From 602e428a35a4ac58099ab3bc6d00a249b23399ce Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 30 Jun 2024 22:06:48 -0700 Subject: [PATCH 01/87] first draft of contrastive learning model --- .../organelle_phenotyping.py | 41 ++++++ viscy/light/engine.py | 134 ++++++++++++++++++ 2 files changed, 175 insertions(+) create mode 100644 applications/contrastive_phenotyping/organelle_phenotyping.py diff --git a/applications/contrastive_phenotyping/organelle_phenotyping.py b/applications/contrastive_phenotyping/organelle_phenotyping.py new file mode 100644 index 00000000..5ec77e7e --- /dev/null +++ b/applications/contrastive_phenotyping/organelle_phenotyping.py @@ -0,0 +1,41 @@ +# %% Imports and paths. +import os +import torch +from viscy.light.engine import ContrastiveLearningModel +from viscy.unet.networks.unext2 import UNeXt2Stem +from pathlib import Path +import torchview + +top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") +input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/patch_final.zarr" +model_dir = top_dir / "infection_classification/models/infection_score" + +# %% Initialize the model and log the graph. + +# %% Initialize the data module and view the data. + +# %% Train the model. + + +# %% Playground +import timm + +available_models = timm.list_models(pretrained=True) +encoder = timm.create_model( + "convnext_tiny", + pretrained=True, + features_only=False, + drop_path_rate=0.2, +) + +encoder.stem[0].Conv +encoder_graph = torchview.draw_graph( + encoder, + torch.randn(1, 3, 512, 512), + depth=2, # adjust depth to zoom in. + device="cpu", +) +# Print the image of the model. +encoder_graph.visual_graph + +# %% diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 33da3552..67877e09 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -4,6 +4,8 @@ import numpy as np import torch +import timm + from imageio import imwrite from lightning.pytorch import LightningModule from matplotlib.pyplot import get_cmap @@ -24,6 +26,7 @@ r2_score, structural_similarity_index_measure, ) +from torchvision.models import resnet18 from viscy.data.hcs import Sample from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d @@ -459,3 +462,134 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 self.validation_step_outputs.extend( self._detach_sample((source, target * mask.unsqueeze(2), pred)) ) + + +class ContrastiveLearningModel(LightningModule): + """Contrastive Learning Model for self-supervised learning. + + :param string backbone: Neural network backbone, defaults to convnext_tiny + :param nn.Module loss_function: Loss function for training, defaults to TripletMarginLoss + :param float margin: Margin for triplet loss, defaults to 0.5 + :param float lr: Learning rate for optimizer, defaults to 1e-3 + :param Literal['WarmupCosine', 'Constant'] schedule: Learning rate scheduler, defaults to "Constant" + :param int log_batches_per_epoch: Number of batches to log each training epoch, defaults to 8 + :param int log_samples_per_batch: Number of samples to log each training batch, defaults to 1 + :param Sequence[int] example_input_yx_shape: XY shape of the example input for network graph tracing, defaults to (256, 256) + :param int z_slices: Number of slices in the input stack, defaults to 5 + """ + + def __init__( + self, + backbone: str = "convnext_tiny", # convnexts are newer "ResNets" informed by vision transformers. + loss_function: Union[ + nn.Module, nn.CosineEmbeddingLoss, nn.TripletMarginLoss + ] = nn.TripletMarginLoss(), + margin: float = 0.5, + lr: float = 1e-3, + schedule: Literal["WarmupCosine", "Constant"] = "Constant", + log_batches_per_epoch: int = 8, + log_samples_per_batch: int = 1, + in_channels: int = 2, + example_input_yx_shape: Sequence[int] = (256, 256), + in_stack_depth: int = 5, # number of slices in the input stack + stem_kernel_size: tuple[int, int, int] = (5, 5, 5), + embedding_len: int = 128, + ) -> None: + super().__init__() + + """ Start of model construction. + Main blocks: + - stem: transforms C_in*Z*Y*X input into C_out*Y*X feature maps. + - encoder: maps C_out*Y*X feature maps into embedding of size E. + - projection_head: maps E to E' for contrastive learning. + + NOTE: If the model variety grows, refactor model constructions into viscy/embeddings.py or similar module. + See viscy/unet.py for comparison. + """ + if in_stack_depth % stem_kernel_size[0] != 0: + raise ValueError( + f"Input stack depth {in_stack_depth} is not divisible " + f"by stem kernel depth {stem_kernel_size[0]}." + ) + + # encoder + self.encoder = timm.create_model( + backbone, + pretrained=True, + features_only=False, + drop_path_rate=0.2, # dropout rate. + ) + + # stem + + """ End of model construction """ + + self.loss_function = loss_function + self.margin = margin + self.lr = lr + self.schedule = schedule + self.log_batches_per_epoch = log_batches_per_epoch + self.log_samples_per_batch = log_samples_per_batch + self.training_step_outputs = [] + self.validation_losses = [] + self.validation_step_outputs = [] + + # required to log the graph + if architecture == "2D": + example_depth = 1 + else: + example_depth = model_config.get("in_stack_depth") or 5 + self.example_input_array = torch.rand( + 1, # batch size + model_config.get("in_channels") or 1, + example_depth, + *example_input_yx_shape, + ) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass of the model. + + :param Tensor x: Input tensor (batch size, channels, depth, height, width) + :return: Projected features + :rtype: Tensor + """ + features = self.backbone(x) + projections = self.projection_head(features) + return projections + + def training_step( + self, + batch: tuple[Tensor], + batch_idx: int, + ) -> Tensor: + """Training step of the model. + + :param tuple[Tensor] batch: Input batch of images and positive images + :param int batch_idx: Batch index + :return: Loss value + :rtype: Tensor + """ + + if self.loss_function.__name__ == "TripletMarginLoss": + anchor, pos_img, neg_img = batch + emb_anchor = self(anchor) + emb_pos = self(pos_img) + emb_neg = self(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) + else: + anchor, pos_img = batch + emb_anchor = self(anchor) + emb_pos = self(pos_img) + loss = self.loss_function(emb_anchor, emb_pos) + + self.log("train_loss", loss) + return loss + + def configure_optimizers(self) -> torch.optim.Optimizer: + """Configure the optimizer for training. + + :return: Optimizer + :rtype: torch.optim.Optimizer + """ + optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) + return optimizer From 438895c3c26e323031f902bae7ad299a554dc554 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 30 Jun 2024 23:41:33 -0700 Subject: [PATCH 02/87] fixed stem and projection head, drafted lightning module --- .../organelle_phenotyping.py | 61 ++++++++++++- viscy/light/engine.py | 51 +++-------- viscy/unet/networks/embedding.py | 89 +++++++++++++++++++ 3 files changed, 161 insertions(+), 40 deletions(-) create mode 100644 viscy/unet/networks/embedding.py diff --git a/applications/contrastive_phenotyping/organelle_phenotyping.py b/applications/contrastive_phenotyping/organelle_phenotyping.py index 5ec77e7e..6f6b9c20 100644 --- a/applications/contrastive_phenotyping/organelle_phenotyping.py +++ b/applications/contrastive_phenotyping/organelle_phenotyping.py @@ -3,6 +3,7 @@ import torch from viscy.light.engine import ContrastiveLearningModel from viscy.unet.networks.unext2 import UNeXt2Stem +from viscy.unet.networks.embedding import ContrastiveConvNext from pathlib import Path import torchview @@ -10,8 +11,32 @@ input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/patch_final.zarr" model_dir = top_dir / "infection_classification/models/infection_score" +%load_ext autoreload +%autoreload 2 # %% Initialize the model and log the graph. +contra_model = ContrastiveConvNext() +print(contra_model) +model_graph = torchview.draw_graph( + contra_model, + torch.randn(1, 2, 15, 200, 200), + depth=3, # adjust depth to zoom in. + device="cpu", +) +# Print the image of the model. +model_graph.visual_graph + +# %% Initiatlize the lightning module and view the model. +contrastive_module = ContrastiveLearningModel() +print(contrastive_module.model) +model_graph = torchview.draw_graph( + contrastive_module.model, + torch.randn(1, 2, 15, 200, 200), + depth=3, # adjust depth to zoom in. + device="cpu", +) +# Print the image of the model. +model_graph.visual_graph # %% Initialize the data module and view the data. # %% Train the model. @@ -21,17 +46,47 @@ import timm available_models = timm.list_models(pretrained=True) + +stem = UNeXt2Stem( + in_channels=2, out_channels=96, kernel_size=(5, 2, 2), in_stack_depth=15 +) +print(stem) +stem_graph = torchview.draw_graph( + stem, + torch.randn(1, 2, 15, 256, 256), + depth=2, # adjust depth to zoom in. + device="cpu", +) +# Print the image of the model. +stem_graph.visual_graph +# %% encoder = timm.create_model( "convnext_tiny", pretrained=True, features_only=False, - drop_path_rate=0.2, + num_classes=200, +) + +print(encoder) + +# %% + +encoder.stem = stem + +model_graph = torchview.draw_graph( + encoder, + torch.randn(1, 2, 15, 256, 256), + depth=2, # adjust depth to zoom in. + device="cpu", ) +# Print the image of the model. +model_graph.visual_graph +# %% +encoder.stem = torch.nn.Identity() -encoder.stem[0].Conv encoder_graph = torchview.draw_graph( encoder, - torch.randn(1, 3, 512, 512), + torch.randn(1, 96, 128, 128), depth=2, # adjust depth to zoom in. device="cpu", ) diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 67877e09..1bb93836 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -4,7 +4,6 @@ import numpy as np import torch -import timm from imageio import imwrite from lightning.pytorch import LightningModule @@ -34,6 +33,7 @@ from viscy.unet.networks.Unet2D import Unet2d from viscy.unet.networks.Unet25D import Unet25d from viscy.unet.networks.unext2 import UNeXt2 +from viscy.unet.networks.embedding import ContrastiveConvNext try: from cellpose.models import CellposeModel @@ -492,38 +492,11 @@ def __init__( in_channels: int = 2, example_input_yx_shape: Sequence[int] = (256, 256), in_stack_depth: int = 5, # number of slices in the input stack - stem_kernel_size: tuple[int, int, int] = (5, 5, 5), - embedding_len: int = 128, + stem_kernel_size: tuple[int, int, int] = (5, 3, 3), + embedding_len: int = 1000, ) -> None: super().__init__() - """ Start of model construction. - Main blocks: - - stem: transforms C_in*Z*Y*X input into C_out*Y*X feature maps. - - encoder: maps C_out*Y*X feature maps into embedding of size E. - - projection_head: maps E to E' for contrastive learning. - - NOTE: If the model variety grows, refactor model constructions into viscy/embeddings.py or similar module. - See viscy/unet.py for comparison. - """ - if in_stack_depth % stem_kernel_size[0] != 0: - raise ValueError( - f"Input stack depth {in_stack_depth} is not divisible " - f"by stem kernel depth {stem_kernel_size[0]}." - ) - - # encoder - self.encoder = timm.create_model( - backbone, - pretrained=True, - features_only=False, - drop_path_rate=0.2, # dropout rate. - ) - - # stem - - """ End of model construction """ - self.loss_function = loss_function self.margin = margin self.lr = lr @@ -534,15 +507,19 @@ def __init__( self.validation_losses = [] self.validation_step_outputs = [] - # required to log the graph - if architecture == "2D": - example_depth = 1 - else: - example_depth = model_config.get("in_stack_depth") or 5 + self.model = ContrastiveConvNext( + backbone=backbone, + in_channels=in_channels, + in_stack_depth=in_stack_depth, + stem_kernel_size=stem_kernel_size, + embedding_len=embedding_len, + ) + + # required to log the graph. self.example_input_array = torch.rand( 1, # batch size - model_config.get("in_channels") or 1, - example_depth, + in_channels, + in_stack_depth, *example_input_yx_shape, ) diff --git a/viscy/unet/networks/embedding.py b/viscy/unet/networks/embedding.py new file mode 100644 index 00000000..586c3a14 --- /dev/null +++ b/viscy/unet/networks/embedding.py @@ -0,0 +1,89 @@ +import timm +from viscy.unet.networks.unext2 import UNeXt2Stem + +import torch.nn as nn +import torch.nn.functional as F + + +class ContrastiveConvNext(nn.Module): + def __init__( + self, + backbone: str = "convnext_tiny", + in_channels: int = 2, + in_stack_depth: int = 15, + stem_kernel_size: tuple[int, int, int] = (5, 3, 3), + embedding_len: int = 256, + ): + super().__init__() + + """ + ContrastiveConvNext model for contrastive learning. + + Parameters: + - backbone (str): Backbone architecture for the encoder. Default is "convnext_tiny". + - in_channels (int): Number of input channels. Default is 2. + - in_stack_depth (int): Number of input slices in z-stack. Default is 15. + - stem_kernel_size (tuple[int, int, int]): 3D kernel size for the stem. Input stack depth must be divisible by the kernel depth. Default is (5, 3, 3). + - embedding_len (int): Length of the embedding. Default is 1000. + """ + + if in_stack_depth % stem_kernel_size[0] != 0: + raise ValueError( + f"Input stack depth {in_stack_depth} is not divisible " + f"by stem kernel depth {stem_kernel_size[0]}." + ) + + # encoder + self.model = timm.create_model( + backbone, + pretrained=True, + features_only=False, + drop_path_rate=0.2, + num_classes=4 * embedding_len, + ) + + # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. + in_channels_encoder = self.model.stem[0].out_channels + stem = UNeXt2Stem( + in_channels=in_channels, + out_channels=in_channels_encoder, + kernel_size=stem_kernel_size, + in_stack_depth=in_stack_depth, + ) + self.model.stem = stem + + # replace the fully connected layer with projection head (Linear->ReLU->Linear). + self.model.head.fc = nn.Sequential( + self.model.head.fc, + nn.ReLU(inplace=True), + nn.Linear(4 * embedding_len, embedding_len), + ) + """ + head of convnext + ------------------- + (head): NormMlpClassifierHead( + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) + (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) + (flatten): Flatten(start_dim=1, end_dim=-1) + (pre_logits): Identity() + (drop): Dropout(p=0.0, inplace=False) + (fc): Linear(in_features=768, out_features=1024, bias=True) + + + head of convnext for contrastive learning + ---------------------------- + (head): NormMlpClassifierHead( + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) + (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) + (flatten): Flatten(start_dim=1, end_dim=-1) + (pre_logits): Identity() + (drop): Dropout(p=0.0, inplace=False) + (fc): Sequential( + (0): Linear(in_features=768, out_features=1024, bias=True) + (1): ReLU(inplace=True) + (2): Linear(in_features=1024, out_features=256, bias=True) + ) + """ + + def forward(self, x): + return self.model(x) From 94cac3293ce7e5ee4adda81ae98417f69cbce12d Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Wed, 3 Jul 2024 08:43:15 -0700 Subject: [PATCH 03/87] Contrastive_dataloader (#99) * initial dataloader.py * Update dataloader_test.py * Update dataloader_test.py * Update dataloader_test.py * Update dataloader_test.py * rename training script --- .../dataloader_test.py | 339 ++++++++++++++++++ ...elle_phenotyping.py => training_script.py} | 0 2 files changed, 339 insertions(+) create mode 100644 applications/contrastive_phenotyping/dataloader_test.py rename applications/contrastive_phenotyping/{organelle_phenotyping.py => training_script.py} (100%) diff --git a/applications/contrastive_phenotyping/dataloader_test.py b/applications/contrastive_phenotyping/dataloader_test.py new file mode 100644 index 00000000..91d0faa4 --- /dev/null +++ b/applications/contrastive_phenotyping/dataloader_test.py @@ -0,0 +1,339 @@ +# %% +import os +import random +import torch +import numpy as np +from torch.utils.data import Dataset, DataLoader +from viscy.transforms import ( + RandAdjustContrastd, + RandAffined, + RandGaussianNoised, + RandGaussianSmoothd, + RandScaleIntensityd, +) +from monai.transforms import Compose +from iohub import open_ome_zarr +import pandas as pd +import warnings +import pytorch_lightning as pl + +# from viscy.data.typing import Optional +from pathlib import Path + +warnings.filterwarnings("ignore") + + +# %% +class OMEZarrDataset(Dataset): + def __init__( + self, + base_path, + channels, + x, + y, + timesteps_csv_path, + transform=None, + z_range=None, + ): + self.base_path = base_path + self.channels = channels + self.x = x + self.y = y + self.z_range = z_range + self.transform = transform + self.ds = self.open_zarr_store(self.base_path) + self.positions = list(self.ds.positions()) + self.timesteps_df = pd.read_csv(timesteps_csv_path) + print(f"Initialized dataset with {len(self.positions)} positions.") + + def open_zarr_store(self, path, layout="hcs", mode="r"): + print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") + return open_ome_zarr(path, layout=layout, mode=mode) + + def __len__(self): + return len(self.positions) + + def __getitem__(self, idx): + anchor_position_path = self.positions[idx][0] + anchor_data = self.load_data(anchor_position_path) + + positive_data = ( + self.transform({"image": anchor_data})["image"] + if self.transform + else anchor_data + ) + if self.transform: + print("Positive transformation applied") + + negative_idx = idx + while negative_idx == idx: + negative_idx = random.randint(0, self.__len__() - 1) + negative_position_path = self.positions[negative_idx][0] + negative_data = self.load_data(negative_position_path) + + negative_data = ( + self.transform({"image": negative_data})["image"] + if self.transform + else negative_data + ) + if self.transform: + print("Negative transformation applied") + + print("shapes of tensors") + print(torch.tensor(anchor_data).shape) + print(torch.tensor(positive_data).shape) + print(torch.tensor(negative_data).shape) + return ( + torch.tensor(anchor_data), + torch.tensor(positive_data), + torch.tensor(negative_data), + ) + + def load_data(self, position_path): + position = self.ds[position_path] + print(f"Loading data from position: {position_path}") + zarr_array = position["0"][:] + print("Shape before:", zarr_array.shape) + data = self.restructure_data(zarr_array, position_path) + if self.z_range: + data = data[:, self.z_range[0] : self.z_range[1], :, :] + print("Shape after:", data.shape) + return data + + def restructure_data(self, data, position_path): + # Extract row, column, fov, and cell_id from position_path + parts = position_path.split("/") + row = parts[0] + column = parts[1] + fov_cell = parts[2] + + fov = int(fov_cell.split("fov")[1].split("cell")[0]) + cell_id = int(fov_cell.split("cell")[1]) + + extracted_combined = f"{row}/{column}/fov{fov}cell{cell_id}" + + matched_rows = self.timesteps_df[ + self.timesteps_df.apply( + lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", + axis=1, + ) + == extracted_combined + ] + + if matched_rows.empty: + raise ValueError( + f"No matching entry found for position path: {position_path}" + ) + + start_time = matched_rows["Start Time"].values[0] + end_time = matched_rows["End Time"].values[0] + + random_timestep = np.random.randint(start_time, end_time) + + reshaped_data = data[random_timestep] + return reshaped_data + + +def get_transforms(): + transforms = Compose( + [ + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.5, 2.0)), + RandAffined( + keys=["image"], + prob=0.5, + rotate_range=(0.2, 0.2), + shear_range=(0.2, 0.2), + scale_range=(0.2, 0.2), + ), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), + RandGaussianSmoothd( + keys=["image"], + prob=0.5, + sigma_x=(0.5, 1.0), + sigma_y=(0.5, 1.0), + sigma_z=(0.5, 1.0), + ), + RandScaleIntensityd(keys=["image"], factors=(0.5, 2.0), prob=0.5), + ] + ) + return transforms + + +class OMEZarrDataModule(pl.LightningDataModule): + def __init__( + self, + base_path: str, + channels: int, + x: int, + y: int, + timesteps_csv_path: str, + predict_base_path: str = None, + train_split_ratio: float = 0.64, + val_split_ratio: float = 0.16, + batch_size: int = 4, + num_workers: int = 8, + z_range: tuple[int, int] = None, + transform=None, + ): + super().__init__() + self.base_path = Path(base_path) + self.channels = channels + self.x = x + self.y = y + self.timesteps_csv_path = timesteps_csv_path + self.predict_base_path = Path(predict_base_path) if predict_base_path else None + self.train_split_ratio = train_split_ratio + self.val_split_ratio = val_split_ratio + self.batch_size = batch_size + self.num_workers = num_workers + self.z_range = z_range + self.transform = transform or get_transforms() + self.train_dataset = None + self.val_dataset = None + self.test_dataset = None + self.predict_dataset = None + + def setup(self, stage: str = None): + dataset = OMEZarrDataset( + self.base_path, + self.channels, + self.x, + self.y, + self.timesteps_csv_path, + transform=self.transform, + z_range=self.z_range, + ) + + train_size = int(len(dataset) * self.train_split_ratio) + val_size = int(len(dataset) * self.val_split_ratio) + test_size = len(dataset) - train_size - val_size + + self.train_dataset, self.val_dataset, self.test_dataset = ( + torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) + ) + + # setup prediction dataset (if needed) + if stage == "predict" and self.predict_base_path: + self.predict_dataset = OMEZarrDataset( + self.predict_base_path, + self.channels, + self.x, + self.y, + self.timesteps_csv_path, + transform=self.transform, + z_range=self.z_range, + ) + + def train_dataloader(self): + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + shuffle=True, + num_workers=self.num_workers, + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + ) + + def test_dataloader(self): + return DataLoader( + self.test_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + ) + + def predict_dataloader(self): + if self.predict_dataset is None: + raise ValueError( + "Predict dataset not set up. Call setup(stage='predict') first." + ) + return DataLoader( + self.predict_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + ) + + +# %% Testing the DataModule + +base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/small_patch.zarr" +# predict_base_path = " " +channels = 2 +x = 200 +y = 200 +z = 10 +z_range = (0, 10) +batch_size = 4 +timesteps_csv_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" + +data_module = OMEZarrDataModule( + base_path=base_path, + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + batch_size=batch_size, + z_range=z_range, +) + +# for train and val +data_module.setup() + +print( + f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}" +) +print(f"Training dataset size: {len(data_module.train_dataset)}") +print(f"Validation dataset size: {len(data_module.val_dataset)}") +print(f"Test dataset size: {len(data_module.test_dataset)}") + +train_loader = data_module.train_dataloader() + +print("Training DataLoader:") +for batch in train_loader: + anchor_batch, positive_batch, negative_batch = batch + print("Anchor batch shape:", anchor_batch.shape) + print("Positive batch shape:", positive_batch.shape) + print("Negative batch shape:", negative_batch.shape) + break + +val_loader = data_module.val_dataloader() + +print("Validation DataLoader:") +for batch in val_loader: + anchor_batch, positive_batch, negative_batch = batch + print("Anchor batch shape:", anchor_batch.shape) + print("Positive batch shape:", positive_batch.shape) + print("Negative batch shape:", negative_batch.shape) + break + +test_loader = data_module.test_dataloader() + +print("Test DataLoader:") +for batch in test_loader: + anchor_batch, positive_batch, negative_batch = batch + print("Anchor batch shape:", anchor_batch.shape) + print("Positive batch shape:", positive_batch.shape) + print("Negative batch shape:", negative_batch.shape) + break + +# Setup the DataModule for prediction +# data_module.setup(stage='predict') + +# Get the predict DataLoader and print batch shapes +# predict_loader = data_module.predict_dataloader() +# print("Predict DataLoader:") +# for batch in predict_loader: +# anchor_batch, positive_batch, negative_batch = batch +# print("Anchor batch shape:", anchor_batch.shape) +# print("Positive batch shape:", positive_batch.shape) +# print("Negative batch shape:", negative_batch.shape) +# break + +# %% diff --git a/applications/contrastive_phenotyping/organelle_phenotyping.py b/applications/contrastive_phenotyping/training_script.py similarity index 100% rename from applications/contrastive_phenotyping/organelle_phenotyping.py rename to applications/contrastive_phenotyping/training_script.py From 0e2fbd2b0743e9cdb1af81ee5f97e20f1c10f3ae Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Wed, 3 Jul 2024 08:54:30 -0700 Subject: [PATCH 04/87] move contrastive network to viscy.representation module --- viscy/representation/contrastive.py | 89 +++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 viscy/representation/contrastive.py diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py new file mode 100644 index 00000000..d93b09de --- /dev/null +++ b/viscy/representation/contrastive.py @@ -0,0 +1,89 @@ +import timm +from viscy.unet.networks.unext2 import UNeXt2Stem + +import torch.nn as nn +import torch.nn.functional as F + + +class ContrastiveEncoder(nn.Module): + def __init__( + self, + backbone: str = "convnext_tiny", + in_channels: int = 2, + in_stack_depth: int = 15, + stem_kernel_size: tuple[int, int, int] = (5, 3, 3), + embedding_len: int = 256, + ): + super().__init__() + + """ + ContrastiveEncoder network that uses ConvNext and ResNet backbons from timm. + + Parameters: + - backbone (str): Backbone architecture for the encoder. Default is "convnext_tiny". + - in_channels (int): Number of input channels. Default is 2. + - in_stack_depth (int): Number of input slices in z-stack. Default is 15. + - stem_kernel_size (tuple[int, int, int]): 3D kernel size for the stem. Input stack depth must be divisible by the kernel depth. Default is (5, 3, 3). + - embedding_len (int): Length of the embedding. Default is 1000. + """ + + if in_stack_depth % stem_kernel_size[0] != 0: + raise ValueError( + f"Input stack depth {in_stack_depth} is not divisible " + f"by stem kernel depth {stem_kernel_size[0]}." + ) + + # encoder + self.model = timm.create_model( + backbone, + pretrained=True, + features_only=False, + drop_path_rate=0.2, + num_classes=4 * embedding_len, + ) + + # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. + in_channels_encoder = self.model.stem[0].out_channels + stem = UNeXt2Stem( + in_channels=in_channels, + out_channels=in_channels_encoder, + kernel_size=stem_kernel_size, + in_stack_depth=in_stack_depth, + ) + self.model.stem = stem + + # replace the fully connected layer with projection head (Linear->ReLU->Linear). + self.model.head.fc = nn.Sequential( + self.model.head.fc, + nn.ReLU(inplace=True), + nn.Linear(4 * embedding_len, embedding_len), + ) + """ + head of convnext + ------------------- + (head): NormMlpClassifierHead( + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) + (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) + (flatten): Flatten(start_dim=1, end_dim=-1) + (pre_logits): Identity() + (drop): Dropout(p=0.0, inplace=False) + (fc): Linear(in_features=768, out_features=1024, bias=True) + + + head of convnext for contrastive learning + ---------------------------- + (head): NormMlpClassifierHead( + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) + (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) + (flatten): Flatten(start_dim=1, end_dim=-1) + (pre_logits): Identity() + (drop): Dropout(p=0.0, inplace=False) + (fc): Sequential( + (0): Linear(in_features=768, out_features=1024, bias=True) + (1): ReLU(inplace=True) + (2): Linear(in_features=1024, out_features=256, bias=True) + ) + """ + + def forward(self, x): + return self.model(x) From 41ba83b3a1f77057618485c238af0e21da00f09f Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Wed, 3 Jul 2024 11:07:03 -0700 Subject: [PATCH 05/87] Update hcs.py --- viscy/data/hcs.py | 176 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 176 insertions(+) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index b3e946b0..0524afb6 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -583,3 +583,179 @@ def _train_transform(self) -> list[Callable]: self.train_z_scale_range = (0.0, 0.0) logging.debug(f"Training augmentations: {self.augmentations}") return list(self.augmentations) + + +# dataloader for organelle phenotyping +class OMEZarrDataset(Dataset): + def __init__(self, base_path, channels, x, y, timesteps_csv_path, transform=None, z_range=None): + self.base_path = base_path + self.channels = channels + self.x = x + self.y = y + self.z_range = z_range + self.transform = transform + self.ds = self.open_zarr_store(self.base_path) + self.positions = list(self.ds.positions()) + self.timesteps_df = pd.read_csv(timesteps_csv_path) + print(f"Initialized dataset with {len(self.positions)} positions.") + + def open_zarr_store(self, path, layout="hcs", mode="r"): + print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") + return open_ome_zarr(path, layout=layout, mode=mode) + + def __len__(self): + return len(self.positions) + + def __getitem__(self, idx): + anchor_position_path = self.positions[idx][0] + anchor_data = self.load_data(anchor_position_path) + + positive_data = self.transform({'image': anchor_data})['image'] if self.transform else anchor_data + if self.transform: + print("Positive transformation applied") + + negative_idx = idx + while negative_idx == idx: + negative_idx = random.randint(0, self.__len__() - 1) + negative_position_path = self.positions[negative_idx][0] + negative_data = self.load_data(negative_position_path) + + negative_data = self.transform({'image': negative_data})['image'] if self.transform else negative_data + if self.transform: + print("Negative transformation applied") + + print("shapes of tensors") + print(torch.tensor(anchor_data).shape) + print(torch.tensor(positive_data).shape) + print(torch.tensor(negative_data).shape) + return torch.tensor(anchor_data), torch.tensor(positive_data), torch.tensor(negative_data) + + def load_data(self, position_path): + position = self.ds[position_path] + print(f"Loading data from position: {position_path}") + zarr_array = position['0'][:] + print('Shape before:', zarr_array.shape) + data = self.restructure_data(zarr_array, position_path) + if self.z_range: + data = data[:, self.z_range[0]:self.z_range[1], :, :] + print("Shape after:", data.shape) + return data + + def restructure_data(self, data, position_path): + # Extract row, column, fov, and cell_id from position_path + parts = position_path.split('/') + row = parts[0] + column = parts[1] + fov_cell = parts[2] + + fov = int(fov_cell.split('fov')[1].split('cell')[0]) + cell_id = int(fov_cell.split('cell')[1]) + + extracted_combined = f"{row}/{column}/fov{fov}cell{cell_id}" + + matched_rows = self.timesteps_df[ + self.timesteps_df.apply( + lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", axis=1 + ) == extracted_combined + ] + + if matched_rows.empty: + raise ValueError(f"No matching entry found for position path: {position_path}") + + start_time = matched_rows['Start Time'].values[0] + end_time = matched_rows['End Time'].values[0] + + random_timestep = np.random.randint(start_time, end_time) + + reshaped_data = data[random_timestep] + return reshaped_data + +def get_transforms(): + transforms = Compose([ + RandAdjustContrastd(keys=['image'], prob=0.5, gamma=(0.5, 2.0)), + RandAffined(keys=['image'], prob=0.5, rotate_range=(0.2, 0.2), shear_range=(0.2, 0.2), scale_range=(0.2, 0.2)), + RandGaussianNoised(keys=['image'], prob=0.5, mean=0.0, std=0.1), + RandGaussianSmoothd(keys=['image'], prob=0.5, sigma_x=(0.5, 1.0), sigma_y=(0.5, 1.0), sigma_z=(0.5, 1.0)), + RandScaleIntensityd(keys=['image'], factors=(0.5, 2.0), prob=0.5), + ]) + return transforms + +class OMEZarrDataModule(pl.LightningDataModule): + def __init__( + self, + base_path: str, + channels: int, + x: int, + y: int, + timesteps_csv_path: str, + predict_base_path: str = None, + train_split_ratio: float = 0.64, + val_split_ratio: float = 0.16, + batch_size: int = 4, + num_workers: int = 8, + z_range: tuple[int, int] = None, + transform=None + ): + super().__init__() + self.base_path = Path(base_path) + self.channels = channels + self.x = x + self.y = y + self.timesteps_csv_path = timesteps_csv_path + self.predict_base_path = Path(predict_base_path) if predict_base_path else None + self.train_split_ratio = train_split_ratio + self.val_split_ratio = val_split_ratio + self.batch_size = batch_size + self.num_workers = num_workers + self.z_range = z_range + self.transform = transform or get_transforms() + self.train_dataset = None + self.val_dataset = None + self.test_dataset = None + self.predict_dataset = None + + def setup(self, stage: str = None): + dataset = OMEZarrDataset( + self.base_path, + self.channels, + self.x, + self.y, + self.timesteps_csv_path, + transform=self.transform, + z_range=self.z_range + ) + + train_size = int(len(dataset) * self.train_split_ratio) + val_size = int(len(dataset) * self.val_split_ratio) + test_size = len(dataset) - train_size - val_size + + self.train_dataset, self.val_dataset, self.test_dataset = torch.utils.data.random_split( + dataset, [train_size, val_size, test_size] + ) + + # setup prediction dataset (if needed) + if stage == 'predict' and self.predict_base_path: + self.predict_dataset = OMEZarrDataset( + self.predict_base_path, + self.channels, + self.x, + self.y, + self.timesteps_csv_path, + transform=self.transform, + z_range=self.z_range + ) + + def train_dataloader(self): + return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers) + + def val_dataloader(self): + return DataLoader(self.val_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers) + + def test_dataloader(self): + return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers) + + def predict_dataloader(self): + if self.predict_dataset is None: + raise ValueError("Predict dataset not set up. Call setup(stage='predict') first.") + return DataLoader(self.predict_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers) + From 2ec3e67e1faa01c03e2c0e893e57ba0dadfc3606 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Wed, 3 Jul 2024 11:28:04 -0700 Subject: [PATCH 06/87] refactored class names --- applications/contrastive_phenotyping/training_script.py | 6 +++--- viscy/data/hcs.py | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script.py index 6f6b9c20..9b31c8f0 100644 --- a/applications/contrastive_phenotyping/training_script.py +++ b/applications/contrastive_phenotyping/training_script.py @@ -3,7 +3,7 @@ import torch from viscy.light.engine import ContrastiveLearningModel from viscy.unet.networks.unext2 import UNeXt2Stem -from viscy.unet.networks.embedding import ContrastiveConvNext +from viscy.representation.contrastive import ContrastiveEncoder from pathlib import Path import torchview @@ -14,7 +14,7 @@ %load_ext autoreload %autoreload 2 # %% Initialize the model and log the graph. -contra_model = ContrastiveConvNext() +contra_model = ContrastiveEncoder() print(contra_model) model_graph = torchview.draw_graph( @@ -27,7 +27,7 @@ model_graph.visual_graph # %% Initiatlize the lightning module and view the model. -contrastive_module = ContrastiveLearningModel() +contrastive_module = ContrastiveLearningModel(backbone = "resnet50") print(contrastive_module.model) model_graph = torchview.draw_graph( contrastive_module.model, diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 0524afb6..c43c2edc 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -586,7 +586,7 @@ def _train_transform(self) -> list[Callable]: # dataloader for organelle phenotyping -class OMEZarrDataset(Dataset): +class ContrastiveDataset(Dataset): def __init__(self, base_path, channels, x, y, timesteps_csv_path, transform=None, z_range=None): self.base_path = base_path self.channels = channels @@ -680,7 +680,7 @@ def get_transforms(): ]) return transforms -class OMEZarrDataModule(pl.LightningDataModule): +class ContrastiveDataModule(pl.LightningDataModule): def __init__( self, base_path: str, @@ -715,7 +715,7 @@ def __init__( self.predict_dataset = None def setup(self, stage: str = None): - dataset = OMEZarrDataset( + dataset = ContrastiveDataset( self.base_path, self.channels, self.x, @@ -735,7 +735,7 @@ def setup(self, stage: str = None): # setup prediction dataset (if needed) if stage == 'predict' and self.predict_base_path: - self.predict_dataset = OMEZarrDataset( + self.predict_dataset = ContrastiveDataset( self.predict_base_path, self.channels, self.x, From 4da85729919013cee3b4fe7e7897b75d92637247 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Wed, 3 Jul 2024 12:41:38 -0700 Subject: [PATCH 07/87] correct imports --- viscy/data/hcs.py | 141 ++++++++++++++++++++++++++++++------------ viscy/light/engine.py | 5 +- 2 files changed, 104 insertions(+), 42 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index c43c2edc..bb015f90 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -6,6 +6,7 @@ from glob import glob from pathlib import Path from typing import Callable, Literal, Optional, Sequence, Union +import pytorch_lightning as pl import numpy as np import torch @@ -585,9 +586,18 @@ def _train_transform(self) -> list[Callable]: return list(self.augmentations) -# dataloader for organelle phenotyping +# dataloader for organelle phenotyping class ContrastiveDataset(Dataset): - def __init__(self, base_path, channels, x, y, timesteps_csv_path, transform=None, z_range=None): + def __init__( + self, + base_path, + channels, + x, + y, + timesteps_csv_path, + transform=None, + z_range=None, + ): self.base_path = base_path self.channels = channels self.x = x @@ -610,76 +620,108 @@ def __getitem__(self, idx): anchor_position_path = self.positions[idx][0] anchor_data = self.load_data(anchor_position_path) - positive_data = self.transform({'image': anchor_data})['image'] if self.transform else anchor_data + positive_data = ( + self.transform({"image": anchor_data})["image"] + if self.transform + else anchor_data + ) if self.transform: print("Positive transformation applied") - + negative_idx = idx while negative_idx == idx: negative_idx = random.randint(0, self.__len__() - 1) negative_position_path = self.positions[negative_idx][0] negative_data = self.load_data(negative_position_path) - negative_data = self.transform({'image': negative_data})['image'] if self.transform else negative_data + negative_data = ( + self.transform({"image": negative_data})["image"] + if self.transform + else negative_data + ) if self.transform: print("Negative transformation applied") - + print("shapes of tensors") print(torch.tensor(anchor_data).shape) print(torch.tensor(positive_data).shape) print(torch.tensor(negative_data).shape) - return torch.tensor(anchor_data), torch.tensor(positive_data), torch.tensor(negative_data) + return ( + torch.tensor(anchor_data), + torch.tensor(positive_data), + torch.tensor(negative_data), + ) def load_data(self, position_path): position = self.ds[position_path] print(f"Loading data from position: {position_path}") - zarr_array = position['0'][:] - print('Shape before:', zarr_array.shape) + zarr_array = position["0"][:] + print("Shape before:", zarr_array.shape) data = self.restructure_data(zarr_array, position_path) if self.z_range: - data = data[:, self.z_range[0]:self.z_range[1], :, :] + data = data[:, self.z_range[0] : self.z_range[1], :, :] print("Shape after:", data.shape) return data def restructure_data(self, data, position_path): # Extract row, column, fov, and cell_id from position_path - parts = position_path.split('/') + parts = position_path.split("/") row = parts[0] column = parts[1] fov_cell = parts[2] - fov = int(fov_cell.split('fov')[1].split('cell')[0]) - cell_id = int(fov_cell.split('cell')[1]) + fov = int(fov_cell.split("fov")[1].split("cell")[0]) + cell_id = int(fov_cell.split("cell")[1]) extracted_combined = f"{row}/{column}/fov{fov}cell{cell_id}" matched_rows = self.timesteps_df[ self.timesteps_df.apply( - lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", axis=1 - ) == extracted_combined + lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", + axis=1, + ) + == extracted_combined ] - + if matched_rows.empty: - raise ValueError(f"No matching entry found for position path: {position_path}") + raise ValueError( + f"No matching entry found for position path: {position_path}" + ) - start_time = matched_rows['Start Time'].values[0] - end_time = matched_rows['End Time'].values[0] + start_time = matched_rows["Start Time"].values[0] + end_time = matched_rows["End Time"].values[0] random_timestep = np.random.randint(start_time, end_time) reshaped_data = data[random_timestep] return reshaped_data - + + def get_transforms(): - transforms = Compose([ - RandAdjustContrastd(keys=['image'], prob=0.5, gamma=(0.5, 2.0)), - RandAffined(keys=['image'], prob=0.5, rotate_range=(0.2, 0.2), shear_range=(0.2, 0.2), scale_range=(0.2, 0.2)), - RandGaussianNoised(keys=['image'], prob=0.5, mean=0.0, std=0.1), - RandGaussianSmoothd(keys=['image'], prob=0.5, sigma_x=(0.5, 1.0), sigma_y=(0.5, 1.0), sigma_z=(0.5, 1.0)), - RandScaleIntensityd(keys=['image'], factors=(0.5, 2.0), prob=0.5), - ]) + transforms = Compose( + [ + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.5, 2.0)), + RandAffined( + keys=["image"], + prob=0.5, + rotate_range=(0.2, 0.2), + shear_range=(0.2, 0.2), + scale_range=(0.2, 0.2), + ), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), + RandGaussianSmoothd( + keys=["image"], + prob=0.5, + sigma_x=(0.5, 1.0), + sigma_y=(0.5, 1.0), + sigma_z=(0.5, 1.0), + ), + RandScaleIntensityd(keys=["image"], factors=(0.5, 2.0), prob=0.5), + ] + ) return transforms + class ContrastiveDataModule(pl.LightningDataModule): def __init__( self, @@ -694,7 +736,7 @@ def __init__( batch_size: int = 4, num_workers: int = 8, z_range: tuple[int, int] = None, - transform=None + transform=None, ): super().__init__() self.base_path = Path(base_path) @@ -722,19 +764,19 @@ def setup(self, stage: str = None): self.y, self.timesteps_csv_path, transform=self.transform, - z_range=self.z_range + z_range=self.z_range, ) train_size = int(len(dataset) * self.train_split_ratio) val_size = int(len(dataset) * self.val_split_ratio) test_size = len(dataset) - train_size - val_size - self.train_dataset, self.val_dataset, self.test_dataset = torch.utils.data.random_split( - dataset, [train_size, val_size, test_size] + self.train_dataset, self.val_dataset, self.test_dataset = ( + torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) ) # setup prediction dataset (if needed) - if stage == 'predict' and self.predict_base_path: + if stage == "predict" and self.predict_base_path: self.predict_dataset = ContrastiveDataset( self.predict_base_path, self.channels, @@ -742,20 +784,41 @@ def setup(self, stage: str = None): self.y, self.timesteps_csv_path, transform=self.transform, - z_range=self.z_range + z_range=self.z_range, ) def train_dataloader(self): - return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers) + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + shuffle=True, + num_workers=self.num_workers, + ) def val_dataloader(self): - return DataLoader(self.val_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers) + return DataLoader( + self.val_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + ) def test_dataloader(self): - return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers) + return DataLoader( + self.test_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + ) def predict_dataloader(self): if self.predict_dataset is None: - raise ValueError("Predict dataset not set up. Call setup(stage='predict') first.") - return DataLoader(self.predict_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers) - + raise ValueError( + "Predict dataset not set up. Call setup(stage='predict') first." + ) + return DataLoader( + self.predict_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + ) diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 1bb93836..89cd2021 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -25,7 +25,6 @@ r2_score, structural_similarity_index_measure, ) -from torchvision.models import resnet18 from viscy.data.hcs import Sample from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d @@ -33,7 +32,7 @@ from viscy.unet.networks.Unet2D import Unet2d from viscy.unet.networks.Unet25D import Unet25d from viscy.unet.networks.unext2 import UNeXt2 -from viscy.unet.networks.embedding import ContrastiveConvNext +from viscy.representation.contrastive import ContrastiveEncoder try: from cellpose.models import CellposeModel @@ -507,7 +506,7 @@ def __init__( self.validation_losses = [] self.validation_step_outputs = [] - self.model = ContrastiveConvNext( + self.model = ContrastiveEncoder( backbone=backbone, in_channels=in_channels, in_stack_depth=in_stack_depth, From 45f73a720ec07921e05f3be8e4a11c6c3cc9784e Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Wed, 3 Jul 2024 13:09:13 -0700 Subject: [PATCH 08/87] cleaner names for model arch and module --- .../training_script.py | 16 ++-- viscy/light/engine.py | 21 +++-- viscy/representation/contrastive.py | 82 ++++++++++--------- 3 files changed, 63 insertions(+), 56 deletions(-) diff --git a/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script.py index 9b31c8f0..701d04eb 100644 --- a/applications/contrastive_phenotyping/training_script.py +++ b/applications/contrastive_phenotyping/training_script.py @@ -1,7 +1,7 @@ # %% Imports and paths. import os import torch -from viscy.light.engine import ContrastiveLearningModel +from viscy.light.engine import ContrastiveModule from viscy.unet.networks.unext2 import UNeXt2Stem from viscy.representation.contrastive import ContrastiveEncoder from pathlib import Path @@ -14,12 +14,13 @@ %load_ext autoreload %autoreload 2 # %% Initialize the model and log the graph. -contra_model = ContrastiveEncoder() +contra_model = ContrastiveEncoder(backbone = "convnext_tiny") print(contra_model) +# %% model_graph = torchview.draw_graph( contra_model, - torch.randn(1, 2, 15, 200, 200), + torch.randn(1, 2, 15, 224, 224), depth=3, # adjust depth to zoom in. device="cpu", ) @@ -27,16 +28,19 @@ model_graph.visual_graph # %% Initiatlize the lightning module and view the model. -contrastive_module = ContrastiveLearningModel(backbone = "resnet50") -print(contrastive_module.model) +contrastive_module = ContrastiveModule() +print(contrastive_module.encoder) + +# %% model_graph = torchview.draw_graph( - contrastive_module.model, + contrastive_module.encoder, torch.randn(1, 2, 15, 200, 200), depth=3, # adjust depth to zoom in. device="cpu", ) # Print the image of the model. model_graph.visual_graph + # %% Initialize the data module and view the data. # %% Train the model. diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 89cd2021..0a79ed27 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -463,7 +463,7 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 ) -class ContrastiveLearningModel(LightningModule): +class ContrastiveModule(LightningModule): """Contrastive Learning Model for self-supervised learning. :param string backbone: Neural network backbone, defaults to convnext_tiny @@ -490,9 +490,9 @@ def __init__( log_samples_per_batch: int = 1, in_channels: int = 2, example_input_yx_shape: Sequence[int] = (256, 256), - in_stack_depth: int = 5, # number of slices in the input stack + in_stack_depth: int = 15, # number of slices in the input stack stem_kernel_size: tuple[int, int, int] = (5, 3, 3), - embedding_len: int = 1000, + embedding_len: int = 256, ) -> None: super().__init__() @@ -506,7 +506,7 @@ def __init__( self.validation_losses = [] self.validation_step_outputs = [] - self.model = ContrastiveEncoder( + self.encoder = ContrastiveEncoder( backbone=backbone, in_channels=in_channels, in_stack_depth=in_stack_depth, @@ -529,8 +529,7 @@ def forward(self, x: Tensor) -> Tensor: :return: Projected features :rtype: Tensor """ - features = self.backbone(x) - projections = self.projection_head(features) + projections = self.encoder(x) return projections def training_step( @@ -548,14 +547,14 @@ def training_step( if self.loss_function.__name__ == "TripletMarginLoss": anchor, pos_img, neg_img = batch - emb_anchor = self(anchor) - emb_pos = self(pos_img) - emb_neg = self(neg_img) + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) loss = self.loss_function(emb_anchor, emb_pos, emb_neg) else: anchor, pos_img = batch - emb_anchor = self(anchor) - emb_pos = self(pos_img) + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) loss = self.loss_function(emb_anchor, emb_pos) self.log("train_loss", loss) diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index d93b09de..841be751 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -42,48 +42,52 @@ def __init__( num_classes=4 * embedding_len, ) - # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. - in_channels_encoder = self.model.stem[0].out_channels - stem = UNeXt2Stem( - in_channels=in_channels, - out_channels=in_channels_encoder, - kernel_size=stem_kernel_size, - in_stack_depth=in_stack_depth, - ) - self.model.stem = stem + if "convnext" in backbone: + # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. + in_channels_encoder = self.model.stem[0].out_channels + stem = UNeXt2Stem( + in_channels=in_channels, + out_channels=in_channels_encoder, + kernel_size=stem_kernel_size, + in_stack_depth=in_stack_depth, + ) + self.model.stem = stem - # replace the fully connected layer with projection head (Linear->ReLU->Linear). - self.model.head.fc = nn.Sequential( - self.model.head.fc, - nn.ReLU(inplace=True), - nn.Linear(4 * embedding_len, embedding_len), - ) - """ - head of convnext - ------------------- - (head): NormMlpClassifierHead( - (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) - (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) - (flatten): Flatten(start_dim=1, end_dim=-1) - (pre_logits): Identity() - (drop): Dropout(p=0.0, inplace=False) - (fc): Linear(in_features=768, out_features=1024, bias=True) + # replace the fully connected layer with projection head (Linear->ReLU->Linear). + self.model.head.fc = nn.Sequential( + self.model.head.fc, + nn.ReLU(inplace=True), + nn.Linear(4 * embedding_len, embedding_len), + ) + """ + head of convnext + ------------------- + (head): NormMlpClassifierHead( + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) + (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) + (flatten): Flatten(start_dim=1, end_dim=-1) + (pre_logits): Identity() + (drop): Dropout(p=0.0, inplace=False) + (fc): Linear(in_features=768, out_features=1024, bias=True) - head of convnext for contrastive learning - ---------------------------- - (head): NormMlpClassifierHead( - (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) - (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) - (flatten): Flatten(start_dim=1, end_dim=-1) - (pre_logits): Identity() - (drop): Dropout(p=0.0, inplace=False) - (fc): Sequential( - (0): Linear(in_features=768, out_features=1024, bias=True) - (1): ReLU(inplace=True) - (2): Linear(in_features=1024, out_features=256, bias=True) - ) - """ + head of convnext for contrastive learning + ---------------------------- + (head): NormMlpClassifierHead( + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) + (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) + (flatten): Flatten(start_dim=1, end_dim=-1) + (pre_logits): Identity() + (drop): Dropout(p=0.0, inplace=False) + (fc): Sequential( + (0): Linear(in_features=768, out_features=1024, bias=True) + (1): ReLU(inplace=True) + (2): Linear(in_features=1024, out_features=256, bias=True) + ) + """ + elif "resnet" in backbone: + # Adapt stem and projection head of resnet here. + pass def forward(self, x): return self.model(x) From a933bddc049fc46aeeaf1efa24905bf9283ef97e Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Wed, 3 Jul 2024 13:14:48 -0700 Subject: [PATCH 09/87] new imports --- viscy/data/hcs.py | 19 +++++++++++++++++++ viscy/light/engine.py | 6 +++--- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index c43c2edc..25dcafb8 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -27,6 +27,25 @@ from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample +import random +from torch.utils.data import Dataset, DataLoader +from viscy.transforms import ( + RandAdjustContrastd, + RandAffined, + RandGaussianNoised, + RandGaussianSmoothd, + RandScaleIntensityd, +) +from monai.transforms import Compose +from iohub import open_ome_zarr +import pandas as pd +import warnings +import pytorch_lightning as pl + +# from viscy.data.typing import Optional +from pathlib import Path + +warnings.filterwarnings("ignore") def _ensure_channel_list(str_or_seq: str | Sequence[str]) -> list[str]: """ diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 1bb93836..0bb206b2 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -26,6 +26,7 @@ structural_similarity_index_measure, ) from torchvision.models import resnet18 +from pytorch_metric_learning.losses import NTXentLoss from viscy.data.hcs import Sample from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d @@ -463,7 +464,6 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 self._detach_sample((source, target * mask.unsqueeze(2), pred)) ) - class ContrastiveLearningModel(LightningModule): """Contrastive Learning Model for self-supervised learning. @@ -482,8 +482,8 @@ def __init__( self, backbone: str = "convnext_tiny", # convnexts are newer "ResNets" informed by vision transformers. loss_function: Union[ - nn.Module, nn.CosineEmbeddingLoss, nn.TripletMarginLoss - ] = nn.TripletMarginLoss(), + nn.Module, nn.CosineEmbeddingLoss, nn.TripletMarginLoss, NTXentLoss + ] = nn.TripletMarginLoss(margin=0.5), margin: float = 0.5, lr: float = 1e-3, schedule: Literal["WarmupCosine", "Constant"] = "Constant", From 247cef38a5fd9a53ea483d113849f520a6ec95c4 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Sat, 6 Jul 2024 19:55:47 -0700 Subject: [PATCH 10/87] Fixed epoch loss logging and WandB integration in ContrastiveModule --- .../training_script.py | 204 ++++++++++++++++++ viscy/data/hcs.py | 65 +++--- viscy/light/engine.py | 160 ++++++++++---- viscy/representation/contrastive.py | 5 +- 4 files changed, 359 insertions(+), 75 deletions(-) create mode 100644 viscy/applications/contrastive_phenotyping/training_script.py diff --git a/viscy/applications/contrastive_phenotyping/training_script.py b/viscy/applications/contrastive_phenotyping/training_script.py new file mode 100644 index 00000000..db010560 --- /dev/null +++ b/viscy/applications/contrastive_phenotyping/training_script.py @@ -0,0 +1,204 @@ +# %% Imports and paths. +import os +from pathlib import Path +from argparse import ArgumentParser + +import torch +import torchview +from torch.optim import Adam + +from lightning.pytorch import Trainer, seed_everything +from lightning.pytorch.callbacks import ModelCheckpoint, RichProgressBar +#from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.loggers import WandbLogger +from lightning.pytorch.callbacks import TQDMProgressBar +import wandb +from tqdm import tqdm + +from viscy.light.engine import ContrastiveModule +from viscy.representation.contrastive import ContrastiveEncoder +from viscy.data.hcs import ContrastiveDataModule + +# %% Paths and constants +os.environ["WANDB_DIR"] = "/hpc/mydata/alishba.imran/wandb_logs/" + +#wandb.init(project="contrastive_model", dir="/hpc/mydata/alishba.imran/wandb_logs/") + +top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") +input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +model_dir = top_dir / "infection_classification/models/infection_score" +timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" + +# Data parameters +base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +channels = 2 +x = 200 +y = 200 +z = 15 +z_range = (28, 43) +batch_size = 32 + +# %% Initialize the model and log the graph +#contra_model = ContrastiveEncoder(backbone="convnext_tiny") +# print(contra_model) + +# model_graph = torchview.draw_graph( +# contra_model, +# torch.randn(1, 2, 15, 224, 224), +# depth=3, +# device="cpu", +# ) +# model_graph.visual_graph + +#contrastive_module = ContrastiveModule() +# print(contrastive_module.encoder) + +# model_graph = torchview.draw_graph( +# contrastive_module.encoder, +# torch.randn(1, 2, 15, 200, 200), +# depth=3, +# device="cpu", +# ) +# model_graph.visual_graph + +# %% Progress bar + +class LitProgressBar(TQDMProgressBar): + def init_validation_tqdm(self): + bar = super().init_validation_tqdm() + bar.set_description("Running validation...") + return bar + + def init_train_tqdm(self): + bar = super().init_train_tqdm() + bar.set_description("Training...") + return bar + + def init_test_tqdm(self): + bar = super().init_test_tqdm() + bar.set_description("Testing...") + return bar + +# %% Define the main function for training +def main(hparams): + # Seed for reproducibility + # seed_everything(42, workers=True) + + num_gpus = torch.cuda.device_count() + print(f"Number of GPUs available: {num_gpus}") + + print("Starting data module..") + # Initialize the data module + data_module = ContrastiveDataModule( + base_path=base_path, + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + batch_size=batch_size, + z_range=z_range, + ) + + print("data module set up!") + + # Setup the data module for training, val and testing + data_module.setup(stage='fit') + + print(f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}") + print(f"Training dataset size: {len(data_module.train_dataset)}") + print(f"Validation dataset size: {len(data_module.val_dataset)}") + print(f"Test dataset size: {len(data_module.test_dataset)}") + + + # Initialize the model + model = ContrastiveModule( + backbone=hparams.backbone, + loss_function=torch.nn.TripletMarginLoss(), + margin=hparams.margin, + lr=hparams.lr, + schedule=hparams.schedule, + log_batches_per_epoch=hparams.log_batches_per_epoch, + log_samples_per_batch=hparams.log_samples_per_batch, + in_channels=channels, + example_input_yx_shape=(x, y), + in_stack_depth=z, + stem_kernel_size=(5, 3, 3), + embedding_len=hparams.embedding_len, + ) + + # Initialize logger + wandb_logger = WandbLogger(project="contrastive_model", log_model="all") + + checkpoint_callback = ModelCheckpoint( + dirpath=model_dir, + filename="contrastive_model-{epoch:02d}-{val_loss:.2f}", + save_top_k=3, + mode="min", + monitor="val/loss_epoch", + ) + + trainer = Trainer( + max_epochs=hparams.max_epochs, + callbacks=[checkpoint_callback], + logger=wandb_logger, + accelerator=hparams.accelerator, + devices=hparams.devices, + num_nodes=hparams.num_nodes, + strategy="ddp", + log_every_n_steps=hparams.log_every_n_steps, + num_sanity_val_steps=0 + ) + + # Fetches batches from the training dataloader, + # Calls the training_step method on the model for each batch + # Aggregates the losses and performs optimization steps + trainer.fit(model, datamodule=data_module) + + # Validate the model + trainer.validate(model, datamodule=data_module) + + # Test the model + trainer.test(model, datamodule=data_module) + +if __name__ == "__main__": + import sys + if "ipykernel_launcher" in sys.argv[0]: + # Jupyter Notebook environment + args = { + "backbone": "convnext_tiny", + "margin": 0.5, + "lr": 1e-3, + "schedule": "Constant", + "log_batches_per_epoch": 8, + "log_samples_per_batch": 1, + "embedding_len": 256, + "max_epochs": 100, + "accelerator": "gpu", + "devices": 1, # Set to 4 GPUs + "num_nodes": 2, + "log_every_n_steps": 1, + } + class HParams: + def __init__(self, **kwargs): + self.__dict__.update(kwargs) + hparams = HParams(**args) + main(hparams) + else: + parser = ArgumentParser() + parser.add_argument("--backbone", type=str, default="convnext_tiny") + parser.add_argument("--margin", type=float, default=0.5) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--schedule", type=str, default="Constant") + parser.add_argument("--log_batches_per_epoch", type=int, default=8) + parser.add_argument("--log_samples_per_batch", type=int, default=1) + parser.add_argument("--embedding_len", type=int, default=256) + parser.add_argument("--max_epochs", type=int, default=100) + parser.add_argument("--accelerator", type=str, default="gpu") + parser.add_argument("--devices", type=int, default=1) # 4 GPUs + parser.add_argument("--num_nodes", type=int, default=2) + parser.add_argument("--log_every_n_steps", type=int, default=1) + args = parser.parse_args() + + main(args) + +# %% diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 3b07c003..13633e01 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -6,14 +6,14 @@ from glob import glob from pathlib import Path from typing import Callable, Literal, Optional, Sequence, Union -import pytorch_lightning as pl +#import pytorch_lightning as pl import numpy as np import torch import zarr from imageio import imread from iohub.ngff import ImageArray, Plate, Position, open_ome_zarr -from lightning.pytorch import LightningDataModule +#from lightning.pytorch import LightningDataModule from monai.data import set_track_meta from monai.data.utils import collate_meta_tensor from monai.transforms import ( @@ -23,13 +23,14 @@ MultiSampleTrait, RandAffined, ) + + from torch import Tensor from torch.utils.data import DataLoader, Dataset from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample import random -from torch.utils.data import Dataset, DataLoader from viscy.transforms import ( RandAdjustContrastd, RandAffined, @@ -41,7 +42,8 @@ from iohub import open_ome_zarr import pandas as pd import warnings -import pytorch_lightning as pl +from lightning.pytorch import LightningDataModule, LightningModule, Trainer + # from viscy.data.typing import Optional from pathlib import Path @@ -629,7 +631,7 @@ def __init__( print(f"Initialized dataset with {len(self.positions)} positions.") def open_zarr_store(self, path, layout="hcs", mode="r"): - print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") + #print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") return open_ome_zarr(path, layout=layout, mode=mode) def __len__(self): @@ -644,8 +646,8 @@ def __getitem__(self, idx): if self.transform else anchor_data ) - if self.transform: - print("Positive transformation applied") + # if self.transform: + # print("Positive transformation applied") negative_idx = idx while negative_idx == idx: @@ -658,13 +660,13 @@ def __getitem__(self, idx): if self.transform else negative_data ) - if self.transform: - print("Negative transformation applied") + # if self.transform: + # print("Negative transformation applied") - print("shapes of tensors") - print(torch.tensor(anchor_data).shape) - print(torch.tensor(positive_data).shape) - print(torch.tensor(negative_data).shape) + # print("shapes of tensors") + # print(torch.tensor(anchor_data).shape) + # print(torch.tensor(positive_data).shape) + # print(torch.tensor(negative_data).shape) return ( torch.tensor(anchor_data), torch.tensor(positive_data), @@ -673,13 +675,13 @@ def __getitem__(self, idx): def load_data(self, position_path): position = self.ds[position_path] - print(f"Loading data from position: {position_path}") + # print(f"Loading data from position: {position_path}") zarr_array = position["0"][:] - print("Shape before:", zarr_array.shape) + # print("Shape before:", zarr_array.shape) data = self.restructure_data(zarr_array, position_path) if self.z_range: data = data[:, self.z_range[0] : self.z_range[1], :, :] - print("Shape after:", data.shape) + # print("Shape after:", data.shape) return data def restructure_data(self, data, position_path): @@ -719,29 +721,28 @@ def restructure_data(self, data, position_path): def get_transforms(): transforms = Compose( [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.5, 2.0)), + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.8, 1.2)), RandAffined( keys=["image"], prob=0.5, - rotate_range=(0.2, 0.2), - shear_range=(0.2, 0.2), - scale_range=(0.2, 0.2), + rotate_range=(0.1, 0.1), + shear_range=(0.1, 0.1), + scale_range=(0.1, 0.1), ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.05), RandGaussianSmoothd( keys=["image"], prob=0.5, - sigma_x=(0.5, 1.0), - sigma_y=(0.5, 1.0), - sigma_z=(0.5, 1.0), + sigma_x=(0.1, 0.5), + sigma_y=(0.1, 0.5), + sigma_z=(0.1, 0.5), ), - RandScaleIntensityd(keys=["image"], factors=(0.5, 2.0), prob=0.5), + RandScaleIntensityd(keys=["image"], factors=(0.8, 1.2), prob=0.5), ] ) return transforms - -class ContrastiveDataModule(pl.LightningDataModule): +class ContrastiveDataModule(LightningDataModule): def __init__( self, base_path: str, @@ -812,6 +813,8 @@ def train_dataloader(self): batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True ) def val_dataloader(self): @@ -820,6 +823,8 @@ def val_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True ) def test_dataloader(self): @@ -828,6 +833,8 @@ def test_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True ) def predict_dataloader(self): @@ -840,4 +847,6 @@ def predict_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, - ) + prefetch_factor=2, + persistent_workers=True + ) \ No newline at end of file diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 39b8d8c2..0e34bc93 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -4,9 +4,14 @@ import numpy as np import torch - +import wandb from imageio import imwrite -from lightning.pytorch import LightningModule +#from lightning.pytorch import LightningModule +#from lightning import LightningModule +from torch.optim import Adam + +from lightning.pytorch import LightningDataModule, LightningModule, Trainer + from matplotlib.pyplot import get_cmap from monai.optimizers import WarmupCosineSchedule from monai.transforms import DivisiblePad @@ -25,7 +30,6 @@ r2_score, structural_similarity_index_measure, ) -from pytorch_metric_learning.losses import NTXentLoss from viscy.data.hcs import Sample from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d @@ -85,7 +89,6 @@ def forward(self, preds, target): loss += (1 - ms_ssim) * self.ms_dssim_alpha return loss - class VSUNet(LightningModule): """Regression U-Net module for virtual staining. @@ -463,27 +466,15 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 self._detach_sample((source, target * mask.unsqueeze(2), pred)) ) - -class ContrastiveLearningModel(LightningModule): - """Contrastive Learning Model for self-supervised learning. - - :param string backbone: Neural network backbone, defaults to convnext_tiny - :param nn.Module loss_function: Loss function for training, defaults to TripletMarginLoss - :param float margin: Margin for triplet loss, defaults to 0.5 - :param float lr: Learning rate for optimizer, defaults to 1e-3 - :param Literal['WarmupCosine', 'Constant'] schedule: Learning rate scheduler, defaults to "Constant" - :param int log_batches_per_epoch: Number of batches to log each training epoch, defaults to 8 - :param int log_samples_per_batch: Number of samples to log each training batch, defaults to 1 - :param Sequence[int] example_input_yx_shape: XY shape of the example input for network graph tracing, defaults to (256, 256) - :param int z_slices: Number of slices in the input stack, defaults to 5 - """ +class ContrastiveModule(LightningModule): + """Contrastive Learning Model for self-supervised learning.""" def __init__( self, - backbone: str = "convnext_tiny", # convnexts are newer "ResNets" informed by vision transformers. + backbone: str = "convnext_tiny", loss_function: Union[ - nn.Module, nn.CosineEmbeddingLoss, nn.TripletMarginLoss, NTXentLoss - ] = nn.TripletMarginLoss(margin=0.5), + nn.Module, nn.CosineEmbeddingLoss, nn.TripletMarginLoss + ] = nn.TripletMarginLoss(), margin: float = 0.5, lr: float = 1e-3, schedule: Literal["WarmupCosine", "Constant"] = "Constant", @@ -491,7 +482,7 @@ def __init__( log_samples_per_batch: int = 1, in_channels: int = 2, example_input_yx_shape: Sequence[int] = (256, 256), - in_stack_depth: int = 15, # number of slices in the input stack + in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, ) -> None: @@ -504,8 +495,8 @@ def __init__( self.log_batches_per_epoch = log_batches_per_epoch self.log_samples_per_batch = log_samples_per_batch self.training_step_outputs = [] - self.validation_losses = [] self.validation_step_outputs = [] + self.test_step_outputs = [] self.encoder = ContrastiveEncoder( backbone=backbone, @@ -524,29 +515,73 @@ def __init__( ) def forward(self, x: Tensor) -> Tensor: - """Forward pass of the model. - - :param Tensor x: Input tensor (batch size, channels, depth, height, width) - :return: Projected features - :rtype: Tensor - """ + """Forward pass of the model.""" projections = self.encoder(x) return projections + def log_images(self, anchor, positive, negative, step_name, step_idx, epoch): + # middle z-slice + z_idx = 7 + + # 7th z-slice from both channels for the first sample + anchor_img_channel1 = anchor[0, 0, z_idx, :, :].cpu().numpy() + anchor_img_channel2 = anchor[0, 1, z_idx, :, :].cpu().numpy() + positive_img_channel1 = positive[0, 0, z_idx, :, :].cpu().numpy() + positive_img_channel2 = positive[0, 1, z_idx, :, :].cpu().numpy() + negative_img_channel1 = negative[0, 0, z_idx, :, :].cpu().numpy() + negative_img_channel2 = negative[0, 1, z_idx, :, :].cpu().numpy() + + images = { + f"{step_name}/anchor_channel1_epoch{epoch}_{step_idx}": wandb.Image(anchor_img_channel1), + f"{step_name}/anchor_channel2_epoch{epoch}_{step_idx}": wandb.Image(anchor_img_channel2), + f"{step_name}/positive_channel1_epoch{epoch}_{step_idx}": wandb.Image(positive_img_channel1), + f"{step_name}/positive_channel2_epoch{epoch}_{step_idx}": wandb.Image(positive_img_channel2), + f"{step_name}/negative_channel1_epoch{epoch}_{step_idx}": wandb.Image(negative_img_channel1), + f"{step_name}/negative_channel2_epoch{epoch}_{step_idx}": wandb.Image(negative_img_channel2), + } + + self.logger.experiment.log(images) + def training_step( self, batch: tuple[Tensor], batch_idx: int, ) -> Tensor: - """Training step of the model. + """Training step of the model.""" - :param tuple[Tensor] batch: Input batch of images and positive images - :param int batch_idx: Batch index - :return: Loss value - :rtype: Tensor - """ + if isinstance(self.loss_function, nn.TripletMarginLoss): + anchor, pos_img, neg_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) + else: + anchor, pos_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + loss = self.loss_function(emb_anchor, emb_pos) + + self.log("train/loss", loss, on_step=True, prog_bar=True, logger=True) + + if self.current_epoch in [0, 1, 2] and batch_idx == 0: + self.log_images(anchor, pos_img, neg_img, "train", batch_idx, self.current_epoch) + + self.training_step_outputs.append(loss) + return {'loss': loss} - if self.loss_function.__name__ == "TripletMarginLoss": + def on_train_epoch_end(self) -> None: + epoch_loss = torch.stack(self.training_step_outputs).mean() + self.log("train/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) + self.training_step_outputs.clear() + + def validation_step( + self, + batch: tuple[Tensor], + batch_idx: int, + ) -> Tensor: + """Validation step of the model.""" + + if isinstance(self.loss_function, nn.TripletMarginLoss): anchor, pos_img, neg_img = batch emb_anchor = self.encoder(anchor) emb_pos = self.encoder(pos_img) @@ -558,14 +593,51 @@ def training_step( emb_pos = self.encoder(pos_img) loss = self.loss_function(emb_anchor, emb_pos) - self.log("train_loss", loss) - return loss + self.log("val/loss_step", loss, on_step=True, prog_bar=True, logger=True) - def configure_optimizers(self) -> torch.optim.Optimizer: - """Configure the optimizer for training. + if self.current_epoch in [0, 1, 2] and batch_idx == 0: + self.log_images(anchor, pos_img, neg_img, "validation", batch_idx, self.current_epoch) - :return: Optimizer - :rtype: torch.optim.Optimizer - """ - optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) + self.validation_step_outputs.append(loss) + return {'loss': loss} + + def on_validation_epoch_end(self) -> None: + epoch_loss = torch.stack(self.validation_step_outputs).mean() + self.log("val/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) + self.validation_step_outputs.clear() + + def test_step( + self, + batch: tuple[Tensor], + batch_idx: int, + ) -> Tensor: + """Test step of the model.""" + + if isinstance(self.loss_function, nn.TripletMarginLoss): + anchor, pos_img, neg_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) + else: + anchor, pos_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + loss = self.loss_function(emb_anchor, emb_pos) + + self.log("test/loss_step", loss, on_step=True, prog_bar=True, logger=True) + + if self.current_epoch in [0, 1, 2] and batch_idx == 0: + self.log_images(anchor, pos_img, neg_img, "test", batch_idx, self.current_epoch) + + self.test_step_outputs.append(loss) + return {'loss': loss} + + def on_test_epoch_end(self) -> None: + epoch_loss = torch.stack(self.test_step_outputs).mean() + self.log("test/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) + self.test_step_outputs.clear() + + def configure_optimizers(self): + optimizer = Adam(self.parameters(), lr=self.lr) return optimizer diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 841be751..e6da09d6 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -4,7 +4,6 @@ import torch.nn as nn import torch.nn.functional as F - class ContrastiveEncoder(nn.Module): def __init__( self, @@ -42,7 +41,7 @@ def __init__( num_classes=4 * embedding_len, ) - if "convnext" in backbone: + if "convnext_tiny" in backbone: # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. in_channels_encoder = self.model.stem[0].out_channels stem = UNeXt2Stem( @@ -90,4 +89,4 @@ def __init__( pass def forward(self, x): - return self.model(x) + return self.model(x) \ No newline at end of file From e7b5121912ef75dc26e678457168728c690f8179 Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Sat, 6 Jul 2024 20:01:24 -0700 Subject: [PATCH 11/87] updated training_script.py --- .../training_script.py | 282 ++++++++++++------ 1 file changed, 193 insertions(+), 89 deletions(-) diff --git a/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script.py index 701d04eb..db010560 100644 --- a/applications/contrastive_phenotyping/training_script.py +++ b/applications/contrastive_phenotyping/training_script.py @@ -1,100 +1,204 @@ # %% Imports and paths. import os -import torch -from viscy.light.engine import ContrastiveModule -from viscy.unet.networks.unext2 import UNeXt2Stem -from viscy.representation.contrastive import ContrastiveEncoder from pathlib import Path -import torchview +from argparse import ArgumentParser -top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") -input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/patch_final.zarr" -model_dir = top_dir / "infection_classification/models/infection_score" +import torch +import torchview +from torch.optim import Adam -%load_ext autoreload -%autoreload 2 -# %% Initialize the model and log the graph. -contra_model = ContrastiveEncoder(backbone = "convnext_tiny") -print(contra_model) - -# %% -model_graph = torchview.draw_graph( - contra_model, - torch.randn(1, 2, 15, 224, 224), - depth=3, # adjust depth to zoom in. - device="cpu", -) -# Print the image of the model. -model_graph.visual_graph - -# %% Initiatlize the lightning module and view the model. -contrastive_module = ContrastiveModule() -print(contrastive_module.encoder) +from lightning.pytorch import Trainer, seed_everything +from lightning.pytorch.callbacks import ModelCheckpoint, RichProgressBar +#from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.loggers import WandbLogger +from lightning.pytorch.callbacks import TQDMProgressBar +import wandb +from tqdm import tqdm -# %% -model_graph = torchview.draw_graph( - contrastive_module.encoder, - torch.randn(1, 2, 15, 200, 200), - depth=3, # adjust depth to zoom in. - device="cpu", -) -# Print the image of the model. -model_graph.visual_graph - -# %% Initialize the data module and view the data. - -# %% Train the model. - - -# %% Playground -import timm - -available_models = timm.list_models(pretrained=True) - -stem = UNeXt2Stem( - in_channels=2, out_channels=96, kernel_size=(5, 2, 2), in_stack_depth=15 -) -print(stem) -stem_graph = torchview.draw_graph( - stem, - torch.randn(1, 2, 15, 256, 256), - depth=2, # adjust depth to zoom in. - device="cpu", -) -# Print the image of the model. -stem_graph.visual_graph -# %% -encoder = timm.create_model( - "convnext_tiny", - pretrained=True, - features_only=False, - num_classes=200, -) - -print(encoder) +from viscy.light.engine import ContrastiveModule +from viscy.representation.contrastive import ContrastiveEncoder +from viscy.data.hcs import ContrastiveDataModule -# %% +# %% Paths and constants +os.environ["WANDB_DIR"] = "/hpc/mydata/alishba.imran/wandb_logs/" -encoder.stem = stem +#wandb.init(project="contrastive_model", dir="/hpc/mydata/alishba.imran/wandb_logs/") -model_graph = torchview.draw_graph( - encoder, - torch.randn(1, 2, 15, 256, 256), - depth=2, # adjust depth to zoom in. - device="cpu", -) -# Print the image of the model. -model_graph.visual_graph -# %% -encoder.stem = torch.nn.Identity() - -encoder_graph = torchview.draw_graph( - encoder, - torch.randn(1, 96, 128, 128), - depth=2, # adjust depth to zoom in. - device="cpu", -) -# Print the image of the model. -encoder_graph.visual_graph +top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") +input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +model_dir = top_dir / "infection_classification/models/infection_score" +timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" + +# Data parameters +base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +channels = 2 +x = 200 +y = 200 +z = 15 +z_range = (28, 43) +batch_size = 32 + +# %% Initialize the model and log the graph +#contra_model = ContrastiveEncoder(backbone="convnext_tiny") +# print(contra_model) + +# model_graph = torchview.draw_graph( +# contra_model, +# torch.randn(1, 2, 15, 224, 224), +# depth=3, +# device="cpu", +# ) +# model_graph.visual_graph + +#contrastive_module = ContrastiveModule() +# print(contrastive_module.encoder) + +# model_graph = torchview.draw_graph( +# contrastive_module.encoder, +# torch.randn(1, 2, 15, 200, 200), +# depth=3, +# device="cpu", +# ) +# model_graph.visual_graph + +# %% Progress bar + +class LitProgressBar(TQDMProgressBar): + def init_validation_tqdm(self): + bar = super().init_validation_tqdm() + bar.set_description("Running validation...") + return bar + + def init_train_tqdm(self): + bar = super().init_train_tqdm() + bar.set_description("Training...") + return bar + + def init_test_tqdm(self): + bar = super().init_test_tqdm() + bar.set_description("Testing...") + return bar + +# %% Define the main function for training +def main(hparams): + # Seed for reproducibility + # seed_everything(42, workers=True) + + num_gpus = torch.cuda.device_count() + print(f"Number of GPUs available: {num_gpus}") + + print("Starting data module..") + # Initialize the data module + data_module = ContrastiveDataModule( + base_path=base_path, + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + batch_size=batch_size, + z_range=z_range, + ) + + print("data module set up!") + + # Setup the data module for training, val and testing + data_module.setup(stage='fit') + + print(f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}") + print(f"Training dataset size: {len(data_module.train_dataset)}") + print(f"Validation dataset size: {len(data_module.val_dataset)}") + print(f"Test dataset size: {len(data_module.test_dataset)}") + + + # Initialize the model + model = ContrastiveModule( + backbone=hparams.backbone, + loss_function=torch.nn.TripletMarginLoss(), + margin=hparams.margin, + lr=hparams.lr, + schedule=hparams.schedule, + log_batches_per_epoch=hparams.log_batches_per_epoch, + log_samples_per_batch=hparams.log_samples_per_batch, + in_channels=channels, + example_input_yx_shape=(x, y), + in_stack_depth=z, + stem_kernel_size=(5, 3, 3), + embedding_len=hparams.embedding_len, + ) + + # Initialize logger + wandb_logger = WandbLogger(project="contrastive_model", log_model="all") + + checkpoint_callback = ModelCheckpoint( + dirpath=model_dir, + filename="contrastive_model-{epoch:02d}-{val_loss:.2f}", + save_top_k=3, + mode="min", + monitor="val/loss_epoch", + ) + + trainer = Trainer( + max_epochs=hparams.max_epochs, + callbacks=[checkpoint_callback], + logger=wandb_logger, + accelerator=hparams.accelerator, + devices=hparams.devices, + num_nodes=hparams.num_nodes, + strategy="ddp", + log_every_n_steps=hparams.log_every_n_steps, + num_sanity_val_steps=0 + ) + + # Fetches batches from the training dataloader, + # Calls the training_step method on the model for each batch + # Aggregates the losses and performs optimization steps + trainer.fit(model, datamodule=data_module) + + # Validate the model + trainer.validate(model, datamodule=data_module) + + # Test the model + trainer.test(model, datamodule=data_module) + +if __name__ == "__main__": + import sys + if "ipykernel_launcher" in sys.argv[0]: + # Jupyter Notebook environment + args = { + "backbone": "convnext_tiny", + "margin": 0.5, + "lr": 1e-3, + "schedule": "Constant", + "log_batches_per_epoch": 8, + "log_samples_per_batch": 1, + "embedding_len": 256, + "max_epochs": 100, + "accelerator": "gpu", + "devices": 1, # Set to 4 GPUs + "num_nodes": 2, + "log_every_n_steps": 1, + } + class HParams: + def __init__(self, **kwargs): + self.__dict__.update(kwargs) + hparams = HParams(**args) + main(hparams) + else: + parser = ArgumentParser() + parser.add_argument("--backbone", type=str, default="convnext_tiny") + parser.add_argument("--margin", type=float, default=0.5) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--schedule", type=str, default="Constant") + parser.add_argument("--log_batches_per_epoch", type=int, default=8) + parser.add_argument("--log_samples_per_batch", type=int, default=1) + parser.add_argument("--embedding_len", type=int, default=256) + parser.add_argument("--max_epochs", type=int, default=100) + parser.add_argument("--accelerator", type=str, default="gpu") + parser.add_argument("--devices", type=int, default=1) # 4 GPUs + parser.add_argument("--num_nodes", type=int, default=2) + parser.add_argument("--log_every_n_steps", type=int, default=1) + args = parser.parse_args() + + main(args) # %% From de7188b628c3c6455820a4940f866867612a4dd3 Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Sun, 7 Jul 2024 11:19:36 -0700 Subject: [PATCH 12/87] Update hcs.py --- viscy/data/hcs.py | 78 ++++++++++++++++++++++++++++++++--------------- 1 file changed, 53 insertions(+), 25 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index bb015f90..93042f24 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -6,14 +6,14 @@ from glob import glob from pathlib import Path from typing import Callable, Literal, Optional, Sequence, Union -import pytorch_lightning as pl +#import pytorch_lightning as pl import numpy as np import torch import zarr from imageio import imread from iohub.ngff import ImageArray, Plate, Position, open_ome_zarr -from lightning.pytorch import LightningDataModule +#from lightning.pytorch import LightningDataModule from monai.data import set_track_meta from monai.data.utils import collate_meta_tensor from monai.transforms import ( @@ -23,11 +23,32 @@ MultiSampleTrait, RandAffined, ) + + from torch import Tensor from torch.utils.data import DataLoader, Dataset from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample +import random +from viscy.transforms import ( + RandAdjustContrastd, + RandAffined, + RandGaussianNoised, + RandGaussianSmoothd, + RandScaleIntensityd, +) +from monai.transforms import Compose +from iohub import open_ome_zarr +import pandas as pd +import warnings +from lightning.pytorch import LightningDataModule, LightningModule, Trainer + + +# from viscy.data.typing import Optional +from pathlib import Path + +warnings.filterwarnings("ignore") def _ensure_channel_list(str_or_seq: str | Sequence[str]) -> list[str]: """ @@ -610,7 +631,7 @@ def __init__( print(f"Initialized dataset with {len(self.positions)} positions.") def open_zarr_store(self, path, layout="hcs", mode="r"): - print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") + #print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") return open_ome_zarr(path, layout=layout, mode=mode) def __len__(self): @@ -625,8 +646,8 @@ def __getitem__(self, idx): if self.transform else anchor_data ) - if self.transform: - print("Positive transformation applied") + # if self.transform: + # print("Positive transformation applied") negative_idx = idx while negative_idx == idx: @@ -639,13 +660,13 @@ def __getitem__(self, idx): if self.transform else negative_data ) - if self.transform: - print("Negative transformation applied") + # if self.transform: + # print("Negative transformation applied") - print("shapes of tensors") - print(torch.tensor(anchor_data).shape) - print(torch.tensor(positive_data).shape) - print(torch.tensor(negative_data).shape) + # print("shapes of tensors") + # print(torch.tensor(anchor_data).shape) + # print(torch.tensor(positive_data).shape) + # print(torch.tensor(negative_data).shape) return ( torch.tensor(anchor_data), torch.tensor(positive_data), @@ -654,13 +675,13 @@ def __getitem__(self, idx): def load_data(self, position_path): position = self.ds[position_path] - print(f"Loading data from position: {position_path}") + # print(f"Loading data from position: {position_path}") zarr_array = position["0"][:] - print("Shape before:", zarr_array.shape) + # print("Shape before:", zarr_array.shape) data = self.restructure_data(zarr_array, position_path) if self.z_range: data = data[:, self.z_range[0] : self.z_range[1], :, :] - print("Shape after:", data.shape) + # print("Shape after:", data.shape) return data def restructure_data(self, data, position_path): @@ -700,29 +721,28 @@ def restructure_data(self, data, position_path): def get_transforms(): transforms = Compose( [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.5, 2.0)), + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.8, 1.2)), RandAffined( keys=["image"], prob=0.5, - rotate_range=(0.2, 0.2), - shear_range=(0.2, 0.2), - scale_range=(0.2, 0.2), + rotate_range=(0.1, 0.1), + shear_range=(0.1, 0.1), + scale_range=(0.1, 0.1), ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.05), RandGaussianSmoothd( keys=["image"], prob=0.5, - sigma_x=(0.5, 1.0), - sigma_y=(0.5, 1.0), - sigma_z=(0.5, 1.0), + sigma_x=(0.1, 0.5), + sigma_y=(0.1, 0.5), + sigma_z=(0.1, 0.5), ), - RandScaleIntensityd(keys=["image"], factors=(0.5, 2.0), prob=0.5), + RandScaleIntensityd(keys=["image"], factors=(0.8, 1.2), prob=0.5), ] ) return transforms - -class ContrastiveDataModule(pl.LightningDataModule): +class ContrastiveDataModule(LightningDataModule): def __init__( self, base_path: str, @@ -793,6 +813,8 @@ def train_dataloader(self): batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True ) def val_dataloader(self): @@ -801,6 +823,8 @@ def val_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True ) def test_dataloader(self): @@ -809,6 +833,8 @@ def test_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True ) def predict_dataloader(self): @@ -821,4 +847,6 @@ def predict_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True ) From 94cd28d894346c2b3318eb533c6b33e0e2f9b396 Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Sun, 7 Jul 2024 11:20:14 -0700 Subject: [PATCH 13/87] contrastive.py --- viscy/representation/contrastive.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 841be751..ba7230ab 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -4,7 +4,6 @@ import torch.nn as nn import torch.nn.functional as F - class ContrastiveEncoder(nn.Module): def __init__( self, @@ -42,7 +41,7 @@ def __init__( num_classes=4 * embedding_len, ) - if "convnext" in backbone: + if "convnext_tiny" in backbone: # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. in_channels_encoder = self.model.stem[0].out_channels stem = UNeXt2Stem( From ebd4a78cce7c0079624528d3f126c15826993065 Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Sun, 7 Jul 2024 11:21:37 -0700 Subject: [PATCH 14/87] engine.py --- viscy/light/engine.py | 154 +++++++++++++++++++++++++++++++----------- 1 file changed, 114 insertions(+), 40 deletions(-) diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 0a79ed27..be9e354d 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -4,9 +4,14 @@ import numpy as np import torch - +import wandb from imageio import imwrite -from lightning.pytorch import LightningModule +#from lightning.pytorch import LightningModule +#from lightning import LightningModule +from torch.optim import Adam + +from lightning.pytorch import LightningDataModule, LightningModule, Trainer + from matplotlib.pyplot import get_cmap from monai.optimizers import WarmupCosineSchedule from monai.transforms import DivisiblePad @@ -84,7 +89,6 @@ def forward(self, preds, target): loss += (1 - ms_ssim) * self.ms_dssim_alpha return loss - class VSUNet(LightningModule): """Regression U-Net module for virtual staining. @@ -462,24 +466,12 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 self._detach_sample((source, target * mask.unsqueeze(2), pred)) ) - class ContrastiveModule(LightningModule): - """Contrastive Learning Model for self-supervised learning. - - :param string backbone: Neural network backbone, defaults to convnext_tiny - :param nn.Module loss_function: Loss function for training, defaults to TripletMarginLoss - :param float margin: Margin for triplet loss, defaults to 0.5 - :param float lr: Learning rate for optimizer, defaults to 1e-3 - :param Literal['WarmupCosine', 'Constant'] schedule: Learning rate scheduler, defaults to "Constant" - :param int log_batches_per_epoch: Number of batches to log each training epoch, defaults to 8 - :param int log_samples_per_batch: Number of samples to log each training batch, defaults to 1 - :param Sequence[int] example_input_yx_shape: XY shape of the example input for network graph tracing, defaults to (256, 256) - :param int z_slices: Number of slices in the input stack, defaults to 5 - """ + """Contrastive Learning Model for self-supervised learning.""" def __init__( self, - backbone: str = "convnext_tiny", # convnexts are newer "ResNets" informed by vision transformers. + backbone: str = "convnext_tiny", loss_function: Union[ nn.Module, nn.CosineEmbeddingLoss, nn.TripletMarginLoss ] = nn.TripletMarginLoss(), @@ -490,7 +482,7 @@ def __init__( log_samples_per_batch: int = 1, in_channels: int = 2, example_input_yx_shape: Sequence[int] = (256, 256), - in_stack_depth: int = 15, # number of slices in the input stack + in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, ) -> None: @@ -503,8 +495,8 @@ def __init__( self.log_batches_per_epoch = log_batches_per_epoch self.log_samples_per_batch = log_samples_per_batch self.training_step_outputs = [] - self.validation_losses = [] self.validation_step_outputs = [] + self.test_step_outputs = [] self.encoder = ContrastiveEncoder( backbone=backbone, @@ -523,29 +515,73 @@ def __init__( ) def forward(self, x: Tensor) -> Tensor: - """Forward pass of the model. - - :param Tensor x: Input tensor (batch size, channels, depth, height, width) - :return: Projected features - :rtype: Tensor - """ + """Forward pass of the model.""" projections = self.encoder(x) return projections + def log_images(self, anchor, positive, negative, step_name, step_idx, epoch): + # middle z-slice + z_idx = 7 + + # 7th z-slice from both channels for the first sample + anchor_img_channel1 = anchor[0, 0, z_idx, :, :].cpu().numpy() + anchor_img_channel2 = anchor[0, 1, z_idx, :, :].cpu().numpy() + positive_img_channel1 = positive[0, 0, z_idx, :, :].cpu().numpy() + positive_img_channel2 = positive[0, 1, z_idx, :, :].cpu().numpy() + negative_img_channel1 = negative[0, 0, z_idx, :, :].cpu().numpy() + negative_img_channel2 = negative[0, 1, z_idx, :, :].cpu().numpy() + + images = { + f"{step_name}/anchor_channel1_epoch{epoch}_{step_idx}": wandb.Image(anchor_img_channel1), + f"{step_name}/anchor_channel2_epoch{epoch}_{step_idx}": wandb.Image(anchor_img_channel2), + f"{step_name}/positive_channel1_epoch{epoch}_{step_idx}": wandb.Image(positive_img_channel1), + f"{step_name}/positive_channel2_epoch{epoch}_{step_idx}": wandb.Image(positive_img_channel2), + f"{step_name}/negative_channel1_epoch{epoch}_{step_idx}": wandb.Image(negative_img_channel1), + f"{step_name}/negative_channel2_epoch{epoch}_{step_idx}": wandb.Image(negative_img_channel2), + } + + self.logger.experiment.log(images) + def training_step( self, batch: tuple[Tensor], batch_idx: int, ) -> Tensor: - """Training step of the model. + """Training step of the model.""" - :param tuple[Tensor] batch: Input batch of images and positive images - :param int batch_idx: Batch index - :return: Loss value - :rtype: Tensor - """ + if isinstance(self.loss_function, nn.TripletMarginLoss): + anchor, pos_img, neg_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) + else: + anchor, pos_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + loss = self.loss_function(emb_anchor, emb_pos) - if self.loss_function.__name__ == "TripletMarginLoss": + self.log("train/loss", loss, on_step=True, prog_bar=True, logger=True) + + if self.current_epoch in [0, 1, 2] and batch_idx == 0: + self.log_images(anchor, pos_img, neg_img, "train", batch_idx, self.current_epoch) + + self.training_step_outputs.append(loss) + return {'loss': loss} + + def on_train_epoch_end(self) -> None: + epoch_loss = torch.stack(self.training_step_outputs).mean() + self.log("train/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) + self.training_step_outputs.clear() + + def validation_step( + self, + batch: tuple[Tensor], + batch_idx: int, + ) -> Tensor: + """Validation step of the model.""" + + if isinstance(self.loss_function, nn.TripletMarginLoss): anchor, pos_img, neg_img = batch emb_anchor = self.encoder(anchor) emb_pos = self.encoder(pos_img) @@ -557,14 +593,52 @@ def training_step( emb_pos = self.encoder(pos_img) loss = self.loss_function(emb_anchor, emb_pos) - self.log("train_loss", loss) - return loss + self.log("val/loss_step", loss, on_step=True, prog_bar=True, logger=True) - def configure_optimizers(self) -> torch.optim.Optimizer: - """Configure the optimizer for training. + if self.current_epoch in [0, 1, 2] and batch_idx == 0: + self.log_images(anchor, pos_img, neg_img, "validation", batch_idx, self.current_epoch) - :return: Optimizer - :rtype: torch.optim.Optimizer - """ - optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) + self.validation_step_outputs.append(loss) + return {'loss': loss} + + def on_validation_epoch_end(self) -> None: + epoch_loss = torch.stack(self.validation_step_outputs).mean() + self.log("val/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) + self.validation_step_outputs.clear() + + def test_step( + self, + batch: tuple[Tensor], + batch_idx: int, + ) -> Tensor: + """Test step of the model.""" + + if isinstance(self.loss_function, nn.TripletMarginLoss): + anchor, pos_img, neg_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) + else: + anchor, pos_img = batch + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + loss = self.loss_function(emb_anchor, emb_pos) + + self.log("test/loss_step", loss, on_step=True, prog_bar=True, logger=True) + + if self.current_epoch in [0, 1, 2] and batch_idx == 0: + self.log_images(anchor, pos_img, neg_img, "test", batch_idx, self.current_epoch) + + self.test_step_outputs.append(loss) + return {'loss': loss} + + def on_test_epoch_end(self) -> None: + epoch_loss = torch.stack(self.test_step_outputs).mean() + self.log("test/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) + self.test_step_outputs.clear() + + def configure_optimizers(self): + optimizer = Adam(self.parameters(), lr=self.lr) return optimizer + From 8162f20784c28e53fbd4f6dbbec85105c63e86d3 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 14:11:24 -0700 Subject: [PATCH 15/87] script to test data i/o speed from different filesystems --- .../dataloader_test.py | 395 ++++-------------- 1 file changed, 71 insertions(+), 324 deletions(-) diff --git a/applications/contrastive_phenotyping/dataloader_test.py b/applications/contrastive_phenotyping/dataloader_test.py index 91d0faa4..89132b45 100644 --- a/applications/contrastive_phenotyping/dataloader_test.py +++ b/applications/contrastive_phenotyping/dataloader_test.py @@ -1,339 +1,86 @@ -# %% -import os -import random -import torch -import numpy as np -from torch.utils.data import Dataset, DataLoader -from viscy.transforms import ( - RandAdjustContrastd, - RandAffined, - RandGaussianNoised, - RandGaussianSmoothd, - RandScaleIntensityd, -) -from monai.transforms import Compose -from iohub import open_ome_zarr -import pandas as pd import warnings -import pytorch_lightning as pl - -# from viscy.data.typing import Optional +import os from pathlib import Path +from viscy.data.hcs import ContrastiveDataModule +import time warnings.filterwarnings("ignore") - - -# %% -class OMEZarrDataset(Dataset): - def __init__( - self, - base_path, - channels, - x, - y, - timesteps_csv_path, - transform=None, - z_range=None, - ): - self.base_path = base_path - self.channels = channels - self.x = x - self.y = y - self.z_range = z_range - self.transform = transform - self.ds = self.open_zarr_store(self.base_path) - self.positions = list(self.ds.positions()) - self.timesteps_df = pd.read_csv(timesteps_csv_path) - print(f"Initialized dataset with {len(self.positions)} positions.") - - def open_zarr_store(self, path, layout="hcs", mode="r"): - print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") - return open_ome_zarr(path, layout=layout, mode=mode) - - def __len__(self): - return len(self.positions) - - def __getitem__(self, idx): - anchor_position_path = self.positions[idx][0] - anchor_data = self.load_data(anchor_position_path) - - positive_data = ( - self.transform({"image": anchor_data})["image"] - if self.transform - else anchor_data - ) - if self.transform: - print("Positive transformation applied") - - negative_idx = idx - while negative_idx == idx: - negative_idx = random.randint(0, self.__len__() - 1) - negative_position_path = self.positions[negative_idx][0] - negative_data = self.load_data(negative_position_path) - - negative_data = ( - self.transform({"image": negative_data})["image"] - if self.transform - else negative_data - ) - if self.transform: - print("Negative transformation applied") - - print("shapes of tensors") - print(torch.tensor(anchor_data).shape) - print(torch.tensor(positive_data).shape) - print(torch.tensor(negative_data).shape) - return ( - torch.tensor(anchor_data), - torch.tensor(positive_data), - torch.tensor(negative_data), - ) - - def load_data(self, position_path): - position = self.ds[position_path] - print(f"Loading data from position: {position_path}") - zarr_array = position["0"][:] - print("Shape before:", zarr_array.shape) - data = self.restructure_data(zarr_array, position_path) - if self.z_range: - data = data[:, self.z_range[0] : self.z_range[1], :, :] - print("Shape after:", data.shape) - return data - - def restructure_data(self, data, position_path): - # Extract row, column, fov, and cell_id from position_path - parts = position_path.split("/") - row = parts[0] - column = parts[1] - fov_cell = parts[2] - - fov = int(fov_cell.split("fov")[1].split("cell")[0]) - cell_id = int(fov_cell.split("cell")[1]) - - extracted_combined = f"{row}/{column}/fov{fov}cell{cell_id}" - - matched_rows = self.timesteps_df[ - self.timesteps_df.apply( - lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", - axis=1, - ) - == extracted_combined - ] - - if matched_rows.empty: - raise ValueError( - f"No matching entry found for position path: {position_path}" - ) - - start_time = matched_rows["Start Time"].values[0] - end_time = matched_rows["End Time"].values[0] - - random_timestep = np.random.randint(start_time, end_time) - - reshaped_data = data[random_timestep] - return reshaped_data - - -def get_transforms(): - transforms = Compose( - [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.5, 2.0)), - RandAffined( - keys=["image"], - prob=0.5, - rotate_range=(0.2, 0.2), - shear_range=(0.2, 0.2), - scale_range=(0.2, 0.2), - ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), - RandGaussianSmoothd( - keys=["image"], - prob=0.5, - sigma_x=(0.5, 1.0), - sigma_y=(0.5, 1.0), - sigma_z=(0.5, 1.0), - ), - RandScaleIntensityd(keys=["image"], factors=(0.5, 2.0), prob=0.5), - ] +data_on_lustre = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") +data_on_vast = Path("/hpc/projects/virtual_staining/viral_sensor_test_dataio/") + + +def profile_dataio(top_dir, num_epochs=2): + channels = 2 + x = 200 + y = 200 + z_range = (0, 10) + batch_size = 128 + base_path = ( + top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" + ) + timesteps_csv_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" + + data_module = ContrastiveDataModule( + base_path=base_path, + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + batch_size=batch_size, + num_workers=8, + z_range=z_range, ) - return transforms - - -class OMEZarrDataModule(pl.LightningDataModule): - def __init__( - self, - base_path: str, - channels: int, - x: int, - y: int, - timesteps_csv_path: str, - predict_base_path: str = None, - train_split_ratio: float = 0.64, - val_split_ratio: float = 0.16, - batch_size: int = 4, - num_workers: int = 8, - z_range: tuple[int, int] = None, - transform=None, - ): - super().__init__() - self.base_path = Path(base_path) - self.channels = channels - self.x = x - self.y = y - self.timesteps_csv_path = timesteps_csv_path - self.predict_base_path = Path(predict_base_path) if predict_base_path else None - self.train_split_ratio = train_split_ratio - self.val_split_ratio = val_split_ratio - self.batch_size = batch_size - self.num_workers = num_workers - self.z_range = z_range - self.transform = transform or get_transforms() - self.train_dataset = None - self.val_dataset = None - self.test_dataset = None - self.predict_dataset = None - - def setup(self, stage: str = None): - dataset = OMEZarrDataset( - self.base_path, - self.channels, - self.x, - self.y, - self.timesteps_csv_path, - transform=self.transform, - z_range=self.z_range, - ) - train_size = int(len(dataset) * self.train_split_ratio) - val_size = int(len(dataset) * self.val_split_ratio) - test_size = len(dataset) - train_size - val_size + # for train and val + data_module.setup() - self.train_dataset, self.val_dataset, self.test_dataset = ( - torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) - ) + total_data_size = os.path.getsize(base_path) # Get the file size in bytes + total_data_size_mb = total_data_size / (1024 * 1024) # Convert to MB - # setup prediction dataset (if needed) - if stage == "predict" and self.predict_base_path: - self.predict_dataset = OMEZarrDataset( - self.predict_base_path, - self.channels, - self.x, - self.y, - self.timesteps_csv_path, - transform=self.transform, - z_range=self.z_range, + print( + f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}" + ) + print(f"Training dataset size: {len(data_module.train_dataset)}") + print(f"Validation dataset size: {len(data_module.val_dataset)}") + print(f"Test dataset size: {len(data_module.test_dataset)}") + + start_time = time.time() + total_bytes_transferred = 0 # Track the total number of bytes transferred + + # Profile the data i/o + for i in range(num_epochs): + for batch in data_module.train_dataloader(): + anchor_batch, positive_batch, negative_batch = batch + total_bytes_transferred += ( + anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes ) - - def train_dataloader(self): - return DataLoader( - self.train_dataset, - batch_size=self.batch_size, - shuffle=True, - num_workers=self.num_workers, - ) - - def val_dataloader(self): - return DataLoader( - self.val_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - ) - - def test_dataloader(self): - return DataLoader( - self.test_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - ) - - def predict_dataloader(self): - if self.predict_dataset is None: - raise ValueError( - "Predict dataset not set up. Call setup(stage='predict') first." + print("Anchor batch shape:", anchor_batch.shape) + print("Positive batch shape:", positive_batch.shape) + print("Negative batch shape:", negative_batch.shape) + break + for batch in data_module.val_dataloader(): + anchor_batch, positive_batch, negative_batch = batch + total_bytes_transferred += ( + anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes ) - return DataLoader( - self.predict_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - ) - - -# %% Testing the DataModule - -base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/small_patch.zarr" -# predict_base_path = " " -channels = 2 -x = 200 -y = 200 -z = 10 -z_range = (0, 10) -batch_size = 4 -timesteps_csv_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" - -data_module = OMEZarrDataModule( - base_path=base_path, - channels=channels, - x=x, - y=y, - timesteps_csv_path=timesteps_csv_path, - batch_size=batch_size, - z_range=z_range, -) - -# for train and val -data_module.setup() - -print( - f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}" -) -print(f"Training dataset size: {len(data_module.train_dataset)}") -print(f"Validation dataset size: {len(data_module.val_dataset)}") -print(f"Test dataset size: {len(data_module.test_dataset)}") - -train_loader = data_module.train_dataloader() - -print("Training DataLoader:") -for batch in train_loader: - anchor_batch, positive_batch, negative_batch = batch - print("Anchor batch shape:", anchor_batch.shape) - print("Positive batch shape:", positive_batch.shape) - print("Negative batch shape:", negative_batch.shape) - break - -val_loader = data_module.val_dataloader() - -print("Validation DataLoader:") -for batch in val_loader: - anchor_batch, positive_batch, negative_batch = batch - print("Anchor batch shape:", anchor_batch.shape) - print("Positive batch shape:", positive_batch.shape) - print("Negative batch shape:", negative_batch.shape) - break + print("Anchor batch shape:", anchor_batch.shape) + print("Positive batch shape:", positive_batch.shape) + print("Negative batch shape:", negative_batch.shape) + break -test_loader = data_module.test_dataloader() + end_time = time.time() + elapsed_time = end_time - start_time + data_transfer_speed = (total_bytes_transferred / elapsed_time) / ( + 1024 * 1024 + ) # Calculate data transfer speed in MBPS -print("Test DataLoader:") -for batch in test_loader: - anchor_batch, positive_batch, negative_batch = batch - print("Anchor batch shape:", anchor_batch.shape) - print("Positive batch shape:", positive_batch.shape) - print("Negative batch shape:", negative_batch.shape) - break + print(f"Elapsed time for {num_epochs} iterations: {elapsed_time} seconds") + print(f"Average time per iteration: {elapsed_time/num_epochs} seconds") + print(f"Data transfer speed: {data_transfer_speed} MBPS") -# Setup the DataModule for prediction -# data_module.setup(stage='predict') -# Get the predict DataLoader and print batch shapes -# predict_loader = data_module.predict_dataloader() -# print("Predict DataLoader:") -# for batch in predict_loader: -# anchor_batch, positive_batch, negative_batch = batch -# print("Anchor batch shape:", anchor_batch.shape) -# print("Positive batch shape:", positive_batch.shape) -# print("Negative batch shape:", negative_batch.shape) -# break +# %% Testing the data i/o with data stored on Lustre +profile_dataio(data_on_lustre) -# %% +# %% Testing the data i/o with data stored on Vast +profile_dataio(data_on_vast) From 2459d8269554eafb2e860666968da6b445b93dfa Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 14:13:14 -0700 Subject: [PATCH 16/87] moved applications folder to viscy.applications so that pip install -e . works. --- .../applications}/contrastive_phenotyping/dataloader_test.py | 0 .../applications}/contrastive_phenotyping/training_script.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename {applications => viscy/applications}/contrastive_phenotyping/dataloader_test.py (100%) rename {applications => viscy/applications}/contrastive_phenotyping/training_script.py (100%) diff --git a/applications/contrastive_phenotyping/dataloader_test.py b/viscy/applications/contrastive_phenotyping/dataloader_test.py similarity index 100% rename from applications/contrastive_phenotyping/dataloader_test.py rename to viscy/applications/contrastive_phenotyping/dataloader_test.py diff --git a/applications/contrastive_phenotyping/training_script.py b/viscy/applications/contrastive_phenotyping/training_script.py similarity index 100% rename from applications/contrastive_phenotyping/training_script.py rename to viscy/applications/contrastive_phenotyping/training_script.py From 72862e9ceadc25a8fc884e406075cadeafc211d5 Mon Sep 17 00:00:00 2001 From: Duo Peng Date: Sun, 7 Jul 2024 16:36:59 -0700 Subject: [PATCH 17/87] add resnet50 to ContrastiveEncoder --- .../training_script.py | 19 ++++++++-- viscy/representation/contrastive.py | 37 +++++++++++++++++-- viscy/unet/networks/resnet.py | 28 ++++++++++++++ 3 files changed, 77 insertions(+), 7 deletions(-) create mode 100644 viscy/unet/networks/resnet.py diff --git a/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script.py index 701d04eb..80c71db3 100644 --- a/applications/contrastive_phenotyping/training_script.py +++ b/applications/contrastive_phenotyping/training_script.py @@ -1,6 +1,7 @@ # %% Imports and paths. import os import torch +# add path from viscy.light.engine import ContrastiveModule from viscy.unet.networks.unext2 import UNeXt2Stem from viscy.representation.contrastive import ContrastiveEncoder @@ -14,10 +15,8 @@ %load_ext autoreload %autoreload 2 # %% Initialize the model and log the graph. -contra_model = ContrastiveEncoder(backbone = "convnext_tiny") +contra_model = ContrastiveEncoder(backbone = "convnext_tiny") # other options: convnext_tiny resnet50 print(contra_model) - -# %% model_graph = torchview.draw_graph( contra_model, torch.randn(1, 2, 15, 224, 224), @@ -27,6 +26,20 @@ # Print the image of the model. model_graph.visual_graph +# %% Initialize a resent50 model and log the graph. +contra_model = ContrastiveEncoder(backbone = "resnet50", in_stack_depth = 16, stem_kernel_size = (4, 3, 3)) # note that the resnet first layer takes 64 channels (so we can't have multiples of 3) +print(contra_model) +model_graph = torchview.draw_graph( + contra_model, + torch.randn(1, 2, 16, 224, 224), + depth=3, # adjust depth to zoom in. + device="cpu", +) +# Print the image of the model. +model_graph.resize_graph(scale=2.5) +model_graph.visual_graph + + # %% Initiatlize the lightning module and view the model. contrastive_module = ContrastiveModule() print(contrastive_module.encoder) diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 841be751..c4714e3b 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -1,8 +1,8 @@ import timm -from viscy.unet.networks.unext2 import UNeXt2Stem - import torch.nn as nn -import torch.nn.functional as F + +from viscy.unet.networks.resnet import resnetStem +from viscy.unet.networks.unext2 import UNeXt2Stem class ContrastiveEncoder(nn.Module): @@ -87,7 +87,36 @@ def __init__( """ elif "resnet" in backbone: # Adapt stem and projection head of resnet here. - pass + # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. + in_channels_encoder = self.model.conv1.out_channels + stem = resnetStem( + in_channels=in_channels, + out_channels=in_channels_encoder, + kernel_size=stem_kernel_size, + in_stack_depth=in_stack_depth, + ) + self.model.conv1 = stem + + self.model.fc = nn.Sequential( + self.model.fc, + nn.ReLU(inplace=True), + nn.Linear(4 * embedding_len, embedding_len), + ) + """ + head of resnet + ------------------- + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1)) + (fc): Linear(in_features=2048, out_features=1024, bias=True) + + + head of resnet for contrastive learning + ---------------------------- + (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1)) + (fc): Sequential( + (0): Linear(in_features=2048, out_features=1024, bias=True) + (1): ReLU(inplace=True) + (2): Linear(in_features=1024, out_features=256, bias=True) + """ def forward(self, x): return self.model(x) diff --git a/viscy/unet/networks/resnet.py b/viscy/unet/networks/resnet.py new file mode 100644 index 00000000..61b180d4 --- /dev/null +++ b/viscy/unet/networks/resnet.py @@ -0,0 +1,28 @@ +from torch import Tensor, nn + + +class resnetStem(nn.Module): + """Stem for ResNet networks to handle 3D multi-channel input.""" + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: tuple[int, int, int], + in_stack_depth: int, + ) -> None: + super().__init__() + ratio = in_stack_depth // kernel_size[0] + self.conv = nn.Conv3d( + in_channels=in_channels, + out_channels=out_channels // ratio, + kernel_size=kernel_size, + stride=kernel_size, + ) + + def forward(self, x: Tensor): + x = self.conv(x) + b, c, d, h, w = x.shape + # project Z/depth into channels + # return a view when possible (contiguous) + return x.reshape(b, c * d, h, w) From 859de821b5679b1d1dd0b5ac02ebaca120622bd4 Mon Sep 17 00:00:00 2001 From: Duo Peng Date: Sun, 7 Jul 2024 16:46:04 -0700 Subject: [PATCH 18/87] rename training_script.py to training_script_resnet.py --- .../{training_script.py => training_script_resnet.py} | 1 - 1 file changed, 1 deletion(-) rename applications/contrastive_phenotyping/{training_script.py => training_script_resnet.py} (99%) diff --git a/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script_resnet.py similarity index 99% rename from applications/contrastive_phenotyping/training_script.py rename to applications/contrastive_phenotyping/training_script_resnet.py index 80c71db3..21d599a4 100644 --- a/applications/contrastive_phenotyping/training_script.py +++ b/applications/contrastive_phenotyping/training_script_resnet.py @@ -1,7 +1,6 @@ # %% Imports and paths. import os import torch -# add path from viscy.light.engine import ContrastiveModule from viscy.unet.networks.unext2 import UNeXt2Stem from viscy.representation.contrastive import ContrastiveEncoder From 23bd0879547379b9209731ee4a5549577cb3256f Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 17:18:12 -0700 Subject: [PATCH 19/87] test dataloader on lustre and vast --- .../dataloader_test.py | 59 +++++++++++++------ 1 file changed, 42 insertions(+), 17 deletions(-) diff --git a/viscy/applications/contrastive_phenotyping/dataloader_test.py b/viscy/applications/contrastive_phenotyping/dataloader_test.py index 89132b45..bcdb5023 100644 --- a/viscy/applications/contrastive_phenotyping/dataloader_test.py +++ b/viscy/applications/contrastive_phenotyping/dataloader_test.py @@ -1,25 +1,36 @@ +# %% Imports and initialization. import warnings import os from pathlib import Path from viscy.data.hcs import ContrastiveDataModule import time +import wandb +from tqdm import tqdm warnings.filterwarnings("ignore") +os.environ["WANDB_DIR"] = f"/hpc/mydata/{os.environ['USER']}/wandb_logs/" data_on_lustre = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") data_on_vast = Path("/hpc/projects/virtual_staining/viral_sensor_test_dataio/") +wandb.init(project="contrastive_model", entity="alishba_imran-CZ Biohub") +# %% Method that iterates over two epochs and logs the resource usage. + + +def profile_dataio(top_dir, num_epochs=1): -def profile_dataio(top_dir, num_epochs=2): channels = 2 x = 200 y = 200 z_range = (0, 10) - batch_size = 128 + batch_size = 16 base_path = ( top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" ) timesteps_csv_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" + wandb.config.data_path = str(base_path) + wandb.config.num_epochs = num_epochs + data_module = ContrastiveDataModule( base_path=base_path, channels=channels, @@ -34,9 +45,6 @@ def profile_dataio(top_dir, num_epochs=2): # for train and val data_module.setup() - total_data_size = os.path.getsize(base_path) # Get the file size in bytes - total_data_size_mb = total_data_size / (1024 * 1024) # Convert to MB - print( f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}" ) @@ -49,24 +57,33 @@ def profile_dataio(top_dir, num_epochs=2): # Profile the data i/o for i in range(num_epochs): - for batch in data_module.train_dataloader(): + # Train dataloader + train_dataloader = data_module.train_dataloader() + train_dataloader = tqdm( + train_dataloader, desc=f"Epoch {i+1}/{num_epochs} - Train" + ) + for batch in train_dataloader: anchor_batch, positive_batch, negative_batch = batch total_bytes_transferred += ( anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes ) - print("Anchor batch shape:", anchor_batch.shape) - print("Positive batch shape:", positive_batch.shape) - print("Negative batch shape:", negative_batch.shape) - break - for batch in data_module.val_dataloader(): + # print("Anchor batch shape:", anchor_batch.shape) + # print("Positive batch shape:", positive_batch.shape) + # print("Negative batch shape:", negative_batch.shape) + + # Validation dataloader + val_dataloader = data_module.val_dataloader() + val_dataloader = tqdm( + val_dataloader, desc=f"Epoch {i+1}/{num_epochs} - Validation" + ) + for batch in val_dataloader: anchor_batch, positive_batch, negative_batch = batch total_bytes_transferred += ( anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes ) - print("Anchor batch shape:", anchor_batch.shape) - print("Positive batch shape:", positive_batch.shape) - print("Negative batch shape:", negative_batch.shape) - break + # print("Anchor batch shape:", anchor_batch.shape) + # print("Positive batch shape:", positive_batch.shape) + # print("Negative batch shape:", negative_batch.shape) end_time = time.time() elapsed_time = end_time - start_time @@ -74,13 +91,21 @@ def profile_dataio(top_dir, num_epochs=2): 1024 * 1024 ) # Calculate data transfer speed in MBPS + print("Anchor batch shape:", anchor_batch.shape) + print("Positive batch shape:", positive_batch.shape) + print("Negative batch shape:", negative_batch.shape) + print(f"Elapsed time for {num_epochs} iterations: {elapsed_time} seconds") print(f"Average time per iteration: {elapsed_time/num_epochs} seconds") print(f"Data transfer speed: {data_transfer_speed} MBPS") +# %% Testing the data i/o with data stored on Vast +profile_dataio(data_on_vast) + + # %% Testing the data i/o with data stored on Lustre profile_dataio(data_on_lustre) -# %% Testing the data i/o with data stored on Vast -profile_dataio(data_on_vast) +# %% +wandb.finish() From 955fea8d665c26aa560e89acf4f063d1f3d60063 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 17:33:48 -0700 Subject: [PATCH 20/87] move training_script_resnet to viscy.applications so that `pip install -e .` works --- .../contrastive_phenotyping/training_script_resnet.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {applications => viscy/applications}/contrastive_phenotyping/training_script_resnet.py (100%) diff --git a/applications/contrastive_phenotyping/training_script_resnet.py b/viscy/applications/contrastive_phenotyping/training_script_resnet.py similarity index 100% rename from applications/contrastive_phenotyping/training_script_resnet.py rename to viscy/applications/contrastive_phenotyping/training_script_resnet.py From f521cebb31bf1a616f72933bab2b61284defbb9a Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 18:27:11 -0700 Subject: [PATCH 21/87] refined the tests for contrastive dataloader --- .../contrastive_phenotyping/dataloader_test.py | 5 ++++- .../contrastive_phenotyping/dataloader_test.sh | 13 +++++++++++++ 2 files changed, 17 insertions(+), 1 deletion(-) create mode 100644 viscy/applications/contrastive_phenotyping/dataloader_test.sh diff --git a/viscy/applications/contrastive_phenotyping/dataloader_test.py b/viscy/applications/contrastive_phenotyping/dataloader_test.py index bcdb5023..1f9dee56 100644 --- a/viscy/applications/contrastive_phenotyping/dataloader_test.py +++ b/viscy/applications/contrastive_phenotyping/dataloader_test.py @@ -8,7 +8,7 @@ from tqdm import tqdm warnings.filterwarnings("ignore") -os.environ["WANDB_DIR"] = f"/hpc/mydata/{os.environ['USER']}/wandb_logs/" +os.environ["WANDB_DIR"] = f"/hpc/mydata/{os.environ['USER']}/" data_on_lustre = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") data_on_vast = Path("/hpc/projects/virtual_staining/viral_sensor_test_dataio/") wandb.init(project="contrastive_model", entity="alishba_imran-CZ Biohub") @@ -101,10 +101,13 @@ def profile_dataio(top_dir, num_epochs=1): # %% Testing the data i/o with data stored on Vast +print("Profiling data i/o with data stored on VAST \n ------- \n") profile_dataio(data_on_vast) # %% Testing the data i/o with data stored on Lustre +print("Profiling data i/o with data stored on Lustre\n ------- \n") + profile_dataio(data_on_lustre) # %% diff --git a/viscy/applications/contrastive_phenotyping/dataloader_test.sh b/viscy/applications/contrastive_phenotyping/dataloader_test.sh new file mode 100644 index 00000000..e317a096 --- /dev/null +++ b/viscy/applications/contrastive_phenotyping/dataloader_test.sh @@ -0,0 +1,13 @@ +#!/bin/bash +#SBATCH --partition=gpu +#SBATCH --nodes=1 +#SBATCH --gres=gpu:1 +#SBATCH --cpus-per-task=16 +#SBATCH --time=2-00:00:00 +#SBATCH --output=/hpc/mydata/$USER/slurm_logs/dataloader_test_%j.txt +#SBATCH --nodelist=gpu-b-[3-4,6] + + +# Activate viscy and run the dataloader_test.py script +conda activate /hpc/mydata/$USER/envs/viscy/ +python /hpc/mydata/$USER/viscy/applications/contrastive_phenotyping/dataloader_test.py \ No newline at end of file From 3f6d3cde0af1733a9933b64b62edc9e759c74414 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 20:10:25 -0700 Subject: [PATCH 22/87] sbatch script for dataloader --- .../contrastive_phenotyping/dataloader_test.py | 7 ++----- .../contrastive_phenotyping/dataloader_test.sh | 10 ++++++---- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/viscy/applications/contrastive_phenotyping/dataloader_test.py b/viscy/applications/contrastive_phenotyping/dataloader_test.py index 1f9dee56..32f4f7b5 100644 --- a/viscy/applications/contrastive_phenotyping/dataloader_test.py +++ b/viscy/applications/contrastive_phenotyping/dataloader_test.py @@ -28,9 +28,6 @@ def profile_dataio(top_dir, num_epochs=1): ) timesteps_csv_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" - wandb.config.data_path = str(base_path) - wandb.config.num_epochs = num_epochs - data_module = ContrastiveDataModule( base_path=base_path, channels=channels, @@ -101,12 +98,12 @@ def profile_dataio(top_dir, num_epochs=1): # %% Testing the data i/o with data stored on Vast -print("Profiling data i/o with data stored on VAST \n ------- \n") +print(f"Profiling data i/o with data stored on VAST\n{data_on_vast}\n") profile_dataio(data_on_vast) # %% Testing the data i/o with data stored on Lustre -print("Profiling data i/o with data stored on Lustre\n ------- \n") +print(f"Profiling data i/o with data stored on Lustre\n{data_on_lustre}\n") profile_dataio(data_on_lustre) diff --git a/viscy/applications/contrastive_phenotyping/dataloader_test.sh b/viscy/applications/contrastive_phenotyping/dataloader_test.sh index e317a096..507abd65 100644 --- a/viscy/applications/contrastive_phenotyping/dataloader_test.sh +++ b/viscy/applications/contrastive_phenotyping/dataloader_test.sh @@ -3,11 +3,13 @@ #SBATCH --nodes=1 #SBATCH --gres=gpu:1 #SBATCH --cpus-per-task=16 -#SBATCH --time=2-00:00:00 -#SBATCH --output=/hpc/mydata/$USER/slurm_logs/dataloader_test_%j.txt -#SBATCH --nodelist=gpu-b-[3-4,6] +#SBATCH --nodelist=gpu-c-1 +#SBATCH --time=0-12:00:00 +#SBATCH --job-name=dataloader_test +#SBATCH --output=/hpc/mydata/$USER/slurm_logs/ +# Make sure that /hpc/mydata/$USER/slurm_logs/ exists!! # Activate viscy and run the dataloader_test.py script conda activate /hpc/mydata/$USER/envs/viscy/ -python /hpc/mydata/$USER/viscy/applications/contrastive_phenotyping/dataloader_test.py \ No newline at end of file +python /hpc/mydata/$USER/code/viscy/viscy/applications/contrastive_phenotyping/dataloader_test.py \ No newline at end of file From 346e7c56893ad5281c93ad2769d58eda65196115 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 20:10:39 -0700 Subject: [PATCH 23/87] delete redundant module --- viscy/unet/networks/embedding.py | 89 -------------------------------- 1 file changed, 89 deletions(-) delete mode 100644 viscy/unet/networks/embedding.py diff --git a/viscy/unet/networks/embedding.py b/viscy/unet/networks/embedding.py deleted file mode 100644 index 586c3a14..00000000 --- a/viscy/unet/networks/embedding.py +++ /dev/null @@ -1,89 +0,0 @@ -import timm -from viscy.unet.networks.unext2 import UNeXt2Stem - -import torch.nn as nn -import torch.nn.functional as F - - -class ContrastiveConvNext(nn.Module): - def __init__( - self, - backbone: str = "convnext_tiny", - in_channels: int = 2, - in_stack_depth: int = 15, - stem_kernel_size: tuple[int, int, int] = (5, 3, 3), - embedding_len: int = 256, - ): - super().__init__() - - """ - ContrastiveConvNext model for contrastive learning. - - Parameters: - - backbone (str): Backbone architecture for the encoder. Default is "convnext_tiny". - - in_channels (int): Number of input channels. Default is 2. - - in_stack_depth (int): Number of input slices in z-stack. Default is 15. - - stem_kernel_size (tuple[int, int, int]): 3D kernel size for the stem. Input stack depth must be divisible by the kernel depth. Default is (5, 3, 3). - - embedding_len (int): Length of the embedding. Default is 1000. - """ - - if in_stack_depth % stem_kernel_size[0] != 0: - raise ValueError( - f"Input stack depth {in_stack_depth} is not divisible " - f"by stem kernel depth {stem_kernel_size[0]}." - ) - - # encoder - self.model = timm.create_model( - backbone, - pretrained=True, - features_only=False, - drop_path_rate=0.2, - num_classes=4 * embedding_len, - ) - - # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. - in_channels_encoder = self.model.stem[0].out_channels - stem = UNeXt2Stem( - in_channels=in_channels, - out_channels=in_channels_encoder, - kernel_size=stem_kernel_size, - in_stack_depth=in_stack_depth, - ) - self.model.stem = stem - - # replace the fully connected layer with projection head (Linear->ReLU->Linear). - self.model.head.fc = nn.Sequential( - self.model.head.fc, - nn.ReLU(inplace=True), - nn.Linear(4 * embedding_len, embedding_len), - ) - """ - head of convnext - ------------------- - (head): NormMlpClassifierHead( - (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) - (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) - (flatten): Flatten(start_dim=1, end_dim=-1) - (pre_logits): Identity() - (drop): Dropout(p=0.0, inplace=False) - (fc): Linear(in_features=768, out_features=1024, bias=True) - - - head of convnext for contrastive learning - ---------------------------- - (head): NormMlpClassifierHead( - (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity()) - (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True) - (flatten): Flatten(start_dim=1, end_dim=-1) - (pre_logits): Identity() - (drop): Dropout(p=0.0, inplace=False) - (fc): Sequential( - (0): Linear(in_features=768, out_features=1024, bias=True) - (1): ReLU(inplace=True) - (2): Linear(in_features=1024, out_features=256, bias=True) - ) - """ - - def forward(self, x): - return self.model(x) From 3a2a865958d69cbde611a88e49e15100369d73f6 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Sun, 7 Jul 2024 20:27:16 -0700 Subject: [PATCH 24/87] nits: updated the model construction of contrastive resnet encoder. --- ...ng_script_resnet.py => graphs_ConvNeXt_ResNet.py} | 12 +----------- viscy/representation/contrastive.py | 7 +++++-- viscy/unet/networks/resnet.py | 2 ++ viscy/unet/networks/unext2.py | 2 +- 4 files changed, 9 insertions(+), 14 deletions(-) rename viscy/applications/contrastive_phenotyping/{training_script_resnet.py => graphs_ConvNeXt_ResNet.py} (84%) diff --git a/viscy/applications/contrastive_phenotyping/training_script_resnet.py b/viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py similarity index 84% rename from viscy/applications/contrastive_phenotyping/training_script_resnet.py rename to viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py index 21d599a4..c6be84e4 100644 --- a/viscy/applications/contrastive_phenotyping/training_script_resnet.py +++ b/viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py @@ -1,15 +1,9 @@ # %% Imports and paths. import os import torch -from viscy.light.engine import ContrastiveModule -from viscy.unet.networks.unext2 import UNeXt2Stem from viscy.representation.contrastive import ContrastiveEncoder -from pathlib import Path import torchview -top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") -input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/patch_final.zarr" -model_dir = top_dir / "infection_classification/models/infection_score" %load_ext autoreload %autoreload 2 @@ -23,6 +17,7 @@ device="cpu", ) # Print the image of the model. +model_graph.resize_graph(scale=2.5) model_graph.visual_graph # %% Initialize a resent50 model and log the graph. @@ -53,11 +48,6 @@ # Print the image of the model. model_graph.visual_graph -# %% Initialize the data module and view the data. - -# %% Train the model. - - # %% Playground import timm diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 5bb678f1..f1fddd4c 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -1,9 +1,12 @@ import timm import torch.nn as nn -from viscy.unet.networks.resnet import resnetStem +# from viscy.unet.networks.resnet import resnetStem +# Currently identical to resnetStem, but could be different in the future. + from viscy.unet.networks.unext2 import UNeXt2Stem + class ContrastiveEncoder(nn.Module): def __init__( self, @@ -88,7 +91,7 @@ def __init__( # Adapt stem and projection head of resnet here. # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. in_channels_encoder = self.model.conv1.out_channels - stem = resnetStem( + stem = UNeXt2Stem( in_channels=in_channels, out_channels=in_channels_encoder, kernel_size=stem_kernel_size, diff --git a/viscy/unet/networks/resnet.py b/viscy/unet/networks/resnet.py index 61b180d4..a34f7271 100644 --- a/viscy/unet/networks/resnet.py +++ b/viscy/unet/networks/resnet.py @@ -4,6 +4,8 @@ class resnetStem(nn.Module): """Stem for ResNet networks to handle 3D multi-channel input.""" + # Currently identical to UNeXt2Stem, but could be different in the future. This module is unused for now. + def __init__( self, in_channels: int, diff --git a/viscy/unet/networks/unext2.py b/viscy/unet/networks/unext2.py index a695c06a..86b05eef 100644 --- a/viscy/unet/networks/unext2.py +++ b/viscy/unet/networks/unext2.py @@ -65,7 +65,7 @@ def _get_convnext_stage( class UNeXt2Stem(nn.Module): - """Stem for UNeXt2 networks.""" + """Stem for UNeXt2 and ContrastiveEncoder networks.""" def __init__( self, From 675c87ce83cf4fa96dd4debcb28e4b30f4a621d1 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Mon, 8 Jul 2024 17:40:51 -0700 Subject: [PATCH 25/87] Updated training script, HCS data handling, engine, and contrastive representation --- .../training_script.py | 19 ++- viscy/data/hcs.py | 146 +++++++++++++++--- viscy/light/engine.py | 82 +++++++--- viscy/representation/contrastive.py | 4 +- 4 files changed, 206 insertions(+), 45 deletions(-) diff --git a/viscy/applications/contrastive_phenotyping/training_script.py b/viscy/applications/contrastive_phenotyping/training_script.py index db010560..501fa5d5 100644 --- a/viscy/applications/contrastive_phenotyping/training_script.py +++ b/viscy/applications/contrastive_phenotyping/training_script.py @@ -25,18 +25,22 @@ #wandb.init(project="contrastive_model", dir="/hpc/mydata/alishba.imran/wandb_logs/") top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") -input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +#input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +input_zarr = "/hpc/projects/virtual_staining/viral_sensor_test_dataio/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" model_dir = top_dir / "infection_classification/models/infection_score" timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" # Data parameters -base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" -channels = 2 +base_path = "/hpc/projects/virtual_staining/viral_sensor_test_dataio/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +channels = 1 x = 200 y = 200 z = 15 z_range = (28, 43) batch_size = 32 +channel_names = ["Phase3D"] + +torch.set_float32_matmul_precision('medium') # %% Initialize the model and log the graph #contra_model = ContrastiveEncoder(backbone="convnext_tiny") @@ -95,6 +99,7 @@ def main(hparams): x=x, y=y, timesteps_csv_path=timesteps_csv_path, + channel_names=channel_names, batch_size=batch_size, z_range=z_range, ) @@ -169,13 +174,13 @@ def main(hparams): "margin": 0.5, "lr": 1e-3, "schedule": "Constant", - "log_batches_per_epoch": 8, + "log_batches_per_epoch": 4, "log_samples_per_batch": 1, "embedding_len": 256, "max_epochs": 100, "accelerator": "gpu", - "devices": 1, # Set to 4 GPUs - "num_nodes": 2, + "devices": 1, # 1 GPU + "num_nodes": 1, # 1 node "log_every_n_steps": 1, } class HParams: @@ -189,7 +194,7 @@ def __init__(self, **kwargs): parser.add_argument("--margin", type=float, default=0.5) parser.add_argument("--lr", type=float, default=1e-3) parser.add_argument("--schedule", type=str, default="Constant") - parser.add_argument("--log_batches_per_epoch", type=int, default=8) + parser.add_argument("--log_batches_per_epoch", type=int, default=26) parser.add_argument("--log_samples_per_batch", type=int, default=1) parser.add_argument("--embedding_len", type=int, default=256) parser.add_argument("--max_epochs", type=int, default=100) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 13633e01..581cef79 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -606,7 +606,6 @@ def _train_transform(self) -> list[Callable]: logging.debug(f"Training augmentations: {self.augmentations}") return list(self.augmentations) - # dataloader for organelle phenotyping class ContrastiveDataset(Dataset): def __init__( @@ -616,6 +615,7 @@ def __init__( x, y, timesteps_csv_path, + channel_names, transform=None, z_range=None, ): @@ -624,12 +624,14 @@ def __init__( self.x = x self.y = y self.z_range = z_range - self.transform = transform + self.channel_names = channel_names + self.transform = get_transforms() self.ds = self.open_zarr_store(self.base_path) self.positions = list(self.ds.positions()) self.timesteps_df = pd.read_csv(timesteps_csv_path) + self.channel_indices = [self.ds.channel_names.index(channel) for channel in self.channel_names] print(f"Initialized dataset with {len(self.positions)} positions.") - + def open_zarr_store(self, path, layout="hcs", mode="r"): #print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") return open_ome_zarr(path, layout=layout, mode=mode) @@ -640,12 +642,15 @@ def __len__(self): def __getitem__(self, idx): anchor_position_path = self.positions[idx][0] anchor_data = self.load_data(anchor_position_path) + anchor_data = self.normalize_data(anchor_data) positive_data = ( self.transform({"image": anchor_data})["image"] if self.transform else anchor_data ) + positive_data = self.normalize_data(positive_data) + # if self.transform: # print("Positive transformation applied") @@ -654,12 +659,15 @@ def __getitem__(self, idx): negative_idx = random.randint(0, self.__len__() - 1) negative_position_path = self.positions[negative_idx][0] negative_data = self.load_data(negative_position_path) + negative_data = self.normalize_data(negative_data) negative_data = ( self.transform({"image": negative_data})["image"] if self.transform else negative_data ) + negative_data = self.normalize_data(negative_data) + # if self.transform: # print("Negative transformation applied") @@ -668,20 +676,23 @@ def __getitem__(self, idx): # print(torch.tensor(positive_data).shape) # print(torch.tensor(negative_data).shape) return ( - torch.tensor(anchor_data), - torch.tensor(positive_data), - torch.tensor(negative_data), + torch.tensor(anchor_data, dtype=torch.float32), + torch.tensor(positive_data, dtype=torch.float32), + torch.tensor(negative_data, dtype=torch.float32), ) def load_data(self, position_path): position = self.ds[position_path] # print(f"Loading data from position: {position_path}") + zarr_array = position["0"][:] # print("Shape before:", zarr_array.shape) data = self.restructure_data(zarr_array, position_path) if self.z_range: - data = data[:, self.z_range[0] : self.z_range[1], :, :] - # print("Shape after:", data.shape) + data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] + + # print("shape after!") + # print(data.shape) return data def restructure_data(self, data, position_path): @@ -717,27 +728,120 @@ def restructure_data(self, data, position_path): reshaped_data = data[random_timestep] return reshaped_data + def normalize_data(self, data): + mean = np.mean(data) + std = np.std(data) + return (data - mean) / (std + 1e-6) + +# def apply_transform(self, data): +# # print("Applying transform to data") +# # print(data.shape) # data shape when 2 channels: (2, 15, 200, 200) +# # transformed_data = np.empty_like(data) +# # for channel_idx in range(data.shape[0]): +# # channel_data = data[channel_idx] +# # transform = get_transforms(channel_data) +# # transformed_data[channel_idx] = transform({"image": channel_data})["image"] +# # return transformed_data +# transformed_data = np.empty_like(data) +# for channel_idx, channel_name in enumerate(self.channel_names): +# channel_data = data[channel_idx] +# transform = get_transforms(channel_data, channel_name) +# transformed_data[channel_idx] = transform({"image": channel_data})["image"] +# return transformed_data + +# def get_transforms(image, channel): +# mean = np.mean(image) +# std = np.std(image) +# if channel == 'RFP': +# if std < 0.1: +# gamma_range = (0.97, 1.03) +# else: +# gamma_range = (0.9, 1.1) + +# if mean < 0.5: +# scale_factors = (0.95, 1.05) +# else: +# scale_factors = (0.93, 1.07) +# elif channel == 'Phase3D': +# if std < 0.1: +# gamma_range = (0.98, 1.02) +# else: +# gamma_range = (0.95, 1.05) + +# if mean < 0.5: +# scale_factors = (0.98, 1.02) +# else: +# scale_factors = (0.95, 1.05) + +# # if std < 0.1: +# # gamma_range = (0.95, 1.05) # Narrower range for low variance images +# # else: +# # gamma_range = (0.85, 1.15) # Slightly adjusted range for higher variance + +# # if mean < 0.5: +# # scale_factors = (0.95, 1.05) # Narrower range for lower intensity images +# # else: +# # scale_factors = (0.85, 1.15) # Slightly adjusted range for higher intensity images + +# # if std < 0.1: +# # gamma_range = (0.97, 1.03) # Even narrower range for low variance images +# # else: +# # gamma_range = (0.9, 1.1) # Narrower range for higher variance + +# # if mean < 0.5: +# # scale_factors = (0.95, 1.05) # Narrower range for lower intensity images +# # else: +# # scale_factors = (0.93, 1.07) # Even narrower range for higher intensity images + +# # normalization for both channels +# # log mean, std for each anhchor, positive, negative +# # mean, std of anchor and positive get closer +# # mean, std of anchor and negative further (0.5 margin) +# transforms = Compose( +# [ +# RandAdjustContrastd(keys=["image"], prob=0.5, gamma=gamma_range), +# RandAffined( +# keys=["image"], +# prob=0.5, +# rotate_range=(0.07, 0.07), +# shear_range=(0.07, 0.07), +# scale_range=(0.07, 0.07), +# ), +# RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=std * 0.1), +# RandGaussianSmoothd( +# keys=["image"], +# prob=0.5, +# sigma_x=(0.1, 0.3), +# sigma_y=(0.1, 0.3), +# sigma_z=(0.1, 0.3), +# ), +# RandScaleIntensityd(keys=["image"], factors=scale_factors, prob=0.5), +# ] +# ) +# return transforms + + def get_transforms(): transforms = Compose( [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.8, 1.2)), + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.9, 1.1)), RandAffined( keys=["image"], prob=0.5, - rotate_range=(0.1, 0.1), - shear_range=(0.1, 0.1), - scale_range=(0.1, 0.1), + rotate_range=(0.07, 0.07), + shear_range=(0.07, 0.07), + scale_range=(0.07, 0.07), ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.05), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.01), RandGaussianSmoothd( keys=["image"], prob=0.5, - sigma_x=(0.1, 0.5), - sigma_y=(0.1, 0.5), - sigma_z=(0.1, 0.5), + sigma_x=(0.05, 0.1), + sigma_y=(0.05, 0.1), + sigma_z=(0.05, 0.1), ), - RandScaleIntensityd(keys=["image"], factors=(0.8, 1.2), prob=0.5), + RandScaleIntensityd(keys=["image"], factors=(0.95, 1.05), prob=0.5), ] ) return transforms @@ -750,13 +854,14 @@ def __init__( x: int, y: int, timesteps_csv_path: str, + channel_names: list, + transform=None, predict_base_path: str = None, train_split_ratio: float = 0.64, val_split_ratio: float = 0.16, batch_size: int = 4, num_workers: int = 8, z_range: tuple[int, int] = None, - transform=None, ): super().__init__() self.base_path = Path(base_path) @@ -764,13 +869,14 @@ def __init__( self.x = x self.y = y self.timesteps_csv_path = timesteps_csv_path + self.channel_names = channel_names + self.transform = get_transforms() self.predict_base_path = Path(predict_base_path) if predict_base_path else None self.train_split_ratio = train_split_ratio self.val_split_ratio = val_split_ratio self.batch_size = batch_size self.num_workers = num_workers self.z_range = z_range - self.transform = transform or get_transforms() self.train_dataset = None self.val_dataset = None self.test_dataset = None @@ -783,6 +889,7 @@ def setup(self, stage: str = None): self.x, self.y, self.timesteps_csv_path, + channel_names=self.channel_names, transform=self.transform, z_range=self.z_range, ) @@ -803,6 +910,7 @@ def setup(self, stage: str = None): self.x, self.y, self.timesteps_csv_path, + channel_names=self.channel_names, transform=self.transform, z_range=self.z_range, ) diff --git a/viscy/light/engine.py b/viscy/light/engine.py index be9e354d..89a8d9c2 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -10,6 +10,9 @@ #from lightning import LightningModule from torch.optim import Adam +import torch.nn.functional as F + + from lightning.pytorch import LightningDataModule, LightningModule, Trainer from matplotlib.pyplot import get_cmap @@ -480,7 +483,7 @@ def __init__( schedule: Literal["WarmupCosine", "Constant"] = "Constant", log_batches_per_epoch: int = 8, log_samples_per_batch: int = 1, - in_channels: int = 2, + in_channels: int = 1, example_input_yx_shape: Sequence[int] = (256, 256), in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), @@ -519,25 +522,42 @@ def forward(self, x: Tensor) -> Tensor: projections = self.encoder(x) return projections - def log_images(self, anchor, positive, negative, step_name, step_idx, epoch): + def log_feature_statistics(self, embeddings: Tensor, step_name: str, batch_idx: int, epoch: int, prefix: str): + mean = torch.mean(embeddings, dim=0).detach().cpu().numpy() + std = torch.std(embeddings, dim=0).detach().cpu().numpy() + + print(f"{step_name}/{prefix}_mean_epoch{epoch}_batch{batch_idx}: {mean}") + print(f"{step_name}/{prefix}_std_epoch{epoch}_batch{batch_idx}: {std}") + + def log_metrics(self, anchor, positive, negative, step_name, batch_idx, epoch): + if batch_idx % 4 == 0: + # Calculate cosine similarities + cosine_sim_pos = F.cosine_similarity(anchor, positive, dim=1).mean().item() + cosine_sim_neg = F.cosine_similarity(anchor, negative, dim=1).mean().item() + + # Calculate Euclidean distances + euclidean_dist_pos = F.pairwise_distance(anchor, positive).mean().item() + euclidean_dist_neg = F.pairwise_distance(anchor, negative).mean().item() + + # Log metrics + print(f"{step_name}/cosine_similarity_positive_epoch{epoch}_batch{batch_idx}: {cosine_sim_pos}") + print(f"{step_name}/cosine_similarity_negative_epoch{epoch}_batch{batch_idx}: {cosine_sim_neg}") + print(f"{step_name}/euclidean_distance_positive_epoch{epoch}_batch{batch_idx}: {euclidean_dist_pos}") + print(f"{step_name}/euclidean_distance_negative_epoch{epoch}_batch{batch_idx}: {euclidean_dist_neg}") + + def log_images(self, anchor, positive, negative, step_name, batch_idx, epoch): # middle z-slice z_idx = 7 - # 7th z-slice from both channels for the first sample - anchor_img_channel1 = anchor[0, 0, z_idx, :, :].cpu().numpy() - anchor_img_channel2 = anchor[0, 1, z_idx, :, :].cpu().numpy() - positive_img_channel1 = positive[0, 0, z_idx, :, :].cpu().numpy() - positive_img_channel2 = positive[0, 1, z_idx, :, :].cpu().numpy() - negative_img_channel1 = negative[0, 0, z_idx, :, :].cpu().numpy() - negative_img_channel2 = negative[0, 1, z_idx, :, :].cpu().numpy() + # 7th z-slice from channels for the first sample + anchor_img_channel2 = anchor[0, 0, z_idx, :, :].cpu().numpy() + positive_img_channel2 = positive[0, 0, z_idx, :, :].cpu().numpy() + negative_img_channel2 = negative[0, 0, z_idx, :, :].cpu().numpy() images = { - f"{step_name}/anchor_channel1_epoch{epoch}_{step_idx}": wandb.Image(anchor_img_channel1), - f"{step_name}/anchor_channel2_epoch{epoch}_{step_idx}": wandb.Image(anchor_img_channel2), - f"{step_name}/positive_channel1_epoch{epoch}_{step_idx}": wandb.Image(positive_img_channel1), - f"{step_name}/positive_channel2_epoch{epoch}_{step_idx}": wandb.Image(positive_img_channel2), - f"{step_name}/negative_channel1_epoch{epoch}_{step_idx}": wandb.Image(negative_img_channel1), - f"{step_name}/negative_channel2_epoch{epoch}_{step_idx}": wandb.Image(negative_img_channel2), + f"{step_name}/anchor_phase_epoch{epoch}_batch{batch_idx}": wandb.Image(anchor_img_channel2), + f"{step_name}/positive_phase_epoch{epoch}_batch{batch_idx}": wandb.Image(positive_img_channel2), + f"{step_name}/negative_phase_epoch{epoch}_batch{batch_idx}": wandb.Image(negative_img_channel2), } self.logger.experiment.log(images) @@ -563,9 +583,19 @@ def training_step( self.log("train/loss", loss, on_step=True, prog_bar=True, logger=True) - if self.current_epoch in [0, 1, 2] and batch_idx == 0: + # if self.current_epoch == 0 and batch_idx == 0: + # print(f"Shapes of anchor, positive, negative for the first batch of the first epoch (training):") + # print(f"Anchor: {anchor.shape}") + # print(f"Positive: {pos_img.shape}") + # print(f"Negative: {neg_img.shape}") + + + if self.current_epoch in [0, 1, 2] and batch_idx % self.log_batches_per_epoch == 0: self.log_images(anchor, pos_img, neg_img, "train", batch_idx, self.current_epoch) + + self.log_metrics(emb_anchor, emb_pos, emb_neg, "train", batch_idx, self.current_epoch) + self.training_step_outputs.append(loss) return {'loss': loss} @@ -595,9 +625,17 @@ def validation_step( self.log("val/loss_step", loss, on_step=True, prog_bar=True, logger=True) - if self.current_epoch in [0, 1, 2] and batch_idx == 0: + # if self.current_epoch == 0 and batch_idx == 0: + # print(f"Shapes of anchor, positive, negative for the first batch of the first epoch (validation):") + # print(f"Anchor: {anchor.shape}") + # print(f"Positive: {pos_img.shape}") + # print(f"Negative: {neg_img.shape}") + + if self.current_epoch in [0, 1, 2] and batch_idx % self.log_batches_per_epoch == 0: self.log_images(anchor, pos_img, neg_img, "validation", batch_idx, self.current_epoch) + self.log_metrics(emb_anchor, emb_pos, emb_neg, "validation", batch_idx, self.current_epoch) + self.validation_step_outputs.append(loss) return {'loss': loss} @@ -627,9 +665,17 @@ def test_step( self.log("test/loss_step", loss, on_step=True, prog_bar=True, logger=True) - if self.current_epoch in [0, 1, 2] and batch_idx == 0: + # if self.current_epoch == 0 and batch_idx == 0: + # print(f"Shapes of anchor, positive, negative for the first batch of the first epoch (testing):") + # print(f"Anchor: {anchor.shape}") + # print(f"Positive: {pos_img.shape}") + # print(f"Negative: {neg_img.shape}") + + if self.current_epoch in [0, 1, 2] and batch_idx % self.log_batches_per_epoch == 0: self.log_images(anchor, pos_img, neg_img, "test", batch_idx, self.current_epoch) + self.log_metrics(emb_anchor, emb_pos, emb_neg, "test", batch_idx, self.current_epoch) + self.test_step_outputs.append(loss) return {'loss': loss} diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index b3c3405a..a9939454 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -10,7 +10,7 @@ class ContrastiveEncoder(nn.Module): def __init__( self, backbone: str = "convnext_tiny", - in_channels: int = 2, + in_channels: int = 1, in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, @@ -44,6 +44,7 @@ def __init__( ) if "convnext_tiny" in backbone: + print("Using ConvNext backbone.") # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. in_channels_encoder = self.model.stem[0].out_channels stem = UNeXt2Stem( @@ -87,6 +88,7 @@ def __init__( ) """ elif "resnet" in backbone: + print("Using ResNet backbone.") # Adapt stem and projection head of resnet here. # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. in_channels_encoder = self.model.conv1.out_channels From 6422060ecdf539a885c760dca2b56a632955f895 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Wed, 10 Jul 2024 17:19:00 -0700 Subject: [PATCH 26/87] Fix normalization, visualization issues, logging and multi-channel prediction --- .../training_script.py | 61 +++-- viscy/data/hcs.py | 40 ++- viscy/light/engine.py | 253 ++++++++++-------- viscy/representation/contrastive.py | 17 +- 4 files changed, 201 insertions(+), 170 deletions(-) diff --git a/viscy/applications/contrastive_phenotyping/training_script.py b/viscy/applications/contrastive_phenotyping/training_script.py index 501fa5d5..fef80f9e 100644 --- a/viscy/applications/contrastive_phenotyping/training_script.py +++ b/viscy/applications/contrastive_phenotyping/training_script.py @@ -6,6 +6,7 @@ import torch import torchview from torch.optim import Adam +from lightning.pytorch.strategies import DDPStrategy from lightning.pytorch import Trainer, seed_everything from lightning.pytorch.callbacks import ModelCheckpoint, RichProgressBar @@ -14,31 +15,43 @@ from lightning.pytorch.callbacks import TQDMProgressBar import wandb from tqdm import tqdm +from lightning.pytorch.utilities.rank_zero import rank_zero_only from viscy.light.engine import ContrastiveModule from viscy.representation.contrastive import ContrastiveEncoder from viscy.data.hcs import ContrastiveDataModule +import logging + +# Set W&B logging level to suppress warnings +logging.getLogger("wandb").setLevel(logging.ERROR) # %% Paths and constants os.environ["WANDB_DIR"] = "/hpc/mydata/alishba.imran/wandb_logs/" +# @rank_zero_only +# def init_wandb(): +# wandb.init(project="contrastive_model", dir="/hpc/mydata/alishba.imran/wandb_logs/") + +# init_wandb() + #wandb.init(project="contrastive_model", dir="/hpc/mydata/alishba.imran/wandb_logs/") top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") #input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" input_zarr = "/hpc/projects/virtual_staining/viral_sensor_test_dataio/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" model_dir = top_dir / "infection_classification/models/infection_score" +# checkpoint dir: /hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/multiple_channels timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" # Data parameters base_path = "/hpc/projects/virtual_staining/viral_sensor_test_dataio/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" -channels = 1 +channels = 2 x = 200 y = 200 z = 15 z_range = (28, 43) batch_size = 32 -channel_names = ["Phase3D"] +channel_names = ["RFP", "Phase3D"] #training w/ both channels torch.set_float32_matmul_precision('medium') @@ -65,23 +78,6 @@ # ) # model_graph.visual_graph -# %% Progress bar - -class LitProgressBar(TQDMProgressBar): - def init_validation_tqdm(self): - bar = super().init_validation_tqdm() - bar.set_description("Running validation...") - return bar - - def init_train_tqdm(self): - bar = super().init_train_tqdm() - bar.set_description("Training...") - return bar - - def init_test_tqdm(self): - bar = super().init_test_tqdm() - bar.set_description("Testing...") - return bar # %% Define the main function for training def main(hparams): @@ -114,7 +110,6 @@ def main(hparams): print(f"Validation dataset size: {len(data_module.val_dataset)}") print(f"Test dataset size: {len(data_module.test_dataset)}") - # Initialize the model model = ContrastiveModule( backbone=hparams.backbone, @@ -122,8 +117,7 @@ def main(hparams): margin=hparams.margin, lr=hparams.lr, schedule=hparams.schedule, - log_batches_per_epoch=hparams.log_batches_per_epoch, - log_samples_per_batch=hparams.log_samples_per_batch, + log_steps_per_epoch=hparams.log_steps_per_epoch, in_channels=channels, example_input_yx_shape=(x, y), in_stack_depth=z, @@ -133,10 +127,11 @@ def main(hparams): # Initialize logger wandb_logger = WandbLogger(project="contrastive_model", log_model="all") - + + custom_folder_name = "multiple_channels" checkpoint_callback = ModelCheckpoint( - dirpath=model_dir, - filename="contrastive_model-{epoch:02d}-{val_loss:.2f}", + dirpath=os.path.join(model_dir, custom_folder_name), + filename="contrastive_model-test-{epoch:02d}-{val_loss:.2f}", save_top_k=3, mode="min", monitor="val/loss_epoch", @@ -149,11 +144,17 @@ def main(hparams): accelerator=hparams.accelerator, devices=hparams.devices, num_nodes=hparams.num_nodes, - strategy="ddp", + strategy=DDPStrategy(find_unused_parameters=True), log_every_n_steps=hparams.log_every_n_steps, num_sanity_val_steps=0 ) + train_loader = data_module.train_dataloader() + example_batch = next(iter(train_loader)) + example_input = example_batch[0] + + wandb_logger.watch(model, log="all", log_graph=(example_input,)) + # Fetches batches from the training dataloader, # Calls the training_step method on the model for each batch # Aggregates the losses and performs optimization steps @@ -174,8 +175,7 @@ def main(hparams): "margin": 0.5, "lr": 1e-3, "schedule": "Constant", - "log_batches_per_epoch": 4, - "log_samples_per_batch": 1, + "log_steps_per_epoch": 5, "embedding_len": 256, "max_epochs": 100, "accelerator": "gpu", @@ -194,13 +194,12 @@ def __init__(self, **kwargs): parser.add_argument("--margin", type=float, default=0.5) parser.add_argument("--lr", type=float, default=1e-3) parser.add_argument("--schedule", type=str, default="Constant") - parser.add_argument("--log_batches_per_epoch", type=int, default=26) - parser.add_argument("--log_samples_per_batch", type=int, default=1) + parser.add_argument("--log_steps_per_epoch", type=int, default=10) parser.add_argument("--embedding_len", type=int, default=256) parser.add_argument("--max_epochs", type=int, default=100) parser.add_argument("--accelerator", type=str, default="gpu") parser.add_argument("--devices", type=int, default=1) # 4 GPUs - parser.add_argument("--num_nodes", type=int, default=2) + parser.add_argument("--num_nodes", type=int, default=1) parser.add_argument("--log_every_n_steps", type=int, default=1) args = parser.parse_args() diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 581cef79..ca2a010a 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -7,6 +7,7 @@ from pathlib import Path from typing import Callable, Literal, Optional, Sequence, Union #import pytorch_lightning as pl +from monai.transforms import MapTransform import numpy as np import torch @@ -16,13 +17,8 @@ #from lightning.pytorch import LightningDataModule from monai.data import set_track_meta from monai.data.utils import collate_meta_tensor -from monai.transforms import ( - CenterSpatialCropd, - Compose, - MapTransform, - MultiSampleTrait, - RandAffined, -) +from monai.transforms import Compose, RandAdjustContrastd, RandAffined, RandGaussianNoised, RandGaussianSmoothd, RandScaleIntensityd, RandShiftIntensityd, RandZoomd, Rand3DElasticd, RandGaussianSharpend + from torch import Tensor @@ -31,14 +27,7 @@ from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample import random -from viscy.transforms import ( - RandAdjustContrastd, - RandAffined, - RandGaussianNoised, - RandGaussianSmoothd, - RandScaleIntensityd, -) -from monai.transforms import Compose + from iohub import open_ome_zarr import pandas as pd import warnings @@ -630,6 +619,8 @@ def __init__( self.positions = list(self.ds.positions()) self.timesteps_df = pd.read_csv(timesteps_csv_path) self.channel_indices = [self.ds.channel_names.index(channel) for channel in self.channel_names] + print("channel indices!") + print(self.channel_indices) print(f"Initialized dataset with {len(self.positions)} positions.") def open_zarr_store(self, path, layout="hcs", mode="r"): @@ -688,11 +679,10 @@ def load_data(self, position_path): zarr_array = position["0"][:] # print("Shape before:", zarr_array.shape) data = self.restructure_data(zarr_array, position_path) - if self.z_range: - data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] + data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] - # print("shape after!") - # print(data.shape) + #print("shape after!") + #print(data.shape) return data def restructure_data(self, data, position_path): @@ -729,9 +719,13 @@ def restructure_data(self, data, position_path): return reshaped_data def normalize_data(self, data): - mean = np.mean(data) - std = np.std(data) - return (data - mean) / (std + 1e-6) + normalized_data = np.empty_like(data) + for i in range(data.shape[0]): # iterate over each channel + channel_data = data[i] + mean = np.mean(channel_data) + std = np.std(channel_data) + normalized_data[i] = (channel_data - mean) / (std + 1e-6) + return normalized_data # def apply_transform(self, data): # # print("Applying transform to data") @@ -821,7 +815,6 @@ def normalize_data(self, data): # return transforms - def get_transforms(): transforms = Compose( [ @@ -846,6 +839,7 @@ def get_transforms(): ) return transforms + class ContrastiveDataModule(LightningDataModule): def __init__( self, diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 89a8d9c2..b5982592 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -1,6 +1,7 @@ import logging import os from typing import Literal, Sequence, Union +import matplotlib.pyplot as plt import numpy as np import torch @@ -9,9 +10,10 @@ #from lightning.pytorch import LightningModule #from lightning import LightningModule from torch.optim import Adam +from PIL import Image import torch.nn.functional as F - +from pytorch_lightning.utilities import rank_zero_only from lightning.pytorch import LightningDataModule, LightningModule, Trainer @@ -481,9 +483,8 @@ def __init__( margin: float = 0.5, lr: float = 1e-3, schedule: Literal["WarmupCosine", "Constant"] = "Constant", - log_batches_per_epoch: int = 8, - log_samples_per_batch: int = 1, - in_channels: int = 1, + log_steps_per_epoch: int = 8, + in_channels: int = 2, example_input_yx_shape: Sequence[int] = (256, 256), in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), @@ -495,11 +496,13 @@ def __init__( self.margin = margin self.lr = lr self.schedule = schedule - self.log_batches_per_epoch = log_batches_per_epoch - self.log_samples_per_batch = log_samples_per_batch + self.log_steps_per_epoch = log_steps_per_epoch self.training_step_outputs = [] self.validation_step_outputs = [] self.test_step_outputs = [] + self.training_metrics = [] + self.validation_metrics = [] + self.test_metrics = [] self.encoder = ContrastiveEncoder( backbone=backbone, @@ -517,50 +520,79 @@ def __init__( *example_input_yx_shape, ) + self.images_to_log = [] + self.train_batch_counter = 0 + self.val_batch_counter = 0 + def forward(self, x: Tensor) -> Tensor: """Forward pass of the model.""" - projections = self.encoder(x) - return projections + features, projections = self.encoder(x) + return features, projections + # features is without projection head and projects is with projection head - def log_feature_statistics(self, embeddings: Tensor, step_name: str, batch_idx: int, epoch: int, prefix: str): + def log_feature_statistics(self, embeddings: Tensor, prefix: str): mean = torch.mean(embeddings, dim=0).detach().cpu().numpy() std = torch.std(embeddings, dim=0).detach().cpu().numpy() - print(f"{step_name}/{prefix}_mean_epoch{epoch}_batch{batch_idx}: {mean}") - print(f"{step_name}/{prefix}_std_epoch{epoch}_batch{batch_idx}: {std}") - - def log_metrics(self, anchor, positive, negative, step_name, batch_idx, epoch): - if batch_idx % 4 == 0: - # Calculate cosine similarities - cosine_sim_pos = F.cosine_similarity(anchor, positive, dim=1).mean().item() - cosine_sim_neg = F.cosine_similarity(anchor, negative, dim=1).mean().item() - - # Calculate Euclidean distances - euclidean_dist_pos = F.pairwise_distance(anchor, positive).mean().item() - euclidean_dist_neg = F.pairwise_distance(anchor, negative).mean().item() - - # Log metrics - print(f"{step_name}/cosine_similarity_positive_epoch{epoch}_batch{batch_idx}: {cosine_sim_pos}") - print(f"{step_name}/cosine_similarity_negative_epoch{epoch}_batch{batch_idx}: {cosine_sim_neg}") - print(f"{step_name}/euclidean_distance_positive_epoch{epoch}_batch{batch_idx}: {euclidean_dist_pos}") - print(f"{step_name}/euclidean_distance_negative_epoch{epoch}_batch{batch_idx}: {euclidean_dist_neg}") - - def log_images(self, anchor, positive, negative, step_name, batch_idx, epoch): - # middle z-slice - z_idx = 7 - - # 7th z-slice from channels for the first sample - anchor_img_channel2 = anchor[0, 0, z_idx, :, :].cpu().numpy() - positive_img_channel2 = positive[0, 0, z_idx, :, :].cpu().numpy() - negative_img_channel2 = negative[0, 0, z_idx, :, :].cpu().numpy() - - images = { - f"{step_name}/anchor_phase_epoch{epoch}_batch{batch_idx}": wandb.Image(anchor_img_channel2), - f"{step_name}/positive_phase_epoch{epoch}_batch{batch_idx}": wandb.Image(positive_img_channel2), - f"{step_name}/negative_phase_epoch{epoch}_batch{batch_idx}": wandb.Image(negative_img_channel2), + print(f"{prefix}_mean: {mean}") + print(f"{prefix}_std: {std}") + + # logs over all steps + @rank_zero_only + def log_metrics(self, anchor, positive, negative, phase): + cosine_sim_pos = F.cosine_similarity(anchor, positive, dim=1).mean().item() + cosine_sim_neg = F.cosine_similarity(anchor, negative, dim=1).mean().item() + + euclidean_dist_pos = F.pairwise_distance(anchor, positive).mean().item() + euclidean_dist_neg = F.pairwise_distance(anchor, negative).mean().item() + + metrics = { + f"{phase}/cosine_similarity_positive": cosine_sim_pos, + f"{phase}/cosine_similarity_negative": cosine_sim_neg, + f"{phase}/euclidean_distance_positive": euclidean_dist_pos, + f"{phase}/euclidean_distance_negative": euclidean_dist_neg } - self.logger.experiment.log(images) + wandb.log(metrics) + + if phase == 'train': + self.training_metrics.append(metrics) + elif phase == 'val': + self.validation_metrics.append(metrics) + elif phase == 'test': + self.test_metrics.append(metrics) + + @rank_zero_only + # logs only one sample from the first batch per epoch + def log_images(self, anchor, positive, negative, epoch, step_name): + z_idx = 7 + + anchor_img_rfp = anchor[0, 0, z_idx, :, :].cpu().numpy() + positive_img_rfp = positive[0, 0, z_idx, :, :].cpu().numpy() + negative_img_rfp = negative[0, 0, z_idx, :, :].cpu().numpy() + + anchor_img_phase = anchor[0, 1, z_idx, :, :].cpu().numpy() + positive_img_phase = positive[0, 1, z_idx, :, :].cpu().numpy() + negative_img_phase = negative[0, 1, z_idx, :, :].cpu().numpy() + + # Debug prints to check the contents of the images + print(f"Anchor RFP min: {anchor_img_rfp.min()}, max: {anchor_img_rfp.max()}") + print(f"Positive RFP min: {positive_img_rfp.min()}, max: {positive_img_rfp.max()}") + print(f"Negative RFP min: {negative_img_rfp.min()}, max: {negative_img_rfp.max()}") + + print(f"Anchor Phase min: {anchor_img_phase.min()}, max: {anchor_img_phase.max()}") + print(f"Positive Phase min: {positive_img_phase.min()}, max: {positive_img_phase.max()}") + print(f"Negative Phase min: {negative_img_phase.min()}, max: {negative_img_phase.max()}") + + # combine the images side by side + combined_img_rfp = np.concatenate((anchor_img_rfp, positive_img_rfp, negative_img_rfp), axis=1) + combined_img_phase = np.concatenate((anchor_img_phase, positive_img_phase, negative_img_phase), axis=1) + combined_img = np.concatenate((combined_img_rfp, combined_img_phase), axis=0) + + self.images_to_log.append(wandb.Image(combined_img, caption=f"Anchor | Positive | Negative (Epoch {epoch})")) + + wandb.log({f"{step_name}": self.images_to_log}) + self.images_to_log = [] def training_step( self, @@ -569,41 +601,38 @@ def training_step( ) -> Tensor: """Training step of the model.""" - if isinstance(self.loss_function, nn.TripletMarginLoss): - anchor, pos_img, neg_img = batch - emb_anchor = self.encoder(anchor) - emb_pos = self.encoder(pos_img) - emb_neg = self.encoder(neg_img) - loss = self.loss_function(emb_anchor, emb_pos, emb_neg) - else: - anchor, pos_img = batch - emb_anchor = self.encoder(anchor) - emb_pos = self.encoder(pos_img) - loss = self.loss_function(emb_anchor, emb_pos) - - self.log("train/loss", loss, on_step=True, prog_bar=True, logger=True) + anchor, pos_img, neg_img = batch + _, emb_anchor = self.encoder(anchor) + _, emb_pos = self.encoder(pos_img) + _, emb_neg = self.encoder(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) - # if self.current_epoch == 0 and batch_idx == 0: - # print(f"Shapes of anchor, positive, negative for the first batch of the first epoch (training):") - # print(f"Anchor: {anchor.shape}") - # print(f"Positive: {pos_img.shape}") - # print(f"Negative: {neg_img.shape}") - - - if self.current_epoch in [0, 1, 2] and batch_idx % self.log_batches_per_epoch == 0: - self.log_images(anchor, pos_img, neg_img, "train", batch_idx, self.current_epoch) - - - self.log_metrics(emb_anchor, emb_pos, emb_neg, "train", batch_idx, self.current_epoch) + self.log("train/loss_step", loss, on_step=True, prog_bar=True, logger=True) + + self.train_batch_counter += 1 + if self.train_batch_counter % self.log_steps_per_epoch == 0: + self.log_images(anchor, pos_img, neg_img, self.current_epoch, "training_images") + + self.log_metrics(emb_anchor, emb_pos, emb_neg, 'train') self.training_step_outputs.append(loss) return {'loss': loss} + @rank_zero_only def on_train_epoch_end(self) -> None: epoch_loss = torch.stack(self.training_step_outputs).mean() self.log("train/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) - self.training_step_outputs.clear() - + self.training_step_outputs.clear() + + if self.training_metrics: + avg_metrics = self.aggregate_metrics(self.training_metrics, 'train') + self.log("train/avg_cosine_similarity_positive", avg_metrics["train/cosine_similarity_positive"], on_epoch=True, logger=True) + self.log("train/avg_cosine_similarity_negative", avg_metrics["train/cosine_similarity_negative"], on_epoch=True, logger=True) + self.log("train/avg_euclidean_distance_positive", avg_metrics["train/euclidean_distance_positive"], on_epoch=True, logger=True) + self.log("train/avg_euclidean_distance_negative", avg_metrics["train/euclidean_distance_negative"], on_epoch=True, logger=True) + self.training_metrics.clear() + self.train_batch_counter = 0 + def validation_step( self, batch: tuple[Tensor], @@ -611,39 +640,38 @@ def validation_step( ) -> Tensor: """Validation step of the model.""" - if isinstance(self.loss_function, nn.TripletMarginLoss): - anchor, pos_img, neg_img = batch - emb_anchor = self.encoder(anchor) - emb_pos = self.encoder(pos_img) - emb_neg = self.encoder(neg_img) - loss = self.loss_function(emb_anchor, emb_pos, emb_neg) - else: - anchor, pos_img = batch - emb_anchor = self.encoder(anchor) - emb_pos = self.encoder(pos_img) - loss = self.loss_function(emb_anchor, emb_pos) + anchor, pos_img, neg_img = batch + _, emb_anchor = self.encoder(anchor) + _, emb_pos = self.encoder(pos_img) + _, emb_neg = self.encoder(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) self.log("val/loss_step", loss, on_step=True, prog_bar=True, logger=True) - # if self.current_epoch == 0 and batch_idx == 0: - # print(f"Shapes of anchor, positive, negative for the first batch of the first epoch (validation):") - # print(f"Anchor: {anchor.shape}") - # print(f"Positive: {pos_img.shape}") - # print(f"Negative: {neg_img.shape}") - - if self.current_epoch in [0, 1, 2] and batch_idx % self.log_batches_per_epoch == 0: - self.log_images(anchor, pos_img, neg_img, "validation", batch_idx, self.current_epoch) - - self.log_metrics(emb_anchor, emb_pos, emb_neg, "validation", batch_idx, self.current_epoch) + self.val_batch_counter += 1 + if self.val_batch_counter % self.log_steps_per_epoch == 0: + self.log_images(anchor, pos_img, neg_img, self.current_epoch, "validation_images") + + self.log_metrics(emb_anchor, emb_pos, emb_neg, 'val') self.validation_step_outputs.append(loss) return {'loss': loss} + @rank_zero_only def on_validation_epoch_end(self) -> None: epoch_loss = torch.stack(self.validation_step_outputs).mean() self.log("val/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) self.validation_step_outputs.clear() + if self.validation_metrics: + avg_metrics = self.aggregate_metrics(self.validation_metrics, 'val') + self.log("val/avg_cosine_similarity_positive", avg_metrics["val/cosine_similarity_positive"], on_epoch=True, logger=True) + self.log("val/avg_cosine_similarity_negative", avg_metrics["val/cosine_similarity_negative"], on_epoch=True, logger=True) + self.log("val/avg_euclidean_distance_positive", avg_metrics["val/euclidean_distance_positive"], on_epoch=True, logger=True) + self.log("val/avg_euclidean_distance_negative", avg_metrics["val/euclidean_distance_negative"], on_epoch=True, logger=True) + self.validation_metrics.clear() + self.val_batch_counter = 0 + def test_step( self, batch: tuple[Tensor], @@ -651,40 +679,43 @@ def test_step( ) -> Tensor: """Test step of the model.""" - if isinstance(self.loss_function, nn.TripletMarginLoss): - anchor, pos_img, neg_img = batch - emb_anchor = self.encoder(anchor) - emb_pos = self.encoder(pos_img) - emb_neg = self.encoder(neg_img) - loss = self.loss_function(emb_anchor, emb_pos, emb_neg) - else: - anchor, pos_img = batch - emb_anchor = self.encoder(anchor) - emb_pos = self.encoder(pos_img) - loss = self.loss_function(emb_anchor, emb_pos) + anchor, pos_img, neg_img = batch + _, emb_anchor = self.encoder(anchor) + _, emb_pos = self.encoder(pos_img) + _, emb_neg = self.encoder(neg_img) + loss = self.loss_function(emb_anchor, emb_pos, emb_neg) self.log("test/loss_step", loss, on_step=True, prog_bar=True, logger=True) - # if self.current_epoch == 0 and batch_idx == 0: - # print(f"Shapes of anchor, positive, negative for the first batch of the first epoch (testing):") - # print(f"Anchor: {anchor.shape}") - # print(f"Positive: {pos_img.shape}") - # print(f"Negative: {neg_img.shape}") - - if self.current_epoch in [0, 1, 2] and batch_idx % self.log_batches_per_epoch == 0: - self.log_images(anchor, pos_img, neg_img, "test", batch_idx, self.current_epoch) - - self.log_metrics(emb_anchor, emb_pos, emb_neg, "test", batch_idx, self.current_epoch) + self.log_metrics(emb_anchor, emb_pos, emb_neg, 'test') self.test_step_outputs.append(loss) return {'loss': loss} + @rank_zero_only def on_test_epoch_end(self) -> None: epoch_loss = torch.stack(self.test_step_outputs).mean() self.log("test/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) self.test_step_outputs.clear() + if self.test_metrics: + avg_metrics = self.aggregate_metrics(self.test_metrics, 'test') + self.log("test/avg_cosine_similarity_positive", avg_metrics["test/cosine_similarity_positive"], on_epoch=True, logger=True) + self.log("test/avg_cosine_similarity_negative", avg_metrics["test/cosine_similarity_negative"], on_epoch=True, logger=True) + self.log("test/avg_euclidean_distance_positive", avg_metrics["test/euclidean_distance_positive"], on_epoch=True, logger=True) + self.log("test/avg_euclidean_distance_negative", avg_metrics["test/euclidean_distance_negative"], on_epoch=True, logger=True) + self.test_metrics.clear() + def configure_optimizers(self): optimizer = Adam(self.parameters(), lr=self.lr) return optimizer + + def aggregate_metrics(self, metrics, phase): + avg_metrics = {} + if metrics: + avg_metrics[f"{phase}/cosine_similarity_positive"] = sum(m[f"{phase}/cosine_similarity_positive"] for m in metrics) / len(metrics) + avg_metrics[f"{phase}/cosine_similarity_negative"] = sum(m[f"{phase}/cosine_similarity_negative"] for m in metrics) / len(metrics) + avg_metrics[f"{phase}/euclidean_distance_positive"] = sum(m[f"{phase}/euclidean_distance_positive"] for m in metrics) / len(metrics) + avg_metrics[f"{phase}/euclidean_distance_negative"] = sum(m[f"{phase}/euclidean_distance_negative"] for m in metrics) / len(metrics) + return avg_metrics diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index a9939454..a1e41055 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -10,7 +10,7 @@ class ContrastiveEncoder(nn.Module): def __init__( self, backbone: str = "convnext_tiny", - in_channels: int = 1, + in_channels: int = 2, in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, @@ -55,12 +55,12 @@ def __init__( ) self.model.stem = stem - # replace the fully connected layer with projection head (Linear->ReLU->Linear). self.model.head.fc = nn.Sequential( self.model.head.fc, nn.ReLU(inplace=True), nn.Linear(4 * embedding_len, embedding_len), ) + """ head of convnext ------------------- @@ -87,6 +87,8 @@ def __init__( (2): Linear(in_features=1024, out_features=256, bias=True) ) """ + + # TO-DO: need to debug further elif "resnet" in backbone: print("Using ResNet backbone.") # Adapt stem and projection head of resnet here. @@ -100,11 +102,12 @@ def __init__( ) self.model.conv1 = stem - self.model.fc = nn.Sequential( - self.model.fc, + self.model.head.fc = nn.Sequential( + self.model.head.fc, nn.ReLU(inplace=True), nn.Linear(4 * embedding_len, embedding_len), ) + """ head of resnet ------------------- @@ -122,4 +125,8 @@ def __init__( """ def forward(self, x): - return self.model(x) \ No newline at end of file + features = self.model.forward_features(x) # extract features + intermediate_embeddings = self.model.head.global_pool(features) # apply global pooling + intermediate_embeddings = self.model.head.flatten(intermediate_embeddings) # flatten features + projected_embeddings = self.model.head.fc(intermediate_embeddings) # apply projection head + return intermediate_embeddings, projected_embeddings \ No newline at end of file From d968334e9b7ffc08a23c848b2cebcfa80dcb00d2 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Tue, 16 Jul 2024 14:56:31 -0700 Subject: [PATCH 27/87] updated training and prediction --- .../contrastive_phenotyping/PCA.ipynb | 443 ++++++++++++++++++ .../contrastive_phenotyping/predict.py | 99 ++++ .../training_script.py | 47 +- viscy/data/hcs.py | 319 +++++++------ viscy/light/engine.py | 87 +++- viscy/representation/contrastive.py | 31 +- 6 files changed, 852 insertions(+), 174 deletions(-) create mode 100644 viscy/applications/contrastive_phenotyping/PCA.ipynb create mode 100644 viscy/applications/contrastive_phenotyping/predict.py diff --git a/viscy/applications/contrastive_phenotyping/PCA.ipynb b/viscy/applications/contrastive_phenotyping/PCA.ipynb new file mode 100644 index 00000000..cf64c678 --- /dev/null +++ b/viscy/applications/contrastive_phenotyping/PCA.ipynb @@ -0,0 +1,443 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2629, 768, 8, 8)\n", + "(2629, 256)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from iohub import open_ome_zarr\n", + "from sklearn.decomposition import PCA\n", + "from scipy.stats import spearmanr\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load predicted features and projections\n", + "predicted_features = np.load(\"epoch97_predicted_features.npy\")\n", + "predicted_projections = np.load(\"epoch97_predicted_projections.npy\")\n", + "\n", + "print(predicted_features.shape)\n", + "print(predicted_projections.shape)\n", + "\n", + "# Load the CSV file\n", + "csv_path = \"epoch97_processed_order.csv\"\n", + "df = pd.read_csv(csv_path)\n", + "\n", + "# Load ground truth masks\n", + "base_path = \"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr\"\n", + "ds = open_ome_zarr(base_path, layout=\"hcs\", mode=\"r\")\n", + "\n", + "background_mask_index = ds.channel_names.index('background_mask')\n", + "uninfected_mask_index = ds.channel_names.index('uninfected_mask')\n", + "infected_mask_index = ds.channel_names.index('infected_mask')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming all masks have the same shape\n", + "# TO-DO:\n", + "# tie the image with projected embeddings\n", + "# test with ER\n", + "\n", + "# Initialize arrays to store the sums\n", + "num_cells = len(df)\n", + "background_sums = np.zeros(num_cells)\n", + "uninfected_sums = np.zeros(num_cells)\n", + "infected_sums = np.zeros(num_cells)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "for idx, row in df.iterrows():\n", + " position_key = f\"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}/0\"\n", + " zarr_array = ds[position_key]\n", + " t = row['Timestep']\n", + " \n", + " # Load a single z-slice, for example the first one\n", + " background_mask = zarr_array[t, background_mask_index, 0, :, :]\n", + " uninfected_mask = zarr_array[t, uninfected_mask_index, 0, :, :]\n", + " infected_mask = zarr_array[t, infected_mask_index, 0, :, :]\n", + " \n", + " # Sum values across each mask\n", + " background_sums[idx] = np.sum(background_mask)\n", + " uninfected_sums[idx] = np.sum(uninfected_mask)\n", + " infected_sums[idx] = np.sum(infected_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize the sums\n", + "max_background = np.max(background_sums)\n", + "max_uninfected = np.max(uninfected_sums)\n", + "max_infected = np.max(infected_sums)\n", + "\n", + "background_sums /= max_background\n", + "uninfected_sums /= max_uninfected\n", + "infected_sums /= max_infected" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Combine the sums into a single array and apply softmax\n", + "combined_sums = np.stack([background_sums, uninfected_sums, infected_sums], axis=1)\n", + "softmax_sums = np.exp(combined_sums) / np.sum(np.exp(combined_sums), axis=1, keepdims=True)\n", + "\n", + "# Separate the softmax values\n", + "background_softmax = softmax_sums[:, 0]\n", + "uninfected_softmax = softmax_sums[:, 1]\n", + "infected_softmax = softmax_sums[:, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN values in combined_sums: False\n", + "NaN values in softmax_sums: False\n", + "Infinite values in combined_sums: False\n", + "Infinite values in softmax_sums: False\n" + ] + } + ], + "source": [ + "# Check for NaN values in the softmax results\n", + "print(\"NaN values in combined_sums:\", np.isnan(combined_sums).any())\n", + "print(\"NaN values in softmax_sums:\", np.isnan(softmax_sums).any())\n", + "print(\"Infinite values in combined_sums:\", np.isinf(combined_sums).any())\n", + "print(\"Infinite values in softmax_sums:\", np.isinf(softmax_sums).any())" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN values in background_softmax: False\n", + "NaN values in uninfected_softmax: False\n", + "NaN values in infected_softmax: False\n", + "Variance in background_softmax: 0.0020539258845222756\n", + "Variance in uninfected_softmax: 0.0039155569854069875\n", + "Variance in infected_softmax: 0.0026512443426509346\n" + ] + } + ], + "source": [ + "# Check for NaN values in the softmax results\n", + "print(\"NaN values in background_softmax:\", np.isnan(background_softmax).any())\n", + "print(\"NaN values in uninfected_softmax:\", np.isnan(uninfected_softmax).any())\n", + "print(\"NaN values in infected_softmax:\", np.isnan(infected_softmax).any())\n", + "\n", + "# Check for zero variance in the softmax results\n", + "print(\"Variance in background_softmax:\", np.var(background_softmax))\n", + "print(\"Variance in uninfected_softmax:\", np.var(uninfected_softmax))\n", + "print(\"Variance in infected_softmax:\", np.var(infected_softmax))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Determine the number of principal components to keep\n", + "#reshaped_features = predicted_features.reshape(predicted_features.shape[0], -1)\n", + "\n", + "pca = PCA()\n", + "pca.fit(predicted_projections)\n", + "explained_variance_ratio = np.cumsum(pca.explained_variance_ratio_)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the explained variance ratio\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, marker='o', linestyle='--')\n", + "plt.xlabel('Number of Components')\n", + "plt.ylabel('Cumulative Explained Variance')\n", + "plt.title('Explained Variance by Number of Components')\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of components selected: 5\n" + ] + } + ], + "source": [ + "# Choose the number of components that explain a significant amount of variance (e.g., 90%)\n", + "n_components = np.argmax(explained_variance_ratio >= 0.90) + 1\n", + "print(f\"Number of components selected: {n_components}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Perform PCA with the selected number of components\n", + "pca = PCA(n_components=n_components)\n", + "reduced_projections = pca.fit_transform(predicted_projections)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PC Background Correlation Uninfected Correlation Infected Correlation\n", + "0 1 0.050334 -0.094879 0.079789\n", + "1 2 -0.002184 0.016340 0.030856\n", + "2 3 0.032372 0.044127 -0.030238\n", + "3 4 0.042380 -0.212249 0.181532\n", + "4 5 0.011044 0.052858 -0.051418\n" + ] + } + ], + "source": [ + "# Calculate rank correlations\n", + "correlations = []\n", + "\n", + "for i in range(reduced_projections.shape[1]):\n", + " pc = reduced_projections[:, i]\n", + " \n", + " background_corr, _ = spearmanr(pc, background_softmax)\n", + " uninfected_corr, _ = spearmanr(pc, uninfected_softmax)\n", + " infected_corr, _ = spearmanr(pc, infected_softmax)\n", + " \n", + " correlations.append({\n", + " \"PC\": i + 1,\n", + " \"Background Correlation\": background_corr,\n", + " \"Uninfected Correlation\": uninfected_corr,\n", + " \"Infected Correlation\": infected_corr\n", + " })\n", + "\n", + "correlation_df = pd.DataFrame(correlations)\n", + "print(correlation_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the PCA results\n", + "plt.figure(figsize=(12, 6))\n", + "plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c='blue', label='Cells')\n", + "plt.xlabel('Principal Component 1')\n", + "plt.ylabel('Principal Component 2')\n", + "plt.title('PCA of Predicted Projections')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# PC1 vs PC3, PC1 vs PC4, etc.