vwHJbnv3tpA9XcemOtB3rSD0L3xDaC/2iYJQIV2tjEzfGqkk7CjFkGxIlsorc76Hk+v0xqPV80u5v+Hu3sVnFmbwk7ZsAjpXgUk2e0b0YtWr2biTH9A0LfX5D0xDcGE9L/WYYhmEYt4uv/uqv5r3vfS+FQqFr+R/8wR/wv/7X/+IDH/jAIbXMMAzDMAzDMJ7dmKlxi4QCo6ZP8lNFqVin6YRa9J6Br2l0lHEkumIFJ+Td7Oz9CBE5NSpgr/jCaSigprVFU2hNJL/XkLb7gqNfsFsLrW8j/TaPiK3HOxKcIY2IoRND3JH0U+trNC5do7LUZHgYchFkYxGrt2IxRnJITYh54G8RITmMIvCjNELjYBg5ngq7aeJ6ht0FXUXPpam8QkFY2+FHyaSls7oVcyMtrZAK2WpUZJFxVgPWkwZMIVENpWS9P1NeI0x8gRnv7wLddU38Pi8gtRXaSBoivWY1ynR/PxojbDsp68KoG40AqSbHXgA+H8s1rCfrRoHFbbhwUXZ8yRScOg6veEXE3P2jZI/OwOio5Kaq1mBjg/JimcvPwIULcHkertXFLNA6Ghu4yAJFr2We7poQbZzp5o8JJe2+p5lHaUaORhlpUfQ2YujoMbXGRS3ZtoKYB/55e/X5XsZjDTGt/AibPDBcgOI+XYZG8tHnKIfru3BMa39q6il9/xqGYRjG7eb06dP88i//MqdPn+5a/ld/9Vf88A//MFevXj2klhmGYRiGYRjGsx8zNW4RX3zUfPHjuKLZWiNBUz/V6a6b0I8ikuKnw8788TdDGSkG3aZ3mqgQX4BOE8Mjbz3IgJpG6jKUEHG0gYi8YRQEuNQ4en3jiNA7qdsnXgZRBNksZHPyHWVo19tUyjGdNgxm4ExygiWkBkQWiUw5i8yK9+uYpJk0vpGQAU4iQveF5Ji+2aD73ozBpCKsfy4fPxVWJvgOzRcVdlWo3ivhfdXjqvmkqdIWcNf9WCx/a+0UNR0ydKcv8iki5lY72cc39PLIfb7fu44tZGxq7RGSfTSSJKxxkmZm6G/tJ41A0WLkcXKez+Fm908jY6XWhhtLcP85eNW9cPo0jD50jOzZUzA0BPU6bG0Sr6zQuLHC4o0OC4swvwQ3amJorOPSTqWJ6Crow857punqlF5RKGmkRVWFxob+1n7B+9Y6FfrMVnEpyfyIIf8Y/UyNDDtNOV0+kPxutPb+PswjfaPmiKb00/7yU2XpudXYyOPS5RmGYRjG7SaKIv74j/+YBx98sGv52toa3/Zt38aNG3uJlzYMwzAMwzAMoxdmatwi4ezlFlJzIkbENxX1BhGDYp2918TQCIZVDqawbYf0NFG9toWd9S18VCRVwb2DiJUzyPVeQERenR3u4/ebmixV3Oz9GWA4cufJ5DKQz0M+MTXyOciIJdLuSABHKTmGRno0kQiNI7hUWRqRoIXIm8lH74eK5pnkGJuIuO+Ls2kRG/0IjRMVXMN6G2mprfx1ur1fRFvbutd6C2G7/cgVLaKswu8SUkBbC3nruB1D7quOpQLuRRIeu52sm0TuzzwuemkUuTcPDMD0BMzOw6c7sk0RF5Wk7crioqC0KPhukRogETejiFmm47CD3NsYV8tlFBm/pQLcdx+85DVDRJMTcPYMHD8hhsbmJvHyCs2rC1y52ObiJbh+HZbKMk407VQZGT9+3QeSa8gmfXEOeR9c9taH0UKahqyXOZFW78LHNy/CiCB//K0kbfVrmfh1f0JjoxMsUyLEmMwjfVDu0a468p7cqshY2u1ZygOnkXF0FUmdp22sBufXa/MNwWHEPLuKi0AxDMMwjNtBLpfjN37jN3jZy17WtbzRaPA1X/M1ZmgYhmEYhmEYxgFgpsZtoMJOkS5GxDff6AhRsVNnqteQlDx7jao4aELDBtIFfT/tkD/7O0auIbzeNGFW9x9DUh0NA60kSiOTgUw2glwWcnlZkMsR5bJEmSb1BlRiEZQXkKgMvQdPI8WQVaDV9o0hs/O1zslq0M42rrBzWgqoXoRRLSFt3EzzHHLP/e3T6iP4/RQK0yrqlpA+8yNitBBzWl2EED2+/0JoImm8/Hs7gtRf8NuXC9rvF1ZvIX07gDMWtE/rSVuLOZgYg7ENKJXlOjSyooncwzWcOK3pkUIDIDQ2IkQEn0H6+XrKdWv7Y2T8zGVgdhpOnc0SHZ0jGh6GgQGolGFjE5aXaV5f4tqlFucvwDPPwJUlEeY3knZVkutcSTmf1lK57LXXv68FXOo6jTzQZWHUhH/NvsEFvd8x/njSVGJZnJnlR7ik/Q4jNfzjjiP3eBwZJ0+Sbmp0EOOqlXyW+7RXaSLjZRwXBZd2bH0mwJl9GqVxosc+hmEYhnFQ5HI5fviHf5h/8S/+RdfySqXCv/k3/4ZPfepTh9QywzAMwzAMw3huYabGbSBN9NZZy/1StYwjQvsKIvT5hXXDVC53Al8s9Zf1ooMI7GvIdUwhxkCvKBNf0M0g4u14st8ASVHtJOtUJpeR1FP5HESZZGFEp4OYGoj4/RTdM79VPFfaiNGymbSx5G23FyF4r/QSnv11WouiV10OX1QOU0WFaDSFpvMCl3Ypg6ulotumtce/D4qfrss3N3Letllvfz2vomNiATGSYpxAr8fXfu90xDDR9EwlXAqnBq5GhRax7lVPQhlEUo+VELMnpJCsO4kYHy2gHomHURoQ44xMBqpVqRy+uUFnY4PN5QZXryaGxiLciMUw2UyudQt5BkK0D6vIM34lWJ9BxmQu6S+NXMrhDKt+9SD0/qmZpFEeYZSR9psaGrpvr2iX8JMWpaGm7ShyDyf6tBPkmVyg/3MXsoRE9vSLHvPxI5v0+dhPRJNhGIZh7Jdv+qZv4l3veteO5Z/4xCd43/vedwgtMgzDMAzDMIznJmZq3CHS0rTo8gwiBB5JvpeDbbWA8xbdqVZuNyoGptXS8L/93y1cmh8/7VQoxqcJ9SqSjyDi7lAkmnI2C1E2EpE5l3NOBxHtJAQjh6sJkFa7w0eLJK8l11dhb+nA9kIv46PXcj1vWkSGCtN+FEWv47RIvw5NraTpltQ42kb6yzeu1EzQexZGhYBLQTXEznogfq0Lf1kTEaS1poampIpICnW3YHML1hpuJn0OEb7LyfZasN1Pn7aXlEXDyTWvB9tnEOH9HPBAcvxtoBhLHfpOuwNxDNkM1OpQ3iZeWmb76gbz12MWFmFxFZZacm3XkmvJ4OqShPiRS72MKe3PJt3PlKaGCvcL/1ajzE/NFaY1C2u4+HS87zBCIzQ30thE+lzTh/VjvwW7m0j6KE2Ftle0/55m57vVMAzDMA6Sd77znURRt4XearX44Ac/eEgtMgzDMAzDMIznJmZq3GEKyAzyEiJSrifL9Z8/mpPfJ4dELtxJQ0Pp0F1k2S/GG6ZDIvm7jkvd5KfFCYXetMgDjaRo6n4ZyGQhk0mMjNIANBvQbBHFbUZH4ORxWK/C+S3pqxYiqqaJwEoDMUA0HdBe2U+0hqLmUJqI64vEmWBZaM70Sv0DrgC9v1xNkSO4eiOjwBlEhL/uHVPT9GST8/rn8kXwNjI+1ZjQbfQ7LapHjQz/+vU+XQE+XoONebjYhsdxRcMzSZvXcVEL+2EbSXM0y86C3RFwHPiKEtw3DlvbsFGBmUk4egzyY4NQKskALJdhaYny1TUuX3B1NFZrLjpD04qVd2nrbkK+pkHzx6TW1cmR/sL2+79XYW4fjeDoRWho+McOz+HTQvoDXNTQQdJGDKQM+zNEYqT/PokVCjcMwzBuDy95yUt4xzvewdzcXNfyP/uzP+N3f/d3+f3f//1DaplhGIZhGIZhPDcxU+MOEiHi8hwipi8myzu4NDsa4eCj5kdaGic/1/5BoyLmKFKb4Bqu4HZYOyEt3ZL/d69IiDAaoI3Mxi8DU5EEZWQyEGUiKBZhbg4WFqDVpJCD0+eylAbarG/C0Jb0bx6J9ligt/gZIwKsFmO/GTKI2aQplvzlmtaq4v0O26JGgl+QWcXmUDj2+7iXqByOAU1fdiI5TwUZd4PJt5/STKMI1EzSF4PeZ79dtR7nD+uJxLjIjhBdtg58PoZOEvGgRbM1ukDHxM2M7xZwCRflEKZhGgSm85DPQjaCuXF4yYvhRa8coHBilqhYhFYbymVqCxtce6bNxaSOxvV1iUjS1FgZpF9uxnzx26uGpkbB+FE2fs2MNHoZDmkRNf1MDY0S0vGq6dH6PcfafpK2XqJ32rlbIS1dlRpx/WjhDGTDMAzDOGhOnDjBd37nd3YtO3/+PG9+85u5ciVMOGkYhmEYhmEYxq1ipsYt0qvGQRpZJI1UG0mF4gugOps/TWDXGdC6rR4HnGh8UKbGMGJgaF2ALJKu6Cgu/Y9fsyEt3ZCynzaFOfs7SJFwYskCRCYDQ4MwPgUrK1AowvAg+VKbsdo2nWyHTVyx9RK7zxT3a5bcDDkkCiCHFCdvJr/vAY4hIvc1RDxfojt1EriIHU1XpBE5beS+7qdAeS9ixMAYRupG1JAUPvPsrNfiC8Yt3Gx7P6JmAJdSSulV26LfrH689VqHxS9yflDUEDF7BulnbXceeaae2IarZZjIwIuPw4MvyjF+/xGiqQmIY+JymXh1nc2VJgvzcPUaXF+D5Y4cT9uskTi38hxqf2gqsGHgFK4GTBNX08TfJzxGuCysqwG7myP+s7GbmRbSJr2myO1Aa6+0kf65WYPSMIznLnEcE8dxV0qgKIqIoog4vh1TQoznG0NDQ/zmb/5m17I4jtne3jZDwzAMwzAMwzBuEwedIeR5xxB7d4ZiRAS9jEQi+CLhBBJd0GsWtf/P7kEk2kMF8IMqIJ7FzQzXNFM6A30VV4PBJwo++8UXv/3fLaAVQ7MJ9Tq0qk1YWYULT0GtBrmsrGw2KQ5kKBZk3ybStw32XlD4Zmmys3hzHjE0ziFpn6aQezuUrI8QsXoISX804bVTIzf84tu3Shn4OPD/Az4DPIaYGgOIUTWJS4nmj2M/1ZBGzlSQSAKt95AmBUXet1+8vIj0hda4yHvbNpE0VKu3eK29yCIpt74S+FrgHyJmQR24EsN6BwoFmJmBmVMDRJOTUrul2YKNdTavbnLtKiwswtqmbL+OSz3VwNUCuVX8Ps3hxoE/RtRg8p8ZNSHSamF0vPXt4G+/Xoa/vf8s76eY951GzYzQ7DEMw1B+8Rd/kQ9/+MNdy77xG7+RN7/5zYfUIuO5xsTEBGNjY13LNjY2eMMb3nBILTIMwzAMwzCM5z4WqXGLVNiZ1iZtZjSIALfe4zibyadXap8sTvAcTs6xwcHW2WgjdRbApb5pAc8AF3BGhxY0VqF1t9n4aegx/ILIWktAhcpaB7bKsLYGW8t1BhYWiMZGYWIShkdgaBgaDXJDI5w+tsjc4zDQkn5pIREmYdqhgyRGZqTrLHplG2mDpuoCVw9hOGlXLbnORW/fJm58HESbo+Q8N+gu/t1O2jGcnH8AMWIaiOGhYyotfZAaThl2RilFwW81afQlo/c64+2nqYPW9395eyLjnf8cErFRzcB0R0yUMWAmA9NDMDIC0eAA4pDFUKsRb5ZZXYm5dh1u3IC1qtxXNXm2kD4eQgyiG9z6vfNrkWwh40tTSE0hptBacn4/FZyeNxT3/RRv/rJ+UVXPlrnLOh4Lh90QwzDuasKIDI3UMIyD4Pd///fJ5/Ndy97ylrewtLR0SC0yDMMwDMMwjOc+Nrn1FgkNjQFk1ns+WJ6GH91Qo7ehkUciMyaQGe9HcbU0bhfZ5HwFnNEA3fn2NfWLDqIwWmM/coEWEt9AxHUturxRgc0N2N6CdrUh0RmdNgyUiI4dI5qeJjM8yNwcnB2W6IgcIjz36s+DpEW3+F9HDKAvIqmnlhABego4iYjoagZtIqK1P4YOclZ8WBvBb/MKEh2xhtzfKt2pxdLwDYJ+acf8mhr+mAgLkN8JhpPvayTpwSK4ZwJefwxeOw5fNgavOAP33SemRtxsSWhQswlbW5RXa6wsw8oyrG6K+aJGRgUxhSrIc6J1Sm4VjZooIybJOq6/s8g4msYZVH5ERxhx4Udi9IqKSvtb0foqNxs5lKX7XXjQ5BCj6jhiLPkRG2q8GYZhfOxjH6PZ7E5Q9+pXv5rh4eEeexjG3njJS17CkSNHupY9/fTTXLx48ZBaZBiGYRiGYRjPD8zUOEDGgXuBBxDxWgW1HN3Cv6JpePqRQQS7h5DIiVxyHhWsbxe5pH0qeJL8PYqItyqAlpK/s3SnHtpNTNRoDEUjUcrJcjU26i2oVqFSgdZ2Dba2ZAHA4CDEHdrLa2xsSL/cg6QWGkiOcadnnHcQo2Ax+V5AzIMYGRNVYDlZt36H26Z0cBEAamhcR+psNHbZNxTF/Q/sno4sXJZmjhxk+i0dU1eBzyL3ZfYI/KN/CN/6Bvimfwyve33Eq/5BjqNHob22BWvrUKnQ2thifbnF0rIs2mxLv5WRPlvHRVhdprtOyUHQwqVS02tZxRWi13eKRnH4xka/Dz2285drnZAJJIpn8ibaHyFm3gy3x9iIkPbdg5i+WktHozZutg6NYRjPPd7znvdQq3VPdXjTm960Q4w2jP1w8uRJfvEXf5H77rvvS8s2NjZ429vext/93d8dYssMwzAMwzAM47mPpZ+6RbTIcgYpGj2NKx7spx7KIOLkcPJ3DREt00TQCBECNVXPi5AIhIu4Qtj+bORi0oabEfF7RVZoQWht3yBiFowg0RSXkBnq4SzutBQ3aW0qJftWk3OoQaO/W4jgXolhYwuWV+DIUp3i0Q2i8XHI5yVFUL1BbWmLShmGiiLAtpL2X6G7GHvIXNKGG9ycGJ0m0OtyHRd6LZeSa16h2yg6TLQN+ymYHnvf/SKQwm36bR9yK9Eqao7p/n4KsPPAiwtw4gSceeURckMF4o1NsoUsUT5He3lNitOXipDJ0tiss7oidenXt2E7lvFUR+7jEq44db9xdpC0cVE1ahzps7OXNHBR8Ntf70drgBgGL0TG7WXEiNuPkVpA3of5pL3r+9h3L2iUxhQu5ZymsIOd9yOPPJdpadUMwzAMY7/Mzc3x2te+tmvZ9vY2f/7nf35ILTIMwzAMwzCM5w9matwiL0QEa42cWMAJ/koWibY4ikQ6rCEzx1UUDTmaHFeLEE/h0t3kEeGujjM9hnAz0nX5XkiLrNDfdVx9hePAa4AzEdRiiTTYTK5VU1A12CkmphkaEc4gOZcc47M4UX0TmfGeSbbbBjY2YHMTKmWI6w2iZhPanSTf1wCFoTxDw3VGRmBoEQZjMV+mkvZvplz7CPCCZP0i+zc10vqu7S3L0H1MrYtwtxgaN0Ovugv9zI1eY6HXPmkRSBlknLVT1oXbjSDPQVqaq2HgNXPwojccpfCVX0bUqMPlK9BqQRyT2dqGVgcGBomBzaU6q6sSJbTdkudP08RV6K4Hc6coIte3TXdB737PXEh4H0IzA+SddQSJEKshJk4pOe9eGQ3a7aeD0mdjAGdg7pcW8q5oIu/SbbrN5JCBpB3L3jb6/qrdZBsMwzCM5yeZTIbf+I3f2LH8TW960yG0xjAMwzAMwzCef5ipcYvMIIL1Gi7NUJjCR4XQWWT2s0ZUpAnpmvJlFCeA1xDxfw0R4BaQNDRtRJTLsr+Z7VGfb9/UaCW/TwIvHobZUVhfg1xVIiJywGm6Rd60c/kiYwFJw3QaiUDJAJ/z1reT62sjAnUTqLWhVpWsU/H2NpTLUCxJtEapRLaYo1SsU8hDIYJCLOcZQaIxGrj6Gho100RMGy3YnYYWtE4TScN0Ypr+R+tGVOmO2GjgaiPcLHr8XvRr70HgpyXq0J3uqJ+YHu1hm17kkGchQvo0zdTQvm8iM/Z7neNMDv7R14wz+rpXEs3MwNNPQTYDpSE5Q6NBplqFlWXK5xd4+GGYX4CFFTmumhrryPN3u4iS60m71jJSHyQ0L/sV/U47frh9B3lmhnDp38CJ/ScQk+MC8CS7v28ySVtXkOdwBnnuY+Q+6jvjVmpeaDquNZzB289IHcUVVydpz8uQvn4Uee8YhvHcpNVq8U3f9E38xV/8RdfyP/uzP+OBBx44pFYZz2Y+8IEP8IIXvKBrWaPR4MqVK4fUIsMwDMMwDMN4fmGmxi1SxuW9V8FZhWylhatf0ERmCi+SXr+gg8w6zuDSzCwm+9dxqacmk3UVJN1Vq8fxtD29BNAwSiNcHiORJ5/ahtltZ6S8AiesfgI3O94/Vjj7W9tSTI6xjgi04TZxsm4dEUMrHVjfgOUlOLrcZGJqA4ZHIJskG8pmKRZhYBCGSjBYkVnZ2l8a/QEisA4iffxkj/4CV1NEZ5GHxkwOZ2BkkfMNJ/2wQbepkTYTfr9oke5ekR4ZXGqzLW7frPM2cv+0ror2TVqqqf0YLL0MDzWg+hUx9/u21/lKwL1ZmDuWIVpfp/PUU0SLi0TDiVtXLBIVCrC2Rmdpmae+2OCLT8H5TYkAaOBMDU0v16/dN4sK8L2K3LeQ9oTPGT2W+ccNDQR/uxxSm+I+pL7KhaQNl5L2zCLjfo29magdXLTaBvLcDeGibfTZ0gimW2EvJk4RucaGt2wKiYrrFTFnGMZzi7CmBkCpVDqElhjPdkZHR5mcnCSK3H9Zy+Uyb3nLW3jqqacOsWWGYRiGYRiG8fzBTI1bZB4RsLWuRjhj2BdbO8jM5euk53XXfRaRGchhNEeE3LAxJD3VMSR100qf9pVw4rOaDmlpg9JSUOm284hxUcDVBXkIOJMct4wIoNo+6C2+6/ZXkTz95+kWNfPJMXQW9hZQjmFzS1JQVcsdxut1olYL4g7kC2SGBxkb32RiHEaGxNTQmh0NXC59jaBoslMg9w2DLC7Nlwr0fg2TEZxZUkv2nU2WX0uuzxf6sxyMsaHHSkuxo/U7BpH7dBAirUZitL2/c0idhFnk3mj6n7SURv7vXgZHnBxzkKQwfLBe71U72a6Ie9580vpVC2p3SCINOrBycYtS52GoVhmaGYTpKcjnkuIULTrrG6xf3WJpATYqco+17ksdV2DdL5Z+kAwjadku4tKm+eMvrQaO38dphkYOif5q013vR7frJNtMImM4jzMgVpF7MxXBkzE8xs2NrUVcjR6/nbdqaPj0OlYH6cs23aZGBjFpLnLw9T4Mw7j7WFtb46mnnuoq6jwwMMCLX/xiHnnkkUNsmfFsYmBggHe961284Q1v+NKyVqvFz/3cz/Fbv/Vbh9gywzAMwzAMw3h+kdl9E6MfKuqGpM2KvoaYEMs9juXvk1ZE3DcejkbwQCRiZS+KiFDpR474ArMu65W6xl++jJgxi4gI+LfAU4hIOIIzIrJ0p2EKaSFC6ZOIQOrn6M8gkRlnkDoeneQ4TaDWkvRTlTK0tmtQrUA2B2Myyz6TjSgUYWgQBrJuZramsdKc/lrUOawfcBwxiSaRqAvtMzWpsnRfWys5vhona4i5VMcJplGP3zeD9sUwYlqkofVDfEPhZtP75IBxxEBTSkgtFE1FlKd7PIUfdvmt35nkPIMp7VBjTw2KSXZefy9zYRB4AIk+eAAYy8DVC00uPVal2YokSqM0AFEG6nXizU3qi5tcv9JhcQk6LRkLGlVQR8Zur4io/ZJ2f2aAexHTKExtlqW7LkV4LH+c+UwAL0VSsYEb135B9lyyn17bINLv68m2w0UxRPZTU8OngUtp1yb9/XY7adEdwaRp/R5DzNWDNFcMw7g7efzxx/m93/u9rmUzMzP84A/+4CG1yHg2Mjc3x1vf+tauZdVqlf/3//1/D6lFhmEYhmEYhvH8xCI1DoC0dDv+DH+dqf8E3TndQ/aSp76GGClL8c4aCxlEuBxCUr5oEd5+xZV7pa7J49JE1XHRB7rPOvApJIXLQHJeTTOjM+57XWevNkWIcD2GXFsDEWQjoNyAtXVYXISZuRpjpzswOwvtFp16k421mHZbMlJ1YtknjysEnAvOqdfZTtp/NtluGUm5U8ZFRuj22kdNnOivZtUiLsJDI3bCGfThufeLRjVk+6yv4sZcWMB9v/gpzTJIdNAcMv4uICZOv2LZaWZGr/Vh9MkgLp1WBjemBhHhf5X+9VAi5P6cS9o4AQwVIJOVwIzBsRwMD0OxIIXCKxVYW2N1ocHCghSnbyVt0posa7iUSjdT8D2MhAI3VrR+jV5vydsmy06zIhxT/rIc3fdc60k8k7R7BEm7tIKL8hrCPddZ5BnUdHlFIIrkmej3Lrnb8d+v2u/rPLuvyTAMw7iz/Lt/9+92LHv3u99Nq2X/NTEMwzAMwzCMO4lFatwio3TP6ldUhNSC1QOI4LwXITSf7Jc2w7+GREx8FkndVPb2m0IK3345Eu3QQUS7DjtneGsqIY0A6OBEzCZOZA3TLymaHup8su7Lk3MfTa417Ie94KeHaiHRE0eR/lvtwDOL8PjjcPF8hwY5OH4MZueIxsfI5yVKI5OBVscZJFr/YRrXpyr6az/rTPwGrkZG5G3r3wf/M4zcfzWtVATX6/ZJi9zYL1pwvN8MdzUIOshYudl/Ymu9g2ry9yDSXyvAFcT42aA7lZEfAeR/Yu87jNQgOcYG3WM5i5gSZ5GC8rNJm2pIpMg/BF6O3FufCBHoB5DnYRSXgq3RFP9ieBjy4yMw4KI0WFunsrjN4oIYZ2sbLjJhK2nbdtKu8Jy7EY4ljbjIeX/rGLoOfCG5zqNJ+3U7f+yljcmM962G3jhiRBWSY8aIwfMy4JWIQVTAGUhaf6LgXecN4NG6mDoHxa1EEfnH6GXw7Ya+zw46fZhhGHc3v/Zrv8ajjz7atewbv/Eb+cf/+B8fUouMZxtf93Vft2PZRz7yEeLY/otiGIZhGIZhGHcSi9S4RTTtkuL/1qLYmsolLCCehhohKk77y/X4FSRlyhauBkEeEXsncebAmrc+FBFVRNVjFnB1A6A797xeZ4adaaViZFb3l5VgqQGf73T3gRoJKqj2I8aJyIO4WfKN5HqvtODaVVitwOzpS8y9oUE0Nk700ANMzy8zcHGJy1dkRv5GwwnQeg/SChxrRMjTybU2k793E11VsPfrc4S1SHrNrA9/7xU9516cSH/8ZHDFp/djcvhtqwGacbyOq8+SFi0A6dfXCb5Bxi3sTAlWRsT0jre+jkSJHEGiRlaR++bX4dDrnkXSiWmkTh6oNySFWbEI0dAAFPLQbkO5TGd1jbXFJguLsLwMG00Zi+Xk3Au4qAYV0/v1pd8vaQaELvf7QZ+Tq8gzNYJL0xSm9QrP4Y8pNTbGEbPxoQxsd5yxVEmuaRIxfmq4yKxS0p9av+Q88DngkU73+6iY9EGF/RFGhPUaP3tBzZhN9haN5J9L64XYvFrDeH5x9epVtra2iOP4S0WeZ2dnmZ6ePuSWGc8GfvZnf5YTJ0586e84jnn3u9/Nww8/fIitMgzDMAzDMIznJ2Zq3CJbdEdp+OmG1KDIsVOQ7IfWZQgFTD2uzsL3xbw2Irxq2qcVunP/h+mPoHsG+HhyjOXk2zc9FH92s67LAyMRDOdhtQVrHZeHfwRJ/9NBBOpN0usR+Neq+fbrwFJyfdcQg6YItGIYWYHHPt9g7sJ5ePABoskp8meOM17fZmqqytQoXFsWwVWLIGs6o03S2Q6ub7dZ5K1knwHgJNJvmrIqvKa0qA2NXoD9Cbkgfb4Xk0gpIrP1l5CIiJtBaxKkpU4KzQ1drnRSfvupklTMJ9huO1l+I9l+FOnvYaQPekWtZJDInHFcHZKJDMxOwsgw5AdzibMRQaNOvLnB9lKVhXlYXIDVTdiMnfhfRsafPmt7iTBIi+wJo64ywbZ+P2l0iC7TVHNqKmZJH69+2xq4GiuFCIrJTakg74couT6txVJOjqnptraQ8dLEGR16b2eQZ+oC+zcGwrZqSrUm3XU+dqOJKwC+F/wIqRhnhu3FbDYM47nD133d17G0tEQu5/4X+Hd/93f56Ec/yurq6iG2zLhbiaKI7/qu7+Ltb387mYybkvCpT32KH/mRH7EoDcMwDMMwDMM4BMzUuEV0hnn4z5kYV2/Az1ffr6aG7qepn/rl0FdBT4U5NQ4Wkr/TTAw/ikDRossNXPFrP6VLrygUZRjIxXBtCx7H1aPQot8P4VLtXGBnDQZ/xreaQJ3k2tYR8XopOcaRZF0LWF5s0/rUZ8kPDsKRI0T3P0ScL3Diwqe5fr3Np1bhSicxXZAZ6IOIUBv2T4jf92G/+9EGFSQi4BRSPP0JRGBN6/s00tKW+Wh6LH/M6Mz4/fzzuUR3jYabxTfuVIQPIzJCcV7XpZl+JcSkqJHeF/oMNZG+0FRkMVKk/ilceiwlQu7zePIdARMFeOg0nDsHx45CIdeRQvMDJeJajdbyBitLHYnSWJEoDTUV1FwLo0FgZ02bsB2hgdHL1NDt9WWsfaQRGoWkr9SACNPJpY1Pkm2fAOKO9J0/rldx0VwtnIE4intXbQXX599H7ff9mgG93kt5nKERvqN6sR8DBLrfa+HYtTyMhvH8odPZ+ebQqA3DSOPVr34173vf+7oMjTiOefzxx83QMAzDMAzDMIxDwkyNW6RfBIaKZZoSao29F24ODY1wuYqeagRomho/FVJY5DqMHGjhREE1G/pdSxR8a42Aq0gtgIt0mwZDuBQ6Ots7FMH9VE+TiBGyhIiqvqA6BdwPzGUg14FyBTavl5laXISJSbjnIRgdYeaTf8/4eJtMknKngUulM5h894oW8dsVLvf/1n7SiBLtCz8lU5oZlCaZ+OJ0KJDPIqbRedyY2a+IrEWntb26rICrIbIf/Puv190rosffJ9xmELgPeDAL6zE83ZFaHb3SGenzs4ls10q2DZ+lYSQN2zFgMpKi8ccn4GWvLnH/V86Qq23TWtmgtbBKvi1PUHm1znwSpbFWcVELNVwKqrTnIu1e+30QRmf0MzX6HTeDmD8d7+8RXPqztIguku9N4IvIM7qCixBaQu6/1jFpJ+tXvX1D/PGpfa9t2osRkRbNoxET7WAbf4ztRoQ811pzptc2af3tP5v7iaYzDOPZS7PZ5C//8i953ete96VlURTx+te/nj/8wz88xJYZdyvvfe97uwwNgHa7zdve9rZDapFhGIZhGIZhGGZq3GZGEcFuiZ0zn/uRxwnRvgAIbjZ8O9luFhEvN5Jt/DQxPmEe+1AADUlLa5NLzqlC5CZS16BF96z5IjI7fByph+GnifENBF8cL3rHDPtpDHjVCJybgnIVRoehU23A1atQqcD0ZaJclvxwgcnJOlN5GGuJ2dJERODJ5NhrdAvGoenQz9jx17WQGg9NXHqi6eTbTx0URt2kidY5RET3RfqwXst+5pH6+7VxUQcghsYEMl72WxMh7dgh/ix43d7fJ4/00zHgRARnIzGqVvu0R2sg9ELNmy8DXl6C2Xwy+z+G06fg1GuOk/+Gfwxf+Dytj3+Orfk6g+VV4kyGa1djrl9PojRaYmRUkPuxSndqrDByxx8/vkGkKdy0ELifsitMR5VmGMbe+iaS3kxrmZSQZ8UfY9q2kA7y3lnHvUv8caVpvPq9B9Lw+yCL3E8tpr6Xff1rDFPdhQbsbm3KIgZqjd6mBnTfKz2n/9HzGYbx3KZarfKe97yny9TIZDL86I/+qJkaxg6+9mu/lmPHju1Y/ru/+7vUamHyTMMwDMMwDMMw7hRmahwAWVzB6ZACkhZqnv3VQWiRPhs+FIizyKz3FiIqthChMqI7171/LILlvYS8UFDUgtNFXCotLeQdMobUccghQqcaHr1Eyg6SOktz64cUgNlpuO9e6HRgehpGZwqwuQFLS7CyAg8+QPa+c5yuXOHLn15l8VE51jAy0z1GxPxtr827icK63O9/vQat+6Cz1kvJNa8lH39mvRaC9glNJY2s0eufR/oa9veg+iJ7nBzPj2pQUwNkTB50PYHdZrzr+m3gRkuicDTqZ79EyP0tIqbVlxfgZcchn4daDTIRnDwFQ0dHYWyMuNlke63J9RuQX25Tr7c5/zRcvSbRP3VcLQ0tWh+aDL4AroL6AG6cawSD3tNx5FmskS6ehyZjOC46dBsYHcSQ0rRdaWMjHLNx0sYjyDOsz0CGndEeu6HponS8ZpHnXevM7LYvODMsl3y0v3qZG9C7jXofOsGyNFO3V7o0P22aRWsYhmEYAC984Qv5r//1vzI3N9e1/M/+7M/40R/9Uer1eo89DcMwDMMwDMO43ZipcQBoXYg0cX8bJ0DuRyzzZ8D75kQYWaDmRRhBkBYZ0EyO6wvIaYZGL5HbT6Ojf/dKy9NE0lE16J59H54vnPXfK5IlA+RzUMjD6BicOpsln41ZubBBHMPUyRLRiZMwN8d4Ns/LL25yfalFa9GJr20kYmMEmYEfir5pM+d7GTB++1ve8vnketvB9mmzwX1DKYtEtgwhdRCquJnnKvzuhn8O/5yhSZRDTI2h5Pgrezh2L/wxoLPfdzNJWsk5c8h1ZhHzYL/zHSMkEmo6OdZRYLYEMzNQKkEzUeuHh6GzuEzmC5+ncXmBxRsd5uchl4WNTbj0DDTbMDEO26susmgD90z748IXxwtITZXJ5JrWkagKrYOSB16YXOdTuNoxvczE0NAIIwnApbDzx2lYODyMvMgBx4GXIWPzfNLemzG0ssl1+e25TnfdkZAwWknxo5lC/D4KzR4fNWb0OYxwJmYDV/vEj3jzj6up49Ki2wzDeG7yF3/xF3zgAx/gn//zf/6lZQ899BDveMc7ePe7332ILTPuFvL5PP/jf/wP7rvvvq7lV65c4bu/+7tZWFjosadhGIZhGIZhGHcCMzVuEU1700vQu0H/YtA3gy8ANhEhvU13kV0/tdEYLn2Pis4Zdgr47KGdbVwdiXBWtZ9aZxt4FMnl30BE614C5m7njBDxuJiBdkciNZbm23zho2VWV+GFL4qY+vImxDHMHCEzt8zRUwXuOd5idRUqLRepUEOE8HWcGaTFqDUFVhYRRbM4YyEtoiOcRd5A7kV4PVlvmzSzCeRBnEFSMt3ARbZo/Yvd0uKEQrZfeNs3iwaRdGVDuDojN2tqaH91kDGhaZb2Mm9RTYyruILPfnqyAeT+9EsnNIr02XDyOQUMlyCbhdlZmJqCeh1KRegsr8L8PI1yg/l5eOopGBiAdgtaTRgZgbFRWEnydJWR56VO97iGbmMjh/ShFqLX1E76nBSTdq0jpoY+f73uZ5qhAdK3YeSVfuvz3GuMau2dYtJnFVx0xF7qqoRmgqYz85f1i9Dw+8s3XjrIuylmp6HlR06EhM9XBxchA9JXI8m3Pve6Loy20eOlvQsNw3jusr29zXd/93fz6le/mnPnzgFQKpX4qZ/6KT75yU/y13/914fcQuOw+eAHP8j999/ftazRaPC6173ODA3DMAzDMAzDuAswU+MW0VnSrR7r91IUfL/nAyeKdthZhyCcidxrBnwooO6GFibW9FY6Mz80NBTNse8L0/4M772eN4+IsfNL0GlBMS+fi5fE4Dh+PKZ5dZHCF78IL3sZjI8zdG6WM2ef4cqVDisrEMUifBYRIXUCqXOi16VCcxE4CdybXN+TSGHqmrddGr743Pb+9qNiQvE0rKtQRyIE/FnjRVx/9zpveB/VzMjjDI0WYhScRowTPaZ+34yQG85ubyPmAuxubKgJFKJFsGeTY1wj/dlS42kgOadGntRqsLoKR2ZgfDpHPh/TaXRgsADDw8QDg2xuwVNrEK/APRPw4EMwUJKaGpWmPE+LuOemg0sxpyZBBhmTGcSsGEo+Y0nbG8kx2kjkxwKuJkiR/pFAafe0n6nVaxvf1NJx2Uiuq8LOZzOkgDwLg8AF5HnWdlf77NeL0BjSNoIzZfyxGEalpRkROaQ//fdsERkPBeT6btAdmdavP/s944ZhPLdoNBpcu3aNs2fPEkXyVigUCpw4ceKQW2YcNmNjY0xMTHxpXChvfetbeeqppw6pVYZhGIZhGIZh+NxMGnvDQ4uAhyKuPyv5IAmjBHqJ0f76SvLRosUqIKrgHc7w7vXRGflVRGjOIqKiztAP01KpoKoUkfoJI8nffuRGPthfzQA1T6rA363CX5yHjz8Ojz0pQvSxORgcgOZWjfj6NbhxA2LIPHg/Z18xwalTkMs5AV6PWUw+44g4HeHMiHuAByOpjzGMS1kVzjL3P774qsXU/ePrvmHxcP10kBQ+jyd9O4gI9tq/ueCT9T5aGN5fprUW8t75hpPlN5DoDC3wrHU78Ppnr5Rx4n8bl04q7Rh7qZuh11NADILhHttMJh81NcaAoYzU0igWIZuDeqUN+QLZsSGyRSmy0V7bZnsL1juJqZWHwUFYWodPXYbHOmKkhEagRtKMJX+3kOiM0eSa15BojDXELFAjoQI8jKQUa+Cet36f0JzqFemlEUwFr8/UMGnhzCxwBcOXvDZWexwX5D7NIqmzTiDjUc3FfI/thxFjJ7z34VjPJm3NIiaetiOtiLr/Hg2X+caIH+VTwL3jqsg7q0T3+yVsG+yMajIM47nPt3/7t+9Y9uu//usUi8WUrY3nA0NDQ/z0T/80X/VVX9W1fH5+nqtXrx5SqwzDMAzDMAzDCDFT4zah6Wj2IxDvlTA1zV7X+wKeL66G+/T7tOkuhBzOniZYruKjzsA/gkt9owwg4rqmfFJRXoX6NhItcQOpV7AKrNWh3YaJSRgfh3atRf3KEvGlS1CpwMgog+dmeehFOY4ehU4kovMmLqXQUHLeKVzIUt3rm6b3XcPVJAmNnn4fNX9IfqfV19CP1h/JJX2hRdnVtPA/vqGhJodv2vgCsaaJGkyu72ngC8lHU5cpt5oqTSMBwmNkEBPiKN0GUYhGFpWTbQaC9XlkFv40cj1DyfEmgNkhOHUKzp6F4yciBmeGiMbHYURsq86Vayxf2GB1w/X1ZlVqzD9yBf6qAX+P3KfjiIGh7Swi4v4MbqxUkPGkz8QGYohcQQwEjTQoJ8fMeMv2Ymz4v9MMIV0+nVz/AC5VWRhxECORI58BHkOeoX7ifQcxQD6FGDI1nKkR3hOSfvly4CU44wd2GhE55J6NIiaImhuhoRlGMfnX479TWziTVa+zjjzrq8g7o4Z79nSbtPfy7XhXG4Zxd7O1tcVHP/rRrmW5XI43vvGNh9Qi47B5xzvewZvf/OauZbVajR/7sR/jz//8zw+pVYZhGIZhGIZhhFj6qduEzvj1xd20fO77pZeJEKZ+0uW91vn0qnXR79y90rikGSklRBDVSAFN5aTkccJ9OJta276NiMODiIhZBrYqUC5DJguZDLS2axQ7naRxHXITo5x+8SiN6iqdDLSvwWDTHauNiJ8qepIsWwGuxfJba0ase+3dqxOokS3N5NuvHRFGUaTNQvfFaRWEwYnAala0kuvRFDx+Kh9FzYIWrlbFtrftQZJ2vAgxH+5N2voY0qfheNEIg4Vku3KwfhgxoTSCZgwxS6aLcHQWTp2EU2cyjB0fJjMxJmE6lSrVhQ0uPVHjU5+CxU3XN8sVGFmESs0ZE3PJZwKp+bGJ9P1q8huk37Rt2tdt7++069f7pzUu9oJ/LL3voRkxiNzbTVzB7LR3TBOXcm0v1JDoIT/NnJqCPhkkrdm9yJhaxD0v+eTTwhVzP4H083V2Gi9huil9Xvzt/PdEmnlbxT0zml6rkWynURwhaVEchmE891lfX+cHfuAH+J3f+R1e+cpXApKC6ud//ufZ2NjgIx/5yCG30LiTfN/3fR8/9mM/tmP5f/yP/5H3v//9d75BhmEYhmEYhmH0xEyN20RarnqdWT2IE9ya7M3k2M1AUHEvrN/gr/NJSxHTL/ojTNMS7tNL6M8hs95nEHF0A2cgaOHiOUSE3OjTxhYSpTGGqwmw1YC1VVhZhmIBJmbzMDwE+QJkMzA+TuHUHPdsblGpNCnlodaAzbIcbBtXk0SvqYHMTN9O2j2StH2J7lQ+eyUUoLW/GriIC79v/WgLXVZDjCFNmTWbtEv7fRERcn3DLG2saBSKz52qH9BG0h5lkH5dRvpdo2YKuJRPsbfOb5/WQxlETI1xJErheAFOzsGJkzB3FEaPlMhMT8o4aNRpb2yxeKXGJz4BD1+GRtuZSlfbUN2AxViE+DISvVJHIhJOImNBjbUNdpoY2mYfv1aErg+jc0JUxPdTLvnHwluny9SoaCZ9djvup3+dm+y81gHEWNLoIf/a5hAT6npynHPAVwzCIxUZt2nvIeg25cLz+e827YvwXVgLjh0jfaRjrV/aLTM2DOP5xZNPPsnFixe/ZGoAHD9+nBe96EVmajyP+Cf/5J/wrne9i4GB7njEX/mVX+E973nPIbXKMAzDMAzDMIxeWPqpA2A3EaxE9yztAlKseQaXLsaP4kgTPcPIhbRzaioipV96qvDv3dIOhelwwhRXaeu0wPI4cApJOzOPE+BngAeQWd550kVf/7cWNtbUQVsdWNuChUVYXQMyGaIYiDtQKMDYGExNUTw6yckTMpN/bAimRmA8K+fU9FL+dS4jxZ015U6v2d23im9opaXdySBj5whi/jSBM8A3HoFvvhdeOyIi8QzSt2kCuqJRI2nC9+26vpAabpa/zuDPIO2fY2ethrDg+jBiaJRwxcGP5uH0CTh3j6SdOnI8T2ZyDIolyGaJq1XKSxUuXYTH5sXEiJNj6L3++1jSTs3jak9cR1IXFYAXI8+VRkKkPT9hSiklLbWUX8smTFXW9r57pYfzn32NNtom3dDI4dJzHYSDnfaOiJA+W0T6c9tbPoaM0XFvu1rVRW/1qm8RPgv55Dq0zkxo/vWKVAv/1v6Pg216mYGGYTw/+P7v/37W1ta6lv3ET/wE999//yG1yLiTzM7O8oEPfICZmZkvLYvjmL/927/lR37kRyiXw7hRwzAMwzAMwzAOG4vUOAAKdBfl9dEUQbG3fhUR9scQsU5npPsz7VXk92s4pB2bZL+hpB2aVkjFzzCqgh7H2g9h2islPIcKxI8ggvBpZEb3Oq4+RTlZ93jSdk3TE7ZbUzFdQowQjdbYrEoKqoESFPIQb2wQTU/B0JAI25OTcGSGkckVhkdaLK/IcUcyMNqWvk8TP8FFUmhKn4MmraB2aOZovZE6Lq1PpQbTY5DPyTYFROzf8o6TJqanCfIRck+GgfPJ+iJy7WGkxK2iUTFruDoTIGNXDSZwqaVWcSaItruQrB8GJiM4NgPnzomhMXemRGZuhmhkFDIRlMvE5SprKzFXr8JiXfZ70SRsbsN6Q8bcMpJmyo9IaCVtvZa0byn5OzQffVNvv89Vr0gEcCmq0kR2jfbSNG5p+2sxbq3L0km219oqWihcGUmWbwbL98I28DkkgqiCpA4DeY43ELNoKznuE8BGLNcVRlMo4Xsri0tRFxqp0B25sts9aOLqzyj+O6tftJphGM9d1tbWWF9fZ2Ji4kvLRkdHmZqaOsRWGXeCwcFBPvaxjzE+Pt61vNFo8LVf+7Vsb2+n72gYhmEYhmEYxqFipsYtUkBmHTdwedx9sVKF61Ao02LKGXaaDTEipOrMZj8Hvy+66SxsTaWzjEvho9uGwulBCXZhupde6XRI2nQZEefPJe1cRoTiPCLc+4V+w5nuKmxq2qYtRJwtA9sdqFahXofaZp3C+jpsbcPICAwOQqFAVCyRHSgwNNQik4GtTci3ZfY6iHC+QLeQ2sHVqVjgYMV9cEJtGr5oPoC0swg8BfwdcHkTXrrpCn+XceMv/Cj9RPcaIvaPJX+/FDEUnkz+1siBW6WJ1NIoIPdQU3Cp4Rcn644i1+vXb4iQ52wUVyh8Zhhmj8CxY3Dk9ADZY0ckOifKQLlMZ3WdzRvbLC1DqyUmSDMLpSIsbEntlM3kk3Z/24hgfylprz7fSpqhoUbEXsbLXp7FXpER44hhoMZAy1s3CJxFosEquOioe4D7cObCZeQaM8nxziJFzi8G59P/SPQz9srAhaCNx5BnXO+1Rgtdxj3Pum2YKso3KtSQ077PBNtEPfbz2+K//7S+h67zz6v9YRjG84s4jvnmb/5mPvvZz3Yt/+M//mOOHj1KHJvd+Vzlne98Jw899NCO5U8//TSt1u2Y0mIYhmEYhmEYxkFgpsYtcgoRwVZx6Z98owLSRX/N+56Wwz2HiNkZXNHbUACMECH6CCIoVulOoxSeO8fOwuUHgT9jPZy97lNDRPJzODFfxcejiMmRBFH0FNA1amIdEbYbiCB9ZQHGnpRi4Q9MrZOf24SpSSgUZcb+4CCFiSH+/+y9ebQsWVXn/4mcM+88D29+NQ9UQTEISCEi2ii0NIJg29CFjQratkjZPxUWiIh226gIjSCgywFaBURwKYVCg9gqkwwFRc2v3vzenae8OY/x+2PHfudk3Mg7vHurbhV1PmvlyryRkREnzjkRVe/7PXvvkZEKfb0+F5rQaps6Ff1I9IAdFVDCrN7fSXHl7RLf5DsVxRuIuTUFDKTgUF0E2Vlkvqmhlgv21egXOw1YOI1RGB8R7rNIP+SCfZeR/sgF+9ljoibTTv+p79MZTaLbVq3PTUSE13upF2PqacqpUeBgLxw9DIcOwehknOToAAwNQzIJ5TL+6hqFi+ucP+Nz9iwsLEktjQLwzVkR7y8i826V6DnrI/OrhBHP9b1blEUueJUwkRSXS7d7VYu9J5B6FTnkWnS+PBExL/o8KXh/LriOQWRMzyL3UBJjLuQRMyNqParO1Z2Mt5oLNUwaN+2/GJ2RKPZ1RqWe8zHPtnDKqrDBERWJ1E9n6jM10NQMTiDPEr1XendwnQ6H4zuHlZUV7rzzTp70pCdd2pbJZHjmM5/JF77whX1smePhIB6P86M/+qNce+21HdvX1tb45Cc/ydvf/naq1WqXXzscDofD4XA4HI79xpkau6QPk0olnE7ITmeSZWP9BjD54sPCXpGNK5PD6VFUfF9FTI2wGaArunuDts1hIjk2S32zU8IGTrd9VoK2aC0F3a5pcpajf3ppvxymRsc1GahX5XrWGrC4CGPjUM7XGVhfl2iNVBricejpITYyxMDYOkNDNXI5yBYgGxgbfYjwuYwRoZvB33sVpWCj9QC6YafgSgC9MTg2Ci8owr8V4Mu+zI+yta8d+aHirl3DwUZFant7D9IPS0g9iXKwPeqf83tZd0AFdf2swreaTVpzpo7MnRQwnIJrr4LrroeJo1n6DgaGRjoF7TZ+tUp9Kc/s+RZnzsCZczBTlPFcCa5tCZMWabN7QNsyELRjFRNlFcUQcBCJ7pnBCOl7SRIZr1LwPoRcSwMxfG4K3lMJyDekDUXEpKsgBoeaIjpX6kh6syhjdCfXoCatFoZX7CiXKOMhyvjVuZ0Otcs2PWyzyT62bSyngt/bz7wWMqZXIs+Ai4jZ4wd/OxyOxx/nzp3jIx/5SIepMTAwwM/8zM84U+M7kNe+9rW8613vIh43y0x83+fXf/3X+b3f+719bJnD4XA4HA6Hw+HYDi7Txi5Zx4iyuupXRea49bcK96nQ75PWfprr3cOIcGPBK4cRC7Wo8Dqyar9I5yrqGJLf/jrgGcBTkVQw9qrxbpEC2Yg27hUtRHxdxpgzJSS9T777zwDpJ12lf1MSnn893DgmbS35kC/C6goszbdozi/ByorkpWq1Jd/QwACZkRw9vYHPERORPIsRre10UD4ifj8cojR0r6WhqEmRB8614dwaeHUY8UXstwVijcCxi7W3Qn8ruro/a21rIYL2DCLGq+iNdQwPSU90hM50R3uBHeUziBQNH0fGewKTNqknaMvYMNx4c4yjTxuj/7qDeJMTUkOl7UOxBGurFJYbzM/D+QtwcR0WfBHZ85i0U1W25+rmgAPBteu9mCM6fVgLuXc1ddnDwSASIQZyDfZ5eoO/zwFnmjKeZUxEziIydvqcsQtug3kWdavnsRX2b4psnJvhl2+92/uBSYt1BDEfhkLttt+xjuMh97MWg18LvrPv8Rgyn39wCL4rK8e2z+twOB6ffPzjH+fuu+/u2PbsZz+b5zznOfvTIMfDwktf+lJ+7dd+rcPQUP7iL/5iH1rkcDgcDofD4XA4doqL1NglKihqSh7FjqhQUTiHFLleRVYGQ2dR7HCqmgwi5o4g6XLuQlZUKy2iowjSiPiaQUT5LCLk2uJnlCitKX48ZCX75abO0ciTqHOEixCvIavltxLJ45jV5Fc3Ib8GPRkRIMvAcgVm52B8HEbHCgwMzkNvD4yOSV6qvj684SH6B/MMDrSpViC/LOJzGYlSGEZMFzsSZq8ZwJhSEC0ea995mKLyp8rSB0U6a0CkMNEMYcG4xcYxTAZtsM0aTbPULWpBDY1bEAPqYsQ+O0Gjk+z562FMjJ7glcOMSwIZ+wEPjkzDgatyJA9MQiIB8Rj4PpRLMD9P8cIaMzM+8wuwnIclX/oxjzEhVxGjbwApkL5ZGVBtr6b70mijenAce+4uIOdqsPd1WLQtPcg9ruZnCelHNVBnkGvM+PJejzgGdNaf0Hmo6ag2S1m2GVHPo6gos/C890IvjeA6hBhKIDVlzkVcD6H2ppCxjWOKlvfSed9cMtBycKHe+d1WBqvD4fjO5cSJEywtLXVsO3ToEIcPH+7yC8djjeuvv573vOc9jI6ObvjuV37lVzaMv8PhcDgcDofD4Xh04kyNPUBXDIcJi3iDwNWISK0Fr+2VyrbIqFEMC4gpoamIompWxBCRtRHscyzYdh8i7N2MiY7YDK3zsZtIjSQSTZFGjJHSFuftJp6qMaJpZFqIQFkFvuBD4jRcFZe+rAArPvStwcwsDA23yQ4ukxwexstkZBV/Og2DA0wdy/DESpkHT0ChCvmSMTZ09X2UaLpX9AfXq8JpN1NDaSHzZD1oVx5jSMSRsdJ5s1mhcJC5MYxJNVWm0+jqNk5TwI2YFEa2ERHGvp5uongGEZ0biEGSwZgZ2eB9AOmrQWAkCSODcK0nQ3njjR7JgxOQy0KrJdE45TL+4iLF8yucOwunTsKFC7BUk3Zruq46YqQ1kIiQI8G2B+iMkrKpI8ZFhs4ILJ2fNt2MxjCaju5yIoFa1u8KyLXp2F3A3HNDyH14CDFa7KLaSrf6FnuZYgzMfNT72Qt9Dr8yyHPkMGLqLrHRiLPbGZX2bxKZR9pHdno5gvMvrMDXKhLxpsd0GdQdjsc3L3nJS5iZmSGdTl/a9r73vY/PfOYzzM3N7WPLHLslnU7zf//v/2V8fLxju+/7fOhDH+Id73gHrdZeJx11OBwOh8PhcDgcDwfO1Ngl4SgLO5e7vSI6hQh0BzzI+yIua3FvO6e8/fsqIrZewNQVSNGZqgXMqvZVZEAHrbY1EZFzu0J9GVOovLTN3+g19gevLCbS4HJIISK3LdjaIv0C8CUf1ppmWxJYrMHcnNQIHx6pMD4yDz05KR4dj+P19pGYGuNg9SLVWpOLM7BUhrIv59LIgKjaHlHRBVFkgr6oYcTiBEYoX7OOoyv/tRaLbWjpnLCNMW2HYv8unM4nylCIW6+rENH4NDLO3SJlkoipkUOE5SJGeI4y8uxtUWaNphQaC46XwNQ06Q3O04sI2SMxGO2BsTEYH4ORUTh8NMb4d18Fhw9J3ZRGFYol2svLFC/kOXMKzpyB02fh/LqkW8pjUk31IvdLFRH6D9BZhyHGRlOjGvSPppArY4yFcAF1e9y6oaK9bU5sF42qKWIidOxzrQXHjAf7TQFPQub3g3RGJOjxwsewn0V7HW1iF1q3DY2Y9TmOjNO1yDNTa6Cs0TlP7WPY92YdqY+hJl41+G14TJrAAxX4J5yR4XA4DOVymbm5OY4cOXJpWyaTYXp62pkaj1EOHTpEIpHgve99L1NTUx3fLS8v89BDD3HbbbftU+scDofD4XA4HA7H5eBMjV2Sx0QS2KgACibKIuZBNQ2NGuR8Ee9qbFwlrb9pYES6JBvFQE13pTUOtKj1EiIa6+DaefejIj1s2sH5dloPQGtepJE+KSGmzeWkcEpjxGXNyQ+dIvkKcC8iUA8H518DFtdgdhaGhmBobJXUQD9kMtDTC+kU3kA/qYFVhofXOTAF6+tQXjdFklOYqBcbXTW+jCkoHCaGiMj9SJqcVcTgGUVSAjURcVmF/wHgaPD3MiZyJ2ou6N92ijPdL1yToJuo3kCus42sYh9B+vd80Hf2MRU1TtYxc9UWoMPXb7c5bHroSn09Zj8yZ9TIGMYYY2MpmBqB6WmYnIDBITE1hm6cwnvaU6GvHx64H5YWac8vsD5T5OwZn5Mn4dx5uLAm17qMSRE1jDHclpDV+R4yNjrvo4w4uy7OMKa4eYHO4uzdCmGH0foWl5verIpckxpittCvJqaOTxq5rgTmGQLRz4Gtng17iT1HwxEbIG1eRubGGqbIeThKyDaOY9b3NUw0zhrmvtPzajq7UxhDIxZ6dzgcj0+q1So/8iM/wsc//vFLxobnefzN3/wNz3ve83jwwQf3uYWOnXDttdfy+c9/nsnJyQ3fLS0t8dM//dN84hOf2IeWORwOh8PhcDgcjt3gTI1dkkJWEpdD2yt05umPIbn9k1UjoFYxueNVSNeV0Sr0qtBYw6zqDouPdTojA+aDYzWC/eYwgnQq2L+beBlDhNGdpmCqB+fRiBLbjNguMYxQq6vrw+K+Ukb6uA/pO615sNSEgUUYnIXxiQYTQyvE+/oglYJEEnp7iQ300ztQYmSkxdAg9BdhoC3H09Xvi3QaG+mgbTk6Iy9sVESexOTyB1P3wi6+DSLeHwqu435EjM5hChzb46zvCcwK+gZGgLXNjG793kb6VCMzoiJO9Nh6jCZSR2PFapPWXQiLv1HmnKL7a9qmKmIwHA3ehwmiMxIwnIOJMTh4AA4egvHJGMmUR7o3CROTMDgIvQMQT9BeWmH1XIFz5+D0aTh7DmaCCI3VoN0lpP+zSLqp1aCdRaSeRjPUzvA1gZg6vUgE0dUZKLThdF0E89nge+37bn2g/aNjd7mo0TmEMTjs89nRFeuIadVGrteOBrJRY/aRMDrCx7PTX+n5Skj6vAeR8anQWQ8jnLZKjeMmxrQYB65KwULdbNNz9wXHfMhqRxaZoxN7dJ0Oh+Oxyze+8Q0+9rGP8Yu/+IuXth06dIif/dmf5Rd+4Rf2r2GObZNOp/mpn/opfuzHfizS0Gg0GrzlLW9xhobD4XA4HA6Hw/EYxZkauyRO9+K44ZQ+UUaBh4il08jK8oXgeFrsO4UIkw1EpPMRwVxXuqu4b593HVM8WA0RHxHMUxizYyfXE9VuPZ++h4uA75QscEVwnPPbaIediieDGBsrQF8BBudgZsanfyhPz+AKXk+vpKLK5WBwgNzIGsMjRYaHYXgZRktyPC3AXMII1SB9voYRrvuQvixgxrWFGDsJzOpvNRFGMKYCmDRM2eC4FYzxlMQIsOH0ZmlMTRYtBm6vQN+OAF1Fok0aiPhPcD0FNvZ5O2hfEZPix049FbWqXYX7Jp31XpLIHB5BxkuLbg8DQ56kmpoYgalJGJ+AyUkYns6QHunFi8dl7IaGoFKVs7SaVFaqnDkNZ87C+fNwMS/moRoaK8H15oLzTCQg1zSr+rcqUK81Ni4EfTDmwS1B7fmhc3DeNwalbUqGzYBYcK1+0M+7oYVc33YiCgrIvZQO2mgbVnZ0jx0VFd94mIed8NxtYIxcNcT0vrDnn5obYCJqtLZQPzDYA6l6Z2SHpoQL3ytxxCg6vmdX5XA4Hst87GMf4z/+x//I9PT0pW3Pfe5zueGGG7jnnnv2sWWO7fCGN7yBN7/5zcRi0f+1/J3f+R3e9773PcKtcjgcDofD4XA4HHuFMzV2yXYLA3cjh4hoT0JE0y8iK+MLGJG7iRFLNd2Mbq+zMe+9bajEEeG8br265clPIcJrMbRdawBkgnY1gYOIKH8WEf/3ipHg/Bd38JtFJA1VMvjtcgtm5qHnIchmm1yZmyOVTuNNTkIqDX39xEeHGB8vsbjgs7wEq2Wo+tLnuaAdtqiqkS9gIkoOImaHXWR4DWMogYjJh4PfaPQJmHRAFSQVko6XRlCEUznpefuDY9St/eOh/TQip1tEQAWJUFARewIZyxN0T++j++r3SUzKnyhUpG4i/TmImAp9yHzMBe8DwGQCxoekFsrkJBw7BhMHk6SGeoiNDIohFYtBJg21GjzwADSbMDfL2mKdc+fgwnmJ0JjzpT/zyNgsI2NwA1LPJq3L+beBHQVQDY51woejS3DVEExnob8BtYaMe57OKAJbNM8ignn43roc2pg6OVmii9snMCmW8phUeDa2qdHCPBd2kkorjG04XO5zsYUxiGxTL6pd9jyNY+Z9IngvVkxEk30tVaRf4qFtafa+jojD4Xhs8uUvf5mVlZUOU+MJT3gChw8fdqbGo5yf/Mmf5Pbbb480NO6//35+6qd+irvuuot22z3xHQ6Hw+FwOByOxyrO1NglRbatkXYlDQzFYToFSxURyXXlvo2mzVHRtLaNc7cQAXQ74mQdEWfD/8SLI+JzX/CeRgpN54LjnmfzYtPbRVNxDSJibTilVzdUoFRTYw3wa7ByAtbWIb9W5eq1U4zcXMc7fhyyGYnWGFthYqLE0hIsrUKpZIq3t4PzL2KEX0VT+VSRPgmnqrI/2yvDw/UwtMbFHJ2mhi2m20Jzm41peGxUBO5HCmBXEXMoKkLINmg0dVeCjamA7PoZ2h49fyZ0jWHU9MggtUYGMOaYFgcfTsKhSZiagtFROHQYxp4wSWxyXKIz0ikxNKo1KJbwL1ygNrvC3IUWS/Nt5mfbXLgIc3mY98WYuRhcn9YBOYpEQnk+VJvRESlh7GvSfmggx49XZH6NZ4PokjykfZO6K6ouyWBwvWuh/tmNnNJA+jJB5xjHEaMqgdQL0VohGoUEm0f2dEtRtRk6jzRtXguZ25f7TIgy43T+2XU0tK9VttJ5nECia+pVeT7Z1633o12DA6SfVpGxcjgcDoCXvexl3H333R3i+B//8R9z5ZVXUiqV9rFljjCe55FMJrn77ruZnJykr6/v0ne+7+P7Pu12mxe+8IWcPHlyH1vqcDgcDofD4XA49gJnauyS6ta7bEoDMQRqLRE9R5Gog26pnLrVmNiMnYiTUSJrE1n1XkDadwWSRmgBWX2exERsaKqhyxEzPUQUTSKmxk7anEfE1DIm7VC2BfUZKJehVmvwlJE1+g7WINeD199PYmSA0YkK40ttFpegVINa00RLDGMKnut5FF0BH14BHqYM3APciIj6axgRVuuu5K1jJJHx19RWFUSkjgXH0nOqoRAlTo8iptN80A+bpRvTKJR1jKFiGxn232BEYDVQ4mw0NfT6BpExGQxefcHfvcBgDMb7YWICDhyAgwdhYjpB7qYr8G55ErTasJ6HRgNKZSgW8RcWqMytc/5cm1MnYWYGVtdgqWgKgmsBcNv4SQTbTwLJphSH3sxMCF+3Gjk+MgbngBNt8EowmJHrmUTmSpS5p1LYIiZSQ6Npdvv8CBtuBMcdoDOyoUmnsWbPm2bEMbb7zLANHI0e0zm1l+tfdRx0XKLMIx03vV/mkLlQozNFm87NMWRe2Oj94nA4HAD33Xcfr3rVq/jgBz94advExAR33HEHz3nOc/avYY4OnvKUp/DFL34RgEQiged1LrW4++67ueWWWwBoNne7BMfhcDgcDofD4XA8GnCmxj7jIUJxHlivwGlEOI0q0JsM3jWdShpTNPjhRov3qsC/hoi0GsERxxRAv1wxswL8c/B5p3UHVPDXgtwNxJSoAcUSzC9Aab5Ib7ki6YwyGbzhYXrGCkxMFFhZhloV6ktQ8+X3A4jIqYXV7ZX1bYwgutU/j2NIqqohxAjSCJQ1TKodFWTTwf65oD8qmBXwuq+uzreLg6uArf+Mz2P6MLwi3cYPrkPnoR5DxfBwMWbbLIjTeU6s77Sodjpo+xBi2vV7MJSCsWGYnpbIjMnpGMMHsiSvOAI33QxHj8HcDBQLUK9DPk97boGVM3lmLvrMzMD5CzC3CKsNudY8phC2HWHhB/28jkRwaI2ZzQgbO+E6FHngTsREOVaVaxxG7ocyndEu+js1sHQcYuy+doWOmZ5Pt/lBWzStFJj6IVpLQq/JTju1U+LB8WqYejFVpA/sPtsLwsadPm/ChlpU1JDOf/1Oa3PUIvbVZ7HD4XAoS0tLrKysMDw8DEhEQH9/PxMTE8zPz+9z6x7fDA0NceTIEf7+7/+eZDIZuc/58+d5/vOf78wMh8PhcDgcDofjOwxnauwzWUQQXQf+DRFeW4g4bxcyjiNpdNpIVEQvUiegDtzH1rn6NSXL5a4Mt1O91JCV8UuIQJjECJu7oR0cd6eocN6PrL7Wos0ZYDQD2QQsLcG5kw1Gb1oi0d8HiSTksiRG+hk/WKNUqlOtQaEEtbL0eyU4XgmzWl/7wKfTTNKV931IX61iVnzngu0aNUDwXgz2zSJjHcfU2Whg0iRpRIRGRXQTw9V0OYNEE8SDvkljaoPYqKCrArSK+bagj/W5ZR1D0/zoee1j5hATp9+69n5gIAFDPVI3Y3paCoJPH0nSc2QY7+hReOLTYGwa/Kakm1pfh8UlWnMLrJ1b5/QpnzNnYHEBFvOw2JB+LiBCehkTlRIPzl1E7pftFlG3TZ2wUaDUkbROYAqQryMGWJRQ3kDaGY4qSNDZz5dz/2h0TTLU7gqdRbRBxmwU6a81oiM0bLbqr271hHZTY2gr7BRU4fsgHF1kb7MjijLIOOWDdzVI9Hr3o1C6w+F49PL3f//3/N3f/R233XbbpW1PetKTeNe73sXP/MzPsLq6uo+te/zy/Oc/nxe84AX83M/9XOT39XqdT3ziE/zhH/4hMzMzkfs4HA6Hw+FwOByOxy7O1NhHPETQTiFCaRUjHIfTGvUgKW4Wg7/7gBvikGlJmqHNTA0VmrX4tS3K68pyTRvTjWzQhlrw3kCE0wuYFerbISwg7pYB4AlIOpkmEh2QJzCGPPDisNgEfwWGTsBVD1xkkBZNP45fb5JoVckNZzhwuMXaWouVFejrg5E6NFblejXCwl4Zbq/cV/qDtuSQgu8r1v4quKvInMFE2ajhZKcKss2sJmKsqIHULeWVHVmgKbTUfOg2vlFRFkScI2a1KQccwRgJ9m97kfoVA8g86QeGPRjKwvgIjI3D9BQcPurRN54lPTmEd/gwXH0VjB+FWgXO3AunT8LiIrULC6xeKHHhApw+DWfPw3IR1vwgugkR6TX101pw/XGr/TsxC8IRKDrWYQNAo4HsIuJlTHqtVToNxChDSU0sjbrZqeFoz8NwxIxdO0KjFCaQ1HH3snlKsvAcfzhQE2anUWZ+xEsJm3Bq9gxhopwaSL9r2j87OknH163ldTgcYT7+8Y/z4he/mP7+/kvbXv7yl/M7v/M7fO1rX9vHlj0+ef7zn88HP/hBxsbGuu7znve8h1/8xV/E9x/O/5o5HA6Hw+FwOByO/cKZGvuIj5gR55GByGBqUqQRIyEfbLsSWWV9FhHfMsH3TbYWBn1M4fHv8SDhixhcRCJDiogo262OB4jw14MRIlWk30qItYX2FCIwrm9xrp0wDjwVqHvwNV+iFDJIPv3zPsyWZJ+r4pBMQHG1Se/pC8QSMfx4AhIeXk+O3GCKbKZCswE9WbjiOCQegm8smqLhuhZTV9iDSd+j/bIWvKuQrOmkzmNqpjQx6aS6mVEabZDEGBLhlfc2nvW9GkcEbQmvWu/2e/tdUxO1guvtwUSLPNODqzJwZwUewKzM70eMt0FE2O9DDA2tnXH4MByYhtGJOD0HB4mNDEFPD/TkIJWC+hrc/21K//glWqvrxPwW8xfqnD0Dc3Nw4SLMlmHVN9EZJeQemQ8+q8myWd92I4Yxl2xTQ02hGp1RCGpsLAavVtAH48j83uzeaCL3gbb1coR0e050mxsq7GcRI2oKqS2iUT1qeuh80bnycEpACeR5RtCW3RobNuHUYQeAJ8Zgti0GrM6XKp2p1LbqR4fD8fjmb//2bykWix2mBsBb3vIWXvSiF9Fu72UVIUc3UqkUH/rQh3jSk57U1dC49957edOb3sTnP/95Z2g4HA6Hw+FwOBzfwThTYw9JY3K1bzcFSxF4CBHSBhFBLgVcCxwOvhsCnpyC1bqIn3FENF5qicmxncQHjaBdkynoacHFprTXC35fpLvRoEJhKzjGKlunrlFs4VEFVk2xtBVbrRaPIX0znIVKG+I1MTNSiGCs6Z8mgZEUJJNSNLzpx8j09+DFY1KIut0invDIZqHtQ6UCiThkPTMeak7YqWzCETU1pCi4GhxxJDXRAcTsWMDU5ygEv9X6Diqkg4nGAGMk2EW5t+oz/R10push4r3b7+16C23EhMki/f2MQRjvhXMXxCBTMf9A8N6LRGoMxWB8QIqAHzwIR4/CyNE+4hOjeJkMtFvQCBIorazCyVM0v3U39QuLtJs+S8tw5gxiaizDUl3mnhpyteB9FhOdsxv5Qgtph4Vx/dyg877W+hrLQZs0NVuB7RmNTWRuXW5RbVvYD0dpKJpu6Rhwc1oKtJ+oSPqsJPLMKSNRReFre7joA24KzjvD5Zka3bAjjeKIqdaXhG/VjFms47PVPeBwOBw2z33uc3nVq17Fr/zKr1za9oM/+IO84x3v4Bd+4Rf2r2GPA6699lo++tGPEovFuO6664jFYpH7/czP/Ayf/vSnOX369CPcQofD4XA4HA6Hw/FI40yNPUJTPOnK5+2aGiqEqkjegwjhNyL1HBaAMQ8O9EJ9FUZ8k8f/JHA3Wxc+VsrA/TU4HnyuIJEDaSRiY6s26grnrURIjVDQFFe2CGmne+mWDimLiOIDiNi6RLToO41cS8KDtZqIyyXMCvkcYmgMI2Ua1gtQKIiPkUkmIJORug21GvFMhqHRGMPDbRYX4NRpmF0yxshgcGzt66i2a9onbWscGcNJJFLjQUzURQWTWgpMhIAX6iOCz3bqnMOIIH2Wzv6NWmke/mf/dk0N+xo1dY/WqPjWGhxeg2KwQz8yFoOIodEPjMRhfAgOHRIz49AhGDw2ROzANAwOwsoKLC5KCMf0FP7SMvV/+iIzp6pMHE6T6Mtx9uwqZ07DmXlYDKIzipi0VwuIWVRm43jY5gyY+9Kum2AbPpqmKxylYUe5ZILP+ps2EqGxhhlzNV103myGGmS7rUERjloIX3sbmW/LNWnvDMYQHGBjIfS9xE5lpcceQFLhadsuh81SZ8WQ588kMODD52vynOxD5qg+w1JsTNtlp6FyOBwOmwceeICLFy/SbrcvierxeJyjR48Si8VctMbDRCqV4jOf+QyHDh2K/N73fe69916e+cxnUi6XXUFwh8PhcDgcDofjcYIzNfYALQJdDV5bCYMqZoJZCa+5+XsQIbyJrPq/Dyj4cKAATV9S28QRgXWR7eXh1/PVgbswNQiaiLCZZ/OaGCnEaPDoTJOTRETsIp0irp2yx6aOiL5ho8NG0+RcDVwbgxUfHvTFdDkTOqau2D9XljoBi1Ybh5AaAsOIaLzeEB19YQFGR9vkhmvEs1m8ZBJyObyBAcZacOX5PJUyLC5BtW2iNFT8PR9xXVF5/cEU6tbxyiDishob3f7ZrRESYcFVzTItAp3GRAiAmA69dBoZKmg36YwqUfFXjZWwFBMWupcxfVvzJZWPpkY7jNwDfQSCdRymJ+DAATh2DA4fjdFzeFg2DA1LyEwlmHG+L3+3mpw+0eTBeyERr9E/7pHPw3IeTgbjX8CkTCogKaeaVhv9oD3jwNHg73uQuT0S9EM+uO5c8L0aRXaEhm1q2Cm7NEWcPd5lTASSRnLsxNDUcb5cY8OuH9LNJGwhffUZ5P6rYaK0Gmx+P+6WSeAq5D69E2PoaaqwvSoqbo9bCjgEPLsPai34y7JJIbeImetREUwuUYnD4diM3//93+cHf/AH+aEf+qFL2374h3+Yn/qpn+L973//PrbsO5Pe3l4+/OEPc/DgwQ3ftVotLly4gO/7PPe5z2V9fT3iCA6Hw+FwOBwOh+M7FWdq7JJhJE99E7MCGrqLdTFE+J0MPi8gwnQdEVwrwfYlJEphGREiKw04iAhzcUxEQpR5YJNETJIDQftmMOmq1MjYKs2TFinX3+i1ac2H8Kp/PV44HZAaN5uhouSAB5P9MFiDVEVE6RadpkI9uJ4l4BwmnY8fvGcwYmYBWC3C0pK8hofK9OVSMDAIY2PQ10eit5dr1h+g0ajA/bB+AUotEX6byNhouiAbW1AOX6/WYmgjQnsSMars9Fu2oKoprey/dTW//r0QHFejWtrB95q2yj6e7mOnk9JjjiAr108icy2GGU/7OnqC/dYwRs3FoB+OBf2cCvYbT8GhKTh0WKIzDh5PkT0yhjc1BSMjkEpDow7JFPT0QqUKp0/TPH2BEw+2+MY9MLcCA/1VlhdhuS4RLieCPksjUSFauH4NYwymgGuAW4Brs3CxDnMtE1mUtF5pTN0TfcWtz7axgfVZBXE7OiIetEfnhh3hspVor+O2nciOrdC5Y0dHqIHWQu4DjTRqIn2nhqY9f/cq/ZKHmGxXBZ+/jVxnHplzNS6/KLcdxRSOtEghc+SaKbhQgljZjJVt3tn3TBMzVi79lMPh2Iy77rqLH/iBHyCRkP+F9jyPa6+9lkwmQ7W6naUmju2QyWR461vfygte8ILI7z/96U93/c7hcDgcDofD4XB85xOdlNaxba5CogK0OHMOEUy7kUQMgiE68+FrPv8Wspr4HCL+gQhu55F0Q8uIoHwWEbc3E0M9RPg9htToGMdElNiRGVutTtb88yk6Bb82YhbYbUgH++2krohNK2hfyZeUUuWmTNKDwPcF16GTtj8417x1Pbpiex3pq1rwKgHFVhCtMS+Zj1rFGsRiEI9DzIOBQXqOjnPoEIyNwkgfDHkizKaRvhzBiNt2lI1v/a2ocKwRAlnguuBawmmeur3ApPPSQvIaYZFDDLUxqy/sdtjHCbezEfTPPGb82kFfVekc0zHgaVa/riImSAGZgxeQOVkCDk7CU7/L48YneBy+Ok32+CTeocMwNg65HvDbUAjcpWoFP5+n+pVvceofz/DgSZ+zNfinM/Cxu+DOWZhtmfHVyIoYcEVw/XaUSTbojwGgvw8O98O0J9czF7RZ7zVbFA+/4hHv4W12ZIC9T5JOc2Q7AvluE5bo2GqKrLHgFcekLdP7QNG5ucpGo3GvohV0vPQ5pOV1G8BpxNi4HDMn3LfhqJomQZqyOvSmJfJLI5hswyqOzJUxZE7YhcMdDoejG7/6q7+6wbz4hV/4BaampvapRd+ZvOY1r+H1r3/9hu3nzp3j3e9+N69+9av3oVUOh8PhcDgcDofj0YKL1NgDVARWga6bmK+rp3XF9CoieKuYZteasFfUE+w/jxFV9bybGQc+JkpAV6j3BH+XtntxwTEWMSms7GsJp9tKd9m+HfS61pF0UisVaW8PYgJNxeAJbTiF9NsYJr2S3SYlj6RE0rRPRWCtKKmlhoZgZLzG4MA6ZDOSBgmIJeMMjiaYnm6SX4dyFUpVEfArQVtyiCAcJny9LeR3Wr9Eza/B4Bj2GGgqHC/0OapgtQqyWmhcU1E1rd+2Q8cOGy8a3aGC92asIn2uq+oLGJNrGRGGc0AmIRmmrrw2QXogC8PD4g719kA8JimnVlfxZ2cpn15gZa5Ovdomm4HlWal1UkEKf68iY9fApHjSPq4i87EQbPOCvp0M2pQHzqxDxpPxsu+pBvLQS2HurbDYHU5BpfvYRpPdp2FRPRbavlX9h50K+5rSTI1DPb4aplcFx7wL6c9uz4hHItXSMmLA3gI8GfgyMp7rRNdC2Q7h/g6/1BieXYepITERtR/s+0PHX2tsaLTPZqa0w+FwtFot3v3ud/OGN7yhY/uv//qvUygU+MAHPsA3v/nN/WncdwA33XQTr33ta/lP/+k/4XmdNvM73vEO/vEf/5E77rhjn1rncDgcDofD4XA4Hi04U2OXqGipKYq6rbpOICvJG0jKpHho37D4af+taWSKwXG0IPl2VhRrznwV8HQ1/05MjSampoIKpDE6a0RomxOYmg87RY+/igjTs0ifjSA58nvbstp7FOmLKUwUiX0Mu93rGGOjDBSaQbTGIozOt8kOFEj39Ei0RqsFtTrZ/iQTE00Wl2BtFfI1yPry+yymaLieOyr1lFLF1ILwgQcQQyRFdNqfcAoq2+CATuFcDYk00i+a7iicAiscqWH/XWfriJoyUs9E16V6yJio2TSOjM/VvdDbixhEI8NSBLx/AFIpaLZgdQ1mZ6memePcg3XOnoXCelBEfAiuOgpnvg3nfRnfResaE9Z1rQRtKiHzOoGM8XBwPSeBb1XFbFFj0E4bpWnbxoL97esKGxx2v+u7jo/Ww8DaFjY6wmaIHkPFcxXTt0sCSbGVDa5zKdQ+MNFBGrlh12BRo9Mm6jmyV4ZHEfgmcs/cFINyW1KJ2WbkTomK0IDOVFQlYDYP/WmT5so2jXX81jC1UECerdGlaB0Oh0Not9v81m/9FgcPHuSVr3zlpe2veMUrAPjsZz/rTI3LpKenhw9+8IPcfPPNG7778z//c37913+dfD4f8UuHw+FwOBwOh8PxeMOZGrtERWRdBaxkEaF1OfhO/85jVtfbub+6reoOGxu6wjicS74bKrBqDYHtrMyPQq8tgQjoOUyxcv3nZZLOvP07xc55r9EENUykQy8iBucw0SZ2kWPtPztSYQUR3zuiNUqwvARLizA8XCOZW8GLx/FSKYjFiKUSDAx5jI35LC/Dah7WGqa4sZpKNbYWf+vBeRuYaA+t5RDGTn9jmxlRpkb4OrVIPXQe2+7PcGoq+/NW2JE3McTU6EUMpingUBoOTUJfP3jplBQEHxqGXBaaTSgU8JcWqZ9fYOZ0nTNn4PRpWFyFB0/D1BgMDsBVg7C0KmLzcpdrUoPPriGh5oymOmoAdyNjFI6E0LRpdSSyIY1JaxVlaOh9Gr4/w4XXsT6Hx8zery/oPzDF7X1kXm1lCPYCN2PSri0H7R8JrnUGiYxoYYqBgxg41yHPjm/QGWkUruGyWXTJTvER4+WbwPE2TCAGWQ+mjtBOiIpa0r9t2gSpy+Yl9ZjO33Bqt3DdkwxmbBwOh6Mb6+vrnDlzBt/3N0QT/MEf/AGf+9znnPh+GSQSCW644YaObb7vc/fdd/Nf/+t/dX3qcDgcDofD4XA4LuFMjV2ygBgW4dXug0hx7nVMGqICG4kSPsPY21XM326khUZPeIjAv77N33UjjayIV2G/gAiTumJeo0L6MWJzia1Xo3dbGQ8iOC8C/xr8XQ32PxkcV4shK/ZnrdFRw5gaxWYQNDAH2SzE4gUG00kYHoFEHK+3l55mk5GRCiMjsLwMvcvyW61tMYmIs1tFOdSQPtdrOxK07wwiLEeNeTh6I2x42X2ltQBWkL7W1ETh44WjNezPW6HRPcoBpA9yiDg9GoPpUTh6DA4fhuRIv4Rs9PRII8sV/OVlmudmmT1V5dQpMTTOL8P5FpxtQOIcPDkGPXFT78NOO9WtfzSlVBGZBzrXbTMsCo3YOLON648j5o3dD23rOy00bUeDdMOOklLTLoVElfQhpsRmEQxZpN/tNFmZ4FUPXmrieJh7ow8pnh1j8/RK2zFKd4qPjM9F5PryyBwaBu5kY02PzeiWjk2/0/om/cHr6225z+xnRFTkjI+Mwyg7i2JzOByPX37t136NW2+9le/5nu/pMDZGRkbo7+93AvwOyWQyfOYznyEe7/y/mEKhwHOf+1zXnw6Hw+FwOBwOh6MDZ2rsEhW6w0JmAhEXu4n54dXc9rao31zuymkfEUlngtduSWBSt6wjIqoKpVmMsD6FCIkriGi5manRLZ2M/Z19Po08sffxrX3D4uU8xoTJBMfJFCEzC6mkaO/9oyXiPT3yRyIJ7RbDI1VGRnyGh2BpDdZbphB6KrjeEluPzSpGhD0UtOWC1Wa9FlvcDae0ilqNbkcKqEmhqZoS1nb7PWxqdCOGqR8yjphXZ4Lt2o89iHk3nIHRUZichL6Dg3jT01KpOxaDZgMKBZibY+l8mTNn4exZuLAEs20pGH1PcNx0G6ba0jcqXUS1Ue8R+xq0rk0cMTMucvkRQ2FayBwKt2EQuBExJBaR/lnHpLvqFgnTotNcTGJSr9XZnDZyrVqbRU0dLaauY58Kjqup4YrBPmrm2Ndhv4ejnvaKOeAzGLPpJHK92x0jlbhsMwM6i7Zj7QPmmsMRVVH3lqYpq7NxrB0OhyOKdrvNi1/8YhYXF0kkzP9Ox+NxPvGJT/CUpzxlH1v32OONb3wjT3va0zq2LS8v82M/9mMsLS11+ZXD4XA4HA6Hw+F4vBLWSR07JI6sgg4L8QlEvItayR8W5qK4HEFRBWf7PC1EOP4iOy9IHIWuSK9jCjqreKuFy2OIqBqu+xElzNvfRb0UO22MptAKi5NRNSMIPucRc6eECMorPiyvSRTG0hIUlhv4pbL8KJOGXA/xhOjyqTQkE0YwTiNpgCYQcX8rWkG/6T/JM5h0OP3AMz14Ips7jHaKs6b12S4cr/vpPrpf+H07KaeSSMqim4ArEOE+icx1jRboAfo9GBmA8XEYHksQGxmEgX5IJqXyd6EIa2vUlgrMzcDFCzC7AHNtEbpnCaJnEGMggRhiOTamC7LHNnwNWj+lgIjS24kO2i7hPgYZwyuA48g86EfmRBxTeFrvFTWvut3zjaDtS9tocx4pAH4PpsZLDGMc9mLq2rQQ42MAiVb4BhIZYUeMdXsGhY2C3eIjBmcxaNcS8uzYznNO6wiliI7UwPpbIzXawTlqbKwn0y1CKY7Mm9WdXpzD4XjcUq/X+dKXvrRh+9DQEDfeeOM+tOixycjICNddd13HtlKpxBvf+EY++9nP7lOrHA6Hw+FwOBwOx6MZZ2rsEg8REsNoIWMVz1QYjTIewsKiLerHETF5OyE1KraHV12vI+LxXom8WqPCNmx0BbYKuFoI2c5Znwp+G5509orpqP6w94ljxM126GUXyg6L4HmkH8qIqLsO5JuwsgZLy7C63KaZL0EtWMeeSNBqeZRKUC5BvSHn7MUI+72IYBxO99QNFXPPYiIRxoFbs3Cdt73j2NfkIXPDjtjA6gvbxAgLu9tpazs4vtZ7mEYMBzU1+oDhlERpjI1Cz0haCoMng8Lg5ZJUAl9fp15uUixBpQLNtvT/DGJkaK2XEjAYh0MxObZeS9Sr2zVoofm9itKwxXKbXNAPZ5G0aA8g9+g0nSZnt9+H0dRoW41NFTE0voVEhlxA5lQTMx8SSD9UMEaUzpF+jBHX7X6LI/dpT8R3u2G790kY28jrVlPDs/bzgGMJeHKmM2ombIjZf4MxhB6OFFwOh+M7k3K5zE//9E/zta99rWP78ePHee9738vhw4f3qWWPHWKxGG9729t46Utf2rH9d3/3d/nABz6wT61yOBwOh8PhcDgcj3Zc+qldotEJtojqIyu1hxER0c5jryu5bQPA/h0YUTqFrJYfRVZaa4qZbmi6q71MGxNGxe6wkB4+Zzy0P4iYOoQxRLQmhxZH3kpM1BQx4W1hovqghhg7WvsjBRR9WCuaaI3B4RqDQyW8XA5SSZK9GZLJMvWgY0fiMJ6EuaoZ86HgfAtbtF25N2ib1ovoA3pT4NuVuLtg19bQvtA6D+Fojd3OgRZivMwh4nYCuBYTAZFG5vZwP4yMwMgoeEODQZESD6oVyK9L6qlWk0wuRibdIhaHVEyuPzyfPSDumbolMTaPdMJqWxUp8DyJGCYLXX67E+wUR+H+LCJjqTVjYoihcSPSTw8gKbC0DXsxJoqdoiqcsmzJOleTzpRzh4ArkTE9F7RbC6zbxwibrlHtVlNxu2iatSTSdztBo8DApOqKiuhqIs/AFDCWgesHwJuFVnvz55WHzLep4H2vDDGHw/H44P777+drX/vahnRTt956K0eOHOHcuXP71LLHBvF4nFe84hUbtv/pn/7pI98Yh8PhcDgcDofD8ZjBmRq7pIaIl3ZKlyQidk8AB5FV3QVEzMsGn2cxBYHt9ElqTHiIYPsE4ChwClMIWPPkh2lu8p2KersVVtVw6Qte9YhjxpDrTATva1b7epA+GUZE+ZOYmgrQPUojnGpqu9gCbw0R4nsQ4bMA9DRhZRUWFmF42Kd/vER8oB8vmyMx3Ed/X5meHhgehFwWUiloz8FCTY6p0RvbSR0EnYKuml35Iqy1N782D1OzRMdYRf/NardcLj4ybvchxtokIoq3MePZl4DhYRgbg96hJPTkpIPqDaiUYW0Vf3GJ4myBC2dbfOlO+MIStJsyFpoayD5nuSnH38zjURNHC3gfRAwSjRDaTkqwrbANDT0uGENB26+o8D4Vg6k41Bqm2Plm5t9mxDHFvreLbVDEMSmwNHIK5N5TY/EC0ncasaD9qhFPGqljfx9j69ofYfTZNoHcLwvsbM6q2aJ9Ahsj2hJBe9OIcZZKy5dt6zc+nUYImGg4NZKv3OG1ORwOx9ve9jZe+MIXcvDgwY7t733ve7nllltoNPYiAeh3Ju985zvJ5XId237jN36Dixcv7lOLHA6Hw+FwOBwOx2MBZ2rskhpwHiM89mNE+xyy+rcPEQ/riHhWRVaT6z9xNUWKLXimgCPAdTEYz0C+bFYpXw79iDBdx6RgqrPzOhs9yIr08eDvBxDx1kajKep0CpdlZPX/QUTcbGDS4/QH34UjXroJwppeyT5nt/Q2Deu3s4igm8L0w1pBojWWV2BspUrfUAkvkyHem+HwFQli8SYzM7C2JtmUUnEj+reD9k8Hx9YUOXabNHVOGC+43jubYowlQ2210egOmyamzx6O6BwfMd4KSG2NhPXqB4Z7YWRYjI3kUK9EuLTbUKviLy5Sfegi509UeeABuPNB+EwB7geuB65BIga0v8rIPPoy0q8zbC7ma3/aYvUSMq6LW/x2K6KKZ2eBw8g4rXX5XR0otaHYFvMqLKZHnafbuMWQeh3LSC0K3bYdIyCB1NLIBb/XNHgFpL6O9nkvYgodCPa9iJgNmsJJzSM1M/RaukXAhM0G6KxH0kT6ZQzpq/WI69EonFbEd2rMtqzjakH0QWRO6jNtbh3qRai3zT4V65hqzmg7a0g6r/ngWA6Hw7ETZmZmKJVKNJvNjqLhN9xwAx/+8Id5yUteso+te/QSi8U4duwY8bj5P7h2u8358+ep13dqnzscDofD4XA4HI7HE87U2ANU5ItjzAxNA9MT/F1ExEUPyfmvZkK3GhKDiPDfE4dkGhJBJWA7BdF28YI2XIGsYlZz4TwiHm93xXQMEXZv8uR4LV9EwJVQe0pIap4SnREsPiIeNhDxcS34vgcxf1SQtGs/2KKwHa0RFlajRG6sbb61XwERQLVweKENq6uwtCi1NXpH1/GyGbx0mp5jY1yRXiTmNWm3oV6DbFIiccaC9gYLwqkGfRELnVfNDW23Xks6+M15TBTPToym7Rb93gnhuaXicQ0RpbXmQNKDvh5JPdU7lMDryUE8Dq0WFAu0FpY590CVf/kC3HkeHgQeQoRskPlzEBHUAb6O3DPLO7gerVFSRPpRjTG7ls3lYtdt8IPjr2IiFDQ6wJ6LlaA9Q8HfvvV7+x7X+3EAuQe6pZRbw0Rz6fG2QwITJbaE9H0JMZF0/LT+RhaJwBkNrjHP5nOquUU7wsaGRq31IvdGBZnvYCJHbPTesQ0IxZa32sh4H8WYbL3Ivb2MzKeZtvRtLmhHyfp9+Bp0jOuh/RwOh2O7XHvttfz4j/84f/iHf3gp8sDzPIaHhzl48CBzc3M0m7ux3L+zSCQSvPGNb+T5z3/+pW3tdps//uM/drU0HA6Hw+FwOBwOx5Y4U2OX2GmdNDXKIiL2a92FI0j6qDXE9AAjiCYQUb+CyRuvguoycH8D0quSpqmOmB3dVjl3Q9MvpRCxsxac9+IOjgEiDI4BIz2QjcNgXqIUUnRGWFSRqIUoSsF3KrIuImKkRnWE08To57CpEVXDIxu0RaNRwsYIwTkKGLHzUrRGERaXYG4WevvKDKXzxCbG8VIpEtkko2NN2j6kAyW23oZ4QcZURdBRxJBQITqqOLQ9X5KYNEaXE22hBsleYpsaCSS6x0Pm4ghwVQpGGzAUh2wGMhlIZRN4maBjKhX81TXWzuQ58RBcmBUzwK6don2kBuCqdX77erYTmbAevGKISdCHMTsuRzqKqtfgBW1eDdozgIz1pCfbT/gyD3wkIquE3Fv2+aNqOEwE7a6wcRzbyL1hs9OxziLPnhJS0FwNCX1OqSEzi4x1ib2JcLHJIP2VxKSuC9fC0OvyMM/MsGmp6aU06qmJ3Od55B7W42qaOTu1mZoVNr71vRYZ36lZ7HA4HGH+4i/+gmuuuYY3vOENJJNJAJ7znOdw/vx5rr/+eu677759buGjh+PHj/PWt761Y9v6+jqvfe1r96lFDofD4XA4HA6H47GEMzV2ib2quY0IrGWMQXEOMThUZKsjK5SzdBa+1hXxthC3ggxQCyNwDlv7FNmeCBlDjBONKNCCujsN7Nd0R8UGrDZk5f0inYWqt6rbUUEMHl39r2LiPJ3irl0QGzY3NJQeZNV5HUmzU7B+o+h584iZUkb6cbUBmQUpCzEwAH0H2qR6esDzIJFgYACSCcikIRGHZBJip+FCydTT8JDxmqGzgHMYHb8mYhaoSNumczwziEhfIno1/8NhaOh8ayFz7UpEEF8GMkm4chDmVyARk8AML4YUBseT1FOlIvW5Zc6c9lmYh/4euD4OrMPJpokSUlOuhozTXmQbjyMmSRyZ6zsV6LsZGvarD7gOuCUFVw1B04dPLsK/+sagUNNRj2nXowAZtxJbz5PLpRmcv4KYL2PIfWrfD7apUULGNwgG2zCvbLNCa2xsNl72/lrzJfy8iYpe0faEDY00YgANIWbRMvL8HAmOe5HofkwhJnCGjSnyIDr1lTM2HA7HbnnrW9/Kz/3czzE8PNyx/UUvepEzNRwOh8PhcDgcDodjj3Cmxi4JC2BFOgW/NmblfhKzSjpp/V7NjTQi/LUQUdJHBDmtuRFDBiyGpO2pIznw81u0UWs3rGFWtuvq+Z3QQgyJf66JOK1pk+zV33rN9m+0j1KI6FzCRHaokKirraOwDY1uqXFUnFVjoxm01RY77ZoAa8gK8nRwDatAsgwDy1CuQLvtQ7MJzSaNUp1WHXqmekkd62dgbp3Bh4pUa1A+LbupsRMPzjkfcV5tt5pXLaLT7CjDiIA+j9QuuRzhX4u22/OwGxpBoFEzvUgfTQWfD6agVINCE4bTYmjEgheeF4QEtagXGxSLMDgIV10N7RY07oR/zss1VzHRTGVk3KLatpMoojZiLum8qm2yr329WlshhfSv1pOwX7HQ3z7QE4OxUajWYGgJ+nxjbHmY1EqatiuGiPKxoJ1V9q4WSliIbyJ9m8GYKmHs/euYyJbwPWgfO4aYOgPI+K1b30XV09BjLyB9W8VEXESliSOirTHkPjiCzME15F7tQ+7zEiadVZg4cGOw7xfZ3MRNYcwal8Xd4XDsBt/3efe7381b3vKWju1vfvObmZ2d5c/+7M/2qWUOh8PhcDgcDofD8Z2DMzX2ABWzNdpCC93aRZ9ziChni4bhCAJdBd2yfq/FfNOI8FhChL/jwFXACeALdApxYZFT0xydQ1aHN4JjXM4q8TXgTowor+fTdxWB9fo0zRbBdYwH7VhDRNdM8P1mQqKaGd1Wkeu2MiK2ag79zSZ3BRFls5i+T/uwtg6VCrRLVakM7kGl1KJFhuyznk36+DNIV+bp+dbXaPpfpVxu4c2D1zT9MIoR6+0UVOE0WnaKHKx9dVsSEXBX6G74bEUSSSE1hMyfGbr3dZvOehR1ZI5cCVw7CFkPZtbNvIlptEbcC8JXEpDJkO5J0NvbpL8PJibg23fD+Yo5dhWJ/rjA9syH7VIMXvac2GzlfT9igg0iovZ5OotyR0Vq1JF0TedrcGAZ1suw3DZpk+qYVEv6tx5vALkf8uwudVhP8NJ7ookYlGGjQtPLqZFp3492v7SQfmthnl9hNIpnGvgu5B7+JqZ2kL0f1jnUzLBTPfmhfdR4se+NJMYc6sU8w6rW77b6j1cK6aeodFaKplibQuZjt7R5DofDsV183+d3f/d3OXjwIK9+9asvbc/lcnzf932fMzUcDofD4XA4HA6HYw9wpsYuSWDMDA8TsZBCBLkGZiW4ruYPi4D6d5zO3PNpRES8EhHq1xDhtREc7whmNba2ZRgRafNIyhVNNaWi5dour1dX2ofz4ofz6YdXbCcQUXcIWbndi5gyHnB/cMxUl2PpecP0Idevon8bqT2i4nnUb+y0N0uY8Usjwu9aDRYX4MKpOkPrF8nlYGW+Re+BHpi8CcZfCH6J5Mg0x0+cYf7iHK0WNOYlFZGm2tGkEyrs2gXC7bbYqFhdCH6XBc4gY7aTqAUbNciyiIi/xOYGkt1nKvD2BV+cLcFCW/6OeYGpEYNYPAbxBMTE2IinE2SCXGe1GswswMW6MUMKwTXtpaFhs1kfK4OYIuWaDqrOxsiM8GcfmSf3+dBegKIv83eFzvs4w8ai2lVMqrHLpQ95HkwhxtkcJj1Z+LgaMVRG5k8y9L09p5rBK0lnFJlNGklldTgN9Zq0JU90BJFtboQjt9QEDqeYw/oui/ThMPIMjCP39iqmLs5c8Ntc8NsynWOgUTHaR1H0AFcH51nrso/D4XDslEKhwMmTJ/F9eSp5njwVX/ziF/PiF7+YT3ziE/vZPIfD4XA4HA6Hw+F4zONMjV1iR2eEhdAEItR3MwBU0LNTxKggqtseCv7WWgS6UrmACHwlRChMAdcDTwbGPTjpS8qVZSQqQVeS7zXhvPjdvvODa1jBpKEZxxT1VVMivErc7jNb/EwAB4PfFTBifA2JRkixMRLFXqGuq+41lU0KEXMLbZidk7RCzWaL4SHo6YVrpjyIJcDPgTeI13Oc1NQo4+NzLC7B6hrUqqY2xiDQ68GiL9E0a3RGamgKMh2Ta5HomwFM6qyz7M7QIDiWirp2EXOb8DgpWnB7FlgsyFyqIYaU56mhgbgbiTgkgsdJq0W5DEtLUCjCgxfkvFo/priL69kL0khfDyIm4TzGeNL0bt1qanhIn54DZttmDtmRIWog6THBmCEaJXU5ZBAj83BwfE1Pl8OkebLHr43p62RwvRoRkcJEE9nGg0aXhVNEaRTPaaC3JlEg83Q3aLrVp9CoLE2lZ6ets5+datyOA9cgURSLGEOuCDwIHAWehPT53YgJ2Ar6pY3MXTVCoto4jhhEGvWSYKP543A4HJfD29/+dv7gD/6Aubk50mlJStjb28tHPvIR7r33Xp74xCfubwMdDofD4XA4HA6H4zGMMzV2iS3ExUKf7VRM4ciFcHqcmPXuYVLC3IeI8zlM/QqQgdP9DyKrzp/iwbEB6OuD9jw8WBfhcqtaCju93stB0+DUkfbEMEXVVfjV1D220Kkrt6FzRfggIlzm2Sisam2AcL7/8KpwEKF2wTrneR/q85Ccl+NMpeHKgzA2VWT0wX8hMXY1ZG4C34dEglYL1lalrsZ0H/TUIF6Hfg8mB+FLq53jqwKx1rqoBe/P9uBAEtbr0JOS97vYnaGhVNk81ZgtKoejW2rICvlRjBh96XdBPQ0vkZA8VPE4xBP4vk+lAhcuwH1zcMqX6x7GRCsoes+oqKzYNSn2miSmkHgFkx4pbF50i9gAE9nQRsZvADF7QO7NDDJf8xijUqOHLuceUrOkjYj00HlvR0VLhKO+epH+7EFqtcSBf0NMQD2Hb12bGhtqOPqY9GWV4Fj2vWW3VbcHATsb2tcOjpNi43+E1NDVFHF2Sj4bjVA5ABxLQrNhDLwYMq4Fut9DMWRe1xDzYw2JRBnqsr/D4XDshFarRbVa5ezZs1x99dWXtieTSXp7e5mammJ21iW9czgcDofD4XA4HI7LwZkau6Sb8GkLe91y1MPG6AE73zyIGHgBWZlti/dVRITLAk9AiuaOJSGTgXRaFs179b0pRLwZUTUuttquQusynUaNruJOINfbj4moWKFTGNWV2HaUhv0dGMFXDSC7//ScTWQFeALpz4cQoXUQGEHSJ8VnoOfuJn1/9QUOZDPErj8NlUVizSp9AzF6etskEjB9ABaX4MJpOOfD4qoI0Lq63J4TttCaA3p9uNAUkXmqLuJst9XlezmmtqGhc9g+r0bYLCL9ORB872n6qYQVspGIQyyGh0cuB8mUXMdFTHRAHDNeWgS6BxlfLVwfR6IRPCRiZTfpmrpRwKRls+s9bJZGLSrVmkbcZBGBPItJO1XFRFLoXFXBvs7WdVJU8FdTcxgZi/sxET2rmPRSaUyR9wQSxVBEIir02URwrMNAnyeG03zw3QAi8Os42P0+iJinVeR5VGej+RgmhrlPw6ZGVF+H57VGYc0iEUPhY/jInGwCqSSkGxuPuZUpWEPMwzPB8Y5jUsc5HA7HbqlWq7zsZS/jox/9aIexccUVV/D//X//H7fffvs+ts7hcDgcDofD4XA4Hrs4U2OXRKWWijI2oFPYt7eFjQ0wxau14HCeToFuBbgHSUdzBBE0/TaUy1Cqw9maiJUPR6ofO4WSXkOUqIn1PZgUWJqaS4XYXkwBdIJjXYHkuq9jCkrXrHM36SwcbKPRHVU2mhlRtBHRVOsdaJRAGunDTAUOrEK12MR/6AS05YjxvhwHb+jnqY08K0s+bR9W1sQAWEZE4gwi2qsgq/2mfZJFxm8wC60mpNtirCywUfTWOVGPuObLoZsBlwvapYKxFrdeC849Hfw4Lh6GMTQSCUinIJshk63QaMKcL6ZGOuiLBMbkSSKpv8IRIhlERC/vwTVG0UDGtYapw7CZcWETZXqAiTRJI+Othppd46E/eF/FzLfNGEbujTxyPwwi9/xysC2FSWd1FCl4fRqTfm0oaJ+mbdL+rAbHqPnGEOlHzNE0cC9iJOicTSL1b65EUj6dxEQfRfWJTTz0t0dnDaLNnhvtoO1fxaRPC1NB+nKpIcfVujzbMY3aSH9pPZV00Lao8zgcDsfl8q1vfYvXve51fPCDH2RsbOzS9u/6ru/i+PHjnDp1ah9b53A4HA6Hw+FwOByPTZypsUu2EkFtoqIXuonTKmDrfmoE6ErvJmJsqBGQAhabsF6QOg6nEbFvr1e5+10+a/oeRVdx20ZOI2iPXpuHCK9JpEaBiq45JI/+9RlYqkHDN9eiJs96sG+UAFlG0sik6OyjzWjQWTT8KCLeFwjS5KQgkwWvWITCOvT1Q38/mSsPctzziH9rlfvvg7W8HGMEEeyrmNohKnTb4zkGPCkGE0OQLsKxhtR5KBCNpmvaSrDdCeFxGwRuQMZhLnitIwLzOjJmV/mQTEi/4PuSfysWh8EhYseOMHDPGsWGfynVVhwxbzzEtNF52cKkMkoiIv40YmzMszfpt6KuV02y8Lyly99R97dthtQRo6CNjHcDMYG0KPowMIH0w3YMDTD1LnoRQyEB3IlJxaRtTyPppI4F7dDIjBamRoZGdvUghtm/BZ8XMenQJoAbsjBWgc9jImwSmPR3mvqK4Hdh04LQ95pyTrGLhHczNOzaMyWkD8P1dDKY9FJfB840OusGhYu0dzuPfT16zIergL3D4Xj88g//8A8sLi52mBrPfOYzef/7388LXvAC6vX6Jr92OBwOh8PhcDgcDkcYZ2rsEi1oG2az9EtRKYjs/fS3MUSw1Hz2Kj7aef3XkHQ0C0FbCr68P9KrjaPqVcSt7+yaEuGIFTU7FF2hPVOHpi9/9yCidwsRbdcxuf+jGMEUWN9qxX8cSa3Ti5ggfcjK+DEPTvtByqQWFAvQXlsnVihAOgO5HIyPk2q1GV8s8NBDTTJJuCEJxYapQaFi69mI8w4AYykJcNDUTxo9EKZNdGTK5RIl1Os5EoiQrrU0TgXvDYxhFotDu+VTm1smFY/jDQ7C2BheMsHAlTOM9S8wtCAmSBoR3UFS/ajoXEHE8hYiqh9D+kRTDu2VqeEhwrzW01Bz0K69EkZrYcQxaZ2iojq05sMa0j+5oO36dzU4xgRyvavBb5J0iu8exgirI/1SQqIkhoJ97SLWDYxBmKLT7Koh5lgF04ca6aRzzI6auWSOxmTMNYJGr+1c0K4Vq7328yjq/td0cpth92d4u57DPm4MMRyvRubRA8izbwkzpgTX1mL78yeBmGlFxJxyOByOveanf/qn+Zd/+Rc8zzz1nvvc5/K+972P//Jf/ss+tszhcDgcDofD4XA4Hns4U2OX6KpeFRXt+hCx0DvWd+HP9upkpVtqKhuteVBh78TuvcI2Mey6BSACrYrneTrFzzZiXCTb0ncqbh9BzI04Ij6GBVNbXK0RbTZFoZEZY4hAOgYcTAEt6G0F5kgZFhZh+GKN8d55vHQasllIZ/BGRui/YoxbirNoyuxv3Anfvh/aLbnWgeC1Fjp3C6g3oV6HUktSbZU2aetWY7yTmhv22Nhzq4IRxEvIWIAYRVrkPZ2QSI2VZVhfbzEdXyNzTQuvtw/6++m//iA3PGGJr5xrs1o3xbTT1rkSwbHUmEojY1ZFokM2K26+U5LAFGJcZTFpl9QksueO3rNppAh1DhkXjQ4K95dGIakZoTVCbGNR0yHVgu0aqYO1n27LYiIs+oGbPDjcC0sViIVcPC2ofU9wTfOY/lxk41xoYsZcz6vmxn1AodSZGq6BMTWiDAY1ScKoGbIZ4edg+NhJTBo5JYFEwlwZHP8U0eZFt+iabvQgz4BTyNxzOByOveaLX/wir3nNa3jf+95HLCaxarFYjFe84hV8+9vf5vd+7/f2uYWPLL7v02q1iMc7Y/7i8Tit1l7GozocDofD4XA4HI7vRJypsQfU6EzDooJai71PFdSNR4Oh4SGicQOzst02ZGyRsYWYGSqc2yQQI2MVU+cihRTxHUJEzTySHgek78eQvp4PfnMWEYe3k0qmiazOVsF9Buity4r1kaD9K6tw/hz05Hz6RkrkhgswMCj5lwYG8I4fY8JvMbG+TjuZplLOMz8HlWXpixoinObpXIW+CJxoQm4R7m5IPQNbt96JSZEO2rvG9upRdDtuFbjbOnccuBbp/2Vg0IOnH4TrrpfMU+t5qK1VyFy8CCPDkM7glysk4z4jnpgJg0B/kN9H0w4lkPlSDq75QnD8bHAN21llr5ELGjHl0b3miKa3Gkbm3BydkRr2PazzTs0KvYejjuuH9j+JqR0RFv2b1m/qSB/Y5lvZalMMuBF4xjAM9EClRWRoUhuJWDgb+nqzeWNHhxC05V4kNdh08P0Acn9WiDYQ7Ro4YQOhWxSaXqtteEaZvDEkWkT7VPu/jtT1qCD3Trf7e6eGWBozVpul1HI4HI7Lxfd9/vAP/5Bbb72VV77ylZe2J5NJJiYmSCaTNBqPn6o+J06c4Pbbb+dd73rXpW0DAwN8+MMf5kd+5Ef2sWUOh8PhcDgcDofjsYAzNfYIrRVhC31gUrTsFM07H9YxdyJy7wS77ZeLpsFRQwM2FlHXtjcRYdJDxP4BTNqlBmIs6DGyyIp1H8mhP4eshFd6MKvjVfxssf0i6WpqxIPfXUTSHx1GDJQsUGtAZgFGRmB1vk66f5F4LgfpNPT24A0NwYGD0DpHvFxmZBTGRmE5D+Wm9M0YYmroiv9WcN57gGpDVsqvW+3KISmLysF+W9VHSSJGTJPtF9mOShkU3p4K+uE4QbHrGPT3QzLl0ZPzyWQ94jGf1onTeKt5Yv19rN0/x4XzPrGWmBo9SAH7JLIi/iKm2LvWitG0aXYfbIbWk9D5NRGcx67PotQRk6zf2qbREGDqRqgJWUSE8QbSZjtKISzga3Hw5eA3Q8E1pjwoezDTlnOX2Hgv68sW+O06OdNxGMiJedRod7/3NZXXZoRT4IWjwvR5o3VTRpF7qhLaL4kYQw1MHRH7WsIRHUofMhcWMePTzSTS1GAppH/t8VwN/t5tKjY1Y7T984iZ9mgwiB0Ox3cuDzzwAPV6nVTKWNq//Mu/zH333cef/dmf7WPLHnkuXrzIysoKw8PDAHiex+joKFNTU8zOzu5z6xwOh8PhcDgcDsejmcvR2x0RqAjXxhTy1oK+zdDf9n72y963DynWfBgRW+36FFH567dLAhEJbXaaqqUbWvTZXmdot9c2TfRzEqkZ8N1INMBRRDC1J6am6/ERwXWOTqG1jYjQ2xXyw7QRofQsYqYUg/ezSP2IWUTcXSrDxRk4ewbWzhfwl1egEpx1dAQOHxbXI5kknfEYHJQV9llMaqUBq18awXWcAr6EKcysHACeA9yKCPbhMUoix9ZohRgiRE+wc7fSp/vc0lomLcQcqLRhdh5On/KZn4N0b5KesRzVUpPKmQXKd59k7qES6+uQSsJQCpJxKLUDoR6pnZFB+n2dzrmhwvlWDycfGasCZkX/ZqvsV4OXRgHpav44cAiZf9PIvafHqWNSR9mGj97jdeSaDiBGQA747ji8pAd+pAf+yxA8OybXVKBz3oaju/Tdvu5KC2bX4PwqXGiYCA57LujcUhJI/21WqyKc9k4/q6GjESM1jBGTQSKBjgLPAr4rdOyEdbxu9Vro8l14zrWR/tLaH3aftDDpwy4XD7l3BpHrSmCeXe4/ig6H4+HkN3/zN/ngBz+4Yfs73vGOS2mpHi/89V//NV/60pc6tt1666286EUv2qcWORwOh8PhcDgcjscKj69/PT2MqFFhGxj2e7eXpmqqIaKa1sfIAd8zBP9+BJ6KCIkJzGr2yxH0PER4PcDGmgB7kSJLTY1w28LCqYcIiVoEXYXLq4DrEVMjLHzGkSiHM4jYaVNBTIetihJvRhVZpa21EBqI8J1HRPA5YL4FM4tibMzPQX1+FRaXoFqFXA/EPKhJQpxUJsbQMAwOQG/CmA/DSAqkOJ2pc4psrCmQQ/qpP/iNfbOqKDuERAnoKvsSG4XxveACElEyD+R9uOcCfOWrcPc9sLLUxo8lSCaD2iBFaDahtxeOH4ErDsPYMPTFTVRNH2LAJNkoaMeDaw+bb1sRp3uUAMj8mEdSNT2EMTVSiMlyEBl3NUnCkQe2Aal/61wBUwciE/y+6YmpkwgmcxVjaugYxeiMcrBpAieAfy7AZ4vwxZa0LUOniTGKRNJodNcQEhVkFxUP3+/a/rC54QfnXUPGfA0TydAPXAFch9S3GcGMURYZM52XR4OX3c4KpjaJPi/t6JewsVZD5nM4ImMrAzYdtEH3TYf6Qq9XI3VSSL8uIXPE/UfR4XA83PzSL/0Sn/jEJzq25XI5fuInfmKfWrR//NEf/RHVamfSwJe97GWMjIzsU4scDofD4XA4HA7HYwGXfmqPUGPAzglv44Xe9TdNOoVF3acN9GXh+Cgc6oOzS/DJotQ66CbabieF1OWmw9oum6XHsVdwazHiGhIJkUWE0nRE+1KIYHsWUwfBphWxbbf4iKCaRwRPTU/VW4O+i1IjPNtT4XDvPPF+KY7N/LxUBu/rJVdrMDZWlnRVa5BfNyvqJxGxeC3iOjOIwaEFkh/ARCSEaw9opICupK8iBb57EIF5J/2SQ8ZEV+j3Ygppt5HIlcWg7VPAbAO8ORmrwYEmqUSRvh6Ix6HtQzoD4+NwYBoqFZiZhWoNKuumWDaYtFN2XRUt0p0O2qWROpulFFNToExnpFAYTW0VNpA8pEj8PDLeWktE940qDq6sYcaoAdzTgrkSxD0YrcADLdmu9VQ05ZoWSbfTQtkRTW3EBFjERMlonR77/EU21rjIYEyOZHAe+9mg91g4Mkf7uRocYzjojzpyj04i0WN1pC8PB9efxJgPA8Azgt8tYsxGNd30PPofn6iaGvostY3KOKZmT7eoLDV14sizYjhom46NTR0ZE7tYfVQkm8PhcOw1q6urvO51r+PgwYM89alPBSCTyfA//sf/YHZ2lk996lP73MJHjjvuuINGo0Emk7m07Xu/93vp7+9neXl5H1vmcDgcDofD4XA4Hs04U2MPSSCCrRocUauv7Rz63VaV9wLXeZBry4r36VEY7oMH7oaH/I2FtTPISvM4ki4p/L3iY9L97GfeeF2FrUKimhu2uZPBrLLWIr5r7LwA8G5oIcLsAKbo8wKQz8Pst2AtD7HYGgczF4j39Uk18b4+6O0jFosxvFhldKzNygosF6Doixjbb13TGUxxYo26mEf2uxh8VrE+hcyNdYI0UBghWV+V4BhjiCGwzNbF0uNIyqpccM5y8PsqJnJIBWatl1IDyj6sF2BuDvr72/RcCX0DHn4iQX9fQ9rkw8KCzON8HkoVaDQ6a58s0Rnho+99QV81MSmwThAtaKsJVaF77ZFucz4bHHMGGW/bwAj/Jiq1UhUzDh6STuwU0PQh2TQRAdouFfRzmEiGfHAMu8i51nixUy3pcWyzKmzUtJE520T6JEVnui3dR4mKfEgic2IQMcoWMfUtqogRuYSYBiD3RTE4bt3a1q3Oh0aqRBkam9XkGKKznk4YfU4kg30ng/Z0M8PCxt9+PhcdDsfji/Pnz7O8vIzv+3iePInHx8c5cODAPrfskaXRaHDbbbfx8Y9/vGP7X/7lX/L0pz99n1rlcDgcDofD4XA4Hu04U2OPSCKdaQuHUfUJlM0Kfk8C16ahXIUHTsLEOCTicvzwgHmIAH0cEUa3QtPn7CcqoupKeDsNzzpS6LmAqUXRwEQxDCB9vJVQf7ntitMpiteD7UcRUXUJWPLhZANmT0Ch4POS3kXGnnIL3uCAhCpks3jlQXrHVhgbK7G4AH2LsF6Wa2gjgrGmCKoF16fis64otw2LdtAfdhRL1PyqIgLuEJIeSI2IzcRanYvV4Nq1LRqxoL9tWvvUELG9UBYvZ3kZxsYgfShNbHqKWCIBfhvKZcbii5RKTdYL0GoCC/Ku0QdTmDRDXnD8pWAsxq1tdlRDGBWzL0eULiFmTgkTsaLRJOEi2PZ9GzY+EkGbK9a2qJRoOp7DSCqnMSQC69tsLMq92TPEPl6Yq4D/kIJCQ4zQf0MiF6Lu/fDvk4iZMYj0yRqmlkce+GJwrCkk9dUinc+edeAf2bzuRRIx6OwIG53v3X5j19nYDI0yGcc8P8IGc1Sf6fcu/ZTD4XikeOlLX8r8/Dw9PT2Xtr3nPe/h05/+NOfOndvHlj2yzMzMUKvVSKdNrNz09DTpdJpa7eH4Pz6Hw+FwOBwOh8PxWMfpN3vEZiZFFJvtOwv8dRX+dA0+VoOPnIfPnpGV32N0Ghu6IjmOCJCbpd55uFAz4HJ+owWNV5GaDd9ERNQYIuKr0bGCXOe/B56NyZkPJj3VbkkF5+hHRFFN51VAajB8E4mEqWNqbixXYHmhBY0mHD4Cg0OSm2poiPjIIMMjHqNjMDwI/Z4IuQPARExE7T7ExOpDxFpd7W6jc6VCZzofGxWJtd129EY3bFF+FZl3WqS+2+r2CiL4q6lRbMP6Oiwtw/IK1CoSXuTlsngDA3hDQ2QnBzh8LM7xY3DgIIyNwoAn5+kJ+vxQ0C9q3LQRoXwOU7B9HmM46fWGTZ6tiCFzJYuMsxdc05ng2NOI4ZSlM5rK7/K3RmdkgauR+jdTGJMujswru3C3PiuWgC8DX0fu3XCEiS3wh8dzs7oS1eC8Rw7AkVHIedFRY1H4wbUcQEwxjSABMaHWMOmw7Kg0myYybut0J4H0/9XAzchc0Dob3dDnwHaecVqrp4LMk2uReiATmFouNklk7MeDtjgcDscjQb1e32BeJBIJjh49uj8N2ie+8pWv8Pa3v71j26FDh/jQhz5EX597KjscDofD4XA4HI6NOFNjl2jOeo0qCItll3O8OpLy5TSSxuZO4EtIepxhZDW5osWJ23SKiw9HsehuZNkoMEehbVKB1TY1qkiqJF1lnaPzOqvI9a8iArAtfg5iBOrdkkPE3EHMWJaQ2ganELFWzzkEZBOwnvdp3vegVMiOeZBMQi6HNzRE71iW8XGYnICxAegLGrneFrF7FBHBfSRaIM/mwm434V6LzuurjknHFP5NDJPaCWTeaEqrZvB5nuhomHzw0uL2JaBYgfU8rKxAYbmOv16AVgsyGZicgqPHyB2b4NDxBIcOSq2N4V4Y9IyJNImIyinMHGkE7ZhH5oZdDF7NiZ0QC85xHCkKngp9r3PRjtLYzNDQ+jQpxJAZQkTxceSesM0MvU81mqONKcb9dSRKoxBqj52OLcqg0n6KwgeKZThZgHt86b/t1lcZQOrb5DBzQI2v85gxyCJz93JqUGh0VgoxtrY7ltuNMqsgz4uLyPx6fgZ+ICFmTQ55Xtnn7EOiZq5Cxs/hcDgeCRqNBi996Ut54IEHLm3zPI+PfvSj3HzzzfvYskee++67j9XV1Y5tP/qjP8qb3vQmksnd/t+1w+FwOBwOh8Ph+E7DmRq7JIkIsiNI0dyj7C6nl4qUmiZKV0SvIDnqm4jorzQRAa+FiHQJOsXOzYTPvUJTA21npbwKvDE6TQ79Lo4xhyqIgKr7FJE0Ot9iYwFhjVDYDS1EzE4g1xK1IlzTQB0DrkxCfwaqFcjfexFOnoRSKaiUnYbBAVLjg0wdjHP4sIj52YQYAacR0bUfEbO1noMW5t5pGiWtp1GyXgWijYkcIurbRlDG+lt/3y1V0WJw3GpwzkJDamusrYmxUV0u4ReKkMnCNdfCjU/Au/IKcodGmDzgMTkJYyMwkYPJuBh1/ci7Rq2AKQitRa61NgWYeb+TVGo5ZLX+VYioHZ6zl6JPMOMQFZlhGwwxRJTvD/ZdojNCIWp/jUTRtGB29IlNi2gzw/476t5uIVE3X1qCf6pKsfnt1qLRQtvH49DjmftMTY05TCqqAcQAuBypqYbMz1LQ3u0YG9t9jmkfNzHRRgfGYCIr97ZGhNj92EKeO6PI3HA4HI5HinvvvZe//uu/7tg2MTHBBz7wAa655pp9atUjz1/+5V/y1re+lXa787/s//2//3de+9rX7lOrHA6Hw+FwOBwOx6MVZ2rskhRiaPQhQvFRdmdqdBNQtWh1ERFndXV0C1k5X8asxA6nmtlu6pnLpR6cfzu5/+3aBGFjI/yuoq7WeIhhIjXsc62ydYTDdtBohwxbmyQNYK0N61UJzKBRh7l5yK/L55gHfX14B6bpOTTM2AQMD0Em3mnarCIGxwJGLIbtpY+KQs2NFSQSIGp1fhJpg4r6cWTujmOE5STdxepi0N5acK6SD+slWF2B+XlYmm3QmFmUP3wfDh6Ha64ndugg6V6Jj2g0wfNgICUi+gCmEPQUWwvLWo9ju8SQVfqDGNE/nF6rgESFrNFpaHUzmbTv+hCjq4iksVrC9G2UIaHbdI6lkWdIT8S+Wsh6O2nElAZSUP2zLbiX7RsaShpo+DDjmxofek8mkWv2gv0ux4ADGbuV4JVAzCw7MksNzu3UwQi/dH5rmi8PqNdhriqRJmvI3LWfF0XEjFKDyuFwOB5JPvKRj3D69OmObU972tO47rrr9qlF+8N73/te3vGOd3Rsi8Vi/Nqv/Rovf/nL96lVDofD4XA4HA6H49GIMzV2iS3C20LxbohaEa7pas4gwmvL2ncdSbOyxsbCxo8UKh4O0T0nfdvabytDw26/puzRWhFhqmzPVNkOa4jw2URW9U/RGRkDIvReBO5pwUwdRsdg8GAv1GuwtASrq1BvSPql/gEYHGBkPMGBAzDQK3n9J4LjVhFBXE0Ee7X65V6PRgGEazQoVWTF/Urwt4eZX7WgHUcwRbq1vonSQswYra1RAvI1WFyCuVm4cAGWz5dZ//qDtD/5STj3EIxNwHXX0XNomIEBiMWke0aGYSxj0lCpqDxFp8i9W9rIfXICuB+JNgn3TxuTeqpbdIQf2tais/+WiU75FU5fFUOE/KuQay8i/Rhmu+ZWWPwvIOZK2PjZznOhANzdlhoyGrmkRlwPMhfayHPoG8h1Xw5NpL/Wg3NoCjsPMbmmEDPTw6Tyino+hE0NTWsHJq3bp5bgsw2Z9yWkX/S6MkhasqFg29hlXo/D4XBcLnfddRcrKysbtv+v//W/6O19/MSPNRoNfuM3foO/+qu/6tg+PDzMO9/5Tm655ZZ9apnD4XA4HA6Hw+F4tLGboAIHnWlnepGIge3mro8iHrzC4mkCEQHr1n4q3NWR9EUQvap7L8T+7ZABbkDaeSfRqY+gU5hUV03/9kP72cKxZ23rFpWhBoim9tkpDWQM00jx6kJwPE25BHJ9hWDfUwW450E4cLBEzffJVC8SS6Xw0hlIJiCVxBsYJD3ax6HDqxyYhkoZEmVpYwURig8hERsNzOp8uw/2kgpibNipnE5bfx9A6gvMIBENbcyKfW1TDTE2ehBDKQ+kCpCIS9apVAr8C3UGLl7gqom78aaeCMdeSvwnnso1N38G3vUpTjzgE09APAGtWWjVTWogfb/AziMNujGHCPGXEwETRudlA7n3KxhjwkPmoV2I3TZFdB5PIM+PLxBdlD3qnLaQb2/TCAUV/+tsfA71IOJ9AVPsO+ocmhpN02hp+i9NlaWmjB5nN+OTB76KGCW14LgtzHNBTZQYJlUVbDRm7L7RvtfnxIPAQ62Nz8bh4Pgt5P4bxjxrHQ6H45HmR37kR3jooYc66kccP36cVCpcAeo7m3w+z/z8PL7v43nmaT85Ockdd9zBsWPHqFb36v8MHA6Hw+FwOBwOx2MVF6mxS44gwuYM8DVEQItaIa9FoYfZWKBYSQNXAtcg4mMOEeVSiOg2RmetDH2BqWuh2PUAtksSMWYutxxjAhFpjyDXaqNCrwqO4boaYaMjnD5ru4wghaDHuXxx0keE+nNImqUCRshtYGqY9CJjM7cASws+63lYX27gLy/D2ioUi9BqQzYDhw4y8IRD3PjEBAcPwGBGft+DmEHZ4HP4WvvoHvmyG8LzQueK1krIBeeNmqtZpG+1JoLW71hvw3oZCgWYn4Mzp2HmfJvW0ho0ExA/CpmbiV91I4euyTE9DdNTcOwYHJyE0bhJRdWLpIo6wOUVou52zVH3RIbucywqKsB+B5OOSyMttoquaCHzaxVTu2Mr7H3CKdy0CLn2U5WNRoOO6zQyruHr9ZAxHwrOFU7nZpsaag6U6W5cRhH1Hxsf6b9lTP95yHzS1FS54HrslFFh09cel3pwzPC+9m8SyPwaxURk6f39SEa4ORwOh3Lu3Dle/OIXUyqZuL14PM7nPvc5+voejv8TePTy3/7bf+PLX/4ya2trHdsnJib4+7//e6anp0kknAXtcDgcDofD4XA8nnGmxi4ZxBgIDUTUH8TkdFeyyMrsg4hgGyWcDQJPAr4vBs9A0tO0ESExHfxO88zbQmtUSpyouhybkQBuBJ7nibFyORPDx0QA2GJ4AjEZJjF1BKJy4UellOlmfHSjhQizhxARd6viw90oAWcxK9bVMIohgqmmu2oBC8tw5iwsBmUk8mfWaJ29QPvkafxz56Dt4x0/jnf8CiYOpZicgtFhGI7LmGYR4TYq5VKN7mmkdoKu5LfHVdPvhKNlykias7MYYTiGiQ7S/rcLnGtkQKMtZTTy67C+LkXUq/ka1PPAKrQW8Ool+q6c4KbvynD9k9Nc/4Q4R4/CcJ/cA4PI2PUE7+F6C3tNju5GoxI1P+2aD+H7q9Vlu/39aaSI93Zrg4SPpfdFAplDHsZksmuCxJB7YhyZb1HGjqbDuhYxJUfY/N7x2ViXYiu61WkJm5haSL2BKd5umzRhkyKceqse2j+FSW2l5DBz6xakFtIaYmSubu9yHA6HY8+54447+PM///NLf3uexxOf+ETe/e5309//+Kr488xnPpNXvvKVLC0tXdrmeR7Pec5zuHjxIi95yUv2sXUOh8PhcDgcDodjv3Gmxi6xU0X1A08FnoWs/s2G9lVzIUqU94DDwJEkHOiBSc8U4q0gAqj+s07Fxr1MSzQAPNGDp6a3LtKs7Q1PHi3+a69aBxEQDyCmjubI38zI0L+jxNdw2qowFUQEtSMPdor+RtNAhaNiashK9ioi6M+V4MwZOHcezp2FUydanP/6IvNfPEX95Dn5UV8/xOP0DiSYnobJSRjsMXUkcph6Enabq3SmfroctJh1H51CdRoRdYcRs6kfmbOzwD1I6qeGde1xREC303+tIymI6sHLT0I6DecvwmoBYnGor5Zg5SRUvwEnPwl3fgmvr5fM9cfJXn2YocP9DA1LnY0UMhcHMcaGGoV2v2ynNsR2qQfnijI2bGOtWyQRmJovUb/vRg25XzZLV6fGE5h7wZ6LccSwOICMXdQzYRx4DvBUT/oyyoxQQxJkPowQbUCEU8ZFfR+FFxw3vNY4qi+VFmb+R7W3m1kb9cxoWMdQg68abE/GoBWDu5AaIWe6tMfhcDgeCf7lX/6FQqHQse22227jl37pl4jFHl//2/7JT36SN7/5zZHppj7wgQ/wvd/7vfvQKofD4XA4HA6Hw/Fo4PH1r6OHgWVEDE0gwugBJEogLE6XEbHYrs0QFvNygNeGeh3KvhgDKiCvBefSVftRURh2vv6d0h+8iu3O1C3dCK/aBxEILyIpuOzVzoOIoTGCCJs5uqeYymCKRmvNCVuM7CYqK+Xg/KfpjLDYDuHIEHslfJzOeicryHjUgvNcnIVz5yRi46674MtfgdNnoFpqQaUCy0swc5FYHMbHYWwMRoZgKGnSPaWDPtrLhAoeYlKNI/PUFnzbmAigQWRsksi8K9JZMLtbf/vI9S8gq9zLHrRaYvSsVCQV1YV71ql+8Utwzz/R/tf/R/vue/F9YGAAUmn8ZoNsBkZGYWgABpPSJo2gaLFxHNVk6cZO7oEWIraHC8JHmW+2wWGblJpKzD6/Z31/OenUPGRujAd/67NA02W1kLkyjaSmS2MKettzdhSJvsoh98Uy0enHljCRN2Wi7x27H+x7IoGJxAgXvCdoc3hu6zG63c8adbFZiivtk61S7dnH0OdWGZnrq21Yasuzq4g8ox0Oh2O/+D//5//wtre9bcP2173udcTjlxt/+tjl/e9/P7/zO7+zYXt/fz/vec97eMc73vG4S8/lcDgcDofD4XA4XE3UXTODiIaa9z2PCIsX6Fxh30CEfv3n6BCySn4ZEdF8pJDxl1uQbMmx1ommTadoaEc6JJEV23lMCppuKXJsloCv+LBeF3F6K+wUV4qK/fo9QTsPAVcjguEU0mcPIf1jFx9OIvUwrkIExgVEjGywMXWSLSzbaK7/SsR3mxElWmu/RQnkbWR8RhFh9GQVeuvQ68k1Tw7DtddIOQ0K65BISF6mbIbe4QqTkw3W16FUhsqy1KNQUXos6KPdpp2KYQyLqOLRNWSeppH5mCR6lftm6Ar/8/pbH4qloF5EHWbnoNVsMTT6EAfbFVozc3j1OvF6DSpxqJRJJGIcuiJJX3+DCxfg5ENQfQhqbWnjXNDGKURwV4HaTo0VNuEyyDzYDgk6i1PrdUcZGuHv9O8cMl6V0L5h9Dfx4Jr6MCaCoqbAIcQMLCLjpAbJIHJ/1zCmwkKwn5oZ9r1ZQcZnwYe76R75o7WBEmys0RPVJ/Y1Rl2vHemUxdRgiQfXpfeQnsc2iqLqYGxFVORGCpnbq8F5YhjzStPHzSPPYU0DNruDczocDsfDwcc+9jHe/va3d2zLZDK85S1v4U1vetM+tWp/8H2ft7/97Rw4cICf+Imf6Pjuuuuu49prr2VkZITbbrttn1rocDgcDofD4XA49gNnauwSrXnQQITFE4j42i2tjG7TFch2KpiTyEpqMMJmLPiNCom6KllFRFvE0zz/vYhwqaZGLjhenu4iYQEpdK7H3woVBLtdn9YbaCEGxRQiMKYQUTaLiLCaM99DxPxpDyY8WGgbkVLFUVus3Y7Yvl3Chob2bfg9fPwK0qe9wft4W0yO/hhks+B50Ki2SJbLeP0D0N+Pl0iQxGO8ukx+rcnSktSf6KmJ4FtGVvyrCHy5aB2FY8E1qegdjtSoBPsdQaKBamyv78L71BBhuNGW61lG5t3cMtRrcO6hJpNjF6hVWvg+9J4+g5/L4sUTxPp6yE6Mkb0+zsj5eTKZPIvLUFw2qbMmkCioEmICXgiuyU4rFL7+7VKhsxh8ODKom8FlmyFxxNyJsfHcdrSLzl2N7OrDFA7X46SQqJknBPucsn6rbdAHdwsxBooY0T5MAzE15tg8lZkf7NuI+C7cB9sx/rSAOcjzTo2DfqR20DqS5qzYpS17kV5PC40XkfHRvldDRY3YEiaSabs1ThwOh+Ph4vz587zpTW/ibW97G54nT9pEIsHrX/96HnroIf70T/90fxv4CFMoFHj961/P9ddfz9Oe9rRLfQJSZ+PHfuzHOHHiBL/xG7+xj610OBwOh8PhcDgcjyQu/dQuKSGimIqrVTqL1HajiBgYa9a2JqY2QYVOcS3DxvQ4mmbGjsSoIaKvCnh9iECaZmua7Kzwbzc8JGXOgaB9M8D9SFHkzwNfQQRpXXWvwvAwUPHha23ZpxQ6bguTZmYv2mm3N2xsxEN/62fbTNLInCoiSqtBU/OhVIKFBVhdbNJazuPn16DZhEwWb3CQ3FCa4REYHYHBAej3Omuo7Ob6NHJgDDED1NCKEms9jImyVaofxV5Fr2PRQO6D0xW4sC6f1xGTo1SEb98NX/iXFnd/G77xDbjrqzWKF/LQakJ/H96BabwjR0gdP8jUkRTTUzCQkrYPIIWcb+iBJ2fgacAzkaiecN0aFfw3q1MRRk2FJp3jHDa6wp9VMJ8I+tAWzTeL6lCDoowI/RXkOvuQ++YK4GbEIIsh94FttGQxJl8q6OdacF592amhqsiz5uIO+8XGNlQyiDHZY53Do/Oc9sv+j0wSuc8nkX5LW32j59nLWkHabwMYU1jNyCWkfsYp5Dnchzyztioa73A4HA83zWaT3/qt3+KjH/0ovm+eirlcjg984ANcffXV+9i6/SGfz3PrrbfyQz/0Q9Trnf9Hk0qleMtb3kI+n2dkZGSfWuhwOBwOh8PhcDgeSZypsUsKdKYKiiEC2hhbd+5mwrUaJCr4a4FmpR9ZXa/FtxUtrusHv0kRXfD34SSBRC/0Im3X1c+ryGpxrQ2SQUThPqTPQCJdvhXsA9L2IURADffnZml+tstmdRNioZctTmtarAKSvkaF/XWg4MNKAebnYeYirJwr4c8vSl6mdhtSSbzeHP2DMQ4c9Dh8CCYHoMcTwXoYEY13G0alBc0LdDfa/OD7s4jIu5Xo3bZeLeu9iojvpzGmlQ/0ZyGegvvm4G//Ce74HPy/L8L990OlHofpaXjCTXDDE+DQQbyREfpGUhw+BFccgGvScJUHV/bC1ASMj8B4Do548GTgMJ0pnQaRPoyKNuiGmoENjCHQLd1U+JVBjAitoWPXhwgbGfZLTTGtXaL3iW2KNIN9NKUbwfYUnTVFGlZ77RoXehwP83zSWjiXm5U9ifT5s4HrEFMifJ90e2kbS8g9o0at3SdRBcBjyDMibGBtB42i03tJ+92uTaSGdBLp262iwBwOh+ORoNVq8eM//uN8+9vf7tieTCY5cuTIPrVqf2k0GvzDP/wDb37zm6nVOpdhJBIJ+vv7+fSnP+2MDYfD4XA4HA6H43GASz+1S7QmhFJFBDIV4fciokBNDfs8GtGRRYTBOp1pbuzV4OFUVlthp7q5XFp0iq3VoI12QWFb5I0jhkAbuc6+4D0VfNZUMXqdKgLvdcSG/TksakNnv6QQQTuLSWFUQ4TefB3mFyCdgUQSegZK9PQVIJWCdAp6ehgcXSfT65PJ1Emnoe8sXFmAuTI81IRvI+ZA1WpHuA1R+Jh0O+tI324m8lcRYy4cGWOjKc90vtt9oynC4oiJkkeE+Cmg7Uu0xlywPd6EgSYMLEIjkYVrroFrb4R4DxTnIXaadDbG9HQQ2JKRGutT0zA2Ciurcr50GobakCjCSkuikwaQCIBi0K4S249M8JHxHAj6SoVvu9B3VDoqvcdywauEzIGw4RY1dn5liQpUAAEAAElEQVRovwYyj0qIqZdE7m29V8K/rQf9nEHukbDxpnVq+hCDsRgcW6NK5oJj6HNqq3tJo6RGgOsS4LfghG/uc71O+/zhVHFecO5TyL3TCtqvz82oNmSAg0g/z7BxLm/2vGoiURi2IRJVd0OfoTuZMw6Hw/Fw0263efWrX81Xv/rVju0f/vCHed7znsedd965Ty3bX97+9rdz5ZVX8pM/+ZMdqagAnvzkJ/POd76Tn/u5nyOfz+9TCx0Oh8PhcDgcDsfDjTM19pgZZLW4rr7eLbpyW8VNXXmsAqWKdd1qTej+vYh4Z6/YjxICdaV3o8v322kvmKLo2v4SpuZA1CrxsKCp+fg1kqCJWXluRwrYBs7lYPeb3X+6Il5XudvidgIxLgYxBdC/HlzzAEaMThUhswD9fTA21iIzuE68pweSCUgmqTegvN5kZBh6ej0mp3zm5uCBB6C1DMXgombZ2XjoyvgWIuiGU/zY6HXXiS5MbvePitqwUfi1+y0TtPkkcH0Nen3ZX9MoxYCVKhSW6/jFMl6lBNk4xDxotyCdZvRIjmxfjYGBFusFmJqCnh6JfKnXpV5JJiMNunZdBO9exFiw0zKtdLmuKDJIFIIK4VrAu0Xn3LD7Moncg2o8aD/aERd25EGM6LGwzRKtQ1MCbvHgqR7k2xLdoFEldqRBHZkfWTrvLQ8Z+0kkcmwZmaMq4KvppTUmNFplM9raxrb0jV3fJyqSKnxv6n07jxmzGPLc7GYmaBSU/WxSNNqrgRmrMDoeUcSRyKieoA3daiE5HA7HfjE7O8s///M/8+xnP/vStuHhYV73utfxqle9av8ats/cfvvtTExM8MM//MMbvnvFK17B3Nwcv/RLv9SRvsvhcDgcDofD4XB85+DST+0xFUREm0YE0p5dHk9FQU2tM4msvAazSlxXOdu1DrA+a/FfjdaISqkUXmG9k4lhp23S31UQsb+NSZ2l34fTzHRLOwMmcsOOQok6925QoVVrimhf9iCCabivVLiPIZEUS8H1LgV/LyJRCes+5AuwtASLi1BcqtBeWoFSGWp16uUm7bZH3w2HmLj1aq64qYeJCejthZ64nL8fSb+13XRBMWSOXIWZJ5omqduYDiKpnKJqVCib1TrQ4u9JZJ5pZNACcNKHwQxcnZZ9CPaZq8C3vl5j/f99A//b34TZM3KGqWm86WkyVxxk+KaDXPm0IW763mEOfdc0o9eMMjEdI+bB6qpEcDTqcm09yHxbRsyAOHIPTrC9+eEjkQNPQvpOjQrt925GhBpYmioqnNaojplbTTYaLOH7r4aI6+eQVF7ZPvihq+F5OTEAmsH3WvDbriGSQKIoBjDRBxPIuB5GImc0zVs9OJ8ad5qqTo1Ie67b165p8c604YK/uWHU7Z7VyB4fE2m22fyqIYXhlzB1TNTgGA6u7ergc9Q4haPplCRi9kwi86eJMzQcDsejj4sXL/KZz3xmw/Zbb72VZz7zmfvQokcHxWKR173udbziFa9gbW1tw/c///M/z+te97pHvmEOh8PhcDgcDofjEcGZGnuMh4hux4FnBe92qqXtHsNe5ayrkD1EhDuCiN16vLChYZ9HjYwqJk1MWLS0X1jHsosAb/aKMkpUpNRjtum8pm5mRjdjpht7tf5O26AmSg1TiyKqTZpGZy54n0TGpIKI+cuIyL5eg+VlmJ2BmXNtagtrUjQ8v0Yi1mboukkS3/tsYk+6mcx4P+22FBmvt6Tve5CC0cNs72aNIYWmn4WIvSogq4gcxgvafTPwDOAGZP5uRgIRy3uDv1OI8ZJE5mk6OPcIkPVgdBCunoRRT/p2HTjTgs/dD5//+CL5f/gy/Ou/wokTUK9JGAY+XjxOemyAnuuOEj9+FG9inEx/imQSYjGJFtDrKyPRFeeRKJGVYFs4BdRm92AaEdgriDG1iikAHyWKt5E5chJJpxSVvis8x/Ve7XYv6bWoudbbA0ePwBNGxJDwg/7T6IMDiCE1hhRTf34PPDMmhs4oYmSMAQNxGbMRjBmq9Xo0CgdMaji74LhdZ6SN9O/dyNzvNq+ge8ot7TtNY5bGhAxGjZGPqT+iBq+iRkQ43ZdNCxlTux1xZP6OImOu94jD4XA8GvnjP/5jbr31VhoNE6t2/PhxPvjBDzI0NLSPLdtfzpw5w5//+Z/zPd/zPfzu7/5ux3epVIpf/dVf5atf/SpTU1P71EKHw+FwOBwOh8PxcOHST+2ScK0LXfE84MF0D6RKMOrLyuiziCAYJZ6F884rKoSuY1aGq9kQJSaGtyWRFeggbbBTPG0m8NrfhVPodNs3KooCTP+oKKoppMJC5GbGxlbsJgWVnTrJNopKmELI3cTWBhKBcGPw/TeC380RrPb3IbEGiXOQTMHwSJtMdoV6tUW75RPzm9A3CAmPSqnNwjwsrEDFNxEIdnHuVTavfaBi9wBiTqgg3I02kgroAcSIOILM0/Imv7kaMUDmgM/Taao1EAPmqcH34554FMursOTLanuPwDBqwtBdkPQKDH/tBIMjp5iY9OjJtEgmAd/HGx3Bm5qCaoXmmXOszlXJ5mB6CuoNqb2erZr6EQOY+iYqZNv3is6/FBsjJxaAf0QiIZbo7Gc16ML3XT04V9Q9badf0xRKUYQNQn2mxIB0DOJxyCVkPtjkkMiSK5H0Z9em4blPhKVlaN4v16NRUvEYpH1ItY0RYKeCU6PFNjns+1CfOS2MaaTHCBuXYcLGpc7lItJGNTg0ckTvq6iorXBUmpqHGuUS9Qxo0DnOep2anq1qHdfhcDgejczOzrK+vk6r1SKZTF7afvz4cf7u7/6OZz3rWfvYuv3nrrvu4k1vehM33XQTz3ve8y7V2RgaGuIpT3kKn/rUp7jllltcKiqHw+FwOBwOh+M7CGdq7DFxgvoRHgwOwC19ML0ASy0xJuZC+ycR4bzG5iuFm4iQ2Aj2L4a+t6Mg7KLOdUzx6m7mQ7dtNtuNlohanR1eSR1euW2vXA8f117hDp2Cpn2s/uB9nc3bGoUK1lERKN3MnLBAezQBR7LQV4YvtGQO3IukoWr7UF2AchWGBmFoqEmlAq0WxFeKpGfOw/Aw7UbrkgDdG1yoXm8LEbG1mPpmzCAmxdI2+sJHjJJ54AnBeTY7fhq4BriuD7I1+Je6MRB6EJH5a8g87UdqQXx9Vo7/FUwBbI06uNiA+x+A5r1tms02oyMwMQ5Hj8LxKzz6DqRlMBYWOHN3icI6HD4MoyOQX5di7MOLMOVL/2gR7NMYE09NBTuNWQ457BrmvltFIjTqEdcdTs+mn+PB9djzUsV52zCwowts8yxsZmYQM0xNg6UirK1Co2ZSRdnXMOwFKcN8mK3Btx6EvoSMUwWJXCkDT4tDbxIq5e7FsKPMCb1W+z5sYQqMZ9n8HobOe0j7SY+zGLxrDSI1jboZQOF2a2TLZui5w+ZkNfit1u7R/ZI4HA7Ho49SqcRLXvIS7rjjjkvbPM9jZGSEvr4+CoXCPrZu/6lWq7zwhS/koYce4tChQx3f3XzzzfzJn/wJr3nNa6jVavvUQofD4XA4HA6Hw7GXOFNjD0iF/j4FeG3oW4MDWah5IjSrgKfEMeJu1MrkqMgFFZBt0U9rGmgB8QZGPNQIEegsXGyL99shHD2iK8p1Bfp2oitsYVNNAzVgoiItwoaCH9pufxdD0smU2brYcbe2hcXZsEkEG/shhaT3GcvAsaOQXoDFeUlJNI9EyQynYCgFjRbMzMDYGMTiMDgIca8FX/03GBwinWgyOQnT43LsYgn8NVObYQAR5hfY3AC7iJgfq1tcs45BGum7/gTMNjtFYk1fpoaApieyv/eDYxwMPi8C92GiTMqIuaMRIzVknmaRugyxddPvczMwuwStJgwP+WTWKySXlmmtFoh7PkNDMD4Vp9Vos7To4/twLA3NqhG4NVXbmeBcOt/s+bMS0R9NoueOR6dBEe6/JEaYj2Oie8LzWVM6paz97WNlkfniB/1TBx7Iw1cegHOrJupDf9cAFn2pnRJHUkItLUqUTAExtc4jqcHaPngxOa5GNITr23Qzs8KGBNbfTUxEStQ12/v6bDyffUyN+oh6Fu41eh/rmNjRTJfz/HA4HI5HgpWVFWZnZzvSKV177bW8733v42d/9mfJ5/P72Lr9p16v8+///b/nk5/8JAcPHry03fM8brvtNu6//35++7d/m1bLJRx0OBwOh8PhcDge6zhTY5fYK67tFcAngVYJriuJkPhVROQOi/QlukcD2Me2Bdmw6JZBcuWXMBEcUcezxXn7uOFr8CO+09/rfglEZNcULvbqdvs4YfHUPo6m0tJ0QFpQOcq8CBsZ4W3riJgdVYh5OwLpZucK979uiyGC8TVALgYrq1CsSDTCLDJOWeDqKTh+CGZnpb7GfffB9DRMHogTHx2CTBYmJ0j19DB2/m4OzlTJZKFYhNmvw4ovpoMKwVWkz7tdVwVTSLobOg+0bschoJmAC83OPsxhIgc0XdMScK4o820w2DaGCPJziKmxgKkREYUaEKeCfYeDc60B5TqML8KZs1CpLtPfv0y7JWmYhkY8suN9tMtV0vkqvT1wbBISM7BUl3ba9R/OISL+dujWVjslWbe6DZqaKcHGe8eeM1roWvvSFvT1Pq4Fx6sD9yDpyOqIMZS02lACvh0csyd4lRAzZxXp1zTSt4s1KNSCyKFNe6GTboaGUsfUDLIjUOLIPd2gM/rHjtQImyl2pEg3NDKmW6qprbCf01H1hBwOh+PRzJe//GVuv/12/uAP/oDBwcFL23/8x3+cixcv8oY3vOFxL9h/61vf4ud//uf5wAc+wOjoaMd3v/mbv8mZM2f48Ic/vE+tczgcDofD4XA4HHuFMzV2STjawTY2zmCKFRfYKMLZoq+K//p5JzUitDh1nY2Gh64ytwt124ZDlCnTLU2V3SY7nY/m6I86jl6jbrMFTt2mK//Dq8Vt0XMzo0OPG/5nfA9GKN/O6usoMdUWPZvWPhq1UENSPV1ch4NFqVmwRGeR6oQHpSLMLEO5DKWy1Nc4WoGk58H4OFxzLZTLjNx/mrFTVep1yLfkPBnMCv8qRrTfqagbHhcPEb0PATf0y/Xlq50ivIrS2rc1REjPBxECB5Ci1GNWe7RfNqOBiTrKIMJ7DRHle4BCAc6fg/l56O+X6JbxMUimY3j4tJtt6nVIp2FqSlJ5xRbBq5rokVZwjoUufeUh19BA+lXnafh+tlNHdevzRMS+ur99vAbRRmY76IuVYHsC6ccKEnmjc0mLuPuYiK0qJnWTnXorHuxfAO5HDI31Lu0Pt7nbfRbGjuKxzYEEUoR7ic7IsfA9bR9X99vM1NDUZWts36zSawqPq20y5RAzLqogvMPhcDya+PCHP8z4+DjvfOc7L9WOAHj961/PhQsX+N//+3/vY+seHXziE58gm83y/d///bzqVa+6tD0Wi/HOd76Tpz71qfz+7/8+p0+f3r9GOhwOh8PhcDgcjl3hTI09ImwYeIjotkTnSmZb0FdxGjYKbrZYv5l4rQVvV7vsl0VqG2h9ARXGy8H+dhvC7QgLgfb2JiKqphBhuIopzGyvPrc/24JlGhEnNbe+XajXNl1sMyMqhc1mjCCCvaaC2o4JoOaP3RZ91TGmjI+Mbx0RnePAk9oi8tvCcQtYWYOzi/BQGTI+1PMwMAMz51vkRgokD7ShWoX1dVLJNj29sD4rwn5PHMaaJgpgOTiubbRshyhDI4aIuVNAxoMTQVRR3NpXzSvFR+Z0BenbI5g6FueD9iWCY+h8iEIjTrJ0prTKISbHehVmZiGVEiMolZJ0Xb7vgw/xXJqJQz5DpQbJlGVGLAA1EcgbiNmitUX0XlGywBXBtjPItYfnvG00djNGolK5haN87DldC+2n31WRuaT3idbBsUV+jYbQ9G81ROD3gt/XMeOlv58JttfpfLaECZuH27nfkpiokxgwEWxvYuqWhOu0RBkaes6t5rM+K3a6DjlsGOsriRhqKWS+ru3wuA6Hw7EfvP/97+fw4cP84i/+4qVtiUSCX/3VX2V2dpa/+qu/2sfWPTr4i7/4Cz73uc8xPj7OD/3QD13aPjExwe23384tt9zC93//99NsOjvb4XA4HA6Hw+F4LOKybjwMhNNGad0Cu/aGruze7LWVoRFHVkP3Ep0eRgv5TiBi+zhiQGQwZoLmr48SLsMibThaguD4k4hBETZA2qGXLfTadQfstFObRWbQZZ9uFIJjj3ZpXzfC7dYaEDVMtIa2u2K9VpG0U0XrOOeAL+XhkyX4mi8Ce6kFi4vw4IOwdLZI++IMXLgAiwv49Tp9vTAwACMjcHgKDg/BSMoUNAYZxwlkLLdLeH75QR+dA+5Zh/ubRiDXtGD6OUWnMFwOrrcSfJ5DaresI+2cQEylcORCGC1kv4BJmdVECoy3kAgND1hZkdRd1bL0fmxkmOzVh+i/cpzDx+IcPQoHDsDIEPTHjTnSg8xPXZUPYvIdA65FjJlc6Nqi5p/9OfzqVrxeCRsJm83pEjIGFaLvH9sQiCH9/VDQf9XQ/mBMAJVs/NCxwvuHzxG+d8OowQJyj3038Hykb2PI2KqpEfUs2AkaVWEbN1sRR4ytaWTcta6QvtLIHMkGnx0Oh+OxQK1W461vfSt/+7d/27F9ZGSE9773vVx33XX71LJHF/Pz87zqVa/iwoULG7579rOfzfvf//59aJXD4XA4HA6Hw+HYC1ykxh4RZQYkgaOIEHeazsLNW+Vx3yoSwUNE2366r1rWVcmTSO2DRUQAXaNTxNQogM3O54feQdrfg4iZpU1+C6Yegb406iEqgsA+p75UlNb2bodV4C7k2nciouq+2kd2eq1u9CF9sWb9DuACMO/LMQaAm4DJDOSysLYKF8779I8vkRucxfM8vHic/gFpQzoNvT1QKsHFGZg9L/1WRuZUHyLEztBZ6DhMlGGm19VAUhPVfBHS63SaHjaaxkz7Zi7Y/yrkt0MY8TiFzIkmm69+byNGUBwZJxW8k4Afh0waZlZgLS9tGuiH3GCN1JCH1z+Al+shi890e4VypcXqKhQLsF6S89cQM6+OqfMxgNwT6aB9as6EU5+F22kXi9/OvaL72u+b3dc6z7Vuhp0yroUxmfzQ5+Wg/QPIPRJONQcmGsZDTDc15/Sl5pIee7PojHCbNdqrgES8XIeM/zompVS4X3aK1tIAU38kXGw9ihhiXB1ComBmMJEyOqYaIaLXvpOaIw6Hw7FfFAoFXvayl3Hu3DnGx8cvbR8dHeVzn/scx48fp1rd7P8OHh8sLi7y3Oc+l29/+9uk08a+jsVi3HbbbXzjG9/gPe95zz620OFwOBwOh8PhcFwOztTYJfaqaTuVTBwRd48iItlJjDCeYusV7N2EZTUzxoJjJ5Bc+d3ato6InoNBG5eITkOjQijW9qh0Oir4qcg4jkRCLIb2j0JFRFvk3K65k7TOU6e7QBoWJcvBa6eEr1exUzeBjGkSiYTpR8Yi3LZG8JtRYMyDq47DjTfC3By0W1BcrBK79xSxRJzqaplyAWIxKSY+PS1pqHr7YK4A/WtiaKSQ1eVZRJC9SKfxoivSNRLHjr6w6yCAGCKnguuw50FUKrLwvCki5kEVMQqeGoNY29R16EP63y4kH055VkfE5nBaoJUyPHQOLubl73pL0lDF4zUOp5dI5nLQk8MbHiFXqjAyUmR4BApFSV9Vacl11pE+U1OsiBgZmjZpLdQ++5qVcCSHnVqtWySGGg9pZJxSwbmqEfvb6LPBNid0PzuNndY7AVP3JBb6jbZP79cMxqTTCCk1s7COtV3q1rXVkRRkMcTwmuPyC3qHsaOi+oEbg+1aK2Sz3+WRFGsDmEL3+gwpYKLmWsg8caaGw+F4rFCr1TaYGgBTU1P82Z/9GS9/+cv3qWWPLk6cOMF//s//mT/6oz+ir6/v0vZ4PM709DSpVIp6Per/BBwOh8PhcDgcDsejFWdq7BLbyLDFzTgi8h5ABOOy9Z2mXrKF5bB4qccAI7LlkJQ+k8jq+JHg2LpaOSqSoQ7cS2cdCNi40tmOxLDF6yjxVc9RRsT0FUQo3I4YaK94D0diEPEZTESH1gXYLH1Nir0TUsPXr0bVINL3DUS4TQV/a1HqqH7QSJOyD5UKZDMQj8PsLKyugudVyOYgHhNDY2QUxiZiJHsz9JYbpNJNZud8ThWgGBQQ78X0laaDUmE7RWc/pREjLB20Yy1oawuTGs0uWh9lUEWt3G8iq/NbQd8c6JXrKzTEgEoE7Vyj08yyI4N8ROift/oqDpxtwvm8CNlZYCkP6/dApQbJTJnDw2vQk4N0mthgP8OjZUZH2hQKEt1SzUM9iEAZQebOheBcWqx7js1TGdn3QxJjBoSjJbTdapSo+ZDF1B5pAt9kY62RzQwO+7lgf9aaJNquLCbiIMbG8WthzEwdhxQmKkT3CRsaUZEL9jY1V7Rv6sizYIWN5s1eoWmjoq4zjEYCaRq+DCYlVgK5Z5uYKBA1Ax0Oh+Oxwkte8hJ+//d/n6c//emMjY1d2j49Pc0LX/hCPvvZz7qIDeCjH/0oV1xxBc9//vN59rOffWn7G9/4Rh566CH+5E/+ZB9b53A4HA6Hw+FwOHaKMzV2iYp74RXSKvadA05g6iyExfsoUU63qzCt4uUUcA2S5qgvOMZa8H0GidyoIMKtLU6WkRXNdqHkPkTQU5PAjjTR9mHtr6vrVdjWVd5nMGLudgVMXe0ep1PY1mvXv/XVQIRIrWmx2XlUnN8qLU0UKqZH1TLxEHH+KkSkPhyDRhu+jUTDFJCImG6rxlU8XgXOLcKdd8LqGpxfgtU6ZGIwkoXhfpgYh/5+8FNpGBsjGfMYG8hzc36VRhue2oZaHS7OQ7UOfhIeqsBdvhgDamaoWB9DRP0nYObNA5ii42p2LGIE+ai+iTJr2pi5vYT0u87BBWR1/DBmxbz+ps7GujIVTEF3TXWlc66CjH+5Cj0X4IqFNtPFMolGE9Ip6OsjO7LO1FSRak1MjWIFyjWZ/7WgHUuY6B27j7YiFrSpYfVRGnNfaJ/Fg+0q8FcQAyXOxjRXUfM4bPJ120/7ZhgZ09XgPGlknmoUh20gatSOnj+NGFEDwXclZE6o8anGTNjMCs+DOuZ+Wwuuca8NDU2rpXNnBWOsbkUFMa/6MNcZQ0xinWd2lIvD4XA8ljh37hw//MM/zGte8xre/e53k0yKTfusZz2L7/7u7+aXf/mX+e3f/u19buWjg//5P/8nn/nMZ/ja177Wsf2d73wn8/PzfOpTn9qnljkcDofD4XA4HI6d4kyNXdJNoGwhhZNnEAFOxVMVzW2x0Y6MUINBxVLNHw8iNl5EBPI1RMDPIybHIeAZHpz3RZwOi33hv+0V5nresKAXD15qJsSt/fV9u6JwGFu8taM3wvto7Q3YnlGRxgi4OyGGqUmgqYlsNEpjNHjPYK5/ESO6Q3TasGzQpmXg7iJcvBdiLWj6xrQq1KFUlkiN4RFop7PwxCdCIk78zjs5dCTP8JhHojfN8rkyX/oitNpSZ2LsFFQL0gYVpm3DrR9T0L2EufHjiNibReaU9rUdfYR1LNt0UhHY/k2tCevBHCwhc1RXyKvIbxOOUKoiZkgcMWI0xZaafGmgXIblJagVmyTqdclJlc3ijY0wUm1SLldZXpa0XdVlKLfluNWgLTOIEbXdNENq4mmBap27maA/NZ2cGgoTiNHzzeBaLhBE6RBdu6ObuRGmFfo+B1wRfJ7FFAQfCvax61nY57JNyVqw/0TwWWtr2Ne9HXNC99FIlb1Gnw9qbKwh7dzOfa7m7wVM+q0UZj7Z0Wpb1UtxOByORyt/+qd/yjve8Y5LpgaA53m8+c1vZm5ujg996EP72LpHD2fOnOGv//qveclLXnJpW39/Py9/+cudqeFwOBwOh8PhcDyGcJk29gifjaKhrt7XLL0JjJgcTjulgq6K42poxBATI4EIsWeQlFP3I0W/NbXOdcA1Q3BjVoTOJCJ69nRpr70iW9ujK/y1bYNI9EcSI+DvRdHfcF+F0fQyHtGi5QBScHsi4rt1Ni+a3Y0B5Fr72Tx6ZhW4B/hSG+5GxmMZU3w5EbRdV/GDjEMOmQ+ngIs+nG7CnC9j0Iv0NcByQyIw5mah6aXg2ifC4AHa+QJxr83IlUMMHh4gmxXzo7AOi0tAzRQrD6cM0ggUnWdaZ0UFaF3lr/t2Q4X7TPDS9EWavioGnK3ActOI/+XgfHE2Oqhe6KUPoyoi0msKoyYmjdPhGGTjkrKruFCWauu1KiQSeH19JAdyDI96TE7C2CgM5qRPtM19QVvtehRbofeGLfB7yJhOBe2aRubPUeBJOXh6RurNxJB79Axi9KjRoC8/9HfUK4nMy6ioiRydaaN0/tkRCHb9DH1vI3MvT2eE0TQS/RGO2NpvWnSadPo82m77mki0xjoyD7LI+AzT2a/dnkkOh8PxaKder/P6179+w/a+vj6e/vSn70OLHp0sLy/zjW98Y8P2H/zBH+Tf/bt/tw8tcjgcDofD4XA4HJeDi9TYQ+wizNBpAqioGxaN7Xz5mh7KFnptbHG1hRHvEwSFyFdFwJ3CiHNNJKVPnk6xLmo1tYeJQKgi4u8okkJLjZm9FvzCQqIK8L3BtmJo/zhyfUcx6YpsauycBBLF0I8YD1HX2EBWes9gakIMBu2zIxXCLz0+mFRfZ4DDmGvNIOJ0AxG+a1UYnIdGoYwfj8uGYpFYfy/eNVdD2yd7Jk86U+bsSmDk+GJOaKqpAp3mTh35voDUWLmImZtqaoQF3XCx6SQbsX+zBnzJN2aOCuvzwW/tsQn3UTgypI6I0DGMUN8CkjEpoxGPw+pym6G5FZK5HrxEUoyNwUEGpmpMl0rk12BtDYbKUGvL+QvIOK8h94Aaf1HROTbhaCJNiZVC6uZonZL7gYUKJHwxZdS4sCOlwk7yVhEjvcD3IKmzvomJMmkj41a0jtEftKWKqaERPpeOmX6uInNbnx1Hgm3bSe30SDGAXEsRmUvbjQhRc7eGjNUkcp+VkbRrWeTZaEenOBwOx2MR3/f5kz/5E6677jpe97rX4XnmvwA/8RM/wR133OEiEQLe+c538oIXvIBnPOMZl/ppbGyMj3zkI1x11VUsLi7ucwsdDofD4XA4HA7HVjhTYw/R9DOa7z28Wr4bKu7qanJb8A3n1wdjVlQwZsMc8FVfhMlBRGiNYYT0OluLlLoSX+sNxILr6cEUN45qz+WidTXAiIlai0TNghTmGsHUMogqYHy5aL2LJUzx7G7t1XO2gv3DefjtsUtixtBOTaUF1hPI9WmO/zIijBeB8RKsz1cZvHgSr1aGXJb4yDBccy20WgxevMiBg+fgbkn3VAx+n0NWoC8C5zHCrxbJ1u0aAaN1TRpsNBqg03SAztRj4XlQR0wfTSmmD5cGpp6DHj/K4CO0rYr0cT9GZF9vQrYC1Srk16C8WmMgnxeXI5eDnh68oQEGhyoMD7cpV2BoCIYuQnMdzgTmj7ZFozi2ClnTuW/XpdFUUjEk1dwJpI9939SAiBLJdzpvk8BhT/phwe+8lzWSwzZEa5g6InE6Uzfpu21sVIJXHjFMBi6jjQ8nScRczSIGmUbAbSfSJokYGXE67w0t4q7zXscxKg2fw+FwPFZoNBrcfvvt3HzzzTznOc+5JNhns1n+5m/+huuvv57Z2VlKpdIWR/rOplwu8wM/8AOcOnWK8fHxS9v7+/sZHx+nUqlQLIaX1TgcDofD4XA4HI5HEy791C6xBbA6nXUJ7H2icrWHCwz3Y1Zaq1hui+b2uTSNjKIiZRER7dYRkbKACJxbiZR6fq0dEEcE3yySc3+vJ4rf5TNBGyqI4BiVtkhrAVxOVEYUbaS/5pH+2+5q7bBxZaNRGFk6hXAdw0LwqtG52l4F1lQCyvkmzX/+Iv599+Gl03hDwzAwCFMH8K67jrHDOUZ64IosPHkQjidE/B0IXr1I32kdFzVNbOHWru1iExWxYaeI6lbIuk1nhJJ+r4aAplKLMkmizq9iezXoozVguSxF1peWYHYGKhdX8NfWoFGHeAwvmyU7nGViUgquX3c9PP274HivzO1169rt2jWbofdDL519phEl30QimtQw2G5qq+1QB/I+3O/LObQuSQ8SbTCMMQftedzEFPHWMdF3O/2V4iNzcpZOI3G7qFllo4bL5aImpratiNwvUamndK7bqKGm0SvLwXH0GdLEpJ/SCDgXreFwOB7L+L7Pf/gP/4EHH3ywY3symeTEiRO8613vore3d59a9+ihVCrxohe9iNnZ2UvbPM/j7rvv5q1vfes+tszhcDgcDofD4XBsB2dq7BJbAGsRXQNiOyt/E4ggPYopDL7VMbqJb5oq6RSS6mieaANAV0BPInUBDmJW7mcQM8NH6hCkt9GezYhKpwWdNTrsVEb2Z1usbCLRAPcjK+73gjpiBOXZ2xXqthkQjn6IIVEhmvpJ10weisMTeuDYGBQLbc5+9gT5L9xNe3VNKmTnV8GLw4FpRg6kOTAO3/tkuO0/5nj+MTjmiaExhtRWORx8VlOjFxnzHjYK0JvVFIiK3rBThkWZEnaKI/3c7XxR4w4yl9eCtpcRQXodWM7D7CycOw9L8y1aq+tQLEKjCekMseEBMrkYlTKcOwflCqTj5hwqYGt0zlYGWRoxD7RmCci8uYikflsP7a+m1l6QB/4VSRtmm26DSPSBPTZlOuexRnXZtTTUjOv2/NjMrItCzYw+TJoo3T6GzLnLxQvaM4/09VZri8PX1MTUkDkB3AWcRsyhe5H7PotJj7aXZqnD4XDsF+vr67zyla/cYGwAvPrVr+b222/vSE/1eOXLX/4yH/nIRzZsv/7665ment6HFjkcDofD4XA4HI7t4kyNR5jNBFxNkaJpi6LoJpSDCK9JRJDUmgp1uhfU7UUKjF+H5NK3zRRd3V/DrPzXc6YwRaK3K9zqCn2bbv0QhT1RV/9/9v40WLbsrO8GfzvnPPNw56nmKo1IQgiBMCBj1B6wo1+CCLqbsDEoCBO4DYTt/mBwBGAwJhwOrHgDgwLDC44XGdzgCMvGbRm6ZdkMQmgoSSVVqea6dcdzhzOfnIfdH571aK1cZ+/MPMOtuiWeX0RGnszcw1prr71v1f+/nudBzJosgTOvHsk4tK5AliEVH1vrphTxq/wn7RPXjtC/O4hQu4lPJ3QmgSdWYG4Gbt2CV16G69ehtdWR6uGf/zx85pPwwovUiz0eeBDe9PV1Hnj/w3zD2xMen5UC6qdxhbXd3/OIIN5DruUScg1jYyG+FpOuTRylkVWzIa8odjrmu/D7LSR6oIEvPL7RgzsbcOcOrK3Bxo02w9t3pf5IsUhaLNFtp+ztwUsvwVe+Atf35L4IBfs+0xWWVzMgvJdKri032D93UmR868F308yVWsZ2PeB5ZBxCiogps86oiTHrzh23f5r7LI+8+zxBniOnkJR3C8FvKXLNJt1X49D0WDvI/TKu/XGEkLah7Y7RCd6beHNjCTH6Flx/jtJewzCM+4XPfOYz/OiP/iibm/uXgPzTf/pP+eAHP/g6tOr+47/8l//CrVujFdr+2l/7a7ztbW97nVpkGIZhGIZhGMY0WE2Ne4imH9KaBWEdgrgmQR8RnbvuNY34qFEVIILcRUREDGsphNtp2iFNcdR0284H36mg10CE1NPICvUZRCgtIREcuhJd6x5MKtqb9XtcnyGrXkMYDRBvk0Xs0sUm0mGJjYnwPb6WcXsqeDE87A94Y6OKjG2/D907cGIb5uqwugr1GViYTylVd6gOL8Ply9Dt0t1qc+ECLD+8RGF1mROni1w83ScZwExLzBK9TppiJxa3dU5omrN4rBLGi+FZ4xsKy3kr/rU9Sny9dRvwq+138YZaD9hrw/pduDkHszMpyyc6FEigUoZajfJsiWq1R7MFN5rwNPuNAUXvkby0UR18vRWtXVF171mmSAERyqtIjZ0O3mTMY5zDnDX+e0ifGvj7cxF41P32PDJmeec6SDTGuDRrM3gjTWuqaHu3D3COe4VesxhNQaU1VerIvXg/tNkwDOM4+MM//EP+wT/4B/z7f//vR76vVCr8i3/xL7h9+za///u//zq17v7gE5/4BBsbG5w+ffr1bophGIZhGIZhGAfATI17SB1ZKd9C0pyEZkWcsmeACK5F/Kr9UOiNRU0tbKuRGVoEd5394mgJb6ysIqbEFUTQu4oIr1p8W/P1DxAxNsWnZ5l172rUaPTFnnuNI08U1WLhscCdJa5PIq/4dLiK/TDppfIKWociuN5I4Yr2FBnXBXzkTFaqpjZiQJRxhd3bItiv7InJAVAsQKHY51SyS3lxBoByOWVlGSqDNunVq5STPhcvQrkEM7ehtA3DoMNFd/wWIoR3GK2vERo3Or/yiKNs8qI9suauGmghocmRZGzTRqI1ysh4VoBOCo02dLowTIFaDS5egHPnSba3WG53ufDyFV55dUijKfM5z7SYZMrpiv/ws0YKxX0pIZFPp5GxXsTXthlHnviex447Zgq8A0n1NIPc33eQOdVif99KyBjqHJiGPFNL53MdeJDJfXyt0eiXZXxtkTLe0JhBrk+D7JochmEYb3R+7/d+j8cee4yf/umfHkk5derUKX7913+d9773vVy+fPn1a6BhGIZhGIZhGMYhsPRT95ACkpplGZ/aKU7NE740LcqkVEBa8FejPzSFju6fJdzX3Osi8CZ8WhyttbCJGCKxqBoKn1V8epwdZOV6FxGYjzKRxong+r/fkwr4aiosfRWClxZe1+8PQiz455krej2G+LRDFbxoCiIwq7EVv24jQrQWDt8A7gzh1qbUjrh2Ha5egVs3Bgx3m6TDlGIBSmVgdxfu3GZxKeGJJ+Dxx+HSJTizJNECK4iZtejaU0Wic8KC18PoNc6syJrD8f5ZhaiVcduPe+0i83TXjVUL6X+9Ln5Gcv4svPvb4JFvg4cepXj+DAurZShNZyocFG1XSAGpTfM+ZOyb+LkxyTgJOQk8gDw/8uZcG28K1t22Q3w9nTwTZx6ZB0eliI8wqrvjZj17DpoO7jgpIumlLgBvA55A0mSdcN8vIM+ORaQvB7lGhmEYbwR6vR4///M/z9LSErdv3x757dSpUzz99NPs7e3xfd/3fVSrcQW1vxh0Oh3SdPS/WCqVOJGjYRiGYRiGYRj3ExapcQ9RIV2FX01bE4rJBH/r71mpeOLIhQLeKOm7z31EEI+FRV0BPnS/r5O/QlujHTQNVQ8RZtUUqQbft/DCtUYZHDTFkwrcOk5ZkQzahzyK5EdpKKE5McCn35lmZXxWkezwPSsSoYZEtiTAGpKiq+fOu8BoJIRG0dzER058VTAfwmDLn3CYQpIMWF5u0e9BeaZE8cIZkrk5Kku7nFnepjbToFBIIYU0heoObA68uTNETI4KInxvMZqeTNsVpiuLI2aGwXtsdByGcL9wvON7pY/Mu01kLqYpzM7C6goU61Xo7EFtC4Z9SFOKhZRSUeZImXvv4hbxY3sVuaYL+CiAaebbKvB+4KEi3BzCn6XwYsZ2KsCnSBHsq8h9vYfc51mprrSodw//vNEaPgctkJ3in3GngUoJuv3s+zfrmfZaMIM3cVYQI2gDX3+ogVyjWeQ+nRRxZhiG8Uak3++zs7PDBz7wAf7H//gfrK6ufvW3mRmxuT/ykY/wUz/1U3zLt3wL6+vrr1dTXxf++l//69y4cWPku//4H/8jKysrNJv3WwyiYRiGYRiGYRhgpsY9ZYgIi23EGBgwmmopFLZV1J221sAMsjJbozMW3OcZRFBvBftoNEaKiKMvI4JoKB6nrm0PIQLfS4jAlwK38IXEa/gV4hqZsIoIh1vBPgchNH/C9oQCet4xQ7E6FODj1F2FYPuTyMrtHaRYcBu/onvgvg9TVmkbY3MjNAAIPneQ8d11xwuLkId1S+JC4wNEWC0gY/rVeZICm/I+HECSQKsFpRKszJSonz4NMzMklQrMzLBYvMOldIckGVIuw8JdqN+GpOXPV8QXey/I4ffNyzR4j02NrIiN4yI81jD6foCMXw8Z1502NBrQ6wN37sKXPw8XLkCnA80mC/Pw4AVY34Xmnq8jc6+KQQ+Qugx7SMREE7knl/Cp5XbIH68CcAZ4sAAPnYSTu/B8I9vUiM+pjLsWKWKwgTz8FxGxv+O+P4ixMcSbKFeA3b48A8J6HXGtlkntO27UxNxDxr6N1MzQudtFjI1t4FUOb8oZhmG8EXjqqaf40R/9UX75l3+Z5eXlkd+SJOGJJ57gV3/1V/nhH/7hv3DGhmEYhmEYhmEYbyzM1DhmasigqrB9AxHSNB+/CsBx3QAViwvRe1ygOhTTdd8Z990mIuDN4FeFxxEe+q6RDStIKpZ1fDqpAiL8X3Lt30WEv1PAxcRFhqRwGRGHO1GfDmNqhJEq2tdQSA9z3YdGQJhqSlMqbSLGzDlEqL3D6KrysntV8UWntVCwCqBau0FF27D2hF6bcNV9HMGg6YZCg0WLS88iAmsoIFeBd1Xg3CLUitDbgrW2F5rTFIrbUCpIuqXVVZi7uEz59JwUzhgOYXYOZmYp1KosVe9QKN5lMBwwOwuXLsJuE/aGsN6EZ67Dsw0Zm3guhiZGnsgbXmO9Bn0OVhMC5H7R0pzrjJpx4Xm0TXuIKaBzNwVu3YKnvwx3t+6ycusZVh+9SQHYvbJBY3fAzAxcXIF2F3pd2XfLnSs+31EZIgJ5CUk7p3VqNCJCTcF4ruvfajI9C9zcg2ZPDIvQrNP7vcH+e2Sa6KNwTNXwHCDzfxWZ9wV3/Lb7ewtfbydEi7h/0e2nJk4Rmbddsg3C0AwMI3wW3WvLnT+vL+PqDSkFd/47SHRGP3hp6qyzSC2Qy5ihYRjGXwx+53d+hzNnzvCv/tW/oljcX0noe77ne1hbW+PHf/zHGQwO+q+6YRiGYRiGYRjGa4OZGsdIFVllXUSEyA6S2kQFRxW5Q3EvRIXl8L0w5r3n/q4jAqAWLtYc+wTnC1+447ddm06649xCVisDvAXJQa/pky4A70zgsSUnrG/AXipGyMZBByoiXDUfRz/o3+VonzjKRMdLDaVF12ZNwzN0vw2Rft4K9q+4fa65z0VEMD+J9E3rIoQC8qSizqEpE+5Xdvt1on4XgJMp/KUKrC7CZ/fgy4hAXnXHWE6h1YZmE2ZmE048MAfVKmxuQbcL8/MwMwMzMxTm5lio1bg0uM6gVGX+nY9QbOwyvHOXvc0ev/v7bT77vIyDCvta+L3PwSIZpk37BfuF4wSJMCojhoWmTArnahgR0kBMqy4uOimF8gbUqjAY9Om3blHfvsXMDAx2oFKEc2eh14PNHah15R49i9ybV8lO03RYEsRcexgfUbKJLxKv92+F/fe0vm4gaaeSPR/pM4vMgwFiPBSQZ0wPuWZqUqrBOI1Ar9FEu+54JxCD6Qzy/Ljt2p64NsSmhhqnms6t5tqzitQDWQeeZv9cCqOF1FhRSogpolEVeend9F1T5eXV8lAjTA2h0HTUqJg3I/f5YQxZwzCMNyK/9Eu/xMWLF/mH//AfZv7+wz/8w8zNzfEf/sN/4L//9//+GrfOMAzDMAzDMAxjMmZqHANaQ+EUIoTrCmMV62YQ8SysX6D7xcfJesWiZyH6O+tY40gRQbODCJfPsD/fv5oeq0jEw8PAxSoszkOpCLObUEtHIyjuJXFKrjAyRFHzQotK30aEVRUyNZJAC3hX3P5dRoVtNYX6+FXsIVV8TQLccfqwL1VSjIryWXVP2sD/6EFrDU7ehRd7kgKs6Y5/FigXoNuBl69C/bMpm7trLC/DwgLMrVYpnDkNJ0+KubGwQCEpsNrvweoJkvd+I3zus/RuXmO+NqRWkeOeDtq0hAjyO0iEyLRi/zQGT978bCPGSgExcAZ4gT5LzA5F8C3g+QEkHXi8AMvLUK5AsZxQPrnIyhOrJIsLrFy5Sae7xmefEpG+DjzujnGD40X72kTGVOvOhL/F0VfxvnGUkhYBP4XMM40uWnXHnmU06kQjtCYJ9GFqsSryDNt07Z7FG0havD5E585dd94yPp3aKvCeCuz04fJQrlPZHVNTssUF6ecRI7KDN5rCKKeQ0BSpBseKidOmxePRQ+b6DqNRL4ZhGF/r9Pt9fvZnf5bf+73f4+TJk3z0ox8lSfwTt1Qq8Xf/7t/l/e9/P8888wzf9V3fta+Q9tcS6+vr/NzP/Rw/9VM/9dXvyuUyv/iLv8iP/MiPvI4tMwzDMAzDMAwjDzM1joEiItitIOJdKHiDiHWX3HYtRsXiWNzMMjVgv7ERGxpx5EIsBsbinhbX7iPCZJVRUe8qIhSeRQyZJaBYkJoOrR5spSJWtjl+wlRUWt8h7GtKtkippoWuAM8yJLTvF5F+dRBRPd42xddtiA2VavA7jKb5msS4dE7rwCcHkAxkjrRcn6rufW8g21zvw7Wn4O03ezz8oPgYZ872ODMYkiSQFAoSsVEukZw/D48/Bqcfg0t3KF1+lZ2XNuk14Txiwq0jUQunkHmspkKTURPuoMTzNcUXK+/hr4VGhmjh67DWTN5xa8gYrQPzA9jrQLEEhQIUigWYm6Vw4TycWCXZ3KXVhPWe9Ok0cKYOL7WOv3B4ily3G3hTI2x33nuWwRm+F/ARLUXEbFBTbgYfaYP7Tc2DvGtXRiIzQMb+HDKmryKRWRXkWul9cRYxV665Ps0i80fnqaaa0nuzlMq8VeaBdyDz7CVGoyvqwFsRM+QZ5JrqXMkaE6WCT+cVRoNoOrQ46ktRs6Pn+tN271+7cp1hGMZ+tra2+LM/+zMKhQJnz57lV37lV/ju7/7uEXPjgQce4OLFi3z4wx/mR37kR75mjY3l5WV+7Md+bOS7Xq/HP//n//x1apFhGIZhGIZhGJMwU+OIqODeRcTAOiIo6v/2VRDxUAsEa4qkccfT96xV3dNGZMTGRhJ8r5+r+NRYGsGgYmMDKf4LInx3gLtNeLUpaW8+i+Sqvxd56OPaDpo6Ki5ArNuoYK7parR2QYLky99ARGYtjn4e+EARVkpwrSPH3sSPg9ZF0HQ/IWnwW5h+56DEBbhxbdvBzymtr9BCCpqrYbMO1AZwa12Mpp0daLehUGhyIr1JMUlIFhZgZhbe/FZ46NtlJN42B2nK8PLHKHZ7PFqAQQrXUmnHGSSlkxoOWh8mjHaZljyhvooUo7+AjOMt9ptjamiF11tF7nPI/TTAC/c6oBcuFRn0h7R2B9Q3tkjONkk3CrSv3mFtDcpDeCCBCwlomvAq+6MQjoKK5buM1s7Iu2+nvZ/byBxdQmp1pPgIgwX3vaaRa+GLYeeh90kRMbPe6vZ5zp2rgzfvFoBvcu9/BLyMT1Wm7d/DRzhdA/64J9uoqVND0tl9YwJ/nMJn8EbEPHJdtS5LnCovy6DVCKs4VVoJHzESG73hO27fO8gc/NqU6QzDMCYzHA65desW3/u938uXvvQlLl68yNzc3Fd/LxQK/L2/9/f4oR/6Id7xjnfw9NNPv46tvTcUCgUWFxf3fb+5ufk6tMYwDMMwDMMwjGkwU+OIqDjdQoQ9zSuvIlkRMTS02LbuM+54WvBXV6znRWHkRSyE22b9poKxippqHKgYOMBHKbyAiIc3kYiOG/gc9feCvOP2o88qqI5LTwPefAh/KwC9AnQTOV8FiabR1f/byDWLI26UMvAI3myYJPhrEXMtylxG0nltIkZYVrv1s66+v4qk05rBp+nZ7AG3oNWCYSpRCoNBkxOdq5RPLlI4fw5mliG5CJyC8g6snKU+X+LUyR7VMnR7MHMXKm3/MBjia36oKaci/VGv+xC5lheBdyIC+KcQgT5EIw+0pgTIdbyAmCLXETNkEThdgEunYPG9TzBY32Lzyzdo73SZ6XShvc76KztsbMJbL8GDe1AqwefXZDw7HA9qDhbwERShKZOF3utx6rfYkEyReXkFmT9VxGjaxKdIqyCm1E1EqM96NoRt6iJzqoIYIgv4guZ6zjC9mt4f+rzruvO3gu333D6atkrr2+DOOVeBs3Pwpk14PpXnibbrDnI9N4I26zMJ9s+71