\n", + "if n_components > 2:\n", + " for i in range(2, n_components):\n", + " plt.figure(figsize=(12, 6))\n", + " plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c='blue', label='Cells')\n", + " plt.xlabel('Principal Component 1')\n", + " plt.ylabel(f'Principal Component {i + 1}')\n", + " plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1}')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the rank correlations\n", + "plt.figure(figsize=(12, 6))\n", + "sns.barplot(x=\"PC\", y=\"Background Correlation\", data=correlation_df, color='blue', label='Background')\n", + "sns.barplot(x=\"PC\", y=\"Uninfected Correlation\", data=correlation_df, color='green', label='Uninfected')\n", + "sns.barplot(x=\"PC\", y=\"Infected Correlation\", data=correlation_df, color='red', label='Infected')\n", + "plt.xlabel('Principal Component')\n", + "plt.ylabel('Spearman Correlation')\n", + "plt.title('Rank Correlations of Principal Components with Ground Truth Masks')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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aNetcPDxu6MqGH/TzPeqXy1Z1DrXVj69YTymE7NTPqQuJ+vG+Ep9jXSdD39DZdP1jkjr2b6s6WWlhuvK/wdbJFNxHb3gpurLpX+ud+HKsPibit2tWMYd76ccufKmnrlznf1us6mz/J1pX/vt7fblca/1jCADHP0rWlS3nc61iTtfRj2/Eb/qY7F7nrOpcu66fD5Ed/tCVc5rVtqoTNPqwrpyxtZxVTMJK/WPb4a3vdOVj14Ks6vx8KklX9hofaBVzvpz++S87RP97yz9WVZDbVt/vS/v17Xqcs37eupGWpSu3SdltFfP16jq6sqHA08fLDy20quNvzNaVJ/Xqoisfecp6n9hzx0f6XBLrWsXsHV9LV670zgl9gEX/PAYAcvKMrpyTmmgVc6yp/jkz/kv9/pcz+YpVnex3Y3TlE+n68Y1dY/08W3D/PF/Owyomp43+wY3w0x9Tj53XP28BgO8X/rpy8Jf6x/H8Av3zIwBcWhuuK2el3LCKCd2of475u4n+uaHSeOvnzIuV9HP+bNcsqxhlu5+unB2u34dNV/TPSQDQsvlWXXn1LzX0uUy1fs70nKk/np8ba30cPnSP/nF75I4NuvKqaXda1fE9rh+rf0P04xS4T3+cA4B9j+j36YppR61isq7r58PFlVG68o0CL0UA4GpV/d8yeVg/jq0S9a+X1n6m36fjFxfYjwAceET/t5Xq+rGMe936MbpUTr8fffX2O1YxdRYP0pXFqN8pmtXdZVVn2+xqurL5ir7OP/dY75/Xs/XP+cp56+cGw3XrPuRXYfpJq23np+rnS9aqCKuYrPr6x8Ro0j/fmbZZv27L0U8PBP+h3ye8Tls/nxwfoN8fo963WMVcTNDPqesF/vTFqtbtBm3Wj1VWM/1+Hj/J+liS/eoFXfnK51FWMbkFnu4KHksulLd+fRKxUb/t+L3W89tve4HX8Rv18+H4Hd5WdSz6dGG4rv87FxOt58a1eP3r/LB11s/f/gf0MefS9K+VLsdaVUFgDf0xKmu9/rXeNeunfHgXmJqBf1mPy7HG+uelHF/9nArbaP04+h/U5x//5l6rmE3H9c+jvov0x5/z91sfF3L26Z/zb/jp94mud/xqVeeLZY105SZttlnF7Bqnf39wNaDA68MeB63qnJqXqCuHbNVPhqQPrF+b7jmv388Pnwq2ion5TL/fNBq1UVdevFLfHwC4v43+eJNxLs4q5uAW/aTxTNE/F2eds57f5WfqnxtqTtuuK3//bkOrOv+G6ud85Ebr49jDM1bqynP+p39Peqa69Xuvq6n6duLD9a9VD+6NtKrjt08/d2O/PmMV88fgQF059V39a6WIGcet6vxyJFFXrh1zxCrm7+fjdeV/KunHN+R363WOqjP17ykyL+tfc2UNtu7jN7+8YrXNHjxTioiIiIiIiIiIbjsuShERERERERER0W3HRSkiIiIiIiIiIrrtuChFRERERERERES3HReliIiIiIiIiIjotuOiFBERERERERER3XZclCIiIiIiIiIiotuOi1JERERERERERHTbcVGKiIiIiIiIiIhuOy5KERERERERERHRbcdFKSIiIiIiIiIiuu24KEVERERERERERLcdF6WIiIiIiIiIiOi2c2pR6saNG/juu+/w/vvv49KlSwCA48eP4/Lly8WaHBERERERERERlU0mRyscOnQId999Nw4fPoyrV6/irrvugp+fH9544w1cvXoV7733XknkSUREREREREREZYjDZ0o988wzqFOnDv755x94eXlp2++//36sWbOmWJMjIiIiIiIiIqKyyeEzpX766Sds2LABHh4euu2JiYk4duxYsSVGRERERERERERll8NnSuXm5iInJ8dq+9GjR+Hn51csSRERERERERERUdnm8KJUq1atMHHiRK2sKAouX76M4cOHo23btsWZGxERERERERERlVEOf33v7bffRuvWrZGWlobs7Gw8/PDD2LdvH0JDQ7FgwYKSyJGIiIiIiIiIiMoYhxelYmNjsX37dnz66af4/fffcfnyZfTu3RuPPPKI7sLnREREREREREREN+PwohQAmEwmdOvWrbhzISIiIiIiIiIiN+HwotS8efMK/X337t2dToaIiIiIiIiIiNyDw4tSzzzzjK58/fp1XLlyBR4eHvD29uaiFBERERERERERFcnhu+/9888/up/Lly/jzz//xB133MELnRMRERERERERkV0cXpSypXz58hg3bpzVWVRERERERERERES2FMuiFJB38fPjx48XV3NERERERERERFSGOXxNqeXLl+vKIoITJ05gypQpaNSoUbElRkREREREREREZZfDi1IdOnTQlRVFQVhYGJo3b4633367uPIiIiIiIiIiIqIyzOFFqdzc3JLIg4iIiIiIiIiI3EixXVOKiIiIiIiIiIjIXnadKfXcc8/Z3eCECROcToaIiIiIiIiIiNyDXYtSW7dutasxRVFuKRkiIiIiIiIiInIPdi1Kff/99yWdBxERERERERERuRFeU4qIiIiIiIiIiG47h+++BwCbN2/GZ599hsOHD+PatWu63y1ZsqRYEiMiIiIiIiIiorLL4TOlPv30U6Snp2P37t344osvcP36dezatQtr165FQEBASeRIRERERERERERljMOLUmPHjsU777yDL7/8Eh4eHnj33XexZ88edOnSBfHx8SWRIxERERERERERlTEOL0plZmbinnvuAQB4eHggKysLiqJg0KBB+OCDD4o9QSIiIiIiIiIiKnscXpQKCgrCpUuXAAAxMTHYuXMnAOD8+fO4cuVK8WZHRERERERERERlksMXOm/cuDG+/fZbVK1aFZ07d8YzzzyDtWvX4ttvv0WLFi1KIkciIiIiIiIiIipj7F6U2rlzJ6pUqYIpU6YgOzsbAPDSSy/BbDZjw4YNeOCBB/Dyyy+XWKJERERERERERFR22L0oVa1aNdStWxd9+vRB165dAQAGgwFDhw4tseSIiIiIiIiIiKhssvuaUj/88AMqV66M559/HlFRUejRowd++umnksyNiIiIiIiIiIjKKLsXpe68807MmjULJ06cwOTJk3Hw4EE0adIEFSpUwBtvvIGTJ0+WZJ5ERERERERERFSGOHz3PR8fHzz22GP44YcfsHfvXnTu3BlTp05FfHw82rVrVxI5EhERERERERFRGePwolR+KSkpePHFF/Hyyy/Dz88PK1asKK68iIiIiIiIiIioDLP7QucF/fjjj5g1axYWL14Mg8GALl26oHfv3sWZGxERERERERERlVEOLUodP34cc+bMwZw5c/DXX38hPT0dkyZNQpcuXeDj41NSORIRERERERERURlj96JUmzZt8N133yE0NBTdu3dHr169ULFixZLMjYiIiIiIiIiIyii7F6XMZjMWLVqEe++9F0ajsSRzIiIiIiIiIiKiMs7uRanly5eXZB5ERERERERERORGbunue0RERERERERERM7gohQREREREREREd12XJQiIiIiIiIiIqLbzuFFqR9//BE3btyw2n7jxg38+OOPxZIUERERERERERGVbQ4vSjVr1gznzp2z2n7hwgU0a9asWJIiIiIiIiIiIqKyzeFFKRGBoihW28+ePQsfH59iSYqIiIiIiIiIiMo2k72BHTt2BAAoioKePXvCYrFov8vJycHvv/+O9PT04s+QiIiIiIiIiIjKHLsXpQICAgDknSnl5+cHLy8v7XceHh5o0KAB+vbtW/wZEhERERERERFRmWP3otTs2bMBAImJiRg8eDC/qkdERERERERERE6ze1FKNXz48JLIg4iIiIiIiIiI3IjDFzo/deoUHn30UURHR8NkMsFoNOp+iIiIiIiIiIiIiuLwmVI9e/bE4cOH8corryAqKsrmnfiIiIiIiIiIiIgK4/Ci1Pr16/HTTz+hRo0aJZAOERERERERERG5A4e/vhcXFwcRKYlciIiIiIiIiIjITTi8KDVx4kQMHToUBw8eLIF0iIiIiIiIiIjIHTj89b0HH3wQV65cQbly5eDt7Q2z2az7/blz54otOSIiIiIiIiIiKpscXpSaOHFiCaRBRERERERERETuxOFFqR49epREHkRERERERERE5EYcvqYUAGRmZuLll1/GQw89hNOnTwMAVq1ahV27dhVrckREREREREREVDY5vCj1ww8/oGrVqti4cSOWLFmCy5cvAwC2b9+O4cOHO9TW33//jTfffBP3338/GjZsiIYNG+L+++/H+PHjcebMGUdTIyIiIiIiIiKiUsLhRamhQ4dizJgx+Pbbb+Hh4aFtb968OX799Ve72/ntt99QoUIFTJo0CQEBAWjcuDEaN26MgIAATJo0CZUqVcLmzZuLbOfq1au4ePGi7ic394aj3SIiIiIiIiIiotvI4WtK7dixA5988onV9vDwcPz99992tzNw4EB07twZ7733HhRF0f1ORPDEE09g4MCB+OWXXwpt5/XXX8fIkSN128qF3oHyYXfanQsREREREREREd1eDp8pFRgYiBMnTlht37p1K2JiYuxuZ/v27Rg0aJDVghQAKIqCQYMGYdu2bUW2M2zYMFy4cEH3Uy6kod15EBERERERERHR7efwolTXrl3xwgsv4OTJk1AUBbm5ufj5558xePBgdO/e3e52IiMjsWnTppv+ftOmTYiIiCiyHYvFAn9/f92PweDwCWBERERERERERHQbObx6M3bsWAwYMABxcXHIyclBWloacnJy8PDDD+Pll1+2u53BgwejX79+yMjIQIsWLbQFqFOnTmHNmjWYMWMG3nrrLUfTIyIiIiIiIiKiUsDhRSkPDw/MmDEDr7zyCnbu3InLly+jZs2aKF++vEPtDBgwAKGhoXjnnXcwbdo05OTkAACMRiNq166NOXPmoEuXLo6mR0REREREREREpYDT33OLj49HfHz8Lf3xBx98EA8++CCuX7+uXSQ9NDQUZrP5ltolIiIiIiIiIiLX5vCiVE5ODubMmYM1a9bg9OnTyM3N1f1+7dq1DidhNpsRFRXlcD0iIiIiIiIiIiqdHF6UeuaZZzBnzhzcc889qFKlis275xERERERERERERXG4UWpTz/9FJ999hnatm1bEvkQEREREREREZEbMDhawcPDAykpKSWRCxERERERERERuQmHF6Wef/55vPvuuxCRksiHiIiIiIiIiIjcgMNf31u/fj2+//57rFq1CpUrV7a6U96SJUuKLTkiIiIiIiIiIiqbHF6UCgwMxP33318SuRARERERERERkZtweFFq9uzZJZEHERERERERERG5EYcXpVRnzpzBn3/+CQCoWLEiwsLCii0pIiIiIiIiIiIq2xy+0HlWVhZ69eqFqKgoNG7cGI0bN0Z0dDR69+6NK1eulESORERERERERERUxji8KPXcc8/hhx9+wJdffonz58/j/PnzWLZsGX744Qc8//zzJZEjERERERERERGVMQ5/fW/x4sVYtGgRmjZtqm1r27YtvLy80KVLF0yfPr048yMiIiIiIiIiojLI4TOlrly5goiICKvt4eHh/PoeERERERERERHZxeFFqYYNG2L48OHIzs7Wtv37778YOXIkGjZsWKzJERERERERERFR2eTw1/feffddtG7dGrGxsahevToAYPv27fD09MTq1auLPUEiIiIiIiIiIip7HF6UqlKlCvbt24ePP/4Ye/bsAQA89NBDeOSRR+Dl5VXsCRIRERERERERUdnj8KIUAHh7e6Nv377FnQsREREREREREbkJpxal/vzzT0yePBm7d+8GAKSmpuKpp55CpUqVijU5IiIiIiIiIiIqmxy+0PnixYtRpUoVZGRkoHr16qhevTq2bNmCqlWrYvHixSWRIxERERERERERlTEOnyk1ZMgQDBs2DKNGjdJtHz58OIYMGYIHHnig2JIjIiIiIiIiIqKyyeEzpU6cOIHu3btbbe/WrRtOnDhRLEkREREREREREVHZ5vCiVNOmTfHTTz9ZbV+/fj3uvPPOYkmKiIiIiIiIiIjKNoe/vteuXTu88MILyMjIQIMGDQAAv/76Kz7//HOMHDkSy5cv18USEREREREREREV5PCiVP/+/QEA06ZNw7Rp02z+DgAURUFOTs4tpkdERERERERERGWRw4tSubm5JZEHERERERERERG5EYevKUVERERERERERHSrHD5TCgB+++03fP/99zh9+rTVmVMTJkwolsSIiIiIiIiIiKjscnhRauzYsXj55ZdRsWJFREREQFEU7Xf5/09ERERERERERHQzDi9Kvfvuu5g1axZ69uxZAukQEREREREREZE7cPiaUgaDAY0aNSqJXIiIiIiIiIiIyE04vCg1aNAgTJ06tSRyISIiIiIiIiIiN+Hw1/cGDx6Me+65B+XKlUNaWhrMZrPu90uWLCm25IiIiIiIiIiIqGxyeFHq6aefxvfff49mzZohJCSEFzcnIiIiIiIiIiKHObwoNXfuXCxevBj33HNPSeRDRERERERERERuwOFrSgUHB6NcuXIlkQsREREREREREbkJhxelRowYgeHDh+PKlSslkQ8REREREREREbkBh7++N2nSJGRmZiIiIgKJiYlWFzrfsmVLsSVHRERERERERERlk8OLUh06dCiBNIiIiIiIiIiIyJ04vCg1fPjwksiDiIiIiIiIiIjciMOLUqqMjAzs3r0bAFC5cmXUrFmz2JIiIiIiIiIiIqKyzeFFqdOnT6Nr165Yt24dAgMDAQDnz59Hs2bN8OmnnyIsLKy4cyQiIiIiIiIiojLG4bvvDRw4EJcuXcKuXbtw7tw5nDt3Djt37sTFixfx9NNPl0SORERERERERERUxjh8ptTXX3+N7777Dqmpqdq2tLQ0TJ06Fa1atSrW5IiIiIiIiIiIqGxy+Eyp3NxcmM1mq+1msxm5ubnFkhQREREREREREZVtDi9KNW/eHM888wyOHz+ubTt27BgGDRqEFi1aFGtyRERERERERERUNjm8KDVlyhRcvHgRiYmJKFeuHMqVK4ekpCRcvHgRkydPLokciYiIiIiIiIiojHH4mlJxcXHYsmULvvvuO+zZswcAkJqaipYtWxZ7ckREREREREREVDY5vCgFAIqi4K677sJdd91V3PkQEREREREREZEbsPvre2vXrkVaWhouXrxo9bsLFy6gcuXK+Omnn4o1OSIiIiIiIiIiKpvsXpSaOHEi+vbtC39/f6vfBQQE4PHHH8eECROKNTkiIiIiIiIiIiqb7F6U2r59O+6+++6b/r5Vq1bIyMgolqSIiIiIiIiIiKhss3tR6tSpUzCbzTf9vclkwpkzZ4olKSIiIiIiIiIiKtvsXpSKiYnBzp07b/r733//HVFRUcWSFBERERERERERlW12L0q1bdsWr7zyCrKzs61+9++//2L48OG49957izU5IiIiIiIiIiIqm0z2Br788stYsmQJKlSogKeeegoVK1YEAOzZswdTp05FTk4OXnrppRJLlIiIiIiIiIiIyg67F6UiIiKwYcMGPPnkkxg2bBhEBACgKApat26NqVOnIiIiosQSJSIiIiIiIiKissPuRSkASEhIwMqVK/HPP//gr7/+goigfPnyCAoKKqn8iIiIiIiIiIioDHJoUUoVFBSEunXrFncuRERERERERETkJuy+0DkREREREREREVFx4aIUERERERERERHddlyUIiIiIiIiIiKi246LUkREREREREREdNtxUYqIiIiIiIiIiG47LkoREREREREREdFtx0UpIiIiIiIiIiK67bgoRUREREREREREtx0XpYiIiIiIiIiI6PaTMio7O1uGDx8u2dnZN93mTIwrtctc3LePrpSLO/TRlXJxhz66Ui7u0EdXysUd+uhKubhDH10pF3fooyvl4g59dKVc3KGPrpSLO/TRlXJxhz4WVy7OKLOLUhcuXBAAcuHChZtucybGldplLu7bR1fKxR366Eq5uEMfXSkXd+ijK+XiDn10pVzcoY+ulIs79NGVcnGHPrpSLu7QR1fKxR366Eq5uEMfiysXZ/Dre0REREREREREdNtxUYqIiIiIiIiIiG47LkoREREREREREdFtV2YXpSwWC4YPHw6LxXLTbc7EuFK7zMV9++hKubhDH10pF3fooyvl4g59dKVc3KGPrpSLO/TRlXJxhz66Ui7u0EdXysUd+uhKubhDH10pF3foY3Hl4gxFRMTp2kRERERERERERE4os2dKERERERERERGR6+KiFBERERERERER3XZclCIiIiIiIiIiotuOi1JERERERERERHTbcVGKiIiIiIiIiIhuO9N/nYCrExEoilKsbV67dg1Lly7FL7/8gpMnTwIAIiMjkZ6ejvbt28PDwwMAcPToUQQGBsLX1xdnz57F77//jurVq8PPzw+rVq3C7t27cfXqVXTu3BmpqakAgOTkZKxevRopKSlYt24d/vrrL0RFRaF169aYNGkSOnXqhISEBIdz3rRpk1W+DRs2RL169bSY3NxcXLhwAV9++SUeffRRHDx4EHFxcTAYDNi/fz8yMjJw9epVtG3bFqGhoQCA5s2bY/bs2YiMjMShQ4dw6NAhREVFwcvLC0OGDIGHhwfKlSuH3r17IykpCdu3b0dGRgaaNm2K5ORk7Nq1C1OnTsWRI0cQFBQEHx8fGAwGmEwm+Pn5IScnp9jyVXNNSEjA1atXnc532LBhOHHiBCIiIpCQkIDk5GQkJCTg2LFjNx3f3NxcGAwG/PPPP7p8Y2JicPLkSURGRuKLL76wO98ffvgBhw4dwokTJ5CQkOBy+e7ZswcHDhxAXFwcqlSpgqlTpzqVb2xsLLKysmAymZCdnV2m9jV1fJcvX47GjRv/J/uaiJTI+KpjW758eYhIsY1vST33FpbvH3/8gQcffNCpuQAAJ0+exMaNG3X51q9fH5GRkbq4rKwsZGRkoHHjxsjJyYHRaNR+t3HjRly9ehUNGzaE2WzGY489htdeew3R0dG4fv06Dh48iPDwcAQEBGDdunXYt28fDh8+jISEBHTu3BkBAQE4ffo0du7cidq1ayMgIACnTp3ChAkTcODAAcTExCA6Oho5OTlQFAXnz5/Xci1s7pbUc8PFixftOk6EhoYiMjISBoOh0LHNP76NGjUqdGwB3HR8RQRvvfUWFEUpcmznzp2Lv//+GwEBATCZTDAYDFruFy9eLLa5UFz5FpwLycnJqFatGv74448Sm7sAdPlu3brV6blb2vLlvlb43L3Zc9nVq1ft2tfsHd//4jWvM6/JnMm3JF/ztm3bFteuXdPlm5aWpj2uqhs3buD48eOIj4+3mienTp3C1atXtd+NHDkSAwYM0MZa3d/MZjNOnjyJHTt2aHOhWbNmMBqNyMnJwaFDh5CYmAiDwYCrV69i4cKFyMzMRHR0NIKDg+Hv749///0Xp06dsnvu/pfH4YSEBMTExODy5cuFjm1h41twbG2Nrzq2AGyOLwCrsV22bBmuXLmCwMBAXL16FQaDAfHx8bBYLLrxvdW5UFi+N27cwNKlS3HhwoUi58LtyrewuetIvgXn7s2OayWZ7y2RMuDMmTPyxhtvSIcOHaRBgwbSoEED6dChg7z55pty+vRpERG5cuWK/PTTT7Jr1y45fPiwPPbYY/LHH3/IrFmzZNu2bTJ37lzZvXu3PPHEE/LYY4/JmjVrRETEbDbLH3/8IZcvX5bhw4dL//79ZfLkyfL3339Ly5YtpUqVKhIbGyuNGjWSBQsWiIjI5MmT5dFHH9XKkyZNktDQUPH29pbAwEAxm81iMBikQoUK0rlzZ+nSpYs0adJEPD09JSUlRX7++WepW7euGAwGMRqN0rx5c7FYLKIoigQFBcnq1asFgISFhUlISIiYzWZJSkqScePGidFolGeffVYSEhIEgISEhIjBYJBKlSqJoiiiKIoEBgZKo0aN5IcffpAvv/xSXnnlFVm/fr2IiKxZs0ZatWollStXlvT0dGnWrJkEBQUJAImIiJB69epJvXr1JCEhQRRFkTvuuEP++usv6dy5s3h6ekpwcLAAkISEBDEYDJKSkiIbN24UAGKxWMTDw0P8/PykcePG8umnn4rRaJQJEyaIv7+/lp+iKGIwGASAmM1mCQkJEYvFImPHjhWj0SghISHi6+sr3377rfj7+4u/v78A0H58fX21/0dFRTmc7/jx423mazAYZMqUKfLZZ585le9nn30mRqNRAIiiKAJA4uPjxWw2CwDx9/e3Gt8GDRpIu3btxNPTU8LDw6Vfv366fJOSkgSAlCtXzqF8AYjBYJDk5GSpUKGCeHt7/2f5ms1meeedd7S50LBhQ+3xMxgMUrNmTafzNZlMWlsVK1YsU/taeHi4dOnSRRub/2Jf8/DwcGh8y5cvLwEBAdr4PvnkkzbH12AwyLBhw2Ts2LESExOjxRgMBomPj9f+ZmRkpIwaNUquXr1qNb4rV66UKlWqSExMjFSuXFmaNGki/v7+4uHhIU2aNJEuXbrclnzV+Zh/LohIkfm2atVKKlSoIAaDQUwmk4SHh0t4eLiYTCYxGo3y8MMPy7PPPivlypWTunXryogRI0RRFGnUqJEYjUZp0KCBAJA777xTG6/AwEBZsWKFmM1mWbRokXTv3l2bByaTSTp06KDNp9jYWAkNDZXIyEiZM2eO+Pj4iKIoEhkZKb/88ot4eXnp5oK6rymKIgkJCUXO3ZJ6bvDw8LBr7qrP/QDE29tbN7bdunWT8+fPy//+9z9tfJ9//nkBIEajURo3bix79uzRtZeQkCB9+/aVjRs3auMbHx8vRqNRm08Wi0XbP319fSUsLMxqbLdt2ybR0dG645iiKOLp6amV/fz8Cs23cuXKAkCbCyWZr625EBQUpDvOFJy79uS7bNkym/maTCb54osvZMuWLRIfHy8mk0nL19m5W1S+zuxrJZkv9zXruWvPc1lQUJB4eXkVua+pr3HsGd/b+ZrXmddkrvaaNzExUXx8fHTjnf/10ssvvyxTpkyRFi1aSOfOneX9998XRVHkkUcekfj4eOnSpYvEx8dLnz59tPrp6ekydepUMZlMsnHjRrlw4YI0atRIOwYHBgZqf8/f318MBoNUrVpVvvnmG4mKihKDwSBVqlSRgwcPSmhoqG6u5f8JCAgocu7+18fh/McJW2Obk5MjU6dO1cb33Xff1eZR9+7d5dixY9r+oI7ttGnT5PTp02I2m2Xjxo0yfvx48fX1FUVRJCQkRJ577jmr8U1JSZGIiAhtbA8fPiyVK1fW5mr+OWFvvnfddZcA0OaCvfk2atRIQkNDtbmgPsYeHh6iKIpUrlzZai6UVL579uyxyreouWtPvoXNXYPBoMu1uPe18ePHS3x8vBgMBgkJCZGRI0c6vZ5T6helNm3aJEFBQRITEyM9evSQIUOGyJAhQ6RHjx4SGxsrwcHBsmjRIu0J2mAwSO3atbUHODg4WNuJvby8JD4+XuLi4gSA3HfffWIwGOSBBx7QnpArVKggwcHB4ufnpz1I4eHh0qhRI/H29pYOHTqIn5+fPPDAAxIZGSlPPfWUKIoi0dHR2ptXf39/6dixowQGBkp6erpcvHhRREQuXLgg7du3l6ioKKlfv7789ttv8u2332pP4IcOHZLx48dLdHS0AJDY2FhJTEzUJnhcXJwoiiI+Pj5iNpvFbDbLl19+KUeOHJFy5coJAKlRo4aUL19e27kURZG0tDTx9/eXjz76SHx8fMTX11c7cKtPoNWqVROj0SgdOnSQs2fPyoULF2Tz5s1Sr149SU5OlpSUFJk7d64MGTJEgLw31RkZGfLss89K+fLldTtGwZ06fzkrK0tq1KghAQEBAkBGjRqlvRhSFEXatm0rf//9tyxYsEACAwOlSpUq0qFDB7lw4YKMGzdOPDw8JCwsTPbs2SNr1qyRoKAgGTdunEP5FnzCuVm+8+bNcyhfs9ksqampcuHCBcnOzpZGjRqJ2WyWhg0bypw5cyQkJETLVc03IiJCfHx8ZO7cuTJp0iSJjIwUAHLvvffK77//rr3YdDRf9SBZs2ZNURRFwsLCxMPDQ1588UURkf8k34K5b9++XdavX6/tn+rY2ptvQECAmM1muXDhgqxZs0ZCQkJk4sSJpWZf69y5c6H72qRJk7Q3jf/FvmY2myUuLs7u8X3vvfcEgISGhmrjq+aSmJioG18AEhMTI35+fqIoisyYMUNERGbOnKn9vm3bthIVFaW9mDYYDNr4vvXWW9obebVNHx8fCQoKEoPBIJ07d5br169rx5GSzjckJEQ3F6pVqyYmk0lq165903wDAwPF09NTDAaDdOrUScv3xo0bsnr1agkODhYvLy8ZP368vPTSS1q99PR0Wb58ubRv377QfS1/+csvv5T58+dri4y9evXSXnzHxcWJj4+P9O/fXy5duiTjx48Xb29viYqKkh07dsi+ffvE19dXzGazbN26VT788EPx9vaWGTNmFDp3S+q5wWQySVBQUKFz12KxiJ+fn3z99dfyzTffaHNXHdsKFSpIzZo1JSIiQhtfdSFg+fLl8uCDD0rdunUL3dfyb9u1a5dUr15dW/Du27evhIeHa/m2adNGzp8/L+PHj5eYmBhJS0uTRo0ayY4dO6R3795isVgkKChIli5dKjNmzBBvb2/5+OOPb5qv+iIyIiKixPMtOBfUDw1SUlJk0KBB4uXlJR9//LFu7tqTb2HHNfUNMQB57bXXtHwBSJ06dRyau/bkeyv7WnHny32t8Llb2HOZh4eHhIaGFrqveXh4SHR0tDa+Xl5ehY7v7XzN68xrMld7zWuxWCQsLEyeffZZCQwMlDfffFOuXLkiBw4ckPfff1+b3wMGDJBu3bppiwKVKlWSSZMmSXp6um6/yr9vFdzXnnjiCVmxYoVUqVJFa8PDw0MCAwMlMTFRQkJCpFOnTrJjxw555plnJCQkRPz8/OSLL76Qr7/+Wjw9PcVsNsuMGTPkpZdeEovFIqtXry507v6Xx2H19c2kSZPkjTfeEC8vL5k9e7Y2tuHh4dKiRQvx9vbWxlcdr0mTJknTpk3l7rvvtjm2+Rc01G1Tp06VMWPGaAuPzZs318ZXXRBXxzY1NVUqVqwolSpVkqVLl0qLFi3EYrGIj4+PjBgxQgYOHCgeHh6yfPnym+Z7zz33CJD3gZ8z+apzQd0v7777bm1B3svLS1q2bFni+Tozd+3Jt+DcDQ8Pl8DAQAkODpa2bdtquZbUvvbqq6/KihUrZMyYMeLj46O9BnZUqV+Uql+/vvTr109yc3O1bcuWLZNly5bJ0qVLpVWrVuLj4yN16tSRjz76SN577z1JS0sTAPLUU0+JiGhvPiIiIqRp06baxFE/IQ8ODtZWfw8ePCiXLl3SFqW+++47eeaZZyQ0NFSMRqMoiiLDhg2TnJwc2bZtmwCQjh07armZTCYxm80iInLu3DmpUaOGPPzww7J9+3bZvn27LFq0SADI/PnztW3qG+UaNWrIyZMntYlQo0YN+eOPPyQjI0MAyKlTp8RkMklSUpIsW7ZMfHx8ZP/+/SIiUqlSJS1GROTUqVPi5+enfWpXsWJFMZvNUqlSJXn88cclNzdXZs+era2Miojs3btX29nVCZr/TID8B2D1k/6zZ89q2+655x45deqU/Pzzz7p8y5cvLxaLRXbv3i0iIhERETJx4kRdvkuXLhUg7xNii8UinTp1EpPJJL6+vrJz504REcnMzBQg71PECxcuiIhof9uRfNWdTP3bar5Go1F27dolVapUcSpfALJkyRJtLuzYsUMAyI8//igiIh999JHNXG09ITRo0ECys7O1Pjds2NChfBVFkeTkZBER2bx5szz88MMC5C3Udu7cWTtD6HbkqyiKxMTEaHPhk08+ER8fHy1f9VMuNd7efNUX0epc+OijjyQhIaHU7GuJiYmFzt384+sK+9prr71W6PguXLhQAEjTpk218VXz/+OPP0REtPFV527FihXF09NTG9v69etrb2DVfMeOHSsmk0kiIyO18VUURRo2bKgdF9T9b8eOHdrYPvHEE1quJZkvkPcppDoX2rdvr71Yqlu3rjz33HNiMBjkjjvusMr3559/1vL19PSUoKAg7Ud9rNWy+mK4bdu2kpubK7t37xYAUr9+fdm9e7ccPHhQ+4DEZDJJSkqKjB07Vjw9PeWvv/6y2teuX78uixYtkiZNmgiQt7A2ZMgQ2bVrlwDQ3riLiHh7ewuQ9wZLRGTKlCl2z93ifm5Q39AWNXeNRqPuucFgMGhjqR7f1YXM/OPds2dP+fvvv3X72u7du+WTTz4RAPLbb79p42s2m7Uz46KiomTMmDG68Z00aZI2ThERETJ48GAxmUwSGBgomzdv1p4L1Oc6dXy9vb3FaDTeNF/1TJXY2NgSz7fgXPjtt98EgHa2+ZQpU3S52puvOj/UuavmazQa5dtvv9U+xVbnblRUlCiKIhUrVnRo7tqTrzP7Wknly33N9ty157lMffPsyL7mzGvIkjoOO/OazBVf8y5evFjL12KxSM2aNbUf9Zskalk9M/rRRx8VEdHeV9WpU0fWrl0r69atk9dff13r9+zZsyU1NVWMRqN2HI6NjRUAUqVKFTlz5oy89dZb2oeHqampMm3aNO34/t5772n5qvt5dna2iIiMGjWq0Ofd//o4rJ4Fl/+5wcvLSxtLtc8JCQnaNnWfeOWVV+TYsWO6ubt27VptbJcsWSJGo1GSk5PliSeeEA8PD2188+erjq8672rVqiUTJ04Uo9EooaGh2lzduXOn9kGcOr5RUVHi4+Nz03zV19YBAQEO5Ws0GrVvLsXGxmoLs2q+6nFMUZQSz1fd1xyZu/bkW3DurlmzRoC8hU517ubPtTj3tSeeeELymzZtmtSsWVOcUeoXpTw9PbUnSlX+J9/CVqWjoqIkMzNTjh8/LkDeJ93qCxP1bAx1hVr9hEc9eKlfs1GfkK9du6adBmkwGCQ6OlpefPFFASCrVq3ScgsPDxcAcvLkSRER+eabb7Q6tvLN/4KiQ4cOUq1aNfH29hZFUWTJkiUSFxcnI0eOFABy+vRpMZlMEhISIjt37pSAgADZvn27iIh2Wp+arzp2np6e8uOPP0qPHj20v7N3714RETlw4IB2YPr7779FJO8FUVBQkKxbt07WrVsn77zzjgCQBQsWyLp162TGjBkC5H3S2rBhQ2nevLl2uumECRMkLi5OZs2apcs3KChI6tevL2+++aaIiKSnp8vbb7+ty3fRokViMBjk559/lnnz5kmjRo20Mdq1a5eI5J01p47h2bNnRSTvoGI0Gh3OF8g7G+bLL7+Uw4cP6w7QoaGhTucbEhKijf+PP/4oQN6ngSL/9wLujTfe0PI1m83i6+urldV8a9WqJc2bN5f9+/cLAHnxxRcdyld9clWp+Y4cOVKaNm2qzcPZs2eXeL6KooiHh4c2F3bu3CnNmzfX8lXPbMw/d+3JNyQkRABoc0HNt7Tsa0uXLhVFUbTxLTh3161bp32i9l/sa+o4quOrjqetT6jyvxjet2+fbnzVfW3y5Mly6NAh3dwNDw8XPz8/bWzDw8Nl1apVunz/+usvAfLObss/vhaLRRv/P//8U/cCTn0BXfC05pLIV53fKjXfzz//XHr06KE933z11VdW+X777be6ufD888/LnDlzZM6cOdon/2pZnZtJSUnyyCOPaC+AevToIWlpabJlyxZtv1ffjO3YsUPq168vH3zwgYiI9jWq/HP3m2++EUVR5KmnnpLk5GRtXD777DMtRv3UXP26vPrmrrC5W1LPDYGBgRIREeHQcUJ9blDH99VXXxUAMn78eN14A5AKFSrII488os2FZ555RtLS0mTFihW6fU0961Ed25o1a2ofgKn5qmO7Zs0aGTVqlLYQbTQateeG7du3i6IoYjQatfH18vISo9Go5VYwX3UubNq06bbkW6lSJW38N2/eLADkm2++0c2F/HPXnnzVr6Woc7fgvhYSEmKVr6Io4uXl5dDctSdfZ/a1ksqX+5rtuWvPc1lAQIAEBQU5tK/5+vqKyWRymde8jr6GdMXXvD/99JMu3x49esiIESNkxIgRYjKZxGQyaeXHH39cgLwzlocOHaotHrVs2VKaNWsmR48elYMHD1rlW6FCBfn0009FRCQ1NVUUJe/MdtWGDRsEgDzwwAPi5+enPd8sW7ZMi1EXmE6cOCEioi0EFTZ3/8vjsPpBbMHnBnV81csPPPPMM9r45l+oHTp0qDYOHTp0kGbNmsmvv/5qNXe3bdsm1apV08ZXXYzJn6+fn5/4+PhIr169tHE0mUySmZkpIqIt1hmNRm181a8Dq7kVzFedC99++61D+RacC3PnztXlu2HDBlEURRYuXFji+aqPkSNz1958mzZtqo2/OlfXrl2rK5fEvrZt2zbJ76+//hI/Pz9xRqlflEpMTJS5c+fqtkVHR8vSpUtFRGTu3LmiKIr2ybaIyNatWwWAdOvWTWJjY7Unyi+++EIqVKggzz//vCiKIhaLRbuWx44dO6Rbt27Su3dvERHttLz8O+HYsWPFbDbL3LlzZfjw4dpXf0aNGqXFqN8VfuONN2T79u2yadMmASAjRoyQl19+WQICAiQkJESmT58uBw8elIMHD0psbKwoiiLXr1+XDh06SFhYmCiKIiIiR48elTp16ggAadGihSiKIv7+/vLll19Ku3btZOjQoSIi2vea8+cbFBQksbGxIiLairOPj49kZGSIiMi6deu0FwMff/yxXLhwQRo0aKB96rVkyRJJTEzUvh8t8n8rqz/99JNcunRJGjZsKDExMVq+W7duleTkZAEgvXv3FkVRtNO5AwICZPjw4TJ58mRt5X3atGny6quvSmBgoNSsWVPq168v8+fPl/vuu0/uvPNO8fT0lFatWsmWLVvkzjvvlICAADGZTLJkyRK5cOGC1KpVS3x9fZ3Kd+vWrZKWliYdO3bUXmx1795dwsPDnco3JCREPD09JSMjQ3bs2CExMTFiNpslISFBlixZImvWrBEPDw954403tHxNJpPce++92mOm5rt69Wpp2LChVK9eXRRFkYsXLzqUL5D33fOPP/7YZr7NmzcXb29vCQ4OLvF8AWhfYQsJCZFvvvlGNmzYoOWrfkowaNAgh/JVP8WaMGGCbN++XVasWCGKopSafW3//v2iKIqMHDnS5twVEe2rxv/FvqaeKq6Or7+/v/j6+sqqVatsjq/6RmLNmjVW43v06FFp3ry5NnaKokjLli21F+/q2LZu3VoGDRqkG98ZM2aIyWTSPtFSXwQ++OCD2jh9+eWXAuR9JW7ChAlaLhMmTCjxfNUPOFQF81Wvl/Luu+9a5Vu9enXZsmWLNhfUr0du2bJFPDw8pEWLFlb72ldffSUVKlTQrmdw9uxZWblypfZpsZeXlxgMBgkLC5MffvhBvvrqKwkODpbZs2fLqFGjtHH5+eefZdasWRIXFycpKSly7733yvr16+Wee+4Rk8kkCQkJcvnyZcnKytI+oVWfd7ds2SJms7nQuVtSzw3q10oLm7u+vr5isVhky5YtIiLy66+/itlslokTJ8qWLVukdu3a4uvrK999952Wr3pmy7Fjx6RChQqSmJio7WsrV66UiIgIASBjxozRxnfcuHHa2M6ePVt7TfDVV1/ZHNt+/fpJhQoVxGKxSL9+/SQrK0s6deqknWmp5lu9enXtDZatfNWx3bhxY4nn6+3tLd7e3tpcqFq1qvbp65YtW2TLli1iMpl0c9eRfNW5O3DgQAHyFoyHDRsmUVFRVvmq88WRuWtPvs7sayWVL/c123PXnucyLy8vKVeuXKH7WmBgoBiNRm1869SpI97e3i7xmteZ15Cu9prXaDRKq1at5MyZM/Lrr7+KyWSSadOmiUje9YE9PT2lYcOG2lxQ3699/vnnEhERId27d9eO/9OmTZPo6Gh58803tX1t8uTJEhsbKy+99JLExsbK999/L/PmzdPm97Fjx2Tt2rVStWpViY6Olt69e8vevXu1M5hr1aql/W31a6/q3N2xY4cYjcZC5+5/eRxWLx9w5swZ7blLHd8zZ87I3XffrX04qlJf0+zatUsiIiJ0+9q0adO0EynmzZsnBoNBIiMjZcOGDTJ79mxtfMeOHas9Rur4+vj4SIUKFeTo0aPy0ksvSXh4uJjNZhkzZoyIiPTv3198fHzEZDJp+aalpYm3t7c2Fwrmq86FjIwMh/ItOBfU/XH79u1Wc+F25evI3LUnX7PZrFt0veuuu0RRFG1fU+duSexrGzZskPx27twp/v7+4oxSvyg1ZcoUsVgs8vTTT8uyZcvk119/lTvuuEO6dOkiTz/9tHh5eUlCQoLMmzdPq6M+aaxatUoGDBgggYGB2hvRS5cuaQ+E+mmNt7e3LFq0SI4dOyaJiYnSuHFj7QLD9erVk759+0rjxo3Fw8NDunbtKmFhYdKnTx9JSkqS8uXLi9lslqeeekoGDRokFotF/P39tYNu/rMzoqKi5I033pAhQ4ZIq1attHyfeOIJAfIequvXr0u7du3EYDBovx82bJgEBgZqLyg6dOggCxculD/++ENCQkKke/fucscddwgA6dSpk7z22mvSvXt37RT8MWPGSL169aRHjx7i7+8vVatWlRkzZkjFihUlMTFRQkNDxcPDQwwG/cU5PTw85Mknn5T+/ftLp06dRETk5MmTUrt2be37pBcvXpT69evr8h01apRYLBZthbtu3boyY8YM2bBhgzRo0MDqrIWYmBiZOHGinDx5Uu666y7x9fWV1q1by/nz5+XRRx/V4oC8U4fvvfdeXb7qKczO5HvlyhWpV6+e9jjVrVtXmjZt6lS+27Zt0z7lVMdw5syZ2qmw6nxQv+bl4eEhVatW1X39M3++tsbW3nzVHBRF+c/z9fPz0+WrPhbFkW+XLl20fa3gWZKuvq+pZ0Gpn2AXnLsiIt27d5f77rvvP9nX/P39pXfv3tr45n+MbI2v+ryrnkZdcHxzc3OladOm2llKHTp0kJ49e8rbb7+tje3o0aO1770PGzZMunfvLhaLRZo3by7ly5fXxrdcuXLi5eUlH3zwgcyZM0dbcBo3bpwuX7X/JZkvAAkPD9fmgr35BgUFyd13360tfhoMBgkNDZXg4GAxGPIugNqtWzftsT58+LDExcXJxIkT5ejRo5KSkqKbCydPnhSz2az1PSYmRt555x0RyfuUW12Qzf84enp6yrPPPiu7d+/WromVmpoqq1at0l0AVD0WqnNX/TuFzd2Sem4YPXq0XceJunXriqLkfT0/KipKvLy8tDcKbdq0kUcffVR69eql5XPXXXdJvXr1RERsju/YsWPFYDBo13lTx1cd2/xn3wG46dgePXpUGjdurHseM5lM2kJCcHCw9kZfnQsF880/F0o6X1tz4ZVXXtHmrre3t1gsFt3cdTTfkydPSlpamm7uJiYmWuXr7NwtKl9n9rWSzJf7mu25W9RzWY8ePeza11q2bKl7Dam+1vmvX/M685rM1V7zTpgwQapUqSImk0ni4+PF399f+7qTeuaQ+uG/SN5ZF8HBwfLee+/Jzp07tQtAq3bt2qV9OJf/mD5mzBh5++23xdvbW3cxcPX9QIcOHWTdunXaDWrCwsJk5syZoih5F7pXX3+p1w8NDg7WFioKm7v/9XE4IiJCTCaT1KxZU9LS0sTHx0f71k+1atWkffv28uyzz2r5NG7cWCpUqCAiYnN81a9w589HXahRxzf/a1V1fJs0aaI9f4aFhcnOnTu1r7OpXxszGAwSFRWl5RsYGKh9ndNWvvnngqP55p8L+W8UcbO5cDvytXfu2pOvrbn76KOPavtaTEyMWCyWEtvX8ps5c6bTX99TRERQyi1cuBDvvPMOMjIykJOTAxGBwWBA3bp18dxzzyEzMxM//fQTVq5cCSDvlp2vvPIKWrRogXvuuQf9+/fHe++9h9zcXK3NSpUqITMzEzk5Oejfvz/uu+8+tG7dGufPn8e4ceMwc+ZMnD17FmazGdHR0WjUqBEGDRqEWrVqYdy4cfjll1+Qnp6Op556Cq1atcKvv/4KAAgPD8fatWtRuXJlHDhwAMuWLcNvv/2GOnXqYNCgQQDybst45coV+Pv7A4B22/cePXpovz927Jh2i/ErV67AaDRi586dWL9+Pbp3746goCAAQGZmJl5++WWsWLFCuz2oyWRC3bp1MXDgQKxdu1bLdfLkyRg7dixGjRoFdVrExcVh2bJlKFeuHDIyMrBs2TKcP38ePXr0QO3ateHv749//vkHx48fR+XKlW0+PpcuXcKWLVvQpEkTAMCBAwfg6emJ3377Dd9//z2GDRuG8PBwLf7MmTP46aefcPbsWbRs2RJJSUmFPv67du3Czz//jIiICDRq1AihoaHabYrz3wLzVvL94YcfsH37dqtcHc33ypUr+Pzzz3H58mU88MAD2i1lb5ZvTk6OQ7nam+/ixYsRGRmJGjVqIDEx0aXz/e233/D7778jNTUV0dHRRea7aNEiXLp0SZfvgQMHcPLkSfzwww+wWCzc1/6/4tjX1L+zcOFC/Pvvv+jZs6fWTv7xLTi2Nxvf7du3Y9OmTXaP7f/+9z/cddddGDRokDa+r7zyCho1aoTDhw8DACwWC1asWIEWLVoAAKZOnYrVq1ejZs2aLp/v7t27MWXKFBw+fBjp6enarb69vLywZ88etG7d2uZjdfz4cXz77be6/Ddt2oQvvvgCe/bsweTJkxEbG6v9LicnB3PmzMHGjRvRuHFjxMbGonbt2vDz89Nizp49i5CQEADAiRMnMH78eFy5cgXdu3dHenp6sT7vqvOquJ4bbM3d3bt349dff7W6jXqlSpVw6NAhh8Z306ZN8Pb2xtq1a/H999/rxjcnJwdbtmzB6tWrcejQIXTt2hX16tW76dgCwGeffYaffvoJsbGxuO+++5CWluay+dqaCwCwZ88e/PLLL8WWb2Fzd8uWLXjvvfeQlJSEO+64w+G5W1i+JbGv3Wq+rryv7dy5Exs2bNDta8U9F242d9XxnTt3Ln799Vebz2X27GvF/RrS3uNwixYtkJycfNOxLew12ebNm3W3qXfV17y5ublYvXq1zeeyyMhIbN26FY899pjWxrlz52AwGBAYGIidO3di8eLFGD58uPb75cuX46OPPsJff/2FJUuW6PI5f/48vvnmG0yaNAnJyclo3rw5GjVqhPLlywPIez+4Z88eVKxYEb6+vti0aRNGjhyJ7OxsPPLII+jVq1exzl2g6OeGwuYuUPhzQ4MGDW46tq1atcLOnTuRkZGhjW/+sQVgNb6rVq2CyWTCqlWr8P3339sc33HjxmHDhg246667EBcXp41vwbHNzs7W3h/HxMTgoYceQosWLW5bvufPn8e3336LFStWIDMzE927d0fTpk1vOhduR75FzV1H8rU1d2/nvqb66quvYDabb7pPFKZMLEqprl+/jr///hsAEBoaCrPZfEvtHT16FBkZGWjZsiV8fHxuqa3s7GzcuHEDvr6+t9SOs0QEp0+fRm5ubpFjk52djd27d8NsNqNSpUowmUy3MVMC8h4vRVHsLhdXjLPt0v/hvlZyHBlbIO8FQFZWFmrXrv2fjG1py5eIiIiI6HYrU4tSZcGRI0cwfPhwzJo1y+4YW3XsibH3b//777/IyMhAcHAw0tLSdOXk5GR89tln6Ny5MzIyMuDj44MdO3ZoZUdi5s+fj0qVKt1yu3Xr1sWvv/6Khg0bIjExES+++CK2bt2KoKAgPPXUU4iOjsaIESNw7Ngx9O7dGz179sSKFSvw1ltvaTH//vuvrmxPHWfbfffdd3H16lV069YNzZs3BwB4eHhg+/btSE1NtatcXDGO1snKysJnn32Gv/76C1FRUWjXrh3WrFlz0/JDDz0ET0/PYq9jKyYpKQm7du3ChQsXEBUVBaPRiPnz5+Pw4cNISEjAU089hQoVKiAoKEhb7R89ejSWLl2K06dPIyEhAdWrV8f69etx+vRplCtXDk899RT27dtnFbNt27ZC2/3oo4/w3nvv6WK6du2q288GDhyILl264M4777zptqLKJVXnZtumTJmCTZs2oW3btujatSs++ugjvP7668jNzUXHjh0xatQovPfee7qY7t27Y/ny5fD29kbPnj1x+PBhfPXVV1q5QoUKGDp0KP799180a9YMixYtwoIFC4ps19bfLriwc+LECUyfPh3r16/HiRMnYDAYkJycjA4dOqBnz54wGo1WMTk5OQAAo9EIAFZlg8GA6OhoeHp64vLlyzh58qRVu6dPny7y79py7do1LF26VPuUNicnB1lZWQAAX19fiIiubDAYEBYWBqPRiJycHJw5c6ZY6tgbExkZifT0dLRv3x4eHh5aP06dOoX3338fr7766k23ORNzq3WOHj2KwMBAZGVl6bapx5LGjRtrMRaLBb/88guSk5N1ZTWmuOsUjKlcuTJ+//13VK9eHX5+fli4cCF+/PFHBAcHo0ePHggLC8OkSZNw5MgR9OvXDw0bNsTevXvx1ltvORRjNBqLpc6HH36Iq1evonPnztqxIzk5GatXr9Y+6S1YtifmdtQREaxbt047lrRq1Qo///zzTcutW7eGyWQqtI49MUXViY6ORmpqKn7//XecOXMGUVFR8PHxwaRJk3DixAmkpKRgwIABOH78OD755BPtGNWoUSN88803hcakpaUhIyOj2OvMnz8fx44dw9mzZ5GQkIABAwZgw4YN6NSpk3bG6dtvv11o+WYxDzzwgO6MraLasacOkPdJ/6ZNm9C6dWs0atQIr732GmbPno2QkBD07t0bKSkpGDJkCP755x888MADePPNN52KOXPmTLHUeeONN3Ds2DEEBATA398fIoLr16/DbDZDURQYDAbEx8fDx8cH586dw4kTJ6xinKnjbLvJycmoVKkSbty4oZ29cfXqVSiKoh03rl69CiDvrGEg7+wOf39/XLx4UVenYExAQAAuXLhw0xh7282fi3pmSb169ZDfP//8gy+//BLdu3e/6TZnYm613W7dusFgMOhi1G8CHT16FPHx8cjNzYXBYEBubi6OHj2K2NhYXVmNURQFR44csVnHnpibtavmEhcXh4MHDyIuLg4GgwGZmZn4+uuv4evri/vuuw/+/v5YsmQJjh07hkceeQSRkZG4evUqPvjgA4di/Pz8HK5jK+batWv44osvcPXqVbRt2xahoaFo3rw5Zs+erT2HFCzb2lYcdZxp98CBA9rxpUqVKkWW7aljK8ZpTn3pj0rMtm3bdN/hFMn7jvJjjz120xhbdeyJKardw4cPS8eOHSUhIUH7Lmv+O5IZDAbtQr/5YwBo34u2N6Z69eq678zeSrseHh4SHBwsnp6e8sILL1h9J1i9M4r6Pd2ePXvqYtTvcztSx9l2LRaLxMfHa99bT0lJkVq1agmQd6va8PBw7cJ9qampUqtWLZvl/HXsiXG2XU9PT+3il3369BGDwaBdM0K9doCvr69WVvtZs2ZNCQ4OlpCQEDGZTLdcx952vby8pG7dutpFxO+77z4ZPXq0PPvss+Lr6yvBwcGSlpYmsbGxUq5cOe3aQNOnT5eWLVtquT399NPy7LPPisVisRlTt27dQts1mUzSo0cPmT59utZObGysREZGSqNGjWTBggXa/FAvXjl9+nRtXpcvX17GjRtXZPnEiRMlUsdWzJAhQ8TPz08eeOABiYyMlHHjxom3t7fUqFFDHnzwQQkLC5PU1FQxGAzi6+srPj4+0rJlS1EURSpXrix+fn7a9RDUsnohSYvFIpUrVxaDwSAtWrQost2xY8daxVSrVk0CAgIkKipKhg0bJq+++qq2z8XGxorRaJQHHnhA0tLSxGQyadcfVMe/UaNG2jVbgoKCtOvpFSy3atVKzGazGI1G8fPzs2rXYrGIwWCQgIAAKV++vBiNRnn00UelXbt22jUGK1SoIOXLl5fy5ctLpUqVpFKlSpKSkiLe3t5iNpulcePG0qZNG/Hx8dGeS9QLTKtlX19fadKkiXZ3F09PT2nSpMkt17E3pm3btlpcSkqK7Nu375aPUUXFOFtHURSpW7euds2Ye++9V9uP1W0ApHbt2lpZvbmJWlZveV7cdW4WExAQIIqiSFBQkHanX7Vti8WiXf9B3T5y5EitbG9M/mud3EqdgIAACQsLk5CQEDGbzdK8eXPp2LGjKEreDQE6duwo1apVsyrnj6lWrZrNmOKu07FjR4mIiBCDIe+C5Ort2QFoFwFW+6uW1d+r1/QoX768+Pv7OxzjTLvqWKtldW488MADcv/992vzpV69evLCCy9I/fr1Bci7bX1RMRUqVCiROoqiSEJCgtStW1dMJpMoSt4d7lq2bCmffvppkeWrV68WS4w9daZMmSImk0lq164t/v7+8thjjwkA7XqPZrNZ64N6p9169eo5HKM+9rdax8PDQ3x9fbXrXKqve9TXoQCkSZMm2nVm1OsrFYxxpo4z7bZo0UK7CY16zFbvHqZuU+8GqL7+qlKlinb9MIvFIlWqVNHVsSfG2XZr1qypvce44447dDewcaXjmojI+vXrtWN0eHi49OvXTwBI586dxdPTU5tXnTp10mL69++vq6PeSKawOvbE2NtuQkKCGAwGSUlJkc8++0wbe/VYp15DSz2+zJkzR3cNK3ti/P39tdeat9Lu6NGjpVy5cmKxWMTDw0O8vLzkiSeeEIPBIP369ZMXX3xROnXqZFV+8cUXtW2dOnWyGVNYHWfbrVWrlhgMBpkyZYp89tln2g0i1OOI+n5O/QkLC9O9rmjcuLF2YfbCYtq3b6+9FjQYDNKsWTO5dOmSOIOLUrfZsmXLCv155513RFEU3bY+ffoIkHf73hdffFF69+5daFndlr8dW+1OnDjRKqbgkx4Aueeee+TMmTOyb98+CQkJEQCydetW2bdvn/amvHnz5nLmzBn55ZdftAORIzHqRC+OdgMCAuTQoUOyYMEC7UWRiMiCBQvE09NTLBaLvPTSSyIi0q5dOwEg6enpWoyiKBIQEOBQHWfbBSA1atSQpk2bSnx8vHYgBKAduNU++Pn5aXc8KVjOX8eemFttt27dutoilvrm88EHH9Tmi1pWF4dOnTolly5d0u5mcqt17G337rvvFpG8u1WlpqZKYGCgfPvttyIi2n7Vs2dPmT59ujYH33jjDRHJu2W3uh8dPHhQRES7jbRaVmMsFkuh7SqKIs8//7yI5N19Tb2VbP7FLgBy//33S7NmzXQX5hwzZow8/fTT2kG8YcOG0q5dO5tl9cBb3HVuFlOnTh358ssvdbc5VhepunXrJgDkoYcekrFjx0pQUJAAebfLzf/8ou4T6osso9EoixcvFhHRbjttT7v5Y9RF7vyLXQaDQZo3by5jx46VsLAwadKkiRiNRqldu7a2wO3l5SWVK1eWwMBASU9Pl3LlyklMTIyIiJw7d04sFot2h5Nz585JfHy8mM1mGTFihJw7d05q1Kgh9evXt2o3NDRUHnzwQQkMDJTy5ctLamqqBAQESI0aNbQXygUXuyIiIiQmJkYCAgIkPT1dmjVrJu3bt5cLFy7IhQsXJCwsTMLCwrRy+/btJTg4WNq3by9HjhzRlW+ljj0xTZs2lYYNG8r27dvl559/lqZNm0q1atXk888/l88//1y7Y4tatrXNVoz6QrYk6lStWlU++eQTef/997W729SuXVt+++037cWxj4+PrFmzRr799lttnqnlatWqlUidm8WEhobKoUOHZPz48dqL54sXL8r48ePF29tbPD09pU+fPnLx4kWpU6eOGAwGqVixokMx6kLvrdYB8j5ASkxM1I4h6rw2Go26N6n5y/lj1N+XdJ38MTExMdrxcdOmTSIi2oW71Rt4PProo9qx+9SpU3LkyBHt2JG/jj0xzrQLQNq1a6cdf2JiYsRkMsn+/fu1Y5SiKFq5fv362g0aCosxGo2SlpZW7HUAyOzZs6V9+/baHaIAyOjRo7VtAOSuu+6SZs2a2SyrrzsLq2NPjD11jEajtGjRQnbs2CFr164VRVHk4YcfFhGRtWvXajEffPCBiIh2V0JHY9TXVbdax8PDQx5//HHJzc2V2bNni4eHh8TFxUlubq7s3btXgoKCxNvbWx5//HH5888/JTExUUJCQnQxJpNJ/P39HarjbLs+Pj7SsGFD+frrryUxMVFSU1OlYcOGsmfPHtmzZ48EBwdLcHCwVk5PT5eYmBhp2LChrFy5Uld2JMaZduvVq6cd5zZv3iz16tWTtm3bypEjR+TIkSPy9ddfCwCtbGubrZjFixcXGuNsu+3btxcAMnfuXJk0aZL2urh8+fLy+eefa6+nfHx8ZMGCBTJjxgzt+Vktq3cHLKyOPTH2thsRESEZGRny7LPPas8NGRkZMnDgQPH19RVPT0+59957ZfPmzZKUlCQmk0kiIyMdijEajdpdIG+lXfU4kf/DgdLwkz/XxYsXS1ZWlnYsuf/++7WyelOHAwcOyPr167X3fPnr2Irx8/OTtWvXSlZWlqxfv17KlSun3YHaUVyUus3UlcT8K4+2JlH+mOKcnPa0W3Cx691339UWrtQnmvj4eMnMzJQTJ04IAImMjJTMzEw5efKkAHmr4Y7EqG9yi6Pdhx56SOLj42Xfvn1an0REcnJytE+i1MWL7du3CwBZunSpFgP83xtle+s4266Xl5ckJSXJmjVrZMeOHeLr6ytJSUliMBhk165d8vrrr+vKIiKvv/66AJAPP/xQKzsa42y76qeKu3btkuTkZFEURfvkSC1HR0dr5XfeeUcAaDHqLcZvtY49MT4+PhIRESEiIuHh4TJnzhzdApN6G+zNmzdrMd7e3pKUlKSVv/zyS12M+iI2fx01prB2zWazpKSkiEjeGwn1DiEFF7vU/K9du6a7pXh0dLS88MILoiiKNG3aVIxGo0RFRQkA7c1BVFSUdOjQoUTq2IpRz+AxGo3a+KuLstu2bdPe+B06dEhERJYsWSIAtMU7EREg7xNKlfrGQa1z8OBBAaDdnaawdvPHAHlnnKll9RPmzMxMrY6np6cYDAY5efKkiPzfm4DMzExtgclkMonJZNJi1MVDtay+MNy4caOIiHzzzTfamZBqjNqGSN5ClrpgNnjwYK2O0WjULX6pC9Q7duzQ5bJjxw5trNSFcNXvv/+u1bFVdraOPTHq8aSo45qr/eQ/WwXIe8F89uxZ7Vhyxx13SI0aNeTs2bPap4xq+dChQyVS52YxTZs2lRo1amhl9bh27do17bj+xx9/iIjIr7/+KgDk008/dSgGgAQFBd1yHU9PT6lRo4b88ccfkpGRoZ0RoR47REQef/xxASDLly/XyvljCpZLqo46f9VyxYoVdcc1tRwfH6+V1Vu2qzHqHYny17Enxpl2PT09teeK8PBw+eCDD8RgMMj27dtF5P+OUWo5PDxcvvrqK902WzE+Pj7i6elZInXU/E+dOqU9z6pnK7711lvaWeIGg0Fq1KghACQ5OVkrd+zYscg69sTY067ZbJZy5cqJwWCQunXr6l7HiVgfow4cOCAA5JtvvnEopqhjnyN19u7dq6tjMpnk77//FhGRzz//XHeMWrp0qSiKootRj2uO1LmVdteuXaurs2XLFq1P6h3HVJs3bxYAWkzBsr0xzrSb/ywZ9ee/PmbZ81Mw1zp16kh2drZ27KhUqZK0atVKsrOztdeEannXrl1F1rEnxt52K1euLK1atZKzZ8/qjmuXL1/WXkts3bpVRES+++47ASCzZs1yKCb/MepW223YsKH2fNagQQPx9PTUHUvUu7j++OOPWvmee+7RYgqW7anjbLv5j2tVqlTRHdeqVKmiPR+q5eeff173fJ2QkKAr3ywmMjJS8lu2bJl2R0dHGUC3VVRUFJYsWYLc3FztR/n/37NWFEW7gHT+OwEC0L6bLSLYunUrAGDp0qU2y/m3FdWu2nb+mLFjx2Ls2LH48MMPAQDPPPMMOnTogA4dOuDy5ctQFAX33XcfmjRpgn379gEAmjVrhiZNmmDv3r0AgJEjRzoUc+XKlWJrd/To0bjvvvvQrFkzXT8NBgMsFovuQt0BAQEAoN19zWDI2yWuXLniUB1n283JycHChQvx5JNPYtKkSbh+/ToWLlyI3NxcjB8/Hs8//7yufP36dQwdOhRGoxGjR4/G4MGDnYpxtt38srOztfmTv3z27FmtnJaWpotRr8dzq3XsiWnQoIF244MmTZogIyMDIqJtO3ToEPz8/DB9+nQtJjY2VrsDWpMmTTBhwgQEBgZqMVFRUbBYLLo6EyZMgNFoLLRdo9GII0eOAACOHTuGU6dO6epcunRJN7ZmsxnPPPMMgLy7HvXt2xeffvopRAQLFy7E/v370a9fPwB5d3JRy+p+X9x1bMVcv34dw4YNw/79+9GxY0cAwGuvvQYAqF69OhRFgdFoxB9//AEA2p3pvv/+ewDQ9l9PT0+tnJOTA4PBoNX55ptvAPzf9R8Kazd/DAAEBwdrZfWaTSdOnAAA1KpVC9nZ2cjNzdX20ccffxxA3l3agoKC8OabbyI3Nxc3btzQYtQ21bL6t9U77JUvXx7Xr1/XtRsREYEbN27g1KlTCAoKwtChQwFAu+NJ+fLlkZOTg1OnTmkxH3zwAQBo18JTczl48KA2R7y9veHt7a2VDx48CIPBoMUULDtbx54YX19fBAQE4MCBAzhw4ABmzJgBRVHw5ptvYv369Zg9ezYAaGVb22zFqDcGKYk633//PQ4cOID9+/djxYoVAICEhAQ0a9YMp0+fBgB88MEHSExMRLNmzXDx4kUA0MoXLlwokTo3i5kxYwYSExPRqlUr5Gc2m+Hl5QURgZeXF4C856n8/9obA/zfXL6VOrm5uXj11VfRunVrfPXVV1o5JycHH3/8MQDgvffeg8FgQL9+/TBlyhS89957upiC5ZKqU9A///wD4P+OJWpZvd7MP//8gwYNGuhi/v33X6s69sQ4027NmjW1OjVr1sSxY8cAAAsWLACQd/0bDw8PrVyzZk3Mnz8fPj4+hcYEBgZqj3Nx1skvPDxcu4bX0qVLkZaWhuHDh0NE8PPPP2PdunXa8/fJkye18urVq4usY0+MPe3euHEDs2fPxrp167RrT3Xu3BlA3p3Ubty4AYPBgEOHDgEAMjIyAEA7RtobA/zfMetW6iiKor1+OHToEAwGA27cuKFdD0kdb/VaStWqVYOI6GLUY5UjdW6lXfV1n1pHfZ4E8u5Sm/+GIJcuXYKiKFpMwbK9Mc60qx4v165di7Vr1+Ltt98GkPdaYeLEiRg8eLCubGubrRh1PBypY0+Meg1NNd8ZM2YAyHt93LZtW+39x2effYZLly6hbdu22vOQWlb358Lq2BPjaLvqPqby8fGBt7c3RER73VWhQgUAQEpKikMxwP8do2613WbNmqFOnTr46quv8Nlnn+H69evIycnBunXrAOTd/U9RFHTp0gVfffUVVq1ahRYtWmgxBcv21HG23fzUY4Z6LDl58iQURdGeV06ePKndLU+NUZ9X8texFaM+1qrq1atr73cc5tRSlouaN2+epKenS1RUlHYGwsMPPyyVKlXSts2bN08SExMlKCjI7hhn6twsxsvLSzw9PbWYd955R4KDg7VPYtRP+V955RWtX40bN5b8D1XBGFt11G2Ftat+rehmdbZu3SoAZN68eVpM5cqVtToDBgzQTu+bN2+eVdmRGPWUwOJod9WqVVqM+mmBas6cOWI2m7WYH3/8UUwmk3z11VdaTFxcnISHhztUx9l2ExMTRUTk0qVL0rp1a/Hw8JAdO3aIyWSSdu3aSbVq1WyWzWazbNq0Sbp3735LMY7WUT85Sk1NFV9fX1EURapWrSo1a9bUyiaTSSurn/yqMerX0m61jj0xgwcPFqPRKI0bN5bnnntOu7ZPxYoVpXHjxmIwGKR9+/aSmJioxahnvEVGRkpMTIwAedfM8PX11a5l5eHhIRaLRRcTEBBQaLvqJxZ9+/aV0NBQ7XvXvXv3FpH/+9Qi//UKxo4dq9uWm5ur+6RDJO+TkIULF2rlgjHFVcdWzMMPPyxhYWHSp08fSUpKkoCAAAkICJDp06fLyJEjBYCkpKRoMREREdpj0KhRI+26GIqiaGU/Pz/tGhmNGjUSo9GoXdemsHaV//9d9/wxdevWFRGRvXv3isFgEC8vL6lSpYqsWrVKG9uaNWtqffz444+1T+5WrVoln3zyiQB5p6SvWrVK1q5dK6GhoaIoilauXr26GI1Grd033njDqt27775be15du3atNGzYUADI+vXrRSTvbCv17K8rV66IiMjGjRu1T/cmTJggK1euFKPRKIGBgTJ48GAZPHiwWCwW8fT01MqBgYHSoEED8ff3l/bt24u/v780aNDgluvYE2MymaRRo0ayfft2mTBhggQHB0u5cuVk9OjRIvJ/xxa1bGubrRh1rEqizqJFi6xiFi5cKB06dNDOVFu0aJFcv35dOnTooF07SS2rMcVd52Yxa9as0WLyfy1dJO9rriaTSdasWSMieWc0GY1GWblypUMx4eHhEhoaest11DN5jh49KnXq1BGLxSInTpwQo9Eo9evXl7vvvltOnDghJpNJ1q5dK82bN9e2FYy5HXXUT8VbtmwpQUFBoiiKtG3bVu6//36t7OHhoZXfeustAaDFqNd9yl/Hnhhn2v3f//4nipJ3XcfRo0eLt7e3eHh4iLe3tyQnJ4vRaNSOUTVr1tTOEKpevXqRMepxuDjrAHnXmnnttdeke/fu2nUZ1WPJhQsXrI43BoNB3nrrLa1cMMZWHXti7Gm3adOmUr58eRkzZozUq1dPuw5g9+7dpWLFihIYGCgeHh4SExMjPXr0EC8vLwkICBCz2exQjHqNqFutYzabpWrVqjJjxgypWLGi+Pr6iq+vr+zevVv2798vzZo1E0VRpEmTJrJ7927tshb5YyIjI0VRFIfqONuuwWCQhIQEWbJkiaxYsUJ8fX0lISFB5s+fL/Pnzxc/Pz/x8/PTyomJiVK1alWJj4+XIUOGSHx8vFStWlVXx54YZ9r19PSU9PR0uXDhgixZskQSExMlJiZGu8SDetxQy7a22YqpU6eOzWPUrbarnnFfsM6iRYukYcOG2lnaK1askEuXLknDhg21/VEtqzGF1bEnxt52f/rpJy2m4HFt6NChYjQa5aeffhIRkYyMDFEURVavXu1QTGBgoAQGBt5yu/7+/nLx4kXZunWrpKWladcjNBqNUq5cOenXr59kZWWJyWSSRYsWSVpamratYIwzdRxtVz2ude/eXcLDw0VRFHn88cdl0KBBWtnT01MrT506VQBoMcHBwbryzWL8/f0lv4yMDN1rAkeUmUWpadOmSWhoqIwZM0a8vLwkMzNTpk2bpn0dysvLS0aNGiWhoaHSsWNHMRgMdsU4U6ewmMaNG4uiKNrXSGbPni0hISHaYtHly5dl0qRJ2uKGiMisWbN0O2rBGFt1Ll++LOnp6YW226RJE6t289dRn9DatGmjxahfKVI9+eSTupiCZXtj1ItkFke7+ReCCi7oDRs2TNLT07WYgmURkdatW8tdd93lUB1n21UXJdRy06ZNtYuu7tq1SxYsWFBoWUSKJcbeOurF3AcMGCAjRoyQbt26yYgRI7Sf1NRUqVKlilZ+4IEHdOX09HRJSEi45Tr2xnTs2FFeeOEF7aLT6uJQeHi4PPzww+Ll5SUNGjTQXkgDedcZ8ff3l4CAAO2ChnFxcZKWliZJSUni6ekpZrNZF6NeD6KwdoG8U+8rVaokFotF6tatq1vsAvK+Vte3b19p3LixeHh4SEREhHbqu0jei47CyvbEOFPH1racnBx57bXX5N5775WxY8fKSy+9JP7+/tr1m6pWrSqxsbHSrl07qVq1qgQEBMgzzzwjDz74oHh6eoq3t7d0795dHnjgAa3co0cP+fDDD7UX47Vq1ZIhQ4YU2W69evV0MerFmfMvdvn6+moXlAbyLnxaoUIF3QJTpUqVpEuXLtpcsVgscs8992jlSpUqSa1atbRyWlqavP3227o6QUFBunarVKki/v7+ujp+fn66hazg4GDd4lf16tXF09NTxo0bJ1FRUbqvx6lzSX3Bkb+s9i3/RSeLo05RMfnbjoqKkjfeeEOWLFkiH330kYjkfSVx4MCBWtnWNlsx8+bNk379+hV7nbZt20qrVq10Meq269eva9cEVGOuX78uKSkp2rGkYExx1rEVoyiKLFiwQItRv/qlevHFFyU1NVWLKVi2N+bOO+/UFnNvpY56nSSRvONa5cqVJTIyUoxGo+zcuVPGjh2rlXft2iW5ubm6bQVjSrqOepHoDh06SM+ePaVJkybSs2dP7ScuLk6io6O18n333acrV6lSRYKCgnR17Ilxtt0777xTunbtql3/Sl0cCg0N1V3wWj3eqftmUTElUUfNz2w2S3p6unzxxRc2PygprFxcMfbUuXz5svTt21eqVKki/fr1k3Pnzkn9+vW1vjZu3FiGDRumPSdGRUXJn3/+6XBMo0aNpFatWrdcZ/Dgwbrn4ujoaKlatar2PB0WFib9+/eXBg0aaI9PYGCgLiYkJEQ7zthbx9l2Bw0aJE888YT2gZTJZNJdD069rqRaVj+Yyn+csVgsDsc4066an/ph5JNPPilTp06Vd999V0RETp48Kffdd59WtrXNVsxbb72lXe/U3jr2xPTu3Vu7xpsaU69ePenUqZNcvHhRe5/VqVMnERG5ePGi9hVztZw/5mZ17Imxt90ZM2ZoMepCier111+XuLg4LaZg2d6YevXqSdWqVW+53bZt22q/v3LlitSrV0+8vb3FaDRKRkaGPP7449oi9q5du+TKlSu6bQVjnKnjSLvq82/dunWladOmUqFCBWnatKn2ExISIgEBAVq5ffv2urK6fpG/jq2Ygl/VGz16tDRp0kScoYiIoAxIS0vD2LFj0aFDB/j5+WH79u2499578fjjj2P06NG4evUqIiMjMX78eKSkpKBq1arIzMwsMiYlJcXhOvbGJCcnY+fOnWjUqBEWLlyIu+++22bfsrKysHnzZjRp0sShMfnpp5+QlZV103a/+eYbbNu2DUOGDLFZx9bfdTYXst/Ro0eRkZGBli1bwsfHp8iyPXVKst3S5vz58xg3bhy+/PJL7N+/H7m5uYiKikKjRo0waNAg1KlTp0TaTUlJsfp9REQE/Pz88O+//+LEiRPFlst/ITc3F+PGjcMvv/yC9PR0DB06FAsXLsSQIUNw5coV3HfffZgyZYrD88aedidNmoTJkydrMUOGDMEjjzyCZcuWwWAwoHPnzmjatCleffVVZGVloU2bNnj77bfx9NNPY8mSJcjJyUHDhg0xf/58JCUlITs7G6tXr8a1a9fQuXNnZGdn48aNG9pXwwqW828DgN69e1u1GxUVhRs3bmDDhg04deoUvvrqKy2mXr16CA0NxerVq5GTk4PU1FT0798fAwYMAADMmzcPR44cQfPmzbWvxKinY9sqJyUl4cCBA4XGOFPH3hhXd+PGDVy5ckX7eknBbTdu3MChQ4cQFhamxWRnZ+Ovv/7SbndcMKa46tiKOXbsmO420AW3XblyBUajUftqT8FyccXcSp2dO3di/fr16N69O4KCgpCRkaErA7DaVlS5pOoUlJWVBaPRqH3VuKhyccUUVUdEcODAASiKgtjYWJjNZogITp8+jdzcXISGhsJkMunK9sSUVJ38X58qbbKzs3H9+nX4+fnZLBdXjLN1du/eDbPZjEqVKsFkMmHfvn24evWqVgZgta2osj11nG334sWLyMjI0I4dal/UrwwVLEdGRqJ8+fLYt2/fTevYE+Nsu7Vr19YdL1zRP//8g+PHj6Ny5co2t126dAk//PADkpKStJjDhw9j7dq16NmzJwBYxdiqY0+MPe1u2bJF916y4LYDBw7A09NT+7p4wXJxxdxKuz/88AO2b9+OYcOGITw8HMuXL8f333+vlQFYbSuqbE8dZ9vNb//+/fDw8EBsbKxdZWdjHFFmFqW8vLywZ88eJCQkaItSlStXxqpVq9CmTRuYTCZcv34df/75J65du4YKFSogMzOzyBiLxeJwHXtjkpOTsW/fPlSrVk27dgAREd0+thaYble79ix2ERERERGVZWXmQudJSUnYtm2b1baPP/4YqampAIC4uDhs27YNX3/9tXbh6aJinKljbwwAfP3111oMERHdXp6eniWyCGRPuwVjHMnlyJEj6NWrl93lkqpT2tplLu7bR1fKxR366Eq5uEMfXSmXwmL+/fdfrF+/HmvWrNGV//jjD2RnZ2PmzJm68rx587SYrVu36sqOxDjTrivl4g59dKVc3KGPxZVLfmqMU5z60p8LmjFjhsTExMinn34qPj4+smDBAunQoYMoiiJPP/20+Pj4SN++fcXf318sFotYLBa7YpypY2/MmDFjtFyJiIjstW3bNjEYDHaXS6pOaWuXubhvH10pF3fooyvl4g59dKVcbhajKIokJCTorlUYGxurldULYKvlBg0aCIBC69gT40y7rpSLO/TRlXJxhz4WZy7Hjx/X9vOTJ09aPRfYy+TcUpbr6dOnD7y8vPDyyy/jypUrePjhhxEdHY3evXtj5cqVuHLlCmbOnInAwEAEBATgzJkzdsU4U8eRmHfffRddu3b9r4ePiIhcyPLly3XljRs36sqbNm1Cbm4uXnrpJQDAqVOnCi07W6e0tctc3LePrpSLO/TRlXJxhz66Ui7OtisiCAsLw5gxY7B7926MHTsWZ8+exZYtW+Dr64v09HQA0MpPPvkkAKBcuXLYvHkz/vrrLzRs2FBXx54YZ9p1pVzcoY+ulIs79LE4c2nUqBHWrVuH+Ph43BKnlrJczPXr12Xu3Lly8uRJERHJysqSY8eO6bZduHBBJk+e7FCMM3XsjSl4BxAiIiJV/rvcFbzTHX/4wx/+8Ic/pfUn/3Gtc+fOEh8fL5mZmRIaGioAtPKJEycEgERGRkpmZqacPHnSqo49Mc6060q5uEMfXSkXd+hjceWiKIo88cQTWsytnClVJhalRES8vLzk4MGDhW5zJqak2hXJu3V1enq6REVFab975513ZOnSpTeNmTdvniQmJkpQUJBW5+GHH5ZKlSoVGlNUu7ZiCiquXIqKcaV2mUvZ7WNp2tdK23g7M772/J2SHN/bka8juUZHR+u2FyyHhYXpbp+8detWAaDFFCw7W6e0tctc3LePrpSLO/TRlXJxhz66Ui630u4ff/xhVR4wYIDExsaKt7e3KIqilX/88UcBIA899JCunL+OPTHOtOtKubhDH10pF3foY3Hloi5A5Y9xdlGqzFzovF69eti6dWuh25yJKal2p0+fjueeew5t27bF+fPnkZOTAwD4448/0L17d0RHR2PMmDF47rnnEBgYiLNnz2LevHl47rnnUKtWLVy4cAE5OTmYPn06li9fjqtXr+L8+fM2Y4pqNycnBx999BGmTp2KLl264NChQwCARx55BKmpqcWaS1ExrtQucym7fSxN+1ppG29bfZw+fTqefPJJJCUlOZ2LPe3aivnoo4+QlJSE4OBgu+uURL62HteC8yV/vmfOnMHatWsBABMnTkRMTAwyMjK0Y0haWhok381zFUUBAC2mYNnZOqWtXebivn10pVzcoY+ulIs79NGVcrmVdjdv3mxVnjJlCtq3b4+rV68CgFZu164dAKBNmza6cv469sQ4064r5eIOfXSlXNyhj8WZi612nOLUUpYLWrhwoSQnJ8vkyZNlw4YNsn37dnnzzTclNjZWhg4dKhs2bNCV582bZ1eMM3XsiUlOTpZ33nlHtm/fLt7e3pKZmSnTpk2ToKAg8fLyEi8vL0lJSZEvvvhCZs+eLUajUSvv2LFDAEhmZqakpqbKxIkTJSQkRHx9fW3GFNXuqFGjJDQ0VAYOHKir4+vrK0lJScWaS1ExrtQucym7fSxN+1ppG29bfUxNTZWBAwdK06ZNnc7FnnYLxqjljh07isFgsKtOSeVr63EtOF/y56soisyaNUtERGbPni01atSQVatWace81atXyxtvvKGVL1++LJMmTdJiCpadrVPa2mUu7ttHV8rFHfroSrm4Qx9dKRdn2+3bt6+0adPGZllEpH79+pL/remTTz4pALSYgmV7Y5xp15VycYc+ulIu7tDH4sol/9mQN9tmrzKzKKV+Pzn/D6D//nL+sq1t9ly3ozjbzZ+n+qZm6tSp4unpKb6+vmKxWOTgwYPaGx+1vHfvXq2Op6enfP/991Z18scU1a76ZqlgnfxvsIorl6JiXKld5lJ2+1ia9rXSNt62+ujp6SmrV6+26qMjudjTbsEYW4tHRdUpqXxtPa4F54utfEVEduzYISEhIf/xUZaIiIiIqPiVmbvvHThwwGrb0aNHtf/Hxsbqyra22YopyJ469sT07NkTQ4YMQatWrVC5cmWtD2fOnEFqair27duHuLg4bNu2DWlpaQCglQ8fPgyDIe+bl0lJSfj444+t6uSPKardI0eOoGbNmli+fLmuTvXq1ZGVlQWTyVRsuRQV40rtMpey28fStK+VtvG21cekpCRkZmZa9dGRXOxpt2CMWr527ZpuLAurU1L52npcC84XW/kCgMViQVZWFoiIiIiIypz/elXMXc2YMUNiYmLk008/FR8fH1mwYIGEhYWJxWKRBQsWiK+vr4wdO1ZiYmKkZ8+eYjAYpG/fvuLv7y8Wi0WL69ChgyiKIk8//bT4+PjYjCmq3ZiYGHnkkUfEx8dHPD09tU/w+/TpIzVr1izWXIqKcaV2mUvZ7WNp2tdK23jb6uOMGTMkICBAEhMTnc7FnnYLxqSkpMjSpUtl0qRJ2tf3iqpTUvnaelwLzhdb+YqITJo0SWrWrPkfH7WIiIiIiIqfIpLvKnSl2Lx586y2rV+/Xvv/HXfcoSvb2mYrpiB76tgbs2HDBixduhSnT58GAAQEBEBRFEyfPh29e/fGzJkzsXjxYixevFirFxgYCLPZjDNnzgAAoqOj0aZNG6xbtw6ZmZk2Y4pqV0RgMBjQp08ffPzxx5g5cyYWLlyIZcuWYeDAgfjwww+LLRd7YlypXeZSNvtY2va10jTetvqYmZmJ0aNHIygoCKdOnXIqF3vaLRjz8MMPY+HChdoFHefMmVNknZLMt+DjWnC+3Czf119/HTNnzkTXrl1BRERERFSm/GfLYcUsMDBQ9+Pj46O7dlNgYKDV9ZzsiXGmTlExAMTDw0O7zkhgYKCcOnVKRETmz58vKSkpWlxkZKTMnDlTLly4IJMnT5aTJ0+KiEhWVpYcO3ZM5s6dq22zFVNUu2qM+nv1OlcxMTHSp0+fYs2lqBhXape5lN0+lqZ9rbSNt60+ApCgoCB5++23nc7FnnZvNr5BQUESHh5ud52SyPdmcyH/fLGVrzo/Zs6cKUREREREZVGZWZSyZe/evdKiRQv5+uuvb7rNmZjiaNfLy0u+//57rXz9+nWrNzVeXl5y8OBBrX7Bsq1tBctFtWvr97beYBVHLvbEuFK7zKVs9rG07WslNXYlMd62+njq1KlbzsWedgvG3GzB7L/Kt6j5UthCJxERERFRWWUooROwXEL58uUxbtw4PPPMMzfd5kxMcbRbr149nD9/XiubTCY88cQTyM7OBgB4e3ujXr162Lp1q1a/YNnWtoLlotq19fvo6OgSycWeGFdql7mUzT6Wtn2tpMauJMbbVh/Dw8NvORd72i0Y4+/vjyFDhjhUpyTzLWq+3CxfAPjoo4/QqFEjREdH49ChQ/joo4+QlJSE4OBgm2UAeOSRR5CamlqsdUpbu8zFffvoSrm4Qx9dKRd36KMr5eIOfXSlXNyhj66Uizv0sbhyAYCJEydi2bJlcMp/vSpW0rZu3Sp+fn6FbnMm5lbbXbhwoSQnJ8sLL7wg3t7esn37dqlTp4688847sn37dhERLWby5MmyYcMGefPNNyU2NlaGDh0q8+bNk+3bt+u23SymqHbz/16NadKkiXzxxRda3sWVS1ExrtQucym7fSxN+1ppG29bfSxqfO35O/a0WzCm4Nja+5iURL62HteC88VWvtOmTZPQ0FAZM2aMeHl5yahRoyQ0NFQ6duwoBoPBqpyZmSnTpk0TX19fSUpKKrY6pa1d5uK+fXSlXNyhj66Uizv00ZVycYc+ulIu7tBHV8rFHfpYXLmoN+aZPXu2NG3aVJxRZhalli1bpvtZunSpPPHEExIXFye1atWSZcuWyYsvvqjbZk+MM3XsiVGvF4J8155Sy4qiyIYNG7RtBWPz1yms7Ey7iqLYfINVXLkUFeNK7TKXstvH0rSvlebxVvtY1Pg6+nfsaVdRFJuLR/Y8JiWR781+8s8XW/kmJydrC1fqHfq++OIL2bFjhwCwKqt3dJw4caKEhIQUW53S1i5zcd8+ulIu7tBHV8rFHfroSrm4Qx9dKRd36KMr5eIOfSyuXNRFqR07dkhISIhTazkmlBEdOnTQlRVFQW5uLgDg6NGj6NChAyTfjQaPHj2Kjh07FhmzZcsWh+vYEwMAISEhSE9Px8svv4zw8HAkJSVBURQAQKNGjSAiUBRF+7fgHfxiY2O1tmyV1W1Ftav+TlWjRg0t33HjxuGNN94otlyKiinov2yXuZTdPpamfe3AgQOlarzvvPNOqz4CuGl/7M0lKSmpyHYLxgwZMgRA3tgC0Ma/sDolla+tx9XWfCmYr4hg0KBBMBgM8Pb2xpEjR1CzZk1cu3YNAKzKAHDgwAFUr14dWVlZMJlMxVKntLXLXNy3j66Uizv00ZVycYc+ulIu7tBHV8rFHfroSrm4Qx+LKxeVxWJBVlYWnFFmFqXURaDS7MCBA4X+PiEhochttmKKateWgm+wiisXe2JcqV3mUjb7WNr2tdI03vaMrTO52NNuwRhbi2pF1bHV7n+Zb8uWLTFkyBC0atUKVapUQVxcHLZt24bDhw/DYDBYlQEgKSkJH3/8MVJTU7Fv375iqVPa2mUu7ttHV8rFHfroSrm4Qx9dKRd36KMr5eIOfXSlXNyhj8WVi+rrr79GamoqnFFmFqVGjRqFwYMHw9vbW7dtwIABmDp1Kl599VUtRlEUjB8/XttWWAwAh+vYE5OYmAgAuHbtGlauXGl1plf37t0xb9483baCZ2/ccccdum0Fy+q2ototqHv37lZvsIorl6JiCvov22UuZbeP+bn6vlYwH1cfb2fG155cCrJ3fAuyp05J5FsUW3MBAF544QWMGDEC/v7+EBE0a9YM3bt3x9WrV2E2m63KmzZtQsWKFfHhhx9i4MCB2Lt3b7HUKW3tMhf37aMr5eIOfXSlXNyhj66Uizv00ZVycYc+ulIu7tDH4spl06ZNWLBgAV5//XXMnDnT4dfAAFBmrillMBisbp9tMBhk9+7dYjAYdDF///23blthMc7UsScmMDBQAgMDJSAgQACIj4+PABCTySTe3t4yd+5c8fb21v2gwDVK1DpqOTAw8KYxhbXr4eFhFdO3b1/tp7hzKSzGldplLmW7j6VlXytt422rj/n742wu9rRbMCb/2NoaX3vbLY58b/a43mwuqPnOnTtXHn/8cYmIiNCuQxUUFCTh4eE3LcfExEifPn0kJSWlWOuUtnaZi/v20ZVycYc+ulIu7tBHV8rFHfroSrm4Qx9dKRd36GNx5RITEyMzZ850ei2nzCxKKYoip0+fttq2ePFiCQ0N1cWsWbNGt62wGGfq2BsjIrqyv7+/mEwm8fX1lcDAQPHx8RFFUcRisUhQUJCIiOzdu1datGghX3/9tc2yrW1FtRsYGKiLsfUGq7hycSbGldplLmWjj6V9X3Pl8bbVx4L9cSYXe9otGGNr8aioOrcz34LzpWC+6gKph4eHBAUFyYULF2Ty5Mly8uRJERGrclZWlhw7dkzmzp170xhn6pS2dpmL+/bRlXJxhz66Ui7u0EdXysUd+uhKubhDH10pF3foY3HlkpWVZXVikDNK/aJUYGCgBAUFaWcfBQUFaSt2hX26fbNPvAvGOFOnqBj1TYm/v78YDAbp37+/1p/ffvtNKlasqJVtvQEsGFOwbE9MUe3a8+a0uHKxJ8aV2mUuZbOPpW1fc6aPJVWnqBhb/SmOXOxp155Ftf8y36Lmi6enp6Snp2tlLy8vOXjwoBZfsGxPjDN1Slu7zMV9++hKubhDH10pF3fooyvl4g59dKVc3KGPrpSLO/SxuHIpDnlXqyrFJk6ciAkTJkBEMHLkSLzzzjvo3bs3evXqBUVR8Oyzz6JPnz5auXv37nj55Zd122zF3Hfffejdu7dDdeyNef/99/HOO+/gvffew/r16zF16lStPyaTCcePH9fK5cuXx7hx4/DMM8/cNKZg2Z6Yotq19fuSysWeGFdql7mUzT6Wtn3NmT6WVJ2iYmz1pzhysafdgjHO1CnJfIuaL/Xr18f999+vlevVq4etW7dq8QXL9sQ4U6e0tctc3LePrpSLO/TRlXJxhz66Ui7u0EdXysUd+uhKubhDH4srl+JQ6i903qNHDwB5V4FPT0+H2WzWtj366KPaNltle2KcqWNPzPLlywEAZ86cwfLlyyEiOHHiBKZMmYJGjRrpYvbv34/Dhw/jpZdewrlz57BixQqUL18ey5Ytw9dff62Vly9fjo0bN1rFFNWurRhA/2apuHIpKsaV2mUuZbePpWlfK23jfbNxUPvjbC43e47M366tGFvjW1SdksjX3rlQMN/+/fvj+eefx5kzZ/DLL7/gnnvuwcCBA7Fx40akpaVZlatXr67b1q5du2KpU9raZS7u20dXysUd+uhKubhDH10pF3fooyvl4g59dKVc3KGPxZWLj4+P7nVstWrV4ChFRMThWi4uOzsb165d023z8PDQbcvOzoanp6dDMc7UuVlMZGSkLkZRFPj5+aFatWro1asXgoODtTvy2XqIFEWBoijIzc3VygVj1Zii2lUUBf7+/lpMUFCQ9gYrLCwMw4cPL7Zciopxpk5Jtctcym4fS9O+ZjAYStV4A7Dq4/33368bA2dysafdgjH79u3TFo/CwsLw6quvFlmnpPLt1auXbrwMBoPVfNm4cWOR+RZ1yC4qX2frlLZ2mYv79tGVcnGHPrpSLu7QR1fKxR366Eq5uEMfXSkXd+hjceWSf7uiKMjJySn079j821JUdqXElStXMGTIEHz22Wc4e/YsgLwX//nfAOQv29pmK6bgwNtTx96Y/HJycqy2KYqCsLAwNG/eHG+//TaioqKcGpui2o2JibH6fcE3WMWVC5Er4L5Wcv6rsY2KirKKKfhcbk8dV8s3ODgY6enpePnllxEeHo6jR48W+ndjY2N1MQXLztYpbe0yF/ftoyvl4g59dKVc3KGPrpSLO/TRlXJxhz66Ui7u0MfiyqWghISEQv+OTVJG9O/fX1JTU2XRokXi5eUls2bNknr16onJZJKnn35avLy8pFmzZhIWFibBwcHi4eFhV4wzdeyJ6dChg7zyyisSGxsr8+fP1/px5coVGTlypIiIjBw5UrKysrTfqeWCMX///bdVnfwxRbVrK6ag4sqlqBhXape5lN0+FpzvrryvlbbxdmZ87fk7JTm+tyNfZ3IlIiIiIirrysyZUvHx8Zg3bx6aNm0Kf39/bNmyBc2bN0e3bt2wbds2rF+/Hr6+vvjkk09w5MgRPPbYY9izZ0+RMeHh4Q7XsSdGJO96I6tXr8aCBQuwcuVKjBo1Co899hgSExORk5MDo9GIEydOwM/PD+PHj8fIkSNx4sQJGI1GhIeHazG7du1C5cqVdXXyxxTVLgAMHjwY//77r67OgAEDMHXqVLz66qvFlktRMQBcpl3mUnb7WJr2NXVbaRnv4cOHW/XRaDRi//79mDt3rq6PjuQyatSoItstGKPmoiiKbiwLq1NS+ap18j+uBeeLWid/vvPmzcO1a9ewcuVKdOjQAevXry/0WHjHHXfoYgqWna1T2tplLu7bR1fKxR366Eq5uEMfXSkXd+ijK+XiDn10pVzcoY/FlUtB3bt3L/Tv2PQfL4oVGx8fHzl06JCIiMTExMjGjRvFx8dHfvrpJ/Hx8ZGYmBjx8vKSQ4cOyf79+0VRFLtinKljT4yiKHL69GnZv3+/+Pj4iIiIwWCQRYsWSWhoqIiIFvP333+LwWDQymvWrNHFLF682KpO/pii2jUYDHLq1CmrOrt37xaDwVCsuRQV40rtMpey28fStK+VtvG21UdFUWTPnj1WfXQkF3vaLRijlguOZWF1SipfW49rwfliK9/AwEAJCAgQABIYGCgAtB9FUazKPj4+unJx1Slt7TIX9+2jK+XiDn10pVzcoY+ulIs79NGVcnGHPrpSLu7Qx+LKxcfHRxRFEYvFIkFBQeKMMrMoVbVqVVm3bp2IiLRo0UKef/55qVq1qgwcOFBiYmKkRYsWEhoaKuvWrZN3331XLBaLXTHO1CksxsvLS/cgent7i6IoEhQUpG1XH1CDwWA1GYqaTPnLajs3a9fPz0+r4+vrKwaDQfr37y8i//cGS83tVnOxN8aV2mUuZa+PRe0TzuZSEvuan59fqRtvi8Wi62P+cfD19XUql/xje7N28z+H5h9fRfm/xaP843uzOiWRr7qt4FzIP1/yz4WCi10iYlXeu3evtGjRQr7++mu7yiVVp7S1y1zct4+ulIs79NGVcnGHPrpSLu7QR1fKxR366Eq5uEMfiysXR5WZRakJEybIu+++KyIi3377rXh6eorRaBRFUWTixIny7bffislkEqPRKAaDQZ588km7YpypU1iMoijSrFkzAaCVzWaztsLo5+cn3t7e4u3trXuTl5qaKoqiSPfu3eXll1+WPn36SO/evUVRFHn22Wd1ZTXG29u7yHZNJpMoiiLe3t7am6X8b7BMJlOx5FJUzH333edwnZJql7mU3T7as0+4yr6mKIqkpqaWmvH29PTUxkDtY/5xKNgfe3PJP7Y3a9dkMulibC0m5R9fW3VKKl9PT0+rxzX/37Y1F/Ivbvn7++sWMVW//fabVKxY0e5ySdUpbe0yF/ftoyvl4g59dKVc3KGPrpSLO/TRlXJxhz66Ui7u0MfiysURJpQRgwYN0v7fsmVL7NmzBxkZGUhJSUG1atUAAH/99Zdu2wsvvOBwjDN1bMUsXLgQiqKgUqVK2Lp1K0QEvXr1wpgxYxAQEAAA2LNnD9LS0pCSkoKGDRvihx9+QHp6Osxms9bXRx99VLctfzklJaXQdvfu3Yty5cqhV69eeP311xEQEID169dDRDBr1iw888wzqFGjRrHkYm+MM3VKql3mUvb6WNQ+4Yr7WmkZ7+bNm2tjoPZRlZmZibvuugt33nmnw3+nRo0aRba7f/9+XcyOHTu0sX300UcRHx+PlJSUQuuUdL75H9e9e/fq5kth+Xp4eCAxMVGbCyqTyYTjx4/bXS6pOqWtXebivn10pVzcoY+ulIs79NGVcnGHPrpSLu7QR1fKxR36WFy5OKLMXOg8v+zsbHh6eha6zZmYkmz32rVrWL9+PerXrw+z2Qx/f3+rNq5du1bo3/Hw8LAZU1S7a9eu1X4PAP7+/jbflBdHLo7EuFK7zKVs9bG07muO9LGk69wsZvPmzVZ9LKxP9uZia+wKtlswZuvWrTcd25vVuR353uw539ZcWL58ua7tjRs34ty5c1ixYgXCwsJw991368qvvvoqvv76a608fPjwYqlT2tplLu7bR1fKxR366Eq5uEMfXSkXd+ijK+XiDn10pVzcoY/FlYv8/xu4TZkyBXFxcVi1apXN192Fcur8Khd048YNGTVqlERHR4vRaJTMzEy5ceOGNGnSRAIDA8VoNMrevXtl1KhR4ufnJwaDwa4YZ+rYE7Nz504ZMGCAdk0p9SLI6s+///4rJ06ckL59+2oX7C349Y6C224WU1S7+X8URZELFy7ofoozl6JiXKld5lJ2+1ia9rXSNt62+ljU+Nrzd+xpt7DxPXXqlN11SiLfwv6uOl9s5at+tc/WV/xslQ0Gg65cXHVKW7vMxX376Eq5uEMfXSkXd+ijK+XiDn10pVzcoY+ulIs79LG4cjEYDBIRESEPPfSQHD9+3Km1nDKzKDVy5EhJTk6W+fPni5eXl2RmZsrIkSMlPDxcUlJSxMvLS5599llJTk6Wp556SgwGg10xztSxJ6Zbt26SmpoqgwYNEoPBILNmzZJXXnlFfHx8tGuIFJw0zZo1k7CwMAkODhYPDw+ZNWuW1KtXT0wmkzz99NPi5eVlM6aodqdPny7169fX3jzZeoNVXLkUFeNK7TKXstvH0rSvlbbxttXH/H1yNhd72i0YU/Bgak+dksrX1uNacL7Yyjf/DxERERFRWVNmFqXKlSsn3333nYiI+Pr6SmZmppQrV05mzZolgYGB4uvrK/Hx8fLdd9/J7t27BYBdMc7UsScmKipKvv/+e628b98+6d+/v0RHR0vNmjXFy8tLgoODpVevXhIbGyuenp5anXnz5onRaJR9+/ZJXFycDBs2TNq0aSN+fn42Y4pq95FHHpHU1FQZOHCgtkBW8A1WceVSVIwrtctcym4fS9O+VtrG21YfAwICJDT0/7F33+FRVO/bwO/Znt47KSQhQOi9Kb1Lk2pBQFFsKKJ8EWxUEUQREbCggIgFRAQBkSoCFpAAERAEQkea9GIgJM/7R96ZXyYbkt2QxCV7f66LS8/sc06ec/bMzuzZ2dlgmTt3bqFzcaTd3DF5LR4VVKe48s3rec09X/LKt3nz5hIZGSlz584VkewPYK5evSrXrl2TUaNG2ZXVmH/++UdXvt06d1q7zMV9++hKubhDH10pF3fooyvl4g59dKVc3KGPrpSLO/SxqHJR5YxxVqlZlLLZbHLo0CER+b9FKZvNJqtWrRIvLy/x9vYWq9Uqhw4dkl27dmmLRwXFFKaOIzGenp5y+PBh2bVrlyiKIps2bZLo6Gj54osvxMvLS/slqMOHD8ucOXPEarWKh4eHHD58WA4cOKDV8fLykg0bNoiXl5dERUXlGVNQu2FhYfLjjz/KgQMHtAWy3G+wiiqXgmJcqV3mUnr7eCfta3faeOfVRy8vL5k0aZK0a9eu0Lk40m7umLwWjwqqU1z55vW85p4veeVrMBhk6tSp0q5dOxERMRgMcurUKfnnn3+0K6hyltWY3bt368q3W+dOa5e5uG8fXSkXd+ijK+XiDn10pVzcoY+ulIs79NGVcnGHPhZVLqqcMc4yoJRITk7Ghg0b7La9//77qFGjBgAgMTERGzZswIIFC2A0Gh2KKUwdR2Kio6Nx8OBBLFiwAL6+vpg/fz7OnTuHv/76C/7+/vD19UVkZCQOHjyIu+66CxkZGfDy8sLBgwexZMkSWCwWzJ8/H/Hx8Zg/fz78/f1RoUKFPGMKavfcuXOIj4/HkiVLYDAYcO7cOZw7dw7t27fH+vXrizSXgmJcqV3mUnr7eCfta3faeOfVx/j4eERERGD9+vWFzsWRdnPHXLhwAfHx8bjrrruQlZXlUJ3iyjev5zX3fMkrXxFBvXr1sH79egCAiEBRFKSmpiIwMNCurMb8+eefuvLt1rnT2mUu7ttHV8rFHfroSrm4Qx9dKRd36KMr5eIOfXSlXNyhj0WViypnjNOklFi0aJH4+fnJ+PHjxdPTUyZOnCitWrUSANK/f3/x9PSUvn37isViEaPRKFar1aGYwtRxJKZjx45Sv359sVgsMn78eLHZbNo9UCZPniwtWrSQpk2byrvvvivvvvuuBAUFiclkEqPRKAaDQZ588kmx2WxiNBq1OqtWrcozpqB2FUURs9ksBoNBkpKS5IUXXpAqVarIM888I1FRUUWaS0ExrtQucym9fbyT9rU7bbzz6uOkSZOka9euEhUVVehcHGk3d0xwcLCsW7dO3n33XbFarQ7VKa5883pec8+XnPkCEJvNJgaDQTw8PPK8KWVe/3LHFFWdO61d5uK+fXSlXNyhj66Uizv00ZVycYc+ulIu7tBHV8rFHfp4u7lYrVYJCAiQgIAA7f6oTz31VKHWchQREZQSGzZswOjRo5GamoorV66gZs2auPfee/HDDz9o2xISEmA2m3Hs2DGHYwpTx9GY1157Da1bt8bhw4cxcuRIhIaGYsKECVi9ejU6duwIEUFGRgYmTZqELl26ICUlBYmJiahatSoOHz6sKwOw2+ZIu//88w/+/vtvDBo0CKdPn0bHjh2RkZGBrKwsvPPOO6hUqVKR5eJsjCu1y1xKTx/v5H3N1cf71Vdftetjzv4MGjSoULk40m7umHbt2kFEICJ4/PHHMWvWrALrlFS+Xbp0sZsvOfNt0qQJNmzYgJs3b0JRFNSrVw+VKlXCzJkz8dBDD+H8+fMIDQ3VyjExMTh58iREBDNnzsSgQYNw5coVrVzYOndau8zFffvoSrm4Qx9dKRd36KMr5eIOfXSlXNyhj66Uizv0sahyqV69urYOY7FYEBcXhwYNGhRqHQdyh0tLS5OsrKx8txUmprjazcu///6b5/ZDhw7JN998I6mpqXYxedVxJKagdnM/Xpy5FBTjSu0yl5JttyRzEblz9jVH/rYrjbeIfR+LIhdH2s0rpjB1iiNfR+aLWh49erRs2bJFi1m3bp3cuHHjlmVHYgpT505rl7m4bx9dKRd36KMr5eIOfXSlXNyhj66Uizv00ZVycYc+FlUuReGOv1LKaDTixIkTCA0NBQD06tULCxYs0G0zGAz4448/ULlyZYdjClPH0ZgpU6YgODgY48aNwwcffIBTp05h7969iI+Px6uvvoq4uDg8+OCDMJvNupjdu3fjq6++wsSJE3H16lXs27cPsbGxaNGiBVJTU3H58uU8YwpqN+ffjoqKwhNPPKEb4/T09CLLpaCYPXv2uEy7zKX09vFO2tf27t17R413znFQ+5iZmZnv+DqSS15jl7vd/MY3PT0dNpvNoTrFke9jjz1W4Gu+zWazyzf3/Lhx48YtY/KqY7FYirzOndYuc3HfPrpSLu7QR1fKxR366Eq5uEMfXSkXd+ijK+XiDn0sqlwAwNfXF04r0iWu/4CiKHLq1Cmt7O3tbbcNgGzatMmpmMLUcTQmLS1NRo0aJfHx8TJ37lzx8PCQtLQ0uXnzpvTo0UPMZrMYjUZ57rnnJD4+Xjp37iwWi0UrDxw4UAwGg9ZOaGioJCYmioeHR54xBbW7d+9eGT16tAQEBAgArU6TJk3E39+/SHMpKMaV2mUupbePd9K+dqeNd159HDVqlPj7+8ujjz5a6FwcaTd3jFr28fHRtZtfneLKN6/nNfd8ySvfq1evSu3atbVtBX3fP3dMUdW509plLu7bR1fKxR366Eq5uEMfXSkXd+ijK+XiDn10pVzcoY9FlUvuf4XBRalbxBSmjjOLUgkJCbJ69WrdtlGjRkl0dLR4enqKh4eHxMTEyOrVq+Wrr74Sg8GglXfv3i0AtHZmzpwp/v7+4u3tnWdMQe2qb5befPNNXZ2cb7CKKpeCYlypXeZSevt4J+1rd9p459XHhIQEefnll6V+/fqFzsWRdnPH5LV4VFCd4so3r+c193zJK9+nnnpKoqKipFy5cuLh4SHNmjWTkJAQCQwMFIvFYleeOXOm1K1bV0wmkzz77LNFVudOa5e5uG8fXSkXd+ijK+XiDn10pVzcoY+ulIs79NGVcnGHPhZVLjNnzpQxY8ZImTJlZO7cuYVa07njF6UMBoOcPn1aK3t7e9ttAyC///67UzGFqeNozIEDB8Rms8mhQ4e0beqbmk8++US8vLzE29tbrFarHDp0SHvjo5Z37dqlvRGy2WyyatUquzo5YwpqV32zlLtOzjdYRZVLQTGu1C5zKb19vJP2tTttvPPqo81mk9WrV9v10ZlcHGk3d0xei0cF1SmufPN6XnPPl7zyjY6Olk8//VT8/f3Fx8dHIiIi5Mcff5Q5c+aI0Wi0K+/bt0+io6Nl+PDh0q5duyKrc6e1y1zct4+ulIs79NGVcnGHPrpSLu7QR1fKxR366Eq5uEMfiyqXffv2iYjInDlzpF27doVa0zHhDici6NevH6xWK4Ds7zpmZWWhSpUqMBgMWly7du1gNpsBAFevXoWI5BsDAI0aNUJQUJDDdRyNqV+/Pmw2G7p164aYmBikp6cDAI4fP46dO3eiRo0a2L59OxITE7FhwwbUrFkTALTygQMHYDQaAQDJycl4//337erkjCmo3VOnTiExMRGffvqprk50dDQyMjKgKEqR5VJQjCu1y1xKbx/vpH3tThvvvPqYnJyMLVu22PXRmVwcaTd3jFq+evWqbizzq1Nc+eb1vOaeL3nle+7cOURGRiIjIwP+/v44d+4c4uPjERsbi6ysLFy4cEFXPnfuHM6dO4f27dtjypQpRVbnTmuXubhvH10pF3fooyvl4g59dKVc3KGPrpSLO/TRlXJxhz4WZS4AcNddd+HJJ59EYdzxi1J9+/bVlXv37o2NGzfqtvn4+MDf318rJyQk4NSpU/Dx8blljPqYl5eXw3UcjQGAmJgYbNiwQdu+cOFCeHl5YcqUKVi+fDm6du2KZ555BgMHDkSdOnVgMBhQu3Zt9O/fH5mZmTCZTFi4cCGCgoKwcOFC9O/fH1u3bs0zpqB2g4OD0bdvX/z666+wWCwA7N9gFVUuBcW4UrvMpfT28U7a1+608c6rj6+99hruu+8+hIeH4/Tp04XKxZF2c8fEx8fbLR4VVKe48s3rec09X/Ja7IqPj8cXX3yBGjVqwGq1IjU1FQcPHkRqaiosFgu8vLx05fnz5yM+Ph7z58+Hv78/KlSoUCR17rR2mYv79tGVcnGHPrpSLu7QR1fKxR366Eq5uEMfXSkXd+hjUeUyf/581K1bF0uWLNGtezilUNdXUZFYv369tGzZUkJCQsTDw0MqVKggXl5eMn78ePH09JSJEydKhw4dRFEU8fPzEw8PD6lcubLUqFFDq9OoUSN56623dO3kjimoXYvFIkajUR555BHt8VatWgkA6d+/f5Hm4kiMK7XLXEpnH++0fe1OGu+8+vjoo4+KyWTK928X9HccaTd3TN++fbVtVqvVoTrFmW/u5zX3fMkr33r16onRaJSVK1fKqlWrxGQyidFoFIPBIE8++aRd2WazidFoFEVRZPLkyUVW505rl7m4bx9dKRd36KMr5eIOfXSlXNyhj66Uizv00ZVycYc+FlUuNptNrFarGAwGmTx5cqHWRbgoVcLS0tIkKyvrlo+vX79eGjZsKMHBwdobndmzZ9vVyd1OYdpdsWKF7vGcbzLVN1hFlUtBMYWpU1ztMpfS28ecXH1fW7FixR013rnHoGXLlhIYGCg2m83h/hS23bxici8eOVKnOPLNby7kl2/O+ZGzzqFDh+Sbb76R1NRUh8rFVedOa5e5uG8fXSkXd+ijK+XiDn10pVzcoY+ulIs79NGVcnGHPhZVLoXBRakSZjAYdL/O17NnT9m0aZPuTU3uGEVRZMeOHbo6jsQU1G779u3lxIkTuvxyv8EqqlwKinGldplL6e3jnbSv3WnjnVcfiyIXR9rNHZPXQlFh2i2KfB15zS9ooVP177//OlUurjp3WrvMpWTbZS4l2y5zKdl2mUvJtstcSrZd5lKy7ZbGXAqLi1IlTFEU3RsU9Rf7cm4DoHvjA0A2bdqkq5O7nbxinG03rzdYRZVLQTGu1C5zKb19vJP2tTt9vHv27FksuTjSbl6LR0XRbmHHJffz6sgi4MmTJ0VE5ObNmzJ69GiJjIwUo9Eoe/fuldGjR4uPj48YDAa7clpamty8eVOaNGki/v7+RVbnTmuXubhvH10pF3fooyvl4g59dKVc3KGPrpSLO/TRlXJxhz4WVS5paWkiIvLKK6/Ixx9/LIXxfz8RR/+Z7PcteteuXSvxdr///vtiy4XIFXBfKznff//9f9auiOjG0pE6xZUvYP+85n7u88pX/QW+119/HbNnz8abb74Ji8WC6dOnY/bs2dqPfOQuq3V2796N4ODgIqtzp7XLXNy3j66Uizv00ZVycYc+ulIu7tBHV8rFHfroSrm4Qx+LKhdV5cqV8fHHH6NQCrWURYVmMBjk9OnTWvlWn7Rv3rxZV/799991dXK3k1eMI+3mLBdnLgXFuFK7zKX09vFO2tfuxPEurvwdaTf3eOd1NVth2r3dfB197nPnq37qlJCQIKtXr9a2x8TEyOrVq2X37t0CwK6clpYmCQkJMnPmTPH39y+yOndau8zFffvoSrm4Qx9dKRd36KMr5eIOfXSlXNyhj66Uizv0sahyUc9Zd+/eLf7+/lIYJlCJEhH069cPVqsVAJCeng4RwcMPP6xtA4ChQ4ciICBAK7dr1w5msxkAcPXqVYgIqlSpAoPBkG9MQe3mLKu5KIqiy7mocskvBgAaNWqEoKCg/7xd5lK6+3in7Gvp6enIysq6o8a7ffv2uPvuu3X553y9K2wueY1L7nZzj/eLL76IH3/8USvnfu11tN3bzTevcclrvuSsD0CbG8ePH0diYqK2/dSpU0hMTNSupMpdVutER0cjIyMDiqIUSZ07rV3m4r59dKVc3KGPrpSLO/TRlXJxhz66Ui7u0EdXysUd+lhUuaiysrKQkZGBwuCiVAnLeekbAPTu3RuzZ8/GH3/8oXtTsnfvXphM2U+Pl5cX/v33X/j7+wMAEhIScOrUKfj4+GjxPj4+2uNqzP79+/NtFwB+++03hIaGAgAyMzMBwO4NVlHkUlCM+piXl9d/3i5zKb19LGifcKV9LTIyEmfPnr1jxhvI/ppaSkqK1kcPDw/s3LlTe9zLy8vpXABg//792jjl1W7uGAD49ddfERkZCSB78chqteZbp7jyVf++Oi6RkZE4cuSI3XzJvQhYv359NGrUCDabDd26dUNMTAwAIDExERs2bMCBAwdgNBrtygCQnJyM999/HzVq1MD27duLpM6d1i5zcd8+ulIu7tBHV8rFHfroSrm4Qx9dKRd36KMr5eIOfSyqXFQLFixAjRo1UBhclCphs2bNKrG6Dz/8cL6Pb9y4EQBw11132W1TJSYm6h4vbC5ErqCgfULFfc15/9XYAtljmd/YJiQkALAf/7zGO7f/Ml8/Pz/UrFkTGzZsAJD9CVTt2rXRv39/ZGZmwmQy2ZUXLlyIoKAgLFy4EP3798fWrVuLpM6d1i5zcd8+ulIu7tBHV8rFHfroSrm4Qx9dKRd36KMr5eIOfSyqXBYuXIi//voLc+bMwdKlS506b1Yp//9+FkREROSCNmzYgNGjRyM1NRVXrlxBQkICzGYzjh07lme5Zs2auPfee/HDDz8UaZ07rV3m4r59dKVc3KGPrpSLO/TRlXJxhz66Ui7u0EdXysUd+lhUudSsWROvvfYaWrduXbiT3ULdiYqIiIiKTVpammRlZTlcLq46d1q7zMV9++hKubhDH10pF3fooyvl4g59dKVc3KGPrpSLO/SxqHIpSlyUIiIicjEGg0H3C32KosiOHTtuWe7Zs2ex1LnT2mUu7ttHV8rFHfroSrm4Qx9dKRd36KMr5eIOfXSlXNyhj0WVS8+ePeXkyZNSFAwFX0tFREREJUlyfbNeRHDt2rVblr///vtiqXOntctc3LePrpSLO/TRlXJxhz66Ui7u0EdXysUd+uhKubhDH4sql++//173q3y3g4tSRERERERERERU4rgoRURE5GIURYGiKLptBoMh33Jx1bnT2mUuJdsucynZdplLybbLXEq2XeZSsu0yl5JttzTmkrtcWPz1PSIiIhdjMBjQrl07WK1WAMC3336L4OBgmM1mAMCJEydgsVgQFBQEADh58iREBGFhYdpJxIkTJ267zp3WLnNx3z66Ui7u0EdXysUd+uhKubhDH10pF3fooyvl4g59LKpcTp48iVatWsHLyws5LVy4EM4yOV2DiIiIilXfvn115cTERF3Zx8cHALQTgYSEBJw6dUrbrsb4+/vfVp07rV3m4r59dKVc3KGPrpSLO/TRlXJxhz66Ui7u0EdXysUd+lhUuSQkJCAyMhJFgVdKERERERERERFRiTMUHEJERERERERERFS0uChFREREREREREQljotSRERERERERERU4rgoRUREREREREREJY6LUkREREREREREVOK4KEVERERERERERCWOi1JEREREt9CvXz8oimL3b//+/bfd9uzZs+Hv73/7SRIRERHdoUz/dQJERERErqxt27aYNWuWbltISMh/lE3eMjIyYDab/+s0iIiIiJzCK6WIiIiI8mG1WhEeHq77ZzQasXjxYtSsWRM2mw3x8fEYNWoUbt68qdWbNGkSqlSpAi8vL0RHR+Opp57ClStXAADr1q3Dww8/jIsXL2pXX40cORIAoCgKFi1apMvB398fs2fPBgAcOnQIiqJg3rx5aNKkCWw2Gz7//HMAwMcff4yKFSvCZrOhQoUKmD59utbGjRs3MHDgQERERMBmsyE2NhZvvPFG8Q0cERERUQF4pRQRERGRkzZs2IA+ffpgypQpuPvuu5GWloYBAwYAAEaMGAEAMBgMmDJlCsqWLYsDBw7gqaeewtChQzF9+nQ0bNgQkydPxmuvvYa//voLAODt7e1UDsOGDcPbb7+NGjVqaAtTr732GqZOnYoaNWpg27ZteOyxx+Dl5YW+fftiypQp+O677zB//nzExMTg6NGjOHr0aNEODBEREZETuChFRERElI+lS5fqFozatWuH8+fPY9iwYejbty8AID4+HmPGjMHQoUO1RannnntOqxMXF4exY8fiiSeewPTp02GxWODn5wdFURAeHl6ovJ577jl07dpVK48YMQJvv/22tq1s2bL4888/8eGHH6Jv3744cuQIypUrh7vuuguKoiA2NrZQf5eIiIioqHBRioiIiCgfzZo1w/vvv6+Vvby8ULVqVfz88894/fXXte2ZmZlIT0/HtWvX4OnpidWrV+ONN97Anj17cOnSJdy8eVP3+O2qXbu29v9Xr15FWloa+vfvj8cee0zbfvPmTfj5+QHIvml7q1atUL58ebRt2xYdOnRA69atbzsPIiIiosLiohQRERFRPry8vJCYmKjbduXKFYwaNUp3pZLKZrPh0KFD6NChA5588km8/vrrCAwMxMaNG9G/f3/cuHEj30UpRVEgIrptGRkZeeaVMx8AmDFjBurVq6eLMxqNAICaNWvi4MGDWL58OVavXo2ePXuiZcuWWLBgQQEjQERERFQ8uChFRERE5KSaNWvir7/+slusUqWkpCArKwtvv/02DIbs35WZP3++LsZisSAzM9OubkhICE6cOKGV9+3bh2vXruWbT1hYGCIjI3HgwAE8+OCDt4zz9fVFr1690KtXL3Tv3h1t27bFuXPnEBgYmG/7RERERMWBi1JERERETnrttdfQoUMHxMTEoHv37jAYDEhNTcXOnTsxduxYJCYmIiMjA++99x46duyIn3/+GR988IGujbi4OFy5cgVr1qxBtWrV4OnpCU9PTzRv3hxTp05FgwYNkJmZiRdffBFms7nAnEaNGoVnn30Wfn5+aNu2La5fv44tW7bg/PnzeP755zFp0iRERESgRo0aMBgM+PrrrxEeHg5/f/9iGiUiIiKi/Bn+6wSIiIiI7jRt2rTB0qVLsXLlStSpUwf169fHO++8o908vFq1apg0aRImTJiAypUr4/PPP8cbb7yha6Nhw4Z44okn0KtXL4SEhODNN98EALz99tuIjo7G3XffjQceeABDhgxx6B5Ujz76KD7++GPMmjULVapUQZMmTTB79myULVsWAODj44M333wTtWvXRp06dXDo0CF8//332pVcRERERCVNkdw3LSAiIiIiIiIiIipm/GiMiIiIiIiIiIhKHBeliIiIiIiIiIioxHFRioiIiIiIiIiIShwXpYiIiIiIiIiIqMRxUYqIiIiIiIiIiEocF6WIiIiIiIiIiKjEcVGKiIiIiIiIiIhKHBeliIiIiIiIiIioxHFRioiIiIiIiIiIShwXpYiIiIiIiIiIqMRxUYqIiIiIiIiIiEocF6WIiIiIiIiIiKjEcVGKiIiIiIiIiIhKHBeliIiIiIiIiIioxHFRioiIiIiIiIiIShwXpYiIiIiIiIiIqMRxUYqIiIiIiIiIiEocF6WIiIio2PTr1w9xcXHF1v7s2bOhKAoOHTpUbH/j0KFDUBQFs2fPLra/QUREROSOuChFREREeVIXfNR/NpsNSUlJGDhwIE6dOvVfp+eyTp06hSFDhqBChQrw9PSEl5cXatWqhbFjx+LChQv/dXp3vO+//x4jR450KDYrKwuzZ89Gp06dEB0dDS8vL1SuXBljx45Fenp68SZKREREBVJERP7rJIiIiMj1zJ49Gw8//DBGjx6NsmXLIj09HRs3bsRnn32G2NhY7Ny5E56envm2kZGRgaysLFit1mLJMTMzExkZGbBarVAUpVj+xqFDh1C2bFnMmjUL/fr1yzf2999/R/v27XHlyhX07t0btWrVAgBs2bIFX331FRo2bIiVK1cWS57uYuDAgZg2bRocOYW9cuUKfHx8UL9+fXTo0AGhoaH49ddf8emnn6Jx48ZYu3Ztsc0bIiIiKpjpv06AiIiIXFu7du1Qu3ZtAMCjjz6KoKAgTJo0CYsXL8b999+fZ52rV6/Cy8sLZrO5WHMzGo0wGo3F+jccdeHCBdx7770wGo3Ytm0bKlSooHv89ddfx4wZM/6j7NyTxWLBzz//jIYNG2rbHnvsMcTFxWHEiBFYs2YNWrZs+R9mSERE5N749T0iIiJySvPmzQEABw8eBJB93yhvb2+kpaWhffv28PHxwYMPPqg9lvOeUur9md566y189NFHSEhIgNVqRZ06dfD777/b/a09e/agZ8+eCAkJgYeHB8qXL4+XX35Zezyve0rFxcWhQ4cOWLlyJapXrw6bzYbk5GQsXLhQ1/a5c+cwZMgQVKlSBd7e3vD19UW7du2QmppaqHH58MMPcfz4cUyaNMluQQoAwsLC8Morr+i2TZ8+HZUqVYLVakVkZCSefvppu6/4NW3aFJUrV8Yff/yBJk2awNPTE4mJiViwYAEA4KeffkK9evW08Vm9erWu/siRI6EoijaWvr6+CAoKwqBBg+y+wnbz5k2MGTNGe17i4uLw0ksv4fr167o4dYw3btyIunXrwmazIT4+HnPmzLHr94ULF/Dcc88hOjoaVqsViYmJmDBhArKysrQYR+dFv379MG3aNADQfbX0ViwWi25BSnXvvfcCAHbv3n3LukRERFT8uChFRERETklLSwMABAUFadtu3ryJNm3aIDQ0FG+99Ra6deuWbxtffPEFJk6ciMcffxxjx47FoUOH0LVrV2RkZGgxf/zxB+rVq4e1a9fisccew7vvvosuXbpgyZIlBea4b98+9OrVC+3atcMbb7wBk8mEHj16YNWqVVrMgQMHsGjRInTo0AGTJk3C//73P+zYsQNNmjTB33//7eyw4LvvvoOHhwe6d+/uUPzIkSPx9NNPIzIyEm+//Ta6deuGDz/8EK1bt9aNAwCcP38eHTp0QL169fDmm2/CarXivvvuw7x583Dfffehffv2GD9+PK5evYru3bvj8uXLdn+vZ8+eSE9PxxtvvIH27dtjypQpGDBggC7m0UcfxWuvvYaaNWvinXfeQZMmTfDGG2/gvvvus2tv//796N69O1q1aoW3334bAQEB6NevH3bt2qXFXLt2DU2aNMHcuXPRp08fTJkyBY0aNcLw4cPx/PPP27VZ0Lx4/PHH0apVKwDAZ599pv1z1smTJwEAwcHBTtclIiKiIiREREREeZg1a5YAkNWrV8uZM2fk6NGj8tVXX0lQUJB4eHjIsWPHRESkb9++AkCGDRtm10bfvn0lNjZWKx88eFAASFBQkJw7d07bvnjxYgEgS5Ys0bY1btxYfHx85PDhw7o2s7Ky7HI8ePCgti02NlYAyDfffKNtu3jxokREREiNGjW0benp6ZKZmalr++DBg2K1WmX06NF2Oc+aNSvf8QoICJBq1arlG6M6ffq0WCwWad26tS6HqVOnCgCZOXOmtq1JkyYCQL744gtt2549ewSAGAwG+e2337TtK1assMt1xIgRAkA6deqky+Gpp54SAJKamioiItu3bxcA8uijj+rihgwZIgBk7dq12jZ1jNevX6/rk9VqlRdeeEHbNmbMGPHy8pK9e/fq2hw2bJgYjUY5cuSIiDg3L55++mm53VPYli1biq+vr5w/f/622iEiIqLbwyuliIiIKF8tW7ZESEgIoqOjcd9998Hb2xvffvstoqKidHFPPvmkw2326tULAQEBWvnuu+8GkH31EgCcOXMG69evxyOPPIKYmBhdXUduTB0ZGal9RQsAfH190adPH2zbtk27SsZqtcJgyD4VyszMxNmzZ+Ht7Y3y5ctj69atDvdFdenSJfj4+DgUu3r1aty4cQPPPfeclgOQfb8jX19fLFu2TBfv7e2tu1qpfPny8Pf3R8WKFVGvXj1tu/r/6jjm9PTTT+vKzzzzDIDsX7PL+d/cVzC98MILAGCXU3Jysva8AUBISAjKly+v+9tff/017r77bgQEBOCff/7R/rVs2RKZmZlYv369rs2C5kVRGDduHFavXo3x48fD39+/yNolIiIi5/FG50RERJSvadOmISkpCSaTCWFhYShfvrxuIQUATCYTypQp43CbuRea1IWI8+fPA/i/RYjKlSsXKufExES7xaukpCQA2fcvCg8PR1ZWFt59911Mnz4dBw8eRGZmphab86uJjvL19c3za3N5OXz4MIDsxaWcLBYL4uPjtcdVZcqUseuPn58foqOj7bYB/zeOOZUrV05XTkhIgMFg0O7HdfjwYRgMBiQmJuriwsPD4e/vb5dT7ucQyH4ec/7tffv24Y8//kBISIhdLACcPn063zZzz4vbNW/ePLzyyivo37+/U4uoREREVDy4KEVERET5qlu3rvbre7eS86ojR9zqF/NExKncbse4cePw6quv4pFHHsGYMWMQGBgIg8GA5557TncTbkdVqFAB27dvx40bN2CxWIo011uN1+2M462uOHPkSjRH/3ZWVhZatWqFoUOH5hmrLhQ602ZhrVq1Cn369ME999yDDz744LbbIyIiotvHRSkiIiJyOfHx8QCAnTt3Fqr+/v37ISK6BZa9e/cCgPZrgAsWLECzZs3wySef6OpeuHChUDfA7tixI3799Vd88803uP/++/ONjY2NBQD89ddfWl8B4MaNGzh48CBatmzp9N8vyL59+1C2bFmtvH//fmRlZWnjERsbi6ysLOzbtw8VK1bU4k6dOoULFy5oOTsjISEBV65cKdL+OLpoltOmTZtw7733onbt2pg/fz5MJp4CExERuQLeU4qIiIhcTkhICBo3boyZM2fiyJEjusccuWrm77//xrfffquVL126hDlz5qB69eoIDw8HkH1VTu62vv76axw/frxQOT/xxBOIiIjACy+8oC2A5XT69GmMHTsWQPZ9uiwWC6ZMmaLL4ZNPPsHFixdxzz33FCqH/EybNk1Xfu+99wAA7dq1AwC0b98eADB58mRd3KRJkwCgUDn17NkTv/76K1asWGH32IULF3Dz5k2n2/Ty8tLqO2L37t245557EBcXh6VLl8LDw8Ppv0lERETFgx8TERERkUuaMmUK7rrrLtSsWRMDBgxA2bJlcejQISxbtgzbt2/Pt25SUhL69++P33//HWFhYZg5cyZOnTqFWbNmaTEdOnTA6NGj8fDDD6Nhw4bYsWMHPv/8c92VS84ICAjAt99+i/bt26N69ero3bs3atWqBQDYunUrvvzySzRo0ABA9qLb8OHDMWrUKLRt2xadOnXCX3/9henTp6NOnTro3bt3oXLIz8GDB9GpUye0bdsWv/76K+bOnYsHHngA1apVAwBUq1YNffv2xUcffYQLFy6gSZMm2Lx5Mz799FN06dIFzZo1c/pv/u9//8N3332HDh06oF+/fqhVqxauXr2KHTt2YMGCBTh06JDTV6WpY/rss8+iTZs2MBqNupvA53T58mW0adMG58+fx//+9z+7m7UnJCRozwkRERGVPC5KERERkUuqVq0afvvtN7z66qt4//33kZ6ejtjYWPTs2bPAuuXKlcN7772H//3vf/jrr79QtmxZzJs3D23atNFiXnrpJVy9ehVffPEF5s2bh5o1a2LZsmUYNmxYoXOuV68edu7ciYkTJ2LZsmX47LPPYDAYULFiRQwbNgwDBw7UYkeOHImQkBBMnToVgwcPRmBgIAYMGIBx48bBbDYXOodbmTdvHl577TUMGzYMJpMJAwcOxMSJE3UxH3/8MeLj4zF79mx8++23CA8Px/DhwzFixIhC/U1PT0/89NNPGDduHL7++mvMmTMHvr6+SEpKwqhRo7Qbszuja9eueOaZZ/DVV19h7ty5EJFbLkqdPXsWR48eBYA8n9e+fftyUYqIiOg/pEhJ3lGUiIiIqJjFxcWhcuXKWLp06X+diksYOXIkRo0ahTNnzhTqXllERERExYX3lCIiIiIiIiIiohLHRSkiIiIiIiIiIipxXJQiIiIiIiIiIqISx3tKERERERERERFRieOVUkREREREREREVOK4KEVERERERERERCXO9F8nUBzq9ptkt81/zxVd+Uqsl678dxP7bzHGLsvSlU8/+q9dzL+XbbqyT6pF/3h4Ht+OFEVXNOtTQ+CfmXZV/J89oiuf+LKsXUzZh/bpyheu63P7+4KfXZ2I6VZ9Lufs+5jprY85U8NDVw6d8otdnZPPNdSVDTf1j2eZ7aoAuYbqhr99iNff+vKF8vpKnif0YwsAbzwxS1d+dllfXTk48axdnbN79T+Znel90y7Gd5e+Exm5hjdsi32dk3X0u5zpml0IAvbqn/9Hxi/Slb9uUcuuzvHp+j8uPwXoypfL2c8pn31GXdl6wX6uXkzQj6ePfhoi7IejdnV2jw7Vb7ipX/uOXWj/HJ2sm+ulKI/dJsNXv7FRgz915f3vVrSr89LYT3XlSYda2cUcOR2kKxvN+rHyXuVtV+dSvL4csEefW9yj+n0RAFI2ldOVs7ztn5OJzb/SlV/9rLeu3KXrz3Z1fh1WV1e+GqkfS9s5/esYAKQH6J+TG10u2MW0jdmtK28YX19XPtfjql2dmzf0f3tojRV2MZPmddGVvXNNIf999q9BF8rpX3PO3n1dV/b8S/8aBdi/rkbM3mEXc7Nagq7s8fpJXXnXsQi7OvKP/m+Jr34/f6XBUrs6cwd11JUrjLXP5c/RVXXlw/oqUCz2z6NfoL6T13YE2sX4HNaXb/jqy1l5nAX8G6H/W4E79PPFlG6/g1ou6+tcijbaxVyqm64rx0ed0ZWPntW/bgHAjbMedtty8ttt/3feH/yernz/mifsYsos19fz/VU/UC1W/WVXZ2U1/WvFka8q2MXEjNeX9z6qz1+5Yf9ZoEeZy7ryv1f0c6z8hFyTGcDZN/XPgYclwy7m4pJIXdn7uP45+ruxXRVE/qRv9+8u+naVM/b7WtLH+mNoh29+tYt572v9hC7zo34unE/Sn68AQNgDh3Tl3Qcj7WL8UvTnXD7H9a+r1/3sx9svTf+3jzXTP0c3/O3nd1au/S84xb7dc9X09ZJmXtQH5HG3jOvv6E8Cjv6j3wdCv7Gf/3/fo3/NKf+O/YmE4d0L+g199PleqRFlV+dcef0+EfPZAbuYPcPidGWvI/p28zrnzQrXv177btY/19f97apg5iP6fbj/1r52Maaf9S9m6frdE2b7QxRu1tDvS1lp+uO7JSnXcwbAkuucQPne/nX2cmyu3HL9bd8G+tc6ADj1t7+uXPFt+7/915P6vxW6WX/+dKrlDbs6H949R1ceNvExXflikv1zlPiVflwuj7afUxnz9Od2/9TXj0tQmQt2dYJf0R9gTjXw15VnDn3Hrs7Dbw/WlQ32L21Qch0O/8112rnrqfft6iSu7acrmyz252AB3+nfG/6jPyzDmnjJrk6ZCfr95mBn+3PG3Gz/6MuXk+zfLyBDv2+F/6p/7k/fo38dA4CIhfrX59zvta73Pm9X58IFT105aJX9a3GWWf+3L+pPneC/z35OGfW7PTzO2D+R/4bqEzTc1Lfzb5D96+yFKvqxCtymn2NX7Q8T8EvTt9t48Ca7mB8+15/jpofo67RusdWuzoq9ybpy9Kf6XM6Xt3+zm9FUv5//e9THLiZ2mb6PucepwsBddnU2rq+kKxuv27/X8j2g71N6oD4m5A/715OD9+ljJNe+F/qz/Unk7zOft9vmCF4pRUREREREREREJY6LUkREREREREREVOK4KEVERERERERERCWOi1JERERERERERFTiuChFREREREREREQljotSRERERERERERU4rgoRUREREREREREJY6LUkREREREREREVOK4KEVERERERERERCWOi1JERERERERERFTiuChFREREREREREQljotSRERERERERERU4rgoRUREREREREREJa5Qi1I3b97E6tWr8eGHH+Ly5csAgL///htXrlwp0uSIiIiIiIiIiKh0Mjlb4fDhw2jbti2OHDmC69evo1WrVvDx8cGECRNw/fp1fPDBB8WRJxERERERERERlSJOXyk1aNAg1K5dG+fPn4eHh4e2/d5778WaNWuKNDkiIiIiIiIiIiqdnL5SasOGDfjll19gsVh02+Pi4nD8+PEiS4yIiIiIiIiIiEovp6+UysrKQmZmpt32Y8eOwcfHp0iSIiIiIiIiIiKi0s3pRanWrVtj8uTJWllRFFy5cgUjRoxA+/btizI3IiIiIiIiIiIqpZz++t7bb7+NNm3aIDk5Genp6XjggQewb98+BAcH48svvyyOHImIiIiIiIiIqJRxelGqTJkySE1NxVdffYU//vgDV65cQf/+/fHggw/qbnxORERERERERER0K04vSgGAyWRC7969izoXIiIiIiIiIiJyE04vSs2ZMyffx/v06VPoZIiIiIiIiIiIyD04vSg1aNAgXTkjIwPXrl2DxWKBp6cnF6WIiIiIiIiIiKhATv/63vnz53X/rly5gr/++gt33XUXb3ROREREREREREQOcXpRKi/lypXD+PHj7a6iIiIiIiIiIiIiykuRLEoB2Tc///vvv4uqOSIiIiIiIiIiKsWcvqfUd999pyuLCE6cOIGpU6eiUaNGRZYYERERERERERGVXk4vSnXp0kVXVhQFISEhaN68Od5+++2iyouIiIiIiIiIiEoxpxelsrKyiiMPIiIiIiIiIiJyI0V2TykiIiIiIiIiIiJHOXSl1PPPP+9wg5MmTSp0MkRERERERERE5B4cWpTatm2bQ40pinJbyRARERERERERkXtwaFHqxx9/LO48iIiIiIiIiIjIjfCeUkREREREREREVOKc/vU9ANiyZQvmz5+PI0eO4MaNG7rHFi5cWCSJERERERERERFR6eX0lVJfffUVGjZsiN27d+Pbb79FRkYGdu3ahbVr18LPz684ciQiIiIiIiIiolLG6UWpcePG4Z133sGSJUtgsVjw7rvvYs+ePejZsydiYmKKI0ciIiIiIiIiIiplnF6USktLwz333AMAsFgsuHr1KhRFweDBg/HRRx8VeYJERERERERERFT6OL0oFRAQgMuXLwMAoqKisHPnTgDAhQsXcO3ataLNjoiIiIiIiIiISiWnb3TeuHFjrFq1ClWqVEGPHj0waNAgrF27FqtWrUKLFi2KI0ciIiIiIiIiIiplHF6U2rlzJypXroypU6ciPT0dAPDyyy/DbDbjl19+Qbdu3fDKK68UW6JERERERERERFR6OLwoVbVqVdSpUwePPvoo7rvvPgCAwWDAsGHDii05IiIiIiIiIiIqnRy+p9RPP/2ESpUq4YUXXkBERAT69u2LDRs2FGduRERERERERERUSjm8KHX33Xdj5syZOHHiBN577z0cOnQITZo0QVJSEiZMmICTJ08WZ55ERERERERERFSKOP3re15eXnj44Yfx008/Ye/evejRowemTZuGmJgYdOrUqThyJCIiIiIiIiKiUsbpRamcEhMT8dJLL+GVV16Bj48Pli1bVlR5ERERERERERFRKebwjc5zW79+PWbOnIlvvvkGBoMBPXv2RP/+/YsyNyIiIiIiIiIiKqWcWpT6+++/MXv2bMyePRv79+9Hw4YNMWXKFPTs2RNeXl7FlSMREREREREREZUyDi9KtWvXDqtXr0ZwcDD69OmDRx55BOXLly/O3IiIiIiIiIiIqJRyeFHKbDZjwYIF6NChA4xGY3HmREREREREREREpZzDi1LfffddceZBRERERERERERu5LZ+fY+IiIiIiIiIiKgwuChFREREREREREQljotSRERERERERERU4pxelFq/fj1u3rxpt/3mzZtYv359kSRFRERERERERESlm9OLUs2aNcO5c+fstl+8eBHNmjUrkqSIiIiIiIiIiKh0c3pRSkSgKIrd9rNnz8LLy6tIkiIiIiIiIiIiotLN5Ghg165dAQCKoqBfv36wWq3aY5mZmfjjjz/QsGHDos+QiIiIiIiIiIhKHYcXpfz8/ABkXynl4+MDDw8P7TGLxYL69evjscceK/oMiYiIiIiIiIio1HF4UWrWrFkAgLi4OAwZMoRf1SMiIiIiIiIiokJzeFFKNWLEiOLIg4iIiIiIiIiI3IjTNzo/deoUHnroIURGRsJkMsFoNOr+ERERERERERERFcTpK6X69euHI0eO4NVXX0VERESev8RHRERERERERESUH6cXpTZu3IgNGzagevXqxZAOERERERERERG5A6e/vhcdHQ0RKY5ciIiIiIiIiIjITTi9KDV58mQMGzYMhw4dKoZ0iIiIiIiIiIjIHTj99b1evXrh2rVrSEhIgKenJ8xms+7xc+fOFVlyRERERERERERUOjm9KDV58uRiSIOIiIiIiIiIiNyJ04tSffv2LY48iIiIiIiIiIjIjTh9TykASEtLwyuvvIL7778fp0+fBgAsX74cu3btKtLkiIiIiIiIiIiodHJ6Ueqnn35ClSpVsGnTJixcuBBXrlwBAKSmpmLEiBFOtfXPP//gzTffxL333osGDRqgQYMGuPfeezFx4kScOXPG2dSIiIiIiIiIiOgO4fSi1LBhwzB27FisWrUKFotF2968eXP89ttvDrfz+++/IykpCVOmTIGfnx8aN26Mxo0bw8/PD1OmTEGFChWwZcuWAtu5fv06Ll26pPuXlXnT2W4REREREREREVEJcvqeUjt27MAXX3xhtz00NBT//POPw+0888wz6NGjBz744AMoiqJ7TETwxBNP4JlnnsGvv/6abztvvPEGRo0apdsWWa01oqq3cTgXIiIiIiIiIiIqWU5fKeXv748TJ07Ybd+2bRuioqIcbic1NRWDBw+2W5ACAEVRMHjwYGzfvr3AdoYPH46LFy/q/kVUaeFwHkREREREREREVPKcXpS677778OKLL+LkyZNQFAVZWVn4+eefMWTIEPTp08fhdsLDw7F58+ZbPr5582aEhYUV2I7VaoWvr6/un8Ho9AVgRERERERERERUgpxevRk3bhyefvppREdHIzMzE8nJycjMzMQDDzyAV155xeF2hgwZggEDBiAlJQUtWrTQFqBOnTqFNWvWYMaMGXjrrbecTY+IiIiIiIiIiO4ATi9KWSwWzJgxA6+++ip27tyJK1euoEaNGihXrpxT7Tz99NMIDg7GO++8g+nTpyMzMxMAYDQaUatWLcyePRs9e/Z0Nj0iIiIiIiIiIroDFPp7bjExMYiJibmtP96rVy/06tULGRkZ2k3Sg4ODYTabb6tdIiIiIiIiIiJybU4vSmVmZmL27NlYs2YNTp8+jaysLN3ja9eudToJs9mMiIgIp+sREREREREREdGdyelFqUGDBmH27Nm45557ULly5Tx/PY+IiIiIiIiIiCg/Ti9KffXVV5g/fz7at29fHPkQEREREREREZEbMDhbwWKxIDExsThyISIiIiIiIiIiN+H0otQLL7yAd999FyJSHPkQEREREREREZEbcPrrexs3bsSPP/6I5cuXo1KlSna/lLdw4cIiS46IiIiIiIiIiEonpxel/P39ce+99xZHLkRERERERERE5CacXpSaNWtWceRBRERERERERERuxOlFKdWZM2fw119/AQDKly+PkJCQIkuKiIiIiIiIiIhKN6dvdH716lU88sgjiIiIQOPGjdG4cWNERkaif//+uHbtWnHkSEREREREREREpYzTi1LPP/88fvrpJyxZsgQXLlzAhQsXsHjxYvz000944YUXiiNHIiIiIiIiIiIqZZz++t4333yDBQsWoGnTptq29u3bw8PDAz179sT7779flPkREREREREREVEp5PSVUteuXUNYWJjd9tDQUH59j4iIiIiIiIiIHOL0olSDBg0wYsQIpKena9v+/fdfjBo1Cg0aNCjS5IiIiIiIiIiIqHRy+ut77777Ltq0aYMyZcqgWrVqAIDU1FTYbDasWLGiyBMkIiIiIiIiIqLSx+lFqcqVK2Pfvn34/PPPsWfPHgDA/fffjwcffBAeHh5FniAREREREREREZU+Ti9KAYCnpycee+yxos6FiIiIiIiIiIjcRKEWpf766y+899572L17NwCgYsWKGDhwICpUqFCkyRERERERERERUenk9I3Ov/nmG1SuXBkpKSmoVq0aqlWrhq1bt6JKlSr45ptviiNHIiIiIiIiIiIqZZy+Umro0KEYPnw4Ro8erds+YsQIDB06FN26dSuy5IiIiIiIiIiIqHRy+kqpEydOoE+fPnbbe/fujRMnThRJUkREREREREREVLo5vSjVtGlTbNiwwW77xo0bcffddxdJUkREREREREREVLo5/fW9Tp064cUXX0RKSgrq168PAPjtt9/w9ddfY9SoUfjuu+90sURERERERERERLk5vSj11FNPAQCmT5+O6dOn5/kYACiKgszMzNtMj4iIiIiIiIiISiOnF6WysrKKIw8iIiIiIiIiInIjTt9TioiIiIiIiIiI6HY5faUUAPz+++/48ccfcfr0absrpyZNmlQkiRERERERERERUenl9KLUuHHj8Morr6B8+fIICwuDoijaYzn/n4iIiIiIiIiI6FacXpR69913MXPmTPTr168Y0iEiIiIiIiIiInfg9D2lDAYDGjVqVBy5EBERERERERGRm3B6UWrw4MGYNm1aceRCRERERERERERuwumv7w0ZMgT33HMPEhISkJycDLPZrHt84cKFRZYcERERERERERGVTk4vSj377LP48ccf0axZMwQFBfHm5kRERERERERE5DSnF6U+/fRTfPPNN7jnnnuKIx8iIiIiIiIiInIDTt9TKjAwEAkJCcWRCxERERERERERuQmnF6VGjhyJESNG4Nq1a8WRDxERERERERERuQGnv743ZcoUpKWlISwsDHFxcXY3Ot+6dWuRJUdERERERERERKWT04tSXbp0KYY0iIiIiIiIiIjInTi9KDVixIjiyIOIiIiIiIiIiNyI04tSqpSUFOzevRsAUKlSJdSoUaPIkiIiIiIiIiIiotLN6UWp06dP47777sO6devg7+8PALhw4QKaNWuGr776CiEhIUWdIxERERERERERlTJO//reM888g8uXL2PXrl04d+4czp07h507d+LSpUt49tlniyNHIiIiIiIiIiIqZZy+UuqHH37A6tWrUbFiRW1bcnIypk2bhtatWxdpckREREREREREVDo5faVUVlYWzGaz3Xaz2YysrKwiSYqIiIiIiIiIiEo3pxelmjdvjkGDBuHvv//Wth0/fhyDBw9GixYtijQ5IiIiIiIiIiIqnZxelJo6dSouXbqEuLg4JCQkICEhAWXLlsWlS5fw3nvvFUeORERERERERERUyjh9T6no6Ghs3boVq1evxp49ewAAFStWRMuWLYs8OSIiIiIiIiIiKp2cXpQCAEVR0KpVK7Rq1aqo8yEiIiIiIiIiIjfg8Nf31q5di+TkZFy6dMnusYsXL6JSpUrYsGFDkSZHRERERERERESlk8OLUpMnT8Zjjz0GX19fu8f8/Pzw+OOPY9KkSUWaHBERERERERERlU4OL0qlpqaibdu2t3y8devWSElJKZKkiIiIiIiIiIiodHN4UerUqVMwm823fNxkMuHMmTNFkhQREREREREREZVuDi9KRUVFYefOnbd8/I8//kBERESRJEVERERERERERKWbw4tS7du3x6uvvor09HS7x/7991+MGDECHTp0KNLkiIiIiIiIiIiodDI5GvjKK69g4cKFSEpKwsCBA1G+fHkAwJ49ezBt2jRkZmbi5ZdfLrZEiYiIiIiIiIio9HB4USosLAy//PILnnzySQwfPhwiAgBQFAVt2rTBtGnTEBYWVmyJEhERERERERFR6eHwohQAxMbG4vvvv8f58+exf/9+iAjKlSuHgICA4sqPiIiIiIiIiIhKIacWpVQBAQGoU6dOUedCRERERERERERuwuEbnRMRERERERERERUVLkoREREREREREVGJ46IUERERERERERGVOC5KERERERERERFRieOiFBERERERERERlTguShERERERERERUYnjohQREREREREREZU4LkoREREREREREVGJ46IUERERERERERGVPCml0tPTZcSIEZKenn7LbYWJcaV2mYv79tGVcnGHPrpSLu7QR1fKxR366Eq5uEMfXSkXd+ijK+XiDn10pVzcoY+ulIs79NGVcnGHPrpSLu7Qx6LKpTBK7aLUxYsXBYBcvHjxltsKE+NK7TIX9+2jK+XiDn10pVzcoY+ulIs79NGVcnGHPrpSLu7QR1fKxR366Eq5uEMfXSkXd+ijK+XiDn10pVzcoY9FlUth8Ot7RERERERERERU4rgoRUREREREREREJY6LUkREREREREREVOJK7aKU1WrFiBEjYLVab7mtMDGu1C5zcd8+ulIu7tBHV8rFHfroSrm4Qx9dKRd36KMr5eIOfXSlXNyhj66Uizv00ZVycYc+ulIu7tBHV8rFHfpYVLkUhiIiUujaREREREREREREhVBqr5QiIiIiIiIiIiLXxUUpIiIiIiIiIiIqcVyUIiIiIiIiIiKiEsdFKSIiIiIiIiIiKnFclCIiIiIiIiIiohJn+q8TcHUiAkVRirTNGzduYNGiRfj1119x8uRJAEB4eDgaNmyIzp07w2KxAACOHTsGf39/eHt74+zZs/jjjz9QrVo1+Pj4YPny5di9ezeuX7+OHj16oGLFigCA+Ph4rFixAomJiVi3bh3279+PiIgItGnTBlOmTEH37t0RGxvrdM6bN2+2y7dBgwaoW7euFpOVlYWLFy9iyZIleOihh3Do0CFER0fDYDDgwIEDSElJwfXr19G+fXsEBwcDAJo3b45Zs2YhPDwchw8fxuHDhxEREQEPDw8MHToUFosFCQkJ6N+/P8qWLYvU1FSkpKSgadOmiI+Px65duzBt2jQcPXoUAQEB8PLygsFggMlkgo+PDzIzM4ssXzXX2NhYXL9+vdD5Dh8+HCdOnEBYWBhiY2MRHx+P2NhYHD9+/Jbjm5WVBYPBgPPnz+vyjYqKwsmTJxEeHo5vv/3W4Xx/+uknHD58GCdOnEBsbKzL5btnzx4cPHgQ0dHRqFy5MqZNm1aofMuUKYOrV6/CZDIhPT29VO1r6vh+9913aNy48X+yr4lIsYyvOrblypWDiBTZ+BbXa29++f7555/o1atXoeYCAJw8eRKbNm3S5VuvXj2Eh4fr4q5evYqUlBQ0btwYmZmZMBqN2mObNm3C9evX0aBBA5jNZjz88MN4/fXXERkZiYyMDBw6dAihoaHw8/PDunXrsG/fPhw5cgSxsbHo0aMH/Pz8cPr0aezcuRO1atWCn58fTp06hUmTJuHgwYOIiopCZGQkMjMzoSgKLly4oOWa39wtrteGS5cuOXScCA4ORnh4OAwGQ75jm3N8GzVqlO/YArjl+IoI3nrrLSiKUuDYfvrpp/jnn3/g5+cHk8kEg8Gg5X7p0qUimwtFlW/uuRAfH4+qVavizz//LLa5C0CX77Zt2wo9d++0fLmv5T93b/Vadv36dYf2NUfH97845y3MOVlh8i3Oc9727dvjxo0bunyTk5O151V18+ZN/P3334iJibGbJ6dOncL169e1x0aNGoWnn35aG2t1fzObzTh58iR27NihzYVmzZrBaDQiMzMThw8fRlxcHAwGA65fv4558+YhLS0NkZGRCAwMhK+vL/7991+cOnXK4bn7Xx6HY2NjERUVhStXruQ7tvmNb+6xzWt81bEFkOf4ArAb28WLF+PatWvw9/fH9evXYTAYEBMTA6vVqhvf250L+eV78+ZNLFq0CBcvXixwLpRUvvnNXWfyzT13b3VcK858b4uUAmfOnJEJEyZIly5dpH79+lK/fn3p0qWLvPnmm3L69GkREbl27Zps2LBBdu3aJUeOHJGHH35Y/vzzT5k5c6Zs375dPv30U9m9e7c88cQT8vDDD8uaNWtERMRsNsuff/4pV65ckREjRshTTz0l7733nvzzzz/SsmVLqVy5spQpU0YaNWokX375pYiIvPfee/LQQw9p5SlTpkhwcLB4enqKv7+/mM1mMRgMkpSUJD169JCePXtKkyZNxGazSWJiovz8889Sp04dMRgMYjQapXnz5mK1WkVRFAkICJAVK1YIAAkJCZGgoCAxm81StmxZGT9+vBiNRnnuueckNjZWAEhQUJAYDAapUKGCKIoiiqKIv7+/NGrUSH766SdZsmSJvPrqq7Jx40YREVmzZo20bt1aKlWqJA0bNpRmzZpJQECAAJCwsDCpW7eu1K1bV2JjY0VRFLnrrrtk//790qNHD7HZbBIYGCgAJDY2VgwGgyQmJsqmTZsEgFitVrFYLOLj4yONGzeWr776SoxGo0yaNEl8fX21/BRFEYPBIADEbDZLUFCQWK1WGTdunBiNRgkKChJvb29ZtWqV+Pr6iq+vrwDQ/nl7e2v/HxER4XS+EydOzDNfg8EgU6dOlfnz5xcq3/nz54vRaBQAoiiKAJCYmBgxm80CQHx9fe3Gt379+tKpUyex2WwSGhoqAwYM0OVbtmxZASAJCQlO5QtADAaDxMfHS1JSknh6ev5n+ZrNZnnnnXe0udCgQQPt+TMYDFKjRo1C52symbS2ypcvX6r2tdDQUOnZs6c2Nv/FvmaxWJwa33Llyomfn582vk8++WSe42swGGT48OEybtw4iYqK0mIMBoPExMRofzM8PFxGjx4t169ftxvf77//XipXrixRUVFSqVIladKkifj6+orFYpEmTZpIz549SyRfdT7mnAsiUmC+rVu3lqSkJDEYDGIymSQ0NFRCQ0PFZDKJ0WiUBx54QJ577jlJSEiQOnXqyMiRI0VRFGnUqJEYjUapX7++AJC7775bGy9/f39ZtmyZmM1mWbBggfTp00ebByaTSbp06aLNpzJlykhwcLCEh4fL7NmzxcvLSxRFkfDwcPn111/Fw8NDNxfUfU1RFImNjS1w7hbXa4PFYnFo7qqv/QDE09NTN7a9e/eWCxcuyP/+9z9tfF944QUBIEajURo3bix79uzRtRcbGyuPPfaYbNq0SRvfmJgYMRqN2nyyWq3a/unt7S0hISF2Y7t9+3aJjIzUHccURRGbzaaVfXx88s23UqVKAkCbC8WZb15zISAgQHecyT13Hcl38eLFeeZrMpnk22+/la1bt0pMTIyYTCYt38LO3YLyLcy+Vpz5cl+zn7uOvJYFBASIh4dHgfuaeo7jyPiW5DlvYc7JXO2cNy4uTry8vHTjnfN86ZVXXpGpU6dKixYtpEePHvLhhx+Koijy4IMPSkxMjPTs2VNiYmLk0Ucf1eo3bNhQpk2bJiaTSTZt2iQXL16URo0aacdgf39/7e/5+vqKwWCQKlWqyMqVKyUiIkIMBoNUrlxZDh06JMHBwbq5lvOfn59fgXP3vz4O5zxO5DW2mZmZMm3aNG183333XW0e9enTR44fP67tD+rYTp8+XU6fPi1ms1k2bdokEydOFG9vb1EURYKCguT555+3G9/ExEQJCwvTxvbIkSNSqVIlba7mnBOO5tuqVSsBoM0FR/Nt1KiRBAcHa3NBfY4tFosoiiKVKlWymwvFle+ePXvs8i1o7jqSb35z12Aw6HIt6n1t4sSJEhMTIwaDQYKCgmTUqFGFXs+54xelNm/eLAEBARIVFSV9+/aVoUOHytChQ6Vv375SpkwZCQwMlAULFmgv0AaDQWrVqqU9wYGBgdpO7OHhITExMRIdHS0ApGPHjmIwGKRbt27aC3JSUpIEBgaKj4+P9iSFhoZKo0aNxNPTU7p06SI+Pj7SrVs3CQ8Pl4EDB4qiKBIZGam9efX19ZWuXbuKv7+/NGzYUC5duiQiIhcvXpTOnTtLRESE1KtXT37//XdZtWqV9gJ++PBhmThxokRGRgoAKVOmjMTFxWkTPDo6WhRFES8vLzGbzWI2m2XJkiVy9OhRSUhIEABSvXp1KVeunLZzKYoiycnJ4uvrK5999pl4eXmJt7e3duBWX0CrVq0qRqNRunTpImfPnpWLFy/Kli1bpG7duhIfHy+JiYny6aefytChQwXIflOdkpIizz33nJQrV063Y+TeqXOWr169KtWrVxc/Pz8BIKNHj9ZOhhRFkfbt28s///wjX375pfj7+0vlypWlS5cucvHiRRk/frxYLBYJCQmRPXv2yJo1ayQgIEDGjx/vVL65X3Bule+cOXOcytdsNkvFihXl4sWLkp6eLo0aNRKz2SwNGjSQ2bNnS1BQkJarmm9YWJh4eXnJp59+KlOmTJHw8HABIB06dJA//vhDO9l0Nl/1IFmjRg1RFEVCQkLEYrHISy+9JCLyn+SbO/fU1FTZuHGjtn+qY+tovn5+fmI2m+XixYuyZs0aCQoKksmTJ98x+1qPHj3y3demTJmivWn8L/Y1s9ks0dHRDo/vBx98IAAkODhYG181l7i4ON34ApCoqCjx8fERRVFkxowZIiLy8ccfa4+3b99eIiIitJNpg8Ggje9bb72lvZFX2/Ty8pKAgAAxGAzSo0cPycjI0I4jxZ1vUFCQbi5UrVpVTCaT1KpV65b5+vv7i81mE4PBIN27d9fyvXnzpqxYsUICAwPFw8NDJk6cKC+//LJWr2HDhvLdd99J586d893XcpaXLFkic+fO1RYZH3nkEe3kOzo6Wry8vOSpp56Sy5cvy8SJE8XT01MiIiJkx44dsm/fPvH29haz2Szbtm2TTz75RDw9PWXGjBn5zt3iem0wmUwSEBCQ79y1Wq3i4+MjP/zwg6xcuVKbu+rYJiUlSY0aNSQsLEwbX3Uh4LvvvpNevXpJnTp18t3Xcm7btWuXVKtWTVvwfuyxxyQ0NFTLt127dnLhwgWZOHGiREVFSXJysjRq1Eh27Ngh/fv3F6vVKgEBAbJo0SKZMWOGeHp6yueff37LfNWTyLCwsGLPN/dcUD80SExMlMGDB4uHh4d8/vnnurnrSL75HdfUN8QA5PXXX9fyBSC1a9d2au46ku/t7GtFnS/3tfznbn6vZRaLRYKDg/Pd1ywWi0RGRmrj6+Hhke/4luQ5b2HOyVztnNdqtUpISIg899xz4u/vL2+++aZcu3ZNDh48KB9++KE2v59++mnp3bu3tihQoUIFmTJlijRs2FC3X+Xct3Lva0888YQsW7ZMKleurLVhsVjE399f4uLiJCgoSLp37y47duyQQYMGSVBQkPj4+Mi3334rP/zwg9hsNjGbzTJjxgx5+eWXxWq1yooVK/Kdu//lcVg9v5kyZYpMmDBBPDw8ZNasWdrYhoaGSosWLcTT01MbX3W8pkyZIk2bNpW2bdvmObY5FzTUbdOmTZOxY8dqC4/NmzfXxlddEFfHtmLFilK+fHmpUKGCLFq0SFq0aCFWq1W8vLxk5MiR8swzz4jFYpHvvvvulvnec889AmR/4FeYfNW5oO6Xbdu21RbkPTw8pGXLlsWeb2HmriP55p67oaGh4u/vL4GBgdK+fXst1+La11577TVZtmyZjB07Vry8vLRzYGfd8YtS9erVkwEDBkhWVpa2bfHixbJ48WJZtGiRtG7dWry8vKR27dry2WefyQcffCDJyckCQAYOHCgior35CAsLk6ZNm2oTR/2EPDAwUFv9PXTokFy+fFlblFq9erUMGjRIgoODxWg0iqIoMnz4cMnMzJTt27cLAOnatauWm8lkErPZLCIi586dk+rVq8sDDzwgqampkpqaKgsWLBAAMnfuXG2b+ka5evXqcvLkSW0iVK9eXf78809JSUkRAHLq1CkxmUxStmxZWbx4sXh5ecmBAwdERKRChQpajIjIqVOnxMfHR/vUrnz58mI2m6VChQry+OOPS1ZWlsyaNUtbGRUR2bt3r7azqxM055UAOQ/A6if9Z8+e1bbdc889curUKfn55591+ZYrV06sVqvs3r1bRETCwsJk8uTJunwXLVokQPYnxFarVbp37y4mk0m8vb1l586dIiKSlpYmQPaniBcvXhQR0f62M/mqO5n6t9V8jUaj7Nq1SypXrlyofAHIwoULtbmwY8cOASDr168XEZHPPvssz1zzekGoX7++pKena31u0KCBU/kqiiLx8fEiIrJlyxZ54IEHBMheqO3Ro4d2hVBJ5KsoikRFRWlz4YsvvhAvLy8tX/VTLjXe0XzVk2h1Lnz22WcSGxt7x+xrcXFx+c7dnOPrCvva66+/nu/4zps3TwBI06ZNtfFV8//zzz9FRLTxVedu+fLlxWazaWNbr1497Q2smu+4cePEZDJJeHi4Nr6KokiDBg2044K6/+3YsUMb2yeeeELLtTjzBbI/hVTnQufOnbWTpTp16sjzzz8vBoNB7rrrLrt8f/75Zy1fm80mAQEB2j/1uVbL6slw+/btJSsrS3bv3i0ApF69erJ79245dOiQ9gGJyWSSxMREGTdunNhsNtm/f7/dvpaRkSELFiyQJk2aCJC9sDZ06FDZtWuXANDeuIuIeHp6CpD9BktEZOrUqQ7P3aJ+bVDf0BY0d41Go+61wWAwaGOpHt/Vhcyc492vXz/5559/dPva7t275YsvvhAA8vvvv2vjazabtSvjIiIiZOzYsbrxnTJlijZOYWFhMmTIEDGZTOLv7y9btmzRXgvU1zp1fD09PcVoNN4yX/VKlTJlyhR7vrnnwu+//y4AtKvNp06dqsvV0XzV+aHOXTVfo9Eoq1at0j7FVuduRESEKIoi5cuXd2ruOpJvYfa14sqX+1rec9eR1zL1zbMz+1phziGL6zhcmHMyVzzn/eabb7R8rVar1KhRQ/unfpNELatXRj/00EMiItr7qtq1a8vatWtl3bp18sYbb2j9njVrllSsWFGMRqN2HC5TpowAkMqVK8uZM2fkrbfe0j48rFixokyfPl07vn/wwQdavup+np6eLiIio0ePzvd1978+DqtXweV8bfDw8NDGUu1zbGystk3dJ1599VU5fvy4bu6uXbtWG9uFCxeK0WiU+Ph4eeKJJ8RisWjjmzNfdXzVeVezZk2ZPHmyGI1GCQ4O1ubqzp07tQ/i1PGNiIgQLy+vW+arnlv7+fk5la/RaNS+uVSmTBltYVbNVz2OKYpS7Pmq+5ozc9eRfHPP3TVr1giQvdCpzt2cuRblvvbEE09ITtOnT5caNWpIYdzxi1I2m017oVTlfPHNb1U6IiJC0tLS5O+//xYg+5Nu9cREvRpDXaFWP+FRD17q12zUF+QbN25ol0EaDAaJjIyUl156SQDI8uXLtdxCQ0MFgJw8eVJERFauXKnVySvfnCcUXbp0kapVq4qnp6coiiILFy6U6OhoGTVqlACQ06dPi8lkkqCgINm5c6f4+flJamqqiIh2WZ+arzp2NptN1q9fL3379tX+zt69e0VE5ODBg9qB6Z9//hGR7BOigIAAWbdunaxbt07eeecdASBffvmlrFu3TmbMmCFA9ietDRo0kObNm2uXm06aNEmio6Nl5syZunwDAgKkXr168uabb4qISMOGDeXtt9/W5btgwQIxGAzy888/y5w5c6RRo0baGO3atUtEsq+aU8fw7NmzIpJ9UDEajU7nC2RfDbNkyRI5cuSI7gAdHBxc6HyDgoK08V+/fr0A2Z8GivzfCdyECRO0fM1ms3h7e2tlNd+aNWtK8+bN5cCBAwJAXnrpJafyVV9cVWq+o0aNkqZNm2rzcNasWcWer6IoYrFYtLmwc+dOad68uZavemVjzrnrSL5BQUECQJsLar53yr62aNEiURRFG9/cc3fdunXaJ2r/xb6mjqM6vup45vUJVc6T4X379unGV93X3nvvPTl8+LBu7oaGhoqPj482tqGhobJ8+XJdvvv37xcg++q2nONrtVq18f/rr790J3DqCXTuy5qLI191fqvUfL/++mvp27ev9nqzdOlSu3xXrVqlmwsvvPCCzJ49W2bPnq198q+W1blZtmxZefDBB7UToL59+0pycrJs3bpV2+/VN2M7duyQevXqyUcffSQion2NKufcXblypSiKIgMHDpT4+HhtXObPn6/FqJ+aq1+XV9/c5Td3i+u1wd/fX8LCwpw6TqivDer4vvbaawJAJk6cqBtvAJKUlCQPPvigNhcGDRokycnJsmzZMt2+pl71qI5tjRo1tA/A1HzVsV2zZo2MHj1aW4g2Go3aa0NqaqooiiJGo1EbXw8PDzEajVpuufNV58LmzZtLJN8KFSpo479lyxYBICtXrtTNhZxz15F81a+lqHM3974WFBRkl6+iKOLh4eHU3HUk38Lsa8WVL/e1vOeuI69lfn5+EhAQ4NS+5u3tLSaTyWXOeZ09h3TFc94NGzbo8u3bt6+MHDlSRo4cKSaTSUwmk1Z+/PHHBci+YnnYsGHa4lHLli2lWbNmcuzYMTl06JBdvklJSfLVV1+JiEjFihVFUbKvbFf98ssvAkC6desmPj4+2uvN4sWLtRh1genEiRMiItpCUH5z9788DqsfxOZ+bVDHV739wKBBg7TxzblQO2zYMG0cunTpIs2aNZPffvvNbu5u375dqlatqo2vuhiTM18fHx/x8vKSRx55RBtHk8kkaWlpIiLaYp3RaNTGV/06sJpb7nzVubBq1Sqn8s09Fz799FNdvr/88osoiiLz5s0r9nzV58iZuetovk2bNtXGX52ra9eu1ZWLY1/bvn275LR//37x8fGRwrjjF6Xi4uLk008/1W2LjIyURYsWiYjIp59+KoqiaJ9si4hs27ZNAEjv3r2lTJky2gvlt99+K0lJSfLCCy+IoihitVq1e3ns2LFDevfuLf379xcR0S7Ly7kTjhs3Tsxms3z66acyYsQI7as/o0eP1mLU7wpPmDBBUlNTZfPmzQJARo4cKa+88or4+flJUFCQvP/++3Lo0CE5dOiQlClTRhRFkYyMDOnSpYuEhISIoigiInLs2DGpXbu2AJAWLVqIoiji6+srS5YskU6dOsmwYcNERLTvNefMNyAgQMqUKSMioq04e3l5SUpKioiIrFu3TjsZ+Pzzz+XixYtSv3597VOvhQsXSlxcnPb9aJH/W1ndsGGDXL58WRo0aCBRUVFavtu2bZP4+HgBIP379xdFUbTLuf38/GTEiBHy3nvvaSvv06dPl9dee038/f2lRo0aUq9ePZk7d6507NhR7r77brHZbNK6dWvZunWr3H333eLn5ycmk0kWLlwoFy9elJo1a4q3t3eh8t22bZskJydL165dtZOtPn36SGhoaKHyDQoKEpvNJikpKbJjxw6JiooSs9kssbGxsnDhQlmzZo1YLBaZMGGClq/JZJIOHTpoz5ma74oVK6RBgwZSrVo1URRFLl265FS+QPZ3zz///PM8823evLl4enpKYGBgsecLQPsKW1BQkKxcuVJ++eUXLV/1U4LBgwc7la/6KdakSZMkNTVVli1bJoqi3DH72oEDB0RRFBk1alSec1dEtK8a/xf7mnqpuDq+vr6+4u3tLcuXL89zfNU3EmvWrLEb32PHjknz5s21sVMURVq2bKmdvKtj26ZNGxk8eLBufGfMmCEmk0n7REs9CezVq5c2TkuWLBEg+ytxkyZN0nKZNGlSseerfsChyp2ver+Ud9991y7fatWqydatW7W5oH49cuvWrWKxWKRFixZ2+9rSpUslKSlJu5/B2bNn5fvvv9c+Lfbw8BCDwSAhISHy008/ydKlSyUwMFBmzZolo0eP1sbl559/lpkzZ0p0dLQkJiZKhw4dZOPGjXLPPfeIyWSS2NhYuXLlily9elX7hFZ93d26dauYzeZ8525xvTaoXyvNb+56e3uL1WqVrVu3iojIb7/9JmazWSZPnixbt26VWrVqibe3t6xevVrLV72y5fjx45KUlCRxcXHavvb9999LWFiYAJCxY8dq4zt+/HhtbGfNmqWdEyxdujTPsR0wYIAkJSWJ1WqVAQMGyNWrV6V79+7alZZqvtWqVdPeYOWVrzq2mzZtKvZ8PT09xdPTU5sLVapU0T593bp1q2zdulVMJpNu7jqTrzp3n3nmGQGyF4yHDx8uERERdvmq88WZuetIvoXZ14orX+5rec9dR17LPDw8JCEhId99zd/fX4xGoza+tWvXFk9PT5c45y3MOaSrnfMajUZp3bq1nDlzRn777TcxmUwyffp0Ecm+P7DNZpMGDRpoc0F9v/b1119LWFiY9OnTRzv+T58+XSIjI+XNN9/U9rX33ntPypQpIy+//LKUKVNGfvzxR5kzZ442v48fPy5r166VKlWqSGRkpPTv31/27t2rXcFcs2ZN7W+rX3tV5+6OHTvEaDTmO3f/y+OwevuAM2fOaK9d6vieOXNG2rZtq304qlLPaXbt2iVhYWG6fW369OnahRRz5swRg8Eg4eHh8ssvv8isWbO08R03bpz2HKnj6+XlJUlJSXLs2DF5+eWXJTQ0VMxms4wdO1ZERJ566inx8vISk8mk5ZucnCyenp7aXMidrzoXUlJSnMo391xQ98fU1FS7uVBS+Tozdx3J12w26xZdW7VqJYqiaPuaOneLY1/75ZdfJKedO3eKr6+vFMYdvyg1depUsVqt8uyzz8rixYvlt99+k7vuukt69uwpzz77rHh4eEhsbKzMmTNHq6O+aCxfvlyefvpp8ff3196IXr58WXsi1E9rPD09ZcGCBXL8+HGJi4uTxo0bazcYrlu3rjz22GPSuHFjsVgsct9990lISIg8+uijUrZsWSlXrpyYzWYZOHCgDB48WKxWq/j6+moH3ZxXZ0RERMiECRNk6NCh0rp1ay3fJ554QoDspyojI0M6deokBoNBe3z48OHi7++vnVB06dJF5s2bJ3/++acEBQVJnz595K677hIA0r17d3n99delT58+2iX4Y8eOlbp160rfvn3F19dXqlSpIjNmzJDy5ctLXFycBAcHi8ViEYNBf3NOi8UiTz75pDz11FPSvXt3ERE5efKk1KpVS/s+6aVLl6RevXq6fEePHi1Wq1Vb4a5Tp47MmDFDfvnlF6lfv77dVQtRUVEyefJkOXnypLRq1Uq8vb2lTZs2cuHCBXnooYe0OCD70uEOHTro8lUvYS5MvteuXZO6detqz1OdOnWkadOmhcp3+/bt2qec6hh+/PHH2qWw6nxQv+ZlsVikSpUquq9/5sw3r7F1NF81B0VR/vN8fXx8dPmqz0VR5NuzZ09tX8t9laSr72vqVVDqJ9i5566ISJ8+faRjx47/yb7m6+sr/fv318Y353OU1/iqr7vqZdS5xzcrK0uaNm2qXaXUpUsX6devn7z99tva2I4ZM0b73vvw4cOlT58+YrVapXnz5lKuXDltfBMSEsTDw0M++ugjmT17trbgNH78eF2+av+LM18AEhoaqs0FR/MNCAiQtm3baoufBoNBgoODJTAwUAyG7Bug9u7dW3uujxw5ItHR0TJ58mQ5duyYJCYm6ubCyZMnxWw2a32PioqSd955R0SyP+VWF2RzPo82m02ee+452b17t3ZPrIoVK8ry5ct1NwBVj4Xq3FX/Tn5zt7heG8aMGePQcaJOnTqiKNlfz4+IiBAPDw/tjUK7du3koYcekkceeUTLp1WrVlK3bl0RkTzHd9y4cWIwGLT7vKnjq45tzqvvANxybI8dOyaNGzfWvY6ZTCZtISEwMFB7o6/Ohdz55pwLxZ1vXnPh1Vdf1eaup6enWK1W3dx1Nt+TJ09KcnKybu7GxcXZ5VvYuVtQvoXZ14ozX+5rec/dgl7L+vbt69C+1rJlS905pHqu81+f8xbmnMzVznknTZoklStXFpPJJDExMeLr66t93Um9ckj98F8k+6qLwMBA+eCDD2Tnzp3aDaBVu3bt0j6cy3lMHzt2rLz99tvi6empuxm4+n6gS5cusm7dOu0HakJCQuTjjz8WRcm+0b16/qXePzQwMFBbqMhv7v7Xx+GwsDAxmUxSo0YNSU5OFi8vL+1bP1WrVpXOnTvLc889p+XTuHFjSUpKEhHJc3zVr3DnzEddqFHHN+e5qjq+TZo00V4/Q0JCZOfOndrX2dSvjRkMBomIiNDy9ff3177OmVe+OeeCs/nmnAs5fyjiVnOhJPJ1dO46km9ec/ehhx7S9rWoqCixWq3Ftq/l9PHHHxf663uKiAjucPPmzcM777yDlJQUZGZmQkRgMBhQp04dPP/880hLS8OGDRvw/fffA8j+yc5XX30VLVq0wD333IOnnnoKH3zwAbKysrQ2K1SogLS0NGRmZuKpp55Cx44d0aZNG1y4cAHjx4/Hxx9/jLNnz8JsNiMyMhKNGjXC4MGDUbNmTYwfPx6//vorGjZsiIEDB6J169b47bffAAChoaFYu3YtKlWqhIMHD2Lx4sX4/fffUbt2bQwePBhA9s8yXrt2Db6+vgCg/ex73759tcePHz+u/cT4tWvXYDQasXPnTmzcuBF9+vRBQEAAACAtLQ2vvPIKli1bpv08qMlkQp06dfDMM89g7dq1Wq7vvfcexo0bh9GjR0OdFtHR0Vi8eDESEhKQkpKCxYsX48KFC+jbty9q1aoFX19fnD9/Hn///TcqVaqU5/Nz+fJlbN26FU2aNAEAHDx4EDabDb///jt+/PFHDB8+HKGhoVr8mTNnsGHDBpw9exYtW7ZE2bJl833+d+3ahZ9//hlhYWFo1KgRgoODtZ8pzvkTmLeT708//YTU1FS7XJ3N99q1a/j6669x5coVdOvWTftJ2Vvlm5mZ6VSujub7zTffIDw8HNWrV0dcXJxL5/v777/jjz/+QMWKFREZGVlgvgsWLMDly5d1+R48eBAnT57ETz/9BKvVyn3t/yuKfU39O/PmzcO///6Lfv36ae3kHN/cY3ur8U1NTcXmzZsdHtv//e9/aNWqFQYPHqyN76uvvopGjRrhyJEjAACr1Yply5ahRYsWAIBp06ZhxYoVqFGjhsvnu3v3bkydOhVHjhxBw4YNtZ/69vDwwJ49e9CmTZs8n6u///4bq1at0uW/efNmfPvtt9izZw/ee+89lClTRnssMzMTs2fPxqZNm9C4cWOUKVMGtWrVgo+PjxZz9uxZBAUFAQBOnDiBiRMn4tq1a+jTpw8aNmxYpK+76rwqqteGvObu7t278dtvv9n9jHqFChVw+PBhp8Z38+bN8PT0xNq1a/Hjjz/qxjczMxNbt27FihUrcPjwYdx3332oW7fuLccWAObPn48NGzagTJky6NixI5KTk10237zmAgDs2bMHv/76a5Hlm9/c3bp1Kz744AOULVsWd911l9NzN798i2Nfu918XXlf27lzJ3755RfdvlbUc+FWc1cd308//RS//fZbnq9ljuxrRX0O6ehxuEWLFoiPj7/l2OZ3TrZlyxbdz9S76jlvVlYWVqxYkedrWXh4OLZt24aHH35Ya+PcuXMwGAzw9/fHzp078c0332DEiBHa49999x0+++wz7N+/HwsXLtTlc+HCBaxcuRJTpkxBfHw8mjdvjkaNGqFcuXIAst8P7tmzB+XLl4e3tzc2b96MUaNGIT09HQ8++CAeeeSRIp27QMGvDfnNXSD/14b69evfcmxbt26NnTt3IiUlRRvfnGMLwG58ly9fDpPJhOXLl+PHH3/Mc3zHjx+PX375Ba1atUJ0dLQ2vrnHNj09XXt/HBUVhfvvvx8tWrQosXwvXLiAVatWYdmyZUhLS0OfPn3QtGnTW86Fksi3oLnrTL55zd2S3NdUS5cuhdlsvuU+kZ9SsSilysjIwD///AMACA4Ohtlsvq32jh07hpSUFLRs2RJeXl631VZ6ejpu3rwJb2/v22qnsEQEp0+fRlZWVoFjk56ejt27d8NsNqNChQowmUwlmCkB2c+XoigOl4sqprDt0v/hvlZ8nBlbIPsE4OrVq6hVq9Z/MrZ3Wr5ERERERCWtVC1KlQZHjx7FiBEjMHPmTIdj8qrjSIyjf/vff/9FSkoKAgMDkZycrCvHx8dj/vz56NGjB1JSUuDl5YUdO3ZoZWdi5s6diwoVKtx2u3Xq1MFvv/2GBg0aIC4uDi+99BK2bduGgIAADBw4EJGRkRg5ciSOHz+O/v37o1+/fli2bBneeustLebff//VlR2pU9h23333XVy/fh29e/dG8+bNAQAWiwWpqamoWLGiQ+WiinG2ztWrVzF//nzs378fERER6NSpE9asWXPL8v333w+bzVbkdfKKKVu2LHbt2oWLFy8iIiICRqMRc+fOxZEjRxAbG4uBAwciKSkJAQEB2mr/mDFjsGjRIpw+fRqxsbGoVq0aNm7ciNOnTyMhIQEDBw7Evn377GK2b9+eb7ufffYZPvjgA13Mfffdp9vPnnnmGfTs2RN33333LbcVVC6uOrfaNnXqVGzevBnt27fHfffdh88++wxvvPEGsrKy0LVrV4wePRoffPCBLqZPnz747rvv4OnpiX79+uHIkSNYunSpVk5KSsKwYcPw77//olmzZliwYAG+/PLLAtvN62/nXtg5ceIE3n//fWzcuBEnTpyAwWBAfHw8unTpgn79+sFoNNrFZGZmAgCMRiMA2JUNBgMiIyNhs9lw5coVnDx50q7d06dPF/h383Ljxg0sWrRI+5Q2MzMTV69eBQB4e3tDRHRlg8GAkJAQGI1GZGZm4syZM0VSx9GY8PBwNGzYEJ07d4bFYtH6cerUKXz44Yd47bXXbrmtMDG3W+fYsWPw9/fH1atXddvUY0njxo21GKvVil9//RXx8fG6shpT1HVyx1SqVAl//PEHqlWrBh8fH8ybNw/r169HYGAg+vbti5CQEEyZMgVHjx7FgAED0KBBA+zduxdvvfWWUzFGo7FI6nzyySe4fv06evTooR074uPjsWLFCu2T3txlR2JKoo6IYN26ddqxpHXr1vj5559vWW7Tpg1MJlO+dRyJKahOZGQkKlasiD/++ANnzpxBREQEvLy8MGXKFJw4cQKJiYl4+umn8ffff+OLL77QjlGNGjXCypUr841JTk5GSkpKkdeZO3cujh8/jrNnzyI2NhZPP/00fvnlF3Tv3l274vTtt9/Ot3yrmG7duumu2CqoHUfqANmf9G/evBlt2rRBo0aN8Prrr2PWrFkICgpC//79kZiYiKFDh+L8+fPo1q0b3nzzzULFnDlzpkjqTJgwAcePH4efnx98fX0hIsjIyIDZbIaiKDAYDIiJiYGXlxfOnTuHEydO2MUUpk5h242Pj0eFChVw8+ZN7eqN69evQ1EU7bhx/fp1ANlXDQPZV3f4+vri0qVLujq5Y/z8/HDx4sVbxjjabs5c1CtL6tati5zOnz+PJUuWoE+fPrfcVpiY2223d+/eMBgMuhj1m0DHjh1DTEwMsrKyYDAYkJWVhWPHjqFMmTK6shqjKAqOHj2aZx1HYm7VrppLdHQ0Dh06hOjoaBgMBqSlpeGHH36At7c3OnbsCF9fXyxcuBDHjx/Hgw8+iPDwcFy/fh0fffSRUzE+Pj5O18kr5saNG/j2229x/fp1tG/fHsHBwWjevDlmzZqlvYbkLue1rSjqFKbdgwcPaseXypUrF1h2pE5eMYVWqC/9UbHZvn277jucItnfUX744YdvGZNXHUdiCmr3yJEj0rVrV4mNjdW+y5rzF8kMBoN2o9+cMQC070U7GlOtWjXdd2Zvp12LxSKBgYFis9nkxRdftPtOsPrLKOr3dPv166eLUb/P7UydwrZrtVolJiZG+956YmKi1KxZU4Dsn6oNDQ3VbtxXsWJFqVmzZp7lnHUciSlsuzabTbv55aOPPioGg0G7Z4R67wBvb2+trPazRo0aEhgYKEFBQWIymW67jqPtenh4SJ06dbSbiHfs2FHGjBkjzz33nHh7e0tgYKAkJydLmTJlJCEhQbs30Pvvvy8tW7bUcnv22WflueeeE6vVmmdMnTp18m3XZDJJ37595f3339faKVOmjISHh0ujRo3kyy+/1OaHevPK999/X5vX5cqVk/HjxxdYPnHiRLHUyStm6NCh4uPjI926dZPw8HAZP368eHp6SvXq1aVXr14SEhIiFStWFIPBIN7e3uLl5SUtW7YURVGkUqVK4uPjo90PQS2rN5K0Wq1SqVIlMRgM0qJFiwLbHTdunF1M1apVxc/PTyIiImT48OHy2muvaftcmTJlxGg0Srdu3SQ5OVlMJpN2/0F1/Bs1aqTdsyUgIEC7n17ucuvWrcVsNovRaBQfHx+7dq1WqxgMuIrNtwABAABJREFUBvHz85Ny5cqJ0WiUhx56SDp16qTdYzApKUnKlSsn5cqVkwoVKkiFChUkMTFRPD09xWw2S+PGjaVdu3bi5eWlvZaoN5hWy97e3tKkSRPt111sNps0adLktus4GtO+fXstLjExUfbt23fbx6iCYgpbR1EUqVOnjnbPmA4dOmj7sboNgNSqVUsrqz9uopbVnzwv6jq3ivHz8xNFUSQgIED7pV+1bavVqt3/Qd0+atQorexoTM57ndxOHT8/PwkJCZGgoCAxm83SvHlz6dq1qyhK9g8CdO3aVapWrWpXzhlTtWrVPGOKuk7Xrl0lLCxMDIbsG5KrP88OQLsJsNpftaw+rt7To1y5cuLr6+t0TGHaVcdaLatzo1u3bnLvvfdq86Vu3bry4osvSr169QTI/tn6gmKSkpKKpY6iKBIbGyt16tQRk8kkipL9C3ctW7aUr776qsDy9evXiyTGkTpTp04Vk8kktWrVEl9fX3n44YcFgHa/R7PZrPVB/aXdunXrOh2jPve3W8disYi3t7d2n0v1vEc9DwUgTZo00e4zo95fKXdMYeoUpt0WLVpoP0KjHrPVXw9Tt6m/Bqief1WuXFm7f5jVapXKlSvr6jgSU9h2a9Soob3HuOuuu3Q/YONKxzURkY0bN2rH6NDQUBkwYIAAkB49eojNZtPmVffu3bWYp556SldH/SGZ/Oo4EuNou7GxsWIwGCQxMVHmz5+vjb16rFPvoaUeX2bPnq27h5UjMb6+vtq55u20O2bMGElISBCr1SoWi0U8PDzkiSeeEIPBIAMGDJCXXnpJunfvbld+6aWXtG3du3fPMya/OoVtt2bNmmIwGGTq1Kkyf/587Qci1OOI+n5O/RcSEqI7r2jcuLF2Y/b8Yjp37qydCxoMBmnWrJlcvnxZCoOLUiVs8eLF+f575513RFEU3bZHH31UgOyf733ppZekf//++ZbVbTnbyavdyZMn28XkftEDIPfcc4+cOXNG9u3bJ0FBQQJAtm3bJvv27dPelDdv3lzOnDkjv/76q3YgciZGnehF0a6fn58cPnxYvvzyS+2kSETkyy+/FJvNJlarVV5++WUREenUqZMAkIYNG2oxiqKIn5+fU3UK2y4AqV69ujRt2lRiYmK0AyEA7cCt9sHHx0f7xZPc5Zx1HIm53Xbr1KmjLWKpbz579eqlzRe1rC4OnTp1Si5fvqz9msnt1nG03bZt24pI9q9VVaxYUfz9/WXVqlUiItp+1a9fP3n//fe1OThhwgQRyf7JbnU/OnTokIiI9jPSalmNsVqt+barKIq88MILIpL962vqT8nmXOwCIPfee680a9ZMd2POsWPHyrPPPqsdxBs0aCCdOnXKs6weeIu6zq1iateuLUuWLNH9zLG6SNW7d28BIPfff7+MGzdOAgICBMj+udycry/qPqGeZBmNRvnmm29ERLSfnXak3Zwx6iJ3zsUug8EgzZs3l3HjxklISIg0adJEjEaj1KpVS1vg9vDwkEqVKom/v780bNhQEhISJCoqSkREzp07J1arVfuFk3PnzklMTIyYzWYZOXKknDt3TqpXry716tWzazc4OFh69eol/v7+Uq5cOalYsaL4+flJ9erVtRPl3ItdYWFhEhUVJX5+ftKwYUNp1qyZdO7cWS5evCgXL16UkJAQCQkJ0cqdO3eWwMBA6dy5sxw9elRXvp06jsQ0bdpUGjRoIKmpqfLzzz9L06ZNpWrVqvL111/L119/rf1ii1rOa1teMeqJbHHUqVKlinzxxRfy4Ycfar9uU6tWLfn999+1k2MvLy9Zs2aNrFq1Sptnarlq1arFUudWMcHBwXL48GGZOHGidvJ86dIlmThxonh6eorNZpNHH31ULl26JLVr1xaDwSDly5d3KkZd6L3dOkD2B0hxcXHaMUSd10ajUfcmNWc5Z4z6eHHXyRkTFRWlHR83b94sIqLduFv9AY+HHnpIO3afOnVKjh49qh07ctZxJKYw7QKQTp06acefqKgoMZlMcuDAAe0YpSiKVq5Xr572Aw35xRiNRklOTi7yOgBk1qxZ0rlzZ+0XogDImDFjtG0ApFWrVtKsWbM8y+p5Z351HIlxpI7RaJQWLVrIjh07ZO3ataIoijzwwAMiIrJ27Vot5qOPPhIR0X6V0NkY9bzqdutYLBZ5/PHHJSsrS2bNmiUWi0Wio6MlKytL9u7dKwEBAeLp6SmPP/64/PXXXxIXFydBQUG6GJPJJL6+vk7VKWy7Xl5e0qBBA/nhhx8kLi5OKlasKA0aNJA9e/bInj17JDAwUAIDA7Vyw4YNJSoqSho0aCDff/+9ruxMTGHarVu3rnac27Jli9StW1fat28vR48elaNHj8oPP/wgALRyXtvyivnmm2/yjSlsu507dxYA8umnn8qUKVO08+Jy5crJ119/rZ1PeXl5yZdffikzZszQXp/VsvrrgPnVcSTG0XbDwsIkJSVFnnvuOe21ISUlRZ555hnx9vYWm80mHTp0kC1btkjZsmXFZDJJeHi4UzFGo1H7FcjbaVc9TuT8cOBO+Jcz12+++UauXr2qHUvuvfderaz+qMPBgwdl48aN2nu+nHXyivHx8ZG1a9fK1atXZePGjZKQkKD9ArWzuChVwtSVxJwrj3lNopwxRTk5HWk392LXu+++qy1cqS80MTExkpaWJidOnBAAEh4eLmlpaXLy5EkBslfDnYlR3+QWRbv333+/xMTEyL59+7Q+iYhkZmZqn0SpixepqakCQBYtWqTFAP/3RtnROoVt18PDQ8qWLStr1qyRHTt2iLe3t5QtW1YMBoPs2rVL3njjDV1ZROSNN94QAPLJJ59oZWdjCtuu+qnirl27JD4+XhRF0T45UsuRkZFa+Z133hEAWoz6E+O3W8eRGC8vLwkLCxMRkdDQUJk9e7ZugUn9GewtW7ZoMZ6enlK2bFmtvGTJEl2MehKbs44ak1+7ZrNZEhMTRST7jYT6CyG5F7vU/G/cuKH7SfHIyEh58cUXRVEUadq0qRiNRomIiBAA2puDiIgI6dKlS7HUyStGvYLHaDRq468uym7fvl1743f48GEREVm4cKEA0BbvRESA7E8oVeobB7XOoUOHBID26zT5tZszBsi+4kwtq58wp6WlaXVsNpsYDAY5efKkiPzfm4C0tDRtgclkMonJZNJi1MVDtayeGG7atElERFauXKldCanGqG2IZC9kqQtmQ4YM0eoYjUbd4pe6QL1jxw5dLjt27NDGSl0IV/3xxx9anbzKha3jSIx6PCnouOZq/3JerQJknzCfPXtWO5bcddddUr16dTl79qz2KaNaPnz4cLHUuVVM06ZNpXr16lpZPa7duHFDO67/+eefIiLy22+/CQD56quvnIoBIAEBAbddx2azSfXq1eXPP/+UlJQU7YoI9dghIvL4448LAPnuu++0cs6Y3OXiqqPOX7Vcvnx53XFNLcfExGhl9Sfb1Rj1F4ly1nEkpjDt2mw27bUiNDRUPvroIzEYDJKamioi/3eMUsuhoaGydOlS3ba8Yry8vMRmsxVLHTX/U6dOaa+z6tWKb731lnaVuMFgkOrVqwsAiY+P18pdu3YtsI4jMY60azabJSEhQQwGg9SpU0d3Hidif4w6ePCgAJCVK1c6FVPQsc+ZOnv37tXVMZlM8s8//4iIyNdff607Ri1atEgURdHFqMc1Z+rcTrtr167V1dm6davWJ/UXx1RbtmwRAFpM7rKjMYVpN+dVMuq///qY5ci/3LnWrl1b0tPTtWNHhQoVpHXr1pKenq6dE6rlXbt2FVjHkRhH261UqZK0bt1azp49qzuuXblyRTuX2LZtm4iIrF69WgDIzJkznYrJeYy63XYbNGigvZ7Vr19fbDab7lii/orr+vXrtfI999yjxeQuO1KnsO3mPK5VrlxZd1yrXLmy9nqoll944QXd63VsbKyufKuY8PBwyWnx4sXaLzo6ywAqUREREVi4cCGysrK0f8r//561oijaDaRz/hIgAO272SKCbdu2AQAWLVqUZznntoLaVdvOGTNu3DiMGzcOn3zyCQBg0KBB6NKlC7p06YIrV65AURR07NgRTZo0wb59+wAAzZo1Q5MmTbB3714AwKhRo5yKuXbtWpG1O2bMGHTs2BHNmjXT9dNgMMBqtepu1O3n5wcA2q+vGQzZu8S1a9ecqlPYdjMzMzFv3jw8+eSTmDJlCjIyMjBv3jxkZWVh4sSJeOGFF3TljIwMDBs2DEajEWPGjMGQIUMKFVPYdnNKT0/X5k/O8tmzZ7VycnKyLka9H8/t1nEkpn79+toPHzRp0gQpKSkQEW3b4cOH4ePjg/fff1+LKVOmjPYLaE2aNMGkSZPg7++vxURERMBqterqTJo0CUajMd92jUYjjh49CgA4fvw4Tp06patz+fJl3diazWYMGjQIQPavHj322GP46quvICKYN28eDhw4gAEDBgDI/iUXtazu90VdJ6+YjIwMDB8+HAcOHEDXrl0BAK+//joAoFq1alAUBUajEX/++ScAaL9M9+OPPwKAtv/abDatnJmZCYPBoNVZuXIlgP+7/0N+7eaMAYDAwECtrN6z6cSJEwCAmjVrIj09HVlZWdo++vjjjwPI/pW2gIAAvPnmm8jKysLNmze1GLVNtaz+bfUX9sqVK4eMjAxdu2FhYbh58yZOnTqFgIAADBs2DAC0XzwpV64cMjMzcerUKS3mo48+AgDtXnhqLocOHdLmiKenJzw9PbXyoUOHYDAYtJjc5cLWcSTG29sbfn5+OHjwIA4ePIgZM2ZAURS8+eab2LhxI2bNmgUAWjmvbXnFqD8MUhx1fvzxRxw8eBAHDhzAsmXLAACxsbFo1qwZTp8+DQD46KOPEBcXh2bNmuHSpUsAoJUvXrxYLHVuFTNjxgzExcWhdevWyMlsNsPDwwMiAg8PDwDZr1M5/+toDPB/c/l26mRlZeG1115DmzZtsHTpUq2cmZmJzz//HADwwQcfwGAwYMCAAZg6dSo++OADXUzucnHVye38+fMA/u9YopbV+82cP38e9evX18X8+++/dnUciSlMuzVq1NDq1KhRA8ePHwcAfPnllwCy739jsVi0co0aNTB37lx4eXnlG+Pv7689z0VZJ6fQ0FDtHl6LFi1CcnIyRowYARHBzz//jHXr1mmv3ydPntTKK1asKLCOIzGOtHvz5k3MmjUL69at0+491aNHDwDZv6R28+ZNGAwGHD58GACQkpICANox0tEY4P+OWbdTR1EU7fzh8OHDMBgMuHnzpnY/JHW81XspVa1aFSKii1GPVc7UuZ121fM+tY76Oglk/0ptzh8EuXz5MhRF0WJylx2NKUy76vFy7dq1WLt2Ld5++20A2ecKkydPxpAhQ3TlvLblFaOOhzN1HIlR76Gp5jtjxgwA2efH7du3195/zJ8/H5cvX0b79u211yG1rO7P+dVxJMbZdtV9TOXl5QVPT0+IiHbelZSUBABITEx0Kgb4v2PU7bbbrFkz1K5dG0uXLsX8+fORkZGBzMxMrFu3DkD2r/8pioKePXti6dKlWL58OVq0aKHF5C47Uqew7eakHjPUY8nJkyehKIr2unLy5Ent1/LUGPV1JWedvGLU51pVrVo17f2O0wq1lOWi5syZIw0bNpSIiAjtCoQHHnhAKlSooG2bM2eOxMXFSUBAgMMxhalzqxgPDw+x2WxazDvvvCOBgYHaJzHqp/yvvvqq1q/GjRtLzqcqd0xeddRt+bWrfq3oVnW2bdsmAGTOnDlaTKVKlbQ6Tz/9tHZ535w5c+zKzsSolwQWRbvLly/XYtRPC1SzZ88Ws9msxaxfv15MJpMsXbpUi4mOjpbQ0FCn6hS23bi4OBERuXz5srRp00YsFovs2LFDTCaTdOrUSapWrZpn2Ww2y+bNm6VPnz63FeNsHfWTo4oVK4q3t7coiiJVqlSRGjVqaGWTyaSV1U9+1Rj1a2m3W8eRmCFDhojRaJTGjRvL888/r93bp3z58tK4cWMxGAzSuXNniYuL02LUK97Cw8MlKipKgOx7Znh7e2v3srJYLGK1WnUxfn5++barfmLx2GOPSXBwsPa96/79+4vI/31qkfN+BePGjdNty8rK0n3SIZL9Sci8efO0cu6YoqqTV8wDDzwgISEh8uijj0rZsmXFz89P/Pz85P3335dRo0YJAElMTNRiwsLCtOegUaNG2n0xFEXRyj4+Pto9Mho1aiRGo1G7r01+7Sr//7vuOWPq1KkjIiJ79+4Vg8EgHh4eUrlyZVm+fLk2tjVq1ND6+Pnnn2uf3C1fvly++OILAbIvSV++fLmsXbtWgoODRVEUrVytWjUxGo1auxMmTLBrt23bttrr6tq1a6VBgwYCQDZu3Cgi2VdbqVd/Xbt2TURENm3apH26N2nSJPn+++/FaDSKv7+/DBkyRIYMGSJWq1VsNptW9vf3l/r164uvr6907txZfH19pX79+rddx5EYk8kkjRo1ktTUVJk0aZIEBgZKQkKCjBkzRkT+79iilvPalleMOlbFUWfBggV2MfPmzZMuXbpoV6otWLBAMjIypEuXLtq9k9SyGlPUdW4Vs2bNGi0m59fSRbK/5moymWTNmjUikn1Fk9FolO+//96pmNDQUAkODr7tOuqVPMeOHZPatWuL1WqVEydOiNFolHr16knbtm3lxIkTYjKZZO3atdK8eXNtW+6YkqijfiresmVLCQgIEEVRpH379nLvvfdqZYvFopXfeustAaDFqPd9ylnHkZjCtPu///1PFCX7vo5jxowRT09PsVgs4unpKfHx8WI0GrVjVI0aNbQrhKpVq1ZgjHocLso6QPa9Zl5//XXp06ePdl9G9Vhy8eJFu+ONwWCQt956SyvnjsmrjiMxjrTbtGlTKVeunIwdO1bq1q2r3QewT58+Ur58efH39xeLxSJRUVHSt29f8fDwED8/PzGbzU7FqPeIut06ZrNZqlSpIjNmzJDy5cuLt7e3eHt7y+7du+XAgQPSrFkzURRFmjRpIrt379Zua5EzJjw8XBRFcapOYds1GAwSGxsrCxculGXLlom3t7fExsbK3LlzZe7cueLj4yM+Pj5aOS4uTqpUqSIxMTEydOhQiYmJkSpVqujqOBJTmHZtNps0bNhQLl68KAsXLpS4uDiJiorSbvGgHjfUcl7b8oqpXbt2nseo221XveI+d50FCxZIgwYNtKu0ly1bJpcvX5YGDRpo+6NaVmPyq+NIjKPtbtiwQYvJfVwbNmyYGI1G2bBhg4iIpKSkiKIosmLFCqdi/P39xd/f/7bb9fX1lUuXLsm2bdskOTlZux+h0WiUhIQEGTBggFy9elVMJpMsWLBAkpOTtW25YwpTx9l21eNanz59JDQ0VBRFkccff1wGDx6slW02m1aeNm2aANBiAgMDdeVbxfj6+kpOKSkpunMCZ5SaRanp06dLcHCwjB07Vjw8PCQtLU2mT5+ufR3Kw8NDRo8eLcHBwdK1a1cxGAwOxRSmTn4xjRs3FkVRtK+RzJo1S4KCgrTFoitXrsiUKVO0xQ0RkZkzZ+p21NwxedW5cuWKNGzYMN92mzRpYtduzjrqC1q7du20GPUrRaonn3xSF5O77GiMepPMomg350JQ7gW94cOHS8OGDbWY3GURkTZt2kirVq2cqlPYdtVFCbXctGlT7aaru3btki+//DLfsogUSYyjddSbuT/99NMycuRI6d27t4wcOVL7V7FiRalcubJW7tatm67csGFDiY2Nve06jsZ07dpVXnzxRe2m0+riUGhoqDzwwAPi4eEh9evX106kgez7jPj6+oqfn592Q8Po6GhJTk6WsmXLis1mE7PZrItR7weRX7tA9qX3FSpUEKvVKnXq1NEtdgHZX6t77LHHpHHjxmKxWCQsLEy79F0k+6Qjv7IjMYWpk9e2zMxMef3116VDhw4ybtw4efnll8XX11e7f1OVKlWkTJky0qlTJ6lSpYr4+fnJoEGDpFevXmKz2cTT01P69Okj3bp108p9+/aVTz75RDsZr1mzpgwdOrTAduvWrauLUW/OnHOxy9vbW7uhNJB949OkpCTdAlOFChWkZ8+e2lyxWq1yzz33aOUKFSpIzZo1tXJycrK8/fbbujoBAQG6ditXriy+vr66Oj4+PrqFrMDAQN3iV7Vq1cRms8n48eMlIiJC9/U4dS6pJxw5y2rfct50sijqFBSTs+2IiAiZMGGCLFy4UD777DMRyf5K4jPPPKOV89qWV8ycOXNkwIABRV6nffv20rp1a12Mui0jI0O7J6Aak5GRIYmJidqxJHdMUdbJK0ZRFPnyyy+1GPWrX6qXXnpJKlasqMXkLjsac/fdd2uLubdTR71Pkkj2ca1SpUoSHh4uRqNRdu7cKePGjdPKu3btkqysLN223DHFXUe9SXSXLl2kX79+0qRJE+nXr5/2Lzo6WiIjI7Vyx44ddeXKlStLQECAro4jMYVt9+6775b77rtPu/+VujgUHBysu+G1erxT982CYoqjjpqf2WyWhg0byrfffpvnByX5lYsqxpE6V65ckccee0wqV64sAwYMkHPnzkm9evW0vjZu3FiGDx+uvSZGRETIX3/95XRMo0aNpGbNmrddZ8iQIbrX4sjISKlSpYr2Oh0SEiJPPfWU1K9fX3t+/P39dTFBQUHaccbROoVtd/DgwfLEE09oH0iZTCbd/eDU+0qqZfWDqZzHGavV6nRMYdpV81M/jHzyySdl2rRp8u6774qIyMmTJ6Vjx45aOa9tecW89dZb2v1OHa3jSEz//v21e7ypMXXr1pXu3bvLpUuXtPdZ3bt3FxGRS5cuaV8xV8s5Y25Vx5EYR9udMWOGFqMulKjeeOMNiY6O1mJylx2NqVu3rlSpUuW2223fvr32+LVr16Ru3bri6ekpRqNRUlJS5PHHH9cWsXft2iXXrl3TbcsdU5g6zrSrvv7WqVNHmjZtKklJSdK0aVPtX1BQkPj5+Wnlzp0768rq+kXOOnnF5P6q3pgxY6RJkyZSGIqICEqB5ORkjBs3Dl26dIGPjw9SU1PRoUMHPP744xgzZgyuX7+O8PBwTJw4EYmJiahSpQrS0tIKjElMTHS6jqMx8fHx2LlzJxo1aoR58+ahbdu2efbt6tWr2LJlC5o0aeLUmGzYsAFXr169ZbsrV67E9u3bMXTo0Dzr5PV3C5sLOe7YsWNISUlBy5Yt4eXlVWDZkTrF2e6d5sKFCxg/fjyWLFmCAwcOICsrCxEREWjUqBEGDx6M2rVrF0u7iYmJdo+HhYXBx8cH//77L06cOFFkufwXsrKyMH78ePz6669o2LAhhg0bhnnz5mHo0KG4du0aOnbsiKlTpzo9bxxpd8qUKXjvvfe0mKFDh+LBBx/E4sWLYTAY0KNHDzRt2hSvvfYarl69inbt2uHtt9/Gs88+i4ULFyIzMxMNGjTA3LlzUbZsWaSnp2PFihW4ceMGevTogfT0dNy8eVP7aljucs5tANC/f3+7diMiInDz5k388ssvOHXqFJYuXarF1K1bF8HBwVixYgUyMzNRsWJFPPXUU3j66acBAHPmzMHRo0fRvHlz7Ssx6uXYeZXLli2LgwcP5htTmDqOxri6mzdv4tq1a9rXS3Jvu3nzJg4fPoyQkBAtJj09Hfv379d+7jh3TFHVySvm+PHjup+Bzr3t2rVrMBqN2ld7cpeLKuZ26uzcuRMbN25Enz59EBAQgJSUFF0ZgN22gsrFVSe3q1evwmg0al81LqhcVDEF1RERHDx4EIqioEyZMjCbzRARnD59GllZWQgODobJZNKVHYkprjo5vz51p0lPT0dGRgZ8fHzyLBdVTGHr7N69G2azGRUqVIDJZMK+fftw/fp1rQzAbltBZUfqFLbdS5cuISUlRTt2qH1RvzKUuxweHo5y5cph3759t6zjSExh261Vq5bueOGKzp8/j7///huVKlXKc9vly5fx008/oWzZslrMkSNHsHbtWvTr1w8A7GLyquNIjCPtbt26VfdeMve2gwcPwmazaV8Xz10uqpjbafenn35Camoqhg8fjtDQUHz33Xf48ccftTIAu20FlR2pU9h2czpw4AAsFgvKlCnjULmwMc4oNYtSHh4e2LNnD2JjY7VFqUqVKmH58uVo164dTCYTMjIy8Ndff+HGjRtISkpCWlpagTFWq9XpOo7GxP8/9u47PKpi/x/4+2zJJtn0SjokoYTeq9JBQVREwIZ0vIogWC6iIggioigiVS9cmljgAgIKioAiIApKiYAgEAhIb4ISpCWf3x/89nxzdpdkd9mEk+z79Tw8Oiczk/fMztky2T2bmop9+/ahevXq6rUDiIio+DjbYCqufl3Z7CIiIiIiKs1KzYXOy5Urh+3btzsc+/jjj5GRkQEASEpKwvbt2/H111+rF54urI4nbVytAwBff/21WoeIiIqXv79/kWwCudKvfR13svzxxx/o3bu3y+WialPS+mUW3x2jnrL4whj1lMUXxqinLAXV+eeff7BhwwasWbNGU/7tt99w+fJlzJgxQ1OeO3euWmfbtm2asjt1POlXT1l8YYx6yuILY/RWlvxsdTzi0Yf+dGj69OmSkJAgn332mVitVvn000+lY8eOoiiKPPPMM2K1WqVfv34SEhIiFotFLBaLS3U8aeNqndGjR6tZiYiIXLV9+3YxGAwul4uqTUnrl1l8d4x6yuILY9RTFl8Yo56y3KyOoiiSkpKiuVZhYmKiWrZdANtWbtiwoQAosI0rdTzpV09ZfGGMesriC2P0ZpZjx46p5/mJEycc7gtcZfJsK0t/+vbti4CAAAwbNgyXLl3Co48+ivj4ePTp0wcrVqzApUuXMGPGDISFhSE0NBSnT592qY4nbdyp8/777+Phhx++3dNHREQ6smzZMk1506ZNmvLmzZuRl5eHV155BQBw8uTJAsuetilp/TKL745RT1l8YYx6yuILY9RTFk/7FRFER0dj9OjR2L17N8aMGYOzZ89i69atCAoKQuPGjQFALT/11FMAgLS0NPzyyy/Yv38/GjVqpGnjSh1P+tVTFl8Yo56y+MIYvZmlSZMmWLt2LZKTk3FLPNrK0plr167JnDlz5MSJEyIikpOTI0ePHtUcu3DhgkyaNMmtOp60cbWO/TeAEBER2eT/ljv7b7rjP/7jP/7jP/4rqf/yP6516dJFkpOTJSsrS6KiogSAWj5+/LgAkDJlykhWVpacOHHCoY0rdTzpV09ZfGGMesriC2P0VhZFUeTJJ59U69zKO6VKxaaUiEhAQIBkZ2cXeMyTOkXVr8iNr65u3LixxMXFqT977733ZMmSJTetM3fuXClbtqyEh4erbR599FGpVKlSgXUK69dZHXveylJYHT31yyyld4wl6VwrafPtyfy68nuKcn6LI687WePj4zXH7cvR0dGar0/etm2bAFDr2Jc9bVPS+mUW3x2jnrL4whj1lMUXxqinLLfS72+//eZQfvrppyUxMVECAwNFURS1vG7dOgEgjzzyiKacv40rdTzpV09ZfGGMesriC2P0VhbbBlT+Op5uSpWaC53Xr18f27ZtK/CYJ3WKqt9p06bhueeeQ/v27XH+/Hnk5uYCAH777Td0794d8fHxGD16NJ577jmEhYXh7NmzmDt3Lp577jnUrl0bFy5cQG5uLqZNm4Zly5bhypUrOH/+vNM6hfWbm5uLjz76CFOmTEHXrl1x6NAhAMBjjz2GjIwMr2YprI6e+mWW0jvGknSulbT5djbGadOm4amnnkK5cuU8zuJKv87qfPTRRyhXrhwiIiJcblMUeZ3drvbrJX/e06dP49tvvwUATJgwAQkJCdiyZYv6GFK5cmVIvi/PVRQFANQ69mVP25S0fpnFd8eopyy+MEY9ZfGFMeopy630+8svvziUJ0+ejPvvvx9XrlwBALV83333AQDatWunKedv40odT/rVUxZfGKOesvjCGL2ZxVk/HvFoK0uH5s+fL6mpqTJp0iTZuHGjZGZmyttvvy2JiYkydOhQ2bhxo6Y8d+5cl+p40saVOqmpqfLee+9JZmamBAYGSlZWlkydOlXCw8MlICBAAgICJD09XT7//HOZNWuWGI1Gtbxjxw4BIFlZWZKRkSETJkyQyMhICQoKclqnsH5HjRolUVFRMnDgQE2boKAgKVeunFezFFZHT/0yS+kdY0k610rafDsbY0ZGhgwcOFCaN2/ucRZX+rWvYyt36tRJDAaDS22KKq+z29V+veTPqyiKzJw5U0REZs2aJTVr1pSvvvpKfcxbuXKlvPXWW2r54sWLMnHiRLWOfdnTNiWtX2bx3THqKYsvjFFPWXxhjHrK4mm//fr1k3bt2jkti4g0aNBA8r80feqppwSAWse+7GodT/rVUxZfGKOesvjCGL2VJf+7IW92zFWlZlPK9vnk/P8A7eeX85edHXPluh3e7Dd/TtuLmilTpoi/v78EBQWJxWKR7Oxs9YWPrbx37161jb+/v3z33XcObfLXKaxf24sl+zb5X2B5K0thdfTUL7OU3jGWpHOtpM23szH6+/vLypUrHcboThZX+rWv42zzqLA2RZXX2e1qv16c5RUR2bFjh0RGRt7mR1kiIiIiIu8rNd++d/DgQYdjR44cUf8/MTFRU3Z2zFkde660caVOz549MWTIELRt2xZVqlRRx3D69GlkZGRg3759SEpKwvbt21G5cmUAUMuHDx+GwXDjk5flypXDxx9/7NAmf53C+v3jjz9Qq1YtLFu2TNOmRo0ayMnJgclk8lqWwuroqV9mKb1jLEnnWkmbb2djLFeuHLKyshzG6E4WV/q1r2MrX716VTOXBbUpqrzOblf79eIsLwBYLBbk5OSAiIiIiKjUud27Yr5q+vTpkpCQIJ999plYrVb59NNPJTo6WiwWi3z66acSFBQkY8aMkYSEBOnZs6cYDAbp16+fhISEiMViUet17NhRFEWRZ555RqxWq9M6hfWbkJAgjz32mFitVvH391f/gt+3b1+pVauWV7MUVkdP/TJL6R1jSTrXStp8Oxvj9OnTJTQ0VMqWLetxFlf6ta+Tnp4uS5YskYkTJ6of3yusTVHldXa72q8XZ3lFRCZOnCi1atW6zY9aRERERETep4jkuwpdCTZ37lyHYxs2bFD//4477tCUnR1zVseeK21crbNx40YsWbIEp06dAgCEhoZCURRMmzYNffr0wYwZM7Bo0SIsWrRIbRcWFgaz2YzTp08DAOLj49GuXTusXbsWWVlZTusU1q+IwGAwoG/fvvj4448xY8YMzJ8/H0uXLsXAgQPx3//+12tZXKmjp36ZpXSOsaSdayVpvp2NMSsrC6+//jrCw8Nx8uRJj7K40q99nUcffRTz589XL+g4e/bsQtsUZV7729V+vdws75tvvokZM2bg4YcfBhERERFRqXLbtsO8LCwsTPPParVqrt0UFhbmcD0nV+p40qawOgDEz89Pvc5IWFiYnDx5UkRE5s2bJ+np6Wq9MmXKyIwZM+TChQsyadIkOXHihIiI5OTkyNGjR2XOnDnqMWd1CuvXVsf2c9t1rhISEqRv375ezVJYHT31yyyld4wl6VwrafPtbIwAJDw8XN59912Ps7jS783mNzw8XGJiYlxuUxR5b7YW8q8XZ3lt62PGjBlCRERERFQalZpNKWf27t0rrVq1kq+//vqmxzyp441+AwIC5LvvvlPL165dc3hRExAQINnZ2Wp7+7KzY/blwvp19nNnL7C8kcWVOnrql1lK5xhL2rlWVHNXFPPtbIwnT5685Syu9Gtf52YbZrcrb2HrpaCNTiIiIiKi0spQRG/A0oXy5ctj7NixGDRo0E2PeVLHG/3Wr18f58+fV8smkwlPPvkkLl++DAAIDAxE/fr1sW3bNrW9fdnZMftyYf06+3l8fHyRZHGljp76ZZbSOcaSdq4V1dwVxXw7G2NMTMwtZ3GlX/s6ISEhGDJkiFttijJvYevlZnkB4KOPPkKTJk0QHx+PQ4cO4aOPPkK5cuUQERHhtAwAjz32GDIyMrzapqT1yyy+O0Y9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKO3sgDAhAkTsHTpUnjkdu+KFbVt27ZJcHBwgcc8qXOr/c6fP19SU1PlxRdflMDAQMnMzJS6devKe++9J5mZmSIiap1JkybJxo0b5e2335bExEQZOnSozJ07VzIzMzXHblansH7z/9xWp1mzZvL555+rub2VpbA6euqXWUrvGEvSuVbS5tvZGAubX1d+jyv92texn1tXb5OiyOvsdrVfL87yTp06VaKiomT06NESEBAgo0aNkqioKOnUqZMYDAaHclZWlkydOlWCgoKkXLlyXmtT0vplFt8do56y+MIY9ZTFF8aopyy+MEY9ZfGFMeopiy+M0VtZbF/MM2vWLGnevLl4otRsSi1dulTzb8mSJfLkk09KUlKS1K5dW5YuXSovv/yy5pgrdTxp40od2/VCkO/aU7ayoiiyceNG9Zh93fxtCip70q+iKE5fYHkrS2F19NQvs5TeMZakc60kz7dtjIXNr7u/x5V+FUVxunnkym1SFHlv9i//enGWNzU1Vd24sn1D3+effy47duwQAA5l2zc6TpgwQSIjI73WpqT1yyy+O0Y9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKO3stg2pXbs2CGRkZEe7eWYUEp07NhRU1YUBXl5eQCAI0eOoGPHjpB8XzR45MgRdOrUqdA6W7dudbuNK3UAIDIyEo0bN8awYcMQExODcuXKQVEUAECTJk0gIlAURf2v/Tf4JSYmqn05K9uOFdav7Wc2NWvWVPOOHTsWb731lteyFFbH3u3sl1lK7xhL0rl28ODBEjXfd955p8MYAdx0PK5mKVeuXKH92tcZMmQIgBtzC0Cd/4LaFFVeZ7ers/Vin1dE8Oyzz8JgMCAwMBB//PEHatWqhatXrwKAQxkADh48iBo1aiAnJwcmk8krbUpav8ziu2PUUxZfGKOesvjCGPWUxRfGqKcsvjBGPWXxhTF6K4uNxWJBTk4OPFFqNqVsm0Al2cGDBwv8eUpKSqHHnNUprF9n7F9geSuLK3X01C+zlM4xlrRzrSTNtytz60kWV/q1r+NsU62wNs76vZ15W7dujSFDhqBt27aoWrUqkpKSsH37dhw+fBgGg8GhDADlypXDxx9/jIyMDOzbt88rbUpav8ziu2PUUxZfGKOesvjCGPWUxRfGqKcsvjBGPWXxhTF6K4vN119/jYyMDHii1GxKjRo1Ci+88AICAwM1x55++mlMmTIFw4cPV+soioJx48apxwqqA8DtNq7UKVu2LADg6tWrWLFihcM7vbp37465c+dqjtm/e+OOO+7QHLMv244V1q+97t27O7zA8laWwurYu539MkvpHWN+ej/X7PPofb49mV9XsthzdX7tudKmKPIWxtlaAIAXX3wRr732GkJCQiAiaNGiBbp3744rV67AbDY7lDdv3oyKFSviv//9LwYOHIi9e/d6pU1J65dZfHeMesriC2PUUxZfGKOesvjCGPWUxRfGqKcsvjBGb2XZvHkzPv30U7z55puYMWOG28+BAaDUXFPKYDA4fH22wWCQ3bt3i8Fg0NQ5c+aM5lhBdTxp40qdsLAwCQsLk9DQUAEgVqtVAIjJZJLAwECZM2eOBAYGav7B7holtja2clhY2E3rFNSvn5+fQ51+/fqp/7ydpaA6euqXWUr3GEvKuVbS5tvZGPOPx9MsrvRrXyf/3DqbX1f79Ubem92uN1sLtrxz5syRf/3rXxIbG6tehyo8PFxiYmJuWk5ISJC+fftKenq6V9uUtH6ZxXfHqKcsvjBGPWXxhTHqKYsvjFFPWXxhjHrK4gtj9FaWhIQEmTFjhsd7OaVmU0pRFDl16pTDsUWLFklUVJSmzpo1azTHCqrjSRtX64iIphwSEiImk0mCgoIkLCxMrFarKIoiFotFwsPDRURk79690qpVK/n666+dlp0dK6zfsLAwTR1nL7C8lcWTOnrql1lKxxhL+rmm5/l2Nkb78XiSxZV+7es42zwqrE1x5rVfL/Z5bRukfn5+Eh4eLhcuXJBJkybJiRMnREQcyjk5OXL06FGZM2fOTet40qak9cssvjtGPWXxhTHqKYsvjFFPWXxhjHrK4gtj1FMWXxijt7Lk5OQ4vDHIEyV+UyosLEzCw8PVdx+Fh4erO3YF/XX7Zn/xtq/jSZvC6thelISEhIjBYJD+/fur4/n555+lYsWKatnZC0D7OvZlV+oU1q8rL069lcWVOnrql1lK5xhL2rnmyRiLqk1hdZyNxxtZXOnXlU2125m3sPXi7+8vjRs3VssBAQGSnZ2t1rcvu1LHkzYlrV9m8d0x6imLL4xRT1l8YYx6yuILY9RTFl8Yo56y+MIYvZXFG25craoEmzBhAsaPHw8RwciRI/Hee++hT58+6N27NxRFweDBg9G3b1+13L17dwwbNkxzzFmde++9F3369HGrjat1PvzwQ7z33nv44IMPsGHDBkyZMkUdj8lkwrFjx9Ry+fLlMXbsWAwaNOimdezLrtQprF9nPy+qLK7U0VO/zFI6x1jSzjVPxlhUbQqr42w83sjiSr/2dTxpU5R5C1svDRo0wAMPPKCW69evj23btqn17cuu1PGkTUnrl1l8d4x6yuILY9RTFl8Yo56y+MIY9ZTFF8aopyy+MEZvZfGGEn+h8x49egC4cRX4xo0bw2w2q8cef/xx9Zizsit1PGnjSp1ly5YBAE6fPo1ly5ZBRHD8+HFMnjwZTZo00dQ5cOAADh8+jFdeeQXnzp3D8uXLUb58eSxduhRff/21Wl62bBk2bdrkUKewfp3VAbQvlryVpbA6euqXWUrvGEvSuVbS5vtm82Abj6dZbnYfmb9fZ3WczW9hbYoir6trwT5v//798fzzz+P06dP48ccfcc8992DgwIHYtGkTKleu7FCuUaOG5th9993nlTYlrV9m8d0x6imLL4xRT1l8YYx6yuILY9RTFl8Yo56y+MIYvZXFarVqnsdWr14d7lJERNxupXOXL1/G1atXNcf8/Pw0xy5fvgx/f3+36njS5mZ1ypQpo6mjKAqCg4NRvXp19O7dGxEREeo38jm7iRRFgaIoyMvLU8v2dW11CutXURSEhISodcLDw9UXWNHR0RgxYoTXshRWx5M2RdUvs5TeMZakc81gMJSo+QbgMMYHHnhAMweeZHGlX/s6+/btUzePoqOjMXz48ELbFFXe3r17a+bLYDA4rJdNmzYVmrewh+zC8nrapqT1yyy+O0Y9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKO3suQ/rigKcnNzC/w9Tn+3FJauhLh06RKGDBmCBQsW4OzZswBuPPnP/wIgf9nZMWd17CfelTau1skvNzfX4ZiiKIiOjkbLli3x7rvvIi4uzqO5KazfhIQEh5/bv8DyVhYiPeC5VnRu19zGxcU51LG/L3eljd7yRkREoHHjxhg2bBhiYmJw5MiRAn9vYmKipo592dM2Ja1fZvHdMeopiy+MUU9ZfGGMesriC2PUUxZfGKOesvjCGL2VxV5KSkqBv8cpKSX69+8vGRkZsnDhQgkICJCZM2dK/fr1xWQyyTPPPCMBAQHSokULiY6OloiICPHz83OpjidtXKnTsWNHefXVVyUxMVHmzZunjuPSpUsycuRIEREZOXKk5OTkqD+zle3rnDlzxqFN/jqF9eusjj1vZSmsjp76ZZbSO0b79a7nc62kzbcn8+vK7ynK+S2OvJ5kJSIiIiIq7UrNO6WSk5Mxd+5cNG/eHCEhIdi6dStatmyJbt26Yfv27diwYQOCgoLwySef4I8//kCvXr2wZ8+eQuvExMS43caVOiI3rjeycuVKfPrpp1ixYgVGjRqFXr16oWzZssjNzYXRaMTx48cRHByMcePGYeTIkTh+/DiMRiNiYmLUOrt27UKVKlU0bfLXKaxfAHjhhRfwzz//aNo8/fTTmDJlCoYPH+61LIXVAaCbfpml9I6xJJ1rtmMlZb5HjBjhMEaj0YgDBw5gzpw5mjG6k2XUqFGF9mtfx5ZFURTNXBbUpqjy2trkv13t14utTf68c+fOxdWrV7FixQp07NgRGzZsKPCx8I477tDUsS972qak9cssvjtGPWXxhTHqKYsvjFFPWXxhjHrK4gtj1FMWXxijt7LY6969e4G/x6nbvCnmNVarVQ4dOiQiIgkJCbJp0yaxWq2yfv16sVqtkpCQIAEBAXLo0CE5cOCAKIriUh1P2rhSR1EUOXXqlBw4cECsVquIiBgMBlm4cKFERUWJiKh1zpw5IwaDQS2vWbNGU2fRokUObfLXKaxfg8EgJ0+edGize/duMRgMXs1SWB099csspXeMJelcK2nz7WyMiqLInj17HMboThZX+rWvYyvbz2VBbYoqr7Pb1X69OMsbFhYmoaGhAkDCwsIEgPpPURSHstVq1ZS91aak9cssvjtGPWXxhTHqKYsvjFFPWXxhjHrK4gtj1FMWXxijt7JYrVZRFEUsFouEh4eLJ0rNplS1atVk7dq1IiLSqlUref7556VatWoycOBASUhIkFatWklUVJSsXbtW3n//fbFYLC7V8aRNQXUCAgI0N2JgYKAoiiLh4eHqcdsNajAYHBZDYYspf9nWz836DQ4OVtsEBQWJwWCQ/v37i8j/vcCyZbvVLK7W0VO/zFL6xljYOeFplqI414KDg0vcfFssFs0Y889DUFCQR1nyz+3N+s1/H5p/fhXl/zaP8s/vzdoURV7bMfu1kH+95F8L9ptdIuJQ3rt3r7Rq1Uq+/vprl8pF1aak9cssvjtGPWXxhTHqKYsvjFFPWXxhjHrK4gtj1FMWXxijt7K4q9RsSo0fP17ef/99ERFZtWqV+Pv7i9FoFEVRZMKECbJq1SoxmUxiNBrFYDDIU0895VIdT9oUVEdRFGnRooUAUMtms1ndYQwODpbAwEAJDAzUvMjLyMgQRVGke/fuMmzYMOnbt6/06dNHFEWRwYMHa8q2OoGBgYX2azKZRFEUCQwMVF8s5X+BZTKZvJKlsDr33nuv222Kql9mKb1jdOWc0Mu5piiKZGRklJj59vf3V+fANsb882A/Hlez5J/bm/VrMpk0dZxtJuWfX2dtiiqvv7+/w+2a/3c7Wwv5N7dCQkI0m5g2P//8s1SsWNHlclG1KWn9MovvjlFPWXxhjHrK4gtj1FMWXxijnrL4whj1lMUXxuitLO4woZR49tln1f9v3bo19uzZgy1btiA9PR3Vq1cHAOzfv19z7MUXX3S7jidtnNWZP38+FEVBpUqVsG3bNogIevfujdGjRyM0NBQAsGfPHlSuXBnp6elo1KgRvv/+ezRu3Bhms1kd6+OPP645lr+cnp5eYL979+5FWloaevfujTfffBOhoaHYsGEDRAQzZ87EoEGDULNmTa9kcbWOJ22Kql9mKX1jLOyc0OO5VlLmu2XLluoc2MZok5WVhTZt2uDOO+90+/fUrFmz0H4PHDigqbNjxw51bh9//HEkJycjPT29wDZFnTf/7bp3717Neikor5+fH8qWLauuBRuTyYRjx465XC6qNiWtX2bx3THqKYsvjFFPWXxhjHrK4gtj1FMWXxijnrL4whi9lcUdpeZC5/ldvnwZ/v7+BR7zpE5R9nv16lVs2LABDRo0gNlsRkhIiEMfV69eLfD3+Pn5Oa1TWL/ffvut+nMACAkJcfqi3BtZ3Kmjp36ZpXSNsaSea+6Msajb3KzOL7/84jDGgsbkahZnc2ffr32dbdu23XRub9amOPLe7D7f2VpYtmyZpu9Nmzbh3LlzWL58OaKjo3H33XdrysOHD8fXX3+tlkeMGOGVNiWtX2bx3THqKYsvjFFPWXxhjHrK4gtj1FMWXxijnrL4whi9lUX+/xe4TZ48GUlJSfjqq6+cPu8ukEfvr9Kh69evy6hRoyQ+Pl6MRqNkZWXJ9evXpVmzZhIWFiZGo1H27t0ro0aNkuDgYDEYDC7V8aSNK3V27twpTz/9tHpNKdtFkG3//vnnHzl+/Lj069dPvWCv/cc77I/drE5h/eb/pyiKXLhwQfPPm1kKq6Onfpml9I6xJJ1rJW2+nY2xsPl15fe40m9B83vy5EmX2xRF3oJ+r229OMtr+2ifs4/4OSsbDAZN2VttSlq/zOK7Y9RTFl8Yo56y+MIY9ZTFF8aopyy+MEY9ZfGFMXori8FgkNjYWHnkkUfk2LFjHu3llJpNqZEjR0pqaqrMmzdPAgICJCsrS0aOHCkxMTGSnp4uAQEBMnjwYElNTZUBAwaIwWBwqY4nbVyp061bN8nIyJBnn31WDAaDzJw5U1599VWxWq3qNUTsF02LFi0kOjpaIiIixM/PT2bOnCn169cXk8kkzzzzjAQEBDitU1i/06ZNkwYNGqgvnpy9wPJWlsLq6KlfZim9YyxJ51pJm29nY8w/Jk+zuNKvfR37B1NX2hRVXme3q/16cZY3/z8iIiIiotKm1GxKpaWlyerVq0VEJCgoSLKysiQtLU1mzpwpYWFhEhQUJMnJybJ69WrZvXu3AHCpjidtXKkTFxcn3333nVret2+f9O/fX+Lj46VWrVoSEBAgERER0rt3b0lMTBR/f3+1zdy5c8VoNMq+ffskKSlJXnrpJWnXrp0EBwc7rVNYv4899phkZGTIwIED1Q0y+xdY3spSWB099csspXeMJelcK2nz7WyMoaGhEhUVJfPmzfM4iyv92tdxtnlUWJuiyuvsdrVfL87ytmzZUuLj42XevHkicuMPMDk5OXLp0iUZOXKkQ9lW58yZM5ryrbYpaf0yi++OUU9ZfGGMesriC2PUUxZfGKOesvjCGPWUxRfG6K0sNvnruKvUbEr5+/tLdna2iPzfppS/v7+sWrVKrFarBAUFicVikezsbNm1a5e6eVRYHU/auFInMDBQDh06JLt27RJFUWTTpk2SlJQkn3zyiVitVvWboA4dOiRz584Vi8UiAQEBcujQITlw4IDaxmq1yvr168VqtUpCQoLTOoX1GxsbK999950cOHBA3SCzf4HlrSyF1dFTv8xSesdYks61kjbfzsZotVpl/Pjx0q5dO4+zuNKvfR1nm0eFtSmqvM5uV/v14iyvwWCQyZMnS7t27URExGAwyMmTJ+XMmTPqO6jyl211du/erSnfapuS1i+z+O4Y9ZTFF8aopyy+MEY9ZfGFMeopiy+MUU9ZfGGM3spik7+OuwwoJSpXroz169c7HJs2bRpq1aoFAEhPT8f69euxcOFCGI1Gl+p40saVOklJSTh48CAWLlyIkJAQLFiwAOfOncPvv/+OsLAwhISEID4+HgcPHsQdd9yBa9euwWq14uDBg/jiiy/g5+eHBQsWIDU1FQsWLEBYWBgqVarktE5h/Z47dw6pqan44osvYDAYcO7cOZw7dw7t27fHunXrvJqlsDp66pdZSu8YS9K5VtLm29kYU1NTERcXh3Xr1nmcxZV+7eucP38eqampuOOOO5CXl+dSm6LK6+x2tV8vzvKKCBo0aIB169YBAEQEiqIgMzMTERERDmVbnd9++01TvtU2Ja1fZvHdMeopiy+MUU9ZfGGMesriC2PUUxZfGKOesvjCGL2VxSZ/HbdJKbFkyRIJDQ2VsWPHSmBgoIwbN07atGkjAKRPnz4SGBgoPXr0ED8/PzEajWKxWFyq40kbV+rce++90rBhQ/Hz85OxY8eKv7+/eg2UCRMmSKtWraR58+by/vvvy/vvvy+RkZFiMpnEaDSKwWCQp556Svz9/cVoNKptVq1a5bROYf0qiiJms1kMBoNUqFBBnn/+ealWrZoMHDhQEhISvJqlsDp66pdZSu8YS9K5VtLm29kYx48fL506dZKEhASPs7jSr32dqKgoWbt2rbz//vtisVhcalNUeZ3drvbrJX9eAOLv7y8Gg0ECAgKcXpTS2T/7Ot5qU9L6ZRbfHaOesvjCGPWUxRfGqKcsvjBGPWXxhTHqKYsvjPFWs1gsFgkPD5fw8HD1+qj9+/f3aC9HERFBKbF+/XqMGjUKmZmZuHjxImrXro0HHngAX3/9tXosLS0NZrMZR44ccbmOJ21crTN8+HC0bdsWhw4dwmuvvYaYmBi89dZbWL16Ne69916ICK5du4bx48ejY8eO2LJlC9LT01G9enUcOnRIUwbgcMyVfs+cOYNjx45h0KBBOHXqFO69915cu3YNeXl5eO+991ClShWvZXG3jp76ZZbSM8aSfK7pfb5fffVVhzHmH8+gQYM8yuJKv/Z12rVrBxGBiOBf//oXZs2aVWib4srbsWNHh/WSP2+zZs2wfv16XL9+HYqioEGDBqhSpQpmzpyJxx9/HH/++SdiYmLUcnJyMk6cOAERwcyZMzFo0CBcvHhRLXvapqT1yyy+O0Y9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKO3stSsWVPdh/Hz80PZsmXRqFEjj/ZxICVcVlaW5OXlFXjMkzpF1a8z//zzj9Pj2dnZsmjRIsnMzHSo46yNK3UK69f+50WZpbA6euqXWYq33+LMIlJyzjVXfree5lvEcYzeyOJKv87qeNKmKPK6sl5s5VGjRskvv/yi1lm7dq1cvXr1pmVX6njSpqT1yyy+O0Y9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKO3snhDiX+nlNFoxPHjxxETEwMAeOihh7Bw4ULNMYPBgF9//RVVq1Z1uY4nbVytM3HiRERFRWHMmDH44IMPcPLkSezduxepqal49dVXUbZsWTz22GMwm82aOrt378Znn32GcePGIScnB/v27UNKSgpatWqFzMxM/P33307rFNZv/t+dkJCAJ598UjPHly9f9lqWwurs2bNHN/0yS+kdY0k61/bu3Vui5jv/PNjGmJubW+D8upLF2dzZ91vQ/F6+fBn+/v4utSmKvP369Sv0Pt/f398hr/36uHr16k3rOGvj5+fn9TYlrV9m8d0x6imLL4xRT1l8YYx6yuILY9RTFl8Yo56y+MIYvZUFAEJCQuA2r25x3QaKosjJkyfVclBQkMMxALJp0ya36njSxtU6WVlZMnLkSElNTZV58+ZJQECAZGVlyfXr16VLly5iNpvFaDTK4MGDJTU1Ve6//37x8/NTywMGDBCDwaD2ExMTI+np6RIQEOC0TmH97t27V0aNGiXh4eECQG3TrFkzCQsL82qWwuroqV9mKb1jLEnnWkmbb2djHDlypISFhUnfvn09zuJKv/Z1bOXg4GBNvwW1Kaq8zm5X+/XiLG9OTo7UrVtXPVbY5/3t63irTUnrl1l8d4x6yuILY9RTFl8Yo56y+MIY9ZTFF8aopyy+MEZvZbH/5wluSt2kjidt3NmUSktLk9WrV2uOjRw5UpKSkiQwMFACAgIkOTlZVq9eLZ999pkYDAa1vHv3bgGg9jNz5kwJCwuToKAgp3UK69f2Yuntt9/WtMn/AstbWQqro6d+maX0jrEknWslbb6djTEtLU1eeeUVadiwocdZXOnXvo6zzaPC2hRVXme3q/16cZa3f//+kpCQIOXLl5eAgABp0aKFREdHS0REhPj5+TmUZ86cKfXr1xeTySTPPPOM19qUtH6ZxXfHqKcsvjBGPWXxhTHqKYsvjFFPWXxhjHrK4gtj9FaWmTNnyuuvvy6JiYkyb948j/Z0SvymlMFgkFOnTqnloKAgh2MA5Oeff3arjidtXK1z4MAB8ff3l+zsbPWY7UXNf//7X7FarRIUFCQWi0Wys7PVFz628q5du9QXQv7+/rJq1SqHNvnrFNav7cWSfZv8L7C8laWwOnrql1lK7xhL0rlW0ubb2Rj9/f1l9erVDmN0J4sr/drXcbZ5VFibosrr7Ha1Xy/O8iYlJcmcOXMkLCxMgoODJS4uTr777juZO3euGI1Gh/K+ffskKSlJXnrpJWnXrp3X2pS0fpnFd8eopyy+MEY9ZfGFMeopiy+MUU9ZfGGMesriC2P0VpZ9+/aJiMjcuXOlXbt2Hu3pmFDCiQh69uwJi8UC4MZnHfPy8lCtWjUYDAa1Xrt27WA2mwEAOTk5EJEC6wBAkyZNEBkZ6XIbV+s0bNgQ/v7+ePDBB5GcnIzLly8DAI4ePYqdO3eiVq1a2L59O9LT07F+/XrUrl0bANTygQMHYDQaAQCVK1fGtGnTHNrkr1NYvydPnkR6ejrmzJmjaZOUlIRr165BURSvZSmsjp76ZZbSO8aSdK6VtPl2NsbKlSvjl19+cRijO1lc6de+jq2ck5OjmcuC2hRVXme3q/16cZb33LlziI+Px7Vr1xAWFoZz584hNTUVKSkpyMvLw/nz5zXlc+fO4dy5c2jfvj0mTpzotTYlrV9m8d0x6imLL4xRT1l8YYx6yuILY9RTFl8Yo56y+MIYvZkFAO644w489dRT8ESJ35Tq0aOHptytWzds2LBBcyw4OBhhYWFqOS0tDSdPnkRwcPBN69h+ZrVaXW7jah0ASE5Oxvr169XjixcvhtVqxcSJE/HVV1+hU6dOGDhwIAYMGIB69erBYDCgbt266NOnD3Jzc2EymbB48WJERkZi8eLF6NOnD7Zu3eq0TmH9RkVFoUePHvjxxx/h5+cHwPEFlreyFFZHT/0yS+kdY0k610rafDsb4/Dhw/Hwww+jTJkyOHXqlEdZXOnXvk5qaqrD5lFhbYoqr7Pb1X69ONvsSk1NxSeffIJatWrBYrEgMzMTBw8eRGZmJvz8/GC1WjXlBQsWIDU1FQsWLEBYWBgqVarklTYlrV9m8d0x6imLL4xRT1l8YYx6yuILY9RTFl8Yo56y+MIYvZVlwYIFqF+/Pr744gvNvodbPHp/FXnFunXrpHXr1hIdHS0BAQFSqVIlsVqtMnbsWAkMDJRx48ZJhw4dRFEUCQ0NlYCAAKlatarUqlVLbdOkSRN55513NP3Y1ymsXz8/PzEajdK7d2/1523atBEA0qdPH69mcaWOnvplltI5xpJ2rpWk+XY2xr59+4rJZCrwdxf2e1zp175Ojx491GMWi8WlNkWZ1/52tV8vzvI2aNBAjEajfPPNN7Jq1SoxmUxiNBrFYDDIU0895VD29/cXo9EoiqLIhAkTvNampPXLLL47Rj1l8YUx6imLL4xRT1l8YYx6yuILY9RTFl8Yo7ey+Pv7i8ViEYPBIBMmTPBoX4SbUsUsKytL8vLybvrzdevWSePGjSUqKkp9oTN79myHNvb9eNLvypUrNT/P/yLT9gLLW1kKq+NJm6Lql1lK7xjz0/u5tnLlyhI13/Zz0Lp1a4mIiBB/f3+Xx+Npv87q2G8eudKmKPIWtBYKypt/feRvk52dLYsWLZLMzEyXykXVpqT1yyy+O0Y9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKO3sniCm1LFzGAwaL6dr2vXrrJp0ybNixr7OoqiyI4dOzRtXKlTWL/t27eX48ePa/LZv8DyVpbC6uipX2YpvWMsSedaSZtvZ2P0RhZX+rWv42yjyJN+vZHXlfv8wjY6bf755x+3ykXVpqT1yyzF2y+zFG+/zFK8/TJL8fbLLMXbL7MUb7+lMYunuClVzBRF0bxAsX1jX/5jADQvfADIpk2bNG3s+3FWx91+nb3A8laWwuroqV9mKb1jLEnnWkmf765duxZJFlf6dbZ55I1+PZ0X+9vVlU3AEydOiIjI9evXZdSoURIfHy9Go1H27t0ro0aNkuDgYDEYDA7lrKwsuX79ujRr1kzCwsK81qak9cssvjtGPWXxhTHqKYsvjFFPWXxhjHrK4gtj1FMWXxijt7JkZWWJiMiwYcNkxowZ4on/+4o4um1uvG7RunTpUrH3u2LFiiLLQqQHPNeKz4oVK25bvyKimUtX2hRVXsDxdrW/7Z3ltX0D3xtvvIHZs2fj7bffhp+fH6ZOnYrZs2erX/JhX7a12b17N6KiorzWpqT1yyy+O0Y9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKO3sthUrVoVM2bMgEc82soijxkMBjl16pRavtlf2jdv3qwp//zzz5o29v04q+NKv/nLRZmlsDp66pdZSu8YS9K5VhLnu6jyu9Kv/Xw7ezebJ/3eal5Xb3v7vLa/OqWlpcnq1avV48nJybJ69WrZvXu3AHAoZ2VlSVpamsycOVPCwsK81qak9cssvjtGPWXxhTHqKYsvjFFPWXxhjHrK4gtj1FMWXxijt7LYnrPu3r1bwsLCxBMmULESEfTs2RMWiwUAcPnyZYgIevXqpR4DgCFDhiA8PFwtt2vXDmazGQCQk5MDEUG1atVgMBgKrFNYv/nLtiyKomgyeytLQXUAoEmTJoiMjLzt/TJL6R5jSTnXLl++jLy8vBI13+3bt8edd96pyZ///s7TLM7mxb5f+/l+8cUX8d1336ll+/teV/u91bzO5sXZesnfHoC6No4ePYr09HT1+MmTJ5Genq6+k8q+bGuTlJSEa9euQVEUr7Qpaf0yi++OUU9ZfGGMesriC2PUUxZfGKOesvjCGPWUxRfG6K0sNnl5ebh27Ro8wU2pYpb/rW8A0K1bN8yePRu//vqr5kXJ3r17YTLduHmsViv++ecfhIWFAQDS0tJw8uRJBAcHq/WDg4PVn9vq7N+/v8B+AeCnn35CTEwMACA3NxcAHF5geSNLYXVsP7Narbe9X2YpvWMs7JzQ07kWHx+Ps2fPlpj5Bm58TG3Lli3qGAMCArBz507151ar1e0sALB//351npz1a18HAH788UfEx8cDuLF5ZLFYCmxTVHltv982L/Hx8Th8+LDDerHfBGzYsCGaNGkCf39/PPjgg0hOTgYApKenY/369Thw4ACMRqNDGQAqV66MadOmoVatWti+fbtX2pS0fpnFd8eopyy+MEY9ZfGFMeopiy+MUU9ZfGGMesriC2P0VhabhQsXolatWvAEN6WK2axZs4qtba9evQr8+YYNGwAAd9xxh8Mxm/T0dM3PPc1CpAeFnRM2PNfcd7vmFrgxlwXNbVpaGgDH+Xc23/ZuZ97Q0FDUrl0b69evB3DjL1B169ZFnz59kJubC5PJ5FBevHgxIiMjsXjxYvTp0wdbt271SpuS1i+z+O4Y9ZTFF8aopyy+MEY9ZfGFMeopiy+MUU9ZfGGM3sqyePFi/P7775g7dy6+/PJLt5432yj//3oWREREpEPr16/HqFGjkJmZiYsXLyItLQ1msxlHjhxxWq5duzYeeOABfP31115tU9L6ZRbfHaOesvjCGPWUxRfGqKcsvjBGPWXxhTHqKYsvjNFbWWrXro3hw4ejbdu2nj3Z9ehKVERERFRksrKyJC8vz+VyUbUpaf0yi++OUU9ZfGGMesriC2PUUxZfGKOesvjCGPWUxRfG6K0s3sRNKSIiIp0xGAyab+hTFEV27Nhx03LXrl2LpE1J65dZfHeMesriC2PUUxZfGKOesvjCGPWUxRfGqKcsvjBGb2Xp2rWrnDhxQrzBUPh7qYiIiKg4id0n60UEly5duml5xYoVRdKmpPXLLL47Rj1l8YUx6imLL4xRT1l8YYx6yuILY9RTFl8Yo7eyrFixQvOtfLeCm1JERERERERERFTsuClFRESkM4qiQFEUzTGDwVBguajalLR+maV4+2WW4u2XWYq3X2Yp3n6ZpXj7ZZbi7bc0ZrEve4rfvkdERKQzBoMB7dq1g8ViAQB8/vnniIqKgtlsBgAcP34cfn5+iIyMBACcOHECIoLY2Fj1ScTx48dvuU1J65dZfHeMesriC2PUUxZfGKOesvjCGPWUxRfGqKcsvjBGb2U5ceIE2rRpA6vVivwWL14Md5ncbkFERERFqkePHppyenq6phwcHAwA6hOBtLQ0nDx5Uj1uqxMWFnZLbUpav8ziu2PUUxZfGKOesvjCGPWUxRfGqKcsvjBGPWXxhTF6K0taWhri4+PhDXynFBERERERERERFTtD4VWIiIiIiIiIiIi8i5tSRERERERERERU7LgpRURERERERERExY6bUkREREREREREVOy4KUVERERERERERMWOm1JERERERERERFTsuClFREREdBM9e/aEoigO//bv33/Lfc+ePRthYWG3HpKIiIiohDLd7gBEREREenb33Xdj1qxZmmPR0dG3KY1z165dg9lsvt0xiIiIiNzCd0oRERERFcBisaBMmTKaf0ajEUuXLkXt2rXh7++P1NRUjBw5EtevX1fbjR8/HtWqVYPVakVSUhL69++PixcvAgDWrl2LXr164cKFC+q7r1577TUAgKIoWLJkiSZDWFgYZs+eDQDIzs6GoiiYP38+mjVrBn9/f3z88ccAgBkzZiAjIwP+/v6oVKkSpk6dqvZx9epVDBgwAHFxcfD390dKSgrefPPNops4IiIiokLwnVJEREREblq/fj26d++OiRMn4s4770RWVhaeeOIJAMCIESMAAAaDARMnTkS5cuVw4MAB9O/fH0OGDMHUqVPRuHFjTJgwAcOHD8fvv/8OAAgKCnIrw9ChQ/Huu++iVq1a6sbU8OHDMXnyZNSqVQvbtm1Dv379YLVa0aNHD0ycOBHLli3DggULkJycjD/++AN//PGHdyeGiIiIyA3clCIiIiIqwJdffqnZMGrXrh3+/PNPDB06FD169AAApKam4vXXX8eQIUPUTanBgwerbcqWLYvRo0fjySefxNSpU+Hn54fQ0FAoioIyZcp4lGvw4MHo1KmTWh4xYgTeffdd9Vi5cuXw22+/4cMPP0SPHj1w+PBhlC9fHnfccQcURUFKSopHv5eIiIjIW7gpRURERFSAFi1aYNq0aWrZarWievXq+OGHH/DGG2+ox3Nzc3H58mVcunQJgYGBWL16Nd58803s2bMHf/31F65fv675+a2qW7eu+v85OTnIyspCnz590K9fP/X49evXERoaCuDGRdvbtGmDihUr4u6770aHDh3Qtm3bW85BRERE5CluShEREREVwGq1Ij09XXPs4sWLGDlypOadSjb+/v7Izs5Ghw4d8NRTT+GNN95AREQENmzYgD59+uDq1asFbkopigIR0Ry7du2a01z58wDA9OnT0aBBA009o9EIAKhduzYOHjyIr776CqtXr0bXrl3RunVrLFy4sJAZICIiIioa3JQiIiIiclPt2rXx+++/O2xW2WzZsgV5eXl49913YTDc+F6ZBQsWaOr4+fkhNzfXoW10dDSOHz+ulvft24dLly4VmCc2Nhbx8fE4cOAAHnvssZvWCwkJwUMPPYSHHnoInTt3xt13341z584hIiKiwP6JiIiIigI3pYiIiIjcNHz4cHTo0AHJycno3LkzDAYDMjMzsXPnTowePRrp6em4du0aJk2ahHvvvRc//PADPvjgA00fZcuWxcWLF7FmzRrUqFEDgYGBCAwMRMuWLTF58mQ0atQIubm5ePHFF2E2mwvNNHLkSDzzzDMIDQ3F3XffjStXruCXX37Bn3/+ieeeew7jx49HXFwcatWqBYPBgP/9738oU6YMwsLCimiWiIiIiApmuN0BiIiIiEqau+66C19++SW++eYb1KtXDw0bNsR7772nXjy8Ro0aGD9+PN566y1UrVoVH3/8Md58801NH40bN8aTTz6Jhx56CNHR0Xj77bcBAO+++y6SkpJw55134tFHH8ULL7zg0jWo+vbtixkzZmDWrFmoVq0amjVrhtmzZ6NcuXIAgODgYLz99tuoW7cu6tWrh+zsbKxYsUJ9JxcRERFRcVPE/qIFRERERERERERERYx/GiMiIiIiIiIiomLHTSkiIiIiIiIiIip23JQiIiIiIiIiIqJix00pIiIiIiIiIiIqdtyUIiIiIiIiIiKiYsdNKSIiIiIiIiIiKnbclCIiIiIiIiIiomLHTSkiIiIiIiIiIip23JQiIiIiIiIiIqJix00pIiIiIiIiIiIqdtyUIiIiIiIiIiKiYsdNKSIiIiIiIiIiKnbclCIiIiIiIiIiomLHTSkiIiIiIiIiIip23JQiIiIiIiIiIqJix00pIiIiIiIiIiIqdtyUIiIiIiIiIiKiYsdNKSIiIioyPXv2RNmyZYus/9mzZ0NRFGRnZxfZ78jOzoaiKJg9e3aR/Q4iIiIiX8RNKSIiInLKtuFj++fv748KFSpgwIABOHny5O2Op1snT57ECy+8gEqVKiEwMBBWqxV16tTB6NGjcf78+dsdr8RbsWIFXnvtNZfrT58+Hc2aNUNsbCwsFgvKlSuHXr16FelGJhEREblGERG53SGIiIhIf2bPno1evXph1KhRKFeuHC5fvowNGzbgo48+QkpKCnbu3InAwMAC+7h27Rry8vJgsViKJGNubi6uXbsGi8UCRVGK5HdkZ2ejXLlymDVrFnr27Flg3Z9//hnt27fHxYsX0a1bN9SpUwcA8Msvv+Czzz5D48aN8c033xRJTl8xYMAATJkyBa4+he3fvz8uXbqEatWqITw8HAcPHsT06dORm5uLzMxMxMfHF3FiIiIiuhnT7Q5ARERE+tauXTvUrVsXANC3b19ERkZi/PjxWLp0KR555BGnbXJycmC1WmE2m4s0m9FohNFoLNLf4arz58/jgQcegNFoxLZt21CpUiXNz9944w1Mnz79NqXzXVOnTnU41rFjR9StWxdz587F0KFDb0MqIiIiAvjxPSIiInJTy5YtAQAHDx4EcOO6UUFBQcjKykL79u0RHByMxx57TP1Z/mtK2a7P9M477+A///kP0tLSYLFYUK9ePfz8888Ov2vPnj3o2rUroqOjERAQgIoVK+KVV15Rf+7smlJly5ZFhw4d8M0336BmzZrw9/dH5cqVsXjxYk3f586dwwsvvIBq1aohKCgIISEhaNeuHTIzMz2alw8//BBHjx7F+PHjHTakACA2NhbDhg3THJs6dSqqVKkCi8WC+Ph4PP300w4f8WvevDmqVq2KX3/9Fc2aNUNgYCDS09OxcOFCAMD333+PBg0aqPOzevVqTfvXXnsNiqKocxkSEoLIyEgMGjQIly9f1tS9fv06Xn/9dfV2KVu2LF5++WVcuXJFU882xxs2bED9+vXh7++P1NRUzJ0712Hc58+fx+DBg5GUlASLxYL09HS89dZbyMvLU+u4ui569uyJKVOmAIDmo6Xusq1JfpySiIjo9uKmFBEREbklKysLABAZGakeu379Ou666y7ExMTgnXfewYMPPlhgH5988gnGjRuHf/3rXxg9ejSys7PRqVMnXLt2Ta3z66+/okGDBvj222/Rr18/vP/+++jYsSO++OKLQjPu27cPDz30ENq1a4c333wTJpMJXbp0wapVq9Q6Bw4cwJIlS9ChQweMHz8e//73v7Fjxw40a9YMx44dc3dasGzZMgQEBKBz584u1X/ttdfw9NNPIz4+Hu+++y4efPBBfPjhh2jbtq1mHgDgzz//RIcOHdCgQQO8/fbbsFgsePjhhzF//nw8/PDDaN++PcaOHYucnBx07twZf//9t8Pv69q1Ky5fvow333wT7du3x8SJE/HEE09o6vTt2xfDhw9H7dq18d5776FZs2Z488038fDDDzv0t3//fnTu3Blt2rTBu+++i/DwcPTs2RO7du1S61y6dAnNmjXDvHnz0L17d0ycOBFNmjTBSy+9hOeee86hz8LWxb/+9S+0adMGAPDRRx+p/1xx9uxZnDp1Cr/88gt69eoFAGjVqpVLbYmIiKiICBEREZETs2bNEgCyevVqOX36tPzxxx/y2WefSWRkpAQEBMiRI0dERKRHjx4CQIYOHerQR48ePSQlJUUtHzx4UABIZGSknDt3Tj2+dOlSASBffPGFeqxp06YSHBwshw4d0vSZl5fnkPHgwYPqsZSUFAEgixYtUo9duHBB4uLipFatWuqxy5cvS25urqbvgwcPisVikVGjRjlknjVrVoHzFR4eLjVq1Ciwjs2pU6fEz89P2rZtq8kwefJkASAzZ85UjzVr1kwAyCeffKIe27NnjwAQg8EgP/30k3p85cqVDllHjBghAOS+++7TZOjfv78AkMzMTBER2b59uwCQvn37auq98MILAkC+/fZb9ZhtjtetW6cZk8Vikeeff1499vrrr4vVapW9e/dq+hw6dKgYjUY5fPiwiLi3Lp5++mnx5CmsxWIRAOrvmThxott9EBERkXfxnVJERERUoNatWyM6OhpJSUl4+OGHERQUhM8//xwJCQmaek899ZTLfT700EMIDw9Xy3feeSeAG+9eAoDTp09j3bp16N27N5KTkzVtXfm4Vnx8PB544AG1HBISgu7du2Pbtm04ceIEAMBiscBguPFUKDc3F2fPnkVQUBAqVqyIrVu3ujwWm7/++gvBwcEu1V29ejWuXr2KwYMHqxkAoF+/fggJCcHy5cs19YOCgjTvVqpYsSLCwsKQkZGBBg0aqMdt/2+bx/yefvppTXngwIEAbnybXf7/2r+D6fnnnwcAh0yVK1dWbzcAiI6ORsWKFTW/+3//+x/uvPNOhIeH48yZM+q/1q1bIzc3F+vWrdP0Wdi6uBVfffUVVqxYgXfffRfJycnIycm55T6JiIjo1vBC50RERFSgKVOmoEKFCjCZTIiNjUXFihU1GykAYDKZkJiY6HKf9htNto2IP//8E8D/bUJUrVrVo8zp6ekOm1cVKlQAcOP6RWXKlEFeXh7ef/99TJ06FQcPHkRubq5aN/9HE10VEhLi9GNzzhw6dAjAjc2l/Pz8/JCamqr+3CYxMdFhPKGhoUhKSnI4BvzfPOZXvnx5TTktLQ0Gg0G9HtehQ4dgMBiQnp6uqVemTBmEhYU5ZLK/DYEbt2P+371v3z78+uuviI6OdqgLAKdOnSqwT/t1cStatGgB4MaF+++//35UrVoVQUFBGDBgwC33TURERJ7hphQREREVqH79+uq3791M/ncdueJm35gnIm5luxVjxozBq6++it69e+P1119HREQEDAYDBg8erLkIt6sqVaqE7du34+rVq/Dz8/Nq1pvN163M483ecebqhcNd+d15eXlo06YNhgwZ4rSubaPQnT69IS0tDbVq1cLHH3/MTSkiIqLbiJtSREREpDupqakAgJ07d3rUfv/+/RARzQbL3r17AfzfN68tXLgQLVq0wH//+19N2/PnzyMqKsrt33nvvffixx9/xKJFi/DII48UWDclJQUA8Pvvv6tjBYCrV6/i4MGDaN26tdu/vzD79u1DuXLl1PL+/fuRl5enzkdKSgry8vKwb98+ZGRkqPVOnjyJ8+fPq5ndkZaWhosXL3p1PJ58254z//zzj8O3ChIREVHx4jWliIiISHeio6PRtGlTzJw5E4cPH9b8zJV3zRw7dgyff/65Wv7rr78wd+5c1KxZE2XKlAFw41059n3973//w9GjRz3K/OSTTyIuLg7PP/+8ugGW36lTpzB69GgAN67T5efnh4kTJ2oy/Pe//8WFCxdwzz33eJShIFOmTNGUJ02aBODGx9kAoH379gCACRMmaOqNHz8eADzK1LVrV/z4449YuXKlw8/Onz+P69evu92n1WpV2xfm+vXrTj/6t3nzZuzYsaPQdwASERFR0eI7pYiIiEiXJk6ciDvuuAO1a9fGE088gXLlyiE7OxvLly/H9u3bC2xboUIF9OnTBz///DNiY2Mxc+ZMnDx5ErNmzVLrdOjQAaNGjUKvXr3QuHFj7NixAx9//LHmnUvuCA8Px+eff4727dujZs2a6NatG+rUqQMA2Lp1Kz799FM0atQIwI1Nt5deegkjR47E3Xffjfvuuw+///47pk6dinr16qFbt24eZSjIwYMHcd999+Huu+/Gjz/+iHnz5uHRRx9FjRo1AAA1atRAjx498J///Afnz59Hs2bNsHnzZsyZMwcdO3ZUr8nkjn//+99YtmwZOnTogJ49e6JOnTrIycnBjh07sHDhQmRnZ7v9rjTbnD7zzDO46667YDQaNReBz+/ixYtISkrCQw89hCpVqsBqtWLHjh2YNWsWQkND8eqrr7o9JiIiIvIebkoRERGRLtWoUQM//fQTXn31VUybNg2XL19GSkoKunbtWmjb8uXLY9KkSfj3v/+N33//HeXKlcP8+fNx1113qXVefvll5OTk4JNPPsH8+fNRu3ZtLF++HEOHDvU4c4MGDbBz506MGzcOy5cvx0cffQSDwYCMjAwMHTpUc/2i1157DdHR0Zg8eTKeffZZRERE4IknnsCYMWNgNps9znAz8+fPx/DhwzF06FCYTCYMGDAA48aN09SZMWMGUlNTMXv2bHz++ecoU6YMXnrpJYwYMcKj3xkYGIjvv/8eY8aMwf/+9z/MnTsXISEhqFChAkaOHKlemN0dnTp1wsCBA/HZZ59h3rx5EJGbbkoFBgaib9+++O6777Bw4UL8888/iI+PxyOPPIJhw4apH10kIiKi20OR4ryiKBEREVERK1u2LKpWrYovv/zydkfRhddeew0jR47E6dOnPbpWFhEREVFR4TWliIiIiIiIiIio2HFTioiIiIiIiIiIih03pYiIiIiIiIiIqNjxmlJERERERERERFTs+E4pIiIiIiIiIiIqdtyUIiIiIiIiIiKiYme63QGKQrN7xzkcmzVtvKb8dMvumrLpv5cc2hz8IlVTTvzmT4c6e3uFacp16u3TlH89EefQ5rkqazTlz55pr81y8ZpDmzw/o6b8d7LFoU5OnKItV7miKZuPOrZJnX9eUw6actqhjr3fviqvKV9Kue5QJ2yHdmldDdH+/EqEk0+NGgv/JGncOm2dk/W18xK9Pc+hTZ5JOy952iYIPOmY/1S/fzTloGXBDnWsx7W3U/YD2o4rTb3g0OZ4iwhNWVo5ril/szZPaHvtmvpjRGOHNmENT2nKF36I0ZRjtjiuqcOP5mp/z0/+DnXMOdr5DjmoXVN+u/9waLN7bIr2gN3tmhx/1qHNkZPhmnKTtAMOdX7I0p6P5aZrb9esLmaHNgc6/UdTTl3V26GOcl7bLmqbtt8L7XMc2pi2BWnb7NDeZn+0cdzvDzymPRb8h+N6P9FYu34jt2vXVKjd/AMA7JZ8lXE7NOWN0+o6NLnY7m9NeUT15Q51pr3QWVO+FKM9p89VczzX4tZry8fvdKgCCdHOVcRPfppy7PeO90Hna0ZqysbL2rn7O9HupHYidtomh2Mnn26g/T3VtOdJhVmO582h9oGacp5ZmyXguHb9AMDFOpc15ZCfHc81o91Na38/dTXMoQnMF+3aOPkzk/WUNt/xltr5DwjTZgMA/++093dRmdrHRxnpeA4Hma9qyjmvxDvUyeqsfQwSP202c4RjljKfaOfqeCPtxFwP0d6PAYA1W1vnah3Hc7hxykFNOfO0Nu+Qit84tPn0eH1NedcRx8f34B8DNOVr2rsKxG7RzhMAZHfQnluJa7TnVq6f4w17rJV27soucTwfr4Zo5yHXol2bEWu0cwAAZ+4qpymfq6z9uekfx/VdvdVeTfmuqJ0Odeb3vtsum/Z+1++C47wc6GR3rsU63v/JNe0YI2L/0pQv7NY+5gJAwAntGBLW2rWpYHejATjZUFuO+NXxNonapn3MP9YsVFO+WMtxfb/baIGmPPr3ezTlvK+0930A8GdN7f1SwGHHx76yE7SPA6e7VtOUzzVzzBK0XXuuJd6X7VDn96OxmrLFX5ulfsJhhzan7tOe92fapWvKf6c4rqlrwdr1bbrkWEfsboKYrdr7Av8zjmtqX3ftuRa9UVv+q5zj7wk8qS1fvNPx/sTvV6umHN7shKZ8bI/2ORkABBzXDuB6nYsOdXLt1nfedW0+v4OOjyURu7X3BVdCtL8nLMvxPAoecURTvvqQ4zwc76R9Dna+rnZ+K73nOC/Z92uf26V8eV5T/uNVx9+Tt0173pjrnHes82OYpmyy+9U5CQ5NELFbu6bOZThZd1Ha9RyxRXtuXdfeJQEAhj35iaY8Zo/2vk752vE+yP9e7XP2k2dCHOoYTmrPm8r1tPfXRz7V3h4AoNg9HAac064FZ8/JAuyem8bf7XgO79uVqCmH/q5tE/G747l29YVzmnLQy46Td7iddtx179mlKf/+p+N5c+q0to1/lnaeon51fE5w5H67+4ZsP4c69vcnBrshBTRyfN7zYMp2TXn+f1tpytbjjo/Lobu1jxN7e4Q51ElapX2edi5Duw7/rlD4856AU46vMR58frWm/OG6lppy9CbHx7UztbX9KJHa+4+8vxznMvvJFxyOuYLvlCIiIiIiIiIiomLHTSkiIiIiIiIiIip23JQiIiIiIiIiIqJix00pIiIiIiIiIiIqdtyUIiIiIiIiIiKiYsdNKSIiIiIiIiIiKnbclCIiIiIiIiIiomLHTSkiIiIiIiIiIip23JQiIiIiIiIiIqJix00pIiIiIiIiIiIqdtyUIiIiIiIiIiKiYsdNKSIiIiIiIiIiKnbclCIiIiIiIiIiomLn0abU9evXsXr1anz44Yf4+++/AQDHjh3DxYsXvRqOiIiIiIiIiIhKJ5O7DQ4dOoS7774bhw8fxpUrV9CmTRsEBwfjrbfewpUrV/DBBx8URU4iIiIiIiIiIipF3H6n1KBBg1C3bl38+eefCAgIUI8/8MADWLNmjVfDERERERERERFR6eT2O6XWr1+PjRs3ws/PT3O8bNmyOHr0qNeCERERERERERFR6eX2O6Xy8vKQm5vrcPzIkSMIDg72SigiIiIiIiIiIird3N6Uatu2LSZMmKCWFUXBxYsXMWLECLRv396b2YiIiIiIiIiIqJRy++N77777Lu666y5UrlwZly9fxqOPPop9+/YhKioKn376aVFkJCIiIiIiIiKiUsbtTanExERkZmbis88+w6+//oqLFy+iT58+eOyxxzQXPiciIiIiIiIiIroZtzelAMBkMqFbt27ezkJERERERERERD7C7U2puXPnFvjz7t27exyGiIiIiIiIiIh8g9ubUoMGDdKUr127hkuXLsHPzw+BgYHclCIiIiIiIiIiokK5/e17f/75p+bfxYsX8fvvv+OOO+7ghc6JiIiIiIiIiMglbm9KOVO+fHmMHTvW4V1UREREREREREREznhlUwq4cfHzY8eOeas7IiIiIiIiIiIqxdy+ptSyZcs0ZRHB8ePHMXnyZDRp0sRrwYiIiIiIiIiIqPRye1OqY8eOmrKiKIiOjkbLli3x7rvveisXERERERERERGVYm5vSuXl5RVFDiIiIiIiIiIi8iFeu6YUERERERERERGRq1x6p9Rzzz3ncofjx4/3OAwREREREREREfkGlzaltm3b5lJniqLcUhgiIiIiIiIiIvINLm1Kfffdd0Wdg4iIiIiIiIiIfAivKUVERERERERERMXO7W/fA4BffvkFCxYswOHDh3H16lXNzxYvXuyVYEREREREREREVHq5/U6pzz77DI0bN8bu3bvx+eef49q1a9i1axe+/fZbhIaGFkVGIiIiIiIiIiIqZdzelBozZgzee+89fPHFF/Dz88P777+PPXv2oGvXrkhOTi6KjEREREREREREVMq4vSmVlZWFe+65BwDg5+eHnJwcKIqCZ599Fv/5z3+8HpCIiIiIiIiIiEoftzelwsPD8ffffwMAEhISsHPnTgDA+fPncenSJe+mIyIiIiIiIiKiUsntC503bdoUq1atQrVq1dClSxcMGjQI3377LVatWoVWrVoVRUYiIiIiIiIiIiplXN6U2rlzJ6pWrYrJkyfj8uXLAIBXXnkFZrMZGzduxIMPPohhw4YVWVAiIiIiIiIiIio9XN6Uql69OurVq4e+ffvi4YcfBgAYDAYMHTq0yMIREREREREREVHp5PI1pb7//ntUqVIFzz//POLi4tCjRw+sX7++KLMREREREREREVEp5fKm1J133omZM2fi+PHjmDRpErKzs9GsWTNUqFABb731Fk6cOFGUOYmIiIiIiIiIqBRx+9v3rFYrevXqhe+//x579+5Fly5dMGXKFCQnJ+O+++4rioxERERERERERFTKuL0plV96ejpefvllDBs2DMHBwVi+fLm3chERERERERERUSnm8oXO7a1btw4zZ87EokWLYDAY0LVrV/Tp08eb2YiIiIiIiIiIqJRya1Pq2LFjmD17NmbPno39+/ejcePGmDhxIrp27Qqr1VpUGYmIiIiIiIiIqJRxeVOqXbt2WL16NaKiotC9e3f07t0bFStWLMpsRERERERERERUSrm8KWU2m7Fw4UJ06NABRqOxKDMREREREREREVEp5/Km1LJly4oyBxERERERERER+ZBb+vY9IiIiIiIiIiIiT3BTioiIiIiIiIiIih03pYiIiIiIiIiIqNi5vSm1bt06XL9+3eH49evXsW7dOq+EIiIiIiIiIiKi0s3tTakWLVrg3LlzDscvXLiAFi1aeCUUERERERERERGVbm5vSokIFEVxOH727FlYrVavhCIiIiIiIiIiotLN5GrFTp06AQAURUHPnj1hsVjUn+Xm5uLXX39F48aNvZ+QiIiIiIiIiIhKHZc3pUJDQwHceKdUcHAwAgIC1J/5+fmhYcOG6Nevn/cTEhERERERERFRqePyptSsWbMAAGXLlsULL7zAj+oREREREREREZHHXN6UshkxYkRR5CAiIiIiIiIiIh/i9oXOT548iccffxzx8fEwmUwwGo2af0RERERERERERIVx+51SPXv2xOHDh/Hqq68iLi7O6TfxERERERERERERFcTtTakNGzZg/fr1qFmzZhHEISIiIiIiIiIiX+D2x/eSkpIgIkWRhYiIiIiIiIiIfITbm1ITJkzA0KFDkZ2dXQRxiIiIiIiIiIjIF7j98b2HHnoIly5dQlpaGgIDA2E2mzU/P3funNfCERERERERERFR6eT2ptSECROKIAYREREREREREfkStzelevToURQ5iIiIiIiIiIjIh7h9TSkAyMrKwrBhw/DII4/g1KlTAICvvvoKu3bt8mo4IiIiIiIiIiIqndzelPr+++9RrVo1bNq0CYsXL8bFixcBAJmZmRgxYoRbfZ05cwZvv/02HnjgATRq1AiNGjXCAw88gHHjxuH06dPuRiMiIiIiIiIiohLC7U2poUOHYvTo0Vi1ahX8/PzU4y1btsRPP/3kcj8///wzKlSogIkTJyI0NBRNmzZF06ZNERoaiokTJ6JSpUr45ZdfCu3nypUr+OuvvzT/8nKvuzssIiIiIiIiIiIqRm5fU2rHjh345JNPHI7HxMTgzJkzLvczcOBAdOnSBR988AEURdH8TETw5JNPYuDAgfjxxx8L7OfNN9/EyJEjNceSy7dG2YptXc5CRERERERERETFy+13SoWFheH48eMOx7dt24aEhASX+8nMzMSzzz7rsCEFAIqi4Nlnn8X27dsL7eell17ChQsXNP+S01u6nIOIiIiIiIiIiIqf25tSDz/8MF588UWcOHECiqIgLy8PP/zwA1544QV0797d5X7KlCmDzZs33/TnmzdvRmxsbKH9WCwWhISEaP4ZjG6/AYyIiIiIiIiIiIqR27s3Y8aMwdNPP42kpCTk5uaicuXKyM3NxaOPPophw4a53M8LL7yAJ554Alu2bEGrVq3UDaiTJ09izZo1mD59Ot555x134xERERERERERUQng9qaUn58fpk+fjldffRU7d+7ExYsXUatWLZQvX96tfp5++mlERUXhvffew9SpU5GbmwsAMBqNqFOnDmbPno2uXbu6G4+IiIiIiIiIiEoAjz/nlpycjOTk5Fv65Q899BAeeughXLt2Tb1IelRUFMxm8y31S0RERERERERE+ub2plRubi5mz56NNWvW4NSpU8jLy9P8/Ntvv3U7hNlsRlxcnNvtiIiIiIiIiIioZHJ7U2rQoEGYPXs27rnnHlStWtXpt+cREREREREREREVxO1Nqc8++wwLFixA+/btiyIPERERERERERH5AIO7Dfz8/JCenl4UWYiIiIiIiIiIyEe4vSn1/PPP4/3334eIFEUeIiIiIiIiIiLyAW5/fG/Dhg347rvv8NVXX6FKlSoO35S3ePFir4UjIiIiIiIiIqLSye1NqbCwMDzwwANFkYWIiIiIiIiIiHyE25tSs2bNKoocRERERERERETkQ9zelLI5ffo0fv/9dwBAxYoVER0d7bVQRERERERERERUurl9ofOcnBz07t0bcXFxaNq0KZo2bYr4+Hj06dMHly5dKoqMRERERERERERUyri9KfXcc8/h+++/xxdffIHz58/j/PnzWLp0Kb7//ns8//zzRZGRiIiIiIiIiIhKGbc/vrdo0SIsXLgQzZs3V4+1b98eAQEB6Nq1K6ZNm+bNfEREREREREREVAq5/U6pS5cuITY21uF4TEwMP75HREREREREREQucXtTqlGjRhgxYgQuX76sHvvnn38wcuRINGrUyKvhiIiIiIiIiIiodHL743vvv/8+7rrrLiQmJqJGjRoAgMzMTPj7+2PlypVeD0hERERERERERKWP25tSVatWxb59+/Dxxx9jz549AIBHHnkEjz32GAICArwekIiIiIiIiIiISh+3N6UAIDAwEP369fN2FiIiIiIiIiIi8hEebUr9/vvvmDRpEnbv3g0AyMjIwIABA1CpUiWvhiMiIiIiIiIiotLJ7QudL1q0CFWrVsWWLVtQo0YN1KhRA1u3bkW1atWwaNGioshIRERERERERESljNvvlBoyZAheeukljBo1SnN8xIgRGDJkCB588EGvhSMiIiIiIiIiotLJ7XdKHT9+HN27d3c43q1bNxw/ftwroYiIiIiIiIiIqHRze1OqefPmWL9+vcPxDRs24M477/RKKCIiIiIiIiIiKt3c/vjefffdhxdffBFbtmxBw4YNAQA//fQT/ve//2HkyJFYtmyZpi4REREREREREZE9tzel+vfvDwCYOnUqpk6d6vRnAKAoCnJzc28xHhERERERERERlUZub0rl5eUVRQ4iIiIiIiIiIvIhbl9TioiIiIiIiIiI6Fa5/U4pAPj555/x3Xff4dSpUw7vnBo/frxXghERERERERERUenl9qbUmDFjMGzYMFSsWBGxsbFQFEX9Wf7/JyIiIiIiIiIiuhm3N6Xef/99zJw5Ez179iyCOERERERERERE5AvcvqaUwWBAkyZNiiILERERERERERH5CLc3pZ599llMmTKlKLIQEREREREREZGPcPvjey+88ALuuecepKWloXLlyjCbzZqfL1682GvhiIiIiIiIiIiodHJ7U+qZZ57Bd999hxYtWiAyMpIXNyciIiIiIiIiIre5vSk1Z84cLFq0CPfcc09R5CEiIiIiIiIiIh/g9jWlIiIikJaWVhRZiIiIiIiIiIjIR7i9KfXaa69hxIgRuHTpUlHkISIiIiIiIiIiH+D2x/cmTpyIrKwsxMbGomzZsg4XOt+6davXwhERERERERERUenk9qZUx44diyAGERERERERERH5Erc3pUaMGFEUOYiIiIiIiIiIyIe4vSlls2XLFuzevRsAUKVKFdSqVctroYiIiIiIiIiIqHRze1Pq1KlTePjhh7F27VqEhYUBAM6fP48WLVrgs88+Q3R0tLczEhERERERERFRKeP2t+8NHDgQf//9N3bt2oVz587h3Llz2LlzJ/766y8888wzRZGRiIiIiIiIiIhKGbffKfX1119j9erVyMjIUI9VrlwZU6ZMQdu2bb0ajoiIiIiIiIiISie33ymVl5cHs9nscNxsNiMvL88roYiIiIiIiIiIqHRze1OqZcuWGDRoEI4dO6YeO3r0KJ599lm0atXKq+GIiIiIiIiIiKh0cntTavLkyfjrr79QtmxZpKWlIS0tDeXKlcNff/2FSZMmFUVGIiIiIiIiIiIqZdy+plRSUhK2bt2K1atXY8+ePQCAjIwMtG7d2uvhiIiIiIiIiIiodHJ7UwoAFEVBmzZt0KZNG2/nISIiIiIiIiIiH+Dyx/e+/fZbVK5cGX/99ZfDzy5cuIAqVapg/fr1Xg1HRERERERERESlk8ubUhMmTEC/fv0QEhLi8LPQ0FD861//wvjx470ajoiIiIiIiIiISieXN6UyMzNx99133/Tnbdu2xZYtW7wSioiIiIiIiIiISjeXN6VOnjwJs9l805+bTCacPn3aK6GIiIiIiIiIiKh0c3lTKiEhATt37rzpz3/99VfExcV5JRQREREREREREZVuLm9KtW/fHq+++iouX77s8LN//vkHI0aMQIcOHbwajoiIiIiIiIiISieTqxWHDRuGxYsXo0KFChgwYAAqVqwIANizZw+mTJmC3NxcvPLKK0UWlIiIiIiIiIiISg+XN6ViY2OxceNGPPXUU3jppZcgIgAARVFw1113YcqUKYiNjS2yoEREREREREREVHq4vCkFACkpKVixYgX+/PNP7N+/HyKC8uXLIzw8vKjyERERERERERFRKeTWppRNeHg46tWr5+0sRERERERERETkI1y+0DkREREREREREZG3cFOKiIiIiIiIiIiKHTeliIiIiIiIiIio2HFTioiIiIiIiIiIih03pYiIiIiIiIiIqNhxU4qIiIiIiIiIiIodN6WIiIiIiIiIiKjYcVOKiIiIiIiIiIiKHTeliIiIiIiIiIio+EkpdfnyZRkxYoRcvnz5psc8qaOnfpnFd8eopyy+MEY9ZfGFMeopiy+MUU9ZfGGMesriC2PUUxZfGKOesvjCGPWUxRfGqKcsvjBGPWXxhTF6K4snSu2m1IULFwSAXLhw4abHPKmjp36ZxXfHqKcsvjBGPWXxhTHqKYsvjFFPWXxhjHrK4gtj1FMWXxijnrL4whj1lMUXxqinLL4wRj1l8YUxeiuLJ/jxPSIiIiIiIiIiKnbclCIiIiIiIiIiomLHTSkiIiIiIiIiIip2pXZTymKxYMSIEbBYLDc95kkdPfXLLL47Rj1l8YUx6imLL4xRT1l8YYx6yuILY9RTFl8Yo56y+MIY9ZTFF8aopyy+MEY9ZfGFMeopiy+M0VtZPKGIiHjcmoiIiIiIiIiIyAOl9p1SRERERERERESkX9yUIiIiIiIiIiKiYsdNKSIiIiIiIiIiKnbclCIiIiIiIiIiomLHTSkiIiIiIiIiIip2ptsdQO9EBIqieLXPq1evYsmSJfjxxx9x4sQJAECZMmXQuHFj3H///fDz8wMAHDlyBGFhYQgKCsLZs2fx66+/okaNGggODsZXX32F3bt348qVK+jSpQsyMjIAAKmpqVi5ciXS09Oxdu1a7N+/H3FxcbjrrrswceJEdO7cGSkpKW5n3rx5s0PeRo0aoX79+mqdvLw8XLhwAV988QUef/xxZGdnIykpCQaDAQcOHMCWLVtw5coVtG/fHlFRUQCAli1bYtasWShTpgwOHTqEQ4cOIS4uDgEBARgyZAj8/PyQlpaGPn36oFy5csjMzMSWLVvQvHlzpKamYteuXZgyZQr++OMPhIeHw2q1wmAwwGQyITg4GLm5uV7La8uakpKCK1eueJz3pZdewvHjxxEbG4uUlBSkpqYiJSUFR48even85uXlwWAw4M8//9TkTUhIwIkTJ1CmTBl8/vnnLuf9/vvvcejQIRw/fhwpKSm6y7tnzx4cPHgQSUlJqFq1KqZMmeJR3sTEROTk5MBkMuHy5cul6lyzze+yZcvQtGnT23KuiUiRzK9tbsuXLw8R8dr8FtV9b0F5f/vtNzz00EMerQUAOHHiBDZt2qTJ26BBA5QpU0ZTLycnB1u2bEHTpk2Rm5sLo9Go/mzTpk24cuUKGjVqBLPZjF69euGNN95AfHw8rl27huzsbMTExCA0NBRr167Fvn37cPjwYaSkpKBLly4IDQ3FqVOnsHPnTtSpUwehoaE4efIkxo8fj4MHDyIhIQHx8fHIzc2Foig4f/68mrWgtVtU9w1//fWXS48TUVFRKFOmDAwGQ4Fzm39+mzRpUuDcArjp/IoI3nnnHSiKUujczpkzB2fOnEFoaChMJhMMBoOa/a+//vLaWvBWXvu1kJqaiurVq+O3334rsrULQJN327ZtHq/dkpaX51rBa/dm92VXrlxx6VxzdX5vx3NeT56TeZK3KJ/ztm/fHlevXtXkrVy5snq72ly/fh3Hjh1DcnKywzo5efIkrly5ov5s5MiRePrpp9W5tp1vZrMZJ06cwI4dO9S10KJFCxiNRuTm5uLQoUMoW7YsDAYDrly5gvnz5yMrKwvx8fGIiIhASEgI/vnnH5w8edLltXs7H4dTUlKQkJCAixcvFji3Bc2v/dw6m1/b3AJwOr8AHOZ26dKluHTpEsLCwnDlyhUYDAYkJyfDYrFo5vdW10JBea9fv44lS5bgwoULha6F4spb0Np1J6/92r3Z41pR5r0lUgqcPn1a3nrrLenYsaM0bNhQGjZsKB07dpS3335bTp06JSIily5dkvXr18uuXbvk8OHD0qtXL/ntt99k5syZsn37dpkzZ47s3r1bnnzySenVq5esWbNGRETMZrP89ttvcvHiRRkxYoT0799fJk2aJGfOnJHWrVtL1apVJTExUZo0aSKffvqpiIhMmjRJHn/8cbU8ceJEiYqKksDAQAkLCxOz2SwGg0EqVKggXbp0ka5du0qzZs3E399f0tPT5YcffpB69eqJwWAQo9EoLVu2FIvFIoqiSHh4uKxcuVIASHR0tERGRorZbJZy5crJ2LFjxWg0yuDBgyUlJUUASGRkpBgMBqlUqZIoiiKKokhYWJg0adJEvv/+e/niiy/k1VdflQ0bNoiIyJo1a6Rt27ZSpUoVady4sbRo0ULCw8MFgMTGxkr9+vWlfv36kpKSIoqiyB133CH79++XLl26iL+/v0RERAgASUlJEYPBIOnp6bJp0yYBIBaLRfz8/CQ4OFiaNm0qn332mRiNRhk/fryEhISo+RRFEYPBIADEbDZLZGSkWCwWGTNmjBiNRomMjJSgoCBZtWqVhISESEhIiABQ/wUFBan/HxcX53becePGOc1rMBhk8uTJsmDBAo/yLliwQIxGowAQRVEEgCQnJ4vZbBYAEhIS4jC/DRs2lPvuu0/8/f0lJiZGnnjiCU3ecuXKCQBJS0tzKy8AMRgMkpqaKhUqVJDAwMDbltdsNst7772nroVGjRqpt5/BYJBatWp5nNdkMql9VaxYsVSdazExMdK1a1d1bm7Huebn5+fW/JYvX15CQ0PV+X3qqaeczq/BYJCXXnpJxowZIwkJCWodg8EgycnJ6u8sU6aMjBo1Sq5cueIwvytWrJCqVatKQkKCVKlSRZo1ayYhISHi5+cnzZo1k65duxZLXtt6zL8WRKTQvG3btpUKFSqIwWAQk8kkMTExEhMTIyaTSYxGozz66KMyePBgSUtLk3r16slrr70miqJIkyZNxGg0SsOGDQWA3Hnnnep8hYWFyfLly8VsNsvChQule/fu6jowmUzSsWNHdT0lJiZKVFSUlClTRmbPni1Wq1UURZEyZcrIjz/+KAEBAZq1YDvXFEWRlJSUQtduUd03+Pn5ubR2bff9ACQwMFAzt926dZPz58/Lv//9b3V+n3/+eQEgRqNRmjZtKnv27NH0l5KSIv369ZNNmzap85ucnCxGo1FdTxaLRT0/g4KCJDo62mFut2/fLvHx8ZrHMUVRxN/fXy0HBwcXmLdKlSoCQF0LRZnX2VoIDw/XPM7Yr11X8i5dutRpXpPJJJ9//rls3bpVkpOTxWQyqXk9XbuF5fXkXCvKvDzXHNeuK/dl4eHhEhAQUOi5ZnuO48r8FudzXk+ek+ntOW/ZsmXFarVq5jv/86Vhw4bJ5MmTpVWrVtKlSxf58MMPRVEUeeyxxyQ5OVm6du0qycnJ0rdvX7V948aNZcqUKWIymWTTpk1y4cIFadKkifoYHBYWpv6+kJAQMRgMUq1aNfnmm28kLi5ODAaDVK1aVbKzsyUqKkqz1vL/Cw0NLXTt3u7H4fyPE87mNjc3V6ZMmaLO7/vvv6+uo+7du8vRo0fV88E2t1OnTpVTp06J2WyWTZs2ybhx4yQoKEgURZHIyEh57rnnHOY3PT1dYmNj1bk9fPiwVKlSRV2r+deEq3nbtGkjANS14GreJk2aSFRUlLoWbLexn5+fKIoiVapUcVgLRZV3z549DnkLW7uu5C1o7RoMBk1Wb59r48aNk+TkZDEYDBIZGSkjR470eD+nxG9Kbd68WcLDwyUhIUF69OghQ4YMkSFDhkiPHj0kMTFRIiIiZOHCheodtMFgkDp16qg3cEREhHoSBwQESHJysiQlJQkAuffee8VgMMiDDz6o3iFXqFBBIiIiJDg4WL2RYmJipEmTJhIYGCgdO3aU4OBgefDBB6VMmTIyYMAAURRF4uPj1RevISEh0qlTJwkLC5PGjRvLX3/9JSIiFy5ckPvvv1/i4uKkQYMG8vPPP8uqVavUO/BDhw7JuHHjJD4+XgBIYmKilC1bVl3gSUlJoiiKWK1WMZvNYjab5YsvvpA//vhD0tLSBIDUrFlTypcvr55ciqJI5cqVJSQkRD766COxWq0SFBSkPnDb7kCrV68uRqNROnbsKGfPnpULFy7IL7/8IvXr15fU1FRJT0+XOXPmyJAhQwS48aJ6y5YtMnjwYClfvrzmxLA/qfOXc3JypGbNmhIaGioAZNSoUeqTIUVRpH379nLmzBn59NNPJSwsTKpWrSodO3aUCxcuyNixY8XPz0+io6Nlz549smbNGgkPD5exY8e6ldf+DudmeefOnetWXrPZLBkZGXLhwgW5fPmyNGnSRMxmszRq1Ehmz54tkZGRalZb3tjYWLFarTJnzhyZOHGilClTRgBIhw4d5Ndff1WfbLqb1/YgWatWLVEURaKjo8XPz09efvllEZHbktc+e2ZmpmzYsEE9P21z62re0NBQMZvNcuHCBVmzZo1ERkbKhAkTSsy51qVLlwLPtYkTJ6ovGm/HuWY2myUpKcnl+f3ggw8EgERFRanza8tStmxZzfwCkISEBAkODhZFUWT69OkiIjJjxgz15+3bt5e4uDj1ybTBYFDn95133lFfyNv6tFqtEh4eLgaDQbp06SLXrl1TH0eKOm9kZKRmLVSvXl1MJpPUqVPnpnnDwsLE399fDAaDdO7cWc17/fp1WblypUREREhAQICMGzdOXnnlFbVd48aNZdmyZXL//fcXeK7lL3/xxRcyb948dZOxd+/e6pPvpKQksVqt0r9/f/n7779l3LhxEhgYKHFxcbJjxw7Zt2+fBAUFidlslm3btsl///tfCQwMlOnTpxe4dovqvsFkMkl4eHiBa9disUhwcLB8/fXX8s0336hr1za3FSpUkFq1aklsbKw6v7aNgGXLlslDDz0k9erVK/Bcy39s165dUqNGDXXDu1+/fhITE6PmbdeunZw/f17GjRsnCQkJUrlyZWnSpIns2LFD+vTpIxaLRcLDw2XJkiUyffp0CQwMlI8//vimeW1PImNjY4s8r/1asP3RID09XZ599lkJCAiQjz/+WLN2Xclb0OOa7QUxAHnjjTfUvACkbt26bq1dV/Leyrnm7bw81wpeuwXdl/n5+UlUVFSB55qfn5/Ex8er8xsQEFDg/Bbnc15PnpPp7TmvxWKR6OhoGTx4sISFhcnbb78tly5dkoMHD8qHH36oru+nn35aunXrpm4KVKpUSSZOnCiNGzfWnFf5zy37c+3JJ5+U5cuXS9WqVdU+/Pz8JCwsTMqWLSuRkZHSuXNn2bFjhwwaNEgiIyMlODhYPv/8c/n666/F399fzGazTJ8+XV555RWxWCyycuXKAtfu7Xwctj2/mThxorz11lsSEBAgs2bNUuc2JiZGWrVqJYGBger82uZr4sSJ0rx5c7n77rudzm3+DQ3bsSlTpsjo0aPVjceWLVuq82vbELfNbUZGhlSsWFEqVaokS5YskVatWonFYhGr1SqvvfaaDBw4UPz8/GTZsmU3zXvPPfcIcOMPfp7kta0F23l59913qxvyAQEB0rp16yLP68nadSWv/dqNiYmRsLAwiYiIkPbt26tZi+pcGz58uCxfvlxGjx4tVqtVfQ7srhK/KdWgQQN54oknJC8vTz22dOlSWbp0qSxZskTatm0rVqtV6tatKx999JF88MEHUrlyZQEgAwYMEBFRX3zExsZK8+bN1YVj+wt5RESEuvubnZ0tf//9t7optXr1ahk0aJBERUWJ0WgURVHkpZdektzcXNm+fbsAkE6dOqnZTCaTmM1mERE5d+6c1KxZUx599FHJzMyUzMxMWbhwoQCQefPmqcdsL5Rr1qwpJ06cUBdCzZo15bfffpMtW7YIADl58qSYTCYpV66cLF26VKxWqxw4cEBERCpVqqTWERE5efKkBAcHq3+1q1ixopjNZqlUqZL861//kry8PJk1a5a6MyoisnfvXvVkty3Q/O8EyP8AbPtL/9mzZ9Vj99xzj5w8eVJ++OEHTd7y5cuLxWKR3bt3i4hIbGysTJgwQZN3yZIlAtz4C7HFYpHOnTuLyWSSoKAg2blzp4iIZGVlCXDjr4gXLlwQEVF/tzt5bSeZ7Xfb8hqNRtm1a5dUrVrVo7wAZPHixepa2LFjhwCQdevWiYjIRx995DSrszuEhg0byuXLl9UxN2rUyK28iqJIamqqiIj88ssv8uijjwpwY6O2S5cu6juEiiOvoiiSkJCgroVPPvlErFarmtf2Vy5bfVfz2p5E29bCRx99JCkpKSXmXCtbtmyBazf//OrhXHvjjTcKnN/58+cLAGnevLk6v7b8v/32m4iIOr+2tVuxYkXx9/dX57ZBgwbqC1hb3jFjxojJZJIyZcqo86soijRq1Eh9XLCdfzt27FDn9sknn1SzFmVe4MZfIW1r4f7771efLNWrV0+ee+45MRgMcscddzjk/eGHH9S8/v7+Eh4erv6z3da2su3JcPv27SUvL092794tAKRBgwaye/duyc7OVv9AYjKZJD09XcaMGSP+/v6yf/9+h3Pt2rVrsnDhQmnWrJkANzbWhgwZIrt27RIA6gt3EZHAwEABbrzAEhGZPHmyy2vX2/cNthe0ha1do9GouW8wGAzqXNoe320bmfnnu2fPnnLmzBnNubZ792755JNPBID8/PPP6vyazWb1nXFxcXEyevRozfxOnDhRnafY2Fh54YUXxGQySVhYmPzyyy/qfYHtvs42v4GBgWI0Gm+a1/ZOlcTExCLPa78Wfv75ZwGgvtt88uTJmqyu5rWtD9vateU1Go2yatUq9a/YtrUbFxcniqJIxYoV3Vq7ruT15Fwrqrw815yvXVfuy2wvnt051zx5DllUj8OePCfT43PeRYsWqXktFovUqlVL/Wf7JImtbHtn9OOPPy4ior6uqlu3rnz77beydu1aefPNN9Vxz5o1SzIyMsRoNKqPw4mJiQJAqlatKqdPn5Z33nlH/eNhRkaGTJ06VX18/+CDD9S8tvP88uXLIiIyatSoAu93b/fjsO1dcPnvGwICAtS5tI05JSVFPWY7J1599VU5evSoZu1+++236twuXrxYjEajpKamypNPPil+fn7q/ObPa5tf27qrXbu2TJgwQYxGo0RFRalrdefOneof4mzzGxcXJ1ar9aZ5bc+tQ0ND3cprNBrVTy4lJiaqG7O2vLbHMUVRijyv7VxzZ+26ktd+7a5Zs0aAGxudtrWbP6s3z7Unn3xS8ps6darUqlVLPFHiN6X8/f3VO0qb/He+Be1Kx8XFSVZWlhw7dkyAG3/ptj0xsb0bw7ZDbfsLj+3By/YxG9sd8tWrV9W3QRoMBomPj5eXX35ZAMhXX32lZouJiREAcuLECRER+eabb9Q2zvLmf0LRsWNHqV69ugQGBoqiKLJ48WJJSkqSkSNHCgA5deqUmEwmiYyMlJ07d0poaKhkZmaKiKhv67Pltc2dv7+/rFu3Tnr06KH+nr1794qIyMGDB9UHpjNnzojIjSdE4eHhsnbtWlm7dq289957AkA+/fRTWbt2rUyfPl2AG39pbdSokbRs2VJ9u+n48eMlKSlJZs6cqckbHh4uDRo0kLfffltERBo3bizvvvuuJu/ChQvFYDDIDz/8IHPnzpUmTZqoc7Rr1y4RufGuOdscnj17VkRuPKgYjUa38wI33g3zxRdfyOHDhzUP0FFRUR7njYyMVOd/3bp1Atz4a6DI/z2Be+utt9S8ZrNZgoKC1LItb+3ataVly5Zy4MABASAvv/yyW3ltd642trwjR46U5s2bq+tw1qxZRZ5XURTx8/NT18LOnTulZcuWal7bOxvzr11X8kZGRgoAdS3Y8paUc23JkiWiKIo6v/Zrd+3atepf1G7HuWabR9v82ubT2V+o8j8Z3rdvn2Z+befapEmT5NChQ5q1GxMTI8HBwercxsTEyFdffaXJu3//fgFuvLst//xaLBZ1/n///XfNEzjbE2j7tzUXRV7b+rax5f3f//4nPXr0UO9vvvzyS4e8q1at0qyF559/XmbPni2zZ89W//JvK9vWZrly5eSxxx5TnwD16NFDKleuLFu3blXPe9uLsR07dkiDBg3kP//5j4iI+jGq/Gv3m2++EUVRZMCAAZKamqrOy4IFC9Q6tr+a2z4ub3txV9DaLar7hrCwMImNjXXrccJ232Cb3+HDhwsAGTdunGa+AUiFChXkscceU9fCoEGDpHLlyrJ8+XLNuWZ716NtbmvVqqX+AcyW1za3a9askVGjRqkb0UajUb1vyMzMFEVRxGg0qvMbEBAgRqNRzWaf17YWNm/eXCx5K1WqpM7/L7/8IgDkm2++0ayF/GvXlby2j6XY1q79uRYZGemQV1EUCQgIcGvtupLXk3OtqPLyXHO+dl25LwsNDZXw8HC3zrWgoCAxmUy6ec7r7nNIPT7nXb9+vSZvjx495LXXXpPXXntNTCaTmEwmtfyvf/1LgBvvWB46dKi6edS6dWtp0aKFHDlyRLKzsx3yVqhQQT777DMREcnIyBBFufHOdpuNGzcKAHnwwQclODhYvb9ZunSpWse2wXT8+HEREXUjqKC1ezsfh21/iLW/b7DNr+3yA4MGDVLnN/9G7dChQ9V56Nixo7Ro0UJ++uknh7W7fft2qV69ujq/ts2Y/HmDg4PFarVK79691Xk0mUySlZUlIqJu1hmNRnV+bR8HtmWzz2tbC6tWrXIrr/1amDNnjibvxo0bRVEUmT9/fpHntd1G7qxdV/M2b95cnX/bWv3222815aI417Zv3y757d+/X4KDg8UTJX5TqmzZsjJnzhzNsfj4eFmyZImIiMyZM0cURVH/si0ism3bNgEg3bp1k8TERPWO8vPPP5cKFSrI888/L4qiiMViUa/lsWPHDunWrZv06dNHRER9W17+k3DMmDFiNptlzpw5MmLECPWjP6NGjVLr2D4r/NZbb0lmZqZs3rxZAMhrr70mw4YNk9DQUImMjJRp06ZJdna2ZGdnS2JioiiKIteuXZOOHTtKdHS0KIoiIiJHjhyRunXrCgBp1aqVKIoiISEh8sUXX8h9990nQ4cOFRFRP9ecP294eLgkJiaKiKg7zlarVbZs2SIiImvXrlWfDHz88cdy4cIFadiwofpXr8WLF0vZsmXVz0eL/N/O6vr16+Xvv/+WRo0aSUJCgpp327ZtkpqaKgCkT58+oiiK+nbu0NBQGTFihEyaNEndeZ86daoMHz5cwsLCpFatWtKgQQOZN2+e3HvvvXLnnXeKv7+/tG3bVrZu3Sp33nmnhIaGislkksWLF8uFCxekdu3aEhQU5FHebdu2SeXKlaVTp07qk63u3btLTEyMR3kjIyPF399ftmzZIjt27JCEhAQxm82SkpIiixcvljVr1oifn5+89dZbal6TySQdOnRQbzNb3pUrV0qjRo2kRo0aoiiK/PXXX27lBW589vzjjz92mrdly5YSGBgoERERRZ4XgPoRtsjISPnmm29k48aNal7bXwmeffZZt/La/oo1fvx4yczMlOXLl4uiKCXmXDtw4IAoiiIjR450unZFRP2o8e0412xvFbfNb0hIiAQFBclXX33ldH5tLyTWrFnjML9HjhyRli1bqnOnKIq0bt1affJum9u77rpLnn32Wc38Tp8+XUwmk/oXLduTwIceekidpy+++EKAGx+JGz9+vJpl/PjxRZ7X9gcOG/u8tuulvP/++w55a9SoIVu3blXXgu3jkVu3bhU/Pz9p1aqVw7n25ZdfSoUKFdTrGZw9e1ZWrFih/rU4ICBADAaDREdHy/fffy9ffvmlREREyKxZs2TUqFHqvPzwww8yc+ZMSUpKkvT0dOnQoYNs2LBB7rnnHjGZTJKSkiIXL16UnJwc9S+0tvvdrVu3itlsLnDtFtV9g+1jpQWt3aCgILFYLLJ161YREfnpp5/EbDbLhAkTZOvWrVKnTh0JCgqS1atXq3lt72w5evSoVKhQQcqWLaueaytWrJDY2FgBIKNHj1bnd+zYserczpo1S31O8OWXXzqd2yeeeEIqVKggFotFnnjiCcnJyZHOnTur77S05a1Ro4b6AstZXtvcbtq0qcjzBgYGSmBgoLoWqlWrpv71devWrbJ161YxmUyatetOXtvaHThwoAA3NoxfeukliYuLc8hrWy/urF1X8npyrhVVXp5rzteuK/dlAQEBkpaWVuC5FhYWJkajUZ3funXrSmBgoC6e83ryHFJvz3mNRqO0bdtWTp8+LT/99JOYTCaZOnWqiNy4PrC/v780atRIXQu212v/+9//JDY2Vrp3764+/k+dOlXi4+Pl7bffVs+1SZMmSWJiorzyyiuSmJgo3333ncydO1dd30ePHpVvv/1WqlWrJvHx8dKnTx/Zu3ev+g7m2rVrq7/b9rFX29rdsWOHGI3GAtfu7Xwctl0+4PTp0+p9l21+T58+LXfffbf6x1Eb23OaXbt2SWxsrOZcmzp1qvpGirlz54rBYJAyZcrIxo0bZdasWer8jhkzRr2NbPNrtVqlQoUKcuTIEXnllVckJiZGzGazjB49WkRE+vfvL1arVUwmk5q3cuXKEhgYqK4F+7y2tbBlyxa38tqvBdv5mJmZ6bAWiiuvO2vXlbxms1mz6dqmTRtRFEU912xrtyjOtY0bN0p+O3fulJCQEPFEid+Umjx5slgsFnnmmWdk6dKl8tNPP8kdd9whXbt2lWeeeUYCAgIkJSVF5s6dq7ax3Wl89dVX8vTTT0tYWJj6QvTvv/9WbwjbX2sCAwNl4cKFcvToUSlbtqw0bdpUvcBw/fr1pV+/ftK0aVPx8/OThx9+WKKjo6Vv375Srlw5KV++vJjNZhkwYIA8++yzYrFYJCQkRH3Qzf/ujLi4OHnrrbdkyJAh0rZtWzXvk08+KcCNm+ratWty3333icFgUH/+0ksvSVhYmPqEomPHjjJ//nz57bffJDIyUrp37y533HGHAJDOnTvLG2+8Id27d1ffgj969GipX7++9OjRQ0JCQqRatWoyffp0qVixopQtW1aioqLEz89PDAbtxTn9/Pzkqaeekv79+0vnzp1FROTEiRNSp04d9fOkf/31lzRo0ECTd9SoUWKxWNQd7nr16sn06dNl48aN0rBhQ4d3LSQkJMiECRPkxIkT0qZNGwkKCpK77rpLzp8/L48//rhaD7jx1uEOHTpo8trewuxJ3kuXLkn9+vXV26levXrSvHlzj/Ju375d/SunbQ5nzJihvhXWth5sH/Py8/OTatWqaT7+mT+vs7l1Na8tg6Iotz1vcHCwJq/ttvBG3q5du6rnmv27JPV+rtneBWX7C7b92hUR6d69u9x777235VwLCQmRPn36qPOb/zZyNr+2+13b26jt5zcvL0+aN2+uvkupY8eO0rNnT3n33XfVuX399dfVz72/9NJL0r17d7FYLNKyZUspX768Or9paWkSEBAg//nPf2T27NnqhtPYsWM1eW3jL8q8ACQmJkZdC67mDQ8Pl7vvvlvd/DQYDBIVFSURERFiMNy4AGq3bt3U2/rw4cOSlJQkEyZMkCNHjkh6erpmLZw4cULMZrM69oSEBHnvvfdE5MZfuW0bsvlvR39/fxk8eLDs3r1bvSZWRkaGfPXVV5oLgNoeC21r1/Z7Clq7RXXf8Prrr7v0OFGvXj1RlBsfz4+Li5OAgAD1hUK7du3k8ccfl969e6t52rRpI/Xr1xcRcTq/Y8aMEYPBoF7nzTa/trnN/+47ADed2yNHjkjTpk0192Mmk0ndSIiIiFBf6NvWgn3e/GuhqPM6WwuvvvqqunYDAwPFYrFo1q67eU+cOCGVK1fWrN2yZcs65PV07RaW15NzrSjz8lxzvnYLuy/r0aOHS+da69atNc8hbc91bvdzXk+ek+ntOe/48eOlatWqYjKZJDk5WUJCQtSPO9neOWT747/IjXddREREyAcffCA7d+5ULwBts2vXLvWPc/kf00ePHi3vvvuuBAYGai4Gbns90LFjR1m7dq36BTXR0dEyY8YMUZQbF7q3Pf+yXT80IiJC3agoaO3e7sfh2NhYMZlMUqtWLalcubJYrVb1Uz/Vq1eX+++/XwYPHqzmadq0qVSoUEFExOn82j7CnT+PbaPGNr/5n6va5rdZs2bq/Wd0dLTs3LlT/Tib7WNjBoNB4uLi1LxhYWHqxzmd5c2/FtzNm38t5P+iiJutheLI6+radSWvs7X7+OOPq+daQkKCWCyWIjvX8psxY4bHH99TRERQws2fPx/vvfcetmzZgtzcXIgIDAYD6tWrh+eeew5ZWVlYv349VqxYAeDGV3a++uqraNWqFe655x70798fH3zwAfLy8tQ+K1WqhKysLOTm5qJ///649957cdddd+H8+fMYO3YsZsyYgbNnz8JsNiM+Ph5NmjTBs88+i9q1a2Ps2LH48ccf0bhxYwwYMABt27bFTz/9BACIiYnBt99+iypVquDgwYNYunQpfv75Z9StWxfPPvssgBtfy3jp0iWEhIQAgPq17z169FB/fvToUfUrxi9dugSj0YidO3diw4YN6N69O8LDwwEAWVlZGDZsGJYvX65+PajJZEK9evUwcOBAfPvtt2rWSZMmYcyYMRg1ahRsyyIpKQlLly5FWloatmzZgqVLl+L8+fPo0aMH6tSpg5CQEPz55584duwYqlSp4vT2+fvvv7F161Y0a9YMAHDw4EH4+/vj559/xnfffYeXXnoJMTExav3Tp09j/fr1OHv2LFq3bo1y5coVePvv2rULP/zwA2JjY9GkSRNERUWpX1Oc/yswbyXv999/j8zMTIes7ua9dOkS/ve//+HixYt48MEH1a+UvVne3Nxct7K6mnfRokUoU6YMatasibJly+o6788//4xff/0VGRkZiI+PLzTvwoUL8ffff2vyHjx4ECdOnMD3338Pi8XCc+3/88a5Zvs98+fPxz///IOePXuq/eSfX/u5vdn8ZmZmYvPmzS7P7b///W+0adMGzz77rDq/r776Kpo0aYLDhw8DACwWC5YvX45WrVoBAKZMmYKVK1eiVq1aus+7e/duTJ48GYcPH0bjxo3Vr/oOCAjAnj17cNdddzm9rY4dO4ZVq1Zp8m/evBmff/459uzZg0mTJiExMVH9WW5uLmbPno1NmzahadOmSExMRJ06dRAcHKzWOXv2LCIjIwEAx48fx7hx43Dp0iV0794djRs39ur9rm1deeu+wdna3b17N3766SeHr1GvVKkSDh065Nb8bt68GYGBgfj222/x3XffaeY3NzcXW7duxcqVK3Ho0CE8/PDDqF+//k3nFgAWLFiA9evXIzExEffeey8qV66s27zO1gIA7NmzBz/++KPX8ha0drdu3YoPPvgA5cqVwx133OH22i0ob1Gca7eaV8/n2s6dO7Fx40bNuebttXCztWub3zlz5uCnn35yel/myrnm7eeQrj4Ot2rVCqmpqTed24Kek/3yyy+ar6nX63PevLw8rFy50ul9WZkyZbBt2zb06tVL7ePcuXMwGAwICwvDzp07sWjRIowYMUL9+bJly/DRRx9h//79WLx4sSbP+fPn8c0332DixIlITU1Fy5Yt0aRJE5QvXx7AjdeDe/bsQcWKFREUFITNmzdj5MiRuHz5Mh577DH07t3bq2sXKPy+oaC1CxR839CwYcObzm3btm2xc+dObNmyRZ3f/HMLwGF+v/rqK5hMJnz11Vf47rvvnM7v2LFjsXHjRrRp0wZJSUnq/NrP7eXLl9XXxwkJCXjkkUfQqlWrYst7/vx5rFq1CsuXL0dWVha6d++O5s2b33QtFEfewtauO3mdrd3iPNdsvvzyS5jN5pueEwUpFZtSNteuXcOZM2cAAFFRUTCbzbfU35EjR7Blyxa0bt0aVqv1lvq6fPkyrl+/jqCgoFvqx1MiglOnTiEvL6/Qubl8+TJ2794Ns9mMSpUqwWQyFWNSAm7cXoqiuFz2Vh1P+6X/w3Ot6Lgzt8CNJwA5OTmoU6fObZnbkpaXiIiIiKi4lapNqdLgjz/+wIgRIzBz5kyX6zhr40odV3/3P//8gy1btiAiIgKVK1fWlFNTU7FgwQJ06dIFW7ZsgdVqxY4dO9SyO3XmzZuHSpUq3XK/9erVw08//YRGjRqhbNmyePnll7Ft2zaEh4djwIABiI+Px2uvvYajR4+iT58+6NmzJ5YvX4533nlHrfPPP/9oyq608bTf999/H1euXEG3bt3QsmVLAICfnx8yMzORkZHhUtlbddxtk5OTgwULFmD//v2Ii4vDfffdhzVr1ty0/Mgjj8Df39/rbZzVKVeuHHbt2oULFy4gLi4ORqMR8+bNw+HDh5GSkoIBAwagQoUKCA8PV3f7X3/9dSxZsgSnTp1CSkoKatSogQ0bNuDUqVNIS0vDgAEDsG/fPoc627dvL7Dfjz76CB988IGmzsMPP6w5zwYOHIiuXbvizjvvvOmxwspF1eZmxyZPnozNmzejffv2ePjhh/HRRx/hzTffRF5eHjp16oRRo0bhgw8+0NTp3r07li1bhsDAQPTs2ROHDx/Gl19+qZYrVKiAoUOH4p9//kGLFi2wcOFCfPrpp4X26+x322/sHD9+HNOmTcOGDRtw/PhxGAwGpKamomPHjujZsyeMRqNDndzcXACA0WgEAIeywWBAfHw8/P39cfHiRZw4ccKh31OnThX6e525evUqlixZov6VNjc3Fzk5OQCAoKAgiIimbDAYEB0dDaPRiNzcXJw+fdorbVytU6ZMGTRu3Bj3338//Pz81HGcPHkSH374IYYPH37TY57UudU2R44cQVhYGHJycjTHbI8lTZs2VetYLBb8+OOPSE1N1ZRtdbzdxr5OlSpV8Ouvv6JGjRoIDg7G/PnzsW7dOkRERKBHjx6Ijo7GxIkT8ccff+CJJ55Ao0aNsHfvXrzzzjtu1TEajV5p89///hdXrlxBly5d1MeO1NRUrFy5Uv1Lr33ZlTrF0UZEsHbtWvWxpG3btvjhhx9uWr7rrrtgMpkKbONKncLaxMfHIyMjA7/++itOnz6NuLg4WK1WTJw4EcePH0d6ejqefvppHDt2DJ988on6GNWkSRN88803BdapXLkytmzZ4vU28+bNw9GjR3H27FmkpKTg6aefxsaNG9G5c2f1HafvvvtugeWb1XnwwQc179gqrB9X2gA3/tK/efNm3HXXXWjSpAneeOMNzJo1C5GRkejTpw/S09MxZMgQ/Pnnn3jwwQfx9ttve1Tn9OnTXmnz1ltv4ejRowgNDUVISAhEBNeuXYPZbIaiKDAYDEhOTobVasW5c+dw/PhxhzqetPG039TUVFSqVAnXr19X371x5coVKIqiPm5cuXIFwI13DQM33t0REhKCv/76S9PGvk5oaCguXLhw0zqu9ps/i+2dJfXr10d+f/75J7744gt07979psc8qXOr/Xbr1g0Gg0FTx/ZJoCNHjiA5ORl5eXkwGAzIy8vDkSNHkJiYqCnb6iiKgj/++MNpG1fq3KxfW5akpCRkZ2cjKSkJBoMBWVlZ+PrrrxEUFIR7770XISEhWLx4MY4ePYrHHnsMZcqUwZUrV/Cf//zHrTrBwcFut3FW5+rVq/j8889x5coVtG/fHlFRUWjZsiVmzZql3ofYl50d80YbT/o9ePCg+vhStWrVQsuutHFWx2MefeiPisz27ds1n+EUufEZ5V69et20jrM2rtQprN/Dhw9Lp06dJCUlRf0sa/5vJDMYDOqFfvPXAaB+LtrVOjVq1NB8ZvZW+vXz85OIiAjx9/eXF1980eEzwbZvRrF9Trdnz56aOrbPc7vTxtN+LRaLJCcnq59bT09Pl9q1awtw46tqY2Ji1Av3ZWRkSO3atZ2W87dxpY6n/fr7+6sXv+zbt68YDAb1mhG2awcEBQWpZds4a9WqJRERERIZGSkmk+mW27jab0BAgNSrV0+9iPi9994rr7/+ugwePFiCgoIkIiJCKleuLImJiZKWlqZeG2jatGnSunVrNdszzzwjgwcPFovF4rROvXr1CuzXZDJJjx49ZNq0aWo/iYmJUqZMGWnSpIl8+umn6vqwXbxy2rRp6rouX768jB07ttDy8ePHi6SNszpDhgyR4OBgefDBB6VMmTIyduxYCQwMlJo1a8pDDz0k0dHRkpGRIQaDQYKCgsRqtUrr1q1FURSpUqWKBAcHq9dDsJVtF5K0WCxSpUoVMRgM0qpVq0L7HTNmjEOd6tWrS2hoqMTFxclLL70kw4cPV8+5xMREMRqN8uCDD0rlypXFZDKp1x+0zX+TJk3Ua7aEh4er19OzL7dt21bMZrMYjUYJDg526NdisYjBYJDQ0FApX768GI1Gefzxx+W+++5TrzFYoUIFKV++vJQvX14qVaoklSpVkvT0dAkMDBSz2SxNmzaVdu3aidVqVe9LbBeYtpWDgoKkWbNm6re7+Pv7S7NmzW65jat12rdvr9ZLT0+Xffv23fJjVGF1PG2jKIrUq1dPvWZMhw4d1PPYdgyA1KlTRy3bvtzEVrZ95bm329ysTmhoqCiKIuHh4eo3/dr6tlgs6vUfbMdHjhypll2tk/9aJ7fSJjQ0VKKjoyUyMlLMZrO0bNlSOnXqJIpy4wsBOnXqJNWrV3co569TvXp1p3W83aZTp04SGxsrBsONC5Lbvp4dgHoRYNt4bWXbz23X9ChfvryEhIS4XceTfm1zbSvb1saDDz4oDzzwgLpe6tevLy+++KI0aNBAgBtfW19YnQoVKhRJG0VRJCUlRerVqycmk0kU5cY33LVu3Vo+++yzQstXrlzxSh1X2kyePFlMJpPUqVNHQkJCpFevXgJAvd6j2WxWx2D7pt369eu7Xcd2299qGz8/PwkKClKvc2l73mN7HgpAmjVrpl5nxnZ9Jfs6nrTxpN9WrVqpX0Jje8y2fXuY7Zjt2wBtz7+qVq2qXj/MYrFI1apVNW1cqeNpv7Vq1VJfY9xxxx2aL7DR0+OaiMiGDRvUx+iYmBh54oknBIB06dJF/P391XXVuXNntU7//v01bWxfJFNQG1fquNpvSkqKGAwGSU9PlwULFqhzb3uss11Dy/b4Mnv2bM01rFypExISoj7XvJV+X3/9dUlLSxOLxSJ+fn4SEBAgTz75pBgMBnniiSfk5Zdfls6dOzuUX375ZfVY586dndYpqI2n/dauXVsMBoNMnjxZFixYoH5BhO1xxPZ6zvYvOjpa87yiadOm6oXZC6pz//33q88FDQaDtGjRQv7++2/xBDelitnSpUsL/Pfee++JoiiaY3379hXgxtf3vvzyy9KnT58Cy7Zj+ftx1u+ECRMc6tjf6QGQ/8fefYdHUbXvA79nS7LJpmwqKaSHEjpIDUpTUbBhATuggGJBVPgivCoIIhYUkaqiNAuCgBRFkCIC6ouCEOklVBECiLRgKMnz+4PfzJvZXZLdZRM32ftzXVx6Js85uc/szJaT3dlbbrlFjh07Jrt27ZKoqCgBIBs2bJBdu3ZpL8rbtWsnx44dk59//ll7IHKnRj3QvTFueHi47N+/X2bMmKE9KRIRmTFjhlgsFgkMDJQXX3xRRERuv/12ASDZ2dlajaIoEh4e7lYfT8cFIA0aNJA2bdpIcnKy9kAIQHvgVucQGhqqfeOJfbt4H1dqrnbcJk2aaItY6ovPe++9Vzte1La6OJSXlydnzpzRvs3kavu4Ou7NN98sIpe/rSorK0tsNpssXbpUREQ7r7p37y4TJ07UjsE333xTRC5/Zbd6Hu3bt09ERPsaabWt1gQGBpY4rqIo0q9fPxG5/O1r6lfJFl/sAiB33nmntG3bVndhzuHDh8szzzyjPYi3aNFCbr/9dqdt9YHX232uVNO4cWNZuHCh7muO1UWqhx56SADI/fffLyNGjJCIiAgBLn9dbvH7F/WcUJ9kGY1GmTNnjoiI9rXTroxbvEZd5C6+2GUwGKRdu3YyYsQIiYmJkdatW4vRaJRrrrlGW+AOCgqS2rVri81mk+zsbMnIyJDExEQRETlx4oQEBgZq33By4sQJSU5OFrPZLK+88oqcOHFCGjRoIM2aNXMYNzo6Wu69916x2WxSrVo1ycrKkvDwcGnQoIH2RNl+satKlSqSmJgo4eHhkp2dLW3btpU77rhDTp06JadOnZKYmBiJiYnR2nfccYdERkbKHXfcIQcPHtS1r6aPKzVt2rSRFi1aSE5Ojvz444/Spk0bqVevnnz55Zfy5Zdfat/YoradbXNWoz6RLYs+devWlc8//1w++OAD7dttrrnmGvn111+1J8dWq1WWL18uS5cu1Y4ztV2vXr0y6XOlmujoaNm/f7+MHDlSe/J8+vRpGTlypAQHB4vFYpGePXvK6dOnpXHjxmIwGKRGjRpu1agLvVfbB7j8B6TU1FTtMUQ9ro1Go+5FavF28Rr152Xdp3hNYmKi9vj4yy+/iIhoF+5Wv8Dj4Ycf1h678/Ly5ODBg9pjR/E+rtR4Mi4Auf3227XHn8TERDGZTLJnzx7tMUpRFK3drFkz7QsaSqoxGo1Sq1Ytr/cBIFOmTJE77rhD+4YoAPLqq69q2wDIjTfeKG3btnXaVp93ltTHlRpX+hiNRrn++utl06ZNsmLFClEURR544AEREVmxYoVW8+GHH4qIaN9K6G6N+rzqavsEBATI448/LkVFRTJlyhQJCAiQpKQkKSoqkp07d0pERIQEBwfL448/Ljt27JDU1FSJiorS1ZhMJgkLC3Orj6fjWq1WadGihSxevFhSU1MlKytLWrRoIdu3b5ft27dLZGSkREZGau3s7GxJTEyUFi1ayKJFi3Rtd2o8Gbdp06ba49y6deukadOm0rFjRzl48KAcPHhQFi9eLAC0trNtzmrmzJlTYo2n495xxx0CQKZNmyZjxozRnhdXq1ZNvvzyS+35lNVqlRkzZsikSZO0+2e1rX47YEl9XKlxddwqVarI+vXr5dlnn9XuG9avXy99+vSRkJAQsVgscuutt8q6deskLS1NTCaTxMXFuVVjNBq1b4G8mnHVx4nifxyoCP+KZ50zZ47k5+drjyV33nmn1la/1GHv3r2yZs0a7TVf8T7OakJDQ2XFihWSn58va9askYyMDO0bqN3FRalypq4kFl95dHYQFa/x5sHpyrj2i13vvfeetnCl3tEkJydLbm6uHD58WABIXFyc5ObmypEjRwS4vBruTo36Itcb495///2SnJwsu3bt0uYkIlJYWKj9JUpdvMjJyREAMm/ePK0G+N8LZVf7eDpuUFCQpKWlyfLly2XTpk0SEhIiaWlpYjAYZMuWLfL666/r2iIir7/+ugCQjz/+WGu7W+PpuOpfFbds2SLp6emiKIr2lyO1nZCQoLXfffddAaDVqF8xfrV9XKmxWq1SpUoVERGJjY2VqVOn6haY1K/BXrdunVYTHBwsaWlpWnvhwoW6GvVJbPE+ak1J45rNZsnMzBSRyy8k1G8IsV/sUvNfuHBB95XiCQkJ8sILL4iiKNKmTRsxGo0SHx8vALQXB/Hx8dKpU6cy6eOsRn0Hj9Fo1Pa/uii7ceNG7YXf/v37RURk7ty5AkBbvBMRAS7/hVKlvnBQ++zbt08AaN9OU9K4xWuAy+84U9vqX5hzc3O1PhaLRQwGgxw5ckRE/vciIDc3V1tgMplMYjKZtBp18VBtq08M165dKyIi3333nfZOSLVGHUPk8kKWumDWv39/rY/RaNQtfqkL1Js2bdJl2bRpk7av1IVw1e+//671cdb2tI8rNerjSWmPa772r/i7VYDLT5j/+usv7bHk2muvlQYNGshff/2l/ZVRbe/fv79M+lyppk2bNtKgQQOtrT6uXbhwQXtc37p1q4iI/Pe//xUA8sUXX7hVA0AiIiKuuo/FYpEGDRrI1q1bZf369do7ItTHDhGRxx9/XADIggULtHbxGvt2WfVRj1+1XaNGDd3jmtpOTk7W2upXtqs16jcSFe/jSo0n41osFu2+IjY2Vj788EMxGAySk5MjIv97jFLbsbGx8vXXX+u2OauxWq1isVjKpI+aPy8vT7ufVd+t+Pbbb2vvEjcYDNKgQQMBIOnp6Vr7rrvuKrWPKzWujGs2myUjI0MMBoM0adJE9zxOxPExau/evQJAvvvuO7dqSnvsc6fPzp07dX1MJpMcP35cRES+/PJL3WPUvHnzRFEUXY36uOZOn6sZd8WKFbo+v/32mzYn9RvHVOvWrRMAWo1929UaT8Yt/i4Z9d+//Zjlyj/7rI0bN5aCggLtsaNmzZrSvn17KSgo0J4Tqu0tW7aU2seVGlfHrV27trRv317++usv3ePa2bNntecSGzZsEBGRZcuWCQCZPHmyWzXFH6OudtwWLVpo92fNmzcXi8WieyxRv8V11apVWvuWW27RauzbrvTxdNzij2t16tTRPa7VqVNHuz9U2/369dPdX6ekpOjaV6qJi4uT4ubPn699o6O7DKByFR8fj7lz56KoqEj7p/z/z1kriqJdQLr4NwEC0D6bLSLYsGEDAGDevHlO28W3lTauOnbxmhEjRmDEiBH4+OOPAQB9+/ZFp06d0KlTJ5w9exaKouC2225D69atsWvXLgBA27Zt0bp1a+zcuRMAMHToULdqzp0757VxX331Vdx2221o27atbp4GgwGBgYG6C3WHh4cDgPbtawbD5VPi3LlzbvXxdNzCwkLMnDkTTzzxBMaMGYOLFy9i5syZKCoqwsiRI9GvXz9d++LFixg4cCCMRiNeffVV9O/f36MaT8ctrqCgQDt+irf/+usvrV2rVi1djXo9nqvt40pN8+bNtS8+aN26NdavXw8R0bbt378foaGhmDhxolZTtWpV7RvQWrdujVGjRsFms2k18fHxCAwM1PUZNWoUjEZjieMajUYcPHgQAHDo0CHk5eXp+pw5c0a3b81mM/r27Qvg8rce9erVC1988QVEBDNnzsSePXvw2GOPAbj8TS5qWz3vvd3HWc3FixcxaNAg7NmzB3fddRcA4LXXXgMA1K9fH4qiwGg0YuvWrQCgfTPd999/DwDa+WuxWLR2YWEhDAaD1ue7774D8L/rP5Q0bvEaAIiMjNTa6jWbDh8+DABo1KgRCgoKUFRUpJ2jjz/+OIDL39IWERGBt956C0VFRbh06ZJWo46pttXfrX7DXrVq1XDx4kXduFWqVMGlS5eQl5eHiIgIDBw4EAC0bzypVq0aCgsLkZeXp9V8+OGHAKBdC0/Nsm/fPu0YCQ4ORnBwsNbet28fDAaDVmPf9rSPKzUhISEIDw/H3r17sXfvXkyaNAmKouCtt97CmjVrMGXKFADQ2s62OatRvxikLPp8//332Lt3L/bs2YNvvvkGAJCSkoK2bdvi6NGjAIAPP/wQqampaNu2LU6fPg0AWvvUqVNl0udKNZMmTUJqairat2+P4sxmM4KCgiAiCAoKAnD5fqr4f12tAf53LF9Nn6KiIgwePBg33XQTvv76a61dWFiIzz77DADw/vvvw2Aw4LHHHsO4cePw/vvv62rs22XVx97ff/8N4H+PJWpbvd7M33//jebNm+tq/vnnH4c+rtR4Mm7Dhg21Pg0bNsShQ4cAADNmzABw+fo3AQEBWrthw4b49NNPYbVaS6yx2Wza7ezNPsXFxsZq1/CaN28eatWqhSFDhkBE8OOPP2LlypXa/feRI0e09pIlS0rt40qNK+NeunQJU6ZMwcqVK7VrT3Xu3BnA5W9Su3TpEgwGA/bv3w8AWL9+PQBoj5Gu1gD/e8y6mj6KomjPH/bv3w+DwYBLly5p10NS97d6LaV69epBRHQ16mOVO32uZlz1eZ/aR72fBC5/S23xLwQ5c+YMFEXRauzbrtZ4Mq76eLlixQqsWLEC77zzDoDLzxVGjx6N/v3769rOtjmrUfeHO31cqVGvoanmnTRpEoDLz487duyovf6YNWsWzpw5g44dO2r3Q2pbPZ9L6uNKjbvjqueYymq1Ijg4GCKiPe+qXr06ACAzM9OtGuB/j1FXO27btm3RuHFjfP3115g1axYuXryIwsJCrFy5EsDlb/9TFAVdunTB119/jW+//RbXX3+9VmPfdqWPp+MWpz5mqI8lR44cgaIo2v3KkSNHtG/LU2vU+5XifZzVqLe1qn79+trrHbd5tJTlo6ZPny7Z2dkSHx+vvQPhgQcekJo1a2rbpk+fLqmpqRIREeFyjSd9rlQTFBQkFotFq3n33XclMjJS+0uM+lf+l19+WZtXq1atpPhNZV/jrI+6raRx1Y8VXanPhg0bBIBMnz5dq6ldu7bW56mnntLe3jd9+nSHtjs16lsCvTHut99+q9Wofy1QTZ06Vcxms1azatUqMZlM8vXXX2s1SUlJEhsb61YfT8dNTU0VEZEzZ87ITTfdJAEBAbJp0yYxmUxy++23S7169Zy2zWaz/PLLL9K1a9erqnG3j/qXo6ysLAkJCRFFUaRu3brSsGFDrW0ymbS2+pdftUb9WNrV9nGlpn///mI0GqVVq1by/PPPa9f2qVGjhrRq1UoMBoPccccdkpqaqtWo73iLi4uTxMREAS5fMyMkJES7llVAQIAEBgbqasLDw0scV/2LRa9evSQ6Olr73HWPHj1E5H9/tSh+vYIRI0bothUVFen+0iFy+S8hM2fO1Nr2Nd7q46zmgQcekJiYGOnZs6ekpaVJeHi4hIeHy8SJE2Xo0KECQDIzM7WaKlWqaLdBy5YttetiKIqitUNDQ7VrZLRs2VKMRqN2XZuSxlX+/2fdi9c0adJERER27twpBoNBgoKCpE6dOvLtt99q+7Zhw4baHD/77DPtL3fffvutfP755wJcfkv6t99+KytWrJDo6GhRFEVr169fX4xGozbum2++6TDuzTffrN2vrlixQlq0aCEAZM2aNSJy+d1W6ru/zp07JyIia9eu1f66N2rUKFm0aJEYjUax2WzSv39/6d+/vwQGBorFYtHaNptNmjdvLmFhYXLHHXdIWFiYNG/e/Kr7uFJjMpmkZcuWkpOTI6NGjZLIyEjJyMiQV199VUT+99iitp1tc1aj7quy6DN79myHmpkzZ0qnTp20d6rNnj1bLl68KJ06ddKunaS21Rpv97lSzfLly7Wa4h9LF7n8MVeTySTLly8XkcvvaDIajbJo0SK3amJjYyU6Ovqq+6jv5Pnjjz+kcePGEhgYKIcPHxaj0SjNmjWTm2++WQ4fPiwmk0lWrFgh7dq107bZ15RHH/Wv4jfccINERESIoijSsWNHufPOO7V2QECA1n777bcFgFajXvepeB9XajwZ9//+7/9EUS5f1/HVV1+V4OBgCQgIkODgYElPTxej0ag9RjVs2FB7h1D9+vVLrVEfh73ZB7h8rZnXXntNunbtql2XUX0sOXXqlMPjjcFgkLfffltr29c46+NKjSvjtmnTRqpVqybDhw+Xpk2batcB7Nq1q9SoUUNsNpsEBARIYmKidOvWTYKCgiQ8PFzMZrNbNeo1oq62j9lslrp168qkSZOkRo0aEhISIiEhIbJt2zbZs2ePtG3bVhRFkdatW8u2bdu0y1oUr4mLixNFUdzq4+m4BoNBUlJSZO7cufLNN99ISEiIpKSkyKeffiqffvqphIaGSmhoqNZOTU2VunXrSnJysgwYMECSk5Olbt26uj6u1HgyrsVikezsbDl16pTMnTtXUlNTJTExUbvEg/q4obadbXNW07hxY6ePUVc7rvqOe/s+s2fPlhYtWmjv0v7mm2/kzJkz0qJFC+18VNtqTUl9XKlxddzVq1drNfaPawMHDhSj0SirV68WEZH169eLoiiyZMkSt2psNpvYbLarHjcsLExOnz4tGzZskFq1amnXIzQajZKRkSGPPfaY5Ofni8lkktmzZ0utWrW0bfY1nvRxd1z1ca1r164SGxsriqLI448/Ls8995zWtlgsWnv8+PECQKuJjIzUta9UExYWJsWtX79e95zAHZVmUWrChAkSHR0tw4cPl6CgIMnNzZUJEyZoH4cKCgqSYcOGSXR0tNx1111iMBhcqvGkT0k1rVq1EkVRtI+RTJkyRaKiorTForNnz8qYMWO0xQ0RkcmTJ+tOVPsaZ33Onj0r2dnZJY7bunVrh3GL91Hv0Dp06KDVqB8pUj3xxBO6Gvu2qzXqRTK9MW7xhSD7Bb1BgwZJdna2VmPfFhG56aab5MYbb3Srj6fjqosSartNmzbaRVe3bNkiM2bMKLEtIl6pcbWPejH3p556Sl555RV56KGH5JVXXtH+ZWVlSZ06dbT23XffrWtnZ2dLSkrKVfdxteauu+6SF154QbvotLo4FBsbKw888IAEBQVJ8+bNtSfSwOXrjISFhUl4eLh2QcOkpCSpVauWpKWlicViEbPZrKtRrwdR0rjA5bfe16xZUwIDA6VJkya6xS7g8sfqevXqJa1atZKAgACpUqWK9tZ3kctPOkpqu1LjSR9n2woLC+W1116TW2+9VUaMGCEvvviihIWFaddvqlu3rlStWlVuv/12qVu3roSHh0vfvn3l3nvvFYvFIsHBwdK1a1e5++67tXa3bt3k448/1p6MN2rUSAYMGFDquE2bNtXVqBdnLr7YFRISol1QGrh84dPq1avrFphq1qwpXbp00Y6VwMBAueWWW7R2zZo1pVGjRlq7Vq1a8s477+j6RERE6MatU6eOhIWF6fqEhobqFrIiIyN1i1/169cXi8Uib7zxhsTHx+s+HqceS+oTjuJtdW7FLzrpjT6l1RQfOz4+Xt58802ZO3eufPLJJyJy+SOJffr00drOtjmrmT59ujz22GNe79OxY0dp3769rkbddvHiRe2agGrNxYsXJTMzU3sssa/xZh9nNYqiyIwZM7Qa9aNfqv/85z+SlZWl1di3Xa257rrrtMXcq+mjXidJ5PLjWu3atSUuLk6MRqNs3rxZRowYobW3bNkiRUVFum32NWXdR71IdKdOnaR79+7SunVr6d69u/YvKSlJEhIStPZtt92ma9epU0ciIiJ0fVyp8XTc6667Tu677z7t+lfq4lB0dLTugtfq4516bpZWUxZ91Hxms1mys7Plq6++cvqHkpLa3qpxpc/Zs2elV69eUqdOHXnsscfkxIkT0qxZM22urVq1kkGDBmn3ifHx8bJjxw63a1q2bCmNGjW66j79+/fX3RcnJCRI3bp1tfvpmJgYefLJJ6V58+ba7WOz2XQ1UVFR2uOMq308Hfe5556T3r17a3+QMplMuuvBqdeVVNvqH6aKP84EBga6XePJuGo+9Y+RTzzxhIwfP17ee+89ERE5cuSI3HbbbVrb2TZnNW+//bZ2vVNX+7hS06NHD+0ab2pN06ZN5Z577pHTp09rr7PuueceERE5ffq09hFztV285kp9XKlxddxJkyZpNepCier111+XpKQkrca+7WpN06ZNpW7dulc9bseOHbWfnzt3Tpo2bSrBwcFiNBpl/fr18vjjj2uL2Fu2bJFz587pttnXeNLHnXHV+98mTZpImzZtpHr16tKmTRvtX1RUlISHh2vtO+64Q9dW1y+K93FWY/9RvVdffVVat24tnlBERFAJ1KpVCyNGjECnTp0QGhqKnJwc3HrrrXj88cfx6quv4vz584iLi8PIkSORmZmJunXrIjc3t9SazMxMt/u4WpOeno7NmzejZcuWmDlzJm6++Wanc8vPz8e6devQunVrt/bJ6tWrkZ+ff8Vxv/vuO2zcuBEDBgxw2sfZ7/U0C7nujz/+wPr163HDDTfAarWW2nalT1mOW9GcPHkSb7zxBhYuXIg9e/agqKgI8fHxaNmyJZ577jk0bty4TMbNzMx0+HmVKlUQGhqKf/75B4cPH/Zaln9DUVER3njjDfz888/Izs7GwIEDMXPmTAwYMADnzp3DbbfdhnHjxrl93Lgy7pgxYzB27FitZsCAAXjwwQcxf/58GAwGdO7cGW3atMHgwYORn5+PDh064J133sEzzzyDuXPnorCwEC1atMCnn36KtLQ0FBQUYMmSJbhw4QI6d+6MgoICXLp0SftomH27+DYA6NGjh8O48fHxuHTpEn766Sfk5eXh66+/1mqaNm2K6OhoLFmyBIWFhcjKysKTTz6Jp556CgAwffp0HDx4EO3atdM+EqO+HdtZOy0tDXv37i2xxpM+rtb4ukuXLuHcuXPax0vst126dAn79+9HTEyMVlNQUIDdu3drX3dsX+OtPs5qDh06pPsaaPtt586dg9Fo1D7aY9/2Vs3V9Nm8eTPWrFmDrl27IiIiAuvXr9e1AThsK61dVn3s5efnw2g0ah81Lq3trZrS+ogI9u7dC0VRULVqVZjNZogIjh49iqKiIkRHR8NkMunartSUVZ/iH5+qaAoKCnDx4kWEhoY6bXurxtM+27Ztg9lsRs2aNWEymbBr1y6cP39eawNw2FZa25U+no57+vRprF+/XnvsUOeifmTIvh0XF4dq1aph165dV+zjSo2n415zzTW6xwtf9Pfff+PPP/9E7dq1nW47c+YMfvjhB6SlpWk1Bw4cwIoVK9C9e3cAcKhx1seVGlfG/e2333SvJe237d27FxaLRfu4uH3bWzVXM+4PP/yAnJwcDBo0CLGxsViwYAG+//57rQ3AYVtpbVf6eDpucXv27EFAQACqVq3qUtvTGndUmkWpoKAgbN++HSkpKdqiVO3atfHtt9+iQ4cOMJlMuHjxInbs2IELFy6gevXqyM3NLbUmMDDQ7T6u1qSnp2PXrl2oV6+edu0AIiIqP84WmMprXFcWu4iIiIiIKrNKc6HztLQ0bNy40WHbZ599hqysLABAUlISNm7ciMWLF2sXni6txpM+rtYAwOLFi7UaIiIqXxaLpUwWgVwZ177GnSwHDx7Eo48+6nK7rPpUtHGZxX/n6EtZ/GGOvpTFH+boS1lKqvnnn3+wZs0aLF++XNfeunUrCgoK8NFHH+na06dP12o2bNiga7tT48m4vpTFH+boS1n8YY7eylKcWuMRjz7054MmTZokiYmJ8sUXX4jVapUZM2ZIp06dRFEUeeaZZ8RqtUqvXr0kLCxMAgMDJTAw0KUaT/q4WjN8+HAtKxERkas2btwoBoPB5XZZ9alo4zKL/87Rl7L4wxx9KYs/zNGXslypRlEUSUlJ0V2rsGrVqlpbvQC22m7evLkAKLGPKzWejOtLWfxhjr6UxR/m6M0sf/75p3aeHzlyxOG+wFUmz5ayfE/Pnj0RFBSEl156CefOncMDDzyAhIQE9OjRA4sWLcK5c+fw0UcfwWazITw8HMeOHXOpxpM+7tS89957uO+++/7t3UdERD5kwYIFuvbatWt17V9++QVFRUV48cUXAQB5eXkltj3tU9HGZRb/naMvZfGHOfpSFn+Yoy9l8XRcEUFMTAyGDx+Obdu2YcSIEfjrr7/w22+/ISQkBNnZ2QCgtZ944gkAQEZGBtatW4fdu3ejRYsWuj6u1Hgyri9l8Yc5+lIWf5ijN7O0bNkSK1euRHJyMq6KR0tZPubixYsybdo0OXLkiIiI5Ofny6FDh3TbTp06JWPHjnWrxpM+rtbYfwMIERGRqvi33Nl/0x3/8R//8R//8V9F/Vf8ca1z586SnJwsubm5Eh0dLQC09uHDhwWAxMXFSW5urhw5csShjys1nozrS1n8YY6+lMUf5uitLIqiSO/evbWaq3mnVKVYlBIRCQoKkn379pW4zZOashpX5PJXV2dnZ0t8fLz2s3fffVfmzZt3xZrp06dLamqqREREaH0eeOABqVmzZok1pY3rrMaet7KUVuNL4zJL5Z1jRTrXKtr+9mT/uvJ7ynL/lkded7ImJCTottu3Y2JidF+fvGHDBgGg1di3Pe1T0cZlFv+doy9l8Yc5+lIWf5ijL2W5mnG3bt3q0H7qqaekatWqEhwcLIqiaO1Vq1YJALn//vt17eJ9XKnxZFxfyuIPc/SlLP4wR29lURegitd4uihVaS503rRpU2zYsKHEbZ7UlNW4EydOxPPPP4+OHTvi5MmTKCwsBABs3boVXbt2RUJCAoYPH47nn38eNpsNf/31F6ZPn47nn38ejRo1wqlTp1BYWIiJEydiwYIFOH/+PE6ePOm0prRxCwsL8cknn2D8+PHo0qUL9u/fDwB48MEHkZWV5dUspdX40rjMUnnnWJHOtYq2v53NceLEiXjiiSeQlpbmcRZXxnVW88knnyAtLQ2RkZEu9ymLvM5uV/vjpXjeY8eOYcWKFQCA0aNHIzExEevXr9ceQ2rVqgUp9uW5iqIAgFZj3/a0T0Ubl1n8d46+lMUf5uhLWfxhjr6U5WrGXbdunUN73LhxuOOOO3D+/HkA0Nq33347AKBDhw66dvE+rtR4Mq4vZfGHOfpSFn+YozezOBvHIx4tZfmgmTNnSnp6uowdO1Z++uknycnJkbfeekuqVq0qAwcOlJ9++knXnj59uks1nvRxpSY9PV3effddycnJkeDgYMnNzZUJEyZIRESEBAUFSVBQkGRmZspXX30lU6ZMEaPRqLU3bdokACQ3N1eysrJk9OjREhUVJSEhIU5rSht32LBhEh0dLX369NH1CQkJkbS0NK9mKa3Gl8Zllso7x4p0rlW0/e1sjllZWdKnTx9p06aNx1lcGde+Rm3fddddYjAYXOpTVnmd3a72x0vxvIqiyOTJk0VEZMqUKdKgQQP59ttvtce8JUuWyJtvvqm1z549K2PGjNFq7Nue9qlo4zKL/87Rl7L4wxx9KYs/zNGXsng6bq9evaRDhw5O2yIizZo1k+IvTZ944gkBoNXYt12t8WRcX8riD3P0pSz+MEdvZSn+bsgrbXNVpVmUUj+fXPwfoP/8cvG2s22uXLfDm+MWz6m+qBk/frxYLBYJCQmRwMBA2bdvn/bCR23v3LlT62OxWOT777936FO8prRx1RdL9n2Kv8DyVpbSanxpXGapvHOsSOdaRdvfzuZosVhkyZIlDnN0J4sr49rXOFs8Kq1PWeV1drvaHy/O8oqIbNq0SaKiov7lR1kiIiIiIu+rNN++t3fvXodtf/zxh/b/VatW1bWdbXNWY8+VPq7UdO/eHQMGDED79u1Ru3ZtbQ7Hjh1DVlYWdu3ahaSkJGzcuBG1atUCAK194MABGAyXP3mZlpaGzz77zKFP8ZrSxj148CAaNmyIBQsW6PrUr18f+fn5MJlMXstSWo0vjcsslXeOFelcq2j729kc09LSkJub6zBHd7K4Mq59jdq+cOGCbl+W1Kes8jq7Xe2PF2d5ASAwMBD5+fkgIiIiIqp0/u1VMX81adIkSUxMlC+++EKsVqvMmDFDYmJiJDAwUGbMmCEhISEyYsQISUxMlO7du4vBYJBevXpJWFiYBAYGanWdOnUSRVHkmWeeEavV6rSmtHETExPlwQcfFKvVKhaLRfsLfs+ePaVhw4ZezVJajS+NyyyVd44V6VyraPvb2RwnTZok4eHhkpqa6nEWV8a1r8nMzJR58+bJmDFjtI/vldanrPI6u13tjxdneUVExowZIw0bNvyXH7WIiIiIiLxPESl2FboKbPr06Q7b1qxZo/3/tddeq2s72+asxp4rfVyt+emnnzBv3jwcPXoUABAeHg5FUTBx4kT06NEDH330EebMmYM5c+Zo/Ww2G8xmM44dOwYASEhIQIcOHbBy5Urk5uY6rSltXBGBwWBAz5498dlnn+Gjjz7CzJkzMX/+fPTp0wcff/yx17K4UuNL4zJL5ZxjRTvXKtL+djbH3NxcvPrqq4iIiEBeXp5HWVwZ177mgQcewMyZM7ULOk6dOrXUPmWZ1/52tT9erpT39ddfx0cffYT77rsPRERERESVyr+2HOZlNptN989qtequ3WSz2Ryu5+RKjSd9SqsBIAEBAdp1Rmw2m+Tl5YmIyKeffiqZmZlaXVxcnHz00Udy6tQpGTt2rBw5ckRERPLz8+XQoUMybdo0bZuzmtLGVWvUn6vXuUpMTJSePXt6NUtpNb40LrNU3jlWpHOtou1vZ3MEIBEREfLOO+94nMWVca+0fyMiIiQ2NtblPmWR90rHQvHjxVle9fj46KOPhIiIiIioMqo0i1LO7Ny5U66//npZvHjxFbd5UuONcYOCguT777/X2hcvXnR4URMUFCT79u3T+tu3nW2zb5c2rrOfO3uB5Y0srtT40rjMUjnnWNHOtbLad2Wxv53NMS8v76qzuDKufc2VFsz+rbylHS8lLXQSEREREVVWhjJ6A5ZPqFatGt544w307dv3its8qfHGuE2bNsXJkye1tslkQu/evVFQUAAACA4ORtOmTbFhwwatv33b2Tb7dmnjOvt5QkJCmWRxpcaXxmWWyjnHinauldW+K4v97WyOsbGxV53FlXHta8LCwjBgwAC3+pRl3tKOlyvlBYBPPvkELVu2REJCAvbv349PPvkEaWlpiIyMdNoGgAcffBBZWVle7VPRxmUW/52jL2Xxhzn6UhZ/mKMvZfGHOfpSFn+Yoy9l8Yc5eisLAIwePRrz58+HR/7tVbGytmHDBgkNDS1xmyc1VzvuzJkzJT09XV544QUJDg6WnJwcady4sbz77ruSk5MjIqLVjB07Vn766Sd56623pGrVqjJw4ECZPn265OTk6LZdqaa0cYv/XK1p3bq1fPXVV1pub2UprcaXxmWWyjvHinSuVbT97WyOpe1fV36PK+Pa19jvW1dvk7LI6+x2tT9enOWdMGGCREdHy/DhwyUoKEiGDRsm0dHRctddd4nBYHBo5+bmyoQJEyQkJETS0tK81qeijcss/jtHX8riD3P0pSz+MEdfyuIPc/SlLP4wR1/K4g9z9FYW9Yt5pkyZIm3atBFPVJpFqfnz5+v+zZs3T3r37i1JSUnSqFEjmT9/vvznP//RbXOlxpM+rtSo1wtBsWtPqW1FUeSnn37SttnXFu9TUtuTcRVFcfoCy1tZSqvxpXGZpfLOsSKdaxV5f6tzLG3/uvt7XBlXURSni0eu3CZlkfdK/4ofL87ypqenawtX6jf0ffXVV7Jp0yYB4NBWv9Fx9OjREhUV5bU+FW1cZvHfOfpSFn+Yoy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKO3sqiLUps2bZKoqCiP1nJMqCQ6deqkayuKgqKiIgDAH3/8gU6dOkGKfdHgH3/8gbvuuqvUmt9++83tPq7UAEBUVBSys7Px0ksvITY2FmlpaVAUBQDQsmVLiAgURdH+a/8NflWrVtXGctZWt5U2rvozVYMGDbS8b7zxBt58802vZSmtxt6/OS6zVN45VqRzbe/evRVqf1933XUOcwRwxfm4miUtLa3Uce1rBgwYAODyvgWg7f+S+pRVXme3q7PjxT6viOC5556DwWBAcHAwDh48iIYNG+LChQsA4NAGgL1796J+/frIz8+HyWTySp+KNi6z+O8cfSmLP8zRl7L4wxx9KYs/zNGXsvjDHH0piz/M0VtZVIGBgcjPz4cnKs2ilLoIVJHt3bu3xJ+npKSUus1ZTWnjOmP/AstbWVyp8aVxmaVyzrGinWsVaX+7sm89yeLKuPY1zhbVSuvjbNx/M+8NN9yAAQMGoH379qhTpw6SkpKwceNGHDhwAAaDwaENAGlpafjss8+QlZWFXbt2eaVPRRuXWfx3jr6UxR/m6EtZ/GGOvpTFH+boS1n8YY6+lMUf5uitLKrFixcjKysLnqg0i1LDhg1D//79ERwcrNv21FNPYfz48Rg8eLBWoygKRo4cqW0rqQaA231cqUlNTQUAXLhwAYsWLXJ4p1fXrl0xffp03Tb7d29ce+21um32bXVbaePa69q1q8MLLG9lKa3G3r85LrNU3jkW5+vnmn0eX9/fnuxfV7LYc3X/2nOlT1nkLY2zYwEAXnjhBbzyyisICwuDiKBt27bo2rUrzp8/D7PZ7ND+5ZdfUKNGDXz88cfo06cPdu7c6ZU+FW1cZvHfOfpSFn+Yoy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKO3svzyyy+YMWMGXn/9dXz00UduPwcGgEpzTSmDweDw9dkGg0G2bdsmBoNBV3P8+HHdtpJqPOnjSo3NZhObzSbh4eECQKxWqwAQk8kkwcHBMm3aNAkODtb9g901StQ+attms12xpqRxAwICHGp69eql/fN2lpJqfGlcZqncc6wo51pF29/O5lh8Pp5mcWVc+5ri+9bZ/nV1XG/kvdLteqVjQc07bdo0efzxx6VKlSradagiIiIkNjb2iu3ExETp2bOnZGZmerVPRRuXWfx3jr6UxR/m6EtZ/GGOvpTFH+boS1n8YY6+lMUf5uitLImJifLRRx95vJZTaRalFEWRo0ePOmybM2eOREdH62qWL1+u21ZSjSd9XK0REV07LCxMTCaThISEiM1mE6vVKoqiSGBgoERERIiIyM6dO+X666+XxYsXO20721bauDabTVfj7AWWt7J4UuNL4zJL5ZhjRT/XfHl/O5uj/Xw8yeLKuPY1zhaPSutTnnntjxf7vOoCaUBAgERERMipU6dk7NixcuTIERERh3Z+fr4cOnRIpk2bdsUaT/pUtHGZxX/n6EtZ/GGOvpTFH+boS1n8YY6+lMUf5uhLWfxhjt7Kkp+f7/DGIE9U+EUpm80mERER2ruPIiIitBW7kv66faW/eNvXeNKntBr1RUlYWJgYDAZ58skntfn8+uuvUqNGDa3t7AWgfY1925Wa0sZ15cWpt7K4UuNL4zJL5ZxjRTvXPJljWfUprcbZfLyRxZVxXVlU+zfzlna8WCwWyc7O1tpBQUGyb98+rd6+7UqNJ30q2rjM4r9z9KUs/jBHX8riD3P0pSz+MEdfyuIPc/SlLP4wR29l8YbLV6uqwEaPHo1Ro0ZBRDB06FC8++676NGjBx599FEoioJnn30WPXv21Npdu3bFSy+9pNvmrOa2225Djx493Orjas0HH3yAd999F++//z7WrFmD8ePHa/MxmUz4888/tXa1atXwxhtvoG/fvlessW+7UlPauM5+XlZZXKnxpXGZpXLOsaKda57Msaz6lFbjbD7eyOLKuPY1nvQpy7ylHS/NmjXDnXfeqbWbNm2KDRs2aPX2bVdqPOlT0cZlFv+doy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKMvZfGHOXorizdU+Audd+vWDcDlq8BnZ2fDbDZr2x5++GFtm7O2KzWe9HGlZsGCBQCAY8eOYcGCBRARHD58GOPGjUPLli11NXv27MGBAwfw4osv4sSJE/jmm29QrVo1zJ8/H4sXL9baCxYswNq1ax1qShvXWQ2gf7HkrSyl1fjSuMxSeedYkc61ira/r7Qf1Pl4muVK95HFx3VW42z/ltanLPK6eizY533yySfRr18/HDt2DD///DNuueUW9OnTB2vXrkWtWrUc2vXr19dtu/32273Sp6KNyyz+O0dfyuIPc/SlLP4wR1/K4g9z9KUs/jBHX8riD3P0Vhar1ap7HluvXj24SxERcbuXjysoKMCFCxd02wICAnTbCgoKYLFY3KrxpM+VauLi4nQ1iqIgNDQU9erVw6OPPorIyEjtG/mc3USKokBRFBQVFWlt+1q1prRxFUVBWFiYVhMREaG9wIqJicGQIUO8lqW0Gk/6lNW4zFJ551iRzjWDwVCh9jcAhzneeeedun3gSRZXxrWv2bVrl7Z4FBMTg8GDB5fap6zyPvroo7r9ZTAYHI6XtWvXlpq3tIfs0vJ62qeijcss/jtHX8riD3P0pSz+MEdfyuIPc/SlLP4wR1/K4g9z9FaW4tsVRUFhYWGJv8fp75bS0lUQ586dw4ABAzBr1iz89ddfAC4/+S/+AqB429k2ZzX2O96VPq7WFFdYWOiwTVEUxMTEoF27dnjnnXcQHx/v0b4pbdzExESHn9u/wPJWFiJfwHOt7Pxb+zY+Pt6hxv6+3JU+vpY3MjIS2dnZeOmllxAbG4s//vijxN9btWpVXY1929M+FW1cZvHfOfpSFn+Yoy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKO3sthLSUkp8fc4JZXEk08+KVlZWTJ79mwJCgqSyZMnS9OmTcVkMskzzzwjQUFB0rZtW4mJiZHIyEgJCAhwqcaTPq7UdOrUSV5++WWpWrWqfPrpp9o8zp07J0OHDhURkaFDh0p+fr72M7VtX3P8+HGHPsVrShvXWY09b2UprcaXxmWWyjtH++Pdl8+1ira/Pdm/rvyesty/5ZHXk6xERERERJVdpXmnVHJyMqZPn442bdogLCwMv/32G9q1a4eHHnoIGzduxJo1axASEoLPP/8cBw8exCOPPILt27eXWhMbG+t2H1dqRC5fb2TJkiWYMWMGFi1ahGHDhuGRRx5BamoqCgsLYTQacfjwYYSGhmLkyJEYOnQoDh8+DKPRiNjYWK1my5YtqF27tq5P8ZrSxgWA/v37459//tH1eeqppzB+/HgMHjzYa1lKqwHgM+MyS+WdY0U619RtFWV/DxkyxGGORqMRe/bswbRp03RzdCfLsGHDSh3XvkbNoiiKbl+W1Kes8qp9it+u9seL2qd43unTp+PChQtYtGgROnXqhDVr1pT4WHjttdfqauzbnvapaOMyi//O0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNGXsvjDHL2VxV7Xrl1L/D1O/cuLYl5jtVpl//79IiKSmJgoa9euFavVKqtXrxar1SqJiYkSFBQk+/fvlz179oiiKC7VeNLHlRpFUeTo0aOyZ88esVqtIiJiMBhk9uzZEh0dLSKi1Rw/flwMBoPWXr58ua5mzpw5Dn2K15Q2rsFgkLy8PIc+27ZtE4PB4NUspdX40rjMUnnnWJHOtYq2v53NUVEU2b59u8Mc3cniyrj2NWrbfl+W1Kes8jq7Xe2PF2d5bTabhIeHCwCx2WwCQPunKIpD22q16tre6lPRxmUW/52jL2Xxhzn6UhZ/mKMvZfGHOfpSFn+Yoy9l8Yc5eiuL1WoVRVEkMDBQIiIixBOVZlGqbt26snLlShERuf7666Vfv35St25d6dOnjyQmJsr1118v0dHRsnLlSnnvvfckMDDQpRpP+pRUExQUpLsRg4ODRVEUiYiI0LarN6jBYHA4GEo7mIq31XGuNG5oaKjWJyQkRAwGgzz55JMi8r8XWGq2q83iao0vjcsslW+OpZ0TnmYpi3MtNDS0wu3vwMBA3RyL74eQkBCPshTft1cat/h9aPH9qyj/Wzwqvn+v1Kcs8qrb7I+F4sdL8WPBfrFLRBzaO3fulOuvv14WL17sUrus+lS0cZnFf+foS1n8YY6+lMUf5uhLWfxhjr6UxR/m6EtZ/GGO3srirkqzKDVq1Ch57733RERk6dKlYrFYxGg0iqIoMnr0aFm6dKmYTCYxGo1iMBjkiSeecKnGkz4l1SiKIm3bthUAWttsNmsrjKGhoRIcHCzBwcG6F3lZWVmiKIp07dpVXnrpJenZs6f06NFDFEWRZ599VtdWa4KDg0sd12QyiaIoEhwcrL1YKv4Cy2QyeSVLaTW33Xab233KalxmqbxzdOWc8JVzTVEUycrKqjD722KxaPtAnWPx/WA/H1ezFN+3VxrXZDLpapwtJhXfv876lFVei8XicLsW/93OjoXii1thYWG6RUzVr7/+KjVq1HC5XVZ9Ktq4zOK/c/SlLP4wR1/K4g9z9KUs/jBHX8riD3P0pSz+MEdvZXGHCZXEc889p/3/DTfcgO3bt2P9+vXIzMxEvXr1AAC7d+/WbXvhhRfcrvGkj7OamTNnQlEU1KxZExs2bICI4NFHH8Xw4cMRHh4OANi+fTtq1aqFzMxMtGjRAj/88AOys7NhNpu1uT788MO6bcXbmZmZJY67c+dOZGRk4NFHH8Xrr7+O8PBwrFmzBiKCyZMno2/fvmjQoIFXsrha40mfshqXWSrfHEs7J3zxXKso+7tdu3baPlDnqMrNzcWNN96I6667zu3f06BBg1LH3bNnj65m06ZN2r59+OGHkZycjMzMzBL7lHXe4rfrzp07dcdLSXkDAgKQmpqqHQsqk8mEP//80+V2WfWpaOMyi//O0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNGXsvjDHL2VxR2V5kLnxRUUFMBisZS4zZOashz3woULWLNmDZo1awaz2YywsDCHMS5cuFDi7wkICHBaU9q4K1as0H4OAGFhYU5flHsjizs1vjQus1SuOVbUc82dOZZ1nyvVrFu3zmGOJc3J1SzO9p39uPY1GzZsuOK+vVKf8sh7pft8Z8fCggULdGOvXbsWJ06cwDfffIOYmBjcfPPNuvbgwYOxePFirT1kyBCv9Klo4zKL/87Rl7L4wxx9KYs/zNGXsvjDHH0piz/M0Zey+MMcvZVF/v8XuI0bNw5JSUn49ttvnT7vLpFH76/yQZcuXZJhw4ZJQkKCGI1Gyc3NlUuXLknr1q3FZrOJ0WiUnTt3yrBhwyQ0NFQMBoNLNZ70caVm8+bN8tRTT2nXlFIvgqz+++eff+Tw4cPSq1cv7YK99h/vsN92pZrSxi3+T1EUOXXqlO6fN7OUVuNL4zJL5Z1jRTrXKtr+djbH0vavK7/HlXFL2r95eXku9ymLvCX9XvV4cZZX/Wifs4/4OWsbDAZd21t9Ktq4zOK/c/SlLP4wR1/K4g9z9KUs/jBHX8riD3P0pSz+MEdvZTEYDFKlShW5//775c8///RoLafSLEoNHTpU0tPT5dNPP5WgoCDJzc2VoUOHSmxsrGRmZkpQUJA8++yzkp6eLk8//bQYDAaXajzp40rNQw89JFlZWfLcc8+JwWCQyZMny8svvyxWq1W7hoj9QdO2bVuJiYmRyMhICQgIkMmTJ0vTpk3FZDLJM888I0FBQU5rSht34sSJ0qxZM+3Fk7MXWN7KUlqNL43LLJV3jhXpXKto+9vZHIvPydMsroxrX2P/YOpKn7LK6+x2tT9enOUt/o+IiIiIqLKpNItSGRkZsmzZMhERCQkJkdzcXMnIyJDJkyeLzWaTkJAQSU5OlmXLlsm2bdsEgEs1nvRxpSY+Pl6+//57rb1r1y558sknJSEhQRo2bChBQUESGRkpjz76qFStWlUsFovWZ/r06WI0GmXXrl2SlJQkgwYNkg4dOkhoaKjTmtLGffDBByUrK0v69OmjLZDZv8DyVpbSanxpXGapvHOsSOdaRdvfzuYYHh4u0dHR8umnn3qcxZVx7WucLR6V1qes8jq7Xe2PF2d527VrJwkJCfLpp5+KyOU/wOTn58u5c+dk6NChDm215vjx47r21fapaOMyi//O0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNGXsvjDHL2VRVW8xl2VZlHKYrHIvn37ROR/i1IWi0WWLl0qVqtVQkJCJDAwUPbt2ydbtmzRFo9Kq/Gkjys1wcHBsn//ftmyZYsoiiJr166VpKQk+fzzz8VqtWrfBLV//36ZPn26BAYGSlBQkOzfv1/27Nmj9bFarbJ69WqxWq2SmJjotKa0catUqSLff/+97NmzR1sgs3+B5a0spdX40rjMUnnnWJHOtYq2v53N0Wq1yqhRo6RDhw4eZ3FlXPsaZ4tHpfUpq7zOblf748VZXoPBIOPGjZMOHTqIiIjBYJC8vDw5fvy49g6q4m21Ztu2bbr21fapaOMyi//O0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNGXsvjDHL2VRVW8xl0GVBK1atXC6tWrHbZNnDgRDRs2BABkZmZi9erVmD17NoxGo0s1nvRxpSYpKQl79+7F7NmzERYWhlmzZuHEiRPYsWMHbDYbwsLCkJCQgL179+Laa6/FxYsXYbVasXfvXixcuBABAQGYNWsW0tPTMWvWLNhsNtSsWdNpTWnjnjhxAunp6Vi4cCEMBgNOnDiBEydOoGPHjli1apVXs5RW40vjMkvlnWNFOtcq2v52Nsf09HTEx8dj1apVHmdxZVz7mpMnTyI9PR3XXnstioqKXOpTVnmd3a72x4uzvCKCZs2aYdWqVQAAEYGiKMjJyUFkZKRDW63ZunWrrn21fSrauMziv3P0pSz+MEdfyuIPc/SlLP4wR1/K4g9z9KUs/jBHb2VRFa9xm1QS8+bNk/DwcHnjjTckODhYRo4cKTfeeKMAkB49ekhwcLB069ZNAgICxGg0SmBgoEs1nvRxpea2226T5s2bS0BAgLzxxhtisVi0a6CMHj1arr/+emnTpo2899578t5770lUVJSYTCYxGo1iMBjkiSeeEIvFIkajUeuzdOlSpzWljasoipjNZjEYDFK9enXp16+f1K1bV/r06SOJiYlezVJajS+NyyyVd44V6VyraPvb2RxHjRold911lyQmJnqcxZVx7Wuio6Nl5cqV8t5770lgYKBLfcoqr7Pb1f54KZ4XgFgsFjEYDBIUFOT0opTO/tnXeKtPRRuXWfx3jr6UxR/m6EtZ/GGOvpTFH+boS1n8YY6+lMUf5ni1WQIDAyUiIkIiIiK066M++eSTHq3lKCIiqCRWr16NYcOGIScnB2fPnkWjRo1w5513YvHixdq2jIwMmM1m/PHHHy7XeNLH1ZrBgwejffv22L9/P1555RXExsbizTffxLJly3DbbbdBRHDx4kWMGjUKnTp1wvr165GZmYl69eph//79ujYAh22ujHv8+HH8+eef6Nu3L44ePYrbbrsNFy9eRFFREd59913Url3ba1ncrfGlcZml8syxIp9rvr6/X375ZYc5Fp9P3759Pcriyrj2NR06dICIQETw+OOPY8qUKaX2Ka+8nTp1cjheiudt3bo1Vq9ejUuXLkFRFDRr1gy1a9fG5MmT8fDDD+Pvv/9GbGys1k5OTsaRI0cgIpg8eTL69u2Ls2fPam1P+1S0cZnFf+foS1n8YY6+lMUf5uhLWfxhjr6UxR/m6EtZ/GGO3srSoEEDbR0mICAAqampaNGihUfrOJAKLjc3V4qKikrc5klNWY3rzD///ON0+759+2TOnDmSk5PjUOOsjys1pY1r//OyzFJajS+NyyzlO255ZhGpOOeaK7/bl/a3iOMcvZHFlXGd1XjSpyzyunK8qO1hw4bJunXrtJqVK1fKhQsXrth2pcaTPhVtXGbx3zn6UhZ/mKMvZfGHOfpSFn+Yoy9l8Yc5+lIWf5ijt7J4Q4V/p5TRaMThw4cRGxsLALj33nsxe/Zs3TaDwYDff/8dderUcbnGkz6u1owZMwbR0dEYMWIE3n//feTl5WHnzp1IT0/Hyy+/jNTUVDz44IMwm826mm3btuGLL77AyJEjkZ+fj127diElJQXXX389cnJycObMGac1pY1b/HcnJiaid+/eun1cUFDgtSyl1Wzfvt1nxmWWyjvHinSu7dy5s0Lt7+L7QZ1jYWFhifvXlSzO9p39uCXt34KCAlgsFpf6lEXeXr16lXqfb7FYHPLaHx8XLly4Yo2zPgEBAV7vU9HGZRb/naMvZfGHOfpSFn+Yoy9l8Yc5+lIWf5ijL2Xxhzl6KwsAhIWFwW1eXeL6FyiKInl5eVo7JCTEYRsAWbt2rVs1nvRxtSY3N1eGDh0q6enp8umnn0pQUJDk5ubKpUuXpHPnzmI2m8VoNMqzzz4r6enpcscdd0hAQIDWfvrpp8VgMGjjxMbGSmZmpgQFBTmtKW3cnTt3yrBhwyQiIkIAaH1at24tNpvNq1lKq/GlcZml8s6xIp1rFW1/O5vj0KFDxWazSc+ePT3O4sq49jVqOzQ0VDduSX3KKq+z29X+eHGWNz8/Xxo3bqxtK+3z/vY13upT0cZlFv+doy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKMvZfGHOXori/0/T3BR6go1nvRxZ1EqIyNDli1bpts2dOhQSUpKkuDgYAkKCpLk5GRZtmyZfPHFF2IwGLT2tm3bBIA2zuTJk8Vms0lISIjTmtLGVV8svfXWW7o+xV9geStLaTW+NC6zVN45VqRzraLtb2dzzMjIkBdffFGaN2/ucRZXxrWvcbZ4VFqfssrr7Ha1P16c5X3yySclMTFRqlWrJkFBQdK2bVuJiYmRyMhICQgIcGhPnjxZmjZtKiaTSZ555hmv9alo4zKL/87Rl7L4wxx9KYs/zNGXsvjDHH0piz/M0Zey+MMcvZVl8uTJ8uqrr0rVqlXl008/9WhNp8IvShkMBjl69KjWDgkJcdgGQH799Ve3ajzp42rNnj17xGKxyL59+7Rt6ouajz/+WKxWq4SEhEhgYKDs27dPe+Gjtrds2aK9ELJYLLJ06VKHPsVrShtXfbFk36f4CyxvZSmtxpfGZZbKO8eKdK5VtP3tbI4Wi0WWLVvmMEd3srgyrn2Ns8Wj0vqUVV5nt6v98eIsb1JSkkybNk1sNpuEhoZKfHy8fP/99zJ9+nQxGo0O7V27dklSUpIMGjRIOnTo4LU+FW1cZvHfOfpSFn+Yoy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKO3suzatUtERKZPny4dOnTwaE3HhApORNC9e3cEBgYCuPxZx6KiItStWxcGg0Gr69ChA8xmMwAgPz8fIlJiDQC0bNkSUVFRLvdxtaZ58+awWCy4++67kZycjIKCAgDAoUOHsHnzZjRs2BAbN25EZmYmVq9ejUaNGgGA1t6zZw+MRiMAoFatWpg4caJDn+I1pY2bl5eHzMxMTJs2TdcnKSkJFy9ehKIoXstSWo0vjcsslXeOFelcq2j729kca9WqhXXr1jnM0Z0sroxrX6O28/PzdfuypD5lldfZ7Wp/vDjLe+LECSQkJODixYuw2Ww4ceIE0tPTkZKSgqKiIpw8eVLXPnHiBE6cOIGOHTtizJgxXutT0cZlFv+doy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKMvZfGHOXozCwBce+21eOKJJ+CJCr8o1a1bN137oYcewpo1a3TbQkNDYbPZtHZGRgby8vIQGhp6xRr1Z1ar1eU+rtYAQHJyMlavXq1tnzt3LqxWK8aMGYNvv/0Wd911F/r06YOnn34aTZo0gcFgQOPGjdGjRw8UFhbCZDJh7ty5iIqKwty5c9GjRw/89ttvTmtKGzc6OhrdunXDzz//jICAAACOL7C8laW0Gl8al1kq7xwr0rlW0fa3szkOHjwY9913H+Li4nD06FGPsrgyrn1Nenq6w+JRaX3KKq+z29X+eHG22JWeno7PP/8cDRs2RGBgIHJycrB3717k5OQgICAAVqtV1541axbS09Mxa9Ys2Gw21KxZ0yt9Ktq4zOK/c/SlLP4wR1/K4g9z9KUs/jBHX8riD3P0pSz+MEdvZZk1axaaNm2KhQsX6tY93OLR+6vIK1atWiU33HCDxMTESFBQkNSsWVOsVqu88cYbEhwcLCNHjpRbb71VFEWR8PBwCQoKkjp16kjDhg21Pi1btpS3335bN459TWnjBgQEiNFolEcffVT7+Y033igApEePHl7N4kqNL43LLJVzjhXtXKtI+9vZHHv27Ckmk6nE313a73FlXPuabt26adsCAwNd6lOWee1vV/vjxVneZs2aidFolO+++06WLl0qJpNJjEajGAwGeeKJJxzaFotFjEajKIoio0eP9lqfijYus/jvHH0piz/M0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNFbWSwWiwQGBorBYJDRo0d7tC7CRalylpubK0VFRVf8+apVqyQ7O1uio6O1FzpTp0516GM/jifjLlmyRPfz4i8y1RdY3spSWo0nfcpqXGapvHMsztfPtSVLllSo/W2/D2644QaJjIwUi8Xi8nw8HddZjf3ikSt9yiJvScdCSXmLHx/F++zbt0/mzJkjOTk5LrXLqk9FG5dZ/HeOvpTFH+boS1n8YY6+lMUf5uhLWfxhjr6UxR/m6K0snuCiVDkzGAy6b+fr0qWLrF27Vveixr5GURTZtGmTro8rNaWN27FjRzl8+LAun/0LLG9lKa3Gl8Zllso7x4p0rlW0/e1sjt7I4sq49jXOFoo8GdcbeV25zy9toVP1zz//uNUuqz4VbVxmKd9xmaV8x2WW8h2XWcp3XGYp33GZpXzHrYxZPMVFqXKmKIruBYr6jX3FtwHQvfABIGvXrtX1sR/HWY274zp7geWtLKXV+NK4zFJ551iRzrWKvr+7dOlSJllcGdfZ4pE3xvV0v9jfrq4sAh45ckRERC5duiTDhg2ThIQEMRqNsnPnThk2bJiEhoaKwWBwaOfm5sqlS5ekdevWYrPZvNanoo3LLP47R1/K4g9z9KUs/jBHX8riD3P0pSz+MEdfyuIPc/RWltzcXBEReemll+Sjjz4ST/zvK+LoX3P5dYveuXPnyn3cRYsWlVkWIl/Ac638LFq06F8bV0R0+9KVPmWVF3C8Xe1ve2d51W/ge+211zB16lS89dZbCAgIwIQJEzB16lTtSz7s22qfbdu2ITo62mt9Ktq4zOK/c/SlLP4wR1/K4g9z9KUs/jBHX8riD3P0pSz+MEdvZVHVqVMHH330ETzi0VIWecxgMMjRo0e19pX+0v7LL7/o2r/++quuj/04zmpcGbd4uyyzlFbjS+MyS+WdY0U61yri/i6r/K6Ma7+/nb2bzZNxrzavq7e9fV71r04ZGRmybNkybXtycrIsW7ZMtm3bJgAc2rm5uZKRkSGTJ08Wm83mtT4VbVxm8d85+lIWf5ijL2Xxhzn6UhZ/mKMvZfGHOfpSFn+Yo7eyqM9Zt23bJjabTTxhApUrEUH37t0RGBgIACgoKICI4JFHHtG2AcCAAQMQERGhtTt06ACz2QwAyM/Ph4igbt26MBgMJdaUNm7xtppFURRdZm9lKakGAFq2bImoqKh/fVxmqdxzrCjnWkFBAYqKiirU/u7YsSOuu+46Xf7i93eeZnG2X+zHtd/fL7zwAr7//nutbX/f6+q4V5vX2X5xdrwU7w9AOzYOHTqEzMxMbXteXh4yMzO1d1LZt9U+SUlJuHjxIhRF8UqfijYus/jvHH0piz/M0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNFbWVRFRUW4ePEiPMFFqXJW/K1vAPDQQw9h6tSp+P3333UvSnbu3AmT6fLNY7Va8c8//8BmswEAMjIykJeXh9DQUK0+NDRU+7las3v37hLHBYD//ve/iI2NBQAUFhYCgMMLLG9kKa1G/ZnVav3Xx2WWyjvH0s4JXzrXEhIS8Ndff1WY/Q1c/pja+vXrtTkGBQVh8+bN2s+tVqvbWQBg9+7d2n5yNq59DQD8/PPPSEhIAHB58SgwMLDEPmWVV/396n5JSEjAgQMHHI4X+0XA5s2bo2XLlrBYLLj77ruRnJwMAMjMzMTq1auxZ88eGI1GhzYA1KpVCxMnTkTDhg2xceNGr/SpaOMyi//O0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNGXsvjDHL2VRTV79mw0bNgQnuCiVDmbMmVKufV95JFHSvz5mjVrAADXXnutwzZVZmam7ueeZiHyBaWdEyqea+77t/YtcHlflrRvMzIyADjuf2f7296/mTc8PByNGjXC6tWrAVz+C1Tjxo3Ro0cPFBYWwmQyObTnzp2LqKgozJ07Fz169MBvv/3mlT4VbVxm8d85+lIWf5ijL2Xxhzn6UhZ/mKMvZfGHOfpSFn+Yo7eyzJ07Fzt27MD06dPx9ddfu/W8WaX8/+tZEBERkQ9avXo1hg0bhpycHJw9exYZGRkwm834448/nLYbNWqEO++8E4sXL/Zqn4o2LrP47xx9KYs/zNGXsvjDHH0piz/M0Zey+MMcfSmLP8zRW1kaNWqEwYMHo3379p492fXoSlRERERUZnJzc6WoqMjldln1qWjjMov/ztGXsvjDHH0piz/M0Zey+MMcfSmLP8zRl7L4wxy9lcWbuChFRETkYwwGg+4b+hRFkU2bNl2x3aVLlzLpU9HGZRb/naMvZfGHOfpSFn+Yoy9l8Yc5+lIWf5ijL2Xxhzl6K0uXLl3kyJEj4g2G0t9LRUREROVJ7D5ZLyI4d+7cFduLFi0qkz4VbVxm8d85+lIWf5ijL2Xxhzn6UhZ/mKMvZfGHOfpSFn+Yo7eyLFq0SPetfFeDi1JERERERERERFTuuChFRETkYxRFgaIoum0Gg6HEdln1qWjjMkv5jsss5Tsus5TvuMxSvuMyS/mOyyzlO25lzGLf9hS/fY+IiMjHGAwGdOjQAYGBgQCAr776CtHR0TCbzQCAw4cPIyAgAFFRUQCAI0eOQERQpUoV7UnE4cOHr7pPRRuXWfx3jr6UxR/m6EtZ/GGOvpTFH+boS1n8YY6+lMUf5uitLEeOHMGNN94Iq9WK4ubOnQt3mdzuQURERGWqW7duunZmZqauHRoaCgDaE4GMjAzk5eVp29Uam812VX0q2rjM4r9z9KUs/jBHX8riD3P0pSz+MEdfyuIPc/SlLP4wR29lycjIQEJCAryB75QiIiIiIiIiIqJyZyi9hIiIiIiIiIiIyLu4KEVEREREREREROWOi1JERERERERERFTuuChFRERERERERETljotSRERERERERERU7rgoRURERERERERE5Y6LUkRERERX0L17dyiK4vBv9+7dVz321KlTYbPZrj4kERERUQVl+rcDEBEREfmym2++GVOmTNFti4mJ+ZfSOHfx4kWYzeZ/OwYRERGRW/hOKSIiIqISBAYGIi4uTvfPaDRi/vz5aNSoESwWC9LT0zF06FBcunRJ6zdq1CjUrVsXVqsVSUlJePLJJ3H27FkAwMqVK/HII4/g1KlT2ruvXnnlFQCAoiiYN2+eLoPNZsPUqVMBAPv27YOiKJg5cyZat24Ni8WCzz77DADw0UcfISsrCxaLBTVr1sSECRO0MS5cuICnn34a8fHxsFgsSElJweuvv152O46IiIioFHynFBEREZGbVq9eja5du2LMmDG47rrrkJubi8ceewwAMGTIEACAwWDAmDFjkJaWhj179uDJJ5/EgAEDMGHCBGRnZ2P06NEYPHgwduzYAQAICQlxK8PAgQPxzjvvoGHDhtrC1ODBgzFu3Dg0bNgQGzZsQK9evWC1WtGtWzeMGTMGCxYswKxZs5CcnIyDBw/i4MGD3t0xRERERG7gohQRERFRCb7++mvdglGHDh3w999/Y+DAgejWrRsAID09Ha+++ioGDBigLUo9++yzWp/U1FQMHz4cvXv3xoQJExAQEIDw8HAoioK4uDiPcj377LO46667tPaQIUPwzjvvaNvS0tKwdetWfPDBB5E16vEAAQAASURBVOjWrRsOHDiAatWq4dprr4WiKEhJSfHo9xIRERF5CxeliIiIiErQtm1bTJw4UWtbrVbUq1cPP/74I1577TVte2FhIQoKCnDu3DkEBwdj2bJleP3117F9+3acPn0aly5d0v38ajVu3Fj7//z8fOTm5qJHjx7o1auXtv3SpUsIDw8HcPmi7TfeeCNq1KiBm2++Gbfeeivat29/1TmIiIiIPMVFKSIiIqISWK1WZGZm6radPXsWQ4cO1b1TSWWxWLBv3z7ceuuteOKJJ/Daa68hMjISa9asQY8ePXDhwoUSF6UURYGI6LZdvHjRaa7ieQBg0qRJaNasma7OaDQCABo1aoS9e/fi22+/xbJly9ClSxfccMMNmD17dil7gIiIiKhscFGKiIiIyE2NGjXCjh07HBarVOvXr0dRURHeeecdGAyXv1dm1qxZupqAgAAUFhY69I2JicHhw4e19q5du3Du3LkS81SpUgUJCQnYs2cPHnzwwSvWhYWF4d5778W9996Le+65BzfffDNOnDiByMjIEscnIiIiKgtclCIiIiJy0+DBg3HrrbciOTkZ99xzDwwGA3JycrB582YMHz4cmZmZuHjxIsaOHYvbbrsNP/74I95//33dGKmpqTh79iyWL1+O+vXrIzg4GMHBwWjXrh3GjRuHFi1aoLCwEC+88ALMZnOpmYYOHYpnnnkG4eHhuPnmm3H+/HmsW7cOf//9N55//nmMGjUK8fHxaNiwIQwGA7788kvExcXBZrOV0V4iIiIiKpnh3w5AREREVNHcdNNN+Prrr/Hdd9+hSZMmaN68Od59913t4uH169fHqFGj8Oabb6JOnTr47LPP8Prrr+vGyM7ORu/evXHvvfciJiYGb731FgDgnXfeQVJSEq677jo88MAD6N+/v0vXoOrZsyc++ugjTJkyBXXr1kXr1q0xdepUpKWlAQBCQ0Px1ltvoXHjxmjSpAn27duHRYsWae/kIiIiIipvithftICIiIiIiIiIiKiM8U9jRERERERERERU7rgoRURERERERERE5Y6LUkREREREREREVO64KEVEREREREREROWOi1JERERERERERFTuuChFRERERERERETljotSRERERERERERU7rgoRURERERERERE5Y6LUkREREREREREVO64KEVEREREREREROWOi1JERERERERERFTuuChFRERERERERETljotSRERERERERERU7rgoRURERERERERE5Y6LUkREREREREREVO64KEVEREREREREROWOi1JERERERERERFTuuChFREREZaZ79+5ITU0ts/GnTp0KRVGwb9++Mvsd+/btg6IomDp1apn9DiIiIiJ/xEUpIiIickpd8FH/WSwWVK9eHU8//TTy8vL+7Xg+Ky8vD/3790fNmjURHBwMq9WKa665BsOHD8fJkyf/7XgV3qJFi/DKK6941PfixYuoVasWFEXB22+/7d1gRERE5DbTvx2AiIiIfNuwYcOQlpaGgoICrFmzBhMnTsSiRYuwefNmBAcHl9h30qRJKCoqKrNsDz/8MO677z4EBgaW2e9wx6+//oqOHTvi7NmzeOihh3DNNdcAANatW4c33ngDq1atwnffffcvp6zYFi1ahPHjx3u0MDV27FgcOHDA+6GIiIjII1yUIiIiohJ16NABjRs3BgD07NkTUVFRGDVqFObPn4/777/faZ/8/HxYrVaYzeYyzWY0GmE0Gsv0d7jq5MmTuPPOO2E0GrFhwwbUrFlT9/PXXnsNkyZN+pfS0dGjRzFs2DC88MILGDx48L8dh4iIiMCP7xEREZGb2rVrBwDYu3cvgMvXjQoJCUFubi46duyI0NBQPPjgg9rPil9TSr0+09tvv40PP/wQGRkZCAwMRJMmTfDrr786/K7t27ejS5cuiImJQVBQEGrUqIEXX3xR+7mza0qlpqbi1ltvxXfffYcGDRrAYrGgVq1amDt3rm7sEydOoH///qhbty5CQkIQFhaGDh06ICcnx6P98sEHH+DQoUMYNWqUw4IUAFSpUgUvvfSSbtuECRNQu3ZtBAYGIiEhAU899ZTDR/zatGmDOnXq4Pfff0fr1q0RHByMzMxMzJ49GwDwww8/oFmzZtr+WbZsma7/K6+8AkVRtH0ZFhaGqKgo9O3bFwUFBbraS5cu4dVXX9Vul9TUVPznP//B+fPndXXqPl6zZg2aNm0Ki8WC9PR0TJ8+3WHeJ0+exLPPPoukpCQEBgYiMzMTb775pu4ddK4eF927d8f48eMBQPfRUlcMHDgQNWrUwEMPPeRSPREREZU9vlOKiIiI3JKbmwsAiIqK0rZdunQJN910E6699lq8/fbbpX6s7/PPP8eZM2fw+OOPQ1EUvPXWW7jrrruwZ88e7d1Vv//+O6677jqYzWY89thjSE1NRW5uLhYuXIjXXnutxPF37dqFe++9F71790a3bt0wZcoUdO7cGYsXL8aNN94IANizZw/mzZuHzp07Iy0tDXl5efjggw/QunVrbN26FQkJCW7tlwULFiAoKAj33HOPS/WvvPIKhg4dihtuuAFPPPEEduzYgYkTJ+LXX3/Fjz/+qHuX2d9//41bb70V9913Hzp37oyJEyfivvvuw2effYZnn30WvXv3xgMPPICRI0finnvuwcGDBxEaGqr7fV26dEFqaipef/11/Pe//8WYMWPw999/6xaSevbsiWnTpuGee+5Bv379sHbtWrz++uvYtm0bvvrqK914u3fvxj333IMePXqgW7dumDx5Mrp3745rrrkGtWvXBgCcO3cOrVu3xqFDh/D4448jOTkZP/30EwYNGoTDhw9j9OjRujFLOy4ef/xx/Pnnn1i6dCk++eQTl2+bX375BdOmTcOaNWtcXsQiIiKiciBERERETkyZMkUAyLJly+TYsWNy8OBB+eKLLyQqKkqCgoLkjz/+EBGRbt26CQAZOHCgwxjdunWTlJQUrb13714BIFFRUXLixAlt+/z58wWALFy4UNvWqlUrCQ0Nlf379+vGLCoqcsi4d+9ebVtKSooAkDlz5mjbTp06JfHx8dKwYUNtW0FBgRQWFurG3rt3rwQGBsqwYcMcMk+ZMqXE/RURESH169cvsUZ19OhRCQgIkPbt2+syjBs3TgDI5MmTtW2tW7cWAPL5559r27Zv3y4AxGAwyH//+19t+5IlSxyyDhkyRADI7bffrsvw5JNPCgDJyckREZGNGzcKAOnZs6eurn///gJAVqxYoW1T9/GqVat0cwoMDJR+/fpp21599VWxWq2yc+dO3ZgDBw4Uo9EoBw4cEBH3jounnnpK3HkKW1RUJE2bNpX7779f97tGjhzp8hhERERUNvjxPSIiIirRDTfcgJiYGCQlJeG+++5DSEgIvvrqKyQmJurqnnjiCZfHvPfeexEREaG1r7vuOgCX370EAMeOHcOqVavw6KOPIjk5WdfXlXe6JCQk4M4779TaYWFh6Nq1KzZs2IAjR44AAAIDA2EwXH4qVFhYiL/++gshISGoUaMGfvvtN5fnojp9+rTDu5OuZNmyZbhw4QKeffZZLQMA9OrVC2FhYfjmm2909SEhIbjvvvu0do0aNWCz2ZCVlYVmzZpp29X/V/djcU899ZSu3adPHwCXLxxe/L/PP/+8rq5fv34A4JCpVq1a2u0GADExMahRo4bud3/55Ze47rrrEBERgePHj2v/brjhBhQWFmLVqlW6MUs7LjwxdepUbNq0CW+++abHYxAREVHZ4Mf3iIiIqETjx49H9erVYTKZUKVKFdSoUUO3kAIAJpMJVatWdXlM+4UmdSHi77//BvC/RYg6dep4lDkzM9Nh8ap69eoALl+/KC4uDkVFRXjvvfcwYcIE7N27F4WFhVpt8Y8muiosLAxnzpxxqXb//v0ALi8uFRcQEID09HTt56qqVas6zCc8PBxJSUkO24D/7cfiqlWrpmtnZGTAYDBo1+Pav38/DAYDMjMzdXVxcXGw2WwOmexvQ+Dy7Vj8d+/atQu///47YmJiHGqByxcfL2lM++PCXadPn8agQYPwf//3fw77ioiIiP59XJQiIiKiEjVt2lT79r0rKf6uI1dc6RvzRMStbFdjxIgRePnll/Hoo4/i1VdfRWRkJAwGA5599lndRbhdVbNmTWzcuBEXLlxAQECAV7NeaX9dzX680jvOXL3mkiu/u6ioCDfeeCMGDBjgtFZdKHRnTHe8/fbbuHDhAu69915t8e2PP/4AcHmha9++fUhISPD67UVERESu4aIUERER+Zz09HQAwObNmz3qv3v3boiIboFl586dAKB9G+Ds2bPRtm1bfPzxx7q+J0+eRHR0tNu/87bbbsPPP/+MOXPm4P777y+xNiUlBQCwY8cOba4AcOHCBezduxc33HCD27+/NLt27UJaWprW3r17N4qKirT9kZKSgqKiIuzatQtZWVlaXV5eHk6ePKlldkdGRgbOnj3r1fm4c6HyAwcO4O+//9YuvF7ciBEjMGLECGzYsAENGjTwWj4iIiJyHa8pRURERD4nJiYGrVq1wuTJk3HgwAHdz1x518yff/6p+7a406dPY/r06WjQoAHi4uIAXH5Xjv1YX375JQ4dOuRR5t69eyM+Ph79+vXTFsCKO3r0KIYPHw7g8nW6AgICMGbMGF2Gjz/+GKdOncItt9ziUYaSjB8/XtceO3YsAKBDhw4AgI4dOwKAwzfijRo1CgA8ytSlSxf8/PPPWLJkicPPTp48iUuXLrk9ptVq1fqX5plnnsFXX32l+/fBBx8AALp3746vvvpKt1BHRERE5YvvlCIiIiKfNGbMGFx77bVo1KgRHnvsMaSlpWHfvn345ptvsHHjxhL7Vq9eHT169MCvv/6KKlWqYPLkycjLy8OUKVO0mltvvRXDhg3DI488guzsbGzatAmfffaZ7p1L7oiIiMBXX32Fjh07okGDBnjooYdwzTXXAAB+++03zJgxAy1atABwedFt0KBBGDp0KG6++Wbcfvvt2LFjByZMmIAmTZrgoYce8ihDSfbu3Yvbb78dN998M37++Wd8+umneOCBB1C/fn0AQP369dGtWzd8+OGHOHnyJFq3bo1ffvkF06ZNQ6dOndC2bVu3f+f//d//YcGCBbj11lvRvXt3XHPNNcjPz8emTZswe/Zs7Nu3z+13pan79JlnnsFNN90Eo9Gouwh8cY0aNUKjRo1029SP8dWuXRudOnVye05ERETkPVyUIiIiIp9Uv359/Pe//8XLL7+MiRMnoqCgACkpKejSpUupfatVq4axY8fi//7v/7Bjxw6kpaVh5syZuOmmm7Sa//znP8jPz8fnn3+OmTNnolGjRvjmm28wcOBAjzM3a9YMmzdvxsiRI/HNN9/gk08+gcFgQFZWFgYOHIinn35aq33llVcQExODcePG4bnnnkNkZCQee+wxjBgxAmaz2eMMVzJz5kwMHjwYAwcOhMlkwtNPP42RI0fqaj766COkp6dj6tSp+OqrrxAXF4dBgwZhyJAhHv3O4OBg/PDDDxgxYgS+/PJLTJ8+HWFhYahevTqGDh2qXZjdHXfddRf69OmDL774Ap9++ilE5IqLUkREROTbFCnPK4oSERERlbHU1FTUqVMHX3/99b8dxSe88sorGDp0KI4dO+bRtbKIiIiIygqvKUVEREREREREROWOi1JERERERERERFTuuChFRERERERERETljteUIiIiIiIiIiKicsd3ShERERERERERUbnjohQREREREREREZU7078doCx0qDbAYdvWF2J07YBjpU89Y+bfuvbRFhEONada/6NrG/YG6dqhBxzHDbvnkK59ZHWirp0ycoNDn32fZOraAT+FOtSEHizStQu66fMHfOaY/0ySfl0y6Jjjpzn/aqDfZjiv6Nr9blvo0Gfkd7fqx83T/56kdx3naIjU58t9PNWhJjhP3z5bVd+O2O6YP2z/eV17722Buna1Ro43Uu4vyfpxtzmUoDBAvx+iNuXrf0+nYIc+RVULdG3j/iCHmtSF+nEO3GzVtS9V0x9zABCw1e6426/fD/X6/O7QZ+ertXXtk+mO54Ry/Qld+8IvkfosdfVZASDwV31eRX9Y4ky9C4599gfof4+tyKHGXs1R+vPo4D1JDjXn9XERfNhxHJPd7jx1o35O1V7Q7wMAuLT/oK59uF+2rn2ukeNtFPqz/jZq0jXHoSZnfD1d+1gT/X6wHDU69DGd07fP1tEf72Eb9ce7M6fqXHTYlpZyVP+77/xL15aF4Q59dq/XnzfVPzjqUHP4xiq6dqH+pkfAWcd8BXa3Y35qoa5dNdPx9xzK09+fdK7zm0PNl5sb6dpyUh8m9Wv97wGAFVM+0rUzZz2uaxeFOvZRzupvt+r1DjrUHJ2Voh/H7nR8oc8XDn2WnKija2+aUseh5lysvn2hpv7YzHzvkkOfnY/a3S8F6o9Dw0mzQx/rAf39YcRux3EPXaffD6Zz+j7i5GH5Ypj+d4enndS1L1x0PCeShurv//bebXOoCTitbyvX6seN67TVoc++Efrz/JLd/TkAhEfoT0jDQv3B+4/+qQgA4FyKfl8Zz+jnlDnD8aTY01//mGraanWoCdQ/BcBNj/6ka//wdguHPmcT9LdJ6i17de0/5qQ59InepN8Pt47/3qHmvVXtdW3zCf0cL8Q63geFbdUfZwXNHR9v0kbpb+udT+jPYcNJx4PKfFK/74x2D0lGx5sV4XvtzmsnF7441lA/J6X2GV3bZnV8XMj/Tn+CGuxOm5BDjvcnf9ys/+WWPx3nWGixayfoJ2XNsSsAUBCrHzd0n+JQU2R36sf9qD+RdjztOK7hb32nqnX1D8QHDjieFPHf6fflx2+Ncqh5dODzuvbRxvq8QXmO+e2f44bv1T9e5t7nuC9rTNSff3+2sznUGPXD4GKIvm094njA9Hlxlq49+q17HWpOVdO3DXanif3zKwCoulx/nJ1N0t8m/8Q47peIHfqBTzzueK7Zpuhfd/xdTX8bFUQ7ZmnSSv/kefsnWbp20HHHCYjdXfrxOx3Pm8I/9c+vQ/bbPR/f7Pg8s8isr9l/u+P7Mmp8pJ/3P/H633Ogo+O+S1yu33asvn7cfp3nOfR5Z9MNurYtxHGOBkV/zBzdpj9PUr51vM/cd5v++K32qX4+ec0cXzvai/vptMO24w3DdO2/GtrdLwU73k89es2PuvbHa691qEmdo28H/ak/13Y/aHPoU2jR75cGjfbo2uf6Ot6fHGqnf74a+ofjcRe+eLuunb5Mf5v89k5Dhz5Hm+hv+/Bd+vapGo6/J7q6/rn08e2OJ44Sr//dRcfs7ledPP6YzuqPu+zrNzvU/LBDf4diydW/Poj7r+N5kzR0l6595B/9MXToO/3zfgDYOuI5x4Au4DuliIiIiIiIiIio3HFRioiIiIiIiIiIyh0XpYiIiIiIiIiIqNxxUYqIiIiIiIiIiModF6WIiIiIiIiIiKjccVGKiIiIiIiIiIjKHReliIiIiIiIiIio3HFRioiIiIiIiIiIyh0XpYiIiIiIiIiIqNxxUYqIiIiIiIiIiModF6WIiIiIiIiIiKjccVGKiIiIiIiIiIjKHReliIiIiIiIiIio3Hm0KHXp0iUsW7YMH3zwAc6cOQMA+PPPP3H27FmvhiMiIiIiIiIiosrJ5G6H/fv34+abb8aBAwdw/vx53HjjjQgNDcWbb76J8+fP4/333y+LnEREREREREREVIm4/U6pvn37onHjxvj7778RFBSkbb/zzjuxfPlyr4YjIiIiIiIiIqLKye13Sq1evRo//fQTAgICdNtTU1Nx6NAhrwUjIiIiIiIiIqLKy+13ShUVFaGwsNBh+x9//IHQ0FCvhCIiIiIiIiIiosrN7UWp9u3bY/To0VpbURScPXsWQ4YMQceOHb2ZjYiIiIiIiIiIKim3P773zjvv4KabbkKtWrVQUFCABx54ALt27UJ0dDRmzJhRFhmJiIiIiIiIiKiScXtRqmrVqsjJycEXX3yB33//HWfPnkWPHj3w4IMP6i58TkREREREREREdCVuL0oBgMlkwkMPPeTtLERERERERERE5CfcXpSaPn16iT/v2rWrx2GIiIiIiIiIiMg/uL0o1bdvX1374sWLOHfuHAICAhAcHMxFKSIiIiIiIiIiKpXb3773999/6/6dPXsWO3bswLXXXssLnRMRERERERERkUvcXpRyplq1anjjjTcc3kVFRERERERERETkjFcWpYDLFz//888/vTUcERERERERERFVYm5fU2rBggW6tojg8OHDGDduHFq2bOm1YEREREREREREVHm5vSjVqVMnXVtRFMTExKBdu3Z45513vJWLiIiIiIiIiIgqMbcXpYqKisoiBxERERERERER+RGvXVOKiIiIiIiIiIjIVS69U+r55593ecBRo0Z5HIaIiIiIiIiIiPyDS4tSGzZscGkwRVGuKgwREREREREREfkHlxalvv/++7LOQUREREREREREfoTXlCIiIiIiIiIionLn9rfvAcC6deswa9YsHDhwABcuXND9bO7cuV4JRkRERERERERElZfb75T64osvkJ2djW3btuGrr77CxYsXsWXLFqxYsQLh4eFlkZGIiIiIiIiIiCoZtxelRowYgXfffRcLFy5EQEAA3nvvPWzfvh1dunRBcnJyWWQkIiIiIiIiIqJKxu1FqdzcXNxyyy0AgICAAOTn50NRFDz33HP48MMPvR6QiIiIiIiIiIgqH7cXpSIiInDmzBkAQGJiIjZv3gwAOHnyJM6dO+fddEREREREREREVCm5faHzVq1aYenSpahbty46d+6Mvn37YsWKFVi6dCmuv/76sshIRERERERERESVjMuLUps3b0adOnUwbtw4FBQUAABefPFFmM1m/PTTT7j77rvx0ksvlVlQIiIiIiIiIiKqPFxelKpXrx6aNGmCnj174r777gMAGAwGDBw4sMzCERERERERERFR5eTyNaV++OEH1K5dG/369UN8fDy6deuG1atXl2U2IiIiIiIiIiKqpFxelLruuuswefJkHD58GGPHjsW+ffvQunVrVK9eHW+++SaOHDlSljmJiIiIiIiIiKgScfvb96xWKx555BH88MMP2LlzJzp37ozx48cjOTkZt99+e1lkJCIiIiIiIiKiSsbtRaniMjMz8Z///AcvvfQSQkND8c0333grFxERERERERERVWIuX+jc3qpVqzB58mTMmTMHBoMBXbp0QY8ePbyZjYiIiIiIiIiIKim3FqX+/PNPTJ06FVOnTsXu3buRnZ2NMWPGoEuXLrBarWWVkYiIiIiIiIiIKhmXF6U6dOiAZcuWITo6Gl27dsWjjz6KGjVqlGU2IiIiIiIiIiKqpFxelDKbzZg9ezZuvfVWGI3GssxERERERERERESVnMuLUgsWLCjLHERERERERERE5Eeu6tv3iIiIiIiIiIiIPMFFKSIiIiIiIiIiKndclCIiIiIiIiIionLn9qLUqlWrcOnSJYftly5dwqpVq7wSioiIiIiIiIiIKje3F6Xatm2LEydOOGw/deoU2rZt65VQRERERERERERUubm9KCUiUBTFYftff/0Fq9XqlVBERERERERERFS5mVwtvOuuuwAAiqKge/fuCAwM1H5WWFiI33//HdnZ2d5PSERERERERERElY7Li1Lh4eEALr9TKjQ0FEFBQdrPAgIC0Lx5c/Tq1cv7CYmIiIiIiIiIqNJxeVFqypQpAIDU1FT079+fH9UjIiIiIiIiIiKPubwopRoyZEhZ5CAiIiIiIiIiIj/i9oXO8/Ly8PDDDyMhIQEmkwlGo1H3j4iIiIiIiIiIqDRuv1Oqe/fuOHDgAF5++WXEx8c7/SY+IiIiIiIiIiKikri9KLVmzRqsXr0aDRo0KIM4RERERERERETkD9z++F5SUhJEpCyyEBERERERERGRn3B7UWr06NEYOHAg9u3bVwZxiIiIiIiIiIjIH7j98b17770X586dQ0ZGBoKDg2E2m3U/P3HihNfCERERERERERFR5eT2otTo0aPLIAYREREREREREfkTtxelunXrVhY5iIiIiIiIiIjIj7h9TSkAyM3NxUsvvYT7778fR48eBQB8++232LJli1fDERERERERERFR5eT2otQPP/yAunXrYu3atZg7dy7Onj0LAMjJycGQIUPcGuv48eN46623cOedd6JFixZo0aIF7rzzTowcORLHjh1zNxoREREREREREVUQbi9KDRw4EMOHD8fSpUsREBCgbW/Xrh3++9//ujzOr7/+iurVq2PMmDEIDw9Hq1at0KpVK4SHh2PMmDGoWbMm1q1bV+o458+fx+nTp3X/iuSSu9MiIiIiIiIiIqJy5PY1pTZt2oTPP//cYXtsbCyOHz/u8jh9+vRB586d8f7770NRFN3PRAS9e/dGnz598PPPP5c4zuuvv46hQ4fqtmVEZKNaVEuXsxARERERERERUfly+51SNpsNhw8fdti+YcMGJCYmujxOTk4OnnvuOYcFKQBQFAXPPfccNm7cWOo4gwYNwqlTp3T/MiKbuZyDiIiIiIiIiIjKn9uLUvfddx9eeOEFHDlyBIqioKioCD/++CP69++Prl27ujxOXFwcfvnllyv+/JdffkGVKlVKHScwMBBhYWG6fwbF7TeAERERERERERFROXJ79WbEiBF46qmnkJSUhMLCQtSqVQuFhYV44IEH8NJLL7k8Tv/+/fHYY49h/fr1uP7667UFqLy8PCxfvhyTJk3C22+/7W48IiIiIiIiIiKqANxelAoICMCkSZPw8ssvY/PmzTh79iwaNmyIatWquTXOU089hejoaLz77ruYMGECCgsLAQBGoxHXXHMNpk6dii5durgbj4iIiIiIiIiIKgCPP+eWnJyM5OTkq/rl9957L+69915cvHhRu0h6dHQ0zGbzVY1LRERERERERES+ze1FqcLCQkydOhXLly/H0aNHUVRUpPv5ihUr3A5hNpsRHx/vdj8iIiIiIiIiIqqY3F6U6tu3L6ZOnYpbbrkFderUcfrteURERERERERERCVxe1Hqiy++wKxZs9CxY8eyyENERERERERERH7A4G6HgIAAZGZmlkUWIiIiIiIiIiLyE24vSvXr1w/vvfceRKQs8hARERERERERkR9w++N7a9aswffff49vv/0WtWvXdvimvLlz53otHBERERERERERVU5uL0rZbDbceeedZZGFiIiIiIiIiIj8hNuLUlOmTCmLHERERERERERE5EfcXpRSHTt2DDt27AAA1KhRAzExMV4LRURERERERERElZvbFzrPz8/Ho48+ivj4eLRq1QqtWrVCQkICevTogXPnzpVFRiIiIiIiIiIiqmTcXpR6/vnn8cMPP2DhwoU4efIkTp48ifnz5+OHH35Av379yiIjERERERERERFVMm5/fG/OnDmYPXs22rRpo23r2LEjgoKC0KVLF0ycONGb+YiIiIiIiIiIqBJy+51S586dQ5UqVRy2x8bG8uN7RERERERERETkErcXpVq0aIEhQ4agoKBA2/bPP/9g6NChaNGihVfDERERERERERFR5eT2x/fee+893HTTTahatSrq168PAMjJyYHFYsGSJUu8HpCIiIiIiIiIiCoftxel6tSpg127duGzzz7D9u3bAQD3338/HnzwQQQFBXk9IBERERERERERVT5uL0oBQHBwMHr16uXtLERERERERERE5Cc8WpTasWMHxo4di23btgEAsrKy8PTTT6NmzZpeDUdERERERERERJWT2xc6nzNnDurUqYP169ejfv36qF+/Pn777TfUrVsXc+bMKYuMRERERERERERUybj9TqkBAwZg0KBBGDZsmG77kCFDMGDAANx9991eC0dERERERERERJWT2++UOnz4MLp27eqw/aGHHsLhw4e9EoqIiIiIiIiIiCo3txel2rRpg9WrVztsX7NmDa677jqvhCIiIiIiIiIiosrN7Y/v3X777XjhhRewfv16NG/eHADw3//+F19++SWGDh2KBQsW6GqJiIiIiIiIiIjsub0o9eSTTwIAJkyYgAkTJjj9GQAoioLCwsKrjEdERERERERERJWR24tSRUVFZZGDiIiIiIiIiIj8iNvXlCIiIiIiIiIiIrpabr9TCgB+/fVXfP/99zh69KjDO6dGjRrllWBERERERERERFR5ub0oNWLECLz00kuoUaMGqlSpAkVRtJ8V/38iIiIiIiIiIqIrcXtR6r333sPkyZPRvXv3MohDRERERERERET+wO1rShkMBrRs2bIsshARERERERERkZ9we1Hqueeew/jx48siCxERERERERER+Qm3P77Xv39/3HLLLcjIyECtWrVgNpt1P587d67XwhERERERERERUeXk9qLUM888g++//x5t27ZFVFQUL25ORERERERERERuc3tRatq0aZgzZw5uueWWsshDRERERERERER+wO1rSkVGRiIjI6MsshARERERERERkZ9we1HqlVdewZAhQ3Du3LmyyENERERERERERH7A7Y/vjRkzBrm5uahSpQpSU1MdLnT+22+/eS0cERERERERERFVTm4vSnXq1KkMYhARERERERERkT9xe1FqyJAhZZGDiIiIiIiIiIj8iNuLUqr169dj27ZtAIDatWujYcOGXgtFRERERERERESVm9uLUkePHsV9992HlStXwmazAQBOnjyJtm3b4osvvkBMTIy3MxIRERERERERUSXj9rfv9enTB2fOnMGWLVtw4sQJnDhxAps3b8bp06fxzDPPlEVGIiIiIiIiIiKqZNx+p9TixYuxbNkyZGVladtq1aqF8ePHo3379l4NR0RERERERERElZPb75QqKiqC2Wx22G42m1FUVOSVUEREREREREREVLm5vSjVrl079O3bF3/++ae27dChQ3juuedw/fXXezUcERERERERERFVTm4vSo0bNw6nT59GamoqMjIykJGRgbS0NJw+fRpjx44ti4xERERERERERFTJuH1NqaSkJPz2229YtmwZtm/fDgDIysrCDTfc4PVwRERERERERERUObm9KAUAiqLgxhtvxI033ujtPERERERERERE5Adc/vjeihUrUKtWLZw+fdrhZ6dOnULt2rWxevVqr4YjIiIiIiIiIqLKyeVFqdGjR6NXr14ICwtz+Fl4eDgef/xxjBo1yqvhiIiIiIiIiIiocnJ5USonJwc333zzFX/evn17rF+/3iuhiIiIiIiIiIiocnN5USovLw9ms/mKPzeZTDh27JhXQhERERERERERUeXm8qJUYmIiNm/efMWf//7774iPj/dKKCIiIiIiIiIiqtxcXpTq2LEjXn75ZRQUFDj87J9//sGQIUNw6623ejUcERERERERERFVTiZXC1966SXMnTsX1atXx9NPP40aNWoAALZv347x48ejsLAQL774YpkFJSIiIiIiIiKiysPlRakqVargp59+whNPPIFBgwZBRAAAiqLgpptuwvjx41GlSpUyC0pERERERERERJWHy4tSAJCSkoJFixbh77//xu7duyEiqFatGiIiIsoqHxERERERERERVUJuLUqpIiIi0KRJE29nISIiIiIiIiIiP+Hyhc6JiIiIiIiIiIi8hYtSRERERERERERU7rgoRURERERERERE5Y6LUkREREREREREVO64KEVEREREREREROWOi1JERERERERERFTuuChFRERERERERETljotSRERERERERERU7rgoRURERERERERE5U8qqYKCAhkyZIgUFBRccZsnNb40LrP47xx9KYs/zNGXsvjDHH0piz/M0Zey+MMcfSmLP8zRl7L4wxx9KYs/zNGXsvjDHH0piz/M0Zey+MMcvZXFE5V2UerUqVMCQE6dOnXFbZ7U+NK4zOK/c/SlLP4wR1/K4g9z9KUs/jBHX8riD3P0pSz+MEdfyuIPc/SlLP4wR1/K4g9z9KUs/jBHX8riD3P0VhZP8ON7RERERERERERU7rgoRURERERERERE5Y6LUkREREREREREVO4q7aJUYGAghgwZgsDAwCtu86TGl8ZlFv+doy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKMvZfGHOfpSFn+Yoy9l8Yc5+lIWf5ijL2Xxhzn6UhZ/mKO3snhCERHxuDcREREREREREZEHKu07pYiIiIiIiIiIyHdxUYqIiIiIiIiIiModF6WIiIiIiIiIiKjccVGKiIiIiIiIiIjKHReliIiIiIiIiIio3Jn+7QC+TkSgKIpXx7xw4QLmzZuHn3/+GUeOHAEAxMXFITs7G3fccQcCAgIAAH/88QdsNhtCQkLw119/4ffff0f9+vURGhqKb7/9Ftu2bcP58+fRuXNnZGVlAQDS09OxZMkSZGZmYuXKldi9ezfi4+Nx0003YcyYMbjnnnuQkpLiduZffvnFIW+LFi3QtGlTraaoqAinTp3CwoUL8fDDD2Pfvn1ISkqCwWDAnj17sH79epw/fx4dO3ZEdHQ0AKBdu3aYMmUK4uLisH//fuzfvx/x8fEICgrCgAEDEBAQgIyMDPTo0QNpaWnIycnB+vXr0aZNG6Snp2PLli0YP348Dh48iIiICFitVhgMBphMJoSGhqKwsNBredWsKSkpOH/+vMd5Bw0ahMOHD6NKlSpISUlBeno6UlJScOjQoSvu36KiIhgMBvz999+6vImJiThy5Aji4uLw1VdfuZz3hx9+wP79+3H48GGkpKT4XN7t27dj7969SEpKQp06dTB+/HiP8latWhX5+fkwmUwoKCioVOeaun8XLFiAVq1a/SvnmoiUyf5V9221atUgIl7bv2V131tS3q1bt+Lee+/16FgAgCNHjmDt2rW6vM2aNUNcXJyuLj8/H+vXr0erVq1QWFgIo9Go/Wzt2rU4f/48WrRoAbPZjEceeQSvvfYaEhIScPHiRezbtw+xsbEIDw/HypUrsWvXLhw4cAApKSno3LkzwsPDcfToUWzevBnXXHMNwsPDkZeXh1GjRmHv3r1ITExEQkICCgsLoSgKTp48qWUt6dgtq/uG06dPu/Q4ER0djbi4OBgMhhL3bfH927JlyxL3LYAr7l8Rwdtvvw1FUUrdt9OmTcPx48cRHh4Ok8kEg8GgZT99+rTXjgVv5bU/FtLT01GvXj1s3bq1zI5dALq8GzZs8PjYrWh5ea6VfOxe6b7s/PnzLp1rru7ff+M5ryfPyTzJW5bPeTt27IgLFy7o8taqVUu7XVWXLl3Cn3/+ieTkZIfjJC8vD+fPn9d+NnToUDz11FPavlbPN7PZjCNHjmDTpk3asdC2bVsYjUYUFhZi//79SE1NhcFgwPnz5zFz5kzk5uYiISEBkZGRCAsLwz///IO8vDyXj91/83E4JSUFiYmJOHv2bIn7tqT9a79vne1fdd8CcLp/ATjs2/nz5+PcuXOw2Ww4f/48DAYDkpOTERgYqNu/V3sslJT30qVLmDdvHk6dOlXqsVBeeUs6dt3Ja3/sXulxrSzzXhWpBI4dOyZvvvmmdOrUSZo3by7NmzeXTp06yVtvvSVHjx4VEZFz587J6tWrZcuWLXLgwAF55JFHZOvWrTJ58mTZuHGjTJs2TbZt2ya9e/eWRx55RJYvXy4iImazWbZu3Spnz56VIUOGyJNPPiljx46V48ePyw033CB16tSRqlWrSsuWLWXGjBkiIjJ27Fh5+OGHtfaYMWMkOjpagoODxWazidlsFoPBINWrV5fOnTtLly5dpHXr1mKxWCQzM1N+/PFHadKkiRgMBjEajdKuXTsJDAwURVEkIiJClixZIgAkJiZGoqKixGw2S1pamrzxxhtiNBrl2WeflZSUFAEgUVFRYjAYpGbNmqIoiiiKIjabTVq2bCk//PCDLFy4UF5++WVZs2aNiIgsX75c2rdvL7Vr15bs7Gxp27atRERECACpUqWKNG3aVJo2bSopKSmiKIpce+21snv3buncubNYLBaJjIwUAJKSkiIGg0EyMzNl7dq1AkACAwMlICBAQkNDpVWrVvLFF1+I0WiUUaNGSVhYmJZPURQxGAwCQMxms0RFRUlgYKCMGDFCjEajREVFSUhIiCxdulTCwsIkLCxMAGj/QkJCtP+Pj493O+/IkSOd5jUYDDJu3DiZNWuWR3lnzZolRqNRAIiiKAJAkpOTxWw2CwAJCwtz2L/NmzeX22+/XSwWi8TGxspjjz2my5uWliYAJCMjw628AMRgMEh6erpUr15dgoOD/7W8ZrNZ3n33Xe1YaNGihXb7GQwGadiwocd5TSaTNlaNGjUq1bkWGxsrXbp00fbNv3GuBQQEuLV/q1WrJuHh4dr+feKJJ5zuX4PBIIMGDZIRI0ZIYmKiVmMwGCQ5OVn7nXFxcTJs2DA5f/68w/5dtGiR1KlTRxITE6V27drSunVrCQsLk4CAAGndurV06dKlXPKqx2PxY0FESs3bvn17qV69uhgMBjGZTBIbGyuxsbFiMpnEaDTKAw88IM8++6xkZGRIkyZN5JVXXhFFUaRly5ZiNBqlefPmAkCuu+46bX/ZbDb55ptvxGw2y+zZs6Vr167acWAymaRTp07a8VS1alWJjo6WuLg4mTp1qlitVlEUReLi4uTnn3+WoKAg3bGgnmuKokhKSkqpx25Z3TcEBAS4dOyq9/0AJDg4WLdvH3roITl58qT83//9n7Z/+/XrJwDEaDRKq1atZPv27brxUlJSpFevXrJ27Vpt/yYnJ4vRaNSOp8DAQO38DAkJkZiYGId9u3HjRklISNA9jimKIhaLRWuHhoaWmLd27doCQDsWyjKvs2MhIiJC9zhjf+y6knf+/PlO85pMJvnqq6/kt99+k+TkZDGZTFpeT4/d0vJ6cq6VZV6ea47Hriv3ZRERERIUFFTquaY+x3Fl/5bnc15PnpP52nPe1NRUsVqtuv1d/PnSSy+9JOPGjZPrr79eOnfuLB988IEoiiIPPvigJCcnS5cuXSQ5OVl69uyp9c/Ozpbx48eLyWSStWvXyqlTp6Rly5baY7DNZtN+X1hYmBgMBqlbt6589913Eh8fLwaDQerUqSP79u2T6Oho3bFW/F94eHipx+6//Thc/HHC2b4tLCyU8ePHa/v3vffe046jrl27yqFDh7TzQd23EyZMkKNHj4rZbJa1a9fKyJEjJSQkRBRFkaioKHn++ecd9m9mZqZUqVJF27cHDhyQ2rVra8dq8WPC1bw33nijANCOBVfztmzZUqKjo7VjQb2NAwICRFEUqV27tsOxUFZ5t2/f7pC3tGPXlbwlHbsGg0GX1dvn2siRIyU5OVkMBoNERUXJ0KFDPV7PqfCLUr/88otERERIYmKidOvWTQYMGCADBgyQbt26SdWqVSUyMlJmz56t3UEbDAa55pprtBs4MjJSO4mDgoIkOTlZkpKSBIDcdtttYjAY5O6779bukKtXry6RkZESGhqq3UixsbHSsmVLCQ4Olk6dOkloaKjcfffdEhcXJ08//bQoiiIJCQnai9ewsDC56667xGazSXZ2tpw+fVpERE6dOiV33HGHxMfHS7NmzeTXX3+VpUuXanfg+/fvl5EjR0pCQoIAkKpVq0pqaqp2gCclJYmiKGK1WsVsNovZbJaFCxfKwYMHJSMjQwBIgwYNpFq1atrJpSiK1KpVS8LCwuSTTz4Rq9UqISEh2gO3egdar149MRqN0qlTJ/nrr7/k1KlTsm7dOmnatKmkp6dLZmamTJs2TQYMGCDA5RfV69evl2effVaqVaumOzHsT+ri7fz8fGnQoIGEh4cLABk2bJj2ZEhRFOnYsaMcP35cZsyYITabTerUqSOdOnWSU6dOyRtvvCEBAQESExMj27dvl+XLl0tERIS88cYbbuW1v8O5Ut7p06e7lddsNktWVpacOnVKCgoKpGXLlmI2m6VFixYydepUiYqK0rKqeatUqSJWq1WmTZsmY8aMkbi4OAEgt956q/z+++/ak01386oPkg0bNhRFUSQmJkYCAgLkP//5j4jIv5LXPntOTo6sWbNGOz/Vfetq3vDwcDGbzXLq1ClZvny5REVFyejRoyvMuda5c+cSz7UxY8ZoLxr/jXPNbDZLUlKSy/v3/fffFwASHR2t7V81S2pqqm7/ApDExEQJDQ0VRVFk0qRJIiLy0UcfaT/v2LGjxMfHa0+mDQaDtn/ffvtt7YW8OqbVapWIiAgxGAzSuXNnuXjxovY4UtZ5o6KidMdCvXr1xGQyyTXXXHPFvDabTSwWixgMBrnnnnu0vJcuXZIlS5ZIZGSkBAUFyciRI+XFF1/U+mVnZ8uCBQvkjjvuKPFcK95euHChfPrpp9oi46OPPqo9+U5KShKr1SpPPvmknDlzRkaOHCnBwcESHx8vmzZtkl27dklISIiYzWbZsGGDfPzxxxIcHCyTJk0q8dgtq/sGk8kkERERJR67gYGBEhoaKosXL5bvvvtOO3bVfVu9enVp2LChVKlSRdu/6kLAggUL5N5775UmTZqUeK4V37ZlyxapX7++tuDdq1cviY2N1fJ26NBBTp48KSNHjpTExESpVauWtGzZUjZt2iQ9evSQwMBAiYiIkHnz5smkSZMkODhYPvvssyvmVZ9EVqlSpczz2h8L6h8NMjMz5bnnnpOgoCD57LPPdMeuK3lLelxTXxADkNdee03LC0AaN27s1rHrSt6rOde8nZfnWsnHbkn3ZQEBARIdHV3iuRYQECAJCQna/g0KCipx/5bnc15PnpP52nPewMBAiYmJkWeffVZsNpu89dZbcu7cOdm7d6988MEH2vH91FNPyUMPPaQtCtSsWVPGjBkj2dnZuvOq+Lllf6717t1bvvnmG6lTp442RkBAgNhsNklNTZWoqCi55557ZNOmTdK3b1+JioqS0NBQ+eqrr2Tx4sVisVjEbDbLpEmT5MUXX5TAwEBZsmRJicfuv/k4rD6/GTNmjLz55psSFBQkU6ZM0fZtbGysXH/99RIcHKztX3V/jRkzRtq0aSM333yz031bfEFD3TZ+/HgZPny4tvDYrl07bf+qC+Lqvs3KypIaNWpIzZo1Zd68eXL99ddLYGCgWK1WeeWVV6RPnz4SEBAgCxYsuGLeW265RYDLf/DzJK96LKjn5c0336wtyAcFBckNN9xQ5nk9OXZdyWt/7MbGxorNZpPIyEjp2LGjlrWszrXBgwfLN998I8OHDxer1ao9B3ZXhV+UatasmTz22GNSVFSkbZs/f77Mnz9f5s2bJ+3btxer1SqNGzeWTz75RN5//32pVauWAJCnn35aRER78VGlShVp06aNduCofyGPjIzUVn/37dsnZ86c0Ralli1bJn379pXo6GgxGo2iKIoMGjRICgsLZePGjQJA7rrrLi2byWQSs9ksIiInTpyQBg0ayAMPPCA5OTmSk5Mjs2fPFgDy6aefatvUF8oNGjSQI0eOaAdCgwYNZOvWrbJ+/XoBIHl5eWIymSQtLU3mz58vVqtV9uzZIyIiNWvW1GpERPLy8iQ0NFT7q12NGjXEbDZLzZo15fHHH5eioiKZMmWKtjIqIrJz507tZFcP0OLvBCj+AKz+pf+vv/7Stt1yyy2Sl5cnP/74oy5vtWrVJDAwULZt2yYiIlWqVJHRo0fr8s6bN0+Ay38hDgwMlHvuuUdMJpOEhITI5s2bRUQkNzdXgMt/RTx16pSIiPa73cmrnmTq71bzGo1G2bJli9SpU8ejvABk7ty52rGwadMmASCrVq0SEZFPPvnEaVZndwjNmzeXgoICbc4tWrRwK6+iKJKeni4iIuvWrZMHHnhAgMsLtZ07d9beIVQeeRVFkcTERO1Y+Pzzz8VqtWp51b9yqfWu5lWfRKvHwieffCIpKSkV5lxLTU0t8dgtvn994Vx77bXXSty/M2fOFADSpk0bbf+q+bdu3Soiou1f9ditUaOGWCwWbd82a9ZMewGr5h0xYoSYTCaJi4vT9q+iKNKiRQvtcUE9/zZt2qTt2969e2tZyzIvcPmvkOqxcMcdd2hPlpo0aSLPP/+8GAwGufbaax3y/vjjj1pei8UiERER2j/1tlbb6pPhjh07SlFRkWzbtk0ASLNmzWTbtm2yb98+7Q8kJpNJMjMzZcSIEWKxWGT37t0O59rFixdl9uzZ0rp1awEuL6wNGDBAtmzZIgC0F+4iIsHBwQJcfoElIjJu3DiXj11v3zeoL2hLO3aNRqPuvsFgMGj7Un18Vxcyi+/v7t27y/Hjx3Xn2rZt2+Tzzz8XAPLrr79q+9dsNmvvjIuPj5fhw4fr9u+YMWO0/VSlShXp37+/mEwmsdlssm7dOu2+QL2vU/dvcHCwGI3GK+ZV36lStWrVMs9rfyz8+uuvAkB7t/m4ceN0WV3Nqx4f6rGr5jUajbJ06VLtr9jqsRsfHy+KokiNGjXcOnZdyevJuVZWeXmuOT92XbkvU188u3OuefIcsqwehz15TuaLz3nnzJmj5Q0MDJSGDRtq/9RPkqht9Z3RDz/8sIiI9rqqcePGsmLFClm5cqW8/vrr2rynTJkiWVlZYjQatcfhqlWrCgCpU6eOHDt2TN5++23tj4dZWVkyYcIE7fH9/fff1/Kq53lBQYGIiAwbNqzE+91/+3FYfRdc8fuGoKAgbV+qc05JSdG2qefEyy+/LIcOHdIduytWrND27dy5c8VoNEp6err07t1bAgICtP1bPK+6f9XjrlGjRjJ69GgxGo0SHR2tHaubN2/W/hCn7t/4+HixWq1XzKs+tw4PD3crr9Fo1D65VLVqVW1hVs2rPo4pilLmedVzzZ1j15W89sfu8uXLBbi80Kkeu8WzevNc6927txQ3YcIEadiwoXiiwi9KWSwW7Y5SVfzOt6RV6fj4eMnNzZU///xTgMt/6VafmKjvxlBXqNW/8KgPXurHbNQ75AsXLmhvgzQYDJKQkCD/+c9/BIB8++23WrbY2FgBIEeOHBERke+++07r4yxv8ScUnTp1knr16klwcLAoiiJz586VpKQkGTp0qACQo0ePislkkqioKNm8ebOEh4dLTk6OiIj2tj41r7rvLBaLrFq1Srp166b9np07d4qIyN69e7UHpuPHj4vI5SdEERERsnLlSlm5cqW8++67AkBmzJghK1eulEmTJglw+S+tLVq0kHbt2mlvNx01apQkJSXJ5MmTdXkjIiKkWbNm8tZbb4mISHZ2trzzzju6vLNnzxaDwSA//vijTJ8+XVq2bKntoy1btojI5XfNqfvwr7/+EpHLDypGo9HtvMDld8MsXLhQDhw4oHuAjo6O9jhvVFSUtv9XrVolwOW/Bor87wncm2++qeU1m80SEhKitdW8jRo1knbt2smePXsEgPznP/9xK69656pS8w4dOlTatGmjHYdTpkwp87yKokhAQIB2LGzevFnatWun5VXf2Vj82HUlb1RUlADQjgU1b0U51+bNmyeKomj71/7YXblypfYXtX/jXFP3o7p/1f3p7C9UxZ8M79q1S7d/1XNt7Nixsn//ft2xGxsbK6Ghodq+jY2NlW+//VaXd/fu3QJcfndb8f0bGBio7f8dO3bonsCpT6Dt39ZcFnnV41ul5v3yyy+lW7du2v3N119/7ZB36dKlumOhX79+MnXqVJk6dar2l3+1rR6baWlp8uCDD2pPgLp16ya1atWS3377TTvv1RdjmzZtkmbNmsmHH34oIqJ9jKr4sfvdd9+Joijy9NNPS3p6urZfZs2apdWofzVXPy6vvrgr6dgtq/sGm80mVapUcetxQr1vUPfv4MGDBYCMHDlSt78BSPXq1eXBBx/UjoW+fftKrVq15JtvvtGda+q7HtV927BhQ+0PYGpedd8uX75chg0b9v/Yu/PwJqq2DeD3JGmT7nuhLYVuLC07sqPsICAgoiIqAgq4gKgoL+LGLuKGgCy+gmyiCAIKIoggKCAqWqACgmDZdxTZioXSPt8f/WbeThLaJKQ1be7fdXHpmT7n5HlOZrKcJDPaQrTRaNQeGzIyMkRRFDEajdr8+vn5idFo1HKzzlfdF7Zu3Voi+VarVk2b/19++UUAyNdff63bFwruu47kq/4sRd13rY+1iIgIm3wVRRE/Pz+n9l1H8nXlWCuufHms2d93HXksCwkJkbCwMKeOtcDAQDGZTB7zmtfZ15Ce+Jp306ZNunz79Okjo0aNklGjRonJZBKTyaS1H3vsMQHyv7E8fPhwbfGobdu20qpVKzl27JgcOnTIJt8qVarIJ598IiIiqampoij532xXbdmyRQDI3XffLUFBQdrjzfLly7UYdYHp5MmTIiLaQlBh++6/+TysfhBr/digzq96+oGnn35am9+CC7XDhw/X5qFbt27SqlUr+fHHH2323R07dkitWrW0+VUXYwrmGxQUJAEBAfLII49o82gymSQzM1NERFusMxqN2vyqPwdWc7POV90X1q5d61S+1vvCvHnzdPlu2bJFFEWRRYsWFXu+6n3kzL7raL4tW7bU5l/dV9evX69rF8extmPHDinojz/+kKCgIHFFqV+USkhIkHnz5um2xcbGyueffy4iIvPmzRNFUbRPtkVEtm/fLgCkV69eUqFCBe2B8rPPPpMqVarIc889J4qiiNls1s7lsXPnTunVq5f069dPRET7Wl7Bg3D8+PHi4+Mj8+bNk5EjR2o//RkzZowWo/5W+PXXX5eMjAzZunWrAJBRo0bJyy+/LCEhIRIRESEzZsyQQ4cOyaFDh6RChQqiKIrk5ORIt27dJCoqShRFERGRY8eOSf369QWAtGnTRhRFkeDgYPniiy+ka9euMnz4cBER7XfNBfMNCwuTChUqiIhoK84BAQGSnp4uIiLffvut9mLgo48+kgsXLkjjxo21T72WLVsmCQkJ2u+jRf63srpp0ya5dOmSNGnSROLi4rR8t2/fLklJSQJA+vXrJ4qiaF/nDgkJkZEjR8q7776rrbxPnz5dRowYIaGhoVK3bl1p1KiRLFiwQLp06SK33XabWCwWad++vWzbtk1uu+02CQkJEZPJJMuWLZMLFy5IvXr1JDAw0KV8t2/fLmlpadK9e3ftxVbv3r0lOjrapXwjIiLEYrFIenq67Ny5U+Li4sTHx0cqVaoky5Ytk2+++UZ8fX3l9ddf1/I1mUzSuXNn7T5T812zZo00adJEateuLYqiyMWLF53KF8j/7flHH31kN9/WrVuLv7+/hIeHF3u+ALSfsEVERMjXX38tW7Zs0fJVPyUYMmSIU/mqn2JNnDhRMjIy5MsvvxRFUUrNsXbgwAFRFEVGjx5td98VEe2nxv/GsaZ+VVyd3+DgYAkMDJTVq1fbnV/1jcQ333xjM7/Hjh2T1q1ba3OnKIq0bdtWe/Guzu3tt98uQ4YM0c3vzJkzxWQyaZ9oqS8C77vvPm2evvjiCwHyfxI3ceJELZeJEycWe77qBxwq63zV86VMnjzZJt/atWvLtm3btH1B/Xnktm3bxNfXV9q0aWNzrK1cuVKqVKminc/gr7/+klWrVmmfFvv5+YnBYJCoqCj57rvvZOXKlRIeHi5z5syRMWPGaPPy/fffy+zZsyU+Pl5SUlKkc+fOsnnzZrnjjjvEZDJJpUqV5PLly5KVlaV9Qqs+7m7btk18fHwK3XeL67FB/VlpYftuYGCgmM1m2bZtm4iI/Pjjj+Lj4yOTJk2Sbdu2yS233CKBgYGybt06LV/1my3Hjx+XKlWqSEJCgnasrVq1SsqVKycAZNy4cdr8TpgwQZvbOXPmaK8JVq5caXduH330UalSpYqYzWZ59NFHJSsrS+655x7tm5ZqvrVr19beYNnLV53bn376qdjz9ff3F39/f21fqFmzpvbp67Zt22Tbtm1iMpl0+64z+ar77uDBgwXIXzB+4YUXJCYmxiZfdX9xZt91JF9XjrXiypfHmv1915HHMj8/P0lOTi70WAsNDRWj0ajNb/369cXf398jXvO68hrS017zGo1Gad++vZw9e1Z+/PFHMZlMMn36dBHJPz+wxWKRJk2aaPuC+n7t008/lXLlyknv3r215//p06dLbGysvPHGG9qx9u6770qFChXkpZdekgoVKsiGDRtk/vz52v59/PhxWb9+vdSsWVNiY2OlX79+sm/fPu0bzPXq1dNuW/3Zq7rv7ty5U4xGY6H77r/5PKyePuDs2bPaY5c6v2fPnpUOHTpoH46q1Nc0u3fvlnLlyumOtenTp2tfpJg/f74YDAYpX768bNmyRebMmaPN7/jx47X7SJ3fgIAAqVKlihw7dkxeeukliY6OFh8fHxk3bpyIiAwcOFACAgLEZDJp+aalpYm/v7+2L1jnq+4L6enpTuVrvS+ox2NGRobNvlBS+Tqz7zqSr4+Pj27RtV27dqIoinasqftucRxrW7ZskYJ27dolwcHB4opSvyg1depUMZvN8tRTT8ny5cvlxx9/lFtvvVV69OghTz31lPj5+UmlSpVk/vz5Wh/1QWP16tUyaNAgCQ0N1d6IXrp0Sbsj1E9r/P39ZcmSJXL8+HFJSEiQ5s2baycYbtiwoQwYMECaN28uvr6+0rNnT4mKipL+/ftLYmKiVK5cWXx8fOTJJ5+UIUOGiNlsluDgYO1Jt+C3M2JiYuT111+XYcOGSfv27bV8H3/8cQHy76qcnBzp2rWrGAwG7e8vvPCChIaGai8ounXrJosWLZLffvtNIiIipHfv3nLrrbcKALnnnnvk1Vdfld69e2tfwR83bpw0bNhQ+vTpI8HBwVKzZk2ZOXOmVK1aVRISEiQyMlJ8fX3FYNCfnNPX11eeeOIJGThwoNxzzz0iInLq1Cm55ZZbtN+TXrx4URo1aqTLd8yYMWI2m7UV7gYNGsjMmTNly5Yt0rhxY5tvLcTFxcmkSZPk1KlT0q5dOwkMDJTbb79dzp8/Lw899JAWB+R/dbhz5866fNWvMLuS75UrV6Rhw4ba/dSgQQNp2bKlS/nu2LFD+5RTncNZs2ZpX4VV9wf1Z16+vr5Ss2ZN3c8/C+Zrb24dzVfNQVGUfz3foKAgXb7qfeGOfHv06KEda9bfkvT0Y039FpT6Cbb1visi0rt3b+nSpcu/cqwFBwdLv379tPkteB/Zm1/1cVf9GrX1/Obl5UnLli21byl169ZN+vbtK2+//bY2t2PHjtV+9/7CCy9I7969xWw2S+vWraVy5cra/CYnJ4ufn5+8//77MnfuXG3BacKECbp81fqLM18AEh0dre0LjuYbFhYmHTp00BY/DQaDREZGSnh4uBgM+SdA7dWrl3ZfHzlyROLj42XSpEly7NgxSUlJ0e0Lp06dEh8fH632uLg4eeedd0Qk/1NudUG24P1osVjkmWeekT179mjnxEpNTZXVq1frTgCqPheq+656O4Xtu8X12DB27FiHnicaNGggipL/8/yYmBjx8/PT3ih07NhRHnroIXnkkUe0fNq1aycNGzYUEbE7v+PHjxeDwaCd502dX3VuC377DsAN5/bYsWPSvHlz3eOYyWTSFhLCw8O1N/rqvmCdb8F9objztbcvvPLKK9q+6+/vL2azWbfvOpvvqVOnJC0tTbfvJiQk2OTr6r5bVL6uHGvFmS+PNfv7blGPZX369HHoWGvbtq3uNaT6Wufffs3rymsyT3vNO3HiRKlRo4aYTCapWLGiBAcHaz93Ur85pH74L5L/rYvw8HB57733ZNeuXdoJoFW7d+/WPpwr+Jw+btw4efvtt8Xf3193MnD1/UC3bt3k22+/1S5QExUVJbNmzRJFyT/Rvfr6Sz1/aHh4uLZQUdi++28/D5crV05MJpPUrVtX0tLSJCAgQPvVT61ateTOO++UZ555RsunefPmUqVKFRERu/Or/oS7YD7qQo06vwVfq6rz26JFC+3xMyoqSnbt2qX9nE392ZjBYJCYmBgt39DQUO3nnPbyLbgvOJtvwX2h4IUibrQvlES+ju67juRrb9996KGHtGMtLi5OzGZzsR1rBc2aNcvln+8pIiIo5RYtWoR33nkH6enpyM3NhYjAYDCgQYMGePbZZ5GZmYlNmzZh1apVAPIv2fnKK6+gTZs2uOOOOzBw4EC89957yMvL08asVq0aMjMzkZubi4EDB6JLly64/fbbcf78eUyYMAGzZs3CX3/9BR8fH8TGxqJZs2YYMmQI6tWrhwkTJuCHH35A06ZN8eSTT6J9+/b48ccfAQDR0dFYv349qlevjoMHD2L58uX4+eefUb9+fQwZMgRA/mUZr1y5guDgYADQLvvep08f7e/Hjx/XLjF+5coVGI1G7Nq1C5s3b0bv3r0RFhYGAMjMzMTLL7+ML7/8Urs8qMlkQoMGDTB48GCsX79ey/Xdd9/F+PHjMWbMGKi7RXx8PJYvX47k5GSkp6dj+fLlOH/+PPr06YNbbrkFwcHB+Pvvv3HixAlUr17d7v1z6dIlbNu2DS1atAAAHDx4EBaLBT///DM2bNiAF154AdHR0Vr82bNnsWnTJvz1119o27YtEhMTC73/d+/eje+//x7lypVDs2bNEBkZqV2muOAlMG8m3++++w4ZGRk2uTqb75UrV/Dpp5/i8uXLuPvuu7VLyt4o39zcXKdydTTfpUuXonz58qhTpw4SEhI8Ot+ff/4Zv/76K1JTUxEbG1tkvkuWLMGlS5d0+R48eBCnTp3Cd999B7PZzGPt/7njWFNvZ9GiRfjnn3/Qt29fbZyC82s9tzea34yMDGzdutXhuf3Pf/6Ddu3aYciQIdr8vvLKK2jWrBmOHDkCADCbzfjyyy/Rpk0bAMC0adOwZs0a1K1b1+Pz3bNnD6ZOnYojR46gadOm2qW+/fz8sHfvXtx+++1276sTJ05g7dq1uvy3bt2Kzz77DHv37sW7776LChUqaH/Lzc3F3Llz8dNPP6F58+aoUKECbrnlFgQFBWkxf/31FyIiIgAAJ0+exJtvvokrV66gd+/eaNq0qVsfd9X9yl2PDfb23T179uDHH3+0uYx6tWrVcPjwYafmd+vWrfD398f69euxYcMG3fzm5uZi27ZtWLNmDQ4fPoyePXuiYcOGN5xbAFi8eDE2bdqEChUqoEuXLkhLS/PYfO3tCwCwd+9e/PDDD27Lt7B9d9u2bXjvvfeQmJiIW2+91el9t7B8i+NYu9l8PflY27VrF7Zs2aI71ty9L9xo31Xnd968efjxxx/tPpY5cqy5+zWko8/Dbdq0QVJS0g3ntrDXZL/88ovuMvWe+po3Ly8Pa9assftYVr58eWzfvh0PP/ywNsa5c+dgMBgQGhqKXbt2YenSpRg5cqT29xUrVuDDDz/EH3/8gWXLlunyOX/+PL7++mtMmTIFSUlJaN26NZo1a4bKlSsDyH8/uHfvXlStWhWBgYHYunUrRo8ejezsbDz44IN45JFH3LrvAkU/NhS27wKFPzY0btz4hnPbvn177Nq1C+np6dr8FpxbADbzu3r1aphMJqxevRobNmywO78TJkzAli1b0K5dO8THx2vzaz232dnZ2vvjuLg43H///WjTpk2J5Xv+/HmsXbsWX375JTIzM9G7d2+0bNnyhvtCSeRb1L7rTL729t2SPNZUK1euhI+Pzw2PicKUiUUpVU5ODv78808AQGRkJHx8fG5qvGPHjiE9PR1t27ZFQEDATY2VnZ2N69evIzAw8KbGcZWI4MyZM8jLyytybrKzs7Fnzx74+PigWrVqMJlMJZgpAfn3l6IoDrfdFePquPQ/PNaKjzNzC+S/AMjKysItt9zyr8xtacuXiIiIiKiklalFqbLg6NGjGDlyJGbPnu1wjL0+jsQ4etv//PMP0tPTER4ejrS0NF07KSkJixcvxr333ov09HQEBARg586dWtuZmAULFqBatWo3PW6DBg3w448/okmTJkhISMCLL76I7du3IywsDE8++SRiY2MxatQoHD9+HP369UPfvn3x5Zdf4q233tJi/vnnH13bkT6ujjt58mRcvXoVvXr1QuvWrQEAvr6+yMjIQGpqqkNtd8U42ycrKwuLFy/GH3/8gZiYGHTt2hXffPPNDdv3338/LBaL2/vYi0lMTMTu3btx4cIFxMTEwGg0YsGCBThy5AgqVaqEJ598ElWqVEFYWJi22j927Fh8/vnnOHPmDCpVqoTatWtj8+bNOHPmDJKTk/Hkk09i//79NjE7duwodNwPP/wQ7733ni6mZ8+euuNs8ODB6NGjB2677bYbbiuqXVx9brRt6tSp2Lp1Kzp16oSePXviww8/xGuvvYa8vDx0794dY8aMwXvvvaeL6d27N1asWAF/f3/07dsXR44cwcqVK7V2lSpVMHz4cPzzzz9o1aoVlixZgoULFxY5rr3btl7YOXnyJGbMmIHNmzfj5MmTMBgMSEpKQrdu3dC3b18YjUabmNzcXACA0WgEAJu2wWBAbGwsLBYLLl++jFOnTtmMe+bMmSJv155r167h888/1z6lzc3NRVZWFgAgMDAQIqJrGwwGREVFwWg0Ijc3F2fPnnVLH0djypcvj6ZNm+LOO++Er6+vVsfp06fx3//+FyNGjLjhNldibrbPsWPHEBoaiqysLN029bmkefPmWozZbMYPP/yApKQkXVuNcXcf65jq1avj119/Re3atREUFIRFixZh48aNCA8PR58+fRAVFYUpU6bg6NGjePTRR9GkSRPs27cPb731llMxRqPRLX0++OADXL16Fffee6/23JGUlIQ1a9Zon/Ratx2JKYk+IoJvv/1Wey5p3749vv/++xu2b7/9dphMpkL7OBJTVJ/Y2Fikpqbi119/xdmzZxETE4OAgABMmTIFJ0+eREpKCgYNGoQTJ07g448/1p6jmjVrhq+//rrQmLS0NKSnp7u9z4IFC3D8+HH89ddfqFSpEgYNGoQtW7bgnnvu0b5x+vbbbxfavlHM3XffrfvGVlHjONIHyP+kf+vWrbj99tvRrFkzvPrqq5gzZw4iIiLQr18/pKSkYNiwYfj7779x991344033nAp5uzZs27p8/rrr+P48eMICQlBcHAwRAQ5OTnw8fGBoigwGAyoWLEiAgICcO7cOZw8edImxpU+ro6blJSEatWq4fr169q3N65evQpFUbTnjatXrwLI/9YwkP/tjuDgYFy8eFHXxzomJCQEFy5cuGGMo+MWzEX9ZknDhg1R0N9//40vvvgCvXv3vuE2V2JudtxevXrBYDDoYtRfAh07dgwVK1ZEXl4eDAYD8vLycOzYMVSoUEHXVmMURcHRo0ft9nEk5kbjqrnEx8fj0KFDiI+Ph8FgQGZmJr766isEBgaiS5cuCA4OxrJly3D8+HE8+OCDKF++PK5evYr333/fqZigoCCn+9iLuXbtGj777DNcvXoVnTp1QmRkJFq3bo05c+ZojyHWbXvb3NHHlXEPHjyoPb/UqFGjyLYjfezFuMylH/1RsdmxY4fuN5wi+b9Rfvjhh28YY6+PIzFFjXvkyBHp3r27VKpUSfsta8ErkhkMBu1EvwVjAGi/i3Y0pnbt2rrfzN7MuL6+vhIeHi4Wi0Wef/55m98Eq1dGUX+n27dvX12M+ntuZ/q4Oq7ZbJaKFStqv1tPSUmRevXqCZB/qdro6GjtxH2pqalSr149u+2CfRyJcXVci8Winfyyf//+YjAYtHNGqOcOCAwM1NpqnXXr1pXw8HCJiIgQk8l0030cHdfPz08aNGignUS8S5cuMnbsWHnmmWckMDBQwsPDJS0tTSpUqCDJycnauYFmzJghbdu21XJ76qmn5JlnnhGz2Ww3pkGDBoWOazKZpE+fPjJjxgxtnAoVKkj58uWlWbNmsnDhQm3/UE9eOWPGDG2/rly5skyYMKHI9smTJ4ulj72YYcOGSVBQkNx9991Svnx5mTBhgvj7+0udOnXkvvvuk6ioKElNTRWDwSCBgYESEBAgbdu2FUVRpHr16hIUFKSdD0FtqyeSNJvNUr16dTEYDNKmTZsixx0/frxNTK1atSQkJERiYmLkhRdekBEjRmjHXIUKFcRoNMrdd98taWlpYjKZtPMPqvPfrFkz7ZwtYWFh2vn0rNvt27cXHx8fMRqNEhQUZDOu2WwWg8EgISEhUrlyZTEajfLQQw9J165dtXMMVqlSRSpXriyVK1eWatWqSbVq1SQlJUX8/f3Fx8dHmjdvLh07dpSAgADtsUQ9wbTaDgwMlBYtWmhXd7FYLNKiRYub7uNoTKdOnbS4lJQU2b9//00/RxUV42ofRVGkQYMG2jljOnfurB3H6jYAcsstt2ht9eImalu95Lm7+9woJiQkRBRFkbCwMO1Kv+rYZrNZO/+Dun306NFa29GYguc6uZk+ISEhEhUVJREREeLj4yOtW7eW7t27i6LkXxCge/fuUqtWLZt2wZhatWrZjXF3n+7du0u5cuXEYMg/Ibl6eXYA2kmA1XrVtvp39ZwelStXluDgYKdjXBlXnWu1re4bd999t9x1113a/tKwYUN5/vnnpVGjRgLkX7a+qJgqVaoUSx9FUaRSpUrSoEEDMZlMoij5V7hr27atfPLJJ0W2r1696pYYR/pMnTpVTCaT3HLLLRIcHCwPP/ywANDO9+jj46PVoF5pt2HDhk7HqPf9zfbx9fWVwMBA7TyX6use9XUoAGnRooV2nhn1/ErWMa70cWXcNm3aaBehUZ+z1auHqdvUqwGqr79q1KihnT/MbDZLjRo1dH0ciXF13Lp162rvMW699VbdBWw86XlNRGTz5s3ac3R0dLQ8+uijAkDuvfdesVgs2n51zz33aDEDBw7U9VEvJFNYH0diHB23UqVKYjAYJCUlRRYvXqzNvfpcp55DS31+mTt3ru4cVo7EBAcHa681b2bcsWPHSnJyspjNZvH19RU/Pz95/PHHxWAwyKOPPiovvvii3HPPPTbtF198Udt2zz332I0prI+r49arV08MBoNMnTpVFi9erF0gQn0eUd/Pqf+ioqJ0ryuaN2+unZi9sJg777xTey1oMBikVatWcunSJXEFF6VK2PLlywv9984774iiKLpt/fv3FyD/8r0vvvii9OvXr9C2uq3gOPbGnTRpkk2M9YMeALnjjjvk7Nmzsn//fomIiBAAsn37dtm/f7/2prx169Zy9uxZ+eGHH7QnImdi1B3dHeOGhITI4cOHZeHChdqLIhGRhQsXisViEbPZLC+99JKIiHTt2lUASNOmTbUYRVEkJCTEqT6ujgtA6tSpIy1btpSKFStqT4QAtCdutYagoCDtiifW7YJ9HIm52XEbNGigLWKpbz7vu+8+bX9R2+ri0OnTp+XSpUva1Uxuto+j43bo0EFE8q9WlZqaKqGhobJ27VoREe246tu3r8yYMUPbB19//XURyb9kt3ocHTp0SEREu4y02lZjzGZzoeMqiiLPPfeciORffU29lGzBxS4Actddd0mrVq10J+YcN26cPPXUU9qTeJMmTaRr16522+oTr7v73Cimfv368sUXX+guc6wuUvXq1UsAyP333y/jx4+XsLAwAfIvl1vw8UU9JtQXWUajUZYuXSoiol122pFxC8aoi9wFF7sMBoO0bt1axo8fL1FRUdKiRQsxGo1yyy23aAvcfn5+Ur16dQkNDZWmTZtKcnKyxMXFiYjIuXPnxGw2a1c4OXfunFSsWFF8fHxk1KhRcu7cOalTp440atTIZtzIyEi57777JDQ0VCpXriypqakSEhIiderU0V4oWy92lStXTuLi4iQkJESaNm0qrVq1kjvvvFMuXLggFy5ckKioKImKitLad955p4SHh8udd94pR48e1bVvpo8jMS1btpQmTZpIRkaGfP/999KyZUupVauWfPrpp/Lpp59qV2xR2/a22YtRX8gWR5+aNWvKxx9/LP/973+1q9vccsst8vPPP2svjgMCAuSbb76RtWvXavuZ2q5Vq1ax9LlRTGRkpBw+fFjefPNN7cXzxYsX5c033xR/f3+xWCzSv39/uXjxotSvX18MBoNUrVrVqRh1ofdm+wD5HyAlJCRozyHqfm00GnVvUgu2C8aofy/uPgVj4uLitOfHrVu3iohoJ+5WL+Dx0EMPac/dp0+flqNHj2rPHQX7OBLjyrgApGvXrtrzT1xcnJhMJjlw4ID2HKUoitZu1KiRdoGGwmKMRqOkpaW5vQ8AmTNnjtx5553aFaIAyNixY7VtAKRdu3bSqlUru231dWdhfRyJcaSP0WiUNm3ayM6dO2X9+vWiKIo88MADIiKyfv16Leb9998XEdGuSuhsjPq66mb7+Pr6ymOPPSZ5eXkyZ84c8fX1lfj4eMnLy5N9+/ZJWFiY+Pv7y2OPPSa///67JCQkSEREhC7GZDJJcHCwU31cHTcgIECaNGkiX331lSQkJEhqaqo0adJE9u7dK3v37pXw8HAJDw/X2k2bNpW4uDhp0qSJrFq1Std2JsaVcRs2bKg9z/3yyy/SsGFD6dSpkxw9elSOHj0qX331lQDQ2va22YtZunRpoTGujnvnnXcKAJk3b55MmTJFe11cuXJl+fTTT7XXUwEBAbJw4UKZOXOm9visttWrAxbWx5EYR8ctV66cpKenyzPPPKM9NqSnp8vgwYMlMDBQLBaLdO7cWX755RdJTEwUk8kk5cuXdyrGaDRqV4G8mXHV54mCHw6Uhn8Fc126dKlkZWVpzyV33XWX1lYv6nDw4EHZvHmz9p6vYB97MUFBQbJ+/XrJysqSzZs3S3JysnYFamdxUaqEqSuJBVce7e1EBWPcuXM6Mq71YtfkyZO1hSv1gaZixYqSmZkpJ0+eFABSvnx5yczMlFOnTgmQvxruTIz6Jtcd495///1SsWJF2b9/v1aTiEhubq72SZS6eJGRkSEA5PPPP9digP+9UXa0j6vj+vn5SWJionzzzTeyc+dOCQwMlMTERDEYDLJ792557bXXdG0Rkddee00AyAcffKC1nY1xdVz1U8Xdu3dLUlKSKIqifXKktmNjY7X2O++8IwC0GPUS4zfbx5GYgIAAKVeunIiIREdHy9y5c3ULTOplsH/55Rctxt/fXxITE7X2F198oYtRX8QW7KPGFDauj4+PpKSkiEj+Gwn1CiHWi11q/teuXdNdUjw2Nlaef/55URRFWrZsKUajUWJiYgSA9uYgJiZGunXrVix97MWo3+AxGo3a/KuLsjt27NDe+B0+fFhERJYtWyYAtMU7EREg/xNKlfrGQe1z6NAhAaBdnaawcQvGAPnfOFPb6ifMmZmZWh+LxSIGg0FOnTolIv97E5CZmaktMJlMJjGZTFqMuniottUXhj/99JOIiHz99dfaNyHVGHUMkfyFLHXBbOjQoVofo9GoW/xSF6h37typy2Xnzp3aXKkL4apff/1V62Ov7WofR2LU55Ointc87V/Bb6sA+S+Y//rrL+255NZbb5U6derIX3/9pX3KqLYPHz5cLH1uFNOyZUupU6eO1laf165du6Y9r//2228iIvLjjz8KAPnkk0+cigEgYWFhN93HYrFInTp15LfffpP09HTtGxHqc4eIyGOPPSYAZMWKFVq7YIx1u7j6qPuv2q5atarueU1tV6xYUWurl2xXY9QrEhXs40iMK+NaLBbtsSI6Olref/99MRgMkpGRISL/e45S29HR0bJy5UrdNnsxAQEBYrFYiqWPmv/p06e1x1n124pvvfWW9i1xg8EgderUEQCSlJSktbt3715kH0diHBnXx8dHkpOTxWAwSIMGDXSv40Rsn6MOHjwoAOTrr792Kqao5z5n+uzbt0/Xx2QyyZ9//ikiIp9++qnuOerzzz8XRVF0MerzmjN9bmbc9evX6/ps27ZNq0m94pjql19+EQBajHXb0RhXxi34LRn137/9nOXIP+tc69evL9nZ2dpzR7Vq1aR9+/aSnZ2tvSZU27t37y6yjyMxjo5bvXp1ad++vfz111+657XLly9rryW2b98uIiLr1q0TADJ79mynYgo+R93suE2aNNEezxo3biwWi0X3XKJexXXjxo1a+4477tBirNuO9HF13ILPazVq1NA9r9WoUUN7PFTbzz33nO7xulKlSrr2jWLKly8vBS1fvly7oqOzDKASFRMTg2XLliEvL0/7p/z/76wVRdFOIF3wSoAAtN9miwi2b98OAPj888/ttgtuK2pcdeyCMePHj8f48ePxwQcfAACefvppdOvWDd26dcPly5ehKAq6dOmCFi1aYP/+/QCAVq1aoUWLFti3bx8AYPTo0U7FXLlyxW3jjh07Fl26dEGrVq10dRoMBpjNZt2JukNCQgBAu/qawZB/SFy5csWpPq6Om5ubi0WLFuGJJ57AlClTkJOTg0WLFiEvLw9vvvkmnnvuOV07JycHw4cPh9FoxNixYzF06FCXYlwdt6Ds7Gxt/ynY/uuvv7R2WlqaLkY9H8/N9nEkpnHjxtqFD1q0aIH09HSIiLbt8OHDCAoKwowZM7SYChUqaFdAa9GiBSZOnIjQ0FAtJiYmBmazWddn4sSJMBqNhY5rNBpx9OhRAMDx48dx+vRpXZ9Lly7p5tbHxwdPP/00gPyrHg0YMACffPIJRASLFi3CgQMH8OijjwLIv5KL2laPe3f3sReTk5ODF154AQcOHED37t0BAK+++ioAoHbt2lAUBUajEb/99hsAaFem27BhAwBox6/FYtHaubm5MBgMWp+vv/4awP/O/1DYuAVjACA8PFxrq+dsOnnyJACgXr16yM7ORl5ennaMPvbYYwDyr9IWFhaGN954A3l5ebh+/boWo46pttXbVq+wV7lyZeTk5OjGLVeuHK5fv47Tp08jLCwMw4cPBwDtiieVK1dGbm4uTp8+rcW8//77AKCdC0/N5dChQ9o+4u/vD39/f6196NAhGAwGLca67WofR2ICAwMREhKCgwcP4uDBg5g5cyYURcEbb7yBzZs3Y86cOQCgte1tsxejXhikOPps2LABBw8exIEDB/Dll18CACpVqoRWrVrhzJkzAID3338fCQkJaNWqFS5evAgAWvvChQvF0udGMTNnzkRCQgLat2+Pgnx8fODn5wcRgZ+fH4D8x6mC/3U0BvjfvnwzffLy8jBixAjcfvvtWLlypdbOzc3FRx99BAB47733YDAY8Oijj2Lq1Kl47733dDHW7eLqY+3vv/8G8L/nErWtnm/m77//RuPGjXUx//zzj00fR2JcGbdu3bpan7p16+L48eMAgIULFwLIP/+Nr6+v1q5bty4WLFiAgICAQmNCQ0O1+9mdfQqKjo7WzuH1+eefIy0tDSNHjoSI4Pvvv8e3336rPX6fOnVKa69Zs6bIPo7EODLu9evXMWfOHHz77bfauafuvfdeAPlXUrt+/ToMBgMOHz4MAEhPTwcA7TnS0Rjgf89ZN9NHURTt9cPhw4dhMBhw/fp17XxI6nyr51KqVasWREQXoz5XOdPnZsZVX/epfdTHSSD/KrUFLwhy6dIlKIqixVi3HY1xZVz1+XL9+vVYv3493n77bQD5rxUmTZqEoUOH6tr2ttmLUefDmT6OxKjn0FTznTlzJoD818edOnXS3n8sXrwYly5dQqdOnbTHIbWtHs+F9XEkxtlx1WNMFRAQAH9/f4iI9rqrSpUqAICUlBSnYoD/PUfd7LitWrVC/fr1sXLlSixevBg5OTnIzc3Ft99+CyD/6n+KoqBHjx5YuXIlVq9ejTZt2mgx1m1H+rg6bkHqc4b6XHLq1CkoiqI9rpw6dUq7Wp4aoz6uFOxjL0a9r1W1a9fW3u84zaWlLA81f/58adq0qcTExGjfQHjggQekWrVq2rb58+dLQkKChIWFORzjSp8bxfj5+YnFYtFi3nnnHQkPD9c+iVE/5X/llVe0upo3by4F7yrrGHt91G2Fjav+rOhGfbZv3y4AZP78+VpM9erVtT6DBg3Svt43f/58m7YzMepXAt0x7urVq7UY9dMC1dy5c8XHx0eL2bhxo5hMJlm5cqUWEx8fL9HR0U71cXXchIQEERG5dOmS3H777eLr6ys7d+4Uk8kkXbt2lVq1atlt+/j4yNatW6V37943FeNsH/WTo9TUVAkMDBRFUaRmzZpSt25drW0ymbS2+smvGqP+LO1m+zgSM3ToUDEajdK8eXN59tlntXP7VK1aVZo3by4Gg0HuvPNOSUhI0GLUb7yVL19e4uLiBMg/Z0ZgYKB2LitfX18xm826mJCQkELHVT+xGDBggERGRmq/u+7Xr5+I/O9Ti4LnKxg/frxuW15enu6TDpH8T0IWLVqkta1j3NXHXswDDzwgUVFR0r9/f0lMTJSQkBAJCQmRGTNmyOjRowWApKSkaDHlypXT7oNmzZpp58VQFEVrBwUFaefIaNasmRiNRu28NoWNq/z/b90LxjRo0EBERPbt2ycGg0H8/PykRo0asnr1am1u69atq9X40UcfaZ/crV69Wj7++GMB8r+Svnr1alm/fr1ERkaKoihau3bt2mI0GrVxX3/9dZtxO3TooD2url+/Xpo0aSIAZPPmzSKS/20r9dtfV65cERGRn376Sft0b+LEibJq1SoxGo0SGhoqQ4cOlaFDh4rZbBaLxaK1Q0NDpXHjxhIcHCx33nmnBAcHS+PGjW+6jyMxJpNJmjVrJhkZGTJx4kQJDw+X5ORkGTt2rIj877lFbdvbZi9Gnavi6LNkyRKbmEWLFkm3bt20b6otWbJEcnJypFu3btq5k9S2GuPuPjeK+eabb7SYgj9LF8n/mavJZJJvvvlGRPK/0WQ0GmXVqlVOxURHR0tkZORN91G/yXPs2DGpX7++mM1mOXnypBiNRmnUqJF06NBBTp48KSaTSdavXy+tW7fWtlnHlEQf9VPxtm3bSlhYmCiKIp06dZK77rpLa/v6+mrtt956SwBoMep5nwr2cSTGlXH/85//iKLkn9dx7Nix4u/vL76+vuLv7y9JSUliNBq156i6detq3xCqXbt2kTHq87A7+wD555p59dVXpXfv3tp5GdXnkgsXLtg83xgMBnnrrbe0tnWMvT6OxDgybsuWLaVy5coybtw4adiwoXYewN69e0vVqlUlNDRUfH19JS4uTvr06SN+fn4SEhIiPj4+TsWo54i62T4+Pj5Ss2ZNmTlzplStWlUCAwMlMDBQ9uzZIwcOHJBWrVqJoijSokUL2bNnj3Zai4Ix5cuXF0VRnOrj6rgGg0EqVaoky5Ytky+//FICAwOlUqVKsmDBAlmwYIEEBQVJUFCQ1k5ISJCaNWtKxYoVZdiwYVKxYkWpWbOmro8jMa6Ma7FYpGnTpnLhwgVZtmyZJCQkSFxcnHaKB/V5Q23b22Yvpn79+nafo252XPUb99Z9lixZIk2aNNG+pf3ll1/KpUuXpEmTJtrxqLbVmML6OBLj6LibNm3SYqyf14YPHy5Go1E2bdokIiLp6emiKIqsWbPGqZjQ0FAJDQ296XGDg4Pl4sWLsn37dklLS9POR2g0GiU5OVkeffRRycrKEpPJJEuWLJG0tDRtm3WMK32cHVd9Xuvdu7dER0eLoijy2GOPyZAhQ7S2xWLR2tOmTRMAWkx4eLiufaOY4OBgKSg9PV33msAZZWZRavr06RIZGSnjxo0TPz8/yczMlOnTp2s/h/Lz85MxY8ZIZGSkdO/eXQwGg0MxrvQpLKZ58+aiKIr2M5I5c+ZIRESEtlh0+fJlmTJlira4ISIye/Zs3YFqHWOvz+XLl6Vp06aFjtuiRQubcQv2UR/QOnbsqMWoPylSPfHEE7oY67ajMepJMt0xbsGFIOsFvRdeeEGaNm2qxVi3RURuv/12adeunVN9XB1XXZRQ2y1bttROurp7925ZuHBhoW0RcUuMo33Uk7kPGjRIRo0aJb169ZJRo0Zp/1JTU6VGjRpa++6779a1mzZtKpUqVbrpPo7GdO/eXZ5//nntpNPq4lB0dLQ88MAD4ufnJ40bN9ZeSAP55xkJDg6WkJAQ7YSG8fHxkpaWJomJiWKxWMTHx0cXo54PorBxgfyv3lerVk3MZrM0aNBAt9gF5P+sbsCAAdK8eXPx9fWVcuXKaV99F8l/0VFY25EYV/rY25abmyuvvvqqdO7cWcaPHy8vvfSSBAcHa+dvqlmzplSoUEG6du0qNWvWlJCQEHn66aflvvvuE4vFIv7+/tK7d2+5++67tXafPn3kgw8+0F6M16tXT4YNG1bkuA0bNtTFqCdnLrjYFRgYqJ1QGsg/8WmVKlV0C0zVqlWTHj16aPuK2WyWO+64Q2tXq1ZN6tWrp7XT0tLk7bff1vUJCwvTjVujRg0JDg7W9QkKCtItZIWHh+sWv2rXri0Wi0UmTJggMTExup/HqfuS+oKjYFutreBJJ93Rp6iYgmPHxMTI66+/LsuWLZMPP/xQRPJ/kjh48GCtbW+bvZj58+fLo48+6vY+nTp1kvbt2+ti1G05OTnaOQHVmJycHElJSdGeS6xj3NnHXoyiKLJw4UItRv3pl+rFF1+U1NRULca67WjMbbfdpi3m3kwf9TxJIvnPa9WrV5fy5cuL0WiUXbt2yfjx47X27t27JS8vT7fNOqa4+6gnie7WrZv07dtXWrRoIX379tX+xcfHS2xsrNbu0qWLrl2jRg0JCwvT9XEkxtVxb7vtNunZs6d2/it1cSgyMlJ3wmv1+U49NouKKY4+an4+Pj7StGlT+eyzz+x+UFJY210xjvS5fPmyDBgwQGrUqCGPPvqonDt3Tho1aqTV2rx5c3nhhRe0x8SYmBj5/fffnY5p1qyZ1KtX76b7DB06VPdYHBsbKzVr1tQep6OiomTgwIHSuHFj7f4JDQ3VxURERGjPM472cXXcIUOGyOOPP659IGUymXTng1PPK6m21Q+mCj7PmM1mp2NcGVfNT/0w8oknnpBp06bJ5MmTRUTk1KlT0qVLF61tb5u9mLfeeks736mjfRyJ6devn3aONzWmYcOGcs8998jFixe191n33HOPiIhcvHhR+4m52i4Yc6M+jsQ4Ou7MmTO1GHWhRPXaa69JfHy8FmPddjSmYcOGUrNmzZset1OnTtrfr1y5Ig0bNhR/f38xGo2Snp4ujz32mLaIvXv3brly5Ypum3WMK32cGVd9/G3QoIG0bNlSqlSpIi1bttT+RURESEhIiNa+8847dW11/aJgH3sx1j/VGzt2rLRo0UJcoYiIoAxIS0vD+PHj0a1bNwQFBSEjIwOdO3fGY489hrFjx+Lq1asoX7483nzzTaSkpKBmzZrIzMwsMiYlJcXpPo7GJCUlYdeuXWjWrBkWLVqEDh062K0tKysLv/zyC1q0aOHUnGzatAlZWVk3HPfrr7/Gjh07MGzYMLt97N2uq7mQ444dO4b09HS0bdsWAQEBRbYd6VOc45Y258+fx4QJE/DFF1/gwIEDyMvLQ0xMDJo1a4YhQ4agfv36xTJuSkqKzd/LlSuHoKAg/PPPPzh58qTbcvk35OXlYcKECfjhhx/QtGlTDB8+HIsWLcKwYcNw5coVdOnSBVOnTnV6v3Fk3ClTpuDdd9/VYoYNG4YHH3wQy5cvh8FgwL333ouWLVtixIgRyMrKQseOHfH222/jqaeewrJly5Cbm4smTZpgwYIFSExMRHZ2NtasWYNr167h3nvvRXZ2Nq5fv679NMy6XXAbAPTr189m3JiYGFy/fh1btmzB6dOnsXLlSi2mYcOGiIyMxJo1a5Cbm4vU1FQMHDgQgwYNAgDMnz8fR48eRevWrbWfxKhfx7bXTkxMxMGDBwuNcaWPozGe7vr167hy5Yr28xLrbdevX8fhw4cRFRWlxWRnZ+OPP/7QLndsHeOuPvZijh8/rrsMtPW2K1euwGg0aj/tsW67K+Zm+uzatQubN29G7969ERYWhvT0dF0bgM22otrF1cdaVlYWjEaj9lPjotruiimqj4jg4MGDUBQFFSpUgI+PD0QEZ86cQV5eHiIjI2EymXRtR2KKq0/Bn0+VNtnZ2cjJyUFQUJDdtrtiXO2zZ88e+Pj4oFq1ajCZTNi/fz+uXr2qtQHYbCuq7UgfV8e9ePEi0tPTtecOtRb1J0PW7fLly6Ny5crYv3//Dfs4EuPquLfccovu+cIT/f333zhx4gSqV69ud9ulS5fw3XffITExUYs5cuQI1q9fj759+wKATYy9Po7EODLutm3bdO8lrbcdPHgQFotF+7m4ddtdMTcz7nfffYeMjAy88MILiI6OxooVK7BhwwatDcBmW1FtR/q4Om5BBw4cgK+vLypUqOBQ29UYZ5SZRSk/Pz/s3bsXlSpV0halqlevjtWrV6Njx44wmUzIycnB77//jmvXrqFKlSrIzMwsMsZsNjvdx9GYpKQk7N+/H7Vq1dLOHUBERCXH3gJTSY3ryGIXEREREVFZVmZOdJ6YmIgdO3bYbPvoo4+QmpoKAIiPj8eOHTvw1VdfaSeeLirGlT6OxgDAV199pcUQEVHJslgsxbII5Mi41jHO5HL06FE88sgjDreLq09pG5e5eG+NnpSLN9ToSbl4Q42elEthMf/88w82b96Mb775Rtf+7bffkJ2djVmzZuna8+fP12K2b9+uazsT48q4npSLN9ToSbl4Q43uyqUgNcYlLv3ozwPNnDlT4uLi5JNPPpGAgABZuHChdOvWTRRFkaeeekoCAgJkwIABEhwcLGazWcxms0MxrvRxNGbcuHFarkRERI7asWOHGAwGh9vF1ae0jctcvLdGT8rFG2r0pFy8oUZPyuVGMYqiSKVKlXTnKqxQoYLWVk+ArbYbN24sAArt40iMK+N6Ui7eUKMn5eINNbozlxMnTmjH+alTp2weCxxlcm0py/P0798ffn5+ePnll3HlyhU88MADiI2NRb9+/bBq1SpcuXIFs2bNQmhoKEJCQnD27FmHYlzp40zM5MmT0bNnz397+oiIyIOsWLFC1/7pp5907a1btyIvLw8vvfQSAOD06dOFtl3tU9rGZS7eW6Mn5eINNXpSLt5Qoyfl4uq4IoKoqCiMGzcOe/bswfjx4/HXX39h27ZtCAwMRNOmTQFAaz/xxBMAgOTkZPzyyy/4448/0KRJE10fR2JcGdeTcvGGGj0pF2+o0Z25NGvWDN9++y0qVqyIm+LSUpaHycnJkXnz5smpU6dERCQrK0uOHz+u23bhwgV59913nYpxpY+jMdZXACEiIlIVvMqd9ZXu+I//+I//+I//Suu/gs9r9957r1SsWFEyMzMlMjJSAGjtkydPCgApX768ZGZmyqlTp2z6OBLjyrielIs31OhJuXhDje7KRVEUefzxx7WYm/mmVJlYlBIR8fPzk0OHDhW6zZWY4hpXJP/S1U2bNpWYmBjtb++88458/vnnN4yZP3++JCQkSFhYmNbngQcekGrVqhUaU9S49mKsuSuXomI8aVzmUnZrLE3HWmmbb1fm15HbKc75LYl8nck1NjZWt926HRUVpbt88vbt2wWAFmPddrVPaRuXuXhvjZ6UizfU6Em5eEONnpTLzYz722+/2bQHDRokFSpUEH9/f1EURWtv3LhRAMj999+vaxfs40iMK+N6Ui7eUKMn5eINNborF3UBqmCMq4tSZeZE5w0bNsT27dsL3eZKTHGNO2PGDDz77LPo1KkTzp8/j9zcXADAb7/9ht69eyM2Nhbjxo3Ds88+i9DQUPz111+YP38+nn32WdSrVw8XLlxAbm4uZsyYgRUrVuDq1as4f/683Ziixs3NzcWHH36IadOmoUePHjh8+DAA4MEHH0RqaqpbcykqxpPGZS5lt8bSdKyVtvm2V+OMGTPwxBNPIDEx0eVcHBnXXsyHH36IxMREhIeHO9ynOPK1d79a7y8F8z179izWr18PAJg0aRLi4uKQnp6uPYekpaVBClw8V1EUANBirNuu9ilt4zIX763Rk3Lxhho9KRdvqNGTcrmZcX/55Reb9tSpU3HnnXfi6tWrAKC1u3btCgDo2LGjrl2wjyMxrozrSbl4Q42elIs31OjOXOyN4xKXlrI80KJFiyQpKUneffdd2bJli2RkZMgbb7whFSpUkOHDh8uWLVt07fnz5zsU40ofR2KSkpLknXfekYyMDPH395fMzEyZPn26hIWFiZ+fn/j5+UlKSop89tlnMmfOHDEajVp7586dAkAyMzMlNTVVJk2aJBERERIYGGg3pqhxx4wZI5GRkTJ48GBdn8DAQElMTHRrLkXFeNK4zKXs1liajrXSNt/2akxNTZXBgwdLy5YtXc7FkXGtY9R29+7dxWAwONSnuPK1d79a7y8F81UURWbPni0iInPmzJE6derI6tWrtee8NWvWyOuvv661L1++LFOmTNFirNuu9ilt4zIX763Rk3Lxhho9KRdvqNGTcnF13AEDBkjHjh3ttkVEGjVqJAXfmj7xxBMCQIuxbjsa48q4npSLN9ToSbl4Q43uyqXgtyFvtM1RZWZRSv19csF/gP73ywXb9rY5ct4Od45bME/1Tc20adPEYrFIYGCgmM1mOXTokPbGR23v27dP62OxWGTDhg02fQrGFDWu+mbJuk/BN1juyqWoGE8al7mU3RpL07FW2ubbXo0Wi0XWrFljU6MzuTgyrnWMvcWjovoUV7727lfr/cVeviIiO3fulIiIiH/5WZaIiIiIyP3KzNX3Dh48aLPt2LFj2v9XqFBB17a3zV6MNUf6OBLTt29fDBs2DO3bt0f16tW1Gs6ePYvU1FTs378f8fHx2LFjB9LS0gBAax85cgQGQ/4vLxMTE/HRRx/Z9CkYU9S4R48eRd26dbFixQpdn9q1ayMrKwsmk8ltuRQV40njMpeyW2NpOtZK23zbqzExMRGZmZk2NTqTiyPjWseo7WvXrunmsrA+xZWvvfvVen+xly8AmM1mZGVlgYiIiIiozPm3V8W81cyZMyUuLk4++eQTCQgIkIULF0pUVJSYzWZZuHChBAYGyvjx4yUuLk769u0rBoNBBgwYIMHBwWI2m7W4bt26iaIo8tRTT0lAQIDdmKLGjYuLkwcffFACAgLEYrFon+D3799f6tat69ZciorxpHGZS9mtsTQda6Vtvu3VOHPmTAkJCZGEhASXc3FkXOuYlJQU+fzzz2XKlCnaz/eK6lNc+dq7X633F3v5iohMmTJF6tat+y8/axERERERuZ8iUuAsdKXY/PnzbbZt3rxZ+/9bb71V17a3zV6MNUf6OBqzZcsWfP755zhz5gwAICQkBIqiYMaMGejXrx9mzZqFpUuXYunSpVq/0NBQ+Pj44OzZswCA2NhYdOzYEd9++y0yMzPtxhQ1rojAYDCgf//++OijjzBr1iwsWrQIy5cvx+DBg/HBBx+4LRdHYjxpXOZSNmssbcdaaZpvezVmZmZi7NixCAsLw+nTp13KxZFxrWMeeOABLFq0SDuh49y5c4vsU5z5Wt+v1vvLjfJ97bXXMGvWLPTs2RNERERERGXKv7Yc5mahoaG6fwEBAbpzN4WGhtqcz8mRGFf6FBUDQHx9fbXzjISGhsrp06dFRGTBggWSkpKixZUvX15mzZolFy5ckHfffVdOnTolIiJZWVly/PhxmTdvnrbNXkxR46ox6t/V81zFxcVJ//793ZpLUTGeNC5zKbs1lqZjrbTNt70aAUhYWJi8/fbbLufiyLg3mt+wsDCJjo52uE9x5HujfaHg/mIvX3X/mDVrlhARERERlUVlZlHKnn379kmbNm3kq6++uuE2V2LcMa6fn59s2LBBa+fk5Ni8qfHz85NDhw5p/a3b9rZZt4sa197f7b3BckcujsR40rjMpWzWWNqOteKau+KYb3s1nj59+qZzcWRc65gbLZj9W/kWtb8UttBJRERERFRWGYrpC1geoXLlypgwYQKefvrpG25zJcYd4zZs2BDnz5/X2iaTCY8//jiys7MBAP7+/mjYsCG2b9+u9bdu29tm3S5qXHt/j42NLZZcHInxpHGZS9mssbQda8U1d8Ux3/ZqjI6OvulcHBnXOiY4OBjDhg1zqk9x5lvU/nKjfAHgww8/RLNmzRAbG4vDhw/jww8/RGJiIsLDw+22AeDBBx9EamqqW/uUtnGZi/fW6Em5eEONnpSLN9ToSbl4Q42elIs31OhJuXhDje7KBQAmTZqE5cuXwyX/9qpYcdu+fbsEBQUVus2VmJsdd9GiRZKUlCTPP/+8+Pv7S0ZGhtSvX1/eeecdycjIEBHRYt59913ZsmWLvPHGG1KhQgUZPny4zJ8/XzIyMnTbbhRT1LgF/67GtGjRQj777DMtb3flUlSMJ43LXMpujaXpWCtt822vxqLm15HbcWRc6xjruXX0PimOfO3dr9b7i718p0+fLpGRkTJu3Djx8/OTMWPGSGRkpHTv3l0MBoNNOzMzU6ZPny6BgYGSmJjotj6lbVzm4r01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNXpSLt5Qo7tyUS/MM2fOHGnZsqW4oswsSi1fvlz37/PPP5fHH39c4uPjpV69erJ8+XJ58cUXddsciXGljyMx6vlCUODcU2pbURTZsmWLts06tmCfwtqujKsoit03WO7KpagYTxqXuZTdGkvTsVaa51utsaj5dfZ2HBlXURS7i0eO3CfFke+N/hXcX+zlm5SUpC1cqVfo++yzz2Tnzp0CwKatXtFx0qRJEhER4bY+pW1c5uK9NXpSLt5Qoyfl4g01elIu3lCjJ+XiDTV6Ui7eUKO7clEXpXbu3CkREREureWYUEZ069ZN11YUBXl5eQCAY8eOoVu3bpACFxo8duwYunfvXmTMtm3bnO7jSAwAREREoGnTpnj55ZcRHR2NxMREKIoCAGjWrBlEBIqiaP+1voJfhQoVtLHstdVtRY2r/k1Vp04dLd8JEybg9ddfd1suRcVY+zfHZS5lt8bSdKwdPHiwVM33bbfdZlMjgBvW42guiYmJRY5rHTNs2DAA+XMLQJv/wvoUV7727ld7+4t1viKCIUOGwGAwwN/fH0ePHkXdunVx7do1ALBpA8DBgwdRu3ZtZGVlwWQyuaVPaRuXuXhvjZ6UizfU6Em5eEONnpSLN9ToSbl4Q42elIs31OiuXFRmsxlZWVlwRZlZlFIXgUqzgwcPFvr3SpUqFbnNXkxR49pj/QbLXbk4EuNJ4zKXslljaTvWStN8OzK3ruTiyLjWMfYW1YrqY2/cfzPftm3bYtiwYWjfvj1q1KiB+Ph47NixA0eOHIHBYLBpA0BiYiI++ugjpKamYv/+/W7pU9rGZS7eW6Mn5eINNXpSLt5Qoyfl4g01elIu3lCjJ+XiDTW6KxfVV199hdTUVLiizCxKjRkzBkOHDoW/v79u26BBgzBt2jSMGDFCi1EUBW+++aa2rbAYAE73cSQmISEBAHDt2jWsWrXK5ptevXv3xvz583XbrL+9ceutt+q2WbfVbUWNa6137942b7DclUtRMdb+zXGZS9mtsSBPP9as8/H0+XZlfh3JxZqj82vNkT7FkW9R7O0LAPD8889j1KhRCA4OhoigVatW6N27N65evQofHx+b9tatW1G1alV88MEHGDx4MPbt2+eWPqVtXObivTV6Ui7eUKMn5eINNXpSLt5Qoyfl4g01elIu3lCju3LZunUrFi5ciNdeew2zZs1y+jUwAJSZc0oZDAaby2cbDAbZs2ePGAwGXcyff/6p21ZYjCt9HIkJDQ2V0NBQCQkJEQASEBAgAMRkMom/v7/MmzdP/P39df9gdY4StY/aDg0NvWFMYeP6+vraxAwYMED75+5cCovxpHGZS9musbQca6Vtvu3VWLAeV3NxZFzrmIJza29+HR3XHfne6H690b6g5jtv3jx57LHHpFy5ctp5qMLCwiQ6OvqG7bi4OOnfv7+kpKS4tU9pG5e5eG+NnpSLN9ToSbl4Q42elIs31OhJuXhDjZ6UizfU6K5c4uLiZNasWS6v5ZSZRSlFUeTMmTM225YuXSqRkZG6mG+++Ua3rbAYV/o4GiMiunZwcLCYTCYJDAyU0NBQCQgIEEVRxGw2S1hYmIiI7Nu3T9q0aSNfffWV3ba9bUWNGxoaqoux9wbLXbm4EuNJ4zKXslFjaT/WPHm+7dVoXY8ruTgyrnWMvcWjovqUZL7W+4t1vuoCqa+vr4SFhcmFCxfk3XfflVOnTomI2LSzsrLk+PHjMm/evBvGuNKntI3LXLy3Rk/KxRtq9KRcvKFGT8rFG2r0pFy8oUZPysUbanRXLllZWTZfDHJFqV+UCg0NlbCwMO3bR2FhYdqKXWGfbt/oE2/rGFf6FBWjvikJDg4Wg8EgAwcO1Or5+eefpWrVqlrb3htA6xjrtiMxRY3ryJtTd+XiSIwnjctcymaNpe1Yc6XG4upTVIy9etyRiyPjOrKo9m/mW9T+YrFYpGnTplrbz89PDh06pMVbtx2JcaVPaRuXuXhvjZ6UizfU6Em5eEONnpSLN9ToSbl4Q42elIs31OiuXNwh/2xVpdikSZMwceJEiAhGjx6Nd955B/369cMjjzwCRVHwzDPPoH///lq7d+/eePnll3Xb7MV06dIF/fr1c6qPozH//e9/8c477+C9997D5s2bMW3aNK0ek8mEEydOaO3KlStjwoQJePrpp28YY912JKaoce39vbhycSTGk8ZlLmWzxtJ2rLlSY3H1KSrGXj3uyMWRca1jXOlTnPkWtb80atQId911l9Zu2LAhtm/frsVbtx2JcaVPaRuXuXhvjZ6UizfU6Em5eEONnpSLN9ToSbl4Q42elIs31OiuXNyh1J/ovE+fPgDyzwLftGlT+Pj4aNseeughbZu9tiMxrvRxJGbFihUAgLNnz2LFihUQEZw8eRJTp05Fs2bNdDEHDhzAkSNH8NJLL+HcuXP48ssvUblyZSxfvhxfffWV1l6xYgV++uknm5iixrUXA+jfLLkrl6JiPGlc5lJ2ayxNx1ppm+8bzYNaj6u53OgxsuC49mLszW9RfYojX0f3Bet8Bw4ciOeeew5nz57FDz/8gDvuuAODBw/GTz/9hLS0NJt27dq1ddu6du3qlj6lbVzm4r01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNXpSLt5Qo7tyCQgI0L2OrVWrFpyliIg43cvDZWdn49q1a7ptvr6+um3Z2dmwWCxOxbjS50Yx5cuX18UoioKgoCDUqlULjzzyCMLDw7Ur8tm7ixRFgaIoyMvL09rWsWpMUeMqioLg4GAtJiwsTHuDFRUVhZEjR7otl6JiXOlTXOMyl7JbY2k61gwGQ6mabwA2Nd511126OXAlF0fGtY7Zv3+/tngUFRWFESNGFNmnuPJ95JFHdPNlMBhs9peffvqpyHyLesouKl9X+5S2cZmL99boSbl4Q42elIs31OhJuXhDjZ6UizfU6Em5eEON7sql4HZFUZCbm1vo7di9bSkqu1LiypUrGDZsGBYvXoy//voLQP6L/4JvAAq27W2zF2M98Y70cTSmoNzcXJttiqIgKioKrVu3xttvv42YmBiX5qaocePi4mz+bv0Gy125EHkCHmvF59+a25iYGJsY68dyR/p4Wr7h4eFo2rQpXn75ZURHR+PYsWOF3m6FChV0MdZtV/uUtnGZi/fW6Em5eEONnpSLN9ToSbl4Q42elIs31OhJuXhDje7KxVqlSpUKvR27pIwYOHCgpKamypIlS8TPz09mz54tDRs2FJPJJE899ZT4+flJq1atJCoqSsLDw8XX19ehGFf6OBLTrVs3eeWVV6RChQqyYMECrY4rV67I6NGjRURk9OjRkpWVpf1NbVvH/PnnnzZ9CsYUNa69GGvuyqWoGE8al7mU3Rqt93dPPtZK23y7Mr+O3E5xzm9J5OtKrkREREREZV2Z+aZUxYoVMX/+fLRs2RLBwcHYtm0bWrdujV69emHHjh3YvHkzAgMD8fHHH+Po0aN4+OGHsXfv3iJjoqOjne7jSIxI/vlG1qxZg4ULF2LVqlUYM2YMHn74YSQkJCA3NxdGoxEnT55EUFAQ3nzzTYwePRonT56E0WhEdHS0FrN7925Ur15d16dgTFHjAsDQoUPxzz//6PoMGjQI06ZNw4gRI9yWS1ExADxmXOZSdmssTceauq20zPfIkSNtajQajThw4ADmzZunq9GZXMaMGVPkuNYxai6KoujmsrA+xZWv2qfg/Wq9v6h9CuY7f/58XLt2DatWrUK3bt2wefPmQp8Lb731Vl2MddvVPqVtXObivTV6Ui7eUKMn5eINNXpSLt5Qoyfl4g01elIu3lCju3Kx1rt370Jvx65/eVHMbQICAuTw4cMiIhIXFyc//fSTBAQEyKZNmyQgIEDi4uLEz89PDh8+LAcOHBBFURyKcaWPIzGKosiZM2fkwIEDEhAQICIiBoNBlixZIpGRkSIiWsyff/4pBoNBa3/zzTe6mKVLl9r0KRhT1LgGg0FOnz5t02fPnj1iMBjcmktRMZ40LnMpuzWWpmOttM23vRoVRZG9e/fa1OhMLo6Max2jtq3nsrA+xZWvvfvVen+xl29oaKiEhIQIAAkNDRUA2j9FUWzaAQEBura7+pS2cZmL99boSbl4Q42elIs31OhJuXhDjZ6UizfU6Em5eEON7solICBAFEURs9ksYWFh4ooysyhVs2ZN+fbbb0VEpE2bNvLcc89JzZo1ZfDgwRIXFydt2rSRyMhI+fbbb2Xy5MliNpsdinGlT2Exfn5+ujvR399fFEWRsLAwbbt6hxoMBpudoaidqWBbHedG4wYFBWl9AgMDxWAwyMCBA0Xkf2+w1NxuNhdHYzxpXOZS9mos6phwNZfiONaCgoJK3XybzWZdjQXnITAw0KVcCs7tjcYt+BhacH4V5X+LRwXn90Z9iiNfdZv1vlBwfym4L1gvdomITXvfvn3Spk0b+eqrrxxqF1ef0jYuc/HeGj0pF2+o0ZNy8YYaPSkXb6jRk3Lxhho9KRdvqNFduTirzCxKTZw4USZPniwiImvXrhWLxSJGo1EURZFJkybJ2rVrxWQyidFoFIPBIE888YRDMa70KSxGURRp1aqVANDaPj4+2gpjUFCQ+Pv7i7+/v+5NXmpqqiiKIr1795aXX35Z+vfvL/369RNFUeSZZ57RtdUYf3//Isc1mUyiKIr4+/trb5YKvsEymUxuyaWomC5dujjdp7jGZS5lt0ZHjglPOdYURZHU1NRSM98Wi0WbA7XGgvNgXY+juRSc2xuNazKZdDH2FpMKzq+9PsWVr8VisblfC962vX2h4OJWcHCwbhFT9fPPP0vVqlUdbhdXn9I2LnPx3ho9KRdvqNGTcvGGGj0pF2+o0ZNy8YYaPSkXb6jRXbk4w4QyYsiQIdr/t23bFnv37kV6ejpSUlJQq1YtAMAff/yh2/b88887HeNKH3sxixYtgqIoqFatGrZv3w4RwSOPPIJx48YhJCQEALB3716kpaUhJSUFTZo0wXfffYemTZvCx8dHq/Whhx7SbSvYTklJKXTcffv2ITk5GY888ghee+01hISEYPPmzRARzJ49G08//TTq1KnjllwcjXGlT3GNy1zKXo1FHROeeKyVlvlu3bq1NgdqjarMzEy0a9cOt912m9O3U6dOnSLHPXDggC5m586d2tw+9NBDqFixIlJSUgrtU9z5Frxf9+3bp9tfCsvX19cXCQkJ2r6gMplMOHHihMPt4upT2sZlLt5boyfl4g01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNborF2eUmROdF5SdnQ2LxVLoNldiinPca9euYfPmzWjUqBF8fHwQHBxsM8a1a9cKvR1fX1+7MUWNu379eu3vABAcHGz3Tbk7cnEmxpPGZS5lq8bSeqw5U2Nx97lRzC+//GJTY2E1OZqLvbmzHtc6Zvv27Tec2xv1KYl8b/SYb29fWLFihW7sn376CefOncOXX36JqKgodOjQQdceMWIEvvrqK609cuRIt/QpbeMyF++t0ZNy8YYaPSkXb6jRk3Lxhho9KRdvqNGTcvGGGt2Vi/z/BdymTp2K+Ph4rF692u7r7kK59P0qD3T9+nUZM2aMxMbGitFolMzMTLl+/bq0aNFCQkNDxWg0yr59+2TMmDESFBQkBoPBoRhX+jgSs2vXLhk0aJB2Tin1JMjqv3/++UdOnjwpAwYM0E7Ya/3zDuttN4opatyC/xRFkQsXLuj+uTOXomI8aVzmUnZrLE3HWmmbb3s1FjW/jtyOI+MWNr+nT592uE9x5FvY7ar7i7181Z/22fuJn722wWDQtd3Vp7SNy1y8t0ZPysUbavSkXLyhRk/KxRtq9KRcvKFGT8rFG2p0Vy4Gg0HKlSsn999/v5w4ccKltZwysyg1evRoSUpKkgULFoifn59kZmbK6NGjJTo6WlJSUsTPz0+eeeYZSUpKkieffFIMBoNDMa70cSSmV69ekpqaKkOGDBGDwSCzZ8+WV155RQICArRziFjvNK1atZKoqCgJDw8XX19fmT17tjRs2FBMJpM89dRT4ufnZzemqHFnzJghjRo10t482XuD5a5ciorxpHGZS9mtsTQda6Vtvu3VWLAmV3NxZFzrGOsnU0f6FFe+9u5X6/3FXr4F/xERERERlTVlZlEqOTlZ1q1bJyIigYGBkpmZKcnJyTJ79mwJDQ2VwMBAqVixoqxbt0727NkjAByKcaWPIzExMTGyYcMGrb1//34ZOHCgxMbGSt26dcXPz0/Cw8PlkUcekQoVKojFYtH6zJ8/X4xGo+zfv1/i4+PlhRdekI4dO0pQUJDdmKLGffDBByU1NVUGDx6sLZBZv8FyVy5FxXjSuMyl7NZYmo610jbf9moMCQmRyMhIWbBggcu5ODKudYy9xaOi+hRXvvbuV+v9xV6+rVu3ltjYWFmwYIGI5H8Ak5WVJVeuXJHRo0fbtNWYP//8U9e+2T6lbVzm4r01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNXpSLt5Qo7tyURWMcVaZWZSyWCxy6NAhEfnfopTFYpG1a9dKQECABAYGitlslkOHDsnu3bu1xaOiYlzp40iMv7+/HD58WHbv3i2KoshPP/0k8fHx8vHHH0tAQIB2JajDhw/L/PnzxWw2i5+fnxw+fFgOHDig9QkICJBNmzZJQECAxMXF2Y0patxy5crJhg0b5MCBA9oCmfUbLHflUlSMJ43LXMpujaXpWCtt822vxoCAAJk4caJ07NjR5VwcGdc6xt7iUVF9iitfe/er9f5iL1+DwSBTp06Vjh07ioiIwWCQ06dPy59//ql9g6pgW43Zs2ePrn2zfUrbuMzFe2v0pFy8oUZPysUbavSkXLyhRk/KxRtq9KRcvKFGd+WiKhjjLAPKiLS0NGzatMlm24wZM1C3bl0AQEpKCjZt2oQlS5bAaDQ6FONKH0di4uPjcfDgQSxZsgTBwcFYvHgxzp07h99//x2hoaEIDg5GbGwsDh48iFtvvRU5OTkICAjAwYMH8cUXX8DX1xeLFy9GUlISFi9ejNDQUFSrVs1uTFHjnjt3DklJSfjiiy9gMBhw7tw5nDt3Dp06dcLGjRvdmktRMZ40LnMpuzWWpmOttM23vRqTkpIQExODjRs3upyLI+Nax5w/fx5JSUm49dZbkZeX51Cf4srX3v1qvb/Yy1dE0KhRI2zcuBEAICJQFAUZGRkIDw+3aasxv/32m659s31K27jMxXtr9KRcvKFGT8rFG2r0pFy8oUZPysUbavSkXLyhRnfloioY4zQpIz7//HMJCQmRCRMmiL+/v7z55pvSrl07ASD9+vUTf39/6dOnj/j6+orRaBSz2exQjCt9HInp0qWLNG7cWHx9fWXChAlisVi0c6BMmjRJ2rRpIy1btpTJkyfL5MmTJSIiQkwmkxiNRjEYDPLEE0+IxWIRo9Go9Vm7dq3dmKLGVRRFfHx8xGAwSJUqVeS5556TmjVryuDBgyUuLs6tuRQV40njMpeyW2NpOtZK23zbq3HixInSvXt3iYuLczkXR8a1jomMjJRvv/1WJk+eLGaz2aE+xZWvvfvVen8pmC8AsVgsYjAYxM/Pz+5JKe39s45xV5/SNi5z8d4aPSkXb6jRk3Lxhho9KRdvqNGTcvGGGj0pF2+o8WZzMZvNEhYWJmFhYdr5UQcOHOjSWo4iIoIyYtOmTRgzZgwyMjJw+fJl1KtXD3fddRe++uorbVtycjJ8fHxw7Ngxh2Nc6eNozIgRI9C+fXscPnwYo0aNQnR0NF5//XWsW7cOXbp0gYggJycHEydORLdu3ZCeno6UlBTUqlULhw8f1rUB2GxzZNw///wTJ06cwNNPP40zZ86gS5cuyMnJQV5eHt555x1Ur17dbbk4G+NJ4zKXslNjaT7WPH2+X3nlFZsaC9bz9NNPu5SLI+Nax3Ts2BEiAhHBY489hjlz5hTZp6Ty7datm83+UjDfFi1aYNOmTbh+/ToURUGjRo1QvXp1zJ49Gw899BD+/vtvREdHa+2KFSvi1KlTEBHMnj0bTz/9NC5fvqy1Xe1T2sZlLt5boyfl4g01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNborlzp16mjrML6+vkhISECTJk1cWseBlHKZmZmSl5dX6DZXYoprXHv++ecfu9sPHTokS5culYyMDJsYe30ciSlqXOu/F2cuRcV40rjMpWTHLclcRErPsebIbXvSfIvY1uiOXBwZ116MK32KI19H9he1PWbMGPnll1+0mG+//VauXbt2w7YjMa70KW3jMhfvrdGTcvGGGj0pF2+o0ZNy8YYaPSkXb6jRk3LxhhrdlYs7lPpvShmNRpw8eRLR0dEAgPvuuw9LlizRbTMYDPj1119Ro0YNh2Nc6eNozJQpUxAZGYnx48fjvffew+nTp7Fv3z4kJSXhlVdeQUJCAh588EH4+PjoYvbs2YNPPvkEb775JrKysrB//35UqlQJbdq0QUZGBi5dumQ3pqhxC952XFwcHn/8cd0cZ2dnuy2XomL27t3rMeMyl7JbY2k61vbt21eq5rvgPKg15ubmFjq/juRib+6sxy1sfrOzs2GxWBzqUxz5DhgwoMjHfIvFYpOv9f5x7dq1G8bY6+Pr6+v2PqVtXObivTV6Ui7eUKMn5eINNXpSLt5Qoyfl4g01elIu3lCju3IBgODgYDjNrUtc/wJFUeT06dNaOzAw0GYbAPnpp5+cinGlj6MxmZmZMnr0aElKSpIFCxaIn5+fZGZmyvXr1+Xee+8VHx8fMRqN8swzz0hSUpLceeed4uvrq7WffPJJMRgM2jjR0dGSkpIifn5+dmOKGnffvn0yZswYCQsLEwBanxYtWkhoaKhbcykqxpPGZS5lt8bSdKyVtvm2V+Po0aMlNDRU+vfv73IujoxrHaO2g4KCdOMW1qe48rV3v1rvL/byzcrKkvr162vbivq9v3WMu/qUtnGZi/fW6Em5eEONnpSLN9ToSbl4Q42elIs31OhJuXhDje7KxfqfK7godYMYV/o4syiVnJws69at020bPXq0xMfHi7+/v/j5+UnFihVl3bp18sknn4jBYNDae/bsEQDaOLNnz5bQ0FAJDAy0G1PUuOqbpTfeeEPXp+AbLHflUlSMJ43LXMpujaXpWCtt822vxuTkZHnppZekcePGLufiyLjWMfYWj4rqU1z52rtfrfcXe/kOHDhQ4uLipHLlyuLn5yetWrWSqKgoCQ8PF19fX5v27NmzpWHDhmIymeSpp55yW5/SNi5z8d4aPSkXb6jRk3Lxhho9KRdvqNGTcvGGGj0pF2+o0V25zJ49W8aOHSsVKlSQBQsWuLSmU+oXpQwGg5w5c0ZrBwYG2mwDID///LNTMa70cTTmwIEDYrFY5NChQ9o29U3NBx98IAEBARIYGChms1kOHTqkvfFR27t379beCFksFlm7dq1Nn4IxRY2rvlmy7lPwDZa7cikqxpPGZS5lt8bSdKyVtvm2V6PFYpF169bZ1OhMLo6Max1jb/GoqD7Fla+9+9V6f7GXb3x8vMybN09CQ0MlKChIYmJiZMOGDTJ//nwxGo027f3790t8fLy88MIL0rFjR7f1KW3jMhfvrdGTcvGGGj0pF2+o0ZNy8YYaPSkXb6jRk3Lxhhrdlcv+/ftFRGT+/PnSsWNHl9Z0TCjlRAR9+/aF2WwGkP9bx7y8PNSsWRMGg0GL69ixI3x8fAAAWVlZEJFCYwCgWbNmiIiIcLiPozGNGzeGxWLB3XffjYoVKyI7OxsAcPz4cezatQt169bFjh07kJKSgk2bNqFevXoAoLUPHDgAo9EIAEhLS8OMGTNs+hSMKWrc06dPIyUlBfPmzdP1iY+PR05ODhRFcVsuRcV40rjMpezWWJqOtdI23/ZqTEtLwy+//GJTozO5ODKudYzazsrK0s1lYX2KK19796v1/mIv33PnziE2NhY5OTkIDQ3FuXPnkJSUhEqVKiEvLw/nz5/Xtc+dO4dz586hU6dOmDJlitv6lLZxmYv31uhJuXhDjZ6UizfU6Em5eEONnpSLN9ToSbl4Q43uzAUAbr31VjzxxBNwRalflOrTp4+u3atXL2zevFm3LSgoCKGhoVo7OTkZp0+fRlBQ0A1j1L8FBAQ43MfRGACoWLEiNm3apG1ftmwZAgICMGXKFKxevRrdu3fH4MGD8eSTT6JBgwYwGAyoX78++vXrh9zcXJhMJixbtgwRERFYtmwZ+vXrh23bttmNKWrcyMhI9OnTBz/88AN8fX0B2L7BclcuRcV40rjMpezWWJqOtdI23/ZqHDFiBHr27Iny5cvjzJkzLuXiyLjWMUlJSTaLR0X1Ka587d2v1vuLvcWupKQkfPzxx6hbty7MZjMyMjJw8OBBZGRkwNfXFwEBAbr24sWLkZSUhMWLFyM0NBTVqlVzS5/SNi5z8d4aPSkXb6jRk3Lxhho9KRdvqNGTcvGGGj0pF2+o0V25LF68GA0bNsQXX3yhW/dwikvfryK32Lhxo7Rt21aioqLEz89PqlWrJgEBATJhwgTx9/eXN998Uzp37iyKokhISIj4+flJjRo1pG7dulqfZs2ayVtvvaUbxzqmqHF9fX3FaDTKI488ov29Xbt2AkD69evn1lwcifGkcZlL2ayxtB1rpWm+7dXYv39/MZlMhd52UbfjyLjWMX369NG2mc1mh/oUZ77W96v1/mIv30aNGonRaJSvv/5a1q5dKyaTSYxGoxgMBnniiSds2haLRYxGoyiKIpMmTXJbn9I2LnPx3ho9KRdvqNGTcvGGGj0pF2+o0ZNy8YYaPSkXb6jRXblYLBYxm81iMBhk0qRJLq2LcFGqhGVmZkpeXt4N/75x40Zp2rSpREZGam905s6da9PHehxXxl2zZo3u7wXfZKpvsNyVS1ExrvQprnGZS9mtsSBPP9bWrFlTqubbeg7atm0r4eHhYrFYHK7H1XHtxVgvHjnSpzjyLWxfKCzfgvtHwT6HDh2SpUuXSkZGhkPt4upT2sZlLt5boyfl4g01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNborF1dwUaqEGQwG3dX5evToIT/99JPuTY11jKIosnPnTl0fR2KKGrdTp05y8uRJXX7Wb7DclUtRMZ40LnMpuzWWpmOttM23vRrdkYsj41rH2FsocmVcd+TryGN+UQudqn/++cepdnH1KW3jMpeSHZe5lOy4zKVkx2UuJTsucynZcZlLyY5bFnNxFRelSpiiKLo3KOoV+wpuA6B74wNAfvrpJ10f63HsxTg7rr03WO7KpagYTxqXuZTdGkvTsVba57tHjx7Fkosj49pbPHLHuK7Oi/X96sgi4KlTp0RE5Pr16zJmzBiJjY0Vo9Eo+/btkzFjxkhQUJAYDAabdmZmply/fl1atGghoaGhbutT2sZlLt5boyfl4g01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNborl8zMTBERefnll2XWrFniiv9dIo7+NfnvW/SuXLlS4uOuWrWq2HIh8gQ81krOqlWr/rVxRUQ3l470Ka58Adv71fq+t5evegW+V199FXPnzsUbb7wBX19fTJ8+HXPnztUu8mHdVvvs2bMHkZGRbutT2sZlLt5boyfl4g01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNborF1WNGjUwa9YsuMSlpSxymcFgkDNnzmjtG33SvnXrVl37559/1vWxHsdejCPjFmwXZy5FxXjSuMyl7NZYmo610jjfxZW/I+Naz7e9b7O5Mu7N5uvofW+dr/qpU3Jysqxbt07bXrFiRVm3bp3s2bNHANi0MzMzJTk5WWbPni2hoaFu61PaxmUu3lujJ+XiDTV6Ui7eUKMn5eINNXpSLt5Qoyfl4g01uisX9TXrnj17JDQ0VFxhApUoEUHfvn1hNpsBANnZ2RARPPzww9o2ABg2bBjCwsK0dseOHeHj4wMAyMrKgoigZs2aMBgMhcYUNW7BtpqLoii6nN2VS2ExANCsWTNERET86+Myl7JdY2k51rKzs5GXl1eq5rtTp0647bbbdPkXfLxzNRd782I9rvV8P//889iwYYPWtn7sdXTcm83X3rzY218K9geg7RvHjx9HSkqKtv306dNISUnRvkll3Vb7xMfHIycnB4qiuKVPaRuXuXhvjZ6UizfU6Em5eEONnpSLN9ToSbl4Q42elIs31OiuXFR5eXnIycmBK7goVcIKfvUNAHr16oW5c+fi119/1b0p2bdvH0ym/LsnICAA//zzD0JDQwEAycnJOH36NIKCgrT4oKAg7e9qzB9//FHouADw448/Ijo6GgCQm5sLADZvsNyRS1Ex6t8CAgL+9XGZS9mtsahjwpOOtdjYWPz111+lZr6B/J+ppaenazX6+flh165d2t8DAgKczgUA/vjjD22e7I1rHQMAP/zwA2JjYwHkLx6ZzeZC+xRXvurtq/MSGxuLI0eO2Owv1ouAjRs3RrNmzWCxWHD33XejYsWKAICUlBRs2rQJBw4cgNFotGkDQFpaGmbMmIG6detix44dbulT2sZlLt5boyfl4g01elIu3lCjJ+XiDTV6Ui7eUKMn5eINNborF9WSJUtQt25duIKLUiVszpw5Jdb34YcfLvTvmzdvBgDceuutNttUKSkpur+7mguRJyjqmFDxWHPevzW3QP5cFja3ycnJAGzn3958W/s38w0JCUG9evWwadMmAPmfQNWvXx/9+vVDbm4uTCaTTXvZsmWIiIjAsmXL0K9fP2zbts0tfUrbuMzFe2v0pFy8oUZPysUbavSkXLyhRk/KxRtq9KRcvKFGd+WybNky/P7775g/fz5Wrlzp1OtmlfL/57MgIiIiD7Rp0yaMGTMGGRkZuHz5MpKTk+Hj44Njx47ZbderVw933XUXvvrqK7f2KW3jMhfvrdGTcvGGGj0pF2+o0ZNy8YYaPSkXb6jRk3LxhhrdlUu9evUwYsQItG/f3rUXuy6diYqIiIiKTWZmpuTl5TncLq4+pW1c5uK9NXpSLt5Qoyfl4g01elIu3lCjJ+XiDTV6Ui7eUKO7cnEnLkoRERF5GIPBoLtCn6IosnPnzhu2e/ToUSx9Stu4zMV7a/SkXLyhRk/KxRtq9KRcvKFGT8rFG2r0pFy8oUZ35dKjRw85deqUuIOh6O9SERERUUkSq1/WiwiuXLlyw/aqVauKpU9pG5e5eG+NnpSLN9ToSbl4Q42elIs31OhJuXhDjZ6UizfU6K5cVq1apbsq383gohQREREREREREZU4LkoRERF5GEVRoCiKbpvBYCi0XVx9Stu4zKVkx2UuJTsucynZcZlLyY7LXEp2XOZSsuOWxVys267i1feIiIg8jMFgQMeOHWE2mwEAn332GSIjI+Hj4wMAOHnyJHx9fREREQEAOHXqFEQE5cqV015EnDx58qb7lLZxmYv31uhJuXhDjZ6UizfU6Em5eEONnpSLN9ToSbl4Q43uyuXUqVNo164dAgICUNCyZcvgLJPTPYiIiKhY9enTR9dOSUnRtYOCggBAeyGQnJyM06dPa9vVmNDQ0JvqU9rGZS7eW6Mn5eINNXpSLt5Qoyfl4g01elIu3lCjJ+XiDTW6K5fk5GTExsbCHfhNKSIiIiIiIiIiKnGGokOIiIiIiIiIiIjci4tSRERERERERERU4rgoRUREREREREREJY6LUkREREREREREVOK4KEVERERERERERCWOi1JERERERERERFTiuChFREREdAN9+/aFoig2//7444+bHnvu3LkIDQ29+SSJiIiISinTv50AERERkSfr0KED5syZo9sWFRX1L2VjX05ODnx8fP7tNIiIiIicwm9KERERERXCbDajfPnyun9GoxHLly9HvXr1YLFYkJSUhNGjR+P69etav4kTJ6JmzZoICAhAfHw8Bg4ciMuXLwMAvv32Wzz88MO4cOGC9u2rUaNGAQAURcHnn3+uyyE0NBRz584FABw6dAiKomDRokVo0aIFLBYLPvroIwDArFmzkJqaCovFgmrVqmH69OnaGNeuXcOTTz6JmJgYWCwWVKpUCa+99lrxTRwRERFREfhNKSIiIiInbdq0Cb1798aUKVNw2223ITMzE48++igAYOTIkQAAg8GAKVOmIDExEQcOHMDAgQMxbNgwTJ8+HU2bNsWkSZMwYsQI/P777wCAwMBAp3IYPnw43n77bdStW1dbmBoxYgSmTp2KunXrYvv27RgwYAACAgLQp08fTJkyBStWrMDixYtRsWJFHD16FEePHnXvxBARERE5gYtSRERERIVYuXKlbsGoY8eO+PvvvzF8+HD06dMHAJCUlISxY8di2LBh2qLUM888o/VJSEjAuHHj8Pjjj2P69Onw9fVFSEgIFEVB+fLlXcrrmWeeQffu3bX2yJEj8fbbb2vbEhMT8dtvv+G///0v+vTpgyNHjqBy5cq49dZboSgKKlWq5NLtEhEREbkLF6WIiIiICtGqVSvMmDFDawcEBKBWrVr4/vvv8eqrr2rbc3NzkZ2djStXrsDf3x/r1q3Da6+9hr179+LixYu4fv267u83q379+tr/Z2VlITMzE/369cOAAQO07devX0dISAiA/JO2t2vXDlWrVkWHDh3QuXNntG/f/qbzICIiInIVF6WIiIiIChEQEICUlBTdtsuXL2P06NG6byqpLBYLDh06hM6dO+OJJ57Aq6++ivDwcGzevBn9+vXDtWvXCl2UUhQFIqLblpOTYzevgvkAwMyZM9GoUSNdnNFoBADUq1cPBw8exOrVq7Fu3Tr06NEDbdu2xZIlS4qYASIiIqLiwUUpIiIiIifVq1cPv//+u81ilSo9PR15eXl4++23YTDkX1dm8eLFuhhfX1/k5uba9I2KisLJkye19v79+3HlypVC8ylXrhxiY2Nx4MABPPjggzeMCw4Oxn333Yf77rsP99xzDzp06IBz584hPDy80PGJiIiIigMXpYiIiIicNGLECHTu3BkVK1bEPffcA4PBgIyMDOzatQvjxo1DSkoKcnJy8O6776JLly74/vvv8d577+nGSEhIwOXLl/HNN9+gdu3a8Pf3h7+/P1q3bo2pU6eiSZMmyM3NxfPPPw8fH58icxo9ejSeeuophISEoEOHDrh69Sp++eUX/P3333j22WcxceJExMTEoG7dujAYDPj0009Rvnx5hIaGFtMsERERERXO8G8nQERERFTa3H777Vi5ciW+/vprNGjQAI0bN8Y777yjnTy8du3amDhxIl5//XXUqFEDH330EV577TXdGE2bNsXjjz+O++67D1FRUXjjjTcAAG+//Tbi4+Nx22234YEHHsDQoUMdOgdV//79MWvWLMyZMwc1a9ZEixYtMHfuXCQmJgIAgoKC8MYbb6B+/fpo0KABDh06hFWrVmnf5CIiIiIqaYpYn7SAiIiIiIiIiIiomPGjMSIiIiIiIiIiKnFclCIiIiIiIiIiohLHRSkiIiIiIiIiIipxXJQiIiIiIiIiIqISx0UpIiIiIiIiIiIqcVyUIiIiIiIiIiKiEsdFKSIiIiIiIiIiKnFclCIiIiIiIiIiohLHRSkiIiIiIiIiIipxXJQiIiIiIiIiIqISx0UpIiIiIiIiIiIqcVyUIiIiIiIiIiKiEsdFKSIiIiIiIiIiKnFclCIiIiIiIiIiohLHRSkiIiIiIiIiIipxXJQiIiIiIiIiIqISx0UpIiIiIiIiIiIqcVyUIiIiomLTt29fJCQkFNv4c+fOhaIoOHToULHdxqFDh6AoCubOnVtst0FERETkjbgoRURERHapCz7qP4vFgipVquDJJ5/E6dOn/+30PNbp06cxdOhQVKtWDf7+/ggICMAtt9yCcePG4fz58/92eqXeqlWrMGrUKIfj+/btq9uP1X/VqlUrviSJiIjIIaZ/OwEiIiLybGPGjEFiYiKys7OxefNmzJgxA6tWrcKuXbvg7+9faN+ZM2ciLy+v2HJ76KGH0LNnT5jN5mK7DWf8/PPP6NSpEy5fvoxevXrhlltuAQD88ssvmDBhAjZu3Iivv/76X86ydFu1ahWmTZvm1MKU2WzGrFmzdNtCQkLcnBkRERE5i4tSREREVKiOHTuifv36AID+/fsjIiICEydOxPLly3H//ffb7ZOVlYWAgAD4+PgUa25GoxFGo7FYb8NR58+fx1133QWj0Yjt27fbfBPn1VdfxcyZM/+l7LybyWRCr169/u00iIiIyAp/vkdEREROad26NQDg4MGDAPJ/HhUYGIjMzEx06tQJQUFBePDBB7W/FTynlHp+prfeegvvv/8+kpOTYTab0aBBA/z88882t7V371706NEDUVFR8PPzQ9WqVfHSSy9pf7d3TqmEhAR07twZX3/9NerUqQOLxYK0tDQsW7ZMN/a5c+cwdOhQ1KxZE4GBgQgODkbHjh2RkZHh0rz897//xfHjxzFx4kS7Pw0rV64cXn75Zd226dOno3r16jCbzYiNjcWgQYNsfuLXsmVL1KhRA7/++itatGgBf39/pKSkYMmSJQCA7777Do0aNdLmZ926dbr+o0aNgqIo2lwGBwcjIiICTz/9NLKzs3Wx169fx9ixY7X7JSEhAS+++CKuXr2qi1PnePPmzWjYsCEsFguSkpIwf/58m7rPnz+PZ555BvHx8TCbzUhJScHrr7+u+wado/tF3759MW3aNADQ/RTPEbm5ubh48aJDsURERFQyuChFRERETsnMzAQAREREaNuuX7+O22+/HdHR0Xjrrbdw9913FzrGxx9/jDfffBOPPfYYxo0bh0OHDqF79+7IycnRYn799Vc0atQI69evx4ABAzB58mR069YNX3zxRZE57t+/H/fddx86duyI1157DSaTCffeey/Wrl2rxRw4cACff/45OnfujIkTJ+I///kPdu7ciRYtWuDEiRPOTgtWrFgBPz8/3HPPPQ7Fjxo1CoMGDUJsbCzefvtt3H333fjvf/+L9u3b6+YBAP7++2907twZjRo1whtvvAGz2YyePXti0aJF6NmzJzp16oQJEyYgKysL99xzDy5dumRzez169EB2djZee+01dOrUCVOmTMGjjz6qi+nfvz9GjBiBevXq4Z133kGLFi3w2muvoWfPnjbj/fHHH7jnnnvQrl07vP322wgLC0Pfvn2xe/duLebKlSto0aIFFixYgN69e2PKlClo1qwZXnjhBTz77LM2Yxa1Xzz22GNo164dAODDDz/U/hXlypUrCA4ORkhICMLDwzFo0CBcvny5yH5ERERUzISIiIjIjjlz5ggAWbdunZw9e1aOHj0qn3zyiURERIifn58cO3ZMRET69OkjAGT48OE2Y/Tp00cqVaqktQ8ePCgAJCIiQs6dO6dtX758uQCQL774QtvWvHlzCQoKksOHD+vGzMvLs8nx4MGD2rZKlSoJAFm6dKm27cKFCxITEyN169bVtmVnZ0tubq5u7IMHD4rZbJYxY8bY5DxnzpxC5yssLExq165daIzqzJkz4uvrK+3bt9flMHXqVAEgs2fP1ra1aNFCAMjHH3+sbdu7d68AEIPBID/++KO2fc2aNTa5jhw5UgBI165ddTkMHDhQAEhGRoaIiOzYsUMASP/+/XVxQ4cOFQCyfv16bZs6xxs3btTVZDab5bnnntO2jR07VgICAmTfvn26MYcPHy5Go1GOHDkiIs7tF4MGDRJnXsIOHz5cnn/+eVm0aJEsXLhQ21+bNWsmOTk5Do9DRERE7sdvShEREVGh2rZti6ioKMTHx6Nnz54IDAzEZ599hri4OF3cE0884fCY9913H8LCwrT2bbfdBiD/20sAcPbsWWzcuBGPPPIIKlasqOvryM+1YmNjcdddd2nt4OBg9O7dG9u3b8epU6cA5J/82mDIfymUm5uLv/76C4GBgahatSq2bdvmcC2qixcvIigoyKHYdevW4dq1a3jmmWe0HABgwIABCA4OxpdffqmLDwwM1H1bqWrVqggNDUVqaioaNWqkbVf/X53HggYNGqRrDx48GED+icML/tf6G0zPPfccANjklJaWpt1vABAVFYWqVavqbvvTTz/FbbfdhrCwMPz555/av7Zt2yI3NxcbN27UjVnUfuGK1157DRMmTECPHj3Qs2dPzJ07F6+++iq+//577SeQRERE9O/gic6JiIioUNOmTUOVKlVgMplQrlw5VK1aVbeQAuSfSLpChQoOj2m90KQuRPz9998A/rcIUaNGDZdyTklJsVm8qlKlCoD88xeVL18eeXl5mDx5MqZPn46DBw8iNzdXiy3400RHBQcH2/3ZnD2HDx8GkL+4VJCvry+SkpK0v6sqVKhgU09ISAji4+NttgH/m8eCKleurGsnJyfDYDBo5+M6fPgwDAYDUlJSdHHly5dHaGioTU7W9yGQfz8WvO39+/fj119/RVRUlE0sAJw5c6bQMa33C3cZMmQIXnnlFaxbt87uTxOJiIioZHBRioiIiArVsGFD7ep7N1LwW0eOuNEV80TEqdxuxvjx4/HKK6/gkUcewdixYxEeHg6DwYBnnnlGdxJuR1WrVg07duzAtWvX4Ovr69ZcbzRfNzOPN/rGmaMnDnfktvPy8tCuXTsMGzbMbqy6UOjMmO7g5+eHiIgInDt3zq3jEhERkXO4KEVEREQeJykpCQCwa9cul/r/8ccfEBHdAsu+ffsAQLsa4JIlS9CqVSt88MEHur7nz59HZGSk07fZpUsX/PDDD1i6dCnuv//+QmMrVaoEAPj999+1WgHg2rVrOHjwINq2bev07Rdl//79SExM1Np//PEH8vLytPmoVKkS8vLysH//fqSmpmpxp0+fxvnz57WcnZGcnIzLly+7tR5HF80Kc+nSJfz55583/AYXERERlQyeU4qIiIg8TlRUFJo3b47Zs2fjyJEjur858q2ZEydO4LPPPtPaFy9exPz581GnTh2UL18eQP63cqzH+vTTT3H8+HGXcn788ccRExOD5557TlsAK+jMmTMYN24cgPzzdPn6+mLKlCm6HD744ANcuHABd9xxh0s5FGbatGm69rvvvgsA6NixIwCgU6dOAIBJkybp4iZOnAgALuXUo0cP/PDDD1izZo3N386fP4/r1687PWZAQIDWvyjZ2dl2f1I5duxYiAg6dOjg9O0TERGR+/CbUkREROSRpkyZgltvvRX16tXDo48+isTERBw6dAhffvklduzYUWjfKlWqoF+/fvj5559Rrlw5zJ49G6dPn8acOXO0mM6dO2PMmDF4+OGH0bRpU+zcuRMfffSR7ptLzggLC8Nnn32GTp06oU6dOujVqxduueUWAMC2bduwcOFCNGnSBED+otsLL7yA0aNHo0OHDujatSt+//13TJ8+HQ0aNECvXr1cyqEwBw8eRNeuXdGhQwf88MMPWLBgAR544AHUrl0bAFC7dm306dMH77//Ps6fP48WLVpg69atmDdvHrp164ZWrVo5fZv/+c9/sGLFCnTu3Bl9+/bFLbfcgqysLOzcuRNLlizBoUOHnP5WmjqnTz31FG6//XYYjcYbnhfq1KlTqFu3Lu6//35Uq1YNALBmzRqsWrUKHTp0wJ133ul0TUREROQ+XJQiIiIij1S7dm38+OOPeOWVVzBjxgxkZ2ejUqVK6NGjR5F9K1eujHfffRf/+c9/8PvvvyMxMRGLFi3C7bffrsW8+OKLyMrKwscff4xFixahXr16+PLLLzF8+HCXc27UqBF27dqFN998E19++SU+/PBDGAwGpKamYvjw4XjyySe12FGjRiEqKgpTp07FkCFDEB4ejkcffRTjx4+Hj4+PyzncyKJFizBixAgMHz4cJpMJTz75JN58801dzKxZs5CUlIS5c+fis88+Q/ny5fHCCy9g5MiRLt2mv78/vvvuO4wfPx6ffvop5s+fj+DgYFSpUgWjR4/WTszujO7du2Pw4MH45JNPsGDBAojIDRelQkND0blzZ6xduxbz5s1Dbm4uUlJSMH78eAwdOtSp86ARERGR+ylSkmcUJSIiIipmCQkJqFGjBlauXPlvp+IRRo0ahdGjR+Ps2bMunSuLiIiIqLjw4yEiIiIiIiIiIipxXJQiIiIiIiIiIqISx0UpIiIiIiIiIiIqcTynFBERERERERERlTh+U4qIiIiIiIiIiEocF6WIiIiIiIiIiKjEmf7tBIpDwrzXbbaNbrpc1/7kZANde8/ueJs+PpHZunbKy5dtYn5/PFrXTqxzTNc+sCvOpk/QAau1QMWq2fqcTZ8Lfwbq2sY/fWxiImr8qWtbfHJ07ZNbY236KFX0NSmK7a852yTs07VX/VRH1w48aLTpk2e1Z+VZpXvvPRtt+iz6vZ6ubdoeaBNzPUDfvhp9Xdf2P2y7Sw968Atde+LqzvrczHk2fQxX9fdRXmCuTUzgPv1thbQ/pWuf/DPEpo/vXj9dO7vCdZuYsB1W42Ze07XP1Pe16ZOd9o+ubTiqv52QP2y6IDtc3zbYpoJcc+Exdz1kez9+vPY2/RhR+vxDwrJs+lw4EaxrT283zybmmfT7dO3+aVv0fTa3tumTOuaorn2wX6JNzLVq+rkzHtLPnSTb5hsbcUHXPvWT/ti6Hmi7T5ku6/epiPqnbWL+uqTfwU3b9MdAVqLtnRRwwGp/aakf9+T+KJs+5nP6XAKP2ITgQoq+HbdZf9tn6toea7lWu+bV2BybGMXq2Kq0Uj9XJx++atPHb5N+Hqz3y5wgmy6w/KVvbx8+wyam6pwndO1r5fT5BmTaPs5eibN9LNARxWaTX6z+cdZ/dbBNzIXKVo+9efpxfnjoLZs+rScO1bUv18m2iSn3lX6yzuif+uw+/pnP6h/Tr1ayOobDbZ8LrZ+jlMu2+0dcNf2+eXxvOX2fCNv7PnGmfh6OtrXo2tf9bZ+zatY/oGtnZNo+v1uC9Lflv0a/E0XM1D++AMCpZ5vq2lkVbecuN0C/f/ic089DwDHb/eNiQ/395v+rvsYrtWzvV98D+pi8araPU4a9+scTSdPfbwY7z/dXj+v7fH7nJF378eefselzuqt+Liu/ZXs/WibrD8iMXQn6gFzbeWlQV//EtX1zFZuY3Fj93ASF6B/PL2eG2vTxO6m/rdC2+ufuc5vK2/S5rn9awN13fG8T8/W0Zrp2dqT+70Z9avm3fVC/v5yppz/2wn63vY9O3W51PKabbWICTur3zcux+sfdy5Vsx/W5pJ+XnCDbmHJpZ/TjfK0/hq2fAwDg+i36/c6Urn+ssPfao2b3Pbr29q9SbcdN0+/zcfP0N34+xfbx2/q1aeAJ/TxdSLL9rL5u19907R832eZi+sdq/61+Sde8mmU7MeZD+vvN3mNZbpT+OUly9LdjPmVbY+1W+tfsu7/UHzfW+zIAWKzedsR3O2gTc2S5/vXT5UT93AX/YTt3l5L0MblWr6VNAbavEYI3+uvafze6ZhPj/7t+PistPqlrH3jd9v1DzhH9NjHZzrfv3/oaDFY3fTXCtk/AUf19UuHOQ7r278f1xwgA5F3TH+eWA7b7R9V2mbp2jeATuvaCnxvb9LGc0O8PAbfoH3eDzbbPJWe+0b9PNdpOt93nuoIMV20fv33/1m+7Utn2vi6/Tj8P1/30ff6sb+d2re8Co36D9XMuAPjqX7IjK8H2ddydDdN17ZX7auhzu2R7HwXv1d9W4HF9vlnlbY8J/w76x9DT+yJtYpRI/XNo9Er9Y0XWfRdt+lw6rn8NYwy3fR6e22iOrt1n+UBd2yfe9rWd/KYf1/r9/PUY29s51Hu4zTZH8JtSRERERERERERU4rgoRUREREREREREJY6LUkREREREREREVOK4KEVERERERERERCWOi1JERERERERERFTiuChFREREREREREQljotSRERERERERERU4rgoRUREREREREREJY6LUkREREREREREVOK4KEVERERERERERCWOi1JERERERERERFTiuChFREREREREREQljotSRERERERERERU4lxalLp+/TrWrVuH//73v7h06RIA4MSJE7h8+bJbkyMiIiIiIiIiorLJ5GyHw4cPo0OHDjhy5AiuXr2Kdu3aISgoCK+//jquXr2K9957rzjyJCIiIiIiIiKiMsTpb0o9/fTTqF+/Pv7++2/4+flp2++66y588803bk2OiIiIiIiIiIjKJqe/KbVp0yZs2bIFvr6+uu0JCQk4fvy42xIjIiIiIiIiIqKyy+lvSuXl5SE3N9dm+7FjxxAUFOSWpIiIiIiIiIiIqGxzelGqffv2mDRpktZWFAWXL1/GyJEj0alTJ3fmRkREREREREREZZTTP997++23cfvttyMtLQ3Z2dl44IEHsH//fkRGRmLhwoXFkSMREREREREREZUxTi9KVahQARkZGfjkk0/w66+/4vLly+jXrx8efPBB3YnPiYiIiIiIiIiIbsTpRSkAMJlM6NWrl7tzISIiIiIiIiIiL+H0otT8+fML/Xvv3r1dToaIiIiIiIiIiLyD04tSTz/9tK6dk5ODK1euwNfXF/7+/lyUIiIiIiIiIiKiIjl99b2///5b9+/y5cv4/fffceutt/JE50RERERERERE5BCnF6XsqVy5MiZMmGDzLSoiIiIiIiIiIiJ73LIoBeSf/PzEiRPuGo6IiIiIiIiIiMowp88ptWLFCl1bRHDy5ElMnToVzZo1c1tiRERERERERERUdjm9KNWtWzddW1EUREVFoXXr1nj77bfdlRcREREREREREZVhTi9K5eXlFUceRERERERERETkRdx2TikiIiIiIiIiIiJHOfRNqWeffdbhASdOnOhyMkRERERERERE5B0cWpTavn27Q4MpinJTyRARERERERERkXdwaFFqw4YNxZ0HERERERERERF5EZ5TioiIiIiIiIiISpzTV98DgF9++QWLFy/GkSNHcO3aNd3fli1b5pbEiIiIiIiIiIio7HL6m1KffPIJmjZtij179uCzzz5DTk4Odu/ejfXr1yMkJKQ4ciQiIiIiIiIiojLG6UWp8ePH45133sEXX3wBX19fTJ48GXv37kWPHj1QsWLF4siRiIiIiIiIiIjKGKcXpTIzM3HHHXcAAHx9fZGVlQVFUTBkyBC8//77bk+QiIiIiIiIiIjKHqcXpcLCwnDp0iUAQFxcHHbt2gUAOH/+PK5cueLe7IiIiIiIiIiIqExy+kTnzZs3x9q1a1GzZk3ce++9ePrpp7F+/XqsXbsWbdq0KY4ciYiIiIiIiIiojHF4UWrXrl2oUaMGpk6diuzsbADASy+9BB8fH2zZsgV33303Xn755WJLlIiIiIiIiIiIyg6HF6Vq1aqFBg0aoH///ujZsycAwGAwYPjw4cWWHBERERERERERlU0On1Pqu+++Q/Xq1fHcc88hJiYGffr0waZNm4ozNyIiIiIiIiIiKqMcXpS67bbbMHv2bJw8eRLvvvsuDh06hBYtWqBKlSp4/fXXcerUqeLMk4iIiIiIiIiIyhCnr74XEBCAhx9+GN999x327duHe++9F9OmTUPFihXRtWvX4siRiIiIiIiIiIjKGKcXpQpKSUnBiy++iJdffhlBQUH48ssv3ZUXERERERERERGVYQ6f6Nzaxo0bMXv2bCxduhQGgwE9evRAv3793JkbERERERERERGVUU4tSp04cQJz587F3Llz8ccff6Bp06aYMmUKevTogYCAgOLKkYiIiIiIiIiIyhiHF6U6duyIdevWITIyEr1798YjjzyCqlWrFmduRERERERERERURjm8KOXj44MlS5agc+fOMBqNxZkTERERERERERGVcQ4vSq1YsaI48yAiIiIiIiIiIi9yU1ffIyIiIiIiIiIicgUXpYiIiIiIiIiIqMRxUYqIiIiIiIiIiEqc04tSGzduxPXr1222X79+HRs3bnRLUkREREREREREVLY5vSjVqlUrnDt3zmb7hQsX0KpVK7ckRUREREREREREZZvTi1IiAkVRbLb/9ddfCAgIcEtSRERERERERERUtpkcDezevTsAQFEU9O3bF2azWftbbm4ufv31VzRt2tT9GRIRERERERERUZnj8KJUSEgIgPxvSgUFBcHPz0/7m6+vLxo3bowBAwa4P0MiIiIiIiIiIipzHF6UmjNnDgAgISEBQ4cO5U/1iIiIiIiIiIjIZQ4vSqlGjhxZHHkQEREREREREZEXcfpE56dPn8ZDDz2E2NhYmEwmGI1G3T8iIiIiIiIiIqKiOP1Nqb59++LIkSN45ZVXEBMTY/dKfERERERERERERIVxelFq8+bN2LRpE+rUqVMM6RARERERERERkTdw+ud78fHxEJHiyIWIiIiIiIiIiLyE04tSkyZNwvDhw3Ho0KFiSIeIiIiIiIiIiLyB0z/fu++++3DlyhUkJyfD398fPj4+ur+fO3fObckREREREREREVHZ5PSi1KRJk4ohDSIiIiIiIiIi8iZOL0r16dOnOPIgIiIiIiIiIiIv4vQ5pQAgMzMTL7/8Mu6//36cOXMGALB69Wrs3r3brckREREREREREVHZ5PSi1HfffYeaNWvip59+wrJly3D58mUAQEZGBkaOHOnUWH/++SfeeOMN3HXXXWjSpAmaNGmCu+66C2+++SbOnj3rbGpERERERERERFRKOL0oNXz4cIwbNw5r166Fr6+vtr1169b48ccfHR7n559/RpUqVTBlyhSEhISgefPmaN68OUJCQjBlyhRUq1YNv/zyS5HjXL16FRcvXtT9k5zrzpZFREREREREREQlyOlzSu3cuRMff/yxzfbo6Gj8+eefDo8zePBg3HvvvXjvvfegKIrubyKCxx9/HIMHD8YPP/xQ6DivvfYaRo8erdsWcmcbhN7VzuFciIiIiIiIiIioZDn9TanQ0FCcPHnSZvv27dsRFxfn8DgZGRkYMmSIzYIUACiKgiFDhmDHjh1FjvPCCy/gwoULun8hnVs5nAcREREREREREZU8pxelevbsieeffx6nTp2CoijIy8vD999/j6FDh6J3794Oj1O+fHls3br1hn/funUrypUrV+Q4ZrMZwcHBun+Kj9NfACMiIiIiIiIiohLk9OrN+PHjMWjQIMTHxyM3NxdpaWnIzc3FAw88gJdfftnhcYYOHYpHH30U6enpaNOmjbYAdfr0aXzzzTeYOXMm3nrrLWfTIyIiIiIiIiKiUsDpRSlfX1/MnDkTr7zyCnbt2oXLly+jbt26qFy5slPjDBo0CJGRkXjnnXcwffp05ObmAgCMRiNuueUWzJ07Fz169HA2PSIiIiIiIiIiKgVc/p1bxYoVUbFixZu68fvuuw/33XcfcnJytJOkR0ZGwsfH56bGJSIiIiIiIiIiz+b0olRubi7mzp2Lb775BmfOnEFeXp7u7+vXr3c6CR8fH8TExDjdj4iIiIiIiIiISienF6WefvppzJ07F3fccQdq1Khh9+p5REREREREREREhXF6UeqTTz7B4sWL0alTp+LIh4iIiIiIiIiIvIDB2Q6+vr5ISUkpjlyIiIiIiIiIiMhLOL0o9dxzz2Hy5MkQkeLIh4iIiIiIiIiIvIDTP9/bvHkzNmzYgNWrV6N69eo2V8pbtmyZ25IjIiIiIiIiIqKyyelFqdDQUNx1113FkQsREREREREREXkJpxel5syZUxx5EBERERERERGRF3F6UUp19uxZ/P777wCAqlWrIioqym1JERERERERERFR2eb0ic6zsrLwyCOPICYmBs2bN0fz5s0RGxuLfv364cqVK8WRIxERERERERERlTFOL0o9++yz+O677/DFF1/g/PnzOH/+PJYvX47vvvsOzz33XHHkSEREREREREREZYzTP99bunQplixZgpYtW2rbOnXqBD8/P/To0QMzZsxwZ35ERERERERERFQGOf1NqStXrqBcuXI226Ojo/nzPSIiIiIiIiIicojTi1JNmjTByJEjkZ2drW37559/MHr0aDRp0sStyRERERERERERUdnk9M/3Jk+ejNtvvx0VKlRA7dq1AQAZGRmwWCxYs2aN2xMkIiIiIiIiIqKyx+lFqRo1amD//v346KOPsHfvXgDA/fffjwcffBB+fn5uT5CIiIiIiIiIiMoepxelAMDf3x8DBgxwdy5EREREREREROQlXFqU+v333/Huu+9iz549AIDU1FQ8+eSTqFatmluTIyIiIiIiIiKissnpE50vXboUNWrUQHp6OmrXro3atWtj27ZtqFmzJpYuXVocORIRERERERERURnj9Delhg0bhhdeeAFjxozRbR85ciSGDRuGu+++223JERERERERERFR2eT0N6VOnjyJ3r1722zv1asXTp486ZakiIiIiIiIiIiobHN6Uaply5bYtGmTzfbNmzfjtttuc0tSRERERERERERUtjn9872uXbvi+eefR3p6Oho3bgwA+PHHH/Hpp59i9OjRWLFihS6WiIiIiIiIiIjImtOLUgMHDgQATJ8+HdOnT7f7NwBQFAW5ubk3mR4REREREREREZVFTi9K5eXlFUceRERERERERETkRZw+pxQREREREREREdHNcvqbUgDw888/Y8OGDThz5ozNN6cmTpzolsSIiIiIiIiIiKjscnpRavz48Xj55ZdRtWpVlCtXDoqiaH8r+P9EREREREREREQ34vSi1OTJkzF79mz07du3GNIhIiIiIiIiIiJv4PQ5pQwGA5o1a1YcuRARERERERERkZdwelFqyJAhmDZtWnHkQkREREREREREXsLpn+8NHToUd9xxB5KTk5GWlgYfHx/d35ctW+a25IiIiIiIiIiIqGxyelHqqaeewoYNG9CqVStERETw5OZEREREREREROQ0pxel5s2bh6VLl+KOO+4ojnyIiIiIiIiIiMgLOH1OqfDwcCQnJxdHLkRERERERERE5CWcXpQaNWoURo4ciStXrhRHPkRERERERERE5AWc/vnelClTkJmZiXLlyiEhIcHmROfbtm1zW3JERERERERERFQ2Ob0o1a1bt2JIg4iIiIiIiIiIvInTi1IjR44sjjyIiIiIiIiIiMiLOL0opUpPT8eePXsAANWrV0fdunXdlhQREREREREREZVtTi9KnTlzBj179sS3336L0NBQAMD58+fRqlUrfPLJJ4iKinJ3jkREREREREREVMY4ffW9wYMH49KlS9i9ezfOnTuHc+fOYdeuXbh48SKeeuqp4siRiIiIiIiIiIjKGKe/KfXVV19h3bp1SE1N1balpaVh2rRpaN++vVuTIyIiIiIiIiKissnpb0rl5eXBx8fHZruPjw/y8vLckhQREREREREREZVtTi9KtW7dGk8//TROnDihbTt+/DiGDBmCNm3auDU5IiIiIiIiIiIqm5xelJo6dSouXryIhIQEJCcnIzk5GYmJibh48SLefffd4siRiIiIiIiIiIjKGKfPKRUfH49t27Zh3bp12Lt3LwAgNTUVbdu2dXtyRERERERERERUNjm9KAUAiqKgXbt2aNeunbvzISIiIiIiIiIiL+Dwz/fWr1+PtLQ0XLx40eZvFy5cQPXq1bFp0ya3JkdERERERERERGWTw4tSkyZNwoABAxAcHGzzt5CQEDz22GOYOHGiW5MjIiIiIiIiIqKyyeFFqYyMDHTo0OGGf2/fvj3S09PdkhQREREREREREZVtDi9KnT59Gj4+Pjf8u8lkwtmzZ92SFBERERERERERlW0OL0rFxcVh165dN/z7r7/+ipiYGLckRUREREREREREZZvDi1KdOnXCK6+8guzsbJu//fPPPxg5ciQ6d+7s1uSIiIiIiIiIiKhsMjka+PLLL2PZsmWoUqUKnnzySVStWhUAsHfvXkybNg25ubl46aWXii1RIiIiov9r77zDo6q6t/2cMzOZSSZlUklvhJJQg9QE6aKgCCKgrwVQwIKiWH4oFpQioigiIvgK0kQRRAREigiiIAoSIFIFQ0eaoAhBWvJ8f+Q7+52ZDMlkmMBJZt/XNResk7XWPGvP3qfs0yQSiUQikUgklQe3J6WqVKmCtWvX4tFHH8XgwYNBEgCgKApuvvlmvP/++6hSpUq5CZVIJBKJRCKRSCQSiUQikVQe3J6UAoCkpCQsXrwYf/31F37//XeQRLVq1RAaGlpe+iQSiUQikUgkEolEIpFIJJWQMk1KaYSGhqJRo0be1iKRSCQSiUQikUgkEolEIvER3H7QuUQikUgkEolEIpFIJBKJROIt5KSURCKRSCQSiUQikUgkEonkmiMnpSQSiUQikUgkEolEIpFIJNccOSklkUgkEolEIpFIJBKJRCK55shJKYlEIpFIJBKJRCKRSCQSyTVHTkpJJBKJRCKRSCQSiUQikUiuOXJSSiKRSCQSiUQikUgkEolEcs2Rk1ISiUQikUgkEolEIpFIJJJrjpyUkkgkEolEIpFIJBKJRCKRXHtYSTl//jxfeeUVnj9//orLPPHRU16pxXdr1JMWX6hRT1p8oUY9afGFGvWkxRdq1JMWX6hRT1p8oUY9afGFGvWkxRdq1JMWX6hRT1p8oUZvafGESjspdfr0aQLg6dOnr7jMEx895ZVafLdGPWnxhRr1pMUXatSTFl+oUU9afKFGPWnxhRr1pMUXatSTFl+oUU9afKFGPWnxhRr1pMUXavSWFk+Qt+9JJBKJRCKRSCQSiUQikUiuOXJSSiKRSCQSiUQikUgkEolEcs2Rk1ISiUQikUgkEolEIpFIJJJrTqWdlDKbzXjllVdgNpuvuMwTHz3llVp8t0Y9afGFGvWkxRdq1JMWX6hRT1p8oUY9afGFGvWkxRdq1JMWX6hRT1p8oUY9afGFGvWkxRdq9JYWT1BI0uNoiUQikUgkEolEIpFIJBKJxAMq7ZVSEolEIpFIJBKJRCKRSCQS/SInpSQSiUQikUgkEolEIpFIJNccOSklkUgkEolEIpFIJBKJRCK55shJKYlEIpFIJBKJRCKRSCQSyTVHTkpJJBKJRCKRSCQSiUQikUiuOcbrLUDvkISiKF7NefHiRcyfPx8//fQTjh49CgCIjo5GVlYWOnfuDD8/PwDAoUOHYLPZEBgYiJMnT+LXX39FvXr1EBQUhCVLlmDHjh24cOECunfvjvT0dABAamoqli1bhrS0NKxatQq///47YmJicPPNN2PcuHHo1q0bkpKSyqx5/fr1xfQ2a9YMjRs3Fj6FhYU4ffo0vvrqK9x///3Yt28fEhISoKoq9uzZg5ycHFy4cAEdO3ZEREQEAKBNmzaYOnUqoqOjsX//fuzfvx8xMTHw9/fHoEGD4Ofnh6pVq6JPnz5ISUlBbm4ucnJy0KpVK6SmpmLbtm14//33cfDgQYSGhsJqtUJVVRiNRgQFBaGgoMBrejWtSUlJuHDhgsd6Bw8ejCNHjqBKlSpISkpCamoqkpKScPjw4Su2b2FhIVRVxV9//eWgNy4uDkePHkV0dDS+/PJLt/V+//332L9/P44cOYKkpCTd6d25cyf27t2LhIQE1K5dG++//75HeuPj45Gfnw+j0Yjz589XqrGmte/ChQvRokWL6zLWSJZL+2ptW61aNZD0WvuW17q3JL3bt2/HXXfd5VFfAICjR49i3bp1DnqbNGmC6OhoB7/8/Hzk5OSgRYsWKCgogMFgEH9bt24dLly4gGbNmsFkMuGBBx7Aa6+9htjYWFy6dAn79u1DVFQUQkJCsGrVKuzevRsHDhxAUlISunfvjpCQEBw/fhxbt27FDTfcgJCQEBw7dgxjxozB3r17ERcXh9jYWBQUFEBRFPz9999Ca0l9t7zWDf/8849b24mIiAhER0dDVdUS29a+fbOzs0tsWwBXbF+SeOutt6AoSqltO336dPz5558ICQmB0WiEqqpC+z///OO1vuAtvc59ITU1FXXr1sX27dvLre8CcNC7adMmj/tuRdMrx1rJffdK67ILFy64Ndbcbd/rsc/ryT6ZJ3rLc5+3Y8eOuHjxooPejIwM8btqXL58GX/88QcSExOL9ZNjx47hwoUL4m9Dhw7FY489JtpaG28mkwlHjx7Fli1bRF9o3bo1DAYDCgoKsH//fiQnJ0NVVVy4cAGzZ89GXl4eYmNjERYWhuDgYPz77784duyY2333em6Hk5KSEBcXh7Nnz5bYtiW1r3PbumpfrW0BuGxfAMXadsGCBTh37hxsNhsuXLgAVVWRmJgIs9ns0L5X2xdK0nv58mXMnz8fp0+fLrUvXCu9JfXdsuh17rtX2q6Vp96rgpWAEydO8I033mCXLl3YtGlTNm3alF26dOGbb77J48ePkyTPnTvH1atXc9u2bTxw4AAfeOABbt++nVOmTOHmzZs5ffp07tixg4888ggfeOABrlixgiRpMpm4fft2nj17lq+88gr79+/P9957j3/++SfbtWvH2rVrMz4+ntnZ2Zw1axZJ8r333uP9998v7HHjxjEiIoIBAQG02Ww0mUxUVZXVq1dn9+7d2aNHD7Zs2ZIWi4VpaWn88ccf2ahRI6qqSoPBwDZt2tBsNlNRFIaGhnLZsmUEwMjISIaHh9NkMjElJYWjRo2iwWDgwIEDmZSURAAMDw+nqqqsWbMmFUWhoii02WzMzs7m999/z6+++oovv/wy16xZQ5JcsWIF27dvz1q1ajErK4utW7dmaGgoAbBKlSps3LgxGzduzKSkJCqKwubNm/P3339n9+7dabFYGBYWRgBMSkqiqqpMS0vjunXrCIBms5l+fn4MCgpiixYt+Nlnn9FgMHDMmDEMDg4W+hRFoaqqBECTycTw8HCazWaOHDmSBoOB4eHhDAwM5PLlyxkcHMzg4GACEJ/AwEDx/5iYmDLrHT16tEu9qqpy/PjxnDNnjkd658yZQ4PBQABUFIUAmJiYSJPJRAAMDg4u1r5Nmzbl7bffTovFwqioKD700EMOelNSUgiAVatWLZNeAFRVlampqaxevToDAgKum16TycR33nlH9IVmzZqJ309VVWZmZnqs12g0ilw1atSoVGMtKiqKPXr0EG1zPcaan59fmdq3WrVqDAkJEe376KOPumxfVVU5ePBgjhw5knFxccJHVVUmJiaK74yOjuawYcN44cKFYu27ePFi1q5dm3FxcaxVqxZbtmzJ4OBg+vn5sWXLluzRo8c10av1R/u+QLJUve3bt2f16tWpqiqNRiOjoqIYFRVFo9FIg8HAe+65hwMHDmTVqlXZqFEjvvrqq1QUhdnZ2TQYDGzatCkB8MYbbxTtZbPZ+PXXX9NkMnHu3Lns2bOn6AdGo5FdunQR/Sk+Pp4RERGMjo7mtGnTaLVaqSgKo6Oj+dNPP9Hf39+hL2hjTVEUJiUlldp3y2vd4Ofn51bf1db9ABgQEODQtvfddx///vtv/t///Z9o32eeeYYAaDAY2KJFC+7cudMhX1JSEvv168d169aJ9k1MTKTBYBD9yWw2i/EZGBjIyMjIYm27efNmxsbGOmzHFEWhxWIRdlBQUIl6a9WqRQCiL5SnXld9ITQ01GE749x33dG7YMECl3qNRiO//PJLbty4kYmJiTQajUKvp323NL2ejLXy1CvHWvG+6866LDQ0lP7+/qWONW0fx532vZb7vJ7sk+ltnzc5OZlWq9Whve33l1566SWOHz+ebdu2Zffu3fnf//6XiqLw3nvvZWJiInv06MHExET27dtXxGdlZfH999+n0WjkunXrePr0aWZnZ4ttsM1mE98XHBxMVVVZp04dfvPNN4yJiaGqqqxduzb37dvHiIgIh75m/wkJCSm1717v7bD9dsJV2xYUFPD9998X7fvuu++KftSzZ08ePnxYjAetbSdMmMDjx4/TZDJx3bp1HD16NAMDA6koCsPDw/n0008Xa9+0tDRWqVJFtO2BAwdYq1Yt0Vft+4S7em+66SYCEH3BXb3Z2dmMiIgQfUH7jf38/KgoCmvVqlWsL5SX3p07dxbTW1rfdUdvSX1XVVUHrd4ea6NHj2ZiYiJVVWV4eDiHDh3q8XxOhZ+UWr9+PUNDQxkXF8devXpx0KBBHDRoEHv16sX4+HiGhYVx7ty5YgWtqipvuOEG8QOHhYWJQezv78/ExEQmJCQQADt16kRVVXnnnXeKFXL16tUZFhbGoKAg8SNFRUUxOzubAQEB7NKlC4OCgnjnnXcyOjqajz/+OBVFYWxsrDh4DQ4OZteuXWmz2ZiVlcV//vmHJHn69Gl27tyZMTExbNKkCX/55RcuX75crMD379/P0aNHMzY2lgAYHx/P5ORk0cETEhKoKAqtVitNJhNNJhO/+uorHjx4kFWrViUA1q9fn9WqVRODS1EUZmRkMDg4mB9//DGtVisDAwPFhltbgdatW5cGg4FdunThyZMnefr0aW7YsIGNGzdmamoq09LSOH36dA4aNIhA0UF1Tk4OBw4cyGrVqjkMDOdBbW/n5+ezfv36DAkJIQAOGzZM7AwpisKOHTvyzz//5KxZs2iz2Vi7dm126dKFp0+f5qhRo+jn58fIyEju3LmTK1asYGhoKEeNGlUmvc4rnCvpnTFjRpn0mkwmpqen8/Tp0zx//jyzs7NpMpnYrFkzTps2jeHh4UKrprdKlSq0Wq2cPn06x40bx+joaALgbbfdxl9//VXsbJZVr7aRzMzMpKIojIyMpJ+fH1944QWSvC56nbXn5uZyzZo1Ynxqbeuu3pCQEJpMJp4+fZorVqxgeHg4x44dW2HGWvfu3Usca+PGjRMHjddjrJlMJiYkJLjdvh988AEBMCIiQrSvpiU5OdmhfQEwLi6OQUFBVBSFkyZNIklOnjxZ/L1jx46MiYkRO9Oqqor2feutt8SBvJbTarUyNDSUqqqye/fuvHTpktiOlLfe8PBwh75Qt25dGo1G3nDDDVfUa7PZaLFYqKoqu3XrJvRevnyZy5YtY1hYGP39/Tl69Gi++OKLIi4rK4sLFy5k586dSxxr9vZXX33FmTNniknGBx98UOx8JyQk0Gq1sn///jxz5gxHjx7NgIAAxsTEcMuWLdy9ezcDAwNpMpm4adMmfvTRRwwICOCkSZNK7LvltW4wGo0MDQ0tse+azWYGBQVx6dKl/Oabb0Tf1dq2evXqzMzMZJUqVUT7ahMBCxcu5F133cVGjRqVONbsl23bto316tUTE979+vVjVFSU0NuhQwf+/fffHD16NOPi4piRkcHs7Gxu2bKFffr0odlsZmhoKOfPn89JkyYxICCAn3zyyRX1ajuRVapUKXe9zn1BO2mQlpbGp556iv7+/vzkk08c+q47ekvarmkHxAD42muvCb0A2LBhwzL1XXf0Xs1Y87ZeOdZK7rslrcv8/PwYERFR4ljz8/NjbGysaF9/f/8S2/da7vN6sk+mt31es9nMyMhIDhw4kDabjW+++SbPnTvHvXv38r///a/o34899hjvu+8+MSlQs2ZNjhs3jllZWQ7jyn5sOY+1Rx55hF9//TVr164tcvj5+dFmszE5OZnh4eHs1q0bt2zZwieffJLh4eEMCgril19+yaVLl9JisdBkMnHSpEl88cUXaTabuWzZshL77vXcDmv7N+PGjeMbb7xBf39/Tp06VbRtVFQU27Zty4CAANG+WnuNGzeOrVq14i233OKybe0nNLRl77//PkeMGCEmHtu0aSPaV5sQ19o2PT2dNWrUYM2aNTl//ny2bduWZrOZVquVr776KgcMGEA/Pz8uXLjwinpvvfVWAkUn/DzRq/UFbVzecsstYkLe39+f7dq1K3e9nvRdd/Q6992oqCjabDaGhYWxY8eOQmt5jbUhQ4bw66+/5ogRI2i1WsU+cFmp8JNSTZo04UMPPcTCwkKxbMGCBVywYAHnz5/P9u3b02q1smHDhvz444/5wQcfMCMjgwD4+OOPk6Q4+KhSpQpbtWolOo52hjwsLEzM/u7bt49nzpwRk1Lffvstn3zySUZERNBgMFBRFA4ePJgFBQXcvHkzAbBr165Cm9FopMlkIkmeOnWK9evX5z333MPc3Fzm5uZy7ty5BMCZM2eKZdqBcv369Xn06FHREerXr8/t27czJyeHAHjs2DEajUampKRwwYIFtFqt3LNnD0myZs2awockjx07xqCgIHHWrkaNGjSZTKxZsyYffvhhFhYWcurUqWJmlCR37dolBrvWQe2vBLDfAGtn+k+ePCmW3XrrrTx27Bh//PFHB73VqlWj2Wzmjh07SJJVqlTh2LFjHfTOnz+fQNEZYrPZzG7dutFoNDIwMJBbt24lSebl5REoOot4+vRpkhTfXRa92iDTvlvTazAYuG3bNtauXdsjvQA4b9480Re2bNlCAPzhhx9Ikh9//LFLra5WCE2bNuX58+dFzc2aNSuTXkVRmJqaSpLcsGED77nnHgJFE7Xdu3cXVwhdC72KojAuLk70hU8//ZRWq1Xo1c5yaf7u6tV2orW+8PHHHzMpKanCjLXk5OQS+659++phrL322msltu/s2bMJgK1atRLtq+nfvn07SYr21fpujRo1aLFYRNs2adJEHMBqekeOHEmj0cjo6GjRvoqisFmzZmK7oI2/LVu2iLZ95JFHhNby1AsUnYXU+kLnzp3FzlKjRo349NNPU1VVNm/evJjeH3/8Uei1WCwMDQ0VH+231mxtZ7hjx44sLCzkjh07CIBNmjThjh07uG/fPnGCxGg0Mi0tjSNHjqTFYuHvv/9ebKxdunSJc+fOZcuWLQkUTawNGjSI27ZtIwBx4E6SAQEBBIoOsEhy/Pjxbvddb68btAPa0vquwWBwWDeoqiraUtu+axOZ9u3du3dv/vnnnw5jbceOHfz0008JgL/88otoX5PJJK6Mi4mJ4YgRIxzad9y4caKdqlSpwmeffZZGo5E2m40bNmwQ6wJtXae1b0BAAA0GwxX1aleqxMfHl7te577wyy+/EIC42nz8+PEOWt3Vq/UPre9qeg0GA5cvXy7OYmt9NyYmhoqisEaNGmXqu+7o9WSslZdeOdZc91131mXawXNZxpon+5DltR32ZJ9Mj/u8X3zxhdBrNpuZmZkpPtqdJJqtXRl9//33k6Q4rmrYsCFXrlzJVatW8fXXXxd1T506lenp6TQYDGI7HB8fTwCsXbs2T5w4wbfeekucPExPT+eECRPE9v2DDz4QerVxfv78eZLksGHDSlzvXu/tsHYVnP26wd/fX7SlVnNSUpJYpo2Jl19+mYcPH3bouytXrhRtO2/ePBoMBqampvKRRx6hn5+faF97vVr7av2uQYMGHDt2LA0GAyMiIkRf3bp1qzgRp7VvTEwMrVbrFfVq+9YhISFl0mswGMSdS/Hx8WJiVtOrbccURSl3vdpYK0vfdUevc99dsWIFgaKJTq3v2mv15lh75JFHaM+ECROYmZlJT6jwk1IWi0WsKDXsV74lzUrHxMQwLy+Pf/zxB4GiM93ajol2NYY2Q62d4dE2XtptNtoK+eLFi+IySFVVGRsbyxdeeIEAuGTJEqEtKiqKAHj06FGS5DfffCNiXOm136Ho0qUL69aty4CAACqKwnnz5jEhIYFDhw4lAB4/fpxGo5Hh4eHcunUrQ0JCmJubS5Lisj5Nr9Z2FouFP/zwA3v16iW+Z9euXSTJvXv3ig3Tn3/+SbJohyg0NJSrVq3iqlWr+M477xAAZ82axVWrVnHSpEkEis60NmvWjG3atBGXm44ZM4YJCQmcMmWKg97Q0FA2adKEb775JkkyKyuLb7/9toPeuXPnUlVV/vjjj5wxYwazs7NFG23bto1k0VVzWhuePHmSZNFGxWAwlFkvUHQ1zFdffcUDBw44bKAjIiI81hseHi7a/4cffiBQdDaQ/N8O3BtvvCH0mkwmBgYGClvT26BBA7Zp04Z79uwhAL7wwgtl0qutXDU0vUOHDmWrVq1EP5w6dWq561UUhX5+fqIvbN26lW3atBF6tSsb7fuuO3rDw8MJQPQFTW9FGWvz58+noiiifZ377qpVq8QZtesx1rR21NpXa09XZ6jsd4Z3797t0L7aWHvvvfe4f/9+h74bFRXFoKAg0bZRUVFcsmSJg97ff/+dQNHVbfbtazabRfv/9ttvDjtw2g6082XN5aFX698amt7PP/+cvXr1EuubRYsWFdO7fPlyh77wzDPPcNq0aZw2bZo486/ZWt9MSUnhvffeK3aAevXqxYyMDG7cuFGMe+1gbMuWLWzSpAk//PBDkhS3Udn33W+++YaKovDxxx9namqqaJc5c+YIH+2suXa7vHZwV1LfLa91g81mY5UqVcq0ndDWDVr7DhkyhAA4evRoh/YGwOrVq/Pee+8VfeHJJ59kRkYGv/76a4expl31qLVtZmamOAGm6dXadsWKFRw2bJiYiDYYDGLdkJubS0VRaDAYRPv6+/vTYDAIbc56tb6wfv36a6K3Zs2aov03bNhAAPzmm28c+oJ933VHr3ZbitZ3ncdaeHh4Mb2KotDf379MfdcdvZ6MtfLSK8ea677rzrosJCSEoaGhZRprgYGBNBqNutnnLes+pB73eVevXu2gt1evXnz11Vf56quv0mg00mg0Cvvhhx8mUHTF8vPPPy8mj9q1a8fWrVvz0KFD3LdvXzG91atX52effUaSTE9Pp6IUXdmusXbtWgLgnXfeyaCgILG+WbBggfDRJpiOHDlCkmIiqKS+ez23w9qJWOd1g9a+2uMHnnzySdG+9hO1zz//vGiHLl26sHXr1vz555+L9d3Nmzezbt26on21yRh7vUFBQbRarXzwwQdFOxqNRubl5ZGkmKwzGAyifbXbgTVtznq1vrB8+fIy6XXuC9OnT3fQu3btWiqKwtmzZ5e7Xu03KkvfdVdvq1atRPtrfXXlypUOdnmMtc2bN9Oe33//nUFBQfSECj8plZyczOnTpzssi42N5fz580mS06dPp6Io4sw2SW7atIkAeN999zE+Pl6sKL/88ktWr16dzzzzDBVFodlsFs/y2LJlC++77z726dOHJMVlefaDcOTIkTSZTJw+fTpfeeUVcevPsGHDhI92r/Abb7zB3Nxcrl+/ngD46quv8qWXXmJISAjDw8M5ceJE7tu3j/v27WN8fDwVReGlS5fYpUsXRkZGUlEUkuShQ4fYsGFDAmDbtm2pKAqDg4P51Vdf8fbbb+fzzz9PkuK+Znu9oaGhjI+PJ0kx42y1WpmTk0OSXLVqldgZ+OSTT3j69Gk2bdpUnPWaN28ek5OTxf3R5P9mVlevXs0zZ86wWbNmjIuLE3o3bdrE1NRUAmCfPn2oKIq4nDskJISvvPIK33vvPTHzPmHCBA4ZMoQ2m42ZmZls0qQJZ86cyU6dOvHGG2+kxWJh+/btuXHjRt54440MCQmh0WjkvHnzePr0aTZo0ICBgYEe6d20aRMzMjLYtWtXsbPVs2dPRkVFeaQ3PDycFouFOTk53LJlC+Pi4mgymZiUlMR58+ZxxYoV9PPz4xtvvCH0Go1G3nbbbeI30/QuW7aMzZo1Y7169agoCv/5558y6QWK7j3/5JNPXOpt06YNAwICGBYWVu56AYhb2MLDw/nNN99w7dq1Qq92luCpp54qk17tLNaYMWOYm5vLr7/+moqiVJixtmfPHiqKwqFDh7rsuyTFrcbXY6xpl4pr7RscHMzAwEAuWbLEZftqBxIrVqwo1r6HDh1imzZtRNspisJ27dqJnXetbW+++WY+9dRTDu07adIkGo1GcUZL2wm86667RDt99dVXBIpuiRszZozQMmbMmHLXq53g0HDWqz0v5d133y2mt169ety4caPoC9rtkRs3bqSfnx/btm1bbKwtWrSI1atXF88zOHnyJBcvXizOFvv7+1NVVUZGRvL777/nokWLGBYWxqlTp3LYsGGiXX788UdOmTKFCQkJTEtL42233cY1a9bw1ltvpdFoZFJSEs+ePcv8/HxxhlZb727cuJEmk6nEvlte6wbtttKS+m5gYCDNZjM3btxIkvz5559pMpk4duxYbty4kTfccAMDAwP57bffCr3alS2HDx9m9erVmZycLMba4sWLWaVKFQLgiBEjRPuOGjVKtO3UqVPFPsGiRYtctu1DDz3E6tWr02w286GHHmJ+fj67desmrrTU9NarV08cYLnSq7XtunXryl1vQEAAAwICRF+oU6eOOPu6ceNGbty4kUaj0aHvlkWv1ncHDBhAoGjCePDgwYyJiSmmV+svZem77uj1ZKyVl1451lz3XXfWZf7+/qxatWqJY81ms9FgMIj2bdiwIQMCAnSxz+vJPqTe9nkNBgPbt2/PEydO8Oeff6bRaOSECRNIFj0f2GKxsFmzZqIvaMdrn3/+OatUqcKePXuK7f+ECRMYGxvLN998U4y19957j/Hx8XzxxRcZHx/P7777jjNmzBD9+/Dhw1y5ciXr1KnD2NhY9unTh7t27RJXMDdo0EB8t3bbq9Z3t2zZQoPBUGLfvZ7bYe3xASdOnBDrLq19T5w4wVtuuUWcHNXQ9mm2bdvGKlWqOIy1CRMmiAspZsyYQVVVGR0dzbVr13Lq1KmifUeOHCl+I619rVYrq1evzkOHDvHFF19kVFQUTSYTR4wYQZLs378/rVYrjUaj0JuRkcGAgADRF5z1an0hJyenTHqd+4I2HnNzc4v1hWultyx91x29JpPJYdL1pptuoqIoYqxpfbc8xtratWtpz9atWxkcHExPqPCTUuPHj6fZbOYTTzzBBQsW8Oeff2bz5s3Zo0cPPvHEE/T392dSUhJnzJghYrSVxpIlS/jYY4/RZrOJA9EzZ86IH0I7WxMQEMC5c+fy8OHDTE5OZosWLcQDhhs3bsx+/fqxRYsW9PPz4913383IyEj27duXKSkprFatGk0mEx9//HE+9dRTNJvNDA4OFhtd+6szYmJi+MYbb3DQoEFs37690PvII48QKPqpLl26xNtvv52qqoq/Dx48mDabTexQdOnShbNnz+b27dsZHh7Onj17snnz5gTAbt268bXXXmPPnj3FJfgjRoxg48aN2atXLwYHB7NOnTqcNGkSa9SoweTkZEZERNDPz4+q6vhwTj8/Pz766KPs378/u3XrRpI8evQob7jhBnE/6T///MMmTZo46B02bBjNZrOY4W7UqBEnTZrEtWvXsmnTpsWuWoiLi+PYsWN59OhR3nTTTQwMDOTNN9/Mv//+m/fff7/wA4ouHb7tttsc9GqXMHui99y5c2zcuLH4nRo1asRWrVp5pHfz5s3iLKfWhpMnTxaXwmr9QbvNy8/Pj3Xq1HG4/dNer6u2dVevpkFRlOuuNygoyEGv9lt4Q2+PHj3EWHO+SlLvY027Cko7g+3cd0myZ8+e7NSp03UZa8HBwezTp49oX/vfyFX7autd7TJq5/YtLCxkq1atxFVKXbp0Ye/evfn222+Lth0+fLi4733w4MHs2bMnzWYz27Rpw2rVqon2rVq1Kv39/fnhhx9y2rRpYsJp1KhRDnq1+stTLwBGRUWJvuCu3tDQUN5yyy1i8lNVVUZERDAsLIyqWvQA1Pvuu0/81gcOHGBCQgLHjh3LQ4cOMS0tzaEvHD16lCaTSdQeFxfHd955h2TRWW5tQtb+d7RYLBw4cCB37NghnomVnp7OJUuWODwAVNsWan1X+56S+m55rRuGDx/u1naiUaNGVJSi2/NjYmLo7+8vDhQ6dOjA+++/nw8++KDQc9NNN7Fx48Yk6bJ9R44cSVVVxXPetPbV2tb+6jsAV2zbQ4cOsUWLFg7rMaPRKCYSwsLCxIG+1hec9dr3hfLW66ovvPzyy6LvBgQE0Gw2O/Tdsuo9evQoMzIyHPpucnJyMb2e9t3S9Hoy1spTrxxrrvtuaeuyXr16uTXW2rVr57APqe3rXO99Xk/2yfS2zztmzBjWrl2bRqORiYmJDA4OFrc7aVcOaSf/yaKrLsLCwvjBBx9w69at4gHQGtu2bRMn5+y36SNGjODbb7/NgIAAh4eBa8cDXbp04apVq8QLaiIjIzl58mQqStGD7rX9L+35oWFhYWKioqS+e723w1WqVKHRaGRmZiYzMjJotVrFXT9169Zl586dOXDgQKGnRYsWrF69Okm6bF/tFm57PdpEjda+9vuqWvu2bNlSrD8jIyO5detWcTubdtuYqqqMiYkRem02m7id05Ve+75QVr32fcH+RRFX6gvXQq+7fdcdva767v333y/GWlxcHM1mc7mNNXsmT57s8e17CkmigjN79my88847yMnJQUFBAUhCVVU0atQITz/9NPLy8rB69WosXrwYQNErO19++WW0bdsWt956K/r3748PPvgAhYWFImfNmjWRl5eHgoIC9O/fH506dcLNN9+Mv//+G6NGjcLkyZNx8uRJmEwmxMbGIjs7G0899RQaNGiAUaNG4aeffkJWVhYef/xxtG/fHj///DMAICoqCitXrkStWrWwd+9eLFiwAL/88gsaNmyIp556CkDRaxnPnTuH4OBgABCvfe/Vq5f4++HDh8Urxs+dOweDwYCtW7dizZo16NmzJ0JDQwEAeXl5eOmll/D111+L14MajUY0atQIAwYMwMqVK4XW9957DyNHjsSwYcOgdYuEhAQsWLAAVatWRU5ODhYsWIC///4bvXr1wg033IDg4GD89ddf+OOPP1CrVi2Xv8+ZM2ewceNGtGzZEgCwd+9eWCwW/PLLL/juu+8wePBgREVFCf8TJ05g9erVOHnyJNq1a4eUlJQSf/9t27bhxx9/RJUqVZCdnY2IiAjxmmL7V2Bejd7vv/8eubm5xbSWVe+5c+fw+eef4+zZs7jzzjvFK2WvpLegoKBMWt3V+8UXXyA6Ohr169dHcnKyrvX+8ssv+PXXX5Geno7Y2NhS9c6dOxdnzpxx0Lt3714cPXoU33//Pcxmsxxr/x9vjDXte2bPno1///0XvXv3Fnns29e5ba/Uvrm5uVi/fr3bbft///d/uOmmm/DUU0+J9n355ZeRnZ2NAwcOAADMZjO+/vprtG3bFgDw/vvvY9myZcjMzNS93h07dmD8+PE4cOAAsrKyxKu+/f39sXPnTtx8880uf6s//vgDy5cvd9C/fv16fPnll9i5cyfee+89xMfHi78VFBRg2rRpWLduHVq0aIH4+HjccMMNCAoKEj4nT55EeHg4AODIkSMYPXo0zp07h549eyIrK8ur612tX3lr3eCq7+7YsQM///xzsdeo16xZE/v37y9T+65fvx4BAQFYuXIlvvvuO4f2LSgowMaNG7Fs2TLs378fd999Nxo3bnzFtgWAOXPmYPXq1YiPj0enTp2QkZGhW72u+gIA7Ny5Ez/99JPX9JbUdzdu3IgPPvgAKSkpaN68eZn7bkl6y2OsXa1ePY+1rVu3Yu3atQ5jzdt94Up9V2vf6dOn4+eff3a5LnNnrHl7H9Ld7XDbtm2Rmpp6xbYtaZ9sw4YNDq+p1+s+b2FhIZYtW+ZyXRYdHY1NmzbhgQceEDlOnToFVVVhs9mwdetWfPHFF3jllVfE3xcuXIiPP/4Yv//+O+bNm+eg5++//8Y333yDcePGITU1FW3atEF2djaqVasGoOh4cOfOnahRowYCAwOxfv16DB06FOfPn8e9996LBx980Kt9Fyh93VBS3wVKXjc0bdr0im3bvn17bN26FTk5OaJ97dsWQLH2XbJkCYxGI5YsWYLvvvvOZfuOGjUKa9euxU033YSEhATRvs5te/78eXF8HBcXh//85z9o27btNdP7999/Y/ny5fj666+Rl5eHnj17olWrVlfsC9dCb2l9tyx6XfXdaznWNBYtWgSTyXTFMVESlWJSSuPSpUv4888/AQAREREwmUxXle/QoUPIyclBu3btYLVaryrX+fPncfnyZQQGBl5VHk8hiePHj6OwsLDUtjl//jx27NgBk8mEmjVrwmg0XkOlEqDo91IUxW3bWz6e5pX8DznWyo+ytC1QtAOQn5+PG2644bq0bUXTK5FIJBKJRCKRXGsq1aRUZeDgwYN45ZVXMGXKFLd9XMW44+Pud//777/IyclBWFgYMjIyHOzU1FTMmTMH3bt3R05ODqxWK7Zs2SLssvjMnDkTNWvWvOq8jRo1ws8//4xmzZohOTkZL7zwAjZt2oTQ0FA8/vjjiI2NxauvvorDhw+jT58+6N27N77++mu89dZbwufff/91sN2J8TTvu+++iwsXLuC+++5DmzZtAAB+fn7Izc1Fenq6W7a3fMoak5+fjzlz5uD3339HTEwMbr/9dqxYseKK9n/+8x9YLBavx7jySUlJwbZt23D69GnExMTAYDBg5syZOHDgAJKSkvD444+jevXqCA0NFbP9w4cPx/z583H8+HEkJSWhXr16WLNmDY4fP46qVavi8ccfx+7du4v5bN68ucS8H3/8MT744AMHn7vvvtthnA0YMAA9evTAjTfeeMVlpdnlFXOlZePHj8f69evRsWNH3H333fj444/x+uuvo7CwEF27dsWwYcPwwQcfOPj07NkTCxcuREBAAHr37o0DBw5g0aJFwq5evTqef/55/Pvvv2jdujXmzp2LWbNmlZrX1Xc7T+wcOXIEEydOxJo1a3DkyBGoqorU1FR06dIFvXv3hsFgKOZTUFAAADAYDABQzFZVFbGxsbBYLDh79iyOHj1aLO/x48dL/V5XXLx4EfPnzxdnaQsKCpCfnw8ACAwMBEkHW1VVREZGwmAwoKCgACdOnPBKjLs+0dHRyMrKQufOneHn5yfqOHbsGP773/9iyJAhV1zmic/Vxhw6dAg2mw35+fkOy7RtSYsWLYSP2WzGTz/9hNTUVAdb8/F2jLNPrVq18Ouvv6JevXoICgrC7Nmz8cMPPyAsLAy9evVCZGQkxo0bh4MHD+Khhx5Cs2bNsGvXLrz11ltl8jEYDF6J+eijj3DhwgV0795dbDtSU1OxbNkycabX2XbH51rEkMSqVavEtqR9+/b48ccfr2jffPPNMBqNJca441NaTGxsLNLT0/Hrr7/ixIkTiImJgdVqxbhx43DkyBGkpaXhsccewx9//IFPP/1UbKOys7PxzTfflOiTkZGBnJwcr8fMnDkThw8fxsmTJ5GUlITHHnsMa9euRbdu3cQVp2+//XaJ9pV87rzzTocrtkrL404MUHSmf/369bj55puRnZ2N1157DVOnTkV4eDj69OmDtLQ0DBo0CH/99RfuvPNOvPnmmx75nDhxwisxb7zxBg4fPoyQkBAEBweDJC5dugSTyQRFUaCqKhITE2G1WnHq1CkcOXKkmI8nMZ7mTU1NRc2aNXH58mVx9caFCxegKIrYbly4cAFA0VXDQNHVHcHBwfjnn38cYpwKtgr2AABY6ElEQVR9QkJCcPr06Sv6uJvXXot2ZUnjxo1hz19//YWvvvoKPXv2vOIyT3yuNu99990HVVUdfLQ7gQ4dOoTExEQUFhZCVVUUFhbi0KFDiI+Pd7A1H0VRcPDgQZcx7vhcKa+mJSEhAfv27UNCQgJUVUVeXh6WLl2KwMBAdOrUCcHBwZg3bx4OHz6Me++9F9HR0bhw4QI+/PDDMvkEBQWVOcaVz8WLF/Hll1/iwoUL6NixIyIiItCmTRtMnTpVrEOcbVfLvBHjSd69e/eK7Uvt2rVLtd2JceXjMR7d9CcpNzZv3uxwDydZdI/yAw88cEUfVzHu+JSW98CBA+zatSuTkpLEvaz2byRTVVU86NfeB4C4L9pdn3r16jncM3s1ef38/BgWFkaLxcLnnnuu2D3B2ptRtPt0e/fu7eCj3c9dlhhP85rNZiYmJor71tPS0tigQQMCRa+qjYqKEg/uS09PZ4MGDVza9jHu+Hia12KxiIdf9u3bl6qqimdGaM8OCAwMFLZWZ2ZmJsPCwhgeHk6j0XjVMe7m9ff3Z6NGjcRDxDt16sThw4dz4MCBDAwMZFhYGDMyMhgfH8+qVauKZwNNnDiR7dq1E9qeeOIJDhw4kGaz2aVPo0aNSsxrNBrZq1cvTpw4UeSJj49ndHQ0s7OzOWvWLNE/tIdXTpw4UfTratWqcdSoUaXaR44cKZcYVz6DBg1iUFAQ77zzTkZHR3PUqFEMCAhg/fr1eddddzEyMpLp6elUVZWBgYG0Wq1s164dFUVhrVq1GBQUJJ6HoNnagyTNZjNr1apFVVXZtm3bUvOOHDmymE/dunUZEhLCmJgYDh48mEOGDBFjLj4+ngaDgXfeeSczMjJoNBrF8we19s/OzhbPbAkNDRXP03O227dvT5PJRIPBwKCgoGJ5zWYzVVVlSEgIq1WrRoPBwPvvv5+33367eMZg9erVWa1aNVarVo01a9ZkzZo1mZaWxoCAAJpMJrZo0YIdOnSg1WoV6xLtAdOaHRgYyJYtW4q3u1gsFrZs2fKqY9z16dixo/BLS0vj7t27r3obVZqPpzGKorBRo0bimTG33XabGMfaMgC84YYbhK293ESztVeeezvmSj4hISFUFIWhoaHiTb9abrPZLJ7/oC0fOnSosN31sX/WydXEhISEMDIykuHh4TSZTGzTpg27du1KRSl6IUDXrl1Zt27dYra9T926dV36eDuma9eurFKlClW16IHk2uvZAYiHAGv1arb2d+2ZHtWqVWNwcHCZfTzJq7W1Zmt948477+Qdd9wh+kvjxo353HPPsUmTJgSKXltfmk/16tXLJUZRFCYlJbFRo0Y0Go1UlKI33LVr146fffZZqfaFCxe84uNOzPjx42k0GnnDDTcwODiYDzzwAAGI5z2aTCZRg/am3caNG5fZR/vtrzbGz8+PgYGB4jmX2n6Pth8KgC1bthTPmdGer+Ts40mMJ3nbtm0rXkKjbbO1t4dpy7S3AWr7X7Vr1xbPDzObzaxdu7ZDjDs+nubNzMwUxxjNmzd3eIGNnrZrJLlmzRqxjY6KiuJDDz1EAOzevTstFovoV926dRM+/fv3d4jRXiRTUow7Pu7mTUpKoqqqTEtL45w5c0Tba9s67Rla2vZl2rRpDs+wcscnODhY7GteTd7hw4ezatWqNJvN9PPzo7+/Px955BGqqsqHHnqIL7zwArt161bMfuGFF8Sybt26ufQpKcbTvA0aNKCqqhw/fjznzJkjXhChbUe04zntExkZ6bBf0aJFC/Fg9pJ8OnfuLPYFVVVl69ateebMGXqCnJS6xixYsKDEzzvvvENFURyW9e3bl0DR63tfeOEF9unTp0RbW2afx1XesWPHFvNxXukB4K233soTJ05w9+7dDA8PJwBu2rSJu3fvFgflbdq04YkTJ/jTTz+JDVFZfLSO7o28ISEh3L9/P2fNmiV2ikhy1qxZtFgsNJvNfPHFF0mSt99+OwEwKytL+CiKwpCQkDLFeJoXAOvXr89WrVoxMTFRbAgBiA23VkNQUJB444mzbR/jjs/V5m3UqJGYxNIOPu+66y7RXzRbmxw6duwYz5w5I95mcrUx7ua95ZZbSBa9rSo9PZ02m43Lly8nSTGuevfuzYkTJ4o++MYbb5AsemW3No727dtHkuI10pqt+ZjN5hLzKorCZ555hmTR29e0V8naT3YB4B133MHWrVs7PJhzxIgRfOKJJ8RGvFmzZrz99ttd2tqG19sxV/Jp2LAhv/rqK4fXHGuTVPfddx8B8D//+Q9HjhzJ0NBQAkWvy7Vfv2hjQtvJMhgM/OKLL0hSvHbanbz2Ptokt/1kl6qqbNOmDUeOHMnIyEi2bNmSBoOBN9xwg5jg9vf3Z61atWiz2ZiVlcWqVasyLi6OJHnq1CmazWbxhpNTp04xMTGRJpOJr776Kk+dOsX69euzSZMmxfJGRETwrrvuos1mY7Vq1Ziens6QkBDWr19f7Cg7T3ZVqVKFcXFxDAkJYVZWFlu3bs3OnTvz9OnTPH36NCMjIxkZGSnszp07MywsjJ07d+bBgwcd7KuJccenVatWbNasGXNzc/njjz+yVatWrFu3Lj///HN+/vnn4o0tmu1qmSsfbUe2PGLq1KnDTz/9lP/973/F221uuOEG/vLLL2Ln2Gq1csWKFVy+fLnoZ5pdt27dcom5kk9ERAT379/P0aNHi53nf/75h6NHj2ZAQAAtFgv79u3Lf/75hw0bNqSqqqxRo0aZfLSJ3quNAYpOICUnJ4ttiNavDQaDw0GqvW3vo/29vGPsfeLi4sT2cf369SQpHtytvcDj/vvvF9vuY8eO8eDBg2LbYR/jjo8neQHw9ttvF9ufuLg4Go1G7tmzR2yjFEURdpMmTcQLGkryMRgMzMjI8HoMAE6dOpWdO3cWb4gCwOHDh4tlAHjTTTexdevWLm1tv7OkGHd83IkxGAxs27Ytt2zZwpUrV1JRFN5zzz0kyZUrVwqfDz/8kCTFWwnL6qPtV11tjJ+fHx9++GEWFhZy6tSp9PPzY0JCAgsLC7lr1y6GhoYyICCADz/8MH/77TcmJyczPDzcwcdoNDI4OLhMMZ7mtVqtbNasGZcuXcrk5GSmp6ezWbNm3LlzJ3fu3MmwsDCGhYUJOysri3FxcWzWrBkXL17sYJfFx5O8jRs3Ftu5DRs2sHHjxuzYsSMPHjzIgwcPcunSpQQgbFfLXPl88cUXJfp4mrdz584EwOnTp3PcuHFiv7hatWr8/PPPxf6U1WrlrFmzOGnSJLF+1mzt7YAlxbjj427eKlWqMCcnhwMHDhTrhpycHA4YMICBgYG0WCy87bbbuGHDBqakpNBoNDI6OrpMPgaDQbwF8mryatsJ+5MDFeFjr/WLL75gfn6+2JbccccdwtZe6rB3716uWbNGHPPZx7jyCQoK4sqVK5mfn881a9awatWq4g3UZUVOSl1jtJlE+5lHV53I3sebndOdvM6TXe+++66YuNJWNImJiczLy+ORI0cIgNHR0czLy+PRo0cJFM2Gl8VHO8j1Rt7//Oc/TExM5O7du0VNJFlQUCDORGmTF7m5uQTA+fPnCx/gfwfK7sZ4mtff358pKSlcsWIFt2zZwsDAQKakpFBVVW7bto2vv/66g02Sr7/+OgHwo48+EnZZfTzNq51V3LZtG1NTU6koijhzpNmxsbHCfueddwhA+GivGL/aGHd8rFYrq1SpQpKMioritGnTHCaYtNdgb9iwQfgEBAQwJSVF2F999ZWDj7YTax+j+ZSU12QyMS0tjWTRgYT2hhDnyS5N/8WLFx1eKR4bG8vnnnuOiqKwVatWNBgMjImJIQBxcBATE8MuXbqUS4wrH+0KHoPBINpfm5TdvHmzOPDbv38/SXLevHkEICbvSBIoOkOpoR04aDH79u0jAPF2mpLy2vsARVecabZ2hjkvL0/EWCwWqqrKo0ePkvzfQUBeXp6YYDIajTQajcJHmzzUbG3HcN26dSTJb775RlwJqfloOciiiSxtwuzZZ58VMQaDwWHyS5ug3rJli4OWLVu2iLbSJsI1fv31VxHjyvY0xh0fbXtS2nZNbx/7q1WAoh3mkydPim1J8+bNWb9+fZ48eVKcZdTs/fv3l0vMlXxatWrF+vXrC1vbrl28eFFs17dv306S/PnnnwmAn332WZl8ADA0NPSqYywWC+vXr8/t27czJydHXBGhbTtI8uGHHyYALly4UNj2Ps52ecVo/Veza9So4bBd0+zExERha69s13y0NxLZx7jj40lei8Ui1hVRUVH88MMPqaoqc3NzSf5vG6XZUVFRXLRokcMyVz5Wq5UWi6VcYjT9x44dE+tZ7WrFt956S1wlrqoq69evTwBMTU0VdteuXUuNccfHnbwmk4lVq1alqqps1KiRw34cWXwbtXfvXgLgN998Uyaf0rZ9ZYnZtWuXQ4zRaOSff/5Jkvz8888dtlHz58+noigOPtp2rSwxV5N35cqVDjEbN24UNWlvHNPYsGEDAQgfZ9tdH0/y2l8lo32u9zbLnY+z1oYNG/L8+fNi21GzZk22b9+e58+fF/uEmr1t27ZSY9zxcTdvrVq12L59e548edJhu3b27FmxL7Fp0yaS5LfffksAnDJlSpl87LdRV5u3WbNmYn3WtGlTWiwWh22J9hbXH374Qdi33nqr8HG23YnxNK/9dq127doO27XatWuL9aFmP/PMMw7r66SkJAf7Sj7R0dG0Z8GCBeKNjmVFheSaEhMTg3nz5qGwsFB8lP9/n7WiKOIB0vZvAgQg7s0miU2bNgEA5s+f79K2X1ZaXi23vc/IkSMxcuRIfPTRRwCAJ598El26dEGXLl1w9uxZKIqCTp06oWXLlti9ezcAoHXr1mjZsiV27doFABg6dGiZfM6dO+e1vMOHD0enTp3QunVrhzpVVYXZbHZ4UHdISAgAiLevqWrRkDh37lyZYjzNW1BQgNmzZ+PRRx/FuHHjcOnSJcyePRuFhYUYPXo0nnnmGQf70qVLeP7552EwGDB8+HA8++yzHvl4mtee8+fPi/5jb588eVLYGRkZDj7a83iuNsYdn6ZNm4oXH7Rs2RI5OTkgKZbt378fQUFBmDhxovCJj48Xb0Br2bIlxowZA5vNJnxiYmJgNpsdYsaMGQODwVBiXoPBgIMHDwIADh8+jGPHjjnEnDlzxqFtTSYTnnzySQBFbz3q168fPvvsM5DE7NmzsWfPHjz00EMAit7kotnauPd2jCufS5cuYfDgwdizZw+6du0KAHjttdcAAPXq1YOiKDAYDNi+fTsAiDfTfffddwAgxq/FYhF2QUEBVFUVMd988w2A/z3/oaS89j4AEBYWJmztmU1HjhwBADRo0ADnz59HYWGhGKMPP/wwgKK3tIWGhuLNN99EYWEhLl++LHy0nJqtfbf2hr1q1arh0qVLDnmrVKmCy5cv49ixYwgNDcXzzz8PAOKNJ9WqVUNBQQGOHTsmfD788EMAEM/C07Ts27dP9JGAgAAEBAQIe9++fVBVVfg4257GuOMTGBiIkJAQ7N27F3v37sWkSZOgKArefPNNrFmzBlOnTgUAYbta5spHezFIecR899132Lt3L/bs2YOvv/4aAJCUlITWrVvj+PHjAIAPP/wQycnJaN26Nf755x8AEPbp06fLJeZKPpMmTUJycjLat28Pe0wmE/z9/UES/v7+AIrWU/b/uusD/K8vX01MYWEhhgwZgptvvhmLFi0SdkFBAT755BMAwAcffABVVfHQQw9h/Pjx+OCDDxx8nO3yinHmr7/+AvC/bYlma8+b+euvv9C0aVMHn3///bdYjDs+nuTNzMwUMZmZmTh8+DAAYNasWQCKnn/j5+cn7MzMTMycORNWq7VEH5vNJn5nb8bYExUVJZ7hNX/+fGRkZOCVV14BSfz4449YtWqVWH8fPXpU2MuWLSs1xh0fd/JevnwZU6dOxapVq8Szp7p37w6g6E1qly9fhqqq2L9/PwAgJycHAMQ20l0f4H/brKuJURRF7D/s378fqqri8uXL4nlIWntrz1KqW7cuSDr4aNuqssRcTV5tv0+L0daTQNFbau1fCHLmzBkoiiJ8nG13fTzJq20vV65ciZUrV+Ltt98GULSvMHbsWDz77LMOtqtlrny09ihLjDs+2jM0Nb2TJk0CULR/3LFjR3H8MWfOHJw5cwYdO3YU6yHN1sZzSTHu+JQ1rzbGNKxWKwICAkBS7HdVr14dAJCWllYmH+B/26irzdu6dWs0bNgQixYtwpw5c3Dp0iUUFBRg1apVAIre/qcoCnr06IFFixZhyZIlaNu2rfBxtt2J8TSvPdo2Q9uWHD16FIqiiPXK0aNHxdvyNB9tvWIf48pH+6016tWrJ453yoxHU1k6ZcaMGczKymJMTIy4AuGee+5hzZo1xbIZM2YwOTmZoaGhbvt4EnMlH39/f1osFuHzzjvvMCwsTJyJ0c7yv/zyy6KuFi1a0P6ncvZxFaMtKymvdlvRlWI2bdpEAJwxY4bwqVWrloh57LHHxOV9M2bMKGaXxUe7JNAbeZcsWSJ8tLMFGtOmTaPJZBI+P/zwA41GIxctWiR8EhISGBUVVaYYT/MmJyeTJM+cOcObb76Zfn5+3LJlC41GI2+//XbWrVvXpW0ymbh+/Xr27NnzqnzKGqOdOUpPT2dgYCAVRWGdOnWYmZkpbKPRKGztzK/mo92WdrUx7vg8++yzNBgMbNGiBZ9++mnxbJ8aNWqwRYsWVFWVnTt3ZnJysvDRrniLjo5mXFwcgaJnZgQGBopnWfn5+dFsNjv4hISElJhXO2PRr18/RkREiPuu+/TpQ/J/Zy3sn1cwcuRIh2WFhYUOZzrIojMhs2fPFrazj7diXPncc889jIyMZN++fZmSksKQkBCGhIRw4sSJHDp0KAEwLS1N+FSpUkX8BtnZ2eK5GIqiCDsoKEg8IyM7O5sGg0E816akvMr/v9fd3qdRo0YkyV27dlFVVfr7+7N27dpcsmSJaNvMzExR4yeffCLO3C1ZsoSffvopgaJL0pcsWcKVK1cyIiKCiqIIu169ejQYDCLvG2+8USzvLbfcItarK1euZLNmzQiAa9asIVl0tZV29de5c+dIkuvWrRNn98aMGcPFixfTYDDQZrPx2Wef5bPPPkuz2UyLxSJsm83Gpk2bMjg4mJ07d2ZwcDCbNm161THu+BiNRmZnZzM3N5djxoxhWFgYq1atyuHDh5P837ZFs10tc+WjtVV5xMydO7eYz+zZs9mlSxdxpdrcuXN56dIldunSRTw7SbM1H2/HXMlnxYoVwsf+tnSy6DZXo9HIFStWkCy6oslgMHDx4sVl8omKimJERMRVx2hX8hw6dIgNGzak2WzmkSNHaDAY2KRJE95yyy08cuQIjUYjV65cyTZt2ohlzj7XIkY7K96uXTuGhoZSURR27NiRd9xxh7D9/PyE/dZbbxGA8NGe+2Qf446PJ3n/7//+j4pS9FzH4cOHMyAggH5+fgwICGBqaioNBoPYRmVmZoorhOrVq1eqj7Yd9mYMUPSsmddee409e/YUz2XUtiWnT58utr1RVZVvvfWWsJ19XMW44+NO3latWrFatWocMWIEGzduLJ4D2LNnT9aoUYM2m41+fn6Mi4tjr1696O/vz5CQEJpMpjL5aM+IutoYk8nEOnXqcNKkSaxRowYDAwMZGBjIHTt2cM+ePWzdujUVRWHLli25Y8cO8VgLe5/o6GgqilKmGE/zqqrKpKQkzps3j19//TUDAwOZlJTEmTNncubMmQwKCmJQUJCwk5OTWadOHSYmJnLQoEFMTExknTp1HGLc8fEkr8ViYVZWFk+fPs158+YxOTmZcXFx4hEP2nZDs10tc+XTsGFDl9uoq82rXXHvHDN37lw2a9ZMXKX99ddf88yZM2zWrJkYj5qt+ZQU446Pu3lXr14tfJy3a88//zwNBgNXr15NkszJyaGiKFy2bFmZfGw2G20221XnDQ4O5j///MNNmzYxIyNDPI/QYDCwatWqfOihh5ifn0+j0ci5c+cyIyNDLHP28SSmrHm17VrPnj0ZFRVFRVH48MMP86mnnhK2xWIR9vvvv08AwicsLMzBvpJPcHAw7cnJyXHYJygLlWZSasKECYyIiOCIESPo7+/PvLw8TpgwQdwO5e/vz2HDhjEiIoJdu3alqqpu+XgSU5JPixYtqCiKuI1k6tSpDA8PF5NFZ8+e5bhx48TkBklOmTLFYaA6+7iKOXv2LLOyskrM27Jly2J57WO0FVqHDh2Ej3ZLkcajjz7q4ONsu+ujPSTTG3ntJ4KcJ/QGDx7MrKws4eNsk+TNN9/Mm266qUwxnubVJiU0u1WrVuKhq9u2beOsWbNKtEl6xcfdGO1h7o899hhfffVV3nfffXz11VfFJz09nbVr1xb2nXfe6WBnZWUxKSnpqmPc9enatSufe+458dBpbXIoKiqK99xzD/39/dm0aVOxIw0UPWckODiYISEh4oGGCQkJzMjIYEpKCi0WC00mk4OP9jyIkvICRZfe16xZk2azmY0aNXKY7AKKbqvr168fW7RoQT8/P1apUkVc+k4W7XSUZLvj40mMq2UFBQV87bXXeNttt3HkyJF88cUXGRwcLJ7fVKdOHcbHx/P2229nnTp1GBISwieffJJ33XUXLRYLAwIC2LNnT955553C7tWrFz/66COxM96gQQMOGjSo1LyNGzd28NEezmw/2RUYGCgeKA0UPfi0evXqDhNMNWvWZI8ePURfMZvNvPXWW4Vds2ZNNmjQQNgZGRl8++23HWJCQ0Md8tauXZvBwcEOMUFBQQ4TWWFhYQ6TX/Xq1aPFYuGoUaMYExPjcHuc1pe0HQ57W6vN/qGT3ogpzcc+d0xMDN944w3OmzePH3/8McmiWxIHDBggbFfLXPnMmDGDDz30kNdjOnbsyPbt2zv4aMsuXbokngmo+Vy6dIlpaWliW+Ls480YVz6KonDWrFnCR7v1S+OFF15genq68HG23fW58cYbxWTu1cRoz0kii7ZrtWrVYnR0NA0GA7du3cqRI0cKe9u2bSwsLHRY5uxT3jHaQ6K7dOnC3r17s2XLluzdu7f4JCQkMDY2VtidOnVysGvXrs3Q0FCHGHd8PM1744038u677xbPv9ImhyIiIhweeK1t77SxWZpPecRo+kwmE7Oysvjll1+6PFFSku0tH3dizp49y379+rF27dp86KGHeOrUKTZp0kTU2qJFCw4ePFisE2NiYvjbb7+V2Sc7O5sNGjS46phnn33WYV0cGxvLOnXqiPV0ZGQk+/fvz6ZNm4rfx2azOfiEh4eL7Yy7MZ7mfeqpp/jII4+IE1JGo9HheXDacyU1WzsxZb+dMZvNZfbxJK+mTzsZ+eijj/L999/nu+++S5I8evQoO3XqJGxXy1z5vPXWW+J5p+7GuOPTp08f8Yw3zadx48bs1q0b//nnH3Gc1a1bN5LkP//8I24x12x7nyvFuOPjbt5JkyYJH22iROP1119nQkKC8HG23fVp3Lgx69Spc9V5O3bsKP5+7tw5Nm7cmAEBATQYDMzJyeHDDz8sJrG3bdvGc+fOOSxz9vEkpix5tfVvo0aN2KpVK1avXp2tWrUSn/DwcIaEhAi7c+fODrY2f2Ef48rH+Va94cOHs2XLlvQEhSRRCcjIyMDIkSPRpUsXBAUFITc3F7fddhsefvhhDB8+HBcuXEB0dDRGjx6NtLQ01KlTB3l5eaX6pKWllTnGXZ/U1FRs3boV2dnZmD17Nm655RaXteXn52PDhg1o2bJlmdpk9erVyM/Pv2Leb775Bps3b8agQYNcxrj6Xk+1SNzn0KFDyMnJQbt27WC1Wku13Ykpz7wVjb///hujRo3CV199hT179qCwsBAxMTHIzs7GU089hYYNG5ZL3rS0tGJ/r1KlCoKCgvDvv//iyJEjXtNyPSgsLMSoUaPw008/ISsrC88//zxmz56NQYMG4dy5c+jUqRPGjx9f5n7jTt5x48bhvffeEz6DBg3CvffeiwULFkBVVXTv3h2tWrXCkCFDkJ+fjw4dOuDtt9/GE088gXnz5qGgoADNmjXDzJkzkZKSgvPnz2PZsmW4ePEiunfvjvPnz+Py5cvi1jBn234ZAPTp06dY3piYGFy+fBlr167FsWPHsGjRIuHTuHFjREREYNmyZSgoKEB6ejr69++Pxx57DAAwY8YMHDx4EG3atBG3xGiXY7uyU1JSsHfv3hJ9PIlx10fvXL58GefOnRO3lzgvu3z5Mvbv34/IyEjhc/78efz+++/idcfOPt6KceVz+PBhh9dAOy87d+4cDAaDuLXH2faWz9XEbN26FWvWrEHPnj0RGhqKnJwcBxtAsWWl2eUV40x+fj4MBoO41bg021s+pcWQxN69e6EoCuLj42EymUASx48fR2FhISIiImA0Gh1sd3zKK8b+9qmKxvnz53Hp0iUEBQW5tL3l42nMjh07YDKZULNmTRiNRuzevRsXLlwQNoBiy0qz3YnxNO8///yDnJwcse3QatFuGXK2o6OjUa1aNezevfuKMe74eJr3hhtucNhe6JG//voLf/zxB2rVquVy2ZkzZ/D9998jJSVF+Bw4cAArV65E7969AaCYj6sYd3zcybtx40aHY0nnZXv37oXFYhG3izvb3vK5mrzff/89cnNzMXjwYERFRWHhwoX47rvvhA2g2LLSbHdiPM1rz549e+Dn54f4+Hi3bE99ykKlmZTy9/fHzp07kZSUJCalatWqhSVLlqBDhw4wGo24dOkSfvvtN1y8eBHVq1dHXl5eqT5ms7nMMe76pKamYvfu3ahbt654doBEIpFIrh2uJpiuVV53JrskEolEIpFIJJLKTKV50HlKSgo2b95cbNknn3yC9PR0AEBCQgI2b96MpUuXigdPl+bjSYy7PgCwdOlS4SORSCSSa4vFYimXSSB38jr7lEXLwYMH8eCDD7ptl1dMRcsrtfhujXrS4gs16kmLL9SoJy0l+fz7779Ys2YNVqxY4WBv374d58+fx+TJkx3sGTNmCJ9NmzY52GXx8SSvnrT4Qo160uILNXpLiz2aj0d4dNOfDpk0aRLj4uL42Wef0Wq1ctasWezSpQsVReETTzxBq9XKfv36MTg4mGazmWaz2S0fT2Lc9RkxYoTQKpFIJBKJu2zevJmqqrptl1dMRcsrtfhujXrS4gs16kmLL9SoJy1X8lEUhUlJSQ7PKoyPjxe29gBszW7atCkBlBjjjo8nefWkxRdq1JMWX6jRm1r++OMPMc6PHj1abF3gLkbPprL0R9++feHv74+XXnoJ586dwz333IPY2Fj06dMHixcvxrlz5zB58mTYbDaEhITgxIkTbvl4ElMWn3fffRd333339W4+iUQikeiIhQsXOtjr1q1zsNevX4/CwkK8+OKLAIBjx46VaHsaU9HySi2+W6OetPhCjXrS4gs16kmLp3lJIjIyEiNGjMCOHTswcuRInDx5Ehs3bkRgYCCysrIAQNiPPvooAKBq1arYsGEDfv/9dzRr1swhxh0fT/LqSYsv1KgnLb5Qoze1ZGdnY9WqVUhMTMRV4dFUls64dOkSp0+fzqNHj5Ik8/PzefjwYYdlp0+f5nvvvVcmH09i3PVxfgOIRCKRSCQa9m+5c37TnfzIj/zIj/zIT0X92G/XunfvzsTERObl5TEiIoIAhH3kyBECYHR0NPPy8nj06NFiMe74eJJXT1p8oUY9afGFGr2lRVEUPvLII8Lnaq6UqhSTUiTp7+/Pffv2lbjME5/yyksWvbo6KyuLMTEx4m/vvPMO58+ff0WfGTNmMDk5maGhoSLmnnvuYc2aNUv0KS2vKx9nvKWlNB895ZVaKm+NFWmsVbT29qR93fme8mzfa6G3LFpjY2MdljvbkZGRDq9P3rRpEwEIH2fb05iKlldq8d0a9aTFF2rUkxZfqFFPWq4m7/bt24vZjz32GOPj4xkQEEBFUYT9ww8/EAD/85//ONj2Me74eJJXT1p8oUY9afGFGr2lRZuAsvfxdFKq0jzovHHjxti0aVOJyzzxKa+8EydOxNNPP42OHTvi77//RkFBAQBg+/bt6NmzJ2JjYzFixAg8/fTTsNlsOHnyJGbMmIGnn34aDRo0wOnTp1FQUICJEydi4cKFuHDhAv7++2+XPqXlLSgowMcff4z3338fPXr0wP79+wEA9957L9LT072qpTQfPeWVWipvjRVprFW09nZV48SJE/Hoo48iJSXFYy3u5HXl8/HHHyMlJQVhYWFux5SHXle/q3N/sdd74sQJrFy5EgAwduxYxMXFIScnR2xDMjIyQLuX5yqKAgDCx9n2NKai5ZVafLdGPWnxhRr1pMUXatSTlqvJu2HDhmL2+PHj0blzZ1y4cAEAhH377bcDADp06OBg28e44+NJXj1p8YUa9aTFF2r0phZXeTzCo6ksHTJ79mympqbyvffe49q1a5mbm8s333yT8fHxfP7557l27VoHe8aMGW75eBLjjk9qairfeecd5ubmMiAggHl5eZwwYQJDQ0Pp7+9Pf39/pqWl8csvv+TUqVNpMBiEvWXLFgJgXl4e09PTOXbsWIaHhzMwMNClT2l5hw0bxoiICA4YMMAhJjAwkCkpKV7VUpqPnvJKLZW3xoo01ipae7uqMT09nQMGDGCrVq081uJOXmcfze7atStVVXUrprz0uvpdnfuLvV5FUThlyhSS5NSpU1m/fn0uWbJEbPOWLVvGN954Q9hnz57luHHjhI+z7WlMRcsrtfhujXrS4gs16kmLL9SoJy2e5u3Xrx87dOjg0ibJJk2a0P7Q9NFHHyUA4eNsu+vjSV49afGFGvWkxRdq9JYW+6shr7TMXSrNpJR2f7L9B3C8f9nedrXMned2eDOvvU7toOb999+nxWJhYGAgzWYz9+3bJw58NHvXrl0ixmKx8LvvvisWY+9TWl7tYMk5xv4Ay1taSvPRU16ppfLWWJHGWkVrb1c1WiwWLlu2rFiNZdHiTl5nH1eTR6XFlJdeV7+rc39xpZckt2zZwvDw8Ou8lZVIJBKJRCKRSLxPpXn73t69e4stO3TokPh/fHy8g+1qmSsfZ9yJccend+/eGDRoENq3b49atWqJGk6cOIH09HTs3r0bCQkJ2Lx5MzIyMgBA2AcOHICqFt15mZKSgk8++aRYjL1PaXkPHjyIzMxMLFy40CGmXr16yM/Ph9Fo9JqW0nz0lFdqqbw1VqSxVtHa21WNKSkpyMvLK1ZjWbS4k9fZR7MvXrzo0JYlxZSXXle/q3N/caUXAMxmM/Lz8yGRSCQSiUQikVQ6rvesmK8yadIkxsXF8bPPPqPVauWsWbMYGRlJs9nMWbNmMTAwkCNHjmRcXBx79+5NVVXZr18/BgcH02w2C78uXbpQURQ+8cQTtFqtLn1KyxsXF8d7772XVquVFotFnMHv27cvMzMzvaqlNB895ZVaKm+NFWmsVbT2dlXjpEmTGBISwuTkZI+1uJPX2SctLY3z58/nuHHjxO17pcWUl15Xv6tzf3GllyTHjRvHzMzM67zVkkgkEolEIpFIvI9C2j2FrgIzY8aMYsvWrFkj/t+8eXMH29UyVz7OuBPjrs/atWsxf/58HD9+HAAQEhICRVEwceJE9OnTB5MnT8YXX3yBL774QsTZbDaYTCacOHECABAbG4sOHTpg1apVyMvLc+lTWl6SUFUVffv2xSeffILJkydj9uzZWLBgAQYMGICPPvrIa1rc8dFTXqmlctZY0cZaRWpvVzXm5eVh+PDhCA0NxbFjxzzS4k5eZ5977rkHs2fPFg90nDZtWqkx5anX+Xd17i9X0vv6669j8uTJuPvuuyGRSCQSiUQikVQqrtt0mJex2WwOH6vV6vDsJpvNVux5Tu74eBJTmg8A+vn5ieeM2Gw2Hjt2jCQ5c+ZMpqWlCb/o6GhOnjyZp0+f5nvvvcejR4+SJPPz83n48GFOnz5dLHPlU1pezUf7u/acq7i4OPbt29erWkrz0VNeqaXy1liRxlpFa29XNQJgaGgo3377bY+1uJP3Su0bGhrKqKgot2PKQ++V+oJ9f3GlV+sfkydPpkQikUgkEolEUhmpNJNSrti1axfbtm3LpUuXXnGZJz7eyOvv78/vvvtO2JcuXSp2UOPv7899+/aJeGfb1TJnu7S8rv7u6gDLG1rc8dFTXqmlctZY0cZaebVdebS3qxqPHTt21Vrcyevsc6UJs+ult7T+UtJEp0QikUgkEolEUllRy+kCLF1QrVo1jBo1Ck8++eQVl3ni4428jRs3xt9//y1so9GIRx55BOfPnwcABAQEoHHjxti0aZOId7ZdLXO2S8vr6u+xsbHlosUdHz3llVoqZ40VbayVV9uVR3u7qjEqKuqqtbiT19knODgYgwYNKlNMeeotrb9cSS8AfPzxx8jOzkZsbCz279+Pjz/+GCkpKQgLC3NpA8C9996L9PR0r8ZUtLxSi+/WqCctvlCjnrT4Qo160uILNepJiy/UqCctvlCjt7QAwNixY7FgwQJ4xPWeFStvNm3axKCgoBKXeeJztXlnz57N1NRUPvfccwwICGBubi4bNmzId955h7m5uSQpfN577z2uXbuWb775JuPj4/n8889zxowZzM3NdVh2JZ/S8tr/XfNp2bIlv/zyS6HbW1pK89FTXqml8tZYkcZaRWtvVzWW1r7ufI87eZ19nNvW3d+kPPS6+l2d+4srvRMmTGBERARHjBhBf39/Dhs2jBEREezatStVVS1m5+XlccKECQwMDGRKSorXYipaXqnFd2vUkxZfqFFPWnyhRj1p8YUa9aTFF2rUkxZfqNFbWrQX80ydOpWtWrWiJ1SaSakFCxY4fObPn89HHnmECQkJbNCgARcsWMAXXnjBYZk7Pp7EuOOjPS8Eds+e0mxFUbh27VqxzNnXPqYk25O8iqK4PMDylpbSfPSUV2qpvDVWpLFWkdtbq7G09i3r97iTV1EUl5NH7vwm5aH3Sh/7/uJKb2pqqpi40t7Q9+WXX3LLli0EUMzW3ug4duxYhoeHey2mouWVWny3Rj1p8YUa9aTFF2rUkxZfqFFPWnyhRj1p8YUavaVFm5TasmULw8PDPZrLMaKS0KVLFwdbURQUFhYCAA4dOoQuXbqAdi8aPHToELp27Vqqz8aNG8sc444PAISHhyMrKwsvvfQSoqKikJKSAkVRAADZ2dkgCUVRxL/Ob/CLj48XuVzZ2rLS8mp/06hfv77QO2rUKLzxxhte01KajzPXM6/UUnlrrEhjbe/evRWqvW+88cZiNQK4Yj3uaklJSSk1r7PPoEGDABS1LQDR/iXFlJdeV7+rq/7irJcknnrqKaiqioCAABw8eBCZmZm4ePEiABSzAWDv3r2oV68e8vPzYTQavRJT0fJKLb5bo560+EKNetLiCzXqSYsv1KgnLb5Qo560+EKN3tKiYTabkZ+fD0+oNJNS2iRQRWbv3r0l/j0pKanUZa58SsvrCucDLG9pccdHT3mllspZY0UbaxWpvd1pW0+0uJPX2cfVpFppMa7yXk+97dq1w6BBg9C+fXvUrl0bCQkJ2Lx5Mw4cOABVVYvZAJCSkoJPPvkE6enp2L17t1diKlpeqcV3a9STFl+oUU9afKFGPWnxhRr1pMUXatSTFl+o0VtaNJYuXYr09HR4QqWZlBo2bBieffZZBAQEOCx77LHH8P7772PIkCHCR1EUjB49WiwryQdAmWPc8UlOTgYAXLx4EYsXLy52pVfPnj0xY8YMh2XOV280b97cYZmzrS0rLa8zPXv2LHaA5S0tpfk4cz3zSi2Vt0Z79D7WnPXovb09aV93tDjjbvs6405MeegtDVd9AQCee+45vPrqqwgODgZJtG7dGj179sSFCxdgMpmK2evXr0eNGjXw0UcfYcCAAdi1a5dXYipaXqnFd2vUkxZfqFFPWnyhRj1p8YUa9aTFF2rUkxZfqNFbWtavX49Zs2bh9ddfx+TJk8u8DwwAleaZUqqqFnt9tqqq3LFjB1VVdfD5888/HZaV5ONJjDs+NpuNNpuNISEhBECr1UoANBqNDAgI4PTp0xkQEODwgdMzSrQYzbbZbFf0KSmvn59fMZ9+/fqJj7e1lOSjp7xSS+WusaKMtYrW3q5qtK/HUy3u5HX2sW9bV+3rbl5v6L3S73qlvqDpnT59Oh9++GFWqVJFPIcqNDSUUVFRV7Tj4uLYt29fpqWleTWmouWVWny3Rj1p8YUa9aTFF2rUkxZfqFFPWnyhRj1p8YUavaUlLi6OkydP9ngup9JMSimKwuPHjxdb9sUXXzAiIsLBZ8WKFQ7LSvLxJMZdH5IOdnBwMI1GIwMDA2mz2Wi1WqkoCs1mM0NDQ0mSu3btYtu2bbl06VKXtqtlpeW12WwOPq4OsLylxRMfPeWVWipHjRV9rOm5vV3V6FyPJ1rcyevs42ryqLSYa6nXub8469UmSP38/BgaGsrTp0/zvffe49GjR0mymJ2fn8/Dhw9z+vTpV/TxJKai5ZVafLdGPWnxhRr1pMUXatSTFl+oUU9afKFGPWnxhRq9pSU/P7/YhUGeUOEnpWw2G0NDQ8XVR6GhoWLGrqSz21c64+3s40lMaT7aQUlwcDBVVWX//v1FPb/88gtr1KghbFcHgM4+zrY7PqXldefg1Fta3PHRU16ppXLWWNHGmic1lldMaT6u6vGGFnfyujOpdj31ltZfLBYLs7KyhO3v7899+/YJf2fbHR9PYipaXqnFd2vUkxZfqFFPWnyhRj1p8YUa9aTFF2rUkxZfqNFbWrxB0dOqKjBjx47FmDFjQBJDhw7FO++8gz59+uDBBx+EoigYOHAg+vbtK+yePXvipZdecljmyqdTp07o06dPmWLc9fnvf/+Ld955Bx988AHWrFmD999/X9RjNBrxxx9/CLtatWoYNWoUnnzyySv6ONvu+JSW19Xfy0uLOz56yiu1VM4aK9pY86TG8oopzcdVPd7Q4k5eZx9PYspTb2n9pUmTJrjjjjuE3bhxY2zatEn4O9vu+HgSU9HySi2+W6OetPhCjXrS4gs16kmLL9SoJy2+UKOetPhCjd7S4g0q/IPOe/XqBaDoKfBZWVkwmUxi2f333y+WubLd8fEkxh2fhQsXAgBOnDiBhQsXgiSOHDmC8ePHIzs728Fnz549OHDgAF588UWcOnUKX3/9NapVq4YFCxZg6dKlwl64cCHWrVtXzKe0vK58AMeDJW9pKc1HT3mllspbY0UaaxWtva/UDlo9nmq50jrSPq8rH1ftW1pMeeh1ty846+3fvz+eeeYZnDhxAj/99BNuvfVWDBgwAOvWrUNGRkYxu169eg7Lbr/9dq/EVLS8Uovv1qgnLb5Qo560+EKNetLiCzXqSYsv1KgnLb5Qo7e0WK1Wh/3YunXroqwoJFnmKJ1z/vx5XLx40WGZn5+fw7Lz58/DYrGUyceTmCv5REdHO/goioKgoCDUrVsXDz74IMLCwsQb+Vz9RIqiQFEUFBYWCtvZV/MpLa+iKAgODhY+oaGh4gArMjISr7zyite0lObjSUx55ZVaKm+NFWmsqapaodobQLEa77jjDoc28ESLO3mdfXbv3i0mjyIjIzFkyJBSY8pL74MPPujQXqqqFusv69atK1VvaZvs0vR6GlPR8kotvlujnrT4Qo160uILNepJiy/UqCctvlCjnrT4Qo3e0mK/XFEUFBQUlPg9Lr+bpamrIJw7dw6DBg3CnDlzcPLkSQBFO//2BwD2tqtlrnycG96dGHd97CkoKCi2TFEUREZGok2bNnj77bcRExPjUduUljcuLq7Y350PsLylRSLRA3KslR/Xq21jYmKK+Tivy92J0ZvesLAwZGVl4aWXXkJUVBQOHTpU4vfGx8c7+DjbnsZUtLxSi+/WqCctvlCjnrT4Qo160uILNepJiy/UqCctvlCjt7Q4k5SUVOL3uISVhP79+zM9PZ1z586lv78/p0yZwsaNG9NoNPKJJ56gv78/W7duzcjISIaFhdHPz88tH09i3PHp0qULX375ZcbHx3PmzJmijnPnznHo0KEkyaFDhzI/P1/8TbOdff78889iMfY+peV15eOMt7SU5qOnvFJL5a3Rub/reaxVtPb2pH3d+Z7ybN9rodcTrRKJRCKRSCQSSWWn0lwplZiYiBkzZqBVq1YIDg7Gxo0b0aZNG9x3333YvHkz1qxZg8DAQHz66ac4ePAgHnjgAezcubNUn6ioqDLHuONDFj1vZNmyZZg1axYWL16MYcOG4YEHHkBycjIKCgpgMBhw5MgRBAUFYfTo0Rg6dCiOHDkCg8GAqKgo4bNt2zbUqlXLIcbep7S8APDss8/i33//dYh57LHH8P7772PIkCFe01KaDwDd5JVaKm+NFWmsacsqSnu/8sorxWo0GAzYs2cPpk+f7lBjWbQMGzas1LzOPpoWRVEc2rKkmPLSq8XY/67O/UWLsdc7Y8YMXLx4EYsXL0aXLl2wZs2aEreFzZs3d/Bxtj2NqWh5pRbfrVFPWnyhRj1p8YUa9aTFF2rUkxZfqFFPWnyhRm9pcaZnz54lfo9LrvOkmNewWq3cv38/STIuLo7r1q2j1Wrl6tWrabVaGRcXR39/f+7fv5979uyhoihu+XgS446Poig8fvw49+zZQ6vVSpJUVZVz585lREQESQqfP//8k6qqCnvFihUOPl988UWxGHuf0vKqqspjx44Vi9mxYwdVVfWqltJ89JRXaqm8NVaksVbR2ttVjYqicOfOncVqLIsWd/I6+2i2c1uWFFNeel39rs79xZVem83GkJAQAqDNZiMA8VEUpZhttVodbG/FVLS8Uovv1qgnLb5Qo560+EKNetLiCzXqSYsv1KgnLb5Qo7e0WK1WKopCs9nM0NBQekKlmZSqU6cOV61aRZJs27Ytn3nmGdapU4cDBgxgXFwc27Zty4iICK5atYrvvvsuzWazWz6exJTk4+/v7/AjBgQEUFEUhoaGiuXaD6qqarHOUFpnsre1PFfKGxQUJGICAwOpqir79+9P8n8HWJq2q9Xiro+e8kotla/G0saEp1rKY6wFBQVVuPY2m80ONdq3Q2BgoEda7Nv2Snnt16H27aso/5s8sm/fK8WUh15tmXNfsO8v9n3BebKLZDF7165dbNu2LZcuXeqWXV4xFS2v1OK7NepJiy/UqCctvlCjnrT4Qo160uILNepJiy/U6C0tZaXSTEqNGTOG7777Lkly+fLltFgsNBgMVBSFY8eO5fLly2k0GmkwGKiqKh999FG3fDyJKclHURS2bt2aAIRtMpnEDGNQUBADAgIYEBDgcJCXnp5ORVHYs2dPvvTSS+zbty/79OlDRVE4cOBAB1vzCQgIKDWv0WikoigMCAgQB0v2B1hGo9ErWkrz6dSpU5ljyiuv1FJ5a3RnTOhlrCmKwvT09ArT3haLRbSBVqN9OzjX464W+7a9Ul6j0ejg42oyyb59XcWUl16LxVLsd7X/bld9wX5yKzg42GESU+OXX35hjRo13LbLK6ai5ZVafLdGPWnxhRr1pMUXatSTFl+oUU9afKFGPWnxhRq9paUsGFFJeOqpp8T/27Vrh507dyInJwdpaWmoW7cuAOD33393WPbcc8+V2ceTGFc+s2fPhqIoqFmzJjZt2gSSePDBBzFixAiEhIQAAHbu3ImMjAykpaWhWbNm+P7775GVlQWTySRqvf/++x2W2dtpaWkl5t21axeqVq2KBx98EK+//jpCQkKwZs0akMSUKVPw5JNPon79+l7R4q6PJzHllVdqqXw1ljYm9DjWKkp7t2nTRrSBVqNGXl4ebrrpJtx4441l/p769euXmnfPnj0OPlu2bBFte//99yMxMRFpaWklxpS3XvvfddeuXQ79pSS9fn5+SE5OFn1Bw2g04o8//nDbLq+YipZXavHdGvWkxRdq1JMWX6hRT1p8oUY9afGFGvWkxRdq9JaWslBpHnRuz/nz52GxWEpc5olPeea9ePEi1qxZgyZNmsBkMiE4OLhYjosXL5b4PX5+fi59Ssu7cuVK8XcACA4OdnlQ7g0tZfHRU16ppXLVWFHHWllqLO+YK/ls2LChWI0l1eSuFldt55zX2WfTpk1XbNsrxVwLvVda57vqCwsXLnTIvW7dOpw6dQpff/01IiMjccsttzjYQ4YMwdKlS4X9yiuveCWmouWVWny3Rj1p8YUa9aTFF2rUkxZfqFFPWnyhRj1p8YUavaWF//8FbuPHj0dCQgKWLFnicr+7RDy6vkqHXL58mcOGDWNsbCwNBgPz8vJ4+fJltmzZkjabjQaDgbt27eKwYcMYFBREVVXd8vEkxh2frVu38rHHHhPPlNIegqx9/v33Xx45coT9+vUTD+x1vr3DedmVfErLa/9RFIWnT592+HhTS2k+esortVTeGivSWKto7e2qxtLa153vcSdvSe177Ngxt2PKQ29J36v1F1d6tVv7XN3i58pWVdXB9lZMRcsrtfhujXrS4gs16kmLL9SoJy2+UKOetPhCjXrS4gs1ekuLqqqsUqUK//Of//CPP/7waC6n0kxKDR06lKmpqZw5cyb9/f2Zl5fHoUOHMioqimlpafT39+fAgQOZmprKxx9/nKqquuXjSYw7Pvfddx/T09P51FNPUVVVTpkyhS+//DKtVqt4hohzp2ndujUjIyMZFhZGPz8/TpkyhY0bN6bRaOQTTzxBf39/lz6l5Z04cSKbNGkiDp5cHWB5S0tpPnrKK7VU3hor0liraO3tqkb7mjzV4k5eZx/njak7MeWl19Xv6txfXOm1/0gkEolEIpFIJJWNSjMpVbVqVX777bckycDAQObl5bFq1aqcMmUKbTYbAwMDmZiYyG+//ZY7duwgALd8PIlxxycmJobfffedsHfv3s3+/fszNjaWmZmZ9Pf3Z1hYGB988EHGx8fTYrGImBkzZtBgMHD37t1MSEjg4MGD2aFDBwYFBbn0KS3vvffey/T0dA4YMEBMkDkfYHlLS2k+esortVTeGivSWKto7e2qxpCQEEZERHDmzJkea3Enr7OPq8mj0mLKS6+r39W5v7jS26ZNG8bGxnLmzJkki07A5Ofn89y5cxw6dGgxW/P5888/HeyrjaloeaUW361RT1p8oUY9afGFGvWkxRdq1JMWX6hRT1p8oUZvadGw9ykrlWZSymKxcN++fST/NyllsVi4fPlyWq1WBgYG0mw2c9++fdy2bZuYPCrNx5MYd3wCAgK4f/9+btu2jYqicN26dUxISOCnn35Kq9Uq3gS1f/9+zpgxg2azmf7+/ty/fz/37NkjYqxWK1evXk2r1cq4uDiXPqXlrVKlCr/77jvu2bNHTJA5H2B5S0tpPnrKK7VU3hor0liraO3tqkar1coxY8awQ4cOHmtxJ6+zj6vJo9Jiykuvq9/Vub+40quqKsePH88OHTqQJFVV5bFjx/jnn3+KK6jsbc1nx44dDvbVxlS0vFKL79aoJy2+UKOetPhCjXrS4gs16kmLL9SoJy2+UKO3tGjY+5QVFZWEjIwMrF69utiyiRMnIjMzEwCQlpaG1atXY+7cuTAYDG75eBLjjk9CQgL27t2LuXPnIjg4GHPmzMGpU6fw22+/wWazITg4GLGxsdi7dy+aN2+OS5cuwWq1Yu/evfjqq6/g5+eHOXPmIDU1FXPmzIHNZkPNmjVd+pSW99SpU0hNTcVXX30FVVVx6tQpnDp1Ch07dsQPP/zgVS2l+egpr9RSeWusSGOtorW3qxpTU1MRExODH374wWMt7uR19vn777+RmpqK5s2bo7Cw0K2Y8tLr6nd17i+u9JJEkyZN8MMPPwAASEJRFOTm5iIsLKyYrfls377dwb7amIqWV2rx3Rr1pMUXatSTFl+oUU9afKFGPWnxhRr1pMUXavSWFg17nzLDSsL8+fMZEhLCUaNGMSAggKNHj+ZNN91EAOzTpw8DAgLYq1cv+vn50WAw0Gw2u+XjSYw7Pp06dWLTpk3p5+fHUaNG0WKxiGegjB07lm3btmWrVq347rvv8t1332V4eDiNRiMNBgNVVeWjjz5Ki8VCg8EgYpYvX+7Sp7S8iqLQZDJRVVVWr16dzzzzDOvUqcMBAwYwLi7Oq1pK89FTXqml8tZYkcZaRWtvVzWOGTOGXbt2ZVxcnMda3Mnr7BMREcFVq1bx3XffpdlsdiumvPS6+l2d+4u9XgC0WCxUVZX+/v4uH0rp6uPs462YipZXavHdGvWkxRdq1JMWX6hRT1p8oUY9afGFGvWkxRdqvFotZrOZoaGhDA0NFc9H7d+/v0dzOQpJopKwevVqDBs2DLm5uTh79iwaNGiAO+64A0uXLhXLqlatCpPJhEOHDrnt40mMuz5DhgxB+/btsX//frz66quIiorCG2+8gW+//RadOnUCSVy6dAljxoxBly5dkJOTg7S0NNStWxf79+93sAEUW+ZO3j///BN//PEHnnzySRw/fhydOnXCpUuXUFhYiHfeeQe1atXympay+ugpr9RSeWqsyGNN7+398ssvF6vRvp4nn3zSIy3u5HX26dChA0iCJB5++GFMnTq11JhrpbdLly7F+ou93pYtW2L16tW4fPkyFEVBkyZNUKtWLUyZMgX3338//vrrL0RFRQk7MTERR48eBUlMmTIFTz75JM6ePStsT2MqWl6pxXdr1JMWX6hRT1p8oUY9afGFGvWkxRdq1JMWX6jRW1rq168v5mH8/PyQnJyMZs2aeTSPA1Zw8vLyWFhYWOIyT3zKK68r/v33X5fL9+3bxy+++IK5ubnFfFzFuONTWl7nv5enltJ89JRXarm2ea+lFrLijDV3vltP7U0Wr9EbWtzJ68rHk5jy0OtOf9HsYcOGccOGDcJn1apVvHjx4hVtd3w8ialoeaUW361RT1p8oUY9afGFGvWkxRdq1JMWX6hRT1p8oUZvafEGFf5KKYPBgCNHjiAqKgoAcNddd2Hu3LkOy1RVxa+//oratWu77eNJjLs+48aNQ0REBEaOHIkPPvgAx44dw65du5CamoqXX34ZycnJuPfee2EymRx8duzYgc8++wyjR49Gfn4+du/ejaSkJLRt2xa5ubk4c+aMS5/S8tp/d1xcHB555BGHNj5//rzXtJTms3PnTt3klVoqb40Vaazt2rWrQrW3fTtoNRYUFJTYvu5ocdV2znlLat/z58/DYrG4FVMeevv161fqOt9isRTT69w/Ll68eEUfVzF+fn5ej6loeaUW361RT1p8oUY9afGFGvWkxRdq1JMWX6hRT1p8oUZvaQGA4OBglBmvTnFdBxRF4bFjx4QdGBhYbBkArlu3rkw+nsS465OXl8ehQ4cyNTWVM2fOpL+/P/Py8nj58mV2796dJpOJBoOBAwcOZGpqKjt37kw/Pz9hP/7441RVVeSJiopiWloa/f39XfqUlnfXrl0cNmwYQ0NDCUDEtGzZkjabzataSvPRU16ppfLWWJHGWkVrb1c1Dh06lDabjX379vVYizt5nX00OygoyCFvSTHlpdfV7+rcX1zpzc/PZ8OGDcWy0u73d/bxVkxFyyu1+G6NetLiCzXqSYsv1KgnLb5Qo560+EKNetLiCzV6S4vzxxPkpNQVfDyJKcukVNWqVfntt986LBs6dCgTEhIYEBBAf39/JiYm8ttvv+Vnn31GVVWFvWPHDgIQeaZMmUKbzcbAwECXPqXl1Q6W3nzzTYcY+wMsb2kpzUdPeaWWyltjRRprFa29XdVYtWpVvvjii2zatKnHWtzJ6+zjavKotJjy0uvqd3XuL6709u/fn3FxcaxWrRr9/f3ZunVrRkZGMiwsjH5+fsXsKVOmsHHjxjQajXziiSe8FlPR8kotvlujnrT4Qo160uILNepJiy/UqCctvlCjnrT4Qo3e0jJlyhQOHz6c8fHxnDlzpkdzOhV+UkpVVR4/flzYgYGBxZYB4C+//FImH09i3PXZs2cPLRYL9+3bJ5ZpBzUfffQRrVYrAwMDaTabuW/fPnHgo9nbtm0TB0IWi4XLly8vFmPvU1pe7WDJOcb+AMtbWkrz0VNeqaXy1liRxlpFa29XNVosFn777bfFaiyLFnfyOvu4mjwqLaa89Lr6XZ37iyu9CQkJnD59Om02G4OCghgTE8PvvvuOM2bMoMFgKGbv3r2bCQkJHDx4MDt06OC1mIqWV2rx3Rr1pMUXatSTFl+oUU9afKFGPWnxhRr1pMUXavSWlt27d5MkZ8yYwQ4dOng0p2NEBYckevfuDbPZDKDoXsfCwkLUqVMHqqoKvw4dOsBkMgEA8vPzQbJEHwDIzs5GeHi42zHu+jRt2hQWiwV33nknEhMTcf78eQDA4cOHsXXrVmRmZmLz5s1IS0vD6tWr0aBBAwAQ9p49e2AwGAAAGRkZmDhxYrEYe5/S8h47dgxpaWmYPn26Q0xCQgIuXboERVG8pqU0Hz3llVoqb40VaaxVtPZ2VWNGRgY2bNhQrMayaHEnr7OPZufn5zu0ZUkx5aXX1e/q3F9c6T116hRiY2Nx6dIl2Gw2nDp1CqmpqUhKSkJhYSH+/vtvB/vUqVM4deoUOnbsiHHjxnktpqLllVp8t0Y9afGFGvWkxRdq1JMWX6hRT1p8oUY9afGFGr2pBQCaN2+ORx99FJ5Q4SelevXq5WDfd999WLNmjcOyoKAg2Gw2YVetWhXHjh1DUFDQFX20v1mtVrdj3PUBgMTERKxevVosnzdvHqxWK8aNG4clS5aga9euGDBgAB5//HE0atQIqqqiYcOG6NOnDwoKCmA0GjFv3jyEh4dj3rx56NOnDzZu3OjSp7S8ERER6NWrF3766Sf4+fkBKH6A5S0tpfnoKa/UUnlrrEhjraK1t6sahwwZgrvvvhvR0dE4fvy4R1rcyevsk5qaWmzyqLSY8tLr6nd17i+uJrtSU1Px6aefIjMzE2azGbm5udi7dy9yc3Ph5+cHq9XqYM+ZMwepqamYM2cObDYbatas6ZWYipZXavHdGvWkxRdq1JMWX6hRT1p8oUY9afGFGvWkxRdq9JaWOXPmoHHjxvjqq68c5j3KhEfXV0m8wg8//MB27doxMjKS/v7+rFmzJq1WK0eNGsWAgACOHj2at912GxVFYUhICP39/Vm7dm1mZmaKmOzsbL711lsOeZx9Ssvr5+dHg8HABx98UPz9pptuIgD26dPHq1rc8dFTXqmlctZY0cZaRWpvVzX27duXRqOxxO8u7Xvcyevs06tXL7HMbDa7FVOeep1/V+f+4kpvkyZNaDAY+M0333D58uU0Go00GAxUVZWPPvpoMdtisdBgMFBRFI4dO9ZrMRUtr9TiuzXqSYsv1KgnLb5Qo560+EKNetLiCzXqSYsv1OgtLRaLhWazmaqqcuzYsR7Ni8hJqWtMXl4eCwsLr/j3H374gVlZWYyIiBAHOtOmTSsW45zHk7zLli1z+Lv9QaZ2gOUtLaX5eBJTXnmllspboz16H2vLli2rUO3t3Abt2rVjWFgYLRaL2/V4mteVj/PkkTsx5aG3pL5Qkl77/mEfs2/fPn7xxRfMzc11yy6vmIqWV2rx3Rr1pMUXatSTFl+oUU9afKFGPWnxhRr1pMUXavSWFk+Qk1LXGFVVHd7O16NHD65bt87hoMbZR1EUbtmyxSHGHZ/S8nbs2JFHjhxx0Od8gOUtLaX56Cmv1FJ5a6xIY62itberGr2hxZ28zj6uJoo8yesNve6s80ub6NT4999/y2SXV0xFyyu1XNu8Usu1zSu1XNu8Usu1zSu1XNu8Usu1zVsZtXiKnJS6xiiK4nCAor2xz34ZAIcDHwBct26dQ4xzHlc+Zc3r6gDLW1pK89FTXqml8tZYkcZaRW/vHj16lIsWd/K6mjzyRl5P28X5d3VnEvDo0aMkycuXL3PYsGGMjY2lwWDgrl27OGzYMAYFBVFV1WJ2Xl4eL1++zJYtW9Jms3ktpqLllVp8t0Y9afGFGvWkxRdq1JMWX6hRT1p8oUY9afGFGr2lJS8vjyT50ksvcfLkyfSE/70iTnLdKDpuceTcuXPXPO/ixYvLTYtEogfkWLt2LF68+LrlJenQlu7ElJdeoPjv6vzbu9KrvYHvtddew7Rp0/Dmm2/Cz88PEyZMwLRp08RLPpxtLWbHjh2IiIjwWkxFyyu1+G6NetLiCzXqSYsv1KgnLb5Qo560+EKNetLiCzV6S4tG7dq1MXnyZHiER1NZEo9RVZXHjx8X9pXOtK9fv97B/uWXXxxinPO48nEnr71dnlpK89FTXqml8tZYkcZaRWzv8tLvTl7n9nZ1NZsnea9Wr7u/vbNe7axT1apV+e2334rliYmJ/Pbbb7ljxw4CKGbn5eWxatWqnDJlCm02m9diKlpeqcV3a9STFl+oUU9afKFGPWnxhRr1pMUXatSTFl+o0VtatH3WHTt20Gaz0ROMkFxTSKJ3794wm80AgPPnz4MkHnjgAbEMAAYNGoTQ0FBhd+jQASaTCQCQn58PkqhTpw5UVS3Rp7S89ramRVEUB83e0lKSDwBkZ2cjPDz8uueVWip3jRVlrJ0/fx6FhYUVqr07duyIG2+80UG//frOUy2u2sU5r3N7P/fcc/juu++E7bzudTfv1ep11S6u+ot9PADRNw4fPoy0tDSx/NixY0hLSxNXUjnbWkxCQgIuXboERVG8ElPR8kotvlujnrT4Qo160uILNepJiy/UqCctvlCjnrT4Qo3e0qJRWFiIS5cuwRPkpNQ1xv7SNwC47777MG3aNPz6668OByW7du2C0Vj081itVvz777+w2WwAgKpVq+LYsWMICgoS/kFBQeLvms/vv/9eYl4A+PnnnxEVFQUAKCgoAIBiB1je0FKaj/Y3q9V63fNKLZW3xtLGhJ7GWmxsLE6ePFlh2hsouk0tJydH1Ojv74+tW7eKv1ut1jJrAYDff/9dtJOrvM4+APDTTz8hNjYWQNHkkdlsLjGmvPRq36+1S2xsLA4cOFCsvzhPAjZt2hTZ2dmwWCy48847kZiYCABIS0vD6tWrsWfPHhgMhmI2AGRkZGDixInIzMzE5s2bvRJT0fJKLb5bo560+EKNetLiCzXqSYsv1KgnLb5Qo560+EKN3tKiMXfuXGRmZsIT5KTUNWbq1KnXLPaBBx4o8e9r1qwBADRv3rzYMo20tDSHv3uqRSLRA6WNCQ051srO9WpboKgtS2rbqlWrAije/q7a25nrqTckJAQNGjTA6tWrARSdgWrYsCH69OmDgoICGI3GYva8efMQHh6OefPmoU+fPti4caNXYipaXqnFd2vUkxZfqFFPWnyhRj1p8YUa9aTFF2rUkxZfqNFbWubNm4fffvsNM2bMwKJFi8q036yh/P/nWUgkEolEItEhq1evxrBhw5Cbm4uzZ8+iatWqMJlMOHTokEu7QYMGuOOOO7B06VKvxlS0vFKL79aoJy2+UKOetPhCjXrS4gs16kmLL9SoJy2+UKO3tDRo0ABDhgxB+/btPdvZ9ehJVBKJRCKRSMqNvLw8FhYWum2XV0xFyyu1+G6NetLiCzXqSYsv1KgnLb5Qo560+EKNetLiCzV6S4s3kZNSEolEIpHoDFVVHd7QpygKt2zZckW7R48e5RJT0fJKLb5bo560+EKNetLiCzXqSYsv1KgnLb5Qo560+EKN3tLSo0cPHj16lN5ALf1aKolEIpFIJNcSOt1ZTxLnzp27or148eJyialoeaUW361RT1p8oUY9afGFGvWkxRdq1JMWX6hRT1p8oUZvaVm8eLHDW/muBjkpJZFIJBKJRCKRSCQSiUQiuebISSmJRCKRSHSGoihQFMVhmaqqJdrlFVPR8kot1zav1HJt80ot1zav1HJt80ot1zav1HJt81ZGLc62p8i370kkEolEojNUVUWHDh1gNpsBAF9++SUiIiJgMpkAAEeOHIGfnx/Cw8MBAEePHgVJVKlSRexEHDly5KpjKlpeqcV3a9STFl+oUU9afKFGPWnxhRr1pMUXatSTFl+o0Vtajh49iptuuglWqxX2zJs3D2XFWOYIiUQikUgk5UqvXr0c7LS0NAc7KCgIAMSOQNWqVXHs2DGxXPOx2WxXFVPR8kotvlujnrT4Qo160uILNepJiy/UqCctvlCjnrT4Qo3e0lK1alXExsbCG8grpSQSiUQikUgkEolEIpFIJNcctXQXiUQikUgkEolEIpFIJBKJxLvISSmJRCKRSCQSiUQikUgkEsk1R05KSSQSiUQikUgkEolEIpFIrjlyUkoikUgkEolEIpFIJBKJRHLNkZNSEolEIpFIJBKJRCKRSCSSa46clJJIJBKJRCKRSCQSiUQikVxz5KSURCKRSCQSyRXo3bs3FEUp9vn999+vOve0adNgs9muXqREIpFIJBJJBcV4vQVIJBKJRCKR6JlbbrkFU6dOdVgWGRl5ndS45tKlSzCZTNdbhkQikUgkEkmZkFdKSSQSiUQikZSA2WxGdHS0w8dgMGDBggVo0KABLBYLUlNTMXToUFy+fFnEjRkzBnXq1IHVakVCQgL69++Ps2fPAgBWrVqFBx54AKdPnxZXX7366qsAAEVRMH/+fAcNNpsN06ZNAwDs27cPiqJg9uzZaNmyJSwWCz755BMAwOTJk5Geng6LxYKaNWtiwoQJIsfFixfx+OOPIyYmBhaLBUlJSXj99dfLr+EkEolEIpFISkFeKSWRSCQSiURSRlavXo2ePXti3LhxuPHGG5GXl4eHHnoIAPDKK68AAFRVxbhx45CSkoI9e/agf//+GDRoECZMmICsrCyMHTsWQ4YMwW+//QYACAwMLJOG559/Hm+//TYyMzPFxNSQIUMwfvx4ZGZmYtOmTejXrx+sVit69eqFcePGYeHChZgzZw4SExNx8OBBHDx40LsNI5FIJBKJRFIG5KSURCKRSCQSSQksWrTIYcKoQ4cO+Ouvv/D888+jV69eAIDU1FQMHz4cgwYNEpNSAwcOFDHJyckYMWIEHnnkEUyYMAF+fn4ICQmBoiiIjo72SNfAgQPRtWtXYb/yyit4++23xbKUlBRs374d//3vf9GrVy8cOHAA1apVQ/PmzaEoCpKSkjz6XolEIpFIJBJvISelJBKJRCKRSEqgdevWmDhxorCtVivq1q2LH3/8Ea+99ppYXlBQgPPnz+PcuXMICAjAt99+i9dffx07d+7EP//8g8uXLzv8/Wpp2LCh+H9+fj7y8vLQp08f9OvXTyy/fPkyQkJCABQ9tP2mm25CjRo1cMstt+C2225D+/btr1qHRCKRSCQSiafISSmJRCKRSCSSErBarUhLS3NYdvbsWQwdOtThSiUNi8WCffv24bbbbsOjjz6K1157DWFhYVizZg369OmDixcvljgppSgKSDosu3Tpkktd9noAYNKkSWjSpImDn8FgAAA0aNAAe/fuxZIlS/Dtt9+iR48eaNeuHebOnVtKC0gkEolEIpGUD3JSSiKRSCQSiaSMNGjQAL/99luxySqNnJwcFBYW4u2334aqFr1XZs6cOQ4+fn5+KCgoKBYbGRmJI0eOCHv37t04d+5ciXqqVKmC2NhY7NmzB/fee+8V/YKDg3HXXXfhrrvuQrdu3XDLLbfg1KlTCAsLKzG/RCKRSCQSSXkgJ6UkEolEIpFIysiQIUNw2223ITExEd26dYOqqsjNzcXWrVsxYsQIpKWl4dKlS3jvvffQqVMn/Pjjj/jggw8cciQnJ+Ps2bNYsWIF6tWrh4CAAAQEBKBNmzYYP348mjVrhoKCAjz33HMwmUylaho6dCieeOIJhISE4JZbbsGFCxewYcMG/PXXX3j66acxZswYxMTEIDMzE6qq4vPPP0d0dDRsNls5tZJEIpFIJBJJyajXW4BEIpFIJBJJRePmm2/GokWL8M0336BRo0Zo2rQp3nnnHfHw8Hr16mHMmDF44403ULt2bXzyySd4/fXXHXJkZWXhkUcewV133YXIyEi8+eabAIC3334bCQkJuPHGG3HPPffg2WefdesZVH379sXkyZMxdepU1KlTBy1btsS0adOQkpICAAgKCsKbb76Jhg0bolGjRti3bx8WL14sruSSSCQSiUQiudYodH5ogUQikUgkEolEIpFIJBKJRFLOyFNjEolEIpFIJBKJRCKRSCSSa46clJJIJBKJRCKRSCQSiUQikVxz5KSURCKRSCQSiUQikUgkEonkmiMnpSQSiUQikUgkEolEIpFIJNccOSklkUgkEolEIpFIJBKJRCK55shJKYlEIpFIJBKJRCKRSCQSyTVHTkpJJBKJRCKRSCQSiUQikUiuOXJSSiKRSCQSiUQikUgkEolEcs2Rk1ISiUQikUgkEolEIpFIJJJrjpyUkkgkEolEIpFIJBKJRCKRXHPkpJREIpFIJBKJRCKRSCQSieSa8/8AFlXy2HvMFGAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the principal components\n", + "components = pca.components_\n", + "\n", + "# Assuming your original features are named, you can list them\n", + "feature_names = [f\"Feature {i}\" for i in range(predicted_projections.shape[1])] # Replace with actual feature names if available\n", + "\n", + "fig, axes = plt.subplots(n_components, 1, figsize=(12, 3 * n_components))\n", + "for i, (component, ax) in enumerate(zip(components[:n_components], axes)):\n", + " sns.heatmap(component.reshape(1, -1), cmap='viridis', ax=ax, cbar=False, xticklabels=feature_names)\n", + " ax.set_title(f'Principal Component {i + 1}')\n", + " ax.set_xlabel('Features')\n", + " ax.set_ylabel('Component Value')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viscy/applications/contrastive_phenotyping/predict.py b/viscy/applications/contrastive_phenotyping/predict.py new file mode 100644 index 00000000..db51c3c9 --- /dev/null +++ b/viscy/applications/contrastive_phenotyping/predict.py @@ -0,0 +1,99 @@ +from viscy.data.hcs import ContrastiveDataModule, PredictDataset +from viscy.light.engine import ContrastiveModule +import os +from pathlib import Path +from argparse import ArgumentParser + +import torch +from torch.optim import Adam +from lightning.pytorch.strategies import DDPStrategy +from lightning.pytorch import Trainer, seed_everything +from lightning.pytorch.callbacks import ModelCheckpoint, RichProgressBar +from lightning.pytorch.loggers import WandbLogger +from lightning.pytorch.callbacks import TQDMProgressBar +from lightning.pytorch.utilities.rank_zero import rank_zero_only +import wandb +from tqdm import tqdm +import logging +import numpy as np +import pandas as pd + +def main(hparams): + # Set paths + top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") + timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/predict_timesteps.csv" + predict_base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr" + checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/updated_multiple_channels/contrastive_model-test-epoch=88-val_loss=0.00.ckpt" + + # Data parameters + channels = 2 + x = 200 + y = 200 + z_range = (28, 43) + batch_size = 11 + channel_names = ["RFP", "Phase3D"] + + # Initialize the data module for prediction + data_module = ContrastiveDataModule( + base_path=str(predict_base_path), + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + channel_names=channel_names, + batch_size=batch_size, + z_range=z_range, + predict_base_path=predict_base_path + ) + + data_module.setup(stage='predict') + + # Load the model from checkpoint + model = ContrastiveModule.load_from_checkpoint(str(checkpoint_path), predict=True) + model.eval() + model.encoder.predict = True + + # Initialize the trainer + trainer = Trainer( + accelerator='gpu', + devices=1, + num_nodes=1, + strategy=DDPStrategy(), + callbacks=[TQDMProgressBar(refresh_rate=1)] + ) + + # Run prediction + predictions = trainer.predict(model, datamodule=data_module) + + # Collect features and projections + features_list = [] + projections_list = [] + + for batch_idx, batch in enumerate(predictions): + features, projections = batch + features_list.append(features.cpu().numpy()) + projections_list.append(projections.cpu().numpy()) + + all_features = np.concatenate(features_list, axis=0) + all_projections = np.concatenate(projections_list, axis=0) + + # Save features and projections + np.save("updated_epoch88_predicted_features.npy", all_features) + np.save("updated_epoch88_predicted_projections.npy", all_projections) + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--backbone", type=str, default="convnext_tiny") + parser.add_argument("--margin", type=float, default=0.5) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--schedule", type=str, default="Constant") + parser.add_argument("--log_steps_per_epoch", type=int, default=10) + parser.add_argument("--embedding_len", type=int, default=256) + parser.add_argument("--max_epochs", type=int, default=100) + parser.add_argument("--accelerator", type=str, default="gpu") + parser.add_argument("--devices", type=int, default=1) + parser.add_argument("--num_nodes", type=int, default=2) + parser.add_argument("--log_every_n_steps", type=int, default=1) + args = parser.parse_args() + + main(args) \ No newline at end of file diff --git a/viscy/applications/contrastive_phenotyping/training_script.py b/viscy/applications/contrastive_phenotyping/training_script.py index fef80f9e..ec580466 100644 --- a/viscy/applications/contrastive_phenotyping/training_script.py +++ b/viscy/applications/contrastive_phenotyping/training_script.py @@ -40,7 +40,7 @@ #input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" input_zarr = "/hpc/projects/virtual_staining/viral_sensor_test_dataio/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" model_dir = top_dir / "infection_classification/models/infection_score" -# checkpoint dir: /hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/multiple_channels +# checkpoint dir: /hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/updated_multiple_channels timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" # Data parameters @@ -55,28 +55,27 @@ torch.set_float32_matmul_precision('medium') -# %% Initialize the model and log the graph -#contra_model = ContrastiveEncoder(backbone="convnext_tiny") -# print(contra_model) +contra_model = ContrastiveEncoder(backbone="convnext_tiny") +print(contra_model) -# model_graph = torchview.draw_graph( -# contra_model, -# torch.randn(1, 2, 15, 224, 224), -# depth=3, -# device="cpu", -# ) -# model_graph.visual_graph +model_graph = torchview.draw_graph( + contra_model, + torch.randn(1, 2, 15, 224, 224), + depth=3, + device="cpu", +) +model_graph.visual_graph -#contrastive_module = ContrastiveModule() -# print(contrastive_module.encoder) +contrastive_module = ContrastiveModule() +print(contrastive_module.encoder) -# model_graph = torchview.draw_graph( -# contrastive_module.encoder, -# torch.randn(1, 2, 15, 200, 200), -# depth=3, -# device="cpu", -# ) -# model_graph.visual_graph +model_graph = torchview.draw_graph( + contrastive_module.encoder, + torch.randn(1, 2, 15, 200, 200), + depth=3, + device="cpu", +) +model_graph.visual_graph # %% Define the main function for training @@ -128,7 +127,8 @@ def main(hparams): # Initialize logger wandb_logger = WandbLogger(project="contrastive_model", log_model="all") - custom_folder_name = "multiple_channels" + # set for each run to avoid overwritting! + custom_folder_name = "updated_multiple_channels" checkpoint_callback = ModelCheckpoint( dirpath=os.path.join(model_dir, custom_folder_name), filename="contrastive_model-test-{epoch:02d}-{val_loss:.2f}", @@ -144,7 +144,7 @@ def main(hparams): accelerator=hparams.accelerator, devices=hparams.devices, num_nodes=hparams.num_nodes, - strategy=DDPStrategy(find_unused_parameters=True), + strategy=DDPStrategy(), log_every_n_steps=hparams.log_every_n_steps, num_sanity_val_steps=0 ) @@ -183,6 +183,7 @@ def main(hparams): "num_nodes": 1, # 1 node "log_every_n_steps": 1, } + class HParams: def __init__(self, **kwargs): self.__dict__.update(kwargs) @@ -199,7 +200,7 @@ def __init__(self, **kwargs): parser.add_argument("--max_epochs", type=int, default=100) parser.add_argument("--accelerator", type=str, default="gpu") parser.add_argument("--devices", type=int, default=1) # 4 GPUs - parser.add_argument("--num_nodes", type=int, default=1) + parser.add_argument("--num_nodes", type=int, default=2) parser.add_argument("--log_every_n_steps", type=int, default=1) args = parser.parse_args() diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index ca2a010a..9f702892 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -8,7 +8,7 @@ from typing import Callable, Literal, Optional, Sequence, Union #import pytorch_lightning as pl from monai.transforms import MapTransform - +import random import numpy as np import torch import zarr @@ -622,7 +622,36 @@ def __init__( print("channel indices!") print(self.channel_indices) print(f"Initialized dataset with {len(self.positions)} positions.") + + # self.statistics = self.compute_statistics() + # print("Channel Statistics:", self.statistics) + + def compute_statistics(self): + stats = {channel: {'mean': 0, 'sum_sq_diff': 0, 'min': np.inf, 'max': -np.inf} for channel in self.channel_names} + count = 0 + total_elements = 0 + + for idx in range(len(self.positions)): + position_path = self.positions[idx][0] + data = self.load_data(position_path) + for i, channel in enumerate(self.channel_names): + channel_data = data[i] + mean = np.mean(channel_data) + stats[channel]['mean'] += mean + stats[channel]['min'] = min(stats[channel]['min'], np.min(channel_data)) + stats[channel]['max'] = max(stats[channel]['max'], np.max(channel_data)) + stats[channel]['sum_sq_diff'] += np.sum((channel_data - mean) ** 2) + count += 1 + total_elements += np.prod(channel_data.shape) + + for channel in self.channel_names: + stats[channel]['mean'] /= count + stats[channel]['std'] = np.sqrt(stats[channel]['sum_sq_diff'] / total_elements) + del stats[channel]['sum_sq_diff'] + print("done!") + return stats + def open_zarr_store(self, path, layout="hcs", mode="r"): #print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") return open_ome_zarr(path, layout=layout, mode=mode) @@ -635,11 +664,7 @@ def __getitem__(self, idx): anchor_data = self.load_data(anchor_position_path) anchor_data = self.normalize_data(anchor_data) - positive_data = ( - self.transform({"image": anchor_data})["image"] - if self.transform - else anchor_data - ) + positive_data = self.apply_channel_transforms(anchor_data) positive_data = self.normalize_data(positive_data) # if self.transform: @@ -652,11 +677,7 @@ def __getitem__(self, idx): negative_data = self.load_data(negative_position_path) negative_data = self.normalize_data(negative_data) - negative_data = ( - self.transform({"image": negative_data})["image"] - if self.transform - else negative_data - ) + negative_data = self.apply_channel_transforms(negative_data) negative_data = self.normalize_data(negative_data) # if self.transform: @@ -727,118 +748,64 @@ def normalize_data(self, data): normalized_data[i] = (channel_data - mean) / (std + 1e-6) return normalized_data -# def apply_transform(self, data): -# # print("Applying transform to data") -# # print(data.shape) # data shape when 2 channels: (2, 15, 200, 200) -# # transformed_data = np.empty_like(data) -# # for channel_idx in range(data.shape[0]): -# # channel_data = data[channel_idx] -# # transform = get_transforms(channel_data) -# # transformed_data[channel_idx] = transform({"image": channel_data})["image"] -# # return transformed_data -# transformed_data = np.empty_like(data) -# for channel_idx, channel_name in enumerate(self.channel_names): -# channel_data = data[channel_idx] -# transform = get_transforms(channel_data, channel_name) -# transformed_data[channel_idx] = transform({"image": channel_data})["image"] -# return transformed_data - -# def get_transforms(image, channel): -# mean = np.mean(image) -# std = np.std(image) -# if channel == 'RFP': -# if std < 0.1: -# gamma_range = (0.97, 1.03) -# else: -# gamma_range = (0.9, 1.1) - -# if mean < 0.5: -# scale_factors = (0.95, 1.05) -# else: -# scale_factors = (0.93, 1.07) -# elif channel == 'Phase3D': -# if std < 0.1: -# gamma_range = (0.98, 1.02) -# else: -# gamma_range = (0.95, 1.05) - -# if mean < 0.5: -# scale_factors = (0.98, 1.02) -# else: -# scale_factors = (0.95, 1.05) - -# # if std < 0.1: -# # gamma_range = (0.95, 1.05) # Narrower range for low variance images -# # else: -# # gamma_range = (0.85, 1.15) # Slightly adjusted range for higher variance - -# # if mean < 0.5: -# # scale_factors = (0.95, 1.05) # Narrower range for lower intensity images -# # else: -# # scale_factors = (0.85, 1.15) # Slightly adjusted range for higher intensity images - -# # if std < 0.1: -# # gamma_range = (0.97, 1.03) # Even narrower range for low variance images -# # else: -# # gamma_range = (0.9, 1.1) # Narrower range for higher variance - -# # if mean < 0.5: -# # scale_factors = (0.95, 1.05) # Narrower range for lower intensity images -# # else: -# # scale_factors = (0.93, 1.07) # Even narrower range for higher intensity images - -# # normalization for both channels -# # log mean, std for each anhchor, positive, negative -# # mean, std of anchor and positive get closer -# # mean, std of anchor and negative further (0.5 margin) -# transforms = Compose( -# [ -# RandAdjustContrastd(keys=["image"], prob=0.5, gamma=gamma_range), -# RandAffined( -# keys=["image"], -# prob=0.5, -# rotate_range=(0.07, 0.07), -# shear_range=(0.07, 0.07), -# scale_range=(0.07, 0.07), -# ), -# RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=std * 0.1), -# RandGaussianSmoothd( -# keys=["image"], -# prob=0.5, -# sigma_x=(0.1, 0.3), -# sigma_y=(0.1, 0.3), -# sigma_z=(0.1, 0.3), -# ), -# RandScaleIntensityd(keys=["image"], factors=scale_factors, prob=0.5), -# ] -# ) -# return transforms - + def apply_channel_transforms(self, data): + transformed_data = np.empty_like(data) + for i, channel_name in enumerate(self.channel_names): + channel_data = data[i] + transform = self.transform[channel_name] + transformed_data[i] = transform({"image": channel_data})["image"] + #print(f"transformed {channel_name}") + return transformed_data def get_transforms(): - transforms = Compose( + rfp_transforms = Compose( [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.9, 1.1)), + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.75, 1.25)), RandAffined( keys=["image"], prob=0.5, - rotate_range=(0.07, 0.07), - shear_range=(0.07, 0.07), - scale_range=(0.07, 0.07), + rotate_range=(0.1, 0.1), + shear_range=(0.1, 0.1), + scale_range=(0.1, 0.1), ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.01), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), RandGaussianSmoothd( keys=["image"], prob=0.5, - sigma_x=(0.05, 0.1), - sigma_y=(0.05, 0.1), - sigma_z=(0.05, 0.1), + sigma_x=(0.1, 0.3), + sigma_y=(0.1, 0.3), + sigma_z=(0.1, 0.3), ), - RandScaleIntensityd(keys=["image"], factors=(0.95, 1.05), prob=0.5), + RandScaleIntensityd(keys=["image"], factors=(0.85, 1.15), prob=0.5), ] ) - return transforms + phase_transforms = Compose( + [ + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.97, 1.03)), + RandAffined( + keys=["image"], + prob=0.5, + rotate_range=(0.05, 0.05), + shear_range=(0.05, 0.05), + scale_range=(0.05, 0.05), + ), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.005), + RandGaussianSmoothd( + keys=["image"], + prob=0.5, + sigma_x=(0.03, 0.05), + sigma_y=(0.03, 0.05), + sigma_z=(0.03, 0.05), + ), + RandScaleIntensityd(keys=["image"], factors=(0.97, 1.03), prob=0.5), + ] + ) + + return { + "RFP": rfp_transforms, + "Phase3D": phase_transforms + } class ContrastiveDataModule(LightningDataModule): def __init__( @@ -877,35 +844,36 @@ def __init__( self.predict_dataset = None def setup(self, stage: str = None): - dataset = ContrastiveDataset( - self.base_path, - self.channels, - self.x, - self.y, - self.timesteps_csv_path, - channel_names=self.channel_names, - transform=self.transform, - z_range=self.z_range, - ) + if stage == "fit": + dataset = ContrastiveDataset( + self.base_path, + self.channels, + self.x, + self.y, + self.timesteps_csv_path, + channel_names=self.channel_names, + transform=self.transform, + z_range=self.z_range, + ) - train_size = int(len(dataset) * self.train_split_ratio) - val_size = int(len(dataset) * self.val_split_ratio) - test_size = len(dataset) - train_size - val_size + train_size = int(len(dataset) * self.train_split_ratio) + val_size = int(len(dataset) * self.val_split_ratio) + test_size = len(dataset) - train_size - val_size - self.train_dataset, self.val_dataset, self.test_dataset = ( - torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) - ) + self.train_dataset, self.val_dataset, self.test_dataset = ( + torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) + ) - # setup prediction dataset (if needed) + # setup prediction dataset if stage == "predict" and self.predict_base_path: - self.predict_dataset = ContrastiveDataset( + print("setting up!") + self.predict_dataset = PredictDataset( self.predict_base_path, self.channels, self.x, self.y, - self.timesteps_csv_path, + timesteps_csv_path=self.timesteps_csv_path, channel_names=self.channel_names, - transform=self.transform, z_range=self.z_range, ) @@ -940,15 +908,104 @@ def test_dataloader(self): ) def predict_dataloader(self): + print("running predict DataLoader!") if self.predict_dataset is None: raise ValueError( "Predict dataset not set up. Call setup(stage='predict') first." ) + + return DataLoader( self.predict_dataset, batch_size=self.batch_size, - shuffle=False, + shuffle=False, # False shuffle for prediction num_workers=self.num_workers, prefetch_factor=2, persistent_workers=True - ) \ No newline at end of file + ) + +class PredictDataset(Dataset): + def __init__( + self, + base_path, + channels, + x, + y, + timesteps_csv_path, + channel_names, + z_range=None, + ): + self.base_path = base_path + self.channels = channels + self.x = x + self.y = y + self.z_range = z_range + self.channel_names = channel_names + self.ds = self.open_zarr_store(self.base_path) + self.timesteps_csv_path = timesteps_csv_path + self.timesteps_df = pd.read_csv(timesteps_csv_path) + self.positions = list(self.ds.positions()) + self.channel_indices = [self.ds.channel_names.index(channel) for channel in self.channel_names] + print("channel indices!") + print(self.channel_indices) + print(f"Initialized predict dataset with {len(self.positions)} positions.") + + def open_zarr_store(self, path, layout="hcs", mode="r"): + return open_ome_zarr(path, layout=layout, mode=mode) + + # def get_positions_from_csv(self): + # positions = [] + # #self.timesteps_df = pd.read_csv(self.timesteps_csv_path) + # for idx, row in self.timesteps_df.iterrows(): + # position_path = f"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}" + # positions.append((position_path, row['Random Timestep'])) + # #print(positions) + # return positions + + def __len__(self): + return len(self.positions) + + def __getitem__(self, idx): + position_path = self.positions[idx][0] + #print(f"Position path: {position_path}") + data = self.load_data(position_path) + data = self.normalize_data(data) + + return torch.tensor(data, dtype=torch.float32), (position_path) + + # double check printing order + def load_data(self, position_path): + position = self.ds[position_path] + #print(f"Loading data for position path: {position_path}") + zarr_array = position["0"][:] + + parts = position_path.split("/") + row = parts[0] + column = parts[1] + fov_cell = parts[2] + fov = int(fov_cell.split("fov")[1].split("cell")[0]) + cell_id = int(fov_cell.split("cell")[1]) + + combined_id = f"{row}/{column}/fov{fov}cell{cell_id}" + matched_rows = self.timesteps_df[ + self.timesteps_df.apply( + lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", + axis=1, + ) == combined_id + ] + + if matched_rows.empty: + raise ValueError(f"No matching entry found for position path: {position_path}") + + random_timestep = matched_rows["Random Timestep"].values[0] + data = zarr_array[random_timestep, self.channel_indices, self.z_range[0]:self.z_range[1], :, :] + return data + + def normalize_data(self, data): + normalized_data = np.empty_like(data) + for i in range(data.shape[0]): # iterate over each channel + channel_data = data[i] + mean = np.mean(channel_data) + std = np.std(channel_data) + normalized_data[i] = (channel_data - mean) / (std + 1e-6) + return normalized_data diff --git a/viscy/light/engine.py b/viscy/light/engine.py index b5982592..d9bc07d2 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -2,7 +2,7 @@ import os from typing import Literal, Sequence, Union import matplotlib.pyplot as plt - +import pandas as pd import numpy as np import torch import wandb @@ -489,6 +489,7 @@ def __init__( in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, + predict: bool = False ) -> None: super().__init__() @@ -503,6 +504,7 @@ def __init__( self.training_metrics = [] self.validation_metrics = [] self.test_metrics = [] + self.processed_order = [] self.encoder = ContrastiveEncoder( backbone=backbone, @@ -510,6 +512,7 @@ def __init__( in_stack_depth=in_stack_depth, stem_kernel_size=stem_kernel_size, embedding_len=embedding_len, + predict=predict ) # required to log the graph. @@ -526,8 +529,8 @@ def __init__( def forward(self, x: Tensor) -> Tensor: """Forward pass of the model.""" - features, projections = self.encoder(x) - return features, projections + projections = self.encoder(x) + return projections # features is without projection head and projects is with projection head def log_feature_statistics(self, embeddings: Tensor, prefix: str): @@ -537,6 +540,15 @@ def log_feature_statistics(self, embeddings: Tensor, prefix: str): print(f"{prefix}_mean: {mean}") print(f"{prefix}_std: {std}") + def print_embedding_norms(self, anchor, positive, negative, phase): + anchor_norm = torch.norm(anchor, dim=1).mean().item() + positive_norm = torch.norm(positive, dim=1).mean().item() + negative_norm = torch.norm(negative, dim=1).mean().item() + + print(f"{phase}/anchor_norm: {anchor_norm}") + print(f"{phase}/positive_norm: {positive_norm}") + print(f"{phase}/negative_norm: {negative_norm}") + # logs over all steps @rank_zero_only def log_metrics(self, anchor, positive, negative, phase): @@ -602,9 +614,9 @@ def training_step( """Training step of the model.""" anchor, pos_img, neg_img = batch - _, emb_anchor = self.encoder(anchor) - _, emb_pos = self.encoder(pos_img) - _, emb_neg = self.encoder(neg_img) + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) loss = self.loss_function(emb_anchor, emb_pos, emb_neg) self.log("train/loss_step", loss, on_step=True, prog_bar=True, logger=True) @@ -614,11 +626,11 @@ def training_step( self.log_images(anchor, pos_img, neg_img, self.current_epoch, "training_images") self.log_metrics(emb_anchor, emb_pos, emb_neg, 'train') + #self.print_embedding_norms(emb_anchor, emb_pos, emb_neg, 'train') self.training_step_outputs.append(loss) return {'loss': loss} - @rank_zero_only def on_train_epoch_end(self) -> None: epoch_loss = torch.stack(self.training_step_outputs).mean() self.log("train/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) @@ -641,9 +653,9 @@ def validation_step( """Validation step of the model.""" anchor, pos_img, neg_img = batch - _, emb_anchor = self.encoder(anchor) - _, emb_pos = self.encoder(pos_img) - _, emb_neg = self.encoder(neg_img) + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) loss = self.loss_function(emb_anchor, emb_pos, emb_neg) self.log("val/loss_step", loss, on_step=True, prog_bar=True, logger=True) @@ -657,7 +669,6 @@ def validation_step( self.validation_step_outputs.append(loss) return {'loss': loss} - @rank_zero_only def on_validation_epoch_end(self) -> None: epoch_loss = torch.stack(self.validation_step_outputs).mean() self.log("val/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) @@ -680,9 +691,9 @@ def test_step( """Test step of the model.""" anchor, pos_img, neg_img = batch - _, emb_anchor = self.encoder(anchor) - _, emb_pos = self.encoder(pos_img) - _, emb_neg = self.encoder(neg_img) + emb_anchor = self.encoder(anchor) + emb_pos = self.encoder(pos_img) + emb_neg = self.encoder(neg_img) loss = self.loss_function(emb_anchor, emb_pos, emb_neg) self.log("test/loss_step", loss, on_step=True, prog_bar=True, logger=True) @@ -718,4 +729,50 @@ def aggregate_metrics(self, metrics, phase): avg_metrics[f"{phase}/euclidean_distance_positive"] = sum(m[f"{phase}/euclidean_distance_positive"] for m in metrics) / len(metrics) avg_metrics[f"{phase}/euclidean_distance_negative"] = sum(m[f"{phase}/euclidean_distance_negative"] for m in metrics) / len(metrics) return avg_metrics - + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + print("running predict step!") + """Prediction step for extracting embeddings.""" + x, position_info = batch + features, projections = self.encoder(x) + self.processed_order.extend(position_info) + return features, projections + + # already saved, not needed again + # def on_predict_epoch_end(self) -> None: + # print(f"Processed order: {self.processed_order}") + # rows, columns, fovs, cell_ids = [], [], [], [] + + # for position_path in self.processed_order: + # try: + # parts = position_path.split("/") + # if len(parts) < 3: + # raise ValueError(f"Invalid position path: {position_path}") + + # row = parts[0] + # column = parts[1] + # fov_cell = parts[2] + + # fov = int(fov_cell.split("fov")[1].split("cell")[0]) + # cell_id = int(fov_cell.split("cell")[1]) + + # rows.append(row) + # columns.append(column) + # fovs.append(fov) + # cell_ids.append(cell_id) + + # except (IndexError, ValueError) as e: + # print(f"Skipping invalid position path: {position_path} with error: {e}") + + # # Save processed order + # if rows and columns and fovs and cell_ids: + # processed_order_df = pd.DataFrame({ + # "Row": rows, + # "Column": columns, + # "FOV": fovs, + # "Cell ID": cell_ids + # }) + # print(f"Saving processed order DataFrame: {processed_order_df}") + # processed_order_df.to_csv("/hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/epoch66_processed_order.csv", index=False) + # else: + # print("No valid processed orders found to save.") diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index a1e41055..43aacb4e 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -1,5 +1,6 @@ import timm import torch.nn as nn +import torch.nn.functional as F # from viscy.unet.networks.resnet import resnetStem # Currently identical to resnetStem, but could be different in the future. @@ -14,9 +15,12 @@ def __init__( in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, + predict: bool = False ): super().__init__() + self.predict = predict + """ ContrastiveEncoder network that uses ConvNext and ResNet backbons from timm. @@ -125,8 +129,25 @@ def __init__( """ def forward(self, x): - features = self.model.forward_features(x) # extract features - intermediate_embeddings = self.model.head.global_pool(features) # apply global pooling - intermediate_embeddings = self.model.head.flatten(intermediate_embeddings) # flatten features - projected_embeddings = self.model.head.fc(intermediate_embeddings) # apply projection head - return intermediate_embeddings, projected_embeddings \ No newline at end of file + if self.predict: + print("running predict forward!") + x = self.model.stem(x) + x = self.model.stages[0](x) + x = self.model.stages[1](x) + x = self.model.stages[2](x) + x = self.model.stages[3](x) + x = self.model.head.global_pool(x) + x = self.model.head.norm(x) + x = self.model.head.flatten(x) + features_before_projection = self.model.head.drop(x) + projections = self.model.head.fc(features_before_projection) + features_before_projection = F.normalize(features_before_projection, p=2, dim=1) + projections = F.normalize(projections, p=2, dim=1) # L2 normalization + print(features_before_projection.shape, projections.shape) + return features_before_projection, projections + # feature is without projection head + else: + print("running forward without predict!") + projections = self.model(x) + projections = F.normalize(projections, p=2, dim=1) # L2 normalization + return projections \ No newline at end of file From 8110e2c05ab07f55e616d1bbef652b242967751c Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Wed, 17 Jul 2024 13:46:48 -0700 Subject: [PATCH 28/87] update training and prediction script --- .../contrastive_phenotyping/PCA.ipynb | 27672 +++++++++++++++- .../contrastive_phenotyping/predict.py | 6 +- 2 files changed, 27627 insertions(+), 51 deletions(-) diff --git a/viscy/applications/contrastive_phenotyping/PCA.ipynb b/viscy/applications/contrastive_phenotyping/PCA.ipynb index cf64c678..a52a0887 100644 --- a/viscy/applications/contrastive_phenotyping/PCA.ipynb +++ b/viscy/applications/contrastive_phenotyping/PCA.ipynb @@ -2,14 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(2629, 768, 8, 8)\n", + "(2629, 768)\n", "(2629, 256)\n" ] } @@ -22,16 +22,21 @@ "from scipy.stats import spearmanr\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", + "import plotly.io as pio\n", + "import plotly.express as px\n", + "\n", + "# Set Plotly default renderer for VSCode\n", + "pio.renderers.default = \"vscode\"\n", "\n", "# Load predicted features and projections\n", - "predicted_features = np.load(\"epoch97_predicted_features.npy\")\n", - "predicted_projections = np.load(\"epoch97_predicted_projections.npy\")\n", + "predicted_features = np.load(\"updated_epoch66_predicted_features.npy\")\n", + "predicted_projections = np.load(\"updated_epoch66_predicted_projections.npy\")\n", "\n", "print(predicted_features.shape)\n", "print(predicted_projections.shape)\n", "\n", "# Load the CSV file\n", - "csv_path = \"epoch97_processed_order.csv\"\n", + "csv_path = \"epoch66_processed_order.csv\"\n", "df = pd.read_csv(csv_path)\n", "\n", "# Load ground truth masks\n", @@ -45,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -63,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -101,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -141,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -171,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -179,18 +184,18 @@ "#reshaped_features = predicted_features.reshape(predicted_features.shape[0], -1)\n", "\n", "pca = PCA()\n", - "pca.fit(predicted_projections)\n", + "pca.fit(predicted_features)\n", "explained_variance_ratio = np.cumsum(pca.explained_variance_ratio_)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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TlW/k119/1bZt2+zXAv9dfHz8TeuuXLlyuhrvuOMOSdevMy5VqpSCgoLUoUMHff3113rzzTclXZ+eHxoaesO+vZHu3bvrzTff1MiRI3PsPu5/fz/TviQqW7Zsuu2pqak6f/68/VIIKeOfuzvuuEOJiYk6deqU3NzcdO7cOX3++ef6/PPPM6zh7+91RnfJyMjhw4dVunTpdGE8bfp92mfDGWmfqwsXLmS5hr9Pef97DX/9Wczt91u6vkJ91apVM6wpNTVVR48eVc2aNW9YU3Z/F6Xx8fFJ97n662c0M97e3ho9erQGDRqk4OBg3X333XrwwQfVo0cPlSpV6qbPB4DcQsAHAIsdOHDA/g/T7du35+ixf/nlF3Xs2FH33nuvPv74Y4WEhMjT01OTJ0/OcLG6jNzo+l7z5+JgaQu9TZs2LcN/2Ob0Kt6PP/64PvroI82YMUMvvviiZsyYoRo1aqhu3bqSrl9X26pVK505c0avvPKKqlWrpkKFCikuLk69evVKtzCdt7d3llY+d/a9vNn7llWpqalq1aqVXn755Qz3pwX1nNCjRw/NmjVLq1atUu3atbVgwQL169fP6ZXh00bxe/Xqpe+++y7DNjf6giklJSXD9+5G72dOvs/S9Z+vnj17Ztgm7drrNFkZvc8t1apVk3T9d0ZOfYnyV7n9fmdHTv8uysraBZkZOHCgOnTooPnz52vRokV6/fXXFRUVpSVLlqhevXq3dGwAyC4CPgBYKDU1Vb169VJgYKAGDhyoUaNG6eGHH85wcbK0LwHSGGP022+/pQsdfzVnzhz5+Pho0aJFDrfBmzx5co6dQ9o09JIlS6ply5Y3bFe+fHlJ6c9Dkvbu3Zvl12vUqJHCwsL09ddfq1WrVtq5c6fefvtt+/7t27dr3759mjp1qnr06GHfHhMTk+XXyEhOv5clSpRQYGCgduzYkWm7sLAwXbx4MdP39mZ+++03GWMcQvW+ffskyWExsDZt2qhEiRL66quv1KhRIyUmJuqJJ57I1ms+/vjjeuuttzRixAh17Ngx3f4iRYro3Llz6bYfPnzYYcp2Tsno527fvn3y8/Ozj+IGBAQoJSXllt7rjJQvX14//fSTLly44DCKv2fPHvt+ZzVr1kxFihTRjBkz9Oqrr940rJYvXz7Dz9mt1JCZrLzffn5+N6zJzc0t3WyBm8nq7yJn3GymU1hYmAYNGqRBgwbp119/Vd26dTVmzBhNnz49R14fAJzFNfgAYKGxY8dq1apV+vzzz/Xmm2+qSZMm6tu3b7rV9yXpyy+/dJiOO3v2bB0/flxt27a94fHd3d1ls9nst32Srk/JzslroyMiIhQYGKhRo0YpOTk53f606bghISGqW7eupk6d6jBNPiYmJt31vzfTvXt3bd68WcOGDZPNZtM///lP+760oPPXEUVjjMaPH+/Ua/xdTr+Xbm5u6ty5s/773/9qw4YN6fan1d+1a1etXr1aixYtStfm3Llz6a59zsixY8ccbkeYkJCgL7/8UnXr1nUY6fTw8NBjjz2mb7/9VlOmTFHt2rUz/QIpM2mj+Fu2bNGCBQvS7Q8LC9OaNWscbgf4/fffZ+nWj9mxevVqh8tAjh49qu+++06tW7eWu7u73N3d9dBDD2nOnDkZfumS0e3Ysqpdu3ZKSUnRRx995LD9gw8+kM1my/QzfCN+fn565ZVXtHv3br3yyisZjqBPnz5d69ats9ewbt06rV692r7/0qVL+vzzz1WhQoUsr4ORVVl5v1u3bq3vvvvO4TKjkydP6uuvv1azZs3STam/maz+LnJG2l01/v5lVGJioq5cueKwLSwsTAEBATe8TSMA3A6M4ANALvnxxx/to2N/1aRJE1WqVEm7d+/W66+/rl69eqlDhw6Srt/vuW7duurXr5++/fZbh+cVLVpUzZo1U+/evXXy5EmNGzdOlStXVp8+fW5YQ/v27TV27Fi1adNG//znPxUfH6+JEyeqcuXK2rZtW46cZ2BgoD755BM98cQTuvPOO/Xoo4+qRIkSOnLkiBYuXKimTZvag01UVJTat2+vZs2a6cknn9SZM2c0YcIE1axZUxcvXszyaz7++OMaOXKkvvvuOzVt2tRhFLpatWoKCwvTiy++qLi4OAUGBmrOnDm3vBBWbryXo0aN0uLFi9W8eXP961//UvXq1XX8+HHNmjVLK1asUFBQkF566SUtWLBADz74oHr16qX69evr0qVL2r59u2bPnq1Dhw6pePHimb7OHXfcoaeeekrr169XcHCwJk2apJMnT2Y4+6BHjx768MMPtXTpUo0ePTpb55Um7Vr8LVu2pNv39NNPa/bs2WrTpo26du2q/fv3a/r06Q4LE+akWrVqKSIiwuG2bdL1+5uneeedd7R06VI1atRIffr0UY0aNXTmzBlt2rRJP/30k86cOZOt1+7QoYPuv/9+vfbaazp06JDCw8O1ePFifffddxo4cGC2z/mll17Szp07NWbMGC1dulQPP/ywSpUqpRMnTmj+/Plat26dVq1aJUkaPHiwZsyYobZt2+q5555T0aJFNXXqVB08eFBz5sxx+jKMm8nK+/3WW28pJiZGzZo1U79+/eTh4aHPPvtMSUlJevfdd51+TWd+F2WVr6+vatSooZkzZ+qOO+5Q0aJFVatWLV27dk0tWrRQ165dVaNGDXl4eGjevHk6efKkHn30UadrB4Acc9vX7QeAfC6z2+RJMpMnTzbXrl0zd911lylTpozDLeOM+d/tr2bOnGmM+d9tq2bMmGGGDBliSpYsaXx9fU379u0dbvlmTMa3dvu///s/U6VKFePt7W2qVatmJk+ebL9t2V/d6DZ5f7+FW1o9S5cuTbc9IiLCFC5c2Pj4+JiwsDDTq1cvh1tlGWPMnDlzTPXq1Y23t7epUaOGmTt3boZ138xdd91lJJmPP/443b5du3aZli1bGn9/f1O8eHHTp08f+23qJk+ebG/Xs2dPU6hQoQyPfyvvpSTTv3//dMfM6NZwhw8fNj169DAlSpQw3t7eplKlSqZ///4mKSnJ3ubChQtmyJAhpnLlysbLy8sUL17cNGnSxLz//vsOtzLLSPny5U379u3NokWLTJ06dey1z5o164bPqVmzpnFzczO///57psdO89fb5P3dXz8Pf71NnjHGjBkzxoSGhhpvb2/TtGlTs2HDhhveJu/v9d7o5zOjW/Kl9cf06dPt/VevXr10P8PGGHPy5EnTv39/U7ZsWePp6WlKlSplWrRoYT7//POb1pSZCxcumBdeeMGULl3aeHp6mipVqpj33nvP4bZvxmT9Nnl/NXv2bNO6dWtTtGhR4+HhYUJCQky3bt3MsmXLHNrt37/fPPzwwyYoKMj4+PiYhg0bmu+//96hze1+vzdt2mQiIiKMv7+/8fPzM/fff79ZtWpVll77Vn4X3eizn9HnedWqVaZ+/frGy8vLfsu806dPm/79+5tq1aqZQoUKmcKFC5tGjRqZb7/9Nt0xAeB2shlzG1ZFAQBk27Jly3T//fdr1qxZevjhh60uBwVAvXr1VLRoUcXGxlpdClyQzWZT//79nR4tBwDcOq7BBwAAdhs2bNCWLVscFigEAACugWvwAQCAduzYoY0bN2rMmDEKCQlRt27drC4JAAA4iRF8AACg2bNnq3fv3kpOTtaMGTPk4+NjdUkAAMBJXIMPAAAAAEA+wAg+AAAAAAD5AAEfAAAAAIB8gEX2MpCamqpjx44pICBANpvN6nIAAAAAAPmcMUYXLlxQ6dKl5eaWvbF4An4Gjh07prJly1pdBgAAAACggDl69KjKlCmTrecS8DMQEBAg6fobGxgYaHE1GUtOTtbixYvVunVreXp6Wl0OnEDfuSb6zTXRb66JfnNN9Jvrou9cE/3mmjLrt4SEBJUtW9aeR7ODgJ+BtGn5gYGBeTrg+/n5KTAwkA+0i6HvXBP95proN9dEv7km+s110XeuiX5zTVnpt1u5TJxF9gAAAAAAyAcI+AAAAAAA5AMEfAAAAAAA8gECPgAAAAAA+QABHwAAAACAfICADwAAAABAPkDABwAAAAAgHyDgAwAAAACQDxDwAQAAAADIBwj4AAAAAADkAwR8AAAAAADyAQI+AAAAAAD5AAEfAAAAAIB8wMPqApBzUlKN1uz/Qyv3n1Lc2csO+2w2m0KCfBTk66Vzl6/q2N/253Zbq18/L9Ua4OWuTYdsip21VTZbxt+xueJ55fda485c0u+/uynm4la5u7ll2taVzis/1JpZ25TUVB2L+1+/5eVa89rrW3leaf3206VtKlPUL0/XmlfaWv36kpRqjC6fclPcLwd14WpKvjmv/Fjr39v+cfGyNu67tf/H5YW2Vr/+7T6vv/8/Li/XanVbm82m0CK+ahJWXHdXKiZ3N9sNn+/qLA34P//8s9577z1t3LhRx48f17x589S5c+dMn7Ns2TJFRkZq586dKlu2rIYOHapevXo5tJk4caLee+89nThxQuHh4ZowYYIaNmyYeyeSB/yw7bhenrNNF5OuWV0KssRdOn7S6iLgNDdtOkO/uR76zTW5adOZE1YXAae56afjv1pdBLLFTfqD35Wuh//HOWPi0v0K8vPUO/+orTa1QqwuJ1dYOkX/0qVLCg8P18SJE7PU/uDBg2rfvr3uv/9+bdmyRQMHDtTTTz+tRYsW2dvMnDlTkZGRGjZsmDZt2qTw8HBFREQoPj4+t07Dcm8v3KV+X28i3AMAAABAJs4lJuuZ6ZsUveO41aXkCpsxxlhdhHR9KsXNRvBfeeUVLVy4UDt27LBve/TRR3Xu3DlFR0dLkho1aqS77rpLH330kSQpNTVVZcuW1bPPPqvBgwdnqZaEhAQVLlxY58+fV2BgYPZPKhclJyfrhx9+0FZbJU1adcTqcgAAAADAZYQU9tGKVx647dP103Jcu3bt5Onp6bAvJ3KoS12Dv3r1arVs2dJhW0REhAYOHChJunr1qjZu3KghQ4bY97u5ually5ZavXr1DY+blJSkpKQk++OEhARJ19/85OTkHDyDnJOcnKzNp22a8ivhHgAAAACccfz8Fa3+LV6NKha9ra+bli8zypk5kT1dKuCfOHFCwcHBDtuCg4OVkJCgy5cv6+zZs0pJScmwzZ49e2543KioKI0YMSLd9sWLF8vPzy9nis9hqUaaddDd6jIAAAAAwCUt/mWt/thtzYT2mJiYdNsSExNv+bguFfBzy5AhQxQZGWl/nJCQoLJly6p169Z5dor+yl/jdWnNFqvLAAAAAACX1PqeRpaM4MfExKhVq1YZTtG/VS4V8EuVKqWTJx1XiTx58qQCAwPl6+srd3d3ubu7Z9imVKlSNzyut7e3vL2902339PRM96bnFWcup1hdAgAAAAC4pJDCPmpcuaRlt8zLKGvmRPa0dBV9ZzVu3FixsbEO22JiYtS4cWNJkpeXl+rXr+/QJjU1VbGxsfY2+UXJgPRfSAAAAAAAbm5YhxqWhfvcZGnAv3jxorZs2aItW7ZIun4bvC1btujIkesLxw0ZMkQ9evSwt3/mmWd04MABvfzyy9qzZ48+/vhjffvtt3rhhRfsbSIjI/XFF19o6tSp2r17t/r27atLly6pd+/et/XcctuZi1cl5YkbIAAAAACASyji56lPH79TbWqFWF1KrrB0iv6GDRt0//332x+nXQffs2dPTZkyRcePH7eHfUmqWLGiFi5cqBdeeEHjx49XmTJl9J///EcRERH2Nt26ddOpU6f0xhtv6MSJE6pbt66io6PTLbznylJSjUZF771pu0rFfFW7TJCk67chDAnyUZCvl85dvqpjZy/f8Hm50dbq189LtQZ4uWvT9j3yK1FaNlvG37G54nnl91rjzlzS778fV+nQELm7uWXa1pXOKz/UmlnblNRUHYv7X7/l5Vrz2utbeV5p/RZaprTKFPXL07XmlbZWv74kpRqjy6eO6c7aVXXhakq+Oa/8WOvf2/5x8bI27jqokNLZ/39cXmhr9evf7vP6+//j8nKtVre12WwKLeKrJmHFdXelYvly5D6NpQH/vvvukzE3HoWeMmVKhs/ZvHlzpscdMGCABgwYcKvl5VnrDp7RiYQkSZn/YL79j3A1Dit2e4pCliUnJ+uHC7vVrl14nl3jAeldv2dpHP3mYug31/S/fqtDv7mQ6/32u9rdU5F+czHJycn64dp+fle6GP4fh4y41DX4uC7+wpUcbQcAAAAAcH0EfBdUMsAnR9sBAAAAAFwfAd8FNaxYVKUCvXWjRfZsun7bh4a3+Z6OAAAAAADrEPBdkLubTUPbVctwX9pV+fn1tg8AAAAAgIwR8F1URM1gPXlHqkoEeDlsL1XYR5/k49s+AAAAAAAyZukq+rg14cWMXu7eXJt/v6D4C1dUMuD6tHxG7gEAAACg4CHguzh3Nxu3wgMAAAAAEPBd3cr9f2hb3AXdWa6ImlUpbnU5AAAAAACLcA2+i1vx2x8aG7NPP/96yupSAAAAAAAWIuC7OGMyvlUeAAAAAKBgIeDnEyyrBwAAAAAFGwE/vyDhAwAAAECBRsB3cczQBwAAAABIBHyXl5bvbQzhAwAAAECBRsDPJ2zkewAAAAAo0DysLgC3psfd5fRgeKiCA72tLgUAAAAAYCECvosrU8RXFUt6Wl0GAAAAAMBiTNEHAAAAACAfYATfxa05cEZ74y+pbtkgNahQ1OpyAAAAAAAWYQTfxS3eHa+3Fu7Wsr2nrC4FAAAAAGAhAj4AAAAAAPkAAd/VGSOJ2+QBAAAAQEFHwHdx5s//ku8BAAAAoGAj4Ls4Y27eBgAAAACQ/xHwXZxJG8Nnjj4AAAAAFGgE/HyCeA8AAAAABZuH1QXg1vS4u7za1Q5V2aK+VpcCAAAAALAQAd/FhZUopGqlPa0uAwAAAABgMaboAwAAAACQDzCC7+LWHTqjQ2euKLxMkGqFFra6HAAAAACARRjBd3ELtp7Qa/N2aOmeeKtLAQAAAABYiIDv8ozVBQAAAAAA8gACvoszf+Z7G/fJAwAAAIACjYCfT9hI+AAAAABQoBHwXRwT9AEAAAAAEgHf5RkSPgAAAABABPx8gxn6AAAAAFCweVhdAG7NE3eXVUStEFUu6W91KQAAAAAACxHwXVyNkECFe3paXQYAAAAAwGJM0QcAAAAAIB9gBN/FbTx8VscSrqp2aGFVCQ6wuhwAAAAAgEUYwXdxMzf8rshvt2rJnnirSwEAAAAAWIiA7+K4TR4AAAAAQCLgu7y0fM9t8gAAAACgYCPg5xM2kfABAAAAoCAj4Ls4pugDAAAAACQCvsszf07SZ4o+AAAAABRsBHwAAAAAAPIBD6sLwK15vFE5taxRSjVLF7a6FAAAAACAhQj4Lu7OckHy9PS0ugwAAAAAgMWYog8AAAAAQD7ACL6L2/r7eZ2+lKzqIYEqX6yQ1eUAAAAAACzCCL6Lm7zysJ6ZvklL98RbXQoAAAAAwEIEfBeXdps8AAAAAEDBRsB3cebPfG+z2awtBAAAAABgKQJ+PkG+BwAAAICCjYDv4pigDwAAAACQCPguz/w5R58BfAAAAAAo2Aj4+QVz9AEAAACgQPOwugDcmn82LKv7qwWrfvkiVpcCAAAAALAQAd/FNQkrJk9PT6vLAAAAAABYjCn6AAAAAADkA4zgu7idxxJ07kqK7ggOUOkgX6vLAQAAAABYhBF8F/fR0v3qNXm9lu09ZXUpAAAAAAALEfBdnLG6AAAAAABAnkDAd3Hmz4TPXfIAAAAAoGAj4OcT5HsAAAAAKNgI+C7O/DlJnxF8AAAAACjYCPguzj5FnzF8AAAAACjQCPgAAAAAAOQDHlYXgFvzaIMyal61pOqVC7K6FAAAAACAhQj4Lq5F9ZLy9PS0ugwAAAAAgMWYog8AAAAAQD7ACL6L23fygi4lS5VKFFJxf2+rywEAAAAAWIQRfBc3etE+df1stZbvPWV1KQAAAAAACxHwXVzabfIAAAAAAAUbAd/FpeV7m83SMgAAAAAAFiPg5xMEfAAAAAAo2Aj4Lo4p+gAAAAAAiYDv8syfk/RtYggfAAAAAAoyAn4+wRR9AAAAACjYPKwuALfmkTtD1bRyCVUPCbS6FAAAAACAhSwfwZ84caIqVKggHx8fNWrUSOvWrbth2+TkZI0cOVJhYWHy8fFReHi4oqOjHdqkpKTo9ddfV8WKFeXr66uwsDC9+eabMvn0YvUH64So//2VdUdwgNWlAAAAAAAsZGnAnzlzpiIjIzVs2DBt2rRJ4eHhioiIUHx8fIbthw4dqs8++0wTJkzQrl279Mwzz6hLly7avHmzvc3o0aP1ySef6KOPPtLu3bs1evRovfvuu5owYcLtOi0AAAAAAG47SwP+2LFj1adPH/Xu3Vs1atTQp59+Kj8/P02aNCnD9tOmTdOrr76qdu3aqVKlSurbt6/atWunMWPG2NusWrVKnTp1Uvv27VWhQgU9/PDDat26daYzA1zZwdOXtCPuvM4nJltdCgAAAADAQpZdg3/16lVt3LhRQ4YMsW9zc3NTy5YttXr16gyfk5SUJB8fH4dtvr6+WrFihf1xkyZN9Pnnn2vfvn264447tHXrVq1YsUJjx469YS1JSUlKSkqyP05ISJB0/ZKA5OS8GZzT6ho6f6fWHT6ncV3rqH3tUhZXhaxI67u8+rOFjNFvrol+c030m2ui31wXfeea6DfXlFm/5URfWhbwT58+rZSUFAUHBztsDw4O1p49ezJ8TkREhMaOHat7771XYWFhio2N1dy5c5WSkmJvM3jwYCUkJKhatWpyd3dXSkqK3n77bXXv3v2GtURFRWnEiBHpti9evFh+fn7ZPMPb48zZs5Js2rx5s2xH8+c6A/lVTEyM1SUgG+g310S/uSb6zTXRb66LvnNN9JtryqjfEhMTb/m4LrWK/vjx49WnTx9Vq1ZNNptNYWFh6t27t8OU/m+//VZfffWVvv76a9WsWVNbtmzRwIEDVbp0afXs2TPD4w4ZMkSRkZH2xwkJCSpbtqxat26twMC8uTp9cnKyYmJiFBQUJCWc15316qkdI/guIa3vWrVqJU9PT6vLQRbRb66JfnNN9Jtrot9cF33nmug315RZv6XNJL8VlgX84sWLy93dXSdPnnTYfvLkSZUqlXFQLVGihObPn68rV67ojz/+UOnSpTV48GBVqlTJ3uall17S4MGD9eijj0qSateurcOHDysqKuqGAd/b21ve3t7ptnt6eub5D4vN7foyCh4eHnm+VjhyhZ8vpEe/uSb6zTXRb66JfnNd9J1rot9cU0b9lhP9aNkie15eXqpfv75iY2Pt21JTUxUbG6vGjRtn+lwfHx+Fhobq2rVrmjNnjjp16mTfl5iYKDc3x9Nyd3dXampqzp5AHpF2+z+bzeJCAAAAAACWsnSKfmRkpHr27KkGDRqoYcOGGjdunC5duqTevXtLknr06KHQ0FBFRUVJktauXau4uDjVrVtXcXFxGj58uFJTU/Xyyy/bj9mhQwe9/fbbKleunGrWrKnNmzdr7NixevLJJy05x9uFfA8AAAAABZulAb9bt246deqU3njjDZ04cUJ169ZVdHS0feG9I0eOOIzGX7lyRUOHDtWBAwfk7++vdu3aadq0adevQ//ThAkT9Prrr6tfv36Kj49X6dKl9e9//1tvvPHG7T49AAAAAABuG8sX2RswYIAGDBiQ4b5ly5Y5PG7evLl27dqV6fECAgI0btw4jRs3LocqzNu61C2tuysVV1hJf6tLAQAAAABYyPKAj1vTtUEZFtUAAAAAAFi3yB4AAAAAAMg5jOC7uLhzl3XNJCmksI8KedOdAAAAAFBQMYLv4l74dptajl2ulb+dtroUAAAAAICFshXwp02bpqZNm6p06dI6fPiwJGncuHH67rvvcrQ43JyxugAAAAAAQJ7gdMD/5JNPFBkZqXbt2uncuXNKSUmRJAUFBRWYlevzEvNnwrfZbNYWAgAAAACwlNMBf8KECfriiy/02muvyd3d3b69QYMG2r59e44Wh6wj3gMAAABAweZ0wD948KDq1auXbru3t7cuXbqUI0Uh6wyT9AEAAAAAykbAr1ixorZs2ZJue3R0tKpXr54TNSEbmKEPAAAAAAWb0/dVi4yMVP/+/XXlyhUZY7Ru3TrNmDFDUVFR+s9//pMbNSIzDOADAAAAAJSNgP/000/L19dXQ4cOVWJiov75z3+qdOnSGj9+vB599NHcqBGZeLBOiO6qWEzlivpZXQoAAAAAwEJOB3xJ6t69u7p3767ExERdvHhRJUuWzOm6kEW9m5SXp6en1WUAAAAAACzmdMA/ePCgrl27pipVqsjPz09+ftdHjn/99Vd5enqqQoUKOV0jAAAAAAC4CacX2evVq5dWrVqVbvvatWvVq1evnKgJTjh1IUnHzl3WleQUq0sBAAAAAFjI6YC/efNmNW3aNN32u+++O8PV9ZG7np62SU3eWaI1B/6wuhQAAAAAgIWcDvg2m00XLlxIt/38+fNKSWEU+XYzrKIPAAAAAFA2Av69996rqKgohzCfkpKiqKgoNWvWLEeLw82l5XubzWZpHQAAAAAAazm9yN7o0aN17733qmrVqrrnnnskSb/88osSEhK0ZMmSHC8QWUO8BwAAAICCzekR/Bo1amjbtm3q2rWr4uPjdeHCBfXo0UN79uxRrVq1cqNGZObPOfoM4AMAAABAweb0CL4klS5dWqNGjcrpWgAAAAAAQDZlK+CfO3dO69atU3x8vFJTUx329ejRI0cKQ9bYr8Fnkj4AAAAAFGhOB/z//ve/6t69uy5evKjAwECHxd1sNhsB/zZrUzNYd5YvqlKFva0uBQAAAABgIacD/qBBg/Tkk09q1KhR8vPzy42a4IQB94fJ09PT6jIAAAAAABZzepG9uLg4Pffcc4R7AAAAAADyEKcDfkREhDZs2JAbtSAbLlxJ1rnEq0pOSb15YwAAAABAvuX0FP327dvrpZde0q5du1S7du1008M7duyYY8Xh5rp+vk6/nbqkGX3uVuOwYlaXAwAAAACwiNMBv0+fPpKkkSNHpttns9mUkpJy61Uhy8zNmwAAAAAACgCnA/7fb4sHa5k/E76Nu+QBAAAAQIHm9DX4yGsYwwcAAAAAZGMEX5IuXbqk5cuX68iRI7p69arDvueeey5HCoNzGMAHAAAAgILN6YC/efNmtWvXTomJibp06ZKKFi2q06dPy8/PTyVLliTg32b/m6JPxAcAAACAgszpKfovvPCCOnTooLNnz8rX11dr1qzR4cOHVb9+fb3//vu5USMywQR9AAAAAICUjYC/ZcsWDRo0SG5ubnJ3d1dSUpLKli2rd999V6+++mpu1IhMtKhWQp3rllYxfy+rSwEAAAAAWMjpKfqenp5yc7v+vUDJkiV15MgRVa9eXYULF9bRo0dzvEBkbnCbqvL09LS6DAAAAACAxZwO+PXq1dP69etVpUoVNW/eXG+88YZOnz6tadOmqVatWrlRIwAAAAAAuAmnp+iPGjVKISEhkqS3335bRYoUUd++fXXq1Cl9/vnnOV4gMpd0LVVXklOUmsrV+AAAAABQkDk9gt+gQQP730uWLKno6OgcLQjOafvhSh09e1lz+zXRneWKWF0OAAAAAMAiTo/gI29h3B4AAAAAIGVxBP/OO+9UbGysihQponr16mV6z/VNmzblWHHIuhv3CAAAAACgIMhSwO/UqZO8vb0lSZ07d87NeuAswxg+AAAAACCLAX/YsGGSpJSUFN1///2qU6eOgoKCcrMuZFFavM9sVgUAAAAAIP9z6hp8d3d3tW7dWmfPns2tepBNxHsAAAAAKNicXmSvVq1aOnDgQG7Ugmxghj4AAAAAQMpGwH/rrbf04osv6vvvv9fx48eVkJDg8Ae31z1ViiuiZrAK+3paXQoAAAAAwEJZugb/r9q1aydJ6tixo8N138YY2Ww2paSk5Fx1uKm3OtWQpyfhHgAAAAAKOqcD/tKlS3OjDgAAAAAAcAucDvjNmzfPjToAAAAAAMAtcDrgp0lMTNSRI0d09epVh+116tS55aKQdc3f/1knEq5owYBmqhVa2OpyAAAAAAAWcTrgnzp1Sr1799aPP/6Y4X6uwb+9UlKNUllJHwAAAAAKPKdX0R84cKDOnTuntWvXytfXV9HR0Zo6daqqVKmiBQsW5EaNyERatv/LeocAAAAAgALI6RH8JUuW6LvvvlODBg3k5uam8uXLq1WrVgoMDFRUVJTat2+fG3XiBoxh+B4AAAAAkI0R/EuXLqlkyZKSpCJFiujUqVOSpNq1a2vTpk05Wx2yzCaG8AEAAACgIHM64FetWlV79+6VJIWHh+uzzz5TXFycPv30U4WEhOR4gcgcU/QBAAAAAFI2pug///zzOn78uCRp2LBhatOmjb766it5eXlpypQpOV0fboIZ+gAAAAAAyYmA//DDD+vpp59W9+7dZftzuLh+/fo6fPiw9uzZo3Llyql48eK5VigydnfFokpIuiZ/72zf8RAAAAAAkA9kORWePXtW7du3V+nSpdW7d2/16tVLlSpVkp+fn+68887crBGZGNetjjw9Pa0uAwAAAABgsSxfgx8bG6sDBw7oqaee0vTp01WlShU98MAD+vrrr5WUlJSbNQIAAAAAgJtwapG98uXLa/jw4Tpw4IBiYmJUunRp9enTRyEhIerfv782btyYW3UCAAAAAIBMOL2KfpoHHnhA06dP14kTJxQVFaVvvvlGjRo1ysnakAX3j/lZtYYt0m/xF60uBQAAAABgoVtame3gwYOaMmWKpkyZovPnz6tly5Y5VRey6GJSii4mXdP/bpgHAAAAACiInB7Bv3LliqZPn64HHnhAVapU0ZdffqmnnnpKBw8eVHR0dG7UiCyxWV0AAAAAAMBCWR7BX7dunSZNmqSZM2fqypUr6tKli6Kjo9WiRQv7bfNw+xlG7gEAAAAAciLg33333QoPD9ebb76p7t27q0iRIrlZF7LI/Jnv+Y4FAAAAAAq2LAf8DRs2cL/7PIx8DwAAAAAFW5avwSfc501M0AcAAAAASLe4ij6sV69sYV1OTpWPp7vVpQAAAAAALETAd3H/16O+PD09rS4DAAAAAGAxp2+TBwAAAAAA8h4CPgAAAAAA+UCWpujXq1cvy/e637Rp0y0VBOe0+OAXJV5N1bx+TVS2qJ/V5QAAAAAALJKlgN+5c2f7369cuaKPP/5YNWrUUOPGjSVJa9as0c6dO9WvX79cKRI3dvriVSVeTZFhOX0AAAAAKNCyFPCHDRtm//vTTz+t5557Tm+++Wa6NkePHs3Z6nBT5s9kn8UJFgAAAACAfMrpa/BnzZqlHj16pNv++OOPa86cOTlSFLKOgXsAAAAAgJSNgO/r66uVK1em275y5Ur5+PjkSFEAAAAAAMA5WZqi/1cDBw5U3759tWnTJjVs2FCStHbtWk2aNEmvv/56jheIzKVde88UfQAAAAAo2JwO+IMHD1alSpU0fvx4TZ8+XZJUvXp1TZ48WV27ds3xApE5pugDAAAAAKRsBHxJ6tq1K2E+j6heKkDXUo283J2+2gIAAAAAkI9kK+CfO3dOs2fP1oEDB/Tiiy+qaNGi2rRpk4KDgxUaGprTNSITs//dSJ6enlaXAQAAAACwmNMBf9u2bWrZsqUKFy6sQ4cO6emnn1bRokU1d+5cHTlyRF9++WVu1AkAAAAAADLh9LzuyMhI9erVS7/++qvDqvnt2rXTzz//nKPFAQAAAACArHE64K9fv17//ve/020PDQ3ViRMncqQoZF3rcSt077tLdepCktWlAAAAAAAs5PQUfW9vbyUkJKTbvm/fPpUoUSJHikLWHTl7WSmpRqmG9fQBAAAAoCBzegS/Y8eOGjlypJKTkyVJNptNR44c0SuvvKKHHnrI6QImTpyoChUqyMfHR40aNdK6detu2DY5OVkjR45UWFiYfHx8FB4erujo6HTt4uLi9Pjjj6tYsWLy9fVV7dq1tWHDBqdrcyU2qwsAAAAAAFjK6YA/ZswYXbx4USVLltTly5fVvHlzVa5cWQEBAXr77bedOtbMmTMVGRmpYcOGadOmTQoPD1dERITi4+MzbD906FB99tlnmjBhgnbt2qVnnnlGXbp00ebNm+1tzp49q6ZNm8rT01M//vijdu3apTFjxqhIkSLOnqpLMIzcAwAAAACUjSn6hQsXVkxMjFasWKFt27bp4sWLuvPOO9WyZUunX3zs2LHq06ePevfuLUn69NNPtXDhQk2aNEmDBw9O137atGl67bXX1K5dO0lS37599dNPP2nMmDGaPn26JGn06NEqW7asJk+ebH9exYoVna7NVdjjPUP4AAAAAFCgOR3w0zRr1kzNmjXL9gtfvXpVGzdu1JAhQ+zb3Nzc1LJlS61evTrD5yQlJTms3C9Jvr6+WrFihf3xggULFBERoUceeUTLly9XaGio+vXrpz59+tywlqSkJCUl/W+RurQ1BpKTk+2XIuQ19rr+TPgp167l2VrhKK2f6C/XQr+5JvrNNdFvrol+c130nWui31xTZv2WE31pM9mY4x0bG6vY2FjFx8crNTXVYd+kSZOydIxjx44pNDRUq1atUuPGje3bX375ZS1fvlxr165N95x//vOf2rp1q+bPn6+wsDDFxsaqU6dOSklJsQf0tC8AIiMj9cgjj2j9+vV6/vnn9emnn6pnz54Z1jJ8+HCNGDEi3favv/5afn5+WTofqzy/+vp3NG/Wv6ZAL4uLAQAAAABkS2Jiov75z3/q/PnzCgwMzNYxnB7BHzFihEaOHKkGDRooJCRENtvtmxs+fvx49enTR9WqVZPNZlNYWJh69+7t8KVCamqqGjRooFGjRkmS6tWrpx07dmQa8IcMGaLIyEj744SEBJUtW1atW7fO9hub25KTkxUTE6OKxa5/ARHRuqGK+JHwXUFa37Vq1Uqenp5Wl4Msot9cE/3mmug310S/uS76zjXRb64ps37L6G51znI64H/66aeaMmWKnnjiiVt64eLFi8vd3V0nT5502H7y5EmVKlUqw+eUKFFC8+fP15UrV/THH3+odOnSGjx4sCpVqmRvExISoho1ajg8r3r16pozZ84Na/H29pa3t3e67Z6ennn+w7J4YLM8XyMy5go/X0iPfnNN9Jtrot9cE/3muug710S/uaaM+i0n+tHpVfSvXr2qJk2a3PILe3l5qX79+oqNjbVvS01NVWxsrMOU/Yz4+PgoNDRU165d05w5c9SpUyf7vqZNm2rv3r0O7fft26fy5cvfcs0AAAAAAORVTgf8p59+Wl9//XWOvHhkZKS++OILTZ06Vbt371bfvn116dIl+6r6PXr0cFiEb+3atZo7d64OHDigX375RW3atFFqaqpefvlle5sXXnhBa9as0ahRo/Tbb7/p66+/1ueff67+/fvnSM0AAAAAAORFTk/Rv3Llij7//HP99NNPqlOnTrppBGPHjs3ysbp166ZTp07pjTfe0IkTJ1S3bl1FR0crODhYknTkyBG5uf3vO4grV65o6NChOnDggPz9/dWuXTtNmzZNQUFB9jZ33XWX5s2bpyFDhmjkyJGqWLGixo0bp+7duzt7qnleqpEe/GiVbDabZv67sQr7MjUHAAAAAAoqpwP+tm3bVLduXUnSjh07HPZlZ8G9AQMGaMCAARnuW7ZsmcPj5s2ba9euXTc95oMPPqgHH3zQ6VpcjZG09+RFSVJqqtM3QwAAAAAA5CNOB/ylS5fmRh24RbfxZgYAAAAAgDzI6WvwkYf8ZdDeJhI+AAAAABRkWRrB/8c//qEpU6YoMDBQ//jHPzJtO3fu3BwpDDfHpHwAAAAAQJosBfzChQvbr68vXLhwrhaEbGIAHwAAAAAKtCwF/MmTJ2f4d+QdXIMPAAAAAAWb04vsIW8pFegtm80mNxI+AAAAABRo2Qr4s2fP1rfffqsjR47o6tWrDvs2bdqUI4Xh5jzcpF9eai5PT0+rSwEAAAAAWMzpVfQ//PBD9e7dW8HBwdq8ebMaNmyoYsWK6cCBA2rbtm1u1AgAAAAAAG7C6YD/8ccf6/PPP9eECRPk5eWll19+WTExMXruued0/vz53KgRAAAAAADchNMB/8iRI2rSpIkkydfXVxcuXJAkPfHEE5oxY0bOVodMJadKD3+2Vp0nrtSV5BSrywEAAAAAWMjpgF+qVCmdOXNGklSuXDmtWbNGknTw4EEZw53Zb6dUI239/by2HD2nVN57AAAAACjQnA74DzzwgBYsWCBJ6t27t1544QW1atVK3bp1U5cuXXK8QGSNTayiDwAAAAAFmdOr6H/++edKTU2VJPXv31/FihXTqlWr1LFjR/373//O8QIBAAAAAMDNOR3w3dzc5Ob2v4H/Rx99VI8++miOFoWs+eukfBsD+AAAAABQoGUp4G/bti3LB6xTp062iwEAAAAAANmTpYBft25d2Wy2my6iZ7PZlJLCau63DevqAQAAAAD+lKWAf/DgwdyuA9lU2NdDko0p+gAAAABQwGUp4JcvXz6360A2+HhIG159QJ6enlaXAgAAAACwmNOL7EnS3r17NWHCBO3evVuSVL16dT377LOqWrVqjhYHAAAAAACyxu3mTRzNmTNHtWrV0saNGxUeHq7w8HBt2rRJtWrV0pw5c3KjRgAAAAAAcBNOj+C//PLLGjJkiEaOHOmwfdiwYXr55Zf10EMP5VhxyNyVFKnH5A2y2Wz68smG8nB3+vsaAAAAAEA+4XQiPH78uHr06JFu++OPP67jx4/nSFHImpRUafWBM1q1/w+rSwEAAAAAWMzpgH/ffffpl19+Sbd9xYoVuueee3KkKDjPxjL6AAAAAFCgOT1Fv2PHjnrllVe0ceNG3X333ZKkNWvWaNasWRoxYoQWLFjg0Ba5x/zl78R7AAAAACjYnA74/fr1kyR9/PHH+vjjjzPcJ10fUU5JSbnF8gAAAAAAQFY4HfBTU1Nzow7cImboAwAAAEDBlqPLricmJubk4XATDlP0SfgAAAAAUKA5HfBbtGihuLi4dNvXrl2runXr5kRNcIKnu01e3B4PAAAAAAo8p5Ohj4+P6tSpo5kzZ0q6PmV/+PDhuueee9SuXbscLxA3FuAp7RreSvvebmt1KQAAAAAAizl9Df7ChQs1ceJEPfnkk/ruu+906NAhHT58WN9//71at26dGzUCAAAAAICbcDrgS1L//v31+++/a/To0fLw8NCyZcvUpEmTnK4NAAAAAABkkdNT9M+ePauHHnpIn3zyiT777DN17dpVrVu3TnfLPOS+S8nSv6dvVp8vN1hdCgAAAADAYk6P4NeqVUsVK1bU5s2bVbFiRfXp00czZ85Uv379tHDhQi1cuDA36kQGrhlpyd5TcndjBX0AAAAAKOicHsF/5pln9PPPP6tixYr2bd26ddPWrVt19erVHC0OWUO8BwAAAAA4PYL/+uuvZ7i9TJkyiomJueWCkHXGWF0BAAAAACCvyPII/rvvvqvLly/bH69cuVJJSUn2xxcuXFC/fv1ytjpkiY0hfAAAAAAo8LIc8IcMGaILFy7YH7dt21ZxcXH2x4mJifrss89ytjpkiY1J+gAAAABQ4GU54Ju/zQf/+2PcfvQAAAAAACCN04vsIQ9iAB8AAAAACjynF9lD3hHkJe0Z0UoeHnQjAAAAABR0TiXD//znP/L395ckXbt2TVOmTFHx4sUlyeH6fNweNpvk7maThzsTMQAAAACgoMtywC9Xrpy++OIL++NSpUpp2rRp6doAAAAAAIDbL8sB/9ChQ7lYBrLjQrI0cOY2+Xh5aEzXcKvLAQAAAABYiLndLiwpRVq444R+2H7c6lIAAAAAABYj4OcDNlbRBwAAAIACj4CfD5DvAQAAAAAEfBdmjNUVAAAAAADyCgJ+PmBjjj4AAAAAFHjZCvj79+/X0KFD9dhjjyk+Pl6S9OOPP2rnzp05WhwylzaAT7wHAAAAADgd8JcvX67atWtr7dq1mjt3ri5evChJ2rp1q4YNG5bjBSILSPgAAAAAUOA5HfAHDx6st956SzExMfLy8rJvf+CBB7RmzZocLQ6ZK+4jbXrtAa0a/IDVpQAAAAAALOZ0wN++fbu6dOmSbnvJkiV1+vTpHCkKWeNmkwJ8PBTg42l1KQAAAAAAizkd8IOCgnT8+PF02zdv3qzQ0NAcKQoAAAAAADjH6YD/6KOP6pVXXtGJEydks9mUmpqqlStX6sUXX1SPHj1yo0bcwPmr0pB5OzV8AYsbAgAAAEBB53TAHzVqlKpVq6ayZcvq4sWLqlGjhu699141adJEQ4cOzY0acQOXr0mzN8Vp/pY4q0sBAAAAAFjMw9kneHl56YsvvtDrr7+uHTt26OLFi6pXr56qVKmSG/UhC1hEHwAAAADgdMBfsWKFmjVrpnLlyqlcuXK5URMAAAAAAHCS01P0H3jgAVWsWFGvvvqqdu3alRs1IYvMn/+12RjDBwAAAICCzumAf+zYMQ0aNEjLly9XrVq1VLduXb333nv6/fffc6M+ZMLcvAkAAAAAoIBwOuAXL15cAwYM0MqVK7V//3498sgjmjp1qipUqKAHHnggN2rEjfyZ8Bm/BwAAAAA4HfD/qmLFiho8eLDeeecd1a5dW8uXL8+puuAEZugDAAAAAJxeZC/NypUr9dVXX2n27Nm6cuWKOnXqpKioqJysDTcR7CutfLm5PD2z3Y0AAAAAgHzC6WQ4ZMgQffPNNzp27JhatWql8ePHq1OnTvLz88uN+pAJdzepZIC3PD09rS4FAAAAAGAxpwP+zz//rJdeekldu3ZV8eLFc6MmAAAAAADgJKcD/sqVK3OjDmTDuSRp5MI9CvLz0qDWVa0uBwAAAABgoSwF/AULFqht27by9PTUggULMm3bsWPHHCkMN3fxmjRt0xEFB3oT8AEAAACggMtSwO/cubNOnDihkiVLqnPnzjdsZ7PZlJKSklO1IYts3CgPAAAAAAq8LAX81NTUDP8OaxljdQUAAAAAgLzCzdknfPnll0pKSkq3/erVq/ryyy9zpCg4x8YAPgAAAAAUeE4H/N69e+v8+fPptl+4cEG9e/fOkaIAAAAAAIBznA74xhjZMhgy/v3331W4cOEcKQpZkzZDnwF8AAAAAECWb5NXr1492Ww22Ww2tWjRQh4e/3tqSkqKDh48qDZt2uRKkchcRl+4AAAAAAAKliwH/LTV87ds2aKIiAj5+/vb93l5ealChQp66KGHcrxA3FiIn/TTC83k7elpdSkAAAAAAItlOeAPGzZMklShQgV169ZNPj4+uVYUssbTTSpf1E+eBHwAAAAAKPCyHPDT9OzZMzfqAAAAAAAAt8DpRfZSUlL0/vvvq2HDhipVqpSKFi3q8Ae3z9kk6b3F+/TZ8v1WlwIAAAAAsJjTAX/EiBEaO3asunXrpvPnzysyMlL/+Mc/5ObmpuHDh+dCibiR81elz385pOlrD1tdCgAAAADAYk4H/K+++kpffPGFBg0aJA8PDz322GP6z3/+ozfeeENr1qzJjRoBAAAAAMBNOB3wT5w4odq1a0uS/P39df78eUnSgw8+qIULF+ZsdciU+fO/NnGbPAAAAAAo6JwO+GXKlNHx48clSWFhYVq8eLEkaf369fL29s7Z6pAlNvI9AAAAABR4Tgf8Ll26KDY2VpL07LPP6vXXX1eVKlXUo0cPPfnkkzleIG7MmJu3AQAAAAAUDE7fJu+dd96x/71bt24qV66cVq9erSpVqqhDhw45Whwy978p+gAAAACAgs7pEfy/a9y4sSIjI28p3E+cOFEVKlSQj4+PGjVqpHXr1t2wbXJyskaOHKmwsDD5+PgoPDxc0dHRN2z/zjvvyGazaeDAgdmuL6+zMUcfAAAAAAq8LI3gL1iwIMsH7Nixo1MFzJw5U5GRkfr000/VqFEjjRs3ThEREdq7d69KliyZrv3QoUM1ffp0ffHFF6pWrZoWLVqkLl26aNWqVapXr55D2/Xr1+uzzz5TnTp1nKrJVZQpJP23f2P5entZXQoAAAAAwGJZCvidO3fO0sFsNptSUlKcKmDs2LHq06ePevfuLUn69NNPtXDhQk2aNEmDBw9O137atGl67bXX1K5dO0lS37599dNPP2nMmDGaPn26vd3FixfVvXt3ffHFF3rrrbcyrSEpKUlJSUn2xwkJCZKuzxZITk526nxul+TkZHm7S2HFfOTp6Zln60R6aX1Fn7kW+s010W+uiX5zTfSb66LvXBP95poy67ec6MssBfzU1NRbfqGMXL16VRs3btSQIUPs29zc3NSyZUutXr06w+ckJSXJx8fHYZuvr69WrFjhsK1///5q3769WrZsedOAHxUVpREjRqTbvnjxYvn5+WX1dCwRExNjdQnIJvrONdFvrol+c030m2ui31wXfeea6DfXlFG/JSYm3vJxnV5kLyedPn1aKSkpCg4OdtgeHBysPXv2ZPiciIgIjR07Vvfee6/CwsIUGxuruXPnOswc+Oabb7Rp0yatX78+S3UMGTJEkZGR9scJCQkqW7asWrdurcDAwGycWe5LTk7WzO9jdCawiooF+Kh7w7JWl4QsSk5OVkxMjFq1aiVPT0+ry0EW0W+uiX5zTfSba6LfXBd955roN9eUWb+lzSS/FU4H/JEjR2a6/4033sh2MVkxfvx49enTR9WqVZPNZlNYWJh69+6tSZMmSZKOHj2q559/XjExMelG+m/E29tb3t7e6bZ7enrm6Q/LmSvShE0HVbmkv3o1rWR1OXBSXv/5QsboN9dEv7km+s010W+ui75zTfSba8qo33KiH50O+PPmzXN4nJycrIMHD8rDw0NhYWFOBfzixYvL3d1dJ0+edNh+8uRJlSpVKsPnlChRQvPnz9eVK1f0xx9/qHTp0ho8eLAqVboecDdu3Kj4+Hjdeeed9uekpKTo559/1kcffaSkpCS5u7tnuca8zHCDPAAAAADAn5wO+Js3b063LSEhQb169VKXLl2cOpaXl5fq16+v2NhY+0J+qampio2N1YABAzJ9ro+Pj0JDQ5WcnKw5c+aoa9eukqQWLVpo+/btDm179+6tatWq6ZVXXsk34f6viPkAAAAAgBy5Bj8wMFAjRoxQhw4d9MQTTzj13MjISPXs2VMNGjRQw4YNNW7cOF26dMm+qn6PHj0UGhqqqKgoSdLatWsVFxenunXrKi4uTsOHD1dqaqpefvllSVJAQIBq1arl8BqFChVSsWLF0m3PL2wkfAAAAAAo8HJskb3z58/r/PnzTj+vW7duOnXqlN544w2dOHFCdevWVXR0tH3hvSNHjsjNzc3e/sqVKxo6dKgOHDggf39/tWvXTtOmTVNQUFBOnYrLMFYXAAAAAADIM5wO+B9++KHDY2OMjh8/rmnTpqlt27bZKmLAgAE3nJK/bNkyh8fNmzfXrl27nDr+34+RX6QFfBuT9AEAAACgwHM64H/wwQcOj93c3FSiRAn17NnT4X72uH2Yog8AAAAAcDrgHzx4MDfqQDaUK2T0bZ+GKuTrZXUpAAAAAACL5dg1+Lj9fD2keuWCuO8lAAAAAMD5gH/lyhVNmDBBS5cuVXx8vFJTUx32b9q0KceKAwAAAAAAWeN0wH/qqae0ePFiPfzww2rYsKFsXABumTNJ0qSVh1SysK+61CtjdTkAAAAAAAs5HfC///57/fDDD2ratGlu1AMnnLxs06fR+1QjJJCADwAAAAAFnNvNmzgKDQ1VQEBAbtQCAAAAAACyyemAP2bMGL3yyis6fPhwbtQDZ5jr/+EqCQAAAACA01P0GzRooCtXrqhSpUry8/NLt4L7mTNncqw4ZO7PfE/ABwAAAAA4H/Afe+wxxcXFadSoUQoODmaRPQuZmzcBAAAAABQQTgf8VatWafXq1QoPD8+NepANNvElCwAAAAAUdE5fg1+tWjVdvnw5N2pBNjGJAgAAAADg9Aj+O++8o0GDBuntt99W7dq1012DHxgYmGPFIXPl/Y2m9qqvwoV8rC4FAAAAAGAxpwN+mzZtJEktWrRw2G6Mkc1mU0pKSs5Uhpvy95SahBVL9yULAAAAAKDgcTrgL126NDfqAAAAAAAAt8DpgN+8efPcqAPZ8McV6et1R1UqyE8RNUtZXQ4AAAAAwEJOB/yff/450/333ntvtouBc44l2vSf/+5WvXJBBHwAAAAAKOCcDvj33Xdfum22vyzjzjX4AAAAAADcfk7fJu/s2bMOf+Lj4xUdHa277rpLixcvzo0acQPmz/9ylzwAAAAAgNMj+IULF063rVWrVvLy8lJkZKQ2btyYI4Uh6/46gwIAAAAAUDA5PYJ/I8HBwdq7d29OHQ5ZYMzN2wAAAAAACganR/C3bdvm8NgYo+PHj+udd95R3bp1c6ouOIHxewAAAACA0wG/bt26stlsMn8bPr777rs1adKkHCsMWccMfQAAAACA0wH/4MGDDo/d3NxUokQJ+fj45FhRyJoKAUaf/rOuigX6Wl0KAAAAAMBiTgf88uXL50YdyIbCXlKL6iXl6elpdSkAAAAAAItleZG9JUuWqEaNGkpISEi37/z586pZs6Z++eWXHC0OAAAAAABkTZYD/rhx49SnTx8FBgam21e4cGH9+9//1tixY3O0OGTujyvS/C3H9PO+U1aXAgAAAACwWJYD/tatW9WmTZsb7m/durU2btyYI0Uha45ctOmlOTs0celvVpcCAAAAALBYlgP+yZMnM73W28PDQ6dOMZJ8O6Xdx4BV9AEAAAAAWQ74oaGh2rFjxw33b9u2TSEhITlSFAAAAAAAcE6WA367du30+uuv68qVK+n2Xb58WcOGDdODDz6Yo8Uha2xiCB8AAAAACros3yZv6NChmjt3ru644w4NGDBAVatWlSTt2bNHEydOVEpKil577bVcKxTpMUUfAAAAAJAmywE/ODhYq1atUt++fTVkyBAZcz1e2mw2RUREaOLEiQoODs61QpHen11AwAcAAAAAZD3gS1L58uX1ww8/6OzZs/rtt99kjFGVKlVUpEiR3KoPAAAAAABkgVMBP02RIkV011135XQtcFKlQKOxj9RWqcJ+VpcCAAAAALBYtgI+8oai3lK7OiGZ3r4QAAAAAFAwZHkVfQAAAAAAkHcR8F3YmSRp0c6T2nDojNWlAAAAAAAsRsB3YfsTbBrwzVZ9uOQ3q0sBAAAAAFiMgO/CjNUFAAAAAADyDAK+K/sz4dusrQIAAAAAkAcQ8PMBGwkfAAAAAAo8Ar4LY4o+AAAAACANAd+FpQV8BvABAAAAAAT8fMDGHH0AAAAAKPA8rC4A2VcpwGhU5xoqU9Tf6lIAAAAAABYj4Luwkr5Su/pl5OnpaXUpAAAAAACLMUUfAAAAAIB8gIDvws4mST//elo74s5bXQoAAAAAwGIEfBe2+5xNT325SeN++tXqUgAAAAAAFiPgAwAAAACQDxDwXZj587/cJQ8AAAAAQMDPB8j3AAAAAAACvgszfw7hM4IPAAAAACDg5wM2xvABAAAAoMAj4AMAAAAAkA94WF0Asq9igNHQdlVVqWSA1aUAAAAAACxGwHdhoYWkdo3Ly9PT0+pSAAAAAAAWY4o+AAAAAAD5AAHfhZ2/Kq0/dFa/xV+wuhQAAAAAgMUI+C5s6x82/fP/1uuDn361uhQAAAAAgMUI+AAAAAAA5AMEfBdm/vyvzdIqAAAAAAB5AQE/H7DZiPgAAAAAUNAR8F2YuXkTAAAAAEABQcDPBxi/BwAAAAAQ8PMBZugDAAAAADysLgDZVzHAaFDLyqoaUtjqUgAAAAAAFiPgu7Dy/lK75pXk6elpdSkAAAAAAIsxRR8AAAAAgHyAgO/CEq5KO48l6OiZRKtLAQAAAABYjIDvwjactqnzJ2v0Qcw+q0sBAAAAAFiMgO/CjLG6AgAAAABAXkHAzw+4TR4AAAAAFHgE/HzARsIHAAAAgAKPgO/C0mbo28j3AAAAAFDgEfBdmD3gW1oFAAAAACAvIOADAAAAAJAPeFhdALKvYoBRv+aVVKdsEatLAQAAAABYjIDvwioHSu1aVpanp6fVpQAAAAAALMYUfQAAAAAA8gECvgu7mCztP3VJ8ReuWF0KAAAAAMBieSLgT5w4URUqVJCPj48aNWqkdevW3bBtcnKyRo4cqbCwMPn4+Cg8PFzR0dEObaKionTXXXcpICBAJUuWVOfOnbV3797cPo3bbtVJm9p8uFIfxOyzuhQAAAAAgMUsD/gzZ85UZGSkhg0bpk2bNik8PFwRERGKj4/PsP3QoUP12WefacKECdq1a5eeeeYZdenSRZs3b7a3Wb58ufr37681a9YoJiZGycnJat26tS5dunS7Tuu2MDdvAgAAAAAoICxfZG/s2LHq06ePevfuLUn69NNPtXDhQk2aNEmDBw9O137atGl67bXX1K5dO0lS37599dNPP2nMmDGaPn26JKUb0Z8yZYpKliypjRs36t577013zKSkJCUlJdkfJyQkSLo+WyA5OTlnTjSH/bWu1FSTZ+tEeml9RZ+5FvrNNdFvrol+c030m+ui71wT/eaaMuu3nOhLSwP+1atXtXHjRg0ZMsS+zc3NTS1bttTq1aszfE5SUpJ8fHwctvn6+mrFihU3fJ3z589LkooWLZrh/qioKI0YMSLd9sWLF8vPz++m52EVY2ySpKNHj+iHHw5ZWwycFhMTY3UJyAb6zTXRb66JfnNN9Jvrou9cE/3mmjLqt8TExFs+rqUB//Tp00pJSVFwcLDD9uDgYO3ZsyfD50RERGjs2LG69957FRYWptjYWM2dO1cpKSkZtk9NTdXAgQPVtGlT1apVK8M2Q4YMUWRkpP1xQkKCypYtq9atWyswMDCbZ5e7kpOT9eOknyRJ5cqVU7t2NSyuCFmVnJysmJgYtWrVilscuhD6zTXRb66JfnNN9Jvrou9cE/3mmjLrt7SZ5LfC8in6zho/frz69OmjatWqyWazKSwsTL1799akSZMybN+/f3/t2LEj0xF+b29veXt7p9vu6enpEh8Wdzc3l6gTjlzl5wuO6DfXRL+5JvrNNdFvrou+c030m2vKqN9yoh8tXWSvePHicnd318mTJx22nzx5UqVKlcrwOSVKlND8+fN16dIlHT58WHv27JG/v78qVaqUru2AAQP0/fffa+nSpSpTpkyunIO1bFYXAAAAAADIIywN+F5eXqpfv75iY2Pt21JTUxUbG6vGjRtn+lwfHx+Fhobq2rVrmjNnjjp16mTfZ4zRgAEDNG/ePC1ZskQVK1bMtXOwUoUAo95Nyqtp5eJWlwIAAAAAsJjlU/QjIyPVs2dPNWjQQA0bNtS4ceN06dIl+6r6PXr0UGhoqKKioiRJa9euVVxcnOrWrau4uDgNHz5cqampevnll+3H7N+/v77++mt99913CggI0IkTJyRJhQsXlq+v7+0/yVxSPcioXduqTMkBAAAAAFgf8Lt166ZTp07pjTfe0IkTJ1S3bl1FR0fbF947cuSI3Nz+N9HgypUrGjp0qA4cOCB/f3+1a9dO06ZNU1BQkL3NJ598Ikm67777HF5r8uTJ6tWrV26fEgAAAAAAt53lAV+6fq38gAEDMty3bNkyh8fNmzfXrl27Mj2eMSanSsvTLl+Tjp+/osJ+UmE/RvEBAAAAoCCz9Bp83Jolx9x07/s/64Of9lldCgAAAADAYgR8AAAAAADyAQK+CysYFyIAAAAAALKCgJ8P2GxWVwAAAAAAsBoB34Uxgg8AAAAASEPAzwdsYggfAAAAAAo6Ar4rYwgfAAAAAPAnAr4LK+dv1K1BGd1ZPsjqUgAAAAAAFvOwugBkX3gxo3btasjT09PqUgAAAAAAFmMEHwAAAACAfICA78KupkjnEpN1+WqK1aUAAAAAACxGwHdhP/7upruiluqDn/ZZXQoAAAAAwGIEfFfGKvoAAAAAgD8R8F1YWr63WVoFAAAAACAvIOC7MAbwAQAAAABpCPj5AUP4AAAAAFDgEfDzARsJHwAAAAAKPAK+C2OKPgAAAAAgDQHfhZUtZNQpPEQ1SwdaXQoAAAAAwGIeVheA7LurhFG7drXl6elpdSkAAAAAAIsxgg8AAAAAQD5AwHdhKalSUnKKrqWkWl0KAAAAAMBiBHwXNv+wm2qNjNX42F+tLgUAAAAAYDECvgtjFX0AAAAAQBoCfj5gs7oAAAAAAIDlCPiujCF8AAAAAMCfCPguzJ7vbYzhAwAAAEBBR8DPB4j3AAAAAAACvgtjhj4AAAAAIA0B34WVKWTUqnpJVS7pb3UpAAAAAACLeVhdALKvSbBRu3Z15enpaXUpAAAAAACLMYIPAAAAAEA+QMAHAAAAACAfIOC7sG8PuKnqG4v10ZJfrS4FAAAAAGAxAr4LSzXX/xiW0wcAAACAAo+Anw/YbFZXAAAAAACwGgHfRaWkGp27ev3vR84kKiWVYXwAAAAAKMgI+C4oesdx3TfmZ+0+d737vt3wu5qNXqLoHcctrgwAAAAAYBUCvouJ3nFcfadv0omEJIftJ85fUd/pmwj5AAAAAFBAEfBdSEqq0Yj/7lJGk/HTto347y6m6wMAAABAAUTAdyHrDp7R8fNXbrjfSDp+/orWHTxz+4oCAAAAAOQJBHwXEn/hxuE+O+0AAAAAAPkHAd+FlAzwydF2AAAAAID8g4DvQhpWLKqQwj660W3vbZJCCvuoYcWit7MsAAAAAEAeQMB3Ie5uNg3rUEOS0oX8tMfDOtSQu9uNvgIAAAAAAORXBHwX06ZWiD55/E4FB3o7bC9V2EefPH6n2tQKsagyAAAAAICVPKwuAM5rUytE91Uppo9mRqtSzboKCSqkhhWLMnIPAAAAAAUYAd9FubvZVKWwUbs6IfL09LS6HAAAAACAxZiiDwAAAABAPkDABwAAAAAgHyDgAwAAAACQDxDwAQAAAADIBwj4AAAAAADkAwR8AAAAAADyAQI+AAAAAAD5AAEfAAAAAIB8gIAPAAAAAEA+QMAHAAAAACAfIOADAAAAAJAPEPABAAAAAMgHCPgAAAAAAOQDHlYXkBcZYyRJCQkJFldyY8nJyUpMTFRCQoI8PT2tLgdOoO9cE/3mmug310S/uSb6zXXRd66JfnNNmfVbWv5My6PZQcDPwIULFyRJZcuWtbgSAAAAAEBBcuHCBRUuXDhbz7WZW/l6IJ9KTU3VsWPHFBAQIJvNZnU5GUpISFDZsmV19OhRBQYGWl0OnEDfuSb6zTXRb66JfnNN9Jvrou9cE/3mmjLrN2OMLly4oNKlS8vNLXtX0zOCnwE3NzeVKVPG6jKyJDAwkA+0i6LvXBP95proN9dEv7km+s110XeuiX5zTTfqt+yO3KdhkT0AAAAAAPIBAj4AAAAAAPkAAd9FeXt7a9iwYfL29ra6FDiJvnNN9Jtrot9cE/3mmug310XfuSb6zTXldr+xyB4AAAAAAPkAI/gAAAAAAOQDBHwAAAAAAPIBAj4AAAAAAPkAAR8AAAAAgHyAgO+iJk6cqAoVKsjHx0eNGjXSunXrrC6pQPv555/VoUMHlS5dWjabTfPnz3fYb4zRG2+8oZCQEPn6+qply5b69ddfHdqcOXNG3bt3V2BgoIKCgvTUU0/p4sWLt/EsCp6oqCjdddddCggIUMmSJdW5c2ft3bvXoc2VK1fUv39/FStWTP7+/nrooYd08uRJhzZHjhxR+/bt5efnp5IlS+qll17StWvXbuepFCiffPKJ6tSpo8DAQAUGBqpx48b68ccf7fvpM9fwzjvvyGazaeDAgfZt9F3eM3z4cNlsNoc/1apVs++nz/K2uLg4Pf744ypWrJh8fX1Vu3Ztbdiwwb6ff5/kPRUqVEj3mbPZbOrfv78kPnN5VUpKil5//XVVrFhRvr6+CgsL05tvvqm/rmd/2z5vBi7nm2++MV5eXmbSpElm586dpk+fPiYoKMicPHnS6tIKrB9++MG89tprZu7cuUaSmTdvnsP+d955xxQuXNjMnz/fbN261XTs2NFUrFjRXL582d6mTZs2Jjw83KxZs8b88ssvpnLlyuaxxx67zWdSsERERJjJkyebHTt2mC1btph27dqZcuXKmYsXL9rbPPPMM6Zs2bImNjbWbNiwwdx9992mSZMm9v3Xrl0ztWrVMi1btjSbN282P/zwgylevLgZMmSIFadUICxYsMAsXLjQ7Nu3z+zdu9e8+uqrxtPT0+zYscMYQ5+5gnXr1pkKFSqYOnXqmOeff96+nb7Le4YNG2Zq1qxpjh8/bv9z6tQp+376LO86c+aMKV++vOnVq5dZu3atOXDggFm0aJH57bff7G3490neEx8f7/B5i4mJMZLM0qVLjTF85vKqt99+2xQrVsx8//335uDBg2bWrFnG39/fjB8/3t7mdn3eCPguqGHDhqZ///72xykpKaZ06dImKirKwqqQ5u8BPzU11ZQqVcq899579m3nzp0z3t7eZsaMGcYYY3bt2mUkmfXr19vb/Pjjj8Zms5m4uLjbVntBFx8fbySZ5cuXG2Ou95Onp6eZNWuWvc3u3buNJLN69WpjzPUvd9zc3MyJEyfsbT755BMTGBhokpKSbu8JFGBFihQx//nPf+gzF3DhwgVTpUoVExMTY5o3b24P+PRd3jRs2DATHh6e4T76LG975ZVXTLNmzW64n3+fuIbnn3/ehIWFmdTUVD5zeVj79u3Nk08+6bDtH//4h+nevbsx5vZ+3pii72KuXr2qjRs3qmXLlvZtbm5uatmypVavXm1hZbiRgwcP6sSJEw59VrhwYTVq1MjeZ6tXr1ZQUJAaNGhgb9OyZUu5ublp7dq1t73mgur8+fOSpKJFi0qSNm7cqOTkZIe+q1atmsqVK+fQd7Vr11ZwcLC9TUREhBISErRz587bWH3BlJKSom+++UaXLl1S48aN6TMX0L9/f7Vv396hjyQ+b3nZr7/+qtKlS6tSpUrq3r27jhw5Iok+y+sWLFigBg0a6JFHHlHJkiVVr149ffHFF/b9/Psk77t69aqmT5+uJ598Ujabjc9cHtakSRPFxsZq3759kqStW7dqxYoVatu2raTb+3nzyIkTwu1z+vRppaSkOHxoJSk4OFh79uyxqCpk5sSJE5KUYZ+l7Ttx4oRKlizpsN/Dw0NFixa1t0HuSk1N1cCBA9W0aVPVqlVL0vV+8fLyUlBQkEPbv/ddRn2btg+5Y/v27WrcuLGuXLkif39/zZs3TzVq1NCWLVvoszzsm2++0aZNm7R+/fp0+/i85U2NGjXSlClTVLVqVR0/flwjRozQPffcox07dtBnedyBAwf0ySefKDIyUq+++qrWr1+v5557Tl5eXurZsyf/PnEB8+fP17lz59SrVy9J/J7MywYPHqyEhARVq1ZN7u7uSklJ0dtvv63u3btLur15gIAPALo+qrhjxw6tWLHC6lKQBVWrVtWWLVt0/vx5zZ49Wz179tTy5cutLguZOHr0qJ5//nnFxMTIx8fH6nKQRWmjT5JUp04dNWrUSOXLl9e3334rX19fCyvDzaSmpqpBgwYaNWqUJKlevXrasWOHPv30U/Xs2dPi6pAV//d//6e2bduqdOnSVpeCm/j222/11Vdf6euvv1bNmjW1ZcsWDRw4UKVLl77tnzem6LuY4sWLy93dPd1qmSdPnlSpUqUsqgqZSeuXzPqsVKlSio+Pd9h/7do1nTlzhn69DQYMGKDvv/9eS5cuVZkyZezbS5UqpatXr+rcuXMO7f/edxn1bdo+5A4vLy9VrlxZ9evXV1RUlMLDwzV+/Hj6LA/buHGj4uPjdeedd8rDw0MeHh5avny5PvzwQ3l4eCg4OJi+cwFBQUG644479Ntvv/F5y+NCQkJUo0YNh23Vq1e3X2LBv0/ytsOHD+unn37S008/bd/GZy7veumllzR48GA9+uijql27tp544gm98MILioqKknR7P28EfBfj5eWl+vXrKzY21r4tNTVVsbGxaty4sYWV4UYqVqyoUqVKOfRZQkKC1q5da++zxo0b69y5c9q4caO9zZIlS5SamqpGjRrd9poLCmOMBgwYoHnz5mnJkiWqWLGiw/769evL09PToe/27t2rI0eOOPTd9u3bHX4hx8TEKDAwMN0/rJB7UlNTlZSURJ/lYS1atND27du1ZcsW+58GDRqoe/fu9r/Td3nfxYsXtX//foWEhPB5y+OaNm2a7tav+/btU/ny5SXx75O8bvLkySpZsqTat29v38ZnLu9KTEyUm5tjtHZ3d1dqaqqk2/x5u4XFAmGRb775xnh7e5spU6aYXbt2mX/9618mKCjIYbVM3F4XLlwwmzdvNps3bzaSzNixY83mzZvN4cOHjTHXb4sRFBRkvvvuO7Nt2zbTqVOnDG+LUa9ePbN27VqzYsUKU6VKFW5Dk8v69u1rChcubJYtW+ZwS5rExER7m2eeecaUK1fOLFmyxGzYsME0btzYNG7c2L4/7XY0rVu3Nlu2bDHR0dGmRIkS3I4mFw0ePNgsX77cHDx40Gzbts0MHjzY2Gw2s3jxYmMMfeZK/rqKvjH0XV40aNAgs2zZMnPw4EGzcuVK07JlS1O8eHETHx9vjKHP8rJ169YZDw8P8/bbb5tff/3VfPXVV8bPz89Mnz7d3oZ/n+RNKSkpply5cuaVV15Jt4/PXN7Us2dPExoaar9N3ty5c03x4sXNyy+/bG9zuz5vBHwXNWHCBFOuXDnj5eVlGjZsaNasWWN1SQXa0qVLjaR0f3r27GmMuX5rjNdff90EBwcbb29v06JFC7N3716HY/zxxx/mscceM/7+/iYwMND07t3bXLhwwYKzKTgy6jNJZvLkyfY2ly9fNv369TNFihQxfn5+pkuXLub48eMOxzl06JBp27at8fX1NcWLFzeDBg0yycnJt/lsCo4nn3zSlC9f3nh5eZkSJUqYFi1a2MO9MfSZK/l7wKfv8p5u3bqZkJAQ4+XlZUJDQ023bt0c7qNOn+Vt//3vf02tWrWMt7e3qVatmvn8888d9vPvk7xp0aJFRlK6vjCGz1xelZCQYJ5//nlTrlw54+PjYypVqmRee+01h1sT3q7Pm80YY5ybgAAAAAAAAPIarsEHAAAAACAfIOADAAAAAJAPEPABAAAAAMgHCPgAAAAAAOQDBHwAAAAAAPIBAj4AAAAAAPkAAR8AAAAAgHyAgA8AAAAAQD5AwAcAII84dOiQbDabtmzZYnUpdnv27NHdd98tHx8f1a1b1+pyAABAJgj4AAD8qVevXrLZbHrnnXccts+fP182m82iqqw1bNgwFSpUSHv37lVsbOwN2504cULPPvusKlWqJG9vb5UtW1YdOnTI9DkFUa9evdS5c2erywAA5FMEfAAA/sLHx0ejR4/W2bNnrS4lx1y9ejXbz92/f7+aNWum8uXLq1ixYhm2OXTokOrXr68lS5bovffe0/bt2xUdHa37779f/fv3z/ZrAwAA5xDwAQD4i5YtW6pUqVKKioq6YZvhw4enm64+btw4VahQwf44baR21KhRCg4OVlBQkEaOHKlr167ppZdeUtGiRVWmTBlNnjw53fH37NmjJk2ayMfHR7Vq1dLy5csd9u/YsUNt27aVv7+/goOD9cQTT+j06dP2/ffdd58GDBiggQMHqnjx4oqIiMjwPFJTUzVy5EiVKVNG3t7eqlu3rqKjo+37bTabNm7cqJEjR8pms2n48OEZHqdfv36y2Wxat26dHnroId1xxx2qWbOmIiMjtWbNGnu7I0eOqFOnTvL391dgYKC6du2qkydPpntfJ02apHLlysnf31/9+vVTSkqK3n33XZUqVUolS5bU22+/7fD6NptNn3zyidq2bStfX19VqlRJs2fPdmizfft2PfDAA/L19VWxYsX0r3/9SxcvXkzXX++//75CQkJUrFgx9e/fX8nJyfY2SUlJevHFFxUaGqpChQqpUaNGWrZsmX3/lClTFBQUpEWLFql69ery9/dXmzZtdPz4cfv5TZ06Vd99951sNptsNpuWLVumq1evasCAAQoJCZGPj4/Kly+f6c8fAAA3QsAHAOAv3N3dNWrUKE2YMEG///77LR1ryZIlOnbsmH7++WeNHTtWw4YN04MPPqgiRYpo7dq1euaZZ/Tvf/873eu89NJLGjRokDZv3qzGjRurQ4cO+uOPPyRJ586d0wMPPKB69eppw4YNio6O1smTJ9W1a1eHY0ydOlVeXl5auXKlPv300wzrGz9+vMaMGaP3339f27ZtU0REhDp27Khff/1VknT8+HHVrFlTgwYN0vHjx/Xiiy+mO8aZM2cUHR2t/v37q1ChQun2BwUFSbr+ZUKnTp105swZLV++XDExMTpw4IC6devm0H7//v368ccfFR0drRkzZuj//u//1L59e/3+++9avny5Ro8eraFDh2rt2rUOz3v99df10EMPaevWrerevbseffRR7d69W5J06dIlRUREqEiRIlq/fr1mzZqln376SQMGDHA4xtKlS7V//34tXbpUU6dO1ZQpUzRlyhT7/gEDBmj16tX65ptvtG3bNj3yyCNq06aN/f2SpMTERL3//vuaNm2afv75Zx05csT+vr344ovq2rWrPfQfP35cTZo00YcffqgFCxbo22+/1d69e/XVV185fFkEAECWGQAAYIwxpmfPnqZTp07GGGPuvvtu8+STTxpjjJk3b5756/8yhw0bZsLDwx2e+8EHH5jy5cs7HKt8+fImJSXFvq1q1armnnvusT++du2aKVSokJkxY4YxxpiDBw8aSeadd96xt0lOTjZlypQxo0ePNsYY8+abb5rWrVs7vPbRo0eNJLN3715jjDHNmzc39erVu+n5li5d2rz99tsO2+666y7Tr18/++Pw8HAzbNiwGx5j7dq1RpKZO3dupq+1ePFi4+7ubo4cOWLftnPnTiPJrFu3zhhz/X318/MzCQkJ9jYRERGmQoUK6d7HqKgo+2NJ5plnnnF4vUaNGpm+ffsaY4z5/PPPTZEiRczFixft+xcuXGjc3NzMiRMnjDH/669r167Z2zzyyCOmW7duxhhjDh8+bNzd3U1cXJzD67Ro0cIMGTLEGGPM5MmTjSTz22+/2fdPnDjRBAcH2x//9WcszbPPPmseeOABk5qaesP3DwCArGAEHwCADIwePVpTp061jwJnR82aNeXm9r//1QYHB6t27dr2x+7u7ipWrJji4+Mdnte4cWP73z08PNSgQQN7HVu3btXSpUvl7+9v/1OtWjVJ10e/09SvXz/T2hISEnTs2DE1bdrUYXvTpk2dOmdjTJba7d69W2XLllXZsmXt22rUqKGgoCCH16tQoYICAgLsj4ODg1WjRo1072Nm71na47Tj7t69W+Hh4Q4zDJo2barU1FTt3bvXvq1mzZpyd3e3Pw4JCbG/zvbt25WSkqI77rjD4b1fvny5w/vu5+ensLCwDI9xI7169dKWLVtUtWpVPffcc1q8eHGm7QEAuBEPqwsAACAvuvfeexUREaEhQ4aoV69eDvvc3NzSBdu/XqudxtPT0+GxzWbLcFtqamqW67p48aI6dOig0aNHp9sXEhJi/3tG0+VzQ5UqVWSz2bRnz54cOV5uvGe38tppr3Px4kW5u7tr48aNDl8CSJK/v3+mx7jZlyB33nmnDh48qB9//FE//fSTunbtqpYtW6ZbRwAAgJthBB8AgBt455139N///lerV6922F6iRAmdOHHCIbjl5L3r/7ow3bVr17Rx40ZVr15d0vUwuHPnTlWoUEGVK1d2+ONMqA8MDFTp0qW1cuVKh+0rV65UjRo1snycokWLKiIiQhMnTtSlS5fS7T937pwkqXr16jp69KiOHj1q37dr1y6dO3fOqde7kb++Z2mP096z6tWra+vWrQ71rVy5Um5ubqpatWqWjl+vXj2lpKQoPj4+3fteqlSpLNfp5eWllJSUdNsDAwPVrVs3ffHFF5o5c6bmzJmjM2fOZPm4AABIBHwAAG6odu3a6t69uz788EOH7ffdd59OnTqld999V/v379fEiRP1448/5tjrTpw4UfPmzdOePXvUv39/nT17Vk8++aQkqX///jpz5owee+wxrV+/Xvv379eiRYvUu3fvDINjZl566SWNHj1aM2fO1N69ezV48GBt2bJFzz//vNP1pqSkqGHDhpozZ45+/fVX7d69Wx9++KF96nzLli3t7+emTZu0bt069ejRQ82bN1eDBg2cer2MzJo1S5MmTdK+ffs0bNgwrVu3zr6IXvfu3eXj46OePXtqx44dWrp0qZ599lk98cQTCg4OztLx77jjDnXv3l09evTQ3LlzdfDgQa1bt05RUVFauHBhluusUKGCtm3bpr179+r06dNKTk7W2LFjNWPGDO3Zs0f79u3TrFmzVKpUKfsChQAAZBUBHwCATIwcOTLddPDq1avr448/1sSJExUeHq5169ZluMJ8dr3zzjt65513FB4erhUrVmjBggUqXry4JNlH3VNSUtS6dWvVrl1bAwcOVFBQkMN16lnx3HPPKTIyUoMGDVLt2rUVHR2tBQsWqEqVKk4dp1KlStq0aZPuv/9+DRo0SLVq1VKrVq0UGxurTz75RNL1qerfffedihQponvvvVctW7ZUpUqVNHPmTKde60ZGjBihb775RnXq1NGXX36pGTNm2GcG+Pn5adGiRTpz5ozuuusuPfzww2rRooU++ugjp15j8uTJ6tGjhwYNGqSqVauqc+fOWr9+vcqVK5flY/Tp00dVq1ZVgwYNVKJECa1cuVIBAQF699131aBBA9111106dOiQfvjhB6f7EwAAm8nq6jgAAAB5kM1m07x589S5c2erSwEAwFJ8NQwAAAAAQD5AwAcAAAAAIB/gNnkAAMClcbUhAADXMYIPAAAAAEA+QMAHAAAAACAfIOADAAAAAJAPEPABAAAAAMgHCPgAAAAAAOQDBHwAAAAAAPIBAj4AAAAAAPkAAR8AAAAAgHzg/wHcO1X8SNzMmwAAAABJRU5ErkJggg==", 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" ] @@ -212,14 +217,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of components selected: 5\n" + "Number of components selected: 1\n" ] } ], @@ -231,18 +236,51 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Perform PCA with the selected number of components\n", - "pca = PCA(n_components=n_components)\n", + "pca = PCA(n_components=2)\n", "reduced_projections = pca.fit_transform(predicted_projections)" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Row Column FOV Cell ID Timestep PC1 PC2 \\\n", + "0 A 3 0 1 2 -0.946861 0.135214 \n", + "1 A 3 0 10 13 -0.795119 0.505766 \n", + "2 A 3 0 11 21 0.793437 0.359740 \n", + "3 A 3 0 12 8 -0.924069 0.261018 \n", + "4 A 3 0 13 26 -0.494323 -0.603584 \n", + "\n", + " Infected Softmax Score \n", + "0 0.220417 \n", + "1 0.220354 \n", + "2 0.228791 \n", + "3 0.243351 \n", + "4 0.222215 \n" + ] + } + ], + "source": [ + "df['PC1'] = reduced_projections[:, 0]\n", + "df['PC2'] = reduced_projections[:, 1]\n", + "df['Infected Softmax Score'] = infected_softmax\n", + "\n", + "print(df.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -250,11 +288,8 @@ "output_type": "stream", "text": [ " PC Background Correlation Uninfected Correlation Infected Correlation\n", - "0 1 0.050334 -0.094879 0.079789\n", - "1 2 -0.002184 0.016340 0.030856\n", - "2 3 0.032372 0.044127 -0.030238\n", - "3 4 0.042380 -0.212249 0.181532\n", - "4 5 0.011044 0.052858 -0.051418\n" + "0 1 0.035215 -0.088615 0.090813\n", + "1 2 0.039682 -0.004987 0.028344\n" ] } ], @@ -282,14 +317,27295 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "PC1" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "PC2" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create an interactive scatter plot\n", + "fig = px.scatter(df, x='PC1', y='PC2', color='Infected Softmax Score',\n", + " hover_data=['Row', 'Column', 'FOV', 'Cell ID', 'Timestep'])\n", + "\n", + "# Show the plot\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to get cell data and plot the images\n", + "\n", + "rfp_index = ds.channel_names.index('RFP')\n", + "phase3d_index = ds.channel_names.index('Phase3D')\n", + "\n", + "def get_cell_data_and_plot(row, col, fov, cell_id, timestep):\n", + " position_key = f\"{row}/{col}/fov{fov}cell{cell_id}/0\"\n", + " zarr_array = ds[position_key]\n", + "\n", + " phase_img = zarr_array[timestep, phase3d_index, 32, :, :]\n", + " rfp_img = zarr_array[timestep, rfp_index, 32, :, :]\n", + " \n", + " fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n", + " axes[0].imshow(phase_img, cmap='gray')\n", + " axes[0].set_title('Phase3D Image')\n", + " axes[1].imshow(rfp_img, cmap='gray')\n", + " axes[1].set_title('RFP Image')\n", + " plt.show()\n", + "\n", + " return phase_img, rfp_img\n", + "\n", + "# example: get data for a specific cell and plot\n", + "row = 'B'\n", + "col = '3'\n", + "fov = 5\n", + "cell_id = 14\n", + "timestep = 4\n", + "\n", + "phase_img, rfp_img = get_cell_data_and_plot(row, col, fov, cell_id, timestep)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -297,26 +27613,82 @@ } ], "source": [ - "# Visualize the PCA results\n", + "# Visualize the PCA results with cells colored based on their infected softmax scores\n", "plt.figure(figsize=(12, 6))\n", - "plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c='blue', label='Cells')\n", + "sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=infected_softmax, cmap='viridis', label='Cells')\n", + "plt.colorbar(sc, label='Infected Softmax Score')\n", "plt.xlabel('Principal Component 1')\n", "plt.ylabel('Principal Component 2')\n", - "plt.title('PCA of Predicted Projections')\n", + "plt.title('PCA of Predicted Projections (Colored by Infected Softmax Score)')\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "ename": "IndexError", + "evalue": "index 2 is out of bounds for axis 1 with size 2", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m2\u001b[39m, n_components):\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m----> 6\u001b[0m sc \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mscatter(reduced_projections[:, \u001b[38;5;241m0\u001b[39m], \u001b[43mreduced_projections\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m, c\u001b[38;5;241m=\u001b[39minfected_softmax, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCells\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mcolorbar(sc, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInfected Softmax Score\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrincipal Component 1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mIndexError\u001b[0m: index 2 is out of bounds for axis 1 with size 2" + ] + }, { "data": { - "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# PC1 vs PC3, PC1 vs PC4, etc.\n", + "n_components = 5\n", + "if n_components > 2:\n", + " for i in range(2, n_components):\n", + " plt.figure(figsize=(12, 6))\n", + " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells')\n", + " plt.colorbar(sc, label='Infected Softmax Score')\n", + " plt.xlabel('Principal Component 1')\n", + " plt.ylabel(f'Principal Component {i + 1}')\n", + " plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by Infected Softmax Score)')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "correlations = np.zeros(n_components)\n", + "for i in range(n_components):\n", + " pc = reduced_projections[:, i]\n", + " correlation, _ = spearmanr(pc, infected_softmax)\n", + " correlations[i] = correlation\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the PCA results with cells colored based on their principal component values\n", + "for i in range(n_components):\n", + " plt.figure(figsize=(12, 6))\n", + " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}')\n", + " plt.colorbar(sc, label='Principal Component Value')\n", + " plt.xlabel('Principal Component 1')\n", + " plt.ylabel('Principal Component 2')\n", + " plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Values)')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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KfpeU9hyr7O88b0aPHu3xPex6neuuu06ZTCZ3M/7du3crQM2aNctj/euvv17Fx8d7baKcX2l+A958800FqK+//rrYbeX33XffKT8/P/Xvv/+696e0zbhPnz6tzGazuu222zymP/HEE+5zWSnl/l7Lb926dQpQH3/8sXuat2bPjRo18vr9UfAzM3/+fGUwGNTq1as9lps9e7YC1B9//KGUUmrGjBleu1d4s2DBgkKfDSFE9SbNnkW117dvX+rUqUNcXBzDhg0jJCSEr7/+mvr16wOO5omhoaGl3p7NZmPhwoUMHTrU3WTsyiuvJDo6mk8//bTE9b///ntiY2O57bbb3NP8/f154IEHyMjI4LfffivjHub56aefqFOnDnXq1KFjx458+eWXjBw5kpdfftljuZtvvtmjFvn06dP8+uuv3HrrraSnp3Pq1ClOnTpFSkoK/fr1Y+/evRw/ftxd/ksvvdSjWVydOnUYMWJEieVbvHgxtWvX5v777y80r6QEUpVVxvxatmxJnTp1aNy4Mffeey/NmjVj2bJlHrUQZrO5ULPX8r7HSikWL17MoEGDUEq59/HUqVP069eP1NRUNm/eDJzbsfTm+++/B2DSpEke012tDpYtW+YxvU2bNlx22WXu53Xq1KFly5YcOHDAPS0iIoJ///2XvXv3Fvm68fHxKKXKVOtb3PviyvpclnP6XFx55ZXUrl2bhQsXuqedOXOGn3/+maFDh7qnRUREcOzYMTZu3Fiu15k8eTJ16tQhNjaWPn36sH//fl5++WV3jX5Z93vPnj28+eabvPrqq5jN5jKXJyUlhcjISI9pZS1DWT9zBdctyzlWVd95+eXPiu1q2WGxWPjll18AaNGiBd26dfP4HTl9+jQ//PADI0aMKNV5XdJvQFnfI4vFwkMPPcR//vMf2rRpU+p9dYmMjGTAgAEsXbqUzMxMwPE99/nnn9OlSxdatGgB4FGLbrVaSUlJoVmzZkRERLi/887Vl19+SevWrWnVqpXHd6urC8HKlSsBx7kK8M0335TYtN11Dpw6dapCyiiEqHrS7FlUe++88w4tWrTAz8+PmJgYWrZsicGQd18nLCyM9PT0Um/vp59+Ijk5ma5du7Jv3z739CuuuILPPvuMl19+2WP7BR0+fJjmzZsXWqZ169bu+eXVrVs3XnjhBfcwMK1bt3b/kOfXuHFjj+f79u1DKcUzzzzDM88843XbSUlJ1K9fn8OHD9OtW7dC81u2bFli+fbv30/Lli3LldmzssqY3+LFiwkLC8Pf358GDRp4zehZv359j+auUP73ODk5mbNnzzJnzpwis+EmJSUB53YsvTl8+DAGg4FmzZp5TI+NjSUiIqJQmRs2bFhoG5GRkZw5c8b9fOrUqQwePJgWLVrQrl07+vfvz8iRI4ttel0axb0vrqaHZTmnz4Wfnx8333wzCxYsIDc3F7PZzFdffYXVavUIfh9//HF++eUXunbtSrNmzbjmmmsYPnw4PXv2LNXr3HPPPdxyyy0YDAYiIiJo27atR9Caf7+9nfMFTZw4kR49enjtH1xaqsAQNmU99mX9zBVctyznWFV957kYDAaaNGniMc0V+OXv1zpq1CgmTJjA4cOHadSoEV9++SVWq7XUzdlL+g0o63s0Y8YMTp065c4aXx4jRozg66+/5ptvvmH48OGsXbuWQ4cOeTRNd/VF/uijjzh+/LjHZys1NbXcr53f3r172blzZ5Hdh1zfrUOHDuX//u//uOuuu3jiiSe46qqruOmmmxgyZEihz5urnDJmuhA1hwS/otrr2rUrXbp0KXJ+q1at2LJlCxaLpVAQ443rrvytt97qdf5vv/3GFVdcUb7CnqPatWvTt2/fEpcr2FfNdXf7kUceoV+/fl7XKXiBWtmqooy9e/emdu3axS5zrv2I83Pt4+23387o0aO9LnOugWNJSnsRV1R/wfwXrb1792b//v188803/PTTT/zf//0fM2bMYPbs2dx1113lLmNx70tYWBj16tVj+/bt5d5+WQ0bNoz33nuPH374gRtuuIEvvviCVq1a0bFjR/cyrVu3Zvfu3Xz33XcsX76cxYsX8+677/Lss8+WKrBo3rx5see2K1nftm3bPGrkvfn1119Zvnw5X331lUfgZbPZyM7O5tChQ0RFRRXbh7FWrVoeNzqg/Me+MgKH6vKdN2zYMB566CF3DolPPvmELl26lDrQLuk3IP/n5IYbbih2W6mpqbzwwgvcd999pKWluWuNMzIyUEpx6NAhgoKCiI6OLnY7+ZNCDh8+nAULFmA0Gt394QHuv/9+PvroIx588EG6d+9OeHg4mqYxbNiwEmtfi/r82O12j+8pXddp3749b7zxhtfl4+LiAMdn5ffff2flypUsW7aM5cuXs3DhQq688kp++uknj226zoGSfieEENWHBL+ixhs0aBDr1q1j8eLFHk3ovMnMzOSbb75h6NChXhNkPfDAA3z66afFBr+NGjXin3/+Qdd1j7vIu3btcs+vbK4aCX9//xKD50aNGnltxrp79+4SX6dp06asX78eq9VaZHbdoi5kKquMFaG873GdOnUIDQ3FbreXuI/nciyLKrOu6+zdu9ddewaOBFxnz54t9+fSlQ15zJgxZGRk0Lt3b6ZMmXJOwW9JBg4cyJw5c1i3bh3du3f32eu49O7dm7p167Jw4UJ69erFr7/+yn//+99CywUHBzN06FCGDh2KxWLhpptu4sUXX+TJJ58s8xi7BQ0aNIjp06fzySeflBj8HjlyBMDdZDq/48eP07hxY2bMmOFOruZNq1at3BmE8yvLsT+Xz9y5fo9W9veJruscOHDAXdsLjqbn4Gj67xIVFcV1113Hp59+yogRI/jjjz+YOXNmqV+nJL169SIyMpLPPvuMp556qtikV2fOnCEjI4NXXnmFV155pdD8xo0bM3jwYHeiqKKYzWaGDBnCxx9/TGJiIl9++SVXXnmlR5K1RYsWMXr0aF5//XX3tJycHM6ePVviPkVGRnpd7vDhwx617U2bNmXr1q1cddVVJX43GgwGrrrqKq666ireeOMNpk2bxn//+19Wrlzp8Xk5ePAgBoPB430VQlRv0udX1Hj/+c9/qFu3Lg8//LD7YiS/pKQkXnjhBQC+/vprMjMzGT9+PEOGDCn0GDhwIIsXLy52mI4BAwaQkJDg0UfQZrPx1ltvERISwuWXX17xO1mC6Oho+vTpw3vvved1yJnk5GT3vwcMGMCff/7Jhg0bPOaXpr/zzTffzKlTp3j77bcLzXPVGLr60xa8mKmsMlaE8r7HRqORm2++mcWLF3utPcu/j+dyLIsqM1DoQttVS3LdddeVuI2CUlJSPJ6HhITQrFkzj/OjIoY6Kuixxx4jODiYu+66i8TExELz9+/fz5tvvllhr2cwGBgyZAjffvst8+fPx2azeTR5hsLHwmQy0aZNG5RSXoeBKavu3bvTv39//u///s9rMGKxWNzDLl155ZV8/fXXhR516tShS5cufP311wwaNKjE19u+fXuh77qyHPtz+cyd6/doVXyf5D9XlVK8/fbb+Pv7c9VVV3ksN3LkSHbs2MGjjz5aqIb0XAUFBfH444+zc+dOHn/88UJN1wE++eQTNmzYQHR0tNfPyRVXXEFAQABff/01Tz75ZKled8SIEVitVu69916Sk5ML9Zc2Go2FyvLWW29ht9tL3HbTpk35888/sVgs7mnfffcdR48e9Vju1ltv5fjx47z//vuFtpGdne3uk3z69OlC8zt16gRQ6PO+adMm2rZtS3h4eInlFEJUD1LzK2q8yMhIvv76awYMGECnTp24/fbb6dy5MwCbN2/ms88+c9dgfPrpp9SqVavIYTyuv/563n//fZYtW+a1VgUcfffee+897rjjDjZt2kR8fDyLFi1y3+GvrEQ9Bb3zzjv06tWL9u3bc/fdd9OkSRMSExNZt24dx44dY+vWrYDj4nb+/Pn079+fiRMnuof9cNXEFGfUqFF8/PHHTJo0iQ0bNnDZZZeRmZnJL7/8wn333cfgwYMJDAykTZs2LFy4kBYtWhAVFUW7du1o165dpZSxIpzLe/zSSy+xcuVKunXrxt13302bNm04ffo0mzdv5pdffnFfmJ3rsSyoY8eOjB49mjlz5nD27Fkuv/xyNmzYwLx587jhhhvK1ZS/TZs29OnTh86dOxMVFcVff/3FokWLPBL/VMRQRwU1bdqUBQsWMHToUFq3bs2oUaNo164dFouFtWvXuofEcfnnn3/cQwXt27fP3dwTHMelpEAQHP0E33rrLSZPnkz79u09ajIBrrnmGmJjY+nZsycxMTHs3LmTt99+m+uuu67CzvmPP/6Ya665hptuuolBgwZx1VVXERwczN69e/n88885efIkr732Gg0bNvTaZ/vBBx8kJiamxOawAIMHD+b555/nt99+45prrnFPL8uxP5fPXEV8j1bm90lAQADLly9n9OjRdOvWjR9++IFly5bx1FNPFeqDet1111GrVi2+/PJLrr322hKbFZfVo48+yr///svrr7/OypUrGTJkCLGxsSQkJLBkyRI2bNjA2rVrCQoK8vpZcC1Tms+Jy+WXX06DBg345ptvCAwMLPT7OHDgQObPn094eDht2rRh3bp1/PLLL4WG6fPmrrvuYtGiRfTv359bb72V/fv388knnxTK0TBy5Ei++OIL/vOf/7By5Up69uyJ3W5n165dfPHFF/z444906dKFqVOn8vvvv3PdddfRqFEjkpKSePfdd2nQoAG9evVyb89qtfLbb79x3333lfo4CCGqgcpOLy1ERXENdbRx48ZSLX/ixAn10EMPqRYtWqiAgAAVFBSkOnfurF588UWVmpqqEhMTlZ+fnxo5cmSR28jKylJBQUHqxhtvLPa1EhMT1ZgxY1Tt2rWVyWRS7du3Vx999FGh5co61FFph5V59dVXvc7fv3+/GjVqlIqNjVX+/v6qfv36auDAgWrRokUey/3zzz/q8ssvVwEBAap+/frq+eefVx988EGJQx0p5ThG//3vf1Xjxo2Vv7+/io2NVUOGDFH79+93L7N27VrVuXNnZTKZCg3VU9Fl9MY1pE5JQ11cfvnlRQ5fU9r3uOD+udYdP368iouLcx+jq666Ss2ZM8djuXM5lgWHOlJKKavVqp577jn39uLi4tSTTz7pMRSMUkV/1gq+3y+88ILq2rWrioiIUIGBgapVq1bqxRdfVBaLxb1MeYY6Ks0QJEoptWfPHnX33Xer+Ph4ZTKZVGhoqOrZs6d66623PPbJ9V3h7VGacinlGLomLi5OAeqFF14oNP+9995TvXv3VrVq1VJms1k1bdpUPfrooyo1NbXY7ZZ0zhaUlZWlXnvtNXXJJZeokJAQZTKZVPPmzdX999+v9u3bV+y6Zfm+UUqpDh06qLFjx3qdV9pjX9rPnLfvktKcY5X9neeNa2ig/fv3q2uuuUYFBQWpmJgYNXnyZGW3272uc9999ylALViwoNht51fW92/RokXqmmuuUVFRUcrPz0/VrVtXDR06VK1atapU+1NWjz76qALUrbfeWmjemTNn3O9lSEiI6tevn9q1a1ehYYy8DXWklFKvv/66ql+/vjKbzapnz57qr7/+8vqZsVgs6uWXX1Zt27ZVZrNZRUZGqs6dO6vnnnvOfS6uWLFCDR48WNWrV0+ZTCZVr149ddttt6k9e/Z4bOuHH35QgNq7d2+Zj4UQ4vylKeWlTYwQQohzZrfb8fPz4/nnn+fpp5+u6uIIUSbz589n/PjxHDlypFQZpkXpPfTQQ3zwwQckJCR4DK0mzh833HADmqbx9ddfV3VRhBAVSPr8CiGEj7j6GkqmUFEdjRgxgoYNG/LOO+9UdVFqlJycHD755BNuvvlmCXzPUzt37uS7777j+eefr+qiCCEqmPT5FUIIH1i0aBEff/wxmqZV2dBYQpwLg8FQqUNK1XRJSUn88ssvLFq0iJSUFI9xcMX5pXXr1thstqouhhDCByT4FUIIH3jsscfQNI0PPvig1GN4CiFqrh07djBixAiio6P53//+584wLIQQovJIn18hhBBCCCGEEDWe9PkVQgghhBBCCFHjSfArhBBCCCGEEKLGkz6/JdB1nRMnThAaGoqmaVVdHCGEEEIIIS5oSinS09OpV68eBkP1qsvLycnBYrH4bPsmk4mAgACfbb+6k+C3BCdOnCAuLq6qiyGEEEIIIYTI5+jRozRo0KCqi1FqOTk5NG4UQkKS3WevERsby8GDByUALoIEvyUIDQ0FHCdXWFhYFZdGCCGEEEKIC1taWhpxcXHu6/TqwmKxkJBk5/CmeMJCK77GOi1dp1HnQ1gsFgl+iyDBbwlcTZ3DwsIk+BVCCCGEEOI8UV27JIaEaoSEVnzZdarn8ahMEvwKIYQQQgghRCWxKx27DwabtSu94jdaw1SvHuJCCCGEEEIIIUQ5SM2vEEIIIYQQQlQSHYVOxVf9+mKbNY0Ev0IIIYQQlUwphc1mw273XdZXIaoro9GIn59fte3TK85fEvwKIYQQQlQii8XCyZMnycrKquqiCHHeCgoKom7duphMpqouSoXT0fFF71zfbLVmkeBXCCGEEKKS6LrOwYMHMRqN1KtXD5PJJLVbQuSjlMJisZCcnMzBgwdp3rw5BoOkKRIVQ4JfIYQQQohKYrFY0HWduLg4goKCqro4QpyXAgMD8ff35/DhwzVyzFq7UthVxffP9cU2axq5jSKEEEIIUcmkJkuI4sk5InxBan6FEEIIIYQQopJItueqI8GvEEIIIYQQQlQSHYVdgt8qIe0JhBBCCCGEKIc+ffrw4IMPnjfbEUIUT4JfIYQQQohqKOl0Ohv+Pcw/e09gs/l+vOA77rgDTdPQNA2TyUSzZs2YOnUqNpvNvYxSijlz5tCtWzdCQkKIiIigS5cuzJw50+vQTp9//jmapnHDDTeU+PoWi4VXXnmFjh07EhQURO3atenZsycfffQRVqu1InfVZ1atWoWmaZw9e9Zj+ldffcXzzz9fNYXy4p133iE+Pp6AgAC6devGhg0bSlznyy+/pFWrVgQEBNC+fXu+//57j/n5Pz+uR//+/X21C+c1V7NnXzxE8aTZsxAVROlZoJ8GQziaIdQxTemg0kEzo2k1K1OhEEKIqpGYks6r839l9Zb9uJK7RoYGcsegbgy75iKfDp3Uv39/PvroI3Jzc/n+++8ZP348/v7+PPnkkwCMHDmSr776iqeffpq3336bOnXqsHXrVmbOnEl8fLxHkHvo0CEeeeQRLrvsshJf12Kx0K9fP7Zu3crzzz9Pz549CQsL488//+S1117joosuolOnTmXeH6UUdrsdPz/PS2KLxVKp48tGRUVV2muVZOHChUyaNInZs2fTrVs3Zs6cSb9+/di9ezfR0dFe11m7di233XYb06dPZ+DAgSxYsIAbbriBzZs3065dO/dyrs+Pi9ls9vn+CJFftav5LeudqJkzZ9KyZUsCAwOJi4vjoYceIicnp5JKK2oapSyo7O/Qzz6Ifvpe9LNPo5+6BT2hAyqpE+rUlaikzugpY9FTn0YlXeJ4JHZAT+yJfvoO9LNPoGfOR89eh376TvSk3ujJ16Knz0HpZ6p6F4UQQpzHTp3N5M6pC/hj6wHyj2pyJj2bGQtW8e6Xa3z6+mazmdjYWBo1asS4cePo27cvS5cuBeCLL77g008/5bPPPuOpp57ikksuIT4+nsGDB/Prr79yxRVXuLdjt9sZMWIEzz33HE2aNCnxdWfOnMnvv//OihUrGD9+PJ06daJJkyYMHz6c9evX07x5cwByc3N54IEHiI6OJiAggF69erFx40b3dlw1rz/88AOdO3fGbDazZs0a+vTpw4QJE3jwwQepXbs2/fr1A2D79u1ce+21hISEEBMTw8iRIzl16lSR5Zw/fz5dunQhNDSU2NhYhg8fTlJSEuAI9l3HIDIyEk3TuOOOO4DCzZ7PnDnDqFGjiIyMJCgoiGuvvZa9e/e658+dO5eIiAh+/PFHWrduTUhICP379+fkyZMlHsuSvPHGG9x9992MGTOGNm3aMHv2bIKCgvjwww+LXOfNN9+kf//+PProo7Ru3Zrnn3+eiy++mLfffttjOdfnx/WIjIw85/JWR66hjnzxEMWrVsGv607U5MmT2bx5Mx07dqRfv37uL5WCFixYwBNPPMHkyZPZuXMnH3zwAQsXLuSpp56q5JKL85HS01H2kyiVi66no2f8H3ry1egJbdATL0VPm46yO35ElNLRs75EJXZBpU6CnO/BshJyvgDbVqDADRXrasj+wlHr637BZLCshZyvIP15SB0NljWgJ4B9PyrzNfSkbtgTWmJPuAT9zCPoaa+hp4xCPz0aPf1NlP1E5R0gIYQQ5525363ndFoWdt37Re7HyzZyPOlspZUnMDAQi8UCwKeffkrLli0ZPHhwoeU0TSM8PNz9fOrUqURHRzN27NhSvc6nn35K3759ueiiiwrN8/f3Jzg4GIDHHnuMxYsXM2/ePDZv3kyzZs3o168fp0+f9ljniSee4KWXXmLnzp106NABgHnz5mEymfjjjz+YPXs2Z8+e5corr+Siiy7ir7/+Yvny5SQmJnLrrbcWWU6r1crzzz/P1q1bWbJkCYcOHXIHuHFxcSxevBiA3bt3c/LkSd58802v27njjjv466+/WLp0KevWrUMpxYABAzyad2dlZfHaa68xf/58fv/9d44cOcIjjzzinu8K9A8dOlTyAXayWCxs2rSJvn37uqcZDAb69u3LunXrilxv3bp1HusA9OvXr9A6q1atIjo6mpYtWzJu3DhSUlJKXTYhKkK1avac/04UwOzZs1m2bBkffvghTzzxRKHl165dS8+ePRk+fDgA8fHx3Hbbbaxfv75Syy3OL8qyGXvGW2BZ7XW+hoamTkPWx6jsxajIjyFzDuR+73X5ipLXSE0BqZC7FIVyzlFgWYue+RZgBEM9DEFDIWgsBoO/T8slhBDi/GCz63z7+79FBr4ABoPGd2v+5d6bevq0LEopVqxYwY8//sj9998PwN69e2nZsmWJ665Zs4YPPviALVu2lPr19u7dS58+fYpdJjMzk1mzZjF37lyuvfZaAN5//31+/vlnPvjgAx599FH3slOnTuXqq6/2WL958+a88sor7ucvvPACF110EdOmTXNP+/DDD4mLi2PPnj20aNGiUBnuvPNO97+bNGnC//73Py655BIyMjIICQlxN2+Ojo4mIiKiyH1dunQpf/zxBz169AAcwX9cXBxLlizhlltuARyB9uzZs2natCkAEyZMYOrUqe7tBAUF0bJlS/z9S3+dcOrUKex2OzExMR7TY2Ji2LVrV5HrJSQkeF0nISHB/bx///7cdNNNNG7cmP379/PUU09x7bXXsm7dOoxGY6nLWBPozocvtiuKV22CX9edKFefEij5TlSPHj345JNP2LBhA127duXAgQN8//33jBw5ssjXyc3NJTc31/08LS2t4nZC+JxSOsq6DVQqaKHolrWo3D9R9qNoKgOUhsZZKCYhgHL+zwCgMuHMGFBV0xxZc4fEmrNMBnTsoB9Fz3gNMt5G1foWzboRPWsB2A+DFo4WeAOGoNvRjLWrpNxCCCEqXmZ2Ltm5JSd2SkhJL3GZ8vruu+8ICQnBarWi6zrDhw9nypQpgCMgLkl6ejojR47k/fffp3bt0v9GlWbb+/fvx2q10rNnXuDv7+9P165d2blzp8eyXbp0KbR+586dPZ5v3bqVlStXEhIS4vW1vAW/mzZtYsqUKWzdupUzZ86g645w5MiRI7Rp06bEfQDYuXMnfn5+dOvWzT2tVq1atGzZ0mM/goKC3IEvQN26dT1aQ3bt2rXYgHX16tXumwQA7733nkfT9Io2bNgw97/bt29Phw4daNq0KatWreKqq67y2esKkV+1CX7Lcydq+PDhnDp1il69eqGUwmaz8Z///KfYZs/Tp0/nueeeq9CyC99SeiZ67u/Yc1ahcn9yBL7elnP+NaCVKhmIQqFhr7LAtyDNGQC7/jrkoFKu8awhVumozHewZc5GV3bAgGa6FL+QuzCYS04qIoQQ4vwUFGDCz2jAZi++ficyNNBnZbjiiiuYNWsWJpOJevXqeSSKatGiRbHBFjiCxkOHDjFo0CD3NFeA6Ofnx+7duz0CurJsuyxczaSLm5aRkcGgQYN4+eWXCy1bt27dQtMyMzPp168f/fr149NPP6VOnTocOXKEfv36uZuGV6SCNbqappXqJoFLly5dPGrfY2JiMJvNGI1GEhMTPZZNTEwkNja2yG3FxsaWeZ0mTZpQu3Zt9u3bd8EFv3YfjfPri23WNNWqz29ZrVq1imnTpvHuu++yefNmvvrqK5YtW1ZsKvknn3yS1NRU9+Po0aOVWGJRWrquY8v5nZxTQ8lJ7IT17H3oOV8UGfh6rIsq1Y+DOg+/QDTn/zypAn+d/1Y2DOiAFWVZjfX0aGzpnn2LyvIjKYQQomr5+xm5ultLjIaib+DadUX/7q19Vobg4GCaNWtGw4YNC2VIHj58OHv27OGbb74ptJ5SitTUVFq1asW2bdvYsmWL+3H99ddzxRVXsGXLFuLi4ry+7vDhw/nll1/4+++/C82zWq1kZmbStGlTd5/d/PM2btxY6lrX/C6++GL+/fdf4uPjadasmcfDW/C8a9cuUlJSeOmll7jsssto1apVobw0rgzSdnvRQ1O1bt0am83m0U0vJSWF3bt3l2s/ihIYGOixT6GhoZhMJjp37syKFSvcy+m6zooVK+jevXuR2+revbvHOgA///xzsescO3aMlJQUrzcSajq78t1DFK/aBL+1a9cu852oZ555hpEjR3LXXXfRvn17brzxRqZNm8b06dPddxkLMpvNhIWFeTzE+UG3nSL71FCyTzYmN7Ep1jOjUdYNgA0Fzoec9S6aRqEabnvGm1jTZpJ7ZhLZJ1uRk9CU7MRLyD1zH5asRVjSZ2FJn4ktZzlKVY8xE4UQ4kJy5/XdMPn7YfASAGsaXN2tJS0aeR+OxtduvfVWhg4dym233ca0adP466+/OHz4MN999x19+/Zl5cqVBAQE0K5dO49HREQEoaGhtGvXrsjhhR588EF69uzJVVddxTvvvMPWrVs5cOAAX3zxBZdeeil79+4lODiYcePG8eijj7J8+XJ27NjB3XffTVZWVqkTa+U3fvx4Tp8+zW233cbGjRvZv38/P/74I2PGjPEavDZs2BCTycRbb73FgQMHWLp0aaEKl0aNGqFpGt999x3JyclkZGQU2k7z5s0ZPHgwd999N2vWrGHr1q3cfvvt1K9f32sysaJs2LCBVq1acfz48TLt96RJk3j//feZN28eO3fuZNy4cWRmZrpz7gCMGjXKoyvixIkTWb58Oa+//jq7du1iypQp/PXXX0yYMAFw1KI/+uij/Pnnnxw6dIgVK1YwePBgd0IyISpLtQl+y3MnKisrC4PBcxddHeqlxqv6sFkOkJV0PTnJXcC6odgAt7QBcHUOkstSdqWURz2xjsKW+Sb2nK+BXBQ6up6ELWcZ1tSHsWa8hDVjBrln7iUr8RJs2T/IuSKEEOeRRnWjmPXkLTSIjgAcAS+A0aBxQ58OTL676gIJTdNYsGABb7zxBkuWLOHyyy+nQ4cOTJkyhcGDB59TkGM2m/n555957LHHeO+997j00ku55JJL+N///scDDzzgHkv2pZde4uabb2bkyJFcfPHF7Nu3jx9//LFcQ+rUq1ePP/74A7vdzjXXXEP79u158MEHiYiIKHR9CVCnTh3mzp3Ll19+SZs2bXjppZd47bXXPJapX78+zz33HE888QQxMTHu4LCgjz76iM6dOzNw4EC6d++OUorvv/++TMmrsrKy2L17t0eG6NIYOnQor732Gs8++yydOnViy5YtLF++3KPr4ZEjRzyGVerRowcLFixgzpw5dOzYkUWLFrFkyRL3+2I0Gvnnn3+4/vrradGiBWPHjqVz586sXr36ghzrV/fhQxRPU9XoynbhwoWMHj2a9957j65duzJz5ky++OILdu3aRUxMDKNGjaJ+/fpMnz4dgClTpvDGG28wZ84cunXrxr59+xg3bhydO3dm4cKFpXrNtLQ0wsPDSU1NlVrgSqT0VHLS3sSeNReFzWOehlZsv11DoWbBhWmAQSv+3o+jgbEfGBuC/WCpy+5LrsC3tAGwUq70Xbj/4uwfrPJNKZZWG7/gO8FQB40sDH7NMJp6oJVw/IQQQhSWk5PDwYMHady4MQEBAeXejlKKv3cfZ/+xU5hNfvTs2Jha4YWb4gpRXRV3rlTX63NXubfsiCY0tOKvo9LTdTq1Sap2x6UyVZuEV+C4E5WcnMyzzz5LQkICnTp18rgTdeTIEY87cU8//TSapvH0009z/Phx6tSpw6BBg3jxxRerahdECay5O8lKGQY4xuMr3L/VEcTpKIzKgMFLAOxKClW8kgNkAPzaQuQcSH8Rcr7F0Viicu6rFdyPsga+4LgLrxe6v1XKfXfS9WSs6S8X2GsNo6kb5tBJ+JkvLdP2hBBCnDtN07i4VQMubtWgqosihCgjHQ17Ga/HSrtdUbxqVfNbFarrnaXqRqlcMlPGYPMy9q6G9yAYwA+D1xrgkmp/i8/4rKGZemAIGgnmPmian6Ppb+4qVPbnYNsHWigEDEQLGoLK3QHp00Df41w/GAJvAVNnyPkO9BQwxoDWyDEUEWlgjIeAvqCnQ84SVO5vgMX16o5jQl6YWp7A13Vq63j+ddFLEcQrpTyW8lpXrEUQGPZfTEG3oGkX1jh9QghRVhVV8ytETVeTa34374ghxAc1vxnpOhe3Sax2x6UyVauaX1Hz6PoZcjPmkpvxPyjQvLlU66MwlvEul4YB73GvH1rAtWhhL2AweDYd0zQNAq5ACyg8/p0W2AMCv/P+YoGl6N8UeA0AyroHZd0Mygr+l2A0tUK3HUGlTUNZfsV7jbMG/l3Buh4wAnZHP1/nDhYMeF1K3Wy6wDpe11JnyU59lNzMjwmtvQjNEFSqbQshhBBCXIh05Xj4YruieBL8iipjs2wnI2UoqLRil3PVeXqr/XUEv568h8IaaMEYDLUxBPbDEDgcyEG37kZTGeDXHs2/VZXWXGr+LdD8W3hMM/g1hKjZjhpY6w5U9hKwbAJNQzNfhiHoVjDURVnWo7K/QNkOgp6O3X7gnJN6FWwUUtLWdNs2Ms8+TkjUW9htx7BZNgIafuauGI31zqksQgghhBBCnCsJfkWlU8pCVvr/yM6YCflqbkvup1tGWjjG8NcxBvQC/LwmaDL6Na/Y1/QRTdMwmtqCqa33+eZLIV/fW0vaS+iZ7zmPqSsM1nA1YtacCa9Kek1XAFzaQNqWs4QzCZvQ9SP5t4Qp4FqCI17BYIgo1XaEEKKmk15nQhSvJp8jdh/1+fXFNmsaCX5FpVHKTlbmHLLSXgeyPeaVFPjm7//quV6++X5d8DP3RDOGoRkbYTBfjqZdmB9xU9gT2M09sGV+iG75CzBgMF+Gbj+Bsm4p1TYcmaLLzjPwBVBYcn7AmrQB/8CbMQdcib+pRzF9roUQouZyDVWTlZVFYGBgFZdGiPNXVlYWQJmGd6ouJPitOhdmZCAqnSV3LWdTxoBKBzSPPrfnUuPrqMEMxj9iNv6Bvc69oDWI0dwbo7m3xzSlssk5/QC65adS1f6Wh6OuuSCF0k+RkzmHnMzZGP1aEx41DzQDup6K0RgrtcJCiAuC0WgkIiKCpKQkAIKCguRmoBD5KKXIysoiKSmJiIgIjEZJpikqjgS/wqeUUqQkX4ctX22jhgKF+8c+r1Fu2X78jaaemEMfxmjqLBcOpaRpgQTWeh/duhtbzjJ0PQ1lz0LpO1HW/SiyQCk0reQ+vkUpaj3H0E2Od9ti3Uli4iV55QJMpp6ER76Bn19cOV9ZCCGqh9jYWAB3ACyEKCwiIsJ9rtQ0utLQlQ+GOvLBNmsaCX6Fz9jtmSQntAdyCgzb43gYnFmJ7WXM2Gzwa4Up5B78A2+WoLecDP4tMfm39DrPbjtBzpl7wLoNPG5MFF9PXGIfYjR0pWP1ui7kWv4gObE7YREzCAq62WsfbSGEqAk0TaNu3bpER0djtXr7VhTiwubv7y81vsInJPgVPmG3nyEx4SI0ctGKqNPVcQTAaJo7OFZeszoHExTxLn4BvdA0haaZfV7+C5nRrx7Bdb5Dt5/AbvkHXU/BbtmMNXspkON1HVfgay8iK7drGXsJr63QST37ABkZb2EOuA6jMZYAcw+MxhgMBhmvTghRsxiNRrnAF+ICJH1+q44Ev6LCZWdvIeX0dRi9jkvrnR1VYMxeDaP/JQSETcbf3ME3BRXFMhjrYQh0DlEUPAIiX0cpHbvlL7LTpmG3/gtYQItAqVzspBf/lau8j1ScN1u5WwXYbXuxZMzMm6fA368joWH3EBx4o9T4CyGEEEKIMpPgV1So5FPjyM5ZgqaVrg9vwebQOgpz4BCCI96QZq/nIU0z4GfuSmidJR7TszJmk5n2Aq4QtuB7r5w3N4rjCny9vy5YbFs5dXo8KdrjBJi6EhjYl+CgmzAawsu9P0IIIYQQlc2OATsVf51bUgs7gQ+OurggKaU4evJasnOWlGtdAKMxnrBaCwmJnCmBbzUTGDwWf/PlKDQokEXa9e+S+guXlGDLFU7rKoOs3F85dfYpDp9oxeETnTiT9g5KSb85IYQQQghRNIkwRIVIPHUXNvsW7DjuOtkV6KrkkEYDNEMwIRGvExm9Bn+zDFdUHWmaP+FRcwkJfwGDsYmzFt+A0sKcOZ69j9Ncttdw/C1YQ2zXEzmT9gKHTrQl17LnHF9FCCGEEMK3lDPbc0U/lGR7LpE0exbnLCX1DTJzv/cIbhSO1Ej+Cvy9nIeuYEgnkDrR/2I0miqlrMJ3NM2fwOA7CAy+A6V0HO+wlayMd8nO/BD0FDTl/XZIaYdVKrb2WKVzPOlawkMngqbh71efIHNP/IwxZd4XIYQQQghfkYRXVUeCX3FOEk8/TUbmB0VW61lxZHQ2OPsA54U+GhBJdN0/MRgk8K1p8pqtmwgOfZCgkAno9uPY7WdISbkdpU57Lk/5xxUGV39xgCxOp7/kseXQwCFER07HYAg6h1cQQgghhBDVnTR7FuV2Jv1TUjM/AIpv0mrB1VRV4fw/QcEPEVv/XwyG0EooqahqmuaH0a8RJnMnomNWEhR0G2W596aU8+FtHsVlkVakZ3/JiVOjnbXRQgghhBBVy64MPnuI4knNryiXzOw1JJ19lNKMTqhwZp9TgOZHvdgtGI21fFo+cf4yGusQEfk64REvY7cfIytzMVlZC0BPQPcSxjrzoRWZwTAvIC76Fky25Q+OnRpDaNAg/IzRBJkuwWAIPJfdEEIIIYQQ1YwEv6LMTqd9SELq0+7At7RDriqgbvQGCXwF4KgN9vOLJyz8YcLCHwYg17KF1NQXyLX84bFsUXW2xQ2P5F5GgQ2w5vxMWs4vjtfGn/DAm4mNnIrRGHxO+yGEEEIIURY6GroPGuCWNKykkGbPoozSs3/lROozQF7goUpxnukKYqP/wd+/rk/LJ6o3s6kT0XUWUTd2E0GBIx1Zw0tcS6OoWl9X4OtoGq2hO//asXE6+3N2nmjJsVMPoZSlIndDCCGEEEKchyT4FaVm1zM4fGoUGo4PjiNbs0NxAbCGgbrRGzCZ6vi+kKJG8DPWo1bUK4SFTnRPKzrELZpOXuCr3FvJ26JCcSbrC7YfbUpmztZzLLUQQgghRMlc2Z598RDFk+BXlFpq5teAcgS67mBXc/fFzB8Au/5tMNSnUb2jmExxlVZOUXNEhT9Bg9ithATdjr9fC0x+bQgJuBmTX3vA6PwYem/8rJQr+HX9EHj7QdBA09BR7Eu6jrOZ3/loT4QQQgghRFWTPr+i1E5nfOFshqphQGFQjv6+Cg07ynEnxRmDKMCuoHndjWil7RQshBd+ftHUjnq10HSlFHb9LMeSBmG1Hyhiba3k3i9KOYfhgsMp4wgNuAyjMfwcSy2EEEII4Z2vMjPbS9MX8QInwa8olczcv0izbsHVWMCKgQAtrzemylcD7EzrTGTIwxL4Cp/RNA0/YyQNY1dw6GQv7PpxL0s5PovFNph23MHBNdrwvqTh2MnBrqcT4NeU2qG3ExHYH00rTW5zIYQQQojiOfKQVPw1si+2WdNIs2dRKoeSJ+Z75uhTYHONvVqgubOjmamZOhGTKruY4gJk0Mw0qbeRmMi3MBgcmcSLGhPYm4I3SbOs/5Bp3UeOPZGzues4cGoc+5PvQSlrxRZcCCGEEEJUKgl+RYmycraRqx9xPsvrP5mLESuGQkGGDjSMXia1vqJShQXfTNN622hW/xDBgf2dUzXnJ7boUNhd8etcHo9/K3QMnMn5hZOpb1V8oYUQQghxwdExYPfBwxfDJ9U0coREifan3If3pqMaVgxkYyRLGclSBixohAeOJNDcpgpKKgRomol6tT4grs5XhAdeh+Yekdp7UiyPlgt5WynwF06mzZbaXyGEEEKIakz6/IpipedsJNd22BH6FlmRm5dUyGioQ71aL1VO4YQogqZpBAVcSlDApcTYk0hMfYXTmQtxZysn7/OcfwgkDbB5vSeooZNLlmU7weaLfF5+IYQQQtRckvCq6kjNryjWodNTAEeAYFMaFmXAogzYlOZ1bN9msT9Ic2dxXvEzRlM/6jXaNThCvYgZKOfNGt3dPz2vYbSdYhtIk2XZVRlFFkIIIYQQPiA1v6JIdj2DLOsOlNIcfQjyxbQ6GjbAX+kYNUe4YCAYs19s1RRWiBJomkat0FuICB7EoeQ7yMhdS16oa8SO7sySWPTNG39jNABW+2kOn32blKyfsemnMWgBRAVeSf2wMYRIk38hhBBCFEP3Uf9cvdTpPi9cEvyKImVa9mBVmrvPZOGQQGHFgKbsGDRFk9pzK7mEQpSd0RBA05jP0VUOudb9gAGlNP5N7F/CmgaCzReRkrWCHUnjAZt7jq5yScr8mqTMJTSvNY3o4OsxGMy+3A0hhBBCCFFGEvwKr+x6Fv8m3e1+7r0lsyMbrg0D8aGTCQu6tLKKJ8Q5M2gBBJraup+HmLqQYfmryOVrBd2IxZ7CjqT7IN+o1vkpFHtSnmR3ylME+MVRP3Qk9cJGYNBMFV18IYQQQlRTdqVhVxXfTdAX26xppM+v8CohYxFWPbWERFeAc5Du2MgxlVQyIXyjWZ13Mfs18jovyL8DjaKmcCJtHkUFvpA/R7Qix3aM/WdeYlviPejKUuHlFUIIIUT15IthjlwPUTw5QsKrY2ffK8PScpdJVH/+xmjaxi6jYeQUAv3b4G+IJtjUifiol2kd+wVGQygpWT+VuB3Ps0FxNmcdx1I/9lWxhRBCCCHK7Z133iE+Pp6AgAC6devGhg0bil3+7NmzjB8/nrp162I2m2nRogXff/99JZX23EmzZ1GIUgqLnuRs1Oz4T1G1v0qBv6FOJZZOCN8xGkKICb2DmNA7vM7X8/XzLS2lFAfOvMLB1Hk0Cr+DuLDhGA2B51hSIYQQQlRXujKg+2CoI72MQx0tXLiQSZMmMXv2bLp168bMmTPp168fu3fvJjo6utDyFouFq6++mujoaBYtWkT9+vU5fPgwERERFbQHvic1v6IQiz0ZpRS6coxDVlyzZ02DmNDbKq9wQlShUFO7Epcp+LOjaY6HRU9mz5nXWHm4JwkZ36OU7ptCCiGEEEKUwhtvvMHdd9/NmDFjaNOmDbNnzyYoKIgPP/zQ6/Iffvghp0+fZsmSJfTs2ZP4+Hguv/xyOnbsWMklLz8JfkUhObaT2NDIxZ8cTOTojmzP+W8muf5tVxr1wsZWQSmFqHz1wkYVO1+5/xZo/Jzv3NHJZmvyw2xJvA9dlb0mWQghhBDVm6/7/KalpXk8cnNzC5XBYrGwadMm+vbt655mMBjo27cv69at81rupUuX0r17d8aPH09MTAzt2rVj2rRp2O1F50M530jwKwo5lDoXK37uC3kr/mTp/tgxoJTjQl4BOboRXQXjbwytyuIKUWmiAq+iTvAgoHANr2fgmxf8us4X0DzmJGX/zoEz7/iyuEIIIYS4AMXFxREeHu5+TJ8+vdAyp06dwm63ExMT4zE9JiaGhIQEr9s9cOAAixYtwm638/333/PMM8/w+uuv88ILL/hkP3xB+vwKD3Y9hwR3Up+8C3g7RrKV0f3ckeNZo0GYNHkWFw5N02hZ+w2MWggJGZ95CYALB76AR/ZFhWuQMMXhtHk0iRwnQyEJIYQQFxAd3wxL5OpQdfToUcLCwtzTzWZzxWxf14mOjmbOnDkYjUY6d+7M8ePHefXVV5k8eXKFvIavSfArPJzN2Y7j1CnuhFQoZy1WkH+zyimYEOcJTdNoXvt56oYO48Dplzmbu9ZdswsaBXNN2NEKNYN2salsfjt2I9m2U/gbQmkQOojGYbcR4Fc4yYQQQgghRGmEhYV5BL/e1K5dG6PRSGJiosf0xMREYmNjva5Tt25d/P39MRrzKsRat25NQkICFosFk+n8v5kvzZ6Fh5OZ31OWoYty7Sm+K4wQ57EQc1s61P2YLvV+pF7o7fgb6mDQglEYHHd00bBiQC/ya9ZxnmVaj2BX2eTYk9h39iNWHr2JdMu+StsPIYQQQlQu3Xl94ItHaZlMJjp37syKFSvyyqXrrFixgu7du3tdp2fPnuzbtw9dz0vauWfPHurWrVstAl+Q4FcUkJS9msK9Gb1w9mMMkZpfcYELMjWlea0p9Gi4lssa/U185EPYMTp/gLzfSFKu/zrPIwXoaOgoLCqDlceGsj1lBrouCbGEEEKImsauDD57lMWkSZN4//33mTdvHjt37mTcuHFkZmYyZswYAEaNGsWTTz7pXn7cuHGcPn2aiRMnsmfPHpYtW8a0adMYP358hR4fX5Jmz8KDXWWhK2diniIrgB09Fm3KQGRgl8ornBDVQMOwkSRk/ECGdbfH9Ly+vjj+pRS6s0m0wpBvCVDY2Z86j0NpX9A5ejp1g/tU5i4IIYQQ4gIwdOhQkpOTefbZZ0lISKBTp04sX77cnQTryJEjGAx5AXVcXBw//vgjDz30EB06dKB+/fpMnDiRxx9/vKp2ocw0pco4GvIFJi0tjfDwcFJTU0tsO18TrDo6iAzrfoxQdACsFDaM2PFjUOPNaJo0IBAiP5ueyb4zb3I07RPy1fPm6xusUErDggHlPtsKc307d4udKQGwEEII4VRdr89d5f7fpksJDKn4OsjsDBsPdP6z2h2XyiRRi/BgNIQBBmwYHLVSBcb2tSsNi7NJp0KRkvN3lZVViPOVnyGYVrWeonfDNUQF9nGeL+AKcnXl6A/sCHyLp4A/Eh7maPqKEpcVQgghhBBFk2bPwoNVt7iHa7EDdhSaMwDOf/Hukm07WbkFFKIaMRuj6Bw7i1x7Cmdz/gYUoaY2rD4+AvTTzqWKTjCnOdtJK+DPpMc4kzuKDrUnVkLJhRBCCOEr5emfW9rtiuLJERIejAYTdpW/xtfVJzFv/NK85pvgb5AmFUKUxGysRUxwX2KCrybIvz5NI0bnm1v6nie7Uz/mZOaaii+gEEIIIcQFQIJf4SEmsJdzqJais9Q6KqM0lNII9KtfqeUToiZoGj6auJAb0fIlufJGqbwB6112n52PTc8hy5aETc/xaTmFEEIIUfHsGHz2EMWrdkfonXfeIT4+noCAALp168aGDRuKXf7s2bOMHz+eunXrYjabadGiBd9//30llbb6aRR2s7tm164cl+bK2exSV2BTBqzKgHLOMxr8q7S8QlRHmmagY50pdK/7fxi1ALylHXRNyz9mn1JwMvtvvjxwJUsODWLRgatYl/gc6dZjlVRyIYQQQojqq1oFvwsXLmTSpElMnjyZzZs307FjR/r160dSUpLX5S0WC1dffTWHDh1i0aJF7N69m/fff5/69aW2sigafujKUbOrY8CuNOy6RobdTJoeSKYeQKYeQLpuJlf3I8BQp6qLLES1pGkadQK70qfBF5iM4YDzRpPKC3xtGJxdDhzTbBidN6fsAOjYOJj+Pd8evoVdZxciyfuFEEKI85+uNJ89RPGqVfD7xhtvcPfddzNmzBjatGnD7NmzCQoK4sMPP/S6/Icffsjp06dZsmQJPXv2JD4+nssvv5yOHTtWcsmrjyxbEjp+zotv5Qh8CcBeaDgWDStGfjkuyXeEOBch/g3p3+gX/AzR6ICOhh1XNmjPwNch7zx0ZWC3KdiQPINvD4/CrtsqfyeEEEIIIaqBahP8WiwWNm3aRN++fd3TDAYDffv2Zd26dV7XWbp0Kd27d2f8+PHExMTQrl07pk2bht1uL/J1cnNzSUtL83hcSMzGcBQadowopZGDqZilNZJz/2Ft4kuVVj4haiKD5k+vuu+i44/d2eqicF/gfEnnXEEvRseQZDhaa5yx7uez/X1JtRyu5D0QQgghRGnpPurvq1ef0K7KVJsjdOrUKex2OzExMR7TY2JiSEhI8LrOgQMHWLRoEXa7ne+//55nnnmG119/nRdeeKHI15k+fTrh4eHuR1xcXIXux/ku2L8uof6NsWMgV/mjClyEK+Xo++tK0W5XGntSl3AobWXVFVqIGiDc3Iwr68/DXyucQb1gAjrdHSBDXlDsWMaOhSWHb+NEZvH5EIQQQghRNXRl8NlDFK9GHyFd14mOjmbOnDl07tyZoUOH8t///pfZs2cXuc6TTz5Jamqq+3H06NFKLPH5oWOtCYAry2xeH0JX00s7fs6Lb8cFuI6RXxOe4Vjmn1VSXiFqiqiANtzQZBXdo18m0tQafy0UkxbpMRiSIwO0cwDgYvx0/EHWJLwi/YCFEEIIIZz8qroApVW7dm2MRiOJiYke0xMTE4mNjfW6Tt26dfH398doNLqntW7dmoSEBCwWCyZT4Sa9ZrMZs9lcsYWvZuqH9KZOwMUkZP+NUhqa5rjgtjr7HDoupQt3qP/x+KNcH/c+dQJbVWp5hahpGoT2pUGoo4tHrj2VLw5c42iD4T7tSpfQYk/aNyTn/MsNjeaiaZIEQwghhDgf2J35PXyxXVG8alPzazKZ6Ny5MytWrHBP03WdFStW0L17d6/r9OzZk3379qHreSNl7tmzh7p163oNfEWey+u9gQGz+2Lb7ky+owokvfLohwj8evJZqWkSogKZDGEE+9X1GHasNDTNcWaeseznu6P3szt1ORZ7pg9LKoQQQghxfqs2wS/ApEmTeP/995k3bx47d+5k3LhxZGZmMmbMGABGjRrFk08+6V5+3LhxnD59mokTJ7Jnzx6WLVvGtGnTGD9+fFXtQrXhbwimcdhNeX18i+hfmMfxPN12kkMZv1ViSYWo2TRNo3XEMMdY2+6ppb/BpBQkZm9hVcJ05u27nr9TFsgNKiGEEKIKSZ/fqlNtmj0DDB06lOTkZJ599lkSEhLo1KkTy5cvdyfBOnLkCAZD3pseFxfHjz/+yEMPPUSHDh2oX78+EydO5PHHH6+qXahWcvRUZ42vS16m2aJaUCoFf6fMo3Fon0oooRAXhpYRt5CU8w+HM37Nl/xKUVLzZ4XzXHWexDo2Npx6jzTrSS6PfdiHJRZCCCGEOP9Uq+AXYMKECUyYMMHrvFWrVhWa1r17d/78UxIxlUeQsRYKA7r7Crpkmganc/ehKxsGrdp9vIQ4Lxk0I5fFPk+DtMtYn/wGVlV88+W8il3Na1PpnalLaRrShwYhnX1RXCGEEEIUw45v+ucWPZircJG6cVGkFuGDnBfOnkMdFaQrsOoGcnUjFt2ITWksPzYZpfTCCwshysWgGWkafi23NvmW+kF5eQ7yn5NK5T3PPzySKvADqwG/JLxIcs5eXxZZCCGEEOK8IsGvKFKkOR5dC0fHe9CrnEGvRfk7m0cb0NGw4ceBzLV8efh+7Mpa6eUWoibzMwTQt/7r9Kv/LrXN7fISYTnPUeUchsxV6wuFxwkGyLafZuGhe1h8+AEybSmVVn4hhBDiQid9fquOHCFRpMTs3eTq2diVsVCrZ7sOFmXEjmsYKa3AX0jM2clXhx+plLIKcaGJDerEdQ3ncEOjBeg4bkDZMaArDaXyzkObY5CkQuu77mclZO/k6yMPYdVzKqfgQgghxAXOrgw+e4jiyRESRTqUsQ4NIzoadnetEliVgRz80T2SYRWkoQHHs7ezN02yPwvhK5HmeG5sNI/apjbu/r0KsCnNa+Drmq/rjh9fi4Lk3JPM2TuUNUkfkGk7U/k7IYQQQghRCST4FUWyK6vzslnDqozYdA0UWJ150rwNeJSfUo75KxLe9HlZhbiQRZrjGRw/h/7138CGAQtG582pwoEvgF3XsLpvYDmaSWfr2axP+YJ5B+7hbO7Jyt8JIYQQ4gLh+u2t6EfB331RmAS/oki1A5qiu/PGGbBhJNedILx0J5cCsuzpbDu93BdFFELk0yCkK51rjQY0iko3pysNK37oODJNOn4sHRSKLHsanx56AJtuqZxCCyGEEEJUEgl+RZGahvQiwBjmbMAMjgvqvGaURTd5di6t4b4L9WPiGxzL3ObL4gohgC617qRL1ChwJqCz538oA3aVlxArj0b+thzZeirLT7xe+YUXQgghLgDS57fqyBESRTIaTPSv9ywGzQ/NndjKRXP3HfTGlX3W3fRSweIjz/q4xEIITdPoWudOrm/wBqF+dXFkfdYcibAAu/MGluPc9d6CQymNnWmrWH78bXJsGZVWdiGEEEIIX5LgVxSrQfDF3NJoFs3DrsCAn0edr6tfQf5hVlzPASwYcV9ca5Crslh05Dl0JUNwC+FrDYIv4vYmC7gx7n9cEfsotcxN0TA4W2+A1wzQCuzORFk2jPyduowZe27l22OvVm7hhRBCiBpMd96U9sVDFE+CX1Gi2gFNuKbeU4xruZybG74JHs2gHc0p8481aseABT+Ul4/X/ox1zNh1KztTf6/MXRDigqRpGnWD2tMmYgC3NHqXjlFD0IrI0q6UY1gkzybRGijF9rRfmX/wMZS3Ab+FEEIIIaoJCX5FqWmagfpB7RhYf6qjGbQCnH16LfiT63zYMHrNNqcBKI1sezaLj77Md8dnSi2wEJXEz2CiV/Q4Otca6pziGcjavYzV7Xjq6LZwLGsbnx74L7peVCotIYQQQpSGHYPPHqJ4coREmTUN7cHIJh8QG5g3rmhJ6a+UcjTxsCgjOn7oGNl8ZgWv7RzBscxdlVFsIQTQKXIgeQGu8+xVrm4MRTSX0jTQNA5mb+XtvWOx6rmVUVQhhBCiRpJmz1VHgl9RLpGmBgyN/x+RpubkKn/HyVZsk0gNi/IrUCOskaNn8tHBJziZvd/XRRZCAGH+0VwRc5/zmYZjgCPXv0t22prCjF1j2Jf+N7qSWmAhhBBCVB8S/IpzMqrJuxg1f2e/X81rn0ClwKYMRdQsaSjsfHX0NWy6tVLKLMSF7uKowdzYYCrRAc0p9Zjd+U7tLD2TTw5PYc7+SWTa0nxTSCGEEKKG0p0JKH3xEMWTIyTOiaZp9K4zGtz9DDwvpF3NKa3Kr9C8fFshxXKc6Ttu48eTH5Jtl6FVhPC1pqGXMqrx20xosQhFICWP3A32/BncgYScg3x2+AVJhCWEEEKIakGCX3HOLo66jlqmhig0bGjY8/U7UGjYVOGg2BursvLHqW95b9/DEgALUUkCjKHEBbaHYvoJOcbsdrTccPXftzsfR7J2czjr38orsBBCCFHN2fP9jlb0QxRPgl9xzkyGAG6Pf5n6ga3BOVSKjoZSjkcpKpQA1wU1JOcm8/aeh0i3nvFpuYUQDl1rX+88Z50TCgzerQNW3ei4seUeKsk16reBTw+9RKYttZJLLYQQQghRNhL8igoR5BfOqMavc03MOIyYHMMfKT8y9ABsGClNNuj8fYLPWk/x9t5J5NqzfV94IS5wTUO6cEnUYBQGdN3ZcsPZisOiG7Ep/wJ99j3/5ugZvLn7fnalbZLhy4QQQogSSLbnqiPBr6hQnWsN4sFWn3Nt3QeIDmgGgFWVHPwWbhqtkW47y4LDr/qsrEIIB03TuDr2bm5s8Bj1glqhMGJXRmzK6Exk57o5VdR5rJGtpzP30Iu8vvtB0qynK7P4QgghhBClIsGvqHAmQyCdIvsTF9QOzVlbZFUGbEpzt6bM/7DpRWSnUxp707ew6fQq7MpW6fshxIVE0zTahPdmTJPX+W/bbxhc7yH88Mu/BMX13VcKNAUplpNM3zmOjSm/Si2wEEII4YVSBnQfPJSS0K4kcoSEzzQKbuMeQVQpR02SRRmxKQN2ZyIsizI6s0QXpjmvsxcefZsXd/yH1cnLJKusEJVA0wx0jLqSR1t/Qi1TgzKs5/irKzuLjr3LKzsfICU3wUelFEIIIaonO5rPHqJ4EvwKn2kVdglhfrXyTVGAI2GOXRmdtb3F1yQpHFlm02xpfHNiHkuOfeDjUgshXMzGIO5tNoPWYZdSUtcFTXM1jc4Lgs9ak3hv/3NY9Fwfl1QIIYQQomQS/AqfMWp+jIx/GrMhqFzraxr5xg52XE3/cfonvj0+H5suzaCFqAwmg5lbGj6GkYAiW164uzEUnI7irPUU7+6bzJGsfb4vrBBCCFEN6MpXSa+qes/OfxL8Cp+KDYznwZbv0DWqP6UZ69fFNZaojgHlPJldJ/XK5G+Z+u94jmcd8lm5hRB5jJqRfrGjcI3zmz8Gdv3bVqAlh6v2Vyk4nrWft/Y8zfbUjZVWZiGEEEKIgiT4FT4X4hfB9Q3upUXoxWhFfOTy36hSylHja1FGbLqGzWPwbgN2ZSDVlsrMPc9y1iJZZYWoDN3rDOCa2FEYNCOQV9uroxUKfF3zwdUcGmwo5h6aQZYts3ILLoQQQpxnfJHsyvUQxZMjJCrNLXETqRsQ7zEtf9BrUxoWZcCCH3aM2JXm7kPomWnW8deiLDz37/2cyDrq45ILIQB6R9/Ik23mMbDevQQYI7BRdN99V9Cbfwxvu7Iz//D/KrvYQgghhBCABL+iEgX5hXJvs+kMbTiJFqEXExsQT5Pgdhg0M7ozEZZyXkjrOgUCXwr8W0MpDZvSeXn3Y5zMOla5OyPEBSrQGMyltfvzaKtZ1DLFel2mYNNoi27Aphuw6Ua2p27j9V2TOZZ1uJJKLIQQQpxfHNe9vnmI4mlKxo4pVlpaGuHh4aSmphIWFlbVxamRsm0ZfHZkJrvSt6DjaFJp0/Nqi7xxfWodwbIji/SkFs/ROKRZpZRZCAFWPZfvT8znz9M/5hvWzDFPV6BjwKL74TiPFQXP5w5hnRnT5AH8DH4IIYQQpVVdr89d5R658jZMIaYK374lw8L8Kz6rdselMknNr6hygX4h3NnkaTqGX45SyvEo4c6VVmC2QvHqnsk8ve1Bfk34UfoVClEJ/A1mBje4i6fbfEi3qH6gjI6mzjiyTuYFvuDtRtY/aZuYsfs5rLq1MosthBBCVCm7Rz6bin2I4knwK84b/ere4myuUdYT17GOBpy2nuLL4/N55J97+eb4F+hKr/iCCiE8BPuFcmODu7m5wX1YlcH5KLk2Vyk4nHWAnxK+qYRSCiGEEOcHSXhVdeQIifNGbXMsg+uNxK5AKzRiqKeCjfXzQmaFpjmaXP6QsJT/bnuQl3ZO5oujn5CQc9JHJRdCAFxS63JizQ1xnMHFd1uwKQ2bM1P0tye/Yfa+N8mwpldqeYUQQghxYZHgV5xX+kQP5M7GDzv78gJFBMGOTLKeF9auZ/kD49OWMxzKOsCqpJ+Z8u9j/J68ouILLYRwG934QUwGc5HzdQU29+2tvHP477N/8fi2BzieddznZRRCCCGqku7sHlThD0l4VSIJfsV5p0NEV/rUGejoP6g0FCovwZVrbFFVuFYpb1gVF83dN1jH0fx5wZG5LDuxlL3pe7EreyXsjRAXltiABjzU4kVC/cIoePPKMYa39z7AmgY23ca0nc9yOjelcgorhBBCiAuKBL/ivHRl9ACC/MLQlQG7bnQHtuA9C7SCAjVJRTe3XHriS17a9QL3bhrLo1sfYkPKeukbLEQFig1owOjG9+PtPKWY5tCaBjZl4fFtD7P17BbfFlIIIYSoIspHwxyVlDBWSPArzlPhpkgeajGZ2MAG2HEkz7G5O/J7u6DOHwAXzVUT7PqbYjnDewdmcc9fd/Gfjf/hv9v+y8bTGyUYFuIctQxtS49aV3hMU+4hj4rm6NKgeHvfm5zKPeXDEgohhBDiQiPBrzhvRQfE8t/WL1HbVAfIGxDc28jUjlDVmfKqFGneNQV2ZxNpHQ07ilxyOJ59gnf2vcuEzRPItMpwSUKci2ENxxJrjst3Y6r0w8rrSvHp4Y99VDIhhBCi6vikv6/zIYonwa84r2maxm0N73AOZOQIfm1o2JzBqx08hkcq6ZQvHDhr7um6MqDQUBjItOdy398PsO3s9ordISEuIJqmcU+zhwgyhlFSlwTI69Pvsj1tO8rb3S4hhBBCiHKQ4Fec99qGd2Bc04eI8I9yTnEEqHbnHS5v18ZFXS97ZonOC3wLTnN5bc8M5uz/QC7AhSinOuYYnmj9PJfW6oOfwR8o/vzMfzPLrnTu+WsCnx5eyClJgiWEEKKGOJ/G+X3nnXeIj48nICCAbt26sWHDhlKt9/nnn6NpGjfccEOZX7MqaUqu6ouVlpZGeHg4qamphIWFVXVxLmi60tmdvoOU3FOczD7G1rN/c8qSjA2Fwd2b0DGEiuvUd/XtVcoZ+DozRTuaUBuc280LfD2zRTtHK9U0/PBHoWE2mokNiOGq6D50rdUFk/NiXghRMqtuYW/6HmbufRmDVjgI1p03tiDvXLUrAwowan482eoRWoY1r/yCCyGEOK9U1+tzV7kH/3Qn/sGmCt++NdPCN9d8WOrjsnDhQkaNGsXs2bPp1q0bM2fO5Msvv2T37t1ER0cXud6hQ4fo1asXTZo0ISoqiiVLllTgXviWBL8lqK4n14Uiw5bBZ4fns/7MevLngVbOMDh/Pa5y9xd2NJ3OC3Y155BK+WudlPuvazsFx07T0Lgk4mJuaziE2gG1MGjSkEKI0nhn7//4O3UThnw9gfNncXf9KtmUAaU0dOd8E0ZmdfkfAcaixxEWQghR81XX6/PzLfjt1q0bl1xyCW+//TYAuq4TFxfH/fffzxNPPOF1HbvdTu/evbnzzjtZvXo1Z8+erVbBr1yti2otxC+Eu5uOY3jDkTjCUYP7QtlRH2xAJ3+WaA3H6L75A1lX4Js/k3TefFc6ego8FLDh7GYe+ucpRm34D+8fmEe6Nd3HeyxE9Te68Z3UDajvyOCOZxZ3d+CrGxw1v+7aYA0Ldp7a9hxWu7XKyi6EEEKcK18Mc6S7r1cdQXb+R25ubqEyWCwWNm3aRN++fd3TDAYDffv2Zd26dUWWferUqURHRzN27NiKPzCVwK+qCyBERbgy+iqahTTj18Rf2Jm+E13p5NiyyFbZaK6szh7jBLtqdp3NK4tMwuO5bFHsumJF4hrWpvxFt6jOtA5tQffaXTAZKv6unhDVXYhfCP9t/SxfHFnIr8m/YdB0d38DHQ277uiW4Dl2t+PfCTnJ3L/lcaa1e5Yoc0Sll70m++74TyxPWMFZaxoKRYR/OE+2eIAGofWrumhCCCHKIC4uzuP55MmTmTJlise0U6dOYbfbiYmJ8ZgeExPDrl27vG53zZo1fPDBB2zZsqUii1upJPgVNUbDoEbc0djzLtTutF18fHguSbmJ6Eo5LqbdsayjP2/JDf+LD3zdTafRyLJZWJW0lt+S1jJ7/1yizdHc1vAmLq19cbn3S4iaKMAYwMj4UexM38fJnJOofL3t85pBF6ZpkG5L5/6/H+OWuBsZXK8/mlb8OSqKppTihX9n8G+640Inf56E09YzPLL9OYyakY+6vInJT27mCSFERfDVsESubR49etSj2bPZfO7dhdLT0xk5ciTvv/8+tWvXPuftVRUJfkWN1jKsFS+2fwmA05bTPL71v1jJcdcyac4guORBkoqnaQqlDLh6G+O8lE/ITWLG3vcI2B9A35jLaB7SBKuyEeoXQtvwFvgb5BQUFy5N07iryRim73wFu7KhO09MvYQWF8p5F+vzI19jMvgzoG5fr8uJkj28ZQonck4CnoEvOFvLKLBjZ/TGCSy49D250SCEENVAWFhYiX1+a9eujdFoJDEx0WN6YmIisbGxhZbfv38/hw4dYtCgQe5puq4D4Ofnx+7du2natGkFlN635MpbXDCiTFG81OEF3to3i4OZBwEqfAgjpRwX7Xq+7vQaimx7Lt+d/CVfNmmNUL8QRjQazFUxvSq0DEJUJ01DmvBs2/+y+NjXbDm7Nd+cooMsV+Z2uzLy0cHFHMg4zt1NbsNslJrJstiYspXjOScx5At67Sp/bgPcMzR03tj1Hg+3/k9VFFUIIWoUX9f8lobJZKJz586sWLHCPVyRruusWLGCCRMmFFq+VatWbNu2zWPa008/TXp6Om+++WahptbnKwl+xQWllrkWU9o+zRnLGf46vQmLbiEuKI6Ze2eTq1vKvV2l8g+T5KgBzp932rGQ448G2FGk2jJ4d/+nLDm+glZhTbkutg/xIQ3OZfeEqJYaBsXxUIsH2JG6i5d2veacWnzNb/7bVr8l/8nu9P280elZaU1RBvMOLXR8SzmHgrMrV6LAPHn57w38eWZLlZRTCCGEb0yaNInRo0fTpUsXunbtysyZM8nMzGTMmDEAjBo1ivr16zN9+nQCAgJo166dx/oREREAhaafz+QqQVyQIk2RXB2b11RyZKNh/N/Bj70sWXyTaFfFsWt84TwFs0arfOGwa7pj3omcRE7kJPJr0lrMBhPD466nT8ylhPgFlXW3hKjW2oS34tk2T/LcjpfR0Ytd1pEhOs/JnGTe3fcxE1vc6csi1ihnrKmAM/DVCyb+y+u+odDcQ74dz06gfmDh5nBCCCFK73yo+QUYOnQoycnJPPvssyQkJNCpUyeWL1/uToJ15MgRDIaaNThQtdubd955h/j4eAICAujWrRsbNmwo1Xqff/45mqa5q/WFyO+K6MsY3/QuwvxCC8wpOfC1O8ciLX75fGOYFrPNXN3CB4cWMXL9o9y/+Xl+SVxb4U2zhTifNQttypOtHgY08PLRz+uPWvg8Wn1qIx8f+krOmVLy0xz3v5UCOwaU87vM1ZLF2/fWvvSDVVNYIYSoQVzBry8eZTVhwgQOHz5Mbm4u69evp1u3bu55q1atYu7cuUWuO3fu3Go1xi9Us+B34cKFTJo0icmTJ7N582Y6duxIv379SEpKKna9Q4cO8cgjj3DZZZdVUklFddSjdjfeufg1hsXdRJAhsMjllHIOj6Q07Mr72MBFrOmsRSl+Wdel5rHsBN7Z9ymz938mF/PigtImvCWPt3yQQGMAkHfOgeP8sepGCp5DrmffnPiF+Ye/rrSyVmc9anVxtFpRzq4b5GWudw0PlxcE48xon1OFJRZCCCHOjaaq0VV1t27duOSSS3j77bcBR6fsuLg47r//fp544gmv69jtdnr37s2dd97J6tWrOXv2bLF3KHJzcz0Ggk5LSyMuLo7U1NQSs6aJmsOm29iaup3k3BRC/YLpFN4Bhc4/qTv45vhyDmcdRWF0L+8a7qh4+TPZFr+snu+CE+C/rccRHVCLjae3cTI7mXoBMXSJakvD4Lrl20EhqgGlFN+d/ImFR7/CqivnHW3v549y/9XQ0JjV+XnqmKMqs7jVTpolnbs3PYJV15zfZwW7eeTrpOHMjD/7ouepE1h9h7gQQtQMaWlphIeHV7vrc1e5+35/L37B5z78UEG2zFx+GfBetTsulana9Pm1WCxs2rSJJ5980j3NYDDQt29f1q1bV+R6U6dOJTo6mrFjx7J69eoSX2f69Ok899xzFVJmUX35GfzoHNmp0PQetbvSo3ZXdqfv443dszlrTS+wRHFBbV6P37LQlMaruz4kJ19CLqVg7qEl1DZFMrntOBqF1CvzdoU432maxqB6/WgX3oop218ny24temFnpmIdDYMGq5L+5Ja4AZVX2GoozBTK5bUv4+fEtc6vroLfX4W/syTwFUIIUZ1Vm2bPp06dwm63uztgu8TExJCQkOB1nTVr1vDBBx/w/vvvl/p1nnzySVJTU92Po0ePnlO5Rc3UMrQZszu/yqMt76Nb1EW0DmtObECdYtbIa/BcUq1v/rYYjr54ihw912MZ11CbpyxnGL95Gvf9NY0dqQfKvB9CVAeNgxtxQ/3rPM6i/FznjM3Z38mmG/jk0HLmHVwqzXRL8GvShhJvySkcxzjWXNx3nBBCiNI6n/r8XmiqTc1vWaWnpzNy5Ejef/99atcu/Z1qs9mM2VzxzRBEzaNpGl2iOtIlqiMAdmVn5u4P+fP0Zgxo6IUGDFHOJs/FD+GSt3z+C/3Cy7sCYBQcyTrJo1tnEOkfTqCfGaUUDYPqMqj+ZXSKaImmyZehqN6uib2MBUe+cfacd54bziF6vPUD1lEsOvYz359czfPtJ9AitFFVFPu89uepf7AqO1qJLVYAdKa1faQyiiWEEEL4TLUJfmvXro3RaCQxMdFjemJiIrGxhYdd2L9/P4cOHWLQoEHuabruGDrDz8+P3bt307RpU98WWlxQjJqRh1qOZfOZbvySuJoT2UmE+AVxWZ2uXFa7K+8d+Jy1KZuKXD9/Qh9PxWecNmiuPsJwxprGGWfL0JM5p1h/ehux5tq0i2hGoF8AvWp3pG1YEwmGRbUT7BfE0LhBfHZ0aV7/XqVh0x1j0xYly57Ds9veYc4lkwnzD66cwlYDutKZueeTUi6tMGn+hAdI/zEhhKgI58tQRxeiahP8mkwmOnfuzIoVK9zDFem6zooVK5gwYUKh5Vu1asW2bds8pj399NOkp6fz5ptvEhcXVxnFFhcYg2agS1QHukR1KDTvwRZjiDgYyvcJqwrN8wx8S//FpWmezaS91Son5J7iROIpQOOb47/RKqQhT7QZg0EzEO4fgslQbb4GxAXu5gb9OZp1ktWn/kLTwKYMpepFn2XP4ZfEddzUoG/JC18AlFI8vvUtMmzZGDQNg6G4o6hQCp5qc1+llU8IIYTwlWp11Ttp0iRGjx5Nly5d6Nq1KzNnziQzM5MxY8YAMGrUKOrXr8/06dMJCAigXbt2HutHREQAFJouRGUwagbGNrmV7rUuYv7hr9mTccg9z2Twx6rrKHQMaNhLmYTdkWW6+ObRrnBYOf+1K+MIo9c7krqZDX5cE3spwxv1I9IktTri/KZpGg+2GIPJYOaXpD9cU0tcT6FYd+ofCX6dPj28nO1pB9z5nV1fNwUbhDgGg9C4KKIlHSJbVXIphRCi5pKa39KzWCwcPHiQpk2b4ud37qFrtQp+hw4dSnJyMs8++ywJCQl06tSJ5cuXu5NgHTlyBIOh2uTwEheoNuHNmd7hMZJzT5NmzSDKFE6kKRyrbmXtqb/ZcPof1pz62z3uZt4wI4VpGjha85c0bnDeRW7+QDlHt7P0xB8sP7meh1sOp3V4PDEBMjyMOH9pmsZ9zYbTMaIVs/d9Qaotk9IEwLvSD/Hu3sXcHt+PMP8Q3xf0PJVjy+WzIz+5nyulYQcMmmOwNs9AWMOo+fFM28Ktq4QQQpSfBL8ly8rK4v7772fevHkA7NmzhyZNmnD//fdTv379Ioe5LUm1Gue3KlTXccRE9aUrnXv+mkpiTgr5Rw8uXCvj+Gt3jwlc/BeevdBYxJp7O67m0wpoFdqI7rXaUi+oDh3CmxJlls+9OD/tSD3A4//MKHE5u+5qIm1AQ2NUfD9GNLrmguv7ftaSwX2bXuGMNRUADYVRy38JoDA4n7u+D55sdTc96nSq9LIKIURxquv1uavcvZaO99k4v2uuf6faHRdvJk6cyB9//MHMmTPp378///zzD02aNOGbb75hypQp/P333+XabrWq+RXiQmDQDAxpcDXv7Pscx+VpXo2M61q9vH2E8bJ8/ut/DdiZdoSdaUdQgBGNK2MuZkzjAYSbQggwmsqxR0L4RpvwJlxXtzfLTv7udb5SYNEdQa/rPq9CMffgclYkbmZWl4cvmM+0Xek8uvUtknNS0TRHojx3yxLnMprmqIlQzlTaHSJaSOArhBA+oJSG8kEtrS+2WVWWLFnCwoULufTSSz1uVrdt25b9+/eXe7sS/ApxHuoX24MT2cl8fXwFBgzOYZNUXtDrrO3N+y4oJiO0x99imkdrrgBboZRj8BM7ip8TN/Nz4mYAwvyC6BLVkjFNrqVeYOmHEBPCV+5tOoRtqfs4nHnC40aOI/A1oqv8n/28BY5kJTF2w3Q+7Pok5gsgAH5z95ccyEgCDKA0Z4sRhR0dP0NeDbCrxreOOYoX299fhSUWQghxIUtOTiY6OrrQ9MzMzHNquSUdZIU4D2maxp1NbmDGRY/RL7Yn7cKbcUlkO8Y3G8bszs/QJ/oSNDT3EEfeBkhybwtXsFzy6e76LtE07/2M02xZrEj8m5F/TuPFf+cjvSZEVdM0jRGNrsOOs2m/86ErDYWWv+NAoXUTcs4y/9BPhabXNHP2fcuyk+udzzy7PugYseoG7BiwKQ2b0tAx8EK78Rdcs3AhhKgsOprPHjVFly5dWLZsmfu56zfp//7v/+jevXu5tys1v0Kcx5qFxNGs+dBC0x9pNZrr6/dh8rbZpNkyPeblZXfO+6s85pRO/lpmj6bRzn//mvQ3GdYcpne6u9TbFMIXutVqR4fwFmxP3Y/d+Ym3KYO7hYR3jrNj0dHfuKvpwEora2XbkXqIhUdXOp95PxYKzZ3ZGeC2uH40CI6pnAIKIYQQXkybNo1rr72WHTt2YLPZePPNN9mxYwdr167lt99+K/d2peZXiGqqRWgjPr50Ko+0HMm1dXvSL7Y7XSPbAHmZnXUcNWB5NTjF19Tmr8h1baO4yp/1p3fy/r5lRS8gRCUwagaea3cPV8V0xYABdzK3fP/1TsOi27n59ymczErxdTGrxEs7FlCaBhp2Zz+xSP9wbo/v7+NSCSHEhc2V7dkXj5qiV69ebNmyBZvNRvv27fnpp5+Ijo5m3bp1dO7cudzblWzPJaiu2eTEhSsxO4WZez9ny5k96HgOdQSOZAjeAtq8JFpaviGRSv4S1RW83mkc6bZsvjiymqNZydiVTlxQHW5s0IMrYjpiMkgjE1E5zljSeHHHXLaePZDvIqCETOi6hlnzZ2Gvp4kw1ZxhkI5lJjNq/fRib2A5OBqIGw0wue1YetRuXxnFE0KIcquu1+eucndb8oDPsj2vv+F/1e64VCYJfktQXU8uISy6ld+SNvPG7s+xKVfjZ809bnDBWl1XwOsKjvVim4zmsekaqphGJCF+AXQIa0ygv5la5jB61GrNRZFNpT+h8Jkcu4VHtrzFzrRjzinFJIRTrnERNfrGXMzT7YZXShl9Lc2axc2rn8OOFSi+BYdrELT7mg3mprg+lVA6IYQ4N9X1+txV7q5fT/RZ8Lvhxjer3XHx5siRI8XOb9iwYbm2K9UxQtRQJoM/V8d2o1lIHO8fWMrG07txDPQCSvcc29d1C8wVkJY28M2rIS5aujWHP1J2OrcPCw//Tm1TGPc0G0CfmPYEXgCZdkXlCjCaeOviSTy29V02nyl6OATPFg6KXxI3c0VMJ3rWaVNJJfWNvekneHDTe2TrOkbNgFHTS1hD4+LI5hL4CiGEOG/Ex8cXW1Fit9vLtV2p+S1Bdb2zJERB2bZcViRt4p+z+9l8Zi+plgznEEreOPtMltDnV1dgV8YSX9szSM7boAGNIXG9GN/iOoyapCAQFW/S32+z9ex+Ct7Mcf3yuWp9XdN0NKa2v50rYjpWbkEryJYzB5i4eQ52ZccV1JsMjgsE7+eyQkPj+94vYzL6V2JJhRCi/Krr9bmr3F2+etBnNb9/3TSz2h0Xb7Zu3erx3Gq18vfff/PGG2/w4osvctNNN5Vru1LzK8QFItDPzMB6PRhYrwfZtlxm7P6SX5P+dtYFF81bAOwKHOylTKzgak4NntvSUSw8sprlJzfxUqc76BARX8q9EaJ0Xut0H4N/f4ZMe3ahz3H+wNdFAa/tWsxlddrhZyj5xs75xK50ntg6N1/gC6Bh1Y34G+xFnsvTO9wtga8QQlQipTT3dVFFb7em6Nix8E3oLl26UK9ePV599VUJfoUQpRfoZ+aptrdzd7OBbDq9B7vSaRXWkDrmcBKyT7Mj7TC/J2/lcGYiZ60ZQF4Q7Ap8bUqjtAnjvQW+LprmGD943MZ3CTIG0Dg4htbhDWkdVp8OEY0xGY1EmUIwSM2wKAeDZuCa2EtYfGx1gTb6hWuCXU2g06zZzD/0K2OaXF15Ba0AvyZuJd2aQ/590zRH3a5FN2LQdIxKucfxVgr6xXala+3WVVdoIYQQogxatmzJxo0by72+BL9CXMDqmCPoX7erx7Qw/2BahMVxQ4NeAFh0G3f8+RInc047AgQFer7hZEqiu/sTF72MK6DOsueyPfUI21OPeGSbrmMK47bGl3Frw57SPFqU2W3xV/DVsTXOVg5FfxB1ZzNhBcw98AsD6l1CTEBEJZXy3BzLSuH1nUsL1GY7sjg7ktxp6MqIDhiVHTS4rHY7Hms9rOoKLYQQFyjlo2GJalLNb1pamsdzpRQnT55kypQpNG/evNzbPeeryCuvvJLDhw+f62aEEOcpk8GP+d2fYmyTAYT7h5QpS7MjybT3oZWK4lo2/yrJljT+t3sZz/7zGboqKXmPEJ5qm8N5uu0IQPPazF8psCuw6wZsdiM2uxGLDk/9Pb/yC1sOfyTt4tY1r5JqzaJwjbbjAksVqPWuY47guQ6jJeu6EEKI81JERASRkZHuR1RUFG3atGHdunXMmjWr3Nstdc3v0qVLvU7//fff+e6774iLiwPg+uuvL3dhhBDnJ6Nm4Pb4vtwe35ccu4XVydt5/t/Pil0nb9xgx3/KEwA78vHlrfhr4jb6JLTj6rrVMxmRqDpXxV6En2bkmW0fe0xXOIbr0pUfuGuGFUpp/Jt2nJf+/Yon2pavX1FlSMg+y6Nb5hUxLnf+567hziDML4jZl0yUVhRCCFFF8o+0UdHbrSlWrlzp8dxgMFCnTh2aNWuGn1/5Gy+Xes0bbrgBTdPwlhz6/vvvBxzNqsqbdloIUT0EGE1cHXsxJ7JT+ODATxg1A/YCtbGu5tHOBpc4gomiA+D8fYnzb8ObxUfXeQS/SinsSq92yYlE5bs8pgPhu8I4Y03HFRjqCnR3xnKtwF/FkmMbGN7oMhqG1Knk0pbs4/2/8c7eH9E0Z+hb5A0mha40jBpEmoJZ0P1RQv2DKq2cQgghRFldfvnlPtluqYPffv36YTQa+fDDD4mOjnZP9/f3Z+vWrbRpU73HRRRClM3oxldzSVRLlhxby870o5gN/nSNasH6U3vYnXHCa01UcZmjCy7rLSAG2Jd+EoA/knbz9p4fOZCeiI4i0j+YYfE9GBbfgyC/ih8+QNQMs7rcx7B1r7g/jLpugCL7Ajtu3Ny9YTY/XvlM5Ra0GGnWbO5dP4d96YkAGLWSWlY49sOAxssdx0jgK4QQVUxHQytl7pSybrc6K6qlsTflbW1cpnF+Z8yYwYwZM3j33XcZOHAgUPOD3+o6jpgQVUUpxd9n9vP1sT9Zm7yLHN3qmgN4XqQX7IfouR28BtCR/kF0jGjGLwnbvb6+WfMjwhxMbEAEg+O60K9uR0xGye0n8sw/sJLZ+38AHP18i0/e5mgCfX39S3iq3Y1V3kdWVzpj1s1iR+px9zSjwdHyoriaX6Nm4I2Lx9C1VgvfF1IIIXysul6fu8rdcdHDGIMq/ka9PSuXrUNer3bHxcVgKF13nHNpbVymK8KHHnqIK664ghEjRvDtt98yY8aMcr2oEKLm0jSNi6OacXFUM5Jz0rh34zsk5px1zfXoOuGofCt8xV5UbTBAtLlWkYEvQK6ykZiTSmJOKlvPHua5bYsJMgbQLDSWXtEtuKFBF6LMIeXfQVHt3dqoFx8fXEWGLbvEZV0fxSXH/iI2MIKxza70beFKsCZpNztSj3vUVetKw6AVdx9bY3zzARL4CiHEeULG+fVO132f1LTM2S46derEX3/9haZpdOrUyWsfYCGEAKgTEMa8Sx/kvubXEhMQib9mJMwvmOah9Yk1RWI2+AP5kmPl+zrx9s2iKc3d1LMkCtCdfzNtOWw9c5h39vzM1b9OZ/Ta2ew4e7yELYiaymz055E2N4I74VMxv2P5MiV/sG8l6daSA2ZfemfPT4UaaXs7f/LPC/cPYlD9SyqjeEIIIUpBd2bi98VDFK9cbQEDAwOZPXs2S5cuZeXKldSuXbuiyyWEqCFC/QMZEd+HEfF9Cs1TSrExZS8v7VxMQvZZIG9cYG+1vsMb9WbewdVlLIFWaFPbU49y+9p3ub5BZ55pfwM5divHsk5jNvjRKLh2lTdtFb7Xr24nlK4zZfsXFNXs2TUusOsjaVV27lz3HvN6jKuSfuXp1hwOZHi7+aNh18FocJTUFQRrGkSYgni/232E+AdUXkGFEEKICpCZmclvv/3GkSNHsFgsHvMeeOCBcm2zTH1+L0TVtU+BENXN0cxTrDm1k/XJe9iWeoQse657Xr3AKCa3H8rZ3Cwe3lzy2Kt5g7oUHcS6vvlah9Vnf3oiubodBfhrfkSagsnVrWhoXBTViBGNu9OlVuNz2T1xntpx5ih3bngHV1Io12fG9fmw62BXRncAbEBxVWxbXu08vFLLqSudaduXsuTYhuI+1Y786poCDZqH1GV+jwfkZo4QosaprtfnrnK3Xfioz/r8/jv01Wp3XLz5+++/GTBgAFlZWWRmZhIVFcWpU6cICgoiOjqaAwcOlGu7kgVGCHFeiAuuzW3Bl3Fbo8uwK51tZw+Tbs2mXmAUTUNjATiWdboMWyz+gt+VTXpn2gmP2marspOUm+pef2XiTlYm7mRiq6sZ07R3mfdLnN/aRMYx55Jx3L1hFvk/M0o5x//FSP7xf3U0fk7YwZYzh+kU2ahSynggPYkJG+dzIvsMfq7Rw7zS0HF0DwgympjWaYQEvkIIIaqlhx56iEGDBjF79mzCw8P5888/8ff35/bbb2fixInl3q6McC+EOO8YNQOdIhtzWXQbd+AL0CAoim61mvnoVbUi/u3w5q6feWjjZ+xOO+mj1xdVpUNUI97vdh8aBue4vxo2ZUB3/0RqzrGrNecDJv31mc9zXlh1G89u/Yqbfn+LE65uAWhe+/a6aEC9wAg+7jGBhsHSJUkIIc5Heb8nFf+oKbZs2cLDDz+MwWDAaDSSm5tLXFwcr7zyCk899VS5tyvBrxCiWnmy3Q1E+AdX+usqBSsSdnDLb+9y9c+v8uhfC/m/Pb/zW8JuDqQnV3p5RMXqENmQDy8dh59mRNdxJg1xBb0Fl9ZIyc3ks4N/+rRM07d/xzdH/3aXQSkNu65hVxreEmIqwGzw56vLH6GRBL5CCCGqMX9/f/fQR9HR0Rw5cgSA8PBwjh49Wu7tSrNnIUS1Uj8oik97TeCj/atYemwTFt2Ghoa/ZsCi8o35lj/rTwH5h1LSy1B552gqrZGYk86PJ//lx5P/uufFB9dmSsfBXFyrcprCiorXJqIB71wylnvWvw8l3j1XvLzje5qHxXJJ7YrvD34y+yyLj2zyMsfxmVVoaCovAnZ9zB9rOwijJve1hRDifCZDHZXsoosuYuPGjTRv3pzLL7+cZ599llOnTjF//nzatWtX7u2W+RfSaDSSlJRUaHpKSgpGo7HcBRFCiNKKDgjn8baD+e3qyfxy1dOs7TeVX69+lmfb30yHiIYEGFz39Qo3EfUYTqmYzNIFFdd1Uik4mHGK0X98wAMbFrA/vfB3pKgeLq7VmB51WgPehw7K4/hATNz4KXZVseMS6rrOgxs/K6LW2fHaCrDpBnQcGdJ14PoGXRhUv3OFlkUIIYSoTHa7oyJj2rRp1K1bF4AXX3yRyMhIxo0bR3JyMnPmzCn39stc81tUH6fc3FxMJlO5CyKEEGXlZzASbgpy/Bsjgxp0ZlCDzuhKZ92pfTy6aQE5uhWl8hJc5amYu6MFvxJXJuxiZcJu+tVry6Nt+7H8+Ha2njmGUTPQvU4Trq3fnkA/+a48n/23/fX8vuK1Ui2bYctlZcIu+tZtUyGvbdVtDP39PfamJWIo9va0IwC26wYMmmJ048t4oGU/SXAlhBDVgK40NB/U0taEcX7r16/PHXfcwZ133kmXLl0AR7Pn5cuXV8j2Sx38/u9//wNA0zT+7//+j5CQEPc8u93O77//TqtWrSqkUEIIcS4MmoGedVrw+WUTuOfPD0nKTS1Uy9syNJZ96UlYS5m0yNtijmmeQ+S4/HTiX34+scM9RI4GLD+xnan/fEeYXxD1giK4qdFFXB/XkQCjf5n3UfhOTEA4DQIjOZp1psRlNQ1e3vYDV8S2qpDmxq/9+xN705LQNOW+aVNCCagbGCmBrxBCVCNFt+w59+1Wd+PHj2fevHm8+uqr9OjRg7Fjx3LrrbcSFBRUIdsv9Ti/jRs7+jQdPnyYBg0aeDRxNplMxMfHM3XqVLp161YhBTtfVNdxxIQQDlm2XL4/sZUVJ/8ly26heUgMQxp1pVV4PTalHGTiX5+QYbOUvCFwJhnyHA7HofRBh2sd3Rk4mw1GWobFcnX9Ntzc6CIiTEHsSU3kaNYZwv0D6BQVh59BupRUttWJu5mw8ZMSl1PKUfsa4R/EF33uoX5QZLlfM9OWS88fXsKudDTN0ZS6pHjWT9P4us9EGgbXKvfrCiFEdVNdr89d5W7x6RM+G+d3z4iXqt1x8WbVqlV89NFHLF68GKPRyK233spdd911zrFmqYNflyuuuIKvvvqKyMjy/8BXJ9X15BJClI5Vt/N/+35j7v7V5Oq2QvPzN5nOq+n1/HdZubP3Flg/2M9EdEAYBzNOuadpaHSp1ZDXLhlCnYDQcr2eKJ+p/3zD4iN/FTnf8R6CrjtuTvhpBpZeOZ5GIeULRNck7WXcn66AW2EwlPzz/HmvcbSOqFeu1xNCiOqqul6fu8rd/JMnMAYFVPj27Vk57L29ZgS/LhkZGXz++efMnTuXtWvX0rp1a8aOHcukSZPKtb0yt9FauXLlBRP4CiFqPn+DkXEtruS3a55iUuv+hBgdd2LzN0nS9XMLdr3xVqOXYbVwMP2UxzSFYmPKYfosf52fTuzg3zMn2JxyhMTsdPSa0L7pPPZsh8E0Cq5VTJN30PW8n1GrrjN6zUflfj2rbvd4XlKzuAH1OkrgK4QQokYLCQnhrrvuYs2aNXz77bckJCTw6KOPlnt7ZU54ZbfbmTt3LitWrCApKQm9wGCDv/76a7kLI4QQVSXA6M+oJj0Z1aQnuXYrnx1azwf7fifVkl3MWoX7+pZG4eRbedO9voqzhnHi+i8LzWscXIvnLhrIJXXiy1wOUbIveo9nwIoZnMpNd09zvU923UD+91/TICkngzUJe+kV27zUr2FXOn8k7mdryvF8UzVnqwPHByV/CwRNgyhTMM92uP5cdk0IIUQVkaGOSi8rK4svvviCjz76iDVr1tC0adPKDX4nTpzI3Llzue6662jXrp0k2BBC1Dhmoz93NO3FqCY92JhyiAPpSSw8tJEDGcnuZfIC2LIHwK5gtqh53r9WvSfWOpCRwsjV84gyBVMnMJRL6zTmjmbdqBsUXqYyCe8CjP7M7DKc21a/7w5E0TXn+1f4jdI0+PTghlIHv5tOHeHhjV+RkJ2GUdNw5cxyfAY0d+2vKwEWQFxgLRZcdq9kDRdCCFFjrV27lg8//JAvv/wSm83GkCFDeP755+ndu/c5bbfMwe/nn3/OF198wYABA87phYUQ4nxn0Ax0q92EbrWbcFvjS0nJTeezA+v5I3kfe1ITseBqpuoZlLrC1OIU1Zy1YODr2dy66CD7tCWT05YsdqcmMm/fn9QNDCPYz0y4KYDr49pzXVx7QvwrPrnGhaB9ZH1C/QJIt+UWuUz+RlDHMk+Xaru7UxMZs+YTbM7mznal0OwaRmP+TM+uPuaOv7XMwXx9xQT8DWX++RZCCHGeUJR8nVDe7VZ3r7zyCh999BF79uyhS5cuvPrqq9x2222EhlZM3pMy/3qaTCaaNWtWIS8uhBDVSS1zKBNa92VC674AHMlI4ZMD61iZsJuknHR058/OxVGNCPE381viHgxo7ume4w2Xpba46Nplb02oFXAiO839/K+Uozy75Xu61GrI7B7DOJmVxqcHNvLvmZOE+Ju5pn5rrpfguEiapvFYu348s2VpoZp5Xc8/rqLj7560Mwz4+V3e7DqE5uHRRW539q7V2JXu/nwAKAzYdR2jofBQRw2CIvmgxx0S+AohhKixXn31VW6//Xa+/PJL2rVrV+HbL3O259dff50DBw7w9ttvXxBNnqtrNjkhROXKtlk4lZtBsJ+ZKHMwSimWHd/G/P3r2JF6AnAEqXoxga/r2zj/V2tph1PyyERdzHJ+mgGb0gsFzFHmID7tPZqmYXWKfZ0L2ft7VjNz5wr387zAt+j35sOet9Mzpkmh6emWHC757lXy/wR7/qQqjAZoFlabi2rFcXlMC3rHtKiQsYSFEKK6q67X565yN/n4KZ9lez4walq1Oy75Wa1W/P39fbb9Mt8+XrNmDStXruSHH36gbdu2hQr31VdfVVjhhBCiugj0MxHnF+V+rmkaAxt0YGCDDtiVzrYzx7ljzVwsyu51fW+Bb3mUdDfT6m6f6xlhn87J4tqfZuGnGWkSWov2kfXoFh1P59pxxAVLhn+Au1tcRqi/malbv0fTSg58Ae5du4D1Ax8lOF+t+uJDW5j+z88FboQ4OoLnvf8aKANtw+OY0lESWwkhRI0i7Z6L5MvAF8oR/EZERHDjjTf6oixCCFEjGTUDnaLiWNTnXt7auZIVJ3d5NIXOz3vCq9LU+p6LvO3blM6etGT2pCWz+PBWANpH1mVWj6FEB8o4w8MadyXQaOKJTUtKtbxF17lj9XwWXjEWg6bx7s7VzPx3VeEFNee4z0q533+FIi44oqKKLoQQQlzwytzs+UJTXZtVCCHOX2mWbLaePsb0bT9yMCMFAxoKhQIui25Kpj2XLaePOaaVon9w/qbRpf1Cd2+3FCsoBX4YMBgMWHQ7JoORLrUb8kDb3qTkZGI2GmkZEUNs4IXxHWnTddoueZ7S9NtWzrv7dzbvzvqkw2w/e9LrcpoGaHk5pDXN8Xdl/weJDbowjqsQQpRWdb0+dzd7nvtfDD5o9qxn5XDgjher3XGpTOXKmmGz2Vi1ahX79+9n+PDhhIaGcuLECcLCwggJCanoMgohRI0SZgrkstjm9IppxuaUo2w7exx/zUiP6KY0Dq0FQKYtlz2piWiaRsOgSMb8MZ89aUkeua88b12Wvr10WRJuuV7Dhu7qsIxFt7M26SBrkw56LHtZTBNe6HId9YLCybXb+DPpEJlWK3Eh4bSLrFtj8kQYNQ0/zYhN6SUvDIDGB3v/dN5o8HYMnMmtcEa8zjsSD7TpI4GvEEIIUYHKHPwePnyY/v37c+TIEXJzc7n66qsJDQ3l5ZdfJjc3l9mzZ/uinEIIUeNomkbn2g3pXLthoXnBfmYuqpU3/ZurxvHtkX94499fSMhJL3dT57KuV5bs1KsTDzD45/+jRVg0G5IPe1Qqh/mZGdG8MxPa9MZsrN7ZijVNo3dMM1ac3ON8XtIaCg0NvcgxnD1FmgJ5uN1V3NL44nMuqxBCiPOPawx3X2y3pjAajZw8eZLoaM9RE1JSUoiOjsZu955DpSRlvgKZOHEiXbp0YevWrdSqVcs9/cYbb+Tuu+8uVyGEEEKUbFDDDgxq2AGADGsuOXYrH+z5g4/2rQfKP7ZwRVEKzuRmsz75cKF5abZcZu1cy9zdG5jWdSB96jbnl+N7OJObRf3gcK6o2xyT0ejbAlagiW36sOLkHvfNAe/jM1Oottd7n27PebN63MZFtRr4othCCCFEtVBUz9zc3FxMJlO5t1vm4Hf16tWsXbu20IvGx8dz/PjxchdECCFE6YX4mwnxN/N4h340CI7i+a0/lGHtsjaRLuVWvYw5XFCW3cZD65bgZzBi1XUMmoauFIFGf7pFN6JDVF1aR8SQY7cRbgqgW52GBPj5NvNjebSKiOXelj15b9cfhZqhuxNW6aCUhlIaBW9NeAbAeRmfG4fUolNUfR+WXAghRFXL+22o+O2W1TvvvMOrr75KQkICHTt25K233qJr165el33//ff5+OOP2b59O//P3n3HN1W9fwD/3CTdew8oLVBWocyypwxBQKaIDBEQ+DoABX4qqCwFEQcgoiLiQsGFDEFk71XKXqWU1b33TJPc+/sjo9mrSdq0z/v1qjZ3nPsktM19cs55DgB06dIFH330kc7jzbFx40YA0lFWW7duVZlSK5FIcPr0abRu3drs9k1OflmW1drNnJqaCg8PqgRKCCG2NqV5V/QOaobfH13B9fxUgONQIhYirbwQlRIxACDUxQvjwztiY/xp6aepRs6/NSahNRWH6iWXWFnjFRIRTqY/wMmMByrHOvL4eC2qN16P6oMKsQipZYVwETggzM271ucQL2o3CGcyHuJuUab0NZKFw0oAlmUAxU2I6pJIHDidL/+yjsNq/XkRQghpGP744w8sXLgQmzdvRvfu3bFhwwYMHToUCQkJGsONAeDkyZOYNGkSevXqBWdnZ6xduxZPP/007ty5g0aNLPPB7fr16wFIe343b94MvtKoMEdHR0RERNRomq3J1Z4nTpwILy8vbNmyBR4eHrh58yYCAgIwevRoNGnSBD/++KPZwdRF9lpNjhBCtHlYnIs5539DSllh9UY97wLKlaSNYegdxdT2pCdpnsNAugTTpz2eRaSXv/FtWVhCURbGHPkOElm1bgbSxFdaC0ua9HKQV3GWfc8BDI8DjydvRbp/WcdheLFFV5s/B0IIsTf2en8ujzvi+6VWq/b85OUPjX5dunfvjq5du2LTpk3S81kWYWFhmDdvHhYvXmzwfIlEAh8fH2zatAnTpk2rcfzKnnrqKezatQs+Pj4Wbdfknt/PP/8cQ4cORVRUFCorKzF58mQkJibC398fv/32m0WDI4QQYlnNPf1xbNg8JBRlYdeTG/jryXWUioQWaduYj1LN6klWjAqu7l5lOeBGfgaePrAFHg5OCHP3xoCQ5pjSojNCbFghuZVXEL7rMwlzzv0OEcuC5aCU+MrDV0r0Zc+dYxlwjLwHmEGwiwclvoQQ0kBYu+BVcXGxynYnJyc4OTmpbKuqqsKVK1ewZMkSxTYej4fBgwfjwoULRl2vvLwcIpEIvr6+NQtcixMnTli8TQDgGT5EVePGjXHjxg28++67WLBgATp16oSPP/4Y165d09o9bmlfffUVIiIi4OzsjO7du+PSpUs6j/3uu+/Qt29f+Pj4wMfHB4MHD9Z7PCGENBStvIKwpMPTuDr6bWzoPg6tPKv/fvPBwF3gBD8nN/DVhuzqYvUKk3raLxEJcTc/C1/fOY/eezbh/y78g78eXEdqaaGVg5LqE9wcl0e/hY4+obL5VsrDnVVJhzTLEniJ9P8ODA9Hhr1mk1gJIYTUf2FhYfDy8lJ8rVmzRuOY3NxcSCQSBAUFqWwPCgpCZmamUdd55513EBoaisGDB1skbmUSiQTff/89Jk+ejMGDB2PgwIEqX+Yya70JgUCAqVOnmn1Rc9XFcemEEGLvhoe1xfCwtsisKEalWIRgV08486VFph6V5OHl0zuQWl6ktw2GAVz5DigTi/QeZ4kk2VAbux7fxq7H0mIcfIbBxGYdMTe6N3ycXK22zNKeR7dxJTsT4Bn+qEDa+y09KtjZA0eeebVOFvUihBBiJRwML9FgbrsAUlJSVIY9q/f6WsLHH3+M33//HSdPnoSzs+WHcL/xxhv46aefMGLECLRr185i9TBMnvMLAImJiThx4gSys7PByoqWyC1btswigWlTG+PS7XVOASGEWArHcbiUk4TdSbeQW1mKSE9/dPRtjEu5SXhQnAsfR1eMCW+H/sGR+OTmcWy9f1FHO8qPTHwTU5r3q/GupeNdTF7XS3mdYm9HF3T0C4GAx4OAx0ffkKYYHdEWbg7mL5vw18MbePviv9LoeNVDmfWRDnnj0MzDF0dHvkJFrgghxAT2en8ujzv8O+vN+U2abdyc36qqKri6umLnzp0YM2aMYvtLL72EwsJC7N27V+e5n332GVatWoWjR48iJibGUuGr8Pf3x7Zt2zB8+HCLtmvyR+DfffcdXn31Vfj7+yM4OFjlDZthGKslv7Yaly4UCiEUVs9/Ux8zTwghDQ3DMOgeGIHugREq24eFtdE4dnHHwRgV3g7zL+zCk9J8tXaApu6+eFRSAM3FfwwFYeoJ6vOLpfOFC6sqcDLjkeKYgykJWHH5CJ5v3h6tvPyx89FtFFRWINjVHbPadMOQsJY6E1OO4/DJ9ZPYfFee7DPgWA7gA+A4PedVP6nHJQVYHncIH3QbZtqTI4QQYrfqwlJHjo6O6NKlC44dO6ZIflmWxbFjxzB37lyd533yySdYvXo1Dh06ZLXEVx5fZGSkxds1OfldtWoVVq9ejXfeecfiweijb1z6vXv3jGrDmHHpa9aswcqVK2sUKyGENGRRPsE4Ovw1PCnJx6mMB8iqLEGoixcGhbZAiJsXjqcnYu2NY3hQnCc9QWmZIJXvobQN0LIDBhNixZq7eo4TSSTYfv+ayraUskLE5aSCBwYLOvTBnKiecFRabgEA9ifFqyS+UjxwLAseT/cNiDwplx/x64OrmBjZEW19g/U/GUIIIcSCFi5ciJdeegkxMTHo1q0bNmzYgLKyMsyYMQMAMG3aNDRq1EgxZ3jt2rVYtmwZduzYgYiICMXcYHd3d5X1eC1h0aJF+OKLL7Bp0yaLjo4yOfktKCjAhAkTLBaArRg7Ln3JkiVYuHCh4nFxcTHCwsJsESIhhNQrER6+iPDQXPh+YGgLPBUSiftFOYjLTsLqG8dQJZFoHylsgznC+nqVWXD4/MYZbLp9AcObtMYzYa3QPagJUsuKsPrKcXAsB0Y90eUYxXZOqQdYVxwcByw4/w8Oj5xj0vMihBBix6xdKNIIEydORE5ODpYtW4bMzEx07NgRBw8eVHQ2Jicng1e9Lh+++eYbVFVV4bnnnlNpZ/ny5VixYoVFYzt79ixOnDiB//77D23btoWDg2ptjF27dpnVrsnJ74QJE3D48GG88sorZl3QXP7+/uDz+cjKylLZnpWVheBg/Z+Wf/bZZ/j4449x9OhRtG/fXu+x2kqBE0IIsSyGYdDKOxCtvAMxJKw1vr17EX8/vo5ScVX1ckAcjJo/a/haNS+0JZSIsevRbex6dFstHtmavkx1L7N8XjLHKm9ToqV3O7EoF9sSrmBaqy41C5QQQggxwdy5c3UOcz558qTK4ydPnlg/IBlvb2+MHTvW4u2anPxGRkZi6dKluHjxIqKjozWy8Pnz51ssOGV1fVw6IYQQ8wS5eGBZlyFY1mUIWI5DcVUlXjrxG24VZAKcng/HjUxojUp8Dcwp5jj1AxnVx8pxcgBk8644HsDIK0DLTmE5ABJG43ofxh3BmKZt4elo+SIohBBC6o66MOe3rvvxxx+t0q7Jye+WLVvg7u6OU6dO4dSpUyr7GIaxWvIL1O1x6YQQQmqOxzDwdnLB3mEzcSs/A0dS7+Ngyj08KM4DDwCfx4OYZSHg8eAucEKBsMLIlg3cEBiV+OpqS5YAs1r2s9LCWPIlkFgWAKttHWAOIpbD2qsnsboHFb8ihJB6zcpLHdUXYrEYJ0+exMOHDzF58mR4eHggPT0dnp6eZudyJie/jx8/NutCllCXx6UTQgixrGjfEET7hmBh+/54VJyHA8nxKBEJEeHhi2fDo+Du4ISrOal4++J+PCrJ1zhftbe2pgwlz3q6jjkGnAQAn9OR+FY/3n7/Ol5t1wON3b3ND5UQQgixc0lJSRg2bBiSk5MhFAoxZMgQeHh4YO3atRAKhdi8ebNZ7Zq1zq+c/NT6vD6hva4jRgghDQXHcbiam4ZHxXl4XJyPa3mpSC0tQl5lOSokYtlROt6n5COYjR7yrOsgIwJlWOk4aAPnufAFWNSpH9wcHPFUo+YIdvMwonFCCGk47PX+XB532OYV4LlYYZ3fikqkvLLC7l4XbcaMGQMPDw98//338PPzw40bN9CsWTOcPHkSs2fPRmJiolntmtzzCwDbtm3Dp59+qrhoy5Yt8dZbb+HFF180KwhCCCHEXAzDoEtAY3QJaKyx73TaQyy+dBCZ5cXa81Mz1g82D6eYByx/qBmIdEeFRIxVl48DYMADg+ci2+GDHk/DmS9AVnkJSqqECHL1gIcjFWckhBBSP505cwbnz5+Ho6OjyvaIiAikpaWZ3a7Jye+6deuwdOlSzJ07F7179wYgLUX9yiuvIDc3FwsWLDA7GEIIIcSS+jVqjvNjX0elWISLWckoFVehmacvKsUifHc3FkfTHkAM1gIJsLYFipXp2qd9+LM8IBYcdj64jUdF+SgWCnG/KFdxZLCrOz7sPgRdg8PgzHeAs8Csz7MJIYTYGs35NYhlWUgkEo3tqamp8PAwf0SUycOemzZtipUrV2LatGkq23/++WesWLGiVucEW4O9DqsghBBiGMdxYDkOHIA9j2/jq1vnkFRaKL0v4VA9R5cBAMXaS+qtyG44jJwCZNQwalkyrVoTS297LnwHcAA8HB0xPKIlpkfFoKmnr3ExEUKIHbHX+3PFsOdvrDjs+dX6Mex54sSJ8PLywpYtW+Dh4YGbN28iICAAo0ePRpMmTcyuBm1y8uvs7Izbt28jMjJSZXtiYiKio6NRWVlpViB1lb3+chFCCDHf97cv4cO4k1Bkloy0YrP0e9n/FQsRc8YltHLGJsryNvUNzdZYgkm17VAXD8SENIafkwuEEgl6hjTB0+Et4MSnXmJCiP2y1/tzRfL7tRWT39fqR/KbmpqKoUOHguM4JCYmIiYmBomJifD398fp06cRGBhoVrtmrfP7559/4t1331XZ/scff6BFixZmBUEIIYTUBaUiIWYe+RuxmSkAlAo6cgwg4QCePBmW/ceUHl8VhoZJy5pWvpzWA5SpHsVxQHpFCf55FK8o2rU94QYAoImHFyLcveHt7IoRTVuhf6OmcHFwMO0pEEIIIVbSuHFj3LhxA7///jtu3ryJ0tJSvPzyy5gyZQpcXFzMbtfk5HflypWYOHEiTp8+rZjze+7cORw7dgx//vmn2YEQQgghtSm9tBjj9/+K9PJSAIyWEc6MbBi0LAk2OefV3jurEwdp0s1x4BgdI671tKVvIYbkkiIklxQBAP55HA8A6BQQCl9nFwgYHjoHhmJW2xgI+HzDcRJCCDENx6gWQbRku/WIQCDA1KlTLdumqSeMHz8esbGxWL9+Pfbs2QMAaNOmDS5duoROnTpZNDhCCCHEFrbHX8O7548AABie/mM5AIx8XV8G8HJwxq9DJuLtC/8hviBbx1nqNySyodJaMlTFZCTOqMONonyexmQn2eNrOemK7w8lJ2JN3Cm4CwR4pmlrPBXWHIPCmsFZQL3DhBBSUxyn5W+xhdqtTxITE3HixAlkZ2eDZVmVfcuWLTOrzRqt89sQ2OucAkIIIcb55e5VvH/hqOKx4eSXkyaTPGBEk1bY2G8U+DzpSdnlpTiTIS38GOUbhAgPH5xIe4gCYSVyKkrx9c0LqOJY1Uqf2hJTea+vMoZTTX7NGHKtK/FVj0Nln9IxAS5uGBfZFu38g9AhIBjhnj4mXZ8QQizBXu/P5XE33rTSanN+U+cut7vXRZvvvvsOr776Kvz9/REcHFw9DQnSKUlXr141q12zKl5IJBLs3r0b8fHSoVJRUVEYPXo0BLTMAiGEEDuSV1GOpUqJLxilOla6yJLB+dG9sLBTX5Vdga7uGN88WmXb8PDWiu9ntInB7od3cD0nDfcLc3E3P0dxPUViysoCUb+o1mnChucOK1O5jmKjroOVmpcl2jkVZfj2VpziEA8HR/QODUdjD0+IJBJkVZTBiS/AsIgWGBIeCQceDZsmhBANtNSRQatWrcLq1avxzjvvWLRdk7PVO3fuYNSoUcjMzESrVq0AAGvXrkVAQAD27duHdu3aWTRAQgghxFo+uXxaa6FmXQmwfLDU4LBIzO/Q2+TreTk6Y3qbLkCbLgCArXcu4cNLJ8ApRnOZOq65Bj2/yj27hhJgxQGqB5ZUVeFgUqLGDdfeB/HwcnTCvrHTEO7pbVKMhBBCSEFBASZMmGDxdg0M7tI0a9YstG3bFqmpqbh69SquXr2KlJQUtG/fHnPmzLF4gIQQQoi1/PckQec+9R5STpYEDmzcHN8NGgcBz+S3UA0vR3VFG58A2SN9iSyDQWHNwVfOyOVrD0P5/2aoSX0UtZyYUdpULBSi/+/fof3PG9Hh540Y8MdWvHP6IG7lZNbggoQQUg/IC15Z46uemDBhAg4fPmzxdk3u+b1+/TouX74MH5/qeT4+Pj5YvXo1unbtatHgCCGEEGvJLCtBqbBKdWivWj6r3gPs6eiEzYPGqMw9qgmGYfBU4+aIz8+FoSHMmwZI5xYfSU7EsZSHuJ6TjqzyUrDgIGY5iNWKgajTWeFD32WNzanVjmOUXtNioRAAUFQlxJOiAvxx7xY6B4Xiy0HPopG7fc9JI4QQYh2RkZFYunQpLl68iOjoaDioLcc3f/58s9o1Oflt2bIlsrKy0LZtW5Xt2dnZiIyMNCsIQgghxFbKRFWYvP8P3FDqgWQ4Bpw8g9OSAAOAI4+PXSOnwolv2foWz7eIxtc3Y6E1A5Vd29vBGQcfJ2JMiyiMbNoGI5u2UYuRw7WcdHx/5zIOPEkAqzaPWG+ubiiPNyYBVl7FSblNHW1fzUrH4D++x6z2MTic9ACPiwogYHhwd3BCsKsHOgYGI8DVDRfTU8Dn8dAlKBSvduoOR1p6iRBSDzCc7ENCK7RbX2zZsgXu7u44deoUTp06pbKPYRizk1+Tqz0fOHAAb7/9NlasWIEePXoAAC5evIgPPvgAH3/8Mfr06aM41t6rjAH2W02OEEKIptMpj/HyoV0QSViNxEwl+ZWP35X1YAa4uGHvs1PRyN3LKnF9euUMNt24IA+k+tocABbgMzywHIdRzVtjw8CR4OnJZjmOw/mMZHxx/RzuF+SiSiJGhUQMVtu7vXLFafUeYJUR1catS2xUf7h6L7HyJk59j6rnWrTF8OYtkFNegeY+vugSFGqxXnhCiP2w1/tzedxhX3xgtWrPKW8ss7vXxZZMTn55SnOc5G848iaUHzMMA4lEYqk4a429/nIRQghRdfDRfbxydK/eYxQJsKyDkWGA/+vcFzPadoGbg6PVYuM4DtsTrmPjtQvIKilVbGfk87eU3qnf6tYXr3fuYfI1qiQS3MzNRIVYhBJRJR4XFeB+YR4uZaQgvaxEdkF5QMr/Ny7xNfJI1fYN7mN0bK8W6OKK/k2aISY4FE29fBHp4wtfF1djIyGE2CF7vT9XJL8brJj8vln/kl/1XLMmTB67deLEiRpflBBCCLGlnLJSzDu6X+vausoYVH+IGxPcCN8NGmuTRIphGExt3QmVVRKsvnhCGqZiKQzV9Yk+jT0LhmHwWqfuJl3Dkc9HTFAjje0sx+F4ykO8f+4wMsqrE2/N7lg9Nx3qw54tQn2dY+3XyK4ox18Jt/FXwm3F/nAvb4yKbI2X2nWCv6ubpQMjhJCasVZxqnpU8AoAtm3bhk8//RSJiYkApNNv33rrLbz44otmt2ly8tu/f3+zL0YIIYTYkkgiwZyDe3Ai+TEATtGja8g/o15Eh8AQq8amze3cTDAMI/2UW2UdSNUbmk8unoGvswteaNO+xtfkMQwGN4nE4CaRSCouwD+P7iG7vBThHt4oqKzAvsf3kFVWikpto7mUhmhb5JZLx/OF8mZ9hbtkkooK8eWVi/jqaiwmtWmPxT374u+Eu9h2+xqSigrhLHDAyOat8HL7LpCAQ7moCs29feHt7GKJZ0EIIaSG1q1bh6VLl2Lu3Lno3Vu6tODZs2fxyiuvIDc3FwsWLDCrXZOHPQNAZWUlbt68iezsbLBq1SVHjRplViB1lb0OqyCEkIZOwrLo9cu3yCorq14WyIjklwFwY/p8eDo6WTlCTYtOHsCexLuQsBzA6k8nnfl83J31hsp0JGviOA5387Kx/uo5XM1OR6GwUhon1KYM6+soNnTHYeIwa2PaZQD4OLugoLJC81C185wFAkR4+iDI3Q2dA0PxYnRHCCUSZJWVIsDVDY086D6AkLrAXu/PFcOe131ovWHPC5fa3euiTdOmTbFy5UpMmzZNZfvPP/+MFStW4PHjx2a1a3LP78GDBzFt2jTk5uZq7Ksv83wJIYTYN47jMGrnr9LEF5BlZsrlj3Wf2z0krFYSXwDo37gp/r5/R/ZI/9JHlRIJ9j64h7Eto2wSG8MwaOsfhK1Pj1Nsu5yZhh33biClpBDJxYXILi9THS2trYCWjXEA8mWJryGVYjHu5efgXn4OTiU/wfrL56sbAdA1tBEW9+yPLsGh1gmWEEIIACAjIwO9evXS2N6rVy9kZGSY3a7Jye+8efMwYcIELFu2DEFBQWZfmBBCCLEGjuMw899duJOTDZUKTvJhxHp6f/kMg0/6DbN+kDoMa9oSoe4eSC8uMer4T2PP2Cz51SYmuBFigqvnEQslYhx8fB+/3r2BGzkZqJJINIYqG54erD/pN6XX1yD1Al8GrheXnobxf+/Aa52741FhAa5mpsOBz8dT4U3RKSgEaSXFAIAejcLQNaQRVaImhGinMq3Fwu3WE5GRkfjzzz/x7rvvqmz/448/0KJFC7PbNTn5zcrKwsKFCynxJYQQUucUVVbi2T9/RXJJkdoeWcrFMQDLVa/lq5SbOPH42DFyIpp4edsmWC0c+Xz8Mvx5DPnjB+1LE6lJLy2BhGXBt9HQZ0Oc+AKMjozC6EhpQn4vLxsPCwtQLKzEjdxMpBQXwsfZFc82b4Vr2RnYfOOS6tJOisxYRwJs65tFjX3SAL++egnKMf56+wZ+vX1D5UhXgQOGN28JDkBuRRn8XFwxrlUUogODUC4SwdfFxeJrRhNCSH2xcuVKTJw4EadPn1bM+T137hyOHTuGP//80+x2Tf6r+9xzz+HkyZNo3ry52RclhBBCLC2jpBhP7fgRlWKxbIt68qSWAMs3McCEVu2wpt9QCOpAEtnc2xdjW0Th74Q7MDj/lQN+vnUVMzvE2CQ2U7X2C0Rrv0AAwCR0UNk3tGlLjGzWGm8c34+HhfnSjboKXhnoDLYKnWsOM1q2aSoXi7Az4Y6iLQbArnt3VVpxc3BA99DG6BwcihZ+/hgY0axO/AwSQqyMen4NGj9+PGJjY7F+/Xrs2bMHANCmTRtcunQJnTp1MrtdkwtelZeXY8KECQgICEB0dDQcHBxU9s+fP9/sYOoie51QTwghDc3Q335GQr5mPQpVsrc8nvRbhgHOTp1T5woZ3crJxLM7f9W+k5V9yTjwGFybPQ/ujtZbh9jaEgty8ee927iTmw0eAyQVFSCltMT4G7ma3vBpO99Q5Wkz29bamuwYD0dHjGnVBo48Ae7kZsHVwQHtAoLwYvtO8HeltYsJkbPX+3NFwavPrFjw6v/qR8ErazG55/e3337D4cOH4ezsjJMnT6rMZ2EYpt4lv4QQQuq+hLxcIxJfoLr3V/poee+n6lziCwDRAcFo4xuA+Pyc6o0cAFlNSUYphRKxHHr8sBm7JkxGSz9/2wZqIS18/PFezwEq2/IrypFUXIT43Gx8c/0SUjSGsluALXpJlCY5MwaKgJVUVeGXW6rDp48/eYyNcRfBAGjm7YMF3XujW+PGKK6sRICbGzydLH8DTQixMlrn1ygSiQS7d+9GfHw8ACAqKgqjR4+GQGD+lBGTe36Dg4Mxf/58LF682GbLK9Qme/1kiRBCGorsslJ8dO4U9iTeM/IM6RjUVzp1xeKedXft+jKhEG2//1L6gAEgln+r/eZGwDA4OGU6mvv42iZAGyupEiKztAQZZSUQsxyuZ2Xg17vXUSisBDjAkc+DM99B+ljG09EJr3TqBhErwfq485pTig3O97XQjaSuIuMWSL7bBwaha2gjCCUSuDo44JnmLdEhKBgMwyCrtBTJxYXwcHRCKz9/KsBF6g17vT9X9Px+usp6Pb9vvW93r4s2d+7cwahRo5CZmYlWrVoBAO7fv4+AgADs27cP7dq1M6tdk5NfX19fxMXFNZg5v/b6y0UIIfWdUCzG//7dg9NJSdLpU7L5u/rzFelb3ldDR2JEZGvrB1lDv96+hvdPHVPMD9OV+Mr1btwEv46dYJvg6qj00mIk5OXCWSBA5+BQRVGpBwV52HHnBv55cA+55eWGG7LUsGelthgt2yxJns/zGQZOfD7KRWLFPgHDoJGHJ/pHNMXzUe3QNpAKlxIpMcuivKoK5WIRiisrcSQxEXEZabiTkw0xKwHLsnAWOGJUy9ZY2Ks3XJxqZyk4ZfZ6fy6Pu8kn1kt+k9+uH8lvz549ERAQgJ9//hk+Pj4AgIKCAkyfPh05OTk4f/68We2anPwuWLAAAQEBGmWn6yt7/eUihJD67EJKMl7a+zfErHTyq/yNjAOk83l1TKxkwODnZ8ehX5OmNonTEp7+7Ufcz80zmPjKnXlpFhp7elk5KvvFcRyOPXmIpaePIqOsVMdB6hssMO9XNsdc9zUsyEDbDIBAVzd4OTuB5YCWvn7o3SQcrfz8kVNeDh9nZ3QJbUTFt+yMhGWRXJCP/IoKFAircDb5Mf6+dxflIpHlftzkDTHA5wMGY2z7DnoPtxZ7vT9XJL9rrZj8vlM/kl8XFxdcvnwZbdu2Vdl++/ZtdO3aFRUVxq3frs7kAdMSiQSffPIJDh06hPbt22sUvFq3bp1ZgRBCCCHGuJ6Zgam7/wIL7XV3ORY6EmAGO0ZPQM/GTWwQpeW81aMvZu/fa/Txs/ftxoHJL9EQVx0YhsHgppEY3DQSKcVFuJqVDrGERai7B3g8Hq5lpuNBYT4upiUjtbhYdlYNyk0rJQs2o7R+sa7LZpeVIbusDGCAh/n5+O9BokYjfB4DjpUuwdUxJBgvd+yCPuHhcKrBfDtivoySEvx47TLOp6Qgo7QUZVUiVLEStaPUxvRb4udO248/Byw6cRSLTh7Fo/mLLHARQlS1bNkSWVlZGslvdnY2IiMjzW7X5L9et27dUpSXvn37tso+eqMlhBBiLRzHYU98PJYcOwKOlfWiqb3tKB6yAKeUAAt4PPw1diI6BYfaLmALGdI0Ek+FR+BE0hOj7mPv5eThSkY6YkIbWT02exfm6YUwtV7y7qGNFd/fys7EuyeP4HZONjhZBuDE50MoUU84DNModmVthrr6tMaiOhFaIhtZUSnhcDE1FRdTUwFIl2hiOQ5VEgkYMPBxdsbwFi3g6uCIhLxcuDk64oV20ejROIzuDY2QVlSEhLw8eDs5Ib+yAr/cuI5L6WkQiiUm/swYM5ndDNpW+pJfjgOm//0Hfho/0bLXJA3emjVrMH/+fKxYsQI9evQAAFy8eBEffPAB1q5di2LFh5MwqZfb5GHPDY29DqsghJD65HJ6Gmbu2Y3SqiqlrZz2rl8ZlgGCPdyl89S69oKz2kgle1IsrESHLV/pP0je0cMyCHZ3x7mXZ1PiYSESlkVxlRAuAgGcBQ7IKC3Bvw/uI72kGI8LC3A9OwMFlZVwFgjg6eiEcpEITgI+Gnl44klBPkqqRKr5iBXyEw16en3Vj6sOyMifF+XebAPPw5nPhwOfDz6PBwnLokoiAZ/Hg4+zMzwcHZFXUYGyqirwGAYOfD44jgPDMHB3dERzX1+09PNHt0aN4SwQ4FpmOrLLyuDv6gaxhIUDn0EjDw+09A9EVECAys87x3EQSsRw4gtq9fegtKoKueXlSCoowNarl3ErKxulVULl1coMM/ofUqnL3+jzjGxS2z4lj96wbe+vvd6f07Bn4ykXVpb/DsvTVuXHDMNAYsIHkjUat5Iq+wSwcePGBo4khBBCzHM7Owsv/PUnWI3Papnq0r1abtAceTycmDITLnac9Mp5OjnDTeCAMrFI/w0pCwAcMktL8cGpE1g+YKDtgqzHpMmai+JxiLsHZnXsonIMy3Hg6UiyEvNysfLMcZxLSZFusFIHnck45W9MyJRMOLRSIkGl+o2pRIJykUjzYKVtBZWVSCkuxsknT7DlymXdFzDwWjIA/FxcwOfxwGMYRAUEokNIMHLKyiFmJagQiVBYUQEfF1c817YteAzwX+IDSDgO/Zo0gYhlwePx0DEkGMHuHtJLchyEYjEeFeTjYGIibmVlIT47B3mVFZDI/k7xGAYufD7KxOLqQMyl7Z9HY96HFRJ8fU0qXvO68INsfxjIRoNYod364sSJE1Zp1+Tkl2VZrFq1Cp9//jlKS6WFIjw8PLBo0SK89957DWL5I0IIIbYz799/wbIGurG03BwOj2xZLxJfueY+vriZnaV9JweAVa0G/fP16xgf1RbtqKqvTehKfAGghZ8/fh3zPAoqKvDuiSM4+DBRJV+wePpgUmN2frusPvpD7blzAHKVCuNklJbi2ONHWpvaHR+v8o+x/eYNrccZg+W46sRXS1waDP0zcFra0Def3FpzfQmxkf79rbMUocnJ73vvvYfvv/8eH3/8MXr37g0AOHv2LFasWIHKykqsXr3a4kESQghpmL69dAlJhYUm34A58Hh4p3c/q8RUW55vG41b2Vng1G+CFSMcNV+k32/dwqpBlPzWFT4uLvhm+CiklxTjamYGGADdQhvDScDHwQf3seHSBWSU6qhAbSxbdsLVtQ4/U+NRTx5NHP1tUYYKo5nz3Kz5POrav7294RjZ+nxWaLceqaysxM2bN5GdnQ2WVZ0sMGrUKLPaNDn5/fnnn7F161aVC7Zv3x6NGjXCa6+9RskvIYQQi/jx6lWsPXtWWrnZEKWhj3weg1/HTUCIbIhifTGudRR+uXkd93NzUT3kG5r/l2OAM0lPbBYfMV6ohydCPVTn4z3ftj2eb9sehRUVuJ6dgaIKIWLTUnA9KxNFwkqwHIvMsjIrRNNAu/d0JZuMnn21xVCiqZj+oRSwsbEb+ufXUenZ+OAIMc/Bgwcxbdo05ObmauwzdZ6vMpOT3/z8fLRu3Vpje+vWrZGfn29WEIQQQggApBYV4csLFxGXlibt8QWMuzeX3bC28vfHlpFjEOZV/9a5dXFwwG/jnsfbRw5WD9vkGMWUZ41yNxyQUliMc0lJ6B0eXisxE9N5u7hgQHgzAMDo1m1U9t3Py8X2WzdwJTNdsVZvUx9feDk5ISogAN9dvYK4tFTFCFmDvzqKRK+BJcCGnm5tTmM155+CA8BjNLdBS1ta6mJpfK/rGhrtSV+oF1pp5gXEAG3D2C3Vbj0xb948TJgwAcuWLUNQkOVGMJlc7bl79+7o3r07Nm7cqBFgXFwcLl68aLHg6gJ7rSZHCCH2hOM4LD16FL/duKW6g5EtWST7XsfZAIDJ0R2watBga4VYp4zc/gvu5uQobqC0vTTyd3dnPg9333zThtGR2lRWVYWLqSmoEIvQ1NsXhx8l4ucb11AsFFYfpCsB0ljHRstPlvrmunSzbWwsxvbs1tZnAnp7Wo09T0s1fG3tmP0cpYmvB4+HG/MWmNNAjdjr/bk87vCPVoPnbIVqz5WVSHr3Pbt7XbTx9PTEtWvX0Lx5c4u2a3LP7yeffIIRI0bg6NGj6NmzJwDgwoULSElJwYEDBywaHCGEkIZBa+ILSG/MJAD40HOTJl3aZ+VTDaey8ceDn8ao7dv1HiMfASkUs/jszBn8X9++NoiM1DY3R0cMalZ9s9g2MBALevRWOUYskeCjM6fwd/xdlFTJk2ITMiBzhwXLCrMpzucb2UZdG4ZcV6m8Toyi+rsKRsu4bqMTYNWu4oszZiPQzhMsUnc999xzOHnypMWTX7PW+U1PT8dXX32Fe/fuAQDatGmD1157DaGhoRYNri6w10+WCCHEXjzOL8DgH37UewwH6LxR7hwcjN8mTIQDn2+N8Oqs9pu+RFmVluVilCkNrTszexZCveh9jGiXV16Oq+npSCspRqiHJ5p6eyO3ohw8APsTE3DwwUMUC4UQsRL9PYjqSw9xkM7bZ2VfUOqIZFA9p59Ra0e5XUD6IZihLhtTen4NzXOVs6eeX3Pa1rVPLT/mA+gaEoLPh41ASB24H7bX+3N53BGrrdfz++S9+tHzW15ejgkTJiAgIADR0dFwUFu9Yf78+Wa1a9Y6v6GhoVTYihBCiEWsO3fW4DEMALCqhSz5PODHsePQJzzCWqHVaS916Iiv4+IMHyhLRObt34+/p0y2elzEPvm5umJIZCQA6TQEABBlszhw7z4S0vPhIGLgy3OGgwMPLMehtEqIMpEIEnmiK08oGen6pRwnzWs5QKVCuXJ+xXAAJ4Huonby8+RFXlmlY00t1KTee2xomaDanPNbW2TX7hQQiF8nPA8XR8daDIY0dL/99hsOHz4MZ2dnnDx5EoxSQTeGYayf/CYmJmLZsmX49ttvNT5JKCoqwquvvopVq1ahWbNmZgVCCCGkYckpLcXLO/fgTna25tw0HeS7+QyDPya+gE4hIdYMsU6b2L49vrokTX51LjHLQJE4XM/ItElcxD6UCIWIS0nDwYRE5FeUo1QoxMO8AhRWVurIv7RsZQCeetEktSRX/sGVrt9txX71q8gSUJX8VXnItK4kVlevtLbvtW2rz4kvA7gJBOgQFIQgDy8MadYMTzVrBieBWX1hpCao4JVB7733HlauXInFixeDxzNm2QfjGP3T/umnnyIsLExrF7qXlxfCwsLw6aef4ptvvrFYcIQQQuqn1MIiDNn6E0QSVvppLgdwyu/aWm6U5fekPs7OWD98eINOfAGgsZcXXAUCVEjE2g9QvwnigId5+Wju52v12EjtKquqwulHT/AoLw+HEx8ivbgEEpaDSCKBUCIBy+n/XdNOSzeqKcWSjDxWcYj2XFt1WLW2pFZpGqu2ZcAYRm2Tlt8Tjce2GPqsdN0Qd3dwALLLylT/rXRw4fMxs3NndA5thHKRCKEeHmjh7w936rkldqyqqgoTJ060aOILmJD8njp1Cr/++qvO/c8//zwmT6bhVIQQQvS7m5WN8b/8BpGE1bipZDhGmgRrudkc1zYK/Zs2xZDISDg2sPm9uqwYNBDvHDxcPb5UvddK7eb/1OPHlPzauaySElx4koykgkKcT0pBbnkF3B0FkHBAblk5ioVCVBm7/qXZSZ1p3aM27UyV/R5oTaINLIPLYxgEuLoi1MMDAj4fTgI+ooOCEOzuARYc+jQJRzNf6e8Px3G4lpGBG5kZcHFwQDMfX3g6OcGBzwefYVBcWYmHBfkQSVgEubsh0scX3165gscFBSipEoLHMCgTCiHgSf+WtQkIQK8mTfBU06bwcXVVxMRyHIqFlXDiC+CiNueR2DHq+TXopZdewh9//IF3333Xou0anfwmJycjMDBQ535/f3+kpKRYJChCCCH102/XbmLp4WPSB+qJr2wDA0Y651Bpf3RQED4ZNsxGUdqP8W3b4lZmFn69dqM6AQY0b4Bkw59/vHQFM2O62DZIYrJHefm4kZ6Bx/kFyCkpx6XUVKQWFqkUSgagfYFnYxPaGvdmyrtQday1ZS2mXEutx9dFIEArf390Dg2Fk0CAFn5+iA4ORoS3t8p8QqPCYBh0Dg1FZz3FXturjU75YNAgk64BSBNyb2cXk88jxN5JJBJ88sknOHToENq3b69R8GrdunVmtWt08uvl5YWHDx8iPDxc6/4HDx7YfVUxQggh1pFcWIiPj57G4QcPDd68ctIx0IrjnAUCfD36WavHaI8YhsGKQQOx5/ZdlIqUKj9rK/TDARklpTj96DH6NWtqyzCJAXcyszB31z6kFpfoPU5rpw6j4/s6RGWIsQVjdODxEOrpiTKhECVVVWAYBp5Ojoj09UPPJmHo1aQJGnt5wcfFBXwLD50kpCYYTvpljXbri1u3bqFTp04AgNu3b6vsM/XDKmVGJ7/9+vXDl19+iYEDta+juHHjRvSlNQQJIYSouZuVjed//h2V8qGYyjfAWuf2Mor5vxHe3vh69CiE0oerOjEMg3f698PSI1p61LnqL3nP+vsHj+H0a7NsHWaDVSkSI7mwCBeTknHhSTKupWWguFIonb/u4oyWAX44l2R45JzGqPaaJpEWSUSNu9NWiduM67o7OuLFjh0wKLI5qiQSCMUSeDk7obmfH81rJfaJY1SXL7Bku/XEiRMnrNKu0cnvkiVL0LNnTzz33HN4++230apVKwDAvXv3FF3S58+ft0qQhBBC7NOj3DyM/XE7WLUKsIqlT3jQuBGWD3mODgjC7mmTa/QJb0PxQsf2WHvyNMqqRFCvj8OovcAZJfp7F4lpOI5Dfnk5RBIW/95NwIkHj5FXVgZHWQXd+7l5ELOs1nNzysqRU1YufWBEpXPOiONsR8vaRTqOUjlEY7oDIODx0MjTE9O7dIKroyO8nJ3RrVEjeFphHVRCiP1JTU0FADRu3LjGbRmd/Hbq1Ak7d+7EzJkzsXv3bpV9fn5++PPPP9G5c+caB2TIV199hU8//RSZmZno0KEDvvzyS3Tr1k3n8X/99ReWLl2KJ0+eoEWLFli7di2GDx9u9TgJIaShO3A3AW/uOaD4IFp9dCYHVK/bqVyIhmHAYxhseHY4Jb5G4jEM3ujTCx8dP20wN+IASFiWhoGaIb+sHMmFRUjOL8Cl5DT8c+ceKsWa1bY15uXqY5c/4rp7fJV7eR15PPi7uaFDSAiebdMKLDi08g9AU18fm0RJSJ1FBa8MYlkWq1atwueff47S0lIAgIeHBxYtWoT33nvP7CrQJi3sNXLkSCQlJeHgwYN48OABOI5Dy5Yt8fTTT8NVqTKdtfzxxx9YuHAhNm/ejO7du2PDhg0YOnQoEhIStBbjOn/+PCZNmoQ1a9Zg5MiR2LFjB8aMGYOrV6+iXbt2Vo+XEEIaqr+u3cR7/x2rXm5Ez9JF6gV7eDwGO6e8gAi6QTbJxPbR+Oj4acMHskB8Vg7ahQRZPyg7VFRRiVKhEEEe7riRnondN+/iTmYWHuUVoEKklOiqF5vSxhqJra2W3tHDWSBAgLsbIny8EerpCQ9HRwR6uKFPeARa+PvRh1aEkBp777338P333+Pjjz9G7969AQBnz57FihUrUFlZidWrV5vVLsNxRiwgVkd0794dXbt2xaZNmwBIPxEICwvDvHnzsHjxYo3jJ06ciLKyMuzfv1+xrUePHujYsSM2b95s1DWLi4vh5eWFoqIiKuhFCCEGnHrwGO/+exg5JdKhnJyWYc3KFG9AsuP8XV2x96UpCPJwt3Kk9VOvTVuQXVam+wBZb8PvU59HTFgjm8VVl3Ach8KKSlRJJHB1dMDRhIe4mpKOs4+eIL2oxPgpczqOM6nX19hjYIU5vwbaYQD4u7miiZc3XBwFeKZlJDo1aoSmvj5woKXGSC2z1/tzedzNln8EnhWG9bOVlXi08l2TXpe6Oqo2NDQUmzdvxqhRo1S27927F6+99hrS0tLMateknt/aVFVVhStXrmDJkiWKbTweD4MHD8aFCxe0nnPhwgUsXLhQZdvQoUOxZ88endcRCoUQCoWKx8XFxTULnBBCGoj39h/BX9dvGz5Qh/ZBQfh72iTqNaqBlUMG4tXd+6QPtL2MLMCHtNBSQyKSSLD1wmVsOR+HsiqR/oPl2aWZPbrKqw5ZksZauTXoAXYWCBDk4YaW/v6okkjQzM8Xz7SKROuAQDg7CMCj30FCGoS6PKo2Pz8frVu31tjeunVr5Ofnm92u3SS/ubm5kEgkCApSHaYVFBSEe/fuaT0nMzNT6/GZmZk6r7NmzRqsXLmy5gETQkgD8s4/B7H7Zrz2nQZu0hkGmB7TCe8O7E+Jbw0Natkcoe7uSC8t1Vz/VQzwZKtIDd+8DdO6dsS7Tw+oN695YnYujiY8RIVIhMgAPwxt0wJOAgGqJBJM+flP3EjX/d5vaSYlvsYk22qHGKqc7OXshBAPD3g6O8Hd0RFeTs5oGxKE8e2i4OHsZEp0hBBrqCNzftetW4fZs2djxowZAIDNmzfj33//xQ8//KB1VO0XX3yBYcOG4a233gIAfPjhhzhy5Ag2bdpk9KhaY3Xo0AGbNm3Cxo0bVbZv2rQJHTp0MLtdu0l+bWXJkiUqvcXFxcUICwurxYgIIaRu+yX2Knbf0JL4MpAWtNI1QpKTJr7H5sxAE19v6wXYgPAYBl+MHYlpO3aiSiKGRCRbT1L9QA74+dJ13ErPwu8zXqiNUE1SVlWFzKISXEpOw+XkVOSXlSPUyxO9m4Wjd7MmeHffERxNeAg+w4BhGIhZFh8ePIFPxwxDfFaOeYmvrg9tavmzAgHDwIHPh6ujI8J9vODt4oJ2wYF4qUsniDgWnk5ONCyZkAZOfeSqk5MTnJxUP/iy1ahac33yyScYMWIEjh49ip49eyqun5KSggMHDpjdrt0kv/7+/uDz+cjKylLZnpWVheDgYK3nBAcHm3Q8oP2HgxBCiCaWZTHn9z048zAJgPZhmQwDcPKKzuo9kQywccwISnwtrFOjEOyaMRmfnzyL4/GPFNu15WxXUzPw7dlL+F8f3fO7bO1o/AOs/O848isqwHEcWI7T2Zmx8/odOPL5iqWEJBwH+VpPJZVCvPbnP3BysPCtjhHDjbUWczNwgpezE4oqq6ddOQn4aB8SjPHRUegVEY4QTw+zQyaE1DGc9INJa7QLQKPjbvny5VixYoXKNluNqjVX//79cf/+fXz11VeKeMaNG4fXXnsNoaGhZrdr1DuCKfNerTXp3NHREV26dMGxY8cwZswYANIbr2PHjmHu3Llaz+nZsyeOHTuGN998U7HtyJEjik8PCCGEmKdSJMaYLb/gcX4hgOr7e23DMhkAnAQqSUCYtxe+Gj8SbYI05xSRmmvh7wcPgSMA2cuu5SZLnqB9czYWc3p3rfXhz7fSMvD8j78r1oQ2VpVEonW7PPksE4os21tralt6EmABj8GA5k2xeFB/hPt41zAwQojdsPKw55SUFJWczF479kJDQ82u6qyLUcmvt7e3wTdFjuPAMAwkOt6ELGHhwoV46aWXEBMTg27dumHDhg0oKytTjFOfNm0aGjVqhDVr1gAA3njjDfTv3x+ff/45RowYgd9//x2XL1/Gli1brBYjIYTUd1klpXj1972KxFcb9SRYOQGb0b0zlgzub70ACQDgeqr0k3hO27BnGQZAeZUYT/IL0NTP1+IxZBWXYP+te/j7+l0kFxZCJJH20AoYBm2CA/HmwF7o3SwcCVm5eO6H3y1+fYl8QQtLLg+kXHaZ0bJdhgHAY4D2oSGIDPCDgMdDdHAgOjcORTM/31r/sIEQUn95enoa7JC01ahaUyUmJmLZsmX49ttvNZ5DUVERXn31VaxatQrNmjUzq32jkt8TJ06Y1bilTZw4ETk5OVi2bBkyMzPRsWNHHDx4UNH9npycrLLgca9evbBjxw68//77ePfdd9GiRQvs2bOH1vglhBAzSFgWa4+cxrZL14z6wFrbMSOiWmLxoH6WDo1oIeAZl1wxADYcP4cvJjxr1nWup6Tj3KMkFJZX4lTiYyQXFAHQ06nBAGKOw62MLLy8fTfGdWyLe1k5Zl3bGBrD8S1BW4MMEODqihc6tUPXiCaI8PGGn5srzb8lhGiqAwWv6uqo2k8//RRhYWFak3cvLy+EhYXh008/xTfffGNW+0Ylv/37151P6OfOnavzH+TkyZMa2yZMmIAJEyZYOSpCCKn/PjlyGj9fvCZ9wMDknrR1Y5/ByLaayxYQ6+gbGYGHeQUGj+MAHIp/gJJKod5KwHFPUvDt6TgkFxbBzdEBvm4uuJ6aibKqKsPB6PlZ2XX9jlWLSDkLBKgQi00/URaTgMdAzHKK5X+cBQI09vbEoJbNEBPWGB1Cg+Hh7EQ9uYQQu1MXR9WeOnUKv/76q879zz//PCZPnmx2+2ZXgSgvL0dycjKq1N702rdvb3YwhBBC6p5T9x9j2b4jyCopUy3kw4PB9U7lH0L/PXMSokMtNyyKGDapSwf8dPGawbxS/u906UkqOoaFwFkgwPZL17H1bBxKhNL3eK2dCcbmerWYE/IZBpNi2oPHY7D1whWDx/MYBgwDuDg6ICooEB8OH4ymfj5gOY7WviWEWAxjpYJXprZZF0fVJicna11jWM7f3x8pKSlmt29y8puTk4MZM2bgv//+07rfmnN+CSGE2NaWM7FYd/S8xlxGQ5Vs5e+/jnwetr/0PCW+taCpnw/efbof1hw+bdTxr//+j3ENm5ID2jBfVB/ezGcYNPbxwit9usPbxRkjolpj7bHTuJmWCRErgbezM0a2a4UZ3bugqFIId0dHNPLWPkeOEl9CSH1V10bVenl54eHDhwgPD9e6/8GDBzUqsGxy8vvmm2+isLAQsbGxGDBgAHbv3o2srCysWrUKn3/+udmBEEIIqVv2Xr8rTXwBja4/RaIhX8ZIifzQoa0j8aWZ80iJZbzUvTM+OXoGEkMllDmlwlj68jxr5YA1LEg1pHVzJOUX4X52LgDpMkFjO7TFmwN6wdvFGQDQNiQQ26Y+p/X8YFpGiBBC6oR+/frhyy+/xMCBA7Xu37hxI/r27Wt2+yYnv8ePH8fevXsRExMDHo+H8PBwDBkyBJ6enlizZg1GjBhhdjCEEEJq3/2sXHxy6BTOPkjWP6yZ054AMwCe69gOq0YOtm6gBACQV1qG3+Nu4FpKBsQSFsWVQjzKyQfHAe0aBSHU0wMphUpLFnIAw0LlAw2OkQ2X01bFWM6c5NTIpNbLRXWNW2OF+3hjXv+eGNmuFQAgvagEFSIRQrw84OboaHJ7hBBCateSJUvQs2dPPPfcc3j77bfRqpX07/u9e/fwySef4NChQzh//rzZ7Zuc/JaVlSnGYfv4+CAnJwctW7ZEdHQ0rl69anYghBBCat+BWwn4v50HwAKGkxZGlitxiofoFt4YS58ZiBYBflaNs6FgWQ6307NQUimEl7MTvjsTh9vpWRCKJQjx8kBaYREKyit1nn8tOR0SAOBDkdgystlJystR8TiloezKO6HjsbGMPG9O725o7u+D1//cV708kZamwnw8MaFTNHo2bYKmfj5wV1u7UtewZUIIqVPqQLXnuqpTp07YuXMnZs6cid27d6vs8/Pzw59//onOnTub3b7JyW+rVq2QkJCAiIgIdOjQAd9++y0iIiKwefNmhISEmB0IIYSQ2pVaUIS3//4PrK4ESJ2sV48B4O7oiD9fnoRm/pZfK7YhySouxcOcfMSnZ+PCoyRcT85AWZVI9SBZEptXWl79WAd5XTJIpB308kMZpQM0TtfWnrE/E7rOUz5XrTd4WJsWmNGjM/g8Hm69Ox+H7z3Ad+fjkFdagTZBflg4sA9aBgWYeGFCCKm76krBq7pq5MiRSEpKwsGDB/HgwQNwHIeWLVvi6aefhqura43aNjn5feONN5CRkQEAWL58OYYNG4bt27fD0dERP/30U42CIYQQUjskLIutZ+Kgo9NNr/4tm2LJ0P6I8POxfGD1DMtyKK6shIDHw83UTHxy4BQe5RZAwrKqHQHGlGg2kcbqVDrmcTOy+b81nd+rdX1dWbs8AM0D/DB/QE8Mbh2pKCjF5/HwTFRLPBPVsmYXJ4QQYtdcXFwwduxYi7drcvI7depUxfddunRBUlIS7t27hyZNmsDf39+iwRFCCLEukUSCLacuYdv5aygWCk1OeNydHPHt5DFWia0+SMzKxbbz13Dq3iMUlFfoHNJrdK5Zw4RUcR0dH3IommchHSqtrQHlAw0E7sDjoVNYCGLCG6FP8wi0Cw2Ck8DsVRYJIaT+qCe9tPamRu9AHMfBxcWlRuOuCSGE1I4KoQjDN/yErJJSpfGxMsYkWQywdeo4K0VnPzKLSnA1KQ3fnYpDcn4hxGJpLy7LsWC1DfvVwhYL6fAZQGLKPDMjMvK2oYHo0zwcGUUlcHF0xKSYaIR4ecLdyRF8Hk//yYQQQoiNmZX8fv/991i/fj0SExMBAC1atMCbb76JWbNmWTQ4QgghlidhWWw4fA4/nLlcPcxZvSfPwBxPhgG+nTwGHcPqf62HKrEElx+noqC8AhlFJSgur4QDnw8/D1fsux6PGymZFruW4p/BAsOOlfF5DLo2aYyLT1KMjoNT6/0N9HBDiwBfOPAFeKpVMzzXqR34fEpwCSHEZFTwqtaYnPwuW7YM69atw7x589CzZ08AwIULF7BgwQIkJyfjgw8+sHiQhBBCLONBZi7Gf7UdIgmruoMDIAHAl835ZNT2KS2B06FxMH6d+Twc+NrGxdonjuNQJqzC7bQsPMnJR6mwCkl5hbiTlo1HOfmoEktUjucxDFjlMtdGXcS4Y3Xeu9QgGeY4YNHQPli86xAeZOcb1VTrIH8sGtYXzf19EezpAR7PFv3ThBBCiPWYnPx+8803+O677zBp0iTFtlGjRqF9+/aYN28eJb+EEFJHlVYIMWrjLzqTMAYAp54AKx3bq3kTvD/8KTQLsN+Kzg+z83DgegLOJyahSixGY18v+Li54NDtRBRXCI0uOMUqz921cC+tTmZUXGYgLSL18fhhaNcoGN9OHYMh634wqnPgcXYe+kZGmB4nIYQQvajas3bFxcWGD5Lx9DRvaTuTk1+RSISYmBiN7V26dIFYLDYrCEIIIdZ19Ukapn/3l8FETbFuLyN9E3VxFGBc53aY1K09mtvZ2r1ZRSVIKyjGg8xcHLyViCtPUiFmVe8M7mXmmr2Kj/kn1ZD6v6GWf1MeAzT198Wwti0xISYaQZ7uAIBGPl74X79u2Hzqkv7YOaBKwiEtvxCNfL0tGz8hhBCihbe3NxhG/xsrx3FgGAYSiUTvcbqYnPy++OKL+Oabb7Bu3TqV7Vu2bMGUKVPMCoIQQoj1XHmchmlb/jR6KpByNeB3hvbHxG7trRSZZXAch+vJGUjKKcCt1AwcufUQeWXl2g/W0eMNyJ5yLY/sNfpDe1nC68jno1/LCJQLRcgvL0eYtxee6xKN3i3CdRacmtS9AzafNJz8Mgww7svtiF3+uonPghBCiF4051erEydOWP0aZhe8Onz4MHr06AEAiI2NRXJyMqZNm4aFCxcqjlNPkAkhhNgWx3FYsGN/9QYTkjtXRweM6tjG8kFZ0JUnaVi28zCe5BZapkFThzDbYMizr5sLujcLw7PRrZFeVILErByE+XpjcveOcHF0MKM9V+k32rq81W7ISiqrkJRbgHB/WsOZEEIshYY9a9e/f3+rX8Pk5Pf27duKpY0ePnwIAPD394e/vz9u376tOM5QlzUhhBDrufwoBR/vP42E9GzpcjsMVIpWGcIA+ObF0WYlV5ZUWinEv9fuIe5xKvJKysEwDNo3CcaEbtHIK6vAy9/thFi9eJc2Rj5ns+4bLJgACxgG3ZuFYVBUc3Rq0gi+bi4I8HCz6HuqgM+Dl7MTiitlc5zVKnyrrAPMAYfvJGJ2/24Wuz4hhBBirPLyciQnJ6Oqqkple/v25o1KMzn5tUV3NCGEEPO989t/2H/9nmYmx0Fl6Rpd/NxcsXXGOLQKDrBGeFpVVInwICsPiRk52HP5LlLyilBcWYlKkUQjsYx7lIrvT15GRIAPJCxnXMJqRIJaow/MjUmAlfZ7uTjj7Wf6ok+LCDzKyQePYdA2NAiuTo41icJoL3Rrjy2n4qQh6cr6OemussoqLTsJIYSYjYY9G5STk4MZM2bgv//+07rfZnN+CSGE1F3v/3lYmvgCGskYw8nWbuVBNVlTGv46vU9nvD3c+sOOSiuFiE/Lxm/nbuDqkzTkl1WAZTlFTMojctXXIpZXWn6Una+yvaYUOaA5Pblajvdxc0b/ls3QMtgfuSVlAAO0DglAl/DGCPH2UBwX4OFuftBmmt2/K7aciNP+syB/IWSve7emjW0eHyGEkIbtzTffRGFhIWJjYzFgwADs3r0bWVlZWLVqFT7//HOz2zUq+R03bhx++ukneHp6Yty4cXqP3bVrl9nBEEIIMc/9jBws3nEQCZm50g3aah0xSgmwlmTtvWf7Y0rPzhaPjWU53ExOx67YWzh0KxFlQh0rAzBav7UMUxs0IgGO8PfGxG4dAAYoLK+Aj6sLIgP9EO7vjUY+XuZGahNuzk7wcHZESWWV5vOUfZgu39wmNNCWoRFCSP1HPb8GHT9+HHv37kVMTAx4PB7Cw8MxZMgQeHp6Ys2aNRgxYoRZ7RqV/Hp5eSnmG3l51e03dEIIaWgOXU/Aou0HwMkq9AJKCa56YiPvXGWqe1T5DPD765PRtlGQReMqKq/E5/tPY3fcnRoNTa5Rj6xy2/LG9OzWFo+Pmwt6NA9Dt6ZhaB7oCz93VzgK+Aj19rTr+haDoiKx9+pdgzdh0zf/hd0Lp4HHs9/nSgghxL6UlZUhMFD64auPjw9ycnLQsmVLREdH4+rVq2a3a1Ty++OPP2r9nhBCSO1hWQ5fHT6Pb49dUixNo8JAwscAcHNyxK75U9C4hmu5puUX4ejNRIhYFq1CAtC6UQCmfPk70gtLatSuVWhJolsF+aGRrxeScgvg6CBAVGggXh3UA/7urnAU1M8ZQoPbyZJfXWQ/Pw+z8rH4twP4ZIp5n7ITQghRRdWeDWvVqhUSEhIQERGBDh064Ntvv0VERAQ2b96MkJAQs9s1+R398ePHEIvFaNGihcr2xMREODg4ICIiwuxgCCGEGIfjOKzceRR/X7qttYdXpbcUmvt9XF3wfPdovNi7M3zcXEy+fmmlEHvj7mJv3B08zMpHlVi18IRAwEDEWvddmKfUe23SlRjASSCAv7srRnZqjdkDutV6Veva0L91U4R4eyCjoET7ByQMFEOgD1y/jwUj+qnMVSaEEEKs5Y033kBGRgYAYPny5Rg2bBi2b98OR0dH/PTTT2a3a3LyO336dMycOVMj+Y2NjcXWrVtx8uRJs4MhhBBinIuJydLEVw/1elbyb2Y/FYM3h/U1+9pJOQWY8fVfyC0u01UkGCIJZ9GJu9oKErMc8Oaw3jh0KxHx6dlaz+MxDJwd+GgZEoCZ/WIwMKq5XQ9VtiQ+j4eX+8Vg1V7ZKg5qSx5B7Z/wv2v3MPOprrYNkhBC6iOa82vQ1KlTFd936dIFSUlJuHfvHpo0aQJ/f3+z2zU5+b127Rp69+6tsb1Hjx6YO3eu2YEQQgjRL7OwBH9duIVz954gObdAWrxKW2ErJeqJb4CHK+Y/3cfwtfJLsPTPw7j2KA1CsQR8hkHfNk0xe3BXLPntEPJLy/W/x1owv9R1nck9O2DWgK6Y/VQ3xKdnIyWvCE4OfHi7usCRz0fzID84CoxY26kBC/f3AZTnh3PVX+r/hHmlFbYOjxBC6idKfk3CcRxcXFzQuXPNi3KanPwyDIOSEs05XEVFRWavt0QIIUS/HWevYe3uU+A4TrljzjiyHr0Qbw8cememRuGirIISbD97DbeTMpFfWo60gmLp+rpKJByHk3cf4eTdR8YltuYWp1I7T+U5yrY3D/TF/KG9MahtdS9um9BAqkpshq7NGsOJz0eVRGLwByrQ0802QRFCCCEAvv/+e6xfvx6JiYkAgBYtWuDNN9/ErFmzzG7T5OS3X79+WLNmDX777Tfw+dJP1CUSCdasWYM+fQz3JhBCCDFeRn4xXtr0BzIKS7UfYKCoFQAIGOD9cYPwXPdoMAwDjuNwNv4JNh++iDspWYp1c5Wbqwlta/OaRCkB9nFzRqiPF/w8XNGrRRO80LMDHPjUm2spDgI+Zg3siq+PXFTdoeUHwZy54YQQQjRRwSvDli1bhnXr1mHevHno2bMnAODChQtYsGABkpOT8cEHH5jVrsnJ79q1a9GvXz+0atUKfftK54ydOXMGxcXFOH78uFlBEEII0ZSWV4SRH/0IsbxwlLaiViyka/rq6Gl14vNw9L3Z8HF3QdyDVOy6eAsX7iejQHkIqxXWwDWnXQZA1+aN8cYzfRDk5Q4PZye4OTuaGBwx1Yt9O+HrQxerhz6zAMOqzhlnAMQ9SMGomKhai5MQQkjD8c033+C7777DpEmTFNtGjRqF9u3bY968ebZLfqOionDz5k1s2rQJN27cgIuLC6ZNm4a5c+fC19fXrCAIIYSo+ufSHSzdcbi6A07PckWccjKq9Kmvq4MAge5uGLXmR5RXiSGWsFoLR8mzG6M/MDYiqVVJzPVwcRCgZ8twTOrdAT0im1AxqlrAcko/RyzA41R/FuT/IvsuxWPOoO4IC/C2eYyEEFKv0Jxfg0QiEWJiYjS2d+nSBWKx2Ox2zVq8MDQ0FB999JHZFyWEEKLd46w8vLjhd5RUVKmOaJYnJFrW8mVYWf7KR/UbHwtUisVIrihSakT/+6LWxFgbI3t+eQywZtIwPMzKx4m7j1BUXgEvV2f0bhmOSb06opGflzFXI1bm6eKEUB9PpOcVK7Zp++dlOQ7TvvwDx1bM0Zg3TgghhFjSiy++iG+++Qbr1q1T2b5lyxZMmTLF7HbNSn4LCwtx6dIlZGdng2VZlX3Tpk0zOxhCCGnI9sTexvLfjigeK6cXHKQ9coq/uGz1MYrjxKonmvQBsIkFqpwdBKgSS1TmCyvzdnPG2inPoFerCADA/Gc0VwkgdQPDMJjarxM+3X3K4LF5JeW4kJCE3m0irB8YIYTUUzTn1zjff/89Dh8+jB49egCQLq2bnJyMadOmYeHChYrj1BNkfUxOfvft24cpU6agtLQUnp6eKkPUGIah5JcQQszw3eFYbDpwXud++bBU+Rub3jxVlsgqenIt3Enn7CDArv97Ebvj7uC/awkoq6yCt5szgrw8EObvhac7tEB3GsJsVyb17ohN/55DeZXY4I/Lv1fvUfJLCCHEqm7fvq1Y2ujhw4cAAH9/f/j7++P27duK40y91zA5+V20aBFmzpyJjz76CK6urqaeTgghRMmjzDzM2rQTecXl0g3yv+Hahjeb0rC5Sw0ZwDDAD689hzB/b8x/pjf16NYTAj4PXq7OqKjSUVVcSZXI/LlWhBBCQHN+jXDixAmrtGty8puWlob58+dT4ksIITX0xT9n8MPRy7rfrCyVvJqQCOub99shIhhfTB8FP1rvtV5qFuSLTF1LailpHuxng2gIIaQeo+S31pic/A4dOhSXL19Gs2bNrBEPIYTUaxzH4dzdJ1jz1zGk5pUotiuSTuU3Lg7V1ZKNrkalSqVdIxNgZwcB2jQORGM/L0Q1DkSfNhFo4udDRY7quan9O+N8QrL+nxUOyC8qs2VYhBBCGohx48bhp59+gqenJ8aNG6f32F27dpl1DZOT3xEjRuCtt97C3bt3ER0dDQcHB5X9o0aNMisQQgipbziOQ2J6LkrKhcgpLsMfp67j5pMMxbq9DKOaZ6jkt+oJiJU/zeXxGLRpHIi3xvRH56aNrHsxUif1ahWBjhEhuP44Q3On/OeRBf48ewtNg/wweUAnW4dICCH1gkqxSgu3a8+8vLwUc3i9vKyzIgTDcTpKderA4+letJFhGEgkkhoHVZcUFxfDy8sLRUVF8PT0rO1wCCF2IL+kHN8euIgDl++hpFwo3ajWc8vJClJpe6dS+aPMUzrG1ARYfe1eWTsCPg+Bnm4Y3a0tBrdvARcnAQI83eHkYNYCAKQeKasUov+SzahiWdW7M9mSWgxXPSDhwMqXEeJL74uEENuz1/tzedxRr30EvpOzxduXCCtx9+t37e51sSWT73TUlzYihBAC5BSXYv3fp3H8xgNUimQfAqp36SolwIzaY+UkWCXXlR3HyeYHmVLUUDnxdXVyQN+oppjavxM6RIQa3whpUBiGgUjCKpJc5R835R9nFsD3hy/h/RcG2zxGQgixezTn16DHjx9DLBajRYsWKtsTExPh4OCAiIgIs9qlj/kJIcRMEpbFqZuP8MOhWNxJzlbdqV61WVcVZx1U3r9Y3b3E2jT284SnmzM8XZwRE9kYE3q3h7ebi3EnkwbNQcCXDsfn9A/LYwCcvPUI779gw+AIIYQ0GNOnT8fMmTM1kt/Y2Fhs3boVJ0+eNKtdo5LfjRs3Ys6cOXB2dsbGjRv1Hjt//nyzAiGEkLqMZTmcuvUIu87eRHJOIbxcnVFSLsST7AKVnl1Fb636ZF5dlHuAtR2n3DOslBFLi09xYDmAxzDg8xlEBvtj/cvP0lBUYjYHPh+tGwciPjnb4Gct4no2zYkQQmyF4WQjwKzQbn1x7do19O6tuZxijx49MHfuXLPbNSr5Xb9+PaZMmQJnZ2esX79e53EMw1DySwipFzLyivHT4TicvPEQZZVVYBgGpZVVYHiyIcjK1Icwyx6CU6u0rC2bMLE6BY8BBnaIRKCXO9xdnPB0x5ZoEepvWiOE6LFwdF/M/vJv6Y+tfLi9fMYTI52vDgaIahJUe0ESQgip1xiGQUlJicb2oqKiGtWYMir5ffz4sdbvCSGkPuA4DrceZ+LApXg8yshDYmouisortR+rlASo7qj+Vn2epMoSRkYku8qHOgj4YMEhyNsdA9o1x9yRveDq5Gi4EULM1K1lE3RrGYa4hBQwSvcXDKQf/PA4aQL8fN8OtRYjIYTYNZrza1C/fv2wZs0a/Pbbb+Dz+QAAiUSCNWvWoE+fPma3a9KcX5FIhNatW2P//v1o06aN2RclhJC6orJKjLe/24+zt2Uf7OlJUnUutatcyEr/bp2UL8sA8HR1wqoXh6FfO1pTndje5tfGofeCTRBKJNqLXnFAalYhEF0LwRFCSH1QjxJVa1i7di369euHVq1aoW/fvgCAM2fOoLi4GMePHze7XZOSXwcHB1RWau8NIYSQuorjOBSWViAjvwQnrz/ErccZEPB46Nk2HDcepVcnvoDBnlmVnlxd83R1BqLleNk2hgFcHR0woltr/G9YD/h7uZvQMCGWdTkxFVUiic4fb44DNu09h0kDO4GvZwlEQgghxBxRUVG4efMmNm3ahBs3bsDFxQXTpk3D3Llz4evra3a7Jld7fv3117F27Vps3boVAgEViyaE1D2pOYU4FHcfRWUVyC0qQ+y9ZBSUVGgcd+7OE+mCpabgqtc6lT9WWYtXB43eX9kDBwEPX746Bt1ahuldR50QW4pLSKl+wKn9X7ZGdZVYgp8PXcbMZ7rZODpCCLFvVPDKOKGhofjoo48s2qbJ2WtcXByOHTuGw4cPIzo6Gm5ubir7d+3aZbHgCCHEFCKJBMt+OIhDl+9Xr5VrqHfWlN5btWXOGaXtHE99o57ryTQP8cWvb02Gi5ODkQEQYhulFULNpBeaj787EIsZw7qCMWUBakIIIcQIhYWFuHTpErKzs8Gyqjdh06ZNM6tNk5Nfb29vjB8/3qyLEUKIJYnEEhyMvYf9F+ORmJKNwnKhdAejdH9upXtyjWZZAHzD5zAM8ExMK8wd1YeWJCJ1VkSQj/Qbfb0IHCCsEuPY1UQM7tLSJnERQki9QAWvDNq3bx+mTJmC0tJSeHp6qnzIyjCM7ZLfH3/80awL1VR+fj7mzZuHffv2gcfjYfz48fjiiy/g7q59Xlx+fj6WL1+Ow4cPIzk5GQEBARgzZgw+/PBDeHl52Th6QkhNVVaJ8ffpm7h4+wlyisqQlV+CYnmyC9nyK4Dpya6Zvb5a6SqWJetd5jHAz/83Ce0igo0Oj5DaMKRLS3zy+0mjjv3p0GVKfgkhhFjUokWLMHPmTHz00UdwdXW1WLtGJ78sy+LTTz/FP//8g6qqKgwaNAjLly+Hi4uLxYLRZ8qUKcjIyMCRI0cgEokwY8YMzJkzBzt27NB6fHp6OtLT0/HZZ58hKioKSUlJeOWVV5Ceno6dO3faJGZCiGVs3X8Rm/de0P+BpqnFp5TPg9q5HCdd11S2j+PJ29d9AQayZZDkw5/Vyjf3j26KpVOGwM/TTev5hNQlfp5ucHV2RHlllcFjH6Tl2iAiQgipP2jOr2FpaWmYP3++RRNfwITkd/Xq1VixYgUGDx4MFxcXfPHFF8jOzsYPP/xg0YC0iY+Px8GDBxEXF4eYmBgAwJdffonhw4fjs88+Q2hoqMY57dq1w99//6143Lx5c6xevRpTp06FWCymYl2E1FFFpRW4dj8VYgmLji0b4dClBHyz94LB8+RrkJqcAMvPkZVwZliAYVU38ySyEUo8zmACDNm5kCXMbcIC8fM7L8CBb2BMNCF1TLCPOx5l5Bs8TsIaMyyCEEIIMd7QoUNx+fJlNGtm2SUfjc4At23bhq+//hr/+9//AABHjx7FiBEjsHXrVqtXKL1w4QK8vb0ViS8ADB48GDweD7GxsRg7dqxR7RQVFcHT01Nv4isUCiEUVg+lLC4uNj9wQohBHMfhaNx9/H7sKm4/yoBE7T66ejiz/qy2Rh92cgDDAziJNPEFlNYzVTqMYQ0nwPJzfNxc8PIz3TBpYCcqBkTsUrMQPzxKzzdYMM6VCrYRQohpaM6vQSNGjMBbb72Fu3fvIjo6Gg4Oqu81o0aNMqtdo5Pf5ORkDB8+XPF48ODBYBgG6enpaNy4sVkXN1ZmZiYCAwNVtgkEAvj6+iIzM9OoNnJzc/Hhhx9izpw5eo9bs2YNVq5caXashBDDhFUibPjzNP49fxflQpHO46qLVhlOHlXW39V2uPp2Ttq68vBmeRvaLqeoHq0vWAYY27stxvVrj1ZhgRDwaekiYr9mDOuKo1cTVX93OPXfAAaje7ezcWSEEELqu9mzZwMAPvjgA419DMNAIpGY1a7Rd2ZisRjOzs4q2xwcHCAS6b5xNWTx4sVgGEbv171798xuX664uBgjRoxAVFQUVqxYoffYJUuWoKioSPGVkpKi93hCiPESU3KwaOMe9H71S/x14gbKK434+2FOr6mepVnAcoCEAyORDmcGJ5uWK+vQ1Xc5RXKtbQcDDO4ciaXTnkbbiGBKfIndaxMeVF2cjePAsBx4YoAv++KJAT7L4YWnOtZqnIQQYm/kc36t8VVfsCyr88vcxBcwoeeX4zhMnz4dTk5Oim2VlZV45ZVXVNb6NWWd30WLFmH69Ol6j2nWrBmCg4ORnZ2tsl0sFiM/Px/BwfqrppaUlGDYsGHw8PDA7t27NbrM1Tk5Oak8R0KIeYQiMWJvJyEuPhnX76chMSUHEpZTrcpsTF7LGR5mLKfo/VUj4DMI8vVATnYJJGJO5RgGkH4MaMQlGEgvwCnHJBsyvXB8P0wZ0sWoOAmxF1+/OQ6TP/wFaVnF4HHQ/N1hgemrdmDn6hnwdHPW0QohhBAVNOy51hid/L700ksa26ZOnVqjiwcEBCAgIMDgcT179kRhYSGuXLmCLl2kN5fHjx8Hy7Lo3r27zvOKi4sxdOhQODk54Z9//tHouSaEWNaBc3fxw/6LSMsphlh98i5MX3u3eiizaQmw8ikDOjSHr4sz9p29I02+oSVJlldpNrZgFis9ztvDGR/NegY9oiKMio0Qe+Pu4oRG3l7IyJTWv9D261FQVIF3vtqHb96eYNvgCCGE2Iwtlp3duHEj5syZA2dnZ2zcuFFvPPPnzzfreRid/NbW+r4A0KZNGwwbNgyzZ8/G5s2bIRKJMHfuXLzwwguKSs9paWkYNGgQtm3bhm7duqG4uBhPP/00ysvL8euvv6K4uFhRvCogIAB8qrxKSI1xHIf7yTnYcegK/rsQrzod0FI1nhTZqhEJMAM4OwrQKiwQrRv54/D5ezhz6YE00WVUw9KYw2tCAizg8/B/Ewfg+YEdTXsuhNiZjNxiXI6XTf/R9XvBAJfjU1BQXA4fT8suSUEIIfWSHfb82mLZ2fXr12PKlClwdnbG+vXrdcbCMIz1k9/atn37dsydOxeDBg1SfNqg/ImASCRCQkICysvLAQBXr15FbGwsACAyMlKlrcePHyMiIsJmsRNSX3AchyOxCdhx6AoSkrIhYaXjIDn1jFJvI0Yep9SkriWMeAyDQZ1boF/HZmgTHoiIYF98u/s8ftp3Cbfj0xSX03ddrYWs1Nb+lZ/KAPi/SU9h4sCOVMGZNAjxSVnGHcgAa385ho9ff9a6ARFCCLE5Wy07+/jxY63fW5LdJL++vr46P1kAgIiICOk8PJkBAwaoPCaEmC+vqAxb91zE/rO3IaySFRlQSgyVH+tjsCKznvP+74UBeJCeh/jkbHg4O+Hlkd3QtXUTaQwchz0nb+KFJT9X9z4zag3oYaiCMwOgU2QoNr/1PPhUyIo0IDxjP+ThgKsJqdYNhhBC6glrFaeSt6m+VGtNaxrZctlZQNqp2bp1a+zfvx9t2rQxO25t7Cb5JYTYTmFJBb7+6wzOXH2E0gohqkQS1SRXPbG0UE+uLrOe7Y4XBnfW2M5xHN75Yh9OXVUd2lyjIddKsbUJD8S0Z7piQKdIOAhoqgRpeJo18pV+Y2CtXwDSkSCEEEJqXVhYmMrj5cuXG1zxRh9bLjsLSFcUqqysNCtWQyj5JYQAAG4lpuPK3RQ8Sc/Hf+fjNW52dVVSNhWn/g0DjWHGcq2bBOCLN8bCz1u1mMKdB+l476sDSM8trj5dX+Kro30obVbukeYxwHsvDcHovtHGPCVC6i0XR9kKCbp+h5T+KIQH+9giJEIIsX9WnvObkpICT09PxWZdvb6LFy/G2rVr9TYZHx9f47BMWXZW7vXXX8fatWuxdetWgz3FpqDkl5AGSlglxtHYBOw9eQs376er/g02opfH3N5VjaHPsvYEAh5ahQVgRO8odI8KR1igD3g81Ytk5RVjyru/oKRcqBKCojdZy4hkjeRYx9MBgEAfd0wbFoMXhmj2MhPSEGXll2pOU9DxN2Boj9Y2iooQQog+np6eKsmvLnVx2Vm5uLg4HDt2DIcPH0Z0dLTK0rqAacvrKqPkl5AGQsKyOHIhAb8dvIK07CKUlVeBlU+Q1dabY2xxKOUNJixh5CDgISLUD13bhGHaMzHw89JeKh8A4h9l4osdp3AtIU1/w/KKzdooJ9tKcTKQFpHeuGg8erQNN+4JENJAZOYVax+lAaXvZf8/cu4exj/VAQKaF08IIXoxHAfGCrWJTG2zLi876+3tjfHjxxt9vLEo+SWkHhJLWBSWlCMxOQe//3cViUk5KCgu1xhlo7UAlTHFoZSOV7kf1pY0K21r1sgXY/q1R+8OTRHi52lwHm1JWSXe+3I/Ym8nG4xLXzEtlX1Kx7g6O+CdFwdieK+2+hsnpIEK8HFX/Z3S1gss+7r1IAP7T9/GmKfa2zpMQgixL3a21FFtLDtrrWV2KfklpB4pLqvED7suYNfRG6gSSzT2cwyjO4c0sQiVMq0JsGyDu5sjhnRrjQUv9IOLk6PBti7efIxVWw6huLQSYrEEiho6xlSd1dG7C7WHw3tHYenLT4PPox4qQvSJbh4KNxcHlFWIqovbyXsWOACs7FdTtt7ZX0euU/JLCCH1kK2WnWVZFp9++in++ecfVFVVYdCgQVi+fDlcXFws8jwo+SWkniguq8TLy3YgJbNA5zEMx2kkkRqFrIwZwqycYMqXA5I17enujCbB3pgzpje6tW1i9Hq4F28+xsJPdlcPxVanJXattA195gAej8HYAe2waMpACKhyMyFG4fEYvDlpAD764Ygs2eXAsNoK4HEAAzxOy6uVOAkhxJ5Ye6kja7DVsrOrV6/GihUrMHjwYLi4uOCLL75AdnY2fvjhB7PiVkfJLyF2pFIowrGLCYi99QQikQRdo8MxZmB78Hg8bPnrnN7EV8HYJFLbqcrfyO5+GQZoHxmCiU93Ru8OzeDiZFwhA7mc/BJ888cZHDgjqyaoY64xZ2rssiFFPB6waNpTmDCok0lxEUKkxgyIxtd/nkFhSQUYtnq7Rg0sDnByoNsKQggh5tu2bRu+/vpr/O9//wMAHD16FCNGjMDWrVvBs8CIPXqXIsQOXLmTjO//voBr91JVhvSejHuADdtOYtGMgdh38pbZ7WtdxkhPD3CbZkEY0q0l+neORICPO5xNTHjFEgk++eEYDp65iyqxpHo0pfy6upYrMmLeLyDNkR0defjsjVHoEd3MpNgIIZraR4bizJWHALT/Gsp/f3t1bGrLsAghxD7Z2ZxfW0pOTsbw4cMVjwcPHgyGYZCeno7GjRvXuH1Kfgmpwx6n5GLu6r+QX1Su8xiRWIKPvzuidzkfQ4z5W+nv7YZ3Zw5BlzZhJie7cpVCEf49dQdf/HoCIrG0C0k9ZMWUQhOei/IIbA83R7z78hAM6trKrBgJIZrEVRKjfiWb0Fq/hBBCakAsFmtUhXZwcIBIJLJI+5T8ElJHcByHmwlpuHj9CSQsi2B/L6zbdgwSiVpqquUO1NRk0RhNG/ni1Ql94OggQFTTIHh5mF9o4Mi5eHy85QgqKkXVSbr8S4nKQy1LEul6ngyA1hEB+HbpRDg7Gi6qRQgxTWJytsFjGA54mJxjg2gIIcS+2eOcX1vhOA7Tp0+Hk5OTYltlZSVeeeUVlbV+aZ1fQuzQ3wev4cddF6XLEMnn0jEAjwFYDnoTRWUMY95IF+Vcksdn4OHqhNZNg/Dm5P5o1tjfjBZVVQhFeHbONyivrP60zpyeXV1cnR0wflBHvPZ8H/B4Fs7+CSEKHMcZnnLPAOeuPgLHcUYXuiOEEEKUvfTSSxrbpk6darH2KfklxEYqKkWIf5iJuFtJOHv5IR4m56oeoLSOJmtGcmh0Uql0U8pngIHdW+KlUd0RGeZvkRvWSqEIG38+gaPnE1BaXlUdnDp5VWYTL+kk4KNruyaYMbo72kWG1jBaQogxoiJDcPbKI801fwGVogESlsPl28noGh1u+yAJIcRe0Jxfnay1vq8cJb+EWBHLclj11X84fCa+er1aZdp6dJVuJLUWotJj7KBo7D6uv/CVv7cbBndvhR4dwtG1XbjF1rp9lJKD+Sv+Qn5xhepSSPqqN7MAjFx1qGVEAFbNfRaBfu5wdjRvzjEhxDzjB3fE2UuPpL+vEoDHQr66kfQeji/7lWeA6wlplPwSQogeNOy59lDyS4gFcRyHh0k52HXoOmKvP0FmTjE4htHIAVWGM2ujvp3Ts0/GzcURb744ELOf640tO88j7nYSKoQiBPp6IDoyFO1bhaBT6zD4+7ib9+S0qKgU4fOtR3Hw1F3NDzFNqMysbe6vclv9ujTHJwvH1CxYQojZhJUi6a+pGOBxmsXXGYns95YHBFrwbwwhhBBiSZT8ElIDZeWV+O/kXfx38jYeJeehSixR2c8BYOQLfKsPKTY03Fe5hLG8c1a5R1XNay/0hZOjAE6OAix+eYiJz8R0O/+7gg0/nFQsYM5ZaI6fYiQlD+jcujHWvzMejrR2KCG1KvbGE4Ct/tOjkvgqfc+xQN8uzW0XGCGE2CMa9lxr6I6SEDPcuZ+Od9buQYGeJYgA9erFhqrF6CGfH6v8R02WBAv4PLzx4gCMG9LRvLZNcP9RFnYfvI7/Tt9RLFWk8pxMfHqc+jcM0D8mEh/MHQEnR/rzREhd4eAgMPrzOoHAyLkMhBBCiI3R3SUhRniYnI3PtxxD4uNsVApF1fN3a9LZaagNpvp/nJZjmoT4YFjfKEwa0cWqc2A5jsPJC/exefsZpGYWVu8wtrqyjp5q5cSXL2AQ2SQAK18fgfBGvjULmBBicaMGRmPngWt6/+TJB6YUl1bA091Zz5GEEEJofm7toOSXEDUSCYuER1kQicQIDfLGx98cRuy1xxrHKaoryx8YQ9b7qzhXV60ppXY7R4XhlUm9cTU+FeCAgT1boXGQtwnPyDx5+aV4c+WfeJySrzoJVz5XWVtPtrbXQ8tQbflhnu5O+OCNkejaLpyWKiKkDosMD1AswWYoAfbxdLVVWIQQQohJKPklRCY3vxSLPtyJh0m5mjt1TNc1tRqzehuc8nBmtSTRyUmASSNj8NLY7nByFKBdy0ZmXsk0hcXlePfjPbh5L10zYOX/Q3M3p22/+gsky5mfHdgOS/43tKbhEkJsJMjPAxk5xTqnb3AcBz9vN7i5Otk4MkIIsTMcp9SDYuF2iV6U/JIGSSgU4dCpu9h76AZS0gsgFrOokkg0ey4NdEaqzFk1d74rC5UsunWzQLw+uR/at2kMBxvPnbtyMwkLPtgJVj6uW28Xj46d6om8Ur2vp/u0wbC+UejeMcJSIRNCbEAoFCEzu1j6ay+/uVL7e8kAcBBYZuk0QgghxBoo+SUNSnlFFdZ9exSHTt1VbNO7PI+BpFZnb6fOExjVVYsYgM9n4Ovthlcn98XTfdqAsVDVZFNl55Zg0aq/qxNfQPvzVyS1mlWsVZYvkh8LoGuHJlg29xn4edMSKITYI6FIAoaTjlbRtTQ5ByA7uwTX76SgY9sw2wdJCCF2gtb5rT2U/JJ6LTO7CBeuPALHcWgU4o21Xx1GTl6p8Q0YPZdX97HqS/TyGKBDm8ZYPm84Av09jI/Fgk6eu4evfz6FgqJy8Pk89OjcDO4ezhDLKzjroEj2lZ+v0txfjuPAMIx0eSeGQae2Yfh08Ri4ODta8dkQQqzN3dUJDg58iKqky7lp+3PHAAAHbP7lNDZ/PMWW4RFCiH2hpY5qDSW/pF4QS1jcupuKvMIynI99gPNXHqG8vEqzV1dLr6reebsGen6N+Rsj4PMwflhHvDAyBgF+HrVW2IllOfz8x3n8/NcFSCRKkTPAsbP3pBWlTel1Vh7yKOvqZQB4eTjjrTmD8VSPVhaKnBBS23g8Bm4ujiiqqtC6X/nv6O37GWBZjorYEUIIqXMo+SV2LSe3BJt/PoXjZ++pJHQcY9oUXJXhuubcrymdw+MxCPL3RHSrUPTv3gJ9YprX6rqXaRn5WLxqN5JS8wFoSdhNfM5ae38BtIgIwMoFz9JSRYTUU3zZtA19q7PJ/zawLAsej9b7JYQQbRhW+mWNdol+lPwSu/PoSTbWbPgPCY+ydR7DyO7QFEktYFqvpjIjkkMnBz66d4jAwjmDEeBbO0OZlUkkLM5deoBvt51CSlohwFSPTlZ+KhoFu7QtX6S8X0b++vbv0QJD+0Whc7smcHejCq+E1GcikcSIz8k4uLs51eoHfoQQQogulPySOk0sluD4mXu4cj0Jd+6nIzm1wCLzcGuKx2PQv0cLPPdMZ0RGBNSZpT1KSiux7psjOHXhvsb8XW05rcqQbw7SCcnqtCxV1CoyEBtXTKwzz5sQYl0SCYuSMiE4cGB0/GGV9/pSsStCCDGA5vzWGkp+SZ0klrB4Z/mfuHwjRTOHVV+OyFi6ejXVmlXZwEhPefXFfopNHdo0RtuWIbVWlVmbrOxirFr3L27cSVVdZsiIEFVWJdL3GnGAv48bPn1/HFo0DbJU6IQQO3D1VjLAcmD0zuOV/hXt2ampbYIihBBCTETJL6kziorL8e+hm9j5zxXk5ZdpLVCl0VNpZP5ZPRdNfwIsb5dhABdXR/Tq0gzvznsGjg51bwifSCTGj9vP4+zFRCSl5qkO7VYe721KTzmAIH93eHi44MGTHACAl6cLRg6KxpzJfcDn0xqehDREufmyKvlaljiTbq5e+5eGPBNCiH601FHtoeSX1Kr7DzJx6ux97Np/BeXlIulGtR5LXWtKmsyINXsjIwLwf68MQdtWoeZcwSYKi8owc+7PyMsrVe22Baon45pA8VrK5gMvmTsMMR0iIBZLAIaBgBJeQho8Px831Q3KHyRynGKZIwAICfSyZWiEEEKI0Sj5JTbFcRzu3EvHoWO3ceDgTYhZLWmsYsiu7iROV8VhXW0p9xY7OPDQOjIYKekFcHDgo22rULz4XDc0bRwAhzrYwyuXnVOEdz/YjcSH2arPWb2Cla59RujTrTliOkQAoN4bQki1zu3D4ejAR5VIovYHWEb2LY8B2rdtXAsREkKIHeG46pE0lm6X6EXJL7GZM+fvY/Vn/6KiQqS6Q1uCJr+xMiYB1oNTO93H2xWb105BaLC3wXjrAqFQhM+/PIRjJ+9BLGENJ7ZailPpOlb5UB4fmDahJ16e1KdG8RJC6icBn4eJo2Pwy85YgOVkf1Oqe37lxVvGjuxMo0UIIcQAGvZceyj5JVYllrD4e89l/LYzFgWF5bKtjOK/RvXe6mDM77eAx8DDwxnNwwMwZngn9OvRok4VqtJFWCXGtRtJeP+D3RCLWdXnqiN8vfOatbzGDIAWTQOx5I1hiKQCVoQQA2ZP7Ysr15JwLz5D9jeJq55xIfv/4L6tayU2QgghxBiU/BKLy8goxDffHcfZ2AdgJdJEjJPdKulaIkMjOdO17I7SblmDWnVo2wgfvDMaPt5u2g+og8QSFr/9GYt9B64hO6cEgOxlYZjqxNZA3i5fqldvrzADhAZ7YcOqFxAc4Gmh6AkhDUFJQblqWQblIXYMgzfe/g27f5sLTw+XWoiOEELsBC11VGso+SUWkZdXigcPs/DRZ/+iqKhCSy+j9qxN79Bl+dA69aqiOg53cRHghTHdMO35HuDz7We+KsdxSMsoxLyFvyr1jlcnvqY3iOreX6VEuFGIN0YNbY8Jo2JoPi8hxGTXbyYjPbNIteKz8t8ojoNEzOH7n89gwdynaydIQgghRA9KfkmN3Lmbho/W7kd6ZqFmUmpk4qZz+LO895eT9x7Ld0u/c3V1QEyHCAwf3A4xnSLg6GA/P85CoQgff3YAZ88nQiSWVO9QrnJt5vBsRe8vAB6PwdQJPTBzcm/w9K7PSQgh+t2JT1f6YI3T6PWVbz9y4i4lv4QQogfN+a099pMtkDpDImFx4OANfLX5OIRCsUr3rbyz0dR5vDp7gOW9v7KlNAL8PfDR+2PRNLxuV2bWpqKiCn/vvow78emIjXuoet8Iw6+bclKrc1izbDyim6sThg1qi1dmDICTI/2aE0JqjseTFbJi5dNYqnFc9Uid8vIqm8dGCCGEGIPuiolJiosr8OFH/+DylceqPbuM8hwwVGdqRvZe6vugysXZAWNHdsLLU/va5XDdwsJy/O/1nxTzeBUYlf9V0/f6KfZpOxEIDvTEr1tm290HA4SQuq9Lp3D9f6yVqvRzHGcXxQUJIaRW0FJHtYaSX2KUA//dwNebj6G8vMpgsSlTe3+1tefp6YxFc4eib88W4NvpshkJCenY9c9VHD5yx6gKzBq931puHFUKWikd7ObqiM8/eh5tWoZaKnxCCFHRvGmAzs/elEev8PgMJb6EEELqJEp+iVYVFVW4cuUJUlPzceXqE1y58gTgKSeqjOEhukZcR/kYBwGDAX3b4J0Fz8DBDnt4KyqqEBf3GLv3XMat26mQSORjwaG7crW+BJjjqg9hGJXDOQ5wdhHgxUm9MHVCD0s/FUII0XAh9iEA3Z9pyrf7+bjaJB5CCLFXNOe39lDyS1QUFpbjy01HcOJEvOZOFvoTOTXSJE330Gd5ItejWzOsfHc0nJwczIy6du3bfw1ffnkEYjGrsl3RQ8IBnI7K1cZQPkPA56FRqDfmvzoIMZ2a1iRsQggxyZlzCUZNZ+nbq6WNIiKEEDtFSx3VGkp+iUJRUTlmzf4O+Xmy5XYYLQPcjPylUllrVvlmiePAF/DQv08rjB7ZCR3ahVkmeBuTSFjs2nUJ32w+qfH8lG8M5d8xnHHr9Gp9eRng1VkDMHRQO3h5udBwQkJIrbh5K8WoD/C6dAq3QTSEEEKI6Sj5JSgtrcS+f67hp59OQyTvvVTc4Gi50ZEneMbmYLJEOMDfHYvmDUWPbs0tEHXtKSoqx/x5vyAlNV+6QaXwl+5ebpX1d43g4uKAzh3CsXD+UPj5utcoZkIIqaniwgr9f8NkUzVKiitsGBUhhNgfGvZceyj5bcBSU/Pwwco9ePggW6XysDRR036DU73cjiyj1ZEby0W1DMGGzybB0dE+hzQDQGWlCCeO38X9hAzwBTxcu5akPfHVQ9EJbsRxy94dhZjOTeHu7kS9vISQOkMg4AGV0LHUmuw9g+VQKRTbOjRCCCHEKJT8NlD79l7Fhg2HdB+gL+lSrrrEQbUXWDa1NTIyEOs/mQR3d2eLxWxLHMfh2NHb+OuPWDx4kA1AVnSKsd50ikahPvhg2Rg0axpopSsQQoj5WrYIxpVrSdXrrzOM6gelrHQ99ohw/9oMkxBC6j6WU6yZbvF2iV6U/DYguTkl+H7rSZw8EY8q+SfzPJhchIkBwLGycwHFBF8HBz66xkTgzblPIyDA03KB2wjHcdi//zq2bzuLvLxSsGp/QDhZss8xAHimL7+kUsVZ9poP6N8ar855Cn6+7na7pBMhpGFY/PZIPD/pK9VpHIDKcB8nJwE6tG9SG+ERQgghBtlN8pufn4958+Zh37594PF4GD9+PL744gu4uxueC8lxHIYPH46DBw9i9+7dGDNmjPUDrkPKy6vw2uzvkZJaoNim6KhlAfC0rUGrf26q8q7pL/XB1Mm9wTOyCnRdcutmCtZ+9A8yM4oMrl+svFtfFWttFMWuZOeEBHliw7rJCAzwMj1oQgipBf7+HhjQvzVOnrqn+R4he7zwzWE0XYMQQgyhas+1xm6S3ylTpiAjIwNHjhyBSCTCjBkzMGfOHOzYscPguRs2bGiQb8bXrj7Bpg2H8ORJLhiG0b02IwtwDKe5hJHWBK+6jHOTJr74vwXD0a5dY0uHbnU5OcV4edoWlJdXAVD7W6Fn/WKV/SYkwAyA5s0C8MknL8DH282smAkhpLYNH9oeZ0/cgxhQrejPAmPHdsaQwe1qMTpCCCFEP7tIfuPj43Hw4EHExcUhJiYGAPDll19i+PDh+OyzzxAaGqrzGtKDbgAAPZVJREFU3OvXr+Pzzz/H5cuXERISYquQa93OP2OxedNR2ZRcPT240KxErNgGqFavYhh4erpi7cfPo1Ur+3st09MLcGD/NcSef4DHj3JU9jEAOPnrZEw+K098DSTAPB6D1q1DsGDBUDRvFmR+8IQQUsuyMouwdMlfYMUseAA4eZEH2f/27ryMoU9Ho6Udvj8QQogtMbBStWfLN1nv2EXye+HCBXh7eysSXwAYPHgweDweYmNjMXbsWK3nlZeXY/Lkyfjqq68QHBxs1LWEQiGEQqHicXFxcc2Ct7GszEK8/eYOpMmGODM8GB7CDKUEWL3qswyPz8OsWf0xdkwXODnZT+VmjuNw9tQ9bPjsPxQVyZbf0PVSmNKTK5/7q/VcDh4ezli96jm0s9N1jAkhRN0/e69CJJIoHjOAxhC7jz7ci59+fcWmcRFCiN3hONUOJku2S/Syi+Q3MzMTgYGqFXAFAgF8fX2RmZmp87wFCxagV69eGD16tNHXWrNmDVauXGl2rLVp/54r2PDZQdWNpgz3Vvt9kZ85eEg7vPXWcAgE/BrFZyv372Xgt1/OISE+AwX5pRCL2eo5txbEcACPz0DCShNfV1dHDB7cFvPmDqHiVYSQeuf0yXiDx6Sm5CM3twT+/h42iIgQQggxTa0mv4sXL8batWv1HhMfb/jNVpt//vkHx48fx7Vr10w6b8mSJVi4cKHicXFxMcLC6nbvXWlJJZa+8wdu3UhR9NhW99yamPHJEmC+gMG0l/pi8uRedlPI6s6tFHy8ci8yMwoV21SGfZsynNkIs+c8hYkvdAfLcpTsEkLqvcqKKqOOS0zIpOSXEEL0YDgrDXumjl+DajX5XbRoEaZPn673mGbNmiE4OBjZ2dkq28ViMfLz83UOZz5+/DgePnwIb29vle3jx49H3759cfLkSa3nOTk5wcnJydinUKvEYgnWr/0Xhw/cBFCd26mkbvLhDwYSOuVhzqGh3vjx5zl1vqe3rLQS1688QWJCJkpKKrF/zxWN5YlUEl8Dhay0Df/WpnGYL16eNQD9+rcGAPD59vHhACGE1ERIqA8KCsoNHkcfBhJCCKmrajX5DQgIQEBAgMHjevbsicLCQly5cgVdunQBIE1uWZZF9+7dtZ6zePFizJo1S2VbdHQ01q9fj2effbbmwdcBS9/+A5djH+ncL12PV0sVZzXydNHLxxXz3ngaAwa0qdPVseMuPMBna/YjP7dUdYd6yDV5Dso5NCNNonv0bI6XZz+Fpk0N/8wSQkh9E9kiEHfvpBk8LjzC3wbREEKIHaOljmqNXcz5bdOmDYYNG4bZs2dj8+bNEIlEmDt3Ll544QVFpee0tDQMGjQI27ZtQ7du3RAcHKy1V7hJkyZo2rSprZ+CxR0+cF1v4qsg/+XSsjQPBw4MGPAY4JutMxEZaVxRsNqQlpKHxw9zcOLIbZw+Hq+Z2BpKfDkARnRGqBf6EjjwMG16X0x4vjscHe3i14UQQiyO4zhcufAIELOAgKf6fiL/nmUBMKgoN254NCGEEGJrdnM3v337dsydOxeDBg0Cj8fD+PHjsXHjRsV+kUiEhIQElJcbHpJlzyrKqzB78jfIyiqW1Uk3YjgzK6sox2NUqsAxDIMRIztg/sJn6tQwNZZlsev3WOz+MxaFBeWQiFmwLGfah1na5u7KG9D3ksnOCW3kjcXvPYu2bev2fG9CCLGFu7dTkZ6SL31PEUkAvnSxI0D2P7GsCjSPgbuHfUwdIoSQ2sJwHBgrVGa2Rpv1jd0kv76+vtixY4fO/REREeAM/IMb2l/XZWcXYeqoL6S3GyYUoZJ3+kL6oTwcnQV4f8VY9Ord0jqBmojjOFRViXHkv+v4deuZ6uHMDKN9zrKxw5mVEmDFa6DctavUDJ/PoHGYH6ZO64MBg6Lq9LBvQgixtb07Lyu+53GQ9gCr4QC0jgqFf4Cn7QIjhBBCTGA3yW9DlxCfjnnTt0rnn0IpkTOCPNdzchbgwzXPo3PXujHs+9a1JHy++h+ky9Yk1kopCWU4Dpw8IdbWq6vt9dCWAKP62JBQb8xbMAxduzWjhJcQQnS4cfUJwHFglHNe+YAhhlG8z7w4s69tAyOEEHvEyr6s0S7Ri5JfO/DD18fw+0/nVJM7jgN4xg9VnjqzD6a/PMDisZkiJ6sYj+5n4o9fzuPOjWSNRFTl+WlLRBlGY06uUZQSYD6PgZePK4aO6ICJk3vC3d3Z1NYIIaRBKSsVojCnVDXxBapvsngcGFkC3LV7cxtHRwgh9oeGPdceSn7rMI7jsGD2j7h7MxUAp5L1MQwjnctrxLzfL759CW3b1c7c1SqhGD9tPo6j/95AoXyJDF1zcQHDvdn61uHVNaeXYdCxSwQmTumJ9h2bUOEqQggxwQfv/Cl9v9FFNvJG+nZEI2gIIaQ+ys/Px7x587Bv3z5F/aUvvvgC7u7uBs/lOA7Dhw/HwYMHsXv3bowZM8b6AetAWUAd9u2Gw7h7I0XxmAGqc2D56tg8WdERLTccXXs0x+Jlo+Hp5WqTeCUSFgm3U/D5h/uQkVYAidKcMGnMRt4U6TvOiDZ8fFwxZkI3ODk7oEu3ZgiP8KcbMkIIMcO1uEe4FvdY/0Gc9MbGxdXRNkERQoi9s8OljqZMmYKMjAwcOXIEIpEIM2bMwJw5c/TWZJLbsGFDnbkXp+S3jpJIWOz+PVbxWKMzU/FLw0rnwcp/2nkMGIbBvuPvwMnJwepxJj/Kxofv/Inkx7lKwanNy9XXW6tMx5JMKofI93Eq3eCKb9t3aoKVHz8Pdw8azkwIITX18+aT1Q9YLZPJmOp5K8+Oi7FFSIQQQmwsPj4eBw8eRFxcHGJipH/rv/zySwwfPhyfffaZYulZba5fv47PP/8cly9fRkhIiK1C1omS3zoq6VGOYpiZRuKr/IAFGL70W072n9XrXrBa4pudWYBVi3ci6WE2KiuqZAmrlkrM2rYZkwQbKOQl/9SIq94AN3cndIyJwKvzn0ZQsJcJz4YQQoguEgmLe3fSpA9kia/8o1bFR66yDyJ5HIMZrzxVC1ESQogd4jjVjhxLtguguLhYZbOTkxOcnMxfhu7ChQvw9vZWJL4AMHjwYPB4PMTGxmLs2LFazysvL8fkyZPx1VdfITg42OzrWxIlv3XUpXP3ARhYkhbVNx+cLClc/tFz6NojssbXLyupxJOH2Yi/nYLj/97Ao8Qs3b+j5vTyGqK1orN0m8CBh3ETu2P6nKcgcODX/FqEEEI0VAnFYFnNGzT1GoUcAE7CgmdCEUZCCCHWExamWutn+fLlWLFihdntZWZmIjAwUGWbQCCAr68vMjMzdZ63YMEC9OrVC6NHjzb72pZGyW8ddfzgTcMrGXGyCpuyuld/Hf4/eJk4v5fjOBTml6EgrxTCShGOH7yB/X/EaY5u01aRWS0WMEyN5+tK24LW4c8t24TiuUk9ENMzkoY1E0KIlTm7OMDN3QllxRUAdP/5l2/PzixEcKiPTWIjhBB7Ji/dY412ASAlJQWentVrruvq9V28eDHWrl2rt834+HizYvnnn39w/PhxXLt2zazzrYWS3zqovEyIpMRscAxjeHK4bMHf3cffhpuBZXsyUvNxeO81PH6QhZyMIpSWVCAvpwSiKonhxNTYzly91Zhlv5F6E2RUT1/mMeg/OArPju+Glq1D4OhEP66EEGIrDMMgKroxLp9LNOr4+3fTKfklhJA6wNPTUyX51WXRokWYPn263mOaNWuG4OBgZGdnq2wXi8XIz8/XOZz5+PHjePjwIby9vVW2jx8/Hn379sXJkycNxmcNlE3UQcVF0iWBGPkkXl3JIsOAk7AAy2Jcn4+0rfCjmJIrEPBRVSXRWCdXcY6+pFV5kpeZpB25hodEu7k7oVuvFhg9oSuiomtneSZCCCHSv9mJd1KNPp7Pp2HPhBBiFCvP+TVWQEAAAgICDB7Xs2dPFBYW4sqVK+jSpQsAaXLLsiy6d++u9ZzFixdj1qxZKtuio6Oxfv16PPvssybFaUmU/NZBXt6yocvKlY21zX+VD5lQr36sdIj8/8qJr87001ACbCw983W17Xd3d8IzYzvjpf89RWvwEkJIHfHgXgaK8soBHgx+cMkwQLtOTWwTGCGEEJtq06YNhg0bhtmzZ2Pz5s0QiUSYO3cuXnjhBUWl57S0NAwaNAjbtm1Dt27dEBwcrLVXuEmTJmjatKmtn4ICZRp1kIurkzSfZQFIOICvlDTKcQBY1YRXX2Krb8quem+wVqb2/GpJxhkAXj6u6NqrBZ6d0BUhjXyqE31CCCF1SkFeafUDAyN3evRrBS9vNxtERQgh9o9hpV/WaNdatm/fjrlz52LQoEHg8XgYP348Nm7cqNgvEomQkJCA8vJy6wVhAZT81lHPjOuCA39dkT6QqM2Kt8YEeX3N1nDIc0CwFxYtG43wZoHw8XOrM4tcE0II0c3DywUAB7CMtPdXPQFWerxk9XO1EiMhhNilOjLs2RS+vr7YsWOHzv0RERHSKY56GNpvC5T81lGvLHymOvkFjEp4rZ5SmpAEN47wwzNjOmPc5J60/AUhhNihsHC/6gXkdSXAEhaNI/zh5GydteUJIYQQS6Lkt45ycnbAxu2zMH/KVptcrybDnt08nBHS2BdNmvrjpVcHUrVPQgipBy6fe6Bae4JVXtkXih6GEeNjaidAQgixVxysMpLTKm3WM5T81mGtosIwaVYf/Lb1rP4DbTWEQP6LygMEDny07xKB5Z+9AGcXR9tcnxBCiM08SsgEn89AIuF0FlYExyGmZ3PbB0cIIYSYgZLfOm7660PQql1jrHjzd+0HmJj4auvANdQCn89Dr4Gt8cr/PQMfP3dazoKQOkAskuDy2fvIzSqGt68buvZrRUNPiYVx4CQcGDFb/T7BZ1SGPTMAnF2daiM4QgixWwzHgbFC55U12qxvKPm1Az37t8Ghaysxd/JmJN5Nl25U+xTemKm48sRXOQFWbGMAhmHg6CRAl56RmDS7PyKaBcDRiW6mCalrTv13ExtX7EFZSaViG4/HYOj4GLyxcmwtRkbqi/ycEhzZdQWcSAJA6T1GAgDSVQgYHoPGTQMQEOxVS1ESQgghpqHk145s2vEKAODhvXR8uOg3ZGcUg2WN/4QntLEP3lw+GuHNg5D6JAelJZUIauSDJhH+4Av41gqbEGJB547ewZpFv2ssX8ayHA7+FYf468nYvPeN2gqP1AOV5VV4ZfQXKCnUslyFfA4wy4FjGEya1Z8q+BNCiKnssNpzfUHJrx1q3joUP/27SGWbWCRGVnohEm6noqxECC8fN7h6OKNRuC9cXZ3g7umiMlzZ25fWYyTE3rAsi69X7dO6brf8+6TELKx4fRtWfDXNxtGR+uLDN7ajuLAcDLSPKpIuVcFg8v/6Y+CIDjaOjhBCCDEfJb/1hMBBgEbh/mgU7l/boRBCrOT+7TTkZRcD0D3VgeM4xB6Px1cf7sXrS0fbLjhSL6Q+ycXVc4lgWFazIAQDcAyjmHbTvnPTWomREELsHgeAtVK7RC+qXEQIIXaiKL8MgP45/vIhqPt/vYB3Z9pmqTRSf1w8dhfgtCS+AMABDFtdpIWGOxNCiHnkBa+s8UX0o+SXEELshKmFha6de4CxnZZCIpFYKSJS35SVVhrujWA5ODgKENk21CYxEUIIIZZCyS8hhNiJpq2C4e7hLJtzqQPHqfTaVZaLMDLqPVRWCK0fILF76U9yjTpu8OhOcHN3tnI0hBBST3GoLnpl0a/afmJ1HyW/hBBiJxiGwbwVowGG0Z4Ay7cp75O9IY7tsBSVFVW2CZTYraQHWUYd9+ykHlaOhBBCCLE8Sn4JIcSO9H+mA0ZM7AZAVnVX+QsA1Jc/UxQoAsa2fx+P76XbOGJiTzKS84xaKoMvoNsHQggxm1V6fa20fFI9Q+9ehBBiZ+YtH4NXlowEA6URThynmfhyHMCygESi+P61EetwbE+cjSMm9iD2RDyqKkTSD0v04PEYNI6glQUIIYTYH0p+CSHEDo2Z1hvvfDoRDCtLetU/7JUnvsqfAsuSms8W/YFNy3baLlhiF35Yux+QsAZ7Djr1bgG+gG+jqAghpB5irfhF9KLklxBC7NSAkR2x8OMJ2neyOt4BZQnwv9sv4u3JX1spMmJPSosr8P7075B8P6v6QxNAexLMcXh3wyTbBkgIIYRYCCW/hBBix4aM7YKRk3uozvXR1XMnHwLNsgDL4taFB3ix50rbBUvqHI7jsGL2D7hyJqF6I8sBYonqaAKl+WSu7i42j5MQQuoTWue39lDySwghdu715WPQbUAr6QNdBS/k837lx8j+n5tRiDFRi20TKKlzLhy5gztxj7UPm5dIgCqR9Ev289OoaUCtxEkIIfUKFbyqNZT8EkJIPbByy0w8/78B2t/81IdAywsaySpBC8uFeCZiAfKyC20RKqkjqoQirJm3Tf/NkvxnRTZ/fNbiEbYJjhBCCLECSn4JIaSemLHoGSyQzwFWX+vXCFO7rsCRvy5YITJSF+354TTEVRLDB8oS4An/ewo9BkZZOSpCCGkAqOe31lDySwgh9cjT47vinQ2TpQ+MfRNU9O5xWLfwdywav8EqsZG6ZcfGw9Ac76yF7Oeo77Bo6wZECCGEWBklv4QQUs8MGNkJn/35utnn3730GM82X2TBiEhdk51WAGFFleEDZYkvwzAIauxr5agIIaSBoJ7fWkPJLyGE1ENtuzTFtnPvSyv3AvrfEDm1dYI5DmKhCM80no+C/CKrxklqx6l9V5Ue6fgZUTzm0G1gG3j6uNkiNEIIIcRqKPklhJB6KiDYG3vvfVS9QVcCzDAAx6o+lpkc/T6O746zUoSkttyOfSjNeRWVv9WKoiltd/dywesfPmfT+AghpF5jrfhF9KLklxBC6jFHR0f893gd3Dxla7NqK4QlYTWnfsoqQQPAp3O3YWYfWg+4PqkSirWsC81Jk2Clr8aRgfjm4FsICPGurVAJIYQQi6HklxBCGoCdNz9Cp34tpQ8Uc4Oguv6vHhmPczG8yXyIRGLrBkpsIrCRj3T5IrHa+s/KPwssh80H34Z/sHetxEgIIfUVw3FW+yL6UfJLCCENxEfbXsXg8V2kCY6Ela3dqucEtTdRTsJhVJM3cObfqzpOIPbgyJ8XcWZPnPSDD4kEqBIBlULZCAD5zwaHmAFtwBfwaztcQgipf6jgVa2h5JcQQhqQRZ9PxaJ1kw0fqGd+8Ecvb8UrAz60bGDEJvZ8dwLr3vgFFaVCzZ1isbQnGACPx2DKgqE2jo4QQgixLkp+CSGkgRk8vht2XNeTvGrMBVXDMEi6l4Fngl9DaXGZ5QMkVrHvx1P4dtlO/T0DLAtHJwHe3TwDrTtF2Cw2QghpUFjOel9EL0p+CSGkAfLx88S/SeulD7QVwTJy6NSEFv+HXz75x8LREUvKSsnDjG5L8fXi36X/rkrVvLX53/Kx6D2svY2iI4QQQmyHkl9CCGmgeDwe/kv5AsHhvtL5vyxr1pyhHZ8dwOgm88HRXKM6pzC3GLN7LkdmUq7BpFfu4a1kK0dFCCENHM35rTWU/BJCSAP347kVeOOzF4w7mGFU31xlCVVVZRWGB76KB5Q41RmZSbl4sf0SiIRioxNfAHD3crViVIQQQkjtoeSXEEIIhk3qjd0PPqveoO3TY46T9g6rkydWDDD3qdWY03s59QLXsiqhCK8/tQqiKjE4cOAkEumXvpNk/2YDx3e1SYyEENJwWavXl957DbGb5Dc/Px9TpkyBp6cnvL298fLLL6O0tNTgeRcuXMDAgQPh5uYGT09P9OvXDxUVFTaImBBC7IuzixP+S/sSjZoHVG80dkiVomeRQ/K9DDwTMAendl2yesxEu9/X/Yey4gpo3GCxrPYPJmRzgVt1jkB4q1Cbx0sIIYTYgt0kv1OmTMGdO3dw5MgR7N+/H6dPn8acOXP0nnPhwgUMGzYMTz/9NC5duoS4uDjMnTsXPJ7dPG1CCLG5raeXYekPs6RrwColTRqJr1IyzHEcOFa6Pqz0eGDNrO8wzG82slNzauFZNFw3z9/Hjs/3Q2sPgGxuN8dx0iRY6d80rEUwVv8213aBEkJIQ0VzfmsNw9nB2LT4+HhERUUhLi4OMTExAICDBw9i+PDhSE1NRWio9k+pe/TogSFDhuDDD81fj7K4uBheXl4oKiqCp6en2e0QQoi9YVkWo5rMh0TMalYJVnvr4MQSvW+6U955Fi++M8paoRKZ1AeZmNVtKcAzYo4vnw9G9m86atYAvPLhBMVjQgipy+z1/lwe9+DwuRDwnCzevpgV4mjSJrt7XWzJLrpAL1y4AG9vb0XiCwCDBw8Gj8dDbGys1nOys7MRGxuLwMBA9OrVC0FBQejfvz/Onj2r91pCoRDFxcUqX4QQ0hDxeDzsT92Elp2aSDfoSG7VexA1sCy2f7QHE5rOx9WTt60QKZF7Z/Tnsk//jThY1ps/Zs5AvLrqeUp8CSGE1Ht2kfxmZmYiMDBQZZtAIICvry8yMzO1nvPo0SMAwIoVKzB79mwcPHgQnTt3xqBBg5CYmKjzWmvWrIGXl5fiKywszHJPhBBC7NAX/y3GN2felz5QL3rFcdLh0dqwbPXQaQAl+aV4d/Q6jPSfjey0PCtH3bBIJCxWTP4SeekF0g3G5LEch8ETu+N/Hzxn1dgIIYSo4VjrfRG9ajX5Xbx4MRiG0ft17949s9pmZTdn//vf/zBjxgx06tQJ69evR6tWrfDDDz/oPG/JkiUoKipSfKWkpJh1fUIIqU8iWjbCf1nfwL+Rl7RTUbm3V1svo7Y5wjJikQTT2vwfPnxxE0RVImuF3KAsHvUpLv57TTo0XX05Kh1cPZyx8IuXbBAdIYQQUjcIavPiixYtwvTp0/Ue06xZMwQHByM7O1tlu1gsRn5+PoKDg7WeFxISAgCIiopS2d6mTRskJ+teh9LJyQlOTpYfg08IIfXBL1c/xoObSZg3aI3+Aw0lXwyDc3uvYNS+/+GNjS9h6Iv9aNitiTiOw43T8fju/T/x8EaSdCPLyuZmMwA4vev7rt21iF5zQgipDdYqTlX3SznVulpNfgMCAhAQEGDwuJ49e6KwsBBXrlxBly5dAADHjx8Hy7Lo3r271nMiIiIQGhqKhIQEle3379/HM888U/PgCSGkgYpsH45/s77Gix2XIC+90OieRm1YsQTrX/sBX765DS9/OAHjXh9m2WDrKbFIjOUTv8DlI7dUE1j5kHQeD+DztBcqYxi8+M4otOgUbtugCSGEkFpmF3N+27Rpg2HDhmH27Nm4dOkSzp07h7lz5+KFF15QVHpOS0tD69atcemSdF1JhmHw1ltvYePGjdi5cycePHiApUuX4t69e3j55Zdr8+kQQojd4/F42H5zLXbc/Rh8R77qTiMSYcXSSDLiKjG+fec3DHWfhiWjP7F0uPXO0vHrcPnILQDQvm4vywIS2eurtn/cq4Mx5a2R1g6REEKILixnvS+il10kvwCwfft2tG7dGoMGDcLw4cPRp08fbNmyRbFfJBIhISEB5eXlim1vvvkmlixZggULFqBDhw44duwYjhw5gubNm9fGUyCEkHrHN9AH/2Z8g1ZdmlZvNJD8cupFs1R2AleP3sZQt2n45aNdFoy0/oj97xquHquumq1z6LI8AVa6KXpzw4uY8+HzNoqUEEIIqVvsYp3f2mSv64gRQoitZaXk4OUu70MskuhObgFpj6++tx61fUMm98H8r2bA0dHBUqHanbLiCpzeGYvEa4/w30+nwbIsGD7f8InyAlgM8HfyJrh5uFg/WEIIsTJ7vT9XrPMb+j/rrfOb/q3dvS62VKtzfgkhhNQf/9/e/cdFVeX/A3/NgDP8BpHfKaBCCAmKuiCWYgsq6iatfjPUTNQvpqlpmaVu/oJ2o9Q11y0z89eaxWprReaP/LmpISqJoQGC6foj0BTlhwgyM+fzB3LzyvBTBpjx9Xw85iFz77nnnnsPR+Y959xzXDs4Y/u11bhdVIrJfRbgt0sF+hM28DvXPZsPYc+nh+Ab3BHxX74GR1eHhy+skdBqtfhL9BKc3Hem2j4htICZsvZJq+7d62cnD2DgS0TUWggYaMKrps/S1BjNsGciIjIO1nZW2HR6KcKeCW5cBvqCOQWQc/I8RnlPR3zM+0jd8aP+Z11NyK+/XEV0u/+vN/AFUPnBSaOVPTutj3+oD156J8YAJSQiIjIu7PklIiKDWPjpdPz263W8FDIfpUVl9T/wwaD2vtmkhRA4/OVxHN5WObmhTVtrJO7+Cx7v7t1EpW55Wq0OR74+gSUTVqGiXFOPA3QQCoXeHuC4v8Vg+NSBXNKIiKg14VJHLYY9v0REZDDOHk7YdnkVNmUuQ6fADo3PSKH4faKs+/64l9y8jWkh8zCs7Xjs/ewQbheW1pJJ67drw0E85z4Jb8e8j/Kyivr3buuZ4fP5WUMxYtogBr5ERET3sOeXiIgMzvkxR3x4JB4ZP2Tj3Ymr8dulG/oT1hDsCZ2u1km0ykrK8N64DwEIWNpaoN+I3pi87EVY21k1QekNq7T4DjYu2ILk1Xuh1Wh/36HVVvZ612diK9l9E3hhzrMYMze6yctKRERNQKcDUPsjK43Pl2rD4JeIiJpNYB8/fJr5d+z992EsmbimMmir6pmsqZdTiLon8bhvaPSd4jLs3nAQu9YfgNJMCft2thgwth9eWPD/YGlt0XQX8xB0Oh3OnvgFx3elY/M726DT3LvAB3tphYAQou7e26p1k4XA+wcWwD/ExzAFJyIiMmJc6qgOxjqVOhFRa6fT6ZAwZiV+SE6rOVHVs77aOpZHktJWBos1Bcu9BgbBt2cn5J3/DU8ND0H4iN6NK3wjZB3PwX+W70DGoUwU5N+CqBqqrKx8AklvgCtE5f4anumVKACVpQqLtsxEz4hAA5SeiKj1MNbP59JSR84TYa5UNXn+Gt1d7P1trdHdl+bE4LcOxtq4iIiMyb/++h98lpj8e0BY5V4gK7Ra/Qc+kFYIXd29xA/82bO0VaPngCB0D+8Kr64d8MRTflBAAfM2jR8cVVpcim9X78WJPem4nJ2Ha5cLaixXvdbrVda8pJFCqcCEhJEYPP5p2La1bnSZiYiMhbF+Pmfw2/IY/NbBWBsXEZExSv/+ZyybtAbX/ne9csO9YdFCV0fPrzQbdD2D3/uHWisUgKj+nJTaSg2zNkpoK7SoKNdAp61MY2ZuBgsbNSws1dBotKgor4BCocCdkjvQafWcXKGUn/PB3fUJfiGgMKsejCuUCqw8vBi+wR3rkQcRkWkw1s/nUvDrNMFwwe/1dQa5LwUFBZg+fTq++eYbKJVKjBgxAitWrICNjU2tx6WkpOAvf/kLUlNTYWZmhu7du2P37t2wtGyZtef5zC8REbUa3fsFYFPWctwpuYNFI99HxqFsaCvqsdwPgMou4nokuz8IrfpZoawWAJeXlus9XKvR4vatUty+VY+ZpRXK6udsjHvP/lZmVfl8c4cuHljx30Wwtm/9k3oREdF9dJWP6RgmX8MYM2YM8vLysGfPHlRUVGD8+PGYNGkSPvvssxqPSUlJQVRUFObOnYuVK1fC3Nwcp06dglLZcgsOsee3Dsb6zRIRkakoKy3DSz3nIu+XqzV/Vqh63rexf/ilP4VN+SexKrCuI/Ct7ZlfQLq2gD5d4OLpBDdvJwwa1x+P+bg1XVGJiIyIsX4+l3p+Hccbrue3YH2T35fMzEwEBATg+PHj6NWrFwBg165dGDJkCC5fvgwPDw+9x/Xu3RsDBgxAQkJCk5XlYXGdXyIiatUsrCywMXM53v9+Mawd9AyTEk3wDbpB1sKteaizPJmQenNrymdIXATe/+8izNs0DRMSYhj4EhEZMSF0BnsBlUH2/a/ycv0jmeorJSUFDg4OUuALAJGRkVAqlUhNTdV7zLVr15CamgoXFxf06dMHrq6uCA8Px+HDhx+qLA+LwS8RERmFgFBffHltLXaVfYrh06Ng46BnuK8hYtiHUZ/BVVXLFD14jBCA0KHngCDM/DDOMOUjIiKT06FDB9jb20uvd95556Hyy8/Ph4uLi2ybubk5HB0dkZ+fr/eYX375BQCwaNEixMXFYdeuXejRowciIiKQk5PzUOV5GAx+iYjIqCiVSkxe9iK2XfsE3939DG9ufBkOzg85vMtQTwDVt0f5XgAsdFpUPrusg5WdBeISx+CdHfMMUzYiImoZQlQ+n9vUr3t/yy5duoTCwkLpNXfuXL3FmDNnDhT3ltKr6ZWVldWoS9Td+1L3pZdewvjx4xEcHIzly5fDz88P69ata9x9awKc8IqIiIxaxOi+iBjdFzqdDttX78Gnf92Gm3m3qiesCnD1BaQ1zPj80Go6p57Znx1c7fGHQd0wYGw4AsJ8obZUN315iIjI5NnZ2dXrmd9Zs2YhNja21jSdOnWCm5sbrl27Jtuu0WhQUFAANzf9j+G4u7sDAAICAmTb/f39cfHixTrLZigMfomIyCQolUoMmzIIw6YMAgAcSPoBGxf9GyW3SmHb1gaXz/56byhx/ZY6ahoC0ljs+3uX751faa5EUD9/TFsxAV4B7Q1UBiIialWaYq6KGvOtP2dnZzg7O9eZLiwsDLdu3UJaWhp69uwJANi/fz90Oh1CQ0P1HuPt7Q0PDw9kZ2fLtp89exaDBw9uUDmbEoNfIiIySU/H9MHTMX1k2z6avQlf/WMHtBrt7587DBb4VqkaiqaAwkwBF09nPPlsCKJfHgiPzpy4ioiIWjd/f39ERUUhLi4OH330ESoqKjBt2jTExMRIMz1fuXIFERER+Ne//oWQkBAoFArMnj0bCxcuRLdu3dC9e3ds3LgRWVlZ+OKLL1rsWhj8EhHRI2PykrGYvGQsAEBTocHx707hxz0/4eg3J3DzWiF0Wi3MzMxQVlr+0F/KK82UEDodFEolnNs7Yvzbo/DH0X1rXtKIiIgeDTodoDDEozaG+zJ38+bNmDZtGiIiIqBUKjFixAj84x//kPZXVFQgOzsbpaWl0raZM2eirKwMr776KgoKCtCtWzfs2bMHnTt3Nlg568J1futgrOuIERFR490uvI0zP5yFVqOFT4+O+DU3D1uWJOO3Szdg42iNwRP/CL+enVFSVIqLZ65AAFAqFXBwsYNXQAe4ebvUeQ4iImocY/18XlXuCJvRMFcYYJ1fcRf7Sj4zuvvSnNjzS0RE9ABre2uEDA6W3js/1g7dwrvqTRsQ6tdcxSIiIqKHwOCXiIiIiIiomQidDsIAw56FweewMH5c55eIiIiIiIhMHnt+iYiIiIiImksrWeroUcSeXyIiIiIiIjJ57PklIiIiIiJqLjoBKNjz2xLY80tEREREREQmjz2/REREREREzUUIAAaYmZk9v3Vizy8RERERERGZPPb8EhERERERNROhExAGeOZXsOe3Tgx+iYiIiIiImovQwTDDng2Qp4nhsGciIiIiIiIyeez5JSIiIiIiaiYc9txy2PNLREREREREJo89v0RERERERM2Fz/y2GAa/dagaPlBUVNTCJSEiIiIioqrP5cY6zFeDCsAARdegoukzNTEMfutQXFwMAOjQoUMLl4SIiIiIiKoUFxfD3t6+pYtRbyqVCm5ubjicv8Ng53Bzc4NKpTJY/sZOIYz1K5NmotPp8Ouvv8LW1hYKhaKli9PsioqK0KFDB1y6dAl2dnYtXZxHFuuhdWA9tDzWQevAemgdWA+tA+uh+QkhUFxcDA8PDyiVxjWFUVlZGe7evWuw/FUqFSwsLAyWv7Fjz28dlEol2rdv39LFaHF2dnb8D70VYD20DqyHlsc6aB1YD60D66F1YD00L2Pq8b2fhYUFg9MWZFxflRARERERERE1AoNfIiIiIiIiMnkMfqlWarUaCxcuhFqtbumiPNJYD60D66HlsQ5aB9ZD68B6aB1YD0TGgxNeERERERERkcljzy8RERERERGZPAa/REREREREZPIY/BIREREREZHJY/BLREREREREJo/BL6GgoABjxoyBnZ0dHBwcMHHiRJSUlNSY/sKFC1AoFHpfW7duldLp25+UlNQcl2R0GloHANC/f/9q93fy5MmyNBcvXsTQoUNhZWUFFxcXzJ49GxqNxpCXYtQaWg8FBQWYPn06/Pz8YGlpCU9PT7zyyisoLCyUpWNbqN0HH3wAb29vWFhYIDQ0FMeOHas1/datW9GlSxdYWFggMDAQO3bskO0XQmDBggVwd3eHpaUlIiMjkZOTY8hLMAkNqYc1a9agb9++aNu2Ldq2bYvIyMhq6WNjY6v93kdFRRn6MoxaQ+pgw4YN1e6vhYWFLA3bQuM0pB70/S1WKBQYOnSolIZtgagVEfTIi4qKEt26dRNHjx4Vhw4dEj4+PmLUqFE1ptdoNCIvL0/2Wrx4sbCxsRHFxcVSOgBi/fr1snR37txpjksyOg2tAyGECA8PF3FxcbL7W1hYKO3XaDSia9euIjIyUpw8eVLs2LFDODk5iblz5xr6coxWQ+shIyNDDB8+XCQnJ4vc3Fyxb98+4evrK0aMGCFLx7ZQs6SkJKFSqcS6devEmTNnRFxcnHBwcBBXr17Vm/7IkSPCzMxMvPfee+Lnn38Wb731lmjTpo3IyMiQ0iQmJgp7e3vx1VdfiVOnTolhw4aJjh078p7XoqH1MHr0aPHBBx+IkydPiszMTBEbGyvs7e3F5cuXpTTjxo0TUVFRst/7goKC5roko9PQOli/fr2ws7OT3d/8/HxZGraFhmtoPdy4cUNWB6dPnxZmZmZi/fr1Uhq2BaLWg8HvI+7nn38WAMTx48elbTt37hQKhUJcuXKl3vl0795dTJgwQbYNgPjyyy+bqqgmq7F1EB4eLmbMmFHj/h07dgilUin7MLRq1SphZ2cnysvLm6TspqSp2sKWLVuESqUSFRUV0ja2hZqFhISIqVOnSu+1Wq3w8PAQ77zzjt70I0eOFEOHDpVtCw0NFS+99JIQQgidTifc3NzEkiVLpP23bt0SarVafP755wa4AtPQ0Hp4kEajEba2tmLjxo3StnHjxono6OimLqrJamgdrF+/Xtjb29eYH9tC4zxsW1i+fLmwtbUVJSUl0ja2BaLWg8OeH3EpKSlwcHBAr169pG2RkZFQKpVITU2tVx5paWlIT0/HxIkTq+2bOnUqnJycEBISgnXr1kFwWelqHqYONm/eDCcnJ3Tt2hVz585FaWmpLN/AwEC4urpK2wYNGoSioiKcOXOm6S/EyDVFWwCAwsJC2NnZwdzcXLadbaG6u3fvIi0tDZGRkdI2pVKJyMhIpKSk6D0mJSVFlh6o/L2uSn/+/Hnk5+fL0tjb2yM0NLTGPB91jamHB5WWlqKiogKOjo6y7QcPHoSLiwv8/PwwZcoU3Lhxo0nLbioaWwclJSXw8vJChw4dEB0dLfu/nW2h4ZqiLaxduxYxMTGwtraWbWdbIGodzOtOQqYsPz8fLi4usm3m5uZwdHREfn5+vfJYu3Yt/P390adPH9n2+Ph4/PGPf4SVlRW+++47vPzyyygpKcErr7zSZOU3BY2tg9GjR8PLywseHh746aef8OabbyI7Oxvbtm2T8r0/8AUgva9v3T5KmqItXL9+HQkJCZg0aZJsO9uCftevX4dWq9X7e5qVlaX3mJp+r6vqqOrf2tKQXGPq4UFvvvkmPDw8ZEFDVFQUhg8fjo4dO+LcuXOYN28eBg8ejJSUFJiZmTXpNRi7xtSBn58f1q1bh6CgIBQWFmLp0qXo06cPzpw5g/bt27MtNMLDtoVjx47h9OnTWLt2rWw72wJR68Hg10TNmTMH7777bq1pMjMzH/o8d+7cwWeffYb58+dX23f/tuDgYNy+fRtLlix5ZD7wG7oO7g+wAgMD4e7ujoiICJw7dw6dO3dudL6mprnaQlFREYYOHYqAgAAsWrRItu9Rbwtk2hITE5GUlISDBw/KJlyKiYmRfg4MDERQUBA6d+6MgwcPIiIioiWKalLCwsIQFhYmve/Tpw/8/f2xevVqJCQktGDJHl1r165FYGAgQkJCZNvZFohaDwa/JmrWrFmIjY2tNU2nTp3g5uaGa9euybZrNBoUFBTAzc2tzvN88cUXKC0txYsvvlhn2tDQUCQkJKC8vBxqtbrO9MauueqgSmhoKAAgNzcXnTt3hpubW7UZKq9evQoADcrX2DVHPRQXFyMqKgq2trb48ssv0aZNm1rTP2ptoSZOTk4wMzOTfi+rXL16tcZ77ubmVmv6qn+vXr0Kd3d3WZru3bs3YelNR2PqocrSpUuRmJiIvXv3IigoqNa0nTp1gpOTE3Jzc/mB/wEPUwdV2rRpg+DgYOTm5gJgW2iMh6mH27dvIykpCfHx8XWeh22BqOXwmV8T5ezsjC5dutT6UqlUCAsLw61bt5CWliYdu3//fuh0OimYqs3atWsxbNgwODs715k2PT0dbdu2fWQ+7DdXHVRJT08HAOlDTlhYGDIyMmQB3Z49e2BnZ4eAgICmuUgjYOh6KCoqwsCBA6FSqZCcnFxtqRF9HrW2UBOVSoWePXti37590jadTod9+/bJerTuFxYWJksPVP5eV6Xv2LEj3NzcZGmKioqQmppaY56PusbUAwC89957SEhIwK5du2TPytfk8uXLuHHjhiwQo0qNrYP7abVaZGRkSPeXbaHhHqYetm7divLycrzwwgt1nodtgagFtfSMW9TyoqKiRHBwsEhNTRWHDx8Wvr6+suVdLl++LPz8/ERqaqrsuJycHKFQKMTOnTur5ZmcnCzWrFkjMjIyRE5Ojvjwww+FlZWVWLBggcGvxxg1tA5yc3NFfHy8OHHihDh//rz4+uuvRadOnUS/fv2kY6qWOho4cKBIT08Xu3btEs7OzlzqqBYNrYfCwkIRGhoqAgMDRW5urmwZC41GI4RgW6hLUlKSUKvVYsOGDeLnn38WkyZNEg4ODtIs5WPHjhVz5syR0h85ckSYm5uLpUuXiszMTLFw4UK9Sx05ODiIr7/+Wvz0008iOjqay7vUoaH1kJiYKFQqlfjiiy9kv/dVy90VFxeL119/XaSkpIjz58+LvXv3ih49eghfX19RVlbWItfY2jW0DhYvXix2794tzp07J9LS0kRMTIywsLAQZ86ckdKwLTRcQ+uhylNPPSWef/75atvZFohaFwa/JG7cuCFGjRolbGxshJ2dnRg/frxsvd7z588LAOLAgQOy4+bOnSs6dOggtFpttTx37twpunfvLmxsbIS1tbXo1q2b+Oijj/SmpYbXwcWLF0W/fv2Eo6OjUKvVwsfHR8yePVu2zq8QQly4cEEMHjxYWFpaCicnJzFr1izZEjwk19B6OHDggACg93X+/HkhBNtCfaxcuVJ4enoKlUolQkJCxNGjR6V94eHhYty4cbL0W7ZsEY8//rhQqVTiiSeeEN9++61sv06nE/Pnzxeurq5CrVaLiIgIkZ2d3RyXYtQaUg9eXl56f+8XLlwohBCitLRUDBw4UDg7O4s2bdoILy8vERcXV20dWpJrSB3MnDlTSuvq6iqGDBkifvzxR1l+bAuN09D/k7KysgQA8d1331XLi22BqHVRCMH1NoiIiIiIiMi08ZlfIiIiIiIiMnkMfomIiIiIiMjkMfglIiIiIiIik8fgl4iIiIiIiEweg18iIiIiIiIyeQx+iYiIiIiIyOQx+CUiIiIiIiKTx+CXiIiIiIiITB6DXyKiR4i3tzfef//9JssvNjYWzz77bJPlBwAHDx6EQqHArVu3mjRfIiIierQx+CUiMkKxsbFQKBRQKBRQqVTw8fFBfHw8NBpNrccdP34ckyZNarJyrFixAhs2bGiy/Bri5MmTeO655+Dq6goLCwv4+voiLi4OZ8+ebZHytFb1/cLj448/Rv/+/WFnZ8cvH4iIyCQx+CUiMlJRUVHIy8tDTk4OZs2ahUWLFmHJkiV60969excA4OzsDCsrqyYrg729PRwcHJosv/ravn07evfujfLycmzevBmZmZn49NNPYW9vj/nz5zd7eUxBaWkpoqKiMG/evJYuChERkUEw+CUiMlJqtRpubm7w8vLClClTEBkZieTkZAC/D0f+61//Cg8PD/j5+QGo3guoUCjwySef4M9//jOsrKzg6+sr5VHlzJkz+NOf/gQ7OzvY2tqib9++OHfunOw8Vfr3749p06Zh2rRpsLe3h5OTE+bPnw8hhJRm06ZN6NWrF2xtbeHm5obRo0fj2rVr9b7u0tJSjB8/HkOGDEFycjIiIyPRsWNHhIaGYunSpVi9erWU9r///S9CQkKgVqvh7u6OOXPmyHrH+/fvj+nTp2PmzJlo27YtXF1dsWbNGty+fRvjx4+Hra0tfHx8sHPnTumYqmHZ3377LYKCgmBhYYHevXvj9OnTsnL+5z//wRNPPAG1Wg1vb28sW7ZMtt/b2xt/+9vfMGHCBNja2sLT0xMff/yxLM2lS5cwcuRIODg4wNHREdHR0bhw4YK0v+r+L126FO7u7mjXrh2mTp2KiooK6fr+97//4dVXX5VGCtRk5syZmDNnDnr37l3vuiAiIjImDH6JiEyEpaWl1MMLAPv27UN2djb27NmD7du313jc4sWLMXLkSPz0008YMmQIxowZg4KCAgDAlStX0K9fP6jVauzfvx9paWmYMGFCrcOrN27cCHNzcxw7dgwrVqzA3//+d3zyySfS/oqKCiQkJODUqVP46quvcOHCBcTGxtb7Onfv3o3r16/jjTfe0Lu/qif6ypUrGDJkCP7whz/g1KlTWLVqFdauXYu33367WnmdnJxw7NgxTJ8+HVOmTMFzzz2HPn364Mcff8TAgQMxduxYlJaWyo6bPXs2li1bhuPHj8PZ2RnPPPOMFHSmpaVh5MiRiImJQUZGBhYtWoT58+dXGyK+bNky9OrVCydPnsTLL7+MKVOmIDs7W7pPgwYNgq2tLQ4dOoQjR47AxsYGUVFRsno+cOAAzp07hwMHDmDjxo3YsGGDdJ5t27ahffv2iI+PR15eHvLy8up9n4mIiEyOICIiozNu3DgRHR0thBBCp9OJPXv2CLVaLV5//XVpv6urqygvL5cd5+XlJZYvXy69ByDeeust6X1JSYkAIHbu3CmEEGLu3LmiY8eO4u7du3WWQwghwsPDhb+/v9DpdNK2N998U/j7+9d4LcePHxcARHFxsRBCiAMHDggA4ubNm3rTv/vuuwKAKCgoqDFPIYSYN2+e8PPzk5Xlgw8+EDY2NkKr1Urlfeqpp6T9Go1GWFtbi7Fjx0rb8vLyBACRkpIiK19SUpKU5saNG8LS0lL8+9//FkIIMXr0aDFgwABZeWbPni0CAgKk915eXuKFF16Q3ut0OuHi4iJWrVolhBBi06ZN1cpfXl4uLC0txe7du4UQlfffy8tLaDQaKc1zzz0nnn/+edl57q/zutR1/4mIiIwVe36JiIzU9u3bYWNjAwsLCwwePBjPP/88Fi1aJO0PDAyESqWqM5+goCDpZ2tra9jZ2UnDkNPT09G3b1+0adOm3uXq3bu3bHhtWFgYcnJyoNVqAVT2ij7zzDPw9PSEra0twsPDAQAXL16sV/7iviHUtcnMzERYWJisLE8++SRKSkpw+fJladv9129mZoZ27dohMDBQ2ubq6goA1YZmh4WFST87OjrCz88PmZmZ0rmffPJJWfonn3xSdh8ePLdCoYCbm5t0nlOnTiE3Nxe2trawsbGBjY0NHB0dUVZWJg07B4AnnngCZmZm0nt3d/cGDSMnIiJ6VJi3dAGIiKhxnn76aaxatQoqlQoeHh4wN5f/l25tbV2vfB4MbBUKBXQ6HYDKodRN6fbt2xg0aBAGDRqEzZs3w9nZGRcvXsSgQYNkQ3lr8/jjjwMAsrKyZAFoY+m7/vu3VQXPVfekKdV270tKStCzZ09s3ry52nHOzs71yoOIiIh+x55fIiIjZW1tDR8fH3h6elYLfJtKUFAQDh06JD3LWh+pqamy90ePHoWvry/MzMyQlZWFGzduIDExEX379kWXLl0a3Es5cOBAODk54b333tO7v2qJHn9/f6SkpMh6io8cOQJbW1u0b9++QefU5+jRo9LPN2/exNmzZ+Hv7y+d+8iRI7L0R44cweOPPy7rpa1Njx49kJOTAxcXF/j4+Mhe9vb29S6nSqWS9TYTERE9qhj8EhFRjaZNm4aioiLExMTgxIkTyMnJwaZNm6RJmfS5ePEiXnvtNWRnZ+Pzzz/HypUrMWPGDACAp6cnVCoVVq5ciV9++QXJyclISEhoUJmsra3xySef4Ntvv8WwYcOwd+9eXLhwASdOnMAbb7yByZMnAwBefvllXLp0CdOnT0dWVha+/vprLFy4EK+99hqUyof/8xcfH499+/bh9OnTiI2NhZOTkzTz9axZs7Bv3z4kJCTg7Nmz2LhxI/75z3/i9ddfr3f+Y8aMgZOTE6Kjo3Ho0CGcP38eBw8exCuvvCIbtl0Xb29vfP/997hy5QquX79eY7r8/Hykp6cjNzcXAJCRkYH09HRp8jMiIiJjx+CXiIhq1K5dO+zfvx8lJSUIDw9Hz549sWbNmlqfAX7xxRdx584dhISEYOrUqZgxYwYmTZoEoHK47oYNG7B161YEBAQgMTERS5cubXC5oqOj8cMPP6BNmzYYPXo0unTpglGjRqGwsFCazfmxxx7Djh07cOzYMXTr1g2TJ0/GxIkT8dZbbzXuZjwgMTERM2bMQM+ePZGfn49vvvlGesa6R48e2LJlC5KSktC1a1csWLAA8fHxDZrV2srKCt9//z08PT0xfPhw+Pv7Y+LEiSgrK4OdnV2984mPj8eFCxfQuXNn2XDpB3300UcIDg5GXFwcAKBfv34IDg6utvQVERGRsVKI+s4cQkREVIf+/fuje/fusrWETc3Bgwfx9NNP4+bNm9KySkRERNT6seeXiIiIiIiITB6DXyIiIiIiIjJ5HPZMREREREREJo89v0RERERERGTyGPwSERERERGRyWPwS0RERERERCaPwS8RERERERGZPAa/REREREREZPIY/BIREREREZHJY/BLREREREREJo/BLxEREREREZm8/wM1+3l79YPkBwAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Additional components if n_components > 2\n", + "if n_components > 2:\n", + " for i in range(2, n_components):\n", + " plt.figure(figsize=(12, 6))\n", + " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}')\n", + " plt.colorbar(sc, label='Principal Component Value')\n", + " plt.xlabel('Principal Component 1')\n", + " plt.ylabel(f'Principal Component {i + 1}')\n", + " plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by PC{i+1} Values)')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -344,26 +27855,81 @@ } ], "source": [ + "\n", + "# Visualize the PCA results with color based on correlation with infected softmax score\n", + "for i in range(reduced_projections.shape[1]):\n", + " pc = reduced_projections[:, i]\n", + " infected_corr, _ = spearmanr(pc, infected_softmax)\n", + " \n", + " plt.figure(figsize=(12, 6))\n", + " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=pc, cmap='viridis', label=f'PC{i+1} Correlation: {infected_corr:.2f}')\n", + " plt.colorbar(sc, label='Principal Component Value')\n", + " plt.xlabel('Principal Component 1')\n", + " plt.ylabel('Principal Component 2')\n", + " plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Correlation with Infected Softmax Score)')\n", + " plt.legend()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize additional PCs if needed\n", "# PC1 vs PC3, PC1 vs PC4, etc.\n", "if n_components > 2:\n", " for i in range(2, n_components):\n", " plt.figure(figsize=(12, 6))\n", - " plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c='blue', label='Cells')\n", + " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells')\n", + " plt.colorbar(sc, label='Infected Softmax Score')\n", " plt.xlabel('Principal Component 1')\n", " plt.ylabel(f'Principal Component {i + 1}')\n", " plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1}')\n", " plt.legend()\n", - " plt.show()" + " plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 100, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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1MyQZw4YNs1lmhQoVjPDwcOvzt956y/D19bXZbns4st9r1qxplC5d2q7l1qxZ0yhRooRx9uxZ4+zZs8a+ffuM7t27G5KMxo0bW6dL77VMGTd48GDr85TX5vZj2TAMo1mzZkauXLmszw8ePGi4uLgYzZo1M5KSkmymTU5Otqnx9tcr5XV67LHHjLi4OOvwr7/+2pBkTJ482TosrWNi5MiRhsViMY4ePZqq5oykvO8mTpyY4XQpJk2aZEgyvvjiC+uw69evG1WrVjVy5MhhrT3lfR4UFGRcunTJOm3//v0NSUa5cuWMGzduWIe3atXKcHd3t3lvFyxY0JBkfPPNN9Zhly9fNvLkyWNUqFDBOqxHjx6GJGP9+vXWYVeuXDHCwsKM0NBQ6+uQso9Llixp81k1efJkQ5KxZ88ewzBuvU5FixY16tWrZ/OaXb161QgLCzOeeeYZ6zB7jwvDMIzs2bMb7dq1S7VP/fz8jDfeeCPV8Ltp1KiRUblyZevz559/3nj++ecNV1dX46effjIMwzB27txpSDK+//5763R3Hnvbtm0zJBmzZ89OtY6aNWsakozPPvvMOiwxMdEICQkxmjdvnmF9PXv2NCQZ0dHRNsMTExOt782zZ88a586ds45LOW58fX2NM2fO2MxXvnx5Izg42Dh//rx12O7duw0XFxfjlVdesQ5r166dUbBgwVT1pPV+kGS4u7vbfNakfH9MmTLFOqxp06aGp6enzfvrzz//NFxdXe/6HrtTWp/V6X0W3fn5nSJlP93+mr3xxhtp1uLId25aUmpYuHChsXTpUsNisRjHjh0zDMMw+vTpY/0uSusz2p7vr++++86QZGzbti3dGlK2YezYscaNGzeMF1980fDy8jJWrFhhM125cuWMRo0aZbg9wKOIU+4BKDIyUkFBQcqfP79eeOEFZc+eXUuWLLE5pdzV1dV6jWNycrIuXLigmzdvqmLFimmeOvriiy/atPCntAz9/fffqaZdvXq16tWrpzp16ujbb7+Vh4dHhvXGxcVJkl0tjtKtzoRcXV3VvXt3m+G9evWSYRj66aefbIbXrFlTpUqVSnNZnTp1sjn1cOXKlbp06ZJatWqlc+fOWR+urq6qUqVKqksS7nT7WQiXL1/WuXPnVLNmTf3999+6fPmyXdt3u+3bt+vMmTPq2rWrzfWZjRo1UokSJVKdpizdakG8XUREhM3r5O/vr4SEBIdPa3R0vzti//79CgoKUlBQkEqWLKkpU6aoUaNGqVqAM3ot05LWvjh//rz1mFu8eLGSk5M1aNAgubjYfoXa05P1K6+8YnPcvvDCC8qTJ4+WLVtmHXb7MZGQkKBz587pqaeekmEY2rVrl93bIt3beyUkJEStWrWyDsuWLZu6d++u+Ph4rV271mb6Fi1ayM/Pz/o85dTvl19+2abTwSpVquj69eupLvvImzevTQu7r6+vXnnlFe3atUuxsbHWmipXrqzq1atbp8uRI4c6d+6sI0eOWE/XTtG+fXub67Hv/OyJjo7WwYMH1bp1a50/f976nk1ISFCdOnW0bt26VHdLuNtxkRF/f39t2bJFJ0+evOu0d65j586d1jOBNmzYoIYNG6p8+fJav369pFut9haLxWbfOCpHjhw2rezu7u6qXLlymp/Vt0vZ9jtvPbps2TLrezMoKEgFCxZMNW/z5s0VFBRkfX7q1ClFR0crKipKAQEB1uFly5bVM888Y/P+cFRkZKT1bImUZfr6+lq3LykpSStWrFDTpk1tzvgqWbKk6tWrd8/rvZOjn0WOcuQ7Nz1169ZVQECAFixYIMMwtGDBApvPgjvZ8/2VcsbU0qVL79pXx/Xr19WiRQstXbpUy5YtS3XGk7+/v/744w8dPHjQ7m0CHgUEegCaNm2aVq5cqUWLFqlhw4Y6d+5cmqF67ty5Klu2rPXataCgIP34449pBs87T4VP+Ufjzk51rl27pkaNGqlChQr6+uuv7eoYydfXV9Kta1PtcfToUeXNmzdVqEk5rfvo0aM2w8PCwtJd1p3jUv6xqF27ts0/sUFBQfr555915syZDGvbuHGjIiMjrdeMBgUFWXvmv5dAn7ItxYsXTzWuRIkSqbbV09PT5h9r6dZrdfvr1LVrVxUrVkwNGjRQvnz51KFDhzSvQU2rFkf2uyNCQ0O1cuVK/fLLL9qwYYNiY2O1dOlSm86cpIxfy7Tc7bg9fPiwXFxc7vkf86JFi9o8t1gsKlKkiPUyF0k6duyYNdik9GtQs2ZNSY4fE/fyXilatGiqHyvSe83u3F8p4T5//vxpDr/z/V+kSJFUP4QUK1ZM0v9d+nP06NE0j2d7a7rzNUx5z7Zr1y7Ve3bWrFlKTExMtZ/t/TxLy5gxY7R3717lz59flStX1pAhQ+wKWREREbp586Y2bdqkAwcO6MyZM4qIiFCNGjVsAn2pUqVsQrCj8uXLl+o1uPMzIC0p7+s7+zqpVq2aVq5cqZUrV6Z7Ccqd78uMPrdKlixp/cHlXqR1Wdbt23f27Fn9+++/qd6b6dVzrxz9LHLU/RyjKbJly6YWLVpo/vz5WrdunY4fP57h3S/s+f6qWbOmmjdvrqFDhyowMFBNmjTR7Nmz07yd4ciRI7V48WItWrTIpg+SFMOGDdOlS5dUrFgxPf744+rTp0+alwIAjxquoQegypUrW3u5b9q0qapXr67WrVvrwIED1taXL774QlFRUWratKn69Omj4OBgubq6auTIkTad56VIqwMdSak6ofPw8FDDhg31/fffa/ny5Tadc6WnSJEicnNz0549exzdVLukd+1+WuNSWvI+//xzhYSEpJo+o1ujHT58WHXq1FGJEiU0YcIE5c+fX+7u7lq2bJkmTpz4QO6pnt7rdLvg4GBFR0drxYoV+umnn/TTTz9p9uzZeuWVV1J1lvagZM+eXZGRkXedLqPXMi32HrdZJSkpSc8884wuXLigvn37qkSJEsqePbtOnDihqKgoh4+JEiVKSFKWvVfS21/O3I93W3fKPhw7dmy6t3G7s9X5franZcuWioiI0Hfffaeff/5ZY8eO1ejRo/Xtt99meI16xYoV5enpqXXr1qlAgQIKDg5WsWLFFBERoQ8//FCJiYlav359qj4EHHWv25ZybO3du1flypWzDg8KCrK+N9O7r7mj78vbpXcmzJ0deaZw9ns6RVrb7Oi2ZCSztrN169aaMWOGhgwZonLlyqX746W9318Wi0WLFi3S5s2b9cMPP2jFihXq0KGDxo8fr82bN9u81+rVq6fly5drzJgxqlWrVqq7ANSoUUOHDx/W999/r59//lmzZs3SxIkTNWPGDL366qsObSfwX0ILPQAbKSH95MmTmjp1qnX4okWLVKhQIX377bdq27at6tWrp8jISF27du2+1pfSU32dOnXUokULu3o39/b2Vu3ata0tCHdTsGBBnTx5MlUr5f79+63j71XKqZzBwcGKjIxM9UirlSHFDz/8oMTERC1ZskRdunRRw4YNFRkZ6dA/fndK2ZYDBw6kGnfgwIF73lZ3d3c1btxYH374oQ4fPqwuXbros88+S7P38Ntryar97iyFCxdWcnJyqtO87XXnqaKGYejQoUPWDr/27Nmjv/76S+PHj1ffvn3VpEkTRUZGKm/evPe0vmLFiql48eL6/vvvM7xrRIqCBQvq4MGDqX44yKrX7NChQ6kCx19//SVJ1n1SsGDBNI/ne60p5T3r6+ub5ns2MjLSrk7E7pTRezRPnjzq2rWrFi9erJiYGOXKlUsjRozIcHkpp76vX79e69evt55CHRERocTERM2bN0+nT59WjRo17rmu+9GgQQO5urpq3rx5972sjD639u/fr8DAQOstQnPmzJlmZ3L3esZPUFCQvLy80jyNO616MlNKK/qd25PWtmTV63in6tWrq0CBAlqzZk2GrfOOfH9J0pNPPqkRI0Zo+/btmjdvnv744w8tWLAg1TSLFy/Wb7/9phYtWujmzZuplhMQEKD27dvryy+/1PHjx1W2bFmbO5YAjyICPYBUatWqpcqVK2vSpEnWwJ7y6//t/3xv2bJFmzZtuu/1pdyWplKlSmrcuLG2bt1613kGDx4swzDUtm3bNIPKjh07rK3HDRs2VFJSks0PFJI0ceJEWSyWDFvJ7qZevXry9fXV+++/n+b1gSm3UEtLWvv08uXLmj17dqpps2fPblePyBUrVlRwcLBmzJhhc0rjTz/9pH379ll7C3bE+fPnbZ67uLiobNmykpTmaZMpsnK/O0vTpk3l4uKiYcOGpQq99rSEffbZZzY/cCxatEinTp2y7ou0jgnDMOy6RWB6hg4dqvPnz+vVV19N8x/kn3/+WUuXLpV06zWLjY3VV199ZR1/8+ZNTZkyRTly5LCe+p9ZTp48qe+++876PC4uTp999pnKly9vPeOlYcOG2rp1q81nTUJCgj7++GOFhoY6fPlDeHi4ChcurHHjxqX52ZHRezYjab1Hk5KSUp2+HxwcrLx582b43kkRERGhLVu2aPXq1dZAHxgYqJIlS1p72E8ZnlFdUurQeL8KFCigDh066Keffkr1Hk9hb+twnjx5VL58ec2dO9emzr179+rnn39Ww4YNrcMKFy6sy5cv25xqferUKZvjyBGurq6qV6+eFi9erGPHjlmH79u3TytWrLinZdqrYMGCcnV11bp162yGf/jhh6mmzarX8U4Wi0UffPCBBg8erLZt26Y7nb3fXxcvXkx1HKScGZPWeyAyMlILFizQ8uXL1bZtW5vP2Tu/i3LkyKEiRYrY9V4C/ss45R5Amvr06aMWLVpozpw5eu211/Tss8/q22+/VbNmzdSoUSPFxMRoxowZKlWqlF0tf3fj5eWlpUuXqnbt2mrQoIHWrl2rMmXKpDv9U089pWnTpqlr164qUaKE2rZtq6JFi+rKlStas2aNlixZovfee0+S1LhxYz399NMaMGCAjhw5onLlyunnn3/W999/rx49eth0mOQoX19fTZ8+XW3bttUTTzyhl156SUFBQTp27Jh+/PFHVatWLd1/duvWrWtt+e7SpYvi4+M1c+ZMBQcH69SpUzbThoeHa/r06XrvvfdUpEgRBQcHq3bt2qmWmS1bNo0ePVrt27dXzZo11apVK+tt60JDQ9WzZ0+Ht/HVV1/VhQsXVLt2beXLl09Hjx7VlClTVL58+QxvL5eV+91ZihQpogEDBmj48OGKiIjQ888/Lw8PD23btk158+a13nYvPQEBAapevbrat2+v06dPa9KkSSpSpIg6deok6dZpzIULF1bv3r114sQJ+fr66ptvvnHoOtg7vfjii9qzZ49GjBihXbt2qVWrVipYsKDOnz+v5cuXa9WqVZo/f74kqXPnzvroo48UFRWlHTt2KDQ0VIsWLdLGjRs1adIkuzvXs1exYsXUsWNHbdu2Tblz59ann36q06dP24SCfv366csvv1SDBg3UvXt3BQQEaO7cuYqJidE333yT6nr/u3FxcdGsWbPUoEEDlS5dWu3bt9djjz2mEydOaPXq1fL19dUPP/zg8LaEh4frl19+0YQJE5Q3b16FhYWpePHiypcvn1544QWVK1dOOXLk0C+//KJt27Zp/Pjxd11mRESERowYoePHj9sE9xo1auijjz5SaGioTeelaSlcuLD8/f01Y8YM+fj4KHv27KpSpUqmXNM9adIkxcTE6M0339SCBQvUuHFjBQcH69y5c9q4caN++OEHu69DHzt2rBo0aKCqVauqY8eO1tvW+fn52bTAvvTSS+rbt6+aNWum7t27W28TWqxYsTQ7aLXH0KFDtXz5ckVERKhr167WH7FKly6dpddo+/n5qUWLFpoyZYosFosKFy6spUuXptn3Snh4uCSpe/fuqlevnlxdXfXSSy9lSV1NmjRRkyZNMpzG3u+vuXPn6sMPP1SzZs1UuHBhXblyRTNnzpSvr6/NDzW3a9q0qfWyLl9fX3300UeSpFKlSqlWrVoKDw9XQECAtm/fbr0lJPBIe4A96gN4yKTcii2t28kkJSUZhQsXNgoXLmzcvHnTSE5ONt5//32jYMGChoeHh1GhQgVj6dKlqW4hdPvtZ+6kO24Vdvtt61KcO3fOKFWqlBESEmIcPHjwrtuwY8cOo3Xr1kbevHmNbNmyGTlz5jTq1KljzJ071+a2YleuXDF69uxpna5o0aLG2LFjbW5blVJjWreYymhfGcatW//Uq1fP8PPzMzw9PY3ChQsbUVFRxvbt263TpHVbpSVLlhhly5Y1PD09jdDQUGP06NHGp59+akgyYmJirNPFxsYajRo1Mnx8fAxJ1tsipXfbo6+++sqoUKGC4eHhYQQEBBht2rQx/vnnH5tp0tr/adW5aNEio27dukZwcLDh7u5uFChQwOjSpYtx6tSpNPfF7ezd747ets6eadN7LVPGpXXburNnz9pMl/K63/5aGIZhfPrpp9b9mzNnTqNmzZrGypUrbWpM67Z1X375pdG/f38jODjY8PLyMho1amRzqyzDuHW7rMjISCNHjhxGYGCg0alTJ+uttm6/jZU9t6273apVq4wmTZoYwcHBhpubmxEUFGQ0btzY5pZnhmEYp0+fNtq3b28EBgYa7u7uxuOPP57qlmfpvc9vvwXW7dJ6/xQsWNBo1KiRsWLFCqNs2bKGh4eHUaJEiVTzGoZhHD582HjhhRcMf39/w9PT06hcubKxdOlSu9ad1i3ADMMwdu3aZTz//PNGrly5DA8PD6NgwYJGy5YtjVWrVlmnceS42L9/v1GjRg3Dy8vLkGS0a9fOSExMNPr06WOUK1fO8PHxMbJnz26UK1fO+PDDD1NtY1ri4uIMV1dXw8fHx+a2kV988YUhyWjbtm2qee489gzj1i3MSpUqZbi5udnsi/TeS+ndGi4tN2/eNGbPnm3Url3bCAgIMNzc3IzAwECjTp06xowZM2xun5nR94NhGMYvv/xiVKtWzfDy8jJ8fX2Nxo0bG3/++Weq6X7++WejTJkyhru7u1G8eHHjiy++SPe2dWl9BhQsWDDVLQbXrl1rhIeHG+7u7kahQoWMGTNmOPweM4z0b1uX3mfR2bNnjebNmxve3t5Gzpw5jS5duhh79+5NdczevHnTePPNN42goCDDYrFY63LkOzct6b1v7pTWsWLP99fOnTuNVq1aGQUKFDA8PDyM4OBg49lnn7X5bkxvGz788ENDktG7d2/DMAzjvffeMypXrmz4+/sbXl5eRokSJYwRI0YY169fz7B24L/OYhgPuFcQAAAeMWvWrNHTTz+thQsX6oUXXnB2OQ+F0NBQlSlTxnq6PwAAcBzX0AMAAAAAYEIEegAAAAAATIhADwAAAACACXENPQAAAAAAJkQLPQAAAAAAJkSgBwAAAADAhNycXcDDLjk5WSdPnpSPj48sFouzywEAAAAA/McZhqErV64ob968cnFJvx2eQH8XJ0+eVP78+Z1dBgAAAADgEXP8+HHly5cv3fEE+rvw8fGRdGtH+vr6OrkaAAAAAMB/XVxcnPLnz2/No+kh0N9Fymn2vr6+BHoAAAAAwANzt8u+6RQPAAAAAAATItADAAAAAGBCBHoAAAAAAEyIa+gBAAAA4AFJSkrSjRs3nF0GnMzV1VVubm73fWt0Aj0AAAAAPADx8fH6559/ZBiGs0vBQ8Db21t58uSRu7v7PS+DQA8AAAAAWSwpKUn//POPvL29FRQUdN8tszAvwzB0/fp1nT17VjExMSpatKhcXO7tangCPQAAAABksRs3bsgwDAUFBcnLy8vZ5cDJvLy8lC1bNh09elTXr1+Xp6fnPS2HTvEAAAAA4AGhZR4p7rVV3mYZmVAHAAAAAAB4wAj0AAAAAACYENfQAwAAAICThIUdeaDri4kJfaDrs0doaKh69OihHj16OLuUTDNnzhz16NFDly5dytL10EIPAAAAAEhTVFSULBaL9ZErVy7Vr19fv//+u7NLgwj0AAAAAIAM1K9fX6dOndKpU6e0atUqubm56dlnn3V2WRm6fv26s0t4IAj0AAAAAIB0eXh4KCQkRCEhISpfvrz69eun48eP6+zZs5Kkvn37qlixYvL29lahQoU0cOBA3bhxw2YZP/zwgypVqiRPT08FBgaqWbNm6a5v1qxZ8vf316pVqyRJV65cUZs2bZQ9e3blyZNHEydOVK1atWxO0Q8NDdXw4cP1yiuvyNfXV507d5YkffPNNypdurQ8PDwUGhqq8ePH26zLYrFo8eLFNsP8/f01Z84cSdKRI0dksVj07bff6umnn5a3t7fKlSunTZs22cwzZ84cFShQQN7e3mrWrJnOnz9v9/69HwR6AAAAAIBd4uPj9cUXX6hIkSLKlSuXJMnHx0dz5szRn3/+qcmTJ2vmzJmaOHGidZ4ff/xRzZo1U8OGDbVr1y6tWrVKlStXTnP5Y8aMUb9+/fTzzz+rTp06kqS3335bGzdu1JIlS7Ry5UqtX79eO3fuTDXvuHHjVK5cOe3atUsDBw7Ujh071LJlS7300kvas2ePhgwZooEDB1rDuiMGDBig3r17Kzo6WsWKFVOrVq108+ZNSdKWLVvUsWNHdevWTdHR0Xr66af13nvvObyOe0GneAAAAACAdC1dulQ5cuSQJCUkJChPnjxaunSp9T7q//vf/6zThoaGqnfv3lqwYIHeeecdSdKIESP00ksvaejQodbpypUrl2o9ffv21eeff661a9eqdOnSkm61zs+dO1fz58+3BvzZs2crb968qeavXbu2evXqZX3epk0b1alTRwMHDpQkFStWTH/++afGjh2rqKgoh/ZB79691ahRI0nS0KFDVbp0aR06dEglSpTQ5MmTVb9+fev2FitWTL/99puWL1/u0DruBS30AAAAAIB0Pf3004qOjlZ0dLS2bt2qevXqqUGDBjp69Kgk6auvvlK1atUUEhKiHDly6H//+5+OHTtmnT86OtoaxtMzfvx4zZw5Uxs2bLCGeUn6+++/dePGDZsWfT8/PxUvXjzVMipWrGjzfN++fapWrZrNsGrVqungwYNKSkqyfwdIKlu2rPXvPHnySJLOnDljXU+VKlVspq9atapDy79XBHoAAAAAQLqyZ8+uIkWKqEiRIqpUqZJmzZqlhIQEzZw5U5s2bVKbNm3UsGFDLV26VLt27dKAAQNsOqXz8vK66zoiIiKUlJSkr7/++r7qdJTFYpFhGDbD7rz+X5KyZctmM48kJScnO7y+zMYp9w9Qzp45nV0C/mMuTrzo7BIAAADwiLFYLHJxcdG///6r3377TQULFtSAAQOs41Na7lOULVtWq1atUvv27dNdZuXKldWtWzfVr19fbm5u6t27tySpUKFCypYtm7Zt26YCBQpIki5fvqy//vpLNWrUyLDOkiVLauPGjTbDNm7cqGLFisnV1VWSFBQUpFOnTlnHHzx4UFevXrVjL9iuZ8uWLTbDNm/e7NAy7hWBHgAAAACQrsTERMXGxkqSLl68qKlTpyo+Pl6NGzdWXFycjh07pgULFqhSpUr68ccf9d1339nMP3jwYNWpU0eFCxfWSy+9pJs3b2rZsmXq27evzXRPPfWUli1bpgYNGsjNzU09evSQj4+P2rVrpz59+iggIEDBwcEaPHiwXFxcrC3l6enVq5cqVaqk4cOH68UXX9SmTZs0depUffjhh9ZpateuralTp6pq1apKSkpS3759bVrj7dG9e3dVq1ZN48aNU5MmTbRixYoHcv28RKAHAAAAAKeJiQl1dgl3tXz5cut14z4+PipRooQWLlyoWrVqSZJ69uypbt26KTExUY0aNdLAgQM1ZMgQ6/y1atXSwoULNXz4cI0aNUq+vr7ptq5Xr15dP/74oxo2bChXV1e9+eabmjBhgl577TU9++yz8vX11TvvvKPjx4/L09Mzw7qfeOIJff311xo0aJCGDx+uPHnyaNiwYTYd4o0fP17t27dXRESE8ubNq8mTJ2vHjh0O7Z8nn3xSM2fO1ODBgzVo0CBFRkbqf//7n4YPH+7Qcu6FxbjzggHYiIuLk5+fny5fvixfX9/7Whan3COzcco9AACAOVy7dk0xMTEKCwu7axBFxhISEvTYY49p/Pjx6tixo7PLuWcZHRP25lBa6AEAAAAAD61du3Zp//79qly5si5fvqxhw4ZJkpo0aeLkypyPQA8AAAAAeKiNGzdOBw4ckLu7u8LDw7V+/XoFBgY6uyynI9ADAAAAAB5aFSpUcPi69kcF96EHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACXHbOgAAAABwkpw9cz7Q9V2cePGBri9FaGioevTooR49etg9z8aNG/Xaa69p//79atSokRYvXpxl9TnqyJEjCgsL065du1S+fHmn1UELPQAAAAAgTbVq1UozhM+ZM0f+/v52L2fbtm3q3LmzQ+t+++23Vb58ecXExGjOnDkOzZuWI0eOyGKxKDo6+r6X9bAg0AMAAAAAslRQUJC8vb0dmufw4cOqXbu28uXL59CPB48SAj0AAAAA4J5FRUWpadOmGjdunPLkyaNcuXLpjTfe0I0bN6zThIaGatKkSdbnFotFs2bNUrNmzeTt7a2iRYtqyZIlkv6vJf38+fPq0KGDLBaLtYV+7969atCggXLkyKHcuXOrbdu2OnfunHW5ycnJGjNmjIoUKSIPDw8VKFBAI0aMkCSFhYVJkipUqCCLxaJatWpZ55s1a5ZKliwpT09PlShRQh9++KHNNm7dulUVKlSQp6enKlasqF27dmXmLrxnBHoAAAAAwH1ZvXq1Dh8+rNWrV2vu3LmaM2fOXU+THzp0qFq2bKnff/9dDRs2VJs2bXThwgXlz59fp06dkq+vryZNmqRTp07pxRdf1KVLl1S7dm1VqFBB27dv1/Lly3X69Gm1bNnSusz+/ftr1KhRGjhwoP7880/Nnz9fuXPnlnQrlEvSL7/8olOnTunbb7+VJM2bN0+DBg3SiBEjtG/fPr3//vsaOHCg5s6dK0mKj4/Xs88+q1KlSmnHjh0aMmSIevfunQV70XF0igcAAAAAuC85c+bU1KlT5erqqhIlSqhRo0ZatWqVOnXqlO48UVFRatWqlSTp/fff1wcffKCtW7eqfv36CgkJkcVikZ+fn0JCQiRJ48ePV4UKFfT+++9bl/Hpp58qf/78+uuvv5QnTx5NnjxZU6dOVbt27SRJhQsXVvXq1SXdOu1fknLlymVdpiQNHjxY48eP1/PPPy/pVkv+n3/+qY8++kjt2rXT/PnzlZycrE8++USenp4qXbq0/vnnH73++uuZuAfvDYEeAAAAAHBfSpcuLVdXV+vzPHnyaM+ePRnOU7ZsWevf2bNnl6+vr86cOZPu9Lt379bq1auVI0eOVOMOHz6sS5cuKTExUXXq1LG77oSEBB0+fFgdO3a0+fHh5s2b8vPzkyTt27dPZcuWlaenp3V81apV7V5HViLQAwAAAADS5Ovrq8uXL6cafunSJWvglaRs2bLZjLdYLEpOTs5w2Y7OEx8fr8aNG2v06NGpxuXJk0d///13hutLb5mSNHPmTFWpUsVm3O0/UDysCPQAAAAAgDQVL15cP//8c6rhO3fuVLFixR5oLU888YS++eYbhYaGys0tdZQtWrSovLy8tGrVKr366qupxru7u0uSkpKSrMNy586tvHnz6u+//1abNm3SXG/JkiX1+eef69q1a9ZW+s2bN2fGJt03OsUDAAAAAKTp9ddf119//aXu3bvr999/14EDBzRhwgR9+eWX6tWr1wOt5Y033tCFCxfUqlUrbdu2TYcPH9aKFSvUvn17JSUlydPTU3379tU777yjzz77TIcPH9bmzZv1ySefSJKCg4Pl5eVl7Uwv5cyDoUOHauTIkfrggw/0119/ac+ePZo9e7YmTJggSWrdurUsFos6deqkP//8U8uWLdO4ceMe6LanhxZ6AAAAAHCSixMvOruEDBUqVEjr1q3TgAEDFBkZqevXr6tEiRJauHCh6tev/0BryZs3rzZu3Ki+ffuqbt26SkxMVMGCBVW/fn25uNxqqx44cKDc3Nw0aNAgnTx5Unny5NFrr70mSXJzc9MHH3ygYcOGadCgQYqIiNCaNWv06quvytvbW2PHjlWfPn2UPXt2Pf744+rRo4ckKUeOHPrhhx/02muvqUKFCipVqpRGjx6t5s2bP9DtT4vFMAzD2UU8zOLi4uTn56fLly/L19f3vpaVs2fOTKoKuOVh/wIAAADALdeuXVNMTIzCwsJsOlfDoyujY8LeHMop9wAAAAAAmBCBHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJEegBAAAAADAhAj0AAAAAACZEoAcAAAAAwIQI9AAAAAAAmJCbswsAAAAAgEfVkbCwB7q+0JiYLF1+bGys2rZtq99++03ZsmXTpUuXsnR9jgoNDVWPHj3Uo0cPZ5eSKWihBwAAAACkKSoqSk2bNrV7+okTJ+rUqVOKjo7WX3/9lSk1hIaGatKkSZmyrP8aWugBAAAAAJni8OHDCg8PV9GiRZ1dyiOBFnoAAAAAwF3VqlVL3bt31zvvvKOAgACFhIRoyJAh1vGhoaH65ptv9Nlnn8lisSgqKkqSdOnSJb366qsKCgqSr6+vateurd27d9ss+4cfflClSpXk6empwMBANWvWzLrOo0ePqmfPnrJYLLJYLNZ5NmzYoIiICHl5eSl//vzq3r27EhISrOPPnDmjxo0by8vLS2FhYZo3b17W7RwnIdADAAAAAOwyd+5cZc+eXVu2bNGYMWM0bNgwrVy5UpK0bds21a9fXy1bttSpU6c0efJkSVKLFi105swZ/fTTT9qxY4eeeOIJ1alTRxcuXJAk/fjjj2rWrJkaNmyoXbt2adWqVapcubIk6dtvv1W+fPk0bNgwnTp1SqdOnZJ060yA+vXrq3nz5vr999/11VdfacOGDerWrZu11qioKB0/flyrV6/WokWL9OGHH+rMmTMPcndlOU65BwAAAADYpWzZsho8eLAkqWjRopo6dapWrVqlZ555RkFBQfLw8JCXl5dCQkIk3WpF37p1q86cOSMPDw9J0rhx47R48WItWrRInTt31ogRI/TSSy9p6NCh1vWUK1dOkhQQECBXV1f5+PhYlylJI0eOVJs2bayd2xUtWlQffPCBatasqenTp+vYsWP66aeftHXrVlWqVEmS9Mknn6hkyZJZvo8eJAI9AAAAAMAuZcuWtXmeJ0+eDFu9d+/erfj4eOXKlctm+L///qvDhw9LkqKjo9WpUyeH6ti9e7d+//13m9PoDcNQcnKyYmJi9Ndff8nNzU3h4eHW8SVKlJC/v79D63nYEegBAAAAAHbJli2bzXOLxaLk5OR0p4+Pj1eePHm0Zs2aVONSwrWXl5fDdcTHx6tLly7q3r17qnEFChTItB72H3YEegAAAABAlnjiiScUGxsrNzc3hYaGpjlN2bJltWrVKrVv3z7N8e7u7kpKSkq13D///FNFihRJc54SJUro5s2b2rFjh/WU+wMHDujSpUv3vC0PIzrFAwAAAABkicjISFWtWlVNmzbVzz//rCNHjui3337TgAEDtH37dknS4MGD9eWXX2rw4MHat2+f9uzZo9GjR1uXERoaqnXr1unEiRM6d+6cJKlv37767bff1K1bN0VHR+vgwYP6/vvvrZ3iFS9eXPXr11eXLl20ZcsW7dixQ6+++uo9nQ3wMDNdC/20adM0duxYxcbGqly5cpoyZYq1B8SMLFiwQK1atVKTJk20ePHirC8UAAAAAO4iNCbG2SVkKYvFomXLlmnAgAFq3769zp49q5CQENWoUUO5c+eWdOvWdAsXLtTw4cM1atQo+fr6qkaNGtZlDBs2TF26dFHhwoWVmJgowzBUtmxZrV27VgMGDFBERIQMw1DhwoX14osvWuebPXu2Xn31VdWsWVO5c+fWe++9p4EDBz7wfZCVLIZhGM4uwl5fffWVXnnlFc2YMUNVqlTRpEmTtHDhQh04cEDBwcHpznfkyBFVr15dhQoVUkBAgEOBPi4uTn5+frp8+bJ8fX3vq/6cPXPe1/zAnS5OvOjsEgAAAGCHa9euKSYmRmFhYfL09HR2OXgIZHRM2JtDTXXK/YQJE9SpUye1b99epUqV0owZM+Tt7a1PP/003XmSkpLUpk0bDR06VIUKFXqA1QIAAAAAkHVME+ivX7+uHTt2KDIy0jrMxcVFkZGR2rRpU7rzDRs2TMHBwerYsaNd60lMTFRcXJzNAwAAAACAh41pAv25c+eUlJRkvc4iRe7cuRUbG5vmPBs2bNAnn3yimTNn2r2ekSNHys/Pz/rInz//fdUNAAAAAEBWME2gd9SVK1fUtm1bzZw5U4GBgXbP179/f12+fNn6OH78eBZWCQAAAADAvTFNL/eBgYFydXXV6dOnbYafPn1aISEhqaY/fPiwjhw5osaNG1uHJScnS5Lc3Nx04MABFS5cONV8Hh4e8vDwyOTqAQAAAEAyUZ/kyGKZcSyYpoXe3d1d4eHhWrVqlXVYcnKyVq1apapVq6aavkSJEtqzZ4+io6Otj+eee05PP/20oqOjOZUeAAAAwAPj6uoq6VbfYIAkXb16VZKULVu2e16GaVroJentt99Wu3btVLFiRVWuXFmTJk1SQkKC2rdvL0l65ZVX9Nhjj2nkyJHy9PRUmTJlbOb39/eXpFTDAQAAACArubm5ydvbW2fPnlW2bNnk4mKatlVkMsMwdPXqVZ05c0b+/v7WH3vuhakC/YsvvqizZ89q0KBBio2NVfny5bV8+XJrR3nHjh3jjQEAAADgoWOxWJQnTx7FxMTo6NGjzi4HDwF/f/80Lx93hMXgIo4MxcXFyc/PT5cvX5avr+99LStnz5yZVBVwy8WJF51dAgAAAByQnJzMafdQtmzZMmyZtzeHmqqFHgAAAADMzMXFRZ6ens4uA/8RnJ8OAAAAAIAJEegBAAAAADAhAj0AAAAAACZEoAcAAAAAwIQI9AAAAAAAmBCBHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJEegBAAAAADAhAj0AAAAAACZEoAcAAAAAwIQI9AAAAAAAmBCBHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJEegBAAAAADAhAj0AAAAAACZEoAcAAAAAwIQI9AAAAAAAmBCBHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJEegBAAAAADAhAj0AAAAAACZEoAcAAAAAwIQI9AAAAAAAmBCBHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJEegBAAAAADAhAj0AAAAAACZEoAcAAAAAwIQI9AAAAAAAmBCBHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJuTm7AAAAzCZnz5zOLgH/MRcnXnR2CQAAE6KFHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJEegBAAAAADAhAj0AAAAAACZEoAcAAAAAwIQI9AAAAAAAmBCBHgAAAAAAEyLQAwAAAABgQgR6AAAAAABMiEAPAAAAAIAJEegBAAAAADAhAj0AAAAAACbk5uwCAPy3HAkLc3YJ+I8JjYlxdgkAAAAPJVroAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmZLpAP23aNIWGhsrT01NVqlTR1q1b05125syZioiIUM6cOZUzZ05FRkZmOD0AAAAAAGZhqkD/1Vdf6e2339bgwYO1c+dOlStXTvXq1dOZM2fSnH7NmjVq1aqVVq9erU2bNil//vyqW7euTpw48YArBwAAAAAgc5kq0E+YMEGdOnVS+/btVapUKc2YMUPe3t769NNP05x+3rx56tq1q8qXL68SJUpo1qxZSk5O1qpVq9JdR2JiouLi4mweAAAAAAA8bEwT6K9fv64dO3YoMjLSOszFxUWRkZHatGmTXcu4evWqbty4oYCAgHSnGTlypPz8/KyP/Pnz33ftAAAAAABkNtME+nPnzikpKUm5c+e2GZ47d27FxsbatYy+ffsqb968Nj8K3Kl///66fPmy9XH8+PH7qhsAAAAAgKzg5uwCHpRRo0ZpwYIFWrNmjTw9PdOdzsPDQx4eHg+wMgAAAAAAHGeaQB8YGChXV1edPn3aZvjp06cVEhKS4bzjxo3TqFGj9Msvv6hs2bJZWSYAAAAAAA+EaU65d3d3V3h4uE2Hdikd3FWtWjXd+caMGaPhw4dr+fLlqlix4oMoFQAAAACALGeaFnpJevvtt9WuXTtVrFhRlStX1qRJk5SQkKD27dtLkl555RU99thjGjlypCRp9OjRGjRokObPn6/Q0FDrtfY5cuRQjhw5nLYdAAAAAADcL1MF+hdffFFnz57VoEGDFBsbq/Lly2v58uXWjvKOHTsmF5f/O+lg+vTpun79ul544QWb5QwePFhDhgx5kKUDAAAAAJCpTBXoJalbt27q1q1bmuPWrFlj8/zIkSNZXxAAAAAAAE5gmmvoAQAAAADA/yHQAwAAAABgQvd0yn1ycrIOHTqkM2fOKDk52WZcjRo1MqUwAAAAAACQPocD/ebNm9W6dWsdPXpUhmHYjLNYLEpKSsq04gAAAAAAQNocDvSvvfaaKlasqB9//FF58uSRxWLJiroAAAAAAEAGHA70Bw8e1KJFi1SkSJGsqAcAAAAAANjB4U7xqlSpokOHDmVFLQAAAAAAwE4Ot9C/+eab6tWrl2JjY/X4448rW7ZsNuPLli2bacUBAAAAAIC0ORzomzdvLknq0KGDdZjFYpFhGHSKBwAAAADAA+JwoI+JicmKOgAAAAAAgAMcDvQFCxbMijoAAAAAAIADHA70knT48GFNmjRJ+/btkySVKlVKb731lgoXLpypxQEAAAAAgLQ53Mv9ihUrVKpUKW3dulVly5ZV2bJltWXLFpUuXVorV67MihoBAAAAAMAdHG6h79evn3r27KlRo0alGt63b18988wzmVYcAAAAAABIm8Mt9Pv27VPHjh1TDe/QoYP+/PPPTCkKAAAAAABkzOFAHxQUpOjo6FTDo6OjFRwcnBk1AQAAAACAu3D4lPtOnTqpc+fO+vvvv/XUU09JkjZu3KjRo0fr7bffzvQCAQAAAABAag4H+oEDB8rHx0fjx49X//79JUl58+bVkCFD1L1790wvEAAAAAAApOZwoLdYLOrZs6d69uypK1euSJJ8fHwyvTAAAAAAAJC+e7oPfQqCPAAAAAAAzmFXoH/iiSe0atUq5cyZUxUqVJDFYkl32p07d2ZacQAAAAAAIG12BfomTZrIw8PD+ndGgR4AAAAAAGQ9uwL94MGDrX8PGTIkq2oBAAAAAAB2cvg+9IUKFdL58+dTDb906ZIKFSqUKUUBAAAAAICMORzojxw5oqSkpFTDExMT9c8//2RKUQAAAAAAIGN293K/ZMkS698rVqyQn5+f9XlSUpJWrVqlsLCwzK0OAAAAAACkye5A37RpU0m37kPfrl07m3HZsmVTaGioxo8fn6nFAQAAAACAtNkd6JOTkyVJYWFh2rZtmwIDA7OsKAAAAAAAkDG7A32KmJiYrKgDAAAAAAA4wOFAL0kJCQlau3atjh07puvXr9uM6969e6YUBgAAAAAA0udwoN+1a5caNmyoq1evKiEhQQEBATp37py8vb0VHBxMoAcAAAAA4AFw+LZ1PXv2VOPGjXXx4kV5eXlp8+bNOnr0qMLDwzVu3LisqBEAAAAAANzB4UAfHR2tXr16ycXFRa6urkpMTFT+/Pk1ZswYvfvuu1lRIwAAAAAAuIPDgT5btmxycbk1W3BwsI4dOyZJ8vPz0/HjxzO3OgAAAAAAkCaHr6GvUKGCtm3bpqJFi6pmzZoaNGiQzp07p88//1xlypTJihoBAAAAAMAdHG6hf//995UnTx5J0ogRI5QzZ069/vrrOnv2rD7++ONMLxAAAAAAAKTmcAt9xYoVrX8HBwdr+fLlmVoQAAAAAAC4O4db6AEAAAAAgPPZ1UJfoUIFWSwWuxa4c+fO+yoIAAAAzpezZ05nl4D/mIsTLzq7BOA/x65A37Rp0ywuAwAAAAAAOMKuQD948OCsrgMAAAAAADjgnq6hv3TpkmbNmqX+/fvrwoULkm6dan/ixIlMLQ4AAAAAAKTN4V7uf//9d0VGRsrPz09HjhxRp06dFBAQoG+//VbHjh3TZ599lhV1AgAAAACA2zjcQv/2228rKipKBw8elKenp3V4w4YNtW7dukwtDgAAAAAApM3hQL9t2zZ16dIl1fDHHntMsbGxmVIUAAAAAADImMOB3sPDQ3FxcamG//XXXwoKCsqUogAAAAAAQMYcDvTPPfechg0bphs3bkiSLBaLjh07pr59+6p58+aZXiAAAAAAAEjN4UA/fvx4xcfHKzg4WP/++69q1qypIkWKyMfHRyNGjMiKGgEAAAAAwB0c7uXez89PK1eu1MaNG7V7927Fx8friSeeUGRkZFbUBwAAAAAA0uBQoL9x44a8vLwUHR2tatWqqVq1allVFwAAAAAAyIBDp9xny5ZNBQoUUFJSUlbVAwAAAAAA7ODwNfQDBgzQu+++qwsXLmRFPQAAAAAAwA4OX0M/depUHTp0SHnz5lXBggWVPXt2m/E7d+7MtOIAAAAAAEDaHA70TZs2zYIyAAAAAODBCgs74uwS8B8TExP6QNfnUKC/efOmLBaLOnTooHz58mVVTQAAAAAA4C4cuobezc1NY8eO1c2bN7OqHgAAAAAAYAeHO8WrXbu21q5dmxW1AAAAAAAAOzl8DX2DBg3Ur18/7dmzR+Hh4ak6xXvuuecyrTgAAAAAAJA2hwN9165dJUkTJkxINc5isXCPegAAAAAAHgCHA31ycnJW1AEAAAAAABzg8DX0AAAAAADA+RxuoZektWvXaty4cdq3b58kqVSpUurTp48iIiIytTgAAAA4x67F/s4uAf81E51dAPDf43AL/RdffKHIyEh5e3ure/fu6t69u7y8vFSnTh3Nnz8/K2oEAAAAAAB3cLiFfsSIERozZox69uxpHda9e3dNmDBBw4cPV+vWrTO1QAAAAAAAkJrDLfR///23GjdunGr4c889p5iYmEwpCgAAAAAAZMzhQJ8/f36tWrUq1fBffvlF+fPnz5SiAAAAAABAxhw+5b5Xr17q3r27oqOj9dRTT0mSNm7cqDlz5mjy5MmZXiAAAAAAAEjN4UD/+uuvKyQkROPHj9fXX38tSSpZsqS++uorNWnSJNMLvNO0adM0duxYxcbGqly5cpoyZYoqV66c7vQLFy7UwIEDdeTIERUtWlSjR49Ww4YNs7xOAAAAAA+3S00rOLsE/OdcfKBru6f70Ddr1kwbNmzQ+fPndf78eW3YsOGBhPmvvvpKb7/9tgYPHqydO3eqXLlyqlevns6cOZPm9L/99ptatWqljh07ateuXWratKmaNm2qvXv3ZnmtAAAAAABkJbsD/cWLFzVlyhTFxcWlGnf58uV0x2WmCRMmqFOnTmrfvr1KlSqlGTNmyNvbW59++mma00+ePFn169dXnz59VLJkSQ0fPlxPPPGEpk6dmqV1AgAAAACQ1ewO9FOnTtW6devk6+ubapyfn5/Wr1+vKVOmZGpxt7t+/bp27NihyMhI6zAXFxdFRkZq06ZNac6zadMmm+klqV69eulOL0mJiYmKi4uzeQAAAAAA8LCx+xr6b775RuPHj093fJcuXdS7d28NGDAgUwq707lz55SUlKTcuXPbDM+dO7f279+f5jyxsbFpTh8bG5vuekaOHKmhQ4fef8Fp2LXYP0uWi0fYRGcXkFqFppecXQL+Yx7slWj24fMcme4h/Dx/WqudXQL+Yx7GG1zzeY5M94A/z+1uoT98+LCKFi2a7viiRYvq8OHDmVKUM/Xv31+XL1+2Po4fP+7skgAAAAAASMXuFnpXV1edPHlSBQoUSHP8yZMn5eJyT33s2SUwMFCurq46ffq0zfDTp08rJCQkzXlCQkIcml6SPDw85OHhcf8FAwAAAACQhexO4BUqVNDixYvTHf/dd9+pQoWsu+2Du7u7wsPDtWrVKuuw5ORkrVq1SlWrVk1znqpVq9pML0krV65Md3oAAAAAAMzC7hb6bt266aWXXlK+fPn0+uuvy9XVVZKUlJSkDz/8UBMnTtT8+fOzrFBJevvtt9WuXTtVrFhRlStX1qRJk5SQkKD27dtLkl555RU99thjGjlypCTprbfeUs2aNTV+/Hg1atRICxYs0Pbt2/Xxxx9naZ0AAAAAAGQ1uwN98+bN9c4776h79+4aMGCAChUqJEn6+++/FR8frz59+uiFF17IskIl6cUXX9TZs2c1aNAgxcbGqnz58lq+fLm147tjx47ZnPb/1FNPaf78+frf//6nd999V0WLFtXixYtVpkyZLK0TAAAAAICsZjEMw3Bkhq1bt2revHk6dOiQDMNQsWLF1Lp1a1WuXDmranSquLg4+fn56fLly2ness8RR8LCMqkq4JbQmIevv9icPXM6uwT8x1yc+PD1c8/nOTLbw/h5HhZ2xNkl4D8mJibU2SWkwuc5MltmfZ7bm0PtbqFPUbly5f9seAcAAAAAwCyyrlt6AAAAAACQZQj0AAAAAACYEIEeAAAAAAATItADAAAAAGBCBHoAAAAAAEzI4UB/+vRptW3bVnnz5pWbm5tcXV1tHgAAAAAAIOs5fNu6qKgoHTt2TAMHDlSePHlksViyoi4AAAAAAJABhwP9hg0btH79epUvXz4LygEAAAAAAPZw+JT7/PnzyzCMrKgFAAAAAADYyeFAP2nSJPXr109HjhzJgnIAAAAAAIA9HD7l/sUXX9TVq1dVuHBheXt7K1u2bDbjL1y4kGnFAQAAAACAtDkc6CdNmpQFZQAAAAAAAEc4HOjbtWuXFXUAAAAAAAAHOBzob3ft2jVdv37dZpivr+99FQQAAAAAAO7O4UCfkJCgvn376uuvv9b58+dTjU9KSsqUwgAAAAAgK4XGxDi7BOC+ONzL/TvvvKNff/1V06dPl4eHh2bNmqWhQ4cqb968+uyzz7KiRgAAAAAAcAeHW+h/+OEHffbZZ6pVq5bat2+viIgIFSlSRAULFtS8efPUpk2brKgTAAAAAADcxuEW+gsXLqhQoUKSbl0vn3KbuurVq2vdunWZWx0AAAAAAEiTw4G+UKFCivn/15qUKFFCX3/9taRbLff+/v6ZWhwAAAAAAEibw4G+ffv22r17tySpX79+mjZtmjw9PdWzZ0/16dMn0wsEAAAAAACpOXwNfc+ePa1/R0ZGav/+/dqxY4eKFCmismXLZmpxAAAAAAAgbfd1H3pJKliwoAoWLJgZtQAAAAAAADvdU6Dftm2bVq9erTNnzig5Odlm3IQJEzKlMAAAAAAAkD6HA/3777+v//3vfypevLhy584ti8ViHXf73wAAAAAAIOs4HOgnT56sTz/9VFFRUVlQDgAAAAAAsIfDvdy7uLioWrVqWVELAAAAAACwk8OBvmfPnpo2bVpW1AIAAAAAAOzk8Cn3vXv3VqNGjVS4cGGVKlVK2bJlsxn/7bffZlpxAAAAAAAgbQ4H+u7du2v16tV6+umnlStXLjrCAwAAAADACRwO9HPnztU333yjRo0aZUU9AAAAAADADg5fQx8QEKDChQtnRS0AAAAAAMBODgf6IUOGaPDgwbp69WpW1AMAAAAAAOzg8Cn3H3zwgQ4fPqzcuXMrNDQ0Vad4O3fuzLTiAAAAAABA2hwO9E2bNs2CMgAAAAAAgCMcCvQ3b96UxWJRhw4dlC9fvqyqCQAAAAAA3IVD19C7ublp7NixunnzZlbVAwAAAAAA7ODwKfe1a9fW2rVrFRoamgXlAADw8Htaq51dAv5jYpxdAADAlBwO9A0aNFC/fv20Z88ehYeHK3v27Dbjn3vuuUwrDgAAAAAApM3hQN+1a1dJ0oQJE1KNs1gsSkpKuv+qAAAAAABAhhwO9MnJyVlRBwAAAAAAcIBDneIBAAAAAICHg8Mt9JKUkJCgtWvX6tixY7p+/brNuO7du2dKYQAAAAAAIH0OB/pdu3apYcOGunr1qhISEhQQEKBz587J29tbwcHBBHoAAAAAAB4Ah0+579mzpxo3bqyLFy/Ky8tLmzdv1tGjRxUeHq5x48ZlRY0AAAAAAOAODgf66Oho9erVSy4uLnJ1dVViYqLy58+vMWPG6N13382KGgEAAAAAwB0cDvTZsmWTi8ut2YKDg3Xs2DFJkp+fn44fP5651QEAAAAAgDQ5fA19hQoVtG3bNhUtWlQ1a9bUoEGDdO7cOX3++ecqU6ZMVtQIAAAAAADu4HAL/fvvv688efJIkkaMGKGcOXPq9ddf19mzZ/Xxxx9neoEAAAAAACA1h1voK1asaP07ODhYy5cvz9SCAAAAAADA3TncQi9JN2/e1C+//KKPPvpIV65ckSSdPHlS8fHxmVocAAAAAABIm8Mt9EePHlX9+vV17NgxJSYm6plnnpGPj49Gjx6txMREzZgxIyvqBAAAAAAAt3G4hf6tt95SxYoVrfehT9GsWTOtWrUqU4sDAAAAAABpc7iFfv369frtt9/k7u5uMzw0NFQnTpzItMIAAAAAAED6HG6hT05OVlJSUqrh//zzj3x8fDKlKAAAAAAAkDGHA33dunU1adIk63OLxaL4+HgNHjxYDRs2zMzaAAAAAABAOhw+5X78+PGqV6+eSpUqpWvXrql169Y6ePCgAgMD9eWXX2ZFjQAAAAAA4A4OB/p8+fJp9+7dWrBggX7//XfFx8erY8eOatOmjU0neQAAAAAAIOs4HOglyc3NTS+//HJm1wIAAAAAAOx0T4H+wIEDmjJlivbt2ydJKlmypLp166YSJUpkanEAAABwjpiYUGeXAAC4C4c7xfvmm29UpkwZ7dixQ+XKlVO5cuW0c+dOPf744/rmm2+yokYAAAAAAHAHh1vo33nnHfXv31/Dhg2zGT548GC98847at68eaYVBwAAAAAA0uZwC/2pU6f0yiuvpBr+8ssv69SpU5lSFAAAAAAAyJjDgb5WrVpav359quEbNmxQREREphQFAAAAAAAy5vAp988995z69u2rHTt26Mknn5Qkbd68WQsXLtTQoUO1ZMkSm2kBAAAAAEDmsxiGYTgyg4uLfY36FotFSUlJ91TUwyQuLk5+fn66fPmyfH1972tZR8LCMqkq4JbQmBhnl5BKzp45nV0C/mMuTrzo7BJSCQs74uwS8B9Dj/IAgNvZm0MdbqFPTk6+r8IAAAAAAMD9c/gaegAAAAAA4Hx2B/pNmzZp6dKlNsM+++wzhYWFKTg4WJ07d1ZiYmKmF5jiwoULatOmjXx9feXv76+OHTsqPj4+w+nffPNNFS9eXF5eXipQoIC6d++uy5cvZ1mNAAAAAAA8KHYH+mHDhumPP/6wPt+zZ486duyoyMhI9evXTz/88INGjhyZJUVKUps2bfTHH39o5cqVWrp0qdatW6fOnTunO/3Jkyd18uRJjRs3Tnv37tWcOXO0fPlydezYMctqBAAAAADgQbG7U7w8efLohx9+UMWKFSVJAwYM0Nq1a7VhwwZJ0sKFCzV48GD9+eefmV7kvn37VKpUKW3bts26/uXLl6thw4b6559/lDdvXruWs3DhQr388stKSEiQm5t93QfQKR4eZnSKh0cBneLhUUCneACA29mbQ+1uob948aJy585tfb527Vo1aNDA+rxSpUo6fvz4PZabsU2bNsnf398a5iUpMjJSLi4u2rJli93LSdkZGYX5xMRExcXF2TwAAAAAAHjY2B3oc+fOrZj/3xp4/fp17dy503ofekm6cuWKsmXLlvkVSoqNjVVwcLDNMDc3NwUEBCg2NtauZZw7d07Dhw/P8DR9SRo5cqT8/Pysj/z5899z3QAAAAAAZBW7A33Dhg3Vr18/rV+/Xv3795e3t7ciIiKs43///XcVLlzYoZX369dPFoslw8f+/fsdWmZa4uLi1KhRI5UqVUpDhgzJcNr+/fvr8uXL1kdWnXUAAAAAAMD9sPs+9MOHD9fzzz+vmjVrKkeOHJo7d67c3d2t4z/99FPVrVvXoZX36tVLUVFRGU5TqFAhhYSE6MyZMzbDb968qQsXLigkJCTD+a9cuaL69evLx8dH33333V3PIvDw8JCHh4dd9QMAAAAA4Cx2B/rAwECtW7dOly9fVo4cOeTq6mozfuHChcqRI4dDKw8KClJQUNBdp6tataouXbqkHTt2KDw8XJL066+/Kjk5WVWqVEl3vri4ONWrV08eHh5asmSJPD09HaoPAAAAAICHld2n3Kfw8/NLFeYlKSAgwKbFPjOVLFlS9evXV6dOnbR161Zt3LhR3bp100svvWTt4f7EiRMqUaKEtm7dKulWmK9bt64SEhL0ySefKC4uTrGxsYqNjVVSUlKW1AkAAAAAwINidwu9s82bN0/dunVTnTp15OLioubNm+uDDz6wjr9x44YOHDigq1evSpJ27txp7QG/SJEiNsuKiYlRaGjoA6sdAAAAAIDMZppAHxAQoPnz56c7PjQ0VIZhWJ/XqlXL5jkAAAAAAP8lDp9yDwAAAAAAnI9ADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACbk5uwAA/y0XJ150dgkAAADAI4EWegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCE3JxdAAAAZhMTE+rsEgAAAGihBwAAAADAjAj0AAAAAACYEIEeAAAAAAATItADAAAAAGBCBHoAAAAAAEyIQA8AAAAAgAkR6AEAAAAAMCECPQAAAAAAJkSgBwAAAADAhAj0AAAAAACYEIEeAAAAAAATItADAAAAAGBCBHoAAAAAAEyIQA8AAAAAgAkR6AEAAAAAMCECPQAAAAAAJkSgBwAAAADAhAj0AAAAAACYEIEeAAAAAAATItADAAAAAGBCBHoAAAAAAEyIQA8AAAAAgAkR6AEAAAAAMCECPQAAAAAAJkSgBwAAAADAhAj0AAAAAACYEIEeAAAAAAATItADAAAAAGBCBHoAAAAAAEyIQA8AAAAAgAkR6AEAAAAAMCECPQAAAAAAJkSgBwAAAADAhAj0AAAAAACYEIEeAAAAAAATItADAAAAAGBCBHoAAAAAAEyIQA8AAAAAgAkR6AEAAAAAMCECPQAAAAAAJkSgBwAAAADAhAj0AAAAAACYEIEeAAAAAAATMk2gv3Dhgtq0aSNfX1/5+/urY8eOio+Pt2tewzDUoEEDWSwWLV68OGsLBQAAAADgATBNoG/Tpo3++OMPrVy5UkuXLtW6devUuXNnu+adNGmSLBZLFlcIAAAAAMCD4+bsAuyxb98+LV++XNu2bVPFihUlSVOmTFHDhg01btw45c2bN915o6OjNX78eG3fvl158uR5UCUDAAAAAJClTNFCv2nTJvn7+1vDvCRFRkbKxcVFW7ZsSXe+q1evqnXr1po2bZpCQkLsWldiYqLi4uJsHgAAAAAAPGxMEehjY2MVHBxsM8zNzU0BAQGKjY1Nd76ePXvqqaeeUpMmTexe18iRI+Xn52d95M+f/57rBgAAAAAgqzg10Pfr108WiyXDx/79++9p2UuWLNGvv/6qSZMmOTRf//79dfnyZevj+PHj97R+AAAAAACyklOvoe/Vq5eioqIynKZQoUIKCQnRmTNnbIbfvHlTFy5cSPdU+l9//VWHDx+Wv7+/zfDmzZsrIiJCa9asSXM+Dw8PeXh42LsJAAAAAAA4hVMDfVBQkIKCgu46XdWqVXXp0iXt2LFD4eHhkm4F9uTkZFWpUiXNefr166dXX33VZtjjjz+uiRMnqnHjxvdfPAAAAAAATmSKXu5Lliyp+vXrq1OnTpoxY4Zu3Lihbt266aWXXrL2cH/ixAnVqVNHn332mSpXrqyQkJA0W+8LFCigsLCwB70JAAAAAABkKlN0iidJ8+bNU4kSJVSnTh01bNhQ1atX18cff2wdf+PGDR04cEBXr151YpUAAAAAADwYpmihl6SAgADNnz8/3fGhoaEyDCPDZdxtPAAAAAAAZmGaFnoAAAAAAPB/CPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJiQm7MLeJSExsQ4uwQAAAAAwH8ELfQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhADwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEJuzi7gYWcYhiQpLi7OyZUAAAAAAB4FKfkzJY+mh0B/F1euXJEk5c+f38mVAAAAAAAeJVeuXJGfn1+64y3G3SL/Iy45OVknT56Uj4+PLBaLs8t5JMTFxSl//vw6fvy4fH19nV0OkCU4zvEo4DjHo4DjHI8CjvMHzzAMXblyRXnz5pWLS/pXytNCfxcuLi7Kly+fs8t4JPn6+vKBgf88jnM8CjjO8SjgOMejgOP8wcqoZT4FneIBAAAAAGBCBHoAAAAAAEyIQI+HjoeHhwYPHiwPDw9nlwJkGY5zPAo4zvEo4DjHo4Dj/OFFp3gAAAAAAJgQLfQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATIhAj4fGunXr1LhxY+XNm1cWi0WLFy92dklApho5cqQqVaokHx8fBQcHq2nTpjpw4ICzywIy1fTp01W2bFn5+vrK19dXVatW1U8//eTssoAsNWrUKFksFvXo0cPZpQCZZsiQIbJYLDaPEiVKOLss3IFAj4dGQkKCypUrp2nTpjm7FCBLrF27Vm+88YY2b96slStX6saNG6pbt64SEhKcXRqQafLly6dRo0Zpx44d2r59u2rXrq0mTZrojz/+cHZpQJbYtm2bPvroI5UtW9bZpQCZrnTp0jp16pT1sWHDBmeXhDu4ObsAIEWDBg3UoEEDZ5cBZJnly5fbPJ8zZ46Cg4O1Y8cO1ahRw0lVAZmrcePGNs9HjBih6dOna/PmzSpdurSTqgKyRnx8vNq0aaOZM2fqvffec3Y5QKZzc3NTSEiIs8tABmihBwAnuXz5siQpICDAyZUAWSMpKUkLFixQQkKCqlat6uxygEz3xhtvqFGjRoqMjHR2KUCWOHjwoPLmzatChQqpTZs2OnbsmLNLwh1ooQcAJ0hOTlaPHj1UrVo1lSlTxtnlAJlqz549qlq1qq5du6YcOXLou+++U6lSpZxdFpCpFixYoJ07d2rbtm3OLgXIElWqVNGcOXNUvHhxnTp1SkOHDlVERIT27t0rHx8fZ5eH/49ADwBO8MYbb2jv3r1ci4b/pOLFiys6OlqXL1/WokWL1K5dO61du5ZQj/+M48eP66233tLKlSvl6enp7HKALHH7pbBly5ZVlSpVVLBgQX399dfq2LGjEyvD7Qj0APCAdevWTUuXLtW6deuUL18+Z5cDZDp3d3cVKVJEkhQeHq5t27Zp8uTJ+uijj5xcGZA5duzYoTNnzuiJJ56wDktKStK6des0depUJSYmytXV1YkVApnP399fxYoV06FDh5xdCm5DoAeAB8QwDL355pv67rvvtGbNGoWFhTm7JOCBSE5OVmJiorPLADJNnTp1tGfPHpth7du3V4kSJdS3b1/CPP6T4uPjdfjwYbVt29bZpeA2BHo8NOLj421+8YuJiVF0dLQCAgJUoEABJ1YGZI433nhD8+fP1/fffy8fHx/FxsZKkvz8/OTl5eXk6oDM0b9/fzVo0EAFChTQlStXNH/+fK1Zs0YrVqxwdmlApvHx8UnV/0n27NmVK1cu+kXBf0bv3r3VuHFjFSxYUCdPntTgwYPl6uqqVq1aObs03IZAj4fG9u3b9fTTT1ufv/3225Kkdu3aac6cOU6qCsg806dPlyTVqlXLZvjs2bMVFRX14AsCssCZM2f0yiuv6NSpU/Lz81PZsmW1YsUKPfPMM84uDQDggH/++UetWrXS+fPnFRQUpOrVq2vz5s0KCgpydmm4jcUwDMPZRQAAAAAAAMdwH3oAAAAAAEyIQA8AAAAAgAkR6AEAAAAAMCECPQAAAAAAJkSgBwAAAADAhAj0AAAAAACYEIEeAAAAAAATItADAAAAAGBCBHoAAJwkNDRUkyZNyrTlRUVFqWnTppm2PElas2aNLBaLLl26lKnLBQAA949ADwDAfYqKipLFYpHFYpG7u7uKFCmiYcOG6ebNmxnOt23bNnXu3DnT6pg8ebLmzJmTactzxK5du9SiRQvlzp1bnp6eKlq0qDp16qS//vrLKfU8rDL7RxwAwKONQA8AQCaoX7++Tp06pYMHD6pXr14aMmSIxo4dm+a0169flyQFBQXJ29s702rw8/OTv79/pi3PXkuXLtWTTz6pxMREzZs3T/v27dMXX3whPz8/DRw48IHXAwDAo4JADwBAJvDw8FBISIgKFiyo119/XZGRkVqyZImk/zsVfsSIEcqbN6+KFy8uKXVrrcVi0axZs9SsWTN5e3uraNGi1mWk+OOPP/Tss8/K19dXPj4+ioiI0OHDh23Wk6JWrVrq1q2bunXrJj8/PwUGBmrgwIEyDMM6zeeff66KFSvKx8dHISEhat26tc6cOWP3dl+9elXt27dXw4YNtWTJEkVGRiosLExVqlTRuHHj9NFHH1mnXbt2rSpXriwPDw/lyZNH/fr1szmLoVatWnrzzTfVo0cP5cyZU7lz59bMmTOVkJCg9u3by8fHR0WKFNFPP/1knSflkoAff/xRZcuWlaenp5588knt3bvXps5vvvlGpUuXloeHh0JDQzV+/Hib8aGhoXr//ffVoUMH+fj4qECBAvr4449tpjl+/Lhatmwpf39/BQQEqEmTJjpy5Ih1fMr+HzdunPLkyaNcuXLpjTfe0I0bN6zbd/ToUfXs2dN6RgcAAPeDQA8AQBbw8vKytsRL0qpVq3TgwAGtXLlSS5cuTXe+oUOHqmXLlvr999/VsGFDtWnTRhcuXJAknThxQjVq1JCHh4d+/fVX7dixQx06dMjw1P65c+fKzc1NW7du1eTJkzVhwgTNmjXLOv7GjRsaPny4du/ercWLF+vIkSOKioqyeztXrFihc+fO6Z133klzfMoZAydOnFDDhg1VqVIl7d69W9OnT9cnn3yi9957L1W9gYGB2rp1q9588029/vrratGihZ566int3LlTdevWVdu2bXX16lWb+fr06aPx48dr27ZtCgoKUuPGja1BeseOHWrZsqVeeukl7dmzR0OGDNHAgQNTXZ4wfvx4VaxYUbt27VLXrl31+uuv68CBA9b9VK9ePfn4+Gj9+vXauHGjcuTIofr169u8zqtXr9bhw4e1evVqzZ07V3PmzLGu59tvv1W+fPk0bNgwnTp1SqdOnbJ7PwMAkCYDAADcl3bt2hlNmjQxDMMwkpOTjZUrVxoeHh5G7969reNz585tJCYm2sxXsGBBY+LEidbnkoz//e9/1ufx8fGGJOOnn34yDMMw+vfvb4SFhRnXr1+/ax2GYRg1a9Y0SpYsaSQnJ1uH9e3b1yhZsmS627Jt2zZDknHlyhXDMAxj9erVhiTj4sWLaU4/evRoQ5Jx4cKFdJdpGIbx7rvvGsWLF7epZdq0aUaOHDmMpKQka73Vq1e3jr9586aRPXt2o23bttZhp06dMiQZmzZtsqlvwYIF1mnOnz9veHl5GV999ZVhGIbRunVr45lnnrGpp0+fPkapUqWszwsWLGi8/PLL1ufJyclGcHCwMX36dMMwDOPzzz9PVX9iYqLh5eVlrFixwjCMW/u/YMGCxs2bN63TtGjRwnjxxRdt1nP7aw4AwP2ghR4AgEywdOlS5ciRQ56enmrQoIFefPFFDRkyxDr+8ccfl7u7+12XU7ZsWevf2bNnl6+vr/UU+OjoaEVERChbtmx21/Xkk0/anNpdtWpVHTx4UElJSZJutV43btxYBQoUkI+Pj2rWrClJOnbsmF3LN247fT8j+/btU9WqVW1qqVatmuLj4/XPP/9Yh92+/a6ursqVK5cef/xx67DcuXNLUqrLAqpWrWr9OyAgQMWLF9e+ffus665WrZrN9NWqVbPZD3eu22KxKCQkxLqe3bt369ChQ/Lx8VGOHDmUI0cOBQQE6Nq1a9ZLHiSpdOnScnV1tT7PkyePQ5cwAADgCDdnFwAAwH/B008/renTp8vd3V158+aVm5vtV2z27NntWs6dYd1isSg5OVnSrdP4M1NCQoLq1aunevXqad68eQoKCtKxY8dUr149m9PIM1KsWDFJ0v79+21C9b1Ka/tvH5byg0DKPslMGe37+Ph4hYeHa968eanmCwoKsmsZAABkNlroAQDIBNmzZ1eRIkVUoECBVGE+s5QtW1br16+3Xhtujy1bttg837x5s4oWLSpXV1ft379f58+f16hRoxQREaESJUo43Jpct25dBQYGasyYMWmOT7l/fcmSJbVp0yabFv2NGzfKx8dH+fLlc2idadm8ebP174sXL+qvv/5SyZIlreveuHGjzfQbN25UsWLFbFrTM/LEE0/o4MGDCg4OVpEiRWwefn5+dtfp7u5uc1YAAAD3g0APAIBJdOvWTXFxcXrppZe0fft2HTx4UJ9//rm147a0HDt2TG+//bYOHDigL7/8UlOmTNFbb70lSSpQoIDc3d01ZcoU/f3331qyZImGDx/uUE3Zs2fXrFmz9OOPP+q5557TL7/8oiNHjmj79u1655139Nprr0mSunbtquPHj+vNN9/U/v379f3332vw4MF6++235eJy//+ODBs2TKtWrdLevXsVFRWlwMBAa4//vXr10qpVqzR8+HD99ddfmjt3rqZOnarevXvbvfw2bdooMDBQTZo00fr16xUTE6M1a9aoe/fuNpcM3E1oaKjWrVunEydO6Ny5c45uJgAANgj0AACYRK5cufTrr78qPj5eNWvWVHh4uGbOnJnhNfWvvPKK/v33X1WuXFlvvPGG3nrrLXXu3FnSrVPF58yZo4ULF6pUqVIaNWqUxo0b53BdTZo00W+//aZs2bKpdevWKlGihFq1aqXLly9be7F/7LHHtGzZMm3dulXlypXTa6+9po4dO+p///vfve2MO4waNUpvvfWWwsPDFRsbqx9++MHaZ8ETTzyhr7/+WgsWLFCZMmU0aNAgDRs2zKHe/L29vbVu3ToVKFBAzz//vEqWLKmOHTvq2rVr8vX1tXs5w4YN05EjR1S4cGGbU/UBALgXFsPe3mwAAICp1KpVS+XLl7e51/1/zZo1a/T000/r4sWL1lvkAQDwqKCFHgAAAAAAEyLQAwAAAABgQpxyDwAAAACACdFCDwAAAACACRHoAQAAAAAwIQI9AAAAAAAmRKAHAAAAAMCECPQAAAAAAJgQgR4AAAAAABMi0AMAAAAAYEIEegAAAAAATOj/AdrMY9mz4X0LAAAAAElFTkSuQmCC", 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" ] @@ -387,14 +27953,25 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 80, "metadata": {}, "outputs": [ + { + "ename": "TypeError", + "evalue": "'Axes' object is not iterable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[80], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m feature_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(predicted_features\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m])] \u001b[38;5;66;03m# Replace with actual feature names if available\u001b[39;00m\n\u001b[1;32m 7\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(n_components, \u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m3\u001b[39m \u001b[38;5;241m*\u001b[39m n_components))\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, (component, ax) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcomponents\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[1;32m 9\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(component\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, ax\u001b[38;5;241m=\u001b[39max, cbar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, xticklabels\u001b[38;5;241m=\u001b[39mfeature_names)\n\u001b[1;32m 10\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrincipal Component \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;250m \u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: 'Axes' object is not iterable" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -402,11 +27979,10 @@ } ], "source": [ - "# Visualize the principal components\n", "components = pca.components_\n", "\n", "# Assuming your original features are named, you can list them\n", - "feature_names = [f\"Feature {i}\" for i in range(predicted_projections.shape[1])] # Replace with actual feature names if available\n", + "feature_names = [f\"Feature {i}\" for i in range(predicted_features.shape[1])] # Replace with actual feature names if available\n", "\n", "fig, axes = plt.subplots(n_components, 1, figsize=(12, 3 * n_components))\n", "for i, (component, ax) in enumerate(zip(components[:n_components], axes)):\n", diff --git a/viscy/applications/contrastive_phenotyping/predict.py b/viscy/applications/contrastive_phenotyping/predict.py index db51c3c9..c2ac7d39 100644 --- a/viscy/applications/contrastive_phenotyping/predict.py +++ b/viscy/applications/contrastive_phenotyping/predict.py @@ -23,7 +23,7 @@ def main(hparams): top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/predict_timesteps.csv" predict_base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr" - checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/updated_multiple_channels/contrastive_model-test-epoch=88-val_loss=0.00.ckpt" + checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/updated_multiple_channels/contrastive_model-test-epoch=97-val_loss=0.00.ckpt" # Data parameters channels = 2 @@ -78,8 +78,8 @@ def main(hparams): all_projections = np.concatenate(projections_list, axis=0) # Save features and projections - np.save("updated_epoch88_predicted_features.npy", all_features) - np.save("updated_epoch88_predicted_projections.npy", all_projections) + np.save("updated_epoch97_predicted_features.npy", all_features) + np.save("updated_epoch97_predicted_projections.npy", all_projections) if __name__ == "__main__": parser = ArgumentParser() From 3961a53a648c9a5e815c90927991e17b1e297576 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Fri, 19 Jul 2024 11:43:29 -0700 Subject: [PATCH 29/87] formatting --- .../graphs_ConvNeXt_ResNet.py | 12 +- .../contrastive_phenotyping/predict.py | 36 +-- .../training_script.py | 44 ++-- viscy/data/hcs.py | 123 +++++---- viscy/light/engine.py | 235 +++++++++++++----- viscy/representation/contrastive.py | 9 +- 6 files changed, 305 insertions(+), 154 deletions(-) diff --git a/viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py b/viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py index c6be84e4..69cab375 100644 --- a/viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py +++ b/viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py @@ -5,10 +5,12 @@ import torchview -%load_ext autoreload -%autoreload 2 +# %load_ext autoreload +# %autoreload 2 # %% Initialize the model and log the graph. -contra_model = ContrastiveEncoder(backbone = "convnext_tiny") # other options: convnext_tiny resnet50 +contra_model = ContrastiveEncoder( + backbone="convnext_tiny" +) # other options: convnext_tiny resnet50 print(contra_model) model_graph = torchview.draw_graph( contra_model, @@ -21,7 +23,9 @@ model_graph.visual_graph # %% Initialize a resent50 model and log the graph. -contra_model = ContrastiveEncoder(backbone = "resnet50", in_stack_depth = 16, stem_kernel_size = (4, 3, 3)) # note that the resnet first layer takes 64 channels (so we can't have multiples of 3) +contra_model = ContrastiveEncoder( + backbone="resnet50", in_stack_depth=16, stem_kernel_size=(4, 3, 3) +) # note that the resnet first layer takes 64 channels (so we can't have multiples of 3) print(contra_model) model_graph = torchview.draw_graph( contra_model, diff --git a/viscy/applications/contrastive_phenotyping/predict.py b/viscy/applications/contrastive_phenotyping/predict.py index c2ac7d39..ec941942 100644 --- a/viscy/applications/contrastive_phenotyping/predict.py +++ b/viscy/applications/contrastive_phenotyping/predict.py @@ -18,10 +18,13 @@ import numpy as np import pandas as pd + def main(hparams): # Set paths top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") - timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/predict_timesteps.csv" + timesteps_csv_path = ( + top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/predict_timesteps.csv" + ) predict_base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr" checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/updated_multiple_channels/contrastive_model-test-epoch=97-val_loss=0.00.ckpt" @@ -35,18 +38,18 @@ def main(hparams): # Initialize the data module for prediction data_module = ContrastiveDataModule( - base_path=str(predict_base_path), - channels=channels, - x=x, - y=y, - timesteps_csv_path=timesteps_csv_path, - channel_names=channel_names, - batch_size=batch_size, - z_range=z_range, - predict_base_path=predict_base_path + base_path=str(predict_base_path), + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + channel_names=channel_names, + batch_size=batch_size, + z_range=z_range, + predict_base_path=predict_base_path, ) - data_module.setup(stage='predict') + data_module.setup(stage="predict") # Load the model from checkpoint model = ContrastiveModule.load_from_checkpoint(str(checkpoint_path), predict=True) @@ -55,11 +58,11 @@ def main(hparams): # Initialize the trainer trainer = Trainer( - accelerator='gpu', + accelerator="gpu", devices=1, num_nodes=1, strategy=DDPStrategy(), - callbacks=[TQDMProgressBar(refresh_rate=1)] + callbacks=[TQDMProgressBar(refresh_rate=1)], ) # Run prediction @@ -68,7 +71,7 @@ def main(hparams): # Collect features and projections features_list = [] projections_list = [] - + for batch_idx, batch in enumerate(predictions): features, projections = batch features_list.append(features.cpu().numpy()) @@ -81,6 +84,7 @@ def main(hparams): np.save("updated_epoch97_predicted_features.npy", all_features) np.save("updated_epoch97_predicted_projections.npy", all_projections) + if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--backbone", type=str, default="convnext_tiny") @@ -91,9 +95,9 @@ def main(hparams): parser.add_argument("--embedding_len", type=int, default=256) parser.add_argument("--max_epochs", type=int, default=100) parser.add_argument("--accelerator", type=str, default="gpu") - parser.add_argument("--devices", type=int, default=1) + parser.add_argument("--devices", type=int, default=1) parser.add_argument("--num_nodes", type=int, default=2) parser.add_argument("--log_every_n_steps", type=int, default=1) args = parser.parse_args() - main(args) \ No newline at end of file + main(args) diff --git a/viscy/applications/contrastive_phenotyping/training_script.py b/viscy/applications/contrastive_phenotyping/training_script.py index ec580466..92f3a375 100644 --- a/viscy/applications/contrastive_phenotyping/training_script.py +++ b/viscy/applications/contrastive_phenotyping/training_script.py @@ -10,7 +10,8 @@ from lightning.pytorch import Trainer, seed_everything from lightning.pytorch.callbacks import ModelCheckpoint, RichProgressBar -#from lightning.pytorch.loggers import TensorBoardLogger + +# from lightning.pytorch.loggers import TensorBoardLogger from lightning.pytorch.loggers import WandbLogger from lightning.pytorch.callbacks import TQDMProgressBar import wandb @@ -19,7 +20,7 @@ from viscy.light.engine import ContrastiveModule from viscy.representation.contrastive import ContrastiveEncoder -from viscy.data.hcs import ContrastiveDataModule +from viscy.data.hcs import ContrastiveDataModule import logging # Set W&B logging level to suppress warnings @@ -34,14 +35,16 @@ # init_wandb() -#wandb.init(project="contrastive_model", dir="/hpc/mydata/alishba.imran/wandb_logs/") +# wandb.init(project="contrastive_model", dir="/hpc/mydata/alishba.imran/wandb_logs/") top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") -#input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" +# input_zarr = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" input_zarr = "/hpc/projects/virtual_staining/viral_sensor_test_dataio/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" model_dir = top_dir / "infection_classification/models/infection_score" # checkpoint dir: /hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/updated_multiple_channels -timesteps_csv_path = top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" +timesteps_csv_path = ( + top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" +) # Data parameters base_path = "/hpc/projects/virtual_staining/viral_sensor_test_dataio/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" @@ -51,9 +54,9 @@ z = 15 z_range = (28, 43) batch_size = 32 -channel_names = ["RFP", "Phase3D"] #training w/ both channels +channel_names = ["RFP", "Phase3D"] # training w/ both channels -torch.set_float32_matmul_precision('medium') +torch.set_float32_matmul_precision("medium") contra_model = ContrastiveEncoder(backbone="convnext_tiny") print(contra_model) @@ -85,7 +88,7 @@ def main(hparams): num_gpus = torch.cuda.device_count() print(f"Number of GPUs available: {num_gpus}") - + print("Starting data module..") # Initialize the data module data_module = ContrastiveDataModule( @@ -100,15 +103,17 @@ def main(hparams): ) print("data module set up!") - + # Setup the data module for training, val and testing - data_module.setup(stage='fit') - - print(f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}") + data_module.setup(stage="fit") + + print( + f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}" + ) print(f"Training dataset size: {len(data_module.train_dataset)}") print(f"Validation dataset size: {len(data_module.val_dataset)}") print(f"Test dataset size: {len(data_module.test_dataset)}") - + # Initialize the model model = ContrastiveModule( backbone=hparams.backbone, @@ -126,7 +131,7 @@ def main(hparams): # Initialize logger wandb_logger = WandbLogger(project="contrastive_model", log_model="all") - + # set for each run to avoid overwritting! custom_folder_name = "updated_multiple_channels" checkpoint_callback = ModelCheckpoint( @@ -146,7 +151,7 @@ def main(hparams): num_nodes=hparams.num_nodes, strategy=DDPStrategy(), log_every_n_steps=hparams.log_every_n_steps, - num_sanity_val_steps=0 + num_sanity_val_steps=0, ) train_loader = data_module.train_dataloader() @@ -155,7 +160,7 @@ def main(hparams): wandb_logger.watch(model, log="all", log_graph=(example_input,)) - # Fetches batches from the training dataloader, + # Fetches batches from the training dataloader, # Calls the training_step method on the model for each batch # Aggregates the losses and performs optimization steps trainer.fit(model, datamodule=data_module) @@ -166,8 +171,10 @@ def main(hparams): # Test the model trainer.test(model, datamodule=data_module) + if __name__ == "__main__": import sys + if "ipykernel_launcher" in sys.argv[0]: # Jupyter Notebook environment args = { @@ -180,13 +187,14 @@ def main(hparams): "max_epochs": 100, "accelerator": "gpu", "devices": 1, # 1 GPU - "num_nodes": 1, # 1 node + "num_nodes": 1, # 1 node "log_every_n_steps": 1, } - + class HParams: def __init__(self, **kwargs): self.__dict__.update(kwargs) + hparams = HParams(**args) main(hparams) else: diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index dc5fbfc7..75411c36 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -6,7 +6,8 @@ from glob import glob from pathlib import Path from typing import Callable, Literal, Optional, Sequence, Union -#import pytorch_lightning as pl + +# import pytorch_lightning as pl from monai.transforms import MapTransform import random import numpy as np @@ -14,11 +15,22 @@ import zarr from imageio import imread from iohub.ngff import ImageArray, Plate, Position, open_ome_zarr -#from lightning.pytorch import LightningDataModule + +# from lightning.pytorch import LightningDataModule from monai.data import set_track_meta from monai.data.utils import collate_meta_tensor -from monai.transforms import Compose, RandAdjustContrastd, RandAffined, RandGaussianNoised, RandGaussianSmoothd, RandScaleIntensityd, RandShiftIntensityd, RandZoomd, Rand3DElasticd, RandGaussianSharpend - +from monai.transforms import ( + Compose, + RandAdjustContrastd, + RandAffined, + RandGaussianNoised, + RandGaussianSmoothd, + RandScaleIntensityd, + RandShiftIntensityd, + RandZoomd, + Rand3DElasticd, + RandGaussianSharpend, +) from torch import Tensor @@ -39,6 +51,7 @@ warnings.filterwarnings("ignore") + def _ensure_channel_list(str_or_seq: str | Sequence[str]) -> list[str]: """ Ensure channel argument is a list of strings. @@ -598,6 +611,7 @@ def _train_transform(self) -> list[Callable]: logging.debug(f"Training augmentations: {self.augmentations}") return list(self.augmentations) + # dataloader for organelle phenotyping class ContrastiveDataset(Dataset): def __init__( @@ -621,16 +635,21 @@ def __init__( self.ds = self.open_zarr_store(self.base_path) self.positions = list(self.ds.positions()) self.timesteps_df = pd.read_csv(timesteps_csv_path) - self.channel_indices = [self.ds.channel_names.index(channel) for channel in self.channel_names] + self.channel_indices = [ + self.ds.channel_names.index(channel) for channel in self.channel_names + ] print("channel indices!") print(self.channel_indices) print(f"Initialized dataset with {len(self.positions)} positions.") # self.statistics = self.compute_statistics() # print("Channel Statistics:", self.statistics) - + def compute_statistics(self): - stats = {channel: {'mean': 0, 'sum_sq_diff': 0, 'min': np.inf, 'max': -np.inf} for channel in self.channel_names} + stats = { + channel: {"mean": 0, "sum_sq_diff": 0, "min": np.inf, "max": -np.inf} + for channel in self.channel_names + } count = 0 total_elements = 0 @@ -640,23 +659,25 @@ def compute_statistics(self): for i, channel in enumerate(self.channel_names): channel_data = data[i] mean = np.mean(channel_data) - stats[channel]['mean'] += mean - stats[channel]['min'] = min(stats[channel]['min'], np.min(channel_data)) - stats[channel]['max'] = max(stats[channel]['max'], np.max(channel_data)) - stats[channel]['sum_sq_diff'] += np.sum((channel_data - mean) ** 2) + stats[channel]["mean"] += mean + stats[channel]["min"] = min(stats[channel]["min"], np.min(channel_data)) + stats[channel]["max"] = max(stats[channel]["max"], np.max(channel_data)) + stats[channel]["sum_sq_diff"] += np.sum((channel_data - mean) ** 2) count += 1 total_elements += np.prod(channel_data.shape) for channel in self.channel_names: - stats[channel]['mean'] /= count - stats[channel]['std'] = np.sqrt(stats[channel]['sum_sq_diff'] / total_elements) - del stats[channel]['sum_sq_diff'] - + stats[channel]["mean"] /= count + stats[channel]["std"] = np.sqrt( + stats[channel]["sum_sq_diff"] / total_elements + ) + del stats[channel]["sum_sq_diff"] + print("done!") return stats def open_zarr_store(self, path, layout="hcs", mode="r"): - #print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") + # print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") return open_ome_zarr(path, layout=layout, mode=mode) def __len__(self): @@ -678,7 +699,7 @@ def __getitem__(self, idx): negative_idx = random.randint(0, self.__len__() - 1) negative_position_path = self.positions[negative_idx][0] negative_data = self.load_data(negative_position_path) - negative_data = self.normalize_data(negative_data) + negative_data = self.normalize_data(negative_data) negative_data = self.apply_channel_transforms(negative_data) negative_data = self.normalize_data(negative_data) @@ -705,8 +726,8 @@ def load_data(self, position_path): data = self.restructure_data(zarr_array, position_path) data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] - #print("shape after!") - #print(data.shape) + # print("shape after!") + # print(data.shape) return data def restructure_data(self, data, position_path): @@ -750,16 +771,17 @@ def normalize_data(self, data): std = np.std(channel_data) normalized_data[i] = (channel_data - mean) / (std + 1e-6) return normalized_data - + def apply_channel_transforms(self, data): transformed_data = np.empty_like(data) for i, channel_name in enumerate(self.channel_names): channel_data = data[i] transform = self.transform[channel_name] transformed_data[i] = transform({"image": channel_data})["image"] - #print(f"transformed {channel_name}") + # print(f"transformed {channel_name}") return transformed_data + def get_transforms(): rfp_transforms = Compose( [ @@ -805,10 +827,8 @@ def get_transforms(): ] ) - return { - "RFP": rfp_transforms, - "Phase3D": phase_transforms - } + return {"RFP": rfp_transforms, "Phase3D": phase_transforms} + class ContrastiveDataModule(LightningDataModule): def __init__( @@ -864,10 +884,12 @@ def setup(self, stage: str = None): test_size = len(dataset) - train_size - val_size self.train_dataset, self.val_dataset, self.test_dataset = ( - torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) + torch.utils.data.random_split( + dataset, [train_size, val_size, test_size] + ) ) - # setup prediction dataset + # setup prediction dataset if stage == "predict" and self.predict_base_path: print("setting up!") self.predict_dataset = PredictDataset( @@ -886,8 +908,8 @@ def train_dataloader(self): batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True + prefetch_factor=2, + persistent_workers=True, ) def val_dataloader(self): @@ -896,8 +918,8 @@ def val_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True + prefetch_factor=2, + persistent_workers=True, ) def test_dataloader(self): @@ -906,8 +928,8 @@ def test_dataloader(self): batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True + prefetch_factor=2, + persistent_workers=True, ) def predict_dataloader(self): @@ -917,16 +939,16 @@ def predict_dataloader(self): "Predict dataset not set up. Call setup(stage='predict') first." ) - return DataLoader( self.predict_dataset, batch_size=self.batch_size, - shuffle=False, # False shuffle for prediction + shuffle=False, # False shuffle for prediction num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True + prefetch_factor=2, + persistent_workers=True, ) + class PredictDataset(Dataset): def __init__( self, @@ -948,7 +970,9 @@ def __init__( self.timesteps_csv_path = timesteps_csv_path self.timesteps_df = pd.read_csv(timesteps_csv_path) self.positions = list(self.ds.positions()) - self.channel_indices = [self.ds.channel_names.index(channel) for channel in self.channel_names] + self.channel_indices = [ + self.ds.channel_names.index(channel) for channel in self.channel_names + ] print("channel indices!") print(self.channel_indices) print(f"Initialized predict dataset with {len(self.positions)} positions.") @@ -964,22 +988,22 @@ def open_zarr_store(self, path, layout="hcs", mode="r"): # positions.append((position_path, row['Random Timestep'])) # #print(positions) # return positions - + def __len__(self): return len(self.positions) def __getitem__(self, idx): position_path = self.positions[idx][0] - #print(f"Position path: {position_path}") + # print(f"Position path: {position_path}") data = self.load_data(position_path) data = self.normalize_data(data) return torch.tensor(data, dtype=torch.float32), (position_path) - # double check printing order + # double check printing order def load_data(self, position_path): position = self.ds[position_path] - #print(f"Loading data for position path: {position_path}") + # print(f"Loading data for position path: {position_path}") zarr_array = position["0"][:] parts = position_path.split("/") @@ -994,14 +1018,23 @@ def load_data(self, position_path): self.timesteps_df.apply( lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", axis=1, - ) == combined_id + ) + == combined_id ] if matched_rows.empty: - raise ValueError(f"No matching entry found for position path: {position_path}") + raise ValueError( + f"No matching entry found for position path: {position_path}" + ) random_timestep = matched_rows["Random Timestep"].values[0] - data = zarr_array[random_timestep, self.channel_indices, self.z_range[0]:self.z_range[1], :, :] + data = zarr_array[ + random_timestep, + self.channel_indices, + self.z_range[0] : self.z_range[1], + :, + :, + ] return data def normalize_data(self, data): diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 028f2792..1d26a91f 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -7,8 +7,9 @@ import torch import wandb from imageio import imwrite -#from lightning.pytorch import LightningModule -#from lightning import LightningModule + +# from lightning.pytorch import LightningModule +# from lightning import LightningModule from torch.optim import Adam from PIL import Image @@ -95,6 +96,7 @@ def forward(self, preds, target): loss += (1 - ms_ssim) * self.ms_dssim_alpha return loss + class VSUNet(LightningModule): """Regression U-Net module for virtual staining. @@ -550,6 +552,7 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 self._detach_sample((source, target * mask.unsqueeze(2), pred)) ) + class ContrastiveModule(LightningModule): """Contrastive Learning Model for self-supervised learning.""" @@ -568,7 +571,7 @@ def __init__( in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, - predict: bool = False + predict: bool = False, ) -> None: super().__init__() @@ -591,7 +594,7 @@ def __init__( in_stack_depth=in_stack_depth, stem_kernel_size=stem_kernel_size, embedding_len=embedding_len, - predict=predict + predict=predict, ) # required to log the graph. @@ -610,12 +613,12 @@ def forward(self, x: Tensor) -> Tensor: """Forward pass of the model.""" projections = self.encoder(x) return projections - # features is without projection head and projects is with projection head + # features is without projection head and projects is with projection head def log_feature_statistics(self, embeddings: Tensor, prefix: str): mean = torch.mean(embeddings, dim=0).detach().cpu().numpy() std = torch.std(embeddings, dim=0).detach().cpu().numpy() - + print(f"{prefix}_mean: {mean}") print(f"{prefix}_std: {std}") @@ -623,12 +626,12 @@ def print_embedding_norms(self, anchor, positive, negative, phase): anchor_norm = torch.norm(anchor, dim=1).mean().item() positive_norm = torch.norm(positive, dim=1).mean().item() negative_norm = torch.norm(negative, dim=1).mean().item() - + print(f"{phase}/anchor_norm: {anchor_norm}") print(f"{phase}/positive_norm: {positive_norm}") print(f"{phase}/negative_norm: {negative_norm}") - # logs over all steps + # logs over all steps @rank_zero_only def log_metrics(self, anchor, positive, negative, phase): cosine_sim_pos = F.cosine_similarity(anchor, positive, dim=1).mean().item() @@ -641,18 +644,18 @@ def log_metrics(self, anchor, positive, negative, phase): f"{phase}/cosine_similarity_positive": cosine_sim_pos, f"{phase}/cosine_similarity_negative": cosine_sim_neg, f"{phase}/euclidean_distance_positive": euclidean_dist_pos, - f"{phase}/euclidean_distance_negative": euclidean_dist_neg + f"{phase}/euclidean_distance_negative": euclidean_dist_neg, } wandb.log(metrics) - - if phase == 'train': + + if phase == "train": self.training_metrics.append(metrics) - elif phase == 'val': + elif phase == "val": self.validation_metrics.append(metrics) - elif phase == 'test': + elif phase == "test": self.test_metrics.append(metrics) - + @rank_zero_only # logs only one sample from the first batch per epoch def log_images(self, anchor, positive, negative, epoch, step_name): @@ -668,19 +671,37 @@ def log_images(self, anchor, positive, negative, epoch, step_name): # Debug prints to check the contents of the images print(f"Anchor RFP min: {anchor_img_rfp.min()}, max: {anchor_img_rfp.max()}") - print(f"Positive RFP min: {positive_img_rfp.min()}, max: {positive_img_rfp.max()}") - print(f"Negative RFP min: {negative_img_rfp.min()}, max: {negative_img_rfp.max()}") + print( + f"Positive RFP min: {positive_img_rfp.min()}, max: {positive_img_rfp.max()}" + ) + print( + f"Negative RFP min: {negative_img_rfp.min()}, max: {negative_img_rfp.max()}" + ) - print(f"Anchor Phase min: {anchor_img_phase.min()}, max: {anchor_img_phase.max()}") - print(f"Positive Phase min: {positive_img_phase.min()}, max: {positive_img_phase.max()}") - print(f"Negative Phase min: {negative_img_phase.min()}, max: {negative_img_phase.max()}") + print( + f"Anchor Phase min: {anchor_img_phase.min()}, max: {anchor_img_phase.max()}" + ) + print( + f"Positive Phase min: {positive_img_phase.min()}, max: {positive_img_phase.max()}" + ) + print( + f"Negative Phase min: {negative_img_phase.min()}, max: {negative_img_phase.max()}" + ) # combine the images side by side - combined_img_rfp = np.concatenate((anchor_img_rfp, positive_img_rfp, negative_img_rfp), axis=1) - combined_img_phase = np.concatenate((anchor_img_phase, positive_img_phase, negative_img_phase), axis=1) + combined_img_rfp = np.concatenate( + (anchor_img_rfp, positive_img_rfp, negative_img_rfp), axis=1 + ) + combined_img_phase = np.concatenate( + (anchor_img_phase, positive_img_phase, negative_img_phase), axis=1 + ) combined_img = np.concatenate((combined_img_rfp, combined_img_phase), axis=0) - self.images_to_log.append(wandb.Image(combined_img, caption=f"Anchor | Positive | Negative (Epoch {epoch})")) + self.images_to_log.append( + wandb.Image( + combined_img, caption=f"Anchor | Positive | Negative (Epoch {epoch})" + ) + ) wandb.log({f"{step_name}": self.images_to_log}) self.images_to_log = [] @@ -697,33 +718,57 @@ def training_step( emb_pos = self.encoder(pos_img) emb_neg = self.encoder(neg_img) loss = self.loss_function(emb_anchor, emb_pos, emb_neg) - + self.log("train/loss_step", loss, on_step=True, prog_bar=True, logger=True) - + self.train_batch_counter += 1 if self.train_batch_counter % self.log_steps_per_epoch == 0: - self.log_images(anchor, pos_img, neg_img, self.current_epoch, "training_images") - - self.log_metrics(emb_anchor, emb_pos, emb_neg, 'train') - #self.print_embedding_norms(emb_anchor, emb_pos, emb_neg, 'train') + self.log_images( + anchor, pos_img, neg_img, self.current_epoch, "training_images" + ) + + self.log_metrics(emb_anchor, emb_pos, emb_neg, "train") + # self.print_embedding_norms(emb_anchor, emb_pos, emb_neg, 'train') self.training_step_outputs.append(loss) - return {'loss': loss} + return {"loss": loss} def on_train_epoch_end(self) -> None: epoch_loss = torch.stack(self.training_step_outputs).mean() - self.log("train/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) - self.training_step_outputs.clear() + self.log( + "train/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True + ) + self.training_step_outputs.clear() if self.training_metrics: - avg_metrics = self.aggregate_metrics(self.training_metrics, 'train') - self.log("train/avg_cosine_similarity_positive", avg_metrics["train/cosine_similarity_positive"], on_epoch=True, logger=True) - self.log("train/avg_cosine_similarity_negative", avg_metrics["train/cosine_similarity_negative"], on_epoch=True, logger=True) - self.log("train/avg_euclidean_distance_positive", avg_metrics["train/euclidean_distance_positive"], on_epoch=True, logger=True) - self.log("train/avg_euclidean_distance_negative", avg_metrics["train/euclidean_distance_negative"], on_epoch=True, logger=True) + avg_metrics = self.aggregate_metrics(self.training_metrics, "train") + self.log( + "train/avg_cosine_similarity_positive", + avg_metrics["train/cosine_similarity_positive"], + on_epoch=True, + logger=True, + ) + self.log( + "train/avg_cosine_similarity_negative", + avg_metrics["train/cosine_similarity_negative"], + on_epoch=True, + logger=True, + ) + self.log( + "train/avg_euclidean_distance_positive", + avg_metrics["train/euclidean_distance_positive"], + on_epoch=True, + logger=True, + ) + self.log( + "train/avg_euclidean_distance_negative", + avg_metrics["train/euclidean_distance_negative"], + on_epoch=True, + logger=True, + ) self.training_metrics.clear() self.train_batch_counter = 0 - + def validation_step( self, batch: tuple[Tensor], @@ -741,24 +786,48 @@ def validation_step( self.val_batch_counter += 1 if self.val_batch_counter % self.log_steps_per_epoch == 0: - self.log_images(anchor, pos_img, neg_img, self.current_epoch, "validation_images") - - self.log_metrics(emb_anchor, emb_pos, emb_neg, 'val') + self.log_images( + anchor, pos_img, neg_img, self.current_epoch, "validation_images" + ) + + self.log_metrics(emb_anchor, emb_pos, emb_neg, "val") self.validation_step_outputs.append(loss) - return {'loss': loss} - + return {"loss": loss} + def on_validation_epoch_end(self) -> None: epoch_loss = torch.stack(self.validation_step_outputs).mean() - self.log("val/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) - self.validation_step_outputs.clear() + self.log( + "val/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True + ) + self.validation_step_outputs.clear() if self.validation_metrics: - avg_metrics = self.aggregate_metrics(self.validation_metrics, 'val') - self.log("val/avg_cosine_similarity_positive", avg_metrics["val/cosine_similarity_positive"], on_epoch=True, logger=True) - self.log("val/avg_cosine_similarity_negative", avg_metrics["val/cosine_similarity_negative"], on_epoch=True, logger=True) - self.log("val/avg_euclidean_distance_positive", avg_metrics["val/euclidean_distance_positive"], on_epoch=True, logger=True) - self.log("val/avg_euclidean_distance_negative", avg_metrics["val/euclidean_distance_negative"], on_epoch=True, logger=True) + avg_metrics = self.aggregate_metrics(self.validation_metrics, "val") + self.log( + "val/avg_cosine_similarity_positive", + avg_metrics["val/cosine_similarity_positive"], + on_epoch=True, + logger=True, + ) + self.log( + "val/avg_cosine_similarity_negative", + avg_metrics["val/cosine_similarity_negative"], + on_epoch=True, + logger=True, + ) + self.log( + "val/avg_euclidean_distance_positive", + avg_metrics["val/euclidean_distance_positive"], + on_epoch=True, + logger=True, + ) + self.log( + "val/avg_euclidean_distance_negative", + avg_metrics["val/euclidean_distance_negative"], + on_epoch=True, + logger=True, + ) self.validation_metrics.clear() self.val_batch_counter = 0 @@ -774,41 +843,71 @@ def test_step( emb_pos = self.encoder(pos_img) emb_neg = self.encoder(neg_img) loss = self.loss_function(emb_anchor, emb_pos, emb_neg) - + self.log("test/loss_step", loss, on_step=True, prog_bar=True, logger=True) - self.log_metrics(emb_anchor, emb_pos, emb_neg, 'test') + self.log_metrics(emb_anchor, emb_pos, emb_neg, "test") self.test_step_outputs.append(loss) - return {'loss': loss} + return {"loss": loss} @rank_zero_only def on_test_epoch_end(self) -> None: epoch_loss = torch.stack(self.test_step_outputs).mean() - self.log("test/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True) - self.test_step_outputs.clear() + self.log( + "test/loss_epoch", epoch_loss, on_epoch=True, prog_bar=True, logger=True + ) + self.test_step_outputs.clear() if self.test_metrics: - avg_metrics = self.aggregate_metrics(self.test_metrics, 'test') - self.log("test/avg_cosine_similarity_positive", avg_metrics["test/cosine_similarity_positive"], on_epoch=True, logger=True) - self.log("test/avg_cosine_similarity_negative", avg_metrics["test/cosine_similarity_negative"], on_epoch=True, logger=True) - self.log("test/avg_euclidean_distance_positive", avg_metrics["test/euclidean_distance_positive"], on_epoch=True, logger=True) - self.log("test/avg_euclidean_distance_negative", avg_metrics["test/euclidean_distance_negative"], on_epoch=True, logger=True) + avg_metrics = self.aggregate_metrics(self.test_metrics, "test") + self.log( + "test/avg_cosine_similarity_positive", + avg_metrics["test/cosine_similarity_positive"], + on_epoch=True, + logger=True, + ) + self.log( + "test/avg_cosine_similarity_negative", + avg_metrics["test/cosine_similarity_negative"], + on_epoch=True, + logger=True, + ) + self.log( + "test/avg_euclidean_distance_positive", + avg_metrics["test/euclidean_distance_positive"], + on_epoch=True, + logger=True, + ) + self.log( + "test/avg_euclidean_distance_negative", + avg_metrics["test/euclidean_distance_negative"], + on_epoch=True, + logger=True, + ) self.test_metrics.clear() def configure_optimizers(self): optimizer = Adam(self.parameters(), lr=self.lr) return optimizer - + def aggregate_metrics(self, metrics, phase): avg_metrics = {} if metrics: - avg_metrics[f"{phase}/cosine_similarity_positive"] = sum(m[f"{phase}/cosine_similarity_positive"] for m in metrics) / len(metrics) - avg_metrics[f"{phase}/cosine_similarity_negative"] = sum(m[f"{phase}/cosine_similarity_negative"] for m in metrics) / len(metrics) - avg_metrics[f"{phase}/euclidean_distance_positive"] = sum(m[f"{phase}/euclidean_distance_positive"] for m in metrics) / len(metrics) - avg_metrics[f"{phase}/euclidean_distance_negative"] = sum(m[f"{phase}/euclidean_distance_negative"] for m in metrics) / len(metrics) + avg_metrics[f"{phase}/cosine_similarity_positive"] = sum( + m[f"{phase}/cosine_similarity_positive"] for m in metrics + ) / len(metrics) + avg_metrics[f"{phase}/cosine_similarity_negative"] = sum( + m[f"{phase}/cosine_similarity_negative"] for m in metrics + ) / len(metrics) + avg_metrics[f"{phase}/euclidean_distance_positive"] = sum( + m[f"{phase}/euclidean_distance_positive"] for m in metrics + ) / len(metrics) + avg_metrics[f"{phase}/euclidean_distance_negative"] = sum( + m[f"{phase}/euclidean_distance_negative"] for m in metrics + ) / len(metrics) return avg_metrics - + def predict_step(self, batch, batch_idx, dataloader_idx=0): print("running predict step!") """Prediction step for extracting embeddings.""" @@ -816,7 +915,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0): features, projections = self.encoder(x) self.processed_order.extend(position_info) return features, projections - + # already saved, not needed again # def on_predict_epoch_end(self) -> None: # print(f"Processed order: {self.processed_order}") @@ -831,7 +930,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0): # row = parts[0] # column = parts[1] # fov_cell = parts[2] - + # fov = int(fov_cell.split("fov")[1].split("cell")[0]) # cell_id = int(fov_cell.split("cell")[1]) @@ -839,7 +938,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0): # columns.append(column) # fovs.append(fov) # cell_ids.append(cell_id) - + # except (IndexError, ValueError) as e: # print(f"Skipping invalid position path: {position_path} with error: {e}") diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 43aacb4e..363d5ad1 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -7,6 +7,7 @@ from viscy.unet.networks.unext2 import UNeXt2Stem + class ContrastiveEncoder(nn.Module): def __init__( self, @@ -15,7 +16,7 @@ def __init__( in_stack_depth: int = 15, stem_kernel_size: tuple[int, int, int] = (5, 3, 3), embedding_len: int = 256, - predict: bool = False + predict: bool = False, ): super().__init__() @@ -141,7 +142,9 @@ def forward(self, x): x = self.model.head.flatten(x) features_before_projection = self.model.head.drop(x) projections = self.model.head.fc(features_before_projection) - features_before_projection = F.normalize(features_before_projection, p=2, dim=1) + features_before_projection = F.normalize( + features_before_projection, p=2, dim=1 + ) projections = F.normalize(projections, p=2, dim=1) # L2 normalization print(features_before_projection.shape, projections.shape) return features_before_projection, projections @@ -150,4 +153,4 @@ def forward(self, x): print("running forward without predict!") projections = self.model(x) projections = F.normalize(projections, p=2, dim=1) # L2 normalization - return projections \ No newline at end of file + return projections From 22640c1b5bb642d1f7b0d0b9decdd5d197dc1667 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 11:16:04 -0700 Subject: [PATCH 30/87] combine the application directories --- .../contrastive_phenotyping/PCA.ipynb | 0 .../contrastive_phenotyping/dataloader_test.py | 0 .../contrastive_phenotyping/dataloader_test.sh | 0 .../contrastive_phenotyping/graphs_ConvNeXt_ResNet.py | 0 .../contrastive_phenotyping/predict.py | 0 .../contrastive_phenotyping/training_script.py | 0 6 files changed, 0 insertions(+), 0 deletions(-) rename {viscy/applications => applications}/contrastive_phenotyping/PCA.ipynb (100%) rename {viscy/applications => applications}/contrastive_phenotyping/dataloader_test.py (100%) rename {viscy/applications => applications}/contrastive_phenotyping/dataloader_test.sh (100%) rename {viscy/applications => applications}/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py (100%) rename {viscy/applications => applications}/contrastive_phenotyping/predict.py (100%) rename {viscy/applications => applications}/contrastive_phenotyping/training_script.py (100%) diff --git a/viscy/applications/contrastive_phenotyping/PCA.ipynb b/applications/contrastive_phenotyping/PCA.ipynb similarity index 100% rename from viscy/applications/contrastive_phenotyping/PCA.ipynb rename to applications/contrastive_phenotyping/PCA.ipynb diff --git a/viscy/applications/contrastive_phenotyping/dataloader_test.py b/applications/contrastive_phenotyping/dataloader_test.py similarity index 100% rename from viscy/applications/contrastive_phenotyping/dataloader_test.py rename to applications/contrastive_phenotyping/dataloader_test.py diff --git a/viscy/applications/contrastive_phenotyping/dataloader_test.sh b/applications/contrastive_phenotyping/dataloader_test.sh similarity index 100% rename from viscy/applications/contrastive_phenotyping/dataloader_test.sh rename to applications/contrastive_phenotyping/dataloader_test.sh diff --git a/viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py b/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py similarity index 100% rename from viscy/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py rename to applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py diff --git a/viscy/applications/contrastive_phenotyping/predict.py b/applications/contrastive_phenotyping/predict.py similarity index 100% rename from viscy/applications/contrastive_phenotyping/predict.py rename to applications/contrastive_phenotyping/predict.py diff --git a/viscy/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script.py similarity index 100% rename from viscy/applications/contrastive_phenotyping/training_script.py rename to applications/contrastive_phenotyping/training_script.py From 5b585aa9250a1b098c0ba93e75436024032fb6b4 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 11:29:37 -0700 Subject: [PATCH 31/87] lint --- viscy/data/hcs.py | 29 +++++------------- viscy/light/engine.py | 46 ++++++++++++++--------------- viscy/representation/contrastive.py | 1 - 3 files changed, 30 insertions(+), 46 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 75411c36..7bc27bb3 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -1,54 +1,39 @@ import logging import math import os +import random import re import tempfile +import warnings from glob import glob from pathlib import Path from typing import Callable, Literal, Optional, Sequence, Union -# import pytorch_lightning as pl -from monai.transforms import MapTransform -import random import numpy as np +import pandas as pd import torch import zarr from imageio import imread from iohub.ngff import ImageArray, Plate, Position, open_ome_zarr - -# from lightning.pytorch import LightningDataModule +from lightning.pytorch import LightningDataModule from monai.data import set_track_meta from monai.data.utils import collate_meta_tensor from monai.transforms import ( + CenterSpatialCropd, Compose, + MapTransform, + MultiSampleTrait, RandAdjustContrastd, RandAffined, RandGaussianNoised, RandGaussianSmoothd, RandScaleIntensityd, - RandShiftIntensityd, - RandZoomd, - Rand3DElasticd, - RandGaussianSharpend, ) - - from torch import Tensor from torch.utils.data import DataLoader, Dataset from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample -import random - -from iohub import open_ome_zarr -import pandas as pd -import warnings -from lightning.pytorch import LightningDataModule, LightningModule, Trainer - - -# from viscy.data.typing import Optional -from pathlib import Path - warnings.filterwarnings("ignore") diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 1d26a91f..bb4c89da 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -1,29 +1,22 @@ import logging import os from typing import Literal, Sequence, Union -import matplotlib.pyplot as plt -import pandas as pd + import numpy as np import torch -import wandb -from imageio import imwrite - -# from lightning.pytorch import LightningModule -# from lightning import LightningModule -from torch.optim import Adam -from PIL import Image - import torch.nn.functional as F -from pytorch_lightning.utilities import rank_zero_only - -from lightning.pytorch import LightningDataModule, LightningModule, Trainer - +from imageio import imwrite +from lightning.pytorch import LightningModule from matplotlib.pyplot import get_cmap from monai.optimizers import WarmupCosineSchedule from monai.transforms import DivisiblePad, Rotate90 +from pytorch_lightning.utilities import rank_zero_only from skimage.exposure import rescale_intensity from torch import Tensor, nn -from torch.nn import functional as F + +# from lightning.pytorch import LightningModule +# from lightning import LightningModule +from torch.optim import Adam from torch.optim.lr_scheduler import ConstantLR from torchmetrics.functional import ( accuracy, @@ -39,17 +32,21 @@ from viscy.data.hcs import Sample from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d +from viscy.representation.contrastive import ContrastiveEncoder from viscy.unet.networks.fcmae import FullyConvolutionalMAE from viscy.unet.networks.Unet2D import Unet2d from viscy.unet.networks.Unet25D import Unet25d from viscy.unet.networks.unext2 import UNeXt2 -from viscy.representation.contrastive import ContrastiveEncoder try: from cellpose.models import CellposeModel except ImportError: CellposeModel = None +try: + import wandb +except ImportError: + wandb = None _UNET_ARCHITECTURE = { "2D": Unet2d, @@ -137,18 +134,18 @@ def __init__( self, architecture: Literal["2D", "UNeXt2", "2.5D", "3D", "fcmae", "UNeXt2_2D"], model_config: dict = {}, - loss_function: Union[nn.Module, MixedLoss] = None, + loss_function: Union[nn.Module, MixedLoss] | None = None, lr: float = 1e-3, schedule: Literal["WarmupCosine", "Constant"] = "Constant", freeze_encoder: bool = False, - ckpt_path: str = None, + ckpt_path: str | None = None, log_batches_per_epoch: int = 8, log_samples_per_batch: int = 1, example_input_yx_shape: Sequence[int] = (256, 256), - test_cellpose_model_path: str = None, - test_cellpose_diameter: float = None, - test_evaluate_cellpose: bool = False, - test_time_augmentations: bool = False, + test_cellpose_model_path: str | None = None, + test_cellpose_diameter: float | None = None, + test_evaluate_cellpose: bool | None = False, + test_time_augmentations: bool | None = False, tta_type: Literal["mean", "median", "product"] = "mean", ) -> None: super().__init__() @@ -574,7 +571,10 @@ def __init__( predict: bool = False, ) -> None: super().__init__() - + if wandb is None: + raise ImportError( + f"wandb is required for logging of {type(self).__name__}." + ) self.loss_function = loss_function self.margin = margin self.lr = lr diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 363d5ad1..10d2c189 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -4,7 +4,6 @@ # from viscy.unet.networks.resnet import resnetStem # Currently identical to resnetStem, but could be different in the future. - from viscy.unet.networks.unext2 import UNeXt2Stem From 5ec5147d9358a0ee0ba4c330cd0afef75604b4f9 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 11:33:30 -0700 Subject: [PATCH 32/87] replace notebook with script --- .../contrastive_phenotyping/PCA.ipynb | 28019 ---------------- applications/contrastive_phenotyping/pca.py | 315 + 2 files changed, 315 insertions(+), 28019 deletions(-) delete mode 100644 applications/contrastive_phenotyping/PCA.ipynb create mode 100644 applications/contrastive_phenotyping/pca.py diff --git a/applications/contrastive_phenotyping/PCA.ipynb b/applications/contrastive_phenotyping/PCA.ipynb deleted file mode 100644 index a52a0887..00000000 --- a/applications/contrastive_phenotyping/PCA.ipynb +++ /dev/null @@ -1,28019 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2629, 768)\n", - "(2629, 256)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from iohub import open_ome_zarr\n", - "from sklearn.decomposition import PCA\n", - "from scipy.stats import spearmanr\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import plotly.io as pio\n", - "import plotly.express as px\n", - "\n", - "# Set Plotly default renderer for VSCode\n", - "pio.renderers.default = \"vscode\"\n", - "\n", - "# Load predicted features and projections\n", - "predicted_features = np.load(\"updated_epoch66_predicted_features.npy\")\n", - "predicted_projections = np.load(\"updated_epoch66_predicted_projections.npy\")\n", - "\n", - "print(predicted_features.shape)\n", - "print(predicted_projections.shape)\n", - "\n", - "# Load the CSV file\n", - "csv_path = \"epoch66_processed_order.csv\"\n", - "df = pd.read_csv(csv_path)\n", - "\n", - "# Load ground truth masks\n", - "base_path = \"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr\"\n", - "ds = open_ome_zarr(base_path, layout=\"hcs\", mode=\"r\")\n", - "\n", - "background_mask_index = ds.channel_names.index('background_mask')\n", - "uninfected_mask_index = ds.channel_names.index('uninfected_mask')\n", - "infected_mask_index = ds.channel_names.index('infected_mask')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Assuming all masks have the same shape\n", - "# TO-DO:\n", - "# tie the image with projected embeddings\n", - "# test with ER\n", - "\n", - "# Initialize arrays to store the sums\n", - "num_cells = len(df)\n", - "background_sums = np.zeros(num_cells)\n", - "uninfected_sums = np.zeros(num_cells)\n", - "infected_sums = np.zeros(num_cells)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "for idx, row in df.iterrows():\n", - " position_key = f\"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}/0\"\n", - " zarr_array = ds[position_key]\n", - " t = row['Timestep']\n", - " \n", - " # Load a single z-slice, for example the first one\n", - " background_mask = zarr_array[t, background_mask_index, 0, :, :]\n", - " uninfected_mask = zarr_array[t, uninfected_mask_index, 0, :, :]\n", - " infected_mask = zarr_array[t, infected_mask_index, 0, :, :]\n", - " \n", - " # Sum values across each mask\n", - " background_sums[idx] = np.sum(background_mask)\n", - " uninfected_sums[idx] = np.sum(uninfected_mask)\n", - " infected_sums[idx] = np.sum(infected_mask)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Normalize the sums\n", - "max_background = np.max(background_sums)\n", - "max_uninfected = np.max(uninfected_sums)\n", - "max_infected = np.max(infected_sums)\n", - "\n", - "background_sums /= max_background\n", - "uninfected_sums /= max_uninfected\n", - "infected_sums /= max_infected" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Combine the sums into a single array and apply softmax\n", - "combined_sums = np.stack([background_sums, uninfected_sums, infected_sums], axis=1)\n", - "softmax_sums = np.exp(combined_sums) / np.sum(np.exp(combined_sums), axis=1, keepdims=True)\n", - "\n", - "# Separate the softmax values\n", - "background_softmax = softmax_sums[:, 0]\n", - "uninfected_softmax = softmax_sums[:, 1]\n", - "infected_softmax = softmax_sums[:, 2]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NaN values in combined_sums: False\n", - "NaN values in softmax_sums: False\n", - "Infinite values in combined_sums: False\n", - "Infinite values in softmax_sums: False\n" - ] - } - ], - "source": [ - "# Check for NaN values in the softmax results\n", - "print(\"NaN values in combined_sums:\", np.isnan(combined_sums).any())\n", - "print(\"NaN values in softmax_sums:\", np.isnan(softmax_sums).any())\n", - "print(\"Infinite values in combined_sums:\", np.isinf(combined_sums).any())\n", - "print(\"Infinite values in softmax_sums:\", np.isinf(softmax_sums).any())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NaN values in background_softmax: False\n", - "NaN values in uninfected_softmax: False\n", - "NaN values in infected_softmax: False\n", - "Variance in background_softmax: 0.0020539258845222756\n", - "Variance in uninfected_softmax: 0.0039155569854069875\n", - "Variance in infected_softmax: 0.0026512443426509346\n" - ] - } - ], - "source": [ - "# Check for NaN values in the softmax results\n", - "print(\"NaN values in background_softmax:\", np.isnan(background_softmax).any())\n", - "print(\"NaN values in uninfected_softmax:\", np.isnan(uninfected_softmax).any())\n", - "print(\"NaN values in infected_softmax:\", np.isnan(infected_softmax).any())\n", - "\n", - "# Check for zero variance in the softmax results\n", - "print(\"Variance in background_softmax:\", np.var(background_softmax))\n", - "print(\"Variance in uninfected_softmax:\", np.var(uninfected_softmax))\n", - "print(\"Variance in infected_softmax:\", np.var(infected_softmax))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Determine the number of principal components to keep\n", - "#reshaped_features = predicted_features.reshape(predicted_features.shape[0], -1)\n", - "\n", - "pca = PCA()\n", - "pca.fit(predicted_features)\n", - "explained_variance_ratio = np.cumsum(pca.explained_variance_ratio_)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the explained variance ratio\n", - "plt.figure(figsize=(12, 6))\n", - "plt.plot(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, marker='o', linestyle='--')\n", - "plt.xlabel('Number of Components')\n", - "plt.ylabel('Cumulative Explained Variance')\n", - "plt.title('Explained Variance by Number of Components')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of components selected: 1\n" - ] - } - ], - "source": [ - "# Choose the number of components that explain a significant amount of variance (e.g., 90%)\n", - "n_components = np.argmax(explained_variance_ratio >= 0.90) + 1\n", - "print(f\"Number of components selected: {n_components}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Perform PCA with the selected number of components\n", - "pca = PCA(n_components=2)\n", - "reduced_projections = pca.fit_transform(predicted_projections)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Row Column FOV Cell ID Timestep PC1 PC2 \\\n", - "0 A 3 0 1 2 -0.946861 0.135214 \n", - "1 A 3 0 10 13 -0.795119 0.505766 \n", - "2 A 3 0 11 21 0.793437 0.359740 \n", - "3 A 3 0 12 8 -0.924069 0.261018 \n", - "4 A 3 0 13 26 -0.494323 -0.603584 \n", - "\n", - " Infected Softmax Score \n", - "0 0.220417 \n", - "1 0.220354 \n", - "2 0.228791 \n", - "3 0.243351 \n", - "4 0.222215 \n" - ] - } - ], - "source": [ - "df['PC1'] = reduced_projections[:, 0]\n", - "df['PC2'] = reduced_projections[:, 1]\n", - "df['Infected Softmax Score'] = infected_softmax\n", - "\n", - "print(df.head())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " PC Background Correlation Uninfected Correlation Infected Correlation\n", - "0 1 0.035215 -0.088615 0.090813\n", - "1 2 0.039682 -0.004987 0.028344\n" - ] - } - ], - "source": [ - "# Calculate rank correlations\n", - "correlations = []\n", - "\n", - "for i in range(reduced_projections.shape[1]):\n", - " pc = reduced_projections[:, i]\n", - " \n", - " background_corr, _ = spearmanr(pc, background_softmax)\n", - " uninfected_corr, _ = spearmanr(pc, uninfected_softmax)\n", - " infected_corr, _ = spearmanr(pc, infected_softmax)\n", - " \n", - " correlations.append({\n", - " \"PC\": i + 1,\n", - " \"Background Correlation\": background_corr,\n", - " \"Uninfected Correlation\": uninfected_corr,\n", - " \"Infected Correlation\": infected_corr\n", - " })\n", - "\n", - "correlation_df = pd.DataFrame(correlations)\n", - "print(correlation_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - 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"backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "PC1" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "PC2" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create an interactive scatter plot\n", - "fig = px.scatter(df, x='PC1', y='PC2', color='Infected Softmax Score',\n", - " hover_data=['Row', 'Column', 'FOV', 'Cell ID', 'Timestep'])\n", - "\n", - "# Show the plot\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Function to get cell data and plot the images\n", - "\n", - "rfp_index = ds.channel_names.index('RFP')\n", - "phase3d_index = ds.channel_names.index('Phase3D')\n", - "\n", - "def get_cell_data_and_plot(row, col, fov, cell_id, timestep):\n", - " position_key = f\"{row}/{col}/fov{fov}cell{cell_id}/0\"\n", - " zarr_array = ds[position_key]\n", - "\n", - " phase_img = zarr_array[timestep, phase3d_index, 32, :, :]\n", - " rfp_img = zarr_array[timestep, rfp_index, 32, :, :]\n", - " \n", - " fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n", - " axes[0].imshow(phase_img, cmap='gray')\n", - " axes[0].set_title('Phase3D Image')\n", - " axes[1].imshow(rfp_img, cmap='gray')\n", - " axes[1].set_title('RFP Image')\n", - " plt.show()\n", - "\n", - " return phase_img, rfp_img\n", - "\n", - "# example: get data for a specific cell and plot\n", - "row = 'B'\n", - "col = '3'\n", - "fov = 5\n", - "cell_id = 14\n", - "timestep = 4\n", - "\n", - "phase_img, rfp_img = get_cell_data_and_plot(row, col, fov, cell_id, timestep)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize the PCA results with cells colored based on their infected softmax scores\n", - "plt.figure(figsize=(12, 6))\n", - "sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=infected_softmax, cmap='viridis', label='Cells')\n", - "plt.colorbar(sc, label='Infected Softmax Score')\n", - "plt.xlabel('Principal Component 1')\n", - "plt.ylabel('Principal Component 2')\n", - "plt.title('PCA of Predicted Projections (Colored by Infected Softmax Score)')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "index 2 is out of bounds for axis 1 with size 2", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m2\u001b[39m, n_components):\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m----> 6\u001b[0m sc \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mscatter(reduced_projections[:, \u001b[38;5;241m0\u001b[39m], \u001b[43mreduced_projections\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m, c\u001b[38;5;241m=\u001b[39minfected_softmax, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCells\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mcolorbar(sc, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInfected Softmax Score\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrincipal Component 1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mIndexError\u001b[0m: index 2 is out of bounds for axis 1 with size 2" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PC1 vs PC3, PC1 vs PC4, etc.\n", - "n_components = 5\n", - "if n_components > 2:\n", - " for i in range(2, n_components):\n", - " plt.figure(figsize=(12, 6))\n", - " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells')\n", - " plt.colorbar(sc, label='Infected Softmax Score')\n", - " plt.xlabel('Principal Component 1')\n", - " plt.ylabel(f'Principal Component {i + 1}')\n", - " plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by Infected Softmax Score)')\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "correlations = np.zeros(n_components)\n", - "for i in range(n_components):\n", - " pc = reduced_projections[:, i]\n", - " correlation, _ = spearmanr(pc, infected_softmax)\n", - " correlations[i] = correlation\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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4ttcykAULFgCUyvjr6b0wf/58v+WdO3fmrLPO8v4eFxdHhw4d2LFjh3dZw4YNWb9+PVu3bq1SXQ4fPgxATEyM33JPBvHKfF4Nw2DRokUMGTLEb/xmYmIi1157Lb///nuZGclXrlxJamoqd955p98Y8YsvvpiOHTuWei2AUkn6Pv/8c6Kjozn//PP9/o49e/YkMjKSn3/+GYAff/wRh8PBXXfd5fd3q+qUbkOGDPFrAe7duzd9+vTx/l3BlZPgwIED3ucGV+txWFgYV111VaWeJ9jn05KefvppfvzxR5555hm/VvqyXHPNNdhsNr9u1r/88gv79+/3+7z79h7xnD/OOuss8vLy2LRp01HX16Mq56eqfC5iYmI4dOjQMddPCCGqk3SxFqKeef3112nfvj1Wq5WEhAQ6dOiArhff64qKiiI7O7vS5S1atIi0tDR69+7Ntm3bvMvPOeccPvnkE5599lm/8kvavXs37dq1K7VNp06dvOuPVp8+fZg8ebJ3aqJOnToFvMhs1aqV3+/btm1DKcWjjz7Ko48+GrDs1NRUmjVrxu7duwN2Te3QoUOF9du+fTsdOnQ4quRQNVVHX19++SVRUVHYbDaaN29OmzZtSm3TrFmzUsnIjvZvnJaWRkZGBm+99RZvvfVWwG1SU1OBY3stA9m9eze6rtO2bVu/5U2aNKFhw4al6nzSSSeVKiMmJsZvfO2kSZO4/PLLad++PV26dOGCCy7ghhtuKLebty+llN/vUVFRAJX6vKalpZGXlxfwb96pUydM02Tv3r3e7uC+PMcaaN+OHTvy+++/+y2zWq2lsmZv3bqVzMxM4uPjA9bP83f0PFe7du381sfFxZW6QVCekvsDtG/fns8++8z7+/nnn09iYiKzZ8/mvPPOwzRNPvnkEy6//PJKB7XBPp/6mjNnDhMmTOCmm26q9KwAjRo1YvDgwXz99ddMnz6d0NBQPv74Y6xWK1dffbV3u/Xr1zNhwgR++umnUjdGMjMzj6q+vqpyfqrK50IpVefnKxdCCAmQhahnevfu7c26GkjHjh35+++/cTgclcq67Gkl9r348vXLL79wzjnnHF1lj1Hjxo3LzfrqUXIsrid51f3338/gwYMD7lMycKpptVHHs88+u8JxkMc6rtmX5xivv/56Ro4cGXCbygaXR6uyF+O+4z19+Qa1Z599Ntu3b+ebb75h0aJFvPPOO7z00ktMnz6dm2++ucyyPeNZ09PT/QJPT9Knf//9lx49elSqnjUhJCSk1M0Q0zTL7VVS1XwCwWCxWLj22mt5++23eeONN1i6dCkHDhyo0jRIwT6fevzwww+MGDGCiy++mOnTp1d6P3B9XubNm8e8efO47LLL+PLLLxk0aJD3Nc7IyKB///5ERUUxadIk2rRpQ2hoKKtXr+ahhx4qN3lfWZ+Hksm9qnJ+qsrnIj09vcJzkBBC1DYJkIU4zlx66aUsW7aML7/80q9LbCC5ubl88803XHPNNQGT0Nx9993Mnj273AC5RYsW/PPPP5im6XdR7enm16JFi6M8kqPn6YJqs9kqDLBbtGgRsGvg5s2bK3yeNm3asHz5coqKirwJikoq64K0puoYDEf7N46Li6NBgwYYhlHhMR7La1lWnU3TZOvWrd6WbnAlDcvIyDjq92VsbCyjR49m9OjR5OTkcPbZZ/P444+XGyB7AuGdO3fStWtX7/ILL7wQi8XCRx99VGGirri4OMLDwwP+zTdt2oSu6yQlJQXc13Osmzdv9mZp9ti8eXOlXos2bdrw448/csYZZ5R7E8VT1tatW/26gqelpZXKdl2eQO/3LVu2lJp6bsSIEbzwwgt89913/O9//yMuLq7MgO5oVOV86rF8+XKuuOIKevXqxWeffVblXhGXXXYZDRo04OOPP8Zms5Genu7XvXrJkiUcPnyYr776irPPPtu73JN5uzyeVvyMjAy/5SV7VFTl/ASV/1zs3LmT7t27V1ieEELUJhmDLMRx5vbbbycxMZH//Oc/bNmypdT61NRUJk+eDMDXX39Nbm4uY8aMYejQoaUel1xyCV9++WWpaXF8XXTRRSQnJzNnzhzvMqfTyauvvkpkZCT9+/cP/kFWID4+ngEDBjBjxgwOHjxYan1aWpr354suuog///yTFStW+K2vzPjrq666ikOHDvHaa6+VWudpefSM7y15QVpTdQyGo/0bWywWrrrqKr788ku/KXo8fI/xWF7LsuoMMG3aNL/lL774IuAaf1tVnrHEHpGRkbRt27bczwdAz549sdvtrFy50m95UlISt9xyC4sWLeLVV18ttZ9pmrzwwgvs27cPi8XCoEGD+Oabb/ymR0tJSeHjjz/mzDPP9HbZLqlXr17Ex8czffp0v7r+73//Y+PGjZV6La6++moMw+DJJ58stc7pdHr/JgMHDsRms/Hqq6/6tb6X/DtUZO7cuX7TNK1YsYLly5dz4YUX+m3XrVs3unXrxjvvvMOXX37JsGHDgjofdlXOp4D39WzZsiXz5s07qh4ZYWFhXHHFFSxYsIA333yTiIgILr/8cu96T28H39fX4XDwxhtvVFh2ixYtsFgs/Prrr37LS+5blfNTZT8XmZmZbN++PeBUWEIIUZdIC7IQx5mYmBi+/vprLrroInr06MH1119Pz549AVi9ejWffPIJp59+OuDqXt2oUaMyL1guu+wy3n77bebPn++dVqekW2+9lRkzZjBq1ChWrVpFy5Yt+eKLL1i6dCnTpk07pgQ3x+L111/nzDPPpGvXrtxyyy20bt2alJQUli1bxr59+1i7di0ADz74IB9++CEXXHAB99xzj3cKJU+raXlGjBjBBx98wLhx41ixYgVnnXUWubm5/Pjjj9x5551cfvnlhIWF0blzZ+bMmUP79u2JjY2lS5cudOnSpUbqGAzH8jd+5pln+Pnnn+nTpw+33HILnTt35siRI6xevZoff/yRI0eOBOW1LKl79+6MHDmSt956y9sldcWKFbz//vsMGTLkqIYNdO7cmQEDBtCzZ09iY2NZuXIlX3zxBWPHji13v9DQUAYNGsSPP/7IpEmT/Na98MILbN++nbvvvpuvvvqKSy65hJiYGPbs2cPnn3/Opk2bGDZsGACTJ0/mhx9+4Mwzz+TOO+/EarUyY8YMCgsLee6558p8fpvNxrPPPsvo0aPp378/w4cP907z1LJly0pNudO/f39uu+02pkyZwt9//82gQYOw2Wxs3bqVzz//nJdffpmhQ4cSFxfH/fffz5QpU7jkkku46KKLWLNmDf/73/+q1LW2bdu2nHnmmdxxxx0UFhYybdo0GjVqxIMPPlhq2xEjRnD//fcDVKl7dWVU5XyanZ3N4MGDSU9P54EHHiiV/KxNmzbebSty/fXX88EHH/D9999z3XXXERER4V3Xr18/YmJiGDlyJHfffTeapvHhhx+WGuMeSHR0NP/3f//Hq6++iqZptGnThnnz5nnHkPuq7Pmpsp+LH3/8EaWUX7AvhBB1Um2kzhZCVJ1nWpK//vqrUtsfOHBA3Xfffap9+/YqNDRUhYeHq549e6qnnnpKZWZmqpSUFGW1WtUNN9xQZhl5eXkqPDxcXXHFFeU+V0pKiho9erRq3LixstvtqmvXrgGnWanqNE8VbeuZmmTq1KkB12/fvl2NGDFCNWnSRNlsNtWsWTN1ySWXqC+++MJvu3/++Uf1799fhYaGqmbNmqknn3xSvfvuuxVO86SU6zX673//q1q1aqVsNptq0qSJGjp0qN9UPH/88Yfq2bOnstvtpaYpCnYdA/FMgZSWllbudv3791cnn3xywHWV/RuXPD7PvmPGjFFJSUne1+i8885Tb731lt92x/JalpzmSSmlioqK1BNPPOEtLykpST388MN+09YoVfZ7reTfe/Lkyap3796qYcOGKiwsTHXs2FE99dRTyuFwBHzNfH311VdK0zS1Z8+eUuucTqd655131FlnnaWio6OVzWZTLVq0UKNHjy41BdTq1avV4MGDVWRkpAoPD1fnnHOO+uOPP/y2KWsqnzlz5qhTTjlFhYSEqNjYWHXdddf5TaWklGtaoIiIiDKP46233lI9e/ZUYWFhqkGDBqpr167qwQcfVAcOHPBuYxiGeuKJJ1RiYqIKCwtTAwYMUOvWrVMtWrSo9DRPU6dOVS+88IJKSkpSISEh6qyzzlJr164NuM/BgweVxWJR7du3L7dsX8E+n/rWvaxHRcfuy+l0qsTERAWoBQsWlFq/dOlS1bdvXxUWFqaaNm2qHnzwQfX999+X+ruXnOZJKdd0aVdddZUKDw9XMTEx6rbbblPr1q0LOD1WZc5Plf1cXHPNNerMM8+s9GsghBC1RVOqErcchRBCiAoYhoHVauXJJ59kwoQJtV2dOsUwDDp37szVV18dsJuyOHqHDh0iMTGRxx57rMyMy6J2JScn06pVKz799FNpQRZC1HkyBlkIIURQeMYqSpba0iwWC5MmTeL1118nJyentqtzXJk1axaGYVSY6EzUnmnTptG1a1cJjoUQ9YK0IAshhDhmX3zxBR988AHz5s1j48aNVZ6jWYiq+umnn9iwYQOPPvoo55xzDl999VVtV0kIIcRxQAJkIYQQx6x169ZomsaECRMYPXp0bVdHnAAGDBjAH3/8wRlnnMFHH31Es2bNartKQgghjgMSIAshhBBCCCGEEMgYZCGEEEIIIYQQApAAWQghhBBCCCGEAMBa2xU4HpimyYEDB2jQoAGaptV2dYQQQgghhDihKaXIzs6madOm6Hr9ahMsKCjA4XBUS9l2u53Q0NBqKft4IQFyEBw4cICkpKTaroYQQgghhBDCx969e2nevHltV6PSCgoKaNUikuRUo1rKb9KkCTt37qxSkPz6668zdepUkpOT6d69O6+++iq9e/cuc/tp06bx5ptvsmfPHho3bszQoUOZMmVKvQnMJUAOggYNGgCuD2BUVFQt10YIIYQQQogTW1ZWFklJSd7r9PrC4XCQnGqwe1VLohoEt+U7K9ukRc9dOByOSgerc+bMYdy4cUyfPp0+ffowbdo0Bg8ezObNm4mPjy+1/ccff8z48eN577336NevH1u2bGHUqFFomsaLL74Y1OOpLhIgB4GnW3VUVJQEyEIIIYQQQtQR9XX4Y2QDjcgGwa27SdXLe/HFF7nlllu8UzhOnz6d+fPn89577zF+/PhS23um37v22msBaNmyJcOHD2f58uXHVvkaVL865AshhBBCCCHEcc5QZrU8wNW67vsoLCwMWAeHw8GqVasYOHCgd5mu6wwcOJBly5YF3Kdfv36sWrWKFStWALBjxw4WLFjARRddFORXqPpIgCyEEEIIIYQQJ4ikpCSio6O9jylTpgTc7tChQxiGQUJCgt/yhIQEkpOTA+5z7bXXMmnSJM4880xsNhtt2rRhwIABPPLII0E/juoiXayFEEIIIYQQog4xUZiooJcJpfMmhYSEBO05lixZwtNPP80bb7xBnz592LZtG/fccw9PPvkkjz76aNCepzpJgCyEEEIIUU8opXA6nRhG9WS4FaK+sFgsWK3WejvGuDZVNm9S48aNsVgspKSk+C1PSUmhSZMmAfd59NFHueGGG7j55psB6Nq1K7m5udx6663897//rRdTbkmALIQQQghRDzgcDg4ePEheXl5tV0WIOiE8PJzExETsdnttVyXoTEzMaiizKux2Oz179mTx4sUMGTLEVYZpsnjxYsaOHRtwn7y8vFJBsMViAVw3+OoDCZCFEEIIIeo40zTZuXMnFouFpk2bYrfbpeVMnLCUUjgcDtLS0ti5cyft2rWrFy2T9dG4ceMYOXIkvXr1onfv3kybNo3c3FxvVusRI0bQrFkz7zjmSy+9lBdffJFTTjnF28X60Ucf5dJLL/UGynWdBMhCCCGEEHWcw+HANE2SkpIIDw+v7eoIUevCwsKw2Wzs3r27SvP61heGUhhBbnE9mvKuueYa0tLSeOyxx0hOTqZHjx4sXLjQm7hrz549fjcnJkyYgKZpTJgwgf379xMXF8ell17KU089FbTjqG6aqi9t3XVYVlYW0dHRZGZmyjzIQgghhAi6goICdu7cSatWrY67QECIo1Xe56K+Xp976r13UzOiGgS3VTwr2ySp4/5695rUNGlBFkIIIYQQQog6pDqzWIvySYAshBBCCCGEEHWIicKQALlWyGh2IYQQQgghasiAAQO4995760w5Qgh/EiALIYQQQpwglFJsXbub1Us2sG9bSsU7HKNRo0ahaRqapmG322nbti2TJk3C6XT61emtt96iT58+REZG0rBhQ3r16sW0adO8U1q9/fbbnHXWWcTExBATE8PAgQNZsWJFhc/vcDh47rnn6N69O+Hh4TRu3JgzzjiDmTNnUlRUVG3HHUxLlixB0zQyMjL8ln/11Vc8+eSTtVKnzz//nI4dOxIaGkrXrl1ZsGBBhfssWbKEU089lZCQENq2bcusWbP81rds2dL7XvF9jBkzppqOom7zdLEO9kNUTLpYCyGEqLeUMsBMBXTQ42XaGyHKsXT+Gt59/AsO7EzzLuvUqzV3TBlG+1NaVtvzXnDBBcycOZPCwkIWLFjAmDFjsNlsPPzwwwDccMMNfPXVV0yYMIHXXnuNuLg41q5dy7Rp02jZsiVDhgxhyZIlDB8+nH79+hEaGsqzzz7LoEGDWL9+Pc2aNQv4vA6Hg8GDB7N27VqefPJJzjjjDKKiovjzzz95/vnnOeWUU+jRo0eVj0cphWEYWK3+l9EOh6NG5+ONjY2tsefy9ccffzB8+HCmTJnCJZdcwscff8yQIUNYvXo1Xbp0CbjPzp07ufjii7n99tuZPXs2ixcv5uabbyYxMZHBgwcD8Ndff2EYhnefdevWcf755/N///d/NXJcQnhIFusgqK9Z8oQQojYpVYAyM8BIBzMF9MZo1tZoerjPNgZgoIw0yHkJiv4BsxC0CCDPtR9GiZI1IATs50PYxaByXUF00XYwd4OxD1S2e79osHcH8zAYB127WzqBrRkQCsY2MHNAC0ULOQMVMhiK1oNzD1hiwXYKmm5H0QDN2A7mQdBiUFosaAVollZoemT1v5jiuHesWax//mI5z97+ruvj4XPlp+saVruVF+Y9SLseLYJXYbdRo0aRkZHB3LlzvcsGDRpEdnY2y5Yt47PPPuOaa65h7ty5XH755X77KqW811glGYZBTEwMr732GiNGjAj43M899xwPP/wwK1eu5JRTTvFbV1RUhMPhICIigsLCQh544AE+/fRTsrKy6NWrFy+99BKnnXYa4Gr5POecc1iwYAETJkzg33//ZdGiRTz++ON06dIFq9XKRx99RNeuXfn5559Zt24dDzzwAL/99hsREREMGjSIl156icaNGwOurtE9evRg2rRpAHz44Ye8/PLLbN68mYiICM4991ymTZtGfHw8u3btolWrVn51HzlyJLNmzSpVTnp6Ovfccw/fffcdhYWF9O/fn1deeYV27doBMGvWLO69917mzJnDvffey969eznzzDOZOXMmiYmJlfuD4pr2Jzc3l3nz5nmX9e3blx49ejB9+vSA+zz00EPMnz+fdevWeZcNGzaMjIwMFi5cGHCfe++9l3nz5rF169Yyb34ez1mst2xMoEGQs1hnZ5u075RS716TmiYtyEIIISpFmVmoovUAaLYuaHoD7zqzaBsq60koWgU4gTA0SwIYaUABaKEQ0h8t8k4wc1FZz4BzVennwIoKGw4hZ0Peh+D4tYzKlFtT13M6vnM9ypUPhcn+i8z9EKDnpXL8AtlPl1mVQFVS3uU2sLQCIsHYjiLXtYEWg4p8BN2WBMYBsDRHs3UEisA4BHoUml46OBCiKhwFRbz+0CeuX0q8UU1T4XQ4mTFhDs/Pe7BG6hMWFsbhw4cBmD17Nh06dCgVHANomhYwOAbIy8ujqKio3FbU2bNnM3DgwFLBMYDNZsNmswHw4IMP8uWXX/L+++/TokULnnvuOQYPHsy2bdv8yh8/fjzPP/88rVu3JiYmBoD333+fO+64g6VLlwKQkZHBueeey80338xLL71Efn4+Dz30EFdffTU//fRTwHoWFRXx5JNP0qFDB1JTUxk3bhyjRo1iwYIFJCUl8eWXX3LVVVexefNmoqKiCAsLC1jOqFGj2Lp1K99++y1RUVE89NBDXHTRRWzYsMF7rHl5eTz//PN8+OGH6LrO9ddfz/3338/s2bOB4psBO3fupGXLlgGfZ9myZYwbN85v2eDBg/1uggTaZ+DAgaX2KWsMtcPh4KOPPmLcuHHSM0jUOAmQhRBClGIULEHlvOJqoTXzgOxytg4FCtDwvYjJASMXhXItV0VQMA9VMK+sQtyckP+h64HlWA+jDikCYwuqZHSiUtGz70O5/3PRAdNnIx0s7dGjHkXTo1HGPtDCwJKEpsf43agQIpDli/4hJzOvzPWmqVj35zYO7kojsWVctdVDKcXixYv5/vvvueuuuwDYunUrHTp0qHJZDz30EE2bNi0VdPnaunUrAwYMKLec3Nxc3nzzTWbNmsWFF14IuMY7//DDD7z77rs88MAD3m0nTZrE+eef77d/u3bteO6557y/T548mVNOOYWnny6+mfbee++RlJTEli1baN++fak63Hjjjd6fW7duzSuvvMJpp51GTk4OkZGR3iA9Pj6ehg0blnms3377LUuXLqVfv36A6wZBUlISc+fO9XZTLioqYvr06bRp0waAsWPHMmnSJG854eHhdOjQwRtQB5KcnExCQoLfsoSEBJKTk8vYo+x9srKyyM/PLxX0z507l4yMDEaNGlVmmcc7E/9vgmCVKSomAbIQQpwATDMbM2sKZuFPrm7NWIFCXF+XCtDRrR0gchxa5n+AnCqUXhwc+wfJLt4gucpKdp2u3zyvQ1mvh2dd6UsYE4xNmOnXBShVg5BBWCLvQbO1RymF6ViJKvwFRSi6/WQ0vRGapQmaJb46DkvUA6n7jqDrGqZZ/qi61H1HqiVAnjdvHpGRkRQVFWGaJtdeey2PP/444Aqaq+qZZ57h008/ZcmSJeV2N69M2du3b6eoqIgzzjjDu8xms9G7d282btzot22vXr1K7d+zZ0+/39euXcvPP/9MZGTpoRXbt28PGCCvWrWKxx9/nLVr15Keno5pus4Be/bsoXPnzhUeA8DGjRuxWq306dPHu6xRo0Z06NDB7zjCw8O9wTFAYmIiqamp3t979+7Npk2bKvWc1endd9/lwgsvpGnTprVdFXECkgBZCCGOI6aZg5H7EapgMcpMB0simqZQjj9KbOkouSemcyNk3IKOVuUubQqFHmBihKMLjI9f5QXHnvVVLZHCHzAKf4GwqzDyPsW3f7iZ67OpngBaLKgc0ELA2hHN2gYIQ7cmYQkbgKYF7rop6rfoRpEVBscA0Y2rpzfCOeecw5tvvondbqdp06Z+ya3at29fpYDs+eef55lnnuHHH3+kW7du5W5b1bIrEhERUeGynJwcLr30Up599tlS2wYa55ubm8vgwYMZPHgws2fPJi4ujj179jB48GAcjpLn6WNXsmVY07Qq36Ro0qQJKSn+GdBTUlJo0qRJlfcJ1GV89+7d/Pjjj3z11VdVqtfxxqiGeZCDXd7xSgJkIYSo40znTpSxB02LRrN1Q9N0DOdenNnPoZx70PQGWMJHYOR/iypc4M7B4/4SNLd7vw4rG6yaKHRFlYLk8so++hbk40tFr0HVg2MPE0UhKu/DCjZLQZFcHIwbW6HQ1WptAEWZng11sLTFGnErur03ujURTZPLhfrs9Au7Yw+14SgIPK2Rpmm06JhIiw6VT9RUFREREbRt2zbgumuvvZZhw4bxzTffVJik67nnnuOpp57i+++/D9iaG6jsRx55hDVr1pSZpKtNmzbY7XaWLl1KixYtvOv++uuvo5pj+NRTT+XLL7+kZcuWpbJcB7Jp0yYOHz7MM888Q1JSEgArV67028aTGds3w3NJnTp1wul0snz5cm8X68OHD7N58+ZKt0JX1umnn87ixYv9Xp8ffviB008/vdx9Sk4FVdY+M2fOJD4+nosvvjhoda6PDOV6BLtMUTGZB1kIIeoY09hPUe5MCjP/S25KH/LTBlB4ZCQFh4dQkHo6+cm9cKSdjVkwD+X8B9OxlKKM21CF8/Fk4NF8/jsaVW/HlG/dYDjqGwmqcrNb+pbv/7MvE4wtOLPux3HobAqS25F/sB35qRdQmDGewqxpFGY+gyP3CwznnqOrr6hREVHhXPfAJYFXuv/4N028qlaSIV199dVcc801DB8+nKeffpqVK1eye/du5s2bx8CBA/n5558BePbZZ3n00Ud57733aNmyJcnJySQnJ5OTU/ZwkHvvvZczzjiD8847j9dff521a9eyY8cOPvvsM/r27cvWrVuJiIjgjjvu4IEHHmDhwoVs2LCBW265hby8PG666aYqH8+YMWM4cuQIw4cP56+//mL79u18//33jB49OmCAe9JJJ2G323n11VfZsWMH3377bam5jVu0aIGmacybN4+0tLSAx9yuXTsuv/xybrnlFn7//XfWrl3L9ddfT7NmzQImQCvLihUr6NixI/v37y9zm3vuuYeFCxfywgsvsGnTJh5//HFWrlzJ2LFjvds8/PDDftnFb7/9dnbs2MGDDz7Ipk2beOONN/jss8+47777/Mo2TZOZM2cycuTISt1gEKI6yDtPCCFqmTIzMI19mEY6RVmPo4xtpbdxhz/KTA4Y+Gref8vuvlulOqEoGTaJY1OZlvSjvdFwLLcnisc+l1W2QuEAYyNmvv+YTFd7pA7EoVmisYT0xRp6Ibq9L5om9+DrkqvvvgBN05g9dR6F+Q5v19qGjRow9vnrOG1g11qpl6ZpfPzxx7z11lu89957PPXUU1itVtq1a8eIESO8c+S++eabOBwOhg4d6rf/xIkTveOZSwoJCeGHH37gpZdeYsaMGdx///2Eh4fTqVMn7r77bu+cvc888wymaXLDDTeQnZ1Nr169+P77772ZqquiadOmLF26lIceeohBgwZRWFhIixYtuOCCC9D10p+JuLg4Zs2axSOPPMIrr7zCqaeeyvPPP89ll13m3aZZs2Y88cQTjB8/ntGjRzNixAhmzZpVqqyZM2dyzz33cMkll+BwODj77LNZsGBBuQm3SsrLy2Pz5s0UFQXubQDQr18/Pv74YyZMmMAjjzxCu3btmDt3rt8cyAcPHmTPnuIbaK1atWL+/Pncd999vPzyyzRv3px33nnH+/f1+PHHH9mzZ49f4rITlSTpqj0yD3IQ1Nd51oQQNUepQpyFv6HMI+iWpljsp6PMZBxZT2MU/I/KJqQqKxmWXonAq6qBsqWKAU6gMcjKp0X7RFfea+G9AXKUoa6p1DG14hc/f4Cyq3hJpZTCRAMsoDfEaj8Ti70rmt4MW2g/NL3hUdfzRHas8yB75GUXsHzRP2QdyaHJSY3peW5nrDZpLxH10/E8D/LfG+KrZR7kHp1T691rUtPkjCiEEEFkGIcpyv8es+hvnI6/UMYBNByUDIA1PQ6NQjSVV2pdeWoq0DyaZykZAJYZsOktwNxdTkmNQbe4ppjCQnGm7eNDyZsVxxocB0NZrchVrZMrUHftCU4wD+EsmIuzYC4ABQBYQIvBGnoRtvChWO2d0TT7MdVfVF54g1DOuap3bVdDCFEBEw0jyN/5ptysrhQJkIUQ4hgppTAcm8nNuAdlbCi1PmCLoZnmnlypasmwymoJDnYirKPtlu0fHOuAFQ0TCIXQs9Ai7wZLG5RjGeRMB+cmUA7QI8F2Klr4VWA/CzCg8EdUwc+AA83aGRVyFhoGStmgaAWY2WDviWbvjaZpmKYDCn8BxxrQ7GDrCJhQ+BuYB0CPB3tfCBmMpoEiHNdLr6GKNrkCcq0Rmq05yixAM/eBFgXW9q7yHX9D1lRw/oOrc7EVtIZg7wLWk8HaClQe5H8HRWvxhIO+r6lvMBwoSD56x7Z/MIJzpSpXisIJKg1H/vs48t93L9VwZdPuhC3sCuyhp2OxlZ4ORwghhKhuEiALIUQVGMYBCnK/pqjwF0xjL5i5aGSglREaVBRmmoCuVKWD5PIC4eAFySFoWtnjz1ys7kc4WkhftAaPoOlRqMKVQA6arTO6tWWZe2sh/SCkXznl6xB6IVrohcX7+P5r71B6D90OYee7Hr7CLgpcB9+f7Z2ATsW/6wD+GX11ew9oPLucOruFXw2AUu6eA8oAM9k1LzEmSpmowtWoot9Abwi2PmhFy11ZpfVGYDsDCr533QSgwFVTvRGYhUBW4KPQwkDlV1y3MmhoBErzVZXAubIdsQM/lwLyMJ2rKMxeRWE26NYuhDd8Bqu9e6XrIIQQxwtTuR7BLlNUTAJkIYSogFKKgryvKch5HtMonbXX832jU7rltTq6RAcKhD0ptQK1TGru8L3imoRiibwLS+TtmEVbUTlPg3EIrJ3QIm5H1wzAAZaWaHpkwBK0sLOP/sCOM95uwxqgt/UP8G0tgSt9tr7Af+eIq0uVp5SJcvyJKloHzi2ghaNZ4tFCL8OCwnFoOKjkMusT+H3jeX+UdYOn/AReR6Oy5ZnOdWQfuhJ7xK0UOX7HNPahlAMNG5qlORFRD2IPlfebEEKI4JIAWQghcHcPVRkAaFpDb4uuo+BnctLv8q4rjwnoZQSoQa9vFYJkE4WuNwXNBoSgh56PFnYjmrEVpfLQrCeh6w1Aj/Met8XeHmJnBb3e4uhpml5uy7s94TeMgiWYeZ+jzF2gTDBSgGzX/mW8Dys3QVTNUyhMCinIfbXEcjCdaWQfuRYFKEJAa0hEgwcIjxxWK3WtSZJbVYhix/PnwaiGMcjBLu94JQGyEOKEppQiP/cjcrNfQZmueR81LQJbyOmEhl5KbuY4jmV8Z3Ul1Spdrg09YhzWkK4YBd+jijaCFoNmP4uQiMvQ9OjShdj6VEvdRO3QNAvWsPMg7DzvMqUUyrkJzMMovQmaZsVwbEY5fsU09qOKtoFKB/ICl1ktLciVU16XbU+9XGUVgEomN+s/5Gb9x50oLJSQkPOJavgsFmvDY61yneCZqicvL4+wsLBaro0QdUNenuvcVZWprOoLCZBrjwTIQogTjlIK0zhEbu7H5Oe+hlK5JTbIRRX8SFHhYjSqFuSW7MpcmWmOKjv+WNObY4t6FIslAWf+t5jO/Wi6HT10CNbQ84pbf0POqHR9xfFN0zQ0Wye/Zbq1JYQPLrWtUk6UkY5SeZiFP+PM+wLMA2jKgaIQcJT5PEopdGpujs2yJrVSSrl7URRQWPgdaSnf4cqEHk9I6KlERj2Mzda6hmoZXBaLhYYNG5KamgpAeHh4lRL8CXE8UUqRl5dHamoqDRs2xGKx1HaVxHFEAmQhxHHNNHNQKhddj8VZtJHcnDcpyF+AKwtxOfvhSp7lSnNc+W7SgVIP6e7WrtJl6OhaCK5ETFa80xnpCaDFYrGehCX0YnRrUzQ9Ft1afGFvt59SqfoIUVmaZkWzxgFgsY3CFjnKu04phbNgIUXZz6KM3bjeq67Phmtf9/zclcxkXZHySilvxufA9TCAgxQWzKewYD5gxR42nKgGY7FYm6Jp9efCukmTJgDeIFmIE13Dhg29n4vjjak0TBXkaZ6CXN7xqt4FyK+//jpTp04lOTmZ7t278+qrr9K7d+D5/AYMGMAvv/xSavlFF13E/PnzARg1ahTvv/++3/rBgwezcOHC4FdeCFFjCguXk5U1hSLHCveS4kC3rIzTJRWP6a38vMCBtjNRpZZrltaENZyCxX4KRv4CTGM3mtYAS9iF6JZmlXw2IWqGpmnYwi7EFnZhqXWmmYXhWA/mAUwjhaKC7zGLNgP5HO3whGMNsotD99LlKpwU5H9IQf6HgJXw8GFENhiD1driGJ+1+mmaRmJiIvHx8RQVVZRpXojjm81mk5ZjUS3qVYA8Z84cxo0bx/Tp0+nTpw/Tpk1j8ODBbN68mfj4+FLbf/XVVzgcxV3CDh8+TPfu3fm///s/v+0uuOACZs6c6f09JCSk+g5CCFHt8vMXkH7kFvwvkYvnn4XKBbwKd5dNrawW4NLK38KONfRi7BHXYbGf5m3tsoZfWe5eQtRluh6FHnq69/eQBnd6f1ZmHo68TynKn4dZtBaFA72MKaV8lRXgUs5yv/01DUok71EB93WSl/cx+fnf0jhuLjZbR5QqQqlcNC0STaubl0kWi0UCAyGOczIGufbUzTN/GV588UVuueUWRo8eDcD06dOZP38+7733HuPHjy+1fWxsrN/vn376KeHh4aUC5JCQkOO2e4YQJxrTzCMj/W7Ku4yuSovw0dL05ujWblhDTsdi64rN3qHMqZGEOF5pejghkTcSEnkjAIaRgVG4BtNMxTS24iz4AdPYRcnRy66bUXZcQyGCM7K57DOCiVK5HDl8K1Z7L/LyvwQcaFoYdvtp2G1nYrd3JjTkLNdc20IIIY5r9SZAdjgcrFq1iocffti7TNd1Bg4cyLJlyypVxrvvvsuwYcOIiIjwW75kyRLi4+OJiYnh3HPPZfLkyTRq1KjMcgoLCyksLPT+npWVVcWjEUIcC8PIITv3A7JzP0WZaWh6FJHhVxMVOZKCgkUoFTgjr6/qCJJ1EgmJuJzQBvej66FBLl2I+s9iaYgl/JziBdETADDNTAzHagzH36BHY7G2Qbd1JvvwcAznJr8yymtd9ig59UuF2+PEYWzFkb8D15hlUCqfgoJfKSj41SdEtxMRdiWNYp5B16W3mRCi+hjoGOhBLlNURr0JkA8dOoRhGCQkJPgtT0hIYNOmTWXsVWzFihWsW7eOd99912/5BRdcwJVXXkmrVq3Yvn07jzzyCBdeeCHLli0rs/vSlClTeOKJJ47+YIQQVWaYuaRnTiEn92vgCOAT5BoZZGY/T1b2GzQIv4DKXUJXjqZpaESjUfJGWAK6NQ5djyUk/HpCwi5A04L7RSbEiULXo9FDz8EWeo7f8ui47yjM+5r8nNcwjT1A+VNPeQLjqrQ5u+Zb9vC/fPT01C7O0O0gN/9TcvI/RdcaER5+IbFRE7BYAkyjJoQQol6qNwHysXr33Xfp2rVrqYRew4YN8/7ctWtXunXrRps2bViyZAnnnXdeyWIAePjhhxk3bpz396ysLJKSkqqn4kKcoJRSGGYaBYX/kpUznQLH7951Wol/vfuQR27+fCxBCo51wB4yiAYxMzCKVmIaB9H0xthC+qFpx9+ci0LUNZoWRmjEtYRGXAtAUcFyCgq+oTD/V5TaGXCfYE815Z1Jyee0ogFKHSY79yOycz/CYmmO1XISYSH9iYq4Bqs1IVBRQghRaaoaslgryWJdKfUmQG7cuDEWi4WUlBS/5SkpKRWOH87NzeXTTz9l0qRJFT5P69atady4Mdu2bSszQA4JCZFEXkIEmakKyc77jtyCReQXrMRUqXjSSXgeHr7hb8lTvakKsVRibtCKtrBaTiKiwVhCw4ehaVb0kH6VOQwhRDWyhfbBFtqHBg1dN9Fys1+mIO8jlJmFqXKP6tbYsdxO03C1OZvGPhzGPvIcf3A4ewoaEcTHPEdUhCTgE0IcHUnSVXvqTYBst9vp2bMnixcvZsiQIQCYpsnixYsZO3Zsuft+/vnnFBYWcv3111f4PPv27ePw4cMkJiYGo9pCiErIyf+ZA4dG4ZmbuDi+9WSPDizQOGIFoDUGdagSz+zpih2J1docm70nIWGXYLefia5Lhlgh6jJN04iMupfIqHu9ywxnDpnpN+NwLAeKc4UEaAQOmuK8+MVnJEUuKeljSc9+F5u1OSH2k4mJvFVyEwghRD1QbwJkgHHjxjFy5Eh69epF7969mTZtGrm5ud6s1iNGjKBZs2ZMmTLFb793332XIUOGlEq8lZOTwxNPPMFVV11FkyZN2L59Ow8++CBt27Zl8ODBNXZcQpyIHEU7Sc+ZRU7eAgxzv3esX6DGXxNXd+dAgXKgINlq7wPGTpzODQGf227r6uoSaetAePi1WKzNj+1ghBB1gsUaSWzcp97fDSOd3Kzp5Od/iVKpgLPkHmg43bfhysl8X8YqV3fu0oM+lHLd3CsoWkNB0Rqy87/jUOazhNi6ER1xDVERV2LRo6p+gEKIE4ahdAwV5CRd1XGX8DhUrwLka665hrS0NB577DGSk5Pp0aMHCxcu9Cbu2rNnD7ru/0bavHkzv//+O4sWLSpVnsVi4Z9//uH9998nIyODpk2bMmjQIJ588knpQi1ENTmc9TaHsqZiqhwAfNtpA/eMdl24lhcklxQaegZRkW+Tnz+fnOxXcDp3AhZCQs4mssHt2O2nHONRCCHqA4slhqiYh4mKcc2AYZrpOBz/UFjwM6aZiq7HERZ+Fbl5X5GT+xaBgmSlyg6dXQGy/2264u1L928pLFpLasZaUjL+S0ToIBpHP0CYvfMxHqUQQohg0lTJuRBElWVlZREdHU1mZiZRUXJHWAgPpRxk5n5Neu5HOJx7MMwMXC04xacdWxWGw+gBuluXHJ8MIZzU9F90vcFR1loIcaJRqogj6feTl/8ZoKOUu21YA7OMANnEk/Pa/wxklnNVpSjZ6gx2a3uaxDxOZGj/YzgCIURJ9fX63FPv+f+0JqJBcId75WYbXNxtR717TWpavWpBFkLUH6aZx+604eQ7VlJ62qXiEYGmAr2SQXJZcxcXL9dJaPyBBMdCiCrRNBuNYl+mQdHt5OZ+TqFjOYWOvzFV4JzYClePlpJzipbX5FAcHPtzOLewJ+1awuxnEBF2JjERV2OzlJ98VAghRPWRAFkIETROI5tD2W+RX7SeoqLtOI0d7jVlXzUagFbG2OOSym491gixn0ajmJew21ofTdWFEAK7rRP2ho8BoJST9MznycqZARSU2rY42C3r1l1Z25feVgF5jqXkOJaSkvmceyqWEEKs7WgSPYGo8H5olTlJCiGOG5LFuvZIgCyEOGZKKfYcepCMfE9yHIUVVYmgt3h8ccWdiJQ3zHYFxqGEh55Pw6j7sdvaoGmSdVoIETyaZiW24XhiG47HNE0KCpeRkf0yBY4/gSJvWOw6L5UfJAcek+zzXD4bFo9rLqTAuY5dh4fBYYgIOYPWcR+i6zIHuxBCVCcJkIUQR8Uws0nP+4703PnkFv4GJcYHG2hYVGWCZCoZIIPd2oGI0HOJDB9KiCS2EULUEF3XCQ87g/CwMwAoMlJwFu0kNf1eiozd3iBZ0zSKU7tUraXGv3u2/74KRU7hUtbua0NkyBnEN7iVqLD+aFpwM9wKIeqO6sliLamnKkMCZCFElSilSM56leTMV1EUlJFZWkPhSsdlrTBIdrXBlJziyfd3u7UjiY1mY7fJ/ORCiNpnsyRgsyTQIvFPHEW7OJTxJPmOpShVhI6GSV6Vy/RMdVc6OAbTO6BEkV34O9mFS91rQ4mLHEXzmIelC7YQxxkTzf3ZD26ZomISIAshKsXhTGF/5ssczp2DThFQufYRg8qdaJT3fx4WIkOvJC5mIlZLbFWrK4QQNcJua0nTuHf9lqVlTOZI9gz803iV37IcaDqp0om9NL91UEBqznRScqZjs7SkbaO3iAjtWPWDEEII4SUBshCiXHmObSRnvk1a3ueAie4eC6xpKsAUSyW5WpJVJbpaK8CqNyM89Axio8YSYmsbnAMQQogaFtdwAo2iHiI772tyC34mO38ROvnu1pvA45VdgbB/d8rigDlQPx3f1mVwGLvZkHoBoJMUPYGEqNHSqixEPWaiYxDcLtZmOUlTRTEJkIUQpRQZ6ew6/Djp+d+jKPRb5+nyY1EGlkpde2moAPMXe+haDM1j3yE87BR0LeRYqy6EEHWCrtuIjrya6MirUcrJkez3OZT9OoaZ4t1G+fwbaAqo8pJ7+QbHfi3LymRPxiT2Zj5PpL03J8XcT0RIl2M+HiGEOFFIgCyEAKDQmcLB7HdJzfoCk/QyLsmK5y820LEFvKQrydXirGvRKFXgDbitlibERt5Kowa3SKIZIcRxTdOsNIq6iUZRN2Ga+WQXLCIn/xfS875wnyGVJxsDlRm8UlZw7HouV3dtQ+WRXvgL6cm/YCGCkxO+IFK6XwtRb0iSrtojV6VCnOCcZjabUu5l5b7T2Z/5NibpldjL1bnarNR5ViM85Cw6NFtPx+bbadd0Fe0SV9Iu8S8aR90mwbEQ4oSi62FEh19Os0Yv0jrhO6LCBpfYItBI5LKWlB1M6z7rDfL4J/lC1uy/lPS8X46q3kKIE9frr79Oy5YtCQ0NpU+fPqxYsaLc7TMyMhgzZgyJiYmEhITQvn17FixYUEO1PXbSgizECUopxe6MN9mb+RKuzn061ipNIa/hRMOmFGilL9M87SCh1pNpHT/bOxbOZmkSpCMQQoj6LdzenRaN38FUheQU/MX+I/fjMPbhOYO6WpUDnZUryP7g7exT3CKt0Mgt2sC61JsItSbRtMFI4iOvwGaJDt4BCSGCxkTHrANjkOfMmcO4ceOYPn06ffr0Ydq0aQwePJjNmzcTHx9fanuHw8H5559PfHw8X3zxBc2aNWP37t00bNgwCEdQMzSlpK39WGVlZREdHU1mZiZRUVG1XR0hyqWUYnf66+zNehncXfs86V7sWmW6TPuz4UQPECADNIq8keaxTxxbhYUQ4gSilCIjdz77Mx7HaaYEzGJtuhMglhcoKwVOn1SKnqs91wV3ceAcYmlK20aTiA0fEMzDEKLW1dfrc0+9P/67C+ENLEEtOy/b4Noe66r0mvTp04fTTjuN1157DQDTNElKSuKuu+5i/PjxpbafPn06U6dOZdOmTdhstqDWv6ZI30YhTiBZBev4fXd3dme9jIn/HHtVz3XqDq41zV0WaFokNstJNI68hS7NN0twLIQQVaRpGjGRl9Cl+Uq6J+2gVaM3ibT3wr/TX/ljlYunjCreRtM8LcvFN0UVkG8c4N/Um1m29yxSsxcg7SZC1A2G0qrlAa4g3PdRWFgYsA4Oh4NVq1YxcOBA7zJd1xk4cCDLli0LuM+3337L6aefzpgxY0hISKBLly48/fTTGIYRcPu6SLpYC3EcM1URyTlz2Zv1AXlF292doqHkhZWJ4mjuUWooDDR0IunUZC5h9vZBqLUQQggATbMRE3EJMRGXoJTB/vRnSc2Z4Z4bwB0FlzGVkxkggPaPfV1duD03SvONFNYdvhcO34tVi6VbwgwahnavhqMSQlSGUQ3TPBnuW2dJSUl+yydOnMjjjz9eavtDhw5hGAYJCQl+yxMSEti0aVPA59ixYwc//fQT1113HQsWLGDbtm3ceeedFBUVMXHixOAcSDWTAFmI45SpHKw8cA05RRsAVc4p1pPERWFRoFeqKdnTNhFKXMTVNGt4NzZL3LFXWgghRECaZqF57CM0j32ErPylbEu7B0NL8673DX4NtFJjl4vXl7xB6umGXdwq7VTprE6+mpjQM+iR8I4kUxTiOLN3716/LtYhIcGbZtM0TeLj43nrrbewWCz07NmT/fv3M3XqVAmQhRC1J6twHX+n3InDcM236ZmvuPzYV8cANOUa8VZGowQaoTSJGk2z6DFY9IhgVVkIIUQlRYWdwaknrcQ0C9h26F4yC34ADO9wl0Bne03D273Sw3/b0gH1kfylLN7VlVBrEm1jx9EkYlCwD0UIUQZT6ZhBnubJdN8pi4qKqtQY5MaNG2OxWEhJSfFbnpKSQpMmgZOuJiYmYrPZsFiK+yZ26tSJ5ORkHA4Hdrv9GI6gZsgtQSGOE04zl31ZX7F456n8eWAohUYaCt3VdqwqzHkKuLKcOgN1ywPAQofGH3HaSes5KeZBCY6FEKKW6Xoo7eOnc9pJ2zntpB00j74fPEm43BfCyv2jqYoTduHawt19M/CYY817Y9VJvnM3/6Tew6KdnViXOhHTrHpCRyFE/WO32+nZsyeLFy/2LjNNk8WLF3P66acH3OeMM85g27ZtfueJLVu2kJiYWC+CY5AAWYh6zVQO9mXPZcnugSzefRrrD/8XJ/koXImzjibVikKnCI0iBYYCA4gOHcSpzVfSMOIM6WonhBB1kKbpNG84lj5J64gPvwZdC/cm6zIDdLn22bOcMn0Te7n+vz/3M37Y3YUNaU9imvUn6Y4Q9Y1nDHKwH1U1btw43n77bd5//302btzIHXfcQW5uLqNHjwZgxIgRPPzww97t77jjDo4cOcI999zDli1bmD9/Pk8//TRjxowJ2mtT3aSLtRD1VJGRyV8HbyS7aKN7iScc9k6A6V2i0FBKldltunQobaFNo0mEWZsTbu+E3dI4iDUXQghRXSx6OG3jnqEtz+BwHmHHkcc5lLcIhRPPOGNP0FyV+QuKvyVc++zN+YS9OZ/QOHQAPRPfCOoxCCHqjmuuuYa0tDQee+wxkpOT6dGjBwsXLvQm7tqzZw+6Xhx4JyUl8f3333PffffRrVs3mjVrxj333MNDDz1UW4dQZTIPchDU13nWRP22JmUsqXlLgPK6unmSaSnslD+22DM7ZlRIHzrGTSPEKkm3hBDieOA0c0jNmcuejDcpNF1jCRV6meOVS+2vtICpHpX7fyY6SVHX0KXRf6WXkagz6uv1uafeM1b3JCwyuG2Z+TlObjt1Vb17TWqatCALUc/kO5M5lLeM5NyfXZ3myr22KV7pRMeKWWL2TM9vOt3jP6NheI/qqbQQQohaY9UjaRp1PU2jriezYDWb0x4h39hRqX1NRZndsz25r3VMdmV9xq6sz2kReQ1d4h5C1+QSUwhRP8nZS4h6Is95kH8PPUVq3m+4LkksaJjYym1BBt+xY0VoWFVxxi4NK3HhF9Kx8VR0/WhmQhZCCFGfRIeeSu+khezLnMW29GcwMSmvFbmirtjem7TuDom7cz5ld85nJEVeRve4J6RFWYijZKK7+/YFt0xRMQmQhajj8ooOsjXjffZmf4HCUWJt5cePebfW7LSPfYLGEQOw6bFo5TdBCyGEOA41jx5Fs6gRpOYsYtOR/2KoHFyj7nz7GGk+t1grmihQuS++FQqTvTnfkJz3G32avEZMaJfqOxAhjlOG0jGCPM1TsMs7XkmALEQddajgH1Yk30+BkQbFEzb5dalWuG7aV9zN2tXKHGntSK+mH2O1yBRNQghxotM0nYQGF5DQ4AIcxhH2Z33MwZzvyHPudrcrB54jORDls61SrmE9RUYGP++/Hp0QkiIv4pS4/6LrcukphKjb5CwlRB2ilCI57zdWp06i0DzsWQq4urmZaFiUQteKM1Yb6FiUWUaQ7EnSpdEs8mo6Np6Artmq+zCEEELUM3ZLLK1ixtIqZiy7MmaxNf1Zn8zVZbcge1K9egJkQ2nelmTPPiaF7M6Zy+6cuSRFXErP+IkyrEeICniu+4JdpqiYtLMLUUcoZfBXyiMsS77HJzgGvOO/3BcfaJg+uedN95zHJUpz/6uTGH4Z57RYSee4JyQ4FkIIUaGWDUdxfquNnBQ1Gq2cthRPcGy4v6OU8r0A970QL/55T+53zN3Zl+3pX6OUzKMshKh7pAVZiDri70PPsS93YQVbuXKGmujeCTpcrcgWNKVj1Szoeghh1mY0b3AVzRtcha7Zq7nmQgghjkcdGz1Ix0YPkuc4wB8HLsepcku1PxlomLhag8trnfJkw1ZoaJrJqsOTWXV4Mp1jbqFLo9ur8SiEqJ9kDHLtkQBZiFrkMLLZlT2f/blLSMv/Cx2tUlM3KVSJscca/ZrNITqkY/VXWgghxAkl3N6UgS3/4kjuKlan3YXDzMB0B7u+nRHLynhtquLsuZ7vLU1ztUBvSH+b3dk/MCjpQ2yW8Bo4GiGEKJ8EyELUgizHbv49/DZ7cxejMDz31F3jiXFChYm3fGk0jRgswbEQQohqFRvRk4ERf1DgPMzv+68h30gBn5HKLiUzYeNOMVn6e80TJOc6d/HlzvNoH30NPRqPlTmUhQAMdIwgj4YNdnnHKzkDCVGD8orSWHJgHBlFWwDlvY5Q7lyhrrQmmndijcDcibc0AJ0WDYbSpfFD1V11IYQQAoBQayMGtviRvKL9rEy5n0zHevfXmfLJZu3imTmqrHu+niDZpIhNmZ+wNesrusTcTOeYG2QaQiFErZAAWYgasiNrIX+kTHR1QHNNSOx3xaDQMDEBHR2j3OmbNBRNwgfSLe6/hFgaVX/lhRBCiBLCbc04u/knOI1cVqbeT0reH7jSbhW3IldmOkJNA125JiQ0zAL+Pvw6/x55j5NjbqRroxHVfhxC1EWm0jBVkLNYB7m845W0swtRzRxGLr8nT+Z3v+A4EOVNYGKUk+hEJ4Q+CS9xWpMXJTgWQghR66yWCPomvsn5Jy0gKWIwJduLK9MQ7O03pXmmi8rn7yOv88m2c0nOWxXU+gohRHmkBVmIavTvkY/4+/BbmBShKQ208rpOe64gFAoLBgZWn67WGlbaNbyBk2PHomlyb0sIIUTdEm5rxmlNnqWT4x4W7x2KQT5QcQuyUv4jmbXir0OKVB4/7B9D99jb6BJ7g4xPFicMsxrGIJvSNlopcpYRohoopVi4byypBWvcSzS0coPjUiXgzVYNnNxwLJ0a3RT8igohhBBBFmlvyuVt/mBf1iL+PvwMeUZmuUGya9yyJeA6DVcA/ffhGfxz5GPOaDKeVg3Oq7a6C1FXmErHDPK0TMEu73glAbIQQeQ0C1mW+hJbsuZhwfS7GFCVCpI9611TZSh0ujW6l/YNr6umGgshhBDVo3nUIJpHDaKwKJ1v9wwGd34NXwrc45bLpnC1fDlUPosPTiQx/SvOb/YcdktE9VRcCHFCkwBZiCBJy9/Ed3vHYFIIuOaD1FXxnMaeRCVQ1l105Z3eqVFIF5pH9qdlg0sItTauieoLIYQQ1SLEFsNVrf9gVdpz7Mqai8JwB72aewqoAJmv3Qyl4/R2C9UAxcGCtXyw/UJOijiLvnFjiLI3raEjEaLmGBXkpDnaMkXFJEAW4hgdKdjOr8nPcsix0b3E9QVuomGioykTCwpN88xzbAboaua6GrBqds5oMoVmkWfV8FEIIYQQ1UfXrJwW/winxT/Cwdw/WJE2hVxnSqntPMGxias7qLNU12t3dmyl2J3zKztzfqV9gwsYkPiITAslhAgKCZCFOEpKKZamvMymzC9dI4b9vpeLf3FlpQarO0u10x0ke1qLXclJNOxaFJe2/IQwaTEWQghxHEuM6Mdl4d+yJfMz/j3yDg4zy2+9CeD+vvSdMsqX5ztXU7AlexEH8tZyUdJUYkJaVHPthagZMga59sirJMRRSM5fz4c7hrEhYHBckmvqpuIuY66WZKfScCpXF7O2UUMY2vp7CY6FEEKcEDRNo0PDa7iq1SI6N7wJQ2kYqjg49tw8DhQce/h2xc4xUvhi143sz11dzTUXQhzvpAVZiCowlcHKQx+y4vAHgEFoFW4xKTQ010hkAEItDWkXfSndYm/Bqturpb5CCCFEXaZpGj0a30qHhtewaN+tZBXt9pn2qSpdphWGcvLd3vtJiuxHj5iraRbRrXoqLUQNMAj+mOGKEuIJFwmQhaikLMdBvt7zH7KdySgUehVPWp4b3UpptIu6jH4JD8l4KSGEEAIIs0Zzecs5ZDv2szz1OfblrcB/ZoeyaH7/OoGdOX+wM+cPbFo4Q1u8TqNQ6XYthKg86WItRCU4TQef7x5LljMZAK2Cbl+laZiAoTTaRl3OGU3GS3AshBBClNDA3oyBzV/mtMb3uL9rA/N0ry5r8kQFFKo8Pt41ml8Pvhb0egpR3TxjkIP9EBWTV0mICpjK4PsDT5NvpPt9VSvAVJSa07EU5dpIx8I5iU9xZpOHqrG2QgghRP13cuwwLm0+HZsWDvh/1xZnui6+Wa2U53d3Bmz3FFImOmsyvua1TRdTUJRTg0cgxLExlF4tD1ExeZWEKEehkct7W29ge/avAQJhDaeyeDNRl0nT6BE7mtHtfqFVgwHVV1khhBDiOBIffjI3tPkfPWJGoGm6536zdw5l3+AY9zIT3HMr+9A0nKqQ6duG8MmOe2vwCIQQ9ZEEyEIEoJTJ76mzeGPLFWQbaWha4EzVJjoO0+Kzn0+3LwU6di5u/iq94m6SLtVCCCFEFem6hV5xt3BDm3kkhp3qbRl2Ud7vXAPdNWOEd53/d67nKzi5cD0vbRxMct6mGqm/EEdLoblv+gTvoYKc9Ot4JUm6hCghLX8nH+0ci6IIUK4WYve6QKcVE50CpWFR7s5cmsJU0DryfM5uMo4QS2QN1l4IIYQ4/oRYGnDJSS+T70xn8cFJ7M9bhWvaRM07HZTr38DzJntoKBSKj3ffTay9Jde3eh2rbquhoxBC1AcSIAvhppTJz8nvsjL9c+8Hw9Xqq1Bo6GWmAgHXlzRYsNIq8mx6N76F6JBm1V9pIYQQ4gQSZo3hkqSXSC/cxYaMb/kn/VsM9+zJFU0NVXJI1GHHLqZtvpTrW7xKk/B21VpvIaqqOsYMyxjkypEAWQhAKcXHOx9kf8E6QEfTzBLrNUwNdJTfvWnfkLl1xNlcnPREDdVYCCGEOHHFhLTkjIS7aR99IXN23e7+doaKWpC93Em9DGDWrrs4NeZSBiWOqcYaCyHqC7mNIASwJPldd3AcaFIJd1ZMpQWcsF0BViK4sPlj1VxLIYQQQviKC23HNS2mY8GKVkFw7B2vrDQKseDE4h63rLEqfT6vbbmBfCO7ZiouRAVMpVXLQ1Ss3gXIr7/+Oi1btiQ0NJQ+ffqwYsWKMredNWsWmqb5PUJDQ/22UUrx2GOPkZiYSFhYGAMHDmTr1q3VfRiijtibu54XNw7jjyNfu6dsUgE7UhcnNdDcXV40DPfdZ6sWyeh2n6BrlgB7CiGEEKI6xYW1444O35MUeop/tkwfvsFxkd/lb3E27Kyiw7y59UYOFeyr/koLIeqsehUgz5kzh3HjxjFx4kRWr15N9+7dGTx4MKmpqWXuExUVxcGDB72P3bt3+61/7rnneOWVV5g+fTrLly8nIiKCwYMHU1BQUN2HI2rZhozf+WDXQ+Sb2bjGEFtxYnEFwGbJ71fXHWbPnTdPpkwr4dzRfi6hkohLCCGEqDWapnFFyxfoF3eb96Z2yVhZoeHEczM7QH8xDQrNXGZsv43P90zGaRbVQM2FCMxAr5aHqFi9epVefPFFbrnlFkaPHk3nzp2ZPn064eHhvPfee2Xuo2kaTZo08T4SEhK865RSTJs2jQkTJnD55ZfTrVs3PvjgAw4cOMDcuXNr4IhEbdmY+Qdf7XsGU4GpdHe3E/dYYzQcWAPchPYEx650XbpmZ2Tr99G0evUxEkIIIY5bpzW+mjvbfYuNKBTuOZOV6ya35zu+om7YGoot2X8ya+d/MCRIFrVEuljXnnpzZe9wOFi1ahUDBw70LtN1nYEDB7Js2bIy98vJyaFFixYkJSVx+eWXs379eu+6nTt3kpyc7FdmdHQ0ffr0KbfMwsJCsrKy/B6ifjiQv413tv2Hz/c+7Z0PzvMF6pqkyfUFqtBxBshbrdwnlqZh3bip7UdE2mNr+hCEEEIIUQ67NZw7O37F1Se9Ali944wrynLtSylIzt/BwoNv4TClV6EQJ5J6EyAfOnQIwzD8WoABEhISSE5ODrhPhw4deO+99/jmm2/46KOPME2Tfv36sW+fa2yJZ7+qlAkwZcoUoqOjvY+kpKRjOTRRA5RSLDr4Hm9tH8f+gq3gM6aYEneTXUGywkTHwEKR0nEqDafSaN/gXG5v9znXtHyRCKsEx0IIIURd1SyiM/d2/B/tG5zjvRlOwEwj/hQamvuy4K/073l+00gWp3yEoYxqrK0Q/kz0anmIih3Xr9Lpp5/OiBEj6NGjB/379+err74iLi6OGTNmHFO5Dz/8MJmZmd7H3r17g1RjUV3+Tl/MH4fnllga6C6y7xhj10+e8ccXN32IS5LGE2aNrs6qCiGEECJINE3jkuYPc3ubTwmzNPTpN1Z5DrOA39I+55XNt5PvzKmeigoh6ox6EyA3btwYi8VCSkqK3/KUlBSaNGlSqTJsNhunnHIK27ZtA/DuV9UyQ0JCiIqK8nuIuivLcYT5+2cUj71QUFEXK98EH+GWaMa0/4TODc+t/soKIYQQIugi7LGM6fAZvWIudy0okena86ty9ypTyj38yp2jxFAaR4rSeHrj9Xyw88karbs4MblmTAn+Q1Ss3gTIdrudnj17snjxYu8y0zRZvHgxp59+eqXKMAyDf//9l8TERABatWpFkyZN/MrMyspi+fLllS5T1G17cjYxddPN5Cune3qmyrzli5Nx3dDqZcZ2+JQIa0z1VlQIIYQQ1W5g4h1cd9IzRNniSs0G5Upg5LoG0DRXgOLJV+Li+ndrzmqmbrwFU5k1V3EhRI2x1nYFqmLcuHGMHDmSXr160bt3b6ZNm0Zubi6jR48GYMSIETRr1owpU6YAMGnSJPr27Uvbtm3JyMhg6tSp7N69m5tvvhlwdbu59957mTx5Mu3ataNVq1Y8+uijNG3alCFDhtTWYYog2Ji1kvn73yG9KAX/8cbueY4V3vFFpbm+Cke2eJ6m4R2qv7JCCCGEqDEnRXZnTPsP+Df9J7498IJ7aXHPMU0DwwQj4JRQ7jmTnYd4ZfM93N7uGUItETVWd3HiqI6s05LFunLqVYB8zTXXkJaWxmOPPUZycjI9evRg4cKF3iRbe/bsQdeLWwjT09O55ZZbSE5OJiYmhp49e/LHH3/QuXNn7zYPPvggubm53HrrrWRkZHDmmWeycOFCQkNDa/z4RHCsOLyIufunu6Zi8i4tDpINpWPTy7vrq3Fzq5dIjGhTndUUQgghRC3qGnMu4baGfL33WQrNXG+3akPpGCVajUtSCtIc+3hi3ShubDmBdtHda6zeQojqpSlVsoOJqKqsrCyio6PJzMyU8ci1LNNxmCkbb0NhAppnJJHrX+93nMKqmd5JnHyXg8agJjfRt/HlNVxzIWpfTlEOO7PXsydnG4eNA+Q4MjniSCPXzMagCE8vDF86FmzYCbNEYNNCaGCLIdbemM7Rp9EqsjPh1gi0srtrCCFErVPKZHvOahanfMjBgl0APmM1y58z2aFcrcytI07m9rZPVHNNRVXU1+tzT71v/eX/sEfaglq2I6eIt/p/Xu9ek5pWr1qQhSiLqUx+TPmcxSlfui/fLXi7U6MDCqsy3cGwhlPp6JhYtOKRRXEhLTi/yY20bXBqLRyBEP6UUuzN305m0REirVEkhbXFUE4KzQI0NDZn/c3azGXkOrNpZE8go+gQKfl7cah8NE0jRA8jPqQZ3Rv2o11kV1YcWcyGzDWkFSWjKJ6qxH+SswprVWqJiUEh+RQa+QCkOfezIx9WZi7x26v4OXR07ICJXQ+lZ8P+nJc4hEirfFELIWqHpum0bdCL+NBWTN92H3lGZlX2BhQ7cjfwyNpreazzu4TawqqrquIEYqD59GQIXpmiYtKCHAT19Q7V8WTBgY9YkvYN4AqLtVInANfb3Irp12KsodA1uKvda8SFNq+x+ooTm9N0sjXnH9IKDxJuaUDL8PbEhsRTYOTzW9p8tuWsJ7lwH/lGZacT8YSg/hOUBRo3h89a19LSW1YX0++Zyn5GK3a6RvehT6MBtI7o7Dd0RgghqtPhwgN8unsKKYV7vBmtA/F0xy5SpduaJnX5gFCLBMm1rb5en3vqfdMvV1dLC/K7/T+rd69JTZMWZFHvZTqOsCTtW+8lf+ng2LUUFCYaFp8tQXFW3P9JcCyCzlQmK9N/4fe0/5FSuA+rZufkqJ5kOTPZnrPO278hOIrf88rbDbq8QNQ/mK6J4Nj/mcp/RicOVmf8xuqM39y5BHR0zYpdD6Njg+5cmngt0SGSWV4IEXyNQpoypv2rTFk/gjwzqzhrVwmaBk6z5M0715n9qQ138GTXWTVRXXEcM1Xwk2qZR3np8frrrzN16lSSk5Pp3r07r776Kr17965wv08//ZThw4dz+eWXM3fu3KN78logAbKo1/KcOczY/iSmT0eI8oY7FgfIru2bhLbm3IRh1VxLcTw6XJjKktT5HHGk0cgez6kxZ7A5ey3bczdzIHcPuSrDvaUrGHWQz8r030uMhw+2ygTHpfeoCSWnSancPu6EOSgMVUSRUcSqjF9ZlfEbAHYthI5RPTi78YW0adAx6HUWQpy4Huz8Hk+tvx6HKvSb+cITLzvdU0D509BQFJi57MreTMsGMhOGqP/mzJnDuHHjmD59On369GHatGkMHjyYzZs3Ex8fX+Z+u3bt4v777+ess86qwdoGh3SxDoL62oWjvstxZjFt86McciT7dINytYpZNYWuBXprK2yaAWj0jjmfy5rfLgmEREAZjiP8k7mCPGcuFs1KuB5JquMAEZZINmX/zc68zaXm0PSEphrKHQf6d2v2vEvrwltOK9HhubqZVRztbCr/juDl8XR1tGuhnNl4EJc0vQaLbil3HyGEqIihnMzd9yar03/ynoVMXFmuFYGGfhT3ULNqkfSMHUDf2AE0C29RMxUWfurr9bmn3iN/HoY90h7Ush05Dt4/59MqvSZ9+vThtNNO47XXXgPANE2SkpK46667GD9+fMB9DMPg7LPP5sYbb+S3334jIyNDWpCFqAlf73ufNEcqJcdZusYE6VgxsPgFyZ6frdzW5mlOimhXY3UV9YfTLGLG9ufZlP0v3qDW5y3m7ZaslQ50NVzJtUoHx67fNVSdCI5r2tF05K7qPhrgUAX8lPYdi1O/Q6FhwUpSeCtGtryTxqEJVXp+IYSwaFauSrqLS5veylMbbiLfLHCvKe/c5FqXZ+Txe9oifk1bSL9G5/F/STeia5JPQdQNWVlZfr+HhIQQEhJSajuHw8GqVat4+OGHvct0XWfgwIEsW7aszPInTZpEfHw8N910E7/99lvwKl5DJEAW9VKOM4tV6X+4fysdiADuTNWGT0CiYdOsjG33DIlhcjf3RFZg5LEk9XtWHP6NjKJ0DOV0r9ExcfpsqaFQKAW6BvgEx4G4ut6VdeFUU8Gxf87osrfSvFOd1YzK1Qso1TJfEU0r3sfVmuxqrTYw2JW3jSc2jAM0zo+/hIuaXoVVD27SEyHE8c1uCeG/nd/h8XUjcWIQ+HxWfOLynIdM5eqps/TQYpSCYS1urrE6i/rPJFA3/mMvEyApKclv+cSJE3n88cdLbX/o0CEMwyAhwf8mc0JCAps2bQr4HL///jvvvvsuf//9d1DqXBskQBb10td7Z3sTDZQXdBhKx6IVdyV9pNNbRNgaVH8FRZ1iKpONWf+yPXcL6zJWc6Bgj3ed/9vH8PnZf42pXBnPy3u/lR8AayhVE0Fy1Sdtqu4qFc+eXPkg+ahq5Q2OA61SLEr9jkWp32EnjDYNOnBJ06toEdG66s8jhDjh2C2hTOr6Ec9svIMsZwaBpr1zneddcyibSnfnUXD1O/r18M+sTF/OQx2fIi607HGbQtSEvXv3+nWxDtR6fDSys7O54YYbePvtt2ncuHFQyqwNEiCLeuezPR+wLP13rFQckJgo14zICq5qfocExyeQ9MIj/C95LtuyN5FWlIKhXHf9PR3cAr91ys747Pm3jISmlaLc/6u+INn3gq2iJ9HcR1X10PVoaO4J2Cr/TFWrkab5ZucMkHHWp9QC8lmfvZZ1m/7GroVwUZMr6df4bMJtEVg0GbsshAjMqtuYcPI7vL31SbbkrQU85xbN24vFVK6pn0w83zjF3x95Zj4TN9zH3W0eoWP0ybVxCKIeMZSGEeQs1p7yoqKiKjUGuXHjxlgsFlJSUvyWp6Sk0KRJk1Lbb9++nV27dnHppZd6l5mmq6HKarWyefNm2rRpcyyHUCMkQBb1yuasDSw59H0lt1buLk4aiaEt6Nv43Gqtm6hdRxyH2JGzjQzHEb5PmU+2MxP/sKh4aHDVv25ceygFxzIlr/cyqVaC5OKLNP9pofyXlNe7uaKez6UOyWcHzd1FvawW3rKfsfLdsitbtiepF2gUqiK+PjiHrw9+CkCcrQm3t7uHZmFJ5RUhhDiB3dLuUT7Z/RqrM37x3vhUgKl0nEpztxr7Z+5XPoHOy9ueZmTSHfSNP7Omqy7qEVPpmCq449arWp7dbqdnz54sXryYIUOGuMowTRYvXszYsWNLbd+xY0f+/fdfv2UTJkwgOzubl19+uVTX7rpKAmRRr3ywawbeLxv3/yoKNMItUdzX4ZnqrpqoQUopdubuIK0wlYa2aH5KXcTazFWete5/S49Nr2p4Vup5KR7rGuh9p0oFhCW5ezVUoRINrY2JC0lkf8FO8o08NDQirVGEWxqQ68zGbrHT2JZImDUMi2alc3RP2kV2QUPnp9Rv+fPIDxQY+WhoRFiiSApvg67phOnhdI3qRYw9gUIKiLU1JtLWEEM52ZS1hmxnJiF6GDbNjl0LIbXoALtyt2CYRSSFt+HsuItJKdjHH0d+4J+MPyk080sF0ArcubJV8QeWsrIGlEyGVrVu2ZoGqIozZfsH0v4J/gDSilKYtP4RBiVcQkJoE7o3PJUG0vNECFHC8BZjOa3ROSxO/potOetxevIgeOcIcPfRKZWR33WumbV3OoWqkP4J59VwzYWomnHjxjFy5Eh69epF7969mTZtGrm5uYwePRqAESNG0KxZM6ZMmUJoaChdunTx279hw4YApZbXZRIgi3rDVCZHig65WwE1DDSs5XZ5dS18uNM06TZ5nNiUtYHZu98nuTC5RBjkuvzQfRKy+SsOiI5tHLBPR+ES7ztvkqgAz15yqeFOdK1rOnbdFYBG22M5LeYc9ufv5HBhCgmhzTgn/nJiQ+KOtrJc3HQ4FzcdXqV9dM1O14Z9Si3vSA/OjrvIb1lSRBuuiWjDNUm34zAK2Zr9L6vSf0PXdbpG9SbbmcHB/H38m7mKbCPDu59fbnlVHAhrCv/QtRJ/J8/fofjmRNkBtX8AX37h36fMd22z+z10dG5rfRenxJ5acYWEECeMtpEn07btyZimydTND3OwYG+p24DlTVf3yd6ZRNgi6RVb+pwrhOnuBRnsMqvqmmuuIS0tjccee4zk5GR69OjBwoULvYm79uzZg34s3evqIJkHOQjq6zxr9Um+M48ZO15nU/ZanyDIlXPYk4m3ZNCjFFzf4nZ6N+pfgzUVwVRoFJJWmEaR6WBtxhrmJX8bsO3PxSw1JVNpCr2KLbi++4LyZrP2bfX0DY596+api45Oj+h+DGtxGztzN5HhOEykLZr2kV2x6ifGfcoi08G6jJWsTv+Djdl/Y7izhfu3roDfhaVvq7NW+nX131/zy2BdFmcltilZjqd+bcPbM7L1TcSHxMt0LUIIP8n5+5i2dSI5znxMd7aLiod+KFA6D3Z8jNaRbWumoieQ+np97qn31YtvwB4R5HmQcx18dt6H9e41qWkSIAdBff0A1hcZjnT+u248hWYhFs1ALzVdjonF5yvI84a+o81DdI7uUaN1FcGR48zhi72fsfTwbyj/9kaAMsIb0+/mSVk0lOv9UqUg2dNCHWhebZcISxSJoc2IsDagZ8yZdI7ugU0P7hfb8ehA3h7+yVzBtuwN7M3fRYGZD5S86eAbjJolwlbwD2QD30LxbO3qBll+cFv83MXbKb9/NWJsMdza+nbaNehQbllCiBPHEUcaL2x8jHTDNcdscetf+TfkNCzc1e5+To6uP11Q64P6en3uqff/LR6BLcgBclGug8/P+6DevSY17cRouhD12tTNz1BgFAAaTqVj0ZSr5dj7faP7TM7jCpSvP+kWCY7rmR3ZO/h478cczD9InpnnTXHiH8i6M0n7/Oa7rjLdp1WJf6uSVzlMjyDEEkanBt04vfG5OMwCom2xxIcmVmJvEUjT8JNoGn4SuF9CQznJLcphR84mvk+Zy4H8fZiYFP+FSs/ebCqFgY6GiTXQ2HDPdkDl/tKlecdEK1c5hx3pTNk0hTMan8nIFqNOmJ4AQoiyxdrjGNdxEhPX34dZTlpDpVy3+gxVfD57fsvzXJZ4GVc0v7JmKiuEKJN8o4s67WD+AZILUjydZwH3HVmlY0Fh0w2fgMj1NdM8tBWnNx5QG9UVVZBv5LP88F8cKTjCj2mLyHe3HLqUl56pOEguGSBXbhomDcPdNlicCCrwdjoaCSFNubL59XSI6oJW/ZMYn/AsmpUoe0N6xPalR2xfADIdGSxOmc/+/F0kFxxwz0Hqy9OCbKFIeWYeBUvxqhJ/5/JvibimjAr8zvMtAeD3Q0tZeugPukf34PoW19EopFHlDlQIcVxqFBLH/e0f57ktEwOuVwqKlB4gWaDi24PfsT1nB/d3vL9G6irqNlNVwxjkIJd3vJIAWdRZSim+3veVu9XHwyfREaBMCyEWwxsUNbBEc1vbe2u2oqJC+/L2s/TQH6QXZVBQlM/6rI04lMM95ZLpHcfrN7bU82/AgDdwkKzQvPlDAwdAyu8nBehY6BzVjbManYumQSN7PAlhTWWMaR0SbW/IlUnXeX93mIVsy95EuuMQOc5s5h38GlMZfh3wFeAElKm8XeM9t9nKu/1S3qAj5S5EKzGu0FSK1RlrWJ2xhh7RPbir3RhpURbiBNYysi0Pd3iapzc94jdUA1wtx4HzILh+X5+9nje2vsGd7e6smcoKIUqRb3BR5zhNJ5/v/Yqf035xTU+jaWUkX9Iw0XAYYNEUgxIGc0GTy4mwRtZCrYUvh+mgyCzCUAYvb3mNbbnbS0+P5El2pZVYXrwS1xZlpTjxb/91hTuaO0guObsv3m1d7yWN+JAERrS4ndaR7Y7yKEVtseshdI7u7v29b+P+LEn9gZ9Sv6fQLKS4u73rItRQvv0FSo5VL91hv7wkXoEycCuf6aXWZPzD6L9uI8oayWMn/5eE0PhjO1ghRL2UFNGCx0+eyuPrH/K74essZx5adwcYVhz5i04pSzgnYUBNVFXUUXVhHuQTlSTpCoL6mgSgLkotSOOJ9VPIcmZRslOkrqkSSZLAcyl7RqN+3NT61pqqpijDluytfLt/AWsz/8WT4khRdpdnT2tv2T2XXX/vsrJOe+7DF4fCJd8jrjUWLJwc1Y0eMT1pHp5EYmgSNt1WlUMT9cT+vL28tu0F0osOB5zz2HPrBHcGfM8NE2+w6w6OS77lvOOY3WUqNNfPqmQiL89UYK5/G9sa83yPp6RFWYgTlGmaPLnhvxwo2AeAQ5U+Fyjl6vpafHPOdTYZnDCQ61oMk+E9R6m+Xp976n35ohurJUnXN4Peq3evSU2Tb2xRZzhNJ89sesEdHEPJ1j9TudsHA3xP9Ik9vSaqKAI4kHeQn9J+Y9WRNaQ6DgE+fzmtrHa4YhWNFy57lLB/F2sFXNX0aqy6Tr5ZgF230yayHa3D2x138/OJsjULT2JKt2kAFBlFfLb3Y34/tASjxGAN1yRxngHKvqnbNL+bLr7vPtPdx1q5d1GqZKuzi+YTlB8qOszov+5gYueHadugdVCOUQhRf+i6zmMnP83nez/mh5TvS61XCncvF/DPwq/4PmUxfx1ezUunPivDfoSoQRIgizpjZfpq0goPlbHW9WVhKB2r5n+hG2YJp3P0ydVeP+Ev05HJkxumcqAgxbvMNZbYdRNDp6zxw/4qs02Avbw/mUC0JZr7OtxP8/CTqlqQOI7ZLDauazmS61qOZH/eXrbkbGFnzg7+OLIU8H3vFV+UKuVK9KZrxWOOPQGxT58HvymlyuyGpTzjDeHR9VM4J+5Mrmp+KY1CYqvhaIUQdZWmaVx90nWEWsL5+sA3nqVAoOAYv98PF6Xz2LrJTO76WI3UVdQdpnsoYbDLFBWTAFnUGWvS11awRaAsxRpj24yVO6s1bH3GJp7e9BKmz6nWdyxxyWVlKR4vXN4WlBq/rGPBqllpHt6cK5v+Hx2jOx3dgYgTRrPwJJqFJ3FO/HmcGtOTd3a+TYGZ7z2fFA820nCig1JYNf/x7yWniSrer/Qb3VS+FyKuf39O+52f05ZyasPu3N5mFA1sEcE+TCFEHXZZsyvIKsphcdpPQNnnD1+aBjtz9/D4umd5vMtD1V9JIYQEyKJuSHdk8nfGeu9FqueytHSXaneLjfui9pLES+gU3blmK3uCm737C+YdXITf6M4S3+/e7qmVbB0ubzsFhFvCiLBG0DayLRclXkKzsOZHXX8hesScymsxb7Ixcz3v7HyHjKJ0wJ3Cy3vBquF0Nx177+GXeFMXB9X+CeGUNzgu3SKkFKxKX8stK+/j5AaduK/DbUTawqvrUIUQdcz1LW8goyiTVRmr/IZplEehsTl7G89teIUHO99dzTUUdYVM81R7JEAWtW5dxiae2vgKUASa5s6wVzyyVFMKq2aieaZqcbfanBbTh6uaX1Vb1T5umcpkbcZG/k5fz47cPVg1G6fGnMyFieew/Mgqd3AMnhb98gJbvcLzsOYOLdwT75RoKU4KO4kxbceQEJpwTMckRCCdok/mhR4vsTFzI69ue9VnLm7fzNYaCotrifsNqqni5HC+Xa09yuvC5nlvKwXrszdy26oHee3Up4mxS7IUIU4UY9uNZcb2Gfxx6M9K76PQWJ25jhnbPuC2tiOqsXZCCAmQRa06UpjOlE2vYWLgGuxXsqu0K3QqUhasGGgaOE2dRvYYRrWUL4hgSndk8seh1czZ8y35ZoHfunVZm/hg9xc0sIZUqizv7Y2SUzsFoFRx1t9Q3U6IxU7riNbc2OpGomwSNIjq1ym6E2/0fIPUglTm7v+GPw7/6U4BV3KqMNfPptJwmmDVTdyj7d3vYJdAQXNJnht9RWYRd6x6iKuaX8IVzS7AqluCfHRCiLrotja3EWeP5+sD8yjvfOHJsO85Z/yc9jsKjdvb3lBjdRW1Q1qQa48EyKJW/ZjyG07lRCmtnGkMXG01ptLRNZOmoQk80HEc4VbplnisTGWSWZTNi5vfZmP29lLrS/5FilvYigXqHl0cIHj7zPtt47uPpkG3qO7c0fZW+ZuKWhUfGs+tbW7h2hbD+WDnRyxP/6vUNq6gVsfEgmEqbJrTPe7eP0iuDE1zXawYCj7dO485e+dz7UmXcWXzC4J3UEKIOuvKpCvYnbeP1Rl/U1aQ7Dtcw/O9uTj1d1qGJ3FB0wE1UU0hTjgSIIta9Vf62uJxx0qVGySbgB0bz3Z7SuYEPEa/pq3guwM/sTN3LwqTkjk0fae4Ke+VDtyN2hMq4L7x4Wpf851xvYE1koSQOPo17ss58QNkjlhRp0RaI7mz3e3cZt7Cj6k/8X3yD6QVHvYm3vKdW7lI2dCUE4snoZf381DRp6d4q+Js2IrZe75hQ+Y2Jpw8NshHJYSoi+5pP4bxax/jQEFyqXXFwzj8acB7uz6leUQiXaI7VHcVRS2RFuTaI1elolZlFWX7/FbhjLlc0exiCY6PwYH8VJ7e+CYHC1LcrbiqVHDs+3PJILl0l2kNUynXlDg+rcKebtMWn+10dHo07MatbW4kQlqKRT1g0S0MbnI+AxPO5e4140l3ZLjX+F+yKqyuhF44seC5RVR+Zn3frNnFy1yfuNUZ63ns35e4t8ONxNqjg3IsQoi6Sdd0nusxmZc3T2dF+krvTbOyhmoUJzOFpza8zsunTCQ+tFFNVVfUIAmQa48EyKJW5TgLKtnOAhoalza7sLqrdNxJLThCuiOT9Zlb+WjPN3gSa3mmYyrv9fcPA1wnal0reT/bFST7TscEMKTZpVzR7CIOO45gwULjkEZyc0PUSxbNwqunPMtzm17mn8wNnpRylP7kWDEUgOH+nGhl9ozx5FPwZLYu/ui4Aut/s7Zy01+PMPKkKxiSNLA6DksIUYfc0+F2Jm94kXWZm8pOfqn8e504VRHv7/qKBzreUlPVFOKEIAGyqDVHCjPIN5xYtYozHisFpzfuiUWTBDaVtTl7F69v/ZjdeQe8y/QS87pC+TcnfDP1unNWe7tPl+RZ2rlBB0a3Gk7z8KYANJEM1OI4oGs64zvdR6FRyGtb33ENDwECt/BYcJomFh1vcOw737KmgaFck0f5B8cl+nEoxazdX7Mtdzf3d7ypWo9PCFH7JnQex0NrJ7Enf1+p/B6+vU58b1OvOLKGrKIcomyRNVhTURMU5c+KcLRliopJgCxqXL5RyLwDv/Dxru8w0DA13R0cu1NRlEz4pEDXNIYlXVYLta2fZu6Yy9f7fyydg1er3LzEZXNNw3VZ4vmsSF9FWuFhNDRi7Q0Z3ORcBjUZgF23H1vlhajDQiwh/KfjGFILDvH0hlc4WJiK55LDdUHrunh1YsVpmugorJryy1lXZOoBumAH+GBqrqRfvx9azd7VqTx28p00CpEu10Icz57u9l9u/esBco0c7zLPjTTXMAxPNn1wumf+mLXzG65OGkyTsMa1UGMhjj+aUkpuJhyjrKwsoqOjyczMJCpKpqUpT4Yjm4fWvsSBgjT872NpgEmIxUDHv6VFKbip1XAGJ/avnUrXE0opUgsOM27182SZni9W34tuhUUz0X2C5LJHOXn2KObZpnlYIi/2eFS6SwsBbM/ZzTMbXyPDnU/Bk3HWVHj/1XDN5e757Bmq+JNnBhiLXJLn4thUOre3+T8ubnp2NR6REKK2FRqF3PjX/RSZjhK5PTw/Q6HpyngAoKNjohh+0oUMP+lC+X52q6/X5556nzv/dqwRlZtes7KcuYX8dPH0evea1LTys4gIEWQvb5nNgYI0d1chzefh+r3QsOI0wTRdXwCGCZc3vUCC43Lsy0vh/r9f4tLf7uXGv54g0wgUHLsYSvcbJ6xK/Osr0LqW4c15utuD8uUrhFubyBa8fdpU2kS0LNUVrjiZjo5TWTAU3oDZX8VzJnu2mL79czZk7ghO5YUQdVKIJYR3T3ueRrZG3kRNnrOGqTQKTR3XJbz7hpx77Sd7/sei5D9qqdZCHD8kQBY1Zl9uCiuOrPf+rpR/UqfipBNWTDQMNCKt0VzXckiN1rM+UEqR4cjm/R3fcdvKp9iYtROFCXjGPAa64HYtM5QWcCyTKvHwLIuxRdE39hRe7P4oU3v8lzBLaPUclBD12DPdxzOgUZ8SS4sHOSg0nMqKw7TgNDVPf8mjeq6H/pnGa1s/o8hwHlOdhRB1V6glhDd6PUW7yNbuHB+uQNlhWgFXPhblvunm+5i+7Qt25R4ot2xRP3hujgT7ISomXayDoL524ahJRWYRd6+eyu68g94TfTGFxZ1Z2fO7jkLX4YmT76ZbQ5njz8NpGkzf/iW/pq0h25kbYBSju+Nmuec/V3dP/22U3/hvBURaIri73Uh6xnYNVvWFOO7lGwW8vuUDlqf/Q5FZVko78Ax58J1TuSyeb2lPDxAT14VwqG7n7dP+S1xobDAPQQhRhzhNg7e2f8LitKUYpo7hHodcdoI/1425RzqN5oy4U2q6unVKfb0+99R7wLw7qqWL9ZJL3qx3r0lNkyRdokZ8svt7ducll3mxaKBhUZ4gWUPTFHe1veGED45znQUsO/wvGY4cHIaDz/b9SKHpwBPQVnaKLH8ahnLdfy4OiF0/hOh22kW2ZEizQXRv2BFdk04mQlRFmCWU+zvdiqFMnt7wJivTN1BWjw5D6aA8066V/WnWNHAaGk53N0vPEIdC5eSG5U9wUZPTubvDsOo6JCFELbLqFu5sdz1Dky7kjW2fsjp9MwpVRnDs+l2heHrjTD5t2J4GtoiarbAIGpkHufIcDgc7d+6kTZs2WK3HHt5KgCyqndM0+GLvj94MjKVbN12hnoGGFVe/67PjenNuQt+ar2wdoZTiq31LeH/XAgqMIldLsebfOhx4qqXKBMwKDdegRgWc3KAt17e8jHBLGEnhiTK+WIggsGg6D3e6nf/++xKbsncSeDqo4oRenl40vlO7eH52ZavVXJ9cz+ffp7gFycvINQp4uPOo6j4sIUQtiQ9txEWJ/VmVvsknj0tgmnvd1I0fMKnbHTVTQRF0EiBXLC8vj7vuuov3338fgC1bttC6dWvuuusumjVrxvjx44+qXGkeEtXuryPrKcIEAgXHHsUXihHWMG5ve3XNVbAO2Z+Xxuxdi/jPmleZsf0b8o0iFGCAu1vVsXIF2boGjewNua31MJ7sei8do9pwUkRTCY6FCCKrbuHpbvfRNao9UJx3oXSiPA2nqeE0i4cme7tVmxqGqYGmBzx/epb9kraGQwUZ1XcwQoha1zO2M43s0d4AuCIr0jex6sjmaq6VELXn4YcfZu3atSxZsoTQ0OIcOQMHDmTOnDlHXa60IItq92va35XuC6yAJ7qMIdwaVs21qlsM0+DFzXNYmPwX4L5doJXsRK0wlOYan62V9ZL6TLZaknLNxXpzqyu5vPm5wT8IIUQpFs3C5G738OW+Rby/81vvct/pnTytQUWmhkUD3d3d2rcF2dW1OvBzeFqe//P3K8zsM0GGRghxnLJoOg92vJFH/n0F0zTL3M6VvEvDUBoP//Mmk7rcTN/GXWqwpiIYlNLcPY2CW+bxZO7cucyZM4e+ffv6NfKcfPLJbN++/ajLlW9RUa2yi3L5NW1NqelPyqLQaN+gRTXXqu6Z+O973uDYn1bqZ9PnhFnW9ExmqQzhEBcSy7RTHpLgWIhacFXzQbxx6gTCLGHe4Sa+FyqG0jCUBYdpodCwYPhc+5ad6KuYpkFy4WFm715UDbUXQtQVnaNb82y3ewKuUwochk6haaFIWTDRMZTOf/99l105B2u4pkJUv7S0NOLj40stz83NPaZekRIgi2r1+LpZOExXv8Hy8qV7uhSe2rDjCdfN98Od37PsiCeRT3HgGyjI9QuS3UtKv6z+ZfSO6cpbvSbyXp9JtIlMqoYjEEJURvOIJszpN5Xnu/+HuJBY13R2yjWlnaE8X8caCp0iZaXQdAXKqqxeIT4859CPd//Ihszd1XsgQoha1SGqFa0jmuN7BaAUOEzd3SBRck52jZtWTCU1P71mKyqOiYlWLY/jSa9evZg/f773d08M8c4773D66acfdbnSxVpUm0KjiL8ztuMN2NzdCDVNYdGUX3dBTzfCm9sMqYWa1iylFJqmsT/vELN2LmRxymr8x2cXd6kuqxu1Z41nEhnf7TQ0IixhnBrbieEtLuCk8CbVe0BCiCrpENWK8Z1u5N6/X/Qu84xF9qdRpHQsWvkBsudGmmseVIM7V74MaHSLbsUTXUYSE9ogiLUXQtQFU7qNZfiyRzAxUcrVC0WV0e7lGYZx3Z+T+eGcF2q4pkJUn6effpoLL7yQDRs24HQ6efnll9mwYQN//PEHv/zyy1GXKwGyqDbzDyxzjbNTOqZ3PC2gNJyARVNYdVc/QgUkhSfQKqJpLdW2ehUaRXyz7w8+3/sbaYUZ7qXFl8SBG809NxZUifXKbxsFhGp2Hu96C+0btCDMEtw584QQwdchqgVnNu7B74f+Lrd3DegYykTX3BkGlP/5wjc4dpoWfDuG/ZO5kyuWPs7tbS9hWItzquEohBC1JdIWzus9x3PHqmdRGBUm8tQ0MJTixmVTee/0B2qoluJYSBbrip155pn8/fffPPPMM3Tt2pVFixZx6qmnsmzZMrp27XrU5UqALKpFkelk7r4/vGPsfP/vYShXs7FFd7V/3tp6SA3XsmZszNjD3avfoFA5AVdI6/tKKHcyHp2SgbBrbaBWZN3dohSmh9A//lTubDMUm0U+zkLUJ490GsUb277guwO/u3uClJXNUEcpE7Oc4Nhh6JQeNeUqdfq2eYRZQri8eb/qOAwhRC05KaIJz3Qdw/3/vFapXAUAO/MOsiT5bwY06VG9lROihrRp04a33347qGXKFbUIOqdp8PDa99iTlwaUnXkVwFA6mmli0630btS5hmpYc97dtpD3d//ot0y5W339RwkpTHeG6vKHYLuuiC9uciZDks6ieVi8ZKwVop7SNI0x7f6PPrFdefjf6e6lgYJk5XPxqzBN93lVQZGpu1sEyjoPuDZ8dctcLm3WV84XQhxnusa0pVloHLvzDnmHcAXi7amiaTy1YTb9E7qfcDlf6hvJYl2xPXv2lLv+pJNOOqpy5ZtSBN23+5ex8sgWoPzg2MU19/F58adVe71qUnZRPnf/9Qbv716Mfyjsn4iruGelpzt1WV9s3m82+jc+hbHtr+Kk8CZysSvEcaBXo4482+0On7lNfftce6Z80tzdqDWf3zVM5d+tOjANpzJ5e/v/fM4lQojjxSun3gdQYcDr+fQXKZOPd/9czbUSovq1bNmSVq1alfk4WnJ1LYJu5o5FFYyp86fQuTLp7OqrUA0rMp3ct3oGf2ftLLGm9JRNqsT6QC+bhkaMvQHdG7bhpVPuYkKXkRIYC3GcOTW2A9+e9SwdGrRwtxp4MlP79jPRUEqnyNBxmlqFYw59KQUf7VrC8D+mcrgwqzoOQQhRSyJt4UzoMAKlVMDrr0DLPtz1E4VGUfVXThw103sjNLiP48maNWtYvXq197F8+XKmT59O+/bt+fzzz4+6XOliLYLqj7SNZDvzKtFyXCxcD6FVZGL1VaqG/ZL6L1ty9ldiS0+QXHrkkKeblE2z8fUZkwizhQa9nkKIuiXUYue1nvfx1d5feWPb197lxVO+ec4ZrkAZzIDllGdv3iGG/fEc886eSIjFFpyKCyFq3TlNT+Hr/b+xLnuXXzI/T3DsnRpSuX7OLnLwzvZFjGl/cW1UV1SCdLGuWPfu3Ust69WrF02bNmXq1KlceeWVR1WuBMgiqD7b85vfCDrPiTlQwOxap7iu1fk1U7lqUmA4mLtvOd/s+5Pkggw0jCrsXbIrpYumaXRokMTz3e+Q4FiIE8yVSWfTtkFz3tw6l83Z+3wudD29TIqHbCilQMOne3bZPN2zc4qKuHbp83xy5gPYdbkMEOJ48cppd3PxkvHkmY4Srcb+E0Ka7nnXP979KxmOXP7b5eqarqoQ1apDhw789ddfR72/9NMUQfVP5i5M07+1A0p37/H83iIigSuan1lj9Qu2zVn7uOq3Z3ll83x25x6i0HDiMKsSIJfsdq04o1FXvjrjSd7odR/hEhwLcULq1rA1z/W4Ax0rhunqFqd8chgYJhQaVleiwwqCY9ccqeD6yndte6Agg+FLX6DAcFTrcQghata8/lOwYHH/5p8DxdN67Opm61q+4OAq5u9bWfMVFRVS1dC9+nhrQc7KyvJ7ZGZmsmnTJiZMmEC7du2OutwqB8imGbhLl2maFWYSC4bXX3+dli1bEhoaSp8+fVixYkWZ27799tucddZZxMTEEBMTw8CBA0ttP2rUKDRN83tccMEF1X0YxzXPuLiygmSlXF0GTaUxvde9hFrsNV/JY5TvdPCf1bMYuew1DhfmYirl6gaJhqH0KozBVn5tyKNbDWZSt9FE2yOqodZCiPqkgS2My5r1Q/MJbMEVHDuV6wLYULpPF2zPuOXiB7jOt4ay+JTsujjen5fO0+u/rJFjEULUDE3T+L7/M9ix+12LKPd1l2u+dN/54uC5TV/hMJ01XlchjlXDhg29cV5MTAyxsbF07tyZZcuW8eabbx51uZXuW5WVlcXNN9/Md999R1RUFLfddhsTJ07EYnF96aalpdGqVSsMoyqtZ1UzZ84cxo0bx/Tp0+nTpw/Tpk1j8ODBbN68mfj4+FLbL1myhOHDh9OvXz9CQ0N59tlnGTRoEOvXr6dZs2be7S644AJmzpzp/T0kJKTajuF45jQNmoc2Zkt2CkWmwqabfkGypikMExyGBV3TaBoeTZi17r/WTtNgScoG/jq8jQLTwY7sFLbmHCyxVfFNAc01vTOWCm/Sub65InQ7Z8V14Y52lxETEhn0+gsh6q/b211CSsERlh7agAUdpzK9wbHnvFNkWrBqhveOt+leo5SG0/QPrn1pGvyQvJaeDVtz+Ul9qvlIhBA1xWKx8GLPOxi78nUcfpfl/o0W3qzWpsFbWxcxtsNFNVlNUQFF4ARrx1rm8eTnn/2zseu6TlxcHG3btsVqPfohRJqq5JwP99xzDwsXLuSpp54iIyODyZMn06VLF7766ivsdjspKSkkJiaW2cIcDH369OG0007jtddeA1yt1klJSdx1112MHz++wv0NwyAmJobXXnuNESNGAK4W5IyMDObOnVvpehQWFlJYWOj9PSsri6SkJDIzM4mKiqraQR0HDGXy0c7f+HjX76Q7stG14nEuFs1Ec/9enD1PQ9dMbm07iFGtz6vNqpdLKcWsHUt4Z9tPFCkDfEb/BU5C5juG2MSqlz+nsYbGQ53+j4uaHl9TXAkhgkspxd/p21l48C/+ydjLrtwjPus8F1Gujta6ZqJrromSPcvLCpB9y7ix9Xnc2q5+54MQQvhbl7GL2/7yb0VTCgxTL5Ee1PXb52f9h5MiGtdoHatTVlYW0dHR9e763FPvU74YhyU8uA1JRl4ha4a+WO9ek5pW6dB67ty5vP/++wwYMACAIUOGcPHFF3PppZf+P3v3Hd9U1cYB/HeS7t1SStl7U7bsJSB7TwVkCopMwQG+ykZky1C2DAUVVBBQ9p5l7w2lLaO0ULpncs/7x81udpN0PV8/keTm5uYpNDfnueec52D37t0ATK+/lh0ZGRm4fPkypk6dqtomkUjQtm1bnDt3zqxjpKSkIDMzEwEBAVrbjx8/jqCgIPj7+6N169aYM2cOChUqZPA48+bNw8yZM637QfIZzjlm3fwT+15cUzwGOFPOiGPisL4sl2A4OGfoWyp3zz1edGcPdkSeVxUTE1NabqStqSyCId6Xcw4psibTTkyCtkXqYELlHvB0ojnGhBDjGGOoE1ABdQIq4JvrW1UJsnLIpBJXnHPlHHCWcou6Cn5+chSditVDCc8A0zsTQvKEGn5lUN4rGI+TXkI5B1kcVaKLQeAcQ8/+iMNtvoVEQiWKcgMBzKwCjJYeM69T5p3m6Natm1XvYXaCHBMTg9KlS6seBwYG4vDhw2jfvj06deqE9evXWxWAuV6/fg25XI4iRYpobS9SpAju3btn1jG++uorFCtWDG3btlVt69ChA3r16oWyZcvi8ePH+Prrr9GxY0ecO3dONXxc19SpUzFp0iTVY2UPckF0KfaJVnIMMGiVsdaLwdPJFZ65eHj1g4QXGsmxmulrQBpJMpdAAMfYip3wNiMJQa5+eKdQJZT2zDodgBBCzOEq0a3ZoO+kJIVckKtG7xijrAkBDky59it+bTreRpESQnKD8ZW6YMKV9ToX1LKeFxhjSJKnY+Hd3fiqeg+HxkiIJXr06GHWfowxq6f+mp0glypVCnfv3kXZsmVV27y9vXHw4EG0a9cOPXv2tCoAR/n+++/x+++/4/jx43BzU/favf/++6r7ISEhqFmzJsqXL4/jx4+jTRv9w39dXV1pnrLCrsiLkDIJZKqh9Zq9qIZwNCpUyc6RZc/sm3/rWSJBcc+si2/iNb9GhSrjg9KtbBobIaTgalWkOv57eRmAsrGrPwkWuASMC5BIuKI2QtZ9xOrWTLXky92EKPzx9Bz6l2lszx+BEOJA9QtVRONCVXD29T2t6tX6cM7xZ8QFDC/XGoXdafhtTqN1kPWz53ReJbPHULRr106rkJWSl5cXDhw4oJV02kNgYCCkUilevXqltf3Vq1cIDg42+tpFixbh+++/x8GDB1GzZk2j+5YrVw6BgYF49OhRtmMuCCKSX0POtX9RTRcVYOhUvJ49w7IY5xxnYx5g0uVf0f34YtyNf6FRFl+jQixM/Wxc9WcVn2KYHvK+sZ0JIcQiTQpXgZeTm8Z5yFBjh4FDCrmgbhBrVroG1KsJKLdxDsy/swezb+60V/iEkBwwr/aH8JSY7thRXkgbc2GDvUMiZrD1Ek/qWkCWs/UqQrmd2T3IM2fOxIsXL/Q+5+3tjUOHDuHKlSs2C0yXi4sL6tWrhyNHjqi61gVBwJEjRzB27FiDr1uwYAHmzp2LAwcOoH79+ibf59mzZ3jz5g2KFi1qq9DzNV8XjywdxspKzso/dZ9zlkjRKND6tclsTc4FzLzxN/59cRXgTLHwkgTqH0wCDg6JolVpvAdZXJBlTMWO6F+6GZwk+ofpE0KINaRMgp/qj8KQ88vFodFG5rRwxdrHAhfAmPq8LHBAEMR1lbV7E8Tz3s7IS6juUxy9Sjew949DCHEAZ4kTltYfgeHnVxvdT1n071HyaxyLuoN3g6s5JkCSq9lrFSFbSU5OxokTJxAREYGMjAyt58aPt27akNlVrHODP/74A0OGDMGaNWvQoEED/PDDD9i+fTvu3buHIkWKYPDgwShevDjmzZsHAJg/fz6mTZuGbdu2oWnTpqrjeHl5wcvLC0lJSZg5cyZ69+6N4OBgPH78GF9++SUSExNx8+ZNs4dR59Uqebaw99llzLypbx1NDolGg0y9FWgdVAPf1x3oqBBN+jXsNBbf2ad4xIxWqJaAQ8I4JBLdCwBiI7WkRyEsqjMYpb1onjEhxH4uvnmEMRfXQ7mmsT6a514GrjofKxeEMjSNRHk++6PZWFT0MT5CixCSd3Q7Ph8vU+ONXuiXCep5ypc7zrZrAV57y6vtc2Xc1f/4wi5VrG/3X4jIyEitvxNj00ftsYqQrVy9ehWdOnVCSkoKkpOTERAQgNevX8PDwwNBQUF48uSJVcfNU2Xq+vfvj0WLFmHatGmoXbs2rl27hv3796sKd0VERODlS/X6tKtWrUJGRgb69OmDokWLqm6LFi0CIK4Td+PGDXTr1g2VKlXCiBEjUK9ePZw6dYrmGJupVRHx6mLWyyzi0GS5oB66Jw5VZhhcvqXD4zTkSWI0lt09JPakGC1moxiGqNhPrhhVrvq5OcPnVbphe7PJlBwTQuzunUIV0K34O4o5almf192WITBlHX49dM994jFHnF/vkLlehBDHWNVgJAD9U8VUBfsAKM8JH5xa6ZjAiMOVLFkSvr6+qpuyc1GXchUhzQLHtlpFyBY+++wzdO3aFW/fvoW7uzvOnz+P8PBw1KtXT5XvWcP6FZRzyNixYw0OqT5+/LjW46dPnxo9lru7Ow4cOGCjyAqm/57fgJwrml1ZhlQrk0q1NsHVUc23hCND1CIT5Pj3+XWseXAcL1LfKmLjYMxw01FNOfxavK8c3igBsLL+CLwTWMF+gRNCiI5h5d/F7meX1ZUPFHeU52HVhUnFUOo0GYeLVHtfQ73PjAFJsjTMuLETs2r3ttePQAhxoOIeAWhauDLOxNzX2q4cWq1bxOt+4iu8SHqLYl7+jg2UALBvkS59Pcj62GsVIVu5du0a1qxZA4lEAqlUivT0dJQrVw4LFizAkCFD0KtXL6uOm6d6kEnusyPiAgTOVENyDA3Y5xxwkThhTq2cK1r1Jj0JfU7+iGnXd+K5KjnWru5q6Uiiyj7FcKTNdEqOCSEOV9wjANNC+gDQ7PlRDKMWgExBAjmXqhq9HBKkyxU7mF6PDwCw+9lVpGSm2SN8QkgOmF2rHxiXQCYwyJU3VTV77XMCY8DQ82tzJlBiVz4+Plo3e42cVa4itHPnTrsUdHZ2dlat2x0UFISIiAgAgK+vLyIjI60+LiXIJFveZiQrhlBLkCmXGhy2wwF4ST1zrGgV5xwTLm5FWFKMapuyaI32fkaPorrnLnHG703H49em4+DlbN8K7oQQYkiXEnWxruEouDFxQJgyOZZzza937UJc3Mi8ZV0cQJ+TP9kqXEJIDvN2dsfU6t2hqExg9HwglwOv0hIRk5bo0BiJSNmDbOubJRy1ipC16tSpg4sXLwIAWrZsiWnTpmHr1q2YOHEiatSoYfVxLU6QpVIpoqOjs2x/8+YNpFKq2FvQeEndVB82DoZMwQnpMiky5BLI5AyZcoYMudiLEeDqlWNxXn8biZtxz4zuw2G6QnUxd198Ua0r9rf5GuWpeA0hJBeoHVAGe1tPQRnPIEVvkPKrXd8Jjal6m02V6FTWj3iWEodvrv6NPFTTkxBiRI9S9Y1eIlN+9sWlLRkW3d5nZG+Sn2muIqSkXEWocePGBl+3YMECzJ49G/v37zdrFSFLyeVyAMB3332nWnlo7ty58Pf3x+jRoxETE4O1a60f/WBxgmzoCzI9PR0uLi5WB0Lyplr+pRX3NE+1DJwrh/ZJFY85+pRy/JIh6fJMRCTF4rew82Ame0zUxW70/Zq3DKqCf1p9jn6lG8PDiYq4EUJyDz8XD2xrNhYuUlcYq2wNiEs8QVHcy9i0GEEQRwdxDvwTeQ17n123S+yEEMdijOHLqp3VibDOFA0AkMvFtpwgAPte3EJ8ekrOBFuA5ZZ1kCdNmoR169Zh8+bNuHv3LkaPHo3k5GQMGzYMADB48GBMnTpVtf/8+fPx7bff4ueff0aZMmUQFRWFqKgoJCUl2ezvpnjx4pgyZQp8fHzw7rvvAhCHWO/fvx8JCQm4fPkyatWqZfXxzS7StXz5cgDih2r9+vXw8lL3Bsrlcpw8eRJVqlSxOhCSNz1IfAVzhup5SF3RvWRd+wekEBoThlX3j+Pim3AAgIQJYIxr9RDrFrQBlFdLs/5E/Us3wuSqHSFlNCuBEJI7OUuc0Dq4Kv57fsPEngwyOcAkirXdkTVRFucmas5J5Jh5fQ/qB5RBUU8/2wZOCHG4/mUbY8X9Q0iWq9eNVSbLMrlEUYIUADjkAjDl6l9Y1ejDnAm2gDJ2ETM7x7RU//79ERMTg2nTpiEqKgq1a9fOsoqQch4woL2KkKbp06djxowZ2QlfZcyYMdi8eTMWLlyIJk2aYMSIEejXrx88PDxscnyz10EuW7YsACA8PBwlSpTQGk7t4uKCMmXKYNasWWjYsKFNAstL8uo6a7bwzr+zkC7ITO43okIzTKjazgERAX+FX8G0a7u1tjEmQCrJ+qtueEi1uG9NvxKYXbsPSnsG2jhKQgixvXvxL9D/1CqT+wkC07hoqL3InbpxrFnISzwnujAn7G07FsU9qKotIXldcmYamhyYA87Fz7u4HKcUWYv4iZ//A20nooRn3vns59X2uTLuSlun2GUd5AcDv89zfyeGHD9+HBs3bsRff/0FqVSKfv364aOPPsp2Pmp2d1hYWBjCwsLQsmVLXL9+XfU4LCwM9+/fx4EDBwpkclyQ3Xr7HKky08kxAFR00HzdZ8mxmH5td5YrZNzAcELN4UWcc9XzDQqVw/H3pmJL008oOSaE5BlVfItBauSrXT10WjHUGoA4L1m8yQXNniPtqTMAQwaX47ML2+0UPSHEkTyd3fBd7T4AuKKdpP3ZV7ePxMe9jpm++EZsR/l3b9tbTv9UttWqVSts3rwZUVFRWLx4Me7evYvGjRujevXqWLJkidXHtXi86LFjx+Dvn3euHhH7+ebKLrPmMjgzKd4t4pjh959f+kscIp0lLOVwQWPDSxg6FquB/a0/x9pGw+Hn4mnHSAkhxD4aBJbXe0FQEMTeYZlcCrkg3jJlEjgxdaNYPKcba0Fx3I5/SVVtCcknOhWvBW8ntyzzkJUX0pRJlSAwJGZmYNfTqzkXLCEGeHl54aOPPsLp06exZ88eREVF4YsvvrD6eGbPQVaSy+XYtGkTjhw5gujoaAiCoPX80aNHrQ6G5B0RybF4kBgDxsT1N1X9EDpzfBkDGgdWgLuT/Qu4XY99hptvnxtZ3VNMkiVMUMXKwOAudUbLIpUxtnJblPAMsHuchBBiT6Mrv4uz0Q+1ToTivELl1CimsZ0hJYOjnI8/IlPewlSBL+VzYUmvUdjN29ahE0IcjDGGweWaYsW9YwB0570ynT85pl79Bz3K1HFwlAWTNcsymXPM/CglJQXbt2/Hxo0bcfr0aZQvX96xCfKECROwadMmdO7cGTVq1AAzvi4OyacOvbijOF0qh2twvfN5BQFoEVzZbnEIgoDT0U+w7M4x3I57qXdtY21MrKzNga4lauL7er3sFhshhOSEWv4lMbFaOyy9c0hdkVbQbehC4zFDeGISptXqilk3/gU3cplRSaZzcZwQkneNqNgcy+8d05sc645EYQw49vIe3i1KhXlJzjt79ix+/vln7NixAzKZDH369MHs2bPRokWLbB3X4gT5999/x/bt29GpU6dsvTHJ2+Iz0iAIgDgyTxyWJ55DOSRMURmViQ2vZkEVbP7+Z6OfYMbV/xCeHCu+l2I75wwSPcW49OlW0vry74QQkpsNr9AcGXI5frx/TFFfwXjPsJxzpMkFjKzYHGsfnjK4n7KxPPbcdqxrOhD1AkvZOHJCiKM5SaSoG1Aal99EIGtirH3e4ByYeH4Hrvf81qExFkQcxie8WHvM/GDBggXYuHEjHjx4gPr162PhwoX44IMP4O1tm5FNFs9BdnFxQYUKtk94SN5yK/YlOBgEQeyZELhEVeBBObdNEIAqPsEo5uFn0/eeefU/DDv9qyo5BtQnEQ71vBljKngXRqPCZW0aFyGE5CafVG6FpfX7m0yOoXg2PDkWIyu1gIfUcNVUxsTza4o8Ex+d2YrXabZb15IQknPm1+ulajsZq9UCAOlcwI03zxwSFyH6LFy4EB06dMD169cRGhqKUaNG2Sw5BqxIkCdPnoxly5bBzNWhSD70Ji0Zoa+fgkN3bgSD5vw1gUtQwTvIpu+9/M4xbAu7ZGQP7UXQ9RWqKezqhV+aD4eE1jQmhORzbYtVQwWfIib34xw4Fx2GTEGO7S1HwlNRN0LzHMo5IJerq1+nyjOx+VGo3WInhDhOMQ8/lMzSoaHvwprYzht8aov9gyrgbF/B2vZzmnPKixcvsHTpUtSoUcMux7c4Qzh9+jS2bt2K8uXLo2vXrujVq5fWjeR/x6IeQM45wKFKkrWXS1IXAXic+MYm75mUmY7x5//EyjunzBgfwiAXGOTyrOXse5SsjYPtJsLH2d0mcRFCSG43vEITmBysx4AniW/w4cnNKOrhh/OdpsCVqZNkmZxBJhdrOHBIFOulArvCrzvkZyCE2N+f734M8wbhcqTKZUiXm7fUJ7ESt9MtH3B2drbr8S2eg+zn54eePXvaIxaSRyRmpoFxBkHvp0y5RIiYnEYmxWXrvS69jsBPd0/h9Ksnqm2my8cA6jk0DEFunuhcogZGVGqKQq5e2YqHEELymm4la+Hg8zs4GvVA7/PKC4kCB+7FR+Of8BvoX64e5AIgl0s0zvQ6cxMZEJ2WhPiMVPi60EVHQvI6Hxd3lPIIQHiysqK9IQxgwPDTv2Jry6EOio4Qx7E4Qd64caM94iB5SBmvQpBnSY51h1qLf6Zl4+riged3Mf7cX4qKqhpHt2B0iK+zGw61mwAXqcW/6oQQki8wxrCy0QdYdvco1tw/DUCd5DKm6FTQmKe87sFZ9C9XD97OrkhVnMNVw/I4oJkoc8bxzu5F+KlxP7Qtbr8VCwghjrG37RiE/DMHxrojlJXuL8ZEODK0gsceQ6LzyRBre7NqEqZMJsPhw4exZs0aJCYmAhDHgiclUbGOgqCsV6DGB8xY8RcOOedWzVdPzEzHZ+f/hpDNue6fVW9NyTEhpMBjjGFitTbwlLiLhQwhJr1yQSyuqJn0RibHIykzHVV8gxXblFm0nguhXALOgTHnduBZcpwDfyJCiD04S6VwlRgfvsqUn38GrLlruOo9IXmVxQlyeHg4QkJC0L17d4wZMwYxMTEAgPnz5+Pzzz+3eYAk94nLSBHvcIjLhwiAIFffNJcGyBDkiE1Psej4ci7gg6ObkCkIenuL9RXe0uUmkeKbWh3wfrn6Fr03IYTkZxLGwCEB5xJwPRc4GQMEzjHw2BZ0LF4dmiOCtP/U3iZwjnnXD9o1dkKIY6xp9L6i51Ld2BKL9DHIZOJNLmMQ5AxLb53IuUDzuaz1fWxzy0+kUimio6OzbH/z5g2kUqnVx7U4QZ4wYQLq16+Pt2/fwt1dPeeoZ8+eOHLkiNWBkLzDQ1XdlIELTCshBiBuE9T7yzUfmGH13TO4Fx8NzsUlm7SXbRJ7MowNsy7p4YeznT/HoPINLHpfQgjJ70p6+cOcKi23417hfryi0cFV/zNAfO7Uq8fZDY8Qkgs0Ci4HKVMu3ym2w5SFT3UrIss4R1RyQk6HTAooQ6NU09PT4eLiYvVxLR57eurUKZw9ezbLm5YpUwbPnz+3OhCSd+yLvKtzFSprj4KYPAtwc3JCoJt5hbFepSTgiwt7cDY6TOe4XGu+HMDABQ4mEWNQvTsDqvgWwcZmg+DpbHgdT0IIKaj6l62Hm29fGnyea1Q5/eXRZfi4uCE+PQ0mC/aAI00uw8P4GFT0LWzDiAkhOWF+/e6YfGEnBAFQFl7NOq2Og3OG5nuX42H/b3IizHzNHssy5ZdlnpYvXw5AnD60fv16eHmpcw25XI6TJ0+iSpUqVh/f4gRZEATI5fIs2589e2bTBZpJ7sQ5x7bHlw0kx1p7Apyhmm8wJCaqaiVnZmD65f3YFX5D1UfBmG7SrZ0kcw5AgLJDGWW9AvBVzffQMrgipLS+MSGE6NWtVA0su3McMWlZa4ZorncMAJmCHO2L1cD2sGsmjspVr3v/6Bac7DIOns7WX7knhOS8rqVCMOnCLgDGkiqxfWbZOEFCsm/p0qUAxLxk9erVWsOpXVxcUKZMGaxevdrq41ucSbRr1w4//PCD6jFjDElJSZg+fTo6depkdSAkb0iRZeKNWXOKxZNpz9K1jO4lEwSMOPk7doXf1EiODR9P2XMtJtAM4AwBLp74p+3HaF20MiXHhBBihJvUGZubD4Kbonih7rw0dS+RKCIpHr4ubtA3xJpzQJAzCHKJ6haXnobfn1yx/w9CCLG7ip6FdOas6mugie2xAxF3HRNUQcKZfW75QFhYGMLCwtCyZUtcv35d9TgsLAz379/HgQMH0LBhQ6uPb3E2sXjxYpw5cwbVqlVDWloaBgwYoBpePX/+fKsDIXmDq9RJIwk19SFjJheRP/z8Pi7ERELQGkJt+HhKyhN2sLs3drb5CG5S+y4YTggh+UUFn8JY/E4vceikRmIsFlqUiEmvor7EuVfhmFevm6JqrUbBHgHgguZ3gWJ+IoD1d0Md+wMRQuxiQ4uBGo+MNdA4ZlyhIn22RkW6TDt27Bj8/f1tflyLh1iXKFEC169fx++//44bN24gKSkJI0aMwMCBA7WKdpH8yUkiQb1CJRAaE5ElmdX80DFFWyo2PdXgsWJSk/DNhf2AwADGTefbGu8jYcCoyk0xuca7OsOxCSGEmNK6WEW4SZyRJpeJya6qd0Gx9innYrVrxrHw+glsajEQn5zZjhRZJsQpL8rhbOrzL2MMnHNEpyXjWVIcSnj55cBPRgixlaKevjC+nKdadFoS0uUyuNLSmsSB5HI5Nm3ahCNHjiA6OhqCoD3g/+jRo1Yd16rfYicnJwwaNMiqNyR5XzEPPwAR6oSYi5WrtYpqMQCM41bsqyyvj0lNwtwrR7An/La6P0JRGIbDcC+yeugfx5aWg9CoSFnb/ECEEFLASJkEn1ZthiW3josnXsHAck4ceBT/Gp5SF5zpOgF1/l6kWDeZQ1+jWUySge+uHcFPzXrb/ecghNjXxGrN8cOdUzCWJHMujjFZdfsMJtZs6bDY8j2Nook2PWY+MmHCBGzatAmdO3dGjRo1bNZpZlWC/PDhQxw7dkxvpj5t2jSbBEZyrwy5XJEQKwpnqRpLIqasosUZTrx4rHVFMSolER3/XYf4zDStY3JFA41JDX9ylYcdXqkRJceEEJJNQyo2wNZHl/AyKRmGEl7lth9vn8Xaln1RyScI9+JijDZCGANOvnxil5gJIY41LqQlfrhzGobPEYr2mRxYdecsJcjEoX7//Xds377d5nWwLE6Q161bh9GjRyMwMBDBwcFaX5KMMUqQCwAGANxQyX91Ty9jYgKdlJkOV6kT4tJT0W7vWiRmpmfpJVb+HnGBq2bGK/fhXJ0cD6pQH1/Xbmu3n40QQgoKT2cXDK/YCHOvGh+CxgGcUiS8n9d6Fx+d2G7y2CmyTMSkJqGwu3nL/BFCcq/a/sVwLfYFAJ5lmJ9yaTjOGTLkHAnpqfBxpSmXtkDLPJnm4uKCChUq2Py4FhfpmjNnDubOnYuoqChcu3YNV69eVd2uXKHKlQXBvbgYRRKs+evDdG5Q/ent7Aa5IKDzvxv0JseaOGfgcuV99bzmwq5eWNe8P2bW60BzjgkhxEbqFy5pch8GIF2QIyolEa2LiUvpcSOVXpQN5g33LtguUEJIjpkU0lJRhC9rcswFpjHNjmHxjZM5EiMpmCZPnoxly5YZ/U6yhsU9yG/fvkXfvn1tGgTJO6JSEvEg7rXikeHhNoC653fRtePYcPcCBHCYXIWJM3H+smLk/gcVamNYpYYo71OIEmNCCLGxkhYU0pp9+RB+bN4LhVw9Ea1nHWVAu1jj74+uYUqd1tmMkBCS05oWLQcGBkEOcM1eZJ1l4QCOveF3MfOd9jkQZT6Vz+YM29rp06dx7Ngx7Nu3D9WrV4ezs/aqNn///bdVx7W4B7lv3744eJBKuRdUqbJMxT1TVQ3Vz627G6paRN68CzwMUiZBVb8imFO/Eyr4BlJyTAghduDt4mbWfpwDR58/QrpchncKl1AMqdQ+p2sWbgSAhMw0ZMjltg2YEJIjOpWsorgn0VhPV7dtxoyuXkKIrfn5+aFnz55o2bIlAgMD4evrq3WzlsU9yBUqVMC3336L8+fPIyQkJEumPn78eKuDIblfUQ9vuEikyNApzpaV2ELiWlcX1YW9DOW7yu1+Lu5Y0bQnJcaEEGJHzhIpmgWXwemop0b3YwxIlclwKeYZBlSsi38j7qkGEWld+NS4LwFDVHICSvnYfo1KQohjLWvSA3vDvze9IwdCX0WgYZFS9g8qn6M5yKZt3LjRLse1OEFeu3YtvLy8cOLECZw4cULrOcYYJcj5nJuTM2oWKoZLMc9M7KlIhuXiXVWeq9Gg0reOMmPAmOpNMLhSfSruQgghDvBF7Xdxev9Ggxcv1T3DDGHxsRhYqQ6aFCmNs6/CDQ7/41ycKdPv4Fbs6zoC/lS0h5A8TSKRIMDVA7FpKVonCs0LZMrNa26fpwTZFmiZJ7PIZDIcP34cjx8/xoABA+Dt7Y0XL17Ax8cHXl7W5RIWD7EOCwszeHvyhJZ1KAj6lKsJcG5kQrz4iRaLNkh0PoxM6zHXGaa3qFFXTK7VipJjQghxkJCAopgY0lzvc8pzMxfEc/eS66eQIsvEz636o7x3IaOvEQQgOjUZ2x5ctUfYhBAH61a6uioLFgt0QVxDXZAAggRcLq5wEvoqImcDJQVGeHg4QkJC0L17d4wZMwYxMTEAgPnz5+Pzzz+3+rgWJ8iauNEkieRXrYtXgETjBKmFc0UlauX8FH2/H2JDiwvQujrWoHBJ9CoXYr/ACSGE6DWuRjMwzsAFjaRYuXyLskItE+cXrrkdChepE/7t9BGKuHnrPc0rvwMEzvHn45uO/FEIIXYypnpjdftNULbzdHAJkjMz8TY9xeHx5T+6K8TY6pZ/TJgwAfXr18fbt2/h7q4eqdSzZ08cOXLE6uNalSBv2bIFISEhcHd3h7u7O2rWrIlffvnF6iBI3lLY3RN9y9cST44alOX+oUyOOfSfPAHofliLuHljW9uB9g2cEEKIXowxeDi5gnMJuCCBIGfgggScS6BqUCkS5l/vX4FcEOAilcLfxROCnKlvMgYul0KzefE8KSEnfiRCiI0FenjBhWmODNRNuJQdIwx7Ht9xdHikADp16hS++eYbuLi4aG0vU6YMnj9/bvVxLU6QlyxZgtGjR6NTp07Yvn07tm/fjg4dOuCTTz7B0qVLrQ6E5C3T6rUFwABBXLeYy6BOjFWUjSr9SbKyl6JxkdI42eNTSCXZGtBACCEkG1oVK6fR1NVOijVH+8SmpyI+Iw0AUMrLT9yXK4ZZcok4+VjVwwRkyOV4lZzosJ+DEGI/VfyKwHhPpPi5X3j9tAOjyqd0z7+2uuUjgiBArme1hGfPnsHb29vq41qckaxYsQKrVq3C/Pnz0a1bN3Tr1g0LFizATz/9hOXLl1sdCMlbwhLeanzYNHqMDQzh4PKs840BoHupqtj23gC4SKUOi50QQkhWw6u+o9124lAnuopkF4qpMW5SscZnaeU6yoYaX4rvhrmXj9k1dkKIY4yo2gCmh+kyJGakOyIcUsC1a9cOP/zwg+oxYwxJSUmYPn06OnXqZPVxLU6QX758iSZNmmTZ3qRJE7x8+dLqQEjecjcuOmuDSO+STIpkWZVAi7gAeEhdsLR5d7vHSgghxLS6hYvjo6rvaCfGWsTzuYfUBZmKpf4iExOgOscb8W/YPaTLZXaImhDiSJ3LVDHdC6k4HbxKoekV2UI9yCYtXrwYZ86cQbVq1ZCWloYBAwaohlfPnz/f6uNanCBXqFAB27dvz7L9jz/+QMWKFa0OhOQtLhLNHl9mIDlWUPYsc4n6Bgl+azeA1jkmhJBc5H/1WqOiTyGdOYbaUmWZWHXzPADAzcnJjAYXg5xzLL16ypahEkJygFQiUU81NuHsS6pmTeyrRIkSuH79Or7++mt89tlnqFOnDr7//ntcvXoVQUFBVh/X4nWQZ86cif79++PkyZNo2rQpAODMmTM4cuSI3sSZ5E8Ni5SEquFkIsflyl24et+xIY1RK7Co3eIjhBBiOcYYOpaugodxZw3uwwFsunsZX9RtgQZFSuLvx7fNOvYv967iy3qtVKsgEELyJh9nVyRkGhlCreip/PvRbfQsX8NhceU73PToHKuOmc84OTlh0KBBtj2mpS/o3bs3QkNDsXTpUuzatQsAULVqVVy4cAF16tSxaXAk90qVaQyVUw2xNrw/52Inc5C7JybWaoYBlWrbMzxCCCFWepmcCCkTe30NSZPLcPpFOLqUrYIpZ/ebddxkWSZi01IQ6O5pq1AJITngyzot8E3oIfGBbtuPq/889fypA6PKf3Tr9tjqmPnNw4cPcezYMURHR0NQTP9RmjZtmlXHtDhBBoB69erh119/teoNSd538lkYPj36j1n7an4Q3Z2ccbb3p3CiatWEEJJrebu4QjDViuLAHw9u4J0ixcG4OGXZnI5hF6lVzQ5CSC4ysHIdfHPukDo5Vv6pPG0IyocMaenpcHN1dXCEpKBYt24dRo8ejcDAQAQHB2tN3WSMOTZBlsvl2LlzJ+7evQsAqFatGrp37w4nJ/riy+/2P32Aj4/sEjNfxTRkpnli1GwgaVxFBAcWNmlPyTEhhORynctUxs93Lhl8Xpk7X3j1DFeiX0DQPfcbeI2zRAIfF2ooE5LXMcbEkYFgAOfgArIO3VXUaF145Qy+bdw6J8LM++xRVCuf9SDPmTMHc+fOxVdffWXT41qcrdy+fRuVKlXCkCFDsHPnTuzcuRNDhgxBxYoVcevWLZsGR3KXdLkMk47vVRfdEvT0GGhWquYA5IqbANyLfeuoUAkhhFipbuHi8HRy0TsUj2v0EDEAGYIcynVPjXU6MwZkygQ8ePvaDhETQhyPgXMOLtecJ6uxzCcX24l7n97NqQBJAfD27Vv07dvX5se1OEH+6KOPUL16dTx79gxXrlzBlStXEBkZiZo1a2LUqFE2D5DkHtsf3ECy1txjJl41hLphxAUAmQAyGJhMAiZIwLgE4Awrrp7D1egXjg6bEEKIBRhj+KBiLfWoSUFcyx4yBqa8CRKU8fJHtQCNKqEaSbLmuvecQ9XDNO/Cccf+MIQQu3BmEnVirOw4UVEnzFHJyY4NLD9RFumy9S0f6du3Lw4ePGjz41o8JvratWu4dOkS/P39Vdv8/f0xd+5cvPPOOzYNjuQu8y9oLtGh+IAJDFwQAImiEJecKUZa6/8ALr50Gr926mf3WAkhhFhvWPX62HT3KmQyGcCZ4pyuOZaa42LUc+x7ch+V/ALxIE7RM8y5mCRrfgUIUG04//KZo34EQogddShVEXvC7itOCwaSLnEcNiF2U6FCBXz77bc4f/48QkJC4OzsrPX8+PHjrTquxQlypUqV8OrVK1SvXl1re3R0NCpUqGBVECT3e/T2DRIz0hUnOt2znWK4teKqlL5zoTJhPvciApxzWv+YEEJyseJePljTujtGHNipcU7P2kM0O/Q4dvUYhK67t2g3lHVqUChfkyLLhEwQqB4FIXncxLpNsefJfePV+ZhijjK1+6zCuHiz9THzk7Vr18LLywsnTpzAiRMntJ5jjDkuQZ43bx7Gjx+PGTNmoFGjRgCA8+fPY9asWZg/fz4SEhJU+/r4+FgVFMldLrx4hg//2w6mmG+SNQNWnADBDfYcK8k5x53YaFQvVMRu8RJCCMm+Cr6BJvcROMepZ0+xqHlHfH5yn3YVW0HseebKjRIA4Pjn0R30rkRroxKSl1XwN31+UErJzIAnFegjdhAWFmaX41p8CbdLly64c+cO+vXrh9KlS6N06dLo168fbt26ha5du8Lf3x9+fn5aQ7BJ3pSUkYHh//2Nfrt/R7pMrirbr78EHjOZHCu9TEq0VYiEEELs5NzLCLP2uxT1DH0rhqCwm6f49SBjgKDZ76yckgNAYNhy66rtgyWE5Axj1fkUc5P/fnDbUdHkL9xONyv8+OOPKFOmDNzc3NCwYUNcuHDB6P47duxAlSpV4ObmhpCQEPz333/WvbEFuGK0gi1YnCAfO3ZMdTt69CiOHj2q9/HRo0dtEqAuW/8Dcc4xbdo0FC1aFO7u7mjbti0ePnxol9jzktTMTPTZtQ1HI54otohVSsVGT/aGycSmpmY3PEIIIXbm7exieicurnEPADMbt1Elxkznoqnm/XuxMbYNlBCSM7iiTahMvOQAZBo35TKfF0/mXIx5WS4p0vXHH39g0qRJmD59Oq5cuYJatWqhffv2iI6O1rv/2bNn8cEHH2DEiBG4evUqevTogR49ethttaMtW7YgJCQE7u7ucHd3R82aNfHLL79k65gWD7Fu2bJltt4wO5T/QKtXr0bDhg3xww8/oH379rh//z6CgoKy7K/8B5o3bx66dOmCbdu2oUePHrhy5Qpq1BCHdy1YsADLly/H5s2bUbZsWXz77bdo37497ty5Azc3N0f/iLnG1jvXcS9WdzkOcSg1BKgnMWgugsyZ6dyZA9+fP4EeFavBRSq1bdCEEEJsplWpcqrSXAYxoEWJMgCAjmUrQ8L3QjDwCmWhr0whn02CI6SAYlB0IHOxDo3mVDsODiYwgHEkpGfmaJwke5YsWYKRI0di2LBhAIDVq1fj33//xc8//4wpU6Zk2X/ZsmXo0KEDvvjiCwDA7NmzcejQIaxcuRKrV6+2eWzffvstxo4di6ZNmwIATp8+jU8++QSvX7/GZ599ZtVxraqSkZaWhgsXLmDv3r3YvXu31s2eNP+BqlWrhtWrV8PDwwM///yz3v01/4GqVq2K2bNno27duli5ciUAsff4hx9+wDfffIPu3bujZs2a2LJlC168eIFdu3bZ9WfJ7bbeua53O1MU5NLqSVb1LMP00A0BiE1Nw4wzR2wUKSGEEHvwdHZBhzKVtM/rmsP0BPHPtdcugXOO2LQUo6sYiBgEzpGSmWHHyAkhjuDv4ir2lyj7TPSNGslnywo5lB2HWCckJGjd0tPT9YaQkZGBy5cvo23btqptEokEbdu2xblz5/S+5ty5c1r7A0D79u0N7p8dK1aswKpVqzB//nx069YN3bp1w4IFC/DTTz9h+fLlVh/X4gR5//79KFWqFBo1aoRu3bqpus179OiBnj17Wh2IKfb4BwoLC0NUVJTWPr6+vmjYsKHRf8T09PQsv1j5SVJGBsIT4gw+z8DAOAOTM9UQmmbFS0Oi/HXSlySrGlPiiXL73Zt4m0ZDrQkhJDf7rlk7caAQh8bQSSbe5ADkwJO3b3HuRSRcJDqD0ozMe6M+ZELyvnF1Gom9x0b34vSBz4VKliwJX19f1W3evHl693v9+jXkcjmKFNEurlukSBFERUXpfU1UVJRF+2fHy5cv0aRJkyzbmzRpgpcvX1p9XIsT5HHjxqFv3754+fIlBEHQusnlcqsDMcUe/0DKPy39R5w3b57WL1XJkiUt/nlyI845joeHocmWtRBkXNX4UfYS6MMAtClRDoOr14HAedYGkQD1nBS5ep6KTM5x5lm4/X8oQgghVnuelACuOZ9QtynMxfP6lajn8HF1RWkfP+1zv5wpblB9l5T09oWnOfObCSG5Wt8qtRT3jI8aocWQrWTHHuTIyEjEx8erblOnTnXYj2VLFSpUwPbt27Ns/+OPP1CxYkWrj2vxHORXr15h0qRJWZLKgmTq1KmYNGmS6nFCQkKeT5ITM9Ix8r9/cP55pMZW5fAYReIrQZZznIRJsLh1J3i7uKKopxdeJieJTygbR5rHURE/nbdjotGlQhVb/hiEEEJsSMKMNW7V25/GxwEAvmrQDJ8e2KsYLaRzZVWxEsLYuo1sHSYhJAd4u7pCMa/CpEy5HM5UeybX8PHxMWs53sDAQEilUrx69Upr+6tXrxAcHKz3NcHBwRbtnx0zZ85E//79cfLkSdUc5DNnzuDIkSN6E2dzWdyD3KdPHxw/ftzqN7SWPf6BlH9a+o/o6uqq+sUy9xcst/vs0D5c0Jcca97X05P8xTvN4O/mDieJBOva94REOUfZYHKs3r7vMVULJ4SQ3CzI09Os4ZFnIsURQTHJKapeZfWfyvtisp2eQQV7CMk/mPFzhOK5NBl97i1mxx5kc7m4uKBevXo4ckRdO0gQBBw5cgSNGzfW+5rGjRtr7Q8Ahw4dMrh/dvTu3RuhoaEIDAzErl27sGvXLgQGBuLChQvZmvprcQ/yypUr0bdvX5w6dQohISFwdnbWen78+PFWB2OM5j9Qjx49AKj/gcaOHav3Ncp/oIkTJ6q2af4DlS1bFsHBwThy5Ahq164NQOwNDg0NxejRo+3yc+RGj2Lf4PDTxyb2UvQGaFwprFekGD6u00C1R43CRVDOxw+PYt9qvMbwsZ7GxyE+PQ2+rgW3WjghhORmb81clu9VcjIEzrHqykWNgj162mIcWHLhHAbXrGvLMAkhOUQKBrlO+1CFq//87/FD9K8W4uDoiC1MmjQJQ4YMQf369dGgQQP88MMPSE5OVlW1Hjx4MIoXL66axzxhwgS0bNkSixcvRufOnfH777/j0qVLWLt2rV3iq1evHn799VebHtPiBPm3337DwYMH4ebmhuPHj4Mx9aeBMWa3BBmw/T8QYwwTJ07EnDlzULFiRdUyT8WKFVMl4QXBkadPIGFiZVGTOODEJBhbrxHG12usGH6nVqVQkCJBNjXeRrziuO3WDYyu18DEvoQQQnKCr6ub6SGUHBDAcf55JF4lJWk9xbR3AwDEpaXZOEpCSE5xkUqRmikTx6QqP+SaV8cUf54KD6ME2VJWrlts8pgW6t+/P2JiYjBt2jRERUWhdu3a2L9/v2q6bUREBCQS9aDkJk2aYNu2bfjmm2/w9ddfo2LFiti1a5dqiV1bk8vl2LlzJ+7evQsAqFatGrp37w4nJ4vTXBWLX/m///0PM2fOxJQpU7T+MhzBHv9AX375JZKTkzFq1CjExcWhWbNm2L9/f4FaAzlDkEMCZnDtSl2L3+2I7pWq6n3u8wbNsPfhA7Pf+0REGCXIhBCSSwV5esHPzU1Mak0kybsf3DWaS2u2mVMyM+GhMwKNEJL3CMoPvZHkGBy4HPXC0aERGxo7dqzBEbv6pt727dsXffv2tXNUwO3bt9GtWzdERUWhcuXKAID58+ejcOHC2LNnj9VJucUJckZGBvr37+/w5FjJ1v9AjDHMmjULs2bNslWIeU61wMKQccGMPRmCPDzQsXwlg3uU8fPXP6wOUM19YBoPb0W/0rcnIYSQXGJISB0su3jO5BDKUxHh4vmfA8xAlqz8fnjyNhY1ggpusU9C8gtnzpCuLzlWUrT93qSkODawfIBx8WbrY+YnH330EapXr45Lly7B398fAPD27VsMHToUo0aNwtmzZ606rsVZ7pAhQ/DHH39Y9WYkd2pVqiyCPb1MrmMnYQy/9+gHFyNVCO+/eQ3OIbaQtF8OJkC9oDwHJBxITs/EnRhKkgkhJLcaX7+xqgI1gKyFXgQAYHiTnAJnicTkDBsGYOut67YOkxCSA/zc3LV7jHV7jxX3zemGITpyQZGu3O7atWuYN2+eKjkGAH9/f8ydOxdXr161+rgW9yDL5XIsWLAABw4cQM2aNbMU6VqyZInVwZCcIZVI8GP7Lhi4+0+ky2R6PzvOEinWd+6Jcv6FjB5ry42rYBzgjKl7GxTJMaC/3TRi9y4cHzICrtmYK0AIIcQ+pBIJpIxBLmhU3wJULV6mqCkhgKNaYBCuvYoynCQravmcDH9q36AJIQ7RqVwlrLtxGVx5flDORVa0/TgXzxFOhoaVEJINlSpVwqtXr1C9enWt7dHR0ahQoYLVx7U4I7l58ybq1KkDALh165bWc4x++fOcqy9fYvutm0jMyMDgarXxMiUR+548hEwQIGEMvq5u6FGpCobXqoeSPr4mj3cy4ql4R2MonrHkmAF4lZyE/x4+QM+q1WzwExFCCLE1D2cXJKZniEOnDfRASBjDyNr1MObAv0YLezEA8enpdoqUEOJI75Ypi3XXLosXygAwuf79VAk0ITY0b948jB8/HjNmzECjRo0AAOfPn8esWbMwf/58JCQkqPa1ZFleixPkY8eOWfoSkguFvX2Lobv+QmS8+hcHTGy4fFz/HYxv2AhuTpYXUOGaHQxc57ERqy6FUoJMCCG5lK+rG5IyMgzvwAC5wJGYkaE+32smyTptY28XF9sHSQhxuKiUZNV9Qx0hAJBJCTKxgy5dugAA+vXrp+qo5Yrko2vXrqrHjDHI5Qau3uiRrTGtz549AwCUKFEiO4chDvYiMRFdt/2CFJksy9mMA1h96SLcnZwxrmEji4/duERJ/HX3tlYxQ3M8io3FzGPHMP3ddy1+T0IIIfbl5exifLknDsgg4OiTx6rHqoI9el7Trrz1Q98IIbkHl4vDBE0UuTe7PUjUGOxQpMu2h8tx9uq4tThBFgQBc+bMweLFi5GkWO/Q29sbkydPxv/+978cq25NzLfqQihSMmXiAwOflB8vhmJYnbrwsvAq/5CadfDn3dvaG5XrxxtpWIEDm69fxbiGDRHg4WHRexJCCLGvUr6+uB/72vhOHIhKThbnJusu9cK0709q2NReoRJCHKicv3++S7pI3tGyZUu7HNeqdZA3bNiA77//Hk2bil9wp0+fxowZM5CWloa5c+faPEhiOwLn2H77lslLSBlyOY4/DUOXSpUtOn6NoCKY3aoNvj1+RNygrJhn7P0YAMWoh8kH9mNjz14WvSchhBD7ql0kGIfCHhtf6glAdFKSeD5niiVKwMAVO3CpuN0ZEvi6uTkockKIPVUNKmJ0aTcAAAdcnQyvgEIM4Ey82fqY+UxaWhpu3LiB6OhoCIJ2vfRu3bpZdUyLE+TNmzdj/fr1Wm9Ys2ZNFC9eHJ9++iklyLlcmkyGTJkAmHGeSrSyiMqgkNqoUqgwJh/ah4i38WIlQ91F5AF1Q0uubEQB5yIirHpPQggh9tO5YhUsPHdGvUHP+dyJMUQnJqtqUEC1i5gkM7m4WQaOdJmMVi4gJB+QC4JZ6597O7k6MixSQOzfvx+DBw/G69dZRzhZOu9Yk8XjoWNjY1GlSpUs26tUqYLY2FirgiCOkZiejsE7/jR7/1K+fla/V/1ixXFiyEco5+svVjYUoL/yqRxgiqtZDECmXMDvN25Y/b6EEEJsr7SfH4q4e6rXslfiyp5iQC7n6m2K/5SUj8XzPUdyZqYDoyeE2EtiRobiM4+s7TyNaRb0mbcCrYNs0rhx49C3b1+8fPkSgiBo3axNjgErEuRatWph5cqVWbavXLkStWrVsjoQYl9JGRnosHETrr58KW4w9iHhgJQxNC5ZMtvvq176SzFMRA5AJt6YnKmSY833nnbkCI4+fpzt9yaEEGI7r5NTVA1hxtU3AIrvFAaAGV3yURxNxBCTnGxwH0JI3uHMGCAwsW2n2xkiDhkBBHEEI7EQJcgmvXr1CpMmTUKRIkVselyLxzctWLAAnTt3xuHDh9G4cWMAwLlz5xAZGYn//vvPpsER2/nhzFmxeIpiKScuQL2Yu2ZbRvHB6V21OiQ2WNe6fvHiCI+Lg1y53pNWn4IeDJBzjpG7/sGijh3Qsxot/UQIIbmBuEoLA+Rcz/cGU983cpJXfhWsCg3FD5072yNMQogDPX37VnWBjAna6x0rW3wcALe+M48Qg/r06YPjx4+jfPnyNj2uxQlyy5Yt8eDBA/z444+4d+8eAKBXr1749NNPUaxYMZsGR2yDc45t169rzRkzliT7ubpiavMWNnnvwbVqY/utW2YEiSxXtb45dBjvVahgcSVtQgghticBg6CcaGhlLwRjYpJ8M+qVbYMjhOQIN2dnrcf6ukHyX1kox9AapWPDY+YnK1euRN++fXHq1CmEhITAWef3cfz48VYd16oKGcWKFaNiXHlIXGoa0jPlWc5QDAAE7XYOA7C+e0+bVRitFhSEGe++ixnHjulvUHExBn3SMmTYe/ce3q9V0yaxEEIIsZ63iwviTRRvVM1DNNIiZgCcpbQkJCH5wYGHD8WPu4kq1vksLyO5xG+//YaDBw/Czc0Nx48f15riwxizOkE2+xvq4cOH+OCDD5CQkJDlufj4eAwYMABPnjyxKghiP3JBwIQ9e8UHBhotDOpGja+rK+oULWrTGAbXroM/+vWDh85VHVVMGjHo3nbduWvTWAghhFgnPUMxh1BPoS4A4sVOQWcbdPZTXBRtWbasfYIkhDjUibCn6gcan3HN84Sp1T6JATQH2aT//e9/mDlzJuLj4/H06VOEhYWpbtnJS81OkBcuXIiSJUvCx8cny3O+vr4oWbIkFi5caHUgxD4OP3qMsxGR6iTYUDVpiEPfhterZ7TAirXeKV4Cyzp2FOencMU8NEUsxt7tUuRzrD5/EZzns080IYTkMTLODX+HCBrncq3CXTr7KxLowXXq2CdIQohDcUFQJ8UCwASxACsT1CuY2KFZSQgAICMjA/3794dEYttRSWYf7cSJE+jbt6/B5/v164ejR4/aJChiO9uuXddunOje17gFeXhiTMOGdoulWZkyWtVPATOuKDJg0YlT2HDxst3iIoQQYpqHk5PqHK7dIFZsUyS/TOMCKKC9LzjgKpWiuJ6L7YSQvEcqkYpJMGd65x8rP/uuNk5gCgTqQTZpyJAh+OOPP2x+XLPnIEdERCAoKMjg84GBgYiMjLRJUMR2rr14qZWFas071jmP/dK3j116j5VcnZzQpUoV7FEUd7PkQ7rs9Dm8X6smvFypYBchhOSETpUr44+bNwEYLvTCFGMpVXORdfdjQGY21qYkhOQur5OSDY9MBANXTEAu5u3t2MBIgSCXy7FgwQIcOHAANWvWzFKka8mSJVYd1+wE2dfXF48fP0bp0qX1Pv/o0SO9w69Jzrn76hWSMxQLs+sr0KVRNMHP3Q0VChWye0z/e7clDj96hFQL18NLzZThyKPH6F69qp0iI4QQYky1wkFaqyFkoZxoaGTCoXK2DOfcrhdkCSGOEZuSZvQzr+xVLuXr77ig8gmqYm3azZs3UUcxZeeWzqo52fmOMTtBbtGiBVasWIHWrVvrfX758uVo3ry51YEQ2xu07S9xaIsZo1o6V6pk93gAoLCnJ/YNHYwPd/yFyLh4cOUcZEONLYhPMgbEpqY6JEZCCCFZ3YuOgRQSyJVjqTWTZc3h1MYqWDMAAhCXlgZ/d3e7xUoIcYw0mXkjQqoFBdo5knyIM/Fm62PmI8eOHbPLcc2eEDB16lTs27cPffr0wYULFxAfH4/4+HiEhoaid+/eOHDgAKZOnWqXIInlLkY+Q4Lmchx6rhhxxf8YBz5v0cxRoaGknx+OjxyBfz4coL6SpRufznbOgWI+NDyHEEJyCgeHBExrPjE4ALl4Y5xlOXdrvFikeJ1cMLC+HyEkz9E791hHkLeXAyIhBdmzZ8/w7NkzmxzL7AS5Tp06+PPPP3Hy5Ek0btwYAQEBCAgIQJMmTXDq1Cls374ddevWtUlQJPvWnr+kqBDNtNcZ1pgTxrhYXGVy86bwsdG6x5aoERyMIE/PrA0qZYwygMkZmBxwYRJUCAhweIyEEEJE9UsUh0wQwKCoUqu8Kf7TTYKzJMmqwl0MAR4eDoycEGI3GkX7mEx90zoPcKB9xYo5GWXeREW6TBIEAbNmzYKvry9Kly6N0qVLw8/PD7Nnz4aQjQuxZg+xBoAuXbogPDwc+/fvx6NHj8A5R6VKldCuXTt40JddrnIvOkZ1X5kkc801lQRxu7uzE0Y3tl/lalNGNKiPecdOqruzlVVRFYGqwpVz9Nn0G34Z2Bc1ihbJoWgJIaTg6lSlEuYcOY74tHS9zzMAnItTYsCRZXk+5Xm9iLcXJDT/mJD8QbHEm2b5AQ6xAwYAuKIrrgiNAiR28L///Q8bNmzA999/j6ZNmwIATp8+jRkzZiAtLQ1z58616rgWJcgA4O7ujp49e1r1ZsRx4lPTsmzTusKvMLxBPQdFpF//WjWw8PgpyARxoTxDyz8JnCMlIxNDt/2FH3p0RpNypaiBRQghDvQmOQU8U6O6o/IUrLGsE5NAvdyTspy1xqmaA+hdo7qDIiaE2NPz2Hit9hrT+VOZKOezTkuHoSJdpm3evBnr169Ht27dVNtq1qyJ4sWL49NPP7U6QaZFyfKhyLdxSE2TiY0UGcT5YbrDKhSdyZ82ybneYwCITUmFXMa1G1gG9uUAEtLTMfy3v9Hup424/jzKcYESQkgBt+7cJaRmZILJkWX4pGptZDnANG5QDrWEooI1B57FxedI/IQQ29py+QoAw+02hizXyAixqdjYWFSpUiXL9ipVqiA2Ntbq41KCnM8InOOTP/5Rr0Gp+JMpiqhoJsqtypWBi1SaE2GqZMjkYAAkigaWyZOoIvZncQkY/OsOPH5t/S8/IYQQ83DO8feNO5ALHIwDEgGQyBQ3uUZPh8YoINVNWcQL4j6vk5Nz6scghNjQ2bAI7XabnjmuJgrbE2NoDrJJtWrVwsqVK7NsX7lyJWrVqmX1cS0eYk1ytzVnLuChImnUe0KSQ7wswoF2lXO+YEJxPx+4OzsjNTPTohOowDkyZHKsO3sR33drb7f4CCGEAOkyOVIzMg3vYGB6jGqbAHABkDgBxX197RAhIcTRIuIStEeSQCP/kgDKWRbuTpRuEPtYsGABOnfujMOHD6Nx48YAgHPnziEyMhL//fef1celHuR8JEMmw6rTFww+r+xVZgLgxBjaVa7gsNgMcXd2Rt9a1SE1dz6x8uzLAbnA8c/Nu0gwUDCGEEKIbbg6SdXnaQO9EfqGUzKd57gA9K1Fc5AJyQ9kmTKN6vQi1edfY3uDUiUcG1h+wdWjc2x1y289yC1btsSDBw/Qs2dPxMXFIS4uDr169cL9+/fRvHlzq49r1iWdhIQEsw/o4+NjdTAke449DEOaTGZ0H+WJ692K5XJkaSd9JrZsggsRz/Eg5jUEbuSTq3MC5hCT5CZLVuPrdq3wQb2aYFS4ixBCbC42JRVyzrV6ilUVaxXbjA2lVJ6+yxcKQO3iRe0ZKiHEQWSqgnzaVD3JAsAZ8GF964e6Fmj2SGjzWYIMAMWKFbO6GJchZvUg+/n5wd/f3+hNuQ/JObtu3DZvRw583OQd+wZjAS9XV/w2uB/GN2+MQE8P/esiK8nVV8CUV8MyZQJm/ncUcw8cy7KsCCGEkOx7phhKqdlDrK9RbIjyufKFAuhCJiH5gKn2luo8wYEWFcrZPyBSoDx8+BAffPCB3k7c+Ph4DBgwAE+ePLH6+Gb1IB87dszqNyCOc+ZxuHp5DY1lOLhOiybQ0wM1iwXnQISGebq44NNmDfFps4ZIz8xEkx/WIjEjQ331TC7uZ6xZ9cuF66hdohi61MhazY4QQoj1PF2cDT9pRhUe5VdS2UJ0IZ2Q/ODa8xcATBfgormc2UA9yAYtXLgQJUuW1Dty2dfXFyVLlsTChQuxatUqq45vVoLcsmVLqw5OHCtdJmitb6Yc+sa4dpI89J06ufoKvquzMwbWr421Zy+KQ67NqW6t8M2eQ2hXtWKOV+cmhJD85FLEc/3nYY0GHGemz9WDG9SxbWCEkBwxZ99xk/swAE4SSpGJ7Z04cQK//vqrwef79euHAQMGWH18q8vKpaSkICIiAhkZGVrba9asaXUwxHrP4uL1VhHVHOIiKDa0rFjWscFZYXijevjvzn08exsPwYKrXakZMvx78z561q5mv+AIIaSAefrmrcHnlN8znOtPkrmih7lh6RIo7OVprxAJIQ50P/q1ujfSyJWxQp7uDoknP1IV1rLxMfODiIgIBAUFGXw+MDAQkZGRVh/f4ss6MTEx6NKlC7y9vVG9enXUqVNH60Zyxlc79wMwXiBFuc5wxaBAR4VlNT93N/wx9H10qVEFZl98VHzoZ+w9jBfx5heWI4QQYtzzOAPnVI3h1cqVEgBFsqzREPNydsaa93vYL0BCiENlZgqqaX16h+0qttG0N2IPvr6+ePz4scHnHz16lK3C0RYnyBMnTkRcXBxCQ0Ph7u6O/fv3Y/PmzahYsSJ2795tdSDEeknp6bga+dLovAJl4tywdAlIcvHwak2FPD2wqEdHLOnZ2fwXcSBNJkebpRvw78179guOEEIKkLTMTPE7RlDc5FDVhtCkWk5Q8z4Hgr294GFsHjMhJM9I11wxRbOgqu6cWQ5MaNXEcYGRAqNFixZYsWKFweeXL19u/2WeNB09ehT//PMP6tevD4lEgtKlS+O9996Dj48P5s2bh86dLUhmiE2Ex8ZBELhZ83QX9uhg93hsLaRYEYv2Vy4vMPnPffBwdsa7VcrbJS5CCCkovF1d1ckxNJZxkcKs9Z2oLgQh+ceMPYcBqFcV4Rxil5vyPCCo93Vxsno2JyEGTZ06FY0bN0afPn3w5ZdfonLlygCAe/fuYcGCBThw4ADOnj1r9fEt7kFOTk5Wjfn29/dHTEwMACAkJARXrlyxOhBiPRep1GRVOs4BJ8ZQxMfbMUHZUEl/XzQpW8qsnm/VHoqT9uhtuzH17wNIzzS+PjQhhBDDKgUFAoLOMk+qLNkIxc6dQ2iYJSH5xb+3HqiSY9WIEQFgcsXNguKqxAhup1s+UKdOHfz55584efIkGjdujICAAAQEBKBJkyY4deoUtm/fjrp161p9fIsv61SuXBn3799HmTJlUKtWLaxZswZlypTB6tWrUbRoUasDIdYrFxigfmDgSj4zo7pobja9U2v0//l3xKWmZX1S3wde4yrmrqt38DIuEZuG97FzlIQQkj/tvXEvyxV1rvsgS3Uu8Q8GYGgj6xsqhJDcJVMmN1jsiSv/x4AADzcHRpX/UJEu47p06YLw8HDs378fjx49AucclSpVQrt27eDh4ZGtY1ucIE+YMAEvX74EAEyfPh0dOnTA1q1b4eLigk2bNmUrGGIdVXVRAeKYAM2GivI+z9sXjcoU8sdfIwfgk23/4GHMG43xfVAvA6VMlDWeU24PDYvE8iNnML5NU4fHTggheVlCahoeRccC0BmlA43TrfIO034eHJje+V040xBrQvIFrlh+09DMCuWpgAvA5HepzUXsy93dHT179rT5cS1OkAcNGqS6X69ePYSHh+PevXsoVaoUAgNzf3Xk/Cg+NV1MEhXzw7gE6sHzyqIqDPByc8mxGG2hhJ8vVr3fDe8t36iqjqoqAiPXeAw9ncocWHXsAvrWC0FRP+ur2hFCSEFz83mU4TWQJerzsKrnSIEBqFg4AB+8U9vuMRJCHOPy02cATI9KZAB61w+xezz5Xl7u3crDsrV6N+cc7u7uqFu3LiXHOejWsygxQVQMxZDIAUmmeGMyReLMgboli+d0qNlWMsAP0zu3Fue7KOe+6CTHyvua9SKUf/Zc+StSMjIdFC0hhOR9d1/GqK86KqtXC4o5h8rvGcV3jXJIIIO4T4NSJXMydEKIjU3e/p9Z+xX28gDLI6umEKLLqgR5w4YNqFGjBtzc3ODm5oYaNWpg/fr1to6NmIFzjnUnLgKAVsEE3SHG4MA3nd7NkRht7YN3auHnD3vBw8VZTP5h/EqmZpIcn5aO1gvW4fhdw2unEUIIUfNxd1UlxJp0iyJKFAV6VCOXOJCYlu7IUAkhdhadmGKyV5MBmNqhpUPiydeoSFeOsThBnjZtGiZMmICuXbtix44d2LFjB7p27YrPPvsM06ZNs0eMxIjXScl4Y+hkpfFBcJNIUNw//wwtblK+NI5PHqmef2wmBiAhLR2f/robSw6ctld4hBCSb8QmpWYZjQONx5qVrZWjepS9yKUK+TkmSEKI3T2Nea1+YCjRUrQ9W9ESmwVObGwsBg4cCB8fH/j5+WHEiBFISkoyuv+4ceNQuXJluLu7o1SpUhg/fjzi4+MdGLV+Fs9BXrVqFdatW4cPPvhAta1bt26oWbMmxo0bh1mzZtk0QGJceIxYoMtoDyqHas5ufiJlzKrK3FzRcFt/4iKCvD0xsHFtGgZECCF6cM7xz5U7OhsVSzzpoSrapajg07NudfsGSAhxmNFbdoufcc2CqJrnAo3ifB4uzo4OL9/Ja1WsBw4ciJcvX+LQoUPIzMzEsGHDMGrUKGzbtk3v/i9evMCLFy+waNEiVKtWDeHh4fjkk0/w4sUL/PnnnwbfJyEhweyYfHys6xy0OEHOzMxE/fr1s2yvV68eZDJaa9bRfgu9YdZ++XGhdjdnJ7g4SZEhk1v0OuXanYwD8/Ycxz+X72DRB51ROtDPHmESQkielZCWjvA3ceID5dQdE9cTle3mj1s0QHEqikhIvhHxVuzZM5UkU5dDwXP37l3s378fFy9eVOWJK1asQKdOnbBo0SIUK1Ysy2tq1KiBv/76S/W4fPnymDt3LgYNGgSZTAYnA7mLn5+fyY4tzjkYY5DLLcsRlCzOmj788EOsWrUKS5Ys0dq+du1aDBw40KogiPXOPAw3a7/yhQNM75THSCUSdK1VBbuu3oFcsOCSmM4cjPtRMfhwzR/YOeFDFPLK3rpphBCSnxy780j9wNC6Ljo4gPbVK2Die7TECyH5CRfUpwGmuMM15lgoTw8VgvJfmzNH2GPOsOJ4ur2wrq6ucHV1tfqw586dg5+fn1Ynatu2bSGRSBAaGmr2Ukzx8fHw8fExmBwDwLFjx6yO01xWdStu2LABBw8eRKNGjQAAoaGhiIiIwODBgzFp0iTVfrpJNLG9pHTzCqAMbFzbvoHkkI9aNMD+mw+QmimDYO44cgaxgIyCXOB4m5yK385dw9j3mtglTkIIyYvWHrugLroF6O8x0sEAjGj+jt1jI4Q4zt6rd8VifZobuVjMiCuWDuGK88NfowfpPQaxjD2HWJcsqb3CwPTp0zFjxgyrjxsVFYWgoCCtbU5OTggICEBUVJRZx3j9+jVmz56NUaNGGd2vZUv7F4CzOEG+desW6tatCwB4/FisBBwYGIjAwEDcunVLtR/N6XQMcztO36tW0b6B5JDShfywZWQ/fL79P4Qp5mOr1uOEnqGAyqWhdLYJnOPvS7cpQSaEEA3P3yaqh1ZzsQcJUuOvKebnjRrFi9g9NkKI43y944Dh62LKi2gSQMIAZ2cTJwmS4yIjI7Xm5xrqPZ4yZQrmz59v9Fh3797NdjwJCQno3LkzqlWrZlWinpKSgoiICGRkZGhtr1mzplXxWJwgO6Jbm5hHJhfEE5JWCVH9XPPxyapq0SDsHT8El8Of4/fQ6/jvxgNImGJ+jGZPh26Je43HDED02yTM3nkEw1vWR/EAX4f/HIQQkpvIBQEyQdDuQVaNr0TW7x0OSCQMqz7sQRfJCclH0jMyIRe4wcEjyjnJTAC+6NjcwdHlY3YcYu3j42NWAavJkydj6NChRvcpV64cgoODER0drbVdJpMhNjYWwcHBRl+fmJiIDh06wNvbGzt37oSzs/kF3mJiYjBs2DDs27dP7/MOm4NMco/nb+PFX3Rji3VxwFUqyfeNFcYY6pcpgfplSqB/g1rYeOoSTj8MFxt3ys+GZuVVjQaf5t/Mn6E3se/afWwe3Q8VgwMd9wMQQkguk5SWAS5oDPFTDqeUAVwK8YHmORXAyBb16dxJSD4z5x+xc8zoiikQk+ShLbIW8iV5V+HChVG4cGGT+zVu3BhxcXG4fPky6tWrBwA4evQoBEFAw4YNDb4uISEB7du3h6urK3bv3g03NzeL4ps4cSLi4uIQGhqKVq1aYefOnXj16hXmzJmDxYsXW3QsTWYlyL169cKmTZvg4+ODXr16Gd3377//tjoYY5RrZe3ZswcSiQS9e/fGsmXL4OXlZXD/6dOn4+DBg4iIiEDhwoXRo0cPzJ49G76+6t5BfYnjb7/9hvfff98uP4ct7bt2X7yjGk+ss4Niu4u0YF0HeadsCbxTtgQ45+AcGLxmO66Gv9D++9GTHAPifOTE1HR8+vMu/PflMDg75d+ed0IIMcbD1RkSIet2BnGtYy7PWqCnQdmSWV9ACMnTDt96aHIfM2v4EUvYsQfZ1qpWrYoOHTpg5MiRWL16NTIzMzF27Fi8//77qgrWz58/R5s2bbBlyxY0aNAACQkJaNeuHVJSUvDrr78iISFBVTyscOHCkEpNt8GPHj2Kf/75B/Xr14dEIkHp0qXx3nvvwcfHB/PmzUPnzp2t+nnMypx8fX1ViaRmculI9lxba+PGjejQoYPqsZ+fnz1/FJvgnGPTiUviFTvlMGsgawEV3YIKBQhjDIwBbaqXx9WIF1lOCob+XjiAl3GJ6LZwM34Z2x+B3p72DpUQQnKdq+HPjT6vrGTLFQ9cnKWoU6a4I0IjhDhIXHIKElIzTE7nYwDKFvZ3VFgkF9q6dSvGjh2LNm3aqDozly9frno+MzMT9+/fR0pKCgDgypUrCA0NBQBUqFBB61hhYWEoU6aMyfdMTk5WFQfz9/dHTEwMKlWqhJCQEFy5csXqn8WsBHnjxo167zuKvdfW8vPzMzk+Prd5nZiMxLRMgCmGDQvQ6klWNlgYB7zcrC/bnh/0rF8Da45eQFJ6umpessmLBhx49jYeQ3/ajkmdm6N6yWAU8dU/WoEQQvKjcT/vMdzboOckOqx5Pbg5F6wRS4Tkd99sP6i+EKZZ90Zn7WMA2DY694++zEvsWcXaHgICAgx2XAJAmTJlwDVWnGnVqpXWY2tUrlwZ9+/fR5kyZVCrVi2sWbMGZcqUwerVq1G0aFGrj2ts9qpeYWFhePgw61CLhw8f4unTp1YHYoyptbXMZWhtrTFjxiAwMBANGjTAzz//bPIfKz09XTUMQHM4gCNlyhXj3rg41E0iqD9ITFA8VpzIqhY1PXcgP/PzcMO6Eb3g4+Zqfm86E3vmw2PiMGHTHrSbsx6TNu/B2+RUe4ZKCCG5QnJ6BpLTMrSG+GkOVNJNnIv6eWPMe40dFyAhxCFO3g0T6w8I6jYmk4s3zfNDgKc7fDwsmz9KSHZNmDABL1++BCAuVbVv3z6UKlUKy5cvx3fffWf1cS2+1Dt06FAMHz4cFStqLxsUGhqK9evX4/jx41YHY4g919aaNWsWWrduDQ8PDxw8eBCffvopkpKSMH78eIPHmjdvHmbOnGn5D2JDQT5eWRopuhfzlEtyFPbycGxwuVBIyWAc/GoEdl+9i9P3w3D2XoRYwMsQncafwDmO3n6Mh1Fv8NuEDwp8rzwhJP96HhuP3ot+UfUaaZbq0JrNo5jOwwDM7N0WUonF19wJIbnY/uv3odlU0mpnKjpouOJjP7dvO4fGViDkoTnIOWXQIPWa2/Xq1UN4eDju3buHUqVKITDQ+oKRFn+bXb16FU2bNs2yvVGjRrh27ZpFx5oyZYpinqjh27179ywNMQtja2t9++23aNq0KerUqYOvvvoKX375JRYuXGj0eFOnTkV8fLzqFhkZme0YLeUklUDKmMEpIapGDAcevXrjwMhyLy83VwxoXBs/De2JMe2M9HTo9JYoyQWO8Ji3+Ov8rSwvIYSQ/CA5LR29F/2CpHSNKTx66LaxapfKOtWJEJK3zd953OC0NFX7UxDXPm5RtZxDYysQuJ1u+RTnHO7u7qhbt262kmPAigSZMYbExMQs2+Pj4y1ea2ry5Mm4e/eu0Zuj19Zq2LAhnj17hvT0dIP7uLq6qtYPM3cdMVtLSc+AoFiTzhDlycvTwOLfBdmIVu+gfyPF4uH6ThoG/mI5BxbvPYmhK/7Asr2nER7z1s6REkKI40zffgjJ6Zl6h1FrUjWOOdC6Wjl4urk4JD5CiGPEJqbgdWKKwYtkgLombB26QEZy0IYNG1CjRg24ubnBzc0NNWrUwPr167N1TIuHWLdo0QLz5s3Db7/9piq/LZfLMW/ePDRr1syiY+XGtbWuXbsGf39/uObypPLs/XDV8DZT6pWlqqK6JBKGb3u1gSBw7LhwU90QNLD8kwoTk+TLYS9w9ekLbDh6ESPbNsDYjk3y/VrThJD8LTYxCQeuP7RovRZnJ4a5/TuY3pEQkqd8uPx3k/soF06ZP6Cj3eMpiPJaka6cMG3aNCxZsgTjxo1D48bi6NBz587hs88+Q0REBGbNmmXVcS1OkOfPn48WLVqgcuXKaN68OQDg1KlTSEhIwNGjR60KwhR7ra21Z88evHr1Co0aNYKbmxsOHTqE7777Dp9//rldfg5bSk7PUJ2YjOEcCKLqywZ91b0VHr96g6tPXwAwY+SJxg6C4v66wxcQ5OuF/k1r2SVGQgixt5i4JLSdtU58YMG1vv++HA5v99x9QZkQYpnUtAxEvI4Xx5kauWDGOeDl5oyi/o4fSUkIAKxatQrr1q3DBx98oNrWrVs31KxZE+PGjbM6QbZ4iHW1atVw48YN9OvXD9HR0UhMTMTgwYNx79491KhRw6ogzLF161ZUqVIFbdq0QadOndCsWTOsXbtW9byhtbVu3ryJChUqoGjRoqqbcs6ws7MzfvzxRzRu3Bi1a9fGmjVrsGTJEkyfPt1uP4etlA5UrDVnbD6BYnv5IoUcEVKe5ObshHWjeuOLri1RopC4xrc5S0DpWn/4AuTGin4RQkgulZCShvZz1qsu+pk73aSovzeKBlDDmJD85r3Z62FOLwxjwPgOWesSERuhOcgmZWZmaq1ypFSvXj3IZDKrj8t4dhegIkhISICvr69qGSlH4Jyj+4LNCHv1VmftDeUO4h9uUikuzB9Hw3/N9Of5m5i547D4QPevTF/jUWO3HZMHoXLxgr2kFiEkb0lMTUeH2euRmJohTiEBjFd+1PD35EGoWMCXESQkv+Gco+akH7K2LQ20iW4u/swxgVkhJ9rntqCMu8q47yB1te3SWfL0NNxb8XWe+zsxZNy4cXB2dsaSJUu0tn/++edITU3Fjz/+aNVxLR5iDQBxcXG4cOECoqOjIej0mg0ePNiqQIhlGGMY2aYh/rd1vzgXWXMsgMZVonRBjqcxb1E2KCBnAs1j2oZUwOw/D4s9KfqGFRm5nJSejStVhBCSE1YfOK9KjlWU5z4j58BP3mtIyTEh+dCP+86YPh8o2kgVgqltaU80B9k8GzZswMGDB9GoUSMA4tLDERERGDx4MCZNmqTaTzeJNsbiBHnPnj0YOHAgkpKS4OPjo9UzyRijBNmBXickAVCX2df8ndc8t1198oISZDP5ebpjSIv62Hj8krjBzBOJs1SCMoX9VY9lcgEvYhPAABQr5EPrgxJCcp3bEVH49fgV8TwngWpNYw5kPfcproV7uEgxuXsr9Gtc05GhEkIcZNORS4AMgFSxQXOotc6fP3/a16GxEaLr1q1bqFu3LgDg8ePHAIDAwEAEBgbi1i31sqyWjqS1OEGePHkyhg8fju+++w4eHh6WvpzY0Mu32sttMQMZ8u3IV+jVyH7zw/ObiV2a4e7zaJx/GGFyXwZAKmHoWLcKfDzccPNpFBbtPIE7ka+QIROXPQvy9cLg1vUwsGUdSCQ01J0QkvMW7zyOLceuqjuHlKs0StVrH2slyhxwd3XC6TmfwtlJqns4Qkg+cOLWY2RkcrEJqbFyK5dCu+dFcTHN34vyALuyx5zhfNaDfOzYMbsc1+JurefPn2P8+PGUHOcCZYI0CnXJASaob5BDdcU/PDo2hyLMmyQShrWf9MLw1lkn/WfZlzGUCPDFhE5N8eXG/zBoyW+4FvZClRwDQHR8EhbtPIHp2w6CpvwTQnLa0l0nseXYVYDrmV4oh6oBpVzrmCn2m9y1BSXHhORj49fuVn/uoXF+0GhTKpO2FSO65kCEBQwV6coxFvcgt2/fHpcuXUK5cuXsEQ+xQLXiQeIJy8AvO+Nir0BUbIJD48oPGGP4rEtzfNiiLv4+fwuhjyKRmJqGjEw5It7EQSYX4Ofphr6Na2JIq3pYufcsDl69n/VAGv82u0Pv4Oj1R6haIgjNq5dF14bVEOBNF5oIIY7z3+V72HTksnqD8hylObxaDu3hlQAK+3iiXxMaVk1IfjVs2R96t6umHwsAFwBIADcXKVpWr+DI8AhR6dWrFzZt2gQfHx/06tXL6L5///23Ve9hcYLcuXNnfPHFF7hz5w5CQkLg7Oys9Xy3bt2sCoRY7nrYC60r/Zo0ewVex6c4MKr8JdDHE6PaNcSodg1V2wSBI10mg5uzExhjeJOYgj/P3sx6nULj34ZzcdhicloGLj16hkuPnmH57tP4vHdLfNCyjqN+HEJIAfYk6g2mbtyn+oLQrLejHGut2qZZmAfA1onv02oIhORTnHNcefzC4PNan3wObBrXz+4xEe1efFseM6/z9fVVfR/5+vra5T0sTpBHjhwJAHoXXmaMQS6XZ9lO7OPU7adm/aKnZ1J1ZVuSSBjcXdQXhs7efZp1DWSdCxf62pVyzjH/z+MI8PZA+7qV7RMsIYQAiE9KQa85W8QHXKeRxNQX8lRLPSnuu0glWD+mL4L98/5yIIQQ/ZbsPGH2vpWLB6J6yWA7RkOIcRs3btR735YsTpB1l3UiOSfNzMRXoPkGdmXo38Gsq3QcmL/9GK4/foFCPp7o3KAqgv29bRofIaRguxcZjffnb1U9zjLiSGf5FuUFvWn92qJnwxpUXJCQfG7r8atm7ccAfD+4k32DIWpUpMuksLAwyGQyVKxYUWv7w4cP4ezsjDJlylh1XFp7Jg+rULSQWftR28a+Khezci1QxYkvNjEVv5+4hh93n0Gnb9bjh50nIdBVDUKIDZy/G473v99qcDqOXhz4rFtz9G4cQskxIfncx8v/hNyCvq/ywea1PQlxhKFDh+Ls2bNZtoeGhmLo0KFWH9esHuTly5dj1KhRcHNzw/Lly43uO378eKuDIZYpGxSgVWBFL64cOsdp/pidhJQJRoWihfAkKhaCFVWqVUMbFTYfvgy5wDG5d0vbBUkIKXD+OHEN8/44Zt5ENo1eZKmEYWgb01X8CSF5W3pGJkLvR4oPBOgpa6/t43bvOCIsosC4zhKuNjpmfnL16lU0bdo0y/ZGjRph7NixVh/XrAR56dKlGDhwINzc3LB06VKD+zHGKEF2oJj4ZLFRI4FW40aXXAa8TkhGYV8vB0ZXcDDG8N3gjhi2bDtS0zNVSTLn+uceq18Ig0Ndfj1yBftC72J8j2bo2rg6XdwghJjteUw8hi3+HTHxKdqFuACzupD3fDvUPoERQnKVvnN+AQTFcFKN9c85g7hR43whlTB82qWZw2MkxBjGGBITE7Nsj4+Pz1ZdLLMS5LCwML33Sc5yc5aqlnJSDZbXqTzKFGtayiwZP0MsVrl4Yfz+xUD8fOgC/r10DxmZcjE5NnThwtQVPAa8SUzFjF8O4ac95xBSNlico9ywKmqUCaaEmRCSRXqmDGv3nsfGgxcBaFeklnBFw1en0auJAWhRvQyKB/rZP1hCSI7adeYmImPixdOB5jmBK/pdBIBL1dt++ex9h8dY4NEcZJNatGiBefPm4bfffoNUKv7CyuVyzJs3D82aWX9Bx6IiXZmZmahSpQr27t2LqlWrWv2mxDaC/X3EkXMc4DLtExk4INHIiW+HR6FoAFUhtadShf0wY0A7fNO/LVIzMnHmThimbNyXNUnm6j/NSXOj45Jw5MojAMD249fRuk55fDe8E1ycLa6xRwjJp14nJGPI97/hxVvxSrrmuUV3iRZ9Jx4OQMoYFo7oar8gCSG5xsxfD4t3dM4HyuvvTFD0JAOoEByA6qWpcnWOyGcJra3Nnz8fLVq0QOXKldG8eXMAwKlTp5CQkICjR49afVyLinQ5OzsjLS3N6jcjttWkWmlx7qoi0ZLKNW6CxrQzDvzw16kcjbUgcZJK4O3uig71qqBNrfLiRo2kWPmn2X3AAtRXETlw9MpjzNhy0IYRE0LysttPX6LdF2vx4k2i0enGyguq+hpcrs5SHJzzEVzpwhsh+d4Hc34R75hoiDBBPGf8PLG//YMixArVqlXDjRs30K9fP0RHRyMxMRGDBw/GvXv3UKNGDauPa/E34ZgxYzB//nysX78eTk70RZqTigb4wM/DFfHJ6UbPcQxAVGzW8fnE/mZ/2AFXHm3A26S0rEkyYHT4NdPdV8P+C/dx++kr/DShJw2HJKQAO3srDGNX7AIA49M6FFQzcDT2KxXog90zRtgzTEJILiEIHPciXwNS0/uCA+WKBsDXy83ucZGsqEiXeYoVK4bvvvvOpse0OMO9ePEijhw5goMHDyIkJASenp5az//99982C44YxxjDx50bY8H240b34xBPiILAackOB/Nwc8GuacMwac1uXH78POsORoZfm5p7Ehkdh67/24gGVUpizrAO8PPygJMTrdxGSEERGROnSo5VzDnFK887AhDo7Y5/pg+3fXCEkFzp8zW7xTsmLqZB8fTf3w6xd0iEZEtcXBwuXLiA6OhoCIJ2zaXBgwdbdUyLE2Q/Pz/07t3bqjcjtte/ZW0s3H7caMVk5eaLDyLQsEpph8VGRL6ebtgwqR9iE1Nw+nYY3iamYM1/oUhNzxR30Cmsphp+beoqn+L5i3cj0f7Ldar3Gt6xAfq3rg1nJ3MuDxNC8qoZmw5Y/BoORQ8CA6qULIyfJ/Wnon+EFBBv4pNx/OoT7eUljczJaBVSzkGREb2oSJdJe/bswcCBA5GUlAQfHx+t7zPGmOMS5I0bN1r1RsQ+JBIGVycp0mU6pcw1eyIVvys/7jyDhlMpQc4pAd4e6NaoOgCgZtliGPPjTqSlZ6rmkQMWzEvm2jk1IH7ZxSelYemOkzh54zFWTuhFhbwIyWfiklKx69QtbD9+XZw6o7luqRk9Qkr9W9TEV/1aU3JMSAHy2Y//qC/CywHuBP3nDUXjYskn3RwaHyGWmjx5MoYPH47vvvsOHh4eNjuu2a1nQRCwcOFC7N69GxkZGWjTpg2mT58Od3d3mwVDrOPn5Y5Xb5NUQ+aUV5y0EigJcOvJq5wKkeioU6E4/pkxFH+fvon/Lt5D5Ks48y/qaeyoValWY02Xy/eeocXYlZjQpwX6vlsLTtSbTEielpCUigW/HcP+C/fEi2qMqUaaaK1xbKRXiEOszPnVB63Rr0Utu8dMCMk9pm/cj9th6nagKknWXAEF6vsjOzagC2g5jOYgm/b8+XOMHz/epskxYEEV67lz5+Lrr7+Gl5cXihcvjmXLlmHMmDE2DYZYp339yuKJTQ5VgqzZqQAotgN48vy1o8MjBhT29cLHnRvjnxnDcOaHsejTLARSW80RZ0CmXMDiP46jz7ebkJ6RaZvjEkIc6vXbRAyc+QtaT1yFfefvqYdF6tKtW6CzHwPQvEYZHF/8KSXHhBQwF24/xd4zd8DBtS+sc4DJAMggthMVNy83Z3zavWmOxEqIJdq3b49Lly7Z/LiMc4Nft1oqVqyIzz//HB9//DEA4PDhw+jcuTNSU1MhkRTswkAJCQnw9fVFfHw8fHwcv9ZwRqYMjT5doXpsqDAyA9CwWin89BnNIc+tMmVynLoVhuuPX+CPY1eRkSlk3cmcJaI417pKWK9yCaz5oh8iXr1FfFIqggN8UNjfy5ahE0Js6GFkDMYs+ROxCakAFOdwBlW9CXWvMVM9Lz5GlhPE1AGt0a1JdVrCiZACSCaToeEnyw0XqtHAmbjb+R/H54s6JjndPreWMu6QEd9B6mLbCuLyjDTc3PB1nvs7MWTDhg2YNWsWhg0bhpCQEDg7O2s9362bddMEzP62jIiIQKdOnVSP27ZtC8YYXrx4gRIlSlj15sQ2XJydIGWA3EjipNx+7cEzR4VFrODsJEXr2hXQunYFfNSxAb5e/x/O3A7Psp8FUw0BAJfvP0O/bzfjyYs3gOK1jWqUwWf9W6JcsUI2iZ0QYht7Tt/CzI0aa50zjVFBTLVJsVwTVw+3hkairDhJ9G4egr4tqceYkIKq7WerLNp/xpB2+SI5JgXDyJEjAQCzZs3K8hxjDHK5PMt2c5jd9SuTyeDmpn0Vw9nZGZmZNHQzNyhe2M+sXsUsxbxIruXt4YYV43thSLt6WYZNGv23NjAoRJkcKw8Teiccg2dvxdaDl3Hu1lOkZ8psETYhxEoHzt9Fy9ErMPPng0bnEsP0UwADRnR8B/8b1NaGERJC8pIdx64hMTnT7MrFbetWRNcm1e0bFDGbcg6yrW/5iSAIBm/WJseABT3InHMMHToUrq6uqm1paWn45JNPtNZCpnWQc8aQ9vUw55cjpncUgGfRcSgR5Gf3mIhtTOjdAv5eHvjhr1OqbQbbzsrk2IwToCBwpKXLsPT3E2AAvNxdMbxrQwxqX48KcxDiIA8jY7D/3B1sPXAZckHx0WUaNyMMfcydGMPfs4eiRGE/W4ZKCMlDMmVyzP/lKCQABA51+0DP97s4N5nhu5EdHRojMYGWecoxZifIQ4ZkXSh80KBBNg2GWK97sxpigmx0QWQGJnAMmLYFJ1ePd2yAJFsGt6+Pdu9Uxj+nb+Hi/UgkJKch+m0SElPTs/YYmzNHWUmxI+dAUmo6lm8/iaSUdIzuRcU5CLGnpJR0jF/yF24+jsr6pJlzKHSXegMAN2cpfhjbg5JjQgq4XlM2qO5LIH7PcwnT205kYGhRsyycpDS0muR+y5cvx6hRo+Dm5obly5cb3Xf8eOvyHbMTZFr/OHeTSCSoXb4orj1+qX8HLq4FwjiQkiHD8+g4FKde5DwlOMAbH3drjI/RGIDYA7z8r1P45aCiep+x4dcWXDH8eW8o2jWsjPLFA7MVLyEkK5lMjn9O3MT8X48qlmvKug+DxnUvI4myer6x2ODt2KgKJvZpgUBfT8MvIoTke/vO3cHLN0laeTDjAAQOnmXNY/FMsmRsd4fFR8xEPch6LV26FAMHDoSbmxuWLl1qcD/GmP0TZJL7Den4Dq4t/ydrg0pxhmSKxhjjwNLfT2DReDoZ5mUSCcPEvi1Q2M8TS/44ASD7ybHSuEV/oW7lkrh4OwJgQEj5oujWojqa1SoPia2WoiKkAHkeHYcfd5zC4QsPtdan16UuvqWz0YhAPy98O+Q9NAspa4tQCSF52Ju4JExbvR9MT2cwUyVcGokyA2YPb09Tq0ieERYWpve+LVGCnI80qVFWffLTWAaECTxL+yr01lOHxkbsZ+B79fBe/Uro/c0mpKSLRfNU/96mkmN9zwvA67fJOHj+nmrTySuPcfLKY0gY4OrijML+XmjXsDJ6vluTlosixAiZXMDY+Ttw5f5z7SdMFN/S+mga6GmuWa4oJvRtgVoVilHjlhACmUxAl0nrjO6j2T7gACqWKIROVJgrV7JHUa38VKQrMzMTVapUwd69e1G1alWbHpsS5HzESSqBv5cb3iamGa7vovhgpGfIaZh1PhLk740933+EFX+fwr9n70AmF9dPLlnED2WLBuDktSdZX6RsdHMjCbXOY4EDqemZiIh6i/X/nMfPu89j0sB30adNbepZJkQhMuotlm49jst3I5GaoagOn2VkjxkH0hxmrZEkSyUMM4a1R8fG1WwSLyEkf+g+aR3kMo1inSYuxDEAv8/MWmOIkLzA2dkZaWlpdjk249zAmjDEbLlpIfLbYVEYMmubeE40dGIUcyf0aFED/xvezkGREUdJSk3H85h4uLo4oXQRf3AOrNp5Blv2X4IgCNrrpELn10TQ2WZmD7SvhytG9GyEkkX8wQFUKxuMAF+P7P8whOQhsfHJmLDwbzwIjwEArSGMmrQqVRug+uhp7sOAyf1b4oN29WwRLiEkH5m6Yg+OXHwIQDxtCBIYPM8ozy97F4xAcKCvgyJ0vNzUPreEMu5ag7+D1MXN9AssIM9Iw/UtX+e5vxNDvvvuOzx48ADr16+Hk5Pt+n2pBzmfqV42GCWDfPEsOl599VAOMCHrvm8Skh0dHnEAL3dXVC4VpHrMGDCmdzMMaFcXRy8/wqNnMdhx9LrYc2ygQBAA8+YuK3q24pPTsfTXE1pPFS/sg1F9mqJtw8pwkpq95DoheQrnHKeuPsGiLUfx6k2izpMwXITLyPPQ2Ky8hN20ZhlMH9EBAT504YkQou3Y5UdayTEgtvu4FFnXhVQ8/uz9Fvk6OSYFw8WLF3HkyBEcPHgQISEhWksPA9YvP0wJcj70vyHvYfSCP8WToQyQcO12mPL+o6cx4JzT3LUCwt/bA71b1QQAJKekY5/GHGMAtqtsqDjO8+gETP9pH+atP4SJA1uhR+sQ+l0j+UJ8YiqiY5PAJMAXS/7Bi9cJhpNdQ7/yGlMcjPUkuzhLsfyznqhftVR2wyaE5EMZmTJM+WG39mmEKU4rcvE+l0D9Hc+BZrXKYGD7+g6OlFiKcQ5m44G+tj5eTvPz80Pv3r1tflxKkPOhelVLwsvNBckpGarJ+Dqj9AAAr94kYceha+jXro6jQyQ5bMrgtgh7EYt7EdFZEmMzl2BV78x1HkO73Z+WIcP8jYfx865zWPl1X5QuGpC94AnJIc+j47Bg4xGE3ngKQDlUmhkfLm1qKScDSTJjwMT+LTGgPQ2nJoQY9vniXVq1RDRHpzBlnRG5+JwAwMPdCUsn9syBSInFaJknk+y1DDGNe8yHGGOYMqSNqkKhQZxj3d9nHRUWyUU83Fywdmp/fNqrKQr7iVWonaQSlC3mL37JmnsCZVp/6HtK9WfM22QM/t+veBETDwCQCwLiE1ORpqi8TUhuxTnHks1H0fuzDarkWONZg68zNV1B9VnTuEmlDBP7N0fohs8oOSaEGDVy5u+4cDNcvNqmuGmdd3TOPQzA1lmDaTQXyfMEQcD8+fPRtGlTvPPOO5gyZQpSU1NtdnzqQc6n2jeuihmr90MQjGQ6jCEhKQ3Ltx7H+IGtHBYbyR3cXZ0xrEtDDOvSEJkyOZykEsQnp6HbpHVIy5CZlyTr9B6b+spNz5BhxbaTcHWW4sTlR0hNywQD0CCkNIb1aITaVUpY/wMRYkO3Hr/E4o2HcfdJtPYTur/kyu4aQw1OE0OplU81DSmNSQNbo1Swf/YCJ4QUCEt/OYabD8Tl47ROLYqpc5o9ycqv6tF9mqJEET9HhkmyIa8t8xQbG4tx48Zhz549kEgk6N27N5YtWwYvL9PLgXLO0alTJ+zfvx87d+5Ejx49jO4/d+5czJgxA23btoW7uzuWLVuG6Oho/Pzzzzb5WShBzsckjEEwI8vZ9u9ltG1UBdXKBzsgKpIbOTtJAQB+Xu5YM7U/xi7YgcSUDOMvUlydZjqbTCXJxy4+FNeLYurXXLgVjgu3wtHnvdpoUKMM3gkpBTcXZ+t+GEKsEPU6AbuO3MCl2+EIf/EWiSnpAHTWJNYdBg11fmwoSdYaSq2k8bhyqcJY/+37cKXfd0KImc5cfYw/9l0xMrVDO0lmDCha2AfDujV0YJSkoBk4cCBevnyJQ4cOITMzE8OGDcOoUaOwbds2k6/94YcfLBrZsGXLFvz000/4+OOPAQCHDx9G586dsX79ekgk2R8gTcs82UBuLSM/dNpW3A17ZXgHxT89EwB3N2cc+3m8gyIjuV1qeiYOnLuH0NvhePbqLZ5HxyMpVZEwG+o11rdslCH6Tjsam7zcXTC0RyME+Hrg+IWH4Jyjce2y6NSiOtxcKZEgtpMpk2PxxiP459hN7Sd0klpDSbLqeQbDvcjK5xU83JzRpXl1TBr4Lq0fTgixyO1HLzBi2m/mfdkyBs4AiQQ4s3GiTRKHvCS3ts9NUcZdZ8BcuyzzdHXb/2z+d3L37l1Uq1YNFy9eRP36YgG4/fv3o1OnTnj27BmKFStm8LXXrl1Dly5dcOnSJRQtWtSsHmRXV1c8evQIJUuWVG1zc3PDo0ePUKJE9kcjUg9yPjb2/RYYM2+H/ieVCYqiEZialokb95+hZmUa4krE4dc9WoWgR6sQ1bbE5DQcvfQQ52+E4ejFR3pfZ3VyrHyx4qnklAz8uO2k1tOnrzzB4k1H8Un/ZhjY5R1KLIjVXr9NwrOoOKSmZWDGT/8hIUnsLc5SzVDfkAg928y5yixhQLNa5TBrdEd4uLtaGzohpABLSErDR9N+y3rSMfJ1yAD8u/yTApccE+MSEhK0Hru6usLV1frvpnPnzsHPz0+VHANA27ZtIZFIEBoaip499ReGS0lJwYABA/Djjz8iONj8kawymQxubtoXD5ydnZGZaZu6NpQg52P1q5VEi7rlcfKyIplR9m5oJMea59QZP+3D38tGOjRGknd4e7qhe8sQdG8ZggdPozFj7T48jnyjtY/ucotZmDNgxcgugsDx02+n8Pu/l9C6YWUU8vMEYwzFgnxQpJAPqlcsCimtuUw0yOQCrtyOwOXbkbh4IxyPwmMgk4sLw3NAXarSUKU5ZTVYxSazhlxpJNEBPu748eu+KFO0EF3UIYRYTRA4hn69BZyrp24YmwLCAUglDHtWjEKAL62fnhfZcw6yZs8rAEyfPh0zZsyw+rhRUVEICgrS2ubk5ISAgABERUUZfN1nWXdBMAAAYwJJREFUn32GJk2aoHv37ha9H+ccQ4cO1Urq09LS8Mknn2ithUzrIBO95oztjBZDlykeaXzK9AyNffkqAZFRb1GSisQQEyqVCcK274bg2as4PIuOw8uYBBwJfYBLdyLEQprQk29kMzlWPc+B2LhU/HXgmt5dPD1cULNSMdSqUgLFgnzg4e6KciULIbiwr+n3J3meXBBwPPQhlm85jpg3SVl30LfmnbG8Vd8wa310nvTycMbQbo3Qs3VNeFGPMSEkm1ZuO4GXMYn6T1t6rk4zALM+7YhCvp4gRFdkZKTWEGtDvcdTpkzB/PnzjR7r7t27VsWwe/duHD16FFevXrX4tUOGDMmybdCgQVbFoQ8lyPmcq7MTalYsipsPXxpu3TGIi+MBGPz5Zhz7daKDoiN5XYkifqqKmD1b1wQAvIiOw9Rle3E/PDrrC0x2MZsYpm3GPOfk5Aycu/oU564+1doeVMgLX41qh8Z1yhp7B5KHvIiOR0xsItxdnfHgaQz2n7yDK7cjHR4HAwDG4OosRafm1THm/ebw8qCkmBBiGzfuP8dv/17W+5xWsUCNL8eGNUujbaMqDoiO2I0d10H28fExaw7y5MmTMXToUKP7lCtXDsHBwYiO1m73yWQyxMbGGhw6ffToUTx+/Bh+fn5a23v37o3mzZvj+PHjBt/TXusfK1GRLhvI7UUABEFAk0FLs86p010fTxCHXgzv3Rgf9Wvi4ChJfvM2IQWnrz5BcmoGShX1x5KNR/A8Jl7dkaxvbqexpaLMGWpkxvNuLk7IlMkhlUhQqWxhfPxBM9StUYrWhcylIl7EIiY2CQmJaXgZEw9vT1cEF/bB6t9O4+5jI0UIjVHONmEw3Yuscc7M8uul2N6xWVUM7tYQZYsXsi4eQggxYP0fZ7Dhr/PidBATX1PKYoClivlj+6Lhdo8tt8vt7XNDlHHX62+fIl2X/7Bfka5Lly6hXr16AICDBw+iQ4cOBot0RUVF4fXr11rbQkJCsGzZMnTt2hVly+Zchwb1IBcAEokE7RpXxsFz9/XvoExKFOvMb955HsP7NKb5ciRb/H080LVlDdVjNqwNJi/4W1yTUfeqqDm/aoaGbptDo+c5PUMGABAEOW4/jML4WX+iZFE/TB/fCYlJaYiKSYCHmwuqVy4KPx8PuLu5WPOOxAwvo+PxOjYJfj7uKFksAGGRb3DhWhhev01CeqYch8/cQ3xiWpbXaSWq2T1NcajnIVuCAS7OTtg8dyDKFg/MZhCEEJLV3JX78O/x2xrnKFMZMiB1Ytj6fdbhp4TYU9WqVdGhQweMHDkSq1evRmZmJsaOHYv3339flRw/f/4cbdq0wZYtW9CgQQMEBwfr7V0uVapUjibHACXIBca3n3TAobP3xauLAlS9yapTraC+L5cJOH3pEVo0qJgToZJ8qnGtslj0eU8s3nwUz6PjtZ4zVDDYYloVS8wX+eItPpqy1eDzgf6emDqmIxrVLmN1aEQU/vwNzl5+gt2HbyDixVvTLzC0trAtglEmx8pfPiMjbJQkEoYyxf3Rr31ddGhWjdbrJoTYxYgpv+Duo1dmFwhUPr97xUg4O0ntGBlxGDsOsbaHrVu3YuzYsWjTpg0kEgl69+6N5cuXq57PzMzE/fv3kZKSYr8gbISGWNtAXhnCMW72dly6FSk2ADXPuLrDWjnAOMfWpUNRpgT1jBDb4pzj5sMXiIlNQiE/TzhLpZi/4RAeRsQYP3GbGoKt3MfAtmxV1lYoEuiNzYuH4OzlJ9h//DZkMgF1qpfAoJ4N4eJSMK83RryIxbGzD5Ccko4SRf1RL6QU0jMy4e/rCV9vd5y/+gS/7rqAh0+jkZqWaclft8F1hU2tSWz6uBrH0Ve0S2dfqYShR5uaGD+wVYH9dyaEOM7HX2/FrQcvta7dcScYXWudMeDXBUNQjtptKnmlfa5LNcS6n52GWG+3/RDr/IYSZBvIKx/AzEwZWg5aJi4NoO8cq/xN4BwQACcpw78bx8Lbk4rNEPu7//QVnkXFIVMmx0+/nUJMrE4F4mwkyLZIjlXH0tN9KZEwfD2mAzq0qp7lJekZMryOTYK7mzMC/PJ+NdGEpDQs23AEx849QEam3Oi+hubsmsVIQzBLYmspfZWpmfZ2dzdnNKldFmMHtkBwIFVAJ4Q4xtEz9/Dtkr3iA81zlQTgUsUcJZ3zo4uzFJu++xBlS1AdBE15pX2uSzNBdnK2bYIsy6QE2Rx0KbwAcXZ2wsKveuCL73dlrSYsiBM8maDeXy7j+GreX/hpzgAHR0oKospliqBymSIAgJb1K2L/6TvYefg6XkTHIzU9E4KgXr87xzD9hbgFgWPOin0oUtgHdaqLawvGxiVj5g//4urtSFXsfj7uKOTnidi4FDDG0KB2GXRpUwNp6TIkp6RDLgjwcHdFlfJFEBjgle1wE5LScOTUPVy5HQG5TIC7uzMyMuSIfpMIL09XvNesCgoX8sKlGxF4E5eCwAAvdGxVDbfuv0TE81iAc9x++BJ3HrxEmmLutu7fh4m/Lu1/LpuMo7ftsZSH8Pd1R+VyRdCqQUU0rVMeAb4eVLiNEOJQv/9zESs2n9BbF4EJADgH1xk9XdjfC8v/1wdlqEggITZDPcg2kNeuUE2cswMXbkRA1cLkHEyjI0izScgBfDKoKT7s1dixQRKiQSYXcOfxSySnZOBJ5GvsPCgmzlnYuwdZ4z30HbNcqULYsnQYDp+6i5nL/gUXkPUDZab6NUthzJBWOHDiDvYdu42klHQE+LojuLAv5HIBTs5SyOUCJIzB08MVjeqWRYdW1SGRMPx75CY2bj+LhKR09fsaq9Ks5wcyOs/XirzRomHRZiSm2RlmLZEy+Hi5IdDPEz3eq4WurUNozh4hJEf9uPk4fvvnknhuM1I4UDmCRjwHMvy5cgSKK5ZbJNryWvtcSdWD3HeOfXqQd3yT5/5OHI0SZBvIax/AzEw5Wg78QTVMh8mUp1kFnd8IDmDj4g9RqVwRB0ZJiGGcc4Q9e4Mnka8x58d9yMgUTLxA/CNLLmXjBBkAls3oiwkzdhhOSi18S8YgJtownmszAB6eYsXt5JQM2/bWar5JNtgySba2knXz+uUxc0JnuLlScS1CSO6wbdcF/LTlJACYTJCVOICO71bHt2M62jO0PC2vtc+VKEHOeTTEugBydpbineolcfFmBCDXSI41Gv6ajU8JgOGTf8G+X8bA28vdscESogdjDOVKBqJcyUC0eKcCtu25hL1HbyEuMRXurk6oU60Eigb54cHTaCSnZODFy7eITUh1SGyrfzllPDm1sAQzN5D761Y25QCSkzPUT9pjdHA2k26LfnSDxRIMHEujJ1wqYXi/Sz28E1IaV25HICEpHT7e7mjTuBIqlgmyJnRCCLGLyzfCVckxoDi3GRjZo6lYsC8lx/kc4+LN1sckpuWZHuTY2FiMGzcOe/bsUZUOX7ZsGby8DM/Ta9WqFU6cOKG17eOPP8bq1atVjyMiIjB69GgcO3YMXl5eGDJkCObNmwcnJ/OvHeTFK1Qvo+PR59N1Wss7GesZ4+Dw8XLDf7+Mc1CEhNiOXC5gxebj2LHvqvYTVhTpUtL3OZEwJs43NpVEWvK2pqpwa+7miCmztuhFtlGxLtXxFFycpejUsjrGDm4JD3dav5oQkrtdvRWJ8dP+yPKVoHWe1HMKlDCGUzsm2ze4fCAvts8Bddz1e9unB/nSX9SDbEqe6UEeOHAgXr58iUOHDiEzMxPDhg3DqFGjsG3bNqOvGzlyJGbNmqV67OHhobovl8vRuXNnBAcH4+zZs3j58iUGDx4MZ2dnfPfdd3b7WXKDokG++HxkWyxac1jcYKIRzsCQmJSOY2fu4d2mVRwSIyG2IpVKMHF4a0wY9i4eh8fg6t1nuHgtHKHXnkAmNzNbNZEcA0CNKsVw487zbMery+x80h7Dqq0KRD+jf9MGh6SLPclSCYOTkwQcgKuzEyqXK4L2LaqhesVgeLi7wkkqgY+XG6RSM8YmEkJIDrt6MwITp+8A1zNaRtWLrHtO5ACTAMd/n+igKAkpmPJEgnz37l3s378fFy9eRP369QEAK1asQKdOnbBo0SIUK1bM4Gs9PDwQHBys97mDBw/izp07OHz4MIoUKYLatWtj9uzZ+OqrrzBjxgy4uOTvHoie7WsjMSkVa7aeMXtE5owl/6J5w4pwooI2JA9ijKFCmSBUKBOEvh3rIjE5DReuP0VycjreJqTgwvVwhD97g/ikNHXVbAUXFyn8fTwQ/TpR77FdXZzwxcdtMXjiZtPD4ywcZp1rmDHsz9RLNV8rkTD4+3pAJpMjPiFNZ0exzViyqD9WfzcAvt40vYMQkj9ERcdj8swdEAQ9BSag3sQVyxsqFQpwx661oyGR0IXAgoAJ2qvL2OqYxLQ8kSCfO3cOfn5+quQYANq2bQuJRILQ0FD07NnT4Gu3bt2KX3/9FcHBwejatSu+/fZbVS/yuXPnEBISgiJF1MWn2rdvj9GjR+P27duoU6eO3mOmp6cjPT1d9TghISG7P2KOGdy7MdZuPWP2/oJcQN+P1uDPDZ9QTw3J87w93dCmiXpExJDe6mrtnHPcfyKuzVyyqD8qlysCQRAwfem/OHH+gVYCXbZkISz8X28EF/ZBq0aVcOz8A9W6zfp6ACxl745hi1mQKGvGLpEAnh6uqFapKFo2qIi2zarCUzEU+sa959i84xwuXHsKDsDD3QVd24RgSJ9G8KHkmBCSTxw7fR8LfzwAWYagUUtCMTVHT0+ykr+vB3au+YSSY0IcIE8kyFFRUQgK0i6s4uTkhICAAERFRRl83YABA1C6dGkUK1YMN27cwFdffYX79+/j77//Vh1XMzkGoHps7Ljz5s3DzJkzrf1xcp3PR7XBojVHzNuZA2/eJqPjB8vw37bx1JNM8i3GGKqUD0aV8uoRKBKJBLMnd0VGhgyh18KQli5DzSrFUaSweh7PJx+2wMUb4UhK1lhiSXVQBwRuaTad3exbI1lmDHBzcQKTSCCTySGRMBTy98KA7u+g23s1ja4rXLNKcSz+tg9SUjOQkpYBP293Or8QQvINQRDQd8QaxLxJ0vMkACkMFid0dXHCPxsoOS5wdEYQ2OyYxKQcTZCnTJmC+fPnG93n7t27Vh9/1KhRqvshISEoWrQo2rRpg8ePH6N8+fJWH3fq1KmYNGmS6nFCQgJKlixp9fFyWo8OdXDuymOcuRgGcbaxCRxIS5Nh8rQdWPbd+w6IkJDcxcXFCc0bVNT7XPFgP6xfMAg/bDiK81fCtJ/U+GKSSiWQyw2PdXJ2lqJEET+ERb5Rjci26VxkMw9odDS44hgBfp6YNakrqlUKhotz9r5WPNxdqMAWISRfkcnkeK/PD5ALWc/5yilughxikqyJA36+7ti1npJjQhwpRxPkyZMnY+jQoUb3KVeuHIKDgxEdHa21XSaTITY21uD8Yn0aNmwIAHj06BHKly+P4OBgXLhwQWufV69eAYDR47q6usLV1dXs980L5n/dB0MmbsLj8Nf6282cA3KuNXfh6o0IJCal0tJPhOgoUdQfi77pjZg3iXgRHQ83FyekpWfi3uNXkEokeKdWaZQqHoCrtyLx579X8CAsGmnpmXBykqCQvxfaNK2MPp3rQiqR4PzVMOw9fBM37zxHfKJ6qSrl51Tz8+rl4YLk1AzteWu660FpkADgTLuYd4CfByaNbIPSJQvB3c0FRQK9kZicDiepBO5uzgi9+hT/HLiOp8/ewMvTFW2bV0Gn1jXg7WnbSpuEEJIfJCSmoOvAH/UW49LEAEAOcI0kudk75TFvag+jo29I/kXLPOWcHE2QCxcujMKFC5vcr3HjxoiLi8Ply5dRr149AMDRo0chCIIq6TXHtWvXAABFixZVHXfu3LmIjo5WDeE+dOgQfHx8UK1aNQt/mrxv09Ih+ODT9Xj+Mk7cwDRazplcq5AXV/yvx8AfsenH4ShZIsDh8RKS2xUu5I3ChbxVj2tV0x5pUjekFOqGlDJ6jKb1y6NpfXHEy8voeNx/9AoXrz/FhatPERuXDKlUgnohpTBiQDMUKeyNXfuvY8+hG4h9mwx/X3fUDSkNXx93eHq4oERRf8QnpiIxKQ1Fg3zRolFFcM5x/kqYalvdkFJZ6gv4eKmT30Z1y6JR3bLZ/ashhJB8Lzk5Hd0+WGnW8nZaFzw5R+8udTFhRGtKjgnJAXlmHeSOHTvi1atXWL16tWqZp/r166uWeXr+/DnatGmDLVu2oEGDBnj8+DG2bduGTp06oVChQrhx4wY+++wzlChRQrU2slwuR+3atVGsWDEsWLAAUVFR+PDDD/HRRx9ZtMxTXl1nTR/OOT6avBkPnsQoN4DJxLtZe5XFk7mbqxN2bRtLwyIJIYQQQgA8eRqNj8ZvhlzOwZU9DGYku1wCDO7bCCMHNrd7jPldXm2fK+Nu0G22XdZBvrD72zz3d+JoeWZCw9atW1GlShW0adMGnTp1QrNmzbB27VrV85mZmbh//z5SUlIAAC4uLjh8+DDatWuHKlWqYPLkyejduzf27Nmjeo1UKsXevXshlUrRuHFjDBo0CIMHD9ZaN7mgYYxh8fR+4rAORXn5LEtAaQzfZADS02Xo/eGPSEnNcHi8hBBCCCG5ybWbERj26SbI5Rb0QSnmG3//dU9KjgkA9RBrW9+IaXmmBzk3y6tXqIxZtfkEfvvrAiCI3cSqBNnQbwvncPdwwZ7fx8PZmSrPEkIIIaTgmbtwLw4evaNuODFF00lqpPdYMbZ688phKFsq0P5BFhB5tX2ujLthV/v0IIfuoR5kU/JMDzJxrNFDWsLfz0N7o77kmHPVPOXU5Ax88tkv9g+OEEIIISQX4Zyje/9lOHjkttguEtTtIwZoPdZ5IQBg6PuNKTkm2ridbsQkSpCJQX9u+BhFgnwM15Xgit5l5Q3Ao0evMGXaDtDABEIIIYQUBDGvE/BuxwWIj0/TmpbGOAA5V7WXxBuHZucCAHRoXR3DBzRzfOCEEL0oQSYGOTs7YfuGjxHg75n1gpPyZK+DATh/4QnWbz7lgAgJIYQQQnLOP3uuoO/AVVm269ZuYVAkzALUybKcY/TQVvj6s06OCJXkMTQHOedQgkyMYoxhzQ+D4aI7r1ijSJc+2/44jzexSXaNjRBCCCEkp6zZcAxLVx4yuo+4bBO0hlsrE5WZU7rhg94N7B0mIcRClCATk4ICvfHn5k/UqxNozqkxgAscHwxejd+2n7d7fIQQQgghjsI5xy/bzuK3P0KhXclUP93eZGdnKXb+OgbvNq9ixyhJnqc5HN+WN2ISJcjELH6+nlgyp59ZybFSRoYMa9cdx4fD1premRBCCCEkl8vIkGHwsLX4edNJcYMZaxsDEIdWC0BgIS/s/GUMAvw97RYjISR7KEEmZqtbpwyaNqwA6J9+rAcDGMOz528x9X/b7RwdIYQQQoj9RD57g+69fsCz52/FDeYmxxA7FmrVKIE/t4yGt7dtl+4h+RPNQc45lCATi8z6tieKF/UFYEGleM5xPvQxIiJe2y0uQgghhBB7uXQpDCNG/oz0dJm4wYLkGAAqVQjG8oUDwCx8HSnAaJmnHEMJMrGIk1SCbRs/QY2qxY0Ps+YA4xxMLoDJOZgADBu6DqNGbkBSUpqjwiWEEEIIyZaVPx7Cl1/+DlmGTD2Hk3ONAlwGXqh4ru27VbF25RCHxEoIyT5KkIlVflw6CN/N6AVfH3ftJ5RLGAgcTM7VSxsonnr8OBrduy3F06fRDo+ZEEIIIcRc9+69QKeOC7Hzr0viBg4wAYr2jU7BIwNJ8sRx7fDNV93sHyzJd2iIdc6hBJlYrUmjivhn+3j4ebsDcq5a249xDomQtZiX5v0RwzdgC62VTAghhJBcaNeuSxjz6WakpYlDqpUX+5WYHGIvsm6SrHFbtWwQenSp47CYCSG2QQkyybYJY9op1vVTDzfS/SLRZ/OmU9j6yxm7x0cIIYQQYg6ZTMCXn/+GFcvE9Y31TRlWbRLUj1U3zlGnVikc2/8lqlYpbv+ASf4lcPvciElOOR0AyfvebVUFu3aXxI2bkaqrqRzmLQX184YTSEhIxegxbe0dJiGEEEKIQWGPozF+3BakpGSImbGJxgxT9BRzjV7khd/3R/16Ze0eKyHEfqgHmdjEsiUD0a1rbctfyIA/t19A/z4rtL5gCCGEEEIc5dy5hxj50QZ1cgwYT4517ru7OeGn5YMpOSa2Q1WscwwlyMRmPhvfAbv+HA9vLzezeo9VX0Cc4010Anp0WYKUlHR7hkgIIYQQovL8eSyGDlqFb6bsABe49phqI8mE5lOt362KPbsmoWrVYnaLkxDiOJQgE5vy9fXA7Nm9xQemrlJxdZVrAEhOTEO3Dotw7epTO0ZICCGEEAL8tvUMhgxYjciIWP3FU0xc7WcM+HpqV3zzdXdIpdSkJrYlzmm38S2nf6g8gj7NxOZq1iyF8RPbmf4UMiYWC1AtDSXePh+3Fd989bv9AyWEEEJIgZOcnI7RIzdgw+rjYjtEOd9Yl4EhqZwDLs5SrFs/Am1bV7dztKTAUi4lZusbMYkSZGIX3bvVw8ZNI/U/qfxwygVVgQtA8+Itx/kzjzFp7Ba7x0kIIYSQguPXjafQs8MiPLobpWqDqNY1BvQnEVz7fkCAB3b8OQ7lygY5KmxCiANRgkzsplSpQGze8rH4QOuLB2JyrFg3GdAtdsEAznHjagQ6tZqHf3dfdmDUhBBCCMlvEhJS0bvTEmxefwKCxlI3quWZBGgnxrpJsmLEW5++7+DPvybA29vdEWGTAszmw6sVN2IaJcjErkqUCMD+A1+gadOKgJwDAsAEnqXnWBdjDAxAZqYcP3y/D58OW+/AqAkhhBCSXyz5fi96d1iMhPgUgDEwfYsbA2BynQ0aF/ednKVYvuJDjP6UlqUkJL+jBJnYnbOzE2bN6YuOnWsC0B6pZHSassYX2MP7UWjXbA6iXsTZI0RCCCGE5DMx0fHo23kJ9u2+ZrrglvKOcqQboJiDzFG1WjH8+fd4VK9Rwm6xEpIFLfOUYyhBJg7z+ZddMHRYc8sr6ClXgxI4Puy9An/+ds7WoRFCCCEkH4mOisfQfqsQ9zZFvdFAz7HqaUAjOeZwc3fGmnUjsPKnoTSkmpAChBJk4lAfDm2OrdvHQKL4zTN6IUtz/g9T/I8xrFl2CD3azkdSUpr9AiWEEEJInpMQn4IdW8/h4yHrkJEhs/j14tI6HL4+7tj6+xhUqFjE9kESYgbGuV1uxDRKkInDFQn2wz/7voC7pzMAC0d7cHE5hpTkDPRquwChZx7YJUZCCCGE5B2cc/y2+RT6dVqCdSsOISkhVU81auMtDuWz3XvVx45dE+Hn52GfYAkhuZpTTgdACiZ3dxfsOfAlPhq8Bk+fvAbnXF00Q98XmHITY+rnGcO3k39H2fJBWLnpIzg7068zIYQQUtC8iUnElPG/IjwsBoCBC+8cYvew4kK71lOKNkjhIG+sXDMMhQp72ztkQkwTFDdbH5OYRD3IJEet3/IxFq8YBFdXJ3DOwY0lx4DeJRjCHkejc/PvcOXCE/sGSwghhJBcIyNdhoN7ruHjD9eokmNAOZfYSHtCpy3BADRoXB6//T2ekmOSa9AQ65xDCTLJcbXqlMZf/02Gh7uLYvknzS8unZ01r/oq7wsckHNMGfMLOjedg2Sam0wIIYTkW4LA8dWYLejabC4WzfoHCbHJJodPq2gmyZzDxdUZM77vi7kL37dbvIQUBLGxsRg4cCB8fHzg5+eHESNGICkpyeTrzp07h9atW8PT0xM+Pj5o0aIFUlNTHRCxYZQgk1zBzc0Zf++fjHIVCgNghkvR6/YgK/Zj4qsgy5Cj57vzsfz7vY4ImxBCCCEONHXsL+jYcCauXQhTz77iEIeOarQRtHqRs8xFFm/detXHv0e/QtPmle0eNyEWy2PLPA0cOBC3b9/GoUOHsHfvXpw8eRKjRo0y+ppz586hQ4cOaNeuHS5cuICLFy9i7NixkEhyNkVlXO+YVmKJhIQE+Pr6Ij4+Hj4+PjkdTp73z1+XsHLxfu2Nul96SjIhy7JRyl/pMhWCMG/lhygUSMOlCCGEkLzs0vmH+N/YraqpxFnmESvvSLSf48p9Nbb5+Lrjx5+HI7iov11jJjkrr7bPlXG3aDYNTk5uNj22TJaGk6dn2fzv5O7du6hWrRouXryI+vXrAwD279+PTp064dmzZyhWrJje1zVq1AjvvfceZs+ebbNYbIF6kEmu0713few/NRU+Pm6AIIg3qHuJAYgJs6C/0gBjDAxA+KNoDOiwCNvWn3BE2IQQQgixMc45Fkz/G/8b8ysA8Tte33rG6vaB3oMAAGrWLoUZ3/fBn/9NouSY5H6KaQA2v0FMwjVv6enp2Qr13Llz8PPzUyXHANC2bVtIJBKEhobqfU10dDRCQ0MRFBSEJk2aoEiRImjZsiVOnz6drVhsgRJkkitJpVL8tf9zdOxWR50Ya3ywAQACsvQeq6gqYjNs/ukoOjaYgYtnHto1ZkIIIYTYhkwmx8i+K9Ch/gwc2XvdrNeIbYWs2xiAiV92wuKfBqNpiyrqVTMIKaBKliwJX19f1W3evHnZOl5UVBSCgoK0tjk5OSEgIABRUVF6X/PkiVhcd8aMGRg5ciT279+PunXrok2bNnj4MGfb7JQgk1xt0tdd8eeBL+DqqrGEExeLcpn8emNM9c0oyDi+GfsreraYi5Tk7F0lI4QQQoj97NhyBp0bzELE49fqZWmsTGr/396dx0VR/38Af80uLDesyJ2CIh54nyBqakGKmvdX88jSTPMsyyy1vDs07dL8aYdnaaaWZt53mhIqeYsKijeIct+w7Of3B7KysMAuLsLi6/l4oOzMZz7zmR1md97zuezsrfDZt0PRo09L4xWQ6CmQRPn8AMDt27eRlJSk+Zk2bZrOMkydOjWvZWYJP5cvXy7T8akftQR96623MGLECLRo0QJff/016tevj5UrV5YpT2PhxLFU6dk7WOGvw9Ow7Kvd2LLxhGa5ph9SaQoMSpCekoW+HT7FS72a4/05/cqhtERERFQWd2/FYewr/4esTJX2CjUAuWF52dhaoGe/1nj1zU5QKHi7S1SQvb29Xn2QJ0+ejOHDh5eYxtvbG25uboiNjdVarlKpEB8fDzc3N53bubu7AwAaNmyotdzX1xe3bt0qtWzliZ8YZDLGvheMV9/shDcHLkVCXFrpwXGBUa4fy9tq359ncGjneSzbOA6etZ3Lp8BERERUqutXYrB0wQ5c+O9m3gJdtcVqAchLbviY/3X/+qjOGDCsHQNjMm2FuxYaK08DODs7w9m59PvkgIAAJCYmIiwsDK1atQIAHDx4EGq1Gv7+/jq3qVWrFjw8PHDlyhWt5VevXkW3bt0MKqexsYk1mRQ7eyv8tvt9tHuhwePYV9fFnr9MXcwHgQSocnIxqu8SrPx2L3JyVLrTERERUblIT8vClzN+x7iBS3Eh7EbewuKaUguUGjBIAEZNDMLQkR0ZHBM9Rb6+vggODsaoUaNw4sQJHDt2DBMmTMCgQYM0I1jfvXsXDRo0wIkTea1BJUnClClTsHjxYmzevBmRkZGYMWMGLl++jJEjR1bk4bAGmUzT7C9eQczdBLzeZ3FeU2shCgzMVUpwDCDvazRv/caVR7Fp1T/oOcgP46a+XJ7FJiIieuZlZmRj5oRfcO5U1OOF+vQxzhWAvMB3/aNthBCwslZg7bZJcFBal0OJiZ4+SZ33Y+w8y8u6deswYcIEBAYGQiaToX///li8eLFmfU5ODq5cuYL09HTNskmTJiEzMxPvvvsu4uPj0axZM+zbtw916tQpv4LqgfMgG4GpzrNWFQgh8NvqY1i9/CBE7qOrXvOUudSNATwKrh/9rrA0x/hpL6Nr31blV2giIqJnUHpqJj4evxaXThfqXyhJEMVM31REftvHAmmHjemEV998wXgFpSrBVO/P88vd2e+jcpkH+fCJT03uPXna2MSaTJokSRg0ogN2h85E42Y182qN9QmOtTPRfNFmZ+bg61lb8MuyA+VTYCIiomeMWq3Gni2n0L/9J0WDY+Bxyy8D6mzkcgn9hwVgz6nZDI6JyKjYxJqqjC9XjMS2jaFY+vnOgi2oiydJ2v2ZCjTR/mXZIQT1aglXDyXnSyQiIiqDnBwVVn69F7t/P4mM9Oy8hcV9p6rVgKyUeptHm3YMaoiPFrxivIISVUZFBpo1Up5UKgbIVKX0GuiP4N6tMLTbIiQnZBSfsKSn1Y+Wjej+JYRawNJKgeb+3pg0py+UjrblUGoiIqKqQ61WI+TQZSyavulxYAzo14S64JgihVjZKPDdurdQw9PJSCUlIiqKATJVOQoLM2w6OBXhZ2/hwzFrkJWZk7ciPxYuUFNc5CtYEzgD4tEgX5np2fj30GUMOvQ5fJt74vMfR8DSSlHeh0FERGRS1Go1/vwlBJtWHkX8gxRNjW/p8zI+aviVHxwXGnjTTGGGWV8Ogt/z9cqr6ESVjiRE3jg5Rs6TSscAmaos32ae2BYyAzs2n8SSz7ZDFKo1LvJ9rS4wyFfBlfm/CyD8zC30aT0HQ8e9iGHjA8uv8ERERCYiPS0Lq7/Zg31b/tOuMZYV+DLVo/ZYAgAhNM+zlY42mDZ/AJr7eRuzuEREJWKATFVej/+1Qbd+rfDP/kv4fuEuxMUmF23CVfCJWnHf4RI0A4Ct+24/Dm07jcDeLTBgZEcoLMzL8xCIiIgqnejbcZg9bi1uRcYCeNRQS5IeDwOiFo+D5BKaThdmp7TC4JGd0P+19uVQaiITUcq832XOk0rFAJmeCTKZDB27NEbHLo2x5NNt2LHxpO6EhWuPC5MkQOTVNN+7FYdfluzHL0v2I3igH96Z09fo5SYiIqpsEuNSMXvcGlw5d0dreX4NcN4L6fG0i3pO4WRmLse0Lwai/YsNjV5mIiJ9MUCmZ87Ej3rhrSndMHn4T4i4cLdMeRT+mt+98QRS4tPw8ZJXn7yAREREldAfq47g5yX7kZmeXWrAq6lFzn/wXEz/4vzfuw9ojQkf9YKstJGsiZ4VAoC6HPKkUjFApmeSQmGOJevH4s6Nh1i1eB9uXYuFEAJ3oh4Uv1FJzVKEwLG9FzC+z7dQWJiheYAPBrzZCda2xp3gnYiI6Gm79N9NfPL2L0h4mJK3wJDRqEWhZQXI5DI0aVULM78ZChs7fl8SFcRBuioOA2R6ptWo5YQZXw0GkDd65lt9vsWtaw90N7OWJCBXrXvk60efN9fDowEhcPnMbWxYdgjPBzfBh18NhlzOJ+JERGQ6srNy8PX0zTi25zxysnMfr5Dp148Y0FGL/IiFpRnGT++Jl3q3hKRnv2QioqeFATLRI5Ik4fOf3sCYPouRkpxR4Kn3o1/UAlLhB28FguMCGWnWHd11Dkd3n8cLPZuh3xsd4dPwufI7ACIioieUnJCGtd/swc4NobobTqlFXrCrR6Cs9TUqAIfq1gjs2QJvTOoCM3PeghKVKL8Pv7HzpFLx04mogOrO9tjw9zTM/+A3HN178dHSvJpjnUr6oCkQKB/68zQO/Xka1rYWmDCvH154uYUxi01ERPRELv13A19N3YS7+V2NSqrZzX84rGc/ZGc3B7z/eX80bePNGmMiqvRMpt1nfHw8hg4dCnt7eyiVSowcORKpqanFpr9x4wYkSdL5s2nTJk06Xes3bNjwNA6JKim5mRwffTUEW0/MQtPWtfNahen6QtfnqV7BNEIgPSUTX0xaj2HPf4KoK9HGKjIREZHBEh4kY0y3RehWdwomD1yKu9djH39vlfYdV9KwHI/+t7RWYMbioVi9930086vD4JjIEPnTPBn7h0plMjXIQ4cORXR0NPbt24ecnByMGDECo0ePxvr163Wmr1mzJqKjtQOQH374AQsXLkS3bt20lq9atQrBwcGa10ql0ujlJ9Njaa3AF6vfRG5uLo7uuYAfPt+B+IcpJc4Cpa+H0UkY1+MrWFor0Onl5hj5YQ/YOVgbIWciIqKShZ++iQXvrcP92wm6EwgBlDaatCh+XkQJQEBgQ0z/ejCbUhORyTGJT63w8HDs3r0bJ0+eROvWrQEAS5YsQffu3bFo0SJ4eHgU2UYul8PNzU1r2ZYtWzBw4EDY2tpqLVcqlUXSEuWTy+Xo3L0Z2gc1wpzxaxH2T4R2goJTVuhSwhO7zLQs7Nl4Ans2nkDwQD+MndUHCgtzI5aeiIgISEtOx6Yf/saBLafwMCap9JGoS/tu00EmlyGoT0tMnNWbgTHRk1KjuGdQT5YnlcokPr1CQkKgVCo1wTEABAUFQSaTITQ0FH379i01j7CwMJw5cwZLly4tsm78+PF488034e3tjTFjxmDEiBElNgPKyspCVlaW5nVycrKBR0SmyFxhhk9+fAPJiWlY++0+RFy8h5SkNETfiNO9gT7NWAr0U979Wyh2b/gX9tWs0W1wAIa9G8zRr4mI6IlcOHkNKxfsRPh/N7VXiEeDbRV3v1NagJw/tzEAO6U13l8wEH6dGhilzEREFckkAuSYmBi4uLhoLTMzM4OjoyNiYmL0ymPFihXw9fVFu3bttJbPnTsXL774IqytrbF3716MGzcOqampePvtt4vN6/PPP8ecOXMMPxCqEuyVNpgwqw8AIFeVi/deWYarF+5o30zo24crnyRp0ibHp+G3pfvx29L96D60HSbM689+W0REpLfcXDU+HbsaIfsvAEIqIQh+9E8Zg2RrO0uMmd4TL/Vt9cRlJiJtnAe54lRogDx16lQsWLCgxDTh4eFPvJ+MjAysX78eM2bMKLKu4LIWLVogLS0NCxcuLDFAnjZtGt577z3N6+TkZNSsWfOJy0mmR24mx1cbx2HBe7/i6O5zugNiQ4LkQul3rjuOXeuPw9lDiQ7dm+GND16G3ExuhJITEVFVIoTAv/svYsn0jUh4kPJ4Ral9iVFyM878ILlAsNyhaxOMn9MHSkfbEjYkoidSHoNqMUDWS4UGyJMnT8bw4cNLTOPt7Q03NzfExsZqLVepVIiPj9er7/DmzZuRnp6O1157rdS0/v7+mDdvHrKysmBhYaEzjYWFRbHr6Nkjl8sw/duhSE3ph1++3YeQA5cQeyfeaB9CQi0Qezsef3x/CH98fwi16rvj662TYGnNv0EiomddTnYOlny0GQd+PwF14f6Fkky/vsR6jKUhySU09auDdz8bANca1Z643ERElVWFBsjOzs5wdnYuNV1AQAASExMRFhaGVq3ymvEcPHgQarUa/v7+pW6/YsUK9OrVS699nTlzBtWqVWMATAaztbPCmI97YczHvXAzIgZv912M7Mwc/TMoKaAusOrG5Wj0bfAhmgXUxZRvh6K6m7LMZSYiItOTk63C5uUHcWT7f7hxOaZoX2JjdcsRAo6uDpj02f/QumN9dvcheppYg1xhTKIPsq+vL4KDgzFq1CgsX74cOTk5mDBhAgYNGqQZwfru3bsIDAzE2rVr4efnp9k2MjISR44cwc6dO4vk+9dff+H+/fto27YtLC0tsW/fPnz22Wd4//33n9qxUdXkVdcNm8LmYsXCHfhr7TEItZ4DduXmai8TAihm27MhEXjVbzaC/tcG4z8dAEsrhRFKTkRElZFKpcL88WsQsucC1LlFqoqh1Ze4DCNQF+bp44pR019G644ceIuIni0mESADwLp16zBhwgQEBgZCJpOhf//+WLx4sWZ9Tk4Orly5gvT0dK3tVq5ciRo1aqBLly5F8jQ3N8fSpUvx7rvvQggBHx8ffPXVVxg1alS5Hw9VfQoLM4z9uDfGfNQL29b+g5+/3Ye0pPQifbk0vxduG1dCcFzQ/s0nEXc/Cb2GP48rp2/CVmmDLgP9YKe0KYejIiKip+3YrjP4ZPSq4hMUnrdYExyLvGbWpSkQTHt4OWHBL2/ByV1ZprISkZGwBrnCSELwnXpSycnJcHBwQFJSEuzt7Su6OFSJxd5LxA+f/omQfRe1awCKdByD3gFycXm4eVbH9OUjULepZxlLS0REFeFCaCT+WnMUqYkZcPJQYu9voaVvVHhaQE3QKxV6ne/R6FwSoHS2xf9Gv4B+IzqyGTVVGaZ6f55f7kDfyTCTG7fLpyo3CwfCvzS59+RpM5kaZKKqwMVDiY+Xvo7cXDVOHQ7HT59vx51r93UnNuTRlY7nXDG34vB290UwtzDD8A9fRp83O0NW2mimRERUIa5dvINfFu3EiQMFHqBKxQW3OqjV2rXImpZKjwLhgt8TkgRzhRkGT3gJ/Ud1hsKCt4NElY4aJY8wX9Y8qVT8RCSqAHK5DP6BjeAf2Ag7fw3Bko826QiIjdO4IydLhR/nbsWpQ+GY98tYyAvXMhARUYXIysjGng0h+HnhDqQmZRRN8KR9iTVB8aP/JQkO1e0wbk5fPN+9GWuLiYh0YIBMVMG6Dw5A98EBOPTnf/hp/jbE3082rGk1oFefktNHr2DLj4fwvzGBAIC4mEQkx6ehmos9lE52ZSk6EREZKPL8bfz69S5cCruOlIQM5BYZcKuMBEoMqKu52mPWjyNRn91uiEyCJAQkI/eENXZ+VRUDZKJK4oXeLfFC75ZQ5eTi9D9XsOCdn5GWqKNGoTADPuw2Ld0P35a1sOqzP3Ex9Jpmua2DFTr1aY1Xp/SAsjqDZSIiY8nKysZ3H/yKi6HXEHs3Abk5uXlBrCHNp/WVHyTL8vI0V8jRqWdLTPxsABQW5sbbDxGVPw7SVWE4SJcRmOogAFT53bkeiykDliDxQUrxiXQN8FUCGURe/zbxuMmdptZBCFjZWmD0nP548X/+vKEiIiqDzPRs7PvtOFZ9+icyUrN0J5LL9c9Qn/EjHgXaNg5W6NSrJUZ8+DJs7a303wdRFWOq9+f55Q6q+265DNK1P+Jrk3tPnjYGyEZgqhcgmY6sjGys+2YPDm05hYcxSdorDQyQkZtbfDO8/I+DR/+bmcvxfO9WeO/bYTAzY4MTIqLixN9Pwtr523Bs5xnd/YkLkyT9At/8tIWnCCykSds6eHfRELh7ORlQaqKqy1TvzzUBcp1J5RMgX/vG5N6Tp40BshGY6gVIpulhTCKuX7wLM3MzJMWn4IsJa/XbML+pTmmXvK40EuDTuCb8ujTBS4MC4ObJGzAiopSENOxefwxblh9AQmzy4xX6NpuWyfRPq6NJttxMhr5vdsZrU3rAXMGHmEQFmer9OQPkisdPUyIT4+SmhJObUvNaqAW+fv9XqLJVJW8oSfrVNktS0QBZLRB57hYiz97C+oU7IMkk+HVpgre/fBWOLvyAJaJnQ1ZGFn5e8BfOH49ATrYK96IeICszp2jCJx19ujBJgkwmYdqyEYh/FIh36tUCDhwzgqjqYh/kCsMAmcjEvdivDV7o2xp/rT6KNV/8hfQU3f3dGrXxxsV/I8q2k/wbvUcfrEItELr7HF7d8wGULvawtFKgc782ePXDnpxrmYiqlHtRD7Dv1+P4e8tJRN94WDSBhKLBsL7BsZ6BtJOHEnNWvwXvhs/ply8REZUZm1gbgak24aCq6eKp61gxbytuR94HAHg3fA4Dx7+E+i29MLjJVKiyc0vOoLgnlkJoTc2c99GhnU6SJLR5qTGad/RF6xcaws3LGQpLDvRFRKbjyn9R+POHA4iLSUJCbDJuR9wveYMnCZBLGM3a2t4STdv64PUPX0atBgyMiQxlqvfnmibW3m/DTGbkJtbqLOy/vtjk3pOnjTXIRFVMo9be+OrP93Sue2ViF6z7clfpg3SVQldwnL/8xN7zOLHnHH4QACRA6WSHDr1aYeSc/rCytjTgSIiInp5zx65i3ohlSIlPe7Qk7zNSKinYLW6Vvk2sC8wmYGVjgZo+rmjfozm6v9oetg7WepediIiMhwEy0TNkyHvdkfggBTvW/qN7mieg+CBZq2+yng1PBJD4IAXbVxzG9hWHITeXo32PFujYtzVaBzWGpZVxn4wSEZVECIHk+FRIkgS7ajaa4Pf88av4sM+iouMTlhbkPnoQWIQBNcgO1W3x7tevwv+lJvptQ0TPBvZBrjAMkImeITKZDBMWDEbvUS9g+cebcPrIZQj1ow/LkgbwKvCBqlevjMIDfT36PTdbhSNbTuLIlpOaZJ4N3NHzzRcRNKgdLK0ZMBOR8QkhsGvNEfz+3V7cvZbXZPo5H1f8b0JXdB3WAQve+snw4LjkHZY4LZOltQIvDvBH75Gd4VnPvez7IaKqS627td6T50mlYR9kIzDVPg5EALBn3TGs/3oXYu/G6/7g1MyNnP9Szw9stWYDvcvi/JwjOvf3Q9DgdvBq4KH3dkREQN7nU/SNWJzcex6piemwc7RBQPcW+HXRDuxc/XdebW+hxjPte7XEse2ndQSykv5BsqyUdDIJ1raW8G5UAzPXvAU7BxtDD42IDGSq9+eaPsheE8qnD/LN70zuPXnaGCAbgalegESFXb94GwvGrsLtiJjHNct6DM6lUxkC5IK1LWbmcnjVd0fd5rXQ5qXGaNujJeRyjpBNRI+pclSIOH0TG7/ZgUsnriE1IQ25qkKtYSQAUimfHTKpSIAslbZNYYUG6zJXmKHZ8/UxdHIPNGhV27C8iOiJmer9uSZA9hxXPgHyrf8zuffkaWMTayLS8G5UE98fmQkASIhNwvZVR7D1x4NIT8owLKOyPncrcHOpysrBtXO3cO3cLexe83feapkEGwcrNPKvi6HTeqNu81pP1gySiEyKEAK7Vv+N//tgHVRZj+Z+L/UzoPimzgAgk0lQqwUg116fX3+gz2eMzEwGe0cbuNdyQesXG6Fzv9bwqO1S6nZERFT5sAbZCEz1CRWRvu5FxWLphxtw8d8IZGepoM5Vo8RaZLVWlbPhO9Q1MEUx+Tg9Vw3zfn8PtRvWMHw/RGQyMtOzMKrNdDy4Hf94oT4PyKSitcNF0wDQOYd76SNZtw5shFk/j4WZOesciCoTU70/19Qg1xxbPjXIt5eZ3HvytPHTnIhK5VHbBZ9ufFvz+sG9BHzQ+0tER8VqJyzUHNuoihll++HdBIxtOwMj5w2Ef3AzLPvgFyTEJMG9tgsmfvs6HF2V5VQgIjKW3NxchOw4jT+X78PNS3eQlZEDGwcrtO3WAr3HvAQv3+cwZ/ASw4PjfKVMu+To6oD4Bym6NgQgQQgBSZIgk8vg5lkdHt7OaBPUBN1e6wBzBed6JyKqSliDbASm+oSK6EmlJqVj76/HcevyXVw6eR23Lt8rGiAb+hEjSum7rGO5KGEqBJlchsYB9dC4XT14N6mJloFNYGNvZViZiMhoEmIT8f20DTh/NBxZGTnISs9GdlZOidu8MXcgVs3e/HiBoV0rSqhFlmQSXpveG7m5aqxbtOPx+AuPmCnkGD69Dzr0bAWlkx0sbTjaPpEpMNX7c00N8nNjyqcG+e5yk3tPnjYGyEZgqhcgkbElxCYj/L8orJy1GXciY/KC5bJ8xJQ05VS+wlNPlbSfQutkZjI4ONnB1dMJvm180OPNF1CzHkfNJioP1y/cxpo5m3Hj0h08vBsPVU6udgI9g12pYBPosgTIOraTyWWwsbfCjyc+gdLJDqlJ6di+6jCunr4Bc4UZur32PJp1aMCxDohMkKnen5tqgBwfH4+JEyfir7/+gkwmQ//+/fHtt9/C1ta22G1iYmIwZcoU7Nu3DykpKahfvz4++ugj9O/f36hlMxQDZCMw1QuQqDypclQ4989V3I6Mxrl/ruLYX2H6Nb8uLdgtmA56BMeF0henmos9LKwsIAGo2cAdgYPao3YTT3g28ODNMVEphBBIikvBV2N+wsXjV5GdkQ0rW0uohUBKQlrxG+obHBceibos1+SjAFtuJgcA5KpyUd1NiXkb34F3k5qG50dElZqp3p9rAmSPt8onQL73fbm8J926dUN0dDS+//575OTkYMSIEWjTpg3Wr19f7DZdunRBYmIivvvuOzg5OWH9+vWYNWsWTp06hRYtWhi1fIZggGwEpnoBEj1NublqrJy1GdtXHkJWRqHmlAX7BxpYG6x3gKxj2yLrdKyuWc8dw+cMQIc+bfTbB1EVFhedgLh78UhLzsCyyb/g5uW7JT/4Kml+YAODXE0NchkfWI1dMARKF3ucPx4BAGjavh7avdyCg2sRVVGmen+uCZDd34KZTGHUvFXqbOyPNn6AHB4ejoYNG+LkyZNo3bo1AGD37t3o3r077ty5Aw8P3a30bG1tsWzZMgwbNkyzrHr16liwYAHefPNNo5XPUPxWIKKnQi6XYdQnAzHqk4EAgPj7ifhj6T6En7yGh/cS8PBeQt78paUFu5L0ZNNIGbjt7avRmDd4MUZ+MhCnD17EuaPhUGWrIDeToZqrEo5uDrB3sseLg9qjxQuN4OimLFvZiCqZtKR0HN4YgnvX7+PB7TicPnwByQ9THyeQZCUPfiWh1MGxDCJBu9tGcfkW2md1DyVGfzIInfr7AQA69fMzTnmIiExUcnKy1msLCwtYWJS9tjokJARKpVITHANAUFAQZDIZQkND0bdvX53btWvXDr/99ht69OgBpVKJjRs3IjMzE507dy5zWYyBATIRVQhHVyXenDtA8zonW4V7UbE4/td/+GPpHqTEP2qWacxGLiXWIJe83YqPNmgtylWp8fBuPB7ezRtV99SeswCAtj1aoIFfXaiyVXCr5YwO/fxgZWP5pCUnMqqcbBUe3I6DmcIMzjUci3Qj2Lv2byx5exWys3Igk8keTe1WgKRHba6Ruya06OSLM3+Ha1/G+cFwwaBZklCznhs69G6Ndj1bwaepJ7tJEJHpMaSFnCF5AqhZU7tbyaxZszB79uwyZxsTEwMXF+25383MzODo6IiYmJhit9u4cSNeeeUVVK9eHWZmZrC2tsaWLVvg4+NT5rIYAwNkIqoUzBVm8KrvAa/6Hhj8/suIvfMQ3769BpdCryEjNdOgUa0NVmqTbv338e/20/h3x2lIMglqlRqLJ65Co3b1cP3sTWSkZcKhuh1sHW2hdLZHzXruaNa5EZp0qA8HJ9Np/kWVnxAC8TGJUGWroLBW4KepvyJ053/ISM2EJEnIVeXmtdgA4NngOQz6sDeChnYAAIRsD8OXo3/Q5FUkOM7bgdED4NK8PrM/3rKxxNzBi3HveqzWiPdyczkCBwag97iX4N24JgNiIqIS3L59W6uJdXG1x1OnTsWCBQtKzCs8PLzM5ZgxYwYSExOxf/9+ODk5YevWrRg4cCCOHj2KJk2alDnfJ8U+yEZgqn0ciExJRlom/vnzFNbM/QMP7yVolts4WMPR1T5viqmSlKH/8ePtDPuYFPrOB60pkwAkwNLaAjZKG1jbWcLCUpE3SFjDGmj6vC9cazlzTmfSEnvrIW6G30Hk6RuIiYpFUlwyLodGIv5+IiCKn9aoOK/N+h+GTu+LsX7TcePCbZR8e6BH/iX1P9Zko18Zx34xFH3GddG8VuWokJ6SCUsbBRQWxu2jR0RVg6nen2v6ILu8WT59kGN/0vs9efDgAeLi4kpM4+3tjV9++QWTJ09GQsLj+zOVSgVLS0ts2rRJZxPra9euwcfHBxcuXECjRo00y4OCguDj44Ply5cbcGTGxRpkIjIJVjaWeGlIB7w0JK+WKzdXjdycXCgszQEAn72+FH//fqLohk/8DLCs/Z1L2bRw4C2AzLQsZKZlIf+rKPLMDZ2bmpnL4du2Lt5aOAzP1XXHzUt3kJ6cAVdPJ7h4OcPCigGDqcrKyELywxRkZebArpoNzh65hLWzNyH6eizUajUUCnPIzCRkpmUjV5WrOxNJhrL83a6dsxmNAuoh6vytJzuIfKX1Fc5PU8L6GnXdMOWHUWjQRru5nZm5Gewdi586hIiInpyzszOcnZ1LTRcQEIDExESEhYWhVatWAICDBw9CrVbD399f5zbp6ekAAFnBKfwAyOVyqPWZ7rMcsQbZCEz1CRVRVaNSqfDHkj2IOB2FtOR0PLybiDsR0cgtPO+qLurimnCX7UO61FrkMuarO6vHO5LJZfDr3gI2dlbISM2Es2d1JD9MxfVzN2Fla4EJi4ejbisfJD1MgcLCHDYO1kYrBxVPrVYjJ1uFnT/ux8XjVyHUAo7uSqQmpuPi8cuIiYot+idRIG7Uu8mwJCs9TTFkchleHNIeB9b9Y7x95Re7tPJLEhyc7aB0skedZl4IHNweLTr5aqZkIiIylKnen2tqkJ1Hlk8N8oMV5TbN0/3797F8+XLNNE+tW7fWTPN09+5dBAYGYu3atfDz80NOTg4aNmwId3d3LFq0CNWrV8fWrVsxZcoUbN++Hd27dzdq+QzBGmQiqjLMzMww8N0eWsvUajVO7T2Hfev/QeTZm3h4Jx7ZmTnF5KBLaVXBZWDE55JCCK0iqnPV+PevsGLTT2g7Q+dyC2sLuNSsjhaBjeHopoRcLoObtwtyVbm4dDwCkgxoEdgUHt4ueK6uOxSWVbuWOv/ZccHANOZGLGJuxOL25bswV5jDq3ENJD9MRXZmNtKT0hF55gYS7iciJSENZuZmcHC2g1fDmjh98ALC9p4zrACPdmtYX1rpifoGq3PVSLyfBLmZvPja6YL02ZdAkdGsLazM4fRc3sBgHnVcMXLeQNRqyHmIiYhM2bp16zBhwgQEBgZCJpOhf//+WLx4sWZ9Tk4Orly5oqk5Njc3x86dOzF16lT07NkTqamp8PHxwZo1ayo0OAZYg2wUpvqEiuhZJYTAnYhoXDweAbm5DBeOX8Xh30KQmZb1OJH0KEaRJEhCDXVxNczF5J/3S7EJSlhpOL37PJecCQpmIklSiX1QzS3MoMrJBcTjIN3ByR4Dp/RC/3dfhlyeV/N3/+YDnD8ajof34hF9LQb3bz6EXTUbBA3rCL9uLSFJEtRqNSLCruP0wfNIepCM9NRMmCvkkJuZwczcDB4+brBVWsNMYQYJEixtLZCrUiP25gPUauyJrMws/PtXGFIT0+BWywXONaqj1UtN4eL5uFlY0sNk/PNHKOLuJeDh3TjE3HiAqPM3kZacAXWuGhIkSDJAlZOrVSOfz8rWMm+wOANov4cG9gmWDA2Oy7CPQmRyGToNaAuZXIbDG0N0D85VeH9AiftUWJpj0Ae90L53a8TfT4R7LRe413YpNj0RkbGY6v25pgbZ6Y3yqUF+uNLk3pOnjQGyEZjqBUhEjwkhkJ6Sgcz0bFwKuYp712Jhq7RG+16tcP3CLXzU8ws9AgY9guO8RKUkMJyuoM7ADIxTEAAunk746shcLH9vNY5tOVlsoF3N1QH9Jr2MzV/9haQHyTrTlJUkSej0SjtMWj4amxZuw29fbM0L6CtE6YFkkS30GeDKCPspbNamd1G/dR283WEmEmKT9AuSC9QMD/2oL5w8HGFla4k2wc1gbs6GakRUMUz1/lwTIDuOKJ8AOX6Vyb0nTxsDZCMw1QuQiPQXc+MBfv7kdxzfdgqZ6VlQq4oPHEoNVitbgFwO5bG0tUR2RrZeDxXKi0wug3ON6rh/80GFlQFA2foFl6kGuYz7AiA3k8HLtwaWhn4KuZkccfcSsHrOJhz89RhU2SoAQI167nmBuwBcvJwwYs5AeDWsAaFWQ2Gp4NRKRFSpmOr9OQPkiscA2QhM9QIkorITQuDi8SuIPHsT6SkZUDrZIzdXjd2rDiEiLApAXi2ghZVCu+m2JgPjBo6VLUCmggxv+ly2GuRH+wKK7q+U/sIN/Hww5/fJqObqoLU8IzUTcdEJsLazgqObsoxlIiJ6+kz1/jy/3IHVXi+XAPlAwhqTe0+eNrZ9IiIqA0mS0Lh9AzRu30Brec/RQYiOikVKQiqca1SHjb0Vjvx+AvvW/o1LoZHISs8Plo0z+JdxnnEyOK5sdA0SpueW0ATJJUyzJJPL4N3UE807N0KHvm3g619X576sbC1Ro667gWUgIiIyXQyQiYiMzL229kBEQUM7IGho3vzN0dfv47+DF5EcnwJ1tgrHtp3CnSv3kJ2dA6EWBtUE69XfWS/lMFI3PZlHca4QwqAgWW7+aARqISDJZfDwdsUrH/ZG0w4NEHXxDhQWZmjaqSEsqvgo5EREJk+I4qegfJI8qVQMkImIniJ3b1f08HbVvB76cX+t9Q/vxiE6KhZRF27j6qlrCA+NwP2bD5CdUXRqKksbC2Sm6mi+TZVMgVrdMmymK0i2r24Lt9quqNPMC77+ddHixcawcbCGjYN1sQG1Rx03w8tARET0jGGATERUiTg9Vx1Oz1VHkw6+Rdbl5qqR9DAZkiRB6ZzXd+j80XCEbDuFqIu34eiqRI167oi9HYdDG44hPTlDv51K0qMKZD5ZrggyuUxrMDOZXIbq7kp0fqUdur8ZiMSHKbhx4RacalZH43YNYOtgXYGlJSKip6I8xgdhDbJeGCATEZkIuVwGR1el1rKmHRuiaceGRdJOWjYKarUakiRh1YzfcGL3adjYW2H0wtdw5UQkDq4/ipuX7iArMwdCrYZMJoPcTA5JAtKT05+onBbWCnj51kDkmRsVOoo1JKBZp0Y4e/jiU21Fnh/wWttbIXBoR9So54Grp64hOSEVCdGJkOQyuHhWR/c3A+Fe2xXP1XWDTFb86NM16gGN29V/OoUnIiJ6xnEUayMw1VHyiIgKE0Lg1uW7SEtMg4uXMyL/u46Te84i+noMMtOzoMpSwbaaLbIzsnEn4h4SYh7PlWtmLscLQzrgjU+HwMbBGsveXY19aw4XO/9wqy7NUL9NHfw2fytyjRxIW9tbof+klzF0Rn+cOXgBK6atQ8R/UYZnJAF2jrboPS4YHnXdcOGfy4gIuw4rOyt4N/aET6vakEky1G3ljWquDshV5ULp4gBJkjjtERFRBTLV+3PNKNZ2Q2EmGXkUa5GNAynrTO49edoYIBuBqV6ARETlLTkuBZdCrkIIAeeajrhzJRqWNpbwaVkbTh6OAICM1AwcWP8PDv92HLE3Y5EUlwK5XA4hBKztrfKCTmd7OFS3R8uXmsDa3hqXjl9BQmwyXDyd0CigHiJO34BQ56J+m7pwreWMBn4+sLCy0CrL3choJMelwsbBCtmZOUiOS4GVnSVcPJ1hq7SGwoJz+RIRVRWmen+uCZBth5RPgJy63uTek6eNAbIRmOoFSERERERUFZnq/TkD5IrHPshERERERESViFCrISTjdj8SogLHBTEhxY8KQkRERERERPQMYQ0yERERERFRZcJpnioMa5CJiIiIiIiIwBpkIiIiIiKiykUtAIk1yBWBNchEREREREREYA0yERERERFR5SIEACOPOs0aZL2wBpmIiIiIiIgIJhQgf/rpp2jXrh2sra2hVCr12kYIgZkzZ8Ld3R1WVlYICgpCRESEVpr4+HgMHToU9vb2UCqVGDlyJFJTU8vhCIiIiIiIiEon1KJcfqh0JhMgZ2dnY8CAARg7dqze23zxxRdYvHgxli9fjtDQUNjY2KBr167IzMzUpBk6dCguXryIffv2Yfv27Thy5AhGjx5dHodARERERERUOqEunx8qlcn0QZ4zZw4AYPXq1XqlF0Lgm2++wccff4zevXsDANauXQtXV1ds3boVgwYNQnh4OHbv3o2TJ0+idevWAIAlS5age/fuWLRoETw8PMrlWIiIiIiIiKjyMZkaZENFRUUhJiYGQUFBmmUODg7w9/dHSEgIACAkJARKpVITHANAUFAQZDIZQkNDi807KysLycnJWj9ERERERETGwCbWFafKBsgxMTEAAFdXV63lrq6umnUxMTFwcXHRWm9mZgZHR0dNGl0+//xzODg4aH5q1qxp5NITERERERHR01ahAfLUqVMhSVKJP5cvX67IIuo0bdo0JCUlaX5u375d0UUiIiIiIqKqgn2QK0yF9kGePHkyhg8fXmIab2/vMuXt5uYGALh//z7c3d01y+/fv4/mzZtr0sTGxmptp1KpEB8fr9leFwsLC1hYWGhei0dzirGpNRERERFRxcu/LxcmOvevCjmAkYuuQo5xM6yiKjRAdnZ2hrOzc7nkXbt2bbi5ueHAgQOagDg5ORmhoaGakbADAgKQmJiIsLAwtGrVCgBw8OBBqNVq+Pv7672vlJQUAGBTayIiIiKiSiQlJQUODg4VXQy9KRQKuLm54Z+YneWSv5ubGxQKRbnkXVWYzCjWt27dQnx8PG7duoXc3FycOXMGAODj4wNbW1sAQIMGDfD555+jb9++kCQJkyZNwieffIK6deuidu3amDFjBjw8PNCnTx8AgK+vL4KDgzFq1CgsX74cOTk5mDBhAgYNGmTQCNYeHh64ffs27OzsIEmSsQ+90ktOTkbNmjVx+/Zt2NvbV3Rxnlk8DxWP56By4HmoHHgeKgeeh4rHc1AxhBBISUkxuVlpLC0tERUVhezs7HLJX6FQwNLSslzyripMJkCeOXMm1qxZo3ndokULAMChQ4fQuXNnAMCVK1eQlJSkSfPBBx8gLS0No0ePRmJiIjp06IDdu3dr/VGsW7cOEyZMQGBgIGQyGfr374/FixcbVDaZTIYaNWo8wdFVDfb29vzgrwR4Hioez0HlwPNQOfA8VA48DxWP5+DpM6Wa44IsLS0ZxFYgSZhqw3yqNJKTk+Hg4ICkpCR+8FcgnoeKx3NQOfA8VA48D5UDz0PF4zkgMi1VdponIiIiIiIiIkMwQKYnZmFhgVmzZmmN7E1PH89DxeM5qBx4HioHnofKgeeh4vEcEJkWNrEmIiIiIiIiAmuQiYiIiIiIiAAwQCYiIiIiIiICwACZiIiIiIiICAADZCIiIiIiIiIADJBJD/Hx8Rg6dCjs7e2hVCoxcuRIpKamFpv+xo0bkCRJ58+mTZs06XSt37Bhw9M4JJNk6HkAgM6dOxd5j8eMGaOV5tatW+jRowesra3h4uKCKVOmQKVSleehmDRDz0N8fDwmTpyI+vXrw8rKCp6ennj77beRlJSklY7XQ8mWLl2KWrVqwdLSEv7+/jhx4kSJ6Tdt2oQGDRrA0tISTZo0wc6dO7XWCyEwc+ZMuLu7w8rKCkFBQYiIiCjPQzB5hpyDH3/8Ec8//zyqVauGatWqISgoqEj64cOHF/mbDw4OLu/DMHmGnIfVq1cXeY8tLS210vBaKBtDzoOu72JJktCjRw9NGl4PRJWIICpFcHCwaNasmfj333/F0aNHhY+Pjxg8eHCx6VUqlYiOjtb6mTNnjrC1tRUpKSmadADEqlWrtNJlZGQ8jUMySYaeByGE6NSpkxg1apTWe5yUlKRZr1KpROPGjUVQUJA4ffq02Llzp3BychLTpk0r78MxWYaeh/Pnz4t+/fqJbdu2icjISHHgwAFRt25d0b9/f610vB6Kt2HDBqFQKMTKlSvFxYsXxahRo4RSqRT379/Xmf7YsWNCLpeLL774Qly6dEl8/PHHwtzcXJw/f16TZv78+cLBwUFs3bpVnD17VvTq1UvUrl2b73kxDD0HQ4YMEUuXLhWnT58W4eHhYvjw4cLBwUHcuXNHk+b1118XwcHBWn/z8fHxT+uQTJKh52HVqlXC3t5e6z2OiYnRSsNrwXCGnoe4uDitc3DhwgUhl8vFqlWrNGl4PRBVHgyQqUSXLl0SAMTJkyc1y3bt2iUkSRJ3797VO5/mzZuLN954Q2sZALFlyxZjFbVKK+t56NSpk3jnnXeKXb9z504hk8m0bpiWLVsm7O3tRVZWllHKXpUY63rYuHGjUCgUIicnR7OM10Px/Pz8xPjx4zWvc3NzhYeHh/j88891ph84cKDo0aOH1jJ/f3/x1ltvCSGEUKvVws3NTSxcuFCzPjExUVhYWIhff/21HI7A9Bl6DgpTqVTCzs5OrFmzRrPs9ddfF7179zZ2Uas0Q8/DqlWrhIODQ7H58Voomye9Hr7++mthZ2cnUlNTNct4PRBVHmxiTSUKCQmBUqlE69atNcuCgoIgk8kQGhqqVx5hYWE4c+YMRo4cWWTd+PHj4eTkBD8/P6xcuRKC03Lr9CTnYd26dXByckLjxo0xbdo0pKena+XbpEkTuLq6apZ17doVycnJuHjxovEPxMQZ43oAgKSkJNjb28PMzExrOa+HorKzsxEWFoagoCDNMplMhqCgIISEhOjcJiQkRCs9kPd3nZ8+KioKMTExWmkcHBzg7+9fbJ7PsrKcg8LS09ORk5MDR0dHreWHDx+Gi4sL6tevj7FjxyIuLs6oZa9KynoeUlNT4eXlhZo1a6J3795an+28FgxnjOthxYoVGDRoEGxsbLSW83ogqhzMSk9Cz7KYmBi4uLhoLTMzM4OjoyNiYmL0ymPFihXw9fVFu3bttJbPnTsXL774IqytrbF3716MGzcOqampePvtt41W/qqirOdhyJAh8PLygoeHB86dO4cPP/wQV65cwR9//KHJt2BwDEDzWt/z+ywxxvXw8OFDzJs3D6NHj9ZazutBt4cPHyI3N1fn3+nly5d1blPc33X+Ocr/v6Q09FhZzkFhH374ITw8PLSCiuDgYPTr1w+1a9fGtWvXMH36dHTr1g0hISGQy+VGPYaqoCznoX79+li5ciWaNm2KpKQkLFq0CO3atcPFixdRo0YNXgtl8KTXw4kTJ3DhwgWsWLFCazmvB6LKgwHyM2rq1KlYsGBBiWnCw8OfeD8ZGRlYv349ZsyYUWRdwWUtWrRAWloaFi5c+EwFBOV9HgoGYU2aNIG7uzsCAwNx7do11KlTp8z5VjVP63pITk5Gjx490LBhQ8yePVtrHa8Hqqrmz5+PDRs24PDhw1oDRA0aNEjze5MmTdC0aVPUqVMHhw8fRmBgYEUUtcoJCAhAQECA5nW7du3g6+uL77//HvPmzavAkj27VqxYgSZNmsDPz09rOa8HosqDAfIzavLkyRg+fHiJaby9veHm5obY2Fit5SqVCvHx8XBzcyt1P5s3b0Z6ejpee+21UtP6+/tj3rx5yMrKgoWFRanpq4KndR7y+fv7AwAiIyNRp04duLm5FRl58/79+wBgUL6m7mmch5SUFAQHB8POzg5btmyBubl5iemfxetBFycnJ8jlcs3fZb779+8X+567ubmVmD7///v378Pd3V0rTfPmzY1Y+qqhLOcg36JFizB//nzs378fTZs2LTGtt7c3nJycEBkZyYBAhyc5D/nMzc3RokULREZGAuC1UBZPch7S0tKwYcMGzJ07t9T98Hogqjjsg/yMcnZ2RoMGDUr8USgUCAgIQGJiIsLCwjTbHjx4EGq1WhNslWTFihXo1asXnJ2dS0175swZVKtW7ZkKBp7Wech35swZANDcCAUEBOD8+fNaQd++fftgb2+Phg0bGucgTUB5n4fk5GR06dIFCoUC27ZtKzLNii7P4vWgi0KhQKtWrXDgwAHNMrVajQMHDmjVjBUUEBCglR7I+7vOT1+7dm24ublppUlOTkZoaGixeT7LynIOAOCLL77AvHnzsHv3bq1++8W5c+cO4uLitAI1eqys56Gg3NxcnD9/XvMe81ow3JOch02bNiErKwuvvvpqqfvh9UBUgSp6lDCq/IKDg0WLFi1EaGio+Oeff0TdunW1prW5c+eOqF+/vggNDdXaLiIiQkiSJHbt2lUkz23btokff/xRnD9/XkRERIj/+7//E9bW1mLmzJnlfjymytDzEBkZKebOnStOnToloqKixJ9//im8vb1Fx44dNdvkT/PUpUsXcebMGbF7927h7OzMaZ5KYOh5SEpKEv7+/qJJkyYiMjJSawoPlUolhOD1UJoNGzYICwsLsXr1anHp0iUxevRooVQqNaOvDxs2TEydOlWT/tixY8LMzEwsWrRIhIeHi1mzZumc5kmpVIo///xTnDt3TvTu3ZtT25TA0HMwf/58oVAoxObNm7X+5vOn+ktJSRHvv/++CAkJEVFRUWL//v2iZcuWom7duiIzM7NCjtEUGHoe5syZI/bs2SOuXbsmwsLCxKBBg4SlpaW4ePGiJg2vBcMZeh7ydejQQbzyyitFlvN6IKpcGCBTqeLi4sTgwYOFra2tsLe3FyNGjNCazzgqKkoAEIcOHdLabtq0aaJmzZoiNze3SJ67du0SzZs3F7a2tsLGxkY0a9ZMLF++XGdaymPoebh165bo2LGjcHR0FBYWFsLHx0dMmTJFax5kIYS4ceOG6Natm7CyshJOTk5i8uTJWtMPkTZDz8OhQ4cEAJ0/UVFRQgheD/pYsmSJ8PT0FAqFQvj5+Yl///1Xs65Tp07i9ddf10q/ceNGUa9ePaFQKESjRo3Ejh07tNar1WoxY8YM4erqKiwsLERgYKC4cuXK0zgUk2XIOfDy8tL5Nz9r1iwhhBDp6emiS5cuwtnZWZibmwsvLy8xatSoInP0UlGGnIdJkyZp0rq6uoru3buL//77Tys/XgtlY+hn0uXLlwUAsXfv3iJ58XogqlwkITiPCBERERERERH7IBMRERERERGBATIRERERERERAAbIRERERERERAAYIBMREREREREBYIBMREREREREBIABMhEREREREREABshEREREREREABggExEREREREQFggExERIXUqlUL33zzjdHyGz58OPr06WO0/ADg8OHDkCQJiYmJRs2XiIiInm0MkImIqqjhw4dDkiRIkgSFQgEfHx/MnTsXKpWqxO1OnjyJ0aNHG60c3377LVavXm20/Axx+vRpDBgwAK6urrC0tETdunUxatQoXL16tULKU1np+1Dkhx9+QOfOnWFvb88HFEREVCUxQCYiqsKCg4MRHR2NiIgITJ48GbNnz8bChQt1ps3OzgYAODs7w9ra2mhlcHBwgFKpNFp++tq+fTvatm2LrKwsrFu3DuHh4fjll1/g4OCAGTNmPPXyVAXp6ekIDg7G9OnTK7ooRERE5YIBMhFRFWZhYQE3Nzd4eXlh7NixCAoKwrZt2wA8bvr86aefwsPDA/Xr1wdQtDZRkiT89NNP6Nu3L6ytrVG3bl1NHvkuXryIl19+Gfb29rCzs8Pzzz+Pa9euae0nX+fOnTFhwgRMmDABDg4OcHJywowZMyCE0KT5+eef0bp1a9jZ2cHNzQ1DhgxBbGys3sednp6OESNGoHv37ti2bRuCgoJQu3Zt+Pv7Y9GiRfj+++81af/++2/4+fnBwsIC7u7umDp1qlYte+fOnTFx4kRMmjQJ1apVg6urK3788UekpaVhxIgRsLOzg4+PD3bt2qXZJr8J+I4dO9C0aVNYWlqibdu2uHDhglY5f//9dzRq1AgWFhaoVasWvvzyS631tWrVwmeffYY33ngDdnZ28PT0xA8//KCV5vbt2xg4cCCUSiUcHR3Ru3dv3LhxQ7M+//1ftGgR3N3dUb16dYwfPx45OTma47t58ybeffddTYuD4kyaNAlTp05F27Zt9T4XREREpoQBMhHRM8TKykpTUwwABw4cwJUrV7Bv3z5s37692O3mzJmDgQMH4ty5c+jevTuGDh2K+Ph4AMDdu3fRsWNHWFhY4ODBgwgLC8Mbb7xRYlPuNWvWwMzMDCdOnMC3336Lr776Cj/99JNmfU5ODubNm4ezZ89i69atuHHjBoYPH673ce7ZswcPHz7EBx98oHN9fo323bt30b17d7Rp0wZnz57FsmXLsGLFCnzyySdFyuvk5IQTJ05g4sSJGDt2LAYMGIB27drhv//+Q5cuXTBs2DCkp6drbTdlyhR8+eWXOHnyJJydndGzZ09NYBoWFoaBAwdi0KBBOH/+PGbPno0ZM2YUaY7+5ZdfonXr1jh9+jTGjRuHsWPH4sqVK5r3qWvXrrCzs8PRo0dx7Ngx2NraIjg4WOs8Hzp0CNeuXcOhQ4ewZs0arF69WrOfP/74AzVq1MDcuXMRHR2N6Ohovd9nIiKiKkcQEVGV9Prrr4vevXsLIYRQq9Vi3759wsLCQrz//vua9a6uriIrK0trOy8vL/H1119rXgMQH3/8seZ1amqqACB27dolhBBi2rRponbt2iI7O7vUcgghRKdOnYSvr69Qq9WaZR9++KHw9fUt9lhOnjwpAIiUlBQhhBCHDh0SAERCQoLO9AsWLBAARHx8fLF5CiHE9OnTRf369bXKsnTpUmFraytyc3M15e3QoYNmvUqlEjY2NmLYsGGaZdHR0QKACAkJ0Srfhg0bNGni4uKElZWV+O2334QQQgwZMkS89NJLWuWZMmWKaNiwoea1l5eXePXVVzWv1Wq1cHFxEcuWLRNCCPHzzz8XKX9WVpawsrISe/bsEULkvf9eXl5CpVJp0gwYMEC88sorWvspeM5LU9r7T0REZKpYg0xEVIVt374dtra2sLS0RLdu3fDKK69g9uzZmvVNmjSBQqEoNZ+mTZtqfrexsYG9vb2myfOZM2fw/PPPw9zcXO9ytW3bVqspb0BAACIiIpCbmwsgr3a1Z8+e8PT0hJ2dHTp16gQAuHXrll75iwLNtUsSHh6OgIAArbK0b98eqampuHPnjmZZweOXy+WoXr06mjRpolnm6uoKAEWagQcEBGh+d3R0RP369REeHq7Zd/v27bXSt2/fXut9KLxvSZLg5uam2c/Zs2cRGRkJOzs72NrawtbWFo6OjsjMzNQ0cQeARo0aQS6Xa167u7sb1GSdiIjoWWFW0QUgIqLy88ILL2DZsmVQKBTw8PCAmZn2x76NjY1e+RQOfiVJglqtBpDXbNuY0tLS0LVrV3Tt2hXr1q2Ds7Mzbt26ha5du2o1Gy5JvXr1AACXL1/WClLLStfxF1yWH2DnvyfGVNJ7n5qailatWmHdunVFtnN2dtYrDyIiInqMNchERFWYjY0NfHx84OnpWSQ4NpamTZvi6NGjmr61+ggNDdV6/e+//6Ju3bqQy+W4fPky4uLiMH/+fDz//PNo0KCBwbWdXbp0gZOTE7744gud6/OnJ/L19UVISIhWjfOxY8dgZ2eHGjVqGLRPXf7991/N7wkJCbh69Sp8fX01+z527JhW+mPHjqFevXpatb0ladmyJSIiIuDi4gIfHx+tHwcHB73LqVAotGqtiYiInlUMkImI6IlMmDABycnJGDRoEE6dOoWIiAj8/PPPmoGkdLl16xbee+89XLlyBb/++iuWLFmCd955BwDg6ekJhUKBJUuW4Pr169i2bRvmzZtnUJlsbGzw008/YceOHejVqxf279+PGzdu4NSpU/jggw8wZswYAMC4ceNw+/ZtTJw4EZcvX8aff/6JWbNm4b333oNM9uRfkXPnzsWBAwdw4cIFDB8+HE5OTpoRvSdPnowDBw5g3rx5uHr1KtasWYPvvvsO77//vt75Dx06FE5OTujduzeOHj2KqKgoHD58GG+//bZWE/HS1KpVC0eOHMHdu3fx8OHDYtPFxMTgzJkziIyMBACcP38eZ86c0QzYRkREZOoYIBMR0ROpXr06Dh48iNTUVHTq1AmtWrXCjz/+WGKf5Ndeew0ZGRnw8/PD+PHj8c4772D06NEA8poGr169Gps2bULDhg0xf/58LFq0yOBy9e7dG8ePH4e5uTmGDBmCBg0aYPDgwUhKStKMUv3cc89h586dOHHiBJo1a4YxY8Zg5MiR+Pjjj8v2ZhQyf/58vPPOO2jVqhViYmLw119/afp8t2zZEhs3bsSGDRvQuHFjzJw5E3PnzjVotG5ra2scOXIEnp6e6NevH3x9fTFy5EhkZmbC3t5e73zmzp2LGzduoE6dOlpNswtbvnw5WrRogVGjRgEAOnbsiBYtWhSZ9ouIiMhUSULfkUyIiIiMoHPnzmjevLnWXMtVzeHDh/HCCy8gISFBM6UUERERVX6sQSYiIiIiIiICA2QiIiIiIiIiAGxiTURERERERASANchEREREREREABggExEREREREQFggExEREREREQEgAEyEREREREREQAGyEREREREREQAGCATERERERERAWCATERERERERASAATIRERERERERAOD/AZEnZCuLQ0mVAAAAAElFTkSuQmCC", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize the PCA results with cells colored based on their principal component values\n", - "for i in range(n_components):\n", - " plt.figure(figsize=(12, 6))\n", - " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}')\n", - " plt.colorbar(sc, label='Principal Component Value')\n", - " plt.xlabel('Principal Component 1')\n", - " plt.ylabel('Principal Component 2')\n", - " plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Values)')\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Additional components if n_components > 2\n", - "if n_components > 2:\n", - " for i in range(2, n_components):\n", - " plt.figure(figsize=(12, 6))\n", - " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}')\n", - " plt.colorbar(sc, label='Principal Component Value')\n", - " plt.xlabel('Principal Component 1')\n", - " plt.ylabel(f'Principal Component {i + 1}')\n", - " plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by PC{i+1} Values)')\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Visualize the PCA results with color based on correlation with infected softmax score\n", - "for i in range(reduced_projections.shape[1]):\n", - " pc = reduced_projections[:, i]\n", - " infected_corr, _ = spearmanr(pc, infected_softmax)\n", - " \n", - " plt.figure(figsize=(12, 6))\n", - " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=pc, cmap='viridis', label=f'PC{i+1} Correlation: {infected_corr:.2f}')\n", - " plt.colorbar(sc, label='Principal Component Value')\n", - " plt.xlabel('Principal Component 1')\n", - " plt.ylabel('Principal Component 2')\n", - " plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Correlation with Infected Softmax Score)')\n", - " plt.legend()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize additional PCs if needed\n", - "# PC1 vs PC3, PC1 vs PC4, etc.\n", - "if n_components > 2:\n", - " for i in range(2, n_components):\n", - " plt.figure(figsize=(12, 6))\n", - " sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells')\n", - " plt.colorbar(sc, label='Infected Softmax Score')\n", - " plt.xlabel('Principal Component 1')\n", - " plt.ylabel(f'Principal Component {i + 1}')\n", - " plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1}')\n", - " plt.legend()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize the rank correlations\n", - "plt.figure(figsize=(12, 6))\n", - "sns.barplot(x=\"PC\", y=\"Background Correlation\", data=correlation_df, color='blue', label='Background')\n", - "sns.barplot(x=\"PC\", y=\"Uninfected Correlation\", data=correlation_df, color='green', label='Uninfected')\n", - "sns.barplot(x=\"PC\", y=\"Infected Correlation\", data=correlation_df, color='red', label='Infected')\n", - "plt.xlabel('Principal Component')\n", - "plt.ylabel('Spearman Correlation')\n", - "plt.title('Rank Correlations of Principal Components with Ground Truth Masks')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'Axes' object is not iterable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[80], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m feature_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(predicted_features\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m])] \u001b[38;5;66;03m# Replace with actual feature names if available\u001b[39;00m\n\u001b[1;32m 7\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(n_components, \u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m3\u001b[39m \u001b[38;5;241m*\u001b[39m n_components))\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, (component, ax) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcomponents\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[1;32m 9\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(component\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, ax\u001b[38;5;241m=\u001b[39max, cbar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, xticklabels\u001b[38;5;241m=\u001b[39mfeature_names)\n\u001b[1;32m 10\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrincipal Component \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;250m \u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mTypeError\u001b[0m: 'Axes' object is not iterable" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "components = pca.components_\n", - "\n", - "# Assuming your original features are named, you can list them\n", - "feature_names = [f\"Feature {i}\" for i in range(predicted_features.shape[1])] # Replace with actual feature names if available\n", - "\n", - "fig, axes = plt.subplots(n_components, 1, figsize=(12, 3 * n_components))\n", - "for i, (component, ax) in enumerate(zip(components[:n_components], axes)):\n", - " sns.heatmap(component.reshape(1, -1), cmap='viridis', ax=ax, cbar=False, xticklabels=feature_names)\n", - " ax.set_title(f'Principal Component {i + 1}')\n", - " ax.set_xlabel('Features')\n", - " ax.set_ylabel('Component Value')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/applications/contrastive_phenotyping/pca.py b/applications/contrastive_phenotyping/pca.py new file mode 100644 index 00000000..9903d520 --- /dev/null +++ b/applications/contrastive_phenotyping/pca.py @@ -0,0 +1,315 @@ +# %% +import numpy as np +import pandas as pd +from iohub import open_ome_zarr +from sklearn.decomposition import PCA +from scipy.stats import spearmanr +import matplotlib.pyplot as plt +import seaborn as sns +import plotly.io as pio +import plotly.express as px + +# Set Plotly default renderer for VSCode +pio.renderers.default = "vscode" + +# Load predicted features and projections +predicted_features = np.load("updated_epoch66_predicted_features.npy") +predicted_projections = np.load("updated_epoch66_predicted_projections.npy") + +print(predicted_features.shape) +print(predicted_projections.shape) + +# Load the CSV file +csv_path = "epoch66_processed_order.csv" +df = pd.read_csv(csv_path) + +# Load ground truth masks +base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr" +ds = open_ome_zarr(base_path, layout="hcs", mode="r") + +background_mask_index = ds.channel_names.index('background_mask') +uninfected_mask_index = ds.channel_names.index('uninfected_mask') +infected_mask_index = ds.channel_names.index('infected_mask') + + +# %% +# Assuming all masks have the same shape +# TO-DO: +# tie the image with projected embeddings +# test with ER + +# Initialize arrays to store the sums +num_cells = len(df) +background_sums = np.zeros(num_cells) +uninfected_sums = np.zeros(num_cells) +infected_sums = np.zeros(num_cells) + +# %% +for idx, row in df.iterrows(): + position_key = f"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}/0" + zarr_array = ds[position_key] + t = row['Timestep'] + + # Load a single z-slice, for example the first one + background_mask = zarr_array[t, background_mask_index, 0, :, :] + uninfected_mask = zarr_array[t, uninfected_mask_index, 0, :, :] + infected_mask = zarr_array[t, infected_mask_index, 0, :, :] + + # Sum values across each mask + background_sums[idx] = np.sum(background_mask) + uninfected_sums[idx] = np.sum(uninfected_mask) + infected_sums[idx] = np.sum(infected_mask) + +# %% +# Normalize the sums +max_background = np.max(background_sums) +max_uninfected = np.max(uninfected_sums) +max_infected = np.max(infected_sums) + +background_sums /= max_background +uninfected_sums /= max_uninfected +infected_sums /= max_infected + +# %% +# Combine the sums into a single array and apply softmax +combined_sums = np.stack([background_sums, uninfected_sums, infected_sums], axis=1) +softmax_sums = np.exp(combined_sums) / np.sum(np.exp(combined_sums), axis=1, keepdims=True) + +# Separate the softmax values +background_softmax = softmax_sums[:, 0] +uninfected_softmax = softmax_sums[:, 1] +infected_softmax = softmax_sums[:, 2] + +# %% +# Check for NaN values in the softmax results +print("NaN values in combined_sums:", np.isnan(combined_sums).any()) +print("NaN values in softmax_sums:", np.isnan(softmax_sums).any()) +print("Infinite values in combined_sums:", np.isinf(combined_sums).any()) +print("Infinite values in softmax_sums:", np.isinf(softmax_sums).any()) + +# %% +# Check for NaN values in the softmax results +print("NaN values in background_softmax:", np.isnan(background_softmax).any()) +print("NaN values in uninfected_softmax:", np.isnan(uninfected_softmax).any()) +print("NaN values in infected_softmax:", np.isnan(infected_softmax).any()) + +# Check for zero variance in the softmax results +print("Variance in background_softmax:", np.var(background_softmax)) +print("Variance in uninfected_softmax:", np.var(uninfected_softmax)) +print("Variance in infected_softmax:", np.var(infected_softmax)) + +# %% +# Determine the number of principal components to keep +#reshaped_features = predicted_features.reshape(predicted_features.shape[0], -1) + +pca = PCA() +pca.fit(predicted_features) +explained_variance_ratio = np.cumsum(pca.explained_variance_ratio_) + +# %% +# Plot the explained variance ratio +plt.figure(figsize=(12, 6)) +plt.plot(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, marker='o', linestyle='--') +plt.xlabel('Number of Components') +plt.ylabel('Cumulative Explained Variance') +plt.title('Explained Variance by Number of Components') +plt.grid(True) +plt.show() + +# %% +# Choose the number of components that explain a significant amount of variance (e.g., 90%) +n_components = np.argmax(explained_variance_ratio >= 0.90) + 1 +print(f"Number of components selected: {n_components}") + +# %% +# Perform PCA with the selected number of components +pca = PCA(n_components=2) +reduced_projections = pca.fit_transform(predicted_projections) + +# %% +df['PC1'] = reduced_projections[:, 0] +df['PC2'] = reduced_projections[:, 1] +df['Infected Softmax Score'] = infected_softmax + +print(df.head()) + + +# %% +# Calculate rank correlations +correlations = [] + +for i in range(reduced_projections.shape[1]): + pc = reduced_projections[:, i] + + background_corr, _ = spearmanr(pc, background_softmax) + uninfected_corr, _ = spearmanr(pc, uninfected_softmax) + infected_corr, _ = spearmanr(pc, infected_softmax) + + correlations.append({ + "PC": i + 1, + "Background Correlation": background_corr, + "Uninfected Correlation": uninfected_corr, + "Infected Correlation": infected_corr + }) + +correlation_df = pd.DataFrame(correlations) +print(correlation_df) + +# %% +# Create an interactive scatter plot +fig = px.scatter(df, x='PC1', y='PC2', color='Infected Softmax Score', + hover_data=['Row', 'Column', 'FOV', 'Cell ID', 'Timestep']) + +# Show the plot +fig.show() + +# %% +# Function to get cell data and plot the images + +rfp_index = ds.channel_names.index('RFP') +phase3d_index = ds.channel_names.index('Phase3D') + +def get_cell_data_and_plot(row, col, fov, cell_id, timestep): + position_key = f"{row}/{col}/fov{fov}cell{cell_id}/0" + zarr_array = ds[position_key] + + phase_img = zarr_array[timestep, phase3d_index, 32, :, :] + rfp_img = zarr_array[timestep, rfp_index, 32, :, :] + + fig, axes = plt.subplots(1, 2, figsize=(12, 6)) + axes[0].imshow(phase_img, cmap='gray') + axes[0].set_title('Phase3D Image') + axes[1].imshow(rfp_img, cmap='gray') + axes[1].set_title('RFP Image') + plt.show() + + return phase_img, rfp_img + +# example: get data for a specific cell and plot +row = 'B' +col = '3' +fov = 5 +cell_id = 14 +timestep = 4 + +phase_img, rfp_img = get_cell_data_and_plot(row, col, fov, cell_id, timestep) + +# %% +# Visualize the PCA results with cells colored based on their infected softmax scores +plt.figure(figsize=(12, 6)) +sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=infected_softmax, cmap='viridis', label='Cells') +plt.colorbar(sc, label='Infected Softmax Score') +plt.xlabel('Principal Component 1') +plt.ylabel('Principal Component 2') +plt.title('PCA of Predicted Projections (Colored by Infected Softmax Score)') +plt.legend() +plt.show() + +# %% +# PC1 vs PC3, PC1 vs PC4, etc. +n_components = 5 +if n_components > 2: + for i in range(2, n_components): + plt.figure(figsize=(12, 6)) + sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells') + plt.colorbar(sc, label='Infected Softmax Score') + plt.xlabel('Principal Component 1') + plt.ylabel(f'Principal Component {i + 1}') + plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by Infected Softmax Score)') + plt.legend() + plt.show() + +# %% + +correlations = np.zeros(n_components) +for i in range(n_components): + pc = reduced_projections[:, i] + correlation, _ = spearmanr(pc, infected_softmax) + correlations[i] = correlation + + +# %% +# Visualize the PCA results with cells colored based on their principal component values +for i in range(n_components): + plt.figure(figsize=(12, 6)) + sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}') + plt.colorbar(sc, label='Principal Component Value') + plt.xlabel('Principal Component 1') + plt.ylabel('Principal Component 2') + plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Values)') + plt.legend() + plt.show() + +# %% +# Additional components if n_components > 2 +if n_components > 2: + for i in range(2, n_components): + plt.figure(figsize=(12, 6)) + sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}') + plt.colorbar(sc, label='Principal Component Value') + plt.xlabel('Principal Component 1') + plt.ylabel(f'Principal Component {i + 1}') + plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by PC{i+1} Values)') + plt.legend() + plt.show() + +# %% + +# Visualize the PCA results with color based on correlation with infected softmax score +for i in range(reduced_projections.shape[1]): + pc = reduced_projections[:, i] + infected_corr, _ = spearmanr(pc, infected_softmax) + + plt.figure(figsize=(12, 6)) + sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=pc, cmap='viridis', label=f'PC{i+1} Correlation: {infected_corr:.2f}') + plt.colorbar(sc, label='Principal Component Value') + plt.xlabel('Principal Component 1') + plt.ylabel('Principal Component 2') + plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Correlation with Infected Softmax Score)') + plt.legend() + plt.show() + + +# %% +# Visualize additional PCs if needed +# PC1 vs PC3, PC1 vs PC4, etc. +if n_components > 2: + for i in range(2, n_components): + plt.figure(figsize=(12, 6)) + sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells') + plt.colorbar(sc, label='Infected Softmax Score') + plt.xlabel('Principal Component 1') + plt.ylabel(f'Principal Component {i + 1}') + plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1}') + plt.legend() + plt.show() + + +# %% +# Visualize the rank correlations +plt.figure(figsize=(12, 6)) +sns.barplot(x="PC", y="Background Correlation", data=correlation_df, color='blue', label='Background') +sns.barplot(x="PC", y="Uninfected Correlation", data=correlation_df, color='green', label='Uninfected') +sns.barplot(x="PC", y="Infected Correlation", data=correlation_df, color='red', label='Infected') +plt.xlabel('Principal Component') +plt.ylabel('Spearman Correlation') +plt.title('Rank Correlations of Principal Components with Ground Truth Masks') +plt.legend() +plt.show() + +# %% +components = pca.components_ + +# Assuming your original features are named, you can list them +feature_names = [f"Feature {i}" for i in range(predicted_features.shape[1])] # Replace with actual feature names if available + +fig, axes = plt.subplots(n_components, 1, figsize=(12, 3 * n_components)) +for i, (component, ax) in enumerate(zip(components[:n_components], axes)): + sns.heatmap(component.reshape(1, -1), cmap='viridis', ax=ax, cbar=False, xticklabels=feature_names) + ax.set_title(f'Principal Component {i + 1}') + ax.set_xlabel('Features') + ax.set_ylabel('Component Value') +plt.tight_layout() +plt.show() + + From b061568b362c7946da2025fc2f182e41311233fd Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 11:34:04 -0700 Subject: [PATCH 33/87] format script --- applications/contrastive_phenotyping/pca.py | 244 +++++++++++++------- 1 file changed, 161 insertions(+), 83 deletions(-) diff --git a/applications/contrastive_phenotyping/pca.py b/applications/contrastive_phenotyping/pca.py index 9903d520..b4395a2a 100644 --- a/applications/contrastive_phenotyping/pca.py +++ b/applications/contrastive_phenotyping/pca.py @@ -1,13 +1,13 @@ # %% +import matplotlib.pyplot as plt import numpy as np import pandas as pd +import plotly.express as px +import plotly.io as pio +import seaborn as sns from iohub import open_ome_zarr -from sklearn.decomposition import PCA from scipy.stats import spearmanr -import matplotlib.pyplot as plt -import seaborn as sns -import plotly.io as pio -import plotly.express as px +from sklearn.decomposition import PCA # Set Plotly default renderer for VSCode pio.renderers.default = "vscode" @@ -27,9 +27,9 @@ base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr" ds = open_ome_zarr(base_path, layout="hcs", mode="r") -background_mask_index = ds.channel_names.index('background_mask') -uninfected_mask_index = ds.channel_names.index('uninfected_mask') -infected_mask_index = ds.channel_names.index('infected_mask') +background_mask_index = ds.channel_names.index("background_mask") +uninfected_mask_index = ds.channel_names.index("uninfected_mask") +infected_mask_index = ds.channel_names.index("infected_mask") # %% @@ -48,13 +48,13 @@ for idx, row in df.iterrows(): position_key = f"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}/0" zarr_array = ds[position_key] - t = row['Timestep'] - + t = row["Timestep"] + # Load a single z-slice, for example the first one background_mask = zarr_array[t, background_mask_index, 0, :, :] uninfected_mask = zarr_array[t, uninfected_mask_index, 0, :, :] infected_mask = zarr_array[t, infected_mask_index, 0, :, :] - + # Sum values across each mask background_sums[idx] = np.sum(background_mask) uninfected_sums[idx] = np.sum(uninfected_mask) @@ -73,7 +73,9 @@ # %% # Combine the sums into a single array and apply softmax combined_sums = np.stack([background_sums, uninfected_sums, infected_sums], axis=1) -softmax_sums = np.exp(combined_sums) / np.sum(np.exp(combined_sums), axis=1, keepdims=True) +softmax_sums = np.exp(combined_sums) / np.sum( + np.exp(combined_sums), axis=1, keepdims=True +) # Separate the softmax values background_softmax = softmax_sums[:, 0] @@ -100,7 +102,7 @@ # %% # Determine the number of principal components to keep -#reshaped_features = predicted_features.reshape(predicted_features.shape[0], -1) +# reshaped_features = predicted_features.reshape(predicted_features.shape[0], -1) pca = PCA() pca.fit(predicted_features) @@ -109,10 +111,15 @@ # %% # Plot the explained variance ratio plt.figure(figsize=(12, 6)) -plt.plot(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, marker='o', linestyle='--') -plt.xlabel('Number of Components') -plt.ylabel('Cumulative Explained Variance') -plt.title('Explained Variance by Number of Components') +plt.plot( + range(1, len(explained_variance_ratio) + 1), + explained_variance_ratio, + marker="o", + linestyle="--", +) +plt.xlabel("Number of Components") +plt.ylabel("Cumulative Explained Variance") +plt.title("Explained Variance by Number of Components") plt.grid(True) plt.show() @@ -127,9 +134,9 @@ reduced_projections = pca.fit_transform(predicted_projections) # %% -df['PC1'] = reduced_projections[:, 0] -df['PC2'] = reduced_projections[:, 1] -df['Infected Softmax Score'] = infected_softmax +df["PC1"] = reduced_projections[:, 0] +df["PC2"] = reduced_projections[:, 1] +df["Infected Softmax Score"] = infected_softmax print(df.head()) @@ -140,25 +147,32 @@ for i in range(reduced_projections.shape[1]): pc = reduced_projections[:, i] - + background_corr, _ = spearmanr(pc, background_softmax) uninfected_corr, _ = spearmanr(pc, uninfected_softmax) infected_corr, _ = spearmanr(pc, infected_softmax) - - correlations.append({ - "PC": i + 1, - "Background Correlation": background_corr, - "Uninfected Correlation": uninfected_corr, - "Infected Correlation": infected_corr - }) + + correlations.append( + { + "PC": i + 1, + "Background Correlation": background_corr, + "Uninfected Correlation": uninfected_corr, + "Infected Correlation": infected_corr, + } + ) correlation_df = pd.DataFrame(correlations) print(correlation_df) # %% # Create an interactive scatter plot -fig = px.scatter(df, x='PC1', y='PC2', color='Infected Softmax Score', - hover_data=['Row', 'Column', 'FOV', 'Cell ID', 'Timestep']) +fig = px.scatter( + df, + x="PC1", + y="PC2", + color="Infected Softmax Score", + hover_data=["Row", "Column", "FOV", "Cell ID", "Timestep"], +) # Show the plot fig.show() @@ -166,8 +180,9 @@ # %% # Function to get cell data and plot the images -rfp_index = ds.channel_names.index('RFP') -phase3d_index = ds.channel_names.index('Phase3D') +rfp_index = ds.channel_names.index("RFP") +phase3d_index = ds.channel_names.index("Phase3D") + def get_cell_data_and_plot(row, col, fov, cell_id, timestep): position_key = f"{row}/{col}/fov{fov}cell{cell_id}/0" @@ -175,19 +190,20 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): phase_img = zarr_array[timestep, phase3d_index, 32, :, :] rfp_img = zarr_array[timestep, rfp_index, 32, :, :] - + fig, axes = plt.subplots(1, 2, figsize=(12, 6)) - axes[0].imshow(phase_img, cmap='gray') - axes[0].set_title('Phase3D Image') - axes[1].imshow(rfp_img, cmap='gray') - axes[1].set_title('RFP Image') + axes[0].imshow(phase_img, cmap="gray") + axes[0].set_title("Phase3D Image") + axes[1].imshow(rfp_img, cmap="gray") + axes[1].set_title("RFP Image") plt.show() return phase_img, rfp_img + # example: get data for a specific cell and plot -row = 'B' -col = '3' +row = "B" +col = "3" fov = 5 cell_id = 14 timestep = 4 @@ -197,11 +213,17 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): # %% # Visualize the PCA results with cells colored based on their infected softmax scores plt.figure(figsize=(12, 6)) -sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=infected_softmax, cmap='viridis', label='Cells') -plt.colorbar(sc, label='Infected Softmax Score') -plt.xlabel('Principal Component 1') -plt.ylabel('Principal Component 2') -plt.title('PCA of Predicted Projections (Colored by Infected Softmax Score)') +sc = plt.scatter( + reduced_projections[:, 0], + reduced_projections[:, 1], + c=infected_softmax, + cmap="viridis", + label="Cells", +) +plt.colorbar(sc, label="Infected Softmax Score") +plt.xlabel("Principal Component 1") +plt.ylabel("Principal Component 2") +plt.title("PCA of Predicted Projections (Colored by Infected Softmax Score)") plt.legend() plt.show() @@ -211,11 +233,19 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): if n_components > 2: for i in range(2, n_components): plt.figure(figsize=(12, 6)) - sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells') - plt.colorbar(sc, label='Infected Softmax Score') - plt.xlabel('Principal Component 1') - plt.ylabel(f'Principal Component {i + 1}') - plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by Infected Softmax Score)') + sc = plt.scatter( + reduced_projections[:, 0], + reduced_projections[:, i], + c=infected_softmax, + cmap="viridis", + label="Cells", + ) + plt.colorbar(sc, label="Infected Softmax Score") + plt.xlabel("Principal Component 1") + plt.ylabel(f"Principal Component {i + 1}") + plt.title( + f"PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by Infected Softmax Score)" + ) plt.legend() plt.show() @@ -232,11 +262,17 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): # Visualize the PCA results with cells colored based on their principal component values for i in range(n_components): plt.figure(figsize=(12, 6)) - sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}') - plt.colorbar(sc, label='Principal Component Value') - plt.xlabel('Principal Component 1') - plt.ylabel('Principal Component 2') - plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Values)') + sc = plt.scatter( + reduced_projections[:, 0], + reduced_projections[:, 1], + c=reduced_projections[:, i], + cmap="viridis", + label=f"PC{i+1} Correlation: {correlations[i]:.2f}", + ) + plt.colorbar(sc, label="Principal Component Value") + plt.xlabel("Principal Component 1") + plt.ylabel("Principal Component 2") + plt.title(f"PCA of Predicted Projections (Colored by PC{i+1} Values)") plt.legend() plt.show() @@ -245,11 +281,19 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): if n_components > 2: for i in range(2, n_components): plt.figure(figsize=(12, 6)) - sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=reduced_projections[:, i], cmap='viridis', label=f'PC{i+1} Correlation: {correlations[i]:.2f}') - plt.colorbar(sc, label='Principal Component Value') - plt.xlabel('Principal Component 1') - plt.ylabel(f'Principal Component {i + 1}') - plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by PC{i+1} Values)') + sc = plt.scatter( + reduced_projections[:, 0], + reduced_projections[:, i], + c=reduced_projections[:, i], + cmap="viridis", + label=f"PC{i+1} Correlation: {correlations[i]:.2f}", + ) + plt.colorbar(sc, label="Principal Component Value") + plt.xlabel("Principal Component 1") + plt.ylabel(f"Principal Component {i + 1}") + plt.title( + f"PCA of Predicted Projections: PC1 vs PC{i + 1} (Colored by PC{i+1} Values)" + ) plt.legend() plt.show() @@ -259,13 +303,21 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): for i in range(reduced_projections.shape[1]): pc = reduced_projections[:, i] infected_corr, _ = spearmanr(pc, infected_softmax) - + plt.figure(figsize=(12, 6)) - sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, 1], c=pc, cmap='viridis', label=f'PC{i+1} Correlation: {infected_corr:.2f}') - plt.colorbar(sc, label='Principal Component Value') - plt.xlabel('Principal Component 1') - plt.ylabel('Principal Component 2') - plt.title(f'PCA of Predicted Projections (Colored by PC{i+1} Correlation with Infected Softmax Score)') + sc = plt.scatter( + reduced_projections[:, 0], + reduced_projections[:, 1], + c=pc, + cmap="viridis", + label=f"PC{i+1} Correlation: {infected_corr:.2f}", + ) + plt.colorbar(sc, label="Principal Component Value") + plt.xlabel("Principal Component 1") + plt.ylabel("Principal Component 2") + plt.title( + f"PCA of Predicted Projections (Colored by PC{i+1} Correlation with Infected Softmax Score)" + ) plt.legend() plt.show() @@ -276,11 +328,17 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): if n_components > 2: for i in range(2, n_components): plt.figure(figsize=(12, 6)) - sc = plt.scatter(reduced_projections[:, 0], reduced_projections[:, i], c=infected_softmax, cmap='viridis', label='Cells') - plt.colorbar(sc, label='Infected Softmax Score') - plt.xlabel('Principal Component 1') - plt.ylabel(f'Principal Component {i + 1}') - plt.title(f'PCA of Predicted Projections: PC1 vs PC{i + 1}') + sc = plt.scatter( + reduced_projections[:, 0], + reduced_projections[:, i], + c=infected_softmax, + cmap="viridis", + label="Cells", + ) + plt.colorbar(sc, label="Infected Softmax Score") + plt.xlabel("Principal Component 1") + plt.ylabel(f"Principal Component {i + 1}") + plt.title(f"PCA of Predicted Projections: PC1 vs PC{i + 1}") plt.legend() plt.show() @@ -288,12 +346,26 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): # %% # Visualize the rank correlations plt.figure(figsize=(12, 6)) -sns.barplot(x="PC", y="Background Correlation", data=correlation_df, color='blue', label='Background') -sns.barplot(x="PC", y="Uninfected Correlation", data=correlation_df, color='green', label='Uninfected') -sns.barplot(x="PC", y="Infected Correlation", data=correlation_df, color='red', label='Infected') -plt.xlabel('Principal Component') -plt.ylabel('Spearman Correlation') -plt.title('Rank Correlations of Principal Components with Ground Truth Masks') +sns.barplot( + x="PC", + y="Background Correlation", + data=correlation_df, + color="blue", + label="Background", +) +sns.barplot( + x="PC", + y="Uninfected Correlation", + data=correlation_df, + color="green", + label="Uninfected", +) +sns.barplot( + x="PC", y="Infected Correlation", data=correlation_df, color="red", label="Infected" +) +plt.xlabel("Principal Component") +plt.ylabel("Spearman Correlation") +plt.title("Rank Correlations of Principal Components with Ground Truth Masks") plt.legend() plt.show() @@ -301,15 +373,21 @@ def get_cell_data_and_plot(row, col, fov, cell_id, timestep): components = pca.components_ # Assuming your original features are named, you can list them -feature_names = [f"Feature {i}" for i in range(predicted_features.shape[1])] # Replace with actual feature names if available +feature_names = [ + f"Feature {i}" for i in range(predicted_features.shape[1]) +] # Replace with actual feature names if available fig, axes = plt.subplots(n_components, 1, figsize=(12, 3 * n_components)) for i, (component, ax) in enumerate(zip(components[:n_components], axes)): - sns.heatmap(component.reshape(1, -1), cmap='viridis', ax=ax, cbar=False, xticklabels=feature_names) - ax.set_title(f'Principal Component {i + 1}') - ax.set_xlabel('Features') - ax.set_ylabel('Component Value') + sns.heatmap( + component.reshape(1, -1), + cmap="viridis", + ax=ax, + cbar=False, + xticklabels=feature_names, + ) + ax.set_title(f"Principal Component {i + 1}") + ax.set_xlabel("Features") + ax.set_ylabel("Component Value") plt.tight_layout() plt.show() - - From c06ab1be62e30aec3cfda039bc077c7f68e1b8c5 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 11:36:05 -0700 Subject: [PATCH 34/87] rename scripts conflicting with pytest --- .../{dataloader_test.py => profile_dataloader.py} | 0 .../{dataloader_test.sh => profile_dataloader.sh} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename applications/contrastive_phenotyping/{dataloader_test.py => profile_dataloader.py} (100%) rename applications/contrastive_phenotyping/{dataloader_test.sh => profile_dataloader.sh} (100%) diff --git a/applications/contrastive_phenotyping/dataloader_test.py b/applications/contrastive_phenotyping/profile_dataloader.py similarity index 100% rename from applications/contrastive_phenotyping/dataloader_test.py rename to applications/contrastive_phenotyping/profile_dataloader.py diff --git a/applications/contrastive_phenotyping/dataloader_test.sh b/applications/contrastive_phenotyping/profile_dataloader.sh similarity index 100% rename from applications/contrastive_phenotyping/dataloader_test.sh rename to applications/contrastive_phenotyping/profile_dataloader.sh From fd12502ba6dfacd435b8f30e24982b32fc432936 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 11:40:55 -0700 Subject: [PATCH 35/87] lint application scripts --- .../dataloader_test.py | 113 ++++++++++++++++++ .../graphs_ConvNeXt_ResNet.py | 6 +- .../contrastive_phenotyping/predict.py | 23 ++-- .../profile_dataloader.py | 8 +- .../training_script.py | 20 ++-- 5 files changed, 136 insertions(+), 34 deletions(-) create mode 100644 applications/contrastive_phenotyping/dataloader_test.py diff --git a/applications/contrastive_phenotyping/dataloader_test.py b/applications/contrastive_phenotyping/dataloader_test.py new file mode 100644 index 00000000..cad32370 --- /dev/null +++ b/applications/contrastive_phenotyping/dataloader_test.py @@ -0,0 +1,113 @@ +# %% Imports and initialization. +import os +import time +import warnings +from pathlib import Path + +import wandb +from tqdm import tqdm + +from viscy.data.hcs import ContrastiveDataModule + +warnings.filterwarnings("ignore") +os.environ["WANDB_DIR"] = f"/hpc/mydata/{os.environ['USER']}/" +data_on_lustre = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") +data_on_vast = Path("/hpc/projects/virtual_staining/viral_sensor_test_dataio/") +wandb.init(project="contrastive_model", entity="alishba_imran-CZ Biohub") + +# %% Method that iterates over two epochs and logs the resource usage. + + +def profile_dataio(top_dir, num_epochs=1): + + channels = 2 + x = 200 + y = 200 + z_range = (0, 10) + batch_size = 16 + base_path = ( + top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" + ) + timesteps_csv_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" + + data_module = ContrastiveDataModule( + base_path=base_path, + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + batch_size=batch_size, + num_workers=8, + z_range=z_range, + ) + + # for train and val + data_module.setup() + + print( + f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}" + ) + print(f"Training dataset size: {len(data_module.train_dataset)}") + print(f"Validation dataset size: {len(data_module.val_dataset)}") + print(f"Test dataset size: {len(data_module.test_dataset)}") + + start_time = time.time() + total_bytes_transferred = 0 # Track the total number of bytes transferred + + # Profile the data i/o + for i in range(num_epochs): + # Train dataloader + train_dataloader = data_module.train_dataloader() + train_dataloader = tqdm( + train_dataloader, desc=f"Epoch {i+1}/{num_epochs} - Train" + ) + for batch in train_dataloader: + anchor_batch, positive_batch, negative_batch = batch + total_bytes_transferred += ( + anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes + ) + # print("Anchor batch shape:", anchor_batch.shape) + # print("Positive batch shape:", positive_batch.shape) + # print("Negative batch shape:", negative_batch.shape) + + # Validation dataloader + val_dataloader = data_module.val_dataloader() + val_dataloader = tqdm( + val_dataloader, desc=f"Epoch {i+1}/{num_epochs} - Validation" + ) + for batch in val_dataloader: + anchor_batch, positive_batch, negative_batch = batch + total_bytes_transferred += ( + anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes + ) + # print("Anchor batch shape:", anchor_batch.shape) + # print("Positive batch shape:", positive_batch.shape) + # print("Negative batch shape:", negative_batch.shape) + + end_time = time.time() + elapsed_time = end_time - start_time + data_transfer_speed = (total_bytes_transferred / elapsed_time) / ( + 1024 * 1024 + ) # Calculate data transfer speed in MBPS + + print("Anchor batch shape:", anchor_batch.shape) + print("Positive batch shape:", positive_batch.shape) + print("Negative batch shape:", negative_batch.shape) + + print(f"Elapsed time for {num_epochs} iterations: {elapsed_time} seconds") + print(f"Average time per iteration: {elapsed_time/num_epochs} seconds") + print(f"Data transfer speed: {data_transfer_speed} MBPS") + + +# %% Testing the data i/o with data stored on Vast +print(f"Profiling data i/o with data stored on VAST\n{data_on_vast}\n") +profile_dataio(data_on_vast) + + +# %% Testing the data i/o with data stored on Lustre +print(f"Profiling data i/o with data stored on Lustre\n{data_on_lustre}\n") + +profile_dataio(data_on_lustre) + +# %% +wandb.finish() diff --git a/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py b/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py index 69cab375..03781217 100644 --- a/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py +++ b/applications/contrastive_phenotyping/graphs_ConvNeXt_ResNet.py @@ -1,9 +1,10 @@ # %% Imports and paths. -import os +import timm import torch -from viscy.representation.contrastive import ContrastiveEncoder import torchview +from viscy.light.engine import ContrastiveModule +from viscy.representation.contrastive import ContrastiveEncoder, UNeXt2Stem # %load_ext autoreload # %autoreload 2 @@ -53,7 +54,6 @@ model_graph.visual_graph # %% Playground -import timm available_models = timm.list_models(pretrained=True) diff --git a/applications/contrastive_phenotyping/predict.py b/applications/contrastive_phenotyping/predict.py index ec941942..06dd3924 100644 --- a/applications/contrastive_phenotyping/predict.py +++ b/applications/contrastive_phenotyping/predict.py @@ -1,22 +1,13 @@ -from viscy.data.hcs import ContrastiveDataModule, PredictDataset -from viscy.light.engine import ContrastiveModule -import os -from pathlib import Path from argparse import ArgumentParser +from pathlib import Path -import torch -from torch.optim import Adam -from lightning.pytorch.strategies import DDPStrategy -from lightning.pytorch import Trainer, seed_everything -from lightning.pytorch.callbacks import ModelCheckpoint, RichProgressBar -from lightning.pytorch.loggers import WandbLogger -from lightning.pytorch.callbacks import TQDMProgressBar -from lightning.pytorch.utilities.rank_zero import rank_zero_only -import wandb -from tqdm import tqdm -import logging import numpy as np -import pandas as pd +from lightning.pytorch import Trainer +from lightning.pytorch.callbacks import TQDMProgressBar +from lightning.pytorch.strategies import DDPStrategy + +from viscy.data.hcs import ContrastiveDataModule +from viscy.light.engine import ContrastiveModule def main(hparams): diff --git a/applications/contrastive_phenotyping/profile_dataloader.py b/applications/contrastive_phenotyping/profile_dataloader.py index 32f4f7b5..cad32370 100644 --- a/applications/contrastive_phenotyping/profile_dataloader.py +++ b/applications/contrastive_phenotyping/profile_dataloader.py @@ -1,12 +1,14 @@ # %% Imports and initialization. -import warnings import os -from pathlib import Path -from viscy.data.hcs import ContrastiveDataModule import time +import warnings +from pathlib import Path + import wandb from tqdm import tqdm +from viscy.data.hcs import ContrastiveDataModule + warnings.filterwarnings("ignore") os.environ["WANDB_DIR"] = f"/hpc/mydata/{os.environ['USER']}/" data_on_lustre = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") diff --git a/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script.py index 92f3a375..98e825de 100644 --- a/applications/contrastive_phenotyping/training_script.py +++ b/applications/contrastive_phenotyping/training_script.py @@ -1,27 +1,23 @@ # %% Imports and paths. +import logging import os -from pathlib import Path from argparse import ArgumentParser +from pathlib import Path import torch import torchview -from torch.optim import Adam -from lightning.pytorch.strategies import DDPStrategy - -from lightning.pytorch import Trainer, seed_everything -from lightning.pytorch.callbacks import ModelCheckpoint, RichProgressBar +from lightning.pytorch import Trainer +from lightning.pytorch.callbacks import ( + ModelCheckpoint, +) # from lightning.pytorch.loggers import TensorBoardLogger from lightning.pytorch.loggers import WandbLogger -from lightning.pytorch.callbacks import TQDMProgressBar -import wandb -from tqdm import tqdm -from lightning.pytorch.utilities.rank_zero import rank_zero_only +from lightning.pytorch.strategies import DDPStrategy +from viscy.data.hcs import ContrastiveDataModule from viscy.light.engine import ContrastiveModule from viscy.representation.contrastive import ContrastiveEncoder -from viscy.data.hcs import ContrastiveDataModule -import logging # Set W&B logging level to suppress warnings logging.getLogger("wandb").setLevel(logging.ERROR) From 6312a18f0cf0d7a3f238dffd68567570a2678b85 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 13:34:46 -0700 Subject: [PATCH 36/87] do not filter all warnings --- viscy/data/hcs.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 7bc27bb3..153a2e80 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -4,7 +4,6 @@ import random import re import tempfile -import warnings from glob import glob from pathlib import Path from typing import Callable, Literal, Optional, Sequence, Union @@ -34,8 +33,6 @@ from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample -warnings.filterwarnings("ignore") - def _ensure_channel_list(str_or_seq: str | Sequence[str]) -> list[str]: """ From 6cbebe89fb3376824bac9f04cf12b0ba253ba8c2 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 13:53:57 -0700 Subject: [PATCH 37/87] log instead of print --- viscy/data/hcs.py | 62 +++++++++++++++++++++++------------------------ 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 153a2e80..b015e3b5 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -33,6 +33,8 @@ from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample +_logger = logging.getLogger("lightning.pytorch") + def _ensure_channel_list(str_or_seq: str | Sequence[str]) -> list[str]: """ @@ -262,7 +264,7 @@ def __init__( # channel_name = re.search(r"^.+(?=_p\d{3})", img_name).group() z_idx = _search_int_in_str(r"(?<=_z)\d+", img_name) self.masks[(int(position_name), int(t_idx), int(z_idx))] = img_path - logging.info(str(self.masks)) + _logger.info(str(self.masks)) def __getitem__(self, index: int) -> Sample: sample = super().__getitem__(index) @@ -439,7 +441,7 @@ def _setup_fit(self, dataset_settings: dict): def _setup_test(self, dataset_settings: dict): """Set up the test stage.""" if self.batch_size != 1: - logging.warning(f"Ignoring batch size {self.batch_size} in test stage.") + _logger.warning(f"Ignoring batch size {self.batch_size} in test stage.") dataset_settings["channels"]["target"] = self.target_channel data_path = self.cache_path if self.caching else self.data_path @@ -467,7 +469,7 @@ def _setup_predict( # track metadata for inverting transform set_track_meta(True) if self.caching: - logging.warning("Ignoring caching config in 'predict' stage.") + _logger.warning("Ignoring caching config in 'predict' stage.") dataset: Union[Plate, Position] = open_ome_zarr(self.data_path, mode="r") if isinstance(dataset, Position): try: @@ -590,7 +592,7 @@ def _train_transform(self) -> list[Callable]: self.train_z_scale_range = z_scale_range else: self.train_z_scale_range = (0.0, 0.0) - logging.debug(f"Training augmentations: {self.augmentations}") + _logger.debug(f"Training augmentations: {self.augmentations}") return list(self.augmentations) @@ -620,12 +622,8 @@ def __init__( self.channel_indices = [ self.ds.channel_names.index(channel) for channel in self.channel_names ] - print("channel indices!") - print(self.channel_indices) - print(f"Initialized dataset with {len(self.positions)} positions.") - - # self.statistics = self.compute_statistics() - # print("Channel Statistics:", self.statistics) + _logger.debug(f"Initialized dataset with {len(self.positions)} positions.") + _logger.debug(f"Channel indices: {self.channel_indices}") def compute_statistics(self): stats = { @@ -655,11 +653,11 @@ def compute_statistics(self): ) del stats[channel]["sum_sq_diff"] - print("done!") + _logger.debug("done!") return stats def open_zarr_store(self, path, layout="hcs", mode="r"): - # print(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") + # _logger.debug(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") return open_ome_zarr(path, layout=layout, mode=mode) def __len__(self): @@ -674,7 +672,7 @@ def __getitem__(self, idx): positive_data = self.normalize_data(positive_data) # if self.transform: - # print("Positive transformation applied") + # _logger.debug("Positive transformation applied") negative_idx = idx while negative_idx == idx: @@ -687,12 +685,12 @@ def __getitem__(self, idx): negative_data = self.normalize_data(negative_data) # if self.transform: - # print("Negative transformation applied") + # _logger.debug("Negative transformation applied") - # print("shapes of tensors") - # print(torch.tensor(anchor_data).shape) - # print(torch.tensor(positive_data).shape) - # print(torch.tensor(negative_data).shape) + # _logger.debug("shapes of tensors") + # _logger.debug(torch.tensor(anchor_data).shape) + # _logger.debug(torch.tensor(positive_data).shape) + # _logger.debug(torch.tensor(negative_data).shape) return ( torch.tensor(anchor_data, dtype=torch.float32), torch.tensor(positive_data, dtype=torch.float32), @@ -701,15 +699,15 @@ def __getitem__(self, idx): def load_data(self, position_path): position = self.ds[position_path] - # print(f"Loading data from position: {position_path}") + # _logger.debug(f"Loading data from position: {position_path}") zarr_array = position["0"][:] - # print("Shape before:", zarr_array.shape) + # _logger.debug("Shape before:", zarr_array.shape) data = self.restructure_data(zarr_array, position_path) data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] - # print("shape after!") - # print(data.shape) + # _logger.debug("shape after!") + # _logger.debug(data.shape) return data def restructure_data(self, data, position_path): @@ -760,7 +758,7 @@ def apply_channel_transforms(self, data): channel_data = data[i] transform = self.transform[channel_name] transformed_data[i] = transform({"image": channel_data})["image"] - # print(f"transformed {channel_name}") + # _logger.debug(f"transformed {channel_name}") return transformed_data @@ -873,7 +871,7 @@ def setup(self, stage: str = None): # setup prediction dataset if stage == "predict" and self.predict_base_path: - print("setting up!") + _logger.debug("setting up!") self.predict_dataset = PredictDataset( self.predict_base_path, self.channels, @@ -915,7 +913,7 @@ def test_dataloader(self): ) def predict_dataloader(self): - print("running predict DataLoader!") + _logger.debug("running predict DataLoader!") if self.predict_dataset is None: raise ValueError( "Predict dataset not set up. Call setup(stage='predict') first." @@ -955,9 +953,11 @@ def __init__( self.channel_indices = [ self.ds.channel_names.index(channel) for channel in self.channel_names ] - print("channel indices!") - print(self.channel_indices) - print(f"Initialized predict dataset with {len(self.positions)} positions.") + _logger.debug("channel indices!") + _logger.debug(self.channel_indices) + _logger.debug( + f"Initialized predict dataset with {len(self.positions)} positions." + ) def open_zarr_store(self, path, layout="hcs", mode="r"): return open_ome_zarr(path, layout=layout, mode=mode) @@ -968,7 +968,7 @@ def open_zarr_store(self, path, layout="hcs", mode="r"): # for idx, row in self.timesteps_df.iterrows(): # position_path = f"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}" # positions.append((position_path, row['Random Timestep'])) - # #print(positions) + # #_logger.debug(positions) # return positions def __len__(self): @@ -976,7 +976,7 @@ def __len__(self): def __getitem__(self, idx): position_path = self.positions[idx][0] - # print(f"Position path: {position_path}") + # _logger.debug(f"Position path: {position_path}") data = self.load_data(position_path) data = self.normalize_data(data) @@ -985,7 +985,7 @@ def __getitem__(self, idx): # double check printing order def load_data(self, position_path): position = self.ds[position_path] - # print(f"Loading data for position path: {position_path}") + # _logger.debug(f"Loading data for position path: {position_path}") zarr_array = position["0"][:] parts = position_path.split("/") From 3b7b5582b31c592af0341f86b49f6152bd0c2bb9 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 13:56:45 -0700 Subject: [PATCH 38/87] split data modules by task --- viscy/data/hcs.py | 433 --------------------------------------- viscy/data/triplet.py | 462 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 462 insertions(+), 433 deletions(-) create mode 100644 viscy/data/triplet.py diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index b015e3b5..ccc64d54 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -594,436 +594,3 @@ def _train_transform(self) -> list[Callable]: self.train_z_scale_range = (0.0, 0.0) _logger.debug(f"Training augmentations: {self.augmentations}") return list(self.augmentations) - - -# dataloader for organelle phenotyping -class ContrastiveDataset(Dataset): - def __init__( - self, - base_path, - channels, - x, - y, - timesteps_csv_path, - channel_names, - transform=None, - z_range=None, - ): - self.base_path = base_path - self.channels = channels - self.x = x - self.y = y - self.z_range = z_range - self.channel_names = channel_names - self.transform = get_transforms() - self.ds = self.open_zarr_store(self.base_path) - self.positions = list(self.ds.positions()) - self.timesteps_df = pd.read_csv(timesteps_csv_path) - self.channel_indices = [ - self.ds.channel_names.index(channel) for channel in self.channel_names - ] - _logger.debug(f"Initialized dataset with {len(self.positions)} positions.") - _logger.debug(f"Channel indices: {self.channel_indices}") - - def compute_statistics(self): - stats = { - channel: {"mean": 0, "sum_sq_diff": 0, "min": np.inf, "max": -np.inf} - for channel in self.channel_names - } - count = 0 - total_elements = 0 - - for idx in range(len(self.positions)): - position_path = self.positions[idx][0] - data = self.load_data(position_path) - for i, channel in enumerate(self.channel_names): - channel_data = data[i] - mean = np.mean(channel_data) - stats[channel]["mean"] += mean - stats[channel]["min"] = min(stats[channel]["min"], np.min(channel_data)) - stats[channel]["max"] = max(stats[channel]["max"], np.max(channel_data)) - stats[channel]["sum_sq_diff"] += np.sum((channel_data - mean) ** 2) - count += 1 - total_elements += np.prod(channel_data.shape) - - for channel in self.channel_names: - stats[channel]["mean"] /= count - stats[channel]["std"] = np.sqrt( - stats[channel]["sum_sq_diff"] / total_elements - ) - del stats[channel]["sum_sq_diff"] - - _logger.debug("done!") - return stats - - def open_zarr_store(self, path, layout="hcs", mode="r"): - # _logger.debug(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") - return open_ome_zarr(path, layout=layout, mode=mode) - - def __len__(self): - return len(self.positions) - - def __getitem__(self, idx): - anchor_position_path = self.positions[idx][0] - anchor_data = self.load_data(anchor_position_path) - anchor_data = self.normalize_data(anchor_data) - - positive_data = self.apply_channel_transforms(anchor_data) - positive_data = self.normalize_data(positive_data) - - # if self.transform: - # _logger.debug("Positive transformation applied") - - negative_idx = idx - while negative_idx == idx: - negative_idx = random.randint(0, self.__len__() - 1) - negative_position_path = self.positions[negative_idx][0] - negative_data = self.load_data(negative_position_path) - negative_data = self.normalize_data(negative_data) - - negative_data = self.apply_channel_transforms(negative_data) - negative_data = self.normalize_data(negative_data) - - # if self.transform: - # _logger.debug("Negative transformation applied") - - # _logger.debug("shapes of tensors") - # _logger.debug(torch.tensor(anchor_data).shape) - # _logger.debug(torch.tensor(positive_data).shape) - # _logger.debug(torch.tensor(negative_data).shape) - return ( - torch.tensor(anchor_data, dtype=torch.float32), - torch.tensor(positive_data, dtype=torch.float32), - torch.tensor(negative_data, dtype=torch.float32), - ) - - def load_data(self, position_path): - position = self.ds[position_path] - # _logger.debug(f"Loading data from position: {position_path}") - - zarr_array = position["0"][:] - # _logger.debug("Shape before:", zarr_array.shape) - data = self.restructure_data(zarr_array, position_path) - data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] - - # _logger.debug("shape after!") - # _logger.debug(data.shape) - return data - - def restructure_data(self, data, position_path): - # Extract row, column, fov, and cell_id from position_path - parts = position_path.split("/") - row = parts[0] - column = parts[1] - fov_cell = parts[2] - - fov = int(fov_cell.split("fov")[1].split("cell")[0]) - cell_id = int(fov_cell.split("cell")[1]) - - extracted_combined = f"{row}/{column}/fov{fov}cell{cell_id}" - - matched_rows = self.timesteps_df[ - self.timesteps_df.apply( - lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", - axis=1, - ) - == extracted_combined - ] - - if matched_rows.empty: - raise ValueError( - f"No matching entry found for position path: {position_path}" - ) - - start_time = matched_rows["Start Time"].values[0] - end_time = matched_rows["End Time"].values[0] - - random_timestep = np.random.randint(start_time, end_time) - - reshaped_data = data[random_timestep] - return reshaped_data - - def normalize_data(self, data): - normalized_data = np.empty_like(data) - for i in range(data.shape[0]): # iterate over each channel - channel_data = data[i] - mean = np.mean(channel_data) - std = np.std(channel_data) - normalized_data[i] = (channel_data - mean) / (std + 1e-6) - return normalized_data - - def apply_channel_transforms(self, data): - transformed_data = np.empty_like(data) - for i, channel_name in enumerate(self.channel_names): - channel_data = data[i] - transform = self.transform[channel_name] - transformed_data[i] = transform({"image": channel_data})["image"] - # _logger.debug(f"transformed {channel_name}") - return transformed_data - - -def get_transforms(): - rfp_transforms = Compose( - [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.75, 1.25)), - RandAffined( - keys=["image"], - prob=0.5, - rotate_range=(0.1, 0.1), - shear_range=(0.1, 0.1), - scale_range=(0.1, 0.1), - ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), - RandGaussianSmoothd( - keys=["image"], - prob=0.5, - sigma_x=(0.1, 0.3), - sigma_y=(0.1, 0.3), - sigma_z=(0.1, 0.3), - ), - RandScaleIntensityd(keys=["image"], factors=(0.85, 1.15), prob=0.5), - ] - ) - - phase_transforms = Compose( - [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.97, 1.03)), - RandAffined( - keys=["image"], - prob=0.5, - rotate_range=(0.05, 0.05), - shear_range=(0.05, 0.05), - scale_range=(0.05, 0.05), - ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.005), - RandGaussianSmoothd( - keys=["image"], - prob=0.5, - sigma_x=(0.03, 0.05), - sigma_y=(0.03, 0.05), - sigma_z=(0.03, 0.05), - ), - RandScaleIntensityd(keys=["image"], factors=(0.97, 1.03), prob=0.5), - ] - ) - - return {"RFP": rfp_transforms, "Phase3D": phase_transforms} - - -class ContrastiveDataModule(LightningDataModule): - def __init__( - self, - base_path: str, - channels: int, - x: int, - y: int, - timesteps_csv_path: str, - channel_names: list, - transform=None, - predict_base_path: str = None, - train_split_ratio: float = 0.64, - val_split_ratio: float = 0.16, - batch_size: int = 4, - num_workers: int = 8, - z_range: tuple[int, int] = None, - ): - super().__init__() - self.base_path = Path(base_path) - self.channels = channels - self.x = x - self.y = y - self.timesteps_csv_path = timesteps_csv_path - self.channel_names = channel_names - self.transform = get_transforms() - self.predict_base_path = Path(predict_base_path) if predict_base_path else None - self.train_split_ratio = train_split_ratio - self.val_split_ratio = val_split_ratio - self.batch_size = batch_size - self.num_workers = num_workers - self.z_range = z_range - self.train_dataset = None - self.val_dataset = None - self.test_dataset = None - self.predict_dataset = None - - def setup(self, stage: str = None): - if stage == "fit": - dataset = ContrastiveDataset( - self.base_path, - self.channels, - self.x, - self.y, - self.timesteps_csv_path, - channel_names=self.channel_names, - transform=self.transform, - z_range=self.z_range, - ) - - train_size = int(len(dataset) * self.train_split_ratio) - val_size = int(len(dataset) * self.val_split_ratio) - test_size = len(dataset) - train_size - val_size - - self.train_dataset, self.val_dataset, self.test_dataset = ( - torch.utils.data.random_split( - dataset, [train_size, val_size, test_size] - ) - ) - - # setup prediction dataset - if stage == "predict" and self.predict_base_path: - _logger.debug("setting up!") - self.predict_dataset = PredictDataset( - self.predict_base_path, - self.channels, - self.x, - self.y, - timesteps_csv_path=self.timesteps_csv_path, - channel_names=self.channel_names, - z_range=self.z_range, - ) - - def train_dataloader(self): - return DataLoader( - self.train_dataset, - batch_size=self.batch_size, - shuffle=True, - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, - ) - - def val_dataloader(self): - return DataLoader( - self.val_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, - ) - - def test_dataloader(self): - return DataLoader( - self.test_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, - ) - - def predict_dataloader(self): - _logger.debug("running predict DataLoader!") - if self.predict_dataset is None: - raise ValueError( - "Predict dataset not set up. Call setup(stage='predict') first." - ) - - return DataLoader( - self.predict_dataset, - batch_size=self.batch_size, - shuffle=False, # False shuffle for prediction - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, - ) - - -class PredictDataset(Dataset): - def __init__( - self, - base_path, - channels, - x, - y, - timesteps_csv_path, - channel_names, - z_range=None, - ): - self.base_path = base_path - self.channels = channels - self.x = x - self.y = y - self.z_range = z_range - self.channel_names = channel_names - self.ds = self.open_zarr_store(self.base_path) - self.timesteps_csv_path = timesteps_csv_path - self.timesteps_df = pd.read_csv(timesteps_csv_path) - self.positions = list(self.ds.positions()) - self.channel_indices = [ - self.ds.channel_names.index(channel) for channel in self.channel_names - ] - _logger.debug("channel indices!") - _logger.debug(self.channel_indices) - _logger.debug( - f"Initialized predict dataset with {len(self.positions)} positions." - ) - - def open_zarr_store(self, path, layout="hcs", mode="r"): - return open_ome_zarr(path, layout=layout, mode=mode) - - # def get_positions_from_csv(self): - # positions = [] - # #self.timesteps_df = pd.read_csv(self.timesteps_csv_path) - # for idx, row in self.timesteps_df.iterrows(): - # position_path = f"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}" - # positions.append((position_path, row['Random Timestep'])) - # #_logger.debug(positions) - # return positions - - def __len__(self): - return len(self.positions) - - def __getitem__(self, idx): - position_path = self.positions[idx][0] - # _logger.debug(f"Position path: {position_path}") - data = self.load_data(position_path) - data = self.normalize_data(data) - - return torch.tensor(data, dtype=torch.float32), (position_path) - - # double check printing order - def load_data(self, position_path): - position = self.ds[position_path] - # _logger.debug(f"Loading data for position path: {position_path}") - zarr_array = position["0"][:] - - parts = position_path.split("/") - row = parts[0] - column = parts[1] - fov_cell = parts[2] - fov = int(fov_cell.split("fov")[1].split("cell")[0]) - cell_id = int(fov_cell.split("cell")[1]) - - combined_id = f"{row}/{column}/fov{fov}cell{cell_id}" - matched_rows = self.timesteps_df[ - self.timesteps_df.apply( - lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", - axis=1, - ) - == combined_id - ] - - if matched_rows.empty: - raise ValueError( - f"No matching entry found for position path: {position_path}" - ) - - random_timestep = matched_rows["Random Timestep"].values[0] - data = zarr_array[ - random_timestep, - self.channel_indices, - self.z_range[0] : self.z_range[1], - :, - :, - ] - return data - - def normalize_data(self, data): - normalized_data = np.empty_like(data) - for i in range(data.shape[0]): # iterate over each channel - channel_data = data[i] - mean = np.mean(channel_data) - std = np.std(channel_data) - normalized_data[i] = (channel_data - mean) / (std + 1e-6) - return normalized_data diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py new file mode 100644 index 00000000..56ac3955 --- /dev/null +++ b/viscy/data/triplet.py @@ -0,0 +1,462 @@ +import logging +import random +from pathlib import Path +from typing import Callable, Literal, Optional, Sequence, Union + +import numpy as np +import pandas as pd +import torch +from iohub.ngff import ImageArray, Plate, Position, open_ome_zarr +from lightning.pytorch import LightningDataModule +from monai.data import set_track_meta +from monai.data.utils import collate_meta_tensor +from monai.transforms import ( + CenterSpatialCropd, + Compose, + MapTransform, + MultiSampleTrait, + RandAdjustContrastd, + RandAffined, + RandGaussianNoised, + RandGaussianSmoothd, + RandScaleIntensityd, +) +from torch import Tensor +from torch.utils.data import DataLoader, Dataset + +from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample + +_logger = logging.getLogger("lightning.pytorch") + + +# dataloader for organelle phenotyping +class ContrastiveDataset(Dataset): + def __init__( + self, + base_path, + channels, + x, + y, + timesteps_csv_path, + channel_names, + transform=None, + z_range=None, + ): + self.base_path = base_path + self.channels = channels + self.x = x + self.y = y + self.z_range = z_range + self.channel_names = channel_names + self.transform = get_transforms() + self.ds = self.open_zarr_store(self.base_path) + self.positions = list(self.ds.positions()) + self.timesteps_df = pd.read_csv(timesteps_csv_path) + self.channel_indices = [ + self.ds.channel_names.index(channel) for channel in self.channel_names + ] + _logger.debug(f"Initialized dataset with {len(self.positions)} positions.") + _logger.debug(f"Channel indices: {self.channel_indices}") + + def compute_statistics(self): + stats = { + channel: {"mean": 0, "sum_sq_diff": 0, "min": np.inf, "max": -np.inf} + for channel in self.channel_names + } + count = 0 + total_elements = 0 + + for idx in range(len(self.positions)): + position_path = self.positions[idx][0] + data = self.load_data(position_path) + for i, channel in enumerate(self.channel_names): + channel_data = data[i] + mean = np.mean(channel_data) + stats[channel]["mean"] += mean + stats[channel]["min"] = min(stats[channel]["min"], np.min(channel_data)) + stats[channel]["max"] = max(stats[channel]["max"], np.max(channel_data)) + stats[channel]["sum_sq_diff"] += np.sum((channel_data - mean) ** 2) + count += 1 + total_elements += np.prod(channel_data.shape) + + for channel in self.channel_names: + stats[channel]["mean"] /= count + stats[channel]["std"] = np.sqrt( + stats[channel]["sum_sq_diff"] / total_elements + ) + del stats[channel]["sum_sq_diff"] + + _logger.debug("done!") + return stats + + def open_zarr_store(self, path, layout="hcs", mode="r"): + # _logger.debug(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") + return open_ome_zarr(path, layout=layout, mode=mode) + + def __len__(self): + return len(self.positions) + + def __getitem__(self, idx): + anchor_position_path = self.positions[idx][0] + anchor_data = self.load_data(anchor_position_path) + anchor_data = self.normalize_data(anchor_data) + + positive_data = self.apply_channel_transforms(anchor_data) + positive_data = self.normalize_data(positive_data) + + # if self.transform: + # _logger.debug("Positive transformation applied") + + negative_idx = idx + while negative_idx == idx: + negative_idx = random.randint(0, self.__len__() - 1) + negative_position_path = self.positions[negative_idx][0] + negative_data = self.load_data(negative_position_path) + negative_data = self.normalize_data(negative_data) + + negative_data = self.apply_channel_transforms(negative_data) + negative_data = self.normalize_data(negative_data) + + # if self.transform: + # _logger.debug("Negative transformation applied") + + # _logger.debug("shapes of tensors") + # _logger.debug(torch.tensor(anchor_data).shape) + # _logger.debug(torch.tensor(positive_data).shape) + # _logger.debug(torch.tensor(negative_data).shape) + return ( + torch.tensor(anchor_data, dtype=torch.float32), + torch.tensor(positive_data, dtype=torch.float32), + torch.tensor(negative_data, dtype=torch.float32), + ) + + def load_data(self, position_path): + position = self.ds[position_path] + # _logger.debug(f"Loading data from position: {position_path}") + + zarr_array = position["0"][:] + # _logger.debug("Shape before:", zarr_array.shape) + data = self.restructure_data(zarr_array, position_path) + data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] + + # _logger.debug("shape after!") + # _logger.debug(data.shape) + return data + + def restructure_data(self, data, position_path): + # Extract row, column, fov, and cell_id from position_path + parts = position_path.split("/") + row = parts[0] + column = parts[1] + fov_cell = parts[2] + + fov = int(fov_cell.split("fov")[1].split("cell")[0]) + cell_id = int(fov_cell.split("cell")[1]) + + extracted_combined = f"{row}/{column}/fov{fov}cell{cell_id}" + + matched_rows = self.timesteps_df[ + self.timesteps_df.apply( + lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", + axis=1, + ) + == extracted_combined + ] + + if matched_rows.empty: + raise ValueError( + f"No matching entry found for position path: {position_path}" + ) + + start_time = matched_rows["Start Time"].values[0] + end_time = matched_rows["End Time"].values[0] + + random_timestep = np.random.randint(start_time, end_time) + + reshaped_data = data[random_timestep] + return reshaped_data + + def normalize_data(self, data): + normalized_data = np.empty_like(data) + for i in range(data.shape[0]): # iterate over each channel + channel_data = data[i] + mean = np.mean(channel_data) + std = np.std(channel_data) + normalized_data[i] = (channel_data - mean) / (std + 1e-6) + return normalized_data + + def apply_channel_transforms(self, data): + transformed_data = np.empty_like(data) + for i, channel_name in enumerate(self.channel_names): + channel_data = data[i] + transform = self.transform[channel_name] + transformed_data[i] = transform({"image": channel_data})["image"] + # _logger.debug(f"transformed {channel_name}") + return transformed_data + + +def get_transforms(): + rfp_transforms = Compose( + [ + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.75, 1.25)), + RandAffined( + keys=["image"], + prob=0.5, + rotate_range=(0.1, 0.1), + shear_range=(0.1, 0.1), + scale_range=(0.1, 0.1), + ), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), + RandGaussianSmoothd( + keys=["image"], + prob=0.5, + sigma_x=(0.1, 0.3), + sigma_y=(0.1, 0.3), + sigma_z=(0.1, 0.3), + ), + RandScaleIntensityd(keys=["image"], factors=(0.85, 1.15), prob=0.5), + ] + ) + + phase_transforms = Compose( + [ + RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.97, 1.03)), + RandAffined( + keys=["image"], + prob=0.5, + rotate_range=(0.05, 0.05), + shear_range=(0.05, 0.05), + scale_range=(0.05, 0.05), + ), + RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.005), + RandGaussianSmoothd( + keys=["image"], + prob=0.5, + sigma_x=(0.03, 0.05), + sigma_y=(0.03, 0.05), + sigma_z=(0.03, 0.05), + ), + RandScaleIntensityd(keys=["image"], factors=(0.97, 1.03), prob=0.5), + ] + ) + + return {"RFP": rfp_transforms, "Phase3D": phase_transforms} + + +class ContrastiveDataModule(LightningDataModule): + def __init__( + self, + base_path: str, + channels: int, + x: int, + y: int, + timesteps_csv_path: str, + channel_names: list, + transform=None, + predict_base_path: str = None, + train_split_ratio: float = 0.64, + val_split_ratio: float = 0.16, + batch_size: int = 4, + num_workers: int = 8, + z_range: tuple[int, int] = None, + ): + super().__init__() + self.base_path = Path(base_path) + self.channels = channels + self.x = x + self.y = y + self.timesteps_csv_path = timesteps_csv_path + self.channel_names = channel_names + self.transform = get_transforms() + self.predict_base_path = Path(predict_base_path) if predict_base_path else None + self.train_split_ratio = train_split_ratio + self.val_split_ratio = val_split_ratio + self.batch_size = batch_size + self.num_workers = num_workers + self.z_range = z_range + self.train_dataset = None + self.val_dataset = None + self.test_dataset = None + self.predict_dataset = None + + def setup(self, stage: str = None): + if stage == "fit": + dataset = ContrastiveDataset( + self.base_path, + self.channels, + self.x, + self.y, + self.timesteps_csv_path, + channel_names=self.channel_names, + transform=self.transform, + z_range=self.z_range, + ) + + train_size = int(len(dataset) * self.train_split_ratio) + val_size = int(len(dataset) * self.val_split_ratio) + test_size = len(dataset) - train_size - val_size + + self.train_dataset, self.val_dataset, self.test_dataset = ( + torch.utils.data.random_split( + dataset, [train_size, val_size, test_size] + ) + ) + + # setup prediction dataset + if stage == "predict" and self.predict_base_path: + _logger.debug("setting up!") + self.predict_dataset = PredictDataset( + self.predict_base_path, + self.channels, + self.x, + self.y, + timesteps_csv_path=self.timesteps_csv_path, + channel_names=self.channel_names, + z_range=self.z_range, + ) + + def train_dataloader(self): + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + shuffle=True, + num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True, + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True, + ) + + def test_dataloader(self): + return DataLoader( + self.test_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True, + ) + + def predict_dataloader(self): + _logger.debug("running predict DataLoader!") + if self.predict_dataset is None: + raise ValueError( + "Predict dataset not set up. Call setup(stage='predict') first." + ) + + return DataLoader( + self.predict_dataset, + batch_size=self.batch_size, + shuffle=False, # False shuffle for prediction + num_workers=self.num_workers, + prefetch_factor=2, + persistent_workers=True, + ) + + +class PredictDataset(Dataset): + def __init__( + self, + base_path, + channels, + x, + y, + timesteps_csv_path, + channel_names, + z_range=None, + ): + self.base_path = base_path + self.channels = channels + self.x = x + self.y = y + self.z_range = z_range + self.channel_names = channel_names + self.ds = self.open_zarr_store(self.base_path) + self.timesteps_csv_path = timesteps_csv_path + self.timesteps_df = pd.read_csv(timesteps_csv_path) + self.positions = list(self.ds.positions()) + self.channel_indices = [ + self.ds.channel_names.index(channel) for channel in self.channel_names + ] + _logger.debug("channel indices!") + _logger.debug(self.channel_indices) + _logger.debug( + f"Initialized predict dataset with {len(self.positions)} positions." + ) + + def open_zarr_store(self, path, layout="hcs", mode="r"): + return open_ome_zarr(path, layout=layout, mode=mode) + + # def get_positions_from_csv(self): + # positions = [] + # #self.timesteps_df = pd.read_csv(self.timesteps_csv_path) + # for idx, row in self.timesteps_df.iterrows(): + # position_path = f"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}" + # positions.append((position_path, row['Random Timestep'])) + # #_logger.debug(positions) + # return positions + + def __len__(self): + return len(self.positions) + + def __getitem__(self, idx): + position_path = self.positions[idx][0] + # _logger.debug(f"Position path: {position_path}") + data = self.load_data(position_path) + data = self.normalize_data(data) + + return torch.tensor(data, dtype=torch.float32), (position_path) + + # double check printing order + def load_data(self, position_path): + position = self.ds[position_path] + # _logger.debug(f"Loading data for position path: {position_path}") + zarr_array = position["0"][:] + + parts = position_path.split("/") + row = parts[0] + column = parts[1] + fov_cell = parts[2] + fov = int(fov_cell.split("fov")[1].split("cell")[0]) + cell_id = int(fov_cell.split("cell")[1]) + + combined_id = f"{row}/{column}/fov{fov}cell{cell_id}" + matched_rows = self.timesteps_df[ + self.timesteps_df.apply( + lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", + axis=1, + ) + == combined_id + ] + + if matched_rows.empty: + raise ValueError( + f"No matching entry found for position path: {position_path}" + ) + + random_timestep = matched_rows["Random Timestep"].values[0] + data = zarr_array[ + random_timestep, + self.channel_indices, + self.z_range[0] : self.z_range[1], + :, + :, + ] + return data + + def normalize_data(self, data): + normalized_data = np.empty_like(data) + for i in range(data.shape[0]): # iterate over each channel + channel_data = data[i] + mean = np.mean(channel_data) + std = np.std(channel_data) + normalized_data[i] = (channel_data - mean) / (std + 1e-6) + return normalized_data From 68d20976d2d8ef541cfa4b47d2fef2cfa6aae3b7 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 14:05:01 -0700 Subject: [PATCH 39/87] clean up imports --- viscy/data/hcs.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index ccc64d54..5622bcf0 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -1,7 +1,6 @@ import logging import math import os -import random import re import tempfile from glob import glob @@ -9,7 +8,6 @@ from typing import Callable, Literal, Optional, Sequence, Union import numpy as np -import pandas as pd import torch import zarr from imageio import imread @@ -22,11 +20,7 @@ Compose, MapTransform, MultiSampleTrait, - RandAdjustContrastd, RandAffined, - RandGaussianNoised, - RandGaussianSmoothd, - RandScaleIntensityd, ) from torch import Tensor from torch.utils.data import DataLoader, Dataset From ac43974e6549fe12770e841b60e70b990398b1a1 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 14:13:56 -0700 Subject: [PATCH 40/87] update typing --- viscy/data/hcs.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 5622bcf0..6484807d 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -5,7 +5,7 @@ import tempfile from glob import glob from pathlib import Path -from typing import Callable, Literal, Optional, Sequence, Union +from typing import Callable, Literal, Sequence import numpy as np import torch @@ -274,9 +274,9 @@ class HCSDataModule(LightningDataModule): """Lightning data module for a preprocessed HCS NGFF Store. :param str data_path: path to the data store - :param Union[str, Sequence[str]] source_channel: name(s) of the source channel, + :param str | Sequence[str] source_channel: name(s) of the source channel, e.g. ``'Phase'`` - :param Union[str, Sequence[str]] target_channel: name(s) of the target channel, + :param str | Sequence[str] target_channel: name(s) of the target channel, e.g. ``['Nuclei', 'Membrane']`` :param int z_window_size: Z window size of the 2.5D U-Net, 1 for 2D :param float split_ratio: split ratio of the training subset in the fit stage, @@ -295,7 +295,7 @@ class HCSDataModule(LightningDataModule): :param bool caching: whether to decompress all the images and cache the result, will store in ``/tmp/$SLURM_JOB_ID/`` if available, defaults to False - :param Optional[Path] ground_truth_masks: path to the ground truth masks, + :param Path | None ground_truth_masks: path to the ground truth masks, used in the test stage to compute segmentation metrics, defaults to None """ @@ -303,8 +303,8 @@ class HCSDataModule(LightningDataModule): def __init__( self, data_path: str, - source_channel: Union[str, Sequence[str]], - target_channel: Union[str, Sequence[str]], + source_channel: str | Sequence[str], + target_channel: str | Sequence[str], z_window_size: int, split_ratio: float = 0.8, batch_size: int = 16, @@ -314,7 +314,7 @@ def __init__( normalizations: list[MapTransform] = [], augmentations: list[MapTransform] = [], caching: bool = False, - ground_truth_masks: Optional[Path] = None, + ground_truth_masks: Path | None = None, ): super().__init__() self.data_path = Path(data_path) @@ -464,7 +464,7 @@ def _setup_predict( set_track_meta(True) if self.caching: _logger.warning("Ignoring caching config in 'predict' stage.") - dataset: Union[Plate, Position] = open_ome_zarr(self.data_path, mode="r") + dataset: Plate | Position = open_ome_zarr(self.data_path, mode="r") if isinstance(dataset, Position): try: plate_path = self.data_path.parent.parent.parent From d03ef5587441ef59833ff9f6e2e4edcd9264e010 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 14:16:11 -0700 Subject: [PATCH 41/87] use pathlib --- viscy/data/hcs.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 6484807d..8ed694bd 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -3,7 +3,6 @@ import os import re import tempfile -from glob import glob from pathlib import Path from typing import Callable, Literal, Sequence @@ -249,8 +248,8 @@ def __init__( ) -> None: super().__init__(positions, channels, z_window_size, transform) self.masks = {} - for img_path in glob(os.path.join(ground_truth_masks, "*cp_masks.png")): - img_name = os.path.basename(img_path) + for img_path in Path(ground_truth_masks).glob("*cp_masks.png"): + img_name = img_path.name position_name = _search_int_in_str(r"(?<=_p)\d{3}", img_name) # TODO: specify time index in the file name t_idx = 0 From a520c60c518a43265dbd13532a5f37bc83dc333e Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 22 Jul 2024 14:30:05 -0700 Subject: [PATCH 42/87] remove redundant file --- .../dataloader_test.py | 113 ------------------ 1 file changed, 113 deletions(-) delete mode 100644 applications/contrastive_phenotyping/dataloader_test.py diff --git a/applications/contrastive_phenotyping/dataloader_test.py b/applications/contrastive_phenotyping/dataloader_test.py deleted file mode 100644 index cad32370..00000000 --- a/applications/contrastive_phenotyping/dataloader_test.py +++ /dev/null @@ -1,113 +0,0 @@ -# %% Imports and initialization. -import os -import time -import warnings -from pathlib import Path - -import wandb -from tqdm import tqdm - -from viscy.data.hcs import ContrastiveDataModule - -warnings.filterwarnings("ignore") -os.environ["WANDB_DIR"] = f"/hpc/mydata/{os.environ['USER']}/" -data_on_lustre = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") -data_on_vast = Path("/hpc/projects/virtual_staining/viral_sensor_test_dataio/") -wandb.init(project="contrastive_model", entity="alishba_imran-CZ Biohub") - -# %% Method that iterates over two epochs and logs the resource usage. - - -def profile_dataio(top_dir, num_epochs=1): - - channels = 2 - x = 200 - y = 200 - z_range = (0, 10) - batch_size = 16 - base_path = ( - top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/full_patch.zarr" - ) - timesteps_csv_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/final_track_timesteps.csv" - - data_module = ContrastiveDataModule( - base_path=base_path, - channels=channels, - x=x, - y=y, - timesteps_csv_path=timesteps_csv_path, - batch_size=batch_size, - num_workers=8, - z_range=z_range, - ) - - # for train and val - data_module.setup() - - print( - f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset) + len(data_module.test_dataset)}" - ) - print(f"Training dataset size: {len(data_module.train_dataset)}") - print(f"Validation dataset size: {len(data_module.val_dataset)}") - print(f"Test dataset size: {len(data_module.test_dataset)}") - - start_time = time.time() - total_bytes_transferred = 0 # Track the total number of bytes transferred - - # Profile the data i/o - for i in range(num_epochs): - # Train dataloader - train_dataloader = data_module.train_dataloader() - train_dataloader = tqdm( - train_dataloader, desc=f"Epoch {i+1}/{num_epochs} - Train" - ) - for batch in train_dataloader: - anchor_batch, positive_batch, negative_batch = batch - total_bytes_transferred += ( - anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes - ) - # print("Anchor batch shape:", anchor_batch.shape) - # print("Positive batch shape:", positive_batch.shape) - # print("Negative batch shape:", negative_batch.shape) - - # Validation dataloader - val_dataloader = data_module.val_dataloader() - val_dataloader = tqdm( - val_dataloader, desc=f"Epoch {i+1}/{num_epochs} - Validation" - ) - for batch in val_dataloader: - anchor_batch, positive_batch, negative_batch = batch - total_bytes_transferred += ( - anchor_batch.nbytes + positive_batch.nbytes + negative_batch.nbytes - ) - # print("Anchor batch shape:", anchor_batch.shape) - # print("Positive batch shape:", positive_batch.shape) - # print("Negative batch shape:", negative_batch.shape) - - end_time = time.time() - elapsed_time = end_time - start_time - data_transfer_speed = (total_bytes_transferred / elapsed_time) / ( - 1024 * 1024 - ) # Calculate data transfer speed in MBPS - - print("Anchor batch shape:", anchor_batch.shape) - print("Positive batch shape:", positive_batch.shape) - print("Negative batch shape:", negative_batch.shape) - - print(f"Elapsed time for {num_epochs} iterations: {elapsed_time} seconds") - print(f"Average time per iteration: {elapsed_time/num_epochs} seconds") - print(f"Data transfer speed: {data_transfer_speed} MBPS") - - -# %% Testing the data i/o with data stored on Vast -print(f"Profiling data i/o with data stored on VAST\n{data_on_vast}\n") -profile_dataio(data_on_vast) - - -# %% Testing the data i/o with data stored on Lustre -print(f"Profiling data i/o with data stored on Lustre\n{data_on_lustre}\n") - -profile_dataio(data_on_lustre) - -# %% -wandb.finish() From 7d984cdf927860da766d29e971eb336620df9c6f Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Tue, 23 Jul 2024 12:03:43 -0700 Subject: [PATCH 43/87] updated predict.py --- .../contrastive_phenotyping/predict.py | 37 +++++++++++-------- 1 file changed, 22 insertions(+), 15 deletions(-) diff --git a/applications/contrastive_phenotyping/predict.py b/applications/contrastive_phenotyping/predict.py index 06dd3924..ab3890b9 100644 --- a/applications/contrastive_phenotyping/predict.py +++ b/applications/contrastive_phenotyping/predict.py @@ -12,10 +12,9 @@ def main(hparams): # Set paths + # this CSV defines the order in which embeddings should be processed. Currently using num_workers = 1 to keep order top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") - timesteps_csv_path = ( - top_dir / "2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/predict_timesteps.csv" - ) + timesteps_csv_path = "/hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/expanded_transitioning_cells_metadata.csv" predict_base_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/all_annotations_patch.zarr" checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/updated_multiple_channels/contrastive_model-test-epoch=97-val_loss=0.00.ckpt" @@ -24,20 +23,21 @@ def main(hparams): x = 200 y = 200 z_range = (28, 43) - batch_size = 11 + batch_size = 12 channel_names = ["RFP", "Phase3D"] # Initialize the data module for prediction data_module = ContrastiveDataModule( - base_path=str(predict_base_path), - channels=channels, - x=x, - y=y, - timesteps_csv_path=timesteps_csv_path, - channel_names=channel_names, - batch_size=batch_size, - z_range=z_range, - predict_base_path=predict_base_path, + base_path=str(predict_base_path), + channels=channels, + x=x, + y=y, + timesteps_csv_path=timesteps_csv_path, + channel_names=channel_names, + batch_size=batch_size, + z_range=z_range, + predict_base_path=predict_base_path, + analysis=True, # for self-supervised results ) data_module.setup(stage="predict") @@ -72,8 +72,15 @@ def main(hparams): all_projections = np.concatenate(projections_list, axis=0) # Save features and projections - np.save("updated_epoch97_predicted_features.npy", all_features) - np.save("updated_epoch97_predicted_projections.npy", all_projections) + # Save in sub-folder instead for the specific FOV + + # for saving visualizations embeddings + base_dir = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/5-finaltrack/test_visualizations" + features_path = os.path.join(base_dir, 'B', '4', '2', 'before_projected_embeddings', 'test_epoch88_predicted_features.npy') + projections_path = os.path.join(base_dir, 'B', '4', '2', 'projected_embeddings', 'test_epoch88_predicted_projections.npy') + + np.save("/hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/ss1_epoch97_predicted_features.npy", all_features) + np.save("/hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/ss1_epoch97_predicted_projections.npy", all_projections) if __name__ == "__main__": From b69eb2f3c39ffbefcc0755b7538fc60a18849384 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Thu, 25 Jul 2024 16:37:00 -0700 Subject: [PATCH 44/87] better typing --- viscy/data/hcs.py | 14 ++++++++------ viscy/data/typing.py | 6 +++++- 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index 8ed694bd..f7e0cb20 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -24,7 +24,7 @@ from torch import Tensor from torch.utils.data import DataLoader, Dataset -from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample +from viscy.data.typing import ChannelMap, DictTransform, HCSStackIndex, NormMeta, Sample _logger = logging.getLogger("lightning.pytorch") @@ -102,7 +102,7 @@ class SlidingWindowDataset(Dataset): :param ChannelMap channels: source and target channel names, e.g. ``{'source': 'Phase', 'target': ['Nuclei', 'Membrane']}`` :param int z_window_size: Z window size of the 2.5D U-Net, 1 for 2D - :param Callable[[dict[str, Tensor]], dict[str, Tensor]] | None transform: + :param DictTransform | None transform: a callable that transforms data, defaults to None """ @@ -111,7 +111,7 @@ def __init__( positions: list[Position], channels: ChannelMap, z_window_size: int, - transform: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None, + transform: DictTransform | None = None, ) -> None: super().