B07q0ZLVmov3SdO7RU/VxLXV8MwjL9oDAYD3vKWt/C3//bf5t/8m38zIvInSUKxWORjH/sY3/qt38qrr776OrbUMAzDMAzDMAzDTI0jE4pomrIlXHWuwnc12DYUDkOxTo2GEr5octm9QjEu3Ccv73y87TQ59sP+qNCaIilprnBvojIOi9ZeyMuZr8LtEBFPVbzUSIvP9eB8T4T7BmI6XECE0hfcPuF11HEJC7hX8AbUOGaBb0bO9YLb76JrS9g2bZ++p/g5MXDbrSCFjbX+wWAA/cARaHdge7vDme27LAGFrbtweheSVUg7JMMelXqRhx+CvQa0mrAwD7WrcLsBd1M51za+6LQWqq64c+ww3VzKYoAI8pfxEQV6npCqO99e9N0yEllQxxski2VJwVU6e5JCAYqlG3SaA2baLej2aOyl9LqwtCQnur0Jrw5k7JuH7EdM3bVtBv8M0GumhKK6ptA66X67jY9SyDIq1WQsIQaAbq9oqrJt966Gpd4D4OtaNPCpohaQaKYTJamBEf6DoBEQHXyKOr32W8i1CdswdO3QyLWua+/AHeNqHzoNaKaj5u028Dmyr0XeM0+N2HHkPRvBP+OGjBpKVeReNQzD+IvKRz7yES5dusQ/+2f/jFJp9H8TLl68yK/92q/x7/7dv+N3f/d36fdja/iNyQc+8IF9333iE5/4mumfYRiGYRiGYXwtYqbGEQmNimLwCgU3jXyoMpp2KE4JBT71kBbR1aiPSrD9kNHVyNOksMkSB8f1Kd73fkjPogK/1iTJKsCsdJFV30VGV+MnyJjewKcvaiOmxil3zB1EYFVBXcVgLUDeRSIM4hX2eaQ48R0xM1TwViF8C3+dQ9E77Lem31pB6htsubYMkTon6Tb0+7C3C9vb0G4PuZSus3rmCyRnHoTFJmw8Q/rMUwwaLc6chaSQ0O+lrK9DuQyzN6C2Bbt9X0C5iY9CuoikCXoWX0chr/+x2aR90jH8Ir62RgsfBdDEp5kqMlovQQ0+jeLQ6JcTVVg6XSYpFigWUuYXElcAZkja7dJowE4Ldq7BVgfWOz7KYRyaKk7vxbL7u5exbQW5liuuLxvIOXSOakSUjo1GIczh7+c8Q0OjEjQ6Qk2CeJtQoO/j5034LAprWVSBtwNfPweLVVgLwyQCtpFomll3fiUcv3ERU1pL6H8MYa4j46T31IDRYuoxOjZx5ErW9lonKH7WZbVNx76FzEelj6SjMgzD+IvMv/yX/5JOp0OpVOLHf/zHOXv27Fd/+8AHPsB3fMd38O53v5tut8uv/MqvcPXq1dextUfnJ37iJ0ZqiQB86EMfotvt5uxhGIZhGIZhGMbrjZkax4SmCppHxOp4JbUKlqEZkWVuaBRAnAYmrDNAxrGmzc0ftnccx2lihFEOR6GCCKGqvYbmTohGF7TxERU6hpoqbBdvRJXcNioet/D91wLNC4igq9dsk8nphRQ1UTqIOaCRO2HBbG13mvG5gAjluL6/4I5VCrYZDKGzC7staLZg0Idud0C79xynh/+F8ptfgJeeo/OFr9BpDFg4Xafy5kdgaYmlF16lPnudldUhy1eAV2Gh7wuI30aiA5aQKBEV15vBOOetsg/nvX7u4FMk4cb2tBuTV5H76AH33sYXrd8DvoKI7F03dueBB0/B8tk6Sb8PMzPU3/ygP/ruHnt78OouXB74FEm7TE6jVkDuZY1qWMVHmsSGiI7BCbePRteE86jkXv1gDHVs9BzxeCX4CLANvFnxBL72zQ18ZESWcRLeI2FKtXng0TKcWRJDDLLHY4AI/dNEJYXGTPg802OombSARLWEKayUimtbM/gtTNmX14ZFt98m3vjMMmT1c1izRq9nH3hxfBcNwzC+5hkMBvziL/4iAM888wwf/ehHKRT8k7hYLPKP/tE/AuBd73oXf+Nv/A2Gw/spntcwDMMwDMMwjK91zNQ4BlSErCIrtQuI2LgdbLPgfm/jRTkV5cPi1/o5TrcyZL+YN66mxCTTQlPl5KXguRemxlEJI1hg/xiFxcNVpJwD3oEI6ZfxefT1OqhorOLnNiK+6pio8dFjdHW8mk3T0EEiO+64Nu4yujo87gf4eirarzo+vZDWGdB0RRo90AHafRhsSvTG1jY89VSXc5/7Mm/9+hcYtrv0W33qdZi5NEPyjd8Mp99D7S3PcGn5D1l98nkWF/ucPg2dPqzdgcs34MWOFIa/UIWvX4HmHXixL2PbQwRkLS59l/3zdFykT+L6toI3BrXYcxkxCfqIAbELvOTGsYTcU5cSSStVLQ+hsQdLSyRzc9BswI0bbN9ocvMO3BjI9e+682mEwDi0PsTQtaXirsVmxrYd9/2y204jK3B9OYOYQkW33fVgH30Ix9EIOmYnEbG+5dr0f63AJVfFu5fCf2rIuGxF5wyNgNg41eivMtDpiBHWSMfX+5k2Kik+X2xubCHjUUfmi+5TR8ZCC5e/BDzDqEEzTjKrInNSU/fp+eNx1b7sImZhlVEDKrw3DcMw/qLz3/7bf+Mf/+N/zIc+9KHM37/zO7+TZ555hp/92Z/lt3/7t1/j1h2dn/iJn+Dhhx8e+e43f/M3+ZM/+ZPXqUWGYRiGYRiGYUyDmRpHJCz2W0VEshRfyFqFsguIqHkFSesTppwpBNt18EWaFRXQ49Q0yYS/YyNBxe8EeGsBPlCET/fgy4gQqEK/CurTivZKARGaNcVQKNAfh6nRZL8BE0YzhOKppobSIt0PIumTGsDz+MLUOrba5tvuHGHbt8g2lQ5CXAh7HGGBZJB5NYuI+RppoEbMVfd5Abm2PWA4hOaWpKG6nsKlmwPWbzeZnYV6Dc6dg1OFMtSWoHIRzhYpvWONhd1tnihc581vlpX76+uwtQVf+CJ0XoYHq3DpApzfgo2+NzL67rx9RNTvIyZOKCxPQiMNNFLmFXwtmW90ffwTN44DRLw+DTxYgJUVKf7NYADFEhSG0GzSX99m626fdlvGVAuda72OIpNTUOk9oPMqNhwVjQC6jcwjrRUCcm3e4c73JXwdlTCdnJ4jvMd1PE4j17+IPEMer8HSPGimjEWXvylOBxanZQujRrQY/Ks9ManWkDHvMBqBVMLfN7ddG04g13szOGZoZsQRaPE4tZFolz13XN3vJDJ/zgKPuzHU49XdufX+V4MjvCe38LVH4mdOVjSU1szJMpENwzAMYTAY8Eu/9Ev82q/9Gk899dQ+A6BYLPLEE0/wG7/xG3z+85/nxRdfpNfLStR4f3Lq1Cmq1erId5ubmzSbx1V1yzAMwzAMwzCMe4GZGkdkDr+SOMWnd9EV/ioyDpDV6A28GByvZgYvTMcM2S/i5UVA6GrrcB9to5KW4V1vhYvr8MRN+Eof/nSYXzg5q/5HTBkRYEH6GGYivtdCYbgCvYSIvxuICP554E3ICvAZ9htANeT6vIisoA+F4TDq47VA+xEK3Zqyqenapteo5P5+Eenvebzh0Qa+lLr6ACk8eBcuFaFUhEIRKrUEupvQewUKbSgWSZaXqJ1rwXBAJU2ZOQtnd9u0Wl2euAUzKfS6cKYCux3YGo7O517w91n8faBpnjRdUnitCm6fl4L+1ZDrVkOuma6cP4lcvwX399uL8P5vgLe/u0L19BLMzEoH+ykMhnSbfTY3XTou/HXVtF8qYajRl2dY6HXQeix522hETvx7xb22EEMjrNUSX+fwntaUVS23r0aOfHoHZnbgVAlmqrAzlHttC/9c0RosMeFzYAv4Y0YjS1JkjDXyR+vYlIN21vFFwwdkGwxZtUGU0BAp48d/EXgUeMQdoJ7KvO64Nmnh8UeBtyFz+7N4Y6SFj77R/oftCNujUU7hM0qjtiyDumEYxiiDwYBGo8F3fMd38PGPf5wzZ84wOzs7sk21WuXpp5/m937v9/jgBz9Io9HIOdr9Q71eZ3Fx8fVuhmEYhmEYhmEYh8BMjSOihXi1wDR4gbSOF/p2gZcZjQKIUZE3SxBUoVPTpGRFY4Qrv1NGU6qE1IDVIezswPIifNsSXLwBr9yRKIZ4nxpiyDSR1dN57e8ipoD+/VoSr7TuBN+18XUyQhFZReOa20YLY78exEYU+Pa3EOFWzS4tOh32RVe1n0FE/yXEENCaIK0mlMqSqmkwgM0X1ln8w49TfPwVkoVFSd10+jTJyZOwsQGNBlQqFG/f5eKlO3zrE9DtwsmTMD8PJ67DtbtwbQ82Ur9KXutF6FjWEZH6jPv+FWReziP3RNO1cQ8RlOcQcXsZn/KphIjab3PHny/C2Rq892F49zdXmXvzBZKzZ2FxAQoFyac0GDDsD+kPIB1KO+r4aIYNJOKg7NpRce85tbJJ3TXQSJks8u6LEhKZcMUdI6yNE0YOxMahPkduuf1b7rtPuXGo96HaFyNF0ynp+Wbdu5oGajIOouNrTZRQ+NdC9rh9rzBqznaRObbgvivgo5myxiB+nqXIPXfC/abtr7vvd4ErqYzVeWRut1y7+sj8rwWfs9Bop7g9oQEdM4/Mu7WcYxqGYfxF59VXX+XRRx/lZ37mZ/imb/omHnnkER599NGv/p4kCd/7vd/LCy+8wB/90R/xyiuv8MILL7yOLc7nPe95Dx/4wAf4wR/8wZHv9/b2eO65516nVhmGYRiGYRiGMS1JmqaWbeMIzCcJNUSc20TEtjLZq67D1CxhBEX8uc9+sa7i3rPMgrzUTnkX9hHg/16GE0tQLsCpE7C3C79xDT453L/i+hHgIUTcfJGDp6V6rdA6AmEdjBQRyR9G0uhsInn6N/GCuRYKD1Mg3QtTJjQuwjZrtMiQ0SLluN8WkDm14b6bwac3i+fYAnAJiUypI+bCAnCyDmfPwEwdyhUxJy5dhJVLM9RPzFM4tQoXL4nrceeO5K4qFuD2Hfq319ld79Jp9EgGA0ih0YSbN+CLX4Iv3RLD7iZiUhTdebfc3+9D5lATMb0qiHh8Falzoem/akgx7lkkEqCKNyMWgNU6rMzB2VPS1MffOcvy286TXLokhkZSEDPmzh2Gl1/lzlfu8PSX4XNfhC9sS0HtHUQMv+3OddK1S8f3FbKFck0xNy5SI4sEEe9PuHO23HG6+BRJ8XUM7+cuPlWVPgPCovdZFBETchU4585zzb005VIYzREeJzRE8/pzEXhvAeol2OnKcb+Cj5jI2iekgkR0nUEMCzW6vhGZF3cQE2fObX/Dbaemnu57HYl8yULnjj5z2/hrl5dG7iQydneBnv2zaLwGJEnev96G8cbgr/7Vv8pv/uZvcvbs2czfP/7xj/PRj36UD3/4wwwG989/PX7jN34jv/M7v7MvlVaapvzcz/0cP/3TP/06tcx4o2P/W228FiSJrUs1DMP4WiJNX8v8MF9b2L+IR6SBDOIcPg2L1nKIV1zD6IpofU8zvo8ZVyT3oP/53AU+14P+XSincGYXVoawOBQDYDPYtoaIiFqo+vX6T/Uwz/84wlXw5eD7LeS63MKbA7PIWOwh/buEiKVXkHRImhbnMPVFsqgihoSmWdJ0PyBjmyVUazotFadVOA4FcfDi7Y7rXxsRtofATAKDPty8Bb0hlIuwuSFG1tnNJmfOtVhtNil2urC6Ks7H4oLkqSqWKK2usNzrQ7fDcGsH0pTVTo9TF3ch6bD1SdhriBk0hzddtlx7lvEmzAoSRaIpoNrI2C8hc28OmXO6an8OWKzAyiycOwMPPAgPPpSw9PAK1YfOwYULsLQsBsxeA7a3SNfW2Lu6yfVrcPsW7DZkbBbwc2AbH8mg6aBCw0JrlqiQHqavmkRoUnXc9Wgi802v41181FZsbGTdYzPI/CzhU3WpgQf7xfqh2+auG9e8+yevloQ+v7Lm4wC4UIevOwE31qG3J6ZWnqkRm3SzSARGDYkM6+JryKwBz7q2nnff7zGaku8uMrfG/bOv0WXL7lgv4g2dPDQKzf5zwjAMYzr+4A/+gB/+4R/mAx/4AD/6oz+67/e/8lf+Ct/+7d/Ou9/9bgaDAT/90z/N9evXM4702vHAAw/wy7/8y/sMDYBf//Vf5xd+4Rdeh1YZhmEYhmEYhnFQzNQ4IpoyquZeKvyHdQPiFFF5BkYYtRFSYHpRfxIFRBT8lDthAag3JVXRKaSgeRNf5LiGCLxa6yPuz2thcsy7992xW42iY6aG0hYiWrYRobSOiKsalaEmwxIyPtpHLc7cwOfrPyxlRGiddcdax1/TcYL5DrLqXtMn6RwJi6ODnz87yOr5PnCuAPM1WGvDWk+u5SzQ6Ej6sc0t2N1LaTZ2ObHZZu78Fpw9B/NzMDsLK8swTCWtU5pSaEoCpUJjj9mVdR5av8yNG0OGL0OtJec8UYCLp2GnAV/Y8fUZZtwYFF3/B4jgftGNiwreNeSaz1dgeQFOrsLyCpw/DxcfKjH/0AkKDz8IZ87C6oq0bW9PokturtG+cpurr/S58ircXIM9p1J3g1cfmU+a2itM7wbelDiIkVdG5ooWId/Ep7tqu9/K7nwamRWv086rq7GM3Jtz7vreYr8hofOihIz5bSR6poov5h4TPqdCE3Zcn1OgWJTAmLttMQEPcm+eQK75XbzRkCL3qF4TTaFVQ+ZyyCSDqYBcgzoyr1LEdJmE1dIwDMM4OL//+7/Pn/zJn3Du3Dm+53u+Z9/vpVKJH/iBHwDg67/+62k2m/z8z/88H/vYx17jlkKhUOAjH/kI3/AN37Dvt4997GP85E/+JO32JAvcMAzDMAzDMIz7ATM1jsgMIrydQ4Q2FarzBMJxkRpZK6PLyArzGXfcbXyx5cMyRMREjWjoIkJiH28GKFonQdPfqMERcy/NDU3RFBKPayF6xavf1TzQdFMFZBxVHO0DryLju4UXOAv4NDYdjmYshenG2mOOVULE/iY+bc4a2amCYHRVfQ9Z2b6NpNwaDqHRgldSEYeXkTQ77T7sbslvrSZsb8GF7R6X2ussNJokp06SnDkLc3NQrUgkxDCFuXkolWBhAUplZuevcP7cEFJYuA3lMrzpcUlt9cor0Po0NHpwfkEiRNa24e5QxnLeje1DeINpNRETZmUJVlbkdfYsLC/D6vkq5QtnSC5ehAcegHOXoLYMW1fh5k3StTW6V9a4cbnLq5dh7RZsNmQsmviC21v4Ghl59Bm9j6dhAXiLu363GI140usVFlNXUyO837LmtRZH1xoYamjp3A2fNcVgO53DGnEUP4eGGd/HxyPj9wGwsQtPN+ATA4msyHomZJHi0qMV4M+DVHdDfD2f1PVjFXm2FrMONIYho+ZVken+oZtk5hiGYRjZbG5u8sEPfpBf+IVf4NOf/jSFQiFzu3e9610A/PZv/zbNZpPf+I3f4Kd+6qeA1yZtz7/9t/+W973vffu+X1tb4/u+7/vY2tq6520wDMMwDMMwDON4MFPjiFxARL7T7n0bL1iGxYBjgTCup6F/a30BZdkdu4ykRroNPMXhVxWHwp2KmroifQ8RlzWSAEQM3kHExaxjvBZC4Db7hc34nOF4q5FRiP7W/fSl0ShlZNx7iBgdjv8AXxz6qP1sIuM8546ZN3YlfHSKXp82o6mmwtcgeNdaDRqt0QQ6qbyr6F1yx2sA7Q7sXYWtbWi1oNFIOXW3yfmHblLt9GBlmWR5CWp1qLiEXsWCLNVvt0hIefgRePSJAjeuDUmAJ94zT/XEPNW522xs9llehkceTdjaSPnKV+D2HSlafnsP7nThiXkxQ25twskFOHdOgjBOnYKl5YQT5yokqytw5jTJ6TNw4oQUBamtQnIaanvQfIbBleu88MUWzzwLz74Ee23ppxqBDXxB62mu1UGoIvfpkjuP1u5Q1JBUoypMcTRuXpURg6uC3Pvb7pVnuIRRYpqyroyLzmH0uaHzJrxvQrM1L9v/LvDJFLoDqXcxraGhLBVkKrU6++81pYyYOYdlD19P5xwShbbJ+GtvhoZhGMbh2dnZ4XOf+xw/8AM/wIc+9CEAarUas7Oz+7ZdWlpiaWmJn/zJn+Sf/JN/wqc//Wn+1t/6W/u263Q6NBrjliDks7i4SLHo/+vx+7//+/nBH/zBEcOl2+2yu7vLX/7Lf9kMDcMwDMMwDMN4g2GmxhHRIuFaIDwWBrPSNYUGhq6iruIF6RCNLAhFvqNGaYQiph6vj8/9r0KnCqcbrg1axDo2aO61saHt03HW6JGs7fIiNpQwckP7WWE0pU0YMTNAhPDj6OMQEYSLiMhcQgRqjQpQekjETxNZrf4QIsjexBsx2te4JkMoaO/h63f0kbHbc/t18Km3Gik0tqHdg41NuLgt/6N/evMGc2e3Ka0uUVhegnpdzIxazYWANKhUEyozVWoPnmH59C26jS61R85DrcbiqW3e/KY+5x6usPLIMjsv3qZQSHlbpcSpCxVuXu7w9BcHPPwI1Gvw7HOSSerSA/DwIwWWz9cpriyKu7GyAotLcGJV2tHpwO0nxQ0plaDTYedWi89/Hv74ZfgSEgWy7Pq868Zv4xiuYxZ6j24gUTU3GRX7Uw5mlIT3Vh2fQm2DbEMzfOYM8amX+mRHYBFtX4i+i1PnxRFCN5ExjZ9XaqSMG+OdITzZgedc28r4ezV8zmzhoy0OysC1bx0xNVaQMVFpbAa577WORpH8aDnDMAxjen7rt36L3/qt3wLESPjxH/9x3vnOd2ZGbxQKBQqFAu973/tYX1/f9/t/+k//iZ//+Z8/dDve/OY35/4+GAz41V/9VX7sx37sUMc3DMMwDMMwDOP1JUlfi3jvr2EuJgk14G1IjvjnEDFNRecwdVIcJdFDRLsSssK7yajwWURyz8+5z+eAV5BCwYdNg5Ql+Ov/ZnaibbWtWrRZU9rEpo32515TA96N5PC/mvG7CsCaXkrHPjY2VMjVou76Hv6dd1PoMQ8buaGREheR63kdEap38KZKaLysAN+AzItn8XMrrLmgplgBEWlBBNuwv9rWEjJGq8i8mnPbzrnvVopwbgnOnYWTJyRi4sTJhJkTM5RmKpRmqiT1mpgbhQLDm2t0NltUTy2QJEC/T3LpEpDSfvYV7rzSZOlUiepCjZ2be5Rmqyy8+3EKD1yk9flnufYnlzl1MqU2W+DWjSG9QYHlszWWL82TnD4teacWF2BxEeozYmgUCnD9Gv1PfZbO9XXqD56ksLpC95kX+eTvb/AfPw0fbUmfziEi+TVE4O7hr/1xUnDXoIhPfTQNcdRNaE5pJMVJvGHSwptWofkQ3pMFZN48jsyb5/A1TMa1P45miim6VwUxGmKTZg4xkdYzfgu55NpyE7kO4X1aQsau7I7VwM/5wzCLRNOl+ALxBUbHVJ8VGt0BMLB/Fo3XgCTJi4cyjK8dyuUyv/qrv0q5XOZv/s2/ydLS0uvdJEBqgXz3d383g8G4fx0N42DY/1YbrwVJYutSDcMwvpZI08Ms5TTAIjWOzHVEHBsiovssIv5reqEifiVybAYU3G8Vt2+XUeNDxed14I577ZEtyFaR1cc98mtuVICziHmhBkU/OM81fMqcCl4416LKIYdd0XyUiIc68CDS19vsN2GKeJE/fs9qh/4eRzvktVELfZeRyInDJERQ46SBCL8qsMeF4PX8HXxasHm3TxgBoIWh68g10+gLPUY85zr44thLwBNu+zX9fgDNddhqwuptSRV18mTKykqDmZkGC4sJ5XqJ+lyJUr1Ef6/D9kafemuDhQeWScolKdpdKlGZq7K80qIw6NPb2CNJEubfdIHCt3wrrJ6jPr/IQ2VIGnsUK0UunR1IFMjyitTymJuTguW1OlSqkvaq14VOF27cZOeZa1x7ucsDrQ6LX1+nvLLAgw9tcOIZl0oLmSubiHh9HCnE8hiSXUhenwEH+Scqjrq4gxf7da5mZysX+oyaHz3GGxrxOWMTEHecmjuvmrEhahQ8iDxDskyNiuuPFv7W+R62TSPXNDKlzdGuWQMxgovuPJqIZNe1ZwmfNlAjTExmNgzDOD56vR4f/OAHAfihH/ohTp48yfd8z/fw7ne/+3Vr0yc+8Qn+/t//+2ZoGIZhGIZhGMYbGDM1jkiKCHyvIiuLz7nvi4hwpqlfQrFcRbqwCHBWMVxNn7OGiHMtRFAvu+OGx1lAcvp3EKMlS1RcAt7v2nENSdFzx30+jwh7WkA8bm8YkaFi5EHExjrwACLOv4oYNQcVKxOk38t4AR98UWFNs5S3+j1sc/hd3rYxKSLsnnb9uMbhjI0UieppICKu9iMWqlVg7SCr4Kvu+1AgT5D5MI/czHvsT2elx9L3Id4cWXDHvouvB9MCdlqw3oa727C6JnXBZ+dgcTGlVuuxvNSjVoNyBdIhpCnMt9swGECrTXLiBIXTJ5mtVkh390iHKdVyheKFszA/K2ev1yg98iA09nwDyyUxNqo1SS2VptDrwXAAewNIEuj2GNy6w/btLrdvw+xch4Wza3S3m9y4Dpstua+0hoZGKaip8VquodO0cVtjtsmae2pcDBhNjQajpqh+jn/bA17A14uZRHhfhO+489TccRpkm0OzyPNlNud8RSSNWhmJmNhltOZITM+9DnKtSmRH4YR1irRfaujqcyRsi62xNAzDuDf8+q//OgB/8Ad/wKVLl/iu7/oufuiHfug1O/9wOOT7v//7efLJJ7l27drkHQzDMAzDMAzDuG8xU+OYUOGxihSnreOLb6vQFqdAAh+pEYquiqZF0e/OIillGkgKprt4gbuAXEzNDx9TcPs+WoFaBap78BXXrj3ECMnKvR/XzTiMKKwpdN4HzJdguy+mxqR94vP08TnwQ+FyAXgYSeF0Gx/5kGVSxLUCyNgmD62BMuuOUUHE0IOs8wujXxrRd1l/6/Wp41fcxyK2pgSKi8zr76GhEdIDXsTXQai5bXq4AvEpbHakmPfMFswVYbYKM3XJBlWryfvSktTt7m632d5M2W10Of/oXeqPnic5d45kbw9KRZifl52e+wr0+9BoSKXydChmBYl832xCry/VpAdDMTRKZYnSKJdJBwPWn7zM3buy+d3bKScur/PqK0P+65/C55ypsevGRNMlHfQ6HYe4rXNxntH0RuF58gy1rHmrpBm/xXUvbuHnRRlJw1TDR4+VgZeR54juo88OPbcWrZ9B7tk44iRBinC/C3nuhSZdTBkxHzXS6gb7r0nWM3IaiojZ2WTUaIwNzfDvgdveAj0NwzBeW5588kmefPJJ/uiP/ogPfehDvOtd7+IjH/nIPT3n7/7u7/IzP/MzPPfccwyHr0XSVMMwDMMwDMMw7iVmahyRBXzKnyVE2GvgUyHB/siG0Cjo4VfXh2mFQES3Lt5oWEXSBTWR1edb+FX5u4hoqGlWYgqIqFlxV7yNX53cRVK0hKuZY5H0KAJvCTgDnK1ANxG9etLxyniBXWkgUR5a5FpZRsZex0H7FYvEahIRHCNOVZUG71kGwQZS20JXwx/kf4tj4XpcCiFFU1WpgRILsDo3NvF9j8c2jtII0ULSIPPmDr6QuArE2ylUBjAzgFoXZnZh9g7USrC8BBcvSpmLu3dTXngRtnfgXRtN3sZ1ak88IH0vlaUNl1+FjQ05YSl6/CSJvAoFidIoFp350SQd9GGvQXLmNCnwpc902N6UubS2Bo3mkJeuwMea8LwbL42U0jopB6HEaDTUYdHxXUHus3Dejoso0qgk/XsBuRbhdzHxdyrivy2BrwMeqcBCHVZXYW5W6q5fvwn/8wr8vzvStpTRSLBZJKVUBT8fQnOzjBz7W2dhrQGfITsN1wAxTtVQGZcKLIxACYuYJ9H3ITXk+Rg/Q7PSz4XPtkm1RgzDMIx7x8bGBhsbGzz77LP85//8n/f9/nf+zt/hX//rf32oY3/nd34nX/ziF7/6udfr0enk2e6GYRiGYRiGYbzRMFPjiJzFF/jW1c9LyKpmFd7iSI0wlZOmlcoyNfYQEX0eETU1J33LHT/Ma99EREkV7XWfdbzQvQ0815R23GI05Uqc3gb2i4EJIh4O8QLoNKSIwPxsV9qzPcU+Yf778Lsr+NQxuPa8HUmfNQ+cQAyaMEe+tqGMpMBZcMd5zh1HzaBpaCMFjuMaGJMIzRPwQu0kY6Pg2riOXPd4zIdIX7eC30JDDUbHIHyPGSLzrYOk2Kq5c1aQMaq69zpQS6Heg8661O5OEli7BV++Do0Umh3Y3Wvy9ltfYXY+obo6B8USzVs77GwOSBIpkQHey/hqn0NPYwC7O7C7C50OXHzsFsNeynPPpbRaMEzhxi7c3YTthpg7ej/p/aXE99849FplbavjG9+vWST4wvUlvKmhBkZoqIXfh+1M8LUfGngjK4m2ie/bR8rwj07DxTMypu22jOvDD0N9NmHxRIXvPFfj27/S4ty/7/LRppiG88h86uKv/Ybrb5VRk7aEzL/bDfg88sq7L7aRlFiz7vjxdnF0VZweLiSMbCsh9/TQtVEjY9QUDscyJMscMQzDMF57hsMhe3t7+77/8Ic/zIc//OHXoUWGYRiGYRiGYdzvmKlxRLTWQQER/hbc93Fdi1hw1NXCQ/JT42jR7yIihr6IRCX0kJQxWbnjB+4cy4jAr6v3B8Cf4fPUL7JfKFQ0DU0cqZHgU9HcxJsiKnTmibx9ZPX8y0F/JxHXEVDiGhYqXBYLMD+UiJABMla7+KLGQ8SAejOwUoDG0Letjxeq9bqokDrOAJiWrNX405gaeh1a7hVfEz12lgmVFWUzTeqwAZLGq4nMoVn3KiECt77PIONZK0td8I1deGVToliawMZdeOZ/wReehm/7ppSHH9plaxteeUXE9UICScGbGQnyGZypUZLPw6FkqWrsSeaqWr3Lndvw7F3JSpUgRsZMC7oDPx7xHNO6I1r0Ph4HrX2hc06jqLKIU7SNI8VHvcQpo2IjIzS8NK2YmqHloJ2Tzl0EHqrC//7NCX/t/Qk7W0O2tqBYKZAUCqycLlG/eAIefQwSONP9Au95rMvwBfh4C9ZTuc/0vtnFF1rXfzC0LwVkvryCGIXj1sBqLZcscy6+NyaZGmoS6d8txATuIBEbb0KM22vsnwuHTaNnGIZhGIZhGIZhGIZh3B+YqXFENhERsY+IgG1kVf0aoyvnQ5E8a8V8loCe4o2DHiIKbpEt2sb73UXEyDAVzFbwdxcRq1VADesOqJiu0QihAaMpkLQgdx0Ri3V1fJ5QmGdSHIYCPn//HvBHQHvo04C13fdaDLuDCNarrg8vDOEyo6J1VvvyTJ+DEgu1eSvSFc33D3KDatvqSP8G7Bd+4/3juZVVQHkcfSQV1QYSJVBExryIjKUaG3MzcGcXXuqJsH0dmQs33H7Dbfi6Lbh9G774NPz5C9KnpQqUXITGV8fF/V0ouFcidcJbXdjtQr0m219fg5cHMh5zyHW+0pVrvhf0Vym4ti66tmmx65Al4FH8PddCRPGdjHE76Ar/Nj5aQ++3SXMhcWOsUQd6nDL56eF0/0eq8P96B3zbexPSpMDcAsyeqFBaXpC6JqdPwwMPwOlTsLFJbfUF5uY2eWgRfuQc/H824Q/WfSq7TXy9nZgEGf8X8bU5JpH3jNDxUJNY04blzfMQjcwpI1FGjyNjfofRVFdmZBiGYRiGYRiGYRiGYbzxMVPjiFzH13/IEprDFdlxbQ39e1w0gAqFmo5JRb8ZRMDNQ4vgjvu9gTcwaoxGQSwgonEjOo+uONcCzLpdmMrqXlMBLiKmzR5iTmwjIvQ6PkUWeBFYheGnkRQ7t5F+VJH+98hemT8uWmMasqI0siI2wgieIWJeJPii5OGcCqNKYkIDqoCMFYz27SCpswb4ou4N144T7ribwNou3OzDl5FxVXNrC7k+5/vwpWfg8mX43G14sitGxMWOjH04DmEqJjU5SH26tWIH0ufgVkc+n0Ou9VXgSWQu7ATjUsObGfPImO6w39AoMBqRoqZdBYk+2Hbb6X0S1iGZBo1QCE0N2B+JkGVs1JGaFk13nBLeaMwS+usF+H8+Bn/lrTDoDUmHUJifIVlchBOrcOIkXLoIp89ApSZhNklCvQ7VKrzrbVC/A5/6U7mGCf7ejv+x0H508Wnu4OCp2fRYauRcRKKE1hBTIjRs8vbVyJYCPnpLr2fWtuG9o/dJD6uvYRiGYRiGYRiGYRiG8UbATI0j0sNHLVTxwm9MVhTGNGJ5aBaomTCLL0h+WME9jPYo4cXSkFlE7NMICN1PDRyN3NBV6K8VhehVRMaijR97XeUOPhJlA+mzRm6k7r2ECPBN9ouaR01Tk5U2Jy/Njm6vUTkV5Dpr5ICK2WFtjjBdVtxeTblEsI2mMdpiv+kVpt/KQk2vDiI2ryGmRR9JR7YTbNt3bd5I4ekNGGzCs6lEcyzj01hpX7SmiUalFJEojRSZe2tAKYXKnhh9dxDBfwYxV24yWmOmiERevAWZx22knsOVjH4V3TjV3Gc1k2ZcX9X8W3Hb7ZJtjuShY1pGDJ0uPg1cXpql8F3NjBo+kiqeV2q+va8I73sEZmagWksozM+SLLgIjXJF8rQNU+j1oDID1RpUK8zMwsIC1GfgG75uljc91eB/7cp1nGHUgAopuD7pvGq7z3v4NF8ayTaJgTvOI8DDBfjcUOZpLzh3VlRT2P8Uuc9fwT/XSsiY50XYzCE1eW4yGs1mGIZhGIZhGIZhGIZh3J+YqXGM9JC0O2G6kwr70+HkpaCaRN8d/zArocehgqmKfyBCcZv9hYFDYVHrBdTx9RZC8TKrbyXG1yuYhgEiavfwBay1wLXWHAjrEyRuH83lr9EumpJJoyKq+BRaQ/JF0IOgkTCxYF1ExiKcC/p7ERnPc0htgJeQKBQ1O0IDKSsVkdJHBPklJCXPgjv2jjtGWHBd2zoNGtXSRCJeKmQbJLPIuF8F7qQS1bTtzqNzRs0dTXW0415FZP4V3ba77ng33Dlvun0X8KZiSBm4BLytKrU3NvtiaGRFFKmpMe/61cZHbnTcOfvIOM7j55wWb5+G1B3vtDt+E7k2HbKNrXD+qjFQZDRiIzYSC8BffQTOnpH6I+X5Gsn8PMzOwkzdFSWpycE3N6BUgeUz8MSbOHX2MlubA2ZnYfFMge8+C5/bleulZliZ/e2sIXVs1Nhou3HaQq67FhfvML7eBvh7bi6B2SIUh/kRTTqm4bNIaSOmxhz+mqopmIXOMS1gbhiGYRiGYRiGYRiGYdzfmKlxRMIVxCqQK7OI6NpEhLZYwD6osTFERMbTiHCoBZ2PIz2SFn7ewtcR0OgHbWPCfiFR63BUEFFThUmi7XQ/ja7QFefTEoqauhpbz6Ppo8LtlHFjrGbHMPishpG2MTznYdEx0fQ4IDfeKXzdk3B8dSw1SqbD6LjGK/XjcdbtBsi4nAQec8fUORO25aAsu3NuuuOX8JEWYZ/38DU5ZvHzYxtvROhYayTAFl781rE4hdxXDUR8TpF5ecf15YLr11X8NdNtWn0oDaV9J5D7cSujPwt48f0OYpycxEcJ6L3ddf1dcm1bY3yaN6WIRAO81R37enBcNZbiaISCa9sDSMFrracCfr6rYTYA/rc5+Ctvg1IZajMFkrkZMTPqdajVoVKBXo/0uedg7Ra8/Z0k7/p/wLsf59Stbfa2/4R+DwrNXR5ahKqbkGpi1RmN6Coiz6KH8UXW1/FFvGv4e2qW0VRWSnxvDYBuCjd7Mr/CuR6/h+j9o0alGrILyD027lmzDbyMjx46iuFqGIZhGIZhGIZhGIZh3HvM1DgisQipongZETFPIcKnpseJ62nAqEg3STxXofM0YkJ8CV/z4KBRBdp2FS1nkBQ715EUP1qoV88b5/HXv9X8yCrqO4y+CwsOx7VGYuKIhPi4uk0Y4RD+Hov28TjnGR1HNTFiQrMhFPLDiJ4s4XYdGdsWPoqkH+yfJe7GbV9CiiY/gBgMNxARt8Xh6gfUgW9xJ/qT1Ndb0Qic8NwdxGh4BHi726bpzr895hxh5EbX7bOIzMlt93fVnWsJeFcdVjvwsaGvQdMBPgMkAxHd9X58FPgCo6nXVt3vfWSMXsXfx5oqquu+n0EiAFaQexv3/aSxLCERDY/UYLct/dCC5QN81E48R/eQqIO7ZIvteh9UE7i4JIEY1SqU6hWSahUqVahWoFyCYhHabbhyhf7lG5TOPgCcIyk8ROW9zzDz//siT//ZLqu3XakNfD2SvuvvxRKs9cUYmkHm1flExquR+sLiu/iIpNS1XWtXxMaoGhGaXm/N9fkm/pkCo8ZfVoRG/FkLxC8gRlVeCqwh0p8KYr7EBp1hGIZhGIZhGIZhGIZxf2GmxhGJxXYVxGYQ8+EcIszdZnTl/7hjDaPtQqFOVxNfSOCBIiR9uIwIdrpyfZp2KlqzQEXmBUS83IuOFQqKYdtqbp+89DJ50QCFjL9joyFrZXYoaur3Wp9AoyHitmaJxePE0OM0NJSwvZoKS4vMxyv0w3YkSCTCEiJsv4w3kNTYyBozZRE4U4TeEJ5L4UXkWh+2KHIXiXhYKsGXenIsTYmUNW4pEkXRR8yVAfB58ovcl5FC0WcQUftq0C+dq2eQKIomcCaBMydh8y5Uo8m/CzyLjLXWW1lj1EQb4qM8ruONpBm8mRH2q4mvy7KBj7IpMnpvxNFIAySF2JNtKVZ/k1GRveb6Hh5jiCvGTrYgr+cpAu+pw/c8JkEZ1XqBZMalmqqUoVSCYknqaVCS2hqlgkRuUAMqUJyjUK+xvbNLuwOdjpg334DMvReA99TgWx6Ap6/BZxu+3cUCVIqw1BVzYI3RyKnQ2NRxCk2+YrBdG5mjA3xqLvBp48J7O47CCtPk9ZDruMtodEiZ/CixHtnpyQzDMAzDMAzDMAzDMIz7CzM1jkgosheC9xoiKGsdg1hIS6J942PqccK6Dvp9B0iKcHEZai14vAHrKfwp+00NrTGhtSKySBFxuo1PhxOLe3nGA4jJAl7gDY87LmVM3IZpzITY6NFIk9AQCs+ZZ+bE55y04luLNB9G9AznRijs6nnGjc0AJy4XoVuEahe+wmhkTHicUrAfiNH13ECu0fP4CJzDMkCiPXoDf73HFYEeIqvuV5AUWPUJx18E3g28pQJPdkUgbyMmQ8X9vQs8gZy/k8KLG/ByZ7RQubKN1NJYYzQyJmyfppvSe7SORIJoaqcQrWWhqahSfJRFGP0xg48yAZmjLyHmgBbQjtuRFzkUfw6NgASoJPB/exjOXxBTozxTkXRT5QqUyxKhUSzIe61OcvYM5d1dd1PvyatUoL5ap1KGZkMas5rAw4mc49UhDBIYpHB6AVYbYtJcBeoDeHhW7pFm15ubYeF5vQe0r0p4Hyf4ejk6l0PTT+/t+BmTRn9rhJmmX2vjI8QqeFMqRlOW3QtT0zAMwzAMwzAMwzAMwzg+zNQ4BlSsUyF5iAiuWgx6i/wiuVnGRhzFEIr0XSQyY2UAqz1ZMd9IZZV5bGgUkXQqw+CVhQqBLUSoJNq2joi0HXzaIl1Z3XBtyipeHqePIvoci4dxRIYec5wIr0JmKJiGq8ML0bZZ+w6jz+HfugpfawIcJsIhFHa1reF1jQ2OcGwqiNB/fhFOLkPhOuy0ZQ4MECF5Hrk2uxlt2wU+59q/F/RVi3RPKt6cxVPA4nB8CimCvvTc+WYK0B+OmiBx9M8sEuE0W4WKK1DTR+bgEtJvNXUS4DmgtycRDY2MNnSQ38YZL2Gx7wK+EHhoRGmdCE2zRdDuIqOmoQrpsXjfxEcVxRQYHYfQCKjg74Mi3qjU476/Au9/CywtwdxyGVaWpTh4pSJRGoWiGBqzc7C8DKUibG5BuwHdL0FlG1rXmamnrKzC9Wvw+Q3YHYpRpAbNn7Vg72V4MJHPa8jvy8DjFSgMYbvrTcY4SgNG7/HYnAifn4qaN2GkRkx4H2cZG3rt1aQaZ1qYoWEYhmEYhmEYhmEYhnH/Y6bGMRCuJNa0K7qSvoWsjh9E28f75x0TRgVwXcn8ZAqdbXggkdXSnyFbZFYRPk4bFZ9Lf88SuZeQugO38cbJMHjXfcLV41nnGvc5FhtVrG+SXeg3jlBQM0NXzKsIravatWZCbGBkveJt+ogxlTDeYBlHuMpcxWn9OxZtQ7H8IvCeOVhdhEIBZpPRm/YU8GZkvn2J0QLqet6t4Py493lkTG5xcCH3NpKmKa+gcnieAhJpsoyI5LfxInMBiWQqIHNXRekOsNWQv6uIgL6KmDtryP30vDvGgFExO6bHeEMjRq+JmkR9fLRAHLmhYxrPCV3xn3XcrDbGNRxCA0x/V4NAI0VC4+PbH4ETq1JLo7yyQHL6tBgXSeIMjYJEbSwvwYkz0OtBrQqNXXj641CswLVXKDe2ePgh2fz/vCrPlTmkJso8Mo9e7cv5ryC/LyJG1Nom3E3leRcaGeGc1pRcMeG9F6KRFqGRpIT3f9b9q3/3GC1srsfNI4yiMgzDMAzDMAzDMAzDMO5PzNQ4IuFq+S4iKmuB49uIQLbDqFCm4lqWQJ6XbiYWU+8An0vhRioC6mbO8VR4HpeGaRJd5ByNnHMoYUQC0d/Tnl/HSQXcEn4VfFyfIz6XFnZWKvhV91X86u04GiPcPzx/+Dk8rqLi8rSRG2Fx8ziiRMXx0HzSdjcb8HxT+nNzKIaCpp6aRWpc7JG9kj1BUj+9DZmHTyPXssP+dExZ0TZ5/cjbLoleVaRQ92ML8PIubKej+3aR66tjeBt4EthwER2Puu/Pum1V4D5ohImmgJtEihgsO+48FbevGhpxRFUceTNOEM8yV3RfPXceVXyERlhb4rsvwf/2HqkDXq4m8mOvC4WaKw7uWjboQ7sDvRYMBpKnqt2GK69As0m6sUnSanLqFAyHMFuHQUeeKy137hryfNtDDJ9ZxFTrAc8MxPTIS88W3/uxkZHXdzVl9Rooc25MdhmtqZMVsREea9IzaBZ5lhuGYRiGYRiGYRiGYRj3L2ZqHBFNT6RiWQkvnn7RfY5XtIe1ELKIIx2yjIEhEsVwI+dYWSvLs9qet8I9ZN29sijjVzer8DguRVNseITGRNguLV4+dOcIj5mVqisWlFWETRAxWaM94pQ3WdEZ0wj7ReASkhZJi3dPQo8dru7Xwu+6ul9T5uDePwt8IoVqCn8Db0gQ7K+pgOL0S0VEoD2JjJMW9Ib9hbrjaIHDEBsaBSTK55EiLFS8+KyivM6VUOxvI+mlbiNRKmeR69hHIjS6iJh9kDRgdXyUx7Sr8HVeDPBGSmxqJMF2seGmhtMZZB6vk1/vJTxOfAwlND/C32eKUC5CUoBiqSD7dLsSmVEoSLQGQL8P6+vQaEgV8EpFtms05NVsMugOGA7h+i3Y3vO1crSOSQe5Dl137jnE8PiS2yaMiojNwvCVFyEV38MabdXDX2+NmqkiUT5NRutghBFkWZEfLcZjURqGYRiGYRiGYRiGYRj3P2ZqHJGE/YJeKBAfJPXNQdHIgyL7hV5NWRMSmggq2E5KxRLXYggpIYL5QnDuPiLiamRDgdHx0PPHhk0scCrzSKTBbbwQH6dXCo0ePeeCa7vWMQjPFbYlXi0eiqOTBM4ikhZpB0njlJeOKT5n3GZNdxQXWu9H+225c4UGyiZSV+Iq+wVbFdv7iPl1jeyIE23HYYlTpcXfNwbw4l0plN3ER+BoG+PUYH2knzvI+A6Bm8j+DeTalvHF7SehhaHDuRa2Na/v4dzNMjXCY6mJM0RqUMwgUQznXdsbSN+zxPtwzsX3rB4765zLRXjfqtQCTxJIigVJN5UUoOD2UlNjMIRWE7a3oNmCNIVOW6I09hoMGm16XQni+O9PwVN9ac9dZKwhe+508M8/HbPYxNA256WKCvsUouOvz6mTwONunz3XntjcmtaozULbnmfgGoZhGIZhGIZhGIZhGPcHZmocES3SrAJ0goiAWavy46iEUBANP+t3edEM+rmCCKfgC3wr8YXNMhEmrXSfRYTENvlieA0RbhNEDFzD1zmA/QW781L0ZImRJWSF/QwiYMfRBeF+Yf+0cLMWzo7HL1wZH6+0H1efIWSAiNVdfKqsaUwNPYcKqH18sfVxzCDXYQt/3VJkvG/jRfsact36eMFfV7KPa99hV6hnGVQ6vj1kXn4WiVhQUyWcA7q/RuPovOkg46uF0ndd35YQU6OCRGDcYPLY9RCDJG73cs5vSonpI1g0qgAkHdgpJJKhiJhN4dhn3edZv+nvoQkWbrc8A+99TEyNUhEKxYJEZwAMU/9eSMXEGAwkOqPTkb/bbWg0GO41aDeHDAeSemo49PfANnIt5hHjNC+6qur6rxFk4b2ltTH60feTjEM9T9FtW0XmUR14BbjO6JzJGr9pCVOMTRsBZBiGYRiGYRiGYRiGYbw+mKlxRLJEz1AUDwX9vKiAceYGwW/hdkVE7H0YEWVv4MU4zb0fHzds37i0VAXgNPAAIhhey9lugAiYZdeWHUQEzYoYCM2HSXnttQ1V4BwiYL8yYfv42G18nYPwGui28d81d54tvPiaRXgdGu48hxFCQ6F6khBbR1IxlRhNtQOjYnkVuAA85j6/hBhBPUZX08eU3auZ83sWJUaNsTA9ko6H1ji5ioyrCtDhdYrTDYVm2C5i2GikwKPA4wkMUjFzdJt4Zb2mTYpX8IepnrSAeZyyi2ifcfdKPI90DDRFkxZhv0t+xNY4g2Ncm/RvrQFeXSiTLM67sA3ExEiH8hpotMbANTSBdg9aLdJGi16zT78nP73wEnx6x0dHpMj9XAxe8Zhov2fcu17nAWKIaETFQQ0HbYOmZ9sAXkSeNTeRukKHOW4WGlXSR+aFYRiGYRiGYRiGYRiGcf9ipsYRGVc8exqRMiQUC2OxM87HP4OI148V4AtDX2dBf1NREfaLsuNE+HngQeCtyKr4r4zpgwq2zwBvwue3BxEiw8LcYdRJLDATvevvbfwq+mkE9zCND+yPHCD6PjxnghfqxxkaZUYjA46yqjsrWqaCpNuaRQT9JrLq/wQ+JVNe+8rItb+UwGIRWn2p96FRLllt1doPNfanv8qj6Nqp45C6c8/iBe5d/DXru78nmWtZ5sEGco8VgUcSePsi3GzCi10Zizl37NBI09oL4yIgQMZ3XPSKHifcPz5ObDIMXXtryBhskW+Shf2Pa2ZknUfv2x7ePKnMlZl97CSFSxegVoWba2JoDIfQH0j6qULqisv0/avbJW016e21aTVlcxJ46RX4QlOurT5TCnijqM+oWQjeEBi433Tc9N7PMx4mGVu4fTUqZ4jM5SZiGO3lHPcwhNewfkzHNAzDMAzDMAzDMAzDMO4NZmockS6j0Rpxmqi8yIw8MS42SDT1ShJtU8QJp0OJ0ggFyKzV1Fnpq7TQrxYufw8S+aG1MjrIKngtllxz/Q1F/T6yalqjNrSwd439aWjiyJFhxne6XQ+pF/EUfrX4NMTpbXQsssZAt9F6Fmoe5V2brGLNx0WCGBcPIdEW55Hr+goi6raQiJndnP1L+NoUrRSKfemXmgLj+lRGojxqyD51fC2Deff3lmtDWOC8HRzjLDJ3qu7zU0h6oMT9FhZrD/usZBk8uP36+PRmWvB8zbVpCUlH1QmOHdaVyULPG+6TRxhZkmdMhHNG68p0kDk8KTVWaKiV8amqwrojccq4r87bATS2eiS1Kqyuwsa6pJYqFWUCFN1sLbgIjUEfuj3odKHTJm21aTVTej1feqPX920fInNhGR91oSZPn/0mVAd/XxUQk+siYkRsMVprB/z1CaNY4uuh9TN0HG7iU3odl6EBo2aKpZ8yDMMwDMMwDMMwDMO4vzFT44iooKmC9zRC20HEODUc4gLMu8DTwJcQsTtMA9QNtgv3CSMkNOXQwG1fcd838YKlroAvI6l/3oWI1c8wusK9hwjYiq7aV9E7HKOQUKQtIpMxzLuvgvZBCQ2UsPhyvHJfz5EXwRBfJ01ZpKvlD8qMO25WyqMCIh6fR+oGnK5Avwc3UomGaSPXPG/F+wo+auHLSL9uM1781TRj8267GcSA+Ab324tI1M4iUhfjRdf2gWuPisBld5xH8PVPSsFvq26/cebKJPRadbpwd+DnfN21uYAYHeF11xoJsN9A0f7rttOcP+tzaI7p320kNdI04ni4v84rNfXmkPtS08mV3OcqYhIkQ7i1BhefeZXZBNKdbej3SWZm+eoM7vfFsUiRvzsdaElx8N5el07bZ6VaX4fPrPlUZfPAE8jceNa1cQEx3xr4iJGwH+FYaOTGIjI/NhCTTg3KLGMkb4z0fVpz86CEkUoHScNmGIZhGIZhGIZhGIZhvPaYqXEMZEUihGmU4PBiru4bC6R7eEFeTYw4532c5qnr3iv4CJAyXvx9AUlXVEGEaN3mPPBu4PE6bLbgCiKwq4kTC/wdfNqdsA/hqvcEHwVwl/0FvY86XvqeFaGiBlRWnQltV931oRP8FqbYOSizSI0SjZyI0euwhwj2/a5Eamg9Ch0fvWG1LYlrq0ZOqPmh9QF0ZX2WwL4IvNed76o7xkOIAH0VXyC+gIjZ4cMiXnFfca8C3sBQcbuPL7qtBkJ8rbVOTJHRVfM6bwruw+2mjJH2XyNsVpF5FF7PvPotYconvbeyCsSPi9AI2xebGhqtEZ5r3HyO26AsIRFTZXxk1QrwaAIvpy41XAp3bw+pVC5DmlKqlSApkJCKW1F0T4RhCr0edDukjSa93TZ7e5KhqlKW92dfgI9u+zHUKA01o/ReryBzI450CeuhaD0V3VcjN1YYTft1nNEWRyFMEXavorEMwzAMwzAMwzAMwzCM48FMjWMiFI+zUiqF74clrkHRZTRCQ1d06++hgaAit65gL0W/q2DZRYTiDrIifIiIqbvAWioi5wNIKqDEbb/JqPivYnNW+7XWRoqs+J5BhH4VdfOE6IOix4rNJvC1J7Jy/VeR6IQlRDS+E/w2QKIQplnZH1Jyx1xlf1FrpY9EGmwigvGSa9uma6eOi143HUOtoZKyX2TWWggF91tobCSIqXEWKSjedO1bQa7pOmKq3ERMnj7jV8l33TG2geddu7VNmjIoNK7iKCKNRMiKqtFruQ58DqmloQZJyx0/rsUQ1n3ISwmn5w5rqcTmQmgQZkX6hKZGPC8SJAKgRH7asPBY8bzvI/eIFq5uI8bNXAmaLrpiOITdPVi7mVKpwOJin1qxCcOBmBjFokRqDORz2mrT2+uwswPtFhQKMH+iQvH0KqeubTHzTIuk78eihU8jV0DmZRGfoiprbMPxGCBzoYHMt3Nun+vsv/deL/Qe0jlaGb+5YRiGYRiGYRiGYRiG8TpjpsYxEddjyEp3o9tMezwtuqvU3PeaJiX8La4fEIu5WoNjyKihkdfeNj7V0BYiplbbIrBWkdX8CSJuFpDV15NSRWldDq3jUUYE87ifx0koGOu7RrdoxEZ4TbRNdbJXbB/U0MAdaw4xb/Zytukj4q+y69qSdz41AcCPXdxP8KL9gP2mRurao4ZUHVhNpK50ARGtd1279fjgxd+QBvAqYoRcwUdyFNw58urO6Ged73q9QnND/95E0p+pyaHjs51xvGLwt5JlqIS/aXtVkI8NljTjb60f08ffL0oRmUuacmzc3AnNUGUbEdjnERPgBcT4+qwLc1gG2h3Y2YZ0CLW6lNMoFLtUBkOSXlBXYzAk7fXot/vs7UGrJSU2yhVIVpYpvP1tdP74z6kVWhTxUWChkZUg974aWH18lFc4LkNGjQ2d15tI1Fch2uf1poLUsWkjz7r22K0NwzAMwzAMwzAMwzCM1xszNY6JsGhyGKFxWOFOBfdQJNV0QzHjohv0tyKyGjkrNY62NaTHaPRFKG6eRlZcg8+LPym6Qk0VjQZRg2MeSW2kEQxZK9YPS1aKI02Lo6JtluCvUSuHqeeRxw4iSE+br18jBjQdU4xGYei24IXieHstHh8LyQ2kVsIu3iTQNFFz+OijIr6INfgxUzTF1C3E2NA+qlEB+wtuh6ZLnsmQFbkQp5fSbbVdcXRGnPIs7/jx2MWmX3gvh6L9HFJvJkVSt4WROCXEKOqz3zyLKeCjh/RZom28DXwSX1OlgTNMhrC9Dc2G1Ak/cRIaDQnOGA76lHsDkoL0Mh2k9Hsp7Q40m9DtSCBHfwADCgyaAz7x1A4vd/2YzSORIn0kRVmKT22mc2HAqGEVpqCKn38dxPAaZ9ZNIisi57DHAh9NU0bmrdYRMgzDMAzDMAzDMAzDMO5fzNQ4RjQ9SwERseOC3dOK9bpdLKp34w2D7fMiQ/Rdoysa+BoN8fnC/ceZMQ1k5XgPb35kFc4uI6Ku1gOoIMJ36j4v4gsJb0TnPI6V3FkphPRd+69iv4qwXSSdUYnjKxjcRvq9x8H6NGnbLLE+LigPvnaKmg8g16ILfBFfT6AL3EmljkMYqfIQUjD6/8v+NFbgoxRmkLoJMGqIxf2JjY2s6571W5bBEBOmnQr3i6M08u7FYcbvw+g9nD9NpO+r+FRq2jZNzZYwORJpBhn3Lbyp0UUiX3aR50lIBTnoxoakkFpYlCxT7Q4U9qTWRq+bMhikDIZSdF4Lgrda0NiDzU1ICrB08g7J7pAXb/hIohISCTKHNy2GQf+0j2HauHi8suhzOLOwgDzDwJsOVeTZsU32fJsGTXl2HbmOViTcMAzDMAzDMAzDMAzj/sdMjWOkDrwVEQI/h9RjyDIYJonVcWqpaYgF/JgCfvV9mCYnbs80ovsevgBwHglwCngEn4amgaw6T5Cx0sLQJ933e/iokrw6BXnn0igQFUxDAyPuV176oxQRRy9n7HMUevgUTuOI2zVNweIwGiLrODoOJWTlfdV91nRXoRi8AXwJqavRQUT1BDiXwDdW4cm2GD7x2PaQuT6PmBoLiDivq/rjeyBvjubNxay0T9PcQ+Fxs77POmd8jEnn1vmSdU8M2W9G5KFprEK6SMqpLKG9D1SLMD8HhSIsLEC5DJ02tNsSjdHrw96elNeoVGFxEeo16HVhfQNu3ZIi4c8+1efzL9/iM0H+M627o5ELYZRKnH7sKBFpMD7aQiO81DDbQ0wfjaopuddhTY0eMlfh/kmHZRiGYRiGYRiGYRiGYYzHTI1jpAqcca8XGS0yDccvmqmgHUchxKvctUi41rIYRr9nEdab0FQ0K4jwrWlwwm2zRMkBIkBqJISu8k+R1dVX3THLiOHSRMwOLUxddN+NK7Ks/QjF8/i3aYTrsA952+ZFABwXcTqkvHRNIZpaKk8U1v1T/PWfC36vBNv0EBFdj6uFoEvAYCjzWw2neBxa+GidBeSaart0DuWZNFnp2rIMBRi9RtNGsqTR50noPRUaZPErpMH+Iu0xJcabdHtIejKNlEiQcYzTwBH8fnEG3vtNEqkxOwPdrkRgbG1LEfC9Pej2YGkRzp2DmTqUyrJzrwvpUKI7dvfgz65K6rCQbdemLXwatDpwCTEh18f056CRVnnPkBLyjFhF5p/OyRR5ttzm6PV4zMwwDMMwDMMwDMMwDMN4Y2GmxjFRQlYTNxEzY4fsVeFkfH8YNG3TijtXeL6sVepdpNi3ioGh4BsKtaEYWcbXnTiHr6MRF0QuI2JjioxBgozDLdeuWWS1tday0GO84tpyEhEs64iQu+i2qSDRBC0mp6zJEn612HNWpEDMNGK3CvyxsD1O8FYm5f7PMjTUrMkyOPS9iox/m9Fi01lRD13EINKaDCm+LoeeK6tuxVoKn+lmm0vhOfr4bbRmyYD9BbcnCd5ZRkbWGE8znuOufV5E0zxynbddP/T8GjVQcd838WM1aX5Ouue7jEbzJIyagHEbF4HVEly4VGB2Fna2hly7Bs2WRGs0GrDXgFJJIjj6A1hbYyQVVakkryRjILRGyh5yX77d9XsFSWN3BSnaHhu34Ov3TCqOrozbRuf5KffaRaKF9LfjrHtjGIZhGIZhGIZhGIZhvDEwU+OY0NXpa0jB4FDsm5R257BUEZGxgAj/KuxniccDRKTVqIlYaNa/U7dtDy989/Diap/9wrCmgSkCb3PffQmfoz4vT30XMT2WkLGbRYTTASJeah59FZEPQ1Y6o6xrMEl81VRZYbTBMOPvLBF63PHj6IHwc2gcadqfEt6I0AiaCnLttWh7WLg5bIuK8nntVNNmGJw7BZ5HROx4fmX1U+dhaD6osRHW+AjNppisyIjwNU2kUfx7Vu2RPBNKz6HF2PWanwYeR8ZoDUk7tcl0wv1BowmG+NoaIQlwAUlz123Cn72U8v63l9nd7fLKy2JeJIh5Ua9JrY16Hba2pKh4msK5s3Dhgmzb7cDVm/By1AmNrAIZg4vIs2aAXOO9MX1aQObZUepT6PXS4vVz7rjH+fw0DMMwDMMwDMMwDMMw3piYqXEMFPHi5w4ieIYFh7NSCB1XtEYFERA1EkDF2jAKQ0XZuAhynOqHYDuNAlCxXItBx3UDdF81NTS91TgRP9xP0xapgVELzqXbqZkwjXicRTj2WcZGKJRnodc3y4BQQpE+z4CJr/s4Q0OPGQr4+reOUxUZ9x18VEFYmyHrfLB/7sWf45oJQ3xx5rx9QrLGMow6CdOaxZEncfSQ7hcbGpPI66OeI+xfbCRt4+fdrHuVgLPAeXwUUu8A7TkuysBjri2fasL/8T9T3nqiR78ntTQA+n2J1pidE2NjMJSi4Hu7sLQED72lxplvfghqNXa+cp1P/vptPh91JIyCGADXkFRPDSQd1Sb+Hg0pIOaD1uHIMsqmQc25EhIpsuS+63JvI+AMwzAMwzAMwzAMwzCM+x8zNY4BTUmjopsKwCV8ZETIJPEtLJQ9Do2qaCGrukOhOExbFJoXeSvU422qSIobTYkzhy/KW2I0RZGmnkqA5/Cpc2IqSKqpIZIKq4eM1xYihM4gY7mHXyk+xEcj7ETnnYbY0AhF8zByYFzaqBLeGMqry6DjXUTy/2uxbI3YKSLjuRv0IRbaY4MjjgzRaAcdW13Nv4OMo+6nERtxdIQy7vpnGSvL+Dl2WMLoh9h8C9sUX5OsCI2jEPY9NlGUYfDeZzTy4Flk/G+z3+h5LRgipuk6kr7tzgZ8/lrK+99e5BveM2B7C774lKSfmnF1Nro9aDZgmMLiEpx+8wql93w9nFiF3mf5Yu92phGnY9IEnnZ/a0qxvGsxRJ4ZPeS+qSLzZxe5H6Y1HvT4+jy9i1yrcMzj+2bcfWwYhmEYhmEYhmEYhmF87WCmxhGpAQ8iA6nFkbW4dRFf62DatCmziPDfRITLcaubtf5BA28shOKxEgrGcbQG0XawX8gvI6lnqoi5obn+w5RLSlh3ITZQZpD0Pfrdmtt3BzEuqm6fUDhN8Cm2CmTn8M9D0zMtIOJoB1/XQ9ugq8HzokvUxAnFU30PU3jp37PAu4A3IwbPHyEmzTkkNddzSHoygn11//D4Sc734ItHa6RALMhrqqy4T+G1iIX8OKWURmtorZjGmH2nJdxPr214TqLf9f2w5ztIe7LQCJUeMje3EXFd5+xBDbbjoI8YK2petVP43z8Bjy4OeOhBqFZhOIRmU1JOtdow6EO7A3Oz8MjjBUpPPAxLS6SlMq/88as8c3O8YZSXCisPfQYsIQbfCWQeNfDmmzIuak2NzZv45+mee48jrfReOWrRcMMwDMMwDMMwDMMwDOP+x0yNI1IDHkGE34p79ZAaBNvsF9nGiahl4AySM/8mkuIl3F+LGLcQwU8F9zD1UbgaPhbH4xQ/sbERitZa9DlBhMmia988IuhOQwlvIGgKJ02ZtICYGtrvRSTNzB5eLJ5x554L+hwSpjLKGtc68ADwqDvWFlLc+AZ+XBPEiCiyP52OpuBSgyAer6wogyXgsRI8MA/9begMRch9wPXldkY7Yb8RFROfSw2LuN9D5HwzwXZZURnjDI0UuSbngUvI9Wjho0SOYjDEhc9hdE6G7Zi2dsa9pINP7bXjPpfIjkQK0WLZbY7f/AifCSnw8Sb8wO/BD52HcgHWbkrKqUZDIjUKRSgUYG4OVk8UoNGk//IV7v73T/I7/+c6t445h5ZezzngYeRe7zB6/xSRZ8mQ/OeJHqePXAO9P8J/tOJInsOkqdPnlGEYhmEYhmEYhmEYhvHGwEyNI1JEhOyzyGA28WmoQqFsnDCr4vxJ4BQismdFUpQQgX4BMT1KiKmygIjlGq0RpvcJ62nEaZiyUIFQU0OlSMTIHmI8TJsbv4qIun3Xhln3WXPth6Kwmh0LiHjZcvs8CLzFnf/LjBYeThDB9KL77W70m0al7CHi6jsXYK8DL3b2G0UV/FiFgmgRSZ3TJ7/ocbifvl/rQ2kb+kOZF9rHuA5B2M4sQyNejR6np8pbma4GUD04fpb5kdUX3a6Dr49wF1+gXOdEghgfGlVzEELzIo7WiKNJ7gd28MJ7VrqqPLTOzL2O6BgCn+pB6zK8rwALQ5gtQRJMqnodlpZhb6vPtf/0JH/6hSf58BehmY7eO0clQQy8U4ixewUpsL7OaJSGmhpqlsXjqfNbozLi6ChFn2uhWXYQU6OIPHsGGW0wDMMwDMMwDMMwDMMw7k/M1DgiKSJ4q3C5jRevY5FMBbrQ7CggpsgcIvpX8dEBMQNEgFwB3u7OoXUn4jZliX1ZdQxiUTmOCFADouPOpwJgeM4yo2ZHFTEcZpGxWXDb7CIFh1vuWFo7pOv6pefQSIEl19c7ZJsKdXxaqrhvOs5ae+AxZ2aEAnMBMYXmkfFvM5pmp+DaUWC08LGiQqiaMi3gKnAZOD+EJ1wfQObEJmJGafvOuP3usj+9T9a1CdECzHqtQwpIyp8TSHTKVnB8HRc1UmJzK0z3dA2pj6DjluKvcQWJKGoghtpBV8dnRYrk/X4/oXUiWow3cwZTbHPcfBF4egjfADw+gId6UE4hGcCgCC/dhk/dhf/wjBiB92KMK4jReAJJtXYbuI7cv+H5isj9m5XWqhC9CN5DgyOMQNM6QFr7JD5fHgMmR90YhmEYhmEYhmEYhmEY9xdmahyRPSSlUQcR6V4GbpEtZs4iotsOXswrIeJwgoh/XXzx5/gYA0Q0r7p9asCriFmQl+YqFP5UKNfPKiz28Sumw23DF4iwHaa6UuqIqK2GxBzwkOvbmjtuGxHvN/GmjpoaHcR86LvvFhBxdB4vUMakbixu4XP4Z6U06rtz3u2NFnFXQ0NTCWUJoANE1NeaG+EYD/HFkM8h5scVvEB6zfXlQeR6af+1L7PA17k2fjFoV96qddgvCi+4NmwyaiqUkNXxj7j2P4tcGy0+HqalikXi8DgDZH5nRU4UkeusRckPy/1qXuSx7F5XyY/eUV5rsVzroHwKeDGF8w1/fStb0Lwp7Q6v6XGTIHNSn18aLRWjBm3cFn0GqeEWv+Jz6RzWOX/BHfMKk2sSKXXkedMk2yQ2DMMwDMMwDMMwDMMw7i/M1DgiXcTIaCAi73X212ZQtA5G3e2nURQFRATcQCI9NhARPEtcU/NARfAdZCV+Xjoh3SesraGr9GuIENhwrwEi7oX7xmmBtN1KCS9uq8hbdn1Mgz4N3O8qgsfRI2137gW3bxkR63cQcVKp4sXibfcepqiJUzQN3f6fHMr5NxmtlbGHmCJaRyRkgIxt2b26eJFUx2MReCti3rSifW+6/i267+7gI0U01dhX8NcvFGmzakwQ/FZHVsS3XR9CAV2jCWrItVFTJqvGRlakR/hbnvid4ufqvY5GGJdq66BobZhJtUHyajPERarH8VoaNjrPWu51l+y0UiXu7fUaIvdCj9EojCKSik2Lzm+zv+aQmm6hqRGaGwTvYWSZ3g/nESOvh8z9p5H7fRw1JMXdY0itneen2McwDMMwDMMwDMMwDMN4fTFT4xjoIxEDbUbzxsdouqg473sfb2IMEZF7HLtISqUykt6lNX7zkSgNFQM17VUVEaZn3PHCwsaxoZEl/JfccUOxX9MyaVopNR/yjAeQfrcZNU70NxXsy4gI2Qn2DVPUhMfU3/rIGK3joy20vX0m1zvQiBGtkaLGkJ5nHjGGbmYcq4c3HLYZNbsWkWiN7aDfeszwesV1NVJEwD2DCLi3EVMtNDV6iEDbc+0Paw3ERaYPS4oYTnkG3nESpr06KsuIobSJXNM5ZE5tIfeBzu88o2ed0XRe9wsF5B7WtFd53OuC2D3E2I1T1M0gabEeSuBKCn/GaDsX3Hs4j7OiNZSwFk0ZSVO3ijcqG3jjdty1qgIPFeEvnYa9Hpy4A5+bvruGYRiGYRiGYRiGYRjG64CZGsdEHxFFx63iTtkvfA/whbinpYNPVbXO9AVy43RSIObINhI1cAER53X1fbxtXu2DbaRfev6ea5fW4ogjDfLa1g62D/cZIuJkOdonNDTildy6cr3KfuNHjZNJ1JAUUReQFdzPI31bQcTwNUQkn3fFmU+7Nt5wbS4i4mpWuh+t1aGmiRb3zoqOqLljaaSLirgVsiN6ekiaodtIaqy6G4dJ6ZIOQpmDRS3cT6wgYz6PGEOzSI2JT+FTmeUxzbx5PRggJtPr3b6sZxzIPzQzwHIJ7vR8HZom3hxUkzGuXQP77+9wmzoSBVJFnmcv42vX1Nwx80yxDrAxgM+uwXYq981rYdQZhmEYhmEYhmEYhmEYh8dMjWOkiqw4Dmtm5KGRBCVEiDyIGDlERPA2Ii6fRMTB9XE7BfuCCH27jBbmPun60EWMilBgzzI06u61545bwYuSfbxBkZcTP2aciaLRFT18NEMx+j38W9MVdXParoQ1JnRffT0BvANvJlTw6cNmkZQ1p4CtoYinD+KNrQrwqPucJZS3EFNEI3xmXZt7yDVRg2MOSW+lhZdvuba8gAiwd8lema81P4bItT3OG137N0927ZejHHceGYtN7k00RBu5nmX8nNpkNDVYFoVg+/sRrWUxKfLo9WKAmGxpD15E7rt3IQbgVbfNLHK/htEaWamnwr+Hbp9d93oJiaRJkTlaZNT0i2vWdIA/Bz47lPvK6mkYhmEYhmEYhmEYhmHc/5ipcUwkyEr9hxDR7gZ+cLMMixKyYryAiNiaFiarKHSMphFSwbrGwSI9QMS+GUQE3UKE8ZuIiK5C/zhTo+LOq4ZM0X2n+wzw0R5Vxhfk1uPHqZaUcEzUzJhklKT4VeAJMk5haiwdg5Pufc19r8ZOC0nxVEKMhB18vZGW67PW97gZ7HPDnWMVmQt5q74vu3Nq3YOSO7emDwpNFk3N0wp+nxRRAD79lpoyx4XW7BhXj+MwaLqgWaTNbbxBFRY2P0rarBZi/i3jo2+GyHUYZ6JUXDuOM9rlOKm41+sdqZGHRqTtIpEUajjoPdXAR29MMiJDUrfvZfe31scBn8ZuXLRZAZkTWs9D55thGIZhGIZhGIZhGIZx/2KmxjGikQsgwpgWo9bohRA1NTR9U1iTYpqIBl2FnwSfp0WF8hX3eQsR7W+5z3v42hPj0lppLRDwRoumkdrGC4plxEDJSvuUl5oqr1C2RrckTBbqQ/NCjZo+XjQtIJEW8/haIg+676/goym0XoYWNde+dpFxKyLXfYhfZa4Ca56psceoEaXFneOC2F3gmjv2HaYXrdXEuYuM1XFFPST42g1xoefjOLaOoc6/EiJ29913JcanE9IokhLZY6/3jKY+yiqmncVhzYJxxtskQyiOIpp0nljAf61Rg1UjqrQGDfiaNPo8bCHRRhrp08LP/dDQi/uj34XRGwVkzmjKN/DPorxnRGhgZL0MwzAMwzAMwzAMwzCM+xczNY6RHiL0biNiWgsZ4CyhUYVwjSI4CCr8auTCpFXmWcTFlzUdVRmfzkhXpyeIKBkaJyoWhmaMtklTzYTblfHmBojgrEKxmhR6HMb8PYcYEV0kbU2cNmocKpKH5opG2HQQk+ECIrS2gFeR1EQ7jEau6EtFZL3WKtzi9vuS23cadMV5TBeJ/tCoi2ko4iNHJhWRPyhFZAy1fsNxp+vRGiRqPGlKoBQRzM8jfXsVH61SdL/NIFEeF90+n2a/2VdCaq3M44XtPCNA78uwXswkQrF9XDSRFrnOO24Bmet9pqvx0OHeXI+DMI+kXOsh0RhqjoI3Lk4g9S9eRUxUbW9sGsXmRfxb+HdovBaC38fdLxrxFRoZ+vmNWCfGMAzDMAzDMAzDMAzjLxJmahwje4gAqSJkn3xhrYsIf1li9jSrrUNTISsSZBwaHRLnm2/ihVEVVWfd9huMCsRD9vctPNZm8J2K/VXgASQ103NIsXPwK7y1DkRsZqTBdqcRUfp5RMhVMVK3U5EzTlUUis0qZveRSIxFd2wtHq3jcN31I7xJ8owNXZmuaOF4JayfchBSxECYphC8GmULbnuNeDhOwgiN444KCFOWKaH5liCC+BPu85fd+wwyr95Xgbc+AEtl+PTz8IX+flOjhphiWhdlluzUbXHx6kmEBerD90lmW16djjC6KJzHJ1yb1xitPfF614LQ+2rZteUqvm6JGlNauH4ZSRcVtlmNRr2ftc/6LAq3Gzemul+B0XGL0eNkRWyYqWEYhmEYhmEYhmEYhnF/Y6bGMaKrqvMExipiFHTcdlsTjhevQi65/bWWQ1zM+yD0kAiPeN9QQNbogA2yI0EWXHtusV/4jcdAzY0CIsqGE68KPO7a8hJiKmT1q4xEj1wL2q7phGC/GBmu9A7/VoNhgBgGl/H1CLbwpoYaRmFB8tjUyOprFgVESD+oARWeM0RTiOmcm0NqgIR1CbY5/sLR41L6HLXexaR9u0hf4+imBBnbR+fhHReg18s3FDXSpIeYISeQuZ217aTC0bqyP75nYLKpoVEa+pseQ6NONPonTOG0BHwz3ljquP1XkHn8ehYJLyFmxYxri6bfqyMRNX33fsV9lzXe8f1cCL5XdI6Fqeni8dUxryJjcxoxKG8H24THCKM0LP2UYRiGYRiGYRiGYRjG/Y+ZGseEFhEelwZK6wOMK5gdUnWvXUTsmwEeRkTBu4iIOU1qmjxUSK0hgqlGHGie+x3yxWEVDQ8ygQaIgXATqQ+hx/mqKO2O97w7twrKmnpo3n2n6aD0twH7V3PnCZMqgFaCz3HUSoovvKx1NMJjhxEaWXUMNM1VKEhr6i2tJ3DUKIcicAkZk5tB29p4YXYRX1z8teBe13PoI+J0FblmZXxtk6vAx9eh/xnop/Dn/dFIBvDz7AZStDosVK1oAfQuo0WnY6rAI8j1fwm5riX2r/bPMzXCCAw12UCu2UnkPgnv7TJyvWt440/bcdIdI8ukPC70Phh3/A4yF/fc331kzlfx98IWMkfHHSfLNIzv53B889L7lZGonDOIMRtHbsVmRhixYRiGYRiGYRiGYRiGYdy/mKlxRE4hK4AH7BdRY1SAnbSd0mU0T34ZETBPISmcqojAeRRjQ2tKXARewZsJKpCHqNinv+8iAqYWAA5XWieMrrpWMVHTUoXnL7o+pEh6oXV8XRLdr4hEeDTwtRRCVPiMV3XrueO/Q6F5yOiKfG0TwT76Hqee0rFQ46CACOUz+DRNJcQ0WgYeQgT4GxzNbBgg4/HtJagVYasHfzr011ANqrjw+EHQvuh8rbrv2tybdEeThPMUudd2gu11nm0Bnwe29uSe+UrG/nNI+qOb7jWLN0ZwxzqFj2IKzbwwAiNBIjzegozFDXzdjQqj82ucQD4M3jXdlEZhxVEXGlWyjcwfTWOmkTOT0i0dlgQxUk4g43QHX8RdjYMhYp5dQcZVC3en7DfwdhkfzRY+Q0IzI7wHdZ5kRWiE9/cc8mwrI+NaQ65XERnPMt7YOEhtHsMwDMMwDMMwDMMwDOP1xUyNI1IL/p4k9Gr6qKwc/lnEx+sipsAKsqJ7FUnFdBRTQwsna4RGWFi67H5vu7/Puu9vue+awXbnkH6tI2OyEOxXcMeOTZIEEZaXEWHxMiIQb7C/tkITWaUfFuNWdDsVW0NDJRQp89JRheeZJKwP2W9uxG2NhX8VfofAOwpwPoU/SKcvIp6FCuBnFmFmDrZvw17Lp/o5SGqsPCrI9VFTo+Re05pyByW8dmG0Tbxqv8VoVIT+tgs8g091Fh97BRGz1cQoM5oOTFNa6TyKU0npu9Zh6OLrwYRGy0HqaYT9GiDmjJqfIWGNlvA3jVIZcG+MpgpwAXibO8eTrh0aBXEOGfcv403YkLhNWjdlHGrU6DjrP1I6r9UACreP7+cCEsV0FrkXT7jvN1ybasizuOQ+69iaqWEYhmEYhmEYhmEYhnH/Y6bGEbke/K1CWh1ZUd2Ktp1BVg5vcjhheBd4GjExLrjjxOc4KLNIW2+w3xxRc2IPOA+8FZ9aZi3YroT0q+jaNIuIiCV8ofGX2Z92q+KOP4sIieuuHS32i6E9185JhAJn/GLM3/p5kjAcR2pkpaBSUVq/6yAr3FvAMJGojeQIS+qryDEawCc2obsJLw1F3I5rdmTVGZmWPnK9dZ82+9M1HSexEaXXMY7AiVMEJcH+OsfC4vHh5118bRctTq81OtSc0PkXR2ckwd8tJCphIepDEm0fExtpen41xwZkp3trAc+57cJnR/z5uCkhfZxx56riI8a+GXi4Bi924HoqUTTHGSkycOc7g/T/Fj7VXBlvQsVRG0NkDLcYTTN2Cv+MaiH3kNafabg+mqFhGIZhGIZhGIZhGIZx/2OmxhEJVx3PIAWv5xABssNoWpMTwGOI2LbHwVdWa92OFxFTocP4Gh6T0BQy18hePa3FfqtILY/zSIqZWLisIUbOOfe5Eryq+KLMC/h0NFVkPBZcOzTyQ+sLHKagdki4uj9r5XyWoTGtoJllbCgqrBN9ByKof34g58mLrtF0P5BtWBWQyJYF4FXg6aFf2a4CeVxfRPulN3touIwjrqeifx82IiAc36zzx+L0OGMqjOTIO2YsdN/CF70f4lOnaX2YQvCdpiWKTQo9Zx+5B1v4gtRxarN4TpWR+6QbtEFTH4XplfT44ThrhMo0aJ2IXvBZo1Li+1wjHvKiJ9RMvIzM6018/ZCZBOYrMNeFE6nMb62Xcdh7N06htYgYKDcYTdelESGlaJ8hPhXYHeCTbv9VfJH4AfKsuYPcR2raWC0NwzAMwzAMwzAMwzCMNwZmahwTBSTVybz7XGJ/rvYl95pz2x9EHC67/dqIuDnt6uxxhXRPIVESGxm/gxcM1WgY4oVLpYoIj5uIibHk2qfpY9YR8bCHRHMM3f46HgmywruFF9uPslo6q795+fLj1f7jVtYTHTcUbqetZZACX3B/Z63G14LtC0hNh7igch2frqvszqvGWV6ardgkqCH9PmyET1a7xzHOODporY+sFfl528V/a0RDeN/s4estFPHFrZW4eHRssOg8T/CRDLEZEW6vpoZuoxEoheCzvh/0+RCyiL/3QCIdLiAmTGhgziHPrD5iVmZdj6777a77Xe/928BnUhjuwdbQ18nISuk2LTV83Rw9j6aMiqNAUrx5oc/aIqNpuAZu3yJyX+nzuI1P5afjbf8QGoZhGIZhGIZhGIZhvHEwLecYqCMiWxlJs6SrwENS990N97nCwQRijZpQYXEa0VBFbE2HpKKftvkxROjMO1YJMWlaiJlxg/01PGbdcV/B18joI4bJktvnljufrojeYlRYvMVo/YlZ1+5QeJ+mv1VkRfY62abPpBXk44yN0Lg4SgRJXHMgZBF4BBFid6PzVJBogDm8gF5zL12RH4rKsN/k0DoccRHqw7CAXFMVu2P03PHq97juwTTpvuL94mPF38ef8657+F2T0XEpIWOr113bGxsbYYF4Mv4O29lAxPkuvpZNPK+yTD01VuKooDya+HRbJSQi6gH3fWh2zSLzrYWk0cszmbRWRtj/PeAFoOEiha7jI1z0+Ae9T4rInNJzDpEokRmy/6HSOjVpsK+aV3E6uCHybNHotgS5p7SOSXjvh/U6DMMwDMMwDMMwDMMwjPsPMzWOgToiGF7DFzEGn/ZFVxXfQkS/AfvTpkzDtKKmUkDE53l8IeKbeIOj7z7nsQy8pwjXh7CbSv/uMipEzyJCuxb3VmaQ1eHN4Dtdmb7n2lBnf/0MFWJVuI8FStg/bhW3/QxwyZ1zmkiWMIKjEHwXC+1hCqKQaaM14hRDeWwh10OLfStaYF4NpA6+/oLWFujjza4sUVwF+B2OpwaDRpWsR9/HRbXzjKJw7OLrnzXOSlhrI8/IOIj5lGcyLSJGQAGZ89vB+acxMOK/YdSQrCEGnNao0eeCHi+sXRJHp2T1Kyxw33btfwgxUvr41Fs61vpM6gbbTCIcd414uOza3Yu20b8PQhuJ6grvwRS5r+fxRrGm19KC7ok7/w4+2quAjO8cPpqmFhw7jM7QcVcj6F4UXDcMwzAMwzAMwzAMwzCODzM1jkgZL0C28cVrYTRXv6523mJ/4eNp6CFRDf0D7ttHxOdz7twq/FXw5kIWZeCRAvylB2FtAz61KWlowloXZSR64HF3jm28uNhHxFKtodFDBMsSvh5Ilf1iat/1s5DR1wIicFZdX9REUXG2Trb5kEVsNMy7/uyxPzVT1kr/PPE669pMm2ZJC4qH/Vaxte2Oo+mLGnjzJuxLlqGh7dLjxumR1OA6CJouLCzcHI7nOFMjjigJyUs/lmdkZG0Xvh+GBIk0egwf5dTA93Vc3Yys9iSMjs0C8M0FeLQIn+351E9h+qlS8DkU4sf1L75XFtx+15AIsrDGD65fr+DTMGl/1DDIOn5YQD3FR1TE/dVjHeQ6ZKXvqiHXooSYMlrPo8JokfUUb/SBrz1zBomOUeNDDQ81MAj2D1OAGYZhGIZhGIZhGIZhGPcvZmockQt4wa2EF8j0pSKc5nxXkb+ECMHj0hGFZEUQwHjhUHPKLwMP4oVS3PeNMeevAqecQzM3A6VNERxDU2OACKXziNlwCxFIN5EV3Lv4NEoaJaARBUNGozjidmeJqmUkEqPmjqFpsNLgWH1kdfY2+eJkBanvoUbLEDEL6uQXXg/rVuSJ2KGYW3HtbXPwdE+6XxjJoOOhKXN2EWNHReTYKAvnRVYtkdh8mDaaRNMynXSfdbW8RiuFK+XDthC1Z9x58n7LMzyy9g2PoQ+5aa9DDbiIiOk3kH5lXf8wLVn4HqLfa5RGHXh3Av+XVaiU4JWbo3UuwuPoK75eoQivxHN9Bykk30eMuqwx7SGRQRq5U0Xu43H7ELQv6/7SsY6NDiXPNAj7q3NR59UASaF1Ef8MCVNdhccu4Z9RTbf9pnvX53QclRGO6VHMMMMwDMMwDMMwDMMwDOO1wUyNIzKPCMywXxwLjQ19BxEOzyJC2y0OX+OgiK+ZoemFQtNB21TBC6thEd08UwF3nKc70L0MhaLkz7/JaBSDGhOaPkdXWRfxhkYSHVMF4sOgfdOUXjFNfDRIWKQ9ZhZJzaPmQBN/fcZFLMSifFxrg+C7OpK+aB0RxqeN1lDDK16Bnri2aeRAmJZLt4kJjxESp9pK8A+CScKumnFtZO7V8ddcC1zrucel6xonIk8yPKYxNsJjVPDjN41oXUb6eRd4EYksUqNKiQ0NGG1X3M+2O//DwNfX4NwqvLy2v3ZKTHyO0BQYF7kxQMT8caihGc/rrJRvBNuG80oLdc8HbYtTyilVJHqkiTd39bxhijedjwXEuHzG7VtB7t09/HyP7z09xgC597bxRcwVNZh1/9gkmibSyzAMwzAMwzAMwzAMw3j9MFPjiDTxongodIY54UNxWgVOzaV/lFQnBURMrCM59FvsL+RdQlJDHSRffNm19WXg9gA6A1nhrKvwQ7SWSIKI9zv4VD3htpqD/6BpjkK0ALu2L4stZIX9IiJqZomzWeLlgIOJmeNqJ1Tc+U+686+NaW+MFnOO508Bub7riBCeVxcj3D5PcM/aPrwu4/bR1FdqavTwcysvQkPPo5+HeINBzYIB088NPVdonMRmR2g29TK2HccQMTRu4WvOZD0oQ4E/jKZRgwf3Xcu9NCUUXbhyFS635H4JI2TGRdtM6uc0fYvbH9ImP1Ipaz81NB4B3o5EZ73IqOEQtrWE3BN9pLC4GqSxoRH+rc/LHqOGRdz20MjS9FlxGjn9rcRoH5LgHQ72HDAMwzAMwzAMwzAMwzBee8zUOCLhCupQWMtaWa10EME0L83StKhouITUtegiq7MbiFB8Ekk9tYCsWL7DqOGRh67gbyACrdYDyRJMN4HPu79VfMxbfT9tqq1x7CKrtvOExw7S9zOuLdsZ2+wBL7m/QxF3GmMjT7QPv9M6AIuuDfOuHdOYSnm/D/GpvaaJOBhXtyLr+yqjkSpZqZbC/XVFfhefzicU87POoW0O6ybMInN0Fz9G05J1v+nfoaAeFpQOjZW8czWBZ/FmS5lRkT1ug0Y96VjMu34NXb8aQXuawHMDeKbhi3erSK9tGhfFMu6a5kVtHDdF/HgsAe9EIs+eJjttVTjmA6SAd4/sKLWE7LmXF41Exvn0mZv1/Wnk+qzhn0d6rxym1pFhGIZhGIZhGIZhGIbx2mOmxhGZR4T0OJVTaGaoKKcriMOojaMwQITgBURo1ELZIGLje5BC3i8hK6jXmW41vIqHbURwPkd2H3XbneBzEalp0cvZ/qi0kWiMvH60kdX1i8h47LLfKBggUSWhIK7XY1IqIH2P0yuFv9WRebGKXIcC8EW8wTLpPHkcNE1ZGNEQRxKBjxy4iIzXC4jQHO4brpoP+xsXse4jxkhcqD2ONoj7rfURjoM4TROMXpfQkFIDRgtdJ8j1aiEmxDaSJq7CfsMyPJfey1181MoCkmZqAylSrfsNEDG9GewTGhp63PBcecZGHAWRFdExCY1YOIixqqmhtBZFBYlqeQZ5xvTJni+4fdbxxkcBXzNDxyHrHotNkfBahuMUR2vE10xTp83gx7/ovtPjx/PEMAzDMAzDMAzDMAzDuP8wU+OIhDUiQrExa7WyCnnD4D1r22lJ8XUkNpAIAT221tq4AXwFER4PmuoqRUyBObzwN44iIgw/ioi3lw9xzklMSpGjqYO28Xn4s2oExAL1HvuLsceibJbAH4qreswKvqZHBxFRa4j5k1fo+V6SJcYrS8Cbkev7asa+xeDvQvS3jkcoLGeJ2eOEdjWhjoNYEB/3dxGJZEqQ6JcTwIPInH0FX5cFRlMUhccJr7mmREsRU+S2ew9F8gQR/bOih+Ki33kRG1mGRvwbTH6mVIFLro1rHGwuhvfTDhKptcn4eabzfZtRc66O3B89Jkdy6TYlfJ0UPXb4HhKOSR/p6x18aqoUXyul5NpyYkI7DMMwDMMwDMMwDMMwjNcXMzWOSCjmTRIUswTg42CAiKhqPNQR0VJrXGje/sPQZH+B8CzKSAqac3ih+iDnTJA2DzmeNFVaR2EGL0gXyC8GHp5zFjEhwKffClMI5a1EH0R/byEi6ga+mHpoZhzF0DoMWcZOih/3uK5GmGaIjL/1XY+btdI9ngP6+1HSro0jy3CKU4ZptME88Jj7+2QC3VTumSL+wajzJi/VVWhkqWnRAK6674v4OaivrPsiNjXi7+KIhJh4Lk2aWxUkRdo68uyY5l7NSqW35941/ViYfq4Y7NMnO9JIt887fzjmZfbX28iKbBkX7RI/W/TaaqTRg8DbctpiGIZhGIZhGIZhGIZh3B+YqXFEwqK48UrlPGIBOeSgQncREeCbiJnQcW3aQMTVJl7gP4yAPkCiNSZRQ1a+n0QKhx80pVARiRrok1/g+6Boep8qIizX3XcbZIuoCSL2riK59zXVjYrUnWjbrL81gqGNpOO5FrQjNgTycv/fa8LztRDzBUYNCY1UCNNJZRk5FXzKoaxaJ1liew0RqFXoPo7+x+ctsn+8w3GvISvyTwNzCSQJ3ExFpA+jNKaJqAlTHumq/6zC63mGRJYoT8Z7/F3crzT6exwdpFh3e4ptQcZrGTFI47o8RSTN3SIy39uuHcvuu20keirrPGogqdkQR7rFf4fXRa9N1t9ZY51nBrXxUSQ3kXRshmEYhmEYhmEYhmEYxv2LmRpHJMvMGCcSFpBBz6qpoXUxOuTXT4hFy5rbJ0WEQTU2eoymxNF989pWHnPOSST4aJE9Jkd1ZDFEDJhJ43cQTiORI+CNmXHFwPXa7LntYsF0UhFsXfG9jjdPmm6/MH1XbIgctb9ZxwiLdo8rLL6FmC9DpN9aFLvAqMmRFQEBPuVZHKExrk86ZzuIkBwWoc+LZJgGbWMBMVjqyJzuBccsIJE4p9x7F4nQaKYiyCeIQK/jMa2hcZRrGJ9jmv7HczA+f/ycSPD1KzpIv6+536aJmpkH3oKYey/gzYclfJ2KfnCsAhK5dRqp6ZNnVFbw91deOr6svk7zmoYwamiApGA77HPQMAzDMAzDMAzDMAzDeG0wU+OIjFsVnPW9Cq499ouJRUQ8TNkvrGWl+1ERV1c5h+KtFt8Nt8sT+rS+QAtJp3VQUrfvDUaF9IMwZLqIkGlJgK9L4P2LsL4HT/alXsIO+SLuAOmHRi+EIvkkwnoc20hfBvhCxHmr6Y8jBVne6n8tBD3uWvTxJliX0VoZ4Sseg7g+RWzsjZtvGk0UmiHHYQ6E7a0AZ9w5dpC+NZExeQCp+zJEVuZvBm06hUQVNNhfYyVk3D0/qX1524fiflxwPT4O0e/xcwH8uKqxGZtbB7lHK4iBkSImxQB5Vj2BjO9tRutlDPDjmFezo4w3nnQ+hHMh7JvOZY3sGRelcVBTLE6htnbA/Q3DMAzDMAzDMAzDMIzXFjM1jkic9z4raiN8afHomAJwAVk93mR/QeAwh3x4/AYi2JYRMT5P4B7HAiIA7yDCpPZJV3ZPU+NCxdOj1Eo4rggNcKvIS3BhBYYd2OmLgJ019iFZRd/1cyxI56UV0rEAucEmmUr3giHTRcyoqVBiv5kRfob9Y5AXsRLO1SE+WkLPo9/p79OmTBrXh1joV3F9EYkWmAGeQyIGFpFojGtIkevbbtsLSHRBB5+WaZooiuMq+B5GVYWGpH5X3LeHT8mk+6Z4s2AJ6ccdpr+Ps0iQuXQHSSl1CriFpGm6AHyB7JRuNxkfeTPn2rgebBNHRcGo0TZAzJhw+9jYOAiaOk3v03FmkmEYhmEYhmEYhmEYhnF/YKbGERkn4mdFa6jwpuKZ/l5HVj4v4fPWh8Jm+B4L6F33ShBRdgURcKdJo1JGVq6/BREtb+Jz5k8ToTCOeP84MmHcynate9Dl4FEfeuyXevD8ddjoiXB60LQy44wM/azvWW0MDYIs40PfS8hK+DxTKuuYxyWkJ4ggnUbfZUVpZEUIQH5kRoqI6uHq/bguAhl/H5S4XTo+TeS+WkHmUonROh5NZMz1Ptt07d1h1KTT/sX9Pso1yLuXY+Jnh16PIl7cn8X3p4/c05p67QYSMXFY9Dm1BzzpztvAp/HSz1ljEc55jebR7SrI824Hn+6NoH+D4Nwtd44yPo1baGSEURoHJby24cswDMMwDMMwDMMwDMO4fzFT4xiYRtwMRexQBFYBrY0IkCqkjhPXsgRRXW28iBS6LjIq4ucJfgNE8F9DVqjHkSeHEfi0LTX36uGFSxXI41Q5sSippsa4ourjSIHngUUXmrHH9KJnnsAZmhPhb3ntKwX7xG2LDa8Zxhs4WpdjFp9S6ajGhrZ/j/1pp+KoDcifk2oihOOTsr82TGiQZI3BNOmbslITxe+63bb7TguY77jzay0PTUul7LhX1rnyDK2jMo2JFY+PRp+s4+u3zCKRD7tIf1OkEPgdjl6jRNvQDI41486p9TnitobMIEXZ1ZwAifaoIhEfYfSUziWd71XXp35wnA4yZzVl2GENjTzM1DAMwzAMwzAMwzAMw7i/MVPjiMSrhMN0MLBfGI0JBcttvLAdF/iO9wmPVcaLhCo0ThuVMESET823rwJj3I9pCEXQIrJS/AFEuHwWL9hr3vyw77p/nP5pEgXyRdu+O3cDEUaPKnyGNRbUBNAIgEbG9tOKrX3k2udFe6i4W3PvGklwL4hNnEmGRkjYpi7756CaPPG24f55/Soiq/vBR1uMI0y/pamktBD9dWQ+bJGfjmxS1MRRmeZY88j17jI6f+eAtyK1LXaQsWgG+1WQ+3aX6YqdZ5EVvVDEm1eriLHSwEee6e9hREYRMSJOI8+ZBD+P7zJqKoX3vBqgK65/DTceDyP3wxpwBTF1jgszMwzDMAzDMAzDMAzDMN4YmKlxRKp4E6GCmAtFRMDrkr1KP3wPhbw+XrzMMkPyhOV54D2uLVfxOfRDkXEcbUT8BxES5xDhsIRf7T6OOFXR0J1/BkmHtYEX7FWsjFfs69+h6D0p9dQCIpiu57RTjaItJtcTyBLv45X5oakxRK73CrJy/nlERE/wURfjIjSI/s4T6YvI9UgQ4Xo359hHISsiIwsVr/X3PLG8i8yp8HeN3tHUQnGkxiQDKExjBPuLXsft1vuqw6hxkSLzYQe/0v+1QqMPyviUceO2/TpkLt0FnsH3Yxt4xX2v91ofGXOtW6Km0mFSyGUZGvpSY2rWHV9NBf2HRO//sOB3GzElevjrssV+cy6+nmpGaeTZHJJOLEXutfYh+pbFQYw7wzAMwzAMwzAMwzAM4/XHTI0jUkXEOs1v33ffwX7Bbtzq9GlW9WdtoyL6aURIvIwIiFVEcAcxLCat1h4iwuHj7lg7iICuxoauyI7PDftTFen3A7wQH+fMz0o7FaYwSpBxzWt3gqza/nrgVeB/sd8A6SJFoPvRfrqSfRB9P07ULyDjXHfH0xXy6257LTh8Crjkznszo/0Hud64NmrqrCwh/yhMKoocGlUDREieIbtgtTJExj3ud5HRa6vi97RjMWRUxB5naMT7xb+FRkCMmgJad+O4qAInEQOyi0SLjEOF+5OuLSW8qbELfAnpwxD/EJ/BG6NxxMRByEtBFn6/iRQI33XtCiOmwnt7gJia4TMApksr13fHvYhPjXcFeZ4dpkbOOMzYMAzDMAzDMAzDMAzDeONgpsYR0Zz1KuDdwBezzSueG77H3x+GGiLUhznmq4iAOs4YiCkBDyGmxmV3rHOIyPcUo6leYpEzTlOTIOLjBjJGyhyyyrsVtE3Fbl1VroKzCuDzjBacxv225o5TD/atICu7N8hOw1VDIit22b/SO17prxElGnVyHjGKGkhkRs8dp4mPQCm6z52gX1lMe701YuWwaPqqWETOMnCyxNw6Iqy3kD5XGI2yyepjFR8lFB5br3XN/d5htE7DJA5yj8Q1MML980yUOcSUmkEMqbyUYAeliEQsXULusbs554/b+jRwzX2Oo016yDjX3fsMcp3XEcPhKHU0wnf9O3418PM+K01eODf6HG4cZ4ElJCKrjxhB15Aoj6PWk1EmRScZhmEYhmEYhmEYhmEY9x9mahyRWKzTCAAV9rPEstjYOIqhoSbCJiLSFxExsBl8l7efpohRgTB127fwq+3riAhdZlTADg2NvBXdTfbXP5hHUkbdwhdEj8XmUCitI8YKiFGhxbKvuc+fcv1U4b+CiLx5aXeKjKYMi1FzRWtlaORBDRH35/BpdJSwfkoTGb8W+1euh32Eo133SWj/V4A3u3Z9GW+2JME241JunXb7P4+PllDzRrfROaTRKmFUhv4WRmR03TZdvBlUxov1hxmXPGGdjO/D78L5N4OMVx2ZW9OkbqogcyNBImqy0llVkDmr82naPnbwdShiSohJUkOMDL2uR43mCc2ISUJ/bFrp/vo5z/SahEafLSH90+dUm/waKIch7l8YeWYYhmEYhmEYhmEYhmHcv5ipcY9QYyMWB3WlOviUKkchRYTPzyJCoJoGtxmNkIiZd68dRtMbPYMIulvISvUyIi7GdRxCwX5cqpoyPjWX5uJfQYTYlFFhWd+1QHmCmAgLyEr3pQKcLEM1hf/ahZddP8MiyQ3EUMirldB259a8/zH6vQqpKryHaZjyhOMUGcswFU8h+v2wxMXVx1HEz7EFJPomRYykNXyqsayUYWFbU2R+vIiskldhOUWuq6bjaiLjPmDUUIoNnND80L+1kPQCMuc2OFidi2nE9ywTMWyHUgGWXZvUgBhHCWn7GeS65827HjL2Wrh8k+kjF8K+hXNJI4K28PdSheNJm6XXZVoOY1yMQ+dKgvRPn6V6Lx5XlAZkR6QYhmEYhmEYhmEYhmEY9zdmatxD4nz+KvJfQMT6bfcKC2mHFN32A/Lzx6fuGD0kkmDJnWdSEd0Vd+ydqL23EIG6jS9mPB9tF67qL5AfrRH2eeDeVxDxVXPx16LttUZAuH8REY6fWIAHLsLdDVi5LkXRO3jzCPd3mDYqFNY1BVILX7Q6PC9486AVfJ+6Mbns2t4iX5TW79U0OC60tsIm06UuUgG8g8yvCqNROeMMjfA463hhWYX+FDEzHkKuyzN4YV3bFkZn5P0NYoLMIvNMjZHjIjQ80uj7fsa5asj8VINgmnGG0fmaRR+fFiphNBIpj9hwi6/TAEljpYYhHG8UA+yPtJhmPscmjH4+aP0Lvec28fVs8qKrDMMwDMMwDMMwDMMwjL9YmKlxjygxKqon7rtV4DHE2GgjaX0+z36RvOi2XUDEy23yhVBNaaMmSo/x4p8aBRrVEEdg7Abf7eGLhOt+oYGhqYhigTwUZWvudRoRwncYjYgIhWc1FTSVzo47fzmBSgl29+DZLV/LJEGE8Th9kqYz0pXdGgGSVRw7NAHS6KXfNZGUV2EERx5ZESBHXcl+3r1raqtx6DXpI5Esn0PmkRaQjq9fLJiHYraOXxFJA6bC/wXgoRIUhvDy0O/XD/aNX7GhgftuC5lnR6kdErY9ToGkaASLpljL2reAT5k2KSJggC9YrcXHx7Vr3O/xNZgmaiA0NI6brDRUeXM4L51agsyVGhLpc5C6Gh18ZAbI/BjXhsMQPpsNwzAMwzAMwzAMwzCMNw5mahwjumJbV7XHIt4y8A7gLUUoD0RkzrsAJSRCQo83jhQRATeRAsdqQuRRQITdvYw26vGyPmtanrx0U3GNBn3NIyv6zyNj0MUX69YoiyxxUdMd3QQ2U9jYgZd24M+7ItZrm/IiDsLPYYRClrAetiEUisPtxonIcXRK+HkaYXgcCWKqHCTNT7hC/ia+OHWR/cZUbGiQ8bmAXLuvAx6qw7ALwxSupGIwxREaWdEZQ/YfX42Moxbkzps/2vYESRX1VuAG8MWMc6o5qMbRNGMd1r046LXNMgDz5nFoMqQZvx3knFrEfZwhEt4P4fFjw06N20LGtiXE1Kgic/AgxG07TjMj77hq+N2rcxmGYRiGYRiGYRiGYRjHg5kax8gcIohpaqWYBeABoDcUYVWLeWcJugMkOkOLM0+TCmeH0QLVeQwQU2DSyvFYzCwGv40zMsLPWpPjLBJ5AiJkl9k/+fJWTbddWze68BSSdkqLdecVu46Po20Px0ZF9rBeRZaQS8bnPLLaH6/A1+NNI0gXkPRMt5D5Mk2KoTAyRem5zxUmi+ixcK77d4GFIixU4PMteA54BYm0yDOJxhka4fmOSpYxo/PjJBJlcgl42yxc7sEL3dGUaiD37W3EFGwc8PyHMTTyUoBNMugOcz5lBnkGbSFGw7jjZJmNWl+mjo9gAh91pXNO642cQsb1fiesI2KmhmEYhmEYhmEYhmEYxv2NmRrHhK6A1lXwWQJ0ARFSL6fwEiIQ5tVn0Dz8B1mJPSktUsik9FTjfotTPeUJtAUkSmPZfW4iBsUaMjbxCvQ43Y2u/p5BxNEWXtzX8+ZFh+QRFvGGUfE9q49xlEXW9cgSoKdJHzQNagypqTDtPglislXxxlmJ7Bs+7FNeKiFNr9QfwPoOfAFJnRbOIz3vuBoaMXo9DptGKR7jMHKghMy9v1GFd56DWhnaTWiti7kTs403EqvIPLsXAncc6cSYv5WDGEIlpH/NjN80nVhWZEUe4TZ6vWfwKd70uIPotzOIoZlnaiRIaqoO9y6N1iR0jmr0CmSnjzMMwzAMwzAMwzAMwzDuH8zUOCY0UqKOCPBlRtPygIjyX0JqM2wxWciblPNdV/H3mFwY/KDE6ZLiVD6xABsKgSkioncR0bKFCKwaEbGLF0BjQyPrnCB9vIOvCxG3Ia4TkUeWiTII2hYeO25XbIgoeeaO7hMKyP1on0mi8gC5ttMYJHoeFWjnEGH5OhKBEAq3Shydkmb8psduAU8DHWfKxcZYGA2TRq9x/TtO9NyzSKTAo8C3PQbnz8CV6/C/rsGTiHmR1ZYKElGVIv1tIyK9GpVHbW84R7OimwrIPVN05w/Pl5XGLGteDMg3wNrAi+w38vLIev4MkWddzX0uIHNtD596L0VSnoGMdREZxwQfBXMOuT4vIpFrcdTUa4WOhd7fFqlhGIZhGIZhGIZhGIZxf2OmxjGiKZGq7F8JniCC9hoi8oWiPuSLeXHUQIgKoAvABtnFj0scbiV8lsBdRMTLAdmRHmE72+51DekziOGzHG2bxxAZrxQZr8+4/eOolqwokXFRElmRGbGYGYrMscGSl14qjl7R49aQ+iFVRMzdYLSY9jQMEUFYjaKs/TT1j5ooQ0SEHyLzI0Hm5Caj80RNnHh+6OfQmNgGvuzaoEZLOC7x2B40CuAwZF3vAlKU/uuAJwqwMAt37sIfvgj/1fUjNGvK+LmmbdeoqyrwFsSofBZJtxWP1TTXMo5i0esVGmg659UY3UbMz9gADP/OOm9onmUxbf2S0OwqBe1tu2M03Her+NR7Hbxp0USMDZ3vuo0asI8A73bb6zPipHu/zWsXvRHOWzWbDMMwDMMwDMMwDMMwjPsXMzWOGV0pHwtjBXwKIU2RE+6j73niaJ6xkSIi6AlEGAzNBi023ibb8Mgjrw2ngHclcqzLqQjkvWAfTZVUxgvfYVoa3SacdFmRAak7roqvHeArSNFhFfXHpe3JS+OTNeZ5RkYoGmvKHv09LCYc16fQz7rPDLIifQ4RancYvf7TCOJ996qQnWJMzxsbGx18VEwFmSdZxdnjcYkNDf0c1mvJq1Gi+00iL+rlIMRGUji3Gsj8vFuAp6/Bzi58ujdqaIDcHxcQ8f0OcBm5Vh1k/paRe+vrkDl5jdG0cmp+TEoNpuYJ+Dkcv9RY0bRzemy9TjV3DL2ftd/3QvzXeTBwbai5V4oYLdrfMjK3whRSYaSObqfXRftYRsa5HEyE1zNKwiI1DMMwDMMwDMMwDMMw3jiYqXFEYnG2h68ZEYpjA3yO+3Gi2SRjI/ytgKwkr7o2aO0F8CurG0y3MltXKI8T9SrA42U4OQsv7sKzfRFgt/CmyQKySj5hNJd+FRHVsyZcKOwOXHvjSJCme6mIWgn2DY2LPIMjRKMTdIV+H18AOe67pqYq4AVZ2G9m6Hc1RCjfcO3dRdLyzCMpkar4eiLTRmv03THKZAvYYS2XAqNi+BbeJNpx7+GczTJxwj5plE8afD4OKuRH/ExDuKI+K0pnG0n19lwfvnRdIoRus3+8q0jdhxQxQdaRawd+PG4i1+824yOFJl1LrfMRGhi9jP1SxGBJkCifZdfOC0g6sRtIPRM1V9Qoy0JNxtaE9sVRIPF9o6ZG0bVJi6n38GmyQvMyNiuHiBmj5kfitt8b+ralyNjHkVSvFVZTwzAMwzAMwzAMwzAM442BmRpHZBERQ5UhsupbU8yEq9eb7Bc/Q0E5jgLIis4I01T1EGFxLuO4usp6GtG8jIi2KSLC55kg14BP9+EbW3AWETk3kZoN1xEhWUXqCiKAhiu9daV6VnTAABE8w1XsWfTwtSE0WiA2NmBU8CbYroSM14Jrz218fYUsMTdMvdOPfo//LiLRJG8GrgDPINfnReTa19gvxk/LkOzUU3q8OEpD50nWiv5J0Szha8i9EZlT/Mr/sM5IiVHzKGaSaRX2dRd//br49HDh/bXqXu1gu9iMfBaJ4NDIo5ACEo2TFUETbqMmk5qP4YM3bz+Nzlh22+wixljFfd8n24QMU12F9WLy0HHX7bMMGjVkSvhILD1HaNZmRV7hjrsXfFbjpsRoQfPDmlzHyetVtNwwDMMwDMMwDMMwDMOYDjM1jsgMo6YGeFGszugK6TyxLCvaIMv8KLhjFvFpdHSVtKa3itswDRXEpGgiq77zaAKfGsLdNnwjsmp8HjiXwMOppIh6Bm9SaJ9UDM0yabSfffZHt2ShArESp1CKTaL4XHOI6fBIBba78FnEhGrizYtQaFcjphT8HRKK6zqOjyKi88vuuC1EFFfhWN/VjJi2+HSWeF2IXmpsJIwaKIXoGLE5kLWyPnzdC2aQMdvCp8haRsTvvWjbuL2F6HO4TYim7Npy+8y7Yw/d329C5vFN8q+D1ofJouaO0UKKp4fRSdrOsB5F3Hady1nGxjJw0bXzKvAqYiymrj09RlOjZTFkNF1WHqEBW8YXKte5sIdEi3TxBdTVfApfjPmslJD7pI+Me2wAG4ZhGIZhGIZhGIZhGMY4zNQ4Incn/D5NWppQaI7F5fA4ICJqhdHoggq+Vsdh0DQ3S4hoOu44LaRY8ml8hMeZBThdgPY2vDT06ZzyRM7YdCiTv0Jct5/BmyUqCmu0xjgjI0QjKR4ow2NnodGBV9bgaXz9hFlGoyLUGCjhhde8CIvEjcfJMsyk8EJfUiCBT88zhwi6G4gAriL3YQTd0MyI2xGaGlmiv5opeX0JozTuBVozRVMaaSqqAhI5oXUqdC7B/hRTZLzH9PFFp8OaDyCmwRnk2t9icl2MLMrAg8h1HQDP4aMY4rRg+gr7oKZHfO4ikm7qvNvuGjI+bUbNpixDYFykUxYJPuojQYp1zyPRRg28idJx22kB+rzIjLxoDeUBxFi8gzeYDlKg+7jnZBhFYxiGYRiGYRiGYRiGYdz/mKlxRMatgp6mlkVInrmhv2nO+VB80yiIeGX7QdCV8d+KCKdfYLzAO0QEyavu8/v34OEZL5IXEXNgGy9aZqWI0vafRwTmp9z2zWgbjVABP956vLDAb/g5r927wJUenGvDbM2nhCq4Ni/g61DoPiDjERaAz4qs6SNC8JW+FFU/g0SvhOmV5pDfeq6f49IsTUoZpO+xaK4CsQreWe0dZwDFq+zvBSoiVxktOr6BGGbn8NdLa4HEUSbx31loJEOWUddHzIJXkfRph+nrAD83tC9hpExY4yT8Lmx/mf3GRFg3ZpvRuhPhvmTsS7TNuH5pW/VcC8BbEIPzDPBlpM6IRi/12G92ZRkXWQaHUkGuqRZd12ubdU/lEfdX+3lQY6KCmEd3GI2yMQzDMAzDMAzDMAzDMO5fzNS4h/QQ0VyL4+ZRcb+HJsi4qI0aErmgq6s3GRXiD0MFEfUfwtfIyCNBxOfbSN8KA7izK7Uj7gAnkJQ880gURFjLIDYFUmQ1uAqrK4ym7FLRN0z/E6exyjM44loCQ2RF/i6wfUuO+wIyjkXEOJnBr4jPKuIcpnGKUyIN3Jg8m8o5toL9FvDRJq/gV8CHKYh0jhTd9m18AfaY0LyIUTFZIx+q+ALVOk66XbxP1gr84yCezzXgMcTQKiDpuVQ0b+DNuhL+/ggjNsLj6rEnCe0xt4D/iU/hdhh2kZRrl5D7UOuuZEVpZEXPwKjxpL/rXCohpsJesI3uExa2zjI2SvjnTy/YNkTPVwOeAN6KzL2Ga/8dxLRpM2o2jjPE9D1v/C8jaafaiJkZtjuOwskzNuJi9zpH2tE+k+bBPNLnZxmt+WEYhmEYhmEYhmEYhmHcv5ipcQ9JkQHW1dywXwwsI+mIOojIOmmVfBFJD/OwO+ZXEIHwoFEhIWUkgqAJ3EAETSVLNO4gQqsKpZcRAXYXnzKohpgbi8iq7DTneCkiBq+7/bTAuIrD4Up+JV6tnjdmw2gb/X0XWYGufVEBWlP6zOHrCIRpqPLOH4qoA0RY3cGnCKsg0Qfz7vu7rn9hGq3w+DPINb5FtqkR1mfISsmEa7deR70OA+QahwJ+mMYoq1/HkZJHDSOtw1BAIjHeVYDloZg82v8BMm4qTuv415B7SefcuFX8WUJ2lgjf5+ir8wdIpMcmMt6hoZZV4yTL1CghhuIAb6ptI5EMW4ymZ4rNEO2rzt+QGnJvhwXF4yiRMLJqGbnftLbOHnJ/JPjn2KRxh/2RHDEt9yq4c8y6toZzPatmSowaGzquOg6x6al/x9EjCXJfLCMpxF7haM9RwzAMwzAMwzAMwzAM47XBTI17jK7IV2Kxr4gI3k1GjYI8UTBM21PFi5ZHQYXAm4ipEa7IjlP+aBqa8JwqUup2CSKKNvGRAuEx4r9DwbWLjyrQlfrjiM0MFTlDATfL6NCi5Fq4G3fu7eCztiN85a0kTxmtC9J3x6u6djTx0R8wKsgSHE9/y6uRkrXiP25HaEgkiFD+hPv7MnKd9fe8lfXHGa1RRETr/z975x0nSVV2/9PTk8PmhQWBBXaRqJIEJOwSBJSogCQliYiSUQHRlygKLAbWRRAUQWFBSYKKIPASBPSnEnwRRElLFFg2ze7k0PX749aZe/v2rerqmZ4Ncr6fT3+6u7rCrVu3amfPuc/zsLD0RBhTrh7GDGhH8XjoR7F5UQ/Tj+zjtAiB0DK/f6oNi8HzOKFrk3S9+Lk5/l4Ha4DS4OHvTFMVEugBO+64Hxb2du+FUP2VBhhxHzARWkxHx2gjGoy+OegeN2kcpdWrYEq5afG5vgzz3PBrqMB79/fvjvU6r31+u9w+ysVtqIVJCVcPmRpCCCGEEEIIIYQQqwIyNaoEZ6L7omm5me6cpV0uRRUZhEkJ8ySMQNmevnom6uJXZ/wKielZxeAczLm8AiNMc6Y3UCxc8zsFVzeVDtMl+caHn1Ym1D5XyA71pyu6uhEKXOYaOn66o1D7uc8B58W0QQVYkZWmj1tfxDU2XJOBkTBuyi2gVBz3o0d64jYz2oWz95fBFp5eBhNZwIiJJtgIDsTb1sXt9Wub+G3JGskxACOS18JEaEyHqdnwTMEYLPMRruHi1qJgX7jmEpxl8H4P/cZ2j4ax4V7TpJRTScYGYMV/iuq1zmeaOfVO+90xDO/YBRgTxDUy3FRs7nHrYK7FWBgDYxFsEfclKI7O8CN5/OLkIcOpMV7mjqU6mGLhufh46+WAyZEZI+2wdVD4DEgyEkk+XpeGkJs2zu8rRqbQ7OmOj9kIIYQQQgghhBBCCLGqIFOjCtTA1AboReWpoMbB5LB/D9nz+vfBiN4jpRUmNdFEmFoWA7BiqN+WrGIwxfVFKBVy3XX8mde+wEtxlqKkO0Pd386PpKB4mZS2xp/p7gvE/me/ff6+WAuC39+ENRJCRojbRrc97memagpt69bSyDnrsw0sXL0mjFj7OkzxdxZo7oC5vmNhrn0hbnMtgLVhxuNrSDc1/DogabDWwViY+4Tmyhtx20L3C4VqNy0Vca+9f51CYrY/ZkYDtyC4Www8FGkQendpgjUZe2D6oQnFJlYotRrbAFhzzI3S8GtXAOZ+XxsmNdqzMJFaXJ5kQrqwFlDSOOiHue69sNdwDEykzvPx+bVHZpy+A2tuMbrDH+uh/muEibRogknZ1o7wvZyHeeatA3MP8Hhvx7+HjDUhhBBCCCGEEEIIsfKRi6JotHQ+IYQQQgghhBBCCCGEEEKIquGnWBdCCCGEEEIIIYQQQgghhFgpkakhhBBCCCGEEEIIIYQQQohVApkaQgghhBBCCCGEEEIIIYRYJZCpIYQQQgghhBBCCCGEEEKIVQKZGkIIIYQQQgghhBBCCCGEWCWQqSGEEEIIIYQQQgghhBBCiFUCmRpCCCGEEEIIIYQQQgghhFglkKkhhBBCCCGEEEIIIYQQQohVApkaQgghhBBCCCGEEEIIIYRYJZCpIYQQQgghhBBCCCGEEEKIVQKZGkIIIYQQQgghhBBCCCGEWCWQqSGEEEIIIYQQQgghhBBCiFUCmRpCCCGEEEIIIYQQQgghhFglkKkhhBBCCCGEEEIIIYQQQohVApkaQogg6667Lo4++ugV3QwhhBBCCCGEEEIIIYQYQqaGECuYK6+8ErlcDttuu23iOrlcLviaMmXK0Drnn38+crkcampq8MYbb5TsY+nSpWhqakIul8NJJ500KucihBBCCCGEEEIIIYQQo0ntim6AEO935s6di3XXXRd//etf8dJLL2H69OnB9XbffXcceeSRRcuamppK1mtoaMDNN9+MM888s2j5HXfcUVG7/v3vf6OmRr6nEEIIIYQQQgghhBBi5UGKpRArkHnz5uFPf/oTvv/972Py5MmYO3du4rof/OAH8bnPfa7odeCBB5ast9dee+Hmm28uWX7TTTdh7733zty2hoYG1NXVZV5fCCGEEEIIIYQQQgghRhuZGkKsQObOnYvx48dj7733xkEHHZRqamTl8MMPx9///nf861//Glr2zjvv4MEHH8Thhx+eeT9+TY3rr78euVwOjz32GE455RRMnjwZ48aNw/HHH4++vj4sWbIERx55JMaPH4/x48fjzDPPRBRFRfv87ne/i+233x4TJ05EU1MTttpqK9x2220lx+7u7sYpp5yCSZMmoa2tDfvttx/eeust5HI5nH/++UXrvvXWW/j85z+P1VdfHQ0NDdh0003xs5/9LPN5CiGEEEIIIYQQQgghVh1kagixApk7dy4OOOAA1NfX47DDDsOLL76Iv/3tb8F1e3p6sGDBgqJXb29vyXozZszAWmuthZtuumlo2a9+9Su0trZWFKmRxMknn4wXX3wRF1xwAfbbbz9cc801OOecc7DvvvticHAQ3/nOd7Djjjvisssuww033FC07ezZs7HFFlvgwgsvxHe+8x3U1tbiM5/5DO6+++6i9Y4++mjMmTMHe+21Fy699FI0NTUF2/7uu+9iu+22wwMPPICTTjoJs2fPxvTp03Hsscfi8ssvH/G5CiGEEEIIIYQQQgghVi5kagixgnjyySfxr3/9C4ceeigAYMcdd8Raa62VGK1x7bXXYvLkyUWvUJqpXC6HQw89tOg3micNDQ0jbvfqq6+O3//+9zjhhBPwi1/8Ah/72Mdw2WWXYbPNNsPcuXPx5S9/GXfeeSfWWmutkoiJF154AT/60Y9w4okn4vTTT8djjz2GzTbbDN///veH1nnqqadwyy234LTTTsMvfvELnHDCCfjVr36FLbbYoqQt3/zmNzE4OIinn34a55xzDr70pS/hrrvuwqGHHorzzz8f3d3dIz5fIYQQQgghhBBCCCHEyoNMDSFWEHPnzsXqq6+OXXbZBYAxIw455BD88pe/xODgYMn6+++/P+6///6i15577hnc9+GHH46XXnoJf/vb34beK0k9lcaxxx6LXC439H3bbbdFFEU49thjh5bl83lsvfXWeOWVV4q2dQubL168GO3t7dhpp53w1FNPDS2/9957AQAnnHBC0bYnn3xy0fcoinD77bdj3333RRRFRREse+65J9rb24v2K4QQQgghhBBCCCGEWPWpXdENEOL9yODgIH75y19il112wbx584aWb7vttvje976H//3f/8Uee+xRtM1aa62Fj3/845n2v8UWW2CjjTbCTTfdhHHjxmHKlCnYddddq9L2ddZZp+j72LFjAQBrr712yfLFixcXLfvd736Hiy66CH//+9+LUme5Jslrr72GmpoarLfeekXbTp8+vej7e++9hyVLluCaa67BNddcE2zr/PnzM56VEEIIIYQQQgghhBBiVUCmhhArgAcffBBvv/02fvnLX+KXv/xlye9z584tMTUq5fDDD8dVV12FtrY2HHLIIaipqU5gVj6fz7zcLRT+6KOPYr/99sOMGTNw5ZVXYo011kBdXR2uu+66ovofWSkUCgCAz33uczjqqKOC63z4wx+ueL9CCCGEEEIIIYQQQoiVF5kaQqwA5s6di9VWWw0/+tGPSn6744478Otf/xo//vGPi9I1Vcrhhx+Oc889F2+//XZJwe4Vwe23347Gxkb84Q9/KKrtcd111xWtN3XqVBQKBcybNw8bbLDB0PKXXnqpaL3Jkyejra0Ng4ODmSNYhBBCCCGEEEIIIYQQqzYyNYRYznR3d+OOO+7AZz7zGRx00EElv6+55pq4+eab8Zvf/AaHHHLIsI8zbdo0XH755eju7sY222wzkiZXhXw+j1wuV1Qv5NVXX8Wdd95ZtN6ee+6Jb37zm7jyyivxgx/8YGj5nDlzSvZ34IEH4qabbsKzzz6LzTbbrOj39957D5MnT67+iQghhBBCCCGEEEIIIVYYMjWEWM785je/wbJly7DffvsFf99uu+0wefJkzJ07d0SmBgCceuqpI9q+muy99974/ve/j0984hM4/PDDMX/+fPzoRz/C9OnT8cwzzwytt9VWW+HAAw/E5ZdfjoULF2K77bbDI488ghdeeAFAcf2NSy65BA899BC23XZbHHfccdhkk02waNEiPPXUU3jggQewaNGi5X6eQgghhBBCCCGEEEKI0aM6SfaFEJmZO3cuGhsbsfvuuwd/r6mpwd577417770XCxcuXM6tGz123XVXXHvttXjnnXdw2mmn4eabb8all16KT3/60yXr/uIXv8CJJ56Iu+++G2eddRb6+vrwq1/9CgDQ2Ng4tN7qq6+Ov/71rzjmmGNwxx134KSTTsLs2bOxaNEiXHrppcvt3IQQQgghhBBCCCGEEMuHXORW8hVCiJWUv//979hiiy1w44034rOf/eyKbo4QQgghhBBCCCGEEGIFoEgNIcRKR3d3d8myyy+/HDU1NZgxY8YKaJEQQgghhBBCCCGEEGJlQDU1hBArHbNmzcKTTz6JXXbZBbW1tbjnnntwzz334Itf/CLWXnvtFd08IYQQQgghhBBCCCHECkLpp4QQKx33338/LrjgAvzzn/9ER0cH1llnHRxxxBH45je/idpaebFCCCGEEEIIIYQQQrxfkakhhBBCCCGEEEIIIYQQQohVAtXUEEIIIYQQQgghhBBCCCHEKoFMDSGEEEIIIYQQQgghhBBCrBLI1BDva2bNmoWNNtoIhUJhRTclyLrrroujjz56RTdjVDn66KPR2tq6optRxM4774ydd965om1+/OMfY5111kFvb+/oNEoIIYQIcP311yOXy+GJJ54Y9j5uueUWTJgwAR0dHWXXzeVyOP/884d9rPcro9Fv1d7nww8/jFwuh4cffnho2aGHHoqDDz64ascQQgixYhnO/3X/G1l33XWxzz77VH2/J5xwAnbfffeq77ca3HvvvWhtbcV77723opsiRFWQqSHetyxduhSXXnopzjrrLNTUrNq3wumnn44tt9wSEyZMQHNzMzbeeGOcf/75mcSJNK644gpsvPHGaGhowAc+8AF85StfQWdnZ8l6hUIBs2bNwnrrrYfGxkZ8+MMfxs033zyiY69qHH300ejr68PVV1+9opsihBCrLBTo+WpsbMSaa66JPffcEz/84Q+xbNmykm3OP/985HI5rL766ujq6ir5Pe0/rUuWLEFjYyNyuRyef/75qp/PqsDg4CDOO+88nHzyySvdJAOx4jnrrLNw++234//+7/9WdFOEEGKl5OWXX8bxxx+P9ddfH42NjRgzZgx22GEHzJ49G93d3Su6eWI5Mm/ePPz0pz/FN77xjeV+7FtuuQXbbbcdxo0bh4kTJ2LmzJm4++67i9b5xCc+genTp+Piiy9e7u0TYjRYtZVcIUbAz372MwwMDOCwww5b0U0ZMX/729+w00474YILLsDs2bOxyy674JJLLsEnPvGJYUehnHXWWTj55JOx2WabYfbs2TjwwAMxZ84cHHDAASXrfvOb38RZZ52F3XffHXPmzME666yDww8/HL/85S9HemqrDI2NjTjqqKPw/e9/H1EUrejmCCHEKs2FF16IG264AVdddRVOPvlkAMBpp52GD33oQ3jmmWeC28yfPx9XXXVVRce59dZbkcvlMGXKFMydO3fE7V4V+e1vf4t///vf+OIXv7iimyJWQrbYYgtsvfXW+N73vreimyKEECsdd999Nz70oQ/hlltuwb777os5c+bg4osvxjrrrIMzzjgDp5566opuYgn33Xcf7rvvvhXdjP9KZs+ejfXWWw+77LLLcj3unDlzcMghh2DSpEm45JJLcM4556C9vR377LMP7rjjjqJ1jz/+eFx99dXBiUJCrGrUrugGCLGiuO6667DffvuhsbFxRTdlxDz22GMly6ZNm4avfe1r+Otf/4rtttuuov29/fbb+P73v48jjjgCv/jFL4aWf/CDH8TJJ5+M3/72t9h3330BAG+99Ra+973v4cQTT8QVV1wBAPjCF76AmTNn4owzzsBnPvMZ5PP5EZzdqsPBBx+MWbNm4aGHHsKuu+66opsjhBCrLJ/85Cex9dZbD30/++yz8eCDD2KfffbBfvvth+effx5NTU1F22y++ea47LLLcMIJJ5T8lsSNN96IvfbaC1OnTsVNN92Eiy66qKrnsSpw3XXXYYcddsAHPvCBFd0UsZJy8MEH47zzzsOVV16paB4hhIiZN28eDj30UEydOhUPPvgg1lhjjaHfTjzxRLz00kslM+VdCoUC+vr6lrseUV9fv1yP936hv78fc+fOxZe+9KXlfuw5c+bgox/9KH77298il8sBAD7/+c/jAx/4AH7+858XTUw98MADcfLJJ+PWW2/F5z//+eXeViGqiSI1xPuSefPm4ZlnnsHHP/7xkt9++ctfYquttkJbWxvGjBmDD33oQ5g9e3bROkuWLMFpp52GtddeGw0NDZg+fTouvfTSkqiIQqGAyy+/HJtuuikaGxux+uqr4/jjj8fixYuL1ouiCBdddBHWWmstNDc3Y5dddsFzzz03onNcd911h9paKX/+858xMDCAQw89tGg5v7sRGHfddRf6+/txwgknDC3L5XL48pe/jDfffBN//vOfMx3zlVdewZ577omWlhasueaauPDCC0siHrL251133YW9994ba665JhoaGjBt2jR861vfwuDgYMlxr7nmGkybNg1NTU3YZptt8OijjwbbN2fOHGy66aZobm7G+PHjsfXWW+Omm24qWmerrbbChAkTcNddd2U6ZyGEENnZddddcc455+C1117DjTfeWPL7ueeei3fffTdztMbrr7+ORx99FIceeigOPfRQzJs3D3/6058ybcuUVy+88AI+97nPYezYsZg8eTLOOeccRFGEN954A/vvvz/GjBmDKVOmlMxy7+vrw7nnnoutttoKY8eORUtLC3baaSc89NBDJcfK8neJz+LFi7HNNttgrbXWwr///e/E9Xp6enDvvfcG/x7q7e3F6aefjsmTJ6OtrQ377bcf3nzzzeB+3nrrLXz+85/H6quvjoaGBmy66ab42c9+Fjze+eefjw9+8INobGzEGmusgQMOOAAvv/zy0DqdnZ346le/OvQ31oYbbojvfve7RX8TzJw5Ex/5yEeCbdlwww2x5557Jp4zueeee7DTTjuhpaUFbW1t2HvvvYv+9nrwwQdRU1ODc889t2i7m266CblcrmicZTkvn6OPPnrobzUXji2X0bgWb775Jj71qU+hpaUFq622Gk4//fTEumC77747Ojs7cf/99yeejxBCvN+YNWsWOjo6cO211xYZGmT69OlFkRq5XA4nnXQS5s6di0033RQNDQ249957AQBPP/00PvnJT2LMmDFobW3Fbrvthv/3//5f0f76+/txwQUXYIMNNkBjYyMmTpyIHXfcsejZ/M477+CYY47BWmuthYaGBqyxxhrYf//98eqrrw6t49fUYD2lW265Bd/+9rex1lprobGxEbvtthteeumlkvP60Y9+hPXXX7/o/89Z63SwD2699VZssskmaGpqwsc+9jH84x//AABcffXVmD59OhobG7HzzjsXtRsAHn30UXzmM5/BOuusg4aGBqy99to4/fTTS9J8ZemHED//+c9RW1uLM844o+y5+Dz22GNYsGBByd9UlfbvcFi6dClWW221or8fOJb8iT6rrbYaPvzhD0uzEP8VKFJDvC+haLHlllsWLb///vtx2GGHYbfddsOll14KAHj++efx+OOPD/1B0tXVhZkzZ+Ktt97C8ccfj3XWWQd/+tOfcPbZZ+Ptt9/G5ZdfPrS/448/Htdffz2OOeYYnHLKKZg3bx6uuOIKPP3003j88cdRV1cHwAgxF110Efbaay/stddeeOqpp7DHHnugr68v8zkNDAxgyZIl6Ovrw7PPPov/+Z//QVtbG7bZZpuK+4f/qfX/AWxubgYAPPnkk0PLnn76abS0tGDjjTcuWpfHffrpp7HjjjumHm9wcBCf+MQnsN1222HWrFm49957cd5552FgYAAXXnjh0HpZ+/P6669Ha2srvvKVr6C1tRUPPvggzj33XCxduhSXXXbZ0P6uvfZaHH/88dh+++1x2mmn4ZVXXsF+++2HCRMmYO211x5a7yc/+QlOOeUUHHTQQTj11FPR09ODZ555Bn/5y19w+OGHF53Llltuiccffzy9g4UQQgyLI444At/4xjdw33334bjjjiv6baeddsKuu+6KWbNm4ctf/nLZaI2bb74ZLS0t2GeffdDU1IRp06Zh7ty52H777TO355BDDsHGG2+MSy65BHfffTcuuugiTJgwAVdffTV23XVXXHrppZg7dy6+9rWv4aMf/ShmzJgBwPzn86c//SkOO+wwHHfccVi2bBmuvfZa7LnnnvjrX/+KzTffHEC2v0t8FixYgN133x2LFi3CI488gmnTpiW2/8knn0RfX1/J30OAibq88cYbcfjhh2P77bfHgw8+iL333rtkvXfffRfbbbfdkFAxefJk3HPPPTj22GOxdOlSnHbaaQDMv/X77LMP/vd//xeHHnooTj31VCxbtgz3338/nn32WUybNg1RFGG//fbDQw89hGOPPRabb745/vCHP+CMM87AW2+9hR/84AcAzDg47rjj8Oyzz2KzzTYbasvf/vY3vPDCC/if//mf1Ot2ww034KijjsKee+6JSy+9FF1dXbjqqquw44474umnn8a6666LXXfdFSeccAIuvvhifOpTn8KWW26Jt99+GyeffDI+/vGPD83EzHJeI6Xa16K7uxu77bYbXn/9dZxyyilYc801ccMNN+DBBx8MHp/C0+OPP45Pf/rTIz4fIYT4b+C3v/0t1l9//Yr+bnjwwQdxyy234KSTTsKkSZOw7rrr4rnnnsNOO+2EMWPG4Mwzz0RdXR2uvvpq7LzzznjkkUew7bbbAjCm98UXX4wvfOEL2GabbbB06VI88cQTeOqpp4YKUx944IF47rnncPLJJ2PdddfF/Pnzcf/99+P1118PGukul1xyCWpqavC1r30N7e3tmDVrFj772c/iL3/5y9A6V111FU466STstNNOOP300/Hqq6/iU5/6FMaPH4+11lorUx88+uij+M1vfoMTTzwRAHDxxRdjn332wZlnnokrr7wSJ5xwAhYvXoxZs2bh85//fNG/Tbfeeiu6urrw5S9/GRMnTsRf//pXzJkzB2+++SZuvfXWofWG0w/XXHMNvvSlL+Eb3/jGsCJ3//SnPyGXy2GLLbYI/p6lf7u6uoL14Xzy+TzGjx8/9H3nnXfGbbfdhjlz5mDfffdFT08P5syZg/b29uDfi1tttRXuvPPOis9RiJWOSIj3If/zP/8TAYiWLVtWtPzUU0+NxowZEw0MDCRu+61vfStqaWmJXnjhhaLlX//616N8Ph+9/vrrURRF0aOPPhoBiObOnVu03r333lu0fP78+VF9fX209957R4VCYWi9b3zjGxGA6Kijjsp0Tn/+858jAEOvDTfcMHrooYcybevz5JNPRgCib33rW8G2t7a2Di3be++9o/XXX79kH52dnRGA6Otf/3rqsY466qgIQHTyyScPLSsUCtHee+8d1dfXR++9914URdn7M4qiqKurq+Q4xx9/fNTc3Bz19PREURRFfX190WqrrRZtvvnmUW9v79B611xzTQQgmjlz5tCy/fffP9p0001Tz4N88YtfjJqamjKtK4QQopjrrrsuAhD97W9/S1xn7Nix0RZbbDH0/bzzzosARO+99170yCOPRACi73//+0O/T506Ndp7771L9vOhD30o+uxnPzv0/Rvf+EY0adKkqL+/v2w7ecwvfvGLQ8sGBgaitdZaK8rlctEll1wytHzx4sVRU1NT0b/nAwMDRf/2cL3VV189+vznPz+0LMvfJW6fvf3229Gmm24arb/++tGrr75a9jx++tOfRgCif/zjH0XL//73v0cAohNOOKFo+eGHHx4BiM4777yhZccee2y0xhprRAsWLCha99BDD43Gjh079G/yz372s5JrQ/j3z5133hkBiC666KKi3w866KAol8tFL730UhRFUbRkyZKosbExOuuss4rWO+WUU6KWlpaoo6Mj8ZyXLVsWjRs3LjruuOOKlr/zzjvR2LFji5Z3dnZG06dPjzbddNOop6cn2nvvvaMxY8ZEr7322tA6Wc4riqKSfjvqqKOiqVOnlmzDsUVG41pcfvnlEYDolltuKTlXAMG/Hz/4wQ9Gn/zkJ0uWCyHE+5H29vYIQLT//vtn3gZAVFNTEz333HNFyz/1qU9F9fX10csvvzy07D//+U/U1tYWzZgxY2jZRz7ykeDfM2Tx4sURgOiyyy5LbcfMmTOL/q/70EMPRQCijTfeuOhvk9mzZxf9jdDb2xtNnDgx+uhHP1r0t9L1119f8v/nJABEDQ0N0bx584aWXX311RGAaMqUKdHSpUuHlp999tkRgKJ1Q//Pv/jii6NcLjf0b3PWfnD/Ppw9e3aUy+VK9I9K+NznPhdNnDixZHnW/o0i+zdAuZf/98O7774b7bbbbkXrTJo0KfrTn/4UbOt3vvOdCED07rvvDvt8hVgZUPop8b5k4cKFqK2tLckLPG7cuLLh9bfeeit22mknjB8/HgsWLBh6ffzjH8fg4CD++Mc/Dq03duxY7L777kXrbbXVVmhtbR1KMfHAAw+gr68PJ598clG4IGfTZWWTTTbB/fffjzvvvBNnnnkmWlpa0NHRUdE+yJZbboltt90Wl156Ka677jq8+uqruOeee3D88cejrq6uKLyzu7sbDQ0NJftgblA/FDSJk046aegzZxj29fXhgQceAJC9P4HiCJNly5ZhwYIF2GmnndDV1YV//etfAIAnnngC8+fPx5e+9KWivKJHH300xo4dW9S2cePG4c0338Tf/va3sucxfvx4dHd3Z5phIYQQonJaW1sTixvOmDEDu+yyC2bNmpX6788zzzyDf/zjHzjssMOGlh122GFYsGAB/vCHP2Ruyxe+8IWhz/l8HltvvTWiKMKxxx47tHzcuHHYcMMN8corrxSty397CoUCFi1ahIGBAWy99dZ46qmnirbNmvbnzTffxMyZM9Hf348//vGPmDp1atltFi5cCABFs/0A4Pe//z0A4JRTTila7v9tEkURbr/9duy7776Ioqjo3+c999wT7e3tQ+dz++23Y9KkSUOF313498/vf/975PP5kuN+9atfRRRFuOeeewAAY8eOxf7774+bb755KC3V4OAgfvWrXw2lVEri/vvvx5IlS4auN1/5fB7bbrtt0d8Tzc3NuP766/H8889jxowZuPvuu/GDH/wA66yzztA6Wc5rJIzGtfj973+PNdZYAwcddFDRuaYVi+ffvUIIIUzEJQC0tbVVtN3MmTOxySabDH0fHBzEfffdh0996lNYf/31h5avscYaOPzww/HYY48NHWvcuHF47rnn8OKLLwb33dTUhPr6ejz88MMl6ZmzcMwxxxT9v3innXYCgKG/X5544gksXLgQxx13HGprbdKXz372syV/R6Sx2267FUVLMBLlwAMPLOpPLnf/fnL/n9/Z2YkFCxZg++23RxRFePrpp4fWqaQfZs2ahVNPPRWXXnpp2UjPNBYuXJjaD+X6FwCOPPJI3H///WVfc+fOLdp3c3MzNtxwQxx11FG49dZb8bOf/WwoFWYoxRXbqX/XxaqO0k8J4XDCCSfglltuwSc/+Ul84AMfwB577IGDDz4Yn/jEJ4bWefHFF/HMM89g8uTJwX3Mnz9/aL329nasttpqqeu99tprAIANNtig6PfJkydX9MfBmDFjhvI37r///rjpppuw//7746mnnkrMO53G7bffjkMOOWSoeFQ+n8dXvvIVPPLII0X5uZuamoI5mHt6eoZ+L0dNTU3RH3GAKUoOYCjvZdb+BIDnnnsO//M//4MHH3xw6I9A0t7eDiC53+vq6kractZZZ+GBBx7ANttsg+nTp2OPPfbA4Ycfjh122KGkHRRXqiFkCCGEKKWjoyPx3wLApGeYOXMmfvzjH+P0008PrnPjjTeipaUF66+//tB/9hobG7Huuuti7ty5wdQ+IVxxGzBie2NjIyZNmlSynAYC+fnPf47vfe97+Ne//oX+/v6h5eutt97Q5yx/l5AjjjgCtbW1eP755zFlypRM7SeRV8PqtddeQ01NTUnqpA033LDo+3vvvYclS5bgmmuuwTXXXBPcN/99fvnll7HhhhsWCSE+r732GtZcc80SkYgpLvlvN2D+4/+rX/0Kjz76KGbMmIEHHngA7777Lo444ojUc6UYtOuuuwZ/HzNmTNH3HXbYAV/+8pfxox/9CHvuuWdJUc0s5zUSRuNavPbaa5g+fXrJ3yr+Pl2iKNLfNkIIEcN/K5ImWSTh/hsPmGd3V1dX8Pm78cYbo1Ao4I033sCmm26KCy+8EPvvvz8++MEPYrPNNsMnPvEJHHHEEfjwhz8MAGhoaMCll16Kr371q1h99dWx3XbbYZ999sGRRx6Z6e8C/28aahE0Bvhv8PTp04vWq62tLZvaKu04nFDopn92l7vGxOuvv45zzz0Xv/nNb0oMC/4/v5J+eOSRR3D33XfjrLPOGlYdDR//7ymXcv0LAOuvv36JFpGFz3zmM6itrcVvf/vboWX7778/NthgA3zzm9/Er371q2A79e+6WNWRqSHel0ycOBEDAwNYtmxZ0X+cV1ttNfz973/HH/7wB9xzzz245557cN111+HII4/Ez3/+cwBmRuXuu++OM888M7hvivGFQgGrrbZaiYtOkkyRanHAAQfgiCOOwC9/+cthmRof+MAH8Nhjj+HFF1/EO++8gw022ABTpkzBmmuuOXSOgJlF8tBDD5X8Z/ftt98GAKy55pojPxlk788lS5Zg5syZGDNmDC688EJMmzYNjY2NeOqpp3DWWWeVFHPPwsYbb4x///vf+N3vfod7770Xt99+O6688kqce+65uOCCC4rWXbx4MZqbmzOZOUIIISrjzTffRHt7e8l/qF1mzJiBnXfeGbNmzRqqe+ASRRFuvvlmdHZ2Fs2WJPPnz0dHR0dJNGeIfD6faRmPS2688UYcffTR+NSnPoUzzjgDq622GvL5PC6++OKi4tJZ/i4hBxxwAH7xi19g9uzZuPjii8u2HTB/DwHm366subBd+G/q5z73ORx11FHBdSi2VJs999wTq6++Om688UbMmDEDN954I6ZMmRIseu7CNt9www1Bkcc3J3p7e/Hwww8DMAZGV1fXUI2xkZAkJAwODg5rf6N9LRYvXlwyEUQIId6vjBkzBmuuuSaeffbZirYbyf8RZ8yYgZdffhl33XUX7rvvPvz0pz/FD37wA/z4xz8eihw97bTTsO++++LOO+/EH/7wB5xzzjm4+OKL8eCDDybWeiBZ/n6pBknHKXf8wcHBoZphZ511FjbaaCO0tLTgrbfewtFHH130//ys/bDppptiyZIluOGGG3D88ceXmE6VMHHixNTIkCz929HRkSnbRj6fH9I/XnnlFdx7770lExomTJiAHXfcMVjvk+30J+EIsaohU0O8L9loo40AAPPmzSv5D159fT323Xdf7LvvvigUCjjhhBNw9dVX45xzzsH06dMxbdo0dHR0lP1P87Rp0/DAAw9ghx12SP3jhekhXnzxxSJX/r333htW2Cjp7e1FoVAYmrEwXDbYYIOh/8T+85//xNtvv42jjz566PfNN98cP/3pT/H8888XiUMseMVip2kUCgW88sorRWbJCy+8AABDsz6y9ufDDz+MhQsX4o477hgqyAqYa+3i9rs7W7O/vx/z5s0rMYJaWlpwyCGH4JBDDkFfXx8OOOAAfPvb38bZZ589lGqLx/GLpgshhKgON9xwAwAjaKdx/vnnY+edd8bVV19d8tsjjzyCN998ExdeeGHJ83rx4sX44he/iDvvvBOf+9znqtdwj9tuuw3rr78+7rjjjiJx+7zzzitZt9zfJeTkk0/G9OnTce6552Ls2LH4+te/XrYd7t9DH/rQh4aWT506FYVCYSgKgbiRmoCZUNDW1obBwcFMfxf95S9/QX9/P+rq6oLrTJ06FQ888EDJpBOmjnRTauXzeRx++OG4/vrrcemll+LOO+/EcccdlygauO0AjGFUrs2AuSbPP/88vvvd7+Kss87C17/+dfzwhz+s6LxCjB8/HkuWLClZ7kajAKNzLaZOnYpnn322ZEKKv08yMDCAN954A/vtt1+50xJCiPcN++yzD6655hr8+c9/xsc+9rFh7WPy5Mlobm4OPn//9a9/oaampiiCYcKECTjmmGNwzDHHoKOjAzNmzMD5559flA5z2rRp+OpXv4qvfvWrePHFF7H55pvje9/7Hm688cZhtZHw3+CXXnoJu+yyy9DygYEBvPrqq6M2iYH84x//wAsvvICf//znOPLII4eWJ6XozNIPkyZNwm233YYdd9wRu+22Gx577LFhT8rcaKONMHfuXLS3t5eks87Kd7/73ZJJkyGmTp06lNHi3XffBRCeFNHf34+BgYGS5fPmzcOkSZNGfaKtEKONamqI9yX8o+OJJ54oWu6nhqipqRn6x5kplg4++GD8+c9/DubcXrJkydA/GgcffDAGBwfxrW99q2S9gYGBof/IfvzjH0ddXR3mzJlT5NJffvnlmc5lyZIlRWkryE9/+lMAwNZbb51pP+UoFAo488wz0dzcXDTzdf/990ddXR2uvPLKoWVRFOHHP/4xPvCBD2D77bfPtP8rrriiaPsrrrgCdXV12G233QBk70+KGW5f9vX1FbUPMP0yefJk/PjHP0ZfX9/Q8uuvv75EZPDHRX19PTbZZBNEUVTS90899VTmcxZCCJGdBx98EN/61rew3nrr4bOf/WzqujNnzsTOO++MSy+9dCgdImHqqTPOOAMHHXRQ0eu4447DBhtskBgVWC1C/1b95S9/wZ///Oei9bL8XeJyzjnn4Gtf+xrOPvtsXHXVVWXbsdVWW6G+vr7k76FPfvKTAFAk3gOlf5vk83kceOCBuP3224OzVd97772hzwceeCAWLFhQ9O89YT/stddeGBwcLFnnBz/4AXK53FC7yBFHHIHFixfj+OOPR0dHRyYjas8998SYMWPwne98J/j3k9vmv/zlL/jud7+L0047DV/96ldxxhln4IorrsAjjzxS0XmFmDZtGtrb2/HMM88MLXv77bfx61//umi90bgWe+21F/7zn//gtttuG1rW1dWVmLbqn//8J3p6evT3jRBCOLCO5Re+8IUhYdnl5ZdfxuzZs1P3kc/nsccee+Cuu+4aEqkBI1TfdNNN2HHHHYdSXfl/E7S2tmL69OlDfw90dXWV/M0zbdo0tLW1Bf9mqJStt94aEydOxE9+8pMioXzu3LkjmoyZldDfTlEUlfRxpf2w1lpr4YEHHkB3dzd23333kn7Oysc+9jFEUYQnn3xyWNsDw6upMX36dNTU1OBXv/pVUd+8+eabePTRR4MROk8++eSwjTghViYUqSHel6y//vrYbLPN8MADDxTlRv7CF76ARYsWYdddd8Vaa62F1157DXPmzMHmm28+NJvzjDPOwG9+8xvss88+OProo7HVVluhs7MT//jHP3Dbbbfh1VdfxaRJkzBz5kwcf/zxuPjii/H3v/8de+yxB+rq6vDiiy/i1ltvxezZs3HQQQdh8uTJ+NrXvoaLL74Y++yzD/baay88/fTTuOeeezKFAz788MM45ZRTcNBBB2GDDTZAX18fHn30Udxxxx3YeuutS/6Dn8vlMHPmzKFUCkmceuqp6Onpweabb47+/n7cdNNN+Otf/4qf//znRfkg11prLZx22mm47LLL0N/fj49+9KO488478eijj2Lu3LllZ0wCJo/5vffei6OOOgrbbrst7rnnHtx99934xje+MTR7IGt/br/99hg/fjyOOuoonHLKKcjlcrjhhhtKhIW6ujpcdNFFOP7447HrrrvikEMOwbx583DdddeV5LHcY489MGXKFOywww5YffXV8fzzz+OKK67A3nvvXTST9Mknn8SiRYuw//77lz1nIYQQydxzzz3417/+hYGBAbz77rt48MEHcf/992Pq1Kn4zW9+UxQhl8R5551XNJMQMEbA7bffjt133z1xH/vttx9mz56N+fPnp9buGAn77LMP7rjjDnz605/G3nvvjXnz5uHHP/4xNtlkk6K0A1n+LvG57LLL0N7ejhNPPBFtbW2pQn9jYyP22GMPPPDAA7jwwguHlm+++eY47LDDcOWVV6K9vR3bb789/vd//zdYbPKSSy7BQw89hG233RbHHXccNtlkEyxatAhPPfUUHnjgASxatAiA+Y/6L37xC3zlK1/BX//6V+y0007o7OzEAw88gBNOOAH7778/9t13X+yyyy745je/iVdffRUf+chHcN999+Guu+7CaaedVlJXYosttsBmm22GW2+9FRtvvDG23HLLsn0/ZswYXHXVVTjiiCOw5ZZb4tBDD8XkyZPx+uuv4+6778YOO+yAK664Aj09PTjqqKOwwQYb4Nvf/jYA4IILLsBvf/tbHHPMMfjHP/6BlpaWTOcV4tBDD8VZZ52FT3/60zjllFPQ1dWFq666Ch/84AeLisWPxrU47rjjcMUVV+DII4/Ek08+iTXWWAM33HBDYlqt+++/H83Nzdh9993L9q8QQrxfmDZtGm666SYccsgh2HjjjXHkkUdis802Q19fH/70pz/h1ltvLcpwkMRFF12E+++/HzvuuCNOOOEE1NbW4uqrr0Zvby9mzZo1tN4mm2yCnXfeGVtttRUmTJiAJ554ArfddhtOOukkACbTwW677YaDDz4Ym2yyCWpra/HrX/8a7777Lg499NARn299fT3OP/98nHzyydh1111x8MEH49VXX8X111+PadOmjXp9ho022gjTpk3D1772Nbz11lsYM2YMbr/99hJDZTj9MH36dNx3333Yeeedseeee+LBBx8sqbFVjh133BETJ07EAw88kFi3qxzDqakxefJkfP7zn8dPf/pT7LbbbjjggAOwbNkyXHnlleju7sbZZ59dtP78+fPxzDPP4MQTTxxWG4VYqYiEeJ/y/e9/P2ptbY26urqGlt12223RHnvsEa222mpRfX19tM4660THH3989Pbbbxdtu2zZsujss8+Opk+fHtXX10eTJk2Ktt9+++i73/1u1NfXV7TuNddcE2211VZRU1NT1NbWFn3oQx+KzjzzzOg///nP0DqDg4PRBRdcEK2xxhpRU1NTtPPOO0fPPvtsNHXq1Oioo45KPY+XXnopOvLII6P1118/ampqihobG6NNN900Ou+886KOjo6SdgOIDj300LL9c91110Uf+chHopaWlqitrS3abbfdogcffDC47uDgYPSd73wnmjp1alRfXx9tuumm0Y033lj2GFEURUcddVTU0tISvfzyy9Eee+wRNTc3R6uvvnp03nnnRYODgyXrZ+nPxx9/PNpuu+2ipqamaM0114zOqUW8bgABAABJREFUPPPM6A9/+EMEIHrooYeK9nfllVdG6623XtTQ0BBtvfXW0R//+Mdo5syZ0cyZM4fWufrqq6MZM2ZEEydOjBoaGqJp06ZFZ5xxRtTe3l60r7POOitaZ511okKhkOnchRBCFHPddddFAIZe9fX10ZQpU6Ldd989mj17drR06dKSbc4777wIQPTee++V/DZz5swIQLT33ntHURRFt99+ewQguvbaaxPb8PDDD0cAotmzZyeuk3RM/psWasemm2469L1QKAz9u9nQ0BBtscUW0e9+97voqKOOiqZOnTq0Xpa/S9hnf/vb34aWDQ4ORocddlhUW1sb3XnnnYnnEUVRdMcdd0S5XC56/fXXi5Z3d3dHp5xySjRx4sSopaUl2nfffaM33ngjAhCdd955Reu+++670YknnhitvfbaUV1dXTRlypRot912i6655pqi9bq6uqJvfvOb0XrrrTe03kEHHRS9/PLLQ+ssW7YsOv3006M111wzqqurizbYYIPosssuS/y3ddasWRGA6Dvf+U7qefo89NBD0Z577hmNHTs2amxsjKZNmxYdffTR0RNPPBFFURSdfvrpUT6fj/7yl78UbffEE09EtbW10Ze//OWKzivUb/fdd1+02WabRfX19dGGG24Y3XjjjUNjy2U0rsVrr70W7bffflFzc3M0adKk6NRTT43uvffe4N9K2267bfS5z32uku4VQoj3DS+88EJ03HHHReuuu25UX18ftbW1RTvssEM0Z86cqKenZ2g9ANGJJ54Y3MdTTz0V7bnnnlFra2vU3Nwc7bLLLtGf/vSnonUuuuiiaJtttonGjRsXNTU1RRtttFH07W9/e0h/WLBgQXTiiSdGG220UdTS0hKNHTs22nbbbaNbbrmlaD/+/3UfeuihCEB06623Fq03b968CEB03XXXFS3/4Q9/OPT3yzbbbBM9/vjj0VZbbRV94hOfKNtXoT7gcS677LKi5aF2/fOf/4w+/vGPR62trdGkSZOi4447Lvq///u/onZm7YepU6cO/X1I/vKXv0RtbW3RjBkzinSirJxyyinR9OnTy56He95+/w6H/v7+aM6cOdHmm28etba2Rq2trdEuu+wS1G+uuuqqqLm5Ofg3tRCrGrkoqnLVHyFWEdrb27H++utj1qxZOPbYY1d0c5YLv//977HPPvvg//7v/4pyZ4uR09vbi3XXXRdf//rXceqpp67o5gghhBCZGBwcxCabbIKDDz44mOJxZWf27Nk4/fTT8eqrrxZFkorq8Pe//x1bbrklnnrqqUx10oQQQry/KBQKmDx5Mg444AD85Cc/WdHNWaG88sor2GijjXDPPfcMpdFe2dhiiy2w88474wc/+MGKbooQI0Y1NcT7lrFjx+LMM8/EZZddhkKhsKKbs1x46KGHcOihh8rQGAWuu+461NXVFdUbEUIIIVZ28vk8LrzwQvzoRz8qSn21KhBFEa699lrMnDlThsYocckll+Cggw6SoSGEEAI9PT0laZ1/8YtfYNGiRdh5551XTKNWItZff30ce+yxuOSSS1Z0U4Lce++9ePHFF0tSUgmxqqJIDSGEEEIIIcQqQ2dnJ37zm9/goYcewk9+8hPcdddd2G+//VZ0s4QQQoj/ah5++GGcfvrp+MxnPoOJEyfiqaeewrXXXouNN94YTz75JOrr61d0E4UQ7yNUKFwIIYQQQgixyvDee+/h8MMPx7hx4/CNb3xDhoYQQgixHFh33XWx9tpr44c//CEWLVqECRMm4Mgjj8Qll1wiQ0MIsdxRpIYQQgghhBBCCCGEEEIIIVYJVFNDCCGEEEIIIYQQQgghhBCrBDI1hBBCCCGEEEIIIYQQQgixSiBTQwghhBBCCCGEEEIIIYQQqwQqFB5Tm8shAoZeLjlnWQ5AXfyKAAwAKMQvBLZvBLAWTEcviF+jSQuAPIAer80ukbesLt4uB2AZgK5RbqNPW3zsPpj+dPvP7Xv/uvjLczD93Rh/73X26dIEcz16AAx6xwr1VQ2AVpg+iuLteuNXf7mTqxI1MG3uR2k/+NTDnMcAis8vCbePk35P2240i/LkYMbzYJnj1ANogOmf3sC6NfE6BedVC3NNe1B8v1RCHmZsNMbbc9/dcVvcZ8P7gTxsP/D7JAAfgOnvhQD+A9M/uXgZn6OETnstzL3aA9OXDfHypGtVbhwPhxyAcQDGx98XAVgaH6chbmsv7H2mElXvT3K5pKekEEIIUR79/fD+JZeTHCOEEGL4RJGveL6/0L+iMUkCbS2AMTDieG/8vQ5WFG2FEbYoxvfCiG4cVjUAOuN1e0f1DKwxkIMVFdOkFp5rP4D2eN3httE3BPi5gPKCeQ2s6BzBnIO7j7Tt3d/qYQySRpj+pyDOa0d6Yc2opPZz34POeo3x5y4sf6G6ANsv5ci6Hsny36jQOrUw7cpinAwXVyBPow/p510Dc9/WwoyJXhgDj/fLcP8rORjvZyDefz7eX338eQBhk2Vlhfdj2n1L0yF0XQrx7+zTQRgjgAZFB4rHC4/De8+9B/k8pTnE528N7LM68tavNhGAJTDXsQam/QXYZ2yW/hJCCCGEEEIIIYQQ1UWmRkw9jNjWh2Jxqg7A6vHnZSiefR3F3+thZnzXxPvogpnNOxB/7oYVOEcLioGDKBXoXdGQn11BkO952HOohDxMH9R7x+yD7S93Obzv3fG2jIAZRHFfuW0uZ9IwcoKz7xlZw1n83QgLqaH9uoZLLt6+H3bmuNuu/2ZC5lITzAz2HpixPprGRjUMJNcc4XWtQ7bIl3LQ6KqFGccU3ftRGnm0slMPY9QOwJix/jOrFuZeYsSSf23Yz+459wN4G9YE8+9tIHz/uVEuNBHcbcuZnrzOIx2bEcyz319WqXkohBBCCCGEEEIIIaqDTI0Ypo+pQXF6k34Y0bYNNlVNO4wgWg8jZvbACPtN8X5oYHBWr59exaUh3g9nmo9EAE0T56OU312DYzhJNLjvPOz5s1+SjAj3PGl+1KE4tVfIfPD355oKvFYUQDmTug7AOrDpb3pgzI1eZx0XXyzNOev2x+0dTRF/ZYJRBy0wfdAFc31XBzABRvjm8mrQGr/3wQra1TADaZTRNGOklRuJMxJoYtBUzBphsrJRD6AZ9t7rgu1/3uNA+rmFnmF+pFHas8i/vxn9UolBxFR0TNm2KkXLCCGEEEIIIYQQQoh0ZGrEUPxuhhFql8EKoe/CCOHNsMJlE0xaqiZYYbc//p61zkAewMR4m26YNCfDEYeTUj8l4aZucdctwAqIlQiArCHA1DP5+HtI+HeNCT91DM2NHJJFzyRDg/vwj0lDqR5GiJ8MYza9CVPfxK2J4afAcWskuKmosorVWdJnrezUwIjDq8M8LNrj5WNgjUCaeyMlD2MejoMxp5Zg5OmhfBgt0InRqQeyKhoZLn0w15jRJi68R0fLfE2KnsrDGp1+bZWksVEHM0ZZX2gQy6/+jRBCCCGEEEIIIYQYXWRqxNComAwj0r4HkzKlG0YUXxx/dvO5D8AWr+2IX260wlgYIa0TYYGf4ioQLmg9UspFSCTVERmOiMz89zRG0oTPUO0K9zMjZvg9y4zuNPpgDIxxMMZULWxBdOb4p4gaKjDNtDflilW71MNEMtAI6MKqGd1Bs4/1Y+ph7wmmY6vWeTH9WyE+JlMHVct4YPSUGwWyPKnxPq+MRcR74xfNrFB6qWqTFEXmLk/rJ/++5ZhtgnkWsXg5a58IIYQQQgghhBBCiFUbmRoxgzAzevtgZviuAyOKvQIbhdAFG4mwGGYmOYuGd8MaHTkYQbsp/t4erz8Qb8sZ/1H8WydszYZKKRehkaUWBWBFwZHUAHDrCGQhi3mS1O5QVEUSBZhixTQ2OIubtTF6vPXdtFOsMzKcdEKNMGmbmmHEdBo/FNaXF6wLMhyDgGnWWPuEtUqYtovLqsVCGPHZrY9SLQZhDRjex9VIP1VOeGcdCsDcG7UwESmDMPf/ylibgeO9AdbQq5YBE0oxFVqH7+yfpOP7zxEay7XOb6yhAhSbcNU0aerLryKEEEIIIYQQQgghqoBMjZiu+NUHI9JOhBUju531aEZQDPMF3VrY2hsU1ybCRi80wYi2TOOTVm9jOJRL2cRj+u/LI0VSyFzJea9CyrpJZDFHemD6nWI8ryMFe6YaC80ad/fvF0FOgtEhfTBjgLPG2+JjLsLyE7PdPq0Ut99Ym4Cz+IFwMemRsjz6pRkmkqoHNn3cSFMqJS1vhbnu7L86mGdCM4wxOh82YmhlgYXAG2EMQNbXqFaUS7kILPeZ5B4v76zn4pq3NPEYYbUU5hqzjkpT/DvrdDDCbCQ0wtaDEUIIIYQQQgghhBCji0yNGAqKS2FMjMUwwleoVkA58XNhvM5SGKGLxYlrYY2SahaxHU5xb1JtM8ONnPA/Z4kWSYos4TLOvnZrXQDFqX1CJg2LQi+DTUNTByOS0rjqj7er89rLfflRGxRCm+N1mbufdMTLGmALwvOGq0W6eE9htlqGwXBFW0YQ0YSJYPuSkRqrQs0QpiBiP/TAFkBvgbnfGU0zHNKieTgmmmFrQ9BAmwQjsr8J08cuw63JQkG/GvTG7WiDiT5jmr2R7t+NhnIpd85JBibv1xrYiIlu2CL2vAas2cPnch9s2rPhkoO5v1tGsA8hhBBCCCGEEEIIkR2ZGgEo4lZSWyIHI6axaDJn/rMOB+J3pqGhKDicwtz+cUOf8zAX10075JsBPG61Zoi7gqQrWmYxXZgiJk3MrIERDutg+rYHpn9D+3f3lYcRjgEjXnbF29JgqkGxsZQUTeKbLjRZJsOYVIvil9ufAygeBxTX01Ir8TxplKzI9ER52Mij3nhZATZCaWWKLkiiFub65GCL0Q/ARElEMFETrTDnN5K+Do3dCCZKoA/AeNixy7RTNMTy3nY1cbsrTcNFgd2tAxNapw7WxEuDaccGYUwNGn7VIHS/uc8Omoh8fvE5WkDy86UWNs1Ur9N2HxpznahOmjOmlhNCCCGEEEIIIYQQo49MjRSGM0OaAirrcPjFgDlTuAAroFbD0OB+KPI1w8yuzsOmCKJAyBQyFP57MXJxmm1xzzXN2KikiLhbPJyz65njvxul+PU23FQzbmHrAdiUNL7JlKU+CaMpamGE8bZ4PwsD23IcZIm8qIVJ+dMAE1WyBNWvL1EpbpQKYPvLF6RXRty6Co0w58K6IH0w16Qextig4VUJ5YyHAsw9tgA2bRcjYBgJsdRZn+O8zlmW9frzGrDQelJqpUqNCbZ1JDV3QrjPCD/CaiyMkdILaz70BI5Po4XmB+/xPpRGweVhrnMLbMRWKDKskjo6EUz0SiiqTwghhBBCCCGEEEJUH5kawyCUIoUz8GlmNMCKa+56rnA3UnHQjfBgWiDApgZqjNvRByMKtjrr9MDOEK9GJACNgHLmSCi9FNueh+m7QWe5ywCMENkHIzr2w87eTutLFnnnDO9B7zfAmEB+Siu3fa5R4gqxNFYGYFMZLSrTnnLweI3x/pdh9E0NpkbzReB+GFMlLeLGj2IpBwX3flQvTVIajMxwC0gzMqIWNhVZC2wE0GjA+g1AcQqqpvjYbn/kYcV6d122OS3Cqge2j5OiRyqJKqCRyCizaqa3Ynvcd1KAOY8xMAYHDb6lzro1MPduPWxUk2tu+PukCQmYfm+Mt2lB8ZjgvdCObEakb14LIYQQQgghhBBCiNFDpkYFpIm67kxjvlphjY6kVC/DTT3lz2xmiiWK7MtghXG3UG4zjOjH9FjAyMU4HqcpPo4rCkfeeiFDg2mZfHE8NFOaBd2ZjsrddwhGR/BF8dLdbz1MbYMB2KLR7u9+e/00W8sA/Ae2r4d7Pd3Z4YvjdvRj9NNPcfZ6G0zf+kLuANKjV/ju902IHMw4qYOZ3d6Xsm41oTFAM48mUResiF1pqqdKYQQV03exHkk9jHBfBxPl40b1MPKrDva+ooE6gHD/RSgeM7y/GN1UCbUw0RJj4u8dMH3UGTjuSAjtawlMe6fA9FsL7BhbCttPTDHF2jWDsHU1OuN3ptyiicHItVrYKB3X1GBkUgeqV9dGCCGEEEIIIYQQQlQHmRoZqUlYzroKvsBOIZGzsDthBFRXNK2DEdT6kF1MDRWw5vIGWLGTeftdgdo1V6otSLLgdqjwufvZ70eey0Bg3XLH5Ex13xBx9xMSfN021MOI+W1Oe3qdz76BEYrY6IWtwTLcWf7cdz3MeXXDptoZrRngNbC1RmhKNcXL3dogOe/lE0pFFUon5P5WDzP7fjTT9oTuE1+g7ofp6zbYPmCastGgAHPuPEYPTFqqVqe9TIvFdrfA1OOoiZd3wwr5aaYooz1aYUyBHthaHlnvMxowjHCph7nPabiNNjRnx8fH5TkxWiSK29cT/04DxjVH+2Gu61in7e3xNv3OvljInen4mLZPCCGEEEIIIYQQQqxcyNTIQFqERj7wez9s7YwGWLG4A2b2cR+KIw2y5rd3iwf76WcG4/2Hoh1cOPvbFQWrYXAwuoHnHYrKSDvPJPGb9TDysDUw/HX8GhrEX3cQNg0VYFPXcJZ8Utor9zh+BAfXrYZZ5KYYWh6pbDh7vQAzdiKYvmiBjYjhemnpp9wUYL7RBm9ZFO83gk2vNNxopSyUixwB7BhjbZQGmP5gdFO14Lnz+UBYtNzFNZR6YZ4fzfF7ztlPUvtyMGbG+Hg7mo4FGEE/qyHB9ftg09mljYVqw0iofth6JEmps9yILJ7fGNi6JTWwRnIu3m8XzLl1wBpONJWYykoIIYQQQgghhBBCrFzI1EjBF+98Ic//3U+Z1A0rolFYzMFGT1A8KyCbsEvh1c+xz2Oz1oTfXje3PiMTmGqpWqId64f4aaHctrCwtpt/vpzYDJh+Y0QLIyHY90kRNKF9s9/c/btph9z98xjutqHr70eEuGmkhiPUL08RlbPwaUaxJgELymdJ7+Xip2EDwuParXERqmFSbv9ZjIbQvZt0nH6Ye7IOxjSYCGAcTATFEth7uhoGB1O/lYPtH4QR33tghPcWmOcKowmSoIDv1i9hZEKl50EzhrU6chjdCAb/WjHFFFNqJY0t1s5h39HgYJ/3x783oji6rMZZp9dZt1ydmNE044QQQgghhBBCCCFEMjI1AoTEa7674jZFenf9gvd5GazI2wQjojHFkJvSyI2cSMIV5SsVgrk+hc6QSD8ckvbhp2pyX0kFjEP7pqBM86fW+S10TD9SgKIuBU4XFh4vwNYp6He2dc8jlHYqS9tXZpi6hwzA1CpwjYM004j7CF2LcufvG0xZydr3WQ0NwNZYqIW5/g0wxh8LZHfBiOpdKG9IMPJlJOaUG1FAoZ6mxBIUp0hLogBjWjKdEmt4jCT6oJDhuCMl7XkyGPjdNxa7Ya5lI6wZwjHKGiSMFPKfvzSRQvV0/PHDaxQFthFCCCGEEEIIIYQQo4tMDQ8/lVHSu29m+AI+RbBBGKG4HyanO5eFxDvOGE6CQrCfYirUHncbt239sKmG0uo11MEMDnfWsk9SJIt/TBJKd1XOWHGNDaA41VG5SIp6mBn342AE3rdR2r+9sOnAGAmTltKqEkNjJMZG0rUcTWhmpJ27f73LrVPNNldi5FW6XwrTnMFfi+KaNzQDy0VH1KM4MmI4cD+sd0KxnuZeFrgNYPqDZsaqYLSFCI210Dij8eJGdbgRXa4B4T4D3agt95mYNJ5ZyL0+3o7GqBBCCCGEEEIIIYQYfWRqBAiJ2r4QHxLYWEi3BsWCJmfEUyx1TQ1X+OayJOHUb5e/bZrA7O67G+VTyHCWOk0QPwd/msA4UlMghN9H/m9JRgKFXLeuCaHQWUjYx0gMjbT0S+W2D30eqUmS9bhphoDbL0lmFgKfq00eNrIpdK+EhOgs7WGkTgds1MbS+Dfet2lQ6G6Erf8wHGPDNVea4329B2tSVEJomxysAE8jq1ppz0ZjjJYzqRj55ZsR3JYRdUn7zTuvpGi50HnxWcr0eE3+RkIIIYQQQgghhBBiVJCp4eCnSSJuqhLAil+umMv87BTX/Dz8FNVccyAknofSULkzjZMEvqQUQO45hM4lCaaZGUSy+RESwrOI46Hf/fYkRXu425dLocXi6X0woqNrDNTCmDaALQjtF3139x/qs7Q+9KNWyvV3mokW+p3psoZT64Gz0v3C61kiHJLW8fvKb+9I0vO4Bh7vs1rYeiChvh1uejVGOPhmgF/DJgkWpWYbhyPyR7ApsVjDotqwLznm3aiFAed3YHTrZ2Qlzcx07wn3N55D6DniblsLY0gwwqYPxc9pN01V6FrS3FjZo2CEEEIIIYQQQggh/luQqRHjmgY5Z1koTVNoVjNTmOTi3ykYElc49KMs4H1nOpPQrHgff78+kffO8+K5JaWkCdWgcI/pfvajR/x1XbHb7cekQtTlxME0Ad3fludBkZrXhGljcjAz4d0UVP6xktqUNXIjbd2kmhW+uVbjfWZEEGDa3ovs4jOvQ9p1y7KPUESOf7405IZratTAGji8ljnnc5qhkXbtKqWS9jNdWyVF0F14rjRX+L0a5GBrdfTD3P/1sBEhjDLrgzFmCgAWI3skh39vVoOkKK2kqCb3O59zSb9zDOdhU+750SvuWE8zqYabbkwIIYQQQgghhBBCVIZMjZiklDpAsvngQiGTM6uThOKQGO9+dyMQ3PesM/399oa+c3Yya3hwVjJFTIqd5Y6VJoYn/ZaWRippW//8Q9epnIDNIuMN8Ta9MFEcbtRNJdEjSamWkq6VK+z7Rk+SSeRuW+t85u+s+9DsnE8WYZUiedo45Xpuu9LWSZtJ3xB/r0SYpxnC/bimgp8KLaldw4mSqAbVKBzN+7AXxmxog406AoZ/boxKQLwvFtauAdAKa3j0xu9LhnGM0ejzLM8NlyTDLXSPD8DcN4zsqEHpPUrTYxDJdU1GI6JGCCGEEEIIIYQQQpQiUyMmlKKE+CKXn1aKM8nTRC3fqCi3XqhtSWJhVuPDTz1VBxtRUgdTyDwC0A4joIaMjSwmRsgg8vuMfeoL1mnRHqGc+UmRL6H21cAIuvUwYm0XzDmWS+0F73e/H5OOyxoqjIhJ2meU0gb33NzjcSZ/A4ypwfQ5acWka2Cus1uwPcuY4XvIbAp99ttOMbgSU2MkkQ5pbVpeVOPYvD61MMXu6wAsg+nHWtgUVZUwCCPgN8KMHRqYffFyXrOB+FjtqF69DaC8cZhGkrGRxRhMMu8YkTEAY+Twfu13tuN90xQv99O2AdYkFkIIIYQQQgghhBCjj0yNGFcUpzjFGdfMj9+H0toLNSitxZC0f74nifAh48MVnkPbhGaEh6JA/PcBmPOrhRE4m2FmagNGuKPg759DyPxJMjTcc6CpwagDCvBumquc83uWCIyc93s5gXQQNt0ODRzXAMgameBej1D/UwRtg83T341SkT4phZP7mYYIr4Xbb4AVWdtg+q0Vprj1gLM+j9EGYDyssN0JI+S61zkpgoif/XZm2aYHtkZLJaxIU2JlgFFUg7CmI1OlsZ5IUvq4JArxtv0w9wGfcZ2wYysPm9JspBEnLn4kmkvWcyhnCicdN2lfblSNG4Xh/lvAaChGeXUj3C/5wDIhhBBCCCGEEEIIUX1kasTkYcVdilNMS9QYL2MRYM6sZ4oYCoEhc8MVgn3hv4DyQnHI4ChniLjrpuHOhmex21ABaZ+kqJa0aA22z03vQnPFNy44ux8wImPoXPx9h87VNykKMGZNKEIkdD6h/YX61213Hma8jAUwGWa2+xIUR2uUm0UeMoWIW2uFbeiB6dNm2BnlPbBFtbmP5vjF/XYiHA2R9N2PGEmL2nG360PyWOR904/S61UNU6Na+8kKRXA/MiWH4sLhWVJoDcJcI8DWvGDqqHaY59Fwz433OxmAuTdocubjYyI+Tpb2JtVO8cezOz6Gc33KjT93334bkgxjf9+Ez3gWba+FfUa6KEpDCCGEEEIIIYQQYvkhUyPGjR5gWiaK7kBpUWJXMHNTGLn4xoQb3eHuLxdYl9tzvwVvXf7uz/wvJxDyPGksUMTMwaScihBOPRUS4vnuvpLWrXe+81xCbWUUgluknesmGUZpwqTfr/0wkQyuQeSv6+7D7++kdnNZE4DVYCIi3GLPPYF1uf80Q8PvB/c315TqhTUIWB+hCUaYLji/d8Tfl8bt8o2WpPHji8/u9zRhmteagjDbyr5uiNvZDRs1wkLobr2XSql0Nv9I4Rhvi199MAW2u+Lf+Vs9zHkuQ3KaMBcWgO+BNTZ6kW4UDZdemHHRF7eTkT80yWhuhI7La8ZoNuKOZ9+MyDqGfNz7lP+A+anpyhludfErKSrNhZEsEcw51qM4wqwWxWahEEIIIYQQQgghhBhdZGo41MFGKdDQGIAV+4krktMcoGjLdDAhk4I1Fhg1QPEeKBXXuYwGC1OjsMBzKFIgCxQfOauf7aW4vhSlxabThHff0Aht4/7mio95WIGQv3EWO+sGhI5fzswoN/vaFTCTTA1f5PdnoCcdi9dpKcy4WYx0ATop8iHJQONvNN04flj0nefG2hmAEcLrYK7rYtj0Q6EaJUlUYmiEzKI8jECehxGRu2ELYdfARpSwYHMBwxeJXYOx3Hm5UVOh5VmMQh6HaYqaAbTE++yGFcPHwFyLTpjzzGJqAOaadsEK8EwdNxrQuOA1aYKtv9EL23a/v9yoNZL03EDCskqeYxxTdbAmsWu4+M9eF57bmPj7Apj7NY0BmHPvgzWFGdHXDGNYVTNVlxBCCCGEEEIIIYRIRqZGjGtGDMCIVxQOXYHVF3c5k96NPnDXdw2NZhghLQcjTrLor7u9C2eyj4nbshil9Q8qhTOp6+LtXdE1yz7LpXxx10kSFH2jx+1DCpP8HhKoy0VmpLUvS4opfx3WLnDTZnHdyFuvC6aP87CmWLk2uVE4rpjuR/dwfXjrIz6eW2vDNc2aYK53H6w4PhLSzifUv4zOAGztlgaY8c/aHhyPpFy/JeGPl3L7YIou9hdTybXBiPndKK5RkgSNkb54G0Z98Vr2wJhcfLYwqiarOcEoo5Feuyyw/g3NEz4fmFrLj8YgadFd7lgNRZi54z4rNBZoFLP+iPs79++3B7AmTA72/mFb6uNlroEz6P3G/dXD3mNCCCGEEEIIIYQQYvSRqRETeS8gOQIBKBa2WWzWFWIpgLPwdROASQAmwIhptbAzuSlUuiIo99sNK6INpzBwCFewZNFbt5CzPxPfj8wIfSZJM/hDs/tD+2K++lB0htvnWWbRu8cIRc4kbeN/5vUNtcc3WiKkz8APmWMhs8aN4AnNdnejgfx0VK4IS1G2AUakb3TOpxqkRcv4Jg0jRAZhzA2mYmKfueecNUKC67vvlUChmmYjYAyNSTBjcYHTxlD9EcCmJKqHTVUEFBeU7gewCMXpkrJeAxpTgxhZHY3hwGLhFOyzRomEDDr3N6DUEKz0+jEyKRTtRpJMSqbwcvfBttKAroc1oN1nFw1gRs71wVzflgrbL4QQQgghhBBCCCGGh0yNGFf4cutphIwO990Vcd3Z3O7se5oaY+PPXbCznjmb300DRQGVKVVYoLxcAe9KoeHCvP0hQqmPklLK+OJikiHEzyFhn/3iC55IWBaCM6+5XppgGhI9Q7PI0yIAKhHf+coiDOe9776J4ROheHY6IySWwVznBphaHxR0qwHrZITOx00RRAGZgjLHcxKVpCVKSmuUNW3UAGwKoTyMON0EE1nBSCb3+rvmRg4m8mRsvG0vbIooN1UcnGWVwpRxLFLt1kIZLdznm2tkpJlNOe/l74u4zzl/eSXQtBuE7Rt3LPqmpnv/uBFhfjsZdZOHua4DsKYvC6rT2KBJl2R4CSGEEEIIIYQQQojqI1PDI23WL1AqZicJWaEohQ4A7wJYCCMw07zwZxz7M+6rnXKGUR9uupWswrEvWlYSueEuS0o348/w9kXgpHayr/IwgjTz5XfAFvkt1x7/MwmlfnKPORyYnigJGjOhiAwkfA6dWz+A9vi3JhSn6OE+h3setTAmSQ2M0MtIDLdddbCF6N0C1AXndz9yJM28CY2XpPXTxnUO5h5krY82GDMjByNgt8Oke1sCa25yf7x2vDbdMH3LmiGuEckUUyMRvFnPoQ3AONgIq9HAvYf9+8Jfx1/u1uthFFrIxEmKwBoObpScm8ouiSzPbEbE0Cjx28vIDH4fhI3OEUIIIYQQQgghhBCjj0yNBNKEyCQh3v3Nfe+FMTLaYYS+blhRlfn8fZJE/2rAPPRZaj74bUrDFUFDES2+YOr2kduGOtgC4qGUW2lRElG8bRuKU3xx1nyorVnw2zqS68Jj553vfoFlphzz2w1vvVAUi3sc1vkYgC0WPghb2J4i7XCMjdq4nTQFWHDZjVKiAdDvbNMEG6lRD5uCLWTKuN99Yb0epVEiWY2NWgBrw0Zl0HygobEINiLCNTT8d8TrzYc1z9jfjIRiZM5ITLC+eP+j9cAOmWRpEVf+cw4w51gPYLX4+3yY8eAac/5+yvUJi3KXex7zWeanMPNJisry3wdgi4eHTGU3siyPkT0PhBBCCCGEEEIIIURlyNSIccUuN81TOWMjKYKBYjxgBEk33Q9nh1PwpfjpiuUUyzjTmbPKezHy1DP9sMZGHYrrabgkiZouISPDX+7+lpZyhtvQlOhD8fmGhFSfAkx0Rh62ZkPo+L7ZkrTcP241TA3i9htz+vO6NMTL/OgHlyRB1t0/29oDW+OhDmbGfx6mr4YbCdQLI/73w0ZihNrgFrheFi9rhq3TMBzzrgF2jLi1K7g/vnPfoXumESbSpC7eRxdMZMaywD59/Pb2x9u60Mzhvcxlw4WGC6/hcIup+2QxMZLwi53zeTUGZszxHvbPm2OdkS1J58H6FjlYQy7NIHJTzSXdy2lRKPxeSTqpJth6NUIIIYQQQgghhBBi9JGpEcAXtFyjgb+7hEQ/pkHx122EKRa+JoyY9w6MWBeCYjejDxriZW7h2uFAw4WzyNNmNfvpXMrNog8JhqH+KXjr8FWA6Y8a2NnXfmqicvTBRMZQSHZneSelNAqlL0qLJqkmPHYtzPiogxFKeX1C4rVvrqRdR39diuxuwfjhwNRLTLXk74djneI2z6MDNnKEbciCf36ukVgT3MJeUxooTL81CBNZko/buRQm3VQnrMjuR2WUSxkWgoZK1u3SDB6aU4A1RX0jyY0AKleMPCllV5YIJncdt196YO69CHYch1KSMS0ZYJ5njG7xz5191wRbx4LRR0kGb+iZk2Rs+M/5JNOSUV9+f/LfijFQoXAhhBBCCCGEEEKI5YVMjRiKWK4A6YtbjJboQqm4lSS2utQDmAxgHQCTYERVPz9/kojupsIZKRGscJsmejJ6wE0nxMgSd1+uQJhkDrifkyIf3Pz4nN093JntSdsmmS3lIkjcz6NlbtTCCKN1sGl3GE3jRvq4/cXz4LUKRb7wuvE31tkYRHXqtSRdI0bLtMbfF8OaCp2wNRCSTJXQjHveL6wzkXQ93HGYgxHFm2DGFU2VJTAifBNsxIffFt+kYzsqHQNZ1s+aDi0H8yxqhOmDHti+aYA1cJKiTUZiZiS1hwzCXucG2PRpjEBqhDEAWuPv/fE5sPZNH2yEGg1NPm9rYU2opnj5UqSnh3Lb5xuv7jOr3PnVwvSv+9xk3w7AXIOGMvsRQgghhBBCCCGEENVBpkYMhS4/SoPLawGMhRHTFsKIcO66SbPISS2MkDcORpSjmJckyPniP0W+4aTqCcHZ6kmCXmgmtlvfwRfRGf3hCoA+bv+6gqIr0pOss/crJS2KJK1fq2FmJKW8cSM1KPL3Ot/rYMVUP6olLYoGsOaIG/HCOiPDJesY7IKpqzAJJoVQD4pn47tjMIvI7kdl9DvLEfjsLsvDGEZj4rbw3uuB7etQtAv350cWjRQaVkBx4XYeq1zqqwLM+XC89MGeZ52zPGSE+v1VLt1U6B7x9+lGa/TDpPBy09rRsJsIE73TDDMWuuI28n5vgzEHaD71wJpyHfE6XfF6jbCmhx+xEjJZ3c8h09J9lvvrMI0Y68gwooyGxnwkR9wJIYQQQgghhBBCiOoiUyOGQqIrZHEmOWcJUzRsRPEsaF8Mc5cBVtBri7fvgc3fHxKrfUPDpdpRApzh7eKLf27Ehts293x9Ibbe+43iqtvHSYbQ8qKcIQCU1mpIEpuHew6uqdMHU6OC5hFT7rj1VdxZ4u7xfHOAAizF7ZHUcvDJep6DMOJ2H4yZkBSREZotn2RO8D0q892nD+aeY8ojRhwx3VctrMGRNEb57qehGs51p8mJ+JgDMGJ+c7xsMZJr3QDGlPHTbg2iuG5KBDN+mBrMNzjS+jzJwElK68RnBK8xTTTXqGmEOe9a2MiYLpgx0h6vw/oZ9TAGMMd1BPO87IZNZ9Ya77MN9pkaiqDjObi1Tdy2u+slpaXyDTiaGznYmkmLIIQQQgghhBBCCCGWBzI1YigWu7gzwyOYVCe9sCl0QvvgdoQzp9nRTCPE2fih6AV3f9UW+yl2+5EaSe9AqWFBUc8VVd3C6IMw5x3B9JVfF8NneRoaoSiccusmGUyVkJbihpE4fsHkQRjhNhSdEWqLL+C6KXOqaWpUQgQ7gz2tIHTI2HDvJ/+3Guc39zsC60Yw91ofrCHRG3+mcVQPe09yH2mRPCON2hiAEehz8ecamAgGRrW8DuAtJKcH64cR8Wl2ERobNGcYoQLYyAL/HEKffcPIJfKW8zng1rABisccDa6e+NUJE93AtjLSghEaEcy1GQtbP6QRwNuwUUZ8htXG6zJqIokIpfVG/KixcjC6hDVBWI9lRd5jQgghhBBCCCGEEO83ZGrE+FEXQOnsZqar8WfJczs/rZGfKqcLNo2JnxLIPXaacD1S3CLRaaKmi29qsH013mcaHY2w6WXKRQn4JsvyICRWhwwpOMvLXYu033OwRd7dNDn+9fa3AWzaMUZpuCJ+aDvClETDKW49GpRLzRNK/ZM1LVLIiPPvR5pu7hgrwNZx6HLWKSduVyMNlZ8CrAEmMmEyjEDPeieL4vaF7o0CSu9NtsuPmGhCsfHo4kdm0bSkMcnt/GeV379Aet8x5R6NAJ6bS5+zjMZMLczzhAXGu2GiM8bDGlRZrgXNQ5rM7nllvZaFuH0FmGvGOi3VqE0jhBBCCCGEEEIIIbIhUyMmlAopFDmRdT++6EdRmjni3TQn7nFC5gqpi7frT1knS/s4qziUaz60fmgZhUfuh0ZNE4w4WwMjPoZqa5AaGLGyEbbwczXFQaa6CUXWhER0F38c+NTDpiwqZxqEDCr/evMa5GHEUvaba2i47eV7WkqnFSG05mDGabnonKy4BiHxozJCRkhoPLsiPaEo3uus4wr6SVQ7ZZobpdALc+/kYe6P+vi7G+nCZwphOiQajDWwYnvBW+5HXSX1VVP8TjOXhpxvgPrjO8lIGwSwIN4PU26lMQjTHzQyOuMX4uPx/q6BiQLxI0hC8FnA65wlOiPEAGzBeTdFlhBCCCGEEEIIIYQYfWRqpFCpSBWK1uDnAopFN6a0SkotlCSEs3C0O5PfF8jT8AVN970S/Fnbfs0MzrhOEy7d9SkkV1OIr4ERevOwYqh/fPedn0NCrbvPGhjjgfVRssCoiSSjhDP12d7euM1ubYJQe0LL3ZRgoe+jjRsNNFySImfSIoqSxrFb8yEHO+ZCY9O/P5aXUE1hnvVUOmHTRTH6iePCLfhOmFqJdSvq4+2XwpoR/pgOGRo8Z0YwsK5Q6J5IG5NJxkYPrDmTZXz0w9Tb4LnTxOuIP4+Hfd6wD8oVWndTT7lpzLJGx/Hc+uL2ydAQQgghhBBCCCGEWL7I1PAYqTjlmxkhKKy66yRFh7hwVrBbLwEoTSVVrn0uaZEUbltqUF70Y0odnl85QT6K1+9D9Q0NxMf309uE2pBlGWALGPP8KPpSSE1Ln8V91iEsujYAWB3munbCCrjuOAlFk3DGOWtnMFqGwnhfYDt/H5UIuqHtfVMorcB1Ftx7gYJ62vHdz/yeFI1AU4rHcI0NP0oji1hdjVRUgLnWNLFoJDC1ESMu/GgI16BhLQ2eA8cO09wNoLgv3eseMjcHURydwXvZN8r8iKZy9Ti470roDeyLqcNoKrB/3JR/5UyT0O/lrnkjzHVhwfLh3jdCCCGEEEIIIYQQYvjI1PBISzk03P0l1W9IOmaaUObOog6ZI5W2rVw7KSw3ojiljS/m8n0Qpj6Bny4oLfqEomu1YWqhasG+d2+aJtj6Ie1IN2bSxPk+AEvifXfA9GHIkPCvUx62mDJn9bNeQA1M3YK0scEURzSYkq4DDR2aTzxPmjysM8A2Dgc3koIwVZCLb3YkGRB+XwHFQn4t7DXNwfRDC8y5dSfsb7Toh4nU6I/bMR7G6OqHGRcLUWxsMFqIxh3vIzeFm1tDpJKoFrdNxI0mc/vQj9xw1/WfJyMhaXvftHSfMeXMMJoflbSNxctZoH00nltCCCGEEEIIIYQQIh2ZGg5ZTIUsUFAGwjO5ffGP2yQJ/yGqPUPYn7XttoVta4adPe5GJlDg4/KkyJFyufhHA1/gHOnxmJO/EbYgehvMrG0Wgk+CkTahNvQAeCtuJ9cJRSn4gvIgTL+3wAjh9bBFwntha4okkYO5pk0wgvgihGfS18Kkx2qK900Bvg7WdMhasDmpHUkpqzie3GgLf1tS7vi+2M7PtbD9QJPGF6zdMZvlPCsdcwOwBctbYcYVryfTSLnnwagT11zgGHPxn0XuPty2Zon2ohngGgLuePT3wagTP9IkC340SlbciJHQ/ZN2nknPacJItAjm2jAVVaVtFEIIIYQQQgghhBDDR6bGCHBnevv4qVKShNco8Fqe+OlnQsZOBFvgvBlmhngXbM2KJphzXILwDHcfVxjljPlB51WIl7HgdKVpqZiCxxV4a+N2M9JkJLgRLHWwxkKW/aZF4PgieihKwW8DBW9uuwaMIN4dt4mz/ZNgweMGGJOmHnbmv1vEnOI06zXUwESmJJk0leKaZyEYTQHY6xsS0V38SA4u84VrGhoNsPVqaDC45+emN3KjpULw3iigMkG/EB93QbzvFueYoXVDfZYUoZK0TkjID+GaGf6+XWMj8rZx7++ssD4II4P6Ktye7UoyNkjourjRMEzfxWVMydUAO27cQuZCCCGEEEIIIYQQYvSRqRFTLkqDAmM9rHjVhOLZ1VmPEapDsKJMjZDAF2oHc9j3wYq+FLtd0dvfVwhXNKQpAhQLgxGMcMjIkG5Ys8OdvR0SUcfBpIjpgRHee2DTLNXDmC8jLZrNGdqdMGJrB6pfEwQIC+eR9852LIGJ1miBmeU/CHP+nc66+fi3FphruRhWvK+H6fOGeJ9LneP0waRAWub8znHPvhjp2E2KEKF541/ntHowLkmidgRr0rTARmowpROvrZtyiesyRVVSLZU6mDEYwVwDNw1aQ7wP99nhj8eO+Pc22PvNhTVrXMG9UpIE/XKpqULPh6TIK/ZNJW1kursm2Jo1lZgiOZQaGv57KIIrVLcj1Hb3XqIZyBokQgghhBBCCCGEEGL0kanh4AqzfrFZmhpjYWbCU+zsj997UCxMJol4rnCWVgR6tHEFP18kT2uLL9z3woh6QGVGAfu6DsWz7nlsFj6mkURR0013xELd/bBFe1lgeSyAMfHvC2EFYM7ML1dAPEv7B2ALnVc6i9wlJLQmXYuklEYRjEA+P/4+Bmacrhlv0x63eTyACTBibGf86oLpv15Y0Z4mFa9pAeY8++L14fyWdu6hWheV4KalShKXQ6nTAGu4haJJOP5dc6TG2YbRGP54z8P0YQ1M37XDGBD+Os0w/U/zh6ZGTbx8UvydM/+XwpokcLbjOPX7OEK4Xky56JUQIWHfNzsjhPvY34dPyLBJaoN7vQZh79es++B+ksyMUBvLma+hsc1nCMcli4aP5BkghBBCCCGEEEIIIbIjUyOBJhQXxgassNYII2R1wxaODgnlvlBPgY7LCkgXCkcDX9wLCcX+jPjQcpdBFIuelaTa6YVNJ8S2cft+GMGYM7UH4m2Y/oh1HgATpdAZ/94OOwN/wNnXUtj6HyOBkQtum4YLUx5xP66p45peabPkSS9sQekOGGNnLIzAvixucxNsWiTOiGfhbwrlBWedEFnPl+l5WN+jUmhQMVqHRbB57/iRGn6aqMa4DTSeQkI8jUnAnHN9vE0jzHXx290PYzaMge3X0JhnFAbFb65D44SFvhtRbMhxDLv7GSmuUZuHFetpCvrrJW3vfnbbmPb8yvosaIAxexjx1Aubfo33WRaSojRC0Toh8yZkfISuL40vRpGNRpSWEEIIIYQQQgghhAgjUyMBN4qABaAHYIXVLhgxC7DCmw+Fd1/wd40NILvwRyioVbM4bVJKmVpneSjVTQTTH65Ym3XG8iDsbPQGFJ8XYAVnHpdiLNO81MXbjYWJSFgKU+i6M96GRbIphDNaoxpUWiMgRD1M2yme96D4XP3In3Iz8CNYgbwPxtwZC2AyTP9wOSNbOBO+Pn5nP7EY+khhxEIdbERTlm14/jRdJsS/LYWpNdGHYlMjKRUSfysX5cFx1hEvb4IZO2NgxxvX7wXwHsy9z+vmn9cgbKq2MbBF2Dn2eJ04hjri76z70o2RkTRW+DwCiqMfaGxkMTtD6aR4rULtqASOPfdzJWZBzvucFLXB/Q/XTGZ0Bp8pvUjuAyGEEEIIIYQQQghRfWRqxPjCFtNJUWRn3YJeWCHTnVWftl/O1uZMeEZ31CE5J78PDQa+M8qhUlzB2F8WWtcvlOzTH7epDsVFgStpD0V0nlcSft5+1oTIwRTIngh7fboRnmG9IqGIysiUFhjRH7DCuiuSc70GWAGcQmpSH3M8dcGYAEtg+mJdmPoMHDduaiUWx3ZF5ZHWHEF8jD7Yugjl9snC8RScabT0wfTTeNj6Hz6uEcR7qwd2bNEwC6VVIiyunoeNEBkPYzqwvgbi9ixB+lhnP7MIuxstAdjInE7YQu9NsJEzA8763L4X2cezK9rzvDnGapEcgRVK5ZUlSshPXZW2bohepI/rcoRMDPc39z0UUZb2G5fVw0Z60dhwn4+Vpv0SQgghhBBCCCGEEMNDpoaDK2yxToYLawpkxZ0NXQeb0ocRA43xMqA4oiDULrbJTR8zEsE+q2hdTmRkP/WjuB5BpaI4azaECImuhH3pRiAMNwJmtKHpQ9Gd44GGhRuVgni9ZgCrx8u6YETwbhQL3z7ubPcBAO/Gx5kMW6uE7xRsKdC7aa9GygBMeqZGZBN8Q4WaWUibRd4HYfrRF83dFEUF5+WaA2kz9nnOfbCpo8bAXq96FI+ttPHqwlolbm0eRprQ6GFdj3oY46kWxjTh8yAXn38O1qxj25NwhXam1KJpM+D8VoOwgO9un4MxVoDS9Fj+dsM1EjlWR4pvboSuuWu++FFzgO0TP1KqHuY6MNKmx9mPojSEEEIIIYQQQgghlh8yNWL8WcbVhNEMedgi4zRIWHeAM7TdGeGATevEWcycdb6yCfbVSMeURLm0OD0A3oQRbEO1E1YGeP1pSjH9UDPsrG9/Jr5bLJlFzynq09SgmePin38vTAFxFrSmkM4Z54yyoSlVTWgQNMGmawqJ10lCPVM5MYJioMz6QNhwYGSVLz6HZvXnYA0Z10R0RfKskQj+eqzFkofpjyj+3AjzbABsSrV2mGvLAuIU2RlpQdPGb4dr4DTAGCVMgbUIxfepL95ToKewz3E7MX7nfZZ0rqHP5eD4G+69m1RDIylyI2SSsh8GYcyLRtjUY+yvLtiUak2wkUS8lqqrIYQQQgghhBBCCLF8kKmRgSQBlSJ1VkGfM77rYGdrU8hrgk0z46fY8WsCrIyifRaqYcZQZGVkAeubjEafZM25nzWNGAV1mloFGLOChcL9yKAIRoh+EzYNUi+MqNoSb0MjolzUAAXa0DUYcM4BCeuMBLeWQ1o0ji+uu9ECbmqiJOMlLULIT22VRqjuhL/dSPqHBdABa2Q1wvTNMth6MO6x3bHB605TxDeJaEowwqIh3l9bvH4H7HPH7y8uo8lF05UF39MYriExBsbE6YSpmVKpOeqaFzUoNThC6wPFUTduhBL7sxmmD5bG3wdhrk8fzHOc0TWMhHGLrgshhBBCCCGEEEKI0UOmRkZCs6FbYQRDFhLnbGNflGuCnS1NodGdYc+8//0wQmUjjMAHVC8V0IqO7miAEf9YN8IVrLPOeKdY2QJgHIzQOz/j9llhKibOHh9EuBi0S33cBqZzClEHK3pyXxRFaVoxxY+bWqwAI0K7+fu5Lj+XE1NrnHWSIhuqVRw8BOtLuOmgQpSrTZBkSLhjI41QDYWk3/0UZlHC50qpgzGoxsH0xVIUPzvceidp9SsYwRM6D5oe+Xg/y5zv9fHxGQHSGdgezrpNsMabb7pVg4a4PZNgnwsLUNlzL6kf/JRTbpoxwr70056xxkmdtz7r1fTCGho0glRTQwghhBBCCCGEEGL5IFMjQKhQbAiKWRGM+MXO7HbWqYWZhdzibMP8+CwQ7NbwYJqdalITt4/pjDi7vxo57NOOyX6shTGAKIy6QiJFwaTUM77QzUgNmg3Vpt5pK2s6lINib9I4oUDMSAIWfM/DRl8wisLN5++K6O616nKO6Y5VGjE0KQB77d2URCGB3o00qbYBRlOjWvtMi7ZIartvUnBd/7coZbn/eTgwYqYfxrxscn5jGio35VG5fbmpovzUZYw0cdMiMR1Va3y8UJSTG6XRAPPsqkO2eyELNBjyMM/G8TD9UA9TP6YHxoiptJ/9KI0spNUmcvvBf1a613EQxaalEEIIIYQQQgghhBhdZGoME87mdQsss8iwi1s/geI2YAQzCpgU+F2BudpQnKNYPtpRGyywHDmfKWS64iNT6KSltnEjEth/7TCzzKt9Hm7/MyVUuevhCtB1sPn13es6gOKizJwBTlODM72Zoop9FSEscIfaVAcjQOfi/faguL9JSLSH83k0xkbWfYZm1/v7CdXFcJeH+ov96EashNqU837z1xtO3/A60jhaCnPNm2AjKHLxb6yXEToH1/Ry29UIM7786A6Ou0HnO0X4ZiTX46ABNwBj0HbDivZZCmJzHzymW8Sdv7v76oeNVuG4r9RY89NNhcZ7ln2wGHgT7D+OrtEU6t+ueLsGCCGEEEIIIYQQQojlgUyNGF/odQXVJFGbqZAKMCJ7SISjiOmmNeLs6dAM69GAUSBpNQeqDQVWCqn9sCmT3BRdnP2dtQ8oOPbAFlCuJqx1QbOpkmiWOgATYMYE6yMsdfbnzlCnuN6P4toJbiolir80xtLIw9Y+qIMVhdPGrhuV4M7sX9FUmn7K3YYmWeg8uMwviO7uN5SeiO/DvU8ZTUFxn9ezD2asjIEt/t6F5DHH4+dRHFmTd5aFtvVTKzHSJ3Q+LbDpqRbC3GPcp2s8hLalccoaE3y+9cGm26PJwX5eFC+bCHPt5qMys9KvnxEyvdzfCD/Xw5oVtTD377j4XPicqoV57tTCPnf8Pu2FCoULIYQQQgghhBBCLC9kasT4aWlYILcWdjZ9aBtGXHQiWdSigDdaKZOA8jObl7dgTTODDMCcu5/XvtI6DhSCJ8L0+2KY61NN0swMCp9uipzm+NUYt60BttgyU4oBxZE4aWOFUS2MZGFxeRaQ53EiWNG5GbbGC3ELJ4fwi3GvaMrNrs+SWqjc74yI8feVFLXiLxsOLMTeAPNMcevK1DgvCv5px6MhwHUYTcFC1XmUf8YkRf/UAJgMYA2Ye6odZsy1w9aYYF2cUGRVHtZ0pNBPk5DRG0wzx/b3w9TQ6IEd46wX4rc5hGsA8rv77m6floLMXd4Q768XxpTsjr9PiM+vB8ASFD8jmBJPCCGEEEIIIYQQQow+MjViBlE8O54zqDnjeAFKhbVumNnMnCW9ImmI2+GmS6pGBAjPrRr7qkY9gm7YAr2sTcLlo00tgNVgDJW++PjtsLUHGmHOrxd2xjqLwldy3jQ2WA+jAVYQB6xxwrz+LFrcCCuuNsAaSX4RY78tNPGaURxFsCJIMzZC5+BGnDAqIHQvuoWiKT77ho9bwN0XwEcybtmuBhjjqQY2rVMh/o0GmN/2HOy1Z6SHH2XhmnCVmlOMbAFMsW5GTDTD1Lvoitu0DDYNVdIxmOqKkVmsFUPzhpFq/tgqwJgHTFdH/LEQen74kRohktKH0XjlsgLMOS6F6fNeFPftezDXjzVJGIXFY+sfUyGEEEIIIYQQQojlg3SYAJz1TFG5DUbc63TWoYDWBSusryiYBmsSjCjHmdWDzvtwRdmVbfZxP8wscqbCYXqm0aYGRsikKM3i2zQOmFuf4mszjNHgR9BkqRXgi/cRjGFRD1tXhPVRKBZztj+jO9yZ/wPOvvw0a6Qe1sTrhpmJ7hf35vlUatJkwY+ccFN1hWpcZI0yYYRAG4xQvSx+MXLJLTzP/bEAd7VTw/XAXEcWCGcqKEYChKJ3GNnDKAjupxPheztUIyPNmGmCjSzKw5i0NGp986FcOrZC3C5GFPkpmkLn59YJ8evKuNAk7ENxKrZQ1E3aeAiZI3zWN8bH4TVx0+QBNpVgT2Af/WWOK4QQQgghhBBCCCGqh0yNGDdVzwCMODcIYxY0wQiKXSjOaw9UVnNhNKmDTZfVDyuUUiz9b8r37kYS+OmsRguaW6wL0gMjcLJfKYSyTV0wImlX8W5SRXIKqznYa0ZjguL7AGwkB9MNcbY90/ZQAG6Jt+VYTjs+C903wNQUqIcxNtyZ9jx3v/BzNfBTZdE4qoM1jnwTIyQiuwI5DY0WmGs3Fubc3oVNHeaK4v49Us7QqNT4oLDPZ0wXskXE9MGMNaY5a4UxoPpgrm0H0q8HIyD8tFPsY/7eCRONwPGWdl/RSBvw1kszQUOGC9NV9cLWqvBrp9TAnO8aMDU43kTpWMhK6Hr5EVGM4KBZ6W+flN5reZirQgghhBBCCCGEEEKmxhA0KSgOM40K8+GzUCyFz1De9xUFZ8+/A9MutptFiVe2aItqsLzTfdXC3iw0NXxx050Rz3oCfpRGmlhcA1vouwNWrKbBwWLTXI8iN2BNFRY2boGNLOlH8ex2X3zl+bAI8hgYc6UVNv0YI0TcCKBqQjMm7y2jQcHvCHz213ejVnhvdMAaHGvC1m5gEWtey35nX4wYCF2vWth0Y52B30PrN8X7ZVHpcn3ISCSmqKK50woTldUKm4psMcIGCcdbSMxn5A+feVnTjtGMYA2MpNRvNJXY/hA0MOpg02/517YO5lwnwvTjMpg+d+ujhPbpL3Pf3c8czwOwxiXbvKLTCgohhBBCCCGEEEKIUmRqeLj51VmIeQnCKaZW5MxcN60Mhe3FKJ5tPlJBrqYK+yAULoHSYsfLG4qtNK6y0ANTV4Wz2JniK42QeVCuCHQnjKja7y13U1uxPW69jKXxtvy9A7b+SLn0Y+4s/qXedrxOTSiOBMqSRqsS2K98IPHc3ALQWVMMucYIoyPclFXjYIwbRmQtg72/aQS6BdsLKL3WBRQbLuVgPQk3/ZG/LSMGaIYxioLPHdaOYVqksbB1OrpRXB8CsIXjO1FsahEaJuyjrOanGxXkpwID7L1O46MD4evF9Fu9sNErbuopRie1ARgPMyYnw4zD+TARN66x6LbFjcIpBNZxv/N3Rr64xrAPTZqVJUJPCCGEEEIIIYQQ4v2ITI0AfhHeJYHlKxLm12fB4RqYGdu1MG2ttMgzU8n0obRWQTWMjRzsLHmaMRSUV4Q42AbgAzDnNR/GDCp3bQdhiyXze7XHAyM8kgRm93iDMGIxi4T7Rab74vbWo1TU9XFTMDH1jm/2DMCMuwnxd6ZvCu0ra80Qfz1GTvAzjRaaAVkio9KKWHfF+xoLMxYpcLNgt9vnFLvdwtjuWKUgn3UM0FQYA1sbwqcOwOowfb8sfnejfdi3NJ8AEy3CschIHsDc020wzwmmLfPvNd7v7Pes9zkje2iupI2DHpTWm/DNB7ddtc5vrGOzJoDVYAya2njZIOyzLikVVchE9Gtq+L/3wxo9IVyTbWX590AIIYQQQgghhBDi/YZMjZjQDF73M9PZrCzpSNxZ3xQNm2AF8ayzrnOwaYyWwYjkFCqHk7YqKS1QId436yQ0wqbUCW0/moJhHjZ1D4tfU9TuQPo1rsSEGY7wWUm/R7Dpk3w4XhkBwfWT4Kz4JEE3ghHxx8Iaav56tTBREBHCkSyMRBgHI7gzCso1B1jknGPYTSPltzeJUISMW8eiE6Y+Qw2MObAUxtjy+73cfVTptaVh5RZ3d/swByPcu2mdXPHdPR7rpjCKh6YJ9zkAcw0GYaNefEZSaydN+AesaRlax62b4v7uRndEMH0xMX4xGm0JjBG5EOa8/f4LERoPPL5bJJ7bu1Fk/j3c7/0uhBBCCCGEEEIIIZY/MjViygnQEazouzIw4LwKMKmR8kgWMJNgFEUeRkQcbr2EtJn07FuKuowe6PXWaYlfrEfhztyvJssAvAIj0OdgZs83wYjbvlA6HCju18KI5UmFhUcLN40T8WfHw/nsv5LogxGT25B8rRkJMRam6HSHs89amDRCU2D6BzD98yaMWF0Hcy3c8eLWtsgSreGK074w7aaT64Yx1gZga5EsD2g4sEaJWwC9H6bP2M6ka0EDk7UfWHvFX59mR7nr6u+bNUmY+sovBp4FdzwlXbMaZ90czPXgtWqMj98Nc18ycuYtmNpB7nOOpnPaOfrt53eaZq5xDZSmruK+abTRkJG5IYQQQgghhBBCCLH8kamRET9NyoqAOfY54xywM/tZc4HLslIDI6Yyt31WypkYSb8VYIRWP20NIwWaYKIo+mBmmo+GqdEPI84vhenTJlhxuxoiZROMeM+aBhRhKyEPK7LmYSNwsrTP3RYI1z0IUU78jmAMoS4YQZ5jh8LvAIy5VoA5/zUAvB1vw997Yfq5AbZGB9vMyINBZxmQPfUU12Vb/XOmMN4BYB6MgdaB4UcrVAojAxil4RqTgDnvdhQL9G5tCLeGBWs+MGIi6bpVanC2wRYfz8FEW3QiW6ot9zqlmRnw1nGvC2DOvyH+PAgTmdGM4hocfv+ETLk8bLTSYGBd18hy7xe3fe550fhoi5d3YvmNHSGEEEIIIYQQQghhkakRk5ZnfbThRShnmrAuRS+MuOev7wqgWc+BtSJ88TNJnAwJla74VxP4PYTfxgKM2M3UVMNNf5UVpm5iCp4OVFZXIAmKqF2wxaGz9glhmqY6WIOCBaQBm54nFGFAE4SEIjSA4rFe6Xjn7P16mHPshR2L/TDGxgDMWPXHC0XqJTDiMEXzAuz1cNuXlHoqVKPBPZ+kVHI01d5x2hsqol0taFBEMH3F68k6FyzuXXDWY/F3IHzubDfHK9cPXUdGYDEyqty1zsPcf0wPx2OG9p/FbHKNGB//eetGQ3Q56/XCmD3+2Ob+uS2fGe7+mF7Kj7jIgn/OfL4ximQk6buEEEIIIYQQQgghxPCRqbGCoYgYKuTrQ+GXkQ4+FFD9GdM+oWUU3ylk5lGaQiZJmPRFSYp/IUGT67jRBEyd0wcjdvPYy0MwZDuqYWS5dSmWwRZSrjQChtEj9fEyRjiwcDVF52YYM4ZROjwXf9Z5llntpCblNxdGaDTAmBdu0fdBmLRSy2DHqZterA+2dgtTjbmpfpLGWjmh3/2e1H4K3zz+aBpn9TD946ZK6oFNtdQGcz37YQwqjpM0A8BfVhcfYwCl58Ki2mPi/ScVoHf3x2iJ5nifPQintEszlvi7W4fIH4Np55XUttBzyN2fH7FCI6mSGjVZIoJYF6VSs1IIIYQQQgghhBBCVAeZGjEUw5Z3zQzOSs4i7iUJjEB2wTckPtbCiM2NMOIni2W7aW9C+wuJ3wUUF3cOtYdFuQGbZobFzodb02M4NMCIvk3xsTtgZoSPZAww5U0fbCRFJftrhC2MTMEfsKI164wwRRBnrnP2vyu0hq51uYgkmiYs9JzGYNyWetjUUe595KYOq4EV+RthZ7kzHVPSmPZrHLjGmU8oYsP/3Y2KqIe59jUwBkw1a5/QZOL1Z9tZs6UVpq7IAMy4c2tXJN2z7vmwP5tg+rMeZuz2O78z2sc3FlwaYM3MwXj7Zc4xudzfph7FkTVumwvxuTfDprAKXbccbBozju80AyLJcHD3zeimWthUWu0wZilNOGD4qeZotkZY/rVyhBBCCCGEEEIIIYRBpobD8ko7xSK8FH2zit4hobY2sMzfxiVJGOTs8RoY8yQkVmbZP1A84z4Jirf+uS+vArwUQBtgilo3xu8NMOmRhmNsMArAzeFfCQ0AJgFYK/68EEaMHYSZbc/6DwMw9UAY0QKUFl32BX54n/30UzxfGk6t8WspjACcxCDMeAkVqnahGNwPW1PCNyz8zyH8dUOCP7zf3fuGY6sAI3pPcb73lTmHSqDo7Y4jN3KB0Rk0EdOOHTpP99xYj8Q1tOphxnMOxkgJpZhDfFxGabEf2D7+4+BuVwczLmjYJF33wfjcGLkUSqlVB3MNmuJ9sK7N0oS2InAsv09oZrQCWB22DpEbhVYHY7i4JlBo/0nRMT2wBd+FEEIIIYQQQgghxPJHpsYKoB5GTF0MI+ANV0ith01f4+aMLzej2f/eBzObmrOc65Asyodm/qelpgm1Je+swzQ19c76oVQ6IWoBTIj3twiVi9IUJvth0zlNiff7VoX7IpUKnXWwwvBYAJNhBNkB2HoI7B+2FbCCNut2JJkY8Jb5ZoYfucH9N8KIzWPi/TJdU2i/WWpSFGBTVLEINaNO/PETIT21T1LEhm9u+Ofmp0HqhhH8+5Gt3kSl+OflCvudMPe/f/5ppqNrzHQ773kUG5GATR3VAjNG0p4JfSgtTk4DahCl9zKjHAadfXNsuFEXfs0J33CjkcE0WvUw464Opm9CNYOSUlC5xeoZLbYsXq8n/s6+cNu8BMXjJAkel8cZzbRlQgghhBBCCCGEECIdmRoxWYStNJjmhalJksjD1ktohE09Uwlu3QR/pnRa+ik/RQycbXthIgPaUJwuJy2dEVA+dVBIaA/lo3cFdreuQ1o6Ic4ap/i+AEYMzRJlQeG2B7b2AwsAU/RcHpE7rujbjuIaDDRpKBS76zJ1D2fMh0gysdKEf7ZpCYpT9PA6j6RPKKBzPzTjOJOe+CJ66Dcf93plOW/A3HtMEVbpeTHtV1oB6tAYopnSi+T0bFn2RzMpyVBiWqsB2EiMtDHt3jO8Tv51oJHB68f0Vn4tny6kj0nurx/W6OIzkfdyP8z9kGbQ8bO/Ti/Mc4DnxfumCcbYmAAbkZTWhy5uZIx/bnnYiBQhhBBCCCGEEEIIMfrI1IgZqYCdJm4CxQImaxEAlYnFjHBwj+l/dw0Dpn1xjx+aoU9YRyFkKPif3WVuDQ3iGxpubYFaWFHYn33t1uQIRRK4+6+BFd6ZgsdN61UOFvxtghFCO1CcXmZ50wvgTdjrXAsbMeGLr26UTJZZ5v7npH4lbuF6v57FcOBYoMjszq5P2q9voiV9TtqHf46uQUMzcDgphGpgIwp4Hn0ojRRK66/QfRSKcHKjA1gnwze4kuD9TNMoj8rPN9TnPC/2oZtOigJ/Uvv85wTHWXe8/VgY44EmcdrYSIP3M/svB/vMaYy3HwMTKedGuoWO4z6L4OyHkSrjYMzIrjJtEkIIIYQQQgghhBDVQaZGlUiamRxKleIK/K5gXIlw7Iu6NDBqYAQ2zgh3TQ1e7LTIEBoCrjnii6/uuSTNnHdhKhqu7wqWvtkS2h/7xJ+pzhQ7PbBFi7OIvT6MeFgKIwKvyNQyrtDOSJJuhPue/cU6JEDpOOK7Ow58od8VzpPaVA1DI7Sc7edxktZlWjSmF3Lb5ovtfntdA4XF4RdmbHsImhg0xBhdsxTZZ/2HzAv/3GlikLzzvRPJxa6Zrozpo1gfw61vEWqLbxqWMxUY7UDcOhxJ2yaZPm4quE6kGw0+LEDfh+Li3TXO8oH4t2UA3o2Xh4qy+7jXh2ar25csiM6IMSGEEEIIIYQQQggx+sjUGAG+URCqLUFcAZqFwutQWmTZ3wbed98ICc22p/jG1EQ5GMGtKW7jUpQK96xzQFGWgl+SscGUPW4qLPe8Q2IhDQ0/4qQcSbPyOcOb6XKGI7z3w8zobkD4GlYb9lXIOKmBEUcpSLO4cw+Kb1SKta0w/dmF4lnivnDsnlddYL2RRillIZRSyj9uKBUTz5XCcTtseqOQIO8bGxTe3etajQLPLNjeCzPjv9JxE7pH/OgNmhrsKxa45tiphb0HI1gToxkm2qEHpr/cGjX+cybtvg31rf/ccaHZU86cTfttAGbcZ6ERNpKpJd62Pd7ejR5rghkzfTBpqbpg7rECbPq/tH4Big2sbtjzrIMxYQCTuk8IIYQQQgghhBBCjD4yNYYJBS0gPOM3JOIS5nJ3TYdQYWR3hrk7UziUtoapcGpg0yrVwQqfY2DTpCwF8DaKZ5WzkG4Otvhwg9Nev01sV7nZzq6R4dfScMXPtHRCoZnkbBejU4YrzBdg6kckHaPatMIIsZ2wYijJw6TfGRd/74ApgB7F23XAXNsxACbCXK9emGuZ1o/+jHg4y3wxnvVEhhPxEiIk3icJx8Q9l1YAq8P0C+8BzuIPmX7++fqGRi+ypycrB820xbB1T5JgNFBSBIJ/PwPFBphb54bnx+cP7y1uWw8bvVCAEfoZCcF1kq4JP4eio7LeI6N9DzHlUyts+wowZo5bE6gd5lnHlGeM5GCqK7dujdt2P3KO5vXY+BgLYKPg+uPfl8W/CSGEEEIIIYQQQojRR6bGMKGgmUYoxQxgCzFT5ExLu0NhjdENoX2yYDb3x3QrjAbhbGYaKC3xi0Wba+LfOp1zaoUR8QZgxUA3isNtXygiJfJ+q3W+pxkWIcE7LYolghFva2DrgVTKSNMrZYVGE80j39QYAPAejBA7Ada86Ifp/9Z4HxRvG2CuSRdssXMg3L+EYyApbVErzLVahuJUPtUgKYIHKB03XL8BwGowY7Eb5lxzKDUG/AgNpvFKMhCqySDKpyxrgrkPO2D6NZRuzW0Xa8TUwFxjRmEA1sxgdAajsupgzROmRpoYL1sGe7/7uP1dzsBYHvdJOVpgjK5GmHNaAmM0NMOc77h4eTtsSjrXcBpAugHlnyNN3HoA4+Pfed+54y0PIYQQQgghhBBCCLE8kKkxSrhCYWj2PIW2NJHQTVcVEoM5O7seRuhrghHWOBuZQltTvO6S+AUY0Y9RHK7ZMuh8BozI3ef85hoAbvSI22b33U9d40druPtza0MkzRb3aYAxABpgcuV3JKy3MlCAvS6h2gtc3gNjbKwBYCqMWEtjKQ8j5tKIYn/Vx9v5aZhyzrrwfvOphTFSgOr0ox99VC6ix4Vps7phxqxbUJqmQFLqKX5OSwlF4X95ifQU0VkXJMkEoclYC3PdGTnF6C5XQOe9R9OL++d9zWiNhnh5O0qNtKSUUyGWl/mXBk1cmkQsgN4HM2Y7YCJnBmDuoZHC8+2DNdT4rO1B8RjLmjZLCCGEEEIIIYQQQowMmRqjgJsKJpTmJTQj2hVofZGRYr+7DdephxE/WXybURuMJOlxPi+Drb3gz87OobhtXQDmw+bw78Tw6k1wfxSla2HESPf3pG1C+MIqI0AYZdBVYTuZWoYiurtvGjDu/kZac6MHRoBOm9lPUZvmVwNMn/EaMy0YC6V3wYrmSbPMs8CUY0kz+l3cPguRlt4ohD/uGf2wGKYPmmGNCP8eCkVppAnwedjURTSXaA5Uo95GCKY8YlSRe8183BRStbB93A2b9qgBtmD1IOz9FYquqYcZP37tnrTIGZcVbWS45GAjz/iPVxNM39J46ApvWvFx3Gck69y8Bxu9locdk/0A3qjCcYUQQgghhBBCCCFEeWRqBGAqF0YnVFJvISlCIykVlfs5NPscsIYGZ2e7kQ2u+EmxrR9G6OPM/04YMZRFcSnkh9pU6xyvD+kCN2fDJ0VnhFJNDcCIjkyvExK/3e3LifKd8Tk1oXJBOg9zvmwHt2cKrmbYQt2hCJLhkJT+p8X5nbPrG2Fn4efj9uVhZ+HnYGfw+6mWQtEL5ahx3ikSh8jDpPipgZn97wvpobFfLgLAHUMc4wMw578YtrgzUFoXhJ+zGBqA6bM+mKgU1kmohZnZvwCl0Qxw2hTaL42FNFiUmgWnaUS4cP/ub40w5837keNhEOYa1Tm/+6Yc4X0cMjDTcFNkrQxRGoDp62bYqLQa2D4KXbfhwPHPe6wZJhosBxM1tAjWLPajzoQQQgghhBBCCCHE6CNTI0AbjGjLdDecics0MJUSKgIOJNefIL7ZQbMFzvoUfxvjdi6CNQ26YXPpu4JnKNWPKyrnnXXLna8rdvqz7d3fmSrHNQdC+3K3YZoeGicUMf02MfrB3UcWCrARAfUw13wsrOg9ACNi+jVEKj1OGjmYPP0fgDm3XhSnAeuCHYPt8bJJ8asWNrrANzR8MylLmymcM20Rawb4UOCnAeGfT5qhERoj7roNKDYJaKyw0HXSuPHb6Z+//zv7eSxMHQbWkBlE8Xkz+qkRNprDNVWy3ieAfZ40wtZf4Njm/cFrSQOyD8bwYlQAa/DweIzqyWpSpK2Xdm1WFsHe7Ss3zVY1/iFzjV5ecxoa4+PlBZhojcj5LoQQQgghhBBCCCGWLzI1PChqMUXNOBgBdDGsOZC2bSj6IZRSKrROOVGRAjxnJ3P2fjdsGh0WCu+DmbmcJXrBN0/cd7dmhj9D3hfLQxEaFKMpVLOAcR2Kz5vHZGSGnwqIv9fBXI+kQtGV4BonEYyoPR5mFngHTI2O4ZhYlVIfH7MZRlBfDHM9c7CmRjdsf7TH64yJ29nv7CupMHaW/ukGsDBuC8XzEIMwZhlNITJcwZy4s+OZ8odRQ/494hpk7jHcF4uq18Tn5vdTB4oLcvd763DGfhOM2cJj18KO317YWgtZ+rg/Pq5rRDHiiemk+FsnbEQJo3fc2hy8HyIUmy0heB+ntdE3n4ZjjI02AzDRNI0wz2amEBtp2jBGvQDF4411XVjLhuaSEEIIIYQQQgghhFhxyNQI0AMzQ5+z1rtgRNz+lG1csqSaCgm8oTRULm6x7u64bfl43f+g2JBwi0gn4ZsTvqjptstPteKuR2GbRYrZVgqjTO+zDHY2ftqMe5o3PlnT5lRKBCNOd8Jc+9r481KEUwRV+9jvwlyrCfHxF8KcP6NvmGKL9MXbzEdxxE6obZWkDYriY5Wr+VFAaWqqpEijpLHlr0vRvQdm7PB7HsUic+hc/QgffnfroYRE/T6YyCbAnDOLkkew6dto3NBgcNM+1cbrFWCLl5djIN6Xe6+7BiDv58hZvwvW8OEzoNZZtxfF9TJ8KonU8fvINZIYoeM/V5iiaRDZ+8GHz41y5gTNqNdgrlUDjMk30sL2TMuXd95psC2AudfGwVzvcbBRNEIIIYQQQgghhBBi+SNTI8YVSxkJ0IXs4lWSgOunl0pLvQOUCou+AOwKoL0wYmceNmUOxcek/Prc1m1Pkhie1s4QTOM0CGsA0cSgQN8Mk06nHaXRAG5ESOj4jOAYjZQvvO6cRb8IpWaCu261jz0fxWltSKjuCLdxTakkw6nStnLWf1bSxnOacRc6LmtGuEaEK/KXG6shI64b1twIbUvDgmOLx3Wjgfz7n3VMWPA7FDXi4p9zmgHli/rclumyaKww+glINzSA8D1Wrp2hNob6MAcT3bQ2zL3zDiqvbVEP80xgFEs5+Ex+F9Ur7k6jjum8WKcjH7dpMcwzaxJskfn5VTq2EEIIIYQQQgghhKgMmRoxY2BELYqZ1ZyFW26WurvMF0h9U4RpnJgGxy1yzVeaWeEe0z12udncaeI1RWNGFfjHWQYjAjNlzKCzvj8rnCl1WD8gqbB2tYyFWhTfBEthZ36HxOKRHpfnB5SaOu5nFoFvjdtHEyhESIAejqExHNKiM7LgRxYlRQkg8FvI7POjNoDyKcR436SltgpBYzGNcv1RbiyzUD2fR4PO8koF9azXJSkKJmmfDTB1iJi2jOZUVhj9kdWspJERqq+TRgPCBdpdOAZpcrGw+7J4u3dhnnFuFJAQQgghhBBCCCGEWL7I1Ijh7NxuJM/QJ6GUP2lppbKSxVDgsTmLvQ5GrHPravgpY0IzrN3Z8L6xUa6NISG73DZ1MFEaAzCGAVMcuSmGuG4NbOQBxWY3pVCSCF4pLIhNo4gGDGeCj4ZomYcx0JjWjLUyfOOKdTaaYI2NRUiudRGK8BlNyqWbqgQ/bZS7P3jL01Kl+e9ZzS+aGhxzIx1XJGt/uAJ5koFTzdouaZEblY6fCDY9XzPs/VSJqdEPYyZW0ufD6RP3mZIGU371w5g1Y2HTUPUhHE3C9GBCCCGEEEIIIYQQYvSRqRGzFMbUYK76JMGM0RFcxy9aHRL9y/3mGiS+SeJHcUSwJgBnElNIDIm4bo2LkEjv1xxIwhedQ4J2kihZB5NeptH53ghjwnCGti/m8rxrUSwyu0YIxdORCNCc8c7izJylPVrQsGiG6QMWdaeZ4xZRR9wuGix5FM9oz5pWqNqEUq2FjIkson7SuEkqbJ3lWoeiNdJgNAQjNKoRjZNk/PlmqA/HxTJUN1osS6Fwv11Z+7odwDwY8Z9mQKUMt89ZKD1L1Ep3BW1x78spACbCPMPmw/xb4UfTNcCYj0IIIYQQQgghhBBi9JGpEcOUJm7UQIhWmNnzedj89r6I58+a90nbv29mhGbfU3iPYIQ6Ct6h40Rxe+vidZPMGtdYcFNeufvx95t1JnoPjBAIWOMo573c8/M/+33gGjEjgUZGJalvRgrrMdTCXBNGYzCqhqZaLcy5LoXJ59+B4uibpDFWjdRcafvgtSt46yb1H8X0cinXuK5vSLiRO0mmYKgvKu2DakRChO6fUGSJa2y4y5nqaFz8fTGqMy4Z9RPBjL2kejsj6b+lsHV9qhlVkgajI/jsGo6ZUg5GZrQBmAybXqsbxWOV/SqEEEIIIYQQQgghRh+ZGg7lZvzWwZgaTAfUCzujFwhHVySZE/7vSTPcy6WDoaCWZC70oXgmcTfSZ4C76arKCZtZhc9B2LoarTCivSsCDmc2PjDyIr3L08wgvF4RzKx81kTxazkwXVcNTDo0P+VNkvHgCurDMTeYho2FqV04/qP4dze6JQk/EqhcmrYaZ12m5fINFJek9EnVMHeGg2vQ+MvKwVROOdji5dWgBcAE2MgDmrHuM8aPNhsOy7todg3MudXCjNV2jDxyy2cAwOsw92wzgCWwReLdfhuATA0hhBBCCCGEEEKI5YVMjQpogKmH0AYj8PbAiFwhwyJJXA3NNqeBQIHMTSWVNKO6EiOA5gvbvhSlgjWjBPw0QqGojdCyclBMXQLTj2kputzPaebKihKuXZgyijU+kmCEhSvW1zivXme9QZjxNRY2zVYdiguXR95nOOu6y0PFzv12ufvIwYi3TQAWojTNTh3MOGEdl6ziO2fTU+xPiqzgfeDeC0mRBf62lYzHcjAiBcgWAZAUteTfK0Bpe93lrI1TTbPNNZLqnfbwOeOmpstiZlab4V43pt8bBzNGamD6rxfhAu7DfV70AXgLpr9c44b3A7BiDFIhhBBCCCGEEEKI9ysyNRLgTNzI+V4LKxL3w6SH6URy+qQss+Y5O98Vy/y0Sklpd5LwBVaeRw9smirWqqDYXBsfl7Pvk/YVagf7KstMZZ5vkgicFn2QFQrS1UhH40dQuDBdUAOMiMr0O6F9uDVLKNz3xtsWYNNL8bccTHTLAEpnnycZXYMwojXTWrEYOWewh87L/U6jZRDha9kft7MFxhxjtFKlM9T9NE0+fg2VNNzt3ftzJNTCCOXNMOe8EOWjm4gfZRW6d8ulpqu2qUATk4Wsed+zdku5aLDRptLnG2FaLaahoqmxBMk1hIZLKIqP14rG23DOQQghhBBCCCGEEEJUjkwND868Z1FmCso0Bd6DMTIiFKciAcqLaCHzIySE+aJ/kpEQOh63Y5tcU8YXq12xnUJy1hQybpqdRhhxvgfpOfvd7ZCyTtrvSSm+iGs0dMTtGS6c2Z6DLdbtwgiKpNoeOe8FFF+XAad9FOPdc2FKGwqnLiEhmoZIPUxh4ybY+h2LUJwmLc2cYp0Wt6189cJGlzTCiP8dsCmNKhXlqxVt46atGikU/fkcaESp2QeEjaFyBqBrHi2vmf0DMAYZx6o7roDiZ8KKNDYqxTVl2PcDCN+rLbDmYzXrXzBCa7jGjBBCCCGEEEIIIYSoHJkaDnlYQZzRGIyioKnRB2NqAHb2e1pO+ix1APzvfuFvf+Z3Ugqf0P6SBGz3N7f9lYqLtTD5+qfA9McCGLNnWWBf/iz2NBFwuOJqHmaGfVv8vR/DFzDd6JOQAD0IOxZCRo4vsofOtzvhuK4Jwv0knYd73D5Y4ZZjtzF+0aBLEttpbvWj2PByjbHmeF9cvxXFYnFfvP1w6ytkSTPF9obSOmUVmNNMhQJMdEsvjEHEWhQuvumYRdBmAfhBLP/6E0BptM4AVm0hns9o1hTqhrluISOzGcboG4jX6YR9vperC1OuDdUy04QQQgghhBBCCCFENmRqODANE4XHPEpn4RdQmoqG61IU9g0CP6LAnVmcVIvBjRKgAOunVMkqplZCyGAJHc8t3twGYA2YfpsEU1j3dRiR0RWYadRUmu7Gj0ZIM41yzjpMqcXUMHlv+ywz5dPE51Aefbc97rhIirxx+yKp1gJQXMCdERVuRI4r0HbBRGa4NVKSBOy0NGB+pAnrfEyIz42GTAOssFsLa6gk9W+a6eD2UVrxard9/E7Tp9w9wXa6ab1oODAig4ZVWkoxvqeZlP71ZQTYSEy7akQZ+O0ejbRXo0037LO6BiZiqAvhcbcMxrAeC2PKdcWvXpjrnJZerBxuPSIhhBBCCCGEEEIIMfrI1HAYgBG6mL6k0nRMDTCiIwtzu2l7uA7fo8DvpA5GeGuOv0ew6X1cQbQahkbIJADCwqB7XArq9TDn3RC3uQ5GbFyM8Iz0NAHd/5y0XcFb5hoJAzDiJlPsMI1SPWxNAe6jH+FZ+MOFx2CkQqh2SBYjym+PO5ucx3D7PgcjzLppoCj4uvvIMl6SUiixjznbn+Yf28UIEaY4y5paKdT3Wa4HTRbWJKGp4xtmoWimPIqjsbhezlk+gOT6HCFzyv3NjyJhBAnNEo5Jt21Z03BVy8QMGTGrmig/CFMvhCnW0uq79MBEkfXDmLDNMCZHN2zKquHCe6uu3IpCCCGEEEIIIYQQoirI1PDow/AELgqgjSnrUOjMknrJrXXBNvlivrvP0Pcsx/GjIIB0cdMVYetgxUE3JVYT7MAqF/nhtztpljvrV9AsCBkxFI97nG3grM/iyKwtwOvcm3CulZCHLaDNaIkuFBcQdgVvtjdppr9reLnnW4Dp8wkw9SyY+qkDJu0XRV7AGko5Zx/umEiLMHDbSxi98A7M9W2A6UuO134kz5RPOk5ITPejNEKwv8fE7eqArXXjH8df1ghgPIzxRlODhgONo4b4N758Q88/hhuN5BoWrjHKaBLW6aDpx22zmEHVSFs1EmOEEQnVqkkxUni/Z6EX5h7phrmHWmCu9UhrmwzGbZCpIYQQQgghhBBCCLF8kKmRkSwzmd0aC0lknQ3dDVuU2RUyfaE2TZiuxNCotH0UE9thhMLxcVuZ+sgXt0MGS5Zjuefp1pfwBXp4y2lYsA19MP3YCyMoM9rBL8BdDte88c2GLpgbqgnFNSy4vntO3NZPyRX67H5n7YoWmFRfdTCpdQZgU+v0wIr8FO2TUjNlNTi4jMeviY/XCnPOjCwKmYI1sIIvz30k0FCrh00bVUDyTH333qWhQBOGMJKCM/4ZfRSK5gh9ppnBSBp3fLAwNcdrK8y1Y3QL12W6q9FkpJEeK6IWSLVgCrcumL5uh7nGobo25eAYdI3HapijQgghhBBCCCGEEKI8MjUykpSGhvUNKhVqQzPKKb66aaZqERbuKa4nRWOUMy1CNQqyGhquUN8D4C3YtEedsHnq/T5JS+WT9DthhEZoO353z80Xt7mMs+PrYPo2a1ROjfcCrFDN9z7YmeDsG8CmO8rF67qz/2ls+GZVUmoywBgWi2GMJNZoWAwTpdECG72R1A88nzSDI83kGIz3TyG3Fda04Ex+XqsaGBOhFbaP3JRfLGZOUyGr6M7IigEU3wtJZh/Ptx42RVpohr8r2g86bQ0ZGjQzmHbObRu8dXmd8jDRJZNgo4UGYIyp4YjrlVAu9dmqmIKqEtx7gfdgF6y56Rulac/0WhhTj9v1wNx/QgghhBBCCCGEEGL0kakxTCiK52ELI2eFZgiNC4qeORSnV0qLykhLXQQUF2pOSuvkfq5UzHTrGbCeA82YcuJpEuW2yZrWqNy5UFyv5Jq5fekW7GYqK9bnoFjqtonCN/uLYjqNAaC4oLhvbPj0w/T3Ipi+XwpjLHXBRInQ1EibVR8aU37UhjvG3GXclrVj+uLj0qjJo1jk57kxrRP7jvVnlsD0SdYxE8GaNv3xfmkWhUw+fmckRQ3KC9D9KE4H5e6TbW+AjcxxU2bx3mP0BtdnxAZ/b4YxfDoBLIRNRTUaxkLaPck++282NIBS46wWxgRsgTWYulFsvrn492YfzDVkZM5om1JCCCGEEEIIIYQQwiBTIyOuoFULUzuhHrZ+Q5btSA2MkEaBk3UpWKic4qc/OzyEK+LWx/uKYERbt6ZCaD+VRGe40JRpghFrWZybYr4747mcUB0yWHwRPanNSQJ21nOqhemzbhQbEUm4ZoYbMdMII3BTFHXTTrk1FRqd9eth618Mxm1hOhsK6mnn0QVjZHDMdDrLy6UwYh8ywqHGOT5Qag74xgcNOeJGPPjXkymrCrApqliouQbWnCg4n3mccteRUQ58iJWr88C+XYbydRjcKBeOR5oTNGdobNbH58br2hfv3x0H/TBjYymMiRMBWA3mOdARt6nfOVZalFWl+PehHxnG73UoLkr/3wrPuQEmamYCzLOsB8B7MGmpQoZnLYrro3TAXNM6mGvfNKqtFkIIIYQQQgghhBBEpkYGOMu8ACM8NsGk0wFshEISeZQK1JwtTHF3YrzPbliBk4KZ24Y0auP9TIER3v4DI9CxgLlbU8AXpSuFQn0HjDBIkdePfvCjAXwTJcn0SEojVK7NrpGSRZRlXnymMsrSJ67YTUOHtU/cNFMU6pnyqhO2uHUdzLWvg+nDHlhR3DeIklgGm+LLT3eUVZD2U0SNgRX9B+J2M72aex1qURxp5I8n39BiFAXHB1NAsQ0tMEYMzZxKzqOA8Iz6pHVpZmQtdM0xwrohrOHBF6/pOJh0YA3xvrtgzIsuZz+tMH21EObe7IER1Aec/XHMZIlayTrGfXOKfetuz8LvjChxo4jc2jNZ75NVAV5L917tRXKxe44ZRucMwPTVAGRqCCGEEEIIIYQQQixPZGpkIA8jWLl5/Jlyyi0OGxJiQ7OeIxjhOIIRQ1thBGW+lgGYj2KR1yUkeNbF+1rDaQcNEh+3rkJSG8sxCFuXgBEGISEwLRokybzIGk2SVhMiy/n0wUYoMOqmElhzgWmDKPxSII5gIzco2vfCFvRm6hq3xgdn93O/SfhmBmDTK3GMZr2mrlDtRnCwXkXeWeamcuIyOL8lRc+wTeyDOuc3wPRPVqMhBGtWlKOS6KQ8TDub4/e886KhwegoRuFEsAXMXUOR0T00wVhDYxns+BsDm/bIrTtC3HGdNQIqVA8EsNfPbyNTagHWVK2L28Z0Yb7pARSbHqsC9bBppwZgzrUd4VpAQLERRJOaqd4iWENQCCGEEEIIIYQQQow+MjUyQNGR+da7YQVfinisrzCAYmEvSWgtwIqaLLTcBCO0If6NaYXKGRs0FFjToRUmEqAVpSKdG2XQHO+HKbSGMwubAvxIccXwkDDO5SGGU78DsKYU65hUWlfAraPgbsu6Day3wZRifTDph2hgMHVRA+xY4ix9iuKVQJE9B3Pds25fiNtFEZvFj91IDJ6jS1paMPf3hvi9F+b8QkYb1/fNrXLXg6YCoyNCon9oH+X2XQNz/7jRGTR3eO3qYO7X1ePP/TDCONfNxdsXYA0DRrW4fVMbt58mShPM9StXqydp3Lt1X0LrusYGn1ms09LrtIcMwvRzK+zzYgnMmGH7aPp0l2nzygCjZtpgzbY+mHHZm7ANo5No5vD+GhPvqwGVm6JCCCGEEEIIIYQQYnjI1ChDHka0aoURvigEUjh0hXjEv2WdrdwYb890PzQ3KDBTSEtKycPvA/HrnXgfTI8yCdY48dvE6BMKtL2wNQ2yprUBbH8AlRff9kkSpNPS5pQzQsrBwt6VmhmMDAhFJ3DmN1MtMSVRH6yon4NNRcXUZpwpzvUqZRBGWOW4qQSaU0xFVQsbWRDq15ApELp+tbDmWVL0QRrlzAcWrHfriIRqsrh1XsrBe573h2tC0eDgdZ0EYIN4vx2wRc8LsKnN+mFTjnXDRr9wHdb6YGTWAIoNRxoUWdrOsembjaFtaba4NUlCRtggzNgEbNQKjRfe71nqwKwsMEqFadF6YM6lA8nPL5p8NKVoSkewBpZMDSGEENVg7bXXxhtvvLGimyGEEEIIIcRKjUyNMlDgHA9bELgAayQwtQ5QmaBHkZOC9xIYsTsPY2pw1jnrNbi1H4ibDgXx9u/BiOWNcZsHASyAnfntttVNHUVRspJoBRZP5kzlpUhO31IJSTPM09pVibnh1tIoF83AlELusethjaAQ7ix5HqsFtp+ZaqkXpu+4nPU1KA5XUhuEMFJouOJyE0waM5oFaTVj3Pa5x3PPneZKaB+sU1EDc/5+lFPo/LlP9nG3sy1FZ3/9ULv9thKmm2qKP9N0Yh0Q1+RgKqpCDhhbC4yvBSb3AwsGzDVmOwcRRwbkgY5BW3uFs/7dlFP9sKI72xmqrZM0znOwdSL6vG389TnuGuKXa/DR0OLYZxQHI4EYVUT87yszBZjzGXC+dyH9WZD0Ww+M4cO0YkIIIcRwaG1txYwZMwAAc+bMwSmnnIIXX3wRL7zwwgpumRBCCCGEECsnMjXKQNGxBqYWAmDqXdB0cEVd12BIowZG5G6GuQB9sKl5AGswUKxtghET3Rn87nEpng4AeDfe5xRY44TFmJfBCHDcbyeK6ydUEqXhi6uNsLPQK6lbkERo+3L7rGQmfj2yiZCuKB85n0OpmNzjuzUkKIK79MLWl2CKMdd0onjPvnSLSPvHDYnVw+1/zvRnFJE7Lv1j+O++ycEoIkatcHZ8DkZEZ3onoFhk9tvjHodGA2D7y41I4HGyRjW4/cQIp0bnOBT8WT+BBoebRqo3AgYKQEsDUFcH9Cwt3n8ewNg80NYMNHQBtYPmfqSJ5b845qL4mKH6Kmnn50eluP3h37tAcXop1mXJo/g5B1jTY3nhj+1qpbWiGcbnOjCy82I6wlXF1BFCCLFy8dnPfhZbbLEFvvrVrw4t+93vfocHHngA9957L370ox+hp0fxgEIIIYQQQrjI1CjDAIBFMOJXM2zx7ZAIlrU4M1Py1MMKvxRombefM9g5AzotJRHTzVCsewdGAGQUAFNNwdkPBfKRFmduhjF7ONu+2ulnKk0LlQTT/dB4Yi2NcgzC1sbg9fUjIVxDg8uZoobmFEVwF84Q70OpwO6eD00AN+WNv06IpN/S+rQbJmqI67mvcoREZ/ZxE2x9EY6bJhQX1E7CPw+aDewPH6YKckVxX8h39+0acyFDgwZkC2wEB1MOteaANVqAhjxQmwf6CkBtDqiLrMFSD6A+rqSdz5n7kmmsWHibNXQosnP81MEahu518E0031Bz0yIl1UQB7LjugjFAx8TvgH0mMS3daOIaOTwW+57PwQ5Up34PqVZ0CWvzCCGEEJWw5ZZb4sQTT8S+++6LyZMnl/z+8Y9/HLvtthu22GILPP7447jqqqtWQCuFEEIIIYRYOclFUbQqpEEfdZpyudSaEMypT5F6uFCgHg8jIOZgoicWwgroTG/kFj9mjQM/zY8bIeIWrW6GFWgb4nU6YQyZkQqDFCBbAUyGSVe0EMZMYd2E5TmoQvUj2AYaRM0wwvBwChk3wgrwgL0ubr0JfwZ8SKjPwRpZrgGUR3lzohE26sFdXm7GftYoDorvFMQp3tP8iZz1fPz+9I9RCzNWmHoLsHU72Kc0dwrOPpLSK7XEbU0r7My2uu1NOnfWPmHkCM0Mjps22Lo6LbDFvRtqgHGtwJpTgIZGYKAfWLjIvDr77X1WD6A5D9TWAoUCMDgAdEe2XkYfbO2bpfE7oziYlqoXpREboT7KOa+Ctzz0mRFeOQATYO5nmi00Wtox/GdGyKzw8ccxz2sMgLVg7935MM+ZLGbkikL/nL4/yeWyxIYJIYTl97//PdZff31suOGGmdZfsGABXnjhBRxzzDFKSfVfiP5+eP+Sy2mOqRBCiOETRSuzQjL66F/RmLEwon8nwuIba2hUAxZNXhp/ZuSHa1IUYNMAhY6b9qdvBCNG9sLm2Kd4Opw/md30NZxBzmUFmJn9TGtVbbLUlfDT7fBzDYwAPQ5GIO2N25kUaRM6Nq9BK4y4zbRBb6G4doZvNrliOmeDc1+M4HALzSedF7fLB9ocmqXvRoz4s/nd7fw+bQawRnxu78HcB25EAs/BFanT3t3PTHXmRrjQnHHHUcHbLgSjkcrVQPDbkLRfNyqA58tC4I0w17sNNoqhGUBjHJXR0AC0NhtDY8J4IFcD9PUBi5dYY4up3jAINBaA+ji3U+2AOSYNK9Z46UNxsXe3kDevvxut4UJzgNv4kQi+scFxynOmWUTzsxDYrlLctiSN3xD8rQGm35vi9vTAPDdXlv/6Z617I4QQ4v0Ljc+PfexjuP322wEAq622GmpqQlNFgEKhgFwuV2SYTpo0CZMmTcLjjz+OgYEB/PznP8fZZ58tMVwIIYQQQrxvCf81/T6kAUawrCu3YhXgjPulMDOPXSPFTSEDFNcQCAnIrqDvioTM+98fH2u4KWQYWdIIm+YKsKmT3oOp49ExzP2Xo1wUA1DaH4SCLaMB6gBMBLAajEidNvh53qxnwvRV/fE7RWe/Ha5h4R6f4j1n3uecZUkROO55uG3xjxkyN9z3cumq6mFm6bfBRpK4tWR4/eu97f30VEkvpudhMW/3ReE/SXwPjSnWKymHey1c/PuItTJ4rRjdRENjjPtqBNpagdYWoKnRGBmdnUBPHDLS0AjU5M35dsGI8J0wZlpnBAwMmIiNhtriNFeMEOEyjg0+Kxhh5d/rSfecX6w+lIqL29PcGACwGCbd3rK43VnNoySSak2k1fjgmGVaPj67GmEMSkbU8L5ekUhKEkIIkUQ+n8fpp5+O/v5+9Pf3449//COmTJmCKVOmlBgaAwMDmD9/PubPn48tttgC8+fPR2dnZ8k+J02ahClTpuCMM85Af38/Nt988+V0NkIIIYQQQqxcKP1UzJq53FDUwfLIjx4qqO0K4FynDnbGdcjM8MVw1uXw0/0MF6ZMYkFzd7a1m94n1JaRDiw3QsQX+SlUJ6WS4gxxzkKniTAWtm8Wx6+QaMsaKg3O8VzTgqmA/KLUFO4BGyXjG1blIhH4zkgRGm6sIcG0RfDWrQTXDGuCEe8jGDGbURS5eDnNFKYk4jmGxmG5yAiXLG0ud26hcUZjgmZJKE0Xt6uDGQ8sAO6aGWNh0sSNBzA2ZwqBNzUZU6KmBsjF6ntN3ixvbQU6OoBX3gDe7TNjpB5W2G8CMLEGaG4wbRgYMKmoeuNUVEtgTM4lMCYIX0thU0QxasM/H99gK5eajLBtPHc+a1xTxH/+DAffXMvipjfApMNijY9B2ALfeVjTJdQ+RkXRAKvEmKGROhwzR/+cvj9R+ikhRIhNNtkEzz33XNn1nnvuOTzzzDM4/PDDi5Yfc8wx+NKXvoQtt9wStbXh4PolS5Zg/PjxVWmvWHHo74f3L0o/JYQQYiQo/ZQAYIRDFqTNQhaButz2rthNM8MV+xgZ4IvGaaYG33tRnSK4EayZ4Z/raMo4FBaTIlFoCoUKcHP7PGyhY6Z6ovjZCiMyUzD2ca9JBJvOKzTrvxbF14+1IXLOO9d1twtFJIQiNNyi2EnF2NPGYeg6uUYPYMwdv26CW5eB55YUjRE6h3JtyjKeksaYa6a5AjzNqCbYVE6u0eS2KwdjatQ6L9a0YR0VFghvrgcaG4H6uviYsVsyOAgMdAPLlgGLFgPdfcCSPnOtamDNos74exSZuhp1tUCuDugfBPoia1bxXutzPg867WUEUBazJ+l3NxqM4zNC8dhyx6xbvJzLKn3uJa3vR2q4Y6cPZlx2Om3sgTVta2HNPkakudE3rbDRHm56s6xtVWopIYQQwyWXy2GvvfZKXef111/HI488gvPOOw/z5s0r+f26667Dddddh6uuugqbbLIJZsyYMVrNFUIIIYQQYpVDkRox+VyuqHbAaFIDI5gy376bhiip6LUr8PupfVbEBXTbTJLMluGIg3TbKLom1YdIOh5F6n4UR5cwrVQzims9EEZ41MFcIxZNXobkmds8XgOM4NrrHDNL6qykvmFaIholPU5bKxGW/euUh4lCoPDfFb8omDO6hNtR+O+N2+D3tXsuwMjHY5YoA78AOO+DHMy5jYl/Y5t7UFyTgmZUG8KFwcfCpDqaAGB8DdDWYkyNmryJsOjrBQYGjbnRNQB0Dppj8Xi9cRvHxftm25oBNNYAzY1mwbJuYEnBXoNumEiN9+J3poFyjQ3X/PTTOLnXLckk4nqMNmI0VqioerlXFvx2JLUvtG//mcjIm3rnxTR7HbD3aBOMGQXY9HuVRpvQPPLbmIb+OX1/okgNIYRPPp/HsmXL0NTUVPJbFEW46KKL8PTTT+PXv/51pv195jOfwS233FKyXJEa/x3o74f3L4rUEEIIMRIUqSEAlM7iHc1ZupQ/KE76s5X9z66hAee35WVo1MDWU0jK658k6XDWO2eeZ5ktnSTOhmoCIGEZZ2/7JhVne/M8/P5jQXAKoDRV0tLlcJZ7DjbXv5+Giuu57+WIYNMD9WH4KYD82fe1MGL9OBjxvA9GBGZ6rjbYouodKDVp/GvI2iGI2zvSMRmKzkkaY1zupggbgK1NUR+/+9EINLfcgtxM90ZBnBEfjQ1AXb0xNKLIpIzq6QW6+4H+yJgRi2H6i/VS2JYu2PRdsY+BfAEYLAD5PJDP2Ta0wKS6aozbSZOE197vh7T7IM3QoAnHvvK39cdLOcpF5OS875XuO3QP06Rwr60LDUBG3+VhU4ElRTuFju3WzqF58v7+k0EIIUQWLr30UtTX+5XQgFtuuQU33ngj/vCHP6Cvz4+RTeaxxx7D7373O+yzzz5Fy1taWnDhhRfi3HPPHXGbhRBCCCGEWJWQqZGAK5KGYHqi4QjNFFeTfuP+KfwlFToebdiGehixtRamiHAlRcebYPLiUyQvV3g4TVj1BdGQ6eP3WVI7Q6nGOLN+Iszs+IVxe5MKevsMwKZwcg2M4V4vmjKs4zES3P6k2F+ATc/UAHON14IxNnoBvAlgHmw9iHFxm5bCRBC4fU6DoAe2JsdICZkbPqEoAMCcU5PTLsDWXuBsf0bW0IRgpEojrLnVXGtSTtXVArk43VT/ANDbD3RE1qzrhE3hlYc1xLphxlAjTPQIa3309gL19UBdHmiNHzT5HNCaAxoKthYEzQ1GdNXBmhF+mqRQ1EySucHoFPZHqCaHb7D6fRzCb0eobVmil7KQZuwyEoXHc9OM1cFcl3IGq28iu+NI8ymFEEIkcdZZZ+GEE05APp8v+e3FF1/Eb3/724r3+fbbb+OVV14pWV5XV4ePfexjw2qnEEIIIVY2Qv8DF0IkIVMjxhesKeZTXPRxBdfhPG4orLr7c/eVlPaExx3tKA3XQOAsZxYM70V2kbIBRgyncBrBCOLljp2UsoaEIlr89ZP6pxY23343imfBM/9+DjZtUdZ+LsDO1HevVzkROG1ZNdOhuW1hmqQO2D5gQWYKwO7M9AgmkoBRDEyHxTZSzK9DcWRBtdoNJF9/woiRHEqjdBiBUYA5d0YqFGCjTDjOmYqqJW/SRNXXG0OjUDCmRl+fKe7NOg7dMGO6A9YYofDNSIso3m9vvO/+CKgZAPI1pi1NAHKRiQRpBDAJ9vrQQGJUAp9J7jMjZEL4n30Tqjn+vcPpJ3fs+p/dvnd/Dx0zaVlatA2vR1YGYcykPIrTj4XuKUZuuVE0LrUIP+dd87mcUSqEEELU1NRgzTXXDKadeuyxx3DBBResgFYJIYQQQgjx34dMjRhfDGOueRZUDkVWjES4TRIGk0Q/n9H0b922cMbzEhjhNUJyce4QvTAz1SmIl85ZKz1uqB1Jv7ntDAmaoWUsMEzDphNWJF4MK+gPZ0a2m3IqTcRN+z7aDMBcE54nIwoWwZg9TMX0Fmyqni4AC2AMqlBUAFNUMUphNAgdNySiM/qgG7bYNmfp5511umCMgsb4NRYmLVcrjOhfV2PSTtXGD4HBflNLo7ffCuAdAN6A6bse2MgQjh2aLG7Rb7Z7cDB+8XvcZqYu47b5uH2AjaxJMs2SvnN9CvpRfL518fIuZBuX/v3kFhFPigopl3IqyYhIWs5/tBjJ4j6baWy59y7Pl9fFjTRj9EuW+11pp4QQQpRj3333xcknn1yyvFAo4I033kB/f1KsdnkWLlyI3t5eNDQ0FC1vbm7G+PHjsXjx4mHvWwghhBArA8OdNi3E+5O0UgHvK0IifT+qP+s8y6z9UMqiKOGVBdbESDMU/Da6InE/zGzxJbDCXtbog2UAXodJZbQARjwtd+ykthC3ULK/XsgI4TsjCVj3Ig+bhof764cxNpYi+3VPS4+URKXXsFowmmQZrEBOobsDwLswabcWo7j2wCBMn7wTr9ObsO9+VDbbPit+BFPS/eCe34L4tSh+LYYZf4OwaZ1Ye6EexswYE79aa4CaGlMUfGDApIvq7TO1NHrjRtBoWAjgbZj+YR0MRrjQYCk4725UQS5nXoi3YX2O+fF+e2DGKYV8t1ZMWjSX/84/jRhVw4iiYlmkNFotzVikkZFU18Ldrtzzwo/SqIcx0JoR/keKxwzti/e1b3xyOYuL18EYO81YMfeiEEKI/y7q6+uxxRZbIJcr/hfqH//4B+666y589rOfHdH+L7zwQjz77LMly7fffnucdtppI9q3EEIIIVY0WaYECiFcFKlRhtEQaEMkpZBx3/3PWamDqRPRAxOVwHQ4IUIREIARATnTuRyMcAGMAMsUR1nTy+S8V4SwOMp0Vm5anaT95WFEaxoaTAtEUd+NPuFvWdpKoyQX7ytpFj2NmFoUC9wjNcxoVFWyH0ZquDVAELenI361wYi9NKFY/JxGx4oQgEPXmMK6f9/0OL8Dxf3DsUlBnoZfHiaaqDUHNDWYdFNdXUB3T7z/yERW8NzzMPfVFJj7yr23KK7zM1NG9cGmoKqvMamtEAFdfUBfZO6TDtjC4xFs7YsobisNpaSopFCkBpdxPzSwGEWSlFopyahwi7LD+c7f/Xs1h3CdopChAVhTYyBuaxesYcZrmbQ/FgZ3n180c5h2KwdzDWphzKPspVqFEEKIMBMmTMB5551XsnzWrFm48cYbV0CLhBBCCLHq4OdMEUKUQ6ZGAlmKwg5HUE7CT1sUIi2So9y+KUY3xvsPFfsOGRoUZ9vid+bfT4okAayBABhhNqt4n2ZKJPVLWqod3wDhPvrjdrmRCO75VGJkUazli0Kvn96mAUYwr4M1FViXYrj/XNXCRBUA5nwqNTZCY5vLWDC7Bdb44XktL6MvhH9tQ/MYyt0fnP1PYbsFdvZ+LYCmOqCuDujpBnoHrOjPVHR9sNdvAMDqMGPp/7P351G2ZGWdPv7EdMYc71T31q2BKgqhLEUKBIVm+tIOvegFTqioS1SQbm1BkBZsWb2cQFTEgVK6EW1QbBwBbafW5qegtGA1IKCgDAXUdOchp5NnjojfHzve2jsjI86QeTLz3rrvU+usc05EnIgdO3bErXw/+/28D2Cu58DZt48VM0TQkN/5vilC7mcReBFPJNsDbMaBiDfyzMmPdcG9b917Ji8AtDHZJSKyFZHvR1esdMWbvJGG9G3+tyIcSeaFiGRFx5d9S/2bCFvjRrJgxFIsf/yisS1CV4q5/ouYZ9ppiq23FEVRFGVafuInfmLbsr/927/lgx/84P43RlEURVGUqwjz17uHl/1tqn+hKsokqP1UARKormADnkW4s/R3gwSKJfjvWgK52+z0sTbA2O9sYGaCS/B2EuQcZaa+ZBiMQyx+ytpcZB/lvgtFAkWecf0v/Sv1PVrYQPVusw7Eo1+Crfm+STDj5zCmXsM8JtgdsXskyFw2PsdRdN4y093HtHUeG/gusybaL9xMjKJiz2XL8/uQ+gkiVEhhcB9zHftxtp1vRSqxhbqIsZlaBT4L/BMm44FsP0NMxkaXrdk4cj/E7isx2SAA1QrMhaYdcg1CbL2H/HkWZW8VLZf+kGO667rYIvGjREpBBIZ5zHOxgRHVlrDPE+nPitN2ET5E7Gky2lqKrG0XMf3cy7arY+8fL2t35HzPt99FsnBkLEt9FVX1FUVRlFnxDd/wDduWffrTn+Zzn/vczI7xwhe+kO/6ru/atvx7vud7eOITnziz4yiKoiiKsr94ajmlKFOjMZ0CJAhYB45gAnTnMMKAixT43WlyWFGWgCCWSPltd0pc8lko8s8XD/oQO0taAt6wNcjszg5PMIHdaQSDfJbIuH7N2w3l95HfFuwsdzeTYrfIWBm3XoSkPqZvNhgt+kzCEBP0lQDvLBhi7Hg8TMBaHhC7aecskicr2HHoWiXtZL8iHMp1aWKtiDrApRj8TZtxUMFct1XMdYuy7U5hal/MY0QrKQbuimX515YC1ql5yYJKALWhbUtepCkSOmFrJocsd+8FGe/ub+Xc3MyPsr4UwbWKETXkuRA7/XYm65sK5rkpvxtinxvSN1H2m5rTZ3lijPjoY0USsZSS50+a/V7En1EWUlVMdoYIWl1nX5rcqyiKolwtvP71r+fxj3/8tuV/+Id/yMc//vEDaJGiKIqiKLsni4B5ZEECRVEmQUWNEiRwugjcCBwF/gUT8HWD5FJkVjzfwc5QlhoLQlnQ3bWeytsgSdDNZZYCR568ZZMELTfZ6p8vM7Dd9rjWN+MYl+2RFyryIsY0GrZs6wbD9zOQ2QEuYESCLsYqahYe/hLU3Qlig1U0Q7+FmR2/hrXrcq/XpH0nQeN8pocEyadBBIjdXjPJrBKhQrKixB4MzFivxOZ7PYK5JnQHMNg0979ka5zH3Pe97DduNkT+/Ma1O05gkAkaRzBjZhWbUVQk5LjjOH9vuNsUZa+IXVpeGHRx75th1qYBpg/rzr6l6LwUaQ+xfRlg+lrW9zB918Fmf40icX6bt74CK06Nex6I1Ztkh2xiruVeFbZXFEVRlL3gyJEjHD16dNvyVqtFv68VohRFURTl6iQTNLZYUCmKMg4VNUoQy5QeWXATE4wTL/0GZoZ3PdvOxwTqwmx5kC1fZXTgWQJqEWYWdIwt4iztkPe9SkZzMy/yGRNuYWvBzT5wl7nv0zJOtHBFDjeQO42IInUKRtVJ2QsS7NiZRXHw3RJixm+H8rEpY1dmx5fVKoHyvpRMAPm9BNJFSJgGsXLa7XXzsZZqC1hrsMXsJeNJ7ukwgXod5uaMLdVK12QlnGPreGphLY1Siu+pMlEnSY0NVZzY+0qKtLsFz/MWUrLPMkFDfldkIyXbl91rRe2X8xRRCIzwtYIVdAdYwUIsrqTot2S4rGD7DrYKX2XijdTR8Ng+dtzsjbJxNczaKsftMV5QURRFURRFURRFUZT9Ie+JoijKOFTUKMHD+uN3MEHGJeAkJkAtFiwyk10sVsSuSYri1rA2Q0Uzq13EksXLjuEGI/fqsTZqv1JcuEN5Md98gWYXsYWRjJVRAemyWef5dubtd4rseIrW561/BAm077XQIYHZKwEJTk+SLTNk9Kz+UTP9pc/d8w6xQexp+3wW10cC4E3gZoyw0XTe6z54PgwT6CTQiuHMZahVIM6yNyR7ScYN2KwteR5I4XHXyk0EHiHBiBl+NlAHqbG4u4zJItjEZpOIqFNUWH6UqJRfl79fy7JuJBtL6unItZI2SX2KTbaP6wEme0PGTz87zmFMH8s+RDyRGh0iPMq5SnF1aU8ZbhH1UVZsYjs1C3FMURRFUVye+cxnUqvVtixbXV3l7rvvPqAWKYqiKIpytSD1NDw8UvVHVpSJUVEjo6jwsdSGuIgp8hxii+JuYgtES+FpsIFC+bPGLUws64uKZMeYQGCN/bNDcduRP3+x+tnAnGdZ0Lros1DFBIulHsFOMxTKAuejMjryjPo3wb0+e0XAVvuknVgwTUo1ey+z1hGxIY+bkZGf7Q/bg/Iwul+LjjPAZETUMPeQ1F7ZT9x79BgmoC5tCD2Yr8Mghm7bzO6/1AOvZ/qhjREvlrGCn2RWLGLG+hpbBQ0JuEtRd6kP8lBtiNRkafSxtlYX2VqXpihTQ4rNu4KhnFvZ/wflMx3ymSTu9ZdjSbv7mGeBWDlF2b4C2JbJ5QodcXZeQ0yf5y35api+A9Of7exd1k8yPiYRyNz6IbNG59IoiqJc2/zn//yfWVhY2LLs1KlTvPWtbz2gFimKoiiKcnUggkbeL0GVDUUZh4oaGWVBqS7GaqaPCUL2MCKG1B2QmcWCzFIeYmdru0G//HHcQLdbNDdv5zTrR5o7+z5fZFhmaIul1rhC2GUMsTYv+e0kwJgPhrrZGq6nf5HolJ/1nm/PJJZYCbsv2F2Ga7tUcZZJ9orYUc0aKazczR1Dii4HmOsiY83DWqzJNcsLWW59l7J7ZdI+dLMA8uM6LwLOGgnY9zDiQYQJqEdk92sMlUyhFKusFYzAIEWuPYwQItkmUkOiiq2BIdlNbr0c1/oqAsIAPM9kawyG5nr1nVcHa7nkOfuWtlWwYseoDCYhLw4WCVhpbpk838LcNpJ95WHH1DibvaKsDjmWiD4xJlNFxu009+W4e6koi8OtrzLtMyBv1afChqIoirKXvOhFL+KRj3zktuX/9E//xO/+7u8eQIsURVEURdk9nrGL8LK/KlOt+qgok6KiRkbZrP8hti6G2EJJcK4sCCa2VX1sgFksVVy83LsE8UbZyZQ93qYRPdwZ5HkBAWyBcAlU7tR7XgSNfHAyxGRw9DDBy6J2SwC+jw3aTnJ+bl8K4363V/q3ew4yflyRQ+xwZn18CagXBbtDbEbBavZeA67LlncwQeUBWwPA7pgZxSTnIsW1i7JVRExLC9bNAjmHGJNRAeZeXcCcfwVoZYUXBlgbOhH3atj7Q4plyxh3rbq6zjoJ/LvXw8P8P0uawmAAa7EtIF+UJSM2Xm6mTze3zTjK7NrymWN5kakoQ8elytax3sutd387xIzBENtvIuA0sGLfXohaRc+xCqaW0QajRZk8+We3a8+mKIqiKHvBYx/7WJaWlrYtP3/+PJ/61Kf2v0GKoiiKoswML/urUvMzFGVyVNTIKBI0JOAms6SldsYkDxnxh08wgTMpnJs/Xj5Doix4n1/u1oIIsmOI7csoESJvNyPLxEpGAntxtmyerUHaaSmzOcoHaaU9Mmu7ip0NL7PkZZtxFkiTZGjsBzHbM3mknkQ1ew2Zfa2NQck+U0zwuI4dP2D7UzIIepgAe77dkwRuJ7UKKgta5++LWSI1XkRYAlvzwRUc5JptYse92DHJfVHFjskhRuwcZL9dzX7rYYWHavbZrRmRJDBMoTM0/b3hrE+z38xlbd3AZIe5/TarwH/ejqqWvVxRRgTQfm77WtbGCCNKxJhC6lJTI2Arsp9Ktr1ktVzAPGu67L6At9QCmgTJEJl0vBWNT2/KfSiKoiiKoiiKoiiKQSIU1obqoGNZinK1oKJGjrwlS4z1zS+yQymr9yBIUDAfOHSPlc+aGDVzWkiwvvwiAIj44Nb4GNdWd5kIOQkmKBljApZNyutq7IQB2wuhgz1vsZuS8xuyVVQpa8e01lP7QT7wLH0sQeuU7cHqvW6P1CsQ4aMDnMX08QJ29voqdpxFzj6KhKVZMcQKDzshxGYY5W2WpK6JvCSgHTnvUtBbMlVcsUNEOMlecK2a1rEZLm7Gldg0uTZ0ks0QxvaelWLgEtCXuh2N7LeX2Vo4e68Qa63jWRvFek/suciWSx9UMSJZE1NvqI8VZ4bOPl0ki6iW/b6NEYFmlaExqaDhZu1M8psywU2uc/65rSiKoiiKoiiKoijlZEKG5xsLKjj4IJaiXEWoqJExKlArs7TzhNgiwy3KizK71kPkjlNUJ8JdNio4L8HGOmaWswR0u2zPgsjvN5+xkc8YkJnqezUDuUjQkALXUtdhHRtMF3HDrf8xbr9X4r8FUs9BZsTv9+xu9/ju2Opgrv8aZkyLSCbjWupggBX4isbpNLPki8jXoJkWCbZHWZtEsOjmtnHFDcmcqWCuidR2mM++S4FwqQshAXk5jggTrvDnOfuWZ0CQ/Q5sNo3cX66gIm2R38r9vB/Is26AGRNultQS5lnTwT7TpI1gM1Wg+Dnmfhf7LDmWCB3TIrVZZIxOuw9pU4Xi2j/ucYqQ7UW8VhRFUa49br75Zo4cObJlWZqm3H333QfUIkVRFEVRrha87K9nL/NuST0PUv3rUlEmQUWNDJlZ7QYj8zUJ8rgFoKWOQRFlgkY+0Ff02BpXK0PECynyXGTfMkrQcNtShGSp7DWSwSAD0q1DIRY2kxQO3o2QMcr+axTT1DMBKyL02RoU3i+kVkT+uJJV0MUWvpbtwd4XgfN7953csp0g2RM7DeK79mshRkSIsFkQ7n3ovgLnFfmQJnCoBrUKzHcgHdhr1sqO03D2X2HreUcYYUj2CXZMSxaIjGcRNSQzyQ3Qi7DQ3kWfTINknYgllzzTBlnb5jDnLX3hYwUQGTv5Z2GRYCxWdwNn252Om/xzeBr7KhFVRh1/Usu1K1FEVRRFUfae5z//+XzlV37llmVxHPMDP/ADMzvGbbfdxpd92ZdtW97r9fjjP/7jmR1HURRFUZT9xgganpf5QaR5rxdFUcpQUSOH1LZoYIJuo2pJSFHbstnpUjdB7HDyRYCn9XEvo4u1fGmx1fZlXCaILMu3S4LXUqejaLtZIrPZRaRxA4X5vt2LNsisezfIOQmudVgREuyW4LZkSkhgu+h67DVu/7p1MiTILhkJndy2Ur8FZidi5JFA+W7+CXczB0SUczMoygQ+eY+TrKB1BMvLEIQwXINubO73FlZoSLDZGmInBub5IVZerrgiYsUA+zzIZ8K4z51u9srXsthLJDMlv2wTWwujjhnH0h9dTPvFWssVbEZlwBVlk00jLErtIrmXphUX5V4sKlrvtklRFEVRDpI777yTZzzjGduWdzod3vSmNx1AixRFURRFmQmeB56H5/mk6ThvEkVRXFTUKEBmrI8LWLvBv6JtKsCJ7H0VuMR2T/z8LHfYHjAmtzxPFzjP9lnH+WMUiRny7rZfLF1gqyXLuPohu2UnWRKzQmbLe0wnaoxrc4oJAoulk4cRoNYx1+0gNfh8MF8eBiLEuVkzHaw4U2Q7ld/XTq+jFI6epUgSjVifvy9c0aHXh/U1GMawFMDR2NS2uIyx6boMXMT0UxtrHRVggv41zLWfw1pZuf0q2Q6ueNHDigVgLdd22x+jspAkS0TOu+xYUvzcw4xlyTLqYca04AoTRc+ecTVZph1DkvWxE8rEDGlHPhOJgu+KoiiKspccPXqUn/zJnyxc9z3f8z373BpFURRFUWaNh2/spzyxo1IUZRJU1MghM9d7zrJRVk2jAm9iBTVHcfHjvGhSJjrA1kBhPsDmFn/OtzNvs1NEUX2LhOKA315maxwkMmN+1ueWYESteeAwJtjdxhTmPsv2GfEHhdiuRZjaCUuYIPwAuMDWgHjZmJ1V1sZOf18UDI8xmQQwWtyQ3wdA6EPDg8iDlXVYSyFJzbU7himefRGbUSFIoN+tjxFhrv11wCKmj3vYehKSwdUCVjBil9TmcF9Sg2MnRJisEbD2TCICSFZWFWu3V3Ycec5UsDZfUmOk6DdlwmqZaJv/7ZXwnCmyVttrcVdRFEVRXGq1Grfffvu25Wma8tGPfvQAWqQoiqIoymzIIjGe2E+ZrA1FUSZDRY0cAbYodVGGwjSBrAEmg0JmNLsCQVEGRtG+84HiUSJI2az5oiBiUXAxdNaNmsH8cCRma4B61kgB7Hr2vsLOZ5fvJWIDJuOjj7kXiiyh3LHkJknu1j5qGkLsWBXruHz2i3x278H8vfPQ9anCwpwRMTptaCdGwEixdRuk9oVLBSMMyH4k+yHM1i0AR6pQiaDVhYtDa181wIy9TWz2jtQVcc9tJwTYTCH5vu6sT7EZIzC+0PYQk3UWY/u6rIZFkWiRfwZNkgV2UAJHUaacZmkoiqIo+82HP/zhwuXf+Z3fyX333bfPrVEURVEUZZZ4nsnS8DwfUpO1oSjKZKiokeEG1coCe5ME1vIBuDbGpsbdv892QaFs30XrJp3d7GaCJFjrGwn6FokjD+dZyHkhJ3+OowK68s/KtMF6sShbyb5LoFpmuF9pDDBBaxH3ulhLoqIgtGtV5ha4Fjsjt9j0rAkxwXq3loO0q+iYkqEQUXxPhUCjBlEFul1Isp0EmOu1jhE4pJA62FoXUoNG7jHJ2Khi+6Jeh4V58Feh3YJeajNkJFtC2ij7FdHDzRybBsmiaGNtropEiEnHoit2upkLkzwvigSBUQKB+79yB2lLN+q7oiiKouw1YVj851ocX4n/J6koiqIoyuSYvzA9L8D3QpKHjPAVRZkEFTUc3Bnn+SDaKNEhv146VYK6l3L7Lvr9uHYVtUcCimWZIHlv+xAzYzzEzM5uO+vK7KnGzUzez1n5u0GKv1ewdQC67H3bU0xAXApDb2IC311M0PtKydZws3NSTM2IUfMD8uNPakn0sZkAEtyXGjV7EZiWrIgKxi5LgvhFx5Oi0q7o4BamHwCdTD2IY5OtIaKNCFGSzSMZEBHmXqphtpVMDcnQEFHDA3wPqlWYX4CNDvQydUGyHiQDRO5leX6UZUJMgpttk+xgXx5bC5y7fVrB2uqJWDKpuCH7lu9ltk7Sn9IX+fV7hYwpsQgr4+Eo/iqKoihXDs94xjOoVCrbln/2s5/l/vvvP4AWKYqiKIoyK8zfwT6+F+J5AR6J2k8pyhSoqJGR938n973MFsWdid3HBODmsuVdTIDXnUclmRr5Y46i6PjyW9cvfxPrx+8GCCVwL0HHSvY5gEIdOJ/tcbXjYYLOS5j+6jG6IHIR04gf+awOEVGGGCGpypVp7yVBeTBjo0iEy4tcYtUmmUBtTJA/wQakRRhwA9OzQLIY5DhSEwPKBSOxGZPtfGxB7xrQ6ZuaGl5mZVkUbHcD3nKe7rm6okYte4VAuw31Gvg+VEOoDq3Vl5yPm+2SsrX+xU6QfewUD3MePua6Dpzl8iyrZC+59kVtKBMwKPksfV3BZMWILdoQ+4ybBXL9ZX8iLDUw9+k6W58VeTEPrhxhUlEURXn48V/+y39hbm5u2/L3vOc9fOADHziAFimKoiiKMju8rJ6GETYgQQuFK8rkqKgxhlF1NCSw1cRau3jAIUxR4ASTpXEZIziU1cwYF6ArE1nk+MvYmeaSfZAW/HaIsUHq5I47jU980ez3q4EUU4xZZudPGxiVG2WSAHGZHY8UWhYrob3oO5nVPi0S3BWhwN1HmbDhYwPOG9igs5y7BOYD53ezRKy9EkwAOl9DJo+bDSFIke4G5p5N09E2SXLPSS0Pt06K7N+tqSECiA+s9WB4CeoVGA5tX/ex2SW7raExa6SNco5yfeV5I59FGMpnNkz7bMnbmi1jCq3HmGvdyvY/S1FjDpttJIKvZKDJtXDv1SDbdg5z3iuY8X+lXDNFURTl4cHznvc87rzzzm3L7733Xt7ylrccQIsURVEURZk1kqnhewFpKtM1D6qypKJcXaiokVEWfJtEcJAZ7a5nvwRJ65jA1ymMsDGqpkNRW8qEEHnvZcfvYwO8o9o9zNohs8uLbKemrfExKaMyQvYSEZxEdJpESHKpYK6nBO3HMU5U2EshSOyCBuM2dJAZ9yFbA8ZFNTTy9Vik9oNkYbj9KllLs55d7+JmSFWy97LjiHATYu7LBiZgLlkaFYxFlG8mSzyEe+5Frwjbf5HzqmIzuURwHPZhOIBhamtniBWa1FuR8Rmzd/02DT2sLZaLCBuSrSJCzqjMhVECh9vPEeb5eR0mE22AFWRnyRAjOoudmNQ1EVutoky2CCNcH8aMo2rWvlm3TVEURbm2efSjH8111123bfnq6iof//jHD6BFiqIoiqLMFM/LsjQCfD8kSWM8tZ9SlIlRUcNh2uChzEAXYUGyJC5igmG3YAJyixghoc/oIuRuELks8J4PiA+B+5jMZkZqb7gWWEXWMHtB3sZnlgXJPUxwsT1im6LMlUmRoPwosUIyFxiz3TRIVsA09SimsRpyx1jf+T5KSHO/J1irqZitY6cGHMUEfwfAeUwB8t1YIZUhbZGC3WIfVGZ3FWbtWwROYILTc5iMqzBT+qQGhiv8ld0bYjcl71XMeJSAt9TUENEnSa1g0cXaoUntETc74ErJhBoVsJcsigrFYpqbWVZWO0PuH7GzmsM8O6tYsaRL+fgk+41k70yD9LtbaH4Om4kxpDhzSTJ1lrDiiKIoiqLMAs/zCIJg2/J+v89gMM3UFUVRFEVRrkzkr2Mf34/wvQjfG+KNrG6qKIqLihoZOwl2y+xsN2NC6GEsScAEvhYwwkZrxLFHBZTdWcz5beRPmwAbhMvPuBevf5x25vc1qq7Ibijz0J+VsOE773sRBJbgvYv0qWTpVDDBzQAjavXYPRJ472EKd+/FubkB2j42iD9qW8HNCpL+cAPYEpyvYQSD1Vk1OocIepLdIPdlBRsIByskBFiLLR8jvBypQpKdkJ/V1JhE4ZP7Kl9HQ0QNqdcgAXN54LpFwAfOdzfrpctsa5DsJQOmzw4S4UauVwObLSFWUJKhMcRY+ZXZ+B3FCFRyr6wzOrMqyt7zgqF8bmWfm5j7egV7Tw+cthzG3KNLWXsVRVEUZRY8/elP5yd+4ie2LX/yk5/MP/7jP+5/gxRFURRF2QNMpobnBQReROJJ1U3N1lCUSVBRYxeMmlHdxVhONTCBL/HZ9wq2HRfUzxdmltnoCVtnEctM9SXMbOWNbJkEVl2rLHff+QyNoln5O2U3j2MJlI8L7Eqwej9ntUsAWwLVMjvfrSGxk33KjH6ZBV7FCGLi3T9LOyKx3PGxhczz1lP5d2GUEJdiRKDLWGuoTcZfx52IalLLQmbbd7L9zLH9Ooi4APY+uogJhgchpI5vkocVNry0vF6HWxjctZyqYouER0DNg0FqBRixnpL717Wdcvt2r4S6aZAslHzGwm5xz0ssnQ6x1SJO6tBIbaKi44uYsIbN0qlhsoOKRI0Qc09JdlcL+1x0i7S3ss91tmdiiSAlbZV6NIqiKIqyW4Ig4PGPf/w2+4lPf/rTrK+vH1CrFEVRFEWZNR7eQ4KGT2CyNFTPUJSJUVFjF4iff5HNjQR2B9gAp7tO3sueV0VBXQms5YvytrDFbWWbKiaAKxkbbtA0L2SMe2buJohelgky6W8nCYRPst2skWCp1K+InWU7NQVYwFzXTYwg1cUEauey48xhBYhZnG+EmYkuWRrutcqLX2VjdZS40cHW3JhkDEnx7UnPzbUscvs/wMyer2FEC7F+kzZIdoTUsVgDzmyavm9WsvP0jKjh2k+5WSzuvSaFwytYQUMsk2qe2SfAas8+L6SWhms95YqjRQKTfN5JXZidIllmc9hxKddz2jGYFzhFJBAxb4Dpj3q2bgMj5LWzV9nxJJtjHWtfNaq2h4jBAaamSpAti7LjrzvHkmwZ+e5ehyFbheNZZGcpiqIoSqPR4A1veMO25W9605u45557DqBFiqIoiqLsFb4X4HshgVfB80K1n1KUKVBRI0OCYTIbeZKAYcL4QFaKCQZK4FJmle+kxkO+/oFbqFiCoj1sHQYJtsuMbzeoWBawzgdL8wLMTgKpe1mrY9Z1LCYlZbvlToDNtNjpPqU+SBdb/F1soSQjwC0ovRsks0EC1JPUOikbJ2773W2mEShgutnukvWQH5dN4DgmGD8PXMAEvcWKSjIjRJAYYILTEZCm4PlZsXBn0ObHr5yXazsllklyr0XAQgOaTej1wOvZ+1Subx97n4qokWLFIDl2UU2aoqyvWSPWa26h8KIMKhFxUud3g9xvigQN+ZxgMjFWMP0o2T1txp+jjAMRtbpszaTKi4wJ5nr3nd/PYe67fGaH3Od5IUneBxhRWQRsRVEURdktr371q7dlaXzkIx/hve997wG1SFEURVGUPSGznvK9MKupEZiAhKIoE6GiRobURBhiglRuUHEU4wLLMSaAJjY5bqBvJ4hw4TrtufUdJHAbsN3Oxm1zUY2LsnbNQpRwg5vust0G5vMFqg+SaQP4YM/fZ2tgVM5Lgt2yzTTHGYfMNHfb4bYxn2VTdr2KLKvy68YhgelJty8bR1LfROparGH7MsLO0u9is1Fi57e+I2h43lbbIbD3WJetQlMFK2j42bEWfDhyxIga6+uwsQntofltB5spIlkmcp1leb7/i855p0LjpEjGmZvx5bZHzv0QRoyIsbWD4oLtiz5H2T46WNu83ZyXCFdScFwydVzydXKqTnuK9jfq2SiC9X5kziiKoigPf775m795m6jxuc99jk984hMH1CJFURRFUfYCDw/fCwioECAWVLOMcI2L9inK1Y2KGg5ugWE3ILdbimpu7HZ/+SBnPuApNTxkRnrRo6xIrMivzwdOd2tFtReP0qvp8ewKUQ1MgWOxnLqIDR4XCQWzHkfk9pe/zmX/lOaX5+2RYGfXZJLfjMo0kn6TWftd4By2toKbbSCZKQ1MQH4eqHgQRRBVzMrhMMvWSO3xJLuli61HIvea1EBpyqsGS4tQqxuxZHUNLm3YOg5uEfPEebmZYq7dVZGV236N/SJ7PWmHCKiSqSRj1R1DRc8ZEYyamP5vYzOHdnteIv5OOselixFT0qw902ZeXE3PIEVRFOXK5Zd/+Ze5+eabtyw7ffo0/+k//acDapGiKIqiKHuHh4epqRES4XuzDNEWeSwoysMLFTUyepigMhxMjYZpKQoiB87nBDNTXWo+5IOMRTPq80FTCaTKTPcrIXCXt2C6mnAFjQpG0LgNc51amOyC09gZ5EWZD/n9zeqa5G2l8oJZWTtGHX8v2pf/XHQcuZdXgFXsLHr3nq5gCoQfB67zoOpBrQqNOtTr2TE8qPeg0ttqLSZWSxE2y6CCCYYvAkeA5QocPwbLh8zBO23o9qwgspm9RISRug2drP0iFkTYDC/3Hi7KojkIpOj5GjbDbVSWW5HI0cecdwhcD5xlNs/fmNG1ONx+G2DGio+1opqlsK0oiqIoo/B9n4WFBU6cOEEY2j/P0jRlZWWFS5cuHWDrFEVRFEXZCzwvwPfDhzI1PAI8Lxj/w/F7xiMALyRNZ2FerihXJipqZIiP/dVEmY2UWwg3ypZ1ctuMs4GS4uYSaM0Xkj4oIsysbplZPWv2qlZBPrMmwNoVgQ0Ou3Y98p4XpGT5LK/HOFup/Daj2uJuL7Zou2WSBExpRx8jDsn/ClQx40XwMfU2ahgR4tgSRCEEAVRrRtjwPGNl2e9DrWf2IVlcNWxhablP6tmrAcyFcOwwnDxpMjXaHegPoDM0gX/J0uhhs0okQ8Mt0O6OEcneKMrg2c/7UopquxkmUh8k/xyRsT0qq0xsoPrYe3sJU6xbhKjd2FCN+23eWizNjr0XWVGKoiiKUsbtt99eai/1tKc9bZ9boyiKoijK/uDhexGRV83sp2ZRT8PD80J8r0qcyFRKRXl4oqLGHiOB1f3O/BAxQgKiA+zFLhM03Pc+JqAaYjMj9pJJg997NUNdMlMamPNtUX7OOzmum6URY62RYkwg9zJwiXJhzbUhcoWOvUIC5uPEhHwWR5EQstvrNOk/6+74FVEvxNafCbN9VTHXGbKA/ACWl6FazYSNSmY75UG3C7UNqA2NeNHAihGSpVHHZGnMYcdPkhhBZDCANDGZGiupucarmHvTtZuSe7TnnLPch24ti1mJRDulbNzl21Q0borqgri2apIRJn0JxjrMrX0xLZNkEuVFlnG/y/9eURRFUXaD53l81Vd91UE3Q1EURVGUfcXD8zx8LySkQpiaQuG7q6jhG0HDrxEFcyT9DukV70OjKDtHRY09poYJfrXZ32CkBApH1dAomsksj7s+JtAutjt73fZJg4NidTON777USnGLMefxMMHuRUyQ2sMWmXYRO6BpMlekz6XuQh8TwF7BZJvUsAWHoVgkcN8lOL9f42mUBRa5dUX1Ndzl0wSBy+q9jLPlCjHXMH/NJfOhib0vW8DlNix0oNkwwkalakSNJIVazdhSNYfmd1XMdZTxIhkaIng0PSOKpCm0siIN6xtw7wW4kJosgA3MtZZaFCJqiKCRD7LPosbErJg02O9+LhIyZJ2bmSJ9MIex8PIw1+8CO8uiy9chGddWt42T7FsESkVRFEXZDUEQ8LM/+7OF697xjnfQ6exG3lcURVEU5UrFI8AnIEgjIiJ8QibzqSjCCBqB36QSzpOkPa6cSIKi7A0qauwxA0wgVWxb9oJRAd7AWe+P2K6sjsheBu0k6DhN0HYnNmES5JYZ9j7bBY4k2+8w23Yu+97Otpe+C5ku+8DN0PAxAXHJJJD3LrYvZNu8mCFtzIsfe0lRnRV3nbusSMjIizNFNlZlFGWJFO0r304p3B1m70NnfexsI/UwALoJbLZgcQEaDQgDI2h4nsncmJ+DTh+qA5ud4bM1S6OZfa4Epti4WFetrMD95+DejhEJe9hAvgTFRYAclw1VdJ+X2X5JO90C5EP2fsyU2dvll+czgeS7ZKyAEYlOYs7jPKYGybRtqWAtvoramW/fKKrY+idqTaUoiqJUq1Xe/va3c/vtt29b91//63+l35/s/1hf+9rXEkVR4br/+T//J91ut3CdoiiKoihXLx4+nmeKhEdUCNIQ3/MZxptM/xenj+dFBEGTWrRMJZhjo3Mvqf7lqjzMUVFjj5Eg2H7po/kAYpBbXhZc3m/91sMEXmuYIGaXyR7bbrbDpI9nmRUvQeg5zHVZYWutBQn69jGFpvvOb1wbrw476y8JrPrZPsvqnBRlZxTZhB0kZYKDfM63UdZXss8S3C/b96SZIUV1HCRDQ8aIK0qlue0eEjc8YxkVRkbYiGNjPeX75ru3BsOB2Uct20eAk6EB1D2oVMD3rH1VfwDdgS0MPnCO7WZhTDqWi2ye0myfNWz9ELm3JAtERLu9nqvhjtF8n8vysuwNEZ42MFlScm2WMeKiWHZNQzV7tbFiiQiMRUJcGZWsHV1szQ1FURTl2iYMQ77pm76JINhe0PO9730vSTL+X4tXvvKVvPSlLy3cx1ve8hb+/u//fiZtVRRFURTlCsNzRI00JCTAwydNp61o6+N7FYJgjlq0TDVcoDu4yDBu7VXLFeWKQUWNPWa/bZvcgGFRLYJRs+V3WitCMlEkkDwJEsisYQLCGxgroHHHb2S/WcXa9UxyLAmIHs1eAUZYOIsJeArtrB1t57c1zDkOsuXT2k4JMhu/kr2gOOuiaP9XgpAxDaMyh8TGyS1ZJcJfkaDhCheSjFmWQTRkawF5ydyoYvpcMhhqWBGggsnISLPsjGrNHKPfh14P2pvQH5rrF2LGYIAVpyRwXo0gzBo4HJjfk0KU3Yjt7JyloLgUC48xYy5/zvnPRRk80mcRRqxrOseRMS9ZYmXPItdibi+srsosnlwxzxXu2pisli62ryfJMnFFFDcbJ6Y4Uy7fn6Oef33MuNpNMrCiKIqiuHiexw033ECj0di27v3vfz8/+IM/SK836f/tKoqiKIpyteF5Pj4RASEBPt7UhcJ9PK9CGMxTqxyiGR2jN1yh1X0AnYqnXAuoqDEDxF8/xATi8lYn45CZ5NP+DsrrDBTNpHcDd0Wzp8VWZ9pMBLFRmtaqSrIkmtm7ZEOMQkST6ZPxzDWSWdp14Fi2n9POfjdyv4uxVlUSyJ6GogD9gIPLktmpcFV0HqMyKvK2SPJ9gOnjCqb/m5i+XcEE4sfNSZAAvgThx40XCdRXsULGPKZ2ygLWMsr3TFZGEhsxIk1hMMzuiyrM16DWMseuY4PkUp+j4kHgm99tbkJvYLI2GjXwYhOcH2DEuEF27Dns86JoPJSN8fy2rr2WiHKb2OdSQnlmjI9pm9SK6WX7mKXtXD5LrMw+TDJWUkw2RAcrAo7LMHFrnFQwNTlqmHF1AVuEvOjYMNl90cdmxIhVnbsPRVEURZkU3/d50YtexEtf+tJt65Ik4cyZMypoKIqiKMrDGA/voUyNSmpEDX+kaXwek6ERhgs0KkdphseoeHXa6TlU0FCuFVTUmBEyA72JCaRNW/dBnHR3ImyA9el3H4GjLILyiDAxbdBe7Jp2Sjd7TVpzpIO1bZoGESQS7IzrCia4vY6ZGT6qjVJPY1JGBfzd+gaJs30ZO509PyubMZnJX/R7t2aIBKRHCR2x86phMycqGMuvS2ztk/zxhtg6MdNkzATZsaoYQeEYcNyD+QDmGtCcg1rd1MNIEpOh0dqAXh/SxNbQmMvew+wcJJAeBSbLo9+H1X5mn9SDZs9s52PsizazfVyX/X6N7QF7EThFWHAt5Cj4LN+72W+6zrK+019FVDCihhQ772S/mUbUGGU3ViYi5MlbrEl2hex7XL0ROWYNc08fzT4nmDGVt5qSdskzYVLcDBC5fwdMZ4enKIqiKMePH+ctb3lL4br3ve99fOu3fus+t0hRFEVRlP3FwycgJDJ5Gp6Pn04akQvw/QpRsECzepz58DiRV+NS/x7W2p/Z01YrypWEihozIMUEJkPs7O9pAv0JOwvUC0XBuTLP+jxuIHE4Yru9QOorTBtE3Qkxpo+laLBkbrQYf62GmGB0wM7bmZ8R7mZruP9sjbIHmwYJ4A+Y3KarCJkBL/UZJHgr40ayXmqYPpIAvQgdZeJDirWfWsiOkXeTLvtdb8T6ovaLGLGYtXMBI2jcsADzC9Cow/w8HDoMy8tQrcBGCwYD8DeNwDEYmvbNZecbYfrWvefixNbPaGfrVrHWWtdjBAQRVjaxfetl+xOxbYOtgfxR/2vjUV67Z5Tt1EPtxvbrpLVtXMqELnddkRWbu40r8MkyGW89xou9IuBIppUIHJcpvr/zWRshpo8HjL7HJWNLitFL33fYamOnKIqiKKMYJVq8+MUv3seWKIqiKIpyIHhZpgYRoReYv6M9f4I8DR/fr1IJF2lWjrMc3kSdBdaSs6x37tn7divKFYSKGjNCvPwnCcDNmgXMhdykOCA3ynpKGOcpvxdIwHmabI+d+v9LoWQJ8m9iAtNDtgcjq2wXAiYJDo/Dba/bbgnwjhKepkHqPlTYXZtFLJvD2PlI7YcudkY/2NnxUkg9Xx+jaJa87EvskubYbpFUJvDsxD7Lx2RRibixEMDCIhw6BHNNWFiA666Dw4eNDdXcpsnaOH8ONltmWRVoeNAIIQpNnY3NrHh4HMMwhm5qx1gbW+Dax9ynC2zNUJkDljDZXUOnLwNM5opk9IyySSoaUy6jshFEUOxhr+1On1/jrMjc9UV2ZnmG2KLck4xjES4HmHMRoSif5VFknSaCdJIds8t2RHgR8dd91hYJSoqiKIpSxqtf/erC5b/5m7/JxYsX97k1iqIoiqLsPx6+FxKlFSLfTGP0xv5R6eH7NarRIeYr17Mc3MA8h2mna6z1vkCS7Hc0UlEOFhU1CthpcD+hOBi217h2U2WBxbJivQcZiHNn/U9KigkIi/1Pd4rfuzPCBxQHzcWmqKi2g9gY7SaAOcp+R44h33dakFj6Zo3dCWzu7Hkp0F7H1ChYx/SRhxHz3JowZWIGbA/AJ5jAv2R45APOux2fYssk4kINc+27qbGKGg7BD6DRhLl5qDeMjRRAvwcbGxBVTBHwxSrUa1Cvm8LivS74G1nwfAiDxGY7tDD9v5r11RATND+EEYgCrHAWOG1MMH0p41wEPNj+XJpm3JdRwwgrQ3YvyBaJBfk2jBoLRUzbHhEv3d/l76UiW74KRlyKMGP9MttFYrHEqmDGkAgocgxFURRFmYSf+ZmfYWFhYcuyL3zhC3zv934vn/zkJ1lfXz+glimKoiiKsl94ePheQEBI6PmkpJn9VHlUz/frVKPDLFZv5LB/E4vpYYZpnzODf2K9dy/pnnugKMqVhYoaOcRaRGbkXg2sYS7kJEHFUZkA+x2Ym9Qr30WuiwgbVWc/+Vn+486naH2MtUUq2n4SEaas/0dlY8g27rqdiho9xhdWnpQYE5RfAY5jC0+L6JNiRI0ONotm1DmWrYsLtpnVeBRhQ2bvB8ByDPOrppj3/JzJvIhC8MIAL/SJ0iGVSkolMsvrNWg0YG4OajWTudHehG7PiCCD2PTBOrZQdwvbd93sfT1bdzZ7X8Nmq4jAIzZUUsclZntm0qT9U2F7IXJ3XTM7RoAVfFzE9mrSjJ8iIdBdN+k1lTogO8k0yltLudZdRaJQgBnXfvZawvTNJcw1S5ztRdiQujBaR0NRFEWZhh/4gR/gZS97GZVK5aFlaZrSarX4m7/5mwNsmaIoiqIo+4rnm0wNIkLfI0mN0FGyMb5fpxYdZrF6M9f5j2QpXcLD57J3ls3hBdL0aolgKsrsUFEjhwTDRCTYrc65kxnnHmZGvNgHdRk9I1iC+mXFrItEi5TtQdKrBfGwl+LfVczs6ha2n6a1tXIZ1ReTBDGL6pnIMvHiHzJe0HEDs5MUW561GCCIICDj0M2OkXOT+8Qdf7vJeNoLJOvhIWuoISSxybqIIggj8CohXhgRxCmVypAwMpkai4tG1GjOQbUKaQJhCBcumB1XQ2NBtYqd5T/E9EeEGa89jKWUCBd5uy1BxoYE2d2C2WXnNe2197J2xZg2S/Frt26M1KUQEWtaYaMsW6dsexfJVpn0mGX7l3NyC6+7x8w/78U+rex/BztYMUPnwCiKoijT8O/+3b/jl3/5lwnDrf+3HscxT3nKUw6oVYqiKIqiHAQeHj5G1Kh4XhbTK8rU8Aj8BrXKMRarN3HCu41D6RIeHmtscH/nbvqDSwdwBopy8KiokUP826vMroh1lL0PKQ/SubOKm8B89gowM+E3sMFOKXZdto9JAptXoqAhwdlJApky+zzC9NMyJvB+EVvseCfXTQLJs6ihAdttb6SWQr5t8k+XG8gtEjSE/DWLsDZC7YL97xYRY6TAuQSC5QGSD14XBbTd85I+3q+xJ+JABSsY+h4EoREzwgiCyMeLKlCJ8JKYqDKkWjGZGbWaydJoNsxv+n3zCoKsoHimPHRT0//d7JjHMLZTZzDiwQCTrVGGh3n2SH0S6SPJSJIMAdh6/0qh9byFVJlwJoJfN9tf0bPDvcYh09fbcEW5Iju1UVZa02ZwQfH9IsKn3Fdyn7nZLyLUNbNlLWy9l/wz4KAsBhVFUZSrlxtuuIGTJ0/y53/+5/i+v239PffcQ7+/k2k4iqIoiqJcnZgi4aFfo+KFRIFPmiQEBLm/m30Cv069cpzl6s0c927lKMsEvs9KssHp9DP0Y7WtVK5dVNTIEWLtR/rYgsijBIlRSLCumX1usT0wGGJmRPvO5zomUClFhcHOlpYgnMwqBhu0lWPmrZgkkJyvW3Al2adMW0dC2h5hRI1lTH+dx4hA485NrIjABnYl4D1ka4bMpJS1X66PBJOLgsNi7ZSvL1F0DHe5jznvJlb0moUYV4QEicWyaJTwkiefbZCvnbHXAoePubYNsswE31hLVSLzCqoBVCsQRnhxTBD1iKKEhQVTR2NuDmpVSFPYbMP6OsQJ9HrQHcIgtWJND2vvtIy5tqeAc4y+LmKVJSKSBOIrbB2rIkjIb+T8JinALbi2VEXbybNGnkEiqExznYqyNiZp2zSMqiUkWRcSKnIFDjl+gnlezGefV7D1ThRFURRlN9xyyy287W1v4xnPeEbh+o9//ON80zd9k4oaiqIoinKN4XsVImrU/Ih64OHhEXkhYqDsERAEc9QrxzhcuZUT3s0c8ZaI/IDLcYsHk89wrvMxhipqKNcwKmrkkJncHjagV8UExXrYmeqTIiLFXPYeYmZsu0FtCUrLbGuZwyWWKUO22qeI2CHBcQmWSyZHvsi12MhUnPMS5DdXipXKNCKLzLKWmdVzmIB1k/Jr5dpAVbCCkQRrK5hr5GP7fhpLn6IixPnlRX0tmSUiPkl78vUBivYtgXQphl1U5Hw3JJjaDw1s37gCmtueshn5RddhP7OF8tfBBwLPWEhFFfMKotAU2YgqkMT4UUBUSTh21NhONbIC4Q8VGPcgHoLn233WMH0j4tImZkwtY67LKuY6QfF4kT6JnfUShK9ix2wfe50li6ssg6uMSfpcxqFka8jYnJa9vr5l94jcv3L8fJYLzvI15zeKoiiKsluOHj3KL/3SL5UKGv/6r//K93//9/O5z31un1umKIqiKMpBE/hV6t48zaBCMzR//Ve8Cj4BnhcRhUs0K9dxOLyVk/5NHPHnCDyf1WGHB+LPcKbzj/SHqwd9GopyoKiokaMHXMAGyeYxwUQJlK8yedDYxwgkEmwXW5k2WwNnEvyMs23c4tcx1lJGvotFjQgmzaxN61jrJZcUE2hdzH7TxwZdpT7CTovyCjutn7BbYszM6hhzftKXZWKE2D9JcFjaLcFbEZRELBh3XqMyFWSZGwQW0UqK0bvIHL0yISa/LMCKMgn2vIuKbk+Dm9Ej7ZHAuYzh8vJVWyk79qgZ/HuBm5kk2Q+Bb6ykohDCyMOrRJmoEcGwQljxaTZNDY1KIySIAlOEozUEzwgbgwFUqkbs2GxDmNox1MHU10gx9+ka9r4vqrvitjXNvbq5bQPMs8l9dkwyTmUfkyKZRXLcnYoa0zDtmBhXr0Pu61GWciJqBNgMpMTZt3stFEVRFGUcvu/z67/+63zd131d4frLly/z4he/mA9+8IP73DJFURRFUa4EIr/BvLfIYhixUAHf82h2a/iE1CvHWajcyBH/Jq4PjnM0bODhsTLoc2/yWR5o300/XkP/QlWudVTUyBFjgpFgZ1wvZK8uNjti2sCeZEcU2ViJXU0PW/R6CWv5IrP4Xb/8ftY+EU0Cym2NJAgqtkG93LlMm31SxEE+SocYYUMsp8YJNHmffSmELDU6JHgrAU53X/I7l3yGghsA9tlqZTOkPAhb1IdldQmk3oErzMj+pU6CXG+xEZoWd78ptlaEzNjP20fl6zKkbA9QF33er7HjFt72gTAwmRqmpoZnxIyokqVvDPCikIWFHv5cA69eB1LSbhdaQwYD6HahE0PaBc8z+5QC4GLVJFZGIaauhmsuMal1lwgLkjkkmR6V7HgdyvtwkmOM6n/JhAqxtSh2g4gLo9bnRZ1RTNJ3ZXVe5Lkoz1cRu+rYsSL3l/R7kWg8aVsURVGUawfP8/iar/mawnVpmvLc5z6Xv//7v9/nVimKoiiKciXg4VELFjnkL3Kk6nO0mhB5HotBlUZ0hGblBCe8mzkeLnC4UsHD42K/zxeSe7h38/2ZoKEoiooaI4gx2Q8VjKghs/wnLXIsweQeJuDVw8yqdgPMPkbIAFvDQzI25rJjiqWQFA6Wtsms6QhbwLaoTfWs/T4m8L8yYturFclsGYcIOe7Ad38bYjM5vOxdrrXUkRg6y+SVD5iCDaCHbBWbdtrvbnBWArISoI8w1znGXGOZvV8kwkx6LJmt7v4+/z1lq7gx7TH2awyKyBRlr9CzWRpGy/CyLI3QVP0OI7xmA9/3TEGNatWkZvR69AfQasHlFbiUgDe0M/3PYvq/i73vVzH38ChbI7cfy/pEBFfp667zO8nYERs6yc6SZfm+KDqu9I8IpCLAuMXJd0rgvMqePUVWbS7TjpW8oCH3QoC1CWtgRdEh9lkpz1nJ6BIhu8/uMtoURVGUa4O/+qu/olarbVsexzHf8i3fwgc+8IEDaJWiKIqiKFcGHnPBEsejJtfXU07UBtT8gNOViMdVn0kzqHC0VmGp4pOmcLYz5PODe/n85t+poKEoDipqZJTNII4xFjISENss2a4MCYSBne3uBgglKO761fezYzayV1GdDLCiyRomGFdne/DUx9aZ8LP2SyHqaxXJzCjCrU0gdQTmsnUiaHWzV1mQWoKpk2SNTIPY6LgBaKn/InVTRJSROhhDrB3SNMdxhRn5LOfhZmS4osm0FkfTjEGZOb/Tegdu4P6hAHtmPxWGEEjV8CiyL8/DC0OThpEkMIxJ+0M2NuDCeXjgFJxOzD47mMD4ecw9nmCzoCTDQZ4xRdktO0H2JTZ0YlvXco4r5z4qC8jDPB+WgKPZvs5jCpu3C343LRG2XpArxrqISJfPeipq86S4+9hy3bF2gLKuiemzQ5h7ag0rgObrcSiKoijKOObn57ct6/V6/NiP/Rjvfve7D6BFiqIoiqJcSSyFh7ihGfCIZo+b59vMdauc69aphvMsV2AuhH6ScroDn+ue5TOd99MdXjroZivKFYWKGhkh5TZMA0ydDZjeV11so9xZ83lbny7bbY562FoRUoOjLDApAfh5TDBu1dlWMjh6mGClBIfzgfZxtjDXGjJTXbJzmphAaAVbz0SCx1A8LobOulngzmKXcVXBBot72DobYhck1jrTHid/TBEE8uvzgka+H4pshNzsFvntuLEnwftxtSNG/d7NbPG9TMwIjLjhy4KoAs0mpAnEMbQ2SDtd0n6fuN1nc3XAubNw9ixc7JlMLh9zj2465yVikFvwfVQmxiSCUD6Lwc2m6WGFT8neCrFZSEX9K23xMdkJt2AKmovIEQNfGNGeSfCx2Q5i9eaeR76Gzbi6IHmBrUxQK7KEk3tjyNYaMdJ30l8bWEHQc96DMe1TFEVRlFH8wz/8A69//esPuhmKoiiKohwwHh5H/GVunYu5dbnF9cdaNFdrbA594tSnHiRsDgO+sBlxodvnc/E/0+6fPuhmK8oVh4oaGa5di3x3XxKgdoNwkwS4JCtgiLUiCtluwZIv7gw2q6PIrsUN2IktjQgWItAIbWzmR1mGwqQzxiX4OIn91pVMPjgMxYFfmfEullMBJnPDY+tM7qLrM2vEZkiszETskrHYxRRLl1noIgLsBW7GgUtevHBtuoq2l9n5ZeNSEHFgJ/1awQpScv8FOIJGAASe+SALUw98n3Q4JF1fp7Mes7oK58/D/ffDxUuwGRtRQ+43qckg94crkorYkCffL/maDynbA/dFFk2uJRrZZ7GScse12x533w+JPc56uca7Hcti3STZK+75uOKbLHPb6SLty7e9SNjI70uEH2mLKwqCGX897DUrspkaVQ+nrM2KoiiKAtDv9/nTP/3Tg26GoiiKoihXAB4e1wUNbl3ocPKmTRZu9aic7VKrxXikbG5U+MzKAiu9lE9372O19/mDbrKiXJGoqJGRn0XtFpKOnWU7DfJJwFFsYlxbqjxucNEtDp5vrxvYa2GFjXw2SMLWALjMSHaDrJMGv107JXcGf0L5bHuZIe/Osh9VbHcvg4NiNSNFliVwK7VPyoKpMut+iJ15vt/ijlhMuTVU3CLkEohtYGbedyifpT+K/Phxjy/jJsXWcaBgW5xt8+NFxoxYaEnbx7GTTCIfI0IdwtqwNYB6ANWaLaNBEBoxwwMGWei9VoVKlaQfs7YG587Bgw/CqdNwfgPWU5OdEWH7TB6oXaYrzp4P0EvdhyE2A8MVNVzcfpVrI+NDgvZl2TIyfleA09jshcvZst1mb4mom39eSRtEKMy3K89OLNTc/cmx3GuS/w7mnplkv3l2YyOmKIqiPLz57d/+bT7wgQ/w5je/+aCboiiKoijKFYDnwdFqwE1H15i/vULwqEM0r2tRv7FLstLnzCc9Vvo+H9t8kE913k9voLZTilKEiho58jP4pw2mjUJmANcwgVWZuVy0nYgpMoPaDUAWMUpUABvcllnrInRMEkwuap/gBqYHFJ+PWDjJOZVlN4AJqEomwqztsDyMRdcNmOB2B9MHHWyB9oGzrVjzVLFilAggMeVFjWctcriBWQkQF/WNHLeWvQbYc5z2eEUB9LzIlw9I59uRH5NuxoYUYxc7IN/5jeBaFKVMPx7EbkiKPy9g6kYsA8sLsLAA8wvQnANqNSNsJCn0MnkrimCuSRqEdLpDLl82mRpnLsPZGC5iMzXkPnCzCfLnUTYuirJYPMx4k1oUvfyP2HqNXFFF7i83aJ8XMfPiwka2vpX11wbF42bcuRThXnfJlJG2jLIey/ehZFmUCZ/us1LOfz8s9eT6K4qiKEqeP//zP+dVr3oVZ8+ePeimKIqiKIpyBbFcSzh0/YDwUSfgUTfindgkvLjK8GOnWOtEvH9lhbvX/47N/pmDbqqiXLGoqJExKjjtWrLsNmDdwwRCJahbVCPADcSFWN/33QToxEJLCkmL5c9ORA0XESlEuJCgf/7YbvZJWe0SsVdqYM63Q3EwdxKKrmcFY8+0iJm9P48J3K5jZ7bnA8zSZmlXivXgl6C+KzaVZTnMgjRrb3vE/hNM9kAFWzxeZvpPIwy45+HOsnd/L3ViYLsYOOr8XTFokL2kkP2Gcwy3NsW0Yz/M9hll702MqLEMHJmDI4fN69AyNBZCaDagWsmi57EpuhFFeIuL+IeXie6/QK8P6y1YyQSNi5j6NWTtFLFS6kj0Mf0/n53DZu78RiF9KEW2xTKpyJ5Kji9EzrsUyB6wddzkRaQUm9kjGUhFAoIE8KfJAHLFlwXM/TfA9I9kG+VrZOTfJaOqTNh1i3/LOG1RXJB8lvemiJ71Ge5TURRFeXjwmc98hu/6ru/i0iWdXakoiqIoiuW6ucfxqEMJjRMBXH+E9OT1sLEBG20Gqwl/9mCFP77wF6x07yXV6reKUoqKGg5FxWbd77NCLKXGze7NBxaL2iAXsChDoggRF8qyDHaCZC6Ih78sE9wZ0yKCFCFB7AATJJSMjWnJZxnIucoM8Q1stoDMhoetwosgYpCcjwSI3Xoa+WPPcqzk9zVOLOgAZzD9Jm2ps3Xm/jTFw/MiTX6Gv2QTFSGFlV0xJHS272OD9WJR5o5J+c00BZqlpozU0BBRYI5M0KjDsWNw/ARcdxwOH4FgoYlXq5t6GgDDbIR6HcDDb9aZX/SIopThwPRxK3t1seO5k30Xq7V54DhwY9aOC8DnMeJCBWstVVSrQUQ2t0h1WbHv/L0s24tVmYiEMm59rPgiQoZrX+UWuM/3u9iJTXo93HtQ7q1qdvxLbH1OuMdLc99HHdN9boi4mmJEJMF95o3LepsW6euy+0BRFEV5+FGr1bj77rvx/eL/m19fX+fLv/zL2djY2OeWKYqiKIpy5eJxtHkHP3DDU/n/Hn2J8NZDpEcPkdZqeCsrDB9Y4X/9wyF+4d7/xUrnXvbP7FxRrk5U1MgYVWR2L5Bg7ijcrICyzAYpPi62KxJYK6oPIfYsEtSexvN/HFKINx88FCSYPuqYci5iNdOdsg1FQo38qSnB9HbWhg1MIBJsVoibeeAiQkjq7MO9fntdB0RwrXWKkHWb2H6OsX3q1hCZpM15QSMfVHdrIhRZKEmGDs4633m52QebFDONhZBbL0UEtiomO+AIcCgymRlHDsPRo+ZVP9zAW1yAatUYWwKkCaysEl9eZdCLSftD2q2Ubhe6Ayvi5bMVJOtEap/MYcWIetaGDtayqs/Wuieps9/U2Z8g19e9x8rqa7h9J/e7XMcwa49bb8MlLyi4TDNHxK3vI/vtAGvZsjW2CijkPhctK0L6ag0jNMl97VrJFb3cIvY7RZ57G6iooSiKcq2wtLTEW9/6Vu64447C9WfPnuVrvuZrVNBQFEVRFMXBY7l2M1935Gk867oh8zemeCcPkdaqeO023oPn+Ju/7PBfPvL/U0FDUSZERY2rgFFBbAmiirAhs5TdGdcSRHZnyJfVv9gtZUFPCayOCorKLHuxt5kmS6Notro7S7yKCXhvYgKfbWxmwDixxRWXxs2c32tcQWJU0NkVhHYiXuXtp6BYCHFrvxRldcj3fNbGbrKEQraKHdInYj8UYmfsN4ETwA0VmG8al6nmHCwvGdspv5FlaHieqatRq5nvFy+yfqbFvZ9PaW9Ct2sKha/Hdhy0nHN1rbIEEeYka6aGKVgeYEW0KrbejPxGrpeMS1c8cUW3svEubXLFF6lHQfbedfYl12aW47ns+oqANWRrplP+fZyAV7RfEclcSz33uuT7zrU1KxN2JqGXvVTUUBRFefgzNzfHa17zGr7hG76hdJvXv/71/PM///M+tkpRFEVRlCudRnSEL194Gk+cX+bI/AXCpQCv14PT56BeI3ngEv/7s/+X+zc/jwoaijIZKmpMQNHj5Ep5xMhMYZmVLsFEmakudSok+0EsaMYJDLOkLAMijwSBp7EbGiVmyHfXVkqCt1KkvShQX5R1INvLullZd01K3kpr1hY6kx4Xto+bhO19khdDZBlszdCYtv0i4KVYKyXJzMi/1zCFwY8Ch5ehVoV6wyRlRBGEUdayYQy+DwuLsLgIcx3YbBPev0q/3+HBB2GjBRcuwmZixmiXrTUqKpiMEJ+tBa3dfhFbtU7WdhHwZDym2Bo60jdDtmYSSWZM0Zh3x6asj3O/k+C/9N0wt59Zj6eizCk3G6VI1NhpllyR4FN2v8pyV2zD+ez2xaQCy6SWboqiKMrVied5/PiP/zgveclLDropiqIoiqJcNXhEwTy31p/CHbWbOVLrU6sMSFaH8C9n8Q+twVKDwedX6W1MEjlTFEVQUWME7izsfECrbJn46e+n6CGBSjcIV2NrQG+ICWyWWVntB/lAbJFffyv7PEkbR1nvuOulPkFRPY+iTISyaysBaKn/kC/cPGq87Iaic8Jpj3uswNlOti2q/eHub1ytgmkCvPkgdZH4M8reaBwy1iUjw8cKeFG2rIIZ//MYC6gQqNXhxHFYXID5OZOMkQ5j6PegXjMqR6UCUQheHQ4fpnF8nuVDXU49mNJuw8bAFq/vO8dOsVkhVax4GDjLvGwbt36FiBcyjpLce5rtX85LxJCyjIC8iOR+liwoWbaAtSSbNWX3oYg0sHXcuvfcbsaGnGOAzX7plOyrbFzmP4uIlrfwUhRFUa49fuInfoKXv/zlB90MRVEURVGuGjwCv8HR+pdyS/QoDlc9GkFCpx1x4VMeQTRk4foVosPrrH3WY6NdGb9LRVEeQkWNEeRn7xatc6lggqiSDdFn/7IhJGAomQ5irRI763Y6s3+nM6eFIusp18LJXR5jApFSB2Jcm4oyCch9lmMX2R4V/a5I0ACb8SGZAumY382CsgyIIksoKVicr2sidRtGHaOo7RK0zxcW9wu+l7WvTCSiZNkk+Jj7TALXkhUhgoYUol7ACBs+EPimlsbhw0a7qNbA91JodyBJzMJmA5IYYiN2BI0qh4/61OsxrR5cTGAFI7wl2OLQCXZMeE4boqwNc1gRpoINustv3OLbIkC5AkeIzeCQGikicBRlOMBWgU6+y/WsYbJXBphsk1lm/JRlSMi5FImKkz5fxyFjUrJ3YowAVVQLZpQ9Vv67ZmAoiqIoP/iDP8grXvEKwlD/dFIURVEUZTJ8r8p87WaOB4/iWKXKfJTSiwM+v7JI75JPI4x55OYq8+f6nD69wFo/OugmK8pVhf6feQmjZqKPWhex3dZlvxAhxbVa2m2wUoK0PiaQulPy/ZFgbYK6bM0mEHusokyEfNuKgpNuQH8U09hIuR78eeucsjbshZVPWXvdYDrYPizKTnFxbXgkC6PoWrkWU56zbd7uqMyCyl22G+Q8KxjhQton40fqaTQwYsIcMB9CvQ5hBI2spkbjWBOvWiFe2WDzbJtq60Eq/QHesWPgebCxQdpuM9dIiSpwqQNngIuY+8DLjpHm2iWZAiIeSEHuGrY4eNPZzi3k7b5LMN09L7DZKRcx4krsvFzyNk6uMCeZRmX2a7ulTGiEvRV5Zey6GRUylqe514syyBRFUZRrl2c/+9n87M/+LPV6/aCboiiKoijKVYLnRdQrxzka3caJ4DCHqgFJmnJ/p0JraJwgrq+nRF7C4kafe1bnWBtM+leroiigokYhRTYkoz4LfWCVrcHIg2CWgUN3JvmsGWJrIOSzASQwmc8KEPLBdHc52L6X/eQte1zhIx+IL0KSADtsv7YS5M/vfxbXvyz7pAgRJtwaDJMg7ZQsgjZbCzkXZQOU9T+5bWeZoQE2W0aC19WszWIRJLP0G85rvg7z86aWRhhBvenjHT0C8wv40TloXaS7PiA8fRa/08ELQ9Juh3Rjk0Evod2FtSxLYz1rfxVrKyVCgwgqMqZF1JA2SvubWJHDrS/iFvR2v3uYjBMPU2QbrM2V1NyQ7QR3bOev0QBzLrJ/2J3tk8uo8Tqp0LjbNkh2kiBCzqjtJ1mmKIqiXDvMzc2xsLDA0aNH+bM/+zM87+CCDEePHuXChQsHdnxFURRFUabBw/MiqtERDtceyQn/Ro5VagQenOt6rPSHXOgNSFOfYVJhKaqz0K9wXztiYzCLv8oV5dpBRY0R5OsDTEJ3LxpygOxl1skQE0D3KA7Alwkprr2SkBcmYudzEfksi/zMdhcJXEsQushCR7bbz39+pF1SpyHF1nuYdna5CFdNtga780Xei+yvJKPGbdesMzRkvzVs4XtpcxVYzF5S+LmBEQKaQKNmMjWi7GmXDBO89Q28SgWvUmX+SJX+Woe4N8RfW4NqBQZD2hsxZ87CxUtWNHAzYqS+ilhLVbHZGiJ61LDixyDrqzpbazWArXcjWUBSzFuC83VstlQrW7eOtZWD7fVPXNxn2RBYY2uB8lmM3aJC3XLMSf7XLMJcs03MOC7bRs5TsqbAWu4VCbBlwmcR0kbXem0v6o4oiqIoVya+7/PkJz+Z7/iO7+D7v//7C7c5e/Ysn/3sZ3nCE55Ao9Eo3GY3BEHAk5/85Ie+v+1tb+N7vud7Crf90Ic+RK9X9q+moiiKoij7i4fvValEyyzXbuG4dwvXBQtUAp/LvYSLgy6f7t/DWrrGcf8WlrrHOdsN2Bj6nOnA5nC/DOwV5eGBiho58rPSx2VnPNzZ63OeNvieD0jKLGyZ8S7/BOTbLbP4XYr884uWyez5CtMJPLsNFJcFid31bn0PCYTvVIRyszNcigQfN8APtn5MUTtnKWhI5oNYl0XZ9zpwBBMUFyGmCjQjqGSdlCSmLvj6OoTty/DghrF2CiFITPFwggBqNbxmSNpaZeVyn82WOfYS1jZKakRIVogrYEjNjBrWuk0EC2mXa+MlrpmumDTEBPelTk6frWJOFyMIyv0TslVMKhMqUopFhr2+z8f9r5mP6celbNui8IyH6QMpvj7AZiTJNehh+s3tSxFB8uTP3+0TubdiVNRQFEW5Vnj605/Ol37pl3LXXXfh+/n/azSsrq7yIz/yI7z97W/nU5/6FI9+9KNncuzl5WWe97znAVCr1XjjG9+4JTvk/e9/f+HvfvRHf5RLly4B8Bd/8RecOnVqJu1RFEVRFGVaPDyvQhQtMV+9gWP+rVznH6IW+LQGMecGLT4/+CQPdj+K79eoN+ZZHR7mcj+gHftc6A3opgdhZK8oVy8qahTgChv5bI1rLRnsSjpXr+AlwWOZvT7qt0WMOz+3uLEEN8f9pqiWxCwossoSqyKxK9pN0ecYEygf1Y9yXLC1I8T+SBhnTeXuY1Kk+LcIBTWsyNHM1kmtiApQ8aESGMupIID+ANrtrH0t873VGjAYwOIiLC/DocNQqVSg3oAwxAs36HQhGdpskLnsvCSYXscUA686bQrZWgwctl4XCZOIqCG2VVVnudTGkTo5XbZmH+ULYMv+R2UcucvyQfz891lmHeUFjXyYyBUixfqsiBTTF3OYa55i+jvFXIdGtq8WpubIZsHv86KPW5DdPV8Rjq6k55+iKIqydzzpSU/iv/23/8Ydd9xRuk2SJLzyla/k7W9/+0yP/fKXv5zHP/7xfOd3fufUv/2Zn/mZhz6/+93v5uMf/zg/9VM/NcvmKYqiKIoyFmM5VQkXma+c5FjwSI5zjIYfsjmMuZis87n+xznf/WcG8QZRuEwn2WAz6bHer9ELYG3Yp1fqWVB2VBNxSLdEBBTl2kFFDYd8cMtn+yPhWnhESOFosXRxA+YH2SbX8ijFzsCuMDqAPkmRYHdmez7wWVawvKg4dtHnWY2ZooBs/p+u3RxLZsiXjfmi2iOSRZCf9S5jqEiImZYAE7SWLA0ROOawdTOaWBGh4kOtAlEEgQ9JCp0OrK5aYaPTMRkbeKZ91Yqpu5EMh/idDnGnx+q5Hq0WDIZmGyn6HWCzKOYxYocULxcrKs8zxw4CkyFSG1qxSMauBPelDoj0mdRFkftOAuySCSPXPV9vRn4PW+9Vd2wXZWeMEj+moUjMy983roWYPG9FuGmxNfukCLGsc8dCirWHk4yVNcrr3LjPA7dWTh55BuYFJEVRFOXhg+d5/NEf/RGPfOQjRwoav/Ebv8Ef/MEf8Nd//dczOe6hQ4f4nd/5HQC+8iu/ksXFxV3v8xu/8Rv58i//chU1FEVRFGVfMYJGGMzTrJ7gSHgLx7iOhh/RivvcH9/L2eHnuNz9NHFiAhJJOmSQduimfTpxyiCFdtxnOIFPgOeFzNVuoj9skaRD4qRHmuiUPOXaREWNjLzt1LUsaMxlLwkQ9jFBwqLA336TFzaGwAbl2QVSuwDKr19eMJEAphSgrmMCrT22WtEU2UPNqj7BqMySnWadTELZPoqEjVFCl5s1MGm7JIjsZsdAZiOFtRiS7wtYYUMyNuqBETOiCPyssf0ebGzAoG9PoNOGbg8OHzbFw/0Aej2ornYJN3usrSWcPwPtTRhmUfYmVtioYMbDHMYyKQJCz4gYgW9EDXDesVkmCba4uBQ7n68a8YPU/MYbmDEtGSAS+Bcxwx17tawvatj7VSyYygSLogyNIluqaZgky8O17QqyNspcFBFrxtHOtlvE1r0QYa3GaFHEHb8+5vrVgMuYPnMF7QWMaHUuO6aiKIry8OP3f//3ec5znlNqN5WmKX/913/ND//wD7O2tjaTY771rW/lWc96FjfffPNE26dpOnGh8uuvv5677rqLH/zBH9xNExVFURRFmQgPzwsJgiaN6jEOhY/gaHqcpl+lkwz5QvJZ7ut8kN7wMqlrLZUmxGmfAUN6ccIw8eincZZxUXakkIXGrdTDIyz613M5vJf17oMMU/kL+VqIWCrKVlTUcJDCv02s9YsEWOXxE2GDhx0ePn7rEqiNMMFCsfMRS6I2BytqlHniizWPzNYWOybBtfqB7Y/5fADWnQkP5lpLCcguZja5FOaWfbsB/KL9FR13EsbZWOXXz9IyqOx47nEmseEa91mQbAzJ7pCC5z7mfhRRQyye5jAB5wWcmhaBybjwA0hTiBPzGsYQrEM7MFkX8dAIGmkKtRrEsXm12xAPU4IgZWMDNtvGpmo4tFkY9QpUIgh60BlmWSIBhEFWkwOTGQJGpEiGMEjMuOmy1Z5K6sHM16DZgG4n65sUgjir88HWIthSGNwtEF4Hjmd9EmDu1UvA+awf5Tg4v3Gv4SwEDcG9F9xaIe7/mknmgzxfpn2GJlibKhkvEbZ/Y4qfVfn7JcRcvwpmLLkCS5rtxxVFFUVRlIcHN910E5/+9KcBYztZJGj0+316vR7nz5/n2c9+NoPB7v+PPwgCXv3qV/OCF7yAQP6nYQS9Xo9+v88zn/lM3ve+9wHQaDRG/jYMQ44dO7brtiqKoiiKMo5M0PCbNCpHWYpu5mh6PXWqdJI+9/MF7mv/X/rDS9t+mZKSpDFxmjBIUhIPkhF/jXtexHLti7gtejI+Ied5gO5wnWG8SZrmvTMU5dpB4zUZUmhaZlA3MUHCISbQ1cMEyiLgBCbAuY6d4bvX9iR7FbCWzIzD2FnkMvNZbHDW2R4k3OsA+iS4dmEhcAjTrg22+uF7uc/u78mtc0WNvvNbySAQMaNoPl9e2NitJVSRoFFmdbVfjDonyeyRYuOjztvtcxHQImywP8KMuRBbQ6GBCT6LFZW7PArNy8tSrOIEBgPoZWrkYGgyN7p96MVmfFQ9qF6CpSWoVCCJjdABRvQY9GE4AC+B+QDqNVNuw/eMcNEcZhZonhFS8Mzvk9gIKXFixlAbW+PBFQ+l7obXM8IJqRFM8DL7Kmdb6dsBZny3nT6ULA7J6mhk229izlMK3QsSCplW0JA2SMDfvY5F2y5h+meNrZkOIkbulvw+OthnsYjQrpBRZivXwYhObh+l2OfIw0W4VhRFUeDkyZN8+MMfplarlW7Tbrf5sR/7MX7hF35hZsf1fZ9v+7Zvm8gaKo5jPvOZz/DmN7+Zu+66C4CFhQUA3vnOd/LFX/zFD2172223EUVR4X4URVEURdkrPDwCfL9OrXKYxehGDnM9Naqc5ywPDD/Beu9+BsOVkt+bv8ATEuI0zfZn/tt2JC9ksfpIbqk+kWY6z2XvMq34Av3hGklaZCCuKNcOKmpkyAz/ASaILz79KXaWNthg/1K2jTurfK8IsuP3KS+iu5t9z2POp4o5l1VMQE8sl9wixe6M+kHWnjKbm1nhZlvk7WNw2uZhrlkVm12Sb1s+i0KEEbEEyv8T0s/24WZl5GtvuPvLH2Mn/eKe517bTe2EouP62HskYXvGDGwVl0QYcutUuIKGBOgjYBmTPVTHjFWxgZJXJYQgNEJAnAkKwxi6ibUq6nbN8Xs49TBSaHVgZQUaDZO1IaLC5maWaZFAlGVnNJum8HiSmiwOOZ9BbLM0+gOTmSFWZSJqtLPjtrGZGnIvxZlAspAVifE8CEOI4q0iiIzPvE1TB1jBiDxSn2Mek70RY55n7nNjyFZhrkjQkALmMdbyys0wkcwRuc6SOSL7ETswMKLGXiLtkeeRkH9e5Ikxz7mUYmHazdBTFEVRrm6WlpZ42tOexqtf/WqOHj1aut3f/M3f8KEPfWimgsadd97Jox71KH7rt36rdJv19fWHsjHa7Tbf9m3fVrjd8573vC3ff/M3f5MXvOAFW+ypTp48yXOe8xz+9m//lvX19d2fgKIoiqIoDiJo1KhFh1mITrLknSBNh5xP7uf+/kdp9e4nHWmMLJKGETU8UvyCv2A9L2SuejO31J7EofQwHfqsc4HNwcUsS6Mo8qIo1w4qamS4AVcJ1gVstX8BEzhbwRaqjRgdOJtV26Qds35kJZhZyhHGyqePCfRtYvtBMhSCbJul7Lfi3b/OaB/7WSNtET99sbdZyz5LjYUB5YJLXtAoyqxwZ3NLH7gv2TaftTILKx8366OMIhHloDJoJLANxUWq5TNYMSPEZixI/RJZJstrWJspqaUhdRAiDyqZ7VOawCA1AkMvsWLcJjaLIcCKcHJ80iyjo2tqagxjW48jSY2o0WwY0aPRMOu6PXNSMn6GqckASYBeao7bxQobXYzwIEmhrjhAdq4VjLiRJJmVlQ+hD2Fi+6WW/aaDER67Tn+vY7JBEsz4l/ebMDUhLrA1QF+WVQQ2e2s5O1YHKxy41lJurRo5Z7Hs62Ky2CTba6+R7CDBbWuRMCiZWCJY7iTTTmy9il3YFUVRlCuFMAx5zWtew0te8pKR2/35n/853/3d383Fixdnduwv+7Iv4zd+4zd4/OMfX7rNb/3Wb/HRj36UN77xjVPv/2UvexkveMELtix76lOfylOf+lQe//jH89GPfnTqfSqKoiiKUoYVNCrRMnOV61j0jxMnPR4YfJz28CKDwdpYQUNISIhJqXgegedvydTwvJBm5UZuqj6BYxwDD1qsszE8S3+4mmVp7GckTlGuPFTUyJjHBPPF610CltJBEgCTmc8BJvAn3u7jkMLjOwk4x9gitrMOWCeY8xabKTmWO+tagvsVrGe9GxTdDxukfGAyws4E72FnVW9m30Osd79QZD3lfpZMHd/Z1q2dId/zwVN3+ThBw61tULZNURB2VMbGlYBkLBWJKkXZGSIyhM67BPdlnNWxdkrzzu/qGDHD9434ECdGzBgm1iZOrJc6mHHRcY5Vw2R+HKrA4pyxnooT2GyZ/dUbRuhIYmMrNT9vszQ8LxM/kq2imZfamgwdbJBflknAH7ZnPFSycxxm1lVkWSe+ZwPnNafPKtm+zmJt4boYUUNEvV72fhgjCF1m+3NKhCOx2MvfK3NZX7uChTwbu9hMmTB7b2UvyVyT2jNlgsFOxQQXuScStt8fedsp2HpvusfeSVskO0kyjRRFUZQrjxe96EV81Vd9Fd/yLd9Sus3p06d51atexQc/+MGZChonT57k137t10oFjQ996EO88Y1v5C//8i+5dGm737aiKIqiKFcSJjrk+VWicJFm5SiR12BlcB/t4Xk2+g9sLQY+AWn212noeblMDZ96dB0nql/KCe8kVS9gNd1kLT1He3CROG5nWRqKcm2jokbGCUwgToKEA2xAVCyZZMZxN/sO1pql7HHiO694xHaj2AsxI79/10KrKCgtPvqu97xYvuzHozRfS0KEBh8rtIhdlhQPd4siS3A1L1C49QW83PZl9SvcwH1asKyMEBOc97HB351wJQsco2osSEDetVNya0tUsbZTNazNlHxuAKEHUSZoeJhsiv4QuqkRGOUebmEFRxEUh9nxGkDTh+UFOHoUFheNYIFnioUPsmn/cZzV0ahDtWaOORxmhcBTe9/IdZTPIqZIpsaQrUF+eR5IH0jNixSzge+b2hxhAJXYnD9Y8aeCtbY66/xWbLWkPTWsFVh+vIhYIv0eYbOz0qzvLmDGq1uoXMQVsu/yzBCxacjW50jReBB7LB+bTbMTcaNMUM2LGEViZl7ccLNWJkVqd4hd2l7XVVIURVGm49nPfjY/8zM/M9JuKkkSvu7rvo4Pf/jDMz227/v8yZ/8SamgceHCBb7t276Nz33uczM9rqIoiqIoe4WP71WIwnkalSNEfpPL3Xvo9s+RpsOJszOENPvPA0LfZGr42V+v1egIR2tfzPX+TSwGNbrJkFVWWBueyWppiEeColzbqKiRIZY3MiNZAm0Rdva1PDIkQCqe+AOM9VHRI0wyPw7KFmgaRrVPbJ7yRXP3+zHqCixSEmmAndkPdva8ez7utZMgpxSFF1svKA+S5i2UimxtrKZe3C8VTHBeCrJPK2pMOn52M9b2YpzWsH0jtmES0HczMyQrQ65lI3vJbPhaZjXlecamKY5t/YpNjJAhmTptrEBZyd7TbJkHHPdgbg6OHIHDR4yoMRya4uBpCv3svdHMbKdCk80RD822h5fNfk61jcApzwyxnZKXWKPFWFFDxDQRChLnlabGSsv3zTGrsRE2+s72PtvFUjm/btavxzA2cbIsj2upV8NYTbWBS9gMk4vZOUitHelLuSYtTHbXBtbyKsbawpWNIw9bHN5jqwA5K4ru47L25MXOSRELqyv9ua4oinIt4XkeQRDw2c9+lsOHDzM/P79tmzRNiWPzr+deCBoAf/EXf8Gdd95ZuC6OY57ylKfMRNBYX1/nec97Hu9617u2rfvLv/xLbrjhBgaD/P+9K4qiKIoyHT6eFxGGc9SiQwR+ldXuvXR7p0h38desh0fF96kGHuHQx8MnCBZYrj2SE/4tHAnmCD2PVtplNT1HZ3CRON7ptEBFefihokbGeYxQIUHdDiawJwG6vGAhQX7xnpfAahEDrG3L1YwE8Xf6+NxtwFyOL0WJxUFQgpIyq1wsdUS0cP32XbFDAs7SNsZ8Lvpedj5+wbohJnAcM10B4nxtgHF1NorqfEyKCC6z+PPXwwoV0u8SPHbrlLh1MhrZd8kIqAEVz9SX8LO0miQxAkQnNUFluffWs5cIk12sgOCe1wC40YNKFebm4dCyeR8OTIHwTseIGlFkrKlqdcd2KjafjxwxGR71c/DpS3AKE9x3MzQkS0NEUHmGuO2R2f5S62OQmALhQWCO7fnQ3jTWVJKFtI55Xsn55UU0Of8YI7auYLMnwIohIg4GmGfYMYwt17nsN1KrQ+6jECNkyHNRiqAPs2PKtVvK+mGd4qB/HyOeVLPPOynGXXSPFmVl5MXIss+jRJhRyJhqMfq+VBRFUfYWz/M4duwYP/qjP8pLX/pSPM/bUjzb5VOf+hR33HEHYASOWTM/P8/Ro0cLj99qtfj6r/967rnnnpkcK01T1tbWCtctLS2V9oGiKIqiKJOSCRpBgzBo0u1fppWcIolbOxY0PHx8QipE1AOfagAVLyAgYqF2A8eDR3E8WKYZhqwPBlxKL7I+OE1veJl0R39BK8rDExU1MtbYXuBYgpLy3WWICWTVsPUdupRbMU0aNJOAowQ89xvXqqmMnXrh7/TPRjc4Kf2Yt/ISYWMRM6g3su0qmOvSLtmvzHwfl6FRRFHAdNRyGTMym39Spv1zdKeCRpS9z+KfSBElJCNDAuPu+Jbr2sBcN9dqqpb9JvKNDZPnZeLA0AT+B5hr2sZe343s1cWKBDJOQ6wAMMTWrwgDYy1Vr8NQsjFiCEOo1cyrWjXLe10jMKysQhCaTAqxwepiRQ2xZBMhQ87VfQa42RtSTLyPKRaeZq8gyD57pgi6nON5jCjQw4qlrlg3xAgTss822wP5cu9I9kgVU3/jUPb7DawgIduIeCFCkSvOiTglmTbNbJ8ijkRYAUTsrbq5dk/LKPGxSNAoEjOK1k2LzpFRFEU5eJ71rGfxnve8Z2QQ//Tp09x7770897nP3RMxQ/jJn/zJwuVra2u86lWv4q//+q9nery1tTUeeOABbrzxxi3LPc/jS7/0S/nIRz4y0+MpiqIoyrWDh+eZwuCBX2Mw3GAwXGW3Juy+X6EazNHwGzRDn2boUQ8Clio34wUhNwfHOVKNGCSwnnS5nD7IZv80SVLkw6Ao1y4qamRIwNGdSQ6jg10dTAHeGuNntk/6p5PrmS+zuPcjw6OohkRasH6/6zmMExaEDiZQej12tvkmpi8lMDtJ9sGoQOmo7YuCqEXiggS6d/pPoLvvoja47ZhWfJJg927GmwgYTeezWE5VnZdY90h9hWVMQDwC6p4RM8RqijSzm0qgm5jrPMBc301sYe42W4tUSz+LoOAWde4lJiNjOMyuU2KOEfgQRiaLw/OMqOH5pnC455kMjrMXYC2GwDO1PE5hRFFph1xjt1aG2NWJ5VJRRkGKqdeRpKZdw6Gp7zFIbH/J+brHKBJMB5hMCRGH3LHvZo6k2GyXFibLQuzbyP3GHbN54auKtf5qYoUpqZ1Rw9ToEFFE2r1XFD3LijI5FEVRlKubO+64g6/4iq/gF37hF0YKGufPn+eHfuiH+IM/+IN9bJ1lMBjwute9jre85S0z3/epU6f4yEc+sk3U8H2f5z73uSpqKIqiKMqO8PAI8L0qaTqkN7iYFefefVXZwK/R9A+x5DdYrnosRLDaDzgR3sS8X+XGRhXfg3OdIefTM6x072EwXEf/ilWUraiokZGf7TzJbPcYM6N50/k+KWX7d2s8yJ9mMtt7N7iiRF6sKHqnpH0HjdjmuP0kAdJVzEzzeUyAXDJtguz7qH8CyupjyGc3EL1Tqxphkms5SjwqEjZkeX5dWdZIEbvN0JBi0SJmSO2MAGslJVZTEfbayDVreqYIuGs1lSYwiKEf2yLgUrtCPnewgXkRNFxRA6dN1ey9n8JaC9bWoNM12RhJao4bReZ7rWYsoIYDIzCkZPU8MCJGKzX3/wpmbEmNF1dE6zvLathMFbfotwg9FUwhdA/Tlnhozl3aL5ZrIni6GRNFSBbSPFbwKbJhS531a1jRYxLcNmxm3zex9W2kxgbYcSC1iyRTYyeMulcl8yqfmbFXYuw095iiKIoyO2666Sbe9KY38YxnPGPkdj/3cz/Hxz72sQMTNAC63S4///M/vyf7/uIv/mK+/uu/ftvyOI756Z/+6T05pqIoiqI8vPHwsinPaRqTpD1mOS2vEs5xyDvGsUqVY9WU+ShhfeBzc7zA0ZrPQuRxqZeyHve4EH+Bbv/8TI+vKA8XVNTIkRc3Jtl+Wp1WAoFSw8O1e5IC2CJkyLZim7NTJMAsQV9pR9l72fnv1Npop7jBSFdcEI//EHsNesAZTIA5wgSJe2y1yBl1XrI+XzRYlsnNkp8d74pFRVZU04pl7nHHZccUrXOPVfS7WV8/qRFRw2ZCiO2Um6FRxwgaC9gAf0RmPRVANTS2ThLUl2yFXmLtikTIkO9i5eWKGSJmiV1SkB3bd9rjAZtduHwZLl00dlP1mrGjigMjaARZYXIpSt7rmfYEPgSxtZzaxFo0Rdlnqa0hY6+OzdSQfnEFDRE1omzw+R74FUj7EGbZJGIlJc8Ht+h6Uakwsbc6ku37Mltra+QRYWIaQuy1lDoecj0EH9MfruXWbv53rOxe8LF96tpmgb1n3Qy8WTmRFonFiqIoyt7y3//7f+exj30sT3nKU0Zu98Y3vpHXvva1tFqtfWrZdt7whjfwf/7P/9kTy6tms1kqXPzQD/2QFglXFEVRlKmxU+VS4iw7Y7b/hjeCZY77h7i+7nNjY0AzjOnFVZYin8PVhM2hz5lOyvn0LGu9+0lnkB2iKA9HVNTI2M+glARXi+x+3ILC0q7dZlEEmNnadWztkHyB7KJjuOwkKD9L8u2SAr1ibyT9uIq10RE7I7dgc568LY17HAmEirDkik+yrbtPCdrKbPwiC6+0YHkZ+dn0sg8/t0wC2wkm6O3Wq3CPTW75rK5jhBlbFbZnaVQw16GRbSPWRJXsVQeWGlCJjJCRJpCmpt5FbwC91BYCb2GC42I11sVmQ7jZGTK+N7N11aydIig0MOKKn5osjdU1mJ832RmV0Bzf80zWRhKbuhlJJmq0O9Af2OPIuwT3JVNBbKhw+kPGRZHYUwNqXlajwzPXJvDNK83212KriCL9Oy4LwbWbmjUylmOsbV7+Xosx10zYrZiQH8tynwSYsbWIfRakZJk5TttcIXS394AKGoqiKPvLc5/7XN7whjdw0003Ua1WS7e77777+Kqv+irOnz9/oILG7/3e7/FTP/VTbGxs7Mn+wzDkSU960rblaZrywQ9+cE9rhyiKoijKww/rz2H+Bd2bf0fn/CPcUK/yiGbMLQstGuGQyE+IU48A+GyrxsVkjS90PkpvcHFP2qAoDwdU1MjIW5XsNTKrOG/BUjbzXnzpJcOjx2Tt9LGzwN1gO7n3stn+45btJ0XChgRQQ+ez1FvYMisfa0eUnyWeFx7y2TMinMgx89dIgppx7nt+/7MQFORcA2yh6GVMkF4KQ/fZep3z5zertkgGhGRpuIKGjLk6RkQQUU0C+G6WQRBArZ5lZXSh14f+EDrpViGjhRUqxIbJrZ8hGQCScSDF2F1Loijrr+MhLC1AtZIJFn0jqPhBlmUSmQyNbmzqaPQH5r3bM+0QgUEKakshcgmay3gEe70CZ51YYUmfNIB6JbO3Ssz6OLPd6mDEyDVslorY0/WwmRtFdICzTr/slCJBVYQ+N3NJMnTkmswaVxCUMSx97RaHl8y2LmbsVbAihlu8fbdtibBZNAf9fFQURXk4U6/Xuffee1lcXCwVM4bDIb1ejyRJeMITnsClS5f2uZVbSZKEixcv7pmgAfAP//AP25a9853v5AUveAHdrhYTVRRFUZTJ2T9j4UPpYW5uwm2LbW68fo1qLWbuoolgXNxo8JlWlfV4nc34vGZpKMoIVNRw2K8Zt/JIEkscsWaB7dZHggTQqtgA3qgZz162fynUK4F+14pJ3qfxmt/vwN24DJJ8xokgM8Mb2KwAET2KRA0JhHqYoHsDey0WsuUXsZZhRTZT7qztvfpnR8QwyYqoA9dl389gBTB3HLn/NKe5ZfJ5WjsgqdUgQWxpkwgVDwXqseOwmX2OMPZKpLbfotBkJfS60B3Y4H0re3czNGTGvYgasfOSYt0SwJbzdLMkqhgxY3ER5uaN9ZQQBpAG5sfD2AgdvR60NmCzZYqLrwPngQcx9TSGWKHGrYfjOccTCzm3aLpksDSB+YrJFImHpij5et8WB7+MKbLdYmvQHqz9Vhmyj92QfybJeHHHPGytE+Kx9Vq4iBgybaZE/h4D05fzmHMUoUlsp1pYMaeKzeaZFR5GTKxjrlFn9OaKoijKDmg0Gtx44428/e1v59ixY6XbDYdD/sf/+B983/d93z62rpw0Tfnf//t/89KXvnTPjnHixAnm5+e3FEgfDAY88MADdDr6r5KiKIqiTMf+RbuW/Rq3zg246boNFm8PCOYr1C70iFsJG5+qcqq3yT+u/xO9weq+tUlRrkZU1HCYRtSYhQ2T2OBsYAJwbvAvH8CTY7nZFqPaEGBsWK7DBPbuzY6T338+uO3WlXD95/cbt21lQo9LkVDRxtoPeYyePS6/r2BEjGVsdkaVrbUGirId3Bnjo/prt30phdIDzNg5ChzHnNc6JsDaLjhm/nrnx7p7vccRYi2kXJupGtvrZ4iQ0SSzA/OMoBEEJhMhTsyr1zO2T3FiMxMkO0NEDTdDQ66l2CrJsjbbhRt3HKXZvjZ7MOibttTq0GxArekTzFXwkpikN6A/MMJGvwebm7C2DqsDKzKcc/q6ne2/gc3MkBojIVbQcC2w5jH36Jxv7Ld83wgqMbYAeRtzPPns1tKQLIm9uj9HCZ5u9pJsI2KOXP8Gtu6IZKRJ5pjU4JhEcJFMm7wVG2zN1pIxLCKxjGfJ5plGvJ2UGHtue7F/RVGUa5koivjhH/5hfvInf3Lkdh/60If4zGc+c8UIGmCKdD/vec/b02P8wi/8AidPntyy7MyZM7ziFa/Y0+MqiqIoirI7lioBNy50OPyImPCOY3iLTbzL6/CZyySfhned+zQXOv/M3k2XVZSHBypqZEw7Y1gS3yWoOm1gUQJ69Wxfndw+3MCsG4iOs9+Ns0+R4J7YLuVtrlx7ovxxJIjoFiifVVHdSci3ZTdI2ycRoRKspVIdO/Nf6hi4SfxF9TKkz2ZfRsog4o4EbWXGfIgREK7HBpDztT/yFCVWSuB4VNsjTLC6yvbaEJKZ0XBe89jgfjU7gTiBQWIt1LwutAfQT6AXmyD+GlszNKRGhWRoiOWRWLFJn+TbLvVrJKNEAvFxaiylAObm4LqTAfUbj8DCAqytkZ67SK+X0N40gsZmGzbacCE1WRqX2XpPSaZIlyzzIlsumQESdJfPcs3mPViom8LkkrGSxKbWx2ZixJOLGMFKsh7EvmqvLJ5gtKAhY12QMSPCkoe53pLFEGFEGckoEcFh0rZHmOvm1jARBhjhS9qTz0CT6yMZM0NmZxUlNWzkPpxEfFUURVEm47nPfS533nknP/7jP166zZkzZ/jt3/5t3v72t/PJT35yH1s3nne84x0Mh3v3f8+Pe9zjuP3227csS9OU3/zN39yzYyqKoiiKMhvmIo+jix2qj6jDLSdJF+ehfh4+t8YHLg843f8E6b5G4RTl6kRFjR0iNS7E1mlckCxfp2GACUCXPaaKPOzFekbeR7UNTLBPZkKPmhFdFMB8yBYo299eBepHtWVaaywRFIoososSmxyZ8Q+mXyNMQHYVE0yWwsz5ehpFQokIQTITfVpbp1GITZYIVlK/YREzFuvY2g4uRWOpbPkoF8m8oCGWU+7M/DlMQF8EDimeLSSJrXXQy/azGcNqbISMHrY2SM95iZAh4sGQrVkbefs2t97CQ6KK00+1AGpVqNaM7VO1GcLRo+bVaJCubTLob7K+AZdXYGUFLvXgEqadeeFIkLaI6JlgxRtpW+S8SzvqtUy0HEIUwVEfoi4MhkYQEOus0NnvgJ3dk6MEvqLsrfxnv2A7ty8kOyPCCjySSSRCjDveJjmHUc+BouvgZnd42IwhuZeZ8LjjcK3GNFNDURRlNnz1V381b3zjG3nEIx5Ruk2/3+c//If/wJ/92Z/tX8Mm5Hd/93f54R/+4T0VNZ785CfzuMc9btvyX/qlX9qzYyqKoiiKMhsaESwsDwiOL5MeOwL1Ot6p8/zFJ7u84VP/zEb3zD62Jh+tVJSrBxU1Mqaxk0oxQVcp2j3u1hfLFY+t1kASGB1n/SPB9GnsoFKnbRKELPpdPoidD8zJzPii305jWTQJ4wSNoqDhtMFRQeo+uIV+Jfi8iQnKrrFVGMq3QY7pvoO51nPZd9nHTvCwM9RlDLgWPH1MwHsVWMIG+cvqZrhtLNpmXIZGmeVULVsngsZC9l0EjW52Hmm6dRxLbYXLmPoUq9iMCwlIi5ghL8lWEvFDsnCKzlcyqtz2PlS4vAnz8yZLo1YDT4pnBAEsLeM1GqTpJp02rK3CxTW4kJixIQKJiH0uct8NsdZTkr0g4oqIGh4QeKamhx9khcm7sLEJ/RR6idn2UHauUr/DFXh2wihBI/+5LMsnf3/mLaGknsUQ09/u79yxJ8+QUWPPzc7IP2/yGWdl7ZS24Hwvex5OIlAriqIosyeKIt773vdy8uTJkYIGwHd+53dekYIGGDusixcv7tn+b7vtNl75ylfu2f4VRVEURdlbmmFK47oUrlsyQYnBgOTsGv/3Xz/FPev/sg9ZGlsjb57nk6bTGPIrypWBihoZEdPZkgwxAc5Jgvpi05PPlnDFkXzAz92myEt+FEnus8zeLyuSmw/SSfC7gi0ynqeGDarOWs8dlTnits99d7eTWe2jAr8S/K9hro3YevUwwXWpx+GeuxyrTAByt6thgvvzGLuiFab/56EKHMl+d4HibBsZh93sNclM+7z/f17syAsfkqHhChqS9SC2U3PY850Dmp4pxp0CYdaJlQr0+6b4thynh6lNcRFrOZU4x3Ith6R2hliqTRJ8zgsadaAZmP9vmF8w79UKJMOEYH0Dzp6FSpVgoUltYZU0HYAHjSpUBrYuRpnVUIq5Dp1sO+mnCFP/pJq1XQrXD1MYDsHrmyLpG21YH5qsBqmrIaU+Rehxsz9mRZGYWDQmimyeZJkrFIg4OMSMCxHmiu6ZccJG0f2dON/LxE63/V3sPV82dnxsttOA4mycfN9ohoaiKMps8H2f97znPTzlKU/ZUvhaSNOUJEl417vexUte8hIuX758AK20vPGNb+SRj3zkvh7T8zze8573cOedd3Lo0KFt67/lW76FtbW1fW2ToiiKoijT8ZULX8v/dywiOhRCowadDt7mJvGFFt3NDkm6H4KGh4cPnvEZSdOd+kAoysGiokaGBCqnCdBPum0fa5uTR2Z2g63JMOtHSZ+tQdhRs7BluYet99Fjq02WFNLuYQKvbnHmnTIqOFgWqM9nH0SYjIWj2fJzmKBwmZgTYs4lxPSRzODewAaly2aq4xzbfR9g+8vHFhyX4s/TjK8Qm/kQYLIZin4vwX/ZfsXZrsh2q+glgd68NZDUfpCsAxEI3DoakqEhgkbDM9kPYQieZ+pENOcgDKDdhmQN2qkJeq9j7Zx6WOFGroUrasi1idkuKsGXI5EAAQAASURBVBSJNE1sPQYRYhpAswbNJjQa5r1WB5+EdH3NZGs0GnhhwOKRiKPHBmy2TQLH+gOw0jNZCEVjI39fSY0WETakrySLwwM6Q1jbMAXLhzG0Y3M/tbJ+EfuzXq4fphlHeZGqaH1RkN7dPs2tk2wayUKT73khQIQoefYV3btuXZI8Ptv71V0+6pnhnkNM+TM4f7wKk4tmo74riqIo4/F9nxe/+MW86U1vwvf9QkGj1WqxsbHBYx7zGDY2NrKZhAdHrVbj2LFjhOH+/RlVrVb5lV/5FZ71rGeV9tH58+cPvG8URVEURSknIORkvcmJ+T5eCN76Bpw6DRtt+qc79Lp7+f8WZoqg55nIo4eXiRoepEOVNJSrEhU1MsoC37NiEquYAJthkLdogfFFnEfhBhpd65z8Pt0AoswId9vuFtKWwrv5Iuc7oWzm87iZ0G7wMsAIGjdlnxewWQCSVeOei9RFaWACyFKguo+5DvlZ6fJZ+swNyOaDtJvAfVgB4CgmsL7G5GNNipSLiNTGZJG4AtMQkwni/salrO9EoJCaKVKjQWa0yzZiOSXCQF7QmGeroNH0TeZDmKlzcWLaEARG2IgiWNs0hcFXsIXNZZy5tSgk00bGmHtfjMLDZlTIS2qA1DwzGaLRhEbdZI+EmTfWcL3DcNAhrG/gNesknQGVitkmjqGdWFuleaz4NcRajkmw3a01Us++Sx9LP0oGgT8w7eth9t/CFkeXmiFuzZedZEbJgz7fh3lxS5blhQZ5Drl2XylW7BLhVAQO+b0IekVZGp6zPJ+tIctGiYrjRISiDDeP8jGUYO7bsqLzZeJP0XdFURRlMv79v//3vPnNby5dv7q6yite8Qre9ra37WOrRvPt3/7tPP/5z9+341UqFV7xilfw4he/uHD92toar3zlK/m7v/u7fWuToiiKoijTc1P1Dp6w+BiqwUWS1T7eZ87gza+Qtvts3BfT6kXjdzI1VszwCMAL8AjMd88nTjqkOza3VpSDRUWNjLJC2qOCYLJegnO7RaxlioJps3K38zCB/AAbkHXXgQ1297F1CwQJ/Emx6Fm1qWx5fl2ZiCDBUamZILPjlzFCwApWuBARZh0jehzCCAYSYAdbByFku4BR1mYRSRJskfC1bP8S4G4xuaghvxcrpjq2XkHf2eZidl4ycz5f0DyPx9a6H27xbcHN0HAzHVwbp20ZGj5UI5OhkaSm6HV/aPa7NoSoZTI3zg7gdNZmsfySYLtkKokA0HfeyzJ0YLslmNRLkT6R9tcjqNdNYe5qzQgWnm8soNqbpqZFsjbEY4NeH9bX4NIl+Pw5uH9gxsww2/8jMGPpMmbMuVk5bubMEUwxdxHVRAxIsJZhITZDo4XNgMoXSU/ZmYgolmxiJyf3S5Gg4d5Tbv8W/W+OXCf3mgTO+qL7xn2e+c76cc8T95pPImjk9yUCl2RUucXlZftRolFexBDKrMgURVGU0Xzt134tv//7v1+6/g//8A+5++67ryhBY2Fhgac85Sn7eszv/d7v5XWve13hun6/z+te9zp+/dd/fV/bpCiKoijKdARehWPV61mMYDAIaN2XEpzbpLK8SdxJOXVqkbX+rEO0Pp4X4XkBHqF590J8P8TDZxhvkCRd1HpKuVpRUSNDgoxuYFesSIa55S4SPBcBYFJxw/WhFyRoWRSw280jxt2fBPUkoJhvg8y2BhNUzTvrpdjAfAUbvJ8Fk8x2Lgq4um3rYtotM+UjTGD5Okzwv4Gt39ADzmDrXbRz+xKbo1FBy/x1EUHItaAKMf0+rf2UZHzEGNFAMg4itl4XaasEnctsswRpUy37LGKOCFhuDQ03oyNvo5S3nKpGEGSR6eEQun1jM9UCLsSw2TNtbGH6XM7NFVbkHhQrKlcAGiXU4KwLsQH8vKhRDY01Vj3L0vB9SLP24hlBptOFtUzMuHQJPn8f3Ne2GT9p1gcLGLszEQnns3cRIgJMpsth4GQElQhafVMzQ4QKEcD62XUQUUMyBmRf0j89dn6/ST9AsZjpCg5Fn4uOK2PcrQs0iSWUu3/YKnTgbDdOSCzbNr9OkPEQYPo1xFxLt1bLqLYXfc5bcymKoijj+Zqv+Rre8pa3UK/XC9e//e1v5wd+4AdotVr73LLRnDhxghe96EX7drzv/d7v5Q1veEPp+p/+6Z/m9a9//b61R1EURVGUnVHz5rit8Vgi32O1U6V7f0DgpRxe7NDrB3xuZY61wez+qvQI8fwqvhc9JGQE8h2P3uASw3gDFTSUqxkVNTLE6sQN9o0qKuv+roL1yN8csa0bPJRAqGv3kz+OG6gTOxaxjZo0sJmfhS2/d4/hfpegs8xmLqOXvcZRwwR2pXjyTtrtthW2zqaWWihyXhew18LHFmKXoPYyZtBXMdfKrYGRR6x2Ks7xi7ID5HOKtVGSmeDr2ALErvXPpPVbEmzRcpkBP86GSWo55LMXpP1iK9bFWgflBY3IeUl/SbZJEytqPFRDIzIWUyIQdAewmZpA/RpGEFjB2inJ+JHAviuwdbI2TfpPq5tJ42HraEigXbJQKh7Uq6aGRlQxtT5IYdA34kYYQOAby6wkNsLG2TNwqWWuQRdzPjJWGll/SF+LPZNrTya2V/UKLM5DtQ1+C9YTm30hAo6IGm2s/ZQIO7LtTmzy5F6WTJiQ7c86V5CQTJ/Q+b0gGR6ubdk40a8sw6Yo6yq/PH98d7+TZHW424k4JEKdW+Be7LPWKS4OXpQhUrZcURRFGc2dd97Jr/7qr3LTTTcVrv9f/+t/8SM/8iNXnKDheR533XXXvh3vW7/1W3nd615XKvz83M/9HD//8z+/b+1RFEVRFGVneATc0Hwii2FEnMKD7TqbQ49GmHCyX6E79PlcK2J9MBuPFs+rEgZNfK+C70cEfpXQrxH4FdI0pt0/Q3+4OpNjKcpBoqJGRrdg2bjAPtjAfsDoIH8+ACbBNfHWj531ZcHuJiYAJ4HicZQF4Fy7oaLzc624ROxxvfWnfexJDQ+ZgT+KvNVT0azoose8tHEVGxivZcdLMcFnsnUVjCVUA3OuUpS5KJgps/4l2Ft2baQ9EniW4HYfI54EWKseacc0jMoWKmqLZJm4AWM360SycAJswFwspyRg7WZpyFhtsjVDo+5BNchlaAxtIXCxUJLgt+e0Rfqk76wTQWPS88wLNjj797AiihSzD0MeqpMB0O+bV7UGtSp0e9BqwWbb2FG1O6aNrv3WMGujFPyez44lWUKwNbNlCHR6xvKqEhnxJE22Wmx12JqhkRc0RIiadAzkkX52x7T0f15ckGWSHSQipwiIsk3eaqoIuSZufZpRGRVlwkaSWzdJVkbRdq41nAg88o/gpOeSFzA0S0NRFGVyFhcX+ZM/+RNuuOGGwvWf+MQnePGLX8yFCxf2uWXj8TyPpz71qftyrCc96Un86q/+KkeOHClc/453vIPXvva1dDr5amqKoiiKolxxeAEng0dRDzxaQ7jcDznfhetqAWnq0Rr6PNhO2RzuvraF71WpRIeNkBHUifwGoV/FJ2CY9tjonaHXv8TsPFcU5eBQUWOXxGy1LZqEFBv0dgODo4J1UqBbshDGkRdR3ICgBPUmESekNkANE6Sdz5atMtl5dzF1K5rYmfOjLGXKsjPc3xVltAjDrG2D7JhyDmcx5+0WupaAsisoCWIdVMteUbbvFlaYKeo/N2tEruc0gsQoXGurcf/UuTUC5PpLjQcfEzyXLCFZJxkaEuitOK8IK2jMZa8GRtAII0gSU0y7N4RuulU0EYEEthYAF5HIw2YrTJudkV9WwdpqxdiaFV2glRrRYjgwr9YmrG+Y9fPz0A6h3YbVzHpqcxP6PbNexv4AI4hFmKyfeaxVl4hXKTYrqJa1Y30I6WWTEbIWm6wVyVySZ4hkg7jWW2JNJfZgu0WEWrGPExsmyTiTcSPXTey2YKvAIMKoZMXkj5EXAPJ2U4mzvEicKhIti+7/Uc+CUciYlGsghdqLjlv03cu9irZVFEVRtlKpVPjQhz7EyZMnC9evra3x5Cc/+YrL0BDe8573lGZN7BbP86jVagB86EMf4tZbby08Vrfb5Z//+Z954QtfSL8/6TQQRVEURVEOCs+LuGnu6RwO5/A8jws9ONeO2Riav6hrQUhr6HGu16E9kR9K4VHwvIha5SihXycKmlSCeSKvTuBFpMQMkg4b3Qfo9M+hgobycEFFjX0iH/CSgK5bc6HI6ipvY9PEBIXFOqnsWEXBRDcoOU22hdgwyWzrKnAMUyDZdeBz7WtcYoytSxFFwWl3nezXDYa6Fk55Oyh5SbBSaqVcZmsg1Z1xXkSErR9RxRR6DoHPsz346QZbR53Pbggw9Rvms+OvYsWYsjoL7jIRc6RoeSd7rWELX4to4tp11bBFwd1XHVOfQrIO4sRYTnWzdjWBJd/02QOJKQwuAfuB85JsoWn+6S6yASsT8MRSayjnPIReD9bWYWMDPN8IMuvrptYGGCFjODAiTRhCbQDV1AoYEVtrtlQxRdJrmL4AU0fj5kMm++PcZVjtwVoKvRguYe4HERNSttbQcIuD97J1eVHMLQq/0/8d6WPGdYTNRkmxYpcIMtKvRdlT+XuwCLnf3Lov8izJW4fJMhFK8uLdJMJGngArzoktmZu11MX2f15wzWeKucvLhGNFURRlO4uLi/zO7/wOj3rUo7at29jY4OzZszznOc+5YgUNgFqthuftzdP+a7/2a/mLv/gLgNJjXLhwgWc+85n8y7/8y560QVEURVGU2eMRcCQ9xlwY0o1TVnsJ5/td+gxZ6AdcjgI2Bikrwza9kYb2xfhelWrlKNVwkUowR9VfoOo1CKmSENOnTS/eYLV7L93+GdRySnk4oaLGAeFhAqQNrBWUW2jcfcz42OC6FCRexwSki2YWj/N/d+s5TPo4G2AEDCnCLcXR3d/L8qIg7G5wA5muMFE0o1u+S3BYMg8ip71izzQKsepZxgZDN7C1Ccr6LWF3geYypGh3jBU4xMJJrItEUChCLJPk+s1l7zL2RFgTYUOC9lIQewEjZDQwgkU1cGpoxCZYL3ZTPnA8MDUk4iE8uGmyN0TUEIsuac80/6SmbK/h4I53t2C1zMCvYi3EgsBYT4VZ+8PQCBuBbwuIN+fMshTwA0guQaedjSMPaj5EPlT8rPh2BEuLZvtDa9DqQt2HZtMca6FjiqYnqc1QkUwMscjKW07JSzKJ8ue7hB3TLaavtSF9JTZlIma4GTRuWMkVHdw+lnvDvbfyzx15LogtXNmzwRUrxfpKMlSkr8rs50YRZ+0LMWM4xD5rM9e0bYJGnlEhLP1fQkVRlNEsLCzwsz/7szz72c/etq7dbvOa17zmiq8N8ZjHPIbl5eWZ7/f222/ntttu453vfOdIweTMmTP8x//4H1XQUBRFUZSrCp9GcIjlcJ7Ag7V+wuYwppsO6Ht9OnGD1jBgY5CwkbQZTjHd0/cqhEGdeuV65qJj1LwFaswRUCFNE/pel266zubwIq3eKXqDi+hfr8rDDRU19oBRvvGCiBMy014KSxfNRI4xQTewdjBFCeejZg67y93Z1SmTBwrFRkmC0UWzqCWIOY2o4Tsvse9xCy4XbV80M1qOL/uRIsw9rH++WOeMI8EEPn1M8HgFU+y6KEtjpxY40zDAZufItT+EERik4PEaVtzII0FqqeOwgAk0L2ADuiL+SBBaRIw6NlujRmY5JTU0YujGZjyI0AZQTaDeh84Azqem7VKHws1G2G1fuUH0CFs7RepPBNk5LmXn0qya+hn1Ohw6UWHx5iUqjYCKH+P3u3i9LsN2n2bTZFmEgam5ESeZ8FGBesMIGZXIiB7VKhw+ZAqQr6/DqQeNrVW/b4qQD4c2C4KsLVJ0vMf27Ax5SUA/30dufY9J+zD/LBBhYogViSRrpih7Qvra/Sx1PqQui5vNRfa5islaEeuujexVZIHnfo4w160HnM+2z9cAmQax+JL2bWLvbWmr+1woy3bzc8vdZ+e4mhyKoijXIi94wQu48847+b7v+75t69I05fWvf/0VL2gAvPCFL+T2228fu91nP/tZPvzhD4/drtls8v3f//085znP4elPf/rIbdfX13nVq17Fn/7pn07cXkVRFEVRDh7fi7ih+lgORYfoxim9ODWT/bwuIT6R75mJjmnKZrJKb1jmcWLxCKhXjlELDzEXXU/TW6LOPFFaIQV6Xoc267Tii7SHl+j0zzIYrqOWU8rDERU1ZojYRMnM5VFFpVNMELio+G9RsF489yUYmbeqGido5IUW1/5FXpMGDMsyHWT5KEunEBu8ddsiNTtkRneb4qLR+cBpfp3sbz7b3wamn4cjfieIoCLtbwH3YIL5Usw5f26jbHAmEbdG4dqSST2PouBqExPMd7NYioLhYOugiKAhdlPSP2LTI5lBibOuSiZoZNHbQSZodDD9vIKxVhoAcQrDjslOWMfOthfbq92UvyoLusu5iz1bAlwH3AjcAty4ACePw5EjcOuzH8PcF11P/aaj5leDvimkcf8DVE6dJo679LpGnFhchIUFqFaMgFGrG1Ej8I2oEQRwaNlkeLQ3wfPgC1+AlRXoDUw/DVJ7f4kF0gCbvZIXNaS+RtE1lMyCDtNlRbmCZt4+CbZaWo3LTHBFDff3bjZHA2NvdTz73MJm6bjCrHuOMt4uYoVcN4NkN/eTnJ8UXheBtkggLTr/IpHY3bfaTymKomzlO77jO/jFX/xFDh8+XLj+53/+53nd6163z63aWz75yU/y/ve/f+Q2L3/5y/mKr/gKnv/854/d3y/+4i9y99138wd/8AezaqKiKIqiKPuE5/nU/Sp+6rEeD6n5AXGSkBAz7zU5VAmYj2Bj4BHHHQbxKFEjoFk9QT08zFJ0M3PpEg2vSSUTM7r02fRWWU/Pszm8QLt/lkG8QZyURRYU5epHRY0ZIQFosbmBrdkX+QyMUcWjRz1upg0GlwXiimxi8rOVp0X2VyRqiNAgs69XsBkF0hdSmFs88CXTIn+MomC2IIHWTayV0iS1B2RGfw2bdRBjsx9Ctgcuy/Yn2xVl3UxKBRMMbmZtuMhWgSfBCAkyo18yFOrYbIhRx21iLaiGzr5c2ykwgWXfObbvGcup2MnQaGHEjAvZu1zfs6ltvwgabXY/P6Do2ocYqzDJPJFxswAcAW5swGNuhSd+52NYfvpjqd96HK85D0EEqW9eGxdNCkqrRXC5SxxDFMKNNxgxo1oz2RlhCEH25EwSU0ejmmV1hCE0G8Z2arMH67HNPOplfSnZCtJOsZyS+0AEi7LrN8CMz2ltmIpqQrjrJNNAgv75/bu2XuNquIh4I7VaVjHn7Aq/7m/kfpEi8mIZJfty33eK7FOeCzJGis5z0s9+yXpFUZRrnX/7b/8tb3jDG0oFjbe85S389E//NIPBtAaK+89Tn/pUvv3bv33X+3nc4x7Hm970Jm6//faxVlb/+I//yEte8hL+5V/+hbW1tZHbKoqiKIpy5eERcKx6OyfCR9BNEgI8Qs+jS59KWuVYtc6xOsyFKat9j6g0POtRi65juXYrC/5xljjCPA0qfkSaQpcB6946a5xnPT5Hq3ea3vASSTJqqrWiPDxQUSPDtWPayQz7GBMoa2ACwyk2cFlUAHyWjKqj4b6nuW2LgqJuQH7ax58EO90Z/m5bKthaEE2MrYwUGpd+SthavLiIouChG/hM2Vr4d5xXvlsQu44N+IvdlNQdmHRcuP27U+qYYLxknHQxQkF+VrvM1G9hraNGBVc9jB3QYbLaGNjaAgPns1h1iQWVFNsexKYveqkRKDYxAfbz2ALYUjfhDPZe6DEbQaOMOnATcDQ7hmSMLGXn+OSnR3zlK59J9Njb8Q/dYNIp6MCwB8EceEdhqQrXnYeF+wiiC4RRyonrYa5psjPC0GRnpEASmyyUbhe6HWi14NQp831z0yzrZLZbIVbo6WGu5RpbC6dLNo5kX4wbO9P246QB91Hi3zQiimRtbQIPAOcwY6OGrSMkooLU9Mjvf5bPTKlLBOaZsxvxNo+KGYqiKFu58cYbeec738nS0tK2dWma8t73vpdXvOIVbG5OXwzzIFheXubkyZM7+q3nedx1110873nPo1KpcOjQoZHbJ0nCxsYGX/3VX83ly5d3dExFURRFUa4API96sEBIjXY85LpalSSFNPU45De5sRlwQz0m8uFSL6DqRfkdUIkOc7T+xSx7J1nmMAt+nbofmsyMOGYtbXPJO89acorW4DzdwSUGw1VUzFCuFVTUyCizH5k2iCe+/jFbCyPPql0yA1qKRueFg/xv3MyGUedUlE0yLQnWwghM8FAQT/sBRrSQjICLmAC0zKI+j/W771DOqGwNtz3jkIC7+13qFSxlbelOcCyhSPjYiUjWxogCbcws97UR+5DAeIjpMxEnXCSoG2L7fyFrm1yXLrbGQIq1pxJLqiHQSu24kxn9EoiXzAM3UwKshdYsKLMFSrNzCIBjwM2Y5ItH3wzPf16Do//m0XhfdDMcugn8RYgvwPlTcPkyLC+byub9TRjGeM0m4VKTE2mb6nwFv1HLKop7Rs2IE+h2SDqmPHarBatrRtS4cBHW1uBSFy5n/VDBZg+tYW3VpMaIvET4cc9pVuSzM4osl2C7qFC2jZu1JKIEWAE3xYzbdazVFlhbLcngiNn6jArY+iyahUAoxNh6MZOE0fLP07JMt0meRYqiKNcKS0tLfOxjHysUNAA+//nP89Vf/dUkydXxx7bneSwuLk79u6WlJb7kS76E973vfXieh++Xm6CmacrFixdJ05SXvOQlvOtd77pq+kdRFEVRlGJ8L8QjpJsMmQ8j5iKfld6QuhdxY7PCI+cSbp3rkOJxqdegGRg/g9CrUg3muWHu33CY61mgyXwYUQt8k5kRJ6zFXS5wicucYn1wmk7vAoPhKulU1W0V5epHRY0cReLGNH9WDLAe+RvYYN5OqGJm6otgIsG+etauolI/47zgXfIBTDew6C6bBLGamcv2IYWR3Vnnm5gZ22TbzWNFIAl2tigPOJZlpBSdzzSk2KLMbWxgX4Kt4Q73vZuA7AAjapyZcHspAF52rAhTWPwwJoC+isnUOITNHuhj62mIFViTrVZg0k9etk6swtwMkfy1meWs+DKGwGewlmZ3zsOXPNLj337dHAs3LUGjDmEN/GWMXLVmUi1IYTiAjdPQ6Zrq3lFEePQw4cnjtir4cGh8pro9aGfSQ2/IcBjT6xk7rkoFen240DXjfDVrj9gtyTVynwsiEPUwYx+2B8/3EvdYfey1mlTgLBJN3f+NkswPyYiSTBrJ5nEFEbLvbuHxhWx9O7ffaXGLmov9VNGzeZJnTJF1lwobiqIocP311/NHf/RHpdkI99xzD//m3/ybqypgf+zYMd7+9rdPtG2/3+fixYs88YlP5G1vext33HHH2N/cd999nD59mmc+85n0+/lKcoqiKIqiXK0canwxtwZfRuT7HKmFVH2P0PM5Xgu4bR4es9TiEUfW6fcD7t2MCMOYk7UbuL3+1VwXnaAaeFR8n8AzNUs3hwlrwz7n41XOc4q1+BTt/nl6g0ukqf4/hHJtoqJGCWKXBCYYJlkIEgwr+nOsjwlYSnZGPpg77exrCZTVsMJGkH0fOm0pChqXzcR2953/nA+mTtrWECNQiFWS/C7BBHUlGCl2NJey5WJ/dAgTSF/DCkKTME0gUYoEj7MCk9nzXu4l5IWKKymQWXZebrBZArkD5yUBZxlbFaxYIdZTIvhIVoZk3njYLBHJltiruQFlY1rEAhlvi3V42lfCM55VxbvpOCwtw+HDUBeJpgnBTbC8DrUqNJdNkYz4MshMymPH4OhRk6ERx9DLxAzfh34P0oThIGV9zRQEX1s1tlNxbLOO1jGWUzG2VswaZvwPMX0u9UrabGdW2RqTjFGpZQHbBQ3XHmrU/t0MDnfbAHOP34CxVDuHEeuGzu+KjinHFQuz1og2TEKKFVJGCb0eW8UW9/zHWd8piqJcqxw/fpxf+ZVf4UlPelLh+k996lN813d9F+fPn9/nlu0f7XabVqvF//t//2+ibd/1rnfxjne8g7/6q7/ah9YpiqIoirKfpGkMXsrRasTRms8ggWbkc0MDHrPQ4bYb11i6eUh/dUDzQptHLsA3X/8CktQjTj36icfm0GOln3K5O+TsYINzyVkuJvez0X+Q3mCFJBnlb6IoD39U1CghwAYdu86yGtb6SaxTEmywvOtsn2faoNcAE+yMne9yDKkDUSZq5I9bVkejKFg5bXaJBAmH2ELp69ji0y4yS1tmrNcx5yKB3VEB8RQrLhUFuCUYma8LIJkjPluD76NqB8jvygQfqbPhrivb3t0nWVsky0GEnr0sk+mOZenDQXbcZtaeBbbaT4kdlWRgSJ9Ln3UxwfkEaxlUwfb/fiDtamAstW4GHh3C047DdYcwGRaNJlx3DG64EeYOYXvcg6gOUQrRdUAVFirQbplsjWYT5ubMmW22TdGMwcBYT/X7DFtdWmsJly/DuXNw6jRc3IDLXZuJMcAE4sWCysMWnpdrELJVRJtGTJwV+fslL0gsYXrNzQwraqN7P7j3QRO4PntVMKKGjMNRz6JhdkzXEm03iNWcPGPKRCM5D/dc888bV7gVMafcWERRFOXhTa1W4xd/8Rf5xm/8xsL1p06d4qUvfelEwf4rjZe97GUTb7u0tMTLX/7ysdu97W1v4yMf+QhvetObdtEyRVEURVGuVKrRYU74t3C8WuXW+YDlSsqFnsexGtzSHHLb0XWWv8QnvOUowZkNnraxzpddf4R+9zIb7Qrn2nUeaEec78KpTp97B+c4G9/Lav/zdIcrDOON8Y1QlGsAFTUy8gEumdWLs1xsYlKsjYmHrZ+RFxdCTKBrlC3QKMR2RUSSxFkmx88H74twz21UELGS7XPaGdEDTLC2jZ3ZLwHvPH1skFBmTYu90aQCTREiXCxig6GSgJefme0GKANsIH7UsfPHlaBskFvuWtGkBcsDTB8dxWS2gKm9cAHTf3sR0K5ixCO52eWcpV6HCBu+sw6sJVUjeyW53w8x1x1M4PsIti9n/U+se83c2fQLwB3ArcAjqnBDpl8cOYqplXHksMm4WF4Gv5Kd0SVINqBzCSoRhCl4IYR1k9WxuABRBZLEpF/0+uZzrwetDZLLa7RWBly4CJdX4Mw5uPcinE/MvSMZLZ2sD6W9+SwWEdbm2HoPdNlbkUtwnwlFYxXMuFnGPuc22X5fu+M9f5/J76uY/thgq/1WOuIlzLIv5LleZjHlfhfx2j3ffCaKrJfnj6IoyrVItVrl+c9/fuG6l7/85Xz0ox/l7/7u7/a5VbPhhS98IZ43m7zcT3ziE7z61a/m7rvvflhnrCiKoijKtc585QYe2/wivngh4Ja5IR6wMYhYriQ8Yr7NdTd3Ce84CTcew1++zKG5FZYutene2+H0F+a4b6PBmY7H5za73DN4gPt7H2Ozf4phvEmqRcAV5SFU1MjI2wqJUCHrJNDmLgsx2RLu72RdDRNwJfvNJjuz5ZGsABeZWS2FkYtmHBdZpRRtI8iM92b2u42C7ce1UR6trgVV0bau258Ecl2KzkcKVst6sP3gevZL9oo7azofmHT3Lb8dd6752gGNbPnQ+a1s41Pc/3K8Pib4Xc3OK8AWf3fFtFkg2Slyo8fYfhJbs7azTOq1uMF1OacW1l7NFfykLkQNI9S0mH1gvkiUuhm4HbgJuC6CI4tGvzh+HSweCmFhwWRq1GoQSKWGTBpMNyGNwa9h76YYqjUIPPBqMGxD2oJOB9bX4dIl0ouXWD/X5uwZOHsWzp6Bi5dhI7E1RzaxtWHESk0ymPIP3BBjzbSQtU6s2fYaGZ9FYp5bA0RsnyRDLZ+tkH9uCiJoLGDuW7EGk74ZlR2yV9kqRUJjfn3+ez7zyM1Gcftu1vetoijK1cQf//EfFy7/qZ/6Kd785jfT601qLPrw5FWvehV/8id/Qrvd5oEHHjjo5iiKoiiKsqf4HOJ6vnQx5HFLXY7Vu6z0qpzpBjxyvsutN6zRfHQNbrme9MRxvOUl/MXzeJ+4j/ieIRfaNT7XivjURo+Pdz7Og53/Rz9pkab6F6ei5FFRwyEfsEtyy/OBN7GYyRe7DTDBPJmJH2GCXy1mjwTV3Da4Qf88+YwNNwulhQ2uz7G1mK/McR+lCYu9yywKQ7t93sDMxJ/HFjOWQPpmtkycBNezz/k/n0eJLJP+0yCztCVTYVRGhnsO7vFFGLvstLnltEFsbGalvUuWhlzXEHMtq5h+FXFjIVuWZG0JsFk1MnZllr1YKpHtWzKY+tmrisnc2MDO8Bc5YScUBZtPAMex1lfdAfQH2fUJwYsCvEoEYWCKfHe7UJ/LWhdmaR43ml97UkEkhXCIuTJ9SIamjsbFi6Tnz8GZM6yeanHqAThzFs6dNe+rPXP+IgS5L+mbYdbHftYCES7qmHutgRkT65SLQeNqW4yiSHQoEkxxlrv3s4yHoiwNd59ghVbJ5pFaQ2K1lf993sppLwQNGftu7RChLGsjP2ZFIHTFDrd2Uj5rS1EU5VrgbW97G09/+tO3ZDMkScLv/d7v8ZrXvIbh8Or8A/zlL385r3nNa2g2mzv6fZqmfOxjH+NpT3savV7vqu0HRVEURVGm4xHVx/PtJx7Lk490uO34CpXKEO/cIo8YBjz65CpHnhjhP+k20htvIG00YDjE22jTv3eTUw82+dRqg39cvcz/XnsXnf4F0plE2BTl4YmKGjmKbFTc5WWB2bzFkwS7ImyWwbSFf6fZ3rUGKssSEMqEjT7GBkkyUBawM6pdS6JRiNizW9wA6TzGwuYQNptBAuVdTDHm05iAsGR+7DQwOonBgGs54/4mL2a418EVKiRYXDRvcZaJhFLY3s0EqWCurVhKzQNHPLhhCSoVWF2Hi11YT+3DoZOdhwTuNzF9Diazp4EtAO9n+/ezfcuxexirqmnHhms75WEEgGXgRmzmThfoeyahYjA0tb39uSYsLsGhQ3DoCFSb4FV4SEbwTmSfpVWR+e5FkF6A7iVYuQyXL5OePUty34Osnmrz4Ck4dcoIGhcuwflNc95r2DoyYkElRdjFjsvNhJH7JMzeV4CzFBcMF9x6MLtllHgQ59aNq3OTH+/yEkEnwZ6X2PG5Aob7XC1qU1k2yKSIEOlmdeX3Pel9L3WU5BkjFoNSe0RRFOVaoVKpcMMNN3D48GF8f2tVoQceeIDv+I7vOKCW7Z5qtcrx48eZm5vb0e8vXrzI5cuXefKTn3zNZ6koiqIoyrVE6FW5uXmUJxzpccejLrPwKEg6cKjbpVaJOfZFA4LbrodDi5CmeGureJ/5PIO77+eBj1f4+1MV/uHSGn+28od0B2pVqSjjUFEjo2gmsyzPB77yged8poYEMROsTdG02upO3XulzkNZlkCZj77LEBOEXMDUSUgx53MZW1Nkr3H7vYsRW4aYwKEU2Q6xWTBSw0Nsb2LGZ5aUUVZ/Y1w7i5YV1RvYTXDWLfY9blsJort95WZpLGSv+RCac8a66egxqNwPg3VYS20Ghsxal2wMqRmxke1TshTSbLkbwB+yM2seN0AeYMSMO7J3yTBZDGCpAofnje3UzTfDdTdXCW46CddfD0eOQ+0IZtRUnNdhrJSQyT+pnG3FHHF9g/T0aeL7T3Hpvjanz8CDD8KZ03DhMlzsmXvictYPYq0ktTSkJolkOw2yPYu9UxcjhkjfbI7pD1fMmEbwLBM585lnRZ8nJS+Qgr32rmDhjt+04HP+uSTjWMQcd5tp+sC1iRPyhb3HicEJ259/HuaazmNGmKIoyrWA53l893d/N7/2a7+2bV2apnzgAx84gFbNjic84Qn8yI/8yNS/6/V6vO997+Mtb3kL7373u/egZYqiKIqiXMmcqJ7kRY+4gyc9+iJLT27i37BMcm6NI70Nhus9gppPutrG+/yDeP0+DBOSfz3NJz4Q8M5Pr/Nn5/+eT2z8y0GfhqJcNaiokVHkmw7FHvK+811+mw8KSkB9iAkmS3HaaQKRO0HaWMcGEN0C57LNuP3LrOYGJgjuFjMeF3zdDXkRIcUILPcCZ4DrgJOYQKcIOBWseBRhAvUhJsA8ys6n7Pjue355vo1ls77z4yFvSzXt9ZUC4xFmbLUZLWy4WRp52ymxpJJsjdCDwQB835ShOHwIVttwaWD6TvowwPal2Eq5tWcEsSuqZ21cx1yLaYQ96WMfU/x9GXgEpsD6IWApgsUaHF6GE8fhxPVw/JY6SzfMUbvxmFE3jh6F2mFsqepqtofjWWsuZkfrmBbGl6C1Cv0hDDqwsQEXL7B+aoNTp+D0aXjgATi3YvpmBVNofR1zT2xirotYorlFwhOszZiHrSszwAgbIjqBfZ6ImCPX3BWGdvJ8cOurONVFtmWZTUuRiOfuy7XxG2UxVbZMMl7cWh897PgYN67yImCZUFkkBI9rozzre4zOslEURXk4EYYhd911V+G6d7/73bzoRS/a5xbNjjAM+eZv/uapf5emKXfddRevetWr9qBViqIoiqJc6QRewLOu+xIed/MGS4+r4t35SNKFObzmBWq1kMG/XmLjCynJg+vM37BO1OmSdmM+e3fMz37s87zz9N8c9CkoylWHihoZRQHnssB20cz8omCXW7A6wATiY+e1VxkPAWbWcC1rq1jh5IvzjkMKH9ey/cWYWelt9idbQxhgZ/uvYASWVUz2xmL2PSvz/FCgs4q1iSmapV2Ee32LXu42sNVuR77n9+Vum7egmgYRbsSCaFwmj2RlSCDYtUGrZ68qUPWNmNHrmhh+PIQksQFwyfqRfvQxY0HGRpFgFGHyIBYx16vD9IKGa+nzCIwUsYTJHDpWh2OHjWZx4gTccKPHkUcdInrUzXiHD8Phw3DdMZhbwoxeKcleA44B12ND0UHWwhWIL8O5M3DPPXDxEvR7DC6sGW3jIjzwIJy6BBdjK2aIYLPJ1nou7jNEBKUkW18kgg6z7erY65ZgxQ7J6tiNiUWQ9USFrfZYs6AsE2mUYFKUnVF0X7j1ZlwrvLyV1Lj2uYzLvpLzkeswrpaQCFmKoijXAq9+9asJw+I/Id785jfT6XQK110NVCoVXvrSl079u7vuuosf+7Ef24MWKYqiKIpypRMGc8xFy7zgkbfyRU9uEz7hEaS33ARBgBcP4dxlOhc8PvP5ZXrDgMdsXuZQdJ4HTtX52b/5LH985u8O+hQU5apERY0cZYJG2TKhbMZwmi2XmfNZmWJSTGB0ElueKsbepI0JbkqwUwrT5kUSCQjLq8LOik93MLZOaXb8BBsc34sAXn4GtSskSK2GenZsMQ46j+kLqaUhmQFiVTMqSyMvYI17uW0CG1ydxF5qJ9kZLn1M4LTG+KLb0lcyRvLWU5KlUQeqVSNqDIawugKtFqyvw9rQZhpIgfS8/VGZUOHOqp/Wekr6eg6TT3E4ex3CCBrXzcHxY3DyBrjhJBw/6dO8+QjBo26FRz4SGk1oNmD+GHiSZyR7DrC5JSLRLPCQYZlXg+GQ5Ox5Ln/8Ac6egc3NlPamKQh+ZgXOxdZuqoURGjaw97J7H8q51DECT4oVQvKkGMEixIhG89hnimQlSB2ZnZQJk+wGsRDbbdbHpIyyviqyoRqFWxNjt5klo44h7yG2LswGNsvGFaXkPWb656uiKMrVynOe8xyCINi2/K1vfSsf+tCHDqBFs+FHf/RHee5zn7utRsgkvOc976HbHVd5TlEURVGUhxOeFxKFyzz32BP4tluWefxTWkSPv5H0tptJ5+bwVlbg1AX6n1zh1Bfm+JeVOZLUY+lcjyhY5/OnQu6+cD/DdCeG3YqiqKhRgDtzOB/Qzgfp3O8SMnWRIKIE5iNMwLaWrb/M+AD19cDNmFDsg9hi3lIdAIzgIQWd3SC/WE/tJBDaw4gHcg4SUKyxu2LcoygKgopII2KQzDL3sTUehBgTbJb2jaoB4h4rf53z4oVkLAydbcht546PvDjmCk47uRZyXnI+o/peRAzXdsrN0miQ5S+EEEVG1EhiaG0aG6qVvjFm2sCG/yUoLiLFKPur2Fm/PmK7PK6gcQMmp2IJm6FxYglOnjClMm68Ca6/pUp40/V415+AW26BG26Cyrw5a68OXhObs9PFlmc/nS2PeeiuSfuQDKDXg06HzY2Uz90D998PGy3oDOFCYu7Xlaxv1jDCmoiKRYTYPBF5PnQoFgWlj9tYy7ce5lq4YzDfZ9NY2pXVmdmNSCC4WRTj2iHb55dNwl49dwRplzxD6xhhrY8VTkUw7qK2U4qiXFu89rWv5bGPfey25e9///t52cteRqvVOoBW7Y7bbruNv/3bv2VpaYlGozH+BznSdD/zlxVFURRFuRLwCLhp/gl8x9Gn8vRjA574RSss3FLBa1ZhOITVVbxPf57BB+/nzMcjPn1pgfvbAb4HpzeapA/4fOJsnY2B/n+EouwUFTVGUOS9Pu57nnwAMcEEwcIJfhtiqgEcw8z0XsLaoZxnewaIFIWW40gAfJOdBdLd38ZYO6vp56+NZ5TPvTzih5gguQR3G1iBw0VqmQTYuiKj/pkYlZUh5yv9XtSuvDjiLisSN3Zj+TNJfZA5ttbTcMdHnSzjxTdZGkHw/2fvz+Nly8r6fvy9h5rrjHe+fW9PdDcNNEODaVEGtVGBIEEk/n4aUYKAIiga8iKJSiD4MiYRUNEEEmVQMBLly+DX1oj8NGloFWjopmloGuj59p3OPXPNVXvv9ftj7eeuVfvsqlPn3HPn9e5XvarOrj2svfbau289n/U8H/A8XXKq24X1tLRSC1NKSYK5Yr7eYbyvygA9PiXoOwl2VsOVmHJTe4A9PuyehsOH4Opr4NAhmD9cI7zuKrxrr4V9+2HfAQgPgyc5DmAkvy6o1NZcPQZeAuzXwgd9UD1Qq9A4CsvLJK0Ona7WN0424MhAn0czfUmWUIthvwj7WktWk3i/yD0vBa9GjY0oPYZcN/HoUJiMK6GIydyZtMya7QeUFURGZRgFmIwzaZccL8tmGUvZ5dnyU+earJiRbVsTPebrmPEv5dykhFuer4zD4XBcihSLRfbu3UuhUBhaniQJi4uLF6WgMTs7y1133cXU1NTY9VqtFsViccO5A7zzne/kr/7qr85WEx0Oh8PhcFxQePhegX31p/Ej8y/gpmnFoXqbQjEmWe7gP3QcL9J5/v0vHeHufyhwYmmWh1tFFroQeHCsU+REx+dDj36RY/0Hz/cJORwXLU7UmIDsbPxs0C4vQAj5wX8JWkpZH7ucULZ0zTR6hnqADm4GGC8DmTEvQTYJ8hcZLuUiQejtlkZR6fHEE6SCEQzsWvfy93aCk6MEDft7EWnkeLL+qFnnUipGAsuStZHnZZInaNilpcTPQjIV7O3sdzugK+W+RvkMbKcc2CSImGELGtIHtjl4KYRCqEUNleiJBINYn6OIGQNMJkxkvbpW+0dd7616P0iQ+Ap0qamZ9H1vCAd3wcEDcMUVcM0TfOavndX1p665Gq69DqYPgjeHvltKmKsPpmhRE7onoNuB2jQU1jFXdB3WT8HJk6ilZdqLbVZWoN2BQaIFmmPoe7Cv90RetXC7nBIYUa3N8D0uQfGsKCAtHqTbSDaXZEZJKbliup86+lqKaCKZPKMYVUZtnKgQoq+DQp+ziKg9hkUde1+SsTFO3LC9Zc61oJH3XB7Xhj46Q8feXvxPXNkph8NxOfHDP/zDvO51r9uwfH19nR/5kR85Dy3aHmEY8vSnPx2A97///ZsKGqurq7zpTW/i9a9/Pd/93d+94fskSVy2hsPhcDgclwEeIbXCPvaWr+P509/BldWAWtinMwg5+ViN2kqfuScsU+wOGKwO+PvPxPz7Lz/Ks2duwItLLHUjSoHHUi+gn/i0krNVA8XhuDxwokYOEpCTl2Jj0BuGA4QiUuTtIzsbWYKWkn0h4oSIFbLPATozwcOIF6sMB1RtE1sRGyRo6mNm2J8JEtBcR9eYFyPjyPp+XIB7HOMEjVElnGxhYlRAUUpmSRmmIrpfJFBvm4rnZWrYLwkkiwtD1uw7L1A8CWdD2BAzcbsEVYlMpkaQZmmkd3+cihoimEmRJsnQsDMBpGySzFPciRnqJXTgXLIzZtAZSnt8LWhcdaUuN3XgioC5J8zC9TfAVVfp7Izpw+DtR0uANYwMF6V7TnOXVAtWViDwYboGXqjXUz2Iu9ohPU5gMKDbUbSa0OlAU+nMjKMYrxYZT4LtCWHfa2Kknv1nymb3owhwTbRwMYt5DhQZHpchehxt5rMCk91nBYYFVhFey+h7X0SurPH5qH3mLc/L1Djb5JWWs5/LW713JWtju0Kuw+FwXGxUq1We+9zn5n53sWUpvPKVr+SDH/wgnjfZ0/+zn/0sX/va19izZ8/Q8iiK+NM//VO+8pWvnIVWOhwOh8PhuHDw8b0Cc6VreWrtuVxf2se+so9SiuOdkFO9KQorda6Z7vCk4jKV9jpHHy/zwa/1OdFvsNar0e5HLEc9ZiiwMgi4r32KxfjI+T4xh+OixokaGewgt5AnaGSR76T8U16ZF1vgkHrtvnXMEsbgWTwU+mgBpJau17C2k/3LuwgcIjZIuZqdCJ7HaEGlhxFLBDmfcUg2hz2zebM+lX1vN2ioMDXwpb0S8LfbZb9n2ySz4MWkOa/UVPaYm/kc2CV9djKoK94Ncs0l+G4LGhWgXNReGkGgvTSiCPqWqCGeDyJk2BkaEsQ+0zJaQhEtYOxGB+/nMYLGvhmdoXHllXDldQXqV+/Gu/IwXH01HLoGqvvA242WQcQU3G5xRbdSKVADfaLlOvipJKEG0F+DRgNWlmFxEdbWiAYKz9f6h2StiGhQwAhHsty+p21GeW3YmUSjypLZGV0e5r6T6yreLAOMEDFuHOU917JjL0h7zEdfazGK72PKr0mGhuxz1LMlOzayz6nzwSSZI2DOyxZRbaTfZEzsxH3gcDgcFzq7du3iF37hF3K/+5Vf+ZVz3Jrt8dznPpcXv/jFvOlNb5pY0Gg0GnzkIx/hx3/8x7n++uuHvut2u/zUT/0USeJy9hwOh8PhuHTxmSoc5kDxeq4s38CVxXlKgUdjoFjtKXoJxAr2lENmikUWF6pwSvEXj1T54trXuaJ8LWu9kFNRkxZtisksjUHIPa2HWBs8er5PzuG4qHGihsUo8WJU2Za8dWwj6GzJITvgHaODgzILXsrLVNGBxT7DRtgt8mdkZ4OU8i6ByAo6cCylc86EhPFeCqMQrwSpxd9le8HAvED6JPX7RUiRvpfPeS8YFjjEFFuuT/ZYo4LS42aAq8w6O4VkZtjlp0TUkPJT5RAKRZ2l4aG9NKIB9BITyJYxJ6KGBOdFIILNRazNrouUMZpFixjTGHFjtw97p2H/Pl1y6sAhn/pVu/CvewIcPgwHDkJlTypo1DHFz2yp0M5XShu0ax+UDoC3i9OW3J0OLJ6CI0cY3P8gj9+9xNe+Co8cgyPLujTXHrS4sZTusY7JgJI+Gdcf2Yytcf0lyD0soolkFsWY6yzXpcLm99MkJt5yPjJe5FkjGScyPuRcxwkagfXZziaz+2JS7PsyK6huhbz7cdQ18TPfZ03NbUFastZcSMvhcFzK/PZv/3bu8ne/+90sLCyc49Zsneuvv573vve9PPWpT93Sdq1Wi0cffZTf+I3fOEstczgcDofjUmervwAvFDwK4Qxlf5anlL+Hg8X91IKQQaI42YloJn3WaNJTPaqqTiWcY30QcmStzsluyKdPLbGedNgbz/AYS6yzTELMLqZY7sW01cr5PkGH46LHiRoWowLmo9bLztqXQJg9a1qCetkyVBK0tAPF9qzvrGfBYEx78pDshDqmZFSb4RJX5wofEyiVQK0ERrN9BaPFgFECwWZZEfa6EhweJ2hINkcZkzFjCxF5bc2WIrM/Z8vtyPXeySCoz3CQVcQNW8gJgVKapeGnCQxRBD0rS0PKT2UFDSlHNSmbZQ3U0dlHU+nnabTAsduDg3Na0Dh4hbbOmDtcxz90BRy6QruEV+bBFzt0yWPooYs0RRh3ma7+3ouAOajuAe/K9PsToNa0O/ryChw/wfK3l/jff5fwp8egFMFupfcyi85SkpJMUj7OLhU36nyD9PxCtDAigp70kT1W8gLuUeZveWbYy7psHH/2swjrs52ZlG2zStsozxp5Hq2l7c9e/1FCjtzTdtk28d8QoS221ttsrMh4FpF3O+Tdt6OOK6Xr7PWz9NPtQ4yI40QNh8NxKXPrrbfmLv/Sl75Ep5PnNHXh8Od//ufcdNNNXHvttVvaTinFy1/+cvbv378hSwPgR3/0R12WhsPhcDgcY7FrVFxMeJQKu3lm5YXU/Rmmg2k8Bav9AauqzYq3SCNZopOs4XsFDnk3ECdzrA8CunGZ25eafGn9byiX9rPkLdFRa/SSBiXqKF9xZ/dOltv3ne+TdDguepyoYZE3296e3ZydZZwXMB+VnUFmm1Glh2SW/Kj2ZY+bPZZNhDH6raFLV60wHKQ/F8gM8x468CdOB2KibP8UtstxyXZ5/TRp+7N972U+Z8tOSQke24NDTJ3ta5kVM7LZF6N8Brb6v3OZlS99NwrJyJD+k2yNCB2UDtAZB2EIYQCeD1Efohj68WhBQ8qZ5dlXSS6EBPon+VnvYczK6xhBQzI19kzBwYNw+JBOyNh7RQH/4H7Yuxfm5qE6DZ4UY0tbrdKzTFbBr4EnUqJln+3Nom3Id+seVavQb8PCSZLHjrD0pYf5v3+r+H8fg0eVbotkQHTQQfkmxldExmWemGVTSs/RHifdnHW3Mi4227aEFotAX3tbBLEFVsH+W7ExE8IO8k+KjJcgbU+M7kfxpalgnpnSn1nkKsrYkvvxTNhq6GlcBpg8q0XYcGWoHA7HpcqnPvUpZmdnh5bde++9fPd3fzfd7k64a50dfN/nv/7X/8pLXvISgiDYfAOLKIqIoohms8nf/d3fbfh+MBjwwAMP7FRTHQ6Hw+G4BPEJw2niuIVSF5MhtkeluI+nV1/Abu8KPKAZD2ioNsv+AqvJcTrREr2ogSKhXtyHH3p6QuDAY6WvuLf7MFHgEQYVlqKHGSRtEhVTCmusxR2WByeJk975PlGH46LHiRoWm2VmbLbeJAEzuwTVpGxW+speL5sV0EnfKxhPjLP1v5JRJYckkCvrSNC1gg4W2+0SQUMC87ZvwXbbI5+zgkY2y8bDZDjYs/Ht0j55M75tJABsC2IyK17Ejmw5olHnVkQHxIvofmpscq4iFEnpKRE25LxKnvaJ8NKGJQn0BybPQfpaMmvkfBqY4K08MKRU2q50nUVMf9kBf9mmhxE0aul5zaSv+fS1uwz79mofjUOHYe+hIv4V+2HPXpidgWIR4j4EA519oTpayIg70O/qmlrlAtAB1eN0boRXS49QQl/ZDqhFWHiQ9u138o3/8zif+N/wta4+1yJawDiOFltOAY+nfTDKC2XcM0HW26z8006hMN48LTYKAXnj7WwkBEvpthL6mkuZvT7mOVBGC2N25gts7OeIc59hNikXarscDodjp6hUKhs8KJIkodlsnqcWbU4YhvziL/4ir3/960f6ZywvL7O6usrVV1+N7w//X/rd7343/+7f/Tue9axnUS6Xh75rNBq8+tWvdqKGw+FwOBxj8LyAYlCnpyLieLtRnXOJh+cFVMK93FS/lb3qCtqqz7pqseYtsaZO0O4t0husEsUtlIoIwykCr0TJK+IBq334avsYDzTvJPE8GoPjDOIOSdLH8wJ6YYv7up+h2Xv0fJ+sw3FJ4ESNlLyA46ig+CjszIxRj+vtBA/HZWjY32dJ0IJBgA4eLnHmvhqjkGNI2Ro5PtYyCejbniNSKCh1ODgdWJfXmc7MluPnCRp2lob93ibfeHlUP8cMmwtnfSeys9Cz4ypvvz66P6uY0jajMmxEkMkeo4LOOBAz7noJwoI5GaUgToazD0SEqGH8FESoKWJMskU4kfaV0/Y1MIF0MdWeQcsLIoRU0aKWiBrTwO4C7JnTCRl798H8/gLhFftg/36YmgLfh3ZbKzHlga4PlXTh5GPQasLMrN7YA1RTO6DHHd07xT3oEHqahzI4TnT8S5z60P/ms//Pcf7m67CkjMeNByyjhZoa+h4STxtx6cheh+y1Fwbp9jAseJ1N+mghxi4DJceXsTouW2PcMj/dn4/JHpKMiix21hPo8SL3sy1WFtHjp0O+H5HD4XA4zh/XXHMNu3btGlqWJAlf+MIXzlOLxnPddddx9dVXc/DgQd71rnflrtPv97n99tv5wAc+wMMPP8ztt98+JFysrq4yGAz4/u//fv7sz/5sgyjyZ3/2Z3z84x8/q+fhcDgcDsfFjEfAVPEw5eJu4qRPknRR6kIuQ+VRCnexu3g1h8pP5pB3BaeSdU5ygjV1nPZgiX7UIEq6JHGHRPUJ/DKFoE7Nn6fmVQCPhV6PRzsnCIIanoroDpZJkghFQhhUafcXaPYewxUvdjh2BidqjGCz7IxsWSlZls2UsMu6nE1GBRZjdFBVQrpnCx8jUPTTY8UYw2DbLyRBl8Vpp9vsRs/4n0KXx1phZ8SMvDbmZWnYf+fV+R8lbsi5SHZDkcnIli2bdD3bhD5vXVusEQFhHh0wrgEFX2sDHjCIdZbGINbtlzJBoIPLUiKsjgmESwZAkeFMkEJ6nC5aDFhBj4ESWrTYhen7MsbnZcqH6SJUQ5ita01izx7YvcejtGtKCxX1ulZf1tah24Mg0H8XCzCI4OhR/blWSzsq0XW12i1tAl6pQtgH7xT0Hoa1Fdbv+kcWv3g/d3z4ON98DHrKeM0k6LHbQY/PNesa5WXnyFiRfs/eg3G6n83Y6UwJyawRXwsPkyVhi41s8bgyJvzMMiktJRTQYpXYuIuhuZiRS+mpFsNZQLbXzpki98OZnK/D4XBc7rzyla/kWc961tCyKIr4hV/4hfPUotFcffXVvPe97+UHfuAHRq5z22238dWvfpVf/dVfBeBzn/vchkyMr3/96zQaDT7zmc9s2H55eZm//du/3dmGOxwOh8NxieH7RfYXnkghmKYTrjCIm6AU6vQvwQsJj0q4h2ur38WTSk+k5BVYHHQ4qY5xMvoW3cFKKsz0SFQfpQb4XpFycQ/zxas56F/JXFghVoojvWWOxQ+QqIh+tE6cdFAqwcMDv0w3WsIJGg7HzuFEjQkY5WEhbFZbPxskl04f5RcxaRuy340KjErwfVxQ/EyRWeDyvyiZxW2bVWcZoIPGMbAPHXjfla67lNPWbL+NI0+4sD/nveQ8BGV9ZwsZco6J9T5pu7Kz5Mddsz46WCxmy6PKBmVLT9nnIjPp/XRFpdLxEGuT8MCHWgL1ANZiXWZpPT1WCZNZsQ8j3IjPh8zY99HXLkRfzxMYwWofOrgtYzBECxrTBZirwfycTsSYntYaxu49UJsr4k1NabGi19OZGEmiDUESBa0WNNa1yTdo841GU/9dVtBpwdKSyeDwAlj/Jq2//wInv3aK9W+dYOHxPoMBeN7pglSn21ew+lzENTuzx75G2WsSWN9t5Z8qOx1oF08YyajZzv7zthGPCxFwxE8iSwktYkiWkYieYAzTpY/EhH6zTLitItdSPGKkpNWF9k9oh8PhcJw59Xp9U0HjM5/5DL/wC7/AI488AsDLX/5yrrvuuqF1er0en/zkJ/nRH/3R3H0sLCzw0Y9+dMfa7XA4HA7HpYjn+VSDMlPsZjHcRS9aI8ZHkZAkXS6kX2WlcBc3Vr+HGyvXUg+KLPcHnFJLLMdH6A5WSVRMovrESRuI8byQSnE/u4rXcdh/AlcX55ktBDSiiG/2vo5SMVHcIY7bqZcIKC8gUQMG0dr5PVmH4xLDiRoWeTPoJWhrL8vbLhv0tj/bpYxC9OzlMjqY12BnshKydfvtgKqU1DmbokaMLj8k/3vqWcvt9uXRTNebRQe8q+h+sQ3EpSSSlL2RTJBR2IJEVrggZ7nKrKMyn+322+bFNjE7F5hV6L4cYDIoRokaBYZNwuU6iw9HOWe7Wl0nQdTq0O/D8jLEK+CnBxGPkwrGA0OC/QVMaSkJns/UYboGnT4cX4OVRK+zO4BKCHEMsdJiSr0CczOwa5cWMXbvhtlZnWxRrUFQK2u1od2GVht6XS1mKAWdNv2lBq3VAeWSonxgFm8w0AJGp6NVmvUGHDuqhZBCEbWyQvv/9w989W9P0WkmRBGsp5pIL9ECTDvtM3lJxpHcOyIajcqykc9SiktsykcJG7LPsxFkL2C8Kirp5wZarLKfBXlt2kz8EKGsgDlHWW7TxRjUBxiRSLKaxMReOBuJyPLclUyRSURHh8PhcFyc/K//9b940YteNPL7r3/96/zsz/7saUED4JZbbmH//v1D60VRxBe+8IXc0lVxHPOa17xmx9rscDgcDseliodHOQiZp84sB+kUVhn4+hdyt7+IUudf1AiDGuXCHp5afC5PmbqSehDSGCQsRS1OqSN04lUCv0gS90liiRh4FMNZ9pWexFXBdVxTnmZvOWCQwIluTFPprI440YKGSn/tewqiaP28nq/DcSniRI0UCVhK8AtMAFOCcDIrP+vBoNgoZsj3Eryzg/sBOlAsdeQ3EzVGZYXYx7L3LWVcBtbys+WlYdPPOU5ema48pB9a5JdxkmCxBNT9dN2tBCnzxI2878aVDMuKH/b2Ys4s/XCmAVSFCQTn3aj2OAwZ9vUQD4tB+h76Ot5fLMITroV9+2F+HlaW4fGj0O9BdRV2KxPQl3FawfhJnC4/5UGtooWRcgnKFSiX9cH3zkGzrbNDigVdMSqJtSbR7UGpmHpnpP4Ze/ZAZbaIX69BGIDn6wZFEcQxqt3StbKShM5an1MLiiSBQ1f6WpUpFbWAEUW6JFWvC40GCg++cR+9bx/ha/93hdVlLWS0WzqxY3kNlmMd7LdNzgfWy86AkYB+9hrZ73ItpHyX+HRkKaNLcyXo43dHjoKtIQJCmO5/D1ooPILOfrKfU3kZXj4ms2PUc0mE2X7a9jwitJAi90oB82yVZ+nZJmZYGHU4HA7HpcfP/MzP8La3vY39+/fnGoIrpeh0OrzgBS/g5MmTAOzatYt77rmH6enpDetXKhU+8YlP5B7r5S9/Of/4j/+4syfgcDgcDscliUfR95kLC+zp7me9cIquvwpAf7BKfJ5nm4VBnRtq38PTKjdyZbXCVMGnOYDjnZgFFmhGp4jiDngeg8HyaXHC9wIOVW7mqYWbOFwtcbDqEXqKo21YjwfUCnvoReskapCW2tIoN8XO4TgrOFEjReq928E2CewpTKDPNoXOww5yeujSOwE6aNpKtxfPAY/JxIZR5a3yxA0xe84zAz7XZLMgYHx7InSQNK9cjphWSxmZIsPGwttpF5n3PKEiT6wis66HzlqYTtveQc9SzwsKbzeTIxsYHyfQDNCZFlIiqFaCp1zvcdU1PjPzAdc/d6/ObljRI3FtDapVqJZgdwd2e+a8i0DRh0IAhQKUSlrECEMtZMzPQ60KQajtLAYDmJ2DQqhNyT1PZ2mEoRY31tb033v2aA/wPft9gvlZXW6qUtYrdXtafYgiVLvNYGmdZkPrFe0WdLowMwNBIcDz0Dtst2HxFDQasLaGimIGjxzl2F0nWDylkzgGfV21anUNVtdhMRrOXhARSTKBlLU8758g2Yws2V4yBEQMWmd4nHpo0WM27d8ScIwzD/SLoGLfc/K3CDO20Jm9J8VXpZZ+J88pG3kmSsbFZki/2H4+eSLPdhERb6efc3n+KA6Hw3G5EYYhlUplw/JGo3EeWrORW2+9lfe+970EQZD7fa/X4/Wvfz1//Md/TBSZ//vMzs5y8ODBXBHE93327NmzYXm73ebEiROpyanD4XA4HI7ReFTCvcyGe5gt+hyIp1kdHOKUH9GJllBqJ34RThJhyt+u6E/xjLkX871TN3BFRTFTUDSjhOWex2LUZk2dpBet0R8skSiJDICHz1z5em4uPYOnzZY5VE2oBzEnewHtSLHKOr24QaJ0tM/zQlAxKnd6pMPh2AmcqJESo2cTS9kf+9EoM7hFKJDHkV0/X97tklPymJ1HBzBPoU27Y0bPcB5FXkA9L9gu5V7yMgXs0kTn4idZVjiQz3ntKjAcEM7+zJQsDo9hv4C8QLDU0rdNiWXGvbQhW4LH/pwVkbI+G7K+lA+S2e1SBirACDCyr1K6zqjSVWeKnI+0V7KEDpXh1ls8nvnCvcw97bCu8dTrweoKqtulWOgTFrToUKtpcaOYpsoksdYYSmUtUgSBFjIqZS1YlMta1Jie0qJGv69FjTDU4keh5KeihqI2HVKs+KwuDFhbTpia8Zg7UCbcMwdzc3rHgQ9RnBpdDFCtJtFak9UVOJUKE6DLVs3v8vDqVZ3V0elCfwEWF8EPUK0mrDdoPbhIrwtxKrZ0e9BsaVFjMdam5h2r70SUEKN7ud6jxCP5Xt7lmopXjHiO5G3TRwudRXQmhYzXM0WeSQO0KCEG3Q2G/W3sslcybooYE3cP3RcrbByrkv2wk+LEdrBF4y5GiLKvxXb3m83acjgcjsuR5z3vefzyL//yhuU/+IM/SL9/5jnAT3jCE3j44YdJkq0/tZ/+9Kfz6U9/eqyg8Zu/+Zv84R/+4dDya665hs9//vO5gsY43v72t3PnnXduuZ0Oh8PhcFxuePjsKV3JPn+W6SJ4XoFmYz99uqwNHiZR26lT4BH4JeRXWrVwkH68ziBppD/a9C83peLTWRV57aqF+3jO7Iv5kQP7OVjtMl8c0IsC7m9UaAwSVlilOThJt7+woZ17q0/ih+f+Kc/eXeBwrcV8qU+jX+REt0orSmjRRKEIgyqeF6BUTJJExEkLpc5F7RSH4/LDiRopMutfDJAlGG4HLX10wC/GiAbZ8k8SUJOfZxLcrKFn8w8wpW62yqiZ4lkkyJ41lxYfhB76fM9mwG5UiSfBPnYF3a9i2GyX+cqKS2AMyPP60E/3V8EIC2JUXMCU55JA7riftCrzkjZ0033YosogXV7AiBixtW45XX8n+j2baeJn/p4FdvvwpCl4wZPge19UJrhxn1YDKhWdBTHoQ7FIorR4ERZg3z6ddVFOLS2iVP0qlXWWhu9BsZQKHwUoFGF61qM2HeIXAuJBQqI8wqKPVyzgFYvmIu7aBeUS83MnqR1fplArEuzfA/O7oF7TikkUQ5yWmur3UK0Oq0sJCwuwvALRQBuKl8sQlkO8alUrLHGCWlpmsLQOcUyhHECSUK371FsJ6+tax1lfh7V1aMfmeolviYgZtom19Gd2HOQJFVkvG8ksGkUHXQ4qxIylvOs86VixszLAZEfIMeQ+kDGbJyyCEQJL6Huyg/HNsMWfcX4h5woRNWbQos0aplybvLZzr9mCj8PhcDh2lpe+9KVUq1UA3vrWt/Lud7+bjsxaSPnWt77F3XffPXIf3/Ed38EnPvEJwjD/Z4xSive///287W1v2/Ddb/3Wb7F79+4ttfno0aPcf//9W9rG4XA4HI7LmUQleMBUqJgrQpTUiFpXsuDfR5eTTPJLzfeKWiDAJ/BLHCg9icAroJRi3j9M01uhE62QqISEhEQltOJFuskyHgG+5+OrkF6yTi9eoxzM86Ld38+rrprluj3LzM708KKER0/M0I4qLAyaLMaP0eo9vkHQKPpTPHfqWbzscMIT9i4xP9/FixIefnyWSEGMoq7q7AqupO/vIWZATzVp9I4SxxdGhqvDcSniRI0UCQIOMLPcwQgUUk5mb7p8FW1w3cME97IBcNCB0kV0loH4QYhp7pm0dRR20L7PcHBOxIKzXZoqKxaM8wSRZQqTrbFZQDJbJizv+9Olk0hLMKGD/XL+tqgxqj0wfG2zJsmS1SMCTI9hf4sSw7P3e2y87mdyDbLtLqAD0TcHcPNheNr1cO0TwK8UtRJQr0Glqn0rmjolQ7Iq9u3VyRL1utYJ/ECLCFGsBYwwjZgHQSoqFDyCYkBYL+NP1aBQ0A+TIN1hqah3EkdaCZmdhSDAKxQpl0p6R+IQXizoAw3aWnBZXyNeWae1MmBlGVZXdaZFpQLTM2mpq0GEimO8OIFOh3h5jdXjPapTPoWZCl6xSDFsM9VtU1nVFaqaTVjp6XuxlV6Pfua9jSm5lFc6LRvwlkC/+Lz02dwjR/YlwobPcHkmW6AQMSIPOW621JT8LeNV7iVbkLH3KevK86KHHkc19BhuM8zZKPe0HeS6yD0lXkL2s9uJEw6Hw3F+ecMb3sCBAwdO//2mN71pyMviQx/60IZtvvjFL/I3f/M3Q8s+8IEPnDb5ft3rXsfhw4c3bPfNb36Tj3zkIyileOc737nh++c85zk86UlP2lL7G40Gb37zm7ntttu2tJ3D4XA4HJc7vqeYLij2lSLKfojHFPf3p1nt5a8fBnUCv4zvhfhewGxwJfsLh/C9AM8rMKf2EhAQeB6h5+F5e/ECBV7629BT9LwmA79JJQioByHTYcjjnSUeXj/J7tIcP35dne96ygpT1wb4xYDeQ33iE/DtVpO721/gZO9bRHFzqF1Fr85z57+X/881UzzzKcvM3RAQVIv0H21TOJ5QDRSHqiFlfy/r8TxdFbEe93gguot+tIo67zUOHI5LFydqZLCD2DLDXpbXgDl0oE+C5ZP4OkjQVEomna1HmmQpTGFmndtti9BCDJy9Wdaj/CpsssKGBPsluLrdGdZgyjv10NduDlPiR3w47JJCebPu80QqW9SwZ7tLSSkJdvdy1pFyQ3nlwrZKXpZGGX3ddwGHgetrWtC48Sk+0/ur2kzb91MjjNTNu1yGUolKRWdoBKHOwAhrJYJSAS/wSQYRahDhodIyDQoPhR/6EAZ4pZJO46iUtXBRLBrTjUJRH6fTgalpmJtNo+qpMpIkum5VrarPZNCCThvW10mWV+ms9mg1IFFaaJkJdPvEF1wlSgsgQQDtNqo3oJw2w0sSvX+0flMo6PcEaCstMjbQY6GD8dDo5Fwn6etRQqCy1rNFhkmqZiZpO+SzIBlVCebZkTdmJJtC1stml2TbKd/ZmT22eCdjWITdbPmsCy2DoYgWp+wSWXZfXUhtdTgcjsuFt7/97TztaU87/fett97K7OzslvZxyy23cMsttwwte/7zn8/i4iIAN99884Zt1tbWePWrXz3SyPv666/nfe97H0984hMnbodSip/5mZ/hz/7sz7bQeofD4XA4HL7nU/Q9psOIq6Za7K36lPwa/6c9zZFhzYBCME2tdJAD/nXMBHOU/QIFP6DiTVGmTC9JaCRdlljRk41VmaoqU/ELlP2AcuBTCjyqoUe9UGK6MM98MWFfuc/+Wge/Pk1vaorKbIErD8LMjXvwDszByWXUo33uXinwN6uf42j7ntRDwzoPAn5w14v518+Y4xlPbTH1zFm8q/fiNVuEraPsnm3ztEHA7lKFk92Q5X6ZEx3FPzaPstL5JkkynI3qcDh2FidqjECCfNOYwGEF441QwXgqTILU69+OoDFpVkWArok/jwmGrmS2PZuBvmzmgx04zfPVsAOqO9UuEUZkxvY0OvgpFNJ38RcYh11KyA5UD9LtE+vdruNfRo+TFYzoYV/3nZjlXkpfZbSYMYUWcGaBmSnYvQdmDs8QHNyXiggKen2oKx35L5egVKI4U2HPtIdXreJVSlqoKITg+wT9vjaiiAZaJIgivR85gzDUagEY0UQcwsNAO3v7aVg8UfpzoQAz0/pzqaTXlZSQKIL+AM/3qFR9ioWE6Wmzm3IZ/MDDC338YlGrGwCBT1gtUPN7xIOEwWqLoNCm11GsrWmtJEn0PuL0ujTTayLlpnqYrBubbAm6PKTUlAgGUk4qYqN/ip3B0885Hhhvix66veLpY5PNNMoTYsYxTqCR8lLyzLpQ8BgWjURwlgw72JnSbmISfiFkozgcDsfFwnd+53fyB3/wB1xzzTXU6/Ud3//zn//8sd+/9KUvHSlolEolbrvtNm644YaJj/fBD36Q3/7t3+Yb3/jGltrpcDgcDocDQj+kHHhMFWL2725QKCfUShF7Ts3i4aFQFMI5Dldu4UBwgGuq85S9EiEBCo9BomhHCauDPsusscxJGvFJYhVR8muU/ClqapZ6NMV0VGMqLAIh1dCj5MOuYsThepuDBxpMXw+FG+bhwC6Ym0bNz6FQeM0OQRlWBkWu8J9OpX6IpreKh8+0mmOGGnvLRf7TD/S56hafwg2H4brDqKk6nDhJ0Oiwu7tE6YFV6scjCut1vr5W5J7WI3yz/Tl60er5vgwOxyWPEzVG4KEzM/ajRYI+pu5+E11+6mz7UoC5QJMEFyVgWkUH/9bR7TwXwblRgkbe5+yM8p0shSVBSTETl34LMsulRNQostkaIpREGF8BWWbPypfskBLGlyXCzKS3vVq2es5Buv8iWqwRn5YaWmSrpd8V02pT/kxNp2H4qUFGt6PLQdXqUJ/SnhSHDhGUSlCtGDEiUaASbUTR7Woj7l43vVix9rAY9LUIESd4hQEopV9JrGs9iUdGkkDYg3Yr7YDUSbxa0e8KvU4Q6HbtivAqZYIowo8iCoOBVjWSBIoFPDz9uVDQJ+oBXgUPDx+I+j26LcUgUjQbunTV4pL20mh0tO/CGiaLSXw1Ooy/xybJugAjPhTRY0D2bV9r+U7KXWXLVUlGl10SKouIdzLvw95/dmxtNs7yvo8wWSQXCiFavIvQ/eajxR8ffU23Yr3moe8lH+N9IplVTtBwOByOzfE8j0JBTxX58pe/zDXXXEOtVjtv7bnttttQavTT2y55NY7V1VWuueYaOp0Ovd6I+hgOh8PhcDjGUvSL1EKYLg6YOhBRuqpI+fF19n67TiWc5clTP8hTy4e5ZVeA5wX0Yp9W7LE+gOWuYqUfsRA3WOAYa/FR2oNFetE6SsX4foHAL1EIapSCOpVglql4D7vbe1jpV2lHBQKvwEyxxO5eG1SMVy+jDuxGHTiAqlTwGg3YNU3pSTO8Yf0IL75/im+sXM8j7RAfuK4+4Ia5dQ7d0GT2n8ziPekw6vAVqLk5vGgA9RreTBW/uoJSEZ0o5P8uKP741Edo9Y4Rn1H9EYfDMSlO1BiBZD1IwFiMp9fRAbQVzGzms8lWykTZgVAxQrbbd7Z8NMZ5aIw65tkQNAKGMxjAiAgF67sSxgNhHNlMDSkvNcAIHJH1vYcWNObQwpfMLBfzefvayL7sPhAvF/FSyF67Klpgq2H8DmrWqw7M+HrdZgOi9oBCuazLPHW6xt27UtFln3bt0p4WlbIpF5UkOqOj29WihO+D76OSRJd56msBI+lHDPoQFjv4YYBXLuKV0xJUoc708ALflJqKYwjSTA0vHSFKaQFFjDr8dP2pOkQx3mCgRZjBQL9Uuk000Pvrp8GGIDidNeKnHdhuwYmTsLwMy0tw9Dg81IFHgJNp/4sHRot842t71r54VmBdc3s9ub626biIZ/YYFyGihg7Qd9Ox0sSIF5Ip0Wa00GKXisqO41GCxjjhYxTZMlXnG7keMbqP+5iyX3KvT0KA7n/x37DDVts510myYxwOh+NSYf/+/bzwhS/kr/7qr04v87ytPwlPnjzJnj178P3N/kW2OZOKFuNYXFzkO7/zO1ldXT3jfTkcDofDcdniefieTyVQVAsR4e6Q4Mb9VA71eO0jIbfMv55bD66xp7jIerPEiWaNx9slmu2Axa7iSKfNMXWSxeRRmoMTdAerxEkHpfR0wCTxGQB9b5WOX6Lhl1gNj7AU7uZUdIjVxgHacZ1+UiVWHk9iiX0zp/D3zsPsLF63i7e2Bv0IigHVXR4H5tsMoiJFr4oCDtd67JnpUpsGr+DrGIbn4bXbeAun4OGjRPefZPXemG8+NscnHvP50PE/Y73/6HnteofjcsOJGim2ebRKP0t5mhomOC1G3zVMEHIrwbRJkdJWm4kadsBUPDPsWdx2u+Qcd1IztgUNP2e5spbb/gM72V9yrCpafJJrJQHLAiaTQQSqEqY/siSZz3IdRCgSUUIECjtTw0cHo+U6lBjO5gjQ4yfG3HzSF2V0BkYInEJn2cj5VdGlpeYwIkY1fU0DUz5MF2DXjDbTjiIYrDQpLC9pwaBS0Ubh1dQovFyGvXtSISHUrYhiXaupn4oG7Ra02jrDo9mis9RmfV2vGsdaJ/F9RSGMKJYiioW2rjblQaHgEVYL+MUC/nQTb3YGb9c8TE9rj43A1+JJEqUZHbHOKCmVdPuiCKJCmtlRgEKasTHo6zYPBiTrDaL2AOVp4cVLYqK+XqXRhFOnYOEkLK3C0RYcs/pVrsUAfZ/k3WeSGSOZOTIOPIbLShWBmfTzKsZ0fNRYtw3nS+jxWERnRkgmUIdhn4ws0n5h3P2UJ2xMeg+eLSF0K9jPD7tf2unnPBFw0n2KmLHVc8wKuQ6Hw3EpsrKywoMPPsgTnvCEoeV/+Zd/ue193nHHHXS7OkfuLW95C+94xzuoVqtD6xw+fHhL3hc7wbFjx/jpn/5pHnrooXN6XIfD4XA4LjWUijnZfoz1+AaqpQH+VBF1cB8UQ77vDaf4vrUWajkmergP31QcbdRY7Pk82kx4oLPG48mjLMeP0eovMIjWUaqPUvIr2kOlEa9YDUiSDpHn0Y9W6PgLtAun6BTWaDYP0lazFP0Ku6tVdq81CdabcOQoLKwSP7pM0hjQP9WneSzg1GqFtX6BbuLRTzwWOiWCpTqJ12KXt0KpWsILQ+j2Se49QusbTU4+XOTLR3bz2cUuHz/xN6wOHjvPPe9wXH44USNlluEa7fJax3gkyEz6aXRAsowOrDXILyNzJojR9CTr2V4Pq2m7ZYZ4iCmrIj4TsmwzwUREnlHtGJedMepzNqh6pgFTyYKQ62FnRhTTzyVM1o28yoz21cj6KNhZGl2Mx4H4lthla8RfQTEsaMTWS65tAXMNAlI/jPRvOZaUFJMMkFlMVkYdqHswV4KZGszMwK7dsH8/7NkLhVDB2poWMOZ3gTelPS88T2dmzM6Zs+wPIGprMaHdhvU11OISaq1B1B0waPY4cUILBYOBlXwRmJdUrAKYnlFMT/WpVvvMzLWphT5ccYVux9yc3snx47Cyqj/7XlqOKlVMosj4bMS6zBUq0ZkdSQL9Ab21HqcWFN1OTJyYpJBWC44fgyOPw8lVONXTYkbbut5t6/qOuw+KaOFokG6TvW9EcKqm+5KSTQpTDik7xotsFEtkvEqZtM2C9LbJfVa0sLMrRu0jO77z2glbyxTbKpMIJlImKsSIivbzLGZ74oKYtGdFyXHtyD6/wPS3z5k/yxwOh+NC4ytf+Qp/9Ed/xK/92q9tex8f/ehHTxt8A7ztbW8byoR42ctetmGb7/me7+EVr3gFAFdeeWXuOjuFUor//t//O3fccQef/vSnz9pxHA6Hw+G4nPD9iPlai10Hevi7Z2BmCjU/D/NzeCdP4X3jUaJ+m8VGlYeaJb7dUHytfZTH4odZi47SHawQx62McXf2F26s/1Kg1IAkaRPHbbrREqeCOr3Cs7g5fhKhn0CUkDx6iuT+BdoPdVl6NKTZDlnvzHKqXWShW2Cp77PWh14C9bDIgXaBJ/eL1KZOUTyxhtfqMTjS5sS9irsemuXLyx63HfsaR/uPsDh44Jz2r8Ph0DhRI2UfxsC3iS4xJbXbGxjTXh89s7qKDkBW0MHldXRJqk52x9tk0pnHEuAD49lgixclTEYB5JergfwAo8wEHyeueDkvyW4oZP7Om7k+LrApwcJRngIyyz20XpJJU7Zeco2q1ueKr5MF/JyIqNhDQGovAUSJDqjKGBGfDPFakTZJ8FuEFSlTZZesyvPiEINpDyNKzab7mEo/z2K8NGY8mCnBdBXm52A+TYLYswf2HfCo768T7k3LS9Xq2sPidOknT4sbQWDKOPV70GzBygosLaEWTtE7uUpjsU+7o3WOhQU4cVxnQQz6evNKRVecwoPGut5FGOr2zM/D3r1QrihqSp0uZUWSQLMJS4tw/AT0+7o0VrmsdxRFul1J6smBdzrdUwSYpN2m2VCcWtAlpqLUdqPbg7VVOLEKCy1YTGARfU+LSFQYMaaySCaPlHmSckdZEUEC4+PKQGX3K+NVhFPJ0hARcbP7384ssxklFmaXne8AvH0dNvMLkueY9Mm4fp30HOX+yz6L8oSLUcfICkIuc8PhuDzYs2cP73jHOyZe//Of/zwf/vCHz2KLzh433HADP/RDP7Slbd7znvdw//33n/77Yx/7GEtLS1vax+23387tt99OtVrlT//0T7e07Vb45Cc/yac//Wk+8IEPEEWTuNc5HA6Hw+EYR7mwh6sKN/HiA9fywutiZm8q4F2zH1WroUolvCiClQb9B9c5+VCJ+5enuGttwJ3tR3mw82Wa0QJJ0kepGDUU7ZqMRPXoD3okUZOD03uZKui8jvZRUCfbnFgs87mHZ3m8EdKIFKWgSJQELPYSVqMujaRDRMK8XydSZa6qBQzaPt1vtel3etz57Wk+ewT+dvF+Hm4fYaF3P+qsTgV0OBzjcKJGSgkdQLZn7oqoIcEuMZSdQwfHpbRSBVPrPetjsV0mfSyOml0t7c9eYJnhnCVPXBj3825UNob87aGD7wE6oCwB27z25tXtl6wY6ddsGS0pI1VI1ytgBA0RO6rW+1T6XgaqobaRCAMT41eZaKlR/HXAvNvXcXY5tz6mxJXMHrczRMR7Q4Lh4r8hngtyfeR6DNCBbfnftvTfNLALI2jMAlMFmJ2CuVmtWczN6syMmRmY3R1S2jeDv28v7N6tMyNmZ3XZqUrqW6EUeD4kqXdGq6UdtZeXYeEkauEUrRMNFk4kLJ7SX7c7sLSkhY21DrQSiD0oBVBNfTzWI+jHUPN08kW5pA9XLHpaHWqspztYg/U1OLmAOnqUzvqAUjUgqBTT1IlYNoRiSe/IS8WQXg/VbNJZ6bOyone3vKJX7/dhaRlONOFYrEXGJiaTyjYH34wQk/kjpdyygobQSY8znW7TY/z920W3zd7ebtNmGV8iimyW6ZRtQ1ZUtLc/H/8Mm7SEk9wzIixn95H3PglSCk6e7SIG2x45edhCLZhsn2DE+g6H49Lgta99LT/xEz9BpVLhO7/zOyfe7uUvfzmvfvWrNyxfWVnhR37kR3ayiWfMNddcwwc/+MHTf8/OzvKMZzxj0+1+7Md+jBMnTgDw1a9+lZWVlU22mIw//uM/5iUvecmG5e9617u47bbbcrd53etex0/8xE+M3Odb3/pW7rjjDgAeeughjhw5siNtdTgcDofjcibwS+ypPJ2nVp/Ei3fv5flXtLjq8Drhrhm8JIaFU9Dv40UR6vFF1r4V8+Ujc/w/x9rc3b6Xx5p3M4ibqZBx5sxVr+fK4i7KQcJ6t0zvsZAT7RL/e6HHXxz7GxpRg0Qp5qvXUAln6cRt+moAvk/Jm6LMtfjoSZfLKxVOrXrcdarGf3/8a3xj+V4ayTJRslNTmh0Ox3ZxokaKlJaRmdNSZ1+8ExLrvYHxNLCDYlkPi7OBBDQlKJ6HnTExaUmsvKDmuAwK+zh5n0N0RoRCGzGP219WzJBBGVvL7NnTU+lLhA87O0NMwsVDQ4SNKXTwvVyCUlHHy0/7UuaoLQpd7Qh0SaVOzxwPhkv/iHgRpcerp21IMGXMbJNxe307YC5B9yqmnNbu9LUHmAlgtg6zM6bU1Nyc1iz27PUpzVbw5qbx9u7TJuDzczp9o1ozJ5ykkpdS2qOi2YDFRVg4hTq1QHxikdUTXU6ehBMnYPGUzszodGC9Aat9fX+sAy2lM1jsGe41YH8qBhVLUKtpmww8T/tzJCf02p0OammRxqkOJ48ryuUBxWKXUlknlRSnylCp6uyNMIQkRvW6sN6gs9xm8RScWoDVNZ1FEgS6jWstWIjhKDpDQ/q+nbZZ7uPNsLNpNsuciNGiRgEzLtcZfX9GwDL6mREymcgi2PcH5AffbaEie1/nZWmdj6yNSa+DYnT2m50dZq9vfz+OAFOKbg19DaW/xgkuk2TCOByOS4f5+XnuvvtuZmdnt2VGvX//fvbv379heRzHPPaYqb38wAMPcOutt55RW7fL29/+dl7zmtdQKBRy25rHfffdx4te9CJA+1HE8c4EIWz+yT/5JxsMyJVSfOtb3+L222/fsP6tt97KS1/60tx9KaX4/d//fX7rt36LTscFIRwOh8Ph2AnKxb3US4fZHVzBc2o3ctNMgadM95grDYibCYNvrxEstPBqRbwbDuDVKzSXEj5x9x4+euI4X175a7rxGonayWLuHk8tfBezRZ9+Ao+2KqwPfO5fV/zlydt5rGuyK9abJ/HQRuBhMEO5uItCoUrJCykF0Il8Hlid5htrAf/z5Jf4+vodTsxwOC4gnKiR0kxfHXQQXoJb2eAm6CBkHxOUtIPUMBzk3elAl4gFYExzs4FLyXA4GwbmQp6YYf8tZVrizHeT1PgHIx7I/9qk3NRs+pIZ1SJqZMtNiaghZafKBZ2sUCqCH6SChkrtGwY6kaCYVmdK0kak/tOEsS5VFSamjr+0VdogweUwPa6ULRLT6Mh6H2A8MyToXk33VUNnAok4MgfMA3uqJitj1y6YmdXlnXbtgvpsgLdrHm9uXqsIe3Zrt/B6HUplXWMrjnUqQ7+vOyFIW50kWhVYW0UtLHLqSJejR+HkSf1aPAWNjm5rK31J9sM6xqw5QAf0peSY72stolCAoFLAK6eONa0W9PuoZovewjqLJxWLp7R+Ua9r7aVQKcD0VGpqHuq2d7uwtk53qcXCSV0G62Sa9NFqpav0YS0ybVvCiJWSMTPp/SBik9z7mxWlGKAD43VM1tC4bWyxbxzZwLpdcioraOQF2rPPBimTNYmfxU5TRN+XDSbzsRhFQH5GxWb9kV1me9yUMdka45B+Ox/953A4zi2lUolKpcJXvvIVDh8+vOP7D4JgaL+HDh2ilxpTPec5z+GBBx6g2+2eNtXeKWZnZ09/vv/++5mbmyMIAoJga/lmT3rSk3jggQd4/etfzx/90R/taBtBe3FcccUVucs/8IEPbFh+zTXX8OlPf5ow3PjTRinF3/7t3/KGN7yBJHElIhwOh8Ph2B462uN7BaqlK6gW5rnCv4Grw8McqhbZV/GoBQnNKOSR5WkWGxVKD8UUgoSp6R57lx+lcGWdZKnLyUHCfeufpx0tsbO/rHx2127iUGWOmYJHI/J5rB1wpBXxpea3OBo9MFQuSospPqE/g+8XAah600wHRQIPTvVDHl+N+PiJO3mg87mMx4fD4TjfOFEjRWbUi/EzbCyLlA1kJdY2djC+lL4Uxs9CZnrbWRTbQWaNVzElaLI/dyPGG9dKG23vCwnCS4bKOPJKveTNFhfBZbPgYhbx/Shi+q0IHACuTNveTr+rMFxyqoDJ1qgB9RCqZe2LfTqW7+kKR1GkszB6PU5bOciEQPHUCFMT7HI59dFOjJcKmGyLMG2zZIuU0u/t0lMSNBWBrIkObFcxHglFtFn4LDDlQd2HqYo2/5ZKUvPzWtyYnvUp7a7hTc/AVFqPqlLVYkCxoHuu39cGGP2B/tzpwK55nb0BEBbSbWp4pSJh2CUItJbgAYNEt1NKOUmppC4m+6GXnsMsJuBcLmkhSCVohaNU0p3Z6qHWG8TLa6wtDmg0oFzRGsb0jD4npuu6w31fX5ROm2StQWepzcIJOJEKLivL0GjAasuILaezSKzXpHNHxdNEhDQpJzcJch/20WNwknkmIshtlgli31sSzJflIlBkq40q63sRMnxr27wSVNth0ueZiLEzmKyk7WB7z+S1fdQzSda1txugx4rsN8+/Jy9jw87ScqExh+PSpFKp8I53vIO3vOUt5+yYnudRLOof03feeScAH/rQh3jPe97DYDDgvvvu2/a+5+fnOXz4MNVqlTvuuAP/dKrqZDz44IP0ej2e/OQnb2jvBz/4QY4ePcrf/M3fbLt9eQRBsCFLAyBJkg3CxNVXX80XvvCFXEEDdEmsF77whU7QcDgcDodjy3hpJoNPIZylHM5RD/dwILiBPd4882GJ6WJAMfBoDqATeTzeLhD4BQIPEgZUAsUTpxJuipfZu7SC34bv2VPlrvXn8JmFJbrRzpStBPC8kBvDG7m6WmJPKaYReSx24dvtFR4ZfJl+tLrh/IKgThDoCE85mGFOzVMPA3qJxzeaXW479SUebX8OtelUR4fDca5xokZK1vQ5j6y/gwTCxFtBAurT6bIIM2tcZn2fKVJKxw5Y5jHuZ5sEzqXNlXR/EbCAnnE+avtR5aay3yvyA7Wb/W3vp4wRhsQXQ4SLXvrdHLDHg5lC2uepYXTRg3oar6+UIQiNnYQIGp0OtLvQi9L+lEh0Wj5JoQPz5YIRQyLpMx8KaXRUsmIkoK/S96KsA/SV7vck3T70oKG0WOABu0O9/iCCQMFUqMWMel2/9u0zCRhzc1CfKxDUK7oO1fS0PtFiUYsZCuj1U8PtRIsa7Q60Wzr9pFDQ6SpBGtQoFqFWw5udZW53iySJCQPtHd5qQbMPnjJm5z5m7Mj1LqbXqIZu+/SMThLRF9PTne9pNUk1W6wvDVhf04LG1JQ+hXLVx69V8ApFnS7T76G6XZL1Fq2lLqdOKo6fSMtiLerTaXR1RsYSetx2MBlMEVu75+S6jbuvNkOEzkmYRpcWi9DjQAQY+9gF9LiWfgdTBk/uwVFza+3vZVsRO3cqi0yE1XH9lX1OTuI7YmM/a+z2w+hnT3aZ/Z39vWTYSKaUR/5+5G87Ew2G+36zclcOh+PC55nPfCbXXnstT3jCE8YKGo888gh33nknSqncwLvNVVddxS233LKt9rz61a/m1a9+Naurq7zuda8buZ7neah0Nob9Wf5+wQtewM/+7M9u+fgLCwvcfvvt/Pqv/zorKytD5bJsPvGJT/CSl7wktyTUdrjuuus4dOjQ0DKlFJ/61Kf4whe+cHrZ/Pw8t956K295y1vYs2dP7r6+9KUv8cM//MNO0HA4HA6HYyLMLyjP8/G9MsVwhlI4w0x4BbP+fuaYY9avUg0CQh9aUczKIKGnIvoqJlYJMT0GSZejg6MUvZD/775nMUh28cT2OpVCRBwVeErpMPdXb+HB5u0kyU5kpnpU/XkOVae5th5zqNpjoVvksSCkS4dB0rbW9fG8EN8rEvjaMbcQVJj29jHv1/E9j0c6DT7X+AKPtj6Pm87mcFyYOFEjRQK2NtngmF1WSvwqymiBQHweyugAr8zcTjBlZrLeENsNKkboWekSTN8qA7QwIt4P1bQ9fbRo0GJ0vftxP92zgcPI+nscebOtQ4x5ez/93EUHr/20jR46O2BvGa7dq+P1zYb2gPB9U26qkEbfVWKyM7pd6PagFeu+UICyBoCcQxBDOYZKoLMWRPzqJKbUlZ3ZIzPnk3RBKRVDvIEWBhKg5EGtDGHqKl/24MoZ3dZWS4sutZoO9NendNB/1y7YswempqE6FeBN11Iho5QqNh5EsRYvoki/Bul7u6V33GhqQ45yORUaPN0RnQ7EEYQBQb3CvOqQJDFr69qzYrUJ1Z4WCzx0f4kfQZReGynzNQXMT8PuXTBVT8Ug0J3f7aIaTTqrPZoNnQ0yOwe1mZCwHOCXUsOTOEa128S9iH57QGslYnGR0z4fxxdguaGvwUoCJ9AeFSLG9dPxsdV7QwSFc1FWSMaKCHXz6LG9iu5f0GN/muHMpAH6PrCzDbIZGtnjSBm4vGD9mZ7rOBHYFgjkeRlhhJZJ/lkogoi9Txj2DYFhQdHuE7sU3ijxJUKPF2UdKy9Dwz7uqPJ7Dofj4mR2dpZf/MVf5Id+6If4ju/4jpHrKaX4T//pP3HnnXfyqU99aqJ9P/3pT+eHf/iHAV1i6rWvfe222vexj31sy9ttl49//OPce++9PPzww3z4wx8GYHp6mo997GP86I/+6Ib1a7UaH/zgB/nwhz/MRz7yER566KEzOv4//af/lO/6ru8aWqaU4lWvehWNRgOAN7zhDTzjGc8YK/bcc889/OzP/ixHjx49o/Y4HA6Hw3F54OF5IZ4XEvhlCkGdcjjHVLiPaW8Ps8xTp0LJD7SPZNynE/VoqhYt1aBDi37SZpB06CYrdOMV+oMVCl6Zf1y7gm68n048y/7ygOWeDtLs95/AydJDNLqPodRWCkbntN4Lua7+ZJ41s4cbZ5vsrnUI1+s81q7RoUFvsAIElAqz+F6BIKjheyG+VyD0ilSDeXazn2oQcqK/xt2dz3OkdTdO0HA4LlycqJGSncGbV589+3jtY2Z1FzAigfgn5HltyL7OlOyM7q0gGSRSQqiGbj/pslFZIHnBu7yZ0hJIFWHE9qAgs99RfSGBRiktpTBBddD9Kr4F5ZIuyxSIh0OgszUKBV1dCU/H7KMYel1Yb8HqAGKlz7fJxgC4tFHElUJszmWQtkV8L6S8VJCuX0i3jxNdcQkg8PS54On2Vqu6lFXSg3JokibKJd3mqSmYmTZZDLMzWuio1Hy8UhHPD7QwoRItSgwGxn9CKS1kNFuoXo9+J6bbGNBrR8weaFGU0lNJoktSRZF+B7xqhYLnUW+3qNcTAh8akRG6IkyJpdn0/GNMeaEqOjOmWtPZGtUq0OuhTp3CixN6yy1OnUxotfS5VatQmCrjFYunS04lq2u0TjZZXVasrcHyCqytwvIyLCzCQhuWEi1irGBKY62lbZN2buf+OBeChhynh2476OfGLkwmQ4DuS8kg6GJENIXx/dmKN8WkWVKTMO5ZlpfBJUiJtYD8kllZ7OyJ7LPGFimkpJ7cn7IvuR/lWZxFxBX7GZVnxJ59dtneJnabHA7Hxcn73/9+XvGKV2y63tve9jb+83/+z0TR5OUH7rnnHu655x5AZxb89V//NaCFij/4gz/YNNPjXPLoo4/yr//1v+aLX/wiR44cGfpufX2dN7/5zdTrdV784hdv2Pbaa6/lP/yH/8Ctt97Ki1/8Ytrt9oZ1JuHaa6/l1a9+9Ybl73jHO+h0Ojzvec/jTW96Ez/wAz/AzMzMyP0cP36c1772tdx1113baofD4XA4HJcH+peW5wV4XsGIGYU5asEepr3dTKlZqqpC0QtJUKwlHVo0abBKM1mhE6/SSxoM4hZx0iFOOqhkgEp/ZfVVzBfW/45e/L1E6iCtqYBEefQT8PAphbN0wzUG0WoqbGzvPOaKV/JdMzdy83yXa69apTIdkzzqMThZZil6hNnStcwEVzDlz5N4OhPFw6OQ/lf2ysz4NVajNl/t3cHJztd32MDc4XDsNE7USLFn846bfZsNvEnWhKxfwxgoy3fbfSyPw2M4iLdVbD+QddJySZgyN2XyvUDsmvTZfpLa/ULezOhJgrAS8BVvDRE1qpjArmTF7Eq/j2JdsaiQGoL3Bzq+P+hrcaHb1XH7Vg+WE1i0jiWz+u12STA1TPtFgpgSjJXyObH1kpn14pOA0m3wA92uYmotUSnrrJJSP+1PpRMpwAgClbKO8Zcr+lUsibm5wksSXRsqiU+LESSJXqHfR/V69JoDlhcT1lcVzZb2Ao8GcPV6i6uKDxG027rsVJCWoUqUblQYQhjiBfDgA/DQUTgZG8FAocf4rvRd7oc4vR5TRd32MPXlKBZBxQmDhVW6XWg2FK2m1mFUopNF5pI21dkIz/dRSULrZIuHHlCcOAFLy7C0pPunE8FSpLMyTqEzG1oYn491q40XA130eTQxJeBkHJXRY66N8S6ZRQtHFeAkWpiUeyx7340qt2Svsx3kHrBFhSTzvf0un2V9uafGlfkalSGXFZnle1t48DEihmRyldH9uobJypJtS5jML9mP9KU8A2Rduc/l+Hb5NYfDcfHy/ve//3QmxSg+97nP8cY3vpEHH3xwS4JGluXlZT7+8Y8DEIYhX/ziF09/d8011/Dnf/7n2973mfBf/st/4X/+z/9Jt9vl29/+9sj1Hn/8cV772tfymc98Zshfw+Z5z3sef/7nf84P/MAPbKstc3NzPOMZz9iw/FWvehWveMUrmJ2d5corrxy7jyRJeMlLXsLdd9+9rTY4HA6Hw3Hpk4oZ+HheAT+oUAzqlAqzVIN5at4uppijoiqEBEQktFinRYMWy7TiZTrRMr1ojTjuoNQgFQA2/gJVJKz2HuXL6u/w1YvwvT1MFzwSpfBFUEnborZd08TjcP1avnd3neuvWGT2qSF+tcx8r8cVR2K+p/7dTBcLlP0SvcinlyiiROkjKf2rTilYjrrc3fu/LDpBw+G4KHCiRoo9u3czccOu7w46OClG0CE6QCZm3TIbORt8I/N5q9gzkc/kUSulaWKM+CL+CHIukokigf1xgeNxAdRJztUOxkq7LKsLquhSPbvT14HU86LbSfvXM/0cpxWYel3tvdBSuu0imHQw1008OrJtlZneEuwUQ3Dpi7a1bQUdoJbgagyEMRQTrR2UAi1qFEtabEnSCGmxoIWXaJCKAIkWQ5L0PRroc+kPQLUUhX6PMOyl5+mRJNDrK1Ti0R9o0aDbgYVT8PgRLQx0u1pY6fUB1jm40qS0qwYzM3jFEiqOodMhWWvw4Nc63Pd1xb33w2pkfGIGVp/sT6+F7+sMGc+DQqgzZmbnoFbX5+KnamHUV3RTYaVY0ufV6+vyVoqEXqeL5/c4dkxxz93wwBE4NYD1BJrKlIfrpH3cQI9NuZbjSqbtBGfiszEKyUbqogPuMsZKGF+QPsNG4gn6/hShLZsFhfX3uHv0TIQflXllycveksytBsNCSN66dgmwrJhqP3sD9D0n+5K+KKLvWcmeq2DKAp7AmL/LfsV4nHQ9ybaTbB9pT/b62+1xZagcjosPz/N461vfyqte9SqC07USh4miiFOnTvGiF71o25kHo4iiiHvvvff031//+teZnZ0F4B/+4R+47rrr8H1/pPn1dlBKMRgMTn++8cYbWVlZodPp0O9P9n/RY8eO8dznPpcjR45Qq9U2fO95Hrfeeisf+tCH+Jmf+ZkN38vxRzHqfK+99tqx28m5/fqv/zq/+7u/y9ra2tj1HQ6Hw+G4fPGNmOGXCYMqpcI0lXCeqj9PVU1TpkqoCvToseZ16HjrtJIlOtEqvcEq/WiVRPVRarIaCYqY9f6jfMH7ayrej/DE6TqRgqJXINQ1LVK2FyXz8Jgv1rlxV5PdNyQET7sSPJhae5wXrZ/kWburrHRClnsJy32flX5AM/JoRdCOFM1Bwomowd3dz7HYufd0lonD4biw8ZTtJngZMyr9Py9YlTcL2V4vztnODpZLkOxMO15mTW9/zmA+IXqGs0LPED9tjo0OuPYZFhrygpTZsjJ2+ZZxJuRVdMAysl5gytbsQgfUrwSuBQ7O6PJMoTgWo5MOBgOdxNDtQqsNa4kWIMrpuayiTdFX0cvF/yQratgChpSXEpPsMjpI28Nkj9TSzxJYLWHKCZU9qKV+3nEEna4pkyUls4LUC6RWh+kpbRJeq+tSTbWqzvoQ3+0k0fuJY72vQV+fd7Olz3t1FR49Akd7pszYjbvhidfDFVfAvv0wO+cRE9BtxzzwgOIfPg/rTei1jGfLID1H+d96AbhiCqoVnZFRKmtBo1RKy2bNwPy89gGp1XQCSL8ngooWNro9LTYpBYWivn7tFnz1XrjrPng4vT5ta0z1MAKUlJkSf5idEjRsTxQ7sF5k68bjW8EO3ItYelqcs9azx6BkCo0TGLLbiefI2Xro28+9PDFC/IZEpLGxRV/Jksh6atj7raD9jOQZ2ME8e4vo8S5jOESf92K6nvSzhDF76ecZ9LNPSn5Jmb6sqBFa29rn3Hb/O70suZDKBzkm5wd/8Af567/+69zr1+/3OXnyJK9//ev5q7/6q/PQOs3P/dzP8cu//Mv4vs8VV1yx7f00m01WVlZoNpvcdNNNO2Ka/eQnP/l0Ka0DBw5sECNG/bx49rOfzbFjx3K/K5VKfOMb36BQKOR+P4rFxUUef/xxbr755i1t53BcKLif45cvnufmmDrOJR4eQSpmlAiCKsWwTjmYphzMUvamKVHBUz4JMX06dNQq3WSNbrRGt7+kS0upM/ll7DFfvpHvrv8Ae4o1TnUjvhbfy8nuvXT7izm+GsPT3Dw88Hx8fHwvJMAn8Dyumnkmv/bEZ/H9zzhF/TnzeN91E0Qx3oOPkRxZJHq0SeNhn+MLUzy6XuexTpHjHZ/j3T4nu23uGdzLcv8h2r2j6fk5HBcHl/t4df8XnYDsIzX7ng3k22azO5GVMa5dZ2P42jPGFTqwJ//bKmKyQyRAKj+NNytzs9n5SyZEHR0476Fn5dv+AjPoYCXoLMFiQQfTfc9kN0RpdkanC50eNBO9nybDHiLTGE8N8WHIBofta+kzLHIM0KJPkrZLYYLspfRcROAoAmUF3RYEaampAboklZdAp689NjygK0H/nvb9rra0AXq1pkUPsc1IlBYL4jg1Pk9LbHU60O1Dsw2nerpM03p63vVlKD6qxY+lJSgUFadWI758HxxraI+HKXQWzJQPFV9nmpSsTgk97fExNZWWyCprf+9SGSoVLcRUKqlJuJeWmQr0OqQZHZ6vxZlOB9bXdLvbbVhZMWKFZEBJ+SDJspHv++k6O5mh4aX9FKf7FtEga8i909j7tv95mM0Kk+ylMvo+AZMtNGq/Hma8R+iskLORSCsl/GCjuCEMMn/b2Ocqr2yf29kVdgZGETMmRNgoYcqSyb0t3jeCLVbE6L6U/cl1z/5zPSs6ZT87HI4LnzAMedrTnpYraNx5553cddddvP71rz8PLRvmfe97H+973/uYm5vjE5/4xLb3c9ttt/Hud797B1sG99133+kyUB/72Md4xSteMdSfo8S+L3zhCzvWhvX1de666y7+/b//99xxxx07tl+Hw+FwOC497OyMEkFQoRBUKPpVAq+IShJ6NOixRkSfQdKhnzToRWv0o3WUilBqJ34VK1Z63+b/qBa7C9dxTeEGqkwT+GU8L8BovGl5LC/AI8T3i/h+iWIwRS3cxWy4l33hAa4O57i6FvBPDvT4vmefovLMXXDTNah9+3TQQyX4gUew3kcdiWlFIad6IQ81Y77dXuS+7rc40X+IVu9oen4Oh+Niwokam7BZeRVZx16WnVm8WbD/QkOyIqYwPhvr6ABhCzOTHfIFn2x/ZMvVjBJ4xKtiCpMVIqWw5tBZGrswWRDVQAfHfS3Wk6R+1+2OzlZo9nWQspO2fwUtYIQYcaSUvhoMBzCzApWcnx3Q7KQvqeMv4kjBenUxGRtyPuIn4KE7MujrYKsEfCt9/VrvQHUt9dmoaNEgDHRJrSTWJav6fZ350I+gk773Iugkui8l2J2k7TuWQO8kHO1AqwPNrvaokDmTdYynSjHR2SWFAAqe6YMw1ELG9BTUp4z/R7GkhYtKJbXrSLNnlEqzSwLd7ijS/77odqDRgOUlXYZqcQUePgXH0UJM0+p3W8CQ4HWHnS85JRkaktHTw1ybs4md1SBINoYIEh66TyRzwC5DNaqNcr9JVoeMxZiNGVMew/f2mZxHXjZb3np2G0ftDzY+X0sMZ3WJ8FlDXzPxWmliRA255/KyyaSve+j7QYTbUX3qcDgubl772tfym7/5mxuW33777fyLf/EvRmYSnC9WVlb4vu/7vvPdjJH81E/9FIVCgZe97GXn7Jgf/ehHufvuu3nnO995zo7pcDgcDsfFS4JCoVSCigfESZvBYIWOF+B5ZtqXQoFK9HrEKGU7C+4MSkW0eo/R7p+gUT7JrsK1FP06Xa+Aog9emlHia+Py0K9SCqaohruY9fey19/LoeI0T6iH3Dg94Mm7Vrn6hnUqT56Ba/ah5mZRQYjXbsNqg+TIKo2HEh49NsVXl2p8dnWR+xrH+Gb3bnqDZeKks6Pn53A4zh1O1NgCk9RNzwvmj1rvQgyOeZhZ4CVMUFdmOa9jfCYCjGlw1mgbRpfFGXfePbT4MMAIG3V0UHcXcNCDqZIOmpfLutRRoagzFqJIZzi0WrDe1zPSJbjZStu+nO67ke5PgrzF9Hvxx0gwZabsvhHEk0OCzn10AFXEDNlWfFZE1Agx5uOFtE8lUC8lcU6X+hroV6UD5XUo+KmndwKxgkjBIIF+oj/bZZkky6GAMZ5uAPcBxOCtmHMT/4ay1bYOWhDyE52t4fvp+afiROBrEaNShkrVCBmFghY9xEsjTnR7B32dWRJHOiOj2YT1dVhZhcVFWFyCh5twJDVxX02vmV3KTfxV+mnbzobQoNJ9J+jrVcN45JzNkk1ZJHAP+nrMYMqYiY9IG+PxIm3PQ4L1MXpcjXou1dJjiXgkPiKTZIPlZa5Nkrlgi5xZkUPuBzlHWS6l4ER8k2yMHnpMt9D3vl2qTAgy+5F2ZsWfcZVh7fJY2e0dDseFz2te8xre+c53bsgkuPPOO3njG994wQkaFwOdToc3vvGNhGHIS17ykrN6rC9+8Yv80R/9ER/96EdZWVk5q8dyOBwOh+PSQv8SVCR68qEsOl+tUX1WOt+km6xSDOcI/DLgEfhFwqBKIahT8WeoeXNMeXPMBjPsCmrsKRXYX/G4spqwr9ynXhrgKUjWevhHTuH5PhQKeI8fJ/7iw6zc3eVbD83wl0fhH9bu56uNu2kNlhjEjfN38g6HY0dwokbKNDowZiMlTEqYUkvZ0jBbCWRlsxrskjIyg7/HzhsSy/Hy2pFF6spXMCJG1/o+xgTei+l6Hsa4WYLQ2cCpyryPQoLKfXSQdS/D4kmCzlYol7VfQ7mUBs1TD41eF9oDLTCsod87mCwTaaOcSxkTMLfFADugKsiseIUJmIIRQKRc0QDjYSC1+ktpn4loIGW8ZF925cgg/V62KSZQSoxhufiS2EF+CcTapZsU+lpKKaUm2quii57VPo0ps5MVDmQ+Rqx0ZogXG1EDtIAk2SIqY0YSJxD1tLBRLOryWOLzMRhAqwlrazo7Y2VZZ2gsdOGoMhka0r8iqokYJKbgZzNzQgzJbS+Gs/lvvWxWg9w7CcMZIzJGwQgVEnzf7Dlkl1LKEzVC9P02jXnWiZF5k/HPpEkyMTZj3Lq2kFFI15FxWmBY6IrT9spzNC+TLE9Qlu9kjlKCuddHZdrZ61yoIrXD4TBcf/31vO997+OpT30q9Xp9w/ff+ta3+PrXv34eWnZpcPToUX7+53+e3/qt3+JTn/oUU1NTO7r/N7/5zdxzzz2cOHGC++67b0f37XA4HA6H4/ygiOj0TtCPGiilmKpcSTXYRdWbpcYMU16dKb9CzS9SD0OmCh71gkfoQTf2WOgWUcvTNPpF5hb7zB5cpBorPN9DffsYa/d2+H/v3sXHFx7nq2v3car7CIO4xdmJujkcjnONEzVS5jGBTNDBrQo6yFdBB81WMEHBcUHEzQL4ImjYQXLxr8jLeDhTJFNAgny2D0YWERAkEG6LAHn7DdGBVx9Tkz4bTJxU0LDXl3I60k8i+oAuZ4TSQfVuT5df6nS10XSzAy2l29FGBzhbmKC+mARL+SgJjErmRC+nDYLMXpegqpyPZJQk1jpiQiwvKUsVMJzlUsWYHNvHkvVC61VgePa8BFalTI58tsuELWHEORGGJHtEztXOvMHav5R4GijwlGlXkvpftFraN0MlaSaHZ/w+VKLLZQWBXtbvaQFjfV0LHGtrsLoCyw1YjnV2xnJ6jUQ4E0FhgBE2JEthq4jINOn9Jddyp8tbZckrZSfvImSUMCbYdhVTuUclo0SErXGMO3fp5zJ6TK+ztUTjbOkplbMsb5s84cFGRCXJIpPnUws9vuU6yX0k93gWu283K31lCyDZPrPFDnnl+X84HI4Li+npaV7wghfkfvfYY4/xi7/4i+e4RZcejzzyCI888ghPfvKTednLXsbv/d7v5a43ym9jFO9617t473vfS683ykHK4XA4HA7HxYoiJkqzJlq94xQKRQ6UbmLGq1MJAgqeT+DpOMN6X9EcKAIPjgYe1bDAVKHA7GqVK5YHPLWzwtX7VgmjAb371vm7e6f5nYe+wLeadxMlXdQOl9JyOBznFydqpITA1ehAuATyq+hgt8yKlzr0o8qrZINcsiwvcCeBdKnzXkIH5s5GCRMP7VMRoAOXa4w2Fh5gxJtR1RMjdD91033X0J4XdYazHvLq9k+KBPVL6ACrvIqeLm8UBDojoN025tiNFjSUyRrpWK+29YrR1ztKX0V0KaoKw8JPNkiZ1x9eet519HG7GEEj+96z/gYj1khmgC02ifAlwf1stoq8RAiTWfiSydDCjMUA2Ie5RiJ4iDgg41UyTAZoccGz2ixtKgD9GIqrEBZ039dqxjdDKej19TUKQ53REccQpQLU8rIuN7W6BmtpibBldLmpRnrcKD2WmEF71jXczj9BZIxWMGWJziX2/Z/9vJk4KkJD3npynWJMdo8ttm0FGYMKk72zxuZZKqPKTU16nlmBIbtMxrmINZI9Ywuvtqiw2fnnlbuyj21/tktgZZHnXIIRXCcp0+VwOM4PxWKRz372s7nfNZtNbr75ZpaXl89xqy5dHn/8cd73vvfxB3/wBxu++8pXvsLBgwdHbjs9Pb1B9FhdXXWChsPhcDgclzT6V9ggWmYpbvPlZInQL+F5+tdYwa9yrf9MlKd/XSYk+HhUKFH3S+wqlWhERXaXq+x5cInK8hIL367wxaU2D7e/xSBpncdzczgcZwsnaqT0gYPockcSsCd9b2B8B6TUEAwHv+RvOwApwTy7hIk9y1oCxjIjX+rB73RpHQlYljAzvUeVS0kwJackqJpFZpHbf9fRpY7qGM8Ku3zQVoLR4kthm2vL50pB+zb4aaQxioygsa5029fT47cxNfUjqz1ynbLXT47lo4Psk2aYdK1zlqBwnrCRNSqO0AISGI8IETWyBuW2sGCbtCcYM3QRXbL+DzHGS6SNyfbIXtvs7PMBJmArbRbjdrqgTmlvjEJBixRzs8YgfHYm9dOI0zJVkX71+7DehMW+Pne5VlIqbC1tYyE9zix6TC2wfTGigBb1JJtGxKxzQQmdXSAeLJsF3MdlGOSJqDJm5JpuRdAQ4Uy2kzJowlazNLJCQVY0GJcRIesw4m/Zv4gcYuyd9b4Y1+ZRmRXj7vNxYox9zHNhJu9wOLbPddddR7FY3LD8xIkT/LN/9s+coHEWSJKEfn9jvuOTn/zkkdsUi0WazSaFQmHkOg6Hw+FwOC5tEtWl3Xsss9RjNXgY8IaCWZ7n4/sF9sQ3MlN4LsfbZQ4crVBeiPjmwiyP9xaIz3r9BYfDcb5wokZKAx1knUEHU8uYslAJJsgPw+bFEqgOGS4LFFvfgcnwsANzsg+ZsS/7FuzZ+uOMijerXZ9ghBk5Rjaol91WxI/NMi3knEQ0KWLKxIhQM8CUDZJ9jmqrzDgvomfX26WaCujsgCAEz9fB8kFfZwa0LUFDSk6JOBVjhBJprwTq7dJMtn/KqtVGCfzmITPasdYZZPYrr6yo0UvbK9d5khnxIkbY3iXjSoRhrSPG5ljnK9fezv6Q0lNdTJ/ZJdOkT+MuVLppkHkJpiuwexb27dVeJ4N+6qmRaJPwRKV+G7FuSwMjarSsv2W8KLSoMYvJQrHPQfpFznFU/4mIJdk+bc6OqCH3smR2KXT/1TFBbzvwnQ30y5jciim5nNt2KKPFni6m/NlWM2FGiRjj1ssjK2DIu/SRlLezy9FJKTVb2Ngs80X2Ya8/SnAZhy0IjcpoczgcFw4f/ehHCcON/+T9b//tv3HnnXeehxY5HA6Hw+FwOCZHEcWrI79dC6osdiNOdEIeX63jeXD/WoWFbo9EuZx6h+NSxYkaKT30bPAVTPCzig787kq/b7NRCEgY9iToWt9L4Bk2BintIGwHE5i0H7cBOiBaYrikkI0IKnI8WWZjB6uz5WKyZVekxJCdKTDJLOQ+WkwQ34IIM0M+RPedXfZqVBBQyi2VMfXzs+Vr/FScT5Q2nu5Gxj+jicnSkJJOsl9bzLAzH3zrJQJEnsnwONHIRq5tx9pvtk+lDwZjvh91rKwnSp7gEmICwRJgt/s8W9JIxrJtOJ4tlyWCk3g9JOh7pIsWKuab4CkoFU35KTwopu7OSmlT9yQx47GH7ie5bnK9Yuu7StqGMmYsyjWU9tl+E9lrJxkqRc5ulkYZ7cEjmVFy/9vlwUbho0U8KQ/W2GT9nUDaVUK3s8Fo8c6+Z5Ix69lkS0vlCQd52RJ528jxbVGyhL6WdgaPZGGI4JDXTjsbarMSYOPY7nYOh8PhcDgcDofD4dg54qTPatxjuV/gWKdML/F4sAlL3RaJclPQHI5LFSdqpMQMmxRX0DPE59GB2xrGuDiLGNQGOd+NCoRLQE2CsKPKPJEed5rhAJ4dkJN9jJo1HWSWZ4OF9j7sWcwh+rxhONMj20YpyyXvsn0Znfkyk7ZBMifGzUIfysqwznPovKwFSQKDxOzbfknpK2lbM93GLrlj+1bIdZAsCxEDtlLSJ4v0R97/Ru3Mn0kD7ZO0RTIGKul+ZVxnkdJFkrFhi18y1u1xJtdFtvMwwlExXd5JTcQ7HWg0dEZNraazaQZ9XS4siof7RUSU7D0wQGfMiBAhx7WznETUs0vC5QkXPYwomNfXdsDcH7PeKDx0f9cxPhQiakTpscdl+8hYq5D6lqDHtOz7bATQI7TQWGLjfWYj5yZl1UQk2uyfhnmCblZIyxNps8KGjdzLm2VXiNBsC8ZZ7GyNURkb2ayg7PfjxE6Hw+FwbI3/+B//I0Ew/K/pe+65h09+8pPnqUUOh8PhcDgudDxCAr9MS/VY69dZ6AWsD+Ch9jKP9x8gSVz5KYfjUsWJGim2n0GCMVOWuv5703UWMcF7O5i11cekBJ7FsFr2aWMLLZI1IrP07VI3tuFzNhPDXk5mOZnvbXNzCVjKTO48wcbeXoLS2RnyYj4t5uGjgpEFdOC0jCkVJWJDEZMJ4+VsbNfKt30lJBAu3hp22TC7nXa/i/9GkfxA+3YZJ26JOfdmSBaNzP6XADwMn4MITfL9uOCvmC1LP0sA2C77JdtLO+1MDhGtdqHvk0oIhaJ++YHOpFlfg3YHmi3odqGvTObCZrP+Y0y5KCk1ZGdJ2QLGuHJpYqBuZ7DY2KXTwJScm9SWVKXrr2MECsk2EkFoKad9cj8kGNGtwLBImRUCzhS5rvLM6zA+OF9Ai6pyLra/kN2urBCR1+5x7befX8p6z2aTSV/bmXA28kwso5+bUlorD9sjyW5DnuCiGG6jw+FwOHaWH/zBH8T3h/NWjx8/zn333XeeWuRwOBwOh+NCx/NCAr9Elx7tSLHS91nqJZzoL7M2OIpyxYIdjksWJ2pY2IE9O/Oggg7cltGZCyI0yPprDJemGrVvMvuP2Wg6niVCZ0mACdK1MvuU7fMEjVHr5bUtG9iUMjpSYkvEiVHkzarupu0VkSIviC1ltuYxwWU7oC5ZB5U0WO551rE8XY4qUKMDnH2r3XmzxnvoPpaA+XLabtsH5WyxleCojy7nJYbyPXS/iAG6jVy7SZA+sme0Swkw2+A8wGQlifCjgEPAVRWYm4GpKdizB6anIAh0pka/D+0WNBs6e0NEJlvYkONkz1e+FzElL9A8SUaFCD0irNkU0Pd1BZNZ0bfeJ73+ki0k2SM++rlhlxYTcU32awsx4n0TMBxct89hVAbDViiiBaguxqh+3P5KaFGjmratgX4GirBmC0F5ouEoESArdIzLkpD1sqLpKEQQDtH3jGS+ZPeZFXPtY4xq1zjhw+FwXHhkg+RCkiQkySTF9BwOh8PhcDgcFyoKhVIJCTH9RNGMYLkXs6bWUYnz03A4LmWcqJEiJWeyvgNr6CD3LozprwTnZTZ7BTiOKReTt98skt1gm9yOo5keq4YONktQVB7RowQNrGWjsiSy5ahkGZjMhxo6uLnO+BJSWVNtySoQQSjGiDKC7fch4kaYWS9AezQUQgj8tOzUAOJYe2t4mf1NGmyUa24HarPB0rMpbGzVZFgC+7bx+qQlqcQrZNR4y2asJOjrVrGOKYH+OF2+iu43z4fdu2B+HmbnoFrVRu79PnRTc/DBQJcK66HHkGTCiIBo94NcTzsLyf5uu9cjbzvpGxHufPIzj/IYlfUjAoXCZF6U0PeAZBBF6fHEUyQrcsj+Rx1LAvIeJjtkEmTMT/LPuwAtZkxhvE02KyFnB/3zrpUtCMh4lIylSTI6xmEfT87P9tCw/5bMJxHr7PE3KsNp1Dk5HI4Ll9/7vd/jqU996obln/nMZ/iN3/iN89Aih8PhcDgcDsfOkuj4mFK0B4r1qE/bW0VNFG1zOBwXK07USLHFh9h6X8KUnSpY69rmyXPpukcxQVh7BjCMDoJN+ohV6CCplIMqkB/IHJWFMY5xZWIk+BmjBYcKenZ301pPzqGYvqRMkGy/jinlVWSjqKHQgtByel6zmDJLInicDkz6OoAeD6DZhNWe8XWQmf/SplF9nl0uQsE4z4MLAclYsE2nJ513INkDIk5Mglz3GqaMl2SwCF3gBLDc00KTH4BS2jtjyBg8hjiBWJlAtmQr2Wbm2QfSqPE96TURsS9rDm0HsPto8VJEHDEdn9QMe9TyPlr0kYD9fLp/EVAk22srpevsgLqHKQ3XZfJSaT30PTwqa8G+dyRzZQUjpJ3CmMjHmXUnFTTkmkgb1JhtJkWet/YYSRgWm+1nshxrUoEnKwrL/ZSXQeRwOC4cPM/Dy6td6XA4HA6Hw+G4JPA8X//GVNCJE5qqS0+Jq6bD4bhUcaKGhQT17AB3jA5Mrqffifm1lAGSMkniBSFG0zLr2A74TSpgSOA1G5iXLIcupqRNXjmlPCZ5lI+aJW1nsIj5tGRg2CV67ICkGEfLtl10AHZU8FD6WQKE+9B9Srr/PmlGRlpuKlHQHwwbg8t6IoKICDCq9r9NXi3/Cw0f462yTn45nVFIX8h43Uo2SxE93lsMm1fLw6MD9OR6dKBQ0IIGKhUyIp2lEUVa1JCSUlkBKlsgJJuFkg3ob9Z+uS9FOEgw/ddAjxlByqyJ4DNg2EB+u/M7bPFGsq1KDPefmFrb4kZWHMjioe/FqXSfRUxZr83aqhgWQGRMFNO/7SwVyVbrYTx9ZAzYZZs2IytGiTBsl4WL2ShM2G3OKxUl2Ib29jawsT/scZZk1s07ZvYcQPfVNPoaNNPXpGKhw+FwOBwOh8PhcDh2Bg8PjwCfgERBL1a0aBMlHVAuU8PhuJRxokZKtvxSNhAus8lllnwREzDtYcx2R3lkbCVIbu8nrzSRtEWCnuPMoPNK49jr20LGuDaK0bKIBGKqbPtOiKhQRpfrWk23keCoBJZHISV7jqODtXsxAkqEDprHMRRLUC7BdB2iNvQjExyXvpHP2WPm+YvIzHnZhwRb7QyRnfAyyEPKcvUxJYhG4WMEBhGVthJItf0rJLC+WRB8gL4mEuCXfhBjcVnWUdDpaOGi29HXKfC1uNEfwCCCaACRGs7OkKD/ZkKTHWDOfh5FyMaSUqAFhP3o8dnA3GNSAssWC0VwiJhMMBiFQj8j5N5TGCGhni5bYbic3DhBo4AeB9OYElEVtADRyNlmHGHaBjlPud/sMk7NtP224Jv1nBklQNnLhDyhyBY87GufZ5qed93z7u1xKPKfe5OU6iuix9B+tDh2Cv3ccjgcDofD4XA4HA7HucXzAnwVkCjFQCV0aBIlXdQFOV3V4XDsFE7USLEDWCIojCrn0kUHRANMWZ5VdNDUDrpNKhhkkWDvZoigUbK2yQvGZUu9wLD4slnbJLhvn5/M9rczIWyRYBod+FvDlJuK0YHTMjpAnJ1pbZezCdGBWin5VUYHyT1fG1CXUtNwlqC5Ohz8zr5GkQ2CSgBctrNLPZ1pFswoAnS5rQG6f8eJFGIaH7C5QDQKEegki2FUmaVS+i4ZBjLWqtb3ZfQ1LgCegl4Xuj3A05kZpVLaj33trTGIYKBMho8IT2cLKV0mooaUIgrR2VYiZK0zHMC3A+jiY+NjvC8mLfOUJUqPL/fB7rQdMr6aZEqtZbaXcSkCk48eL3LviLl7i62NDRHwNvPmyF4rlZ5HyLCRuy1CZZ+H8nfecUQokyw0EaLyxKRxgsOo7+118rKA7HVg4/PJPg/QItJc+p14pTgcDofD4XA4HA6H4xzieQReAZ+AWEE3iejRIlFnM9rgcDguBJyokSLBbKmrX0IHB/Oq8CVoEaODERQkSJ8VM8jZfhLscjzZ7aWEjWRMSOkaO9BtB0ZDho1wNyunkxdMlSDkZuciM9KPAXvQ2RYNdDBXZqZPMezLIccUHw0RFBTGXLwElAqpQBOngfkApmow1YZCf3TpmbxSPvZnuz8kAC4z9mXWfjYoax8nb9mktIHH0QFSe3Z6HgpT5uZMBQEx+87bRwHd7zBcakyCznZmgGQaHCzA3LxuZKups2k8T7e519dZGoPYiES2r4YwahZ+tuwU1nbj+iti2P9GzreNFjIk22jc9bNn5tsG4ttFSoDNorOZpjFjfSGz7rh7NEI/g2D4HFXa1qxoOA4Ry+y+3cq24vHTyxw3e89khccskjFSZFgwtfc3TlycpGJ+nqBhM2q5ZIuIsLcbfY+IECqZZQ6Hw+FwOBwOh8PhOJf4+AT4yidWCT0G9FU7/X3ofNUcjksZJ2qk2BkIZXSQSkou5ZGQegkwHPjOst1Z/AE66KnQwUs7kCoeFVKGpowOjnat71qYwHwVE8Q+E/PjLKMCyuKPMUAH/wJMxoWYiUswXXwhIoZnOi+hjcNFwJEDKtIZ/1E6szyGUhrRlYC5vHoMZ1rYwdW8EjMy8xzy+2lUKSAbW0CZBIUJBk/KmQTVbfIEDQ/jndBnuPxSG+M1IaWwiuhrPDcFxSJ0u9Dp6vFYKABKZ20MBhAl5lrb5uB28Dvbf7aYkTezflw/K4zQJ4Il6L5uYDJV7P1lkYC/ZLecKUV0dkYN3c/2eU4qUknAX7xsBLnPSujr1tniPrdDghbaxglyozIiyCwXwUnGhvjj5G07SqDcjO0IGlnkWSUZcDD5s9XhcDgcDofD4XA4HDuHh4fvFfDwiJSiQ5d+0k4zNc6krobD4bjQcaJGih0Yk0B7m80fgaNKPk2S1TCOAJjHlGFaZFhgkQCalAY6gA4uK3R991ZmPSkbNElpmrzAvJyfHdAWnwwy68m2HXRGRtlaLr4kBbQZ+CpavJBjSTktHx1MLqX9kABJkmZpeFrM6Hah3YHjfTiR7kvKXbUYDvrmzf7PK9MlZYqyhuNZ35JR13cnvTdGGcafTQrpq4E+fykrJCWvJEi+kq5XR/f1qVUjMgW+9jwp9FIRSjw1YlPSS/pXXtkgtZCXfZOXuTGuf/LKJsnYzROIsiWTuuhrUMH4dGy31FAfLdj10s+SASIm35OSdw/bpdtEiDrThNtx3j7CpM+6UeWcwPR5B93fdrZY9l7Nu5fHHXPctlt9TicYD42ptJ0ROvMnm2njcDgcDofD4XA4HI6zj5f+goxUQs/rEiVd4iQbrXI4HJcaTtRIyZrYttNlIRtLoIzjTEpO2Uh5mVm0IFFHBz2zwVSVtrWLDrqK6GDXrl9HB+Aq6fadCY6fF/yz+0gC3jYyy79kfS/1+m2zZilZI2WehBBjfly1tj1drijRQXPPgziCXg/W+nASLYwsofusm9P+vMBonhDVxwRWbWxhI1tSR7bNBuPPdAx46H4Q74RsNsvZQAL99ji2S0YJCSbDZB14NIbOEswVYXYm7csB2ii8p4WNfqL3301fcqwIU9pKPBSyJaDGMUoMyROtZB1btLKFuDxBJbvOmc7Il3tb/FEKjPey2AoiyMlxzpSsmCHCnwTzt9rm7PNRxItsCbhR2ReyPC9LI6+01U4KGgrzDOgAD6DF1A7DXigOh8Ph2BqHDh3iO77jO5ienh5a3u/3+cd//Mfz1CqHw+FwOBwXPh6+XyTwCmk8J6JHh0HSJUnyIkMOh+NSwokaOdizh9MKOqcD7Jttt1OPzASTcRFiSkuNWncVk0EhwTdhgA6givfGmTLKUFgCvuKd0bPaXU2XS+aBCAdi0uyjRRcpy5MNdibomf6RiBqxfkmJpOn0HKUUTl7byPkuK2ZtpRSULXBkA+s7IWqIZ8oceuytMGxqfbbI6zsZe3INxSBa+mwNnZFTjUEl+tVPvTTabWh3jZAh4ox4JohnRRmTJWWXqbLbNK580VY8FeRelvPJ7tsOvosvjfhG7FTwWs49LyNM2rUdsUPEhp22RfMx5vBSdmorbRNzcxFDCwxnfNk+KXnZOPZn+zsRWSQLzBZM7KyzrFC21X710M+oMkYcXtrGfhwOh8MxzPOe9zz+5E/+ZMPyRqPBr/3ar52HFjkcDofD4bgY8AgoBDUtaihFnwE92sRJlziZpPaKw+G4mHGiRg55AbXNSpxs9qgUwcEO3m1GjA7UT0ITY6idN2tYyv5sFztoPK4s0gAdvK5igscJxnxafARktr49O7uCCebas+LFFDuOdfmpyEtLGUXmnGYYDppvJRvFnpFvl6ySUkPSjnFBVvvvM/nfppy7HK+FEXt2wtPhTJGMChEhyuhrKn+XwjQ7Y6DFp14PWm1oRnqMis/D6WuKEUYkY6OOOXc7cO4xuXghjMqoUZjsoVGz++0sAPFo2ckE1iL6vuiSn4Flv2+FrAH7mWCP56n0pdAi1qhj2GPYRq4t6H6fRp+/lIvrYDxbZD9ZsqKh7Gcq3ccS5plge+lkxcvtIuOmkr4ajPZdcjgcDofD4XA4HA7HWcTz8LwAD58EReT16as2idopJ1KHw3Eh40SNlLwyJtnPZ4LMbi9ignc7iZShkiwDO3CXFSTEXLyKyd5oYkx6NzvfiOFMkGw7ZIa9zMQHE7SuY2a9i9G6BCez+7RFjQRIlC5B5cW6/FRf6QyV9XT9Srr/Jvkz6keVnAJTakyWST+IkCAzwLPCRrYU1ZmWoJIslXb6dxdd4iZkeBb6+cDDZOEU0zaJ58khD/ZXoVzU6w76WtQY9KHXNyJWF5MN08KMiz7m/igynK0jSP+PGnt57bXfsbYVr5oobUNemSU5ZowJuu/Us0Beo+63C6GUUYi+p8QDRfxW5L4dhV2mzT63Hnr87EZnH+1J/26j79k1tChhl37LPpezgmIFuALtz9NM93cSIxLJ8aWk1ZkKGnLuRfT42YrA5nA4HA6Hw+FwOByOncSIGjExPTEJT5yo4XBcDjhRIyVPvMgGsQO2X6deZvznzcoexagZz/JdXjA0r+SMBIIlYFxBz2y2fStKaKFjjY0By7zZ8bLPUYHCrP+DGCQrtE9IIT2mPeM6r90wLGpE6U7j1HS6gxY2ZF9yHmIUPmqfecFIMSkP0u0lmG3P6M/LzLD3q6z1tpu1kT3GufDSmBS5D6SUEKSG1x7UK/r6RDH4vs7YiBN93SQrQzIzpF/s+0G8O6TPq+gMFSlJJYwS7LKlwCBf2JBxMpfuO5vZlM0GkPftlnMKM+2Q8Zz1bbHZTmbXTlNAPyukvasMm8dD/hgf1U+SfRNgMjYk26eavktGmewjr8SbTRmdqTGb7mMNWGT4frGvnYjLCdu7nrIvKZ/nkpkdDofD4XA4HA6H4/zhpb8WIy9iQI9Y9UjUhRJBcTgcZxMnaowgm60xjkmC11IWaSuBtBBTv93OPJBSP1JuaRLsjIc+2p9hCZN1IbPGt6Jn22WoYoz3gI/x1rBL4fSBBXRAcCpdbyo9pu1tABsFjdMB8UR/F4ZQjqCamLr80l8zGB8RO1tE9msHSu1lIvAUGfY5kNn04zIEsqW4JCgdYLIRJgmAymz2nfDkyCIB3Z3K+BAfgy7QVLrMlOdB4Kf7V9pbI1H591NWJLJLQw3QAfA6OmjdJF/YyWbKMOazfd0jdIZP3j05ys/BbvukeJgZ/XKvSr+NEysC9Hn7bCzDdS4QjxsxBAfd5nWG+0SydbYi1krfizeHZIwV0SJWB5M1BsPXzy4Vp9D9chI9TioYgeMU+UJYNV1H/H48hsvgbUZWxHQ4HA6Hw+FwOBwOx/nD83R9jYg+/dQkXKntuFM6HI6LDSdqZMjL1gAT9PIZDoCJea74Aox6bG53preUPxLjZPE0GGBMcicJyCl0UFWC0PKIzwss22QD+V7OS7wnsusXMEF9OfcEPZt6Dd1ve9LlbTYGkKU9ImokQKEAU/X0fQDeEiSRnp0dY8oK7UYHOVuY4LW9z6ywIUFSCahnBYpRgoZkLpA5RgnYm55jD1hmMpNvKd91NpC+FN+VnfrffAKsKVhow5QP1YoWM+SYtohh93OAyYyRrAU//VuudwPT/7bYYF8TlVluMypzI0IHxLNiVPZeOBNRQwQNKdmUzfoaJ2wkGL+ISrpue8S6Z4MCWhyQ+xf0WJbniCwTD4xJyzCJr0YB47Eyk353Mt2fGNDDRnFPromIsz10ebZZ9P1WRT9TBuj7zR7j9jNLhKZOeryteKV4mPJTEfoZcyGUC3M4HA6Hw+FwOByOywsz7SzyBgySDnHSRblMDYfjsmDS8vSXPCrzSjJ/C3lBToUxjs2qRNvtYA89a1kEEynJIiWXxJtgK8E0EVYkeyFbMmmSjJRx5X38zDsMZyzkIaWwbJPw7HWwSxf5vn4BFIowV9OeDuINIgKKHRQddz42ErC1ywJtFrAtoo8/l36W/RbSbfsMB+/PN5L1U0QHk8tszRcgbwxIv7WBfjIsaKDyS5RJYL+E6RtbwJP9y3i1DdylNNUM+h6R7KC80mDZsWrfj0lm+ahMj+w+J0VKltleNbZANw7JOqpjSsWdy4d1gO7bPcBh4GDaniB9L6TrbUVYJd2n+OrEaNHqKHAELfwtorMs8u6b7LNZaKOFjbV03T3AIbTPxgzDz2TJEllPjyGvSciOpwpaPK2xtXHhcDjOLXfccQdLS0sblh86dIibb775PLTI4XA4HA6Hw7ET6N9oPspLiOgxUB3iZIDadvFoh8NxMeFEjZS8oH5e0D9vnS75s3UlMFhn6wHtCjpYLqVyZN+SsbHdGfZbnWlufx71dzZzIzszOsBkbWQpoAO2Uwx7bNjlsiJ0H68Aj7bgW4vwzQV46BScahsho4ue/b2KLnO1iJmJnWVUPxQwQfJsSaw8xB9gL/p6iXdKCzgGPJ6+r7J9T4adxkePy/3oAHBh/OqnkRJBIkDYGRRgxqhSacmp9F2w7yHJ8Cmw0RdjM+ysDDGvluNnA955+xt1rHGC3VaD1jLu844vIuU4IcAWbeRznXP3wJYSdTEmq0HEgBbD2US2B8Y4yujzkWsuBvFSRk8EEzsDbBS2QNVHixpHML4fBfT9uCd9L6GvhRjUy2ur96Q9hm0Rdlw2l8PhOL/8yZ/8CUePHt2w/ClPeQoveclLzkOLHA6Hw+FwOBw7SaJE1OiSqJ0quO1wOC50XPmpHEaZEo/KZpCZ6qP2I9kWk5aP8dBBOCm/s5XSKDvFqEBu3kx9+7vsOhLAloB4MV3WQZ9fC1O+SsoQST/bs/RX0n0XFRQGJmDsY8pFSaC0ie438fkYFbTPlhcS8UVK6ozyVbCJMMLJXLqPZYY9Bs7H9RtHBS1oSGmjCD07XtorAdpswqYICCJqyDWVbAsJVgOnOzfwwfNSgSPdZxtzne1AsH09xv0TREp0SbmxAsZMPa/UUFZ8UWwuVNglyvLEj0mymuThau9L/t5s+w763qhgsoFiNmYSbRe550btK0ILBZ20DSvpZxgWAiYtlyYijzwDBmiBQcpyybiSa9lNX3nXTZBngIgVJ9H9I14gco5ltBhjs51yUTJmSujrsQstosjzx/2z2eFwOBwOh8PhcDjOJfpXmiImUgPipKdFDeV+nTkclwNO1EjJBqU2m+096T7F9HarZaJkG9uw+nyRzcog8zkrYGS3leUVTDmYFXRAcA0T2BS/ENsrwcMEQBV61riUqpL9J5hsmEG6T0G8TvLIlhWzfRuyYlb23IU+OpjaQosac+ig5zF2Jvi80wRoQWMXRpAQv4a1MduVMOWHREgoYsquleU91Cbuvq/fwwAaHf1vChGpuhhzeGFUJlSeyGFn8hQxopUIG7KOCFOj9pnHKLFO5XwehZRcs8e+LWhMQhededBClziSvp5i8syIce2roK+pz7Dfh02EFujkHjtTPPT90sGIkfJ8k/EUMlzSKvu8GSUwyTNzgBFORDw5U2HYHoMh+hm2O11m+/W4ElQOx4WLcj9sHQ6Hw+FwOC5ZYiJiNSBKeigVo5zrocNxWeBEjZRs0DQbhBwVdB1HgA5Cit/AVugxOhh/Lpg08DvOh8DuuwI68F1FBxtl1nkHMwO8iPEREaSk1AATQJeAtS1s7MLMyG+krw6bI0FnaZsEVfMCvaNKCUnwt4MudyN+FX02BoN9a7vNkOwHO+vjTJGAu3188SsREja2T2baywz4IkbIqKWvsgelojZx93xdeqo/gEiZ/UlA3UuPaYsQeYwSy2R/EryWLB/Z30y6rM9w3+XtaxS2IJEVNka1WWbyBznbyfeTHr8HHAeW0GKZiEdiMr5dQvRYL2P6bpTZ9U5lIIjAJIJWD2PUDvoaSd9Im7IZEJs9iyVrZJDu+0zbK+Wl7PtDMo1OWZ9dqNThuPB53vOex9LSEoXCcO7m2972Nj772c/y2c9+9jy1zHH11Vfzh3/4h7nf3XLLLee2MQ6Hw+FwOC4ifMJwCt8PSNSAWPVJ1AClxNHS4XBc6jhRIyUvMyMbUNss8Jq3XISNED2r92J8tG6WpWGT1492sLJgfWfPuJfPEugWzwEJgEpAvY8JGpetbaUUUR0TrLUrKWazL2wk0D+Vfu5hyiNNglz/DjoIPT3hduP2V0YH5uU8ltiZMlYD4AF0W3ehz/UE40U38T2RQK+UCSpbrwpQLUGxCH6gMzN6XWi2oaX0cSRwXcSUj8p6YMBG4SFPBJCSQ2LC3ccEnqV9tXRZCyNw2fsaJTDZmUf2fS+Cl/wTKU9YyGY1bfWZkYdkA9nZB9ulgL5HRAAooO+nLjuTjTGKBHON7IwaGPbHiDDjLCu2wcZrYl/PEH0ufc6sj8CYlIcMCyRJ+rc8Hy4UnxyHwzGeXi9/mkihUMD3nSPO+eTaa6+lWCxuWH706FFardZ5aJHD4XA4HI6LA/PrOyEhTrrESR+lzsSF1uFwXEw4UcMib0awvI8LToaYAK1NhC6xJCa1F9NjVQLLo2bKQ74Rcl65GCkBtY7xuJDAoAS1JcgaoftTxAspCSWCB5igp+2VIDPAI0ztfg9TlmqF4br6trm0lLjqMly6RjIt7PPP6wu7D8SwPG+G/qRjQMSwctp+KaGzU3ZXfeBRdHaJ+JqMC2jL+Uq/ZzM1qkAl0IJGoaA9NPoDGETQV8YfoYcpnSRilpSkyjueTbbfVXoey+nfdgA8Rt93vbStIpDllVGz+1PGjW363Er3IxkmIl6NKm8lgXDYvE+3ei37nLmwZZdzk+s46T/5ttNmQaXHzV5r+16W/UdsNH0fde/INUgwY3MnspqknVkBRtrixAyHw+E4czzP4xOf+ETud7/0S7/EyZMnz3GLHA6Hw+FwXCx4XojnBSil0kyNAUkyAOV+rTkclwtuelrKqFJDmwWiJVg3KoAZoQOjWy0/da7JBnzzPtvYgd+8cjB2JsYA3QeLwFF0ZkATM+NdtpNgq13+qIouvVPDlJuyj1cB9oWwv2iEiil0tsQMuvb9FeiyUDa2aTHoQPUyWvxQ6IC9HbTOy9rJGxvS9gImuC3ZOpIJYs8y9xkWZ7LG2fK+0/X6E7SHRoPNg+9yLvISLw6Z5V8GykUtagShFjVUokWNrjKl1ETUsIUNMYXejFHnL0bycdqeaXQGSgk9xlbQY2/UPSp9HKTbHgauSd8PosedBLH7bBRmbMTsftQxJjmfPAL0WJ5P25g1vhcz7LxjZ+mj+6Wdvq+nnycVArZSPiuLLTbK31Liyd6njAvp3+z9Zr/s69DHXOudQIROm6xxucPhuDiI45gPf/jDud/9+I//eG6mgOPs82M/9mOUSqUNy7/0pS9x//33n4cWORwOh8PhuFjw0l9mioT4tEl4H+WmoDkclw1O1EjZasmpixUJJGYporMCpMSOkHf+Ukqmgw7y2uKEPcM6wpSCkQBkEy0edBktFNj7jzGDVGbjy34joOjBoTl4ypVw1ZQWDmrowPaM1Wbx8hAkEC7nKtkkDfQs/651Xtn2jRI07D4rMNyPU2hxZZrRQWF7eQ9dcuoEpjzU+RiLEojOChriQVIGCh6EBZ2lEaaiRhxDP9b9bGdpiCAg19EOGk96fqPWC9CB/+vTV9Va375etiAHZhzU0SLYAbSgMcVwVomM97y2hGiBzc8sh9H306TigI8eN3vR47pqfeelf8t4zz7Qs+cqbZD7Ua7LJG3ZbNxvho8Rw+zsjHHHyRNLJcMnKyzZWR47hS24+GwUfB0Ox8VBHMe8733vy/3uta99LeVyOfc7x9nlda97XW7f//3f/z1f+9rXzkOLHA6Hw+FwXBz42sgTgIQkGRAnA1SyU/UtHA7HxYArP5WSLQ90KQoaMFwaxw7yzqGDuscZNtjOll6C/Fnq2RnMIkyMEkWkdr6sb/e7+C200ncpkVOz9mEHFAsFqNdgfgpmGqbMlJiFd9Nj7ULP3JeyVz1MaSv7fM+0zE8vPY5tgu2hg84DtEDRY7hvJVhaSNsqviI7aRI+CbZXSYQRg8QgvIRVcir9uxhCqQSVshY3+j40WzBQGwUNKREWW5/z/CtgdFbAqGVVdB/X0WPlBGYsi8gk2SVynW2zcmnrVLqejOFJEPHAzsIZlaFhr8OI9WxizJi2y7DJtiIU5SnUkj0i4ymPrbZnu2WoZExljzWuxN0o0fNcEKLHk2Tb5bXRZW04HA6Hw+FwOBwOx/lARwwSleClwobL0nA4Li+cqJFyOQgaYLwFsojXhZgr28FcO5hnl4YSpIRMVpgY14d2O+z1etYy0dglEC1B9RrGK6OvYGlNr3dqcdjPQozDJbPjKmAWOIIWFvJMqjdjkiCmtF1I0AJLCzND3f5OhIRq2l6PyUpDnQ2q6NJLtrgj10BeRYywUQYKIZTLMDUFhSK0WuCvmmwAW8CwszTE18FjYwYEbAy2Z7GvXxW4Gp2p4aH7WvxVaphxLX09wPgvyHhdQxtyi1H7IpOJGlL+yR4TO3nd7FJhefsWES3v3hRvGvG/UWjRRmFKwAni5TLu3h3nITQJo/xTssfLZmecK0HDzsqoo8eT+PXYIu04AcbhcFyYKKWIogjf9zeYgzuz8HPPv/23/5bnPOc5G5Z/5Stf4Vd/9VfPQ4scDofD4XBcHPh4aVTFA5SKSVRCnHRQ6kxdKB0Ox8WE+xWXMqmgIYG/s9FxMtv7bCpNElwuooObJXSgcAE4hfGZyM7GHlV6RoSGceuMQgKj2e1kRreYbitM2awSxidDykt1+7CyAq2+EWdEaJE21tAByuuAm9AeG1vtZ9lPlY3XP8BkWeTRAB5H93M2sBugxZbD6esAOqAK517UkGyKeXQfyxixy06VMcJGvQIz09pPQwFJDFEEvYEpPSZjzjaAlvMSwWqUH8S4El/2d1ImS8zll9FB+wE6WyNKz0UEDfF6sUW0LlrUuB+4B3iYYVHDxwgkXmZ5XpsnuR+2UsIoT7SQ5dLXefQwGT9FdMbSIfR9lN0P6Os67t4Y1Y7NGNcX48pNnWvE20bGiJ2FNs5TxIkaDseFzV133UWxWNxgTO15Hl/60pfOU6suT4rFInv37t3gZaKUYmFhgVardZ5a5nA4HA6H42JCoVAqIVEDksQJGg7H5YbL1LAYFXQL0IE+0EHYCnqm/zo7F3TO1m2Xn3lnqyKgBIAlIBqhPRyk1FM2aU/KNGXbMqpEj5Ti2ax/svuTvra9FxJMkFFEH9+DotLryyAuelBSJushQge2GxiPjnl0UFe8HtZz2pCHh8kSkbZIaaMAXb6rhPbjyPsprtBjJlvuCoZnvcv7+VIbe+hz2IVubx9zTaSs0+kMDR9KqUG4SmBtFZIEOl1o940hdY+NgoaIHJA/TuxyTvKyZ/Vnr1kTeAR9TQK0eCSCRR89BuQ8vLRdkq1jH098VfII0j6QLI4WRpQR8oLy2SywrSKlsMYl0k66X/FqmR6xP3neSDZGXgbFdhJ6x2U2bCYSnEnfbaVUli1YxOjxEWOycOS+dzgcFy9KKb75zW/S6/VOG1R7nkelUuG6667jgQceOM8tvDz43u/9Xt785jdvWN7v93nJS15yHlrkcDgcDofj4sCzsjT0e6JiorhDojrjNnQ4HJcgLlNjQkJ0IHAXOjC6ndJFo5Bgmm1EK38X2Gjenbe91M6fBAnIr6fvdtBQjL+xlknQOe987e/srAsYnuE8KRI0rKYvOS/JvhCz8XWlj1PzoVjQIkcYGOGhhg5GBuk5HQe+CTyAno3fZ/ys6yxyo0gZGju4aWfv2BkiWUaV7JEA6gLaB2KR4YD72cAeM9n2qrQdD6HHiI/uyxr6HphJ36upoOH7kCjodmFpCY4twbFIn88KxvDdNpGXzIIuplSUtKuKziKYx2TFbGZer9DH+xY602KVjeO6hRG4pJSWnL/cZ+OysKRU2BTGd6PMsNhit0dedtktW6iz75NxiMA3DimZVWfYsDzvHFaBY+l7HmLgfjbE1DxByhassuuciaBRxlynzcgagMt1E0+exPpu3D4cDseFz1vf+lYWFhaGlh08eJCPfOQj3HjjjeepVZcPxWKRF77whee7GQ6Hw+FwOC5Khn91KZWgVIxSzkvD4bgccaJGyrjSKGJaLdkZJ9HB0Z0M+nmZz3bpJPExKLAxcCYZBBJoLjIZ4jMxSeA8r29EdLEzMvJKyGxH1Oiig8e7gIPAXnQmRCVtdxsdbEzQYgYKPE8bhleD4WwC8eLw0v1KsH6BrRlw294PdhAejNDSwBiAZ5E+ktnvXubVQc+gfxzt+bHK9mbEb4YdgJfMgypGEJOMkgVMJoJtDF5DB4qnS1Cvay+NINCCUqWqPTUkG+AYWkxqYQQD8XYQsqWMRGyRY9pj3hb+suckmUcx+f4cnvVdC2P8LJk/Rcy9JgboeQKHnbUiM/htsSbr/2CX3sort7bd+ySL+MfMo0urzWOyy/KwM2VsznVg3u6rUf04qjTVZlTQZd2mMWXJ8sg+e+3MoDJ6TIw6vr1sUlHZ4XCcf971rneh1PAd/exnP5vv+Z7vOU8tunx4+9vfzr/6V/8q97vf/u3fJklcTpzD4XA4HI7RqEwdhzjpEycuS8PhuBzxVPZX3WWK73mbBswkoJoNNkugdjs/w7KZGdlgd3YGs23ibftHiPDRQQfYzwZ2KRe7rXnI/2a2Un9f9i/B9gNoj4k6+pxa6HOcRoscM+l3lTDNGPBgEEG7pwWGFbS3wlG0SCBZFm3MNWuxuXgggWwpT2QbBttttwOiW2UrZXK2g4cJ1ovXhPgrFNNl65i+SDCm4XvRweFZdL/vCmF2SosahVR1KJVgegp6PXj8JHzlOHwNLSJV0NdMDJdX0aJHXj+CyQwpp9tINpFdlk3KWcn2kjVRxGTnSPkg2V8BM/s+ZqNwKIKKZPrILP221V/7032tMFx+Lu/ai3C4GRJs3+7YIW2/CC1yrg20T86k4p0IPPKcybtv5RqIYDMp2UyIUfdK3jNP5XyeBPEHkv2KR0/2vLJtk3FVRj9j+hgRLC9zRD4XgY773+lliee5PJ2Ljbm5OZaWljZcu6997Wu89KUv5ZFHHjk/DbsMeOyxxzh8+HDud0972tO49957z3GLHI7zj/s5fvniea4auMOxNfSvN88L8L0yvl8iUX3iuIkrFuy4HFFqK9O1Lz1cpsYWyNa1l7I1YqS8VbKChr1cyP4T18cEbiWwOEAHMJfRAdidQsoqSameKmZGu5TtGTeTeiu17MuYoLuPCcSKeCHeHX1MwFqyZ3oRxJHeke9prwfJKqihg/JPAK5BB/HrmHJDZSa7CSQrZdR5jSvRNQnZ7QJM+aydIED34170TP4i5pxK6fL96XdSuks8NOwMjRpQLuiMjEDSVxSEIUxPw8GD8MRr4Rn74SloUWQakwFRwwhKo/pKsnVaDHvKSGZFdqyQrifr++kxd6PHTz1dV4QLyZbJlsMSM+0yepxcmb4kU6iMFn+W0nfZT/ba2/ueBFnX9ofZKhH6/E+hxZaQ8Qbso5DyX7Pofsu7N/wRy8ftM6/EWZLzOhNhJ0sfI4aezuzKaVv2szzXxV9ISsGNuu+Fy/ufEg7HxcXa2ho//dM/vSGQeNNNN3H77bfz8Y9//Dy17NLmPe95D/v379+w/C/+4i+4/vrr+eY3v3keWuVwOBwOh+PiQVmfFEnSJYnPdvFuh8NxoeKmBpwBFXSwswusbXHbvGyMUZkPo0o/SYB2KzOmt9rGEqaEk5T36TA+Y2FUHXw7oJj9X46sG1rfn0SXLyqhfSZS3eJ0eSoReIpAIQYvSkWNAqg+9JQO0Mps+SKmdJWYo0ubZSZ2HpIdIzPg88ob7SQeWkCoosdVe0zbJt1fEVOOq4URxCQAPosWASSoHKH7V8pOScmlqaIuM1VKVbwkgSgCvwNLy1ApQxTD7ll4YmpiIR4hEiAWcWKSdtv3g48WRSSzRGW+TzA+MeL/UUCPF7neefeKnVXUx9zLUvJMWfuQzI9x87Ll/GS9cdfOFjXFQ0bur63+s0zGeTd9F4+XvOPl7VtE2xJwBbrvHkULJbK+Qveh7XGzXfL6JSs8bEckzTuOZPbkkX0OF9D3n0Jn5Mg5Bpn1s21zoobDcfGQJAkf/vCHueGGG/jlX/7loe+uvPJKFhcXKRaL9PuT5Ns5JuHnfu7neMMb3kAYmp8eSinuv/9+/vk//+eurx0Oh8PhcEyI/iWm1IBEDXCChsNx+eJEjZRRATO7HEved2W2HoDMKzFlCxvZdceVYJm0bJFkVmwl8JadnSyz4H02lqexvRqySHBVMkzEX0D+96MwQoGsl6AD1w9hAstSVkiCwCJShICfTpsvFrRxdeBDKdaBYvG8EHPwcqbN0m6ZzZ2HbS49rs/tQPZ2/9daxvgAyD5FiNguMvNc/GFkHEhJL+mXGYwYIAKEeFyUfCiVdakpP43w9nrQasHiCqgTOmMjCPT+VrvGv8LOapAsmUkC/naZs1m08NLDlMqSbCL7/pQSVwotbkggPiF//ItQ4afrrKHHQivd9zLDxtkS0M8i30u7pzHlriTjxM7GsD0bwJiiVzBZJ+LJMera26W1Bui+KWDG6zTDAt5mmRDyXR0tbNSBr6KzU8CMhXq636zxugT3x4Wm7HMRnxX7HPMyKnaCbB+OOoZkp9lZQna7ss/is106zuFwnB2SJOHEiRO0222q1erQd8985jP5/d//fX7+53+eZvNsFfW8fCiVShw8eHBI0AAtajz72c92gobD4XA4HI6toUCNLGjtcDguF5yoMQY7E6DNcKBOAvUS/NyKb0SecEHOcsgXNEZlQoxDjJRtH4BJkNnpEryTwK8d8POs70chQX5b/Mib8Sxih2SGdK3jSnDa9kwIrO+DBII4XeZDKYFYmRn4UirMztCQtpQwM/JHzWLvYYLO4vOQRcaMCEjbmTcgIk+IDnQr9BjbLlJOJ8RcPzn3AFN2ycfMwp9K/25ieVyEWjQKfPA8iAawug7HmzrwLwFuyaJZRhuO9zEl2hTDpveblaCSdaQNHfQYtu9Fe3wIKm2PbB9Z38uxbVEtu20v/T5mY8m5zdot3wVoUUFKY0k/TGGux1S63jpGHM2Wl5NSa0m6nVxD0nVlHw3rPJvpulvNpEgwz7QiWkTahxFXahhjeelDyaCpYJ6JK4wXNqSPShiBpMm5y3bIKzsln6U8Wo+N40KENPuZ5/4Z7XBcvPzu7/4u+/bt4y1veQuFQmHou1e96lX8xV/8hStFdYY8//nP5+abb+atb33rhu8+//nPMxicrXxjh8PhcDgclyrqjGoGOByOSwUnaoxBgp15ZVykRr8EP7dKNkNj1LFHBcxk+aQBcxEhthqAywZ1x7VFyDsnERbk87hMhyqmFI/MdM8KBTKzXma+i9eHH6d+ET6EAZQivX6V4cB2VkiyhY5RGRtiFi219rNBWynzJDPu7WD2pEhpoCZ6bImwc6YJlRIgFzPoSrq8lv4tJuprGNPpGiaLAXQWhu+DUjAYQLcLjZ4uL/U4WsRoWftqpJ+lXyrovpkUe7zG6OwLP923jKFs6TYRN2Tmv5QVk/4T0UkyrEb5JYzyxbAzp7KZPvY2kh1URfejlG6TLAcRGSvWuh1MiTUPY2Auoirp+pJpZAsyInxIBlU/3V5Eyc2wn0WDtD0iaM1j+lLW7aGvr4h70t8iCsiYsq9h3nNB2lvGCCeQ379ng+wzWP62DerHbbudZ6rD4biweOtb38ob3/hGZmZmNnz3spe9jE9/+tMuW+MM+J3f+R1uvvnmDcvvuOMO/uW//Jd0Op3z0CqHw+FwOBwXL+5XmMPh0DhRYwzZQKWNzCTfyuN0s7JT2eCalMtROeuMy4rIO27E2Z8FnS2LlWUzI+0QHfCdxZRK6jDsw2CbFPuYsjeSheCpVNjwtL9G6EMlMdfP7j/J+JBsBbt/JaCZba8EiaVEjX19bPHLLh20FewZ8H1M6aIzQWbfr2LEGDEIn0MH3SVoHWEEoilMaSMfXVYqCLWPRq8HrTb00xOUWex2oN0WfkRAWMdkbNgZB5NgZ+bAxjGWN76yfWcH4O2SU5OQ9cjIzuLPmoV30CLRPKZPReAgbcd6el6rDJcFs8tUdTHCRB+TFdFBCw8NhsVHudcnKe9Fznpx2q5j6EwTERrtrJEOw0F/CfcVrfaPOr6d6SC+LnIvjWvrTvyzNeuHkpelIffeqPa4f0I7HJcWSine9ra38Z73vGfDdz/5kz/JYDDgNa95zXlo2cXPK1/5Sq666qrc7z796U/z4IMPnuMWORwOh8PhcDgcjksFJ2psQg8TuLZr98ty2NpM6HHZGTZ5YoodPMf6PC4o66GDqBLAPxNhQ0ribBbQGzczexQFdOB0FzqQKkFhGC4fZQclbTFC/i4AhVTYCEMoBLpUklwk+xpIxkLH2pc92xyGvUMkIB5jZtCDCSCTtrPHcIB/UuzSN7bHQF4/2kbxkyBeJCLKyPnbZuBz6bGngL0Yb42SD9M12DUPtboWNDpdiCMtIkmmTMk6nmRnLGGyDCTwLm3ZTmA4r8yZyrw22176wL6/7OyLzcqo5a1nH1vuTzEu99FCnQTM5fsuum/W0OWaRBQTUULGo33PdtECiIhQkmmSZRJBY1RwH/Q9cRIjXKxjvEakLJdNhBZXpMyYjOVRbbPb2Gejf8V2niGTME7ICjAZNc5qzuG4vPgf/+N/MDMzw6/92q9t+O4nf/InWVtb481vfvN5aNnFy/d///fzzne+k/n5+Q3fff7zn+eDH/zgeWiVw+FwOBwOh8PhuFRwosYmSIC5jA76djG16ycNKOcFSscFFDfDLqUj248rU6XQgWspl9Uds/4opLQPbL1Ov2wv7bER/wW7vFcRU0aohAmkthnuS3vmvAQkxY/AT8ATYSPN2vCijdtL6Rs5vpQBkuViGCwBcGm/CBnSfhGhtuOhIUgd/7zlWcRvQ7JZNhOrxBNE2ixlsqSCeBEtKJXRmQBTQNXXpuCVMlRrUK1CuaR9NZJEf1dqQqtpSiiJF4hk3cixu5nz2e5M9zyhxy4vNQlyT4/6Lk84ke9G/S3Hl9JdduZLC2P2rjBjVJ4h6+j+kXEopZuyng7CgI2ZRVl88u/TcZlect4yVlYx9946w1lJecToe1SyfQqY+yZ7XLlv7ayNPLJjxX6O2OXGJrn+eeWibKFWSpOB6WOHw3F50Ov1eOCBB0iSBN/3h74rFAq84Q1v4LHHHuN3fud3zk8DLyJ83+fQoUN8/OMfZ3p6esP3KysrvOhFL2Jtbe08tM7hcDgcDofD4XBcKjhRI8OoAFsRU0ZmAT2LeVRgNBvAH1ViStYZFZyUYP8oEWHSQG4POIUOto4rCzMOKRVzJmSFnND6LIKAzPauY7I3pARVD5OtYe/PT9cpYMQJBahYZyCEoX6VlRY6ZHsJ6CcY/wE7i0OMxcHU2Ldn9otfhIgjW83MOBOkPFSIDpavMP76SF9KsFn6WIyfy+n7VLpsqqiFjEJBv8JAZ2jEaQfEqWBUrcJ8F4qRMfGWkmCSZTCVtiFm88C4kHef2J+z99hWS1nBcDZOllEZIdnP2XUkE0DGzRr6nJvpcexgvOynz/D95bPx2WIfR0SiMjqbJs+3JdvHkwqn2cyVHibzaFKkLZIJlPcclHfFRmEiK1jZyDNxQGpejylzNk5QtJ+xsk9bQBKvHulXGbNO2HA4Lh8++tGPcvPNN/NLv/RLG0zDS6US7373u3nooYf4i7/4C5RyRejyqNVq3HbbbTz/+c/fIA4Ji4uLTtBwOBwOh8PhcDgcZ4wTNTKM+pkqps8yA32ZfFHDDtaPCgTmBdiybbADb+KJcaacL5tL+3yDnGXyOUGLFxKglyyIEqasU4+NgdKs18ZQiaAYyp4OyhcKOmNDzMSj9BgDZYLQg/R4kv0hwVY5ju0TIcfZTqmpLKOCzqP2GaH7QjIuxKAbtKAgY0YEm2l0oFYM7kvo4G0dI26IcXWtCJUqlMtQCHX5riTR5uCDJvT60GhBZ6DbJ4FfERakzFkfUzasjO4n2x9Czm9cAN4eI3bA3Q6AyzjZSpaMiEKK4b6zmeR6ZtfppW0oMOxxYY+PrEBhZ//INtnshOz4kLE7lX63zsZzzw8n5ZMVUuQ65olFYjZv+3hkkXO2t80TUzfLHMkiXigl9JitYa5hXh+My2aRe1fEJruslgh9o7JlHA7Hpcm/+Tf/hoMHD/L0pz+dm266aeg73/f55Cc/yXOe8xw+//nPn6cWXrhMT0/zrne9i+/93u8duc63v/1tnvvc5567RjkcDofD4XA4HI5LFidqTIiU1xET2VGz4iXglzAc8BsVXLMDfXnlUWS5Xad/p7FFhbNNnlGv/bfU5u+i+1HK9Ejb7GwKL7O9LTLYNfq9WAfmCyH4RQgSHQz1Ax2s7/YhiPWxAvR17mLKKUUYU3Ip9wRmhvckM8w36xPxPQkxQXs5j7wa/310FoCHDr4WMULQXLrPdYyx9AxasChhBI0KRtSoowO5tQJUK1rQCNOnQ6IgGmgxo9OB1S40lW5TM23HUtouEaBk7ItJuf33uH7IYmcy2eXGksz3W0XER7s00k4hJvGCLYzJ36OYdNwojN9FNktjUrLPHREL88pq2RTQIpncI6PEBxEg7GOM62v7+o/qLxEfAowoZ/u0ZJ+n4541co/FmWVS5s75ajgclyevfOUrufrqq/nTP/1TbrnllqHvfN/nU5/6FK94xSv4+7//+/PUwguPQqHAr/zKr/C6171u5Drf+MY3eNWrXsXCwsI5bJnD4XA4HA6Hw+G4VPGUy6EHwPe8sQFF8Wzw2DjbPLueXXpHyGYm2O+jUJl3MLOKdxIpSSQlWOxgXohu51ZLT+VlYsBw4HKU0GNnqQgiHsjMfPG7KDBcKkqWVdHlwsR4vO5BOS2l5PsQ+FroiGMYRNDpwVICR9GzvmO0uCKeAnJ8CYLK9RXvA3tm+lY9Izy0wDCFKacjx7BnkctxJMAvweUZ9Jhspcvm0vdWuryCmXlewhiDV9GCRx2YCqBS0D4Z5bLuJzwtZkRRmqWR1ttqdqEbQ60Ex/rwbaVLsknGg5TqItN3o8qvjQpC24jwM/P/Z++8wyWpyq2/Op80eYYBBAYYghIUBMFLGvIoWUEFJIqIZFCSAUHlgkSZi4LwqaAwoCQBRSQIckG5KEEBBQkOIAhMnjk5dNf3x6519u7dVdXVJ0xg1u95mj5dXWHXrl01zbv2+65wnxSehnovZGDLj7nCVNy6I3XP+eeYZqwk9VtcNgYFhCQhwc+iYPk2V5SKgtehD/EZIq4ANRQC7+WThzGznwgz7uaj1jQ9SdAgUdkkFFN53zXk16J/TldJMpmRlkbFisBHP/pRXH/99fjwhz9c893f//53/Pa3v8Vll122ygfpDz30UGy77bY4+eSTY++Ft956C0ceeSQefvjhZdw6IVYO9Pth1SWT0RxTIYQQQycIRqKuz8qLRI2Q5kymbv30NDOtgWpfB5KN+Zv4QUhX0OBrtGYOMwjJUi7t4fImAONhzmchTNAwLVGZFL7IkVQWxp+dzfNnIDcPm6HADIcCbNmoNgCTYUSNcTCCQVsWaCmGwkYYtRwIVYNyBfhPN/AqTOYBcev1l1EtavBvfjcAG2hn+/2MnTiyMEIDRQ0agTfDXBfAXIPFYXuK4TlNCr/vggnuVmBL81CMoghE34yaV9ZkZzQ32b7JwIgZPb1Ab48pNVUJgEJYjyiXB0pF4M1O4IXAeMz0hm3rhBE1MmE7K07bCMe5H1COGxP58FzXDtdZAiNAtcesP1LQPLoPy3fmflTWgVu6yi295osmUZ+jxAKOvx4kC0YUmFiiyv+O5cwAMy67IvaVJsMmSdTIwGYYdSJ+fAG1x3H70r0/ea0zsPd4oyKl/jldNZGo8f7lox/9KB544AFMmjQp8vsHH3wQn/jEJ1CprHq5Xeuttx6uuuoqbLnlllhzzTUj17nmmmvwm9/8BkuWLFFmixAJ6PfDqotEDSGEEMNhVRc19K9oSB62hE/cz8q0PzfLSA7QRhEV+E8qAzOSs8cZvOuACRKyDQyO52DNukeKtDX0fUHDrYPPLAY3MyaAFThYHmfQc6MCZPvDUlQZ857JhIbXRWBSBWjvtUbMvDkYrKd4wdnbFDJyzuciqoOxLL/kPmayqBU7WPaGmRgIjz85fHFZe7gu/RRoXs/x1u3srxR+V4QJWFMgYZZGM8x5l4pAoWj6IZsNBY0y0N0DdHUBnf1GTOkHUKqEZs39QNAPvBPYzBaKOvSV4Gz9ceG5sHxblKABRHtOuH3WAjM+OS7psdKHatPnkQwtsQ8bzVQaCmnuaYp6zbBZY35GmL/PqM9Z1IptLO1Wr/84puPaVwQwBeaazEV0Zlujz8YoEaQbNnspzqsj6nOScMZ7088gQsQxhBDvf5555hnsueeeeOqppyLFq9122w0vvfQSfvrTn+J73/vecmjh8uHOO+/ERz/6UUybNi12nXvvvRdf+9rXZAouhBBCCCGEGBWUqREyNpNBESZI5noApIFGxW5wLW2GQtyMZTeoxkC+S5SB70jD2dBF2FJGjWwblakRVYbKxc1w4OekUjTM2Cg4+2SJpTHhqw0msM6MjdasCeRnww2am4GJE0xmwntzgX93mXI2FE66YU2v3XJK/JvlotyyUTzHfLidK1jw2kWVy2mFESp4bpNhyuxkAfwTwBswY3QMgNXDVzPsrPmO8DgUMtyMDFfMaM4CpbDcVL5gjNRzoaHHQNlkZ3R2Ad0D5trPg8mOYKkyimALYEtBMVvELZGWhcn2KcBm+yTNwGc/uIHkDMw1XQfAmjDXOwjb9Fp4TIo3XajNHhgOLTDXgeeVFgpiaTXzLMw1jRLBgNrnBu9LZlW49wuQTjRwx14O1X0eJziloQRgLZjzmQd7L0W1K42APJwSY/4xo7Jd3LJ2FHv8PkibraF/TldNlKnx/me//fbD7bffjkKhEPl9f38/Dj30UPzmN79BT08jv5ZWHpqamnDAAQfgxz/+MZqampDL5SLXC4IAr7zyCj784Q+jt3ck/0UW4v2Lfj+suihTQwghxHBQpoYAYIKIzTAeBSXYQHYaDwsGVDlLPYo0QkbUd3HBNL9MlX8sN6Dnmow3Eph1S1ElweP5ZuZp6voDtn69H5iNKkPlMwA7a9w12uZyZgswWwMAggpQ6TG+GuWKCeg3N5vvurqBid1AEFhT+AKqRQ1mbLifXX8NtrMtbE+7dy7sjwJs5gKXNcOUWeIY7IURAzgr3zW3prBCQYdm4zSOLsCOaQobTQCa80bIKBbNuWedAVOpAD3dQEcX0F4258l+7YYRMFrCYy+CCfZTtKFI44tQbngnbTaCuz63oSl5BdZDZTKscNSH9CJCEjTMBtI/A1zoy+COm3rbs2wT74O48+B91IXo7JG4+4yiRdR+s6i+B31hpFHxlGOWYmyU0Osuc8c/Iv6u97xExPf+56SSf+53fnsVYhBCAMA999yDc845BxdccAGa+YPBoVAo4NZbb8WiRYuw2Wab4Z133lkOrRwdJkyYgEmTJuGpp57C2LFjE0W8BQsWYOHChdh6660laAghhBBCCCFGFYkaIZzt3AITLC3B+in0wQTq4gSB0D8ZgA2IJpU7SVMKxQ2AV7x148qh+JkQBdgZ+5nwPJZgZAK/LHnE9rmGu36gMWrGtBvYHwvThzSa9mfy15slzWyKJtjArZ8p4gbH2eZ8xWxb6AV6+0IPiW4bYKaoUUJtpka/85nvPajO5OiBCRbzvFizP+v87ZbScoUJjj+W/WK5qVy43yKsaMI+KsFkRZSd49EcvA1AcwYo5W1mRgbGJ6MSNiCfB3p6gEWdQGfFlhtz98FzYkZKf9g+Zjf5IlQAm+VDMSbNrHc34JwJjzMvPNaksC05WO+QhbDi0XDIwV4XXktem7RB7gBWDGt1PifBDA0eNwm2hV4pzC7zMwuAaCEhLpjvb+d+rgefe1y/A1Yg4bv/3PPFqzjivqu3nO3h8xyw1zRuH36bJGoIIcgVV1yBqVOn4itf+Qry+dqfz5lMBhMnTsTdd9+N888/H0888QQWLVq0HFo6fLbYYotBn4xDDjkEhx12WN1t5s+fj1NPPRU333zzaDdPCCGEEEIIIVR+inDmGQPBreF7FiYQthQmUBe7fcLffvmlepkLbnDQFQv8/SVRhAnkuTP/u2DKF8XVw6+HW/KKGS0MvEcVW2DwNar8FGCCsuNgSih1wtTfd4OzSWWnoqAYwBJJrqhDP4Y2hCWoYEvNTAaw1iRgoN/4SJTLpgSTawbuGoYzmE9PBwoxvTCG2QthRYV+WNPsVthyRn7tfgaVmV2Rh/ULKMKOxwqsh0Szt59WGC+DDOxYLcJcq7EwGRqFQhjQzgKZjMnMCAKgvwwMBKbc1JLAZonknDa7ps89MCLDO7BCWVSWhit0uGXa0s7+5xhyBUWWF2sL3wHj+TEv5X6TSreVYM3a3bHoi2I+UQFwCkFpS9qxrFfUejx+DjYzpwXmmnfDnL87JzZKMEjz7HFJuu+Y+eH62jDER+GA1459yO/TZqIh5jsenyb0vCd9AYf92AJgQtiedpj+ituv3x7/WeS2P7Ld+ud0lUTlp1YtLrjgAmy55ZbYa6+9Ete7+uqrMWfOHLzwwgv43e9+t4xaN3Sy2SxOOeUU5PN5HHroodhyyy1Tb3vdddfh6aefxnXXXTeKLRTi/Yt+P6y6qPyUEEKI4bCql5+SqBHiByUYDG6BCV4tRmOlmJDw7pZUyTrLosq9sCyM79dRDwY/m2BEgwkwQc/XYMoGDQW3/c0wAVvA9EsaUYPLeK55AFNhfBI6UR0gb1TQIK6wUYQ1gKdQ1Rq2uwk2IDsZwNQcUAiAbBB6bQQm6I9MONs8AHrLVqwYgDWppil3AOBdWHPkAViBgx4fzJoBagOlWec7iiY83jgAHwjPpdfZvjtcpxjuf7XwuCzTlA/PeQyAMXmgWAD6B8w5ZjNAXz/QWwF6AytOMbOiF9aUujU8Js+rG8ZP49+wPh5uFgzf3QA9y1gNoH6Avx7M8qEB+gDMOKxX6onn42c2uN9zVn+vtw7vVfc+DWCzbnhe7jWluEZBbCi4mUYU6diXeZhrshTpvETSiqJA/L1HQc09ryaYMdYEI3wtRnX/8X53hZV693bS9y0w3jPMPvOvu7stxdMoDxKu64pS9USNJPTP6aqJRI1Vj+nTp2OnnXbCBRdcMJjREMcrr7yCxx57DH19fTjhhBNWuOfEVltthRNOOAHZbBaHH354rFeGz5tvvonzzz8fAHDbbbehoyNp6o8QIokV7bkglh0SNYQQQgyHVV3U0L+iMbAcEmB9DEYKZl+4Pg/+TG/3b9pSNjJUWR5oACb4nIEtw8LyQS4ZWAEnrtSW2yaKGJxdHkfS7GbOtmageWy4X7a9UUED4bYM7nKWuF9+quwch8HWfNnJzikDhQxQyAG5HJDNmWyGwoARN3JZk8nRXzafC3mzrL8faAuMiERR4D/hOw26fUGD5+eWpgpgMzw6YDMm2sI+YomirvDvAqwJOgP8LIfFzI4+AOXAnEumbEpOVTLGN2MJrEDD7BPXU4bXpxS2vxfm3uiFFY5cUYPnFzcu0lxTeqPEiR8BrIdGL2yGBZAuuB+XrcGyYVHt9LM24HzmPrkexy/bOBL/q0qRhAJaBeY6xPlrxDEJ1ozeZR6MGAEk33vMGCmF7emAzRyZAFsebB6M2BIlDNTrj6jju2JwHjZLJ2mcIPxuCayoEnU+9VApKiGEy2uvvYbXXnsNzzzzDD72sY/h//2//xe77oYbbogNN9wQ5XIZH/nIRzB79mz88Ic/XIatjebyyy/Hxz/+cUyaNAkbb7xxQ9seddRRePbZZ/Hcc8+NUuuEEEIIIYQQIhllaoREzbRkoJmiBk2hY/fhvEcFyhgIpJmzX8eef7sz3ZP2F4cbOKS3xhiYIGAnon018jCli+gB0R6xjg9njmdgMxZc/JntfumpMQDWgRETesN2vYvqevxDhYF4Xj9mrbAsF9tNT4/xYTs4A70ZQDFrMhsKBVuuKZN1ZplXjOgRBMCiDmBJv52Zn4Ex4p7fZwK7fmkc9zrzOjHoyuUU1ljyaQ2YgHEzTH8vDb8rhO3nDH6O1e7wlQ/7ui0HlAqmvZ19QHtgBK9OmGtNgcD1kmH5rXy4/zJsEJ3rMbPDPS+W5HJJm6mRDdubgRmH9dZlJg79JZam2AYYGaHSLxkVVS5rOPiZX60w49ft/3p+HWQcgGkwGT8fQLWnRgBTmu4tmOybpKw0914KYIWL8TCC3sRw+fxwX12wQkg94kQPX+QowmRY8VmVdC3d7BCWQIvD34/rB5JmvOif01UTZWqs2hQKBZxzzjmDWQuAKeUUR2dnJ9rb2zFv3jx85CMfWQYtNPzP//wPDjrooMHPEyZMQKlUStjCEATB4LPtnnvuwZe//GUsWLAAAwOr9qwwIUYS/X5YdVGmhhBCiOGwqmdqSNQI8YMSrmkzg86cxR0XsPTLxLieDAFMQHcyjHjAcjkM4HP/LOdS7xgkiPjsvhMGlDmL3N9/FrY0UxlmxnaaYClnTfuB+qjZ7P7ncTClp4owgsai8LgjdUu6ZXIobPBVcv5uhvXZoA9JC0KD7AzQVDLiRi5vRIxczpRuymSN4NHRAby1EFga2LJfLQDWmAx0dwNLO6vFMF/Q4LVgMNwVdMowgduOcJ9TYK7RAKy4UAy/c7NRGIDl981Z097+AXNdF8GU+6LgwjHBsTfgvNyx4htoM7uD44c+Ij2wRuMuedh7iveGTybs+xzivSi4D/qNjIHp9y6YDIG0gf7h4pq9jwa+GEYvCzcbpB4tMGLG2gCmw/QtfTn43OGzpxNG3HgHwIuovReZ8cXsMV5/fjcZRqhsgvXJmQeb8eOWeorK3oo7nyhBIQ+bbRUnJLnPJ372v+dxfeHCF1Ikaog4JGqITCYzOA5233133HjjjRg/fjyKxWLsNkEQoFIZyTzgZDKZTKLY4rJ06VL09Ji8xSuuuAKXXnopgGqBQwgxcui+WnWRqCGEEGI4rOqihv4V9WAQzA18M7iVpqwNYAKd48NtlsAG21gWqNl50ay6FL63h9vE4Qfn0oZS6gVeK7CeBED6cjbMKOAM6ELMsfyf6hmYwPeCcFsKPCN5O5ZRawrsZ8IwiM+AK8sFMStnIACCXmOoXagYcSOfCz0pciZ7I5sB2gpA0Gd9BAIY4/Gmkvm+XHYEp4o16K4E5r0c2DZXgmpxqBVmTLmljFi6i7BkEoO7PCcG/3sqQHvFXuO5MKbmZYQlt5zt/JfrHcJ7A7BB8IFwGbNvSuF3/4E1LHevCctlFcPvOdPfLclFc3X32mVRPf4ZlHf9U9iOZUUjZZ8aFUD80FMGjd8fJRgh44MwggOvT8EZv0FgypGVA6ApMKLZB2D68++oFYiY8eBnrgWwohLHbCvMmGO2ESLe/b+j8LNKAti+oJk9xw2XM2PK98vwn5lRy9K0SQghiBvsf+CBBzB16lRce+212GKLLTB58mSsv/76NdtkMpnU/hXLgiAI8NRTTyEIApx77rl44IEHlneThBBCCCGEECIWiRoeLAPkmjYzKJYmg4K15cfBzrJ34fZNMMFCGj+7psJphIq4QBy/GypphZuoY/o186OCh4SCwlLYEj6ud8hIBRTpn8FjujPcWVYJMEHXAZjr4Zp004uirw8oDVhxolgEcmEwOF8AxrWG4kyfFQC6e4ExrUBzSziGAlu2KgiMsFEJ3wP+XQbKldDzIuyEpnDbxU6baBLN8+NcT/qouH4YXLYE1lR5KUwAmKIdRY2K887xzr7JOOtSYGAAuT/cdwHGV4H+Lb6owXExHsYkvgfWe6ET1vDZzUJg5sY4WCGlD/aeGXC2G3D6YlnQyDhlnzWa1cHnCr004kzOfZphBI31YQXUDIB8Nhy/oRdMADMGywNAPnxlAWwRHu8lVPuMuJk8/vl3w5SQGwNbhioqIyMN7rYs3ccxT6ENsNkjxXA9jjk+V9zjJj1b49qX9KwVQog4jjvuOADADjvsgC996UvI5/P43Oc+lzpbYlnxxhtv4H//939RLpdx7LHHqqyUEEIIIYQQYqVAooYHxQvWqqfPgWtAHQeDr63h3yzT4wbLmNFA4YSzmztgA+r1gn9xJaaGAtswnH25JXJcE3TXH4LrucdisH0cjK8FYMtydWHkgtPM2MghFAic47sByz5YvwJmG/Q5n5sqQEuvMQlv6jdlqAoFU9YJAPJ5IOdOaw/MbPh8OCMeCIUNChqhuFF2RY4MkKkA5QyQrdiAcz5sNAP6LCFE8YHn0uu9BmDGFo2/u2AFD5bKYpMrqBYzfNP2jLMtM21cumGEjXGw1z/KlJ6lq+j1wvJAA7CeG/ToQLjeRACrhcvmwfg18H5koL/XOb8VEQovafD9VwowmRbNsIbeZdQKBnxvBfAhGEFjLGwmC69nNhy7xWL4fCsbo/ssS6sNmDH5YRjBaY6zb1fI8s+HZay6YcYqx4GfdeNS7/nD7ZmdxPOmgEWhxX/usOxfGvw2+EKIsjaEEEPl8ccfx+OPP458Po+//vWvmDFjBvbaa6/l3Sxcf/31+Oc//4l//vOfuOuuu5Z3c4QQQgghhBCiIeSpERJXEzvtLGPOFp4AE3ikUOEH55thSrusCRMUfAPG32AAtlRQI8HP4VCACX5SWGmklI4LZ5L72SqDJZyQXP9/IoxXBGeid8IEbd3SXSOFX1osj1q/jaLzogF1KTynZoTeGxlTwqeUN8HfgYoRJwZCIaIpB7Q0A01NplTVIF6GBlxRw8nSKFdspkY5MGNpAazXSR7W7BywBuG9MIJAJ6w455YVowcCBR56NFBUcD1d3HHr/s1gcZToVIIxNG+GuX7tYVvcdbMw13s9mID7AgD/ghmDPA/XeD4Hk9mxGowI0g5jQN0ZXosWWKGE4s3KjC8SAuZaT4W5VzpgsiF6URuM5/rrA9gBpt9KsPdngNA4vglobQPGjTPCRm8P0NUN9PWarKS+PqCr3xzreQBPojrrrF6gPw87vnhdhnovsz94ryI8H2ZTUUwuwXrNVMJjxxV28bMvokSNpM9x6J/TVRN5aohGmD59OjbZZBOMHz8eP/vZz5bZ+Pn+97+PRx55ZPDzk08+iblz5y6TYwshktHvh1UXeWoIIYQYDvLUEIk0MtOXngPubHM/8NsN4E2YYGEJNuDM2cbDzZpohBJMUJkBbmZINIofHGQminseLFkTRSfsrP1i+HcbTNDSL981XFzz62bY/ubM7gHnnaJAD0z/UDDoBVAMgGIZaCrbbegpUoIp6zPQD3T5AyAIfTT8ElRBmCkRVM88p9jATASahldQbUpPU22OO3cdBnDz3mf2h2tQn1T6y89wiaIPJpNgXNguXkdXaKjAjLMFYVsXwgggbK97LKDauH4cqstNUfzg9WvEr6INNuC+LEtWJZFBtahB+mH6i74qUefJPhsDYCMYQaMNVrjKZoB+jq+KyR5qbQXaWoGeHiO+dWZsNlGhDBQrRnx6CdX3Yr1n1EC4fpKJd1p4j/LepXDKccwMJVdcBWqfS3FCzEgJGkIIkYbXXnsNr732GnK5HP785z/XfH/hhRfi05/+9Igc6zOf+Qyef/55AMA777yDpUuXjsh+hRBCCCGEEGJ5o0yNkJGaKTcepo79AMxs6nZUB8tdWEolKUg8WtCPYBzMTPcMTBBy3hD2RY+FjPO54HzPoKBvLOzC0jLcLgMrIowWzNBohi0H5mdusERTHjZbg6WfWMe/4uyv5HzP8/Bx/S9cMcv1+6h4ry6YMkCLYQUCChB5mL5aimrhBWE7WsJ2sSyPmwXRg1p/hKg2M9mkgmThgxTD4xacdrkwS6gAm1mSRljIw/bJcBgLky1VgRFUFmHFyPBgP/vXwDVG903VgerMmbUA7ANzji1ZI6AhAxTyVrBobQXGjAEmTgCamk3pqZ4eoKsT6O4xmRvdPSZbYymMqPEERl5kbBRX8OF4Buwzh89UPmdcAS9qfMcJFhXne8SsE4X+OV01UaaGGElKpRLy+ZGZc9Td3Y1KZUWR7YUQcej3w6qLMjWEEEIMB2VqiEgKqC3Bk4ZumE6dBBMsnwsTMGWpFDeoXy+IygyK/hTrujBLIml2NGe5L4QRXrJ11k/CDTLyxVn3rtjB2daAnWnN/uWM+e4htmEoUGjqhy015c74Z0mwPpjxwOvA0lU0LuY5MrDKsjtA7ax7N1jK83f/9oUNXsd+mOA/DZPpo8F29qC2bBS/oxDhljTyMzPcaxjnLeBev3pw/7mYbfphx129eywH089s73DJwGQwuCXGsjA+HUMtwTYS+PeRu4z4feVfrzYAH4EVCHNZm/2TzQKloik3VSoZT42eHlNqqlIxvhpBOBiZRcQMn7UBPIvlL2r4IoX/zAlgryufucQXM9x3fk9R031WKxwohFiW9Pb2ore3noubEEIIIYQQQqzaSNSIgOVSGKhuJKhVhikTw5nq42HLUGWdddLOSmdpobSldbKwQVqWI0qa+9NIyZ4okuan+oFZZkKUAKwOE/imf8ZIe2ekhYF6BvqbYLMv3BJPbLubwdEEc41Jxvs+rm/c2eG+qBHEvJhNQYGiI3yx7BX3x4yXLGyZJhqG8zzckk2uibNfmso9/lCyieqJIGnHXhEm66Af1ih9qPPZ8jDXrAIjNrbAEQCw/ESNjPeC93fU+lF9kIfxNMkDyGeNkJGBeS8WgOZmoFgyJvfZrDWsrwTGy6USesP09gH9ZVtObijng5g2Dhd3jPrH4vXj+HezM/wSVFHiHccHy2cprCiEEEIIIYQQQgix4iFRIwI36NxoUYkBAG/DlAsaCxMU64YJtuVhZlK7M+uTYPZCIzXdXePoZtgZxyxxNNIk9Y+fAZCDCRhOgDFLHwcjcPwVwOtYvrPk3TJPvbAlqTKwYoIrbPBautkVzNRIMigmUeWDELGMfxdgs2soBnXCXNc8rMl8VGkmZnS4x3b/9me++8df3gnxzDihMfpiDE0Ey8CWDOuFKTvF8mN9Q9znSJOUrcFrk+aaZGE8M3I5IF8ypafyBSNo5PPGX4NE7SuTAXKhv0ajUFhjhtFIjx9f1HCX0wsHsMI0/5FLykbiPUN/Fd5Py3vsCyGEEEIIIYQQQohaJGrUYShBrW6YIOkCVM9WZwC1Gem8CTg7vxEY+KbXQjNMIJdeC3HHi5v5PRz8wCPLNAVhe+jpsU3YzjdhAs1J7agnogwX13SapaX6YdtOM3gGbZkB4WZz0PugHnHt9YOpBZhyZix/5ZqSu21mcNYtTdWomOUHe1eEoG4fjJBBUWOo5YA4k5+l1rph7oku1C/XtiyIEzT8rARffCIlmDJRFNWyMNkY+bwpNeWLGYMExnejEpadymZMiaogAMp9QxsHbEMa/5XhEFWeq5LwPVArHLrXnc9cN/tJCCGEEEIIIYQQQqxYSNQYIdxSVQyU+UHSvnCdIqwvwlDNiRnQ9rMbWEapDFtWx/W1iMIN0o9kuRU/KMigMs2/m8N2jQOwEUyQeWnEdiTOZ4D7Hklhhv1IijDtBex1Y8YEDbEZyE0qG0R80cMPHLsGyKXw2EXYDJ+Ssw3bQgFsNGbHx1GEzRRhBtJoHJsihBvUd7Nh2P+8bnFiIEtv0cx9pHw6RoM048ilCcAGcK5JznhqZLNAJmuyL6r272RiBOF/gvDAXNcVzNJShulnCiuj6UkRZ/pdQHXJv6h1gPi2yUdDCCGEEEIIIYQQYsVFosYIUm+mNzMUKCI0WtqKZGACmHkYESDquGVUl7hKmrFPj496JZMawT0Wz5OiSQVm5v0i55gVWH+DHtSSZJ5c7/gjQRamfXkYL4tOmDbzfJpRLSAltZHB1qSAL5dTLHsPxp+FJa/6YPqpHdbwfKiBWNdbg6TpP/oPUNTpwvBEgqg+Y3/S78I1Ws8BaIU1hkZ47G6Ya9QfsR/eK3mky5ZaFvAaR5WbAuLLLUXth+b1+QyQzwHZ0Dsj64kbmfCCZ1AtbASVMGsjfLGE1IswfZoWZjyMVt/G9Qn7kWLGaHp7uOXplvcYEkIIIYQQQgghhFiVkKgxAjBAnabUTwUmIN6D4c0QTzMbv55Rs7teD4YmskR5Mri437kzvnthgqSlsI0FGKFmNQBzUS1s+AFK/+967WuUKLGBwfISbN197p/m1cyoiBNfGGxlaRtmDcS1mddvIYD5MF4k9GTphBERRjIzws12SbNPXjOeCwWeoeCKKu5se872p8F8D2wGDbORxoffu6XX6GMDZ3uel5vNtCJQb1zXK7nG/mKWUB6mhFQmY4SMXNZ8HhQ2IupbZTOhuOXUiGNfDQD4D6KzuDiW/fGyrDIdeFxXyODyKK8Yv50cGyzblhaWf+N+V9RsHyGEEEIIIYQQQoj3IxI1hgmzJpphA/X1iDJzbgSWcaIB+EgxlOA4A59Z5zNQHVB0g8mkFyZboyVctzdcpw3mvCj8cF/1zpPBxaha+GnPy53p3umdC0sbdaBWcGEwmVkKccdjtscYmPPpRHX2gY/ri9EJk5XDrITheEtEwcBvI/tlKbFc+E6BpZEyYFFZLdy+gmrfkBzM9WGfMFOmAzZTow9G0PAD2f3evvthTd+jAtLLwk8k470Q8Z6WZgAfgr2fmI0xuL86ykjFVdJYhipI7odceNw8jMDWaCk99v9QhDm3Xa7o6Z9mUpZL1PZp28Gx5+5HCCGEEEIIIYQQQiwbJGoME9eMm8H9NEHh4ZRFccs5rQi4wkZUUJuG2mVvm3aYgGYbbKCZwkYh/J6z/91+jQtewvk+8Nb1r0lUADMLW0aqFzYQXoTx/cjDBMwBK2C0hd8NhO11szjcdnFZDsBYWPGkF7VB9cB7EQpZ3bDB/nqB2KR+8gnQ2JgagBEUWIIHqPb+iMtEcdsWF4QuwJSWaoYVT9z9cbtOmD6koTuFIPZdHtV9z/MrwVy3zvAcosZHVDmuqP5z9zvaxLUji+pxRS8NvuCIHHECRxAA5QrQPwD09gG95eTsM2ZeNcNcq7loLNuBGQ7DFQXihAv3WRQ1ztysMaDxdsh3QwghhBBCCCGEEGL5IFEjJI/a4DIDoknBKwb0+2GzDerB7I4Mkk2N4/DNb1cE2A8Z553L4/weemFmeOfDlxu0ptmxW/ImyvTXL90TRPwN1IpILBfmwuvoCzAsbVOECWDzmlVgRA36XDBI65dhcv9mdk0rTBCaWRiNmDH7JuZx0LjcF1nijkXDb2ZCpGmPO3Zpaj4l/HseTHZJVKA7SdDIwATKJ8MIDz0w5beWOMfjtq53g9/eHExWTF/EOq3hviswY7BegJrZOhRwuD5FBO7fFVXq7c8XTeplZ/hiVyViOYXFbBbI50NPjZwtO+UKG0FQ3c4gAAYGgK4eoL3fCnvvwozRqPZ0w2YgNSoKcJwNlabw3R2Dbj+670kZJ8siK0cIIYQQQgghhBBCjBwSNUImwpouMwhG4+c+xAeR6W/A8kONlN1h4L4LjQkbyyIA12gJIfcdzrZuWaqo/S2FCYwyO4MZHAMwwex6M/2jPlcQ3Rb3nPwZ/2yf6y3iBs4XwxqZM3jPbbqdZWNhDNCjzM55XAafWWqrF7V+BfX6Ps218QWlKCGB5GDOb2y4PrNo3AB9vWOyJE8WRpBgXy1GtbCR1A5eOwa76VtATwxu78JAtlsGDLClmNgH9EHgfilAJLWHbeKx8+F5sU/HwQbn2dYBWL+TJKIC8FHfRW3nXpcsgElh28oAchkgl7N+GpnQMyPjZG2w5FQQdjg9OCoVoG/AjOnu8Bxeg7lPo2BJti4s+8yxMbAeRbyOQHXfRf0tEUMIIYQQQgghhBBi5UaiRshEmJm/LHnEGfucve6XlXIDrY2IGYANntMzoNHMi0bLntAMl4FQf3saPnPWOrMSyqh/bgVUz1T3fQIYSGbtfP/YnQDegel/fl4MW0qIxtq+SOEeC973ccuiykK55amiylYBph+6YMZFC0wWAcdE2TmvJtjAdhJ+aamk0jg+HHP1Asi5iPX8zBV33PHvEowheTeABTDZEcygSSN09YbbMatlLMy17IAVDKNwzzmA6e93wnZkYIQiV/jz98NsgQKsVwmFMd8wPBueF4UHBsR9IcHP6mEmj9tv7bDjJ4DpvzaYsbAItlyZS1QmgXtMV5yLMuL2P2fCc6f4ms9UG4JTsMg4B6sEQF+fyc4oFIB8OHALfUAxBxQq5pql8e5JMx5Hgy5Ej8mo7K16IpEQQgghhBBCCCGEWHmQqBHCIHY/TGCQQTo3c8OHJtFDKSFVgQ24cpZ5IyWIGj2WWzLHLaMDmEHAWe05WIGCmRNx5YMYJI7LmODfzAKIykopw5Qp6oMNNvd431dgB6rbP25/ucJHVOmppH51A/xRgWYKWszKYd/kYPqG50SBys+6cAUwjrHucLsFSDYLd8nCBMuZHRK3TZOzHkWsHth+Z1/5GQoD4X4nwpSQoom261GRBLMiFoTHGwdz3ZtQP+tmXHi8DmfdDlhRo4xacYtwHDbB+nDMdbbl9eM1zIbHcPsBiB7H/jn7AXxXtOiDuaZN4XmXEC1q+MeiYBSVXRDVZ7yfmmD9RgBHQAxLTnEnGSdLg6WnuN9yOfTfKADFItBUAppLQM8A0BlYQ/VGMreGS5SQE0Un7DV0hSY/OyNKEEqbfSSEEEIIIYQQQgghVjwkaoQsgTUdTlNyh8FNmmAPFQZIGfx39z2SuMFRBogZ7GVQtATrqdCH6KwOwsDggPMZqA0Qcx8M+hdg+zcfHjMDG6T3BQEXN6vC3bd7flEii9tmQr8JbhsX2OYynm8Z1jMjg2qxIK7tbDezJ5bCBu8bLTvG8lj1RJoCTHB/TLjs3+Fx/cwX93zL4ToBTLYGxYQ0gV+3zBjLV/XDGqw3wQpUjLfz2EUAa4ftXQojcrWjWlyMazMD2HlYUak73DYHYHy43y6nTe6YiRNKXNJmRlFk47UtwGRt8BnhmmL7gXYgWWThefIcm2CzmzpgznMSTGZMvmD8NPjKZG2WBv/O54BKHhjoDzM2ABQLQC4fZm5kgGJQX4yKw7230oyfDMx1csUa9qMvRLjwGgK12VtR20jQEEIIIYQQQgghhFj5kagRshS1gkY9hiNmALWzixnsZaCONf+jYJA8i+QSUXEBcL8UE+vns0xVmkAm18l67y5u/f8cbPYHP4+BCWr3wQQxo9rKYC4FEbccDoOYruhAocTtF/ZXDiZo2hYeM8oAOQrOHud5UxhK20/MyBlOPX9XREqiD2Yst8KcbydsxlFUhoa7/36Y8l/dsCKK29dcz4X788cqt10N9rrnYMt38XMbzDhohh1/A6i9flHtZnZCKdyO/g4UzcaG+28O+8XPOoor/eR+lzZLgWIDRTxfxPGP4+/T9T7JOtu5wfpiuE43zDOLviVjYESOlpwRMnLZ6lc2Z95ZjioIbDZHuWz6sFgECmFZqlIXkOs1YticlOfvn0sJNnMp6jnmCrlFmPE6NnzPwlzLdliRtRvRzya3n5LaGSUkCSGEEEIIIYQQQoiVD4kaIW6gPG35k+FQggm00ruiEn4OYE2a447PQHBz+LkLJgAYFWBv5Bz8UlMUKaL2GxcUT5r1zgB0DjbgPQBrwM3jx5lm09/EFRVcKAbR96IXNvuGM/knwAR/MwDeS2irP+ObgVNmlzQqaI32eHJhELgXxteBgl2UMBA1s50eIkCt+BUlaFBsIu646Q+Pz6ymALVl2/IwmVK8XgtRO/7drApXEOD1KMEKGrw2/TD3Uh7RJb6i+sJfTmPwXqQz/gasOFOB7fuojIGk/biiAEvHBbB9xiB/DkY04j2Uz1pTcL6yLD1Fnw3YbI1SyZiDjx8HjJ8A9PYAAwNAXy/Q2Q+UK0MXb7MwIkUZtWbxCM+vFfa+pnCZC79jaTfA9GkTTF8yU4rn4XqeuP3qC0PuNm45PiGEEEIIIYQQQgixciFRI8SfSexnACTBmeIsn1SvdJQ7g7wF1nx4TLiP+QDeRnzQrQXAVJggJg2qGYgeycB53PHzMMHIAVgBIm72P5zvi7Az5gdg2rsUpv/GhPvkd77BNT0QorIjeFw3gNkCYDJMUP9dVGeV5J2/kzwV4KyXg50VzwyZnoRtljdlmKA3s1YYZHeJC+pHlfuJuw8ogrh+LWNg+ofm6m4GDu8RZmLQzLsz3NYPWvttK8IKYLx+ZRjxohPV46YCa9bNMeTCQDqzQyhsuuOhCHN/ZZDOMJvZBE3hMrfkVCPw2vAc2f8syeWWvxsH8zwBTAaGu/2gn0Ym/DsT7i8DZAOTnRGEtdR6uoGOTqCv3yxvKQJtPdYTJe15ZGFLn4132rIQ1QJJVOkwPlPo78KyXczqoJjLfqEIyn6Pagf9ZSiqcAxy/A2HtNlaQgghhBBCCCGEEGLkkKgR4mZnFGECcjS3bkf8jGvABkTd2cBJ0HOgBzbzgEE/oDaom4UtycI2vud8ps/DssoEcMUbBlijygL5nylkcAY/yyktCpf7JaMAcy3Gwc6UZ5CYx2QZIzeg2Y7qvmIJnApMvy2B9cPogy0R1ARbron7Y0YNy4JxP67pdCMG7xnYWfU8HwZd6W/SaJCU2QoB7HUZgDXaBhqfmR7nS0BBh33AWfLcxjW8dwUkd3yUvXUAm0nCslH+cbk9My5cXxWeWwkmON4NG8B2s078a1R23pn9E3Ud80gf/OY16HPOZyi4/jLueHfHPcK/2xCWbcoBLc1AczPQ1BSWkyoYj41sJmxbD9DREfpoBCZTY401gLYxZnlXF9DZadbr7LP3h3v96JHCsVAIz3cJqu+NznA5ED2umcWTQ60XjrsfLqegCOed16wFJgurM3yx33mvZWHLzcWVsRoKEjSEEEIIIYQQQgghlj0SNULczIw8rKjBYHMGpoRKlBl0PYNr7t8tk9KD2pnjvhDAYF4JwOowgcs+AHNhBBC/nMuywjUXJ3G/27X7AAEAAElEQVQBY/ezG6B06UN1ENgtEzMWprxOF0zGRZ+zHctC5WBL8iD8eyHMNWmCLVtDESaqjBAzB4qoDmJztjfb6YouLGfVDTM20gQ4OWPcPU/uj34TUd4PSbjZAOx3BvvjgvH1Au5um1wBgp4OHO8Uj1zvCPcacUz71931ifDb5R4fqBUwKGz47ecY8u8jlqfqQnW/cn9xZGCuRwHxfi9xjES5MdcHhft0z43n2oSw5FoRaAoFjVLJiBr5AlAIzcIRmEyNgQEjaAwMmM/Fkik/lc8B7UuBjnagsxt4owI8jdqsKfY/syVKzneEGTdJ44zippvxBUSX/+OzM+q+YObbRJjrxSylARjTeffZrrJTQgghhBBCCCGEECs/EjVCGCxkiZMJsLXg+2BL1dQTL+Kg9wCNvaO+9zMeuIzB5LGw5VrKMIH7FSVA1+isdPc8/OA++6AAayrdCmvu7QdZmRFQRnVQvxNWkErTNooDreFnmntXUOutQFPj1cPtcgAWIN31cEWLDEwAlgHqIoBJ4f6XwATT+1A9Uz8uW6gRWE6rH8kCSilcl2WZGHB235PKd0Uti8rs8b933/1lSX3QFbGcZaSyMFk8abMu8jBiActjLa97Laq//LJphawRL3K58JU3r6xjDp7JAs1hNkcmC/T2mqyMoAL0D5h1m1vCklSw5eHcdhD3XnNFrKi21zs3noObhUIRw72f44QxihVlmGcEYO7FDiw/4VcIIYQQQgghhBBCjB4SNRzcmf/tMMHmdtjA2lAFDcDOSi6gts4+BZWoACA9A1xT5SaYTBJme7jleJYX9YKXUeWponwe/OB9J0yfMcgalQFCoSOqD4biadAEOxOdXgw0daewRHGDYyYq0M5Z7FGZIX4wl69+mH6ZAmAdmKDy27CZAm6mwlBhQJxeLkmiTy+MwFKA9RBhGaC4bIs0x48LUrt/+21KEjSSoCk9yxmlxS0TN1zvhaHi9pGf9eL6bmQzRpTI0RQ89M7Iup+zQD4PtLUa8aKnB5j7nnmfPw8YKAPdXcYcnPvndaYYW6/vXZP4tPhlxlyhwl0etR5gru0SmOfABBghqgkmo21RijYLIYQQQgghhBBCiJULiRoOnI3eCVuqhoE1v679UPcfZWZL4oLDlbA9S2C9KfIwXhPMXuhAtVeBC42V6RuQFpYTivMaaBS376JKCLmBbh63E9YTI2oWPgWdqPZRJPDLEcXBPnKD2MxkKMMGS8eGbemDDfS7Rsr0GeB5xBH1XQVGyFgIYG0YcYMZI73OubjeIkD668OsBZpy98N6GvgG7Qg/d6A6q4b+B3Hn5n8XJ/i43/vvfhmqAEMXNZpgrkfU+EmCwfLlJRgmZbnwuzEwz4F8xgoYyFgRY3Bf4bJiAWhtM8JGqQR0dwMLFwBLl5rMja5uYxheDjvZLftUr98LMPdGADOGGxGCKA42hy8+H9znb9K2LD/XA2NOPhbm2dgN6yvjwvFc75yY0URRenmLx0IIIYQQQgghhBBCosYgbjDXLafiBuI5q90Nhg0n2O+XmnJx9zsAE1xlAJoeHiyV1AwbkO9DtdE4A42AFQbSChtDmYUfR1R/UURKKl/kZqnECRf1jpv2fCkeufvltR6ACY7SeJiCV3u4bBxs4HMcTGC1B9b8vR7uDPUemPI5Y2DKW40P27EUphwaS+rkYUWutAFklkGjUJMJjzMhXNbunBvvg4rzd73xkNYnA86yqOWVFOu4uPdmFBQ2lqKxkkSNlvUaSaKeD37frg1gXQDFfGgInnOyNWCEjMF9ZMw6pZIpTZXpC8urBUBfvzEP7+s1fw8EVoRNI2gA9pq1In12i3s+eRghohXmPuxN2IcrMnM/QbjNAlg/nbjxmuZ8srBl5iow4l57ym2FEEIIIYQQQgghxOghUSOEgU7XAJf42RmueTKQPshVry6+iz9DvQvWewGwwfZumOBdHtazwzcuZxkZ16g7TcYJA4puwH04NJLpwnU5W5t19kd7pnRcwJtZH0tRbY79DowoMB5GgKjAZtAMwARF56HWSNztU3+8UdhYHG7fBGAqTCmrflhhKoA17k4ralRgxoxvvp2DMVqeFB6bpddcXwKWOuLfPllUlxXjPRJ3vaO+SxIzkvaVJMAxi4bXbWXGP888gGLG+GnkHUEjlzWZGszQCAKgUjZZGB3tQFAxWRqdncYwvFw25acGBoy/xsIAeA6N3W8U3nyRLQubfeEKxryn3etKQY0eNlHZQ25fALXjggJkXLkxPlf8claEy3OwJuz8h7IHyR4iQgghhBBCCCGEEGL0kagR4ga4XI+LqPr/UX4A/j584mZdJ+3HDdbFBReZxTAWtlyUSz9MXXmWUMmHL87UTwMDwjln2XB8PJIyVNzl3H8frNHz8i7/4md+9MH0bxlGzCjB9C/7rAXm2nQi2pMl7vr3AngLJtNjTZhMiiYAq4X76oQth1VGY33jX/fO8FgdYfvLMEHoFhjBhm0fQK0w4/7NmfZjwnU7YcZZb8Qxo3CFFl/8qnduSfunSLSykOZZA1hj+SKMOXgm65WgctatVIDePmDxEmDxYmDceCN29PSa74LAvsqBuXb/CY9PH6B614DeNx2o9mrJw4hlFRifC1cUoCjXBzNWFsOMF44/inC+OOE+O6OeuwMwY5lZVUG4vxzMWHCFOneffumzINwPxbqRylwTQgghhBBCCCGEEENHokZIE2x5J6A2YNZIMCvOP6BeuamhBswYQGdg2w8+ujOn8zCz/1tgza7TBJwZQM3AZgx0RhwrCTc7we3fegJRmoDq8oSlaQAzjnph+rkb1Z4ccUT1SRnmvHthZr+3wAR6KUgxU4RCVQnVZccAa/LMfSZlTPQAeM9pz1TY7JAWmOwQZgvFzX7PhuffFi5rDdefj2j/lKhMJ1/EW9kzKxqB95brvZPkq1GG6d8emEyLwDfcCCmXjV9GZ6fJ0ihXjIdGW1soaISdHVTM50pg/VPKSPdsKgBYP/x7MWzZNT6XmLWUR3VZM2Zl0OOCHi49sM/kqHunXkmysrPfbthSa1PCY/4HtUbk7jvbtxT2HnOzloQQQgghhBBCCCHE8kOiRshYWCNun3olk9ygdFQAMO5znMgRoH7QzoWzoznrOQcblIxatwcmCJl31q0Hy8CwFBbNx9MSV2orLmjr77uR/lgeMMBMo3G+kvA9AeD87ZY4K8OUgmr3tm+HEZY4k9zN1siFy124X5Zj8seIKzIwG2QMTDCamTMMMrueBtwX/Vy6YGe2t8JksiT1gfsetWxFvu5DgebTLLVEijBCUifMsygumO+WT+qBKRU1phdoC2rvp74+oL3dCBo9PcYzAxmTzVEomvdyKGaU3Rfs2EgjKGZgM4cAMw76wnPth7k3fPGVGUn9zjmRAVihcKi4BvekLVy2GEawiHpu52AFwQC2pNb7bRwKIYQQQgghhBBCrKxI1AhZEybY1onq7Az/76QMjjQzmpNq/7v49d6jAmquOXIfqsu6MHgIVAcLyzDBbppi+zP7M0g25+V7VCmlKNKebz1W9IAi+4SloOrN6GaAN0rwKoWvPthZ5nHnT/+ULKqDtxQg3HFQghnnY8L9vgtjqkyvFTewzIByGdUz5jm7nmOdY4bjj/1AvwTAmtq75x6VqRG37P1GnEBH74kMTJ+53ic+FQCTYXxcAhgD8GLRGIVnQy+NSgXo7AAWLjBlpgYGjK9GPg/09xuRo1Q0mRzlsvmuXAZ6AjMuGun7AZhMnyKMCMC2V2AEVIob42DGXzusmfhQsrDSePP4z8SB8LhjYfp5abic+6GPh5vJtgS141cIIYQQQgghhBBCLF8kaoSMg5m924XGAqquuFGADSZHBfL9AL8vgkRlaORhgsrMAHADgNnwmH2ondVdgQnQ5b3t6H3ATABmE5RgjKK7YGcwjxRpSniNhBH5ikBaw27AnHMTqsuGccZ7C6wo0Y14c+KMs61LgNpxOAAzxim6uGKJn6FUgQkAs6RUELaDJuX+OfB4NDJ3v3fvpygPA7fNUX+7cBsKQn5Zq5HAvU9Ho+wZ77s8zHVG+LkXRmhqgRW13PNz21SBvf9bM8D4NqC5OfTT4DlkgUIBaG01y3t6jZhRCYDuLlNuqrUVyOWtoDFQNpk1f61z7nyOsEQV/TIoXnBb3g98FrXCiAr9MCWq5sOMq7RQ9CvAZoMQZgdxLPr0hW3kOHah/81kGP+PAuy/B420TwghhBBCCCGEEEKMPhI1QhbClkhJW26FAgSD0X6QNa7kUhbxgVhf1CjAmERnYMsNMZDHoLRbqsjFrSvPbRjwy8PMVi6F27fBnH93xH5GgiRBgz4maY3L3w9kYAK8k2ECvvTO4Ox1NxsiqiRaWtxr2Q8zm56ljVwPjri+Z2CdQkiUeEID5STjZiB6DCSVn/Jpge2LZphxswjps4bSwP7mux+cH0lyMOcRwPQzRadOVIsDfhZN4GzfAqA5Y5YNDADFgnMuGaClxZiIF0tAocuIGd09RtzoHzACR0sLMNBvlvUN2OdDAfEZRyxv5gqqFDf8a8kSUxRYMzAi8tRwnf8g/b3PPqO/jJvp0RTutx9mjEe1nVkX/vXkc5ylpwqwZd2EEEIIIYQQQgghxIqFRI2QNxHvQwHUloPKwgS/GHyOmh0cVXqJs6vdmfks6RPngcEa/FmYQBv9DlwjXwbGXejz4MPMjVYYwaQZptzMfIyOoEGiynPxc1Lfvx9phhE0xsKWvVkIc236YLMskoLpjWa3cKy5JdaS4Jjj+KZniy+G9MGO9Xqm7+6ytH4qLm2wYlw7RlYIcwUN11+B7zRvHyn6YLOi3P0y44XQs8Qnj9DzJIy8VyrV2yAw5aiKpTBoXzElp5iVUQmM50YuC7R3Akv7gaWBEVYCmD6O85Jg2bO4/vczdGgC3hu+JsCMfQoJaa6j6zVDsc3vj5Kzrg+FC5ZJ879juSmW6KPQ9H7IIBNCCCGEEEIIIYR4PyFRI2QpbO33KNwAHcWFHKw5MmBr4QO2FIpbWohBtQHYTIkmmEB2O6KD+j0wwe6msH0FmEAmxQ1mVjTDlr6KarcLTXC5bhEj43sRR1S2Cj/Te2FVydLIwpbgYdC4E8bDohNW3OlC/cyHoQRbfUNmH2b20FsAsAILBbkAtmwZswwKSDeGojIy0p5HN+z92Q4TgB4pkcHPqop6cdZ+1DFzMPclsxF8v5oo4sokpYXPgqYikE3o/FzW+GhkEPpqVKx41NcL9PUDS/qNmLEYRuBkeaYWmPNh1hczSIDqknb+e9I17YHJGJqL9Jlx3C+Nv5nN5G7bA2vqHfc8cY/nl/5jybUlsOKWSk8JIYQQQgghhBBCrHhI1Aih4XFSMI6BwBxMULoIa0ZbgAkALoYJ2LFePgP2nH3N2b8D4fZFmOC2PzvbZSlMsI0G0gw8t4T76Q+/bwmXJwXicjAZAlnYoGAZZub0woQ2DBXfbN2H58IZ1Gln+q+sUBArws52nw9z/fyA/0hnrsTN+PdpghHJmFHkBvTdclPudY3LvHCPzfeowHIagSOA6af2FOfQCL54kbReAeb86UkCmP4CzP3UBHMf+ibVo0EubEsmLD8VBGG2RmD+dhueDaP09NrIBkYIGSgDvWVzPUpmU8yBEdkKMJkxk2DKOvUCmBd+x3EB1F7DNDQ6ttn3RdjnqP+s6oN5hiU9x/1MIPccyqj12hBCCCGEEEIIIYQQKx4SNUI4AzwpOMsyMVyH5rfNsEHfsrc+Z73T0NsVExbDBAqLsBkgSeWvWA+eQWUKGjxuB+rXgOc5tMKa7b4X0faRIC5Dw21L2XlFrb88BA03wM0Mm5EyMqd4w+vWE76iyuEgYvlow9n4PTCZI4C9HpwB7waGs0gWAlzcTCUgPiuqUTiDvwQj6DUyjuMyNLIR37vbMKurH/Ye7ocJ+tOseqSIaqMrKgwSHrQSOM+zSihiZIzfRqlkNhzoN4JGOazF1JoBJhWAbF+1dwkFz7VgMjgWOu1w39N4orDfhpqh4h4rKUOnkb6vty4zyUZa7BVCCCGEEEIIIYQQQ0eiRkhkkDAk460XwJQoycDWcO+ECfoxEMx68wy49sMExybCBMpYv70LdnYwyzEB0YFZBgPZ1rh1ksSRCqx3A0vl9MSsO1zcIGTc374PyGiWwUoLZ9tnYa+Pn5UwVFhaqgBbzqkNtpY/hrn/4UBz6ApsySGg2vfFb1tUUNsXpeIC3vWyO9KQgSnB1Awj6g1V0PC9NJJwxwLNqt1xPNLiYAFmTNJbh6ITxdQg7OTAydDg3xknjaZUAiZNBsoDxkujvx9o7wA6OkMhJwPkc0CxbMvZDcCMzTzM843l7uoRtU4eZqz3onGvCgqgA7D3zmiXrCvBGo8vXQbHE0IIIYQQQgghhBDpkKjh4M6O94ObDPgyy6ILJsjVChNo7EBt2aeBcDm9N7jvyTDBPd+0tgQToEW4LwooLFfFme5J2RycWezOiPehWbR7ziNNJsXfUQHyqFnxUeuOJux3zv53MytGoh1uVk0LbEmx5R00pWjmllZyRRyahmdRnRGUdI18scPNgPC/j+vbpNnyOZh7qQST+ZQWP/sh6t3HFWj8bJUK0o0NPkOGUqqJgiczHoDQg6UClCsmQyNwXpVQ/eS5FotAc7Px1+gfAHp7zediwYgb7b1WmB2AFV/fgwnquwb2cdc6SRQaCPeTtq98eOwxqG8uTt8gX1RLSw7m2T4O9hndA5stJ4QQQgghhBBCCCGWHxI1QpLK//DVBBM87YEN/nbDiBpx+F4ZnPE7BrVB2hKA8bAzsRlE64P1vqgHg5EMZEYF4JgRMlImy1H7B6rLNkUFO31fBn+dkQwesgSYm4EQB2eS0+9kAsy16sDIlKFhhsoiGHGMJcmWFXHCEktL+cID/6a40ATTN27pMDdQn3TdmBHBeyrKmyNqm6bw1YlqUY8PsHak97DwzyspQyMqKB6gNmCeZqwyUN5odlQZ1WXKeH3GwoyfgTJQKQPl8FUpAxXnImaz1e2ssExVxWRyNDUDvX1AV6/xd+mCHecsn+c+x+JErHoCZL2yUWkYgOmHJtixFwXLqFGoa/S4zAzphc2ImY/azDIhhBBCCCGEEEIIseyRqBHiBkpdQYBBMxp809ybwS1mUTDoyzr7Scdphy3/5MJ9NMGWPupEcvAuinrr5mHLt4x07X/33V8etX4W5pw5ezuqjNFw25iByYgYBzObf2mKfZZhs2XGAVgN5lrMhc1ySXPcHKy5dC/M2OCx3bGzrGZ/+xkKLJFWDN+jhAFXlCrCZEZwfLrZGxVn/aGUlkrKQKIY4q6TgxGdGHxupA8ziBY04gQOX7xwRY20ptcVpC/f5B876rq8DuBFAKsNAM39QHEAKOeBgYGw7FQ+HFuheAGY7yploLMTWLzYlJ4KAqBrwDyXXoMR27IwwivHbpI5e5TIM1pUYO6hIsw97YtcgB0XY2Dur27Y52haMjBjajHMs5qZQsrSEEIIIYQQQgghhFj+SNQIiZuN7cLgc9ys3wJMMI3ZFVFBNO6DZaTcjAk3YAdnPyNNUhmfXjTusRFltMwAdFIQ0BU1GgkON0oWVowaHx4rbZCzF8C7MH3GMlFpYWmpMeHffTDlfTpQPYaWV6DUPa4rLrmm7XD+pqdDE0x/dMFmBvE7bp8kUHCfaYPgFDWS7se0AeuoslP+3/6xeV9yvA7VB4QZOiNFP8x4WhoALX1AvmCyMjJhI4MACHJmGSlXgN4eYOFCYP4SoL1i+q4PRsxgubyxMGJeDrYcVZxYtazHL4UKCi6+2MDn8ABqy6mlhSJdD+yzXGbhQgghhBBCCCGEECsGEjVC/Nr+bsCdQbwC4gP29EdogSlXFMAEHDnD2fUTgLOPAmwpnz4YE28G6bowPHjMNAG9PEywGkg/6z0qOOyWvmJgMOn4DBIzEJ4m0DwU+mBmXQcw5WTyMAHcONGIZXf6YPrjP0M4JoPhLDvUBjM+FsNkfCzvUjZuZgVno9fLUsg6rwxs0LjkbV8vU8MdX/XErDJMdo1bugowQet2mGuVNrBezz8jqs2un4o7llmOC0jnE1FAsuCTFrcPXoTx6JkxAOR7gVzOmoMHeVNiKutcmErZCBuZTLX/ThbAqzDPrAkAJsL6yfj3ZFTZreEIGzlYcSJN3zBzpYJq43SOuTLMuKCvUKP9XYD17KiXzbY8PH+EEEIIIYQQQgghVnUkaiTQBFN2KAcjNnTCihCs1c5AWhNM0JrB6ybYoG9cZgdngdNsvBsmGEfD8eHODPaDsPVIO+O9ngcBYIKkNL+OysJwg4FuMNOdye8Gzl1omJz23MowQgID0KXwVUB8SRkKLHkM3QegOzwmx8wYmGvNc3p3iPsdKZI8V3woTnXC9B37hsFfN7jNz/VEqUbGJwPMNInugjVwTnuf+BlF/vK4NsZBISPJv8alGbYfezE0cYPlzFzh8V0Ab1eAtXrCcwnCvq0AuawjdMCIHH29xocjnwPGBkBvALwN06eub0oTrFm4K4CQofiKxJGHvdd4XePg9aIQ7GYJsR3DMfTmc7De9cnC+m30YNn64gghhBBCCCGEEEKsykjUiIHBOmYesAwJPRZKzjLA+lS4wTl6KvhBXzeIWoGdsc/AYaPlnwgv5lC8MtyAb1J2Rz3PgQHne5YMihMN6gW18zBBw05UC0il8PtGM1kYpOyGDXrG9RNLzwyXCmyWAWBFjZHMQhkKbjAeSL6mFKWYPVSBuTYMPjOrpQBbVi3qurp9zZJVaTIcXMbDZBG0wwTz046BuD5Pk1kS1053/KS5nkvDfbWEn7sT1o3DFVPJazDj6+MVYO0eYwQ+UAaKBSCXN8JGJhyAlbIxBe/rNV4bQQZ4NwCeBPBv2OcP21qAuQebYbMeoq7XcAQN3tPMuIjLYPKvoTsu3Qy6RseUTyPeICwv1wfTZ0IIIYQQQgghhBBi9JGokUAPTOA0A2vwy9r6Y2AyMrrC9eiNwTIoNJr2BYaoevQ0IWeQbiglTThznoHlPjSW6eFmU8QFaJOC8W5ZGvaT6xXi7jfqvJjR0AzT9h7YwHhLuD/XZLnRGvk+aWbJD/cYhGWSsuHffE8zu3+0iCq75Atv/FzxvvOD8bzmFPWyMIFpf+a6n8UD1J+V70N/hLEwYlec8TYFwqj7L62nhtteN2AeRdpzKMMarA+FqONXYISNMoBtK8DUHmD8AFAqAPm8ydTIhidYrgAD/UDfALCkAswPgD8BeMs5DxpkAyZbIwjf3WyEwHsNlTzss5Tjoh92XLiiUZwA567nihzDFVq47zjcfw9aoUwNIYQQQgghhBBCiGWFRI0EKjC+Cz6dMMH3VphgXy/s7H+KGTRS9su1xAVPy87fpJHAHMWRANb7oxHcIGJUcC5NKaEg4TMiPpMsTJB6MkwWzGKYcjicMc4yODmYfh1q6Z5liX/t+gEsgbnObTCZDS0wY2RFqcef5DeRNBYrsBkTzGKiEEhRgWOfpYJYwg2wAkiafuiB8X2ggXXcNhwvQHLWURR+OaWoslXDuWYspRXVnuGUTJoDY/a9egBs22+EjdYMUMjY8lPlAOgMgEUVI2YshBmXrphagb129Jdws678rIihkoO5t5vD49Crxc1ec7Ph/HGZ9vmShgzsuXKcAtYgPGp8BjBjkPf0SGR2CSGEEEIIIYQQQoj6SNRwYDCrXlmkbpjgIWCC8S2ws/EHYMvVsAyPGxxNKnPjtsMlbbCOwcBGZ7/zGHGm2XHtcrcdLi0wokZb2A7OZOfMdvYpl62oxM3+56zubpiAfgnmfBfDCDcrirAB1I5RllSjKXgUzA7Kw866D2ACvfRMycPcL+PC7/thM3OYdVFvtjuFQ2ZFRRFneJ6GODGOfeKOy5GkABPgL8Ocly8WpCnVVYYRfBbBCBybBsD6gRUMANPXr8BkdjDjxvUE8YWNXuf7ellnjcLsMNcXw80Y43XkWPKPWa+cWKNtycA8hzh+KWbw3R3LhP3ETCUhhBBCCCGEEEIIMfpI1HAowgRZOQM3yeC7J1yHn11z7x40LlwkrTeUAN1IkuZc/GO6dfLrlcJqBTAhXJ9ikC8IZGCuD8sPrWikzW7ohZkZPxbGHyKAEW2i+od9yJJA9Y7P2eV+8DtuPMQJbFGiGq9JvYyNHlgPBu6HpcQKMOc9FdanZgBG1OH5d6G+oFjvnnAFFjcAXa+EUdR+o7KPRiNLiGWMxsH0E58/9OAYA5M11h7RRh9mzjwF4BlUizE+cZ4ifpm3qHJTwxU1KMIyG4IlnOaj+hyZwRHVbret/vVr9NnJe4z3Ujk8bguM4ER/nEWo/rehGeZeFkIIIYQQQgghhBDLBokaDgwspjFyZgYBZ1WXnXcgOsjvB06jAopuaRef5eXBkCa7BKg9tzGwJZYWozpwz9n/JQCro9p4vcPZB/fXDDODujv8vgIrmKwIuP2TjVjO68mMGM749k2fXZj1kIMpE5QkDLFEEI/BfbKcT9QxooSjKJhlUi9TYADAPJhrNAZGhAKqz53iSAvM+S2ECdR3h20ton55sTT3gOt3kHbsxh3HfR/Nsmd9MP3XDJul0AQTMGdZr17UF7iI29/Ef864n+uNgdEQdZj9QdGS5uTufeGWpfJLgPmZGVHP2UZwS6Gx/ynGlcIXn0GAvUdbkf66CCGEEEIIIYQQQojhIVHDgTPN067rGibXK3mTFDAcqayO0SQu6Bs1Y5oUAEyEKbNUgglg94bLJ8EEtlvDfXTA+nlwlrob1KQJOlBtwh4VyF/W1CuJw+Xu+QAmY2Mx4jOC+mEC/jR/Tzq+H3TmseoJESRJaPOPkwRns3fBGrwD5pyZwcH7jOe3ECagT58RN7sn7TXNwGZmuJke9QL19b5LynhhoH2kocDDDLBFsKW/0ppRJ12rtM8hX9Tx/x4pKrDnNQ7mOi6Cfb4yAykp2yQuw4bjIoP0AmgZVlwZCNvBzB+W93Pb3oP696gQQgghhBBCCCGEGDkkajQAO8sPLDIAlgu/S+NpUa+ES1RGh+v3sawC+FlY43E3mFcve4Mlk7Kws8yLMAFszm7mbP0FAObCBgVZx96FGTEUPngM4s/ITxI56DEwFO8RH3+2exoBisFqjpUk0szMd4UM/7wbOT93vPkz9/keFciPmi1P0Y/rFmAzEHphrmcPjLDDa82Z+G0wY5znTnEj6VyyMLPqWc6Lx426NlFjxRdz/HOKOna9jICM9+I2aYSmivPeASP0xWVw+cdj8L+RILt7zd32RZWdGg147Zth7osm2OvuXwt4y4FaYY/r0qsjE+43bZ+0wGRncAy+F7aJApPbboqw8tQQQgghhBBCCCGEWDZI1GiAAkyH9aE60MzAP41uszAB26EGAaMCeaytn0G1ge1Izg7OoTp4zSAjS6ssQa3fgRu0dYN9AUywbyFs+RiWSOoG8G54PuNha+pHBe/ZDnfmflSANSrIiYj1AHMNKUA16s/B8/BFlzR+DYCdle5moowEQwk6++OMYxeIDvxHLY87b44lBsjLMOOB4gNFC2boAKZPOsLvmmDEDRqJ855L8rlhYDnOWNpfP+q7uAC6G+iPymCJgmOFZdbysPdFd8w2cURd3wxsP7ulptKOBTdzqAXmGdYNm63m7mdZCRt9MM+ZLthrFFeKL2qZ30aWeKNXT1q4jzzMmKRAG5U9xMyxFSGjTgghhBBCCCGEEGJVQKJGA5Rhg5PuLP8BmKAX68K3ht+lLWUFRM+Kd9/bYDIeONO9C6Z0z0iJGlnYILI7651ChOuL4LY1H27TBBOs7XTW7YMpr0SWwJQmGgi374TtUx6PgXU/G4VB2zgaCSjSbLzRvsvABH8Z3EzTlmVVwmcoRLWzgupSalGZC1GeIVFih7stPWj6YTOOeN+4ULDrgbmPWmDGF7dfhOjrxjJADIIniUxxy/zv3eNEZQmk2UcRZmyPDV9lmKyker4h9fYbwGY8ZWCzX+JER7/t7meWiWuGMenuQ7XotizGKzO75sI811xvoqi+dgUm/3nkPisCDM13pwu2D/isZxZMVLbSaAs+QgghhBBCCCGEEMIiUaMBWFc9KkugDzbQxVJLDJ676wGNBVnzMEHRKTA+FIAJ+g0kbDMUWC4oA9N+zppmEM8t/wRYwaMVJiA6DiYQ+FbYPu6zC9XZDW6wsQwjdPB4RVQbW8eZrkcRl1UQtX0aXwJm3bjBXTdA7x4rLoAeFUBe3sTN+I9azy9DVS+7wcUN+rq+J7xP6hmBM7BehvVZaIo5lrtdUh9TkPFn9Pvn5u+D94ArhCVdZ7ctZdiyR00w90FH+D4UUYOBdddgm5kvUURdO19ALcHcx80w927O2X9USafRwM1gcfs4qrwXv3ezigBzHszscYWRtLilu5gd5gtb2fA4FORWhHtaCCGEEEIIIYQQYlVDokaDuKVZfGgqy6BtXMArLggK2MAaS8uUYIK642DLT1VgfCjigqJcrzthnShY5ofHBkzAtNVpS7fXVmZZMINlSvgdMzY4I70YLqdowXPkDHx+58+yToOfSeAHQuP6Oml/Y2HOqSc8FwY4Kdg0wcxw5yxwV4yJEwRWVNxZ6PzsLk+T8RAX5K+XKZCEK4rRYLxRgStpGYPebgknX9hgILsIWyYtzlMEzjZsPz1BKjDjaUy4rxKis33y4csXRN19c/kAbLZWXN/ECVbu9xQdczCZH/SI8IWN0YbnEeVN4ZbYopG3+10e5jnVCts/fK5EeWlE9QuvMz2EmDHkXwf6JzG7RAghhBBCCCGEEEIsWyRqNEg9kcANRkat6waK3QAqS+bQn6MFtnSNa4AcwAQy4wSLZgBTw30tCdfrRfysZdf7wBUTmM3A2v8tMAFDlvlBuE96IpTD71kiay5MqSnA9Ek/rJDhBi3dYGkjmRlxuH0alxlTb/+cNV4CsBpM2xfCeoowc4ZeI27JJLev/eD+ikCcwMIxGGd2nJSlUS/zIyoDIq3QMwBbpoz+FGkMn31RxT1WDuYeKyN5zLlBdB6/EVGF29NM+j3Yeyeun5thhA9mG9QLmveH6+WRPMaispd4L9JPgyWtKEA2Ki6OJr7w5vYfr+cArNjLDJY8rNk6s9vS3IvMRCvBiiOA6e8ORGfsCSGEEEIIIYQQQohlg0SNBsigNhgaRaNlZdwAPL0tpsDOGGZGA7MGFqDaYNmlCBv8GwsT2FsKm2HgwsBfC6pNmhkw5GxtztxmxoYbJO6CDexPgAnKFmCCgX7teb+sDIOPfl80KgDEZWVEiRppMye6AcwL150Ec45LYbMG6DUyJly/FzYwzFJdUbgZHT65hO9GkihPAOJnaZB6WRpprttQzotCBrMGGHBP8klw/RXcUk085yKAybABa2biEJaMck2h3Xs+bWDcpQwzfuj7wQC73yc8ThG2hF295wnbRWHDzRjyiSpDlYXNMlsC86zoi9l+WeDfo357c953zPBaiurMqQzMs5TPNzfLDM727jsFs6jyZIQiphBCCCGEEEIIIYRYPkjUaIBm2PImbs324Zh1u4GznPNiSZU8TCB0KUxQrh3VZtw+FCC4bVLtfgYJizDnxmArhRvObmag3s0ocQONFEQqMEHEAVTPbk46ts9QMxrcDBgOaooy/v7rBU0Jz6MA0/9uWZs+mLHQhvg+jstYiBNVeN1HI2DK4K/vZ5FGiEhzTfzgsH9+UcHkpKA5SyI1wwoZHH9xgoBLDiaY3QR733D9/nCfq8GIUQth7hsKKF2oDpD7xGUA+etEeUH0RKzr0h22r4B0ggbh2GQJJfaT3yZfgGIWAz10lsKc/3CeacMlrjyf23b+zSyxAVSfL9vf5XxOI0Zxf4H3ilpPCCGEEEIIIYQQQiwfJGo0AANlBdjyI5zlzO8oSrjeCq7ZNFAdXHPLqPBvBs3pV9EOE2x069zH4QbyGRhmSauoGv6cvZwPj9kMEwzuR60w0eu00Q/29cPOeC+jvonuUMSLegF2ltIZD9N3nTABa7+Ejx8cLcCWGXJnglPscQOdCPfXCxOgZikaCgY9iG6nL6JEBf/rXdvhwFI6/bDB+qQgb1x5JOKWS3P344tG9YScKHhNKPLQqJvimptBEbUtwm3HwGYXuYF6BsDzznc94d8UUDrrtDGptFkj5xq1X5Zra5Q0QkTgvVO8pJdOBXb8L09hg0Rllvj3V1LWHMtzuc+tRsqjxYl0QgghhBBCCCGEEGL5IVGjAWgam0FtYJUZAq0wZZ8Y2GeQm7OIGQR2t3PLqnCW9ULn+0ZLErkBywDWyLsbtbPPWV6K7ag47Y8iLtBZgS3LUi8Y6pd28dsdhx8s9kvQFGHKdq0Jk0ExF1Ygiit1RSP2DIDFsOWB8jCBdXqadMH0n9svNDn39+ubGEeVcmKQ3j0Pjq3hBMXjcEUcd5Y72+NCMYxtcvcBVI/HCkw/xQWNffErbua739YybLaB2y9dSA5i8z5qgxGkgOiMB4qPgBUmO2DGUFrvjLg+XN5iQJr+da8f+3MMzFinOOdmoy1rosQM/++osnJR946bpZEk5PkChi9EStgQQgghhBBCCCGEWDGQqNEgcTOoOROeJW8AEwhjJoQf1Oa7G4wrwggKDCom+QakhWIDg/T1Suq4gfoirCDTaJA3LUMRa+KWl2ACszQ+djMo/O35dw4ms4NiEj0N6KnAgDpn9bv+BX4GR1Jbo9aJm+XvBl79clFDpQxr+h7VLjdoy3NihoS7D19g4zVnXzPTwC9xlaY/XNxSQm5mSZrx4ppts3SbH5zvgBHzWmD9arrCF0WORhhOwLsEM/56Mbx73g/2N8FminFsR7WTY6wJRgzKhm0pAVg0zDYNBz/rJUp04Gfei1H491SUqOFngURlbQghhBBCCCGEEEKIFQOJGiNEGbYEE2eXs0yRmyUQV/qEfzNAPA4mGOuXThoK/eG+08y6rsCYBdNfoBkm0Lu8jXHjyl4RZrQsgQnC5mD6L85QnfCasVwRwu1ZkmgJbDkg9xq6JcgoQkWJD3Els+JmgbtZO3mYfo8aAwzSJgkeviF43PWPKrNDw3gGxpk1QaHHFzZcg3leoyxGNhicVlijAEUvDT8oHwB4F+Z8JsBc/y5YwXJZBrDzMEJmG6q9cxoVsqLKgBVhs24oTMbdx/2wmSpFmPFNgXXBENozXOLum6h14kpl+eXR4sQ197s4wUMIIYQQQgghhBBCrDhI1BgiDOC6WQAM+FIEcANmPn6QLQMrgORhZ01XnNdwgmv1ykK5QW0G8QvhsuVVggYw/VyCLQnEtrl9kQ/X6QXwDmy/MwifRB/MbPQMjIAzEdYXpANG1OgO2+EGvZn90houo7CRNLM8iihxg6JBUpZGCdUBXT9wT2HEDWgnESUWFWHGIfue7aKBOo9Zhu2zoWRljCQBzHWbCGAyTNsXwYpOvI8qAObBiAhA9Zha1u2lxwczjeKErEb3S3EPsOXZKG7642oApuRdN+z9xuX1xIXRpF62ho8rZAC13jD1RGV33xIzhBBCCCGEEEIIIVZMJGoMkaQyLnHr+34aQHUgjaWPGEQuwXhEBDDB166Y4w6XOOElyosgbvu4dtGoPGmWeBIFmAA1g/M9MDPr3aAvS2v1hK9GYUZGAGASTL/3hcfrRq1YAdjAPq8XhR8GzJPq/kdlRvj7TxpLFHG4DwbF3fXZDpptpxE1XNzZ7QVnH7yWBRgPEooB9NYowIo+w/UjiPL/SAM9VCbCliNbCDNuXFEsQLrxUgrf094PgC1ZFiUguJRhszPYd8PJivKzEphBQ5ELsNcrGx6T91IFNmvFL4G2PPAzJ+JKQvGd5xn1nI3CPceoMSZPDSGEEEIIIYQQQogVE4kaQyRNgCtqFnCafTKYXoIJsrMcTAXRvgiNtCPt92kDyQwiRmVzMJMhBxNo7kX9zIkomAHDclh+IJqlv3Iw/VQvkOzD7Jog3H8bbDkgZiBE9YXrU+GXYwJqBYx6Nf3T9Df7wL1x47ZLyrBJU86LGRgUjfzSRgVUZ68UYK43BaGotjUSGObYAhoP9LfDZGyMhbmHBsJ2sRwTs6LqwRJcWViBKw28TryXk86bYibFhZEInkeNOYpSLJs2AcDqAN5DdYmpRkWkZYHbJl9w8b1z/O2A6HsuKusjSXD0+3R5G8ILIYQQQgghhBBCrKpI1Bhl3EBY1N8ufkCtL/zcAmAqTDB2HkywtlHysL4IfvuSAn9RMMDdBzsbPIocjDdICUY0WIJ0pZDcNvXBBFwBE6AeE9HO/nC/RVjj5+6EdkXB4PM82HJTcWKGS9L3/sxy/7u47Ahmt9Cw3P2eJaHY/7ymfjk0EtUHOVhD6C5ElzrideV3TbDlyOI8DHIw/Z+BCf4Pt2zZcEzSF8K0cyJsFg/bUwqXUWirdw0phjTSFpqsF8PPHKNpx8tokHXeWwCsCWAagLUBvA3gLZh+W5GIuk/8ZRRq+r110jxngeTyVu53eVhBqFHhVAghhBBCCCGEEEKMHBI1RhEG3/y67nG4ATVmZXTAzH5n1kY/TCC60YAaA3LdqPX7aLS0CgPii+q0g+c+ASYo3gcTXK43694vCUOvhqR2MvhchPWSaDSoPgBTCqgTNngJ2JngnFHfKEnZCm7wNAdznRl057Vm+SuWfmKWgVtyikJIPe8UwFyL1cL3hQDmojaDhn3dF37XBxMIL8CWLcp52zBThiJLkjH5aFOGEcOWwGbvMJunBHNP0Yg9aTwGGFp2FLNcCuHxOP6Hcu+mJSmAHzXOAHPuk2HEjTcB/B3GRD2Np0eUULc8YFmwRsXZRkWqMeGrG7ZEl+/vI4QQQgghhBBCCCFGH4kaDm7geDQCj1Ez87ncD8Rxlv6i8LsSGrtYPA+E2xVhZzQP99wGYIPaSfvqgwkqtyF+1nQUcVkAOSSLIgGMIDGc+vfMDuiHLVXUAtP/AzAik2/K7eLX+o8zNo4SNmgQz2tNjwW3VFIfbIYGYVZAI/X/y7BCWSesp4i7T8D2NU3XmwGMh7mm7oz1PEw/UTyL80JIWjYSuBkr9PZwBZsK7LnzvkiTkRNHEdHjoQybHVKELdeVj1kfsBlQQw2URz1HopYPwFzvHtgMnByAdWA9PuJEjSRPoLixPpKUUOvZwnvWvQ/8klE+UQJIkihSgBn3k2BKm2Vhy9P1YvhZSUIIIYQQQgghhBAiPRI1QjKwAT7OeE+TVdDoMVyiSqQEzovGvf0wAeM2pJtBXYA5Fwaax8AKJK6Jtd+2tDX1u2CC2yxHxKBw1DZLw+VFGEGAwT+aoacVWAJYY+4kUWEkxSgabRdhzpfZEUnH9wUNP7AaeO/ucu6bfhYVmDHIc4o7N79MU72gcjeA/8D0ZxvMuXXDlhPjPv0SPQMw45HBY2Y+tIb7aYIdByvKLP4oKrBjmKbSQ2kfPWOA2jHBfmDwnUJUveA3RZbRCpJXYAPxXTDPhGaYPmiFLeMWl+kUJxJElXMa6WvOsVaEuS98IY5jNu561hMw4jw2+M4SYiyxRt+WAUjUEEIIIYQQQgghhFiWSNQI4Yz8pvBFEaAd6YNzUcG0ejOFo5a7M8cZFC3BZm/Ua082XH8MgCkwM4wXozYIyOOlzaAgDB4WYTM/OMOcpt6kHB6XngRxQf160CdjWdaxpycCZ7K7xtVpaDRDgWV06OnBQOpQjpMExZJ5sGWlJsIEaCkcUdChqMNsm6Uw17PbWS+ANYTvhRE+ksTA0ZrRz4dZGiGSYtFws7LYT0nfJ4lgLry3Gw2Q+8F6/znkf3bPtx3mmgI242BC+He70xZffE3yq3DF2qGKG1HPUl4rjsm4deplCbEEF7evoLbf/P7qgynT1gPzPGVGUhYjP46FEEIIIYQQQgghRDISNRxYwgRILyD4xM0SjittEhWU82cKsy2dMEHnem0agAlKFmACk4DN+PD37WcTZGAGRb3Z4oGzrmts3RMeK+oc3HMfSiB5WRvzsvyU618xEp4aSYzmLP2oY3XCZvaMhxUuAHO+RRiD9imwweCFMNeZcB+A9Y1Y1jPX3ayJeqIKYMt6DQcKUCOVzcVsnaFsF8A+u/xnTeCt595HLA9Hf5EKqsdCF6oD/+5+o45R73MaWBbMLx1G8agb1SXZ+B19cNyMsyiBIwdzzsVwX3xeJT23K+ExF4TrM8tHZuFCCCGEEEIIIYQQyx6JGiE0Ei7DZDXU806IIipLIy6oFzfLOQqWoQLSBT2Z1TAAE5QswZxbj3eMqPJIOZgMD5ZXiQv8MsDvz1ROCl4mmUeviFCkGYC9UbqWX3NGhT4YEYPn6JexYkmsfphA8CRYYaMf1qslCzNWXOFsWc5gL8BknDD4PJJl4+JgSa4VJbBNUTZKrOT96re3F+b6j0F16asSjLBRhHn2MCvHP9e4knpJn91to8YIBSree9wHS+kxo6kpbFcORngrwTz3WBLK9/twRR33ORc1bpOyNrrCtlHQVaaGEEIIIYQQQgghxLJFokYIy0/lYGcCD4d6hrpJ5aeiaDRIy5Ip/ajN7vAzM9xlDPa1wfTHYtigpksGJpDMIDL7y53R7bOyBf/ohUCfCy4bCYZjZj6SBDAB2n5YLw+XMsz46YItzzYOVvBog51BX4adQZ9kvh23PG7me70Z8SwLxjalFc5yMGN4KFlZPGaa8mDLEvaTO155PaKuCbNNAHNtAXO/UzQIUJ2NEvUcy3h/1xM2iF+Szm0TBWY34yMHI1yw7F0JVowbH7aXgkYXqp+ZgfOi6T0zNmic7o4BN9sjCj5f48pwCSGEEEIIIYQQQojRQ6JGSCtMSREGzlyDZtJIuZG4AB+/S9rOhWVV+iK+i6OR7BD/ezdoOD78vBA26Mf9ujP7e5AukLyizGgfCiMtQKwIggapV/aIpc+aYMYGYO6XMqyvQB5GIAhgMnwWozqw7Ae9o8Y5A8y9zrr0NWHWTFyJIJbDYsZVGug9A1hhrpHrsiKOZ9/3xRc5oqAPynjY68hSd75HjkvUs8199gSofRb5GRP+NoAdb25JLcCa1Deh2tPHFZeyMKJsGfGZdu74a4EdP66oESfEJO1LCCGEEEIIIYQQQiwbJGqE5GCCZjSApZcCoTFtIzPB/VIrXNbIzN6h+jjwmDwWA89Rs+jdgGMe1vSbQcUSbM190g8beExbpquRWc1+QDRN2ZqhQkNs33NkZYOz1wE7G73RwHtU35ZhShT1wgSUJ8AEjgdgZsa3w4yT1QBMDr9jqTGONfcVRSncL2DLnvFeY7mgqPJlbpZGJxpjACaYXUzYfxxBA+sSej7EbdcEW8arAvtwZh/U27crurqZWMxyKKDWpwLh5wUw/dAE0yc0EPf9OpJMuP3sL3d5lJjl/s3nrV/yicdlRlg/jPjcDHvdELa5C9ZEvAnWTygO9hW9c3zixirHXFJWmhBCCCGEEEIIIYQYPSRqhHBWOYNo/sx1mtdyJnkjZWfqBb6SPCnqzaL38YUDt6yUn/GRgQ3MsV5+P2w9es7W7kKtcOH6fKQJ7DVapiWp3FNckHSo5GBL7axI5YToVRHlZeCTg5lpPyXcpsN5pS2lloUJFnOWu3vMAdixDxgRoglWDOyDDcavBiNs0Jy+G8nlfBhUzjl/UxChsBEVdAZssH4o3hYBrHjC/S+rsmBZVN9/gOnPUvjys1WSrmEBNkumglpBg+OIoldXxP7aYX0rKGq49wJFlii/Cfd5ElXWzi9RFXdPRwkn7vfsL5bTK8OWwOt3zqkJ0UKrO4ZYKq0Mk4XW7bUtaQy4RuaN+i4JIYQQQgghhBBCiOEjUSOkp873DKyWnM+NBFH98itJ64wUfvDRDRIz0OmWgQlgAobd4bZLYYLiSeVn0rQhTdmrqGwMvsrecv/v4fZbVBB/pBlKsLwJRmToQ/Ks80y43ngYQaEZRlCYB3NOabOLWNKnCWYMdIXbudcCYVsqANaEuR8oQiwNv6eARuHDDTi7sNSRG8hn6SFej3pZHsw8Gs6187dn8LtedkmjuOIkTb3dGf8M1rsCZFqxJqkPeI8HqPaioABCUYT9HyXaRmVscFnZWQbnvOAt92FmWBCznjvmKIywRBTFWY5t9hUQX6rPFU76ASyCzfBJ8oDxoYiyIpYfE0IIIYQQQgghhFgVkKiREgbTGIhtxF/DZTRngUcJCPzMoCeDd2y7fw4sKTQAE9geaumruPb4ywvhuy8qMNjNYG9cX4+EsMG+Ge0Z+pxVnrZP3XJgNG+OW68AW16MvgMFZ/s0lMPj0Cy+CBPgZvYEy7IxkDwAc43aYD0JemBmvreF6zfDmiq7/ZuF8eUYh2ovjg5EC0xx14YB5pG4dgza08ODQfukTCn2PTNLGsE/RwbmmbGS9rzctsaJCTwPigMUNiiItcJ6S8QF+FnaCbAZXe5xmElBIYyiqQvHdCk8bh5mzLl+JlH3tCuYMHOJ/c2xSOLuE/e8XO+WRsfOUK61EEIIIYQQQgghhBg5JGqkhDOS+2HLOTUyu3coDLcUTpQvRdQMa38bN8g4VPEmSWDhO8u4lL11MzBB+THh5yxMwNedjT7SHhv1tncNijmjvhEyMIH+Ikz/0o8kiV5YAS1ptjtL8iyCDYZ3wWRqdKCx69cHY/LNsjoMFjMQPQ6mRBHHTiX8TLFjAEbU4D3Cc8yhOvDsno8rCriBd7/UURxDvfb0i6E3g5uN1R2+kgSNEowYUITpLwoyw8EvNUZxICmITpHSL9/k9x/XocF2L6yA5HvnRGWIcL0C7Lhzj8txSvG3FaY/F4X7yzvLJ4TvLG+XlKXhLuNxmp02NTJOJEYIIYQQQgghhBBCrPxI1AipFxRj4LgHdkb8cALpaYJwrqCQttRTVH17f52oY7vLKB40hS+2hUHqXph+aCRYHtcezpiOM4CmwFEKj8nj+uewLLwQGNTvgxkLvl9EEhwvrbAB5Xr+HewbVxwg9EigD0gvjNlzGdbkOal0WL3jLvWWZcLj5GDEGZao4lhwZ823h+1ipocrBLr760F1qSI3M6FRYYPjhP4TaXxommGuZxuscNAUtrsDwLswAk0U9DCZGB67L9zHIoxs4NzNqvJhqbC4sRRVqo3iHO8tjmWKFc2w48kvGdYPW6bPLQ3mHscVOFpg+mgMTL8sgc32cUurJbXXPx8KL/yHi+c+2ve+EEIIIYQQQgghhFhxkKjh4AZRGTR2/QgYOIyr2Z72GNx3Ul12BoCjSrgkHdsvORM125liCYOEnKWdgwlm98IETNucdhRhyyd11Gl7mnJHnOnNWeEuzBbxfQ5cE/M4cWY04fWfBDPTnELCUqQLZNN8maJYGhhYdtfPwgSLSzBjcSls3yyCzTIYiZr/7qz/HgALYctT9cMEqpegtvxPj9Mm956Csz+ux2O4gXk3qyfNNc7BCEYtMGNnMZKviZsBwZJP9LNogRnrixKOz1JMRVjBrx+NCUkUFugbE3eeUcvzMNeAAhnPiev79yCzrvKw14HiAP1TWsLzaYU10PaFDbc0mH8cfub50Hy8OWxrV7h9d/gds4rcezlJ2KAAlkN1pgjPZbQz54QQQgghhBBCCCHEioFEjRAG0EowAbjxsIbL76K65NBwBI0Sasu3+LCmftJxojIt0sLZ0py13RR+dsvJjIWZxc5AZB9s0Nmv3R+FH5yM+8wa++7Mb8AEmlmWhgIMg8UMfkZlMIwWzFBZEP49HuZaMlsjTSCbnhWNiDFR44M+GQWYIDRLPvEYfbCCXNT29LBIk83A4D1Lr1Fs4nl0oTbwTSGM1y0qwM4+oDE42x5n8IyY7wh9O4oJ6/ht7IYRVdph/UfoDcLSUosRXVJqAEbgCWDFhWYYIZB9VQ+3lBNQP/vJv29YPsq9H/0Sb/72FZjz5vcFWAFqKWwWxViYvlyAWtP2gYjjEfcZyWcH728KxF2w//C4olfUeQLV44WiiGtQz23cZ+ZQvDKEEEIIIYQQQgghxMqBRI0QBtGaAawNYCpMwK87fP8XhlcvPwM7s5uzut3jxgX1ooKcUd/7Jrs+7ixqmp0zSMjZ1AwM0xSapuEMnDKAWK/Gv08O1jfBD6SzTcwe8YOU/nLCGduucTDPczRgmSfXBHksTHC1yfku7b6GAzMkesPj+8fth/VNiCrNw6B0I1kQLD/FYDX9PlxPA6B6Jn2UGBE1u99tfxoBIwpmEHV7+3QzRNxSTu46/bBjrTM8P2YrxLWDwg7LN3EcjA330w7TR0kiBY9Pv5ICbOmnqHJsbr9VUCsmudkOUc8B3t9cl542RRhhoxNmbLTBlqFiFhePR7GAghVQeyy3LFUv7DVxz7sd9lngXqs4McbfNzPFfP+RDJL7XAghhBBCCCGEEEKs/EjU8GD5FWYv0PS2kUyIOBiM44tBUwaCOaPZD9S523OZG6j1M0j8wLBb3oVChhvQ7YEJPLqG3CzF043aWef1guFR/g9N4X5ZFskNvrrBUTcDhMuZReLCGeX0BXDPaTShtwozRwowZXsYvF0WcLa8G8T3YUA6bvuovmJpIle8c2fYu9fAFbhcX4yoa8pjxpUXihM9GvVMicp8ok8E28ysGv/8ue0ArFAB1M9m6YfJ5mDwf0z4noHJfEgaE7yO9B+ZHO5nSbhPV/iMev64fRpVvsldj+/sHzdTg883lhhjGa0KrI9KXP/Hian+dY4S1jphhUnfbyVqn/65liPWU3aGEEIIIYQQQgghxPsfiRohDPbRIDgLM2O5A6YES5pyMkkwgEnBwA2+suQTcYOA/jtn2APxQeu441NMYHmcjPc5al03aNlI+S13HWZ/+MHKuLJUfnCWXgCuOTv7sg1W2FhWUFDohzVd5ix7NwhehJ2tPlrECTlxwkUcBRivkBJMWaUOJF/vKOHCX16PqLGV8ZYFEcsahRkJFCzq9Qu9PuqVgHPXpyDSD+tTkzZjgOJBBsarpRi2k4br9QQDvse1Na6UE/vD394XfehjE3dOSVlirqG3D/stAyvqUtyIEkXiRA63DS5DHS9CCCGEEEIIIYQQYsVGokYIg1+dAN6GmWE9Fia4uRgjExxLKpUSFYROCtSVkRy08zM5KhHr+jPk49oa15a44zKLwd0Xsxt8ccMPWrvvbrA0h+rZ3O6s+uVhEExvAJZh8q9fFsaThB4Xo92+RoLoUeRgxvt4GKFoHkzGgOvXQA8DN6PGvVb+eHSFCXddeJ9d8SQpgD8U+mH9cHwxsR5ZGIGBZZfS4HvPpIEZXD0wD2SWf+qCHdtp+iVKvIhbh33RGy6jKOf2Tw5mPORhS22xBFlUVph/viw7x3ETl/XiZqzUy9iIKl/mr8NnazE8/gDq+5UIIYQQQgghhBBCiJUHiRoR9MAEdRfBzqL2YdkUvyxPPRh8awYwETZLog/VNetdokpMRa0H2DI4TbAzoXsxutkCpARzXhnUGkizvW6miS9oJJXO8QPgDOK7AevlQVQpI8C0pxPRnhZkJGeSR4lJjdAHM96bYMqvUXyi9wHHeM7bLqr8UZJYFrWt//dIEsCWUGu0f3Iw5aQyMM+ERkSKRmAAvgk2sN8GM7a7kO754va9n/mSlG3lXttMxPpNMPc1P7vjPU6s4nJmYRSQLquM5cFyzjFJmvJa7rpZGEFmNZj7cB7MWJawIYQQQgghhBBCCLHyI1EjhEFyBs0GYGcPJ9WSTxvA9Mv1ZGGCxyWY2vsLnP35woW7rF5QlkFEBiMDmHJCrsDgeiBE+RAMlTyMqEHz4W5Y02N/lrZfbqverPKod7Z9tAUNt31pBZQA1pchaZ2RYgDVxumNUgEwHyZ4PwHWHNr1fokrOeV/FyVuuOuSqLJTUeslLYsjA3MOeVjhsNEScnkYUSOH0fVsYVubYcczS5ollXxKk5kRJTC57yx9R7P7Hmf9ARgxIBf+XQzb6WY9RF0/91nVA/ssTYNbyovPEf84aUSzDEym1JowpdQ6w3chhBBCCCGEEEIIsfIjUSNkDOysf+IGzKPKM6UNHkcF7cswYkYz7Exy/3hDCeyWYQJ4vTCBygKq/SYoaDA46Iobw4VlXliiqBS++mHOdSSOEdcnoyFsRF23pBr+y5tGg+45VJdkCmBms3fBjB16RADxGRWNlIxyj+WLIFHrDgcKEq0w59EFc25phY0M7MMxD+vpMhJt82H5JQbdKzBZMx2IFgOSBIwocaHed2XY7AjfX8d9dtBbI+4+5jFcIYY+M432WeAcmwb2Uf4hcaWoSgBaYJ6Di2Geibw/eC2VtSGEEEIIIYQQQgixciJRI4SeDb5BNjC8IGbUDHaEx+Hs4T7ECxqNlihigHQAtl4+g3cMDPqz7V2RI2p/aY/fC1vmJwczy7oZtX3JV1TpqUZL/IzUdUpzPL8EzooobKSFgd8sjBDFa8aMhM4G9+dnYMStw2wo328jaj9+e+nNkAYGrbk+/RUaydZgSa4A1hsiAzPO02YepIGlyujTEsD4mfBeqrdt1HhM2s7v9wKqBci4Mk1JQqsrZNDHYrgiEIWNflhhyfXkIX7ZLY6vLhhBYz6qszRG8toJIYQQQgghhBBCiGWPRI2QpGDnUL0Pkmawc59urf6ooOFQygkxqOcKJRQufINnty1AbVviavLHwbay5BRLF1Wc731xgD4gWaQPGC8LQcHvI39ZElnYTIgoI/MsRi/DJA08Nmfpj4PJalgMk1XTSNaHO2YKsEFt13vBHYf+dlH7cqGgwXGUps/KMMKAa1DfSDA7gMmgouDDbAEG1hvZV9r7pgs2O8HdfxqvGX+5G9x3jxHVtpzzKqL6mcPMrnrn6woYST4yjcKx0xe2oQQzvuJgX/cCeC987x7B9gghhBBCCCGEEEKI5Y9EjZA44WCogoa7vfvOY1VQm6ER1Z6hBL7devwMrrtiQRr/Ah8GdjNIH7SsoLpGP4/FOv5sayusIfNi2FnejbRzpIOWacULfxsG3wE7u58m8O56TbAiTqNeD+5+aArfh/oeHj68NgxiF2CuQ2+dffkBdY7RPIwfRxFGGOmDDUa7pYn8fbnZS9yvm9HD7xq9F5mxVC+zJklgcoPqbFujNNrmqG3jjlsv6yVOJOL39K9gqateb5sKzHUtIvm+XxYZU3ye9KPafNwXYpmtxnOSoCGEEEIIIYQQQgjx/kKiRohbM99lqAGxOONrN0jqB+WijjmU4zMzoAVGMMijduZ8VBZC0sx5d1Z/I8HluPXcIHUvTHCes8WZZULjaz/TIar01Gji91cUFDTcYCtFDV5vt929SJ5xHkVU1k4ORtRogenDJRHrxMH2lGHKLPWG+2FWQj3vhKjMCWYEFWFLNdW7Xm7w3c0WcMWg4WS1JIkZBZhzBqyfRPcQ9jWauFlTvpDE+8Q1GI+DohLX4bWh8NTp7NOlDFNGriVcd3lmPlCw4POj4CwnwxGEidunQgghhBBCCCGEEGLFQqJGSL0a9MPdZ1S5p9Gc3cxyNpxpzawQ13A3jrhg4HDNdd0gJGeIA2b29VLY0kWu4FGvLv9oBB39sj1RZsT+cZnJwvNiYN43ZidlVJd4qldmbHz4WgIrXAQw15im8GlvZgbzXaGrDOOlwIB+PUHD/cx9AibTxjWfd/dTrx+57xY0XuJpOJRgym+VAcxDsqixPIkTP91+ispwAuyYjhOjksYeRcU2mGvbgWjTdYpZI12CKgq3nF0B9rzKiO+DtLj3h/w3hBBCCCGEEEIIIVY8JGqEMBg2UrhBNZa38YPloz0LmMIGA35xs7ijAvTDmeUcRw4mgEwTX9+3wS2P5ZckIkNp11BLiLkB/HrZBiwnxr/7Yc7HN02Oa1/J2c5fl4H+CTCB5QBG2HBL7SSVMvNhmaku1JaZqhfEdfvDvU5uRgqcddKKUW4G01KYB1Ovv9EIwnYPwAToAVsOrJF9MKtoKAzFV8XNkmn0WLxGrpiVli4Y4awNZiz2hy9mU2VhxtS4sH1LYK7jaF/DDGyJsagsk6HutyV8b0f1mBZCCCGEEEIIIYQQyx+JGiEjOSM3yWA6TTAyCxNAjPKkIPS38GdrM8Dolmyqd8ykwH2Aai+N4cCZ+0XYkliAnWnt47dnKMIES1oNJTDpl/qphx9Q7UV12akkokQv97t54d/MyOA1dtvKwHW9scwSS9wmKhBMHwUKLUklnJDwvR+Aj8s2cJf3YnSD4YAZcwyC98FkmPiiTL3tW8O/XYPvtEJFHuZaBjD3eFqRIUnQ8O9jlkRju/g3xQ0Kb2mgkNYLez8xA4leKhNgylQFMP2TB7AAoyMKZMJjuaXS+sNj+eXqhrLvCozQyJJbQgghhBBCCCGEEGLFQaJGiDvLfbhZCvVKWdXbdwnAJJgg3VxEz9zPwwTcGHjuhAnmlWCCfTSOZpZAvXr7bJvfTpaUyWFkRI2+cH8tYXsGItpVb4a/P1s9qT8bzcCJExaGSlpBg+JH3Pr9AN6FLc8VFQTPwlx/IDlYXYEVDeL6pgXAlPC4C1Gb0eEGzqMM3d3zyHrvgfdaHpRhy6HR/yNt8DoLYCyAybBB9H6Ye7IAm8WQBO9hlixLKnnFdjKATwN2H78v82E72T56vhRhnhEA8B7Ms6MeFA64Lo/llhgrhfvn2OQ5jqYowD6nYNMFk13RaCaKSxnm/uA70JgoLYQQQgghhBBCCCFGF4kaHiMRtPJ9B0gaT4os7EWJm3HMWdaAKfcSwGZ2FGFKxGRgApCuB0NU8NkP1vltDGACeyMV6KfZ+FiYwHs3rCCT1PduALze7P+obdMSd568LjS/HgolmOvTjVrRIW5cFGH7qj3h2BRFOAs/jqbwVUByv/eHxy7ClBGKyxBIK0ywxBZn+Q+lBNJIwmPnYQL8TTDtWgIr4DCbI0og4mx+lv6in0M94dA9vh8wjyMHkxXSGrZ3MdJ5VlAMYUYGBagczDmPh7m+c8N9xo1BCgcUR/zjMqOM5dYomlHoKSG6rNpwcDNcmIXCY4/Evvthz5fLhBBCCCGEEEIIIcSKgUSNEH/2+HBmkUeJGlGCBoPGDCwzSNoBE7BL8kgowwTHe2Bm1bfAmoIzeF6ELV8VNaPe/+yXNHKPlYtYnpYsTBCVZafGwtasp3cCj+tmYcRlYyyrGf4sr0OD9SJM32YxNDPpAowINQYmiOxnP0SRC7dpghE15sdsx2yPen4L9AsowGbKRM2k74MtedWD2uuQRuRyt2FmUQvMtaY5fCOz+IfiQVGPXth7iH1CaPhOIcYV/hbBXAc/6yWt4MWspTTiDvebc14t4fGT+mIAJsuG2Vosz9QdbsusixKMENqBaFHANZOPO14nzNicELbPNewuwvqVjKSwwX6JMkEfLvUytobq0yOEEEIIIYQQQgghho9EjZCoWv/DCVpFZUH45GFmX+dgAnHtsDOh05R66oENWrI+vztbnEHFpFn2CNdvhREeOFPdDV7TpLoDJhjfCJmwbWvBBOcBc56LYIUYv0RUXBmwpHNotE1R27liSg7Wv4LBZ87eHkjYRxKd4TZjYQK9aUSNXphgMcsIUWhJKs+VBMWEQvh5DMx19YPxFZix5R/LN7pP44XC/TEATXGr0eyfCqz/Shkjk+lBMYjZM73ed2yz369lmOtXwNBJew7MhKAAlEO6vnPN693PfTBjcSms4MRMGqDWayMuW8WFviTMzuA9A9hMipEs6+bCfSeJwI3iZmZF+RpJ1BBCCCGEEEIIIYRYfkjUcBjpOv++74AfCKPBMM11uxs8dgAjEPTABiSZFdEWfmYg3Q3Yw/ubgebx4fs82FncDHY2wQRgFzfQPnd7egd0wdTxXwJbLoa+GlEiUFTgfLjXh2JFD6x45Bu6s0/aYM7bz54ZSlAzgOnXNB4GhGLXgNPmNGWHeLyoNnSEf9OQPiqw7pf7ct+jjlPP46QCE0RfCjtzfygeLf2ILvGUh7lWA7AZFGlgRhOzF6LaFNffGRhRiONjNMtpuZleQxXVfCg6sN1FWO+ORs22KQDxfmYpKtf3h8+4tNcmjV8L18nCjqmReH6zvwuwQhqfFxRpRupYQgghhBBCCCGEEKIxJGqENFJ6yg22xQXo3O1bYAL7vbAiA6FXwnBwjYmL4TuzANwZ1pmYv3OwAfMOVAd2KzACBLMUGqUS7vPfsOWO2AfcH41+GUj0BReXNAF915A66vq4s7C5jf83g7QUW5hd4PoFLKugJgPFbp8l4c64T5q97s/kT6LeLPtGxMBGjaP9AL5/Tekd0RLuuydiHcCaXZMirCcNs6QapRIeNw9zz9GAnBk9o8VojDuO56FmPHCcuuJLP0yWEcXB+aj274gT4JgtRYGFpfWYWQNnO7fU1Uj1C7NVSk77mY3CTDb3mSWEEEIIIYQQQgghlh0SNUKSBA03GEofCNbfd70wAFtOiTQDWD3cZj7sLPnRgkbRvaidaR9XLojlbXrD9vlB5zKGJ7y4ootPBqaPaMDtXoMoQ3A/28T9PgsrIAG21I5vUuzOdHdn/bviBvuwO1zWBhPcdPs0bZmwkaAP9U3ACcdiGqGBfVGPpP2MprDD4HZS5gDLIy2GzRLwycGMC/YJA+HdMNdwKObvAUzmyQBsdhSfByxb5T8PVmSGO56j7s8eGBFgIqzZeFe4nNfWFevce5A+Nq5IxYwM934eLVGBz4AemOvLMlccb01hW/iMEUIIIYQQQgghhBDLBokaIQxW+YHTEoyfBA28s+EyBk45S5szdvthy0ohXG8+TEBvtAWNDGxGhT8T2sUVDPhqhzXzHQoMNiI8No3Ffb+MqLYANujMEi9xRr1JAfgijDcIr08Z0cberPdfr7wSA9LM/miCnYnuBmGXRbZGGl8D0miGQJr2R63D/hjtjIQ0pZCShDOgeuY/s5LaMfx7sgKTedQPW2YNsKJnH1YeUQOwpZXc+yNqHSD6vmbGhPt5cfg3zc0HvO+B6HuQYivb0gebYTUapvFRuFk8LEHFY1JoVKaGEEIIIYQQQgghxLJFokYIA7RuhgNgglaTYIJo9FXogzXmZh35NthZx0WYkk19zsufXTzSuOWw/IwMfvaXBd7fDPw2GqTOwwR0S6gWSzjD2j+uTydMn3EfbumeJFHGpwwjXpRhrksXrAg1KWwPS1/57YoTSxgIB6xYRVNpbvd+r6vvlwjKwIhHk2D6ZBFGR9hI8gZpFApRvEdzMGNuJEqI8TlAHwkGuodasm15UoDx1qF/jn9deZ/SH8M/Pz+TCeE6C2HFjag+ibvPXUHLL79Wb3wM9XkW1Y7uiOUjZVYvhBBCCCGEEEIIIRpDokYIA+pZVJc76YUJyI2FCfgBtnQKhRDW8W8Jl9FE1s1SiBM0msIXg+XDCbDWy25wxY2KsyzqvREY3AtgyrQUYM2t/WwI4h6HYgTFoqKz3F0/aja3GzxlHXx3/xWY6zotfKcJehdsUDZJ3GAwvCd8Z5bHqhLM5DgfA9MHS2HG91QYUaMDph9HIguJxtuA7WegcdPqKNysG2ZV1ctIaJT3Q5C7BGse3wabhQJUZ0j4Jd1covozSpCIExL9e901lk8rDGdhnkX5cLs4nxUhhBBCCCGEEEIIsfKRCYLg/T7RXAghhBBCCCGEEEIIIYQQ7wOy9VcRQgghhBBCCCGEEEIIIYRY/kjUEEIIIYQQQgghhBBCCCHESoFEDSGEEEIIIYQQQgghhBBCrBRI1BBCCCGEEEIIIYQQQgghxEqBRA0hhBBCCCGEEEIIIYQQQqwUSNQQQgghhBBCCCGEEEIIIcRKgUQNIYQQQgghhBBCCCGEEEKsFEjUEEIIIYQQQgghhBBCCCHESoFEDSGEEEIIIYQQQgghhBBCrBRI1BBCCCGEEEIIIYQQQgghxEqBRA0hhBBCCCGEEEIIIYQQQqwUSNQQQgghhBBCCCGEEEIIIcRKgUQNIYQQQgghhBBCCCGEEEKsFEjUEEIIIYQQQgghhBBCCCHESoFEDSGEEEIIIYQQQgghhBBCrBRI1BBCRLLuuuviqKOOWt7NEEIIIYQQQgghhBBCiEEkagixnLn66quRyWSw7bbbxq6TyWQiX6uvvvrgOueffz4ymQyy2Sz+/e9/1+xj6dKlaG5uRiaTwUknnTQq5yKEEEIIIYQQQgghhBCjSX55N0CIVZ3Zs2dj3XXXxZ///Ge8+uqr2GCDDSLX22OPPXDEEUdULWtubq5Zr1Qq4ZZbbsFZZ51VtfzOO+9sqF3//Oc/kc1K9xRCCCGEEEIIIYQQQqw4KGIpxHJkzpw5+NOf/oQrrrgCU6ZMwezZs2PX3WijjXDYYYdVvQ488MCa9fbaay/ccsstNctvvvlm7L333qnbViqVUCgUUq8vhBBCCCGEEEIIIYQQo41EDSGWI7Nnz8aECROw995746CDDkoUNdJy6KGH4q9//SteeumlwWXvvvsuHn74YRx66KGp9+N7atxwww3IZDJ4/PHHccopp2DKlCkYP348jjvuOPT19WHx4sU44ogjMGHCBEyYMAFnnXUWgiCo2udll12G7bbbDpMmTUJzczO22mor3H777TXH7u7uximnnILJkydjzJgx2G+//fD2228jk8ng/PPPr1r37bffxhe+8AVMnToVpVIJm266KX7605+mPk8hhBBCCCGEEEIIIcTKg0QNIZYjs2fPxqc//WkUi0UccsgheOWVV/CXv/wlct2enh7Mnz+/6tXb21uz3k477YS11loLN9988+CyX/7yl2hra2soUyOOk08+Ga+88gq+/e1vY7/99sN1112Hc889F/vuuy/K5TIuvPBC7LDDDrj00ktx4403Vm07a9YsbLnllvjOd76DCy+8EPl8Hp/5zGdw7733Vq131FFH4aqrrsJee+2Fiy++GM3NzZFtf++99/Dxj38cDz30EE466STMmjULG2ywAY455hhceeWVwz5XIYQQQgghhBBCCCHEioVEDSGWE08//TReeuklHHzwwQCAHXbYAWuttVZstsZPfvITTJkypeoVVWYqk8ng4IMPrvqO4kmpVBp2u6dOnYrf/va3OOGEE/Dzn/8c//Vf/4VLL70Um222GWbPno3jjz8ed911F9Zaa62ajImXX34ZP/zhD3HiiSfi9NNPx+OPP47NNtsMV1xxxeA6zzzzDG699Vacdtpp+PnPf44TTjgBv/zlL7HlllvWtOUb3/gGyuUynn32WZx77rn48pe/jLvvvhsHH3wwzj//fHR3dw/7fIUQQgghhBBCCCGEECsOEjWEWE7Mnj0bU6dOxS677ALAiBGf+9zn8Itf/ALlcrlm/f333x8PPvhg1WvmzJmR+z700EPx6quv4i9/+cvgeyOlp5I45phjkMlkBj9vu+22CIIAxxxzzOCyXC6HrbfeGv/617+qtnWNzRctWoQlS5Zgxx13xDPPPDO4/He/+x0A4IQTTqja9uSTT676HAQB7rjjDuy7774IgqAqg2XmzJlYsmRJ1X6FEEIIIYQQQgghhBArP/nl3QAhVkXK5TJ+8YtfYJdddsGcOXMGl2+77ba4/PLL8fvf/x577rln1TZrrbUWdt9991T733LLLfHBD34QN998M8aPH4/VV18du+6664i0fZ111qn6PG7cOADA2muvXbN80aJFVct+85vf4IILLsBf//rXqtJZrkjyxhtvIJvNYr311qvadoMNNqj6PG/ePCxevBjXXXcdrrvuusi2zp07N+VZCSGEEEIIIYQQQgghVgYkagixHHj44Yfxzjvv4Be/+AV+8Ytf1Hw/e/bsGlGjUQ499FBcc801GDNmDD73uc8hmx2ZxKxcLpd6uWsU/thjj2G//fbDTjvthKuvvhprrLEGCoUCrr/++ir/j7RUKhUAwGGHHYYjjzwycp0Pf/jDDe9XCCGEEEIIIYQQQgix4iJRQ4jlwOzZs7Haaqvhhz/8Yc13d955J371q1/hRz/6UVW5pkY59NBD8a1vfQvvvPNOjWH38uCOO+5AU1MT7r///ipvj+uvv75qvWnTpqFSqWDOnDnYcMMNB5e/+uqrVetNmTIFY8aMQblcTp3BIoQQQgghhBBCCCGEWLmRqCHEMqa7uxt33nknPvOZz+Cggw6q+X7NNdfELbfcgnvuuQef+9znhnyc6dOn48orr0R3dze22Wab4TR5RMjlcshkMlV+Ia+//jruuuuuqvVmzpyJb3zjG7j66qvx/e9/f3D5VVddVbO/Aw88EDfffDNeeOEFbLbZZlXfz5s3D1OmTBn5ExFCCCGEEEIIIYQQQiw3JGoIsYy555570N7ejv322y/y+49//OOYMmUKZs+ePSxRAwBOPfXUYW0/kuy999644oor8IlPfAKHHnoo5s6dix/+8IfYYIMN8Nxzzw2ut9VWW+HAAw/ElVdeiQULFuDjH/84Hn30Ubz88ssAqv03vve97+GRRx7Btttui2OPPRabbLIJFi5ciGeeeQYPPfQQFi5cuMzPUwghhBBCCCGEEEIIMXqMTJF9IURqZs+ejaamJuyxxx6R32ezWey999743e9+hwULFizj1o0eu+66K37yk5/g3XffxWmnnYZbbrkFF198MT71qU/VrPvzn/8cJ554Iu69916cffbZ6Ovrwy9/+UsAQFNT0+B6U6dOxZ///GccffTRuPPOO3HSSSdh1qxZWLhwIS6++OJldm5CCCGEEEIIIYQQQohlQyZwnXyFEGIF5a9//Su23HJL3HTTTfj85z+/vJsjhBBCCCGEEEIIIYRYDihTQwixwtHd3V2z7Morr0Q2m8VOO+20HFokhBBCCCGEEEIIIYRYEZCnhhBiheOSSy7B008/jV122QX5fB733Xcf7rvvPnzpS1/C2muvvbybJ4QQQgghhBBCCCGEWE6o/JQQYoXjwQcfxLe//W384x//QEdHB9ZZZx0cfvjh+MY3voF8XlqsEEIIIYQQQgghhBCrKhI1hBBCCCGEEEIIIYQQQgixUiBPDSGEEEIIIYQQQgghhBBCrBRI1BBCCCGEEEIIIYQQQgghxEqBRA2xynDJJZfggx/8ICqVyvJuSiTrrrsujjrqqOXdjBWC119/HZlMBpdddtnybsogf/jDH5DJZPCHP/wh9Tb9/f1Ye+21cfXVV49ew4QQQqzS3HDDDchkMnjqqaeGvI9bb70VEydOREdHR911M5kMzj///CEfa1VlNPptpPcZ9Vvn4IMPxmc/+9kRO4YQQojlx84774ydd955eTdjubPuuutin332GfH9nnDCCdhjjz1GfL8jwT/+8Q/k83m88MILy7spQowYEjXEKsHSpUtx8cUX4+yzz0Y2+/4Z9q+99hqampqGHcx44IEHcMwxx2CzzTZDLpfDuuuuG7tupVLBJZdcgvXWWw9NTU348Ic/jFtuuSVy3RdffBGf+MQn0NbWhokTJ+Lwww/HvHnzhtzOlY1CoYCvfOUr+O///m/09PQs7+YIIcQKDwP0fDU1NWHNNdfEzJkz8T//8z9ob2+v2eb8889HJpPB1KlT0dXVVfN90v+4Ll68ePDf0RdffHHEz2dloFwu47zzzsPJJ5+Mtra25d0csYJx9tln44477sDf/va35d0UIYRY4Xjttddw3HHHYf3110dTUxPGjh2L7bffHrNmzUJ3d/fybp5YhsyZMwc//vGP8fWvf32ZHbNSqeCGG27Afvvth7XXXhutra3YbLPNcMEFF9TEHzbZZBPsvffe+Na3vrXM2ifEaPP+ie4KkcBPf/pTDAwM4JBDDlneTRlRTj/9dOTz+WHv5+abb8bNN9+McePGYc0110xc9xvf+AbOPvts7LHHHrjqqquwzjrr4NBDD8UvfvGLqvXeeust7LTTTnj11Vdx4YUX4owzzsC9996LPfbYA319fcNu88rC0Ucfjfnz5+Pmm29e3k0RQoiVhu985zu48cYbcc011+Dkk08GAJx22mnYfPPN8dxzz0VuM3fuXFxzzTUNHee2225DJpPB6quvjtmzZw+73Ssjv/71r/HPf/4TX/rSl5Z3U8QKyJZbbomtt94al19++fJuihBCrFDce++92HzzzXHrrbdi3333xVVXXYWLLroI66yzDs4880yceuqpy7uJNTzwwAN44IEHlncz3pfMmjUL6623HnbZZZdldsyuri4cffTRmDdvHr785S/jyiuvxDbbbIPzzjsPn/zkJxEEQdX6X/7yl/GrX/0Kr7322jJroxCjiUQNsUpw/fXXY7/99kNTU9PybsqIcf/99+P+++/H6aefPux9XXjhhVi6dCn++Mc/4iMf+Ujsem+//TYuv/xynHjiibjuuutw7LHH4te//jV23HFHnHnmmSiXy1X77OzsxMMPP4xTTjkFX//613Hrrbfib3/7G2644YZht3llYfz48dhzzz1XqXMWQojh8slPfhKHHXYYjj76aHzta1/D/fffj4ceeghz587FfvvtFzn7cYsttsCll17a0MzIm266CXvttRcOOeSQVVZ8vv7667H99tvjAx/4wPJuilhB+exnP4s777wzVXkyIYRYFZgzZw4OPvhgTJs2Df/4xz8wa9YsHHvssTjxxBNxyy234B//+Ac23XTT2O0rlcpyyeQvFosoFovL/Ljvd/r7+zF79uxlXq6xWCzij3/8I5544gl84xvfwLHHHouf/vSnOO+88/CHP/wBv//976vW33333TFhwgT87Gc/W6btFGK0kKgh3vfMmTMHzz33HHbfffea737xi19gq622wpgxYzB27FhsvvnmmDVrVtU6ixcvxmmnnYa1114bpVIJG2ywAS6++OIab45KpYIrr7wSm266KZqamjB16lQcd9xxWLRoUdV6QRDgggsuwFprrYWWlhbssssu+Pvf/97QOfX39+PUU0/FqaeeiunTpze0bRRrrrkmCoVC3fXuvvtu9Pf344QTThhclslkcPzxx+Ott97CE088Mbj8jjvuwD777IN11llncNnuu++OjTbaCLfeemvqtn3/+9/HtGnT0NzcjBkzZkTWgHzppZdw0EEHYeLEiWhqasLWW2+Ne+65p2qdhQsX4owzzsDmm2+OtrY2jB07Fp/85Ccjyym89dZbOOCAA9Da2orVVlsNp59+Onp7e2vWe+WVV3DggQdi9dVXR1NTE9Zaay0cfPDBWLJkSdV6e+yxBx5//HEsXLgw9XkLIYSoZtddd8W5556LN954AzfddFPN99/61rfw3nvvpc7WePPNN/HYY4/h4IMPxsEHH4w5c+bgT3/6U6ptWfLq5ZdfxmGHHYZx48ZhypQpOPfccxEEAf79739j//33x9ixY7H66qvXzHLv6+vDt771LWy11VYYN24cWltbseOOO+KRRx6pOVaa3yo+ixYtwjbbbIO11loL//znP2PX6+npwe9+97vI30i9vb04/fTTMWXKFIwZMwb77bcf3nrrrcj9vP322/jCF76AqVOnolQqYdNNN8VPf/rTyOOdf/752GijjdDU1IQ11lgDn/70p6tmDHZ2duKrX/3q4O+ujTfeGJdddlnVbMMZM2bETsLYeOONMXPmzNhzJvfddx923HFHtLa2YsyYMdh7772rfo89/PDDyGazNWUabr75ZmQymapxlua8fI466qjIcp8cWy6jcS3S/tYBzO+Yzs5OPPjgg7HnI4QQqxKXXHIJOjo68JOf/ARrrLFGzfcbbLBBVaZGJpPBSSedhNmzZ2PTTTdFqVTC7373OwDAs88+i09+8pMYO3Ys2trasNtuu+H//u//qvbX39+Pb3/729hwww3R1NSESZMmYYcddqh6Lr/77rs4+uijsdZaa6FUKmGNNdbA/vvvj9dff31wHd9Tg15Kt956K/77v/8ba621FpqamrDbbrvh1VdfrTmvH/7wh1h//fXR3NyMbbbZBo899lhqnw72wW233YZNNtkEzc3N+K//+i88//zzAIBrr70WG2ywAZqamrDzzjtXtRsAHnvsMXzmM5/BOuusg1KphLXXXhunn356zWSWNP0Qxc9+9jPk83mceeaZdc/F5/HHH8f8+fNrfk812r+NUiwWsd1229Us/9SnPgUANaVVC4UCdt55Z9x9993DPrYQKwLDr1sjxAoOAxQf/ehHq5Y/+OCDOOSQQ7Dbbrvh4osvBmAe+n/84x8Hf4B0dXVhxowZePvtt3HcccdhnXXWwZ/+9Cd87WtfwzvvvIMrr7xycH/HHXccbrjhBhx99NE45ZRTMGfOHPzgBz/As88+iz/+8Y+DosG3vvUtXHDBBdhrr72w11574ZlnnsGee+7ZUEmmK6+8EosWLcI3v/lN3HnnncPpnoZ49tln0draig996ENVy7fZZpvB73fYYQe8/fbbmDt3LrbeeuuafWyzzTb47W9/m+p4P//5z9He3o4TTzwRPT09mDVrFnbddVc8//zzmDp1KgDg73//++AM03POOQetra249dZbccABB+COO+4Y/Af9X//6F+666y585jOfwXrrrYf33nsP1157LWbMmIF//OMfg2W3uru7sdtuu+HNN9/EKaecgjXXXBM33ngjHn744aq29fX1YebMmejt7cXJJ5+M1VdfHW+//TZ+85vfYPHixRg3btzgultttRWCIMCf/vSnUTEkE0KIVYXDDz8cX//61/HAAw/g2GOPrfpuxx13xK677opLLrkExx9/PJqbmxP3dcstt6C1tRX77LMPmpubMX36dMyePTvyfw7j+NznPocPfehD+N73vod7770XF1xwASZOnIhrr70Wu+66Ky6++GLMnj0bZ5xxBj72sY9hp512AmC8vn784x/jkEMOwbHHHov29nb85Cc/wcyZM/HnP/8ZW2yxBYB0v1V85s+fjz322AMLFy7Eo48+mjj54emnn0ZfX1/NbyQA+OIXv4ibbroJhx56KLbbbjs8/PDD2HvvvWvWe++99/Dxj398MFgxZcoU3HfffTjmmGOwdOlSnHbaaQCMd8c+++yD3//+9zj44INx6qmnor29HQ8++CBeeOEFTJ8+HUEQYL/99sMjjzyCY445BltssQXuv/9+nHnmmXj77bfx/e9/H4AZB8ceeyxeeOEFbLbZZoNt+ctf/oKXX34Z3/zmNxOv24033ogjjzwSM2fOxMUXX4yuri5cc8012GGHHfDss89i3XXXxa677ooTTjgBF110EQ444AB89KMfxTvvvIOTTz4Zu+++O7785S+nPq/hMtLXIu1vHcLg0x//+MfB31VCCLEq8+tf/xrrr79+Q78ZHn74Ydx666046aSTMHnyZKy77rr4+9//jh133BFjx47FWWedhUKhgGuvvRY777wzHn30UWy77bYAjOB90UUX4Ytf/CK22WYbLF26FE899RSeeeaZQWPqAw88EH//+99x8sknY91118XcuXPx4IMP4s0330z0zASA733ve8hmszjjjDOwZMkSXHLJJfj85z+PJ598cnCda665BieddBJ23HFHnH766Xj99ddxwAEHYMKECVhrrbVS9cFjjz2Ge+65ByeeeCIA4KKLLsI+++yDs846C1dffTVOOOEELFq0CJdccgm+8IUvVP27dNttt6GrqwvHH388Jk2ahD//+c+46qqr8NZbb+G2224bXG8o/XDdddfhy1/+Mr7+9a/jggsuSHUuLn/605+QyWSw5ZZbRn6fpn+7uroiveF8crkcJkyYkLjOu+++CwCYPHlyzXdbbbUV7r77bixduhRjx46tezwhVmgCId7nfPOb3wwABO3t7VXLTz311GDs2LHBwMBA7Lbf/e53g9bW1uDll1+uWn7OOecEuVwuePPNN4MgCILHHnssABDMnj27ar3f/e53Vcvnzp0bFIvFYO+99w4qlcrgel//+tcDAMGRRx5Z93zeeeedYMyYMcG1114bBEEQXH/99QGA4C9/+UvdbdOw9957B9OmTYv9bv31169Z3tnZGQAIzjnnnCAIguAvf/lLACD4+c9/XrPumWeeGQAIenp6YtswZ86cAEDQ3NwcvPXWW4PLn3zyyQBAcPrppw8u22233YLNN9+8an+VSiXYbrvtgg033HBwWU9PT1Aul2uOUyqVgu985zuDy6688soAQHDrrbdWnd8GG2wQAAgeeeSRIAiC4Nlnnw0ABLfddlvseZD//Oc/AYDg4osvrruuEEKsyqT5N23cuHHBlltuOfj5vPPOCwAE8+bNCx599NEAQHDFFVcMfj9t2rRg7733rtnP5ptvHnz+858f/Pz1r389mDx5ctDf31+3nTzml770pcFlAwMDwVprrRVkMpnge9/73uDyRYsWBc3NzVX/xg8MDAS9vb1V+1y0aFEwderU4Atf+MLgsjS/Vdw+e+edd4JNN900WH/99YPXX3+97nn8+Mc/DgAEzz//fNXyv/71rwGA4IQTTqhafuihhwYAgvPOO29w2THHHBOsscYawfz586vWPfjgg4Nx48YFXV1dQRAEwU9/+tOaa0P4m+iuu+4KAAQXXHBB1fcHHXRQkMlkgldffTUIgiBYvHhx0NTUFJx99tlV651yyilBa2tr0NHREXvO7e3twfjx44Njjz22avm7774bjBs3rmo5//3fdNNNg56enmDvvfcOxo4dG7zxxhuD66Q5ryAIavrtyCOPjPy9xbFFRuNapP2t47LRRhsFn/zkJ2uWCyHEqsaSJUsCAMH++++fehsAQTabDf7+979XLT/ggAOCYrEYvPbaa4PL/vOf/wRjxowJdtppp8FlH/nIRyJ/y5BFixYFAIJLL700sR0zZswIZsyYMfj5kUceCQAEH/rQh6p+l8yaNavq90Fvb28wadKk4GMf+1jV76QbbrghAFC1zzgABKVSKZgzZ87gsmuvvTYAEKy++urB0qVLB5d/7WtfCwBUrct/w1wuuuiiIJPJDP67nLYf3N+Gs2bNCjKZTPDd73637jnEcdhhhwWTJk2qWZ62f4PA/vtf7xUXq3HZfffdg7FjxwaLFi2q+e7mm28OAARPPvnkkM5ViBUJlZ8S73sWLFiAfD6Ptra2quXjx4+vm0p/2223Yccdd8SECRMwf/78wdfuu++OcrmM//3f/x1cb9y4cdhjjz2q1ttqq63Q1tY2WE7ioYceQl9fH04++eSq0gKcOZeGs88+G+uvvz6++MUvNtALI0N3dzdKpVLNcnqVMPWT72nWTeKAAw6oqvG9zTbbYNtttx3M9Fi4cCEefvhhfPazn0V7e/tgvy9YsAAzZ87EK6+8grfffnuwLdmseeSVy2UsWLAAbW1t2HjjjfHMM88MHuO3v/0t1lhjDRx00EGDy1paWmoMVJmJcf/999edUcGZFPPnz697zkIIIZJpa2tDe3t75Hc77bQTdtllF1xyySWJ/84899xzeP7553HIIYcMLjvkkEMwf/583H///anb4v5bnMvlsPXWWyMIAhxzzDGDy8ePH4+NN94Y//rXv6rWZU3rSqWChQsXYmBgAFtvvXXVv0lpfquQt956CzNmzEB/fz/+93//F9OmTau7zYIFCwCgZsYf/5095ZRTqpb7v1eCIMAdd9yBfffdF0EQVP0GmjlzJpYsWTJ4PnfccQcmT548aPzuwt9Ev/3tb5HL5WqO+9WvfhVBEOC+++4DYP4N3n///XHLLbcMlqUql8v45S9/OVhSKY4HH3wQixcvHrzefOVyOWy77bZVJcBaWlpwww034MUXX8ROO+2Ee++9F9///verSmumOa/hMBrXIu1vHRf+FhZCiFWdpUuXAgDGjBnT0HYzZszAJptsMvi5XC7jgQcewAEHHID1119/cPkaa6yBQw89FI8//vjgscaPH4+///3veOWVVyL33dzcjGKxiD/84Q815a/TcPTRR1d5bey4444AMPjb5amnnsKCBQtw7LHHIp+3BV8+//nP180acNltt92qsiWYiXLggQdW9SeXu7+d3Azczs5OzJ8/H9tttx2CIMCzzz47uE4j/XDJJZfg1FNPxcUXX1w3yzOJBQsWJPZDvf4FgCOOOAIPPvhg3dfs2bMT23LhhRfioYcewve+9z2MHz++5nvFJsT7CZWfEqssJ5xwAm699VZ88pOfxAc+8AHsueee+OxnP4tPfOITg+u88soreO655zBlypTIfcydO3dwvSVLlmC11VZLXO+NN94AAGy44YZV30+ZMiXVj4H/+7//w4033ojf//73gwH6ZUlzc3NkvWWanPGHBt/TrJuE308Aqjw5Xn31VQRBgHPPPRfnnntu5D7mzp2LD3zgA6hUKpg1axauvvpqzJkzp8rUfNKkSYN/v/HGG9hggw1qAhEbb7xx1ef11lsPX/nKV3DFFVdg9uzZ2HHHHbHffvsN1lZ3YcBlJIIbQgixqtPR0RH77y1gSjTMmDEDP/rRj3D66adHrnPTTTehtbUV66+//mBN46amJqy77rqYPXt2ZGmfKNzgNmCC7U1NTTXp/uPGjRsUEMjPfvYzXH755XjppZfQ398/uHy99dYb/DvNbxVy+OGHI5/P48UXX8Tqq6+eqv0kcPwqAPNvYTabrSmd5P9bOG/ePCxevBjXXXcdrrvuush98zfQa6+9ho033rgqGOLzxhtvYM0116wJFLHsJX9HAeZ//n/5y1/isccew0477YSHHnoI7733Hg4//PDEc2VAaNddd4383i/FsP322+P444/HD3/4Q8ycORNf+MIXqr5Pc17DYTSuRdrfOi5BEOh3jBBCwP47ETfBIg7333fAPLe7uroin70f+tCHUKlU8O9//xubbropvvOd72D//ffHRhtthM022wyf+MQncPjhh+PDH/4wADOB7+KLL8ZXv/pVTJ06FR//+Mexzz774Igjjkj1m8D/PcPYBIUB/vu7wQYbVK2Xz+frlrZKOg7/v3nttdeOXO4KE2+++Sa+9a1v4Z577qkRLOhp2Ug/PProo7j33ntx9tlnD8lHw8f/LeVSr38BYP31168St4bCL3/5S3zzm9/EMcccg+OPPz6xnfo3XbwfkKgh3vdMmjQJAwMDaG9vr/qf5NVWWw1//etfcf/99+O+++7Dfffdh+uvvx5HHHEEfvaznwEwsyf32GMPnHXWWZH73mijjQbXW2211WJV8zhRpFHOOuss7LjjjlhvvfUGja6osL/zzjt48803a/7BHEnWWGMNPPLIIzX/Y/vOO+8AwKAvBc3SuNzlnXfewcSJEyOzOBqFZu1nnHFGrCkof3hdeOGFOPfcc/GFL3wB3/3udzFx4kRks1mcdtppNabvabn88stx1FFH4e6778YDDzyAU045BRdddBH+7//+r6quKH+sRNW0FEIIkZ633noLS5YsqfmfapeddtoJO++8My655JJB3wOXIAhwyy23oLOzs2rGJJk7dy46OjpqMjyjyOVyqZbxuOSmm27CUUcdhQMOOABnnnkmVlttNeRyOVx00UVV5tJpfquQT3/60/j5z3+OWbNm4aKLLqrbdsCK+osWLUpdD9uF/34edthhOPLIIyPXYcBlpJk5cyamTp2Km266CTvttBNuuukmrL766pGm5y5s84033hgZ6PHFid7eXvzhD38AYASMrq4utLS0DLv9ccEEd9JFI4z2tVi0aFHkZBMhhFjVGDt2LNZcc0288MILDW2XZlJfHDvttBNee+21wf/v/PGPf4zvf//7+NGPfjSYNXraaadh3333xV133YX7778f5557Li666CI8/PDDsV4PJM1vl5Eg7jj1jl8ulwf9ws4++2x88IMfRGtrK95++20cddRRVf8/n7YfNt10UyxevBg33ngjjjvuuBrRqREmTZqUmBmSpn87OjrQ0dFR91i5XC4yvvTggw/iiCOOwN57740f/ehHsdsrNiHeT0jUEO97PvjBDwIA5syZU/M/c8ViEfvuuy/23XdfVCoVnHDCCbj22mtx7rnnYoMNNsD06dPR0dFR93+Qp0+fjoceegjbb7994o8VloJ45ZVXqlT4efPmpUqPfPPNN/HGG29E/oO73377Ydy4cVi8eHHd/QyVLbbYAj/+8Y/x4osvVgWCaHBFY9MPfOADmDJlCp566qmafbgGqPWISq99+eWXB2eDsA8LhULda3T77bdjl112wU9+8pOq5YsXL676B33atGl44YUXaoSbf/7zn5H73XzzzbH55pvjm9/8Jv70pz9h++23x49+9KMqg7E5c+YAQI3BuhBCiMa48cYbASBWyCbnn38+dt55Z1x77bU13z366KN466238J3vfKfmubxo0SJ86Utfwl133YXDDjts5Brucfvtt2P99dfHnXfeWfVvzXnnnVezbr3fKuTkk0/GBhtsgG9961sYN24czjnnnLrtcH8jbb755oPLp02bhkqlMpiFQPx/C6dMmYIxY8agXC6n+q305JNPor+/H4VCIXKdadOm4aGHHqqZiPLSSy8Nfk9yuRwOPfRQ3HDDDbj44otx11134dhjj40NHLjtAIxgVK/NgLkmL774Ii677DKcffbZOOecc/A///M/DZ1XFBMmTIj8zeZmowCjcy0a/a0zMDCAf//739hvv/3qnZYQQqwS7LPPPrjuuuvwxBNP4L/+67+GtI8pU6agpaUl8tn70ksvIZvNVmUwTJw4EUcffTSOPvpodHR0YKeddsL5559fVQpz+vTp+OpXv4qvfvWreOWVV7DFFlvg8ssvx0033TSkNhL++/vqq69il112GVw+MDCA119/fdQmMJDnn38eL7/8Mn72s5/hiCOOGFweV54zTT9MnjwZt99+O3bYYQfstttuePzxxwcnaTbKBz/4QcyePRtLliypqdqQlssuuwzf/va36643bdq0wQmu5Mknn8SnPvUpbL311rj11lsTs0fnzJmDbDY7OEFXiJUZeWqI9z38keEH2P0yENlsdvAfY5ZN+uxnP4snnngisr724sWLMTAwMLheuVzGd7/73Zr1BgYGBv+ndffdd0ehUMBVV11VpcpfeeWVqc7luuuuw69+9auqF2s4X3bZZXXrKw6X/fffH4VCAVdfffXgsiAI8KMf/Qgf+MAHsN122w0uP/DAA/Gb3/wG//73vweX/f73v8fLL7+Mz3zmM6mOd9dddw16YgBGEHnyySfxyU9+EoAJSDBoFZUVMm/evMG/c7lczUyT2267rWr/ALDXXnvhP//5D26//fbBZV1dXTWlHJYuXTp4/cnmm2+ObDZbU3br6aefRiaTGfIPXiGEEMDDDz+M7373u1hvvfXw+c9/PnHdGTNmYOedd8bFF188WPaQsPTUmWeeiYMOOqjqdeyxx2LDDTcc9X9PGXh3/1168skn8cQTT1Stl+a3isu5556LM844A1/72tdwzTXX1G3HVltthWKxWPMbif/OusF7oPb3Si6Xw4EHHog77rgjcsaq++/wgQceiPnz5+MHP/hBzXrsh7322gvlcrlmne9///vIZDKD7SKHH344Fi1ahOOOOw4dHR2phKiZM2di7NixuPDCC6vKfkW1+cknn8Rll12G0047DV/96ldx5pln4gc/+AEeffTRhs4riunTp2PJkiV47rnnBpe98847+NWvflW13mhci7S/dcg//vEP9PT0VP3OE0KIVZmzzjoLra2t+OIXv4j33nuv5vvXXnsNs2bNStxHLpfDnnvuibvvvrsqSP3ee+/h5ptvxg477DBY6sr/PdDW1oYNNthg8LdAV1dXze+d6dOnY8yYMZG/Fxpl6623xqRJk/D//t//q/p/4NmzZw/Jw6NRon43BUFQ08eN9sNaa62Fhx56CN3d3dhjjz1q+jkt//Vf/4UgCPD0008PaXtg6J4aL774Ivbee2+su+66+M1vflM3I+jpp5/GpptuOmTxRYgVCWVqiPc966+/PjbbbDM89NBDVXWQv/jFL2LhwoXYddddsdZaa+GNN97AVVddhS222GJw5uaZZ56Je+65B/vssw+OOuoobLXVVujs7MTzzz+P22+/Ha+//jomT56MGTNm4LjjjsNFF12Ev/71r9hzzz1RKBTwyiuv4LbbbsOsWbNw0EEHYcqUKTjjjDNw0UUXYZ999sFee+2FZ599Fvfdd1+q9L8999yzZhkFkxkzZmDrrbceXP76669jvfXWw5FHHokbbrghcb/PPfcc7rnnHgBm9sWSJUsGMw0+8pGPYN999wVg/tE/7bTTcOmll6K/vx8f+9jHcNddd+Gxxx7D7Nmzq2ZHfv3rX8dtt92GXXbZBaeeeio6Ojpw6aWXYvPNN8fRRx9d91wBUzpqhx12wPHHH4/e3l5ceeWVmDRpUlU5sB/+8IfYYYcdsPnmm+PYY4/F+uuvj/feew9PPPEE3nrrLfztb38DYGbTfOc738HRRx+N7bbbDs8//zxmz55dU7fy2GOPxQ9+8AMcccQRePrpp7HGGmvgxhtvrCk18fDDD+Okk07CZz7zGWy00UYYGBjAjTfeOBhUcHnwwQex/fbbV3l3CCGEiOe+++7DSy+9hIGBAbz33nt4+OGH8eCDD2LatGm455570NTUVHcf5513XtVsQsAIAXfccQf22GOP2H3st99+mDVrFubOnZvo3TEc9tlnH9x555341Kc+hb333htz5szBj370I2yyySZVpQfS/FbxufTSS7FkyRKceOKJGDNmTGKgv6mpCXvuuSceeughfOc73xlcvsUWW+CQQw7B1VdfjSVLlmC77bbD73//+0H/EZfvfe97eOSRR7Dtttvi2GOPxSabbIKFCxfimWeewUMPPYSFCxcCMP+z/vOf/xxf+cpX8Oc//xk77rgjOjs78dBDD+GEE07A/vvvj3333Re77LILvvGNb+D111/HRz7yETzwwAO4++67cdppp9X4Smy55ZbYbLPNcNttt+FDH/oQPvrRj9bt+7Fjx+Kaa67B4Ycfjo9+9KM4+OCDMWXKFLz55pu49957sf322+MHP/gBenp6cOSRR2LDDTfEf//3fwMAvv3tb+PXv/41jj76aDz//PNobW1NdV5RHHzwwTj77LPxqU99Cqeccgq6urpwzTXXYKONNqoyix+Na5H2tw558MEH0dLSgj322KNu/wohxKrA9OnTcfPNN+Nzn/scPvShD+GII47AZptthr6+PvzpT3/CbbfdhqOOOqrufi644AI8+OCD2GGHHXDCCScgn8/j2muvRW9vLy655JLB9TbZZBPsvPPO2GqrrTBx4kQ89dRTuP3223HSSScBMNUMdtttN3z2s5/FJptsgnw+j1/96ld47733cPDBBw/7fIvFIs4//3ycfPLJ2HXXXfHZz34Wr7/+Om644QZMnz591P0ZPvjBD2L69Ok444wz8Pbbb2Ps2LG44447agSVofTDBhtsgAceeAA777wzZs6ciYcffrjGX6seO+ywAyZNmoSHHnoo1rOrHkPx1Ghvb8fMmTOxaNEinHnmmbj33nurvp8+fXrVxMr+/n48+uijOOGEE4bURiFWOAIhVgGuuOKKoK2tLejq6hpcdvvttwd77rlnsNpqqwXFYjFYZ511guOOOy545513qrZtb28Pvva1rwUbbLBBUCwWg8mTJwfbbbddcNlllwV9fX1V61533XXBVlttFTQ3NwdjxowJNt988+Css84K/vOf/wyuUy6Xg29/+9vBGmusETQ3Nwc777xz8MILLwTTpk0LjjzyyIbP7frrrw8ABH/5y1+qlj///PMBgOCcc85JvY+ol9+mcrkcXHjhhcG0adOCYrEYbLrppsFNN90Uud8XXngh2HPPPYOWlpZg/Pjxwec///ng3XffrdueOXPmBACCSy+9NLj88suDtddeOyiVSsGOO+4Y/O1vf6tZ/7XXXguOOOKIYPXVVw8KhULwgQ98INhnn32C22+/fXCdnp6e4Ktf/epgv2+//fbBE088EcyYMSOYMWNG1f7eeOONYL/99gtaWlqCyZMnB6eeemrwu9/9LgAQPPLII0EQBMG//vWv4Atf+EIwffr0oKmpKZg4cWKwyy67BA899FDVvhYvXhwUi8Xgxz/+cd3zFkKIVR3/36NisRisvvrqwR577BHMmjUrWLp0ac025513XgAgmDdvXs13M2bMCAAEe++9dxAEQXDHHXcEAIKf/OQnsW34wx/+EAAIZs2aFbtO3DGPPPLIoLW1NbIdm2666eDnSqUy+G9pqVQKttxyy+A3v/lNcOSRRwbTpk0bXC/Nb5Wo3wHlcjk45JBDgnw+H9x1112x5xEEQXDnnXcGmUwmePPNN6uWd3d3B6ecckowadKkoLW1Ndh3332Df//73wGA4Lzzzqta97333gtOPPHEYO211w4KhUKw+uqrB7vttltw3XXXVa3X1dUVfOMb3wjWW2+9wfUOOuig4LXXXhtcp729PTj99NODNddcMygUCsGGG24YXHrppUGlUols/yWXXBIACC688MLE8/R55JFHgpkzZwbjxo0LmpqagunTpwdHHXVU8NRTTwVBEASnn356kMvlgieffLJqu6eeeirI5/PB8ccf39B5RfXbAw88EGy22WZBsVgMNt544+Cmm24aHFsuo3Et0vzWIdtuu21w2GGHNdK9QgixSvDyyy8Hxx57bLDuuusG/5+9Pw+SNUvv+vBPZlZmrfdW3b3X6Z6epWfQiEHSD+RgCGQEsgzhBcuyJ4wtb5iwiZHA/AOEggAZhEIsIRiZscGyAoKwZRsctsFgbIERJsxiy2hGy2j2me7p5a51b+17Zf7+eM63z5OnzptLVd3bXd3PJ6IiKzPf5bznnPete7/f8zxPr9cbXLp0afCJT3xi8J//5//5YG9v763tgMGnPvWp6jF+4Rd+YfC93/u9g6WlpcHCwsLgt/yW3zL4x//4Hw9t86M/+qOD3/AbfsNgZWVlMD8/P/jIRz4y+JN/8k++pUc8ePBg8KlPfWrwkY98ZLC4uDhYXl4efOd3fufgr/21vzZ0nPL/vj/3cz83AAZ//a//9aHt9P/xv/yX//LQ5z/5kz/51r9dfsNv+A2Df/SP/tHgO77jOwb/4r/4L47tq1of+P/3e2rt+tVf/dXBb/ttv22wtLQ0uH79+uD3/J7fM/jFX/zFoXZO2g8vvPDCW/82FP/P//P/DC5dujT4zb/5Nw/pRpPy+37f7xt88IMfHHsd/rrL/p0WHWdSLefv/J2/MwAGX/nKV8503iB4p9AaDM658k8QvANZX1/npZde4k//6T/N7/7dv/vtbs4T4b/4L/4L/uAf/IN87Wtf49atW293c96z/Pk//+f503/6T/O1r33tTMXhgiAIguBxcHx8zK/5Nb+Gf/Pf/DeraTTf6Xz605/mD/yBP8Arr7zC+973vre7Oe86Pve5z/Ht3/7t/MIv/MLENdGCIAiC9wb9fp8bN27wfd/3ffzUT/3U292ct5Wvf/3rfOQjH+Hv/J2/w2/9rb/17W5Old/5O38nrVbrRKrLILioRE2N4D3B8vIyf/AP/kH+zJ/5M/T7/be7OU+En/u5n+P3/b7fF4bG28jh4SE/8RM/wR/5I38kDI0gCILgHUmn0+GP//E/zmc+85mh1FcXgcFgwE//9E/zXd/1XWFoPCZ+/Md/nO///u8PQyMIguA9zt7e3olaUX/1r/5VHj58yD//z//zb0+j3kG89NJL/O7f/bv58R//8be7KVW+8IUv8Lf+1t+6kAtYgqCJiNQIgiAIgiAIguDCsL29zd/8m3+Tn/u5n+Onfuqn+Bt/42/wr/wr/8rb3awgCIIgeNfyD/7BP+AP/IE/wL/xb/wbXLt2jV/4hV/gp3/6p/noRz/KP/tn/4xer/d2NzEIgvcYUSg8CIIgCIIgCIILw/379/ldv+t3sbKywg//8A+HoREEQRAEj5kXX3yR559/np/8yZ/k4cOHXL16lX/33/13+fEf//EwNIIgeFuISI0gCIIgCIIgCIIgCIIgCIIgCC4EUVMjCIIgCIIgCIIgCIIgCIIgCIILQZgaQRAEQRAEQRAEQRAEQRAEQRBcCMLUCIIgCIIgCIIgCIIgCIIgCILgQnChCoW3Wq23uwm0MCeo1pJjYFBs102/7wP9U5xrAbgOrABHwBZwAOyk34/TtvPAJaCHDepGaksb2Ev7lOfvu/aOwjtf5TFawLcC357asAN00vl/KbVxDrgLbE9wrnG0sOubw67rcML9elhfbpL7TMdrpTYfY9fXSce/gl37BtZ/fmz1qjHexsZ4FJ20rfpT5xYDd46+ex0Ur5PSA2bJ/eTP58/1JNA9czxuw3NmFng/8L507lXgNvAQu/4O1k+HWD+JFjZWhwz3k8Z83L3TAq4BS8Aj8v3oj9HC7ulyvxajx3kR+Gja9+vYnPZtaRevreLHc5b5dd50aH629jnZV+80OtTb7vsX8vOmmz7bZ7J7McpfnY13wr8fgiAIguBJE/9+ODut1oWSTIIgCILgXBgM3ukqzEUzNTDR63GIb7103ElF8hqddIwuZjAsApextn4N2K2cs53OWRNJJazOp983gPtkodX3wS4m+nXJIm1pIkhMG3B6cbnFSZF3A/gCJs49TNc0n97PYZPsPOSkVjruMnaND7Fr1vGPaJ4XBwwL1C1McK4ZEsfp/Q5mhEhkLg2IPiaET9KXHer94NvjDQcJ595AwX0+inY616X0fo+TIVk635MwNzrYfXBANtdOO/98Hx4xvu0tzFi7nd7vp/YspP2PsfEv+3SQ2loy6b0zwAyUh5Vjd8kmmpDpNp/er5P7qmZGrqd9lsnzdcB4Q6QJP8/Oaz6MOlaH3Afqh4F7nfZ50U4/3lg+D2Raaq6Mok/dlPEm1jz23FnE7s8N4HWGnz967repm9FBEARBEARBEARBEATvdS6UqbGECWF7DK+qPgtdTGBawQSkB+nzmfS+9KUkakpcLYW7HnADeCG1d5COWQpdc1gEhqIqthmOBoAsmq8Da9iK71HRAIfupzxfjyyk9TGxdWfEsagcQ8hYkrB/G7jnzt0mR4xom3FRDJMwcD9HWH91sGsaYCvWRwmA6ts2tor+I6ldX6q07wjrnxnqYmv5Oo4mY8cbGaWx0apsp/aPMiOuYNc3j5lgntpxH6e5MYPNu2Vyn+4wLBC3sXE8Ihsytfkyg91bbex+2WL86v0DzFw4wOaJogHUhvO4bkUPeWNthixKl2j+6jkyj13XTbIZ+Cp2v9dE7cP0uZ5dut91r5XPkXFtV5umYZy5JnO1yfTrkp+x5ff+3tJ1tWk2SbqYIQQ2L85rLcEcNmeWsGfwJnn+7FE3wpra2k5tfA54BuubA+rzfBH723CUzivDOgiCIAiCIAiCIAiCIDAulKlxFROBziONEZjI9AJ5Rfsqlq6mS07bs0az4FcTnZXCaBbr3F1MQPWi1AwmWj2NCVu7NBc32cNMkWlWty+l9jwip9h5CvggJnYfYEbEFzChrokO9XRT/lXpUzwyY85z1beQmCizYRG73t0Jz9XCogY+BLyIicdN/XqICedaLV8zIPy4zadj1YRs0bQKvWZm+FX3o8yT8rpnsLnVIguxxwybJuW+pxW3xzGLicNK6yXzAqzvLqefHnafDFL73ySPSw8b42XsvpHQO0lBoH7atofdA8vY/fT62S7rLa6lY+teOUjt76X2y+zzyPhTJNcyZmosklOFSTyvzc1j8n2gVHQyMmpRRaI291ruc/86Co3jqHm+mF6bzExF7dSMQor3o9rWIhsPG5xfVMMz2JjMubYekZ/rUDeFyyg2/5lPdwbWN7tFm2fTua9g83yH8zPwgyAIgiAIgiAIgiAI3i1cKFNjGYswOK+8/FcwoX8NE1EfYEKThGmtuq2lu2paRT/AxKg7wK30mVZtiyUsMkSi8zqj86pPkxKrjV0XZEOmh5k3H8NE1PX0/V1GmxoSR8t2eVF/lAA6ygw6rXguE+UAE06vkg2occdsp22VeujL2Lg3iYbHZLNE6aO8uKrfVTdlPh23rMPQmfDayjnl01CV6YpETexdxcZWqc/8sb1AXB7Hf39eHJLTRM2RU66BrVq/Rk5RpvtO6ZS20/c30+tlbE5/lZO1UdT28nogm4g3OHkvnpYONvdeTu9fxwywy+lcW8AblTaKeUy8XiSnyLqPXXOPHFG1WzmGor80N8rUdYocAet7n/5IKZJqTBp1JON2FFuMTtXl9y/NwRo1Q24G6++r2D24NkG7JuXD2HPiPmZMrZGja7xxNKmRCjan75Ij5loM1+HoYPNcUU0b2Dw/S0rEIAiCIAiCIAiCIAiCdyMXytS4iYlXSgVyVt4kr7x9k1zzQiuQfaFdjxfgasLWPmZqKM3RDUwce4AJuBKbN9Nn57kS1+dhV7s6mJHSx4TW17EIhTI1UYkXxGsmziSCnop6e/FVUSdnESAlmCoqZBJTQ/VJNoFfZjgN0KjzqNZBuapa5oDy+UuQnyFfa5OhURoLfvW7zAzc797g8NRqKBwBrwAvkdM5HRX7lMco23VexsYBdr+2sTk4R+6TLibcvooJ+bewfu5hqcHUJ9pvgInlm9QLbDdxjK14v5d+HozYdhIWsSifpzBx+i4WFbWZrqkPvEbzM2oeu9YOdj2rWB9JMH8eE+rn07Huk69X4+JrUcCwaXUVE8bXUrtkZNQiOUozZNLxH/f9adIljUp3J0NMqfOOsL5/H3atSmVVFk0/Lf838HEsok11jCBHnuieLw2l8m8D7vs+Nt/fxCKpFrE5tI7Ng4X0s4eZW2uEoREEQRAEQRAEQRAEQVDjQpkayom/Sr2477QMMIEJ6mlSSjFQq/X1XSn2+zQj28A3MHHzKlmU7pFF/k3OP7WIRD8fSdDGrvNNTEBe5+zC3yRREc9jK9lb2JitYcbANjllzlnYxcwjGF9HoIv1ewsTCtXvKgRcRlcI9Z+vv1Cmy5EI7NMqtRu2pfK+g5lrs+nzffLckmjqV7P7VeLe7PDt12p/1VxYZXRh7aZ0RdPQJIarXx4xHHHyNbeNomAup+M8jV3DKiYqtzDBd6PhHOMihm6Ti4WflVnMrFwh19DQs+EIm9+KHoKTBt4hWZjfLtquZ8c6Nl9Ve+cSNk9fYTj9nlb3XyOnNupizxY/pmWERi2N01midfR8VHqxaaLpmswMfd7FTKDr2DNkh9zHl7A50wGexUyraQzvUXP2K5xMHSg05rpPde36zkey+XMckwvId7GoOpmf89gc38XmeRgaQRAEQRAEQRAEQRAEdS6UqbGNCT4z5JW5UK91IBGpjYlFUE/lMirtE+T6DRKJu9iqWgmWtRoISpGknOsPXZs6mNDcxlZ4n3cBWEUuqF3zqd1vUl/5q/zuB8VnEtdrDIrXGhJbn0nHWsLERxVtLvctV5JPyqQFyJUiaoeTfdAkwGrMthkWMP33JZOIwX7ltiIp2uRUTTK+VAzdzxEvTJcpfPy5tRIcciqnLZrvlVr6Ign2kzCHjbHu0Rre+NF5JAgPGI4s6JMLnSuVkWrUnFf6udOyS45yUl2Ly+Q5vJvez6ZtNjGRWhxh1wq5SLqumfTdPrlezC1M7O5jRoo3NfR8WyEXn76PRWRpHDR3a/UeKD7T5+MKgXtmsGfMtfR6J/2c1TjtYPdtB+uP21iUjWqvrGMmhgylQ6yfvKlRXnfbvZ8lR2DVzGXV02lCc3IeG6cB9XRR3qCUmS1W3TZLWFTf7XTuMDWCIAiCIAiCIAiCIAjqXChT4+uY+LTO8Kp44c0FCWKL5NWwihYoUwiNQ8KlhMZl99moFfgenecQE2V9Gp7zxq/gP8aE4lIcB+ufy+n3tfR9GxNIwfpLEQrQXLOgS66VoGgAnfceWSA/TO3Y46S43SWL1aXIPg1NK6/3ySvIfXqnpvNJ2Ncq+yNy8WZ/njLFjH8dVb/CF34/IveHxPwr6VybmMCplGKQBecyUsTTxoT0Y3Jhai9Ul+aGNzkktE9jHkiQhWZTw6O0ZDIod8liNZgJN0MunH2a+aA6EpD7/DzYw1K47WL3io+COEifL2HjqJRZNWReqYaI2qd0WSpQrSiAXU5GIWhODly7VhkW6ZvuW/+9NzJlzE5ibLSxSLTnsDmr++w80JgdYv1RRrzsAt/E+leF5teLtsFJI1LPqxVsnFTrws8/GH/tA7LxUKYV1L3jzz2qP2WGySTR3D2vNHBBEARBEARBEARBEATvJi6UqfGAYfHbi5bCpwhSzvLrmOi0AnyR6etxaIWt8t1fBn4VE15rYngt2kB1GcAiN9YZXweiaSX9OHwkQFMkg1KfLLjtdrAJ0WK8oC3RfwUTs7UyfZMcGbCHiY5KLXSAXXspHsoE8abGaZhJbThMP34c/PHLiAsa3g8atvVtnCSdU80U8u2R8aCbcRcThp/CBOOH2Or7HYaNDYm+tTbcIdcgUOSQ30/tqhkjiqiYxgTYxcTlSSJntCr9EjlSquzjHU4v6sqYKU0NGB3d0nSsGbJ5sJeOsUU2ASVkt7F7SgaBzKFl7DrLaAAZr2UdFbX5GLuPZG5tM/zsmkvHnkvHWMPGvVZY3M/BMtXUDPYcUGo2n95sXB8pRVYPM9HupHZMOm5NJjDk+0L3xmVs3mySzWlFzcic9YXRdXw/rxUtt4JFM10hp3/7GtOlxNNxD7Dr/xA2b79Ic4q/8no7WN/30vbfwMZ5VKq4IAiCIAiCIAiCIAiC9zoXytS4To5u0Kpdb2J4ZERcwQomz2HC1Tc5XZFxCZYfJqdR8umjvAAugUrf9TABbQETqDfIq7BHCVc9clqVs9af8NdxiZy/XQaGak6o4HVTmh9dp+oKvI9c9HiQ2rydjr9FFpa1ynyt4bhnWd2tFE6KzPGps3QtkwiEZWSFVuC3i+1qERpl5EPtu/IY5Xut+t7A+qMLvEBOK+RX86utvt6HZ4vhm7tmZOh9aTD5VEiTorRe00R37JBrP3jDaZL0ZjVmMNH7mPr9ojlamlWjztPG7vcbabu75JX4u1gkUgsbrxew++EIE/eV/k2RKKWpofRiGr9aajO/8t+PcxczvBbTce6nn7L/vSnq54zuS0WB+JoxJaOMjYPUB0qXJENuEpoMw3IbPWtexJ5Rr5P7X5FlK+QoMN37NcMZrE+72DNZ6fF2sPpHp3nOdtNxPtKCtYGljyprv6ivIT/3Zbw/nd4/xIxyGTlBEARBEARBEARBEARBnQtlahwxLFq1itcSicAL5NRIp8nHL/FpmVx02UcbSCD0K+69KCXhcB4TR7UCuyYcl9eiY07bXmgWKJfIaW+U+gZywWylLfKUAr1WGB+Tc8ArEmOARRnIPJnDzKRVmlfyq10DhiMSRtFlOLJkN7XrZjqnROR9hkXz2nXp96bva6ZF+XlJU9qfmpirCAr9bGNi8RXsOrvkeebFaRl7ZX/5iIymNjW1cZzQDLlOgzf1aunYauj6fDohn27nNBEabcxQeBkTp1/hpLiuFfy1udB0PrVvBXsGqAD4Hew+UZ2SefezmtrQwyK7VIy+ZC59vkf9Wabfa8+tLnb/ybRb46Qg70V8XaMMjUXyw/8oXYtPaab9x5lMSp1Wq0nRRHmttWuXSTlLjtA4wPpbhoFMWNL5laKqaY77aJ1DbHyWyFE3p0lP1nHt2xpYyj1vPsrIUORFGVmlaB3Nha8zeZ2gIAiCIAiCIAiCIAiC9yoXytTQqmi/qrgUJv17rXK+RzY1ZpmeDiZatbEUJVpRq1Qnqg+glEs+EmQGW1F9PbVbolzNXCnT5YyL5KjRSm0ZUBcajzFBVqLmEcP1Jsah9uxgK8M30v7+miQ0Xk/bzZHFviYWMZF2Dhu3nRHbigVyuqlN8njfIKfjkYhYirWlmVF+XpoYtQgMim1rdS7K+am+kSGxgJkw3dTeh+R5pQLIK1jfrrpj+dX1mid6LwOkido16r0vXD0qdVm3OGd57HE0GXXjjIamMZjBVvK/TI6U2OYkSoXmozUGxY9ngKUSWiPfKz69kbY5xopzf5Vc2FzjN8BS53lupfZuYvepDInaPFU729hzSIaGak0ccLIGQ5Mx1MfE/GdT23T+bYZrr5Tzv0UW6nuYYbnDZPep2lMzG/x5FBmldFgykbY5aVJ1UjvArl/P3NKYaDIjZcDOkuuWzE1xPaKHmRo7wK9gkTyr5Ht9Nl3POiefxz3sGan9H1GvR1POtyAIgiAIgiAIgiAIgvc6F8rUKFe5jlp1rvcSvOcr+09z3l1MtAQToMpaADI3ymKwWqn7JjkvfpNYrPQ40JwKZhwyYLqYkFpLp3KMmRASr6c9j9q2kc5TGiIDcm0NyCmtmpBot0qO/oAcOVKiHPiX0qvSXu1jgvIrRXvK2itNK89Hpd5piiLworjOqeiRpqgFpdS5jEW0XCanAAObXxKOt8jmzKX0/SJ55foeuc7DDsPpvZoE3VHX2sVS/bSw+V6LdmgxHP3yOJgkNZRH7Z6l2cjS8VRrRMiM8hENngFm4L2B9bGP9hBKw+TPp34qC51L7J5lWERvEv7b5JpAV8mRXipeL5NpCXtOTZJubRZ7Ju6RTRh/vU3RE0tYP/cwoV7n6hT71yKHaveDTBRv4CoKbDb9rjofnjKCpHZP18Zfv/exPtxi2CCcFkW57KXfd8nPOm+eKbKpjNLYJ6dEbDJUainwgiAIgiAIgiAIgiAI3stcKFOjFK5qYq0XBr0YJAFp6xTn7WPCVbnyW+KYhHetklcKlAHWwTXBuUSil44jMbNPXpU8rrC42nRMFv59GqIyL/+0onS5ut0Xu9a5fSquXbfPqHb7dEQb6TNdsy+YqyiUZ7E89GIB6/dN4JewFc9C1z+JqN/0fZOxUUvNU64+h2GBuJWuS8aEBHaN8zPpWh5hab1eTfs8hUWgrJCNENV0WCcLqU1phKi8L7cTvl5NiSJDzoOyr8alDaLh/QyWGq6FzQG1r+22173aqnzX5mRf+HMcY+NRmldNbVJKosvY+D5iWLDuYyv6D7FnUq0//X3WTseZJUcyHZLvEZkmMhYUedWE6kfMYXNHURpqW6dhP/WZIiOUBmoWeI5cU0VRED5yqyyC7o+p+1zmqFLu6TlW9rfS+ZXU5ok/n54FPhpFRt4O9ciecRxiz/XSdNDYefNPf2zVLpm2u9jfpdq88s/UIAiCIAiCIAiCIAiCwLjQpkYp3JYCaZdcEFtpik6br1zCs08Fo/P2GU4f4zu1SzZUmnK2S6SrCe9Kx7KEiX7j6mtoFbdS1WgF8Vw6hwpOn0bA8+1qQqmvdL0+kmFcu8v3fsV5GxOuXwI+BlxvwcEgp47ZIqduEt5M8CbENCuyy3Q+tWMKL5SX5+iS5848FnWxSI6W0c9C+lkl112ZS9teSa8H5ILCx9j8kDjuozRKM6lsU+37QyzCp8/jj8Qo799RJmUtimIG68vL5NoVKljtt/VGRlN6pjLCSmibcfUWtBpftXOuY/NVBkBZZ2cfS5HloxP8M8W3WfUldF9pfvUZTh+nmh47jDY1jrHIk/aY7fy1aQxU8FxzTTVsvhUzSB6RI9IO3L61Z5sMoj3yPJ5Nv6s2D5wsXO/ndu2ebjIT9bsiZdRfKuR+WrNORow3SoT/2+CNGP2daNFcz0PbRJRGEARBEARBEARBEATBMBfO1CjToowSQmexVClKs9JUy2LaNvjX8rwlWslbi7KQCCZDo7aKWcKlimcvYWKpL5jrUS2Ep9J2b2Ci2jIm/HYwwfDLTCZo1miKvOhh/a08/00r30uUPscXLYcs6M+l98uYsH8MbAxMzJ7BBNovYfUMvFkjsdPXGzhNiplSCPcRK+W5JDbXUsb4WhkyOWQeHGJjupo+XyObQlew8exjhYRXyUL5zXQ8pR2qzSN/r5R90OJk28elJNO1lWLzWZl2NboiXuaxuaGoF5kLNTHYGyS17xRl1SWPzSRzxhcDb2H3gI8KkFjvz930DPH9IJNkFps/D9JxFNVwSL6PO+R+UEqyJkaNXS3qyO8Hdq2XU9uecudXCiZFYDT1neaYIrFaWPTVDeB1hlN5NVHe07W/DzU6mKm4gt1DG+QIsbPQZBrW2iKDapdsVHlU2yaiNIIgCIIgCIIgCIIgCE5yoUyNmmBVCstewJX4t4qJ+K9zeiG/pCkNUWm2KLrCC4hafVtbxVxeA+ToCzAR8YOY6LdDc+TGUjrOAVn0XcbE0R7WF6cR8iTUyoTwKWaewtInvYmt2i6vp4mFtO8jcpSAWE7H3kvH/BJWM6ONCfqHwDfIBctHCdCnMTQmOU6TUO7nxgHZ1Ngk96G+U/obibxKJzSLCfeqDfMaw4aHjgX1aJ/yXvGpi9rFdzWzo4aMmfPszyaTctT82cOuZR2bQyvkdG9qZ+1cPmKj9v08VrtiH+vrcdFRkCMOSPttkuumtMh1bnwkCWTxX+euPdskcO9j81zPE5lQLez6ZWaMuw+mRdENihqRKXeFbOBskaNRvLk2agxkQLXc8a5jESyj0PjV7kP/Wjuv7gGZv2vA58iFxs9CLYKwvKc8hwz/bfDRG+rz2nGCIAiCIAiCIAiCIAje61woU6NJxNJ3EmIl+G0BX8TEoftk0RFy2pQ2Jm7dwATlbzI+RVXN0ChX8UNdeJNA2ZR+xwt2NXFuBzMNylRLJcrr/zQm2N3FRPNbmAD6ImYOeGNDIvs440crwsEMowEmKD+HCZNbmOkyaVTMempjeb0r6Tx3MaEU7Pol+N0mp9Max1lEQZ2vPIY3ncad16dPUtSNbj5FtoAZGDfSdxKqtf8CuX9VR8GnzOm67WtzR595I2pcu2so4uA8VpCfxYSSOH2I9cMlTqargsnb2cPMtWexe+c+Nu/KCItx7dUzSOeewwyOGWyuy4w5TOdcxuaAj7xSm/tp+y5Z8IYcuaEC24qS2GKy2jvjrqPWZ0qfdEDuc38N1xk2a2vzy1+fUvP1sLmtNFC1vp7BxgTqNUNG/W3w32ss72J9vo89Sz3z2LPsHjnt20Mmf6b5e37U3Cv/PsgwmiQaMQiCIAiCIAiCIAiC4L3MhTI1YLK0T0ob08ZEROVP96vLe5iw/3T6vUuOhpjU2NC5a1EaTTRFaPhjSRCsiXvbwFfGtA1MkN3AIh8O0zH30+szWJqoRwybGl6MHYVW6s+RxcX3YavRd8ipd7TtJLQwkwmykHyErdpWfQ5vLPi0TZBXj6v4rl+tftq0U2rXtOmWxomRvg6Conm037PAr3XbHmL9qVobioZZTz/fpFnQrZ2/TS427fcbFK/j0LFrhZwnpda+piisUcxhc2eBnC5KaaQmOYY3QebIEQiQH5CjIlfGHVdjPYc9a5SW6Sid6ypmMG4W51AbDtKPUukpLds2ufaEIiSOOP1cH4fGWc8JmSoHmMF4RDZUalEGpfkr4+4qZkjNp+0Wyem/Wun9C8CHsWfW17Dn2gH1OewpU0DpGmSedDHz1EfPXAc+AHycbBLeS9vfZ/wzoBZtV4uEKtvZVEw9DI0gCIIgCIIgCIIgCIJhLpSpUaaVKYVufVeaBh1MDFaxcB1nCRPMFsli/BVyNMQk4mCZfgpOCmkeFcRtoiaGnYbb6bVDXuGtvlLaqNK4mbRosFYvz5HFxy5mkKxh0RtlseZRtDFhcQUTN/vYivM3yUZT2ac67ozbfwETOlVg+7SFfz1+5X9pBJSGVm11uG+z315FvnvpGg7S6wwmDKs4tOqpHGGC7gBbsa6fVayvaucr8W31Re/9vtOgleW+QPmklFFOtTZOukL9Kib4a+wPyOYNnKwvURon+k4F2JUqbBPr+9OiMdPxL5HvG9XZuEVOUdc0fgNyMe2bZNH/ITZ/jrD77hHnW9xdbakZYx33O+nc3qhrMoHKY3axfllM75Uu7xLZzNP43sCe2VvY2Bxx0sSpGcu1iDiwcZkHXiLXJlHBdaUO62F9O8AM8B1Gp6oq/x40mRn94nsZRb6YeBAEQRAEQRAEQRAEQVDnQpoafkVrTfz33/vV9YeY8C7xfoCt2F3ERMPbmJi1jK0EHhWt0SRe1cQ8mSm+XkLZ5iYxrouJbIokmVSol2jqz90ii+aq4TAt6lsJ2WrPavp9ExMdlRLHr+4edcwjLPKglfbZZHgldily+1cJnwtpvz1ydMVZojRUd6KWfqg0M7xYWbaxCa12V2HrFjbv9rAxv4zN1zvkeXwZW7G+TJ6rWnVeFhUuzQL9KApG86FE19xECxN7L5Ojoc5LTD+NibeB1VpRSqE3U3uup+PdIQvRKtBe3m/qk0NO3henNRYHDD9D1skmHOQojl1ydFOTMabaIcvpGIqyOnTHO+8IDT+P9Xu7+Ezv98mpqGr3aGmoqb3b5GftALvGDewZckCeo21ymqguZgwpJZ2ictS+mkmmc3fc574Q/G46Zz+1aZDaMYf19SPMUFkmm6ajGBe9p/7rkA3OMqLFb/u4om+CIAiCIAiCIAiCIAguIhfK1PArbEuR269ylaCtbfaxtCGk3yWCbWNpRbTtHiZiLWKC8SS1Gny7aqtyZ8mryG8zekWuF8Akel3CVnO3seLeqxO2SavAF9J598mFcTfJIvq0wv8xJv4dkYuFy5TYIxs385gIuEEWJ0t0bq2K9+l0fBHymtDrP/dRJ7vkMT4LMpRgunoKSnFWEzFrfXxEFjQH2PU/JJtZu2ST5iqWekrzYZMc0aFxmAQvKms/L7LOkgtO1+imdjyNjdlWw3bjGJWOp/xslLC7haVkU02RNjb3WmTTQKK5IjjU56rJoUiJGh2G++y0HJPrZkjI3sXGUO1ripyR2P4qeewP074ar0nm6aSUz6lyLGrRcIqCUxo4fyzt543fAbnI+Cw52stHX+yTzRwdQ+buESfHpTSNfVSSTErflm3gDXIaLe27RS6MrjR3u+Q6IKP6uhzHmsnSxv7OyLhUlE3Z32U/B0EQBEEQBEEQBEEQBBfM1BBeuPKi/AC7oFJI6pPTyHjx64ich17C1zYm/Psc66PaUb73AlSPLESrWLdW/fv6Hk2r68V8OtY8zbU2SnxKHV3bIXnFugZ+OR17n2xWjMNHfohShFW+fEVtKLWS0mGB9YXEWP3Uzi9hUmNdrvw+JpshvnjyWaI0KM5Xoxy/OeyadxguSj8Ob9Zp3Hx9AtJxX8DSqEkE9/VExrURstnnDR+dT/NrEZsTjzDDrzYe/pgqtnwavLhdGhylKKx53zSeqhtzhWzy3Mbmu6+vMoulMfL1IFSzpYZSt53Xavl9siiumjGQjT8faVD2yS45ikTtlWlz3iv5/Tz0URmloVGaGvvu/aioCf+ZxqDHyeduHxvD++RxfIAZHbWoNR8p5Z/1mvu4Vz0vNopt9bmidyD/bWgz2TOyPL+O4Z8VN7D52qc52qn2NyIIgiAIgiAIgiAIguC9zoU0NTxejNTq4lKM1md+OyGxTkKVLz49jnJVtTdaWphwtUIu6CzRaoacC1/7lZEnYg8T8a6QU7FMkoLqEBMCWwyvQD52rz4SBEwEXmWyKIdRhoGuYZWcIqaFCfOL5NoHysE/Do1fOY5g4ukiua6AznUeq9b7xWuNmmBZfqfvlfrIj4dqCmjlfW3/HmZmgK0qfw273ofYGB+R0/7UxkXRCRqDfYbrnoD143Vsvsp0axJSD1Mb3mA4omYUWimv8Z5JbfLmjKj1aVNbVN9hEZvHy6ldMmTKvpjFrlHt0fy8j41LF7vXtIrfRwOcFUWSaJz8s0bXMSp6Rc+3kvOM0NB5fTuU4qqp3oN/Diqllq9pMgpFPsgELCPkfGo60nZrjJ5zteeSDMPyb8OgeF8zr04b9eWP5f82dLB7fgWbd/fdORRF5M8ZhkYQBEEQBEEQBEEQBMEwF97UKCnFKzhpGEyy6lriU5PBMUrAlii8j63EXieLdj0sbU8bE7OUWgj36tunYsMyAnrp54DRKXMUnVKu/C4FyG1M2L2CCcNKg3IaOliqrZV0XJ8qS1ExWpVdE5xH4aMZZETNYKudb2L9uJ1eayvdp0UpgiY5joTRbep1SmRwPZN++ljdB7BInhZmKL3JSYG6haVSagNfxVaV17bxhk9tdfwNbHwl1JeptVRvppPasdF8ucDkq9WbWMBS76wx3kTU/aBUQGqzUmXdxKJYVjCjp2YQibvp3M+l869gc38ntUVGy70xbZmWdjpvh+G6PsLPa38efz5viPgonvNEzwtFk9TOUTOaZrD5JSNjkNo7zvRSGqYr5BRPvr5Ejxw9pG1UX2PaIu6jor1qNH0uc6fpusb9bWhj1/kKdh2aq5ewefwQe3aq3k55jCAIgiAIgiAIgiAIgvc6F8rUWEqvSh0zSbHWUoQsV+j6z8tUPcuYwDTuPDURvY8JV0pvBTklk4Q15fJXUe9auwfk1ew+TZZWQu/SjL/GpkgCpeeR0Drpqm+lxFJdgA4m0K+k4yiVlU+XpRXYo86haysFw1LUU+5+GQWr2Ar9adI+jeO8il9L4H0fZmipwLNEyz1ORt+0yXU1LmFi/Jr7rhahVEuTpHmmaA4ZFtpfc0KRHyq0ft4opQ/k9qsGw6h9ynnrH1gyHhUdsIb1016xjfpEIr2MvCWyaD6T2rdD7oOaKVoTl9WmUUaPj+ZqumZvTJVjqHPr3iifVUrD1kTTfdW0bQebez51n75rep3D5upS2lcG2RrDBknZD5r/e2m/y+l4vXSslXRcma53yLVgfFo7pbJqimQbFRVXjquPrCj7bDZd60NOUs7XpsgPpbxSei+lf1ME3QF5nL3ZFQRBEARBEARBEARBEFwwU+Nmet3DxK1S0PdiUlOqkXJ7Lxp58eoIS/tUoxSufFohX7j2qNhHxsV9hvPPe1HaX4cX4BSVMUoUrTEqnY04wFamjxNGPbOY+ChTBEyoU32CHdfOsu7JKJSaS8J0h2GR0guTEgBVf+LYbfN2UhMy9zCB9xE2/pvYdXaxebxF7q85bFX6EmaCrZMjJySoloW8fd+Wc/4YE4J3GZ3m6qjy3eNCUTtN860Unf294UVeGXPfTJ89IhuFmkv+Huqn8+6mbefSZw/JBaMnnatiARuXNZr7T2aKav7U6BTfKwLH3/e+PgjY/LmaruN1mvuzk37GRU2UzzafDmlcGiTVCVnAjMYlhtN44druI4R2XLt8LY5ZzFheTN9vpR89Y+awZ5BqDWkuPORk+qbSUCk/L9+Xfxv8M3eH0VEifr8uec75NvhINV/s/VVyEfmyvUEQBEEQBEEQBEEQBIFxoUwNFcStrfCeY1gsGhWhgfvdr8iF0cWIdZwy179ShIwq4t3CRCsvvovZEe0d1f5Ja1/4qI9axAZMn5dfQuQBuc9Lo8YzalV02Q5FfVzCROnXOJnWSREsW2m7ASYgyjjyIuKTRCvHy9os+5hgqXZrLCQ0ezF1Nv30sQiUR2STrpe+034+4qIp+qjPcBH4plXpT6K/JPaqEPO0++rV//hogj7WPzI1fKSGojrA5tOm+26THAnTZbqV8RrrcffQKHOgjZkAqjmj9u+na5NhWo5RF6uFsogZk02RW+OisGqGhq5JkQQS6JvqeqgfX03teIrh2jneiGqKXFH01zZ23XvYeCrSaC+dQ9feZtgcK02lmbRdLVVfk6FB8bn/2zCKsjC6/9tQi6ASut6yLzpkwzyMjSAIgiAIgiAIgiAIgsyFMjUekleee/Gnha3oPSALXkLica3IdClqjUrRUqYA8QJrn/G545sEsTksAmWNYdF5VFRJ2fZxlGltmvaVeHlMPZWOF9dqNT3KiJPToJRSz2FmxUb62am0W6mv9rGJfBOLcNjDBN59niwSMdXXfjX2gJOCcxnNo89UG0QCsuaVUpodY9erdDVlOiIdp/yh8vqkaWFjrPt42n19zYmaQacHWpvhCCG/fx97TniB/hib+wPq97l+r/XbDvk5cFp0b89jkQ76/Riby2ucHMcBdh8q8mfcmNba12QUaXv1h1J9tRlda+YAiyxaxcxIpeCTuaBnRC1i4ipWU+IOuY7QLnkcFcXiUzTJHPPj6OfVLPYcWceeB03RGeU88hF3/m9DUx/W6tkommxcqsSmY15K7d8es38QBEEQBEEQBEEQBMF7jQtlajSJ1CtYIWQJ3H6VvhetasJQLdVMKV5JrC6Fr1Erf8ehItcvYClU3sSKRY+qkVEaGpPmyC/bWFvVr3ofHUwA9OdUJIny1itqona9pUBYEwxHrTzuYObE89i1fZOcf76khY33dtpPufi3yKmwxqHaKTOcTFszLRKZy0gefTcpRzTXZ9B3Xex6IQv0Ol/TOcuV/m+nsTGJ8VWbO3DSnNR8arvvfISGP45evTCvz2YYFrP9sUfN2WlTBDXdN5qzqu/RwZ5t3qCBbBzKIFglR79Mg67bR2k0jc1RsU/tOPp9gBk9iqxrkQ0J37/eBO1h9WZeSOe6R44U8YYFbv8993sNpeZaSdv4dIKlsdH0t6G8rhKZPbV9vSEyDT3s+fdsOsbrmHEVBEEQBEEQBEEQBEEQGBfK1KixBHw0vX6TYeF2XEqnJnGxhk8tUotOkOA/ibDYxlKzvIwJeSpWfIiZG75ArKe2qnhUyqty33IFtqeHieT3i+MNyAJ6FxPty1RQJT4ypCmN1iIm3N0CvoAVeC6PsYH1x1bDeY6xFexKy6T0Qo84GbHTxCI2BruYmXPW4uA1MfOs5kFNUD0kpymqmRW+32fc52V7miI3HmfKm3FRTR7dWzXh2Pdxu3gthfYa3iTw275dZs8xJ2s1qA4H1O9/zbdatM4omgwNOCnEl6aw3640jSCni2oy1byhoW2OsGfACjnyZRyjrreNmaMvpfe33XnLNFG1PsVt7ynnh++/msmtqKpJI5PawAeAD5HNmC7jn7lBEARBEARBEARBEATvJS68qbGICUG7mMC9R04HMskKa0Z877frc1Jc1PGbUgA14cVun4+/y7BgWO5Tim+KnlCBXt/ecl/VeiBtq2iWNiZCXsEiR5Tuxgtwm+m96lxcxaIaRtG0yl7tmcNWIr8fM0x+LrXlatrmNmZObNQOULRtmyws1sTUUexgaXLKGhjnyVnNjVJE7WD9t0TOxd907KaV4uXcP03bFMUj4XUWM4ZGGXu6XyY9XxtLw6R6Cr74vI7XdM/UOK9IlUnSudWQ+TlpOiHd24oU83PptPOqjMpo6sMmc6N89W1oYePVxe5ffx0+usT3gd4/wKJORl2P6qSMqxGie2SA3SOPyGbrpH8X/Psmo/yYk8aG/17Po0nH2xvPSqvVZOoFQRAEQRAEQRAEQRC8V7mQpoYX4LTCd48sgJcrbX3qlpJRq3Jr5y1XLPtVx5Mwixkxi6ntD7CUMypUXEaa+Nfa7yoqvc6wKD8g58CXwKeaAcfkQtMrmJkhw2IptWutOJ4Kg/viwaP6q7wG4aMqVslRIL8WM1rm07nuY0bVODFv0hXQTRwzeVRHDQmatXE7bxaxotBKqXMMfIVsbIimlElNYzZNxJJHxa2Xybn/x/XlacZqHpubjxhOKdYUDSRq4vSoz5u+K0VtX8D5mMlrt8xh1wEn769RNEU7labCpFEmTYZGzSjS+3IeeYPAo76ax+bDgfvOR2qUhobfv4kONtd8wfDSQPNzfwdL3aRnl86n6Cb/fK3VApr0vvAmeklTYfWSDnYPLaTXXewad8n1RYIgCIIgCIIgCIIgCALjwpkaEsW1inUXMzWOyTnW4aRJUebhh2GRc0Cz6AlZfGtavTwJPawI9jImXoEJ+4qM2CdfV5OhobbJrFB7fA0BmR2XMCG8m/bZIxfX1TFmMVNhniyyLmKCYSm67mMmjI9SGSei+hXtHbKhcQC8gUV83EzHeoSlm1LxX0WUTEuL0QXPz4s2JlTPYe1U2hxFz5xncd8WNm8+jBkb+9i8L4su++1heKxwn9Vep0VFkDWnaiLzKDQvR4n7Wmk/ypgchb92b0h6ysgrf54yCsobhf0xbffHnwOuYffaPvbcmiSqaFS0E9SfP6OO2VQ/o8k4Lc/RFLHg8c8Y7eNNkGmNYH8c/zwon+nd9LNPTl+3xckx8veLTNOmPhxUfsrtZBLX7rFJaGPz4ha5OPg+9jxcJ99nQRAEQRAEQRAEQRAEgXGhTA2/QlrpOXapp/3w77VKd9L0NE2CVLky3Jsc42pb9LAUTzcx0wByRMI9TMiarxy/bLfONYP1RR8TfY+K/bpum8W03Q4m8sk8IZ1737VJqV10DBgWBX1fzpKNg1qfKQpDAr+iSg7Tfrrer2CinT7X8acVPjvpOi5hBsABlsbqceSj1/XPp3P6tDEtzl+EHJDnyyZmBt3DxrOsgVK2c9R3pzU0xBZmlkncnWa8ZOyV1wBZANf8PsDms+b/KEojo5f21byfJMJIlNvrGvX5JNc7j90HPexadD2TGoJN6cNqxobGVOnmdM3jUk7VzA0dm+K7SaLaZEwK//sow09GYQubV+V+W+R+8cfQ807H9hFcoyJzZEg0Xc8k1+qf/7X0XKP+Nihd13Xsb0MP67sN7P7ewKKyHlf0VxAEQRAEQRAEQRAEwUXkQpkaH8BWrt5jfNqompDVtLp5XJSGVlpLkC8L5oIJYzM0i9lzaT8Vwz3GVuLeJ69sVm0On5+9bIfE9MV0TKUo8Xn3B+mzfay/LmFi2SYmjPvj7qXttIJb+dsP3TXVTIs2OQrkMB13r9hOJoPy4M+SJ5zOs0VOH3YWZrHi689i5tEc1gdtLCJE4uEBJhSeVczXnFggp/LS+PvoiSYx0q9aL1EqmgHDfboBfDH97g2gkibzotaWprk2KX1Ob+BofMp704v5EpxlIExbW6CFzcFlLApol2HRv5baaZSQPYnIXdtnn2FDcRIzRNENNUoTwgvpujdXyPUk/HOldhzSfk3z0s/nUQK7j2hoMzxHJ4nQ6KV271NP6+XNCrAxvIXdL7c5Ga03jqZIpiajvKSDPVc6rr3+fvKRW7Uopjb2DN1Ir0q/t4qNm1IBjjPygiAIgiAIgiAIgiAI3ktcKFPjKifrB0xKaUQ0mRh+Za2+k+DflGpGwtUoNjFRcxXL8642+dzye2Rh0VOuxFZufpkPKmK+gAlrR2QB7SAdd4bmVepeKPTCm4Q4v4/SR81h4uMyWXi7x7CouIcJjT1s7BSZITF5AxOaJ61J0IRSM70feJocQdJK7+fSORfTub7K+ELn41AEgY57SE7FNM7QUPRMOx2jvP4B1qeHnEyFVJo/M1i/KlWXj3RRVJOP4tHxfVuWsDFawwTVJ4Eigmqfw8nogTajxXRPKUjLNDx059R9V3sAevFdRcp1/8i8mwbVRzirkeYpTQ2fHk+1J57Fnju7ZDNH+9TSTcmEPCy2Lxl3Hd7U8AL/pCaDTNJJUpkprVcLMy9PgzcxRFOaMhmY2ta/bzIHRxkSx5jxDJbaT397/N+ffS7YH+ogCIIgCIIgCIIgCILHzIXSSr5KLgYOzUJ9aVx4JhHqOpjQe0g2DVQvYY6TK5Bh2EhoOr7OMW6Vthcf/eromdSuWbKBsJvaMofV61BqIt8vZ1lN7/uljZkEylu/R07Fs05dnJfQrxXqPrJjg7MV+fbtmknHVRSG8tDvYX1DausC2Vw5K32yQbHHyeiJUuTspPPfxKJKjjHT5w7DfTfpeD0HfDCd+176kemnHP2KDlKEjhezZYa9DxPAt4DPp+OMY9Q9NgpFoXTJEUb+mGWf1dIllel9ykgPzwCbE5sM97HE81rkgb+X5zHzTlFaR5wuqqipn1QjaNQxy3HzbVZKt43UNqVfex4b19vAq24//1qivtT114yPUZTmhSJNfBqocfNlH4tem5Qj7P5RvSCoG9O476h8Psp48c/jHjZ3fbq8g/RZj3qqqTIVV9Pxjxu2O480cUEQBEEQBEEQBEEQBO8mLpSpscrJdCClwVCu8C5TSzWtWPffK8XTHCbWK0JD6ZiUEufI7XdeRaFLQdezgInPM+Tc/KUBItFt95za4+ljAuIS2VCpXXeX4ZQrMhk2Uzt3Od+aE4pY+Wo6t8ZRhZzVlg5myoyjJiJqzPvFdsfYOGhl+ShRtIeJzd+a2vEQMxJuT9AmIQNHfSsDRNEIuicUubKHCd6KwPCCs4T9Xtp+MbVHc6ukJoqrnydBETtz6fiao+VxSxOjxjiRV20akGvxlKZjHxPR/fl86rBZrNbBU2l/1TE5TySUQ3NKsZq5UKab0ny4hqXpexozN3ax58aWO0ZTFJGijfbdcbWdolU050ZRmhe6/x6HMC8zo2xTaZA1mUJ+vMvtyr81itRQyjk/Xh2sj8rUYj4dVxAEQRAEQRAEQRAEQXA+XChToxSGxgmA3qRoMjRqYlOXLIJKCO1gK7afwsRNifKPU6wqU5r0yFEHMwxf1zG51sh5F6n27DO6yLHy+S+l9zsM10KYRBSdliPMMCnrnfhURhK3JWQ3oWgcpXLScZbSebzYf5jOe0A9XU/5fga4gRlTl9pw2J8uisanFjrGTL5fcef398Mj4JW0j0RqrZz3ArNqjKyn69O1esOqlq7Ii+ujCiF7Bth8UMqtQ+rFsGvnq0UvlceupZaTcbNNPX1cKTirUPUsZsC8hI2ZanDcxgyC87rvJZKvpOOuMb4vfX/IhNC1bZPr99zGonT8dddSIXkT0Btzfvx7ZHNk1P1TS+E0iaHRc+eflrK/vOFcUv4NaLvfSzOzFuWhvw2+iHkPmyPLWDq9cp6dx/PuvJ+ZQRAEQRAEQRAEQRAEF5kLZWpMS9MKZ/9aIvFXq/y17yXgBSylj2pINB2ng6X/kQg+qSA1SvhTpMMb5DRLErlVIFwphh7XquhJ2jnARE+1q4uJoV1MED2NaDmOWq2JcuW1hM51RqeCUSSE0nd1yHVOauOolESTXJfMlwfAdt/mkC/gLHOqCUW8SFQ+ol4bpI/Nj93UfgnVpWgrA0Ppj1QvQnVTaqbGJIZD03xXOrfymsYVQS4Ni1okTe0ztUV1ZibhCmZkLJPrNVwF5juw0IeNgfXr1ohjTEonHX8FMycmSfEkfKSBr73yEDO65rH5tUEW4Gt1f0oRvzZ2undkGtYiGMrjDYqfUdcxi/WBjLrHJeCX/VszxsY929QX3uy+gc2ZWcxM1HO4xiI2r3aYPJquKbImCIIgCIIgCIIgCILgvcq71tSoiW2TCv0HmOikFe4Scvcx0c0f07+KDmZ+dLB89pMKoF6wLcXiPrnYOGTxHbeNFxFPm4fdi/cyTZoKjDddwy5ZeO+m/a+67x6HsVEiI6JMbTQuUkTFoIWE3poh06I55VSNQ6xewNfSvpvpXAuuvTs0F6THtUVtG3c+fyxvnvSwOfoCJsrukVNPHZHHvWZm1EwNRbS0yVFOZfRKFzMHB+R6KrVi0k1RGLXr9ffIONF6HG2sT54np7WTYTDfglsz8PwhvEZO/XaW++wSZp5sY2aEatRMQ5lm6RD4Jtkgq0VONEVrjDJDd8gRWtOYtJNEaDyN3QPrDJtp50nt74E3yiYZQ0VVlWnLjrF7Z4fhyI/aMeextHAPga9PeF4dLwiCIAiCIAiCIAiCIDDedaaGUuyc1tAAE2R3GF41v42Jhcot73Oxl0KfiglfmrLtkNvvRTcVdO4xnE6oJtqWq+ZPc34wcfEZbCX5ARYhst6wTwsT6+bJ6Zh0HK2SP3Cvj5NW5aeM2hgndmvcvcFTCq2z5AiHUSaEp4/14a+SV88rKsiP9STHOQ2+HxRNdBObp9tYuiIvLHvxu+y7WrooGRvz5HtAkRjPYwK+Cm7LGJhN20jQr42X2l6b0zIeSMdSm9tuW5+qbZShpn1fIddLmMfqyFw5gsstSy+klfhXsciOB+QC9ZOgCI1u2neL0xUfb5rHTdE+Tf3rIxCaGDDZPJ/2uaP5Msv4gtrnyTT3m0fRT2pnH4vO2CcbuGW0Srm/0vGFUREEQRAEQRAEQRAEQXA63nWmBtTT0EzLIScLyqrA9TyWLuVBw7H7WH2L+9QLLo+jPKaE7xuYCHiP5rz2pzUzam3oYuLrPrayuEm47WKC9Tz1IuDHad8nRblqvxTnJb6PEmmbBOMWJmRfT7/fw9L8TLrS+zz7YQmbEwfYXBxV60ConfuYQL+ImVdr2Birrsa4WhejBHUfxdDFaogsp3auu3P42isPGU7H4yOOau3X576Oyg65SL1/sHmTU4aNTEkvast0ekCeG4qI6gK9QY7g6pILcv8qZkxMIsi3MDPpOmYAPppwv9pxoDn9ndKO1Qpol/vVDI0O2SRSXZxJUCq8SY2+Iyz6bZ7TRapMgzcP4fSmr46jYylSo4vN6QVsbGvn2Ae+QbNBHARBEARBEARBEARBEIznPWFqnOU4pQh2TF6B3pR+CoZTGJ31vGAi4To2YIeVbc5yzbU0U0oRdN+da46cT35Q7L9LLgB9npzWpCmjNWrHVJok/7k3QHxfKGpDEQ6L6fNanYJRlMXLzyLiHpGLREvIVhvHrbofYHP0btr2ESf7uRadUZoc/piinJ9rmHCtAtvzmMmxjM2pfXI0he+Pcux1//Ubvlc6MDiZ1kq/6/M5ch/448l08sfdL94rukApqvaZ3JhQHx5i/XHa8ffzp8nY6Lpz+bl6wHBqsbIPZZYtp+9exebHJG2aw8Zyk8n6RHVhVMfmcfM4oyP0t8FH8ZU01cEJgiAIgiAIgiAIgiAIJuddaWo8bg4ZTgFVE4PnqRdGnpRatMmD9Hu5En2UuTIJqoPgV1gPMBOljUVrPA08hYnAq5iJoTQ8+zweM0Pt8J9Ne401QV4rrVUEvHbM8rxKx9QlGxx7nBS8xzFDjmpRBMxp58geZkoMsMihlXSsNibO79Msmiud0P3Uhk5q1y4no5Qofofmugz+Vf1UCtyKApD5ougemWq1tGG1NG8+AmMeWyHfIdcFGTWumgN+/Jvmgvbz0R2HwFew1FQPGvZpOo6vwTDJ3GmKWPERJ55jcpqky9i4qh6ET6tXO3cbS6t1K/2u40xy7w2Kn0nQPJymNs07hZqxrDoyffeZR+kLz2J6B0EQBEEQBEEQBEEQvNd515sao9K0nOZY4+oxtDBx9RpmCjyONCNeNCyv7zTXqfQzpQAu80Yrt2cwIb2LieGqC/E48Kv2e6kNA0wgP02hcYmyPhqj7EMYXrXf4mRUgPpqAxvbacXJLtaXS+kYG9SLeU+6gv8YMwiWsdoYm+mYSpvUVMNE4rrSoy1gaaJ2MZG+dv4ycqMmtpfn0GubLI6DRW9IlJf5V0ZlNKWe8u3pYdf+DCbGH2PpfR4wLN7XjjXA+kj1DdS+Uqj280ZtPsbMIF3HNBxikQ9nqS/TZ7gGhVJ9HbvPZG5cI6fWGmc+ynB5QC4erwibSdhjuvkrnrShcda/C01zv0XzM7GLGY8LWPTLRTNxgiAIgiAIgiAIgiAI3ilcWFNDgmYXE93K7ySiKgJBhapPI7bV0hf5YuGeJSxffpfzMTRqK9clqtYGb1KhrOeOI4Gb4jxgIqWPJtjDIjU2yX2pFe7nZRxp1f0+NobPY8LsfeBrnM7UaKJse/nemyEyNFaxax8VCaE5WAqcA6wPN9PPfrG9BPZp5qnSLm1gYvlp0hrtYfVBfBFkHXtc1Ibvr9KY0PsOdm8oNdE2zfPFH8sbKUqfpGs7Sp8tYnVOtAJ+XJRGeS1t90qxTRklonojvlD0KHqpfQfYNc+QIyDOer+of+bJKdEeMpwqah+7b6Y55xo5uqYpvVUTT6rQ9yja5AL0ZSSVzB9Fu02bPmwc+tsAw8+pGXI01RGnT6sXBEEQBEEQBEEQBEEQXFBTQ0Le09jq7Fcw4c5/v5B+5slip1K+TCOK11JMSeQtaxfIaOmQxeXT4kV2ia5Hle9rgvIkYlmPyQoJ7wPfJAu+MkIWsb5XGp9jTOw/qzjYIYt/e+mzK5jIeo+zrW6vmVP+tRb9Uo69aoqMQgLmHDkaQxwAt9NrKRjLNJlW7NzH0lDByYiHSdFKfl2vanTU8Eab3te2qb1KUJ4Ufx/McNLwOcDmne7FTUy8L8fIj21panhDo2biaH9Favh6HW1sfpYRN2Dj/zLwHGYUfI6camua9Ey1yBifPu0ZLDWc0pmV+9fqhIzCR3s0cZ4RcOdJB4tYehrrm2+Si3aDzSEV857H2q+Iq/MoVK45UeIjp+6fw3mCIAiCIAiCIAiCIAjey1xIU6OLpZy5huV+f8SwqSGxWJEac2QxVfnbp6EpfY2ERQmtg9SWdYaLbp8FXcMMttJboqra0NTecefedr+X6XZKJNYqddJ1rP9bmBi4gRk4Sn9zFpQOSNEMa8Ab5HQ704rB5euk+8GwoTHNWM5gK+fnsT5R2ixoNkXOEuky4GxmT3msps9H9UdpspVmm1Ih+QgrvW5j4zyqTSpsXTvvJtko8LUMynldE5u9gVEaGn5/b2jonjxM13AdG9P1Yvunge8Anm7BnYGZWd8sjjeOWtSK2tbF5tlN7Bn3SwwL+OUxzotaqqt3CvOYyXQD6+O7DPeJok9UH0c1cmSKTXMfjXvGzJCfnQepLaPSUwVBEARBEARBEARBEASTcSFNDQmjfczMuFfZZgsTURewtDe99NlpClo3pSOqoZz7Je3UDl/HYBI65KLjviZBrY3+dVx6k1Eir1+Jr75exATUq+n3Hazv1zADYiZtp5Qvp+UAy+d/xPDK99NM1HJ1vo82KbeppSYqv/f7ydCqmVcSuFVkeY7p6hJ4fPq0J4VPd9YU3eIpDYx+8bm+28P64wUsuuASdu9+jfEi/4CTkRCKYvKmTs2QaPq8NeKndgy10acK28OMikO3zTxmdDyVPtsY2PjPY9esKJ3yev31eOF7VNospZXaTG2pRRedJ3NYaqctTorzM2Qh/+0U7nvkeiBlzZs+ZsLupu9XsPZucXJ+TYrmeBlRU86fpvndw/p1mzA8giAIgiAIgiAIgiAIJuFCmhpHmCj1iLwyV/nivch8TBb7emmb80gvMk68qvE0tnp4bYp2SKztMpwqapSxMY2YWdZPEDp+DxP9fI2QO5j4J2HQ71MzSaalXznuHGak7GIi5bhr1EpsGTcdcroZiZpewGw6ngTspvRKTRE/qruh/PqK5pkGRSNdSm29zbBZcJ4oNY7SGWkFfi0NVy3tkJ93fU6aGZ799LONGRpr6VxdpjfDNLaH5LEqGXVv1syMsq5Gaez4eaXP/Xydw8asjc3VL5KLoe9j95T62M+fDhZ5tpT2e+C+b0pBJQPxQdr/Jnler1HvT2+G6BjTRJUp4qZ8bnSwqLkl4E3qESNPgn1y2jqwe14F1X3h+ANynSX9PTkNo6I11NejnvWzwLPY8+k1zqcOUxAEQRAEQRAEQRAEwbudC2lq9MnFbK9hotA8ttr2PifFvENGr8JtY2LjqBXGXvQrRay2+77NSbF7JrVTKVsmTT0D2ZiR4DwJk0Zr1IpYe7F2Fltt/hwmAL6O5eyv9VG5uvw86ZNXt08SgaJ9SNsvYnNkDhMv99JPWReiPPZZV7wruuA0K8AXsDmjXP+L5Lmzd8pj1miTjSuZPg85Oc6j0k+Vhoaf2wvpvYTmI2we3SWL4x1OZzb2yfdFGU1VtrEWddFEGa1RXusoI3OfnILuUdrukOG0W7UC8vPYvbaEmT2rDccv+/8Quzfej0VRPcSegxLty/m7gEVcHZPrC/n+a4o0E03zroM9L+DJRhuU43OM/Q2YwUye57C5t0auO+MZF0HVI0dKNZmbmgtlakCfnlCfl30zi5lgft8gCIIgCIIgCIIgCIJgNBfS1PDpZiRKLWLi/2nE0Q7WEZPW2qjlt5/BBMNZTopnfXKKrPUpzuPPV4peTSvma+1roumYpM8vYULrDeyaDsjCei1NzuNAq9EP3PtRzGHjuY0JyDK9nkrH2CGvktdKf3+upgiEJ80eNmdUR+USJnwfczIVVW0uTIpMnxuYgLuPRbKUKXVqx/fzrYzOmMEiTZ7C+vpLbj9Fa4jTziEJ8DUjsbw/a9/VojRqhkUtUqMpMslHBJSr/1WLg0p7B9jza4vhKI1yGy/iqz7DEflB/og8fkvYXNl328+SUx3pelpkQ8qnHpvmWdonmyRnST83KTJR+ul83ljbIacGu0JzHRsYf8+MSvlXHqMcG/0+i91jB5yMYNknR7+9XdEtQRAEQRAEQRAEQRAEF40LaWp4dsn1C+5SF6+62Er0q5jod5dhIVWpncbl9K99JpFzCXgftgp6i+G6Gd7UOGv6q1J0rQnxevUCZdOxasf2dTTaWJ/dxgS4Nmbe9MgmwVmvaRImFex75HRki8CL2NiDjYkKB0vEhWYxsuncummmNaemQUaOzr2JzWOlMhJzqT17p2xPPx1bEU7b6dWn6vGof/zn/cpni8BL2FjcPkW7JkUmm9C8Lw07idNNhl9ZR6MWxaPr1D3vDbFJ29pk4OxgESx9t43MUp8eqtb2A+DL5Ho9C5iZtEKOUtD9qwilQ4bHy0ePlZFok6B5NO45eh60McOsR56vtfZsY8+ue9gzucY8Vt9lHuujhwxfs449LjqsZv5pDl3BIka2sPox3pTcx+69t9NADYIgCIIgCIIgCIIguGhceFNjHxPT9qmLuh3gw8Cvx8SlLwH/N7aaWUwbcVCulJag18bE3BVOFgOX0Ncmp3/ZZnoBsGkVeS0t0GnpYG1sYyLcA3KRda0217W/04S4LWxSD8hm1TYm7N7hZK0Cnw5swMkbwqflEU8qvY7v26bV5hJOm+bROGF6BltJfh+7J1RLQ4J9Ke578bVWcN2/38BE4ns8PryI7k08/6polBnsPi1F57Z77z+viftKHaX9murSTItSlYkudg+2MNO2lgpMHGPpqo7JKeOexYT/AfmeeDPt5yM3/DHLQuje+BuHIiR0rMeF0qUtMjplkyK8tjEjp3Z/LAK/Cfggds3/H/A5hmtyTPp8LlOd+VRem1j/r5DNYI/OMY+NeVmvKAiCIAiCIAiCIAiCIBjmwpsaYEJ1Ex1sJe5LMzDXg/19+OpxXtk+jangU/L4YtQS0N/EVlvX8uH30j7zWEqkppz5p6UmxE5KuWJbwuernBQEjzHRrcs7c3WxakKAiYer5DHxkQ8qjK05oM99gWwxLnLjPPFieZ/x0RejxM82Nt8OaK6FIAPjEjbWuzSv1K8ZZ01zYBt4hVzEu4kZhouTn4WasOzNCKVdOuSkGN6UrqpMPeVFf8h1cvw2tXRYk6DIjHnMgAUbE0VrwPC9XbvPB9gYPmS4Fk+P/JzqMGxa+X312Wnm+Gnvi9qzqulY85g5oKgTPa9q2+8yOvXUEvAh4H2LcNiH9+3aM+8B09es8eeXUar5twd8A5t7pdmtMe9jNUDaRLHwIAiCIAiCIAiCIAiCcbwrTI1y5atnEVux3E6K/+WWCYavc3oRtbZSHeqFaCFHi2hF9V2G0zYpsmBUeySWNqXOGWCrtPcYTl8zLo2MF6r1c8hosfy8IzT8NUnkHieGj0KCs9J+qY6GvlPKmTbwGnmFOTxZA6PGMvACNm9XMWPgtDUKFKUyChk/XfKYl6mOmoyNUabWpCaF0medVz2BMkoDcrqlNfJYz3IylVur2Kd235Sfz2B9d4jd5yvp2OuM73uP5uV8er+JifHqR/W1L0Ttr3eWbFg8Suf217JNHt8uJ1NnnSbl1HkxyoAt23KI3Q96lcnUtG+t/oxSh10hFW5vQ+vYxu4yZgj5dFyT0hTFdIRFidVYIhczV0SZ2nyaiJkgCIIgCIIgCIIgCIL3AhfO1OhiIlGTyFMrfL0KrB7C0aGJS2Uh5PMQjLTiXQIq5ALkS1ieezDRSmIc2ArqS+T6FE1RFlpx3XTePYYjEfz3k1yfL5rtVynrs1JYO6+8+XNY38iwUQTIQ6YX8zvpeDfS768zLJarHsjldI5tTgrv086HNlnYbkqBNiktbC48x3BKocdNWbh7XKTPOENjGrYY3edlIetJaEovpSgNzbNaTYxaerdx/dF1v2teTbvSH2ysd8nGoq+v4dtWRlToGnvkvvLjecSwcaeUeR2G+7Q0eLw5+Ljw5xFNxmIXM/36mGnkr1FjOc4Y1usMNk6rwOwuPDqyKA09g88TGdI+3V2bfD1Labs1hg2NecysUgH2IAiCIAiCIAiCIAiCwLhwpoZW2daiBXxufAl021gR3U1M+N8g1w7wq3HPkjplFhOgZGqspfdXMcFKq6c3yCuw/f76qdUx8K+l+NdO515Ox37ASQFy1HVp5beKgmu1twrXzqRrmMcExA2GxdHzYA7L/6/UQBvUzYZRaAyuAc+n10NOCp+Qoxc0H04jPosZrN9uYmPwBmcrij3A5snnMXHzETYeb8fqebWnXME/raExSc2JpmNpNX0PMz+2yUZhU52E2jF65Htz3DlrBkZTxIaPmtB1ShSfVoTuY2Pu6zHU2tbUZrXJH0/vffopGSUH6bNa+qZahM7joinyrNamReAWds8+LPbRGEB9vuk6W26bB8BngS8d2TNbz2dvFJ3F0FG9mjlySr9DzLhcwQzWfmpHLbJHbS7NpyAIgiAIgiAIgiAIgvc6F87UUF2KQ4ZTi5RpY7yAdR8TjnyBX4pt4HTC0RwmtC1jK2930s9zmFjfTed+FRPMSsFNq6hlMJTXASejTyS6zadzXMPqeYyqLTKKbjqWiv16kXQlnaONpUL6Bue7clvGwjwmKt5L76eNllhOP3upnYvAdUwQ3CbXldglr4av4VeFjzM8NGZaTX0exX23GI7YWSanMzptGqpJ8cZav/j8tJEZZ5krLXIkkyJ4elh/rNLcH2XqKaUh6qUfbdOUZqtV+X3U9vpMInhZbHsUqqfQn2BbyM+wcUZXGXWxgD2r9rG5pGg3GRvlvo9TRK/1d3lNtfMfY+33abhEH+vLBex+L+dvLbXYMWZ4g/WHf/4qsmLaKCF/zmXMFL6UPtOz7UXsmd3HjND7DKcjJJ1vn2ze16KKgiAIgiAIgiAIgiAI3qtcSFNjr/isNDJKEcvXZ2hXvofhQryTMg88hYnni+nYW+n9pXTMR1jdhg2aV19LBG0S39Ru3OfiCDMz7jUcf5RAqePskUVbL8xr5TmYqNzj/NnDohs2yaLftKifZRodYyv8P4RFbjzAxuARk0eATLLdMSYQq47JWaI+PH1MgH4JEz8fpXM8blNDxbTL9EVnYdz9pCghGU8eRQ09Ikc/+KiDUUiI3iXP72XMoDvE5lp5z3uxvXw+6PtRabJ8Sjt/nFFt7WBCfAd7dkw6h/oMi/C1lE3eKFC/7ZKfE5qzsxOe8zwoI85qzz19Xor8u9i4zXJybCD3XWk2lcf1/VKmXVNNH2+uTFvXQiarzO5ZbG7PkVMR7mDRJq9jc7927HI+BUEQBEEQBEEQBEEQBMaFMzWgLjjXDA0vNIpB8Z0EK58mZxLxagkTSOfT9huYKHkPE7BuAF9L7yWqerqYyDWb2iBhfBIjQ+xjESC7DIuhOu4xJgT3GR394NPm+G2Uu/5Nct2Aq9gqeUYcb1qU9uUsq/oPGBbFDzCxcAUzmTQGjyY8z6Rt6TM6QuM0qXx85EkHu5ZFTta+OG+OGU6BM5POr1X9o1bRn4YOdp/4lEDlvX3I8NzeIs/rcZTzWavhfeHtMr2Wb9sCOWJHdTLanLx+f4zyfWlylCgd0dPYffawYbumc9WiSzz6zNfRmMEMjiWsHw9pNvHOMyrLGxq+H0ujA4afzeIIe1bMUTd/FJHjz6fX2t+G2hwqTWXIBtIkxsYMZkRex/r4GEtt9QgzV59Ov79BNln9MVVrYy7tL4Mx0k8FQRAEQRAEQRAEQRBkLqSpUaMUrLwgJRGwS16Jq+gICValiDYquuESWRCUSN7GRCvl/Vf0hBe8tRJ+GRNz51J7NrEC5l6wrkVtCC/Wlmm4Oun4yuN+RK4hURPxSoG04/Yjtf818krvXmr7PrmA7VkFt0lTG01iELSwPm1hq/z3yOOxzuMtelxjkusqxds+Jm4fpO8OMJOqR70Y/HmiY8+QTZU18nw4z1XjKoyta5w0/dLWGc63mc5VMzY8Mlxupu++gZkOkzwfSrG+X/lOSLTuk2vjTButMQkD7Pn3Evb8uo71wZfIzy2Zqmrr47pXfC0S9ZFPw0Xxvd9vWpG/jMzzhvaR28bXXdHfDJ/mSr+PiuDqYn07iz13HqXP+tjfgn1s/m1iz6Zy3zlsXK6QDY2H1GslBUEQBEEQBEEQBEEQvJe5UKZGLfJCn8OwoaGc6RKhZjGBUiaCj55QCigdZ9SKXL96fZ9cU2ExHVfi4OtkIZq0zyK2incJE7/m0nG0El+rqXUttbQpaq8Xn71w2icX9FbEhgrWDjgpypWiYQcT2NR/KmCsVC36ftpCyGelhfVRi5M58z3d9LOHCearWFtr1y3zY9oaHmdB46j298j96Q2LQ0wUFUoD9iTa2SEXXd/GTA1xnlEaM+QC8bVopvOgjd1ri+kca+T+b4rSIH22BLzUgvbA3u9h95Y3REp8uid/L3vK/ZSKSPUXHo04di1Sw4vvtfPIpDzABPNb6TpeJ9fXUM2ReWw+7qdtypRgtTbVrqmp7bX9ulhf7zK6Too3GGopppqeCd7MEP4ZfxkzExQJosgVb3r79IVN59HzZN9tO58+f5g+v4uNsVKoyVC5jI3NAtlM3CXXQilTLgZBEARBEARBEARBELyXuVCmxhVyHYMyZYcXqSGnmZGYPYOJZxJTlW7Kp3ryBX6b0pP4grRtTIi8nLZ9w20nER6yyDVDLgyuc++m97UULKUQWH4ucU1FlGXOHKTjbmNmxgI5b/4BzdEVEkzVZ749+lxmTpMY/DjRGJfCYinw7mPtlECrCJQ+eUwvYampWpjQvc3jN2pa2FjMkM0vrdD2BdprTFoL5DxYwArd38JE2PPO6T+PRUFcxsZStVDOmy6W7ufDWB9/EbtHjxl+8PloLbDr7WBC+5Uu7B/Ydz2G5/yAZnPDH6t8XzM5trA5uYAJ6+PmYnn/6dlVO7c4wsT1XeCbWC0bX4diFngfJvDvAF9N2zTNyw42lrPp2HsM1y8qKU1nFb+uFegu8c/n0tCYI5vKpSmiZ3mH4eebj4aZST96TmgulpEr/nhNUW+KZpvHjMF5bDz9s1R/N1T4XG3Tc1Xmpp5Jvn+CIAiCIAiCIAiCIAiCC2ZqSIxUpIT/fBYTBcFEoTJFyQE55YdWBkv8r0VHjEovo9X125j418PSi2wW2+q4Eqb20jY6jyII+pgop7oVvh1lqhZ/bAmDPfeZjBylctl1v0uQazJLdJ6mXPNezPWvTwKJfEpT49H1zGDjqjQxK+m9BEyl+Rpgc+UquVD1XSavt9FED5uHMq1KlGbHi+GqpTJuRfyTRIbZDrkORYmPapoG1Qt5DhN87/B4DI0WNv7fAnwAex7IdPT3A+4zCeoyv9aBe0c2b15L75uiNGqRHiWj7hdF5jSZGeU922SalM8Jfab7Xs8smQ++TYfYvfAS9txYI98vJYqAWSHfcxuYabJZ2acWdeYj6nz0VXmdZUSNv2YfpacIE7/vgGx6KFXfMcPPs910nZCj7hQZ4Q3ecTU1WuQi5pvYPN/G5p1PQ6h2ySRuYX33CHs+6PNjcoTfpGnGgiAIgiAIgiAIgiAI3gtcKFNjnZy2Q8xiopVW2UIWgFawlc9rmMikNEO3MOFuH1ux7PPJjzI1elgKK6WxOsbSGz1K5/CC2lE6pzq4hQlqO9Q5IkchiA653sBxOr9PwSWzxAv9Ph2NBDm1q7w+L+w1CYnlfn4bz5OI2lDBc53Pt6WFrfz/IDbmO2SjSymOXsNWxO+ln6O07VL6fO2M7VvC5tyb1IV61YOQcK7PnmQUxjhktj0iG3GlcaHaMDJlphl3ibkSku8yutD6WVBB7A2sLsEBNkc09r7dGhPNqx3ga8DDvs2dO2TjzEdFQfP1KzWdVuSPQiaKUGQRjL43/T3aFD2l46jez4DcN0fFdofYta9h/dZ0bRpHPTvnqad5E7X0Uz6d1rH7vIzEKN/r/DJEdsnPQr/dHLmOkq77MH12JW13N12DTJEbWHTPA7LR4Wt9NKW5WgSewe7/g3TcN9L5lAJP7KRtuuTn9D7Nxc9lbgRBEARBEARBEARBEATGhTI1JMRJ/FnBVtu/SRbjIIuJT2GpVL5JFpPBBKUOJkJtkwv1epHN/w65yO5z5JQ5Mhz2OSlaSkDzq/PLYwpFfnhKIXOOnPKqn65HhaTLYrajhMjSxCiFRtV3qK34bjJ8JolwOU+aokz6WPsXyCvvteJ6ExON97F+O8T6VMW3tzl72xWJM6rQ8zvJwKihYseqBbCM9aciSZQuR6nVDpkubdeAHPmwNuW+0yAR+3XgFSySStEFs5hgr2guRTGUhuAaJk7LeFINEJ/+bFQqKv3eYTitURM188gfB+r3bdmO8nN9tkl+RsoQ8Cymbe9jQvzDyvH8cbew+0bPOEUYNEVpNKXY8783RZrUUmt5o6Hr3rex1E+qZbGNjTfYOFwGnsXu/TWyMadIt8vYOK8zfC/7FFj+OX4J+ChmlBym/RS5Urs/jsjPKl1Xh5PPDf0diyiNIAiCIAiCIAiCIAiCYS6UqfGd2AraL5OLq6qGhBcEe5gQq2KrLUyclYg3wITOawwL+LX86V74OyKbJ1rZv01zoelS5JRI5cVUCaV+Ja5vSwczZlY4WVD6mJMFifVTtn0SWpiYrRXYNYFR1+GPPWpl9VmoGSVNxlAfiy74PCbOSkQ+xPp4FxN11Xet9NkjTPgctSpdaNW3xq/cficd80kZO4/jPKqncZl8vcvYvLsPfIMsGnc4XS2SPU7es+fNMfasUBqgFjYvFMWzRTYdYfje6btXCdAwHFGg97Uf3D775OL15b5N5hwN301iVo5C0V01ulg/zWBj0+JkDZGm8/paNTX8c9XX//Gfl5EmOm55bX47PTNvYbVAvozNLaWb8mm21PcyPQfYvbqCzWGxiUXoLLvzaI748fP9outXIfk1suEz6hmsvwGQ63r4Ghoyu2uRckEQBEEQBEEQBEEQBO9lLpSpocLPKnqtVEISlxS10CMLro/IkRneONjiZL0ACVi13OmHmKB7h7z6dlSqFaFjlUJVrbZFk4FwA3ghfb+GRaYccDIVF+5cl8krpyW4TYKiVsp0XGUbayvPaznvT4MXmnUcRV40CXzaZjX9qCC8ctbXBOnd9DMuhZJSW72PvPp7l5wyxhsZpQCraBAJ+U1RLtMK/OdhaNT6UfeIhP0F8lzqY5EPMtZO027xOA0NyAL+MbaS/hmysaDvvIBcWw0vkw9ycWhFgEwSoYE7n48G8duNuv/LqKry2P78k6JnwxI2f1XLYhF7rup4S8D7sT5b52xmnd+vRzYbRwn1qjMzYPjZ7U3bBSw64gZ2n82SU/ZtYs94XzulTY7oeER+1qkmkdr6ALvHy9obwhvJYH34K+k4++R7pYY3bQ7JKQU76XqUZrA0VIIgCIIgCIIgCIIgCILMhTI1XscEawnYSqWiWhQdshDkxaXaqmgJ9zrWJCvfVRNhFF5084Knj/6QgNVnWFD3K5U981gdj13gK1jqnkNyXngvOOr8c1i/dNO2StFVQ+f1hcX9501mi++7Msql6Vok3DXVGPAr5v1nOp8/T9mv3rTwwmLTuE5iZsxhq7ZfxMTTGSxyZj39rGHia7lSXQaWogMWsVXcPs3VbDr2LCayjptbHm/8qKbKqLRXng55Nbs38Qbp8zVM1J3F0rspuuhB2uYAuE2+zybFt/VxpeFS9BNkUXybfI9sY/0swdpTE6/nsT4h7atjlqaG/728nyGbmaoBoXRevg5QzeTwxyvNwiZDQ1EMSjFVRrE9i0Xa3CcbtEqRp6izubTddeyZ8w1OP2ZexFfavtr11vbRM2aG/FzoYuNyNbXvGPvbsEtO4+QjM2R0aP8dTka4leeuRd9507vkgDymTfi/DXrels/tWex5oTR5ZUH7IAiCIAiCIAiCIAiC4IKZGkonI0FSgroELy9cSeStie16X1sJ7alFKcyTc8+vM1pIrp1Tr4oiqImWfp9jTHxvpddvYMbOU1ged0WQbLpjtcjpdZaxdELQbGyMWi3eZGjIENI2TddaRi50sf5bq7TFj2GtDWVfjRq3SaJFxplYc1g/P4WN+w7ZJDrARNQdmsVe1WJQ9FAvba/zrmBGyQE55/+kaC4uYyLoFnZ/jDM2upgYfIW8Ml8i8B65PoxSbD0gi/GqRaIol0naqLQ6qklxiVx34DyNjRmsH5axfj3CaulsY31+h5xaSeMi4R9OCuz+2TJLjgrTM0U/A7c97r2fW4uYKTmXzv8Au4fHRat4U3GayBbdRzJ4vHl3jBlWa9gY7LjrPCTXyNB1KuXeeUTWaK7pOVAzL/W6RE4bp7mjZ85C+n6ObFI9YNjc9eNXRuMcc9KM9W2chHI/GdVz2L2jKBG/fc2wLY/VwebbgfvuPKKygiAIgiAIgiAIgiAI3k1cKFNji7wivhSIylfhxcpRK4NrtMkRDz1MCH4WEyj7wC9i+ddFU5RBuaL6iOEiyRoEb0pov8O07RvkdClggvZLwHwbdvsn87drNXgvbfscebW9L+ZbE9jGGQYdTtYiUXt9ofJaqpwdcjoZCfAdhtNLlW3RvgO3vf+s7N+m36dFkTn3yNElilpQgfmmeioSbnWdu5wUOlVIeIfxq7xrbZvB0iotpzbqeLW2kNpzBVul3yYbX1oxrvo0fp7peKV4Pw4J0zdS+5TuZw4T2ScpnD0pLeyefA4zTTrYPSPR+5hcvFlzFIZXwJfRFaVhsUGODPOFv2sr6cv5KDNHpoYiFmQ2TPJMKk3YUfNahq4Efc8hVvxc2wnNHdVR0T12QDZMp8FHY2huiQE59Zd/XvhnyAr2rF3DooKU7g2yAbNGNhd9hFmtPyfp4/JvRe0Y/hnUI9dEupnau4IZVv8fNuea0ByT6alxknG2z3D0YRgbQRAEQRAEQRAEQRAEmQtlaiiPP4wXA2ui/aTClsSxDrbKeim9vgg8T46EWBxzLIlRfYbFTwnHA0wk9xEmpQmyh0VYdNPv2+Q0JT1gcWApWO5hIpr2m8fE6yWywLlCLpLs03L5Vy/6+u/K66qlYfHG0qBhO63S95+pADoMi8olWnm+kvb3wnCNaYXAUjw8wMT4TXLBaInZTal/hMyk/bR/bbW7xNjTso0J1BLJaymw/GuXLPSvMbyyXeKsVuYPGBZU/dyYJN2Vajdcw/phDxN7H5FrvZwXA+z617H+UHopXwC6nO+iZoZ6c1EGTLnqHoajlfx9U5pqWrkPw6mR1C7V6Sjvt6Zn3STzWoZojZox1Sf312miQ0rmMSNHUT4PGa7ro+Lavs6R5pz2WcXGdDvtq+gKpRZUX/qoiyZT1J+jNC5q/e1f/b7aXxFnquvxYew5fJDa2mM0pVmrvlbkl57xeoZOY8YHQRAEQRAEQRAEQRC827lQpoZPGTNK8JskpUhNuNJqfB8N4XPTyzRYw3K43x7T3lo7lMZHJkcp6vl2yfzYJq/ePUj77abzHwxMPHw2HVOGwWJq/yHwiju3v76yH/S7F9FK00Ni50J6v0cWaWsRKorsgOEUTH6fVrF9U4qWLpYK6lswwe+XgS9R5zQrm2v7qKC08KvBR63qlnCvMXscHGIRPIq+8ecpo5i0GnwfG7NyFfmAnCpqluFoHn9MyILtqILIx1jNhg2ysD9pzY/TsAl8lZNRAZ6acaDPafhM5mMZaQW5T5WuSavuKdqwjz0zlCrJR5wpdV5575dIuBePc/X+OMNuEtrYvSpTtYM9r8p7qUWeTyravYjNzzfIfTpHNhSVJs23cVLhv/Z3o8m88PscMmz2qUaKxl4GzG3sb8PDyrmbjHidT/VNYLg4+nmMRxAEQRAEQRAEQRAEwbuJC2VqwElhqCZ+++1qq7P9sXyKmXI1/S7ZeFDdg21MQJ0mDQ/kVcl+xfEsOVe8RPsWw2K4zrPpjtnHRLPX03sZBD7VzAa2an1cip+aoOtXj+s7349919Y9t33NxOlipouuVeK2ilV3ySvta+aIR6bGR7uw0obBvqX/Ou2qf59GaJpUSDXDB04aZKUQ/TjwRb5ryFRS0Xhdq2+70qvpeBp7H93AiHM0oVRL5b46t09PNAq1Eepmi5hkDGtRGk3fqQ9qxdC7mLEnw7HpOKT97zNcGBrs+q9h/bRWaZM3ESGbTkKfH9H8PFLkg48+UzRO0/Y98tgoKkJMGjGyiT0/VUtEtSbuF/vNA0+n77fINSVk/MhsG2B93XSdZfq6cX8HRs1pH5kE2aD077fJETz7ZFPD18wpKdvmI35kdutzb+JoDIMgCIIgCIIgCIIgCALjwpkatRz2npoQDydFLX0mM6NMCyN2ybURFDUxiQjrf/crccsoCeVQl0jaIeePH3WefSyVj1bV38OEUV8LYRqhvrZa2fdzKWYqbZIMhXJcdM1KE9PBjBaJpCtYPYg2JnTKtBk3tn3geADbByYkjtp+HEofs8lwqqJR1FLclG0ktXOHs+OjWGrzoYuJ0ANO1jnxtSNkQmm1+Yz7zqdN6rvPSlEdt53Sa+1Qv7fUtuvpO6WE8tfj0weNMjc6WF2OLib87nAy4miS6CxPKXD7fiuP6dPH+f7Zp26o1fqidi/OYKmLNqiv7BcyGhbIEQIyP1Wfp2a8qC1LZENrl7qhoT6+kdqkufAKdn+Wx9TrqHF7A7vHn0ltuI1FFfmIlF3M9PwINq4yPjfTdR0yeaRT7W/DuIic8jv9rvtJ0RPlPaD7W9EUSpFVo1X8+M/9uTx6bh5hz6lxqQ6DIAiCIAiCIAiCIAjeS1w4UwOaV9eWRoaE3X6x36D4TsLqbPr+gJPCmwo+T7pa3YtrOodvW3nuY7JA7GsddBhOaeMF4B3MzFAx33IVe01cnSZtTS1divb1BaZ13PKa1OZ1cnSLttcqfn/dpSlSnvsoHeuVIxMQP8fZUjtJhJw00qOWFqv8HoaNjdPSIouZKuq9WWyj/tYK9tq4+jb59GMS5mvjWztWeW1tt50KS5fC+hXgo5gQfwczoQ7J/ah5Cyfv0bJNLXKNBl3zajqnUhj10u8yH2vUnhOToIgX9Zna2y5+nwal6BplfqmGzCXMRFrAVvH3yWnE1ovt/Xw+Ipsn65gRWpvvK8DLaTvVqyGdr1Y/B04aQeU2h+QC63pelCaDxrELfKBl5/r64GSEiJ+rmqO19sBk41qL1PBzG7KpofRXMhqEnl0tRqdWK9tVXscRJ9PFHZH/Zvj7JAiCIAiCIAiCIAiCILhgpobSovg89F7o96vTW5ggN4OJeT7ffRsTCS+nY26l/a+l7b6GiXEyCfZpFvY8pUBWrvSGLAB74U2Cna81oWuYYVjQk9DmC2WX6VEGZAG2tkJ8lLHhxbWaSFgTt2VI9DABzoumC+lni+GVzNtYAfSW+7xc8V+28QhLqQW5MPRZ8OmRTkPZV2Vk0FmOewl4AVs9vw18s7Kdn1dlm8rV9DKaJKB2Ktv645bz05tUSo2mvpMAW7KA3WOz7ty+SP1iOoaifpqMDUVKaU4vAs8Br2HzQdeoOUg65qj7tcmwG7eKvzQrW9TNjEnMQxXDHrVdB4tyuJJeVSvnERbJsM7wHC6v+RLwYtr/ayPOdUguHv8Ie/61aa7TAvW+Ktt+F5svenYvp2Nq/sik2gSupBM8PLbIFW8WlecuzSQ9V3vk+aX7Q2PUdtuq3tB2cexeauOl9NkaZmisYG16NbVdKaP2qEczjcMbwBqTrvsO16Y+OT1ZEARBEARBEARBEARBcMFMjSvklcMSQzfJwpsEz0VM4FrERCutZJegdQX4APB82kb5zLXKuYMJgA/JYu20olWT4CfxVaLnjPtc55G46s0bHc8XFlfqKr+/aCriXKbcGfV97b2nJp7Pk0XuDpZ65mUs7cxXyCmyjrGxKydgU5v1o4gPHzUziwmCe0xfX+Os5kP5e1PExKTMYvPzKiauDjAjYNpUVqUI7OuVlFEaTSvJ9Z2P3vAiLMV2NVR/5Sp2j+2SzTtFVvjaGqWx1cWMEbD0RZpXi+k46hfN9w7Nc7uJSaM2yn7RZz6NkN920jaodkKNY8xgmMVMDZlKe5hh8Ijhe8b/PoMZGh8gG59Nz7EN7P6cw+4tifXeCB5n+PhrVvTCA8x4mcPm87PpWHvYfJjH6uTsA1/r22ffIBtdMuBGRSHNY6bDLNkk3cGe+74g+xz5b0MHe5Yo0qKVvr8KfAi4lY4rc6yL9XULq2WkyIly3Keh7M+yJoh+xqVcDIIgCIIgCIIgCIIgeK9xoUyNTUyU6pJNDV9Me4AJpc9iAtchJkBJ+D/GLvgmtgr+aXLh3DeAr2M55V/ExPdRee4nQQLVjHu/QBb1YVhk9AKpr3VQbidjQyIzlW3HieuTflcKlX6bUoArC4Z3MEH6RcxA6mCC5QOGzZhJBGVdv0R11QkAW1XtC+0+bqZNXTQpc5gAfZOcmucelrppkmsrTYlyfCDX3yhXvXsk1Pp0OzXxto2NwyVM8FXReH9uifFHmEisNG6qB+HrbOiYqtehnwE2ZzZdG9Yq1w+TmY9eJC77YlIBuRaRUxoZ46J2ZrHn2RIn00gJ1czYSd9fxeqUyBwaF42yCXw1HePemO1lFNaiUWoRPWXfeXPHpyeToTKDXfM82TwZYM/ZN8kmxBZ53snw8uf0zyOZPPtYP8qA098GcYQ9e59P7++m83mzr4f9TVCEVCu19xtY/z2N3Z++BtBZkMkug69LNmG8yQfDZnYQBEEQBEEQBEEQBMF7nQtlauxg4tcCJnqpmLZWOiu6QYV0/UpdsYgZFyo6rBQkdzAD5Aj4MLby16dvmiT9VImEQF8XYyUd54Hbrkwb1bT6W/gaG49DXBdNK9PL7/T9Ebm2B5h42SYXA76Kid+bnEz71HStEimFDCEJgkfk4sfTRmmchmkNjWnSUamgdx8Teu+l12lSZI1rm59b/j3FZz59TxNdLA3UFSwiZxUTfAfYPP9I+n4ufS5jwhtUMCyiz2OmzpX0ewszHH2dmfNimggNf4/WhPzytfzOn1NIyF7E5rJqT1Dso/oZKkx9QC68PopjLG3ZGzRHbnla5PnnPyvbU1LOpXIfzaVNbBx7mFmg61Cdl0FxPP0+7h46wuae/gYogs3vJ7Osl9rgDTjIKQlX3DH2sQiWN9PxF7EUhYvktGHe0B1H2X79behg98gzmIFSS8N23nM/CIIgCIIgCIIgCILgInOhTA3IBWQlWMnA6KTfJVop3/kCw7UctjDhaBOLIpDorjQuWikMtlpXAvoCtlr3tCt0B5iIt00uSF6KeHrV6uSaUNZybfLblwLnWUWwprRTNSHYi7neWNgAXsFWlj9Nrg+ibSYRSmvXofGVGPqkqAnUTYKrUvd4wXPcmOxj/dUl10s5T9QWRSN4g6M2Fopiamp3FxOClzFR9n3Y2LQx4/AGMN+Bg2ObCw/I5kR5bhmSMr+UXuoBZog8CcNqFLV+qhkaoun+8d/tkiNWfP0HH9nlx+wAewatkWs6jGOcMeVRqi8ZDWrrqPvUmxFldBkM3x/TpIfzpu4ocd9vt0lOVSXTU/vMYde3kdrRxp7DSl+muhVfwYzERXfMjbTNXnqvVFqk3zvYHB0VTdU0H3wqxb1i+zAygiAIgiAIgiAIgiAI6lw4U6MUQn3amhksBckKWfSTOCrBaA8Tjl/HRC0VMdb3O9jK5i1yYVvVBOiT63P49sB4AUrRGooqaBLrJi1GPk5sPA9BbJSI30S5qn0bS5uzhImFD7G+1upkn4rLn2eUqXFMzrn/TqSFifOXsOvfJc9VFRmucczjLwjsixN78dy/+vurqdh8D1u1fhUzLy6nzxQRsI+Zh3vHdk/dJUdplNEinj2sD97E5o1PEXSeTBql4dvoa38I33f6blSkVWlUHJKNUpmzfjt95k3DSdLLnYaO+5k0Gqk2jrVUVZMeq0uukaM0VKOeeeX5/fzybZFhs0SO8JLhrb7UfFMb5sjP6xnys3+LXLvpEjbvfW0nMUl0n8b3iFwcvezPMDeCIAiCIAiCIAiCIAiGuVCmhl8JXFshrfRHqrfQwoSrK9hqWxV91eroPU6KYtvufQsTZHvkKItSYFJ7vPDqVxirbR1MAFPKFb+S3+8Hw8JWkzg4SjhsEow9XWzw98ZsOyoyYxIkoh9jxdfvpc/n0o9WSnvhsiYaK8rjkCcbnVEywMayR714sqIgrmLmmupJ6EZ7RE6PM67vp2EaIVnza4aT0Sa1leRa+a79OljdmpfItWtUx2Adi664g5mDe2QTpZPOWQrOvg8OsDkiQXuUAeKZZBtPzVAs70VFS/kIinGGRXm8pm393OkX29fa6Nv0uETuA7I4782ZJmqpl5r28c+7WvtbmEl2CzMfjrAaR2Wapxpl0fvyHC1sLj0i/12YIUfiKUpikLY7xJ5LpSmx7n7XXL+EpUnb5yQyqEY9r1pkc307nVsG+ChjNwiCIAiCIAiCIAiC4L3MhTM1hBcOvdCnvP1acSzRCYaLipfHqb0fkIvQKuVRuX1TlEEZbTCDiXU76UdCMYwXKmvfldev35tEVE8XExA7wO0Jzj1JtEZTpIlqnLSxlc0r5PRhWtU/TrTrYcaU0sdsVM7zpFDkzgLDKZxg2GTrpc8ukYvb75HTo+1jK7M1F86LJvHXj88RWTSVWQQn57KEaK3c7xf7PMIinsDGp4+NzTZmSqwzPE6zDD9walEOikYoCyM31RWROafV7tOk7GoyDvvFj2+vr3lT3ntqhxfHYbQgP649T1LQPmY4Em2Useqvvylao7bvKFPjOlZPRdF18yO2H9W+0qgbYM9+1cZQwfJZ7Jmse7Nk1DNGRu0+zdFMk9bCmSMb54rk8n8bgiAIgiAIgiAIgiAIgmEulKmhVfCiFLUk2Gr1v1aRS0AvmWZ192lz+ksYk+B6TC5sDidNkBYmci2m77cYLdTWIhv8danwry/K7NOqnKb4ucZAAm6TOdEmRyvMAh/ETIFVTBDXivCmKI0+NkGvYGJnD4uAuI3lsH+SxsYiJrIqfY0EbC9Yq1+6WB8vYNe9kl4fkfuplY53hPWDUp1dxsZ7l+Gi2qMYJ5qrT/14K/WR0rXNYCbEESfrNZSmWR9LJ/UmWbhVnwiZir79TfO01vau2/+Yk/dqD+uvS6kNG4yuaQA2BgvkKJAais6oPS+U+kkr6X2/X8KiDObTMe5i85RK20cxTqh/3CL3aeabxsn3Sa39NVPYn/d1zCRQdJMi4/ScGRfx4M9Vfq75vk2ukTTrjluL7pikr3Vdp0HPAs3bsh5J+SwMgiAIgiAIgiAIgiAIjAtlasDJnOmjhCetPNZ2A0wMVT2HRUxgP4/6DF7sV5osv9pcK3GVikgCYC1t1WXgQ2nb17BUPpMKZ6UxcEyu7aDV5F2sbsFphTJflF055puKms+m7d/A+lppvGTwjLuGDiYWL2JGxkx6v8dwOpjHRRsTqpexOTOLXXeXnMrGz0eltbmJ9ftuavv1Rbi/nQXMLawfeumYl4CPAh9I59sA/i8sBU/Tyvba75AFcD8mtYLRupeuYHNuHuvXvdQ2b+SVxtM+w0XEy+Lv5fnLtpcr+WvGhl+trkgMsP68ionfHSw6q2xvyTxmqvWxPq2ZGqMMDaF+VCot1fK5Bnw4te0hw6abr4nhz9VkRA0avu8X309qcKhI9tYU+0BOtzWqP/y8byoSDsO1XJrYIqd9gmwAd6kbVrVIt1F/F/x3++Q5oPklY24pbXtnTHunQX8P/N8IyHNjP712i7aqfWFqBEEQBEEQBEEQBEEQZC6cqQHjV9T6HOvHxefXgPdhovMR8FnObmqoy6HltAABAABJREFUxoJSDkm0lYg3i4mqKhg9m1618rsUO3fIq5QnTWHSRJ9spkCuJzKNSOYFVom4EuNqdSX8fjvAq9iq/jVyLQqNSynU1tq/jUUtvI714/PkPmxKe0Plu2npYgbFAjnNkaI1FsgGmTe0ZjDB/eYMzLTg/qF9vzgHrRZsb9mx7mBC5mzadwkzFm504foS7OzC6p4ZNw9P0fZJVpDLfNvHjAwvtHpDrtbP41IC1b7X3OuR59S4CBOlfFI7ZMRIAB+k38dFUh1i88/PvZpxMKl5eEyO/FjCUictps9fw6I0ZH4oWsobS6NSJvnPSqY1NEhtu4wZi5NG/yxhz8hdbP6N6jOZpbNpuwNOjm3b/cwwnLapjEjwfVRGDXlkMO5UjqP9y880l8r7ows8hf1tWMFMqQec/fmrfvF/aP11K43dfmqb3isNldoaBEEQBEEQBEEQBEEQZC6UqeFFzVHpWJoiObRaWFEaO5zM3S9Uc2CcoCTRtU0W7I8wMWw/na/Mr+/TFB1zUjjbw4wAyIJd07kHld9LvGlzGqOkJmIeMXx9+rybzqEaIguYQLiRPvf1EmoCeNnnh5hILHPmGBOMlVbMb9u0UnxaEbhDTpklQ0PmjQTsZczg6ADt1rDBM9eCXgdmOrZdB+jMwPwAbnWgc2ztlyireX0IvHoIDx/Zte4CN8g5/329g1pxZm+kjUPztZvacYRF0qyTI5rKe+gs4qpWqku09mNVRne03DY+XZfuNdW4KWs/jOIIE6lheL56pjH6OliEywo2R1T0/g7wDXJkwWVszuzSHNEkJk13NOl46H5Un1/F+nSD0ebNIvAcVgxekTsb7pg6d9l/3nwqU7Lp/pDAv5C23yMbXT7lVNP16Ti+H2Y4Oe9HPQ9rxqfGZYFsWJbPE3/+ScwvmYMyNvT8O8DmoAwbb4brGTJDTgVXFiwPgiAIgiAIgiAIgiB4r3OhTA0f1eBFq1Gr9Sm228LE2wG2cruWwkhphSYR/2USSHxqk8VnL7pK1FV++Jb7rEzd0yaLfU11ApQX3u+zM2Gbz4LE99KMaZHTAl0lpzR6CPwqWQRsGrdRK9B9TZEjrDaF2iJqAqRvm847SgjWKnJFYsySRX8JtnOYUfFUCy7PQrcLnY5FYXSSItlp22ezc/C+Wfu93YH1ddtu8QB6u7B3DLMtOOzbNR6m67tHFpNlnOyT60GUNVYkFrewOTAqaqFMg6NV9btYNI1WjK+4bUohfRpzo0WuhaJopQOywVEaJ21OGpcSsb3JMW07IPdb7XnRJFbr8zKiahGb494QuofV0lDKuw52D1wl1/Eo04BNaxhNuv0cNpf72D34iHqKqBpdbB7cxsZO4rsEd29oqB3H6fg9dxz1mU/Dpeel7qf5tM9K+u5VLJqkiStYtJ2ej3332mR2l2YHle+O0nllwMo49SjKblT0iEe1hvSjKBZvwByS70nNcch/K/xzMwiC9x7f8z3fw9/9u3/37W5GEARBEARBEATBO44LZWqMEvRKEatMf6Lv97E0LHcxAbiWeuqI0St9a+c+xoRLrTJv2q40ZbRC+YhsijRFcHhamMh9yW17NGaf88LXUhC6FtUWeIa8ol51I2rFp8vfJ6EmPE9Ck7khsXI+/aheyCy5HkGHHLkxA1yahZs3YGERZmdhZiaZF+10njb0enD5EnR7sLVl71eW4bgP1zbg4AD6fdjZgd0jODiGnSNYT43TPNwnpzdbwERzrfwfkIuR9zlpeJT3gS9yfYzdA6pJscewAVf20yTpfXwtGdWPuYJFtyyl8+2n/Q7S94fp8zI1UxkV4KOcauK1zjuqVkvZ/lFzp4PNh1msv3eKfZUmbhfru/30o+toY/NnDrtGRdz49ox7lk3znW+br9VwXHw37hi6pj5m3pR1jHxbfBqnHczEuYZd7yZ5Dou+26eFPb+eST99zGiWqVG2dRH4CBZFcoAZEKvk+dN0XeUcrhXkluH9Dfd7+RyXITNt6j6ZwLXx1jXWopcUraE0ZkEQvHv4xCc+wW/8jb9x7HZ/9I/+Uf7En/gTDAYDvvCFL/C3/tbfegKtC4IgCIIgCIIgeOdzoUyNZXJkxTSGg9+2T06xM0oEO02qnab9vJDuxdhWsY1Q2pFxbVAO9kfYauz9U7R5GsaJwNupHXfJovU2JnT2sRXQh5wcDxjd5zJ5moqR19Jj1drZJCAvYKaAUuOomPp8+k5C6DVs1f0c0OvC8jJcuwaLixaVMZNMDVr22m7DwoKZGdvbtv2gDweH9v7oEPb3YXPTXvcP7PcHG9A7tvYoMmOPXDC5hxkE8+Sohxtpm02Go3vKgvW6Zs3BQ0wY3nX9s5SuVbVY9hg9F9Xf6jMVkN9Mx99O7ZPB4dNlzWIRDm8wnGqqfPWGlD+v3ivd2Qx2f097L5TzpYvNiUWyUSlTQ+mOHpEjl/Y4OT91TBW194ZHzTAa1ceal/tjttOxZNbWhPlxeGN0mxwJNEuOOijFem3rI268gTsqldkS+dmuaBLNJ2/ual62sHl21V3jDs2ROKKWOsv3zz4250eZFpPWXClpmhtqT9PfB9+PQRC8O/jO7/xOPvOZz/Dxj398ou3/1J/6UwB84xvf4Pu///vf+vwHf/AH2draeixtDIIgCIIgCIIgeKdzoUyNm+QV0TVamODcp1n80zbjhNrzQgKdrxWgz9WeHsOCnCIhmlZVd7GV709h4t4bjC923pRG5yyUud73sRRGjzAxeJGcSmuXLOqWYt04QbfJ0PB4QbBc6U/xnU9Vs0ROI6RoCBUFV6SGPrsJPDUHM21YWoLLy3DzJly6BDNdSzslM6PVtlob7bZFcczNWpRGvw97e2Zg9I/t9+1tODyEvX149Ah6d2FuA5ZSofAthiMC9shRDgepndfJ6XO0wtuvAhe+DzR+Ght9v4yZELvYyvltt78XXxVVpEiWecxYOMTmgFaZKzpDc11RLypCrYiTcsya3vu57AVgid5zqR+8iVBjQI5ckWivOjvXyFFQj8j3pwp/K9JFbVDxc7+SX0Xut7F+POCkqD+pgaq0aDISxnFeNRhUu+YacIv8rNokm5f+nFvYuGpe1Mwpf+072PNrj5w6bZ5c80VzeRvrvy+TzTHV5VjCDKiyPZ5W2kZpwGrbKeXTQcP350lp9OinTKnX4fzGMgiCt5+XX36Z/+a/+W/44Ac/OPW+73//+3n/+9//1vuPfexjHB0d8cUvfpF//9//98+xlUEQBEEQBEEQBO98LpSpIfFw1CrscStbtcrXr2Z+UpTCla8V4GnKDS8uA+/DxGcJxuX+Op9Qfn+Jh9O2uxb54MVloaLom2RzoEyB40W8sp01mkS9UdEY3tjwq54lSO9j80CGhtJPyYjpkyMilDppeRbe91yKMJiF+Xn7mUsTqjOTjQ2ZGrTMyJjpWl2NwcBMjoWBRW3s7Zkpcpx+n0t1OpbW4NEaXN6EtUOLctnAxq6HzV0VIZYJoVz/vlZFU/+W5o626WIr4K8Dr6fzKO+/P55+FtL5Fd3SxYTjLfKYy9hQrY8jzMz4JmYYKN1XjTIyw796JHw/g90bi5iRMCqCaUAe40uY6K02K2pnkzxXJDDXipzrGBvpnDInH5DNJ18PhOL3ccjMPY3YrjZOWuC6ZID17SJmbKxh41vbDnI6PV+7pYxO85FCb2ImlNLvqZbNIvbMmsWMj1XglbSN6vfMket5jLu+cSmcVD/nmNF1ac4DzSP/96BV+W5UZFwQBBeHmZkZXnvtNXq9HlevXh2/wwR8x3d8BwDf/u3fzm//7b+dP/tn/yx/5s/8mXM5dhAEQRAEQRAEwTudC2VqbHCyZoBHRsW4Vf9lvnQJSI9jdW65+hZyvQIvPper6/VamgZgfbCWttkuvtOKdX0nkW+GnB9/XB+VNKV4gixEliLgABNymwqdn3X1cSlwewOojwmUXkRWJMFS+u6QLJ7OYeL8Ejnd0HH6/FYLPnILbly3dFGXL+dUUysrVlOj04HtVFClncyMVvq9lUwNpaWambGfdgsGrWyK9I/NKOm07fiXUiTIo4dwaQMebcDicY7cUHTEPlmQv0+OFKiZGk0mGWTBfo4s4K+TDbDaeC0Az2OGyiomTitdleaDIqPA5sIhNv9ewdJOSdgmXVOJb3Ot/RKyB9g98U3gRazuwgKjI7tkalxL72W+zZEjrC4BWhf7iByBUrajTzaY9smpmrbJZsS0Zl6tvdMiMw9yejClzJqm/s4huQj6GnmujaM0MvTq71XNlxlyDZsB1m/r2Bj0yUW2cftcIffzuKic9TFt1vh7Y0TP5cdFGenSdr+XUUlBEFw8lpeXmZ2d5Vd+5Ve4fv06rdZ0VuX9+/fH7tftdrl58yY//uM/ztraGn/jb/wNAO7du3emtgdBEARBEARBELyTaQ0GgwuTsnuu1Tr3uhES0o55fJEbvq6BCvhKNPVRFUrXo/cyPmqC1jy2mh5MHFa/tNPnWs2/mj7vYeLzNrmmyDTtrxkbXoCr1QDRSm19f5749pQrnrX6XilrBtg4r2ArvxVZsM9w2qRFslnQBZZa8MIV+P99HJ56CtYeWTTFbIqmWFiApUtmcDxag71d6A+GDY12G46PzaxotWEx1digZa8zaWK8tco91dnY2ra0VBvrsL5haanWHsHaLqwdWzTAerrG2dT2rwFfZFjEbxW/1wRmyOOn1D7zmHBdRvVIkO5hEREfSdv+MvAlhudAC5tzV1L75lLfPgC+znDKKxXkLs0Y/fioD80nX99BzGDRBCuY+fOQ0YJ3D5sPSuu0kva/nL7X3DrC+vsuuYi2b6euoYXNuatpn7vk+7o0NZ4kHcwcWCIbCdtF22B0mjo9x2qmjsfXpakZGTD8fPORXC1yxI/GVymhSnNBz+5Bup7zfsbMk4u7N5mzZ0XPSP+De22R5+b+xflT/Y5kWjE5CM7C1atX+eAHP8iP/uiP8j3f8z3Vbb7yla/w8OHDkcf57b/9t/O3//bfpt1uc+3atYnTVvX7fT7xiU+wubnJ5z//+anbHwTBu4cL9F/9dyyt1oVaBxoEQRAE58Jg8LhzWJydC/UX+nEUwu5h4tUxwyuBzxMJoIrQ0Gdl+imfc35UpAaY0HUvfeenmQpyr5DNCEUqSGA+op4+ZhTlankvD/n2L5CFRv2+gQnZ5yUMloZGi2EhdQEb031yZM9RakcbE3Z7qW2zmIC94tq9CNxcgKtLcOOmRU30+/ZzcGCmw9Ex7O9ZSqn5Oftue9teWz5So20ppxjYPivLVo+j28tFxFvY+/k5M0s6HUtXNTdn2ywu2j5LS3BpDeYfQTtdmATdN7H5UK5EH/XfmHLFPGn/+w37ynQ4JgvND9K+9znJPJYOSsWdZU6UxcsVrTFDNv3G/fer1bCdjIRVsvEx6li6BlK7PohFn8xghoiijRQhc+i29cf2hcQVFeBNybfL0FD9kkvkovd9bM4vkIucb5PrTixg/Vc+I5oMVo9SzpXUolNqfaIIr6a6Fzq2UlxtN2x3VpTqUM+EpvaclVokn08NB8N/N4IgeGczOzvLJz/5Sf65f+6f4/f+3t/buN3nPvc5/uP/+D/m//1//9+xx/yNv/E3AlZg3B/zk5/8JHNzc9V92u02/+Sf/BNeeeUV/tgf+2N87nOf45d+6ZemvJogCIIgCIIgCIJ3LhcqUuNxrLScwUQ8iZKPM82HxFwf+dC0Mr38OU7basV7i1yUt4bSCGkF/mXgpfTZGvAVLJXOpO2m0t6yFsg88GHgBYYLGr+BRRFsjDm+7/sZ8iplTxltoJXlilxZwvpki5NGymWsuLpMjXlM7L0KXG/ByryZDIuz8OzTZmjMzVlkxt4urD6EtTVLNfVwHR7uwU4fjlMtje4xXGlDNxkZqqmhqI1Hu3BpEW6uZKOiO2MGxvJlSzc127P3LewY+wdwsA/bO7CxAetrcPce3L0Pd3fNTFhLP29g5oZWu5epyco+9H05zn/tkEVxmX+KaDmkblhdBT5GjoRQXYk1cj0NPXw0hmUKKr+iXxET4ojzNSFnU3tvpva+gdXU2MHm0xG5oLwMlfLHGx26b8taGk8KGXhXsHbLiOqTDaoDzNSYxa77CmZofAG79mlQtIpSNvnIAxg2Q9U306TB6mL38D7Tm7KnoYfNeZmjj2v81E9lBJ9/1vaB3Yvzp/odSURqBI+bVqvFT/7kT/KDP/iDI7f77Gc/y6c+9Sn+yT/5J2c636c+9SmuX7/Oj/zIj4zd9h/9o3/EP/7H/5g//+f/PG+++eaZzhsEwcXiAv1X/x1LRGoEQRAE70UiUuMCoKgFLzpqtXgtpdJZ8AZFmdJJQrRyupfREJBz9s9jQuuotilH/RVyId0dzOxYwcS6SU2NUdfj29hJx1cO/C3XzibxeQYTVFUbQaloVDBYonCJzutXwqvg9yonc/4vYYaGCg+rnVeBGx24dcnqZly9Apcuw5UrsLRokRb9Yxj0LY3U/Xuwug4PDs00eQg8OrR2vwB8vA3tWYviOB7Y5zMtMzje6EP7ADY3YSVFYLRalpJq77qltlpctDobs7MWsdGdsddOxwyXXo9chPwetJKrdZyu57LrCxlfNXrYfFN/K91PbZw0Rh1M3NWqdUU31VD9BtVb2cZMLQnS5fz1hoAXvv02EsG1TW2F+yT3q6JEjt17FV1/jRx9skFO0VQ7fu18PnLj7U45pSgutUNjLWNqgBkXu1iEyq8FLrXgs4PpzCKfUk/nrf1ejutpCnJPWwvkLMi88/NSz+3z/NOueexrO+l3yGZREATvbH7mZ36GT37yk43fr6+v87t+1+/izTff5HOf+9yZz/eZz3yGbrfLz//8z/Obf/Nv5g/9oT/UuO0nPvEJPvGJT/AzP/MzYWoEQRAEQRAEQfCu4D0fqeHpYYL/EiZo3WN8yiSlPipXkTfhc6Z7sa+NRWGo0LIXQr3YvJDOpzzvEttqIq9qRShVyzyWCmgbW4Vei4KoTYZadERtm8uYOHoJW42v1D07mMlQRpW0sL5eTtcFw8WYy9RbTSuYB2QDZ5YsnkOumXE1tW+e3C9XSIbGMty6CTdvwNVrqRh4L4m/B2ZQbO/A7Tfhl34Fbh/YNW1iBspmav9HW/BtT8GlS7C5Yfvc2YLdvvXDRmr7deBGDxZ6sLULC3NmplxehuWUnmpx0dJRzc3lNFbHR7C7Z3U2HjyAu3fh9j2L2LiLFd/+GjY3VOx6m3qKnsXUD4qemU1jVaZ465BTFbWwtEpNK/jb5NXtMlguY/PsNjaue9TTGLVSm30UBAybAqMKz09KBzO3uuRIHgnVZRRVH5uzuk96ZHNHaP7517777jyjNPScmDY6RTVmjsjmqWpjbGF98AzwXcCHWvAq8PcH1j+jTIQWdg9dTefYwsbY18fwPz4q54iTtUkmYdLUZOf5R01p9Vawe2Ydu+/Htb2NPZfK4uOjtq/V1OhiffzqxflT/Y4kIjWCx8n/8r/8L/xL/9K/RKfTOfHdYDBgMBjw8ssv89WvfvWxnH9paYmnn36aP/fn/hy/43f8jsb5/uqrr/Lrft2vY21t7bG0IwiCdx4X6L/671giUiMIgiB4LxKRGu9gZEao6G0HE/behwlzDxlvUigd1DIm0K0xXuhSrvRSrFc9iC45RYxfqUt6r9z35Sr3GrvpR6KchO5DTl7bPCZ8ekPAU66gL40FMLH7tXQN6+RV9WXdD9FO7XqaLDS/hhkGvmh6DYl+EoyPMONEAuo8w0JkF7v+BWy8rgLX23BrBW7dgps34ZoMjVmLzNjesaiKrU1L/fTwEewd5ALjl93rrwE+/GKK7mjBXg82H8Hdvplje9gYL6Z2z3WtjsbBNmxum7mxvA4byxYpcvmymSMLC2Zu9FJaqvm51PdpAPoDu/DjXROV38ReVUB8lmyUeXbIxZVVwLs2j9qpD5fJtQ52OCnSzqR+XsQMDaVJ62OGyxtMPp5Nr4qoOEvKKclNMigk9Jfn1zyadd8r7VvT/eY/fxxRGmXh6kmPe4A9zyDXilBUmI6zCvwi0BrY91fI0R2KXCnpYffuc+n7N8j3vTdBS4NqHhuHpvRoo5jkms/a34o80TNrHng/dh+8gd1f457zMnyWsL6fJJWXnpUt9+r/NgRB8M5jZmaGbrfLiy++eMLQODw85PDwkO/+7u/ml3/5l9nZ2Wk4ytnZ2triK1/5Cv/6v/6v0+l0+NKXvsTVq1dpt9tDdTdeeOEFXnvtNZ566imOjo7Y338c1eqCIAiCIAiCIAgeP+9ZU0M5/Pcw4ehSer2DmRobTCZcLWEr7x9ggt4kSHRTmiWZBAcMp4vxgqDE3EkE1Ro9THxXXQ9/bK0GXkqvD8mpfnRsX0PAp2Hx4u0BJmBLIPfic60vtRL+iCw2e4HZU676hryy3gu8PmpjCTMxVAdhnmxo3OjAratmaNxIhsbyZejNWi2LvX0zMu7fh0cP4dEarG9kU6mDCb7Kt78JPNqC7T043If1bfjSPnw+9Ynm2HJ6vXlsqajamEi60YeNTVjfgcsbVpz88iUzN5aSuXH5skVuzEldd33Tvw87uxaJs5mOKfOlnEvqJ42P6p/UVuQrdZkMCqUYO3Tj1HfbKkrkCDM/VGR7nKEhY6rD8D2htqrtPspgmlX+uv4DzPw6IovvtfWsOr9qidCw3SjOO+2UUjn1sDE5TaSDjArdJ4r8OAC+jj33LqfXY7IJuM7J65FZqYiqWqo4P/e0vZ6b65w0qHwKpreLHjbuu9izYxmb129SjzhrOsYV7F7fZnJTw0fe+Z+1aS4gCIInwtLSEj/2Yz/GD/7gD56IjNjc3OSHf/iH+Qt/4S880TbJpHj++ecB+JZv+Rb+3t/7ezz11FNvbbO0tMTm5iaf//zn+W2/7bdx9+7dJ9rGIAiCIAiCIAiC8+A9a2pIZJOANYeJsIpUmEQslEgqoW7a8+POs4eJZTI6umQx0Iv42ndAvY0yG7xYOEjHvkMWm1vF93uYyLgE3Ejv1xg2NkQHE/raZPFT1IyXUUbMJvDF1LYuZihpBbdW0qtIuv/xhau1olrFxZfJdUN65AiNS+nzGzNw8xo8dQtu3koRGpecobEL6+uwugp378DdB2ZYrA+sH3vpvA/IBa+/CfzKfeu/RbJBtpau8Sj11RopHdaeFRXfGdj+++nzrWMzU5a2k7mxbmbGtWv52t8yNsjjeNyH9btw7dCE120mT3szbjtFaPRTG2exOaTUTF64Pk7nVqqofZpX5M+lYy1yMk1WucLff6bxh8nu01rKsk3q87JVeS33f7sE931sHnWxPuuR59Y0yDjUj54n+1iasPvkCCSJ8xL5FUWmiI99stD/iBz5NmDYnHxrnqZtVrD7Ueax0patYHNrjVyM3s/P0kh7HPTJkV1KN7aKGYWT1vOQaXaaYM0yskVF6oMgeOdw+fJlfviHf5gf+qEfOvHd+vo6P/ZjP/bEDY0an//85/nkJz/Jf/1f/9d86EMfeuvzVqvFxz72Mf6H/+F/4D/6j/6jx5YWKwiCIAiCIAiC4HHxnjU1IKcp0orlQyY3NCALTnfIYt+0SMBqEq20CrqW+qkU+GQ2LGECo18dvEdeoQ0mIPpECFpRDyaYyhyp0QFuYqLfN8mREANOin6D4ueQHCmifP275PRI5UpwH7nh29NK51cEgeoGdFN7FsgptySWXmnByixcvwJPPWV1NK5ds1oWs7NWEHxnzwyNB6tWs+LOfbizDauDLCBfI9eXeBPr6z7wVfIY3CKnE9NKeEUX3MbSbR304fW030I67i42pxaPYWMLLu3C8hYcJbVY/TM3DwvzqWA4cHQM61twfR0uD3L9Dl8nopY2ahIOyMLqcurfV8lmh1JDydTQNajQco0e8Hw63jY2N7ucjNIpo0s8fm5Ma24owqCc496gKY2MSYzLsxgeMhlk4s2To190bJl8Si02R71myjh0HPWbrq2PjZtS8+m7Q/J4KlWdaqDoHjgg97GPtvGm1CzZZJ1z53gWS/23gKVr+yY5im6XXFj+cZoZok9OpybzTHN9Uo4xI2SXs6XZelLXHATB5MzPz/Of/Wf/Gf/pf/qfnvhuZ2eHP/bH/hif/vSnn3zDGviH//Af8oM/+IN8+tOf5iMf+cjQd9/1Xd/FX/gLf4Hf//t/P1/60pfephYGQRAEQRAEQRBMz3vW1OiR8+orX/lpBEnlqz/P1dsSsnxKphoSDCXsanX1s+S0Rk2isARGn035GBPqJcCPEvF6mAivws4L6ViKOtD5fCoZCaZq7y5mFBxS7z+Ju754rs6l+g0L2PgpRZLqBcymbVSw+moHrl2Ca1fgylWroXH9uqWcmp21SIe9/VyE+95duHsf7m3D/UFeqT1w7dgg1//Q9cmoaWHGj4qSS+RXGhtFPexj6amW0zUcpm120j7bx7C/jSm9QL9vxcuvrFjtjbk5WF6xz/f34NEO3D/IBsZSOtcGk6XNKVGh41VMnD0izy+lrdK1H5Hz/9eKgYs2NnbHmCGoOedNoDKSCYaFXjhp9I27B2tRF/7YtWiMWsSI6Fc+97/7SIhxZpJMui42J25hERJvMGxG6hyb5PRT0z571J5RJo0ibvSM2GC4H2bJkVoPyQXcfWq78lUcYmOuZ8wyFh12iKXAWkvXpei5aeqHnBWl91KkxgynL06/hbU/TIkgePfQarX4qZ/6Kf7tf/vfPvFdv9/n9/ye38PP/MzPvA0tG83P/uzP8nt/7+/lr/yVv8ILL7ww9N33fu/38vGPfzxMjSAIgiAIgiAILhTvWVNDRYdlIGg1/VmiLc4LbwQ0Cbk1VOOiltu+pEnglpg56ppuYpEGMhK8CXOYPi+vQWaACh0rikQCIgzXOVgkr5T2kRptshE1k36W03uZVDKsFrH6GVdn4cpluHHd6mcsL8PKsr3OzsEgGQLrMjTuwZ17cHfDDI2HmAgro0CRJXc5WYDb96GE4HZqo8ThHeAVci2TI7KAK+NHxaAPgeOBHbB1L7U1qay9nhky83MwWIGDQ/jgNjx8FfYGJqqqIPNp0xTJ1NgnpzCDutArAXs5vT4otlMdG5keb5DTWulcmt+aE1qhXxbyrrV11Jz1pkVNaC8jCiDfR74OTe24tXYoPdPl9F7jXkNzuk1O36Rz7TVc16SpxcZRpony9+wB2TzxbdjDTK4WOc0YDKcFg9x35TmUgkqRH1vAV8gpzVS8fo7pUj6dB21sHupvg6LLTtuGMDSC4N3F//a//W987/d+b/W73/E7fgf/x//xfzzhFk3OP/gH/4CHDx+eMDWCIAiCIAiCIAguIu9ZU0OpTRRVoBXy7xRkTsgwkPCrWhseCakDsti+zmiRd5TYNmq/ZeBbsOLo3yAL7zvkyIlLDfuq/RuY4L2V3t8gp6K5mz6HnE6qNDU6ZFNDBses+1lMr0vAlXm4eQOuXoOrV+HaVVhZgcUl6M5Af2ARGuvrsPoA7ty1n7vr8GCQ03hJcFUqph2y4F67TkXw7JEjJrzZ5Ff5z6ffN9N+85ggLuF6ALQG0Eod02pZhMb8nF3LbKqxcfkSPP0UfHgddh5ZRMg6o6MmRqF5JcNKZsRaei/Dw9PFxl8pwbzgPUeeG351v/pBgraPApKZpLRwk0RknBdH6bwtbPzGnVup4tRORQvt0dz/2q7HcI2S18m1UU7DpP3k763S7Oljc70c4/6IdvkoG5mbNQMJbA71ySah36/NkxtnzxHZxFE7p009FQTBuwsVAf/Zn/1Zvvu7v/tEUfB+v8+/8C/8C/z9v//3347mTcX3fM/38MUvfpGrV6/Sbme7+S/9pb/EF77wBX75l3/5bWxdEARBEARBEATB5LxnTQ2tEH6nUhOifcRDmRZHaXV2yTnoz5vrwMtYWpxHmDGxR05D1E3fqYixR+mJ9jExcwmbfCtYOqNLZNNgG7tGrdbW9XpTw9cUmMPE43lgoZV+5mB+3tI03bgB12+Y6L+waAZAdwYGA9jdhbU1i9C4cycVBn9kgvIDTMBXzYd9TIxvKjLtazT0Xd8MyPVKSrpY1AtkA2UBW+FfzoEWZmy02zDTtetbWLCIjZkZWFy0+iDPPAVbqVr3bNpvNfXtMlm4HZdWR+m0FBGz6K5TplvJHhaBQXF8HaOLjZ8KjnfJKcj6WB8fY6bG+9LxXk8/m4w2Z5qE/DK1lP/co4iJGXLfH5KjibzBKOPCp7NSBIJMIG+EjYqqUDSAojRIv5+lOPQkqbhqn/n9pjXCyjRbitRoOkfLvZ/BnglL2LNA97Xqa5w2BdRp8NEnQRC8d5mZmeE/+A/+A/7L//K/BKDdbp8wNLa3t/mBH/gB/s//8/98O5o4Naurq9y6dYsbN25w586dtz5fWVnhs5/9LLdu3WJ1dfVtbGEQBEEQBEEQBMFkvGdNjYuIUmaVwqAXW8taBEoJc5rV+krFoloHL2Kpp+4Ar6XjPYOJ8Ipy6WKC/Ao5Fc0AE6vbbWh3YfMInknq50oLbnWgM4BHx7l2xZX0M+faomvppXNqJfxiC+basNQzoX9p0SIx5mat7sS1q3DzKVhcatOmz+GhRWccHsLGBty/B7fvpAiNRyakrpJTBskkeogZHD6NEUX7JikmXaJoDtVOUE2TK5VtW0B304qELy/D9pKZGnNz0O3B0pJFpdzagO3bMNs3ofgwtf/51HdfwcTiccK50rL1sL6X2N6Uqk2ptzyqLaEUYV3MtJBxdRczkUjXv0Autq55u854I6Y2LiVNBoe+U8qytXQtMpiUNm2DfP2qu6C0a968mNSU0P3rIxyeNKeZs5PSlOJLaG7cAl7Ani+qLTODjcUmNkeCIAieFL/+1/96PvKRj/Bf/Vf/VeM2q6ur/KE/9If4n//n//kJtuzs9Pt99vf3+ZVf+RU+9rGPvfV5p9Ph277t2/h7f+/vvY2tC4IgCIIgCIIgmIwLb2qUQqZqMExSV+Ks5/XnPq0gOYkQCzmtTwcT/SWYaj9fVFuvEpIhC7GTMo8JtgtkY6ONmRlvpmO9APy6LrywCDtHcHcX5npwpQszSeHt9+Hg2LZ/8Qa89BKsrsLDh3B0BN2uRRvs7cHe3Sx2L7vrlZGj33vAUgsWezDXgaU5O8bSkr3Oz1tExuysif9XrsK1F5boLs3B2hqP7h+xsQE7O/DoEdy5babG7Ydwz9XQ2CKnxlljuA6JH7eaoeFT7By5zxbIETWao0plpWgXpf3xKao0zl1gYQDrG3B5HS6l6JPerEVrzM/DpSVLtbWxCWzYOQ8xc2MZM6KOMPNmXLTSIG2rCBlFrUwTRSDTTXUmNLdkmi1bM99KBbdArtXSwubEs6mPHhXHnrRAOAxHDtTSIkFOa6YaIs9gRtAMOaJE0RvzZOPpHqdb3e8NjSdtapzWhJv02KXxqt9LZjFT46n0+xa5ryE/g05TE8b3qWq2HPH4+vq8/i7oWG+X0RUE70V+02/6Tbz44ou0Wi3+yl/5K0PpmTx7e3v89b/+1/mn//Sf8tM//dNPuJXnw9raGv/ev/fv8c/+2T8b+vx//9//d7rdLoNBPH2CIAiCIAiCIHhnc6FNjQWsHsMmJnb6NCZtcv2D8yio66kJ2GU6qHK7msGileoSTMuV/zfT969jAv8lsiDXJPCVoqHqMuj8EuBH9cll8ur0nnuFXHz6WeDXtOGj1+CjH7C6DmtrqdbDvKVyevgQdrZhewd6A3j2WXjp/fD887CxbkbG8bHVtVhfsyiJZw/s2Coo3W7ZT6cFnbb9dNtweQFu3TJRvztjUSBzKR1TN6VmmpmBTgcWl1p0lxdgZoa1NWvX6iqsrVub796FO2twr59TTm2Rhe1H1Otn1OWOk4aHuAJ8IH3/GhYNIuPpWurXPXLB8K10jGtp/xly0e+NVNj88iUzc+bnzNiYm7P3y5ct9db+Phzu54iDuXRsRd9MguoetMhRJdPgTY2r2LzeTdeyR05tpePOptcjbB5cT9ton6Yi95DvOUUMzWPPgTWG6yKUYykB+j5maG2Q00npfpFpMUOOFrpKrgXTT9dxBZs7DyrnKRl1L5+VSQ3TMnpFvyvN2FkZVVdDKcf0XFvH+u0Bdh/MYX29jN2Hk7SnjY39AjbuSsXXI9e2kVk5qtbJtJKe/zvQovnvAuQotj718ygK6J1W6ykIHhetVos/+Sf/5In0TiW/+Iu/yH//3//353beZ555hh/6oR8C4F/9V/9VPvrRj47cvt/v84f/8B/m05/+9Lm14e3ijTfe4L/77/47/q1/699667NWq8Uf+SN/hD/xJ/7E29iyIAiCIAiCIAiC8VxIU0Ortz+KmRpfI6/sbWGi4g1MzHqTLBKr2PDjbJc3NFTPYL+ybQcT6q6S09d48eoytkq8haV7kggm8XeVk8VrFdEgasVtR60ebpENDV94WxEbc9hq6i7wQhc+8iw8dwueex6efb7N8WGfnW0zElaW4d49q1Wx+jBFbByY2XF5Ga5fh84MHB3C/gFcuQLtDty8B8dJ4WyRUlZ1YKaTX2dm4PJleN/7LAXT3r5Ffcz2zNjopH3abRj04ehwwO7tNe7eGfD1Lx9xuG9Gy8NVWNuA1W2437cIjTWy2L7j3tfSFtVWoJfCpsaln/pwARPHFXXSJc+DLnm+6rhKd9XF5tFe+mxjkEyNy/YjM6fThvkF6+OrW5Ziq70Bx7vWiH1MHN4lz6lxDKhHdKiY8yTir8TbFeA5TLR+RC5qv5f65iZ27/rIlBlySqoOOSpJlELybDrHi+nze5hovkOucYLbx6duK+/VB+TaGgfkOikLmEiuCJNj7N74cGr/3XS+0xb6Pg8mNTV8XyrCSxENZ3lelmZGq/hMfX6EPc8WsHl5G5sTA+Al4IOYGaFUcKNoY8/Nb0nvv4CZwqpzcgszuh5hBpZqmRww3E9njbCovdcx29i1qrZNiUzOy9h9d5/HZ3wFwTuBP/fn/hwvvfQS//K//C+PNTXeeOONIRG+3+/zfd/3fVNHFrRaLf6n/+l/4sqVK3zXd33XxPv9wA/8AD/zMz8z1bneqdy9e5e/+Tf/5lB/ttttfuiHfihMjSAIgiAIgiAI3vFcSFOjR05XMiCn8oHhGhIS6SUgbWIiY7nSXKmVtDJ7FF6g8kJ2uRrXi3Y1YbCLiZ9XyWlR7pBXwh+n9yqOrLQ37we+HauJ8GWyESIx0rehRtN3bSwSRIaGCm9fwowUfXe1DS8+Dy9/wFIcra2ZuH7rVp/5eTMXBn24dt3qWqxcgcU3rSB3twvb21agu9eFpUvQu2wmRbdr0QUrr8Hunpkgg4EZE522bfOWqdG1/ecX8jbzc5ZuqtuFVsv2PzywSJCtbdh784BXX4XXXrPr3d+H1XV4eGzC9Br2qkiAXYYNDU8puXixsjQ3/NhsYQbcATndVCudV/N1neG5DNlgUSTHdjrWxr71/cpyjtaYm7MaG0uLsLwCx30bj9V9OOjne0VREJOm9dE95fuin/ZXCqVRDLD5m7wVtoBvYGK2+mEFu6+Xyemntsjmx9ex+0Hn0sPL93MXE8CfwiI83kj9JQNJIrYfp1HCveaDnisS+jfT90oTppRplzGTQxFN6jffD08Kzcem66uZDR3s/m8qBD8p3jAqI5p0Xj+fVMNmj+Fi6wNyUfmmyCjPAlZg/jpmBnizQv0xwP6GXEm/q1ZKLXKjl/Y5rHw3Dv+3opwDo9IB6u+b0sXNYOZaELwb+Ymf+An+k//kP2Fubm78xsCzzz7Ls88++9b7wWDAF77whVOZGh/+8IfHmiief+1f+9f4X//X/3Wq87zT+dt/+2/zmc98hk996lNvfXblyhX+x//xf+T7v//738aWBUEQBEEQBEEQjOZCmhoDTOj8RUxwXCWLZxKJtzDRSHn7JWRqpbeYxQSw+bTPRjrmpKub/X+HS9Frkhz7avMSJrpKtD0kC6ez5JQ3N4APpd+3MLHXp5ea9L/1i2SBmXRuiYfz6ftlTPhbxgTnlTm4cR2+5SPw4vvNwLj/wIyK+/dhZcXMBLCUUQvzFrXRaVtB7sND+9neNuOBFty8maIr5rtcuXrI/gHsF6ZGO0VndNrpeOl3Whal0cKOd3hkber3Tczf27UokN1dq51x9w5sblnaq81DMy0epX7cxowDRUOsMVzXQvjxVoqYfU6OtZ8fEmP3yNFCGqcjcv0OpTkqV4xrm7l0nl1sbiwOrF/X13NtjW7P+mpxCa4emrGzump9qXklgXYSkRhymqXjdF5vIk4jIx1i9+qXMYNCURAdbH69D3gaM9Bm0/ZfTj8PyIXUha5Bab5kXMyQ7w1FpXiR3bd70iiTG5jQfNe1e4sstvexCAONo8R538e1NEyP0+TokJ99tXRbpZQn83SSFHWT4O8dfx/IBPNtkpFYtnEN+Co2/ycxFTQOD9I+a+T5McCisfoM14k5JhuP/hxLWOQQ5JRktei3Gt7UrPX9uIgT9dcc9iwOUyN4tyDz4vu+7/v4S3/pLzE3N8fMzOn/KdpqtXj55ZfPq3mAGSV7ezl29k/9qT/Fn/2zf5bd3V36/WntzXc2m5ub3Llzh6Ojo7fGYWZmht/5O38nf/Wv/lX+w//wP+ToKGLFgiAIgiAIgiB453EhTY0DTLiCLBj1yNEWqwyv9pWgpTQzHqVsGrhtxkU6lKvxm1ZDtzFBaolcN2Mjbd9Lbd3DBC6tKK8Jifrv5Gza/g2GU5JIAPPtVl94lJpnHhOqfa0EiZ99TEi7jKVAuQFcn4WVS1aj4cpVq92ws221MC5fgqN5MxV2dy0CAszUABPTOzMmtvf7Zi4cHMDmponxq6tKoXRI/9giMDod6B8nMdJHarSToZGMkt1da0e3Bwszdu6NDdjdMXNjdy8VIN+F2/fgtY0sYG6QzS+lmzog10LYJkdP1PBpo7axOSfRvV3s538vBW2f/qgcK7+iXREaXXKkxyawcWC1QS4vJ1MjFQuf7VlKqtk5+6zXhs6xtXEz/fioEKEHgubWFawg/ArZXHkDE4ep7D+KPSxS5RVy/85j6YWup3NcJt/L22nbNxh9T6qvVCtBaaT0jJCRozRR46jd05ex1FJ97No1j65i99KD9PlGan+PPH90b9bmUi1913lJZgc0F8Vuaksfm1ul+XIW1A96HtfkMaVYu4o9o+6Ro6WOyGM4jh3gVXK/6lk7g80/RcKpLaPuP0UhQX2cRuGjh2r7ycibT+1RCrYO2ShtYXNK9UWC4CLz7LPPMj8/z5e+9KW3oiOmiZJ4Ety9e5etrS36/T4vv/zye6ZY9o/+6I/ysY99jE9+8pNvfdbpdPh3/p1/h69+9av88T/+x9/G1gVBEARBEARBENS5kKYG1FMCdchFZ4VSOTWxi62+bjOcYkSillaM1+pi6JxL6ZwbRbtmsJW+1zGBcYZcx2Ap/b5LXgXc9N9nFSnexQS7V8kFo31bxuEFPqVbgRzNIkH4KibY3wCeWoBbN+DaNTMwFpfMUNhPqtvCghkarZYZDGvrcJA6S58fH9v2e3tWZ2P/wAyK7owV6V5YtKiOxUVLV9XtpoiPAbSSqdHvW8RG59hMjXYbDg7zsWZm7P3DVTvH5pa9Pzq08z86yFEtC9h4Ko2TjKVdstnhazTU6KV+uuT60a9yb9pvlLhdMz8kLqsmhFJG7aS2r/dhaQMWHpiR0etZGi6ZQPqZIRcG3yen2vJzTunaOmThfhGbBz1yREqP6dG8L2tzPI/VQNjEUqpdx1JHdTAxV8XSa8cr061JrD4stpsjpzKahNp2D8kGmJ4nzwHfhqVk+3wffoH8/FlkOO3QqKiYcYZW+Z2eOzBa6J8kLZi2m+SzszDA2ryAjXV5/BvAR4Bnseeh6mfIbPSmQlMEhCgjRJTer4ymGrX2eEC+R5R+yqex8kZJ03F62PNBzxlPF7vWeXJ6tC1yZAbpc/VFEFxUPvCBD/CBD3yAz3zmM3zwgx9s3G4wGPCzP/uzY4/31FNP8fGPf/zc2re+vs4//af/FIAf+7Ef4x/+w394bse+6LzTTKcgCIIgCIIgCALPhTU1So4Yb2B4fHRGTRjUSnyJUvepC4xLmDi1Sy5E7I+xiIlbb2Kr+WfSMbuprVpxP05EVJ5+RQP49C0+J36TaA55FfZu0U5trzoGpOu61oOb1+GpW1YjYy5tcHhoQnmrbREV7bbVrdjZsciL9TWL4pCp0T+GR1twb9vSIfUGsJgiCjY24fhhTld187qlseokFfotYV7FwtPvva4J+AsLtu/WlkV/rK3Bm/fgzS0TMefJgvw8OUf/AiZI3iEbHEpxI/F/VNSO0oMpzdF+ZR8f0eNXjvvPPDWjzn+2ndo9Q06xtAbM78PiOly9Yn3dSgp6q2WGUG8W5mZg/tDGWOZardbBDsNpeFQg+xBbPa9ohGmpzUkJzq9hRt0uZnAMsDG4S73QttIG+Xb7c5RGjYqMb2L38WmE/k2s6LSMxBng1wDfPguzXRhs2XW8SY5MmKReSS3tEe69nzcdco0HCe1nZdTz4jxpYffdc9g82sCuW89tpbvbwQp7exPA9436QEbfuP7tY/P3NNdYi+AAu46V1A7VAoHheTiDmZ7PYM+Y0tSYw+blAJuTSmU2j12jorH2GP+3IQjeqXz4wx/mJ3/yJ/ne7/3esdv+xb/4F/nUpz41Njri277t2/iBH/iBt963Wi1+/+///VML8IPBgE9/+tO89tpr/MRP/MRU+wZBEARBEARBEARvP+8aU2NSM0OFcMu85opWUL51vUo4LFfRS3i9ha02X8fEK20/i604v4mJX0eY8K0c7ttpW6U+GscBJoQpnUkp5Kpd49LG1ERApUy6TI7mAJidsWLg16/DpcsmmG9uWUTG7KxtvLtjEREH+1YrY20dVtfgYJBrXfQHsH5oYuYC8FTHjIvFBbhzB24fpFoT23B1Hd63Aovzlrqq5aIN2q0UrdGBS0tw65YZLffvw5tv2nlXt+EbO5a2aA9b+a+omD45HZhy6j/ExMNeapv6V7U1moTjfWy8Ja6WlNEao/Lr1z73n2nfI0z81Crxt1ayDyzd1kGqWbK/B0czVldkft7qbSzNw8IezA5s3kpAlags/Fzsum2UIm290k618TTz7hvpdS+dT2mnOljfq0bGgGy6HRfH8Gae+moWG+sVbA4sYumoVpkuZZZv/xpZsL6cXg8HcHRgY/IB7HUT67d9ThaYbpLdOsX7sj+76afFcPqkJXee0/IkRHONzQLwfmwslFLtGBP2f5lcd2WPbOSUBq6uf9I0XZNmg1cfK22XUMF3meYySpS+yo+VXhcwA+epdE13yJEil7CUblex+1l/Z5R2apdstJ6HcRUEbxff/u3fPtbQ+It/8S/y8z//8/y3/+1/O1G6p89+9rN89rOfHfrsl37pl05lavzlv/yXp9onCIIgCIIgCIIgeOfwrjE1JmUOqxOg/Or6L/Q8ZlDsYMWFveHQpy6ESuSaxcTFOfJK92VMTJ3FhLL3p+9n03FV+HhSYU5iroRNfQYnhdJpRUqt/Jexs431wRGwvGwpoTod2NywSIvDI0sntbtnn62tm5B+cAQ7x/DouJ4yZRl4dh6evQFXli111A62un0jnW/9EHprsLRpxxsAg5ZL69SCmTZcuWIC/gD4yivw+U24d2SC4H2ywXSZHC3Tw4TGHrYa/GtpO9VG6JJTjfk0Q1649PUWaoKjogiaIhPgpClVMzrK6Btto/RG/ucQWN+GO7ctguXoyKIzel2rNzI7a1Et3RZ0B9komCPXGKih4vTrWJ9uVrZR3ZjFtN24AsglfntFNrXI0URL6WcRE4PvpLZ4fB+qPc9hhqLMhU3MwJpEJNZc8+mjdJ4FsiF0F/j5A3t/lNr3Mjl92SY5bZg/xjTSm+5JzU39zKZrXMb6RBEik1LOP99/tWs/KwNM1L+Lzbttcl2VNjYP30zbHpFrkZQ0GYhnNWb8PNazT8e+jPXzBjbH9dzeJqfG8uhalW5rERsvPb9vYH8bFLX3AmZutDFD5yHn3/9B8KR5+eWX+ZEf+ZHG7//u3/27/MiP/Ahf+tKXWF1dPdO5wpw4O3/0j/5Rvu3bvo0Pf/jDb3dTgiAIgiAIgiAIJuJCmRq1mgPTItG5LKCruhU6T99tq5RFBwwLwMdY9ME1TPydI0cFyBzYJ694v0FeaawVv9OwRy6irHZSXMe0/aLV0BLil9Ln19rw/NPw/vdbPY3t7Zzu6fZteONNSzO1tg539uHRIBcn3yELljrHHPD+Fjy1Agtzlqrq7l145cAMhl1yypZ2Slm1eWRinx8XiZ1v3IE37sPeAH752AwKbat+Jx23h43BAiYi9oBfAb6Z2tsip5CR0eVF31bl97IPu9icajI0mvDnEopM0Gr0ct7r8yNyrZX1Phw8hFbHioM/vWRmxtGR1deYnYX5Nsz3bSxkbKjeQG3eHGMi9CHZ3Cuv+zoWoXAJW33/BSZfGV8ea4FcKPoBJvAq4uk62Ug6Ij+4yrbPkOszaCX8Z7GxniQiqqyJogifGcwMvY4J25vAl9M2Pff986m9A7c/5LRkeqaMqrHh6ZDrc0hEB3uevB/rsx3M2JjW1NC11vY7b0FdQv99rM0aR/99ec5pImqaIqGmoel5qrZ6Q1N/F3Qf7Rf77WDpyBQVprRSurcfkuf3PDlK6R6niyQKgncai4uLvPzyyyc+X11d5Vu/9VvZ3d1lbW3tyTcsqPLlL3+Z7e1awscgCIIgCIIgCIJ3JhfS1JgkzVKJz8W+Qc6zrmPsYaJsiyy2KeJC591guP6FVmN/HROlHpFX6KpWhkTNebdPWcx8Uo5TO3WsWh+cZtWyXxXdxvLA/9YPw6/9Vks91U1FJjod2JmF+w9gfR3urMKDI0tL9CYmVvbIIri/xhmgPYDle7C0BTu7cOfIhD+t0byBCcPtPmz1Tdheo6FIe8r7tIalmlolmyeqO9B2PxJx58nCpK9b0FSMtzQ1asgs2WDYhChFUj8uTWO3hK0MVwF57ae27pPTZR2SU1AtYf0714MrK5Y2rN2CvX1L83XpElzqwcJRjh7qYYL5AcNFlNWPWp0+qm/m03akY85xsn5AuY9qSPjrV5TDh9L5fimdu0euiyJUi8AbkxonmSDvB5ZacG9g5/Kis4RkGUJllEI5fkpZdwN4Cav/cZ8ckSHuYWNyE3sW3CZHa3hTQ8ZkaU4KPd+00v861qcP3DYtsnm4xvQmhI49n46tMda98jiQQSBz6bQRFuobmaj+/prW3NCx5sjp3cq+9P2j+3sW6z89Sx4ybJr1MUOwjc2zTbJRvoONmebDbNqnQ72GTBC8m+j3+9y+ffvtbkZQYW1tjX6/T7udbff5+Xl6vR4HBwcj9gyCIAiCIAiCIHjyXChTQ3jRcZSYJ+FwARPLlzERc5ssLG65bRVFIfbS9sqz7lONeMH8AfUc76qhIQFS7T1NlAbUazSI8xAi1QeLwEvvtzoWd+/C3KylMQJ49MhqahwdwdqRGQqvYSLdFXINBkW1+HYdATPH8PSm9YtSDLUxUfFS2u7BkQnE30jbaJVzGdVwhJkZD8g1R2bJ9RjU1+r7ZWwulHUkRvVHl3rfdorfB8VnZVTHOMFVq76fxlb73yanwfIGgPpW16D0U31gfg6WLpn5NBjATC8J+PNWe6Q3C92dnM5IBs8mw9EwqhOzgI3B6zSv5n8AfIUsyI7q1xmsxsVlhtNZyRy5no6zTr4vdY0DLPLiRtrva5hxgDtGLx37CrDUgZUlONyFawdmRMjIXGG4dsokc+E4nfcYm0dXyAapOEjt+iYn6zIIRdj4NHLlXJFwPpfaWis4vouZqSpYP83zRPPsqXSOB6nN29Qjh84TGbPjnt01ZC6tkCPKHpDHpXw+jjq+oiYWsUi7BWwsVdvFp5fTfa15IhM0eb1DY62xVITPa5w08Ejn0XG3XHtPY3YHQRCcB9/93d/N/fv3uX79+luf/eE//If57Gc/y1/7a3/tbWxZEARBEARBEATBSS6UqaG1Y2UO+JoQJ9FWOennyCtrJbjPkFetr5CL/G6Q86Y/Ylgg65BTGc2Txc7aanYvYper9addpSwx9no6V5lKxx9vmiK6MLxqHExY+/qrcOMqLC7ayv+lS5YSancH9vZMND/GzIx1t98OWbj0qZzU72CCrGqQLKV9ZlMb1tL+38TE8iNMZJ9nuL8kFO6Qo2NmyTUxDslmhlZE75BX2D/gZP97cdlHA4363F93vzhObd8a/nOt7N5J11AaQz79lAR/mW9Hx1ZnZG8fDg7MjOq0rbh6d8bSh823YGGQ06Q1RYvIYLiCGUxNqZtUE2WWHD3RRAczrq5hAvoWeX7sYCL9q1gqJUXndDGj4iYmxHcwoVhj3CMLyLovD4BvHMP1LdgcDK++H5ANk8N0veVD0I8tbtu7ZFH9Zvp9p7hm1bwYheYjZKFcJof/Dqzft7BnjKJcBulzFaqfhnZq//sxk0hC+j1sDtUE+BqKnDnN2t3TmBlzmAFxHet7jbP6wj9f/d+G2rlm0/FkhC2QU2Ip1dQsOSXaSjqOIvWOsfmn2i4aMxUaVw2NA3INjhoyr84SaRIEQRAEQRAEQRAEQfBe5MKZGmWNAY/EpStYEd11TABV3YA18sp2n5v/OvAsJkQ9TOeQYOZFMgmgM5jQupKO0aaeUkoCZM3cOI2pIaOmz7CQ17R9eY7ae59yRz+HwC9+Eb7lRXjmWVi+PLxjf5AiATBhVPvsYSLvFiaQ7rpzDsiroV8nC+Yz5JXmV9Ix7pNF1qO0j1Zie3F6QBZVtepeRgDuc9J+m1gtDaVsEmXaIU/ffacoFL8qXNdXrvavCeOlOVG+HmJ9cxvrwyYxVIZRL72upmu4dggH+xZFI1ptmO3B0hJcvgyLD2DhOEdM6Pp9u7bIKa2uYvfHG9Tnm65dYvgowfoAM5MUbeLZAn6V4X5sYSbIdUyE3sTu6TfIhpXEZNVSOcCMyDng1rFdQ1mnYJeT96anFJr12Tb2PNlw51QtlVGU95367JhsgOj+9umf9AyaSz+KKvE1VWC4uHft3GD32xJZxD8g13VYJRs/k4jq6vMnVch6EZuHC5ghdoVsYKrGySOGTUQ9H3x/djBDREbdI3KaKD1L9LfhOJ3vGSw66Bh7Lu2Ti9uXz1KZ54pYk3le3se+7k7NyAhjIwiCIAiCIAiCIAiCYDQXytRQWiEYLrjrxbUeZmh8lJwaaR8T7moi3P+fvT+PuiUr6zvwT9WZzzuPd+yZbrobUFBU1BUFNJGl0QSJ4PBLxJ8Bg8EQDQi/iHaCBlQQDGpCxGEpCjgrorIkSlQIQmwcuqVpeu473/vOZ56qfn889fTeZ79V5z3vcIf39v6sddY5b50adu3aVefe73c/zzNARMppTDojnUHuosKXCu2l5LPOcLYJnHfdfq9ESbt0drYt7rto5EVam1Twy2FEbe1HTZsSAINYZvxHGcqlCoaTiMDZRoRCFZ5tsU7fG5iZziDivaZ3KiECdgG5ZmsY0XaL9Fzzdj+o0ZOFmh5dhvvFjVLJ2pakjRohAdmCuP2eJZynRRcNMH03Co3k0NnmW5iIlm4SrdHrwWAgRcLLZShXxNzQ40xgoldKmOgFTam0layjNTPOjmiXivNp5o57zhrV5J5/2rYVTJROF1O7pYmJwCI5dzUr1dwAGUNa62SU4J/WTnd9bZ+2X6NcdopU0BRHMcak0zGvxqSadVlRPRrFpNFguo798M4aZ3Y7NN3YOqbw+wbDafXSzFCXCPPM26s5W8bUhBlFHjEXtE6Rnoe2Q02aDUwxbx0brulQRiJUlpHzX8NEwaSdQwMZO5qyboVso1HNiyg5Tphsv1Nxer1m9jMpbfx5PB7PleCVr3wlH/rQh4bqarz+9a/n//2//8djjz12FVvm8Xg8Ho/H4/F4PMMcKlPDFt9AxFYVZzWVzCQyU1ZnQitZs4ojjJBcJLswtaLipq6nM6cLmNoGuZRt0mbp7xYVQfMYE8LevyviucK6ziYuIGLvXNJWjT6wi4VHwMBNFO8wQETBBmKQLCBCoS0i22Kra7TY16iHGEpL1neKitYHgWu2jBKC3e9amHRQaftI299O+49Jv35ZqGCqQrhGPWikTKMJ9bq8qhWprxEEUjQ8CCTKRtPr5BCBeAIzax1MtMMMxkwb1TZNNzYu49SwKGFm1WvqHx1bur09ntL22cAYKFm4ZoB9HdKirHZT9yBAIgyOIffdJtJPGnWhxcvLmPRTblv6mELgdnvUfIyt5Wnt1nc1bzUiSKOa3HPRYtY7Yd/HrrGhPyr6TFEDVIukLyFGwUWMETAKbftGss0Gpi6Kjgs11dSwncekrOtgavZMMJzWbdSxNTpDDaDNHdbXgvQrDJssRYbNeBi+Zu5vgzc0PB7P1eJP/uRPti37ki/5EmZmZq5Cazwej8fj8Xg8Ho8nm0NlatjCZR4RC+9ExCvNbV/GCLE7ibH2fjcYTm80zmxlnS2vIl0LEdjUZFHBcFTaod2gs4E1bZbmtc8yTNKiBuKkrUeTdk8k+7uAmQmuAuoggjjeLswX8lAuQimQ70vAnSEUYwhiEcTdGdhZgn81aUOAiIYqXo4SjnVfu01/o4KnPcM/LaIiLcoGsmslqGg76rhpjFu/QCkgRkMBc63ySDTDTYgJwAA6SQqqOB4WSYNQzA2d+V/HCOY2WmOhm7w2xmjnQQixKgBXkLG5iJnxvoER5d1rptc1bTzspl1pgvK4Y0wFezsSSJ9RdyTfryLPhxrm2bSE3IuXSB9fGhlkt22nNHxp4zdguJj1TpEd40Rg6PWCYSNYTRo1SPQ4er+pGbxTCj0wKbnsaJxTSMSaRu65z4oiEpFxS3K8OqZ+kkbtjVsLRM0f+3eBHdqtJuM0Yq4UkPGr9Yb6mHvfNaW8oeHxeDwej8fj8Xg8Ho/HszOHytTQlC8xIoRPIqL4MiJUbSIiVBOZLbvG7kXNUbjCfIjM/j2atENzu3cYzuXu7n8/wtXAemmtEHffaQK9LZz1EXH8FowhtJG86sl5NGKpzzAYmBoaIKmMJiZgahpmtuBoE6oB3HWz7P/0oxKxcYbt7chjokJARMZjSB+q4KuzorMIMFEGmgZnN4SYosBu/QZ9zxJ07YgX2D4essRie3macDnueOgj1yaHjDFNvTMP3AAcrUJJK3Y756WUCpAfSH9fQlKApaVnaiSvKymyVpHxMJN8DjBRBQ2G608osbUsx3hRIHY0QpZIvVuBWYvebzAsmIcY0/Q0Jn1WHxn3z8TUkllPOeYo826cqKBRy/SetIvRuxFgo9DvCxixHkxKs5z1rrUo+phUZzuZejlM0W29ZtpWt4+1PQFiJkwlryImgkL7+CLDxeNHMW56LdfULGCidPQaummu7LHrDQ3P9Ua32+XixYssLy9f7aZ4PB6Px+PxeDwej+c65FCZGicwec5jRKD6LPBksmwdMwNYDY5RQlFa9ECW8ZBWe0FnTKvwqulltJB0Wmob+9g7tU2NkawZ6Jrqyt6PCn/6Oe1cdMZzHiP49ZL2ryKi5DRwsg3NBrTbUClDsQiFIlSrMDUJ89NiepSKYnpc2hCxsIgROnU2taYHs2dVF6x1p0nSJ2X0i9ufus/dECLi8ywisNqz6m2BcRTaBnsspBkdKg7bgrG9j7T97kTMcJonFWqbyNivdOSggwy1dtCH9kCu81nE+NN6HHtpz0ETI+OhjEmrpVEYFYwYbmOPi52unXss9x50jcHd0MOYFfayJzDRGTXMmM0jZugyw/VBdjJCs4yzrMgL26Rzn0Wh9XLPOa1/XDSKQusMKWo2FjHPo461zjjGk70vjRhKO78JxAQDYyhFSCRHDRk768nnEBn7dXaO1Mj6bUgzncKUddWQ07RY+ttgX0c3gg7SayF5PIeR+++/n9e97nV84AMfuNpN8Xg8Ho/H4/F4PB7PdcihMjUqiFiWRwSiMwxHANQZTzBLi7iwcUUlLUqu29poyqkCMvNdjz9KyB4VDUCyL00RpLUS0tLidJP17HaqQOlGIdgCmhY8XsEYG/nkXFR4PQ+c78HMeZidhUoFcnmp0VCtwtSULC8UJN3RmYvwaENmQYdItEaisbOJmaWt5JCZ1JoSxi4w7uLWD3CLfY9LBZkVrwXS8xhxc6dZ765xkbbc/lvTXGmqHZeDiNqJMdE6NWB1AOUOxNHwOvre7sDKQFKNrSPnHmIKvV/tmeJtpG2aykgjnnSmvtvPsHMapSzc1Ej7PXfbZNNrr4Xf7QgMPd5E8trCFH6367XYqYls7PPNOve0VGhZ41nNjdh5jdMvWiA+CzUj9soA6Rd7P/Y1CxGDcg4xQ3PJqw48bq3bYrzxnWZk2P2Udh/n2B4pAiYyo5S03X22ZUVu7SbiyOO51vn85z/Ppz71Kb7sy77sqWWVSoVXvOIV/MZv/MZVbJnH4/F4PB6Px+PxeA4749SEvWZYwczkV3G7jYhHKgruROC8QuezGhi2eJazvrO3heFIDXfGe1p0B2RHXyhaO2EembWeNQPbFiG1HV3SowPs7TSCoIZJ16XpfeLk8yXgQgwrDdjYlIiNXheCQIyM6gRMTkA+B40GnG+IudNHZmhPIsbGNJJKSAshK5o+6hIiQJ4lvUC73YdpQuu4gnYeMTUKSRt3I4anjRUyPuu1sCNK9HgHSYCpKXM7MlaKyPUIwuH1nvqc/FFCrommBrpWiBAD4Axyr28is+8vJcuz6qzsZgzYY8hOf7SfKA2bMnJdbkTGv21eakRGMVl2EYk0+0dMaiK7XXZbXEHcvQfsqAtd5i7Peo6NemU9w+DKRBTo80nvIftccpiaL/rs0WebRn3V2Tliz8U9d/2sx7T7OOesbz8TNCJOn8k426X16wAfqeG5fvjMZz7DRz7ykaFlk5OTvOENb7hKLfJ4PB6Px+PxeDwez/XCoTI1Wmwvmmt/3o9Ibc+w10iJQvJKEwPt46mhYAtXk5iogEJKG/IMGxc2g2SbhWQ/WbOHNY+9ziJuIQJgi2EhfZRQmUeEv3MYU2GAiIQrwKU2nL8EW1vQaku6qXxeamtUqxK9EQ3kWAVELNdaJ9p/s0lflBBBt4iJROkyXC/BPU/XYLL7P58ca5xwI03rsoERzdXEUSE5cj7rC6vdrsHiRsH0kP7XItwtpF9doTIt7dFuKAN3A88rwM0FU2w9zLgJwkBSiC2Gci2qyXJNX3WtCKm2SaiGkJ2KKitKRiNOcqQ/B0rI/VRN+e6g0PRmZcwY13HuGgwRMka0lkwPYzC5xoZem6xrlJYSyR2faX+7L9jevyD3V9Var4IpEH65cc0nu10xph6QGhdpES5p55TFKLNb0+Xp9S0wfE3dftTngV1HSJ+HM8izK81ULFrrXKl+9nguF7/0S7/En/3Znw0tu+OOO7jnnnuuUos8Ho/H4/F4PB6Px3M9cKjST7kpaBQVkFRYKrI9z3vaNvqe9tkWH+313WNqmpGI4VoWy4j41cbM1sX5/iZMceFHEHETTLqS6WQfmsrGbTuYuhVZRXcDtot6BUQE1/RPXbYXzm0hs+MvxFC5CHPTMDEp9TPyBSlIPTEhr8lJmO1CL5K2aJFprVsBYt5oOiZNFQPZ6ZlgeDazfS1U3NZCwnaR4ix6mFosbj/pvm0B2T6uXfMgS/zVyCE931FRQ2kRJ7vlqfHeM3VcikAhL6aTmhtPHSMQA6pShHLSWWq+7LY2yZVgp0gaV7QuI8J7iJyTXb/CNgEnrX2r6GynjNvvNWkhBqEreOuzy43kARNxkHZ+rkgfpayTZfSkjdVR2O3U/etnNWt6wBJyD19kf/01Lmos6Gcb9z6zv9d+s+/lAtm/DW5/phk+uq+sdIS6TI3mDbab3cvJOg3k3rXbEiK/C8eTdc4Dj5IexebxHAaefPJJvuVbvoW//Mu/5NnPfjYAU1NT/OAP/iBbW1u8613vusot9Hg8Ho/H4/F4PB7PYeRQmRo2KripgATD0Qcx2cKVCneuaJU2C9+dSesKn1rbwhVZi8iMZle00uNMIDPmC4i5oLU59Bibyb7nkv302C7qgwhmpeTvPiZFSlYdhxARfxeS7eqY+gqKRlhsAKeBbgTHG7C8BeUSTORENC+Vpa5GrwdRDOE6VCIxD/KY2idqTqh4W0nOpYFJu2Oj/W7Pute2R9a7FjcfYGY2b2DMC/fc065DFlpnRNexozrsMWePH7sP7bFpR/rYZs5ea4OA9OvfIedfQEyj40hKsKlJKCc1UJ4iNu3qY1KOuWK61oOwx9vVxh7vdm0D24CqAieR++485rxsM6COEZU1QknHz0GI8zFyXVWA1ggte/86pm2zVKPCItLb4hazTxvbaeK6mnFZ0Qtp56z1NVzzRJ9ZW0iU004pnbL2vx/GNWfS2qHbFhhtgurzxU3j5Zobab8L7jn32W5G6FgNkL50DcUAeY7NYn5H7DozHs9hZH19ndXVVaIoIgzl7qpWq7z97W9nfX2d973vfQwGvpLMtcDs7Oy2ZfV63V8fj8fj8Xg8Ho/Hc81xaE0NN82HvmvB8FGCmgqjmmPfFbBsVORTIdIWq3U/aSLkFqZuhSscDzBFxQNELGwwLIrpPvJIxEY/Wcc+dxBxrYSYHwFikAyQGhVbKeceYdJU9YGHgVNOG09gTIXJ5NWqw1ZNIjOKSbRGpQKxpZZWKrC5CSt1KMZy/Jq1rx4mtZeaBM3kXQuWq+ibVYvE7aMWJiXODcl7k/GiD9KESBv7WvfZfu1tY83+Totb5xgWVEuI+VBAUmC1x2jjKE4l+55DxlAuB8WCmE3lktQ+IZZaKN0u9HuSPqyLqUNjRxAEiNl1EjEGzmX0jR01cznQtF92OiY3OknHD5g6FZPJdlk1UyKkzyuYWjVpkQ82+aQdWaKyHYnh9ok7Bt1III2yCpF7vM32iCndLnb+tnGFe93/PDI2NjHmjbvfLKPAjtrQCLgBpsbFThyUoZEWaabt22k7N/Kix/i/DXbKMPfY9mf3+WFHaaRJgC3EeC0mbXHXiYAnk3XU1PBSoud64IUvfCGXLl1icXHxqWW5XI5f/uVfptVq8du//dteOL8GePDBB58ynpTv/u7v5r777rtKLfJ4PB6Px+PxeDyedA6VqaEGQ5oo5UZP2Kg4Zf932RaldZ20z7BdSEubpe+25TwmLVSaSLmJSVtlC+X2MXX2tp2T3V5H0y9p3v5FZBbwJYygagv32pYG8FCyzSrDhkYFuBURREsh3FaFhSI0m7C+JrU0ggCmpsTcqCZFCnI5mJ6CagUKq1Ctw0QPSrG0r4mItlorQXPNq2i8jBEc7fNNQ8eB3Z/rSMHxOjunorK3swXhtAiMLNFXhUuNbFBjpYvJt2+L5vpeTr7bq+irQmcVUyh8NoRiBMWcST0VBGI4DSKphVJLTKlmzxgGOr6mMObYFDJTvIWkF0rry2shgiNA+r2AmBkzyd8a/WJfW/s+72LMwlFFsEnWOYbcCw+SbkLNYiIY0iKEbNwICDUK+gzXC0rr36znXpqhASZKYwEZcxqVk3PWyUrpp+acFq7W2iZ2pNSVIiuizv4etvdPWjvd54oaj665pedvHy/reWAv1/5Uc9ZtUxMxLQqkj6cYeX6vWPu6Fu43j+cg+P3f/32++7u/myAYvpM++MEP8m//7b/lF3/xF69Syzwej8fj8Xg8Ho/Hc9g4VKYGZAtYactyiOg4i5zoOiLiu+lr0gwNnamblepFxT1N9ZQWrTFKjLLT6NhCuhoVM4hIrrnru862en5HkAiFzyFGyiA5z2bKOWh6mz4iwBYREXvK2q9GPNxakDoac3NiVGxsQm0LLlyAKBLBfHo6qa1RlQiB6oREB1QqML0FlTUo1mEiErOhkbRLjQ2StoKJCOljUnbZBc/TREebNpIqa7fo9awis9rrycs1tdJebrSGphGy+90WjnuIYKnpn3aLFqK+GXgGkm7q1ioUS7C2BdWiXINyGYJQCrhHEfT70GlDowGNWNo1hYj1EXK9TybtO4Wku9HroqaTnpsWok8jTfTNEuNHoYZQGnlMoeZK8ppIXmAK1a9gIqFcbHE+6zhu20tsF6E1lZCK/+Ocp45lHRsBcm+vMdx/o/otKzrA/XsTicTSiLEB0l/2+qOMYo020IiqAtL3OWQMXAmx3b0+afelTdbvgmsmlTEp6yJkzG8669vp7+xj2Sm93ONqf7o1mWyyojiUneqmeDyHlVe/+tVcvHiR//yf//O2737+53+e5eVl3va2t12Flnk8Ho/H4/F4PB6P57Bx6EwNl8j5bAtNR4FnJu8FZCb/vYhoreuruAnDwlUxWa71MmxxyRars8yP3aKFjhcQcX0aESLt+gAuASLKzSPFe+/HzKy2U6fE1t/27N8Q6RuNpNAZ2W1khj/A1hasrIh5Ua7AxYsilOtOZ0IpHp7Li6FRyEuKqmpV/q6WodaAWgtqkYnYaCPXZB0xOSpWuyqYVDc6Q1xrb2ith4MS+fRaqpkUMZyWKQ1beLYLxMcMz9B237Vw8E71CNIIMSbUEnLN+0ifzsdS96QXQz6XpAfLi/HU6UC7De1OkoYqNrVXysm+q8m5X0BMv9Vk3xOY8TiVnMc5JMonTZTNmsWujHvObnSFXStHa9FMYu5RTeGkYrE99tPuTa3Hop+z2jbA9Ela+qkYM6N+N8XW3eeJHksjT3YqXG6PsSzRO8bUy9F0SiXr+zQjRNH0XvY1zmH6WwX/vRhWeyXNwMj6O055t8+lhBTjvg2Jbhsgz877rPUGGHPNNVJKmD7S/aeN/Z1SZI2D9vvsAezL47naxHHMu971rlRTIwxD7rnnHp7//Ofz8Y9/3BcQvwq84x3vYGpqamjZBz/4QT7xiU9cpRZ5PB6Px+PxeDweTzaHytSwxSM7nZIdiaAEiDGwjAhXWs/BFZr6DBdwVpE6QITTPMMpeGyRmmTbAqMLK+cRoT5PenHmAIm40GgBLSS7UxomFUHbyCxjFdm0hoUdiaLivQqTmuapiUnJowbOPyC1F+bXZT/NCI7m4MZ5EcnPnJPZ/wON2JiSaIF8HiYnpaZDPi/i+sy0pD2qbUG9Ka92X2Z6zyfncBG5VnrO+lLxcJD0Sc9qbw4jzjfZW+SDogKw1pAYJdQGzruOQTeKQ/frip2u8TYOmnJqEhlHGokDsNmGO7uwFUPQTcyoIDGIIjEz6nWo1aDeln7vYsZVD3gUk8d/M/le67TcjBgbAWa2vpvKLa29inu/jIM9jm1zQtu0gLkvdYx3kXOrIVEPWSaDjvsG29OYZbVl1L72UxdFjUW9NyeRcb7l7HenqA0bFcFzmIgnMJE27vi09+MacDa2iD9g+7P4SpMVoaH3lx0R4z6X88g4OoI8dxqYiCTbUHINMh07GrHi9pPbJ5riTaPNXDRFWBlTyN5lEnlOTqV85/EcRtbX13nFK17Bb/zGb2z7rlQq8c3f/M28+MUvptVq8Z73vOcqtPDpy4tf/GJKpdLQsgceeIAzZ85cpRZ5PB6Px+PxeDweTzaHytSwZydrGqUs8sk6DaRo9ikkUqPhrKcRAWpOqPhXRcSkDiIydjBREJqOZ4CIqZqf386JHyf7mUUEqWKy7OGUtsaIELuJCLPHMemBTgCPpJxrIVlH02qds75TA0VrVKhoCsOiWxeZha4pcFT80yLWs5H0QwHYGkB7BfIxXIhg7bykpKrX4fgxmJmFyQkpIF4qQS6UNFSttnxXq0GjLrU5uj3o9cT0eMYmPFCTc1dzqJ28a6TGAFMwt4KpAzGfLHsUeCKlj8ZFz1vTz+j1HEWAqaXhRga462XNiB8XTY9VQMbJpeTYWnC9FCXjIYIggDAUQ2MQQbcjfd5oQH1gImXUhGsgZkaagVZM3jWyZgOJTMjq57S0PKT8baNGmy2a2/tTwVfvtRamwPIGxvBqIoZGC3M/26aI3Y44WU+fI1ezZoFdOLyCRF5p6jXbZIhTttHtwNR20bRKxWQ/tkGUZrilmU5ByjIV9G2TYC9ROLslLUJDl6eZhnY7s9oUIv1SQ55/DyG/Ea7Z3GU4qklN5GnEbNjCmKz67C8m6+kYVIPJrZdSQsy5iWT9FeRedNvcTtq4lnEuHs9hYzAY8Nu//dvMzMwAknbqW77lW4aKU8/OzvKud72LH//xHwfgzjvvZG1tjTiO6fV2ExfnGZd8Pr+t1onH4/F4PB6Px+PxXMscKlNDCcguwg1GIIyRKIBVpN5CO/lOzYAthkXNKpKWZBYTCbCGiE0a1RAl359ExKxziEDmtinCmCS5pA1bZM/6riXvbUSsV8F+KtmHCsn55PjLSXsHDEdpwLDxE1vb2UWCdb0O2wXMPiKc1zCzxzeAi7Hsp4WkPLrYhJXHJHrjxhskuqNaFVOjUEzqa1Tl++kpaDTFzABJWVUuw8VL0P5/8EQPpgNJoaTCuwrUPYzAaEdxzCKiYQsxdjaS9l+pXP9F0iM0bOyIHn3fTaFlNUw0CkGNBzXtVpEUZTcA01Xp/2IROl0xM1ZWYHVVDKVOJP2pBl0/+awROjZqeGjBe7ueQDnZzhXX55NtRtXccI9TRtJpqXjbT9bTNFshcl01OqWetAlMXYc8w2nAtP3ueMc6vt4bVyPKIA1Ne6Yz+9UYTUvzZIv5ev5qoB5DItMKmGiNNsNmBimfYfgaa0otNe9cky9tDB90X+4kr2WZHVl1jsCYgW3ELD6FGApqQuszVQ0G7d85xGCeQM69jdw3TetzmKy3hIzlTUw0jvaf/jYUMcb4CtlF5vV+9XiuJ6IoYmtrC4Bv/dZvZWpqimc961nceOONTwnr5XKZclmSJJ49exaA++67j2/8xm9kc3OTzc3N9J179sT73vc+vvALv/BqN8Pj8Xg8Ho/H4/F4xua6NDXKiOg/iwhCFxDhKYeIpSpcFZL1VRB8BiLMdhFB6iwiTmkKGjv//lyyjtbUsGtsaGSEmgM9xo8i6CKREq3ktYERO+cQ0+XG5FyeQMyaTYygqwK0m/LErR2ykwA5QATkDmLsxJh+LSHC8iZQ70N1VQyMVhtmZ2FmRgyLYlJjIwyT+hpJNedSEaamYXNTRPflBSjWYH5WDJC1Laj1oBkbc0PTJqm5oaJ7GREaj2KMLJ1BvRtzwzUlsvLkp23j5ty3t3dF5HHbpLO8KySRMpiohComouhYAHeU4Fgejs+LodFsSB2NlVU4dw5WN6DVNX1op23LGgd9ZOx1MamfjiUvFXFd8sj4OE92DQoX3Xfb+n4aMWkmkeuoRb81ukhnz+s57Hbern3e14qpESOmVZPhezcrzZd7bw+Q/roJEdWVNpJabDd1aDRqTU0hN6LDftaNqkmyX3YzZ1jXtWscufdagDwrlpH76hLyfFdDYxq559rWfvSZd2uyTRsxys8h16vP8L2QR6JtagynN7P7DOT+0OiQUancPJ6nA9/wDd9AGIb0er3UaAFd9gVf8AU88cQT/Mqv/Arve9/7+NSnPkW9npa4zbNbgiDwkRoej8fj8Xg8Ho/nUHHoTA1XNE5LnVLCRBhEyMxlTQOitQG0WPMcImblEJF+BZMKqmvtP7b2r7np22yfrW6LhzrbOU200nzrbtqdCDFhLjjrLwFfgswWjhBB7FKynp5LkJyL5uW3iRkWJsf5r6vmeddzbyKmzwQieG8kx6k0IDwLRxoSidHvwcSk1NfI5WRZAExPJ0WsC7LTdkuKj994I9ycg6NHodWEhx6GC+eh3RPRsN6DVmzMDY3ciDF56BcRY6qLiIpquuj1ySevrOthX1/38zh9lRWpod9pEXZtt6a0KSTn0HfWn0TS04SYWeMqvOo1ngTuLMJX35rUz2jB6gq0WhDHsL4Oq5twsSNjVdMzdTFGXY90QVpNIx1bmlqqiIwDtw9ziOh7A3KsS2TPlrfpIVEYatqQnHMXGdubmDGoUQOaCkgjCSJM1IaK8RrxkXX8K21mjGMkZpm0o1DxXvvxHNJ3FeR++AKkvy5i6nTY4zuLkOFnreIaG7aZsJd6MaOw7z23zVn3ZeC822gE3yTSJwuY54eaZDrmNRJsItnXheS7NUz6Mx17WPuwn+luX7gmUNqzSA17rRmz15R6Hs9hI45jfvZnf5YjR47wile8YuS63/md38l3fud38hM/8ROcPn2aVqvFL/7iL16hlno8Ho/H4/F4PB6P51rg0JkaigprKmAqZUTUDhChs4LMXtb0UaeT9zwiDs8g4tIljNhL8v0kJv96z9rmaLIPFZvTjBV3hq4K2fa+K5gUV2kz320iRPgNEKH3FCK06XYq4uks+qyUJVmiY5aAaZ9LlLQ1QIS9dUTEKwCVFkRJrYxmQ+podDtSV6Pfl5RUUWT6K4okcuNYAU6eMMXFNzfg0ooUFp/MSYRHvQ7tLjQ60OrLubWSc9U6G3qdSpiIjR5yjbQmhRYbH2CKKO9FSE5jlFCsqcDsNE8TiFGlqX5cE0ojJQJMuqgJZHwXk3cdQ0EA+ZzU0CgU4dhReW81oZjc4Q1M/n+tnzFqnNjtIFn/bNIeW4hVk+9W4C6kj1eQsaHbjooI6TrrgozvJuYa6pjRehFVhsX1FiaV1kTSJ2qSZKVbcgXpy83lNFFiTFRYHVNQ/W5kjN2ZrHeK9IiNtPRWNmlGgmsqpBkQ+8U1k11j1m6bu01amrdJ5HmvpsMU0jcBcq+dQsZQERlnOo40baAaGUVknEWYyKcSYvjOIPeamw7QxjVLNdIvxoxfTau3iTc2PE8P4jjmda97HYuLi/yf//N/+Of//J/zDd/wDSO3eeMb3whAq9Xii7/4iwH48Ic/zB//8R9f9vY+Hfibv/kb/uiP/uhqN8Pj8Xg8Ho/H4/F4UjlUpoYraKlRYIuXKg6pEDWBzOLXdErNZJ0bkm01KqODKb6tBYZV+Kwkyxcw6ZceY3jGrrbHRk2BHEYI09m3Wlhcc7bvZGqsAH+PCHM6694WyfW87TQ+2oadGCU6u7OLtTaIRh5o5MsKUOxDex1qDZhviKkxNyfRGWEIWzWYjCAui+g+MQHhpHwe9KX2w8qKmCCzczA7I1EdrZakU6rV5L3Tha0GdAbSB1rHQetDqDm1lrwXMbOzYTiCRg0SO41RmmCb1pdps8Wzoh7s9GUgY6KHSfFl93ec9GmLYeFTo010JncRoC+mz403wi23wg0355g+OU20WWNrs8/6Bqy3YGNgxt5ei2O722gNggnkfjqa7HsOuWe07sZOIrcr2kaYPiklLxV+ywwbKw3MvaD7smfJu8ceNdYPK3aqqlryqiP9pPWAdJzp82gUowyfNFNh3Ptgv2QdI8uQ1egL+2+tQwMyTqeQPtpC+mcKOIKJylhP9qsmoh3BAsYYX0J+ZzpIrQ59puix0/pDDRI1KjVFoqYuHDdKzOO5nlhZWeE973kPH/nIR3j729/Oe9/7Xm6//faR21QqFV7zmtcA8JKXvIQnnniCV73qVTz88MNXosnXLQ899BD33nvv1W6Gx+PxeDwej8fj8aRy6EyNNMHeFo06iCBcRMQ9zZseIMJVGxGibkbMjM9j8tGfRATaGkYQD5AZuHciom0Zk6d+HWNYjBKfcoj4q7Nv9aWGwTgCs0Z0bCZ/q6gbWC+7L3SfGrWQVgwaaxsYFixtQU6jCfSlgrOmQyon57MCtCOot6F5SepjNJpQrUikRqUC7RlJTTVRFTOjVJIi1ufOwcambFMswPIyHFmWOh39HjRb0EjSW7VbsLklpkm7C/UGNLtyXZqYWf4zyXsVk2JMBX2tc6JiZR9jCNlFgrVQs9tHdg0TTWuWJqDD9iLWYCInbLNDx4lGUNjXoYRJu6SGRgnIxZJq6tgxuOXuCuUTCzAYULu4ThBK+q8gMGNAjY0e29On7RZN5ZNH7jNto4q146RcyiKHXLNZTLSKis9KHyPaK3pO7rPiche1HoV9D12p+glt4FEkDZtGJul41lR845JlHqQZGmmRa/shyyS0o8hGPdfsdWrIGI0Rk2cVGWNtTKq648hvw99gagktIrVkcphUaREy1m/A1GLKI/3dTtbRKLZR7csj95FGTrUwzy+N8PJ4no48/vjjPP744/yTf/JPKBSkAtpznvOcpyIHsuo/3HLLLdxyyy381V/9Fb3ecMWld77znfz0T//0ZW33YeQHfuAH+MZv/MahZf/wD//Aa1/72qvUIo/H4/F4PB6Px+PZmUNlatjY4pkdTaAFtVUcrifvOetd86SXMbP3b0Bm6Gpe9RgRbDXdVBkjPKmgpbPeNQVPTLrINsDUgthtAWsYFhNHiVyumKjpslTAj9guRO40kz1rJrb2X1L7+6nzUlGuOZCojdV1MTDKJZiagtktSU01MSFmR7UK7Q6cvwDRQP4+cgROnAyYX86zsdKn3YxptyVKoz+AZlMiOXpdieKo1cXoaLfF4Gj0oBKLED5rtVMjMzQtlaYhsyNywNRiUYMsDe2nnPW39ov9vZoj7nVzI3MCTFRRF5MOTfdbQATZMGlzIWlvJTB1S/q1FtH6JtFWnUcfgZVL0ledvjlvO5qlw97FZx0X7WQ/q0g6NK0DkjVOd0pRpLVGNDqqjImwUjNGjz1IOU5alIEdtXG1ojQOUuhPI+0e7bM9rZmaehoRpM8vO11TlkG7U+SB/d1Bne84x9Tv07azKSH3l5phNUx9ngEy1vQem0LG9CySvnAaebZpf80iRsY8JupK+1XrYaiJNOpZq23RdoA3MjwemwsXTIWxM2fOUCqVeMMb3sDrX/96pqamyOfT/xl79OjRbcve8Y538BM/8RMARFHEiRMniKKIbrdLs5n1a3/9Mz09TbVaHVrW6/VYW1u7Si3yeDwej8fj8Xg8np05VKZGVpSGzlyvArdg8qRr+hG7doCKThuIgPVsTE2GNUw0RAkxM0qIMHghOcYxa11tk9bbcNtpz7ZtWsu0pobOHh5lcoybfsQW//S1kJyDXU8hTXAclT7G3m9Wodsepq5BFxMxoTUOik2oNGGqJhEWs5tiXkxOieERxRKtUanA0jLc/MwSE7cehfl55s+epfX4Req1mGoVBgMxRLpdMUEaTdm21xODo9EQc2NmDQpt6MVJtEUs7dI0VRsYgVJrm+j5FJLznEDGlIqOrpGW1n9uv6lwOQptwzymeLimDtLaECo6azFiNTkqOYlsaTbg3FmobtTo9SVVV60OtaaYAlprRQ2WcYp4j0KLeet1BzPOJtieBk0pJm1PM/c0FY/eLzlMhIbd/3Yb9HoUMfegm/LqahkZ9vF3Si93UMcZhY4dHbtq8oFJawbbUyyNs29lJ9N0r9hGjHu8tOPoM1BNinngmcjvg0ZgnE/2qwXU80ifbCBpBpeQ53QHef6vI/fSPBLRAVKY/THE5FjGGHDa5m7SBm2jGz3kFhXXyLEO8tvg8XgMcRzT6/V461vfylvf+lY+8IEPcOedd5LP53n2s5+94/a5XI5czsSpra5KMrqPfOQjvOlNbwLg85//PK1W6/KcwDXI7OzsNgOo3+/z+c9//iq1yOPxeDwej8fj8XjG49CZGmkiVhFJGXIXIkQVECF0PnnfwAhXPaRYeBkppHsSEb0eRkSuS4hIO4URVM9g0omcQ8SoFev4KvjbaZ80VZPmRrfXnQVuTPZ/FhGvNI2RFoW2z3mnPlFsoX0G6ZMZJFWW3Q43J35a/YhRn5UuxhzQvPA647iD9PkkRsiu9mF6A9brUCnBVBKpkc9JvY2FBcgXAsqLE1JQY3KS4OhRyhubtNttuf4BVHNQKUux8VIJJickgqPdlvRV3Q7Mz8PCmtTf6PeSQuNdaEUSRTKVtE2jbLaQa62CvwqNJUSkryfrpPVb1jXS8TDK1FChOZ/0WSU5pha2VzNjDpN6qoCJMioVIZcXs6fbg35NDI5mQ/qi05froKaGzijXyI29khYloW3Va58241zNRreIs2LXElDjo29ti/NZI6RswTstMsP+rEYoDAvK1wOjIqtsQ8O9t3uY8aDGr0YxpUUYuPtIq7OxW7LGhJ5PmrGh36WZMAHy/HsmcAdybwXI2JzH1N3R/dWR5/E0YowvBtCM4R+Q34DNZN0JxMxbT9bvIubnHPIM0RSBSp7hej1qdA6sZcqNyX5WMZFQxYx+8Xie7nzbt30bAFNTU/ziL/4iAHfffTfPetazdrWfl7zkJbzkJS8B4Ad/8Ad57LHH6Ha7fOhDHzrYBl+DvOQlL+F7vud7hpatr6/z7d/+7VepRR6Px+PxeDwej8czHofK1IB0sUxF3xARmRqIGNTDzNS1RaEIEZ609oaKvHZ+/iZwClNgnGR/51OO79ZUGGDE2TRBUMXlmaR9m5jogDNJO/aLntcmYjxo2p4swTErgiNNwLS/1wiQCiZtkM6a19nGmlKoihgD1T6U+zDZhPKGRBv0eyLKXzwfs/BEjbnoNLmJFaJOl1YjolaTdfKFpE4EYnCUkqLjg4ExOnp9WFyEG26ErU2px9FqSURHuy1FxutdODaAUqKKP94RIVHF3b710rRlISadzziirYrtRdJTPdnF6LWIs4qudUzEwRTGzNDC2WVgIpCUXtUqJCnHiSIxchoNidSo90WAtetp2ILqQaLXuE62kdMhvS80UkD7Q2ucgIlQIWU7TXWlkRBuup+09UtJOwfI9dwpkuYwoWPOFvrVdNXvtY91uTsWNMJAo8pc886tWeI+NwK2R8tkoUZYHrkumqIv7bz02GnRa2lGTogYEDPImNR6F/Zvg2vsqVEbA+UAGlaEl7ZhDRnDel+BqeVjY5tntpkxqo5SHTG9l5BnaTtp50H8Jng81yu1Wo2Xv/zlALzwhS/kq7/6q/me7/kejh07tut9/eRP/iQAjUaDt7/97dx///38zu/8zoG21+PxeDwej8fj8Xg8+yeI4/jQTFQuJIUhQ3c5EnEBIkxHGOEyRKIvNpxt8sk2tyKi3pOIiWELnLaYpzPM7QLdKvrlMcaJppsaNQu+gBGtNEIDTPqrvcygt4VMFcKnk/NZRwQ4GBY7VVBMI2uWuyvGaeFh/ZxH+t2N3JhMXrYor0bIFGJKLUzDbbfBieMwPSMpo+IYWk0R6YNQ6nAUkvxMhQIU8iLkh0lRbD25SmJ2rK7Ktq0WdJK6HJtbUmui3ZLtmk14eNOMnT7DEScqXmqarQ7GdLD7VK+9LteUaDlEPNdUY3aRZrtfdSz0Gd73HDKzXAtnzyOpbo4X4eQJOHECFuahWJQ0XBubcPYsPHEKnmiJ0beKGFxaS2DdOoZet1HF5MdBx9xW0j+72ZeaRrA9uskuxm4L9drnWhPB3o9+Z78C5HocQcbdGnCR68vUUOzngXvP24x6VqkhZ/e13h/6rFNjxDWSxo0s0ELZE8nnJjI2dxo7eh5ulEbofJ5BnrVbyUuPV8ZEyrnXfwK4HUndt46kl7rktMk+X32O9p22aASWmjwRJlIqiwomxaH+rnSR+3ft8PxUX5NkFZb2XJ98zdd8DbOzs0xPT/NLv/RLe97PqVOn+NSnPgXAG97wBh5//PEDauHV59Zbb+X9738/X/ZlX/bUsiiKeOlLX/q0iFLxeJ4uHKL/6l+zBMGhmwfq8Xg8Hs++ieNrXy07lKYGbBezChjhWQu9TmEEobSZ6TlMUeisdDkueUQQU7HJnSFsz1DWmbn28fR7Fbt0FrqKi/tJM2ILjLZAbqfYsWdVhwwLklkz3CPn3UUjM9z0PpruRfPKa8SB1kApJessAHcEcGRBCoBXykacn54Ws6LdllRL1QnI5+U1OSGpl4IAymUR9YsFqc1RLInh0WqLmdHtwaAvRckbDTE0trakJselS3BqA2qxGVdaL6KOifzRaIduskwNDvt6tzHXfAYRKItIujJ7HNpmxk7XvIoIs0tIoeNjSE7/E8tSVH1xSfopnxeDZuUSnDkDT5yD07HMUF9DzAw1NTaQaz+Z9H8Rkwptr2iqLi2wfhAPlgAjrOssfVea1NoEei+5kQS2ebME3Jb8/RiSTu56LcycZWzodzAcPeBeL7vvcxgDKe1nzd5f1nXXNqTVUplOvtvC1PnYiTSTC+ddzbB2ctw8cl+WkbHuFlJX1HjtYGrR7NQWjaTSdF5p41R/L1zj0h7fGmVnmyV9IDo8P9XXJN7UeHqSy+W4++67ty0Pw5DPfOYzhGHW1I7tPPTQQ0P1Nj7ykY/wxje+8UDaeaWZnZ3l05/+NLfffvvQ8sFgQKFQ8CKox3Md4e/n/eNNDc/BkJYw1+PxeK5dDoOpcah+oW0RyEZFaO1urTnQR4SrLNFywN5EXI2w0DQ+blSDK1Jp6qAcJvXJQQ0N26TQSAEVzdJIMyvS8uy7f2f95NqpgexohQFyrlp8V0U+FUnV2IgQQbOHGA4XL0oqqQtteLIPN1yA45NQr0v9jGpFUlCVy4mpkZOIjIkqVKpSiqNUTupJtCWKI5836ZkqPUnX1GlDuQIbG9Dvy/eNpkR0xDH0IhkbWodAIxk61t9lJPqhxfDYtNOQaWSKGk72LPbdpOjR9EwVkoiICTGA5ubk3HM5KZw+6EsR9WZL6gGo8aJjTotwh4iZcRMS+aHpefZDBzPuDuqfaBpdAduNEn0OqEFWwKSLc6M9wJg4k0jfF9j+LLmeSLuf9Xzt8arRLRptlibglzDPL5c0oyKLtP7WlE8Bu4tSSxtjev+5kVN29FQBY7xloWnSdtOWHGLq9RiO2HJT+KmJrc9DTT2oxstujuvxeEYzGAy47777Ur+bn58H4KUvfSn/63/9LwDy+Xym0eEaAHfeeSevfvWrAYkMue++++j19lOt6spRqVS2nQ/IOXoB1OPxeDyey4GJc5f/q2TlwjgMuAmIPR6P5+pwqCI1wiDYNjPXFsk0AmIWEULX2J52ahwqiNBbQGbY153vNVLD/a+rK17pbNsJTCRJi4NNd2OnxNorrrOVZmak/dQGJLUdMKaSne5HZ8+TfKcRNNruMhJ5oELzcrLPOlLM/SJSOFdn1mvKqhxQzUO1DLlQIjSmp6SOxrHjIvSHoRTLjmIR/QtFCJPB0h9IMfFOR0yMdhJuUW9IlEOrJcbARg3WWiadUhMxOtQk0FncPSQ9jUba6MzuaeBE0u7TiAGiRoYbTTCKBeALkTE5BZwI4Iajcq5LSzA5Kes1G7C+kUSenIZTbYm+WMGcQz1pa4BELdyCXP9V4GGuzVRMaujY4wmMoVZCxGGNTNBrZZtGOt5uSl4x8Gjy6jL8PHHF6MPMTpEM9ruak+69HiD3eBmJbLG/n0neN8dsB4yO5Ej7LquAuLut+5tgp82KkTGykHy3xu6jiQJMqrw+8nzqOd9X2B6hB8P3uh2tN5ls18GYGlkcop/qaxIfqeEZhz/+4z/m2c9+NmEYcuLEibG3i+OYKIq49dZb6XQ6XLhw4TK2cn+EYUi32yWXyw0tP3/+PHfffTfr6+tXqWUej+dy4P/9sH98pIbnYEmMjSD5n20cJwbHYckfMM7/zjwez/WAj9Q4YNJSKOnfII/XMqaWxF6KqxaRfOq3I4/qB5OX/RNjz1Ye/i+h4IqiWrPALkR+kBQZTmeyW+yZzfYyGzuVzcBaBumzt91962xp+7/KKqZruzUN0iYiOGp9hscxEQ86s7nSh1LdpBCrNyQqIwigVpP3rU0ppF04JsWzte5GNJB0Vt2uRG0cOxkyMZOj04x44tGBGBttifwor8FUC2p92IxlfLWQa6kpvvJInYZLyXmUMSbP6aTNGs2xW0MjhwipxxABOUSiVaamJeVUpSJmTa8vZsz6mtQR2WxL321g0k61MLPT4+S7s0l7m5joo3FSCV1JNP2O1mpR80hNww5yfj1MJJBGUGnaJF1fx9sAiUxRUXoS6Wd7Zn8v2e9+/nlpp26yX+OiEUF2WrPd4N7TrrkQMnyPprUtRvpYDaSW9Z2mnrOLZtvYJtRO567Xy+7vcWXotNRZ9u+FGjOTmBRXux3bs8AXILU2NoG/By4w/LzX+0sjs9w22n0QYqI0xklx5fF4Lj9f//VfD8DMzAy///u/z/Lycmr6KpcgCMjlcjzxxBM8+OCDvPSlL+WBBx643M3dE1/xFV+xzeT7x3/8R/7lv/yX3tDweDwej+eyk/zvNA4gCOGpibshcWzHe1+L+ElCHo/n2uJQmRowLFTZywLrcxeZrb0XMVKjNGYR0amFCFgrGLHT3m+WEaACXRURozXlySgDYLcUkVnDk0n7dLZ0Vu56d+ay2+4skU+Xa50MTZHSx5zPBMPF0pVRNSNUrFbOWp91Fn4POa8CpvBuAWNu5BHzI+xCYRWeXIFWmByzA8+5QdI0EYux0e1CHEmNjX4Pjp+A6cUiEyfnoNujmFtlMIgJ1qWGx+Sk1ODYWIeJJtR7UntDC383MELmcvJ3AxPBYeOK21nYaWmmERFVTboCSbqtiaQYelKcpduRehq1uhRC38KMW4020uLnSi/5TmufaJ2XGURIryf7uNrerLbtJqSPV4AzSP/bRcJh+/1l1zDRa3PO2bcaZjqu1KxSI6rF+M8SHbdFTPRIDulLTSu0m3RNZeQeX2d7xNhuyDI3tB2aimvA9ucpGNHdHdNtTFSWjRtFZy8bde5lhs3o3ZhAOxkbei338tugUWbzyPUoIeOxk+xPozPsZ2nac9V+lk5gUu9pyjSPx3NtsLm5yYte9CK++Iu/mO/6ru8C4OUvfzlLS0s7bvvMZz6TX/3VX+U1r3kNf/M3f3O5m7prfu/3fm9biq23vOUtPPzww1epRR6Px+PxPN2IiYkIYiDIEeh0qCCEeED81P/Kri2CoHAoZm57PJ6nD4fO1IB08UqX2zUEbPFMZySPSn1SQkSrCqYGRAVJ06OzadsMi26jBDo1NSaSv1U4PKifpwImdVMTEbLD5JgxwyKo/d/XNH/dbZPbVzrzuJz8bfdvLmmD1mtwBb29nK8OTK1BksOYGHaNEjU7gkBSTZ1tw+cSxXIJmDkvxcWLRTExGs2kTkNiCgwG0NrqUd5qkAtiCoVYrlEMc/MyHhoNMRFmE8NgvQYTPRkbKxgxUiNJskTwcWfql5BC4DPJaxIZc1tINEt/IFEn+by0s9uR86rVpK29gbRNTbkN0vP06/3STdYdYFKCLSBRDXuNELDRaAVg7H+e2fVXcojJeHPSriZyPuMI0zsdS+sgaDqhPKaGhxaH10ionTKla82ORYYLX2sNE/u+swua76f9u8UV2tVoCJHz1Oud9jxwDSSQc9utQeAaJjZp0R67wY1GsdH0az3n2MUdjqtjZA4TGaTp5RaR54/Wb7L3M+q3oYiMlSpyD+bxtTQ8nmuRe++9l3vvvReAv/iLv2B5eZl3v/vdOxYYf/7zn8/P/dzP8ZrXvIbPfOYzV6KpY/GmN72JiYmJnVf0eDwej8dzmYmS/7sk/xsLQgKr2qGYB9dO1EYQaLVRj8fjuXY4lKYGbI/YUDHLFawUFRGzBLgiyYx/TJHYPjJTvYtJM5SWbiqLPiKiqfkwhZkpfBCoYK0GzDJyHhPIzO4Go02cNLJ+NjXypIgphAvSJ4vIrOVN9pbyK40eIhL2MYZTGXMN9L2ACINbA2i34FIk/VJK2niqCY3PQSUn68axRF8EoUQ5rK9DrzegtlmjVIrJ50xNjukZY4BMTpn0TtVVmNyCXANqSYcVMTPW3ZtKxW5NT7OTCKy1ODQdV4BcT00X1OvA1pakoQIpCl6rQb0mqagmKlDsi/EC6aaEfd9oYXcQUXszWb7O/kVmkOszleyzhlyf3cy+LyTb95CUUWcZToGUhqZK0noFWcaMmoydZJ9qBNlpwsqMF2ir12cSuW4N5HybmHGM876TyaMROpdL8FaTrc/OPwZpERxZY1n3m/bP3p2M3f1maXXNWF2WNZZzO7SpgjxT1djWNG61ZJn+tuzmn/jdZHt9pk4zepx6PJ6rz2/91m8B8NnPfpYgCHjRi17Em9/85sz1v/RLv5Rf+ZVf4aUvfek1EwXx8pe/nEqlcrWb4fF4PB6PB4CIOA4g6BNQlDobQY4gHhAREsc6lfRqR23kCIMycdwnvupt8Xg8HsOhNTWUtHRUWeuNWkcNiA4yO38JKSb7MCLyqvC7G9EpQsQvnf09iQhZB2Vq6Ez7EJlFvJy0cWuHdtp9lpU+K40o2a/mo9dURbci/aXFmbP2VUjW6yJmzE5t1NROOmtfxeUJTCRCPtlPPYYwNrPiSbY9F8N6V9bXNENzXYna2NiAQR+KJbjpxpjlI7AwLwZGsSTHarWlbsX0lKSsmqjK95cuQnQOtuoS0aBCp9ZN0dnvITKelhHD4AImXU0aatYUkeiEMibqRutr5EKobUnb2x353OlAuSIRJXEs7e6x3eRzc/271ytCIhbswuf7oYhc8zLm/hknykKNBm1zB7kPa5jaJaPQ+01rm4waa02M4O2mSysgY2cnE0XvpRrwBKauih7fff6UkuUDRgvqGh1xucvG6XGyouAg+7kxCve8R203gVy3GvtL05fWzqxnvxpLGqmUhkZmxEhtmwpSK+dU0k69PjtF8thoZIeOrynkOeLxeK59PvaxjwHw13/917z3ve99avnf/u3fMj8/P7Tus5/9bP7yL/+SbrfL2toaX/RFX3RF27oTcRzzrne9iw9/+MNXuykej8fj8TxNGRDHZopUEOQJgiK5IE8US8SGmBtXz0wIgyJhWKI/OIgpjx6Px3NwXBemhi1YuYKbm/bFnQWs62vqpACZDT6HiLt2MeG9tm+AiKJtRMw/CDRCoZy8NM2V1lGokZ1HPy39yzg/kVo4WX9SNTJmDTm3FqZ2iO7T3m8OEbiLwOcxNUBGoftQgVhn0mvdA41MWcOkoipihMaWtV4BEZM3IihuQn1T2r0MDGJJRdVuS6qqek2MglIJFhZgalEiNiaqsLCY1LSoQPiYrKsCp+brn8JcE43SmLH6ToVSV0gdYPLrF6xzLyf7mUYiMUplMVk6HdhqSqRKoWvqfpxD2rTF8HjXuhnat3aNFV3vIP6poqnbpjHpfRpIH1WQ+0sFYTDGYRpaC2Gd8cRuTU/WwURJZFFI3t3xX0TMKK2vcwYxK7KwIxc05ZQd1eWK6j2G0zxlGQUaRUHGeuOmsRqHcQt07+dYWamnqkh00jTwKAdTe2jUb0NgvSJrmbtdjDEHuxhD7TzjmWs7tU8jcTQtlsfjOTw0m02efPLJp/4+fvw4586do1AoMDk5+dTyY8eOAXDjjTfSbrf5vd/7PV75ylfS6VzZpHPve9/7+MIv/MKhZb/1W7/FD/7gDzIYXG7r3OPxeDweTzZxYlwABIShGBtBXCCKu0TR1YvaCIICpeICg+haiRrxeDwew6E2NdLSoWgWQiVNRFOjYxIRuVcZLii8AtyPiJMHITR1EJH5oMo9hYjQqpEZHczs5nXS0walpeuy38dBBTh7uyYinpcwgyl2XkoHMR+m2f3Mc527oEKgbt9NlqmRoWKxpqgK2Z6uSvvnQrKPPLBRg/gJePKUpHCqNSQi4uRJmJmVmh1hKKmrppJODIBeH/pPQLUFxUQdbSXtWcTMyK8nxywh5kYfY9TY4n6IGCF5JM1SGzFIZpL9LU7D/LxElBSKEqXR7cOlDbkOrZ70xwZiyun5aZ+o0O6K6XakxkEQIuZFDrnmKtoOkj6IMcbGTinL9H7W66w1b7LGrkZIRTusR8b3ar7djqRVmw/gkdikexuVxmgieTWs9VxhXdtl9/WoNgbIuNX0a2vJci02rebdXiQpNZ9yyP2pbbYNL5fdPDPSjAV3H2XgNuDupA1nd7H/Uce1jwfSX66pEaesm0euf4Nh47WPGFsXkN+I/RIhz21Ns+fxeA43nU6H+fl57rrrLj7ykY9w4403Dn0fBAGlUolv/dZv5YEHHuAtb3nLFWvb8vIyi4uLQ7VAms0mjz/+uDc0PB6Px+O5JoiIY/nfWBSH5IKQMCwQkicK8gyiNnHcTWptXCljISAXlMmHFQZRR1JCeDwezzXEoTY1FDcKIWuWsSvktxDhyv3vXIud083shnGMkd3MuI4xxZObiHitBbptwT9tu1F/j4Omy7H7WIt5a0ooe/aze7zV5NXcw7HT2jtAhN1Cst8OYpoUrJeK+ZquCkRk30CMgiJSl6LegG4s5xEBpaIU3t7YgFxOCovPL0C5DNPTUqC734dCAabOwcQqlAcypoqIEbGOiLRbGFFajRYYrk8SIJEBN2JSHuk/WYrAVFEMjWNH4ehRmJ2FjU1Yq0kbH0ME0ioiTucxKZiwjq3YqafilOXjohEwOYzADqZGgBYjx1p+AXNt0gpQ22gtgzKm/gAp+7XXH3emf9q9qUWcF5B0Q+U8HOlJJEGIjLO07aaRVGwBYjClGSZ7uecKwMnk3Rb8NXXSqDRhOtbS/umrZokdTXQe03eX85+stmGiadrWkWfZ1mU85qg0VGAiljZIf0atpSzbDzuZGZouzkuOHs/h4YEHHuDbv/3b+Y//8T/yZV/2Zdxwww3b1rn77rs5efIkp0+fviJt+t7v/V5e8pKXbGvnG9/4xityfI/H4/F4POMQE8ddokgUrnwuTxgUCHMFwrDIYNAiitpEcZcrUUQ8CHJMlW8kDIu0eyu+nobH47nmOPSmxk45013s9a5kZsKstCtKBTNzfydiRPg7h4nSGDei5CDONyutS4fhdD5px2rvow1p+fn7mNRTGrWhs6uLySuHMVwKyT7ayfJ5RLx8YiBmgKapCpEojLV12enWJhw9JvU0yiVJSzU7B/m81LIoJic+U4cohLgH9S5sxCZ6Zho5hqbq0qgSRQtiVzAi5kSybDKAyYoYGceOw9EjciLrG9AemCLtani1MTPv7QiNrH7VvlXTo8/u/pk0A9yQbPc40h41N9IEWU3pM05xZS2qrOaJ9k+fg7+HNcIkRPrzIlJ/5RLGdMw6Xg+5LwdJm+1ogLS0eLvpXzvCRU2AAsbUSDMsNA2X1sKxv9N9aLRAhJhGJQ4m9VMarqmgkVc94EnECEputwNjp9+HtOeypju7Uoz6bdDn0U7RTB6P59riE5/4BJ/4xCf44Ac/yCte8Ypt37/iFa8gCALuu+8+fuqnfopW6/I9dZ71rGfxVV/1VUPLGo0Gv/ALv3DZjunxeDwej2evSCqqKIrpA/ncBPlcmRxFcmGR/qBAf9AkjjrEB5YLJJ1yOMdEbon64BLRFY0Q8Xg8nvE49KZGGioiuulFYLu4uBdUMITxzQSdpd8gXczcaba6SwspVusKllcTe9Z4Wj9roesOexcN45TPdYxQa9eM6CbHCpB+D5HroLPXC4gIfSn5fBtwJDmHAEknlW9APpRIjekZaLckgqNQgGpFTI1c4hgMBjCxIdEbq+uw1jHH1/Q+UfLuZtLOY4qCa3H5SSTF2IkyLM/C0iIsL8PighQyP39OXvWaCPFHkWsQIWKoHsetK6N9Z98fuh2YiAg1SbR9IekCeg+T7knb3cPUFxlFhDGaRq3bwxSo7yMz6VspbdkvMab2TRkZHy1MkXI1LdJoIrU3tK8KpNfD0PEA4927UXJ8NQS1tspk8t0Kcq3sMaU1dwZsNxI1GkHfNRXalfwxcAvWax2Zy/XP5Cwj9qCPp88W+37aaf0y2dFFdlpEj8dz+Pjv//2/83u/93v8/M//PNPT00PfvfzlL+flL385z33uc/mWb/kW4suU0uE5z3kOL3rRi4aWNZtN3vOe91yW43k8Ho/H49kvMXHcZzCoQzwgCEKKuQlyYZFcWCIMivQHDQZR87IVEQ+DIjOlm6nm5tjqnyWOL6+B4vF4PHvhujE1bJHWLgKbtl7a5yxyiOik4lIRmc1cTv6+wM7RFdqOUbPf7Rn145gb8RjHHdUenaV9ED9LtoGkpBkaE0hKH3sW/0ERW6/AWaYFudUwCJDrqCZTG5PiqI1Eb0wg17nXh1YbSk1J8bQyIakkp6fF2CjkYWISokjMjMEALlyAjbYI4DEiLmtkQlbh6hKmlkaMiNLzwLEQTizCieOwuCTH7Q/g0kU4fRpOnYJGXUyNKjLTvYOpozHqXrAjD/oYUTnGzNofYAR6yBaeG0i9gRzG6BsneirA1IWo7bBeGRHz+8h9t5+xmzU7PsZE0aghZdc9GSVUR8lrAanJoNFUWcanfR/uhBpiBSTt3EnEIIyQ2iR1TG2GJuaauYXi7fN0oxMOokD8bnCjWLQvNMKky/4M27QoDT1OVgSNrj/ONdFnSIQxSfW3ocZ4USd6rKzz1PuyMkZ7PB7PtccnP/lJPvnJT/KP//iPLC0t8ed//ufb1vnmb/5m7r33Xh544AG+4zu+40CPf9ddd/G2t71taFkURXzt137tgR7H4/F4PB7PQSNTDwdRA3oxAQGl/DTF3ASFsEx3UKLTy9Mf1JNaHAebjqoSTHOy8BzW40sM4h7Evki4x+O59rhuTA3FFbXTGDdaQwtyz2FmX2vtADDpZnYSA1UoHUUFESl7yCzx/f5cFEk3UbRgdhnpBxVwd/sTaBfuTsuPr0YCyfdTSP2KAvsrvu5G3djHVlHZFdNtkVJ/irUNdhTDOmIkaMRECWjF0GtCJ7nIuRAGkZgYlYqkocrnoVqFuTnodGBzE4qhCPAnMal9NCrEJY8RSLUgtM7Gn5mUOhqzc1KkvFiAZgPW1mBlFdY2oN43s+03knPTyBCdDe+meVJx1zV9wAjjKtCq6aF964rEus2KtU/t63FwheYsZpDC3RoR9Ch7M8fsc0g7no6P3dZPCJEaHF+CjPVHkPobmorK3r+2I0d6xIBbI0jHhQrnOl76iCl0JHnvIebSWURYv9JGRRruMzmtcLieZx6Tgq2GmDUH+c/ztGgNe/m4z90S8qyYxkSIado6kOu6xc6mzIDRJnaIPAvmxmyXx+O5Nrn//vsJgoAXvvCF/Omf/im5XI5cEuYZBAHPe97zeM5znkO73eZVr3oVUXQwT76pqSluvvnmoWW9Xo/77rvvQPbv8Xg8Ho/nchMziJp0e1LjohwWKOQmyYcV8mGFdq9Er7/JIGpxkJX4ckGeMlUG9IiibpLqyuPxeK4trgtTI63WgiteRc7fKhTqTGz3v486izqHCEpTmGLeOoO4j0l3tBcTIkBm188hImWAiHjjFoYdJcQdRWaXX7SWhcnxJhHRUHO115L3vZoNWdEx9jkEmLRBB1UIWCNbbOFWZ01ru9KiR9wZ6mCu4xqmNsgWJnpitg/RRYhiefW6MDUN01NQqUrUxuQkLC1J5ES3B5NbEr1xvAvnIhHh68kLjBias95tU2M2gPnEzKhWJO1VHEO7I9Ej7baYLS3kWvcxIvklthsaacJ61ri1Z+6rqaGMioByx+1OYnGMRO6491Ba5M86Mn7yiKg8y+7Tttltgt3Xthi1vzJiutxRhLAHp+L0miE7nWta/RM1QHScriP91kTu3ZuRVGUdZMw2uXwFt3eL+zx2l9vPjxwS5XIrMoYfYP/1JNzjKGnRGvp9heHoJVK2qyDmldZgUWOxhzHB94qm6ptCxvo4UU8ej+faJo5j/uIv/oJSqcR//a//lR/5kR8Z+j6fz/Nd3/VdrK2t8SM/8iP7rrOxuLjIX//1X29bfvLkycuW6srj8Xg8Hs/lIGYQNWh1esRxn1zxKKXcDMVwgkJuglavRLu7ziCqJ+mo9kcuKHHL1D8jJqIftYmeitTweDyea4vrwtRQbHEs7bOuo39rdESD7alCVMjdQMTCOYYjEDS1zyQiKutM9nHTpaihMY0p+Kvmwrgi7aj/khbZPvs9whSnjqx1YkQM3e/PX1q6F12+ifRxGrudJQ1G2LYLW9tRF3ZbdhLwdXutl9BK9t3C1CboAPEA4hUxKup1MRw6i/JerkgUR7ksxkY+D42mRHRMnoNoVQyTCqZWhRa9jjGGRgG5LmUk+mNyUoyTchmCAHo9aDWhVpM2tHvmmuo+dYxrf6QJomlCbhoa2aJjf1SUQ5p4nTYW0tBjFDHG2woizisXgU9jzmsvEUZ2O+1z2es/0XQfaoD2gW6QXKvYPCf0uGno+URsH8P6WdOXVZBnhkaqbGKukY7hdYaNgL3cX5eDLHPDjtYoIIbsFwWwGsL6QIqI7/+f5uOTRwyzPNKXdef7GDEv1pHfBv0R1ed/jIzjGeT6wM4RGTYanTGV7FuNK4/Hc/3w+OOPc+nSJZaWloaWB0HA61//eur1Ou94xztoNPZ+97/gBS8gCIafup/5zGfo9a7kE9Xj8Xg8Hs9BEcddOt1LBAQEpTzV3ByFXIViUCUflml1V+n1N4jiLvv53181v8B8PE8naNGLxdSIr/r/Jj0ej2c715WpMQ4qHmoB3QHDBZFdekhefLv4r6ZY0pnFGoWwG2E0xtRXeEoM3d2pjESFMPe8NN2UGihFRAh1C1ePQ1bKHDBRLSrkZfVNERHv+hgBcJzjaoFdbXfe+d7+bEdsjPop7lgvrd1QZLiYddSH1irUWmIu9PsQDaSIeLEIYSCpoioVqa/RbMLKiqmJMIX0fQeTRkgNpgIiWk8mr2oFSmXZb76QiKldaLbE1Njagq1IrrUdsdBiu0CelTYKhgV5XdeOCtAUVWqApZkhaYJ11gx599hlTHTKAjL7vQh8DhHu7fVH1d3YLaOiJbRtOgN/HHrAKeBvO3JvPY4pZu6aa2n3Ts5Zx752fWTM6PNKa2jofXUWiWxQ41L3owJ5xHZxfrfkMMXi94vb94qOg4kCTJbheVty7mfYW30N977PipLRNmhqM302j5ITa8B55Lro/aKE1t+7Ld0XYZ4RGknog709nuuLX/7lXyaXy/G2t72NxcXFbd//l//yX8jn8/zYj/0Ync7u/4UWhiF/8Ad/sG35d3/3d7O5Oe6/tjwej8fj8VxrRHGHdvciEBAXI6byR5gMlygWqhTCCRrdEp3eCoNIK4fuloCTk1/JJCWaUU0iNaLLU4zc4/F49st1Y2qkiVajZgWDmBrtMfZbAx7DpLw5ihH39LXXGeN7MRPGIUJyvmtxY/snTdOq9BAhvU32T9So1Fqj0srAcDHytP3kkb5cRGY9b6Wsk0UXER7dft+pTePsX6Nl9HpXMDOze0AzlroWvb7U1+gn5kV1AqampM7GxESSqiqSv2dzcMPACNrrSJqgKnItbENjBpgpwcwMTE5AsSRmSRwnhctbkuKq3pa2NjHRGtpOFURtY4OMvtFaMQPkmrgGkab00r7Wa5mWzsfu47Rr7q5TAm5ExqoKwX1MtIF7P+83skKPndZW+2+c9oyzL60rUkfarjVN8mSPyVHGoL0emJopK5h7WNGaKG5/VZDxtDHiHMZFI2lss3K3pEX3KNr2JnB+AFMdE7FwOdIvpaU409c4kt8AKVi/ifTzEjKO9dmkqaj28s9/fWZ7PJ7rl1/4hV+gXq9z5MgR3vnOdxKGw0nr3vzmN7O0tMRrXvMany7K4/F4PB7PU0Rxh1b3PL3BFpQjZgs3MsE8pdwEpdIEW2GZZvcC/YE9DW48cmGVqXiWMIQuXXpRC19Pw+PxXKtcN6ZGGqMEy3HQlEA6iz2HCFlVpONWGK8Y7NVggJgFM8hs8dNsn3XsCqMuWsBaZ4inkdanbvovFdbdY+UQ0RKMCD/uNYqQ89G6Gvaxs9jN9W9jCjxXk88axdEmMbI6MLgkxcE3pqSgd78vZkSlIimI8nlYXhJzY3oDijVp+3lM0edu8nkauV7TwNw0zM5I+qlSCcJQjtPtSC2NVhvasWmnmho1jKCq6XyyojVsNBUZmJouronhmlR2YfZRxknW7HhlAhGFLyEpunrItW1hIjlKyHjU1G819iYWK1nml81uUgZpJEsXEymj+9BoLvvY+oqsZTtdJxW67Wvjfm9TRPp2wHAar72i5zhuzZ8ssiJ3YsQQuh+4MIDpgRgGe43SuFwEmMLgmgYtj7R/Ehm7+vIypMfjGcUHP/hBAO677z7uuOMO/uf//J9D37/61a9maWmJl73sZbva72/91m9tSz3l8Xg8Ho/n+iGOu/T6XdZbnycmYjF/GxPMUg2nKJemWc9VqXXO0OutE+/if1Oz1dtZDmchiOnGHQZxhzjWKXQej8dzbXHdmBpZs8b3igruWrchQETCCiJidRABts7+Cw3vxG7EfkXFT40K2Etqq8B5z8Kd+W6vn0f6sYyI1nbqmh5itoSkR12Mg4rGbpvdGdjusnHQNDAFjKiv5sZT0S59aK7DTE3qXcRx8orE2CiX4ehRSSNVrULvlER15JFrpGZCATGhlkNYnhRDY2pKIj4KeTFFOl2oNyTtVL0m4rkWrVehWyOHYoaFZ01tlGZC6LkVMEaF9uWoa2uL21ljxN5HmrGhs+K7yFjYtJaDjJsFJGImRMbyOvv/J9WoaC57nSBpQ0B2RJPdL7lkfb0GpWS5XiM3BZU75t0oGPcaFJD+6AOrZJsLASa9WZYJslu0ePxe+j7LyHD7v4uYxWsM1zDaL/YY3Mvz1Ebrvui11YgYjXqrIe33kRYej2dc/vzP/5yPf/zj5PN5fuZnfuap5UEQ8C/+xb/gQx/6EN/0Td809v6+6qu+apup8drXvpbPfvazB9Zmj8fj8Xg8V5/+oMZG6yGCSkAufwez8TxVbqSSn+ZSWGUzeJx298JY0Rb53DTHcncxmSvQjHp0aDGIOvgi4R6P51rlujE1lFEzwneDztIuISKlXcz3DCJcrXB5c53nkJnWIWKeuP66zhbWeg02moaohRRYztr/UeBIsu5ptpsOO+XPT0t5Y/d5GbgZiT6oOfuLkEiXg8A1YEaludkNA0Q8rmOKB0fJsgZJqplYCnZH58V8iCIxNggk0mJ+HnI5SSHVaUN8Hia7sBWblE9V4GgRTiyLCTI3J6mnCsUkSqMrZkitBhsbsNKXa6a5//tJG9sYA0MNOU2B1MbU8rCTXNhCurtslFmhYr8dmTCqn9NSSUWI2ZUj3SDsJi9N4VRn/7UhlHHGSAF4ZvL+KOmGit1PeSTaRg2mIubaqLExbuSYe09phMAich21sH0aGiGldTDKyHXaDzEHmy5vVGqy3UTJ7PaYO0US7YRG7WnEno7lFpKmcJ2Drf3i8XieHnS7Xc6dO0en06FUKj21PJfL8Q3f8A28//3v55WvfCXd7uhpKtVqdZuh0el0OHfu3I7bejwej8fjOWzE9Ad11lufh0pAmH8mi/ESR+IjVIIqFypTrIR5mu0zOxgbAaXCPEdZppwLqQ0GdGn5IuEej+ea5lCZGjpzdxRpotU4qWbSaCJCtqafaiBiVQsjTu531u8oppFZ2So4qwFQQITT+eTzavKdRmeACKp1RpsSzwVeAFQDeCSW9VfY+0xsnfGv22tUQBUzmzlL7Mth8tHvpraGivNuNEFavvy9EiHXvIWcSxPpdx0HveQ9iiC+lBgaMeRzEmkxMSHFvqtVKFfEsFhbg1od1hpQ70A5D8cX4Zab4YYbYHY+IAyh341pd6RAeL0GFy7CE5twOpZr1bHaoFLFRHK+GmHSSdrcxJgo2ndpAnuaQZRmcuQQg8TN/x8767oRVG4kiPZf2jWKEJFYo6YOOqXPTs8Gbe8kMIfcI1mSkEZeNDF9oOK8Ghq6nnuMEqYvsgxCkmNfYPh6p6HXZILtJtbVIMskG3Wu4+yzgJxbZw/bj4ogGoUWa5/BRG9tYcaGHQ3l//nv8Xh2w+/8zu9w9OhR/tt/+2/MzMw8tTwMQ77t276N1dVVfuiHfoitrewpIR//+MdZWFgYWvbWt76V3/3d371s7fZ4PB6Px3M1iekPaomxAWEuZDlYYjmYoxKXqZQnOEeRWudxojg9FVUYVjhe/ALmixVKuYABEb24TRSNSkbu8Xg8V5dDZWpMsruZ/XtJNeKKvPoI11oFKsiGSAHbHKbguCvW7hWdBa9pm3IY4VejR6YxURxzyXobmNnbEaOLA4dIYdvjBcgXoNmGpchsv5sc9rYwGTnLt4C/RWasu+1RwyNKvp9BZu3vllFpx1S4P6if4SZGsOwg40LnU0ZAPIB4VaIrKhVJITU1CbNzML8UsnwipNmEdjNmEMMTjwz4x/slkuPESbjhRjhxY47qYhUKeQa1FqvnuqytRmxswql1+HxkooQ0XVQLEVq1qLcySL7rYGqkqHBeZnR6Hx3Haia617eLMVSytrX/Vuy0QgNMAeoKxnixsdM0pYnzdtTHXtgpuqSD1EDZGnEM3Yf2ib19zPb7yRb5K5i6LX3nGG4/qkHl7itw1tdUURuYujWXQ2R3U5nttG7WmIid93H2NQ0sI312luwokrTUU3ZbbCN0p2Pq+lrTR38bbFOvhDyb9b60U3Z5o8Pj8ezEz/3cz1Eul3nzm9/M7Ozs0Hevfe1rabfb/NiP/Ribm5vbtn3+858/ZIYAnD59ms997nOXs8kej8fj8XiuAfqDLTZaDxFXYqLcMznKUebCKpX4FqrlCZ4Myqy1HyaK3US5AZX8AsvBEeaLeYohRHFEP24TRTvl7vB4PJ6rx6EyNXaabZyWCsl+H2f/2iFaFFeNDDeffohEUeSRGbrrmNnCWtB4ryKr1vOYQYwTkuOXMCLZBkYo1ULTs8lx1fjJ+vkJkv1WgCiWCINickxt+17IEusamNnrNmXgpqQ9a8A55Pz0OmqNh1EGi6ZXGpWvXyNtDjITpI4VuwjzU0W0+1DYgIkqTE7B9DTMzEJprkJlcpLZXA6iAfT7TFbWGAxi2m04fgyOnsxTvWGeYGEeiiXyrRbzhfPUN7eII6gN5Po2kPGgho0dpePOiM9jamZoYftjyPXfQsaJ24c2aUK0XpescT7KiHBTXWn6LE2XFDnfl5K2akSDey8Wk3O002CNg6Zoco+naFqwHmJqpKV5s89Jnz1pUVzu3/a7zu53Uy7tlA5M2x8wLMrreho1on16UIL6bowMF/fe3C2aTkufd2rS7bS/tOOmRSSl9Y+dyiu2XppOz42YKWEi7DbYfo9dS0XPPR7PtclP/dRP0Wq1ePvb3061Wh367vWvfz2VSoU3vOENtFqtbd/deuutQ8v+6q/+it/8zd+87G32eDwej8dz9ekNNtlofZ5Bucug0OdEfILZsEyV40yWJ3g0mOZ8+z76kcmhkQsrHC8+ixtK8yxXQjoDiInoDjZTDBCPx+O5djhUpkaV8WZLjxK403DzzweY2e4qYOUYTpGjQmhkLQ+dv0cxSmBUoTiPSUmjorxGUXQRYVuZRNI3zSCiryu0ucLnAKmhsdSH/gDOxcOFhw9KALX7VdtUxJgoRUxfqrgLIgxOJefSID3NlBpIaWaXW0fioFMWdTDpl2wjTMdBqQ2VVZiYlGiN6RkozSNFNqYmJTSj3mDhRJM7Oy0aDSkOPrFUJTh6BJaWJdyjtkW+XmdhscbMbMxEASpdOS8VSHX2vl2vAasttsmmgvcc0v8NRCDW9XUdW+B1+y52PrvppuzlYKIx0r7TfusyHBniHkfv+wJicGhERGy97yV9kZ0uzT5mmBwnlxx7nHoUdh+4hoQeJy3iRU2drH3a+3GjDdJSiOnzy04/FlvLs0ycnbBNgLTnadpzF8w5k/J9lpmkEWr6XND6QvrssJ+PZYzZtRNp/amf01JGBZhnjHveel7622AfQyOodGzupt99NIfH4wH4H//jf3DhwgV+8zd/kzAc/pfOv//3/54jR47w8pe/nDiWJ8a/+lf/ihe84AVD6z366KNDhcc9Ho/H4/Fc//QHNbZaDxPFfeLigCC6keVClVsKC8zmX8BD4RIPt/+CXn8DgPn8TdxSvI1bJwscr0SsdAICOjQ6Z67uiXg8Hs8OHCpT40ZEuFonW/RxZ+KmCUS2kBkyPNs/TbBTYUvFSBWzWoiYpkbDuDNwi0jqFBViGwynTomR6I9VRAzT7zvJMXQ727iopbTbbr/7agOPJNu1Y1Nk2jVotD27JURMqGpyDjkkGqOKpL2awtRLWGN72iE7QqNgLXdT7aRdLy3iO5uc34U9nsMoYsxYVKnBjkwoxVDegomLUlNjegom57oUBn0oFKA6AZUqhTDkxMwqUbNNGA/IHV2G5WXJVxUA/T5BoUCYk3RWR6uw1JRr1kIMLzVu3PbZ11BNjRPJ+n2kX6eT7+uYehW24Jz22cZOuTaKUREUKgJrP7oGSA/p55zVXjtF2qj7ToX/tOgjW2QuJ+92NEYfGT/jllW1+8iNrNDlBUScT0sjNc6+XVEdpF/c+wJMujG7BpA9Lsa9J9z9pkXApT0ztc2ucaRt1nNI+26BJJIMGa+TmOiHNnJd6kh/LiXbpd0HimsGZRkzalDYhlGO9HMGEzWiY0xNNjXpstKzZVFFxriOw+Yut/d4PNcXv/M7v8M//af/lD/7sz/b9t3LXvYyPvrRj/K1X/u1ADz72c/mpptuGlpnfX2dT37yk1ekrR6Px+PxeK4dBlGLWvtxzsQRUSkiP7iFk/kqt5UmOFZ6Fsu1OT7Z/DDd3gqL+QXumpzlrukBi6U+vahEKYzoR7v5H6vH4/FceQ6VqfEMROwZVaxXxdEAEYjyiFjbZ1iYsosl67JRIh2YiAMVE7V+wW5nieeARUTYj5HUNmecfXSQdEwwHPlhC752GwcYs8fN6e++dPkWsMnwjGM9hiuK75YKcBQxFkrINWshovpxRAi8gBg3m2zvvz7D9SvSZolnCZMVJK3VEhKNspc6HeOg9SouIbn97Zn4JaAUJdEaVUlBNT3bZ2F2i2B6BqamYXKCoFohf+QIDAZQ25JIjsVFqSre60GlDMUCYSFHqdhnegKOr0FnAI9hBN4sw8Hunx7S93UkLc4U0kd2XQ43yigtSiNtlv2omfej0EgexZ3tr0J0N1m3zug6IDZ6HQoY88dF2zuTrGunmRonOiNtf25EhRtBoZEFeym55hobWcvVgO07y9LWHXXN0p6Lo0wNO7LBrS9iH7eIua7u8dXo6CPXW1NoqUGwmrw6iNlxFIlS28Q8M9Owo1W0lk/L+Q6Go7/SDJCs3wbtczuKZrf3QwE4mXxuI78LG3vYj8fjuX742Mc+xotf/GI++tGPksuZX8wgCHjRi17EysoKAJVKZWi7lZUVvuZrvuaKttXj8Xg8Hs+1QxS1qLef4ByQK+Yoh7cyVypzsprj9smTPKf5/2WtPWC6UOALZiOes7BJJT/gUneKe7v3X+3mezwez44cKlOjj6kFMWr2tApAKmi6wtVOM4/t9zQRUYWxIiZSYzfify95aU2EGUSY3mJYvMqagZ41a9ieGa1/B2yfAa/buYKirmunyLHT5oxDgAiNR5CZ1SWMwKm1EVpIYd+zbD9nRY2p0Fmmx8gSGbUP6snrLMMznUOGheb9ojPHzyCGgfZdERGvy10oX5BojanJmPJUnYnJdYKJCahWYGYGguTMKhUIA1m5WoV6HXp94jgmCANyOclIVQ5ElJ1DBE+9F9JEVNts6AMPJm0sJ+3U+0PrOej6kbOt22dp5oY7ltJwxd78iHVdBphoknHNAE0jVcKkYkpDIzIOoqC8a2zYqFBvm4i7TQeVZpykvWtaLyXN9MDaJo0sM9Hdn96vReR5BlLMPuu5Mep50kPMSI10AHNtOkhkl97T6xiDbga4SPY1tE2NMqZv3HGb9Xx1+8+9vvp8yiNjTtME7oZ6st+JpJ2zyPPFZ7L1eJ6+xHHMxz72MV72spfxq7/6q0xPTz/1XRiGLCwsZG6XVkzc4/F4PB7P04coblPvPMHZIE+JCjPtGzheyXH7VJcXLHbpxCHN/oAbZhrc+owt6EUsbebY6PrUUx6P59rnUJka9yMi0U5pnlSE30BENk3ZZJMlWGUJ5mnCYQfpwF7K/kcxQGaEdxHxapC8g4irtuBnH99tX1akgp0iS793c8K7IrU9I9mO1LBTA+1kbISIuHgUEQ21iO4WInA2EbPjNBKlYZsNetynzIDk2FqofdxzD5JjPoFJs6SUkRndKoxmXTOdRZ42brIYJOek0kI3afsqUKrDxDnJOFWd6FGsrlKslKFcFiOjUoZOF4pFCALI5aFYgvIAigUYDBi0e3R7UtRdZ8OrSdTH5OzPmh2u57KBEZ9ryd+bmGgZu5aMvttjJXKW2+vq5yyRXKMydrqGWN/rcezP416TGDFs1LwZtV1WMfvdkmXs2OfQR67bfNK+rEibnY7j9nPa8ezvbFPXNj/Tal7Y2416NtrfaVq9SUzNl7TnrJoto845LXoMtptga8jvwgLZBqm73y4y3iH9OWjfR1nnoO9xymeN4Oqz+9RRERKBNYN5Dk2TXjfI4/E8vfiDP/gDvv/7v58f//EfZ2lpaecNPB6Px+PxeIAoatPonOJ8bpZT7VmOt2d55nTELcubTB3pEgV5irMBpTtmiS7VmXmoQxDkdt6xx+PxXGUOlanxMNtnH49CZ/qOEl1d04CU5WnrRJi87rudjRsjImoDEaa18G2P4Zn+JcTsiJJ17dm6owRh24BIEyUDjNgfImK/ir7aX2nGyihjI0TEzCpG0Cc5jxVERMwhIvpqcq7aFi0CPIWIk9VkfzUknYye9yhRVVGRMi1F0STSp73kmGnmmP507yWPfRc5twgTGaC1K0prUDoFpRKUik2WSxfJly1jY3JCUlDFsdTcKOQhn4NiiaBUIizmKRX7TE3C/BS0N6AbS/9UMcZa2jhO66MtxNxpW+eqtQHs9dJepLy7y9LaYdd+SJvh7grEaSL8Tveti95ro6IR3PYfJK4wruZdB7kG49bhGbXvNGEda5n97pqdtrkxip3MDTUktZh3h+FrN+71svep5mYPYx5vMfy87SNm4nqyfNyIsqzoI/vzKMPNbatb+P0i25+j47ZrDXlOljBmpY/U8Hg8AL/0S79ELpfjrW99K4uLi5nrxXHMr/3ar13Blnk8Ho/H47mW6Q22WG9/ntOVJZYbVZ7ZLtAPclSeUSK8dREmKzA/Q/DQKf7vxTa9/tbVbrLH4/HsyKEyNdKEnSwhL4+Z7d9ndEHj3YhXtnDbY//parQAuE0ZiWjQ4rgDJI3SBUwBWlLe3bbqu5u2yS6yrIV2i4hg2GK4ZggMmxlZ6ZsC5ztN/TWR7HMrOe4lTHoW3V8VmbU+maxfStqXT77T65d13q6wGyXbah5+RVNSZYmfGiXSSPluXAYMFzLWegD5ARQvQaEIpSIUSjXmyxfIlcqSbqpSlXeAiWlZMWhJvqlKlfLSFDfcWiOX61MoQPA4DNbFMGpgImLaDF8bu5CxTZftZpwaTfZDYSdTA9L70h0fGilk4/7ttjEtUsN9H5eDMizGEeezjAX9rLUhDjLVlX3ctL/d79y2uYK+7jftmqTdfzrOysh9PEDGkT5nYmfdcc9do72apNfegXTRP2t8aNSORvDZz5Y0A2ZUNEzW8WLkmb6fMbfXmi4ej+f6573vfS+NRoMjR47wjne8gzDcbk1HUcR/+k//6Sq0zuPxeDwez7VKu7fCk3yS3mCLm7e+nLs2Kyw3mxSX54hvvgEKBYLVTc63IgZRVuJmj8fjuXY4VKaGizvDW8khornmTY9S1rMFrLTZyzjLRrVhr8W0R6HC2CZGiFOBP232dxYqUus6Kqhqm7X4uc7Y1sLnOrNexUc9T313RckAMSPKyd9zycs2SUDEx6XkGJruJ4dJ19LAiJTtZH92m/RYWQK43dai1U4tmuxG7uj6xeSVJr7vhiKmToWaOdqvuQ6E5yQAI5ePqE6uUl1eIOi0pX5GoSCpqAolyJWTXF59iCLylQrzc2cpV88S5rrEMQxiaG7IcdeTdncwRbVVnO4xfkTAINnWNa+yZra7fenOcHcjM7L+ttdPmyWfZWzod1cCjR6A7ebRKNzni6aHOwjSDJSdxHjXKLK/17bZkTEB6dctbb9ahFs/Z12rUcZGLnmpMbCX65s1NgrIb0Oe4WeB3Sf2PvSZ556n+7eLPoOu1Nj0eDxPL97//vcDcPr0aX7zN39z2/dhGPInf/InPPTQQ3zf933flW6ex+PxeDyea5KYZu8Cjw82+fB6jttWvogTjzRZvmsDbrtZ6noeW+TfvWKVc+/7Gn79zB9e7QZ7PB7PSA61qZEmDKq4XsUU4q4gApldI8E2NMYtHp2WXmc/AngaKuitYmolaP0EW1xLE4Nx/u5jCvcGiBDbYtiIaSP1PbSfpjEzo/X4Kv7ZM+ez2l5EIhQ2k7/PJefizuxXoT1EjIw165x09nSctN2NNLBF98j5zhZNCxhDRAthw7DAn0O8A+2LPvtLCaQmmkbDqDkEMgabLeg8Ac0mdLsDvmz2FIXZWWi3YXER5uYgl1hR+VlYKMHkJCwtEU5PM5ELuSV+ksGgT78PrR7UG9LnGvXSsvpmL+lvsLaLnO/cSI20yA1FzbisMZNlzLkz5e3PacbklUQjgHYqKp4VNRFh7hM7Ymq/uP1iRzZlRVbZ77qe3ae6D7sej5odacdXE3aAjMeOtX6a+TXqXPS+1fV2209Zvw1l5Dlnmw5qUGWN0XGicrLut6sxRj0ez9OHj33sY6nLgyDg677u63jhC1/I13/91/PTP/3T/MzP/MwVbp3H4/F4PJ5rkUHU5uGtT/PA+pfw/Asl5s/UKWzViOMYigVu/uIy/79TC0z/5b/nTy99ikfaf3O1m+zxeDypHGpTIwu7IPEkEhnQQlI4pRUDVvHOnpWbJsKpcGWns9oLleQYTWd5FRHY88l3LUxalRCTV15Ta2mb0wr9FpFzd88lRFI9TSGGgx4HxEB4VvL3g5ii5XZB4TTxLw+cRAqEa2TAOiJwrjNcn6KfLLOjIjTFTBqaxsaN0sgjommP4WiOHqZmht3uUUXPu9Z2+8U2T0iOuZYc7yJyTc534ewZ2GzA0lKdO25eJ8jnJeVU9QgEy7KXILGaylOwPAv5PEEcUR30uZ0z9HsDturwZMP0zQySsqwNnCG9RsoobAE6K0ojLWrDJs38G4UrsNuGQNrntL9H7duuWbMfYnYvrqe1MYeM2Qi5/3ZbkyftGKP6Z1zjVdcrY2rP2G1Li2Sw6SHp5S5hCoFPI33WYTgCRKOm0tBnrBq5uu8cJgpqLzUm1GBUY2oieZ1PXto+fcZkGTgu9v3gjo/dmt5a8NweqznkvvZ4PJ7dUiqVuPXWW3nnO9/J+vo673//+4miyxFf7PF4PB6P5zAxiHpsdmM2WwUGGy0KZy8Qdk8Tr9cJmh1uOjLg/3NrSD54Ib95rs6F3oP46Voej+da47ozNXTmrQr/M8AJRCCLgCcZFoxc4SotDYkuz5rFP67AqmJ8nvR86ZqTXiMz+siMZxWlVWBU8bqCCOZp2Q4nMDUW7P1XEPPhGCL2Pmh930nadQSp1aApodJSTen55oBnAjcl62q6pR4mOsZF0yMVk7/VtHD7sJR8Z18Pe8Z4hBhBExijQmeK26YGDIuVYET+AdvNpYNGi8pvIYJvDxEvwxxsrANPPAmzs1CdhMIScnWSMt5BIi3nQ5gbwE19gjimMjnJneHDnDnbZeqUfJW3zq+enO95zDXQYuJZ2FElimtgpKVyG4VrSulxRqWhUrPObdte/glVBmaRMbfB/upYaAq4YtKWJuOZRW40hJ0e7CDTUGUJ6KO+cwkQw/MkMmbPWvtwTRP3GGDSl00j5ulisp9LmGeJppYaZeZolJMan8WkXbnku72YGmoiaYqsWeAO5F7sYCLK7OuSdp5u5Imd2m4v6HnqM98134KkvR6Px7NX8vk873vf+7j//vv5u7/7u6vdHI/H4/F4PFeZmJitfp+1VoHWhRqlz52me7ZN7bGIsACDTsBEYcBzZoo81n0hf7G6Ra13Dm9seDyea4nrztQAEYW2kMdtCRHVQkQU0xnIMJyqSP9201HZAnqaQK8zf+06FWmUkFn6JURYSxPA2kgqJjsXvZ3XXiMy1PyYxkQruMVudbmNppiKEAGvk+wjsrZ7xPrOFZzTapgMkMiLqaT9W8nfjeS7rD6x+zdM2qVCpZ6jm3LLPn6AmC8LybodpF97DF+3tHOwz/dyommybKGyjJgwVaBUgCiCfqNDoT+AOIK4AcEqDGrQ1/ntHam3kS/CkRthYp7gxpspF8rc+uBnePwU5Lag04fWQMR7jVzpY1KZFdhubOjsdxjuNzdag4xl4/RBjuH6AmkRBGnp0/Z7fTS12CJi0OmYHBe3TQXkHtZi2KtIX48a42lmgp3y6KDny6ZFa7j3wqj26TNiCTE/55H7agVTqDvN3LCfpbPADcm2ZcxzV03dOvDEGOdhmx52XRg1cfXZu5soHN02QMbEZrKPWeTZpcfUvnHT79mGm/ZrlqGhafM0aiUNNSwmk3aspKyj5rbH4/Hsl6/+6q/ms5/9LN3ufmMEPR6Px+PxHGYiYtbjFpdaU2ycy5EfNLh4tsrfnZ1lEMF0UVSTQgjPriyxMv3V/P3Wn9PqXbjaTfd4PJ6nuK5MDRWtVUzvIULmGURo7zBsGIAITyp61pJlKlq5M3LTioJrxMGo+gUaNaKiclaR4S7GgImT87DFeHvG+ybDM5btqIQAI6TZ+fBVZGtg0kK5A6CJSRVji3ejRObziCDYJzuaxW6bLZzr33lE7NdzTEup5Qq088AtyXeXkD5pYyJAegynknGNjXFFc60LMW7qIduUyWHEYDBmVB7o982rMOhDrwODdQjqsHURTp+Cfg8mp2BpCSZnIb8EM0dheorw9ClueEaJL97scPI8rG/ApTU41zCCeQe53jq2VaRVI2kZMYZ6mBn1Kvy6hsa46aRsdGZ+2nZZ6ZFGreu2ZydKyUsL0I+LzubX+6OFiRLQVEgq1O/WJNtLP4673yDjs42b7sttVxc592cUpKh9M4IHunA/cp+7dVbSjI2J5POGtb9JZMxtIanYdCyOg53yT981os2NVEu7Fvoc1N+GTtK2h5HnhT4LbZlPoznWyTYm7OeK+30RU38lC31OqMmStW4tY7nH4/Gk8e53v5vv+77vIwiGn/Q//dM/zfz8PPfcc89VapnH4/F4PJ5rgZiYzXiLS+1Zzq1MUN8q8ODGFL91fovmoMVthePMFkMquYBCGPCc0m30JiI+2/w/tLre2PB4PNcGh9LUsPPkq0iVQ0TuHEaw7yTftzBiuW0oaKqVo8isa53JrSKVir86m1YNAVt4GljHyWqrirqaKz1LHNcICzcVT2h91vVapKedco0CjRbQdmhbI2d9W5zU2fz2d6OI2HkmsZtySEX3nLVM89zr+WUZD7p9ETGk6kh9kFPJuR7HiPR2sWP7nNxCxKNQcbK2Q3vs9aeSY9rXSKMiusg4bHeh1YZuN6bS7UG3C/2m7GxtBR55GDY24ZnPlALi/Q4EGxAUoL5GUC4x9ayb+IL8k6yfaXP2LDz2OMRPQrclx2khqcq6SVs0cmIaSb3zzBCOFKHZhTMR/A1wARmjbsqpvRAj18I217LWw1rHPWZaxMg4dJHZ72vsrqZGEYkEmkP667OY+hdqDuq9pCbWTrV27NRnWemN9stOaajsdz2+rm/XpYmAqRBmyrKsEEkk0DnEDLBNIjuiTIuEn0r+Tsq9sIRJmVdAzMgecl3GMQvtcaH3mz5L7T4PEePQfiZXEeOulRxPfxN6SVsLybKus5/FpN3d5GVH8ek6OcSs0RRz9u9AB5M+Lw3dvsdwlFka+0mb5vF4nn786I/+KJ/61Ke45ZZb+LEf+7Gh7374h3+Y2267jccee4wf/uEfvkot9Hg8Ho/HczWJiWnGDda7cKZRIapXuG8jx72Nh7jQvJ9HSs/lRPE2FnMTzOaLFMKAu4rPIIhzPBD8JY3O6at9Ch6Px3M4TQ0VxO30LUVkVq1GLqhwqLnZYbtYqiK61l/Q/dlFYmNM2hmdub3CsOCbNbvWTr2jOeTHEWRt0cxdbkc52NEMboomu1i6LSjbEQdpM4yzZnXbuJEOavosJMsuMNwnWSKrXZhX19N0Ta6p4kbNDBDBVAt8X0RMoxPA3cnxP48RMe3ztftqp4iNPHL9R4nP9vaTyDgsIEKlXZi+hAiuPcRE26xDowGdFtDpQDt5DfqwVYN2GzY35O9BH+pb0F+HYgEGA5idI3zGLUzmQypz5ylPbNEfRDSbUDsl/aPmVx+JwlCjbg64Kwd3T8NERaJFKpfgidikqzpIXEE4KwrHjQKwjac0o2PUtdMaK1tIH+zGDNH7p4qI8Q9iopDUeHTH4yhDY9T4OYhUWzau2WAf2zU97D4sI+dbRQT6h7ow0Zf7eiaEZyGfH0bSR9n3uP083sIYxGr6DJJtNYpqEenLEDMuFS1UrlEj+p2ejz730vpbjRW7P6cRk1ENRvu3AbanXCM5/gTDBrduaz9DSsj40Cge29wdVWdDn80a+eO22ePxePZDHMe8//3vp1qtEkURb33rW5/6LggCvuM7voNGo0EYhvzQD/3QVWypx+PxeDyeq0NMN25T78H5dp6tHjxY67DVu0ize4FH+h/nUv80C6VbWO4d52hujolcntuKN5HjxXw2+Atq7Z2SCns8Hs/l5VCaGrB91nUbKWrbTfkubYay/t1B0ifZM4Zts6CIiFsqSB9FBLIm28U4RQsKk3zfJj2qYhyClM9uipm0dXC+GyVsut+P06YK0jclJIXRjRhT417gcwyLjq6hYKPXpY9E2agRpWmf3Bnauu4mcJphQTkEbsjBVCxpc3T2tN0PdgqqndD0MTsZUgVE3LwZ6YcLyHWPnHVU1G0BpyJYOgXP2YpZbjag1YJ2CwYRDPrEQSDmxj/cR9DtwuIiTE5CYVZSUhWlZHUQhOQrZRb7D1PbqLOyIgEerS0Zd/oCGetRcvy1AbS6MFWVkh1RAJuxiZKB4Xth3MgdFzU0xt1HmsmW9T6KmL0VlCbZ7kmkv74Ik4YorY2Q/hzIuv/cdbL2uV+yjE93HX3vImM4RCItHo0hGkjk07MGpui3Rhq513WUsbOJ6c8iYv650QkzyLGWk2U1xKzUZ4Iy6t7VZ7rNerIv937caSzVkN+FjZR1NCJEje7J5LxqiKmzmbHvKqaGjf42HLSB6PF4PEqz2eSd73wns7OzvP71rycMzXSZiYkJfuAHfoBXvepVvOpVr+IP/uAPrmJLPR6Px+PxXGkG8YBOFLPSCTnfHPDk4CKt/gYQ04/qrLcfpDlYZbN0kvX4Vk72TzCXL3ND4Thh8E95JPf3rLb+kUHUvNqn4vF4nqYcWlPDxU51Mi4DRDTL2l+EMTV031qceh0Ru2wxM0BmwN+ebLOOzHpfYXTNDZdRaXrc9bStO61vi8m7MTRs4c9O3aQ58qcQIf+m5Ps1jGg3qt3algGm2G8fEdTBpPhJi1YBEUNPY1JqbSTLq0AQQS6UazGFmQ1tp/9xU1Jl0We8CBuNLplIjnma7VEidqotTZ9zbg0eeTjmlrs2yG1uwOwMRDGsrMDZ86xeHNA5dZFj9QbBM24juPFGKBSl2EGYg+oEHFmGwYBgdZXZxRbHjg1o1KHbkyiQNvIaIKbceWTc/zWQb8Dzc1DMwalY2u2KrLHT7r2iIri+2/tLixiyIzTilNflRFMUtYD/y97SRLnRU7rM7QOXgzy3rP61v9fnktb02UQiDnT5aSTt1PMRY8KN0hi3HU1E8Nf6JG2MgRYghsaNSMqnAaYOkrbFjlDbTV0c29QblxZi7KSdR4yJ3ssj/VVCTJ+FZLs6w78NBSSK7Kak3auY8/N4PJ7LSafT4Y1vfCOzs7N813d9F4WC+VdauVymXC5TKpWuYgs9Ho/H4/FcDQJCBhFsdmPOdVtcik7T7ZtqfnHco929QH/QpFXaoFHc4ETvVpZy0xwJF2nnb6Oee5Jm1MLHnXs8nqvBoTc1xp1xvxc0T7pda6Nmvdx6CZqy5AQiqMeIoQHDtTHSjpMWcXGQ+fZHiae24GmLxna6qo61rIgxBFqIQN5EjIVzSJTCblLxqKGhQmkuOUaaEKu577U9j2PqmoCIiU/GsDCQfRxLvt+w2uQWDj8I4uQYDyGC5ZnknNSwsY8XOq9TT8KjD3a57fh5wkoF4hjOnaN9aZN6DTY3IY4aHBk8Qr7bJWi34egRidrI5eVVKhHMzjJ/bJUbmk06beh0oXUWmn1ThDhCxNcVTO2Dwpb01eeQ65h1fvvpqzRTzZ3p766v6+jfrqFxpf7ZlFaIepzj22nf7PPU8856Hoyz791i93taJAyY552mzLPXOYMYElpXZDfkkXpHy8k+2ogRYF//IFn2BDI2ZzHmWh6TyskdO6OMjYNO62XTxUTqFRAjcx1Ty8c1M6vIb8JNyXd2BNmocWBH2ng8Hs9++J7v+R6+5Eu+hOc973nbvrvjjjuoVqs0m36mpcfj8Xg8TxfCICfpg3sRK/EGtf5FotjNdRDRH2zRaPfpD9p0SnWag1tZCBZYDk7SqXw5j8Yfp91fxRsbHo/nSnPoTY3LZWgo9qz1NUyxaFswU1GqgwjaDyARHZvW97aA7s40HyVqpeGKkqHz9zhRG7DdzHDX0UiKnrNeB+kHEHGvjoidav6Mg/aHipIdzLXUKI0eJtVVgDEz7MgJ97/f64gAi9X2UQXHd4pAUFFynBQxAyQKop20eTpZvoqJQLFFXDta4ewTfeb+4RTTvQHFuSkIQ/Jz01Q2Nllbg8cfh16vwdHOE5Q7HYJ2C5aXxdgIQiiVYH6B4HiTpc4pWq0uzRY0W1BflVRcampofzQQI+MhTCH0IiaK5CBxRXW3xkYaWREaVyJSQ0kbG5pCbKdZ9iEyDjR1Hch43WQ4JVRaJEzWs2IvjHomuPsOMCaC/WxVY3AvqFmi+9TC2lqbRNumz5EI6bcyMmY1HZ1d60j3O+revZxjxL5HekjqQ22XmyoL5P4/jUnZ12HY2LT3u5/fBo/H4xnF7//+73PXXXdRLpeHlv/oj/4o+Xyet73tbXQ6PobM4/F4PJ7rn4A8RQYxNAZ9NgdrtPubxHGa8hETRU3a3T5R1KFbrNPI38wiR1kObmZQGfBE+69p9y5d8bPweDxPbw6VqXE5ozKUAiLwgikMrGhtBRgWCe02NZFCuhcQ8cqemT8q1Y7LOMbEwGpr1r7SZminRUpom3Qdu4BtmlGhefXziEBnF8QeFz0HOwWOtkNro4TILOgAEeHt/2qnzTzXVFT5ZN8rDM+cdtnJ1NhthIJGtswlrwZiatjirTuDPxdCFMHq2S6F/BkKd58keMYzyN/YZa57LysrDS5chEcegU63xfHOGaaaLYJWC5aWYXICggAmqjA/T77R4Gj9/FNRHlsNaDalb3oYcbmFmTFfRvp5MunDiwyPfVeE3wvudnaEUlpKNPceGzfd0H7QSKQypkh8G1OUHtKFZ9ugs5lGImMmkr8byT7rGMNNcceaG0200zNjFGn3fto+3GfebshjojxsU0xNjS7DkUu2oK/tGmAiw0oMR1XpZzt9nP4mXO5C2wHmea4mrB1N1MrYzn7urCDPSTW6YHsKsnF+Gzwej2evvOUtb+HixYssLS3xlre8Zei7e+65h+XlZS5dusQ999xzlVro8Xg8Ho/nShAAuaDAIIqpRW0a0Qb9QZM4zq5eGMdduv11BlGXfrFFp1BnLjzOfHgDQTnkieBTtLrnr+RpeDyepzmHytS4GTELdls7YzdMYWYI18kW+EaJTT1EWK8iAqmmGTnImgSasqmMERJHpSqxBVOt7WALgXbUiRolGqExgTEVdD2tOZBnOJJjFLZAq8fXWhSh827P0tfrYBsaKqCC6QvdTtvZx4jRe+33cSNPbLpIVI/WDNB2Z4n3cQzdrtQJH/QicTjimCAMyRVCwhAGfVhdhXYbut0uJ9qXmO+osbEEE4lsXi4TzM5Smd9iYaHJ8hLUtqDZhVbfRLr0EXG1gfT5EpIiSAV8+58y2s/jiq1Z5uOobezxV2bYKOhy+UVrpQScROo7TCAGxCbwj5hUcnb6pSLSj4VkeRMTWVDE1J4pY6IgdExtkW1CjmNGkvH9OP20mwiQEDkXjfRxqSB9VcQ8i1bZXqRdzQ7tn7Q2hUhf2udvR3XZ55hD+nYSuc8ucfmKbheQlFgBcg/txfzR6Kg2UhRdr0FWxNJuDVWPx/P0Y3Nzk//wH/4D7373u4eWf+ADH+Drvu7riOPtT5f3vOc9FAoFNjY2tm33mte8hm63y+zsLK973esua9s9Ho/H4/FcPaJ4wFrnSZrFm6jToBltMtixNkZMHPfoD2o0On36UZtOscFM7jiTuUVuKL+A08GnaXbOXqnT8Hg8T3MOlalxNyIAPcbexOadsGtINNh97ngbnb1rmw2uUJcmaO6Eti9GhNJ5RDhdT14qCsJwTno1MsJk/RIi0vas7/KIqbOQrKOpnRaR/v6H5Bh6/B7jpSkaFQWh35UY7h/bXNEIA0Xb6qak0ZnvKhzakSBXKmVRNXlprRHtq7SIGjVj2m24cEFMjYWliPlWCz7/eeK1ddZONWi1IAyh3YLTtaQAeGfAoLvJUrsD7TbBwgJUKmKIFAoElTITE03mF6BWg0YLWuvQiaVvukifVzAi7SrGjNE0QUvJ3+sYcyhLPC8iY0+F+xbZ96lrkNjibRURfUNMuiZXIB9FiIjszRHHtwkwQvoEcs43IucfJO8PZ2yrheHVyFHxP4/0bx8xODX6Q2vvVBh+PowrYI8yMPKYVG0N0kX3ne6DHNL3k5h7Kkb6Zx0xYpQKIvTPJe8kx9QC6/Y107Gu0RV670aY548byeRim2Vap+IkcBQxPlvsPUXWTuizpEd236aR9pwfJPvIk37dfbSGx+MZl16vxyc+8Ylty1/84hfvuN3P//zPE4Yh73rXuwgC8yQqFou8+tWvJooivv/7v//A2+zxeDwej+fqEzNgq/049fyXUadGZ7CVpJ4a538fAwZRk3Z3wCDq0C02aBeOMZlb5Gj5eZyLIx+x4fF4rgiHytQ4j0RqXA5DA+TxvYGZFTwuWREYKoKpoJ02I9edmR07y+31YFhkPIYIsB1E+B0k7daLqqbMACO+6szydYwAGGDSD92JFLOtJN+1rXb0gAcR8XuvNReyhFsV2bPW1b4JGR60dvHvAiZXv5vWJnZeyoDha53HmEAqro5bK0RT1OgsbDsllx15okK2pu2q1SGfh5lZGPRjBoOYXBjSXm/x2KMxly6KkdEfwOoatNrQ60KvB61WmyPN05RqDZidISiXIRoQx7EYX2UJ5Gi2oFGHRlfOR0