__init__() self.positions = positions @@ -179,6 +179,7 @@ def _read_img_window( def __len__(self) -> int: return self._max_window + # TODO: refactor to a top level function def _stack_channels( self, sample_images: list[dict[str, Tensor]] | dict[str, Tensor], @@ -234,8 +235,9 @@ class MaskTestDataset(SlidingWindowDataset): :param ChannelMap channels: source and target channel names, e.g. ``{'source': 'Phase', 'target': ['Nuclei', 'Membrane']}`` :param int z_window_size: Z window size of the 2.5D U-Net, 1 for 2D - :param Callable[[dict[str, Tensor]], dict[str, Tensor]] transform: + :param DictTransform transform: a callable that transforms data, defaults to None + :param str | None ground_truth_masks: path to the ground truth masks """ def __init__( @@ -243,8 +245,8 @@ def __init__( positions: list[Position], channels: ChannelMap, z_window_size: int, - transform: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None, - ground_truth_masks: str = None, + transform: DictTransform | None = None, + ground_truth_masks: str | None = None, ) -> None: super().__init__(positions, channels, z_window_size, transform) self.masks = {} diff --git a/viscy/data/typing.py b/viscy/data/typing.py index d02463d8..898e05f6 100644 --- a/viscy/data/typing.py +++ b/viscy/data/typing.py @@ -1,10 +1,14 @@ from __future__ import annotations -from typing import TYPE_CHECKING, NamedTuple, Sequence, TypedDict, TypeVar +from typing import TYPE_CHECKING, Callable, NamedTuple, Sequence, TypedDict, TypeVar if TYPE_CHECKING: from torch import Tensor + +DictTransform = Callable[[dict[str, Tensor]], dict[str, Tensor]] + + T = TypeVar("T") OneOrSeq = T | Sequence[T] From 60ad63c166b3cecc5cca9a0e03105bdf0c5ec62d Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Thu, 25 Jul 2024 16:37:11 -0700 Subject: [PATCH 45/87] wip: triplet dataset --- viscy/data/triplet.py | 464 ++++++++++++++---------------------------- 1 file changed, 157 insertions(+), 307 deletions(-) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index 56ac3955..c42c4c22 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -1,13 +1,12 @@ import logging import random from pathlib import Path -from typing import Callable, Literal, Optional, Sequence, Union +from typing import Callable, Literal, Sequence import numpy as np import pandas as pd import torch from iohub.ngff import ImageArray, Plate, Position, open_ome_zarr -from lightning.pytorch import LightningDataModule from monai.data import set_track_meta from monai.data.utils import collate_meta_tensor from monai.transforms import ( @@ -24,341 +23,192 @@ from torch import Tensor from torch.utils.data import DataLoader, Dataset -from viscy.data.typing import ChannelMap, HCSStackIndex, NormMeta, Sample +from viscy.data.hcs import HCSDataModule +from viscy.data.typing import ChannelMap, DictTransform, HCSStackIndex, NormMeta, Sample _logger = logging.getLogger("lightning.pytorch") -# dataloader for organelle phenotyping -class ContrastiveDataset(Dataset): - def __init__( - self, - base_path, - channels, - x, - y, - timesteps_csv_path, - channel_names, - transform=None, - z_range=None, - ): - self.base_path = base_path - self.channels = channels - self.x = x - self.y = y - self.z_range = z_range - self.channel_names = channel_names - self.transform = get_transforms() - self.ds = self.open_zarr_store(self.base_path) - self.positions = list(self.ds.positions()) - self.timesteps_df = pd.read_csv(timesteps_csv_path) - self.channel_indices = [ - self.ds.channel_names.index(channel) for channel in self.channel_names - ] - _logger.debug(f"Initialized dataset with {len(self.positions)} positions.") - _logger.debug(f"Channel indices: {self.channel_indices}") - - def compute_statistics(self): - stats = { - channel: {"mean": 0, "sum_sq_diff": 0, "min": np.inf, "max": -np.inf} - for channel in self.channel_names - } - count = 0 - total_elements = 0 - - for idx in range(len(self.positions)): - position_path = self.positions[idx][0] - data = self.load_data(position_path) - for i, channel in enumerate(self.channel_names): - channel_data = data[i] - mean = np.mean(channel_data) - stats[channel]["mean"] += mean - stats[channel]["min"] = min(stats[channel]["min"], np.min(channel_data)) - stats[channel]["max"] = max(stats[channel]["max"], np.max(channel_data)) - stats[channel]["sum_sq_diff"] += np.sum((channel_data - mean) ** 2) - count += 1 - total_elements += np.prod(channel_data.shape) - - for channel in self.channel_names: - stats[channel]["mean"] /= count - stats[channel]["std"] = np.sqrt( - stats[channel]["sum_sq_diff"] / total_elements - ) - del stats[channel]["sum_sq_diff"] +def _scatter_channels(channel_names: list[str], patch: Tensor) -> dict[str, Tensor]: + return {name: data for name, data in zip(channel_names, patch)} - _logger.debug("done!") - return stats - def open_zarr_store(self, path, layout="hcs", mode="r"): - # _logger.debug(f"Opening Zarr store at {path} with layout '{layout}' and mode '{mode}'") - return open_ome_zarr(path, layout=layout, mode=mode) +def _gather_channels(patch_channels: dict[str, Tensor]) -> Tensor: + """ + :param dict[str, Tensor] patch_channels: dictionary of single-channel tensors + :return Tensor: Multi-channel tensor + """ + return torch.stack(list(patch_channels.values()), dim=1) - def __len__(self): - return len(self.positions) - - def __getitem__(self, idx): - anchor_position_path = self.positions[idx][0] - anchor_data = self.load_data(anchor_position_path) - anchor_data = self.normalize_data(anchor_data) - - positive_data = self.apply_channel_transforms(anchor_data) - positive_data = self.normalize_data(positive_data) - - # if self.transform: - # _logger.debug("Positive transformation applied") - - negative_idx = idx - while negative_idx == idx: - negative_idx = random.randint(0, self.__len__() - 1) - negative_position_path = self.positions[negative_idx][0] - negative_data = self.load_data(negative_position_path) - negative_data = self.normalize_data(negative_data) - - negative_data = self.apply_channel_transforms(negative_data) - negative_data = self.normalize_data(negative_data) - - # if self.transform: - # _logger.debug("Negative transformation applied") - - # _logger.debug("shapes of tensors") - # _logger.debug(torch.tensor(anchor_data).shape) - # _logger.debug(torch.tensor(positive_data).shape) - # _logger.debug(torch.tensor(negative_data).shape) - return ( - torch.tensor(anchor_data, dtype=torch.float32), - torch.tensor(positive_data, dtype=torch.float32), - torch.tensor(negative_data, dtype=torch.float32), - ) - - def load_data(self, position_path): - position = self.ds[position_path] - # _logger.debug(f"Loading data from position: {position_path}") - - zarr_array = position["0"][:] - # _logger.debug("Shape before:", zarr_array.shape) - data = self.restructure_data(zarr_array, position_path) - data = data[self.channel_indices, self.z_range[0] : self.z_range[1], :, :] - - # _logger.debug("shape after!") - # _logger.debug(data.shape) - return data - def restructure_data(self, data, position_path): - # Extract row, column, fov, and cell_id from position_path - parts = position_path.split("/") - row = parts[0] - column = parts[1] - fov_cell = parts[2] - - fov = int(fov_cell.split("fov")[1].split("cell")[0]) - cell_id = int(fov_cell.split("cell")[1]) +def _transform_channel_wise( + transform: DictTransform, channel_names: list[str], patch: Tensor +) -> Tensor: + return _gather_channels(transform(_scatter_channels(channel_names, patch))) - extracted_combined = f"{row}/{column}/fov{fov}cell{cell_id}" - matched_rows = self.timesteps_df[ - self.timesteps_df.apply( - lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", - axis=1, - ) - == extracted_combined +class TripletDataset(Dataset): + def __init__( + self, + positions: list[Position], + tracks_tables: list[pd.DataFrame], + channel_names: list[str], + yx_patch_size: tuple[int, int], + z_range: slice | None = None, + anchor_transform: DictTransform | None = None, + positive_transform: DictTransform | None = None, + negative_transform: DictTransform | None = None, + fit: bool = True, + ) -> None: + self.positions = positions + self.channel_names = channel_names + self.channel_indices = [ + positions[0].get_channel_index(ch) for ch in channel_names ] - - if matched_rows.empty: - raise ValueError( - f"No matching entry found for position path: {position_path}" + self.z_range = z_range + self.anchor_transform = anchor_transform + self.positive_transform = positive_transform + self.negative_transform = negative_transform + self.fit = fit + self.yx_patch_size = yx_patch_size + self.tracks = self._filter_tracks(tracks_tables) + + def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: + filtered_tracks = [] + y_exclude, x_exclude = (yx_patch_size[0] // 2, yx_patch_size[1] // 2) + for pos, tracks in zip(self.positions, tracks_tables, strict=True): + tracks["position"] = pos + tracks["fov_name"] = pos.zgroup.name + tracks["global_track_id"] = tracks["fov_name"].str.cat(tracks["track_id"]) + image: ImageArray = pos["0"] + y_range = (y_exclude, image.height - y_exclude) + x_range = (x_exclude, image.width - x_exclude) + filtered_tracks.append( + tracks[ + tracks["y"].between(*y_range, inclusive="neither") + & tracks["x"].between(*x_range, inclusive="neither") + ] ) + return pd.concat(filtered_tracks).reset_index(drop=True) - start_time = matched_rows["Start Time"].values[0] - end_time = matched_rows["End Time"].values[0] - - random_timestep = np.random.randint(start_time, end_time) - - reshaped_data = data[random_timestep] - return reshaped_data - - def normalize_data(self, data): - normalized_data = np.empty_like(data) - for i in range(data.shape[0]): # iterate over each channel - channel_data = data[i] - mean = np.mean(channel_data) - std = np.std(channel_data) - normalized_data[i] = (channel_data - mean) / (std + 1e-6) - return normalized_data + def __len__(self): + return len(self.tracks) - def apply_channel_transforms(self, data): - transformed_data = np.empty_like(data) - for i, channel_name in enumerate(self.channel_names): - channel_data = data[i] - transform = self.transform[channel_name] - transformed_data[i] = transform({"image": channel_data})["image"] - # _logger.debug(f"transformed {channel_name}") - return transformed_data - - -def get_transforms(): - rfp_transforms = Compose( - [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.75, 1.25)), - RandAffined( - keys=["image"], - prob=0.5, - rotate_range=(0.1, 0.1), - shear_range=(0.1, 0.1), - scale_range=(0.1, 0.1), - ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.1), - RandGaussianSmoothd( - keys=["image"], - prob=0.5, - sigma_x=(0.1, 0.3), - sigma_y=(0.1, 0.3), - sigma_z=(0.1, 0.3), - ), - RandScaleIntensityd(keys=["image"], factors=(0.85, 1.15), prob=0.5), + def _sample_negative(self, anchor_row: pd.Series) -> pd.Series: + candidates: pd.DataFrame = self.tracks[ + (self.tracks["global_track_id"] != anchor_row["global_track_id"]) ] - ) - - phase_transforms = Compose( - [ - RandAdjustContrastd(keys=["image"], prob=0.5, gamma=(0.97, 1.03)), - RandAffined( - keys=["image"], - prob=0.5, - rotate_range=(0.05, 0.05), - shear_range=(0.05, 0.05), - scale_range=(0.05, 0.05), - ), - RandGaussianNoised(keys=["image"], prob=0.5, mean=0.0, std=0.005), - RandGaussianSmoothd( - keys=["image"], - prob=0.5, - sigma_x=(0.03, 0.05), - sigma_y=(0.03, 0.05), - sigma_z=(0.03, 0.05), - ), - RandScaleIntensityd(keys=["image"], factors=(0.97, 1.03), prob=0.5), + # NOTE: Random sampling + # reproducibility relies on setting a global seed for numpy + return candidates.sample(n=1).iloc[0] + + def _slice_patch(self, track_row: pd.Series) -> Tensor: + image: ImageArray = track_row["position"]["0"] + t = track_row["t"] + y_center = track_row["y"] + x_center = track_row["x"] + y_half, x_half = (d // 2 for d in self.yx_patch_size) + patch = image.oindex[ + slice(t, t + 1), + [int(i) for i in self.channel_indices], + self.z_range, + slice(y_center - y_half, y_center + y_half), + slice(x_center - x_half, x_center + x_half), ] - ) - - return {"RFP": rfp_transforms, "Phase3D": phase_transforms} + return torch.from_numpy(patch) + + def __getitem__(self, index: int) -> tuple[Tensor, ...]: + anchor_row = self.tracks.iloc[index] + anchor_patch = self._slice_patch(anchor_row) + if self.fit: + positive_patch = anchor_patch.clone() + if self.positive_transform: + positive_patch = _transform_channel_wise( + transform=self.positive_transform, + channel_names=self.channel_names, + patch=positive_patch, + ) + negative_row = self._sample_negative(anchor_row) + negative_patch = self._slice_patch(negative_row) + if self.negative_transform: + negative_patch = _transform_channel_wise( + transform=self.negative_transform, + channel_names=self.channel_names, + patch=negative_patch, + ) + if self.anchor_transform: + anchor_patch = _transform_channel_wise( + transform=self.anchor_transform, + channel_names=self.channel_names, + patch=anchor_patch, + ) + if self.fit: + return (anchor_patch, positive_patch, negative_patch) + else: + return (anchor_patch,) -class ContrastiveDataModule(LightningDataModule): +class TripletDataModule(HCSDataModule): def __init__( self, - base_path: str, - channels: int, - x: int, - y: int, - timesteps_csv_path: str, - channel_names: list, - transform=None, - predict_base_path: str = None, - train_split_ratio: float = 0.64, - val_split_ratio: float = 0.16, - batch_size: int = 4, + data_path: str, + tracks_path: str, + source_channel: str | Sequence[str], + split_ratio: float = 0.8, + batch_size: int = 16, num_workers: int = 8, z_range: tuple[int, int] = None, + yx_patch_size: tuple[int, int] = (256, 256), + normalizations: list[MapTransform] = [], + augmentations: list[MapTransform] = [], + caching: bool = False, ): - super().__init__() - self.base_path = Path(base_path) - self.channels = channels - self.x = x - self.y = y - self.timesteps_csv_path = timesteps_csv_path - self.channel_names = channel_names - self.transform = get_transforms() - self.predict_base_path = Path(predict_base_path) if predict_base_path else None - self.train_split_ratio = train_split_ratio - self.val_split_ratio = val_split_ratio - self.batch_size = batch_size - self.num_workers = num_workers + super().__init__( + data_path=data_path, + source_channel=source_channel, + target_channel="", + z_window_size=z_range[1] - z_range[0], + split_ratio=split_ratio, + batch_size=batch_size, + num_workers=num_workers, + architecture="UNeXt2", + yx_patch_size=yx_patch_size, + normalizations=normalizations, + augmentations=augmentations, + caching=caching, + ) self.z_range = z_range - self.train_dataset = None - self.val_dataset = None - self.test_dataset = None - self.predict_dataset = None - - def setup(self, stage: str = None): - if stage == "fit": - dataset = ContrastiveDataset( - self.base_path, - self.channels, - self.x, - self.y, - self.timesteps_csv_path, - channel_names=self.channel_names, - transform=self.transform, - z_range=self.z_range, - ) - - train_size = int(len(dataset) * self.train_split_ratio) - val_size = int(len(dataset) * self.val_split_ratio) - test_size = len(dataset) - train_size - val_size - self.train_dataset, self.val_dataset, self.test_dataset = ( - torch.utils.data.random_split( - dataset, [train_size, val_size, test_size] - ) - ) - - # setup prediction dataset - if stage == "predict" and self.predict_base_path: - _logger.debug("setting up!") - self.predict_dataset = PredictDataset( - self.predict_base_path, - self.channels, - self.x, - self.y, - timesteps_csv_path=self.timesteps_csv_path, - channel_names=self.channel_names, - z_range=self.z_range, - ) - - def train_dataloader(self): - return DataLoader( - self.train_dataset, - batch_size=self.batch_size, - shuffle=True, - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, + def _setup_fit(self, dataset_settings: NormMeta): + dataset = ContrastiveDataset( + self.base_path, + self.channels, + self.x, + self.y, + self.timesteps_csv_path, + channel_names=self.channel_names, + transform=self.transform, + z_range=self.z_range, ) - def val_dataloader(self): - return DataLoader( - self.val_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, - ) + train_size = int(len(dataset) * self.train_split_ratio) + val_size = int(len(dataset) * self.val_split_ratio) + test_size = len(dataset) - train_size - val_size - def test_dataloader(self): - return DataLoader( - self.test_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, + self.train_dataset, self.val_dataset, self.test_dataset = ( + torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) ) - def predict_dataloader(self): - _logger.debug("running predict DataLoader!") - if self.predict_dataset is None: - raise ValueError( - "Predict dataset not set up. Call setup(stage='predict') first." - ) - - return DataLoader( - self.predict_dataset, - batch_size=self.batch_size, - shuffle=False, # False shuffle for prediction - num_workers=self.num_workers, - prefetch_factor=2, - persistent_workers=True, + def _setup_predict(self, dataset_settings: NormMeta): + # setup prediction dataset + self.predict_dataset = PredictDataset( + self.predict_base_path, + self.channels, + self.x, + self.y, + timesteps_csv_path=self.timesteps_csv_path, + channel_names=self.channel_names, + z_range=self.z_range, ) From 0cb8df0210db1db6d6a4b2abf1bb6fd140578df6 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Fri, 26 Jul 2024 09:50:12 -0700 Subject: [PATCH 46/87] avoid forward ref this might increase code analysis time a tiny bit but should not have any effect at runtime --- viscy/data/typing.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/viscy/data/typing.py b/viscy/data/typing.py index 898e05f6..0f246d1f 100644 --- a/viscy/data/typing.py +++ b/viscy/data/typing.py @@ -1,10 +1,6 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING, Callable, NamedTuple, Sequence, TypedDict, TypeVar - -if TYPE_CHECKING: - from torch import Tensor +from typing import Callable, NamedTuple, Sequence, TypedDict, TypeVar +from torch import Tensor DictTransform = Callable[[dict[str, Tensor]], dict[str, Tensor]] From 2ac0eef72684898f9ec4497c2ae1c0b39960ed54 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Fri, 26 Jul 2024 09:50:34 -0700 Subject: [PATCH 47/87] check that z range is valid and fix indexing --- viscy/data/triplet.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index c42c4c22..8b28b75b 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -54,7 +54,7 @@ def __init__( tracks_tables: list[pd.DataFrame], channel_names: list[str], yx_patch_size: tuple[int, int], - z_range: slice | None = None, + z_range: slice, anchor_transform: DictTransform | None = None, positive_transform: DictTransform | None = None, negative_transform: DictTransform | None = None, @@ -75,12 +75,18 @@ def __init__( def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: filtered_tracks = [] - y_exclude, x_exclude = (yx_patch_size[0] // 2, yx_patch_size[1] // 2) + y_exclude, x_exclude = (self.yx_patch_size[0] // 2, self.yx_patch_size[1] // 2) for pos, tracks in zip(self.positions, tracks_tables, strict=True): - tracks["position"] = pos + tracks["position"] = [pos] * len(tracks) tracks["fov_name"] = pos.zgroup.name - tracks["global_track_id"] = tracks["fov_name"].str.cat(tracks["track_id"]) + tracks["global_track_id"] = tracks["fov_name"].str.cat( + tracks["track_id"].astype(str), sep="_" + ) image: ImageArray = pos["0"] + if self.z_range.stop > image.slices: + raise ValueError( + f"Z range {self.z_range} exceeds image with Z={image.slices}" + ) y_range = (y_exclude, image.height - y_exclude) x_range = (x_exclude, image.width - x_exclude) filtered_tracks.append( From a2bc2a5c6e8e800b31c27c44f89c8108d09061f0 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Fri, 26 Jul 2024 09:55:19 -0700 Subject: [PATCH 48/87] clean up and explain random sampling --- viscy/data/triplet.py | 27 ++++++++------------------- 1 file changed, 8 insertions(+), 19 deletions(-) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index 8b28b75b..73bd539e 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -1,30 +1,16 @@ import logging -import random -from pathlib import Path -from typing import Callable, Literal, Sequence +from typing import Sequence import numpy as np import pandas as pd import torch -from iohub.ngff import ImageArray, Plate, Position, open_ome_zarr -from monai.data import set_track_meta -from monai.data.utils import collate_meta_tensor -from monai.transforms import ( - CenterSpatialCropd, - Compose, - MapTransform, - MultiSampleTrait, - RandAdjustContrastd, - RandAffined, - RandGaussianNoised, - RandGaussianSmoothd, - RandScaleIntensityd, -) +from iohub.ngff import ImageArray, Position, open_ome_zarr +from monai.transforms import MapTransform from torch import Tensor -from torch.utils.data import DataLoader, Dataset +from torch.utils.data import Dataset from viscy.data.hcs import HCSDataModule -from viscy.data.typing import ChannelMap, DictTransform, HCSStackIndex, NormMeta, Sample +from viscy.data.typing import DictTransform, NormMeta _logger = logging.getLogger("lightning.pytorch") @@ -105,6 +91,9 @@ def _sample_negative(self, anchor_row: pd.Series) -> pd.Series: (self.tracks["global_track_id"] != anchor_row["global_track_id"]) ] # NOTE: Random sampling + # this is to avoid combinatorial length growth at fitting time + # since each cell can pair with any other cell + # (3e4 instances will make 1e9 pairs) # reproducibility relies on setting a global seed for numpy return candidates.sample(n=1).iloc[0] From b89303462c0e381df7a5d5b1a3b636587d1ffe4d Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Fri, 26 Jul 2024 13:24:19 -0700 Subject: [PATCH 49/87] sample dict instead of tuple and include track index --- viscy/data/triplet.py | 116 ++++-------------------------------------- 1 file changed, 10 insertions(+), 106 deletions(-) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index 73bd539e..8410b1c1 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -1,7 +1,6 @@ import logging from typing import Sequence -import numpy as np import pandas as pd import torch from iohub.ngff import ImageArray, Position, open_ome_zarr @@ -112,7 +111,7 @@ def _slice_patch(self, track_row: pd.Series) -> Tensor: ] return torch.from_numpy(patch) - def __getitem__(self, index: int) -> tuple[Tensor, ...]: + def __getitem__(self, index: int) -> dict[str, Tensor]: anchor_row = self.tracks.iloc[index] anchor_patch = self._slice_patch(anchor_row) if self.fit: @@ -137,10 +136,15 @@ def __getitem__(self, index: int) -> tuple[Tensor, ...]: channel_names=self.channel_names, patch=anchor_patch, ) + sample = {"anchor": anchor_patch} if self.fit: - return (anchor_patch, positive_patch, negative_patch) - else: - return (anchor_patch,) + sample.update( + { + "positive": positive_patch, + "negative": negative_patch, + } + ) + return sample class TripletDataModule(HCSDataModule): @@ -149,10 +153,10 @@ def __init__( data_path: str, tracks_path: str, source_channel: str | Sequence[str], + z_range: tuple[int, int], split_ratio: float = 0.8, batch_size: int = 16, num_workers: int = 8, - z_range: tuple[int, int] = None, yx_patch_size: tuple[int, int] = (256, 256), normalizations: list[MapTransform] = [], augmentations: list[MapTransform] = [], @@ -205,103 +209,3 @@ def _setup_predict(self, dataset_settings: NormMeta): channel_names=self.channel_names, z_range=self.z_range, ) - - -class PredictDataset(Dataset): - def __init__( - self, - base_path, - channels, - x, - y, - timesteps_csv_path, - channel_names, - z_range=None, - ): - self.base_path = base_path - self.channels = channels - self.x = x - self.y = y - self.z_range = z_range - self.channel_names = channel_names - self.ds = self.open_zarr_store(self.base_path) - self.timesteps_csv_path = timesteps_csv_path - self.timesteps_df = pd.read_csv(timesteps_csv_path) - self.positions = list(self.ds.positions()) - self.channel_indices = [ - self.ds.channel_names.index(channel) for channel in self.channel_names - ] - _logger.debug("channel indices!") - _logger.debug(self.channel_indices) - _logger.debug( - f"Initialized predict dataset with {len(self.positions)} positions." - ) - - def open_zarr_store(self, path, layout="hcs", mode="r"): - return open_ome_zarr(path, layout=layout, mode=mode) - - # def get_positions_from_csv(self): - # positions = [] - # #self.timesteps_df = pd.read_csv(self.timesteps_csv_path) - # for idx, row in self.timesteps_df.iterrows(): - # position_path = f"{row['Row']}/{row['Column']}/fov{row['FOV']}cell{row['Cell ID']}" - # positions.append((position_path, row['Random Timestep'])) - # #_logger.debug(positions) - # return positions - - def __len__(self): - return len(self.positions) - - def __getitem__(self, idx): - position_path = self.positions[idx][0] - # _logger.debug(f"Position path: {position_path}") - data = self.load_data(position_path) - data = self.normalize_data(data) - - return torch.tensor(data, dtype=torch.float32), (position_path) - - # double check printing order - def load_data(self, position_path): - position = self.ds[position_path] - # _logger.debug(f"Loading data for position path: {position_path}") - zarr_array = position["0"][:] - - parts = position_path.split("/") - row = parts[0] - column = parts[1] - fov_cell = parts[2] - fov = int(fov_cell.split("fov")[1].split("cell")[0]) - cell_id = int(fov_cell.split("cell")[1]) - - combined_id = f"{row}/{column}/fov{fov}cell{cell_id}" - matched_rows = self.timesteps_df[ - self.timesteps_df.apply( - lambda x: f"{x['Row']}/{x['Column']}/fov{x['FOV']}cell{x['Cell ID']}", - axis=1, - ) - == combined_id - ] - - if matched_rows.empty: - raise ValueError( - f"No matching entry found for position path: {position_path}" - ) - - random_timestep = matched_rows["Random Timestep"].values[0] - data = zarr_array[ - random_timestep, - self.channel_indices, - self.z_range[0] : self.z_range[1], - :, - :, - ] - return data - - def normalize_data(self, data): - normalized_data = np.empty_like(data) - for i in range(data.shape[0]): # iterate over each channel - channel_data = data[i] - mean = np.mean(channel_data) - std = np.std(channel_data) - normalized_data[i] = (channel_data - mean) / (std + 1e-6) - return normalized_data From da4fe266cb404690986ee7ba9678d646414e4981 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 29 Jul 2024 11:43:24 -0700 Subject: [PATCH 50/87] take out generic HCS methods for reuse --- viscy/data/hcs.py | 68 ++++++++++++++++++++++++++++++----------------- 1 file changed, 44 insertions(+), 24 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index f7e0cb20..5f915d3a 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -341,6 +341,10 @@ def cache_path(self): self.data_path.name, ) + @property + def maybe_cached_data_path(self): + return self.cache_path if self.caching else self.data_path + def _data_log_path(self) -> Path: log_dir = Path.cwd() if self.trainer: @@ -379,9 +383,15 @@ def prepare_data(self): f"Skipped {skipped} items when caching. Check debug log for details." ) + @property + def _base_dataset_settings(self) -> dict[str, dict[str, list[str]] | int]: + return { + "channels": {"source": self.source_channel}, + "z_window_size": self.z_window_size, + } + def setup(self, stage: Literal["fit", "validate", "test", "predict"]): - channels = {"source": self.source_channel} - dataset_settings = dict(channels=channels, z_window_size=self.z_window_size) + dataset_settings = self._base_dataset_settings if stage in ("fit", "validate"): self._setup_fit(dataset_settings) elif stage == "test": @@ -391,25 +401,22 @@ def setup(self, stage: Literal["fit", "validate", "test", "predict"]): else: raise NotImplementedError(f"{stage} stage") + def _set_fit_global_state(self, num_positions: int) -> torch.Tensor: + # disable metadata tracking in MONAI for performance + set_track_meta(False) + # shuffle positions, randomness is handled globally + return torch.randperm(num_positions) + def _setup_fit(self, dataset_settings: dict): """Set up the training and validation datasets.""" - # Setup the transformations - # TODO: These have a fixed order for now... (normalization->augmentation->fit_transform) - fit_transform = self._fit_transform() - train_transform = Compose( - self.normalizations + self._train_transform() + fit_transform - ) - val_transform = Compose(self.normalizations + fit_transform) - + train_transform, val_transform = self._fit_transform() dataset_settings["channels"]["target"] = self.target_channel - data_path = self.cache_path if self.caching else self.data_path + data_path = self.maybe_cached_data_path plate = open_ome_zarr(data_path, mode="r") - # disable metadata tracking in MONAI for performance - set_track_meta(False) # shuffle positions, randomness is handled globally positions = [pos for _, pos in plate.positions()] - shuffled_indices = torch.randperm(len(positions)) + shuffled_indices = self._set_fit_global_state(len(positions)) positions = list(positions[i] for i in shuffled_indices) num_train_fovs = int(len(positions) * self.split_ratio) # training set needs to sample more Z range for augmentation @@ -439,7 +446,7 @@ def _setup_test(self, dataset_settings: dict): _logger.warning(f"Ignoring batch size {self.batch_size} in test stage.") dataset_settings["channels"]["target"] = self.target_channel - data_path = self.cache_path if self.caching else self.data_path + data_path = self.maybe_cached_data_path plate = open_ome_zarr(data_path, mode="r") test_transform = Compose(self.normalizations) if self.ground_truth_masks: @@ -456,15 +463,13 @@ def _setup_test(self, dataset_settings: dict): **dataset_settings, ) - def _setup_predict( - self, - dataset_settings: dict, - ): - """Set up the predict stage.""" + def _set_predict_global_state(self) -> None: # track metadata for inverting transform set_track_meta(True) if self.caching: _logger.warning("Ignoring caching config in 'predict' stage.") + + def _positions_maybe_single(self) -> list[Position]: dataset: Plate | Position = open_ome_zarr(self.data_path, mode="r") if isinstance(dataset, Position): try: @@ -478,9 +483,17 @@ def _setup_predict( positions = [plate[fov_name]] elif isinstance(dataset, Plate): positions = [p for _, p in dataset.positions()] + return positions + + def _setup_predict( + self, + dataset_settings: dict, + ): + """Set up the predict stage.""" + self._set_predict_global_state() predict_transform = Compose(self.normalizations) self.predict_dataset = SlidingWindowDataset( - positions=positions, + positions=self._positions_maybe_single(), transform=predict_transform, **dataset_settings, ) @@ -538,9 +551,11 @@ def predict_dataloader(self): shuffle=False, ) - def _fit_transform(self): - """Deterministic center crop as the last step of training and validation.""" - return [ + def _fit_transform(self) -> tuple[Compose, Compose]: + """(normalization -> maybe augmentation -> center crop) + Deterministic center crop as the last step of training and validation.""" + # TODO: These have a fixed order for now... () + final_crop = [ CenterSpatialCropd( keys=self.source_channel + self.target_channel, roi_size=( @@ -550,6 +565,11 @@ def _fit_transform(self): ), ) ] + train_transform = Compose( + self.normalizations + self._train_transform() + final_crop + ) + val_transform = Compose(self.normalizations + final_crop) + return train_transform, val_transform def _train_transform(self) -> list[Callable]: """Setup training augmentations: check input values, From ac71bddd93a8f0f88ef6ccd72464510f0450b58c Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 29 Jul 2024 11:43:47 -0700 Subject: [PATCH 51/87] implement TripletDataModule --- viscy/data/triplet.py | 108 ++++++++++++++++++++++++++---------------- 1 file changed, 68 insertions(+), 40 deletions(-) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index 8410b1c1..ac2672a3 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -1,10 +1,11 @@ import logging +from pathlib import Path from typing import Sequence import pandas as pd import torch from iohub.ngff import ImageArray, Position, open_ome_zarr -from monai.transforms import MapTransform +from monai.transforms import Compose, MapTransform from torch import Tensor from torch.utils.data import Dataset @@ -15,7 +16,7 @@ def _scatter_channels(channel_names: list[str], patch: Tensor) -> dict[str, Tensor]: - return {name: data for name, data in zip(channel_names, patch)} + return {name: data[None] for name, data in zip(channel_names, patch)} def _gather_channels(patch_channels: dict[str, Tensor]) -> Tensor: @@ -23,7 +24,7 @@ def _gather_channels(patch_channels: dict[str, Tensor]) -> Tensor: :param dict[str, Tensor] patch_channels: dictionary of single-channel tensors :return Tensor: Multi-channel tensor """ - return torch.stack(list(patch_channels.values()), dim=1) + return torch.cat(list(patch_channels.values()), dim=0) def _transform_channel_wise( @@ -38,7 +39,7 @@ def __init__( positions: list[Position], tracks_tables: list[pd.DataFrame], channel_names: list[str], - yx_patch_size: tuple[int, int], + initial_yx_patch_size: tuple[int, int], z_range: slice, anchor_transform: DictTransform | None = None, positive_transform: DictTransform | None = None, @@ -55,7 +56,7 @@ def __init__( self.positive_transform = positive_transform self.negative_transform = negative_transform self.fit = fit - self.yx_patch_size = yx_patch_size + self.yx_patch_size = initial_yx_patch_size self.tracks = self._filter_tracks(tracks_tables) def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: @@ -98,12 +99,12 @@ def _sample_negative(self, anchor_row: pd.Series) -> pd.Series: def _slice_patch(self, track_row: pd.Series) -> Tensor: image: ImageArray = track_row["position"]["0"] - t = track_row["t"] + time = track_row["t"] y_center = track_row["y"] x_center = track_row["x"] y_half, x_half = (d // 2 for d in self.yx_patch_size) patch = image.oindex[ - slice(t, t + 1), + time, [int(i) for i in self.channel_indices], self.z_range, slice(y_center - y_half, y_center + y_half), @@ -111,7 +112,7 @@ def _slice_patch(self, track_row: pd.Series) -> Tensor: ] return torch.from_numpy(patch) - def __getitem__(self, index: int) -> dict[str, Tensor]: + def __getitem__(self, index: int) -> dict[str, Tensor | dict[str, int | str]]: anchor_row = self.tracks.iloc[index] anchor_patch = self._slice_patch(anchor_row) if self.fit: @@ -136,7 +137,10 @@ def __getitem__(self, index: int) -> dict[str, Tensor]: channel_names=self.channel_names, patch=anchor_patch, ) - sample = {"anchor": anchor_patch} + sample = { + "anchor": anchor_patch, + "index": anchor_row[["fov_name", "id"]].to_dict(), + } if self.fit: sample.update( { @@ -154,10 +158,11 @@ def __init__( tracks_path: str, source_channel: str | Sequence[str], z_range: tuple[int, int], + initial_yx_patch_size: tuple[int, int] = (384, 384), + final_yx_patch_size: tuple[int, int] = (256, 256), split_ratio: float = 0.8, batch_size: int = 16, num_workers: int = 8, - yx_patch_size: tuple[int, int] = (256, 256), normalizations: list[MapTransform] = [], augmentations: list[MapTransform] = [], caching: bool = False, @@ -165,47 +170,70 @@ def __init__( super().__init__( data_path=data_path, source_channel=source_channel, - target_channel="", + target_channel=[], z_window_size=z_range[1] - z_range[0], split_ratio=split_ratio, batch_size=batch_size, num_workers=num_workers, architecture="UNeXt2", - yx_patch_size=yx_patch_size, + yx_patch_size=final_yx_patch_size, normalizations=normalizations, augmentations=augmentations, caching=caching, ) - self.z_range = z_range + self.z_range = slice(*z_range) + self.tracks_path = Path(tracks_path) + self.initial_yx_patch_size = initial_yx_patch_size - def _setup_fit(self, dataset_settings: NormMeta): - dataset = ContrastiveDataset( - self.base_path, - self.channels, - self.x, - self.y, - self.timesteps_csv_path, - channel_names=self.channel_names, - transform=self.transform, - z_range=self.z_range, + def _align_tracks_tables_with_positions( + self, + ) -> tuple[list[Position], list[pd.DataFrame]]: + positions = [] + tracks_tables = [] + images_plate = open_ome_zarr(self.data_path) + for fov_name, _ in open_ome_zarr(self.tracks_path).positions(): + positions.append(images_plate[fov_name]) + tracks_df = pd.read_csv( + next((self.tracks_path / fov_name).glob("*.csv")) + ).astype(int) + tracks_tables.append(tracks_df) + return positions, tracks_tables + + @property + def _base_dataset_settings(self) -> dict: + return { + "channel_names": self.source_channel, + "z_range": self.z_range, + } + + def _setup_fit(self, dataset_settings: dict): + augment_transform, no_aug_transform = self._fit_transform() + positions, tracks_tables = self._align_tracks_tables_with_positions() + shuffled_indices = self._set_fit_global_state(len(positions)) + positions = [positions[i] for i in shuffled_indices] + tracks_tables = [tracks_tables[i] for i in shuffled_indices] + self.train_dataset = TripletDataset( + positions=positions, + tracks_tables=tracks_tables, + initial_yx_patch_size=self.yx_patch_size, + anchor_transform=no_aug_transform, + positive_transform=augment_transform, + negative_transform=augment_transform, + fit=True, + **dataset_settings, ) - train_size = int(len(dataset) * self.train_split_ratio) - val_size = int(len(dataset) * self.val_split_ratio) - test_size = len(dataset) - train_size - val_size - - self.train_dataset, self.val_dataset, self.test_dataset = ( - torch.utils.data.random_split(dataset, [train_size, val_size, test_size]) + def _setup_predict(self, dataset_settings: dict): + self._set_predict_global_state() + positions, tracks_tables = self._align_tracks_tables_with_positions() + self.predict_dataset = TripletDataset( + positions=positions, + tracks_tables=tracks_tables, + initial_yx_patch_size=self.yx_patch_size, + anchor_transform=Compose(self.normalizations), + fit=False, + **dataset_settings, ) - def _setup_predict(self, dataset_settings: NormMeta): - # setup prediction dataset - self.predict_dataset = PredictDataset( - self.predict_base_path, - self.channels, - self.x, - self.y, - timesteps_csv_path=self.timesteps_csv_path, - channel_names=self.channel_names, - z_range=self.z_range, - ) + def _setup_test(self, *args, **kwargs): + raise NotImplementedError("Self-supervised model does not support testing") From 0e4165823b7cffd1a877874f3e067833d520fe09 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 29 Jul 2024 13:16:46 -0700 Subject: [PATCH 52/87] use new batch type in engine --- viscy/light/engine.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/viscy/light/engine.py b/viscy/light/engine.py index bb4c89da..8983daf4 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -599,10 +599,10 @@ def __init__( # required to log the graph. self.example_input_array = torch.rand( - 1, # batch size + 1, in_channels, in_stack_depth, - *example_input_yx_shape, + *example_input_yx_shape, # batch size ) self.images_to_log = [] @@ -708,12 +708,13 @@ def log_images(self, anchor, positive, negative, epoch, step_name): def training_step( self, - batch: tuple[Tensor], + batch: dict[str, Tensor], batch_idx: int, ) -> Tensor: """Training step of the model.""" - - anchor, pos_img, neg_img = batch + anchor = batch["anchor"] + pos_img = batch["positive"] + neg_img = batch["negative"] emb_anchor = self.encoder(anchor) emb_pos = self.encoder(pos_img) emb_neg = self.encoder(neg_img) @@ -771,12 +772,13 @@ def on_train_epoch_end(self) -> None: def validation_step( self, - batch: tuple[Tensor], + batch: dict[str, Tensor], batch_idx: int, ) -> Tensor: """Validation step of the model.""" - - anchor, pos_img, neg_img = batch + anchor = batch["anchor"] + pos_img = batch["positive"] + neg_img = batch["negative"] emb_anchor = self.encoder(anchor) emb_pos = self.encoder(pos_img) emb_neg = self.encoder(neg_img) @@ -833,12 +835,13 @@ def on_validation_epoch_end(self) -> None: def test_step( self, - batch: tuple[Tensor], + batch: dict[str, Tensor], batch_idx: int, ) -> Tensor: """Test step of the model.""" - - anchor, pos_img, neg_img = batch + anchor = batch["anchor"] + pos_img = batch["positive"] + neg_img = batch["negative"] emb_anchor = self.encoder(anchor) emb_pos = self.encoder(pos_img) emb_neg = self.encoder(neg_img) From 673eb92e962cc57da9ab6cf65f0fad2b39ea4fae Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 29 Jul 2024 13:38:43 -0700 Subject: [PATCH 53/87] better typing --- viscy/data/triplet.py | 4 ++-- viscy/data/typing.py | 26 +++++++++++++++++++++----- viscy/light/engine.py | 16 ++++++++-------- 3 files changed, 31 insertions(+), 15 deletions(-) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index ac2672a3..d4da91f0 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -10,7 +10,7 @@ from torch.utils.data import Dataset from viscy.data.hcs import HCSDataModule -from viscy.data.typing import DictTransform, NormMeta +from viscy.data.typing import DictTransform, NormMeta, TripletSample _logger = logging.getLogger("lightning.pytorch") @@ -112,7 +112,7 @@ def _slice_patch(self, track_row: pd.Series) -> Tensor: ] return torch.from_numpy(patch) - def __getitem__(self, index: int) -> dict[str, Tensor | dict[str, int | str]]: + def __getitem__(self, index: int) -> TripletSample: anchor_row = self.tracks.iloc[index] anchor_patch = self._slice_patch(anchor_row) if self.fit: diff --git a/viscy/data/typing.py b/viscy/data/typing.py index 0f246d1f..4d9469f4 100644 --- a/viscy/data/typing.py +++ b/viscy/data/typing.py @@ -2,6 +2,9 @@ from torch import Tensor +# TODO: use typing.NotRequired when upgrading to Python 3.11 +from typing_extensions import NotRequired + DictTransform = Callable[[dict[str, Tensor]], dict[str, Tensor]] @@ -50,14 +53,27 @@ class Sample(TypedDict, total=False): norm_meta: NormMeta -class _ChannelMap(TypedDict): +class ChannelMap(TypedDict): """Source channel names.""" source: OneOrSeq[str] + target: NotRequired[OneOrSeq[str]] + + +class TrackingIndex(TypedDict): + """Tracking index extracted from ultrack result + Potentially collated by the dataloader""" + fov_name: OneOrSeq[str] + id: OneOrSeq[int] -class ChannelMap(_ChannelMap, total=False): - """Source and target channel names.""" - # TODO: use typing.NotRequired when upgrading to Python 3.11 - target: OneOrSeq[str] +class TripletSample(TypedDict): + """ + Triplet sample type for mini-batches. + """ + + index: TrackingIndex + anchor: Tensor + positive: NotRequired[Tensor] + negative: NotRequired[Tensor] diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 8983daf4..86455ad1 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -30,7 +30,7 @@ structural_similarity_index_measure, ) -from viscy.data.hcs import Sample +from viscy.data.typing import Sample, TripletSample from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d from viscy.representation.contrastive import ContrastiveEncoder from viscy.unet.networks.fcmae import FullyConvolutionalMAE @@ -708,7 +708,7 @@ def log_images(self, anchor, positive, negative, epoch, step_name): def training_step( self, - batch: dict[str, Tensor], + batch: TripletSample, batch_idx: int, ) -> Tensor: """Training step of the model.""" @@ -772,7 +772,7 @@ def on_train_epoch_end(self) -> None: def validation_step( self, - batch: dict[str, Tensor], + batch: TripletSample, batch_idx: int, ) -> Tensor: """Validation step of the model.""" @@ -835,7 +835,7 @@ def on_validation_epoch_end(self) -> None: def test_step( self, - batch: dict[str, Tensor], + batch: TripletSample, batch_idx: int, ) -> Tensor: """Test step of the model.""" @@ -911,12 +911,12 @@ def aggregate_metrics(self, metrics, phase): ) / len(metrics) return avg_metrics - def predict_step(self, batch, batch_idx, dataloader_idx=0): + def predict_step(self, batch: TripletSample, batch_idx, dataloader_idx=0): print("running predict step!") """Prediction step for extracting embeddings.""" - x, position_info = batch - features, projections = self.encoder(x) - self.processed_order.extend(position_info) + features, projections = self.encoder(batch["anchor"]) + # FIXME: fix in prediction writer + self.processed_order.extend(batch["index"]) return features, projections # already saved, not needed again From 52df395ee1e96ad59e7333d60d293684954c9287 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Mon, 29 Jul 2024 13:59:56 -0700 Subject: [PATCH 54/87] read normalization metadata --- viscy/data/triplet.py | 34 ++++++++++++++++++++++++---------- viscy/data/typing.py | 2 +- 2 files changed, 25 insertions(+), 11 deletions(-) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index d4da91f0..c44eb748 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -9,14 +9,19 @@ from torch import Tensor from torch.utils.data import Dataset -from viscy.data.hcs import HCSDataModule +from viscy.data.hcs import HCSDataModule, _read_norm_meta from viscy.data.typing import DictTransform, NormMeta, TripletSample _logger = logging.getLogger("lightning.pytorch") -def _scatter_channels(channel_names: list[str], patch: Tensor) -> dict[str, Tensor]: - return {name: data[None] for name, data in zip(channel_names, patch)} +def _scatter_channels( + channel_names: list[str], patch: Tensor, norm_meta: NormMeta | None +) -> dict[str, Tensor | NormMeta] | dict[str, Tensor]: + channels = {name: data[None] for name, data in zip(channel_names, patch)} + if norm_meta is not None: + channels |= {"norm_meta": norm_meta} + return channels def _gather_channels(patch_channels: dict[str, Tensor]) -> Tensor: @@ -28,9 +33,14 @@ def _gather_channels(patch_channels: dict[str, Tensor]) -> Tensor: def _transform_channel_wise( - transform: DictTransform, channel_names: list[str], patch: Tensor + transform: DictTransform, + channel_names: list[str], + patch: Tensor, + norm_meta: NormMeta | None, ) -> Tensor: - return _gather_channels(transform(_scatter_channels(channel_names, patch))) + return _gather_channels( + transform(_scatter_channels(channel_names, patch, norm_meta)) + ) class TripletDataset(Dataset): @@ -97,8 +107,9 @@ def _sample_negative(self, anchor_row: pd.Series) -> pd.Series: # reproducibility relies on setting a global seed for numpy return candidates.sample(n=1).iloc[0] - def _slice_patch(self, track_row: pd.Series) -> Tensor: - image: ImageArray = track_row["position"]["0"] + def _slice_patch(self, track_row: pd.Series) -> tuple[Tensor, NormMeta | None]: + position: Position = track_row["position"] + image = position["0"] time = track_row["t"] y_center = track_row["y"] x_center = track_row["x"] @@ -110,11 +121,11 @@ def _slice_patch(self, track_row: pd.Series) -> Tensor: slice(y_center - y_half, y_center + y_half), slice(x_center - x_half, x_center + x_half), ] - return torch.from_numpy(patch) + return torch.from_numpy(patch), _read_norm_meta(position) def __getitem__(self, index: int) -> TripletSample: anchor_row = self.tracks.iloc[index] - anchor_patch = self._slice_patch(anchor_row) + anchor_patch, anchor_norm = self._slice_patch(anchor_row) if self.fit: positive_patch = anchor_patch.clone() if self.positive_transform: @@ -122,20 +133,23 @@ def __getitem__(self, index: int) -> TripletSample: transform=self.positive_transform, channel_names=self.channel_names, patch=positive_patch, + norm_meta=anchor_norm, ) negative_row = self._sample_negative(anchor_row) - negative_patch = self._slice_patch(negative_row) + negative_patch, negetive_norm = self._slice_patch(negative_row) if self.negative_transform: negative_patch = _transform_channel_wise( transform=self.negative_transform, channel_names=self.channel_names, patch=negative_patch, + norm_meta=negetive_norm, ) if self.anchor_transform: anchor_patch = _transform_channel_wise( transform=self.anchor_transform, channel_names=self.channel_names, patch=anchor_patch, + norm_meta=anchor_norm, ) sample = { "anchor": anchor_patch, diff --git a/viscy/data/typing.py b/viscy/data/typing.py index 4d9469f4..fb7b6b73 100644 --- a/viscy/data/typing.py +++ b/viscy/data/typing.py @@ -5,7 +5,7 @@ # TODO: use typing.NotRequired when upgrading to Python 3.11 from typing_extensions import NotRequired -DictTransform = Callable[[dict[str, Tensor]], dict[str, Tensor]] +DictTransform = Callable[[dict[str, Tensor | dict]], dict[str, Tensor]] T = TypeVar("T") From 7654d71a03dfb26744d899ada88d9fca5d95e455 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Mon, 5 Aug 2024 11:56:42 -0700 Subject: [PATCH 55/87] training script w/ tensorboard logging --- .../contrastive_phenotyping/demo_fit.py | 2 +- .../contrastive_phenotyping/predict.py | 74 ++++++++++++++----- .../training_script.py | 27 +++---- viscy/data/hcs.py | 5 +- viscy/data/triplet.py | 4 +- viscy/light/engine.py | 11 ++- viscy/representation/contrastive.py | 18 ++++- 7 files changed, 97 insertions(+), 44 deletions(-) diff --git a/applications/contrastive_phenotyping/demo_fit.py b/applications/contrastive_phenotyping/demo_fit.py index 27f9f532..aba11397 100644 --- a/applications/contrastive_phenotyping/demo_fit.py +++ b/applications/contrastive_phenotyping/demo_fit.py @@ -20,7 +20,7 @@ def main(): final_yx_patch_size=(224, 224), ) model = ContrastiveModule( - backbone="convnext_tiny", + backbone="resnet50", in_channels=2, log_batches_per_epoch=2, log_samples_per_batch=3, diff --git a/applications/contrastive_phenotyping/predict.py b/applications/contrastive_phenotyping/predict.py index 6055eed2..d54f21f2 100644 --- a/applications/contrastive_phenotyping/predict.py +++ b/applications/contrastive_phenotyping/predict.py @@ -17,22 +17,31 @@ RandScaleIntensityd, RandWeightedCropd, ) +from monai.transforms import NormalizeIntensityd, ScaleIntensityRangePercentilesd +# Updated normalizations normalizations = [ - # Normalization for Phase3D using mean and std - NormalizeSampled( - keys=["Phase3D"], - level="fov_statistics", - subtrahend="mean", - divisor="std", - ), - # Normalization for RFP using median and IQR - NormalizeSampled( - keys=["RFP"], - level="fov_statistics", - subtrahend="median", - divisor="iqr", - ), + NormalizeIntensityd( + keys=["Phase3D"], + subtrahend=None, + divisor=None, + nonzero=False, + channel_wise=False, + dtype=None, + allow_missing_keys=False + ), + ScaleIntensityRangePercentilesd( + keys=["RFP"], + lower=50, + upper=99, + b_min=0.0, + b_max=1.0, + clip=False, + relative=False, + channel_wise=False, + dtype=None, + allow_missing_keys=False + ), ] def main(hparams): @@ -40,7 +49,7 @@ def main(hparams): # /hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/expanded_final_track_timesteps.csv # /hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/uninfected_cells.csv # /hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/expanded_transitioning_cells_metadata.csv - checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/contrastive_model-test-epoch=09-val_loss=0.00.ckpt" + checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/infection_score/multi-resnet2/contrastive_model-test-epoch=21-val_loss=0.00.ckpt" # non-rechunked data data_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/2.1-register/registered.zarr" @@ -50,20 +59,47 @@ def main(hparams): source_channel = ["RFP", "Phase3D"] z_range = (26, 38) - batch_size = 15 # match the number of fovs being processed such that no data is left + batch_size = 1 # match the number of fovs being processed such that no data is left # set to 15 for full, 12 for infected, and 8 for uninfected + # infected cells - JUNE + # include_fov_names = ['/0/8/001001', '/0/8/001001', '/0/8/000001', '/0/6/002002', '/0/6/002002', '/0/6/00200'] + # include_track_ids = [31, 8, 21, 4, 2, 21] + + # # uninfected cells - JUNE + # include_fov_names = ['/0/1/000000', '/0/1/000000', '/0/1/000000', '/0/1/000000', '/0/8/000002', '/0/8/000002'] + # include_track_ids = [25, 36, 37, 48, 16, 17] + + # # dividing cells - JUNE + # include_fov_names = ['/0/1/000000', '/0/1/000000', '/0/1/000000'] + # include_track_ids = [18, 21, 50] + + # uninfected cells - FEB + # include_fov_names = ['/A/3/0', 'B/3/5', 'B/3/5', 'B/3/5', 'B/3/5', '/A/4/14', '/A/4/14'] + # include_track_ids = [15, 34, 32, 31, 26, 33, 30] + + # # infected cells - FEB + # include_fov_names = ['/A/4/13', '/A/4/14', '/B/4/4', '/B/4/5', '/B/4/6', '/B/4/6'] + # include_track_ids = [25, 19, 68, 11, 29, 35] + + # # dividing cells - FEB + # include_fov_names = ['/B/4/4', '/B/3/5'] + # include_track_ids = [71, 42] + # Initialize the data module for prediction data_module = TripletDataModule( data_path=data_path, tracks_path=tracks_path, source_channel=source_channel, z_range=z_range, - initial_yx_patch_size=(512, 512), + initial_yx_patch_size=(224, 224), final_yx_patch_size=(224, 224), batch_size=batch_size, num_workers=hparams.num_workers, normalizations=normalizations, + # predict_cells = True, + # include_fov_names=include_fov_names, + # include_track_ids=include_track_ids, ) data_module.setup(stage="predict") @@ -130,6 +166,6 @@ def main(hparams): parser.add_argument("--devices", type=int, default=1) parser.add_argument("--num_nodes", type=int, default=1) parser.add_argument("--log_every_n_steps", type=int, default=1) - parser.add_argument("--num_workers", type=int, default=15) + parser.add_argument("--num_workers", type=int, default=8) args = parser.parse_args() - main(args) + main(args) \ No newline at end of file diff --git a/applications/contrastive_phenotyping/training_script.py b/applications/contrastive_phenotyping/training_script.py index 3b67a1f9..a027945e 100644 --- a/applications/contrastive_phenotyping/training_script.py +++ b/applications/contrastive_phenotyping/training_script.py @@ -8,7 +8,6 @@ from torch.utils.data import DataLoader from lightning.pytorch import Trainer from lightning.pytorch.callbacks import ModelCheckpoint -from lightning.pytorch.loggers import WandbLogger from lightning.pytorch.strategies import DDPStrategy from viscy.transforms import ( NormalizeSampled, @@ -25,13 +24,11 @@ import pandas as pd from pathlib import Path from monai.transforms import NormalizeIntensityd, ScaleIntensityRangePercentilesd +from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.callbacks import DeviceStatsMonitor -# Set W&B logging level to suppress warnings -logging.getLogger("wandb").setLevel(logging.ERROR) - # %% Paths and constants -os.environ["WANDB_DIR"] = "/hpc/mydata/alishba.imran/wandb_logs/" # @rank_zero_only # def init_wandb(): @@ -201,20 +198,18 @@ def main(hparams): margin=hparams.margin, lr=hparams.lr, schedule=hparams.schedule, - log_steps_per_epoch=hparams.log_steps_per_epoch, + log_batches_per_epoch=2, # total 6 images per epoch are logged + log_samples_per_batch=3, in_channels=len(source_channel), in_stack_depth=z_range[1] - z_range[0], stem_kernel_size=(5, 3, 3), embedding_len=hparams.embedding_len, ) - # Initialize logger - wandb_logger = WandbLogger(project="contrastive_model", log_model="all") - # set for each run to avoid overwritting! - custom_folder_name = "test" + #custom_folder_name = "test" checkpoint_callback = ModelCheckpoint( - dirpath=os.path.join(model_dir, custom_folder_name), + #dirpath=os.path.join(model_dir, custom_folder_name), filename="contrastive_model-test-{epoch:02d}-{val_loss:.2f}", save_top_k=3, mode="min", @@ -223,8 +218,14 @@ def main(hparams): trainer = Trainer( max_epochs=hparams.max_epochs, + # limit_train_batches=2, + # limit_val_batches=2, callbacks=[checkpoint_callback], - logger=wandb_logger, + logger=TensorBoardLogger( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/test_tb", + log_graph=True, + default_hp_metric=True, + ), accelerator=hparams.accelerator, devices=hparams.devices, num_nodes=hparams.num_nodes, @@ -253,7 +254,7 @@ def main(hparams): # Argument parser for command-line options # to-do: need to clean up to always use the same args parser = ArgumentParser() -parser.add_argument("--backbone", type=str, default="convnext_tiny") +parser.add_argument("--backbone", type=str, default="resnet50") parser.add_argument("--margin", type=float, default=0.5) parser.add_argument("--lr", type=float, default=1e-3) parser.add_argument("--schedule", type=str, default="Constant") diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index e8ba12fa..7cfc6262 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -25,7 +25,8 @@ from torch.utils.data import DataLoader, Dataset from viscy.data.typing import ChannelMap, DictTransform, HCSStackIndex, NormMeta, Sample - +import warnings +warnings.filterwarnings("ignore", category=UserWarning, message="To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).") _logger = logging.getLogger("lightning.pytorch") @@ -550,7 +551,7 @@ def predict_dataloader(self): self.predict_dataset, batch_size=self.batch_size, num_workers=self.num_workers, - shuffle=False, + shuffle=False, ) def _fit_transform(self) -> tuple[Compose, Compose]: diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index a8354331..035bf886 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -260,7 +260,7 @@ def _setup_fit(self, dataset_settings: dict): self.train_dataset = TripletDataset( positions=train_positions, tracks_tables=train_tracks_tables, - initial_yx_patch_size=self.yx_patch_size, + initial_yx_patch_size=self.initial_yx_patch_size, anchor_transform=no_aug_transform, positive_transform=augment_transform, negative_transform=augment_transform, @@ -271,7 +271,7 @@ def _setup_fit(self, dataset_settings: dict): self.val_dataset = TripletDataset( positions=val_positions, tracks_tables=val_tracks_tables, - initial_yx_patch_size=self.yx_patch_size, + initial_yx_patch_size=self.initial_yx_patch_size, anchor_transform=no_aug_transform, positive_transform=augment_transform, negative_transform=augment_transform, diff --git a/viscy/light/engine.py b/viscy/light/engine.py index 312f72b5..acfbdf28 100644 --- a/viscy/light/engine.py +++ b/viscy/light/engine.py @@ -620,6 +620,7 @@ def __init__( ) self.training_step_outputs = [] self.validataion_step_outputs = [] + self.validation_losses = [] def forward(self, x: Tensor) -> Tensor: """Projected embeddings.""" @@ -728,12 +729,16 @@ def validation_step( self.validation_step_outputs.extend( _detach_sample((anchor, pos_img, neg_img), self.log_samples_per_batch) ) + self.validation_losses.append(loss) return loss def on_validation_epoch_end(self) -> None: super().on_validation_epoch_end() + val_loss_epoch = torch.stack(self.validation_losses).mean() + self.log('val/loss_epoch', val_loss_epoch, prog_bar=True, logger=True, sync_dist=True) self._log_samples("val_samples", self.validation_step_outputs) self.validation_step_outputs = [] + self.validation_losses = [] def configure_optimizers(self): optimizer = Adam(self.parameters(), lr=self.lr) @@ -785,10 +790,10 @@ def on_predict_epoch_end(self) -> None: combined_features = np.array(combined_features) combined_projections = np.array(combined_projections) - np.save("embeddings2/multi_resnet_predicted_features.npy", combined_features) + np.save("embeddings4/1_multi_resnet_predicted_features.npy", combined_features) print("Saved features with shape", combined_features.shape) np.save( - "embeddings2/multi_resnet_predicted_projections.npy", combined_projections + "embeddings4/1_multi_resnet_predicted_projections.npy", combined_projections ) print("Saved projections with shape", combined_projections.shape) @@ -803,4 +808,4 @@ def on_predict_epoch_end(self) -> None: } ) - df.to_csv("embeddings2/multi_resnet_predicted_metadata.csv", index=False) + df.to_csv("embeddings4/1_multi_resnet_predicted_metadata.csv", index=False) diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 1fdb8d45..f5de8cca 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -3,6 +3,9 @@ import torch.nn.functional as F from viscy.unet.networks.unext2 import StemDepthtoChannels +import warnings + +warnings.filterwarnings("ignore", category=UserWarning, module="torch") class ContrastiveEncoder(nn.Module): @@ -57,6 +60,8 @@ def __init__( encoder.head.fc = nn.Identity() + intermediate_projection = None + elif "resnet" in backbone: print("Using ResNet backbone.") # Adapt stem and projection head of resnet here. @@ -65,8 +70,10 @@ def __init__( in_channels_encoder = encoder.conv1.out_channels encoder.conv1 = nn.Identity() + intermediate_projection = nn.Linear(encoder.fc.in_features, 768) + projection = nn.Sequential( - nn.Linear(encoder.fc.in_features, 3 * embedding_len), + nn.Linear(768, 3 * embedding_len), nn.ReLU(inplace=True), nn.Linear(3 * embedding_len, embedding_len), ) @@ -80,15 +87,18 @@ def __init__( # Append modified encoder. self.encoder = encoder + self.intermediate_projection = intermediate_projection # Append modified projection head. self.projection = projection def forward(self, x): x = self.stem(x) embedding = self.encoder(x) - projections = self.projection(embedding) + embedding_reduced = self.intermediate_projection(embedding) + embedding_norm = F.normalize(embedding_reduced, p=2, dim=1) + projections = self.projection(embedding_reduced) projections = F.normalize(projections, p=2, dim=1) return ( - embedding, + embedding_norm, projections, - ) # Compute the loss on projections, analyze the embeddings. + ) # Compute the loss on projections, analyze the embeddings. \ No newline at end of file From 37b07a14aee67bc8a366ecac8689d543bcafc906 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Wed, 28 Aug 2024 10:59:55 -0400 Subject: [PATCH 56/87] Merging code related to figures (#146) * notes on standard report * Add code for generating figures --------- Co-authored-by: Alishba Imran --- .../figures/figure4/classify_feb.py | 119 +++++++++++++ .../figures/figure4/classify_june.py | 119 +++++++++++++ .../figures/figure4/figure_a_1.py | 167 ++++++++++++++++++ .../figures/figure4/figure_e_2_feb.py | 86 +++++++++ .../figures/figure4/figure_e_2_june.py | 83 +++++++++ .../contrastive_cli/plot_embeddings.py | 7 + 6 files changed, 581 insertions(+) create mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py create mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_june.py create mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_a_1.py create mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_feb.py create mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_june.py diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py new file mode 100644 index 00000000..9a6cf87c --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py @@ -0,0 +1,119 @@ +# %% Importing Necessary Libraries +from pathlib import Path +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, confusion_matrix, accuracy_score +from sklearn.decomposition import PCA +from tqdm import tqdm +from viscy.light.embedding_writer import read_embedding_dataset +from imblearn.over_sampling import SMOTE + +# %% Defining Paths for February Dataset +feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr") + +# %% Function to Load Annotations +def load_annotation(da, path, name, categories: dict | None = None): + annotation = pd.read_csv(path) + annotation["fov_name"] = "/" + annotation["fov ID"] + annotation = annotation.set_index(["fov_name", "id"]) + mi = pd.MultiIndex.from_arrays( + [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] + ) + selected = annotation.loc[mi][name] + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + return selected + +# %% Function to Compute PCA +def compute_pca(embedding_dataset, n_components=6): + features = embedding_dataset["features"] + scaled_features = StandardScaler().fit_transform(features.values) + + # Compute PCA with specified number of components + pca = PCA(n_components=n_components, random_state=42) + pca_embedding = pca.fit_transform(scaled_features) + + # Prepare DataFrame with id and PCA coordinates + pca_df = pd.DataFrame({ + "id": embedding_dataset["id"].values, + "fov_name": embedding_dataset["fov_name"].values, + "PCA1": pca_embedding[:, 0], + "PCA2": pca_embedding[:, 1], + "PCA3": pca_embedding[:, 2], + "PCA4": pca_embedding[:, 3], + "PCA5": pca_embedding[:, 4], + "PCA6": pca_embedding[:, 5] + }) + + return pca_df + +# %% Load and Process February Dataset +feb_embedding_dataset = read_embedding_dataset(feb_features_path) +print(feb_embedding_dataset) +pca_df = compute_pca(feb_embedding_dataset, n_components=6) + +# Print shape before merge +print("Shape of pca_df before merge:", pca_df.shape) + +# Load the ground truth infection labels +feb_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track") +feb_infection = load_annotation(feb_embedding_dataset, feb_ann_root / "tracking_v1_infection.csv", "infection class", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) + +# Print shape of feb_infection +print("Shape of feb_infection:", feb_infection.shape) + +# Merge PCA results with ground truth labels on both 'fov_name' and 'id' +pca_df = pd.merge(pca_df, feb_infection.reset_index(), on=['fov_name', 'id']) + +# Print shape after merge +print("Shape of pca_df after merge:", pca_df.shape) + +# Prepare the full dataset +X = pca_df[["PCA1", "PCA2", "PCA3", "PCA4", "PCA5", "PCA6"]] +y = pca_df["infection class"] + +# Apply SMOTE to balance the classes in the full dataset +smote = SMOTE(random_state=42) +X_resampled, y_resampled = smote.fit_resample(X, y) + +# Print shape after SMOTE +print(f"Shape after SMOTE - X_resampled: {X_resampled.shape}, y_resampled: {y_resampled.shape}") + +# %% Train Logistic Regression Classifier with Progress Bar +model = LogisticRegression(max_iter=1000, random_state=42) + +# Wrap the training with tqdm to show a progress bar +for _ in tqdm(range(1)): + model.fit(X_resampled, y_resampled) + +# %% Predict Labels for the Entire Dataset +pca_df["Predicted_Label"] = model.predict(X) + +# Compute metrics based on the entire original dataset +print("Classification Report for Entire Dataset:") +print(classification_report(pca_df["infection class"], pca_df["Predicted_Label"])) + +print("Confusion Matrix for Entire Dataset:") +print(confusion_matrix(pca_df["infection class"], pca_df["Predicted_Label"])) + +# %% Plotting the Results +plt.figure(figsize=(10, 8)) +sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["infection class"], s=7, alpha=0.8) +plt.title("PCA with Ground Truth Labels") +plt.savefig("up_pca_ground_truth_labels.png", format='png', dpi=300) +plt.show() + +plt.figure(figsize=(10, 8)) +sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["Predicted_Label"], s=7, alpha=0.8) +plt.title("PCA with Logistic Regression Predicted Labels") +plt.savefig("up_pca_predicted_labels.png", format='png', dpi=300) +plt.show() + +# %% Save Predicted Labels to CSV +save_path_csv = "up_logistic_regression_predicted_labels_feb_pca.csv" +pca_df[['id', 'fov_name', 'Predicted_Label']].to_csv(save_path_csv, index=False) +print(f"Predicted labels saved to {save_path_csv}") \ No newline at end of file diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_june.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_june.py new file mode 100644 index 00000000..8977e3bc --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_june.py @@ -0,0 +1,119 @@ +# %% Importing Necessary Libraries +from pathlib import Path +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, confusion_matrix, accuracy_score +from sklearn.decomposition import PCA +from tqdm import tqdm +from viscy.light.embedding_writer import read_embedding_dataset +from imblearn.over_sampling import SMOTE + +# %% Defining Paths for June Dataset +june_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr") + +# %% Function to Load Annotations +def load_annotation(da, path, name, categories: dict | None = None): + annotation = pd.read_csv(path) + annotation["fov_name"] = "/" + annotation["fov ID"] + annotation = annotation.set_index(["fov_name", "id"]) + mi = pd.MultiIndex.from_arrays( + [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] + ) + selected = annotation.loc[mi][name] + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + return selected + +# %% Function to Compute PCA +def compute_pca(embedding_dataset, n_components=6): + features = embedding_dataset["features"] + scaled_features = StandardScaler().fit_transform(features.values) + + # Compute PCA with specified number of components + pca = PCA(n_components=n_components, random_state=42) + pca_embedding = pca.fit_transform(scaled_features) + + # Prepare DataFrame with id and PCA coordinates + pca_df = pd.DataFrame({ + "id": embedding_dataset["id"].values, + "fov_name": embedding_dataset["fov_name"].values, + "PCA1": pca_embedding[:, 0], + "PCA2": pca_embedding[:, 1], + "PCA3": pca_embedding[:, 2], + "PCA4": pca_embedding[:, 3], + "PCA5": pca_embedding[:, 4], + "PCA6": pca_embedding[:, 5] + }) + + return pca_df + +# %% Load and Process June Dataset +june_embedding_dataset = read_embedding_dataset(june_features_path) +print(june_embedding_dataset) +pca_df = compute_pca(june_embedding_dataset, n_components=6) + +# Print shape before merge +print("Shape of pca_df before merge:", pca_df.shape) + +# Load the ground truth infection labels +june_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking") +june_infection = load_annotation(june_embedding_dataset, june_ann_root / "tracking_v1_infection.csv", "infection class", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) + +# Print shape of june_infection +print("Shape of june_infection:", june_infection.shape) + +# Merge PCA results with ground truth labels on both 'fov_name' and 'id' +pca_df = pd.merge(pca_df, june_infection.reset_index(), on=['fov_name', 'id']) + +# Print shape after merge +print("Shape of pca_df after merge:", pca_df.shape) + +# Prepare the full dataset +X = pca_df[["PCA1", "PCA2", "PCA3", "PCA4", "PCA5", "PCA6"]] +y = pca_df["infection class"] + +# Apply SMOTE to balance the classes in the full dataset +smote = SMOTE(random_state=42) +X_resampled, y_resampled = smote.fit_resample(X, y) + +# Print shape after SMOTE +print(f"Shape after SMOTE - X_resampled: {X_resampled.shape}, y_resampled: {y_resampled.shape}") + +# %% Train Logistic Regression Classifier with Progress Bar +model = LogisticRegression(max_iter=1000, random_state=42) + +# Wrap the training with tqdm to show a progress bar +for _ in tqdm(range(1)): + model.fit(X_resampled, y_resampled) + +# %% Predict Labels for the Entire Dataset +pca_df["Predicted_Label"] = model.predict(X) + +# Compute metrics based on the entire original dataset +print("Classification Report for Entire Dataset:") +print(classification_report(pca_df["infection class"], pca_df["Predicted_Label"])) + +print("Confusion Matrix for Entire Dataset:") +print(confusion_matrix(pca_df["infection class"], pca_df["Predicted_Label"])) + +# %% Plotting the Results +plt.figure(figsize=(10, 8)) +sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["infection class"], s=7, alpha=0.8) +plt.title("PCA with Ground Truth Labels") +plt.savefig("june_pca_ground_truth_labels.png", format='png', dpi=300) +plt.show() + +plt.figure(figsize=(10, 8)) +sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["Predicted_Label"], s=7, alpha=0.8) +plt.title("PCA with Logistic Regression Predicted Labels") +plt.savefig("june_pca_predicted_labels.png", format='png', dpi=300) +plt.show() + +# %% Save Predicted Labels to CSV +save_path_csv = "june_logistic_regression_predicted_labels_feb_pca.csv" +pca_df[['id', 'fov_name', 'Predicted_Label']].to_csv(save_path_csv, index=False) +print(f"Predicted labels saved to {save_path_csv}") diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_a_1.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_a_1.py new file mode 100644 index 00000000..c688a7cf --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_a_1.py @@ -0,0 +1,167 @@ +# %% Importing Necessary Libraries +from pathlib import Path +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from sklearn.preprocessing import StandardScaler +from umap import UMAP +from viscy.light.embedding_writer import read_embedding_dataset + +# %% Defining Paths for February and June Datasets +feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr") +feb_data_path = Path("/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr") +feb_tracks_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr") + +# %% Function to Load and Process the Embedding Dataset +def compute_umap(embedding_dataset): + features = embedding_dataset["features"] + scaled_features = StandardScaler().fit_transform(features.values) + umap = UMAP() + embedding = umap.fit_transform(scaled_features) + + features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) + ) + return features + +# %% Function to Load Annotations +def load_annotation(da, path, name, categories: dict | None = None): + annotation = pd.read_csv(path) + annotation["fov_name"] = "/" + annotation["fov ID"] + annotation = annotation.set_index(["fov_name", "id"]) + mi = pd.MultiIndex.from_arrays( + [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] + ) + selected = annotation.loc[mi][name] + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + return selected + +# %% Function to Plot UMAP with Infection Annotations +def plot_umap_infection(features, infection, title): + plt.figure(figsize=(10, 8)) + sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=infection, s=7, alpha=0.8) + plt.title(f"UMAP Plot - {title}") + plt.show() + +# %% Load and Process February Dataset +feb_embedding_dataset = read_embedding_dataset(feb_features_path) +feb_features = compute_umap(feb_embedding_dataset) + +feb_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track") +feb_infection = load_annotation(feb_features, feb_ann_root / "tracking_v1_infection.csv", "infection class", {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) + +# %% Plot UMAP with Infection Status for February Dataset +plot_umap_infection(feb_features, feb_infection, "February Dataset") + +# %% +print(feb_embedding_dataset) +print(feb_infection) +print(feb_features) +# %% + +import matplotlib.pyplot as plt + +# %% Identify cells by infection type using fov_name +mock_cells = feb_features.sel(sample=feb_features['fov_name'].str.contains('/A/3') | feb_features['fov_name'].str.contains('/B/3')) +zika_cells = feb_features.sel(sample=feb_features['fov_name'].str.contains('/A/4')) +dengue_cells = feb_features.sel(sample=feb_features['fov_name'].str.contains('/B/4')) + +# %% Plot UMAP with Infection Status +plt.figure(figsize=(10, 8)) +sns.scatterplot(x=feb_features["UMAP1"], y=feb_features["UMAP2"], hue=feb_infection, s=7, alpha=0.8) + +# Overlay with circled cells +plt.scatter(mock_cells["UMAP1"], mock_cells["UMAP2"], facecolors='none', edgecolors='blue', s=20, label='Mock Cells') +plt.scatter(zika_cells["UMAP1"], zika_cells["UMAP2"], facecolors='none', edgecolors='green', s=20, label='Zika MOI 5') +plt.scatter(dengue_cells["UMAP1"], dengue_cells["UMAP2"], facecolors='none', edgecolors='red', s=20, label='Dengue MOI 5') + +# Add legend and show plot +plt.legend(loc='best') +plt.title("UMAP Plot - February Dataset with Mock, Zika, and Dengue Highlighted") +plt.show() + +# %% +# %% Create a 1x3 grid of heatmaps +fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharex=True, sharey=True) + +# Mock Cells Heatmap +sns.histplot(x=mock_cells["UMAP1"], y=mock_cells["UMAP2"], bins=50, pmax=1, cmap="Blues", ax=axs[0]) +axs[0].set_title('Mock Cells') +axs[0].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[0].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Zika Cells Heatmap +sns.histplot(x=zika_cells["UMAP1"], y=zika_cells["UMAP2"], bins=50, pmax=1, cmap="Greens", ax=axs[1]) +axs[1].set_title('Zika MOI 5') +axs[1].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[1].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Dengue Cells Heatmap +sns.histplot(x=dengue_cells["UMAP1"], y=dengue_cells["UMAP2"], bins=50, pmax=1, cmap="Reds", ax=axs[2]) +axs[2].set_title('Dengue MOI 5') +axs[2].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[2].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Set labels and adjust layout +for ax in axs: + ax.set_xlabel('UMAP1') + ax.set_ylabel('UMAP2') + +plt.tight_layout() +plt.show() + +# %% +import matplotlib.pyplot as plt +import seaborn as sns + +# %% Create a 2x3 grid of heatmaps (1 row for each heatmap, splitting infected and uninfected in the second row) +fig, axs = plt.subplots(2, 3, figsize=(24, 12), sharex=True, sharey=True) + +# Mock Cells Heatmap +sns.histplot(x=mock_cells["UMAP1"], y=mock_cells["UMAP2"], bins=50, pmax=1, cmap="Blues", ax=axs[0, 0]) +axs[0, 0].set_title('Mock Cells') +axs[0, 0].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[0, 0].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Zika Cells Heatmap +sns.histplot(x=zika_cells["UMAP1"], y=zika_cells["UMAP2"], bins=50, pmax=1, cmap="Greens", ax=axs[0, 1]) +axs[0, 1].set_title('Zika MOI 5') +axs[0, 1].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[0, 1].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Dengue Cells Heatmap +sns.histplot(x=dengue_cells["UMAP1"], y=dengue_cells["UMAP2"], bins=50, pmax=1, cmap="Reds", ax=axs[0, 2]) +axs[0, 2].set_title('Dengue MOI 5') +axs[0, 2].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[0, 2].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Infected Cells Heatmap +sns.histplot(x=infected_cells["UMAP1"], y=infected_cells["UMAP2"], bins=50, pmax=1, cmap="Reds", ax=axs[1, 0]) +axs[1, 0].set_title('Infected Cells') +axs[1, 0].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[1, 0].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Uninfected Cells Heatmap +sns.histplot(x=uninfected_cells["UMAP1"], y=uninfected_cells["UMAP2"], bins=50, pmax=1, cmap="Greens", ax=axs[1, 1]) +axs[1, 1].set_title('Uninfected Cells') +axs[1, 1].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) +axs[1, 1].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) + +# Remove the last subplot (bottom right corner) +fig.delaxes(axs[1, 2]) + +# Set labels and adjust layout +for ax in axs.flat: + ax.set_xlabel('UMAP1') + ax.set_ylabel('UMAP2') + +plt.tight_layout() +plt.show() + + + +# %% diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_feb.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_feb.py new file mode 100644 index 00000000..e3791417 --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_feb.py @@ -0,0 +1,86 @@ +# %% Importing Necessary Libraries +import matplotlib.pyplot as plt +import pandas as pd +from pathlib import Path +from sklearn.preprocessing import StandardScaler +from viscy.light.embedding_writer import read_embedding_dataset +from umap import UMAP # Add import for UMAP + +# %% Function to Load Annotations from GMM CSV +def load_gmm_annotation(gmm_csv_path): + gmm_df = pd.read_csv(gmm_csv_path) + return gmm_df + +# %% Function to Count and Calculate Percentage of Infected Cells Over Time Based on GMM Labels +def count_infected_cell_states_over_time(embedding_dataset, gmm_df): + # Convert the embedding dataset to a DataFrame + df = pd.DataFrame({ + "fov_name": embedding_dataset["fov_name"].values, + "track_id": embedding_dataset["track_id"].values, + "t": embedding_dataset["t"].values, + "id": embedding_dataset["id"].values + }) + + # Merge with GMM data to add GMM labels + df = pd.merge(df, gmm_df[['id', 'fov_name', 'Predicted_Label']], on=['fov_name', 'id'], how='left') + + # Filter by time range (3 HPI to 30 HPI) + df = df[(df['t'] >= 3) & (df['t'] <= 27)] + + # Determine the well type (Mock, Zika, Dengue) based on fov_name + df['well_type'] = df['fov_name'].apply(lambda x: 'Mock' if '/A/3' in x or '/B/3' in x else + ('Zika' if '/A/4' in x else 'Dengue')) + + # Group by time, well type, and GMM label to count the number of infected cells + state_counts = df.groupby(['t', 'well_type', 'Predicted_Label']).size().unstack(fill_value=0) + + # Ensure that 'infected' column exists + if 'infected' not in state_counts.columns: + state_counts['infected'] = 0 + + # Calculate the percentage of infected cells + state_counts['total'] = state_counts.sum(axis=1) + state_counts['infected'] = (state_counts['infected'] / state_counts['total']) * 100 + + return state_counts + +# %% Function to Plot Percentage of Infected Cells Over Time +def plot_infected_cell_states(state_counts): + plt.figure(figsize=(12, 8)) + + # Loop through each well type + for well_type in ['Mock', 'Zika', 'Dengue']: + # Select the data for the current well type + if well_type in state_counts.index.get_level_values('well_type'): + well_data = state_counts.xs(well_type, level='well_type') + + # Plot only the percentage of infected cells + if 'infected' in well_data.columns: + plt.plot(well_data.index, well_data['infected'], label=f'{well_type} - Infected') + + plt.title("Percentage of Infected Cells Over Time - February") + plt.xlabel("Hours Post Perturbation") + plt.ylabel("Percentage of Infected Cells") + plt.legend(title="Well Type") + plt.grid(True) + plt.show() + +# %% Load and process Feb Dataset +feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr") +feb_embedding_dataset = read_embedding_dataset(feb_features_path) + +# Load the GMM annotation CSV +gmm_csv_path = "june_logistic_regression_predicted_labels_feb_pca.csv" # Path to CSV file +gmm_df = load_gmm_annotation(gmm_csv_path) + +# %% Count Infected Cell States Over Time as Percentage using GMM labels +state_counts = count_infected_cell_states_over_time(feb_embedding_dataset, gmm_df) +print(state_counts.head()) +state_counts.info() + +# %% Plot Infected Cell States Over Time as Percentage +plot_infected_cell_states(state_counts) + +# %% + + diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_june.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_june.py new file mode 100644 index 00000000..ef3fd076 --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_june.py @@ -0,0 +1,83 @@ +# %% Importing Necessary Libraries +import matplotlib.pyplot as plt +import pandas as pd +from pathlib import Path +from sklearn.preprocessing import StandardScaler +from viscy.light.embedding_writer import read_embedding_dataset + +# %% Function to Load Annotations from CSV +def load_annotation(csv_path): + return pd.read_csv(csv_path) + +# %% Function to Count and Calculate Percentage of Infected Cells Over Time Based on Predicted Labels +def count_infected_cell_states_over_time(embedding_dataset, prediction_df): + # Convert the embedding dataset to a DataFrame + df = pd.DataFrame({ + "fov_name": embedding_dataset["fov_name"].values, + "track_id": embedding_dataset["track_id"].values, + "t": embedding_dataset["t"].values, + "id": embedding_dataset["id"].values + }) + + # Merge with the prediction data to add Predicted Labels + df = pd.merge(df, prediction_df[['id', 'fov_name', 'Infection_Class']], on=['fov_name', 'id'], how='left') + + # Filter by time range (2 HPI to 50 HPI) + df = df[(df['t'] >= 2) & (df['t'] <= 50)] + + # Determine the well type (Mock, Dengue, Zika) based on fov_name + df['well_type'] = df['fov_name'].apply( + lambda x: 'Mock' if '/0/1' in x or '/0/2' in x or '/0/3' in x or '/0/4' in x else + ('Dengue' if '/0/5' in x or '/0/6' in x else 'Zika')) + + # Group by time, well type, and Predicted_Label to count the number of infected cells + state_counts = df.groupby(['t', 'well_type', 'Infection_Class']).size().unstack(fill_value=0) + + # Ensure that 'infected' column exists + if 'infected' not in state_counts.columns: + state_counts['infected'] = 0 + + # Calculate the percentage of infected cells + state_counts['total'] = state_counts.sum(axis=1) + state_counts['infected'] = (state_counts['infected'] / state_counts['total']) * 100 + + return state_counts + +# %% Function to Plot Percentage of Infected Cells Over Time +def plot_infected_cell_states(state_counts): + plt.figure(figsize=(12, 8)) + + # Loop through each well type + for well_type in ['Mock', 'Dengue', 'Zika']: + # Select the data for the current well type + if well_type in state_counts.index.get_level_values('well_type'): + well_data = state_counts.xs(well_type, level='well_type') + + # Plot only the percentage of infected cells + if 'infected' in well_data.columns: + plt.plot(well_data.index, well_data['infected'], label=f'{well_type} - Infected') + + plt.title("Percentage of Infected Cells Over Time - June") + plt.xlabel("Hours Post Perturbation") + plt.ylabel("Percentage of Infected Cells") + plt.legend(title="Well Type") + plt.grid(True) + plt.show() + +# %% Load and process June Dataset +june_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr") +june_embedding_dataset = read_embedding_dataset(june_features_path) + +# Load the predicted labels from CSV +prediction_csv_path = "3up_gmm_clustering_results_june_pca_6components.csv" # Path to predicted labels CSV file +prediction_df = load_annotation(prediction_csv_path) + +# %% Count Infected Cell States Over Time as Percentage using Predicted labels +state_counts = count_infected_cell_states_over_time(june_embedding_dataset, prediction_df) +print(state_counts.head()) +state_counts.info() + +# %% Plot Infected Cell States Over Time as Percentage +plot_infected_cell_states(state_counts) + +# %% diff --git a/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py b/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py index 40cf36db..c87a0980 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py +++ b/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py @@ -43,6 +43,13 @@ plt.xlabel("n_components") plt.show() +# TODO: Include the followiing in the standard report. +# * Explained variance of the features and projections. +# * The UMAPs of the features and projections. +# * 2D image of the embeddings of features and projections of test tracks (e.g., infected, uninfected, dividing, non-dividing). +# * Heatmaps of annotations over UMAPs. + + # %% # Extract a track from the dataset and visualize its features. From 8ebe86c6a9cafa057bdabf86c60d96e873839c25 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Wed, 28 Aug 2024 11:02:26 -0400 Subject: [PATCH 57/87] produce a report of useful visualizations to assess the dimensionality and features learned by embeddings (#140) * notes on standard report * add lib of computed features * correlates PCA with computed features * compute for all timepoints * compute correlation * remove cv library usage * remove edge detection * convert to dataframe * for entire well * add std_dev feature * fix patch size --------- Co-authored-by: Soorya Pradeep --- .../contrastive_cli/PC_vs_CF.py | 219 ++++++++++++++++++ .../contrastive_cli/computed_features.py | 115 +++++++++ 2 files changed, 334 insertions(+) create mode 100644 applications/contrastive_phenotyping/contrastive_cli/PC_vs_CF.py create mode 100644 applications/contrastive_phenotyping/contrastive_cli/computed_features.py diff --git a/applications/contrastive_phenotyping/contrastive_cli/PC_vs_CF.py b/applications/contrastive_phenotyping/contrastive_cli/PC_vs_CF.py new file mode 100644 index 00000000..9bd3f064 --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/PC_vs_CF.py @@ -0,0 +1,219 @@ + + +# %% +# from viscy.data.triplet import TripletDataModule +from viscy.light.embedding_writer import read_embedding_dataset +from viscy.data.triplet import TripletDataModule + +from pathlib import Path +import numpy as np +from skimage import io +from computed_features import FeatureExtractor as FE +from sklearn.decomposition import PCA +import pandas as pd +from sklearn.preprocessing import StandardScaler + +# %% +features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr" +) +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/2.1-register/registered.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr" +) + +# %% + +source_channel = ["Phase3D", "RFP"] +z_range = (28, 43) +normalizations = None +# fov_name = "/B/4/5" +# track_id = 11 + +embedding_dataset = read_embedding_dataset(features_path) +embedding_dataset + +fov_names_list = [name for name in embedding_dataset["fov_name"].values if name.startswith("/A/3/")] +unique_fov_names = sorted(list(set(fov_names_list))) +correlation_sum = pd.DataFrame() +ii = 0 +features = pd.DataFrame() +computed_pca = pd.DataFrame() + +for fov_name in unique_fov_names: + + all_tracks_FOV = embedding_dataset.sel(fov_name=fov_name) + + unique_track_ids = list(all_tracks_FOV["track_id"].values) + unique_track_ids = list(set(unique_track_ids)) + + for track_id in unique_track_ids: + a_track_in_FOV = all_tracks_FOV.sel(track_id=track_id) + indices = np.arange(a_track_in_FOV.sizes["sample"]) + features_track = a_track_in_FOV["features"] + time_stamp = features_track["t"][indices].astype(str) + + scaled_features_track = StandardScaler().fit_transform(features_track.values) + + # perform PCA analysis of features + + pca = PCA(n_components=5) + if scaled_features_track.shape[0] > 5: + pca_features = pca.fit_transform(scaled_features_track) + ii += 1 + else: + continue + + features_track = ( + features_track.assign_coords(PCA1=("sample", pca_features[:, 0])) + .assign_coords(PCA2=("sample", pca_features[:, 1])) + .assign_coords(PCA3=("sample", pca_features[:, 2])) + .assign_coords(PCA4=("sample", pca_features[:, 3])) + .assign_coords(PCA5=("sample", pca_features[:, 4])) + .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"], append=True) + ) + + # load the image patches + + data_module = TripletDataModule( + data_path=data_path, + tracks_path=tracks_path, + source_channel=source_channel, + z_range=z_range, + initial_yx_patch_size=(256, 256), + final_yx_patch_size=(140, 140), + batch_size=1, + num_workers=16, + normalizations=normalizations, + predict_cells=True, + include_fov_names=[fov_name], + include_track_ids=[track_id], + ) + # for train and val + data_module.setup("predict") + predict_dataset = data_module.predict_dataset + + whole = np.stack([p["anchor"] for p in predict_dataset]) + phase = whole[:, 0, 3] + fluor = np.max(whole[:, 1], axis=1) + # phase = np.stack([p["anchor"][0, 3].numpy() for p in predict_dataset]) + # fluor = np.stack([np.max(p["anchor"][1].numpy(), axis=0) for p in predict_dataset]) + + # Compute Fourier descriptors for phase image + data = { + "Phase Symmetry Score": [], + "Fluor Symmetry Score": [], + "Sensor Area": [], + "Masked Sensor Intensity": [], + "Entropy Phase": [], + "Entropy Fluor": [], + "Contrast Phase": [], + "Dissimilarity Phase": [], + "Homogeneity Phase": [], + "Contrast Fluor": [], + "Dissimilarity Fluor": [], + "Homogeneity Fluor": [], + "Phase IQR": [], + "Fluor Mean Intensity": [], + "Phase Standard Deviation": [], + "Fluor Standard Deviation": [], + } + + for t in range(phase.shape[0]): + # Compute Fourier descriptors for phase image + phase_descriptors = FE.compute_fourier_descriptors(phase[t]) + # Analyze symmetry of phase image + phase_symmetry_score = FE.analyze_symmetry(phase_descriptors) + + # Compute Fourier descriptors for fluor image + fluor_descriptors = FE.compute_fourier_descriptors(fluor[t]) + # Analyze symmetry of fluor image + fluor_symmetry_score = FE.analyze_symmetry(fluor_descriptors) + + # Compute area of sensor + masked_intensity, area = FE.compute_area(fluor[t]) + + # Compute higher frequency features using spectral entropy + entropy_phase = FE.compute_spectral_entropy(phase[t]) + entropy_fluor = FE.compute_spectral_entropy(fluor[t]) + + # Compute texture analysis using GLCM + contrast_phase, dissimilarity_phase, homogeneity_phase = FE.compute_glcm_features(phase[t]) + contrast_fluor, dissimilarity_fluor, homogeneity_fluor = FE.compute_glcm_features(fluor[t]) + + # # Compute edge detection using Canny + # edges_phase = FE.detect_edges(phase[t]) + # edges_fluor = FE.detect_edges(fluor[t]) + + # Quantify the amount of edge feature in the phase image + # edge_density_phase = np.sum(edges_phase) / (edges_phase.shape[0] * edges_phase.shape[1]) + + # Quantify the amount of edge feature in the fluor image + # edge_density_fluor = np.sum(edges_fluor) / (edges_fluor.shape[0] * edges_fluor.shape[1]) + + # Compute interqualtile range of pixel intensities + iqr = FE.compute_iqr(phase[t]) + + # Compute mean pixel intensity + fluor_mean_intensity = FE.compute_mean_intensity(fluor[t]) + + # Compute standard deviation of pixel intensities + phase_std_dev = FE.compute_std_dev(phase[t]) + fluor_std_dev = FE.compute_std_dev(fluor[t]) + + # Append the computed features to the data dictionary + data["Phase Symmetry Score"].append(phase_symmetry_score) + data["Fluor Symmetry Score"].append(fluor_symmetry_score) + data["Sensor Area"].append(area) + data["Masked Sensor Intensity"].append(masked_intensity) + data["Entropy Phase"].append(entropy_phase) + data["Entropy Fluor"].append(entropy_fluor) + data["Contrast Phase"].append(contrast_phase) + data["Dissimilarity Phase"].append(dissimilarity_phase) + data["Homogeneity Phase"].append(homogeneity_phase) + data["Contrast Fluor"].append(contrast_fluor) + data["Dissimilarity Fluor"].append(dissimilarity_fluor) + data["Homogeneity Fluor"].append(homogeneity_fluor) + # data["Edge Density Phase"].append(edge_density_phase) + # data["Edge Density Fluor"].append(edge_density_fluor) + data["Phase IQR"].append(iqr) + data["Fluor Mean Intensity"].append(fluor_mean_intensity) + data["Phase Standard Deviation"].append(phase_std_dev) + data["Fluor Standard Deviation"].append(fluor_std_dev) + + # Create a dataframe to store the computed features + features = pd.concat([features, pd.DataFrame(data)]) + + # compute correlation between PCA features and computed features + + # Create a dataframe with PCA results + pca_results = pd.DataFrame(pca_features, columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"]) + computed_pca = pd.concat([computed_pca, pca_results]) + +# %% + +# Compute correlation between PCA features and computed features +correlation = pd.concat([computed_pca, features], axis=1).corr() +# correlation_sum = correlation_sum.add(correlation, fill_value=0) +# correlation_avg = correlation_sum / ii + +# %% find the best correlated computed features with PCA features + +# Find the best correlated computed features with PCA features +best_correlated_features = correlation.loc["PCA1":"PCA5", :].idxmax() +best_correlated_features + +# %% display as a heatmap +import seaborn as sns +import matplotlib.pyplot as plt + +plt.figure(figsize=(20, 5)) +sns.heatmap(correlation.drop(columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"]).loc["PCA1":"PCA5", :], annot=True, cmap="coolwarm", fmt=".2f") +plt.title("Correlation between PCA features and computed features") +plt.xlabel("Computed Features") +plt.ylabel("PCA Features") +plt.show() + +# %% diff --git a/applications/contrastive_phenotyping/contrastive_cli/computed_features.py b/applications/contrastive_phenotyping/contrastive_cli/computed_features.py new file mode 100644 index 00000000..f633eaec --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/computed_features.py @@ -0,0 +1,115 @@ +import cv2 +import numpy as np +from skimage import color +from numpy import fft +from skimage.feature import graycomatrix, graycoprops +from skimage.filters import threshold_otsu, gaussian + +class FeatureExtractor: + + def __init__(self): + pass + + def compute_fourier_descriptors(image): + + # Convert contour to complex numbers + contour_complex = image[:, 0] + 1j * image[:, 1] + + # Compute Fourier descriptors + descriptors = np.fft.fft(contour_complex) + + return descriptors + + def analyze_symmetry(descriptors): + # Normalize descriptors + descriptors = np.abs(descriptors) / np.max(np.abs(descriptors)) + # Check symmetry (for a perfect circle, descriptors should be quite uniform) + return np.std(descriptors) # Lower standard deviation indicates higher symmetry + + def compute_area(input_image, sigma=0.6): + """Create a binary mask using morphological operations + :param np.array input_image: generate masks from this 3D image + :param float sigma: Gaussian blur standard deviation, increase in value increases blur + :return: volume mask of input_image, 3D np.array + """ + + input_image_blur = gaussian(input_image, sigma=sigma) + + thresh = threshold_otsu(input_image_blur) + mask = input_image >= thresh + + # Apply sensor mask to the image + masked_image = input_image * mask + + # Compute the mean intensity inside the sensor area + masked_intensity = np.mean(masked_image) + + return masked_intensity, np.sum(mask) + + def compute_spectral_entropy(image): + # Convert image to grayscale if it's not already + if len(image.shape) == 3: + image = color.rgb2gray(image) + + # Compute the 2D Fourier Transform + f_transform = fft.fft2(image) + + # Compute the power spectrum + power_spectrum = np.abs(f_transform) ** 2 + + # Compute the probability distribution + power_spectrum += 1e-10 # Avoid log(0) issues + prob_distribution = power_spectrum / np.sum(power_spectrum) + + # Compute the spectral entropy + entropy = -np.sum(prob_distribution * np.log(prob_distribution)) + + return entropy + + def compute_glcm_features(image): + + # Normalize the input image from 0 to 255 + image = (image - np.min(image)) * (255 / (np.max(image) - np.min(image))) + image = image.astype(np.uint8) + + # Compute the GLCM + distances = [1] # Distance between pixels + angles = [0] # Angle in radians + + glcm = graycomatrix(image, distances, angles, symmetric=True, normed=True) + + # Compute GLCM properties + contrast = graycoprops(glcm, "contrast")[0, 0] + dissimilarity = graycoprops(glcm, "dissimilarity")[0, 0] + homogeneity = graycoprops(glcm, "homogeneity")[0, 0] + + return contrast, dissimilarity, homogeneity + + # def detect_edges(image): + + # # Apply Canny edge detection + # edges = cv2.Canny(image, 100, 200) + + # return edges + + def compute_iqr(image): + + # Compute the interquartile range of pixel intensities + iqr = np.percentile(image, 75) - np.percentile(image, 25) + + return iqr + + def compute_mean_intensity(image): + + # Compute the mean pixel intensity + mean_intensity = np.mean(image) + + return mean_intensity + + def compute_std_dev(image): + + # Compute the standard deviation of pixel intensities + std_dev = np.std(image) + + return std_dev + From 6e7d61fe62d36bd53344cd5c458fa94ced7f972c Mon Sep 17 00:00:00 2001 From: Ziwen Liu <67518483+ziw-liu@users.noreply.github.com> Date: Fri, 30 Aug 2024 11:14:39 -0400 Subject: [PATCH 58/87] Remove obsolete scripts for contrastive phenotyping (#150) * remove obsolete training and prediction scripts * lint contrastive scripts --- .../contrastive_scripts/demo_fit.py | 2 - .../contrastive_scripts/predict.py | 157 ------------- .../predict_infection_score_supervised.py | 12 +- .../contrastive_scripts/profile_dataloader.py | 7 +- .../contrastive_scripts/training_script.py | 216 ------------------ 5 files changed, 8 insertions(+), 386 deletions(-) delete mode 100644 applications/contrastive_phenotyping/contrastive_scripts/predict.py delete mode 100644 applications/contrastive_phenotyping/contrastive_scripts/training_script.py diff --git a/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py b/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py index 27f9f532..14e0a8ac 100644 --- a/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py +++ b/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py @@ -1,8 +1,6 @@ from lightning.pytorch import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger -from lightning.pytorch.callbacks import DeviceStatsMonitor - from viscy.data.triplet import TripletDataModule from viscy.light.engine import ContrastiveModule diff --git a/applications/contrastive_phenotyping/contrastive_scripts/predict.py b/applications/contrastive_phenotyping/contrastive_scripts/predict.py deleted file mode 100644 index a2136491..00000000 --- a/applications/contrastive_phenotyping/contrastive_scripts/predict.py +++ /dev/null @@ -1,157 +0,0 @@ -from argparse import ArgumentParser -from pathlib import Path -import numpy as np -from lightning.pytorch import Trainer -from lightning.pytorch.callbacks import TQDMProgressBar -from lightning.pytorch.strategies import DDPStrategy -from viscy.data.triplet import TripletDataModule, TripletDataset -from viscy.light.engine import ContrastiveModule -import os -from torch.multiprocessing import Manager -from viscy.transforms import ( - NormalizeSampled, - RandAdjustContrastd, - RandAffined, - RandGaussianNoised, - RandGaussianSmoothd, - RandScaleIntensityd, - RandWeightedCropd, -) -from monai.transforms import NormalizeIntensityd, ScaleIntensityRangePercentilesd - -# Updated normalizations -normalizations = [ - NormalizeIntensityd( - keys=["Phase3D"], - subtrahend=None, - divisor=None, - nonzero=False, - channel_wise=False, - dtype=None, - allow_missing_keys=False - ), - ScaleIntensityRangePercentilesd( - keys=["MultiCam_GFP_mCherry_BF-Prime BSI Express"], - lower=50, - upper=99, - b_min=0.0, - b_max=1.0, - clip=False, - relative=False, - channel_wise=False, - dtype=None, - allow_missing_keys=False - ), -] - -def main(hparams): - # Set paths - # /hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/6-patches/expanded_final_track_timesteps.csv - # /hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/uninfected_cells.csv - # /hpc/mydata/alishba.imran/VisCy/viscy/applications/contrastive_phenotyping/expanded_transitioning_cells_metadata.csv - checkpoint_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/test_tb/lightning_logs/copy/contrastive_model-test-epoch=24-val_loss=0.00.ckpt" - - # non-rechunked data - # /hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/5-infection_pred/infection_score.zarr - # /hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr - # /hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/2.1-register/registered.zarr - data_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/5-infection_pred/infection_score.zarr" - - # updated tracking data - # /hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking/test_tracking_4.zarr - # /hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr - tracks_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking/test_tracking_4.zarr" - - # MultiCam_GFP_mCherry_BF-Prime BSI Express for June dataset - source_channel = ["MultiCam_GFP_mCherry_BF-Prime BSI Express", "Phase3D"] - z_range = (28, 43) - batch_size = 1 - - # infected cells - JUNE - # include_fov_names = ['/0/8/001001', '/0/8/001001', '/0/8/000001', '/0/6/002002', '/0/6/002002', '/0/6/00200'] - # include_track_ids = [31, 8, 21, 4, 2, 21] - - # # uninfected cells - JUNE - # include_fov_names = ['/0/1/000000', '/0/1/000000', '/0/1/000000', '/0/1/000000', '/0/8/000002', '/0/8/000002'] - # include_track_ids = [25, 36, 37, 48, 16, 17] - - # # dividing cells - JUNE - # include_fov_names = ['/0/1/000000', '/0/1/000000', '/0/1/000000'] - # include_track_ids = [18, 21, 50] - - # uninfected cells - FEB - # include_fov_names = ['/A/3/0', 'B/3/5', 'B/3/5', 'B/3/5', 'B/3/5', '/A/4/14', '/A/4/14'] - # include_track_ids = [15, 34, 32, 31, 26, 33, 30] - - # # infected cells - FEB - # include_fov_names = ['/A/4/13', '/A/4/14', '/B/4/4', '/B/4/5', '/B/4/6', '/B/4/6'] - # include_track_ids = [25, 19, 68, 11, 29, 35] - - # # dividing cells - FEB - # include_fov_names = ['/B/4/4', '/B/3/5'] - # include_track_ids = [71, 42] - - # Initialize the data module for prediction - data_module = TripletDataModule( - data_path=data_path, - tracks_path=tracks_path, - source_channel=source_channel, - z_range=z_range, - initial_yx_patch_size=(224, 224), - final_yx_patch_size=(224, 224), - batch_size=batch_size, - num_workers=hparams.num_workers, - normalizations=normalizations, - # predict_cells = True, - # include_fov_names=include_fov_names, - # include_track_ids=include_track_ids, - ) - - data_module.setup(stage="predict") - - print(f"Total prediction dataset size: {len(data_module.predict_dataset)}") - - # Load the model from checkpoint - backbone = "convnext_tiny" - in_stack_depth = 15 - stem_kernel_size = (5, 3, 3) - model = ContrastiveModule.load_from_checkpoint( - str(checkpoint_path), - predict=True, - backbone=backbone, - in_channels=len(source_channel), - in_stack_depth=in_stack_depth, - stem_kernel_size=stem_kernel_size, - tracks_path = tracks_path, - ) - - model.eval() - - # Initialize the trainer - trainer = Trainer( - accelerator="gpu", - devices=1, - num_nodes=1, - strategy=DDPStrategy(find_unused_parameters=False), - callbacks=[TQDMProgressBar(refresh_rate=1)], - ) - - # Run prediction - trainer.predict(model, datamodule=data_module) - -if __name__ == "__main__": - parser = ArgumentParser() - parser.add_argument("--backbone", type=str, default="convnext_tiny") - parser.add_argument("--margin", type=float, default=0.5) - parser.add_argument("--lr", type=float, default=1e-3) - parser.add_argument("--schedule", type=str, default="Constant") - parser.add_argument("--log_steps_per_epoch", type=int, default=10) - parser.add_argument("--embedding_len", type=int, default=256) - parser.add_argument("--max_epochs", type=int, default=100) - parser.add_argument("--accelerator", type=str, default="gpu") - parser.add_argument("--devices", type=int, default=1) - parser.add_argument("--num_nodes", type=int, default=1) - parser.add_argument("--log_every_n_steps", type=int, default=1) - parser.add_argument("--num_workers", type=int, default=8) - args = parser.parse_args() - main(args) \ No newline at end of file diff --git a/applications/contrastive_phenotyping/contrastive_scripts/predict_infection_score_supervised.py b/applications/contrastive_phenotyping/contrastive_scripts/predict_infection_score_supervised.py index f20901b9..fb64b9f0 100644 --- a/applications/contrastive_phenotyping/contrastive_scripts/predict_infection_score_supervised.py +++ b/applications/contrastive_phenotyping/contrastive_scripts/predict_infection_score_supervised.py @@ -1,13 +1,13 @@ +import os +import warnings from argparse import ArgumentParser -from pathlib import Path + import numpy as np -import os -import torch +import pandas as pd from torch.utils.data import DataLoader from tqdm import tqdm -from viscy.data.triplet import TripletDataModule, TripletDataset -import pandas as pd -import warnings + +from viscy.data.triplet import TripletDataModule warnings.filterwarnings( "ignore", diff --git a/applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.py b/applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.py index 57fe0a03..e4fbcbe0 100644 --- a/applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.py +++ b/applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.py @@ -1,13 +1,10 @@ # %% Imports and initialization. -import os import time -import warnings -from pathlib import Path -from tqdm import tqdm -from viscy.data.triplet import TripletDataModule from monai.transforms import NormalizeIntensityd, ScaleIntensityRangePercentilesd +from tqdm import tqdm +from viscy.data.triplet import TripletDataModule # %% Setup parameters for dataloader # rechunked data diff --git a/applications/contrastive_phenotyping/contrastive_scripts/training_script.py b/applications/contrastive_phenotyping/contrastive_scripts/training_script.py deleted file mode 100644 index a9f9a19e..00000000 --- a/applications/contrastive_phenotyping/contrastive_scripts/training_script.py +++ /dev/null @@ -1,216 +0,0 @@ -# %% Imports and paths. -import logging -import os -from argparse import ArgumentParser -from pathlib import Path - -import torch -from torch.utils.data import DataLoader -from lightning.pytorch import Trainer -from lightning.pytorch.callbacks import ModelCheckpoint -from lightning.pytorch.strategies import DDPStrategy -from viscy.transforms import ( - NormalizeSampled, - RandAdjustContrastd, - RandAffined, - RandGaussianNoised, - RandGaussianSmoothd, - RandScaleIntensityd, - RandWeightedCropd, -) - -from viscy.data.triplet import TripletDataModule, TripletDataset -from viscy.light.engine import ContrastiveModule -from viscy.representation.contrastive import ContrastiveEncoder -import pandas as pd -from pathlib import Path -from monai.transforms import NormalizeIntensityd, ScaleIntensityRangePercentilesd -from lightning.pytorch.loggers import TensorBoardLogger -from lightning.pytorch.callbacks import DeviceStatsMonitor -from lightning.pytorch.callbacks import LearningRateMonitor - - -top_dir = Path("/hpc/projects/intracellular_dashboard/viral-sensor/") -model_dir = top_dir / "infection_classification/models/infection_score" - -# Data parameters -# 15 for covnext backbone, 12 for resnet (z slices) -# (28, 43) for covnext backbone, (26, 38) for resnet - -# rechunked data -data_path = "/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr" - -# updated tracking data -tracks_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr" -source_channel = ["RFP", "Phase3D"] -z_range = (28, 43) -batch_size = 64 - -# normalizations = [ -# # Normalization for Phase3D using mean and std -# NormalizeSampled( -# keys=["Phase3D"], -# level="fov_statistics", -# subtrahend="mean", -# divisor="std", -# ), -# # Normalization for RFP using median and IQR -# NormalizeSampled( -# keys=["RFP"], -# level="fov_statistics", -# subtrahend="median", -# divisor="iqr", -# ), -# ] - -# Updated normalizations -normalizations = [ - NormalizeIntensityd( - keys=["Phase3D"], - subtrahend=None, - divisor=None, - nonzero=False, - channel_wise=False, - dtype=None, - allow_missing_keys=False - ), - ScaleIntensityRangePercentilesd( - keys=["RFP"], - lower=50, - upper=99, - b_min=0.0, - b_max=1.0, - clip=False, - relative=False, - channel_wise=False, - dtype=None, - allow_missing_keys=False - ), -] - -augmentations = [ - # Apply rotations and scaling together to both channels - RandAffined( - keys=source_channel, - rotate_range=[3.14, 0.0, 0.0], - scale_range=[0.0, 0.2, 0.2], - prob=0.8, - padding_mode="zeros", - shear_range=[0.0, 0.01, 0.01], - ), - # Apply contrast adjustment separately for each channel - RandAdjustContrastd(keys=["RFP"], prob=0.5, gamma=(0.7, 1.3)), # Broader range for RFP - RandAdjustContrastd(keys=["Phase3D"], prob=0.5, gamma=(0.8, 1.2)), # Moderate range for Phase - # Apply intensity scaling separately for each channel - RandScaleIntensityd(keys=["RFP"], factors=0.7, prob=0.5), # Broader scaling for RFP - RandScaleIntensityd(keys=["Phase3D"], factors=0.5, prob=0.5), # Moderate scaling for Phase - # Apply Gaussian smoothing to both channels together - RandGaussianSmoothd( - keys=source_channel, - sigma_x=(0.25, 0.75), - sigma_y=(0.25, 0.75), - sigma_z=(0.0, 0.0), - prob=0.5, - ), - # Apply Gaussian noise separately for each channel - RandGaussianNoised(keys=["RFP"], prob=0.5, mean=0.0, std=0.5), # Higher noise for RFP - RandGaussianNoised(keys=["Phase3D"], prob=0.5, mean=0.0, std=0.2), # Moderate noise for Phase - ] - -torch.set_float32_matmul_precision("medium") - - -# %% Define the main function for training -def main(hparams): - num_gpus = torch.cuda.device_count() - print(f"Number of GPUs available: {num_gpus}") - - print("Starting data module..") - # Initialize the data module - data_module = TripletDataModule( - data_path=data_path, - tracks_path=tracks_path, - source_channel=source_channel, - z_range=z_range, - initial_yx_patch_size=(512, 512), - final_yx_patch_size=(224, 224), - batch_size=batch_size, - num_workers=hparams.num_workers, - normalizations=normalizations, - augmentations=augmentations, - ) - - print("data module set up!") - - # Setup the data module for training, val and testing - data_module.setup(stage="fit") - - print( - f"Total dataset size: {len(data_module.train_dataset) + len(data_module.val_dataset)}" - ) - print(f"Training dataset size: {len(data_module.train_dataset)}") - print(f"Validation dataset size: {len(data_module.val_dataset)}") - - # Initialize the model - model = ContrastiveModule( - backbone=hparams.backbone, - loss_function=torch.nn.TripletMarginLoss(), - margin=hparams.margin, - lr=hparams.lr, - schedule=hparams.schedule, - log_batches_per_epoch=1, # total 2 images per epoch are logged - log_samples_per_batch=2, - in_channels=len(source_channel), - in_stack_depth=z_range[1] - z_range[0], - stem_kernel_size=(5, 4, 4), - embedding_len=hparams.embedding_len, - ) - print("Model initialized!") - - lr_monitor = LearningRateMonitor(logging_interval='step') - - trainer = Trainer( - max_epochs=hparams.max_epochs, - # limit_train_batches=2, - # limit_val_batches=2, - callbacks=[ModelCheckpoint(), lr_monitor], - logger=TensorBoardLogger( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/test_tb", - log_graph=True, - default_hp_metric=True, - ), - accelerator=hparams.accelerator, - devices=hparams.devices, - num_nodes=hparams.num_nodes, - strategy=DDPStrategy(), - log_every_n_steps=hparams.log_every_n_steps, - num_sanity_val_steps=0, - ) - - - print("Trainer initialized!") - - trainer.fit(model, datamodule=data_module) - - # # Validate the model - trainer.validate(model, datamodule=data_module) - -# Argument parser for command-line options -# to-do: need to clean up to always use the same args -parser = ArgumentParser() -parser.add_argument("--backbone", type=str, default="convnext_tiny") -parser.add_argument("--margin", type=float, default=0.5) -parser.add_argument("--lr", type=float, default=0.00001) -parser.add_argument("--schedule", type=str, default="CosineAnnealingWarmRestarts") -parser.add_argument("--log_steps_per_epoch", type=int, default=10) -parser.add_argument("--embedding_len", type=int, default=256) -parser.add_argument("--max_epochs", type=int, default=100) -parser.add_argument("--accelerator", type=str, default="gpu") -parser.add_argument("--devices", type=int, default=1) # 4 GPUs -parser.add_argument("--num_nodes", type=int, default=1) -parser.add_argument("--log_every_n_steps", type=int, default=1) -parser.add_argument("--num_workers", type=int, default=15) -args = parser.parse_args() - -main(args) - From 1f269c73653bc6aa1b379012099699b5c4e9bb7f Mon Sep 17 00:00:00 2001 From: Ziwen Liu <67518483+ziw-liu@users.noreply.github.com> Date: Sat, 31 Aug 2024 09:22:44 -0400 Subject: [PATCH 59/87] SSL: fix MLP head and remove L2 normalization (#145) * draft projection head per Update the projection head (normalization and size). #139 * reorganize comments in example fit config * configurable stem stride and projection dimensions * update type hint and docstring for ContrastiveEncoder * clarify embedding_dim * use the forward method directly for projected * normalize projections only when fitting the projected features saved during prediction is now *not* normalized * remove unused logger * refactor training code into translation and representation modules * extract image logging functions * use AdamW instead of Adam for contrastive learning * inline single-use argument * fix normalization * fix MLP layer order * fix output dimensions * remove L2 normalization before computing loss * compute rank of features and projections * documentation --------- Co-authored-by: Shalin Mehta --- .../contrastive_cli/fit.yml | 43 +++- .../contrastive_cli/plot_embeddings.py | 22 +- .../contrastive_cli/predict.yml | 2 +- .../contrastive_scripts/demo_fit.py | 2 +- .../graphs_ConvNeXt_ResNet.py | 2 +- .../classify_infection_covnext.py | 2 +- .../predict_infection_classifier.py | 2 +- examples/configs/predict_example.yml | 2 +- .../VS_model_inference/demo_vscyto2d.py | 7 +- .../VS_model_inference/demo_vscyto3d.py | 6 +- .../VS_model_inference/demo_vsneuromast.py | 6 +- .../dlmbl_exercise/solution.py | 4 +- .../img2img_translation/solution.py | 4 +- tests/data/test_data.py | 2 +- tests/light/test_engine.py | 2 +- viscy/_log_images.py | 38 +++ viscy/cli/cli.py | 4 +- viscy/cli/contrastive_triplet.py | 2 +- viscy/data/hcs.py | 2 +- viscy/representation/contrastive.py | 116 ++++----- .../embedding_writer.py | 0 viscy/representation/engine.py | 164 +++++++++++++ viscy/scripts/count_flops.py | 2 +- viscy/scripts/network_diagram.py | 2 +- viscy/scripts/visualize_features.py | 2 +- viscy/{light => translation}/engine.py | 231 +----------------- .../{light => translation}/predict_writer.py | 0 viscy/{light => translation}/trainer.py | 0 28 files changed, 335 insertions(+), 336 deletions(-) create mode 100644 viscy/_log_images.py rename viscy/{light => representation}/embedding_writer.py (100%) create mode 100644 viscy/representation/engine.py rename viscy/{light => translation}/engine.py (70%) rename viscy/{light => translation}/predict_writer.py (100%) rename viscy/{light => translation}/trainer.py (100%) diff --git a/applications/contrastive_phenotyping/contrastive_cli/fit.yml b/applications/contrastive_phenotyping/contrastive_cli/fit.yml index 19ebe0e7..c153ee47 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/fit.yml +++ b/applications/contrastive_phenotyping/contrastive_cli/fit.yml @@ -1,4 +1,5 @@ -# See help here on how to configure hyper-parameters with config files: https://lightning.ai/docs/pytorch/stable/cli/lightning_cli_advanced.html +# See help here on how to configure hyper-parameters with config files: +# https://lightning.ai/docs/pytorch/stable/cli/lightning_cli_advanced.html seed_everything: 42 trainer: accelerator: gpu @@ -8,16 +9,19 @@ trainer: precision: 32-true logger: class_path: lightning.pytorch.loggers.TensorBoardLogger + # Nesting the logger config like this is equivalent to + # supplying the following argument to `lightning.pytorch.Trainer`: + # logger=TensorBoardLogger( + # "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations", + # log_graph=True, + # version="vanilla", + # ) init_args: save_dir: /hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations - version: chocolate # this is the name of the experiment. The logs will be saved in save_dir/lightning_logs/version + # this is the name of the experiment. + # The logs will be saved in `save_dir/lightning_logs/version` + version: l2_projection_batchnorm log_graph: True - # Nesting the logger config like this is equivalent to supplying the following argument to lightning.pytorch.Trainer - # logger=TensorBoardLogger( - # "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations", - # log_graph=True, - # version="vanilla", - # ) callbacks: - class_path: lightning.pytorch.callbacks.LearningRateMonitor init_args: @@ -34,12 +38,29 @@ trainer: enable_checkpointing: true inference_mode: true use_distributed_sampler: true + # synchronize batchnorm parameters across multiple GPUs. + # important for contrastive learning to normalize the tensors across the whole batch. + sync_batchnorm: true model: - backbone: convnext_tiny - in_channels: 2 + encoder: + class_path: viscy.representation.contrastive.ContrastiveEncoder + init_args: + backbone: convnext_tiny + in_channels: 2 + in_stack_depth: 15 + stem_kernel_size: [5, 4, 4] + stem_stride: [5, 4, 4] + embedding_dim: 768 + projection_dim: 128 + drop_path_rate: 0.0 + loss_function: + class_path: torch.nn.TripletMarginLoss + init_args: + margin: 0.5 + lr: 0.0002 log_batches_per_epoch: 3 log_samples_per_batch: 3 - lr: 0.0002 + example_input_array_shape: [1, 2, 15, 256, 256] data: data_path: /hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr tracks_path: /hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr diff --git a/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py b/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py index c87a0980..624d4304 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py +++ b/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py @@ -11,7 +11,7 @@ from umap import UMAP -from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.embedding_writer import read_embedding_dataset from viscy.data.triplet import TripletDataset, TripletDataModule from iohub import open_ome_zarr import monai.transforms as transforms @@ -19,13 +19,13 @@ # %% Paths and parameters. features_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations/predict/2024_02_04/tokenized-drop_path_0_0-2024-06-13.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations/predict/2024_06_13/l2_projection_batchnorm-128p.zarr" ) data_path = Path( - "/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/2-register/registered_chunked.zarr" ) tracks_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking/test_tracking_4.zarr" ) # %% @@ -34,8 +34,8 @@ # %% # Compute PCA of the features and projections to estimate the number of components to keep. -PCA_features = PCA().fit(embedding_dataset["features"].values) -PCA_projection = PCA().fit(embedding_dataset["projections"].values) +PCA_features = PCA(n_components=100).fit(embedding_dataset["features"].values) +PCA_projection = PCA(n_components=100).fit(embedding_dataset["projections"].values) plt.plot(PCA_features.explained_variance_ratio_, label="features") plt.plot(PCA_projection.explained_variance_ratio_, label="projections") @@ -50,11 +50,15 @@ # * Heatmaps of annotations over UMAPs. +# %% +print(np.linalg.matrix_rank(embedding_dataset["features"].values)) +print(np.linalg.matrix_rank(embedding_dataset["projections"].values)) + # %% # Extract a track from the dataset and visualize its features. -fov_name = "/B/4/4" -track_id = 71 +fov_name = "/0/1/000000" # "/B/4/4" FOV names can change between datasets. +track_id = 21 all_tracks_FOV = embedding_dataset.sel(fov_name=fov_name) a_track_in_FOV = all_tracks_FOV.sel(track_id=track_id) # Why is sample dimension ~22000 long after the dataset is sliced by FOV and by track_id? @@ -253,7 +257,7 @@ def load_annotation(da, path, name, categories: dict | None = None): # %% ann_root = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking" ) infection = load_annotation( diff --git a/applications/contrastive_phenotyping/contrastive_cli/predict.yml b/applications/contrastive_phenotyping/contrastive_cli/predict.yml index 038cbbc7..763d8b71 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/predict.yml +++ b/applications/contrastive_phenotyping/contrastive_cli/predict.yml @@ -6,7 +6,7 @@ trainer: num_nodes: 1 precision: 32-true callbacks: - - class_path: viscy.light.embedding_writer.EmbeddingWriter + - class_path: viscy.representation.embedding_writer.EmbeddingWriter init_args: output_path: "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations/predict/test_prediction_code.zarr" # edit the following lines to specify logging path diff --git a/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py b/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py index 14e0a8ac..b3c75b30 100644 --- a/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py +++ b/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py @@ -3,7 +3,7 @@ from lightning.pytorch.loggers import TensorBoardLogger from viscy.data.triplet import TripletDataModule -from viscy.light.engine import ContrastiveModule +from viscy.representation.engine import ContrastiveModule def main(): diff --git a/applications/contrastive_phenotyping/contrastive_scripts/graphs_ConvNeXt_ResNet.py b/applications/contrastive_phenotyping/contrastive_scripts/graphs_ConvNeXt_ResNet.py index 03781217..5ddb2252 100644 --- a/applications/contrastive_phenotyping/contrastive_scripts/graphs_ConvNeXt_ResNet.py +++ b/applications/contrastive_phenotyping/contrastive_scripts/graphs_ConvNeXt_ResNet.py @@ -3,7 +3,7 @@ import torch import torchview -from viscy.light.engine import ContrastiveModule +from viscy.representation.engine import ContrastiveModule from viscy.representation.contrastive import ContrastiveEncoder, UNeXt2Stem # %load_ext autoreload diff --git a/applications/infection_classification/classify_infection_covnext.py b/applications/infection_classification/classify_infection_covnext.py index 5eddb236..397e822d 100644 --- a/applications/infection_classification/classify_infection_covnext.py +++ b/applications/infection_classification/classify_infection_covnext.py @@ -15,7 +15,7 @@ from torch import Tensor from viscy.data.hcs import Sample -from viscy.light.engine import VSUNet +from viscy.translation.engine import VSUNet # # %% Methods to compute confusion matrix per cell using torchmetrics diff --git a/applications/infection_classification/predict_infection_classifier.py b/applications/infection_classification/predict_infection_classifier.py index 458fc670..bcf4ec7f 100644 --- a/applications/infection_classification/predict_infection_classifier.py +++ b/applications/infection_classification/predict_infection_classifier.py @@ -6,7 +6,7 @@ ) from viscy.data.hcs import HCSDataModule -from viscy.light.predict_writer import HCSPredictionWriter +from viscy.translation.predict_writer import HCSPredictionWriter from viscy.transforms import NormalizeSampled # %% # %% write the predictions to a zarr file diff --git a/examples/configs/predict_example.yml b/examples/configs/predict_example.yml index b2556139..1face7f0 100644 --- a/examples/configs/predict_example.yml +++ b/examples/configs/predict_example.yml @@ -8,7 +8,7 @@ predict: num_nodes: 1 precision: 32-true callbacks: - - class_path: viscy.light.predict_writer.HCSPredictionWriter + - class_path: viscy.translation.predict_writer.HCSPredictionWriter init_args: output_store: null write_input: false diff --git a/examples/virtual_staining/VS_model_inference/demo_vscyto2d.py b/examples/virtual_staining/VS_model_inference/demo_vscyto2d.py index 1c920a28..e4be2d9e 100644 --- a/examples/virtual_staining/VS_model_inference/demo_vscyto2d.py +++ b/examples/virtual_staining/VS_model_inference/demo_vscyto2d.py @@ -11,11 +11,12 @@ from iohub import open_ome_zarr from plot import plot_vs_n_fluor + # Viscy classes for the trainer and model from viscy.data.hcs import HCSDataModule -from viscy.light.engine import FcmaeUNet -from viscy.light.predict_writer import HCSPredictionWriter -from viscy.light.trainer import VSTrainer +from viscy.translation.engine import FcmaeUNet +from viscy.translation.predict_writer import HCSPredictionWriter +from viscy.translation.trainer import VSTrainer from viscy.transforms import NormalizeSampled # %% [markdown] diff --git a/examples/virtual_staining/VS_model_inference/demo_vscyto3d.py b/examples/virtual_staining/VS_model_inference/demo_vscyto3d.py index 928de1b7..95d82b01 100644 --- a/examples/virtual_staining/VS_model_inference/demo_vscyto3d.py +++ b/examples/virtual_staining/VS_model_inference/demo_vscyto3d.py @@ -13,9 +13,9 @@ from plot import plot_vs_n_fluor from viscy.data.hcs import HCSDataModule # Viscy classes for the trainer and model -from viscy.light.engine import VSUNet -from viscy.light.predict_writer import HCSPredictionWriter -from viscy.light.trainer import VSTrainer +from viscy.translation.engine import VSUNet +from viscy.translation.predict_writer import HCSPredictionWriter +from viscy.translation.trainer import VSTrainer from viscy.transforms import NormalizeSampled # %% [markdown] diff --git a/examples/virtual_staining/VS_model_inference/demo_vsneuromast.py b/examples/virtual_staining/VS_model_inference/demo_vsneuromast.py index 017ad4ef..a097418c 100644 --- a/examples/virtual_staining/VS_model_inference/demo_vsneuromast.py +++ b/examples/virtual_staining/VS_model_inference/demo_vsneuromast.py @@ -13,9 +13,9 @@ from plot import plot_vs_n_fluor from viscy.data.hcs import HCSDataModule # Viscy classes for the trainer and model -from viscy.light.engine import VSUNet -from viscy.light.predict_writer import HCSPredictionWriter -from viscy.light.trainer import VSTrainer +from viscy.translation.engine import VSUNet +from viscy.translation.predict_writer import HCSPredictionWriter +from viscy.translation.trainer import VSTrainer from viscy.transforms import NormalizeSampled # %% [markdown] diff --git a/examples/virtual_staining/dlmbl_exercise/solution.py b/examples/virtual_staining/dlmbl_exercise/solution.py index 93fc4917..ff0ba819 100644 --- a/examples/virtual_staining/dlmbl_exercise/solution.py +++ b/examples/virtual_staining/dlmbl_exercise/solution.py @@ -118,8 +118,8 @@ from viscy.data.hcs import HCSDataModule from viscy.evaluation.evaluation_metrics import mean_average_precision # Trainer class and UNet. -from viscy.light.engine import MixedLoss, VSUNet -from viscy.light.trainer import VSTrainer +from viscy.translation.engine import MixedLoss, VSUNet +from viscy.translation.trainer import VSTrainer # training augmentations from viscy.transforms import (NormalizeSampled, RandAdjustContrastd, RandAffined, RandGaussianNoised, diff --git a/examples/virtual_staining/img2img_translation/solution.py b/examples/virtual_staining/img2img_translation/solution.py index c1f0f7e2..a71d5329 100644 --- a/examples/virtual_staining/img2img_translation/solution.py +++ b/examples/virtual_staining/img2img_translation/solution.py @@ -128,8 +128,8 @@ # HCSDataModule makes it easy to load data during training. from viscy.data.hcs import HCSDataModule # Trainer class and UNet. -from viscy.light.engine import MixedLoss, VSUNet -from viscy.light.trainer import VSTrainer +from viscy.translation.engine import MixedLoss, VSUNet +from viscy.translation.trainer import VSTrainer # training augmentations from viscy.transforms import (NormalizeSampled, RandAdjustContrastd, RandAffined, RandGaussianNoised, diff --git a/tests/data/test_data.py b/tests/data/test_data.py index 8eb06352..ead8afc0 100644 --- a/tests/data/test_data.py +++ b/tests/data/test_data.py @@ -5,7 +5,7 @@ from pytest import mark from viscy.data.hcs import HCSDataModule -from viscy.light.trainer import VSTrainer +from viscy.translation.trainer import VSTrainer @mark.parametrize("default_channels", [True, False]) diff --git a/tests/light/test_engine.py b/tests/light/test_engine.py index 9ce182f5..db93998b 100644 --- a/tests/light/test_engine.py +++ b/tests/light/test_engine.py @@ -1,4 +1,4 @@ -from viscy.light.engine import FcmaeUNet +from viscy.translation.engine import FcmaeUNet def test_fcmae_vsunet() -> None: diff --git a/viscy/_log_images.py b/viscy/_log_images.py new file mode 100644 index 00000000..cc6b0fe4 --- /dev/null +++ b/viscy/_log_images.py @@ -0,0 +1,38 @@ +from typing import Sequence + +import numpy as np +from matplotlib.pyplot import get_cmap +from skimage.exposure import rescale_intensity +from torch import Tensor + + +def detach_sample(imgs: Sequence[Tensor], log_samples_per_batch: int): + num_samples = min(imgs[0].shape[0], log_samples_per_batch) + samples = [] + for i in range(num_samples): + patches = [] + for img in imgs: + patch = img[i].detach().cpu().numpy() + patch = np.squeeze(patch[:, patch.shape[1] // 2]) + patches.append(patch) + samples.append(patches) + return samples + + +def render_images(imgs: Sequence[Sequence[np.ndarray]], cmaps: list[str] = []): + images_grid = [] + for sample_images in imgs: + images_row = [] + for i, image in enumerate(sample_images): + if cmaps: + cm_name = cmaps[i] + else: + cm_name = "gray" if i == 0 else "inferno" + if image.ndim == 2: + image = image[np.newaxis] + for channel in image: + channel = rescale_intensity(channel, out_range=(0, 1)) + render = get_cmap(cm_name)(channel, bytes=True)[..., :3] + images_row.append(render) + images_grid.append(np.concatenate(images_row, axis=1)) + return np.concatenate(images_grid, axis=0) diff --git a/viscy/cli/cli.py b/viscy/cli/cli.py index f9a55f12..af8fb517 100644 --- a/viscy/cli/cli.py +++ b/viscy/cli/cli.py @@ -10,8 +10,8 @@ from lightning.pytorch.loggers import TensorBoardLogger from viscy.data.hcs import HCSDataModule -from viscy.light.engine import VSUNet -from viscy.light.trainer import VSTrainer +from viscy.translation.engine import VSUNet +from viscy.translation.trainer import VSTrainer class VSLightningCLI(LightningCLI): diff --git a/viscy/cli/contrastive_triplet.py b/viscy/cli/contrastive_triplet.py index 1d2a41aa..f890b59c 100644 --- a/viscy/cli/contrastive_triplet.py +++ b/viscy/cli/contrastive_triplet.py @@ -8,7 +8,7 @@ from lightning.pytorch.loggers import TensorBoardLogger from viscy.data.triplet import TripletDataModule -from viscy.light.engine import ContrastiveModule +from viscy.representation.engine import ContrastiveModule class ContrastiveLightningCLI(LightningCLI): diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index e8ba12fa..9ee37185 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -229,7 +229,7 @@ def __getitem__(self, index: int) -> Sample: class MaskTestDataset(SlidingWindowDataset): """Torch dataset where each element is a window of (C, Z, Y, X) where C=2 (source and target) and Z is ``z_window_size``. - This a testing stage version of :py:class:`viscy.light.data.SlidingWindowDataset`, + This a testing stage version of :py:class:`viscy.data.hcs.SlidingWindowDataset`, and can only be used with batch size 1 for efficiency (no padding for collation), since the mask is not available for each stack. diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 13be2b15..4e635357 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -1,103 +1,91 @@ -import logging +from typing import Literal import timm import torch.nn as nn -import torch.nn.functional as F +from torch import Tensor from viscy.unet.networks.unext2 import StemDepthtoChannels -_logger = logging.getLogger("lightning.pytorch") - class ContrastiveEncoder(nn.Module): + """ + Contrastive encoder network that uses ConvNeXt and ResNet backbones from timm. + + Parameters + ---------- + backbone : Literal["convnext_tiny", "resnet50"] + Name of the timm backbone architecture + in_channels : int, optional + Number of input channels + in_stack_depth : int, optional + Number of input Z slices + stem_kernel_size : tuple[int, int, int], optional + Stem kernel size, by default (5, 4, 4) + stem_stride : tuple[int, int, int], optional + Stem stride, by default (5, 4, 4) + embedding_dim : int, optional + Embedded feature dimension that matches backbone output channels, + by default 768 (convnext_tiny) + projection_dim : int, optional + Projection dimension for computing loss, by default 128 + drop_path_rate : float, optional + probability that residual connections are dropped during training, + by default 0.0 + """ + def __init__( self, - backbone: str = "convnext_tiny", - in_channels: int = 2, - in_stack_depth: int = 12, + backbone: Literal["convnext_tiny", "resnet50"], + in_channels: int, + in_stack_depth: int, stem_kernel_size: tuple[int, int, int] = (5, 4, 4), - embedding_len: int = 256, - stem_stride: int = 2, - predict: bool = False, - drop_path_rate: float = 0.2, - ): - """ContrastiveEncoder network that uses - ConvNext and ResNet backbons from timm. - - :param str backbone: Backbone architecture for the encoder, - defaults to "convnext_tiny" - :param int in_channels: Number of input channels, defaults to 2 - :param int in_stack_depth: Number of input slices in z-stack, defaults to 12 - :param tuple[int, int, int] stem_kernel_size: 3D kernel size for the stem. - Input stack depth must be divisible by the kernel depth, - defaults to (5, 3, 3) - :param int embedding_len: Length of the embedding vector, defaults to 256 - :param int stem_stride: stride of the stem, defaults to 2 - :param bool predict: prediction mode, defaults to False - :param float drop_path_rate: probability that residual connections - are dropped during training, defaults to 0.2 - """ + stem_stride: tuple[int, int, int] = (5, 4, 4), + embedding_dim: int = 768, + projection_dim: int = 128, + drop_path_rate: float = 0.0, + ) -> None: super().__init__() - self.predict = predict self.backbone = backbone - encoder = timm.create_model( backbone, pretrained=True, features_only=False, drop_path_rate=drop_path_rate, - num_classes=3 * embedding_len, + num_classes=embedding_dim, ) - if "convnext" in backbone: - _logger.debug(f"Using ConvNeXt backbone for {type(self).__name__}.") - in_channels_encoder = encoder.stem[0].out_channels - # Remove the convolution layer of stem, but keep the layernorm. encoder.stem[0] = nn.Identity() - - # Save projection head separately and erase the projection head contained within the encoder. - projection = nn.Sequential( - nn.Linear(encoder.head.fc.in_features, 3 * embedding_len), - nn.ReLU(inplace=True), - nn.Linear(3 * embedding_len, embedding_len), - ) - - encoder.head.fc = nn.Identity() - elif "resnet" in backbone: - _logger.debug(f"Using ResNet backbone for {type(self).__name__}") # Adapt stem and projection head of resnet here. # replace the stem designed for RGB images with a stem designed to handle 3D multi-channel input. - in_channels_encoder = encoder.conv1.out_channels encoder.conv1 = nn.Identity() - - projection = nn.Sequential( - nn.Linear(encoder.fc.in_features, 3 * embedding_len), - nn.ReLU(inplace=True), - nn.Linear(3 * embedding_len, embedding_len), - ) - encoder.fc = nn.Identity() - + # Save projection head separately and erase the projection head contained within the encoder. + projection = nn.Sequential( + nn.Linear(encoder.head.fc.in_features, embedding_dim), + nn.BatchNorm1d(embedding_dim), + nn.ReLU(inplace=True), + nn.Linear(embedding_dim, projection_dim), + nn.BatchNorm1d(projection_dim), + ) + encoder.head.fc = nn.Identity() # Create a new stem that can handle 3D multi-channel input. - _logger.debug(f"Stem kernel size: {stem_kernel_size}") self.stem = StemDepthtoChannels( - in_channels, in_stack_depth, in_channels_encoder, stem_kernel_size + in_channels=in_channels, + in_stack_depth=in_stack_depth, + in_channels_encoder=in_channels_encoder, + stem_kernel_size=stem_kernel_size, + stem_stride=stem_stride, ) - # Append modified encoder. self.encoder = encoder # Append modified projection head. self.projection = projection - def forward(self, x): + def forward(self, x) -> tuple[Tensor, Tensor]: x = self.stem(x) embedding = self.encoder(x) projections = self.projection(embedding) - projections = F.normalize(projections, p=2, dim=1) - return ( - embedding, - projections, - ) # Compute the loss on projections, analyze the embeddings. + return (embedding, projections) diff --git a/viscy/light/embedding_writer.py b/viscy/representation/embedding_writer.py similarity index 100% rename from viscy/light/embedding_writer.py rename to viscy/representation/embedding_writer.py diff --git a/viscy/representation/engine.py b/viscy/representation/engine.py new file mode 100644 index 00000000..c4a04aba --- /dev/null +++ b/viscy/representation/engine.py @@ -0,0 +1,164 @@ +import logging +from typing import Literal, Sequence + +import numpy as np +import torch +import torch.nn.functional as F +from lightning.pytorch import LightningModule +from torch import Tensor, nn + +from viscy._log_images import detach_sample, render_images +from viscy.data.typing import TripletSample +from viscy.representation.contrastive import ContrastiveEncoder + +_logger = logging.getLogger("lightning.pytorch") + + +class ContrastiveModule(LightningModule): + """Contrastive Learning Model for self-supervised learning.""" + + def __init__( + self, + encoder: nn.Module | ContrastiveEncoder, + loss_function: ( + nn.Module | nn.CosineEmbeddingLoss | nn.TripletMarginLoss + ) = nn.TripletMarginLoss(margin=0.5), + lr: float = 1e-3, + schedule: Literal["WarmupCosine", "Constant"] = "Constant", + log_batches_per_epoch: int = 8, + log_samples_per_batch: int = 1, + example_input_array_shape: Sequence[int] = (1, 2, 15, 256, 256), + ) -> None: + super().__init__() + self.model = encoder + self.loss_function = loss_function + self.lr = lr + self.schedule = schedule + self.log_batches_per_epoch = log_batches_per_epoch + self.log_samples_per_batch = log_samples_per_batch + self.example_input_array = torch.rand(*example_input_array_shape) + self.training_step_outputs = [] + self.validation_step_outputs = [] + + def forward(self, x: Tensor) -> Tensor: + "Only return projected embeddings for training and validation." + return self.model(x)[1] + + def log_feature_statistics(self, embeddings: Tensor, prefix: str): + mean = torch.mean(embeddings, dim=0).detach().cpu().numpy() + std = torch.std(embeddings, dim=0).detach().cpu().numpy() + _logger.debug(f"{prefix}_mean: {mean}") + _logger.debug(f"{prefix}_std: {std}") + + def print_embedding_norms(self, anchor, positive, negative, phase): + anchor_norm = torch.norm(anchor, dim=1).mean().item() + positive_norm = torch.norm(positive, dim=1).mean().item() + negative_norm = torch.norm(negative, dim=1).mean().item() + _logger.debug(f"{phase}/anchor_norm: {anchor_norm}") + _logger.debug(f"{phase}/positive_norm: {positive_norm}") + _logger.debug(f"{phase}/negative_norm: {negative_norm}") + + def _log_metrics( + self, loss, anchor, positive, negative, stage: Literal["train", "val"] + ): + self.log( + f"loss/{stage}", + loss.to(self.device), + on_step=True, + on_epoch=True, + prog_bar=True, + logger=True, + sync_dist=True, + ) + cosine_sim_pos = F.cosine_similarity(anchor, positive, dim=1).mean() + cosine_sim_neg = F.cosine_similarity(anchor, negative, dim=1).mean() + euclidean_dist_pos = F.pairwise_distance(anchor, positive).mean() + euclidean_dist_neg = F.pairwise_distance(anchor, negative).mean() + self.log_dict( + { + f"metrics/cosine_similarity_positive/{stage}": cosine_sim_pos, + f"metrics/cosine_similarity_negative/{stage}": cosine_sim_neg, + f"metrics/euclidean_distance_positive/{stage}": euclidean_dist_pos, + f"metrics/euclidean_distance_negative/{stage}": euclidean_dist_neg, + }, + on_step=False, + on_epoch=True, + logger=True, + sync_dist=True, + ) + + def _log_samples(self, key: str, imgs: Sequence[Sequence[np.ndarray]]): + grid = render_images(imgs, cmaps=["gray"] * 3) + self.logger.experiment.add_image( + key, grid, self.current_epoch, dataformats="HWC" + ) + + def training_step(self, batch: TripletSample, batch_idx: int) -> Tensor: + anchor_img = batch["anchor"] + pos_img = batch["positive"] + neg_img = batch["negative"] + anchor_projection = self(anchor_img) + negative_projection = self(neg_img) + positive_projection = self(pos_img) + loss = self.loss_function( + anchor_projection, positive_projection, negative_projection + ) + self._log_metrics( + loss, + anchor_projection, + positive_projection, + negative_projection, + stage="train", + ) + if batch_idx < self.log_batches_per_epoch: + self.training_step_outputs.extend( + detach_sample( + (anchor_img, pos_img, neg_img), self.log_samples_per_batch + ) + ) + return loss + + def on_train_epoch_end(self) -> None: + super().on_train_epoch_end() + self._log_samples("train_samples", self.training_step_outputs) + self.training_step_outputs = [] + + def validation_step(self, batch: TripletSample, batch_idx: int) -> Tensor: + """Validation step of the model.""" + anchor = batch["anchor"] + pos_img = batch["positive"] + neg_img = batch["negative"] + anchor_projection = self(anchor) + negative_projection = self(neg_img) + positive_projection = self(pos_img) + loss = self.loss_function( + anchor_projection, positive_projection, negative_projection + ) + self._log_metrics( + loss, anchor_projection, positive_projection, negative_projection, "val" + ) + if batch_idx < self.log_batches_per_epoch: + self.validation_step_outputs.extend( + detach_sample((anchor, pos_img, neg_img), self.log_samples_per_batch) + ) + return loss + + def on_validation_epoch_end(self) -> None: + super().on_validation_epoch_end() + self._log_samples("val_samples", self.validation_step_outputs) + self.validation_step_outputs = [] + + def configure_optimizers(self): + optimizer = torch.optim.AdamW(self.parameters(), lr=self.lr) + return optimizer + + def predict_step( + self, batch: TripletSample, batch_idx, dataloader_idx=0 + ) -> dict[str, Tensor | dict]: + """Prediction step for extracting embeddings.""" + features, projections = self.model(batch["anchor"]) + return { + "features": features, + "projections": projections, + "index": batch["index"], + } diff --git a/viscy/scripts/count_flops.py b/viscy/scripts/count_flops.py index 206d744f..ec461089 100644 --- a/viscy/scripts/count_flops.py +++ b/viscy/scripts/count_flops.py @@ -2,7 +2,7 @@ import torch from ptflops import get_model_complexity_info -from viscy.light.engine import VSUNet +from viscy.translation.engine import VSUNet # %% model = VSUNet( diff --git a/viscy/scripts/network_diagram.py b/viscy/scripts/network_diagram.py index 8e1b8b96..2d7985a8 100644 --- a/viscy/scripts/network_diagram.py +++ b/viscy/scripts/network_diagram.py @@ -1,7 +1,7 @@ # %% from torchview import draw_graph -from viscy.light.engine import FcmaeUNet, VSUNet +from viscy.translation.engine import FcmaeUNet, VSUNet # %% 2D UNet model = VSUNet( diff --git a/viscy/scripts/visualize_features.py b/viscy/scripts/visualize_features.py index c331bf46..00f1a653 100644 --- a/viscy/scripts/visualize_features.py +++ b/viscy/scripts/visualize_features.py @@ -17,7 +17,7 @@ from sklearn.decomposition import PCA from sklearn.manifold import TSNE -from viscy.light.engine import VSUNet +from viscy.translation.engine import VSUNet # %% # prepare sample images diff --git a/viscy/light/engine.py b/viscy/translation/engine.py similarity index 70% rename from viscy/light/engine.py rename to viscy/translation/engine.py index 4c474c4a..b14c5133 100644 --- a/viscy/light/engine.py +++ b/viscy/translation/engine.py @@ -7,12 +7,9 @@ import torch.nn.functional as F from imageio import imwrite from lightning.pytorch import LightningModule -from matplotlib.pyplot import get_cmap from monai.optimizers import WarmupCosineSchedule from monai.transforms import DivisiblePad, Rotate90 -from skimage.exposure import rescale_intensity from torch import Tensor, nn -from torch.optim import Adam from torch.optim.lr_scheduler import ConstantLR from torchmetrics.functional import ( accuracy, @@ -26,9 +23,9 @@ structural_similarity_index_measure, ) -from viscy.data.typing import Sample, TripletSample +from viscy._log_images import detach_sample, render_images +from viscy.data.typing import Sample from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d -from viscy.representation.contrastive import ContrastiveEncoder from viscy.unet.networks.fcmae import FullyConvolutionalMAE from viscy.unet.networks.Unet2D import Unet2d from viscy.unet.networks.Unet25D import Unet25d @@ -51,38 +48,6 @@ _logger = logging.getLogger("lightning.pytorch") -def _detach_sample(imgs: Sequence[Tensor], log_samples_per_batch: int): - num_samples = min(imgs[0].shape[0], log_samples_per_batch) - samples = [] - for i in range(num_samples): - patches = [] - for img in imgs: - patch = img[i].detach().cpu().numpy() - patch = np.squeeze(patch[:, patch.shape[1] // 2]) - patches.append(patch) - samples.append(patches) - return samples - - -def _render_images(imgs: Sequence[Sequence[np.ndarray]], cmaps: list[str] = []): - images_grid = [] - for sample_images in imgs: - images_row = [] - for i, image in enumerate(sample_images): - if cmaps: - cm_name = cmaps[i] - else: - cm_name = "gray" if i == 0 else "inferno" - if image.ndim == 2: - image = image[np.newaxis] - for channel in image: - channel = rescale_intensity(channel, out_range=(0, 1)) - render = get_cmap(cm_name)(channel, bytes=True)[..., :3] - images_row.append(render) - images_grid.append(np.concatenate(images_row, axis=1)) - return np.concatenate(images_grid, axis=0) - - class MixedLoss(nn.Module): """Mixed reconstruction loss. Adapted from Zhao et al, https://arxiv.org/pdf/1511.08861.pdf @@ -231,7 +196,7 @@ def training_step(self, batch: Sample | Sequence[Sample], batch_idx: int): batch_size += source.shape[0] if batch_idx < self.log_batches_per_epoch: self.training_step_outputs.extend( - _detach_sample((source, target, pred), self.log_samples_per_batch) + detach_sample((source, target, pred), self.log_samples_per_batch) ) loss_step = torch.stack(losses).mean() self.log( @@ -262,7 +227,7 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 ) if batch_idx < self.log_batches_per_epoch: self.validation_step_outputs.extend( - _detach_sample((source, target, pred), self.log_samples_per_batch) + detach_sample((source, target, pred), self.log_samples_per_batch) ) def test_step(self, batch: Sample, batch_idx: int): @@ -467,7 +432,7 @@ def configure_optimizers(self): return [optimizer], [scheduler] def _log_samples(self, key: str, imgs: Sequence[Sequence[np.ndarray]]): - grid = _render_images(imgs) + grid = render_images(imgs) self.logger.experiment.add_image( key, grid, self.current_epoch, dataformats="HWC" ) @@ -525,7 +490,7 @@ def training_step(self, batch: Sequence[Sample], batch_idx: int): batch_size += source.shape[0] if batch_idx < self.log_batches_per_epoch: self.training_step_outputs.extend( - _detach_sample( + detach_sample( (source, target * mask.unsqueeze(2), pred), self.log_samples_per_batch, ) @@ -556,190 +521,8 @@ def validation_step(self, batch: Sample, batch_idx: int, dataloader_idx: int = 0 ) if batch_idx < self.log_batches_per_epoch: self.validation_step_outputs.extend( - _detach_sample( + detach_sample( (source, target * mask.unsqueeze(2), pred), self.log_samples_per_batch, ) ) - - -class ContrastiveModule(LightningModule): - """Contrastive Learning Model for self-supervised learning.""" - - def __init__( - self, - backbone: str = "convnext_tiny", - loss_function: Union[ - nn.Module, nn.CosineEmbeddingLoss, nn.TripletMarginLoss - ] = nn.TripletMarginLoss(), - margin: float = 0.5, - lr: float = 1e-3, - schedule: Literal["WarmupCosine", "Constant"] = "Constant", - log_batches_per_epoch: int = 8, - log_samples_per_batch: int = 1, - in_channels: int = 1, - example_input_yx_shape: Sequence[int] = (256, 256), - in_stack_depth: int = 15, - stem_kernel_size: tuple[int, int, int] = (5, 4, 4), - embedding_len: int = 256, - predict: bool = False, - drop_path_rate: float = 0.2, - ) -> None: - super().__init__() - self.loss_function = loss_function - self.margin = margin - self.lr = lr - self.schedule = schedule - self.log_batches_per_epoch = log_batches_per_epoch - self.log_samples_per_batch = log_samples_per_batch - self.training_step_outputs = [] - self.validation_step_outputs = [] - self.test_step_outputs = [] - self.training_metrics = [] - self.validation_metrics = [] - self.test_metrics = [] - self.processed_order = [] - self.predictions = [] - self.model = ContrastiveEncoder( - backbone=backbone, - in_channels=in_channels, - in_stack_depth=in_stack_depth, - stem_kernel_size=stem_kernel_size, - embedding_len=embedding_len, - predict=predict, - drop_path_rate=drop_path_rate, - ) - self.example_input_array = torch.rand( - 1, in_channels, in_stack_depth, *example_input_yx_shape - ) - self.training_step_outputs = [] - self.validataion_step_outputs = [] - - def forward(self, x: Tensor) -> Tensor: - """Projected embeddings.""" - return self.model(x)[1] - - def log_feature_statistics(self, embeddings: Tensor, prefix: str): - mean = torch.mean(embeddings, dim=0).detach().cpu().numpy() - std = torch.std(embeddings, dim=0).detach().cpu().numpy() - _logger.debug(f"{prefix}_mean: {mean}") - _logger.debug(f"{prefix}_std: {std}") - - def print_embedding_norms(self, anchor, positive, negative, phase): - anchor_norm = torch.norm(anchor, dim=1).mean().item() - positive_norm = torch.norm(positive, dim=1).mean().item() - negative_norm = torch.norm(negative, dim=1).mean().item() - _logger.debug(f"{phase}/anchor_norm: {anchor_norm}") - _logger.debug(f"{phase}/positive_norm: {positive_norm}") - _logger.debug(f"{phase}/negative_norm: {negative_norm}") - - def _log_metrics( - self, loss, anchor, positive, negative, stage: Literal["train", "val"] - ): - self.log( - f"loss/{stage}", - loss.to(self.device), - on_step=True, - on_epoch=True, - prog_bar=True, - logger=True, - sync_dist=True, - ) - cosine_sim_pos = F.cosine_similarity(anchor, positive, dim=1).mean() - cosine_sim_neg = F.cosine_similarity(anchor, negative, dim=1).mean() - euclidean_dist_pos = F.pairwise_distance(anchor, positive).mean() - euclidean_dist_neg = F.pairwise_distance(anchor, negative).mean() - self.log_dict( - { - f"metrics/cosine_similarity_positive/{stage}": cosine_sim_pos, - f"metrics/cosine_similarity_negative/{stage}": cosine_sim_neg, - f"metrics/euclidean_distance_positive/{stage}": euclidean_dist_pos, - f"metrics/euclidean_distance_negative/{stage}": euclidean_dist_neg, - }, - on_step=False, - on_epoch=True, - logger=True, - sync_dist=True, - ) - - def _log_samples(self, key: str, imgs: Sequence[Sequence[np.ndarray]]): - grid = _render_images(imgs, cmaps=["gray"] * 3) - self.logger.experiment.add_image( - key, grid, self.current_epoch, dataformats="HWC" - ) - - def training_step( - self, - batch: TripletSample, - batch_idx: int, - ) -> Tensor: - """Training step of the model.""" - stage = "train" - anchor_img = batch["anchor"] - pos_img = batch["positive"] - neg_img = batch["negative"] - _, anchor_projection = self.model(anchor_img) - _, negative_projection = self.model(neg_img) - _, positive_projection = self.model(pos_img) - loss = self.loss_function( - anchor_projection, positive_projection, negative_projection - ) - self._log_metrics( - loss, anchor_projection, positive_projection, negative_projection, stage - ) - if batch_idx < self.log_batches_per_epoch: - self.training_step_outputs.extend( - _detach_sample( - (anchor_img, pos_img, neg_img), self.log_samples_per_batch - ) - ) - return loss - - def on_train_epoch_end(self) -> None: - super().on_train_epoch_end() - self._log_samples("train_samples", self.training_step_outputs) - self.training_step_outputs = [] - - def validation_step( - self, - batch: TripletSample, - batch_idx: int, - ) -> Tensor: - """Validation step of the model.""" - anchor = batch["anchor"] - pos_img = batch["positive"] - neg_img = batch["negative"] - _, anchor_projection = self.model(anchor) - _, negative_projection = self.model(neg_img) - _, positive_projection = self.model(pos_img) - loss = self.loss_function( - anchor_projection, positive_projection, negative_projection - ) - self._log_metrics( - loss, anchor_projection, positive_projection, negative_projection, "val" - ) - if batch_idx < self.log_batches_per_epoch: - self.validation_step_outputs.extend( - _detach_sample((anchor, pos_img, neg_img), self.log_samples_per_batch) - ) - return loss - - def on_validation_epoch_end(self) -> None: - super().on_validation_epoch_end() - self._log_samples("val_samples", self.validation_step_outputs) - self.validation_step_outputs = [] - - def configure_optimizers(self): - optimizer = Adam(self.parameters(), lr=self.lr) - return optimizer - - def predict_step( - self, batch: TripletSample, batch_idx, dataloader_idx=0 - ) -> dict[str, Tensor | dict]: - """Prediction step for extracting embeddings.""" - features, projections = self.model(batch["anchor"]) - return { - "features": features, - "projections": projections, - "index": batch["index"], - } diff --git a/viscy/light/predict_writer.py b/viscy/translation/predict_writer.py similarity index 100% rename from viscy/light/predict_writer.py rename to viscy/translation/predict_writer.py diff --git a/viscy/light/trainer.py b/viscy/translation/trainer.py similarity index 100% rename from viscy/light/trainer.py rename to viscy/translation/trainer.py From 4bfbf8b7c4018791adef536903cffc1620870649 Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Sun, 8 Sep 2024 14:19:26 -0700 Subject: [PATCH 60/87] created and updated classify_feb_embeddings.py --- .../figure4/classify_feb_embeddings.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py new file mode 100644 index 00000000..f3198cf3 --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py @@ -0,0 +1,86 @@ +# %% Importing Necessary Libraries +from pathlib import Path +import pandas as pd +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, confusion_matrix +from viscy.representation.embedding_writer import read_embedding_dataset +from imblearn.over_sampling import SMOTE + +# %% Defining Paths for February Dataset +feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/febtest_predict.zarr") + +# %% Function to Load Annotations +def load_annotation(da, path, name, categories: dict | None = None): + annotation = pd.read_csv(path) + annotation["fov_name"] = "/" + annotation["fov name "] + annotation = annotation.set_index(["fov_name", "id"]) + mi = pd.MultiIndex.from_arrays([da["fov_name"].values, da["id"].values], names=["fov_name", "id"]) + selected = annotation.loc[mi][name] + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + return selected + +# %% Load and Process February Dataset (Embedding Features) +feb_embedding_dataset = read_embedding_dataset(feb_features_path) +print(feb_embedding_dataset) + +# Extract the embedding feature values as the input matrix (X) +X = feb_embedding_dataset["features"].values + +# Prepare a DataFrame for the embeddings with id and fov_name +embedding_df = pd.DataFrame(X, columns=[f"feature_{i+1}" for i in range(X.shape[1])]) +embedding_df["id"] = feb_embedding_dataset["id"].values +embedding_df["fov_name"] = feb_embedding_dataset["fov_name"].values +print(embedding_df.head()) + +# %% Load the ground truth infection labels +feb_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred") +feb_infection = load_annotation(feb_embedding_dataset, feb_ann_root / "extracted_inf_state.csv", "infection_state", {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) + +# %% Merge embedding features with infection labels on 'fov_name' and 'id' +merged_df = pd.merge(embedding_df, feb_infection.reset_index(), on=['fov_name', 'id']) +print(merged_df.head()) +# %% Prepare the full dataset for training +X = merged_df.drop(columns=["id", "fov_name", "infection_state"]).values # Use embeddings as features +y = merged_df["infection_state"] # Use infection state as labels +print(X.shape) +print(y.shape) +# %% Print class distribution before applying SMOTE +print("Class distribution before SMOTE:") +print(y.value_counts()) + +# Apply SMOTE to balance the classes +smote = SMOTE(random_state=42) +X_resampled, y_resampled = smote.fit_resample(X, y) + +# Print class distribution after applying SMOTE +print("Class distribution after SMOTE:") +print(pd.Series(y_resampled).value_counts()) + +# Train Logistic Regression Classifier +model = LogisticRegression(max_iter=1000, random_state=42) +model.fit(X_resampled, y_resampled) + +# Predict Labels for the Entire Dataset +y_pred = model.predict(X) + +# Compute metrics based on the entire original dataset +print("Classification Report for Entire Dataset:") +print(classification_report(y, y_pred)) + +print("Confusion Matrix for Entire Dataset:") +print(confusion_matrix(y, y_pred)) + +# %% +# Save the predicted labels to a CSV +save_path_csv = "feb_test_regression_predicted_labels_embedding.csv" +predicted_labels_df = pd.DataFrame({ + "id": merged_df["id"].values, + "fov_name": merged_df["fov_name"].values, + "Predicted_Label": y_pred +}) + +predicted_labels_df.to_csv(save_path_csv, index=False) +print(f"Predicted labels saved to {save_path_csv}") + +# %% From 634b955d8fb58f9042f351e5f45ef20b90ef9d64 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Tue, 10 Sep 2024 09:55:12 -0700 Subject: [PATCH 61/87] Module and scripts for evaluating representations (#156) * docstring * move scripts from contrastive_scripts to viscy/scripts * organize files in applications/contrastive_phenotyping * delete unused evaluation code * more cleanup * refactor evaluation metrics for translation task * refactor viscy.evaluation -> viscy.translation.evaluation_metrics and viscy.representation.evaluation * WIP: representation evaluation module * WIP: representation eval - docstrings in numpy format * WIP: more documentation * refactor: feature_extractor moved to viscy.representation.evaluation * lint * bug fix * refactored common computations and dataset * add imbalance-learn dependecy to metrics * refactor classification of embeddings * organize viscy.representation.evaluation * ruff * Soorya's plotting script * WIP: combine two versions of plot_embeddings.py * simplify representation.viscy.evaluation - move LCA to its own module * refactor of viscy.representation.evaluation * refactored and tested PCA and UMAP plots --------- Co-authored-by: Soorya Pradeep --- .../contrastive_cli/computed_features.py | 115 ----- .../figures/figure4/classify_feb.py | 119 ----- .../contrastive_scripts/demo_fit.py | 43 -- .../PC_vs_CF.py | 65 +-- .../evaluation/analyze_embeddings.py | 114 ++++ .../plot_embeddings.py | 73 +-- .../evaluation/plot_embeddings_soorya.py | 487 ++++++++++++++++++ .../predict_infection_score_supervised.py | 0 .../{contrastive_cli => examples_cli}/fit.yml | 0 .../fit_slurm.sh | 0 .../predict.yml | 0 .../predict_slurm.sh | 0 .../figures/classify_feb.py | 99 ++++ .../figures/classify_feb_embeddings.py | 94 ++++ .../figure4 => figures}/classify_june.py | 10 +- .../figure_a_1.py => figures/figure_4a_1.py} | 4 +- .../figure_4e_2_feb.py} | 7 +- .../figure_4e_2_june.py} | 6 +- .../predict_infection_classifier.py | 2 +- .../dlmbl_exercise/solution.py | 2 +- pyproject.toml | 1 + tests/evaluation/test_evaluation_metrics.py | 2 +- viscy/cli/curator_script.py | 2 +- viscy/cli/metrics_script.py | 2 +- viscy/evaluation/__init__.py | 0 viscy/evaluation/evaluation.py | 204 -------- viscy/representation/contrastive.py | 12 +- viscy/representation/evaluation.py | 392 ++++++++++++++ viscy/representation/lca.py | 75 +++ viscy/scripts/fit_demo_contrastive.py | 45 ++ .../scripts}/graphs_ConvNeXt_ResNet.py | 10 +- .../scripts}/profile_dataloader.py | 0 .../scripts}/profile_dataloader.sh | 0 viscy/translation/engine.py | 2 +- 34 files changed, 1391 insertions(+), 596 deletions(-) delete mode 100644 applications/contrastive_phenotyping/contrastive_cli/computed_features.py delete mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py delete mode 100644 applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py rename applications/contrastive_phenotyping/{contrastive_cli => evaluation}/PC_vs_CF.py (86%) create mode 100644 applications/contrastive_phenotyping/evaluation/analyze_embeddings.py rename applications/contrastive_phenotyping/{contrastive_cli => evaluation}/plot_embeddings.py (83%) create mode 100644 applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py rename applications/contrastive_phenotyping/{contrastive_scripts => evaluation}/predict_infection_score_supervised.py (100%) rename applications/contrastive_phenotyping/{contrastive_cli => examples_cli}/fit.yml (100%) rename applications/contrastive_phenotyping/{contrastive_cli => examples_cli}/fit_slurm.sh (100%) rename applications/contrastive_phenotyping/{contrastive_cli => examples_cli}/predict.yml (100%) rename applications/contrastive_phenotyping/{contrastive_cli => examples_cli}/predict_slurm.sh (100%) create mode 100644 applications/contrastive_phenotyping/figures/classify_feb.py create mode 100644 applications/contrastive_phenotyping/figures/classify_feb_embeddings.py rename applications/contrastive_phenotyping/{contrastive_cli/figures/figure4 => figures}/classify_june.py (99%) rename applications/contrastive_phenotyping/{contrastive_cli/figures/figure4/figure_a_1.py => figures/figure_4a_1.py} (99%) rename applications/contrastive_phenotyping/{contrastive_cli/figures/figure4/figure_e_2_feb.py => figures/figure_4e_2_feb.py} (97%) rename applications/contrastive_phenotyping/{contrastive_cli/figures/figure4/figure_e_2_june.py => figures/figure_4e_2_june.py} (98%) delete mode 100644 viscy/evaluation/__init__.py delete mode 100644 viscy/evaluation/evaluation.py create mode 100644 viscy/representation/evaluation.py create mode 100644 viscy/representation/lca.py create mode 100644 viscy/scripts/fit_demo_contrastive.py rename {applications/contrastive_phenotyping/contrastive_scripts => viscy/scripts}/graphs_ConvNeXt_ResNet.py (93%) rename {applications/contrastive_phenotyping/contrastive_scripts => viscy/scripts}/profile_dataloader.py (100%) rename {applications/contrastive_phenotyping/contrastive_scripts => viscy/scripts}/profile_dataloader.sh (100%) diff --git a/applications/contrastive_phenotyping/contrastive_cli/computed_features.py b/applications/contrastive_phenotyping/contrastive_cli/computed_features.py deleted file mode 100644 index f633eaec..00000000 --- a/applications/contrastive_phenotyping/contrastive_cli/computed_features.py +++ /dev/null @@ -1,115 +0,0 @@ -import cv2 -import numpy as np -from skimage import color -from numpy import fft -from skimage.feature import graycomatrix, graycoprops -from skimage.filters import threshold_otsu, gaussian - -class FeatureExtractor: - - def __init__(self): - pass - - def compute_fourier_descriptors(image): - - # Convert contour to complex numbers - contour_complex = image[:, 0] + 1j * image[:, 1] - - # Compute Fourier descriptors - descriptors = np.fft.fft(contour_complex) - - return descriptors - - def analyze_symmetry(descriptors): - # Normalize descriptors - descriptors = np.abs(descriptors) / np.max(np.abs(descriptors)) - # Check symmetry (for a perfect circle, descriptors should be quite uniform) - return np.std(descriptors) # Lower standard deviation indicates higher symmetry - - def compute_area(input_image, sigma=0.6): - """Create a binary mask using morphological operations - :param np.array input_image: generate masks from this 3D image - :param float sigma: Gaussian blur standard deviation, increase in value increases blur - :return: volume mask of input_image, 3D np.array - """ - - input_image_blur = gaussian(input_image, sigma=sigma) - - thresh = threshold_otsu(input_image_blur) - mask = input_image >= thresh - - # Apply sensor mask to the image - masked_image = input_image * mask - - # Compute the mean intensity inside the sensor area - masked_intensity = np.mean(masked_image) - - return masked_intensity, np.sum(mask) - - def compute_spectral_entropy(image): - # Convert image to grayscale if it's not already - if len(image.shape) == 3: - image = color.rgb2gray(image) - - # Compute the 2D Fourier Transform - f_transform = fft.fft2(image) - - # Compute the power spectrum - power_spectrum = np.abs(f_transform) ** 2 - - # Compute the probability distribution - power_spectrum += 1e-10 # Avoid log(0) issues - prob_distribution = power_spectrum / np.sum(power_spectrum) - - # Compute the spectral entropy - entropy = -np.sum(prob_distribution * np.log(prob_distribution)) - - return entropy - - def compute_glcm_features(image): - - # Normalize the input image from 0 to 255 - image = (image - np.min(image)) * (255 / (np.max(image) - np.min(image))) - image = image.astype(np.uint8) - - # Compute the GLCM - distances = [1] # Distance between pixels - angles = [0] # Angle in radians - - glcm = graycomatrix(image, distances, angles, symmetric=True, normed=True) - - # Compute GLCM properties - contrast = graycoprops(glcm, "contrast")[0, 0] - dissimilarity = graycoprops(glcm, "dissimilarity")[0, 0] - homogeneity = graycoprops(glcm, "homogeneity")[0, 0] - - return contrast, dissimilarity, homogeneity - - # def detect_edges(image): - - # # Apply Canny edge detection - # edges = cv2.Canny(image, 100, 200) - - # return edges - - def compute_iqr(image): - - # Compute the interquartile range of pixel intensities - iqr = np.percentile(image, 75) - np.percentile(image, 25) - - return iqr - - def compute_mean_intensity(image): - - # Compute the mean pixel intensity - mean_intensity = np.mean(image) - - return mean_intensity - - def compute_std_dev(image): - - # Compute the standard deviation of pixel intensities - std_dev = np.std(image) - - return std_dev - diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py deleted file mode 100644 index 9a6cf87c..00000000 --- a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb.py +++ /dev/null @@ -1,119 +0,0 @@ -# %% Importing Necessary Libraries -from pathlib import Path -import matplotlib.pyplot as plt -import pandas as pd -import seaborn as sns -from sklearn.preprocessing import StandardScaler -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import classification_report, confusion_matrix, accuracy_score -from sklearn.decomposition import PCA -from tqdm import tqdm -from viscy.light.embedding_writer import read_embedding_dataset -from imblearn.over_sampling import SMOTE - -# %% Defining Paths for February Dataset -feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr") - -# %% Function to Load Annotations -def load_annotation(da, path, name, categories: dict | None = None): - annotation = pd.read_csv(path) - annotation["fov_name"] = "/" + annotation["fov ID"] - annotation = annotation.set_index(["fov_name", "id"]) - mi = pd.MultiIndex.from_arrays( - [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] - ) - selected = annotation.loc[mi][name] - if categories: - selected = selected.astype("category").cat.rename_categories(categories) - return selected - -# %% Function to Compute PCA -def compute_pca(embedding_dataset, n_components=6): - features = embedding_dataset["features"] - scaled_features = StandardScaler().fit_transform(features.values) - - # Compute PCA with specified number of components - pca = PCA(n_components=n_components, random_state=42) - pca_embedding = pca.fit_transform(scaled_features) - - # Prepare DataFrame with id and PCA coordinates - pca_df = pd.DataFrame({ - "id": embedding_dataset["id"].values, - "fov_name": embedding_dataset["fov_name"].values, - "PCA1": pca_embedding[:, 0], - "PCA2": pca_embedding[:, 1], - "PCA3": pca_embedding[:, 2], - "PCA4": pca_embedding[:, 3], - "PCA5": pca_embedding[:, 4], - "PCA6": pca_embedding[:, 5] - }) - - return pca_df - -# %% Load and Process February Dataset -feb_embedding_dataset = read_embedding_dataset(feb_features_path) -print(feb_embedding_dataset) -pca_df = compute_pca(feb_embedding_dataset, n_components=6) - -# Print shape before merge -print("Shape of pca_df before merge:", pca_df.shape) - -# Load the ground truth infection labels -feb_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track") -feb_infection = load_annotation(feb_embedding_dataset, feb_ann_root / "tracking_v1_infection.csv", "infection class", - {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) - -# Print shape of feb_infection -print("Shape of feb_infection:", feb_infection.shape) - -# Merge PCA results with ground truth labels on both 'fov_name' and 'id' -pca_df = pd.merge(pca_df, feb_infection.reset_index(), on=['fov_name', 'id']) - -# Print shape after merge -print("Shape of pca_df after merge:", pca_df.shape) - -# Prepare the full dataset -X = pca_df[["PCA1", "PCA2", "PCA3", "PCA4", "PCA5", "PCA6"]] -y = pca_df["infection class"] - -# Apply SMOTE to balance the classes in the full dataset -smote = SMOTE(random_state=42) -X_resampled, y_resampled = smote.fit_resample(X, y) - -# Print shape after SMOTE -print(f"Shape after SMOTE - X_resampled: {X_resampled.shape}, y_resampled: {y_resampled.shape}") - -# %% Train Logistic Regression Classifier with Progress Bar -model = LogisticRegression(max_iter=1000, random_state=42) - -# Wrap the training with tqdm to show a progress bar -for _ in tqdm(range(1)): - model.fit(X_resampled, y_resampled) - -# %% Predict Labels for the Entire Dataset -pca_df["Predicted_Label"] = model.predict(X) - -# Compute metrics based on the entire original dataset -print("Classification Report for Entire Dataset:") -print(classification_report(pca_df["infection class"], pca_df["Predicted_Label"])) - -print("Confusion Matrix for Entire Dataset:") -print(confusion_matrix(pca_df["infection class"], pca_df["Predicted_Label"])) - -# %% Plotting the Results -plt.figure(figsize=(10, 8)) -sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["infection class"], s=7, alpha=0.8) -plt.title("PCA with Ground Truth Labels") -plt.savefig("up_pca_ground_truth_labels.png", format='png', dpi=300) -plt.show() - -plt.figure(figsize=(10, 8)) -sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["Predicted_Label"], s=7, alpha=0.8) -plt.title("PCA with Logistic Regression Predicted Labels") -plt.savefig("up_pca_predicted_labels.png", format='png', dpi=300) -plt.show() - -# %% Save Predicted Labels to CSV -save_path_csv = "up_logistic_regression_predicted_labels_feb_pca.csv" -pca_df[['id', 'fov_name', 'Predicted_Label']].to_csv(save_path_csv, index=False) -print(f"Predicted labels saved to {save_path_csv}") \ No newline at end of file diff --git a/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py b/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py deleted file mode 100644 index b3c75b30..00000000 --- a/applications/contrastive_phenotyping/contrastive_scripts/demo_fit.py +++ /dev/null @@ -1,43 +0,0 @@ -from lightning.pytorch import Trainer -from lightning.pytorch.callbacks import ModelCheckpoint -from lightning.pytorch.loggers import TensorBoardLogger - -from viscy.data.triplet import TripletDataModule -from viscy.representation.engine import ContrastiveModule - - -def main(): - dm = TripletDataModule( - data_path="/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr", - tracks_path="/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr", - source_channel=["Phase3D", "RFP"], - z_range=(20, 35), - batch_size=16, - num_workers=10, - initial_yx_patch_size=(384, 384), - final_yx_patch_size=(224, 224), - ) - model = ContrastiveModule( - backbone="convnext_tiny", - in_channels=2, - log_batches_per_epoch=2, - log_samples_per_batch=3, - ) - trainer = Trainer( - max_epochs=5, - limit_train_batches=10, - limit_val_batches=5, - logger=TensorBoardLogger( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/test_tb", - log_graph=True, - default_hp_metric=True, - ), - log_every_n_steps=1, - callbacks=[ModelCheckpoint()], - profiler="simple", # other options: "advanced" uses cprofiler, "pytorch" uses pytorch profiler. - ) - trainer.fit(model, dm) - - -if __name__ == "__main__": - main() diff --git a/applications/contrastive_phenotyping/contrastive_cli/PC_vs_CF.py b/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py similarity index 86% rename from applications/contrastive_phenotyping/contrastive_cli/PC_vs_CF.py rename to applications/contrastive_phenotyping/evaluation/PC_vs_CF.py index 9bd3f064..1c2112b9 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/PC_vs_CF.py +++ b/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py @@ -1,18 +1,17 @@ - - # %% -# from viscy.data.triplet import TripletDataModule -from viscy.light.embedding_writer import read_embedding_dataset -from viscy.data.triplet import TripletDataModule - from pathlib import Path + import numpy as np -from skimage import io -from computed_features import FeatureExtractor as FE -from sklearn.decomposition import PCA import pandas as pd +from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import ( + FeatureExtractor as FE, +) +from viscy.representation.evaluation import dataset_of_tracks + # %% features_path = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr" @@ -35,13 +34,16 @@ embedding_dataset = read_embedding_dataset(features_path) embedding_dataset -fov_names_list = [name for name in embedding_dataset["fov_name"].values if name.startswith("/A/3/")] +fov_names_list = [ + name for name in embedding_dataset["fov_name"].values if name.startswith("/A/3/") +] unique_fov_names = sorted(list(set(fov_names_list))) correlation_sum = pd.DataFrame() ii = 0 features = pd.DataFrame() computed_pca = pd.DataFrame() + for fov_name in unique_fov_names: all_tracks_FOV = embedding_dataset.sel(fov_name=fov_name) @@ -77,23 +79,13 @@ # load the image patches - data_module = TripletDataModule( - data_path=data_path, - tracks_path=tracks_path, + prediction_dataset = dataset_of_tracks( + data_path, + tracks_path, + [fov_name], + [track_id], source_channel=source_channel, - z_range=z_range, - initial_yx_patch_size=(256, 256), - final_yx_patch_size=(140, 140), - batch_size=1, - num_workers=16, - normalizations=normalizations, - predict_cells=True, - include_fov_names=[fov_name], - include_track_ids=[track_id], ) - # for train and val - data_module.setup("predict") - predict_dataset = data_module.predict_dataset whole = np.stack([p["anchor"] for p in predict_dataset]) phase = whole[:, 0, 3] @@ -140,8 +132,12 @@ entropy_fluor = FE.compute_spectral_entropy(fluor[t]) # Compute texture analysis using GLCM - contrast_phase, dissimilarity_phase, homogeneity_phase = FE.compute_glcm_features(phase[t]) - contrast_fluor, dissimilarity_fluor, homogeneity_fluor = FE.compute_glcm_features(fluor[t]) + contrast_phase, dissimilarity_phase, homogeneity_phase = ( + FE.compute_glcm_features(phase[t]) + ) + contrast_fluor, dissimilarity_fluor, homogeneity_fluor = ( + FE.compute_glcm_features(fluor[t]) + ) # # Compute edge detection using Canny # edges_phase = FE.detect_edges(phase[t]) @@ -189,7 +185,9 @@ # compute correlation between PCA features and computed features # Create a dataframe with PCA results - pca_results = pd.DataFrame(pca_features, columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"]) + pca_results = pd.DataFrame( + pca_features, columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"] + ) computed_pca = pd.concat([computed_pca, pca_results]) # %% @@ -206,11 +204,18 @@ best_correlated_features # %% display as a heatmap -import seaborn as sns import matplotlib.pyplot as plt +import seaborn as sns plt.figure(figsize=(20, 5)) -sns.heatmap(correlation.drop(columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"]).loc["PCA1":"PCA5", :], annot=True, cmap="coolwarm", fmt=".2f") +sns.heatmap( + correlation.drop(columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"]).loc[ + "PCA1":"PCA5", : + ], + annot=True, + cmap="coolwarm", + fmt=".2f", +) plt.title("Correlation between PCA features and computed features") plt.xlabel("Computed Features") plt.ylabel("PCA Features") diff --git a/applications/contrastive_phenotyping/evaluation/analyze_embeddings.py b/applications/contrastive_phenotyping/evaluation/analyze_embeddings.py new file mode 100644 index 00000000..b7dda83c --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/analyze_embeddings.py @@ -0,0 +1,114 @@ +# %% Imports +from pathlib import Path +import seaborn as sns +import matplotlib.pyplot as plt +import plotly.express as px +import pandas as pd +import numpy as np +from sklearn.preprocessing import StandardScaler +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler + + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import load_annotation, compute_pca, compute_umap + +# %% Jupyter magic command for autoreloading modules +# ruff: noqa +# fmt: off +%load_ext autoreload +%autoreload 2 +# fmt: on +# ruff: noqa +# %% Paths and parameters + +path_embedding = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_51.zarr" +) +path_annotations_infection = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/extracted_inf_state.csv" +) +path_annotations_division = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/" +) + +path_tracks = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking/test_tracking_4.zarr" +) + +path_images = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/2-register/registered_chunked.zarr" +) + +# %% Load embeddings and annotations. + +dataset = read_embedding_dataset(path_embedding) +# load all unprojected features: +features = dataset["features"] +# or select a well: +# features - features[features["fov_name"].str.contains("B/4")] +features + +feb_infection = load_annotation( + dataset, + path_annotations_infection, + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +# %% interactive quality control: principal components +# Compute principal components and ranks of embeddings and projections. + +# compute rank +rank_features = np.linalg.matrix_rank(dataset["features"].values) +rank_projections = np.linalg.matrix_rank(dataset["projections"].values) + +pca_features, pca_projections, pca_df = compute_pca(dataset) + +# Plot explained variance and rank +plt.plot( + pca_features.explained_variance_ratio_, label=f"features, rank={rank_features}" +) +plt.plot( + pca_projections.explained_variance_ratio_, + label=f"projections, rank={rank_projections}", +) +plt.legend() +plt.xlabel("n_components") +plt.ylabel("explained variance ratio") +plt.xlim([0, 50]) +plt.show() + +# Density plot of first two principal components of features and projections. +fig, ax = plt.subplots(1, 2, figsize=(10, 5)) +sns.kdeplot(data=pca_df, x="PCA1", y="PCA2", ax=ax[0], fill=True, cmap="Blues") +sns.kdeplot(data=pca_df, x="PCA1_proj", y="PCA2_proj", ax=ax[1], fill=True, cmap="Reds") +ax[0].set_title("Density plot of PCA1 vs PCA2 (features)") +ax[1].set_title("Density plot of PCA1 vs PCA2 (projections)") +plt.show() + +# %% interactive quality control: UMAP +# Compute UMAP embeddings +umap_features, umap_projections, umap_df = compute_umap(dataset) + +# %% +# Plot UMAP embeddings as density plots +fig, ax = plt.subplots(1, 2, figsize=(10, 5)) +sns.kdeplot(data=umap_df, x="UMAP1", y="UMAP2", ax=ax[0], fill=True, cmap="Blues") +sns.kdeplot(data=umap_df, x="UMAP1_proj", y="UMAP2_proj", ax=ax[1], fill=True, cmap="Reds") +ax[0].set_title("Density plot of UMAP1 vs UMAP2 (features)") +ax[1].set_title("Density plot of UMAP1 vs UMAP2 (projections)") +plt.show() + +# %% interactive quality control: pairwise distances + + +# %% Evaluation: infection score + +## Overlay UMAP and infection state +## Linear classification accuracy +## Clustering accuracy + +# %% Evaluation: cell division + +# %% Evaluation: correlation between principal components and computed features diff --git a/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py b/applications/contrastive_phenotyping/evaluation/plot_embeddings.py similarity index 83% rename from applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py rename to applications/contrastive_phenotyping/evaluation/plot_embeddings.py index 624d4304..9f411a59 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/plot_embeddings.py +++ b/applications/contrastive_phenotyping/evaluation/plot_embeddings.py @@ -6,15 +6,12 @@ import pandas as pd import plotly.express as px import seaborn as sns -from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler from umap import UMAP - from viscy.representation.embedding_writer import read_embedding_dataset -from viscy.data.triplet import TripletDataset, TripletDataModule -from iohub import open_ome_zarr -import monai.transforms as transforms +from viscy.representation.evaluation import dataset_of_tracks, load_annotation # %% Paths and parameters. @@ -103,51 +100,16 @@ # %% # Create the montage of the images of the cells in the track. -# normalizations = [ -# transforms.NormalizeIntensityd( -# keys=["Phase3D"], -# subtrahend=None, -# divisor=None, -# nonzero=False, -# channel_wise=False, -# dtype=None, -# allow_missing_keys=False, -# ), -# transforms.ScaleIntensityRangePercentilesd( -# keys=["RFP"], -# lower=50, -# upper=99, -# b_min=0.0, -# b_max=1.0, -# clip=False, -# relative=False, -# channel_wise=False, -# dtype=None, -# allow_missing_keys=False, -# ), -# ] - -normalizations = None source_channel = ["Phase3D", "RFP"] z_range = (28, 43) - -data_module = TripletDataModule( - data_path=data_path, - tracks_path=tracks_path, - source_channel=source_channel, +predict_dataset = dataset_of_tracks( + data_path, + tracks_path, + [fov_name], + [track_id], z_range=z_range, - initial_yx_patch_size=(256, 256), - final_yx_patch_size=(256, 256), - batch_size=1, - num_workers=16, - normalizations=normalizations, - predict_cells=True, - include_fov_names=[fov_name], - include_track_ids=[track_id], + source_channel=source_channel, ) -# for train and val -data_module.setup("predict") -predict_dataset = data_module.predict_dataset phase = np.stack([p["anchor"][0, 7].numpy() for p in predict_dataset]) fluor = np.stack([np.max(p["anchor"][1].numpy(), axis=0) for p in predict_dataset]) @@ -166,9 +128,10 @@ plt.show() # %% display the track in napari -import napari import os +import napari + os.environ["DISPLAY"] = ":1" viewer = napari.Viewer() viewer.add_image(phase, name="Phase", colormap="gray") @@ -239,22 +202,6 @@ y=sample_id, # show fov_name as y-axis ) - - -# %% -def load_annotation(da, path, name, categories: dict | None = None): - annotation = pd.read_csv(path) - annotation["fov_name"] = "/" + annotation["fov ID"] - annotation = annotation.set_index(["fov_name", "id"]) - mi = pd.MultiIndex.from_arrays( - [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] - ) - selected = annotation.loc[mi][name] - if categories: - selected = selected.astype("category").cat.rename_categories(categories) - return selected - - # %% ann_root = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking" diff --git a/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py b/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py new file mode 100644 index 00000000..b7c3a717 --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py @@ -0,0 +1,487 @@ +# %% +from pathlib import Path +import pandas as pd +import seaborn as sns +import plotly.express as px +from sklearn.preprocessing import StandardScaler +from umap import UMAP +from viscy.light.embedding_writer import read_embedding_dataset +import matplotlib.pyplot as plt + +# %% +dataset = read_embedding_dataset( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/Ver2_updateTracking_refineModel/predictions/Feb_test_2chan_128patch_128projDim/2chan_128patch_20ckpt_Feb_test.zarr" +) +dataset + +# %% +# load all unprojected features: +features = dataset["features"] +# or select a well: +# features - features[features["fov_name"].str.contains("B/4")] +features + + +# %% perform principal componenet analysis of features + +from sklearn.decomposition import PCA + +pca = PCA(n_components=4) +# scaled_features = StandardScaler().fit_transform(features.values) +# pca_features = pca.fit_transform(scaled_features) +pca_features = pca.fit_transform(features.values) + +features = ( + features.assign_coords(PCA1=("sample", pca_features[:, 0])) + .assign_coords(PCA2=("sample", pca_features[:, 1])) + .assign_coords(PCA3=("sample", pca_features[:, 2])) + .assign_coords(PCA4=("sample", pca_features[:, 3])) + .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4"], append=True) +) + +# %% plot PCA components + +plt.figure(figsize=(10, 10)) +sns.scatterplot(x=features["PCA1"], y=features["PCA2"], hue=features["t"], s=7, alpha=0.8) + +# %% umap with 2 components +scaled_features = StandardScaler().fit_transform(features.values) + +umap = UMAP() + +embedding = umap.fit_transform(features.values) +features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +features + +# %% +# scaled_features = StandardScaler().fit_transform(features.values) + +# umap = UMAP(n_components=4) + +# embedding = umap.fit_transform(scaled_features) +# features = ( +# features.assign_coords(UMAP1=("sample", embedding[:, 0])) +# .assign_coords(UMAP2=("sample", embedding[:, 1])) +# .assign_coords(UMAP3=("sample", embedding[:, 2])) +# .assign_coords(UMAP4=("sample", embedding[:, 3])) +# .set_index(sample=["UMAP1", "UMAP2", "UMAP3", "UMAP4"], append=True) +# ) +# features + +# %% +sns.scatterplot( + x=features["UMAP1"], y=features["UMAP2"], hue=features["t"], s=7, alpha=0.8 +) + +# %% +def load_annotation(da, path, name, categories: dict | None = None): + annotation = pd.read_csv(path) + # annotation_columns = annotation.columns.tolist() + # print(annotation_columns) + annotation["fov_name"] = "/" + annotation["fov_name"] + annotation = annotation.set_index(["fov_name", "id"]) + mi = pd.MultiIndex.from_arrays( + [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] + ) + selected = annotation.loc[mi][name] + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + return selected + + +# %% +# ann_root = Path( +# "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track" +# ) + +# infection = load_annotation( +# features, +# ann_root / "tracking_v1_infection.csv", +# "infection class", +# {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +# ) +# division = load_annotation( +# features, +# ann_root / "cell_division_state.csv", +# "division", +# {0: "non-dividing", 2: "dividing"}, +# ) + + +# %% new annotation + +ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred") + +infection = load_annotation( + features, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +# %% +sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, s=7, alpha=0.8) + +# %% +sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=infection, s=7, alpha=0.8) + +# %% plot PCA components with infection hue +sns.scatterplot(x=features["PCA1"], y=features["PCA2"], hue=infection, s=7, alpha=0.8) + +# %% +ax = sns.histplot(x=features["UMAP1"], y=features["UMAP2"], hue=infection, bins=64) +sns.move_legend(ax, loc="lower left") + +# %% see the histogram distribution of UMAP1 and UMAP2 for each infection state +sns.displot( + x=features["UMAP1"], + y=features["UMAP2"], + kind="hist", + col=infection, + bins=64, + cmap="inferno", +) + +# %% +# interactive scatter plot to associate clusters with specific cells + +fig = px.scatter( + data_frame=pd.DataFrame( + {k: v for k, v in features.coords.items() if k != "features"} + ), + x="UMAP1", + y="UMAP2", + color=(infection.astype(str) + " " + division.astype(str)).rename("annotation"), + hover_name="fov_name", + hover_data=["track_id", "t"], +) +fig.update_traces(marker=dict(size=3)) + +# %% interactive PCA plot + +fig = px.scatter( + data_frame=pd.DataFrame( + {k: v for k, v in features.coords.items() if k != "features"} + ), + x="PCA1", + y="PCA2", + color=(infection.astype(str) + " " + division.astype(str)).rename("annotation"), + hover_name="fov_name", + hover_data=["track_id", "t"], +) +fig.update_traces(marker=dict(size=3)) + +# %% cluster cells in PCA1 vs PCA2 space using Gaussian Mixture Model + +from sklearn.mixture import GaussianMixture +import numpy as np +import seaborn as sns + +gmm = GaussianMixture(n_components=2) +PCA1_array = features["PCA1"].values.reshape(-1, 1) +PCA2_array = features["PCA2"].values.reshape(-1, 1) +gmm.fit(np.concatenate((PCA1_array, PCA2_array), axis=1)) + +GMM_predict = gmm.predict(np.concatenate((PCA1_array, PCA2_array), axis=1)) +features = features.assign_coords(gmm=("sample", GMM_predict)) +# display the clustering results +fig = px.scatter( + data_frame=pd.DataFrame( + {k: v for k, v in features.coords.items() if k != "features"} + ), + x="PCA1", + y="PCA2", + color=features["gmm"].astype(str), + hover_name="fov_name", + hover_data=["track_id", "t"], +) +fig.update_traces(marker=dict(size=3)) + +# %% +# cluster features in heatmap directly +inf_codes = pd.Series(infection.values.codes, name="infection") +lut = dict(zip(inf_codes.unique(), "brw")) +row_colors = inf_codes.map(lut) + +g = sns.clustermap( + scaled_features, row_colors=row_colors.to_numpy(), col_cluster=False, cbar_pos=None +) +g.yaxis.set_ticks([]) + +# %% +# interactive scatter plot to associate clusters with specific cells +df = pd.DataFrame({k: v for k, v in features.coords.items() if k != "features"}) +df["infection"] = infection.values +df["division"] = division.values +df["well"] = df["fov_name"].str.rsplit("/", n=1).str[0] +df["fov_track_id"] = df["fov_name"] + "-" + df["track_id"].astype(str) +# select row B (DENV) +df = df[df["fov_name"].str.contains("B")] +df.sort_values("t", inplace=True) + +g = px.scatter( + data_frame=df[df["infection"].isin(["uninfected", "infected"])], + x="UMAP1", + y="UMAP2", + symbol="well", + color="infection", + hover_name="fov_name", + hover_data=["id", "t", "track_id"], + animation_frame="t", + animation_group="fov_track_id", +) +g.update_layout(width=800, height=600) + +# %% video frame for scatter across supervised infection annotation + +df = pd.DataFrame({k: v for k, v in features.coords.items() if k != "features"}) +df["infection"] = infection.values +df["division"] = division.values +df["well"] = df["fov_name"].str.rsplit("/", n=1).str[0] +df["fov_track_id"] = df["fov_name"] + "-" + df["track_id"].astype(str) +df.sort_values("t", inplace=True) + +for time in range(48): + plt.clf() # Clear the previous plot + sns.scatterplot( + data=df[(df["infection"].isin(["uninfected", "infected"])) & (df["t"] == time)], + x="UMAP1", + y="UMAP2", + hue="infection", + palette={"uninfected": "blue", "infected": "red", "background": "black"}, + s=12, + ) + plt.legend().remove() + plt.xlim(-7, 15) + plt.ylim(2, 15) + + plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Supervised/scatter_infection_" + str(time).zfill(3) + ".png") + +# %% video frame for scatter across virus type or wells + +# for time in range(48): +# sns.scatterplot( +# data=df[(df["t"] == time)], +# x="UMAP1", +# y="UMAP2", +# hue="well", +# palette={"/B/3": "blue", "/A/3": "blue", "/B/4": "red", "/A/4": "green"}, +# s=12, +# ) + +df_well_B4 = df[df['well'] == '/B/4'] # DENV, MOI 5 +df_well_A4 = df[df['well'] == '/A/4'] # ZIka, MOI 5 +df_well_Mock = df[(df['well'] == '/B/3') | (df['well'] == '/A/3')] # Mock + +for time in range(48): + plt.clf() + sns.scatterplot( + data=df_well_B4[(df_well_B4["t"] == time)], + x="UMAP1", + y="UMAP2", + hue="infection", + palette={"uninfected": "black", "infected": "black", "background": "black"}, + s=12, + ) + plt.legend().remove() + plt.xlim(-7, 15) + plt.ylim(2, 15) + plt.title(f"Time: {(time*0.5)+3} hours post infection") + plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Dengue/scatter_Dengue_infection_" + str(time).zfill(3) + ".png") + +for time in range(48): + plt.clf() + sns.scatterplot( + data=df_well_A4[(df_well_A4["t"] == time)], + x="UMAP1", + y="UMAP2", + hue="infection", + palette={"uninfected": "black", "infected": "black", "background": "black"}, + s=12, + ) + plt.legend().remove() + plt.title(f"Time: {(time*0.5)+3} hours post infection") + plt.xlim(-7, 15) + plt.ylim(2, 15) + + plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Zika/scatter_Zika_infection_" + str(time).zfill(3) + ".png") + +for time in range(48): + plt.clf() + sns.scatterplot( + data=df_well_Mock[(df_well_Mock["t"] == time)], + x="UMAP1", + y="UMAP2", + hue="infection", + palette={"uninfected": "black", "infected": "black", "background": "black"}, + s=12, + ) + plt.legend().remove() + plt.title(f"Time: {(time*0.5)+3} hours post infection") + plt.xlim(-7, 15) + plt.ylim(2, 15) + + plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Mock/scatter_Mock_infection_" + str(time).zfill(3) + ".png") + +# do the plot next for the baove three conditions with palette: "Mock": "black", "Zika": "blue", "Dengue": "red" +for time in range(48): + plt.clf() + sns.scatterplot( + data=df[(df["t"] == time)], + x="UMAP1", + y="UMAP2", + hue="well", + palette={"/B/3": "black", "/A/3": "black", "/B/4": "red", "/A/4": "blue"}, + s=12, + ) + plt.xlim(-7, 15) + plt.ylim(2, 15) + plt.title(f"Time: {(time*0.5)+3} hours post infection") + plt.legend().remove() + + plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Well/scatter_well_" + str(time).zfill(3) + ".png") + +# %% video frame for scatter across division state for 30 cells + +# div_csv_path = '/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/track_Feb.csv' +# df_div = pd.read_csv(div_csv_path) + +# plot for well A3, FOVs 0, 1, 10, 11, 12,and 13 +selected_fovs = df[df['fov_name'].isin(['/A/3/0', '/A/3/1', '/A/3/10', '/A/3/11', '/A/3/12', '/A/3/13'])] + +for time in range(48): + plt.clf() + sns.scatterplot( + data=selected_fovs[(selected_fovs["t"] == time)], + x="UMAP1", + y="UMAP2", + hue="division", + palette={"non-dividing": "blue", "dividing": "red"}, + s=12, + ) + plt.legend().remove() + plt.xlim(-7, 15) + plt.ylim(2, 15) + plt.title(f"Time: {(time*0.5)+3} hours post infection") + + plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Division/scatter_division_" + str(time).zfill(3) + ".png") + +# making videos +# ffmpeg -r 2 -f image2 -pattern_type glob -i "*?png" -vcodec libx264 -crf 20 -pix_fmt yuv420p output.mp4 + +# %% display flow field plot for df over time for one dengue infected cell + +import matplotlib.pyplot as plt + +# Group the features by track_id and fov_name +grouped_features = df_well_B4.groupby(["track_id", "fov_name"]) + +# Create a new column for the UMAP1 and UMAP2 coordinates +df_well_B4["UMAP1_track"] = np.nan +df_well_B4["UMAP2_track"] = np.nan + +# Iterate over the groups and assign UMAP coordinates to each track +for group_name, group_data in grouped_features: + track_id, fov_name = group_name + umap1 = group_data["UMAP1"].values + umap2 = group_data["UMAP2"].values + df_well_B4.loc[(df_well_B4["track_id"] == track_id) & (df_well_B4["fov_name"] == fov_name), "UMAP1_track"] = umap1 + df_well_B4.loc[(df_well_B4["track_id"] == track_id) & (df_well_B4["fov_name"] == fov_name), "UMAP2_track"] = umap2 + +# Compute the flow field for each cell +flow_field = np.gradient(df_well_B4[["UMAP1_track", "UMAP2_track"]].values, axis=0) + +# Plot the flow field with reduced density +plt.figure(figsize=(10, 10)) +plt.quiver(df_well_B4["UMAP1_track"], df_well_B4["UMAP2_track"], flow_field[:, 0], flow_field[:, 1], scale=10) +plt.xlim(-7, 15) +plt.ylim(2, 15) +plt.show() + + +# %% show the umap flow field of cell 30 in well B4, fov 4 with time as velocity + +df_well_B4_4_30 = df[(df['fov_name'] == '/B/4/4') & (df['track_id'] == 30)] +df_well_B4_4_30.sort_values('t', inplace=True) + +flow_field = np.gradient(df_well_B4_4_30[["UMAP1", "UMAP2"]].values, axis=0) + +plt.figure(figsize=(10, 10)) +plt.quiver(df_well_B4_4_30["UMAP1"], df_well_B4_4_30["UMAP2"], flow_field[:, 0], flow_field[:, 1], scale=10, color='r') +plt.xlim(-7, 15) +plt.ylim(2, 15) +plt.show() + +df_well_A4_9_5 = df[(df['fov_name'] == '/A/4/9') & (df['track_id'] == 21)] +df_well_A4_9_5.sort_values('t', inplace=True) + +flow_field = np.gradient(df_well_A4_9_5[["UMAP1", "UMAP2"]].values, axis=0) + +plt.figure(figsize=(10, 10)) +plt.quiver(df_well_A4_9_5["UMAP1"], df_well_A4_9_5["UMAP2"], flow_field[:, 0], flow_field[:, 1], scale=10, color='r') +plt.xlim(-7, 15) +plt.ylim(2, 15) +plt.show() + +# %% use linear classifier to predict infection state from UMAP coordinates + +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import train_test_split +from sklearn.metrics import classification_report + +X = features[["UMAP1", "UMAP2"]].values.astype(int) +y = infection.values.codes + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +clf = LogisticRegression() +clf.fit(X_train, y_train) + +y_pred = clf.predict(X_test) +print(classification_report(y_test, y_pred)) + +# %% use linear classifier to predict infection state from PCA coordinates + +X = features[["PCA1", "PCA2"]].values +y = infection.values.codes + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +clf = LogisticRegression() +clf.fit(X_train, y_train) + +y_pred = clf.predict(X_test) +print(classification_report(y_test, y_pred)) + +# %% use gaussian mixture model to cluster cells in PCA space +from sklearn.mixture import GaussianMixture +from sklearn.metrics import f1_score + +gmm = GaussianMixture(n_components=2) +PCA1_array = features["PCA1"].values.reshape(-1, 1) +PCA2_array = features["PCA2"].values.reshape(-1, 1) + +gmm.fit(np.concatenate((PCA1_array, PCA2_array), axis=1)) + +GMM_predict = gmm.predict(np.concatenate((PCA1_array, PCA2_array), axis=1)) +features = features.assign_coords(gmm=("sample", GMM_predict)) + +# display the clustering results +fig = px.scatter( + data_frame=pd.DataFrame( + {k: v for k, v in features.coords.items() if k != "features"} + ), + x="PCA1", + y="PCA2", + color=features["gmm"].astype(str), + hover_name="fov_name", + hover_data=["track_id", "t"], +) + +fig.update_traces(marker=dict(size=3)) + +# %% diff --git a/applications/contrastive_phenotyping/contrastive_scripts/predict_infection_score_supervised.py b/applications/contrastive_phenotyping/evaluation/predict_infection_score_supervised.py similarity index 100% rename from applications/contrastive_phenotyping/contrastive_scripts/predict_infection_score_supervised.py rename to applications/contrastive_phenotyping/evaluation/predict_infection_score_supervised.py diff --git a/applications/contrastive_phenotyping/contrastive_cli/fit.yml b/applications/contrastive_phenotyping/examples_cli/fit.yml similarity index 100% rename from applications/contrastive_phenotyping/contrastive_cli/fit.yml rename to applications/contrastive_phenotyping/examples_cli/fit.yml diff --git a/applications/contrastive_phenotyping/contrastive_cli/fit_slurm.sh b/applications/contrastive_phenotyping/examples_cli/fit_slurm.sh similarity index 100% rename from applications/contrastive_phenotyping/contrastive_cli/fit_slurm.sh rename to applications/contrastive_phenotyping/examples_cli/fit_slurm.sh diff --git a/applications/contrastive_phenotyping/contrastive_cli/predict.yml b/applications/contrastive_phenotyping/examples_cli/predict.yml similarity index 100% rename from applications/contrastive_phenotyping/contrastive_cli/predict.yml rename to applications/contrastive_phenotyping/examples_cli/predict.yml diff --git a/applications/contrastive_phenotyping/contrastive_cli/predict_slurm.sh b/applications/contrastive_phenotyping/examples_cli/predict_slurm.sh similarity index 100% rename from applications/contrastive_phenotyping/contrastive_cli/predict_slurm.sh rename to applications/contrastive_phenotyping/examples_cli/predict_slurm.sh diff --git a/applications/contrastive_phenotyping/figures/classify_feb.py b/applications/contrastive_phenotyping/figures/classify_feb.py new file mode 100644 index 00000000..7b883fd1 --- /dev/null +++ b/applications/contrastive_phenotyping/figures/classify_feb.py @@ -0,0 +1,99 @@ +# %% Importing Necessary Libraries +from pathlib import Path + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +from imblearn.over_sampling import SMOTE +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, confusion_matrix +from tqdm import tqdm + +from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import compute_pca, load_annotation + +# %% Defining Paths for February Dataset +feb_features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr" +) + + +# %% Load and Process February Dataset +feb_embedding_dataset = read_embedding_dataset(feb_features_path) +print(feb_embedding_dataset) +pca_df = compute_pca(feb_embedding_dataset, n_components=6) + +# Print shape before merge +print("Shape of pca_df before merge:", pca_df.shape) + +# Load the ground truth infection labels +feb_ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track" +) +feb_infection = load_annotation( + feb_embedding_dataset, + feb_ann_root / "tracking_v1_infection.csv", + "infection class", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +# Print shape of feb_infection +print("Shape of feb_infection:", feb_infection.shape) + +# Merge PCA results with ground truth labels on both 'fov_name' and 'id' +pca_df = pd.merge(pca_df, feb_infection.reset_index(), on=["fov_name", "id"]) + +# Print shape after merge +print("Shape of pca_df after merge:", pca_df.shape) + +# Prepare the full dataset +X = pca_df[["PCA1", "PCA2", "PCA3", "PCA4", "PCA5", "PCA6"]] +y = pca_df["infection class"] + +# Apply SMOTE to balance the classes in the full dataset +smote = SMOTE(random_state=42) +X_resampled, y_resampled = smote.fit_resample(X, y) + +# Print shape after SMOTE +print( + f"Shape after SMOTE - X_resampled: {X_resampled.shape}, y_resampled: {y_resampled.shape}" +) + +# %% Train Logistic Regression Classifier with Progress Bar +model = LogisticRegression(max_iter=1000, random_state=42) + +# Wrap the training with tqdm to show a progress bar +for _ in tqdm(range(1)): + model.fit(X_resampled, y_resampled) + +# %% Predict Labels for the Entire Dataset +pca_df["Predicted_Label"] = model.predict(X) + +# Compute metrics based on the entire original dataset +print("Classification Report for Entire Dataset:") +print(classification_report(pca_df["infection class"], pca_df["Predicted_Label"])) + +print("Confusion Matrix for Entire Dataset:") +print(confusion_matrix(pca_df["infection class"], pca_df["Predicted_Label"])) + +# %% Plotting the Results +plt.figure(figsize=(10, 8)) +sns.scatterplot( + x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["infection class"], s=7, alpha=0.8 +) +plt.title("PCA with Ground Truth Labels") +plt.savefig("up_pca_ground_truth_labels.png", format="png", dpi=300) +plt.show() + +plt.figure(figsize=(10, 8)) +sns.scatterplot( + x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["Predicted_Label"], s=7, alpha=0.8 +) +plt.title("PCA with Logistic Regression Predicted Labels") +plt.savefig("up_pca_predicted_labels.png", format="png", dpi=300) +plt.show() + +# %% Save Predicted Labels to CSV +save_path_csv = "up_logistic_regression_predicted_labels_feb_pca.csv" +pca_df[["id", "fov_name", "Predicted_Label"]].to_csv(save_path_csv, index=False) +print(f"Predicted labels saved to {save_path_csv}") diff --git a/applications/contrastive_phenotyping/figures/classify_feb_embeddings.py b/applications/contrastive_phenotyping/figures/classify_feb_embeddings.py new file mode 100644 index 00000000..da63c52a --- /dev/null +++ b/applications/contrastive_phenotyping/figures/classify_feb_embeddings.py @@ -0,0 +1,94 @@ +# %% Importing Necessary Libraries +from pathlib import Path + +import pandas as pd +from imblearn.over_sampling import SMOTE +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, confusion_matrix + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import load_annotation + +# %% Defining Paths for February Dataset +feb_features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/" +) + + +# %% Load and Process February Dataset (Embedding Features) +feb_embedding_dataset = read_embedding_dataset( + feb_features_path / "febtest_predict.zarr" +) +print(feb_embedding_dataset) + +# Extract the embedding feature values as the input matrix (X) +X = feb_embedding_dataset["features"].values + +# Prepare a DataFrame for the embeddings with id and fov_name +embedding_df = pd.DataFrame(X, columns=[f"feature_{i+1}" for i in range(X.shape[1])]) +embedding_df["id"] = feb_embedding_dataset["id"].values +embedding_df["fov_name"] = feb_embedding_dataset["fov_name"].values +print(embedding_df.head()) + +# %% Load the ground truth infection labels +feb_ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" +) +feb_infection = load_annotation( + feb_embedding_dataset, + feb_ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +# %% Merge embedding features with infection labels on 'fov_name' and 'id' +merged_df = pd.merge(embedding_df, feb_infection.reset_index(), on=["fov_name", "id"]) +print(merged_df.head()) +# %% Prepare the full dataset for training +X = merged_df.drop( + columns=["id", "fov_name", "infection_state"] +).values # Use embeddings as features +y = merged_df["infection_state"] # Use infection state as labels +print(X.shape) +print(y.shape) +# %% Print class distribution before applying SMOTE +print("Class distribution before SMOTE:") +print(y.value_counts()) + +# Apply SMOTE to balance the classes +smote = SMOTE(random_state=42) +X_resampled, y_resampled = smote.fit_resample(X, y) + +# Print class distribution after applying SMOTE +print("Class distribution after SMOTE:") +print(pd.Series(y_resampled).value_counts()) + +# Train Logistic Regression Classifier +model = LogisticRegression(max_iter=1000, random_state=42) +model.fit(X_resampled, y_resampled) + +# Predict Labels for the Entire Dataset +y_pred = model.predict(X) + +# Compute metrics based on the entire original dataset +print("Classification Report for Entire Dataset:") +print(classification_report(y, y_pred)) + +print("Confusion Matrix for Entire Dataset:") +print(confusion_matrix(y, y_pred)) + +# %% +# Save the predicted labels to a CSV +save_path_csv = feb_features_path / "feb_test_regression_predicted_labels_embedding.csv" +predicted_labels_df = pd.DataFrame( + { + "id": merged_df["id"].values, + "fov_name": merged_df["fov_name"].values, + "Predicted_Label": y_pred, + } +) + +predicted_labels_df.to_csv(save_path_csv, index=False) +print(f"Predicted labels saved to {save_path_csv}") + +# %% diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_june.py b/applications/contrastive_phenotyping/figures/classify_june.py similarity index 99% rename from applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_june.py rename to applications/contrastive_phenotyping/figures/classify_june.py index 8977e3bc..b8d63208 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_june.py +++ b/applications/contrastive_phenotyping/figures/classify_june.py @@ -1,15 +1,17 @@ # %% Importing Necessary Libraries from pathlib import Path + import matplotlib.pyplot as plt import pandas as pd import seaborn as sns -from sklearn.preprocessing import StandardScaler -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import classification_report, confusion_matrix, accuracy_score +from imblearn.over_sampling import SMOTE from sklearn.decomposition import PCA +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, confusion_matrix +from sklearn.preprocessing import StandardScaler from tqdm import tqdm + from viscy.light.embedding_writer import read_embedding_dataset -from imblearn.over_sampling import SMOTE # %% Defining Paths for June Dataset june_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr") diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_a_1.py b/applications/contrastive_phenotyping/figures/figure_4a_1.py similarity index 99% rename from applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_a_1.py rename to applications/contrastive_phenotyping/figures/figure_4a_1.py index c688a7cf..aa3883fe 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_a_1.py +++ b/applications/contrastive_phenotyping/figures/figure_4a_1.py @@ -1,11 +1,12 @@ # %% Importing Necessary Libraries from pathlib import Path + import matplotlib.pyplot as plt -import numpy as np import pandas as pd import seaborn as sns from sklearn.preprocessing import StandardScaler from umap import UMAP + from viscy.light.embedding_writer import read_embedding_dataset # %% Defining Paths for February and June Datasets @@ -63,7 +64,6 @@ def plot_umap_infection(features, infection, title): print(feb_features) # %% -import matplotlib.pyplot as plt # %% Identify cells by infection type using fov_name mock_cells = feb_features.sel(sample=feb_features['fov_name'].str.contains('/A/3') | feb_features['fov_name'].str.contains('/B/3')) diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_feb.py b/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py similarity index 97% rename from applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_feb.py rename to applications/contrastive_phenotyping/figures/figure_4e_2_feb.py index e3791417..870bd016 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_feb.py +++ b/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py @@ -1,10 +1,11 @@ # %% Importing Necessary Libraries +from pathlib import Path + import matplotlib.pyplot as plt import pandas as pd -from pathlib import Path -from sklearn.preprocessing import StandardScaler + from viscy.light.embedding_writer import read_embedding_dataset -from umap import UMAP # Add import for UMAP + # %% Function to Load Annotations from GMM CSV def load_gmm_annotation(gmm_csv_path): diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_june.py b/applications/contrastive_phenotyping/figures/figure_4e_2_june.py similarity index 98% rename from applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_june.py rename to applications/contrastive_phenotyping/figures/figure_4e_2_june.py index ef3fd076..bbaeb958 100644 --- a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/figure_e_2_june.py +++ b/applications/contrastive_phenotyping/figures/figure_4e_2_june.py @@ -1,10 +1,12 @@ # %% Importing Necessary Libraries +from pathlib import Path + import matplotlib.pyplot as plt import pandas as pd -from pathlib import Path -from sklearn.preprocessing import StandardScaler + from viscy.light.embedding_writer import read_embedding_dataset + # %% Function to Load Annotations from CSV def load_annotation(csv_path): return pd.read_csv(csv_path) diff --git a/applications/infection_classification/predict_infection_classifier.py b/applications/infection_classification/predict_infection_classifier.py index bcf4ec7f..444038a8 100644 --- a/applications/infection_classification/predict_infection_classifier.py +++ b/applications/infection_classification/predict_infection_classifier.py @@ -6,8 +6,8 @@ ) from viscy.data.hcs import HCSDataModule -from viscy.translation.predict_writer import HCSPredictionWriter from viscy.transforms import NormalizeSampled +from viscy.translation.predict_writer import HCSPredictionWriter # %% # %% write the predictions to a zarr file diff --git a/examples/virtual_staining/dlmbl_exercise/solution.py b/examples/virtual_staining/dlmbl_exercise/solution.py index ff0ba819..8d295818 100644 --- a/examples/virtual_staining/dlmbl_exercise/solution.py +++ b/examples/virtual_staining/dlmbl_exercise/solution.py @@ -116,7 +116,7 @@ from tqdm import tqdm # HCSDataModule makes it easy to load data during training. from viscy.data.hcs import HCSDataModule -from viscy.evaluation.evaluation_metrics import mean_average_precision +from viscy.translation.evaluation_metrics import mean_average_precision # Trainer class and UNet. from viscy.translation.engine import MixedLoss, VSUNet from viscy.translation.trainer import VSTrainer diff --git a/pyproject.toml b/pyproject.toml index 57248fa4..e1579612 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ dynamic = ["version"] metrics = [ "cellpose>=3.0.10", "scikit-learn>=1.1.3", + "imbalanced-learn>=0.12.0", "torchmetrics[detection]>=1.3.1", "ptflops>=0.7", "umap-learn", diff --git a/tests/evaluation/test_evaluation_metrics.py b/tests/evaluation/test_evaluation_metrics.py index b0c36b43..af1c8411 100644 --- a/tests/evaluation/test_evaluation_metrics.py +++ b/tests/evaluation/test_evaluation_metrics.py @@ -4,7 +4,7 @@ from skimage import data, measure from skimage.util import img_as_float -from viscy.evaluation.evaluation_metrics import ( +from viscy.translation.evaluation_metrics import ( POD_metric, VOI_metric, labels_to_detection, diff --git a/viscy/cli/curator_script.py b/viscy/cli/curator_script.py index 1c35da2d..00071d97 100644 --- a/viscy/cli/curator_script.py +++ b/viscy/cli/curator_script.py @@ -11,7 +11,7 @@ import numpy as np from PIL import Image -import viscy.evaluation.evaluation_metrics as metrics +import viscy.translation.evaluation_metrics as metrics import viscy.utils.aux_utils as aux_utils # from waveorder.focus import focus_from_transverse_band diff --git a/viscy/cli/metrics_script.py b/viscy/cli/metrics_script.py index a9cb1afd..b4739534 100644 --- a/viscy/cli/metrics_script.py +++ b/viscy/cli/metrics_script.py @@ -10,7 +10,7 @@ import iohub.ngff as ngff import pandas as pd -import viscy.evaluation.evaluation_metrics as metrics +import viscy.translation.evaluation_metrics as metrics import viscy.utils.aux_utils as aux_utils # %% read the below details from the config file diff --git a/viscy/evaluation/__init__.py b/viscy/evaluation/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/viscy/evaluation/evaluation.py b/viscy/evaluation/evaluation.py deleted file mode 100644 index becc7cee..00000000 --- a/viscy/evaluation/evaluation.py +++ /dev/null @@ -1,204 +0,0 @@ -import numpy as np -from torch.utils.tensorboard import SummaryWriter - -import viscy.evaluation.evaluation_metrics as inference_metrics - - -class TorchEvaluator(object): - """ - Handles all procedures involved with model evaluation. - - Params: - :param dict torch_config: master config file - """ - - def __init__(self, torch_config, device=None) -> None: - self.torch_config = torch_config - - self.zarr_dir = self.torch_config["zarr_dir"] - self.network_config = self.torch_config["model"] - self.training_config = self.torch_config["training"] - self.dataset_config = self.torch_config["dataset"] - self.inference_config = self.torch_config["inference"] - self.preprocessing_config = self.torch_config["preprocessing"] - - self.inference_metrics = {} - self.log_writer = SummaryWriter(log_dir=self.save_folder) - - def get_save_location(self): - """ - Sets save location as specified in config files. - """ - # TODO implement - return - # TODO Change the functionality of saving to put inference in the actual - # train directory the model comes from. Not a big fan - - # model_dir = os.path.dirname(self.inference_config["model_dir"]) - # save_to_train_save_dir = self.inference_config["save_preds_to_model_dir"] - - # if save_to_train_save_dir: - # save_dir = model_dir - # elif "custom_save_preds_dir" in self.inference_config: - # custom_save_dir = self.inference_config["custom_save_preds_dir"] - # save_dir = custom_save_dir - # else: - # raise ValueError( - # "Must provide custom_save_preds_dir if save_preds_to" - # "_model_dir is False." - # ) - - # now = aux_utils.get_timestamp() - # self.save_folder = os.path.join(save_dir, f"inference_results_{now}") - # if not os.path.exists(self.save_folder): - # os.makedirs(self.save_folder) - - def _collapse_metrics_dict(self, metrics_dict): - """ - Collapses metrics dict in the form of - {metric_name: {index: metric,...}} - to the form - {metric_name: np.ndarray[metric1, metrics2,...]} - - :param dict metrics_dict: dict of metrics in the first format - - :return dict collapsed_metrics_dict: dict of metrics in the second format - """ - collapsed_metrics_dict = {} - for metric_name in metrics_dict: - val_dict = metrics_dict[metric_name] - values = [val_dict[index] for index in val_dict] - collapsed_metrics_dict[metric_name] = np.array(values) - - return collapsed_metrics_dict - - def _get_metrics( - self, - target, - prediction, - metrics_list, - metrics_orientations, - path="unspecified", - window=None, - ): - """ - Gets metrics for this target_/prediction pair in all the specified orientations - for all the specified metrics. - - :param np.ndarray target: 5d target array (on cpu) - :param np.ndarray prediction: 5d prediction array (on cpu) - :param list metrics_list: list of strings - indicating the name of a desired metric, - for options see inference.evaluation_metrics. MetricsEstimator docstring - :param list metrics_orientations: list of strings - indicating the orientation to compute, - for options see inference.evaluation_metrics. MetricsEstimator docstring - :param tuple window: spatial window of this target/prediction pair - in the larger arrays they come from. - - :return dict prediction_metrics: dict mapping orientation -> pd.dataframe - of metrics for that orientation - """ - metrics_estimator = inference_metrics.MetricsEstimator(metrics_list) - prediction_metrics = {} - - # transpose target and prediction to be in xyz format - # NOTE: This expects target and pred to be in the format bczyx! - target = np.transpose(target, (0, 1, -2, -1, -3)) - prediction = np.transpose(prediction, (0, 1, -2, -1, -3)) - - zstart, zend = window[0][0], window[0][0] + window[1][0] # end = start + length - pred_name = f"slice_{zstart}-{zend}" - - if "xy" in metrics_orientations: - metrics_estimator.estimate_xy_metrics( - target=target, - prediction=prediction, - pred_name=pred_name, - ) - metrics_xy = self._collapse_metrics_dict( - metrics_estimator.get_metrics_xy().to_dict() - ) - prediction_metrics["xy"] = metrics_xy - - if "xyz" in metrics_orientations: - metrics_estimator.estimate_xyz_metrics( - target=target, - prediction=prediction, - pred_name=pred_name, - ) - metrics_xyz = self._collapse_metrics_dict( - metrics_estimator.get_metrics_xyz().to_dict() - ) - prediction_metrics["xyz"] = metrics_xyz - - if "xz" in metrics_orientations: - metrics_estimator.estimate_xz_metrics( - target=target, - prediction=prediction, - pred_name=pred_name, - ) - metrics_xz = self._collapse_metrics_dict( - metrics_estimator.get_metrics_xz().to_dict() - ) - prediction_metrics["xz"] = metrics_xz - - if "yz" in metrics_orientations: - metrics_estimator.estimate_yz_metrics( - target=target, - prediction=prediction, - pred_name=pred_name, - ) - metrics_yz = self._collapse_metrics_dict( - metrics_estimator.get_metrics_yz().to_dict() - ) - prediction_metrics["yz"] = metrics_yz - - # format metrics - tag = path + f"_{window}" - self.inference_metrics[tag] = prediction_metrics - - return prediction_metrics - - def record_metrics(self, sample_information): - """ - Handles metric recording in tensorboard. - - Metrics are saved position by position. - If multiple scalar metric values are stored for a - particular metric in a particular position, - they are plotted along the axis they are calculated on. - - :param list sample_information: list of tuples containing information about - each sample in the form - (position_group, position_path, normalization_meta, window) - """ - for info_tuple in sample_information: - _, position_path, normalization_meta, window = info_tuple - position = position_path.split("/")[-1] - sample_metrics = self.inference_metrics[position_path + f"_{window}"] - - for orientation in sample_metrics: - scalar_dict = sample_metrics[orientation] - pred_name = scalar_dict.pop("pred_name")[0] - - # generate a unique plot & tag for each orientation - main_tag = f"{position}/{orientation}_{pred_name}" - - # Need to plot a line if metrics calculated along an axis - if scalar_dict[list(scalar_dict.keys())[0]].shape[0] == 1: - self.writer.add_scalars( - main_tag=main_tag, - tag_scalar_dict=scalar_dict, - ) - else: - axis_length = scalar_dict[list(scalar_dict.keys())[0]].shape[0] - for i in range(axis_length): - scalar_dict_i = {} - for key in scalar_dict.keys(): - scalar_dict_i[key] = scalar_dict[key][i] - self.writer.add_scalars( - main_tag=main_tag, - tag_scalar_dict=scalar_dict_i, - global_step=i, - ) diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index 4e635357..bfc7c6d5 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -11,9 +11,17 @@ class ContrastiveEncoder(nn.Module): """ Contrastive encoder network that uses ConvNeXt and ResNet backbones from timm. + Returns + ------- + tuple[Tensor, Tensor] + A tuple containing the embedding tensor and the projection tensor. + + - embedding (Tensor): The embedded feature tensor. + - projections (Tensor): The projected feature tensor. + Parameters ---------- - backbone : Literal["convnext_tiny", "resnet50"] + backbone : Literal["convnext_tiny", "convnextv2_tiny", "resnet50"] Name of the timm backbone architecture in_channels : int, optional Number of input channels @@ -35,7 +43,7 @@ class ContrastiveEncoder(nn.Module): def __init__( self, - backbone: Literal["convnext_tiny", "resnet50"], + backbone: Literal["convnext_tiny", "convnextv2_tiny", "resnet50"], in_channels: int, in_stack_depth: int, stem_kernel_size: tuple[int, int, int] = (5, 4, 4), diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py new file mode 100644 index 00000000..9b1dfc7f --- /dev/null +++ b/viscy/representation/evaluation.py @@ -0,0 +1,392 @@ +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +import torch +import torch.nn as nn +import torch.optim as optim +from numpy import fft +from skimage import color +from skimage.feature import graycomatrix, graycoprops +from skimage.filters import gaussian, threshold_otsu +from sklearn.cluster import DBSCAN +from sklearn.metrics import ( + accuracy_score, + adjusted_rand_score, + normalized_mutual_info_score, + silhouette_score, +) +from sklearn.neighbors import KNeighborsClassifier +import umap +from torch.utils.data import DataLoader, TensorDataset + +from viscy.data.triplet import TripletDataModule + +""" +This module enables evaluation of learned representations using annotations, such as +* cell division labels, +* infection state labels, +* labels predicted using supervised classifiers, +* computed image features. + +Following evaluation methods are implemented: +* Linear classifier accuracy when labels are provided. +* Clustering evaluation using normalized mutual information (NMI) and adjusted rand index (ARI). +* Correlation between embeddings and computed features using rank correlation. + +TODO: consider time- and condition-dependent clustering and UMAP visualization of patches developed earlier: +https://github.com/mehta-lab/dynacontrast/blob/master/analysis/gmm.py +""" + + +""" +Utilities for loading datasets. +""" + + +def load_annotation(da, path, name, categories: dict | None = None): + """ + Load annotations from a CSV file and map them to the dataset. + + Parameters + ---------- + da : xarray.DataArray + The dataset array containing 'fov_name' and 'id' coordinates. + path : str + Path to the CSV file containing annotations. + name : str + The column name in the CSV file to be used as annotations. + categories : dict, optional + A dictionary to rename categories in the annotation column. Default is None. + + Returns + ------- + pd.Series + A pandas Series containing the selected annotations mapped to the dataset. + """ + # Read the annotation CSV file + annotation = pd.read_csv(path) + + # Add a leading slash to 'fov name' column and set it as 'fov_name' + annotation["fov_name"] = "/" + annotation["fov_name"] + + # Set the index of the annotation DataFrame to ['fov_name', 'id'] + annotation = annotation.set_index(["fov_name", "id"]) + + # Create a MultiIndex from the dataset array's 'fov_name' and 'id' values + mi = pd.MultiIndex.from_arrays( + [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] + ) + + # Select the annotations corresponding to the MultiIndex + selected = annotation.loc[mi][name] + + # If categories are provided, rename the categories in the selected annotations + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + + return selected + + +def dataset_of_tracks( + data_path, + tracks_path, + fov_list, + track_id_list, + source_channel=["Phase3D", "RFP"], + z_range=(28, 43), + initial_yx_patch_size=(256, 256), + final_yx_patch_size=(128, 128), +): + data_module = TripletDataModule( + data_path=data_path, + tracks_path=tracks_path, + include_fov_names=fov_list, + include_track_ids=track_id_list, + source_channel=source_channel, + z_range=z_range, + initial_yx_patch_size=initial_yx_patch_size, + final_yx_patch_size=final_yx_patch_size, + batch_size=1, + num_workers=16, + normalizations=None, + predict_cells=True, + ) + # for train and val + data_module.setup("predict") + prediction_dataset = data_module.predict_dataset + return prediction_dataset + + +""" Methods for evaluating clustering performance. +""" + + +def knn_accuracy(embeddings, annotations, k=5): + """ + Evaluate the k-NN classification accuracy. + + Parameters + ---------- + k : int, optional + Number of neighbors to use for k-NN. Default is 5. + + Returns + ------- + float + Accuracy of the k-NN classifier. + """ + knn = KNeighborsClassifier(n_neighbors=k) + knn.fit(embeddings, annotations) + predictions = knn.predict(embeddings) + accuracy = accuracy_score(annotations, predictions) + return accuracy + + +def dbscan_clustering(embeddings, eps=0.5, min_samples=5): + """ + Apply DBSCAN clustering to the embeddings. + + Parameters + ---------- + eps : float, optional + The maximum distance between two samples for them to be considered as in the same neighborhood. Default is 0.5. + min_samples : int, optional + The number of samples in a neighborhood for a point to be considered as a core point. Default is 5. + + Returns + ------- + np.ndarray + Clustering labels assigned by DBSCAN. + """ + dbscan = DBSCAN(eps=eps, min_samples=min_samples) + clusters = dbscan.fit_predict(embeddings) + return clusters + + +def silhouette_score(embeddings, clusters): + """ + Compute the silhouette score for the DBSCAN clustering results. + + Parameters + ---------- + clusters : np.ndarray + Clustering labels assigned by DBSCAN. + + Returns + ------- + float + Silhouette score for the clustering. + """ + score = silhouette_score(embeddings, clusters) + return score + + +def clustering_evaluation(embeddings, annotations, method="nmi"): + """ + Evaluate the clustering of the embeddings compared to the ground truth labels. + + Parameters + ---------- + method : str, optional + Metric to use for evaluation ('nmi' or 'ari'). Default is 'nmi'. + + Returns + ------- + float + NMI or ARI score depending on the method chosen. + """ + clusters = dbscan_clustering(embeddings) + + if method == "nmi": + score = normalized_mutual_info_score(annotations, clusters) + elif method == "ari": + score = adjusted_rand_score(annotations, clusters) + else: + raise ValueError("Invalid method. Choose 'nmi' or 'ari'.") + + return score + + +def compute_pca(embedding_dataset, n_components=None, normalize_features=True): + features = embedding_dataset["features"] + projections = embedding_dataset["projections"] + + if normalize_features: + scaled_projections = StandardScaler().fit_transform(projections.values) + scaled_features = StandardScaler().fit_transform(features.values) + else: + scaled_projections = projections.values + scaled_features = features.values + + # Compute PCA with specified number of components + PCA_features = PCA(n_components=n_components, random_state=42) + PCA_projection = PCA(n_components=n_components, random_state=42) + pc_features = PCA_features.fit_transform(scaled_features) + pc_projection = PCA_projection.fit_transform(scaled_projections) + + # Prepare DataFrame with id and PCA coordinates + pca_df = pd.DataFrame( + { + "id": embedding_dataset["id"].values, + "fov_name": embedding_dataset["fov_name"].values, + "PCA1": pc_features[:, 0], + "PCA2": pc_features[:, 1], + "PCA3": pc_features[:, 2], + "PCA4": pc_features[:, 3], + "PCA5": pc_features[:, 4], + "PCA6": pc_features[:, 5], + "PCA1_proj": pc_projection[:, 0], + "PCA2_proj": pc_projection[:, 1], + "PCA3_proj": pc_projection[:, 2], + "PCA4_proj": pc_projection[:, 3], + "PCA5_proj": pc_projection[:, 4], + "PCA6_proj": pc_projection[:, 5], + } + ) + + return PCA_features, PCA_projection, pca_df + + +def compute_umap(embedding_dataset, normalize_features=True): + features = embedding_dataset["features"] + projections = embedding_dataset["projections"] + + if normalize_features: + scaled_projections = StandardScaler().fit_transform(projections.values) + scaled_features = StandardScaler().fit_transform(features.values) + else: + scaled_projections = projections.values + scaled_features = features.values + + # Compute UMAP for features and projections + # Computing 3 components to enable 3D visualization. + umap_features = umap.UMAP(random_state=42, n_components=3) + umap_projection = umap.UMAP(random_state=42, n_components=3) + umap_features_embedding = umap_features.fit_transform(scaled_features) + umap_projection_embedding = umap_projection.fit_transform(scaled_projections) + + # Prepare DataFrame with id and UMAP coordinates + umap_df = pd.DataFrame( + { + "id": embedding_dataset["id"].values, + "fov_name": embedding_dataset["fov_name"].values, + "UMAP1": umap_features_embedding[:, 0], + "UMAP2": umap_features_embedding[:, 1], + "UMAP3": umap_features_embedding[:, 2], + "UMAP1_proj": umap_projection_embedding[:, 0], + "UMAP2_proj": umap_projection_embedding[:, 1], + "UMAP3_proj": umap_projection_embedding[:, 2], + } + ) + + return umap_features, umap_projection, umap_df + + +class FeatureExtractor: + + def __init__(self): + pass + + def compute_fourier_descriptors(image): + + # Convert contour to complex numbers + contour_complex = image[:, 0] + 1j * image[:, 1] + + # Compute Fourier descriptors + descriptors = np.fft.fft(contour_complex) + + return descriptors + + def analyze_symmetry(descriptors): + # Normalize descriptors + descriptors = np.abs(descriptors) / np.max(np.abs(descriptors)) + # Check symmetry (for a perfect circle, descriptors should be quite uniform) + return np.std(descriptors) # Lower standard deviation indicates higher symmetry + + def compute_area(input_image, sigma=0.6): + """Create a binary mask using morphological operations + :param np.array input_image: generate masks from this 3D image + :param float sigma: Gaussian blur standard deviation, increase in value increases blur + :return: volume mask of input_image, 3D np.array + """ + + input_image_blur = gaussian(input_image, sigma=sigma) + + thresh = threshold_otsu(input_image_blur) + mask = input_image >= thresh + + # Apply sensor mask to the image + masked_image = input_image * mask + + # Compute the mean intensity inside the sensor area + masked_intensity = np.mean(masked_image) + + return masked_intensity, np.sum(mask) + + def compute_spectral_entropy(image): + # Convert image to grayscale if it's not already + if len(image.shape) == 3: + image = color.rgb2gray(image) + + # Compute the 2D Fourier Transform + f_transform = fft.fft2(image) + + # Compute the power spectrum + power_spectrum = np.abs(f_transform) ** 2 + + # Compute the probability distribution + power_spectrum += 1e-10 # Avoid log(0) issues + prob_distribution = power_spectrum / np.sum(power_spectrum) + + # Compute the spectral entropy + entropy = -np.sum(prob_distribution * np.log(prob_distribution)) + + return entropy + + def compute_glcm_features(image): + + # Normalize the input image from 0 to 255 + image = (image - np.min(image)) * (255 / (np.max(image) - np.min(image))) + image = image.astype(np.uint8) + + # Compute the GLCM + distances = [1] # Distance between pixels + angles = [0] # Angle in radians + + glcm = graycomatrix(image, distances, angles, symmetric=True, normed=True) + + # Compute GLCM properties + contrast = graycoprops(glcm, "contrast")[0, 0] + dissimilarity = graycoprops(glcm, "dissimilarity")[0, 0] + homogeneity = graycoprops(glcm, "homogeneity")[0, 0] + + return contrast, dissimilarity, homogeneity + + # def detect_edges(image): + + # # Apply Canny edge detection + # edges = cv2.Canny(image, 100, 200) + + # return edges + + def compute_iqr(image): + + # Compute the interquartile range of pixel intensities + iqr = np.percentile(image, 75) - np.percentile(image, 25) + + return iqr + + def compute_mean_intensity(image): + + # Compute the mean pixel intensity + mean_intensity = np.mean(image) + + return mean_intensity + + def compute_std_dev(image): + + # Compute the standard deviation of pixel intensities + std_dev = np.std(image) + + return std_dev diff --git a/viscy/representation/lca.py b/viscy/representation/lca.py new file mode 100644 index 00000000..43be307a --- /dev/null +++ b/viscy/representation/lca.py @@ -0,0 +1,75 @@ +# FIXME: this is a method from previous version at (viscy.representatin.evaluation) +# and needs to be turned into lightning module. + +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader, TensorDataset +import numpy as np +from sklearn.metrics import accuracy_score + + +def linear_classifier_accuracy(self, batch_size=32, learning_rate=0.01, epochs=10): + """ + Evaluate the accuracy of a single-layer neural network trained on the + embeddings. + + Parameters + ---------- + batch_size : int, optional + Batch size for training. Default is 32. + learning_rate : float, optional + Learning rate for the optimizer. Default is 0.01. + epochs : int, optional + Number of training epochs. Default is 10. + + Returns + ------- + float + Accuracy of the neural network classifier. + """ + + class SingleLayerNN(nn.Module): + def __init__(self, input_dim, output_dim): + super(SingleLayerNN, self).__init__() + self.fc = nn.Linear(input_dim, output_dim) + + def forward(self, x): + return self.fc(x) + + # Convert numpy arrays to PyTorch tensors + inputs = torch.tensor(self.embeddings, dtype=torch.float32) + labels = torch.tensor(self.annotations, dtype=torch.long) + + # Create a dataset and data loader + dataset = TensorDataset(inputs, labels) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) + + # Initialize the neural network, loss function, and optimizer + input_dim = self.embeddings.shape[1] + output_dim = len(np.unique(self.annotations)) + model = SingleLayerNN(input_dim, output_dim) + criterion = ( + nn.CrossEntropyLoss() + ) # Works with logits, so no softmax in the last layer + + optimizer = optim.SGD(model.parameters(), lr=learning_rate) + + # Training loop + model.train() + for epoch in range(epochs): + for batch_inputs, batch_labels in dataloader: + optimizer.zero_grad() + outputs = model(batch_inputs) + loss = criterion(outputs, batch_labels) + loss.backward() + optimizer.step() + + # Evaluate the model + model.eval() + with torch.no_grad(): + outputs = model(inputs) + _, predictions = torch.max(outputs, 1) + accuracy = accuracy_score(labels.numpy(), predictions.numpy()) + + return accuracy diff --git a/viscy/scripts/fit_demo_contrastive.py b/viscy/scripts/fit_demo_contrastive.py new file mode 100644 index 00000000..7a55c5fe --- /dev/null +++ b/viscy/scripts/fit_demo_contrastive.py @@ -0,0 +1,45 @@ +# %% Imports and paths. +from lightning.pytorch import Trainer +from lightning.pytorch.callbacks import ModelCheckpoint +from lightning.pytorch.loggers import TensorBoardLogger + +from viscy.data.triplet import TripletDataModule +from viscy.representation.engine import ContrastiveModule + +data_path = "/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr" +tracks_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr" +log_path = "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/test_tb" + +# %% Define data module, model, and trainer. +dm = TripletDataModule( + data_path=data_path, + tracks_path=tracks_path, + source_channel=["Phase3D", "RFP"], + z_range=(20, 35), + batch_size=16, + num_workers=10, + initial_yx_patch_size=(384, 384), + final_yx_patch_size=(224, 224), +) +model = ContrastiveModule( + backbone="convnext_tiny", + in_channels=2, + log_batches_per_epoch=2, + log_samples_per_batch=3, +) + +trainer = Trainer( + max_epochs=5, + limit_train_batches=10, + limit_val_batches=5, + logger=TensorBoardLogger( + log_path, + log_graph=True, + default_hp_metric=True, + ), + log_every_n_steps=1, + callbacks=[ModelCheckpoint()], + profiler="simple", # other options: "advanced" uses cprofiler, "pytorch" uses pytorch profiler. +) +# %% Fit the model. +trainer.fit(model, dm) diff --git a/applications/contrastive_phenotyping/contrastive_scripts/graphs_ConvNeXt_ResNet.py b/viscy/scripts/graphs_ConvNeXt_ResNet.py similarity index 93% rename from applications/contrastive_phenotyping/contrastive_scripts/graphs_ConvNeXt_ResNet.py rename to viscy/scripts/graphs_ConvNeXt_ResNet.py index 5ddb2252..f2daf6c8 100644 --- a/applications/contrastive_phenotyping/contrastive_scripts/graphs_ConvNeXt_ResNet.py +++ b/viscy/scripts/graphs_ConvNeXt_ResNet.py @@ -4,15 +4,18 @@ import torchview from viscy.representation.engine import ContrastiveModule -from viscy.representation.contrastive import ContrastiveEncoder, UNeXt2Stem +from viscy.representation.contrastive import ContrastiveEncoder, StemDepthtoChannels # %load_ext autoreload # %autoreload 2 # %% Initialize the model and log the graph. contra_model = ContrastiveEncoder( - backbone="convnext_tiny" + backbone="convnextv2_tiny", + in_stack_depth=15, + in_channels=2, ) # other options: convnext_tiny resnet50 print(contra_model) + model_graph = torchview.draw_graph( contra_model, torch.randn(1, 2, 15, 224, 224), @@ -28,6 +31,7 @@ backbone="resnet50", in_stack_depth=16, stem_kernel_size=(4, 3, 3) ) # note that the resnet first layer takes 64 channels (so we can't have multiples of 3) print(contra_model) +contra_model(torch.randn(1, 2, 16, 224, 224)) model_graph = torchview.draw_graph( contra_model, torch.randn(1, 2, 16, 224, 224), @@ -57,7 +61,7 @@ available_models = timm.list_models(pretrained=True) -stem = UNeXt2Stem( +stem = StemDepthtoChannels( in_channels=2, out_channels=96, kernel_size=(5, 2, 2), in_stack_depth=15 ) print(stem) diff --git a/applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.py b/viscy/scripts/profile_dataloader.py similarity index 100% rename from applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.py rename to viscy/scripts/profile_dataloader.py diff --git a/applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.sh b/viscy/scripts/profile_dataloader.sh similarity index 100% rename from applications/contrastive_phenotyping/contrastive_scripts/profile_dataloader.sh rename to viscy/scripts/profile_dataloader.sh diff --git a/viscy/translation/engine.py b/viscy/translation/engine.py index b14c5133..a6a78b67 100644 --- a/viscy/translation/engine.py +++ b/viscy/translation/engine.py @@ -25,7 +25,7 @@ from viscy._log_images import detach_sample, render_images from viscy.data.typing import Sample -from viscy.evaluation.evaluation_metrics import mean_average_precision, ms_ssim_25d +from viscy.translation.evaluation_metrics import mean_average_precision, ms_ssim_25d from viscy.unet.networks.fcmae import FullyConvolutionalMAE from viscy.unet.networks.Unet2D import Unet2d from viscy.unet.networks.Unet25D import Unet25d From 9639961fa960df8fcaa6c38455721399a83bea65 Mon Sep 17 00:00:00 2001 From: Shalin Mehta Date: Tue, 10 Sep 2024 10:16:32 -0700 Subject: [PATCH 62/87] delete duplicate file --- .../figure4/classify_feb_embeddings.py | 86 ------------------- 1 file changed, 86 deletions(-) delete mode 100644 applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py diff --git a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py b/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py deleted file mode 100644 index f3198cf3..00000000 --- a/applications/contrastive_phenotyping/contrastive_cli/figures/figure4/classify_feb_embeddings.py +++ /dev/null @@ -1,86 +0,0 @@ -# %% Importing Necessary Libraries -from pathlib import Path -import pandas as pd -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import classification_report, confusion_matrix -from viscy.representation.embedding_writer import read_embedding_dataset -from imblearn.over_sampling import SMOTE - -# %% Defining Paths for February Dataset -feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/febtest_predict.zarr") - -# %% Function to Load Annotations -def load_annotation(da, path, name, categories: dict | None = None): - annotation = pd.read_csv(path) - annotation["fov_name"] = "/" + annotation["fov name "] - annotation = annotation.set_index(["fov_name", "id"]) - mi = pd.MultiIndex.from_arrays([da["fov_name"].values, da["id"].values], names=["fov_name", "id"]) - selected = annotation.loc[mi][name] - if categories: - selected = selected.astype("category").cat.rename_categories(categories) - return selected - -# %% Load and Process February Dataset (Embedding Features) -feb_embedding_dataset = read_embedding_dataset(feb_features_path) -print(feb_embedding_dataset) - -# Extract the embedding feature values as the input matrix (X) -X = feb_embedding_dataset["features"].values - -# Prepare a DataFrame for the embeddings with id and fov_name -embedding_df = pd.DataFrame(X, columns=[f"feature_{i+1}" for i in range(X.shape[1])]) -embedding_df["id"] = feb_embedding_dataset["id"].values -embedding_df["fov_name"] = feb_embedding_dataset["fov_name"].values -print(embedding_df.head()) - -# %% Load the ground truth infection labels -feb_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred") -feb_infection = load_annotation(feb_embedding_dataset, feb_ann_root / "extracted_inf_state.csv", "infection_state", {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) - -# %% Merge embedding features with infection labels on 'fov_name' and 'id' -merged_df = pd.merge(embedding_df, feb_infection.reset_index(), on=['fov_name', 'id']) -print(merged_df.head()) -# %% Prepare the full dataset for training -X = merged_df.drop(columns=["id", "fov_name", "infection_state"]).values # Use embeddings as features -y = merged_df["infection_state"] # Use infection state as labels -print(X.shape) -print(y.shape) -# %% Print class distribution before applying SMOTE -print("Class distribution before SMOTE:") -print(y.value_counts()) - -# Apply SMOTE to balance the classes -smote = SMOTE(random_state=42) -X_resampled, y_resampled = smote.fit_resample(X, y) - -# Print class distribution after applying SMOTE -print("Class distribution after SMOTE:") -print(pd.Series(y_resampled).value_counts()) - -# Train Logistic Regression Classifier -model = LogisticRegression(max_iter=1000, random_state=42) -model.fit(X_resampled, y_resampled) - -# Predict Labels for the Entire Dataset -y_pred = model.predict(X) - -# Compute metrics based on the entire original dataset -print("Classification Report for Entire Dataset:") -print(classification_report(y, y_pred)) - -print("Confusion Matrix for Entire Dataset:") -print(confusion_matrix(y, y_pred)) - -# %% -# Save the predicted labels to a CSV -save_path_csv = "feb_test_regression_predicted_labels_embedding.csv" -predicted_labels_df = pd.DataFrame({ - "id": merged_df["id"].values, - "fov_name": merged_df["fov_name"].values, - "Predicted_Label": y_pred -}) - -predicted_labels_df.to_csv(save_path_csv, index=False) -print(f"Predicted labels saved to {save_path_csv}") - -# %% From 083897cf22980afa394b8cae53dfd6c9ba73407c Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Tue, 10 Sep 2024 13:36:29 -0700 Subject: [PATCH 63/87] lint --- viscy/representation/evaluation.py | 43 +++++++++---------------- viscy/representation/lca.py | 4 +-- viscy/scripts/graphs_ConvNeXt_ResNet.py | 2 +- 3 files changed, 19 insertions(+), 30 deletions(-) diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py index 9b1dfc7f..cbf8ead0 100644 --- a/viscy/representation/evaluation.py +++ b/viscy/representation/evaluation.py @@ -1,15 +1,12 @@ import numpy as np import pandas as pd -from sklearn.decomposition import PCA -from sklearn.preprocessing import StandardScaler -import torch -import torch.nn as nn -import torch.optim as optim +import umap from numpy import fft from skimage import color from skimage.feature import graycomatrix, graycoprops from skimage.filters import gaussian, threshold_otsu from sklearn.cluster import DBSCAN +from sklearn.decomposition import PCA from sklearn.metrics import ( accuracy_score, adjusted_rand_score, @@ -17,8 +14,7 @@ silhouette_score, ) from sklearn.neighbors import KNeighborsClassifier -import umap -from torch.utils.data import DataLoader, TensorDataset +from sklearn.preprocessing import StandardScaler from viscy.data.triplet import TripletDataModule @@ -43,6 +39,18 @@ Utilities for loading datasets. """ +__all__ = [ + # re-exporting from sklearn + "silhouette_score", + "load_annotation", + "dataset_of_tracks", + "knn_accuracy", + "clustering_evaluation", + "compute_pca", + "compute_umap", + "FeatureExtractor", +] + def load_annotation(da, path, name, categories: dict | None = None): """ @@ -118,8 +126,7 @@ def dataset_of_tracks( return prediction_dataset -""" Methods for evaluating clustering performance. -""" +"""Methods for evaluating clustering performance.""" def knn_accuracy(embeddings, annotations, k=5): @@ -164,24 +171,6 @@ def dbscan_clustering(embeddings, eps=0.5, min_samples=5): return clusters -def silhouette_score(embeddings, clusters): - """ - Compute the silhouette score for the DBSCAN clustering results. - - Parameters - ---------- - clusters : np.ndarray - Clustering labels assigned by DBSCAN. - - Returns - ------- - float - Silhouette score for the clustering. - """ - score = silhouette_score(embeddings, clusters) - return score - - def clustering_evaluation(embeddings, annotations, method="nmi"): """ Evaluate the clustering of the embeddings compared to the ground truth labels. diff --git a/viscy/representation/lca.py b/viscy/representation/lca.py index 43be307a..663b64a7 100644 --- a/viscy/representation/lca.py +++ b/viscy/representation/lca.py @@ -1,12 +1,12 @@ # FIXME: this is a method from previous version at (viscy.representatin.evaluation) # and needs to be turned into lightning module. +import numpy as np import torch import torch.nn as nn import torch.optim as optim -from torch.utils.data import DataLoader, TensorDataset -import numpy as np from sklearn.metrics import accuracy_score +from torch.utils.data import DataLoader, TensorDataset def linear_classifier_accuracy(self, batch_size=32, learning_rate=0.01, epochs=10): diff --git a/viscy/scripts/graphs_ConvNeXt_ResNet.py b/viscy/scripts/graphs_ConvNeXt_ResNet.py index f2daf6c8..5444a47a 100644 --- a/viscy/scripts/graphs_ConvNeXt_ResNet.py +++ b/viscy/scripts/graphs_ConvNeXt_ResNet.py @@ -3,8 +3,8 @@ import torch import torchview -from viscy.representation.engine import ContrastiveModule from viscy.representation.contrastive import ContrastiveEncoder, StemDepthtoChannels +from viscy.representation.engine import ContrastiveModule # %load_ext autoreload # %autoreload 2 From 4521afcb984ca63be784b5885183be9deb738a7d Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Tue, 10 Sep 2024 13:50:34 -0700 Subject: [PATCH 64/87] fix import paths --- .../evaluation/plot_embeddings_soorya.py | 14 ++++++++------ .../figures/classify_feb.py | 2 +- .../figures/classify_june.py | 2 +- .../contrastive_phenotyping/figures/figure_4a_1.py | 2 +- .../figures/figure_4e_2_feb.py | 2 +- .../figures/figure_4e_2_june.py | 2 +- .../virtual_staining/dlmbl_exercise/exercise.ipynb | 4 ++-- .../virtual_staining/dlmbl_exercise/solution.ipynb | 4 ++-- .../img2img_translation/solution.ipynb | 4 ++-- .../virtual_staining/phase_contrast/solution.ipynb | 4 ++-- .../virtual_staining/phase_contrast/solution.py | 4 ++-- 11 files changed, 23 insertions(+), 21 deletions(-) diff --git a/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py b/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py index b7c3a717..3553fcb3 100644 --- a/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py +++ b/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py @@ -1,12 +1,14 @@ # %% from pathlib import Path + +import matplotlib.pyplot as plt import pandas as pd -import seaborn as sns import plotly.express as px +import seaborn as sns from sklearn.preprocessing import StandardScaler from umap import UMAP -from viscy.light.embedding_writer import read_embedding_dataset -import matplotlib.pyplot as plt + +from viscy.representation.embedding_writer import read_embedding_dataset # %% dataset = read_embedding_dataset( @@ -177,9 +179,9 @@ def load_annotation(da, path, name, categories: dict | None = None): # %% cluster cells in PCA1 vs PCA2 space using Gaussian Mixture Model -from sklearn.mixture import GaussianMixture import numpy as np import seaborn as sns +from sklearn.mixture import GaussianMixture gmm = GaussianMixture(n_components=2) PCA1_array = features["PCA1"].values.reshape(-1, 1) @@ -430,8 +432,8 @@ def load_annotation(da, path, name, categories: dict | None = None): # %% use linear classifier to predict infection state from UMAP coordinates from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report +from sklearn.model_selection import train_test_split X = features[["UMAP1", "UMAP2"]].values.astype(int) y = infection.values.codes @@ -458,8 +460,8 @@ def load_annotation(da, path, name, categories: dict | None = None): print(classification_report(y_test, y_pred)) # %% use gaussian mixture model to cluster cells in PCA space -from sklearn.mixture import GaussianMixture from sklearn.metrics import f1_score +from sklearn.mixture import GaussianMixture gmm = GaussianMixture(n_components=2) PCA1_array = features["PCA1"].values.reshape(-1, 1) diff --git a/applications/contrastive_phenotyping/figures/classify_feb.py b/applications/contrastive_phenotyping/figures/classify_feb.py index 7b883fd1..e6b34e1a 100644 --- a/applications/contrastive_phenotyping/figures/classify_feb.py +++ b/applications/contrastive_phenotyping/figures/classify_feb.py @@ -9,7 +9,7 @@ from sklearn.metrics import classification_report, confusion_matrix from tqdm import tqdm -from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.embedding_writer import read_embedding_dataset from viscy.representation.evaluation import compute_pca, load_annotation # %% Defining Paths for February Dataset diff --git a/applications/contrastive_phenotyping/figures/classify_june.py b/applications/contrastive_phenotyping/figures/classify_june.py index b8d63208..ca51f2b1 100644 --- a/applications/contrastive_phenotyping/figures/classify_june.py +++ b/applications/contrastive_phenotyping/figures/classify_june.py @@ -11,7 +11,7 @@ from sklearn.preprocessing import StandardScaler from tqdm import tqdm -from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.embedding_writer import read_embedding_dataset # %% Defining Paths for June Dataset june_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr") diff --git a/applications/contrastive_phenotyping/figures/figure_4a_1.py b/applications/contrastive_phenotyping/figures/figure_4a_1.py index aa3883fe..a670db0d 100644 --- a/applications/contrastive_phenotyping/figures/figure_4a_1.py +++ b/applications/contrastive_phenotyping/figures/figure_4a_1.py @@ -7,7 +7,7 @@ from sklearn.preprocessing import StandardScaler from umap import UMAP -from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.embedding_writer import read_embedding_dataset # %% Defining Paths for February and June Datasets feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr") diff --git a/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py b/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py index 870bd016..d3052018 100644 --- a/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py +++ b/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py @@ -4,7 +4,7 @@ import matplotlib.pyplot as plt import pandas as pd -from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.embedding_writer import read_embedding_dataset # %% Function to Load Annotations from GMM CSV diff --git a/applications/contrastive_phenotyping/figures/figure_4e_2_june.py b/applications/contrastive_phenotyping/figures/figure_4e_2_june.py index bbaeb958..1605ba27 100644 --- a/applications/contrastive_phenotyping/figures/figure_4e_2_june.py +++ b/applications/contrastive_phenotyping/figures/figure_4e_2_june.py @@ -4,7 +4,7 @@ import matplotlib.pyplot as plt import pandas as pd -from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.embedding_writer import read_embedding_dataset # %% Function to Load Annotations from CSV diff --git a/examples/virtual_staining/dlmbl_exercise/exercise.ipynb b/examples/virtual_staining/dlmbl_exercise/exercise.ipynb index e18f85ea..7e5fadff 100644 --- a/examples/virtual_staining/dlmbl_exercise/exercise.ipynb +++ b/examples/virtual_staining/dlmbl_exercise/exercise.ipynb @@ -158,8 +158,8 @@ "from viscy.data.hcs import HCSDataModule\n", "from viscy.evaluation.evaluation_metrics import mean_average_precision\n", "# Trainer class and UNet.\n", - "from viscy.light.engine import MixedLoss, VSUNet\n", - "from viscy.light.trainer import VSTrainer\n", + "from viscy.translation.engine import MixedLoss, VSUNet\n", + "from viscy.translation.trainer import VSTrainer\n", "# training augmentations\n", "from viscy.transforms import (NormalizeSampled, RandAdjustContrastd,\n", " RandAffined, RandGaussianNoised,\n", diff --git a/examples/virtual_staining/dlmbl_exercise/solution.ipynb b/examples/virtual_staining/dlmbl_exercise/solution.ipynb index a61d1935..c0c18c06 100644 --- a/examples/virtual_staining/dlmbl_exercise/solution.ipynb +++ b/examples/virtual_staining/dlmbl_exercise/solution.ipynb @@ -167,8 +167,8 @@ "from viscy.data.hcs import HCSDataModule\n", "from viscy.evaluation.evaluation_metrics import mean_average_precision\n", "# Trainer class and UNet.\n", - "from viscy.light.engine import MixedLoss, VSUNet\n", - "from viscy.light.trainer import VSTrainer\n", + "from viscy.translation.engine import MixedLoss, VSUNet\n", + "from viscy.translation.trainer import VSTrainer\n", "# training augmentations\n", "from viscy.transforms import (NormalizeSampled, RandAdjustContrastd,\n", " RandAffined, RandGaussianNoised,\n", diff --git a/examples/virtual_staining/img2img_translation/solution.ipynb b/examples/virtual_staining/img2img_translation/solution.ipynb index bc525038..2ce50ffd 100644 --- a/examples/virtual_staining/img2img_translation/solution.ipynb +++ b/examples/virtual_staining/img2img_translation/solution.ipynb @@ -89,8 +89,8 @@ "from viscy.data.hcs import HCSDataModule\n", "\n", "# Trainer class and UNet.\n", - "from viscy.light.engine import MixedLoss, VSUNet\n", - "from viscy.light.trainer import VSTrainer\n", + "from viscy.translation.engine import MixedLoss, VSUNet\n", + "from viscy.translation.trainer import VSTrainer\n", "\n", "# training augmentations\n", "from viscy.transforms import (\n", diff --git a/examples/virtual_staining/phase_contrast/solution.ipynb b/examples/virtual_staining/phase_contrast/solution.ipynb index af7b800a..4e017df0 100644 --- a/examples/virtual_staining/phase_contrast/solution.ipynb +++ b/examples/virtual_staining/phase_contrast/solution.ipynb @@ -54,8 +54,8 @@ "from viscy.data.hcs import HCSDataModule\n", "\n", "# Viscy classes for the trainer and model\n", - "from viscy.light.engine import VSUNet\n", - "from viscy.light.trainer import VSTrainer\n", + "from viscy.translation.engine import VSUNet\n", + "from viscy.translation.trainer import VSTrainer\n", "from viscy.transforms import NormalizeSampled\n", "from lightning.pytorch import seed_everything\n", "\n", diff --git a/examples/virtual_staining/phase_contrast/solution.py b/examples/virtual_staining/phase_contrast/solution.py index 0a475870..b455aced 100644 --- a/examples/virtual_staining/phase_contrast/solution.py +++ b/examples/virtual_staining/phase_contrast/solution.py @@ -18,8 +18,8 @@ from viscy.data.hcs import HCSDataModule # Viscy classes for the trainer and model -from viscy.light.engine import VSUNet -from viscy.light.trainer import VSTrainer +from viscy.translation.engine import VSUNet +from viscy.translation.trainer import VSTrainer from viscy.transforms import NormalizeSampled from lightning.pytorch import seed_everything From 19c45591bc96f6326077eaba9910cebacc424b24 Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Tue, 10 Sep 2024 14:04:15 -0700 Subject: [PATCH 65/87] rename translation tests --- tests/{light => translation}/__init__.py | 0 tests/{light => translation}/test_engine.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename tests/{light => translation}/__init__.py (100%) rename tests/{light => translation}/test_engine.py (100%) diff --git a/tests/light/__init__.py b/tests/translation/__init__.py similarity index 100% rename from tests/light/__init__.py rename to tests/translation/__init__.py diff --git a/tests/light/test_engine.py b/tests/translation/test_engine.py similarity index 100% rename from tests/light/test_engine.py rename to tests/translation/test_engine.py From 63d9f5abcde37cba4a17aa6225fd5ba3c70bb64d Mon Sep 17 00:00:00 2001 From: Ziwen Liu Date: Tue, 10 Sep 2024 14:06:39 -0700 Subject: [PATCH 66/87] rename translation metrics --- viscy/{evaluation => translation}/evaluation_metrics.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename viscy/{evaluation => translation}/evaluation_metrics.py (100%) diff --git a/viscy/evaluation/evaluation_metrics.py b/viscy/translation/evaluation_metrics.py similarity index 100% rename from viscy/evaluation/evaluation_metrics.py rename to viscy/translation/evaluation_metrics.py From ee826b504321338f2793fc31b989f9bd8c41ff18 Mon Sep 17 00:00:00 2001 From: Ziwen Liu <67518483+ziw-liu@users.noreply.github.com> Date: Wed, 11 Sep 2024 12:16:44 -0400 Subject: [PATCH 67/87] Sample positive and negative samples with a time offset for the triplet contrastive task (#154) * wip: sample positive and negative samples from another time point * configure time interval in triplet data module * vectorized anchor filtering * conditional augmentation for anchor anchor is augmented if the positive is another time point * example training script for the CTC dataset this is optimized to run on MPS * add example CTC prediction config for MPS --- .../contrastive_cli/fit_ctc_mps.yml | 113 +++++++++++ .../contrastive_cli/predict_ctc_mps.yml | 44 +++++ viscy/data/triplet.py | 177 +++++++++++++----- 3 files changed, 292 insertions(+), 42 deletions(-) create mode 100644 applications/contrastive_phenotyping/contrastive_cli/fit_ctc_mps.yml create mode 100644 applications/contrastive_phenotyping/contrastive_cli/predict_ctc_mps.yml diff --git a/applications/contrastive_phenotyping/contrastive_cli/fit_ctc_mps.yml b/applications/contrastive_phenotyping/contrastive_cli/fit_ctc_mps.yml new file mode 100644 index 00000000..983744d8 --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/fit_ctc_mps.yml @@ -0,0 +1,113 @@ +# See help here on how to configure hyper-parameters with config files: +# https://lightning.ai/docs/pytorch/stable/cli/lightning_cli_advanced.html +seed_everything: 42 +trainer: + accelerator: gpu + strategy: auto + devices: 1 + num_nodes: 1 + precision: 32-true + logger: + class_path: lightning.pytorch.loggers.TensorBoardLogger + # Nesting the logger config like this is equivalent to + # supplying the following argument to `lightning.pytorch.Trainer`: + # logger=TensorBoardLogger( + # "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations", + # log_graph=True, + # version="vanilla", + # ) + init_args: + save_dir: /Users/ziwen.liu/Projects/test-time + # this is the name of the experiment. + # The logs will be saved in `save_dir/lightning_logs/version` + version: time_interval_1 + log_graph: True + callbacks: + - class_path: lightning.pytorch.callbacks.LearningRateMonitor + init_args: + logging_interval: step + - class_path: lightning.pytorch.callbacks.ModelCheckpoint + init_args: + monitor: loss/val + every_n_epochs: 1 + save_top_k: 4 + save_last: true + fast_dev_run: false + max_epochs: 100 + log_every_n_steps: 10 + enable_checkpointing: true + inference_mode: true + use_distributed_sampler: true + # synchronize batchnorm parameters across multiple GPUs. + # important for contrastive learning to normalize the tensors across the whole batch. + sync_batchnorm: true +model: + encoder: + class_path: viscy.representation.contrastive.ContrastiveEncoder + init_args: + backbone: convnext_tiny + in_channels: 1 + in_stack_depth: 1 + stem_kernel_size: [1, 4, 4] + stem_stride: [1, 4, 4] + embedding_dim: 768 + projection_dim: 32 + drop_path_rate: 0.0 + loss_function: + class_path: torch.nn.TripletMarginLoss + init_args: + margin: 0.5 + lr: 0.0002 + log_batches_per_epoch: 3 + log_samples_per_batch: 2 + example_input_array_shape: [1, 1, 1, 128, 128] +data: + data_path: /Users/ziwen.liu/Downloads/Hela_CTC.zarr + tracks_path: /Users/ziwen.liu/Downloads/Hela_CTC.zarr + source_channel: + - DIC + z_range: [0, 1] + batch_size: 16 + num_workers: 4 + initial_yx_patch_size: [256, 256] + final_yx_patch_size: [128, 128] + time_interval: 1 + normalizations: + - class_path: viscy.transforms.NormalizeSampled + init_args: + keys: [DIC] + level: fov_statistics + subtrahend: mean + divisor: std + augmentations: + - class_path: viscy.transforms.RandAffined + init_args: + keys: [DIC] + prob: 0.8 + scale_range: [0, 0.2, 0.2] + rotate_range: [3.14, 0.0, 0.0] + shear_range: [0.0, 0.01, 0.01] + padding_mode: zeros + - class_path: viscy.transforms.RandAdjustContrastd + init_args: + keys: [DIC] + prob: 0.5 + gamma: [0.8, 1.2] + - class_path: viscy.transforms.RandScaleIntensityd + init_args: + keys: [DIC] + prob: 0.5 + factors: 0.5 + - class_path: viscy.transforms.RandGaussianSmoothd + init_args: + keys: [DIC] + prob: 0.5 + sigma_x: [0.25, 0.75] + sigma_y: [0.25, 0.75] + sigma_z: [0.0, 0.0] + - class_path: viscy.transforms.RandGaussianNoised + init_args: + keys: [DIC] + prob: 0.5 + mean: 0.0 + std: 0.2 diff --git a/applications/contrastive_phenotyping/contrastive_cli/predict_ctc_mps.yml b/applications/contrastive_phenotyping/contrastive_cli/predict_ctc_mps.yml new file mode 100644 index 00000000..993267af --- /dev/null +++ b/applications/contrastive_phenotyping/contrastive_cli/predict_ctc_mps.yml @@ -0,0 +1,44 @@ +seed_everything: 42 +trainer: + accelerator: gpu + strategy: auto + devices: auto + num_nodes: 1 + precision: 32-true + callbacks: + - class_path: viscy.representation.embedding_writer.EmbeddingWriter + init_args: + output_path: /Users/ziwen.liu/Projects/test-time/predict/time_interval_1.zarr + inference_mode: true +model: + encoder: + class_path: viscy.representation.contrastive.ContrastiveEncoder + init_args: + backbone: convnext_tiny + in_channels: 1 + in_stack_depth: 1 + stem_kernel_size: [1, 4, 4] + stem_stride: [1, 4, 4] + embedding_dim: 768 + projection_dim: 32 + drop_path_rate: 0.0 + example_input_array_shape: [1, 1, 1, 128, 128] +data: + data_path: /Users/ziwen.liu/Downloads/Hela_CTC.zarr + tracks_path: /Users/ziwen.liu/Downloads/Hela_CTC.zarr + source_channel: DIC + z_range: [0, 1] + batch_size: 16 + num_workers: 4 + initial_yx_patch_size: [128, 128] + final_yx_patch_size: [128, 128] + time_interval: 1 + normalizations: + - class_path: viscy.transforms.NormalizeSampled + init_args: + keys: [DIC] + level: fov_statistics + subtrahend: mean + divisor: std +return_predictions: false +ckpt_path: /Users/ziwen.liu/Projects/test-time/lightning_logs/time_interval_1/checkpoints/last.ckpt \ No newline at end of file diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index 4e056851..1ffb9cd0 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -1,6 +1,6 @@ import logging from pathlib import Path -from typing import Sequence +from typing import Literal, Sequence import pandas as pd import torch @@ -61,7 +61,43 @@ def __init__( predict_cells: bool = False, include_fov_names: list[str] | None = None, include_track_ids: list[int] | None = None, + time_interval: Literal["any"] | int = "any", ) -> None: + """Dataset for triplet sampling of cells based on tracking. + + Parameters + ---------- + positions : list[Position] + OME-Zarr images with consistent channel order + tracks_tables : list[pd.DataFrame] + Data frames containing ultrack results + channel_names : list[str] + Input channel names + initial_yx_patch_size : tuple[int, int] + YX size of the initially sampled image patch before augmentation + z_range : slice + Range of Z-slices + anchor_transform : DictTransform | None, optional + Transforms applied to the anchor sample, by default None + positive_transform : DictTransform | None, optional + Transforms applied to the positve sample, by default None + negative_transform : DictTransform | None, optional + Transforms applied to the negative sample, by default None + fit : bool, optional + Fitting mode in which the full triplet will be sampled, + only sample anchor if ``False``, by default True + predict_cells : bool, optional + Only predict on selected cells, by default False + include_fov_names : list[str] | None, optional + Only predict on selected FOVs, by default None + include_track_ids : list[int] | None, optional + Only predict on selected track IDs, by default None + time_interval : Literal["any"] | int, optional + Future time interval to sample positive and anchor from, + by default "any" + (sample negative from another track any time point + and use the augmented anchor patch as positive) + """ self.positions = positions self.channel_names = channel_names self.channel_indices = [ @@ -76,13 +112,15 @@ def __init__( self.predict_cells = predict_cells self.include_fov_names = include_fov_names or [] self.include_track_ids = include_track_ids or [] + self.time_interval = time_interval self.tracks = self._filter_tracks(tracks_tables) + self.valid_anchors = self._filter_anchors(self.tracks) self.tracks = ( self._specific_cells(self.tracks) if self.predict_cells else self.tracks ) def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: - """_filter_tracks Select tracks within positions that belong to this dataset and remove tracks that are too close to the border. + """Exclude tracks that are too close to the border or do not have the next time point. Parameters ---------- @@ -93,7 +131,6 @@ def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: ------- pd.DataFrame Filtered tracks table - """ filtered_tracks = [] y_exclude, x_exclude = (self.yx_patch_size[0] // 2, self.yx_patch_size[1] // 2) @@ -110,6 +147,7 @@ def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: ) y_range = (y_exclude, image.height - y_exclude) x_range = (x_exclude, image.width - x_exclude) + # FIXME: Check if future time points are available after interval filtered_tracks.append( tracks[ tracks["y"].between(*y_range, inclusive="neither") @@ -118,6 +156,17 @@ def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: ) return pd.concat(filtered_tracks).reset_index(drop=True) + def _filter_anchors(self, tracks: pd.DataFrame) -> pd.DataFrame: + """Ensure that anchors have the next time point after a time interval.""" + if self.time_interval == "any" or not self.fit: + return tracks + return pd.concat( + [ + track[(track["t"] + self.time_interval).isin(track["t"])] + for (_, track) in tracks.groupby("global_track_id") + ] + ) + def _specific_cells(self, tracks: pd.DataFrame) -> pd.DataFrame: specific_tracks = pd.DataFrame() print(self.include_fov_names) @@ -129,12 +178,29 @@ def _specific_cells(self, tracks: pd.DataFrame) -> pd.DataFrame: specific_tracks = pd.concat([specific_tracks, filtered_tracks]) return specific_tracks.reset_index(drop=True) - def __len__(self): - return len(self.tracks) + def __len__(self) -> int: + return len(self.valid_anchors) + + def _sample_positive(self, anchor_row: pd.Series) -> pd.Series: + """Select a positive sample from the same track in the next time point.""" + same_track = self.tracks[ + (self.tracks["global_track_id"] == anchor_row["global_track_id"]) + ] + return same_track[ + same_track["t"] == (anchor_row["t"] + self.time_interval) + ].iloc[0] def _sample_negative(self, anchor_row: pd.Series) -> pd.Series: - candidates: pd.DataFrame = self.tracks[ - (self.tracks["global_track_id"] != anchor_row["global_track_id"]) + """Select a negative sample from a different track in the next time point + if an interval is specified, otherwise from any random time point.""" + if self.time_interval == "any": + tracks = self.tracks + else: + tracks = self.tracks[ + self.tracks["t"] == anchor_row["t"] + self.time_interval + ] + candidates: pd.DataFrame = tracks[ + (tracks["global_track_id"] != anchor_row["global_track_id"]) ] # NOTE: Random sampling # this is to avoid combinatorial length growth at fitting time @@ -160,25 +226,30 @@ def _slice_patch(self, track_row: pd.Series) -> tuple[Tensor, NormMeta | None]: return torch.from_numpy(patch), _read_norm_meta(position) def __getitem__(self, index: int) -> TripletSample: - anchor_row = self.tracks.iloc[index] + anchor_row = self.valid_anchors.iloc[index] anchor_patch, anchor_norm = self._slice_patch(anchor_row) if self.fit: - positive_patch = anchor_patch.clone() + if self.time_interval == "any": + positive_patch = anchor_patch.clone() + positive_norm = anchor_norm + else: + positive_row = self._sample_positive(anchor_row) + positive_patch, positive_norm = self._slice_patch(positive_row) if self.positive_transform: positive_patch = _transform_channel_wise( transform=self.positive_transform, channel_names=self.channel_names, patch=positive_patch, - norm_meta=anchor_norm, + norm_meta=positive_norm, ) negative_row = self._sample_negative(anchor_row) - negative_patch, negetive_norm = self._slice_patch(negative_row) + negative_patch, negative_norm = self._slice_patch(negative_row) if self.negative_transform: negative_patch = _transform_channel_wise( transform=self.negative_transform, channel_names=self.channel_names, patch=negative_patch, - norm_meta=negetive_norm, + norm_meta=negative_norm, ) if self.anchor_transform: anchor_patch = _transform_channel_wise( @@ -187,14 +258,11 @@ def __getitem__(self, index: int) -> TripletSample: patch=anchor_patch, norm_meta=anchor_norm, ) - sample = {"anchor": anchor_patch, "index": anchor_row[INDEX_COLUMNS].to_dict()} + sample = {"anchor": anchor_patch} if self.fit: - sample.update( - { - "positive": positive_patch, - "negative": negative_patch, - } - ) + sample.update({"positive": positive_patch, "negative": negative_patch}) + else: + sample.update({"index": anchor_row[INDEX_COLUMNS].to_dict()}) return sample @@ -216,26 +284,46 @@ def __init__( predict_cells: bool = False, include_fov_names: list[str] | None = None, include_track_ids: list[int] | None = None, + time_interval: Literal["any"] | int = "any", ): """Lightning data module for triplet sampling of patches. - :param str data_path: Image dataset path - :param str tracks_path: Tracks labels dataset path - :param str | Sequence[str] source_channel: list of input channel names - :param tuple[int, int] z_range: range of valid z-slices - :param tuple[int, int] initial_yx_patch_size: - XY size of the initially sampled image patch, - defaults to (384, 384) - :param tuple[int, int] final_yx_patch_size: output patch size, - defaults to (256, 256) - :param float split_ratio: ratio of training samples, defaults to 0.8 - :param int batch_size: batch size, defaults to 16 - :param int num_workers: number of data-loading workers, defaults to 8 - :param list[MapTransform] normalizations: list of normalization transforms, - defaults to [] - :param list[MapTransform] augmentations: list of augmentation transforms, - defaults to [] - :param bool caching: whether to cache the dataset, defaults to False + Parameters + ---------- + data_path : str + Image dataset path + tracks_path : str + Tracks labels dataset path + source_channel : str | Sequence[str] + List of input channel names + z_range : tuple[int, int] + Range of valid z-slices + initial_yx_patch_size : tuple[int, int], optional + XY size of the initially sampled image patch, by default (512, 512) + final_yx_patch_size : tuple[int, int], optional + Output patch size, by default (224, 224) + split_ratio : float, optional + Ratio of training samples, by default 0.8 + batch_size : int, optional + Batch size, by default 16 + num_workers : int, optional + Number of data-loading workers, by default 8 + normalizations : list[MapTransform], optional + Normalization transforms, by default [] + augmentations : list[MapTransform], optional + Augmentation transforms, by default [] + caching : bool, optional + Whether to cache the dataset, by default False + predict_cells : bool, optional + Only predict for selected cells, by default False + include_fov_names : list[str] | None, optional + Only predict for selected FOVs, by default None + include_track_ids : list[int] | None, optional + Only predict for selected tracks, by default None + time_interval : Literal["any"] | int, optional + Future time interval to sample positive and anchor from, + "any" means sampling negative from another track any time point + and using the augmented anchor patch as positive), by default "any" """ super().__init__( data_path=data_path, @@ -257,6 +345,7 @@ def __init__( self.predict_cells = predict_cells self.include_fov_names = include_fov_names self.include_track_ids = include_track_ids + self.time_interval = time_interval def _align_tracks_tables_with_positions( self, @@ -286,6 +375,7 @@ def _base_dataset_settings(self) -> dict: return { "channel_names": self.source_channel, "z_range": self.z_range, + "time_interval": self.time_interval, } def _setup_fit(self, dataset_settings: dict): @@ -300,15 +390,18 @@ def _setup_fit(self, dataset_settings: dict): val_positions = positions[num_train_fovs:] train_tracks_tables = tracks_tables[:num_train_fovs] val_tracks_tables = tracks_tables[num_train_fovs:] - - print(f"Number of training FOVs: {len(train_positions)}") - print(f"Number of validation FOVs: {len(val_positions)}") - + _logger.debug(f"Number of training FOVs: {len(train_positions)}") + _logger.debug(f"Number of validation FOVs: {len(val_positions)}") + anchor_transform = ( + no_aug_transform + if (self.time_interval == "any" or self.time_interval == 0) + else augment_transform + ) self.train_dataset = TripletDataset( positions=train_positions, tracks_tables=train_tracks_tables, initial_yx_patch_size=self.initial_yx_patch_size, - anchor_transform=no_aug_transform, + anchor_transform=anchor_transform, positive_transform=augment_transform, negative_transform=augment_transform, fit=True, @@ -319,7 +412,7 @@ def _setup_fit(self, dataset_settings: dict): positions=val_positions, tracks_tables=val_tracks_tables, initial_yx_patch_size=self.initial_yx_patch_size, - anchor_transform=no_aug_transform, + anchor_transform=anchor_transform, positive_transform=augment_transform, negative_transform=augment_transform, fit=True, From e2175b4108ddc8c403b37030940e227b185a28f1 Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Tue, 17 Sep 2024 13:28:27 -0700 Subject: [PATCH 68/87] add fig for mitosis --- .../figures/cell_division.py | 166 ++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 applications/contrastive_phenotyping/figures/cell_division.py diff --git a/applications/contrastive_phenotyping/figures/cell_division.py b/applications/contrastive_phenotyping/figures/cell_division.py new file mode 100644 index 00000000..32838fb2 --- /dev/null +++ b/applications/contrastive_phenotyping/figures/cell_division.py @@ -0,0 +1,166 @@ + + +# %% figures for visualizing the results of cell division + +from pathlib import Path +import pandas as pd +import seaborn as sns +import plotly.express as px +from sklearn.preprocessing import StandardScaler +from umap import UMAP +from viscy.light.embedding_writer import read_embedding_dataset +import matplotlib.pyplot as plt + +# %% +# single channel. with temporal regularizations +# dataset = read_embedding_dataset( +# "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval_phase/predictions/epoch_186/1chan_128patch_186ckpt_Febtest.zarr" +# ) +# dataset + +# single cahnnel, without temporal regularizations +# dataset = read_embedding_dataset( +# "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_1chan_128patch_32projDim/1chan_128patch_63ckpt_FebTest_divGT.zarr" +# ) +# dataset + +# two channel, with temporal regularizations +# dataset = read_embedding_dataset( +# "" +# ) +# dataset + +# two channel, without temporal regularizations +dataset = read_embedding_dataset( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest_divGT.zarr" +) +dataset + +# %% +# load all unprojected features: +features = dataset["features"] +# or select a well: +# features - features[features["fov_name"].str.contains("B/4")] +features + +# %% umap with 2 components +scaled_features = StandardScaler().fit_transform(features.values) + +umap = UMAP() + +embedding = umap.fit_transform(features.values) +features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +features + +# %% + +def load_annotation(da, path, name, categories: dict | None = None): + annotation = pd.read_csv(path) + # annotation_columns = annotation.columns.tolist() + # print(annotation_columns) + annotation["fov_name"] = "/" + annotation["fov ID"] + annotation = annotation.set_index(["fov_name", "id"]) + mi = pd.MultiIndex.from_arrays( + [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] + ) + selected = annotation.loc[mi][name] + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + return selected + +# %% + +ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/9-lineage-cell-division/lineages_gt") + +division = load_annotation( + features, + ann_root / "cell_division_state_test_set.csv", + "division", + {0: "interphase", 2: "mitosis"}, +) + +# %% +sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, palette={'interphase': "steelblue", 1: "green", 'mitosis': "orangered"}, s=7, alpha=0.8) +plt.show() +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/UMAP_cellDiv_GTtracking_sc_woT.svg" +# ) + +# %% +no_inter = division[division == 'interphase'].count() +no_div = division[division == 'mitosis'].count() + +# %% plot the trajectory quiver of one cell on top of the UMAP + +from matplotlib.patches import FancyArrowPatch + +cell_parent = features[(features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([13]))] +cell_daughter1 = features[(features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([14]))] +cell_daughter2 = features[(features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([15]))] + +# Adding arrows to indicate trajectory direction +def add_arrows(df, color): + for i in range(len(df) - 1): + start = df.iloc[i] + end = df.iloc[i + 1] + arrow = FancyArrowPatch( + (start['UMAP1'], start['UMAP2']), + (end['UMAP1'], end['UMAP2']), + color=color, + arrowstyle='-|>', + mutation_scale=8, # reduce the size of arrowhead by half + lw=1, + shrinkA=0, + shrinkB=0, + ) + plt.gca().add_patch(arrow) + +# tried A/3/7, 8 to 9 & 10 +# tried A/3/7, 13 to 14 & 15 +# tried A/3/7, 18 to 19 & 20 +# tried A/3/8, 23 to 24 & 25 + +sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, palette={'interphase': "steelblue", 1: "green", 'mitosis': "orangered"}, s=7, alpha=0.8) +# sns.lineplot(x=cell_parent["UMAP1"], y=cell_parent["UMAP2"], color='black', linewidth=1) +# sns.lineplot(x=cell_daughter1["UMAP1"], y=cell_daughter1["UMAP2"], color='red', linewidth=1) +# sns.lineplot(x=cell_daughter2["UMAP1"], y=cell_daughter2["UMAP2"], color='blue', linewidth=1) + +# Apply arrows to the trajectories +add_arrows(cell_parent.to_dataframe(), color='black') +add_arrows(cell_daughter1.to_dataframe(), color='red') +add_arrows(cell_daughter2.to_dataframe(), color='blue') + +plt.xlabel('UMAP1') +plt.ylabel('UMAP2') +# plt.title('UMAP with Trajectory Direction') +# plt.legend(title='Division Phase') +plt.xlim(-3, 13) +plt.ylim(1, 11) +plt.legend([],[], frameon=False) +# plt.show() + +# single channel, with temporal regularizations +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel.png" +# ) + +# single channel, without temporal regularizations +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel_woT.png" +# ) + +# two channel, with temporal regularizations +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel.png" +# ) + +# two channel, without temporal regularizations +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_woT.png" +) + +# %% From 2a6cd20dece53525916af7e715a916d85f8eeddc Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Tue, 17 Sep 2024 13:54:35 -0700 Subject: [PATCH 69/87] add script to save image patches --- .../figures/save_patches.py | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 applications/contrastive_phenotyping/figures/save_patches.py diff --git a/applications/contrastive_phenotyping/figures/save_patches.py b/applications/contrastive_phenotyping/figures/save_patches.py new file mode 100644 index 00000000..30f0353b --- /dev/null +++ b/applications/contrastive_phenotyping/figures/save_patches.py @@ -0,0 +1,53 @@ + +# %% script to save 128 by 128 image patches from napari viewer + +import napari +import numpy as np +from pathlib import Path +import sys + +sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") +# from viscy.data.triplet import TripletDataModule +from viscy.representation.evaluation import dataset_of_tracks + + +# %% input parameters + +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" +) + +fov_name = '/B/4/8' +track_id = 12 +source_channel = ["Phase3D", "RFP"] + +# %% load dataset + +prediction_dataset = dataset_of_tracks( + data_path, + tracks_path, + [fov_name], + [track_id], + source_channel=source_channel, +) +whole = np.stack([p["anchor"] for p in prediction_dataset]) +phase = whole[:, 0] +fluor = whole[:, 1] + +# use the following if you want to visualize a specific phase slice with max projected fluor +# phase = whole[:, 0, 3] # 3 is the slice number +# fluor = np.max(whole[:, 1], axis=1) + +# load image +v = napari.Viewer() +v.add_image(phase) +v.add_image(fluor) + +# %% save patches as png images + +# use sliders on napari to get the deisred contrast and make other adjustments +# then use save screenshot if saving the image patch manually +# you can add code to automate the process if desired \ No newline at end of file From 767b12cbafe673e45c9d778b012ef7bcd5ec4ad9 Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Wed, 18 Sep 2024 09:05:39 -0700 Subject: [PATCH 70/87] add save patches as npy --- .../figures/cell_division.py | 30 +++++++++---------- .../figures/save_patches.py | 22 ++++++++++---- 2 files changed, 31 insertions(+), 21 deletions(-) diff --git a/applications/contrastive_phenotyping/figures/cell_division.py b/applications/contrastive_phenotyping/figures/cell_division.py index 32838fb2..ee134875 100644 --- a/applications/contrastive_phenotyping/figures/cell_division.py +++ b/applications/contrastive_phenotyping/figures/cell_division.py @@ -25,17 +25,17 @@ # dataset # two channel, with temporal regularizations -# dataset = read_embedding_dataset( -# "" -# ) -# dataset - -# two channel, without temporal regularizations dataset = read_embedding_dataset( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest_divGT.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178_gt_tracks.zarr" ) dataset +# two channel, without temporal regularizations +# dataset = read_embedding_dataset( +# "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest_divGT.zarr" +# ) +# dataset + # %% # load all unprojected features: features = dataset["features"] @@ -138,8 +138,8 @@ def add_arrows(df, color): plt.ylabel('UMAP2') # plt.title('UMAP with Trajectory Direction') # plt.legend(title='Division Phase') -plt.xlim(-3, 13) -plt.ylim(1, 11) +plt.xlim(-7, 11) +plt.ylim(5, 17) plt.legend([],[], frameon=False) # plt.show() @@ -154,13 +154,13 @@ def add_arrows(df, color): # ) # two channel, with temporal regularizations -# plt.savefig( -# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel.png" -# ) - -# two channel, without temporal regularizations plt.savefig( - "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_woT.png" + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel.png" ) +# two channel, without temporal regularizations +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_woT.png" +# ) + # %% diff --git a/applications/contrastive_phenotyping/figures/save_patches.py b/applications/contrastive_phenotyping/figures/save_patches.py index 30f0353b..e230d5a0 100644 --- a/applications/contrastive_phenotyping/figures/save_patches.py +++ b/applications/contrastive_phenotyping/figures/save_patches.py @@ -5,6 +5,7 @@ import numpy as np from pathlib import Path import sys +import os sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") # from viscy.data.triplet import TripletDataModule @@ -20,8 +21,8 @@ "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" ) -fov_name = '/B/4/8' -track_id = 12 +fov_name = '/B/4/6' +track_id = 52 source_channel = ["Phase3D", "RFP"] # %% load dataset @@ -42,12 +43,21 @@ # fluor = np.max(whole[:, 1], axis=1) # load image -v = napari.Viewer() -v.add_image(phase) -v.add_image(fluor) +# v = napari.Viewer() +# v.add_image(phase) +# v.add_image(fluor) # %% save patches as png images # use sliders on napari to get the deisred contrast and make other adjustments # then use save screenshot if saving the image patch manually -# you can add code to automate the process if desired \ No newline at end of file +# you can add code to automate the process if desired + +# %% save as numpy files + +out_dir = '/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/data/' +fov_name_out = fov_name.replace('/', '_') +np.save((os.path.join(out_dir,"phase"+fov_name_out+"_"+str(track_id)+".npy")), phase) +np.save((os.path.join(out_dir,"fluor"+fov_name_out+"_"+str(track_id)+".npy")), fluor) + +# %% \ No newline at end of file From 275958449ce0c44522406ee8e48c0e1c791c0466 Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Wed, 18 Sep 2024 10:34:34 -0700 Subject: [PATCH 71/87] save figure at 300dpi --- .../contrastive_phenotyping/figures/cell_division.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/applications/contrastive_phenotyping/figures/cell_division.py b/applications/contrastive_phenotyping/figures/cell_division.py index ee134875..306618bf 100644 --- a/applications/contrastive_phenotyping/figures/cell_division.py +++ b/applications/contrastive_phenotyping/figures/cell_division.py @@ -145,22 +145,26 @@ def add_arrows(df, color): # single channel, with temporal regularizations # plt.savefig( -# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel.png" +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel.png", + dpi=300 # ) # single channel, without temporal regularizations # plt.savefig( -# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel_woT.png" +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel_woT.png", + dpi=300 # ) # two channel, with temporal regularizations plt.savefig( - "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel.png" + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel.png", + dpi=300 ) # two channel, without temporal regularizations # plt.savefig( -# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_woT.png" +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_woT.png", + dpi=300 # ) # %% From 74fa3d75737e52608bd0980c745e6cc12403cf4f Mon Sep 17 00:00:00 2001 From: Ziwen Liu <67518483+ziw-liu@users.noreply.github.com> Date: Thu, 19 Sep 2024 23:43:23 -0400 Subject: [PATCH 72/87] Linear probing (#160) * refactor linear probing with lightning * test convenience function * always convert to long before onehot * use onehot only during training * supply trainer through argument to avoid wrapping * only log per epoch * example script for linear probing * add comment about loss curve * fix sample filtering order for select tracks * add script to visualize integrated gradients * plot integrated gradients over time * Use sklearn's logistic regression for linear probing (#169) * use binary logistic regression to initialize the linear layer * plot integrated gradients from a binary classifier * add cmap to 'visual' requirements * move model assembling to lca * rename init argument * disable feature scaling * update test and evaluation scripts to use new API * add docstrings to LCA --- .../evaluation/grad_attr.py | 171 +++++++++++++++ .../evaluation/linear_probing.py | 54 +++++ pyproject.toml | 10 +- tests/representation/test_lca.py | 23 ++ viscy/data/triplet.py | 2 +- viscy/representation/lca.py | 196 ++++++++++++------ 6 files changed, 394 insertions(+), 62 deletions(-) create mode 100644 applications/contrastive_phenotyping/evaluation/grad_attr.py create mode 100644 applications/contrastive_phenotyping/evaluation/linear_probing.py create mode 100644 tests/representation/test_lca.py diff --git a/applications/contrastive_phenotyping/evaluation/grad_attr.py b/applications/contrastive_phenotyping/evaluation/grad_attr.py new file mode 100644 index 00000000..169345dd --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/grad_attr.py @@ -0,0 +1,171 @@ +# %% +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch +from captum.attr import IntegratedGradients +from cmap import Colormap +from lightning.pytorch import seed_everything +from skimage.exposure import rescale_intensity + +from viscy.data.triplet import TripletDataModule +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.engine import ContrastiveEncoder, ContrastiveModule +from viscy.representation.evaluation import load_annotation +from viscy.representation.lca import ( + AssembledClassifier, + fit_logistic_regression, + linear_from_binary_logistic_regression, +) +from viscy.transforms import NormalizeSampled, ScaleIntensityRangePercentilesd + +# %% +seed_everything(42, workers=True) + +fov = "/B/4/6" +track = 4 + +# %% +dm = TripletDataModule( + data_path="/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr", + tracks_path="/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr", + source_channel=["Phase3D", "RFP"], + z_range=[25, 40], + batch_size=48, + num_workers=0, + initial_yx_patch_size=(128, 128), + final_yx_patch_size=(128, 128), + normalizations=[ + NormalizeSampled( + keys=["Phase3D"], level="fov_statistics", subtrahend="mean", divisor="std" + ), + ScaleIntensityRangePercentilesd( + keys=["RFP"], lower=50, upper=99, b_min=0.0, b_max=1.0 + ), + ], + predict_cells=True, + include_fov_names=[fov], + include_track_ids=[track], +) +dm.setup("predict") +len(dm.predict_dataset) + +# %% +# load model +model = ContrastiveModule.load_from_checkpoint( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/epoch=178-step=16826.ckpt", + encoder=ContrastiveEncoder( + backbone="convnext_tiny", + in_channels=2, + in_stack_depth=15, + stem_kernel_size=(5, 4, 4), + stem_stride=(5, 4, 4), + embedding_dim=768, + projection_dim=32, + ), +).eval() + +# %% +# train linear classifier +path_embedding = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +path_annotations_infection = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/extracted_inf_state.csv" +) + +dataset = read_embedding_dataset(path_embedding) +features = dataset["features"] +infection = load_annotation( + dataset, + path_annotations_infection, + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +# %% +train_fovs = ["/A/3/7", "/A/3/8", "/A/3/9", "/B/4/7", "/B/4/8"] + +# %% +logistic_regression, data_split = fit_logistic_regression( + features.copy(), + infection.copy(), + train_fovs, + remove_background_class=True, + scale_features=False, + class_weight="balanced", + solver="liblinear", +) + +# %% +linear_classifier = linear_from_binary_logistic_regression(logistic_regression) +assembled_classifier = AssembledClassifier(model.model, linear_classifier).eval().cpu() + +# %% +# load infection annotations +infection = pd.read_csv( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/extracted_inf_state.csv", +) +track_classes = infection[infection["fov_name"] == fov[1:]] +track_classes = track_classes[track_classes["track_id"] == track]["infection_state"] + + +# %% +def attribute_sample(img, assembled_classifier): + ig = IntegratedGradients(assembled_classifier, multiply_by_inputs=True) + assembled_classifier.zero_grad() + attribution = ig.attribute(torch.from_numpy(img)).numpy() + return img, attribution + + +def color_and_clim(heatmap, cmap, low=1, high=99): + lo, hi = np.percentile(heatmap, (low, high)) + rescaled = rescale_intensity(heatmap.clip(lo, hi), out_range=(0, 1)) + return Colormap(cmap)(rescaled) + + +# %% +for sample in dm.predict_dataloader(): + img = sample["anchor"].numpy() + +# %% +with torch.inference_mode(): + probs = assembled_classifier(torch.from_numpy(img)).sigmoid() +img, attribution = attribute_sample(img, assembled_classifier) + +# %% +z_slice = 5 +phase = color_and_clim(img[:, 0, z_slice], cmap="gray") +rfp = color_and_clim(img[:, 1, z_slice], cmap="gray") +phase_heatmap = color_and_clim(attribution[:, 0, z_slice], cmap="icefire") +rfp_heatmap = color_and_clim(attribution[:, 1, z_slice], cmap="icefire") +grid = np.concatenate( + [ + np.concatenate([phase, phase_heatmap], axis=1), + np.concatenate([rfp, rfp_heatmap], axis=1), + ], + axis=2, +) +print(grid.shape) + +# %% +selected_time_points = [0, 4, 8, 34] +class_text = {0: "none", 1: "uninfected", 2: "infected"} + +sps = len(selected_time_points) +f, ax = plt.subplots(1, sps, figsize=(4 * sps, 4)) +for time, a in zip(selected_time_points, ax.flatten()): + rendered = grid[time] + prob = probs[time].item() + a.imshow(rendered) + hpi = 3 + 0.5 * time + text_label = class_text[track_classes.iloc[time]] + a.set_title( + f"{hpi} HPI,\npredicted infection probability: {prob:.2f},\nannotation: {text_label}" + ) + a.axis("off") +f.tight_layout() + +# %% diff --git a/applications/contrastive_phenotyping/evaluation/linear_probing.py b/applications/contrastive_phenotyping/evaluation/linear_probing.py new file mode 100644 index 00000000..5cf8a85e --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/linear_probing.py @@ -0,0 +1,54 @@ +# %% Imports +from pathlib import Path + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import load_annotation +from viscy.representation.lca import fit_logistic_regression + +# %% +TRAIN_FOVS = ["/A/3/7", "/A/3/8", "/A/3/9", "/B/4/6", "/B/4/7"] + + +model_embeddings = { + "no-track": Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" + ), + "cell-aware-2ch": Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr" + ), + "cell-aware-1ch": Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_1chan_128patch_32projDim/1chan_128patch_63ckpt_FebTest.zarr" + ), + "time-cell-aware": Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" + ), +} +path_annotations_infection = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/extracted_inf_state.csv" +) + +# %% +for model_name, path_embedding in model_embeddings.items(): + print(f"Model: {model_name}") + dataset = read_embedding_dataset(path_embedding) + features = dataset["features"] + + infection = load_annotation( + dataset, + path_annotations_infection, + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, + ) + + log_reg = fit_logistic_regression( + features, + infection, + train_fovs=TRAIN_FOVS, + remove_background_class=True, + scale_features=False, + class_weight="balanced", + solver="liblinear", + random_state=42, + ) + +# %% diff --git a/pyproject.toml b/pyproject.toml index 36c44ee6..039bdc41 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,15 @@ metrics = [ ] examples = ["napari", "jupyter", "jupytext"] -visual = ["ipykernel", "graphviz", "torchview", "seaborn", "plotly", "nbformat"] +visual = [ + "ipykernel", + "graphviz", + "torchview", + "seaborn", + "plotly", + "nbformat", + "cmap", +] dev = [ "pytest", diff --git a/tests/representation/test_lca.py b/tests/representation/test_lca.py new file mode 100644 index 00000000..f64b5771 --- /dev/null +++ b/tests/representation/test_lca.py @@ -0,0 +1,23 @@ +import numpy as np +import torch +from sklearn.linear_model import LogisticRegression + +from viscy.representation.lca import linear_from_binary_logistic_regression + + +def test_linear_from_logistic_regression(): + """ + Test ``linear_from_logistic_regression``. + Check that the logits from the logistic regression + and the linear model are almost equal. + """ + rand_data = np.random.rand(100, 8) + rand_labels = np.random.randint(0, 2, size=(100)) + logistic_regression = LogisticRegression().fit(rand_data, rand_labels) + linear_model = linear_from_binary_logistic_regression(logistic_regression) + logistic_logits = logistic_regression.decision_function(rand_data) + with torch.inference_mode(): + torch_logits = ( + linear_model(torch.from_numpy(rand_data).float()).squeeze().numpy() + ) + np.testing.assert_allclose(logistic_logits, torch_logits, rtol=1e-3) diff --git a/viscy/data/triplet.py b/viscy/data/triplet.py index 1ffb9cd0..b816b28c 100644 --- a/viscy/data/triplet.py +++ b/viscy/data/triplet.py @@ -114,10 +114,10 @@ def __init__( self.include_track_ids = include_track_ids or [] self.time_interval = time_interval self.tracks = self._filter_tracks(tracks_tables) - self.valid_anchors = self._filter_anchors(self.tracks) self.tracks = ( self._specific_cells(self.tracks) if self.predict_cells else self.tracks ) + self.valid_anchors = self._filter_anchors(self.tracks) def _filter_tracks(self, tracks_tables: list[pd.DataFrame]) -> pd.DataFrame: """Exclude tracks that are too close to the border or do not have the next time point. diff --git a/viscy/representation/lca.py b/viscy/representation/lca.py index 663b64a7..bcd10816 100644 --- a/viscy/representation/lca.py +++ b/viscy/representation/lca.py @@ -1,75 +1,151 @@ -# FIXME: this is a method from previous version at (viscy.representatin.evaluation) -# and needs to be turned into lightning module. +"""Linear probing of trained encoder based on cell state labels.""" -import numpy as np +from typing import Mapping + +import pandas as pd import torch import torch.nn as nn -import torch.optim as optim -from sklearn.metrics import accuracy_score -from torch.utils.data import DataLoader, TensorDataset +from numpy.typing import NDArray +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report +from sklearn.preprocessing import StandardScaler +from torch import Tensor +from xarray import DataArray +from viscy.representation.contrastive import ContrastiveEncoder -def linear_classifier_accuracy(self, batch_size=32, learning_rate=0.01, epochs=10): - """ - Evaluate the accuracy of a single-layer neural network trained on the - embeddings. + +def fit_logistic_regression( + features: DataArray, + annotations: pd.Series, + train_fovs: list[str], + remove_background_class: bool = True, + scale_features: bool = False, + class_weight: Mapping | str | None = "balanced", + random_state: int | None = None, + solver="liblinear", +) -> tuple[ + LogisticRegression, + tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray]], +]: + """Fit a binary logistic regression classifier. Parameters ---------- - batch_size : int, optional - Batch size for training. Default is 32. - learning_rate : float, optional - Learning rate for the optimizer. Default is 0.01. - epochs : int, optional - Number of training epochs. Default is 10. + features : DataArray + Xarray of features. + annotations : pd.Series + Categorical class annotations with label values starting from 0. + Must have 3 classes (when remove background is True) or 2 classes. + train_fovs : list[str] + List of FOVs to use for training. The rest will be used for testing. + remove_background_class : bool, optional + Remove background class (0), by default True + scale_features : bool, optional + Scale features, by default False + class_weight : Mapping | str | None, optional + Class weight for balancing, by default "balanced" + random_state : int | None, optional + Random state or seed, by default None + solver : str, optional + Solver for the regression problem, by default "liblinear" Returns ------- - float - Accuracy of the neural network classifier. + tuple[LogisticRegression, tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray]]] + Trained classifier and data split [[X_train, y_train], [X_test, y_test]]. """ + fov_selection = features["fov_name"].isin(train_fovs) + train_selection = fov_selection + test_selection = ~fov_selection + annotations = annotations.cat.codes.values.copy() + if remove_background_class: + label_selection = annotations != 0 + train_selection &= label_selection + test_selection &= label_selection + annotations -= 1 + train_features = features.values[train_selection] + test_features = features.values[test_selection] + if scale_features: + scaler = StandardScaler() + train_features = scaler.fit_transform(train_features) + test_features = scaler.fit_transform(test_features) + train_annotations = annotations[train_selection] + test_annotations = annotations[test_selection] + logistic_regression = LogisticRegression( + class_weight=class_weight, + random_state=random_state, + solver=solver, + ) + logistic_regression.fit(train_features, train_annotations) + prediction = logistic_regression.predict(test_features) + print("Trained logistic regression classifier.") + print( + "Training set accuracy:\n" + + classification_report( + logistic_regression.predict(train_features), train_annotations, digits=3 + ) + ) + print( + "Test set accuracy:\n" + + classification_report(prediction, test_annotations, digits=3) + ) + return logistic_regression, ( + (train_features, train_annotations), + (test_features, test_annotations), + ) + + +def linear_from_binary_logistic_regression( + logistic_regression: LogisticRegression, +) -> nn.Linear: + """Convert a binary logistic regression model to a ``torch.nn.Linear`` layer. - class SingleLayerNN(nn.Module): - def __init__(self, input_dim, output_dim): - super(SingleLayerNN, self).__init__() - self.fc = nn.Linear(input_dim, output_dim) - - def forward(self, x): - return self.fc(x) - - # Convert numpy arrays to PyTorch tensors - inputs = torch.tensor(self.embeddings, dtype=torch.float32) - labels = torch.tensor(self.annotations, dtype=torch.long) - - # Create a dataset and data loader - dataset = TensorDataset(inputs, labels) - dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) - - # Initialize the neural network, loss function, and optimizer - input_dim = self.embeddings.shape[1] - output_dim = len(np.unique(self.annotations)) - model = SingleLayerNN(input_dim, output_dim) - criterion = ( - nn.CrossEntropyLoss() - ) # Works with logits, so no softmax in the last layer - - optimizer = optim.SGD(model.parameters(), lr=learning_rate) - - # Training loop - model.train() - for epoch in range(epochs): - for batch_inputs, batch_labels in dataloader: - optimizer.zero_grad() - outputs = model(batch_inputs) - loss = criterion(outputs, batch_labels) - loss.backward() - optimizer.step() - - # Evaluate the model + Parameters + ---------- + logistic_regression : LogisticRegression + Trained logistic regression model. + + Returns + ------- + nn.Linear + Converted linear model. + """ + weights = torch.from_numpy(logistic_regression.coef_).float() + bias = torch.from_numpy(logistic_regression.intercept_).float() + model = nn.Linear(in_features=weights.shape[1], out_features=1) + model.weight.data = weights + model.bias.data = bias model.eval() - with torch.no_grad(): - outputs = model(inputs) - _, predictions = torch.max(outputs, 1) - accuracy = accuracy_score(labels.numpy(), predictions.numpy()) + return model + + +class AssembledClassifier(torch.nn.Module): + """Assemble a contrastive encoder with a linear classifier. + + Parameters + ---------- + backbone : ContrastiveEncoder + Encoder backbone. + classifier : nn.Linear + Classifier head. + """ + + def __init__(self, backbone: ContrastiveEncoder, classifier: nn.Linear) -> None: + super().__init__() + self.backbone = backbone + self.classifier = classifier + + @staticmethod + def scale_features(x: Tensor) -> Tensor: + m = x.mean(-2, keepdim=True) + s = x.std(-2, unbiased=False, keepdim=True) + return (x - m) / s - return accuracy + def forward(self, x: Tensor, scale_features: bool = False) -> Tensor: + x = self.backbone.stem(x) + x = self.backbone.encoder(x) + if scale_features: + x = self.scale_features(x) + x = self.classifier(x) + return x From 10219d348bd4ec5f061f8899be83ede1502d150f Mon Sep 17 00:00:00 2001 From: Ziwen Liu <67518483+ziw-liu@users.noreply.github.com> Date: Wed, 25 Sep 2024 19:40:38 -0700 Subject: [PATCH 73/87] Tweak attribution visualization (#170) * add maplotlib style sheet for figure making * add cell division attribution * add matplotlib style sheet * move attribution computation to lca * tweak contrast limits and text * add captum to optional dependencies * move attribution function to a method of the classifier * add script to show organelle dynamics * add occlusion attribution * more generic save path * add uninfected cell * tweak subplot spacing --- .../evaluation/figure.mplstyle | 8 + .../evaluation/grad_attr.py | 194 ++++++++++---- .../figures/organelle_dynamics.py | 238 ++++++++++++++++++ pyproject.toml | 1 + viscy/representation/lca.py | 39 +++ 5 files changed, 430 insertions(+), 50 deletions(-) create mode 100644 applications/contrastive_phenotyping/evaluation/figure.mplstyle create mode 100644 applications/contrastive_phenotyping/figures/organelle_dynamics.py diff --git a/applications/contrastive_phenotyping/evaluation/figure.mplstyle b/applications/contrastive_phenotyping/evaluation/figure.mplstyle new file mode 100644 index 00000000..7e609568 --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/figure.mplstyle @@ -0,0 +1,8 @@ +font.family: sans-serif +font.sans-serif: Arial +font.size: 10 +figure.titlesize: 12 +axes.titlesize: 10 +xtick.labelsize: 8 +ytick.labelsize: 8 +text.usetex: True diff --git a/applications/contrastive_phenotyping/evaluation/grad_attr.py b/applications/contrastive_phenotyping/evaluation/grad_attr.py index 169345dd..321a7a00 100644 --- a/applications/contrastive_phenotyping/evaluation/grad_attr.py +++ b/applications/contrastive_phenotyping/evaluation/grad_attr.py @@ -1,11 +1,11 @@ # %% from pathlib import Path +import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch -from captum.attr import IntegratedGradients from cmap import Colormap from lightning.pytorch import seed_everything from skimage.exposure import rescale_intensity @@ -24,8 +24,8 @@ # %% seed_everything(42, workers=True) -fov = "/B/4/6" -track = 4 +fov = "/B/4/8" +track = 44 # %% dm = TripletDataModule( @@ -69,28 +69,43 @@ # %% # train linear classifier -path_embedding = Path( +path_infection_embedding = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" ) +path_division_embedding = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178_gt_tracks.zarr" +) path_annotations_infection = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/extracted_inf_state.csv" ) +path_annotations_division = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/9-lineage-cell-division/lineages_gt/cell_division_state_test_set.csv" +) -dataset = read_embedding_dataset(path_embedding) -features = dataset["features"] +infection_dataset = read_embedding_dataset(path_infection_embedding) +infection_features = infection_dataset["features"] infection = load_annotation( - dataset, + infection_dataset, path_annotations_infection, "infection_state", {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, ) +division_dataset = read_embedding_dataset(path_division_embedding) +division_features = division_dataset["features"] +division = load_annotation(division_dataset, path_annotations_division, "division") +# move the unknown class to the 0 label +division[division == 1] = -2 +division += 2 +division /= 2 +division = division.astype("category") + # %% -train_fovs = ["/A/3/7", "/A/3/8", "/A/3/9", "/B/4/7", "/B/4/8"] +train_fovs = ["/A/3/7", "/A/3/8", "/A/3/9", "/B/4/6", "/B/4/7"] # %% -logistic_regression, data_split = fit_logistic_regression( - features.copy(), +logistic_regression_infection, _ = fit_logistic_regression( + infection_features.copy(), infection.copy(), train_fovs, remove_background_class=True, @@ -98,32 +113,54 @@ class_weight="balanced", solver="liblinear", ) +# %% +logistic_regression_division, _ = fit_logistic_regression( + division_features.copy(), + division.copy(), + train_fovs, + remove_background_class=True, + scale_features=False, + class_weight="balanced", + solver="liblinear", +) # %% -linear_classifier = linear_from_binary_logistic_regression(logistic_regression) -assembled_classifier = AssembledClassifier(model.model, linear_classifier).eval().cpu() +linear_classifier_infection = linear_from_binary_logistic_regression( + logistic_regression_infection +) +assembled_classifier_infection = ( + AssembledClassifier(model.model, linear_classifier_infection) + .eval() + .to(model.device) +) + +# %% +linear_classifier_division = linear_from_binary_logistic_regression( + logistic_regression_division +) +assembled_classifier_division = ( + AssembledClassifier(model.model, linear_classifier_division).eval().to(model.device) +) # %% # load infection annotations infection = pd.read_csv( "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/extracted_inf_state.csv", ) -track_classes = infection[infection["fov_name"] == fov[1:]] -track_classes = track_classes[track_classes["track_id"] == track]["infection_state"] - +track_classes_infection = infection[infection["fov_name"] == fov[1:]] +track_classes_infection = track_classes_infection[ + track_classes_infection["track_id"] == track +]["infection_state"] # %% -def attribute_sample(img, assembled_classifier): - ig = IntegratedGradients(assembled_classifier, multiply_by_inputs=True) - assembled_classifier.zero_grad() - attribution = ig.attribute(torch.from_numpy(img)).numpy() - return img, attribution - - -def color_and_clim(heatmap, cmap, low=1, high=99): - lo, hi = np.percentile(heatmap, (low, high)) - rescaled = rescale_intensity(heatmap.clip(lo, hi), out_range=(0, 1)) - return Colormap(cmap)(rescaled) +# load division annotations +division = pd.read_csv( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/9-lineage-cell-division/lineages_gt/cell_division_state_test_set.csv", +) +track_classes_division = division[division["fov_name"] == fov[1:]] +track_classes_division = track_classes_division[ + track_classes_division["track_id"] == track +]["division"] # %% @@ -131,41 +168,98 @@ def color_and_clim(heatmap, cmap, low=1, high=99): img = sample["anchor"].numpy() # %% +img_tensor = torch.from_numpy(img).to(model.device) + with torch.inference_mode(): - probs = assembled_classifier(torch.from_numpy(img)).sigmoid() -img, attribution = attribute_sample(img, assembled_classifier) + infection_probs = assembled_classifier_infection(img_tensor).sigmoid() + division_probs = assembled_classifier_division(img_tensor).sigmoid() + +# %% +attr_kwargs = dict( + img=img_tensor, + sliding_window_shapes=(1, 15, 12, 12), + strides=(1, 15, 4, 4), + show_progress=True, +) + + +infection_attribution = ( + assembled_classifier_infection.attribute_occlusion(**attr_kwargs).cpu().numpy() +) +division_attribution = ( + assembled_classifier_division.attribute_occlusion(**attr_kwargs).cpu().numpy() +) + # %% +def clip_rescale(img, low, high): + return rescale_intensity(img.clip(low, high), out_range=(0, 1)) + + +def clim_percentile(heatmap, low=1, high=99): + lo, hi = np.percentile(heatmap, (low, high)) + return clip_rescale(heatmap, lo, hi) + + +g_lim = 1 z_slice = 5 -phase = color_and_clim(img[:, 0, z_slice], cmap="gray") -rfp = color_and_clim(img[:, 1, z_slice], cmap="gray") -phase_heatmap = color_and_clim(attribution[:, 0, z_slice], cmap="icefire") -rfp_heatmap = color_and_clim(attribution[:, 1, z_slice], cmap="icefire") -grid = np.concatenate( - [ - np.concatenate([phase, phase_heatmap], axis=1), - np.concatenate([rfp, rfp_heatmap], axis=1), - ], - axis=2, +phase = clim_percentile(img[:, 0, z_slice]) +rfp = clim_percentile(img[:, 1, z_slice]) +img_render = np.concatenate([phase, rfp], axis=2) +phase_heatmap_inf = infection_attribution[:, 0, z_slice] +rfp_heatmap_inf = infection_attribution[:, 1, z_slice] +inf_render = clip_rescale( + np.concatenate([phase_heatmap_inf, rfp_heatmap_inf], axis=2), -g_lim, g_lim +) +phase_heatmap_div = division_attribution[:, 0, z_slice] +rfp_heatmap_div = division_attribution[:, 1, z_slice] +div_render = clip_rescale( + np.concatenate([phase_heatmap_div, rfp_heatmap_div], axis=2), -g_lim, g_lim ) -print(grid.shape) + # %% -selected_time_points = [0, 4, 8, 34] -class_text = {0: "none", 1: "uninfected", 2: "infected"} +plt.style.use("./figure.mplstyle") + +selected_time_points = [3, 6, 15, 16] +selected_div_states = [False] * 3 + [True] sps = len(selected_time_points) -f, ax = plt.subplots(1, sps, figsize=(4 * sps, 4)) -for time, a in zip(selected_time_points, ax.flatten()): - rendered = grid[time] - prob = probs[time].item() - a.imshow(rendered) + +icefire = Colormap("icefire").to_mpl() + +f, ax = plt.subplots(3, sps, figsize=(5.5, 3), layout="compressed") +for i, time in enumerate(selected_time_points): hpi = 3 + 0.5 * time - text_label = class_text[track_classes.iloc[time]] - a.set_title( - f"{hpi} HPI,\npredicted infection probability: {prob:.2f},\nannotation: {text_label}" + prob = infection_probs[time].item() + inf_binary = str(bool(track_classes_infection.iloc[time] - 1)).lower() + div_binary = str(selected_div_states[i]).lower() + ax[0, i].imshow(img_render[time], cmap="gray") + ax[0, i].set_title(f"{hpi} HPI") + ax[1, i].imshow(inf_render[time], cmap=icefire, vmin=0, vmax=1) + ax[1, i].set_title( + f"infected: {prob:.3f}\n" f"label: {inf_binary}", ) + ax[2, i].imshow(div_render[time], cmap=icefire, vmin=0, vmax=1) + ax[2, i].set_title( + f"dividing: {division_probs[time].item():.3f}\n" f"label: {div_binary}", + ) +for a in ax.ravel(): a.axis("off") -f.tight_layout() +norm = mpl.colors.Normalize(vmin=-g_lim, vmax=g_lim) +cbar = f.colorbar( + mpl.cm.ScalarMappable(norm=norm, cmap=icefire), + orientation="vertical", + ax=ax[1:].ravel().tolist(), + format=mpl.ticker.StrMethodFormatter("{x:.1f}"), +) +cbar.set_label("occlusion attribution") + +# %% +f.savefig( + Path.home() + / "gdrive/publications/learning_impacts_of_infection/fig_manuscript/fig_explanation/fig_explanation_patch12_stride4.pdf", + dpi=300, +) # %% diff --git a/applications/contrastive_phenotyping/figures/organelle_dynamics.py b/applications/contrastive_phenotyping/figures/organelle_dynamics.py new file mode 100644 index 00000000..9a7448a4 --- /dev/null +++ b/applications/contrastive_phenotyping/figures/organelle_dynamics.py @@ -0,0 +1,238 @@ +# %% +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import xarray as xr +from cmap import Colormap +from lightning.pytorch import seed_everything +from skimage.exposure import rescale_intensity +from sklearn.preprocessing import StandardScaler +from umap import UMAP + +from viscy.data.triplet import TripletDataModule +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.transforms import NormalizeSampled, ScaleIntensityRangePercentilesd + +plt.style.use("../evaluation/figure.mplstyle") +seed_everything(42, workers=True) + +# %% Paths and parameters. + +features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/jun_time_interval_1_epoch_178.zarr" +) +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/2-register/registered_chunked.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.2-tracking/track.zarr" +) + +# %% +embedding_dataset = read_embedding_dataset(features_path) +embedding_dataset + +# %% +# Compute UMAP over all features +features = embedding_dataset["features"] +# or select a well: +features = features[features["fov_name"].str.contains(r"/0/[36]")] +features + +# %% +scaled_features = StandardScaler().fit_transform(features.values) +umap = UMAP(random_state=42) +# Fit UMAP on all features +embedding = umap.fit_transform(scaled_features) + + +# %% +# Add UMAP coordinates to the dataset + +features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +features + +# %% +ax = sns.scatterplot( + x=features["UMAP1"], y=features["UMAP2"], hue=features["t"], s=7, alpha=0.8 +) +fmt = mpl.ticker.StrMethodFormatter("{x}") +ax.xaxis.set_major_formatter(fmt) +ax.yaxis.set_major_formatter(fmt) + +# %% +fovs = ["/0/3/002000", "/0/6/000000", "/0/6/000002", "/0/6/001000"] +tracks = [24, 14, 34, 38] + + +track_features = xr.concat( + [features.sel(fov_name=fov, track_id=track) for fov, track in zip(fovs, tracks)], + dim="sample", +) + +# %% +dm = TripletDataModule( + data_path=data_path, + tracks_path=tracks_path, + source_channel=[ + "Phase3D", + "MultiCam_GFP_mCherry_BF-Prime BSI Express", + "MultiCam_GFP_mCherry_BF-Andor EMCCD", + ], + z_range=[10, 55], + batch_size=48, + num_workers=0, + initial_yx_patch_size=(128, 128), + final_yx_patch_size=(128, 128), + normalizations=[ + NormalizeSampled( + keys=["Phase3D"], level="fov_statistics", subtrahend="mean", divisor="std" + ), + ScaleIntensityRangePercentilesd( + keys=[ + "MultiCam_GFP_mCherry_BF-Prime BSI Express", + "MultiCam_GFP_mCherry_BF-Andor EMCCD", + ], + lower=50, + upper=99, + b_min=0.0, + b_max=1.0, + channel_wise=True, + ), + ], + predict_cells=True, + include_fov_names=fovs, + include_track_ids=tracks, +) +dm.setup("predict") +ds = dm.predict_dataset +len(ds) + + +# %% +def render(img, cmaps: list[str]): + channels = [] + for ch, cmap in zip(img, cmaps): + lo, hi = np.percentile(ch, [1, 99]) + rescaled = rescale_intensity(ch.clip(lo, hi), out_range=(0, 1)) + rendered = Colormap(cmap)(rescaled) + channels.append(rendered) + return np.sum(channels, axis=0).clip(0, 1) + + +renders = [] + +f, ax = plt.subplots(4, 12, figsize=(12, 4)) +for sample, a in zip(ds, ax.flatten()): + img = sample["anchor"][1:].numpy().max(1) + rend = render(img, ["magenta", "green"]) + renders.append(rend) + a.imshow(rend, cmap="gray") + idx = sample["index"] + name = "-".join([str(idx["track_id"]), str(idx["t"])]) + a.set_title(name) + a.axis("off") + +# %% +track_df = ds.tracks +selected_times = [2, 6, 8] +track_df = track_df[track_df["t"].isin(selected_times)] +selected_features = track_features[track_features["t"].isin(selected_times)] +selected_renders = [renders[i] for i in track_df.index] + + +# %% +fig = plt.figure(layout="constrained", figsize=(5.5, 2.7)) +subfigs = fig.subfigures(1, 2, wspace=0.02, width_ratios=[4, 7]) + +umap_fig = subfigs[0] +umap_fig.suptitle("A", horizontalalignment="left", x=0, y=1) +umap_ax = umap_fig.subplots(1, 1) +umap_ax.invert_xaxis() + +sns.scatterplot( + x=features["UMAP1"], y=features["UMAP2"], s=40, alpha=0.01, ax=umap_ax, color="k" +) + +sns.scatterplot( + x=track_features["UMAP1"], + y=track_features["UMAP2"], + ax=umap_ax, + hue=track_features["fov_name"], + s=5, + legend=False, +) + +sns.lineplot( + x=track_features["UMAP1"], + y=track_features["UMAP2"], + ax=umap_ax, + hue=track_features["fov_name"], + legend=False, + size=0.5, +) + +hpi = (track_df["t"].reset_index(0, drop=True) * 2 + 2.5).astype(str) + " HPI" +track_names = pd.Series( + np.concatenate([[t] * 3 for t in ["Track 1", "Track 2", "Track 3", "Track 4"]]), + name="track", +) +sns.scatterplot( + x=selected_features["UMAP1"], + y=selected_features["UMAP2"], + ax=umap_ax, + style=hpi, + markers=["P", "s", "D"], + s=20, + hue=track_names, + # legend=False, +) +handles, labels = umap_ax.get_legend_handles_labels() +umap_ax.legend( + handles=handles[1:5] + handles[6:], + labels=labels[1:5] + labels[6:], + loc="upper center", + ncol=2, + bbox_to_anchor=(0.5, -0.2), + labelspacing=0.2, + handletextpad=0, + fontsize=8, +) + +img_fig = subfigs[1] +img_fig.suptitle("B", horizontalalignment="left", x=-0, y=1) +img_axes = img_fig.subplots(3, 4, sharex=True, sharey=True) + +for i, (ax, rend, time, track_name) in enumerate( + zip(img_axes.T.flatten(), selected_renders, hpi.to_list(), track_names) +): + ax.imshow(rend) + if i % 3 == 0: + ax.set_title(track_name) + if i < 3: + ax.set_ylabel(f"{time}") + ax.set_xticks([]) + ax.set_yticks([]) + +for sf in subfigs: + for a in sf.get_axes(): + fmt = mpl.ticker.StrMethodFormatter("{x:.0f}") + a.xaxis.set_major_formatter(fmt) + a.yaxis.set_major_formatter(fmt) + +# %% +fig.savefig( + Path.home() + / "gdrive/publications/learning_impacts_of_infection/fig_manuscript/fig_organelle_dynamics/fig_organelle_dynamics.pdf", + dpi=300, +) + +# %% diff --git a/pyproject.toml b/pyproject.toml index 039bdc41..f01529a4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,6 +32,7 @@ metrics = [ "torchmetrics[detection]>=1.3.1", "ptflops>=0.7", "umap-learn", + "captum>=0.7.0", ] examples = ["napari", "jupyter", "jupytext"] diff --git a/viscy/representation/lca.py b/viscy/representation/lca.py index bcd10816..7c521619 100644 --- a/viscy/representation/lca.py +++ b/viscy/representation/lca.py @@ -5,6 +5,7 @@ import pandas as pd import torch import torch.nn as nn +from captum.attr import IntegratedGradients, Occlusion from numpy.typing import NDArray from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report @@ -149,3 +150,41 @@ def forward(self, x: Tensor, scale_features: bool = False) -> Tensor: x = self.scale_features(x) x = self.classifier(x) return x + + def attribute_integrated_gradients(self, img: Tensor, **kwargs) -> Tensor: + """Compute integrated gradients for a binary classification task. + + Parameters + ---------- + img : Tensor + input image + **kwargs : Any + Keyword arguments for ``IntegratedGradients()``. + + Returns + ------- + attribution : Tensor + Integrated gradients attribution map. + """ + self.zero_grad() + ig = IntegratedGradients(self, **kwargs) + attribution = ig.attribute(img) + return attribution + + def attribute_occlusion(self, img: Tensor, **kwargs) -> Tensor: + """Compute occlusion-based attribution for a binary classification task. + + Parameters + ---------- + img : Tensor + input image + **kwargs : Any + Keyword arguments for the ``Occlusion.attribute()``. + + Returns + ------- + attribution : Tensor + Occlusion attribution map. + """ + oc = Occlusion(self) + return oc.attribute(img, **kwargs) From 42a0cb5755e61d39b4cc175043a93a548d0f0725 Mon Sep 17 00:00:00 2001 From: Ziwen Liu <67518483+ziw-liu@users.noreply.github.com> Date: Fri, 27 Sep 2024 11:35:33 -0700 Subject: [PATCH 74/87] UMAP line plot to assess temporal smoothness in features space (#176) * add maplotlib style sheet for figure making * add cell division attribution * add matplotlib style sheet * move attribution computation to lca * tweak contrast limits and text * add captum to optional dependencies * move attribution function to a method of the classifier * add script to show organelle dynamics * add occlusion attribution * more generic save path * add uninfected cell * tweak subplot spacing * lower case titles * reduce UMAP components to 2 and add indices * add script to make the bridge gaps figure --- .../figures/organelle_dynamics.py | 4 +- .../figures/track_smoothness.py | 111 ++++++++++++++++++ viscy/representation/evaluation.py | 8 +- 3 files changed, 117 insertions(+), 6 deletions(-) create mode 100644 applications/contrastive_phenotyping/figures/track_smoothness.py diff --git a/applications/contrastive_phenotyping/figures/organelle_dynamics.py b/applications/contrastive_phenotyping/figures/organelle_dynamics.py index 9a7448a4..4ee9980a 100644 --- a/applications/contrastive_phenotyping/figures/organelle_dynamics.py +++ b/applications/contrastive_phenotyping/figures/organelle_dynamics.py @@ -154,7 +154,7 @@ def render(img, cmaps: list[str]): subfigs = fig.subfigures(1, 2, wspace=0.02, width_ratios=[4, 7]) umap_fig = subfigs[0] -umap_fig.suptitle("A", horizontalalignment="left", x=0, y=1) +umap_fig.suptitle("a", horizontalalignment="left", x=0, y=1) umap_ax = umap_fig.subplots(1, 1) umap_ax.invert_xaxis() @@ -208,7 +208,7 @@ def render(img, cmaps: list[str]): ) img_fig = subfigs[1] -img_fig.suptitle("B", horizontalalignment="left", x=-0, y=1) +img_fig.suptitle("b", horizontalalignment="left", x=-0, y=1) img_axes = img_fig.subplots(3, 4, sharex=True, sharey=True) for i, (ax, rend, time, track_name) in enumerate( diff --git a/applications/contrastive_phenotyping/figures/track_smoothness.py b/applications/contrastive_phenotyping/figures/track_smoothness.py new file mode 100644 index 00000000..51796ebc --- /dev/null +++ b/applications/contrastive_phenotyping/figures/track_smoothness.py @@ -0,0 +1,111 @@ +# %% +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +from cmap import Colormap +from iohub import open_ome_zarr +from skimage.color import label2rgb +from skimage.exposure import rescale_intensity + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import compute_umap + +# %% +t_slice = slice(18, 33) +y_slice = slice(16, 144) +x_slice = slice(0, 224) + +phase = open_ome_zarr( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr/B/4/8" +)["0"][t_slice, 3, 31, y_slice, x_slice] + +segments = open_ome_zarr( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr/B/4/8" +)["0"][t_slice, 0, 0, y_slice, x_slice] + +# %% +features = read_embedding_dataset( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) + +# %% +_, _, umap_df = compute_umap(features) +umap_df + +# %% +track_ids = np.unique(segments)[1:] +track_ids + +# %% +selected_umap = umap_df[ + (umap_df["fov_name"] == "/B/4/8") + & umap_df["track_id"].isin(track_ids) + & (umap_df["t"] >= t_slice.start) + & (umap_df["t"] < t_slice.stop) +] + +selected_umap["HPI"] = selected_umap["t"] * 0.5 + 3 + +# %% +plt.style.use("../evaluation/figure.mplstyle") +fig = plt.figure(figsize=(5.5, 4.5), layout="constrained") +subfigs = fig.subfigures(2, 1, wspace=0.02, height_ratios=[3, 2]) + +img_fig = subfigs[0] +img_fig.suptitle("a", horizontalalignment="left", x=0, y=1) +img_ax = img_fig.subplots(3, 5) + +clim = 0.03 +cmap = Colormap("tab10") + +labels = label2rgb( + segments, + image=rescale_intensity(phase, in_range=(-clim, clim), out_range=(0, 1)), + colors=cmap(range(10)), +) + +for t, (a, rgb) in enumerate(zip(img_ax.flatten(), labels)): + a.imshow(rgb) + a.set_title(f"{(t+t_slice.start)/2 + 3} HPI") + a.axis("off") + +line_fig = subfigs[1] +line_fig.suptitle("b", horizontalalignment="left", x=0, y=1) +line_ax_1 = line_fig.subplots(1, 1) +line_ax_2 = line_ax_1.twinx() +sns.lineplot( + data=selected_umap, + x="HPI", + y="UMAP1", + hue="track_id", + palette=[c for c in cmap([2, 4, 6])], + ax=line_ax_1, +) +sns.move_legend(line_ax_1, "upper right", title="Track ID") +sns.lineplot( + data=selected_umap, + x="HPI", + y="UMAP2", + hue="track_id", + palette=[c for c in cmap([2, 4, 6])], + ax=line_ax_2, + linestyle="--", + legend=False, +) + +fmt = mpl.ticker.StrMethodFormatter("{x:.1f}") +for a in [line_ax_1, line_ax_2]: + a.xaxis.set_major_formatter(fmt) + a.yaxis.set_major_formatter(fmt) + +# %% +fig.savefig( + Path.home() + / "gdrive/publications/learning_impacts_of_infection/fig_manuscript/si/appendix_track_smoothness.pdf", + dpi=300, +) + +# %% diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py index cbf8ead0..1dded467 100644 --- a/viscy/representation/evaluation.py +++ b/viscy/representation/evaluation.py @@ -250,8 +250,8 @@ def compute_umap(embedding_dataset, normalize_features=True): # Compute UMAP for features and projections # Computing 3 components to enable 3D visualization. - umap_features = umap.UMAP(random_state=42, n_components=3) - umap_projection = umap.UMAP(random_state=42, n_components=3) + umap_features = umap.UMAP(random_state=42, n_components=2) + umap_projection = umap.UMAP(random_state=42, n_components=2) umap_features_embedding = umap_features.fit_transform(scaled_features) umap_projection_embedding = umap_projection.fit_transform(scaled_projections) @@ -259,13 +259,13 @@ def compute_umap(embedding_dataset, normalize_features=True): umap_df = pd.DataFrame( { "id": embedding_dataset["id"].values, + "track_id": embedding_dataset["track_id"].values, + "t": embedding_dataset["t"].values, "fov_name": embedding_dataset["fov_name"].values, "UMAP1": umap_features_embedding[:, 0], "UMAP2": umap_features_embedding[:, 1], - "UMAP3": umap_features_embedding[:, 2], "UMAP1_proj": umap_projection_embedding[:, 0], "UMAP2_proj": umap_projection_embedding[:, 1], - "UMAP3_proj": umap_projection_embedding[:, 2], } ) From d5017abb2bd94a389c268a08f49ed037a87e5b8f Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Tue, 24 Sep 2024 21:39:24 -0700 Subject: [PATCH 75/87] fixed import error --- .../figures/cell_division.py | 50 +++++++++---------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/applications/contrastive_phenotyping/figures/cell_division.py b/applications/contrastive_phenotyping/figures/cell_division.py index 306618bf..23f87085 100644 --- a/applications/contrastive_phenotyping/figures/cell_division.py +++ b/applications/contrastive_phenotyping/figures/cell_division.py @@ -8,15 +8,15 @@ import plotly.express as px from sklearn.preprocessing import StandardScaler from umap import UMAP -from viscy.light.embedding_writer import read_embedding_dataset +from viscy.representation.embedding_writer import read_embedding_dataset import matplotlib.pyplot as plt # %% # single channel. with temporal regularizations -# dataset = read_embedding_dataset( -# "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval_phase/predictions/epoch_186/1chan_128patch_186ckpt_Febtest.zarr" -# ) -# dataset +dataset = read_embedding_dataset( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval_phase/predictions/epoch_186/1chan_128patch_186ckpt_Febtest.zarr" +) +dataset # single cahnnel, without temporal regularizations # dataset = read_embedding_dataset( @@ -25,10 +25,10 @@ # dataset # two channel, with temporal regularizations -dataset = read_embedding_dataset( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178_gt_tracks.zarr" -) -dataset +# dataset = read_embedding_dataset( +# "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178_gt_tracks.zarr" +# ) +# dataset # two channel, without temporal regularizations # dataset = read_embedding_dataset( @@ -104,16 +104,16 @@ def load_annotation(da, path, name, categories: dict | None = None): # Adding arrows to indicate trajectory direction def add_arrows(df, color): - for i in range(len(df) - 1): + for i in range((df.shape[0]) - 1): start = df.iloc[i] end = df.iloc[i + 1] arrow = FancyArrowPatch( (start['UMAP1'], start['UMAP2']), (end['UMAP1'], end['UMAP2']), color=color, - arrowstyle='-|>', - mutation_scale=8, # reduce the size of arrowhead by half - lw=1, + arrowstyle='->', + mutation_scale=20, # reduce the size of arrowhead by half + lw=2, shrinkA=0, shrinkB=0, ) @@ -124,7 +124,7 @@ def add_arrows(df, color): # tried A/3/7, 18 to 19 & 20 # tried A/3/8, 23 to 24 & 25 -sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, palette={'interphase': "steelblue", 1: "green", 'mitosis': "orangered"}, s=7, alpha=0.8) +sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, palette={'interphase': "steelblue", 1: "green", 'mitosis': "orangered"}, s=7, alpha=0.5) # sns.lineplot(x=cell_parent["UMAP1"], y=cell_parent["UMAP2"], color='black', linewidth=1) # sns.lineplot(x=cell_daughter1["UMAP1"], y=cell_daughter1["UMAP2"], color='red', linewidth=1) # sns.lineplot(x=cell_daughter2["UMAP1"], y=cell_daughter2["UMAP2"], color='blue', linewidth=1) @@ -138,33 +138,33 @@ def add_arrows(df, color): plt.ylabel('UMAP2') # plt.title('UMAP with Trajectory Direction') # plt.legend(title='Division Phase') -plt.xlim(-7, 11) -plt.ylim(5, 17) +plt.xlim(2, 18) +plt.ylim(-2, 18) plt.legend([],[], frameon=False) # plt.show() # single channel, with temporal regularizations -# plt.savefig( -# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel.png", +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel.png", dpi=300 -# ) +) # single channel, without temporal regularizations # plt.savefig( # "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel_woT.png", - dpi=300 +# dpi=300 # ) # two channel, with temporal regularizations -plt.savefig( - "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel.png", - dpi=300 -) +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel.png", +# dpi=300 +# ) # two channel, without temporal regularizations # plt.savefig( # "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_woT.png", - dpi=300 +# dpi=300 # ) # %% From a58ab836fcf9c68d5d91dcf196df1dc9e7b82e64 Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Tue, 24 Sep 2024 21:40:16 -0700 Subject: [PATCH 76/87] formatted with black --- .../figures/cell_division.py | 68 +++++++++++++------ .../figures/save_patches.py | 19 ++++-- 2 files changed, 58 insertions(+), 29 deletions(-) diff --git a/applications/contrastive_phenotyping/figures/cell_division.py b/applications/contrastive_phenotyping/figures/cell_division.py index 23f87085..71111df7 100644 --- a/applications/contrastive_phenotyping/figures/cell_division.py +++ b/applications/contrastive_phenotyping/figures/cell_division.py @@ -1,5 +1,3 @@ - - # %% figures for visualizing the results of cell division from pathlib import Path @@ -58,6 +56,7 @@ # %% + def load_annotation(da, path, name, categories: dict | None = None): annotation = pd.read_csv(path) # annotation_columns = annotation.columns.tolist() @@ -72,9 +71,12 @@ def load_annotation(da, path, name, categories: dict | None = None): selected = selected.astype("category").cat.rename_categories(categories) return selected + # %% -ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/9-lineage-cell-division/lineages_gt") +ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/9-lineage-cell-division/lineages_gt" +) division = load_annotation( features, @@ -84,23 +86,37 @@ def load_annotation(da, path, name, categories: dict | None = None): ) # %% -sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, palette={'interphase': "steelblue", 1: "green", 'mitosis': "orangered"}, s=7, alpha=0.8) +sns.scatterplot( + x=features["UMAP1"], + y=features["UMAP2"], + hue=division, + palette={"interphase": "steelblue", 1: "green", "mitosis": "orangered"}, + s=7, + alpha=0.8, +) plt.show() # plt.savefig( # "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/UMAP_cellDiv_GTtracking_sc_woT.svg" # ) # %% -no_inter = division[division == 'interphase'].count() -no_div = division[division == 'mitosis'].count() +no_inter = division[division == "interphase"].count() +no_div = division[division == "mitosis"].count() # %% plot the trajectory quiver of one cell on top of the UMAP from matplotlib.patches import FancyArrowPatch -cell_parent = features[(features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([13]))] -cell_daughter1 = features[(features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([14]))] -cell_daughter2 = features[(features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([15]))] +cell_parent = features[ + (features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([13])) +] +cell_daughter1 = features[ + (features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([14])) +] +cell_daughter2 = features[ + (features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([15])) +] + # Adding arrows to indicate trajectory direction def add_arrows(df, color): @@ -108,45 +124,53 @@ def add_arrows(df, color): start = df.iloc[i] end = df.iloc[i + 1] arrow = FancyArrowPatch( - (start['UMAP1'], start['UMAP2']), - (end['UMAP1'], end['UMAP2']), + (start["UMAP1"], start["UMAP2"]), + (end["UMAP1"], end["UMAP2"]), color=color, - arrowstyle='->', + arrowstyle="->", mutation_scale=20, # reduce the size of arrowhead by half lw=2, shrinkA=0, shrinkB=0, - ) + ) plt.gca().add_patch(arrow) + # tried A/3/7, 8 to 9 & 10 # tried A/3/7, 13 to 14 & 15 # tried A/3/7, 18 to 19 & 20 # tried A/3/8, 23 to 24 & 25 -sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, palette={'interphase': "steelblue", 1: "green", 'mitosis': "orangered"}, s=7, alpha=0.5) +sns.scatterplot( + x=features["UMAP1"], + y=features["UMAP2"], + hue=division, + palette={"interphase": "steelblue", 1: "green", "mitosis": "orangered"}, + s=7, + alpha=0.5, +) # sns.lineplot(x=cell_parent["UMAP1"], y=cell_parent["UMAP2"], color='black', linewidth=1) # sns.lineplot(x=cell_daughter1["UMAP1"], y=cell_daughter1["UMAP2"], color='red', linewidth=1) # sns.lineplot(x=cell_daughter2["UMAP1"], y=cell_daughter2["UMAP2"], color='blue', linewidth=1) # Apply arrows to the trajectories -add_arrows(cell_parent.to_dataframe(), color='black') -add_arrows(cell_daughter1.to_dataframe(), color='red') -add_arrows(cell_daughter2.to_dataframe(), color='blue') +add_arrows(cell_parent.to_dataframe(), color="black") +add_arrows(cell_daughter1.to_dataframe(), color="red") +add_arrows(cell_daughter2.to_dataframe(), color="blue") -plt.xlabel('UMAP1') -plt.ylabel('UMAP2') +plt.xlabel("UMAP1") +plt.ylabel("UMAP2") # plt.title('UMAP with Trajectory Direction') # plt.legend(title='Division Phase') plt.xlim(2, 18) plt.ylim(-2, 18) -plt.legend([],[], frameon=False) +plt.legend([], [], frameon=False) # plt.show() # single channel, with temporal regularizations plt.savefig( "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel.png", - dpi=300 + dpi=300, ) # single channel, without temporal regularizations @@ -167,4 +191,4 @@ def add_arrows(df, color): # dpi=300 # ) -# %% +# %% diff --git a/applications/contrastive_phenotyping/figures/save_patches.py b/applications/contrastive_phenotyping/figures/save_patches.py index e230d5a0..2b39df93 100644 --- a/applications/contrastive_phenotyping/figures/save_patches.py +++ b/applications/contrastive_phenotyping/figures/save_patches.py @@ -1,4 +1,3 @@ - # %% script to save 128 by 128 image patches from napari viewer import napari @@ -21,7 +20,7 @@ "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" ) -fov_name = '/B/4/6' +fov_name = "/B/4/6" track_id = 52 source_channel = ["Phase3D", "RFP"] @@ -55,9 +54,15 @@ # %% save as numpy files -out_dir = '/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/data/' -fov_name_out = fov_name.replace('/', '_') -np.save((os.path.join(out_dir,"phase"+fov_name_out+"_"+str(track_id)+".npy")), phase) -np.save((os.path.join(out_dir,"fluor"+fov_name_out+"_"+str(track_id)+".npy")), fluor) +out_dir = "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/data/" +fov_name_out = fov_name.replace("/", "_") +np.save( + (os.path.join(out_dir, "phase" + fov_name_out + "_" + str(track_id) + ".npy")), + phase, +) +np.save( + (os.path.join(out_dir, "fluor" + fov_name_out + "_" + str(track_id) + ".npy")), + fluor, +) -# %% \ No newline at end of file +# %% From df9a5cd086dad1f061326a1b0cc0b2f3a861f0ea Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Fri, 27 Sep 2024 10:00:41 -0700 Subject: [PATCH 77/87] reduce to single arrow on plot --- .../figures/cell_division.py | 119 +++++++++++++++--- 1 file changed, 104 insertions(+), 15 deletions(-) diff --git a/applications/contrastive_phenotyping/figures/cell_division.py b/applications/contrastive_phenotyping/figures/cell_division.py index 71111df7..5473aa34 100644 --- a/applications/contrastive_phenotyping/figures/cell_division.py +++ b/applications/contrastive_phenotyping/figures/cell_division.py @@ -1,5 +1,7 @@ # %% figures for visualizing the results of cell division +import sys +sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") from pathlib import Path import pandas as pd import seaborn as sns @@ -11,10 +13,10 @@ # %% # single channel. with temporal regularizations -dataset = read_embedding_dataset( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval_phase/predictions/epoch_186/1chan_128patch_186ckpt_Febtest.zarr" -) -dataset +# dataset = read_embedding_dataset( +# "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval_phase/predictions/epoch_186/1chan_128patch_186ckpt_Febtest.zarr" +# ) +# dataset # single cahnnel, without temporal regularizations # dataset = read_embedding_dataset( @@ -29,10 +31,10 @@ # dataset # two channel, without temporal regularizations -# dataset = read_embedding_dataset( -# "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest_divGT.zarr" -# ) -# dataset +dataset = read_embedding_dataset( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest_divGT.zarr" +) +dataset # %% # load all unprojected features: @@ -118,9 +120,9 @@ def load_annotation(da, path, name, categories: dict | None = None): ] -# Adding arrows to indicate trajectory direction +# %% Plot: Adding arrows to indicate trajectory direction def add_arrows(df, color): - for i in range((df.shape[0]) - 1): + for i in range(df.shape[0] - 1): start = df.iloc[i] end = df.iloc[i + 1] arrow = FancyArrowPatch( @@ -149,9 +151,6 @@ def add_arrows(df, color): s=7, alpha=0.5, ) -# sns.lineplot(x=cell_parent["UMAP1"], y=cell_parent["UMAP2"], color='black', linewidth=1) -# sns.lineplot(x=cell_daughter1["UMAP1"], y=cell_daughter1["UMAP2"], color='red', linewidth=1) -# sns.lineplot(x=cell_daughter2["UMAP1"], y=cell_daughter2["UMAP2"], color='blue', linewidth=1) # Apply arrows to the trajectories add_arrows(cell_parent.to_dataframe(), color="black") @@ -162,8 +161,8 @@ def add_arrows(df, color): plt.ylabel("UMAP2") # plt.title('UMAP with Trajectory Direction') # plt.legend(title='Division Phase') -plt.xlim(2, 18) -plt.ylim(-2, 18) +plt.xlim(-5, 10) +plt.ylim(-5, 10) plt.legend([], [], frameon=False) # plt.show() @@ -191,4 +190,94 @@ def add_arrows(df, color): # dpi=300 # ) +# %% Plot: display one arrow at end of trajectory of cell overlayed on UMAP + +sns.scatterplot( + x=features["UMAP1"], + y=features["UMAP2"], + hue=division, + palette={"interphase": "steelblue", 1: "green", "mitosis": "orangered"}, + s=27, + alpha=0.5, +) + +sns.lineplot(x=cell_parent["UMAP1"], y=cell_parent["UMAP2"], color="black", linewidth=2) +sns.lineplot( + x=cell_daughter1["UMAP1"], y=cell_daughter1["UMAP2"], color="blue", linewidth=2 +) +sns.lineplot( + x=cell_daughter2["UMAP1"], y=cell_daughter2["UMAP2"], color="red", linewidth=2 +) + +parent_arrow = FancyArrowPatch( + (cell_parent["UMAP1"].values[-2], cell_parent["UMAP2"].values[-2]), + (cell_parent["UMAP1"].values[-1], cell_parent["UMAP2"].values[-1]), + color="black", + arrowstyle="->", + mutation_scale=20, # reduce the size of arrowhead by half + lw=2, + shrinkA=0, + shrinkB=0, +) +plt.gca().add_patch(parent_arrow) +daughter1_arrow = FancyArrowPatch( + (cell_daughter1["UMAP1"].values[0], cell_daughter1["UMAP2"].values[0]), + (cell_daughter1["UMAP1"].values[1], cell_daughter1["UMAP2"].values[1]), + color="blue", + arrowstyle="->", + mutation_scale=20, # reduce the size of arrowhead by half + lw=2, + shrinkA=0, + shrinkB=0, +) +plt.gca().add_patch(daughter1_arrow) +daughter2_arrow = FancyArrowPatch( + (cell_daughter2["UMAP1"].values[0], cell_daughter2["UMAP2"].values[0]), + (cell_daughter2["UMAP1"].values[1], cell_daughter2["UMAP2"].values[1]), + color="red", + arrowstyle="->", + mutation_scale=20, # reduce the size of arrowhead by half + lw=2, + shrinkA=0, + shrinkB=0, +) +plt.gca().add_patch(daughter2_arrow) + + +# single channel, with temporal regularizations +# plt.xlim(-5, 8) +# plt.ylim(-6, 8) +# plt.legend([], [], frameon=False) +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel_arrow.png", +# dpi=300, +# ) + +# single channel, without temporal regularizations +# plt.xlim(0, 13) +# plt.ylim(-2, 6) +# plt.legend([], [], frameon=False) +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_singelChannel_woT_arrow.png", +# dpi=300 +# ) + +# two channel, with temporal regularizations +# plt.xlim(-2, 15) +# plt.ylim(-5, 5) +# plt.legend([], [], frameon=False) +# plt.savefig( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_arrow.png", +# dpi=300 +# ) + +# two channel, without temporal regularizations +plt.xlim(-3, 12) +plt.ylim(1, 10) +plt.legend([], [], frameon=False) +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/cellDiv_trajectory_2Channel_woT_arrow.png", + dpi=300, +) + # %% From 17a2e4828b970ab7fa32cce26bb525c99b7a0292 Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Fri, 27 Sep 2024 13:25:28 -0700 Subject: [PATCH 78/87] remove reduntant script --- .../evaluation/plot_embeddings_soorya.py | 489 ------------------ 1 file changed, 489 deletions(-) delete mode 100644 applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py diff --git a/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py b/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py deleted file mode 100644 index 3553fcb3..00000000 --- a/applications/contrastive_phenotyping/evaluation/plot_embeddings_soorya.py +++ /dev/null @@ -1,489 +0,0 @@ -# %% -from pathlib import Path - -import matplotlib.pyplot as plt -import pandas as pd -import plotly.express as px -import seaborn as sns -from sklearn.preprocessing import StandardScaler -from umap import UMAP - -from viscy.representation.embedding_writer import read_embedding_dataset - -# %% -dataset = read_embedding_dataset( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/Ver2_updateTracking_refineModel/predictions/Feb_test_2chan_128patch_128projDim/2chan_128patch_20ckpt_Feb_test.zarr" -) -dataset - -# %% -# load all unprojected features: -features = dataset["features"] -# or select a well: -# features - features[features["fov_name"].str.contains("B/4")] -features - - -# %% perform principal componenet analysis of features - -from sklearn.decomposition import PCA - -pca = PCA(n_components=4) -# scaled_features = StandardScaler().fit_transform(features.values) -# pca_features = pca.fit_transform(scaled_features) -pca_features = pca.fit_transform(features.values) - -features = ( - features.assign_coords(PCA1=("sample", pca_features[:, 0])) - .assign_coords(PCA2=("sample", pca_features[:, 1])) - .assign_coords(PCA3=("sample", pca_features[:, 2])) - .assign_coords(PCA4=("sample", pca_features[:, 3])) - .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4"], append=True) -) - -# %% plot PCA components - -plt.figure(figsize=(10, 10)) -sns.scatterplot(x=features["PCA1"], y=features["PCA2"], hue=features["t"], s=7, alpha=0.8) - -# %% umap with 2 components -scaled_features = StandardScaler().fit_transform(features.values) - -umap = UMAP() - -embedding = umap.fit_transform(features.values) -features = ( - features.assign_coords(UMAP1=("sample", embedding[:, 0])) - .assign_coords(UMAP2=("sample", embedding[:, 1])) - .set_index(sample=["UMAP1", "UMAP2"], append=True) -) -features - -# %% -# scaled_features = StandardScaler().fit_transform(features.values) - -# umap = UMAP(n_components=4) - -# embedding = umap.fit_transform(scaled_features) -# features = ( -# features.assign_coords(UMAP1=("sample", embedding[:, 0])) -# .assign_coords(UMAP2=("sample", embedding[:, 1])) -# .assign_coords(UMAP3=("sample", embedding[:, 2])) -# .assign_coords(UMAP4=("sample", embedding[:, 3])) -# .set_index(sample=["UMAP1", "UMAP2", "UMAP3", "UMAP4"], append=True) -# ) -# features - -# %% -sns.scatterplot( - x=features["UMAP1"], y=features["UMAP2"], hue=features["t"], s=7, alpha=0.8 -) - -# %% -def load_annotation(da, path, name, categories: dict | None = None): - annotation = pd.read_csv(path) - # annotation_columns = annotation.columns.tolist() - # print(annotation_columns) - annotation["fov_name"] = "/" + annotation["fov_name"] - annotation = annotation.set_index(["fov_name", "id"]) - mi = pd.MultiIndex.from_arrays( - [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] - ) - selected = annotation.loc[mi][name] - if categories: - selected = selected.astype("category").cat.rename_categories(categories) - return selected - - -# %% -# ann_root = Path( -# "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track" -# ) - -# infection = load_annotation( -# features, -# ann_root / "tracking_v1_infection.csv", -# "infection class", -# {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, -# ) -# division = load_annotation( -# features, -# ann_root / "cell_division_state.csv", -# "division", -# {0: "non-dividing", 2: "dividing"}, -# ) - - -# %% new annotation - -ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred") - -infection = load_annotation( - features, - ann_root / "extracted_inf_state.csv", - "infection_state", - {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, -) - -# %% -sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=division, s=7, alpha=0.8) - -# %% -sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=infection, s=7, alpha=0.8) - -# %% plot PCA components with infection hue -sns.scatterplot(x=features["PCA1"], y=features["PCA2"], hue=infection, s=7, alpha=0.8) - -# %% -ax = sns.histplot(x=features["UMAP1"], y=features["UMAP2"], hue=infection, bins=64) -sns.move_legend(ax, loc="lower left") - -# %% see the histogram distribution of UMAP1 and UMAP2 for each infection state -sns.displot( - x=features["UMAP1"], - y=features["UMAP2"], - kind="hist", - col=infection, - bins=64, - cmap="inferno", -) - -# %% -# interactive scatter plot to associate clusters with specific cells - -fig = px.scatter( - data_frame=pd.DataFrame( - {k: v for k, v in features.coords.items() if k != "features"} - ), - x="UMAP1", - y="UMAP2", - color=(infection.astype(str) + " " + division.astype(str)).rename("annotation"), - hover_name="fov_name", - hover_data=["track_id", "t"], -) -fig.update_traces(marker=dict(size=3)) - -# %% interactive PCA plot - -fig = px.scatter( - data_frame=pd.DataFrame( - {k: v for k, v in features.coords.items() if k != "features"} - ), - x="PCA1", - y="PCA2", - color=(infection.astype(str) + " " + division.astype(str)).rename("annotation"), - hover_name="fov_name", - hover_data=["track_id", "t"], -) -fig.update_traces(marker=dict(size=3)) - -# %% cluster cells in PCA1 vs PCA2 space using Gaussian Mixture Model - -import numpy as np -import seaborn as sns -from sklearn.mixture import GaussianMixture - -gmm = GaussianMixture(n_components=2) -PCA1_array = features["PCA1"].values.reshape(-1, 1) -PCA2_array = features["PCA2"].values.reshape(-1, 1) -gmm.fit(np.concatenate((PCA1_array, PCA2_array), axis=1)) - -GMM_predict = gmm.predict(np.concatenate((PCA1_array, PCA2_array), axis=1)) -features = features.assign_coords(gmm=("sample", GMM_predict)) -# display the clustering results -fig = px.scatter( - data_frame=pd.DataFrame( - {k: v for k, v in features.coords.items() if k != "features"} - ), - x="PCA1", - y="PCA2", - color=features["gmm"].astype(str), - hover_name="fov_name", - hover_data=["track_id", "t"], -) -fig.update_traces(marker=dict(size=3)) - -# %% -# cluster features in heatmap directly -inf_codes = pd.Series(infection.values.codes, name="infection") -lut = dict(zip(inf_codes.unique(), "brw")) -row_colors = inf_codes.map(lut) - -g = sns.clustermap( - scaled_features, row_colors=row_colors.to_numpy(), col_cluster=False, cbar_pos=None -) -g.yaxis.set_ticks([]) - -# %% -# interactive scatter plot to associate clusters with specific cells -df = pd.DataFrame({k: v for k, v in features.coords.items() if k != "features"}) -df["infection"] = infection.values -df["division"] = division.values -df["well"] = df["fov_name"].str.rsplit("/", n=1).str[0] -df["fov_track_id"] = df["fov_name"] + "-" + df["track_id"].astype(str) -# select row B (DENV) -df = df[df["fov_name"].str.contains("B")] -df.sort_values("t", inplace=True) - -g = px.scatter( - data_frame=df[df["infection"].isin(["uninfected", "infected"])], - x="UMAP1", - y="UMAP2", - symbol="well", - color="infection", - hover_name="fov_name", - hover_data=["id", "t", "track_id"], - animation_frame="t", - animation_group="fov_track_id", -) -g.update_layout(width=800, height=600) - -# %% video frame for scatter across supervised infection annotation - -df = pd.DataFrame({k: v for k, v in features.coords.items() if k != "features"}) -df["infection"] = infection.values -df["division"] = division.values -df["well"] = df["fov_name"].str.rsplit("/", n=1).str[0] -df["fov_track_id"] = df["fov_name"] + "-" + df["track_id"].astype(str) -df.sort_values("t", inplace=True) - -for time in range(48): - plt.clf() # Clear the previous plot - sns.scatterplot( - data=df[(df["infection"].isin(["uninfected", "infected"])) & (df["t"] == time)], - x="UMAP1", - y="UMAP2", - hue="infection", - palette={"uninfected": "blue", "infected": "red", "background": "black"}, - s=12, - ) - plt.legend().remove() - plt.xlim(-7, 15) - plt.ylim(2, 15) - - plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Supervised/scatter_infection_" + str(time).zfill(3) + ".png") - -# %% video frame for scatter across virus type or wells - -# for time in range(48): -# sns.scatterplot( -# data=df[(df["t"] == time)], -# x="UMAP1", -# y="UMAP2", -# hue="well", -# palette={"/B/3": "blue", "/A/3": "blue", "/B/4": "red", "/A/4": "green"}, -# s=12, -# ) - -df_well_B4 = df[df['well'] == '/B/4'] # DENV, MOI 5 -df_well_A4 = df[df['well'] == '/A/4'] # ZIka, MOI 5 -df_well_Mock = df[(df['well'] == '/B/3') | (df['well'] == '/A/3')] # Mock - -for time in range(48): - plt.clf() - sns.scatterplot( - data=df_well_B4[(df_well_B4["t"] == time)], - x="UMAP1", - y="UMAP2", - hue="infection", - palette={"uninfected": "black", "infected": "black", "background": "black"}, - s=12, - ) - plt.legend().remove() - plt.xlim(-7, 15) - plt.ylim(2, 15) - plt.title(f"Time: {(time*0.5)+3} hours post infection") - plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Dengue/scatter_Dengue_infection_" + str(time).zfill(3) + ".png") - -for time in range(48): - plt.clf() - sns.scatterplot( - data=df_well_A4[(df_well_A4["t"] == time)], - x="UMAP1", - y="UMAP2", - hue="infection", - palette={"uninfected": "black", "infected": "black", "background": "black"}, - s=12, - ) - plt.legend().remove() - plt.title(f"Time: {(time*0.5)+3} hours post infection") - plt.xlim(-7, 15) - plt.ylim(2, 15) - - plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Zika/scatter_Zika_infection_" + str(time).zfill(3) + ".png") - -for time in range(48): - plt.clf() - sns.scatterplot( - data=df_well_Mock[(df_well_Mock["t"] == time)], - x="UMAP1", - y="UMAP2", - hue="infection", - palette={"uninfected": "black", "infected": "black", "background": "black"}, - s=12, - ) - plt.legend().remove() - plt.title(f"Time: {(time*0.5)+3} hours post infection") - plt.xlim(-7, 15) - plt.ylim(2, 15) - - plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Mock/scatter_Mock_infection_" + str(time).zfill(3) + ".png") - -# do the plot next for the baove three conditions with palette: "Mock": "black", "Zika": "blue", "Dengue": "red" -for time in range(48): - plt.clf() - sns.scatterplot( - data=df[(df["t"] == time)], - x="UMAP1", - y="UMAP2", - hue="well", - palette={"/B/3": "black", "/A/3": "black", "/B/4": "red", "/A/4": "blue"}, - s=12, - ) - plt.xlim(-7, 15) - plt.ylim(2, 15) - plt.title(f"Time: {(time*0.5)+3} hours post infection") - plt.legend().remove() - - plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Well/scatter_well_" + str(time).zfill(3) + ".png") - -# %% video frame for scatter across division state for 30 cells - -# div_csv_path = '/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/track_Feb.csv' -# df_div = pd.read_csv(div_csv_path) - -# plot for well A3, FOVs 0, 1, 10, 11, 12,and 13 -selected_fovs = df[df['fov_name'].isin(['/A/3/0', '/A/3/1', '/A/3/10', '/A/3/11', '/A/3/12', '/A/3/13'])] - -for time in range(48): - plt.clf() - sns.scatterplot( - data=selected_fovs[(selected_fovs["t"] == time)], - x="UMAP1", - y="UMAP2", - hue="division", - palette={"non-dividing": "blue", "dividing": "red"}, - s=12, - ) - plt.legend().remove() - plt.xlim(-7, 15) - plt.ylim(2, 15) - plt.title(f"Time: {(time*0.5)+3} hours post infection") - - plt.savefig(f"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/obsolete/videos/Division/scatter_division_" + str(time).zfill(3) + ".png") - -# making videos -# ffmpeg -r 2 -f image2 -pattern_type glob -i "*?png" -vcodec libx264 -crf 20 -pix_fmt yuv420p output.mp4 - -# %% display flow field plot for df over time for one dengue infected cell - -import matplotlib.pyplot as plt - -# Group the features by track_id and fov_name -grouped_features = df_well_B4.groupby(["track_id", "fov_name"]) - -# Create a new column for the UMAP1 and UMAP2 coordinates -df_well_B4["UMAP1_track"] = np.nan -df_well_B4["UMAP2_track"] = np.nan - -# Iterate over the groups and assign UMAP coordinates to each track -for group_name, group_data in grouped_features: - track_id, fov_name = group_name - umap1 = group_data["UMAP1"].values - umap2 = group_data["UMAP2"].values - df_well_B4.loc[(df_well_B4["track_id"] == track_id) & (df_well_B4["fov_name"] == fov_name), "UMAP1_track"] = umap1 - df_well_B4.loc[(df_well_B4["track_id"] == track_id) & (df_well_B4["fov_name"] == fov_name), "UMAP2_track"] = umap2 - -# Compute the flow field for each cell -flow_field = np.gradient(df_well_B4[["UMAP1_track", "UMAP2_track"]].values, axis=0) - -# Plot the flow field with reduced density -plt.figure(figsize=(10, 10)) -plt.quiver(df_well_B4["UMAP1_track"], df_well_B4["UMAP2_track"], flow_field[:, 0], flow_field[:, 1], scale=10) -plt.xlim(-7, 15) -plt.ylim(2, 15) -plt.show() - - -# %% show the umap flow field of cell 30 in well B4, fov 4 with time as velocity - -df_well_B4_4_30 = df[(df['fov_name'] == '/B/4/4') & (df['track_id'] == 30)] -df_well_B4_4_30.sort_values('t', inplace=True) - -flow_field = np.gradient(df_well_B4_4_30[["UMAP1", "UMAP2"]].values, axis=0) - -plt.figure(figsize=(10, 10)) -plt.quiver(df_well_B4_4_30["UMAP1"], df_well_B4_4_30["UMAP2"], flow_field[:, 0], flow_field[:, 1], scale=10, color='r') -plt.xlim(-7, 15) -plt.ylim(2, 15) -plt.show() - -df_well_A4_9_5 = df[(df['fov_name'] == '/A/4/9') & (df['track_id'] == 21)] -df_well_A4_9_5.sort_values('t', inplace=True) - -flow_field = np.gradient(df_well_A4_9_5[["UMAP1", "UMAP2"]].values, axis=0) - -plt.figure(figsize=(10, 10)) -plt.quiver(df_well_A4_9_5["UMAP1"], df_well_A4_9_5["UMAP2"], flow_field[:, 0], flow_field[:, 1], scale=10, color='r') -plt.xlim(-7, 15) -plt.ylim(2, 15) -plt.show() - -# %% use linear classifier to predict infection state from UMAP coordinates - -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import classification_report -from sklearn.model_selection import train_test_split - -X = features[["UMAP1", "UMAP2"]].values.astype(int) -y = infection.values.codes - -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - -clf = LogisticRegression() -clf.fit(X_train, y_train) - -y_pred = clf.predict(X_test) -print(classification_report(y_test, y_pred)) - -# %% use linear classifier to predict infection state from PCA coordinates - -X = features[["PCA1", "PCA2"]].values -y = infection.values.codes - -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - -clf = LogisticRegression() -clf.fit(X_train, y_train) - -y_pred = clf.predict(X_test) -print(classification_report(y_test, y_pred)) - -# %% use gaussian mixture model to cluster cells in PCA space -from sklearn.metrics import f1_score -from sklearn.mixture import GaussianMixture - -gmm = GaussianMixture(n_components=2) -PCA1_array = features["PCA1"].values.reshape(-1, 1) -PCA2_array = features["PCA2"].values.reshape(-1, 1) - -gmm.fit(np.concatenate((PCA1_array, PCA2_array), axis=1)) - -GMM_predict = gmm.predict(np.concatenate((PCA1_array, PCA2_array), axis=1)) -features = features.assign_coords(gmm=("sample", GMM_predict)) - -# display the clustering results -fig = px.scatter( - data_frame=pd.DataFrame( - {k: v for k, v in features.coords.items() if k != "features"} - ), - x="PCA1", - y="PCA2", - color=features["gmm"].astype(str), - hover_name="fov_name", - hover_data=["track_id", "t"], -) - -fig.update_traces(marker=dict(size=3)) - -# %% From ad741769183baa4a2313cd4d4c069984a9048735 Mon Sep 17 00:00:00 2001 From: Soorya19Pradeep <101817974+Soorya19Pradeep@users.noreply.github.com> Date: Fri, 27 Sep 2024 14:41:27 -0700 Subject: [PATCH 79/87] Fixes on correlation of PCA and UMAP components to computed_feature script (#159) * reduce initial patch size * add radial profiling * add function descriptions * add umap correlation * add def comments * change umap for all data * add script for 1 chan * add p-value analysis * add PCA analysis * remove duplicate script * Refactor and format code * Format code * Removed umap correlation * note for future refactor --------- Co-authored-by: Ziwen Liu --- .../evaluation/PC_vs_CF.py | 463 +++++++++++++----- .../evaluation/PC_vs_CF_singleChannel.py | 252 ++++++++++ viscy/representation/evaluation.py | 92 +++- 3 files changed, 676 insertions(+), 131 deletions(-) create mode 100644 applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py diff --git a/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py b/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py index 1c2112b9..f43b121b 100644 --- a/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py +++ b/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py @@ -1,9 +1,19 @@ +""" Script to compute the correlation between PCA and UMAP features and computed features +* finds the computed features best representing the PCA and UMAP components +* outputs a heatmap of the correlation between PCA and UMAP features and computed features +""" + # %% from pathlib import Path +import sys +import os + +sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") import numpy as np import pandas as pd from sklearn.decomposition import PCA +from umap import UMAP from sklearn.preprocessing import StandardScaler from viscy.representation.embedding_writer import read_embedding_dataset @@ -12,15 +22,22 @@ ) from viscy.representation.evaluation import dataset_of_tracks +import matplotlib.pyplot as plt +import seaborn as sns + +from scipy.stats import spearmanr +import pandas as pd +import plotly.express as px + # %% features_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" ) data_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/2.1-register/registered.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" ) tracks_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" ) # %% @@ -34,50 +51,61 @@ embedding_dataset = read_embedding_dataset(features_path) embedding_dataset -fov_names_list = [ - name for name in embedding_dataset["fov_name"].values if name.startswith("/A/3/") -] +# load all unprojected features: +features = embedding_dataset["features"] + +# %% PCA analysis of the features + +pca = PCA(n_components=5) +pca_features = pca.fit_transform(features.values) +features = ( + features.assign_coords(PCA1=("sample", pca_features[:, 0])) + .assign_coords(PCA2=("sample", pca_features[:, 1])) + .assign_coords(PCA3=("sample", pca_features[:, 2])) + .assign_coords(PCA4=("sample", pca_features[:, 3])) + .assign_coords(PCA5=("sample", pca_features[:, 4])) + .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"], append=True) +) + +# %% convert the xarray to dataframe structure and add columns for computed features +features_df = features.to_dataframe() +features_df = features_df.drop(columns=["features"]) +df = features_df.drop_duplicates() +features = df.reset_index(drop=True) + +features = features[features["fov_name"].str.startswith("/B/")] + +features["Phase Symmetry Score"] = np.nan +features["Fluor Symmetry Score"] = np.nan +features["Sensor Area"] = np.nan +features["Masked Sensor Intensity"] = np.nan +features["Entropy Phase"] = np.nan +features["Entropy Fluor"] = np.nan +features["Contrast Phase"] = np.nan +features["Dissimilarity Phase"] = np.nan +features["Homogeneity Phase"] = np.nan +features["Contrast Fluor"] = np.nan +features["Dissimilarity Fluor"] = np.nan +features["Homogeneity Fluor"] = np.nan +features["Phase IQR"] = np.nan +features["Fluor Mean Intensity"] = np.nan +features["Phase Standard Deviation"] = np.nan +features["Fluor Standard Deviation"] = np.nan +features["Phase radial profile"] = np.nan +features["Fluor radial profile"] = np.nan + +# %% compute the computed features and add them to the dataset + +fov_names_list = features["fov_name"].unique() unique_fov_names = sorted(list(set(fov_names_list))) -correlation_sum = pd.DataFrame() -ii = 0 -features = pd.DataFrame() -computed_pca = pd.DataFrame() for fov_name in unique_fov_names: - all_tracks_FOV = embedding_dataset.sel(fov_name=fov_name) - - unique_track_ids = list(all_tracks_FOV["track_id"].values) + unique_track_ids = features[features["fov_name"] == fov_name]["track_id"].unique() unique_track_ids = list(set(unique_track_ids)) for track_id in unique_track_ids: - a_track_in_FOV = all_tracks_FOV.sel(track_id=track_id) - indices = np.arange(a_track_in_FOV.sizes["sample"]) - features_track = a_track_in_FOV["features"] - time_stamp = features_track["t"][indices].astype(str) - - scaled_features_track = StandardScaler().fit_transform(features_track.values) - - # perform PCA analysis of features - - pca = PCA(n_components=5) - if scaled_features_track.shape[0] > 5: - pca_features = pca.fit_transform(scaled_features_track) - ii += 1 - else: - continue - - features_track = ( - features_track.assign_coords(PCA1=("sample", pca_features[:, 0])) - .assign_coords(PCA2=("sample", pca_features[:, 1])) - .assign_coords(PCA3=("sample", pca_features[:, 2])) - .assign_coords(PCA4=("sample", pca_features[:, 3])) - .assign_coords(PCA5=("sample", pca_features[:, 4])) - .