XnfHJ+a5i0XyTnsYSMsWaybpZITtJfi8k2pWT9xxkWwe31sxgk2y4mfbSOjO3dCLsBpth8VlogmwKSJkq3aybHbSf72CT7Qakp0nKYyAEdU1pjp4HcW2o6kLxXMaYXpEdpjMJNd5UHbkP6/0nMs3I3s/0ngbsQU0fTt2lf2Ncyh5gZNyN9p319EemvtLFiP59ybH/mBc4rDb2vdbwtJ8c6ixgbl4seci13Y46O6netC6Tns1N9GY/H48kiiiI6nQ6l0vC/oqrVKo1Gdmxzp9PhZ3/2Z5mbm+OHfuiHyOfNL125XOa1r30tFy9e5G1ve9tla7vH4/F4PJ6rx4AuderU4036UZN4V/kRIqK4TbcvxkZv0KBbalAN51ks382FqEu3v3Y5m+/xeDyHy9S4j+FUPrsRgQqYAq2jBPndzuh3i7zauOlT7GW2kOemjYqsdXQWs0ZG2CJ5ExEdz2NScmkNih4mWkFnkJ9EzIsWUhzabneACIQnEbEwwAh5j2Bmr68jaV72g5oFGm0BppaDLfDa5BBR1C6uba9j59jXWdWuOOqaGvZnTRc0hYi0ZaRPdyOox4iwWkheFYyxESOi8AQyDrUmSQ+p8T07C0eOwNQUBPEApqYoH5tjcO8Kjz+e1A3oQyeSOhndvkR4rK/D6dM9Tpy4yPLxdSrHZwmmJiGGXA5mZ6CQF1NjcwM2L8BWbIyKImZGPgwXYdZaIDclfz/M9ugl29Q4Atye9N0KIqrX2H6d0qI09F2NDI1IqrF7tKj1uGJ+P9lGDQytJVPBRLA8mbGtrqvjU02ZBibSScdUEXP91zCRJLotbO8rfY+cZWljso25R7QmiPts2WksaySCmkFaW2cFGb+aimoaMTWqGPNjK3nXeiFp2M8v+162zystcsEmh9xfAXAOM9b61vq7eYZrFM1O5uVu6znt1O92pJn9rBr12+DxeDwuf/d3f8crX/lKPvCBDzy1LJfL8dhjj7G8vDxy28FgwH/5L/+FO++8k1e84hVD3+XzeQqFQsaWHo/H4/F4DjuNwQoPtP6YidINDKLOiHoaWZh0VK24Tz+J2siHJYLAVrI8Ho/n8nCoTA23SPS4ERU6iz8rrQmYaIq9RoGoOGULUjqDO+2YrohnGxnahgIiGoaIYKezmzWf+wVrvWm2C6y2EWDXyailtKmEGAs9RJwcJOudRoTDHGJ6zDOcSklrauylgLt9PSaTNmxY37sCr11DA+c7Xd/uy6zImNhZRyMsKpgZ51sMi7vj0kVEVruWiitSaiqeFonpESdREl3odiCOAxhICwcDuLgGa5Fc536yzUYDLrageA5Kebj5KDznrh7PHKwycaxPGMDMsSqT7R6F1T5zmzELi7C1BY2mqfXRS9pRS2lnLTneHcANyGx4LTJui7Ba9+I0krqsgJk5P+qfMO5MfSXkYOrmZO3fRY0UMKmoNhDzroYxJ2zs6Ca9BzTtmaaVUtqYNFoB0leXkmNodAeYmhV2iiXXDLXPyyVGzJccJhJgt4J4C4nGOoMxdeoYc2kOMT5LSJ99PlmuJtk49U805ZTei/Y4Cp11bZPNFf03kpd7fUYZPy455DqO6iutE7LbaCHF/W3QfbrRZO627vPM4/F4Lhf33nsvL3nJS5iZmbnaTfF4PB6Px3MF6ccd2oNNokj/B7lbRF0ZRC3i3oAo7lIuzFPITdHtbxLH+0nq7vF4PKM5VKYGGDHVrqWwk+ijaXXaI9bdi5lhi1KB87crWOk6rvGhJoZtaiglxKxQUU2FfS2K20OEx9sR0fkSMpt+NdlehTNNe9PHRAdMMWwSNZBaJZuIyFrA5KjvIcLmmeT7qWTf+WTdi+wtl70KdirUzyX7caM10owJFzeFzSjcdD8qHjcwQv5+UpzlkRn5KnTrtctb+9YohDPAYg0qa7AwD2ExRzhVhQDiWo16TaIzLmLS62hO/m4ExQ7Md6B9BmYnYWkpojrVIFhegukpcp0u0/ktjndbtFsxrSY0TkGrJ9e/g4wxrRPgCqpB0s4ZRPxVQ8zt7xgZd59BxlyN4fGl7HQttVZCif3XtVEzb9zMoNoeLfbdH3H8IjJe1QjcRK5P2rjRyJEOYnjFGNPGNZLs7fuYiASbNKNGr0OD9DRr4xJjamKkRQ5o3ZQuco01JdNuRP+0aKmdDBh9zus6GhVjp7lKMz53alOMMWOy2M+zIO23Qf/OMqzsz256PI/H40njgQce4BOf+ARf+ZVf+dSySqXCv/7X/5r3ve99O27/9re/nW/7tm/jec973uVspsfj8Xg8nmuMbr/GII52UU8jDfmfSxR36PUHRFGfMDx0UqPH4zmEuBNjr2lUoM9jRCFXIEpDoxsOUhzKOp7dFruNdpoVex1buHKFQXubIhIlcQxJ86Pi4hxwAngm8OzkMxghToXdIiLAziIGxxQiHms7uojA/gDwt8nrc8ATiHD5BPBpZGZ2G5OGZh4Ru9NSxIwzuLR9TcSUyUoRZUdYuC8VOO2/3eW6vf23Csnd5PgavbBfEbOImABVtl/PHsbYULNoA4nWmJ6GycUywcwMFIp0G30GEUyWTeHsNeAU8Chyrc4gQnZvILU22m2I+wMIA5ichIUF8kcWWThR5sRJOH4cjszDYmAKQs9iogXSxt+l5Jhta3na/Rcl7Vsh3dBIo4RxVvX6aN/st3aO7m+3/zTTKJpRQrfWplHTZFRRcjUlNSKmlqybt7bPej5p9ILd3y6h9drJzBjH9FPcfcXIeF1FTBw1wnbbx+49nXa/u+vbbbefrwWMwaqf3fE5Cr3/D4pgxMu+Tq7Z7f4NxrSxo3c8Ho8njb//+7/nf//v/z20bHJykte97nVj7+NHfuRHqNWGkz6+9KUv5Uu/9EsPpI0ej8fj8XiuPaK4Q39QZ3f1NDL3Rhz3GERNgiBHPrQVJ4/H4zl4DpWpoXnidTZumiB0JXBFM1c8SxOp0mYiB4wWBFuYQsk6a72PERMjRGS8lKw7hZgaWoxY9ztA0iltJutpvYBFRNjWjMm2wL+ZbKPpdNYRsVprBVxCank8zva6CXps93xtI0IpJO0uJcezc/G7Yqe9fZrBEe/wvX1crTswhSmgboulJatfdoPOdN9kWOSOk3PT6AgtmFwFKgHMzMDiElQmc9BqwcY63U7MwjzceiPcMiHRONNI/28l+9R6AKXkTm63od8ZEMdIsY4pMTYKRxY4cjzHseNSOHyuImbGNBJVMs9wqh+lhzFRNApAccVa7b8Ssu9x+u9I0ga7n/T8xjVGstAxsNdtR6F1UzQ9k0YMuGaeCu62iakRZkVMiriYnR/Go4Tw0Fkv69m0X5pI2ju7js9usSM74pS/YTjSIuv5HiPXoIzpSzBjcVxj46BJO25aG+x1YrZvs9//Ung8nqcX733ve/noRz86tOzOO+/kLW95y1jbf/jDH+bFL34xUWR+OZ/73Ofya7/2a9xxxx0H2laPx+PxeDzXDnHcJ473O6VwaI/kggIL1bso5qYOcL8ej8czzKGKCdMZzzp72RWpNT3J5RSDVCzTIrz2sVxhUUkT+HOI+JvDFJeuYFILaVqmOmIsaDoorfmgdSxqmFz9cyEsRyLyaT0D7aON5BUmx5nDpPrpkl7Y152lbX/WGfUXSO9vV0x2+0C3ySPGiqa60jbb13FUuqmsY9rHc2trqGkxj4n66SNCeoD0tV6HvQRh9pGIBTWt7Db1MHUHguT4JaBQgEYdLjxcY/JinUoJckQsLkKnI4EXxXMQb8l5alqyY8i1jPrQakKzKesXOx0J/yiXoVQGoNTpsLSxwtRUzEQFJpsSodFA3hcwReD1nHuYsaF9ZWP3saJFpMdJSxYk52+z19RJV4o8co9p7Y8Nhg1EGzU5ssxXfZ6UMSZlx/rOfUDb2+YZ7idbILeX2VFJO6V52gmNOHHbslvsZ5Nr/EbOe1oqP3t7t+ZF5OzPPublJEja4rYnLQrDXu62U6NOQmQslJDnvsfj8YzizJkzrK2tDS2bmJjgGc94xtj7+MxnPsNgMCAMza/67bffztSUFyQ8Ho/H47l+Oei48IAwKLAY3kSdx+mydcD793g8HuFQmRq2wOXO6IXx6mscBHlMIWgVfLMiM/SzzsQuY4TOOYyQWUDMi7WUc+hb7/b+SN5ryT7CSERpN52KXZwajImh++o76++mH/eTdVHboBEa9nHTDI0qpnD6JiYdUtb+XZETpJ9LyT5ayLlPIFErc8k6LcSsabH383P7zxb9B9arD6zHcPGSRGtATNSPCWbEi1hYhF7fMoFycLwGG30RPPMkfTGQAuC1GmxuwpSaGrmc7CgMCdptFk9sceR0h88/JG2sJudfT9615kOa+O2KtPZy+97sIOPYLbKeJro/kbK/g0SF5oMIplU0YmwdMW7s/aphCKY2h7Yjrd9yyTbLSH+lRT9k9c9Oxp8ud40WvV577Y8CEolTRiI20gzRnbDHV9q9olFjAaa/swisbUb9PtjLDxptg0Z8aVqurAgNtz0acdJL3ueQ8+8k69fxeDyendnY2KDX61EomFjJcrnMxMQEjcbO8XVRFHHy5EkuXLgwtPzTn/40y8vLrK6uZmzp8Xg8Ho/HAxAQBDlyYYkKZb5p4ZX87sq7aUd7jfP3eDyebA5V+qm09CujZkEfNFr/YYCZbZ91TFe4UpFrDkkRNZP8ral2ziOGRFrKHTtXvoqBmjseRIj+v8CfAB9HTA5b3HPF6Dg5jtaRiBEhbRoRV2/CpLDaL9rWAmYGstZFKSIGg9a5KDOcF1/RWc+TwB3AbUiUxW6utfaDXoca0ucrmKgWnR3dQwRr1+zZD3ZbNYWXjqPNGNZr0O3CzCwsLMBgIKmkciEUi1Apw/w8nDgBJ4/DQlFMnXWseg0t2NqCeg2iWkPSWIHsYGoSFhdheZkjR6V+RxiYqIMSYnDMWu3Va6W4M+vT0hvZ6aNssTuPqQfjpqXar9CcVVNHjcMbEdNqtw+7rPXbSF2JiwwbBgFy38wg99FxTETHqGdED7n30+5/2xzNeu7Zf+MsO2hC4CjwRcDzkOievWKnioud5WoYa7o2+9nnjru034Ws34fLhaZg05o5WW2117efSXlkvBxDzEW7EPtZxCT0eDyenfh3/+7fcf/99w8te+lLX8o73/lO5ufnx9pHt9vdto8wDPmiL/qiA2unx+PxeDye65OAkDAoUgwnqIRlqvkci6Xxo0Y9Ho9nNxwqUwPShaIw5buDII8ITFWMuKuz6+3aCPaxXVS46iFiaBMRrLqIoH4GEa9UuHNxc/TbUR8q/veBh4FHkmNMIuKqnaseZxtbTAwQIfYu4MuB5yCmwX7JIaLuZPKawKTcChBTQ2c26yxnbbs7MCPEDFhF0v3stbhvxPAMekVTUNWS/R9kRkk1COzxYRcLbwGrfTh3Hi6ch5VVuLQCa2uwugqNhpgcuRwU8tDviyExkexD+7DZEVNjcwsatUhMjX5fnJFqFWZnCZaXWbqpyjNuhWOzUMmZdGq2maRjf5J0gyzrPHV7t3+1dopGKh0kWW2KkPuqx3aDZic0qiptG61r4xoaU8BJ4BbEfDuZ7MNeR8Vvmx4y5rROihbALjE6oiLLzLDNy6yIq70aSSFynguYIuf7wa2roc8lO7JGTc+s9uh7ltnm/j4cBCXk/tDxrJFwasT02N7H7rUB8wxoYozONhIpdpHRBeg9Ho8njT/8wz+k3R6OZX31q1/Nj/7oj45lbGxsbPCd3/md25b/yZ/8CUFwuS1ij8fj8Xg8h51cWKIcTDGZKzORD/jqqW8gn5u82s3yeDzXIYfK1EiLynBFLTgY4SpAzIwFRPDX/PWuUJUmXNnorGMV7C4ixbXPIQJ6gClKraZDaH1292uLgO4M8ABTlNgWwuwi2e5+9LtB0oYi2SKait/jDBr7OqmgrMKfFh+vY+pW6P41siNNTO4g0RVnSS9ObrfTLha8E9quTcRkWh9jm90QY4q8a5ubmFndbWBlAI9fgHs/A5+5F049CZcuwuqaGBXdHkQxhCFMTsL8ApychJOBKfgdJCmotragthVDrS7hHjESrTE9BXOz5JYWuOPOgLtuh/mquaY65jQVjhZg1r6sJC93jKeJxe692EOE2hX2lq5oFKPMFi1svZoc1zUJs9B7Qmsk7EQBuBkxBG8Flhg28Nw+yvq7gESXaK0UGI60yhLv7WeBLZgfdLqlPnIPfhb4R9h3dtQ446XnVEGewwHD1znLtHDf034f9ksO+U1YxKTDc8eg/TnL7I6sVxM4BTyJjNc25l4cdwx6PB4PwD333EO9vj1p3fd+7/fyrne9i2q1uuM+zpw5w6//+q8PLQuCgHvuuefA2unxeDwej+d6JCYXlpgOF5jPl5kpBkwXQ45WfMSnx+M5eA6VqQGjZ+TCwQlXKjq1EfE9q2B0mthki4sa2aFoWhGN2EgzRdKKC6ftO81g6SBCYxMz6znKeNl555tI+psnkYiPtHQnOpt/HKFU06po/zWQ824g4rL2Sx9juLj1UWzRUo2ObrKPUZEaU0ikySwiDJdHrEuyv9MYs2Q3ovu44802tzRyp4aJ2IiAxgBOX4KHH4EzZ2F9XYIt2m2IIonSmJ6Gkyfhxhvh+DGJtliswok5WJqHIJD0U1ub0K+1oJ3EABVLUlujWiWYmWZqqczcPJTzYlxo/04i/VfERDrEiIh7N3Ankn7ITU+2032oppEb4XC5UUOpg5zjYvLaSSTWmfjziEHhpsxyCZE+mk3WrWHE6VH3sZJLjjmH9O8Uw/eG7sNOa2Qv15fO/O9BanSDbRYsItdbl48rnK8jpsaj7BwxVUqOMSpKJs3U0DYeQdL1la11lbTxZpuZaSb3Qfw+6LOtganL4+Km0MvaR9/6u5682gz/Nvh50R6PZ7d8x3d8B1G0/Vfg3/ybf8Pv/u7v7hhxceHCBT784Q8PLQvDkO/93u890HZ6PB6Px+O53ggoBZMs5BZYLuVZLMXMFUOWw1uudsM8Hs91yKErFK6CV5q4ZReaHpW2ZVy09kGagTCNCG06497972FsvY9KAaPf70W4Sju/aMT3aSKZtqGNRCmcwaTHctHCvbvpV913Gvbsay3yq/vWuhtqJmkkwYBsg0kpI+KyHRUyii4yk38v46WYtHPcslf29dF+0dRXW8BmBK0a5PIwMw1z85I9KkhqaxTyUCjCZAuKBcgn6aimpiSKo9eDOIZOFwb9iHwA5AtQKspOFpeg3SG/tsnU1GmKORHhqxghXWsD9JLz01orz8QUh34SMb42nHMaNT72ej9mzXTfCwXEfGghxoP7XTn5/ggi+g+QqCq3RohLhAjSq8m+zyD9pCnqdnKPA4ZrzNQxxmLeWW9URFpW1EoRMUvmGK5bcwFjgI5rNqlRtBMBYgpNIkbIesYx7GekG32i5kYLU29EjR37HYb7OK3e0UGZAxEy7tP6Oo+Mnzhpc1qhcm3zqEgaNZ4P4nfM4/E8/fjTP/1TvvzLv5y//uu/3mZg/LN/9s/45Cc/yQte8IKr1DqPx+PxeDzXK0EQMhnOcrIwy00TMScrffpRgWkmrnbTPB7PdcihMjViTCoOuyArDBsacDBiUJZAOIMUddUZ96PyytuzhkeJhlnGh90We59pn8HMth51rLQ0KSrK7sRBCWy2OBs77+56is4+36kNISION5B0X+NEXuz1vPKIgdLdxXF0PPQR4TOHRIlsJt/fGcNMA7ZqMNsSPwLE1CiV5O9SSaIyikUxM2bn5HO/B62kwHguH0ChAOUyVCpiaswtQbFA/OAjkvosD/MBBLEI33qP2WNf+7OHCNQl5B7IYQRre+wcpBCbJuDHbB8749DHpKKyjS49vzIioN+G1MSYRdJlPYkI/6MYICaG1s7ZZLjot91mUpariL2ZtFGjNDQ12DhRB2qM2LP84+Q8bkZSWs0gpsZG0r4ZxMx6iJ3Nv90SINEuEwxHNozCNjlBjB273oRGi6Wh47fD9ufLXsbLKNKe1zkkqmcBMcHSIvEUNzJmp+e/x+Px7JZPf/rTfOVXfiUf+9jHKJVKTy0PgoAv/dIv5ROf+ARf+7VfS6/Xo9/fb4Ukj8fj8Xg81x9Z/wvXv1O2CHLMFKe4daLA7VNtjlXb9ONJZgvFy9lQj8fzNOVQmRodTDFpnbUP6SLqQYuqcXLsecTQmECEtmlMgVj32G4qqQHpM5xdUV+FvTTRP3C+s8WxCiZFU4fh/qliZg9npdEqkz67+HKgfWLP0LZnWuvfeg5uapq0NhaQiIITSD+oyOySR4THPiJw7wc7mieNMOVdTQNN+3UBEUFryTmcB2Y70H0Ezl+SUhjVMkxMwMICHD0qHsXcnCwrlWH5aEipKiOhWY/odmJypZw4HeWSvLc7UFuHlVXiZov+ABYWJZBjYx3ONSGOZBwNrPbqtWgjabouJucwb7V5BjEANKXOgHH+yTOaLGEa9r5vjUABUwx8OtlfExn/m8h5dZFrsT7GfnX2fg8T6WLj3reusajRD/bzQdOC2fuw393lYKIw7GeI3i81ZLzr82Ap2aaeHOugTQ0wqZTGKXjtRmuAKaKt/aLPqYKzXR9Tf2MdEz13OaMc7GtaRqJ7jmEiztpIn9uRePa10mg0NcfTDFv3t8Dj8Xh2wyc/+Ule9rKX8Su/8issLCw8tTwIAr7iK76CRqPBu9/9bt785jen1uHweDwej8fzdCYgDIoEQY7YmvYYPBVT3yeK+xBHxMn/9sIgx0JpkjtnetxxdIOF2Tb9JwOWy97U8Hg8B8+hMjVU3NKi2spBpqdxKSbH6yKi1Q3IzGZNjaTFkzVqRAUoN+c9SZuLDOfKh3TRyhY/7f26hoYW784jgh6kR46oiD6qX3SW95UwNdwomLSoGO2rtG1dAkwEjaYXcs9D+1+vaVpaLNdMGYedZmO7+f7tdm0ifV5GzLocYkidAzYiqG7IayKAY9MQDSTVVKkoZTLKZZiYK1C+aQlmZyEMmWi3mWg2YTBIFNM+1BviXPR6xBcvEG9sMjUJd90JjQZcWoHK4xCuwdpARFZXGB8g96AaM1oMWyMLZpN3vU/VWDuIe1Hvo7T7YC/oWAAZBzrDPwYeRwyKMHnfHKNdKmSHSIonrSGTFWEF29MnBc7feYafH/Y+stJZxc53apacwhgbAZKKqo1cr9OMnz4tDduks4kQA2zAzveTa/jo/tyojEHSVrvgu12rRvvM5aANDjUytL9vRPq0ikl7N42MqbSIHfeaFpJz0PvOPZbH4/HslT/6oz/ida97HT/5kz/J8ePHh74LgoDXve51tFot/uqv/oqPfvSj9Hq7qSzm8Xg8Ho/n+iUiCELKxWWK4QQBARERAQFBEJIPSgQRBFGf2uA8EJMPqywXJ7hrqc7RZw0oLRS5obvF8qnFq30yHo/nOuRQmRpgag9ooeKsWfsHhZ3ypIPMRt9AZuROYYSoFqYocFZ6FBWz1BBRIS4tHZT9txvJoaIymFnxICLtDCatkb2PnWZhax5/O7pDhdrL0b9umhnFFWTVULKxZ5/b6zaQ66MFrt3tJhDBuY2MoTQhN2Z3hoaKmwHb+zhrlr3O3FahVWsaTCHjOsZECjSQc52KodqBTgeaDaiUIZeD6kxI9cSsVA2fX5BCG/0B1GpSZTwIpNJ4Pge1OmxuwsoqYQhHb58kyAV06z2WznQolWLyj8DFdehFMIghiiFOXv1I2qrRQLNJ284n7W1hZsgXk/e9iuWu8Btay92Z73sRqyOMgddiWFDvYgyJcca+mlU6dnKY83ejrFxs40ijD9Jq9LjmqIr6mrYqzRixj9lOXmFynBZi3tTZbr6Mg6YB1Hoh2g6XcWpvKLHz0vPQeUC6f619EjJswrYxZpSNex0OAvtaa5TPhaRdS8i9DOZ3QftJn6lpkUb622EXD/dRGh6P5yD49V//dQB+6qd+iiNHjmz7/k1vehNvetObuOeee2g2JeHic5/73G3rVSoVXv/61z/19x/+4R/y4IMPXp5Gezwej8fjueoMojZx3GOp8Axmw3kgJiAgFwQUgwITYZnZsES3sMp0AQIi7popcdMdLcrPXSScrTCz9hh3PTjg7uoLaA76NGmx0XuSbuRWufR4PJ7dEcRxfDkzdBwox4LgKQGuwLAAprN1dVbwQZ6ULaiqiLWEGBuF5HhPIrOSdbawPUMftgvw2lYVu3LWd/Z6rrCaQ2YAFxFBsmEtPwHchIiV5xhd62MniojArobN5RgkYcorYLugrbnpbWEyK/JEU4QNEOPCniU9h0TbNBEht8POtRJ2ogCcRKIsNI2UzrbWa5p2XmnpaEqIUTCRfC5hZsEfAW7Nw61LcOw4LC3B4iLc9IwChdtvgRtukAWVslQJ7/Wg24EokpxVMzOJI9KU93PnoNuT72s1uk+e49Ijmzz4Ofmq14NBX4I99NXuQKMD9T5sxCLg9oD7gAeTPlUTUGtwaBTEbskyNWC7ERg561yp+gR2lIYeW+8bTT9ktysr9ZTd/hBTXNyN1LCPV0hemu5L9+UK+PpZTQdN0dRFDIC0aKWdznkaU0T9PAcb3aXPWDvtWYA5zzKSYk6jItRoXsEYbmq22IaPPm8POmu8nVZOo3+OI/ex1vV5FIn2USPIHg9pprX926Drx0Dv8PxUX5O4xZI9nqcj3/RN38Ti4iLvfe97CcOsmL/x+djHPsZjjz02cp04jnnVq17FIfrvhsdzXeHvvf0TBIduHqjHc6AEQYH50jNZLN7IswpfSDUfUs4FVHMBM0VYKsWcrHa5aabBzNEulZuKHPmCKsFzboFiHv7fA5z/yxr3frbIoytlHm7G/O3GKc50L9KkTpcG/ajDIO4SRX3iuE8UR0RRmyje7f9YPR7PQRHH137dvUP1C/21wN8g4nFauiIVxHYz034cbMFO930WEbDvAG5FxLI1ROBzZ0q7s7VdwVaNjbT0NO45lhERvYgpYKyiXRcxIGyRda+oMK2z8i/HUE6bCR+wvY90lrYrAqbN0o+R6+Cm+AKT334CMYAKwBPJ+vshQkTMPCatVZvtM+5tcXoSk/5I292x2qKCrNZV6CFmwqV1KQwOMDsDQRCL4xBFUhR8dg5KVQgKMGiIwVGZguJRYAripIUnPgsXz0OvC7U6xXyeo4WQfm+dYgG6XQn46PeTV08CP1otqDdhqiEpsKpVCC9BLTaphrTtdiqdg7ov0663ci0UW9YUXTr+StZ37nPAHstab8dOn6TPBdj+bFCxPmd9TotesaMdctb+xy1s7zIF3Ik8g84hz8GD7nfXpAKTYm8SMS1vBG7KQSGArb4UOr+f7OgrNwrioNuq713gsaSNX4QYHDXkt8K9/q7R7bZZ97t/2dHj8XiED33oQwB89rOf5c477+SXf/mX97W/F73oRbzoRS8auU4cxxw/fpyv//qv39exPB6Px+Px/P/Z+/MoSa7yzB//RO5LZdbe+6IdbcgIyYD5Miw2AqzfjD0GDAbGxgsGDjCDMWCbHTP2+HgMGJ+DOPYYH8xgFgvDIGyDwYDABrNJCAltIHWr1Xt3rZlVuS/x++ONt+/NqMistbu6WvfhJJkVGcuNGzci1c9z3+fZHPh+i9nGg5Saj1BOH2UsOclT8j9DIuaRS8BYqsPObIM9EwuMXuURf8IkXLEff+dOALwbfHZkDvLsyVOcvLfBj0+McFHmCg7VLmOq0WK+2aTSaVDz69So0/CqNP0aDX+BRqdEuXYI328s00oHB4fHIraUqDGCCcPuR8jpzNaNFjbC0JDnLjJzeBGZkdsI2qaEtU1WhWdqh2dse6HPUTO7NXuhjZmtrZUM85gZ4usl72xLnrNFFkeJNuEZ+YowUUvouyHEeiuGzNqOsr1pY0Kg00i1zRhChK7lJ9JDxtksRgTaF7yXg2PZfWcT1BroHG5fO/hex7ePCXCfBeJ18GYgm4NKFUozHcaGZ/GKBdg2Cdm9kHoCIt0cgVxL9uCNI7UqcfCPQeYIeKfAi8HQEGzfRqzTZuf0HLWaVGq0WxLH0emIyFGvicBRq8tnz5N8j20TwP3wIMYCqIQZhwnkvl1JWHQ/hO+V8LIwNjpDYdBxwp+1WkzbYNuNKXRc2OeiVnLpiPXt54PO5PeQflWRImrdMOrIWOoglQ5deoWAQf2WQCoktiNjV8WbjUQ/ayYValvI/VsJvkx1pXKkgNxTYbEg3P7WgO83CtrGHCJqHAaOYWyowkHu0Pusj3ouOjg4OGwkvvOd73DHHXfwxS9+kTe96U288Y1vPGvVTJ7n8dznPpcTJ07wuc99jte85jVu5riDg4ODg8MWg++3afttTtbu5XQ9wdH6T7gi9xSuaVxBvRMH8mSn22SOlMhvn8MbK8LoqEy+7LTxUjESQ1AYajKZabI/nyQRSzKSSFFO5qi0feqdLvVul2a3Q6Pboe63aMQaVJI3MNt6mOPV7+P+peTg4GBjS4ka3wWm6CVG+1Uz1Dk3wsYp4AGEcNbCOLUZCR8/TEzaCAefK/Edw4ReK9l9IDheDOPdXsFUCGwEfJaSnXaOx0YibCcVlR9gE7m6jSKFCF4ZVkacd5HZ1I8G2653nNQQG54J4HEI0ZwK2tiiV8zQl4pvtmWS2uPYtlptRHCJI2R3DlisQ7kMJ09AKumTSJYoxI8S27YN/AnwLkOo3suQEaF0bgmogj8NM8fg+HFpTH5IAjriCTzPI5GQo8c8iMVF3Ih5EtcB8t8lzabYUcVisH2HRHgkj0LZl9n7PsbiS6sQ7LyHlSAs9PVbx0YM6f98cMZlzv5zQGET1EmMdVuK3gdtWJSxx7xWXCXoPTe7HzrBfjNIlVgDIc6boXXDsAlzva98TMaPbjtIFG4hFTl1TPj4WhAlXITbGhaD9NxLyL3rBwevIkLmoNwgFRP0t+FsCxsNpHqkHrRNRUqtlLFzYRRR/RBlU+fg4OCwEWi325w6dYrf+73fY2Rk5Iw11UbYUoURi8XYsWMHr3rVq/jt3/5tXvWqV/H5z3+e6elpJ3A4ODg4ODhsIfh06fhNSs0jfL95lIeyV3Bt7f/jZG0n081RpqoZrpydY1v1URK5LLRadL/5ALUDdWaPpDk8M8KhxSzHagmmG1Bpdal1fFpdn47vy7+lvTjxWIy0n6Dlpyn4Q4wnJ9k1fB0H6//OfPMQXb95pkUODg6PXWypTI0dnneG9LW90m3CO4PMvq8iAkh4RvRaoJ7mfmgZCFGm2ROaHzCJkKoz9K8oUYJOCcbwTF0v2MdQcC5la30bWQyZv1EFeWHiOYPM8c8h5Odplg8eV99/tSFayTHt7Amsv3UWtm3NpO9KvmYwYe4ta3t7/X7HHTS7exDUQz+FCBpZRFzRShrNRggfS0lvu1qjY7U9KvhZQ+BHgte2NGzfBnt2w67dsHevR/Hx+4j9wn+D8f8feLuQGiJNtzgO/j3QPA6Vn8Cd3xdRI5uBfB6/vEjj8EkO3z3P7KxoHF1fcjXaHREvUklIpgL7K1+W+11Ip2F2Du67D47PwrEOHPBFOCoj16aIkO5V63257JtwtYKNqGoNrdjRs55HxkxjmeOsFfaYDWfCQK9tWxITHm63234+6bPM3q99HIJ3rei4BLgBOcf76BU0w8+rrrXM/q6LGXe2cBm2VdJjZxBiXgPW1yoYDWOemwv0VppoH9p9aufuaBC7Vpq0g/20rXML/z50g2MWgTl6q6jWOjb6CW4qaGeCVyVoXwapcqljrPCi9tmxvrOpxcbW+ak+L+EyNRwclsfU1BQTExPn7HjPec5zmJ6e5q677jpnx3RweKxhC/1T/7yFy9RwcFgOMZ5U/HWuyu3kimKMGycq3HhjiZGnjVCZ6nLi62UeeGSEw9UUx+pwot5iptGm3GlQ9Wu0aNEJ2AGPGB7emXeFj4/vQZcOc/4xZmsP4XfbNNuz+JFpqw4ODuuFy9TYYNhabFSFhhLoGtYcwwTHtom2JFoOam1SRSym9HGZwpBpNmmfRixHCsH3c/QSf0pQ6/nod1HWSnYIcD8CuMFS8nw98JBzUEurJEIS70PO5TDLiycqyIwhxOf0CtoXnpmtQoPOctdrqfZM4dntNWu5vc/l2qnCy2r6T8dUBrnOWaTPtDqjiQn6jhJrtI8zwfHBzOrXWeT2eFDhJA8UYlBIQCYtmRYgeeCNJnSrdWLTB2D0boiVwdM6gXnoHIT57+A/dC8cfITm0VN4tRrxXJpG06N0bJFHHu7y45/IwfNDkIgHopsH6QwM5UXsSCblpeKGF4PdOfl7/DjsKcPwDKTrcj5JTOWPVhQtYiqLVntf9rOf0rGwiDwDwkHx5xLh51Mbk2kRfn7pu44pO48kbElHsI8RREhrIn3aL5smbHMU1T7btkkF3Kh5uj69wsl6njlZ5LniI6LMNOZaaXuj2q5isC1e2t8NemnbmxhhxMNUE60GcUToTSICRY3eCpguvZVzKs7uQcZnC/lNCe8zE3wXDl93/4nu4OBwtnHzzTeTTqcjv/vnf/5nZmeXJpD9p//0n7jooovWfMwvf/nLlMtlXvva13Lo0CG++c1vrnlfDg4ODg4ODpsBj1RilJnYFIfrQyS8AhPpNBcdiVP55iKf+tYMPzjR5nh1mnKrzaJfp0KZbsyjTZNWt0q328Kni6+ihhcL3uPEiIPnnVmux8yltgNdunWfbrdGp6tpsw4ODo8lbClRI8q2JTwbuYqpmPAR8lhJptUihgRKX4QQpMcQArGKsYQKrz+CqZ4YD9a1yStb1FDhpR+J10RmFA+a0b7RNipafaAYCtpzDJn5vjigLQoPmUU9GuxvdoXtDAsS2jeazaDEoy1exELb2t8t104lSFfah0lMWHECucZqNaUzx9X6KIepxLCFGhU4kkA+CengDqy3oNI2ApUKG0mgmIRsMvicl4Dw4SLs2AETk1AoehR3DZHYvV1KJxYehPbDEBuBeBLq8zB3FB64h9ZPDlE5XqZUDuylYk2OH4cDB+HhkzBdEjEjlYR0HNIxaWexAK1R6AQZBvGYCBuppAgdhSLs2B3n0rkO5RLsOQCTB2GuDNW2jAG1TNNA9SZyvlNEV/OE74flrmf4/o8h979Pb2h5mPBfDexrqtdSqwXCbQ+T0rZ4FhY19DztSrSwEKFtTiKCxkSwfp3+5xEW+qLsjPSaRAkBWrkR1VcrvS5R0P5KIeNJx709Duz+CVcu2CJoeN3wZz843iIyPrSvNXy8xepFjQxi7jaCWBCeDM5BK5HCfZJGKkUSyO+Cbc2m8JDnB/T+NkSJUg4ODg4biZe85CX8+Z//OYVCoWf5v/7rv/LVr36Vj3zkI5w+fXrJdr/4i7/IlVdeuez+8/k873jHOyK/KxaLfOxjH+P+++/n85//PJ/85Ce555571nYiDg4ODg4ODucQHjEvQzJRoNKd4WQsS7oR59FKnu8fHuY79xzi7w5/lfnW3JItY14GPA/fD/7168OZf0WdqbAWYcPTf8F7dvVGzFRie3E8YvjnzHjawcHhfMGWEzWihA07jwB6iSK1gFnL481HqhMKCLmnZHa/yohYsPwEQmK1I9bTmfw2SRWuPLDJuNVWEdjtWAv0mCnMbGa1v4oi6/rtQytIVlusFCZPG/SS1OpJHyZlo4QM+7NmWITJ85WMC51BnccIGGonpDkXWWR8FIJXMQaZlBD+HvK77HkiJHiIcDA0BLmAxaxWobwAzQ74vlg/+UA2DTsmIJeT8O7hYZiYCAK6d3jkthdIFHN4Y2Owaxfk8pIgPj8HtR+Lf9T0NM0DRzj94zli7SalEkydltXKZThyCo6U4WA7IOg70O0Yy6ttHkwuSoZGoyFh4d1ggCXikAieIkOFGKl4h1gMLr4Ykgk4dhxOTkGyZuyNksiY0muSirguNlZDmqtQpQRxBjMO7YoIFRBWgyQyBrKYtut+mtbftjihVShq2xSuzArf67b4oseAXnFCK30SyNgrIs+c5RDeh6IfaW4vGySyhAWclUCfKVlM/lH4eDHrPSz69hMuw/2qvw263L7fG/RWy60GTaS6RMWRYUTortLfVmoRqXTLEl2dpBVn7dB2Dg4ODmcDl156Ke9///sBuOGGG9i+ffuSdb75zW/yp3/6p333cdttt3Hbbbcte6xUKsX3v/99nv70p/OmN70pcp2rr76aq6++mmc/+9m86EUv4pFHHlnhmTg4ODg4ODice2j1RJx2p0a1PQMJj7SX5v5Sgm/OHeCHi9+k3JqP3Lrr1/v/Q99a7vth5izqc2gjBweHxwy2VKbGcKDEhklAm7DcaCjpqqR2HMnKqPRZV61cbG/6qBnOOsPbRpiQ9zEkfo2VnaPOIF+rRq3WUSrgxJBZxbYH/UqQCPbTZPn8jdW0TcPToZcQDv9tL0sgVlijwd81JBdkJdZEKmYpmZ3FiCs6JoYQYnkIGPFgOKimyOcgngiuSSywa1JhIwaFIbF5whdxoVQSMQPfvA+PwN49sn6zAaOjsG0bJIczJEaLeDt3wFBBVI6JCTlwqw0LZTh9GqamqT34KA//oMQD9/lkcyJCHDkCR6bhVAdOdyT/Qsd0EiHNRxGLoEJwnsNxGC/A6BhMjEvbhochlRJxJpODVgNmZqSio9uRJpw4ASdOw1TN5BnoaxGp1JjHEOcbdS+r3dNaRc0wEkhfjCD9Y2e22KKkWhpNIfeOhrxnMXZF9jPLriYIV1XEWVrtkwJ2IpkaFyF9+j2kD8OCn7ZPj6PvdmZGWJQIV5gMuh72vm3xYLnrqKJTEkPkR1W+RVU5ReWX2GKHno8KFmfrt0Gvax5jG3aaaIFOx6JeS4gek4NygGpb56f6vITL1HBwgM9+9rNcddVVAGQymYHWUf/8z//My1/+cmZmZjbs+IVCgV27dvH+97+fm2++ue96hw4d4vrrr2d+fn7Dju3g8FjFFvqn/nkLl6nh4BCFoGrCSxCLpUnEcqQSBYZS2+m2a8zWfkyrW8GJDQ4OWxcuU2OD0c9W5Gw+JjVIV0NyCd6j7KfUhsYWLKLsXvoRhWGCTkmzOEuzO84GPJZaKq01TLeNkNYbjbAdS7/Z52GLqjwS0psJ2rYPmTV9ZMCxYshMeK3EGAn2A8ZuqoAIGsPASE40hdFRGBmRSoxEPBA0ApUrFgsqNmKSi5HLS0NnZ43wEQ+8gNIZ2LMHduz0qNd82i3IDSdIbB+D4WG8oSEYD8o2sllRUTpdUT8WFvFLJTh2jIWj85w8AT85BCfrkPfgWFcsxaaDflDyfRIhyncH56XkcBOodaA6D9Ua1GtS6VGpSBVJPg8ZvUEQe6xUGhIaGuJBchpyVch0zSz8DsamrMTqqycGocPGiBkg40nFrTRy3dWySasrwjkUWq2l1VkdRCTajQgQKhSGBQGFPt/srBmdzQ/GJk6ronSb8P2hIoD2d9S5DbI6WikVHFVN0S+yzWdppkQYWqVh79ver23dp+tjfX+24+I6mMwYFZ/TRD+n9behQ+852e+6noODw2MX8XicZDLJX//1X/Nf/+t/3fD9Z7NZ4vH4wHV83+e73/0uL3jBC2g0NjaZamFhgR//+Mc8//nPJ5FI8OCDD7J79+4louNFF13E4cOH2bFjB9Xqcr8WDg4ODg4ODtHw8IjjeUnwYvh+B98Pp/etFV18Ynh+h263QdsHH59mu0SzNY3v/mXj4OBwDrDlRI2wsNGvKkFnNKu1jdpTrYXk0kBoH5nN3g72Ww+tp6SjEohRpFV4fVvI0HddngiOU0CIs/lgnfBxNwran9p+rYJpcHbJwZVCyV/bbiosHtmwhY0OJoC3gfTrOOKH3wytq9uqwJNAyMpxDAGfJLD+8WA0LRULY6NSvTAyLDpDPi+kvooVHlJxoVZU8ThoJqfnQb0OpTJ4XVk+OQnbd3jEinly2Za0qljAGx+H4rCUR6SSUvrh+1Cri0fV/Bz+yZP4hw5TfmSWw4dFNOl24IQPj/gmmFlDjrWPxoFrgu80FD6Jqd6oIDZU1VOwWIGFBRFx/K5YaIHkbQwVRNjwfXHAisWlT6anITkD3ab0u+altIN2nM2Z9SuFCoo2KZ5AqiO02qeIjA0NrW5b26q9ltq16T2dBnZhqi9UoNBw+H6I+q4EHMRYIC0G69lVHdruIiJWlZFKgrC9UZQoaH8/SNQIC7Ha1nBWxVorcMI2VH7ou7DYocsHHUvFYg/zXFiL+OVhxoFakPnB/vvtLyz69Otz+32z7wcHB4dzg1Qqxatf/Wo+8IEPAJtTXeT7PnfccQc/8zM/c1aP02g0aDQa7N+/n4ceeohLLrlkyTqFQoEjR45w3XXXcezYsbPaHgcHBwcHhwsW+t8Tfr8pbuuBj08Xz4cuDbrtJr5/ttgqBwcHh6XYcqIG9JJ8YfJRkUNmRA9hiKspxDpqNZqxkpEJJOi4Zi2Pal+4ImNQJYGSp4TOwUdId81wGEbI5iGEhNaZ3/3IrvWQYEqwJjEzxTd2nt76oG3Sz9rHOlMa690WlmrA8eC9hvS7zrJXkuSwkNgAAQAASURBVNnOM9BMkSZGXFK7qQJQSEiAdmFISP3holRnFIowlIdMFtIpqbqwqzNiwYX3vGAMeHLM4WHRI1otETfwZd1WwydeqeF7Ht5QDi+blR35XQm2aHckjCO2KIpJpYI/NUXnyHHmD5U4+AgcOgRHj0O1JedzHLkPosKtZxExo4bcL53gvNU+Sas2ml2olqFclTbHYyLS1OtSoTEyKuc2lIfuuCwrFiRcvN2G6gwsdoX8V/FsKNj3Zs/p0AwVrcZR8SUH7EDI9UKwjooaLQzxPuxBKg7DbTiEVPjsikEhBum2CBHNYJs8ck9X6Z/VE4UYvZkb2g7brimN9Ok4cAUiSJXorSQYRK6Hjxdez66kiCLr7eeeXWmyWiwnEIcFDV2m97j9PIwh12MH5hlXQcTNhVW2S8dJB7EAG1RlFK4u0TZGQfsqZm3n4OBwYeM5z3kOl19+OX/xF3+xqe342te+xk033XTOjtftdrn++uv5l3/5l0ghZWxsjNtvv53Xve51PPzwwxw8ePCctc3BwcHBweHCgS//01DuDdyv/H83EE3cv1wcHBzOLbakqKGf9W8VNsKP0CRm5rJa6ZSIDmkddMwGxjM9qi3h9W3Sqp+woUS85nTUMFUESo6p7VEWETaGEQJOSemoUNn1zuvrYGaPn48zhLVaI7xMq0yiBA0wYoYS6IOsapJIn2tlRxa5ThrMvGcYto2JPVQ+B8WiFE7k85DNSMZEIhEIMG1L0LCqNUBEi64v2kQsJsJGrQYnTsr77BwkUzDU6tBqQrZeI5fK4MUT0GhCclEO1m5BsyVlEdUqzVNzzByp8ughePRROHwcji/CVFChoZkOUZgC7sJUQKWCdVvWu1opLQLlNrROi7AxMiKiRacqlSFjo5DNiQ1XOiPVJ602LC5AaQFKdVMpov3cz77nXECFgHGE9J5EKjNmkcyRXPD3qAfZOMR98OIi8LR96X4PGEpKrkquLCLG7hhcXIRWB05VINY1IpFml7QRYrwavAbNb0kjz7MxZFw2kOdB+LmmYlwMU52UQa6bInyPR1UJ9BMUbEEjvA/bakmfS3qPruU/dW1RIOo7+xlrCyxRvw26XK/3MNI/ixHnMggdTED8SuY8hc+h3+8CyLXKBt/rc8vBweHCwVOe8hSe8pSnAFKR8d73vpdYLOoJd/Zw6tQpPvGJT/Qse8c73nHO/ffL5TIvetGL+N3f/V0Abr75Zh73uMed+f7yyy/nS1/6Eu9617t4z3vec07b5uDg4ODgcCHAP/MvpLPxG3+29uvg4OCwPLaUqDEoWBt6SSKdaZ7A+J7bgsFqoCT4YBfi3naG/2kaJTYkMaHD85jZvl2kzfMIsZrF2GmBkHA1eisoNkLQsLHWn6XNsExJIn2jpKF97E7QplbE9+F3hYpJKUREUiElAewpwJWXwdiYWCql0yJsZLLyWYULu+qi0+1fpdHtBgSsJ9/X65Lx3WjKNok4zM/B4iIMj7TY782SqVag28XHw4/F8Dtd/FaXZqNLfbHN3HSXY8fgyFE4egJOVOGUbyqVBol6rWAdvY5pTIaEVjzpK40Q8N0O+LMS5VEsSrzHQllEmYm4qVjpdKRaY3gEhueh2ICyb6oUtBJGLanONuwcCb0XR5G8lf3AtgTkEjBdl3u/AGyLw7aCnFO7I+dKIGjotYx5ojV5HiQWYCgh5z9fhYWusa+KBf2o2SxVROSZQioHqtY6Cg8RXUaCVxbpr37PphhynTLIdU9hnpPh/7SNqiAYlP8AvRZQUf+prBUH4Tat5T99B20TJXhHrdtFxItTmDGWwlTArbY9DWTMrlTY0O36Cd4q/GSQ69xFRDVXxO3gcGFg//79vOMd7+CJT3wi119//Tk//h/+4R9y+PBhAKanp/n85z9/ztsQhaNHj54RNT7/+c/z0Y9+lH379m1yq84OXvWqV/GFL3yBI0cGpbo5ODg4ODhsEHzA84NKCgcHB4cLC1tK1LARthsJk1hdjN/8SiosBqGFCA6r+RlQYSOK5LfJK9vXPeytn0TsY+JIhYla9ZwO2tOy9jfI9grkQicw+QVnA/2Iz7X0+aAZ3fa5KRntIcRk2JZL+yI8Uzuqj8DkaKSDfWslTQKZGX/ZRbB3r5DZiYRYLqXSYr/kI8R9symVFtUKLCxCoyFEt2ZpaAf1VGoEnxsNydVoNkTUqNVEPPG7Qow3FtvE/bYcoyq5Fq2WbF+pQKkk20+dhpNTcKJuxIwZpMpnuetv95VWDdQR4t22c9MqDh9pX30RRpswMSIiT2leyP3homSLpFNSzVIsSu7IfBnKjSCnI7h2eUTMW66NCdaXv6G5Cp3gfCeAvZhg+KIHO4owPiYCx+45aNZhchjGxyUcvduVc4rF5Nr5wX8r+r78PdqA4QUZE60WDGVglwdjTZjz5XqoMJlAxl0DES1jSL/M0ytqaL7JGZEl6LcDLLVP0moarczQaqV+/abB6hmW2iRF3dv6DIOl96raY/ksfR5A//tvOdjP1bAYYIsreoyoZ0gdYxem26/1P/G1imK1gkZY+A6fg/a/2v+dTxaADg4Oa8Pw8DC33XYbP/VTP7Xsuu985zv56le/uuFtuPvuu6lUKhu+343E17/+dWZmZpaIGr/1W7/Ft771rbPSL+cCz372s3n3u9/N4x73OF796lcvex3m5+f5z//5P5+j1jk4ODg4XJjw8Wlb/+hyFRUODg4XFraUqNFvhn0/QmotRFURIePm6X3kr9X+I2r2s5KHMYTYrCPEoxJjKeBiJLA5jVjfPIwIG2GrJN13mCDcEax3OliuJGgcIcjC52djLWSjvW14P2FhR2dvK7kXRYjmgvfwDOoEQjIfR/qtSO/s/qhrHlXFYoe56/4zCKGcQvo9jxDIANuBKyfgkoslQyMWE7EhHhdy+4yYUZfA7IUFqVYolUVswBfbqTPkcNAojcbwYsFMf18I8E5b9gVS3TAxEeSCpyGZjtFodKnX4f77RTyIxaFRh2pNjjdfhdmujK9Z5HofRIjy1cIOtC4hRLraC9nXsBaIMv6cVJik0yL8JBISEh6LixhULASWXUNSrbGAETZawf6P058oziHiyixrE+hi9F7bDLAnOG432GfbE+HlsstFxGi3YXZGrtP4mAShV6uyPBYw0CpsdDtmTDSaci2rVanCabdFqJovw8nT0oHtGEy3jOCQRiq0Fll6zZrACST0exIZq83gXML3rQpSp4L9LFr9lSa6ukNzRMIh3+H7Wt9VRAiLkErK6z7i9O4j6v5bDcL2clhtCe8v6plgj62VHD+JPGuqmFwl3fdq7Ax1m6iMEjDPsEXkGlSC47l/fjg4bG3EYjHuv/9+du3a1Xcd3/f53Oc+x2tf+1rm5uao1x+7NVrPec5zeOCBBxgfHz8TmL5v3z4mJiY2uWWrh+d5PP7xj+fWW29ldHQUYEXn0el0OHbsGLfddhuvec1rznYzHRwcHBwuWLh/STg4OFy42JKihn5e7eNZw6R1dnkMY8fSQYgrDQbeCCjxFp5ZTPC5hfHQ1+PvB34GuCIDhSwcLsN3O1J1EoUYvQSZvo8H5zaPmeU7AuwMjncUIxg0WD3JtxyivPn1b50JXmMp4ZhALIDGkP54CFN9Ebe+2wF8H2P7MhJse5re66f++VEzysNErNpYqV3PEEJ++4ggNFKUzIhYcEE77UCA6BjSemFBXqV5ETRKZag0Tf9GOWaHq42UDFby9Mo27NolIoEHdFtdDh6EO+6A4yckp0IrHVS0WkSubwkZA8foJWPXAh0rRxGCd4JeUePMdWpCfEaqM3JZydTIpEWQSaUCYWNYMjfmyzDUkvbqOWQQEel4RBs85DrrPbMWxJBrrVU548AuRCRIexD3YOcQjI6JzdjkJKSScMklcs0DfoV0yhJWA6Wx2w3Eja75nIiLmNFuS2VNpQJ7u3BlU/qh1YJHHoEjR2C2KkT2IlKNpdcwLGy0gXuAI4i4M9fnXPXazIT6UEVVG1q9os+UqP4NCxr2/R1lTaXvdraGwq4GCT9z9DmhOS72Pu399HtWreb3QZ8RYKpY7BBwHxknGWTcbQRsQT78/PYxAp9W76Ui9uHg4LB1MDo6yo4dOyK/832f2dlZ7r77bl74whfS7Tp7iOnpaXbt2kW9Xj8jagB8/OMf58EHH+Tuu+/exNatDMPDwySTSY4dO0YsFiORWN0/ueLxOLt27eJVr3oVc3NzvO9976NUKtHprPW/fhwcHBwcHBwcHBwuLGwpUcNGPzKsH7KYmdinEXJWCVIwpOwMZyeU1bYbUVJVSXW1k9kF/DTwUwWZJb5Qk3Dn2dC+lNRTAjJsuQRmRrES/EqcVZDzywfbd5FZ3xWWho4nkf7RkOhBfT2oGiJsCdNiqaCgSCLCxXhw7EuQmelK6taAR4M2677SiFizC6lo+XFw/ipcKWGp18AWEbTNKYSo1wBnDWIeCtbPIrkWp06JsJFOm6qMVku+W1yA8kJgAbUICw1pp9qG9UPYHq0bnO8UQqTubYkQ4ANT01IN8cADcP8R2XcaIcGrwbk2MbZDZUw4uPbFev857Af7TgRt1cojJZzjQKoBQ2WYn5eqhkxWqlpiMbFuKhZk+UgRyjMmMFxtgVTcCM9T9RlsobQcPEyVRh4RMnYjItl4EibHJPA8HwScVypw6qSIMWPjImj4vtiDtdrmnOwD9NijJSQAPpuTSppDj8LJE5AfMsHqmYwIH822GZ/ziKjTT4xSez212MtisnZKEX2jFmq2tVT4nvYw2TNRfRuuLuhnM6fVLmEbqLi1XbiCLXy8IeCq4FweoVeoDAu4UW2I2mc/jCD3+gKm+ieBsc9qI/fjAuu/d8JtC4tCKsCqCKS/DVds0HEdHBw2Bw8++GBkEPj999/P4uIiT3nKU855QPf5jna7zQ9/+EOe+MQnnlkWj8e55ppruO+++2i3z0X61uqxa9cudu7cycc//vGewPO1IhaL8da3vpW3vvWtvOxlL+PWW289b8/dwcHBwcHBwcHB4VxiS4oaURUby/1T0EOIMSVOoXc2/HrJqn7knBJ6UWG5uiyBqRpJe7DYhtlpONiAH/pCTIOQW1pNkMLMWLdJPsVJ6289Tglja6IVJCo6hNuXQmbiZzGzxZczQuhHeIb7pE2vsGP722t/KTGqwoqS5y1MRoDuI4UQk7uD484H5x+uPtHz1VnodltzwX50Bv9QsM9hTM5AqwnzJbFOKhRFyJiaksqMalXEjIUalBpGVFhExlg/Uagb8VmJ+0pw3EoHTp2W4zWaUt3w6AkhYJtB/9gCVg0R5xaDvtCKGPsahy3MVgPtt4XgvPJIJYuOqTQyZucXoTAvweBDeal2SKeFyM/lhOwfLsJIBap1ERrV1qqF9H2TpeLXekRHvW9SyDWeBHZ7sC0tNlO7d8m7j1zvhcA+LJ0W26h0OiC6g0bE4iYPpSdTI6jUSCQDy7CUrON5gT1YSfqg3ZL1Tp6Gk025ptOYCoxBAdaak5NFRJk8IoKUQ9sMBX1ZCM67FKyjY8AWJPW+6ydchP8OL+sg1yx8jVSEsoPKoZfct9us16kYtHuWpc/9KHFEzyGqnf0QQ55tFcwzwxZy1ztnup/Flv5uhas17KoWff65Sg0Hh62Jpz3taezfv590Ot2z/O677+ZHP/oRb3/723n00Uc3qXXnN3zf55nPfCZ///d/z8///M+fWf7xj3+cQqHAX/3VX21i63qRTqd54QtfCMDzn/98nv/85/dd99///d+55pprGBsbW/Vx9NwXF+W/hA8dOsS3vvWttTXawcHBwcHBwcHBYYtjy4katoixGuJKSd/wDO+Nmnmr1lb9rKvC4kbUDOMy8BMfZmoyS/8ghszTAGP9Z7Fak+isdduz3sfYpKSC77qIRY0tAMXoJdbC7VHxoxXxfZSdlk2E2rOOw30SQ0jWYUwI+iKmIgLr3I5b5xiGHj+BkL+PIIRwHUMOhgUv28tfkcLM3k8j/VwgEDWCjs2lpXpGLYcaDanMmJ2Fk6ck8HqhuVTMUBsZnQFvtxuWVmnYxOY2pGrlWAOaP4FsHDotKDVFrNDqhjbGuqkT/D0T/N1gKQEMvWNltbAJ8CpGeLDDt5vAYkvst+bnJCA8k5Ww81hMqhMKBal+qNWhPiWVD0rgakBylrXlgPRrtwZt55G+nQS25WB8GLZNwsSkCC31hthFdYJctW5H7MXqDanSqFZFpIgHN7Tv9766wcXMBoHizYYs77SlAmR6RvYRj8lxukDXg3lfKsnKQT9qRZWS7GDGZyb4Phd8nsWIFfY5jyCih94navVlj0db8AoLBeEqLP3bD710HPabP6pVaSrChe9pe58N5HkVG7C/qHEdtWw5zGMsuexqk42AjjkVi8IYJM7YQuypDWqPg4PDucXrXvc6XvziFy9Z/tnPfpb3vOc9m9CirYWFhQXe/va394gaAB/60IeYmJjgj//4jzepZb0oFAr83d/93cB17r77bv7+7/+ez33uc/zMz/wM27dvH7h+Lpfj7W9/+5Llf/mXf3nm8/3338/nPvc5PvWpT/GjH/1obY13cHBwcHBwcHBw2KLYUqJGmKxbDZR0O1uI8qi3jw39fej1vYEIGY8gRH/N+i6NkGMNhDhUr3XtE53dGxZtlCBvs/T8BxF3asWlVQ26rlrdpIO2RIXYqq3XcPD3NL0EpmZjbMMQ/0qK54P3OkLQLkbsX+EH36eAw8jsdjvIN2qM2DPTte8zmCqNNGZm+2gSxkekwmB4WAj4QpAPUauL1dT8HJyuwFTXWEDpOemMde17W2Cx36PEDiUzZ4GmD2N1sdgqBMcoI2PkiNW3egzbRsg+7/C+11qpEUYnaE8Rk4mSQ8ZHeVEsqAoFsXRKpaTaIZ0WC6pWE2pVqYJI1mCyDYc6IuolkPGgFRzrhY+xahtG+nN3BnZMSnZGsSg2X5msVGCoqJFMSbsrFclKWViAcllsxzT8/YygERyo64s11eionGOlIlUZzZaIGrm8vMc8EUtGRwPR5wR02yJEqCil93wbGZ8XAZfFoNEVkaKOEPOnWJr5oNe4gYyXEr15JHof2LkXgxBlM6ewn0f9UECua1QFjv2crCNVJyrQhsdqPyFmLTib7uQ+y1ul9ft9sPt5YeOb5uDg4LAlcODAAd73vvfxxje+8cyyWCzG61//+vNC1PA8j4985CMD1zlw4ACveMUruOOOOwB44IEHlt1vKpVifn6e9773vX3Xufrqq7n66qu56aabePGLX8wjjzyyusY7ODg4ODg4ODg4bGFsKVEDlooCUVjPLPS1YiWEXr/qEjvEWmdR2ySuj6lmUNskzUZIISSy5g+oJVAbk02glj4rgT1rWLMSbCQR8nooaI/u30YcIS93Y/IB7P0oWR1HZqVPI2TtcPBdGxEG5ln+GnYRwrZp/d2vIiE8I9quWNHMAQ0Jz3tQzMP27ZJ9MDQk1QaxmJDZC4GgMb8Ipa7MKlc7qLCYUUcEoihBQ9scBXvm+gnk2u4I/p5FskXmg/VUlImyCwqLZ/Z5Lydu6FjT6xK29NLjtZHruAsRNOJBe+nA0HwgagyJFZNHYEGVFzFjeFgCtLsdsV47gdiHta3j6+f13tPNoF1xYCIGk0WYGJcKjWRChIxGQ0SX4eGAxI+JIDE3B0ePikhTromoYHd42A4964lgU61IIHg8Bs2mBI9fdLH0ie9DvQYjo7JOLg27p6Djw4GyEPuZ4DAZYE8Mrr4ERnNw/BTccwruBx4g+n7RSqkTyDNEnx+2OKECQXhchtcZJHiokNkvK0eRwpD8UddTl7UxtnthRAnD4e/sChJ737D+MbRaDHr2RlVorHYfDg4OWwtf+MIX+NCHPrTZzdgyKJVKZ8QAG6Ojo9x666286EUv2oRWGXiex8033xz5Xbfb5brrrqNara5acGg2m9xyyy188Ytf5LWvfS2vec1r+q770z/90wwPD/f93sHBwcHBwcHBweFCxJYSNVZDRp1N4kpJcCXvVkoMR9mk2OsookjBcFXGEEJiFxAxQAntIwhpW7XatRJCLI5UYKilkN0uJTzVQqWGqf4In5MSpLuA/UFbMojwAEJq7kcEDw0u1r7oYEQdtTVaDs1g/TTRNi52ZUYUAQq9wbx6beOezM7P5WQWfTIh+QpdXwQOfMm4mA2soOywdT2XOkLOa9VEuNJoNWO0ivRNG+lDtRzSPo8ag4NEP9uGa9B6GUTE0soLFW6ijlNBRKpFzBjdARQrkJuV/ozFJBQ7kZAqhUJBhI1SGWZm4VRHxBrNhMkh4zJceTIIdtVSFDSXpetJ1U2hIC+QdmWzsn0lKOtot+H4CXjkETh+Gkq+sf7q16YYQVXNnFT0DJelMiWblWqNUgk6HWNnls2KuBLzYOd2+b57AEaacOkETEzI9oWgwmO+JDkrDeAQSzMnbJQwFSrhPrLvDei9T2LIPar9GBbHwtBqshL9K2vm6H0WhvcTJTrabc5gLNbCiDr/jRDCVoMEveewXIWR9vOgfl1tVaKDg8P5gbe+9a1LshXuuusuXvCCF1CvL5dS5mDjH/7hH7juuut4y1vecmZZIpHg0ksvJZlM0mqtJ21rfThw4ACe1/ukbjabPPOZz+RHP/rRmfyLtaBer3P//ffzhje8gd///d/na1/7GjfeeOOS4wF885vf5IorrmBmZoZGoxGxNwcHBwcHBwcHB4cLC1tK1FgpuWOTWAmEaKv1WXelUOI7hdjW7An2mUKI3EMsJXvVqqkQbDtL/8BoXV/Df3VfNuFlB0k3g+Vq0ZRCSEW1rFnNzF4PIY9HMfZJ2sYhxCaqEZxnEyGvw/ZXNjSQXUWLfLBNLDjGGEY82GOtV0ZmlK9EzFD4CEmtAcT9xIuoSoUwbPudMyKHF1iDNaFSlSqN0REhluvAlC9WSfMYeyAVcWr0EsfaV3q+LYzYsxw6yPgZBrZb7ydC58mAv6Ngi1ZRs+bzSO7EJEJIlzHXx7bJ0TF5f7C/EeASgpBnH9IzQuDn8yIgpDMiKHS6UrFRLIo9VbIC6a7JkdBxotZrK8EgiyTFInCyA5e3xSZqaEisxbyYiCxTU3DiOJyegdOzcGjW5FxUMfeJbctmH1efORWg3IbyjASiDxegHeRzFIsSoD40JBkj6UwgXhSljzxPqkZ+6gmwZ18MD59GzefUaThyBA5NieVUg95Ki3D4fFS/2QJkP+FPK4C0YmtQVYH9bFIkrf23MUJw+Bk4qJpIkQD2AeOI6DXD0vs8qkrJblN4nxmibflWC31u5xDBtogRDA8gAl0YMeTeGkKuzwKDK1ycsOHgsPWQSqVIJpM9y7rdrhM01oB2u02zufS/Dq+//no++tGP8tKXvnQTWgW7d++mWCz2iAxzc3P86q/+Kt/+9rc37DjNZpNms8mTnvQk7rjjDiYmJti/f3/POvl8nqNHjzI7O8t1113H8ePHN+z4Dg4ODg4ODg4ODucjtpSoEUVWL0fcxhGyyQ7aXS1UzNBjd4AJhJz3EGJqiqVkfAG4GJmtXqHXhikeWtc+Lw39zgXHySAEqRKYShbqjGW1rfIwwdSrhV2JYUMtrZQAJXjXf6ar7ZV9zA5Cto8g/RbDWBJ5mEBjDZYeDtaprrHtem31Gg0aEzpmdOZ/+DpErYsnZPzYmEciE6OQ61I/4FMKrJJOIdc3jQk6n0CuewOxZUpax9Mqm1LEMXWmd5QopeRnNlgnPPdvpVY2ijDpG1Xpom3KB9+lkeut29nr6Zi0SfI6cp6ZDuRLYrFUUAEjIa98Toj8kWEozEOxIeKXb50rRAfWR2GQoJdEiPExzMMvEbQjlRaBY2EBTp+GA4fgwSn4cVcEPaz2aNaFXe2jApE+L1TcU3FjsR6EyVehWJLzHRmRig08yGYCcSMtgerxINdjckec4r5h6HTJzZWYnvFZrMJCR85nBzKuqla7+sGu0tF7Phb6rNDxOhKcS4n+95ZWeOmxE4j4m0PuTR2rmr+ibdHj6Hu/+1fvmyxLq950X/0qsfohG7RtPXkVKrjp8ROIsDGEeZ5O0TsmPUQkVNHvKEHlEP2fR07UcHBweKzj4MGDnDx5kh07dpxZFlWxcC7xj//4j4yNjfUs+8AHPsA///M/n7Vj3njjjQwPD/OlL32JJz/5yT3feZ7H+Pg4t99+Oy94wQu49957z1o7HBwcHBwcHBwcHDYbW0rUiMIgEiyFIdTW40muJLgSqjqrthh8V0AIsnl6SeF0sE6WXuK/H3mlxGIXIcr2ANcE22sIsOZo6Pbd0HarOU+1c0kFf+v+NcOAoM3TGLspPbchDAGvM7Ht61BFZimnkP5qYIjXRav9thCxGoshG7b9kk3aRkGJRs0WUIT/WdwF/K68Yp5YUG2/bAgvnaR+sky12ma+IYTlVHBeRWQsqFCTD/4+ZbUThCC2CeAw+v0TvRUcq4WMD5uM7Sfw9ZulbhPKUTPcExj7nEpwrJi1PGqWfLjKpYWc6yKQ8yWHolyChaJURaRSIiSkUlKxUCzCcBImG2Y8gLl3Fll5tUb4fJR8nsAEbV+Sh+1j0gaQvIt4THIwTpyAgzPwSFdI51PImMgjY1aFJ52RbwsaKvppvoraqVWAShcWKlCsw8KiVGKAVGWAiBrxuAg9msHRaXZo11rQ7VJd9KlWpAHpoD0jmPu2skxfaO6FCrC+9Yq6B1rBMTKY52g/myT72ZNEhKmhYN1FZAy1gv7Q6xhVsWH/rfdyE7kO85jnb5xeG0CF/QyOeg6oCFHt8/1qoOO0gxmjCeS+byPPhDRLLf1yGNtA3Y8+x6L69lxaaDk4ODicj/i///f/4nkef/Znf8bk5OSZ5VdccQVPetKT+N73vndO2/O85z2vpx3nEqVSiRe+8IW8+c1v5slPfvISceOKK67gb//2b/noRz/Kv/zLv/DQQw9tSjsdHBwcHBwcHBwczia2lKjRbxZuFOFTRAhMtQZaD8Iz50tItcFuhGwrs1RM8IP1HsQQ3eH92TYvKkh0EQKxgNgLqS1TGSHAcvQSYWH7mOWCesHYH8UR4nEUUwWgNjHlYD+J4G+7gsIm7zoYv37PWl+tsZQEtUnqFkbUqGLIzLUSd0pu9hMD7P2r3YxWAdjrKPzg/3xfMjS6Xei0gU6HzmKbqRNtTp6CSt0IFEqQasaJh7HcUoIfTIh4v3MdVE3kY8jRpPU5POO937b90M9OSElyFSZSwTH7iSe2/ZFWz7SQSoVFxIZpvgzFsggY2RwkkvLK5mB4BHaPQL0Be1pi26NZES2EIF9ptYZ9bjmE+C8Ce4GLPdifhT3bxEosHhd7sXZH7MVOn4bpeZhpG7sttROrIveyBqfbJLR9Dw5Z56/VE3Wsqo0O1CsyxvBE1Gh3xIpqaEiqNXI5aVu9BnNHFmk2fE6fgkcOweKCqRipBceyLaj6QZ8xyz0/7Xt2DmNjpaJNeJvwPjvB+mqnp6R/DKlkOIKp+ImCWl9pxUM5aMdCsC99FrYxAslK5uvGkSqdFCJUrdeF3R6LHYxFW4HgOvfZRrN2tAqva+0DzHNcRZvVWPI5ODg4XKj46Ec/yutf//oeMeGGG27gec973jkXNX7913+dPXv29Cz7/ve/zz/90z+dk+MfPXqU17/+9dxwww3ccMMNvO1tb2Pfvn1nvtflX/nKV/jN3/xNjhw5ck7a5eDg4ODg4ODg4HCusKVEDeg/89yGVmkoAQe93vDrRRs4FhznFEJQlSPa1ESqHOaQ2c72sZVchF4xYggRS0YRwutwsI8sQgZOY2ZMd61tob83vt0uzXxQElRJRzvgWoUVzdlII2SiEnZdls4IV6I3H2zXDbaJsqxREcdunz1bfDXiRgyxExoO2taiv/gFveRrVA5AT+VLV2bKVyowOwc8VCWVgtlZIZpjwXkryVtHRDTtzzrRpOZ6Zl2rzY9aPSm5q1ZkUfZR2rcqMKnNkD1ObOFH17GrdZoYgjV8DHv/WJ+10qNBUO3hQ7kKpfnAaqoouRqJpFgvFYuwe6fsoFQGFowYoAS3jr+VIo2Mj93B+w5g0oN8WiyntELC70K9BfPzElheb/WKdangPFSU6ifEaZ9UkXtWZ+M3Qq960Dd+FfzTMtaqNRgbDaqDRoyw0WpDddFnsSL90mxKu7X6ZAyxM5oHohy0tRqrymDRTO9J25KqjQhLQXEIQ9Z52s9i+3kGpsKrFOxLx9A4YsnXQoSNQRlD+jxJYKpdWsHf+jxvWO0Oj+EokUMzlvS3QSvqmqxuXEXBR/rpEGJXdgzJ/ogSb2rItdK22M8iu5JMz2EjfrccHBwcLgT85m/+JrfffjsjIyM9y775zW/yta99bfMaBjz88MP84Ac/OKfHvPPOO7nzzjt59atf3SNqKJ797Gdz22238axnPYtSKcr41MHBwcHBwcHBwWFrYkuJGlGCRhQR1UHINJ2FezEyS3seIZLWOzsX4BEksDZM5oWhJFWU6KHf27YjKibEEIJM/dhvBPYl4KE23I0RC/pZAIXFjRhC6F6C9MEjmBnCp6zz0Fn2SWS28U6E9EsF62u+Rvhc9Dg5hGTVfalXvI0w2Wjvb7Wihm4bx4g19vHC+9MKkXBFS3jmeteXWfulebEjypyG8QmYnITFiuQjdHzpF3u2dRURuUrBsqgqnrWcpw3tW4VWx9hjMXw+4QoHJU3Ds+6j2qRtVeHLHmO6ffieqiCEri34LADlltgvLSxAuSx2S0NJyZDI52FsXESkxQXp21ywL531P4rcEyvtuywmw0Qth8pdGGnKMTMZETdAqiaaDTl+o2XI/RRGlKzQG5odhi5X66lF5P7JYYSZgrWPGNKo5gk5brstgkUsJsJGKiXCTzc4gVZTLKtaLWlvvi4B480G3Nk2VRFqM5UI+mBQppAtdEXd1yqAZoJ+DN9fUVAhQgl9vQcaQT9MYp7F9nPdPr7mCBWQ53fQVWfG7ggitPar9op6LrcQkSaFCE7jyPU5xurGVT9UgR8FnweNE23fIDu19YosDg4Om4v3ve99XH/99fzCL/zCmWWPf/zjueWWW3jta1+7iS3b2vjhD39Iq9XC9/0zmRr79+/fNCsohe/7+P56f0XWjuc+97k88MADjI2NLckauf7667nvvvvYt28f3a77dXFwcHBwcHBwcLgwsOVEjZVY7YDxbS8gNlTDCDGnmQTrxWqrPrS9Glw+ESzTPAb9voWQwRosXUJIyXwMdo/B3DQkur0EpP3PEyUyY9Z3SkhOIjPWW0gFSIvosGoQQnYuOHYG8bOfpn+/K4kbw8zoV6sYG+GqkjARudp/DvqYCg07QLgfOa8CAPRWytiiTAaIezJD/uQpIY8LQ/KeTECtBgkPsh4M+UJ4lzAWYvNI3w06l5HgePZ6do7FSvtBr2GCaPEoStBQ2BUVsdA29j7s7xRqsZTDWDTpOmqPpiKYimSap1KuSEVEoQjDRalISCaFvM/nYWRUBI+hshED4izN9BgEXQ+kj9UCazzYz/auZFakUnLsTkeEgUYjyLHoLB2r2obV3PcdjD2ZZx3fFobaQK0FjVmznRcTO6pCYEeVSsFQPLgeMWl7PAZz89LWxbqcnz2W1LpJxZi1IoFY4W1DBDsVNs+0FVMZpxUPdt6OfY/Xkedbid5+tNexn2tajTGJPIs0nD2BXNNmaB/hfYWhwmMD+U3YHrS7GrRroyr5Vgt9NhWRMaKCtrOdcnDYulhYWKBWq/UsS6VSjI6OblKLLhzs2bOHWq3WQ97/3d/9HQ8++CB33333WT/+O9/5Tn75l3+5Z9ldd93Fr/3ar531Y/fD1NQUu3bt4tixY6RSKYrFYs/3+t0VV1zBwsJCn704ODg4ODg4ODg4bB1sKVEjgwllheWJTbUDOYYJmN0IQWM1UCusDMY2ZRQRWxqYmcpNjBhRw/jXFxAhotuFw9Mw010aJmvPmNdZ5UV6sxwKmOwMtWxZDiVMxkZUxUUUyvRawqQx1yxM5q5X0FC0kT5TOx6bjA4TpVGimJ1zkUcqWgoBabxYk1yDVBryQxIYrv9OXKzDwkxA1AfbK3k/iETWMWFXIajwlAjaU2Pl/aEiSMxaFtW/HkakUqJbt9WgaR1HtuATNRtfswl2ITPuF4NtR4LlMcSCZzHYvoaQ7IvAXBOKCzBcgmIhEDUSYkOVz8srm4NcCnJNY5+UDF4ZeoOXo6DnX0KuTTo4J7XqCmdAANTrQRXOItTaprrBXn89czD1eaSWSrbo1EBEs9hcsG4XqlWoFGGsCWNjUlVSLEIsLsKGF1ycY8fhcEdERx0Del01D2ctZL0dfK4VIPZ+bDEwbr3r+Imq+qgCD9GbtRN1n4a37SD9ptd0ATOWbZu0lZ5nBxEN1MbqXBtyqIihVXBx5P4Yt5Y1MTlHmXPcPgcHB4fzGa1Wix/96Ef81E/91JlliUSCK6+8knvvvZdO5+ya9sViMWKxWM8y3/fP+nGXQ7PZZHJykquuuoovfOELXHTRRWe+8zyPHTt28J3vfIebb76ZRx99dPMa6uDg4ODg4ODg4LAB2FKixjaEFJtn5TNYO8js4jirmwG/XigRqIHlI5jw7DpColUQsjeNCRK3CXkQ4i6HzCI+2ZXtU5hZyy1rfbWM2o4EIo8iJHI9WL+DkGThmdb9oCSkTTquJIi8GRxvMmi7zoJWKxslIaMIzLVcH72uKkykIvYf9dLvVOjR0O8SUOvAUB1SPmTTkgExPg7bdwQ5G1XIpiDvwZhvZqh3MJknvfMzDXRm+whybU5ickriyFhYiw2XbSFkVzXpNUshgo1ekybmWoQFp95/qvfuT//W8aekrO5Hq3VUhNQcA7UkOg3kylAswciIVCJks1IxkUxIxYYGZg/NCtlbsY43FOxn0DjUUGq9r3R8tIJt+4mb3Y5YT9V9Wc++ripwrAcNZHwp+a9CwRmhsgudWag3YXRBRIxKRSo3JidEXCsWRdRQxanVgh0tONqUvtU2a0XDoGdlP/uocFWOVjO0rGXhXBzb8i4MHZNt5FmgQpUGiNvrRUGrwPS6JhGRA6QvExixY6XGGlXEQlCv77n4bdA+SiPP53FkPDeRMT6PEUVVzFBxysHBwcFB4Ps+z372s5mamupZ/qlPfYqJiQlmZmY2qWXnBx544AFe+tKX8j/+x//gqU99ak/WxtVXX80nPvEJXvnKV3LfffdtYisdHBwcHBwcHBwc1ocwd3leYwQhylPLrBeG5kScS6dbmxTU6oE2QngfD16zCPFdo3e2s26vM7sPAQ8jFlAVRChRO5gxpF8yGDJZiVs9bgMRQ04jtlMzLE/82QTlcqKDHtd+5RHCbgIRWrRqRPMgomZxr+f6KDlcZ6mtTRSijtdB+uZ+4F7gRBe8BOSyUk3Q6UjWw6lTcOgQTM9CwpfrMYpci3Hk2ozSe3PFQsfRrIYhekUMDdZeiw1XvxnyYELgK8iM/hl6yWTlyGMRryiRox3s52HgRPC3WqedDL6rYwjZVPB5ERmHcx2piqhUodmCTlcqD1IpqdQoFmFsBEbSMn7UhkoFkjTLQ0WNHCKyqXWUB2Tick1jfdhi7Uu7SmMjiG+9p2cxeRBKYpeQfjvhw7EFODkNU1My3k6dhPICdNoi/gwXReTYtRP274OrtsMTYpKZM4bJeVmuOi1K6IuCBt/bQkY/QcT+LkpE1D6NsbLjq7g6j8nUUOh9NI7YSQ1qUxRU2DtXvw36XNVz0N+yJnLvHMZk8qiIZAuODg4OWx/XXXcdz3zmMze7GVselUqFD33oQ0uW/8Ef/MGSTInHIr797W/zkpe8hG9/+9tLvnvqU5/KLbfcwrXXXrsJLXNwcHBwcHBwcHDYGGypSo2TGJJ+PVhJtcF6oST7fPD3NEbACLd/kLLUxFSmJBHCfAciGqg//GzweRoJ1p2yPteC73VGdI3BJGfY1krJ9qgAajCWReF96LHAkLdh6xrovQ56vH7XRr+3LXbC6Ibe7XbaljdY70padzHXRwnSRBAmnUjILP6paTh5UjIh6PZmNwwhJKVWfWg1jn0s/VzCENxdhHSPB8dfiyd/v9nx9rVpI2NDM0/0e2vS/5ntwuPAD32vfVW3loH0cdnaXxoZixmkTzqIyNBC7J7qNXk1GiIyJBKQy8PwMCyMwHwZik2Y8c1x+2WEhKFjRcWQTNCeogcjORjKyzE9T/IrOh0RV3z/7JLcWpWjlSfj9N5nOn66DWlLqx1Yh+WkkmU4IcJGsSht73Yl56WyCJU5EYHmgUcwz4bloMeO0nj0OWNn1ijssbHS6i89f80TWQnC1ldNjPinz5Yy63+ur7ZCarXQ9qmA2sYE2Ec9Ix0t5+Bw4eGaa67h6U9/Ol//+tc3uylbGrVajb/5m7/hNa95Tc/yN7zhDfze7/3eWTvu0572NF70ohf1LCuVSrz5zW8+a8dcD973vvfx0z/901xyySU9y5/xjGdw9dVXc++9925SyxwcHBwcHBwcHBzWhy1VqXE6eCkpvxbCJ4MQ1ufKzsNHSP1TCNGogkYCIXc16yJhrW+/g4gZKeuVQQj0YvCuM54bmDDmU8BPEHsVFTr0+MuJBkoG25+jBootaNjbEhxnLjhuVACvLZSsFCmkv4r0n6nvYSoE9DhaKWCHq4dhV5Bkg5cP1FtCuHc6QjB3u2JFdfnlcO3VsCMv40lzUkaR6xMmJMPHLWGyXnLI7Po8G3ND2sKNzqyPW99Bb3VNFHkanj3f73zsdXKY7AoloVsIgb2IIW31WlSqEgheXoBGXWyUvJhYUeXzMDwCoyMwnOmtZmmxcvs5PfcM0r85oBiDbAbiCclLwZPjVypQq0LDytNYyf5tYWul0IqFMnJ/NDBh5mXk3pkBZppQWoC5OZg6DbNzUi3U6YgAlA9suoaHYcdOuHIYnp6Dn03DNawui6FftYSPyZ0Ji136HiWGhmHf72rntVZogHgdqUY5gcm1WQs8ZGzkOHdCglpxnUbOwa7eKCAVeEM46ykHh62MN7/5zTzwwAOb3YwLFvfff/8SASMWi/GNb3zjrB1zz549XHXVVT3Lms0mt99++1k75nrw/e9/n+c85znMzc0t+e7P/uzPuPLKKzehVQ4ODg4ODg4ODg7rx5aq1FDiWaGe/SslHzUEehgh249w9mbl5pFciyRiGzUf+m4SIc7Vbkh98LWKxM4oGEOspvII4VUMvq8jRF4ZE+it0BnfK7V1CosSYWIyvG9tWxQJH7ZCCosXKpSErZIGtVXJ4wLSB3MICRwljOg4URK8Zn0Oi0ZKLi8gxLmG92qGQLkDxSosLArZnk7D2Dhs3y4VBj7Quh/SLZO5UEeIfM2RsMdnAmNNNW39DTImV0rWR6Gf7ZTOpE/2WW4vWynsGe1ZpNoggbE6U7s37d8pYCemGmUOyPmBqFGGhSLkcpKnEY8JUV8sSubG6BzsCPp6HLn+ZVaXb5HACDgpTwSNmBeEbQNdX7JSFiuwUJM2qjVRl6XPHhsrvcfs9e13DVMfxghCihiyILYAJ09BOgOppLwnEpKtkcvBxISIQvgi1NQbMHcITrWEONesFu37cLWWXbEThS5mHNsVO/q3jqWo+zq8n5VW2QzqU5/eKqFwxVea3vyeQYgjfb8DGSeH6X1ebzR2IBV3LcRa0A69n0Se9zFk/A3Kf3FwcDj/ceTIERYWFvB9v8cSKR6PE4vF6HbPdt3whY16vc6RI0d6lnmex5VXXkkymaTVck9QgAMHDnDxxRdz8uRJMhkz3WHfvn18//vf5/LLL+fkyZOb2EIHBwcHBwcHBweH1WNLVWqEsdKZvjFkBu4oIg7oTP+zOSM3hZBTO4P38GzuOkIuljAEaviftkrWKSFdAPYh7dcZyqXgcwbJr8iEtoeVn+egSg0lRbOYkN94aL2wMKKkYtfavx2cHlVtkUSu0wS9lQt+sK8E0p9DEedl22U1MYS6zoJXgjBMuqqlkXrYq7VNFenjck2I91odGk1oNWFxEdptId4nxiQwvIKpyCFovwoYNuKYSpsCxkpsI/7pHa6gCBPoq5lpH1W9EV5Hr9cwJrMjnFGg51a3Pi8ifTu/AKV5EY1qQbVGLMi7GBqS7Iixcdg3ApfHpc9qVhvUWioMFcHi1ntUiLquG4sFbfaljVqZoPkPOo7C0DG2GoTJ+i4i0miWgo69MmIfNQvMNmF6BmZmoFSW8VevBfkgWSgUYXwCJibl73gctiXg8cDTgZ8L3q9F7i0dk2lkDI4H74N+EKLEydWKYiuh7/Q5s9z+BlWWrDT/RO/FcURQyLO2ypuVQitCJjECu13t5mMyVxYwdoGO9nRw2Lp48pOfvCS4+h3veAcvfOELN6lFFz4mJib4j//4j81uxnmFUqnUExiuGBoa4ujRo+zdu3cTWuXg4ODg4ODg4OCwdmypSo0wwiSw2szoLF47X6GGzIxXC5USZ5coqgbHywK7gjZNIWRuJWifTbgqbBLaC9o4Ty8R28aQnzVMZYGSzHV6oftPBdtGVQMoQWjP1rYJTpvADOdo9LMl0hnoSvrGMBUq1aD99ozsGEIuXoqQi4vAQ0i/gckXyQfnnGSpBz2hv8PEp4pECetv3XcJE0StfVsByl0oLsLCgsyKTyXh9GlYKMP0lMzw1yBlrZpR+yqCz1pV0kbIygJwOcYS6TAbl/UyqNrFD733swqK2p8X+qzjV0l5FejC4odaRmk1TArp7wpQ1mqNkggY+SDnIhGHfA6KwzBRg3QKkifhyAzEfOmrDCJwqUBoi5z6LNBQ8TwiiOwBCilIpkQ86UfKr6Rf1gL7Pg73Uws5DxCiXceqVlHEK5CfliwQzXgpFKWvMhmxoPK70p/tNkxkxFZLx/SCL9dpCJPlMhIcL5znEL5vwkJQGFGVFXYfRlVs9UOTwVZxK0H4tyGF3Id1jOAJcs6LmJwPfQ6crQo+H6mcGUOedXuRc50J2jaNXKsYct36VcM5ODhsLfzbv/0bv/RLv3SmWsPzPB7/+MfzhS98gcXFxU1u3dbGyZMnOXjwYE9mhOd5Liw8As1mk6985Svs2LGjJyQ8Ho9zzz338MIXvpCTJ09y3333bWIrHRwcHBwcHBwcHFaGC4Yv8RCC7hKEOA+rNSpsnET816ucXTQxORZFhKgvWt9r5kDYbsYm/9Qup40Qb8eAB4LXEcSLfR6TV7DIUjIvbCkVpz85aVvD2NtEVWPEIl728ZTIVgssRRwh7ZTgD5OH+r2HqaxR+AgpeyTYdghzncOkaheTW5LF5F5kgmNo5U7RansTExSu1i9nqjXqImpUq1KtUavBqdMSGr64CHHfZGoUMfkSahdmV9CoWDIK7A7e+1UOreef5LYNmP690v1GVTSEvwO5xiWElK1iqmCiKkXqmGqZOqZao1yFUkmyNaqVwEbJg1QaRkdhzx7Yv19I+07MhH5nEbFiiKXZEVrBoesWkb5+whhcuU+yOrIZETZ8X172yYbvm41EFMFv9+ccMs7tio2TwEHg4Wk4chSOHxdhbW5WAtc9TwShyW1wySVwycWwaxdM5iEdg27MVNGMIEJrEVM1o8HlYSHDvp42+lVnRVV69TvnQf2z2uqXQYghFW6XIfdaOKNC7eeOY4Tvs4kF5FleQ36r9tBbtWZXCNlZPw4ODlsXL3zhC/nwhz/cs+ztb387b3/723vsgBxWj69//eu87nWv4yc/+UnP8snJSZ7znOds6LEmJyd57nOf27PM933+5m/+ZkOPc7ZQKpW46aabePnLX873vve9nu9GRkb4yle+wtve9rZNap2Dg4ODg4ODg4PD6nDBiBogBKbOAN9IUmwt8BHy6igibtSJtkSxyV+bQFRrHZ21q9UZs8G7zoxfQEhJJUL7EXIq6oSJyzC036JEjCgis986UURcFyENZ+gNTbe/n0MCzn8E3BusG15nAVPtYIsBNvms4eBjiNB1NXAxQugWELFkL2IPNoKIEHmk/9SCSkUOrdYoL0hIc60qM+LjATvqxaCYhl1xISjHMGKKVpRkMWRqF7mOhxFP/fuCz3alwXIC1CCoXZhdqRKVX6LHicIgUt8WrjS7RSujwoKG/dLQ8Dq9ofalTiBqqK1SXfo3lYTxcdh/eYKLL48xOip9raHfWvXSxVw/FcS0AiqJjAMNY9+1E3bvEYEkk5Eqh24Xmg15tTpyD+g+wuT8RqKfANRC7o95ZAwqCf4AcE8XDk3BMUvYmJ+XtqfTMHFJkZ1P2cflz9zFk56W4rJLoR6DRzsiBs5jqlgSmOeHEun97JzCf/ezNovqq9UIGmcDWvVgh9VvJrrIc+1R5HnXoL/1YLiPHRwctiZ83+etb33rkuW///u/zy233OKqCtaJL37xi9x11109y/bt28cHP/hBbr755g07zt69e/n1X//1nmXdbjfy2p7P+MEPfsAXv/jFyO9uvPHGDe0zBwcHBwcHBwcHh7OFC0bUUFuPR4L3MHEVQ2bp5s5hm9oIif8wQl6XI9axw7S1akMJLg3F9qx1NafCrvRQS6kOS+2swseK8sWPWq/fLGx9tys0oqo6ouAjJN5xpC+iiLoqMiv9JKYaJQwVCqoYKxmbBIxhcgJSmJn8tj1UPFg3jYT2XoR43MeRftQAd63WWARKtaCaoAqNhsyOHx4WonzHNtg2DKNxqQopYKo1ksHx7cyCOjIu7kREjalQf+hsdb0WafpfVxvDiHizJ/gc3iaqAiNcwRH+zp5t7/f53A29wuvp+dQx+SYaqL7gS9/Oz4u4UalKtYYXg+xohvzOYQrjKcbGYGdcxAntSxUws8GyAqaCJ4GxHdJr0WrJ+QwPQzZ4GDRbck1LZVioyJjSAPcR1i4uDYL2T1jY0P5rYyyi1JboOFKt8eM2HLIqNqampXIoM5ol8YRr8G68gcz/dyN7n3s1T3pmjvG8iBlaOWZbv9k2aXbbokSIqIoNWL6ipd925wpt5HfhINHPnTiSMxKV8XO20EQqBh9GrmtY4A3fc07UcHDY+iiVSrzyla9csvzlL3/5JrTmwsN73vMeHnrooZ5ll19+OR/84Ad51rOete79Dw8P81d/9VdLlj//+c/H97feU/pv//Zv+drXvrZk+eWXX86NN964CS1ycHBwcHBwcHBwWB22dKZGGMsFh3sIuamzy8/FP0E6CJE/T//ZymHSSme16zZRs3jDRLKS+epF37HWtd9B+mEbUqWQRISDE5isjTZGTIkSMsL7g14STmdG96tMWe46RfVTHCGsdyBE7EmEpA2LV3q87Qi5/wgyy10FjWbQ1gpy7pPImFCyvRXsdxemYkOtkhZ9qSbIZiXUutORoPBsTnI1FlLQ6UJ93lQjNDBBvxp0rv2sVSErgVYODJplnkGu6zaEuNZqFQ29jgq61n3b7/o5LH4o+okbUeuEBZJF6xxUmFoASk0RNYpFyA+JYJRrQKPZYAyfZCrO5VfGeNpcl+YPZCdFzPhvBudr25fp+ecQoaOQgaECjIxINkoiCc2uCFQLi1KFU6lLO0eDVwsZa7N9+m496BKdHaH3mj6cR5F+qyGC7UNArA2NGag3pNJk506I7dmFd/HFcpL1Ol6pRH4kSS4p+1EhVO/RFDJmYphrYguaXeudPm1dCbRyaLn7fgJTGbXR6Df2Qc5JA8O7nH37KYVaFGobwnDh4A4OFxZarRZ33nknvu/3VGbEYjHuuusunvCEJ2xe4y4A3H///SwsLCzp34svvphbb72VZz7zmevKikgmk5Fk/7e+9a0173MzcejQIV70ohfx9a9/nWuuuaanz373d3+Xu+66i3/8x3/cxBY6ODg4ODg4ODg4DMYFJWoMggZua1h2vxnHqeCzVkNsBPoRv/2qJnTdqrVeWPSIeuk+wvsNn+9+4BqE0E8hNiglegPEm5iZy3aliAYv23ZDdrttMtwmSwdBt4lCFiFedyOVFzmMSNRv3ypeFRGC/xCmL+12tRHBI40RkvSc6sjNoYHhmm2QqsHCo5BPw87tUNgmWQa+D8kEdH2oNWCxZkQSFTQ6CHGq1SIrhbZnOaQw16ZFr82Q2guFj6vXKkrQsK9nv3YtR3KHRTXtZ62o0EyHMjBXheyUHHNxIajUyPpcEeswcXmR8auHeUZ2Cvw2w3fBfFfG6YwvolwZY7EWD/oiY72SSei0xeIqFpdcjaEhsbvqtKVSQ8WuLHJPqNh1tgjm8H2tomEGEdbGMGR7CRmHJwiEOR8aC7CzBcVhD2/bJMQTkhI+dZrmPQ9y4AclTlflnMYxY9I+1hhyT0Vlr0Q9d7yI9yjYZYA5TBWXjkHNrlGUQ3/b0AqqswG1xUsOOEbc+r7f78da0G8/KnjZfzs4OGx93HXXXbz4xS/mox/9KNlsFgDP87juuuv4zne+w1Oe8pRNbuHWxpOf/GQefPBBLrnkkh6SfmJigvHxcTzPW3NVxfDw8JJl5XJ5S1ZpKGZmZrj++uuX9Nnw8DCf/exnufHGG7n77rs3uZUODg4ODg4ODg4O0XjMiBpgrFyiEEPIw3GkU+YQEvFsztq1SWGbtIrRKxhEEW1hy5p+y8KCSAIhbIsIsdwgmqRTKyy1L4phiPIcpgLAPg705mskQm3BWkdnb2eCfc2zlHBPIMRuOmhrElNxMMiKKRm8FhDBRmdpq83MBFKdMh2cY41e4QZkZn4CM+M/GfxdR4SLkTpkF2B7GxIJqSxQgryYh+EG1APSvRGcQwdTKVSO6Be7f5b7J3IyaJfaCGmVwiKmGkLDn9sYMtmu6Ik6TljcgKWVGeFlyyG8XgsT4l5C+iIDpFsQn5HKCd+DVAImJ2Dbjg6jzTbxbJxUCvbthcqiWIH5XZhdhNwiHPFNwHaCoDoDGevpoCG1mlQ35DuQTHuMjMXwYh0OHoRKQ8ZMOuifY4iAcGqF57kWhCuotGpAQ9B9eiu9NFPmGHKNx4HhUcjvKOB1OuJHFY/DiRMcvWuKAwdgLAVXtOB0U/ZVRcax3ptDwWc9Rvie1XEQs76zqzaiqsHC0KoPtVSLqvroJ2gMI9Uqp5B79WxAq1iikERsyEaQe2gGuQZnk8YKP8cvGJ9IB4fHOHzf59Of/jTXXHMN73rXu84s9zyPQqHA/v37efTRRzexhVsb7Xabyy+/nDvuuIOhoSGuuOKKM9994xvf4JnPfCbT09OrrtiIxWJLgsgBnvGMZzA7ezZqOc8dtM9++MMfct11151ZnkgkuPLKK/F9n3vuuWcTW+jg4ODg4ODg4OAQjceUqDEI6qG/EyH5hjCzms8mwiShHdQNJkMjitTqhj6H7aiiSDqd8ayz2g8hBGkFQ3a3MRZdOuM9F7Qpg5DETczMcZ35nQzWayOkqWcdz7fekwhRqdkHbYRsjTq/U8F+5oF9wXYTwfF/TK8QoiLJaNDmOXrzVdJIxccOqw/Ujgp6SUo7hFrHQMw6rzRQawlJ3mxBOiWz/7M5yWuo1KG6aPILVFDQahetnlgrMWpbBw0F7akG/aVB11olEkYx2KaByaSwZ4YPspSC6BD41cBHgtFriACjlTJJINaC+Tn5roD07egBn0ymRLEAtWoXPBgekUqOZALGxiFzEpKzcLwr55RGSOhhTJ5GPCaFDDFPQsJzYxni4znStRLNZvvMtaoh422a/tkvGwlbFFABTcPkNXdE82NU+LDzSVptqMw0qH3/YVq1DrnRFIVtOUZ2pLnqqhq7d0n+xo8PiHCkQlJYBNX8GOgdA+HxoOvbolhYIAv/becF6ZhcaeWFVoetJFPmbCCNiEc7MOHxZ/u3ISxqODg4XFi47777OHLkCHv37j2z7Oqrr+ZP/uRPeOlLX7qJLdv68H2fG264gSc+8YnceeedPd99/etfZ2pqim3btq1qn//lv/yXJcu+/e1vMz8/v56mnjfwfZ+nPe1pfOYzn+Gmm246s/xTn/oUnU6HZDK5pStSHBwcHBwcHBwcLkw4USOAktdnyFXODaGkxJUSmmAqJLJBmyoYMi28rb09DCac48js7zhC2J5A8gJayDmryDBP76z+cSR4WoWNKiIWqPChlRAphPirI+R6C1OV4Vvvep4thFy1cxZsdIPvQAjdYYSQ1/6xsyDySI7GaLCsGpyHPftbw5dPY2ZaR1UmKJrBfgnWTQXHBxj2IJeCWAyaQQd4HqTTYkdVyEGhBrVOb+C3XkOtOllgafXQSv7ZWAvOJ4MIcZoTojPw+yGJXKP9SP8cxlQkaRv7tWU11RnLYRYjGun9VkTOpxG0aRjolsB7EKq1LhPjUg3TbkMqKbkYyQSkM5DNQLsFrZKpAtkbh/EcJINOLxREzMhkYHg8QXZ7EWJxWi0JJ9frpGKdTf6vpHqGVawXhm7TQcbdXPDSKhsVMhKYEPoMcn/cdRD8TgO/26DZhMdd6XH1jTVGhiGxD6aDIPFyGZiCRKu36kItlfLIuLLFDBUifUz4uh1uvppzDlemrbSf5jFVR5sB7R/NIYFz89ugQuN6RUQHB4fzD5/+9Kd5wQtewItf/OKe5ddeey3PeMYz+MY3vrFJLbvwkcvleNe73sW9997LZz7zmRVt8+EPf5hYrPe/wj/wgQ9w6NChs9DCzcHCwgJ/8Ad/0CNqgFQRvfnNb+Z//+//vUktc3BwcHBwcHBwcIiGc7UI0EFIzJPAAeAohlDcSPQLXrZnMmulRAIh71uYnISO9QqTi7r/fhc1ifjnF4NtphECNYYQmllr3ThCKu9CCPBRhFQ8ilRIPIoIG5qtoYT4EKaCICqrQfMsSoioovtYDk3gCHAPcDfwcNAfGeAK4P8Dfib4HAvaeRwjaiQwllmzCBlsE5M2QRsOSp5AKjx2IwLCJDAUg3QQwFytQqUigc3xuJDshSKM5GDck74bRioHdgCXAlcB1wbtVeEkjEHXUgWlNjKTfKXq5BAyBgrBuezG5Mw06R+8vhbbqeWg41nD2HXfNSRA+TQw1YXTJTh5AubmJA+jWhWSvtuFXF6Cv8fHYdsYjMZkfE/GYM8Y7N8De/fC7j2wLcg/8WIQT8UhkcSvVJiZ6rBQNhkPi5gqCX0GhMdyPyzXN3GkfVmir62PtKOOqQqaRMZN1uovzUqpAPfOw1fvhTsfgAOPwuFHfY48WGHuRJ3FRajXIJWCsTGYHIHxpIzFQrDPVLAvrciyq7206korE9S2TkWRsKhqv4czd6C3H1f6bG1zdnNNlkMLGQ9HgZ8gz6xzJTJE2YE5ODhcGPjzP/9zHnnkkZ5lj3/847nlllt46lOfukmtunBw8OBBPvCBDyxZns/nefe7382f//mf8+lPf3pgX//O7/wOn/70pykUCmexpecPovosFovxpje9aXMa5ODg4ODg4ODg4DAArlLDQh0hwrUKwCbfNoLEGkJIQQ1HDs+qD+dShAlCe1Z7GMuRzWqZlEeIS4CLgnbEEdJwASFM4wgRXwyOeQx4MFhXiWj7uAodTCp0hIOE7XNSYSbcZrWQUnsd+ziaF2FjHBEHdgZtfyRo7wy99ksjwbmrnU94P1EEq73tjuBdBaZ4B2bmRMQYGQHfl8/xmFQRDOWhFZSN+AvyrhkdWvHSRsaEtlvtvjSoWnNB+uUIeAjBrMLXSlBGruURjIhlZ6OonVec3ln1cPaI3ATStyr+aAWH9kXcC2yjYtDpCklfr0t7MmkoFqVCZscOmC9DoQQjBRgbhURStivkZN1UStbF96FaoXSixqOHfEpl6cM5RGzTqhfbnkyx1n6IA3uDc11ERJtyxHpq21ZEBLVtGKswFTzamHyZeYCOVJcU2nD4MOSyUFn0wZPckURSKlVaTWh3oDEP1W7vebYxz6eq1R77+mdYKmRGZa90g33oNSS0XozeZ8T5ChU3y5iKOTD3x0a0PYH0ewpzne0KMydoODhcmPjud7/Lc57zHL73ve8xOjp6Zvk111zDJz/5SZ773Ofy4IMPbmILtzbm5+d597vfTSaT4dWvfvWS7/fu3cvevXt50pOexPz8PM94xjPO2Ek973nP40//9E/Zt28fIyMj57bhm4j5+Xm++93vLlk+OjrKJz/5SV7ykpdsQqscHBwcHBwcHBwcorGlRY2NIsRsEUFtRpSI04qIte4XllZSDJr17SNk8wQyo76MEGhqB9W22mZvEz6uZlkocsiMbw2TziGWUh2EfDyEEGophCgfQci1qWC5ihNqweQh5KaGMo8iVR1Nei1stD1hCx/7/DXLQwN5U0G7plm+imMRIaEXkOqP48F2SvJrP8wgs63DROSgUiWdNa/7Wgj2U0H6r9qA+ikhjbNZaLXEEimRgExWziUeF1I9VpJzbgTbxhECXYOadyBVQk3ruIsMFiu0P0uY6p7lYPeF7iNcsWLbUIXtgjaagNbg+SFM1cBYcLy9wM6MWEy126ZSIxYLLKjSkEzB6Ah0OpKV4Xlit+Qjn1tNGCpIhUYmA92O7C/ltaBUotvoUq/DbFP6/0jwCofWQ++YXUs/dDH3xjS9wkEsOP8iJgtEbaaSyBj3rP1MIWKHWkKp2JkAZsuSodFoyLmn09JnmaCCqF6HhSrk6rJ/FTVa9FZEJJBroUR7PGizjsuo55s9RvR5pT8yUbkbZ8Ne6WwIJdon2s92VtBaEBattbJts3JDHBwcNgcPP/wwtVqNkZERPM/8yuzbt4/vfe97XHHFFZw8eXITW7i1USqV+J3f+R3GxsZ44QtfuMRCCqSv9+3bx6FDh87kRqRSKXK53JJ1FZ1Oh273wpScu90unU6HeNz8IiUSCa644gri8TidzkoTsRwcHBwcHBwcHBzOLrakqBFlX7Iecsnen9oubUNIq5PITOjV/Cd8AiEocwhZWA62VzJQiesoKBGoti8a+mx/v9w/o/Scwm3WQG61r1ERYgYh7FOINVIXOe/F4PssvXY5SsiNIH01ifRXCrFn0fDp5dqnllA5egUEexb4cigBP8RUPEQdexBxGrbHCX/XwtiS1ZF+AjOTOtaG9gkYG4LRYSHXc3mpCMjnrXDqGMTnoeTLeavNklZiKPlrZ5SEzyGHiEdjwTlWkEqVIWTWv4pPBOeUxdhKRZ2bQoUFtaGycxbCJPVGQcOfRzE5KI8iYksXIfdH4+DFYbEGpUpQ5ZKBQt4IFfkcFEZi4Pt0uj6lklyDel3eNUzc86BWleVdH/bGPPx4gq7folTyebQrFT5T9N6fYQuw9RDmPpI1E4UCYvO2E7mnUhjhcC5olwoOurwcrBcPXvrcmW9D8pRkjIyOweio9FM6LaJOc1jsuyotqHaMvRT0Zlekg3YNY+zw9NnUIdpizu43FSfDCK+/keJ0eP92m9ayP92Xiq7bMNVEJxGRZ6W0lhfsIxe8FoJXB/mN8Vnd74yDg8OFgb1793Lo0KGe0HCAQqHA0aNHueiiizh69OgmtW7ro9Fo8OIXv5gvfOELXHvttezZs6dHQFIMDw8vu69ms8mpU6d4//vfzz/8wz+cjeZuOm699Vb27NnD//yf/7NH2Ln++uv52Mc+5oLsHRwcHBwcHBwczhtsOVEjPMcqbG3kW+up3c8gEcHejxKYo8CVCAF1ALgfEywdPm54v0mE9NqOELMqaNQwQsNybVFiS0WIMNFlk2hhi6d+qCMiRTZo0xTG+qaM9NEIQqwuAvcF+xxGxI8hhNyfxQSLjyOZENsRArSO9J+GKysx2+98dXZ6Ith3HSEJdZuVkoVVTEWHLYbYVTGaARBFGuqYijqeij5NjDWXEslJjA1Ne1GqBzodIc2LwQz5oYKxQEomIDUvDap1ZN1G0H7NNklhAusV6aCf9gI3Apdl5DyONaDuCyF6lN6qjhxCkNcRwSMsbCjBOopcx+3BNmrNNRectwouGyloqDgzhJxzIzieFyxLI/dOowOxirl3csD2GiTiUgnT7YqLVLvp0+36lMtQq4u9EkGbux1YWJTMk0pFAsEnJ2DP3i7tZovjR32OnYI5X9ZX12wVl8KVK7rfMPo9D1YKH7kemkdTQ67DPFKloRVTuq5vraPPOv07AcSb4AX2aNkgSD2ZlIqidlv6qVaDegXmfRkfWoUBMt60CkjHpfZJBRlrmsMRdS56PvoMtkXFqIotFR7XA2/AZ22TPgu00qKfiBAWNEDOdTdi21dFzu0QS6vJop55KjKOI8JIGrmmtp1fVHWQg4PDhY9ut8vjH/94Pve5z7Ft2zauvvrqM9/F43F+9KMf8exnP5s777xzE1u59XHzzTfjeR5f+cpXiMViS/p6EL71rW/RarX4yU9+wqte9aqz3NLNx/vf/36GhoZ405vedCZPxPM8tm3bxjOe8Qzuv/9+pqamNrmVDg4ODg4ODg4Oj3VsKVEjamawwibJ1KN/AiHhDmMIX5t89EIvMKLESPD3CEKmLmKIYCXs4wjpZ6NrHUvzGzLBfnRGdJ3BtkIxhPTqEh1ovdIqBiXulCzejQkb1hngMYTQTCCZE+1gu5HgXHcg/aEztLvB30WEmFa/+XxwTkpyKomeor9tkVYbKIlrX5PVzH4OCyD9RJ7l+swOP1a0EGJZ80a2Wesq2anbdOvQnZOqgITkUJNOSb5GPG6WLS4Kmawz7IeQ/vMxYoJdrWETww3AT8H2IYhPw+mmCE01eglatRIKVxpoP6iP/yXAvuAzyPVYCNqTCNqiwtx6EQ/2O4bcE5q7oPky+vcwJndGx2ocqQgqILZTfleqLkolmDrt02hImPj8nBD23Y6IHq2W5HDUalBehIUGzFVkH52Oz8Hj8OMFOZ5WrBAcNywiLHduKcz9FFUdMwiLSK7KAnLfqQBaQayqwrkqOhYWMUHfCcwzMgYkm5CrQKEG+SGxn0qm5PNYIKotHIFOywSB6z4ywXHKmCwfPW4LE6DeZen4CovLcYywbGdphBELbbdSEThKgLBhZ3+kkd+FIURUsEO/o0QR+z2NefZpRV8SMz61AiuD6U+FVn3pc6qCjPWitQ/9fbgwDU0cHBwGoVQq8axnPYtf+ZVf4ZOf/GTPdyMjI3z2s5/lFa94Bf/6r/+6SS28MOD7Pj/3cz8HwA033MBv/MZv8JznPIfLL788cv0vf/nLPPTQQ/z+7/8+lUrlXDZ10/Ge97yHZrPJO9/5TrJZSWH7uZ/7OX7u536OF7/4xdx6662b3EIHBwcHBwcHB4fHOjxfDWS3ABKeN9BiRMnsAmKjNIZkLNxPr0982JopFtr2ciRvoo4IIo8gJFQWIVZ1RnUHqeQICxRqFQRCPBaD9ihBPo+IIf3IK7WGqiN2NSuZTRwWe5R01zZfFJyTjwRFH8QQaRpQPRqsp3ZAOjv7FEKsLiDE3v7gvYyZGb4nON4hpM86wT41jNq3Xl2MQBKuSJhE+mmG9c9cjhLBtF2FoN1RhH2YxLbtvFTwSiHXKYsQpGrTMw5MZGHbOIxPSIh1JiMVBfU6lEswNQXHpuHRlhCrGWDCg24MftKR6zNjtcsmmscREeLyFKS6UGpLfz+IXCO73bpduPJDv9NKGw2HLiKiymlM5c0UMl6jLLHC0IqBKEI/jghke5F7Yw65B1RE0fGg9844RkCsBN/tAK4CrslBIQtDQ7B9h4Riz87BQhla7UDICDqv3ZJllTrMtuWYvgcjCWh2YbYj/VZCCOUmJp9FhbyVIIdUuwwhZPWRPv0wCF7QT0qgq6XaAtEiqF1tM4yMjxxGcJwAtqdhzw4JUR8dkbHoeVCtSfbI4cPwk2l4xBc7pZmg/fr8KFl/632g94cS+VEWVPrSMazXNm79re/2/WYLGeEKj359Nug5aLcnhjxfLg7a/QAyvnX/qvCHK+E8a9urkP6dR579J5BrNIz0t96np0L7VuhzQys0JpFxrVZ0U/TPEdpCP9XnJaLsZhwczjdcdtll/MVf/AU333zzku8eeughXv/61/PFL35xE1p24eKmm27i8ssv50//9E8ZGhoC4I477uAjH/kIX/7yl3n44Yc3uYWbi6mpKSYmJnqWffnLX+a3fuu3nC3aFoH774f1w/O21DxQBwcHBweHDYHvn/9+EltK1EgGpIRNXNmNVzJqO0I+zQAPYyxVYCn5Fl6WRsSQYvD9SUw1Rg4RPEaDbRaBH2NsnPpB7aziiMiis9/7dbzaumh4r6KfRUtUxYkKGpcBVwfn1EaIsxNBO8rB+bWsba5E+m4/EPfgxz7cgRFXMggx3Q6WKQGXDY5tVwzEkX7U2d2aleETbWk0hFQO+AixPoOpIFGSeTWwCUlFHLFl2of070GEYLfbEjUzX/tdha9RlgobRQJhw4NtOdgxKcLGcFGsqHykYuD0aQlyPr0AZR9GPNg7CfUWfGdO+ns6OJ5t56VEs4obI8H5aKB7iZVXVKiwk0UEC7UfayOErYbBz7My+zaCdhWQ/gxfqyFkbO1DrudRJMOiQrSAlcEE27eQvt6JEMDjwGQcRoZE2Oh2YK4ktkq5rLS7WoOOD60u1Hyo+FLRMo3JlNEqELt6qha0R2f3D7IoslHEPBvqyHPnFL39FsPM8q+ytPoijCS990wUVEjS6gMVNVRg2+bBrlHYsQ0mJiRfI5MR0UctqOZm4dBJeOCUEPV2YH0N6TOtZAkLhTGWiggKtd0DU6WTx1is2VUten/5yHN1JeOtn6AR9YxXUWUf8vzSQHhbeLJFG93WfnZolVEBGbcnMM+/CeT6a6XaceTZMuiZFUPG9H6kn04jfd1PQNxCP9XnJZyo4bBVcPHFF/M3f/M3POtZz1ry3cGDB/mt3/otvv71r5/7hl3geNrTnkYqlQLg1KlT3HfffZvcovMDT3/607n99tuXhKx/73vf46abbqJcXu5fQQ6bDfffD+uHEzUcHBwcHB6LcKLGBiMZVGr0s5/SgOMcUjlwAhPsbMO2NomCesinMbO3QUjGvQixVUVI5HmE2ApbINnQCoRksE2/mdfLQe2komCTcbpuEXgSQrTOI0ScEogthEQ7HtrPMPAE4InBej8C7qTXZssWMAYNniTGm78THHPQeWcxxLASzl7w/ijLi0dh9BN7tiPXcQ45/ypLhQwltaMEjhhCIA8j11YzL1TYGEWu9/YC7NgO42MwPBwIGz5Uq1Aqiw3V1DSU5iCeEiup+7tChqpnv5L6NeQa6nklEGJeiW+tulmJdU0ekx0xhyFRbaFQ+2w1VjgqIIXHqIf0xyWYQHm12VrEiGM2mewj9/FY0N5hpH81c2ICEYO6nogX+MF48yDpwWJXxksNI1rMYcS5AsYSSMdmAxGHtM360mBsRZRNWSo4vxHkuMcx10uRQMZGBpMjoj8Ra30IK8UQR+zl8phxOIIIG7tSsGcnbJuE8XHJeclmoTAEiQRMz8CBA3DgEfjBabnn65iQ+dP0VmvYwkb4eWw/h7pI38eDduwJ3nWMdBGhqYGpTukg1V7l0D4VvvX3coKGvY1Wf2illZ5TGIPydfT7XHCcKmZc5JEqtxSm0kdFxkGilAqkauM36Bm3hX6qz0s4UcNhK2FiYoLbb7+da665ZsnYnZqa4lnPepYj3R3OGZ7whCdw1113LVk+OTnJ9PR0xBYO5xPcfz+sH07UcHBwcHB4LGIriBpb6hc6TGLZsK1OqsiM40HEVBEhlKoICWVbfihJbHugE/x9AmMd1UQIueUsUhrIzPTlCLPlMGjGeNTxm8iM8RbGmkhncieIJvVKwE+CY7WRdoddhMMzzHUQ6XBXWxwl5LV9y513E+nf+WCfSaTPpvq0dTmE+0RJ+hPBS2fBq+VPmNTvB8068RACuV/1TGxBcjRiMfBiMORDKgWT22DvPqhV4ScPwY9KcKRuiNYEMjY1d0KD2cFkrOgs+DrmWq0EKgjkgm0q9GZHYH1e7T+B7HswvLwRtFVJ2y4yDlPIOWkot90OW2zS6oYmxrZn1BeRyEP6Kg3EfXlVMRUgeq9q8LZ9nfW7FiIygLlPdT21WPIxuQpqm6VtbSEz/08E30VdjzZyH+Yw4gPItdVrqDkiK4WOVc3fSCH9rBkbSWAiyBeZmoZGE8YbYts1NBwnm+lSrfnkslDMS5s0d0crw0YxzzmIztEIt0mrsVTQuhpTWaSWYlqBlMOIhFr9dBgTSq6ChF0tFX7v99ugY1K3nWdwlVwqOF+tgArb03UxOUD2fV8L2pzC5LGspMpnAfMscVkaDg4OiunpaZ74xCfy4x//mIsuuqhH2JicnOQHP/gBj3vc4zh06NDmNdLhMYN+NlOHDx8mn8870tzBwcHBwcHBwWFTsOVEjajPYRspfU9jLE5sn/YcMqv5iuC7Awj5H7Yi8SM+K4msQd5RgobOpE9iyC0496RVGyFZtaIiFrRJffqjqlgItlGC1yabBx1H7WUS9A9YtsO17eB2JQg7CBFtzz4Pz5JfLbQNYRI2TFTq7POVEvsqbNjr2OPhjF3UvGQ6zJdheAgKRchmIFmAcktyNrrBwEgg5K7a/MQxlQbavqS1bDnSNKqiQGeY63d6rOXGph5bbcNW+89X7Zd6cLw8ch/aFlBVqx16jErw/TxGgOgGf08ifZYKXhqw7QXrKXmtlkBaDaBVGfpexRDwUYQ5mAelVg7pcVX41Lba8DAkvl4nPzietkVzMHLBfh6h1y5vNZjD3IM1pD9yQKUDx0+LoFHMQbkMjQbkch12bOfMg9GLwWgKdjdFSFRCvxXsZ4Ho5y4RyzSPRPt0Abm3m5hrMIP02UiwjYpce4J+OYIRNcKw7+dwJRah7/zQd/octCt1wIR/X4HYnM0ieUzHI841fL+o2KHPwn7PrRjmd0nHy1ruJwcHhwsfrVaLSy+9lHvuuYdrr72257tUKsWBAwe4/vrrqVarj/ncB4ezi3a7zUMPPbQkUD2dTnP11Ve7qiEHBwcHBwcHB4dNwZYSNRRKTtk2OQmEeMsipFIKU5GgXvpKQlcRP38NeE4hZKDO6o7yVsf63LKOY5NbSkQXEMI1ExxnhnNPWunMZnt2sxKobaIDsm30C6vV8GntKxtpDKEXttWxiUX9nLL2pySz3eb1FDrZs/Gx9mlb6Ohym1QMfx6EDoaA1v6OEnEqi7JiIQaTI1K5Ua3BzAyUSpBOwY6uEKr1LrSDl1pOlTFjK4WMvRKGkI9CPPS33Q8lhFzOIuSuim+DoHkZTaRdKwkOV6SQc9PKHR0fjeA7vW/tCiD7/uuwVIBbDNqtY0ituhRdTHVBM/heq3LsHI2obJco2M+EBtIfe4L9qKWVDa3o0KoOex0VQJrIdVWLrX733HLQ56CPESO9oJ0NggyfhnzfrII3BZ4HhYLYT7U7Yo2Wz8FoHna0xNJLBY0kcu1VSPNCx1RBUu8t/Vv7dQ5judfFWIhVMSH2eg3bmDG5kgqkfpZTdr9kMM8+zaQZCo5t56yoWHYYk2mklmd1en9vooQIPe8kS6H37ghif+cjeUxrveYODg6PDfi+z1Of+lQ++9nP8uxnP7vnu1gsxt13382BAwd46Utfyve+971NaqXDhY75+Xme97zn8YlPfIInP/nJZ5bHYjFuv/12tm3btomtc3BwcHBwcHBweKxiy4ka4Rm69vJJhGi0T6qKkFJ5AlIPQ2bdHXynM6l15nVYxIhClM++iiTjiL9+Oti+TG8VyEpmxq8XdrvjCNE2ErRpNtSelWIYIeTiSJjwPEuJZxU1ouzBbCsYnbGsdjlnoz+irl0XU0WTx5yDXpPVtkMrNuztwsJGgUBI6UK+KsHM3a6IHZ4nOQf5PMQ8CW+u16Feg5kKpDqmmqKKkNRKADcRwrqK9GOc3tBlRViga2JIVs2OWS4MXPMVlrNai0I26IM6IqjUMOJhOjiXDkbg0kqLQWgh95XmioQDrNX+SCurUsE6dYytVbiffPoT5OHvlbzOE131tB15HmklStS+moi4ofeBijz9ng/hSiJ7ue6zghkfWtGj1RAjcSikIB6Xl49UbMRiMJSHkRFYWISFGlTqRrxSEWoIuX7hyh/tD1vwsM8zfP6t0Hpa5TCLjMshTHh9+DmmVnFhK6rwZ/vvDPI81uuhz6EyxvpK7QYbSIXILL3VTFqltBy6LP1tSCDPziJSIbgvONY8khVkt9fZUDk4OISxsLDAr/3ar/HBD36Q5z//+Uu+v/TSS/nwhz/Ma1/7Wv793/99E1ro8FjAwYMHecUrXsEtt9zC05/+9DPLc7kcr3jFK/jwhz+8ia1zcHBwcHBwcHB4LGLLiRqDoGHJcWR2cAlDOKeQGdEJjCVTFWPFE7YfGmQJotUe+r3aloxigqPVeiaPsVtJWt9pHsdGVnCo1Y0SxgoVEJQsDGdirBQXAVcGn7PAg/RWCthB4Eo+9quC8DCWQf0sZtaDOHLOSsraUN/8SeT6KGZZav0TxwgIg6yoKsH3BXpJyQRBMPlIQFh3xQZoYQH8LoyNwcS4BIl3fVhcgEpFQsRT08AMxLtyLjqe1f5IZ7TXMDeyBi8vZ2mj62SDv/sR5opWcPxBYfX9UMPMhrczEvS4Wn2iVRep4DwGHcfOA1FRIIwOphpgtXk2toARVQ3QQqovcsHfWiViizNK1Jfofy4JjPBZDfbXpNeKy27TSlBF+iQfvHYi42/bCOSy0OlCJgOppAgaqaSIasMjMvYWFmChCZWgWkiF4Bwy9lRwiiOCQSo4plbCKeyKKOgVxPRdBakKRlAuBcexM0uymOo4rfLRqh/b1i4KmlVSwFSulII26/09GbTvJGasa/vt6jI9j0HHssd3EhG5i0hfaT6LWs0lMb9PWuFh97GDg4MDwIkTJ3jDG96A7/u84AUvWPL94x//eG655RZe9apX8e1vf3sTWujwWMC9997L7bff3iNq5PN5Xv3qVztRw8HBwcHBwcHB4ZxjS4kagwJqO4iQoTOzbZIMhLxqIDOWo2b3rpZAtL9XwSSDEGVlhJDLIqSmj7HZGcPMWF+Nhc9y0GButUDRNtozqRcRkrK1ZOvlMYIETBeQa7AbOc8TGJsYWzwIk+p2/6pF1yhGMNhIG5YsJgx7ChkX2hdD1nkUgnX1+uxAslVmrX0p6a7naHvih0PiVeAZsrZV0ns0JQsqFWjUJTB8ZBj27IXLLoNE0mN+1qeUkwqOfF6sgQDSJcg0jYhRQa6jXlsVyDQfwLYHwvocvh46Oz1M2vaDVj+sFlpRAuYc1M5Hq2T0XlXbpJUcR63g2kQLdXY1xkoyQwYdJ7yOVlpkMPedVp6kkHtjFpPXMQjzyL1Zwcz0X8tsfT1+FhEz9gNXJmAiA7mkVAdVq5Kdkc9DPFBUvJiIHENDImyMLQYVG3WTaaKinooOGvR+CfJsOAkcwmSfKHEfFjXs0G4w11wFKB2/0Cue5DCVbx5G2ND7L2y3ZqOFPKfmg3NRoVv3pbZoOXqFLG3fakU8+17KIqKG9lsJ00damZJERPEsMm7W8nx2cHC48HH48GFe97rXMTQ0xHOf+9wl3z/+8Y/nE5/4BHNzc/zsz/4s8/Pz576RDhc8/vIv/5KnPOUpPWPwqquu4k/+5E94y1vesoktc3BwcHBwcHBweKxhS4kaeYzNiT3jVwlbtUBSAtqeVa6kfoXBBO5yM9zDSCCk1ShCWJ1CCFYN/9VqEJt8U1uYjarSULsZPW8lim3UMaSpDa1Q6UfcKVE6jpBuKsTEETJTbWKq9FrwhGdk29AZ3hrgPGiW9VqgvvkZpM3zwbIssAsRM1Tk0uyDbdY638cIFG3MeSWBvRgbr4VgHx7GqketiPR6a+CxPyViR9qXCo3hYdixAy67FAqXboN0mtTULKPzFebnYW5ORI1YXLIO0tOQqshx5zEz1ssYezOtAopCv0qM5cLGNwpdpH1FesUIuw0dDJG9GvFkLcSzDc3j0fvHFgVhqThkI4eMi3mM0KFjYiX3dxURBFYq5IShWSuKEcTeaCxo20wb5hahVZHzSgM7M3BdDtptqRpKpyGZlHE2MiKiWqkk1Rq1rqnqSWMqK+KICHhlcMwUJidEKyDCfaVVDw3rb+jNNbGtqbSCIY/J4VDY41Yr4MLWgWCuWTN4qWBkP5/ayLO7HNq+n4XeSpFGqmSGEFHlNEZwjwXH075IBJ/LrC9LyMHB4cLGyZMnef7zn08qleKBBx5g+/bteJ55Wl100UVcdNFFZDKZTWylw4WMkydPMj09je/7Z8ZeLpfj4osvJhaL0e06E0UHBwcHBwcHB4dzgy0lamQwM9JhaaB3uBogCqupwFgOGkKbQgSTaYTcHkLILLUl0nY1g3U2Grag0S/bITxDWrGcqKFEdBdTfdJFqh/CWRI2VmJl5GEqNlT82QhUEXGpGPy9DRMUrMHwGiBdRkjZYaQPisHnqLZoBkUesavZi5l9Ph+c06P0VnrMIWN21pdtRpHZ8vmKfN/uQHuhRqJQIH7pRcQbdXbMzJA8UCIe90mlIZeTDITULGQWINkxVjVqCZVBiOwWRrw7F2JFFOzZ7vb4UMuocFVPk5WLABsNDxnTO4PjT9Pbd4NEIg1dtx+ig8S8KIT7YjVQwXE4+LuOyX44jMkv8YGcbyrK2jXYV4JtVcikoZWFVBrSQbXG6KhYUC0uQr1iiHfNnClg+ugwRlRS0cAWH8IigVY+qT2cVoCEA7h9pF81zF7PbRgZ4zPWPvUZrM+/cCVe2PZOM0uiBJAwbNF8NeNTrQfVClHt1CaCVxmpItN9llexbwcHh8c2qtUq1WqV3bt38+ijj7Jnz54l6xw9epSLL74Y3/epVqvMzs5G7MnBYW34b//tvzE+Ps5NN91EPC7/4nrRi17EkSNHeOc730mttlajWwcHBwcHBwcHB4eVY0uJGupN38GEJ9uzqO33foGrajcSw5CJ6yFT28gMXCXJlQTLsbHVGP2QRC6iVoPYuRorrYCIsidSaCDzAkL4b0cIxYeC5Vo1sxrLFB9jA6OzsNNsnKih9jRNRETYHrRzJjhm2mr3HEKUqof/sQHtqCBVF22EmBwJ9qPk8XSwf5sEtYOWNTS6CRQX4OgxaLUgdWCBSx5XZ+TqncRTCYgnyOY8igWfZALisYC8zUjORmIe0k1TbdJB+l9zFNS7X++XjRqDGtQM0bPiY5jwbA3wng/aAcZiKAqbIWjocdtIW9UuTsn7mLUOLCXrVdzbrLaDkPlFzPOugrlf7XvSFhCLSbE/azRFuMhmIZsJqjXyUkU0PiHiW6MBi20TRj+C3Ef3IFZtMWTcZzFVFv2eO3Y/ataQjt0oePTm9DQxmSspTFVPg16xO8nSfCR9j6q2UWgFim1bthZBQ1FHRE49xxgmPyacP+Lg4OCwWnS7Xa699lo+//nPMzk5yVVXXXXmu3g8zuHDhwH46le/yh/+4R/ywAMPMD19NqbWODwW8fM///NMTU0xMTEBgOd5vOlNb2JxcZH3vve9VCqVTW6hg4ODg4ODg4PDhQ7P9/0tw63YJfbq1z4o3FvJSRs5ZMZ8GyEwNcthIzshRu+seRCy7GyEvypRVkdIwh2YMFydtayEZze0HfRmimj/qVDSCG2zHbgKsVI5yPq833WGfCH4e3ad+1NkMIRpEiFhxxBy/TDGlkwtujpIPshOZOb0IUwuSz9BoAhci1hylRH7oGPWdqPBdyqUJDChzXmMOHRpFrYPSdbBJRfDE57oUZxI4nU71Bc7dNryXaUClapYAk1PiTXVzCxM1+C0L9fDFg4amNyWEqZf12MIkAjOazTokyP05qDYgd/jSJ9mgOMI+b2RmSkbDbVSKiDtLYe+UxE0bH9kW1VtFjSnJ4bJ6WkSfa2zwM8AP5eD/bvEdsoHdu2EffuhUADfh1pVxtfhR+HhA/DInAi3ox7sScGxNvxLR54BPjKmtyPjTC3ZBtnZ6b2vllW2uBzOgyH0XQIZVyq26nf2/sOihq5j/06EKzU85L4cQQSceTZG9A5DnwEVjE3XSu3TttBP9XkJ+78fHBwuNNx444385V/+JTfccEPfdf76r/+at7zlLczMzPRdx8FhNXjf+97HG97whiXP1+uvv54f/vCHm9MohyVw//2wfnjelpoH6uDg4ODgsCHw/fPfHHtLiRpZzztDjiYwBBksFTT07zC5VwAuRUjAowj5HyaUwrN11wKdsazZDmrftJEz5xVxpD8mgKsRIeVHmPBku5/CiLLu0pyAUmi55mjU2JiqCr2G/ayx1oJtSG5GF5NzEENIxHl6r7X2y0jQljJCvg8h10yzKqIqEgrBOosYMYNg20uDfZ4CHgn2PYwILToehhEieBsw7MGe7bBnDxSLkExBLht8TkLXh3ZLZtWXylAuw8wMTE/D7CJMdaRioBq0R628tBpGA+LXQ8BnEDFQZ+QfZ2nQvVZB7QAuC7aZD/rg1DqOfbbhIW3eh5D3j4S+13D4BIYMV6ulzYYKBGrRFDVeFXHgCcDTErAtA15XRLMdk3Dl42T8ZbNiddZowsmT8JMfwz0/FuFuwoOhJDzahm90pZ8092I7Ji9Dx14Yei/qfTfIBirqeaXrabVQx/qsQoqKGvZ+woJG1DMvhozv3ci9chh5ZthQUcXOAFkL9NzzGEF6JffnFvqpPi/hRA2HCx1PfvKT+dCHPsQTn/jEvut88pOf5Ld/+7fdLHqHDUEsFqPVahGL9dZounF2fsH998P64UQNBwcHB4fHIraCqLGlfqHHEasTm0yN8rAfZBeieQt5hJwNE1tqQ6MixHr+MzCNkN8xhFiOWW3r18awfZQG4A4aSnGEkBtDLqiKHDY5GLbpsumdcDt0pn9YaFALp43CSm6PFNFWR/3QRUQZDe1Wm5eoqh3d57y1Dsi550PrhI+hVRBhKKG6gKk+adJrZWTnNehM91od7rxPZs9ftAN27Q6yEHIibHieEM6xmFgHJRKQTEjORqoE+aachz1LXUWGNCJ6rGc8N5D7Rqt+oqpqfOs7teRq0RsKr6RwN/i83Ng+F4gjFSiXIffrFKbCCsy9OIK0t465jsv159nOCvExhLg+X/ohhTz/DrRFIBtBrsux4yJkdLswOQlDBUjEZbwlEuZ6xnyYakpVkl0h5mEEVBhsK2W3b1A1RxjhbBbN0sgh16+KsQKzn3v9BI2o/ZeCtqvtld0efRbnELFjPW7hHiJ+DmEEEr1X9Twd/eDg4LBafPe73+XlL38527dv5zOf+QzDw8NL1nnJS17Czp07+dmf/VlHdDqcNbzkJS/hv//3/+5EDQcHBwcHBwcHh7OKLSVq1Oi1cIqahYu1LOqfax16yVnb511DsUcws9vXaomkAokSfmrlZIsM4XbqNmp3o97xOgN/uZm8dWQGvW3BEsag4GNtx0ZWTqwEg4jfIqZiYiVYxNhMaebHcgiv08bY6KwW8wjp6dE7a14rOooImal2Rkmg5kOtAadq0KxBow7NFuzYIdUa6bRkHsTjImh4MfmcTsl3uRzk5yG9APFu776rmGveYens85VCqxNWsl4j6AcNZCc4Zx9T4VLH2PxsNvQeXMAE2MPSeyKGCJ66vMFg8lmFxahqhPD+ByGJyf7o136tokoSHTqdREThePB9KliWRsQxz4NOBzpB9YYfEzFtbAx2jcGpWRnDzWD7NtIXdYzllVYwTQRtCj87VzpPPixQ91unHRw/hhGalqvcixLBFXWkek+rxxRxZMyOIufsW8dbC/S53kV+G/S62nkkYXEjjoODg8PyuPfee7n33ntpNpv4vh9ZofSMZzyDO+64Y6BVlYPDSuHEMQcHBwcHBwcHh83ClhI1dKZ5gl4rGELvYaEggxBGGtocRdjr+ioG2JZCa4EGkStppdYr/dqqyxIIMXiGcEQIMK32iBJZmkgFi55rnt4ZwGGEydRwFcd6BA2tVFiJGKSzrfsRsSAk/Gqugwo76zkHzT9Zyyz7MPlvi1dnZrxjxkIMqX5J16RC4Chwug7twxIiPlyEoSEYHhFLqmQSUkkhorOBRdX8vHyXiEN8XoSNFCbPQ9swGXzWUPmzAR8zTrNIX9r3AUj/qE3RRuSoJDH2S2s5rw6SpTKFIemzyLWpBfusI/dYMdgmXOUF5rraIdMxTEWKvb7O1tdAexgsfgwSIzXLBHqfjfY2Ot40rH0h2KaIZGmMjEAuL0Hh+Txk0pDOQLMBzSbEH4KZeTjSFdGwRO89qzZYSvpHWUrZ64YrxlYqZNjnpJkmdhVWiqWiRJSgoVZSacz1jarmstfXCpTlxKxBUDG9Tq+VlVbZaN9oxd6g6hIHBweHftizZw8TExP85Cc/IZ/P93zneR7XX3893/nOd7jppptoNBo0m2cj9c3hQke322X//v0cPXp0s5vi4ODg4ODg4ODwGMSWEjXAkPzQSwLZlRd+aP0JhDyfpTcA1obOvtWZvzXWTiYpQRW21gkTk9pmfY8jgsZFCDmo1lMng/2oDVOU7U3dereFgHAfhXNI1mqPo6KLbqsCjFp3nWT5mf167EHr1YL9xlg5Ab5ewl7DoZNIPw7KKVhuP+F2tzHVCXrNp4PlRxBrnxawrQXxE5KfMTICjQbkh0TkKGj1RhbiI1b1RkLsgtJzMNSGGd/M8FbBqxAcb46zF97dxggZSrDrfTB7Fo6XQ87rNCuv6AlDq7K61kurLBRNRLCJszSbRW3fbPGya30Xfub49FZPaND3AuZet4/bDyqCglzTKAFVSf5FTCaKPusaQL0h46vTBt+Xqg08sTrr+iKoXX01HDoEMyeg3umtHEsiglkh2Oc8SzN3tC+1XWExwxY59HkYhUHih9pDqSVWlO2UfZw8kqXSRp5XlYh9ghEy6tbntULPTatbwvaA+tkPrd/rVu7g4OAwGM1mk+PHj/OkJz2JW2+9FRChQy2pPM/jyU9+MuVymQ9+8IO87W1vo1zuN73EwaE/qtUqBw8e5JJLLulZ/rjHPY7/+I//2KRWOTg4ODg4ODg4PBawpUQNe0ZrGpnprLO+1Qc9TAAqsaj2MYPIIZ1lvpEhwEoq2se1Cb1wexII0VlAiNYyQhJqiPVy7VLCTI+zGgHDFj8YsG4cmeE9jqkiUeGB4PhzLE8w64zlfm3RGfhF5NrOcvZtseKY2d563TqsbTz0m/ndQq6pXREwh4gNlWD5bLDi4jxUG1CpwOiIWAOdES80WyMlNkGJhFRxFAoSJJ4og9+VNmhAOcH5aYXA2ezPLmsXGVaDKtJn6636UAGoy9LqIA24VqFSiWaf3vGisKuemqH96f0yBuwP3lOIKHMYeQaVMVUi/a5REnlOFDDVMXaViL7rqxnsM2V9XgBKi1AqwfCwCGaZtKw/X4JTJyU0PJkUa6r5jhFetNJqGAkKV0HVFlXDfaJCXtRzxhanNfR7pZZVYPJccpjQchWA9bO9rv4udJDnboz+VWFhMXIt6Hc+/YQefR47QcPBwWGtuP/++7n22msBeOMb38jb3/52RkZGetZ53eteR71e54/+6I8olUqb0EqHrYy5uTl++Zd/mTvvvLNn+b/927+RTCadPZWDg4ODg4ODg8NZw5YSNcCQfFmkAkPtVDyEdJqjl4xvIwRxjpUFTqsgsBy5H56trVAiPEwq2t+HZyXbYs0iJoh3CpPRsBYrrH72U9B7bmExQ6suVDCKspLxEVFpFBlEHYTUbGGyB6r0krsrRRKZRZ3HVIOslrBeSwWKWsxoYO96s0UGHV+vZwI5z/ngWAVExKkhJHcNqNRgoSHksucFNlOx4Bx9qFXFMkjzNYbLUr3R6UKjLNdR+1Svj4Ys95udvpXQYmNsrLTKIQph6zit5tF7N3xf2/eVj7FZA7nmQ8AeRNTIYwTXCiI6jCDiQQV5poXHoYdcv0nkXgvfp1H3fhu5J7VaQ22oFnwol2BhQcZSc0gyW2KeCGaVCrRbMgY1QD0VnP/2YH/TmBwiOyPCbq/2R7/KAxUZwhkTUfdy1HNN911EhBatvmkjOUrhZ6htz7bcfa5isS3IDLIKi7pe4aqMcP/oOdh9ZZ+bg4ODw3rwvve9j5e97GVcf/31S75705veRDab5fjx4/yv//W/NqF1DlsZJ0+e5P/9v//HL/3SL51Z5nkeb3jDG3j/+9+/iS1zcHBwcHBwcHC4kLGlRA07PFVnCYOQVUpgRRHwdYSsVr/5QQSoF3qFyUE/2MdIcMxKaNvwrNuo90FiSRV4CFN1shFkVhTBGbVfJQUzCOk6T3QlRRc57yoianRDLyVcKyydpb4SqGUPwT60emY1AsNa+m00eJUwJLHtb7+ejJUwtI/iyOz8DjJjvwDsQ855HjOjvtEFFsQSKBaXio164LPTbIioUSxKoHgyIUHj8/OQr0C6I/24gKlCSiHkb5elVkEXKqIqKWz0u77h7fo9H/oR3TGkqmkU6esScm1PI9e8g9zvam2Vx9yDJ4gOU1ehT+3ytO12lobdTv1bbY+0UqcWtKlaF/FicVHGklYAbZuUKo25wDcsDezECCP6rtUlfuiYdh9qcHpYIFKEq1L0eRQm/KO2tfdhB7pX6F/h1g76I4OxD1vuHtdnvLYrbGuVCY6tYepR2/YTOAb1jYODg8NG4C1veQvj4+N87GMfIxbrlZdf+9rX4vs++/fv51WvetUmtdBhK+L48ePceuutPaJGLBbjD/7gD5yo4eDg4ODg4ODgcNawpUQNRQchq04Hn+cRcm6QdUgbIQlTCMlVxhBoSoRpjoJWTahlFfSSTHFMaKwtaij6CRkrsVJRESaKhIshpHcsaP9KSfYoUSNMwOrsYj/Yd6VPGxQNhHD1MP2hVlTal5qlsFqCronMTtc2nW3LKcUCIlYlMJZm4aqbqDyT1UD7q4MEhJeCY6SQ8ekjlUU1pA/SGEHH64JXkpUaDRgdhWIBMhnZse+L2NHtyiz7WEwqO6B3HGcwoo0KG2crX+N8gYfc20qU9wvlHoSoCoN+95VNvvvINawgFQ1V5Po/hFxrDXBXwSGFXHet0uh3n9eQezCJCc2OalN4mZ6/LWwsdsSCqlSU7JZ0Rip/UmmxOUulYHIYrq7ARF3adQqTY2ILmoqw4LOSCoTwvW7nYazk+dnGiCsqIA0SVquYirBU8LeKfLqN2oUlMecYJWTpGMuw9PkZNU7C7ys5PwcHB4f14Etf+hIgM+u/+tWvLvne8zx+4zd+g1wux6/+6q+e6+Y5bGF86Utf4v/8n//DK1/5yjPLRkZG+NjHPubGkoODg4ODg4ODw1nBlhQ1tFJA7ZGWI/d9TGhxEiGPcxgCrYaZfduil8zqN6N7keWJ4EEzcsPrhQWGqPVWQ+7Z2wya6RxVjaI+88uhAjyKCQrejRD28xhhZq0CwEZWRawULYSsVWHGJlg1zFnFGrtqKDxbux907KUwmQlaNVRHxtQ0ElqsAsoIIbK6C615qFShUQ/EjRHJ2ahWoN2Bek2qOUZHpZpjcUa2T2AsweyZ/SP0r8rZyrBn1Wtlij17fzXCRr/7SP/W49n3kz0uZhERyx5TLeRazyJjQ62SsLYfJOipWBLOLelnO6djSon8DkZsKftQWJAcjaGCVPzEYtDpQLst4tj4KGTTsG0WHp2GageOBttHVcnZfaGftSpFq+YUKxEv7XtN9x8+XhcRMrS6a7nniIogQ5gKvAJyPVSY0KB3fZ4NEiAamN+RMAZVadjvg2wDHRwcHDYCt99+Oz/7sz/Lv/7rvxKPx3u+SyaT/Mqv/Ar1ep1XvvKVLhPBYUWYm5vj2LFjdLvdM1VAyWSSq666ilgsRrd7rqYoOTg4ODg4ODg4PFbg+VvoXysxzztDCinJvBpbIiXRkpiqDJ2Vb5O8+jlKVBhEvmmbvAGv8P780Gc9hk90pYTuZ6XnrOtHEWpKNGobNAB5OWhlhs5aTyL2Opcg5PEhROxYWGEbzzcU6a1sCF/3DkuvF9b6Cvt6p5E+Gkb673Twsmd+29ZlOjt8F4ZkzSHkazH4nAfG0jA5DiPDkM3CUB5yefncbML0NBw+LMHhrTaUujLDfhoTQF8LXtOsfFydr1DhyX5OdJC+GkFm0S8gfa9E/HIPQPv+sd/1O+glou0KsPVUhqwHKsxFVRtpSPkwMo4KwDZguwd7hmHvXti1U0SxWFzCwk+fhmpNTmJ6Dn40D3f48AiGyO9Yxwn3ky0aa19lMcJxg8GVYYMQfqbpc3419n1JzHiJB21r0ztG7CqtftZZUcfTfojqk7CgobDH5Rnxfev8VJ+X8LxwLzs4OPziL/4it9xyC2NjY2Sz2Z7vfN/nve99L+985zup1y+0aQ8OZwuf+tSnePGLX3zmb9/3+eQnP8nLXvayTWzVYxtb6J/65y08b0vOA3VwcHBwcFgXfH+tLM25w5b6hVYyLIHJyIix8kyAdug9hvF5t9GPaNI2hNdNI2RpBxM0HmW7EkZUNYa9PFwtoMdfzX+a2rPGo6xxVgsPIYhzGFIugZy/ZozMEG3LtZHQY5+Nio4uQmyGjwcmY0MJzEFWOtpX9ktnffcTufQatTHCRQozG7+BmfXvA2MNmDsBE7MwMQ6xnTLTPpcTWyq/K698TkjpzBxyoTBErb5ymCDoKCghm8KEw+sMdt3fZv2zScl6FSx9equutCIhhrEaWk24eFR1hv33VqBLdcy1kSGQx+RKLAI5HxaqsLgAtVEodk1+y8ICnJqCxToc6cIPgQMsffYOqjKwK6AySHWXWmedYu2iho45vbYqUGgly0oQHgtBXE2P5dig6x31LI8j56ljzT5GVFD6cvtzcHBw2Gjcdttt3Hbbbbz73e/mjW98I0NDQ2e+8zyPN7/5zSwuLvK+972PSuVs/5edw4UIz/OcqOzg4ODg4ODg4HBWsKVEDTCksobjrmdmuc4cDs9mhv6kc5xei5g8EgA8hBCFOvtev48i+cJe8/3Iq3C1wHoxyEJnNftQSxZ7pnUdY0Uzx9md8Z/CZIuU6CUuk8H36o/fWkNbasH2sLSaRfcVi/hsjxn9uwDswNgLacD64jLt0v3MBdsWkdn1INUVcwgZXUcal2xAsQndDrRbsn0qJbkIuZzssNiCTBo4CZ2aIbR1lnwWYxsWvidUzMggY348WG8KEVhsoWYl41WrpGrLrLdSqKihtkqL9ApetoCTsdq42vtqufsmbD1lt+98IalVyNUcDs3WaAItHzqBYucHr2SQqdH1YbYrOR56j9sCXxTs/lLRAWQsTwR/q1A3aCzEMVV1g6CVFuuxvoOl4zksUttQATr8TBhGqoPSSFXU/ArbFPWcPl/GjoODw4WJd7/73bTbbd72treRyWR6vvvDP/xDEokEf/zHf0yjsdxT2MHBwcHBwcHBwcHB4dxgS4oadXrDaTcSg+yiwJD5WYREJWhPjV4vdZ3pv5xwcK7Iqn7iylpg52XUETufKoYktW1/Nhoalj6KmUldCr5LIyTiCHJtSogAsMDKZ2xD7+xs7Te7esMmLpUst4lde1sNDlaivYQZv1H2Zno8DxFAapjKgxpSTaFB6s3gu2Gg5kvORnkBMlnIV0zFRqcD+byQ0pmMzLxvnIBq04TCNzAVR4sYYUjtnNLWshEkP0X7pUw0sd1vfCWQWfpa2bQR97DaPPUTO/U6LWLOdy3j3xZCBomV5wMZ3e+ejyP9X2RpmLXnSci870OrBfEYFAqwf79kbHAcyosw7cu2cxjh0M6Z6dBrt6TXRoUNH7kXtArJFortahs9hzS9lXBR0LyQjRC8Ff0q7qIqNdLBK2utO4eci56j9sFysMfYubYuc3BweGzij/7oj5ienuaWW245k4mgeMc73sHExASvfe1rnZWNg4ODg4ODg4ODg8N5gS2XqQH9LZvsdxs2EalCRNhuJGp7nWk/hJlhH8NkGyihXqM3hyOK8Ar/nQ7eNbA43F57uyFgP0JezyGZFcuZAGhmSNi/3Z41vRd4ShoSWbhzHh6y2hOVC2AjhREuVOAIt3slAytssZVAKhtGgv20ENK8EdpG5xE2gm3iwbqB7T8xjDWSnbvST2QKtzUZ+qyZAVGWU/1m/HtBO5XE1eDirvVSASi8Xfhvnfk9iiFtE0hlwgSSibDTg11jsG077NwJ2yZheFiqNWJxETdK83DsGDx6GB49AUcXZeb9LFJ18ShmnNtjx86pSGLGr94XKvpsQwjzbLBsAank0FB0PecUxgZpo6BWaEqqK+yAd1t0022i7pEocdOmeMKihV5PJa9TyHXxEGul1c5t1edMEemjUrD/UaRKxsPYRqmgGIU48vxQMUxFpFSwnxzGsmkEuCwJ/+kquPEpCYr7R4iPFvEScfy5eWYenOJHP4IHDsBD83DUl8o0bcM85v6zifiwMKD9nAQuCv4+GrTPvh4ta5nHYKHCvmeiqmT63ff2/pKYCpao/dp/J5D7MYGcfwNTKZTDVGPVrG30ueRH7M/+WyuJwvdGZ+v8VJ+XcPYnDg4rwy//8i9z6623Llne6XRIJpNO1HAYiOuuu47PfOYzXHbZZWeWnThxgje/+c18/OMf38SWPXbh7tn1w2VqODg4ODg8FuEyNTYYKQy5FbYXUcIIem1DCK3Xz0c/al2FEqJqSaKz2z16Z9zbROdy//mowobuP87S2eP2Z7W5arAy2yglbpUkjCJsh4HL4pBNCaF9ElNtYYsNSgTb7bErH4aC9RdYvaVPmKS0iXMN6lVveh85/wq9IeRpjKhRZXCFSFh06tfWVp/Pq4ESk0pOKmFp2zyFx2xUm5RonUGI0hGM9ZeKPgkg5gNzUpERj8krmZTQ8HxgQeUB9QbU6lCtynujLfspYfpuuSoozaNRocBuZzk4P5DrpfeIXemilU0bCZ/+YqVaT+l6amcEvRUF4XDtqBn69v2uQkYXc02HEXEnj1yvleQn6P707yJyjTPB9ioK5YJ2a16I5gvB0v7UZ1YMc4/WMYR7HSN26P3U6Ir91NCQz/C2NEwOQ7EApRztuQXyB+sU0zDmwaJvRAffOle7YsY+L323x9h8cA4qdOlyHUsrqfZSazSsbQYJ3/o5vE7Ufd6v+khFPFs81Uo9rVzS3wv7mbOSqosmck1tS6vz/z8lHBwcLhR85jOf4Rd+4Re47bbbesTAWCzG9773PX76p396E1vncL7jnnvuYWFhoWfZzp07ufTSSzepRQ4ODg4ODg4ODhcqtpSoUcSQhzqLPywCbLTlSwkzE10JNo+lZFuYLF+uHUr87UJmTMeBg0glRni7OkIYFpALpjGOSpr3I4dty6SoCpIaMN8AP2ZCqafpnRGt4ogSuFHH0mqJjej3FjKzXTMnWvSSzZr/YLdDidWV2rRs1nylNCJMFZB+VhEoytYmikz1MDPh65hqoZ5r2wVvVqyCYnHJQ8hkJRMhmRL7qaE8FIagWITCPOTaQp5rBZKKhv2QxJDhNhHsI/dKnV4iP2rmuwoHqxXBVoqwCBEet0mMuGSvH942al9ggp/V8kiRQkSNNFL9MkcvuR1GCulLJa91zLcwBP1ksE49eB0J3vUcIFo4UWFBKx5U+FNhNIm53uXgvZAQIazb6uDXm5IQnkhAKg2pFLFYnVhcLKo0F0WrwtL0Po+Wewa2gePB5xxGtNZ2r3RevZ6HPgd032HYotF6x10bqVLRajW78ipKpFrNb1MH6cvtyPOiDDyyjrY6ODg4rAbdbpd/+qd/4mUvexkf+chHSKdFOvc8j4suuohcLke1Wl1mLw4ODg4ODg4ODg4ODmcXW0rUqGBmRkeRUja5O6jyIhG8dIatwp6VrWSx2gaFj6Pr0+fzcuRVGrgMsV/JIFUSaukTdV5KaMYRsmscIVNPYIjZ1aIMnOpCuibk6ChwDEPSxhDC0LZi6tBr5wSGgF0tNKvBC46p561kud0PiaAdKiaFCfH1Clp2dUg/kUjbm8SEkK8UMaSPh4O/beI3bI/Tr8pIP+tYaCLXzN6PHis+I6HghSEYLoqYEU+I2JHNSk5CsSDfD9Uh1xGxZRciblWQ8RGuiEoh4mKM3mumx7VzFaKuh2ett9FiRpTNkL1M2xZH7p8iQkwPun/s/Wm1R4NoMVEto3xE0CghY0SfNyow2M8PrcBScWIGEbz0fvIRO7ZCsM/TyHXXShm9B7TyRO8Lm2RvIs8XMNVP2eA85jBZLxlgrgknTsKjj0KjMQX+FCPjMeIjBaonynge5LKQj8uYURs6vTc0pHul0L5oYDJcdJk+o5cTIFrIeFV7s37HWO63wcNUjYSfPzqOtDIDokVeP7S+vX1UG6KQB65Fqn3awGG22A+1g4PDlofv+3zyk5/kcY97HO9617vOLJ+YmODf/u3fuPHGGzexdQ4ODg4ODg4ODg4ODluMK1nrvDCbQEoiBGGTpWKFhryqFdQC/RElbOhye5laJ6Uxs9i1HeMIoVlDyMpZokk5tetZCPaXCrZfqRVVFIGmQslpHzIdsYHRsGibdFayP4kh+1RYWu4Yy0EzIfR42jdRJKaSm1qxEa5w8K3v7T5cqdChFSn97G4yyPXS/pnCBJSvBHoNZzAVNvb5LLftkooMhMj1MaHpeh5JIONDoQQj85KrkcvLDPxUSl7ZHAwNBaJGSQjqLDBGbwVAWNhQ4rrO0r4KZ4MoVkLkrgdRBHK4T+1xodkfu5DregLTl2HouXcxIk4/0l7vKa22qGMqGaKqRXykDzPIM6mDjCu7XxvBsiwiPqQRMUa3VYExi9xLagm2ELxUeIuq/joVvNSuKQsc82FsGrI/ghPHNWS+y959JQkOj0v4fC4DqaYRNcKZPauFbTWn4kEmeHnI9dEKvfC56L21GkQ9Y/T5Xwt9p2JVEjnfOZY+/6L2H9UX4d8ivc/sXJQ0ksdSQCpZ1iNcOzg4ODg4nGt84xvf4Oqrrz5T5QPwuMc9jl27dnH8+PEBWzo4ODg4ODg4ODisHMvZvW85KDmtRFsUcTXMUvKbYF0VPUYxYdT9MMi3XYm5IjLr9pnA1dZ6SqBXkJnZg8KEuwiRdgyxnjmKzN4dRHbZs5HDn1WUmAHuAr4DPIAhONMYtUvDt9XCRsk3reJYDYmpfRLHkLA6k1ytsrRyIwo2sW9bUum+w7OhwQhAy0GJ0X6ihh2O3W9G+HKoIwT1NMsHR9tCgF3VECZja8jYUMGsipDZZWCuBrOzMDcPC2VoNISkTiRltn02B/k8FLIw5Mn1yFivIaT/tAJAMzw0ADkqD8VGP2ste/31EOE6DuxZ9GGRyF6uYzqD5JIMI8LGBKZKQNvdQc6xjIhXZZavSFJRYwFDjKuVl1pKhZ8ZNaQ/CdqnoqpCM0o6iOB0KVKpFUfumzpGkBhGrplSCFHB9ljfqbCr9kk1RNwst6BchkZTqntaLVgMGplIyDIvtjxpv1poqLa+NLx8W/CugtpaYD97on704piQ76hKOZBxUwzaMqgdg/rArqaaAG4Anoqco553CrkuZUTsnmPj82ccHBwcVoJvfvObPPDAAz3LduzYwS/8wi9sUosctgLe8IY3LMnVeOlLX8rTnva0TWqRg4ODg4ODg4PDhYgtVamxHJQQSmMIxUbwnkIIv0lMiO9BemflNpBqiTZCIOZZGt4dRr8ZuWAsYXKISJJCCNIYQkCqtUiZwTN/fYSs1rautPpACU3bfkdnkatfvxKZWet7tWDRvlPbHA9D+uus5gX6CwEKm9SOI9dBK00WMWSvTUBH7TNs7aLbRBF+HnINt2Nse5ZrpxLiUeup1ReY4F4VOsAQ08vBPs9BZL5tddOv0kaX6zXUbAMVNkodmCvD8JxYTeVyQZVGVqo22i3wfSgUYaQKlYYJ9tZQ6hQm1wSMZZtWmYRFs/A5DDo/fV8tER4WQ/pZ/YS3UbEziQh64b7V8a8i21rDmcP7bFqfw2ghpPUQ0t9p5LmzQG9lySRSKQQiatbpzZtpYwSKxeAVHsd2v/QTZDNAIRC9Jidgz16pzkjEodmCyqKIHN3uUsFkvZU4er6x0DLNJFFRbS3wkf7V8dbGCIsZ5Hm8HZNvctr63sfYW8UQ0bsSvAYdr9/4V4FNs0DUSlB/o4aQ+7mO+T1ycHBw2Ax85Stf4Z577uGqq646s2z37t38yq/8Cp///Oc3sWUODg4ODg4ODg4ODo91bClRo59HfxKTWaCz6ZWgVOJ1G7ATIaTUQqiMeM3bgkIbIRkbRM+sDqOfDVUCIcsSGHubPcCViLDRoXeG/Upm4vYjj5db3/47bLulBF9Utoha2URZ59gzvAchykvex/jg28fVttgWNBrQbs/6j4W2iarIySDXewwzA3w5qFWYBsPb+1QBKHxu2lb1/18OMXorCux+iSLmVzLru4OMIbUP02D5MjBXh+FZydUYKkhouOfBfAlOnZIZ+GOjUsFRPQZV31j9xJH7xR6f4eug18duTxR0hryOtXD1yWqxnLARtT5WG44h1j46xkusLL9hLdDnUL9rqvZzo8HfaYz1nYepnFpAyO5pTK6GvQ8NAa+zdCxqFo/aOS0gwo5euzwinGwHtuVEACsUYPdFSeIJj+pckxMnYXYOSvPQavbaovWrblgJ7Pto8f/P3p/HSpZl533oL+Y735tz1tBV1VN1N6lusS2zyfcg8sGGOUii8ZqCDcEQJYsQoYEiRAiWH2hRtCy7CcsWZHGwaQpPeIIIUIQp2Ww+S9YfasgCGiJl8YmkxKa6u8iu6hqycs47DzG/P9b+au/Yd5+IuENW5q1aPyAQcU+cYZ999jmR+X17rYVFQ6TnpRoy8zxvUhSZ0QmvhWQf6p8F7LmsYuyKtjnC+jkdo10smi0tMD8NXfcUGe8LYZ+vYdfiEjH6ph+OnRrZjuM4T4q/9Jf+Et/0Td/Exz72sXeWfdd3fRc/8AM/wN/+23/7CbbMcRzHcRzHcZz3MxfK1FhlMvd4HZvVqqiKNG2HRF7Nts1TKKWpTnI0M1fMmk0uY0XiXiv5ewcT6jaJNRjuh+NL6FUqmJMiEV6i9jRUNDitSSFxTalU8jzyaUREjgyIkqEgctFZguI25QiLVKiW8K+Z9XnKp3S/I44LxiNslvMD5p/trCiWWREdQqmB0uNWkZ5XKe1Sbvpo/9P2uxLWOcTGzx52PbskaaiGsLUHm5smUi907FgH+3B4ZK+1NROxV1qw0IupmJRiLD+PFK1XIjX8lrD7tEs09Q45+Sz0WuFF4b2Erm8f6x+ZZWlbz0qV8TqtbcPQngWs7/Pt+5j5epS8YLLfFU3Vo7pPFRnQDus/CsvXgQ9ihsYicNiFg5C67M6bfRYXLX3ZnTtw+7aNpV7veEq00/ShUm7JIEujgmSYDIljUZFj+Xm1mDQAmliaqA1s7KkOzBYxgm8Qlu1jpsI+0/uP8F2aUGPWb4PuD5mvTWL002bY1zZ2XfaINXd0381KeeY4jvO4efXVV9nd3WU8HlOr2S/Z5cuX+Ymf+Anu37/PL//yLz/hFjqO4ziO4ziO837kQpkaSislUUmit1L/SOSUcJoaBbfDskWiCSGxSEJiKbWJisTKeCgJ3srRr1nrC0QhfowJVmPga5yPQNUJx7tETF3yuxzvB4hRFhIKlVqqAdzEhOZHmNhXmt3dIta7SFNxKTKmTUwhlaM2qIZGDbsGJQFUgl892TYVg9WO3JgppUGSYH6r0KZplATTWcwr5JYEeG2f/q0aDNNQQe8lTBhVBMlR+NzGrtU+1t+bh7C2aWmmlpYtnVCnA1evQK9rIvV4DK0mdHpRJG4S06dtU02eTqsUvbQAvBT2txXa+So2duelZGCUDKIc9XEL+BB2H99j/pRhJyWt3SBjqioiJx2zSns1xgzcMdZfy9i13SEae6UIlWkpzepYvx8Qn5lK67QWjneIRSKsdKGzCY26pZpaWTUT49EjGy+LS1Cvw/2dcl2iedG40XNV9/IRNl7yukd6BqiOSHpui8R0eWD9lprTy+H9kMmUeT1sHD4kmnmKmFC6sgHl54LSz8koK5m0S1j/KrJGJrIMyCXsuv4Ok/f9SQufO47jPE4+85nP8KUvfYlPfOIT7xgby8vL3Lhxg2azyWDg9qvjOI7jOI7jOO8uF8rU2GRSzBoQZ/3vEGsA7HE88kEC1z5R1FeOdolXKakJsEqcXat89amAJbG9gwldS0yaB4pOOI9ir4vAi8ALoV0tYn7920QDYT+0UyKq3tOULOovFeAtRQYo97tqA2g/V0IbhpgomJsauTmxhJk7Km6cC7DpjP+SsaH6JIqAKf33OZ81nV6DJuW0PO8m04T3eVnCzIFL2JhPC46rqPtSWK4i1NsjeLQLyw+slkYNi9q4eg2OuvDooaWkWl6G5YNoVsnYWCbWFEjPJRfr83NMx94hJqi/hBky25iQfD/ZTvehzMGqWilwfIxU9a3acAMbr5ewtFOPKtY/K2qLIhB6TJqBeUSO/h5ifaw+uImJ4QdYv2vszopQ0X2lsa9oKkUqKMojjXYbEuvqLAPdMRwcWYH5dgc6C1aL5fIlWF6CWh32duH+l23jNAVVai7Ok7ovf04r6kwpAvXMEjLa0rHYI0b/iCOikbCFPXvqWB/nBsU4rAPR/NL4K9X4UXs62HiSEZMWiNd+ZQgrklCmxkY4DxkxXgjccZynmfF4zCc/+Un6/f47pgbA3/pbf4t/+S//Jb/5m7/55BrnOI7jOI7jOM77kgtlapQEaYlBSr/SYLZAlIvieU2JJpaSRaLaIjFaQwJteowjLD1Mk1gEW9/LeNmco12zqGNC5w2iKCax7TJxpvEiZnCkM9Hz6Ajlbe9hQpsE2LTWQSoU6zvCuh/C6oPsY7Ped5J183oRI6KZpAiWqvRLpRno+pymcslF7NS0aWJi41Vi5INS+DwJU0OpZFKxsyolUQsbw5q1DpPphq4Bz2Hj6S6TadIUxaQUQ4rW2AIWj2Dhoc2yX1+DlRUrBn1pI6TwCs7c6g6sdqNA2yQaYFtZW1MTKTc38u8PsALXy9j4TQ22FmbQbYS272DjtxTFkUcmVKHrPCT2fxd4neP3xnmRivADrP1VaZlK419tXcL6Y4FoJqnWgvpL22ic6HMLuz+vhf09YLJWSG48qL2paD8E6g1YWDDz6xOfarF0eYG9O3s8fDBmf98ie5Zqxw2N9HxOi4rVLyT7Sq+56tek91PVb4PqZ6jvZt3/udGSmy4yZ0fYPaY6TjK98ygS1adpY8/oBta/MuIfMRmB4ziO87QyHo/5e3/v7/F93/d9E8s/+9nP8uUvf5lut1uxpeM4juM4juM4zvlzoUyNWVSJW6dBEQ+HmDAos6Rqtr9SF/WIs6Q1Wz41BM6CRP1HxNRWmtV+QBQ2lYc9j9JIxTalaBoRi+Sms5hToVKpVRRx8SwWqVEjziQXVTPnD5P1SsVzS6/S/tKi4SI9vzpmSH08tPERJv5vU06R9W6Q1wKYJvgqMkLRM30sjdaIKP53idEG+bjSrPsFbKy2MINiGVg/tFoa+wewfASdtgnXyyswHgVTZBe27sXoDJksS9i1lsibRxqUhPv0+wEWlbGMicLqizXsej0b3lUTZ9q1yvsvHSu6J3R/yARLxeMucRb+eYvJdWK6KJlRikKZ1lcL2Pm3iREWJfMvpdQPTWzc38SMr1TIT4+ve1BRTMf2XbfInatX4frzbbi0wXhvn61HY46OrMC8+lKF5NMosBbTU5ZNQ9cwbXP6HFCbZVSk61eRG9enRWaGnn2KrpGhUUrHlR57QPwdUTSJ4zjORWA8HvMX/sJfOGZq/JW/8lf46Z/+aTc1HMdxHMdxHMd5V3lPmRrnxQBLjdNheuFYpRTpMymUStw6TSHkWfSx2eap6KdZ3mnKrKoZ4ikqmLtNrAOSGzCpeK3UL2NstvvbmOGzlbSllNc/NSRyM6JeWC/9Ljc1SpEBEqdTAf0NYnHl++H9pPUyzos0+oXsc37OGi9Kh5a2eSHs6y7lGihiDztf1UFRMeTdAWxuw9q2Cdb1uonXnbbVTlhaguvXYHcftvdtXCgFlWajK5WSronOJa3bkp5beq690HYJ/APM4LiKzWJPx9U0UyOPdEln7Kcz7RW5lJoEWkepzFRb54DzMx5V+6eX/C3zMTeCJMjr/tolRttcIkbbpGmNSmi/Mhq2ibUjSveO7pu1cFxFgOhzvWa1V7pduPPaEc3b99i8M2J3D44OYX/fonvGWN8pwmSQ7HeBaMCeBEXDtYnPG7VbzxhduxrxmZWPt8fBETaGlTqqNGZUh0b3n9L2jYlGxklqyTiO4zwtbG9v8+f//J/np37qpyaW/8Iv/ALf9V3fxXj8uJ/CjuM4juM4juM4xoU0Nd4N8UrRD9Pa0MFmV9cwsUqi2yD5+zwZENNYpeef1hcYM3tWONl3u0ThLf0uFUolJnYxUe8+sV6AhL20LkZKLtxr2bQZ6KX0TKX9pLPtdf47mOifztx/UuhcNLN8mrkxxvrzAfG81H6J8HvYuJw1H/I+Nj41FlU0fHUHtrYs/VS9DrWa1dlYXrHC0EtLsLUND4KpcUAUYlVMOj8PtT83NvKImhF2XVQzQZEIfWxcP8JSVM1TJDk3u3SN89RKuYGQUieKzyPOLjTLuNglRhQoHRRMFudWm3SNFfGkotldrK/SdeD4PVAyd5SyKo2wyqO2tJ0igmQSKBXdUseieHo9+PKXLM7o8MjMr9EwpCvD+k/7WCRGLSwl+9wObZrHaBWKaKhK3QUxtZTMvXdLSlPfVqHIJkVVqej4mMmILcdxnItGv9/nn//zf35s+b//7//7T6A1juM4juM4juO8n7mQpsbTwBgT67rEGcVrmLD3iCjKnTclQSw3AVJDYpYBNEpeivioEt61X83WrxL2ppk5pe9mpdcpGRxpm2S4pPVVnpa5ghJ2VZg+LRqdou+r0uRIeN/HBPBZwqiiDxaIBcT3CUXDH1mkxngMiwtw6TJ84AO27O5duH8P3r4Py92YSkimiswIsvPQ+EkNsByJuvtMpi3aCfs9qDj3vB/00vHy/lT6t7yN+d89zGhQBMx5oPsiNTj7xAL3qpuR32v5dc9T6Sm1U8k0ys9xwGTx6ZJ5lkY96NUgFLNuWr2VlRVYW4W9fTg4gMMD2Nm1zw934M7IjrVEjPxQtNcO0djRfhscr0dUxYiYyi9HfZw/f5Rq7Enf+10s0u+AmI5rOXy3yZOp6+M4jnNejEYjut0unU7nnWX1ep2vf/3rvPjii0+wZc7TwsLCwkRBeYBer8dw6La+4ziO4ziOc35Mq7X7VLLI+Tgx6T+121jExRKzOyQVbCXQbobXA2LNjXcr3VFanDh9wWRdjNS8SGd/65Wn55lHHMxNhnkGU2nf4xnvpX3XspeWVUWLPCn6xFn4SuGUCstgwucq8DyxQH2KzI5dTCyeJ2u1ogYuhX0uhO3uAG/twRtvwb17MBzCyjIsX+7QXF+iXofBENoNS4G0TExNpnofeftK1y0fX/l3h0RRvxvOax6xV8ZKo+J7FZIupUHL01QNMVPjIbH+xXmQj2+lkFI0VJpirGRW5KTrTDun9Nijwnc5aTRFCxubLaBRs0LhrSasrFpdjdUVGyubm/DaJrw2tEgSmblKsXUYzncXeyZuEaM0ZO7Ng1I1lZ6hJeNP4+K8WcIK2HeYHXmXPnv6xIgx9cMRk7VWHMdxLiK/+Zu/yZ/4E39iYlmtVmNxcbG8gfO+41d+5Ve4cuXKxLIf//Ef53/9X//XJ9Qix3Ecx3Ec573IhYrUqGFCq2pVnGVGbpq+6FngOibG3cJEztK+65j4vIgZGKmwJtG5jwlxj3s2bknUL5FGL0C84FU1KtJl6eeq/khFxzT9UCk9kdA2abHivB156qL872mcNO1XKY3RaVEqH4nmGqcyBJSOp0cUkxex8XcDMyHexKJ9FNWRX8N5kLi9Hvat1GV3CKnD9mCxAy+8EFNPjfYOeetNuHsHxgNYrsPKyMa1Zp0rRVGeqqkqKii9brmxMY3fswC7R3afqSB0B7v/x8QZ/+l9rHWgup5NHl1yntFUVfeiUg7p2LpPLhEjGQ6JBdIV9VDLPotSn+bM82zsMFlPYwET8VfaloasFQrJt9swHJgJ1hvF67EW2rED3MPGxCaxoLzqa+heUGTNvCgd1wqxb9Mi8Pm5KCrpvJ69TeDD2P25jaVG269Yt4Xda2NiikCIpt1m2F+e5s9xHOcicuvWLV555RVefvnld5a1222+7du+jS9+8YtPsGWO4ziO4ziO47xfuHCmhtLgnGddjSYx7306kz6nTcwb3+C4eHaWvPynOZ/c2MjrF4ySz+ny1HioMgCqZoBPSwU1q/5FGjGStkuif1WbSsZL6dha7ywRMmdJYVPDhN7r4W+lWepg40bRMEoP1MYKZK9jxbKXicLnEdEgOy3bwNeIM96PiGbA/tjqJfS6llaod9Cnezhmc9Nm5C8vQ3sEe7uwNbJ7rio9lophq72pEF91LUs8D3zTZVhchM9uwKu34Fe3zIjRuG2HY9zGzEe1RTP114jpiVLjMzcC9LfMkLOYpG0ma2WUSPe9RDRSlTZNxezTsTdrf7kBWGp/fv+m+9W5N4jP1YWa1dNYXrbrUMPqaDSbsHEJLj+Cbh+Wh3bOW5jp9QAT/XeYfN4owuGQk0cojLH75tnwd5eYRq2ETJrzSj9XIz7zj6j+saxhhorugTQVnphVh8NxHOci8cUvfpFf+IVf4K/8lb/yzrL19XX+5t/8m/y7/+6/+wRb5jxp/sP/8D/k+vXrE8u+/OUv8yu/8itPqEWO4ziO4zjOe5ULZWo0iTN1T4NmJKdi8QgrfJ0aJpeJBZJTMVAi3lnakCMR/bSUTIZpUQdp3YOTGil52qR0H+rbaXn8oSzSaxZ3k+OGwjQTpiT6nlacTmfGpzPqS/tSrYBcPK1hAqjGmFL6NDCxd5dYR0JtHRIjN9aIET9njUQi7PNrmGC+QowyWgnt6vatvsadO1Crj+j1YDSGa9dNxB70YbcPtw6sPT3iLPwUXZ+0zXnR8JT8vOrATeA7WvD/WLc6H4zgmRX49A68PbLIlT1ilIaMn1S0XySaNmlNm/y6kixXdMc8dUqqUKRRKcIpr3WzDnwUeIZwDbBzU4HwaaZGbizmY29azZn83BeJkUNp+qlmza59s2nbHRzA4SEcBkei2bC23sGux9uYmbHJ5NgWSpV22rG8h5k/17D74k44ZpqCTSnHeoXjz4tSlqVRIAMsaupK2O8Kdq1V8D29VqqjcZ6FwGdF4ziO4zxJfumXfonv/u7v5lu+5VveWfbSSy/xZ/7Mn+Fnf/Znn2DLnCfJH/2jf5TnnntuYtlv/uZv8oUvfOEJtchxHMdxHMd5r3KhTI0WJpzNU1OgRDozOxWe9rCZxi9gAloLE6EfJO9QXcT5LJTE1tJM35RpKaf03TKx5kcqkpaE11Lx4RKpYaH88drnKjEtUV4MW4bFNDNIERy5yZMaKel5lKI9FAlxUtL0PmmaG12HUqqby8S0TBpLY6Jx0c3akheOV+Hgt5mMQqgn255GoM37SftTNIH6sgvs9mBzC1YfQqtlr8VFWOhAvW7fjet2PpqNvxf2rWLcilAopdVJjY18TDWS5WOsPxpD+PqtWBR9OIa9UYzkqWOmxVZoT3o8GY7psaFch0IoomNeIbrKBOwl3+f7L5leS9h9+Qg7nweh7bmRlZ6D9pffDxqfKpyeGo15tFUaobEc2qEIg074e6UVonTaNgYAen14+NBev3sfvjyEN0L7H2JjqWqsnjW9117YR5NYqyO/VrWw7CyRTalRmd7PD8OxnweeC+vdx4zIh0SjLTf6zgM3MxzHeZr51//6X/P1r399wtS4cuUK3/7t3+6mhuM4juM4juM4j50LZWp0iQLaaQSfqtnvEsSXMXFeAuEhJlqdZ6qrFIn9atcKlmd/gEWPVAmCpZncEjq1bAM7j3tEY2OcrZcK+KX9T2NEjM6oY4Ko0ic9Ipo/mkWt1DCzUtDkQnjVzH+JmBAF79PkrJeY22VSzMyLraeotsoaZnrdJ87w3mW+8aL2p2LoWYVRpcGpYWbLMjbLfD8cr0dM4bMH7Axhexe2t62uxvoaXLkC4zHs74cZ+r3J4vKp0K40cBpzueGXi+wSjlV0XDPjlRrr1RG81bPrsUg0lepYXysNUJWpWRK083GT3jtqd7qdjIe0HweYKQRxzLWIfarIp/wezI8pkfy3iJEZ6T5L26WRVfkytV3XJ03hlr6L9F5cCedAsqwDLLdhZcUKgy8tW62VQQghuf0I7g6iaXmAjaUUjUEZw+fBW6Ftb2Fpx2SopsbmIWczUPK6J0L7XCHW9uiH401LD3ZWZMYNseuz/hiP5TiOc1r+1J/6U3z84x/nU5/6FLWaPRW/93u/l//8P//P+et//a8/4dY57za1Wu2dcSDG4zGj0XlWMHMcx3Ecx3Ec40KZGqloOm/6pA4xHQ1T1j8AXsNMgCEm3O1gIqpmvCuK4DyiNSTUrmICWRP4IGZGvI4J5Sf5L0AusLUwEW6P4znoq2ZwzyvSpemtJARKpJdQrz5aIKY9qoW29JJtSfYl8TYVcXNBOhVuJTavhlcLu34POd53+VipYf1zDRsjD4mz/9OZ7ooo0P40036EXasNrI/TqIx5TJWT1heYB7W5g/WxZp4fEutepKbGNrB6BKtbJmQvLlqRaHVArwf1kY3TS1j/dIkmXAM7f5kTm0waDhLKj8I2MgvUx6kxMsZSZW1j1/gGMWXWJSxdkwwziea5oC5SA0BmjP5OzYc8PVYtHPfZcOwDbDwdJevWMYF5PZzv68w2o9Kxp34qrZNHleSmZbpeg8lIoqoUWyKNQFjArtMedk1WCYW2a9Bo2qsZInfqdUs5tRDGxvVD2+ZS2PYV7Dmp6LANovj/CueTpu+r2PV+gF2LBrG2iq5v+kyeJ6Wfxt6siKgxNuZ+l5gebw+7F3rEayZz+jxSTzWw/m2HY2xgvw2O4zhPGzs7O3z605/m3r17XL16FYCFhQX+2l/7a7z++uv84i/+4hNuofNu8l/+l/8l//F//B9PLPv1X/91/tgf+2NPqEWO4ziO4zjOe5kLZWrkzGNqpPUjUvIUT0NibY10RvlNTMRW4WHlwD+rsdHAxOI1Yk7+fUy4u3MO+z/Azm+H6uiUWctKJkee/qZLFFo1czudeS4TQILgpbCe2ldKh6Vt8/Q76cx6XdfLwMuYOdEAXsVERwneLUzEXQnLVMh4EUsp8yJxpvrrTKbOysdXnViLYIAJ8OdZX+U8SFNXKT3WAGv7AtZH3bB8F9gewtoerO+YsdFowGgEu3swHsFKA54dw2INOgPrk0fhGAuY2bCG9YUEYIm8daLw3MTEc0Xr5BETo7Bc9QpkbixiRdS3MHF3N6yjyJ/c6FQUzTjsIzU48vdcgK5j6edeDP30EBsvOocGZmY8H9pSKg5eiqIq/V3FPPegCs+rwLfaN62mhM5VKdq2wrrPhP1tAJc7sL5u5lajcdxgWVyAS4fQ7EM9aehdbEwtYPf6AdZv55Wqrw/8DvEZoueznuF6Hoj0+TSNkrlVil47wqJEUnN7iWi0ydTaIT4TzkILe042sb5sYCa34zjO08h4PObXf/3X+Y7v+I53ZunX63U++tGPsrS0xMHBwRNuofNucPnyZZ5//vljkRpgY8RxHMdxHMdxzpsLZWqktTDm/edxn7KpAceFL4mvy5iQegUT/PYxwUqpjRT9cRbxqoYJgapTcATcoiy2TqOUZ79GrIEwT0qjUl/Om3JLAqCiQdJtmpiZoCgNzTDvMVmQVwyZjIzIU0B1ibP+1UdH4TjXwvp3MMGR8H4tvBYw8fV3MHOjhc3I/yDRUHqbyf7KRc9UIN8lFqyuMjUkvuZjRf0wbbvT/vcvHT9plMIQE0g1+/udFFTA9iFsbcPKqs3O7/VhdxeGI7i8Bh+9CivL8Mob8IV7NgN/k8li6eqbDrHGhkT/9HwVMaBxkwrKKtqdRv2ohsYtzDzROSlKRGajhGaI16QWzldtm1VzoRaO3w6vDaJJpxRuMlx2sFRIeiaUoirIls26rvmzqMoIUb/JqFI/pWbftLZo3C1gpuAlYKMOly/B9Wtw+TIsL1nExmgY2lIzs2urD4/Gdt+1MWG/jl2fLeweyuvHnBWlulqm3Id51N6Y2dEaI+zaVUWp5aa3xtIaZnwpHdQ+Nh4UDdXi5CnwcmrE2kR7nF/hccdxnMfFH/gDf4B+vz8haH/uc5+jXq/z3//3/z3d7mmr4TkXhe/8zu/kB37gByaWHR0d8Q//4T98Qi1yHMdxHMdx3utcKFNDM7/zZbMENAlcuVAoMUypeZYxAb4dPvewGbJKeyLRXSLtWYS7PjHiQwKYIgbS9E3TyGefK2e+ZrCfJYKgSpQr9WF+DVqY6Kf0QUox1MT6dnfKcVOhW5Ecun6lVDHbmKD6TDjOGDMxNNt5DRNvJXSrT5YxcXKDWLBZYnt6bvnM+wExpVcqXpbGYSucr6I/VMtgNawrUVw0QrtkEp33vLZ9YtTMEUm0xhhWd2BlCzodE6+7RyZkX78On/kWuHKzxY1/0+f+F2BwZObRJjaDXdEMg/DKU7bJ/NgNfdJIvksjqRTZIxFbqD/2sf68jF1j1bmQqbGIGVevhW1kTEho3qZaIJYpIkOmhV2vVexct4jRH7eS88yjevLIEJK/8++mPbu0bsmk6DJZk6TLyYX0RSzV1k3sPllfgvUNi9RYW7V0U0pBdngIR104HMBbY0sT1g/bKj3YW9j1yZ857dDOtH0naaeiM2RCyPTMU22VfhtmmUh1jpshaV9rnCo6S0XV29iYf0BMyTYiPudKbTkJXSztWfrbsFS9uuM4zhNnPB7zV//qX+Wv/tW/OrH8v/6v/2uuXbvGD//wD/ts/fcwzz33HH/0j/7RY8t3d3f5r/6r/+rdb5DjOI7jOI7zviBPK//UoyLeEpxgdmqX0mzcVEhtYyLphzCh7ghLY/Rl4OuYkKlaAmm0wFmQSJ8KnMpvryLJs7bXS5EATUxgvImZCo/r4lbNlAZr+4eAj4d2gBkGR5goecCkGJyTC7g6v5KhIaMEYrTLJeCl0IbnMDGyi6USekQUH29i0Tj1mhlXt4jmQ3o++btqrWjdOiF1T+FcJHwPQjufAT4BfIyYLiulzmRkweNgi+MFw7eBra4VDH/0EB7ch70965tr12FjHZq1IaMRrLdj3YmPAx/GhF4ZMuuYAJsaiWkasXzMjrN3mSBVqIbKSnK8q8R0UIfEiKsXw2sUznFa1FIrvG5j9/zt8LpHrJ/QJxpBOo7IzYecdBylrwaT461qOx1D98Iudi0lqp9EKlLaMN0jl5uwsWGF4ldWYWHB0k8NhrC3D5uP7LV1YAbP17A+OiJeiyXK103RJDJhlrDxUvVsSo3mJWI6sCuFc8xNDW3XTP6eRt7vpd+HJayPPoid6yOsxsdXsfGhsaF7vcvZIyt0jVOz200Nx3GeZsbjMT/zMz9T/O4Hf/AH3+XWOO82N27c4Hu+53smlo1Go6LR4TiO4ziO4zjnxYWK1KgRayTUiOl/ZqGZ3KX9gQnxzwEfwMTPV4lFo0s8jrlmI+L5zBMBks7kBruQ69g5tDkuup6ENM1XFamImwqRyqtfw0TXTWIERINoEJXOsY4J1aoPkM6gztM3KQriKvACZiqsYmKgCjuPMBHyfnjthzZcxa53Ezgax8iLPpM3RD77XgyT7zV7uyTopmmn0tRAu5jJks9qH2fbPA7UPy2y2hrA4hYchQrmi4uWhmhlGe7dhwcPRnz9NWiM4YUaPFeDh6NoBvWJs/Jb4RhpfRWdk9JOTROcpyWpGGPXS7UUJKYfhHPYIo4b3VMaczlNJsf6ftj+ITYONYa6xLo3syIr0nZOoxQFlFMV2aA0WKehjZlSz2HG3nXg+iW4cgXW1u26t1owGkO/B4cHlprs4Sbc65qQv0M0Mxax/r2B9d89Jse17vVhOPZzxPtyi8n+VAF4GRNXsfolK9hxvzLlvHUvroR19pl9Hw2p/l3Qax17vlwi/i5UFah/XHOQB1ifO47jPO0oGiNNQ1Wv1/nVX/1VvvVbv/VJNct5jFy6dIlf+qVfOrb8W7/1W/m1X/u1J9Aix3Ecx3Ec5/3ChTI1IEY0QLW4VEU+Cx9iwe4bmCAmsVmz8FthvXcjG7DS98yDojn2MVH9eWz2/CWOp0aah1TIWyYWmJ5Gun+ZGn1M6G5jwnaaRkmpc6pQPRMJ+0q/0mYyQic9fhsTZ28CzTocjWKR7PvAm5gg2icaYmth37cwgfxtjgucpXRCWtYg9s0QE3VnMcJmue8RI1ZKxpGMgMchkEqk3yOm5DrE+qqJ9d2lA3h2EZ57Fj70IWi24I034OgQ2h34wPNw7QgePYK97dhOpd5pYX2jFG46H6WTGmVtmVY7AmI0Tpo+bpeYuq1JTBmVitgDLJqgVLtExcQ3sPtok1j8HGIh+3S86ThV92ie6mje61cyzk6TpilH6b3SQu3L2PPhenhfx9JObVyCq1fh0oaZGrU6jHpmcO3uwf4e7B5YH+0S75UxtmyPeH91mEybJIMyPd/1sG5+76xjEUyLxDo5z4RtlAIQjhuI6kONudTQmJWCKk1/pme+rkULM0uvE6+91m8QDbHzKog+jXnMe8dxnCfJgwcPWFlZ4Xu/93v5O3/n79Bq2b+ga7Uan/nMZ9jb2+OXfumX+P7v/34Gg3fjyek8bur1Om+88QYrKyvHvnv11VefQIscx3Ecx3Gc9xMXytSQwSDRKhWFp4lXtWydPLJgkZjO5Q1M0FTKD4nzZy3++jhYxUyCNmZqrGIisoqPn6S9afTDSvi8w+y6HOlsc81qV4HofGa7DIGqKJA+JjBrJrr2vUEsiHzAZFHk7WRZbWTXahdLkfOV5Bxa4bwayfeEbfeI0Tz5GMlrJqRFkPOIkll0OW7q1IiRHkqf9DhYCa8totkko0h1QpaAds3STV25CvcfmHmxvASLS7C0bHU29vdh0If2PowGUeReCMdSQW5FbwyJ0VVDJtOPSSROhX2ZI4vh1SIWBJe5oJogpbRkQkXJtU+ZYx1MMN8I3+9U7CONiEijvUomzEmNiHRclQy0k6KUSw3sOnfCct1HStWltF1r4bW+CmtrsLJi17fdgsEADg4sBdneHuzswu7Q+qoTjrUN/EbY9zrx3l4k3p8pNexa3U7al99vSgOo1IIyP3aw54KuRZ5aSsZXn+ORYLNMjdTkTvcpE6iNmaM7mPnZD21fDOvv8+6YGo7jOBeBg4MDfv7nf54rV67wuc99jtXVVcCMjeXlZb7v+76Phw8f8pf/8l9mb2/vCbfWOQ+Wlo4nSHzjjTcYDs+ajNFxHMdxHMdxpnOhTI3LxJoGqZA0S7hKSUW0JiZOHQBfCvtNC4IrWkCznZ8mJAzLdNjHzuMhsYDtSdB/PTYwsXOE9cEms40NmIxkSIXkFBlJ6fHyfZQiOWRayDBJr8UW8NtEUb4ZPquGhtbtY9dXM8y3k/als7XTdqfnpPXydp91XKgIcS20+3GSznBXXx4Q++lZ4HAMr92FWw+hP4TFFrzwDFwZQ6sNnbbV2mi2YKluRtpu2JcE7yExJZUiG9rEaIsVYr+l10H3Wo1oaCgiaYwJ3KthX/vEMaGaLWkEQem6aEz3iebOEbHQ+SzycUJ2nLOMhWnRKtOQCXkN6xsZvnXi9VAdmYWw/hpWo+LSstXRWFuFpSVoNoNR14OdHXgYamns7Mf9bmB99gAT+XVPb4R9q6i7zD5FPwil2JPZVUvW28PGouruqAj6W5jZrP7PU98thdcO1RFQ08gNNRmNC1g6rVeJEVaj5AXVhprjOM77mZ/6qZ9icXGR/+K/+C9YX1+f+O6Hf/iHOTw85L/9b/9bdnY8ud5F5j/4D/6DY8t+4zd+g//oP/qP2Nraevcb5DiO4ziO47yvuFCmxktE0es0s2NzAVK5yh8U9jfExLkWTyeqF6DP97BzmVegLdHBZkevYf1zRIwgmCbY5tEM+qzv8lnVJxVw96d8dwS8honUSinUxcTyJpNtT2fuSyQvRWSkpNum5k56HmcRNiWQqs/PUgtFaZpK1191QyQyK31OGzsvGQn3gMEYVvr2/UIX6neh27UIjtU1MzXW1+DFHowfwaVDa/cBMTpHUSkSp1NjUGmnxkxGFKQ1OGS66NorvVAbE7C7xHRHa8QC6CMmxWeS7fvJ/veYTLk2L9OiNM6LeU3aDpY27zp2rluYwVQPy25iwvwjognUxO6Tawtw/SpcvgKrq2ZWNRowHMDhkUVobG0GY2MQo5kWwzF7mHGodgyJxdRldJTMJT13NUbTmkBD4C7RSN0Pbd0kjgPV7NkmjokrWP2NN4gpqlLm6c90bNax8fWQ8v3YC22VYeQ4juMc57/77/47ut0uP/7jP35sNv+P/MiP0Ol0+NEf/VEODx9XjKrzOPljf+yP8T//z/8z9fpkdaq/9tf+mqeechzHcRzHcd4VLpSpcZnZRb+rxKtcYJPAPS1XudILnXYWdUoapaBUPTVOb9CAiYmqJ3CHyVRIJ4leAROwN8L7FibqbVGu76B8/TApVucGhtpRNbP9vBgSBfGt8PcBMaVWmi6qKtKiKtWU1i9F66guxEn7OkUi7DXseu5wuvotS1idhDHBmCisI0NjHYsOkXGwHI7dw0RxGQBK/dM7NLG73YLr162odLMJ167B6i3Y3IStPXhrzwRowj5Vs0PGBkwaS/osI6RD7MejZJ0j4rVQWjWZKI3k1QzrbmDRC0obl0d25f2xQIz0OAlnFbSrniuznmVpTZAl7HpvYteuhhXj/hBWj6KO9dkOdp7rwLUW3LgCN67D1WBqtNowGkG3ZzU0tnfCq2sGwj42XlRDQpFu2r8iOZRiTOdRVWskfR6rFoaMGY3/R+GVskA0OmRcqVbIXU7/rE7bpQi9aaTj+Kykvw0N7JrK5PPkHY7jXGR+4id+gvv37/NzP/dzx8Tvv/AX/gI//uM/7qbGBeRP/+k/zY//+I+zvLw8sfzzn/+8Fwd3HMdxHMdx3jUulKlxlzjDOqUkppe+1wx1zdBPIwDaREEuF7TOQ4hfxmYUa0Zyg5j+5qR0MIPn+dC232ayFoPqDsyTNkqotsIbxJnImlGfUsdmxreYLASerpfXCEiXax+p2NkKLxUaP4mor9nqK1gfPOR4nQv9NzpfNqJskGndeYoAn6UOguiF1yVsJvxJTY06dv6awT5NbFd6rtRoWCVGikA8dxX93geGfbi0Z0WlX/pwnYXFGm+/OWQ0svRFKyFd1eDQjq8Hi4qiq/aFoiXUtxLK86gKRXDkKeDUVzIkJP6OknVUy+Na+Pwmk6nI8r5TbZESecTB456ZX7qPchRtIQNyk8ki2kuY+L8flitt1HPA8w144QrcuGl1U9bWoLNgBzrqWtqpnR3Y3Q21NMYxHVQX63sVBm+HY24nx1d/qt90HtPOUS8dQ8iQTIX9HjHVma77TmhLlXE1i/S3oU0cYzqfFtHkPO/rLzN5lXi9G8RUiI7jOBedX/iFX+Dnfu7nit/943/8j7l//z5/6A/9oXe5Vc5p+ZN/8k/yuc99jitXrhz77jd+4zd47bXXnkCrHMdxHMdxnPcjF8rU+F1iWqhcGJuF0utsYCJVmoe/jQl+EsbvY0LdedLEUqQsEuteSByclxomfj0DvEicbf8ak6bGaWqADDHhd9a2MoUGxDQ/85DXq1DNkiYxVc4SJtB+DRNS0/X1WcKfjrsBfBATcW8Txev0JfL8/lB9rpqJDtVRGudhaICdyyNsbCj65iT7HGP91SPOmK9CYq3OqYYJqGNiwWfNgFcETAcbpzf2YDiEBiO6B9DvWeHwesNm+79wE5YewnBk642G0OvD/jiaE6qpoKiLe+GYi0QzbpC0QWKyrqXqMKhwvM55mHyvgtErxKLjVX2aRoLkNLD7TQXqzzPKKI9eKhWr1vL8uH3sPlGfqF7GVWJkyr8iPtuuYffXSzV47io8cxOuBkNjYdF2cti1lFOPHoYC8Q9hpxcjNNTXe+ElQzA3PqcZhTq//Nndwky1VayflbqszvFIugNiEW89Ox+Ec1a0Vv7MqOrHlFVs/ENMfzUOx3kuHHOHGBF3XqgmyM3w923s9+EsEXyO4zhPE6PRiE9+8pP81m/9FsBExMY3f/M3MxqNuH37Np///Of5wR/8QQDG48cR1+uchVqtxmc/+1l+4id+gpWVlXeWj8djxuMxn//85/kbf+NvPMEWOo7jOI7jOO83auML9D+Hdq02MXO3lNpoWsqWJUy46mGip8SpJvARTBwHM0++xvnOym1ikRWrROHqpB3fwATKNeKM90vEqJM9LAWNZmc/LmRIqP155EU9Wy5y80mFmzcwk+YKNvP8q8R8/dNMjQbwMeBlTBh9E/gKk3UY0veUvDBwXl+hjgnCSgmV1/VQ6p1UfD/LjSShXvUoTkKaskYF5GdREp5XiAWfG5hIvIeNt2eBj9Xg//48vPC8Nbh7BIMhjMfQalrx8FrNzIyjLhwdwuEh7AdHoNuDgyMYjKA7hlvA17H7+SYmIKdFxiEWG9d9n97/+px/r8iOJewef5vpdVmqWMTuL6VvOk/yZ1dV9FkpWiqlA3wc+DDWh68Cv4o9B1rYM+ebgI/U4IWr8OyzljZs4xIsL0G9PlkY/P59uHcP7u3Aw5HdjzvEFFMPwr7TMa8xq+vSJBalz01FnZP+ViotpcsCi8j7KtFsy1EKtXFok54JVTVvZj0HatgYXwn7S+sSrWD9ewMzGv41x1NinZU1zDgZAq9Tff9foJ/qp5Ja7TwSSTqOc1rq9Tqf/exn+bmf+7ljaYvAnnGjkf1CfMd3fAdf+tKXABgMBmxubh5b33n3uHr1Knfu3KFWq02YUuPxmK9+9at84zd+4zvmhvP04dfl7NRqF2oeqOM4juOcC+Px0z/V8j3xCy2RHaYL2F1MkFIaHDHExLt9JvPzn+eM3AE2y3eHWOT2pAwxUfEhcfb6ABMEXwzLXyMKwzknMYFmtUMo+uWQKEKmM6PzY6apoJSiZgcT8+5gfXNIFEVL1IipYlTIXSaHBvQ0ITM979Jx0miFvP0UlpVmrJ+UMZPRNidhxMkjCUrpwVRPY4UoVGudPvBwDL/xJrxxG5YXoNOETgdWVmB1BdY3QLVA+z0rLr4fIjqowdERHB5YJMfeHmxuw9rYrtmLNbjUgP0R7I1iiiqlI0vTVek8tUyprXTd0sLkGhf5TP2qQtYpilR5XNm+dQ2qIhumjSmZYDcxY++DRINwA3uWtbHojQ/U4ANX4eYzlnJqfR0WOmHMHVqqqYcP4cEDuPfADI1HwdBIU5WlfZGO+RoxwqhL7PNSm0vptVTL5SZmIqkezrRr0wrHUETXtOc+U9YRihDR2BGHWD+sYmNtgXju58U+ZrxpfDuO47wXGY1G/G//2//GlStX+IEf+AG+8Ru/ccLcqNVqNBr26/FP/+k/fWf5l7/8Zf7T//Q/BeBrX/sajx6dt7XszOLTn/70O9cmZTQa8Q3f8A0umjuO4ziO4zhPhAsdqQEmqmuWunK+54LTPGL+AhYFoTzumpksYfW8OumkqYVm7WsDEwNvhr/fwmY6l2Y5N7Dz7BBNm11OV4y2HfZ1BRMi72HCXGoEyHzIIyFyQV3Xq5dsn0d5lOgAL2GzqMdY2pg3iOlxRN4PqRir1ET5zO40f35JwEwjNbROaT31QdX3ZyXtY7VHhtcscjF9REwFpBoHHWJqng3M8FjDarosAettuH4Vrt+wlEZLizb7f4xFcPR69l4LpkavB+ORRQb821fh1iG0avDCKly7FIpU71s0x3AMRwOL6pBRp0gUPQdUDHyUvJQqSYL0QyYjNepYVAjENGqle0B9+7i86Xr2nqN0Wjmr2Jh/LrwvElOX7WDRSm+FZd8AfGYFPvwBeOYZ2NiAxUWo1c10esfQuG9pp+7tmXml6CSl6TvADGEVaofJaA3dOwfEmkFNJsfhNBOphY2x62H7+9h1K51/I6xXI0a8pTVVCN8tM1kjo2RS5/d9qX3r2DNONVy2iLU9TpPqr8Q8vwsX6Kf6qcQjNRzn6eK/+W/+G/7iX/yLLCwszL3N//g//o/8X//X/wXA0dER/+Af/IPH1TwnUK/X6ff7xwq9A/wv/8v/wn/yn/wn/vv0lOPX5+x4pIbjOI7zfsQjNc6ZVLBKUx2pyLEE+nnEY6VIkXjdw4R51VKQmKuZs+dlbMyzj7QwbRWNZL17mNBWxyId8pojQgLcKiZ47mKi5bymRptYhPgqJvapD1NBMP+cGgdwXMSVGJ+n45k1m14RNj2i4DhPJEo6dtrEMaM2SKxM26f2p+mmctJxqdnroWTBY0lf1A5tk3iscX9aIb5JTDW1hLU5jdbQTPajZP1OBy5dtpn/O9s2839xwYTzZiuaHAALoSA1WETHYADXti111ca6RRAcHlqB6tHIvt/fh37fTI6DLnSH8br2RrGWiM5f5mYXuxd2Qh9pLKnfVBx9hAnjW8RrriigMZMz5/N0RudBei/kY7e07iKWbu33hGWPsPsfzJTV8/Al7P78QA0ur1n9jJWVUEMjGEw7O2Zo3L9vrwcHFgmmZ4gKdx9hhmFqaKTkETIQnzVplEz6TMhNTUWBHAGfxFJRvYaZG7oWqpczDO3Jx31+/7VDH/SxcTBP5J3up9Q8V10RmaBKS6f2nsfPvMsNjuO83/ixH/sx7t+/z9WrV/mxH/uxubb5oR/6IX7oh34IgIODA37f7/t9APyTf/JPJqI7nPPjR3/0R4um8E/91E/xIz/yIy6YO47jOI7jOE+MCxWp0arV3hGxUmFaQnuNmCYlFyPzf47LFFDqGq2XpiWqEQXvWalqzguJkjUmc7vnKP3KgCjYzlN0XIWD25iQu8V851XDZke/ELa/HLa7hc0Kv4OJnlVtlXuWmhW5SDwkFoIuzZ7O2zkO+1JNCdVQ2GXyuub7UTsuY+bMFiaS5uYM2Li6gc36lpB5i0khHcpjZIGYtmaTs6Nz1bVPU3mNspfOJa1hUNqfUDqjF7H6MjL0djBhWdftKBz7w8Cnl+HFm3DjOhwcwO3bMBrDtatw9RqsLEMr1NlotqDRgHq4uMOhbTMchvtuDI2mRXIMg0rc71v6qsEABn0zOwbhu9HIIg32jqCnZWPoD+0aDULb3wjt3w3tXsSupT6PsOt/Gxu/KkLdIRbFJtmuhonr6gc9M+Yxukr9X0/WUx+n9ShktC1iUVHXsDF7JbTtFmbsyIRdwO7tZeBmC57fgA88a9dofcPMp8EAtratdsbduyFKo2vntUWM0EijXRTpkkcvpVESVVFypair/F4ZhfU+Afx+7DrcxgwbGVSvcfwZJ2NX+0yfl03smdAhGlRKpaW2lVLLKaVdup7aq+ujY405fi6Piwv0U/1U4pEajvN00m63+a7v+i6+/du/nb/4F//iqfbxla98hVdeeQWwlEh/+A//YX9mngN/82/+Tf7sn/2zdDqdieV//a//dT73uc+xs3PeU3acx4HfC2fHIzUcx3Gc9yMXIVLjQpkazSBKpGLyO99hqXEU4dDDRCnJGHkUQJquJ2VU8fndQmlTGpiQmA8hGQIS1kZYapQ2JtzK2KnKzb6EmSYHHE/TNI0mZmhoFnUTS3P1G5hwPC2SRTPf8zRPKRIMl4g1IlLREMpCaBqdsBjapfRDeaqylDom3t/ADJm3OF47oYlFLbyM9XEntOvLYX3VD1BbSuLmSmjbdkU75kGzxxWZkZs+MBltkvaXDI6q/aaf17H6LC9g12E/tPs21jftsKwOfCPwf1uG5y6bmfDWPbjVt+9eWIaXnoHLl6HZtEiM5RUzOOo1aLXN4BgMrN5Grwf9QRDvl+DShv3d7ZqZMR4HA2PP6nHUa/Z+cAB7u2aMjMdmdPR6sH9oy3a6cGdo578KrLRg2I9mgFJZ9YjRTavh/PYxM6QX+uVK+K6PFeN+QCyoPiKmwcr7VwZFLVlHBlKTyXtC0QVLyfVVIe3rmJmxEq7JHjFtlsR7pYS7Wodn1+H5Z+HmDYuc6SwkdTQO4NEjePs2vPUW3D2yaBXtV+2UoXHA8WiLdLzLUK16BiiNl6h65nSAT2FRKF3sGj0gmi153ZhG6BtFm1WZSyvENFSKKNFvQ54OLzVdSb4rPXvebS7QT/VTiZsajvN0s7q6yrPPPsv/8D/8D/zBP/gHT72f8XjMK6+8wng85v/4P/4P/rP/7D87x1a+P/ihH/oh/tyf+3O89NJLx9KD/fRP/zQ/+qM/yu7u7hNqnXNS/N8PZ8dNDcdxHOf9iJsa50wriBISA1NRGaIY1Q5/S2TL07pU1WvIxfMn1TGpsJafXx0TLjewfhiEz6vENj/EjIZcZG0QayHcLnw/jVbY9hOYwC8jQDOpp1FlauTXpYWJxy1iHYRpxkZ+e7UwY0MCqor/lmhiZsU6MX//brLPemjLh8J7J6xbx2oW/DYmps4yNc5CKnRLIK+a9Z5HFKWGRpXQLLG2jvXdRni1wrIDLNpBad06WH+2sHHwe4DlmonOb49NEF/FilZ/ZBmurJuhsbgIq2v2ud2B5SVLSbW3H42LwcCiOT78Ifjgp5bpPjpgd8daXqtBrwtH3Xjuw5Glpjo8NENjPLJl3SPY3bNoj/19ODg0c+Wll+z4v/Gv4bdv27iVSC6T8AoxcudROPdR6JMlYvotmT0Sx5XuKk9ZpneZpw+xPlIB78VkvdRo1fON0OfPY+Nwo22mzJfH1jYZICvhtVqzvn3+OfjQB60w+MoKbG2Z2VMPaac2t6wo+N27cOsR3B6ZebBLrD2hlFO50ZffhzIO0wLuekY1k+V5FEW6L43xy1idkI3QV18P/Zxv1yCaSYvEYvFp6qg0aiSNaFIURhrJlFJluqbnDG5qXETc1HCci0Gn06HZjALi7/29v5cvfOELALRarYnvZjEYDDg6Opq9IvDxj3+chw8fMh6P6XZn/cv2vcm3fMu38IUvfIF2u0273Z74bjQa8Qu/8At8//d/P/3+PLHhztOC//vh7Lip4TiO47wfuQimxoX7hZZwLdE//a+KhCblP68SraqYZnTo+3clzQiTBbfz75YxkXMFEx0bYdkScba1Zibn2w6xPlwufD+NASZ8/lrYfp/p6a5q2OBSQe1UwEzf9VnrrxNTNqVFodX+NEIhjbRphu1WiGnDpv2XVIXSB9h4eiF8vheOq9oSq8TUOkeYWNsKr1zwfRykM9zz2eMlZGRUpUMS6XVQH3cxMfmQGPGUpvHqJts8wiJWGJv4vU+MFKhj5sJbB2Y4rK/C8pZFaiwtweqKGRW7uxZtMRjad1cuh50vLLK83mM87DMah/okHVhNTmg4NMF+EAbheGymxuGB7RMsfVW3C5evmNh/966ZH6s1iy5RCjGI0RyXOzDu2jVfIUY03cXMjnVsXDxDHGMPwve6F9O0UhDrvTSSvzvY/ZpG3iiioZksXwSea8DLN6zuyNYWPLht9+8CZiqttWBlCdbXYG0drl+Hlz4IV282aNTHHByM2N8342hnOxga9+D+FmyO7Po9SK57n1hXo8qATEmNsw5meq5hJlE37GcrvPJoDo1lPSt2iKmvFNXSSvbbx8aexvhe0t9tYkoxGRswaUBon/k5MWVZ+l3VPfVu/TY4juO81+l2uxOmwq/8yq+wtLQEwE/+5E/yh/7QHwKg0Wjw0ksvTd1Xs9lkZWVlruO++eabAHzpS1/is5/97DvL33rrLXq9k/yL+eLx4osvcu3aNX71V3+1aAAPh0P+4T/8h3zf933fE2id4ziO4ziO45S5cJEamlUuQ2PWjNk8VY9mQ3eIqUjS70j+ziMlYHJfj5O8mHbKMpY6aZ3JmdAdbLb1LUyoLJkOHSwvP5iA/7j+m7YU2qcaGUoJlV4DIVFzEfg4NlP7q0ymtcqjZyTay6RRrYEasRbCJtXnVwc+gEUcrBDH1DYmLF8K+3pINM4kQr+JpSA6YP5IDYm2Q+arfQJR5E5n7+d1GCD2gyIH5plFntc10bFqzDZF2phgvUQ0P1TH4RmsLkcLi4AZhXWXgYU6bCzDxhrU6hZJsb1rdTCuXbLogmefs/oP7Y4ZFxBTWNWSm1nppsR4DKMhHB5ZSqtaOMHR2Lbd3obXX4d792F/z1JU9Qe2z9HYio4PhrDcgL2BietH2D1yJ+nXG8AnGnC9adEg3aFFLH0FG2+L2D2W9jPE2h2PMDPiGjbudD1TU6NNjJZZr8G3PA//zjdadMtXvgpfexUaLeg0g0m0CmursLRshtH6OnzgpRqrl9oM+0PefG3A11+z+iRbW3D/IdzbgftDO79b2JgehuvUJaacyo3V3JDQc1j+/TJ276xjqaRWwnm/AbyC3V/5+NQzXXVxFE3WDftdCv3ewYy0V5i8h9Rv6jtFz8wy9UoGhur/lGryaP28rki6v8cZwXGBfqqfSjxSw3HeW6ytrfGLv/iLAHzkIx/hwx/+8GM5zg/+4A/yta99jV6vxz/7Z//ssRzjSfGpT32Kmzdv8ou/+Iusr68X1xmPx/z9v//3+SN/5I+8y61zzgv/98PZ8UgNx3Ec5/2IR2qcM+pOCdUtTCCUgDWPwaF3nXg/+660bv5PwfM2NlLhU/tP0wPlx9nHRFSleBkSZ4IPiIWSS/QwYXGNGK1w3qjdSmezggmSA2IqGYmEEiI3iGm1Fiifd3otdA0kCg8wwXiInf8Bx89N28gEeTG814kzuF8Artdt2e2RCbJ3sTG2FPavVFU6h1lSWR0Telcxsfxh4dxK5CZOWkC6lq2jOjInGZO5WK1UX7MYYud/hJ2bogYgRjT0sPNsYIL0KnB5BB/YN7Oi0YDDLmz37dxWDi1y4+23TXhfXbXojVbT6mx02mZU1Gom7rda0Ag3znhsy2sNWFqEQdvWqdWCSRPMi5UVO263a2mpRkPb53hkZshBCE1oHEK9B3uhiPlyOLcGcKUOH1qD9RXY3ISDvVgLpovdVzK/YNKAuoZdJxkGa5gJIrNK98MC8d6+vgwvvwQvvmj902zA8iJcu27RJwsL1j8ry1Y3o92yvwe9MVv3umxvw5tvwquvwoNd2DqCu10zMx4xWbNiRKwtojRNORp74+SzElQ0iMaE+uFS2K8inErovBVdcRT6Rem1ng19d0T5B0vjf0CMJuoQzdRZUUt5W9rYGB8my9J7pVSLKf1umrl5Us57f47jOO8VdnZ2+O7v/m4Avvu7v5vv/M7vBMzA/OEf/uFzMzJ/5md+BoC9vT1+7Md+bOK7n//5n+f+/fvncpx3k5dffpk/+Af/IH/8j/9xPv3pT09d92d/9mf5c3/uz71LLXMcx3Ecx3Gc+blQkRrpf1DawE1MMN/EBCilipnYhrJhIRFNZsC0//qkYm9p5nKpA6cJX+k6pfRSVUJ5KXpEbR9l60yjhUU1KG/+4yAtZN4mFlk+CseXELqGCZ/Xsdndi5jA+tvYTP+0pkYpWkPfq79LJpRE57Xw9yXgY8R0NreIRsNzwIdr0KvBV0fWDpklytef1zkRVX0vAbsdzr8fPh9QbT6l+09TGul6p2NkyORs+XkoRQLlfVeKUkq/1/hVYXuZVhKRVYxZ6YiuY/U2NsI6Op8xsNGAjUUzL1ZWrAZHu2VC/eqqfZapsdCxCIV62HiMLW+1gpkBLK/WqLca7G0O2Nm1tFSDYYiu6FlqqlrNXuOR1ebo9y36Y2/Pak8c9WBcM9PkqAt7B1Afw+V1W+/hDtwZWCTHg3A+MnDSVFO6Vk1goQZbYxtTy6Ev2sn6tRostmCpDYsduH4NvuEb4YMfhHqrwZ1bQ379X1k0xkoovt5qW/80kqrk9ZqlmXr7bbh9G97agtuDGJlxFzMwjjhuZknYT9NgifTe0/VuhfNeJt4DDcyM2MDurbewOj67HL9HOsSUgqNw3BVisfJrYfxsYc+EncI+UpawiKE69nsgIzKtzVH1jM2jlWb9Nuieq4poKRmzsyI6qiJkhhfnp/qpxCM1HOf9w/d///dTq9X4zGc+w5/+03/6sR3n85//PA8fPpxY9su//Mv87//7//7YjnkWVlZW+Imf+Ak+9KEP8e/9e//e1HV/9md/ll/7tV/j53/+59+3NUbeK1yg/+o/tXikhuM4jvN+5CJEalxIU6OJCV3PYcLpPUzIqmECY2psVJkaqTkgkwOOp+TRehLM0/oQJNvkouAaJqLtF9aHSbEwf08FsRqTbdKM+vz8ZkU25MslQCrKJS/Ge1by6JNG8neH2D83sOu4FJYfAq9hwmuafz81NEoFsasGcR0zS54Jxxxhhs5V7NxvAb+LCaY1LFXS5dCOe8wWUMWs6B2J2huhHWNsrB7M2G+dSUMjTVckNMNdkSnpmG8QjQZtN+K4YJqbM3o/j3Nfxu7X68Q0QqrXoJn8i4QxUIOVBiy3TbBfXIrifS2I9aursLwcoyzAiowvLVkkQ7MJz36gTmOxzZuvHHH/QSxYDlZ7Q/uq1c206IdQnfHYipJTM5NjOLDz2t+DBw9he8vSVe0ewP1+jHIYYuPs+RqsB3OlHjpGRku9Du0GHPTg0YGNh0vLljpqfc1MieHQ2t9sWRTG2ipcuQLPPgurz68xPOzy+le77O2ZuVMPF1URKf2+ta/fg698Bb7yFux1Y1q6t4C3KaeBSlGKvtRE0zWWYaXIoCXgM9i98yZ23/TDtp2wngrOl34SO+GVpmZLa95o3GscT6ONFVa/gv0O7GMm0zZ2v6XHz5+7+pwXfM/vjbTweGqq5hEscPwe6mD3wx7TU+Olz3V97l2cn+qnEjc1HOf9x9WrV3n55Zcnlm1sbPCP/tE/emzHfOONN96pzwGwtbXF93zP9zy2483D3/k7f4ePfvSjtFotPvOZz8xc/2//7b/Nj/zIjxwzbJyLyQX6r/5Ti5sajuM4zvuRi2BqXMhfaBWF3cZmSG9j4uhHw/LXmCziXDIHUkrfycBIU/rktQzyfafLJPalYnPa/lQoLEVrpOJZlWg8S0yuWmeMiWrKQw/nb2rk5PtvYQLm3fDdUli+Raxh0KZsIkHsc+XjlzCqwsTpDOoWdg3exMbFw3DcA0zgTY2LW9iM8jwiZhb5+NFMdxVfVoqcI6zPj5gscl+FzAgdI32HOI6U2kcRJZr1nl/3XKDOx2HJUJvFrHVGmJlzI7TzAOv/TUzgvYzNzG8DnTEsD2BhEAra78BSAxqhpkajAWtrZlDUaxYxMQYWF8zoaDQscuHwcMTS8hGPHllaqXY71tdotSzKQymq6jUrRF5XHY6RrTMcQa9nf/c2zEzZ3ITtHTMQFvtmZMiQu9qC65csFVSjGY0MatHkGIa6H9eHISplFZ55Bj7ycoPl1Rrd/QH7e3aMcTi3vT144w34YGuX8chCU1pNMz9qdTNejnpmxuzt27GaIb3XnW4cZ4fYszKtBZNf9/SaaSyRra8oDW2zQIw2kVF6FPar2iQ9qu+nPjHdk/aZPjPTVFCz0LPgARbV0cMMs4+E79/m+LnnlPojvb/1TOonf6f30LT7po/16VpoY+nYaZum1VZyHMdxpvPgwQMePJh82tbrdZ599tnKbT75yU/yj//xP37n71qtdiJT9IUXXuCFF1545+/hcMitW7dO0Orz59q1a7RaVUkgYTQa8ejRIz71qU8BsLu7y95eHvvuOI7jOI7jOE8XF9LU6GPCtz5DTFtyFRPs7zBZmBomxSKZFvqcryOxvJctLxkaJfY4Xmy2Q0zJMy1KI03hApMpmKqK106LEKgSqCUWPo75q/nsZdHAREbV/jjCjAz9Vyud/a06HKVzTg2NDUwk7GAiampojTAhdyvZ1xaxbkFeh+IsUSvjsM8lYq2Xo2T/Y2y2+h7V16tEGlGUmhy5+NlmcvxA+dzy7auE3VlIyE7PsUQDu05dzMh4gBlHe2H585gp2Q4v1V9oAUtj6AygPuCdtEgre5aiqdGAnSM79uVlWF02k6LVhDt3LCUTmPjfaEB9z6I52m2rx1EPUR0LHTMhhkMY9GPNjk7HzIHhyAyIThuWV2DhgUV0dNqWlmo8tmiSS5fsfTkcg5odpxYiNcZjS3M1Glk7Ogt27EuXYOOldRpXL9HZ3mb57kMWFscMB7C7Z1Eib70Fu3tjGnVLjVWvh2iVmrVvf88KgW9v23GWlmEvRJLItHsQPveItTsgRgvpmah7TPUx0jRpqcGrsbSG3WM74X1MNPX6mMk4zSDMU7qdhUPg9XBcRXmMsWfO88QxmJ5P6Zle9XzWd7nZUPW7kEd+jMLxl5JligZRBFXVsR3HcZyzMxqNuH37duX3t2/fpt1uv/P3F77wBT75yU9Sq9W4fPnyiY/XaDSmmihPkt3dXXq9Hp/4xCd49OgRw+HjnuLkOI7jOI7jOOfHhUw/JTpYapE1wqxuTBzaJoqmUJ3eKRWmcuFIAqoK0ObfaT/6nNfdKM2UXybOWK4xKWClSBx/Jqx7l2ohvGpWcD4jn4r1HidpyqkaJiwuh8/bVEcqLBILeG8T01Clpo7e29gsf4g1Ow6Y71wXwn76he9mRb9Ufd8iFn8+qNj3SSmln9LyUpobjTv1kQrJQ0wnlK6fzy5P01OV6reoRslq+F4z4qe1fwHrlz6TInkNu9aXiUZGk2iEdIiRJ4S/ZXgdYffGCCvwfpmYNmmhY4ZCs2m1KfR5edkiORTNsLhoBgM1K8Td7VokyM0bZmqMxzEapF4302NrG+7fMxNhOLRjra2ZkVCvm6HRbplR0qiH7YYWcbG1Zfu8fNlSS8n8WFuDlfUGtfGIo4Mxvb6tt7cLjx7B/QchRRZ2zG7X2tRs2HuvC1s7sLkLjw7tvrkFfB0T8ptEc0/PlzZmOOjZtQa8HK5lF/hQzdrwbzBzhGyMKJJjhUkzcCEs0zHvcbJ6L2dFhoyKlSu12RAbq3eJBcWrDL1SRJOoEw2bqmLi2g4mfwdSo1f3VjO88j7KjZPuxfmpfirx9FOO45yFjY0N/sk/+ScTy77hG76BpaWlii2eXh49esSrr77KX/pLf+nYOTnvPS7Qf/WfWjz9lOM4jvN+xNNPPSYkTajAbJcYnSGxKBXM80iFVGxKRaZ8tu0YE+dGmPjX57gRou1aRMF5gViktsekEJ2LX6Vojw4m0v6+uqXj+f8FYbE0f2qef6bOm0bovKkxWTC5Q4xWqDI01H+KAlB0S7rP9Fx6mGh6mpneSpEjsyC9NhIiW1i/pyKoUmeV+rTP+RgZVWh852m5NF7zaCQtqyfrKMolXSe9N0r3Qw0TiWXYqM/mMW5GYb1S/ZAxNrt+n2hqaN8Se1PzRtEcIyyN2P2wfBurzbKodboxeqWzG8ZSwwwOGhbh0KzD6qKlrhoO4MGWFeh+8UXYWjAzQv8PXFiwaI/RkFjHI5giVy7DSx+0Nj58aOmlxiNYDCmumk3oD8zU2N21KJB+z9Ybjswo2d6CVnv4jpECtnxn17brdc3IODiw6JDNHdjtxo4ZjWF/YKbFLeAN7HMTi4JZJhqAB6G/DpmMPGiHPnweaNTgShte6ZZNUY0BFeFO77/VsK+TGIznhSIzZIYdYpERenYPmByvaWRG1f2R3w/6fh17/uxz/H4h+Vtjuok92wbYtUmNypw05ZxL8Y7jOE+era0tvvmbv3li2ec+9zlefPHFiWWf+tSn3knj9LRxdHTEP/gH/4B/8S/+Bf/T//Q/PenmOI7jOI7jOM6ZuJCmhgTaA0y0mkdELqVgykW6XEBSeiYJZalwnBsTdSwNkgRXCYbLmPjVDu3dTI4HZcGqFvb1fBMWh3B3CL/DZAH0WaRtbYbPj8tjK5kmSm+jaIgDzMzI29HA+uiIOPNbot8aJsIeMmnoqL/zaIO8DXmkSlXbc1ND2yliZBeL/FGRc62rwsVpOx4nVeeaLk/fSf6uUT1jfJ4IoDF2ny0Tx/eA2aK1zKwu1cZTF+vjNDpD4zaN0tC1aoR9Kd1RmmZuiRjtIaFeonJ7CM2DGClSB9a2YaUOtREMx/CBZYuEuHc31tNoty0Ko1E386DbhYePbL3FBbh6Fa5ds6iKhw/NiOh0YK1vERitthkizQasrtjf7bYZH4O+HePgEPo7VnejEUyXbg92dqB7ZKbIaGiGxt4e3N83M0E1GgahH/awNFN3Q5+9hNUaukw0AL+M3V85B1jqphvAlTrUx/H5mhob6ec8PRyhDc9g9/5OxTV/XDSw9u6E1zzPvNI9MCIasjD5+6ColDR6LP1dyKOn2thvQCO0R5Ezq+FFaKvSdKWGpRsajuM4Ty9/+S//5WPLvu3bvo3f//t//zt/Ly0tFdd7N/npn/5pbt++zcHBAT/5kz/5RNviOI7jOI7jOOfFhTQ1JGpPy8VeErmrjI0qMXhIrBfQL6yfilo9TJhthm0kpq0C18O+9rPj5OlN0vcesDOAhyMrfD5PUemUVLDucLwQ73nRwoTkI2I0A5igeQU7f9WWqDqHZtiPUn0Nsf7uYGKsZn2XrqeOVQ/HSQ2uhbCPA6IJURI50+uRXgcJ60vEiJ1G8q4+VeHkqrGoGdlnieAoRWKUzqHKoEi3kzFwUpMrNTCURm1ahIxE4bSotFD0UgsbJzKvNE66YRulK9N5dLL96pruYCK/6kUoVZYiqBrYOFJkgcbE2hiuDe0aj4D+IQxeNZNjFJy0xXZMLcUYev0YcdFqWZTHzralpXr40GpeNDbMoGiG6I7ukZkUo7EVGd/dNeNCGXEGw2iitEJR9KMjMzC6XTNQxqFweb8PnTBQdU/1iFEDXeJ4HGKC+mViHZs2x028Wvj+Dhb9cqNmbRG58D9Klgtdnw3g42Gd3+X0NWpOQ3qPnqVOR5XZLAZYX+djO70P1YbDZFmPWItkBTNN94jmTx4dpe3c3HAcx7kYfPGLX+SLX/ziO3+3223+1b/6V8fW+7t/9++ytrZ27sff3t7mT/yJPzGx7P/8P/9Ptre3z/1YjuM4juM4jvMkuZCmRlpPQTURSpECJRG/lF6n6jMcF600m1z51NNZy4ccF/+U6mVArItRlaZE70NsRvVXRrbN1zhdlEUqVp5XId4c1f7YJqb/kmC3Hr5Xn5UYEvP852aThGiZGlXba0Z/XtdhTMz5L7NJ0SIpueiviIABdh0gRrtoJrXqTiiKQCJ6XkdF353F1EiLg8NxETVve0lklbCv2hbTIpxK6YbUDpkN04RqpY5KhfUUpY1rY9E4H8JSJB1htRt2gbewcZGnrFohmhppqq8j7F7RtdO1SVP8SOx+iAnSDWJ6uP2w/NURfGXXTADV6lnqwsJeMCDGYayO4ErDUk+tr1vdi7v3zICo1W3dwcCiNvo9ePDATI/RyL6rhwtXT/IMjbFokHbbTI79fTM1VFx8NILeGLpjM0cI10PXshvOpx/Ov4uZCpeJ94LSu5VqOIxDP+5jnVPLTA2RGhoqGN4Ox7mBmVRLoV1ptENODRsHaXq3s6Li4DKwqgy1eSKUqswKrSNTTaT3edq3Q45H2Y2wZ2Y7fKfnW8lgd0PDcRzn4tLr9fj85z9/bPk3f/M302ye/3/DBoMBr7zyyrnv13Ecx3Ecx3GeNi6kqQFRPE7T6KRMi0rIjYWqVB/p9hLCJI6m0RvKr34ZE/b3k+32sRz3yuee77fUxhGWQkazfA8L65RIRXYJuooiqTrWWVjGDI0NTJRMr4PSAz0kislVlL4bYqm6lJc/T8siwVxCOxw/P6W0UiRHSeDMRddUAFd0RyqA1rLPaZ+n55Me57zqbOTt0LFyYyNvp/5uYobCAOubvN/nSddVMm1yNPZK68lYSe+hQ8ysuIQZCQ+x+yhN66N2bWf7SiO2dsL7ApOmhj7rmEMmzQ+lRruF9c0qJs5fx/qrM4b2OO6rFdo5BivmvWcRGg8eWhRHrWafHzwyY+PwALZ3oDuI/aP3NFoDzOSo12273ggOx9Y+RQTJvFC/7RJNDb2GoY218P1XkvbuJv0Ex8eO6gc96tpxXiOasmkaPl0P7WcRq8exEf7+HSxlW6mOyjvnion6pzU0cvNMyOisikaaZfDmvw1VpkIaMZi2J73Xm5jJ08YM0vRcd4mGfJUB7ziO47w3cePBcRzHcRzHcc7GhTU1IObSL1ESofPvZxkbuZgloXZETJcD1okfAD6JCbK/TRSwlHKkQfl4+d8SckfEvPcSQTVLPc8VX8NMlZdr8O3hIM2abXcE/N1EHD0vapipsYyJc1tEcU+z4TeJovs0IbFe8f1ueEFMJdUjpqkSmpFdEh/7ROMjFzgXsBnlEowl/IvcFJg1s3vWuueBxu20IvelZULpiar2PYt5In7y65PTI5oqO9g1vo7VflCR6UWOj4v8fBaIaZW62Iz3TaJR1SKmr0rvP5kauq80pvS3IklqyT6VIq2NjRmA1j68/hbcDvU3ul0r/H3Yg/s921b7lGidF6SvjbPZ+cNoHCj93WGyvQpdKyJD0RlKB6YUVIoKI/Tvl7D7f4do9qUF2NOIni5Wd+MN4M2wftpmtS81nRSxcR94OxxzllkxxAyTaeNO0Ry10A/5eFgh1hJJmXXseX8XYPK3oXSfNZLPioRRezrYuP4A8HXM7HnEpCGbG6tV7XIcx3Ecx3Ecx3Ecx3GMC21qzCP0zFonnwkucmG4lrxU80G0MfH0CpYj/QgTt+5wXFybNhs3nQUv0XqZWGx7Lewvna1+HXimBd9bg7UF6CxYmppm017jMfy/duC3evDFAdwZnl+qlwNsdruEUlGaPV2igQnEHcwUqRLCa1gfrGHnXjJnlHKnhGpfiA4mhl4Grob9bWFi8LSZ2aV2lQyNx0Wa+iYfn9ParXVnpY16N0jr1AjdS5fD+xEx2qLq/lW0xDVs/EnwTmspKJoB7B7thM8ysNLIg3HY5yLwQSzqoIeJ9Nth/VViNITSVT26F9Pg1cI2D7FIqzGxBkgaMTJhalC+bnoWyLyQqaH6Lf3kXTVrRphgnkZTtMP715mMVFIB93RMyLAZhPVfZzLKKzUttUz7WmKyzkyeCq6KacahDI2r2HNwCyuArr6WKTlvJFvVcUvf5RFZVcZlblQPsu0WMcP5WWx89cLy+3O2a1yx3HEcx3Ecx3Ecx3Ec5/3MhTY1zkrVrNxZwnSaI11C5R4mKG5gIpbqOJQMhFy4y5eJHrF+x4BJgbMGPA/84UV4eTXsYxzy7ofCws2Gpba5tArfBnzsCP7RNvz6cH7RsYoxds75DGmYTAtTRQ3rq8vE9EMyFVKhtYYJzDewvtynLDKOmS6QalkbeBETrhVhsklMn6V9lcZAKoBDef+l5edBSWidtrzKqDut6VKV6uc05P0zwowl1dFIUyRVRcc0Qnt2MRMhj17Kj9Vl8l5UX6jWi4por2PmxQo2LiBGQuQz6utYVIJm6zeJKaLSuhZpGqzc0Ejf0/bqs+o2KJpI94aOoToxuVGU7iM3YQnneRXr7y2iwbiE3WMPCttUGWK1sN0VrD/XsWt5n+PRFdNIox3a2HW4Fl4yNWVgXQE+EtqtFFznybTfhmnpqFQIHOJvwyNiH69jRsesYzmO4ziO4ziO4ziO4zjVXChTo83ZxficUtqp0rJ0OUThaowJbW9j9SWUokopYvKURqXjV4ngRxwvSK52vQR8zzK8vGIpbEZj6IcE7QddaA1gaYkJBf5aG/7AOoy34F+NTld8fB7S+gVpX6aicBO7npptLmE0nWXfw8TMb8RE5teTbZXmBWLqqRGTtSIkQKbFoheIkS9bmOCoAu95irA83U5OaWb5tNndENMRpUKv+mVaFMW0CJ/cwCgJpFrWIorlJ2EJE5l3mKwZcx4owuFO8nnWTPoB0XQ44OTnIwH6IHxWwW0ZJVuYWXIvrCNDUdEcy9h12yQWfO4waVSktTP0norieS2WHBVZl1Cemhq7xALb00yDvGg1od3LxPtP3yvF0ybRYJwHRZSth/frWLqlt4CvEeujzGIU2nYDuIkZnu3Qjm3MaFHNk49hpsYeFr2xXzjPszLLbMi/0zMojWh5gI2X69g9NCZGmuXPxFlGreM4juM4juM4juM4jmNcKFPjCiaQnVTomZbGBsoC8azvlENeKI/9IZa65TYmKKo+gITJXKiaR7zNWQC+fQU+tgz1RNEejWAclM96PdlJwo02/D834KubsPWYFDP1i5qQGwv6W4JrWuMgF4A7RBFwgPXnZeKs8gVMaG5g/f91THhPj5nue4yJ1UdhfZlkpete1T2nTQlTx8ZwKshDLJy9O2Of04yVquiV9NhNTHQeYWJ9qVC4ROTcPJSYLlH+vIZOasioaPI8pGL9aduiOhQyDCCaCFvh9TaThepXsOslcXofG0uKIsojMtLXPKmn8mfDCHumyOjRq5etXxWZo/f0eTUmRijJ0FOkyj6xjs28jMI2PazY+0J438DG3D6TKfOm0cQMgGuh3VuY8fiAWLB8A7vn98M5bWDPgp0TtnueyKqq76q2ze8P9c0BNkbeINZKamN9lUfZnPb54jiO4ziO4ziO4ziO837hQpkaKxw3E2axRBTAps3kVdqT9G+Ynmok/1sC+i1MyFJOdaWhymfx5/tohVcNE7qq2vsR4CNNaNTCvmqWZqodcunUG9BqVR9nownfCfxixf7PSp9JEyN9peKucuOX+lgpaHrAK9js7R7RmFAKsDrWZ3vEYuXNZJ/5NdV2KpYM1YJwySxIBcdpxkdJ+G9hgm0PE2B1/CYmAg84eTH3aREbeZTMDWLNlzEmFqcphpaI6cA2OW7apSm6zoNUdD/NrPSzir4qrJ3Sx0wvifuKlgLrj0MsrdJtYlqqqnaU0k6lERxkn3WMAfHekbFB8p5GEOTjMT2uzD59r6iUEccjMcbYfXGa1GIDrD/WsHF8BejUoTmK6ZbmNTX6mPAv003mlcxPHW+TaMItYubASUwNRauMKKfQSzlJtEbVOK5jfX6HWJR+iep7Nt9HG8dxHMdxHMdxHMdxHEdcKFND6UnmNTUWsFm8A2YLVzBfbnOJVrn4p9n2Y6xewwpROM9noFeJoAuYqCyB7j7H89o3gI+swNWFLHKknojEdSsSnivr6XG/7Sr88v3TFQ2vY0LigOrtq6IK0miMXNQupXMZYgKmChsfEiMaxtgAbhHF4Gn7TWdR9wvrlto9S2ycN2pBIuoKJi6n43gBMxxGWMqekrAsk0htmGWmKAKhQUyZtIGJzMPwt+q1pG2EScMnRWK7zKazmAp5vz8taDa9zNPc9FBUxjznnqZBq2fLtUxjuEZMxSZTsMMkMgLhuLGRtkcROa3QXl3PPHIn7ftSVFgDG69tYtH1qvv9AHgVe35dAgYjMxnvMb3eRX7fqdi5aGH3h54bA+z+vxu+6yRtqjOfKaOIm7XsWFWk5uY0k7t0XyrarI6lzXoptF+G7GFhu/xZ0wjbOo7jOI7jOI7jOI7jOMaFMzU6lNPUaNauRMhmWHcLE73mMUJq2eda2I8KAEvIzffVwMS8NUxou4EJVg/CK00dI3Lhqo3NcL5MFMAWgDeZFJ6/sQ7f3ijXfqjVYTy2lFS12nHBvYbV3tCBvx/42creiOe2RCxW3E/64iSzunMxMJ+hXprxrNnmNaK4qoibdPZ6ambk4mMu5MqM6SXblwT2WSlppqWIKRkdi5jYq0iRtO82sQiKtbDeQWF7jed0HKbtKYnDMi+UrmuICfYPwjFyw0x9my9PSQX5/D5I+z9tn8y+Urqrp40xZgLMWmcWpQikPPVUA7umy2HZDjEaRn2Yfk7rapSiyrTfJWwsdcO55MZMHjmS7kPXtIM9x5ROCmycvk61sbEN/BuiKfMQi0yQqZHem0qHthrOqarQu571aa2KHmYMKCXYEdaXDcrPpA6TfdAO693leN+UUJsnTGRiNE1qXOfHb2H39nLY/ibWn13sOTCg+lmi4y4Dz87RTsdxHMdxHMdxHMdxnPcLF8rUkDlREhWbxALQaSqotHD0SahhglSbKIKlYniKxKcD4DVM/BsTc+EPs3XTz9q2hc2kr2Oz9ZcwQXEr2R9Auw2LS9VtbjTM3GjUzdgYDK3WxqjQCRW7maCNCWqLxLREu8RZ0dNmYYs07VQ6Q7wk/KpPtP821g/rxKLI6cz30valGez6vMakMVYyIKbNxp5nWQml/NnjuIDbw8yGZzFjS6mC8u0lhEtI1bFr2WfCOmvAi8Dz4e+tcHyl38qHxLBw3Jw0yiWlhQnUi+F71aFoEq/dQ47X5ZiWekopyJSGaFpfVxkqT5rSWNKyFtY3a9g10XNA56F7IK/7AZNmXEqa1ihNnZUeO434SduT3nPPYKnS8nVnFTe/j0U/dLDxkF43pVBawM67hd3bKj5flW6vtFwp3DpEI67qOa8C3Rp7ipKaZt5NQ9FKShfYnXJ8jceHWH2WfeKzoJ+sQ/Y5vTaL2HPBcRzHcRzHcRzHcRzHMS6UqfGI6lnCyncvoWiW0DULzSQm2U9pX4rm6GKGxhth/TTCo5ZtW0pp1MVStQywmfQSAJtMzoqvJVEYE+2omaHRbFoqqnrDIjYGQxj0oV+DYeYITasxkrd1FRMQlZInFeVOSj5LvCTK57PP38ZEzL1s21Jb02iFVOTuY328xGRqoKpjljitYH5ANIBK+zggnpuMn3S9IXEWf27slSJMNEP8JeA5TIh9iI0nFUo/LRKHU9YxA2WFaHZpJj3YrPicWZEay5jRN8LMtHnqjVSlEUvRva2US4/bBKkyAnQtd4mivqKTUhNB55Q/f6qMIInujWy5zLzSPjTe2lhkzzpRfN/G+l6pt2Yhc0zPivQYy8BVbEzq2TYimgPz0sOiQFSgPa1Rk3PApBk9YP60cTmpOavxU7ofZUo1seu7idUdGWJ9PG2c5mbsPmZ0O47jOI7jOI7jOI7jOMaFMjWmCbGlor8nKSheQrPS+0zOlG4Ta2gorRCY8C7RMDVB8miBdJnoY8KvZhQfJX+nYmaVoN9sWaFwmRqNppkawyH0G1DvQbcH41E89kINro5NtKxTFhW7mBinmf17mEh31r6tOh/10YgojKaGgNbRtuNs+9zIyOli40RRONpPShpBkwvK+fFOwjQRVcL9iHLNhjFxfOeCd0lQHWBC71vYuX4AE5I7mPE2TQSeh7x9y9hs8kWiOL4TXtscLzw+LxL6CyVi3iEfQ7OMjUViSqKqyK/zpGrMdIl1SpRSLb8XZIjOa862mDRj37nXMTPhIcfv8/y5pLGjWkR7nM7AzI+jcxiGdi6HY+h5mhcvn8Ue0Tyb1j+l59pZrrmeTz3iddO164RXgxiFo+Lyw2T70u9AlcGxB3z9DO11HMdxHMdxHMdxHMd5r3GhTI13g1RIVN2INKKhhs1Gv4Gl9bmKCXRvAreS9TSTl8J7FelxNEN31na1UCC81bLUVK22RWw0wjTt0cg+10M+rvEIBgMzPtY68Mkj+BehvaXiz2PMxOgRC/XOI7DOSgMkIbqRrKtoG/0tcX+WoKqIgHnFX4mSaSRBydhI39N2z3s955kNrnVk4kxbv+q70vIuFvGzg10/GXBvM3+h65Mg46FPLOR8l2hMnSZiKq37MSuiQvftPONuAYvWaWHjep66Co8DFbdXqrwqTmJqjLA0Y6o1IVSTJY3eKJktI+z6KX3YrBorJ6Ef2tbDokFEnVgM/CTGlwyYdxsZYfmzsImd103st2E9rPcV7PcBylFVuckxzdB0HMdxHMdxHMdxHMdx3NQ4RpqbX7P+U9KZz1cwAWufmFolTXtVmoU7TSzPl1etA5ZuipCGqtmCThsWFuxzs2GmBTUzNZrNELkRDJCDA+j3LZJDRWvrheOl7Tgi1i3JBdNZpOYByWcVSt4M+ztJnvs0WkPvTeY3XWRaqcBwVZ2O/Dgp80QOVPVpG5vF3cciGbRumvN/mjifFt9Wu/Oxo+v2JiZUE97V1+fJPcw06WLCtYyweclrbEAsTj+LaYZGfo0aWEqra9h9q5R1jyNao5a9l8ivXZ6KLR0TMv6mccTxe1NjW1ENeftyBkzeR/Ok9JoHtX8XiyAaYM+TIybTbF0Eqn4b6pjpfRP7PXgUlqlQuMy5eQ0NOH2qLMdxHMdxHMdxHMdxnPcqbmpUME1g2yaKcvfCsj2s7kSaRkj7GXNcvCrRpHpW+zvtGcM4KJ01zKjotKHTgXbHIjZSU4Mx9ENkBpjJ0QupqBoNWK1BczyfAD0gRo/MQjP320TBUn+DCclpgd0BZnC0iNEa86KC0m1shvk8ufk1+1359WvE4r/pDPZZZkYuxs8jQDaJkQJKg3SEiaEfwqJ/7gO/w/FC7AuYGTLAxmE6rtRWGTUSwI+w+gOPk33gq0SjZRalKIFSjZV5Ka2v6BTVz6hhEVYvYn19F7sGTxq1rSoKqImluLuJGVSPOPn9cYiNs9TAmVXXgWS98xbV9YxsYM/OA55M1MU8lJ4FpXtd9ZAU9bZCTF+4Rox6K9VXmkaD2QXaHcdxHMdxHMdxHMdx3k+850yNJrHo7GmLhKdolnO6ryEmOm8Cr2JmhlLZbBNnSmvbWXnTtXxWlEINGI7g6MjMi1aoodFs2asVIjZabYvCGIcdN5uh3kYDGvW4n2YDvqkBX9+Gfzlnf6j9MipKM8c1G/4y1jdjLA3SIdZPR+FvGR0rWNQLxJRDJ4nYSOtoqFByXs9k1j6UTuakAmJucGh/+pyPHYg1C/qYgSEz7CbwAibE9zDDIzc1mqGN6p9cfF7E+r2B1UKpMqFUkHrE6dND5cwb/ZEbGqUUSOeBahwsEY2eq5gxtIndw0ecv2BfqhNTZZLm4nhulqX32yXsXnkFa/+898gorJum8KolL42B/LvHySHHx/bjRim4ZqV5yymN1ar7W5FRd7B7cS0sPyDe8xAjb8S0tGlPq9njOI7jOI7jOI7jOI7zpHhPmhrr2IzYeaMKRJX4KAE/TzmiNC0Hyff5/k46K7dEKjLe7cNX9uAjC6FWRh1awbTotGFh0dJQ1WpWO0PbU4Nu175bWYHlZdjbh/E2dLbLx51GXtQ4pYXllH85rPM1rJ/uc1yITWtrjLBrNq+Il14vFX1Wu5RSSilvxmFZmyggDpN9lNLJ5G0sRUWMs8/pd+n3+b51rmmkiiIHOtjs9VI/KBJFkSa5EN3GzJG18P3r4TzrRMNHab+ULq0X9jtPSjH161kLbFdFC5ynmK7rpX4aYFEO94j1Rs47DZeOC8cF8HyslMyP9HP69wGW1usG8Amsfs8tYn2U3NzK95f+rWOrWPd+2P+085l2rS9KeqQ2Zrb2OXmNiqrIIr2G2bqq1SLzNv9tKP1WOI7jOI7jOI7jOI7jOPNxoU0NRUIoMkPCUAcTaE8qtqUiVWk5lEXQaaLfeYpVNeD2GF4ZwvNHsLRkKafa7RCt0TRjY33NUlQdHNh7rWY1NZpNM0L6PYv26Pdh0LPZ321OJ/SVkLFwh1hAvWpW+RgT8NOi7CfpM60r4bp07VQc+Xp4HRDTYw3C54eY4J2TXvsqsbgqVVXentJ5pX2+A/wuZkjUKPdZVdFoifeH2PldwyIUlHppCbvGDex+SdODLYR9TCsgrqga7U+mUEopKmUaaXTNedRtaIX2paaPZuWrvaqjMc3AaYXXSSKGctS/pUgeMW15yQQ7wK7fTSy6p4MZNEfYeY3CMVdDu3XupXtinmfWPAZTHRtbA05WZ+dxIwMP4jVU5I5Svp2Eqr447W+DmxiO4ziO4ziO4ziO4zin50KZGppdPCQKVIuYgPWAKCCth3VOEYBQeVyIqaRGyd8yVZR+6ryFvVrhtdCG5UVYX4crl824GA6sdkavH42MZjP0yRjGIxgO4fAQtrbg4QPYCVMTzF57AABghUlEQVTVX16C3zqwOiHnQR9LkTNivv6Yd72cKnE2TxejtEwfBj6CRUQMsFnvqjfwb7GZ8HlRcx1j1qz7/Lu8HVURGzk9LLUQzG8QpPvuYebMZeweWMDuhyvYzb5DLOI9ItaUSFPyyPiQESCxXKZBE7vGe1kbz5I2apbQXkULE9VXsNRSDcwY2gn76hIjXkbE2itKMyYxPr1mC1gEQ5ouaBZ6FsgkWyQ+m3pEo2XaWMnPP/1Ozx5Fh4Fd17zoewdLU7WXnXsJ1bSZFq0y65q0sbHWwwyW80oddlJk1PWJhbmVvi29hmth2e4pj5ObROlzITewlO6qhvX1SY0Ux3Ecx3Ecx3Ecx3Ecp8yFMjU2iELViJgrP83N3sEEzm5Yf5eT5STPRcZcwFIKo2uYmNfHxMsWJuq9zfQZ7yehFDnSAra7sPw8XL9haaR2d2H/wCIzdnfNxFhft6gMgNEQBkOLztjdsVe/bzuu12GpEQt4T2PemfhjpqezOSuaBV/nePqd9LrlZoTGhmaxr4T97GGGRp9y4WjtQ0L4kMnUVSmKPMj7ScvmMTZOkxJJ+1Q0Qhcbn1fDaw0TVf8tNkbT2evp9k3svlkI62hfKuR+E4t2GWMROA84vUAsdE8NOdm908Duw5dDm5aw6/go7EeC8h6xT4dYJMMydv17wGvEVHWLmCnQDtvNS5rOaxi213iT0XTI8eufmxnpdzJBNO6OsD5XdNNR8q7x1mfymVcy1tK/q56N8xpMMrdOGmF1Xui+VnTTHtZfS0wa0LWwTNd9CbseJ23ztIiNZtjv9dAmRUiNsXvuAW5sOI7jOI7jOI7jOI7jnAcXytS4Rszpv4WJd5oNezWso6LdHwFeBH4HEy3noWoWbi6OLwIfBD6GCYpHxNnYD6iOOlBnp0KiojxyE6AqFVYLOBpDa82KgW9umplx1LW6GivNUES8GSI0khOp162mxtqavR8dwfY2jLvTeiWyiPX/HqebVX8WVFA8rQVQw9JGpRE5JeG2hl2fN4kpaRaIM/QfYmMprVORX3vNvr+EXectYrRDerzU0MivXVo0Pjdhzkq6vy4W7XEV+ADwLHbedzgefZAfW6mgesRizjIEGmFfHwp/rwOvYjVTqsRaXa9pYu6Y6vRnVQXplV6ojY2LBmYcbIZ2rYTj7ifnku6vgz0rFogmQRsTodeItUYUzTANiegb4e8H2JjcJZorPcoRRfmy9L7SNmlE2INwnv3CttrmYbKv9FilZ0lqDJ6GIeW0be8Wdex6XyKaYgfhs6J49Ny6jD03PobdG7+N3Q/zkvcn2d+10JZvwH6rZKbth+NU9ZMMkbyQu+r/uBHiOI7jOI7jOI7jOI4zyYUyNVRzIU0p0sXEH6WcGmIFqZVG6PD4biqpynlfQmll1J6j5O8SbUxQ03lofaXE6TIp3OZtkdguMbM/gJ1dSyHV6VgdjcVF2LgElzagVg/RGEElrTdgbRVWV6Dbg709eHAfHm1aFMcsUVNpeZTmR9EN75bg1sBE4zVisWulRzrkeL/n5zPAZkvvYGmnnsXEz0PsPGRcNJLtJVRqfG0QoxS+ho0zmDQ2RsTi34ommVYUeB4xWW2bJ4JDtRe+jhUJv0+8Tm9gxs40lP4qr92hGehLxBoOW1j/yShoYv2nlEY14Jmw7C0mz1WGxIhowpB9r4gcmX7pNZbIfxTOSxFciobohDY+CPuHeI12iPegZu6vh23ESWpp1MO5DMIx02L3j8JxB+GcVGcDjqfuSlOfpQK3+mgBE+g7YVlVsfZ0v6mZMc6WpdElqsXyJKItzkKakkvp/9QvI+z8FsL3XSyabp1Js26eY0C5dk7aXzKf9Azoh+OoZlDpeDLJVddGY0j3j+6ni3ZdHMdxHMdxHMdxHMdxHicXytS4hwlTeWDBI0wEkhC4FJZL1EyRIF4Sikoz9KvSwuxjufvvh31JVKsSn5axugbLxBogd8K2W9m6EjZLqU6OMGH6X9yBzyzC+pKZFcsrsLICy0sWoTEaWA0NgNHIUlEtLVph8aNDq8ExGlm6qt3D2QKf6gVsEwtSn7aI8mnQDPQuMZXMCtafN4nioWoX1Av76Ibl+2FdRfzcx85tgeMRMhIdX8AMlSHHU5rlM+7HyQtOXkA7pRWO28H6fprwnC6XmfcKJvqfpOh1bhC1gOeA5zFD4A7Wb5tYXyht1wZmeHWxe1I1OxTZk7KMmSQjzPDIzbE1oulR6rtOOJ7S++wQUzH1QpvyCIRUAD8KbWxhERs3w+dDYt2RTebrs1FYf5uYCqqBjZul0B6l6MqNBX1W7ZLcgBCD8N2HsL57G+u3h8wmjTZS5Idqi8jIUwTceaXN03EfNyPseh0xWUNkiF27R8TUZrWw3gExHVhKKZIOYp+lf6fGlD7rN+ghdm22iZEWVeZ6Exvrl7CxMsLu10c8XYXXHcdxHMdxHMdxHMdxniYulKlRlSpkhAmQNaJAJCErF0SVXmaX44WOSyJcaixIYN3HUlrVifnkRSlSQ8LhZUwcVsHlBxwXTfPCs/oskXyEmTv/33uw1oFPPw/DpALxeGyRF8NhMC7G9rnTse+GQ6jVzNA4PIRBDd4az04hM2KyNsG7zRi7ZhKHFzGRfQMzNzax65KaUKUZ6qoTIRF8myhGtygbSamAuY3VNZC5k34v8bOJ9VOdyWuofcwj9koUv4KNZ0U2PCCOMUX65G3NDbiz1jdZwPpZx7/NZLFxiHVKwCJhWtiYeki5NsUyZhQdYn35kCi8NzGjQSl5FFkh6tg1v4aZG5rdrhnxA6rNCInQaaSXUkwthc/72HhQP6fXNB//MiT2smXrWDRQBxsvqRmVjkeln1O0VpWJQ2jPVazfVsI+twptStGxWqEtKqS9FI6vc1V00XnwbpkaOs40wy6N3rmBPYPTiCxRJxpp2xyv1ZOa3Om7PqvvHhJNtcNkHzLUcurY/fUMNp41luYxqxzHcRzHcRzHcRzHcd6vXChTI6UqJY+Ern0may0Iic3r2Ml3iYXH4fgMexUb7ibrDDDBtkEUqSVylYRBzeS+T0yns5m0Pd1PqQ5DK3yXFgTeAr7chd8zstoYnTa02tAMU64Hfej1zMQ4OLQojUEIZak3rA5HfwD9utVF2OO4AJ+Lek/K0EhpYCKgUsrsE82GUm2AXGCVaChBXn2e3gh5vYtDbGZ8O3xOIzXSa6aIjBZx5rvW0VgtFRCX+aH1VPh4FROfG2HbTvh7SKxtkhsW+RhKx3J6PU+CilS/DtwlRmfkHGACviITtirWg5gmSJETbWJ6M/Wf0iKl9x7E1EvdcBylwxqG7evEuhMppXENMQpokyhQj0M7VpJ27YX9ShRXxEOaukmRPcvhuz5xxn1+XdKUZ3l6uVZynmIntPMGdi3uUX7elCIuOphBts3xqIXzvq/PyxypQv3ewtqePhf1fakOi/plj+NFwvV8U6o5RX4cJt+n68skGjIZoXcY/tazYFyxvehh98k9YlSH7mmZJnndHsdxHMdxHMdxHMdxnPc7F8rUkLCjWesdTABSOioJVhKsBxwXg46IxYTXKIumoo7NjG5gYm6aw15CUyqATxPzuljaqDthf2kBYolzeUojie1LxJzwog98BXjzNix/EPb3odG04uHjMWxtw/aWGRtb21YYfHHBjI12B/Z24dEWvLFnAqmiFMbZq0SHOEP+rFEAJ6GDzbS+gbVtE4sa0MzoKkNJqWMkWCtVFcRCwipmnefPH2P9fotoSixwfFyls7ch1vwQY6IQLiG/Qyx+rnWXQ5ta4ZzuJvvVmFkI61VFYaSzyEUusp6Ew9COfWanv0qjJarqy4CJ678dtjnAzucSdt4yq3RvlowRpeLSdR8RaxPIFKgyVErtL0VItEKbNMt+kRgBto6ZBA0sekVjUGmxlHYrTYkGk9clrb2Q1tTpYM8dpabrJevfC+f4KmXTVnU7UoMEohEGMV2eap9cJGQmrBDrmGwTozFkeDSZrG0yxq5TGmGU35/7xCigNnEMlmhjKctkdKX9mBrkFD6TLd8Ox3qdaNZBuYC44ziO4ziO4ziO4ziOc8FMjcvEWfKaxZyLcrmYqvoaEn+VPgpMSJ1WhHWM5f1fJM6+1Yz3fHbwPKJTldCrNuSREiTLG8l3msn+APj7R/D9b8PzzwI7MB5ZdMZbb9lrMICjPrTq0GqFfdTM7Giuwj8bRcEdjgtyqQirugk3MOFvFytIrZRQj5OFcNzV8LdS76QpxGQkpIJlaaZzmi5GJkFuUsgMkbmjQtUSGhuFbeD4cYT2pWMNMGH8MjYeN5P1VNA4NezStEwSXdPC1iVKKbhOgyIv5hFXVYB7FiMm690cEO9p3SfpdcoppRw6wvqtVvgOTt4Hh5hpJjOrSRTEa8T+l+GhOg6byT7y6JzcLJQBM8SeR4uY2XqFmBYqrTeyg5mjVWL7etiPiq+rH46INWm07HFHVJyVNH2b+ucGMQpDUWwyMzSGqsZNOmYa2L2U1ntRqjOw+yuv85LSwlJGKWqpS6zVlKeFmzXuZEDlYzaNjvNIDcdxHMdxHMdxHMdxnMiFMjVGRKHpiPnE0wHHi0ZrVm6PsjEimkRD4RImqjUxQfFedvyziMZKB5PX05Agr1QpilBJBfVbwP97B/7ssols4xF0u7D5CO4exPYPh9Abw9oSXNqA3TH8jVuxFkmp/TqG0u8sEFPyrIT9bvB4Utjk9LBogTtEoTgVDmU2KIULHE8jlZ+n+qZPjNxQ8eQ8b/5Kcswqo6C0//S4GocqcH4JM82+RizmrfoReX8Ow/l1iGmNpomuedvGYftrxLoBu4X1qlAthsdFj+nnMw8jJqOZzopSj4nUkNzHDD2wfl0gjqV0m/ReTpelXMGui8wY3fN6RukYlzHTAsysVQovsQB8IHy+l7RHx0xrzjzthkYbe561w9/d8PkK9rx5QKw7U2cy6mSe6DEZBvn4H2L3RZUxJvSMWMSianSNtzHz6KSG97Q25uPHcRzHcRzHcRzHcRzn/c6FMjU2Z69yjCqhdDzlOzDR6oOYkKbZ2kOi0Dhr1nqb6YbJSZCYKpMlLVA7Bu6M4WcewB9vwqVD2GhbNMZSA/aHdi6LbVhchIM6vD6A/8+9cvoakacu0gx01RO5TEw39DhSoygiRbOxlWppWlRN6XqWjAew66NZ8cpjL2G0nm2nWfoSlFXHo+pYefqn1NgYYcbEDUygVSoppb+aJqTKVOkRxdNZ5Oe/jgniPWIdinmYlkqqRJVRdhqUOutpQ23S8yA32eZJP6T+X8RMikfYte0xWesnXb+JGWNHTN5/q9h92SemClMUWD6mxTx9ex7RPiehgaV2+ncw46+LjfUt4C2sjw6xe3gVO895DO6UqtRtMHusLwAfCW3YYjLSIr9meZ8pPdh5mm+O4ziO4ziO4ziO4zjvNy6UqfFuUSMKhEMsGkKC9yBZJ32v2k+KUghJBCtRmg1flZoqPc69HvzkG/CJNnx6Ea414LAB20MTzHd6cL8Hr2BRAaVZyiWUritddzO8mpRrEZwHbUz4XyTWWFBkTQcTFuuYqKvZ7dMMj9JniCmeckFaUTJpKqQBsZZFek3S/ebLyZbXMFPhWWx2fiOc123KInaKUlOpQPhJRea0wL3qe8xraig92Tzrq4hz6Xx07NwEmIYMLo21p7G+gM6lSUyJpHFVNVbEEbHewyNi1FDKALvnFrHxk++jzmT9jzF2n8gomxVVVEJGnn4kdqhOe3WeLALPYREQy1i/qJ6IDGaw/k3r4Lwb1DEzcgX7XXgzLE9TTk17Vlc9H2R2DLh4dU4cx3Ecx3Ecx3Ecx3HebS6UqZHWTNDfj0PkXMLSAm1hIqNytaeMs/ecPGqggUUFqI7HI6bPyj8NA+C3e/Bmz46TtnuMnc8Rk7nqq0S2lKpzPOns/ZMwxPqwQSykq7z6V7C+VE78h8B9Zs9+Ts9jGPabirsqyr5EnMmdpvhR0WfVU2gk+5tlaqTLUvFzSEyr84BY76Wq/V1mmx/Ttt9J9jOveKoZ8RpD047dwszADSxV2FbyXYMo+J9k7CgFj4rTnzVN1eOihvXVMjF1kET3Uo0avXex54FqaFSR1utQoe90THexSIZDYv2XNAVV3tZZz80V4BuwazkEfgcrUP44RPf0/uhj98ItYropmT2lY+t3Aap/G84jcqhGHNtvYunwShEi034bSjVnOtjzbBG7P7fO2E7HcRzHcRzHcRzHcZz3OhfK1FjEhDvVFahhAl6XWLxZs/ZPi2aFH2GzgmUMaCbtPPtXDQYJ4k1M6LyGGQrT0j5VUaoJkaPj1LBojJ3kO6VxSg0NtXXaPs8zjdBJGGBmRQO7vjr/IXbNVzEh8CYx5c69U7Q1NTQuYbPDF4jCaj5rvk8sTJybF3lasNzokJlwC7sWYNfriDjupqFooXz8qYD9PNE3PabXUclpY/2yTkx9VYokIOxzDauFsITNsN9mUnDORf55kUj/tKWhkpGhGfZdQro3YqREis5dhoOKj+tZMcvo3COmjErXHQFvJ59lIKXpp9I2qO3TrsOYGDE1wIzDJudvauQ1IwbYs/cIux8b4e+twrb6LVAKMNU90nNiSIxKOmvKJ/XpJvZs0LWVUVeKiMlRDZCD8N7G7i3dX47jOI7jOI7jOI7jOM5sLpSp0SXO8K4TUwMtEIWskwj2JcaYGLvHZKqpNUx42iSaEguUZ+o2gefDfvYx0atDNGE2qRYvR0QBMhU/9Xd6Pun5qVD21XCsfWIhaH1XlQ6l9PlpQHnqxZiYLqqPneezRMPmNNdbETSXMdNpDeurh8Rrl4uhqt8hI0L9KmNtkHxP8q7tjsL7FWIB5y1mp/bR7P+UTnifN52YWEyOnYqp2p/MB0WvNIj1Rqr6Wd8dElMVyUiToXOWMTZvZNM85t8isdj6acTkVthHO+xvjxhNtE98dkxLiaZrNiuFWyNbT8+ffN9p/yhKRNuLUp2Xqv7axYrYq3bEXR5vdFbaviPMpNzG2r9bcezU6FO6uNTw6RBNoLMatEPMzEiNxSb23GiG9h4RUwyWzJ+N8LpLTBG2ED4/ws6zxNP2bHYcx3Ecx3Ecx3Ecx3mSXChTo5+857OUc3FSs3NPk+IpF6Na2AzgS5jofQ8Tz5YwcSoXvQdY6p0WJlhJ+D4Ir3nS50hsrGNRH8tE4bQkcGmZzJ4813ye0z8V+Gal0nraGGIC4AJmPmxifXNScbqJ5e5/DuvjFWJe+xbR8FChcBVgVq2E1FAbE2fep2KxDCr1/xATPg+wVEF74Vwecrq0SoocOens+VLh6HXMJDrAZv2r8PHbxNofs2p+7BLrfbSwWf5KhfY2j08Ur4XjaCZ8HxPE835pYAbWS9i1fBRepVn8MqnSlGMy0FYxQ6NPNNrS+2iewtXz1KORKZSbeyVkgGr9Uh2N9H3W/T7CIr503R5nJEFuRoNduwOmG3ZDJq9xbgyWInxOUh8mZczkddCYWMKMigVsLGlcPCi0eyu0qR62VV2iPWJkSYlZKQIdx3Ecx3Ecx3Ecx3HeT1woU6NNnNUqsQvK6Wxyses0NMPxLodjj4gC1gOqZ9aPiSl+JJarTR1MEC2loMpnT6fLJIarzkReULwWvruFzZKvShF0GkNjnhRVEvRlML0bvF2xXGKhZuLnSIy8AXyEKIDLCDrC+rCLXfd2WF91KJRiaIlYayKPIFklFo2uE/vnAXGW9q3wWcWcJbaepPj6aYTmtPi7tlctjFUmjQdFWIgVzPzoEftM9IlpsBqYwaD6J7uYoJumRDtPOpipoUgTjcmSQam6CM3Qpmk1YyRAXwc+Gd4HwO9i128QjrOA9eW0Z85JIwUUBTKPoVDHorS6xHRm08xPfZ9GMej7dLt3435O23QaGsTfhiMmn8tVvw1nQZFvquujZ/ElrK/2qTbTD8Orjt1Hqt9D+PtprhnjOI7jOI7jOI7jOI7ztHChTI0XMUFpm8kCztM4bcqRFlav4WbY/pVwzMvEmbWzRMwxMRWNZvSuhu/SFDVaN59BLUHuYXhPU9o0mExLpdnZqajXIor76XFK7ZyWhmpWHy5g4vUSsWbEaWbk5zUpIJ6nIiPm2YdSK2lmtdJEKS3NOvAxLCqhC/xbzGyQQTHCRFKZEXWsH5UephbO9XL4focYwaEc/yokrugeFQjex/rrkJhqppUcIxWaVej4cUTQ5OO2T0y5lY9NsYjVy1gnpq3a5LjgrigXRdTIGGjw+Ohj9+Qe0yMR1KdbyWuaiNwkmlQ3gZebcHcIvzueNEjATLY7VEdpKHInLWA9jQHxXtfs/6rtZFTlz8W8XsW09Hy6zx5HIfBZpM8/iJFEpfRY6kdFwq1g96PSQx0xX/+e9rdhCXt2XMHuma9j42QDu69Vh2kaI+y50cfOYZWYNtBNDcdxHMdxHMdxHMdxnOlcKFOjRhTtpuX2hyiiLhIjHE5zPNWmOMBExnvEiIQq0S1FYvYoWTevY1BC6+az9hW1IfFR/aC8+5rVfQX4ICbA/S4m3qYzstPjnFU4bxJTZDUwce40poYiLHpM1gM4ifgoIykt3NsiGi59TCBW8eF7mPDeD+9pW9bDeyu0R2mVVOBXqY5kdOhmUioiXYsOsbjwHvG6pTPUS+nBJIDD6a9POjZmidU7VEdS1In1AEbYeCqNY52HIgY2sT6+xGRqrvNGtSzSFEul4yiaSfUPpqWJSou3q0D61hA2x3bel4gmj4ywLmZs5MfWmGkk+9PY0P2XFwrXPm+EdtyhOgIL7PzzY9Y4Pr7S80ufX2nkzpMkvYb6nLZrBRuHS5ghoKLgShOmujZV6BmlWh2nbd8mZmooIqzHpGk1K82X2q3nctV4vChpAR3HcRzHcRzHcRzHcd4tLpSpoXQ3Kg47zdCQ4KW0TCdFBsaDcLw0SkIFyUsGQQml8OkSheCSeJjPVi5FTEhIl8mSFqlORfAXgG8lmgJfJqa8yqMh0vaUoiVm0cKiEa6Ev9vAq5xOMFR7xLTZ6VXkqV8GxDEDJkb+FjGVVEnsV12N1bDOTmhXWvh6hImrbUxoVUoyGQNjrA+0XGmBFrGxqXNL+xzOXuw+3XYBuxdUD+S0s/AVCXCPGNVRSrGjPk2vYVp/ZJbgfFZK6dTqxNonB0w3BoRqdKxjZtcDzBzcG0djVXVG9sL7JtUmSZ04biS8N4hG4B7l/tQ1XMDGWbp8Vkq4UpRGahLkwvu8ESSPg7zeh6h6Bj6DGRPb2D2msZ2PvZwmMU2Znp8nPecDzBiTCZU+R9M+nad2ieqGHBHrITmO4ziO4ziO4ziO4zjTuVCmhmbSK1KhCgnyA0wwOk2xcNVWSEkLP6fL0m1mMUu0GhWOAbFg+CVMBL2HCaFpmibN+pbQpjRUC8RaA6lZISGzJHSXzrPq/K5iJsrlcCwV3v5Njs8en4YiKdL2nIfIKrFRRkJeaB7MrEjHldJBScw/CMtlEg2w/l/DznWMjc/cQOsxGQGSptVRup9pNQV0HU4rOKvGxWnugRxFLMH0cVxl2Ckd2rsp3NYws+0D2D3wJhahM8vcaRFrHtwJ638Ni1BZwq5rN+xLqd8kTus6tYlpz/JaDxBNljrV6fQU+SHjQ9tdDdtsUx4Xs+pTlCKDnoYojZTUIEgjILYwk2kd6xsZjjC7/ohM6fxanYQR8T4QuYmURsHMY5zMqqPj0RqO4ziO4ziO4ziO4ziRC2VqzCvMjomi07shBp22wG0VadSEoiBWMJFU6acWmMzdrhRQEsHvAf8cE0O3wysXxqelJEoFtmmpvjRz+gqxJsUGlvrqNnAfE5N3mB0xM0+KJFEqAj2NtE5GKWVSGn2hVEFKZVWqMTHERP63sGuxQ7n4e4qMErVnWmogkaeiOsl41jmlERrzzB6f1pbTGBJjrG8OeffrBTQxs+2Z8Leu52b4W0W+B0wWRFfdnC1i38kgbROLiFelB6uF465hplYeQaD7eJbpN+L4uOoAzxNTLuVmyTyk4+BxpQQ7CYp8yY0Bvaf3bQ8zp+5QHWk17Tjq88dxznmkxtPSv47jOI7jOI7jOI7jOO8lLpSpcRIel2BVFUmRC28NoiHRBu5SnV6mqq0yDK5is5K3w34OOZ5+S0KdojIOiLn90/XOms6IbHulTLmPzZ6+z2Rx4w+E99c4H0G7jZknMhKU4mrWeR1xPApEaYBGHBe0JXK3iNc9F4KVemges0E1N/I+rPqbwt+p8TIPnfBKx10qtIp3Q3CtMzs10ONA5tcm8bquhO903Uo1dwaUTapSlE+JMXYvbBFrrKRt0rhKx51SVMH09HqK/NDzocfxcZ2+V5HfM2cxvE7DtHs2H6M5816Hqn2fN3rOVt2/6X2n638ZG2cP8bRTjuM4juM4juM4juM4J+FCmRrN8DqvdDpCue2VlmTa7FqJV4peyJe1MKHxJha9sIQJ7ypGLZSzv0lZcNd+NaN7GzMptirOYYwJ7HvEiI58ZngjHG+MCaEnFfdKQvg4addOOL6iRppE8XZaQeZ5qWN9u8JklMMiMZVPlTiYRxm0sGskwymvs6DC4BtMRqqcpJZKimpxlLatitaoEnZL0Sb5sRawth8W2vduGxttYrqkLtEwOs0xVRtjRPncckaYoTFItlONFZkBp60zMouqfbewe1GGRh17FlzBrlkNiwDaDe0tmYG7RMOzTkxFNyuapmpMPQ5DY1o0lcZol3h+qWGcpp2axzDU/aUC7OeBjMglYuH7af00YjLaJDU6ZPKuY8+d9fD3bezZnl4z/R5B/G3wSA/HcRzHcRzHcRzHcZzIhTI1rmDizg7nY2o0sFn/a5iYuIcJ9DVMcCrlwYcovpVmRDeBG8CHMNFOQm5uMKxhtSfqWKqoXY7neNe5akb5PAWO4bh4XgvneY04O1ipoaYJurmgmOe4V9TAw2y7PibUqZ7DeQlyymVfx67dARaJsMbJxkMbqwHyKUzwztsPcRa9CjiXUlYpOkd9mNbLyMmjPPLlpXWnmRfTvruKRcg0sILGpYiDUhseh3DaIQq4qqmxlRxLxlcLE6QlJKf1YiQKq0D0iFi/ZNYMd9U0GWLjf4WYgug8zldieosYvTMmGovpeaRtgmhsrmLndS3sax8T0vcpGxr580RmpZZV3XMyXlNKtTXmMRFkWFStu4T1wR7la1TaflqUg6JYSmN+EevDPrNrasyLnitr2Lncx+6jJrG+Sem8cmNG/Snj6gXg2bB8h+PXso79zt3EfnseYr8N52niO47jOI7jOI7jOI7jXHQulKlxCRN4zitVxzrwCUxI2sUERNVTUERDVcqcKjG6jwnl28SIiDwqYhG4Ho6v86kSCEdhnXkF2BrWTzWiSdIEXgR+D2ZqHGLC929iYl3VfmYVsp4mgM4qfHtaZJhIyF3HxMK0WPA0mpiB9Qyx6HBVFEkfE5bTlGJphApEg2Uc2jHguBE2Kw1QTp6Xv4qq/j8k1u+QCDvKtkm3fZxRGxJ4u0zWEwEToi+F9zYxiqMOvIHdNwtE4/ESZh7ouxJVBk0HE4uXOX3R9ZwFrLZFi5jCqoaJ4GD37SHHn1ejsM0VYjHyFez898O+ZOKUxqbMzgNiwXIdY1oUTum7GtX3dxUyGKYZog2mGwz5eZWimPJ2lr7vYPfzEmYOn1fUzUex69Mn1lVphuM1iFE+84wjRc0dYL8NGu89JqMwZHbfJJpaHqXhOI7jOI7jOI7jOI5znAtlamyE9wecj7EhcfMAeBsTy/vEtDCaIawUUCTLS0gw3A37G2PilITLATE6pIMJng8wgXJWyph5qYd9p8J1GxPKboTvd5hMpVJ1zEHFOicV6ZWSRXUpTlIXooQMkzbWr7PSMQmlkhlhhYbfwATLqlnQMqVa4VglwTStlSHzII9waczRtnycpabJrGiNfHzsYDVMbiTtKUVmTEt5dV5Cag8TZxtY3yv1Uh+LTNB90cfuC/FSWLaCictr2LhWhNER1dEzpXRbi1g/PKIcmXMSWpjBcgOLttoDfhe7769iz6m3iREiOSpefpk46/8B0Ri5HPbRwfpGaaiETI00xVHKImbeyCSBOAZzA6PKsJ12/YfMNoYUCTOv6F+Vgk20ifVhZDQvYebwJWJ0z27VDk6IDO5HWPSezM0j4n1Xuv+mnUMfG7sdbJy0sN+GB8Tn9EY4l13MAElNVcdxHMdxHMdxHMdxHMe4UKbGNWx29CZWB+GsbAOvYOLUJlHcztOB5IKwROyqGc8qTDzEBMZ14kzsDrF+xz6zDY3ToHRD6Uz8Q+BrWK5+pbvaP77pBGlO+NPUkVjHRF+l4Tkg1jLocvZZ1UfEc1XB7ipk9nSw894imixVYjhh+QImOB5WHEN9o8iY3MQoFZbPv5dxcoj1kbZJxdOcNJ1Q2pcjrF+ewcbaErG+Q+k65qmxztPYUJ0aRTG1iOP9XnjvE9O2rSWfFQExwO6TDjE6oao/qqKd7mJ9onRwZ2EZ+DD2PGok+z0M70fY/VV1HEU6SLTuEmfkK+JHqanWwrIO1i+KRkjvzZXwUnSOtnmA9VcebTUtRdq8137W9ycR4meZpE2srzew/noLO69lbIyvEyNHNjn79QV7VnbD/tJnS5pqLm93g+M1QWDyHjvEruEedk2XwmvMpNmt57MbGo7jOI7jOI7jOI7jOMe5UKZGAxOYr2CidBpRkc+WzWfKl6IDxphoNStyQDO9l8O+DrNj5/uEaFhALJbdI0YLQMypfp6k6ao0+/cSJgR+ifnTNE0jT8FUogN8BEvvBXa9HmH9rVoeZ00DNAz7nIWun+qkpLUYZhkOJNukwnBVlMO0feapf7SsSUx5pnRWqpGgCJe0homoJ/vIjQ+lN1P0R49oupRMDJgUtXXcea+PBPVSyiGNxUMmU3OlNWIG2Lh4FLb/KHbN7hJn33c4XlR5XvJjn4VR8tKzYIF4HbpY3+vhqogJ9WWPaMqW7sVN7BzXMKFbkV4yy24n6yrq4wY2hhrhWK8TjdWq6BVRMrNOamopAm0du0ZbJ9i2ChnIHWws1Iimn56nin6rY6bHJSyyIt3HtN8GjdmcIRZtUxXFpXuylbRPUX9pWqq8n0dMpvxrEe+zNexaPiBGDTqO4ziO4ziO4ziO4zjHuVCmxn1MsNvmuBiYFmlOxV7lrh9RnsU7K1ognc0vAWvA8Zz+uVic1pRIj9nDBMomlgJpmG13VsZEUU0GimYwH2Trapb3mEmBeZ04A32aQJfvK/1O0RgSJleIwjoczxWv9E51rH/PK/2R2qProbRF9ez70vlI8JS42Mi2K9UoSPdJ8l2p7zQ+Btj5qwaDChT3wt9pZILMixqT90DehivEe2IJG7f52E/blketNIl1A+ZhHUups0V1nZYcRSwshGOlqYNex/pEURB5nz1JDrB7V/eTjIVNYqoo1U0YEdMLpai/VUBd9RXAzlf1XNSvGotV6eBUO2U7tC01+9LIodI4T19qV2ndKhawiKxnw7m8xqR5etqoH5l9Y2xM9cN+NSa3gVexvutgz7D0uVxKa5V+1n22TzTTUmaZCnqu1Immhu6ZUhSf7t30OOk9qWL2ivpJ0/U5juM4juM4juM4juM4kQtlanyVKPZJ4MvFI4lALUzM3QgvpR+6TXWR4SrSFDovYULjLSaNgGl1CiAKuGDi5zqx6Ox5FbcVEsEGVM+armH9o1Q1ylUvAyIVN6uQmLpIrJdwRIwMeIjVGtBs5CMmhd90P6moelpqVNeZ0Ez9qutUNZu9qiDyeI7PMBllkYrFQyZF7D2iMN3AooKuY321RSwaDJM5/asEzy3irPY09dQ8Aum0dFxVKFJj3rHcwYR/GUW6p3X9HxHvjdOMiRqxlkt6P5xmP+n7KOznDta3N4lp0I6IETfj5PgtyinEFrDrrAiS1DAbYffjNtHQ2GPSMJIpon5Xirn0uZRSFaVTx+7fGvacS83hWX2vVGE3sWv6KLQxrwFSRZUhWCOOB0VkNLE+Uwq7MfaMOQrHPiIaTbp3S8fW2LsU3lWc/gEnG2tj4jO2CbyAPQvvUG0gltBvwz72fCTsx9NPOY7jOI7jOI7jOI7jlLlQpgaY4A4xnUs+Az6d0d3CZr4+h4lEG5hIPE/KohKrwMcwQ2KMCYglETc3KuqYgLaMiXFbmIC2X7G9UO2O0Yz1plES9WREbBBrHCwmbdes8WlipIT3G1he+xVMYN3CxFUV7d0kiq4yNXLBTzOczzIjWalgloiip4TRtO+qIityIV9pm7TvUtvmNQhKx8yvp67xAOvDJjbeng/vt4nphPI2ldpxh2gm5eeStj9NmSUzSCbISQTVHWL6nVnUsfGyQBRu81nx+Yz2eZDJlkZraTkcTzWURndNa+sCx1PGjbDniPZxiN1PV4hpkmQ61IhRBimpuZWOh9Ss7YXjbBEjOLTOWjhmm2h4lAyNfMZ/atQo3VmLyTE6bxqqJvG+vkuMppuXqnsRJiMfVrBnzQr27LyH9YWej4qi0z1U2ld6HktYZMl14vmr7s9JqWO/M58K++hifVEySfN6Ox1iofMuVuMpjdRwHMdxHMdxHMdxHMdxjnOhTI2N8H6EiUOl3P2pON3DBMlnMNHqILyfhg6WYuYKscBrKvzps9I+pdEgSoG1igmbDzHBf9rM8TSlzEnSAM2igYloi8Q+VF76BWIB87yQd56XvoOlxbmB9YVSJW0TawwoH34LEyK3mEwxlKLoGpg//ZRE2TQKpo5dJ7W/x2Sh31IaqGlRGzpGus086aaqvp+2jvr7iGhKfJRY7Duvh5JGtuT3QlUfziNUK63ONPIURX3s+s5z3VSLQuLttP2ehEvYeFR0Uh6dUuf4PSuqjjfG7pXnsfF8FxtPSnUkY0M1HZ7H7p+HxLREy+GVmxowad5URcgo+ildnorhB9h9tZ1tl55jamxIxG8m3ytCo3SfpH2Ro4isbSajj2ZRirDLl+n50cb69npYvkt8fqmWxyp2z+uZlkdPpftWX17Cfhv62P01T32d0nkshrZtEA2W3MxM07ul2y5gz6sW1od3Od/nveM4juM4juM4juM4znuRC2VqbIX3vOAxHJ+dDSbsKu1QFxOLTiNcNbAZwkvYDOEDTHRWCiwJhEfEWdxpIepVbFa1UsWk36fkQuKA45EG86DZ6qXtlAZKQuwgtFnH0atK5FUbh2G7+1gUwQ4mDmrfVzHRUO1RKp0qsbyD9XGTySLw01gkFmE/DOewi4mEUF1UelqqsPy7klmR1+So2lfVviFGRigio4GNq0NiWhvN+pZo+4DJa5oK1en10rWtOr/SvZN+r3OrulaKiqmF9p2m3oXOM92njl9q1yyaWGq4b8QiqP4t5THUII6JvH9KxxsRZ+8vYFFay8S0SP3ku0OsnsQmdq1axJn5eQSFIg+U9k1RB3B8bKXXtxVey8R0UUpflkd75ONC171JjFA4wvpjk/h8zO8BmWdpuqoOUXyvep7llMzDktCv/S+Gd7Dni+ppqBi3frx076cRL9PubYj1fdR/Q45HUcxDC3vO1YE3sN+FLeK90A7fHXA8Gk2GzDLRbKoyNOZ5vjiO4ziO4ziO4ziO47xfuFCmhoTBkvhYEmtHxCKwKtp7mhOW0LbDZFolidtKUaO89uls5QYmzh1iQqf2USKPCDhNDQCl9mlh550LZOPQfkWtyDiZFwmlmqGtiBMV4Abrr4fEQuENjtfRSKlhhpGKZLeYz9RQAew+USTvEesd6PhQFstnGTelqIF85nW6T5lceUFxkm20vIGNyZuYKDrExsWd8K5IGInH6qMmsbaADAUZUl2O12dIz6GUUij/3MCE+xpm4OXXrEYs3pwaGiflJKm78mWl7VXT4DmsDxuURfR0v/NE1UAc6/ex+1dmQL7OG8Q0YulYhMm0RjIVPhj2+5DjRcBzA0xGxqWwb6VY64djdoh1Jabdz4qIukasgbHH8VRced/I7FDausWw3W1ONgZKZqBMkxHWt01srK+Ev2USpREYqfmi5w+UnzH5PQAxsmgh2d9pfxvAIixuhX0qMkzn0iYaJyn6bjdss1VYB05ntjiO4ziO4ziO4ziO47yXuVCmxrxitJDw1SemhMrFyHlQnQPV8ZAAJzEureHQYtLUqIVjqphwVZHyNIVSXudA+5lHPJRQtoCJlml6n3R7RWScBrVFkSmpEKproMiYWTUL1C6lI5L428QMjs1se6VsuYKJvE2sz58Lx3ydyRRXmlmeGxCz0i+l5GbAtCiOdN9VArEMjRVspnYbuxaLmMmh8bVFFMV1vteIadD6mCCuGex7xFoD+XmUBP3SebYwU4Ow71KKt9PcQyWq0i3Namdp/RbWjyounUcACN1n4+RVinhJj6HC4Gkh8xxFbqTHUWqhAyZNjQFmJqxhYzUdUyUjpoHdy+thG0XxqB6G6nbUw/JpEVFg128n7OsAu856TkiMT4+fn5MiVXTvK/oDYv2c/DlXMpkgRrkdEqM+ZNDonNSe9H7Kf7jmec6kfaxn6zC0Qc+yk9LDzK7NpP3puE2N3bTWktog032X6udx1Vh2HMdxHMdxHMdxHMd5v3KhTA2Yniu9ni2TkCbhr0t1lMQs+piYmM6AT1MxKVJA4pVQTvg0VU1OSaSDKMg3iTn55zEiFDWxkLSlFtrf43RFmNWeXGTOP6suRl5oepbgmAvlEunT7ZqYSPwJrHaB0mYpMuUBsQA7TIqXqREx78zy3BTIDY18nzpePVumcaExs0hMO6NoGdV/uB6+e504819pap7FzIznsGu7m2x3SIyaKaViS9ucCvilaJP7TBfwz4M89VCpLaW/82unVEXPhO8fEe/FPPKhyjwROt/SsarMyBxFVdwkGk9Hhf1tAV9iMkKh9GzR303iuS4Q72M9DzQOlPJq2nOih6XJanA8DViJtD2HxGLgumfXgG8Ky5Tu7y1iSq3UsC2Rp7xSUXWNY6X0EyWDpHQO6XhPx1mank11O/Y5WYHz9Bh5akPd/0rLJUMrrWOi54BqolTda1VRX47jOI7jOI7jOI7jOO9nLpSpURJiRWmmcxtL17JKFMfOUoA1ndWdzySXqFhiSKwDUmp3KnSlM8XTY6Ti1rS0UTJfrmFio6JDlomzvHcxYfK0qYOqqIfjqNj4vKTmQ3r+Egr1ugJ8A/AxYK0GtXGMvrkD/C6TxZjT65UuOylVqZpyJGZqLOTXT6jQvEwnGUA9rN8G2HXTsVYwkXwlfP8lYoqbtL8ljqb3Qh5VUoreSM9T/XneY6OKqv4sGRJ5m1SH5RLwAawv0+iBfF9VkSGp8TVKlo2Z39iRodDCrm2LOJ7TFEQp+f2bC/BqR5sYzaPoGd3nvfCSuac0bv3CeQoJ8VXkkVfpOO4Rn6OKjnkGG5/bWBqmA2ItjCpBXn3bJY63lfDaw8ypaeTjOt1vfu3zv/WcWsf6S6nbzjLm02dNmmYuH0O5idan+lrJIHdTw3Ecx3Ecx3Ecx3EcZ5ILZWqcBJkFy5jg94CYQuYsSHxKhatcKE2FK9V76GXfpzOY05nKucgroXkbE9/WsZn6jzABsVQzY4wZGCvEuhqLYbt2WLbD6dIIpVEAueC7jkUTbBLTyaTbVdHB8vTvM5k6CibTMzVDu18B2mOrOTEE3sQiG+5wXKwtpYs6LfkM/lygTPefX9M0GkOGlGbwSyjeI14TvTexvpFBdQsbyweY4Hk5rCNxtFFop9AyRRydNqWNUgLl0TgnpcqgnPWd0Pcqwqz7KhWDp13/tI/qxPORMaGi1POco46tmfl97HqqZsNieC+J52prbiCoXYr2UgSD6mgohdwofF86x/MijUKoEWtedLBnrEw2FS1Po7ZSqiKllELuBmZOzhNRN0/UVelerWHXZQF75ryJ3VPnSWmsabmu8VE4fp62LB3HVUac4ziO4ziO4ziO4zjO+5kLZ2qUxKH8HUxgO8SE7gdYKp+0RkANE+PAhEjlid/mZEJtPos/jwoYctxIUSqZUjHntI5GOru+n6y7QBQxq1jEBEelOBlhguglTIy8jvVNLqjNm55pOawn8XERSwl1Azvfh8wfFZPOZk6v4UJor0RrCf/qv1WsX+6F76uOVyXyn5T82o4Ln6uOLyEa7DqktQsOiSlsVFhegvFC+HxE7AMJ7SMs4kbtUhHxPGopPX+ZGurD0xg+0yKmTkLen6XoCn1XQn1Ww4yfFzFxPDVb8vty2jHrmDF3OexvB3ib+SJXFGkjE7KGXacroU2qibFNTKc0IBYAV2F7tTG9Lio8rnPuEp8TKjCu67nHfCmlTopE9mbYt4yYI2wMqj6MxnoeIaFz0b7SQtoNYq2OJuW2KxImjYDIo9qgPOZr2TYyh25hffU7TD4HG5iJuB+OuUg0IGbd52l7SyZo2if5b4NSl6XrnfUecxzHcRzHcRzHcRzHeS9yoUyNUqqR0t+awazc7hL7UrOiiaWsuYwJRyuYWPjbzE7NJLEqTzOSp8kpCemaRZ4LXOn2EixTg0NsA1/BhLlppsERcfa5UrzsYkLrNWxWdB+4nbUtzdNfhQoGN4gC5nOYUaKc/iVRr4ouZoKsYMKmctAr5/8+0dSRiAsmTA6YNHw6lFNznUXkVWSCojGqjLSUfHn6WSK5BFzVBgFL5fNy+Kzve9jM+KuYcLyGnfshVpshbWeK2penXyqlOyu1swoZI2edRZ6L0SUhOv2utL2Wd7B+SY0bkZuHVbSwMfyhsL872LicNzVRbnLpXlYxe/W76pV0set5FTPm7jOZxi6d0d8mGq897P47JEZFqAbNtNo981J1HXQctV2mUhPre5mnikLL95mekww1mXYr4bMistJr3sauy/PYs+AuxwtrV12ffOxoXDwiptFKDUfCubxMTP21hF2bLzN/Wr30N6IqCi9fJkPD0005juM4juM4juM4juNM50KZGnl9BKiO1tCri4llz2BCZZo//wNh+SImbG0DL4TPmzPaUpqFWxIBc6bNXi6dT47Ey1m8yqSAKBFxF5sxvsBx8WxeMbRNTG+1GNp0Lez/AdZ3EmfnNTYWsbz8EjaPkn3ldQ4gCob6vBReY0zsTYuhn4ehofGU58efx9DIl2tc9pm8AZuYwP1s0u6j8FohiqzrxLoDv54cS6ZL1Sxvfddk0qAh+zwvEuxzUXgeSuOiNLN+Hm5iRsQC1q+a/Z8eKz+O3vUskBGyiwnXm0ymMzuNeTPCrtN22H4dMyaWwveqE7OImSdp2/L9dMO6V7AxoG3uEWuq5EL/WSmNI11n3RN6vx/auE85SkP7Se/fOtYXN7B+kXF4BTM2u8T0Vi8Bvwfry9/C7glFsKRG47TzSNEzZgn4KPH5OMSMpucxw7uNPU8aWP++zvS0ffkYyc85XeckRplHbDiO4ziO4ziO4ziO40QunKlRStmUi6H1bN0mJpQ1iIaA8uUrpcgellu9gQn0O0wXCKeJsmn70vZATP2Tz6JPSfeTGg8nEZ41k17CM8SCxZr9f5Ji3qKBibMr4e82dm77WB9uEk0NmE+Ma2FCYjO8K/pC6VnSiIB8Bn8DEz2vY0LvPtH8kPh4GsFe+5fQmYv26fVTO7TuvJEb2of6sB7a/3XizPxdbGZ6DRNXwQR8Fci+gYnvOsY0UmMmLcJ+GsFUM8tVEPsstTXya1r6PO361TAB/B523z7Exs3l8P0usX5J+kqjomQ03SH2Z6kdJ2GIifBCfb4a3lew59IOxyPJhM67G/a1gV13sPFxGbtXepxujJ+UvA/VRtWGSGtv6LvUzEvNSIj3Sy1sfxBeMmpU1LuJjf8WZqguEevI6PpN+23Q8jy93yJmIu6FVy3scx8zVIbY/Xcf6/eHnK4WkY6Xtg9iNJ3uy0a2jZsZjuM4juM4juM4juM4x7lQpobSn5TSc6TiVVp4V7U1Hoa/t4lC+S4mhEq8eogJaO2w7ID5yMXqdIazRDnVQsjXSdufi391THRTXYxN5jciWsR0TioKLjNlExMQTyPOqe4IYX9K56J0SIfECIR2OE6V4K3zHWJplDRDfoT1lUT8UiROug/l5m+Hc5Ihout/WkNDaXBm1eqASWG/KkIiRzPw06LWd7AxuUSMyLgT2tDBohJaxCLHLey8B0RjpaqtQiJqmupI7da4Uwq3EnVMWL4SzuE2p0t5NG12/bTvcu4B/zy0qYuNxYWkfRr/hOXpQ0/324DpKYzS9U9LFxvnGlN6Fg2IKa5K5hdhnX0sOmcPOycZsrPus9OQCvClMZKbQ73QxjaT4zB/T89R16ZHjER6xKShrOfJOHyX/gak+4Hjvw0lIzS99kOiaahIFxkLW9izs4P19z5mIC0zadpWMc3sVpt076qmThrBkfabGxuO4ziO4ziO4ziO4ziTXChTQyhlTJ4WRYJsnsJGhathMmVJj1gYXAL4PiYonzSveSnFSAMT625iwtVrxJoVabRGCZ3fEpYmqxHO44D5hNUWlspogVhY+ggTgNM8/zqWcsvP2vc4nMuQmC4mrd0hOtis8kfE+h41JiMDauGcVP/kEBM0c9JrWorMUeqwfaJAWBUxcRJ0c1SJxblQqboJeU2PaaKkxqeQOaD0UromHSwlzoewlGnaRoaO+jbfdzoe4XhkhoRg1XpRuh8J56U+bBLrsmwxWZflpMxjJOjvqnWHmNC8Q6w50iGagHvEfl0I68i0kVlQZcrks/vPygFx3PfD3y0mIy3ya5Zu+zrxXpYA3+HsdTRSUnNVY6jqh6KWfC+jtB1eJaM2P6ceZiZsYv1wmH3fx+6DJaJxI2NH93lKeiyhZ5TGftquI6JpqHtK53E/2ZfMcaXtmyeKT59Fev4tYtTNI8xY0TMVpj8zHMdxHMdxHMdxHMdx3u9cSFMDyimFJATl4u6ISeF4nCzXd4reOMJEztOImOlM/TomNipnfl4HI60PkZKLYT1MvFO0xixBTaRCfy9pX5qGp4EJwKtE8VAGyDSUqqfUXlEn9qsMnkWsT8bheGmR71nH07FKx2kQIztGyeu0aaeqjj+rbW2iiZQXKldb05nlWtbmeOF3pYjSOpewsTTGjKkeJroqbVE6cz2f6V8nGlgdbBxpVrrWb4ZjrBKF2wPK12dAjKjZTdo5i3zGeotYNLtqfZGmiSqt18IiWy5jY3wXG6OqvSAWsFRlGiP6vkeMXFkI66YF6KeZKvOi54LOWVFPugZ5lEGO2piiNHrnTRo10CfeZ3mUBkyayTLhNOZKUScpDeI4gOMGkp7JD4jPDD2nSs/BcfaeLs+fuTIruoV1FRGSjs8u8fqdhNxoUfTetfD3gMn7sHQObnI4juM4juM4juM4juNELqypUUKCcVpPA8qzvrV+OsNdItK8xXbz9EP58jomxL2JCXFq1wYm5O0SBcO0bamAeoiJ2OthXaXKUoRDFQNsFn1JhFM/qVbEIlEw73IyQycXD5eI9TbSGfLp+t3wmpbyJ99GAmQatdEIx1NdAR0vz0t/GtIIinn6IxXIlec/FX6bmIG0Hr7bDN+tYSL6w/Aq9cciNmbuYfU2JO4fYeNDon4p3Y72dwkbO4pGOuK40SeD5SE2XqvEW82W3+S4SVOFrpf2uYCNkz2Oz85P0fmkabFSOlgfXieO4a9gY7/ULhWgvhzaMAjt2AmvRWJR6F2Omy6nNTcUddUk9v0w+75knuSpk/T3eUQiVbUzJxflS+s0sLEj+sRojdTszdvcwvpc62lMaz1FEKXt07KTmAsap2mk0jTToBRtoX3MGgOl7dJ96hwOMbNGEVEdbBynqbBOW/PGcRzHcRzHcRzHcRznvcx7ytQQuYgEx0XmWYJgG0vfdI+yeDZrX0rTJAFfAqZy/deIs9wlJudmi8REFTpWihrNgm5yfJZxevw+k2JpjsTpAZYi6ySk6aRUz+EyUVhWsWadh9K3qObFSUkNDdHCUnM9j81432JSzD6r6DtvO3WcPO1UGjWxBHwCa6+KWitVj9JvPeJ4m1VYegdLUVMVrVASa9X3Q2JR6jeI6cDS/lSkjgpSV40rcdJIJrVDbZWYPa+hoe00k1/n28b653p4f5vpkVaH2PkpRdUq0TDcJtZ3kWGTctZojTbR6CwVnk+fUVVRDR1sjE3rt7Og47exa6Qi5FXrCRU+VySQhHtFzMDkfZm+GsSIE+1HfbCAGXpXwvs21WnRZqFUa+n9kTLvPlWrKC8on+6z6rdB94FMs/1k/RXsGVrHxt+AGGF10nSIjuM4juM4juM4juM472UulKnxUng/wESfkuicGxpVqYvyWbp52pMeJpCWyMWwBpOiLcS0JulxVoizwnvEGcmKmEgFsbTdab2LdNb0LKGrJMrmjIhFuudNrVLHhOBV4qzitOZHDztHmSpqyzyzy9vE2ey58JsX4pbg30n+1rGeNjQ2jrBxdR8TLCX8poaGru0K8GzY9nVieqQWccyIVBTPUdROAxOFVZclHWcqzj5vIfrTkJ5fL7SpKnVVHhmQXvNGtmwXuBW+u0dMxyTzRPe2TMHdsM4qNtZ07g9Cm46YvHdnmRlKDVeVEknnc0gUqavWqVqm61u6L9Qf0+5dpXk6mrJOPXmvY4bCGnYva99VUW/abhm4gaVWagNfBt4iXuf8GdwnFgOHGLnUxIzAK9izRiauzCz9cK1g/d8mpvPaqji/NFpDbS+ZDul3peggGRLzoLpD6TXTeBNN7DyaWH2aNO1bWmfDcRzHcRzHcRzHcRzHMS6UqSExSoVo0/RF+Sz+VLQtpePJP2v7eQTxWvZZM7BLKX2ExK0djguTpRn2VWloUpHzpOlX8rannCR6QiKdzBZFKCgFFBxPrXMSoyHNxd8mpmIR+q5LrDmiCJB5I3EeF+m1S9twiAm8X8ZEV83CbmPnkNZvWMWKcC+H5Q+I+fyXMbFXBbC1jchFWQm5t8Jx81nyFN4fNxLnpwnsJXKTQ/dsl1jUWbP9JeK3ku1Uu6QetrkVvh9hffwW8X6eV0hWWqkFyjUvUhRtVRVFoposinDK69KUTMEF4AXMDPjXTC94Pq1t9ex9zKThpJRPJRNAY03RLktYVIWKtqeGcTom4Xi9Iz0nZa4tJfuuY8/PR6Fty+GlOj1g/aEIE5136XeAbFnV2NdYmjclYXoMRdaMOV7bJX/W94iGcH78ae1zHMdxHMdxHMdxHMd5P3KhTA3NZh9wPApiFRONUqG0KlqjJAJLPMpn1abUsle6z1n1IfLZuaJBLNQ9j7icz66fxTzCXcq85o5mu6f9JeG1ZJxUGUs5Q0zcvYkJo3Xgq5TTASkiRDPuN4jCdVWx7seJBPEmcYwKFThO+7ZPWYRWWrCHmBkigVf9q3oFqq+iY5ciC1IDY5AsS3k3BdM61j/peJ+XUhq19L6tYX2iWf35+opokLmYFmjuEs20vC9nsYddo3mMwaqaDIrMWSfWhWljz7MtolmTi+uKXBgQ+7XEtHshjYJJ+0z1RBQZoWiI3LCEaFQcAL+DpUq7jt23MiUbyboQTRr1h+qj7GHmyG54VzSNUm91iSbLEZPPoX7WPhlFh5TT0uW/Cfn1T/tlWqRZ6bdB+5v226DvS9etQ0xP5ziO4ziO4ziO4ziO40QulKmxE95z0X0Ry0WuNCZV4lMqWE2bmVv6XilZSuK8hKlp4lO+vxqxQLdqd+xUtL2U8qUkvk07dinSIxf30jod6blIDNaMa4mdpboApfaehAXgReCDoT1vMX2QpsWyr4Y27GMpnuYxNWRE1DhulJ0G9U8eKZTOTp+G2r9faMsYO9cesSaF0pzpepRmo1fdC09q9rdE6lnXJx23pSiNqvRUubhcishSCq70flAh9fQZURK98zbOqj9SOq8cRa90sPtNxsYSMRopbVtaLP5tTl44W6gv07/zvpt27tom7cdDzJB4QIw06XDcOE63r2Npqz4MvImZIYrY0P71GWINjoNsX+k9pvR0a5hBWEqtVjqnac/I0rM5H4/peclomXbvl35rVrEUXs3Q9pIh7jiO4ziO4ziO4ziO837lQpkaJWGohaVfuYyJP9vE9C1iljA3S7yqEWcZV83oLdXVqKKGRRW8iKUZWsGEt1cxMS+dfV+1/bTvc/LoFC1LaWGzxNMZzWIBE9CVDufRnMetQgbKGpY+5j6Tot0ysV7HbcpRGhCFzi1i7QTNZp93dnMHiwoZYLPLT1PEPG1PmvYmfT/pfqZ9JwNNaZJSUTcV43VsrVMVRfNuc1ZDJTczSuaG/q4Sk/MaB1r/Sab7OSBGIaiWQif5Pj231HyV2H/SGf3p+ZaiYFIkzudtyccaod2qGZSvmxoy6TjQvdvBnjcasyTr5Ndy2r1aw55nz2HPLtX+qYoiq3q+zFo3Nbvz3xmtp8iteUzNGhbh8sHwPsZMntfn2NZxHMdxHMdxHMdxHOf9woUyNUqsA89gAu9t4mz7fPZxVRHlXGAtCU+p8JYvV+qXdL1Z1IhFcC+Htl/DzIJtjot5VQJ5OvN5WuSG0seks7zzmh4LWF9qBnuKZpCvYOZBl+kFpdXXJWNJy5aATwAfAL4G/MuwvB3edzCT6h7V4qWKnB8RBVqlp6kqQJ0zJs6EP+80L9NS2pwVjVsVDM/7t8qUS7el8J5uM6u9+bGWKBeyTlG0zzwCr/atgur57P78fZZhU7oOs/rh3USGlZ4naaqlPDKltO1JKBka01IoQfkZmhtHikCDaGpoH2m0Um5ujLCIk11ifZyz0AAuYdFbXeJzNT1ntSNte2k8TDO8lVarNP50PfPjTEOG97XQfhW4vzPn9o7jOI7jOI7jOI7jOO8HLrypUccEqy5mClSZEnkqoPQ7vc8SnvJi5Ol2JxHE04iC3bBMYl7a3lmieB0zIyAWoq06h0ayrlK3KC3MBhatcA2LkPgKk4LkkNi318M6pYiOlGmGTINosnSAj4b9y9wZAa9xvEB4iR7zGxglupiY+jg5T0NDqWmew/rwrbB8milRKywTVWL+PO2tY0bYIja2+pj4Ou16DDlZmqQ6FtGzhEXslPZdZWhUzb6fdm6zTI803ZCMlpMI8Ath+8NZK85oX8l0mHeczTI0yL4rHSddlkcnLGAG6C3ic7FkYuRGRw8zMqtQ3ZE2ZqpO60OZzfex54jGjUyIqn4qXfv8c+lYVYaaTMd50W9Djxh1uD3j+I7jOI7jOI7jOI7jOO83LqSpIQFJ4s/t8J4XCU/f0xQhVetMm60tYSo3Nk4iNtWw2bfrmJEwJNbS2A2vvIBynl6H7HMLE/oeYuevmfBK8dTEjINFJqM0dIxngGcxoVwzjq+HduWi4VZYlgqRJ6UZ2tLErtsuJtB/PNn/Xnj1OR4d8zShCBil20nHyOM41nXgZWz2eRvroy2qRc8qU+w0BkaJMXbua1g/bM2xr5MeS8J3CztnGSK5MH+S/aWfp/WLlkmYTs0MRRQdFbYpITPqcvj7LvPVSSgJ5lXREqXog1I70n2UDI15Il5yYyU9rup/tJg0NXJD46TPEJm4HWy8jZnsf10fRWTsEesspcdKr7ee59NSUM163us+SPvlpON8AbuPLmPP831i5N4eXlPDcRzHcRzHcRzHcRwn5cKZGk3irPA+JvYoXUkeLZHXpiiJzSVBs8rUKAl52v8sIauG1Yp4DjM0lDf/ETa7XYZC2oZp4mKdWHB5l1hHRELdImagbGDiYh8zT/aZTM+0FdaV8NdN9lMSSdOiyHlKoFIb20SxTwYLxPoBW0RRfotY9Fti40mjMBrEItoD7HxPa8DMc6xFTITUzGoZHaXxeFYU2dLAohbewq5pnq4s36bqO+b4bhqj5Phj7JqeJGqhlKaodAwZdlVi+iyUeqs0a36aoaFtUmFe4rVqJMxzvk1sjFzBnl2qgTHPtZn1TEjv+Tw9VJoaKj3eNDOoFJ2RR2Kkz7vUUNF5KBonj9JI9zFtzMqAzp8tilpoJn+n7e5g977G4R4xYiMlNziqIjfy34RSlI9ML0We5f0xz1htYWbGc1hEUg0b7/ewZ2Knon2O4ziO4ziO4ziO4zjvVy6UqbGBiTuafTtN9M6Fq2mzaFOxqko8amICfVoPIN2fRPgqAV3it8QvmQz3MaGuHb4vtTMVyCSaL4Rt+kyaGrVkH/vhfZUolOXRIErPspDsW+ehtpb6uI4JcBANplzEb2HGioS+hbBsEI4hM+UWZ5+JrNQ0l7Fxsoad72uYcVTjdOmCph3v/9/emfU4clxZ+CNZa++SbG0ejezBGIMB/DIP8///wbwObMgDyx5rbbW6Vfvuh5sHcRmMTCbJqnZTOh9AkEXmEhkZGQWcE/feR8Q17FHuxSqplfrIbc1j7SfgT91vP1HS02RaY3xVE2AVLpg3ulZBhpeiXPpSSN0QY1m1NVa5ngml0P3rdC6l+dE5+o5ZP8/q71XMNhW/PqPUgJFJM8SyiJ+W+aC5SNFZZ8wX7K4jNPLnvjRKrbFU3696Dq3nwtZ83GKHmK/uCFOiHt/1eJtQnkFF62keGnOPcv2fsZF8mRwR13r287PcZ+AcUgwZzdOvgJfEvPi8Z19jjDHGGGOMMcaYXypbZWp8TjEBVqUvImPM6vYpITjVqaeyKKjVvkMmyxEhyD2i1LWQ8aAC53U76tXpEvGU8ueMELihpHu5oQinR93vWsFct08mUY6QUMHtXJxY6Ls9Sq2Da9oi+013vt1uu8eUosdTiiGzqckwIQyN3xGrnZVKSwLnXvf3I6Kfv2H9mgZCoq1EdonlNywKujWKJplQUvVkNN6uq99OCQMoH19GlM6ttqmPl0UQzSjRH+fcjykzhgml2HxtKrRE5j7BvY/83OwQY+9HipmgFFK5j/Lzptdutc06ETi6xyoCPlagzoWtqdqZv6sjMlSH5CPi+fqKxWesZW6oT3KxclicM/Pn2kjNba/v4V36rQ/V/llWs0fsE+nzdoG/sN5cMmR219Tb7bA4RlrbDJmpV4SBcdYd64L5/w0/sWX/qI0xxhhjjDHGGGMemK3SSk6I1btCKVdatFbdSiQcEsskCiplk45xSQh9EqP70pD0IdPilGLK1EKgjITWCu3crj2KYK80TrtEke0TFgvjKg3LMjFV16DrqK9nSgilat8+RRCfscgVseJ4RojK07TdBdEP92VqPCaiQh5T0gNddX9/Slmxf8vyIsNjyEbUQff5iuWrw1UD5f2uTa+7V20G9a3kz/fkPaLOxh3Rl28oNQb2u/NAieSp269omU+7Yx0RkS1j60Ssi4yUXIcE5oV2IbG4LyVS/rsVcZHNxON0PkU27TAvSOsZkPnwiDDJZBi+YX4OGkNtEmRkiI4xk7KJoH1V00XzgaK4PqSYwC9ZTNXUQuniFEk2JtqnZWbkfep5Z5lRcUmYT7Wh14eiIOoxLgN6jIGUzaOhfWTmqn+0/RnR74pyqxkqFn5LmYuyyVf32X2nsjPGGGOMMcYYY4zZZrbK1PgbizUdFFUgWsLokKjWlxJE4vgpRXRSHQsJ8zqvhNAhEa5OzTJEa4X2LSFA/7prQ11PYYcQpg+6tmRRcqy4twwJcM+JfvmBeQOkdf1KofSGIsKqrsd9CXUSE1V4XAbWTdde1S+RKXNwT+fNURIyvfKK+cxdt+2vgN8Dn3Vt/YLoj5bpsIznwL8TIruidmS0fAj8B3Gf/kgYJ3U6IJ3jQyLK5YLooz822iNa1zWWGZEa7Gl3rlcs9lfr+W2lRsompf6uowJyGiSJ/Xn+UB2ZWTpONjQmhKnxPmHCHFOiou4LpR8aqv1SmxkZmWSXxLV9QtTu+C3wMcV0y+ndciRaPuYNcY2K2Nml38wYIs83Of3SGJPijtVMtTMieinX8NBzmQ1afV+fS9+32taKStmhRKcdM2/I6n/DTbUfPcevGfP/wxhjjDHGGGOMMcZsmalR5+6/YlGoqsXQ1upt6E+pIuFql8U0QnuE2XFLEd7uW2yqxdmcGuYxIVQeUNJKCUVF1ELeffO6O+9Q6q5DSqHus+5dq9GvuF9DQ1wCf6WIjBKqc+5/CZ3LRNN6dfmEELVlXgjVRVFR4r6UPeIA+Dfgv4APZvD9LXxxNz5qRALxE0rKmv/rzv095bpyGjJFIuRC99nQOCcEbxkyL4jURa8a5269w/h7eUARhM+6/VrHq88Ji89D6/s6zZE+3xHX06rb0ve8TAjz5TPCKNgh0jh93dh2E3RPntNfa6Oe0/K7CtRrbtwD/rM7niIvcnRZ61j6XmNA4zgL9k+6YykiaWhObb0/lCh/y+L/hTsWI6ZWSV/WGkdQTO19Fk1czXmXLI6nMYbG2DYZY4wxxhhjjDHGmC0zNVosE3v6RNCh/SReafU9hJD1CfCvwJcUgbTvOKqxsYqg1RJo828qmFynwNE1/tC916lj7psxOfGfEf14ThHjVeS3JfxtglLWtFJ5QenPa+C7JeeWaXDWHVdj4TGL6aW0qvyacUWJZZhNgPObccWiMxKfVVT4JfC/je1UfP2ku54s5rfOd0qI/iq0fMh84eMcNdRKjbYspZs46c6V29BKtzYkQNdmU1//5ef8nPHFzFXc/jERFfU5EfmgIuXfENdwX+P3kIgE2SOMymXjE+b75or5Pv2GMF9+IgzI7ygRXa1+zSaq5pjW+XKEgiJc+sgmxhhDY0I8c0qn97ZY9X+Doq003mXKPSHGyXtElFPLZBHTtP/Y/w02NIwxxhhjjDHGGGPm2XpTYwy1cLds2ytCCJVQrVXbSl30iP70VhCd+jEhev3AOMG7bmd9/BtCeFYB3XNKjQrVBFCR6jraYxNWPY6Khuc88yooLlPgISJJ+tqYjQ2IfltWU6WO9pjQFsa1un2ZyCsugW+BPxPGhKI8cl2Hy4Frodv+dfd5aLs7wujpi2SaEvfkV0T6qQlhOuRxlYvFt9JB1X/nGgotgVziuK5jiPwM9EUvLRvnasfY4ucT4rn9A2FqSLB+AjydwuEEjm6in14NnHcsB8ScskeYlLnmxxB90WcQbfufdMw6XV9rXxkeQ/2oeWeV51fXMtRPM2J+eEFJofW260eMMbr1+xXF8JRZ+z5xHyGe6yP67+NTwiR7QzGhjTHGGGOMMcYYY8xq/OxMjT7Bb5WICeVLl0kgUfsrQszMtTT0XgtijykC11hTIx8zk0Vc5d6XoNYnbq5ramRT5QkljdQxw9ehfPJXzBdGzt9dUMTRhyYXJc/9twy1XQK9VlTXxoWOv6yWSuaKEDIl+J8S4yqbJ1OWi7pjow5gMbJB7/tEgfDfE4Lyj91LBZc1vrT9UPoptVv77HfHUH0TCDH3EfFc3BARBJeE+A6ljoXaPJR6Ss9bXjEPizUgtN80fR66V1NCYL+hFG9X3YTrSUlLp37ZJ4wJidxjmVD64paIuHnDfB8s21/vuci5OOrZry9aQ78NRZbltFOtNtb3bOwzsUtExBwS/TA2RdSqDBnGY+dJRf1cMd8XPwF/ocwHQybJlDASd1jNGHO0hjHGGGOMMcYYY0zhZ2lqwKIAugo3LArH54QQu0sImapr0RKwFFWx2zjOMvpEtymxwndCEUAfgtx/HxDC9yUh2v1Av1D3nOgTRZMIpczSCu+3YWjM0ucsAEuElMg9dlU81baHhPi9Q4jfJyPbpYLpMg7qlewTVuufPcIsuKZdbLymPo+iT2SwfEPcO/VPn5GxLD2U9tVxPiCEXNX6uCQEfaX1ghCG69Re+V61rgOKYQbzNURyW2W43aZXa16QaP1nyv1RGx/fxPj+lujrCVFv4yOirsm3jL93Sm11TamFsk50wpBxWReshvm5qp5jWumQZJqqf5ddn+7XKsaEoh92ePgojVxYPp9/FfJ40/4nRNt3u5dSt7XG2Clxz9+scW5jjDHGGGOMMcYYE/zsTA1Yz8iokYCXRTAJnRNC4Ow7j1L/9OWoH3PunDZJgqNWiT/UauaM0vbktEJ97BMCe141Xx9rlZXsmZY4O0ROr9SKMNBnRUTkldX5N5GNAInB7xEiPYSYWdeJGGKo3smqY3aXiLLQyvExRpeuQVEjf+++lxml/qjTdg31ZW63zJJsbMwI00Jm3F73ekaJ3FAkQC7sTvpb57iutslCu/pAERV12/XKJkeuazJh/rnV90rjpZRct13bPyFMja8Zf++mlAiPnE5uVdQnuei7UDFrPcN69lTPIUegtcy9GWGWvei2/5rxxez3u/2Pl23YcUWI/HusXmNmHTY1VbNpl8egnusZ86ZqfT0XwP/zcKa0McYYY4wxxhhjzC+Bn6Wp8ZDcUdIG3aVXzaaiVV0v4Bb4K/NpftZNo1IzI0RF1YfQ8V5RUhU97b4/I1Ym53OdEf0B9ydKSlTfdOV2X00JaPdhK31R/n6PiNTYZXy6oMyMEk2w6cr0U8KUuCTu0SFx//YZjh6RKSBj44wwHepraZlZfcYGzD8Puc9eMm8STAnB/Bml9kNfyp5sJmXTrC9iaFZ91zpe3jabQRPa5puMjBxZc02Jzmj1XR8yTE4ZX0NjiJapoXG5Qzy3+X5csvj8ZnYpZs3j7ljnjDMcJsT93KEYZGPaf9zt87ZradwX2SjM47Ovv5bVkzHGGGOMMcYYY4wxw9jUGElevQ7zq59bK6VfUAyATc5Zi+w3zIu3fUL8OtQC9hmRKkmRCQcUIf2IWGE9JDJvQhYFc/TEusJnX6SJiifD8DXUKZXUN8dL9qvZI0Tj590xviT6dB2ULmmHKFb8nGJMvabUg+jb94pIqQYxXn9LGFmtyJMhg0PH03e31fe31e8ydY4pdSv0uVX3QMep23RAuSfvda9Zd03L0vvIyJh27amf75bZpWu7IgypPxFp2VaNQrolxs75sg2r89f9okijFvptjygEf0P0ybJC9DrejDK+x45xRWRlcX8s2xi5UEdsTCj93jffPCXMx+96fjfGGGOMMcYYY4wxy9lqU0NRCy0BLRfRVV74+xD89b5De5X1LiEuv6DUk7hvJBTXRYJXidbIq+tbNUT02zUlT7xEPEV1PFSqGEUz6L4+IwTrS2J1/DqprFoiY506qVWHJd93ifOnhOB7xOo1U6aEGE+3bx5DisCB1URe1XmZdm16QzG/hsjXfkIYI88oNR5a9Ujq9Fz1cervtY+igXRf/07bcKkjlLJwXN9DRaV8BHxOPHfnRB+MiZ6QwVLT1291Si5YPhZ17XrOtKJ/nSiNenzqGZxSUnqdV23Svbxj2OQSt0T0jqIzTlltLI5NU/XQ6F61+lhjWAZlnYJuDC3jLT+/OneeRw6JeWyfiF56G/WFjDHGGGOMMcYYY36ObK2psUusen1ESaEjlLd+lxD6ZpQUSUM1DVrUIisUgVX56fP3TygC/KqCd31eHVPiWxZ6b5mPEllVlNunrDrP1Nd6TkQTfNv9dkEI4DeUuiIS/C/WaEfNlBDWJa4D/K4711cbHjtTi+UtIyNvKy6J8dZXP0T7a/xdMC8yX1NE49PqNwmxq/ahVuG/IYTrsREAmSvCaFBKptrIgHlRva+PWuPxrjvuI6JPXjMsrreuXwaExj6UouPXRD9/1x37iCIot4yq2rTIhsm0+q5ui6Kk8r2XydeqTfE+USj9lDJ+62iWIfrEc73nc+wS9/BV2vaKqIkxZXzaoxMexox9W+wT84dqpOTrnlFqAO0R/XtCSUO2yfyVDTyYv8c7XZsOiPH5UIawMcYYY4wxxhhjzC+BrTQ1lCv+UyK1yhdEvQmhfPJPCCF1jzA1jokV3KuYDS1RUchwkICq9CsqAHwfq5Yl5u5S8trXERqtNi8jr6Kvz5ePIRFe13pI9P1TQqCTeXPebbdpvvgdIpf/ISEcnxFpri5YFCg3oSXY90VtZHJthT52CZH5kBhzP1LG3CWLNSbysdcROy8pBa43Wf2dnwsJs62x3xcRVBtv+f22a+eM6B+ZcrssNwBb90VcE/15STHWVHC8b9+WUTOpXvU+OVJHkUQqev5x14Y6jdh7wH8T89T3xDPyktUiH+p+zO3ZIZ7B33Tn+pL2nLOJudqHok/etWiDfaIvfkMx6H5Mv8tw1P+GGdGHJ4TZsMr8Us+fmkNkjGXT+5YYHxesnrLOGGOMMcYYY4wxxsyzlabGjCJ8Kx1QRjUPtJr+GaUo9H2KcDnFlUSqU9o1CaB09jqphW7Sa0xaoWWimYTOvlRCtaiv1c8fEWl+pkRkgCI37oj7AZsZD7qfx8Q9VH2IVQXUHEEyTX/ne9Un2Pd9X6eKyoZWjcaFzJmX6Rh917GJ0PnQRZaH+qX+nI2M/H7aHeMDSqHwI+BvLBfeZQK1Iidk6uR2tiJJWoZFXROhNjv0WeNIx1LUyA1hVuR7OiOekc+AfyEiKa4Isf2EeP6nbDYXydR4RDyb50QkyDpROquwQxg5MpHeNfaIcaU+qeci1XBR2x8Tcyysfz15bORxkyN6boixDotjWNEd21hXxBhjjDHGGGOMMeafwVaaGhKfrojIi5YYdd29JJAfEiL8OoJ7Fq1E30r+PmH6MVEg+oxIETMGibi7zBc2Hlo9nwX7ZSL5LkUsbom5M6LfnnXvykP/BviGEJK1v1bdb4pWVue2T4mV1TI5lpHFdQnVO5TBrpX8ObXYkNGRj5n/7jMSLikpf1TrYh3DYgL8utv3+zX234S+CB5YHC91YfBsAOXfbohx/D4h9kP01ROKGThE/ZzvdOfPYzDfzz6Toi9Sg/S53j6bEHkl/hVxb3R/HxHmxTPimX9JRG4dU8aDnuXayDkgxPhL5iMuWmMzm3Yvq2ONNUz65rCh7fdpm3kTwsh5SlzvPyt91WXXFqV3a13bFTGHHRHPV2vO2YQxBqBQkfsXhCn1rtQkMcYYY4wxxhhjjHmXmdzd3TkThjHGGGOMMcYYY4wxxhhj3nmWZTIyxhhjjDHGGGOMMcYYY4x5J7CpYYwxxhhjjDHGGGOMMcaYrcCmhjHGGGOMMcYYY4wxxhhjtgKbGsYYY4wxxhhjjDHGGGOM2QpsahhjjDHGGGOMMcYYY4wxZiuwqWGMMcYYY4wxxhhjjDHGmK3ApoYxxhhjjDHGGGOMMcYYY7YCmxrGGGOMMcYYY4wxxhhjjNkKbGoYY4wxxhhjjDHGGGOMMWYr+AedclDFV/LW5wAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ], + "source": [ + "bead_counts = list(BEAD_COUNTS)\n", + "seeds = [int(BASE_SEED + k) for k in range(len(bead_counts))]\n", + "\n", + "samples = [\n", + " generate_one_sample_multilength(\n", + " seeds[i], n_beads=bead_counts[i],\n", + " noise_source=noise_source, noise_asset=noise_asset, add_decoy=True\n", + " )\n", + " for i in range(len(bead_counts))\n", + "]\n", + "#---------Plotting-------------------------------------------------------------\n", + "\n", + "fig, axes = plt.subplots(len(samples), 3, figsize=(16, 5 * len(samples)))\n", + "\n", + "for r, s in enumerate(samples):\n", + " extent = s[\"extent\"]\n", + " img = s[\"afm_img\"]\n", + "\n", + " print(f\"\\n{'='*20} SAMPLE {r+1} (Seed: {s['seed']}, \"\n", + " f\"{s['n_beads']} beads) {'='*20}\")\n", + " print(f\"Image window (nm): X=[{extent[0]:.1f}, {extent[1]:.1f}], \"\n", + " f\"Y=[{extent[2]:.1f}, {extent[3]:.1f}]\")\n", + "\n", + " if s.get(\"decoy_coords\") is not None:\n", + " d = s[\"decoy_coords\"]\n", + " print(f\"Decoy XY bounds (nm): \"\n", + " f\"X=[{d[:,0].min():.1f}, {d[:,0].max():.1f}], \"\n", + " f\"Y=[{d[:,1].min():.1f}, {d[:,1].max():.1f}]\")\n", + " print(f\"Decoy Z range (nm): {d[:,2].min():.2f} – {d[:,2].max():.2f}\")\n", + " oob = (d[:,0].min() < extent[0] or d[:,0].max() > extent[1] or\n", + " d[:,1].min() < extent[2] or d[:,1].max() > extent[3])\n", + " print(\" Decoy out-of-bounds!\" if oob else \"Decoy inside extent.\")\n", + " if d[:,2].max() < 0.1:\n", + " print(\" Decoy height ≈ 0\")\n", + "\n", + " ax1, ax2, ax3 = axes[r]\n", + " ext_list = list(extent)\n", + "\n", + " ax1.imshow(img, cmap=\"afmhot\", origin=\"lower\", extent=ext_list)\n", + " ax1.set_title(f\"AFM image\\n(seed {s['seed']}, {s['n_beads']} beads)\")\n", + " ax1.axis(\"off\")\n", + "\n", + " ax2.imshow(s[\"dna_mask\"], cmap=\"gray\", origin=\"lower\", extent=ext_list)\n", + " ax2.set_title(\"DNA mask (decoy excluded)\")\n", + " ax2.axis(\"off\")\n", + "\n", + " ax3.imshow(s[\"cross_mask\"], cmap=\"magma\", origin=\"lower\", extent=ext_list)\n", + " ax3.set_title(f\"Crossing mask (n={s['n_crossings']})\")\n", + " ax3.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3YoKkN3e5dIo", + "metadata": { + "id": "3YoKkN3e5dIo" + }, + "source": [ + "## 13. Benchmarking\n", + "\n", + "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "WLAYkyIQlhuX", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WLAYkyIQlhuX", + "outputId": "90abc53a-e966-419e-ea05-0bdd22aeaff8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==============================================================\n", + " SYSTEM PROFILE\n", + "==============================================================\n", + " OS : Linux 6.6.113+\n", + " CPU (logical) : 2 cores\n", + " RAM available : 12.1 GB\n", + " Mode : Non-MD (2D Walk)\n", + "\n", + "==============================================================\n", + " RUNNING BENCHMARK\n", + "==============================================================\n", + " Rep 1/3: 0.41s\n", + " Rep 2/3: 0.40s\n", + " Rep 3/3: 0.37s\n", + "==============================================================\n", + " Median Wall Time: 0.40 s\n", + " Mean Wall Time : 0.39 s\n", + " Peak RAM (RSS) : 209.9 MB\n", + " Projected Total : 0.0 hours for 400 samples\n", + "==============================================================\n" + ] + } + ], + "source": [ + "import time\n", + "import tracemalloc\n", + "import os\n", + "import platform\n", + "import psutil\n", + "import threading\n", + "import statistics\n", + "\n", + "class _PeakMemTracker:\n", + " \"\"\"Track peak resident memory usage in a background thread.\n", + "\n", + " This helper periodically samples the current process RSS and stores the\n", + " largest value observed during the tracked interval.\n", + "\n", + " Attributes\n", + " ----------\n", + " peak_mb : float\n", + " Peak resident set size observed during tracking, in megabytes.\n", + " \"\"\"\n", + "\n", + " def __init__(self, poll_interval_s: float = 0.05) -> None:\n", + " \"\"\"Initialize the memory tracker.\n", + "\n", + " Parameters\n", + " ----------\n", + " poll_interval_s : float, optional\n", + " Sampling interval in seconds, by default ``0.05``.\n", + " \"\"\"\n", + " self._process = psutil.Process(os.getpid())\n", + " self._poll_interval_s = float(poll_interval_s)\n", + " self._stop_event = threading.Event()\n", + " self._thread: threading.Thread | None = None\n", + " self.peak_mb = 0.0\n", + "\n", + " def _run(self) -> None:\n", + " \"\"\"Continuously sample RSS until tracking is stopped.\"\"\"\n", + " while not self._stop_event.is_set():\n", + " try:\n", + " rss_mb = self._process.memory_info().rss / 1e6\n", + " if rss_mb > self.peak_mb:\n", + " self.peak_mb = rss_mb\n", + " except Exception:\n", + " pass\n", + "\n", + " self._stop_event.wait(self._poll_interval_s)\n", + "\n", + " def start(self) -> None:\n", + " \"\"\"Start background memory tracking.\"\"\"\n", + " self._stop_event.clear()\n", + " self._thread = threading.Thread(target=self._run, daemon=True)\n", + " self._thread.start()\n", + "\n", + " def stop(self) -> None:\n", + " \"\"\"Stop background memory tracking.\"\"\"\n", + " self._stop_event.set()\n", + " if self._thread is not None:\n", + " self._thread.join()\n", + "\n", + "\n", + "def _bench_stages(\n", + " seed: int,\n", + " n_beads: int\n", + ") -> dict[str, float]:\n", + " \"\"\"Benchmark the major stages of one sample-generation pipeline run.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " Random seed for the benchmarked sample.\n", + " n_beads : int\n", + " Number of beads in the benchmarked chain.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, float]\n", + " Stage timings in seconds. Keys are:\n", + " ``\"1_chain_init\"``, ``\"2_md_relax\"``, ``\"3_crossing_detect\"``,\n", + " and ``\"4_afm_render_masks\"``.\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + "\n", + " timings: dict[str, float] = {}\n", + " t0 = time.perf_counter()\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n", + " timings[\"1_chain_init\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " frames = run_md_relaxation(\n", + " coords0,\n", + " seed=seed,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME,\n", + " )\n", + " timings[\"2_md_relax\"] = time.perf_counter() - t0\n", + " else:\n", + " timings[\"1_chain_init\"] = 0.0\n", + "\n", + " t0 = time.perf_counter()\n", + " frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n", + " timings[\"2_md_relax\"] = time.perf_counter() - t0\n", + "\n", + " last_coords = frames[-1]\n", + "\n", + " t0 = time.perf_counter()\n", + " crossings = find_polyline_crossings(last_coords[:, :2])\n", + " timings[\"3_crossing_detect\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " chain = {\n", + " \"seed\": seed,\n", + " \"n_beads\": n_beads,\n", + " \"coords\": last_coords,\n", + " \"crossings\": crossings,\n", + " }\n", + " render_chain_and_masks(\n", + " chain,\n", + " noise_source=\"none\",\n", + " noise_asset=None,\n", + " )\n", + " timings[\"4_afm_render_masks\"] = time.perf_counter() - t0\n", + "\n", + " return timings\n", + "\n", + "\n", + "def benchmark_pipeline(\n", + " seed: int,\n", + " n_beads: int,\n", + " n_reps: int = 3,\n", + " total_samples: int | None = None,\n", + " print_report: bool = True,\n", + ") -> dict[str, Any]:\n", + " \"\"\"Benchmark the sample-generation pipeline and report timing statistics.\n", + "\n", + " This function prints a system profile, runs repeated benchmark passes for\n", + " one representative sample configuration, tracks peak RSS memory usage, and\n", + " estimates the total runtime for the full dataset.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " Base random seed for the benchmark runs.\n", + " n_beads : int\n", + " Number of beads used in each benchmarked sample.\n", + " n_reps : int, optional\n", + " Number of benchmark repetitions, by default ``3``.\n", + " total_samples : int or None, optional\n", + " Total number of samples to use when projecting full runtime. If\n", + " ``None``, the projection is omitted.\n", + " print_report : bool, optional\n", + " Whether to print the system and benchmark summary, by default ``True``.\n", + "\n", + " Returns\n", + " -------\n", + " dict[str, Any]\n", + " Dictionary containing benchmark results, including wall times, stage\n", + " timings, median runtime, peak RSS memory, and projected total runtime.\n", + "\n", + " Notes\n", + " -----\n", + " When ``USE_MD`` is enabled, the function also reports available OpenMM\n", + " platforms and uses the same MD settings as the rest of the notebook.\n", + "\n", + " Examples\n", + " --------\n", + " >>> results = benchmark_pipeline(seed=0, n_beads=300, n_reps=3)\n", + " >>> results[\"median_wall_time_s\"] > 0\n", + " True\n", + "\n", + " \"\"\"\n", + "\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + " n_reps = int(n_reps)\n", + "\n", + " cpu_logical = psutil.cpu_count(logical=True)\n", + " available_ram_gb = psutil.virtual_memory().available / 1e9\n", + "\n", + " platforms = None\n", + " active_platform = None\n", + " if USE_MD:\n", + " platforms = [\n", + " openmm.Platform.getPlatform(i).getName()\n", + " for i in range(openmm.Platform.getNumPlatforms())\n", + " ]\n", + " active_platform = (\n", + " \"CUDA\" if \"CUDA\" in platforms else\n", + " \"OpenCL\" if \"OpenCL\" in platforms else\n", + " \"CPU\"\n", + " )\n", + "\n", + " if print_report:\n", + " print(\"=\" * 62)\n", + " print(\" SYSTEM PROFILE\")\n", + " print(\"=\" * 62)\n", + " print(f\" OS : {platform.system()} {platform.release()}\")\n", + " print(f\" CPU (logical) : {cpu_logical} cores\")\n", + " print(f\" RAM available : {available_ram_gb:.1f} GB\")\n", + " if USE_MD:\n", + " print(f\" OpenMM Platforms: {platforms}\")\n", + " print(f\" Active Platform : {active_platform}\")\n", + " print(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\n", + " print()\n", + "\n", + " print(\"=\" * 62)\n", + " print(\" RUNNING BENCHMARK\")\n", + " print(\"=\" * 62)\n", + "\n", + " wall_times: list[float] = []\n", + " stage_timings: list[dict[str, float]] = []\n", + "\n", + " mem_tracker = _PeakMemTracker()\n", + " mem_tracker.start()\n", + "\n", + " for rep in range(n_reps):\n", + " rep_seed = seed + rep\n", + " t0 = time.perf_counter()\n", + " rep_stage_timings = _bench_stages(rep_seed, n_beads)\n", + " elapsed = time.perf_counter() - t0\n", + "\n", + " wall_times.append(elapsed)\n", + " stage_timings.append(rep_stage_timings)\n", + "\n", + " if print_report:\n", + " print(f\" Rep {rep + 1}/{n_reps}: {elapsed:.2f}s\")\n", + "\n", + " mem_tracker.stop()\n", + "\n", + " median_wall_time_s = statistics.median(wall_times)\n", + " mean_wall_time_s = statistics.mean(wall_times)\n", + "\n", + " stage_keys = stage_timings[0].keys() if stage_timings else []\n", + " median_stage_times_s = {\n", + " key: statistics.median(t[key] for t in stage_timings)\n", + " for key in stage_keys\n", + " }\n", + " ts = total_samples\n", + " projected_total_hours = None\n", + " if ts is not None:\n", + " projected_total_hours = (median_wall_time_s * int(ts)) / 3600\n", + "\n", + " if print_report:\n", + " print(\"=\" * 62)\n", + " print(f\" Median Wall Time: {median_wall_time_s:.2f} s\")\n", + " print(f\" Mean Wall Time : {mean_wall_time_s:.2f} s\")\n", + " print(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\n", + " if projected_total_hours is not None:\n", + " print(\n", + " f\" Projected Total : {projected_total_hours:.1f} hours \"\n", + " f\"for {int(ts)} samples\"\n", + " )\n", + " print(\"=\" * 62)\n", + "\n", + " return {\n", + " \"seed\": seed,\n", + " \"n_beads\": n_beads,\n", + " \"n_reps\": n_reps,\n", + " \"mode\": \"md\" if USE_MD else \"non_md\",\n", + " \"cpu_logical\": cpu_logical,\n", + " \"available_ram_gb\": available_ram_gb,\n", + " \"openmm_platforms\": platforms,\n", + " \"active_platform\": active_platform,\n", + " \"wall_times_s\": wall_times,\n", + " \"stage_timings_s\": stage_timings,\n", + " \"median_wall_time_s\": median_wall_time_s,\n", + " \"mean_wall_time_s\": mean_wall_time_s,\n", + " \"median_stage_times_s\": median_stage_times_s,\n", + " \"peak_rss_mb\": mem_tracker.peak_mb,\n", + " \"total_samples\": int(ts) if ts is not None else None,\n", + " \"projected_total_hours\": projected_total_hours,\n", + " }\n", + "\n", + "\n", + "BENCH_SEED = int(BASE_SEED)\n", + "BENCH_N_BEADS = int(BEAD_COUNTS[0])\n", + "BENCH_REPS = 3\n", + "\n", + "benchmark_results = benchmark_pipeline(\n", + " seed=BENCH_SEED,\n", + " n_beads=BENCH_N_BEADS,\n", + " n_reps=BENCH_REPS,\n", + " total_samples=int(N_SAMPLES) * len(BEAD_COUNTS),\n", + " print_report=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_12", + "metadata": { + "id": "cell_md_12" + }, + "source": [ + "## 14.Dataset Generation\n", + "\n", + "Finally, we are ready to generate the full dataset. The amount of samples and the lenghts of the chains is defined in the global variables and will be referenced here.\n", + "This notebook generates the full dataset (default: 1000 samples × 4 chain lengths). There is added functionality to preview the images for a gives set or range of indices.\n", + "\n", + "\n", + "Progress is printed every 10 samples and a `manifest.csv` tracks all file paths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_12", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cell_code_12", + "outputId": "218ea67b-51a4-4573-90f0-4127e2f3c1f8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Starting dataset generation: 1000 samples…\n", + "Special override for index 832: seed = 10005\n", + "Progress: 10/1000\n", + "Progress: 20/1000\n", + "Progress: 30/1000\n", + "Progress: 40/1000\n", + "Progress: 50/1000\n", + "Progress: 60/1000\n", + "Progress: 70/1000\n", + "Progress: 80/1000\n", + "Progress: 90/1000\n", + "Progress: 100/1000\n", + "Progress: 110/1000\n", + "Progress: 120/1000\n", + "Progress: 130/1000\n", + "Progress: 140/1000\n", + "Progress: 150/1000\n", + "Progress: 160/1000\n", + "Progress: 170/1000\n", + "Progress: 180/1000\n", + "Progress: 190/1000\n", + "Progress: 200/1000\n", + "Progress: 210/1000\n", + "Progress: 220/1000\n", + "Progress: 230/1000\n", + "Progress: 240/1000\n", + "Progress: 250/1000\n", + "Progress: 260/1000\n", + "Progress: 270/1000\n", + "Progress: 280/1000\n", + "Progress: 290/1000\n", + "Progress: 300/1000\n", + "Progress: 310/1000\n", + "Progress: 320/1000\n", + "Progress: 330/1000\n", + "Progress: 340/1000\n", + "Progress: 350/1000\n", + "Progress: 360/1000\n", + "Progress: 370/1000\n", + "Progress: 380/1000\n", + "Progress: 390/1000\n", + "Progress: 400/1000\n", + "Progress: 410/1000\n", + "Progress: 420/1000\n", + "Progress: 430/1000\n", + "Progress: 440/1000\n", + "Progress: 450/1000\n", + "Progress: 460/1000\n", + "Progress: 470/1000\n", + "Progress: 480/1000\n", + "Progress: 490/1000\n", + "Progress: 500/1000\n", + "Progress: 510/1000\n", + "Progress: 520/1000\n", + "Progress: 530/1000\n", + "Progress: 540/1000\n", + "Progress: 550/1000\n", + "Progress: 560/1000\n", + "Progress: 570/1000\n", + "Progress: 580/1000\n", + "Progress: 590/1000\n", + "Progress: 600/1000\n", + "Progress: 610/1000\n", + "Progress: 620/1000\n", + "Progress: 630/1000\n", + "Progress: 640/1000\n", + "Progress: 650/1000\n", + "Progress: 660/1000\n", + "Progress: 670/1000\n", + "Progress: 680/1000\n", + "Progress: 690/1000\n", + "Progress: 700/1000\n", + "Progress: 710/1000\n", + "Progress: 720/1000\n", + "Progress: 730/1000\n", + "Progress: 740/1000\n", + "Progress: 750/1000\n", + "Progress: 760/1000\n", + "Progress: 770/1000\n", + "Progress: 780/1000\n", + "Progress: 790/1000\n", + "Progress: 800/1000\n", + "Progress: 810/1000\n", + "Progress: 820/1000\n", + "Progress: 830/1000\n", + "Progress: 840/1000\n", + "Progress: 850/1000\n", + "Progress: 860/1000\n", + "Progress: 870/1000\n", + "Progress: 880/1000\n", + "Progress: 890/1000\n", + "Progress: 900/1000\n", + "Progress: 910/1000\n", + "Progress: 920/1000\n", + "Progress: 930/1000\n", + "Progress: 940/1000\n", + "Progress: 950/1000\n", + "Progress: 960/1000\n", + "Progress: 970/1000\n", + "Progress: 980/1000\n", + "Progress: 990/1000\n", + "Progress: 1000/1000\n", + "\n", + "Done. Dataset written to: dna_dataset_100_4lengths_MD\n", + "Manifest: dna_dataset_100_4lengths_MD/manifest.csv\n" + ] + } + ], + "source": [ + "import os\n", + "import csv\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "manifest_path = os.path.join(OUT_DIR, \"manifest.csv\")\n", + "print(f\"Starting dataset generation: {N_SAMPLES} samples…\")\n", + "\n", + "# ============================================================\n", + "# Optional preview PNG export settings\n", + "# ============================================================\n", + "EXPORT_PREVIEW_PNGS = False\n", + "\n", + "# Choose indices to export as PNG previews.\n", + "# Examples:\n", + "# set(range(4, 41)) -> export indices 4 through 40 inclusive\n", + "# {0, 3, 10, 25} -> export only specific indices\n", + "# set() -> export none\n", + "PREVIEW_PNG_INDICES = set(range(4, 8))\n", + "\n", + "# Separate folder for preview PNGs\n", + "PREVIEW_PNG_DIR = os.path.join(OUT_DIR, \"preview_pngs\")\n", + "if EXPORT_PREVIEW_PNGS:\n", + " os.makedirs(PREVIEW_PNG_DIR, exist_ok=True)\n", + "\n", + "# Define the header including global parameters\n", + "header = [\n", + " \"index\", \"seed\", \"n_beads\", \"bond_length\", \"persistence_bonds\",\n", + " \"image_npy\", \"dna_mask_npy\", \"cross_mask_npy\", \"meta_npz\",\n", + " \"n_crossings\", \"has_decoy\", \"afm_params_json\"\n", + "]\n", + "\n", + "with open(manifest_path, \"w\", newline=\"\") as f:\n", + " w = csv.writer(f)\n", + " w.writerow(header)\n", + "\n", + " for idx in range(int(N_SAMPLES)):\n", + " # Seed override for a known-problematic sample\n", + " if idx == 7:\n", + " seed = int(BASE_SEED + idx + 9997)\n", + " print(f\"Special override for index 832: seed = {seed}\")\n", + " else:\n", + " seed = int(BASE_SEED + idx)\n", + "\n", + " n_beads = int(BEAD_COUNTS[idx % len(BEAD_COUNTS)])\n", + " add_decoy = (idx % 4 == 0)\n", + "\n", + " s = generate_one_sample_multilength(\n", + " seed,\n", + " n_beads=n_beads,\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " add_decoy=add_decoy,\n", + " )\n", + "\n", + " img_path = os.path.join(\"images\", f\"img_{idx:04d}.npy\")\n", + " dna_path = os.path.join(\"dna_masks\", f\"dna_{idx:04d}.npy\")\n", + " cross_path = os.path.join(\"cross_masks\", f\"cross_{idx:04d}.npy\")\n", + " meta_path = os.path.join(\"meta\", f\"meta_{idx:04d}.npz\")\n", + "\n", + " np.save(os.path.join(OUT_DIR, img_path), s[\"afm_img\"])\n", + " np.save(os.path.join(OUT_DIR, dna_path), s[\"dna_mask\"])\n", + " np.save(os.path.join(OUT_DIR, cross_path), s[\"cross_mask\"])\n", + "\n", + " # Save detailed metadata\n", + " np.savez_compressed(\n", + " os.path.join(OUT_DIR, meta_path),\n", + " seed = s[\"seed\"],\n", + " n_beads = int(s[\"n_beads\"]),\n", + " extent = s[\"extent\"],\n", + " n_crossings = s[\"n_crossings\"],\n", + " has_decoy = add_decoy,\n", + " bond_length = BOND_LENGTH,\n", + " persistence = PERSISTENCE_BONDS\n", + " )\n", + "\n", + " # ------------------------------------------------------------\n", + " # Optional PNG export for selected indices\n", + " # ------------------------------------------------------------\n", + " if EXPORT_PREVIEW_PNGS and idx in PREVIEW_PNG_INDICES:\n", + " png_path = os.path.join(PREVIEW_PNG_DIR, f\"img_{idx:04d}.png\")\n", + "\n", + " fig, ax = plt.subplots(figsize=(6, 6), dpi=150)\n", + " ax.imshow(s[\"afm_img\"], cmap=\"gray\", origin=\"lower\")\n", + " ax.set_title(f\"Sample {idx} | beads={n_beads} |\"\n", + " f\" crossings={s['n_crossings']}\")\n", + " ax.axis(\"off\")\n", + " fig.tight_layout(pad=0)\n", + " fig.savefig(png_path, bbox_inches=\"tight\", pad_inches=0)\n", + " plt.close(fig)\n", + "\n", + " # Write to manifest including JSON-serialized AFM parameters\n", + " w.writerow([\n", + " idx, seed, n_beads, BOND_LENGTH, PERSISTENCE_BONDS,\n", + " img_path, dna_path, cross_path, meta_path,\n", + " s[\"n_crossings\"], add_decoy, json.dumps(AFM_KW)\n", + " ])\n", + "\n", + " if (idx + 1) % 10 == 0:\n", + " print(f\"Progress: {idx+1}/{N_SAMPLES}\")\n", + "\n", + "print(\"\\nDone. Dataset written to:\", OUT_DIR)\n", + "print(\"Manifest:\", manifest_path)\n", + "\n", + "if EXPORT_PREVIEW_PNGS:\n", + " print(\"Preview PNG directory:\", PREVIEW_PNG_DIR)\n", + " print(\"PNG preview indices:\", sorted(PREVIEW_PNG_INDICES))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "openmm-cuda", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file