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"], append=True) - ) - - # load the image patches prediction_dataset = dataset_of_tracks( data_path, @@ -87,31 +115,9 @@ source_channel=source_channel, ) - whole = np.stack([p["anchor"] for p in predict_dataset]) + whole = np.stack([p["anchor"] for p in prediction_dataset]) phase = whole[:, 0, 3] fluor = np.max(whole[:, 1], axis=1) - # phase = np.stack([p["anchor"][0, 3].numpy() for p in predict_dataset]) - # fluor = np.stack([np.max(p["anchor"][1].numpy(), axis=0) for p in predict_dataset]) - - # Compute Fourier descriptors for phase image - data = { - "Phase Symmetry Score": [], - "Fluor Symmetry Score": [], - "Sensor Area": [], - "Masked Sensor Intensity": [], - "Entropy Phase": [], - "Entropy Fluor": [], - "Contrast Phase": [], - "Dissimilarity Phase": [], - "Homogeneity Phase": [], - "Contrast Fluor": [], - "Dissimilarity Fluor": [], - "Homogeneity Fluor": [], - "Phase IQR": [], - "Fluor Mean Intensity": [], - "Phase Standard Deviation": [], - "Fluor Standard Deviation": [], - } for t in range(phase.shape[0]): # Compute Fourier descriptors for phase image @@ -139,16 +145,6 @@ FE.compute_glcm_features(fluor[t]) ) - # # Compute edge detection using Canny - # edges_phase = FE.detect_edges(phase[t]) - # edges_fluor = FE.detect_edges(fluor[t]) - - # Quantify the amount of edge feature in the phase image - # edge_density_phase = np.sum(edges_phase) / (edges_phase.shape[0] * edges_phase.shape[1]) - - # Quantify the amount of edge feature in the fluor image - # edge_density_fluor = np.sum(edges_fluor) / (edges_fluor.shape[0] * edges_fluor.shape[1]) - # Compute interqualtile range of pixel intensities iqr = FE.compute_iqr(phase[t]) @@ -159,53 +155,145 @@ phase_std_dev = FE.compute_std_dev(phase[t]) fluor_std_dev = FE.compute_std_dev(fluor[t]) - # Append the computed features to the data dictionary - data["Phase Symmetry Score"].append(phase_symmetry_score) - data["Fluor Symmetry Score"].append(fluor_symmetry_score) - data["Sensor Area"].append(area) - data["Masked Sensor Intensity"].append(masked_intensity) - data["Entropy Phase"].append(entropy_phase) - data["Entropy Fluor"].append(entropy_fluor) - data["Contrast Phase"].append(contrast_phase) - data["Dissimilarity Phase"].append(dissimilarity_phase) - data["Homogeneity Phase"].append(homogeneity_phase) - data["Contrast Fluor"].append(contrast_fluor) - data["Dissimilarity Fluor"].append(dissimilarity_fluor) - data["Homogeneity Fluor"].append(homogeneity_fluor) - # data["Edge Density Phase"].append(edge_density_phase) - # data["Edge Density Fluor"].append(edge_density_fluor) - data["Phase IQR"].append(iqr) - data["Fluor Mean Intensity"].append(fluor_mean_intensity) - data["Phase Standard Deviation"].append(phase_std_dev) - data["Fluor Standard Deviation"].append(fluor_std_dev) - - # Create a dataframe to store the computed features - features = pd.concat([features, pd.DataFrame(data)]) - - # compute correlation between PCA features and computed features - - # Create a dataframe with PCA results - pca_results = pd.DataFrame( - pca_features, columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"] - ) - computed_pca = pd.concat([computed_pca, pca_results]) + # Compute radial intensity gradient + phase_radial_profile = FE.compute_radial_intensity_gradient(phase[t]) + fluor_radial_profile = FE.compute_radial_intensity_gradient(fluor[t]) + + # update the features dataframe with the computed features + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Fluor Symmetry Score", + ] = fluor_symmetry_score + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase Symmetry Score", + ] = phase_symmetry_score + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Sensor Area", + ] = area + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Masked Sensor Intensity", + ] = masked_intensity + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Entropy Phase", + ] = entropy_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Entropy Fluor", + ] = entropy_fluor + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Contrast Phase", + ] = contrast_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Dissimilarity Phase", + ] = dissimilarity_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Homogeneity Phase", + ] = homogeneity_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Contrast Fluor", + ] = contrast_fluor + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Dissimilarity Fluor", + ] = dissimilarity_fluor + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Homogeneity Fluor", + ] = homogeneity_fluor + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase IQR", + ] = iqr + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Fluor Mean Intensity", + ] = fluor_mean_intensity + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase Standard Deviation", + ] = phase_std_dev + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Fluor Standard Deviation", + ] = fluor_std_dev + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase radial profile", + ] = phase_radial_profile + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Fluor radial profile", + ] = fluor_radial_profile # %% -# Compute correlation between PCA features and computed features -correlation = pd.concat([computed_pca, features], axis=1).corr() -# correlation_sum = correlation_sum.add(correlation, fill_value=0) -# correlation_avg = correlation_sum / ii +# Save the features dataframe to a CSV file +features.to_csv( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/features_twoChan.csv", + index=False, +) -# %% find the best correlated computed features with PCA features +# # read the features dataframe from the CSV file +# features = pd.read_csv( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/features_twoChan.csv" +# ) -# Find the best correlated computed features with PCA features -best_correlated_features = correlation.loc["PCA1":"PCA5", :].idxmax() -best_correlated_features +# remove the rows with missing values +features = features.dropna() -# %% display as a heatmap -import matplotlib.pyplot as plt -import seaborn as sns +# sub_features = features[features["Time"] == 20] +feature_df_removed = features.drop( + columns=["fov_name", "track_id", "t", "id", "parent_track_id", "parent_id"] +) + +# Compute correlation between PCA features and computed features +correlation = feature_df_removed.corr(method="spearman") + +# %% display PCA correlation as a heatmap plt.figure(figsize=(20, 5)) sns.heatmap( @@ -219,6 +307,151 @@ plt.title("Correlation between PCA features and computed features") plt.xlabel("Computed Features") plt.ylabel("PCA Features") -plt.show() +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/PC_vs_CF_2chan_pca.svg" +) -# %% + +# %% plot PCA vs set of computed features + +set_features = [ + "Fluor radial profile", + "Homogeneity Phase", + "Phase IQR", + "Phase Standard Deviation", + "Sensor Area", + "Homogeneity Fluor", + "Contrast Fluor", + "Phase radial profile", +] + +plt.figure(figsize=(8, 10)) +sns.heatmap( + correlation.loc[set_features, "PCA1":"PCA5"], + annot=True, + cmap="coolwarm", + fmt=".2f", + vmin=-1, + vmax=1, +) + +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/PC_vs_CF_2chan_pca_setfeatures.svg" +) + +# %% find the cell patches with the highest and lowest value in each feature + +def save_patches(fov_name, track_id): + data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" + ) + tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" + ) + source_channel = ["Phase3D", "RFP"] + prediction_dataset = dataset_of_tracks( + data_path, + tracks_path, + [fov_name], + [track_id], + source_channel=source_channel, + ) + whole = np.stack([p["anchor"] for p in prediction_dataset]) + phase = whole[:, 0] + fluor = whole[:, 1] + out_dir = "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/data/computed_features/" + fov_name_out = fov_name.replace("/", "_") + np.save( + (os.path.join(out_dir, "phase" + fov_name_out + "_" + str(track_id) + ".npy")), + phase, + ) + np.save( + (os.path.join(out_dir, "fluor" + fov_name_out + "_" + str(track_id) + ".npy")), + fluor, + ) + + +# PCA1: Fluor radial profile +highest_fluor_radial_profile = features.loc[features["Fluor radial profile"].idxmax()] +print("Row with highest 'Fluor radial profile':") +# print(highest_fluor_radial_profile) +print( + f"fov_name: {highest_fluor_radial_profile['fov_name']}, time: {highest_fluor_radial_profile['t']}" +) +save_patches( + highest_fluor_radial_profile["fov_name"], highest_fluor_radial_profile["track_id"] +) + +lowest_fluor_radial_profile = features.loc[features["Fluor radial profile"].idxmin()] +print("Row with lowest 'Fluor radial profile':") +# print(lowest_fluor_radial_profile) +print( + f"fov_name: {lowest_fluor_radial_profile['fov_name']}, time: {lowest_fluor_radial_profile['t']}" +) +save_patches( + lowest_fluor_radial_profile["fov_name"], lowest_fluor_radial_profile["track_id"] +) + +# PCA2: Entropy phase +highest_entropy_phase = features.loc[features["Entropy Phase"].idxmax()] +print("Row with highest 'Entropy Phase':") +# print(highest_entropy_phase) +print( + f"fov_name: {highest_entropy_phase['fov_name']}, time: {highest_entropy_phase['t']}" +) +save_patches(highest_entropy_phase["fov_name"], highest_entropy_phase["track_id"]) + +lowest_entropy_phase = features.loc[features["Entropy Phase"].idxmin()] +print("Row with lowest 'Entropy Phase':") +# print(lowest_entropy_phase) +print( + f"fov_name: {lowest_entropy_phase['fov_name']}, time: {lowest_entropy_phase['t']}" +) +save_patches(lowest_entropy_phase["fov_name"], lowest_entropy_phase["track_id"]) + +# PCA3: Phase IQR +highest_phase_iqr = features.loc[features["Phase IQR"].idxmax()] +print("Row with highest 'Phase IQR':") +# print(highest_phase_iqr) +print(f"fov_name: {highest_phase_iqr['fov_name']}, time: {highest_phase_iqr['t']}") +save_patches(highest_phase_iqr["fov_name"], highest_phase_iqr["track_id"]) + +tenth_lowest_phase_iqr = features.nsmallest(10, "Phase IQR").iloc[9] +print("Row with tenth lowest 'Phase IQR':") +# print(tenth_lowest_phase_iqr) +print( + f"fov_name: {tenth_lowest_phase_iqr['fov_name']}, time: {tenth_lowest_phase_iqr['t']}" +) +save_patches(tenth_lowest_phase_iqr["fov_name"], tenth_lowest_phase_iqr["track_id"]) + +# PCA4: Phase Standard Deviation +highest_phase_std_dev = features.loc[features["Phase Standard Deviation"].idxmax()] +print("Row with highest 'Phase Standard Deviation':") +# print(highest_phase_std_dev) +print( + f"fov_name: {highest_phase_std_dev['fov_name']}, time: {highest_phase_std_dev['t']}" +) +save_patches(highest_phase_std_dev["fov_name"], highest_phase_std_dev["track_id"]) + +lowest_phase_std_dev = features.loc[features["Phase Standard Deviation"].idxmin()] +print("Row with lowest 'Phase Standard Deviation':") +# print(lowest_phase_std_dev) +print( + f"fov_name: {lowest_phase_std_dev['fov_name']}, time: {lowest_phase_std_dev['t']}" +) +save_patches(lowest_phase_std_dev["fov_name"], lowest_phase_std_dev["track_id"]) + +# PCA5: Sensor area +highest_sensor_area = features.loc[features["Sensor Area"].idxmax()] +print("Row with highest 'Sensor Area':") +# print(highest_sensor_area) +print(f"fov_name: {highest_sensor_area['fov_name']}, time: {highest_sensor_area['t']}") +save_patches(highest_sensor_area["fov_name"], highest_sensor_area["track_id"]) + +tenth_lowest_sensor_area = features.nsmallest(10, "Sensor Area").iloc[9] +print("Row with tenth lowest 'Sensor Area':") +# print(tenth_lowest_sensor_area) +print( + f"fov_name: {tenth_lowest_sensor_area['fov_name']}, time: {tenth_lowest_sensor_area['t']}" +) +save_patches(tenth_lowest_sensor_area["fov_name"], tenth_lowest_sensor_area["track_id"]) diff --git a/applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py b/applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py new file mode 100644 index 00000000..aac8855c --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py @@ -0,0 +1,252 @@ +""" Script to compute the correlation between PCA and UMAP features and computed features +* finds the computed features best representing the PCA and UMAP components +* outputs a heatmap of the correlation between PCA and UMAP features and computed features +""" + +# %% +from pathlib import Path +import sys + +sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") + +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA +from umap import UMAP +from sklearn.preprocessing import StandardScaler + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import ( + FeatureExtractor as FE, +) +from viscy.representation.evaluation import dataset_of_tracks + +import matplotlib.pyplot as plt +import seaborn as sns + +from scipy.stats import spearmanr +import pandas as pd +import plotly.express as px + +# %% +features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval_phase/predictions/epoch_186/1chan_128patch_186ckpt_Febtest.zarr" +) +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/9-lineage-cell-division/lineages_gt/track.zarr" +) + +# %% + +source_channel = ["Phase3D"] +z_range = (28, 43) +normalizations = None +# fov_name = "/B/4/5" +# track_id = 11 + +embedding_dataset = read_embedding_dataset(features_path) +embedding_dataset + +# load all unprojected features: +features = embedding_dataset["features"] + +# %% PCA analysis of the features + +pca = PCA(n_components=3) +embedding = pca.fit_transform(features.values) +features = ( + features.assign_coords(PCA1=("sample", embedding[:, 0])) + .assign_coords(PCA2=("sample", embedding[:, 1])) + .assign_coords(PCA3=("sample", embedding[:, 2])) + .set_index(sample=["PCA1", "PCA2", "PCA3"], append=True) +) + +# %% convert the xarray to dataframe structure and add columns for computed features +features_df = features.to_dataframe() +features_df = features_df.drop(columns=["features"]) +df = features_df.drop_duplicates() +features = df.reset_index(drop=True) + +features = features[features["fov_name"].str.startswith("/B/")] + +features["Phase Symmetry Score"] = np.nan +features["Entropy Phase"] = np.nan +features["Contrast Phase"] = np.nan +features["Dissimilarity Phase"] = np.nan +features["Homogeneity Phase"] = np.nan +features["Phase IQR"] = np.nan +features["Phase Standard Deviation"] = np.nan +features["Phase radial profile"] = np.nan + +# %% compute the computed features and add them to the dataset + +fov_names_list = features["fov_name"].unique() +unique_fov_names = sorted(list(set(fov_names_list))) + +for fov_name in unique_fov_names: + + unique_track_ids = features[features["fov_name"] == fov_name]["track_id"].unique() + unique_track_ids = list(set(unique_track_ids)) + + for track_id in unique_track_ids: + + # load the image patches + + prediction_dataset = dataset_of_tracks( + data_path, + tracks_path, + [fov_name], + [track_id], + source_channel=source_channel, + ) + + whole = np.stack([p["anchor"] for p in prediction_dataset]) + phase = whole[:, 0, 3] + + for t in range(phase.shape[0]): + # Compute Fourier descriptors for phase image + phase_descriptors = FE.compute_fourier_descriptors(phase[t]) + # Analyze symmetry of phase image + phase_symmetry_score = FE.analyze_symmetry(phase_descriptors) + + # Compute higher frequency features using spectral entropy + entropy_phase = FE.compute_spectral_entropy(phase[t]) + + # Compute texture analysis using GLCM + contrast_phase, dissimilarity_phase, homogeneity_phase = ( + FE.compute_glcm_features(phase[t]) + ) + + # Compute interqualtile range of pixel intensities + iqr = FE.compute_iqr(phase[t]) + + # Compute standard deviation of pixel intensities + phase_std_dev = FE.compute_std_dev(phase[t]) + + # Compute radial intensity gradient + phase_radial_profile = FE.compute_radial_intensity_gradient(phase[t]) + + # update the features dataframe with the computed features + + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase Symmetry Score", + ] = phase_symmetry_score + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Entropy Phase", + ] = entropy_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Contrast Phase", + ] = contrast_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Dissimilarity Phase", + ] = dissimilarity_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Homogeneity Phase", + ] = homogeneity_phase + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase IQR", + ] = iqr + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase Standard Deviation", + ] = phase_std_dev + features.loc[ + (features["fov_name"] == fov_name) + & (features["track_id"] == track_id) + & (features["t"] == t), + "Phase radial profile", + ] = phase_radial_profile + +# %% +# Save the features dataframe to a CSV file +features.to_csv( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/features_oneChan.csv", + index=False, +) + +# read the csv file +# features = pd.read_csv( +# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/features_oneChan.csv" +# ) + +# remove the rows with missing values +features = features.dropna() + +# sub_features = features[features["Time"] == 20] +feature_df_removed = features.drop( + columns=["fov_name", "track_id", "t", "id", "parent_track_id", "parent_id"] +) + +# Compute correlation between PCA features and computed features +correlation = feature_df_removed.corr(method="spearman") + +# %% calculate the p-value and draw volcano plot to show the significance of the correlation + +p_values = pd.DataFrame(index=correlation.index, columns=correlation.columns) + +for i in correlation.index: + for j in correlation.columns: + if i != j: + p_values.loc[i, j] = spearmanr( + feature_df_removed[i], feature_df_removed[j] + )[1] + +p_values = p_values.astype(float) + +# %% draw an interactive volcano plot showing -log10(p-value) vs fold change + +# Flatten the correlation and p-values matrices and create a DataFrame +correlation_flat = correlation.values.flatten() +p_values_flat = p_values.values.flatten() +# Create a list of feature names for the flattened correlation and p-values +feature_names = [f"{i}_{j}" for i in correlation.index for j in correlation.columns] + +data = pd.DataFrame( + { + "Correlation": correlation_flat, + "-log10(p-value)": -np.log10(p_values_flat), + "feature_names": feature_names, + } +) + +# Create an interactive scatter plot using Plotly +fig = px.scatter( + data, + x="Correlation", + y="-log10(p-value)", + title="Volcano plot showing significance of correlation", + labels={"Correlation": "Correlation", "-log10(p-value)": "-log10(p-value)"}, + opacity=0.5, + hover_data=["feature_names"], +) + +fig.show() +# Save the interactive volcano plot as an HTML file +fig.write_html( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/volcano_plot_1chan.html" +) + +# %% diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py index 1dded467..f459e8b4 100644 --- a/viscy/representation/evaluation.py +++ b/viscy/representation/evaluation.py @@ -2,7 +2,6 @@ import pandas as pd import umap from numpy import fft -from skimage import color from skimage.feature import graycomatrix, graycoprops from skimage.filters import gaussian, threshold_otsu from sklearn.cluster import DBSCAN @@ -103,7 +102,7 @@ def dataset_of_tracks( track_id_list, source_channel=["Phase3D", "RFP"], z_range=(28, 43), - initial_yx_patch_size=(256, 256), + initial_yx_patch_size=(128, 128), final_yx_patch_size=(128, 128), ): data_module = TripletDataModule( @@ -273,12 +272,18 @@ def compute_umap(embedding_dataset, normalize_features=True): class FeatureExtractor: + # FIXME: refactor into a separate module with standalone functions def __init__(self): pass def compute_fourier_descriptors(image): - + """ + Compute the Fourier descriptors of the image + The sensor or nuclear shape changes when infected, which can be captured by analyzing Fourier descriptors + :param np.array image: input image + :return: Fourier descriptors + """ # Convert contour to complex numbers contour_complex = image[:, 0] + 1j * image[:, 1] @@ -288,16 +293,23 @@ def compute_fourier_descriptors(image): return descriptors def analyze_symmetry(descriptors): + """ + Analyze the symmetry of the Fourier descriptors + Symmetry of the sensor or nuclear shape changes when infected + :param np.array descriptors: Fourier descriptors + :return: standard deviation of the descriptors + """ # Normalize descriptors descriptors = np.abs(descriptors) / np.max(np.abs(descriptors)) - # Check symmetry (for a perfect circle, descriptors should be quite uniform) + return np.std(descriptors) # Lower standard deviation indicates higher symmetry def compute_area(input_image, sigma=0.6): """Create a binary mask using morphological operations + Sensor area will increase when infected due to expression in nucleus :param np.array input_image: generate masks from this 3D image :param float sigma: Gaussian blur standard deviation, increase in value increases blur - :return: volume mask of input_image, 3D np.array + :return: area of the sensor mask & mean intensity inside the sensor area """ input_image_blur = gaussian(input_image, sigma=sigma) @@ -314,9 +326,12 @@ def compute_area(input_image, sigma=0.6): return masked_intensity, np.sum(mask) def compute_spectral_entropy(image): - # Convert image to grayscale if it's not already - if len(image.shape) == 3: - image = color.rgb2gray(image) + """ + Compute the spectral entropy of the image + High frequency components are observed to increase in phase and reduce in sensor when cell is infected + :param np.array image: input image + :return: spectral entropy + """ # Compute the 2D Fourier Transform f_transform = fft.fft2(image) @@ -334,6 +349,12 @@ def compute_spectral_entropy(image): return entropy def compute_glcm_features(image): + """ + Compute the contrast, dissimilarity and homogeneity of the image + Both sensor and phase texture changes when infected, smooth in sensor, and rough in phase + :param np.array image: input image + :return: contrast, dissimilarity, homogeneity + """ # Normalize the input image from 0 to 255 image = (image - np.min(image)) * (255 / (np.max(image) - np.min(image))) @@ -352,14 +373,13 @@ def compute_glcm_features(image): return contrast, dissimilarity, homogeneity - # def detect_edges(image): - - # # Apply Canny edge detection - # edges = cv2.Canny(image, 100, 200) - - # return edges - def compute_iqr(image): + """ + Compute the interquartile range of pixel intensities + Observed to increase when cell is infected + :param np.array image: input image + :return: interquartile range of pixel intensities + """ # Compute the interquartile range of pixel intensities iqr = np.percentile(image, 75) - np.percentile(image, 25) @@ -367,6 +387,12 @@ def compute_iqr(image): return iqr def compute_mean_intensity(image): + """ + Compute the mean pixel intensity + Expected to vary when cell morphology changes due to infection, divison or death + :param np.array image: input image + :return: mean pixel intensity + """ # Compute the mean pixel intensity mean_intensity = np.mean(image) @@ -374,8 +400,42 @@ def compute_mean_intensity(image): return mean_intensity def compute_std_dev(image): - + """ + Compute the standard deviation of pixel intensities + Expected to vary when cell morphology changes due to infection, divison or death + :param np.array image: input image + :return: standard deviation of pixel intensities + """ # Compute the standard deviation of pixel intensities std_dev = np.std(image) return std_dev + + def compute_radial_intensity_gradient(image): + """ + Compute the radial intensity gradient of the image + The sensor relocalizes inside the nucleus, which is center of the image when cells are infected + Expected negative gradient when infected and zero to positive gradient when not infected + :param np.array image: input image + :return: radial intensity gradient + """ + # normalize the image + image = (image - np.min(image)) / (np.max(image) - np.min(image)) + + # compute the intensity gradient from center to periphery + y, x = np.indices(image.shape) + center = np.array(image.shape) / 2 + r = np.sqrt((x - center[1]) ** 2 + (y - center[0]) ** 2) + r = r.astype(int) + tbin = np.bincount(r.ravel(), image.ravel()) + nr = np.bincount(r.ravel()) + radial_intensity_values = tbin / nr + + # get the slope radial_intensity_values + from scipy.stats import linregress + + radial_intensity_gradient = linregress( + range(len(radial_intensity_values)), radial_intensity_values + ) + + return radial_intensity_gradient[0] From 582952dc0cd4adaf62f07b0b9de009337de26e32 Mon Sep 17 00:00:00 2001 From: Alishba Imran <44557946+alishbaimran@users.noreply.github.com> Date: Fri, 27 Sep 2024 17:48:47 -0400 Subject: [PATCH 80/87] updated eval module & cosine sim figures (#168) * updated files * format fixed for tests * updated scripts * umap dist code * bug fixes and linting * logistic regression script * add infection figure script * Add script for generating infection figure and perform prediction on the June dataset * Format code * Black format evaluation module and fix import in figure_cell_infection script * Refactor scatterplot colors and markers * Calculate model accuracy * Add script for appendix video * formatted code * updated displacement funcs for full embeddings * script for displacement computation * fix style * fix docstring format --------- Co-authored-by: Shalin Mehta Co-authored-by: Soorya Pradeep Co-authored-by: Ziwen Liu --- .../evaluation/cosine_similarity.py | 546 +++++++++++++++ .../evaluation/displacement.py | 118 ++++ .../evaluation/log_regresssion_training.py | 111 +++ .../evaluation/pca_umap_embeddings_time.py | 220 ++++++ .../figures/figure_cell_infection.py | 652 ++++++++++++++++++ .../Infection_classifier_accuracy.py | 71 ++ viscy/representation/evaluation.py | 248 +++++++ 7 files changed, 1966 insertions(+) create mode 100644 applications/contrastive_phenotyping/evaluation/cosine_similarity.py create mode 100644 applications/contrastive_phenotyping/evaluation/displacement.py create mode 100644 applications/contrastive_phenotyping/evaluation/log_regresssion_training.py create mode 100644 applications/contrastive_phenotyping/evaluation/pca_umap_embeddings_time.py create mode 100644 applications/contrastive_phenotyping/figures/figure_cell_infection.py create mode 100644 applications/infection_classification/Infection_classifier_accuracy.py diff --git a/applications/contrastive_phenotyping/evaluation/cosine_similarity.py b/applications/contrastive_phenotyping/evaluation/cosine_similarity.py new file mode 100644 index 00000000..78a4906c --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/cosine_similarity.py @@ -0,0 +1,546 @@ +# %% +# Import necessary libraries, try euclidean distance for both features and +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from sklearn.preprocessing import StandardScaler +from umap import UMAP + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import ( + calculate_cosine_similarity_cell, + compute_displacement, + compute_displacement_mean_std, +) + +# %% Paths and parameters. + + +features_path_30_min = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) + + +feature_path_no_track = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) + + +features_path_any_time = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr" +) + + +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" +) + + +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" +) + + +# %% Load embedding datasets for all three sampling +fov_name = "/B/4/6" +track_id = 52 + +embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) +embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) +embedding_dataset_any_time = read_embedding_dataset(features_path_any_time) + +# Calculate cosine similarities for each sampling +time_points_30_min, cosine_similarities_30_min = calculate_cosine_similarity_cell( + embedding_dataset_30_min, fov_name, track_id +) +time_points_no_track, cosine_similarities_no_track = calculate_cosine_similarity_cell( + embedding_dataset_no_track, fov_name, track_id +) +time_points_any_time, cosine_similarities_any_time = calculate_cosine_similarity_cell( + embedding_dataset_any_time, fov_name, track_id +) + +# %% Plot cosine similarities over time for all three conditions + +plt.figure(figsize=(10, 6)) + +plt.plot( + time_points_no_track, + cosine_similarities_no_track, + marker="o", + label="classical contrastive (no tracking)", +) +plt.plot( + time_points_any_time, cosine_similarities_any_time, marker="o", label="cell aware" +) +plt.plot( + time_points_30_min, + cosine_similarities_30_min, + marker="o", + label="cell & time aware (interval 30 min)", +) + +plt.xlabel("Time Delay (t)") +plt.ylabel("Cosine Similarity with First Time Point") +plt.title("Cosine Similarity Over Time for Infected Cell") + +# plt.savefig('infected_cell_example.pdf', format='pdf') + + +plt.grid(True) + +plt.legend() + +plt.savefig("new_example_cell.svg", format="svg") + + +plt.show() +# %% + + +# %% import statements + + +# %% Paths to datasets +features_path_30_min = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +feature_path_no_track = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) +# features_path_any_time = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_1chan_128patch_32projDim/1chan_128patch_63ckpt_FebTest.zarr") + + +# %% Read embedding datasets +embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) +embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) +# embedding_dataset_any_time = read_embedding_dataset(features_path_any_time) + + +# %% Compute displacements for both datasets (using Euclidean distance and Cosine similarity) +max_tau = 10 # Maximum time shift (tau) to compute displacements + + +# mean_displacement_30_min, std_displacement_30_min = compute_displacement_mean_std(embedding_dataset_30_min, max_tau, use_cosine=False, use_dissimilarity=False) +# mean_displacement_no_track, std_displacement_no_track = compute_displacement_mean_std(embedding_dataset_no_track, max_tau, use_cosine=False, use_dissimilarity=False) +# mean_displacement_any_time, std_displacement_any_time = compute_displacement_mean_std(embedding_dataset_any_time, max_tau, use_cosine=False) + + +mean_displacement_30_min_cosine, std_displacement_30_min_cosine = ( + compute_displacement_mean_std( + embedding_dataset_30_min, max_tau, use_cosine=True, use_dissimilarity=False + ) +) +mean_displacement_no_track_cosine, std_displacement_no_track_cosine = ( + compute_displacement_mean_std( + embedding_dataset_no_track, max_tau, use_cosine=True, use_dissimilarity=False + ) +) +# mean_displacement_any_time_cosine, std_displacement_any_time_cosine = compute_displacement_mean_std(embedding_dataset_any_time, max_tau, use_cosine=True) +# %% Plot 1: Euclidean Displacements +plt.figure(figsize=(10, 6)) + + +taus = list(mean_displacement_30_min_cosine.keys()) +mean_values_30_min = list(mean_displacement_30_min_cosine.values()) +std_values_30_min = list(std_displacement_30_min_cosine.values()) + + +mean_values_no_track = list(mean_displacement_no_track_cosine.values()) +std_values_no_track = list(std_displacement_no_track_cosine.values()) + + +# mean_values_any_time = list(mean_displacement_any_time.values()) +# std_values_any_time = list(std_displacement_any_time.values()) + + +# Plotting Euclidean displacements +plt.plot( + taus, mean_values_30_min, marker="o", label="Cell & Time Aware (30 min interval)" +) +plt.fill_between( + taus, + np.array(mean_values_30_min) - np.array(std_values_30_min), + np.array(mean_values_30_min) + np.array(std_values_30_min), + color="gray", + alpha=0.3, + label="Std Dev (30 min interval)", +) + + +plt.plot( + taus, mean_values_no_track, marker="o", label="Classical Contrastive (No Tracking)" +) +plt.fill_between( + taus, + np.array(mean_values_no_track) - np.array(std_values_no_track), + np.array(mean_values_no_track) + np.array(std_values_no_track), + color="blue", + alpha=0.3, + label="Std Dev (No Tracking)", +) + + +plt.xlabel("Time Shift (Ï„)") +plt.ylabel("Displacement") +plt.title("Embedding Displacement Over Time") +plt.grid(True) +plt.legend() + + +# plt.savefig('embedding_displacement_euclidean.svg', format='svg') +# plt.savefig('embedding_displacement_euclidean.pdf', format='pdf') + + +# Show the Euclidean plot +plt.show() + + +# %% Plot 2: Cosine Displacements +plt.figure(figsize=(10, 6)) + +taus = list(mean_displacement_30_min_cosine.keys()) + +# Plotting Cosine displacements +mean_values_30_min_cosine = list(mean_displacement_30_min_cosine.values()) +std_values_30_min_cosine = list(std_displacement_30_min_cosine.values()) + + +mean_values_no_track_cosine = list(mean_displacement_no_track_cosine.values()) +std_values_no_track_cosine = list(std_displacement_no_track_cosine.values()) + + +plt.plot( + taus, + mean_values_30_min_cosine, + marker="o", + label="Cell & Time Aware (30 min interval)", +) +plt.fill_between( + taus, + np.array(mean_values_30_min_cosine) - np.array(std_values_30_min_cosine), + np.array(mean_values_30_min_cosine) + np.array(std_values_30_min_cosine), + color="gray", + alpha=0.3, + label="Std Dev (30 min interval)", +) + + +plt.plot( + taus, + mean_values_no_track_cosine, + marker="o", + label="Classical Contrastive (No Tracking)", +) +plt.fill_between( + taus, + np.array(mean_values_no_track_cosine) - np.array(std_values_no_track_cosine), + np.array(mean_values_no_track_cosine) + np.array(std_values_no_track_cosine), + color="blue", + alpha=0.3, + label="Std Dev (No Tracking)", +) + + +plt.xlabel("Time Shift (Ï„)") +plt.ylabel("Cosine Similarity") +plt.title("Embedding Displacement Over Time") + + +plt.grid(True) +plt.legend() +plt.savefig("1_std_cosine_plot.svg", format="svg") + +# Show the Cosine plot +plt.show() +# %% + + +# %% Paths to datasets +features_path_30_min = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +feature_path_no_track = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) + + +# %% Read embedding datasets +embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) +embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) + + +# %% Compute displacements for both datasets (using Cosine similarity) +max_tau = 10 # Maximum time shift (tau) to compute displacements + + +# Compute displacements for Cell & Time Aware (30 min interval) using Cosine similarity +displacement_per_tau_aware_cosine = compute_displacement( + embedding_dataset_30_min, + max_tau, + use_cosine=True, + use_dissimilarity=False, + use_umap=False, +) + + +# Compute displacements for Classical Contrastive (No Tracking) using Cosine similarity +displacement_per_tau_contrastive_cosine = compute_displacement( + embedding_dataset_no_track, + max_tau, + use_cosine=True, + use_dissimilarity=False, + use_umap=False, +) + + +# %% Prepare data for violin plot +def prepare_violin_data(taus, displacement_aware, displacement_contrastive): + # Create a list to hold the data + data = [] + + # Populate the data for Cell & Time Aware + for tau in taus: + displacements_aware = displacement_aware.get(tau, []) + for displacement in displacements_aware: + data.append( + { + "Time Shift (Ï„)": tau, + "Displacement": displacement, + "Sampling": "Cell & Time Aware (30 min interval)", + } + ) + + # Populate the data for Classical Contrastive + for tau in taus: + displacements_contrastive = displacement_contrastive.get(tau, []) + for displacement in displacements_contrastive: + data.append( + { + "Time Shift (Ï„)": tau, + "Displacement": displacement, + "Sampling": "Classical Contrastive (No Tracking)", + } + ) + + # Convert to a DataFrame + df = pd.DataFrame(data) + return df + + +taus = list(displacement_per_tau_aware_cosine.keys()) + + +# Prepare the violin plot data +df = prepare_violin_data( + taus, displacement_per_tau_aware_cosine, displacement_per_tau_contrastive_cosine +) + + +# Create a violin plot using seaborn +plt.figure(figsize=(12, 8)) +sns.violinplot( + x="Time Shift (Ï„)", + y="Displacement", + hue="Sampling", + data=df, + palette="Set2", + scale="width", + bw=0.2, + inner=None, + split=True, + cut=0, +) + + +# Add labels and title +plt.xlabel("Time Shift (Ï„)", fontsize=14) +plt.ylabel("Cosine Similarity", fontsize=14) +plt.title("Cosine Similarity Distribution on Features", fontsize=16) +plt.grid(True, linestyle="--", alpha=0.6) # Lighter grid lines for less distraction +plt.legend(title="Sampling", fontsize=12, title_fontsize=14) + + +# plt.ylim(0.5, 1.0) + + +# Save the violin plot as SVG and PDF +plt.savefig("1fixed_violin_plot_cosine_similarity.svg", format="svg") +# plt.savefig('violin_plot_cosine_similarity.pdf', format='pdf') + + +# Show the plot +plt.show() +# %% using umap violin plot + +# %% Paths to datasets +features_path_30_min = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +feature_path_no_track = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) + +# %% Read embedding datasets +embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) +embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) + + +# %% Compute UMAP on features +def compute_umap(dataset): + features = dataset["features"] + scaled_features = StandardScaler().fit_transform(features.values) + umap = UMAP(n_components=2) # Reduce to 2 dimensions + embedding = umap.fit_transform(scaled_features) + + # Add UMAP coordinates using xarray functionality + umap_features = features.assign_coords( + UMAP1=("sample", embedding[:, 0]), UMAP2=("sample", embedding[:, 1]) + ) + return umap_features + + +# Apply UMAP to both datasets +umap_features_30_min = compute_umap(embedding_dataset_30_min) +umap_features_no_track = compute_umap(embedding_dataset_no_track) + +# %% +print(umap_features_30_min) +# %% Visualize UMAP embeddings +# # Visualize UMAP embeddings for the 30 min interval +# plt.figure(figsize=(8, 6)) +# plt.scatter(umap_features_30_min[:, 0], umap_features_30_min[:, 1], c=embedding_dataset_30_min["t"].values, cmap='viridis') +# plt.colorbar(label='Timepoints') +# plt.title('UMAP Projection of Features (30 min Interval)') +# plt.xlabel('UMAP1') +# plt.ylabel('UMAP2') +# plt.show() + +# # Visualize UMAP embeddings for the No Tracking dataset +# plt.figure(figsize=(8, 6)) +# plt.scatter(umap_features_no_track[:, 0], umap_features_no_track[:, 1], c=embedding_dataset_no_track["t"].values, cmap='viridis') +# plt.colorbar(label='Timepoints') +# plt.title('UMAP Projection of Features (No Tracking)') +# plt.xlabel('UMAP1') +# plt.ylabel('UMAP2') +# plt.show() +# %% Compute displacements using UMAP coordinates (using Cosine similarity) +max_tau = 10 # Maximum time shift (tau) to compute displacements + +# Compute displacements for UMAP-processed Cell & Time Aware (30 min interval) +displacement_per_tau_aware_umap_cosine = compute_displacement( + umap_features_30_min, + max_tau, + use_cosine=True, + use_dissimilarity=False, + use_umap=True, +) + +# Compute displacements for UMAP-processed Classical Contrastive (No Tracking) +displacement_per_tau_contrastive_umap_cosine = compute_displacement( + umap_features_no_track, + max_tau, + use_cosine=True, + use_dissimilarity=False, + use_umap=True, +) + + +# %% Prepare data for violin plot +def prepare_violin_data(taus, displacement_aware, displacement_contrastive): + # Create a list to hold the data + data = [] + + # Populate the data for Cell & Time Aware + for tau in taus: + displacements_aware = displacement_aware.get(tau, []) + for displacement in displacements_aware: + data.append( + { + "Time Shift (Ï„)": tau, + "Displacement": displacement, + "Sampling": "Cell & Time Aware (30 min interval)", + } + ) + + # Populate the data for Classical Contrastive + for tau in taus: + displacements_contrastive = displacement_contrastive.get(tau, []) + for displacement in displacements_contrastive: + data.append( + { + "Time Shift (Ï„)": tau, + "Displacement": displacement, + "Sampling": "Classical Contrastive (No Tracking)", + } + ) + + # Convert to a DataFrame + df = pd.DataFrame(data) + return df + + +taus = list(displacement_per_tau_aware_umap_cosine.keys()) + +# Prepare the violin plot data +df = prepare_violin_data( + taus, + displacement_per_tau_aware_umap_cosine, + displacement_per_tau_contrastive_umap_cosine, +) + +# %% Create a violin plot using seaborn +plt.figure(figsize=(12, 8)) +sns.violinplot( + x="Time Shift (Ï„)", + y="Displacement", + hue="Sampling", + data=df, + palette="Set2", + scale="width", + bw=0.2, + inner=None, + split=True, + cut=0, +) + +# Add labels and title +plt.xlabel("Time Shift (Ï„)", fontsize=14) +plt.ylabel("Cosine Similarity", fontsize=14) +plt.title("Cosine Similarity Distribution using UMAP Features", fontsize=16) +plt.grid(True, linestyle="--", alpha=0.6) # Lighter grid lines for less distraction +plt.legend(title="Sampling", fontsize=12, title_fontsize=14) + +# plt.ylim(0, 1) + +# Save the violin plot as SVG and PDF +plt.savefig("fixed_plot_cosine_similarity.svg", format="svg") +# plt.savefig('violin_plot_cosine_similarity_umap.pdf', format='pdf') + +# Show the plot +plt.show() + + +# %% +# %% Visualize Displacement Distributions (Example Code) +# Compare displacement distributions for Ï„ = 1 +# plt.figure(figsize=(10, 6)) +# sns.histplot(displacement_per_tau_aware_umap_cosine[1], kde=True, label='UMAP - 30 min Interval', color='blue') +# sns.histplot(displacement_per_tau_contrastive_umap_cosine[1], kde=True, label='UMAP - No Tracking', color='green') +# plt.legend() +# plt.title('Comparison of Displacement Distributions for Ï„ = 1 (UMAP)') +# plt.xlabel('Displacement') +# plt.show() + +# # Compare displacement distributions for the full feature set (same Ï„ = 1) +# plt.figure(figsize=(10, 6)) +# sns.histplot(displacement_per_tau_aware_cosine[1], kde=True, label='Full Features - 30 min Interval', color='red') +# sns.histplot(displacement_per_tau_contrastive_cosine[1], kde=True, label='Full Features - No Tracking', color='orange') +# plt.legend() +# plt.title('Comparison of Displacement Distributions for Ï„ = 1 (Full Features)') +# plt.xlabel('Displacement') +# plt.show() +# # %% diff --git a/applications/contrastive_phenotyping/evaluation/displacement.py b/applications/contrastive_phenotyping/evaluation/displacement.py new file mode 100644 index 00000000..a0d46c28 --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/displacement.py @@ -0,0 +1,118 @@ +# %% +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import plotly.express as px +import seaborn as sns +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +from umap import UMAP +from sklearn.decomposition import PCA +from matplotlib.font_manager import FontProperties + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import dataset_of_tracks, load_annotation +from viscy.representation.evaluation import calculate_normalized_euclidean_distance_cell +from viscy.representation.evaluation import compute_displacement_mean_std_full +from sklearn.metrics.pairwise import cosine_similarity +from collections import defaultdict +from scipy.ndimage import gaussian_filter1d + +# %% paths + +features_path_30_min = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) + +feature_path_no_track = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr") + +features_path_any_time = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr") + +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" +) + +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" +) + +# %% Load embedding datasets for all three sampling +fov_name = '/B/4/6' +track_id = 52 + +embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) +embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) +embedding_dataset_any_time = read_embedding_dataset(features_path_any_time) + +#%% +# Calculate displacement for each sampling +time_points_30_min, cosine_similarities_30_min = calculate_normalized_euclidean_distance_cell(embedding_dataset_30_min, fov_name, track_id) +time_points_no_track, cosine_similarities_no_track = calculate_normalized_euclidean_distance_cell(embedding_dataset_no_track, fov_name, track_id) +time_points_any_time, cosine_similarities_any_time = calculate_normalized_euclidean_distance_cell(embedding_dataset_any_time, fov_name, track_id) + +# %% Plot displacement over time for all three conditions + +plt.figure(figsize=(10, 6)) + +plt.plot(time_points_no_track, cosine_similarities_no_track, marker='o', label='classical contrastive (no tracking)') +plt.plot(time_points_any_time, cosine_similarities_any_time, marker='o', label='cell aware') +plt.plot(time_points_30_min, cosine_similarities_30_min, marker='o', label='cell & time aware (interval 30 min)') + +plt.xlabel("Time Delay (t)", fontsize=10) +plt.ylabel("Normalized Euclidean Distance with First Time Point", fontsize=10) +plt.title("Normalized Euclidean Distance (Features) Over Time for Infected Cell", fontsize=12) + +plt.grid(True) +plt.legend(fontsize=10) + +#plt.savefig('4_euc_dist_full.svg', format='svg') +plt.show() + + +# %% Paths to datasets +features_path_30_min = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr") +feature_path_no_track = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr") + +embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) +embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) + + +# %% +max_tau = 10 + +mean_displacement_30_min_euc, std_displacement_30_min_euc = compute_displacement_mean_std_full(embedding_dataset_30_min, max_tau) +mean_displacement_no_track_euc, std_displacement_no_track_euc = compute_displacement_mean_std_full(embedding_dataset_no_track, max_tau) + +# %% Plot 2: Cosine Displacements +plt.figure(figsize=(10, 6)) + +taus = list(mean_displacement_30_min_euc.keys()) + +mean_values_30_min_euc = list(mean_displacement_30_min_euc.values()) +std_values_30_min_euc = list(std_displacement_30_min_euc.values()) + +plt.plot(taus, mean_values_30_min_euc, marker='o', label='Cell & Time Aware (30 min interval)', color='green') +plt.fill_between(taus, + np.array(mean_values_30_min_euc) - np.array(std_values_30_min_euc), + np.array(mean_values_30_min_euc) + np.array(std_values_30_min_euc), + color='green', alpha=0.3, label='Std Dev (30 min interval)') + +mean_values_no_track_euc = list(mean_displacement_no_track_euc.values()) +std_values_no_track_euc = list(std_displacement_no_track_euc.values()) + +plt.plot(taus, mean_values_no_track_euc, marker='o', label='Classical Contrastive (No Tracking)', color='blue') +plt.fill_between(taus, + np.array(mean_values_no_track_euc) - np.array(std_values_no_track_euc), + np.array(mean_values_no_track_euc) + np.array(std_values_no_track_euc), + color='blue', alpha=0.3, label='Std Dev (No Tracking)') + +plt.xlabel('Time Shift (Ï„)') +plt.ylabel('Euclidean Distance') +plt.title('Embedding Displacement Over Time (Features)') + +plt.grid(True) +plt.legend() + +plt.show() diff --git a/applications/contrastive_phenotyping/evaluation/log_regresssion_training.py b/applications/contrastive_phenotyping/evaluation/log_regresssion_training.py new file mode 100644 index 00000000..0bb7a4b3 --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/log_regresssion_training.py @@ -0,0 +1,111 @@ + +# %% +from pathlib import Path + + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import plotly.express as px +import seaborn as sns +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +from umap import UMAP +from sklearn.decomposition import PCA + + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import dataset_of_tracks, load_annotation + + +# %% Paths and parameters. + + +features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" +) + + +# %% +embedding_dataset = read_embedding_dataset(features_path) +embedding_dataset + +# %% +# Compute UMAP over all features +features = embedding_dataset["features"] +# or select a well: +# features = features[features["fov_name"].str.contains("B/4")] + +# %% OVERLAY INFECTION ANNOTATION +ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" +) + + +infection = load_annotation( + features, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +# %% plot the umap + +infection_npy = infection.cat.codes.values + +# Filter out the background class +infection_npy_filtered = infection_npy[infection_npy != 0] + +feature_npy = features.values +feature_npy_filtered = feature_npy[infection_npy != 0] + +# %% combine the umap, pca and infection annotation in one dataframe + +data = pd.DataFrame({"infection": infection_npy_filtered}) + +# add time and well info into dataframe +time_npy = features["t"].values +time_npy_filtered = time_npy[infection_npy != 0] +data["time"] = time_npy_filtered + +fov_name_list = features["fov_name"].values +fov_name_list_filtered = fov_name_list[infection_npy != 0] +data["fov_name"] = fov_name_list_filtered + +# Add all 768 features to the dataframe +for i in range(768): + data[f"feature_{i+1}"] = feature_npy_filtered[:, i] + +# %% manually split the dataset into training and testing set by well name + +# dataframe for training set, fov names starts with "/B/4/6" or "/B/4/7" or "/A/3/" +data_train_val = data[data["fov_name"].str.contains("/B/4/6") | data["fov_name"].str.contains("/B/4/7") | data["fov_name"].str.contains("/A/3/")] + +# dataframe for testing set, fov names starts with "/B/4/8" or "/B/4/9" or "/A/4/" +data_test = data[data["fov_name"].str.contains("/B/4/8") | data["fov_name"].str.contains("/B/4/9") | data["fov_name"].str.contains("/B/3/")] + +# %% train a linear classifier to predict infection state from PCA components + +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import train_test_split +from sklearn.metrics import classification_report + +x_train = data_train_val.drop(columns=["infection", "fov_name", "time"]) +y_train = data_train_val["infection"] + +# train a logistic regression model +clf = LogisticRegression(random_state=0).fit(x_train, y_train) + +x_test = data_test.drop(columns=["infection", "fov_name", "time"]) +y_test = data_test["infection"] + +# predict the infection state for the testing set +y_pred = clf.predict(x_test) + +# %% diff --git a/applications/contrastive_phenotyping/evaluation/pca_umap_embeddings_time.py b/applications/contrastive_phenotyping/evaluation/pca_umap_embeddings_time.py new file mode 100644 index 00000000..5f59da3e --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/pca_umap_embeddings_time.py @@ -0,0 +1,220 @@ +# %% +from pathlib import Path + +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +from umap import UMAP + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import load_annotation + +# %% Paths and parameters. + + +features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" +) + + +# %% +embedding_dataset = read_embedding_dataset(features_path) +embedding_dataset + + +# %% +# Compute UMAP over all features +features = embedding_dataset["features"] +# or select a well: +# features = features[features["fov_name"].str.contains("B/4")] + + +scaled_features = StandardScaler().fit_transform(features.values) +umap = UMAP() +# Fit UMAP on all features +embedding = umap.fit_transform(scaled_features) + + +# %% +# Add UMAP coordinates to the dataset and plot w/ time + + +features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +features + + +sns.scatterplot( + x=features["UMAP1"], y=features["UMAP2"], hue=features["t"], s=7, alpha=0.8 +) + + +# Add the title to the plot +plt.title("Cell & Time Aware Sampling (30 min interval)") +plt.xlim(-10, 20) +plt.ylim(-10, 20) +# plt.savefig('umap_cell_time_aware_time.svg', format='svg') +plt.savefig("updated_cell_time_aware_time.png", format="png") +# Show the plot +plt.show() + + +# %% + + +any_features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr" +) +embedding_dataset = read_embedding_dataset(any_features_path) +embedding_dataset + + +# %% +# Compute UMAP over all features +features = embedding_dataset["features"] +# or select a well: +# features = features[features["fov_name"].str.contains("B/4")] + + +scaled_features = StandardScaler().fit_transform(features.values) +umap = UMAP() +# Fit UMAP on all features +embedding = umap.fit_transform(scaled_features) + + +# %% Any time sampling plot + + +features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +features + + +sns.scatterplot( + x=features["UMAP1"], y=features["UMAP2"], hue=features["t"], s=7, alpha=0.8 +) + + +# Add the title to the plot +plt.title("Cell Aware Sampling") + +plt.xlim(-10, 20) +plt.ylim(-10, 20) + +plt.savefig("1_updated_cell_aware_time.png", format="png") +# plt.savefig('umap_cell_aware_time.pdf', format='pdf') +# Show the plot +plt.show() + + +# %% + + +contrastive_learning_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) +embedding_dataset = read_embedding_dataset(contrastive_learning_path) +embedding_dataset + + +# %% +# Compute UMAP over all features +features = embedding_dataset["features"] +# or select a well: +# features = features[features["fov_name"].str.contains("B/4")] + + +scaled_features = StandardScaler().fit_transform(features.values) +umap = UMAP() +# Fit UMAP on all features +embedding = umap.fit_transform(scaled_features) + + +# %% Any time sampling plot + + +features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +features + +sns.scatterplot( + x=features["UMAP1"], y=features["UMAP2"], hue=features["t"], s=7, alpha=0.8 +) + +# Add the title to the plot +plt.title("Classical Contrastive Learning Sampling") +plt.xlim(-10, 20) +plt.ylim(-10, 20) +plt.savefig("updated_classical_time.png", format="png") +# plt.savefig('classical_time.pdf', format='pdf') + +# Show the plot +plt.show() + + +# %% PCA + + +pca = PCA(n_components=4) +# scaled_features = StandardScaler().fit_transform(features.values) +# pca_features = pca.fit_transform(scaled_features) +pca_features = pca.fit_transform(features.values) + + +features = ( + features.assign_coords(PCA1=("sample", pca_features[:, 0])) + .assign_coords(PCA2=("sample", pca_features[:, 1])) + .assign_coords(PCA3=("sample", pca_features[:, 2])) + .assign_coords(PCA4=("sample", pca_features[:, 3])) + .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4"], append=True) +) + + +# %% plot PCA components w/ time + + +plt.figure(figsize=(10, 10)) +sns.scatterplot( + x=features["PCA1"], y=features["PCA2"], hue=features["t"], s=7, alpha=0.8 +) + + +# %% OVERLAY INFECTION ANNOTATION +ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" +) + + +infection = load_annotation( + features, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + + +# %% +sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=infection, s=7, alpha=0.8) + + +# %% plot PCA components with infection hue +sns.scatterplot(x=features["PCA1"], y=features["PCA2"], hue=infection, s=7, alpha=0.8) + + +# %% diff --git a/applications/contrastive_phenotyping/figures/figure_cell_infection.py b/applications/contrastive_phenotyping/figures/figure_cell_infection.py new file mode 100644 index 00000000..b14ae3ae --- /dev/null +++ b/applications/contrastive_phenotyping/figures/figure_cell_infection.py @@ -0,0 +1,652 @@ +# %% +from pathlib import Path +import sys + +sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import plotly.express as px +import seaborn as sns +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +from umap import UMAP +from sklearn.decomposition import PCA + + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import load_annotation + + +# %% Paths and parameters. + + +features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" +) + + +# %% +embedding_dataset = read_embedding_dataset(features_path) +embedding_dataset + +# %% +# Compute UMAP over all features +features = embedding_dataset["features"] +# or select a well: +# features = features[features["fov_name"].str.contains("B/4")] + + +scaled_features = StandardScaler().fit_transform(features.values) +umap = UMAP() +# Fit UMAP on all features +embedding = umap.fit_transform(scaled_features) + +features = ( + features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +features + +pca = PCA(n_components=4) +# scaled_features = StandardScaler().fit_transform(features.values) +# pca_features = pca.fit_transform(scaled_features) +pca_features = pca.fit_transform(features.values) + + +features = ( + features.assign_coords(PCA1=("sample", pca_features[:, 0])) + .assign_coords(PCA2=("sample", pca_features[:, 1])) + .assign_coords(PCA3=("sample", pca_features[:, 2])) + .assign_coords(PCA4=("sample", pca_features[:, 3])) + .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4"], append=True) +) + +# %% OVERLAY INFECTION ANNOTATION +ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" +) + +infection = load_annotation( + features, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +# %% plot the umap + +# remove the rows in umap and annotation for background class +# Convert UMAP coordinates to a DataFrame +umap_npy = embedding.copy() +infection_npy = infection.cat.codes.values + +# Filter out the background class +umap_npy_filtered = umap_npy[infection_npy != 0] +infection_npy_filtered = infection_npy[infection_npy != 0] + +feature_npy = features.values +feature_npy_filtered = feature_npy[infection_npy != 0] + +sns.scatterplot( + x=umap_npy_filtered[:, 0], + y=umap_npy_filtered[:, 1], + hue=infection_npy_filtered, + palette={1: "steelblue", 2: "orangered"}, + hue_order=[1, 2], + s=7, + alpha=0.8, +) +plt.legend([], [], frameon=False) +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/umap_infection.png", + format="png", + dpi=300, +) + +# %% plot PCA components with infection hue + +pca_npy = pca_features.copy() +pca_npy_filtered = pca_npy[infection_npy != 0] + +sns.scatterplot( + x=pca_npy_filtered[:, 0], + y=pca_npy_filtered[:, 1], + hue=infection_npy_filtered, + palette={1: "steelblue", 2: "orangered"}, + hue_order=[1, 2], + s=7, + alpha=0.8, +) +plt.legend([], [], frameon=False) +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/pca_infection.png", + format="png", + dpi=300, +) + +# %% combine the umap, pca and infection annotation in one dataframe + +data = pd.DataFrame( + { + "UMAP1": umap_npy_filtered[:, 0], + "UMAP2": umap_npy_filtered[:, 1], + "PCA1": pca_npy_filtered[:, 0], + "PCA2": pca_npy_filtered[:, 1], + "PCA3": pca_npy_filtered[:, 2], + "PCA4": pca_npy_filtered[:, 3], + "infection": infection_npy_filtered, + } +) + +# add time and well info into dataframe +time_npy = features["t"].values +time_npy_filtered = time_npy[infection_npy != 0] +data["time"] = time_npy_filtered + +fov_name_list = features["fov_name"].values +fov_name_list_filtered = fov_name_list[infection_npy != 0] +data["fov_name"] = fov_name_list_filtered + +# Add all 768 features to the dataframe +for i in range(768): + data[f"feature_{i+1}"] = feature_npy_filtered[:, i] + +# %% manually split the dataset into training and testing set by well name + +# dataframe for training set, fov names starts with "/B/4/6" or "/B/4/7" or "/A/3/" +data_train_val = data[ + data["fov_name"].str.contains("/B/4/6") + | data["fov_name"].str.contains("/B/4/7") + | data["fov_name"].str.contains("/A/3/") +] + +# dataframe for testing set, fov names starts with "/B/4/8" or "/B/4/9" or "/A/4/" +data_test = data[ + data["fov_name"].str.contains("/B/4/8") + | data["fov_name"].str.contains("/B/4/9") + | data["fov_name"].str.contains("/B/3/") +] + +# %% train a linear classifier to predict infection state from PCA components + +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import train_test_split +from sklearn.metrics import classification_report + +x_train = data_train_val.drop( + columns=[ + "infection", + "fov_name", + "time", + "UMAP1", + "UMAP2", + "PCA1", + "PCA2", + "PCA3", + "PCA4", + ] +) +y_train = data_train_val["infection"] + +# train a logistic regression model +clf = LogisticRegression(random_state=0).fit(x_train, y_train) + +x_test = data_test.drop( + columns=[ + "infection", + "fov_name", + "time", + "UMAP1", + "UMAP2", + "PCA1", + "PCA2", + "PCA3", + "PCA4", + ] +) +y_test = data_test["infection"] + +# predict the infection state for the testing set +y_pred = clf.predict(x_test) + +# %% construct confusion matrix to compare the true and predicted infection state + +from sklearn.metrics import confusion_matrix +import seaborn as sns + +cm = confusion_matrix(y_test, y_pred) +cm_percentage = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] * 100 +sns.heatmap(cm_percentage, annot=True, fmt=".2f", cmap="viridis") +plt.xlabel("Predicted") +plt.ylabel("True") +plt.title("Confusion Matrix (Percentage)") +plt.xticks(ticks=[0.5, 1.5], labels=["uninfected", "infected"]) +plt.yticks(ticks=[0.5, 1.5], labels=["uninfected", "infected"]) +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/confusion_matrix.svg", + format="svg", +) + +# %% use the trained classifier to perform prediction on the entire dataset + +data_test["predicted_infection"] = y_pred + +# plot the predicted infection state over time for /B/3 well and /B/4 well +time_points_test = np.unique(data_test["time"]) + +infected_test_cntrl = [] +infected_test_infected = [] + +for time in time_points_test: + infected_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/3")) + & (data_test["time"] == time) + & (data_test["predicted_infection"] == 2) + ].shape[0] + total_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/3")) & (data_test["time"] == time) + ].shape[0] + infected_test_cntrl.append(infected_cell * 100 / total_cell) + infected_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/4")) + & (data_test["time"] == time) + & (data_test["predicted_infection"] == 2) + ].shape[0] + total_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/4")) & (data_test["time"] == time) + ].shape[0] + infected_test_infected.append(infected_cell * 100 / total_cell) + + +infected_true_cntrl = [] +infected_true_infected = [] + +for time in time_points_test: + infected_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/3")) + & (data_test["time"] == time) + & (data_test["infection"] == 2) + ].shape[0] + total_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/3")) & (data_test["time"] == time) + ].shape[0] + infected_true_cntrl.append(infected_cell * 100 / total_cell) + infected_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/4")) + & (data_test["time"] == time) + & (data_test["infection"] == 2) + ].shape[0] + total_cell = data_test[ + (data_test["fov_name"].str.startswith("/B/4")) & (data_test["time"] == time) + ].shape[0] + infected_true_infected.append(infected_cell * 100 / total_cell) + + +# %% perform prediction on the june dataset + +# Paths and parameters. +features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/jun_time_interval_1_epoch_178.zarr" +) +data_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/2-register/registered_chunked.zarr" +) +tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.2-tracking/track.zarr" +) + +# %% +embedding_dataset = read_embedding_dataset(features_path) +embedding_dataset + +# %% +june_features = embedding_dataset["features"] + +scaled_features = StandardScaler().fit_transform(june_features.values) +umap = UMAP() +# Fit UMAP on all features +embedding = umap.fit_transform(scaled_features) + +june_features = ( + june_features.assign_coords(UMAP1=("sample", embedding[:, 0])) + .assign_coords(UMAP2=("sample", embedding[:, 1])) + .set_index(sample=["UMAP1", "UMAP2"], append=True) +) +june_features + +pca = PCA(n_components=4) +pca_features = pca.fit_transform(june_features.values) + +# %% + +# sns.scatterplot( +# x=june_features["UMAP1"], +# y=june_features["UMAP2"], +# hue=june_pred, +# palette={1: 'blue', 2: 'red'}, +# hue_order=[1, 2], +# s=7, +# alpha=0.8, +# ) +# plt.legend([], [], frameon=False) +# plt.xlim(0, 15) +# plt.savefig('/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/june_umap_infection.png', format='png', dpi=300) + +# %% plot June and Feb test combined UMAP + +june_umap_npy = embedding.copy() +june_pca_npy = pca_features.copy() +june_data = pd.DataFrame( + { + "UMAP1": june_umap_npy[:, 0], + "UMAP2": june_umap_npy[:, 1], + "PCA1": june_pca_npy[:, 0], + "PCA2": june_pca_npy[:, 1], + "PCA3": june_pca_npy[:, 2], + "PCA4": june_pca_npy[:, 3], + "infection": np.nan, + } +) + +# add time and well info into dataframe +june_data["time"] = june_features["t"].values + +june_data["fov_name"] = june_features["fov_name"].values + +# Add all 768 features to the dataframe +june_features_npy = june_features.values +for i in range(768): + june_data[f"feature_{i+1}"] = june_features_npy[:, i] + +# use one mock and one dengue infecected well only +june_data = june_data[ + june_data["fov_name"].str.contains("/0/6") + | june_data["fov_name"].str.contains("/0/2") +] + +# add the predicted infection state +june_pred = clf.predict( + june_data.drop( + columns=[ + "infection", + "fov_name", + "time", + "UMAP1", + "UMAP2", + "PCA1", + "PCA2", + "PCA3", + "PCA4", + ] + ) +) +june_data["predicted_infection"] = june_pred + +# %% combine the june and feb data + +combined_data = pd.concat([data_test, june_data]) + +# perform the umap analysis again with the 768 features +features = combined_data.drop( + columns=[ + "infection", + "predicted_infection", + "fov_name", + "time", + "UMAP1", + "UMAP2", + "PCA1", + "PCA2", + "PCA3", + "PCA4", + ] +) +scaled_features = StandardScaler().fit_transform(features.values) +umap = UMAP() +# Fit UMAP on all features +embedding = umap.fit_transform(scaled_features) + +# overwrite the umap coordinates on combined data +combined_data["UMAP1"] = embedding[:, 0] +combined_data["UMAP2"] = embedding[:, 1] + +# plot the combined data with 'fov_name' starting with '/A and '/B' hue 'infection' and '/0' hue 'predicted_infection' +Feb_split = combined_data[ + combined_data["fov_name"].str.contains("/A") + | combined_data["fov_name"].str.contains("/B") +] +June_split = combined_data[combined_data["fov_name"].str.contains("/0")] + +sns.scatterplot( + x=June_split["UMAP1"], + y=June_split["UMAP2"], + hue=June_split["predicted_infection"], + palette={1: "blue", 2: "red"}, + hue_order=[1, 2], + s=7, + alpha=0.8, +) +sns.scatterplot( + x=Feb_split["UMAP1"], + y=Feb_split["UMAP2"], + hue=Feb_split["infection"], + palette={1: "steelblue", 2: "orange"}, + hue_order=[1, 2], + s=7, + alpha=0.8, +) +plt.legend([], [], frameon=False) +# plt.savefig('/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/combined_umap_infection.png', format='png', dpi=300) + +# plot the scatterplot hue well name '/A' and '/B' are blue and '/0' are red +combined_data["color"] = combined_data["fov_name"].apply( + lambda x: "brown" if x.startswith("/0") else "green" +) + +sns.scatterplot( + x=combined_data["UMAP1"], + y=combined_data["UMAP2"], + hue="color", + palette={"green": "green", "brown": "brown"}, + data=combined_data, + s=7, + alpha=0.2, # Increased transparency +) +plt.xlim(-5, 5) +plt.ylim(-2, 20) +plt.legend([], [], frameon=False) +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/combined_umap_well.png", + format="png", + dpi=300, +) + +# plot the predicted infection state with combined data +sns.scatterplot( + x=combined_data["UMAP1"], + y=combined_data["UMAP2"], + hue=combined_data["predicted_infection"], + palette={1: "blue", 2: "red"}, + hue_order=[1, 2], + s=7, + alpha=0.8, +) +plt.xlim(-5, 5) +plt.ylim(-2, 20) +plt.legend([], [], frameon=False) +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/combined_umap_predicted_infection.png", + format="png", + dpi=300, +) + +# %% plot % infected over time + +time_points_june = np.unique(June_split["time"]) + +infected_june_cntrl = [] +infected_june_infected = [] + +for time in time_points_june: + infected_june = June_split[ + (June_split["fov_name"].str.startswith("/0/2")) + & (June_split["time"] == time) + & (June_split["predicted_infection"] == 2) + ].shape[0] + total_june = June_split[ + (June_split["fov_name"].str.startswith("/0/2")) & (June_split["time"] == time) + ].shape[0] + infected_june_cntrl.append(infected_june * 100 / total_june) + infected_june = June_split[ + (June_split["fov_name"].str.startswith("/0/6")) + & (June_split["time"] == time) + & (June_split["predicted_infection"] == 2) + ].shape[0] + total_june = June_split[ + (June_split["fov_name"].str.startswith("/0/6")) & (June_split["time"] == time) + ].shape[0] + infected_june_infected.append(infected_june * 100 / total_june) + + +# plot infected percentage over time for both wells +plt.plot( + time_points_test * 0.5 + 3, + infected_true_cntrl, + label="mock true", + color="steelblue", + linestyle="--", +) +plt.plot( + time_points_test * 0.5 + 3, + infected_test_cntrl, + label="mock predicted", + color="blue", + marker="+", +) +plt.plot( + time_points_test * 0.5 + 3, + infected_true_infected, + label="MOI true", + color="orange", + linestyle="--", +) +plt.plot( + time_points_test * 0.5 + 3, + infected_test_infected, + label="MOI predicted", + color="red", + marker="+", +) +plt.plot( + time_points_june * 2 + 3, + infected_june_cntrl, + label="mock new predicted", + color="blue", + marker="o", +) +plt.plot( + time_points_june * 2 + 3, + infected_june_infected, + label="MOI new predicted", + color="red", + marker="o", +) +plt.xlabel("HPI") +plt.ylabel("Infected percentage") +plt.legend() +plt.savefig( + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/infected_percentage_withJune.svg", + format="svg", +) + +# %% appendix video for infection dynamics umap, Feb test data, colored by human revised annotation + +for time in range(48): + plt.clf() + sns.scatterplot( + data=data_test[(data_test["time"] == time)], + x="UMAP1", + y="UMAP2", + hue="infection", + palette={1: "steelblue", 2: "orangered"}, + hue_order=[1, 2], + s=20, + alpha=0.8, + ) + handles, _ = plt.gca().get_legend_handles_labels() + plt.legend(handles=handles, labels=["uninfected", "infected"]) + plt.suptitle(f"Time: {time*0.5+3} HPI") + plt.ylim(-10, 20) + plt.xlim(2, 18) + plt.savefig( + f"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_feb_true_infection_" + + str(time).zfill(3) + + ".png", + format="png", + dpi=300, + ) + +# %% appendix video for infection dynamics umap, Feb test data, colored by predicted infection + +for time in range(48): + plt.clf() + sns.scatterplot( + data=data_test[(data_test["time"] == time)], + x="UMAP1", + y="UMAP2", + hue="predicted_infection", + palette={1: "blue", 2: "red"}, + hue_order=[1, 2], + s=20, + alpha=0.8, + ) + handles, _ = plt.gca().get_legend_handles_labels() + plt.legend(handles=handles, labels=["uninfected", "infected"]) + plt.suptitle(f"Time: {time*0.5+3} HPI") + plt.ylim(-10, 18) + plt.xlim(2, 18) + plt.savefig( + f"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_feb_predicted_infection_" + + str(time).zfill(3) + + ".png", + format="png", + dpi=300, + ) + +# %% appendix video for infection dynamics umap, June data, colored by predicted infection + +for time in range(12): + plt.clf() + sns.scatterplot( + data=June_split[(June_split["time"] == time)], + x="UMAP1", + y="UMAP2", + hue="predicted_infection", + palette={1: "blue", 2: "red"}, + hue_order=[1, 2], + s=20, + alpha=0.8, + ) + handles, _ = plt.gca().get_legend_handles_labels() + plt.legend(handles=handles, labels=["uninfected", "infected"]) + plt.suptitle(f"Time: {time*2+3} HPI") + plt.ylim(-8, 10) + plt.xlim(-5, 5) + plt.savefig( + f"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_june_predicted_infection_" + + str(time).zfill(3) + + ".png", + format="png", + dpi=300, + ) + +# %% diff --git a/applications/infection_classification/Infection_classifier_accuracy.py b/applications/infection_classification/Infection_classifier_accuracy.py new file mode 100644 index 00000000..97958b01 --- /dev/null +++ b/applications/infection_classification/Infection_classifier_accuracy.py @@ -0,0 +1,71 @@ +# %% script to compare the output from the supervised model and human revised annotations to get the accuracy of the model + +import numpy as np +from iohub import open_ome_zarr +from scipy.ndimage import label + +# %% datapaths + +# Path to model output +data_out_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/supervised_test.zarr" + +# Path to the human revised annotations +human_corrected_path = "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred/supervised_test_corrected.zarr" + +# %% Load data and compute the number of objects in each class + +data_out = open_ome_zarr(data_out_path, layout="hcs", mode="r+") +human_corrected = open_ome_zarr(human_corrected_path, layout="hcs", mode="r+") + +out_medians = [] +HC_medians = [] +for well_id, well_data in data_out.wells(): + well_name, well_no = well_id.split("/") + + for pos_name, pos_data in well_data.positions(): + + out_data = pos_data.data.numpy() + T, C, Z, Y, X = out_data.shape + + HC_data = human_corrected[well_id + "/" + pos_name + "/0"] + HC_data = HC_data.numpy() + + # Compute the number of objects in the model output + for t in range(T): + out_img = out_data[t, 0, 0] + + # Compute the number of objects in the model output + out_labeled, num_out_objects = label(out_img > 0) + + # Compute the median of pixel values in each object in the model output + for obj_id in range(1, num_out_objects + 1): + obj_pixels = out_img[out_labeled == obj_id] + out_medians.append(np.median(obj_pixels)) + + # repeat for human acorrected annotations + HC_img = HC_data[t, 0, 0] + HC_labeled, num_HC_objects = label(HC_img > 0) + + for obj_id in range(1, num_HC_objects + 1): + obj_pixels = HC_img[HC_labeled == obj_id] + HC_medians.append(np.median(obj_pixels)) + +# %% Compute the accuracy + +num_twos_in_out_medians = out_medians.count(2) +num_twos_in_HC_medians = HC_medians.count(2) +error_inf = ( + (num_twos_in_HC_medians - num_twos_in_out_medians) / num_twos_in_HC_medians +) * 100 + +num_ones_in_out_medians = out_medians.count(1) +num_ones_in_HC_medians = HC_medians.count(1) +error_uninf = ( + (num_ones_in_HC_medians - num_ones_in_out_medians) / num_ones_in_HC_medians +) * 100 + +avg_error = (np.abs(error_inf) + np.abs(error_uninf)) / 2 + +accuracy = 100 - avg_error + +# %% diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py index f459e8b4..343519d7 100644 --- a/viscy/representation/evaluation.py +++ b/viscy/representation/evaluation.py @@ -1,3 +1,5 @@ +from collections import defaultdict + import numpy as np import pandas as pd import umap @@ -12,6 +14,7 @@ normalized_mutual_info_score, silhouette_score, ) +from sklearn.metrics.pairwise import cosine_similarity from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler @@ -439,3 +442,248 @@ def compute_radial_intensity_gradient(image): ) return radial_intensity_gradient[0] + + +def calculate_cosine_similarity_cell(embedding_dataset, fov_name, track_id): + """Extract embeddings and calculate cosine similarities for a specific cell""" + # Filter the dataset for the specific infected cell + filtered_data = embedding_dataset.where( + (embedding_dataset["fov_name"] == fov_name) + & (embedding_dataset["track_id"] == track_id), + drop=True, + ) + + # Extract the feature embeddings and time points + features = filtered_data["features"].values # (sample, features) + time_points = filtered_data["t"].values # (sample,) + + # Get the first time point's embedding + first_time_point_embedding = features[0].reshape(1, -1) + + # Calculate cosine similarity between each time point and the first time point + cosine_similarities = [] + for i in range(len(time_points)): + similarity = cosine_similarity( + first_time_point_embedding, features[i].reshape(1, -1) + ) + cosine_similarities.append(similarity[0][0]) + + return time_points, cosine_similarities + + +def compute_displacement_mean_std( + embedding_dataset, max_tau=10, use_cosine=False, use_dissimilarity=False +): + """Compute the norm of differences between embeddings at t and t + tau""" + # Get the arrays of (fov_name, track_id, t, and embeddings) + fov_names = embedding_dataset["fov_name"].values + track_ids = embedding_dataset["track_id"].values + timepoints = embedding_dataset["t"].values + embeddings = embedding_dataset["features"].values + + # Dictionary to store displacements for each tau + displacement_per_tau = defaultdict(list) + + # Iterate over all entries in the dataset + for i in range(len(fov_names)): + fov_name = fov_names[i] + track_id = track_ids[i] + current_time = timepoints[i] + current_embedding = embeddings[i] + + # For each time point t, compute displacements for t + tau + for tau in range(1, max_tau + 1): + future_time = current_time + tau + + # Find if future_time exists for the same (fov_name, track_id) + matching_indices = np.where( + (fov_names == fov_name) + & (track_ids == track_id) + & (timepoints == future_time) + )[0] + + if len(matching_indices) == 1: + # Get the embedding at t + tau + future_embedding = embeddings[matching_indices[0]] + + if use_cosine: + # Compute cosine similarity + similarity = cosine_similarity( + current_embedding.reshape(1, -1), + future_embedding.reshape(1, -1), + )[0][0] + # Choose whether to use similarity or dissimilarity + if use_dissimilarity: + displacement = 1 - similarity # Cosine dissimilarity + else: + displacement = similarity # Cosine similarity + else: + # Compute the Euclidean distance, elementwise square on difference + displacement = np.sum((current_embedding - future_embedding) ** 2) + + # Store the displacement for the given tau + displacement_per_tau[tau].append(displacement) + + # Compute mean and std displacement for each tau by averaging the displacements + mean_displacement_per_tau = { + tau: np.mean(displacements) + for tau, displacements in displacement_per_tau.items() + } + std_displacement_per_tau = { + tau: np.std(displacements) + for tau, displacements in displacement_per_tau.items() + } + + return mean_displacement_per_tau, std_displacement_per_tau + + +def compute_displacement( + embedding_dataset, + max_tau=10, + use_cosine=False, + use_dissimilarity=False, + use_umap=False, +): + """Compute the norm of differences between embeddings at t and t + tau""" + # Get the arrays of (fov_name, track_id, t, and embeddings) + fov_names = embedding_dataset["fov_name"].values + track_ids = embedding_dataset["track_id"].values + timepoints = embedding_dataset["t"].values + + if use_umap: + umap1 = embedding_dataset["UMAP1"].values + umap2 = embedding_dataset["UMAP2"].values + embeddings = np.vstack((umap1, umap2)).T + else: + embeddings = embedding_dataset["features"].values + + # Dictionary to store displacements for each tau + displacement_per_tau = defaultdict(list) + + # Iterate over all entries in the dataset + for i in range(len(fov_names)): + fov_name = fov_names[i] + track_id = track_ids[i] + current_time = timepoints[i] + current_embedding = embeddings[i] + + # For each time point t, compute displacements for t + tau + for tau in range(1, max_tau + 1): + future_time = current_time + tau + + # Find if future_time exists for the same (fov_name, track_id) + matching_indices = np.where( + (fov_names == fov_name) + & (track_ids == track_id) + & (timepoints == future_time) + )[0] + + if len(matching_indices) == 1: + # Get the embedding at t + tau + future_embedding = embeddings[matching_indices[0]] + + if use_cosine: + # Compute cosine similarity + similarity = cosine_similarity( + current_embedding.reshape(1, -1), + future_embedding.reshape(1, -1), + )[0][0] + # Choose whether to use similarity or dissimilarity + if use_dissimilarity: + displacement = 1 - similarity # Cosine dissimilarity + else: + displacement = similarity # Cosine similarity + else: + # Compute the Euclidean distance, elementwise square on difference + displacement = np.sum((current_embedding - future_embedding) ** 2) + + # Store the displacement for the given tau + displacement_per_tau[tau].append(displacement) + + return displacement_per_tau + + +def calculate_normalized_euclidean_distance_cell(embedding_dataset, fov_name, track_id): + filtered_data = embedding_dataset.where( + (embedding_dataset["fov_name"] == fov_name) + & (embedding_dataset["track_id"] == track_id), + drop=True, + ) + + features = filtered_data["features"].values # (sample, features) + time_points = filtered_data["t"].values # (sample,) + + normalized_features = features / np.linalg.norm(features, axis=1, keepdims=True) + + # Get the first time point's normalized embedding + first_time_point_embedding = normalized_features[0].reshape(1, -1) + + euclidean_distances = [] + for i in range(len(time_points)): + distance = np.linalg.norm( + first_time_point_embedding - normalized_features[i].reshape(1, -1) + ) + euclidean_distances.append(distance) + + return time_points, euclidean_distances + + +def compute_displacement_mean_std_full(embedding_dataset, max_tau=10): + fov_names = embedding_dataset["fov_name"].values + track_ids = embedding_dataset["track_id"].values + timepoints = embedding_dataset["t"].values + embeddings = embedding_dataset["features"].values + + cell_identifiers = np.array( + list(zip(fov_names, track_ids)), + dtype=[("fov_name", "O"), ("track_id", "int64")], + ) + + unique_cells = np.unique(cell_identifiers) + + displacement_per_tau = defaultdict(list) + + for cell in unique_cells: + fov_name = cell["fov_name"] + track_id = cell["track_id"] + + indices = np.where((fov_names == fov_name) & (track_ids == track_id))[0] + + cell_timepoints = timepoints[indices] + cell_embeddings = embeddings[indices] + + sorted_indices = np.argsort(cell_timepoints) + cell_timepoints = cell_timepoints[sorted_indices] + cell_embeddings = cell_embeddings[sorted_indices] + + for i in range(len(cell_timepoints)): + current_time = cell_timepoints[i] + current_embedding = cell_embeddings[i] + + current_embedding = current_embedding / np.linalg.norm(current_embedding) + + for tau in range(0, max_tau + 1): + future_time = current_time + tau + + future_index = np.where(cell_timepoints == future_time)[0] + + if len(future_index) >= 1: + future_embedding = cell_embeddings[future_index[0]] + future_embedding = future_embedding / np.linalg.norm( + future_embedding + ) + + distance = np.linalg.norm(current_embedding - future_embedding) + + displacement_per_tau[tau].append(distance) + + mean_displacement_per_tau = { + tau: np.mean(displacements) + for tau, displacements in displacement_per_tau.items() + } + std_displacement_per_tau = { + tau: np.std(displacements) + for tau, displacements in displacement_per_tau.items() + } + + return mean_displacement_per_tau, std_displacement_per_tau From 0d6a473964041710f587d54cecbb0de4ba5da1c5 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Tue, 8 Oct 2024 14:49:23 -0700 Subject: [PATCH 81/87] GMM clustering and fixed compute pca --- .../evaluation/GMM_clustering.py | 154 ++++++++++++++++++ viscy/representation/evaluation.py | 101 +++++++++--- 2 files changed, 232 insertions(+), 23 deletions(-) create mode 100644 applications/contrastive_phenotyping/evaluation/GMM_clustering.py diff --git a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py new file mode 100644 index 00000000..dd34e30d --- /dev/null +++ b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py @@ -0,0 +1,154 @@ +# %% import statements +from pathlib import Path +import pandas as pd +from viscy.representation.embedding_writer import read_embedding_dataset +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from viscy.representation.evaluation import load_annotation +from sklearn.mixture import GaussianMixture +from sklearn.metrics import confusion_matrix +from sklearn.decomposition import PCA +from viscy.representation.evaluation import GMMClustering +from viscy.representation.evaluation import compute_pca +# %% Paths and parameters. + +features_path_30_min = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) + + +feature_path_no_track = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) + + +features_path_any_time = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr" +) + +features_path_june = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/jun_time_interval_1_epoch_178.zarr") + + +# %% visualize distribution of embeddings +feb_embedding_dataset = read_embedding_dataset(features_path_30_min) +features_data = feb_embedding_dataset['features'] +n_samples, n_features = features_data.shape + +random_dimensions = np.random.choice(n_features, 5, replace=False) + +plt.figure(figsize=(15, 10)) +for i, dim in enumerate(random_dimensions, 1): + plt.subplot(2, 3, i) + sns.histplot(features_data[:, dim], kde=True) + plt.title(f"Dimension {dim} Distribution") + +plt.tight_layout() +plt.show() + +# %% initialize GMM clustering and ground truth labels + +feb_embedding_dataset = read_embedding_dataset(features_path_30_min) +features_data = feb_embedding_dataset['features'] + +ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" +) + +infection = load_annotation( + features_data, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + +cluster_evaluator = GMMClustering(features_data, infection) + +# %% Find best n_clusters + +aic_scores, bic_scores = cluster_evaluator.find_best_n_clusters() + +plt.figure(figsize=(8, 6)) +plt.plot(cluster_evaluator.n_clusters_range, aic_scores, label='AIC', marker='o') +plt.plot(cluster_evaluator.n_clusters_range, bic_scores, label='BIC', marker='o') +plt.xlabel('Number of clusters') +plt.ylabel('AIC / BIC Score') +plt.title('AIC and BIC Scores for Different Numbers of Clusters') +plt.legend() +plt.show() + +# %% +# Choose the best model (with the lowest BIC score) +best_gmm = cluster_evaluator.fit_best_model(criterion='bic', n_clusters=2) +cluster_labels = cluster_evaluator.predict_clusters() + +# %% the confusion matrix with ground truth states +ground_truth_labels_numeric = infection.cat.codes + +cm = confusion_matrix(ground_truth_labels_numeric, cluster_labels) + +cm_df = pd.DataFrame(cm, index=["Background", "Uninfected", "Infected"], + columns=["Cluster 0", "Cluster 1", "Cluster 2"]) + +plt.figure(figsize=(8, 6)) +sns.heatmap(cm_df, annot=True, fmt='g', cmap='Blues') + +plt.title('Confusion Matrix: Clusters vs Ground Truth') +plt.ylabel('Ground Truth Labels') +plt.xlabel('Cluster Labels') +plt.show() + +# %% +# Reduce dimensions to 2 for vis +_, _, pca_df = compute_pca(feb_embedding_dataset, n_components=2) + +pca1 = pca_df["PCA1"] +pca2 = pca_df["PCA2"] + +color_map = {'background': 'gray', 'uninfected': 'blue', 'infected': 'red'} +colors = infection.map(color_map) + +plt.figure(figsize=(10, 8)) + +# Plot Cluster 0 with circle markers ('o') +plt.scatter(pca1[cluster_labels == 0], pca2[cluster_labels == 0], + c=colors[cluster_labels == 0], edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (circle)', marker='o') + +# Plot Cluster 1 with X markers ('x') +plt.scatter(pca1[cluster_labels == 1], pca2[cluster_labels == 1], + c=colors[cluster_labels == 1], edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (X)', marker='x') + +plt.xlabel('PCA 1') +plt.ylabel('PCA 2') +plt.title(f"Ground Truth Colors with GMM Cluster Marker Types") + +handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, + markerfacecolor=color_map[label], markersize=10, markeredgecolor='black') + for label in color_map.keys()] +plt.legend(handles=handles, title="Ground Truth") + +plt.show() + +# %% Visualize GMM Clusters in PCA space (without ground truth) +_, _, pca_df = compute_pca(feb_embedding_dataset, n_components=2) + +pca1 = pca_df["PCA1"] +pca2 = pca_df["PCA2"] + +plt.figure(figsize=(10, 8)) + +# Plot Cluster 0 with circle markers ('o') +plt.scatter(pca1[cluster_labels == 0], pca2[cluster_labels == 0], + c='green', edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (GMM)', marker='o') + +# Plot Cluster 1 with X markers ('x') +plt.scatter(pca1[cluster_labels == 1], pca2[cluster_labels == 1], + c='orange', edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (GMM)', marker='x') + +plt.xlabel('PCA 1') +plt.ylabel('PCA 2') +plt.title(f"GMM Clusters") + +plt.legend() +plt.show() +# %% diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py index 343519d7..c36df06c 100644 --- a/viscy/representation/evaluation.py +++ b/viscy/representation/evaluation.py @@ -17,7 +17,7 @@ from sklearn.metrics.pairwise import cosine_similarity from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler - +from sklearn.mixture import GaussianMixture from viscy.data.triplet import TripletDataModule """ @@ -128,8 +128,71 @@ def dataset_of_tracks( return prediction_dataset -"""Methods for evaluating clustering performance.""" +"""Clustering algortihms.""" + +class GMMClustering: + def __init__(self, features_data, infection_data, n_clusters_range=np.arange(2, 10)): + self.features_data = features_data + self.infection_data = infection_data + self.n_clusters_range = n_clusters_range + self.best_n_clusters = None + self.best_gmm = None + self.aic_scores = None + self.bic_scores = None + + def find_best_n_clusters(self): + """Find the best number of clusters using AIC/BIC scores.""" + aic_scores = [] + bic_scores = [] + for n in self.n_clusters_range: + gmm = GaussianMixture(n_components=n, random_state=42) + gmm.fit(self.features_data) + aic_scores.append(gmm.aic(self.features_data)) + bic_scores.append(gmm.bic(self.features_data)) + self.aic_scores = aic_scores + self.bic_scores = bic_scores + + return aic_scores, bic_scores + + def fit_best_model(self, criterion='bic', n_clusters=None): + """ + Fit the best GMM model based on AIC or BIC scores, or a user-specified number of clusters. + + Parameters: + - criterion: 'aic' or 'bic' to select the best model based on the chosen criterion. + - n_clusters: Specify a fixed number of clusters (overrides the 'best' search). + """ + # Case 1: If the user provides n_clusters, use it directly + if n_clusters is not None: + self.best_n_clusters = n_clusters + + # Case 2: If no n_clusters is provided but find_best_n_clusters was run, use stored AIC/BIC results + elif self.aic_scores is not None and self.bic_scores is not None: + if criterion == 'bic': + self.best_n_clusters = self.n_clusters_range[np.argmin(self.bic_scores)] + else: + self.best_n_clusters = self.n_clusters_range[np.argmin(self.aic_scores)] + + # Case 3: If find_best_n_clusters hasn't been run, compute AIC/BIC scores now + else: + aic_scores, bic_scores = self.find_best_n_clusters() + if criterion == 'bic': + self.best_n_clusters = self.n_clusters_range[np.argmin(bic_scores)] + else: + self.best_n_clusters = self.n_clusters_range[np.argmin(aic_scores)] + + self.best_gmm = GaussianMixture(n_components=self.best_n_clusters, random_state=42) + self.best_gmm.fit(self.features_data) + + return self.best_gmm + + def predict_clusters(self): + """Run prediction on the fitted best GMM model.""" + if self.best_gmm is None: + raise Exception("No GMM model is fitted yet. Please run fit_best_model() first.") + cluster_labels = self.best_gmm.predict(self.features_data) + return cluster_labels def knn_accuracy(embeddings, annotations, k=5): """ @@ -199,7 +262,7 @@ def clustering_evaluation(embeddings, annotations, method="nmi"): return score -def compute_pca(embedding_dataset, n_components=None, normalize_features=True): +def compute_pca(embedding_dataset, n_components=None, normalize_features=False): features = embedding_dataset["features"] projections = embedding_dataset["projections"] @@ -210,35 +273,26 @@ def compute_pca(embedding_dataset, n_components=None, normalize_features=True): scaled_projections = projections.values scaled_features = features.values - # Compute PCA with specified number of components PCA_features = PCA(n_components=n_components, random_state=42) PCA_projection = PCA(n_components=n_components, random_state=42) pc_features = PCA_features.fit_transform(scaled_features) pc_projection = PCA_projection.fit_transform(scaled_projections) - # Prepare DataFrame with id and PCA coordinates - pca_df = pd.DataFrame( - { - "id": embedding_dataset["id"].values, - "fov_name": embedding_dataset["fov_name"].values, - "PCA1": pc_features[:, 0], - "PCA2": pc_features[:, 1], - "PCA3": pc_features[:, 2], - "PCA4": pc_features[:, 3], - "PCA5": pc_features[:, 4], - "PCA6": pc_features[:, 5], - "PCA1_proj": pc_projection[:, 0], - "PCA2_proj": pc_projection[:, 1], - "PCA3_proj": pc_projection[:, 2], - "PCA4_proj": pc_projection[:, 3], - "PCA5_proj": pc_projection[:, 4], - "PCA6_proj": pc_projection[:, 5], - } - ) + pca_df_dict = { + "id": embedding_dataset["id"].values, + "fov_name": embedding_dataset["fov_name"].values, + } + + for i in range(n_components): + pca_df_dict[f"PCA{i + 1}"] = pc_features[:, i] + pca_df_dict[f"PCA{i + 1}_proj"] = pc_projection[:, i] + + pca_df = pd.DataFrame(pca_df_dict) return PCA_features, PCA_projection, pca_df + def compute_umap(embedding_dataset, normalize_features=True): features = embedding_dataset["features"] projections = embedding_dataset["projections"] @@ -687,3 +741,4 @@ def compute_displacement_mean_std_full(embedding_dataset, max_tau=10): } return mean_displacement_per_tau, std_displacement_per_tau + From 8e4a028ad3aa531e878bd55003a0548ab9146253 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Tue, 8 Oct 2024 14:55:52 -0700 Subject: [PATCH 82/87] format issues fixed --- viscy/representation/evaluation.py | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py index c36df06c..a6bbe5f6 100644 --- a/viscy/representation/evaluation.py +++ b/viscy/representation/evaluation.py @@ -15,9 +15,10 @@ silhouette_score, ) from sklearn.metrics.pairwise import cosine_similarity +from sklearn.mixture import GaussianMixture from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler -from sklearn.mixture import GaussianMixture + from viscy.data.triplet import TripletDataModule """ @@ -130,8 +131,11 @@ def dataset_of_tracks( """Clustering algortihms.""" + class GMMClustering: - def __init__(self, features_data, infection_data, n_clusters_range=np.arange(2, 10)): + def __init__( + self, features_data, infection_data, n_clusters_range=np.arange(2, 10) + ): self.features_data = features_data self.infection_data = infection_data self.n_clusters_range = n_clusters_range @@ -155,10 +159,10 @@ def find_best_n_clusters(self): return aic_scores, bic_scores - def fit_best_model(self, criterion='bic', n_clusters=None): + def fit_best_model(self, criterion="bic", n_clusters=None): """ Fit the best GMM model based on AIC or BIC scores, or a user-specified number of clusters. - + Parameters: - criterion: 'aic' or 'bic' to select the best model based on the chosen criterion. - n_clusters: Specify a fixed number of clusters (overrides the 'best' search). @@ -169,7 +173,7 @@ def fit_best_model(self, criterion='bic', n_clusters=None): # Case 2: If no n_clusters is provided but find_best_n_clusters was run, use stored AIC/BIC results elif self.aic_scores is not None and self.bic_scores is not None: - if criterion == 'bic': + if criterion == "bic": self.best_n_clusters = self.n_clusters_range[np.argmin(self.bic_scores)] else: self.best_n_clusters = self.n_clusters_range[np.argmin(self.aic_scores)] @@ -177,12 +181,14 @@ def fit_best_model(self, criterion='bic', n_clusters=None): # Case 3: If find_best_n_clusters hasn't been run, compute AIC/BIC scores now else: aic_scores, bic_scores = self.find_best_n_clusters() - if criterion == 'bic': + if criterion == "bic": self.best_n_clusters = self.n_clusters_range[np.argmin(bic_scores)] else: self.best_n_clusters = self.n_clusters_range[np.argmin(aic_scores)] - self.best_gmm = GaussianMixture(n_components=self.best_n_clusters, random_state=42) + self.best_gmm = GaussianMixture( + n_components=self.best_n_clusters, random_state=42 + ) self.best_gmm.fit(self.features_data) return self.best_gmm @@ -190,10 +196,13 @@ def fit_best_model(self, criterion='bic', n_clusters=None): def predict_clusters(self): """Run prediction on the fitted best GMM model.""" if self.best_gmm is None: - raise Exception("No GMM model is fitted yet. Please run fit_best_model() first.") + raise Exception( + "No GMM model is fitted yet. Please run fit_best_model() first." + ) cluster_labels = self.best_gmm.predict(self.features_data) return cluster_labels + def knn_accuracy(embeddings, annotations, k=5): """ Evaluate the k-NN classification accuracy. @@ -292,7 +301,6 @@ def compute_pca(embedding_dataset, n_components=None, normalize_features=False): return PCA_features, PCA_projection, pca_df - def compute_umap(embedding_dataset, normalize_features=True): features = embedding_dataset["features"] projections = embedding_dataset["projections"] @@ -741,4 +749,3 @@ def compute_displacement_mean_std_full(embedding_dataset, max_tau=10): } return mean_displacement_per_tau, std_displacement_per_tau - From 1643d669560bd66612c59aa57412d24ea24dd368 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Fri, 11 Oct 2024 12:40:43 -0700 Subject: [PATCH 83/87] umap weights and labels --- .../evaluation/GMM_clustering.py | 105 ++++++++++++++---- viscy/representation/evaluation.py | 5 +- 2 files changed, 87 insertions(+), 23 deletions(-) diff --git a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py index dd34e30d..11931f25 100644 --- a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py +++ b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py @@ -11,6 +11,7 @@ from sklearn.decomposition import PCA from viscy.representation.evaluation import GMMClustering from viscy.representation.evaluation import compute_pca +from viscy.representation.evaluation import compute_umap # %% Paths and parameters. features_path_30_min = Path( @@ -31,8 +32,8 @@ # %% visualize distribution of embeddings -feb_embedding_dataset = read_embedding_dataset(features_path_30_min) -features_data = feb_embedding_dataset['features'] +embedding_dataset = read_embedding_dataset(features_path_30_min) +features_data = embedding_dataset['features'] n_samples, n_features = features_data.shape random_dimensions = np.random.choice(n_features, 5, replace=False) @@ -48,21 +49,10 @@ # %% initialize GMM clustering and ground truth labels -feb_embedding_dataset = read_embedding_dataset(features_path_30_min) -features_data = feb_embedding_dataset['features'] +embedding_dataset = read_embedding_dataset(features_path_june) +features_data = embedding_dataset['features'] -ann_root = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" -) - -infection = load_annotation( - features_data, - ann_root / "extracted_inf_state.csv", - "infection_state", - {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, -) - -cluster_evaluator = GMMClustering(features_data, infection) +cluster_evaluator = GMMClustering(features_data) # %% Find best n_clusters @@ -79,10 +69,23 @@ # %% # Choose the best model (with the lowest BIC score) -best_gmm = cluster_evaluator.fit_best_model(criterion='bic', n_clusters=2) +best_gmm = cluster_evaluator.fit_best_model(criterion='bic') cluster_labels = cluster_evaluator.predict_clusters() +# %% ground truth labels (if available!) +ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" +) + +infection = load_annotation( + features_data, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) + # %% the confusion matrix with ground truth states + ground_truth_labels_numeric = infection.cat.codes cm = confusion_matrix(ground_truth_labels_numeric, cluster_labels) @@ -100,7 +103,7 @@ # %% # Reduce dimensions to 2 for vis -_, _, pca_df = compute_pca(feb_embedding_dataset, n_components=2) +_, _, pca_df = compute_pca(embedding_dataset, n_components=2) pca1 = pca_df["PCA1"] pca2 = pca_df["PCA2"] @@ -130,7 +133,7 @@ plt.show() # %% Visualize GMM Clusters in PCA space (without ground truth) -_, _, pca_df = compute_pca(feb_embedding_dataset, n_components=2) +_, _, pca_df = compute_pca(embedding_dataset, n_components=2) pca1 = pca_df["PCA1"] pca2 = pca_df["PCA2"] @@ -151,4 +154,68 @@ plt.legend() plt.show() + + +# %% Visualize UMAP embeddings colored by GMM cluster weights +umap_features, umap_projection, umap_df = compute_umap(embedding_dataset) + +gmm_weights = best_gmm.weights_ + +plt.figure(figsize=(10, 8)) +plt.scatter(umap_df["UMAP1"], umap_df["UMAP2"], c=gmm_weights[cluster_labels], cmap='viridis', s=50, alpha=0.8, edgecolor='k') +plt.colorbar(label='GMM Cluster Weights') +plt.title('UMAP Embeddings Colored by GMM Cluster Weights') +plt.xlabel('UMAP 1') +plt.ylabel('UMAP 2') +plt.show() + + +# %% Visualize UMAP embeddings colored by cluster labels +umap_features, umap_projection, umap_df = compute_umap(embedding_dataset) + +plt.figure(figsize=(10, 8)) + +plt.scatter(umap_df["UMAP1"][cluster_labels == 0], umap_df["UMAP2"][cluster_labels == 0], + c='green', edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (GMM)', marker='o') + +plt.scatter(umap_df["UMAP1"][cluster_labels == 1], umap_df["UMAP2"][cluster_labels == 1], + c='orange', edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (GMM)', marker='o') + +plt.xlabel('UMAP 1') +plt.ylabel('UMAP 2') +plt.title(f"GMM Clusters in UMAP Space") + +plt.legend() +plt.show() + +# %% UMAP vis (w/ ground truth colors and GMM cluster markers) +umap_features, umap_projection, umap_df = compute_umap(embedding_dataset, normalize_features=True) + +umap1 = umap_df["UMAP1"] +umap2 = umap_df["UMAP2"] + +color_map = {'background': 'gray', 'uninfected': 'blue', 'infected': 'red'} +colors = infection.map(color_map) + +plt.figure(figsize=(10, 8)) + +# Plot Cluster 0 with circle markers ('o') +plt.scatter(umap1[cluster_labels == 0], umap2[cluster_labels == 0], + c=colors[cluster_labels == 0], edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (circle)', marker='o') + +# Plot Cluster 1 with X markers ('x') +plt.scatter(umap1[cluster_labels == 1], umap2[cluster_labels == 1], + c=colors[cluster_labels == 1], edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (X)', marker='x') + +plt.xlabel('UMAP 1') +plt.ylabel('UMAP 2') +plt.title(f"Ground Truth Colors with GMM Cluster Marker Types in UMAP Space") + +handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, + markerfacecolor=color_map[label], markersize=10, markeredgecolor='black') + for label in color_map.keys()] +plt.legend(handles=handles, title="Ground Truth") + +plt.show() + # %% diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py index a6bbe5f6..a2b2322a 100644 --- a/viscy/representation/evaluation.py +++ b/viscy/representation/evaluation.py @@ -133,11 +133,8 @@ def dataset_of_tracks( class GMMClustering: - def __init__( - self, features_data, infection_data, n_clusters_range=np.arange(2, 10) - ): + def __init__(self, features_data, n_clusters_range=np.arange(2, 10)): self.features_data = features_data - self.infection_data = infection_data self.n_clusters_range = n_clusters_range self.best_n_clusters = None self.best_gmm = None From dc101cecb8fa2acf1f801d8583558c2b2077a16b Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Wed, 16 Oct 2024 12:54:16 -0700 Subject: [PATCH 84/87] Ruff and black formatted --- .../evaluation/GMM_clustering.py | 254 ++++++++++++------ .../evaluation/PC_vs_CF.py | 17 +- .../evaluation/PC_vs_CF_singleChannel.py | 13 +- .../evaluation/analyze_embeddings.py | 8 +- .../evaluation/displacement.py | 144 ++++++---- .../evaluation/log_regresssion_training.py | 44 ++- .../evaluation/plot_embeddings.py | 5 +- .../figures/cell_division.py | 8 +- .../figures/classify_june.py | 67 +++-- .../figures/figure_4a_1.py | 172 +++++++++--- .../figures/figure_4e_2_feb.py | 85 +++--- .../figures/figure_4e_2_june.py | 84 +++--- .../figures/figure_cell_infection.py | 22 +- .../figures/save_patches.py | 9 +- 14 files changed, 609 insertions(+), 323 deletions(-) diff --git a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py index 11931f25..46cca6dd 100644 --- a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py +++ b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py @@ -1,17 +1,20 @@ -# %% import statements +# %% import statements from pathlib import Path -import pandas as pd -from viscy.representation.embedding_writer import read_embedding_dataset + import matplotlib.pyplot as plt -import seaborn as sns import numpy as np -from viscy.representation.evaluation import load_annotation -from sklearn.mixture import GaussianMixture +import pandas as pd +import seaborn as sns from sklearn.metrics import confusion_matrix -from sklearn.decomposition import PCA -from viscy.representation.evaluation import GMMClustering -from viscy.representation.evaluation import compute_pca -from viscy.representation.evaluation import compute_umap + +from viscy.representation.embedding_writer import read_embedding_dataset +from viscy.representation.evaluation import ( + GMMClustering, + compute_pca, + compute_umap, + load_annotation, +) + # %% Paths and parameters. features_path_30_min = Path( @@ -28,12 +31,14 @@ "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr" ) -features_path_june = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/jun_time_interval_1_epoch_178.zarr") +features_path_june = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/jun_time_interval_1_epoch_178.zarr" +) # %% visualize distribution of embeddings embedding_dataset = read_embedding_dataset(features_path_30_min) -features_data = embedding_dataset['features'] +features_data = embedding_dataset["features"] n_samples, n_features = features_data.shape random_dimensions = np.random.choice(n_features, 5, replace=False) @@ -50,38 +55,38 @@ # %% initialize GMM clustering and ground truth labels embedding_dataset = read_embedding_dataset(features_path_june) -features_data = embedding_dataset['features'] +features_data = embedding_dataset["features"] cluster_evaluator = GMMClustering(features_data) -# %% Find best n_clusters +# %% Find best n_clusters aic_scores, bic_scores = cluster_evaluator.find_best_n_clusters() plt.figure(figsize=(8, 6)) -plt.plot(cluster_evaluator.n_clusters_range, aic_scores, label='AIC', marker='o') -plt.plot(cluster_evaluator.n_clusters_range, bic_scores, label='BIC', marker='o') -plt.xlabel('Number of clusters') -plt.ylabel('AIC / BIC Score') -plt.title('AIC and BIC Scores for Different Numbers of Clusters') +plt.plot(cluster_evaluator.n_clusters_range, aic_scores, label="AIC", marker="o") +plt.plot(cluster_evaluator.n_clusters_range, bic_scores, label="BIC", marker="o") +plt.xlabel("Number of clusters") +plt.ylabel("AIC / BIC Score") +plt.title("AIC and BIC Scores for Different Numbers of Clusters") plt.legend() plt.show() # %% # Choose the best model (with the lowest BIC score) -best_gmm = cluster_evaluator.fit_best_model(criterion='bic') +best_gmm = cluster_evaluator.fit_best_model(criterion="bic") cluster_labels = cluster_evaluator.predict_clusters() # %% ground truth labels (if available!) ann_root = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" ) infection = load_annotation( - features_data, - ann_root / "extracted_inf_state.csv", - "infection_state", - {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, + features_data, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, ) # %% the confusion matrix with ground truth states @@ -90,15 +95,18 @@ cm = confusion_matrix(ground_truth_labels_numeric, cluster_labels) -cm_df = pd.DataFrame(cm, index=["Background", "Uninfected", "Infected"], - columns=["Cluster 0", "Cluster 1", "Cluster 2"]) +cm_df = pd.DataFrame( + cm, + index=["Background", "Uninfected", "Infected"], + columns=["Cluster 0", "Cluster 1", "Cluster 2"], +) plt.figure(figsize=(8, 6)) -sns.heatmap(cm_df, annot=True, fmt='g', cmap='Blues') +sns.heatmap(cm_df, annot=True, fmt="g", cmap="Blues") -plt.title('Confusion Matrix: Clusters vs Ground Truth') -plt.ylabel('Ground Truth Labels') -plt.xlabel('Cluster Labels') +plt.title("Confusion Matrix: Clusters vs Ground Truth") +plt.ylabel("Ground Truth Labels") +plt.xlabel("Cluster Labels") plt.show() # %% @@ -108,26 +116,52 @@ pca1 = pca_df["PCA1"] pca2 = pca_df["PCA2"] -color_map = {'background': 'gray', 'uninfected': 'blue', 'infected': 'red'} +color_map = {"background": "gray", "uninfected": "blue", "infected": "red"} colors = infection.map(color_map) plt.figure(figsize=(10, 8)) # Plot Cluster 0 with circle markers ('o') -plt.scatter(pca1[cluster_labels == 0], pca2[cluster_labels == 0], - c=colors[cluster_labels == 0], edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (circle)', marker='o') +plt.scatter( + pca1[cluster_labels == 0], + pca2[cluster_labels == 0], + c=colors[cluster_labels == 0], + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 0 (circle)", + marker="o", +) # Plot Cluster 1 with X markers ('x') -plt.scatter(pca1[cluster_labels == 1], pca2[cluster_labels == 1], - c=colors[cluster_labels == 1], edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (X)', marker='x') - -plt.xlabel('PCA 1') -plt.ylabel('PCA 2') -plt.title(f"Ground Truth Colors with GMM Cluster Marker Types") +plt.scatter( + pca1[cluster_labels == 1], + pca2[cluster_labels == 1], + c=colors[cluster_labels == 1], + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 1 (X)", + marker="x", +) -handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, - markerfacecolor=color_map[label], markersize=10, markeredgecolor='black') - for label in color_map.keys()] +plt.xlabel("PCA 1") +plt.ylabel("PCA 2") +plt.title("Ground Truth Colors with GMM Cluster Marker Types") + +handles = [ + plt.Line2D( + [0], + [0], + marker="o", + color="w", + label=label, + markerfacecolor=color_map[label], + markersize=10, + markeredgecolor="black", + ) + for label in color_map.keys() +] plt.legend(handles=handles, title="Ground Truth") plt.show() @@ -141,32 +175,56 @@ plt.figure(figsize=(10, 8)) # Plot Cluster 0 with circle markers ('o') -plt.scatter(pca1[cluster_labels == 0], pca2[cluster_labels == 0], - c='green', edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (GMM)', marker='o') +plt.scatter( + pca1[cluster_labels == 0], + pca2[cluster_labels == 0], + c="green", + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 0 (GMM)", + marker="o", +) # Plot Cluster 1 with X markers ('x') -plt.scatter(pca1[cluster_labels == 1], pca2[cluster_labels == 1], - c='orange', edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (GMM)', marker='x') +plt.scatter( + pca1[cluster_labels == 1], + pca2[cluster_labels == 1], + c="orange", + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 1 (GMM)", + marker="x", +) -plt.xlabel('PCA 1') -plt.ylabel('PCA 2') -plt.title(f"GMM Clusters") +plt.xlabel("PCA 1") +plt.ylabel("PCA 2") +plt.title("GMM Clusters") plt.legend() plt.show() -# %% Visualize UMAP embeddings colored by GMM cluster weights +# %% Visualize UMAP embeddings colored by GMM cluster weights umap_features, umap_projection, umap_df = compute_umap(embedding_dataset) gmm_weights = best_gmm.weights_ plt.figure(figsize=(10, 8)) -plt.scatter(umap_df["UMAP1"], umap_df["UMAP2"], c=gmm_weights[cluster_labels], cmap='viridis', s=50, alpha=0.8, edgecolor='k') -plt.colorbar(label='GMM Cluster Weights') -plt.title('UMAP Embeddings Colored by GMM Cluster Weights') -plt.xlabel('UMAP 1') -plt.ylabel('UMAP 2') +plt.scatter( + umap_df["UMAP1"], + umap_df["UMAP2"], + c=gmm_weights[cluster_labels], + cmap="viridis", + s=50, + alpha=0.8, + edgecolor="k", +) +plt.colorbar(label="GMM Cluster Weights") +plt.title("UMAP Embeddings Colored by GMM Cluster Weights") +plt.xlabel("UMAP 1") +plt.ylabel("UMAP 2") plt.show() @@ -175,45 +233,89 @@ plt.figure(figsize=(10, 8)) -plt.scatter(umap_df["UMAP1"][cluster_labels == 0], umap_df["UMAP2"][cluster_labels == 0], - c='green', edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (GMM)', marker='o') +plt.scatter( + umap_df["UMAP1"][cluster_labels == 0], + umap_df["UMAP2"][cluster_labels == 0], + c="green", + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 0 (GMM)", + marker="o", +) -plt.scatter(umap_df["UMAP1"][cluster_labels == 1], umap_df["UMAP2"][cluster_labels == 1], - c='orange', edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (GMM)', marker='o') +plt.scatter( + umap_df["UMAP1"][cluster_labels == 1], + umap_df["UMAP2"][cluster_labels == 1], + c="orange", + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 1 (GMM)", + marker="o", +) -plt.xlabel('UMAP 1') -plt.ylabel('UMAP 2') -plt.title(f"GMM Clusters in UMAP Space") +plt.xlabel("UMAP 1") +plt.ylabel("UMAP 2") +plt.title("GMM Clusters in UMAP Space") plt.legend() plt.show() # %% UMAP vis (w/ ground truth colors and GMM cluster markers) -umap_features, umap_projection, umap_df = compute_umap(embedding_dataset, normalize_features=True) +umap_features, umap_projection, umap_df = compute_umap( + embedding_dataset, normalize_features=True +) umap1 = umap_df["UMAP1"] umap2 = umap_df["UMAP2"] -color_map = {'background': 'gray', 'uninfected': 'blue', 'infected': 'red'} +color_map = {"background": "gray", "uninfected": "blue", "infected": "red"} colors = infection.map(color_map) plt.figure(figsize=(10, 8)) # Plot Cluster 0 with circle markers ('o') -plt.scatter(umap1[cluster_labels == 0], umap2[cluster_labels == 0], - c=colors[cluster_labels == 0], edgecolor='black', s=50, alpha=0.7, label='Cluster 0 (circle)', marker='o') +plt.scatter( + umap1[cluster_labels == 0], + umap2[cluster_labels == 0], + c=colors[cluster_labels == 0], + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 0 (circle)", + marker="o", +) # Plot Cluster 1 with X markers ('x') -plt.scatter(umap1[cluster_labels == 1], umap2[cluster_labels == 1], - c=colors[cluster_labels == 1], edgecolor='black', s=50, alpha=0.7, label='Cluster 1 (X)', marker='x') - -plt.xlabel('UMAP 1') -plt.ylabel('UMAP 2') -plt.title(f"Ground Truth Colors with GMM Cluster Marker Types in UMAP Space") +plt.scatter( + umap1[cluster_labels == 1], + umap2[cluster_labels == 1], + c=colors[cluster_labels == 1], + edgecolor="black", + s=50, + alpha=0.7, + label="Cluster 1 (X)", + marker="x", +) -handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, - markerfacecolor=color_map[label], markersize=10, markeredgecolor='black') - for label in color_map.keys()] +plt.xlabel("UMAP 1") +plt.ylabel("UMAP 2") +plt.title("Ground Truth Colors with GMM Cluster Marker Types in UMAP Space") + +handles = [ + plt.Line2D( + [0], + [0], + marker="o", + color="w", + label=label, + markerfacecolor=color_map[label], + markersize=10, + markeredgecolor="black", + ) + for label in color_map.keys() +] plt.legend(handles=handles, title="Ground Truth") plt.show() diff --git a/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py b/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py index f43b121b..51c0e13d 100644 --- a/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py +++ b/applications/contrastive_phenotyping/evaluation/PC_vs_CF.py @@ -4,17 +4,16 @@ """ # %% -from pathlib import Path -import sys import os +import sys +from pathlib import Path sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") +import matplotlib.pyplot as plt import numpy as np -import pandas as pd +import seaborn as sns from sklearn.decomposition import PCA -from umap import UMAP -from sklearn.preprocessing import StandardScaler from viscy.representation.embedding_writer import read_embedding_dataset from viscy.representation.evaluation import ( @@ -22,13 +21,6 @@ ) from viscy.representation.evaluation import dataset_of_tracks -import matplotlib.pyplot as plt -import seaborn as sns - -from scipy.stats import spearmanr -import pandas as pd -import plotly.express as px - # %% features_path = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" @@ -341,6 +333,7 @@ # %% find the cell patches with the highest and lowest value in each feature + def save_patches(fov_name, track_id): data_path = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" diff --git a/applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py b/applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py index aac8855c..3810afd4 100644 --- a/applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py +++ b/applications/contrastive_phenotyping/evaluation/PC_vs_CF_singleChannel.py @@ -4,16 +4,16 @@ """ # %% -from pathlib import Path import sys +from pathlib import Path sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") import numpy as np import pandas as pd +import plotly.express as px +from scipy.stats import spearmanr from sklearn.decomposition import PCA -from umap import UMAP -from sklearn.preprocessing import StandardScaler from viscy.representation.embedding_writer import read_embedding_dataset from viscy.representation.evaluation import ( @@ -21,13 +21,6 @@ ) from viscy.representation.evaluation import dataset_of_tracks -import matplotlib.pyplot as plt -import seaborn as sns - -from scipy.stats import spearmanr -import pandas as pd -import plotly.express as px - # %% features_path = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval_phase/predictions/epoch_186/1chan_128patch_186ckpt_Febtest.zarr" diff --git a/applications/contrastive_phenotyping/evaluation/analyze_embeddings.py b/applications/contrastive_phenotyping/evaluation/analyze_embeddings.py index b7dda83c..074b39f1 100644 --- a/applications/contrastive_phenotyping/evaluation/analyze_embeddings.py +++ b/applications/contrastive_phenotyping/evaluation/analyze_embeddings.py @@ -16,8 +16,8 @@ # %% Jupyter magic command for autoreloading modules # ruff: noqa # fmt: off -%load_ext autoreload -%autoreload 2 +# %load_ext autoreload +# %autoreload 2 # fmt: on # ruff: noqa # %% Paths and parameters @@ -95,7 +95,9 @@ # Plot UMAP embeddings as density plots fig, ax = plt.subplots(1, 2, figsize=(10, 5)) sns.kdeplot(data=umap_df, x="UMAP1", y="UMAP2", ax=ax[0], fill=True, cmap="Blues") -sns.kdeplot(data=umap_df, x="UMAP1_proj", y="UMAP2_proj", ax=ax[1], fill=True, cmap="Reds") +sns.kdeplot( + data=umap_df, x="UMAP1_proj", y="UMAP2_proj", ax=ax[1], fill=True, cmap="Reds" +) ax[0].set_title("Density plot of UMAP1 vs UMAP2 (features)") ax[1].set_title("Density plot of UMAP1 vs UMAP2 (projections)") plt.show() diff --git a/applications/contrastive_phenotyping/evaluation/displacement.py b/applications/contrastive_phenotyping/evaluation/displacement.py index a0d46c28..a14ce11d 100644 --- a/applications/contrastive_phenotyping/evaluation/displacement.py +++ b/applications/contrastive_phenotyping/evaluation/displacement.py @@ -3,87 +3,115 @@ import matplotlib.pyplot as plt import numpy as np -import pandas as pd -import plotly.express as px -import seaborn as sns -from sklearn.decomposition import PCA -from sklearn.preprocessing import StandardScaler -from umap import UMAP -from sklearn.decomposition import PCA -from matplotlib.font_manager import FontProperties from viscy.representation.embedding_writer import read_embedding_dataset -from viscy.representation.evaluation import dataset_of_tracks, load_annotation -from viscy.representation.evaluation import calculate_normalized_euclidean_distance_cell -from viscy.representation.evaluation import compute_displacement_mean_std_full -from sklearn.metrics.pairwise import cosine_similarity -from collections import defaultdict -from scipy.ndimage import gaussian_filter1d +from viscy.representation.evaluation import ( + calculate_normalized_euclidean_distance_cell, + compute_displacement_mean_std_full, +) -# %% paths +# %% paths features_path_30_min = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" ) -feature_path_no_track = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr") +feature_path_no_track = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) -features_path_any_time = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr") +features_path_any_time = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr" +) data_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" ) tracks_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" ) # %% Load embedding datasets for all three sampling -fov_name = '/B/4/6' +fov_name = "/B/4/6" track_id = 52 embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) embedding_dataset_any_time = read_embedding_dataset(features_path_any_time) -#%% +# %% # Calculate displacement for each sampling -time_points_30_min, cosine_similarities_30_min = calculate_normalized_euclidean_distance_cell(embedding_dataset_30_min, fov_name, track_id) -time_points_no_track, cosine_similarities_no_track = calculate_normalized_euclidean_distance_cell(embedding_dataset_no_track, fov_name, track_id) -time_points_any_time, cosine_similarities_any_time = calculate_normalized_euclidean_distance_cell(embedding_dataset_any_time, fov_name, track_id) +time_points_30_min, cosine_similarities_30_min = ( + calculate_normalized_euclidean_distance_cell( + embedding_dataset_30_min, fov_name, track_id + ) +) +time_points_no_track, cosine_similarities_no_track = ( + calculate_normalized_euclidean_distance_cell( + embedding_dataset_no_track, fov_name, track_id + ) +) +time_points_any_time, cosine_similarities_any_time = ( + calculate_normalized_euclidean_distance_cell( + embedding_dataset_any_time, fov_name, track_id + ) +) # %% Plot displacement over time for all three conditions plt.figure(figsize=(10, 6)) -plt.plot(time_points_no_track, cosine_similarities_no_track, marker='o', label='classical contrastive (no tracking)') -plt.plot(time_points_any_time, cosine_similarities_any_time, marker='o', label='cell aware') -plt.plot(time_points_30_min, cosine_similarities_30_min, marker='o', label='cell & time aware (interval 30 min)') +plt.plot( + time_points_no_track, + cosine_similarities_no_track, + marker="o", + label="classical contrastive (no tracking)", +) +plt.plot( + time_points_any_time, cosine_similarities_any_time, marker="o", label="cell aware" +) +plt.plot( + time_points_30_min, + cosine_similarities_30_min, + marker="o", + label="cell & time aware (interval 30 min)", +) plt.xlabel("Time Delay (t)", fontsize=10) plt.ylabel("Normalized Euclidean Distance with First Time Point", fontsize=10) -plt.title("Normalized Euclidean Distance (Features) Over Time for Infected Cell", fontsize=12) +plt.title( + "Normalized Euclidean Distance (Features) Over Time for Infected Cell", fontsize=12 +) plt.grid(True) plt.legend(fontsize=10) -#plt.savefig('4_euc_dist_full.svg', format='svg') +# plt.savefig('4_euc_dist_full.svg', format='svg') plt.show() # %% Paths to datasets -features_path_30_min = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr") -feature_path_no_track = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr") +features_path_30_min = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" +) +feature_path_no_track = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" +) embedding_dataset_30_min = read_embedding_dataset(features_path_30_min) embedding_dataset_no_track = read_embedding_dataset(feature_path_no_track) # %% -max_tau = 10 +max_tau = 10 -mean_displacement_30_min_euc, std_displacement_30_min_euc = compute_displacement_mean_std_full(embedding_dataset_30_min, max_tau) -mean_displacement_no_track_euc, std_displacement_no_track_euc = compute_displacement_mean_std_full(embedding_dataset_no_track, max_tau) +mean_displacement_30_min_euc, std_displacement_30_min_euc = ( + compute_displacement_mean_std_full(embedding_dataset_30_min, max_tau) +) +mean_displacement_no_track_euc, std_displacement_no_track_euc = ( + compute_displacement_mean_std_full(embedding_dataset_no_track, max_tau) +) # %% Plot 2: Cosine Displacements plt.figure(figsize=(10, 6)) @@ -93,24 +121,44 @@ mean_values_30_min_euc = list(mean_displacement_30_min_euc.values()) std_values_30_min_euc = list(std_displacement_30_min_euc.values()) -plt.plot(taus, mean_values_30_min_euc, marker='o', label='Cell & Time Aware (30 min interval)', color='green') -plt.fill_between(taus, - np.array(mean_values_30_min_euc) - np.array(std_values_30_min_euc), - np.array(mean_values_30_min_euc) + np.array(std_values_30_min_euc), - color='green', alpha=0.3, label='Std Dev (30 min interval)') +plt.plot( + taus, + mean_values_30_min_euc, + marker="o", + label="Cell & Time Aware (30 min interval)", + color="green", +) +plt.fill_between( + taus, + np.array(mean_values_30_min_euc) - np.array(std_values_30_min_euc), + np.array(mean_values_30_min_euc) + np.array(std_values_30_min_euc), + color="green", + alpha=0.3, + label="Std Dev (30 min interval)", +) mean_values_no_track_euc = list(mean_displacement_no_track_euc.values()) std_values_no_track_euc = list(std_displacement_no_track_euc.values()) -plt.plot(taus, mean_values_no_track_euc, marker='o', label='Classical Contrastive (No Tracking)', color='blue') -plt.fill_between(taus, - np.array(mean_values_no_track_euc) - np.array(std_values_no_track_euc), - np.array(mean_values_no_track_euc) + np.array(std_values_no_track_euc), - color='blue', alpha=0.3, label='Std Dev (No Tracking)') +plt.plot( + taus, + mean_values_no_track_euc, + marker="o", + label="Classical Contrastive (No Tracking)", + color="blue", +) +plt.fill_between( + taus, + np.array(mean_values_no_track_euc) - np.array(std_values_no_track_euc), + np.array(mean_values_no_track_euc) + np.array(std_values_no_track_euc), + color="blue", + alpha=0.3, + label="Std Dev (No Tracking)", +) -plt.xlabel('Time Shift (Ï„)') -plt.ylabel('Euclidean Distance') -plt.title('Embedding Displacement Over Time (Features)') +plt.xlabel("Time Shift (Ï„)") +plt.ylabel("Euclidean Distance") +plt.title("Embedding Displacement Over Time (Features)") plt.grid(True) plt.legend() diff --git a/applications/contrastive_phenotyping/evaluation/log_regresssion_training.py b/applications/contrastive_phenotyping/evaluation/log_regresssion_training.py index 0bb7a4b3..7456b284 100644 --- a/applications/contrastive_phenotyping/evaluation/log_regresssion_training.py +++ b/applications/contrastive_phenotyping/evaluation/log_regresssion_training.py @@ -1,34 +1,22 @@ - # %% from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np import pandas as pd -import plotly.express as px -import seaborn as sns -from sklearn.decomposition import PCA -from sklearn.preprocessing import StandardScaler -from umap import UMAP -from sklearn.decomposition import PCA - from viscy.representation.embedding_writer import read_embedding_dataset -from viscy.representation.evaluation import dataset_of_tracks, load_annotation - +from viscy.representation.evaluation import load_annotation # %% Paths and parameters. features_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" ) data_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr" ) tracks_path = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr" ) @@ -44,15 +32,15 @@ # %% OVERLAY INFECTION ANNOTATION ann_root = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" ) infection = load_annotation( - features, - ann_root / "extracted_inf_state.csv", - "infection_state", - {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, + features, + ann_root / "extracted_inf_state.csv", + "infection_state", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, ) # %% plot the umap @@ -85,16 +73,22 @@ # %% manually split the dataset into training and testing set by well name # dataframe for training set, fov names starts with "/B/4/6" or "/B/4/7" or "/A/3/" -data_train_val = data[data["fov_name"].str.contains("/B/4/6") | data["fov_name"].str.contains("/B/4/7") | data["fov_name"].str.contains("/A/3/")] +data_train_val = data[ + data["fov_name"].str.contains("/B/4/6") + | data["fov_name"].str.contains("/B/4/7") + | data["fov_name"].str.contains("/A/3/") +] # dataframe for testing set, fov names starts with "/B/4/8" or "/B/4/9" or "/A/4/" -data_test = data[data["fov_name"].str.contains("/B/4/8") | data["fov_name"].str.contains("/B/4/9") | data["fov_name"].str.contains("/B/3/")] +data_test = data[ + data["fov_name"].str.contains("/B/4/8") + | data["fov_name"].str.contains("/B/4/9") + | data["fov_name"].str.contains("/B/3/") +] # %% train a linear classifier to predict infection state from PCA components from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import train_test_split -from sklearn.metrics import classification_report x_train = data_train_val.drop(columns=["infection", "fov_name", "time"]) y_train = data_train_val["infection"] diff --git a/applications/contrastive_phenotyping/evaluation/plot_embeddings.py b/applications/contrastive_phenotyping/evaluation/plot_embeddings.py index 9f411a59..7e770c8d 100644 --- a/applications/contrastive_phenotyping/evaluation/plot_embeddings.py +++ b/applications/contrastive_phenotyping/evaluation/plot_embeddings.py @@ -1,7 +1,9 @@ # %% +import os from pathlib import Path import matplotlib.pyplot as plt +import napari import numpy as np import pandas as pd import plotly.express as px @@ -128,9 +130,6 @@ plt.show() # %% display the track in napari -import os - -import napari os.environ["DISPLAY"] = ":1" viewer = napari.Viewer() diff --git a/applications/contrastive_phenotyping/figures/cell_division.py b/applications/contrastive_phenotyping/figures/cell_division.py index 5473aa34..2844ff58 100644 --- a/applications/contrastive_phenotyping/figures/cell_division.py +++ b/applications/contrastive_phenotyping/figures/cell_division.py @@ -3,13 +3,15 @@ sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") from pathlib import Path + +import matplotlib.pyplot as plt import pandas as pd import seaborn as sns -import plotly.express as px +from matplotlib.patches import FancyArrowPatch from sklearn.preprocessing import StandardScaler from umap import UMAP + from viscy.representation.embedding_writer import read_embedding_dataset -import matplotlib.pyplot as plt # %% # single channel. with temporal regularizations @@ -107,8 +109,6 @@ def load_annotation(da, path, name, categories: dict | None = None): # %% plot the trajectory quiver of one cell on top of the UMAP -from matplotlib.patches import FancyArrowPatch - cell_parent = features[ (features["fov_name"].str.contains("A/3/7")) & (features["track_id"].isin([13])) ] diff --git a/applications/contrastive_phenotyping/figures/classify_june.py b/applications/contrastive_phenotyping/figures/classify_june.py index ca51f2b1..21373fef 100644 --- a/applications/contrastive_phenotyping/figures/classify_june.py +++ b/applications/contrastive_phenotyping/figures/classify_june.py @@ -14,7 +14,10 @@ from viscy.representation.embedding_writer import read_embedding_dataset # %% Defining Paths for June Dataset -june_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr") +june_features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr" +) + # %% Function to Load Annotations def load_annotation(da, path, name, categories: dict | None = None): @@ -29,29 +32,33 @@ def load_annotation(da, path, name, categories: dict | None = None): selected = selected.astype("category").cat.rename_categories(categories) return selected + # %% Function to Compute PCA def compute_pca(embedding_dataset, n_components=6): features = embedding_dataset["features"] scaled_features = StandardScaler().fit_transform(features.values) - + # Compute PCA with specified number of components pca = PCA(n_components=n_components, random_state=42) pca_embedding = pca.fit_transform(scaled_features) - + # Prepare DataFrame with id and PCA coordinates - pca_df = pd.DataFrame({ - "id": embedding_dataset["id"].values, - "fov_name": embedding_dataset["fov_name"].values, - "PCA1": pca_embedding[:, 0], - "PCA2": pca_embedding[:, 1], - "PCA3": pca_embedding[:, 2], - "PCA4": pca_embedding[:, 3], - "PCA5": pca_embedding[:, 4], - "PCA6": pca_embedding[:, 5] - }) - + pca_df = pd.DataFrame( + { + "id": embedding_dataset["id"].values, + "fov_name": embedding_dataset["fov_name"].values, + "PCA1": pca_embedding[:, 0], + "PCA2": pca_embedding[:, 1], + "PCA3": pca_embedding[:, 2], + "PCA4": pca_embedding[:, 3], + "PCA5": pca_embedding[:, 4], + "PCA6": pca_embedding[:, 5], + } + ) + return pca_df + # %% Load and Process June Dataset june_embedding_dataset = read_embedding_dataset(june_features_path) print(june_embedding_dataset) @@ -61,15 +68,21 @@ def compute_pca(embedding_dataset, n_components=6): print("Shape of pca_df before merge:", pca_df.shape) # Load the ground truth infection labels -june_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking") -june_infection = load_annotation(june_embedding_dataset, june_ann_root / "tracking_v1_infection.csv", "infection class", - {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) +june_ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_06_13_SEC61_TOMM20_ZIKV_DENGUE_1/4.1-tracking" +) +june_infection = load_annotation( + june_embedding_dataset, + june_ann_root / "tracking_v1_infection.csv", + "infection class", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) # Print shape of june_infection print("Shape of june_infection:", june_infection.shape) # Merge PCA results with ground truth labels on both 'fov_name' and 'id' -pca_df = pd.merge(pca_df, june_infection.reset_index(), on=['fov_name', 'id']) +pca_df = pd.merge(pca_df, june_infection.reset_index(), on=["fov_name", "id"]) # Print shape after merge print("Shape of pca_df after merge:", pca_df.shape) @@ -83,7 +96,9 @@ def compute_pca(embedding_dataset, n_components=6): X_resampled, y_resampled = smote.fit_resample(X, y) # Print shape after SMOTE -print(f"Shape after SMOTE - X_resampled: {X_resampled.shape}, y_resampled: {y_resampled.shape}") +print( + f"Shape after SMOTE - X_resampled: {X_resampled.shape}, y_resampled: {y_resampled.shape}" +) # %% Train Logistic Regression Classifier with Progress Bar model = LogisticRegression(max_iter=1000, random_state=42) @@ -104,18 +119,22 @@ def compute_pca(embedding_dataset, n_components=6): # %% Plotting the Results plt.figure(figsize=(10, 8)) -sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["infection class"], s=7, alpha=0.8) +sns.scatterplot( + x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["infection class"], s=7, alpha=0.8 +) plt.title("PCA with Ground Truth Labels") -plt.savefig("june_pca_ground_truth_labels.png", format='png', dpi=300) +plt.savefig("june_pca_ground_truth_labels.png", format="png", dpi=300) plt.show() plt.figure(figsize=(10, 8)) -sns.scatterplot(x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["Predicted_Label"], s=7, alpha=0.8) +sns.scatterplot( + x=pca_df["PCA1"], y=pca_df["PCA2"], hue=pca_df["Predicted_Label"], s=7, alpha=0.8 +) plt.title("PCA with Logistic Regression Predicted Labels") -plt.savefig("june_pca_predicted_labels.png", format='png', dpi=300) +plt.savefig("june_pca_predicted_labels.png", format="png", dpi=300) plt.show() # %% Save Predicted Labels to CSV save_path_csv = "june_logistic_regression_predicted_labels_feb_pca.csv" -pca_df[['id', 'fov_name', 'Predicted_Label']].to_csv(save_path_csv, index=False) +pca_df[["id", "fov_name", "Predicted_Label"]].to_csv(save_path_csv, index=False) print(f"Predicted labels saved to {save_path_csv}") diff --git a/applications/contrastive_phenotyping/figures/figure_4a_1.py b/applications/contrastive_phenotyping/figures/figure_4a_1.py index a670db0d..c1c2befc 100644 --- a/applications/contrastive_phenotyping/figures/figure_4a_1.py +++ b/applications/contrastive_phenotyping/figures/figure_4a_1.py @@ -10,9 +10,16 @@ from viscy.representation.embedding_writer import read_embedding_dataset # %% Defining Paths for February and June Datasets -feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr") -feb_data_path = Path("/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr") -feb_tracks_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr") +feb_features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr" +) +feb_data_path = Path( + "/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr" +) +feb_tracks_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr" +) + # %% Function to Load and Process the Embedding Dataset def compute_umap(embedding_dataset): @@ -20,7 +27,7 @@ def compute_umap(embedding_dataset): scaled_features = StandardScaler().fit_transform(features.values) umap = UMAP() embedding = umap.fit_transform(scaled_features) - + features = ( features.assign_coords(UMAP1=("sample", embedding[:, 0])) .assign_coords(UMAP2=("sample", embedding[:, 1])) @@ -28,6 +35,7 @@ def compute_umap(embedding_dataset): ) return features + # %% Function to Load Annotations def load_annotation(da, path, name, categories: dict | None = None): annotation = pd.read_csv(path) @@ -41,19 +49,30 @@ def load_annotation(da, path, name, categories: dict | None = None): selected = selected.astype("category").cat.rename_categories(categories) return selected + # %% Function to Plot UMAP with Infection Annotations def plot_umap_infection(features, infection, title): plt.figure(figsize=(10, 8)) - sns.scatterplot(x=features["UMAP1"], y=features["UMAP2"], hue=infection, s=7, alpha=0.8) + sns.scatterplot( + x=features["UMAP1"], y=features["UMAP2"], hue=infection, s=7, alpha=0.8 + ) plt.title(f"UMAP Plot - {title}") plt.show() + # %% Load and Process February Dataset feb_embedding_dataset = read_embedding_dataset(feb_features_path) feb_features = compute_umap(feb_embedding_dataset) -feb_ann_root = Path("/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track") -feb_infection = load_annotation(feb_features, feb_ann_root / "tracking_v1_infection.csv", "infection class", {0.0: "background", 1.0: "uninfected", 2.0: "infected"}) +feb_ann_root = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track" +) +feb_infection = load_annotation( + feb_features, + feb_ann_root / "tracking_v1_infection.csv", + "infection class", + {0.0: "background", 1.0: "uninfected", 2.0: "infected"}, +) # %% Plot UMAP with Infection Status for February Dataset plot_umap_infection(feb_features, feb_infection, "February Dataset") @@ -66,21 +85,47 @@ def plot_umap_infection(features, infection, title): # %% Identify cells by infection type using fov_name -mock_cells = feb_features.sel(sample=feb_features['fov_name'].str.contains('/A/3') | feb_features['fov_name'].str.contains('/B/3')) -zika_cells = feb_features.sel(sample=feb_features['fov_name'].str.contains('/A/4')) -dengue_cells = feb_features.sel(sample=feb_features['fov_name'].str.contains('/B/4')) +mock_cells = feb_features.sel( + sample=feb_features["fov_name"].str.contains("/A/3") + | feb_features["fov_name"].str.contains("/B/3") +) +zika_cells = feb_features.sel(sample=feb_features["fov_name"].str.contains("/A/4")) +dengue_cells = feb_features.sel(sample=feb_features["fov_name"].str.contains("/B/4")) # %% Plot UMAP with Infection Status plt.figure(figsize=(10, 8)) -sns.scatterplot(x=feb_features["UMAP1"], y=feb_features["UMAP2"], hue=feb_infection, s=7, alpha=0.8) +sns.scatterplot( + x=feb_features["UMAP1"], y=feb_features["UMAP2"], hue=feb_infection, s=7, alpha=0.8 +) # Overlay with circled cells -plt.scatter(mock_cells["UMAP1"], mock_cells["UMAP2"], facecolors='none', edgecolors='blue', s=20, label='Mock Cells') -plt.scatter(zika_cells["UMAP1"], zika_cells["UMAP2"], facecolors='none', edgecolors='green', s=20, label='Zika MOI 5') -plt.scatter(dengue_cells["UMAP1"], dengue_cells["UMAP2"], facecolors='none', edgecolors='red', s=20, label='Dengue MOI 5') +plt.scatter( + mock_cells["UMAP1"], + mock_cells["UMAP2"], + facecolors="none", + edgecolors="blue", + s=20, + label="Mock Cells", +) +plt.scatter( + zika_cells["UMAP1"], + zika_cells["UMAP2"], + facecolors="none", + edgecolors="green", + s=20, + label="Zika MOI 5", +) +plt.scatter( + dengue_cells["UMAP1"], + dengue_cells["UMAP2"], + facecolors="none", + edgecolors="red", + s=20, + label="Dengue MOI 5", +) # Add legend and show plot -plt.legend(loc='best') +plt.legend(loc="best") plt.title("UMAP Plot - February Dataset with Mock, Zika, and Dengue Highlighted") plt.show() @@ -89,27 +134,48 @@ def plot_umap_infection(features, infection, title): fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharex=True, sharey=True) # Mock Cells Heatmap -sns.histplot(x=mock_cells["UMAP1"], y=mock_cells["UMAP2"], bins=50, pmax=1, cmap="Blues", ax=axs[0]) -axs[0].set_title('Mock Cells') +sns.histplot( + x=mock_cells["UMAP1"], + y=mock_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Blues", + ax=axs[0], +) +axs[0].set_title("Mock Cells") axs[0].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[0].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) # Zika Cells Heatmap -sns.histplot(x=zika_cells["UMAP1"], y=zika_cells["UMAP2"], bins=50, pmax=1, cmap="Greens", ax=axs[1]) -axs[1].set_title('Zika MOI 5') +sns.histplot( + x=zika_cells["UMAP1"], + y=zika_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Greens", + ax=axs[1], +) +axs[1].set_title("Zika MOI 5") axs[1].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[1].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) # Dengue Cells Heatmap -sns.histplot(x=dengue_cells["UMAP1"], y=dengue_cells["UMAP2"], bins=50, pmax=1, cmap="Reds", ax=axs[2]) -axs[2].set_title('Dengue MOI 5') +sns.histplot( + x=dengue_cells["UMAP1"], + y=dengue_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Reds", + ax=axs[2], +) +axs[2].set_title("Dengue MOI 5") axs[2].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[2].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) # Set labels and adjust layout for ax in axs: - ax.set_xlabel('UMAP1') - ax.set_ylabel('UMAP2') + ax.set_xlabel("UMAP1") + ax.set_ylabel("UMAP2") plt.tight_layout() plt.show() @@ -122,32 +188,67 @@ def plot_umap_infection(features, infection, title): fig, axs = plt.subplots(2, 3, figsize=(24, 12), sharex=True, sharey=True) # Mock Cells Heatmap -sns.histplot(x=mock_cells["UMAP1"], y=mock_cells["UMAP2"], bins=50, pmax=1, cmap="Blues", ax=axs[0, 0]) -axs[0, 0].set_title('Mock Cells') +sns.histplot( + x=mock_cells["UMAP1"], + y=mock_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Blues", + ax=axs[0, 0], +) +axs[0, 0].set_title("Mock Cells") axs[0, 0].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[0, 0].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) # Zika Cells Heatmap -sns.histplot(x=zika_cells["UMAP1"], y=zika_cells["UMAP2"], bins=50, pmax=1, cmap="Greens", ax=axs[0, 1]) -axs[0, 1].set_title('Zika MOI 5') +sns.histplot( + x=zika_cells["UMAP1"], + y=zika_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Greens", + ax=axs[0, 1], +) +axs[0, 1].set_title("Zika MOI 5") axs[0, 1].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[0, 1].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) # Dengue Cells Heatmap -sns.histplot(x=dengue_cells["UMAP1"], y=dengue_cells["UMAP2"], bins=50, pmax=1, cmap="Reds", ax=axs[0, 2]) -axs[0, 2].set_title('Dengue MOI 5') +sns.histplot( + x=dengue_cells["UMAP1"], + y=dengue_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Reds", + ax=axs[0, 2], +) +axs[0, 2].set_title("Dengue MOI 5") axs[0, 2].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[0, 2].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) # Infected Cells Heatmap -sns.histplot(x=infected_cells["UMAP1"], y=infected_cells["UMAP2"], bins=50, pmax=1, cmap="Reds", ax=axs[1, 0]) -axs[1, 0].set_title('Infected Cells') +sns.histplot( + x=infected_cells["UMAP1"], + y=infected_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Reds", + ax=axs[1, 0], +) +axs[1, 0].set_title("Infected Cells") axs[1, 0].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[1, 0].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) # Uninfected Cells Heatmap -sns.histplot(x=uninfected_cells["UMAP1"], y=uninfected_cells["UMAP2"], bins=50, pmax=1, cmap="Greens", ax=axs[1, 1]) -axs[1, 1].set_title('Uninfected Cells') +sns.histplot( + x=uninfected_cells["UMAP1"], + y=uninfected_cells["UMAP2"], + bins=50, + pmax=1, + cmap="Greens", + ax=axs[1, 1], +) +axs[1, 1].set_title("Uninfected Cells") axs[1, 1].set_xlim(feb_features["UMAP1"].min(), feb_features["UMAP1"].max()) axs[1, 1].set_ylim(feb_features["UMAP2"].min(), feb_features["UMAP2"].max()) @@ -156,12 +257,11 @@ def plot_umap_infection(features, infection, title): # Set labels and adjust layout for ax in axs.flat: - ax.set_xlabel('UMAP1') - ax.set_ylabel('UMAP2') + ax.set_xlabel("UMAP1") + ax.set_ylabel("UMAP2") plt.tight_layout() plt.show() - # %% diff --git a/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py b/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py index d3052018..bf16fdff 100644 --- a/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py +++ b/applications/contrastive_phenotyping/figures/figure_4e_2_feb.py @@ -12,52 +12,72 @@ def load_gmm_annotation(gmm_csv_path): gmm_df = pd.read_csv(gmm_csv_path) return gmm_df + # %% Function to Count and Calculate Percentage of Infected Cells Over Time Based on GMM Labels def count_infected_cell_states_over_time(embedding_dataset, gmm_df): # Convert the embedding dataset to a DataFrame - df = pd.DataFrame({ - "fov_name": embedding_dataset["fov_name"].values, - "track_id": embedding_dataset["track_id"].values, - "t": embedding_dataset["t"].values, - "id": embedding_dataset["id"].values - }) - + df = pd.DataFrame( + { + "fov_name": embedding_dataset["fov_name"].values, + "track_id": embedding_dataset["track_id"].values, + "t": embedding_dataset["t"].values, + "id": embedding_dataset["id"].values, + } + ) + # Merge with GMM data to add GMM labels - df = pd.merge(df, gmm_df[['id', 'fov_name', 'Predicted_Label']], on=['fov_name', 'id'], how='left') + df = pd.merge( + df, + gmm_df[["id", "fov_name", "Predicted_Label"]], + on=["fov_name", "id"], + how="left", + ) # Filter by time range (3 HPI to 30 HPI) - df = df[(df['t'] >= 3) & (df['t'] <= 27)] - + df = df[(df["t"] >= 3) & (df["t"] <= 27)] + # Determine the well type (Mock, Zika, Dengue) based on fov_name - df['well_type'] = df['fov_name'].apply(lambda x: 'Mock' if '/A/3' in x or '/B/3' in x else - ('Zika' if '/A/4' in x else 'Dengue')) - + df["well_type"] = df["fov_name"].apply( + lambda x: ( + "Mock" + if "/A/3" in x or "/B/3" in x + else ("Zika" if "/A/4" in x else "Dengue") + ) + ) + # Group by time, well type, and GMM label to count the number of infected cells - state_counts = df.groupby(['t', 'well_type', 'Predicted_Label']).size().unstack(fill_value=0) - + state_counts = ( + df.groupby(["t", "well_type", "Predicted_Label"]).size().unstack(fill_value=0) + ) + # Ensure that 'infected' column exists - if 'infected' not in state_counts.columns: - state_counts['infected'] = 0 - + if "infected" not in state_counts.columns: + state_counts["infected"] = 0 + # Calculate the percentage of infected cells - state_counts['total'] = state_counts.sum(axis=1) - state_counts['infected'] = (state_counts['infected'] / state_counts['total']) * 100 - + state_counts["total"] = state_counts.sum(axis=1) + state_counts["infected"] = (state_counts["infected"] / state_counts["total"]) * 100 + return state_counts + # %% Function to Plot Percentage of Infected Cells Over Time def plot_infected_cell_states(state_counts): plt.figure(figsize=(12, 8)) # Loop through each well type - for well_type in ['Mock', 'Zika', 'Dengue']: + for well_type in ["Mock", "Zika", "Dengue"]: # Select the data for the current well type - if well_type in state_counts.index.get_level_values('well_type'): - well_data = state_counts.xs(well_type, level='well_type') - + if well_type in state_counts.index.get_level_values("well_type"): + well_data = state_counts.xs(well_type, level="well_type") + # Plot only the percentage of infected cells - if 'infected' in well_data.columns: - plt.plot(well_data.index, well_data['infected'], label=f'{well_type} - Infected') + if "infected" in well_data.columns: + plt.plot( + well_data.index, + well_data["infected"], + label=f"{well_type} - Infected", + ) plt.title("Percentage of Infected Cells Over Time - February") plt.xlabel("Hours Post Perturbation") @@ -66,12 +86,17 @@ def plot_infected_cell_states(state_counts): plt.grid(True) plt.show() + # %% Load and process Feb Dataset -feb_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr") +feb_features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/June_140Patch_2chan/phaseRFP_140patch_99ckpt_Feb.zarr" +) feb_embedding_dataset = read_embedding_dataset(feb_features_path) # Load the GMM annotation CSV -gmm_csv_path = "june_logistic_regression_predicted_labels_feb_pca.csv" # Path to CSV file +gmm_csv_path = ( + "june_logistic_regression_predicted_labels_feb_pca.csv" # Path to CSV file +) gmm_df = load_gmm_annotation(gmm_csv_path) # %% Count Infected Cell States Over Time as Percentage using GMM labels @@ -83,5 +108,3 @@ def plot_infected_cell_states(state_counts): plot_infected_cell_states(state_counts) # %% - - diff --git a/applications/contrastive_phenotyping/figures/figure_4e_2_june.py b/applications/contrastive_phenotyping/figures/figure_4e_2_june.py index 1605ba27..e33a0f36 100644 --- a/applications/contrastive_phenotyping/figures/figure_4e_2_june.py +++ b/applications/contrastive_phenotyping/figures/figure_4e_2_june.py @@ -11,53 +11,72 @@ def load_annotation(csv_path): return pd.read_csv(csv_path) + # %% Function to Count and Calculate Percentage of Infected Cells Over Time Based on Predicted Labels def count_infected_cell_states_over_time(embedding_dataset, prediction_df): # Convert the embedding dataset to a DataFrame - df = pd.DataFrame({ - "fov_name": embedding_dataset["fov_name"].values, - "track_id": embedding_dataset["track_id"].values, - "t": embedding_dataset["t"].values, - "id": embedding_dataset["id"].values - }) - + df = pd.DataFrame( + { + "fov_name": embedding_dataset["fov_name"].values, + "track_id": embedding_dataset["track_id"].values, + "t": embedding_dataset["t"].values, + "id": embedding_dataset["id"].values, + } + ) + # Merge with the prediction data to add Predicted Labels - df = pd.merge(df, prediction_df[['id', 'fov_name', 'Infection_Class']], on=['fov_name', 'id'], how='left') + df = pd.merge( + df, + prediction_df[["id", "fov_name", "Infection_Class"]], + on=["fov_name", "id"], + how="left", + ) # Filter by time range (2 HPI to 50 HPI) - df = df[(df['t'] >= 2) & (df['t'] <= 50)] - + df = df[(df["t"] >= 2) & (df["t"] <= 50)] + # Determine the well type (Mock, Dengue, Zika) based on fov_name - df['well_type'] = df['fov_name'].apply( - lambda x: 'Mock' if '/0/1' in x or '/0/2' in x or '/0/3' in x or '/0/4' in x else - ('Dengue' if '/0/5' in x or '/0/6' in x else 'Zika')) - + df["well_type"] = df["fov_name"].apply( + lambda x: ( + "Mock" + if "/0/1" in x or "/0/2" in x or "/0/3" in x or "/0/4" in x + else ("Dengue" if "/0/5" in x or "/0/6" in x else "Zika") + ) + ) + # Group by time, well type, and Predicted_Label to count the number of infected cells - state_counts = df.groupby(['t', 'well_type', 'Infection_Class']).size().unstack(fill_value=0) - + state_counts = ( + df.groupby(["t", "well_type", "Infection_Class"]).size().unstack(fill_value=0) + ) + # Ensure that 'infected' column exists - if 'infected' not in state_counts.columns: - state_counts['infected'] = 0 - + if "infected" not in state_counts.columns: + state_counts["infected"] = 0 + # Calculate the percentage of infected cells - state_counts['total'] = state_counts.sum(axis=1) - state_counts['infected'] = (state_counts['infected'] / state_counts['total']) * 100 - + state_counts["total"] = state_counts.sum(axis=1) + state_counts["infected"] = (state_counts["infected"] / state_counts["total"]) * 100 + return state_counts + # %% Function to Plot Percentage of Infected Cells Over Time def plot_infected_cell_states(state_counts): plt.figure(figsize=(12, 8)) # Loop through each well type - for well_type in ['Mock', 'Dengue', 'Zika']: + for well_type in ["Mock", "Dengue", "Zika"]: # Select the data for the current well type - if well_type in state_counts.index.get_level_values('well_type'): - well_data = state_counts.xs(well_type, level='well_type') - + if well_type in state_counts.index.get_level_values("well_type"): + well_data = state_counts.xs(well_type, level="well_type") + # Plot only the percentage of infected cells - if 'infected' in well_data.columns: - plt.plot(well_data.index, well_data['infected'], label=f'{well_type} - Infected') + if "infected" in well_data.columns: + plt.plot( + well_data.index, + well_data["infected"], + label=f"{well_type} - Infected", + ) plt.title("Percentage of Infected Cells Over Time - June") plt.xlabel("Hours Post Perturbation") @@ -66,8 +85,11 @@ def plot_infected_cell_states(state_counts): plt.grid(True) plt.show() + # %% Load and process June Dataset -june_features_path = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr") +june_features_path = Path( + "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/code_testing_soorya/output/Phase_RFP_smallPatch_June/phaseRFP_36patch_June.zarr" +) june_embedding_dataset = read_embedding_dataset(june_features_path) # Load the predicted labels from CSV @@ -75,7 +97,9 @@ def plot_infected_cell_states(state_counts): prediction_df = load_annotation(prediction_csv_path) # %% Count Infected Cell States Over Time as Percentage using Predicted labels -state_counts = count_infected_cell_states_over_time(june_embedding_dataset, prediction_df) +state_counts = count_infected_cell_states_over_time( + june_embedding_dataset, prediction_df +) print(state_counts.head()) state_counts.info() diff --git a/applications/contrastive_phenotyping/figures/figure_cell_infection.py b/applications/contrastive_phenotyping/figures/figure_cell_infection.py index b14ae3ae..30e7cd31 100644 --- a/applications/contrastive_phenotyping/figures/figure_cell_infection.py +++ b/applications/contrastive_phenotyping/figures/figure_cell_infection.py @@ -1,27 +1,24 @@ # %% -from pathlib import Path import sys +from pathlib import Path sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") import matplotlib.pyplot as plt import numpy as np import pandas as pd -import plotly.express as px import seaborn as sns from sklearn.decomposition import PCA +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import confusion_matrix from sklearn.preprocessing import StandardScaler from umap import UMAP -from sklearn.decomposition import PCA - from viscy.representation.embedding_writer import read_embedding_dataset from viscy.representation.evaluation import load_annotation - # %% Paths and parameters. - features_path = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" ) @@ -178,10 +175,6 @@ # %% train a linear classifier to predict infection state from PCA components -from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import train_test_split -from sklearn.metrics import classification_report - x_train = data_train_val.drop( columns=[ "infection", @@ -220,9 +213,6 @@ # %% construct confusion matrix to compare the true and predicted infection state -from sklearn.metrics import confusion_matrix -import seaborn as sns - cm = confusion_matrix(y_test, y_pred) cm_percentage = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] * 100 sns.heatmap(cm_percentage, annot=True, fmt=".2f", cmap="viridis") @@ -588,7 +578,7 @@ plt.ylim(-10, 20) plt.xlim(2, 18) plt.savefig( - f"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_feb_true_infection_" + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_feb_true_infection_" + str(time).zfill(3) + ".png", format="png", @@ -615,7 +605,7 @@ plt.ylim(-10, 18) plt.xlim(2, 18) plt.savefig( - f"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_feb_predicted_infection_" + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_feb_predicted_infection_" + str(time).zfill(3) + ".png", format="png", @@ -642,7 +632,7 @@ plt.ylim(-8, 10) plt.xlim(-5, 5) plt.savefig( - f"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_june_predicted_infection_" + "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/infection/video_umap/umap_june_predicted_infection_" + str(time).zfill(3) + ".png", format="png", diff --git a/applications/contrastive_phenotyping/figures/save_patches.py b/applications/contrastive_phenotyping/figures/save_patches.py index 2b39df93..ebba6c32 100644 --- a/applications/contrastive_phenotyping/figures/save_patches.py +++ b/applications/contrastive_phenotyping/figures/save_patches.py @@ -1,16 +1,15 @@ # %% script to save 128 by 128 image patches from napari viewer -import napari -import numpy as np -from pathlib import Path -import sys import os +import sys +from pathlib import Path + +import numpy as np sys.path.append("/hpc/mydata/soorya.pradeep/scratch/viscy_infection_phenotyping/VisCy") # from viscy.data.triplet import TripletDataModule from viscy.representation.evaluation import dataset_of_tracks - # %% input parameters data_path = Path( From 9bcb67563a8b8abfaa7398b0b9e4606b73cc6535 Mon Sep 17 00:00:00 2001 From: Soorya Pradeep Date: Wed, 16 Oct 2024 13:21:39 -0700 Subject: [PATCH 85/87] remove extra paths --- .../evaluation/GMM_clustering.py | 23 +------------------ 1 file changed, 1 insertion(+), 22 deletions(-) diff --git a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py index 46cca6dd..fe1566c5 100644 --- a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py +++ b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py @@ -21,21 +21,6 @@ "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr" ) - -feature_path_no_track = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_random_sampling2/feb_fixed_test_predict.zarr" -) - - -features_path_any_time = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/negpair_difcell_randomtime_sampling/Ver2_updateTracking_refineModel/predictions/Feb_2chan_128patch_32projDim/2chan_128patch_56ckpt_FebTest.zarr" -) - -features_path_june = Path( - "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/jun_time_interval_1_epoch_178.zarr" -) - - # %% visualize distribution of embeddings embedding_dataset = read_embedding_dataset(features_path_30_min) features_data = embedding_dataset["features"] @@ -52,15 +37,9 @@ plt.tight_layout() plt.show() -# %% initialize GMM clustering and ground truth labels - -embedding_dataset = read_embedding_dataset(features_path_june) -features_data = embedding_dataset["features"] - -cluster_evaluator = GMMClustering(features_data) - # %% Find best n_clusters +cluster_evaluator = GMMClustering(features_data) aic_scores, bic_scores = cluster_evaluator.find_best_n_clusters() plt.figure(figsize=(8, 6)) From c3368bae4eaee53ba90ed3b9bd7516a115fa2beb Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Thu, 17 Oct 2024 20:06:10 -0700 Subject: [PATCH 86/87] pulled changes from main --- .../evaluation/GMM_clustering.py | 54 +- viscy/representation/evaluation.py | 748 ------------------ .../representation/evalutation/clustering.py | 70 ++ .../evalutation/dimensionality_reduction.py | 33 +- 4 files changed, 126 insertions(+), 779 deletions(-) delete mode 100644 viscy/representation/evaluation.py diff --git a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py index 11931f25..2fe2d351 100644 --- a/applications/contrastive_phenotyping/evaluation/GMM_clustering.py +++ b/applications/contrastive_phenotyping/evaluation/GMM_clustering.py @@ -5,13 +5,12 @@ import matplotlib.pyplot as plt import seaborn as sns import numpy as np -from viscy.representation.evaluation import load_annotation from sklearn.mixture import GaussianMixture from sklearn.metrics import confusion_matrix from sklearn.decomposition import PCA -from viscy.representation.evaluation import GMMClustering -from viscy.representation.evaluation import compute_pca -from viscy.representation.evaluation import compute_umap +from viscy.representation.evalutation.clustering import GMMClustering +from viscy.representation.evalutation.dimensionality_reduction import compute_pca +from viscy.representation.evalutation.dimensionality_reduction import compute_umap # %% Paths and parameters. features_path_30_min = Path( @@ -30,6 +29,41 @@ features_path_june = Path("/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/jun_time_interval_1_epoch_178.zarr") +# load annotation +def load_annotation(da, path, name, categories: dict | None = None): + """ + Load annotations from a CSV file and map them to the dataset. + Parameters + ---------- + da : xarray.DataArray + The dataset array containing 'fov_name' and 'id' coordinates. + path : str + Path to the CSV file containing annotations. + name : str + The column name in the CSV file to be used as annotations. + categories : dict, optional + A dictionary to rename categories in the annotation column. Default is None. + Returns + ------- + pd.Series + A pandas Series containing the selected annotations mapped to the dataset. + """ + # Read the annotation CSV file + annotation = pd.read_csv(path) + # Add a leading slash to 'fov name' column and set it as 'fov_name' + annotation["fov_name"] = "/" + annotation["fov_name"] + # Set the index of the annotation DataFrame to ['fov_name', 'id'] + annotation = annotation.set_index(["fov_name", "id"]) + # Create a MultiIndex from the dataset array's 'fov_name' and 'id' values + mi = pd.MultiIndex.from_arrays( + [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] + ) + # Select the annotations corresponding to the MultiIndex + selected = annotation.loc[mi][name] + # If categories are provided, rename the categories in the selected annotations + if categories: + selected = selected.astype("category").cat.rename_categories(categories) + return selected # %% visualize distribution of embeddings embedding_dataset = read_embedding_dataset(features_path_30_min) @@ -49,12 +83,12 @@ # %% initialize GMM clustering and ground truth labels -embedding_dataset = read_embedding_dataset(features_path_june) +embedding_dataset = read_embedding_dataset(features_path_30_min) features_data = embedding_dataset['features'] cluster_evaluator = GMMClustering(features_data) -# %% Find best n_clusters +# %% Find best n_clusters, can skip this if already known aic_scores, bic_scores = cluster_evaluator.find_best_n_clusters() @@ -69,10 +103,12 @@ # %% # Choose the best model (with the lowest BIC score) -best_gmm = cluster_evaluator.fit_best_model(criterion='bic') +# set n_clusters to the best number of clusters +best_gmm = cluster_evaluator.fit_best_model(criterion='bic', n_clusters=2) cluster_labels = cluster_evaluator.predict_clusters() # %% ground truth labels (if available!) +# need to update path to this ann_root = Path( "/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/supervised_inf_pred" ) @@ -156,7 +192,7 @@ plt.show() -# %% Visualize UMAP embeddings colored by GMM cluster weights +# %% Visualize UMAP embeddings colored by GMM cluster weights (without ground truth) umap_features, umap_projection, umap_df = compute_umap(embedding_dataset) gmm_weights = best_gmm.weights_ @@ -170,7 +206,7 @@ plt.show() -# %% Visualize UMAP embeddings colored by cluster labels +# %% Visualize UMAP embeddings colored by cluster labels (without ground truth) umap_features, umap_projection, umap_df = compute_umap(embedding_dataset) plt.figure(figsize=(10, 8)) diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py deleted file mode 100644 index a2b2322a..00000000 --- a/viscy/representation/evaluation.py +++ /dev/null @@ -1,748 +0,0 @@ -from collections import defaultdict - -import numpy as np -import pandas as pd -import umap -from numpy import fft -from skimage.feature import graycomatrix, graycoprops -from skimage.filters import gaussian, threshold_otsu -from sklearn.cluster import DBSCAN -from sklearn.decomposition import PCA -from sklearn.metrics import ( - accuracy_score, - adjusted_rand_score, - normalized_mutual_info_score, - silhouette_score, -) -from sklearn.metrics.pairwise import cosine_similarity -from sklearn.mixture import GaussianMixture -from sklearn.neighbors import KNeighborsClassifier -from sklearn.preprocessing import StandardScaler - -from viscy.data.triplet import TripletDataModule - -""" -This module enables evaluation of learned representations using annotations, such as -* cell division labels, -* infection state labels, -* labels predicted using supervised classifiers, -* computed image features. - -Following evaluation methods are implemented: -* Linear classifier accuracy when labels are provided. -* Clustering evaluation using normalized mutual information (NMI) and adjusted rand index (ARI). -* Correlation between embeddings and computed features using rank correlation. - -TODO: consider time- and condition-dependent clustering and UMAP visualization of patches developed earlier: -https://github.com/mehta-lab/dynacontrast/blob/master/analysis/gmm.py -""" - - -""" -Utilities for loading datasets. -""" - -__all__ = [ - # re-exporting from sklearn - "silhouette_score", - "load_annotation", - "dataset_of_tracks", - "knn_accuracy", - "clustering_evaluation", - "compute_pca", - "compute_umap", - "FeatureExtractor", -] - - -def load_annotation(da, path, name, categories: dict | None = None): - """ - Load annotations from a CSV file and map them to the dataset. - - Parameters - ---------- - da : xarray.DataArray - The dataset array containing 'fov_name' and 'id' coordinates. - path : str - Path to the CSV file containing annotations. - name : str - The column name in the CSV file to be used as annotations. - categories : dict, optional - A dictionary to rename categories in the annotation column. Default is None. - - Returns - ------- - pd.Series - A pandas Series containing the selected annotations mapped to the dataset. - """ - # Read the annotation CSV file - annotation = pd.read_csv(path) - - # Add a leading slash to 'fov name' column and set it as 'fov_name' - annotation["fov_name"] = "/" + annotation["fov_name"] - - # Set the index of the annotation DataFrame to ['fov_name', 'id'] - annotation = annotation.set_index(["fov_name", "id"]) - - # Create a MultiIndex from the dataset array's 'fov_name' and 'id' values - mi = pd.MultiIndex.from_arrays( - [da["fov_name"].values, da["id"].values], names=["fov_name", "id"] - ) - - # Select the annotations corresponding to the MultiIndex - selected = annotation.loc[mi][name] - - # If categories are provided, rename the categories in the selected annotations - if categories: - selected = selected.astype("category").cat.rename_categories(categories) - - return selected - - -def dataset_of_tracks( - data_path, - tracks_path, - fov_list, - track_id_list, - source_channel=["Phase3D", "RFP"], - z_range=(28, 43), - initial_yx_patch_size=(128, 128), - final_yx_patch_size=(128, 128), -): - data_module = TripletDataModule( - data_path=data_path, - tracks_path=tracks_path, - include_fov_names=fov_list, - include_track_ids=track_id_list, - source_channel=source_channel, - z_range=z_range, - initial_yx_patch_size=initial_yx_patch_size, - final_yx_patch_size=final_yx_patch_size, - batch_size=1, - num_workers=16, - normalizations=None, - predict_cells=True, - ) - # for train and val - data_module.setup("predict") - prediction_dataset = data_module.predict_dataset - return prediction_dataset - - -"""Clustering algortihms.""" - - -class GMMClustering: - def __init__(self, features_data, n_clusters_range=np.arange(2, 10)): - self.features_data = features_data - self.n_clusters_range = n_clusters_range - self.best_n_clusters = None - self.best_gmm = None - self.aic_scores = None - self.bic_scores = None - - def find_best_n_clusters(self): - """Find the best number of clusters using AIC/BIC scores.""" - aic_scores = [] - bic_scores = [] - for n in self.n_clusters_range: - gmm = GaussianMixture(n_components=n, random_state=42) - gmm.fit(self.features_data) - aic_scores.append(gmm.aic(self.features_data)) - bic_scores.append(gmm.bic(self.features_data)) - - self.aic_scores = aic_scores - self.bic_scores = bic_scores - - return aic_scores, bic_scores - - def fit_best_model(self, criterion="bic", n_clusters=None): - """ - Fit the best GMM model based on AIC or BIC scores, or a user-specified number of clusters. - - Parameters: - - criterion: 'aic' or 'bic' to select the best model based on the chosen criterion. - - n_clusters: Specify a fixed number of clusters (overrides the 'best' search). - """ - # Case 1: If the user provides n_clusters, use it directly - if n_clusters is not None: - self.best_n_clusters = n_clusters - - # Case 2: If no n_clusters is provided but find_best_n_clusters was run, use stored AIC/BIC results - elif self.aic_scores is not None and self.bic_scores is not None: - if criterion == "bic": - self.best_n_clusters = self.n_clusters_range[np.argmin(self.bic_scores)] - else: - self.best_n_clusters = self.n_clusters_range[np.argmin(self.aic_scores)] - - # Case 3: If find_best_n_clusters hasn't been run, compute AIC/BIC scores now - else: - aic_scores, bic_scores = self.find_best_n_clusters() - if criterion == "bic": - self.best_n_clusters = self.n_clusters_range[np.argmin(bic_scores)] - else: - self.best_n_clusters = self.n_clusters_range[np.argmin(aic_scores)] - - self.best_gmm = GaussianMixture( - n_components=self.best_n_clusters, random_state=42 - ) - self.best_gmm.fit(self.features_data) - - return self.best_gmm - - def predict_clusters(self): - """Run prediction on the fitted best GMM model.""" - if self.best_gmm is None: - raise Exception( - "No GMM model is fitted yet. Please run fit_best_model() first." - ) - cluster_labels = self.best_gmm.predict(self.features_data) - return cluster_labels - - -def knn_accuracy(embeddings, annotations, k=5): - """ - Evaluate the k-NN classification accuracy. - - Parameters - ---------- - k : int, optional - Number of neighbors to use for k-NN. Default is 5. - - Returns - ------- - float - Accuracy of the k-NN classifier. - """ - knn = KNeighborsClassifier(n_neighbors=k) - knn.fit(embeddings, annotations) - predictions = knn.predict(embeddings) - accuracy = accuracy_score(annotations, predictions) - return accuracy - - -def dbscan_clustering(embeddings, eps=0.5, min_samples=5): - """ - Apply DBSCAN clustering to the embeddings. - - Parameters - ---------- - eps : float, optional - The maximum distance between two samples for them to be considered as in the same neighborhood. Default is 0.5. - min_samples : int, optional - The number of samples in a neighborhood for a point to be considered as a core point. Default is 5. - - Returns - ------- - np.ndarray - Clustering labels assigned by DBSCAN. - """ - dbscan = DBSCAN(eps=eps, min_samples=min_samples) - clusters = dbscan.fit_predict(embeddings) - return clusters - - -def clustering_evaluation(embeddings, annotations, method="nmi"): - """ - Evaluate the clustering of the embeddings compared to the ground truth labels. - - Parameters - ---------- - method : str, optional - Metric to use for evaluation ('nmi' or 'ari'). Default is 'nmi'. - - Returns - ------- - float - NMI or ARI score depending on the method chosen. - """ - clusters = dbscan_clustering(embeddings) - - if method == "nmi": - score = normalized_mutual_info_score(annotations, clusters) - elif method == "ari": - score = adjusted_rand_score(annotations, clusters) - else: - raise ValueError("Invalid method. Choose 'nmi' or 'ari'.") - - return score - - -def compute_pca(embedding_dataset, n_components=None, normalize_features=False): - features = embedding_dataset["features"] - projections = embedding_dataset["projections"] - - if normalize_features: - scaled_projections = StandardScaler().fit_transform(projections.values) - scaled_features = StandardScaler().fit_transform(features.values) - else: - scaled_projections = projections.values - scaled_features = features.values - - PCA_features = PCA(n_components=n_components, random_state=42) - PCA_projection = PCA(n_components=n_components, random_state=42) - pc_features = PCA_features.fit_transform(scaled_features) - pc_projection = PCA_projection.fit_transform(scaled_projections) - - pca_df_dict = { - "id": embedding_dataset["id"].values, - "fov_name": embedding_dataset["fov_name"].values, - } - - for i in range(n_components): - pca_df_dict[f"PCA{i + 1}"] = pc_features[:, i] - pca_df_dict[f"PCA{i + 1}_proj"] = pc_projection[:, i] - - pca_df = pd.DataFrame(pca_df_dict) - - return PCA_features, PCA_projection, pca_df - - -def compute_umap(embedding_dataset, normalize_features=True): - features = embedding_dataset["features"] - projections = embedding_dataset["projections"] - - if normalize_features: - scaled_projections = StandardScaler().fit_transform(projections.values) - scaled_features = StandardScaler().fit_transform(features.values) - else: - scaled_projections = projections.values - scaled_features = features.values - - # Compute UMAP for features and projections - # Computing 3 components to enable 3D visualization. - umap_features = umap.UMAP(random_state=42, n_components=2) - umap_projection = umap.UMAP(random_state=42, n_components=2) - umap_features_embedding = umap_features.fit_transform(scaled_features) - umap_projection_embedding = umap_projection.fit_transform(scaled_projections) - - # Prepare DataFrame with id and UMAP coordinates - umap_df = pd.DataFrame( - { - "id": embedding_dataset["id"].values, - "track_id": embedding_dataset["track_id"].values, - "t": embedding_dataset["t"].values, - "fov_name": embedding_dataset["fov_name"].values, - "UMAP1": umap_features_embedding[:, 0], - "UMAP2": umap_features_embedding[:, 1], - "UMAP1_proj": umap_projection_embedding[:, 0], - "UMAP2_proj": umap_projection_embedding[:, 1], - } - ) - - return umap_features, umap_projection, umap_df - - -class FeatureExtractor: - # FIXME: refactor into a separate module with standalone functions - - def __init__(self): - pass - - def compute_fourier_descriptors(image): - """ - Compute the Fourier descriptors of the image - The sensor or nuclear shape changes when infected, which can be captured by analyzing Fourier descriptors - :param np.array image: input image - :return: Fourier descriptors - """ - # Convert contour to complex numbers - contour_complex = image[:, 0] + 1j * image[:, 1] - - # Compute Fourier descriptors - descriptors = np.fft.fft(contour_complex) - - return descriptors - - def analyze_symmetry(descriptors): - """ - Analyze the symmetry of the Fourier descriptors - Symmetry of the sensor or nuclear shape changes when infected - :param np.array descriptors: Fourier descriptors - :return: standard deviation of the descriptors - """ - # Normalize descriptors - descriptors = np.abs(descriptors) / np.max(np.abs(descriptors)) - - return np.std(descriptors) # Lower standard deviation indicates higher symmetry - - def compute_area(input_image, sigma=0.6): - """Create a binary mask using morphological operations - Sensor area will increase when infected due to expression in nucleus - :param np.array input_image: generate masks from this 3D image - :param float sigma: Gaussian blur standard deviation, increase in value increases blur - :return: area of the sensor mask & mean intensity inside the sensor area - """ - - input_image_blur = gaussian(input_image, sigma=sigma) - - thresh = threshold_otsu(input_image_blur) - mask = input_image >= thresh - - # Apply sensor mask to the image - masked_image = input_image * mask - - # Compute the mean intensity inside the sensor area - masked_intensity = np.mean(masked_image) - - return masked_intensity, np.sum(mask) - - def compute_spectral_entropy(image): - """ - Compute the spectral entropy of the image - High frequency components are observed to increase in phase and reduce in sensor when cell is infected - :param np.array image: input image - :return: spectral entropy - """ - - # Compute the 2D Fourier Transform - f_transform = fft.fft2(image) - - # Compute the power spectrum - power_spectrum = np.abs(f_transform) ** 2 - - # Compute the probability distribution - power_spectrum += 1e-10 # Avoid log(0) issues - prob_distribution = power_spectrum / np.sum(power_spectrum) - - # Compute the spectral entropy - entropy = -np.sum(prob_distribution * np.log(prob_distribution)) - - return entropy - - def compute_glcm_features(image): - """ - Compute the contrast, dissimilarity and homogeneity of the image - Both sensor and phase texture changes when infected, smooth in sensor, and rough in phase - :param np.array image: input image - :return: contrast, dissimilarity, homogeneity - """ - - # Normalize the input image from 0 to 255 - image = (image - np.min(image)) * (255 / (np.max(image) - np.min(image))) - image = image.astype(np.uint8) - - # Compute the GLCM - distances = [1] # Distance between pixels - angles = [0] # Angle in radians - - glcm = graycomatrix(image, distances, angles, symmetric=True, normed=True) - - # Compute GLCM properties - contrast = graycoprops(glcm, "contrast")[0, 0] - dissimilarity = graycoprops(glcm, "dissimilarity")[0, 0] - homogeneity = graycoprops(glcm, "homogeneity")[0, 0] - - return contrast, dissimilarity, homogeneity - - def compute_iqr(image): - """ - Compute the interquartile range of pixel intensities - Observed to increase when cell is infected - :param np.array image: input image - :return: interquartile range of pixel intensities - """ - - # Compute the interquartile range of pixel intensities - iqr = np.percentile(image, 75) - np.percentile(image, 25) - - return iqr - - def compute_mean_intensity(image): - """ - Compute the mean pixel intensity - Expected to vary when cell morphology changes due to infection, divison or death - :param np.array image: input image - :return: mean pixel intensity - """ - - # Compute the mean pixel intensity - mean_intensity = np.mean(image) - - return mean_intensity - - def compute_std_dev(image): - """ - Compute the standard deviation of pixel intensities - Expected to vary when cell morphology changes due to infection, divison or death - :param np.array image: input image - :return: standard deviation of pixel intensities - """ - # Compute the standard deviation of pixel intensities - std_dev = np.std(image) - - return std_dev - - def compute_radial_intensity_gradient(image): - """ - Compute the radial intensity gradient of the image - The sensor relocalizes inside the nucleus, which is center of the image when cells are infected - Expected negative gradient when infected and zero to positive gradient when not infected - :param np.array image: input image - :return: radial intensity gradient - """ - # normalize the image - image = (image - np.min(image)) / (np.max(image) - np.min(image)) - - # compute the intensity gradient from center to periphery - y, x = np.indices(image.shape) - center = np.array(image.shape) / 2 - r = np.sqrt((x - center[1]) ** 2 + (y - center[0]) ** 2) - r = r.astype(int) - tbin = np.bincount(r.ravel(), image.ravel()) - nr = np.bincount(r.ravel()) - radial_intensity_values = tbin / nr - - # get the slope radial_intensity_values - from scipy.stats import linregress - - radial_intensity_gradient = linregress( - range(len(radial_intensity_values)), radial_intensity_values - ) - - return radial_intensity_gradient[0] - - -def calculate_cosine_similarity_cell(embedding_dataset, fov_name, track_id): - """Extract embeddings and calculate cosine similarities for a specific cell""" - # Filter the dataset for the specific infected cell - filtered_data = embedding_dataset.where( - (embedding_dataset["fov_name"] == fov_name) - & (embedding_dataset["track_id"] == track_id), - drop=True, - ) - - # Extract the feature embeddings and time points - features = filtered_data["features"].values # (sample, features) - time_points = filtered_data["t"].values # (sample,) - - # Get the first time point's embedding - first_time_point_embedding = features[0].reshape(1, -1) - - # Calculate cosine similarity between each time point and the first time point - cosine_similarities = [] - for i in range(len(time_points)): - similarity = cosine_similarity( - first_time_point_embedding, features[i].reshape(1, -1) - ) - cosine_similarities.append(similarity[0][0]) - - return time_points, cosine_similarities - - -def compute_displacement_mean_std( - embedding_dataset, max_tau=10, use_cosine=False, use_dissimilarity=False -): - """Compute the norm of differences between embeddings at t and t + tau""" - # Get the arrays of (fov_name, track_id, t, and embeddings) - fov_names = embedding_dataset["fov_name"].values - track_ids = embedding_dataset["track_id"].values - timepoints = embedding_dataset["t"].values - embeddings = embedding_dataset["features"].values - - # Dictionary to store displacements for each tau - displacement_per_tau = defaultdict(list) - - # Iterate over all entries in the dataset - for i in range(len(fov_names)): - fov_name = fov_names[i] - track_id = track_ids[i] - current_time = timepoints[i] - current_embedding = embeddings[i] - - # For each time point t, compute displacements for t + tau - for tau in range(1, max_tau + 1): - future_time = current_time + tau - - # Find if future_time exists for the same (fov_name, track_id) - matching_indices = np.where( - (fov_names == fov_name) - & (track_ids == track_id) - & (timepoints == future_time) - )[0] - - if len(matching_indices) == 1: - # Get the embedding at t + tau - future_embedding = embeddings[matching_indices[0]] - - if use_cosine: - # Compute cosine similarity - similarity = cosine_similarity( - current_embedding.reshape(1, -1), - future_embedding.reshape(1, -1), - )[0][0] - # Choose whether to use similarity or dissimilarity - if use_dissimilarity: - displacement = 1 - similarity # Cosine dissimilarity - else: - displacement = similarity # Cosine similarity - else: - # Compute the Euclidean distance, elementwise square on difference - displacement = np.sum((current_embedding - future_embedding) ** 2) - - # Store the displacement for the given tau - displacement_per_tau[tau].append(displacement) - - # Compute mean and std displacement for each tau by averaging the displacements - mean_displacement_per_tau = { - tau: np.mean(displacements) - for tau, displacements in displacement_per_tau.items() - } - std_displacement_per_tau = { - tau: np.std(displacements) - for tau, displacements in displacement_per_tau.items() - } - - return mean_displacement_per_tau, std_displacement_per_tau - - -def compute_displacement( - embedding_dataset, - max_tau=10, - use_cosine=False, - use_dissimilarity=False, - use_umap=False, -): - """Compute the norm of differences between embeddings at t and t + tau""" - # Get the arrays of (fov_name, track_id, t, and embeddings) - fov_names = embedding_dataset["fov_name"].values - track_ids = embedding_dataset["track_id"].values - timepoints = embedding_dataset["t"].values - - if use_umap: - umap1 = embedding_dataset["UMAP1"].values - umap2 = embedding_dataset["UMAP2"].values - embeddings = np.vstack((umap1, umap2)).T - else: - embeddings = embedding_dataset["features"].values - - # Dictionary to store displacements for each tau - displacement_per_tau = defaultdict(list) - - # Iterate over all entries in the dataset - for i in range(len(fov_names)): - fov_name = fov_names[i] - track_id = track_ids[i] - current_time = timepoints[i] - current_embedding = embeddings[i] - - # For each time point t, compute displacements for t + tau - for tau in range(1, max_tau + 1): - future_time = current_time + tau - - # Find if future_time exists for the same (fov_name, track_id) - matching_indices = np.where( - (fov_names == fov_name) - & (track_ids == track_id) - & (timepoints == future_time) - )[0] - - if len(matching_indices) == 1: - # Get the embedding at t + tau - future_embedding = embeddings[matching_indices[0]] - - if use_cosine: - # Compute cosine similarity - similarity = cosine_similarity( - current_embedding.reshape(1, -1), - future_embedding.reshape(1, -1), - )[0][0] - # Choose whether to use similarity or dissimilarity - if use_dissimilarity: - displacement = 1 - similarity # Cosine dissimilarity - else: - displacement = similarity # Cosine similarity - else: - # Compute the Euclidean distance, elementwise square on difference - displacement = np.sum((current_embedding - future_embedding) ** 2) - - # Store the displacement for the given tau - displacement_per_tau[tau].append(displacement) - - return displacement_per_tau - - -def calculate_normalized_euclidean_distance_cell(embedding_dataset, fov_name, track_id): - filtered_data = embedding_dataset.where( - (embedding_dataset["fov_name"] == fov_name) - & (embedding_dataset["track_id"] == track_id), - drop=True, - ) - - features = filtered_data["features"].values # (sample, features) - time_points = filtered_data["t"].values # (sample,) - - normalized_features = features / np.linalg.norm(features, axis=1, keepdims=True) - - # Get the first time point's normalized embedding - first_time_point_embedding = normalized_features[0].reshape(1, -1) - - euclidean_distances = [] - for i in range(len(time_points)): - distance = np.linalg.norm( - first_time_point_embedding - normalized_features[i].reshape(1, -1) - ) - euclidean_distances.append(distance) - - return time_points, euclidean_distances - - -def compute_displacement_mean_std_full(embedding_dataset, max_tau=10): - fov_names = embedding_dataset["fov_name"].values - track_ids = embedding_dataset["track_id"].values - timepoints = embedding_dataset["t"].values - embeddings = embedding_dataset["features"].values - - cell_identifiers = np.array( - list(zip(fov_names, track_ids)), - dtype=[("fov_name", "O"), ("track_id", "int64")], - ) - - unique_cells = np.unique(cell_identifiers) - - displacement_per_tau = defaultdict(list) - - for cell in unique_cells: - fov_name = cell["fov_name"] - track_id = cell["track_id"] - - indices = np.where((fov_names == fov_name) & (track_ids == track_id))[0] - - cell_timepoints = timepoints[indices] - cell_embeddings = embeddings[indices] - - sorted_indices = np.argsort(cell_timepoints) - cell_timepoints = cell_timepoints[sorted_indices] - cell_embeddings = cell_embeddings[sorted_indices] - - for i in range(len(cell_timepoints)): - current_time = cell_timepoints[i] - current_embedding = cell_embeddings[i] - - current_embedding = current_embedding / np.linalg.norm(current_embedding) - - for tau in range(0, max_tau + 1): - future_time = current_time + tau - - future_index = np.where(cell_timepoints == future_time)[0] - - if len(future_index) >= 1: - future_embedding = cell_embeddings[future_index[0]] - future_embedding = future_embedding / np.linalg.norm( - future_embedding - ) - - distance = np.linalg.norm(current_embedding - future_embedding) - - displacement_per_tau[tau].append(distance) - - mean_displacement_per_tau = { - tau: np.mean(displacements) - for tau, displacements in displacement_per_tau.items() - } - std_displacement_per_tau = { - tau: np.std(displacements) - for tau, displacements in displacement_per_tau.items() - } - - return mean_displacement_per_tau, std_displacement_per_tau diff --git a/viscy/representation/evalutation/clustering.py b/viscy/representation/evalutation/clustering.py index d87d3968..051ac965 100644 --- a/viscy/representation/evalutation/clustering.py +++ b/viscy/representation/evalutation/clustering.py @@ -7,6 +7,76 @@ normalized_mutual_info_score, ) from sklearn.neighbors import KNeighborsClassifier +from sklearn.mixture import GaussianMixture +import numpy as np + +class GMMClustering: + def __init__(self, features_data, n_clusters_range=np.arange(2, 10)): + self.features_data = features_data + self.n_clusters_range = n_clusters_range + self.best_n_clusters = None + self.best_gmm = None + self.aic_scores = None + self.bic_scores = None + + def find_best_n_clusters(self): + """Find the best number of clusters using AIC/BIC scores.""" + aic_scores = [] + bic_scores = [] + for n in self.n_clusters_range: + gmm = GaussianMixture(n_components=n, random_state=42) + gmm.fit(self.features_data) + aic_scores.append(gmm.aic(self.features_data)) + bic_scores.append(gmm.bic(self.features_data)) + + self.aic_scores = aic_scores + self.bic_scores = bic_scores + + return aic_scores, bic_scores + + def fit_best_model(self, criterion="bic", n_clusters=None): + """ + Fit the best GMM model based on AIC or BIC scores, or a user-specified number of clusters. + + Parameters: + - criterion: 'aic' or 'bic' to select the best model based on the chosen criterion. + - n_clusters: Specify a fixed number of clusters (overrides the 'best' search). + """ + # Case 1: If the user provides n_clusters, use it directly + if n_clusters is not None: + self.best_n_clusters = n_clusters + + # Case 2: If no n_clusters is provided but find_best_n_clusters was run, use stored AIC/BIC results + elif self.aic_scores is not None and self.bic_scores is not None: + if criterion == "bic": + self.best_n_clusters = self.n_clusters_range[np.argmin(self.bic_scores)] + else: + self.best_n_clusters = self.n_clusters_range[np.argmin(self.aic_scores)] + + # Case 3: If find_best_n_clusters hasn't been run, compute AIC/BIC scores now + else: + aic_scores, bic_scores = self.find_best_n_clusters() + if criterion == "bic": + self.best_n_clusters = self.n_clusters_range[np.argmin(bic_scores)] + else: + self.best_n_clusters = self.n_clusters_range[np.argmin(aic_scores)] + + self.best_gmm = GaussianMixture( + n_components=self.best_n_clusters, random_state=42 + ) + self.best_gmm.fit(self.features_data) + + return self.best_gmm + + def predict_clusters(self): + """Run prediction on the fitted best GMM model.""" + if self.best_gmm is None: + raise Exception( + "No GMM model is fitted yet. Please run fit_best_model() first." + ) + cluster_labels = self.best_gmm.predict(self.features_data) + return cluster_labels + def knn_accuracy(embeddings, annotations, k=5): diff --git a/viscy/representation/evalutation/dimensionality_reduction.py b/viscy/representation/evalutation/dimensionality_reduction.py index 0a906bf4..047ee3b0 100644 --- a/viscy/representation/evalutation/dimensionality_reduction.py +++ b/viscy/representation/evalutation/dimensionality_reduction.py @@ -8,7 +8,7 @@ from xarray import Dataset -def compute_pca(embedding_dataset, n_components=None, normalize_features=True): +def compute_pca(embedding_dataset, n_components=None, normalize_features=False): features = embedding_dataset["features"] projections = embedding_dataset["projections"] @@ -19,34 +19,23 @@ def compute_pca(embedding_dataset, n_components=None, normalize_features=True): scaled_projections = projections.values scaled_features = features.values - # Compute PCA with specified number of components PCA_features = PCA(n_components=n_components, random_state=42) PCA_projection = PCA(n_components=n_components, random_state=42) pc_features = PCA_features.fit_transform(scaled_features) pc_projection = PCA_projection.fit_transform(scaled_projections) - # Prepare DataFrame with id and PCA coordinates - pca_df = pd.DataFrame( - { - "id": embedding_dataset["id"].values, - "fov_name": embedding_dataset["fov_name"].values, - "PCA1": pc_features[:, 0], - "PCA2": pc_features[:, 1], - "PCA3": pc_features[:, 2], - "PCA4": pc_features[:, 3], - "PCA5": pc_features[:, 4], - "PCA6": pc_features[:, 5], - "PCA1_proj": pc_projection[:, 0], - "PCA2_proj": pc_projection[:, 1], - "PCA3_proj": pc_projection[:, 2], - "PCA4_proj": pc_projection[:, 3], - "PCA5_proj": pc_projection[:, 4], - "PCA6_proj": pc_projection[:, 5], - } - ) + pca_df_dict = { + "id": embedding_dataset["id"].values, + "fov_name": embedding_dataset["fov_name"].values, + } - return PCA_features, PCA_projection, pca_df + for i in range(n_components): + pca_df_dict[f"PCA{i + 1}"] = pc_features[:, i] + pca_df_dict[f"PCA{i + 1}_proj"] = pc_projection[:, i] + pca_df = pd.DataFrame(pca_df_dict) + + return PCA_features, PCA_projection, pca_df def _fit_transform_umap( embeddings: NDArray, n_components: int = 2, normalize: bool = True From 6996bbd7847c42eddecaf7ca7d88492f8e7ca7e7 Mon Sep 17 00:00:00 2001 From: Alishba Imran Date: Thu, 17 Oct 2024 20:23:44 -0700 Subject: [PATCH 87/87] merging bugs fixed --- viscy/data/hcs.py | 11 ++++++++--- viscy/representation/contrastive.py | 3 +-- viscy/representation/evalutation/clustering.py | 6 +++--- .../evalutation/dimensionality_reduction.py | 1 + 4 files changed, 13 insertions(+), 8 deletions(-) diff --git a/viscy/data/hcs.py b/viscy/data/hcs.py index db518a3c..54837bb3 100644 --- a/viscy/data/hcs.py +++ b/viscy/data/hcs.py @@ -3,6 +3,7 @@ import os import re import tempfile +import warnings from pathlib import Path from typing import Callable, Literal, Sequence @@ -25,8 +26,12 @@ from torch.utils.data import DataLoader, Dataset from viscy.data.typing import ChannelMap, DictTransform, HCSStackIndex, NormMeta, Sample -import warnings -warnings.filterwarnings("ignore", category=UserWarning, message="To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).") + +warnings.filterwarnings( + "ignore", + category=UserWarning, + message="To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).", +) _logger = logging.getLogger("lightning.pytorch") @@ -551,7 +556,7 @@ def predict_dataloader(self): self.predict_dataset, batch_size=self.batch_size, num_workers=self.num_workers, - shuffle=False, + shuffle=False, ) def _fit_transform(self) -> tuple[Compose, Compose]: diff --git a/viscy/representation/contrastive.py b/viscy/representation/contrastive.py index dd89d4cf..dd4186fc 100644 --- a/viscy/representation/contrastive.py +++ b/viscy/representation/contrastive.py @@ -1,3 +1,4 @@ +import warnings from typing import Literal import timm @@ -5,7 +6,6 @@ from torch import Tensor from viscy.unet.networks.unext2 import StemDepthtoChannels -import warnings warnings.filterwarnings("ignore", category=UserWarning, module="torch") @@ -84,7 +84,6 @@ def __init__( ) # Append modified encoder. self.encoder = encoder - self.intermediate_projection = intermediate_projection # Append modified projection head. self.projection = projection diff --git a/viscy/representation/evalutation/clustering.py b/viscy/representation/evalutation/clustering.py index 051ac965..b7065339 100644 --- a/viscy/representation/evalutation/clustering.py +++ b/viscy/representation/evalutation/clustering.py @@ -1,14 +1,15 @@ """Methods for evaluating clustering performance.""" +import numpy as np from sklearn.cluster import DBSCAN from sklearn.metrics import ( accuracy_score, adjusted_rand_score, normalized_mutual_info_score, ) -from sklearn.neighbors import KNeighborsClassifier from sklearn.mixture import GaussianMixture -import numpy as np +from sklearn.neighbors import KNeighborsClassifier + class GMMClustering: def __init__(self, features_data, n_clusters_range=np.arange(2, 10)): @@ -78,7 +79,6 @@ def predict_clusters(self): return cluster_labels - def knn_accuracy(embeddings, annotations, k=5): """ Evaluate the k-NN classification accuracy. diff --git a/viscy/representation/evalutation/dimensionality_reduction.py b/viscy/representation/evalutation/dimensionality_reduction.py index 047ee3b0..130c8634 100644 --- a/viscy/representation/evalutation/dimensionality_reduction.py +++ b/viscy/representation/evalutation/dimensionality_reduction.py @@ -37,6 +37,7 @@ def compute_pca(embedding_dataset, n_components=None, normalize_features=False): return PCA_features, PCA_projection, pca_df + def _fit_transform_umap( embeddings: NDArray, n_components: int = 2, normalize: bool = True ) -> tuple[umap.UMAP, NDArray]: