diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml new file mode 100644 index 00000000..3796291d --- /dev/null +++ b/.github/workflows/black.yml @@ -0,0 +1,35 @@ +name: Black linter + +on: + pull_request: + paths: + - '**.py' + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + with: + ref: ${{ github.head_ref }} + token: ${{ secrets.GITHUB_TOKEN }} + + - name: Install Black + run: pip install 'black[jupyter]==23.11.0' + + - name: Run Black + id: black_check + run: | + black --check --verbose . || echo "CHANGES_NEEDED=true" >> $GITHUB_ENV + continue-on-error: true + + - name: Apply Black reformatting + if: env.CHANGES_NEEDED=='true' + run: | + black . + git config --global user.name 'github-actions' + git config --global user.email 'github-actions@github.com' + git add -A + git diff --quiet && git diff --staged --quiet || (git commit -m "Apply Black formatting" && git push) + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/alphadia/__init__.py b/alphadia/__init__.py index bfef8ecd..83c347d4 100644 --- a/alphadia/__init__.py +++ b/alphadia/__init__.py @@ -2,7 +2,7 @@ __project__ = "alphadia" -__version__ = "1.4.0" +__version__ = "1.5.0" __license__ = "Apache" __description__ = "An open-source Python package of the AlphaPept ecosystem" __author__ = "Mann Labs" diff --git a/alphadia/cli.py b/alphadia/cli.py index c75cea55..100bf9f2 100644 --- a/alphadia/cli.py +++ b/alphadia/cli.py @@ -11,6 +11,23 @@ from alphadia.workflow import reporting from alphadia import utils +from alphabase.constants import modification + +modification.add_new_modifications( + { + "Dimethyl:d12@Protein N-term": {"composition": "H(-2)2H(8)13C(2)"}, + "Dimethyl:d12@Any N-term": { + "composition": "H(-2)2H(8)13C(2)", + }, + "Dimethyl:d12@R": { + "composition": "H(-2)2H(8)13C(2)", + }, + "Dimethyl:d12@K": { + "composition": "H(-2)2H(8)13C(2)", + }, + } +) + # alpha family imports # third party imports @@ -101,6 +118,7 @@ def gui(): "--config-update", help="Dict which will be used to update the default config.", type=str, + default={}, ) @click.option( "--neptune-token", @@ -124,7 +142,7 @@ def extract(**kwargs): kwargs["neptune_tag"] = list(kwargs["neptune_tag"]) # load config file if specified - config_update = None + config_update = {} if kwargs["config"] is not None: with open(kwargs["config"], "r") as f: config_update = yaml.safe_load(f) diff --git a/alphadia/fdr.py b/alphadia/fdr.py index e63e5464..4171b242 100644 --- a/alphadia/fdr.py +++ b/alphadia/fdr.py @@ -27,6 +27,7 @@ def perform_fdr( competetive: bool = False, group_channels: bool = True, figure_path: Optional[str] = None, + neptune_run=None, ): """Performs FDR calculation on a dataframe of PSMs @@ -123,7 +124,7 @@ def perform_fdr( classifier, psm_df["qval"], figure_path=figure_path, - # neptune_run=neptune_run + neptune_run=neptune_run, ) return psm_df @@ -339,6 +340,7 @@ def plot_fdr( ax[0].set_ylabel("true positive rate") ax[0].legend() + ax[1].set_xlim(0, 1) ax[1].hist( np.concatenate([y_test_proba[y_test == 0], y_train_proba[y_train == 0]]), bins=50, @@ -376,7 +378,7 @@ def plot_fdr( if figure_path is not None: fig.savefig(os.path.join(figure_path, "fdr.pdf")) - # if neptune_run is not None: - # neptune_run['eval/fdr'].log(fig) + if neptune_run is not None: + neptune_run["eval/fdr"].log(fig) plt.close() diff --git a/alphadia/fdrexperimental.py b/alphadia/fdrexperimental.py index 6f1cf366..c705ba56 100644 --- a/alphadia/fdrexperimental.py +++ b/alphadia/fdrexperimental.py @@ -3,18 +3,24 @@ import warnings from copy import deepcopy import typing +import warnings +from abc import ABC, abstractmethod +from copy import deepcopy # alphadia imports # alpha family imports # third party imports +import numba as nb import numpy as np import pandas as pd import torch import torch.nn as nn import torch.optim as optim from sklearn import model_selection +from tqdm import tqdm +from torchmetrics.classification import BinaryAUROC class Classifier(ABC): @@ -122,13 +128,17 @@ def __init__( input_dim: int = 10, output_dim: int = 2, test_size: float = 0.2, - batch_size: int = 1000, + max_batch_size: int = 10000, + min_batch_number: int = 100, epochs: int = 10, learning_rate: float = 0.0002, weight_decay: float = 0.00001, layers: typing.List[int] = [100, 50, 20, 5], dropout: float = 0.001, - metric_interval: int = 1000, + calculate_metrics: bool = True, + metric_interval: int = 1, + patience: int = 3, + **kwargs, ): """Binary Classifier using a feed forward neural network. @@ -144,14 +154,20 @@ def __init__( test_size : float, default=0.2 Fraction of the data to be used for testing. - batch_size : int, default=1000 - Batch size for training. + max_batch_size : int, default=5000 + Maximum batch size for training. + The actual batch will be scaled to make sure at least min_batch_number batches are used. + + min_batch_number : int, default=100 + Minimum number of batches for training. + The actual batch number will be scaled if more than min_batchnumber * max_batch_size samples are available. epochs : int, default=10 Number of epochs for training. learning_rate : float, default=0.0002 - Learning rate for training. + Base learning rate for a batch size of max_batch_size. + If smaller batches are used, the learning rate will be scaled linearly. weight_decay : float, default=0.00001 Weight decay for training. @@ -162,13 +178,20 @@ def __init__( dropout : float, default=0.001 Dropout probability for training. - metric_interval : int, default=1000 - Interval for logging metrics during training. + calculate_metrics : bool, default=True + Whether to calculate metrics during training. + + metric_interval : int, default=1 + Interval for logging metrics during training, once per metric_interval epochs. + + patience : int, default=3 + Number of epochs to wait for improvement before early stopping. """ self.test_size = test_size - self.batch_size = batch_size + self.max_batch_size = max_batch_size + self.min_batch_number = min_batch_number self.epochs = epochs self.learning_rate = learning_rate self.weight_decay = weight_decay @@ -177,6 +200,8 @@ def __init__( self.input_dim = input_dim self.output_dim = output_dim self.metric_interval = metric_interval + self.calculate_metrics = calculate_metrics + self.patience = patience self.network = None self.optimizer = None @@ -186,11 +211,18 @@ def __init__( "epoch": [], "batch_count": [], "train_loss": [], - "train_accuracy": [], "test_loss": [], - "test_accuracy": [], + "train_auc": [], + "train_fdr01": [], + "train_fdr1": [], + "test_auc": [], + "test_fdr01": [], + "test_fdr1": [], } + if kwargs: + warnings.warn("Unknown arguments: {}".format(kwargs)) + @property def fitted(self): return self._fitted @@ -257,6 +289,32 @@ def from_state_dict(self, state_dict: dict): self.__dict__.update(_state_dict) + def _prepare_data(self, x: np.ndarray, y: np.ndarray): + """Prepare the data for training: normalize, split into train and test set. + + Parameters + ---------- + + x : np.array, dtype=float + Training data of shape (n_samples, n_features). + + y : np.array, dtype=int + Target values of shape (n_samples,) or (n_samples, n_classes). + """ + x -= x.mean(axis=0) + x /= x.std(axis=0) + 1e-6 + + if y.ndim == 1: + y = np.stack([1 - y, y], axis=1) + x_train, x_test, y_train, y_test = model_selection.train_test_split( + x, y, test_size=self.test_size + ) + x_train = torch.from_numpy(x_train).float() + y_train = torch.from_numpy(y_train).float() + x_test = torch.from_numpy(x_test).float() + y_test = torch.from_numpy(y_test).float() + return x_train, x_test, y_train, y_test + def fit(self, x: np.ndarray, y: np.ndarray): """Fit the classifier to the data. @@ -271,6 +329,10 @@ def fit(self, x: np.ndarray, y: np.ndarray): """ + batch_number = max(self.min_batch_number, x.shape[0] // self.max_batch_size) + batch_size = x.shape[0] // batch_number + lr_scaled = self.learning_rate * batch_size / self.max_batch_size + force_reinit = False if self.input_dim != x.shape[1] and self.network is not None: @@ -289,64 +351,851 @@ def fit(self, x: np.ndarray, y: np.ndarray): dropout=self.dropout, ) - # normalize input - x = (x - x.mean(axis=0)) / (x.std(axis=0) + 1e-6) + optimizer = optim.AdamW( + self.network.parameters(), + lr=lr_scaled, + weight_decay=self.weight_decay, + ) + + loss = nn.BCELoss() + + binary_auroc = BinaryAUROC() + + best_fdr1 = 0.0 + patience = self.patience + + x -= x.mean(axis=0) + x /= x.std(axis=0) + 1e-6 if y.ndim == 1: y = np.stack([1 - y, y], axis=1) - x_train, x_test, y_train, y_test = model_selection.train_test_split( x, y, test_size=self.test_size ) + x_train = torch.from_numpy(x_train).float() + y_train = torch.from_numpy(y_train).float() + x_test = torch.from_numpy(x_test).float() + y_test = torch.from_numpy(y_test).float() - x_test = torch.Tensor(x_test) - y_test = torch.Tensor(y_test) + num_batches = x_train.shape[0] // batch_size + batch_start_list = np.arange(num_batches) * batch_size + batch_stop_list = np.arange(num_batches) * batch_size + batch_size - optimizer = optim.Adam( - self.network.parameters(), - lr=self.learning_rate, - weight_decay=self.weight_decay, - ) + batch_count = 0 + for epoch in tqdm(range(self.epochs)): + train_loss_sum = 0.0 + test_loss_sum = 0.0 - loss = nn.BCELoss() + num_batches_train = 0 + num_batches_test = 0 - batch_count = 0 + # shuffle batches + order = np.random.permutation(num_batches) + batch_start_list = batch_start_list[order] + batch_stop_list = batch_stop_list[order] - for j in range(self.epochs): - order = np.random.permutation(len(x_train)) - x_train = torch.Tensor(x_train[order]) - y_train = torch.Tensor(y_train[order]) + train_predictions_list = [] + train_labels_list = [] - for i, (batch_x, batch_y) in enumerate( - zip(x_train.split(self.batch_size), y_train.split(self.batch_size)) - ): - y_pred = self.network(batch_x) - loss_value = loss(y_pred, batch_y) + for batch_start, batch_stop in zip(batch_start_list, batch_stop_list): + y_pred = self.network(x_train[batch_start:batch_stop]) + loss_value = loss(y_pred, y_train[batch_start:batch_stop]) - self.network.zero_grad() + optimizer.zero_grad() loss_value.backward() optimizer.step() - if batch_count % self.metric_interval == 0: - self.network.eval() - with torch.no_grad(): - self.metrics["epoch"].append(j) - self.metrics["batch_count"].append(batch_count) - self.metrics["train_loss"].append(loss_value.item()) + train_loss_sum += loss_value.detach() + train_predictions_list.append(y_pred.detach()) + train_labels_list.append(y_train[batch_start:batch_stop].detach()[:, 1]) + num_batches_train += 1 - y_pred_test = self.network(x_test) - loss_value = loss(y_pred_test, y_test) - self.metrics["test_loss"].append(loss_value.item()) + train_predictions = torch.cat(train_predictions_list, dim=0) + train_labels = torch.cat(train_labels_list, dim=0) - y_pred_train = self.network(x_train).detach().numpy() - y_pred_test = self.network(x_test).detach().numpy() + auc, fdr01, fdr1 = self.get_auc_fdr( + train_predictions, train_labels, roc_object=binary_auroc + ) - self.metrics["train_accuracy"].append( - np.sum( - y_train[:, 1].detach().numpy() - == np.argmax(y_pred_train, axis=1) - ) - / len(y_train) + if not self.calculate_metrics: + # check for early stopping + if fdr1 > best_fdr1: + best_fdr1 = fdr1 + patience = self.patience + else: + patience -= 1 + + if patience <= 0: + break + continue + + if epoch % self.metric_interval != 0: # skip metrics if wrong epoch + continue + + self.network.eval() + with torch.no_grad(): + test_predictions_list = [] + test_labels_list = [] + + test_batch_size = min(batch_size, x_test.shape[0]) + test_num_batches = x_test.shape[0] // test_batch_size + test_batch_start_list = np.arange(test_num_batches) * test_batch_size + test_batch_stop_list = ( + np.arange(test_num_batches) * test_batch_size + test_batch_size + ) + + for batch_start, batch_stop in zip( + test_batch_start_list, test_batch_stop_list + ): + batch_x_test = x_test[batch_start:batch_stop] + batch_y_test = y_test[batch_start:batch_stop] + + y_pred_test = self.network(batch_x_test) + test_loss = loss(y_pred_test, batch_y_test) + test_predictions_list.append(y_pred_test.detach()) + test_labels_list.append(batch_y_test.detach()[:, 1]) + num_batches_test += 1 + test_loss_sum += test_loss + + # log metrics for train and test + average_train_loss = train_loss_sum / num_batches_train + average_test_loss = test_loss_sum / num_batches_test + + self.metrics["train_loss"].append(average_train_loss.item()) + self.metrics["test_loss"].append(average_test_loss.item()) + + self.metrics["train_auc"].append(auc.item()) + self.metrics["train_fdr01"].append(fdr01.item()) + self.metrics["train_fdr1"].append(fdr1.item()) + + test_predictions = torch.cat(test_predictions_list, dim=0) + test_labels = torch.cat(test_labels_list, dim=0) + + auc, fdr01, fdr1 = self.get_auc_fdr( + test_predictions, test_labels, roc_object=binary_auroc + ) + + self.metrics["test_auc"].append(auc.item()) + self.metrics["test_fdr01"].append(fdr01.item()) + self.metrics["test_fdr1"].append(fdr1.item()) + + self.metrics["epoch"].append(epoch) + + batch_count += num_batches_train + self.metrics["batch_count"].append(batch_count) + + self.network.train() + + # check for early stopping + if fdr1 > best_fdr1: + best_fdr1 = fdr1 + patience = self.patience + else: + patience -= 1 + + if patience <= 0: + break + + self._fitted = True + + @torch.jit.export + def get_q_values(self, decoys_sorted: torch.Tensor): + """Calculates q-values for a dataframe containing PSMs. + + Parameters + ---------- + + scores : torch.Tensor + Score to use for the selection. Ascending sorted values are expected. + + decoys : torch.Tensor + Decoy information. Decoys are expected to be 1 and targets 0. + + Returns + ------- + + torch.Tensor + The q-values. + + """ + decoy_cumsum = torch.cumsum(decoys_sorted, dim=0) + target_cumsum = torch.cumsum(1 - decoys_sorted, dim=0) + fdr_values = decoy_cumsum.float() / target_cumsum.float() + return self.fdr_to_q_values(fdr_values) + + @torch.jit.export + def fdr_to_q_values(self, fdr_values: torch.Tensor): + """Converts FDR values to q-values. + Takes a ascending sorted array of FDR values and converts them to q-values. + for every element the lowest FDR where it would be accepted is used as q-value. + + Parameters + ---------- + fdr_values : torch.Tensor + The FDR values to convert. + + Returns + ------- + torch.Tensor + The q-values. + """ + reversed_fdr = torch.flip(fdr_values, dims=[0]) + cumulative_mins = torch.zeros_like(reversed_fdr) + min_value = float("inf") + for i in range(reversed_fdr.size(0)): + min_value = min(min_value, reversed_fdr[i].item()) + cumulative_mins[i] = min_value + q_values = torch.flip(cumulative_mins, dims=[0]) + return q_values + + @torch.jit.export + def get_auc_fdr(self, predicted_probas: torch.Tensor, y: torch.Tensor, roc_object): + """Calculates the AUC and FDR for a given set of predicted probabilities and labels. + + Parameters + ---------- + predicted_probas : torch.Tensor + The predicted probabilities. + + y : torch.Tensor + True labels. Decoys are expected to be 1 and targets 0. + + roc_object : torchmetrics.classification.BinaryAUROC + The ROC object to use for calculating the AUC. + + Returns + ------- + torch.Tensor + """ + scores = predicted_probas[:, 1] + sorted_indices = torch.argsort(scores, stable=True) + decoys_sorted = y[sorted_indices] + qval = self.get_q_values(decoys_sorted) + + decoys_zero_mask = decoys_sorted == 0 + qval = qval[decoys_zero_mask] + + y_pred = torch.round(scores) + auc = roc_object(y_pred, y) + return auc, torch.sum(qval < 0.001), torch.sum(qval < 0.01) + + def predict(self, x): + """Predict the class of the data. + + Parameters + ---------- + + x : np.array, dtype=float + Data of shape (n_samples, n_features). + + Returns + ------- + + y : np.array, dtype=int + Predicted class of shape (n_samples,). + """ + + if not self.fitted: + raise ValueError("Classifier has not been fitted yet.") + + assert ( + x.ndim == 2 + ), "Input data must have batch and feature dimension. (n_samples, n_features)" + assert ( + x.shape[1] == self.input_dim + ), "Input data must have the same number of features as the fitted classifier." + + x = (x - x.mean(axis=0)) / (x.std(axis=0) + 1e-6) + self.network.eval() + return np.argmax(self.network(torch.Tensor(x)).detach().numpy(), axis=1) + + def predict_proba(self, x: np.ndarray): + """Predict the class probabilities of the data. + + Parameters + ---------- + + x : np.array, dtype=float + Data of shape (n_samples, n_features). + + Returns + ------- + + y : np.array, dtype=float + Predicted class probabilities of shape (n_samples, n_classes). + + """ + + if not self.fitted: + raise ValueError("Classifier has not been fitted yet.") + + assert ( + x.ndim == 2 + ), "Input data must have batch and feature dimension. (n_samples, n_features)" + assert ( + x.shape[1] == self.input_dim + ), "Input data must have the same number of features as the fitted classifier." + + x = (x - x.mean(axis=0)) / (x.std(axis=0) + 1e-6) + self.network.eval() + return self.network(torch.Tensor(x)).detach().numpy() + + +class BinaryClassifierLegacy(Classifier): + def __init__( + self, + input_dim: int = 10, + output_dim: int = 2, + test_size: float = 0.2, + batch_size: int = 1000, + epochs: int = 10, + learning_rate: float = 0.0002, + weight_decay: float = 0.00001, + layers: typing.List[int] = [100, 50, 20, 5], + dropout: float = 0.001, + metric_interval: int = 1000, + **kwargs, + ): + """Binary Classifier using a feed forward neural network. + + Parameters + ---------- + + input_dim : int, default=10 + Number of input features. + + output_dim : int, default=2 + Number of output classes. + + test_size : float, default=0.2 + Fraction of the data to be used for testing. + + batch_size : int, default=1000 + Batch size for training. + + epochs : int, default=10 + Number of epochs for training. + + learning_rate : float, default=0.0002 + Learning rate for training. + + weight_decay : float, default=0.00001 + Weight decay for training. + + layers : typing.List[int], default=[100, 50, 20, 5] + typing.List of hidden layer sizes. + + dropout : float, default=0.001 + Dropout probability for training. + + metric_interval : int, default=1000 + Interval for logging metrics during training. + + """ + + self.test_size = test_size + self.batch_size = batch_size + self.epochs = epochs + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.layers = layers + self.dropout = dropout + self.input_dim = input_dim + self.output_dim = output_dim + self.metric_interval = metric_interval + + self.network = None + self.optimizer = None + self._fitted = False + + self.metrics = { + "epoch": [], + "batch_count": [], + "train_loss": [], + "train_accuracy": [], + "test_loss": [], + "test_accuracy": [], + } + + if kwargs: + warnings.warn("Unknown arguments: {}".format(kwargs)) + + @property + def fitted(self): + return self._fitted + + @property + def metrics(self): + return self._metrics + + @metrics.setter + def metrics(self, metrics): + self._metrics = metrics + + def to_state_dict(self): + """Save the state of the classifier as a dictionary. + + Returns + ------- + + dict : dict + Dictionary containing the state of the classifier. + + """ + dict = { + "_fitted": self._fitted, + "input_dim": self.input_dim, + "output_dim": self.output_dim, + "test_size": self.test_size, + "batch_size": self.batch_size, + "epochs": self.epochs, + "learning_rate": self.learning_rate, + "weight_decay": self.weight_decay, + "layers": self.layers, + "dropout": self.dropout, + "metric_interval": self.metric_interval, + "metrics": self.metrics, + } + + if self._fitted: + dict["network_state_dict"] = self.network.state_dict() + + return dict + + def from_state_dict(self, state_dict: dict): + """Load the state of the classifier from a dictionary. + + Parameters + ---------- + + dict : dict + Dictionary containing the state of the classifier. + + """ + + _state_dict = deepcopy(state_dict) + + if "network_state_dict" in _state_dict: + self.network = FeedForwardNN( + input_dim=_state_dict.pop("input_dim"), + output_dim=_state_dict.pop("output_dim"), + layers=_state_dict.pop("layers"), + dropout=_state_dict.pop("dropout"), + ) + self.network.load_state_dict(state_dict.pop("network_state_dict")) + + self.__dict__.update(_state_dict) + + def fit(self, x: np.ndarray, y: np.ndarray): + """Fit the classifier to the data. + + Parameters + ---------- + + x : np.array, dtype=float + Training data of shape (n_samples, n_features). + + y : np.array, dtype=int + Target values of shape (n_samples,) or (n_samples, n_classes). + + """ + + force_reinit = False + + if self.input_dim != x.shape[1] and self.network is not None: + warnings.warn( + "Input dimension of network has changed. Network has been reinitialized." + ) + force_reinit = True + + # check if network has to be initialized + if self.network is None or force_reinit: + self.input_dim = x.shape[1] + self.network = FeedForwardNN( + input_dim=self.input_dim, + output_dim=self.output_dim, + layers=self.layers, + dropout=self.dropout, + ) + + # normalize input + x = (x - x.mean(axis=0)) / (x.std(axis=0) + 1e-6) + + if y.ndim == 1: + y = np.stack([1 - y, y], axis=1) + + x_train, x_test, y_train, y_test = model_selection.train_test_split( + x, y, test_size=self.test_size + ) + + x_test = torch.Tensor(x_test) + y_test = torch.Tensor(y_test) + + optimizer = optim.Adam( + self.network.parameters(), + lr=self.learning_rate, + weight_decay=self.weight_decay, + ) + + loss = nn.BCELoss() + + batch_count = 0 + + for j in range(self.epochs): + order = np.random.permutation(len(x_train)) + x_train = torch.Tensor(x_train[order]) + y_train = torch.Tensor(y_train[order]) + + for i, (batch_x, batch_y) in enumerate( + zip(x_train.split(self.batch_size), y_train.split(self.batch_size)) + ): + y_pred = self.network(batch_x) + loss_value = loss(y_pred, batch_y) + + self.network.zero_grad() + loss_value.backward() + optimizer.step() + + if batch_count % self.metric_interval == 0: + self.network.eval() + with torch.no_grad(): + self.metrics["epoch"].append(j) + self.metrics["batch_count"].append(batch_count) + self.metrics["train_loss"].append(loss_value.item()) + + y_pred_test = self.network(x_test) + loss_value = loss(y_pred_test, y_test) + self.metrics["test_loss"].append(loss_value.item()) + + y_pred_train = self.network(x_train).detach().numpy() + y_pred_test = self.network(x_test).detach().numpy() + + self.metrics["train_accuracy"].append( + np.sum( + y_train[:, 1].detach().numpy() + == np.argmax(y_pred_train, axis=1) + ) + / len(y_train) + ) + + self.metrics["test_accuracy"].append( + np.sum( + y_test[:, 1].detach().numpy() + == np.argmax(y_pred_test, axis=1) + ) + / len(y_test) + ) + self.network.train() + + batch_count += 1 + + self._fitted = True + + def predict(self, x): + """Predict the class of the data. + + Parameters + ---------- + + x : np.array, dtype=float + Data of shape (n_samples, n_features). + + Returns + ------- + + y : np.array, dtype=int + Predicted class of shape (n_samples,). + """ + + if not self.fitted: + raise ValueError("Classifier has not been fitted yet.") + + assert ( + x.ndim == 2 + ), "Input data must have batch and feature dimension. (n_samples, n_features)" + assert ( + x.shape[1] == self.input_dim + ), "Input data must have the same number of features as the fitted classifier." + + x = (x - x.mean(axis=0)) / (x.std(axis=0) + 1e-6) + self.network.eval() + return np.argmax(self.network(torch.Tensor(x)).detach().numpy(), axis=1) + + def predict_proba(self, x: np.ndarray): + """Predict the class probabilities of the data. + + Parameters + ---------- + + x : np.array, dtype=float + Data of shape (n_samples, n_features). + + Returns + ------- + + y : np.array, dtype=float + Predicted class probabilities of shape (n_samples, n_classes). + + """ + + if not self.fitted: + raise ValueError("Classifier has not been fitted yet.") + + assert ( + x.ndim == 2 + ), "Input data must have batch and feature dimension. (n_samples, n_features)" + assert ( + x.shape[1] == self.input_dim + ), "Input data must have the same number of features as the fitted classifier." + + x = (x - x.mean(axis=0)) / (x.std(axis=0) + 1e-6) + self.network.eval() + return self.network(torch.Tensor(x)).detach().numpy() + + +class BinaryClassifierLegacyNewBatching(Classifier): + def __init__( + self, + input_dim: int = 10, + output_dim: int = 2, + test_size: float = 0.2, + batch_size: int = 1000, + epochs: int = 10, + learning_rate: float = 0.0002, + weight_decay: float = 0.00001, + layers: typing.List[int] = [100, 50, 20, 5], + dropout: float = 0.001, + metric_interval: int = 1000, + **kwargs, + ): + """Binary Classifier using a feed forward neural network. + + Parameters + ---------- + + input_dim : int, default=10 + Number of input features. + + output_dim : int, default=2 + Number of output classes. + + test_size : float, default=0.2 + Fraction of the data to be used for testing. + + batch_size : int, default=1000 + Batch size for training. + + epochs : int, default=10 + Number of epochs for training. + + learning_rate : float, default=0.0002 + Learning rate for training. + + weight_decay : float, default=0.00001 + Weight decay for training. + + layers : typing.List[int], default=[100, 50, 20, 5] + typing.List of hidden layer sizes. + + dropout : float, default=0.001 + Dropout probability for training. + + metric_interval : int, default=1000 + Interval for logging metrics during training. + + """ + + self.test_size = test_size + self.batch_size = batch_size + self.epochs = epochs + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.layers = layers + self.dropout = dropout + self.input_dim = input_dim + self.output_dim = output_dim + self.metric_interval = metric_interval + + self.network = None + self.optimizer = None + self._fitted = False + + self.metrics = { + "epoch": [], + "batch_count": [], + "train_loss": [], + "train_accuracy": [], + "test_loss": [], + "test_accuracy": [], + } + + if kwargs: + warnings.warn("Unknown arguments: {}".format(kwargs)) + + @property + def fitted(self): + return self._fitted + + @property + def metrics(self): + return self._metrics + + @metrics.setter + def metrics(self, metrics): + self._metrics = metrics + + def to_state_dict(self): + """Save the state of the classifier as a dictionary. + + Returns + ------- + + dict : dict + Dictionary containing the state of the classifier. + + """ + dict = { + "_fitted": self._fitted, + "input_dim": self.input_dim, + "output_dim": self.output_dim, + "test_size": self.test_size, + "batch_size": self.batch_size, + "epochs": self.epochs, + "learning_rate": self.learning_rate, + "weight_decay": self.weight_decay, + "layers": self.layers, + "dropout": self.dropout, + "metric_interval": self.metric_interval, + "metrics": self.metrics, + } + + if self._fitted: + dict["network_state_dict"] = self.network.state_dict() + + return dict + + def from_state_dict(self, state_dict: dict): + """Load the state of the classifier from a dictionary. + + Parameters + ---------- + + dict : dict + Dictionary containing the state of the classifier. + + """ + + _state_dict = deepcopy(state_dict) + + if "network_state_dict" in _state_dict: + self.network = FeedForwardNN( + input_dim=_state_dict.pop("input_dim"), + output_dim=_state_dict.pop("output_dim"), + layers=_state_dict.pop("layers"), + dropout=_state_dict.pop("dropout"), + ) + self.network.load_state_dict(state_dict.pop("network_state_dict")) + + self.__dict__.update(_state_dict) + + def fit(self, x: np.ndarray, y: np.ndarray): + """Fit the classifier to the data. + + Parameters + ---------- + + x : np.array, dtype=float + Training data of shape (n_samples, n_features). + + y : np.array, dtype=int + Target values of shape (n_samples,) or (n_samples, n_classes). + + """ + + force_reinit = False + + if self.input_dim != x.shape[1] and self.network is not None: + warnings.warn( + "Input dimension of network has changed. Network has been reinitialized." + ) + force_reinit = True + + # check if network has to be initialized + if self.network is None or force_reinit: + self.input_dim = x.shape[1] + self.network = FeedForwardNN( + input_dim=self.input_dim, + output_dim=self.output_dim, + layers=self.layers, + dropout=self.dropout, + ) + + # normalize input + x = (x - x.mean(axis=0)) / (x.std(axis=0) + 1e-6) + + if y.ndim == 1: + y = np.stack([1 - y, y], axis=1) + + x_train, x_test, y_train, y_test = model_selection.train_test_split( + x, y, test_size=self.test_size + ) + + x_test = torch.Tensor(x_test) + y_test = torch.Tensor(y_test) + + optimizer = optim.Adam( + self.network.parameters(), + lr=self.learning_rate, + weight_decay=self.weight_decay, + ) + + loss = nn.BCELoss() + + x_train = torch.Tensor(x_train) + y_train = torch.Tensor(y_train) + + num_batches = (x_train.shape[0] // self.batch_size) - 1 + batch_start_list = np.arange(num_batches) * self.batch_size + batch_stop_list = np.arange(num_batches) * self.batch_size + self.batch_size + + batch_count = 0 + + for epoch in tqdm(range(self.epochs)): + # shuffle batches + order = np.random.permutation(num_batches) + batch_start_list = batch_start_list[order] + batch_stop_list = batch_stop_list[order] + + for batch_start, batch_stop in zip(batch_start_list, batch_stop_list): + x_train_batch = x_train[batch_start:batch_stop] + y_train_batch = y_train[batch_start:batch_stop] + y_pred = self.network(x_train_batch) + loss_value = loss(y_pred, y_train_batch) + + self.network.zero_grad() + loss_value.backward() + optimizer.step() + + if batch_count % self.metric_interval == 0: + self.network.eval() + with torch.no_grad(): + self.metrics["epoch"].append(epoch) + self.metrics["batch_count"].append(batch_count) + self.metrics["train_loss"].append(loss_value.item()) + + y_pred_test = self.network(x_test) + loss_value = loss(y_pred_test, y_test) + self.metrics["test_loss"].append(loss_value.item()) + + y_pred_train = self.network(x_train_batch).detach().numpy() + y_pred_test = self.network(x_test).detach().numpy() + + self.metrics["train_accuracy"].append( + np.sum( + y_train_batch[:, 1].detach().numpy() + == np.argmax(y_pred_train, axis=1) + ) + / len(y_train_batch) ) self.metrics["test_accuracy"].append( diff --git a/alphadia/features.py b/alphadia/features.py index 1b3aabe1..ce8abf4c 100644 --- a/alphadia/features.py +++ b/alphadia/features.py @@ -546,11 +546,8 @@ def precursor_features( dense_precursors: np.ndarray, observation_importance, template: np.ndarray, + feature_array: np.ndarray, ): - feature_dict = nb.typed.Dict.empty( - key_type=nb.types.unicode_type, value_type=nb.types.float32 - ) - n_isotopes = isotope_intensity.shape[0] n_observations = dense_precursors.shape[2] @@ -569,14 +566,17 @@ def precursor_features( sum_precursor_intensity * observation_importance_reshaped, axis=-1 ).astype(np.float32) - feature_dict["mono_ms1_intensity"] = weighted_sum_precursor_intensity[0] - feature_dict["top_ms1_intensity"] = weighted_sum_precursor_intensity[ - np.argmax(isotope_intensity) - ] - feature_dict["sum_ms1_intensity"] = np.sum(weighted_sum_precursor_intensity) - feature_dict["weighted_ms1_intensity"] = np.sum( - weighted_sum_precursor_intensity * isotope_intensity - ) + # mono_ms1_intensity + feature_array[4] = weighted_sum_precursor_intensity[0] + + # top_ms1_intensity + feature_array[5] = weighted_sum_precursor_intensity[np.argmax(isotope_intensity)] + + # sum_ms1_intensity + feature_array[6] = np.sum(weighted_sum_precursor_intensity) + + # weighted_ms1_intensity + feature_array[7] = np.sum(weighted_sum_precursor_intensity * isotope_intensity) expected_scan_center = utils.tile( dense_precursors.shape[3], n_isotopes * n_observations @@ -601,31 +601,37 @@ def precursor_features( mass_error_array = (observed_precursor_mz - isotope_mz) / isotope_mz * 1e6 weighted_mass_error = np.sum(mass_error_array[mz_mask] * isotope_intensity[mz_mask]) - feature_dict["weighted_mass_deviation"] = weighted_mass_error - feature_dict["weighted_mass_error"] = np.abs(weighted_mass_error) + # weighted_mass_deviation + feature_array[8] = weighted_mass_error - feature_dict["mz_observed"] = ( - isotope_mz[0] + weighted_mass_error * 1e-6 * isotope_mz[0] - ) + # weighted_mass_error + feature_array[9] = np.abs(weighted_mass_error) - feature_dict["mono_ms1_height"] = observed_precursor_height[0] - feature_dict["top_ms1_height"] = observed_precursor_height[ - np.argmax(isotope_intensity) - ] - feature_dict["sum_ms1_height"] = np.sum(observed_precursor_height) - feature_dict["weighted_ms1_height"] = np.sum( - observed_precursor_height * isotope_intensity - ) + # mz_observed + feature_array[10] = isotope_mz[0] + weighted_mass_error * 1e-6 * isotope_mz[0] + + # mono_ms1_height + feature_array[11] = observed_precursor_height[0] - feature_dict["isotope_intensity_correlation"] = numeric.save_corrcoeff( + # top_ms1_height + feature_array[12] = observed_precursor_height[np.argmax(isotope_intensity)] + + # sum_ms1_height + feature_array[13] = np.sum(observed_precursor_height) + + # weighted_ms1_height + feature_array[14] = np.sum(observed_precursor_height * isotope_intensity) + + # isotope_intensity_correlation + feature_array[15] = numeric.save_corrcoeff( isotope_intensity, np.sum(sum_precursor_intensity, axis=-1) ) - feature_dict["isotope_height_correlation"] = numeric.save_corrcoeff( + + # isotope_height_correlation + feature_array[16] = numeric.save_corrcoeff( isotope_intensity, observed_precursor_height ) - return feature_dict - @nb.njit() def location_features( @@ -636,21 +642,23 @@ def location_features( frame_start, frame_stop, frame_center, + feature_array, ): - feature_dict = nb.typed.Dict.empty( - key_type=nb.types.unicode_type, value_type=nb.types.float32 - ) - - feature_dict["base_width_mobility"] = ( + # base_width_mobility + feature_array[0] = ( jit_data.mobility_values[scan_start] - jit_data.mobility_values[scan_stop - 1] ) - feature_dict["base_width_rt"] = ( + + # base_width_rt + feature_array[1] = ( jit_data.rt_values[frame_stop - 1] - jit_data.rt_values[frame_start] ) - feature_dict["rt_observed"] = jit_data.rt_values[frame_center] - feature_dict["mobility_observed"] = jit_data.mobility_values[scan_center] - return feature_dict + # rt_observed + feature_array[2] = jit_data.rt_values[frame_center] + + # mobility_observed + feature_array[3] = jit_data.mobility_values[scan_center] nb_float32_array = nb.types.Array(nb.types.float32, 1, "C") @@ -662,18 +670,15 @@ def fragment_features( observation_importance: np.ndarray, template: np.ndarray, fragments: np.ndarray, + feature_array: nb_float32_array, ): - feature_dict = nb.typed.Dict.empty( - key_type=nb.types.unicode_type, value_type=nb.types.float32 - ) - fragment_feature_dict = nb.typed.Dict.empty( key_type=nb.types.unicode_type, value_type=float_array ) n_observations = observation_importance.shape[0] n_fragments = dense_fragments.shape[1] - feature_dict["n_observations"] = float(n_observations) + feature_array[17] = float(n_observations) # (1, n_observations) observation_importance_reshaped = observation_importance.reshape(1, -1) @@ -763,44 +768,32 @@ def fragment_features( o_fragment_height, fragment_height_weights_2d ) - if np.sum(fragment_height_mask_1d) == 0.0: - feature_dict["intensity_correlation"] = 0.0 - else: - feature_dict["intensity_correlation"] = np.corrcoef( + if np.sum(fragment_height_mask_1d) > 0.0: + feature_array[18] = np.corrcoef( observed_fragment_intensity, fragment_intensity_norm )[0, 1] - if np.sum(observed_fragment_height) == 0.0: - feature_dict["height_correlation"] = 0.0 - else: - feature_dict["height_correlation"] = np.corrcoef( + if np.sum(observed_fragment_height) > 0.0: + feature_array[19] = np.corrcoef( observed_fragment_height, fragment_intensity_norm )[0, 1] - feature_dict["intensity_fraction"] = ( - np.sum(observed_fragment_intensity > 0.0) / n_fragments - ) - feature_dict["height_fraction"] = ( - np.sum(observed_fragment_height > 0.0) / n_fragments - ) + feature_array[20] = np.sum(observed_fragment_intensity > 0.0) / n_fragments + feature_array[21] = np.sum(observed_fragment_height > 0.0) / n_fragments - feature_dict["intensity_fraction_weighted"] = np.sum( + feature_array[22] = np.sum( fragment_intensity_norm[observed_fragment_intensity > 0.0] ) - feature_dict["height_fraction_weighted"] = np.sum( - fragment_intensity_norm[observed_fragment_height > 0.0] - ) + feature_array[23] = np.sum(fragment_intensity_norm[observed_fragment_height > 0.0]) fragment_mask = observed_fragment_intensity > 0 - if np.sum(fragment_mask) == 0: - feature_dict["mean_observation_score"] = 0.0 - else: + if np.sum(fragment_mask) > 0: sum_template_intensity_expanded = sum_template_intensity.reshape(1, -1) observation_score = cosine_similarity_a1( sum_template_intensity_expanded, sum_fragment_intensity[fragment_mask] ).astype(np.float32) - feature_dict["mean_observation_score"] = np.mean(observation_score) + feature_array[24] = np.mean(observation_score) # ============= FRAGMENT TYPE FEATURES ============= @@ -810,44 +803,81 @@ def fragment_features( weighted_b_ion_intensity = observed_fragment_intensity[b_ion_mask] weighted_y_ion_intensity = observed_fragment_intensity[y_ion_mask] - feature_dict["sum_b_ion_intensity"] = ( + feature_array[25] = ( np.log(np.sum(weighted_b_ion_intensity) + 1) if len(weighted_b_ion_intensity) > 0 else 0.0 ) - feature_dict["sum_y_ion_intensity"] = ( + feature_array[26] = ( np.log(np.sum(weighted_y_ion_intensity) + 1) if len(weighted_y_ion_intensity) > 0 else 0.0 ) - feature_dict["diff_b_y_ion_intensity"] = ( - feature_dict["sum_b_ion_intensity"] - feature_dict["sum_y_ion_intensity"] - ) + feature_array[27] = feature_array[25] - feature_array[26] # ============= FRAGMENT FEATURES ============= mass_error = (observed_fragment_mz_mean - fragments.mz) / fragments.mz * 1e6 - fragment_feature_dict["mz_library"] = fragments.mz_library[ - fragment_height_mask_1d - ].astype(np.float32) - fragment_feature_dict["mz_observed"] = observed_fragment_mz_mean[ - fragment_height_mask_1d - ].astype(np.float32) - fragment_feature_dict["mass_error"] = mass_error[fragment_height_mask_1d].astype( - np.float32 + return ( + observed_fragment_mz_mean, + mass_error, + observed_fragment_height, + observed_fragment_intensity, ) - fragment_feature_dict["height"] = observed_fragment_height[ - fragment_height_mask_1d - ].astype(np.float32) - fragment_feature_dict["intensity"] = observed_fragment_intensity[ - fragment_height_mask_1d - ].astype(np.float32) - fragment_feature_dict["type"] = fragments.type[fragment_height_mask_1d].astype( - np.float32 + + +@nb.njit() +def fragment_mobility_correlation( + fragments_scan_profile, + template_scan_profile, + observation_importance, + fragment_intensity, +): + n_observations = len(observation_importance) + + fragment_mask_1d = np.sum(np.sum(fragments_scan_profile, axis=-1), axis=-1) > 0 + if np.sum(fragment_mask_1d) < 3: + return 0, 0 + + non_zero_fragment_norm = fragment_intensity[fragment_mask_1d] / np.sum( + fragment_intensity[fragment_mask_1d] + ) + + # (n_observations, n_fragments, n_fragments) + fragment_scan_correlation_masked = numeric.fragment_correlation( + fragments_scan_profile[fragment_mask_1d], + ) + + # (n_fragments, n_fragments) + fragment_scan_correlation_maked_reduced = np.sum( + fragment_scan_correlation_masked * observation_importance.reshape(-1, 1, 1), + axis=0, + ) + fragment_scan_correlation_list = np.dot( + fragment_scan_correlation_maked_reduced, non_zero_fragment_norm + ) + + # fragment_scan_correlation + fragment_scan_correlation = np.mean(fragment_scan_correlation_list) + + # (n_observation, n_fragments) + fragment_template_scan_correlation = numeric.fragment_correlation_different( + fragments_scan_profile[fragment_mask_1d], + template_scan_profile.reshape(1, n_observations, -1), + ).reshape(n_observations, -1) + + # (n_fragments) + fragment_template_scan_correlation_reduced = np.sum( + fragment_template_scan_correlation * observation_importance.reshape(-1, 1), + axis=0, + ) + # template_scan_correlation + template_scan_correlation = np.dot( + fragment_template_scan_correlation_reduced, non_zero_fragment_norm ) - return feature_dict, fragment_feature_dict + return fragment_scan_correlation, template_scan_correlation @nb.njit @@ -864,84 +894,16 @@ def profile_features( scan_stop, frame_start, frame_stop, + feature_array, ): n_observations = len(observation_importance) - - feature_dict = nb.typed.Dict.empty( - key_type=nb.types.unicode_type, value_type=nb.types.float32 - ) - feature_dict["fragment_scan_correlation"] = 0.0 - feature_dict["top3_scan_correlation"] = 0.0 - feature_dict["fragment_frame_correlation"] = 0.0 - feature_dict["top3_frame_correlation"] = 0.0 - feature_dict["template_scan_correlation"] = 0.0 - feature_dict["template_frame_correlation"] = 0.0 - feature_dict["top3_b_ion_correlation"] = 0.0 - feature_dict["top3_y_ion_correlation"] = 0.0 - feature_dict["cycle_fwhm"] = 0.0 - feature_dict["mobility_fwhm"] = 0.0 - feature_dict["n_b_ions"] = 0.0 - feature_dict["n_y_ions"] = 0.0 - - fragment_mask_2d = np.sum(fragments_scan_profile, axis=-1) > 0 - fragment_mask_1d = np.sum(np.sum(fragments_scan_profile, axis=-1), axis=-1) > 0 - - fragment_weights_2d = fragment_mask_2d.astype(np.int8) * np.expand_dims( - fragment_intensity, axis=-1 - ) - - feature_dict["f_masked"] = np.mean(fragment_mask_1d) - - # stop if fewer than 3 fragments are observed - if np.sum(fragment_mask_1d) < 3: - return feature_dict - - non_zero_fragment_norm = fragment_intensity[fragment_mask_1d] / np.sum( - fragment_intensity[fragment_mask_1d] - ) - fragment_idx_sorted = np.argsort(non_zero_fragment_norm)[::-1] - - # ============= FRAGMENT MOBILITY CORRELATIONS ============= - # will be skipped if no mobility dimension is present - if dia_data.has_mobility: - # (n_observations, n_fragments, n_fragments) - fragment_scan_correlation_masked = numeric.fragment_correlation( - fragments_scan_profile[fragment_mask_1d], - ) - - # (n_fragments, n_fragments) - fragment_scan_correlation_maked_reduced = np.sum( - fragment_scan_correlation_masked * observation_importance.reshape(-1, 1, 1), - axis=0, - ) - fragment_scan_correlation_list = np.dot( - fragment_scan_correlation_maked_reduced, non_zero_fragment_norm - ) - feature_dict["fragment_scan_correlation"] = np.mean( - fragment_scan_correlation_list - ) - - # (n_observation, n_fragments) - fragment_template_scan_correlation = numeric.fragment_correlation_different( - fragments_scan_profile[fragment_mask_1d], - template_scan_profile.reshape(1, n_observations, -1), - ).reshape(n_observations, -1) - - # (n_fragments) - fragment_template_scan_correlation_reduced = np.sum( - fragment_template_scan_correlation * observation_importance.reshape(-1, 1), - axis=0, - ) - - feature_dict["template_scan_correlation"] = np.dot( - fragment_template_scan_correlation_reduced, non_zero_fragment_norm - ) + fragment_idx_sorted = np.argsort(fragment_intensity)[::-1] # ============= FRAGMENT RT CORRELATIONS ============= # (n_observations, n_fragments, n_fragments) fragment_frame_correlation_masked = numeric.fragment_correlation( - fragments_frame_profile[fragment_mask_1d], + fragments_frame_profile, ) # print('fragment_frame_correlation_masked', fragment_frame_correlation_masked) @@ -952,11 +914,9 @@ def profile_features( axis=0, ) fragment_frame_correlation_list = np.dot( - fragment_frame_correlation_maked_reduced, non_zero_fragment_norm - ) - feature_dict["fragment_frame_correlation"] = np.mean( - fragment_frame_correlation_list + fragment_frame_correlation_maked_reduced, fragment_intensity ) + feature_array[31] = np.mean(fragment_frame_correlation_list) # (3) top_3_idxs = fragment_idx_sorted[:3] @@ -964,11 +924,11 @@ def profile_features( top_3_fragment_frame_correlation = fragment_frame_correlation_maked_reduced[ top_3_idxs, : ][:, top_3_idxs] - feature_dict["top3_frame_correlation"] = np.mean(top_3_fragment_frame_correlation) + feature_array[32] = np.mean(top_3_fragment_frame_correlation) # (n_observation, n_fragments) fragment_template_frame_correlation = numeric.fragment_correlation_different( - fragments_frame_profile[fragment_mask_1d], + fragments_frame_profile, template_frame_profile.reshape(1, n_observations, -1), ).reshape(n_observations, -1) @@ -978,32 +938,33 @@ def profile_features( axis=0, ) - feature_dict["template_frame_correlation"] = np.dot( - fragment_template_frame_correlation_reduced, non_zero_fragment_norm + # template_frame_correlation + feature_array[33] = np.dot( + fragment_template_frame_correlation_reduced, fragment_intensity ) # ============= FRAGMENT TYPE FEATURES ============= - fragment_type_filtered = fragment_type[fragment_mask_1d] - b_ion_mask = fragment_type_filtered == 98 - y_ion_mask = fragment_type_filtered == 121 + b_ion_mask = fragment_type == 98 + y_ion_mask = fragment_type == 121 b_ion_index_sorted = fragment_idx_sorted[b_ion_mask] y_ion_index_sorted = fragment_idx_sorted[y_ion_mask] if len(b_ion_index_sorted) > 0: b_ion_limit = min(len(b_ion_index_sorted), 3) - feature_dict["top3_b_ion_correlation"] = fragment_frame_correlation_list[ + # 'top3_b_ion_correlation' + feature_array[34] = fragment_frame_correlation_list[ b_ion_index_sorted[:b_ion_limit] ].mean() - feature_dict["n_b_ions"] = float(len(b_ion_index_sorted)) + feature_array[35] = float(len(b_ion_index_sorted)) if len(y_ion_index_sorted) > 0: y_ion_limit = min(len(y_ion_index_sorted), 3) - feature_dict["top3_y_ion_correlation"] = fragment_frame_correlation_list[ + feature_array[36] = fragment_frame_correlation_list[ y_ion_index_sorted[:y_ion_limit] ].mean() - feature_dict["n_y_ions"] = float(len(y_ion_index_sorted)) + feature_array[37] = float(len(y_ion_index_sorted)) # ============= FWHM RT ============= @@ -1034,11 +995,9 @@ def profile_features( cycle_fwhm_mean_list = np.sum( cycle_fwhm * observation_importance.reshape(1, -1), axis=-1 ) - cycle_fwhm_mean_agg = np.sum( - cycle_fwhm_mean_list[fragment_mask_1d] * non_zero_fragment_norm - ) + cycle_fwhm_mean_agg = np.sum(cycle_fwhm_mean_list * fragment_intensity) - feature_dict["cycle_fwhm"] = cycle_fwhm_mean_agg + feature_array[38] = cycle_fwhm_mean_agg # ============= FWHM MOBILITY ============= @@ -1078,11 +1037,9 @@ def profile_features( mobility_fwhm_mean_list = np.sum( mobility_fwhm * observation_importance.reshape(1, -1), axis=-1 ) - mobility_fwhm_mean_agg = np.sum( - mobility_fwhm_mean_list[fragment_mask_1d] * non_zero_fragment_norm - ) + mobility_fwhm_mean_agg = np.sum(mobility_fwhm_mean_list * fragment_intensity) - feature_dict["mobility_fwhm"] = mobility_fwhm_mean_agg + feature_array[39] = mobility_fwhm_mean_agg # ============= RT SHIFT ============= @@ -1098,9 +1055,9 @@ def profile_features( delta_frame_peak = median_frame_peak - np.floor( fragments_frame_profile.shape[-1] / 2 ) - feature_dict["delta_frame_peak"] = np.sum(delta_frame_peak * observation_importance) + feature_array[40] = np.sum(delta_frame_peak * observation_importance) - return feature_dict + return fragment_frame_correlation_list @nb.njit diff --git a/alphadia/grouping.py b/alphadia/grouping.py index ffa7c7d0..a53ef1f0 100644 --- a/alphadia/grouping.py +++ b/alphadia/grouping.py @@ -87,7 +87,7 @@ def group_and_parsimony( # check that all precursors are found again if len(return_dict) != len(precursor_idx): raise ValueError( - "Not all precursors were found in the output of the grouping function." + f"Not all precursors were found in the output of the grouping function. {len(return_dict)} precursors were found, but {len(precursor_idx)} were expected." ) # order by precursor index @@ -100,6 +100,7 @@ def group_and_parsimony( def perform_grouping( psm: pd.DataFrame, genes_or_proteins: str = "proteins", + decoy_column: str = "decoy", ): """Highest level function for grouping proteins in precursor table @@ -112,22 +113,28 @@ def perform_grouping( raise ValueError("Selected column must be 'genes' or 'proteins'") # create non-duplicated view of precursor table - duplicate_mask = ~psm.duplicated( - subset=["precursor_idx", genes_or_proteins], keep="first" - ) - upsm = psm.loc[duplicate_mask, ["precursor_idx", genes_or_proteins, "_decoy"]] + duplicate_mask = ~psm.duplicated(subset=["precursor_idx"], keep="first") + # make sure column is string + psm[genes_or_proteins] = psm[genes_or_proteins].astype(str) + upsm = psm.loc[duplicate_mask, ["precursor_idx", genes_or_proteins, decoy_column]] + + # check if duplicate precursors exist + if upsm.duplicated(subset=["precursor_idx"]).any(): + raise ValueError( + "The same precursor was found annotated to different proteins. Please make sure all precursors were searched with the same library." + ) # handle case with only one decoy class: - unique_decoys = upsm["_decoy"].unique() + unique_decoys = upsm[decoy_column].unique() if len(unique_decoys) == 1: - upsm["_decoy"] = -1 + upsm[decoy_column] = -1 upsm["pg_master"], upsm["pg"] = group_and_parsimony( upsm.precursor_idx.values, upsm[genes_or_proteins].values ) upsm = upsm[["precursor_idx", "pg_master", "pg"]] else: - target_mask = upsm["_decoy"] == 0 - decoy_mask = upsm["_decoy"] == 1 + target_mask = upsm[decoy_column] == 0 + decoy_mask = upsm[decoy_column] == 1 t_df = upsm[target_mask].copy() new_columns = group_and_parsimony( diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py index 6ed73991..bea8a73f 100644 --- a/alphadia/libtransform.py +++ b/alphadia/libtransform.py @@ -28,11 +28,11 @@ def __init__(self) -> None: Processing steps can be chained together in a ProcessingPipeline.""" pass - def __call__(self, input: typing.Any) -> typing.Any: + def __call__(self, *args: typing.Any) -> typing.Any: """Run the processing step on the input object.""" logger.info(f"Running {self.__class__.__name__}") - if self.validate(input): - return self.forward(input) + if self.validate(*args): + return self.forward(*args) else: logger.critical( f"Input {input} failed validation for {self.__class__.__name__}" @@ -41,11 +41,11 @@ def __call__(self, input: typing.Any) -> typing.Any: f"Input {input} failed validation for {self.__class__.__name__}" ) - def validate(self, input: typing.Any) -> bool: + def validate(self, *args: typing.Any) -> bool: """Validate the input object.""" raise NotImplementedError("Subclasses must implement this method") - def forward(self, input: typing.Any) -> typing.Any: + def forward(self, *args: typing.Any) -> typing.Any: """Run the processing step on the input object.""" raise NotImplementedError("Subclasses must implement this method") @@ -262,6 +262,12 @@ def forward(self, input: SpecLibBase) -> SpecLibBase: decoy_lib.calc_fragment_mz_df() decoy_lib._precursor_df["decoy"] = 1 + # keep original precursor_idx and only create new ones for decoys + start_precursor_idx = input.precursor_df["precursor_idx"].max() + 1 + decoy_lib._precursor_df["precursor_idx"] = np.arange( + start_precursor_idx, start_precursor_idx + len(decoy_lib.precursor_df) + ) + input.append(decoy_lib) input._precursor_df.sort_values("elution_group_idx", inplace=True) input._precursor_df.reset_index(drop=True, inplace=True) @@ -347,8 +353,24 @@ def forward(self, input: SpecLibBase) -> SpecLibBase: class FlattenLibrary(ProcessingStep): - def __init__(self) -> None: - """Convert a `SpecLibBase` object into a `SpecLibFlat` object.""" + def __init__( + self, top_k_fragments: int = 12, min_fragment_intensity: float = 0.01 + ) -> None: + """Convert a `SpecLibBase` object into a `SpecLibFlat` object. + + Parameters + ---------- + + top_k_fragments : int, optional + Number of top fragments to keep. Default is 12. + + min_fragment_intensity : float, optional + Minimum intensity threshold for fragments. Default is 0.01. + + """ + self.top_k_fragments = top_k_fragments + self.min_fragment_intensity = min_fragment_intensity + super().__init__() def validate(self, input: SpecLibBase) -> bool: @@ -361,7 +383,10 @@ def forward(self, input: SpecLibBase) -> SpecLibFlat: input._fragment_cardinality_df = fragment.calc_fragment_cardinality( input.precursor_df, input._fragment_mz_df ) - output = SpecLibFlat(min_fragment_intensity=0.0001, keep_top_k_fragments=100) + output = SpecLibFlat( + min_fragment_intensity=self.min_fragment_intensity, + keep_top_k_fragments=self.top_k_fragments, + ) output.parse_base_library( input, custom_df={"cardinality": input._fragment_cardinality_df} ) @@ -471,3 +496,32 @@ def forward(self, input: SpecLibFlat) -> SpecLibFlat: logger.info(f"=======================================") return input + + +class MbrLibraryBuilder(ProcessingStep): + def __init__(self, fdr=0.01) -> None: + super().__init__() + self.fdr = fdr + + def validate(self, psm_df, base_library) -> bool: + """Validate the input object. It is expected that the input is a `SpecLibFlat` object.""" + return True + + def forward(self, psm_df, base_library): + psm_df = psm_df[psm_df["qval"] <= self.fdr] + psm_df = psm_df[psm_df["decoy"] == 0] + rt_df = psm_df.groupby("elution_group_idx", as_index=False).agg( + rt=pd.NamedAgg(column="rt_observed", aggfunc="median") + ) + + mbr_spec_lib = base_library.copy() + mbr_spec_lib = base_library.copy() + if "rt" in mbr_spec_lib._precursor_df.columns: + mbr_spec_lib._precursor_df.drop(columns=["rt"], inplace=True) + mbr_spec_lib._precursor_df = mbr_spec_lib._precursor_df.merge( + rt_df, on="elution_group_idx", how="right" + ) + + mbr_spec_lib.remove_unused_fragments() + + return mbr_spec_lib diff --git a/alphadia/numba/fragments.py b/alphadia/numba/fragments.py index 72835d0a..bd5bc5c4 100644 --- a/alphadia/numba/fragments.py +++ b/alphadia/numba/fragments.py @@ -119,6 +119,24 @@ def filter_by_min_mz(self, min_mz): self.position = self.position[mask] self.cardinality = self.cardinality[mask] + def apply_mask(self, mask): + """ + Apply a boolean mask to the fragment container + """ + self.precursor_idx = self.precursor_idx[mask] + self.mz_library = self.mz_library[mask] + self.mz = self.mz[mask] + self.intensity = self.intensity[mask] + self.type = self.type[mask] + self.loss_type = self.loss_type[mask] + self.charge = self.charge[mask] + self.number = self.number[mask] + self.position = self.position[mask] + self.cardinality = self.cardinality[mask] + + if np.sum(mask) > 0: + self.intensity = self.intensity / np.sum(self.intensity) + @overload_method( nb.types.misc.ClassInstanceType, diff --git a/alphadia/outputtransform.py b/alphadia/outputtransform.py new file mode 100644 index 00000000..8f0f5fe2 --- /dev/null +++ b/alphadia/outputtransform.py @@ -0,0 +1,754 @@ +# native imports +import logging +import os + +logger = logging.getLogger() + +from alphadia import grouping, libtransform +from alphadia import fdr + +import pandas as pd +import numpy as np +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPClassifier + +import multiprocessing as mp + +from typing import List, Tuple, Iterator, Union +import numba as nb +from alphabase.spectral_library import base + +import directlfq.utils as lfqutils +import directlfq.normalization as lfqnorm +import directlfq.protein_intensity_estimation as lfqprot_estimation +import directlfq.config as lfqconfig + +import logging + +logger = logging.getLogger() + + +@nb.njit +def hash(precursor_idx, number, type, charge): + # create a 64 bit hash from the precursor_idx, number and type + # the precursor_idx is the lower 32 bits + # the number is the next 8 bits + # the type is the next 8 bits + # the last 8 bits are used to distinguish between different charges of the same precursor + # this is necessary because I forgot to save the charge in the frag.tsv file :D + return precursor_idx + (number << 32) + (type << 40) + (charge << 48) + + +def get_frag_df_generator(folder_list: List[str]): + """Return a generator that yields a tuple of (raw_name, frag_df) + + Parameters + ---------- + + folder_list: List[str] + List of folders containing the frag.tsv file + + Returns + ------- + + Iterator[Tuple[str, pd.DataFrame]] + Tuple of (raw_name, frag_df) + + """ + + for folder in folder_list: + raw_name = os.path.basename(folder) + frag_path = os.path.join(folder, "frag.tsv") + + if not os.path.exists(frag_path): + logger.warning(f"no frag file found for {raw_name}") + else: + try: + logger.info(f"reading frag file for {raw_name}") + run_df = pd.read_csv( + frag_path, + sep="\t", + dtype={ + "precursor_idx": np.uint32, + "number": np.uint8, + "type": np.uint8, + }, + ) + except Exception as e: + logger.warning(f"Error reading frag file for {raw_name}") + logger.warning(e) + else: + yield raw_name, run_df + + +class QuantBuilder: + def __init__(self, psm_df, column="intensity"): + self.psm_df = psm_df + self.column = column + + def accumulate_frag_df_from_folders( + self, folder_list: List[str] + ) -> Tuple[pd.DataFrame, pd.DataFrame]: + """Accumulate the fragment data from a list of folders + + Parameters + ---------- + + folder_list: List[str] + List of folders containing the frag.tsv file + + Returns + ------- + intensity_df: pd.DataFrame + Dataframe with the intensity data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + quality_df: pd.DataFrame + Dataframe with the quality data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + """ + + df_iterable = get_frag_df_generator(folder_list) + return self.accumulate_frag_df(df_iterable) + + def accumulate_frag_df( + self, df_iterable: Iterator[Tuple[str, pd.DataFrame]] + ) -> Tuple[pd.DataFrame, pd.DataFrame]: + """Consume a generator of (raw_name, frag_df) tuples and accumulate the data in a single dataframe + + Parameters + ---------- + + df_iterable: Iterator[Tuple[str, pd.DataFrame]] + Iterator of (raw_name, frag_df) tuples + + Returns + ------- + intensity_df: pd.DataFrame + Dataframe with the intensity data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + quality_df: pd.DataFrame + Dataframe with the quality data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + """ + + logger.info("Accumulating fragment data") + + raw_name, df = next(df_iterable, (None, None)) + if df is None: + logger.warning(f"no frag file found for {raw_name}") + return + + df = prepare_df(df, self.psm_df, column=self.column) + + intensity_df = df[["precursor_idx", "ion", self.column]].copy() + intensity_df.rename(columns={self.column: raw_name}, inplace=True) + + quality_df = df[["precursor_idx", "ion", "correlation"]].copy() + quality_df.rename(columns={"correlation": raw_name}, inplace=True) + + df_list = [] + for raw_name, df in df_iterable: + df = prepare_df(df, self.psm_df, column=self.column) + + intensity_df = intensity_df.merge( + df[["ion", self.column, "precursor_idx"]], + on=["ion", "precursor_idx"], + how="outer", + ) + intensity_df.rename(columns={self.column: raw_name}, inplace=True) + + quality_df = quality_df.merge( + df[["ion", "correlation", "precursor_idx"]], + on=["ion", "precursor_idx"], + how="outer", + ) + quality_df.rename(columns={"correlation": raw_name}, inplace=True) + + # replace nan with 0 + intensity_df.fillna(0, inplace=True) + quality_df.fillna(0, inplace=True) + + intensity_df["precursor_idx"] = intensity_df["precursor_idx"].astype(np.uint32) + quality_df["precursor_idx"] = quality_df["precursor_idx"].astype(np.uint32) + + # annotate protein group + protein_df = self.psm_df.groupby("precursor_idx", as_index=False)["pg"].first() + + intensity_df = intensity_df.merge(protein_df, on="precursor_idx", how="left") + intensity_df.rename(columns={"pg": "protein"}, inplace=True) + + quality_df = quality_df.merge(protein_df, on="precursor_idx", how="left") + quality_df.rename(columns={"pg": "protein"}, inplace=True) + + return intensity_df, quality_df + + def filter_frag_df( + self, + intensity_df: pd.DataFrame, + quality_df: pd.DataFrame, + min_correlation: float = 0.5, + top_n: int = 3, + ) -> Tuple[pd.DataFrame, pd.DataFrame]: + """Filter the fragment data by quality + + Parameters + ---------- + intensity_df: pd.DataFrame + Dataframe with the intensity data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + quality_df: pd.DataFrame + Dataframe with the quality data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + min_correlation: float + Minimum correlation to keep a fragment, if not below top_n + + top_n: int + Keep the top n fragments per precursor + + Returns + ------- + + intensity_df: pd.DataFrame + Dataframe with the intensity data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + quality_df: pd.DataFrame + Dataframe with the quality data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + """ + + logger.info("Filtering fragments by quality") + + run_columns = [ + c + for c in intensity_df.columns + if c not in ["precursor_idx", "ion", "protein"] + ] + + quality_df["total"] = np.mean(quality_df[run_columns].values, axis=1) + quality_df["rank"] = quality_df.groupby("precursor_idx")["total"].rank( + ascending=False, method="first" + ) + mask = (quality_df["rank"].values <= top_n) | ( + quality_df["total"].values > min_correlation + ) + return intensity_df[mask], quality_df[mask] + + def lfq( + self, + intensity_df: pd.DataFrame, + quality_df: pd.DataFrame, + num_samples_quadratic: int = 50, + min_nonan: int = 1, + num_cores: int = 8, + ) -> pd.DataFrame: + """Perform label-free quantification + + Parameters + ---------- + + intensity_df: pd.DataFrame + Dataframe with the intensity data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + quality_df: pd.DataFrame + Dataframe with the quality data containing the columns precursor_idx, ion, raw_name1, raw_name2, ... + + Returns + ------- + + lfq_df: pd.DataFrame + Dataframe with the label-free quantification data containing the columns precursor_idx, ion, intensity, protein + + """ + + logger.info("Performing label-free quantification using directLFQ") + + intensity_df.drop(columns=["precursor_idx"], inplace=True) + + lfqconfig.set_global_protein_and_ion_id(protein_id="protein", quant_id="ion") + + lfq_df = lfqutils.index_and_log_transform_input_df(intensity_df) + lfq_df = lfqutils.remove_allnan_rows_input_df(lfq_df) + lfq_df = lfqnorm.NormalizationManagerSamplesOnSelectedProteins( + lfq_df, + num_samples_quadratic=num_samples_quadratic, + selected_proteins_file=None, + ).complete_dataframe + protein_df, _ = lfqprot_estimation.estimate_protein_intensities( + lfq_df, + min_nonan=min_nonan, + num_samples_quadratic=num_samples_quadratic, + num_cores=num_cores, + ) + + return protein_df + + +def prepare_df(df, psm_df, column="height"): + df = df[df["precursor_idx"].isin(psm_df["precursor_idx"])].copy() + df["ion"] = hash( + df["precursor_idx"].values, + df["number"].values, + df["type"].values, + df["charge"].values, + ) + return df[["precursor_idx", "ion", column, "correlation"]] + + +class SearchPlanOutput: + PSM_INPUT = "psm" + PRECURSOR_OUTPUT = "precursors" + STAT_OUTPUT = "stat" + PG_OUTPUT = "protein_groups" + LIBRARY_OUTPUT = "speclib.mbr" + + def __init__(self, config: dict, output_folder: str): + """Combine individual searches into and build combined outputs + + In alphaDIA the search plan orchestrates the library building preparation, + schedules the individual searches and combines the individual outputs into a single output. + + The SearchPlanOutput class is responsible for combining the individual search outputs into a single output. + + This includes: + - combining the individual precursor tables + - building the output stat table + - performing protein grouping + - performing protein FDR + - performin label-free quantification + - building the spectral library + + Parameters + ---------- + + config: dict + Configuration dictionary + + output_folder: str + Output folder + """ + self._config = config + self._output_folder = output_folder + + @property + def config(self): + return self._config + + @property + def output_folder(self): + return self._output_folder + + def build( + self, + folder_list: List[str], + base_spec_lib: base.SpecLibBase, + ): + """Build output from a list of seach outputs + The following files are written to the output folder: + - precursor.tsv + - protein_groups.tsv + - stat.tsv + - speclib.mbr.hdf + + Parameters + ---------- + + folder_list: List[str] + List of folders containing the search outputs + + base_spec_lib: base.SpecLibBase + Base spectral library + + """ + logger.progress("Processing search outputs") + psm_df = self.build_precursor_table(folder_list, save=False) + _ = self.build_stat_df(folder_list, psm_df=psm_df, save=True) + _ = self.build_protein_table(folder_list, psm_df=psm_df, save=True) + _ = self.build_library(base_spec_lib, psm_df=psm_df, save=True) + + def load_precursor_table(self): + """Load precursor table from output folder. + Helper functions used by other builders. + + Returns + ------- + + psm_df: pd.DataFrame + Precursor table + """ + + if not os.path.exists( + os.path.join(self.output_folder, f"{self.PRECURSOR_OUTPUT}.tsv") + ): + logger.error( + f"Can't continue as no {self.PRECURSOR_OUTPUT}.tsv file was found in the output folder: {self.output_folder}" + ) + raise FileNotFoundError( + f"Can't continue as no {self.PRECURSOR_OUTPUT}.tsv file was found in the output folder: {self.output_folder}" + ) + logger.info(f"Reading {self.PRECURSOR_OUTPUT}.tsv file") + psm_df = pd.read_csv( + os.path.join(self.output_folder, f"{self.PRECURSOR_OUTPUT}.tsv"), sep="\t" + ) + return psm_df + + def build_precursor_table(self, folder_list: List[str], save: bool = True): + """Build precursor table from a list of seach outputs + + Parameters + ---------- + + folder_list: List[str] + List of folders containing the search outputs + + save: bool + Save the precursor table to disk + + Returns + ------- + + psm_df: pd.DataFrame + Precursor table + """ + logger.progress("Performing protein grouping and FDR") + + psm_df_list = [] + + for folder in folder_list: + raw_name = os.path.basename(folder) + psm_path = os.path.join(folder, f"{self.PSM_INPUT}.tsv") + + logger.info(f"Building output for {raw_name}") + + if not os.path.exists(psm_path): + logger.warning(f"no psm file found for {raw_name}, skipping") + run_df = pd.DataFrame() + else: + try: + run_df = pd.read_csv(psm_path, sep="\t") + except Exception as e: + logger.warning(f"Error reading psm file for {raw_name}") + logger.warning(e) + run_df = pd.DataFrame() + + psm_df_list.append(run_df) + + if len(psm_df_list) == 0: + logger.error("No psm files found, can't continue") + raise FileNotFoundError("No psm files found, can't continue") + + logger.info("Building combined output") + psm_df = pd.concat(psm_df_list) + + logger.info("Performing protein grouping") + if self.config["fdr"]["library_grouping"]: + psm_df["pg"] = psm_df[self.config["fdr"]["group_level"]] + psm_df["pg_master"] = psm_df[self.config["fdr"]["group_level"]] + else: + psm_df = grouping.perform_grouping( + psm_df, genes_or_proteins=self.config["fdr"]["group_level"] + ) + + logger.info("Performing protein FDR") + psm_df = perform_protein_fdr(psm_df) + psm_df = psm_df[psm_df["pg_qval"] <= self.config["fdr"]["fdr"]] + + pg_count = psm_df[psm_df["decoy"] == 0]["pg"].nunique() + precursor_count = psm_df[psm_df["decoy"] == 0]["precursor_idx"].nunique() + + logger.progress( + "================ Protein FDR =================", + ) + logger.progress(f"Unique protein groups in output") + logger.progress(f" 1% protein FDR: {pg_count:,}") + logger.progress("") + logger.progress(f"Unique precursor in output") + logger.progress(f" 1% protein FDR: {precursor_count:,}") + logger.progress( + "================================================", + ) + + if not self.config["fdr"]["keep_decoys"]: + psm_df = psm_df[psm_df["decoy"] == 0] + + if save: + logger.info("Writing precursor output to disk") + psm_df.to_csv( + os.path.join(self.output_folder, f"{self.PRECURSOR_OUTPUT}.tsv"), + sep="\t", + index=False, + float_format="%.6f", + ) + + return psm_df + + def build_stat_df( + self, + folder_list: List[str], + psm_df: Union[pd.DataFrame, None] = None, + save: bool = True, + ): + """Build stat table from a list of seach outputs + + Parameters + ---------- + + folder_list: List[str] + List of folders containing the search outputs + + psm_df: Union[pd.DataFrame, None] + Combined precursor table. If None, the precursor table is loaded from disk. + + save: bool + Save the precursor table to disk + + Returns + ------- + + stat_df: pd.DataFrame + Precursor table + """ + logger.progress("Building search statistics") + + if psm_df is None: + psm_df = self.load_precursor_table() + psm_df = psm_df[psm_df["decoy"] == 0] + + stat_df_list = [] + for folder in folder_list: + raw_name = os.path.basename(folder) + stat_df_list.append( + build_stat_df(raw_name, psm_df[psm_df["run"] == raw_name]) + ) + + stat_df = pd.concat(stat_df_list) + + if save: + logger.info("Writing stat output to disk") + stat_df.to_csv( + os.path.join(self.output_folder, f"{self.STAT_OUTPUT}.tsv"), + sep="\t", + index=False, + float_format="%.6f", + ) + + return stat_df + + def build_protein_table( + self, + folder_list: List[str], + psm_df: Union[pd.DataFrame, None] = None, + save: bool = True, + ): + """Accumulate fragment information and perform label-free protein quantification. + + Parameters + ---------- + + folder_list: List[str] + List of folders containing the search outputs + + psm_df: Union[pd.DataFrame, None] + Combined precursor table. If None, the precursor table is loaded from disk. + + save: bool + Save the precursor table to disk + + """ + logger.progress("Performing label free quantification") + + if psm_df is None: + psm_df = self.load_precursor_table() + + # as we want to retain decoys in the output we are only removing them for lfq + qb = QuantBuilder(psm_df[psm_df["decoy"] == 0]) + intensity_df, quality_df = qb.accumulate_frag_df_from_folders(folder_list) + intensity_df, quality_df = qb.filter_frag_df( + intensity_df, + quality_df, + top_n=self.config["search_output"]["min_k_fragments"], + min_correlation=self.config["search_output"]["min_correlation"], + ) + protein_df = qb.lfq( + intensity_df, + quality_df, + num_cores=self.config["general"]["thread_count"], + min_nonan=self.config["search_output"]["min_nonnan"], + num_samples_quadratic=self.config["search_output"]["num_samples_quadratic"], + ) + + protein_df.rename(columns={"protein": "pg"}, inplace=True) + + protein_df_melted = protein_df.melt( + id_vars="pg", var_name="run", value_name="intensity" + ) + + psm_df = psm_df.merge(protein_df_melted, on=["pg", "run"], how="left") + + if save: + logger.info("Writing protein group output to disk") + protein_df.to_csv( + os.path.join(self.output_folder, f"{self.PG_OUTPUT}.tsv"), + sep="\t", + index=False, + float_format="%.6f", + ) + + logger.info("Writing psm output to disk") + psm_df.to_csv( + os.path.join(self.output_folder, f"{self.PRECURSOR_OUTPUT}.tsv"), + sep="\t", + index=False, + float_format="%.6f", + ) + + return protein_df + + def build_library( + self, + base_spec_lib: base.SpecLibBase, + psm_df: Union[pd.DataFrame, None] = None, + save: bool = True, + ): + """Build spectral library + + Parameters + ---------- + + base_spec_lib: base.SpecLibBase + Base spectral library + + psm_df: Union[pd.DataFrame, None] + Combined precursor table. If None, the precursor table is loaded from disk. + + save: bool + Save the generated spectral library to disk + + """ + logger.progress("Building spectral library") + + if psm_df is None: + psm_df = self.load_precursor_table() + psm_df = psm_df[psm_df["decoy"] == 0] + + libbuilder = libtransform.MbrLibraryBuilder( + fdr=0.01, + ) + + logger.info("Building MBR spectral library") + mbr_spec_lib = libbuilder(psm_df, base_spec_lib) + + precursor_number = len(mbr_spec_lib.precursor_df) + protein_number = mbr_spec_lib.precursor_df.proteins.nunique() + + # use comma to separate thousands + logger.info( + f"MBR spectral library contains {precursor_number:,} precursors, {protein_number:,} proteins" + ) + + logger.info("Writing MBR spectral library to disk") + mbr_spec_lib.save_hdf(os.path.join(self.output_folder, "speclib.mbr.hdf")) + + if save: + logger.info("Writing MBR spectral library to disk") + mbr_spec_lib.save_hdf(os.path.join(self.output_folder, "speclib.mbr.hdf")) + + return mbr_spec_lib + + +def build_stat_df(raw_name, run_df): + """Build stat dataframe for run""" + + base_dict = { + "run": raw_name, + "precursors": len(run_df), + "proteins": run_df["pg"].nunique(), + } + + if "weighted_mass_error" in run_df.columns: + base_dict["ms1_accuracy"] = np.mean(run_df["weighted_mass_error"]) + + if "cycle_fwhm" in run_df.columns: + base_dict["fwhm_rt"] = np.mean(run_df["cycle_fwhm"]) + + if "mobility_fwhm" in run_df.columns: + base_dict["fwhm_mobility"] = np.mean(run_df["mobility_fwhm"]) + + return pd.DataFrame( + [ + base_dict, + ] + ) + + +def perform_protein_fdr(psm_df): + """Perform protein FDR on PSM dataframe""" + + protein_features = [] + for _, group in psm_df.groupby(["pg", "decoy"]): + protein_features.append( + { + "genes": group["genes"].iloc[0], + "proteins": group["proteins"].iloc[0], + "decoy": group["decoy"].iloc[0], + "count": len(group), + "n_peptides": len(group["precursor_idx"].unique()), + "n_runs": len(group["run"].unique()), + "mean_score": group["proba"].mean(), + "best_score": group["proba"].min(), + "worst_score": group["proba"].max(), + } + ) + + feature_columns = [ + "count", + "mean_score", + "n_peptides", + "n_runs", + "best_score", + "worst_score", + ] + + protein_features = pd.DataFrame(protein_features) + + X = protein_features[feature_columns].values + y = protein_features["decoy"].values + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_test = scaler.transform(X_test) + + clf = MLPClassifier(random_state=0).fit(X_train, y_train) + + protein_features["proba"] = clf.predict_proba(scaler.transform(X))[:, 1] + protein_features = pd.DataFrame(protein_features) + + protein_features = fdr.get_q_values( + protein_features, + score_column="proba", + decoy_column="decoy", + qval_column="pg_qval", + ) + + fdr.plot_fdr(X_train, X_test, y_train, y_test, clf, protein_features["pg_qval"]) + + return pd.concat( + [ + psm_df[psm_df["decoy"] == 0].merge( + protein_features[protein_features["decoy"] == 0][ + ["proteins", "pg_qval"] + ], + on="proteins", + how="left", + ), + psm_df[psm_df["decoy"] == 1].merge( + protein_features[protein_features["decoy"] == 1][ + ["proteins", "pg_qval"] + ], + on="proteins", + how="left", + ), + ] + ) diff --git a/alphadia/planning.py b/alphadia/planning.py index f506b872..f03e6285 100644 --- a/alphadia/planning.py +++ b/alphadia/planning.py @@ -10,17 +10,33 @@ import typing # alphadia imports -from alphadia import utils, libtransform +from alphadia import utils, libtransform, outputtransform from alphadia.workflow import peptidecentric, base, reporting import alphadia +import alpharaw +import alphabase +import peptdeep +import alphatims +import directlfq # alpha family imports from alphabase.spectral_library.flat import SpecLibFlat +from alphabase.spectral_library.base import SpecLibBase # third party imports import numpy as np import pandas as pd import os, psutil +import torch +import numba as nb + + +@nb.njit +def hash(precursor_idx, rank): + # create a 64 bit hash from the precursor_idx, number and type + # the precursor_idx is the lower 32 bits + # the rank is the next 8 bits + return precursor_idx + (rank << 32) class Plan: @@ -63,6 +79,7 @@ def __init__( logger.progress("") self.raw_file_list = raw_file_list + self.spec_lib_path = spec_lib_path # default config path is not defined in the function definition to account for for different path separators on different OS if config_path is None: @@ -92,13 +109,19 @@ def __init__( self.config["output"] = output_folder logger.progress(f"version: {alphadia.__version__}") + # print hostname, date with day format and time logger.progress(f"hostname: {socket.gethostname()}") now = datetime.today().strftime("%Y-%m-%d %H:%M:%S") logger.progress(f"date: {now}") + # print environment + self.log_environment() + self.load_library(spec_lib_path) + torch.set_num_threads(self.config["general"]["thread_count"]) + @property def raw_file_list(self) -> typing.List[str]: """List of input files locations.""" @@ -126,6 +149,15 @@ def spectral_library(self) -> SpecLibFlat: def spectral_library(self, spectral_library: SpecLibFlat) -> None: self._spectral_library = spectral_library + def log_environment(self): + logger.progress(f"=================== Environment ===================") + logger.progress(f"{'alphatims':<15} : {alphatims.__version__:}") + logger.progress(f"{'alpharaw':<15} : {alpharaw.__version__}") + logger.progress(f"{'alphabase':<15} : {alphabase.__version__}") + logger.progress(f"{'alphapeptdeep':<15} : {peptdeep.__version__}") + logger.progress(f"{'directlfq':<15} : {directlfq.__version__}") + logger.progress(f"===================================================") + def load_library(self, spec_lib_path): if "fasta_list" in self.config: fasta_files = self.config["fasta_list"] @@ -148,15 +180,16 @@ def load_library(self, spec_lib_path): prepare_pipeline = libtransform.ProcessingPipeline( [ libtransform.DecoyGenerator(decoy_type="diann"), - libtransform.FlattenLibrary(), + libtransform.FlattenLibrary( + self.config["search_advanced"]["top_k_fragments"] + ), libtransform.InitFlatColumns(), libtransform.LogFlatLibraryStats(), ] ) speclib = import_pipeline(spec_lib_path) - if self.config["library_loading"]["save_hdf"]: - speclib.save_hdf(os.path.join(self.output_folder, "speclib.hdf")) + speclib.save_hdf(os.path.join(self.output_folder, "speclib.hdf")) self.spectral_library = prepare_pipeline(speclib) @@ -183,22 +216,57 @@ def run( keep_decoys=False, fdr=0.01, ): + logger.progress("Starting Search Workflows") + + workflow_folder_list = [] + for raw_name, dia_path, speclib in self.get_run_data(): workflow = None try: workflow = peptidecentric.PeptideCentricWorkflow( - raw_name, self.config, dia_path, speclib + raw_name, + self.config, ) + workflow_folder_list.append(workflow.path) + + # check if the raw file is already processed + psm_location = os.path.join(workflow.path, "psm.tsv") + frag_location = os.path.join(workflow.path, "frag.tsv") + + if self.config["general"]["reuse_quant"]: + if os.path.exists(psm_location) and os.path.exists(frag_location): + logger.info(f"Found existing quantification for {raw_name}") + continue + logger.info(f"No existing quantification found for {raw_name}") + + workflow.load(dia_path, speclib) workflow.calibration() - df = workflow.extraction() - df = df[df["qval"] <= self.config["fdr"]["fdr"]] + + psm_df, frag_df = workflow.extraction() + psm_df = psm_df[psm_df["qval"] <= self.config["fdr"]["fdr"]] + + logger.info(f"Removing fragments below FDR threshold") + + # to be optimized later + frag_df["candidate_key"] = hash( + frag_df["precursor_idx"].values, frag_df["rank"].values + ) + psm_df["candidate_key"] = hash( + psm_df["precursor_idx"].values, psm_df["rank"].values + ) + + frag_df = frag_df[ + frag_df["candidate_key"].isin(psm_df["candidate_key"]) + ] if self.config["multiplexing"]["multiplexed_quant"]: - df = workflow.requantify(df) - df = df[df["qval"] <= self.config["fdr"]["fdr"]] - df["run"] = raw_name - df.to_csv(os.path.join(workflow.path, "psm.tsv"), sep="\t", index=False) + psm_df = workflow.requantify(psm_df) + psm_df = psm_df[psm_df["qval"] <= self.config["fdr"]["fdr"]] + + psm_df["run"] = raw_name + psm_df.to_csv(psm_location, sep="\t", index=False) + frag_df.to_csv(frag_location, sep="\t", index=False) workflow.reporter.log_string(f"Finished workflow for {raw_name}") workflow.reporter.context.__exit__(None, None, None) @@ -214,58 +282,26 @@ def run( logger.error(f"Workflow failed for {raw_name} with error {e}") continue - self.build_output() - - def build_output(self): - output_path = self.config["output"] - temp_path = os.path.join(output_path, base.TEMP_FOLDER) - - psm_df = [] - stat_df = [] - - for raw_name, dia_path, speclib in self.get_run_data(): - run_path = os.path.join(temp_path, raw_name) - psm_path = os.path.join(run_path, "psm.tsv") - if not os.path.exists(psm_path): - logger.warning(f"no psm file found for {raw_name}") - continue - run_df = pd.read_csv(os.path.join(run_path, "psm.tsv"), sep="\t") + try: + base_spec_lib = SpecLibBase() + base_spec_lib.load_hdf( + os.path.join(self.output_folder, "speclib.hdf"), load_mod_seq=True + ) - psm_df.append(run_df) - stat_df.append(build_stat_df(run_df)) + output = outputtransform.SearchPlanOutput(self.config, self.output_folder) + output.build(workflow_folder_list, base_spec_lib) - psm_df = pd.concat(psm_df) - stat_df = pd.concat(stat_df) + except Exception as e: + # get full traceback + import traceback - psm_df.to_csv( - os.path.join(output_path, "psm.tsv"), - sep="\t", - index=False, - float_format="%.6f", - ) - stat_df.to_csv( - os.path.join(output_path, "stat.tsv"), - sep="\t", - index=False, - float_format="%.6f", - ) + traceback.print_exc() + print(e) + logger.error(f"Output failed with error {e}") + return - logger.info(f"Finished building output") - - -def build_stat_df(run_df): - run_stat_df = [] - for name, group in run_df.groupby("channel"): - run_stat_df.append( - { - "run": run_df["run"].iloc[0], - "channel": name, - "precursors": np.sum(group["qval"] <= 0.01), - "proteins": group[group["qval"] <= 0.01]["proteins"].nunique(), - "ms1_accuracy": np.mean(group["weighted_mass_error"]), - "fwhm_rt": np.mean(group["cycle_fwhm"]), - "fwhm_mobility": np.mean(group["mobility_fwhm"]), - } - ) + logger.progress("=================== Search Finished ===================") - return pd.DataFrame(run_stat_df) + def clean(self): + if not self.config["library_loading"]["save_hdf"]: + os.remove(os.path.join(self.output_folder, "speclib.hdf")) diff --git a/alphadia/plexscoring.py b/alphadia/plexscoring.py index 9f1a2e33..486b1616 100644 --- a/alphadia/plexscoring.py +++ b/alphadia/plexscoring.py @@ -24,6 +24,8 @@ import numpy as np import numba as nb +NUM_FEATURES = 41 + def candidate_features_to_candidates( candidate_features_df: pd.DataFrame, @@ -159,6 +161,7 @@ def multiplex_candidates( @nb.experimental.jitclass() class CandidateConfigJIT: + collect_fragments: nb.boolean score_grouped: nb.boolean exclude_shared_ions: nb.boolean top_k_fragments: nb.uint32 @@ -170,6 +173,7 @@ class CandidateConfigJIT: def __init__( self, + collect_fragments: nb.boolean, score_grouped: nb.boolean, exclude_shared_ions: nb.types.bool_, top_k_fragments: nb.uint32, @@ -184,6 +188,7 @@ def __init__( Please refer to :class:`.alphadia.plexscoring.CandidateConfig` for documentation. """ + self.collect_fragments = collect_fragments self.score_grouped = score_grouped self.exclude_shared_ions = exclude_shared_ions self.top_k_fragments = top_k_fragments @@ -202,9 +207,10 @@ class CandidateConfig(config.JITConfig): def __init__(self): """Create default config for CandidateScoring""" + self.collect_fragments = True self.score_grouped = False self.exclude_shared_ions = True - self.top_k_fragments = 16 + self.top_k_fragments = 12 self.top_k_isotopes = 4 self.reference_channel = -1 self.precursor_mz_tolerance = 15 @@ -215,6 +221,16 @@ def jit_container(self): """The numba jitclass for this config object.""" return CandidateConfigJIT + @property + def collect_fragments(self) -> bool: + """Collect fragment features. + Default: `collect_fragments = False`""" + return self._collect_fragments + + @collect_fragments.setter + def collect_fragments(self, value): + self._collect_fragments = value + @property def score_grouped(self) -> bool: """When multiplexing is used, some grouped features are calculated taking into account all channels. @@ -239,7 +255,7 @@ def exclude_shared_ions(self, value): @property def top_k_fragments(self) -> int: """The number of fragments to consider for scoring. The top_k_fragments most intense fragments are used. - Default: `top_k_fragments = 16`""" + Default: `top_k_fragments = 12`""" return self._top_k_fragments @top_k_fragments.setter @@ -312,6 +328,10 @@ def validate(self): self.fragment_mz_tolerance < 200 ), "fragment_mz_tolerance must be less than 200" + def copy(self): + """Create a copy of the config object.""" + return CandidateConfig.from_dict(self.to_dict()) + float_array = nb.types.float32[:] @@ -327,6 +347,8 @@ class Candidate: failed: nb.boolean + output_idx: nb.uint32 + # input columns precursor_idx: nb.uint32 channel: nb.uint8 @@ -352,9 +374,10 @@ class Candidate: # object properties fragments: fragments.FragmentContainer.class_type.instance_type - features: nb.types.DictType(nb.types.unicode_type, nb.float32) fragment_feature_dict: nb.types.DictType(nb.types.unicode_type, nb.float32[:]) + feature_array: nb.float32[::1] + dense_fragments: nb.float32[:, :, :, :, ::1] dense_precursors: nb.float32[:, :, :, :, ::1] @@ -369,6 +392,7 @@ class Candidate: def __init__( self, + output_idx: nb.uint32, precursor_idx: nb.uint32, channel: nb.uint8, rank: nb.uint8, @@ -384,6 +408,8 @@ def __init__( precursor_mz: nb.float32, isotope_intensity: nb.float32[::1], ) -> None: + self.output_idx = output_idx + self.precursor_idx = precursor_idx self.channel = channel self.rank = rank @@ -413,22 +439,12 @@ def __str__(self): def initialize(self, fragment_container, config): # initialize all required dicts # accessing uninitialized dicts in numba will result in a kernel crash :) - self.features = nb.typed.Dict.empty( - key_type=nb.types.unicode_type, - value_type=nb.types.float32, - ) self.fragment_feature_dict = nb.typed.Dict.empty( key_type=nb.types.unicode_type, value_type=float_array ) - self.fragments = fragment_container.slice( - np.array([[self.frag_start_idx, self.frag_stop_idx, 1]]) - ) - if config.exclude_shared_ions: - self.fragments.filter_by_cardinality(1) - self.fragments.filter_top_k(config.top_k_fragments) - self.fragments.sort_by_mz() + self.assemble_isotope_mz(config) self.assemble_isotope_mz(config) @@ -444,31 +460,32 @@ def assemble_isotope_mz(self, config): * 1.0033548350700006 / self.charge ) - self.isotope_mz = offset.astype(nb.float32) + self.precursor_mz + return offset.astype(nb.float32) + self.precursor_mz - def build_profiles(self, dense_fragments, template): - # (n_fragments, n_observations, n_frames) - self.fragments_frame_profile = features.or_envelope_2d( - features.frame_profile_2d(dense_fragments[0]) - ) + def process( + self, + jit_data, + psm_proto_df, + fragment_container, + config, + quadrupole_calibration, + debug, + ) -> None: + psm_proto_df.precursor_idx[self.output_idx] = self.precursor_idx - # (n_observations, n_frames) - self.template_frame_profile = features.or_envelope_1d( - features.frame_profile_1d(template) - ) + isotope_mz = self.assemble_isotope_mz(config) - # (n_fragments, n_observations, n_scans) - self.fragments_scan_profile = features.or_envelope_2d( - features.scan_profile_2d(dense_fragments[0]) + # build fragment container + fragments = fragment_container.slice( + np.array([[self.frag_start_idx, self.frag_stop_idx, 1]]) ) + if config.exclude_shared_ions: + fragments.filter_by_cardinality(1) - # (n_observations, n_scans) - self.template_scan_profile = features.or_envelope_1d( - features.scan_profile_1d(template) - ) + fragments.filter_top_k(config.top_k_fragments) + fragments.sort_by_mz() - def process(self, jit_data, config, quadrupole_calibration, debug) -> None: - if len(self.fragments.mz) <= 3: + if len(fragments.mz) <= 3: self.failed = True return @@ -482,8 +499,7 @@ def process(self, jit_data, config, quadrupole_calibration, debug) -> None: scan_limit = np.array([[self.scan_start, self.scan_stop, 1]], dtype=np.uint64) quadrupole_limit = np.array( - [[np.min(self.isotope_mz) - 0.5, np.max(self.isotope_mz) + 0.5]], - dtype=np.float32, + [[np.min(isotope_mz) - 0.5, np.max(isotope_mz) + 0.5]], dtype=np.float32 ) if debug: @@ -498,7 +514,7 @@ def process(self, jit_data, config, quadrupole_calibration, debug) -> None: dense_fragments, frag_precursor_index = jit_data.get_dense( frame_limit, scan_limit, - self.fragments.mz, + fragments.mz, config.fragment_mz_tolerance, quadrupole_limit, absolute_masses=True, @@ -521,7 +537,7 @@ def process(self, jit_data, config, quadrupole_calibration, debug) -> None: _dense_precursors, prec_precursor_index = jit_data.get_dense( frame_limit, scan_limit, - self.isotope_mz, + isotope_mz, config.precursor_mz_tolerance, np.array([[-1.0, -1.0]]), absolute_masses=True, @@ -555,14 +571,14 @@ def process(self, jit_data, config, quadrupole_calibration, debug) -> None: if debug: # self.visualize_precursor(dense_precursors) - self.visualize_fragments(dense_fragments, self.fragments) + self.visualize_fragments(dense_fragments, fragments) # (n_isotopes, n_observations, n_scans) qtf = quadrupole.quadrupole_transfer_function_single( quadrupole_calibration, frag_precursor_index, np.arange(int(self.scan_start), int(self.scan_stop)), - self.isotope_mz, + isotope_mz, ) # (n_observation, n_scans, n_frames) @@ -584,8 +600,6 @@ def process(self, jit_data, config, quadrupole_calibration, debug) -> None: # DEBUG only used for debugging # self.template = template - self.build_profiles(dense_fragments, template) - if dense_fragments.shape[0] == 0: self.failed = True return @@ -594,62 +608,168 @@ def process(self, jit_data, config, quadrupole_calibration, debug) -> None: self.failed = True return + fragment_mask_1d = ( + np.sum(np.sum(np.sum(dense_fragments[0], axis=-1), axis=-1), axis=-1) > 0 + ) + + if np.sum(fragment_mask_1d) < 2: + self.failed = True + return + + # (2, n_valid_fragments, n_observations, n_scans, n_frames) + dense_fragments = dense_fragments[:, fragment_mask_1d] + fragments.apply_mask(fragment_mask_1d) + + # (n_fragments, n_observations, n_frames) + fragments_frame_profile = features.or_envelope_2d( + features.frame_profile_2d(dense_fragments[0]) + ) + + # (n_observations, n_frames) + template_frame_profile = features.or_envelope_1d( + features.frame_profile_1d(template) + ) + + # (n_fragments, n_observations, n_scans) + fragments_scan_profile = features.or_envelope_2d( + features.scan_profile_2d(dense_fragments[0]) + ) + + # (n_observations, n_scans) + template_scan_profile = features.or_envelope_1d( + features.scan_profile_1d(template) + ) + if debug: self.visualize_profiles( template, - self.fragments_scan_profile, - self.fragments_frame_profile, - self.template_frame_profile, - self.template_scan_profile, + fragments_scan_profile, + fragments_frame_profile, + template_frame_profile, + template_scan_profile, jit_data.has_mobility, ) - self.features.update( - features.location_features( - jit_data, - self.scan_start, - self.scan_stop, - self.scan_center, - self.frame_start, - self.frame_stop, - self.frame_center, - ) + # from here on features are being accumulated in the feature_array + # (n_features) + feature_array = np.zeros(NUM_FEATURES, dtype=np.float32) + feature_array[28] = np.mean(fragment_mask_1d) + + features.location_features( + jit_data, + self.scan_start, + self.scan_stop, + self.scan_center, + self.frame_start, + self.frame_stop, + self.frame_center, + feature_array, ) - self.features.update( - features.precursor_features( - self.isotope_mz, - self.isotope_intensity, - dense_precursors, - observation_importance, - template, - ) + features.precursor_features( + isotope_mz, + self.isotope_intensity, + dense_precursors, + observation_importance, + template, + feature_array, ) - feature_dict, self.fragment_feature_dict = features.fragment_features( - dense_fragments, observation_importance, template, self.fragments + # retrive first fragment features + # (n_valid_fragments) + mz_observed, mass_error, height, intensity = features.fragment_features( + dense_fragments, + observation_importance, + template, + fragments, + feature_array, ) - self.features.update(feature_dict) - - self.features.update( - features.profile_features( - jit_data, - self.fragments.intensity, - self.fragments.type, + # store fragment features if requested + # only target precursors are stored + if config.collect_fragments: + psm_proto_df.fragment_precursor_idx[self.output_idx, : len(mz_observed)] = [ + self.precursor_idx + ] * len(mz_observed) + psm_proto_df.fragment_rank[self.output_idx, : len(mz_observed)] = [ + self.rank + ] * len(mz_observed) + psm_proto_df.fragment_mz_library[ + self.output_idx, : len(fragments.mz_library) + ] = fragments.mz_library + psm_proto_df.fragment_mz[ + self.output_idx, : len(fragments.mz) + ] = fragments.mz + psm_proto_df.fragment_mz_observed[ + self.output_idx, : len(mz_observed) + ] = mz_observed + + psm_proto_df.fragment_height[self.output_idx, : len(height)] = height + psm_proto_df.fragment_intensity[ + self.output_idx, : len(intensity) + ] = intensity + + psm_proto_df.fragment_mass_error[ + self.output_idx, : len(mass_error) + ] = mass_error + psm_proto_df.fragment_number[ + self.output_idx, : len(fragments.number) + ] = fragments.number + psm_proto_df.fragment_type[ + self.output_idx, : len(fragments.type) + ] = fragments.type + psm_proto_df.fragment_charge[ + self.output_idx, : len(fragments.charge) + ] = fragments.charge + + # ============= FRAGMENT MOBILITY CORRELATIONS ============= + # will be skipped if no mobility dimension is present + if jit_data.has_mobility: + ( + feature_array[29], + feature_array[30], + ) = features.fragment_mobility_correlation( + fragments_scan_profile, + template_scan_profile, observation_importance, - self.fragments_scan_profile, - self.fragments_frame_profile, - self.template_scan_profile, - self.template_frame_profile, - self.scan_start, - self.scan_stop, - self.frame_start, - self.frame_stop, + fragments.intensity, ) + + # (n_valid_fragments) + correlation = features.profile_features( + jit_data, + fragments.intensity, + fragments.type, + observation_importance, + fragments_scan_profile, + fragments_frame_profile, + template_scan_profile, + template_frame_profile, + self.scan_start, + self.scan_stop, + self.frame_start, + self.frame_stop, + feature_array, ) - def process_reference_channel(self, reference_candidate): + if config.collect_fragments: + psm_proto_df.fragment_correlation[ + self.output_idx, : len(correlation) + ] = correlation + + psm_proto_df.features[self.output_idx] = feature_array + psm_proto_df.valid[self.output_idx] = True + + def process_reference_channel(self, reference_candidate, fragment_container): + fragments = fragment_container.slice( + np.array([[self.frag_start_idx, self.frag_stop_idx, 1]]) + ) + if config.exclude_shared_ions: + fragments.filter_by_cardinality(1) + + fragments.filter_top_k(config.top_k_fragments) + fragments.sort_by_mz() + self.features.update( features.reference_features( reference_candidate.observation_importance, @@ -662,7 +782,7 @@ def process_reference_channel(self, reference_candidate): self.fragments_frame_profile, self.template_scan_profile, self.template_frame_profile, - self.fragments.intensity, + fragments.intensity, ) ) @@ -712,7 +832,13 @@ def __len__(self): return len(self.candidates) def process( - self, fragment_container, jit_data, config, quadrupole_calibration, debug + self, + psm_proto_df, + fragment_container, + jit_data, + config, + quadrupole_calibration, + debug, ) -> None: # get refrerence channel index if config.reference_channel >= 0: @@ -733,20 +859,28 @@ def process( # process candidates for candidate in self.candidates: - candidate.initialize(fragment_container, config) - candidate.process(jit_data, config, quadrupole_calibration, debug) + candidate.process( + jit_data, + psm_proto_df, + fragment_container, + config, + quadrupole_calibration, + debug, + ) # process reference channel features if config.reference_channel >= 0: for idx, candidate in enumerate(self.candidates): if idx == reference_channel_idx: continue - candidate.process_reference_channel( - self.candidates[reference_channel_idx] - ) + # candidate.process_reference_channel( + # self.candidates[reference_channel_idx] + # ) # update rank features - candidate.features.update(features.rank_features(idx, self.candidates)) + # candidate.features.update( + # features.rank_features(idx, self.candidates) + # ) score_group_type = ScoreGroup.class_type.instance_type @@ -872,6 +1006,7 @@ def build_from_df( self.score_groups[-1].candidates.append( Candidate( + idx, precursor_idx[idx], channel[idx], rank[idx], @@ -896,7 +1031,7 @@ def build_from_df( raise ValueError("precursor_idx must be unique within a score group") current_precursor_idx = precursor_idx[idx] - idx += 1 + # idx += 1 def get_feature_columns(self): """Iterate all score groups and candidates and return a list of all feature names @@ -981,9 +1116,7 @@ def collect_features(self): feature_columns = self.get_feature_columns() candidate_count = self.get_candidate_count() - feature_array = np.empty( - (candidate_count, len(feature_columns)), dtype=np.float32 - ) + feature_array = np.empty((candidate_count, NUM_FEATURES), dtype=np.float32) feature_array[:] = np.nan precursor_idx_array = np.zeros(candidate_count, dtype=np.uint32) @@ -995,13 +1128,8 @@ def collect_features(self): for j in range(len(self[i].candidates)): candidate = self[i].candidates[j] - # iterate all features and add them to the feature array - for key, value in candidate.features.items(): - # get the column index for the feature - for k in range(len(feature_columns)): - if feature_columns[k] == key: - feature_array[candidate_idx, k] = value - break + feature_array[candidate_idx] = candidate.feature_array + # candidate.feature_array = np.empty(0, dtype=np.float32) precursor_idx_array[candidate_idx] = candidate.precursor_idx rank_array[candidate_idx] = candidate.rank @@ -1095,9 +1223,93 @@ def collect_fragments(self): ScoreGroupContainer.__module__ = "alphatims.extraction.plexscoring" +@nb.experimental.jitclass() +class OuptutPsmDF: + valid: nb.boolean[::1] + precursor_idx: nb.uint32[::1] + rank: nb.uint8[::1] + + features: nb.float32[:, ::1] + + fragment_precursor_idx: nb.uint32[:, ::1] + fragment_rank: nb.uint8[:, ::1] + + fragment_mz_library: nb.float32[:, ::1] + fragment_mz: nb.float32[:, ::1] + fragment_mz_observed: nb.float32[:, ::1] + + fragment_height: nb.float32[:, ::1] + fragment_intensity: nb.float32[:, ::1] + + fragment_mass_error: nb.float32[:, ::1] + fragment_correlation: nb.float32[:, ::1] + + fragment_number: nb.uint8[:, ::1] + fragment_type: nb.uint8[:, ::1] + fragment_charge: nb.uint8[:, ::1] + + def __init__(self, n_psm, top_k_fragments): + self.valid = np.zeros(n_psm, dtype=np.bool_) + self.precursor_idx = np.zeros(n_psm, dtype=np.uint32) + self.rank = np.zeros(n_psm, dtype=np.uint8) + + self.features = np.zeros((n_psm, NUM_FEATURES), dtype=np.float32) + + self.fragment_precursor_idx = np.zeros( + (n_psm, top_k_fragments), dtype=np.uint32 + ) + self.fragment_rank = np.zeros((n_psm, top_k_fragments), dtype=np.uint8) + + self.fragment_mz_library = np.zeros((n_psm, top_k_fragments), dtype=np.float32) + self.fragment_mz = np.zeros((n_psm, top_k_fragments), dtype=np.float32) + self.fragment_mz_observed = np.zeros((n_psm, top_k_fragments), dtype=np.float32) + + self.fragment_height = np.zeros((n_psm, top_k_fragments), dtype=np.float32) + self.fragment_intensity = np.zeros((n_psm, top_k_fragments), dtype=np.float32) + + self.fragment_mass_error = np.zeros((n_psm, top_k_fragments), dtype=np.float32) + self.fragment_correlation = np.zeros((n_psm, top_k_fragments), dtype=np.float32) + + self.fragment_number = np.zeros((n_psm, top_k_fragments), dtype=np.uint8) + self.fragment_type = np.zeros((n_psm, top_k_fragments), dtype=np.uint8) + self.fragment_charge = np.zeros((n_psm, top_k_fragments), dtype=np.uint8) + + def to_fragment_df(self): + mask = self.fragment_mz_library.flatten() > 0 + + return ( + self.fragment_precursor_idx.flatten()[mask], + self.fragment_rank.flatten()[mask], + self.fragment_mz_library.flatten()[mask], + self.fragment_mz.flatten()[mask], + self.fragment_mz_observed.flatten()[mask], + self.fragment_height.flatten()[mask], + self.fragment_intensity.flatten()[mask], + self.fragment_mass_error.flatten()[mask], + self.fragment_correlation.flatten()[mask], + self.fragment_number.flatten()[mask], + self.fragment_type.flatten()[mask], + self.fragment_charge.flatten()[mask], + ) + + def to_precursor_df(self): + return ( + self.precursor_idx[self.valid], + self.rank[self.valid], + self.features[self.valid], + ) + + @alphatims.utils.pjit() def _executor( - i, sg_container, fragment_container, jit_data, config, quadrupole_calibration, debug + i, + sg_container, + psm_proto_df, + fragment_container, + jit_data, + config, + quadrupole_calibration, + debug, ): """ Helper function. @@ -1105,10 +1317,24 @@ def _executor( """ sg_container[i].process( - fragment_container, jit_data, config, quadrupole_calibration, debug + psm_proto_df, + fragment_container, + jit_data, + config, + quadrupole_calibration, + debug, ) +@alphatims.utils.pjit() +def transfer_feature( + idx, score_group_container, feature_array, precursor_idx_array, rank_array +): + feature_array[idx] = score_group_container[idx].candidates[0].feature_array + precursor_idx_array[idx] = score_group_container[idx].candidates[0].precursor_idx + rank_array[idx] = score_group_container[idx].candidates[0].rank + + class CandidateScoring: """Calculate features for each precursor candidate used in scoring.""" @@ -1266,8 +1492,6 @@ def assemble_score_group_container( """ - validate.candidates_df(candidates_df) - precursor_columns = [ "channel", "flat_frag_start_idx", @@ -1369,7 +1593,7 @@ def assemble_fragments(self) -> fragments.FragmentContainer: ) def collect_candidates( - self, candidates_df: pd.DataFrame, score_group_container: ScoreGroupContainer + self, candidates_df: pd.DataFrame, psm_proto_df ) -> pd.DataFrame: """Collect the features from the score group container and return a DataFrame. @@ -1389,16 +1613,55 @@ def collect_candidates( A DataFrame containing the features for each candidate. """ - ( - feature_array, - precursor_idx_array, - rank_array, - feature_columns, - ) = score_group_container.collect_features() + feature_columns = [ + "base_width_mobility", + "base_width_rt", + "rt_observed", + "mobility_observed", + "mono_ms1_intensity", + "top_ms1_intensity", + "sum_ms1_intensity", + "weighted_ms1_intensity", + "weighted_mass_deviation", + "weighted_mass_error", + "mz_observed", + "mono_ms1_height", + "top_ms1_height", + "sum_ms1_height", + "weighted_ms1_height", + "isotope_intensity_correlation", + "isotope_height_correlation", + "n_observations", + "intensity_correlation", + "height_correlation", + "intensity_fraction", + "height_fraction", + "intensity_fraction_weighted", + "height_fraction_weighted", + "mean_observation_score", + "sum_b_ion_intensity", + "sum_y_ion_intensity", + "diff_b_y_ion_intensity", + "f_masked", + "fragment_scan_correlation", + "template_scan_correlation", + "fragment_frame_correlation", + "top3_frame_correlation", + "template_frame_correlation", + "top3_b_ion_correlation", + "n_b_ions", + "top3_y_ion_correlation", + "n_y_ions", + "cycle_fwhm", + "mobility_fwhm", + "delta_frame_peak", + ] + + precursor_idx, rank, features = psm_proto_df.to_precursor_df() - df = pd.DataFrame(feature_array, columns=feature_columns) - df["precursor_idx"] = precursor_idx_array - df["rank"] = rank_array + df = pd.DataFrame(features, columns=feature_columns) + df["precursor_idx"] = precursor_idx + df["rank"] = rank # join candidate columns candidate_df_columns = [ @@ -1431,7 +1694,20 @@ def collect_candidates( "flat_frag_stop_idx", "proteins", "genes", + "sequence", + "mods", + "mod_sites", ] + utils.get_isotope_column_names(self.precursors_flat_df.columns) + + precursor_df_columns += ( + [self.rt_column] if self.rt_column not in precursor_df_columns else [] + ) + precursor_df_columns += ( + [self.mobility_column] + if self.mobility_column not in precursor_df_columns + else [] + ) + df = utils.merge_missing_columns( df, self.precursors_flat_df, @@ -1440,23 +1716,15 @@ def collect_candidates( how="left", ) - for col in ["delta_frame_peak"]: - if col in df.columns: - df.drop(col, axis=1, inplace=True) - - if self.dia_data.has_mobility: - for col in [ - "fragment_scan_correlation", - "top3_scan_correlation", - "template_scan_correlation", - ]: - if col in df.columns: - df.drop(col, axis=1, inplace=True) + if self.rt_column == "rt_library": + df["delta_rt"] = df["rt_observed"] - df["rt_library"] + else: + df["delta_rt"] = df["rt_observed"] - df[self.rt_column] return df def collect_fragments( - self, candidates_df: pd.DataFrame, score_group_container: ScoreGroupContainer + self, candidates_df: pd.DataFrame, psm_proto_df ) -> pd.DataFrame: """Collect the fragment-level features from the score group container and return a DataFrame. @@ -1477,16 +1745,23 @@ def collect_fragments( """ - ( - fragment_array, - precursor_idx_array, - rank_array, - fragment_columns, - ) = score_group_container.collect_fragments() - - df = pd.DataFrame(fragment_array, columns=fragment_columns) - df["precursor_idx"] = precursor_idx_array - df["rank"] = rank_array + colnames = [ + "precursor_idx", + "rank", + "mz_library", + "mz", + "mz_observed", + "height", + "intensity", + "mass_error", + "correlation", + "number", + "type", + "charge", + ] + df = pd.DataFrame( + {key: value for value, key in zip(psm_proto_df.to_fragment_df(), colnames)} + ) # join precursor columns precursor_df_columns = [ @@ -1530,38 +1805,70 @@ def __call__(self, candidates_df, thread_count=10, debug=False): """ logger.info("Starting candidate scoring") - score_group_container = self.assemble_score_group_container(candidates_df) fragment_container = self.assemble_fragments() - # if debug mode, only iterate over 10 elution groups - iterator_len = ( - min(10, len(score_group_container)) if debug else len(score_group_container) - ) - thread_count = 1 if debug else thread_count - - alphatims.utils.set_threads(thread_count) - _executor( - range(iterator_len), - score_group_container, - fragment_container, - self.dia_data, - self.config.jitclass(), - self.quadrupole_calibration.jit, - debug, - ) + candidate_features_list = [] - candidate_features_df = self.collect_candidates( - candidates_df, score_group_container - ) - validate.candidate_features_df(candidate_features_df) - fragment_features_df = self.collect_fragments( - candidates_df, score_group_container - ) - validate.fragment_features_df(fragment_features_df) + validate.candidates_df(candidates_df) + + for decoy in [False, True]: + candidates_view_df = candidates_df[ + candidates_df["decoy"] == (1 if decoy else 0) + ] + self.config.collect_fragments = not decoy + + if decoy: + logger.info(f"Processing {len(candidates_view_df)} decoy candidates") + else: + logger.info(f"Processing {len(candidates_view_df)} target candidates") + + score_group_container = self.assemble_score_group_container( + candidates_view_df + ) + + # build output containers + n_candidates = score_group_container.get_candidate_count() + if not decoy: + psm_proto_df = OuptutPsmDF(n_candidates, self.config.top_k_fragments) + else: + psm_proto_df = OuptutPsmDF(n_candidates, 0) + + # if debug mode, only iterate over 10 elution groups + iterator_len = ( + min(10, len(score_group_container)) + if debug + else len(score_group_container) + ) + thread_count = 1 if debug else thread_count + + alphatims.utils.set_threads(thread_count) + _executor( + range(iterator_len), + score_group_container, + psm_proto_df, + fragment_container, + self.dia_data, + self.config.jitclass(), + self.quadrupole_calibration.jit, + debug, + ) + + logger.info("Finished candidate processing") + logger.info("Collecting candidate features") + candidate_features_df = self.collect_candidates(candidates_df, psm_proto_df) + validate.candidate_features_df(candidate_features_df) + candidate_features_list += [candidate_features_df] + + if not decoy: + logger.info("Collecting fragment features") + fragment_features_df = self.collect_fragments( + candidates_df, psm_proto_df + ) + validate.fragment_features_df(fragment_features_df) logger.info("Finished candidate scoring") del score_group_container del fragment_container - return candidate_features_df, fragment_features_df + return pd.concat(candidate_features_list), fragment_features_df diff --git a/alphadia/testing.py b/alphadia/testing.py index b34c0165..bccdf793 100644 --- a/alphadia/testing.py +++ b/alphadia/testing.py @@ -74,7 +74,7 @@ def filename_onedrive(sharing_url: str) -> str: # pragma: no cover try: remotefile = urlopen(encoded_url) except: - logging.info(f"Could not open {sharing_url} for reading filename") + print(f"Could not open {sharing_url} for reading filename") raise ValueError(f"Could not open {sharing_url} for reading filename") from None info = remotefile.info()["Content-Disposition"] @@ -107,10 +107,10 @@ def download_onedrive( output_path = os.path.join(output_dir, filename) try: path, message = urlretrieve(encoded_url, output_path, Progress()) - logging.info(f"{filename} successfully downloaded") + print(f"{filename} successfully downloaded") return path except: - logging.info(f"Could not download {filename} from onedrive") + print(f"Could not download {filename} from onedrive") return None @@ -135,18 +135,18 @@ def update_onedrive( filename = filename_onedrive(sharing_url) output_path = os.path.join(output_dir, filename) if not os.path.exists(output_path): - logging.info(f"{filename} does not yet exist") + print(f"{filename} does not yet exist") download_onedrive(sharing_url, output_dir) # if file ends with .zip and zip=True if unzip and filename.endswith(".zip"): - logging.info(f"Unzipping {filename}") + print(f"Unzipping {filename}") with zipfile.ZipFile(output_path, "r") as zip_ref: zip_ref.extractall(output_dir) - logging.info(f"{filename} successfully unzipped") + print(f"{filename} successfully unzipped") else: - logging.info(f"{filename} already exists") + print(f"{filename} already exists") def encode_url_datashare(sharing_url: str) -> str: # pragma: no cover @@ -188,7 +188,7 @@ def filename_datashare(sharing_url: str, tar=False) -> str: # pragma: no cover try: remotefile = urlopen(encoded_url) except: - # logging.info(f'Could not open {sharing_url} for reading filename') + # print(f'Could not open {sharing_url} for reading filename') raise ValueError(f"Could not open {sharing_url} for reading filename") from None info = remotefile.info()["Content-Disposition"] @@ -223,11 +223,11 @@ def download_datashare( try: path, message = urlretrieve(encoded_url, output_path, Progress()) - logging.info(f"{filename} successfully downloaded") + print(f"{filename} successfully downloaded") return path except Exception as e: - logging.info(f"Could not download {filename} from datashare") + print(f"Could not download {filename} from datashare") return None @@ -259,26 +259,26 @@ def update_datashare( # file does not yet exist if not os.path.exists(unzipped_path): - logging.info(f"{filename} does not yet exist") + print(f"{filename} does not yet exist") download_datashare(sharing_url, output_dir) # if file ends with .zip and zip=True if filename.endswith(".zip"): with zipfile.ZipFile(output_path, "r") as zip_ref: zip_ref.extractall(output_dir) - logging.info(f"{filename} successfully unzipped") + print(f"{filename} successfully unzipped") os.remove(output_path) # file already exists else: # force download if force: - logging.info(f"{filename} already exists, but force=True") + print(f"{filename} already exists, but force=True") # remove file try: os.remove(output_path) - logging.info(f"{filename} successfully removed") + print(f"{filename} successfully removed") except: logging.error(f"Could not remove {filename}") return @@ -289,7 +289,9 @@ def update_datashare( if filename.endswith(".zip"): with zipfile.ZipFile(output_path, "r") as zip_ref: zip_ref.extractall(output_dir) - logging.info(f"{filename} successfully unzipped") + print(f"{filename} successfully unzipped") os.remove(output_path) - logging.info(f"{filename} already exists") + print(f"{filename} already exists") + + return unzipped_path diff --git a/alphadia/workflow/base.py b/alphadia/workflow/base.py index 1245e50f..8e2ba451 100644 --- a/alphadia/workflow/base.py +++ b/alphadia/workflow/base.py @@ -31,8 +31,6 @@ def __init__( self, instance_name: str, config: dict, - dia_data_path: str, - spectral_library: SpecLibBase, ) -> None: """ Parameters @@ -62,6 +60,11 @@ def __init__( ) os.mkdir(self.path) + def load( + self, + dia_data_path: str, + spectral_library: SpecLibBase, + ) -> None: self.reporter = reporting.Pipeline( backends=[ reporting.LogBackend(), @@ -82,9 +85,9 @@ def __init__( # initialize the calibration manager self._calibration_manager = manager.CalibrationManager( - config["calibration_manager"], + self.config["calibration_manager"], path=os.path.join(self.path, self.CALIBRATION_MANAGER_PATH), - load_from_file=config["general"]["reuse_calibration"], + load_from_file=self.config["general"]["reuse_calibration"], reporter=self.reporter, ) @@ -94,9 +97,9 @@ def __init__( # initialize the optimization manager self._optimization_manager = manager.OptimizationManager( - config["optimization_manager"], + self.config["optimization_manager"], path=os.path.join(self.path, self.OPTIMIZATION_MANAGER_PATH), - load_from_file=config["general"]["reuse_calibration"], + load_from_file=self.config["general"]["reuse_calibration"], figure_path=os.path.join(self.path, self.FIGURE_PATH), reporter=self.reporter, ) diff --git a/alphadia/workflow/peptidecentric.py b/alphadia/workflow/peptidecentric.py index ce682bd0..db0ae75e 100644 --- a/alphadia/workflow/peptidecentric.py +++ b/alphadia/workflow/peptidecentric.py @@ -42,6 +42,7 @@ "base_width_mobility", "base_width_rt", "rt_observed", + "delta_rt", "mobility_observed", "mono_ms1_intensity", "top_ms1_intensity", @@ -83,8 +84,10 @@ "mobility_fwhm", ] -classifier_base = fdrx.BinaryClassifier( +classifier_base = fdrx.BinaryClassifierLegacyNewBatching( test_size=0.001, + batch_size=5000, + learning_rate=0.001, ) @@ -93,12 +96,18 @@ def __init__( self, instance_name: str, config: dict, - dia_data_path: str, - spectral_library: SpecLibBase, ) -> None: super().__init__( instance_name, config, + ) + + def load( + self, + dia_data_path: str, + spectral_library: SpecLibBase, + ) -> None: + super().load( dia_data_path, spectral_library, ) @@ -128,14 +137,14 @@ def init_calibration_optimization_manager(self): { "current_epoch": 0, "current_step": 0, - "ms1_error": self.config["extraction_initial"]["initial_ms1_tolerance"], - "ms2_error": self.config["extraction_initial"]["initial_ms2_tolerance"], - "rt_error": self.config["extraction_initial"]["initial_rt_tolerance"], - "mobility_error": self.config["extraction_initial"][ + "ms1_error": self.config["search_initial"]["initial_ms1_tolerance"], + "ms2_error": self.config["search_initial"]["initial_ms2_tolerance"], + "rt_error": self.config["search_initial"]["initial_rt_tolerance"], + "mobility_error": self.config["search_initial"][ "initial_mobility_tolerance" ], "column_type": "library", - "num_candidates": self.config["extraction_initial"][ + "num_candidates": self.config["search_initial"][ "initial_num_candidates" ], "recalibration_target": self.config["calibration"][ @@ -241,7 +250,7 @@ def norm_to_rt( else: lower_rt = ( dia_data.rt_values[0] - + self.config["extraction_initial"]["initial_rt_tolerance"] / 2 + + self.config["search_initial"]["initial_rt_tolerance"] / 2 ) else: lower_rt = active_gradient_start @@ -251,7 +260,7 @@ def norm_to_rt( upper_rt = self.config["calibration"]["active_gradient_stop"] else: upper_rt = dia_data.rt_values[-1] - ( - self.config["extraction_initial"]["initial_rt_tolerance"] / 2 + self.config["search_initial"]["initial_rt_tolerance"] / 2 ) else: upper_rt = active_gradient_stop @@ -283,9 +292,9 @@ def norm_to_rt( def get_exponential_batches(self, step): """Get the number of batches for a given step This plan has the shape: - 1, 1, 1, 2, 4, 8, 16, 32, 64, ... + 1, 2, 4, 8, 16, 32, 64, ... """ - return int(2 ** max(step - 3, 0)) + return int(2**step) def get_batch_plan(self): n_eg = self.spectral_library._precursor_df["elution_group_idx"].nunique() @@ -381,13 +390,13 @@ def calibration(self): self.start_of_calibration() for current_epoch in range(self.config["calibration"]["max_epochs"]): - self.start_of_epoch(current_epoch) - if self.check_epoch_conditions(): pass else: break + self.start_of_epoch(current_epoch) + features = [] fragments = [] for current_step, (start_index, stop_index) in enumerate(self.batch_plan): @@ -447,15 +456,13 @@ def end_of_epoch(self): pass def end_of_calibration(self): - self.calibration_manager.predict( - self.spectral_library._precursor_df, "precursor" - ) - self.calibration_manager.predict(self.spectral_library._fragment_df, "fragment") + # self.calibration_manager.predict(self.spectral_library._precursor_df, 'precursor') + # self.calibration_manager.predict(self.spectral_library._fragment_df, 'fragment') self.calibration_manager.save() pass def recalibration(self, precursor_df, fragments_df): - precursor_df_filtered = precursor_df[precursor_df["qval"] < 0.001] + precursor_df_filtered = precursor_df[precursor_df["qval"] < 0.01] precursor_df_filtered = precursor_df_filtered[ precursor_df_filtered["decoy"] == 0 ] @@ -492,10 +499,10 @@ def recalibration(self, precursor_df, fragments_df): top_intensity_precursors["precursor_idx"] ) ] - median_fragment_intensity = fragments_df_filtered["intensity"].median() - fragments_df_filtered = fragments_df_filtered[ - fragments_df_filtered["intensity"] > median_fragment_intensity - ].head(50000) + + fragments_df_filtered = fragments_df.sort_values( + by=["correlation"], ascending=False + ).head(10000) self.calibration_manager.fit( fragments_df_filtered, @@ -555,9 +562,6 @@ def check_recalibration(self, precursor_df): self.com.accumulated_precursors_01FDR = len( precursor_df[precursor_df["qval"] < 0.01] ) - self.com.accumulated_precursors_001FDR = len( - precursor_df[precursor_df["qval"] < 0.001] - ) self.reporter.log_string( f"=== checking if recalibration conditions were reached, target {self.com.recalibration_target} precursors ===", @@ -568,7 +572,7 @@ def check_recalibration(self, precursor_df): perform_recalibration = False - if self.com.accumulated_precursors_001FDR > self.com.recalibration_target: + if self.com.accumulated_precursors_01FDR > self.com.recalibration_target: perform_recalibration = True return perform_recalibration @@ -592,6 +596,7 @@ def extract_batch(self, batch_df): config.update(self.config["selection_config"]) config.update( { + "top_k_fragments": self.config["search_advanced"]["top_k_fragments"], "rt_tolerance": self.com.rt_error, "mobility_tolerance": self.com.mobility_error, "candidate_count": self.com.num_candidates, @@ -621,6 +626,7 @@ def extract_batch(self, batch_df): config.update(self.config["scoring_config"]) config.update( { + "top_k_fragments": self.config["search_advanced"]["top_k_fragments"], "precursor_mz_tolerance": self.com.ms1_error, "fragment_mz_tolerance": self.com.ms2_error, "exclude_shared_ions": self.config["search"]["exclude_shared_ions"], @@ -673,13 +679,10 @@ def extraction(self): ) precursor_df = self.fdr_correction(features_df) - if not self.config["fdr"]["keep_decoys"]: - precursor_df = precursor_df[precursor_df["decoy"] == 0] - precursor_df = precursor_df[precursor_df["qval"] <= self.config["fdr"]["fdr"]] self.log_precursor_df(precursor_df) - return precursor_df + return precursor_df, fragments_df def log_precursor_df(self, precursor_df): total_precursors = len(precursor_df) diff --git a/coverage.svg b/coverage.svg index 2d1c7436..cb3cdc0e 100644 --- a/coverage.svg +++ b/coverage.svg @@ -9,13 +9,13 @@ - + coverage coverage - 37% - 37% + 41% + 41% diff --git a/gui/package.json b/gui/package.json index 62076239..3b079ca2 100644 --- a/gui/package.json +++ b/gui/package.json @@ -1,7 +1,7 @@ { "name": "alphadia", "productName": "alphadia-gui", - "version": "1.4.0", + "version": "1.5.0", "description": "Graphical user interface for DIA data analysis", "main": "dist/electron.js", "homepage": "./", diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg index cca14fea..59cfb78a 100644 --- a/misc/.bumpversion.cfg +++ b/misc/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 1.4.0 +current_version = 1.5.0 commit = True tag = True parse = (?P\d+)\.(?P\d+)\.(?P\d+)(\-(?P[a-z]+)(?P\d+))? diff --git a/misc/config/default.yaml b/misc/config/default.yaml index 8e9a36dd..095e56dc 100644 --- a/misc/config/default.yaml +++ b/misc/config/default.yaml @@ -9,6 +9,7 @@ general: astral_ms1: false log_level: 'INFO' wsl: false + mmap_detector_events: false library_loading: rt_heuristic: 180 @@ -25,23 +26,19 @@ search: target_mobility_tolerance: 0.04 target_rt_tolerance: 60 -fdr: - fdr: 0.01 - group_level: 'proteins' - competetive_scoring: true, - keep_decoys: false, - channel_wise_fdr: false, +search_advanced: + top_k_fragments: 12 calibration: min_epochs: 3 max_epochs: 20 - batch_size: 4000 + batch_size: 8000 recalibration_target: 200 final_full_calibration: False norm_rt_mode: 'linear' search_initial: - initial_num_candidates: 2 + initial_num_candidates: 1 initial_ms1_tolerance: 30 initial_ms2_tolerance: 30 initial_mobility_tolerance: 0.08 @@ -54,7 +51,6 @@ selection_config: sigma_scale_mobility: 1. top_k_precursors: 3 - top_k_fragments: 12 kernel_size: 30 f_mobility: 1.0 @@ -74,7 +70,6 @@ selection_config: scoring_config: score_grouped: false - top_k_fragments: 12 top_k_isotopes: 3 reference_channel: -1 precursor_mz_tolerance: 10 @@ -87,6 +82,20 @@ multiplexing: reference_channel: 0 competetive_scoring: True +fdr: + fdr: 0.01 + group_level: 'proteins' + competetive_scoring: true + keep_decoys: false + channel_wise_fdr: false + library_grouping: false + +search_output: + min_k_fragments: 3 + min_correlation: 0.5 + num_samples_quadratic: 50 + min_nonnan: 1 + # configuration for the optimization manager # initial parameters, will nbe optimized optimization_manager: diff --git a/nbs/debug/debug_lvl1.ipynb b/nbs/debug/debug_lvl1.ipynb index 7e450f7e..a811242c 100644 --- a/nbs/debug/debug_lvl1.ipynb +++ b/nbs/debug/debug_lvl1.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:WARNING: Temp mmap arrays are written to /var/folders/lc/9594t94d5b5_gn0y04w1jh980000gn/T/temp_mmap_8wbttgfv. Cleanup of this folder is OS dependant, and might need to be triggered manually! Current space: 135,225,167,872\n", + "WARNING:root:WARNING: No Bruker libraries are available for this operating system. Mobility and m/z values need to be estimated. While this estimation often returns acceptable results with errors < 0.02 Th, huge errors (e.g. offsets of 6 Th) have already been observed for some samples!\n" + ] + } + ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -16,13 +25,13 @@ "import os\n", "\n", "from alphabase.spectral_library.base import SpecLibBase\n", - "from alphadia.extraction import data, planning\n", - "from alphadia.extraction.workflow import manager, peptidecentric" + "from alphadia import data, planning\n", + "from alphadia.workflow import manager, peptidecentric" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -62,30 +71,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0:00:00.000299 \u001b[32;20mPROGRESS: _ _ _ ___ ___ _ \u001b[0m\n", + "0:00:00.000810 \u001b[32;20mPROGRESS: /_\\ | |_ __| |_ __ _| \\_ _| /_\\ \u001b[0m\n", + "0:00:00.001200 \u001b[32;20mPROGRESS: / _ \\| | '_ \\ ' \\/ _` | |) | | / _ \\ \u001b[0m\n", + "0:00:00.001430 \u001b[32;20mPROGRESS: /_/ \\_\\_| .__/_||_\\__,_|___/___/_/ \\_\\\u001b[0m\n", + "0:00:00.001851 \u001b[32;20mPROGRESS: |_| \u001b[0m\n", + "0:00:00.002328 \u001b[32;20mPROGRESS: \u001b[0m\n", + "0:00:00.002817 INFO: loading default config from /Users/georgwallmann/Documents/git/alphadia/alphadia/../misc/config/default.yaml\n", + "0:00:00.010220 INFO: Applying config update from dict\n", + "0:00:00.010736 \u001b[32;20mPROGRESS: version: 1.3.2\u001b[0m\n", + "0:00:00.010906 \u001b[32;20mPROGRESS: hostname: Georgs-MacBook-Pro.local\u001b[0m\n", + "0:00:00.011244 \u001b[32;20mPROGRESS: date: 2023-11-14 16:30:39\u001b[0m\n", + "0:00:00.011553 INFO: Running DynamicLoader\n", + "0:00:01.958157 INFO: Running PrecursorInitializer\n", + "0:00:01.959985 INFO: Running AnnotateFasta\n", + "0:00:01.960559 INFO: Dropping decoys from input library before annotation\n", + "0:00:02.046945 INFO: Running IsotopeGenerator\n", + "0:00:02.047515 \u001b[33;20mWARNING: Input library already contains isotope information. Skipping isotope generation. \n", + " Please note that isotope generation outside of alphabase is not supported.\u001b[0m\n", + "0:00:02.047836 INFO: Running RTNormalization\n", + "0:00:02.055838 INFO: Running DecoyGenerator\n", + "0:00:05.156083 INFO: Running FlattenLibrary\n", + "0:00:11.293920 INFO: Running InitFlatColumns\n", + "0:00:11.294946 INFO: Running LogFlatLibraryStats\n", + "0:00:11.295227 INFO: ============ Library Stats ============\n", + "0:00:11.295416 INFO: Number of precursors: 470,599\n", + "0:00:11.358480 INFO: \tthereof targets:235,310\n", + "0:00:11.359254 INFO: \tthereof decoys: 235,289\n", + "0:00:11.363403 INFO: Number of elution groups: 235,310\n", + "0:00:11.363838 INFO: \taverage size: 2.00\n", + "0:00:11.377859 INFO: Number of proteins: 9,904\n", + "0:00:11.380199 INFO: Number of channels: 1 ([0])\n", + "0:00:11.380608 INFO: Isotopes Distribution for 6 isotopes\n", + "0:00:11.380883 INFO: =======================================\n" + ] + } + ], "source": [ - "test_lib = SpecLibBase()\n", - "test_lib.load_hdf(speclib, load_mod_seq=True)\n", - "plan = planning.Plan(output_location, raw_files, test_lib)\n", - "\n", - "plan.config[\"general\"][\"reuse_calibration\"] = False\n", - "plan.config[\"general\"][\"thread_count\"] = 10\n", - "plan.config[\"general\"][\"astral_ms1\"] = False\n", - "plan.config[\"calibration\"][\"norm_rt_mode\"] = \"linear\"\n", - "\n", - "plan.config[\"extraction_target\"][\"target_num_candidates\"] = 5\n", - "plan.config[\"extraction_target\"][\"target_ms1_tolerance\"] = 3 if MODE == \"astral\" else 15\n", - "plan.config[\"extraction_target\"][\"target_ms2_tolerance\"] = 5 if MODE == \"astral\" else 15\n", - "plan.config[\"extraction_target\"][\"target_rt_tolerance\"] = 150" + "config_update = {\n", + " \"general\": {\n", + " \"reuse_calibration\": True,\n", + " \"reuse_quant\": True,\n", + " \"thread_count\": 10,\n", + " \"astral_ms1\": False,\n", + " },\n", + " \"search_initial\": {\n", + " \"initial_num_candidates\": 2,\n", + " },\n", + " \"search\": {\n", + " \"target_num_candidates\": 5,\n", + " \"target_ms1_tolerance\": 3 if MODE == \"astral\" else 15,\n", + " \"target_ms2_tolerance\": 5 if MODE == \"astral\" else 15,\n", + " \"target_rt_tolerance\": 120,\n", + " },\n", + " \"fdr\": {\"library_grouping\": True},\n", + "}\n", + "plan = planning.Plan(output_location, raw_files, speclib, config_update=config_update)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0:00:13.456621 \u001b[32;20mPROGRESS: Loading raw file 1/1: 20230815_OA1_SoSt_SA_Whisper40_ADIAMA_HeLa_5ng_8Th14ms_FAIMS-40_1900V_noLoopCount_01\u001b[0m\n" + ] + } + ], "source": [ "for raw_name, dia_path, speclib in plan.get_run_data():\n", " pass" @@ -93,19 +156,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "11it [00:15, 1.43s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None True\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0:01:34.889078 INFO: Loaded CalibrationManager from /Users/georgwallmann/Documents/data/performance_tests/outputs/astral_lf_dia/.progress/20230815_OA1_SoSt_SA_Whisper40_ADIAMA_HeLa_5ng_8Th14ms_FAIMS-40_1900V_noLoopCount_01/calibration_manager.pkl\n", + "0:01:34.889792 INFO: Initializing CalibrationManager\n", + "0:01:34.890751 INFO: Disabling ion mobility calibration\n", + "0:01:34.891526 INFO: Loaded OptimizationManager from /Users/georgwallmann/Documents/data/performance_tests/outputs/astral_lf_dia/.progress/20230815_OA1_SoSt_SA_Whisper40_ADIAMA_HeLa_5ng_8Th14ms_FAIMS-40_1900V_noLoopCount_01/optimization_manager.pkl\n", + "0:01:34.892314 INFO: Initializing OptimizationManager\n", + "0:01:34.893053 \u001b[32;20mPROGRESS: Initializing workflow 20230815_OA1_SoSt_SA_Whisper40_ADIAMA_HeLa_5ng_8Th14ms_FAIMS-40_1900V_noLoopCount_01\u001b[0m\n", + "0:01:34.893844 INFO: Initializing OptimizationManager\n", + "0:01:34.894256 INFO: initial parameter: current_epoch = 0\n", + "0:01:34.894679 INFO: initial parameter: current_step = 0\n", + "0:01:34.895098 INFO: initial parameter: ms1_error = 30\n", + "0:01:34.895443 INFO: initial parameter: ms2_error = 30\n", + "0:01:34.895790 INFO: initial parameter: rt_error = 240\n", + "0:01:34.896160 INFO: initial parameter: mobility_error = 0.08\n", + "0:01:34.896496 INFO: initial parameter: column_type = library\n", + "0:01:34.896792 INFO: initial parameter: num_candidates = 2\n", + "0:01:34.897099 INFO: initial parameter: recalibration_target = 200\n", + "0:01:34.897395 INFO: initial parameter: accumulated_precursors = 0\n", + "0:01:34.897697 INFO: initial parameter: accumulated_precursors_01FDR = 0\n", + "0:01:34.897994 INFO: initial parameter: accumulated_precursors_001FDR = 0\n", + "0:01:34.898446 INFO: Initializing FDRManager\n", + "0:01:34.898759 \u001b[32;20mPROGRESS: Applying channel filter using only: [0]\u001b[0m\n", + "0:01:34.953161 \u001b[32;20mPROGRESS: 219,866 target precursors potentially observable (15,444 removed)\u001b[0m\n", + "0:01:35.043559 \u001b[32;20mPROGRESS: Skipping calibration as existing calibration was found\u001b[0m\n" + ] + } + ], "source": [ "workflow = peptidecentric.PeptideCentricWorkflow(\n", - " raw_name, plan.config, dia_path, speclib\n", + " raw_name,\n", + " plan.config,\n", ")\n", + "workflow.load(dia_path, speclib)\n", "workflow.calibration()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -114,11 +223,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0:03:13.549251 INFO: Duty cycle consists of 76 frames, 1.34 seconds cycle time\n", + "0:03:13.549875 INFO: Duty cycle consists of 1 scans, 0.00000 1/K_0 resolution\n", + "0:03:13.550268 INFO: FWHM in RT is 3.59 seconds, sigma is 0.57\n", + "0:03:13.551165 INFO: FWHM in mobility is 0.000 1/K_0, sigma is 1.00\n", + "0:03:14.343229 INFO: Starting candidate selection\n", + "100%|██████████| 1000/1000 [00:21<00:00, 46.29it/s]\n", + "0:03:37.596133 INFO: Finished candidate selection\n" + ] + } + ], "source": [ - "from alphadia.extraction import hybridselection\n", + "from alphadia import hybridselection\n", "\n", "config = hybridselection.HybridCandidateConfig()\n", "config.update(workflow.config[\"selection_config\"])\n", @@ -129,9 +252,7 @@ " \"candidate_count\": workflow.com.num_candidates,\n", " \"precursor_mz_tolerance\": workflow.com.ms1_error,\n", " \"fragment_mz_tolerance\": workflow.com.ms2_error,\n", - " \"exclude_shared_ions\": workflow.config[\"library_loading\"][\n", - " \"exclude_shared_ions\"\n", - " ],\n", + " \"exclude_shared_ions\": workflow.config[\"search\"][\"exclude_shared_ions\"],\n", " }\n", ")\n", "\n", @@ -152,11 +273,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0:05:34.568625 INFO: Starting candidate scoring\n", + " 0%| | 0/1991 [00:00).\n", - " Convert the value to a supported type, such as a string or float, or use stringify_unsupported(obj)\n", - " for dictionaries or collections that contain unsupported values.\n", - " For more, see https://docs.neptune.ai/help/value_of_unsupported_type\n", - " self.neptune[f\"eval/{key}\"].log(value)\n", - "0:00:22.924721 \u001b[32;20m PROGRESS: === Epoch 0, step 0, extracting elution groups 0 to 4000 ===\u001b[0m\n", - "0:00:22.931979 \u001b[32;20m PROGRESS: MS1 error: 30, MS2 error: 30, RT error: 240, Mobility error: 0.08\u001b[0m\n", - "0:00:24.458850 \u001b[38;20m INFO: Duty cycle consists of 13 frames, 1.39 seconds cycle time\u001b[0m\n", - "0:00:24.459340 \u001b[38;20m INFO: Duty cycle consists of 928 scans, 0.00065 1/K_0 resolution\u001b[0m\n", - "0:00:24.459644 \u001b[38;20m INFO: FWHM in RT is 5.00 seconds, sigma is 0.77\u001b[0m\n", - "0:00:24.459992 \u001b[38;20m INFO: FWHM in mobility is 0.010 1/K_0, sigma is 6.57\u001b[0m\n", - "0:00:25.253378 \u001b[38;20m INFO: Starting candidate selection\u001b[0m\n", - "100%|██████████| 7936/7936 [01:25<00:00, 92.99it/s] \n", - "0:01:52.319552 \u001b[38;20m INFO: Finished candidate selection\u001b[0m\n", - "0:01:53.376656 \u001b[38;20m INFO: Starting candidate scoring\u001b[0m\n", - " 0%| | 0/13888 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:02:59.624536 \u001b[32;20m PROGRESS: === checking if recalibration conditions were reached, target 200 precursors ===\u001b[0m\n", - "0:02:59.626199 \u001b[32;20m PROGRESS: ============================= Precursor FDR =============================\u001b[0m\n", - "0:02:59.626529 \u001b[32;20m PROGRESS: Total precursors accumulated: 3,979\u001b[0m\n", - "0:02:59.626850 \u001b[32;20m PROGRESS: Target precursors: 2,448 (61.52%)\u001b[0m\n", - "0:02:59.627200 \u001b[32;20m PROGRESS: Decoy precursors: 1,531 (38.48%)\u001b[0m\n", - "0:02:59.627568 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:02:59.627807 \u001b[32;20m PROGRESS: Precursor Summary:\u001b[0m\n", - "0:02:59.630128 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 428; 0.01 FDR: 356; 0.001 FDR: 264\u001b[0m\n", - "0:02:59.630410 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:02:59.630701 \u001b[32;20m PROGRESS: Protein Summary:\u001b[0m\n", - "0:02:59.635015 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 384; 0.01 FDR: 321; 0.001 FDR: 241\u001b[0m\n", - "0:02:59.635433 \u001b[32;20m PROGRESS: =========================================================================\u001b[0m\n", - "0:02:59.637120 \u001b[38;20m INFO: calibration group: precursor, fitting mz estimator \u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:03:00.254198 \u001b[38;20m INFO: calibration group: precursor, predicting mz\u001b[0m\n", - "0:03:00.309053 \u001b[38;20m INFO: calibration group: precursor, predicting rt\u001b[0m\n", - "0:03:00.424371 \u001b[38;20m INFO: calibration group: precursor, predicting mobility\u001b[0m\n", - "0:03:00.470987 \u001b[38;20m INFO: calibration group: fragment, predicting mz\u001b[0m\n", - "0:03:01.314373 \u001b[32;20m PROGRESS: === Epoch 1, step 0, extracting elution groups 0 to 4000 ===\u001b[0m\n", - "0:03:01.324511 \u001b[32;20m PROGRESS: MS1 error: 15, MS2 error: 15, RT error: 150, Mobility error: 0.04\u001b[0m\n", - "0:03:01.326967 \u001b[38;20m INFO: Duty cycle consists of 13 frames, 1.39 seconds cycle time\u001b[0m\n", - "0:03:01.327432 \u001b[38;20m INFO: Duty cycle consists of 928 scans, 0.00065 1/K_0 resolution\u001b[0m\n", - "0:03:01.328012 \u001b[38;20m INFO: FWHM in RT is 4.35 seconds, sigma is 0.67\u001b[0m\n", - "0:03:01.328465 \u001b[38;20m INFO: FWHM in mobility is 0.010 1/K_0, sigma is 6.38\u001b[0m\n", - "0:03:01.342323 \u001b[38;20m INFO: Starting candidate selection\u001b[0m\n", - "100%|██████████| 7944/7944 [00:14<00:00, 563.14it/s]\n", - "0:03:16.033999 \u001b[38;20m INFO: Finished candidate selection\u001b[0m\n", - "0:03:16.377162 \u001b[38;20m INFO: Starting candidate scoring\u001b[0m\n", - "100%|██████████| 29497/29497 [00:05<00:00, 5408.77it/s]\n", - "0:03:22.561646 \u001b[33;20m WARNING: base_width_mobility has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.562374 \u001b[33;20m WARNING: base_width_rt has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.563392 \u001b[33;20m WARNING: rt_observed has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.564243 \u001b[33;20m WARNING: mobility_observed has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.565186 \u001b[33;20m WARNING: mono_ms1_intensity has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.565724 \u001b[33;20m WARNING: top_ms1_intensity has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.566317 \u001b[33;20m WARNING: sum_ms1_intensity has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.567011 \u001b[33;20m WARNING: weighted_ms1_intensity has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.567678 \u001b[33;20m WARNING: weighted_mass_deviation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.568292 \u001b[33;20m WARNING: weighted_mass_error has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.569224 \u001b[33;20m WARNING: mz_observed has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.570052 \u001b[33;20m WARNING: mono_ms1_height has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.571037 \u001b[33;20m WARNING: top_ms1_height has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.571584 \u001b[33;20m WARNING: sum_ms1_height has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.572468 \u001b[33;20m WARNING: weighted_ms1_height has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.573044 \u001b[33;20m WARNING: isotope_intensity_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.573706 \u001b[33;20m WARNING: isotope_height_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.574476 \u001b[33;20m WARNING: n_observations has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.575037 \u001b[33;20m WARNING: intensity_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.576104 \u001b[33;20m WARNING: height_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.576921 \u001b[33;20m WARNING: intensity_fraction has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.577664 \u001b[33;20m WARNING: height_fraction has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.578308 \u001b[33;20m WARNING: intensity_fraction_weighted has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.578937 \u001b[33;20m WARNING: height_fraction_weighted has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.579844 \u001b[33;20m WARNING: mean_observation_score has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.580365 \u001b[33;20m WARNING: sum_b_ion_intensity has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.580882 \u001b[33;20m WARNING: sum_y_ion_intensity has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.581252 \u001b[33;20m WARNING: diff_b_y_ion_intensity has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.581696 \u001b[33;20m WARNING: fragment_frame_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.582565 \u001b[33;20m WARNING: top3_frame_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.583327 \u001b[33;20m WARNING: template_frame_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.583953 \u001b[33;20m WARNING: top3_b_ion_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.585574 \u001b[33;20m WARNING: top3_y_ion_correlation has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.588456 \u001b[33;20m WARNING: cycle_fwhm has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.589977 \u001b[33;20m WARNING: mobility_fwhm has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.591341 \u001b[33;20m WARNING: n_b_ions has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.593934 \u001b[33;20m WARNING: n_y_ions has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.597026 \u001b[33;20m WARNING: f_masked has 16 NaNs ( 0.05 % out of 29497)\u001b[0m\n", - "0:03:22.708919 \u001b[38;20m INFO: Finished candidate scoring\u001b[0m\n", - "0:03:22.849346 \u001b[38;20m INFO: number of dfs in features: 1, total number of features: 29497\u001b[0m\n", - "0:03:22.849837 \u001b[38;20m INFO: performing precursor_channel_wise FDR with 39 features\u001b[0m\n", - "0:03:22.850059 \u001b[38;20m INFO: Decoy channel: -1\u001b[0m\n", - "0:03:22.850605 \u001b[38;20m INFO: Competetive: true,\u001b[0m\n", - "0:03:22.884568 \u001b[33;20m WARNING: dropped 7 target PSMs due to missing features\u001b[0m\n", - "0:03:22.885054 \u001b[33;20m WARNING: dropped 9 decoy PSMs due to missing features\u001b[0m\n", - "0:03:22.888583 \u001b[38;20m INFO: Pre FDR iterations: 13\u001b[0m\n", - "0:03:28.647933 \u001b[38;20m INFO: Post FDR iterations: 27\u001b[0m\n", - "0:03:28.724244 \u001b[38;20m INFO: Test AUC: 0.595\u001b[0m\n", - "0:03:28.724723 \u001b[38;20m INFO: Train AUC: 0.680\u001b[0m\n", - "0:03:28.725011 \u001b[38;20m INFO: AUC difference: 12.44%\u001b[0m\n", - "0:03:28.725188 \u001b[33;20m WARNING: AUC difference > 5%. This may indicate overfitting.\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:03:29.063180 \u001b[32;20m PROGRESS: === checking if recalibration conditions were reached, target 400 precursors ===\u001b[0m\n", - "0:03:29.064603 \u001b[32;20m PROGRESS: ============================= Precursor FDR =============================\u001b[0m\n", - "0:03:29.064917 \u001b[32;20m PROGRESS: Total precursors accumulated: 3,981\u001b[0m\n", - "0:03:29.065249 \u001b[32;20m PROGRESS: Target precursors: 3,003 (75.43%)\u001b[0m\n", - "0:03:29.065588 \u001b[32;20m PROGRESS: Decoy precursors: 978 (24.57%)\u001b[0m\n", - "0:03:29.065968 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:03:29.066322 \u001b[32;20m PROGRESS: Precursor Summary:\u001b[0m\n", - "0:03:29.068538 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 1,052; 0.01 FDR: 812; 0.001 FDR: 641\u001b[0m\n", - "0:03:29.068819 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:03:29.069049 \u001b[32;20m PROGRESS: Protein Summary:\u001b[0m\n", - "0:03:29.071608 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 887; 0.01 FDR: 700; 0.001 FDR: 566\u001b[0m\n", - "0:03:29.071947 \u001b[32;20m PROGRESS: =========================================================================\u001b[0m\n", - "0:03:29.074611 \u001b[38;20m INFO: calibration group: precursor, fitting mz estimator \u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:03:30.209704 \u001b[38;20m INFO: calibration group: precursor, predicting mz\u001b[0m\n", - "0:03:30.266174 \u001b[38;20m INFO: calibration group: precursor, predicting rt\u001b[0m\n", - "0:03:30.390364 \u001b[38;20m INFO: calibration group: precursor, predicting mobility\u001b[0m\n", - "0:03:30.442486 \u001b[38;20m INFO: calibration group: fragment, predicting mz\u001b[0m\n", - "0:03:31.239442 \u001b[32;20m PROGRESS: === Epoch 2, step 0, extracting elution groups 0 to 4000 ===\u001b[0m\n", - "0:03:31.248153 \u001b[32;20m PROGRESS: MS1 error: 15, MS2 error: 15, RT error: 150, Mobility error: 0.04\u001b[0m\n", - "0:03:31.250409 \u001b[38;20m INFO: Duty cycle consists of 13 frames, 1.39 seconds cycle time\u001b[0m\n", - "0:03:31.250748 \u001b[38;20m INFO: Duty cycle consists of 928 scans, 0.00065 1/K_0 resolution\u001b[0m\n", - "0:03:31.251128 \u001b[38;20m INFO: FWHM in RT is 4.34 seconds, sigma is 0.66\u001b[0m\n", - "0:03:31.251422 \u001b[38;20m INFO: FWHM in mobility is 0.008 1/K_0, sigma is 5.22\u001b[0m\n", - "0:03:31.253447 \u001b[38;20m INFO: Starting candidate selection\u001b[0m\n", - "100%|██████████| 7947/7947 [00:14<00:00, 535.21it/s]\n", - "0:03:46.926533 \u001b[38;20m INFO: Finished candidate selection\u001b[0m\n", - "0:03:47.275622 \u001b[38;20m INFO: Starting candidate scoring\u001b[0m\n", - "100%|██████████| 28290/28290 [00:05<00:00, 5237.04it/s]\n", - "0:03:53.072988 \u001b[33;20m WARNING: base_width_mobility has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.073683 \u001b[33;20m WARNING: base_width_rt has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.074578 \u001b[33;20m WARNING: rt_observed has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.075462 \u001b[33;20m WARNING: mobility_observed has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.076235 \u001b[33;20m WARNING: mono_ms1_intensity has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.076791 \u001b[33;20m WARNING: top_ms1_intensity has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.077656 \u001b[33;20m WARNING: sum_ms1_intensity has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.078616 \u001b[33;20m WARNING: weighted_ms1_intensity has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.079220 \u001b[33;20m WARNING: weighted_mass_deviation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.079969 \u001b[33;20m WARNING: weighted_mass_error has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.080617 \u001b[33;20m WARNING: mz_observed has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.081228 \u001b[33;20m WARNING: mono_ms1_height has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.082094 \u001b[33;20m WARNING: top_ms1_height has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.082657 \u001b[33;20m WARNING: sum_ms1_height has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.083206 \u001b[33;20m WARNING: weighted_ms1_height has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.084077 \u001b[33;20m WARNING: isotope_intensity_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.084765 \u001b[33;20m WARNING: isotope_height_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.085627 \u001b[33;20m WARNING: n_observations has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.086194 \u001b[33;20m WARNING: intensity_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.086744 \u001b[33;20m WARNING: height_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.087991 \u001b[33;20m WARNING: intensity_fraction has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.088772 \u001b[33;20m WARNING: height_fraction has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.089625 \u001b[33;20m WARNING: intensity_fraction_weighted has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.090209 \u001b[33;20m WARNING: height_fraction_weighted has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.090996 \u001b[33;20m WARNING: mean_observation_score has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.091840 \u001b[33;20m WARNING: sum_b_ion_intensity has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.092689 \u001b[33;20m WARNING: sum_y_ion_intensity has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.093350 \u001b[33;20m WARNING: diff_b_y_ion_intensity has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.094223 \u001b[33;20m WARNING: fragment_frame_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.095168 \u001b[33;20m WARNING: top3_frame_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.095919 \u001b[33;20m WARNING: template_frame_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.096659 \u001b[33;20m WARNING: top3_b_ion_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.099917 \u001b[33;20m WARNING: top3_y_ion_correlation has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.102238 \u001b[33;20m WARNING: cycle_fwhm has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.103628 \u001b[33;20m WARNING: mobility_fwhm has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.105456 \u001b[33;20m WARNING: n_b_ions has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.107104 \u001b[33;20m WARNING: n_y_ions has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.108417 \u001b[33;20m WARNING: f_masked has 9 NaNs ( 0.03 % out of 28290)\u001b[0m\n", - "0:03:53.228018 \u001b[38;20m INFO: Finished candidate scoring\u001b[0m\n", - "0:03:53.370600 \u001b[38;20m INFO: number of dfs in features: 1, total number of features: 28290\u001b[0m\n", - "0:03:53.371060 \u001b[38;20m INFO: performing precursor_channel_wise FDR with 39 features\u001b[0m\n", - "0:03:53.371387 \u001b[38;20m INFO: Decoy channel: -1\u001b[0m\n", - "0:03:53.371758 \u001b[38;20m INFO: Competetive: true,\u001b[0m\n", - "0:03:53.404692 \u001b[33;20m WARNING: dropped 5 target PSMs due to missing features\u001b[0m\n", - "0:03:53.405158 \u001b[33;20m WARNING: dropped 4 decoy PSMs due to missing features\u001b[0m\n", - "0:03:53.409294 \u001b[38;20m INFO: Pre FDR iterations: 27\u001b[0m\n", - "0:03:56.582933 \u001b[38;20m INFO: Post FDR iterations: 15\u001b[0m\n", - "0:03:56.650237 \u001b[38;20m INFO: Test AUC: 0.590\u001b[0m\n", - "0:03:56.650781 \u001b[38;20m INFO: Train AUC: 0.637\u001b[0m\n", - "0:03:56.651094 \u001b[38;20m INFO: AUC difference: 7.26%\u001b[0m\n", - "0:03:56.651432 \u001b[33;20m WARNING: AUC difference > 5%. This may indicate overfitting.\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:03:56.994045 \u001b[32;20m PROGRESS: === checking if recalibration conditions were reached, target 600 precursors ===\u001b[0m\n", - "0:03:56.995615 \u001b[32;20m PROGRESS: ============================= Precursor FDR =============================\u001b[0m\n", - "0:03:56.996089 \u001b[32;20m PROGRESS: Total precursors accumulated: 3,978\u001b[0m\n", - "0:03:56.996489 \u001b[32;20m PROGRESS: Target precursors: 2,842 (71.44%)\u001b[0m\n", - "0:03:56.996964 \u001b[32;20m PROGRESS: Decoy precursors: 1,136 (28.56%)\u001b[0m\n", - "0:03:56.997317 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:03:56.997702 \u001b[32;20m PROGRESS: Precursor Summary:\u001b[0m\n", - "0:03:56.999967 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 914; 0.01 FDR: 708; 0.001 FDR: 573\u001b[0m\n", - "0:03:57.000267 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:03:57.000527 \u001b[32;20m PROGRESS: Protein Summary:\u001b[0m\n", - "0:03:57.003411 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 783; 0.01 FDR: 611; 0.001 FDR: 506\u001b[0m\n", - "0:03:57.003745 \u001b[32;20m PROGRESS: =========================================================================\u001b[0m\n", - "0:03:57.006153 \u001b[32;20m PROGRESS: === Epoch 2, step 1, extracting elution groups 4000 to 8000 ===\u001b[0m\n", - "0:03:57.018497 \u001b[32;20m PROGRESS: MS1 error: 15, MS2 error: 15, RT error: 150, Mobility error: 0.04\u001b[0m\n", - "0:03:57.020920 \u001b[38;20m INFO: Duty cycle consists of 13 frames, 1.39 seconds cycle time\u001b[0m\n", - "0:03:57.021349 \u001b[38;20m INFO: Duty cycle consists of 928 scans, 0.00065 1/K_0 resolution\u001b[0m\n", - "0:03:57.021664 \u001b[38;20m INFO: FWHM in RT is 4.34 seconds, sigma is 0.66\u001b[0m\n", - "0:03:57.022107 \u001b[38;20m INFO: FWHM in mobility is 0.008 1/K_0, sigma is 5.22\u001b[0m\n", - "0:03:57.023607 \u001b[38;20m INFO: Starting candidate selection\u001b[0m\n", - "100%|██████████| 7943/7943 [00:14<00:00, 548.75it/s]\n", - "0:04:12.056673 \u001b[38;20m INFO: Finished candidate selection\u001b[0m\n", - "0:04:12.400753 \u001b[38;20m INFO: Starting candidate scoring\u001b[0m\n", - "100%|██████████| 28448/28448 [00:05<00:00, 5268.81it/s]\n", - "0:04:18.189541 \u001b[33;20m WARNING: base_width_mobility has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.190689 \u001b[33;20m WARNING: base_width_rt has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.191322 \u001b[33;20m WARNING: rt_observed has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.192491 \u001b[33;20m WARNING: mobility_observed has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.193418 \u001b[33;20m WARNING: mono_ms1_intensity has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.194143 \u001b[33;20m WARNING: top_ms1_intensity has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.194895 \u001b[33;20m WARNING: sum_ms1_intensity has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.195853 \u001b[33;20m WARNING: weighted_ms1_intensity has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.196626 \u001b[33;20m WARNING: weighted_mass_deviation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.197614 \u001b[33;20m WARNING: weighted_mass_error has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.198039 \u001b[33;20m WARNING: mz_observed has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.198835 \u001b[33;20m WARNING: mono_ms1_height has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.199738 \u001b[33;20m WARNING: top_ms1_height has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.200515 \u001b[33;20m WARNING: sum_ms1_height has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.201212 \u001b[33;20m WARNING: weighted_ms1_height has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.201679 \u001b[33;20m WARNING: isotope_intensity_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.202637 \u001b[33;20m WARNING: isotope_height_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.203389 \u001b[33;20m WARNING: n_observations has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.203887 \u001b[33;20m WARNING: intensity_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.204483 \u001b[33;20m WARNING: height_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.205281 \u001b[33;20m WARNING: intensity_fraction has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.205724 \u001b[33;20m WARNING: height_fraction has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.206290 \u001b[33;20m WARNING: intensity_fraction_weighted has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.206990 \u001b[33;20m WARNING: height_fraction_weighted has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.207892 \u001b[33;20m WARNING: mean_observation_score has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.208481 \u001b[33;20m WARNING: sum_b_ion_intensity has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.209095 \u001b[33;20m WARNING: sum_y_ion_intensity has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.209997 \u001b[33;20m WARNING: diff_b_y_ion_intensity has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.211109 \u001b[33;20m WARNING: fragment_frame_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.211826 \u001b[33;20m WARNING: top3_frame_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.212910 \u001b[33;20m WARNING: template_frame_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.213835 \u001b[33;20m WARNING: top3_b_ion_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.214550 \u001b[33;20m WARNING: top3_y_ion_correlation has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.215624 \u001b[33;20m WARNING: cycle_fwhm has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.216304 \u001b[33;20m WARNING: mobility_fwhm has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.217035 \u001b[33;20m WARNING: n_b_ions has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.218064 \u001b[33;20m WARNING: n_y_ions has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.218648 \u001b[33;20m WARNING: f_masked has 11 NaNs ( 0.04 % out of 28448)\u001b[0m\n", - "0:04:18.322059 \u001b[38;20m INFO: Finished candidate scoring\u001b[0m\n", - "0:04:18.464559 \u001b[38;20m INFO: number of dfs in features: 2, total number of features: 56738\u001b[0m\n", - "0:04:18.465039 \u001b[38;20m INFO: performing precursor_channel_wise FDR with 39 features\u001b[0m\n", - "0:04:18.465354 \u001b[38;20m INFO: Decoy channel: -1\u001b[0m\n", - "0:04:18.465653 \u001b[38;20m INFO: Competetive: true,\u001b[0m\n", - "0:04:18.532874 \u001b[33;20m WARNING: dropped 10 target PSMs due to missing features\u001b[0m\n", - "0:04:18.533515 \u001b[33;20m WARNING: dropped 10 decoy PSMs due to missing features\u001b[0m\n", - "0:04:18.540811 \u001b[38;20m INFO: Pre FDR iterations: 15\u001b[0m\n", - "0:04:27.858964 \u001b[38;20m INFO: Post FDR iterations: 23\u001b[0m\n", - "0:04:27.985665 \u001b[38;20m INFO: Test AUC: 0.603\u001b[0m\n", - "0:04:27.986218 \u001b[38;20m INFO: Train AUC: 0.643\u001b[0m\n", - "0:04:27.986622 \u001b[38;20m INFO: AUC difference: 6.27%\u001b[0m\n", - "0:04:27.986998 \u001b[33;20m WARNING: AUC difference > 5%. This may indicate overfitting.\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:04:28.364064 \u001b[32;20m PROGRESS: === checking if recalibration conditions were reached, target 600 precursors ===\u001b[0m\n", - "0:04:28.366573 \u001b[32;20m PROGRESS: ============================= Precursor FDR =============================\u001b[0m\n", - "0:04:28.367096 \u001b[32;20m PROGRESS: Total precursors accumulated: 7,930\u001b[0m\n", - "0:04:28.367629 \u001b[32;20m PROGRESS: Target precursors: 5,770 (72.76%)\u001b[0m\n", - "0:04:28.368172 \u001b[32;20m PROGRESS: Decoy precursors: 2,160 (27.24%)\u001b[0m\n", - "0:04:28.368872 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:04:28.369300 \u001b[32;20m PROGRESS: Precursor Summary:\u001b[0m\n", - "0:04:28.372486 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 2,024; 0.01 FDR: 1,686; 0.001 FDR: 1,272\u001b[0m\n", - "0:04:28.372887 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:04:28.373179 \u001b[32;20m PROGRESS: Protein Summary:\u001b[0m\n", - "0:04:28.376802 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 1,516; 0.01 FDR: 1,272; 0.001 FDR: 1,004\u001b[0m\n", - "0:04:28.377301 \u001b[32;20m PROGRESS: =========================================================================\u001b[0m\n", - "0:04:28.380450 \u001b[38;20m INFO: calibration group: precursor, fitting mz estimator \u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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tMjx08biTRKjcBkRAEFg4fDMqCyUb707FwurLznORkJD49NSuXfuNagCCIDBu3DjGjRv36Yz6SKQkpmJupUIm+3Q1h7HhcQysPgq9Vk94QCTfjmz7QebV6w3sWXQY0SBycMXxNzp+vlcfZ5BVUZpnb6vYxsGatv9rxqmtF2jeuwGl6xR/4/gpXeYTHRLLIx9/Fl/LmArgf+cZ/Sv+iiiKzDk7kYLl8hEVFE1SXAreZfNmmsvcUsWABT2zZafEPw+pqvc90KRpidfJuGfuCUC5lKegUIBCbvwPkMllCEil8RISEhKvY9/SI7S278KQ2mM/qeyVTC5DLjd+DSrNP1xajlwuo+uEDuQrmZt2g5u/dtyprRf4sdIIpnaZz9i/hjDn7G+Uql0s2+fp+0cXNgYs4tuRbTiw4jhfOXVjydC1WY4tXr0wACVqFM50LOxpBDqtHr3OwJPbAYT6R9CjxBD6VxrO2R2Xs23P+3Dmr4sMrj2GszsufZLz/ZeRHL/3wDWnI5NW9SKiREUAyqUGIMpkRqdPLkflaEvLPvW4fvo+vWuMZ+20PW+d02AwMKffMobUGUuwX+jHfgsSEhISn53rx24jinD3vC+aNM0nO6+dsy2LfaYzce9w2v4vcy7b+/DN8K9Y4jONsvWzrpwFiI8yaijqtHo887tRtMrfz0k7tPIkyQmp7F1yJMvjI9YNYNOzxfSb1TXTsUpNy+DpbawSP/DncVISUtGld++IDY/72za9C0t+XsPt0/dNWogSHw9pq/c9KVOtIJc6tYMrf1EqNQAzmYhWpgBBQK3RsXnRcWSiHkOalg0z9lOnbUVy5n+91ELA3WfsXXwYgP3LjtFr6nef6q1ISEhIfBa6jPsauUJG+YalP7mAs1chT7wKGXdtfI7fQSYT3inq9j4061UfhUKBk4cDeUtkvyo3K74Z+RWrx22lUZdaWR4XBAFHN/ssj8lkMuycbAh+FIZWrSV/qdyM++tnYsLiaNK9Tpav+dA0/L42G6fsoOH3tT/J+f7LSI7fe5AQl8KEQesJehrJAoUVTrpkiqqDuWmdBwQZIgoEvQGDFpAbQBRN4pivw7OgByVqFuXZg2Cqt6n0Sd6HhISExOckd5GcjFg/6LPacO3ILX5t/DsAM06MpWTNoh//pIJAXFQC4c+iKFO/BErVm/P70lLUzO67FI1ay+AlfbC2tzIdq9i4DBUbl/nbpozZOoSLe69Rqalxjqoty//tuf4OXcZ9TZdxX3/Sc/5XkRy/9+DquYfcvh4AMhnXbfLTIPYW5ZL9uWGbDzAgiDJAAIMBRDmI4OLh8MY5lSozZp4c/0nsl5CQkPi3o9VoWTt+KzK5jO9Gt3vvyt2Le68x+4dlVG1VgQHze7zXXFcP3WDVmM0AeBZwo+FronWiKKLT6rl66CbHN54DoEKj0jTpXve9zv8yjm72NO1Z74PNJ/HPRcrxew/KVvGmYDFPEASu2hYAoHzSE1DIQC5HlIEoF5Ap5CCTYWaRvWqtXQsPsWDgygyVXhISEhIS786ZbRfZOHkH6yf+xYU91147rlyDkkw9NJLpR0e/Mdp34M/jRIfEsmfRYdSp75eP6FXIA3MrFWZKxWsFmLUaHf0qjqCVQzdSk9W453PF2dOR0llsR+9aeIj+VUZy5bnMywdCr9NzYe81/hy5kf4Vh7Ng4Io3XstXiQiM4tzOK2jU2g9ql8TfQ4r4vQf2jlbM2/gDR/bc4PhaO/QBO8iXGo6TLoEoMzsEmQJRq8cznwvPHoTgkcflrXMGPghm/k8rALC0taDbbx0/9tuQkJCQ+GLYs/gwGydtp8MvrWnVv/Fbx+ctmRulhRKZTCBv8Te3pntTIcZzWv/UhGC/MKq2qvDW1J234ZHfjY2BizDoDdg4WGc5JiY0lsc3AwB4eO0Jq33nvHa+ZcM3oknVsGHyDio0+nA6dmsmbGPj5B0AiAaRh9eesHP+IVbcm4VXoTfr1Oq0On6o8CsJUYk07lYHJ09HKjcrS+GKBT6YfRLvhhTx+wA0aFGasat/5I6N8Y6tWrwvopkcg0yGqFQgKBWI5irM7bP+YL+Mk4cDzp6OCDLhi291JCEhIfGh2frHLiKDotk8dWe2xuctnostocvYHLKMnAXfX0y/TN3iLL89g9Y/NibgXtB7z2dla/lapw/ANbcLPX7vSOXmZSlQJg97lh5lTJs/eOTjn2ls8971sLK1oHG32u9t18to0iObMrkMZfrOlcpSiaWtxVtfK4qg1+oBuHb0Fusn/sXwppM+qH0S74YU8ftAmFsoOetSjFKJAVSLu892z6oIZnIwGPAPiEIml/Hg9tsXCStbS+ac+43IwCiKVcustyQhISHxX6bjr23YNGU7Xw9tne3XWNlaflAbYsPj6FZkEKlJaYzcMJBa7at80PlfpcPQlnQt+j8u9fYBjFG36JBY5l+YmKG9W9/pnek7vfMb5wp8EMyin9dStJI3nUdnT7C664QO5CnmhXfZvOQrmRs/H39snW1wcn9zzjoY8xO7/94JbZqWZw+D2bf0GG7Z2P2S+HhIjt8HIj4+hbNuxen/ZD8l4gOwIZUEhQWCQY5MFBANGhRK+VvnSYhOpE+ZYSTHpzBsVX/qf1sDAL1ejyAIn1TVXkJCQuJTk5KYiqXN6yNJTXvW++xFCImxyaQmpQFw98LDLB2/4xvPEfggmPZDmmdwPGPD47Gys8i2YLRWo+P+xUckx6cCYO1gRVJsMn43nnLl0E0qZqO928tsn3uAa4dvce3wLZp0r4Ozp+NbX6OyUNKoa23T4wJlMnfzeB1Lfl7D7oWHcXR3YJ3/fJr2rI+X1Mb0syJ5ER+IM2cfEmrpgK+NJ3JEqsQ9QFTKMZgJGGRgMFeiykZYPCku2dRzMexpBAD+dwJp59KD7/L2IzYi/qO+DwkJCYnPxdz+y2hl14VFg1d9blPeSK7CntT/riYIMvYsPmrsc/sSIY/DmfL9AjZM3sm2mfsASE1OY/X4bXTw+oHuxYaw9Nf1tPfsw9F1Z954rtn9ljO04UTsc9jSf/b3/LLyB9Mxq5e+U5Liklk/aQdXDt1443xVW5ZHZamkZM0iOLxG1+9Dok4xbhNr0jTIZDIKlssntTD9zEgRvw/A8dMPWLDyJCjlnHYvRqHEYGpG3mW/V0WQC4CAoDOQmKZj98aLFC+bm3yF3LOcyyO/G2O2DCboUSit+jcC4NapeyTFJZMUl4yfjz8VGpX+ZO9NQkJC4lNxad91AC7v9+GHmV0/rzFvIX/pPBxdfxZBIMN2K4CtkzW2TjYkRCeSq4gnd8/7MqP3UoIeGrsxRTyLZteCw2jVWvYsPUr972q89jwx6U5lclwKLfs2BGDhpd/RpGmJDo0jOiQWJw8H1v2+nR3zDiKTCWwMWIi9i22GeW6dvs+dc740712P3bErCXkczoqRm6j+VQWKVPp4+eT9ZnelaJWCFKtWyNQeT+LzIjl+H4BDx+6QkqJBBE55lqDXw8OUi/ZDhZpUMxUIMhBBZjCwcMo+LKxUbD01HIVZ1lu/1b+qmOFx3W+qc//SQ6ztrChd59MoyktISEh8agYv68v+5cdo+UOjjzL/4xtP2TpzLzXbVTYJFD/zDSHwQTDVWlUwjUuIScLC2hwzZcavSFEU+XPkJp7eC6LfzC64582Bez7XTB0xrO2tWHV/JokxSQB0LToY0WDsQezoZs/349uTHJ/CoVWn6Di05Rtt/nlZX45tPEvFJi/EmfOXysPMvss4tPo0zp6OrHs0G/d8xo5Qts42mFtl7H6SlqLm1yaT0Gn1hAdEUqZuceYPXElCdBKH15xia8iSd7iK74aljcVn35qXyIjk+H0AvmlXifiEVAoWdGXbkRsEWLuQOymSqtH3OeJZFlEQwYAx+pcMllYqBJnw1nmfY+Ngza9rBny8NyAhISHxD6Bcg1I453TCIYfdR5l/8c9ruHnqHud3XWF3/GoiAqPoUfJnEKFR11oMWdqHszuv8FvH2dg52aBQKmjcrTZdxrQD4NmDELb8Yey5nruwJ72mfPPac1nbW2Ftb0XEs2hkchl6g57qrSvQ94/OKJQKQv0jaDuwaYZo4cmtF5g/YCU12lRi4AKjOLSThwNfD2mRaX6N2thL97mWYMu+DShZozDOnk6YW2Z0/BRmcuRKOXq9gYAHwZzccsGUo6jV6Ai4F0Tuojn/5lWV+NKQ4q4fgFIlvFg8qzNKKzMMZjKO5jbqJzUIvmHM8VMK6JQy9GYy6nxVlhLVvUlO/9BJSEhISBg5suYUPYsPoWvhQSTFJX/w+cvUKwFA4crGrc2o0FgwBuIIehgGwL2LDxENInGRCUQFx7B5+m7T693yupC7aE5kcoELe68xoMYYzu688sZz5vByYtHlyUzeP5zRmwdh62RN7zLDGFxnPH/N2Z9h7NF1Z0iMTebAyhPo9YY3zvvtiNYozc1IjEni0oEbCIJA3uK5sHGwyjRWYaYwFZikJaVRtWU5SPc3UxJS2bPkSKbXXD18k2ldF/Dw6uM32iHx5fFOEb/4+Hh27NjBmTNnePr0KSkpKbi4uFCmTBkaNWpE1apVP5adXwTe+VwRFQKH8palx92jVAp7iK0+mXiFFTIRRBkcOnoXuQE8vRz5rlsN5gzfyqk912nRpTrdf2lumkuv07N97gFkchmtf2ws5UZISHwmpHXv0xH8OByAxJgkkuNTsLa3Qq83cGzdaexz2GXY7nwXbpy8S0RgFHU7VWPT1N3cPHUfn+N3KFO3OJ1+bYXv1ScMW2Esmmj/v+akJaVhMIjcOHGXRt+/aKOmNFdStWV5Nk0LJtjP6CiuGLWJ6q0rZHne5+QplpM8xYwRNU2alpREY4VubHjGYr12/2tGXGQCNdpUynLN12l1yOQyZDIZmlQNmjRjJ4ygh6HQ9M3X5peV/Tiy7gwt+tSnSCVvBizowYT2s3jk40/NtpUzjZ/6/XzioxJ55hvCvAu/v3Hu7HJpvw9/Dl9Pgy61aJ9FFFPi05AtbyI0NJRevXrh7u7OhAkTSE5OpnTp0tSrV4+cOXNy4sQJGjRoQNGiRdm8efNHMXTy5MlUqFABGxsbcuTIQevWrfH19c0wRhRFxo0bh4eHBxYWFtSuXZu7d+9+FHuy4sD5e4gyAX9HV+475kQhGqgbfOulqJ+As4MlcoWM4iW9eOATwKFtV0hT69mx8nSGuc7uvMLyERtZ+st6Lh/w+WTvQUJCwsg/Yd37krl25CaX9l1DFMVsv6bD0JZ0/a0DY7cNwTW3Uevt8KqT/NFzMaNaTjV1sHgdoiiya8FBNk3bhU5r3AoN9gtjWKPf+aPnErbN2kdaihrRIHL/0iMAuk3owJT9w015eg6udgyY34NBC3uy6v4sOv3aOsM56n1TjbzFvchV2BOVhfKdxZJtnWyYemAEfaZ/x3cj22Q4Vrp2MWp8VYlzu69xL90+gB0LDtHCqQctXXrRveRQkuKSyV8yNz8v6833Y9vSok997l/yo2fpX5jd/88sr3mZusUZtuIHUyGHhZU5k/cPZ1voUkrWLJJpfMlaxrZ1peoU49bpezy59eZrnx02T9uF/51nrBy16b3n+hjsX36MbkUGcmjVic9tykclWxG/UqVK0aVLFy5fvkzx4sWzHJOamsrOnTuZOXMmz5494+eff/6ghp46dYr+/ftToUIFdDodI0eOpGHDhty7dw8rK2Noe9q0acycOZNVq1ZRsGBBJk6cSIMGDfD19cXGxuaD2pMVFUvk5tzdQEQZHMhfliIxQTR6ep2tRaohCiCTwcjf2lMwjysWlkrCnsWgMJOj0+pp1D5jQYdHfleE9Ds+M1X2evxKSEh8OP4J696Xyq3T9/i10UQAJu75lUrNymXrdRbW5nw7IqMzFOpvjAIKMjIVLbyKz/E7LBi0CgBHVzsafl8bpbkZCjMFOo0OpfmLtVSm+Hu7KLkKe7L46pQMz2nSNMRHJeKS0wlRFNkweSdPbgfQZ+p35MjlnGmOEjWKUKJGZmcrLTmNFWO2ADC+/Sw2By4E4NCa06boXqh/JPevPMa7dB4avFQNvH/FCZ75hvDMN4TOI9vg5PFCXDkuXQbM/h1yJ0dtHERCdCI3Ttzl53oTEGQCVVuUp1rrCkYpG4yai4sGr0ZloaT39M4o3/Jd1axXPQIfBNOwS80Mz2vUWvYvO4pbnhxUbp69v5WPwboJW4kMimb9xG006lrns9nxscmW43f37l1cXN6stG1hYUGnTp3o1KkTkZGRH8S4lzl48GCGxytXriRHjhxcu3aNmjVrIoois2fPZuTIkbRpY1w4Vq9ejaurKxs2bKBPnz4f3KZXaV6zBDM2n0GUw6ECpRl0ZQ+lI57imhpDmKUjogCxiSkkpKRhYanEzcuR1adGkJaqwSN3xsUhp7c7ICAaRI5vPE/5Bm/vISkhIfHh+Cese18qLysWyM3er4Zw14JDxh9EAc8Cbm8c65rLGaW5GTqtHk9vo2SWS04nll6fSkxYHPlK5eLOOV/iIhOp3qriG+fKLjqtjj7lfiXkcTgD5/egVO2irJmwDYAcuZzpM/W7bM+lslTh4GpHbHg8ibEpiKKIIAh0HduOtZN2YGamIFdhD8Z9PRuAeafHka+EsVVovY5VOb/nGl6FPHBwe+HgPb4VwE/VxoAgsPDCb+Qp9uZ+xc8RBAE7Z1vUKWrA2C3k3K4rnN9zlaqtKmBpY8HpbRc5tOokYOxz/LxS+nXU+7YG9b7NLF2za/5Blg5bB8DK+7OybK2XmpTG1K4L0KRp+HX1j9g6ffhgTrvBLdg0bSdt//fv3obO1i3P2xa/9x3/d4iPN97BODoaVcf9/f0JCwujYcOGpjEqlYpatWpx/vz5j24PQFKqGtFMAJlAuJ09VzyNTagb+/tgUIDBTGDojD20+3EZ0bHGMn/HHLZ45HYmITaZGT9vZNX0fRgMBpQWSvKmf0CLVJKaWUtIfGr+ievel0LRKoWYc24if5wYR/mGpd5rrucRMxvHzEULr+Lp7c7ax/NZ93g+hSrk58DKE/icuEvOgu6UrFkEazsr5p79jTW+s8lZ8IWWqk6rY/+fx/E5fued7UtJTCP0iTEq+cjHHxcvJ/KWyIWZUkH5+u92wy4IAlP2/0r9b6pRunZRupX4mdPbL1O5aRkWnJ3AzGOjKF6tIDqNDp1Gx7OHIabXPnsUSlJ8Cvcv++F79Ynp+a0z96HXGdBr9QQ9Cnvn91e/c01GrB9Am4FNAaOUzPPIa9EqBbF2sMLB1Q7vstnv5vEqz1u/qSyVWLyma8vVwzc5t+MyVw7c4OyOy3/7XG+izaBmbAlZRqv+jVGnqgkP+HfezP2tW7Hg4GDOnTtHREQEBkPGyqMBAz6+7IgoigwePJjq1aubtmDCwox/0K6urhnGurq6EhDw+twEtVqNWq02PU5ISPjbdh2/7oeICHIBUQb7CpWjUvAjWj68zJ/l6iHKAAsBtRbOXXuMOjKVFUtOoDKTk8PGnECfpwhApXrFKFI2D/POjScxJhkH148jbSAhIZF9Pve696VRtEqhDzLPEp9pXD96i5I1i2Zr/HMpmG2z97F02HoApuwfTqk6xV5bJLdz/iGW/rIeBFh1bxYe+V98j9w978vMPssoWasIA+Z1zyzW7GjN8LU/cXDVCZ49DOPexUcsujyJZ74hbP5jL7GRCdT/pnq232+eojnJU9yLo6OMeaPrJu2gZpuKiKLIsKZTuH3Wl5wF3Ql6FMbuJceo2aYScREJRgdZMLZXc3K3N833XO5FJhPI9TdapclkMmp/XZXaX1flmxFtsLKzNLUOzVXYk60hS0EQ3qsAse431fEq7IGdi+1r+/+WqFGEPMW80KRpKPfSDtjDa0/YMn03NdtVpma7zEUqz0mITuTmqXuUa1DyjS0BwXgj0LvkEEIeh/PT/J607PdxdCU/F+/s+K1cuZK+ffuiVCpxcnLK8CEQBOGTLIA//vgjt27d4uzZs5mOvfqhfB4qfx2TJ09m/Pjx723T8Qu+3LkTBDIBBIzbvYVKMezsDnLHR1Em/DHX3QsgmoEBkYCIGE5tvIFOZ0CnM/A0JhnB2gIHSyU58+cAjCX4ktMnIfH5+Sese/9Vlg5bx4EVx+kzrfM7CQE/lzURZAK/Np1MnY5VGb7mx6zHpkcTlSozVJYZe+geWHGCoEehBD0KpcvodlmuydeP3eH6sXsArBi1mdb9GrJh2m6ePQjh2MZzVG9VHvN3aFOm0+lNPzf+vhZXjtzC72YA9y/5ARAVHAPAvUt+7Fx4iMW/bMQtjwvLrk/B1t4a+xwvunb0nvIN1naWnNx2kV5lf2XCX0Oo1KR0tm0xGAzsmHeQ1KQ0OgxtaXLwNGot14/exrts3tc6a++Cd9l8bzxu72LLslt/ZHp+6bB13Dx5lwt7r73R8fu1ySQeXXtCxaZl+H3Pr288V1qymlB/Y8vUxzf8s2H9l8U7O35jxoxhzJgxDB8+3OT1f0p++ukndu/ezenTp8mZ84XgpJubMfcjLCwMd/cXIfyIiIhMUcCXGT58OIMHDzY9TkhIwMsrezkQz0lO1TB21j60ChEsBaNQswxSVCoOFCpD+9sXaXP/Eldz5Qe9gCDA5mM3ERR6ZDYKVEk6SFd1r9ayDBcO3uLZ4wg69K+PtZ3lW84uISHxsfnc695/mX3LjqFOUXNo9cl3cvwadqmFZwE3pnVfRJh/JH43nr5xbE5vd1SWqkxOTKOutblz3pdSNYtmcKgAbp/1JTo0lnO7r5qeK1O3OFO7LzY9VlkqSUlKe6PjZzAYuLjPhxxeThQonYev/9cM9zw5yOnthqO7Pd94D0I0iFRrWZaH158SGWR0/Ax6A6vG/wVA2NNIzC2UnN97DfsctlRNL5Jwz5uD1v0bcXitsSfw0Q1nsXOypnDF7KUQ+Ry/y5Khxvw7l5yONPq+NgCLh6xl79KjOHk4sP7JPGJC4wh5HE7x6oU+6WekYpPS3Dx596158Jr0yOfzf9+Etb0Vo7cM4e65B3z9ls4qXyLv7PilpKTQsWPHT774iaLITz/9xI4dOzh58iR582bMJ8ibNy9ubm4cOXKEMmWMekYajYZTp04xderU186rUqlQqd5cKfY2zJUK8uR04nFQFF4ONjxLSUIUABn8VbIy7W9fpIHfTSbV/YokpSXoBVJT9ZjLQLSQs3/3z/w56yCP7oVQsXpBxn63CAClSkHnIU3fyzYJCYn353OtexLQe+q3HF5ziu9GtX2n1wmCQPFqhRm9aRCH15zOVEn66tjjG8+zZ8kRWvRtwE9zu5mOlahemFX3ZmV6TdCjUH5uOBFEaNqjDjFhcbQZ0BjvMnk5tPqUSaMvLUnN1cO3aNg54/kfXPZjZt/lFK9emDxFPVnwvzXIFTJW3Z1BjlzO1G5vjF6lJKZibWdJYmwyZeoUJz4qyeT4gVH1oWHnmhQsm5crh28xd8AqABae/438pXIDkL9UbvpM+5b9fx7n9LZLnNt1lQ2P52bq55sVbnlcUFkq0Wl05CrsaXo+LdnYhECdqiElKY1eZYaRHJdCj9870iHdWYoKjsH36mMqNCqF0lyZ5fxg/H5PSUw1iUy/C1//3JLmfRpgYf3miOqk/cO5evAmVVq9vgDl2pFbhPlH0LBrbWq0qUSNNpXe2Z4vgXd2/Hr06MHWrVv59dc3h0o/NP3792fDhg3s2rULGxsbU06fnZ0dFhYWCILAoEGDmDRpEt7e3nh7ezNp0iQsLS355pvXt9X5EMjlMlZM/Y74pFT0iDQetdx07LarFw9c3CkcGUrTh9fZVLI6CKBVyhCtDahSYNXWC/QeamzdkxSfgp2TNfHRSeQt8u75GBISEh+ez7XuSUCLvg1p0bfh2we+Bu8yefEu8/bCg2tHbxn/PXIrW/PKFXLkchl6nYGilb0zSKsMmNuVrbP3Ex+ViJWtBRWyKHDZ9+cJAu4HE3A/mK7j2gPwqvxeXEQCwY/DWHptEjGh8eQvmYtC5fIxqs0MPPK50rBzDUrVKGyqdj6/5xpgrKq2tM2Yx3Z4zWme+YYCYGamQK7Iulf8q3gWcGP943nodYYM29z9Z3elWLVClKhWGFFvIC3ZmCufEG0sXDQYDPxYZQQxoXE0612fgQt6Zjm/KIoMbzKJ68du88OMLnw14N2DHW/L2QPI4eVM016vjxgH+4Xxa5PfQYSUpFTaD/73Vva+s+M3efJkmjdvzsGDBylRogRmZhl1e2bOnPnBjHuZRYuMUbDatWtneH7lypV07doVgGHDhpGamkq/fv2IjY2lUqVKHD58+JNo+JmZyXF2sOaq7zNjX17R+CEWzQT+KlWZkUd30P7mBTaWqYZBEBBEEEUBnWhg5V8XKFsiF2VL5MLazpKV58aQnJCK80sJus+Z3f9PHlzx4+dlfShQKs9Hf18SEhKfb92TgEc+T5jRczEFyuTlf0v6fPAuRhHPotFrdfxvcS/2LTtGszc4By8TFRyLmcoMt7wO1GyXMTK0cuxWAh+E4OhmT8ehLXh04ykVGpbMkBvasHNNbp66R/Fqhfj652bkKuxBDi8nUxWzRq2lT8URxEUm8P3YdnwzzBhFK1g2L1uezs/SpqotyjH/7ASs7S1xz5sjw7Hn/eELlM7NyPU/ZdnaDYy9e09uuYBXQXfTdrCNozXhAVHo9QbT9beys6TZS1vvfxwdzeMbT2nwUmRVm95PWJP2+u1Vg0HE54SxmnrH/IPZcvzO775KyONwWvRtgMri9ZHElxFFkad3nuFRwC3L15hbqVCqzNCkabFzensk9EvmnR2/SZMmcejQIQoVMlZsvZrk/LHIjvq7IAiMGzeOcePGfTQ73kZ+T2fc7K0JSktKNwp2lyjH4JN7KRwZSrlQf6555gMDGAygTRNQKAQ801XjASysVFi8IlT64OoTbpy8x4EVJwEY23Yma3xns2PufgwGkbaDmklt3SQkPhKfa92TgIkdZxP6JIIntwJp1LU2JapnFj7OLsuGb8Dn2B1+mt+dIhUL8PRuED9UHI5Bb2Dm8TGMWPdTtuc6ufUCaclqgh+FcWLTeRp3rW06VrNtJdZP2oFHfldm9F4GQNHK3sw8Ptr091KieiHWPHixhVztlS1InUZHUnwKALHhcSTHpzC+01zUqRrGbhqAo6t9lnZ5l8mT5fPjt/2P8R3nAiB7w3fFtln7WDV2C3KFjLWP5uLk7sCiIWvYtfAwlZqWYcL2n0mKS+bGyXuUrl0Ua3ujA1msSkGKVSlomkcmkzHnzATunPN9Y9GFXC7DIYcdMWFxpqjhmwj2C2NcW2ORx7WjN5m8b8RbXwPw54iNbJ62C7e8ObC0Madx97p89VMT03EndweW355BbHg8RdJ7Of9beWfHb+bMmaxYscIUZZPIiIO1BXt/70GVX+eTrNcjChBvacnuEuXo4HORb6+d4UrufMaIn14AG6OC/PQlR2jZoCQ1K2X+g7t06BZj0z+wyGVgMOCQw46Le6+ZRC/d8rhk2W9RQkLi/ZHWvc+Hg5s9oU8ikMllWNtbcX73Vco3LPnGnLGsSIhOZOuMvYBRFLpIxQJEhcSg1xoraMMDoihWNfsSNHnThZMhc8/dzqPa8M2vrTi6/ix3zhlbi96/7Idep0eRTUFrSxsLpu3/lQdXHtO4a21unLzLzdP3AehXdQyJMUmYW6koUrEAjm529J/R5Y3Rr9CXClxObbtEhyHNsxz3vKpZrpCbbPW9+hiAh+n6gOPaz+T2mQeUqFGYP46Mfu05cxb0yFKM+VUGL+vDjjn7aZ6NLX1LWwsEmbG5wbXDt9BqdJgp335NAx8EAxAeEIloEPlz+IYMjh+Aez5X3PO9vhj038I7O34qlYpq1ap9DFv+NchlMtLUejAD0os81lWqQQefi9R/cBv3xFhCrR3QqUCWKpAqM3D2jj8+twLp921N2rQsmyGKkJKQmvEEgsAPMztjaWWOwkyOCHjmf7OivYSExN9HWvc+PbsXHWbfsqOkJRmLCMytVIxsMYWooBiadK/D/5Zk7sak0+p4dN2fvCVyYW75YtckJTGVh9f9qdW+MrdO36d+ej5eufolGLSoJ+pUDbW+rpJhLlEU0aq1r3UwQ/xeiCGXrZu5pZ9cIadhl5pcOnCDGyfv8dWPjbLt9D3n5ShaqZpFKFIhPw+uPjE5mklxKVw5bMxJrNS4NNXe0DmjUPl8lKpVhJiweKq/ocChdf9G5CrsiVseF+ycjWlSgxb2ZO/SY+Qp5sWq8duIjTDq3WpSte/0fp7jf+cZi4asoUT1wnQe3ZaKjctQsXGZbL3WIYcdDq52xITGYW1vlS2nD+DHud3IXTQnArB36VGa9Kj7t2z/N/DOe4MDBw5k3rx5H8OWfxXmZgoEQ3qenwAP3dw5n88bhWig07VziHIQFWBQgUEhoLUQ0KTqmLf4GL4PM6qr5yvpBQqZMdonl4NMxuG1p8lbIhfr/Rew/skC8pfO83neqITEfwBp3fv0rBy1Ef/bgWg1OuRmchp2qYU2vV+t+jUOx8zeSxhYfTTDm0zK8PyoVtMZ0WwK6lQNm58tMkl/CIJA0x51+erHxhlSZfR6AwNqjKGVUw9ObbuY5bmqtSyPtb0VxasWJF969eyrqFM1nNt1leT4FJ7eDcpwbN2kHXznPZCTWy5k63pY21sxYk3/DGlPivQCDUEQcMnpmGG8KIpcOXKLx7eMDQzMLVVM2z+c5denvLH1nUwmo3yDkultQ43kLZ6L/rO/Z/nITWycsough2F4l8vP2K3/y5btr7J97gFunLjL2t/+IiYs7p1fX6iCMfewVO0Xot6iKL4xlzCHlzM9fu9E9987sT3yT3pN+fadz/tv4Z0jfpcvX+b48ePs3buXYsWKZUpy3r59+wcz7ktGb9AjiICIUdBZgNWVa1D1ySPaX7/I/FoNUSuUCCJoVWCJAq21Fgu9GTle0YpKjE81zgMIgvF/lw4a7/IcXpPnISEh8eGQ1r1PT6sfm7Bn8WE6j25nis60/KEht888eG3O2PMWWxGBURmeT4hOBCA+MnudmRJjkkzbmpf2+1Ari/MVrlSARt/XJDwwisSYJBxfytN+jspCSZ2OVbm834c6HTJGFDdP34MmTcuOBYeo/Uq08XXk8HJi3OaBBNwPpkztYvhee8KCIWsRRZG4SON7FEWRleO3cfngTfzvPEOQCfSa2AHP/G4cXn+GfMW98Dlxlwbf1aDx97UACAuIZFa/P/HM70b/WV2yzBcXBAHX3M4E3AsGASKfRf8t4ebE2CRunrwLgjGi+ao2YnYYvWkQ/refkbeEUXNXr9MzoNoo/HyeMnLDwDfmFEr8DcfP3t6eNm3afAxb/lV0rFGaNWd8QA/IQJTBiSJFCXRwJFdsDC3uXmNL2SrIRGPUL1Gnx8xSho2dNY4vVVud3uPDlB9X45THjU7967L0lw1o1Vo6j2j92d6bhMR/DWnd+/R0Hf81Xcd/neG5t+WMDV3RjyNrT1OtVYUMz0/Y/jPn91ylcIUCnN99lYpNSr9x29XexZY2A5uwY95hzu66SpeASNxyZ+zF/OjaE/6acwAw6uR980sr07HY8HiiQmIoUDoPv678IctzdPq1FXsWH6Vhl1qvtSMrqjQrS5VmZQHIXcSTZw9Djbl+FfKzZdY+7J1t2fzHXtN40SCydMQm0+Nzu6+BKPL4VoDJ8Tu05jQ3Tt7jxsl7NOlWK5P8jV6nR5AJzD09jq7FhxIbEW/Uqs0GUcExLPp5DTm9Peg6vj0PLj8m7KnRQa/Vvsrf0sZUmCky9AZOiE40OerXjtySHL+38Ldatkm8nbolCrDujI9xSzd9u9egEFhdvQaj9+yi95njbCtfAb2ZAkEEvbmAwgBPohLYsd+Hy2cf0bpVOR74PEUUISYigbJ1irM9eCEGg4hSZfZ2IyQkJD4I0rr36dFqdNw59wDvMnlNlaNvwy1PDjqPbpfpeY/8rrTo04BOefqRHJ9Kh59b0H1iR5LjU1jz23acPRxo9z+jlmpKYio7Fx5Gk6ZDFEXUKRpCn0Rkcvy8CnuSs6A70SGxGXL8kuNT6FHmF5LjU/hpTlea98w6l8ygNxATEc+KMZvxLOCKS04nPPO/W2GBuZWK/jM6A7Bk+Ea2zzuIIJPhWcCN6NBY2g1swtmdV3l678U2s52zNSkJqVRvXdH0XNm6xdg6ez/mViqcPTNuGT/yecrQxpOwcbTmlz/74OntRnJiCq1+aJBh3O2zDzj11yWa96xHnmIvumrtWXKUszuuAFD768qUqlWEOh2qkhiXTK32b3bQRFHEYBDfqljh4GrPDzO7cPf8Qzqky94kxCRxaNUJSlQvku0uJf8V3tnxe05ERAS+vr4IgkDBggXJkSPH21/0H+JBUAToADPSu3iIIINNlSvR/9gRcsdE0+j+LfYVK4NeFBANYFADFgIb1p8jKiyJwMBo5s/6jhO7fYiLTmLtrIN0G9aEBYPXYWVnid/1J7jmdmb0hgGSIygh8QmQ1r1Px7wf/+TgyhPkLpqTZTcz92h9V0RRRK81AKDVGvXl9i0/zs4FhwAoUaMwhSvkZ+vMfWyYsguAcg1KUKxKQUrVyighYzAYE7iX35iKKIoZolZpKRpSEo0Fec976r5KckIq9y49AiA1KY1fmk5BoVSw8vZ0cuR0yjD28e1A5v9vDcWrFqTHBGME9Pn8LwsXu+R0TM8FMs6/M2wJgiDQsnd9rh27TVqKBr+bAbjndWHNb9s5uvEc9b+pTulaRYgNT0Cr1qFV67h65DYNvq1umvfWmfukJqWRmpTGjB/+JORxOK65nfn219YZ7Pzt23nERyXy+GYAs46PMV6L5DRsnaxRWpjhntdYMas0VzJ8bcaeyaH+EUQFx1C8WiFTYaNeb2BYg9+4f+kRI9YPyOCoZkWbgc1oM7CZ6fHSoWs5tOokKksl26NWSN+RL/HOjl9CQgL9+/dn06ZN6PXGMni5XE6HDh1YsGABdnaZG1j/F/mqSnHCY5OwNFcw48olRMG43ZtqqWRVrRoMPnCIfieOsbd0KQzIwCCgURnb/MpFJTprGR55nXBwsUFpbvyDjQ6Lo1/N34zaTjo9olZLwP1g/G48pWgWMjASEhIfBmnd+/TERRorV+OjEtGkaQh6GEae4jn/dts8c0sVc85OwM/Hn5ptjYLLhSrkR66QYe1gTQ4vo8Plns/ozAsKGdeO3UFpbpbhnKIoMqzpFG6f9eXHmV0oUb0Qm/7YS7OedShRtRBO7vZM3D4E/7tBtHiNGPQfvZdy7ahRtLhg2bzcv/wYnUbHn6O30G/6d6ZqWoBdi45w7+Ij7l18REJMEjVaV+C3b+Zi0IuMWv8jlRqXZu+fJ0CEGq0qcHb3VfKV8DI5UHbONtTtUJW0FDVLft2QQSsv2C+M0rWKULRyAVxyOqLXGShRLaOkTYNvq/P4VgD2LnbERSYQ8jicvMUz97PPVdiD22d9Uadp2TprH/U6VaN/ldHEhMVRuk4xpuz7JUvNy7jIBHqXGYY6RcOA+d1p3rs+YMyzvH3GKF9zad/1tzp+r/K8y4iNg7WkcfsK7+z49ezZkxs3brB3716qVKmCIAicP3+egQMH0qtXL7Zs2fIx7PzisFQpGfxVTeKSU5h5+RIAYnrUb1WtqvQ+fpLCoaHUfnSfEwWNlUkGnYBMAP/oRGQqGXEaY4VS8261OHvkDm161WbcMWNRh4WtBS6uOXDPm4MCUkWvhMRHRVr3Pj3/W9ybYxvOUb5hSYbU+w3fK49pM6AJff/o/LfnzFM0J3mKvtiGLFWzCOsezWFKt4V0yj+A9v9rSs+JHSlUPj9j288i1D8CvT5j8wCtRsfdC8Zo3Y1T91gxdispiamc3n6Z/XErAKNY855lx/A5cZdf/uybwZGDjA0JlOZKuoxuw5qJOzi59SIe+Vz5fvSLfNJa7Spxfs81khNSObjqFA+v+5OWogFBYGyHOTTtVpv96cL+vSZ15PuxbfDIm3Uk+nn3jlI1ClOwfD4afGeM7Dl7OLL2/kxEUeTSgRsEPAihUmNjmzlbJxuGLe8LgEajo1StIpSrl1G+5s55X+6ef4hMLuPxzQAe3wwg6GGoqWI3Mij6tULn4QFRqFON33WJMUmm5+1dbOk55Vtun7lP+yEZ26cF3AsiMiiacg1Kvnberr91pGKTMuQq4pnt9nT/Fd7Z8du3bx+HDh2ievUXoeBGjRqxbNkyGjdu/EGN+zdw4bGxhZsgCiCIiIJIgo0F62pVoe/hk/Q/eozjxQpjEGTodSATBdLsZJilGsBMRlJCKivmHkYU4fzpBwyZ35WHPk/p+L+mJMUl4+rlLIWwJSQ+MtK693HxOXGH+T+toHLzciaZDQdXe9r9z7h1F/TI2GP2uQjvh+TYpvPcPP3A+PPG89TvVJ3o8DimHxrOjZP3qdw0o76cUmXG0KW9uHL4Fp2GtsTn5D0AFEqjc5GWrObCPh8u7r8BwPm912jyUlcPgKFLe7Nh6i6iQmLpNLQlLl5O7F95ipiwOIpWyp9hbLm6xdn2bCGD6v3G/Ut+lKtXHM/8rpzddQ1Eo1P1HAtrFV4vybC8jLmlivmnx+Nz4i7Lx2zhzoVHVGhYilI1CgPGqt2zu6/xe+cFAEzdO4xC5fNhbqni+vG7nNtzDb1Oz8HVp3FwtWPd/RkEPgjh8LozhDwJx2AwyljIZALWDlZUalqaYxvPYW6pws7ZhtFtZvDryh+wsrM02SSKIuPbzwQRilUrRNv/Nctg89dDWvD1K05fVHAMfcsOQ6fVM3BhT5r3yZhr+By5XEaJGn+/y8u/mXd2/JycnLLc1rCzs8PB4d1Lu//tVMyb0yjFIopGMWeMW77LG1Sn6/GzlPMPoPoTX856F8KADJ0B5OYCMq2Ag7MlPw5ej+huixiaQJGSuWjQtjwNOlZly6z9rBi3Dfe8Liy/NlkKZUtIfESkde/jsnvhIZ75hvDMN4TOY9plEF8GmLhrGBf3Xs92H92XEUWRHfMOEvEsms6j2mRwPAwGA0/Sde4EAdoObMIPVUdh0IvU61iVrmPbsWvxESo3LUOBl7T66naoSt0OVQGYd2osy0Ztplm32jy9F8TgBr+jVevwLOCKIAiUq18ik01Wdpb0mtSJuKgEdi8+RrEq3qy8NQ1NqgZreys0ai2bpu/FwlpF2wGNkclk/HFwOOGB0Xikb0Uf2XAOuVxGxUalWDtpBy6ejjR9xcF8lZzebsRFJaBOMUbYgh6Gmhw/gK2z9pl+3jb3IFcO36LzyK/YNucAqUlpOKX3j09NTCXIL4wfqo5GNBijlxWblKZS49LUbFMRpbkZ5pYqtocv5eTmC8zoY2xbd+XwLWq/VNBh1N4zajK65XHJVhBDr9Nj0BtzNd+k2yfxet7Z8Rs1ahSDBw9mzZo1uLsb7yzCwsIYOnQoo0e/vnXLfxVHa0sEHYh60jX9RJCLRDpYs65uJXoePsfQ3Qc5M7wABhGwlqERBQyCQGBsPCEhMcgMIlUbFOXgbh+8i3lSoLA7z9LvgCOexaBVa5G/slCC8QPy+GYAeYrlfOf2RhISEi+Q1r0Ph06rY+mwdSTEJFG8WmGuHrpBqdrFeHjtCZWbl8vk9ImiiEwuo1W/hvz+7VwC7gUzYccQilcr/JozZMTvRgBLftkAwOObAQxc0N0kTnztyG2ObzYKKPed9h25i+bEkL61e2zTecICorh3yY+9y0+w6fGcLOc/teMKFw/c5MqRO8YqVJ0xB7T9oGY0/r4mof6RaNK0plztl1k1/i8Orj6NXCFjW+ACU/Xy8c0X2DBtNwD5S+ambN1iKMwUGap+i1TMz8y+f3LrrC8D5nyf5c2/Rq0FkQznLlbZmx9ndiYhJsm01Qvw4MpjIoOMxSie+V3xTZdH2bHgEPr091S5aRl8rz0hbzEvjqw/a3L6AEpUK4Sjqx02Dlam7Velyoxy9UuQI5cTep2BYlUz5qLLZDLmnB7H7XO+1GiTvRw+19wuzDw1nvCnkZm6rUhkj3d2/BYtWoSfnx+5c+cmVy5jr8LAwEBUKhWRkZEsWbLENPb69esfztIvFEEQKOOZg+uREcZQ+EvO36IWteh06gqlngbT6M5tDpUogV4AQQMygwzfqBjMnOXYC2ZcPemLIMK4wRtZs3cQ3ca2w9HVnpLVC2VaKJ8zo9cSjq4/S6laRZie3k9x09SdbJ62m+9Gt6XtoGZZvk5CQiIj0rr34bh1+j475x8E4OTm8xj0BlKT0lj/ZEGW47f8sYc/R27CxsGKxNhkAM7vvmZy/FISU7GwNn9trpdrLifsc9gSF5HArTMPmN5jCXNOjwOMhRxKCzP0WgNKczNGt5uJIJOBaMAlpxMe+V25d8kPWydrpvdZRt2vq2TKb5OZzisaI1GCQM2vKlC3QxW2zNrPyvF/Yedsw5/XJ2NtZ4nvNX/+mneQWm0rmhw5QSbjfw1+5/cdQ3BysydPel6amUqBuZWSQfUmYmGtYtymgaZ+vIfWnOH+lcfcv/IYO2cbQp6E03VMW3Kmd+UI9Y/gx1oTMOgNzDk+ilyFjPqHgiDQvGddDAYD03sv4+F1fwYv7MGIVn+gTtWQv1QuRq3pz5rfd3Biy0WS4lIAqNW2Eo5udvjdCMDvRgAt+xijrzK5wNAlvZna0/gZGLa8N/U6vmhvqE7TEPEsGkQ489dl2gzI2B/X09sdz9dsT7+OYlULvVNfZYmMvLPj17p1649gxr+bnAobbsZHoLMytmkD47ZCtJ0VK5pU4aedp/h5+1GOlC2CAdBZgigIyJJAIQiY25pj72ZHeGg8keHxREcm4uJqR7exbd943pAnEQCEpv8LxubkSXHJ7F54SHL8JCSyibTuvTuaNA1hTyPxKuSRwSnLVzI3OXI5kxSXTKEKBbhx/DaVm5d77TzRobEAJCem0rxPfYIfhZq2fP+as5+lv6ynQqNSTNw1DICjG86yZsJ2WvdrQJsBTbB1smHNg1mMajWdW2ce4FXohQB0Tm931vvO5tzu68wZuMr0fJfRbWk/qCkymUDzHnWYP2QdxzZd4OrRO6y5Mx2luZnpPbX/X1O8Crnj7OnAyW2XsXWwosOQZshkMm6d8wWZjPiYZOYOWYs6WUPY0wgC7gVz5chttgctAEHgzzFbeXovmOvH79Lgm2oUrpCfDY9mIZfL2bX0KA/So28rx22j16SOyOUyarQuz4mtF/HI68KWWfsBo7xLi5512bP8OM6eDiTHG522hz5PTY7fc8IDojixxdiO7tim89g52xDxLNoUXYxMl6KRK2S4583BV/0aoNcb2DJzP07u9nQe8RXFqxREnarGwdUOQRAQRRG5PGMhhVJlhplSgVatw9ohe3qMf4fUpDRWjt6ErZMNHX9thULxt9Xq/vUI4svlRRIkJCRgZ2dHfHw8trbv3komKyr2m41WFEnIJaI3NyBa6xGUIoJCj506mZMDZmOfnMqQfm34q0oZxBQzZMkKhCQBixSBma0aER6cwJWdNyleOhe9Bzc2LToPfZ6ya+lxSlQrSKNvq2VYYEMeh3Nk7Wmqf1WR/On5KYdXn2L7nH18PbQVdTtJTecl/p18jM/xv5WPca1EUaRvuV/wvx3Id6Pa0mVse9Mxg8FA4INg3PK6ojI3Q6vRvTG3Kzk+hX3Lj1G4QgFK1nyRrC+KIu09+pAYm4xMLrAvcQ0ymYw+5Yfz9G4Qto7WbA1eZBr74MpjUhPTKFW7aKZt0U0z9rJq/F8AdBndhq8HNcnQ2WPx8I3sXHiE/KVy8fhOEIXL58M1lzM587vy3a8tXxttvHP+EcNaTsegNyBTyDDoDNg72xAXEU+N1uUZubofSXEpTO6+GE2alp8X98A1l3PGOS485OfGUwCo0KAE147fwdbRmqT4FNr+1JgTWy4Q8czopPWd3JFjWy7yyOcpKksl6hQNVnYWrL03A0tr8wzzGgwG/ui7nIfX/Rm2tDfueVwIfBjKrsVHOb3jCtVblsNMpaDmVxVMnULAuFUvk8uQyWTsW3GCeYPWYG6pZMyGAYiigbJ1i2e6HiFPwokJjaNY1YKvvVbvy875B1iQ7rwLArT6sTH9Z3f7KOf6p5Ldz/LfdomvXr3K/fv3EQSBIkWKUK7c6+/Y/kskxadw/uAtSlX1xjVdF0qnMSCYCcjSBAxmAqIopIt+iiTbmLOsdTWGrj/K/7Yd52DVIiRbCohyEVFUoNXLGLb2EKoEkRL53KjTrDRHDt6mdr2iKJUKJvVcSlhAFMe2XCA2LI4arcubQv3Ong50GNoCcytzEmOTCLgXRL3vatDw+3drESQhIWFEWveyR1J8Ck/vBgECD338Mxxb/PNads4/RIkahZlxbMxbE/qt7CwzVXYCpCSkmrZ+i1TyNmnttRvUlDUT/qJVv4amsQdXn2J2/xVY2liw+t4MbB2tM8zV+ocGIIJ7XhdqpWv8PUer0XF4w3mQyQh8GAYiPLjyhAdXjFG4as3LorJUojQ3w+WlrheLh29i55JjOHs4EBUSa8odrNq8LD0ntMMi3RGztrekz+SO/FRnIr0rj2HeiVEZonPFqxTk9+2DeXLnGQ+uPMagf9GXd8fCI2hSXxQ4BD4Mo2T1QjzyeYqtozWRKTGoUzTI5TLSUtT43QykULm8mCkVyGQyhi3tneG9FqvszdSeSwEIfhzOovMTSI5PQRRFk8P2skMcH2W0Q52qxaugGy6viE/rdXo0aVo88rnike/dupK8KwXL58dMqUCvN2DQGzix8dx/zvHLLu/s+AUFBdGpUyfOnTuHvb09AHFxcVStWpWNGzfi5ZVZ2PG/xOyhGzm3/yYuHvasuTwBAHmaiF4GiiTQqwCNAOlrnSAT2disHF0OXCJnRBw9D15gcesaqEU92IhoZAoEnRy9HPyCo+j1vzWgF3kWGE2PPnXIXcidsABjQ/LVE3ew+re/GL32RwqWzcMPFYajTtMy4+hoJnaaQ9jTSJr1qoeljQXlG5akTN2MuSoPLvsxf8AKyjcsRdcJHT7lZZOQ+EcjrXvvhs+xO4giIAiUekVSw//OM8CoxfY6EmOTCHsaSYHSebKMEB3bcJZZPyynQOk8OHs60O23jqZjDb6rQYPvamQYHx1i3C5OSUolLUWNraM1lw7e5PGtAKq3Kk9KYiodhjTL8lwxYfGkJBg7ZRheKmZQKBW4ejlx95IfC4YZi0em7xtGiSre6PUGjv91GQTQ6/UULJuHh9efgkwgPiYpQ8cNgKf3Q0xadk/vBfPQJ4BFv2ygzteV+XH6t5SrV5xy9Yrz5M4z4iITuHvRD4BG31Zj74qTpiILW0crvh/dhnodqxITHs/tsw8oVbMIKgslQ5tP4/a5h9RoVY6Rq34gLCCS37stxtndgeF/9jEVgPyyvDdHN5yjSddabPhjL2t+30m1FmUZvaZfpmvTbkATbBys8Srohlajo0eZX7B1tKZl3/rMG7g63fHTMGHrYCo0KvXa37dGrX1vWbKilQuyLWK5MYd03gGadM+6VZ7E33D8unfvjlar5f79+xQqZEyu9PX1pXv37vTo0YPDhw9/cCO/JBRm8vR/X1xahR7kiSJ6g4BOKWCwEsDwXOFFRGulYO73tfl91h56/3WWPfWKE2Jri0YGBhHU9gKCKCclTYfCUo6gN3Do2G0Orr9Aveal6F6tMBd2XeH+JT9A4OENf8wtzEx3w75XHxMXmQDAhb3XiQmNZdfCQ/Sb9T3xUYm0HdQUpcqMHXP343vlMb5XHtNucPNs98eUkPi3809e98aNG8f48eMzPOfq6kpYWNhHOd/ts/cRDSIlaxZ97ZgSNQqTI7czOo2OWl9XzXBs0IIe7Ft+nGqtymf5Wq1GR+8yvxAdEkvPSZ34+ueM0T6NWsvaidvRqnX43QxgzpnxGdbbrGg3qCmWNhaoLFVsnLqb0rWLMKX7UkRRZMO0Pei0epr3qEOIfwSt+tSjUuPSPLj6hC1zDlC7TUWqtSjLub0+iCJUbl6G5PgU6n1dhYbfVGVG/1Wm89y75EeJKt5cPXaHhJgkEATK1StOl+GtGNxkClEhcZzb50NaijpDUV7VZqXp9HNz48/NyzCy3WySE1I5sOoU/ad9Y3JI8xX3YsahEYT6RyCK4JEvB70nd2THwiPEhsfTflBTkuJT+LnpVFIS0xgwqzNl6xQDIOBBCADh6bp/J/+6wiOfAB75BHBo3VmObblIVEgsnX9tycC5XQFYPGIzgEmrcN/KUwQ8CObbYS2wczJ2lWrRy+hgbZ9/kKBHxr85uZnclF8IcPuc72sdvzUTtrFu4naa96nPgHnd3/h7fBuWNhZUblaWyi9tTUtk5p0dvzNnznD+/HnT4gdQqFAh5s2bR7VqUs7YoOmdqNakFMUqvhDhtNJAmt6AzFaOeaqAVi2AUkBUpRf5CiKH6xaj0/6rFPcNZdCG44wZ0BRBEElDhkEvoNXIiAfskwXkehlhyWlYxqeyc90FZGodvy3rysbJO9BpDXw9sCnm1uZ8M/wrUhJSaPBdDQqWy8exDWfZtdD4BWXrbMPsH5YDoLIwo82AptT9pgZXD9+ibP0SGbSuJCT+6/zT171ixYpx9OhR0+NXE+w/FAdWHGdmL2P15o/zutGqX9bi1TdP3iUifSfi0v7rtHhJZNfT253eU7997Tk0aRriIozt2sICIjMdP7/rKqH+kSAINPiuOgozBVqNDr1O/1qFA3NLFW1+asywplO4efo+x7dewNbRmvjoRHTpUiUntl0iOT6FkCfhyBUKpvX9k4ToRK6fuEev39pzbq8PBr2BHJ6O7D5wk9vnH1G0Yn5+mNyR0KcRWFqb0/ZH4/v0KuCGuaUSjUbPsc2XOLXzGsUq5iMqJA6ACV0WE/w4nIbfViOHpwPnD9zgm8HN8C5tzMXuOLgZqYlp1GlfKcsopHveHIiiyM7FR4kMjuGboS2wsjVGESODY0hJSgMgOtR4vh2LDpMQkwyCgKObUXeyesuyHFx7BnWqhg1/7CU2whgcWDN5F43SZV7qdayCuZWKFj1qE/IknHmD1wJgYWVOtzFtXjaJmm0qcm73NWwcrWnzUyP+HLkZSxsLnD0daP3StvurnN15BYBzO69kcPwinkWRkpBKnmJSNP1D886OX65cudBqtZme1+l0eHp6fhCjvmTMLVXUaJ5R6b1v51qs3nSBWsVys+vJY+RaAb1ahmgpQzTIEABBbmBu3zos/d8Gmh+9w57WJbiZ1x2ZAMlaJTq9HFGQkegpYB0O5no5ru52hAdEI4oiKckaAv0iSYxLYULXxYxe1Yfvx7Yz2VCofH4in0Wzc76xIXnHYS1ZOGg1BoPIX7P302ZAUyo1LcNfEctNr0lOSMHKVnIAJST+6eueQqHAzc3to58n6GGI6ednvqGvHed3I8D086Nr/lmOuXLoJlO7LqBU7WKM2jDA5OBY2Vryy6p+LPllPQH3gkhJTM2wNZqvVG7MrVQIgkDHYS2Ji0ygb8WRJMUnM/3gCIpULPBau4pV9ubm6fsUqZCfkav7c/Psg3RnTCQyKIbtCw5Tq20lZg5YZYzYAZUalaRxl5qYKc1wdLVDrdaye/kJbOwtsXe2ITEumVpfVaRiwxJM67uSpLgUfl7Qla6jvuLasbtcPX4PnVrHrbMPUZgp0OsM+Jwy9qBdN3UPAKJowO9mIPOOjsDexZYytYpQ5vgookPjmP/zegqVy0uDThkjp343A1g8fBMA9s42tB9olEk5tO4sIFC4Ql7aD2zMzbO+LBm51fQ6+/T2cTkLuFGscgGObb5obC4gACLkKmSUVjm39zrzBq8DAXr91h6HHHY4ezgQHRpLgdK52LnkKDKZjOY9aiOTyXD2cGT6weGmXMs5J8cSExZHXGQCjm72Wf4+UhJT6ftHZ3bOP0jjl8SnwwMi6V5sMFq1lvE7hlLlDVXfEu/OOzt+06ZN46effmLBggWUK1cOQRC4evUqAwcO5I8//vgYNn7x6GNSMTyOJTyHNbIUEZm1cetWa61HpwWDCmSCyP3ibhyrV5B6xx7yv4UnGDinLQq5HoNoTQpyDDI5WrUZKS4yGubxJi4kkaI1CnD3tB9rlpwgJU0HwK2zD+lbYwLrbk3NYEeVluXp+0dnBAGa967PilGbSUlINYl2vsyc/svZu/gIbQY25YeZ35ueF0WRsKcR5Mjl/NGiChIS/zT+6eveo0eP8PDwQKVSUalSJSZNmkS+fPk++Hm6jP2amLB4DHoDPSZ1eu0477J5TT8XfqUF2XOObzxHQnQSZ/66RGJMErZOL/rZxkcnERMaR0xoHLfPPqBSkxc307kKebA50Kj59+DyY/pUGIFOa4za3b/kh2d+Ny4dvEHZesVxesXh+H5MW5r2qIOjmz0B94L5vetiRBHaDWhE93Ht6D6+PXK5jJiIBI5sPI/MTEGtthWRy2U0/PZFZHfarp9xyemAraM1gxpNIfRpFKsn7yI1SQ3A+ul72b/6DAigsjRDnaJFFEGvM5jmUJjJTXYLgoyokDi2LzpG9zFfmcZsnLmPvStOsnfFScrXK45DjheVmi45nYzVvXHJJtUGgPP7fAAIexqFuaXKlDsI4F0mD11HtTY9LlW9MMe2XDSWwQLWdhZ0G92G41sv4fu8KEc0VhZvmL6XbmPa4p7PheiQWBb/anQ6LaxU5C7qycIh63h8K4CRa/pTuUlp4iIT6F5qGKlJaQxb3od6r6hIHNtwluk9F1OgdB7mnJmQodI6IToJrdp4oxX5LBqJD8s7O35du3YlJSWFSpUqmXRydDodCoWC7t270737i1BtTExmh+K/yJE9N9Dp9PgGRqGylmFIBJ1cQJsswyBXojHXYqkUkQt6VvWpQpXz/hS9G0a7g7fY16IYSkFPhCCQopSj0wgIgpI993yxjBHx0YJNeDwCApZujqRGxoNOR3KCMYl5Rt8/SUtO4+elvbBzsqHNgCY8uOzH45sBNO5am+1zD1AivWVPxLMoFvxvNZWbleXKgRsAXD7gk8HxWzBwBbvmH6RSs7JM3DP8c1xOCYlPzj953atUqRJr1qyhYMGChIeHM3HiRKpWrcrdu3dxcnLKNF6tVqNWq02PExISsn0ulYWSX1b1f+u4ik1KU6J6YbQaHTXaVMpyzFc/NSboUShl6hbP4PQBVG1RjiNrTmNlb0mJ6pk7dDzf0r2w7/pLzpNAoy41mfDtPG6cvEe+El4sujAx02ufV96mpWp4Lma2be4hbB2s+XqQMWrWfWxbjmy6gEFv4MKBm1R9qV/vlrmHWPnbDqzsLFhx+Tf06e3DUpPUuOV2RhRFytcrxv61ZwFwzeWCQafHLY8LgQ9C0OkNdBzUmJvnHnLx0C1kgoAggFato0BJL3xOGSOS5pZKbBytQAB7F1us7TPuvtg727Dm1lTUaRrsXrp+fSZ1ZPuCw7jmdmZk+9nkKuzBsMXdmTFgNY9uBrBq4k4Gze4CQMNvq1GwbB5+bTMTvc7ArAO/8FvXRQQ8MEZzv/2lBVq1jgW/bAJR5MxuY3/gzr+2RCaXYdAbmNF/JRjSk9aB83uuUblJaVISU0lN33KOCMrsvPmcuItoEHl03Z/k+JQMldbeZfMycsNAYsLiaNrz4xVpPL37jKuHblDv2xo4uNp/tPP803hnx2/27NkfwYx/Nz0HNWTbmnNEy/Xcj4/HLFXAYA6KBBl6uQy9tRydXo5gpiXO1ZINvSvQc855vlt8iRs1PVA42GAQEklUKInVy9EKMvQGGcoUGYJSJMXNGrlGhyE6FZmFCtQyqjQrxc6lxzmTnj9xdudVmvWog8/xO/zadDIA1b+qQL9ZXWjVrxEAI5pPJfB+MBd2X2PUhgGc2nqBlj9kzM3wvfIYgIfXnnzCKygh8Xn5J697TZq86IRQokQJqlSpQv78+Vm9ejWDBw/ONH7y5MmZikE+NI+u+3P77AMA7p5/mGWyfcFy+Zh37rcsX++S04n5WThtr9KybwNun3tAeEAUtdpVYlizKSREGyVGBFnmFmYX9t9gyYhN5PBy4qsfGjBsaU9m9FuJXqcnOTGVEP8IPPLmwMHFlq6jvuL2eV/a9m+IwWBg/i8buXHal9CnkSBAcnwqCTFJjF3bjzGd5uOQw5bJfw3COj0/ul77Slw6cpvvhjajRkvjVmX7goNJik/lr4VHCE/X3tOn2/bjtE7sX32Gm2d9qVC/OBM2/sitsw9BEEhNVrNiwnaObblE30lfU7ed0ZlWWSo5vOk8MeHxdBzYBHMrFaWqF0Kv1zOi7WwArp24x5D5XXHIYUdUSCw26b2An1fR5iniyfo70wFjRfFzpw+gcqNSrJ+x1/hAEIzVhhgjl4vPjaNPlTGm6u3nOHk4YDAY8Mjnyrgtgwj2C6dF78w9lr/5tTV6rZ4S1QuTlpSGuZUqQ2Vvrfbv145NFEVWjtrEpf3XKVA6D31mfJ9JxmdovfHERcRz4+RdJu7+9b3O9yXxzo7f999///ZBEhlIjoonwS+Ext9Uwep2ELfjoolTiBiUcmQJIho7BRqljBQN2ChFjrQtQt3DvuS7H033uRdY9HsNBNGAUmZJqsaCNEGNHiWpKWbI1KBIk0OqgE4P8kQ1MhFO7L4OWh32Ho6oFIKpzVBayos7/bM7rnB2xxWqNC+Ha24X012jIBOo0KQ0NdtVzvReBi/tw57FR6jT8c0J7c98g/m18e/YOlnzx7GxWNlJFcISXy5f0rpnZWVFiRIlePToUZbHhw8fnsEhTEhIeC85mssHfPhz5Cbqf1eD9oONlalmyhdfLWZZyHRsnr6bAytO0OP3Ttnu0ZoVnvldWXje6CDO6v+nKbfwp1ldqN66Qqbx2xceJiwgirDAaG6ff8jGBzNYdHYsV4/eYdm4v9gy9xA9xrbFPV8OLG0sGDizC+f2+hAZHMOBNWdfTCQIVGhQHCd3ey4eus3M/cPIkdMxw7l+nt810/mbdavF3hWnKFWjsFEbMH0uuVygQMlc7Fl+EoDQgEg6FR+Grb0VgkygWouy7F5+EoPewIJhGylTswj2Ljasm7aX9dONeYIWViq+6lsfpcoMpcrM1EnDTKXA0saC+cdHsnfladZP28veVadZfmE8Tu72+N8NYuXEnZSrW4x6X1fC3FJFWoqGOu0rUqBULrqPaUOgbwhWNpZUbV6aiMBo2v3UCHMrJX0md8Ln5F2c3ew5tPYMOp2ebXMPcnzTBeafHoudsy2pSWkmtYuX8cjvyi+r+rFzwUE6FxxIriKeLL46Jcuew3+Hp3eesXHKTgCe3ArENU+ODELiAHbpYtr2Lv8tkfdsOX7JyclYWWX/i/tdx//bmTN0I1q1jlWTdqF3d0YpA0Uha/RJIqJcgDQ5qSoVKjMdWoMapULG2hGVGP39ASocDeRq80BuV/PAQqElVask2UxFrEGLJtYWhUIg2UWGZaiIQi5gsFYhqHWgMpYMqzV6ZHLl8xs1StcuRusBTUiJS+Hw2tPYOdlgn8MOgKmHR7B3yTFK1ymKhZV5lu8lb4ncDFjQ863v+fJ+HyICIokIiMT3ymPK1i/5wa6nhMSn4Etd99RqNffv36dGjRpZHlepVKhUWVe//h02/7EH/zvPWDlmi8nx8yrkQcFy+dBpdRnyz56zbuJ21Kkats3a+16O33NEUaRCw1JcOniTYpW9adazbpbVsC171eXJ3SCS41NxcLXDwtqce1eecPP8I6PCAgIrftvBc7W+vStP8exRGE5udlSoV5w7F/1ITVGDKFK+bjF6VhtPTFg89s7WbLg9NdM59689y9b5h2n/Y0Oadq7O97+2pGDpPFjamPPgmr9REBpo92NDCpbOzdi1P3B+/w387wdzbMsl4iITqd2mIkF+EZSrV5Qrh++QkpjGhYM3sXO0ZsMf+xAEGSCybupe/lpwhFqtKxi3ZBGo0bIsGo2OiV2XUL3li6hrWrKafatO02V4SzbO3M+Vo3e4cvQOhcrmQZ2mRRAEtGk6OpccTmxEPL8u64lbLmd+avA7iMZt7VPbr4AAot7AL0t78e2vrdg8cy+7Fh0lPDCKG6cfMKnLfAwGkdiIBNr+lLkCfPvcAyaliaCHoWhSNSZh6/fFPb8ruYp4Eng/GEGAwlkU/cw68xuPrvub0p3+K2TLtS5QoACTJk0iJCTktWNEUeTIkSM0adKEuXPnfjAD/w24pXfwcHS2A1FEMECOBLDQyVEkgxCnQKuWk6pVkKaToxPlBBVy4GQnbwA6TbqCc2oSTqpk8trFYG+ZiL1jGpo8alJcdOiUkOoqQxRkGJQydOYKRJkAMoG0JDWxEQncPvcQgAU/r2XnwiOc2nkFEIiPTiIh2li9plAoaN2/EWH+kcwftIow/whWjN5M/8ojuX8p6+jB66jdsRpl6pegTqdqFH9FwFVC4kvgS1n3fv75Z06dOoW/vz+XLl2iXbt2JCQkfLIoZbOe9bBztqH1j41Mz10/epuH157w5FYgF/ddz/SaDkNb4uzpSOsfs5aDyS56nZ6/5h2kf43xTOyykDzFc9F/ZpfXtgXzuxVIrsKejFnXj2UXfyMhNpnfvl/M5SO3jVvDgrGqVakyxkTsnIxbg7aO1oxZ3ReNTgeCgKCQk7+EFzHhCSAIpKVqMRgMRIfFo0kvSji65RJLx24jLDCazXONagqnd1/nt+5LGd5+Li9pQbN5ziEe3gjAPY8Lbfs1oF2/BhQqm4fabcpzcsdVHt0M4Nb5R8jM5NjnsGHd9L34nL6fXo0rUKNVeXRaPYmxKexdeYr49DU9PiaJkMfGXu2BvqE061oLc0slCjM5lRqVANKreNOreheN2IR3qdwIglFEOzosDoNB5NxeH1KT03juEV8/eRdRFBHT8xsTY5OZ0n0xuxYfw97VjrYDGuPq5WgSvE5LTsv0uwh7GsmSYesIexpBvpK5GLVhwAdx+kRRJPBBMKJBZPntGexOWM22iD+p+FKB0HNsHKwpW68EZsr3E4/+0shWxO/kyZOMGjWK8ePHU7p0acqXL4+Hhwfm5ubExsZy7949Lly4gJmZGcOHD6d3795vn/Q/xOITI/D1eUqBkl6M/Wk9Plee8nP3etRsXJxuUzdxNTacVGszUs2UpKgUWJmJGEQ48ENxSp4OxvlZMm2n3WT772VQyOJRCWkEyhzRaM1IwYJUrYAYqyAxtwKrcD1yBwsMMhlNO1Yi+HYg5uaKF3d76R9crUYPchn2TtampOEbJ+7y19z9XD18C4PeQGRQNBd2XwNg9fhttO7fiEpNy2Sr16KTuwPTDo/5KNdTQuJT8KWse8+7ikRFReHi4kLlypW5ePEiuXNnjrR9DOp2qpap73fpOsUoWD4fOo2OCg0zC/d+N6oN341qk+n554iiyL2Lj8iRyzlDG7RXObj2DMtGbTE+EARunH7AjP4r+H3b/zKNjY1IYPOcgwBcPGgs2HjmFwZyWbqavoBDDlvmHR1BbEQCSfEp5Czgyt6Vp9FodLQrMhRBJgcMeORxwbtULirUK8a9q/4kJ6bRr/4UAh+G4Z7bmfFr+jBz8DpEvQEzlYL2/Y36fqYtcAH6T/6aiT2Wk5zeFeT3Xsv5Y+dgcuR0JE8RT2Yf+IWFIzab8ufUKcbqXIMBEqISOLPnuulYzgKutOhRm9SkNE78dRmZQo5Wo+P2BT9UFkrsc9iiUWsZ3m42fX5rR9Pva5ry6dzyupjmeXgjkOJVCqAwNzMV3JipFHw9oBH5insxYnlvlo7eQkxYPAVK5aJKk9I4ezhQpnYRFg5dB0BSbDK9JnbgzI4rpmvvnMXv0NHdnlxFPAl6GEqXMe2ylGzRqLUE+YaSu1hO5HIZWo0OhZn8jd9B22btZenQdbjlzcGqB7OxsDLH4vMH4v9RZMvxK1SoEFu3biUoKIitW7dy+vRpzp8/T2pqKs7OzpQpU4Zly5bRtGlTk4aPxAtkMhlFyhmlFSYt7opep0eukGMwiAT6RWOjB7W1HL3SnASVFSpFEjZmApYWZmydWIY+3c5Sam8QD+u74FvPHRVaFIIWtV5FtKAlSWOHRgsyvdxY3RuuQ2al5MHdEAJuBmFuoUSWnjfR74/vuHToFomxyQgyGT/M7GK6y1r08xqe3g1CrjCODXoYSo22lfA5fsf039A/+1L/2xdbSJcP+BDsF0azXvVQmitNz0cERrF63BYKV/SmRd8X4q0SEl8KX8q6t2nTps927lcJehRK0KNQKjQqzfzzby/OeB27Fh1h0dB1WNiYs953dgZBeZ1Wx9N7weQp6kmof4TpeZWlEnWq1pTAbzAY0OsMJmfLztmaKk1KceeiH7W+Mub/+Zx6YKpGBYiNTGR4+zlUblyKx3eDCfILx+/2swy2DV/ag+rNSiOTyZiwrh8jOs7H54wvQemRtdDAKHrXnmTcghUM9B7flubf1wSgapNSTNryE7ERCdi72JqcPoDI4FguHb1Ni64veqk/F3wG8CroRlhANIXL5SXgQTBx6YLMBUp40aRLDVw8jMLMvX9rz/61Z1j1+y4A1Kka1KkaBJnRWfK7/SxDEUWFusUpUNKLJ3eDMOhF/O8Fo1XreHo/hGm7B+Pk7oBHHhfAWAgSFWxsf1emVhG+HWbsqqLXG/Auk4fHtwLpMKSZcd5GJSlSMT+JscmUrpW5y4tSZcbiq1PQpmkwT08tSktOw0xlhlxhzAkc2XwqN0/do0mPOpSqWYRp3RZSonphph0Z/Vrn7/YZY1FR5LMo1KmaTO3xJN6xuCNnzpz873//43//y3w3JfF6zuy4wuY/9tCqX0MafFvd9Ed95PBtCEsFeznKFDmaOEhRmBMtyLA3U2MtTyCwlCMXuuWj2p9PaDrhDhFlrVE6KpHLdKQZorCUm+OntkSrMJBsZo4oypBpFch1IkWLuRPg8xSANTMPUqZ6QUpUyEtSnLGVjrmVitI1X2zDlqlTnKf3gilatSC3Tz8g6GEo3w7/iu/HtqN3mWGIIhm+4EIehzGqpVErMC1FTadfWpuObZyyk8OrT3F49Smqta7wWgHPl/E5fpuAe0E06VEXlcWHy0GSkHgfpHUveyTGJtOv4gjUqRq6/9aBDkNb/u254qOMEjPqZDWaNC1Wdi+OTemxhLO7rlGlWRl6TGjP4bVnUSjl/DirC0qlgqiweBYM28CNM76EBUYzfn0/ytQqws6lx7Gyt6b72LaUqFIQgLptK3Jo43mS4lONLcYEgbtX/Ll79WkmmwqVyY13qVxcOnqXkKdRdPzJqHjQZ3xbti89Tskq3lw9eZ+LR26TlqxBFEVm7B5C0fIZ9RTPH7zF3tVnKVQmN2361sXv9jPuXvZHrzeweMx2dq88g0FvQK6QkRibgspSiYOzDXqtAa1Gx41zvvy+8UeGtp4FQPUWZUxOH4CNgxUeL0Xx8hR2p0zNIuQtlpNjWy7SJL0rx3PsnKzpNuor5g7dQKlq3pSsUpCt8w9TuXFJfmk1C6W5GYvPjMEttzNeBd347pcWBDwIoWWvuiTGJnF86yVK1yzCb1sHIVfIsXEwhteiQ+O4f9moAnFw1Sm+H9M2w3mT41O4cugmJWsWwdzKnGtHbzO69TSs7a1YfG0Kjq72PPM1plkE3g8mLSkNg97AzVP3SElMzbK5wNmdl7m49xpyhYyBi3pJTt9reOeqXol3Z9XYrQQ9CmXFqM00+PbFh04QBBRqEblWRJkgYlDI0apUpMhlRFuqsDZTYq1I5dQPBfA+E0GOh0k0GXuPffOLY6bQg0UsKpk1KY4xRCisSdPISdWZITPIsDSzpMv/GlG2cgGO7bjKztVn2b3mHH/dmsiw5b24cfI+BUrn5szuazTpUgO5Qs7pnVdBJsPOxY5chY3dCErVLoqTuwOzT08gIToxQ79FCxsLzK1UpCWrMbcyR683mCqyStUqyr5lR8ldNCe2ThlL6LMiKiSGXxr+hmgQSYhKpMu4rz/wb0FCQuJjoNcbeHTdHwdXW/Tp7c+ei+9q1FpGtpxOsF8Y47cNxrtMHvYsO4bfjaf0mtTJJH3yKh2HtsQ+hx15i3nh4GqX4VjQo/D0f8Nwy+2MS14XHt96xoQui6j1VXlO78yYU+hz6j7W9lYsG/sXAMe2XiLIL5xe49piEEWTrIqThwPRoXE4u9sTFRaPXC5DkAno0kWX4+NS2LvmnGnefEU9qVivGLkLufO/Gd8S4BvKjMEbEEURuZmCnPlcUJor+b3vCi4cuk39dhUpViEfdy4ZnaFHtwLxu/WM4Uu6EewfRXS6MHaQX3gG+yvUK8r41X3pWnEMCAKaNB0LR2zhp2mdCAuMpkX32ln+Tp47fvYudjT8piqbZh3g5rmHBPRYRq5C7iTGpjB2dR+iw+JYNHIL4YHRHA6MRq/VE+AbSlxUIqIook7VEBkci1tuZwRB4LtfWnLvkh9Tei0jNTGNJ3eeYWlrQVpiKkqVgsUXfsMtjwv2LrY4uNoRGxGfZYHP1O6LuLT/Bl6FPFh+Yyq3Tt9DrzMQH5XIosFrGLl+AOO3D+Hs9ss07lYbnVaPOlVD2brFX9tRKj69J73BIFKw7IcXMP+3IDl+n4BmPeuw5rftNOuVUYiyYaMS+PmFs//kbXSxBkQZaFUyRJmSaAs7rMz0KORpWJtr2T+5OJ07XiLfiWjKrgviVhcPHMySUApqUgwKRBREIiNVsCRVq0QbrUdEoHrjkuxYex7RUoVeb2D3+vN83bM2Ob3d+amOcStGJpfR9PuaGNITdcMDo5lxfEwGzaPCFYzq+8nxKcRGxJPT253okFi8yxmfXzR4Dae2XmTWybEIgkDtDlWp0KQ05lbm2SrPV1koMbdUkZqUZqoylpCQ+Oez6Oe17Fl8lHwlcjH79HgC7wdTvU1Flo3cSGRQDLdOG9uTnd15BU2ahvmDVgMQHRLHxB1DspxTZaGk/jfV2b30GEkJqVRtZkzMT01KY9D877mw9wZ12lfi2LZLPL71Yis2IiiGHF6ORDyLoVLjksRFJVGpcSly5HTE1tGKhFjjbodcIWfPqtM8uP4UVy9HwkPiaPJddVp0r4GtvTWP7wRhplIwuvNiosLisLQ2JywwozC3jYPR+bh1wY/xvf/EwlJpqgY2GAwEPArnp6bpXV1EkcObL3Fo00UAqjYtxfn9NwGRm2cfMe/gME7uvMrp3T5Eh8eTkpRGcrxxG9jK1hJBEIgOizOdOzYqkQMbLtBr7FdYZlEQER0ab/r5xllfJvX+k0Klc6WbInLrvLFY7+w+H9ZN20daihqVhRnWdpYc22bMzdNp9Xw/ohXW9pYUqZiPyOAYnD0cEASBzbMPcPein+kcZmZyUvQG0lI0hPhH4JbHBStbC1bdmk5yYmqmDioAYnrhh5i+1d6ibwP+mr0fTZoWu/S2coXK56dQ+RedX8Zty/rv5TmNu9VBJpNhn8OW/KXzvHHsf5kvyvE7ffo006dP59q1a4SGhrJjxw5at25tOi6KIuPHj2fp0qXExsZSqVIlFixYQLFixT6f0RiTY1UqBSrzzJVDYkIaaQHxWDlboEoAwaBErTWQZmNGpLkNaq2InUJPUEFbTv9SgDoTH1H5jydElrUisoQNSkFLLot4RNEMURQJ0crR6WVotZa0Hr+SFT934FlQLMhkIAismHWE1p2rYWVrjkypwKAXEUVY/ftO7NzsiYtOwu9mIJ0KDGTJpd/J6f2i/2dacho9Sg0jNjyeQQt7cP3YLe6c8zUdv3fhISe3XKBOB2NPyed3ZSmJqWycshO33C40610/y2tk42DNn/dmE/ksiiKVC37Iyy8hIfERCX8aBUBkUDTeZfLiXSYv2+bsZ9vsAwDkKeGFlY0FDbvUJDG9/y2QqRPFq6ybsosdC4xSH4PmdqVkzcL0rzUBvdZA3Q6V6Vd7IpY2FgiCDFE0oLIwY+iC7jh7OJAYl8xPjaYQG57A0NazmHNgGKuvTeLZo1AigmLIXyIX3aqMA6BC3aKIcgXrZh3kwMYLrLsygfzFc3LrwiMi0vPZkhLSjJWvMhm5vF1p3b0Wj+8Gs2HuYaxtLUhJTCMlMQ1kAp55XAh/FmPqKAKAIGBhrTKOEQSKVchH3sLuBD+OxLuUF+tnH6JgyVzERiUS+TyvLz1iV662UWpk8KzO7F93lnxFPdmz+ixx0cn8tdi4xfwqLbrX4tTu6zy5G4Req8czXw76T+lEiSrehAREsXmu8bpGBsfh6uVIgG8oBcvk4Xa6Q6iyUDFk3vdUaVwKvd5AxyI/kxSbQrm6xYgJj8XK1gKVpZIqTUpTvUVZVk7YTlqKmmbda1O61ov0IXMrFeZWGdN2DAYDJzZfoG6natRsV5kytY35f84ejiy/9QcPrz6mcvPMgt9v4+45X05vv0jz3g3wKuTxzq//L/FFOX7JycmUKlWKbt260bZt20zHp02bxsyZM1m1ahUFCxZk4sSJNGjQAF9fX2xsbLKY8dOwY95BYiPi2TZrn0nn6jn5C7sjM4jIU/TobRTIdCLKRBmacDPi5eaINlqi0qyxlGvx6eCJ15VYChyKot4gX/bsLIHcRoGbMhYlGgyiAYMIYXoZkWlgFaTkxwU7yFfGg7jTjxGVCoQ0PReP32PbomOIZmYIMgMbZu4nJjjj3axOq+fh9SekJqdx5fAtAu8HkadoTuIijHeSz3xDqNysLKe3XTK+IL3B97mdV0yO33N2LzzEpnQhzcKVCpC/VJ4sr5NLTidccmZuMSUhIfHPZeD8bhxcfYpKjUsDcHzLBUJeKrpo+F0N2g5I7y6S35XxW/7Hs0ehtB3wZikXu5dSRO5d9sPe1c7UC/f4tkuIIqbiiKIVCzByRW9TZMkhhy0JscmmG95VU3YzadNPeJfKjXep3GjUWszMzdCqddy+/Bid1rjbEReVaDpnsQr5aN2jFrtWnTFF8gQBAh+FM2/UNlPEytJaZTyQHrkqViEf3X5twa6Vp8jh6chT3zDyF/Xg8JZLIBNQmMlZ9tsuPPI4s+zkCFp7D0Wr0bEPMhSaPOf5e67briKP7wUT8jSKOm3Kc/X4PQoU98L3RgCFSmfcSpUr5AQ8DEOvFylZrRDFKhVgfLelPLjuT1p6dTCCwPUzD5i7fxhBj8M5s+e6yfFTp2mY3Hs549b1xzOvC0npkdKbZx+YtvFHrexD9RbluHXOl+DHxu1pBxfbtxY6ndh8gWndFwMw5/S4DBW/bnlccEsvJHlXxrX7g/ioRPx8/Pll9Y9smb6bUrWLvbZl4H+ZL8rxa9KkSYb2RC8jiiKzZ89m5MiRtGljlAlYvXo1rq6ubNiwgT59+nxKUzPw7Yiv2DB5J20GZra98Vfl8Lnsz/ET9zCTgzJJgVYUsZArSbGBBEEk3EKDpVyHjVUSRyYUIsfdRGyD1FQb+ZhT8wpgKzegUOrRiXK0BgUGg5wImYE0jRlPUpIIUYvYuVuiSDIgKBXMm7ibxPAEBIUC0BMbmQhyGUqlAqW5FZo0Dbo0LT6nffmj75/otTrTgtTr944kJ6bR5qfGWNtbsn3uAfx8nhorrAQoWSuzZl/e4rkQBLB2sMbJI2tpBp1Wx5WDN8lbwgu3PDk+6PWXkJD4eDh7OvLdiK8AmN57Kcc2XQDALZ8rbrldqPdNNXYtOUqJaoXIV9yLys3KUJnMmmrPt/yeV2t2GNyM5PhUgh+H0+nn5uTwcuLbYS3QanTcv/qEOxf8kClkuOR0pE67iozoMI/CZfMwaOZ3aNQ6chfx5MmdYACSk9QEPQ4nKiyemIgESlbx5quetdmy8BhpqTryF/cgOT6Nbwa90CKUK+T0GdeGfMU8WTRuBxq1llzebvjfD0E0vLA7JTm9G1K63TFRicwatonkxDTAH4DoiASjEyqKxpxBmUBMZCKXj9019foFQCaQt7AHRcvnZd9aY3Sv8TdVuXX+EZN+WGnS5ytdrSCpqRo2zDnIhjkHqdmiDD/P6WyqYJbJBLwKuPL4ThAFS+fiz4k7M15sQcDO2Zo+49tibqVCpzOwZf6RF63XRBGtRs/Ir+ey7sYkarQsy51LfpSqVpATf10GXuzoFK2YnyZdaxIRGI13mTyIophlxa0oilw6eJPwwCiTja/q9t04cZewp5HU/646CrN3c0/yFM/FzZN3yVcyN6tGb+bI2tPsWXSYHTErpSKPVxBEMYtbjLcQFxfH5cuXiYiIwGAwZDjWpUuXD2bcmxAEIcNW75MnT8ifPz/Xr1+nTJkXi0qrVq2wt7dn9erVWc6TVcNyLy8v4uPjsbX9cG1c0lLUpsbiLxPwIISdy0/gFxTPwwehGMxkJOW3JclNINHDgGilw9ozkRw2yZS2D8RZmUQR3zBafXMHuVbEZ4gnvn3c0YoQpzMjVOOCb1IOAhKciQ63QB5uhTJKwCJGxDzOgCJFj1mcFplaiywpDUFvgNRUHJ2tiQmLR0Q0OnnPFyNRRHypAXeJat78cXCkyX6dVseFPdeZ+M0cBJnA3DMTKFguc1JtVEgMFtbmr03K/XPkRjZN2Ym1vSWbgpagslBmOU5CIjskJCRgZ2f3QT/H/4R172PwIa9V38qjeXovyPhAEEAQKF27CDdO3MPSxoKtAfOyzPkND4xmUMNJAMw+PALXXBkj/4lxyVham5sUEZITUjm04RzLxm0HwCNfDkL8IwFYdXkCj2494/c+KwCjA6c3GEwaps9tc3S1xWAQjVG+dEel8+DGfDOgES8THhTD9bMP2TDvCI45bHl48yV5F9GAq5cD4c9iUVmYoU7VUqF2Ye5cfWqK1IFotEGnB1E0jStY0gvPvC6c2GHUSv1pUnvypDt90WHxqCyVWNta8MDnKaO+XZRB+qVi/eJcPnong51D53ZGp9VTunpBcng6khyfyryRm/E57UtqUppRu/UlKtUvzug/eyKXy5jafxUndxrtwPBi7be0MWfNtd+xsn3hON2/+gSD3kCxSi86YahTNXQqNJiUxDTK1inKpO2Ze0Sf33ud8R3nADBoXjeKVvYmdxFP0/GQx+F0Kz4YROg99VvaDmyaaY43odPqCH0SQc6C7uxaeIgFA1aSu2hOltyYjlwuQxRFUhJSM0gD/dvI7mf5nSN+e/bs4dtvvyU5ORkbG5sMnr0gCJ9tAQwLM7a+cXV1zfC8q6srAQEBr33dp2hYvnz4BjZP20Xzvg0Y+Eq7s3nDNnD30mNwsAULFYIBlCl6LKJlqJUy1KKc5CQl4aKMYJU1gkxGQFENl0bmpuq4p5SeGUxiIQtCa9vgqBCBaNRWIKAHwYmoFBWiRoEgylBbCtgEiSiUMkQUYFChksGs/YM5sfUSW+cdQhBkyGQCMjPQanSg12e8exMz3skpzBTUaFORxVcnI5PLyFXYk1tn7uOWJwc50juWpCanceXQTYpU9MaqWNYfOm2acftAq9Fl+lKVkPjc/FPXvX8aQxb1YN3kncgUci4cuAm8SOK3trPg1UDQnYuPuHz4No45bIkNN6aR3Lvsl8HxO7b1EjN+WkWuQh4sODYCuUKOpY05xasVRGmlQpOmw6ugO6JBpHC5vLh4OqAwU6A0N0OTpsXZzY7w4NgX0az0bhcxkenbui8Z5f8gNIN9yYlp/NBkBqnpUb2o0HgUZvIX+XsyGeEhCfQY0YJ6X5Xn4c1A1s89Qmqy5qXoGej1Ijnz56Bxh0oUr5ifcwdu0rhjZVIS04gMjqV09YI0/c4ogn16jw+T+6/GydWOZSdHsH/deWP0MH0+pbkZw+Z1Zsv8I/jdDeLW+UfotAZmD9uEVq3D2s6CQdM7MXPIBmNOIcbo2suYqRRcOnqH+SM2M2BKRyrWL25y/GRyGbXbVKBhh0rkzO+awekDKJIuT5OSmMa+1afwzOdK8coFTOfyOXWfYS2n0/i7GtT9+kW/9+cV3wD5SubK4PQ9vhnAw+v+xmur0WNt/+6KywozhSm3r3X/xlRrVQH7HHamG43p3RdxZM0pOgxtSc8p377z/P8m3tnxGzJkCN27d2fSpElYWv7zPOdXQ8yvCzs/50M3LM+KC3uMH6iLe65lcvwKl83L3UuPMTMYUKu1WDtZIUakoYiWYaOwJjQZks0tSNHpCLayRyZTYCFL4ebXHjg9SKbQpkiqDH7Cib+8ScxnjpNZEloM6A1KZAKo3ZQkWtmQFmqGRYyMRDcFMkHALF6LPFWLWoSQ4FiKVPEm72lf/O+HYEDEoNYiyGRYWKlIiU/G0tYChVJBxSalTQLUL/P0XjBLftmAVyF3bp26j7m1ig2P52Fla8GyXzewd8lRLKzN2Rq6JIN46HO6TexInuJeFCyX77V9giUkPhf/9HXvc3D9xF2Wjd5KyeqFSUlOo3rzslRqVJLxWwYR5BfG9ZP30esN9BjXjqS4FPKV9MqU/zX667mkJqVRrm4x6nWsgiAIuOfJwapJu6j/9f/ZO+s4Oars7X/vLenu8ZlMJu6uJBAgBIJLcAmLuy66sLjLAstiwd09WHBICJIQIE7cZSKTcZeWqrr3/ePWdJIFdpf9wcK+myeffKalqvt2ddXt555znufsTOfe7Vn03Uq0NtmRpvo4uW2yuPyIcSyZvTb9OhUltVz/7Dn0GNARIQRt2ufy5FfXcebut1O+qY6+w7pSV9VEm3a5FK8sSxM5ACdq065TAdl5mZx1rTEkLl1fzewpyxg2qk+6BVvbjnkM2qEHR521OxOem8pXH8xLl+QVLy8jvzCbnfcZxJQP57Fy0UYQgjZFOTTUNOOlfPY6fAcOPXU08aYEfYd1ZencYh66/i0G79ST4y/ePz2e1YtNxLS6vJ6X7vuExvqWtDk1WpNKeNSU13P6NcYn8ZHr3+TDF6bhJX0Amurj3HbOs1sd510OGEJ+UQ4fvjANMObJXtLni7dm8d0nC7lnwp+47ZXzWDR9NfsduzMde2wut6mrbOTBK18lrzCL8+84Dtsxc/99f3qBaWFnp2uePJsTrzqU9574gqb6ZhZ8vZzV89enid+aRRu4+5yniGZGufyJs+i3RVaourSOi0bfROAFHHfFoex4wHYMGvV/F/j9fb347ElmITJr4vxtxO/n7lBSUsLFF1/8u5v82rc36tOysjI6dOiQfryiouIHUcAt8Us3LP8xXPzwGbz78KcceObeP3juzJuOwnZtxj/wKRK45O5jue22D0AK1Po4mT2iJOstfEtTWZ+JbQkynQRZVjNfX9OD/FUtFM1uZpc/rmXqO72xsi06Oi3YVGBLj+bCGBtcQUNLDgkfbEeQTFgoS6AcidPsc/Pl47ESKYSvwjoUBa6DbQse+vI6Ak9RW1nPVYfew7O3vENOm2zGnLy5e0cQKO459yn8lE9tKP5INCV59vrxnD/ulDRJdCLOD1aerYjEXDr2bs/kV75mTMSmqaaZ3sO7b9UNZBu24bfC73Xe+y1Qtr6KmrJ63nzwU9Yu3sjapSUgBFPfncXh5+zLxy9+zZHn7sMrS+9BK0V2/k/7eHbu1Y6V89fRfWAnzr71DwCcsfMNlBZXMferpTw46WqOu/RAkkmP7XbtlxZ8rFy4YYsInmDtslIuGHM3Z99wOEedvRdgsgeB0iAFK8Jav1teOIen7viAed+uIjc/k2592zPvu1W4GVGWzt/AGfvcxZhjduaL9+aQaEkxdOee3Pnqedz6x+eprmxkyC69+Hj8DL78YD626+B7AV17F3HiFsTt1MsP5MsP5gHQrV97zrrqEG4462leemASLz34mSmbaW3UqxVzpiznqds/4PQrD8aN2Bz9x72ZM3UZq5dsYsIzU0Drza3ews9772Wvct1jp9O2Yz7HXrAfXtJn8aw1pnvIFkKTVpx46YG8/9zU9P2TLjuQZXOLmfLeXFLJJuZ9s4JDThnNDqHCNt6c5OlbJ6C1ZtoHc2msbQZg98NHMGy3fq3DSKO6rI6Trz6cWGaUp298E4Dhe26u9149fx2pMKvj/MjCvxW5hdkM3rXfTz7/f8EVz5zHpBencPgFB/zzjf8/x88mfgcccACzZ8+mZ8/flzlijx49aN++PZ999lm6xi+VSjFlyhT+9re//aZj227PQWy3549byggh2OPwHfj4xa/JK8ymz5DORJWiOQCpJF3qJfFqSCqHlJNFhW0R+JJIoY8bqWLqgz04aOxystem2Pn8dcx4tivSkRTZtaamISOKF0gSBRYpN4pf7+DWWyhXYMUl2BIdc0z5S8JDaAd8H1D4vuKzN2Yy4bHPUIFC2BbaD2isbWb254vYYe9BCCFoaYzjp/wffLYPn/qcWHaUD578nK4Du3Dbe5cz76slPPbnF+k2qDNjTtuTHQ/YDiEEWmuuP/ROEs1JPntpCvWVjew4Zhi3f3D1zz7eG1ds4rE/v8CAnftw0g1H/+z9t2Eb/h6/13nvP43aigbO3eUmkvEUB522O6sXbgBL0lgXJ+Vp3nr0M7SGl+/9iOLlpWitueTeE3/UqLm5Ic6NL59PU30L3fpvtt/o2KOI0uIqOvUqQinFn4+4j5qyBopXlLPvsSNNvZaUIENyswXRKVtfnX6drz6YZxayQKsa45GbJrBolokU1lY2khGmMUtCSxqlNB+/Pt28ppQIKRFC0FgfByF44vYPyco1GYnWdG9JcRUdtkhNt+tUwI2Pnsqdl7/O3G9WcdO5z1Fd0bgVUUVo0FDQLpeaikbefe5reg3qxL5HjSA7LxON2Py5hKBDtzaUbqgxUT2tWf79ei46+F5emXULhR3yOO3qQzh++A1bv4f54GTlxIhmukwcPwOEoOeAjhx62u7s+4edWDF/PYmWFB17FvHag5MYfcgwOvcs4qt3Z/Pxy6FZtTJm0Fl5GfQavDkbdsQ5+7Ly+3UopdIm1/ufuCtrF28gvyiX028yzhtBoBi+5yAOP28/MrKi7LjfEAAaqpuoLKmh55AuPDTtFsrWVv5bNi7/KnYcM4wdQ+X5/zp+NvE7+OCDueKKK1iyZAlDhgzBcbZm74cd9u+36flnaGpqYtWqzaaRa9euZd68eRQUFNC1a1cuueQS7rjjDvr06UOfPn3SaZkTTjjhVxvTL4EegzrzxjJj9PnKAxNJ1TZjuzaDdu7JMeftzUUPT0AKiZcdIW5Dpe+wLhYnx0oSzYsz9bGe7HviSgq/a2b41SUsuK8jjq2wRA2edsi0EzSkorRk+jQQo7lzDLdW4tQr3EZt5iBbIhwLbUlIaISUEAQsnL6KVDjZICSjDh/GM7e8DRquf+GP9BjYmbvPfQphWeggwIk6bLdrf+Z+vpCM3AzWL9mIVpoNyzeRmZvBu49MTPfz/ObdWViOxbUvXcRuR+xINCtGosWjpcGkYSpC9dfPxdvjPmTmx3OZ+fFcDjh9r20WMdvwf8ZvOe/9npBMpEgmUiBgyvtz6TOsO0tnF4NlgdZI1ybwAixbMu2jeQDsuPcgDjhhs8XTgm9XMvG1b/nmo3n4ns/dEy5l09oq7rrweTr2aMt1z5xDyepyegzsRNn6GmrKDWlau3QTD149ntL11fiBieQBIckRHHn67lulTHO28Ans0L0tu44ZwltPT02Tou1H9+GY8/fm2tOeJpn0sSMOfqBwozapuFnIHnDMTgwY3hUnYuOlAlIpn5qqJgZs35Wl80xKNkDw+B0fkkym8L2AREuKi246gqycKDUJj8qyBhCSLaXAfYZ2odeAjhx6yq5cdvTDBL5CWoIpH81jtzFD6TWoM2uWbiKnIJM9Dh3OkJ17ccf5L2zxefVWPoGt0bTWp6MZEeLNSSzHZr9jdia/bQ49B3Zk9eISDj5lNyxLsmzOOkrXGaJ836WvUF1Wz1fvzuaJL641fX1byWNInseety/ZWxzT8Q98ku568txfJtClTzv679CTKx7fXM6kteaKg/7GkrB129gL96dyYw2rF67ngYuep6GmifPvPpHDzt2XXkN/2N3jHyGV9HBcm+WzV9O2UxvabNG2bhv+MX428Tv77LMBuPXWW3/wnBCCIAh+8PgvhdmzZ7PXXnul77fW5p166qk8//zzXHnllcTjcc4///y0gfOkSZN+Uw+/n4tIxEEkU5BMsXzaCh5aW4mb54BW2BGJj0tSWpRn5pJjx4nKBNF+AdMf6sKos9fR8b0GEh1tVl9RSK6l6BatxJKandquZWlDRzzPIu7bxKUFykbl2xRuClCJgKC6xSyitQZfIYDSkhqw7VDpG7BuWWm4YoXPx39HLDPKsllr0gq+wFf85d3LSLaksGzJ3ec8mZ6gj+t+ISdefTgr5qxJt9YJvIBvJsykub4lreTNzMvgsD8ezh5/2OUfHquWxjiv/+092ndvy0Fn7ZN+fOShI5j4/Ff03aHnv9QjeBu24Z/ht5z3fk8o6lzAyIOGs3jmaprqWpj/9XLcrNjm6z/QICXtuxdREqpsvS0IyvLvi7n59CeJNyXMnKI1xctKqSipZeWCDaxcsIGxf9yHPkNNlwnHtcLom3n9SWFXCQCEYIfR/ZkzbRlt2+dxyuUHbuWccNipuxFvSVFb2cDJl46hpSnB5He/p66qkaJOedz+wjnUhopeLQWeNq+ZTAa4rgVScs/17zBjygrufvU8nr3rYxbMKUYLQVVNC32GdGLlklK0ELz78rcI2Ox+sGMPHnjrIi4a+xB11U1oIcgpyKKwKIfDThpFIuGxy94DKeqYxyvTb6RiUx0XHDYOFWjOubaRvY7cgWmTFtKlbwfOveEIlNIM2rFHuodwXttsbnvu7HQZTWGHPHbebzCLZ65m5P5D6Du0C5++Pp01Szcx4dmpVJU38LfxFxIEipywl26/YV3JbpNJY12cREgcg0BRX9NE5pb2J2HksbK0DqUUL9/9EeuWlzJ4ZG9mTV6Ufv7Oc57GTwX89Z1LGBqma30vYPnczbWYMz6dz8fPfkW8KUFrTLJ8iwX+/KlL+eT5KRx0+p4MHd3/J8/DJ656mbfHfczwfQbz/eeLyMiJ8fKqB8nKy6SmrI5IhvuTDhLb8G8Qv99Scbnnnnvyj9xnhBDcfPPN3Hzzzf+5Qf3CaKiqh0AhHBuloHpDLW2jban2AjJjNkltkfShJhJlrWhDxPKxhCQyKkXW7R7Drt5Ez8dqCDpKSk/KxhIpcMASAZYQWCRZL4posTOIaxDaRmaCi0C0y8Bq9HA0CO3h2o5Rp9kWKEX3AR3JzotRsqocIQTTJy5Ca00sJ4POPdsSzXSRUvLwn1/i/HtOwrYtctuEpFubFeoX47/jjQ2PsWT6Sr5841tWfV/Miu/X8sX4bwHY6aDhjL34QIbvPfifHqt3H/6U1/46AYABI/vQY7D5sdj5oO35sPnlf2okug3b8K9im9LcYN3STcyYtBCANu3zGLpbX7587/s08QNAa9p3b0PJOvODXh52v/j20wX8ZQvRwaCRvRi6c2/2OXonStZWMuW9OXTsXki3fptrtLPzM3Eyo8aKRGtotYKRAsexuPKBE03XjHjqBxZQX09axKtPT6WgbTabKt5g5lcrQCn2OWoEp//5AJ6462M+fGMGyrXBU0btiwAN5910OA/f+gFBoPjq0wWcd90hnPin/Vl69rOk/ICKsnqy8zIYOrIX82esMR9bCrAkUcdi2MjeFLbP5alPLuPcQ8dRXdVMQ0OChoYEz90/iYbaFp7460f0HdyZ+149l6ycWChC1NiOxfP3fko87rN4zjo2rqlk2bz1rFxSmiZZddUtXH/6Mzz1+VUIICM7ys3PnMV7z0/l8ZsnMPntrQny1x/Pp8eAjhx/4X4AVG6qY/2qcpIJE9lsbkwgbIuS4iru+tPL3PbiuYzYayDfT1vOdqP6MPerpXz84jQGjejJa/d/ChgLneF7DuD7KcuQliQVvtbaJZvSxE9rzZVPns2ExyZTW1nPgaeM5pmb3wKg24BOdB/cmROuOjw91HEXPEvp2gqWz1nDc/Pv+snzcNoE8/lWhqQy3pQg2ZJixZw1XHPwnWTkxHhmwT3bFv4/gf8qA+f/BewyZjvef+YrUijjn2dJWqqasbMjOLUabIHjWzS5WVQoiyw3hSs0maKJFUdCRlmKvvdX0fumKsiD6kMyce0GoiKFS4CvQeGwSSiaVC4tjo/vWmRUCXLKAlSGTaBAW4JTL92fZ240FylSUryyHAJFdttsmqqbIFRMO1GXwm6FFHUs4L3HPmP+18uJZkY45/bj2Pmg4Xzw5OfpH4W6qgaS8RQDR/Zh4Mg+3H/+03zy7JfmLSzBH+8+ic59OvzE0dkaWxpD//0Fvo30bcM2/PLo1LsdA3fqRcnqcs6+9Wg2rK2kY69NbFpfw8AdumEJ2PuoHVm+cAM4DmhNPOwUUbyFVcq+x43kzGsP58nb3+el+yey/9E7ktMuj5x2+ZStr8Z2HTp2a8PGNZV4vpkHs3JjNLZ64ymN5ylsx+Kqs56lvKSOLr3acsP9J9C1p1GkvvTw5yQTHqUlNZRuqgUJaMjMjvLYXR8zbfISAITWYG2up0NruvVuz7BdejJn2iq236U3Zx3+AA0NCYaO6MHCWWtBa7JzYpx0/j5opcnKjjJj2goCpbFjLm3a5zL7u1Vsv3NP9jtyBK89NSWd+bDdzY4IKxZtpKGuhTbtcnj4vUtYv6ocJ+qwOp1ZEWxaX827L3xNKuljuQ5u1CHemKCuuonXH5nM209NYYfd+3Hb82fTpVc7hBC4URvLNQt32wIvFdC5h+mIEW9Oct6B9xiLGK3Sn1mHn39DuLBfOnctga9Yt7wsPX8XtMuh58BObFxTwYi9BgKaVQs3MvKAIfQa2Jm6qkYOONHY0kx+4zvuu/B5Bu/Sh/qqBsrXVfPmQ5OMTaCUFC8toXhpCUN3688HT05m6G79GTq6P6VrK9juRxoBbImLHjydD5+YzJgz9qRsbSXdB3WmTcd8vnzjW7TSNNe1ULmxehvx+wn8W8RvypQp3HPPPSxduhQhBAMGDOCKK65g9OjR/3znbfiHGDCiJx27FLBmcQmxgmxOveogVm+o5ZOJC4k0SYiAigoilZJ4JMr6SB5SQKA1rrWOpecXklWepONrjfS6tAoRg7p9I8RkE45IEJVNoDQFkRZW2UkqqwpJaRDCQqQE0SaLAIFrCx59cDLHnb8f875YzOrFJen2R252BjQkcVwLryVJY0Oc7z5ZQK/BndPblIbF0t0Hdmb43oOJxBzmTF5IY00zZw6/knGf30jbTgXsfNBwPntpKl36d+Laly78l0kfwC6H7sCr6x4jlm2MoZVS2wjfNvxq2DbvGRuQk648lPWrynhx3KdsKq7CjkbAkiyZtwE3I8KiW9+ne5+2aCkRaLoNMMKNw8/cnfFPTyGV9Jn97RrynvqKL9+dC0BtZQPL529g+fwNfPnhPLAEg0b0JDs3lp5TpGOD9EzXiYIoR5w8iqfvn0R5WT1I2LC2kreen8b51x7Cl58uZIfd+7A+VLnqsKVk197tOOPPY7jszGfStWtaBfQb1pUVS0rRgeKgI7fn+ktfpak5SbdBHdnnyB2Yc91baCFYMGcdCIEQgh33HMCj933KngcM4ZhTduW0g+6jrKSWZMJj7B53oDV061XE2BN2AddOE8veQ7uy6x79mDl1OT37deBPJz1BQ32cQcO7UVVax7oV5WRkRmgJBSW+r8hrmwOrKnFjrkmTS8E51xzKp2/MBCGY9+0qtNZsP7ofL3xzA99OWsTjt74HUhBowd9ev4ChO/eiclMdN53zLM2hnU2/Yd3QWuM4FotnrwUEyZTPkrnF+ArsqMOQkb3SHn93XfwinXoU8erCO6ncWMNbj0wG4LPXp3PcdzdvZQMze/JilNIs/HYF2+85kNLiKtp3L6SusgHLlgSeyd59Mf5b1i7ayNpFG9PnyoZlm0glvR+1/gLYacwwdvoRocagXfoSiblk5MTo1Lv9D3fcBsCsgX4WXn75Zfbdd18yMjK4+OKLufDCC4nFYuyzzz68+uqrv8YY/+eQW5AFShGvbuD5m99m1fSV2M0p3LoUkdoAp1ETq4asVRbJ2hirK9qwoaUN6xLtWO0VMf/mQiqOyET60PP8KgqnxYkJRUc7RQ+3hh1y1zIgu5ze+VXkFzRAboBXAM1dLBL5Fol2NqkcBz/bYdKUZQzYfQD5nQtMAbdlUVPZBFLiecrU/1kWQkr2O36UEYVgVpXX/eF+bjzuQeZNWcrMSQtNwTBQuaGG958wE8Yuh+zA+7XP8djMO+jav9NWx2Hii1N58S9vk2hOpB9b/N0KztruCh6+5Hl8P6CpztQGfv/FQg7JOpmDs05m4gtf/We+qG34n8G2ec+gfEM11570GI/f8i6bNtWDbSMsYcpBLEnSC1BA8dpqsC20ZdEjTN0qpUmlAhCCuuomPN+UtODYCNumXecCuvdvXfgJFs9dx/QvlpLX1pSL7LhHP7Ql0bZERx2mTl3Bh2/NBinJyc/EdiwiWRGu/dMrjLv9Az6YMJdzbz6cw07ZFR1xsbNjXD/uBKIZLocdu7l/a9subVi+pNQ4G0hBeUUjTS1JkIJ1xVXcffME2nYpgIhFZl4UN9vFyonw5KOfs2plOU8/NJnZ361inyO3J6d9DjvvO5BASrQl2bC+msryBjQmi6IFFK+pZMXqSvYdO4LvvltFeXkD8bjH7G9XUbymEm1LrIhDp95FYEnuvv4tasJWbbZr065TPoecuAuFHfNYv6YSLMnR5+6Z9qvNyI7y4gOT0mlxpTUFRTkEgeLiox5kbRjBy8iNsfdRO1JfH6e0pNYIUISgoTbOZUc/TDLh4XsBxSvKDEmWktrKJhbNWE3x0k0UFOUQyzQ1lU7E4bHr36ImtPMCOP6ygxh10HAOP3sf/vzQqTz69U3c+/FVPD/vr+x22A7piOaIfYekb7f2Sl707Qou3ecv/7C068ewcNoykvEUteX1rFmw/mft+7+En92ybcCAAZxzzjlceumlWz1+33338dRTT7F06dJfdID/afwarZ5+LuJNCa458j6WzS02RCojio5F8WMOOjeGloJkhqSph0NTG03QwSOzsI6ebRsYmrOePrEyuohatr+klPxP4qgolLxUQGJnl0BDg7LY4OWxLNGOhY09WFjRgZbyTHTSwi2VRJoFORsCpK9xGn3suAdKI+tbEK1ni1LQ2sNXKYaO6s3f3vwTx/S/PO35pL3Q4kVrbNei73bdWPztcgCuef489jx6s6v7+mUlrF++iV0O3h7LtihevIFzR1wLwOm3HMNxVxpj1esOuyttxDli/+2Y9ek8djpwGJ16t2PCg58A0L5HES+uePDX/pq24XeMX/o6/v953vs5x6quqpETd7oJhTCLPjBOAK3WI3Z4O9CGSGnNK19eRWG7XACmfDSPF8ZNIis3xg2PnMzZY+4lGffYYXRfbnv6DKZ8uoBxN00gKytKdakhEQ+9eT5ZORm07ZjLITv9BV9oQzQ1IExXiutvG8unH85j5nerQRp7KB0uNDMzXVpqE2AJrrjxcGrrmtl7n4FsXFOFG7W49po3aW5KYiUD7KhNTmEWVeUNZn8/IC8WJeEHeCmfbj3aUhzWLmodlgUqzS579OP7mauJJ3xjAxP+rEYjNm98/Geuv/QVFiwwXoJoHQpBzL5b+foRNhwI9GaxiDYuCwcfMZztdulDh64FfPzmLDp1KeCZOz8CCSdeuB877tGPvoM7U11ez8mj70BrzdCRvTj+/H3ov11XkgmP43a+xQy69e3C/sFp37/A9BFGa2zbwnFtsvNiVISfuW3HXDp1b8sOew3kgGN3xrItvvnwe+679GUAzrj+CPY7ZiRvPz4ZIaCuooHPXvuOwo55PD/3jnQXjQ0rSrl479uINyfZ97hdGLBjLzauKmPf43fhb2c8zrqlm3CjDu+WP4llSVbPX8ejl7/E0N0HcOoNY6ktr2fmxPnsPGY78opy0+dnbXk94/74FHlFOZxz14m0NCT49LkvCfyAg87cm3bd2v6rl8V/Jf7Va/lnE79IJMLixYvp3bv3Vo+vWrWKwYMHk0gkfmLP/w78HogfwJqFG3j4ilcpWVtB/5F9WDx/I41xjyAnBq5FKscl3saiuZ0kmQ9Bxzh5bRrpVVjDoKwN9I+W0Jlahp5XSd6XSVSGoPy5PFKjIgQKWjQU+9ksaOlCcaIdS2rbUVGfQ6osA7fSIXttQKRZY8UDIg0+2lNs1689S75fZyYpL0AoBYFCCs0dr5xH4Adcd8xD5gMI0KHjvWnD45PbJotdxgylc98OjL1oTHqF2lTXzPG9LyYV9zj5+qM46ZojqKts4JT+l5KMp4hlR3l2wd3UVTRw3k7XbXWcdBCQ2zabB7++lRuOuIuK9VWcc9dJdOzVnobqRnY/euS29O//IH7p6/j/53nv5x6rOVOXceefX6OxMay3c0LlrQpr5YTAjtikAlN68dir59Kjz+a023MPT+bdV6eTV5BJZVk9g4d04fhz92DK50tYPn8Da5eVIoTg2rv+QKfubenSs4i1q8rp0buIc096kg0bjYXIlj53Ugp22KE7M2etAUui0QShElhqELZEBcqU+SnN4EGduPPu47j8sldZtrwMtGb0Ln35ZtpylAiFGkIgBFx5xcFkug7vvzeHIcO78uJTxghZW0ZtLDBRtVjUIRH3wsWxYaXCEkhLGp873/QLllqnyZUIWklXKIxRysyLSiFayXP4VP/BnVi2YCNSCnTY/uyG+45nwYw1vP/qdCxbMuYPO9GmKJvOXdtwzzVvkkz65LfJoq6mmcL22ViWRdnG2rSOxfRkN2PJyYtx5OmjeeFeI96QUqBCf75jzt2TITv14sNXvmHG5MWgNHsesT1XPXgKzQ1xLjv8XuoqG/nrGxfz3tNfMvFVI9aTlkClAtyozZhTRnPo6XvQOUzBnjDgMmrK6tnpgKHc+vrF6fOjbF0l7z0+mR33H8r2exn/23vOeZLPXjFdR8YXP8yNR93L8tlrGLBTb+6fchNNdc1MeHgivYd1Z/t9BnPByOvYtLqMdt3aUrLStHPtPbw7j86445+e3//N+NV69Xbp0oXPP//8BxPg559//ou3OvtfRs8hXbjv06sAmPPFYmYe+xDSdcjMy6Qx4WO5FtFaQdIVICRxK0KdtlimzGpRonAzApY8Ktnu3DIyp6Zod0ottU/nk9orQkSDKxrJzlxDhoiTYfkskD5rk5KUo6mN2cQqJZFai1SWjRVXzFtXheVYSD+cLIIAISVZOVFmf7mUtx7/wkQBgoD8ohz2P34XMjMjBCmfNx/6lJ0OGErv7Xuy/4m7/qCNngrM+kMrxSOXvcQ378+mz/Y9WfTtcuJNSd4c9xH7nrDbD46TkIKG6mZmT17I0wvuBWD1vGLO29EYP6cSHvufssev+E1tw/8Cts17m7FxfQ0+0qQShTARP8sYHuflxaipaiZoJVsIbrn6TSJRhwsuG8PQ7bsz4ZXpJFMeZaV1oCAQ8Ml73zPls8UIKejepx2j9hmIm5tBh65tOO6IB2ioj9Opcz7ry+vAkUhfoySoiI0WILXgwssP5IVnpvL554vRjkRZEiyN8sASGhyJr43dzKI15Rx+zEPEHJvANaqP7Uf2pCGe4PslJYBOR/Qee+Jz7rj9GOYs2cicJRs58tid2Limktnz1+FbMl0v1RIo05loC7amdKgIl6Ads2WgNMI8xMAhndhr/8E8NG5iqJ+w0VrjRhz6927PpuIqqsMMSvGaSrSUqJDsCkALQZt2uWggAD4cPwM0nHHp/iTDtHptjdm/sqwxnVJNU00Rjte2uOzu4xgxui/NDXE2rasmFU8xe8pybMfioJNG0bZDHjef9bT5sALyQseGzJwY97z7Z6IZESxbIrdo5+lGHY6+9EBevucj3n/6K7756HtenncnADe+dD6Lp69in2M323bFmxK4UZdz/3r8VufcHmN35tsP5jBkt/7kFman28ZZ4d9X73yPtx8wGZ+/fnQVG5ZvAtiqsUDaYWIb/r1evRdffDHz5s1j1KhRCCGYNm0azz//PA888MCvMcb/eTSEFz4pj6NO2JmXH/4cHa4Kc5RDi7ARSUlCuDTpLFZaAQntkCETxN0o9hMpBl1US+bkJAVn1NLwWD7emBiOpYmJBFbmBgqcFFp7ZDiatVV5NIscmh1JypXkSAtcQVIIMpSGFg8c0HGNEJrGhM+sKcvMGKUALAI/YPwDEynskMcZ1x9Oytd8Nn4Gn42fzscvTOXRKTemP9/3Xy42ZqRSsMsh23PBKPNchx5tTbTQV7zz8CS+/3IJt79/BdM/+Z76ykYKO+Xzzv2fgBCMv/sDDj1nX2BzazilNNLammD+PbTWfPD4JOqrGjn2isO2tYjbhh/FtnnPYM2yUh67/YOt7Ft0a8QPqGlMQoaLRqcFFRtL60AKnnz0cx5++kz+cNquvPLUV3iWxMl0ye6YSxBG2fr0a8+DL5zNxRe8yAvjZzBs+67UN8TRlqCsqhHtSjP3ZUo8FAJh3kvDupIattu1J/EIrF1ZwbryWtPhw4EgpQzHcSy00gQCkAptKwILBJKvpq8gkh0JHV0EWIZYZednpltNaglvf7GAvLwMZJ6LTikCpbFCq8LCdjkccdj2rF1TyZLFGymtbCDYQjEsdNi0Q8NBY4ay176DuPyyV9ERC+0p46NqSVKeYsHSTXTvUUh1c9Koo30FtkRqyIg49BvchV33GWjq4KTg2QcmpSOVs6evCok5RKI2fkoRtPopWq0qXhi+a18WzlqD7ytuPP8l7nr+LLr06cDw3fry6K3vM3LMUC65fSyT357Nc/d8TP8RPVg+dx3Slhx4giFs33wyn9vPfY7svAz6D+/KzMmLEY7N6Vcdwg57DTTmzWFesba8AaUUX741k3sueJYufTtw+LnGg7WxpomzRlxHQ00TN79+ETtvId7Y8YDteKf0ifT9m9/6M99/sZjhe5uIYOcwouxEbK45+G/ssN925LXN5oRrjqB48QaklAzf559bhP0eMOez+Xz63JccfsEYBu/6016G/xf8bOJ33nnn0b59e+69917eeOMNwNS/jB8/nsMPP/yf7L0N/w72OHIEga+IZUUYtnt/5n6ziiWLShBaoy1JrEpgZ0ncpKBBR6gP8hBCMN/tTnNGBZkZcZxHNX3/XE/mhwlyzq2l+SEIDs/ElZqejk+WLCFfNNLGiZPntme27kwiEsWzXZqxyKyUKFuQpSIEWkOgsUQElTCNw4vX10JG1JhPSyjs3IaG2mbTLP3Gt/F9hZACrWDNkhImPD6ZYbv3J68wm/sueC6dtpnxyXxOuPpwpr07ixOvPoKu/TtyYl9TV7V28UaWz13LB09+ARh3+u32GsT8r5YQy4py6V63ctVzf6TrgE5c+sS5jPvjUzxy6YsM32vIVq7uLY1xHr30eSKxCKOP3ImHLjTeYrmF2Rx2nunjuPjb5ZStrWDPY0elTVJbkYwnUYEilhVjG/43sG3eM5i8hWefBvoM7sTy4iq0HSpkwdT5EdbYCZAKUJoNpbUceth9dOlcwK5HDOfLzxbjKc3X01aYCFRuhE6DOqI11DfEURHJnKUluG2ixP2Ajh0KWF9STeAYQiOQBBCqczXXjHufVJgCjboOSgiUY0hcNDtCylOQDNBRibLMYJsChcyw0WiKa+uRjoUscMnTNtVNCXw0dVbA6xO/56abj+CRF6dSUl5PZUMzWoCwBdIH3wYnYjFirz5YuREuu+pgLCm45OrXWLioxBBJKRBaIz1Tv7fbnv159/25hhhaAssNSWlIAIWGjRtqUJZAKBBhKr1Tj7ZEozYbK+u5/fq3OevCfdnviO2Z8PK31NS2gBDMn7M+Le5IJEPC51iba/mUIrcgk+9nrTGRv5DYXnP2swSpAClMOrmkuIrlx+zEJ+OnE/iKlYtK8AMFfsCK+RuIxFwWzViN1pqG2mZmfmFqXbXSTP98MW88MommujiRrCjJpgQF7c1v07xpy9Aa1i8vpamuhby2OVSX1VFfbcQdq+atY/bkRVSW1HDx/adS0G5zHR9ATkEWexy9WaBz0Jl7M3i3/ly82014SZ94c4I7PrgS4Gc5RfwecM+Zj1K1sYbiRet5asF9v8p7/Ft2LkceeSRHHnnkLz2WbfgJSCnZ99iR1JTXU7x4I/eOP59rT3+aOXOLseMWwlcI7eIkJcksiRIudTqP7z2LxsIoKWUjskGOg94uZL6TIPOCWlIVGv/sHHKQuHZAVDSgrXW0t+tIKZuNDTlURLNJWBlox8FuEXSIZFCvTK2KbjTdOUj6m2v+XAeUpmRDLbgue/1hJC/d+b75sbAshDRikCdveAuN5qCTdyPenDSpXyH4fsoSOvduz42v/4ku4QV7/FWH8e4jE8lrm0tWfiZCSrRSJFqSpFIeFz98Og9e+BwIePjSF7ltwuUUL9mICv2cyoortiJ+U974jonPfQVAr+26Ec2MkIqn0qrisuIK/rzHTSilqa9q4Kg/HZzet2J9JecOu4JU0uOBb26j97Ae/7kTYRt+U/yvz3tlG2t458Vv0qRP5sVYvr4K4VooMLlDq5UAmogZQpBXkEFLQ4K4VqSSPktXlLJsZRkqaqElSF+bPCXw2eeLGTt2R867cF+uvn0CyhYmsudI1lbXIWJG1BFoUBlhIE2D5QlSvkpbtzQLn8yiCE0pH601TTIwkS7XCrObmz37lA9IwYbGJtNZzYI9du3NJ98uRWtNZXMLk75bxuxF66lpaEFHBRqBEK3qDolSGl9pJny2EKHhwy8WsXZDFSrQuBELX2m0BVoLsrNs4imfx16eSllZPdoWiAB8wtpCV5oPKCASdVBNSbAEtiXRCZ/1xVWbexNvqmfOjNW8PelKXpp0BWcd9RCbNhrPQiFahRth/WVrlFZAm3Y5tC5nW9XMaE0QmJo+pTUZ2VEGDuvCzee/gA40Xfu2p2RNhUnpF2bx1YfzuPfK8bTvXMAeh23PdxMX4PuKobv2Yd60FSyZtdaIAKXEcWxueP0ieg3uTFVpHd98NA9hWTiORX11E3ltc+g+sDMXjTuF0rUV9NuxJ9cfNQ6AHoO6cNLVh/1gAf736NqvI1c+cy5fvTmdsX866Bc66//z2GG/7Zj43JeMOGDYr/Ye2wyc/0sQb0pw9ohraW6IM/biMSybsQqZNPULwrUhFZBqn0HmpoBAWARJlxaRyQrt4AgfhIXKXI1zD3TLkWQ+30Lk5jpkqSK4IZ8sYRGzAmzqKZLN+BrmOz1ZHfHYaPkkItmkqm3WkCIrR+IkwPdsdKARtsRqSYG20J5AeIGpL7EsXntkMurvxRWW6QQCgo9f+NrcDwKEFCyesYpF36xg7aKN3PjqhbTpkM9pN4wllhHh2Zve5PErjXVGq23MstlrWTpjNdK1UX7AnMkLaahp5P3HP0NIwdA9BjBwl75bvf2gXfuRlZ+JG3HY6cDh7HbUzqTiKQo7FQBgOzbSkigV/CD1W7x4I011JvW+YvaabcRvG/5nkJUTIzs3RmN9nENPGMl7H3wPlsSzMIIKKcA2wgrta4iaa7TMS0JMYBH66bmtmc9QGGHDofsMZvq3q4jkRbnsbxOob47jxwwxRGuULZCBplu7fNbUmBSuskzqFaHxhCBHW8TjPlqBtgUNnk8QAaENsdKt22uwfCPcUEKgI5iImjYkSAt4b/ZSLEHYKk5jB4LqIIGfAUJJRMh0A0sbIawnwAHlgECzvLwSHJPa7dmvPevXV9EUuhzUBwHYsHxDpUn9OpKoDSndWucsULa53eSZem4ChQ/ImPG169Ami7L1RuTiewHfTlvB+uJKOvdvT0lVI6BN6jhsd+dELVJxzxBVKaiuaealDy7hlkteZdUKI24pKsqhd/+OTJ+6HK00BxyzE5+On2kEx0Kw25ihvP/iNzTVt1BX3cz86atBQFlJDTkFmbw061a01jQ3xLntnGdoqo9TGXZtwbLIaZNFTpssZn22iHhoxO0lfRZ8s4Ju/Y1/38Fn7AlAc0OcbgM6Urauitfu+YBp78/mpKsO457znqHv8O4ccd5+jDp0B6QUW9WLjzpsBKMOG/GLnvf/aVz29Hn88d5TycrL/NXe418ifgUFBaxYsYLCwkLy8/N/UJi/JWpqan6xwW3DZtx/8XM01beY2qIPvydR24iwbaxm0J6LdC2oS2JlOlAmSCUt/IYYqdooS+hAwo9iCw+Z4ZG4Efp1sMj8ayPOEw3IsgB1f5HxjkKSJ5M4mesospto4/Qi1y1imSNosjNoES7Ct8ioAemA3aIQKYlM+UbNpqRRr4V2C/U1zYbkuU5oX6AgCNLFydgS7QWcetNYXrz9XXS4Ml0+Zw0X7n4LLy+9F8u20ivRVuQX5VBb0RCK5wRagxN12O+E3XBcB9u18VM+w/YY+IPztWv/TrxV9hQIkbYX2BLJliQHnb0PvYf1YLejduLZ61/DcR16DevOTgcO48TrxpJoTrD3j4hNADauLOWDRyey65E7MXT3gb/I978N/3lsm/e2RlZOjGc//jMN9S106lbIynVVLFy2CeVYobjDXP4ISTTTJtlaWB9G/gKNIVKhAEJhCJ1QMHp0Py46b1/2OflBPAnKBe2GYmEhjTgCwRq/kVSWBcooXrUt0mnKOhQ6IunZLp/i8loT6Epf3ibyJTQgTYBRo9MkUCuNipCuwRMagogZt1ACJ2KTSPnokERaCfO5tGPIrA7AUiatrRHpjKq0BYs2VhBzLFCh4MPSaCFQyuxj24K8tjmUlNVBKPoQHmEEFUhq8nIzaGhM4IfEtUkrLr/5CJ586kvQcPMNbxuVsAqPMQIiEsexSCY8ZIZDQZtMaqqMgb/WUFnRwMDtu7FqVTkgiOZmsMdBQ/nuK1Ovvfj7dcRTxjAbKXjl8S/TETyhFPlF2VRuqgOtWbFgA6Xrq+k7tAu5BVk8MvEqTht1Szqq2lTfwj2XvEwsarN83jpGHrgdFeur6dSzLXuO3fEH51pmToxz7zyBe857hmTcY8PyUj585iu8pM/i6atYPH0Vh52zN5NenkaXPh24d9K1uNEfN3v+b4MQ4lclffAvEr9x48aRnZ2dvv2PJsBt+OVRsqqMr96Ybu7YNuXFlURyM0glA0iBsGxIgdvkEyQVVpNF3HNpKbSwWiTxskyKLY2wOmELRXOkiti5G+ncXpJ1WT3We82IylLU0+1x8mxysSjScbKj1dgScp1GbJGiONKOCplLwo+SzIZorUWsRmAnNEnpYrcoolGHKJrm6uZw/hFG6as1+IbwFXYqoKqkJpwUBDJmsWL+etyoQ7IlZWp2lCLZkkobeB5z6UF07FFEl/4dKeyYz4RHJvH6PR8CpM9HpWDgyD589dZ0s0q1rM32WH+H1rSBUopUwtuquftfjr+fNfPXkdMmiw0rS3nr3g8AU7dy5fMXcNpfjvuH39cD5z3JvC8WMenFr5hQ/fy/96Vvw2+ObfPeD5Gdl0F2XgaASdW6Fso1xEBp0BERKj5jbKxtNJYnYUZU+2BlWqQ8jZvQRDMjNGkPLQRfLl7LxAUrKeiUzab6RrQWKBcQoAJTS6d8jedoE7VDpL3wlBVuB6BgVX0tKhq+pzCPaTv87kJSJwPzGlqC5xqyJlpJYjhnCB1u70Oj9tFREz1Eg4pBPjb1SR8lQTjga43TYvZVjiHC0geSmr6927Fo2SaTebUxi9VAo5QxZd5YWw8RAYEgAIQ0dX5aC3Lyoxx35M48/uSXaSVvVXOc+x7/jHiLj/H+A0eYTilB2irGRDSxJC0Jj5akt9luB8jOzWDPA4bw/luzQWvWFVdzxw0T2PvQYVSVN7Bo1pqwRCcUp0gR+v4Jhu7Yi7rqJjpFHbSvyG2TxSVjH2LkPgM58eL9uPqEx4hEbQo75gOaqpJa8gqzWTBtGWiIZUZ55Iutrbn+Hi/c/i415Q1Yrs2Fd53AuqUlLPxmefr59ctLSTQnWTmvmNLiCrqFpTqphMei71awcUUpB5yy+w96OP8Ypk2YyVvjPuKIC8ew5zG7/NPt/9vxLxG/U089NX37tNNO+7XGsg0/gdmfLUirwlrRvXc7dj96Z9p0yOOl+z9j04YaMrRDswfKtXAbFSiwlIVWLs0ii2VxBx1ImvIiOCJB4rAquhVa5J9bi/w2gTioBP1CR+jr0sHOIKlTuKKaXCtJnhVnrptkrupObVTh1Tp4WQ6+a2HHNRnloKIa1Rzgtfg4BZkM7NGWdasraKxrgUAhLEksajNwl37Ul1cz/5tV4QQYMH3iQnILs3AzUjRWNeFmRbn00TOwHZtEc5JVC9ax6+EjWDF3LVPensm3H5oWQmkvL0AFivsvep7AD9J9J6e8PZOTrztqq+NZvHgDt53wIF36daRsbQVrF23g2pcuZPfQULp15dhQ3UQybG3U+h6t6r5/hN7DujPvi0X0GNLt//S9b8Nvi23z3j/GsrXlKFugbEMulATtmvq/jY3NeFGzuBIiTLMK8ITxv8vukI1Gk6r3wYI3vlmAFiG5CGvcfEeAHaZnJSgEOhRlYLfWppmx6NbL0gNpb9YwYJnHhJXmc8aJKmWe0zYQpnmFMhFCIUAr87wII3m0OlhtQQ6rpI+2QoIZktGMbDN3NDV7ICE3GmO/Qb1587uFZLR1AU2yxXj9CQTSg04d81m1ttJYsrjmMwllYXkajabaT/LA+Km0KcqgsTJuRCWWoCVMGYNA2zaBEOw1qi9ffb4EjenXe9QR2/PGq9PTx0eHwcCjjhtJ1+6FvPj0FBOxDZQRj0jBF5MWgzJiwmRjku5921G8sjx9/ISA+TPXAhCNObw+7TpuOOMZAOZPX8WAYV1pbojT3AAde7Rl05oKsC0WTF+NFY3QZ1AnmpoSLJ+3jn7DfnqO3OfYXVizaANjTh7NgaftQUNNE82NcYQQjBwzjO6DO/PEVa/SbWAntNI8cMmLdOvXkWdveoNUwnjI1lc3cfK1R2z1usl4iu+/WsyAHXuTW2gWdk9f+xqbVpdTubF6G/H7MViWRWlpKUVFRVs9Xl1dTVFREUEQ/GKD2waDjStKTXrUsszyF8HOY4aydv46ls5czaYVm8BxiNe2QIaLcCS2BOlJYjUejh2jyY/iV7iskJomP0K9H6Elew3eLiX0eKcNbc6sRRZ7cPAG9OPtkftkEiVCO1sSFU1kySaiMkWAw0q3gJqMLBpFFgnh4tYKVMTCbVBELBCpAK1h4foqVNwzAhBLogNFPBkw9eN5HH7qbiyeVWyMTaUpPq6vaTHGpRGXVNLnhb++z+jDR3DtkfeyZMYqdjtsB777aC6Br9hx/yE0VDebFj8hIbbdzbN7LDNiPKFiLs31LWTmZqSP50u3vc2G5ZvYsHyTSS0DX705ne33HUJWXiaXPn4OF+1iVqOHnLMvaxetZ9G0ZUQy3B9N72qtaahuJKdNNkIIzrn7FA4+Zz/a9yj6wbbb8N+JbfPeZlRUNPDQo5NJKQ2ORNumtk1JgXKEyWham0mGDiNyhCpf7WvKW5pNw4oME9Xa8nkRQOc2uRQ3NSAs0qRL2Yaoaczrt8JE90ELjbDDWKA2u2kbcMMWv2G0TUuQliF5rTWEQpvbilD/oI1NjMSkZDWY6J+CVs2EUua1tTLPyQBqtWcikZlmzGV+nDcWL8TLhJT2IAARE4RvCxFY0VJH976F0BSwurYOMK8pFIZUhxHTSi9FRoFL0g+MV6sCoc38KQHf1yxcXYqXZYPS+IHi9U/mEm2XSZucTDasrwq9CQXDdzS1ydOmmAiaHbGNWjckf/iadt0KKd9Uj4y5uNlRki2e+dDaqItRmkTco3RDDedefxjvPDOF3Q/ajr7bdWHZvHWsWryJTetrNtd0C41SmmULNhjiXFbPoxOv+sH51dKUYMKTX9C1T3s+KH0s/XgsK8qG1ZWsXVLCXsfsQsceRdzyxiUAXHXYPcz/ehlOxMZLeOmAQCux2xL3X/gsX7z+LV36duDp7/8GwJjT9+Ll29+mc9+OHN7mDE64+ggOOWdfls1cxeDd+v9LUcO/RyqRYtnMVfQd0WurjNLvAT+b+P1Uo49kMonrbvNA+zUw9uIDqSmrpfugLrxy94foQPHp81OoKKkzNSgZMVrb7shUgNWURCsXEbVNWH+TT6TIwnYEDZtiFCcjCAQ2khbtoHpvIHi/DUXn1SOnJ+HkTegbChF/zMMVDgWWxJEJYmITmdKjyO3GupY2LKKI+qxsEpk2GesjJAskQgmUGyVSl8LyBVbMQTe3tgFqFcEpPnh1OkpaGOOq1nyOUf6aicWi+0BT8Fu2vgoE1JTXE4m5tDQmmPXZIpyIjZvhkGoxqzs/ZX58O/Qs4vYJl3HxnreyZuEG7r/oOa578QKUUlyy560sn7OGcJ3Ojgdsx6yJ85k2YSbfvj+LOz66mu33HsL4kidAQ2ZuBoN37ceiacvoM7zHj6b7xp3zBJ88+yWHXXAAFz14BkIIOvft+J84NbbhP4Rt895mPPn0V3z93UpwRBilEmhLoCwTsdJhPR7hYQk05nYraYoIgpA8tZI91RrF803adK3XgI6ZsjhtbSaPhoyFkTfHvGY66idDthfX2IAfCV9TAeG6T6QAS6AzWvPPQBCuPUOianLG5nmjQQtVHwLwhbE6aXV3DrYgmtqMz/bEZnKqoUWF854yqV+pwukuMMeqWQYsbqjhnD1HsPr9WQBEozZxN8xcKEMqLQEtfhCKUYQp83FtlFbgQ/e2OZRWNpjhOwIsSGlIplLUl6cQrkQojfChqqaJBx6cxOqyGmSWQ2Y0Qm1ds3kTXyMcmzVrqxBas3JlOShlMr22hQ7rrSNRi70OHEK33kVoDZf89Rjji/rKd2wsqSeWE4OyevMbZdt069uOeGOc8vXVIKAoFNP9Pd55/HNeufdjAJ6d3pUO3QoBqCypZfncYgBmTFrI8D0GpPcZsFMv5n+9jP4jerJs9hq8pE+vIV348LkpzP1yCXWVDYy98ABGHzGClsY4YPrJt+K4Kw/juCsP4+S+fyLRnOSjZ77g6wkzWTl3LaOP2onrX/3Tj45Va803787Ccix2OWSHrZ677bgH+O6D2QzfZwh3Tbr+R/f/rfAvE78HHzS9T4UQPP3002RlZaWfC4KAqVOn0r//r2M2+L+O9j2KuOG1SwBobkzw2avfsNcxuzDhscnktc1BRl3KS+qMoCIQyEBiK5NS0I6FTNlkVCoCFxL5Efx4QLEsoDaRQVOhS5DrkMhei/9yHu1uasZ5pQlxaxV6QQLuboeVZZGjYlh2EltUkiE8sq0WUtqhIpZko51DiyWwKm38qCRSJ/CdCNEmRfuibOIb6kjFPSxLokKfLbOyDJfeWm3uGQmgNZGYy4mXH8rC6StpqI+DbbPXsbvwyQsp1izcCFrjJX2G7TGAeVOWkpmbQXNjHJSmtqKBjj2KyMqJ0VTbjO2a03zNgvUh6QM74nDaTWPJzMtg1sT5ZkiB5rbjHmT8xsfIzNkcITz9L8dx4Bl707ZLm/RjWus0Cfz+i0Xm7+SFv9YpsA2/EbbNez9Ez55tEV8uwY44NLuawAEcgS9ARURaPJEmewJDzkLxh7JJ9/wWmBq91hSuipCO5mml0TYEjk7/UmlLE7TuKDCssvW5cE2mouCZqj2zkUmSoC0FURCeUR4TpnmJakPEtDREURumJpBp4pVuz6E0OjACDxBoNywaTJIehxfVSM/UD4pWYqkFCm1Ib8qIQ1TMjEv4Rszx+LTZHLPHIL5Zvo6KeAtBACiBDEAozY49uzB7yQajkbMgK2bTFPcQQqIdzfa79+bjLxYSxH0QIAOBSmlTZ+kAgUB4GmnDXY9OJPA12OaxvMIsahqMB6CQGq3M8dNah5FOSffeheTkRlkwdx1aaU68cF+OOXVXli3cyNXnPk8i7uHYAp0KCLyAfkM6M3RkL8rWVxPLjlK8qgIhBNJ10MCYE0f96PnVqaeJqmflxsjaIlPToXshx//5IFYvXM9hZ+1lvnOtue4PDzJv2nJ2Omg41z17DufufANl66porG+hYn0165dtAqV45qY3GH3ECC577CymvDOT7ff+oaHzmbcdxzsPfsLYSw7i2RvGA9BQ0/RTlwLTP5rLrcfeD8Bdk65j2J6D0s9VlxrBV3XJ70/49S8Tv3HjxgHmQD/++ONY1hZtWVyX7t278/jjj//yI9yGrfDHv53IH/92IgC7H7kjl+xzGxrY/aiRLJu/gYpNtYggIFAaGXNBaaJVini7GBaSnOKAVL6kPhqlrsViIe1RaOLZNsnYGppuh+79bSK31CHebYKFSXimA7KfS0w7dEIQcevIlAmavSht3Dxy3Tgr7I7EIw5+rY22XQIHpBCUN8aRuS5k2Tg1CSzLAccm8HxE0gvb+1hm9lNhqsG22PnA7bhg37+afu+tjvNCsG7ppvTtP/7teD586gsQgubGBLltsqmvamCPsTsxa/IiRo/dmW59O7Db4Ube37lvB9p1LaR8fRW+HzB+3Ec8v+hedKB5c9xHbFpVRktjnFQ8heNuvjSEEHTo2Q4A3/O5fO9bWD1/HbsduRPD9hrM5c+cxyfPfMEh5+73nzoNtuE/hG3z3g9x/LEj2WP3/vzt6cnMXLbepHcto8LVdhiNsw3BA3NpW2HELIhCYGNUukobUhOSKt/Rxg4lDPprpzVFEEbz0MYEXhC+oAZpzJAJRKgeTleioX1txBi2QlvaPK+MfYvZF9LqLwkatZlQKkMAhRDoFGaQoZpEh4RTeMKE7ASmk5FSYQo0TF/bIk1ig8CMRSmNcCHIwhj3CRC+QIQK5iUt1WzQzRAJo4MafA34gkU1lUSjNs3aRwtBDQEiM1Qqa8HrX88HCVZUIj3jG5gbdWnwPTNeAdoxqesgFGsIJXAcwQUX7cOlV443aeOwP68OS3BEmE3qO7QzE9+bC9L4GD792BeUlNbyxUfzjYJbQjIVsMNOPVi3vIwjTtmVPQ/eDoCrT32KdWuqjBI7PBcmvjWLnfYagJSSOV8vZ+OaSg48bmcWzSmGiEtCsVWGRQjBKdcclr7/8r0f8/WH37N+yUYAZk1exKLvVnL/Z9eyfG4xKlA8csUr5BflsG7JRsacvDsAOW2yOfTsfX703N7zmF3SNX69h3Vnxiffs0dY+/1jiGZG07cjf5fOvf71S/lq/DeMHvvT+/9WEPqnchg/gb322ot33nmH/Pz8X2tMvyl+6ebuvxSa6pqJZkawHZvq0loevOhZfF8z58slAFzz7B+J5mZw8+lPGRNV10VFbHAsNJDKjaJiFl6mIMi0qWsPXoFGZ3tkd2yiZ34NO+Uto3+0lCGROjp875N1Xi2iLEDHBPpvbVF/yEZpRUr5VCmPVV6EmiCTJfHOzK/vwbKKNqRSUfx6B1nvkFEpceoUmTUKocCqSeGkwtW0Fxjj6SBApHyEFxK/VndWIcD3QWv2OGJ7GmtbqK2oZ93STSjPp9/23anYUENNeT1u1CEVT5GVn4kOFM0N8fRxyynIQquAgTv1Zvu9B7H3H0Zy19lPMPPT+enawKuePpcd9h3MhIc+ZeAufdlpi1ZBf4/StRWc2vfirR7LaZPNpU+cw66H/9CWYBt+G/zS1/H/z/Pev3us5ixaz51PTWJFogHtmvStioR1dHZopSLN7QzH5sFDDuCMDz/cTK4E6ECT6dg0CT+s8wOENkRPKrBD8tfayiIUUaBNZEoQIGSYOpWt6dvQTzTcVyuNEAppGxsTlOl3Ky0wZBLS/XVFGMnXmsCz0NpCmJBbOF4Zvn9Yh5cQhqDahLlibcYGRskSmG4cRk1rnhYBEAepJdoyhFD4YnOaOTCpXJEKP67ht1gpeO38Yzn17vF40hBkoU36Op1aDkwqXBi/GjJtm2STj5bajDeMMNphb3QCsC3JCYeN4OXx05FAbjSCSvjEw65MIpyWEZBhSVTCN/WdQhhRSEgMTRtRuPHOP+C4Fj16FdGuQx4AX344j7uuGE9em0wG79Cdrz8zv1tZuTGuuec4rj/9KdBwyiUHMPG1bynfWANCkpEd5YBjduLjl7+hfbc2uBGH3gM7sXjWWtaH/Xgt20Ilk0QzXB6fciNFnX88hfx/he/5vHDr26TiHqff+od03d6yWauwbIs+w3v8Ku/7c/CvXss/m/j9/47fI/Gb8uZ33H7CA3Ts1Y4n593N8ze9ydsPmBqIA8/e14T1haCgQz6T3pxFdXWzKWx2bHAclCVQGRH8bAcVkSTyLLyoJN5GoDIh1S5ORrsEvduU0i2jnlE5SxjoVtO+XlF4cQPW1AQA6sRsglvb4Mc0gVLUa48yX1DmZ7A42YNVTQUsq+9IaU0eqaYIVpVFpN7CrdVEGjROEvAVTkOA5SuEF2C1pBBa06V7IU3VzdRV1EOg0kXEA3foztKZq8zkGZ6q2+3al4vvPoEzRvyIHcCPnc5qswfgkN360XdYN94KG3qjNfudNJrLnzj7J49/ycpSrtj/NjKyY9z7xY2898hEZnz8PSvnrklvs+OYYdz+wdU/74vdhl8Nv8fr+PeKn3us1pVU8/zbMxjcrwM7DO3G2LtewMPwHJUZijAk6NCEOE2MhPHuM0V6pjZO24QFbGGqVrZG5hTCDQwXs5QhaQqkpU1ViAJhhdE+NJYMiZkOo21hmlLKzfOBEBKlFJZUSGn0cn5gm1SpGR522HQ3UAGuLfB80NhI49iMFxD2AQeQaCW2rFBBCJEuVw58AZ4Mo4EaUkbklpbHJglLXDDK4zByJ5U06V9lUsACgQzL0QZ2asuSkkqTQldgtZoOhKTR8s3hTKucNeBrY63lG4WyUMbqxfIwEU/LWMtIQPianJhLKhWgGlJIDQW5GdRUN4OArKwo48adwFlnPkOrgCLqWKQak4hW/0ABBIrs7Civf/hnZnyzggmvTmevAwazz0Hb8fH4GTw9bmJ4kDSdehRSs6GaeEuKvY7Ynu136cWjN7xDIm5qt1u7ihAKqIQUaD9ko+GxHL/wDlzXIZphCktTSY9lc4rpO6zrjwornrrxTRZ8s4KL7jmRvsO7px/XWjP1nVkIKRh9xIitIo7TP/6em/5gMgB/fuwsDjhld7TW/OX4B/j+yyUc/acDmfv5QvY5YTcOOnPvH7lyfn38q9fyv9W5Y+PGjbz//vusX7+eVCq11XP33Xffv/OS2/APsOgb46ResrKMusoG5kxegNYa27E5547juGDXG9m0pgIrGjGdeSIuODbCN3kUoQQyUFgtPgQWkUBDvg1CkgxAORHiccHyoAPV2dm40qchWkaf7DL6vSBo85BLZFwD8pVGmJlAPVKIHuySo2xs2ycmWxBiPe2sWjLtgDXRFjY15VKdmUNLlYXnOnh5hgC6TUb5JxMKKymJaI1QmnXrqk2KIZwYReipsOT7dWDZQNA6s1K2sZaX7/2IEfsPZemMlTTXhxG+1olHayzbon23NiSakmRmRakqraWlIc7CactZOG05VsQhSPkIKTj+ysP+0eFnzuSFVJXUArUs+W4F+5w4mpNvPJrKDdW8//gkZnw0l6P+i1sEbcO/hm3znsHT47/l8+kr+PD7ZejxoNoY7zkVCdW9tqnNa03VaqlM7ZwVhq4I9RI+EAujdEqA5WO5hhhqP8CJhvVlUmBJTaA0GREfEcp8zW9ygCUEngIpZChKVWhMJ1/XVgRK4CtBxAYvMOIMIQxp9AMP11ZIqQiUDDmGIUqBlihXEpiebqQCgZSamBsQKEHKs5ECUr7AdTarugMl8QIbN+KhkahAorUFMYWfAmEJdADKtsLaQA0RjVYmkhl4yog3kmFtnhSIGGS2CBZVVELU1CMDZDkuTSkvJMtA0tQ40koaBWALggCk1KgECMe8bkbUoiUZmOCrLdCeBkfQ1JJCC9h9n/5cd9GBxOMpnn1mClNnrKJb77YsWrEJbW021W5Rip1G92H27LW05nJlAJ4XoLTmqQc+o2xTHaUltUz9ahllJTUo2zKRQmDjxhq69Cgi0Zzgy48XsmljLQ++fyk3nPE0ZaV1JuqIANe0fOneuy1l62uQQtPcYIISZ+11B/uO3ZFzrj+CIFCcOfovVJXUUtgxj1te+CNCwFfvzGbvo3ciJz+Ttx/9DIC7L3iOI87em4NO253pn8zjrnOfId4YB6257e0/M2LfzXWAPQZ3ISsvAy/l02+HngA017fwzXvGWuzdRyfRUNXAyrlrfzPi96/iZxO/zz//nMMOO4wePXqwfPlyBg8eTHFxMVprtt9++19jjP/zOPbKw0i2JOmzfU+KuhTSc0hX1i7cwKBRfcnIjtFn+x5sWlOB7UhSCX/z6kgDMkAK29iqhNF5qUz4XnoWMglKW8icCElXsynpMBOf6twsyrwcvKxVtL9Y0GOETfYl9ciVHu7BpfhX56POySQqbYrwUXYjuTJBpkyQ6fSkwG1hXbSF0kgucTsHP6XQvmXaBzVJhCNMIbiIYCd8bCnQCQ9hCYStIB5a16fTP1Yom9OUb6yhfEM1aM0uBwwhJ89USU985du02i7wFXd/cCXrl5cSb07Qf0QPPnv5G569+S0AlK8QUpJbmMVdZz3J9S+dT9vObX70+O/xh5HMnjSfzNwM3hz3EYu/XcEh5+7LgaftxVl3nMBZd5zwHzoTtuG3wrZ5bzO2H9yVz2esQElTs+cBgWUifcoxpE+5IdkTClxliEmYxpVhhM/UnVmoIFysRQIiEYUUGqI+USdAI0imLKKuwpFJYk6AEOAFEsdSSK2wLUMgEoFJwUZtE9XzAomnXdABGY4hOEpDIrABTZaTQorARB4RJHxJxA47ahj1Bg2pKEJqbBGgtaQ+KcmJaFMjFzXRxUAbYucrYZxQlCQVBFhSI4WHDomn0hLPtkh5NpYLSgXoKKa6xZfowAGt0C6QAlxpFMm+QDsSLypIpcx400pf1yZo8Iy3nwbtbEH4wlQ72jymhcCPgkyZyGCQaZNqNqlau8Vko9t1zKGiqRm/JeCLeav55rzHSCUDencooAaf2mWbmLtsE4VFmdTWt6CFQASaGfPXIWxpxqE0o0b1pinhs2x5KfscOJRXXvqG6qYEVXOKzeAiDjpQJpWtFBs2VJuTSwpc16ZLryJ6DetGWVUzjmMhggDPM1G+jRtq8VIBO+7Rj4aKBuprmyjbUMMHL0zjnOuPYOmctVRtqgMhqCqt5/Ij7ye/TSab1lYy6/PFPPL5NXTr35H1y8vYsKKMh654hb7DuzP59e+INyXSkczM3NhW5327roV0HdiZpTNWM/XdWXQf1JmsvExOvWkssz9byOBRfXnnwY/Z75Tdf8Wr75fBz0717rTTTowZM4Zbb72V7Oxs5s+fT1FRESeeeCJjxozhvPPO+7XG+h/Bf0OKKPADVs0rpsfgLrhRF6UU5euquHS/26mrbARgpzHb8f2MtfheABEXbdtoS6IdCyvTJRFzUI7Ey5K05EmSuQI/W+PlKWibJDe3kW65tYzOX0WXaCX9nSqK6hWFVzXgTjSrLH+3CPH7C/A7SDytiCtNXQAr/Ww2pjqyIVHAysZCNjXlU1Wdi26RZK5xsJtNDYzVrIm0KKy4xooHZKUUXm0Lbdpksese/Zn4+ndm/EKEwg9lakrCuh2AiGtx0hUHM3vyIuZ/bTyptFZYtuTej67kkgPuBGD3I0Ywb8pS9jhyBIlQGR1uDMB+J+3GZY+e+YNjXbK6nNXz1zHy4OG4EYcji86ipSGOkEahfOnjZ3PgGXv9y9+dl/KZ9PxXdOzdnuE/oirbhl8Gv/R1/P/zvPfvHKum5iSH/+U5qpviJLNC0YalURkmVatdDW6AlgrhAAQIIbGcACHDFKzwiTjmei6ws2hQ9QhMdM+1UkTCtKsUioilUVoTsQNsoVBaYaOwLCO5DTRINFIIU78nIdAWqUDgysCoSY2uFkdqUgHEbEPgXKGQIqAlsLERWFLhaws7bPORVAIviKCFIFAaISSBkkbvoTWeAl/bYZTQRPwCrbCkBmWRDEDjoNF4ShAol0AJPCWRQhMoQdKTCARBIFBhE2KlQAcCAmHS14FEBGFWI8AIWrxQd6LDx+ICS4iwHjEsiwxb9IrQiFqEtjIiTDEjIcuxGd6pA9OXbqDV17BVECKSoYm1xKSNwxpH4ZnuIvg6TPPqsEZRY2nQmHrJJ+4/mQvOeg4AYSQ0aYGHjUCnfJOtCRRCQ5u22bz4wSXcfPErzJq6nKIOuTTUt4Q+gpqMqEW8Mcn+R4/g0r8ew6wvl/L8XR/SZ7uuFK8sZ/ncYtyIjUr5+F5AZm6MQcO7MXPyIkYfOpxrnzyLC/a5nTWLjCgkErV4dubtbFpTwV1/fJqKkloGj+zN3R9ckU71fvDUF3z++rcsm7XafA4pePTbW+gxsMv/7eL7hfGrpXqXLl3Ka6+9Zna2beLxOFlZWdx6660cfvjh/9UT4H8LLNui34he6ftSSjr0KOKCe0/m9lMeBWDQzr1ZsbiE+qom0KGIQgrwAlTCN+mOQBI4NrFakIHE8wWWZ5PwBfUtDktbIjR6MXYoWE88I0rv7EqSTwgKX4+QdUsD9rQkmfuW0XJbHsERUaJSkI1mgGgiXxZjiQQxK8CyIOakKK/Po9kBGixiFRLXEuBYWFIR09AsBaJtJl5mhPfe/57uAzqzcWmJWaYLQTTD5eQ/7c/MyYuY/63p+pH0FM/85V269C4yaWIhGDSiN5eMO5FEc2s6TjD13TmA5vM3ZvBO8YNISzLxpa/DamfB1HdmpYnfhEcm8e2Hczj52iO4+Zj7aW6Ic9RFYzj3zhPoPrgrS75bmSaela0rVaC+qoF5Xy5hxP5DtzKM3hJ3nfYIX43/FgTc/NZlWLbFzgdvv1UtidaaaRNmIgTsesRO21qF/Q6wbd4z0Frz3HszWLephuxolOrGOMoJBR2WNvVsliF92AqBxokacYQtU9h2gC01lqWRQuFKhZABQjbTJmQmMUehFMQcDwsfRwZEQiGHK1WogVAYb+MAWxiiZQuBp2wCLLRQWHi4AgJlAQFOyHZsIWgJLDItj0AL3LBGMKSZJJTAEgAmSqe0BtGCpyS+srBkQEIJJBYSTTywaA4yiFl+SGoEAoXSkFQ2mUi0TqEQJANJSgUEytQVKi3xA0mmKwgUJAOXIFAopVFaEvgWSpttCcLawiD0xQnVvlpLMx/5gCvQvlHrtlrJBDZYQViLGJK/wAZ7C0V1Iz7flmwgcFudawypTGeJvFC8YYP2MKzRktgBoYm1SctbPmAJfLSxj1Hwl/s+ol3ftlRUNJiIpVKIwJBLXwMRy9QdSgu8gKbGBO+Pn8GsmWsgYlNRa4z9sSTZ2Q53Pn0m61eVM2q/wVSW1fPFxwtYX1LPmrXfm7aglkXKUxx68m588MLXNDclOfmqQzjlqkPo3r8jX38wlzVLNpn6SjR/m3AZBe1yKWiXS5uO+VSU1LJ4xmoCP8B2DEV66vrxpBIelmMReAFaaa478j5eXT7uV7/mfg38bOKXmZlJMmkqSjt27Mjq1asZNMh411RVVf2yo9uGn4XRh4/gmbl/paG6ib479GDE/kNZ8M0Kirq15fbznkcnUxBxESkfqTU6w8Wt8wiidtiHEVMvqyVJNJ6IUWKZREiz79CSaVEXqafbsfW03dmh8E/1OAs8si6sxXk/SuNfc4i1k9hCI0WSmCil3KmijV1PSUYOa2PtWOkW0OhmEs9wSdbaROokVpZFUA2Z5RrpSqriKWRuhA0ltRS0z6Gm3EQxA2kx/pmvaaxtBscxUj4FWJINa6rSPX6XzVnLxy9+w+CRvRGt9htKo7Vm2O7Gc+3k647AsiXTPphLQ01z2tXB93yeuOZVtIYH/vSCmfTZHGF0I6YlU1ZBFsdcehCHnb8/YFTXV+x/O8WLNrDDfkP560c/FHqsW7rRkD4MWb957L0AXPnc+ex3yh7p7WZ9Oo9bj74HgDs+uY4dDxj2y5wg2/BvY9u8Z7C2pJon3zLn8Jg9BlBLghYRNxGmsLcuTgCOj3QBFK7jIWVAxAmMw4n0ido+tkgRCVuOCTQxO4kjNY5QpAJNvpvClh6O8IkIY2Fi/J4VMWF61AahS7IRX0iaAxsLiStT+NrCET6OUARIfC3JlCmjPXDM6yik0Z0Ik/IMtMS3Ba5QaK1QCDxtE2ATaJtACYRQJJVAYSGFJlNZtNFJPG2Fa1SFr2y0ECidQiOI+w4+Ftm2JiCOpyx8ZeMpC6VNKthXEulrtC3DdLaJ7qV8QaAEWhkSqJTA9wQ6sM0veEoZP1Qbo671hUkRt3oTKqDZROqEDNveOYYjtUYACbYwsA7ChaYdCmU8E5kj0GgpUNJUUBJoPMAKCH8/BL6lkL5ZTGNpRKBZt6m21QnHGHhLGZpt67BVngRfo4H2HfO4/qYjmDltZfqca22/KSQMG92Ph+/6mLGnjOLxuz/m+29XUr6hNl0vCNDq1lDQNjt9/5ZznuOlb2+kZE0Fd17wQrqbyK4HD6fXkK4AlK+vYvmcYoSQ7HrI8DTpAxhzyu5MfGkq7bq3Zf2SEsDU97U0xpn23mwGj+pLx9Dyq2RVGTcffR+FnfK5+a3L/q2uH782fjbxGzlyJN988w0DBw7k4IMP5rLLLmPhwoW88847jBz5+/Or+f8NHzw2icmvfs1ptxz7g1RhECg+efZLytdVsmj6Shpqmtlj7M4ccOKuaM8UfsRyosTjAVagUJYEW5rajAYfmZLYCQvhg/Rtkr4k2SwoTtlUNWVTXpDF8JyNtMQi1HerpvEdi/aPNZP9YBORSQmcGUkabs4hGBslKgSFVoqo8HDFRrKttmTaHjluM8uj7ahtyaLFdUlEotiNFkpYqEyJ06iwEmC3mNZPZQqszAhSa5LJAK+2JVSiydBuX5mJKqxrFJaFE7F498kvmPnZwnS9RiwnxqCde3Hkefty13nPMuXdWcQyo4w8YCiTX/uOXkO78tFzX9F7u27EMqPEm5PUVzXy0NRbWDWvmF0PM67sR108hnlTl9JU30LbroXEMqPUVTZw5pDLaaprBgx5/DHkFuYYo+n6Fo6/5khevf3tHxUhZ2RHt7gd++EG2/Afx7Z5z6BDYS7dOuRTUlnPoroqqpvj6BwM8RMa7ShwNXbEx7IxxCgSx7E0rkyQ6Zr6u6g0EThbKtCQVBb5boKo9BFC4wqfDMtDEhCTcSxhIn4xEaC0MN7vKCxh0rIKhYtGOpBSFlpYBFoTFR4pbWGJAFcEocjV9Mw1r2n+WqE8VAEqbNcmhEZri6SyCRBoJCltIzBRxyYtcbSFZ0m0NpITX0szHi1RwtT9AWTKBAEOgW59fUkisPC1Tdy30LaJ3EVtP4wyCnwt8QKJa9mhQMXH9y2QEs/GRAUVBK5EB6YGUfvCzI3hh9GBgkCiLRN9kynSfYhNnaAwrd+Cza3vSIYaHMsQSCkg4UD7SCYVjS2GHPogbYGwDTFrTSNLJQlaldSBwJLGpMHyN1fstPopCq3RgUZYhiSioLS6iSefncqllxxAVnaUxQvW8/XXKwGN9jVTv1wGSrHkyjdMb2Fl0tFoyCvMZvT+A/nw9Vn0HtCB4y7cj5fun4hSmqryBu669BV69GuPal3lC8E3H8/n4WvHc+TZe5GTl4m0LQI/ILsgM33Oz/xsIc1NCdp2KWT90k3p35Rhewzkvgue5et3ZpKdn8mb6x9BCMHX78xg/bIS1i8rYcXs1QwZvbnDSOnaCq4+wDhE3DX5BrLzNxvC/yfxs2v81qxZQ1NTE0OHDqWlpYXLL7+cadOm0bt3b8aNG0e3bt1+rbH+R/B7r/E7KHYiXspnyOgB3PfVzVs9t3DaMi7f73Zzp/XiB/704GnU1Mb56MVp1FU3QcQFy0JZEh1zUBEbbUvz35U0tLXxsyTaFSQzIVHkQ65Pbpsm2mY30i+njO0y19PLrabAjtN2RYJ2l9fjLjCEJ76XS/WduSQ7WvgKWrSgPMikxs+gwstnTbyQdc35rGtsS0OTg6rOwGpwsBsho0rjtoD2NG5cIX2NaPRxvADhBdjNSTOTiLR3A3jGAyy/MJvL7j2eNx+axPxpy3GiDl4yJGG6NWJnk2pJhQ9prn32bDr3bMcnL37NB09/iROxOf+vx/HSne8hpaChponTbxzLkWFkb9akBdxw1L1orek+sDPtuhfiJTzmfrYQ0Ox2xI6cfecJTJswi97Du2/l5A7QUN1IY20znXq3Z8HUJdSU1bHHH3b5QTp3xZzVCCHos33PX+K0+Z/DL30d//887/3cY6WUZtG6Mk545HW0gGQ+IMFzPcgPEE5ANMPHtkxUr01mHMfWZDspItIjw0pih5nKDOkRkT6uNNdpUlnkWXEiVopsGQc0EeERkz5aB7gSBAHSOPiF2q8AHwe0RAojCKlXESLCxw2Vvw6G3Xha4AC2MMICH0UECBAktENEKDSKVus6pW0CpFH4IkgpG4WNj1EQKwRKm8ihpy0EJurnYZPSFhphttMShSSlLZKBhQA8bTxukkoQYOMpaVLV2iLhO8b+RoNSgkBDi2+jsU0K2ZOGPHo2Sc9ElLSSRiQSkk2tMGE8X4SLY0E+MepakhDWBQol0p5/IhAIH9xkWFIddh4RgRGSOFJAs1FDa926jzakLjD1jqiwX0rY8cMsyAUZSEYP78HUWavxAlMCYN5bhduZfWQAoCkoyOLPl47hw3dmM2P6apO29hWy1ZlLa0Ro+XXGBfuw8+796NKjLRcf8yirl25CCMG4185jzpSlvPzgZ+l9UIpI1CEjy6W2rCH9GxLNcHlt/h2cOfJGasob6NC9Lc/OuBWAwztfQLIlRSRmk2wyUf97PrmK/jv24rQhV1C5sQaAj+qewbZtStdUcNuJD1DYsYDrXrkIN7o54vf+YxN56MJnAfjLe1cy8u/avP1f8avV+PXsufmHKCMjg0cfffTfG+E2/Fs44LQ9+ezlqex70ugfPNdtQCdyC7Opr2o0KyHbtBz64o3vuOPdy8ltk8Wj170Z+iGJsOZPIfARykb7GqXBbQiwfPAyTBrErbPwFDR4GaQC44/V6Lk0Zzt0cWtp6tVEwzs27Z9upmBcE7EvU3Tcu4qay7OoPy2DmCXoIJrJFAHZVoKYSJHlxmkbbWBZpDP1sRSNlTkEWZKUa5FRL7E9QdAksJIa1xWIBontWbTtWUjZinIzuXhG8CEsCSmP2uombjzjaf5405FceOdx3POnF01vRyHSaWDPV+naDqGh/w49KepUwPdTlhrXeimpq26ipnzzpDD+vo/Izs8kIzvGo5e/ZFIS3dtSvGQjxUs2pm1mRh06gsuf/iMv/eVt3hr3EdKSvLHxMXLabG4UntMmm5w22dRW1POXY8bRVNdMYccCBu+2dduvvjv04pdAEAS8cdf7JONJTrhubDpVvQ0/D9vmvc2QUtC7YxsijqRFmK4YylIQVQhbYbsaxwqIuR5ZkQRZjk/M9sm2k9gywBGKTCtFppXAkQpPSdrYTURFCkcERISHJRSuCLCFj4tR+mqEoVxCY+Gb8lxt0sQBJkKmsFBoCqxGlJEhGMWwUFhoFBKJxjfufCZ6JyQBAXk6bsQhgK8FgbZR0jO1dlh4WqKEhw8kdYS4hgigQ3GJMqNCa0lCKWKh4MHTAk+7aCQZpPAkhjwq87oZWpJQPsLerP5N2AJfOyQDG61tAsC1Anzt4WsL17LwlMC2FBmRAC8wtYx+IAgCC6UtAk+ilATLmDejJbV+M2Qaiy8dkDaXbvX+ExYkbZDKiEWMX6Kx3lFoRBRsH7QkTBML04JPmYS98I09l+WFKWRp3j87N8ba2nri0ljliMBEbbEsE/XT2ohTlFF91NQ0c/31b2/+HVMay7bRLb75TqUIDakF075aRrvOBSitWb26HMLt160q5w/n7sW3ny1mTUgGNZCMp0jGUyZyGdp/Ka1JxFMccvqevPnQRA46Zbf0+d4aPMgtzGHPs3dkx32HMHhUPwAyc2NUbjTnWFlxFZ17t6dDzyIe+e72H712Ro8dybQJs8jIiTHsNxT3bTNw/jv83iN+/wzJRIr7z3uG4iUbaaproSLsE3jXx1czZLd+vPXY5zx327umRs4xdSg64oBroyxBEHUIYhZBxKQfWgokQaYgkQFBtjb9JTN8inqU0TGrkV6xarrE6untllFkNVCwJknHq+qJzTHmm8lBNmW359K8vUsqELRoSa3KojmIsNHLpziey/p4O9bX5VLfnEXQaGPVRYmWhumBFDhNRvkbqfOwEEgvwG7xIeWbcH8Q4EqB15IEBNKS/Omvf6Bbn3Yk4ymuOvrBdN1HOkoYGLVwmw55nHXTUdxz/jP4KYXj2gzYsTsLvl5Oqzlo++6FlK2pACHIzI7QVNtMx57t2LSmPP2ajmvz1Nw76dCjiLfu/4gnr3yF7IIsXl79ILHMKFprZn06jzYdC+i1XTcWfbOMS3e/CYBz7zmZoy895Fc5H2Z8PJfrD/krAFc8dwH7n7rnr/I+vzf8t1/H/0n8u8fqgHueYW1TA362RsUUOpLCzlS4EZ9st5lYNEV+JEWmnSRiJcmSATHbzAtd3UoiwicqPWwREBUeUeljkSQiNBpNJEzFmjo9QOswgkfr7zWtZWkqfDAUlRIKWwn1DcYEGaM0tTDRfmvLILsgTNdqlKEw6X2DMO2a0MYfJUAaUUY6/WuhsYyiV5tIpIng2SBM+ldr43WY0mYkSks8DEHztYkeetrC0za+tvC0RaCNetbTFiltkwpsNIJUYNLPKWVSwb6yCbSxkfEDy0QNA0HKt9CBRRCYOmcVtKqEw7o/LcCXoQhEpKN+6RSwH6qFW9XArQ1MWjuKhPaBrQdKKEyZUKBxhSBIhn6LtEYDCev6jPBEeNo0ZAlr/QAiliTlBRC2jSOlTXRKm+9s330G8fWkRQS+MYiONyXwk4YMduqUx6Z1NensTvdeRYzaeyAdu+SzflUFo8cM4eNXv2Xi+BkmBS4EBx8/kpXz17Ni/nqGjurD39646Afn+TVH3sf3U5Zy4pWHcvLVmz1f77/oOaa+M5OWhhay8zNp0yGPU647ilGHjfiXr6FfGr9oxK+goIAVK1ZQWFhIfn7+P1QZ1tTU/PzRbsP/GVprUgkP27X5+u3ppJI+Qpo6uJ5Du9JvRE+klBx1zt6sWbSB0nVVbFhXQ7w5aSJmxvkUIQRCmSbdxCwyajR+i8TKlgQtklS2JhXYlG0qpKVNjJrMDOoCl6aYpJsbpah7A7Vv2HR8s4V2f20kstin65HV1B2fwaarcsjIk1iimQwZx5EeGVaCArcFR3akOqOZqlg+9Q40ZdlkbLTNilJKHFcTuC7RuEbGzaQsXIkV95FSMGSXXvTq2ZY3n/gKpTTjrn4DgINP3GWzsXMI27HwPfPLUV1ax30Xv8iZN43lyevfwvcCFn672hRLY2pIytZVh1FCaG5IIKSktLgyTSZtx+LR6X+hQw/TXHzsnw5i0Kh+tO/elljYy/Hjpz/ngfOfxrIlzy97gEGj+nH6bcdRW1bHgX9n9umlfGpKa2nXre3/+bzo3LcD0cwIvhfQffDvy3rg945t895PozmZYl1tAyKCsW+xFNgB0jI+e5FIkkwbotIn34kTFUnaus1ELZ8c0UTM8omKJBnSR+ITFQopFFGRwhXmx761y5kFOFvcbo1UCKMvAFqJnxEbKB3q1IRJVZrSNU1rltAKbVda9WxBaC3SurEO/wUYoaxCYAtNRCtSCAJtEQjz1zM0EIWPEpJAG7GGQuFbpr7Q1ia7EiCJIfG0QCGJaN+kdbWVbhvsY+MrQYBFoGUYLXTwtI8vJSllYQsLV1n42HiBRaNnvFhtqUgJU9NnS4FlaTxfIXyF0pYRVtgCrTQ6ECYlbGm0Cg9kEB6QMM3bevxaW8jJ0CJGuqFVjAirbnzCWj8zTQZKmD67UiJ80xHEdGoxHaZ0+IVZljQ9jBHgaWwNcV8ZO5rWDie2MsbSAhCCz79cQoeiHMrK62loSpjx2YbI+gFplwaUonhVOcUrysE3Ho7fTFzIpuIqpG2jlY9lC469cD9uP8ekXmvK6/nm43k8d9sEMrKiXDruZHoM6sztb19CXWUjBe1y0+d/Mp7ik+e+AmDkQcNZ+PVS1i7YwMt3TPhNid+/in+J+I0bN47s7Oz07W32Er8vaK25fO9bWDhtGefddxqphFlVSynoObQbd028Nt22xnYsrn7sDAAa61t48d5P+Hj8DJTnIxwbnQqwlEYECs/TyJjECfsBBZ4gsCDiS1oCh4ZUDs25UZSyqfdyqIxm0S+jggannsQxLlX7xujytwby34yT/2oL2RMTbLo6l8qjM4kJjWW1EBUpsmULGVJTEsunJJpkYySPuuYo8YiLqIxhSwvdJJGuRkiFIyyUI3GSAdqysAJNCpg+ay25XdvQUtOMFx6Dj16dDtGoqQMMazx6DunCitYUsBAUtMvl8LP3Zt3STXw2frpxxQ9XjZFMh0jUxU/5tDQmw5RxgBtx0sd5h/2GUtixgE+en0L/HXuSW5jNS3dMILdNNpc+cgZu1CVobXyutGnULgQnXHPkj36Xf9rtBlbOWcPptx33o9v8HHTq3YHXNjyBCtRWKedt+OfYNu/9NFpSqTRp0miwNVZEY9sa1/bJsgOyHJ9ct4VcO0HMSpElE+RaLWTIBBkiiSN8I/IQATaaDGEsWlwRkj2MrYrRkIrNUbyQqJmInrmjQmLnaGVq48I0nhamP29r1y8wl71D67pNGL8+QkIZroFbbeukhkCYKKGHxkXgIbHQeK39QQRhBC/kTQKTUtbSJJ5tE+ELtCLACqOJRmmshMLSvhkrkpQOlbPaxwujgYFW+EqQlC4RrfACRUoFJFWA1Da+DUJYpDywhQ14hughsByNb0EiJdBCmrSmMClvIVv765r3NK3bBCIl0qxba8OidajcFRKUZSxdhAprAC0gtfkYi9btARwIfJOSNa3rwv2Exgu0SSNrkJYwpthSEPittX6w376D2G3n3vzl1vcAyMyM0L5LHqWVDYaY+hrbkmilOOy4nXh63KSwN3M4GEk69VsdetyqMNoX+Iqz9v4rh5y8K6MOHMquB27HNX94gMqSWtCaF+58n4r1VWTkxLjl1Qu3Ov8jMZcTrz6c7z6ay9iLxtBzUGfee+yz333Hjlb8f5nqffTRR7n77rspLS1l0KBB3H///Ywe/cOauB/Df2OKKN6U4PC8U9Ea8tvn0bFXe3zP56KHztjK7w/g7Yc/5Y1xnxBvSjB870H0HtqNHcdsx9UnPU4ipcxFYltox0YJ0K6FyHBIRCVBVKKikmS2IJ4Dfo7Gz/YhL0UsI0W3wlqKMhrom1FKN6ecXMejUDZROCtB1+vriK00tRLNQxyKb8yncUQGKS1IaEl94FLr57IplUVJsojSlhzW1efT2BiBFgdRG8FpFtgtIBOajGqNndLYTQFOQiEDhYwbg8/C3Bg1pfXhJ9akvVq0NktUHa79A0VumyyS8SQHn7wrY44bxdm73ZI+Vp17FrJxZQUARV0KKF9XCRoOOHk3TrnmME7s+2cABo/qy7qlJTTWNqOVIpYdJd5g2sjt+YeRnHnbsRR2zGfahJm07dyGATv3+cnvMvADDs0+BS/ls9tRO3HTm5f9X0+P/0n8N17H/xf8p+e8D+cv48o3PkEDLR18yAywM+JkZgRkRVN0iDWQ5SbJtZvo6DZgyYCOdjXZVpIskcAVAVnSIyo0TsgzIoAjBFboJOcIE99rvQ9h4lCEUblW0onx9fPCe+Y5jWm0ZqC3+PtT9H3LbRQ6rNtLexIThHpeXwt8pEmthiIThY0fbtOa9lXGUjqsETRpYB2minWr4EOYaJ3x/zO1gn5aTGL+mtSvEY/4yialJZ52SCpBMnDxQ+WxpyxSgcRTDi2+Y4yklWk1FygLz5eAsYYxbeQEKhBhxK/1L6AkMiFMv18d1lH6RhUsAjbfbr0f/keD2DL962MUw5otzKN1qP4NHwsFIMIzBtzCHHyK8jOpqmhChiKRDoXZdCrM5aIL9uPKP79KTW0zaM2g/h246fZj8FI+C+cWc89NEwBD2LUftvnUQKA46YJ9aK5voaG6iS/enZMu+8nIivDCNzfy/dTlfPnODL77ZAEA+xy9I5Nf/w6AW169kJ33H/ovXRu/JFoa4zx15Utk5GRwxu3HY9nWP9z+VxN37LXXXpx00kkcffTR5Obm/vMd/sMYP348l1xyCY8++ii77rorTzzxBAceeCBLliyha9euv/XwfhXEsqL86dGzeeO+DyhdXU5dRT3PLb2fTr3bb7VdvCnBk1e/lva2m/HpAmZ8uoDxD08i0ALhOmY5FxjFlLQs8BVFuTHWx31kYMLpMd9MBH5SoisdGttL4vkWKz2LuvwYCc+lJpZNl2gNzU6Uxh2aKXk/i14v1NL54QYyF3oM+kMF1QdnsPbqAnTnCPn4RGQdjtRk2opsO4FtBVRnZFNRn0FSS1IxC69JEm2QpFIandB4UUnQEOB4JhjnpBQtlqRzv/ZsXGVIW9j9fPOBaM0P2Rb1dcYe5u0nvmLMcaPCZat5uqS4Or2MrdhQA0IgLNi0poJ3Hv2MzPwsLFvyh0sO5KZjHgjfSxBvTKTf6qs3p9PSGOeYPx/MZ69Mo2JdFRtXbOLyp/7IXseNAmD+1CWsXbCeA07fk4p1VcRyYmQIOO0vx/7Sp8o2/Jv4Pc97v8WcN2ZQX17tMo8NdfWUOE0Ewhi12zLAEpqISIZWKSZrV2g1kSWTZIkUWVaKKAGZ0kT3JBATEhmSPiO6MARPhrV2Mqy7AzZHogQEqJDMCbQOQtIGSgjs1iJAzPZ/Nwv8AK2EUG9xP/2/Ne25xXatUUepQaOwhElPKsIiQ92agg4lJa1Ti1ZoIUPvQAkiQGgLJRQIidQKHwgwNjNGgGKb1Kw03qUKj4i0Qfvh+4uwwtH0xotZipRw8IU5cilB2CXEQgiTxg6UDDO7Ch0KXbQw/isqKsAXCG1Sw9iEHURA2ptTva1R2NZMuRAQeGbaFLapKxRBWCmzxYEc2qsDC1aWmsMY5pRVaC0DUFnXDBbmMaC0spFEKiA7N0pzwvQSFgi69mpHVnaERFyyallpWrh32NE78t4r3xHLihBvSmJHHHYbM4RuvYpoqG1hj0OGcdNZTwOCHgM6cfs5zzBv2gqs0A2joF0up113BBtWlpGVE2PoqL7/4pXxy+Lzl6fy4RNGlTxs78G/mKer/OebbI0hQ4Zw/fXX0759e8aOHcu77777g4blvyXuu+8+zjzzTM466ywGDBjA/fffT5cuXXjsscd+66H9qjj4nH358xPnkJETY/Bu/WnXrfAH26SSHu26tAGtyG+Xg5SCWFbEpCGDwKRDfR/h+QhP4UoBnk/5xjrsxgRWs4/V5CObFU5NQKRO4bZosiokTlUEVR2jvKKARRUdWNTQkVn1PVgR78TqVDvKZC5Lzi5kxuedKTs+Cy2gzUctbL9PCT3uqcaNK2JSUWjX08WtpE/GRvpnb6JXTiV9iqoo6FyD3a4RXZgk2U6RzNIkCiSJPEG80CKRKUjluaRci6aWJDVKYbXNQlkCHXVQro12LGNg7drGwNMynlhmlhK8++wUU9sXPq7TheSC/Pa5hjBrwcLvVvL2o5NpbojTUNPM4FH9OOu2Y8JNBdHMaEgSzeXVoXtbrjn4b8z8ZD7FizfiJX2mvj0dgJdve5sr9rmVRy99nldue5vxd71HY00TDdVNzPho7k9+3xMe+oQjCk7jpb+89cueSNvwo/g9z3u/xZxn25LhvTtRppoJvACkiWXZlsYSAZalcESAH9hYeDhCEZE+EREQFZqoNHV7ESGICIkrLBxh4QpTw2YLC0fY2MLGETZSyPR/QViLLAQy3WdCpMmhbBUUbBHaE0KkCcpPQaf/6zSBTD/WGjhKk7m/31f8JKlsJUcyvaVJ7Jqat7CFcWgvIzEt7CyhsYV53txWuNKYUdtSEREaR/q4MsAJVdCWAFcqHOnjWArH8g0RtzSOVEipsGSADFvmWZYyty3T4g5pxoHUxlfPCf/KUMUrABuU1GhLb37MIuzcYuoCtW3+KrnF4+FzSEx41xbo8HW1wKSct3wftjw45n9dXQtjj3+UliBAWyYiOXnSIg4/4G6OPvg+3nlnTtjQRDBgWFee/ejStPmz7wdsXFPJnZe+ynEjb+WzCXPSDH/5/PU01rUAoJT5dpPxFIUd8nlg4jXc/uYlxLI2e6v+JzFwVD+imRHy2ubQY8gvt4j72cTvwQcfpKSkhPfee4/s7P/H3lnHyVFlX/z7XlV19/hkJsnE3YV4QhJiEAIEDW6LO8vi7u6+LO4S3AkSCBCSEHd3m8hkZjLaVlXv/f541T3JAvsjLCywm8snTEtVdXV11avz7r3nnBxOPvlkGjVqxFlnncW33377q+3YL4lkMsmsWbMYNWrUTq+PGjWKKVOm/Og6iUSCqqqqnf79WaPHsK68X/Y8D3x9E7ZjU1pczhX73cbdp/wdN+nx/t8/Z+vabWjXR7k+fiJJbn4GzdsVmXKoH6T0kgmOOX0v2rVvaKx1lMZyFZlaE4n5hKIekZjGqVE41ZpIKYTKBM5GB1kaJro5mzUlDVhRUcTsymZMr27HglgLVscL2VQvh9m3NmL6R02o2DOCTGpaPFbBgBFrafZKBWFfky19GtkxOka20D17E60yS+hQr4wWhduJ1K9BN6jBa6DxIxo/R5CoJ0kWShJ5FvHmGbj1M9heEyOKxi/MwpUCHbJRlmUGFtveYboa/LMka1ZsrbtbCGGaVywLhGB7SVXd66kI1OqfvOZ1nrnxHbLqGdHP9r1aAVCvYR7Xvfo3zr7nBAoa5adXE1IgHXPpTflwZvr1vAa5DDy4TtepbY9WP/lbf/zkeGorY3z0+Be/+HzZHT8//qjj3u855r0+ez6eoxG2CBSTNBJFxPYJyyRZtkthuJZ8O4ovhCnrSo+QUDgY0CeFwBESGwsbC0uYfymAl4qfepwCUimwVrfMzqG13gmw7Zj9S2cJ0fhBiTcF9JQ2tmKmxGv+mpJtHSBMiT772pSCVXp7qWXEj4BF8YNHKYBY95oRkQ4wUYCZAoAolMntCSNA7QjPCFELs47BVjoYojQyAJIieCwDkeXU8IdQxr5NgpDaPBagRYopQ91OSILE4g6gMPinLX4A4NKgz6ojjcxZvSm9jE4hcrnD4x1/q2AZnTow6Z0G1/Vw1Q6/ffD6A7d+yILZ64hHTR92OMNh68YyvvtsAQCTPltAyq/OS3qsWrxpp+0e8Jc6OZcdo3jlFj5/eSLR6tiPvv9jUbqpnNuOe4gXb3qTXe2sa9ujFe9se46xG5+kfpOCXVr3X8Uul3rB2E2NGjWKUaNG8cQTT/DRRx9x++238+yzz+L7/v+/gd8oSktL8X2foqKinV4vKipiy5YtP7rOnXfeyc033/yj7/3ZIlodY+m0FXQd3JFwRpgJYycxd8JCAA44bW869GmDEAInbFNRUglCsGXNNuo1ctGeR3ZeNrXVRqDyjQc/Bcsip1E+vYZ1BgTffrbAMK6yQ5BQgI2WApFlIcqMDZJXK4jnWCS9DLbFJNXRDAqzq9ieYRPNtqlS1RRa1dR2yKD05Syajq+hw12lZK536XxDCS2e3c6yixuycXQeeXYSR5YTkdVsDiXIdnLIDCXYFs2l3IFoVQhqbKwai2RE4NQYt6ikI4mEQlhxZXSl8sNo1zzWCUFh/WzywhZSSg46cU/+ft07+AoWz1lPVmEOteU15oCmaGopaXhfkZkToffQjsyduIzqilqkJZn88RwQhvGb2yCXVt1asHzOGraXVPHhU19SWVpF75Hdada+Ma/c9g6xmgTTx81Da81Z95zIy7e8TY9hXTjy4oNQSnPJU2fTsHkhvUf+dE/JKTcfw9i73uOQ8/bb6fV53y6moqSSIUcMQMpdntftjn8Rf8Rx7/cc80Z2aMubqxaRcrkAkEGtzg4ARo6MYQNOoLBnoXGoY+naCOwA5Ml/mY/7YRgwt0NfX/A3BeCUrsvckWL1BvfdFBAzgiOpEixB+bgO0HkY4OZCunfPxfTDpTT7PJHq8zO9e4bna3J6SpMGhGb/Urm+utbjlBRNCmzuHKJu/4L9EgH7WFHX4yhSpVwtTGdhOpNIGjyavymSTLAXwWvmidphMpwCfMGnS2FIadpMXLWpSqf/QvB4x/o4dX/TDOEdUK6xbatbR2vj5mHZgeVcilgS6PWRLqEHByqFiHfs4Q4eJmJJPnt/Nm06NmL10s207dSYlx4aX+eUlGoD0DseXajfOJ+nvr2OyI/YrCmluGifW6kqq2H+d0u5/KmzfrBMKtYvLebGw++jQbMC2vVqw7dvml7BoUfuSetuu5a521EA+teKXwT8UrFlyxZef/11XnnlFebPn0+/fv1+rf36t+Kf2Xda659k5F199dVccskl6edVVVU0b/7nlL24+oDbWTxlGYPH9Oemdy5nwOjefPDY59QryqNdr9aEM8M0aJqf7lezwha+p6gqrwGlqSmvTZc+RUCJ96pqKVu1he01ydSog4x52CGFrNKosIWV1HgRgbIDayAtkZaFX22RKPDYHIeq7AziKsTWUD2KIttpFKql0K6iamQGG4bl0vaNCto+VkrWOpfeFxXT9qkyFl/aiE1DcpGWxhHbyJFVZFsNKAzFWGt7VGRkUJ0Vxs8O49fYSNuGBPgWWEriKw1hC8dTBvglNNqRbK2KEWpWQPGqEh647RNEKGQGJdenNuaBHVDN/B3n6WbkGnxQLxIxl6JWDaieG0UrqK1JsX01VeW1fPzs16RGmPpN6vHoRS+aTWhNKGIElH1f4Xs+PYd3Tbt7bFlbwmX73ELJ+lIKGufz6urHfrKZd8gRAxhyxADKt1Tw/PWv03nPDjRq1YDL9jY39EufOYf9Tx3xm5xn/+vxRxz3fo8x75QBvdkQrWRyzXqTNLdJFzxNGVPjBtdBtjSlNDsAHGbamLrjmz8KjbUD+NNB51rqcSpbUleKNd1pxjljR8AHnlaBV65ZJ52B0+Ze7+8AslKf6BGweIPMHkGWz7gCyyCzZxmdPm0kV1LgL7WOcfKwAlIHuBrTn0cqg2hSW6lpgskemhK10qBFCjwSyMIEwDG9fXY4wgT2byYlpoKspg6WTx0tEVjQ6SBlJlJdkUKkMZNIZdx8dsRBdZFq4Etl/1LLBOsI2OmAamG6ZrRXt+0fHPAA0u4ECCX4flB6VqmN7dBcqXbeuZBjUZCXSUlxZR1gDbKCWTlhDjqiL4/d9iFL565Huf7OUwsp6Tu0HWuXbCIctjnklCGMGNPvR0FfKlL+vc7/I4T/3bvTKF6xmeIVmxl0SF9CEYdGrRvSKJD8+r1jl4FfVVUV77zzDq+99hrffPMNbdq04fjjj+f111+nXbt2v8U+/uyoX78+lmX9YKZbUlLygxlxKsLhMOFw+D+xe795bF5lBIU3LDUm0i27NOPVNY+l30/Gk2zfWglo6jct4LRbjyFaHScjJ4MvX5/C3O+WBmaKdeXOWE2CRdNX0XqPlgjXGKULxzazSe1heQo/bIGwUVrhSUmoygxAIlui4zZ+Ioua2jirkwVsz0mwOZZLy+xt1A9lUhSqItuKUntChJWH16PDi+W0fbqUvMVxBp6+lm39s1jwt8Zs7p+PJZKEZCkRKwuJojSUQ1koQmkol6QTIhkHHIGVIfGyJKEagV2r8VxJJCqwtI/vWCgpWF9SCWEb6WvznT2FUMrcMG0blCIUCZGsCVL6AvYc3ZNtxduZO2k5aE2rLk1Zt3RTek6ug2OWFokWgkQsSUGjfMq3VABGo++A04az5+idTcABxr88kZL1pQBUldfge34a+FWVVfPW/R/Rvncbhh5Z5w37yF+fZcoHMwA478GTkcHM/LeYJf4vxx913Ps9x7wzXn2XbfEoNCSllIQUgrhnKAkWxpcXjEWaEnWgLXVfF2i8gPwgCLJ4QhmwEmQRDTEjpa2n0yAwDfa0SpdqvQD8+ICn6jJ+KdCXyqylAJRH2rYWpQ0A87XpOdNaBBk/GWT0wNcSLWySGjxtIzR4wsi6qMCirY7RK3C1ZTJZAdhSiDSrN9V1mMowukiElsExEQGAMzIsvpYk9Q46gVoGmUTjI+xqk5HUOmDjqoBhnAZ7/5xpFAGQ3hEKibo/aXAndnprp/r4jusGP2oaBAafI1IoP5WkSy2ud8Z/gQpP3eY05GaG8V1FPJo0dm5SgBd8iwDZJpM+W7dWmeOrISPDIV5jem+3ba7k3mveTmcCs3MzqK2MsceANvQf3omW7Yp4/5lvKA0s1+o1yCUnPxOtNSvmrqOwUT6FjfPT+ySl5JFvb2T5rNX0+38YvnsfO5ipH8+iYfP6jD5zJAedvS+Wbf1hJKF2GfgVFRVRr149jj76aO64444/xGw3FaFQiD59+jB+/HjGjBmTfn38+PEceuihv+Oe/WfCTZp+hp/qIwhFQtzx8VXMn7iYAQf25s37PqJtj1YcdObejDxuEB898zWPXf4qKFMYadSyPm17tGLGhEWsWbgehEQ4NuEMh3gsgcQxDC7tGJNvVyJiCpSN8CVeQmNlSZQP8ZwMvIRDSTRMJCdJTSJMg8xKyjJzaBiqosapIjcUp+zsbJYdU0iXp7fR5uUyGkyvZe8TV1LSN5t55zdl7YBCpFNLRECW45JlZxFyfMpDWdRGfPxYGK9K4EUttJB4GQKrVqJCGivTAEE7JNFJhWULo9kX97GEMGVrQLs+wlcktOb218/nngteoqq8lmXzNxCtjKbt39Yt32Ieaw1KYofMzLppqwasX2YYa1M+nsMeQzrRvGNj5n27BIDz7v/LjwKzQYf0Y9zTX2GHbC5/7tydlnntzvd49+FxALy+1xPpnsFoVTS9zPzvlvCPmXdRVVZDzxE7OzWkEwAA8mhJREFUewT/cyybsRIn7NBmj5Y/7+T6H48/6rj3e4551fEk0hfGZUGZzJcCHGGAi68FvjIgrFaFyBIuSgjTR4dhrYLBBZb2jaWW1gFI2SFTRQD+tMYVCqFJgz6lNZ4wOCCpdwZ58UAo2UOjdYo6YYCDFzC3BJDEyLOIoAztI3GVhYeFFexBUkmEsEhokc5Kpjx8U84cKYu4FPAzmCdV5tUBsJRpTKUDAGhEogN9P61J0T98RBrkeVoS90OmRKohoUXg6yvwtG2WU0FGUkkjLaMCh5EdMoDG3jYAiMERSM1TdfrgGdC5E8jbEailHu+Y+dM7A7edyr3/tB2AbMcmGnfr3lM7LAfkZIT47MULmDx1Bd9MXk44ZNG4YR7PPDcxPamu2/FAzVFr4rUuDRvmsm1TBetXl6a3F86widXEQWuatqrPPof25q8H3EfZ1sr0tjJzDIHjs1cm8fDFL5ORHeGlOXeSE/RuAzRoWkCDpv9/r13jNkU8OuXHbdv+CLHLwO+DDz5g5MiRf9j+oUsuuYS//OUv9O3bl4EDB/LUU0+xfv16zjnnnN97137zOOjsfXnvkU856KxRP7nMHkM7s8fQzjx+6Ut8+9ZUvn1rKolYgpNvPIoDTxtGMpbg+8/msXDKCrasLuHaZ8+ianstC6euBAtyssNc8eiJXH/K0+gaH2lb5tpLKohLZNjCAkLV4GZIZD0bLyGQcYmbcEjGJfFYiHhWglhSUhbLZXNGLkWRGhpEKqnvRKnOzaT08myWnFRE5ye30uatMhrOrGHfU5extWcOM89twdLBRtzVER4h6ZJlJSi18yi3FEkZggxNXEpCCRvfAT8O4UqJFwYrpnBiID2JlVDYQqATPjgWOukhwjY66SF9zWuPT6CyJoGwLLaX1ZqR07Jo1bkJ6xZvNFIHQf+J55kCzub1ZUbDIihL9B7RlYnvTUdIiWVL5I+Ub92kR2GTehx0ziiSsSSd/knrr1VXU4orbFKPzNyM9OsXPHoa5/S5Eq3hmCsOpW2PVmitWThpKY3bFv1oQ/Cs8fO4ar/bEAIem3E37Xu3+dFzZd63i5BS0n1I53953v0vxB953Pu9xrzTB/Xh6amGnKSDDFXCNee21hbG58IAQlc7gcetTVi7SHRaOBkCAkMAxuwA8KV68zxtcoNKK9MzR9CHB0SVMZwQOvDXJaW3ZwVeuCLQxTP1RgOybJKBW4ZOb8lcw2b7dlB09vG0RGCElBFGTFkEWbtU1iyp7eD7S7Qw++ph42sj72KLVEbS7INOybVoGxtS9rQGQAbbNYpTMl3Z9LDxEChlgJynJK62jbWbEkbDT9v4SuIqO1jGLKeUNG4gSuCrwD5OgVLmN9PK1Ga1L1LK1wFIEyltnDrQ9k8ADY3R40s9Dh6mFbTqDu0O24XahEeKQEyQ7XOkwE9ohIKaaILS8hquufU9ABxL8uzfT+HDd2dSsq0GAXTr1JjFC4sh6A1MIf6c3AhlWwX162enxZhtKckuyKK8pJr6jfJ4+YFPDegDWnVuwkX3HEurjo3ZsGILa5eYilmsJk6sNr4T8Pul8emzE9i0eivHXnkoWbmZ//b2/t3YZeA3atQoPM9jwoQJrFq1iuOPP56cnBw2bdpEbm4u2dnZv8V+/uw45phjKCsr45ZbbmHz5s1069aNcePG0bLlf39m4/Q7TuD0O074Wcv2Gdmd9x79DICpH83m5BuPQkrJERfsTzgzxIblW2jVuSlFLeoz+sTBaKXIb5DLcRftz/QvF9G0VX2K15aaCznpEcq0SLoeWgb9PVJi+RqpIO5ZhGKCRFIg4jbJuELFw9TGw8QyPKoyI1RlWlRkhakMV1EvlKDAiZJs4LDthizmn9OMLs9spv0bJRTNrebAsxfRt1sOU85ozYLhjbEiRtbAsgS2A1XhJNFoBM9xcGtAuBZ2hcCPgIgKnJDEDwucOGAJsmwHNxkzLLOsEHgKaUtUbYKF8zYgQiEjBuorM8DYkkhOBve8ezFfvTWNEYf35d2nvmbhtJWgFLXbo4CgYYsC/v7VteQW5jD2ng8B09un/6lPJRFLcnafK9m8uiT9XtP2jRh10rD0MvufOoIew7qQ1yA37cICxkC83/496bdfTzr2MWLdbz/wMU9d8QpZ+ZmMXff4D6QIooG4tNb8JDtt9lcLuHLfWwC4/5ub2WNol591Xv23xh953Pu9xrwL9x7MyG7tGf3OC6A1nm/EizUaz+hqkNQWMWUoHVEdwkGR0F5QYt2BnBEIwwmtSQa2XikmrofA1aouOaQhDohAGFlpQUwLanWEEJpkUAJNaInQgnigbecHnrhCaGLKQWClcYkf9NgZhw6BLUw+0lN20NpmMnYpEWcDykwpWGAFvXspXJP6T5osaMBs0Fgm+xcQGlJkEx8R6PhpAzAxmoQyyPr5OigbqxSgtfCDz3SVJKmMv6/nCzxtEfctlJbEPfO9VQD4fCWCxwbs6ZSIs8KAviDLp7VIg72U8HJgR5L22UVT588bPLeFMH3VPjsDRB203u3wLx3p9TW+p7FSGqsaps1enQaOrqs4+fRncIQMtqFZsnQzLZoXsGFNKSiTJw2HbUo3VaBdn+3bqgmHLBIx47qkQ5KLbj+CfQ7rzV1/fSn4HM3aJcV89Nw3rJyzjnVLN5mKWVAevnT/uygoyuOOdy8hK++XAba1izbwwFlPAhDJCnPCNYf/ou38mrHLwG/dunXsv//+rF+/nkQiwb777ktOTg733HMP8XicJ5544rfYz12K8847j/POO+/33o0/dPQ/oBcXPX4Gn7/4LSdcU1cimjdxCY/+7QUADn/0ZC4aeTubVm3FzjR+rzO/Wkgi5pqRK+QY3T8hcKsVwrHB1+iwgpANwijAhyUkI5KwL7ESYCUEKuaQzFD49SCekJTEHKriCbZGcijKilIvVEODcCaFdg219ZOUXZ3BrDOb0/25TXR5fQtFC6sZc9F8hrVcyaST2jLlgNYQNn1FmXaESselJhwm6oRRiRBJWyBdGysEOi7wI8YayK2VDOzYCrUpxuzFG8zgZ0tUwsfJihhfy6QxAccKiC9Ks2LJZmZ/v4LpE5dRURFj2viFOx1fIQRlmysp31pJbmEOjVo1YN3STQD4ro8Tqrv0KrdVsXm1EZuWtoUQ0LJrs/T7Wmu2bSijYYv6PyB7vHzL20z5YCZTP5rFvicNIxR2qNhm5DniNXHchPsD4Ne2V2ukJVG+4tNnvmLOlws47poxhDPqAKXagdiilWbzmq3k1c8lMyeD/8X4o497v9eY17VhQ2wknq/QvnGziAAJ5RDzHbKtMHHtIZQgrsJGT057hNFE8XGCwqYLSG3yb1bgupEiYqRkVJJK4uKg8EEHgBCHmHJI9c9FtcDDOF0ktcTTYVSQ8VNakNQSIcDVFq62AYk03XXElMQS0oA8CICXDogyZpsCA1h9LXGx0dSJLGtMDyDCgDQpAlCpDWBDG18PhcmAWsIUgV0l8Y3fBVLooLyrsVOPkbipUnWQyXOVQGNKvK4WeMoyINA3mUaDXWx8BZ5vPt/1RBqI+p5A+UF21sNQc1OgzxMG1AUAUCiR9utNO3AkRdDnTRog+kmN1HWvEbhzpMHjTu+ZpECqXy/lAmJ2yPzwedkZdWVmbUggvu/TvWtTFs7fgNaK9WtKESrl+KFI1vokdVDOd31U0LuN0sRcj+/HLwKlmDxu3g6fpfnqrek4codidVD+3bahjG0bylg4dQUD9uvxi66Rgkb55BZmU11es8uM3t8qdhn4XXjhhfTt25d58+ZRWFiYfn3MmDGcccYZv+rO7Y7fNkafvjf7nzqchZOXUVpcTv2mBUirrpSllTY2ZRitI4QgXhNPO3+kWa9CEIo4JJOeKWcK0yenbAtHguWGkVGJDktURODELfwwOBmCuGfjZ2pUxKK2xiGWFaEymkVeViYVmRlUR2zqhWrJsRwy6nmUX96Bqae3oueLG+nxxiYK1kU55NYF7P33ZXx7THs+P7wTmzMLsYSHJXyktIiHPNxQGBXXaMtGhwUqKVG1AhvBl6vXYSU1omGIUI3CiSu0LUi6CjvhI+wQJD2Ep4IBzMdXMPaxCeArpo5fyI76fuG8LBKVtSiluWj0PbTs0JjjrjiYZ657k/779yCStXNjfcMW9RkwujfTPp0b9NooFk5als7gvXjTm7x2x3v0GNaFe7+8Yad1++3fkykfzaTXPt3TYPLE64+gQbMC2vZoRW5hDsl4ksv2uYWNyzdz6wdXkFuYnRYq/erV7wDIL8rjsL8ekN5u/aYF7DGsC1327MDWddu4bO+bKGhcjxeWP0JG1u8jZvp7xu5x76fj7B79eWzR9/iexPcFCc8m5kvCvkOtcojoEJnSJ65sHOEgVRglfHwhCAnDBpAYP1xPA0KmS7paSSOfos1fV9kkRQZJ5aAQxLSD1AKFokaF0VgG3CGp8cPEVQgp/HTLWUJrPBXC1zYahS0UnsrC05aZ16GodB2ynaTx2dUpOSdBUglsYQSW60gXCktoPGVkaXytUVoGJW6I6xC28AAPrcMgjOWbQAVZwhTFQ6OFDD6jrifPC3oKFQpPhQIAaL6L64OnrYDwIfF8U/JN+DYy6Hv0fJMJ1Nr8NlpbphdQGXCYEtbTPiarFwC8dHnXDzJ9nsn0SQ+kFyDaFAj0DKCTqddSmUCCx9qAOpFqaNTGgMSAQlOitVOMXQ14ELbggQc+RSiNbQm8RHCv0bDP3l3Ysr6MstJa8xmCnRUYUkwjpRl5aE+mfrWE6kCia9r4BWRlhUCAZUmE8vESPt0Htee4iw7gk+e/ZcmMlTRrW8TWtSVsWbuNNt1b0GOvTqxZuIFnr3uDXiO6csSFdWPl/xe5hTm8tPJRYtVx6v+M/sD/ROwy8Js0aRKTJ08mFNq5Ob1ly5YUFxf/aju2O/4z8db9H/HsNWPJys/ktTX/oPvgjtz7+TXEqmN07NOWZm0bEqtNgGNTsr6MSFaYTv3bM2/y8oABbMKtiSOCbJR2fYQUWBq0LZG1SdM3pyw8YYHykb7xhbSUJBEWJHMtdETixyXxuMRN2sTiYSqyMskP15AfrqFhKEGOTGDlhZlwYWe+O7M93d8ppv/L66i3KcaBTyxi5AtL+e7gdrx3ZA9WNyww6jQyi7hM4koLJcGzBSJuoWyJnynwa4wGoCUhgTQzSEuCJ1GOxEoqM3uPgPAUIuEhlYakkX4palRIfHsNVYH6u+v6iHAI7fskYi7L566juiLGK0vuTx+vZNxlzaKN1G+az7yJS1kxb91OvTC+V6cLt2TqCgCWz1r9g99v9Bn7MPLEIThhJ80Yy8iKcNDZ+7JlTQlaa4pXbElvY8qHMznzrhO476ubWL1gHc9fN5ZENPkDVfjnrxvL/G8Xs3DS0jQgLN+8ndqK2v9J4Ld73Ns5yqui5GVHsKRkePPWPDZ/mtG78ywcW5NwbeJWhJibIC5D1OBiEUIIjRYaTyqEdskUhpBmBfU/NwAyJgMm0Np0Cro6RFzbaAzRQZHqa7MMuMOUaGv9sGHbIkgELFhPm944hUVSSZLKwlcQtpQpnWrj/2EYxQaKxRKZaZKcCMq2rpYBU1mjEIiAkJL0jd2cqw1r08Y4abjKyLrY0kISwg1sLUwyzIA7T5nvKUWqlJ0qGStTjsUysjUaXF8Stgzo9LQk6afgsunXi7uACBns5IPnywDwBRIxXpDg8oNycgrgKQzA8wNbPN88T2frtDDALQCGaRHEALxJDbg7ZPfMFzCg0Afp6/R2ZVAilkHWT/jQOC+LkpIaAww1SE/ju5pyL2YwnauC7KImHLJ59J5xdUdKB9lCrUFpcjIcaqrjAfjTDB3dgwtvPYJ7Lx3LxI/norUmHHEYOroHEz+YlT6fO/dpTbs9mjP1k9l4rk9Z8XZjaAC026MFkawwb9z3ETM+n8eMz+ex/ynDdqn0m5Wb+Yfo7UvFLgM/pdSPipVu3LiRnJycX2Wndsd/LipLqwGI1yZwkx4RrXn5lreZP3EJw44eyNpFGwHot19PStZsI14V44ATB7Nm0UbcpEss5hHKCJFM+ggfnJBFMpEE2w6kBEwa3sbD95Tp+wtJLFdjC4GbJyAisH0M4y/bwo1b+DUWVdkW1dVhynMzqJeRRW1mNXlOkiw7TpblYocUE49rz7dHdaTL+E0Me2EFzZdVMPKtZez9znJmDGrJO2N6MqVbSywZImE7xBAox0JnKPxKG21Z+A6oMNgxgZUFKtNCJsFKahwp0LZAOBLLU0itERkhlGuYwCjN1i3VdOxURNXcdWbAsSTaV4b44iqy87Pos7dh2VaV1zB/ynIevfhlKksqycyNEK2MoZVp/Nn72IEMOKAn5VsqOH/gtRx7xaGc//CpvP/opww67MeZpD/GEL76gDuY9+1iBh7Sl72PHcyhf92PDUs3cdBZIwHoMbwrPYZ3ZdRJw0jGXeoV5e+0fu+RezDlgxnsMbQLx193OKGIQ7teranftPAHn/W/ELvHvbp4Y/xs7n/1G7q0LuL5G47n0nGfIXxTIvSVJJ60iIbCOL7AFhmEbBdJBFsoo30nIKJ9bGHjChtLgKcCJqu20MKQG5LKIqozMGIpkAh622LKNr1/WhLzw7japtoPYwmNUpqkMm6/nhbElYWvJVIolLZwfQshJFHPuIaARgY9hSogkcR9C9cLEbE9wraPGzBhJTogexih6lRlUGnjQqa1jy0FVUoaeRohSHrCSNoIjadMti0kddCjF3B3hSFfaIxCgAh8fo3Gn8koqgDE1iZl8JowZOqgjzBF2HA9AcL08umAyKFV3XO8lO4fQTZvhyxfKruXFEaUWYk0UBMq6O0LvHfTZdwA9FnBhDWV3cMHmXo/DcxIl3VlMs2JY+vW6uC3qAOPApCWQHuKtJWKr8kKO1QSrzvwSuMIgRfIctVUxw3LHLONeDSBZVtc9fCJjDl1CItnrmHfowZw9VEPA5Cdn8mpVx/CPkcNYFtxOZ5rrvFWXZtx6JkjWDRlOcdfcQgAAw/qw8R3p9NzWOc0wS6ZcPnsuQkUNilg8KF/DKb/zwmhd9FD5JhjjiEvL4+nnnqKnJwc5s+fT4MGDTj00ENp0aIFzz///G+1r/+RqKqqIi8vj8rKSnJzc3/v3fnNI1Yb54sXvqHNHi359LkJrJi9ho0rtqCUpmWXpgjLJpThcOmTZ/Hx0xPo2LcN+x4/mDtPf4pv3zP6cdc8fy53nvMsAPWaFLC9rMb0jFgWWgqTSgvZhDNDRH3j2O07Eh2x8T2FWz+EcgR+GOKZAi9L4GVoVKZGOQocH1HPJTcvRmYoSW44RoNILZlWnIjUKT90PCVpN30b+768hO5TN6W/45pWBbx1SG/eH9qNCiuLRDyE79qmoTkuEQkJrsCKSayEwIpBpBpkUmHHTMOx8EEkNdJVSFdh+QrhavB8bE9x9F8G8vErk4lWRM3sVmvOvvYQDjtjOACTx81lyqdzmfDmNFAarXwzaNkSN+GSUy+LRi0LuWHsheQVZnNw/qlm5wW8tvrvZGRHePqq18jKzeTUW4/+SWHnVBxRdAbV5TVBozJc/8bFO+n//ZyIVsfIyI78YbSndiV+7ev4v3nc29Vjdc0/PubL6cuRUnDT30Zz+ZefU+0kIdtFZPg4IZ/CnGpywi4RK0ZRJEqOnSDD8siQcfKsKGHhYwmPsPCMwInWuMIKWK1Q4YWwhMTTtsn0aajyMvCxAU3St0AYMBhTYQN8tCTqWYCF0FCRCGNJnR4bNALXN2zYFMDylQgszkwTmhCmPIqQCKGwpY/nG0BqvG81yg8YukKSEoIx5Wll3DGC8q2vBa7ShCwjSuzpEJ5vSspCGOFpIwMd9Oj5FlJohDS+u74yalFAIL9i9tX3QSnz/XXQl5cSbQaB8gW+KxBSgJZoz7CuTZuKDLJ2oq6k65v1hG/KtpYX9PsFAMxk7cxzmRJk9nYga+xA8Ehl84RnMqWprF96ORVkAD0ImC2G0JHalq/NPw2F+RnEyqMkEqZUnxGy6dC2IcuXbiYRc4PWG01WVpgLrjuYZ+4bR9mWKlM+9nzQmkNOGsy5NxwGQGVZDRPemY4U8MR1b4EQXPGPUxlxeB1g++K1yRSv3MrRF+7/oxk9pdROzP6Xb32bl28xfumn3X4cx15x6M+76H6j+LnX8i5n/B588EFGjBhBly5diMfjHH/88axYsYL69eszduzYf2und8d/PjKyIhx6/v4Ur9jMl6+Yfq96Rfls31bFusXFfLj9ecKBkvlfH/hLer0hh/bh+8/mEskI8/yt75BSV9++qdyQPjAzNaGkGcF8RSKWRDoWOmRj+UYGxrIkusJDZVuQgIgvSSTAzgYVNaVWFTF9O5UJi+qIT022Q23SJtMJUy/sE5IujjSDw/y+TZnVuzlFa6rZ962lDP9sGa3XlnPFI19yznPf8f7IHrwyqh9r6tfH9yx8KdGOBQmJ7whUXKMcgcoQyKjASQisqCYcNeUoFZiMy4Qwo5glUI5m7KtTsXwBWZnopEc4ZIFjc8HBD7BlfSk1ZdU79EQCUtKoVSG9hnYikhlmxOH9ad6xMdKSfPf+DIpaNWDrulJA47s+X702iU+e/gqAniO6UFFSydpFGzjmikPJqfdDRulN71zKx0+O5+uxk9Hwg77CnxP/q0SOH4vd415dnHvEYGxLUlod5YZ3xpOUPjIzcO5xDDcg7lpIqbBFiKiXDEgKCXyl0VoEnr2eyQIG5AcLHw9wdQhX2SSUjdaSGm1jaVO6LUtk42kReNCarFfSt0kq02dnhJQlMS9sMmwB0cFceiazJkVQxRMGTJnet8D31iJgv5reMt8XSOFj2z5J11xDKtBfsSwBhDA8XhEIWGsItq98ieP4xDSQElfWBjgZPT0jgWAIHdr020lj6mbma0GWLugBMXwDc/y0bzJiSpsyr5SgPMyyqWwfBCVXsQMzN0XQqHssNJCoy+6lWk5kinCRNPN4oUC4YKUYu8GyQpnXDDHbZPQcIfASOi3gLLQhxaVBn4awJfE9w4vG18ZeUwdlYK0p31JNs8b5VJTXEqtJkEgmWDh7PaeevzevPP41roZh+3XjkGMH8Pkb02jdqgG1pbXkF2Yx/MAerF68iX7DOvLVW9N476kJ2I7Fstlr68ZCrWnSusFO5/ao4wf/y3P/n+WcdhxXV81b+3Munz9E7HLGDyAWizF27Fhmz56NUorevXtzwgknkJHx579R/Ddn/DzX48uXJ9K4TRE9hu8s8Ov7ijtOeJiVc9Yy4tjBjL37A3oM68LVL53PnSc/RiQzzFUvnp8GA5vXlnDZAXdTtqUSgM4D21PYuB6Tx80jOy+T2riH9o3mHUIYG7Qgc6SCDCAa7PwIcUA5xu7NzbbxHHAjAj9DoG3wMgSepXHra7TtQ3YSKydBJAIRJ0lOOE62k8SRPrZIqfWbWbRd5TFy3DIOeX8+zTaZfVUCvunTgVf37ce3Xdvj+Y7J/iUluBIR6A5aSUGo1jQgiyjYSU24VuMkAV8jE34wkGlkUiFcDyvpoz2F8PygvKEMKPa8OuDnmWab7LzMoOnY3JnqN8mn/77d+eSZCVi2he96OGGb97c+xbrFxVw8/CYiWWFufudSLtzregCOvfJQTrvtuJ/8zVfOWUMy7tJlYIdf7Tz6M8RvcR3/t457v+RY/eO9yTz3yTSSWZDMFXiZ4EU8yNJoSxPJryE7wyPiuBSGagnbHtl2kgwrSYblIaUOCB0+lgBXC2x8YlpiBVZotZ6Np0IklY2PwlUONW7EEBeUIqmCfj9lyqAJ18LzTWnXSMuQJlloLU2PsauRVtBHp00PoQlDckAItKcD/9lUyq0OeClPYzmgtQo8a4MSqeUHGTjLkCW0Tq+qfIUMMo8CmZYo0SolnSLNdjCtI8LCjBtOAKYkgEYrU4LVO2TX0ka3SpPW0kq9l+rX00GpVgWgJXhNJs1xcHyB71PnsqHqyBekiRnB6ztk96SXej3Vw6fTWUAZMHxTrFyNWc4O1tFKY/sEac1g3YDoYUggGqk0gwa158xz9ub2a95izYqtCA1/OXMYIw/uSfm2Gjrv0Yyxj33Fy4+MN98t6Dvf++BebFhWzIr5G8jIChGripGRHSZWHadew1wcWzJgVHfOu+unx86fE1prXrr5LTat2srptx9Hwxb1/63t/bvxc6/lXwT8/pvjvxn4vXXfhzx1xcsIKXhp5d9p1OrHfQPLNm9n8ffLGHBgHz74xxc8c/VrANz45sUMOqQvW9aWcNfpT7J0xmozMArB3sfsybfvziCcHSFWa3r8jACWkUBJP7Ys8zyYOSkB5ETwA3NwbYGbaeNmWniOQFrQJC+TDfEY0fqSZIbGy1KQpSHiIrJ8IhlJMmyXjHCMiKWwLW3aPIQph7q+aXAeMG09R34wi0Gz16S/68YG+bw+og9vDu7L1tw88EzZVySlkSyICpy4QLogXY1TC1bCDEp2VGFFFUIIZMLHjntYURdhSbTShJTCiyWNzZ3vpxuOUcFUOWZ6VdKXoFbsd9wgPnvhG5ywgxtP0rRdI56de7fpF0q4SClIxl1O63Ix5VsquPqVvzHimEH/72+/vaSSj58YT6M2DRly+ICdtAD/G+O/+Tr+teOXHKvJC9Zw8aPvU1Qvh3WRGqKOxsv0IVOjLYWVGycc8ok4SfIyEoSkT5YTJ2L5OFITlglCIoGURitPYyGFHxAWbKKexNOm0hD3DbBL+hZxP4Tn24GciSCRlEHJ1pQ/TQYPlGcbpiqkBYoNcgv6QlwNdnDdyeD9VHYsqRBhDDhDpLNweU4GlYk42gr61ZDmr1GfJq1EHBA0RBrxmLd0kOkPTHkJmCR1gn6QnhwTZNcQAi11apOmmS712WLn7RslawKfdeokWVJ/AxUu4QUALuj1k/FgqAyye1iBtW4KIKYyfF6Ab4OvityhlJveZ421w+6mtQC9INsX9Pqlv1Iy8ClRypSTAXxD5sBV5EQcOrQrYt6M1aRq244jee/7G7Adi6/em8V9l7++g4OSmWgXNcmjsqSKeDRJ0zYNqNhaydEXjKL9Hi14/ua3WT5nHeGMEB8U11ma7kqsWbiBCWMnsc/xe6XF9f8I8auWej/88MOf/cGHHHLIz152d/xnI9Wz4ITsnzSZVkpxVs/LqS6vMVpyQqK1JjMng26DO/LJMxN45ILnCWdHDGDxfTJzIuTkZ+F7imhlDDvi0KJDI5ysMBtWlRhWcGBzZma7wgxgljTjZTSBdmy0lKAkTsxHKk0oZKGVpixaixMRhCs1dgy8pIUb1agMCz+qiGZKYlk2TixMRoaLE9xchDQjvw56hCb2bcuE3u1pvn47R306h0O/nkezbRVc9uZXXPT213zZqxOvDevPxI4d0GFAGgsqLwQyIRAJgZKakC2wXUHSElgZFlatjxWy8TMsMvMzSMZccD0OOrg349+aQdL10b6FCEreuIBStOvbBq8mztqlm4IB22b2N4sRoRCDx/Rj47JNhDNDVG+vJbcgm1Dwm9mOzfNLHqJ6ew0Nm//0DHP1gnXcftzDVJVX065na2Z+Pg+A1+98n6cX3PeHdKH4I8Xuce+nY3D31kx4+Dwijs0DX07i2UmzTYtEAgiDn7RxpTTlTwkhy8Vo1hnUELEkUmQCPo408i3Ggs1k8BLKIu5LtLKJejaecvCUmcQJwPMCZ46kKQejNSppqghYpidXezJAMKR15NK6cElpJmhoSF0G6QmYDVFABL29UkISotqHiG2YySkOrhX44aYyapbxGBYB8YsdLjGhQHjS9NYBWgbb0QasaYUBoxpDlpGpIrJIJ/LMigTNdyLIzkm0p1NV50B/z2TtgDpGrq57nt6YB5ZrysSp0q6DQFZr/FQSdEdyRvDZdRlBTUQKEjEFtjDbcM3GO7VuyLKVW5Ha9PURgE7DajHgVyhtehG94Hj6CpHq+fM1tTUJ5s1YQwr0GaFnhet62I5F8VojN4ZSOI7EjZt7UvcBbSlZt435U1aSXy+Tp7+9jncfG8/0z+ax7/F7sWn1Nvb7y16/8OyH2457iA1Li5n2yWyemnvvL97O7xU/C/gddthhOz1PeSr+82vAjzLfdscfIw44Yx+admhM/aYFFDauRzKe3EkGBMy1FQvcHNYt2kjbXm1AQ4febcgtzGH1gvUAJKIJQpkhkjGXaFWMRi0LjZZf3CUnN0Kz1vU59tIDmTd5OU/d+A6FTepRtq0GCMq/SoE22T+RVHTv3YoFs9chHAulJNIVEDLZNFcaxm1YaryQRCQ0VobAiwn8qMSXEfxc35Sdwi5WxMeyfUKOqUtYwYzd16bZeVWTBtx+yv7ce+xI9vt+Ccd+OZO+y9az/6zF7D9rMRvq1+P1vfrz5p592ZaVjxfTWEgzyNump08mIRQ32QXbsfBjGsuHqKuQvkTYDh9NWAS2DHRivEDg2kfYEmIJ+u7djb2P6M05I+5EeQrt+2zbbMrR3747I122+OiZCZxwxc7AIiM78gNh5lT4ns+Hj3/B8ze8Trwmgdaa6u216fc3rd6K7ylkaAfNRq2Z+flccgtz6Niv3b9/sv0XxO5x719HdiD4ffl+Q8mwHJ6dMYuquGv6ZW1w/SQ60yaWULiWxPVtIo4LSGwZMWxXba5LIYyzRMKXRshFQtK3cD0b1zdpMd+XJGKOuQ590MpCu0bYeCcfNyVplZvP2vJKCGGu3WqNlqBCpFmzJI0fMAJj2RGUW4U0pch0pcI1ZU1fm75CLXXafUIoUZe+8gFpgJ95T4IHOsgsCkRdKixdfhXpkqoGSIh00nBI8xZMWr/BLO6aUrNIlal9bUCZZdaTSIgFld5UWdYNPkvsUKrdoUwsNZAMvmZA3JAKQlLgeSpNLEmDZV+bbQavG/0+jXIVDkZ/sFnjehQXb6dJgzxCcYWdVGmAmhJhRmksBXsNaMvk75YxZEgHZk5bQyyaDMq8piIiUs2ZWpOdm4EfSxCOOPztlsPJyAxTUxmlYeO8oKFSk5mdSWWNqaL85dIDuO3Up0AptpdUsXzOWp6+4S0ATr/pCN5e8/AvPu8BmndozIalxTTv2OTf2s7vFT8L+Kkd9Nq+/PJLrrzySu644w4GDhyIEIIpU6Zw3XXXcccdd/xmO7o7/v0QQtBjmOnte/ehT3ji0hcYPGYAN759WXoZy5Jc+PiZPH/96ww/ehDHXHEos79cQL/9ewJwwtWHsX5JMXn1c1g4dQXJaBJpS9bMX49tCXIb51O6pYKJ789k4vszadWtBWhNWXF5XflXBQ0lngYpySnIYsmUFUjLZOYsJ/DtTGUHfYUXtgi5kpCtSYYlXgKsiMaPCFRIkhASndR4IYGfofAjPm4oYMjZfqoibcYwbUo7UTvMu4N78s6g3rRfX8JxE2Zw+OQ5NC/dzuXvf87FH47n626deLt/X77u2AXPdsygJwXYkBSm78+XAssCOwnKlghHYCU1vq+RWTaOq81MVmFkbgCyMhn7zLe89exElJBgCyJZYXoOaM33n8ylYbMCSjaUAfDynR/Sbo+WDNi/x8/6nb946Vsev+TF9PPc+jmcdvuxeAmPmePns+foXjs5hwB8PXYSd574CEIKnp5/Py27/HHKF79X7B73fl5U1MR4d+ICotpDZhhw40cFZIXwogoVEjghH9fTJP0QlvSxpR8IrKfTV7i+IOlZAWlD4Hqm7Ov7JoOIFijXTN5IERlSjFQvYKl6JmtVURrFdgwgPKpzV96du9iUW5NmvokHcgenhlQyC4uAgWoycDoYsggqwyoF7vxUH2CQYdOCwFzDAMVURg7QXgAGdV15VQe7L9wgM5eq3Oqg7Kph87pKZNTsE1qYzSUDhq1lMoJEISD4IlLl5KAvMC2tEvxNHWl8s8lU6RdfY3smo2YLgaryEDZI33xP6RJk5sz9QSU0Ecci4XlpgWbQ5PkWDUWYV964GICR+99jyt0QlLc1lhDkhGyaN67H1g3lSFcxd/oa7nv4eF567juEUkyfuByhNW07NiJaGSUeTTJ8VFc+eGEy8ao4OTkR3KTH3w66n83rygiFbZIJLy1NlpkTIb8gh8v/fgq3nfYkXsLDTXrk1c+hpqKW9j1b/tvn/HWvX8S6RRt3clj6M8Uus3ovuuginnjiCfbaqy5Nut9++5GZmclZZ53FkiVLftUd3B2/TXz/0Uy0hmmfzP7Be/udPJwBo3tTVVZNvYZ57HN83W+9bvFG5n2zCIBRJw9j4fcrOOy8fXn8CtMH6CfdHWoSUFlWRZrKlqa0BVNIS4Lv48YSZoatXMKRCMmka0rMjgTbwgpEQD1boiMWjqcRvkTEQGZJsEHEwM8UWCELL0Oisiy0rdCORjkabGUGdRloPIkAAPpmcF7epBE3n3AIdx11AKOnLeD4b2fQb+Va9p2/mH3nL6YsK4sP+vTmnV79WNagadCnCK4jsJPg2wJsjY6D5QiUrbGSGi0diPnGDqgqaQ5NyDYzcxc8pSEjjIgaR5QbXz6f2qoo7z7+Ja898KnR9/N81i3dlAZ+WmuUr7Bsi+rttdx1yj8QAi587HRi1TEaNDdae3bI5rwHT+aVW9/hqv1u55pX/8Z5D5z8o+eDHxBPDGvwl55V/72xe9z76Tjrnrcoq4wiskzWCAEyaaGlAhuUsEgoC9vx8ZQCjKQJKKSlDXNVG2Dh++ArC+VJfDdgR6Rqjqk+tjgIYUCdSASac57Jxhl5EEEyrgjFzfMpM1ebPrVUP50M/qbmliponVMYQkWAB2VAdEAHAE3UKaKke/FE0AtnVGZMD5wv8GzS2T0pgz7Df44gGZYCY2CycNo3OnfF1VXYIUP2Ta2d7qXzzXe2/qnNzxBDSBMwNCbzl/LDFUIERIpUCdf8iwCJpOnDA0w7SlKlK8pok+G76KTh9OjajOuuf5tNsUT6CwgPqqtjzCtfzzlnPktuRphM26Y2ngTPR2hBKGQxsG9rJn29lGVlUU45ewR+0mP9si1cceqzPPPhhXz02jRmJBYDgqNPHMjdFxvGfL2CbPoO7UhBgxw692zJB899y+a1pSAE+Q1yKFlXavbb94lur2Hp7DVk5URYF+jQTvtsHi/Nu4tkwiUnP+vnndhBTP90DjUVtQw/ZhDKV7x530d8+9b3dOzXlgsf+3O69uwy8Fu1ahV5eXk/eD0vL4+1a9f+Gvu0O/4DccZdJzD2rvcYeuRAILjxa41lW1Rsq+LkDn8jWhXjurEXMezogWzbWEZOQTaNWjckkhXGTbiMPn0Elz19Nlpr3nxwHGWbK9hjSCfa9GjJG/ePAyE44tyRLJi6kq0byikvqWTU8YN558mvg0HYgI14jRlAhBDYvsJNBnWbpDCl4JCNUhaWMr6ZOBLL04QFuAmFH7FwHIFfo0lmClSmxKvVuFkS5Wi0Y5iGhIK/VjB4E5RoUtNvIG6FeHfPvrw7oC/tNpVw1JRZjJk2m6LKKk6b+B2nTfyOxY2b8G6vfnzUpQ8VkWz8YKavLYFvgZXSupKgLIEfEviupuseTVi3eCuu6yPjHhIb7WuwNC1bFHDcefsARuV97dLNAFiOzcjjB7F6ySYmfzKHAaO6c/HI21m7uJjrXjmfmvIaZn4xH4Dz9ryWqtIazrzzOJ5d+ADhzBDKVzxyvtFYXDVvHcOP/nEiyMgTh5KVl0luYc4fqln5jxK7x72fjjXFZYiI0b9UgHZMT6wimLxpMyny3ACkBc4d0jaow2iXS7SrUcp46WoljC6MEuCaDJ/0rLoyrTJgQ/qizuPVM5hOJOuAj9BQXh03ZUsLk+VKgaMAyKVQlYVZx7bATQaALXjfEC0CHoiEkGXkSgjaClvk5bG1tIqkNn19dqAxrO26TB7UOVeksoU6lYbzSZNy0+BOgEzsNI+GHd5PZyiD724FADjldZti14rUl3NBouvIGEHfnp0AlCZF91KY58LTgQ2bmcQO7N+GIw/szfRpq4hWJw3ID3YkYlu4wZdcsXSLkXcRRnLGlLUVfpXHpC8WIxxJt14tOPTo/rzx+ASEr0j4irKSanoNbMvbz39HOGLTql3DdDavdcfGHHPO3uljkFtgJKykFJx82QGsWrCB8WOnUlMVJSMrTOOW9Zn2xfx0sqHrwPaEM0JpabKfG0unr+S6Q+5O/1YVJZW8cMMbAKxZsJ6DzhxJh75td2mbf4TYZeDXr18/LrroIl555RUaN24MwJYtW7j00kvp37//r76Du+O3iY792nHTO5cDUL6lgnP7XkW8Ns5DE2/BDtlEq0yf35yvF/LgOU9RWxmlfrMCnl/yEK+t+Ttu0qNeQ3MjFELw6vKHqK2Mkp2fxZdjJ6cbcbsP7MDh5+3LI5e8wmevTOKdRz9PM3qtkB20cNRNfeM1MYx4qjBN1b4G10f4viEjOMHgH7FBCuy4sX2rthXCEUQSgmQ1eLbEzxIkswRuRtBI7Wh0xDxOUc+0BXUNO9TN5DWsKirirsMO5N4D92fIkhUcNXUGIxcsosvmTXTZ/AFXfvYR33Townt79GdSy0740jb7HYA+kQBpmSyAbwkWbihFRgTCsoxPpK8QrpGE2ZrwkDtYoZ1x4+EUFOXSd+9ufPjs18z+ZgnffTyHMWeNYMXcdQDM+nIhY87bl5z6xjki5Ue5YflmjrxoNEunr2TG5/M4576TKN1UzlGXHgwY95AHznyCcGaIi588m0hmGCEEgw758yjP/6dj97j302FrgZ/Q2FKgYqBjQJbA06lhwEK72vS7CpOF10KikkFzWSoTF5RwNRjA58m60mQgKizNsISljH7djlkrPLM5J+htSym1WBJUwmTwsrIcqhIu0jcfmbIdF1DnSOGD4wRjA4FsiUy3D4KC7JBNteumly9ZVUmjBjkUV1cboWPM52kBXjDGpPsJg9JuilcivB2yicFyKcFjrTTaDo6PZaoUQhpgZ0CjTm8nxcgVKZawCMrIqSyn0GnnDXNcNVKJuiyf0jTOziIzM8SaTduNPSV1bTfTJ69k+fIt/OORL4iW1xKK2HhJ4118yUX7c+8dHxsZmxSrWaV69IJScXBPkK5iyKD2fPHWdAob5LBpfRnd+7amY/dm3HL28+hYgngswVfvz+a5L68kVpugWZsGxKMJHrjoZZJxl0se+gv7HtmX8a9N4d5znyevMJvqsmoQcNkjJ9OgaUHaDUQKaNG+0S86tyNZYaQ0uo6ZuRmmAhNE+96tafknnSTvMvB77rnnGDNmDC1btqRFixYArF+/ng4dOvD+++//2vu3O/4DsWruWso3bwdg4aSlHHzuKK57/SI2r95KyYYyaiuNB23pxnJqttf+wGj6vUc/Zeq4OZx+27F06NOGYUf0Z843i6nXIJeOfVrz8p0f8NlL39VNoYOZoZ/0zMibKgETNFXjm9Hassw6QX9cVl6Y2piLJQWqVqMDcoKKuhQ2yaW2PIG2BMIW6HoCEpqQEsikQIeEGVgzBb7QkGEGZR3o/mGnsoAEd5LUY/CFxTedO/Ntx87k18Q4ePYcjpwxgz02bmDfpQvZd+lCKjIy+axjD8Z17M2cRq3BN83q2tZo11g6IU0PoC01vq0RvrGCU66ixvW59aq32O+gFWxZu43+Qzty/l3H8f7z37FhQyU4Dkpr3nliAl336oSbcEn6inmTllFTae6GR196EEIIDj13JPFogiv2v4NENEnXQe15YMKN6d/ru3emMuXDmQAMPXLgn8pq6PeK3ePeT8fe3dvy5cwV2AlNMk8a8BEDlEDFwc8A4QTXgpQIz/j1GiZ7ClFhLkAV1CeVCBwjUhkj89wOxH9tl0AHz6xrJYOypgCRNOSBVKYsJAXCtujRrgmz5q8nJE31OJUhM+sFqf+gzmv5QUEiAGBYGIcPz2wzHk2Sl+lQE3fT65RsqTIZMifV1mL22/G0AaHSlFhtBJ4wr4mg31CIOpIFwZgkUrg4HmBjYd5LWZ+hQaTEGVJVhpQAcyAZI4TR/5MBUzakBb4bHBxPG8FpPwBrSlO6pdpwXTwV9BYCrgpcQKCstIqOnRqzcX0ZujaJHeD2vNxM+vZuxcK56xFaEU/4Rpu0KJcefVuzcOoqtpVUYwlBSMKTt30EgFaKTr1a0rh+Nk/d+j5Txy9MS4R169eGytJq3nzscw4+eRjlJZV899EcAF5/+HMmfTgrfQ6GMpyg7K544bZ32bRqCx16t+aGV86nsFEeTdoU/eC8VUrxxr0fUVsV5cRrD/+BxFVtVZSyzdt5dOrteAmfznu2B6Bpu8bkF+XRuPWPy6H9GWKXgV+7du2YP38+48ePZ+nSpWit6dKlCyNHjvxTWjvtDui1TzcOv3A0tZVR9j7B9DANO8qUgNcu2sCSaStQns9Rlx3yA9CXjCd54rKX0RrGZn/Aabcfw6fPf5t2ARl0cG/Kt1aSUh3VGpPFs2SdwDHAP0uLpESPA/2/5u2K2Li2DCkESgqkLc2NwjI3iz1aN2TazLVoKbAcQUM3TNs2jaiqjbN4aynKNsAvngcyA0iAkgLtCFTKaFKZ3jycgL0p2ekKEVpQmZHJq4MG8+qAwXTYtIUjZ83g4HmzaVhTxbFzv+fYud+zOSefTzv04pP2fViZ38QATKmRnkB6ZtCXrjJlKhsDVF2BtASffTYf4Svmz1lHLJpk7MNfmOvKtsE1qYyew7rw2v2fsGJhMRPeno6TFSESsjjg1OE0bFGfi0bcwsq5a1GeyaRuXLmVaHUsLb7de5/uNGhWSCgj9D8n7PxLY/e499Ox3+DOfD1tBWjIi9t4uZJyN4EtBdozGSMdAt/C3JwlYIugBBxM9rTRkzNan4APdtzIg6R0ioWCNnn5DGzTnLcnLCCwtiXDkiSTKdG+oB9PazIzQ0RjLkP7tMWvdPn++zXILKOxKVIECR1kBlNjwA7ae1aqR8/T5GVHqIwl6ggSWhNSEith9r9bmyIWrdmKI0B5Op3h067CjgjTy0uQyfOUwYaC9LkTClvE09/BZOb27tuO+Ss2UV4dJUUyTpWvUwRhHSedPRRA++b1WbeqFBdtgKaGlk3y+cuYAQzp044b7v6AWQvWE3YsEsbqA+FqhNDkZYap9ZKELEnSU0brMLB8xNegNAtnrmPzqm0IVwUlZ0XDojx69WvNy49+iVcZM1k/S1LUtB7X33sM7Ts3oboyyrsvTWbsPyaQACNQ7xm3pXVLN7Nsxqq6E0opcvIiDNi7C2PaXUK8NsHkT+Zx0hUHppMGi6etIFZlEPHgg3pxwIlDuO6oB0Fp1i3eyNPXvI7tWLy59tGfdCCa+/ViXrjxTQAat2rIgWfus9P7V+53O8tnrmLokXty3diL0q+nAOCfOXYZ+IE5WUeNGsWoUaN+7f3ZHb9D2I7NuT/R9N+qa3Men3EXtVVRtm0oQ6cahINwwg4DD+nL9E/n0G1wR87qdSUqcOywHIvcgmxOu+kIcvKz+OCpL0nEvaDcoY2wMZDyMApnhuk1tCPl26o56pyRTP9qAePfnglKc/GdR3H1SU+RTPpG/d7XhmLnmylnz96tmDFpBUjjt+tWbGf5ukrieSHCGRZeRODbgqxqoFqgLIhna7QDSoh0xk96AhKghUA7GH0sCEolqTIKoAUrGzbm7v0O4b59DqL/6pUcMn82o5bOp3F1BafN+prTZn3NioJGjGvXm89b92JLZoHpabIEUkrTC6g0QkgsIfARaCmQrkBYklefn4RwLFMyyQjTuF0jzrzqAJq0bMBrD4wDy8J1FSgYcuSeXH3kw5x2wxiWBwLVwpZoX1FZVsPZ/a9lyGF92bahjP1PHsara36ZcOn/cuwe90wsX7iR8e/PZt/DetOhWzO6d2iCFVMoC2r9JJ4HdthkvXRIQDKQbguud+PfbUqcnoXRf9PUOUR4dUAPTdrtoXfzRvx11CDueeErpB9kzHyNG1M4EYkbiABbvimznnBQb046pD+RsMNeR91nypa1CiwRVJe1KedKAv/auvLuTrIrGloU5JHwfVZuLCUlh5IRkSjL4YDhnenRsSk3PDbOALR054g2una1GgfIyQkRr06StqmVZnIpPY2qUabsbNd95tCebenaoiFPvDEFmdIElCYzl9KKFkJgWRI/aUDU1lXl6KQKyDOQl5PB0G6tefiucdzvKTQwsG9rZk5bjSMCzeeg2bG2qtaQknUgHSNMiVNoU7Oul5fJe69+j+/5NGqSz9bNFQil2bZhO9ee9TyrFhWbCT2A5zNi3644luSxm95DSsHBfxlMVVkttiMZeWhvpo5fyLatVbTu0Ijn7v4INHixJAjBQYHGXupnCIVtChrmmT50SzLq+EGUbChn8EG9OP/u41g8bWVQLSIgDxrdWjv00xCnWftGZOVlkIgmaduj5Q/eryozLOEUW/i/KXY7d/xT7Fb8/2G4SY+T2/+NbRvLOOf+kzjiogN/sIzWZqZ1dp+r0q9d/sw5jDxxCADP3/w2b9z/SZ2Fm4D8BrlEaxK4CTPz7Ni7FQ99fhXbt1Vx6oAbSMTctCJ7dk6E2oSHFjKdBURKtBA0bpaH8jTbY66ZNacGM0vihSRuhg22RNsC1zFWcNgCfFCOwMswJAwhTAkIp66fR1ugQzuUgVOSDSnmXKrHKGDJhRMuw1cs5sAlsxm2ejGhHfTd5hS15vPWvfi6xR6Uh7KRPkjXMOlk0syuTcO6QroK4SoyfI1bHUtnNoWnEEqRmxuhalsVKMXeh/VmwhtTATP77dyrJV+8+h1rA0YbwQxcuWkrA+75/Fp6DOvy650kf7DYfR3//NjVY3XGQQ+ycU0pzVrV55lPLkZrzWEnPkZ5VRQ/LNFSEGoQoibh4lugIgHYE6ZcqVOM16CvLV1OlTuwa3XQWweQ1NgasrWNTGpirm+y9AE4dHxB20YFrA5YntISHH9oPwZ0acE1t7xL2LGxch22ltWk20qkECit0wxe49tr9mlHUWWUNv1zgR2Ztoxsi9ZgeYY40bQoj7uvPZyTL32RhAq8doVA7ECk0ARjhAAsiZ9qQAzKs2n5F6XNsdKao/fvzXsfz8YLihuOlPhx3xw/KdBKYyUx0iq+H0i5aJPJDD60feMC1q0tNd8tyBRKMKQyUq01GuEG3zOQzwLAVzRtWg/bkmxYvc1MQINtn3fFaJo0K+TJe8ZRvGabmeyn23hMdvDsqw/i5Qc+MwL+QPtuTbnrlXPIzI5w3n53s2bxJoYd0purHjuZqvIarjvxcVbM30Cn3i158MNL+eSFiTx29Ru06NiIm146l6LmhcyfvJz8+jk0bt2Ab9+exou3v0erzk25cewFfPHqZKZ9No+Bo3vSrH0jmrdvTH6Df30+x2ri+J5P9o8wfYtXbGbqJ7MZfvRACpsU/Mjaf7z4VZ07dsf/Tsz7dhFfj51M627N2e+0vYlkhklEE5QFPYAbl2/+0fWEELTq2pwb3ryYjx7/gg592zLiuDrD64GjexnglwJCQlCxtRKEIJQZxk14DD6oFw9d9BLffzaPRDRplgsseGoqo4EOYNAPGNwhhNZsWVtmQCBBr07IRluGIieTEFYeyhIox8K2BKoG/CyJkgJfSyPOammwjRSLTgbZCtsM9H5co+y6rKBO9b4E4+uO8glJ22F8hx582bYHufEYI1fM58Cls+m3cSW9tq6h19Y1XD7tPWYXteWrFnvwbZPubLeywcH05FjaeAZr812iQIbIwHONSKkWhv5XGXUR4RCRsGTVihLymhQQq03gC3jpgU9x4y5terUmmXApalZIr6EdefmWd0lEzSC8e763O35pdOjalI1rSmnftSkAlRVRkttjSBVoVQoY2roln85bgSXNBEs7plYpLSBpMk3SSidnTChoYIcpj8UNIzSYTElfgydIKDdw0jDXtQGGmivP3Ic2zRvw6gczGDagPYP7tKGmJsEJpz+FAmIxl0IpyUAQ90yqTHsKywrAny0MsBN1fYIIgWMLXE+l5UxSIsZpD9uAkluyrZqTz34OZYOdrseSlowh9Scom8qUJVkgnSQcYfQBlUqD2bzsCL26N+fdD2YZyRYftO8ZwOoGYE2DRJBMeAgpzHxUpfbLVBPWriqhDu2ZfVJe0LOnNCFbov0dNCt9VbevwNB9uvLm09+kM2+4CsuWzPpqCe+tKqFr31ZsXBbcE4LKjdAQyXDoNagdL9xTN/FdMW895x9wL898cy21QV9yTWWUhVNXcs0xj5BbL4szbzwM7flMeHsa37w7A+35bFq5lUaBB26PvToCcOMxDzHt03kgBKXF2yleVcKBpw7nwFOH79K5nJEdYXtJJeOe+Yr+B/TCTbg8fP6ztOrajLPuOfFHkxz/DbE74/dP8b+cKVBKcXDOicZyDOP0cclT5wAw47O5LJ66nDEXHEBuYc4v2v5bD4/j2euNeno6ayfAcSxOu/FIhhzWlxP3uBKAekV5ZOdnsXH5FpDCeNcGGavhR/RnwbRVlG2tMiNqygPY1CcCsoY04E8Z0yMiNr4UEHEM6BHgRyQqZJtyrxT4jkFyWgsImeyECnS9lCNRNijbzOYJ5CF08FVSY33KIzOtFhOUrxpWV7L/8jmMWjmX7tvWp4+JLwRzGrThq2Y9mNi4OxVOFsLTRiU/MCoXrjIlYVeZjJ+vkJ4PnkK4Qbd50P+XUrrHV1hS4yWNgmtmdpgX59zFoinLCWc49Nq72y/6Df8s8b98He9q7OqxUkqxZeN2GjWrh5SS2657h68nLsN3BDpkenf79G/N5KUbzITLBmQqm26u0bQTRJCKUrZJo1mJYGIlMCVfabJTEnMtOULgJQPgFJAc7JiiR+emPHTf8Xz+2QLy8jJYv7GcJ577NtBoISAwgLYljmPhJv2gjze4loVAp2ZzZg5oQJ8tTCYtuNAtC/yUpZgCIQXaU8bRI/gojQGA3g6v2EhU4NVtWdLoGQZOFlpjMmmpBkJf88w/TmHrpgreen8Wc5cWG1AXkCxSt2wz3pjWm0ED2tGoUR7vvj8LlOa4Ywcwd/Y6li7dnNbwS/cHknoejCk6GNOCbeYXZLH3qG7s0bsl7To24uK/PEXZtur0Oo5j4wUT8wOOGUBFeQ1Tv1yYziIC4Pm07dKYrv3b8OELk0gTTpTmqQlXIy3BzK+XMPTgXnzw9Ne88cjnwe/kp79f90HtsWybEYf3Z78T65IIAGf3v5Z1SzcRyggx4qg9+dvDJ2Ol6tu7EC/c8AZv3v8RXtKjdfcW9B3Vg7fuN8STp+be+6eTttqd8dsduxxCCJp3bMqquWt/8F6//Xum3Tt+SZQWlzPx7alo37h2GKkWH6TEjbu8dPt7jDh6ANKWKE/RunNTlsxYDUBeQTaVpdUIS9K6WzP2O2YA37w9zdxAbNsoykvwPJ+i5gVs3VwJSY+2XZqwekUJKB8VU1iOXZdisC2kq/AjGuVYRnbFloZYaJmalPY1hAS+BN9RwQ3JEECUNA3rOihZpQCg2GH0T0lNoGBbZh6v7jGcV7sNp0llGSNXz2PfNfPoWraBviWr6Fuyist4jzkN2vJ1k+58W9SdKicT5Qd6XH7Q1yMF2jdEFSkDcJs0EvwCSDX35BZkEbYE24q3A4JYbZLzht/KCZcfSPPCxr/4d9wdu0NKSZMWhennXbo145sJixEpIQ8NQ/u2Z8GyTdQkfVM5lAJLGdKFkymJK2WyXSlZk6RCWJKQLXEsiUj41CY9bG1Y78oyzNTrz9ufJs0Kuf2hj9m4qcIwUzUsXLSRc89+nlWrSgC49LIDkCpg0yoDmIQQnHb8IMZ9Np/NJVWk6qG2lFzy11E89I/xhiTia5QQJvPn6bqMn9DoqMl4AQwf1pE5U1dT6fvGeceRDNurE/vs3YXrr38b3zNZNFNa9ZBotCXp17s5+fVy+Pzz+WYPFCb7lyJ/CPjso7l88N4sEIKIFGRkhah0vSAjqI11W9DnZ9mCa64+iE/enU22ZWFFLLxal+VzN+CEbTzPgLvC+tmUl1Sb/fFM6jSV3RNa06VHczp0acJZF+/H4jnrufLkZwxIDUgYACiNl0zQf0QnyrZWc+hJg6gur+X7cSb71qR1IZvXlqHRrFq4kcKGubwy9UZWLS7m0avepEvvljRp3QDLkjQNWLELp6wApXHCNknPS59XG5Zv5vWlD/zoOXjty+fzzVvTGHH0njTv8MvHs7ce+BgvaT7TCTsMOrgv4579ipadmtKk3S+TgPkzxM8CflVVVT97g7tn13/eEELwyJTbmT9xMVWl1Qw6bNf1yWK18bQu3I7x7LWvs3zGqjR7V6dmnKn1qmN4SZ/npt/GginLuf+859PrRjJDVALK8zn16kPoPqgD/Ud2Y/3yzex5QE/mf7eExm2KmPzZAkrWBKUNIVg9b72Z0ds2Uiu08kBZRovL10hbIms0Unr4SqMiNtqWoBR+hmXAnScRGmzHsImxjWk6rk9Bg2yK41FUpkiXfXXwv6BNp05jLP1Pszm7gFe6j+DVriNoWlnGPuvmsfe6+XQt30DfbSvpu20ll/A+cwvb8HXRHkxp0JlyJydtr4QGGRb4Cc94dEZCCMdG+yYbiFIUtqhPv6Ed+fSFiSTiHolYgm3F5Tx04Uug4d6PL6dL/7Y8e8ObVG6r5px7TiCn3q4p2v+3x+5x76fDTXrc9teX2LyujGv/fhLPvHIWG9aW8dU3S6hfP4cF8zbgbYsjMywj5WKZdJYAcrTDFacP4/5nvqTWNaBCKE2/No2ZO3cDHibbblsCPB/b06a1w4eHHvmcpvXz2HvP9oxdMS1gxgJKs2rlVlKyUI0b5VGQHWb79qjxWA560D7/cC7JhGtKtmh6dGpGu9YNyLBssrGodF1D7AoIDqmMWiodZ8gnZuxaMWcDNdUJLKBT2wa0aFrAmEP60KpNA+pnZ7IlIAdIQAXZPaEUMyeu5NDD+yI8UyawhEAntdnHYPI4+bvl6WPt+4qairgpkwcTSdPpYoDl6WcMY9JXS3jm0S/RwIGH9WbhnLUG4MVdcrPCHHBYb955aUq6xGyOUl15u237Ih587gwScZfLT3yKdStL0FrjeQGBLpBzQSkyMkNceNuRFDTISZ8LQ0bvwZTPF7B55Vb+euuRfPDid6xfvoXc/Cy2ri/lw6cmEK+soVOvlliWZNmctTx04Ut07t+W8pJK0Jr6jfNp1q4hc75Zght3CUd+Wmy5RccmnHTdmJ98v7YqihOyCf2LbQCcdOORfPb81ww+tD9HX3YwuYU5vLftuX+5zn9D/KxSr5TyZ0sW/NnNyneXiH55vPvwJzx+yUvseVAfbv3gip3ee+PeD3nu+jcIZ0dwwg5HXDiatnu04Onr3mTLmhL2PnYQF//9VIQQfPriRB6+6CVSNm9dB7Rlydx1hCMOT3x3ExXbqrho1J1opSlsUkD51koy87OIpvoCg5JvymRdhhyatW3I+uVb0CltQFG3nHZMylADOBZaCgMAAS0ESgRgL2wRdwwJxPJME7WbIfEiAm2LtDZrejqVKgHv2COkdu4LTAvQ+pom1WXsvX4+e2+cT5ftG9LHTiFYnNecSQ26MrmgE5sihSbTkfAC8VVtbJF8U+JFGQJIqj8SpQL2s0DH4qA1t775N0IhmytG3wXA2Xcdx+F/3Z/ayih3nvIPlK+46oXz0gr5f7b4Na7j/5Vx75ccq+ULNnDhmEcA6D20IyuWbaGqOk5+QRYvfHopN9/0HtOnrzYZdGmuDwVgC8JS4sd8lAVehkEzQgsGdW3B1HlrSfl425YwZVUg5TIhfYX0NI0b5ZLwFGWlRrRcYHpilW96zCxf039oB76fsiLtmmHbhv0K0G9gWzp3a8ZLz0xEAJk5YWpqE2kSCghycyJUV8fTBdudvAwD0odWQdbSN2CqfcdGlJVUUb49WqcrKEXQgaHTVYFOXZpQsrmCmtoERUW5lJVHSSZ9vKB1o9+ANixdupmE6+ElfJTnp6jRZGaFueiK0dxx/bsAdOnalCXzNwSVFMHZF47iifs/DZoVzdiC0mTnZVBTE6+reviKcMgiGTOfedHNh/H9+EVMn7gMgO792jB0dHcyMkIsnrOOMacMQQBZuRHy/2lcWDB1JVcc9SgAGVkhhh7Ui8/Hfm/6OpUy5A+M08aD465g3AsT+fzVyeawBlWgg04bxl/vPYGJ78/kk+e+5vDzRv1Lf/LJH8xg85oSDjln350A3sLJS7li1O1k52fx5Jy700YD/wvxq5Z6v/766/TjtWvXctVVV3HKKacwcKDRevv+++958cUXufPOO//N3d4df8bwXI+KbVVM/3QuADO/mMf6pcXM+mIew48dTL2GeRx92cG02aMFz9/wJq26NOOYSw/CsiQDfqR8bDuyrgfFkiz6fgX1ivJ47LsbKSjK562HP0tT91t1bkK0Js7hZ40gr2EeE96ZzpJZawMmq43yzKCZqKghLz+DyvJakBInI0wy4QYzbAttWwZn+grhWOD5aCmRMlVCFagan3DYRtsipc9KKKEQrvHq1SGJI4AYYGHM04OJciolmDYJSQNBnQaFmzMLea3jCMa2H06T6jKGF89n+KaFdK1YT7dK8+8cPmVNZkMmFXZhckFnVkWKDIi0JCIZ6GAoCYlEnTg2pG8auU0KSVRHufu8F7j99Quo37QelWW1ZAcD+Ywv5jPjs3kAPHbRi3Ts24ZDz9/vF/XP/Nlj97j309GmUxMG79eNTevKWLawmJraBFiSyu21fDB2GktmrDGtHDol7xHgEAWe8s28zAen2jPgCZg5ZRWNGufiARHHZvO6spTBThp85edEqKyKsWVDBUJD+05FtGzdAM/1mfTFYmTQt6eBoUM7snLhBkpLa0EIwo5FLOij8+IuLz/xNVbIxrYtWrQoYMniTXUCyFpTU1prrlkp6NSpMcuWbaZeXgblFTFEwPxNWbCl+vOqymspL60xIEyY/kRT0hZB/1wmTZsVsnrJJuJx4zG3cW0Zdz50PC89/S2LFxYDMHPSyvSxLijMonx7bbpiMPrgngzZuzMdX2nM8sWbWDJnPcIyPYoPPHUqmZkhQ/DwNULK9Fh58BH92GtUV+687HWK15UBmgZFeZRvq6F731Y8csN7aF8RznSIZIRYMGkZxcs3c/Kl+3PcOSOo3yh/p3Ngycw1SFvSsWdLxr30nTkIGsJhh6LmAQvWV3XEEa1RvubCfW/nr/eewNdvTyMUccjJz6BkQzl7BiBv6GF9GXpY358896LVMb56bRJ/v/AFs1mlOeqSg9LvL52+Cs/1qdhWxaZVW/+ngN/PjV0md+yzzz6cccYZHHfccTu9/tprr/HUU0/xzTff/Jr79x+P3Rm/XQutNef3v5oVs9dw5MUHUrKhjL3G9Ofpq16ltLicZh0ao5XmzLtPZPH3K3gzaJxt1KoB+50ynBOuMen6Leu2Ua9hHuGMEJtWbeWC4bcA0LFPa2Z/vRi05pCz9uG8+05k0kezuO3kJ7AsyfNz7qBhs7p+o/KtlZy5183EaxPs95e9+OKNaabXJnWaBwmchk3yKatI4PvK9MlJwSkX78+LD32BFtC2W1NWLNsa2EyZ7KH2FTpkYUVsjBuRpqgwm63ba/EiFtoSWAkfN8tChYxDgTTiDrghkdYPMwduBzIIqeyf3jkbGEhINIhVMKR4EYNLFtFr+2rslBQEsDWUx5SCzkzO7ciizGYo16yD6wW+pkH3eCK5AwhU4Ptc+8yZLJi8lA+e+AqEYORxAzni3H25+9THqS6voXTTdgCuG3sB8eo4GTkZ7HXYn8Pl49e+jv+bx71/91i9/dxE3ntxMg2bF7Bkzjq0Y5tyachkz5UTTBqCDLwOsu3SEoZhusO2skI2H312Od9+s4RbbnqPrMwQtdXxtH5dyoEiVQ049sSBnH7+SMa9N5OH7hxnNiKg94A23P7A8dxy1Zt8/91yIOj3Cwhf4YhNMumhNZx53t6MfX4SLoqEr43As0ql6gGtadasgOINZQaAKk2Hrk1Zt3ILibhHUeN8mreqz8zJK9J9tpYtjf85kNKMiWQ4dOjQiAWz1xLKCpOIe1iOZNjIrmxes40lC4vTPbrtOzZiw/py4rFkmqxl2mTMMegzqB17H9iDe657FykFp/1tJD37t6F95yb4vuLuq99i7vTVVJXVpI9tOGzz/oyb+GjsNJ6482P6DunA1fcdw9K567n+jGfxveA7pysFqfHCp6hZAS9Muj69rXmTl3PV0SbD98AHl3DJIffVoXPgxZm3Mu6l73jjoc8AGHRAD+ZPWU719lrQmlOuG8MLN78DwNl3HMOBpw4nFHH4OXHlAXcw9+tFaSu1q1/+K8MDwwEwZd4XbnyLgqI8jr3y0F0SWE/GkyyftZoOfdsSCv+8/fkjxc+9lnd5Gv/999/Tt+8P0Xjfvn2ZPn36rm5ud/zJw024rJ6/DoBtG8u5/o2LGXHs4LQuUvHKLRSv3MLbD37M8KMHUtSyPrmF2WxZu42Xb3kbpRQfPfUlJ3e6hLP7XI2bNFy4M289iufn3c0NYy8gO88or6d60Lau3Qa+j590Wbe4mK/fnkr51goAsvMyCYct/KTLl69Oxq+Nk5sdwkkJeQbZtZLiCtp3aQy+j3A98rIc9j+ynymZuj7FizYy+pCeyISHiCcRCQ+pNVbcZUCHxuw3sD1XX3UQ1996BHt0bMIeDfOpbzlITxOq9gnVeFgJU3IVvsZJKuy4wkoorKTCShrtPumZf8IzFgVpJq/SadBW6uTyQbM9uaL36Rw+9Dru6HwUE+t3ISYdipKVjNkylfuWv8jrCx7gki3jGBBdSdgxjeQ6uNkScowcjhRG5DojzLN3fMiHz3+HCDkgJV++NoWX7vyAJ2bcwZgL9jdZREuyet467jvzSW499iEWTVn2nzy9/jCxe9z76TjytKG8+u3VNG6cHzhT+FhSIJI+0lfYSd/oUnq+eS/p06ggi9EjuyE9BcE/4SqaB85A2ZEQMuoRK41iuQqZ8AkLgXADLzXPB8/ni/dn4fuKzRu2mwyjUhxyRB+uueVwtm6uQCc9I7HiGwKD8A3RIRlNpjPuLz31DdHaBG6ti4h7yACMjjqgOxlhC3xF8eqSgFmskEpTsbkSN+YhNWwr3s7sictTGu8IXxG2JH85bSgtmheSGZLg+sSr4iyYtQaAZCxJo8IsmjfMpV52hKULitOuHo+9fAaPvHAmDz17Kj16NjefqVNlbgMmZ01ZyZ5DO4Lvo1yPlYuKad+5CQDrV25l7nfLSVTHzQ8UjHluwuMft7zPvMnLefaTi7jlHyeRkRnm07FTAxs3HWRp9c6TREiXoFORiCXTj9987Au69W8LGIJc72GdKGyUz/GXjKbrgLbUK8qlfqM82nRpSiTTod/Ibhx82nC6DWxP667NGHRg758N+oA0+TCcGebJWXftBPoAsnIzOf/BkznuqsN22VXnxjH3cvGQ67n92Ad3ab0/W+wyq7d58+Y88cQT3H///Tu9/uSTT9K8+Z+L+rw7/v0IRUJc9/rFzBo/n6MvOzj9+v1f38iyGauY980ivnptEoeetx/terXi5RWP8N2703nsohcoalmfB895Jk3f37JuG7VVMS4adhNVZTUs+n4Flz55Jk9Ov50NyzbRfa9OALTr0RJpSTJzMrj1L38nGTeD0rn3HM/wIwewfatpyk8JQ1eV1TDs8D54vmbyJ/MCWQaBTroQMOWiZTV89/FsI4siBAlX8/mrUxC2HTBpg9mwr5jxjQE/+x3ehy7dmlG2fAulW6uQtqRFp0Zs2FwBSpryiwtIQSTDJprwEQiUTKX7RKo5Ka0jliqXmPJY0Fu4Q7Kyxs7gy6KefNWgJ2EvSd/ylexVtpg9ty8lz4uxX9lc9iubS0LYzM1uxbSsdsyItKbUzjbZBCkMaPYUWzaUm3KwUnUis1JSvGoLT1/3hvlQrZn8kfHElFKQlZf5659Ef4LYPe7969iyvgw3avpHhaewHdvIlyR940aT6qm1zETklpuPoE3HRhTmZzJtygpWLiwGKaiXaXq13nl5iilXBtecALxaH8uSKL9uUpSVFeaY4XdSXRVPu0189OpUPn9zBoVFuWwprjDntS2JZNjEogEDXmvDbAXcuEpn9ywp6di1KRddezCt2jakZGM582euNZdlIsVu1ZRurjAXpC3rStF+6sLV9N2zDcNHdaN45VbKircb0CkFWkEoYpGMu5RsqgBg/eptFDTIoby0Bsex+Nsxj5NfmE1R43yWLthowEsqew+gNXn1MsnMDjPigO5Mn7icFQs38sHLUxgwohOvPDaB6kAnD22knKI1CZyQxcevGqH32ZNXcNLf9uXL92YSjoTIyAwRjyVp1LQe+4zpw4Lpq6muqGX1omLQmpMvG73T791vn67Uq5/N9pIq1i/bzOMTruXuc56lorSai+4/AcuSWJbkvg8vY/XCDZw35GYATrjyYP5y1aEA3Dfuyl90ruU3zKOqrIacguxfXW6lZEOp+bu+9Ffd7h8tdhn4PfjggxxxxBF8/vnn7LnnngBMnTqVVatW8c477/zqO7g7/vix15j+7DWmf10vB5BTL5u+o3rQd1QPTr/j+J2WH3J4f+o3rceFQ25kybSVHHfVYRxx4QF02bM9uQVZWLYR37Id87ewUT6FO/SXZOdnMWxMXxZMXk5NRW369fcfH49lW3Tt14asvEwWTFtFtCoGaL55ZwZt9miRnsH6PiybtSZtCF6/KJels9eZTEKqn83302UloY0NHQBSEskM0aRlfb7/YgGxGjOzVkmferZkU23S6Ho5trGmchVqe4KIbRltwEiwnRQBJemiwnZA1SNdjk6LxKZn4KbcleoTTFoO3xd25vt6nZDKY4+KdQzevpg9K5bRKFnJgOqVDKg2vUIrww2ZltmWaZltWWk3SOt21ekpmjL0lE/nUtgk38hAuAqtNbXVcR6bejuhjBAtOpqswuQPZjD27g845Jx9GXXSMAA2rylhygczGHLEABo2r79rJ9EfPHaPez8dkz+dxyNXvUHV9iiEQyAEbiyJsHco8QYevNozJcRrzn6BLr1aMO3bZTRpVWh65RREqxNorSlqnAeeoqBBNl7SM0BGa1QysPOQpmS7abW5QadcNlJzKdf1zcRGGl1B7foM2a8b2fUyee/VqaAhFLFxkz7hDAc37qI8hcJn6ay1CCAeTTJqdA/y8jLJzIqwvayaWVNW4qd9ajURyyIeTdR9fkCoWL1oM2ce+GBwfZl96tilKSMP6UOrDkVcfuozO5VGt2+t5MCj+jPu7RnmeWkNVRVR82Zw/TduXs9kNpWmRYtCXNfn0GP3ZPncDWxeW8Yz933K289NpHRLJZGsMPHaBEJrYpUxhBBpfVaC7/b0XR8HPsl12b3N60p55f5PAcgtyEy7eNRUxijfWklBUV76eF/1+Kl8+spkDjhxLxZNX8WUT+YC8O37Mzny/Dpbw2hVzDgHKUWnPq3/7fPt+rEX8uUr3zHi2MH//8K7GDe9cxnfvvn9TuYD/43xiwScN2zYwOOPP76TWfk555zzXzHz3d3j98vivtMf5/MXvqGoZQNeWf33/3f5qvIazu17NZWlVdz9+bV0Hdgh/V7JhjKWTF/J9M/msmXtNi576iwaB5pPABcMuZHls9cgAiBmORY6aBzeKSxTfMlrkGP8FjV07NeWdcs2kYgm003XaI0TdgCB6xltwaZti6gsryUaTRpWnpsyEK2beWflRIjVJlBKmzIqcMRZw3j7+cngWCitadKykE2bTNaBkJNuHDdZCIuGDXMo2VplJGakQGNIIqmGbBEsLwKxWtiBIOLu0B+VXsaUl1tFS9izchl7Vq6gY6x4p56OMiuL6RmtmRZpzdxQMxLCTpd5tOcjhKBpywI2rtgCwJhzR3L2XQa8l24q594zn2LZzFXEKmNYjuTD7c9j2xZn9riMdYs30rFfWx6dcvv/ew78lvFbXMf/rePev3usjutxNRVlhjSV2yCHmpokGTkRampNObBB03xCEYeNa8rMNSlAKIW0zDViO5LB+3Thm88WgNI88c4FNGtdnyfv+ZRPXpua1srcKYLrMCMrTCQzREGDXCq31xIK2ZRsqQiEy3cOx5E4YYdosF9pIeR0uttsU0jBu1Ou4+Eb3+ebcfNp1LQej793AVed+gzLFm7EcmxGHLwHX34wF5SmbefGhDNCLJ69ni69WnDkqUO4/eLXTG+x3GHHtfneBx2/Jzn1snjtia/N56YuTj8AjqlVgklgvQbZ1G+Qy2EnDOT5h76gdGsVQmuOP29v3npmIm5A6gqFLHxP4fvKtESmiWN1mUKE4KATBuLGXT5/c1p6v5q0LKRxiwI2rS1l85ptgCnb+oHGXceeLVg2aw0nXDqaEy87EDfpcftpT1G8qoRrnzuThy99hSXTVyMEnHrdGD56ZgI9hnTi0sdO5bkb3+athw2Y/Mfkm2nT7c99vfyR4zcVcG7evDl33HHHL9653fHfFVprvnxlIgBb120jmfQI/QtzbIDcgmxuff8ypnw0iwbNdvZBbNi8kMrSar58ZRIA9535FJVl1Qw6uDcbl2+hYYv6LJ+9hmbti2jZuRlZuRFmfrWY8i0V6W1ISwY4SXPiFQfzzI1vk4gm2b5lO/GqWFr6QPs6mA0nzaw9KIV269OSzn3b8NAlr5CZE+Hkmw9n/FszWLlgQ3pgrq2MkZaF8TxatG9EvDKGiMXBtbCk5LDD+/Dac99RWRGFuGt67kIOEsixJHsNbMe7b80wNx9bGsu57Umys0JU+apO0R+Mowd1iQLBDhlBdiByCMHajIasDdXnjcJB5CVr6B9k/3pH11Do13JAzUIOqFlIQljMDTdneqQV08OtKLUioDS5DfNh5Vbz+wZ3po3LN3P+kBuI1ySCHRD4nubIJufw4pIH0nu1bskmFk5eRiKWoPc+3SnfUsGlw2/Ac30e+OZmGrZosEvnlvmoXevV+S1i97j34zF4dE8+eWkSPQe3Y+53y0FA+36tmTt1FQDDRnahqjJO8epSk1E3prDYUpB0FRaCpk3rYQF59bOpX5SH49isW7o5+P3Nby8syMqKUBNk/xBw6gX7csgJA/n8nZk8FMibpBw5eg1qy5ypq0FpOvdqwZJ5G3BTgDAleK7N9a93AEdawRdvzzAadhgdvTnfr2T5go3GgCLp0alLMyaPW0A85oKvufLOo3j8tg/p2qcVz939CSrhIoRE+4qOPZuzbEFxGoAtmbOeO587nc/ensn2spp/yrrtPHk1uoGKlQs2ct9Vb2EFpWWA7NwMMrJCBvgpRTJaB3atkEXL9kWsWRLYqQWVjlYdiui8RzNitUk+Dz6qz9AOdO3diiEH9SQUdnjjsfFUllZTvKaEzWtL0VqzaoGRl1o4zVQQ1i4pZtrn8wH4/NXJLJm+KnX4mPfdErZtLOfLsVM445aj2PeEwcz5ZjHNOzamZacmu3x+7Y5fP36RRsN3333HiSeeyKBBgyguNvTzl19+mUmTJv2qO7c7/hyRiCXxPQ+0pkHzgv8X9KXihjH38tJNb3LPqf9Iv7Zx+WbuPuUfrJyzmm6DO5LfIJeFk5exYekm3rzvEyZ/MJNt60t5ackDPDXjDlp3acrnL06kbGMp+Q2yyc7PpH6zAoYe0R+Uonn7RrTu2txoRWlNyfoy80FaM/TQPoBGG+l8mrZtSMpqs6YiysZVBvhEa+LsObIbj35yKY+Pv5LTrzk4kKZQZqAORE6H7L8HtQFrDdfHcl2evOEdqtaW0LhhLlJp414QSyLiSWpLqpj6yVwiGmTU5aRj+mPFXCQQrY5jJTxk3CfiaVo1rme0+bRG+D7CN5Zt3bs3Q4pAWDbwyUwx8lI3iEo7iy/z9+D2JodxbJu/cm3jI/kwpydbrVzC2mdAfC0XVHzDy1tf4O/lb3NKbDrW95ONz6qUvP/kl3w/bg43HfdwHejboVAQq44z9u4P6TK4EwhBvDbBJSNu4uoD7uCbN79n4aSlFK/Ywta125gzYeHPPq+KV27m6MZncELLcyndVP6z1/utYve49+Px1zuP4cM1DzLqaFMCR0PT1vXTFl0F9bNp17mxIcgrBa5P525NTNnW9UlUxRj76JdccvNhDBneiXsvHcu2TRWccvF+9BvSgQYNcsDzybRtHnvrr4QdC6E1GRGH/Y40hJuGjfPT0isCQ9pwLAsLA5767dWBUWP6BCVZ6q5dDQWF2Sbb5tVdz0/c+QlHnz6US28/guPPHs6E92cTDtvGtcNXPHHrh+Za0JoNyzdz3enPMu2rxTx3zziqK6PpTJsADjtxMMedNQwBFBblcvHtR5CTn0lBvYyAfGImcCKluRlM4AaP7MIBR/XbKdnpe4Emp9L02rMtew7vnH6eioKGOTz41vnc/9q5nHv9IWRm1hEnyrZUcu8lr/H4ze+C8kH5zJu0jJfuG8fNpz1Dw6b1OPDEwaxZsom1i01lJBlz6T6wPfsfP4hzbzuKdcs2cffZz5LfIIeWnZsw+uShHH7evjRsXsip1x/OCZcfTMtOTTj07H3Ir5/DuiXFdOjVkpOvHZNu4/m146MnxnPniY+wadWW32T7/22xyxm/d955h7/85S+ccMIJzJ49m0TC3Aiqq6u54447GDdu3K++kwC33347n3zyCXPnziUUClFRUfGDZdavX8/555/PhAkTyMjI4Pjjj+e+++4jFPrX6t2749+LSGaYi588h1nj53HSjUf97PXCGeZ3sSzJzUfdT21VjMycTKZ8OJMJr0/m/bJnWTp9FVcFIsO2I3GTPrGaGEWBaXeH3m3SQl/bN1dw+dNn89yNb1JTXs1jk26iRccmvPnwp2kCCEKQW5BNXmE25919HGfeehTKV+TkZxHJCrNi3npmf72YPffbg0sOuAvt+wzYbw8yskJsWltCi/ZFNGpeQNuuzXj3yQnM/GoRAP327kLDJnm8eu9H6c/RliQjy5SDjzl9CO17tOSCA+8zJepAYy8zK0xJcQUW8OYT39SVnKyUfZOiICOMiCaRCQ98RetOjVm9uoQMx2LE3l2on5vBN58tMGXr4LNT2Yy6Mo957gqb2ZGWzA634PGC4bR2y+hXs5IB8TV0Sm6hrVdKW6+UY5hDjQgxx2nK3IwWvPzXf7Cx1DM9U+idfTmBvMJs3n/sM4SU9BzehTlfmWyA73rseVg/hh61J17SZ/AuuMEsmLiEihJD1Fk6bQV7jRnws9f9teP3Gvf+LOGEbIYf1oeMrDDZ+Zk0b1fEktlr8RIenqt4+ragD9KyEFKydPpqsK2AvGTOpVh1go9engLAp29O46SL9uPWJ0/hwavf4uuP5tB7YDuuPuEJmjWrR0ZOhEUz1vCPG9/lojuPJiMzxMW3Hs6DV70FUpCdl8HJF47iuHP2pnhdKcNH98B2LEYd2pu7Lnud0q1VgGbYgXuQmxNh3JvTTatIIOOSkRni/ite54yrDuLGM55PZwSdkIWvNdIS+D5k50SoqYhSvLY0IG8oqsprsGxTds2pl8nQA7pj2T059C+D2ba5glbtiwAYNronq5dsIS83g/JAdiU1cdtzny6cecVobj3/JbaXGAeQtl2bsGpRcTrl/9B173Da5aP5fvxCWndqzOJZa/CSPuVbKnnwyje5+enTyM3LJFoVsHvRpmUFdmqLqdcwl20bt1OvoSkNPn/XR2xasw2EoKBhDoWN8jjt2kNp192UaF+++yOKA2u8G146l+btG3HmzUeiXI9v35lGzyEdefJ7I8eViCW542QjCF9bGeXqF877Fc86E1Vl1Txy/jPm9wk7XPbsuf/vOpPem8ayGas48pKDyKv/v9fStcvA77bbbuOJJ57gpJNO4vXXX0+/PmjQIG655ZZfded2jGQyyVFHHcXAgQN59tlnf/C+7/sceOCBNGjQgEmTJlFWVsbJJ5+M1ppHH330N9uv3WFi9Bn7MPqMfXZpncpSc1PfunYbm1ab7NrwYwYjBHTq15ZwRojNq7dCoFvnJszfTSvrZnX99+/Bw9/cxFNXvUarrs34/uPZlG2uoGxzBefecyJOyOaAk4aybnExDZsXMvrUodxw9MMIrfE9RUFRHh888SWfvzKJxq0acPVz55CRFWL+pGVEAzmEaePmckz7i1G+JrteFtHqONc8exZ/u/tYTul3PcpXLJq6gqpyM0AjIK8gi+57tmfSh4YR6yc9pnw8x2QHtabjHs0YMGoPxpw6hAsOeZCNq0qQtjDlXCnQHhB3EUBpVQxtVSOC2fLe+3Vnw4OfkaxN8tRdH+Mm/TrNPiDlcpAyRhfK6DnYjsS2JfHqBKm7R3n9Zrwp8nkzpy95foze8XX0S6yjT2I9uSrOkOQahiTXQOW3rJL12NKpL91uu5CaTt1Zv7qURi0bMP2zuTx3/ZvBdxesXVzMZc+dS1ZuJstnreKMPS7jnPtOYsjhuwbchhy5J3MmLCAUcf4tn+hfI36vce/PFEIIeg/rxOa128itl8Vjn1wGwC1nPFO3kO+bf0KA6zH6uD0ZtH8Pwpkh2nVtyudvTWfLhnIiEZsT+t+IZUvKt9Xi+4ppXy5Ml2rDAft38ex1fPDiJJ687UMiwWsoTU15Lfdf8QZ/vXkMrds3YuK4eQzYuwvd+rTi4GMH8MJDXyClYMXc9WzZUL6TKcfBx+/JRy9PZn1VjLsufHWndoN9j+hL1z6tad+tGQVFuaxevImn7viIaFWUaG3C9DoCvusz+viBHHnGsHSW677LxjLru+XkF2Zz1rUHc9SZw3jv+W8pL6lKs4q7D2jDUWcOp2P3Zvz9pvdYHZRqm7SqT5cezVkVCDyjNTl5GezRvw1vzrgJgJOH3U5JcQVozaqFxTx5y/sMP6yP6dVzjcp0tDrOmNOHsnDaKlYs2EhR8wIuuedYHr3qDTr3agFA32GdmTlhMX1HdOaaJ07ls1enEKuJ43s+lm0x4sj+fPPuDDav3cZDF73Iw19cQ7Q6zvtPfAXAJ899Q8fehsThhG1ad2vOqnnr6NSv3S6fUyvnrKF6ew09R3T7yXaPrPwsOvRtw4rZa+i9T/f/d5sV2yq55cj70BriNXHOf+S0Xd6vP3vsMvBbtmwZQ4cO/cHrubm5P5qF+7Xi5psNHfyFF1740fe/+OILFi9ezIYNG2jSxPQR3H///Zxyyincfvvtu4kaf7DwXC8tt9KwZX2y8jOprYxy6q3HcOFjp5ORE0FKyV6H9eObN7+ndFM5xQHZoH7TnXsCO/VtwwNfXgfA0S3OA63JLcxmztcLqamopWOfNlz5zFlcdfA9fPzMV8SD5u553y3h6zenMj3oVVm7aCMT3vqev1/8Mp5r2L22Y+MlvfQMuSZg2s2duBQE+IkkSEltZZRls9aaHVKaypIqpn8+n3BmiGQsyd8vfQXpWIDAcRxufPp06jUw5+QdL57FV+/NpPuAtiycvoaufVujtOalBz5jUeBC0rBRLtU1CQ4+cRBHnroXY//+JXEvSYNGeWxas81kET2ddhQB0g8GDO/EtK8WGemzlP5WkA1Mxt3080oZ4euMDmzsvw/3zVlNF2s7PatX0yexno5eKW3VdtouHg+Hjyc3N5dkk85UD9gLp1HnnX6PipJKHjj7aR6ffge3HfcQvuvzwWOf7TLwy8rN5OpXLtyldX6r+L3GvT9bXDXmARZPX8Xh547krFuPZt2yzXz/6bw6koOUNGpRyB6D2jPpk7mMe2UKZVsqufSB43n0yjcYProHR5w9giuPfcwAIoDAe9uNu1iOje8rbFuSUIrKkio2rzPtG4lYcocGWMGapZu59OjH0qAqnOFw1jUHMenT+bTv0oQVCzaadYOKgROycOMuH7082YAlT6UFlJ2QxcjDetNvSEd6D+nI8vnradg0n+7929C5R3M+esmU+3MKc7BtyfZt1Yx7ZQp77dedxi0K2bqxnIUz1wBQUVbDPZeMxbIsQuEArGpNJCvMbc+cRijscNtfX2LyZwuQtkWbzo254r7juOyYx0wpWIDj2Jx4QR1zVinF9Y+dxOqlm/m/9u46Lur7D+D4645ukEYUVCzs7sDA7pzdbs7Ouc2YMVt/mzVrs2t2dysIdqESKiildMfd9/fHwSEDFBT783w8eMy7+9Z94T573yfe7yWTdoIkcfHwLS6mrbR9XURoDM+9Q0ChxMhIl8ObLvPcN5Sdy0/R9cemnP7XHRQKDI112Tz/MHtWqQI6DU05k1cNpIhzQQoVt+KFbzDPHgYR+OQlRZwL0qBjNe5d8aZq47Ls+vMoNVpUpFBxW/48N5WosNhMmRnCAiOYP3AlJuZGjF87NNuauv4PX/BjzZ+RlBKTN4/EpVvtbP/mNDTkLHWbTVJCMnoGum/5CwV9Iz3M7Qrw6kU4DvmcDiY3rp+8zfSOCyleuQgLTk/7YMPfb5LnwM/W1hYfHx8cHR0zPX/p0iWKFi2aX9eVZ25ubpQtW1Yd9AE0a9aMpKQkrl+/jouLyye7NiGr+JhEEmISkCSwLWLNmFVDst3O2NyIsKAIXngHU7ikHdZFLOnxUweeeT1Hz1APq0LmmbYvXd0J98M3sbArwNJRG9DQ0mDuwUkgg7uXVPn3dPS0KVXDiSsHr+Nz+5l6X01tTf6atE0V9AEolDQfUJfKLmVwO3qLB+4+VG1SjvCQSJr3rsecgavS5taoeiIlpTKt5021u2vP2hiZ6rN14SEAlCkKek5sTbNe9dRBH0BEcCTJkbFYWBnTbVhGr+mC7cNwP3kfTW0NqjXMHFwt3zeSux5+6OlrM2fk5owVv+lpJdJqSNkVNqO4sy1XT95V76uto0lynGqoMj0VjeoClchk4FSuEN63nnJPac49vQJs0q2ChXYKrqYxtLKJx/zOVWRhYRSJvgoPr1IecLFxJLxiTXysSrB0+yNSlBokxCbR85eOnNp0gY6jWmX53d658ICz2y7R5odmFC3vkO3v/3PxubZ7n5tnjwIBePJA1TOlb6T7Wg+0Kidf2PNwhk3vyKPrT3kWHYyRmQGzh/7D7SvesPcaleqVoP2ABjy+7Z+WKFhCW0eL5IQUKtcuyoCf2zOsxXxQSsRExuFx+h7t+9WlQi0ndv51lqjwWGwKW3Dj0uNM15YUn8zyaftQKiUMTfUyXkirY52S9iVIU1POop0/AnB2/w32/XORlIRkjm5z5/iOq1SsU5wbFx5RoXZx+oxtQVJCEhoaciRJIiYsvddfNX3+r+l7eBkUqcofCBmLwYDYmAQW7/yR+9f8WPHbPiJfxfJz39XUb1kBPX0dAKxsTRg3tys/9fyL6PC4tF58SFGk8NwvlBLl7Tm61Y3da84S+PQVHQY2oNPA+hzYcEk9QpLO2t4MMwsjHt14gq6BNslJKTTpUgObQgW4cd6LSvVKggy8b/sDcG7PNVr2qavaWZJQpCiY1X8V2jqaJCUkY2pphGuPOhibGXBiy2WGze+BibkRE1vO5c6lRxxac4b1dxegqaWZKegDOPevG7fOqqbJNO/vQuXGZbP8LSlSFOrsBilJKVleT4hNJD4mAXNbM+Ryea6CPlDlnl334H9EhERS0Mk2V/vkpyv7PUmMS+TuRS9ePg/DxtHq7TvlszwHfkOHDmXUqFH8/fffyGQyAgMDcXNzY/z48UydOvVDXGOuBAcHY21tnek5MzMztLW1CQ7OecJnUlKSer4OqJZDCx+ecQFDJm8eyf0rj+j+U/s3bpteOsfawYJAnxDGNvoNSSmhpaPJ2tsLsXHMWCU6dcdoQv1fcWbHFfzu+iOXy5nQ/HfM7UzR0dcmKT6ZpIRk7l1+pJoo/Rp9Q12iI1RzbeyL21C0XCG+G98acxtTareqxO0LXqyfuRufW/5c2X+NAgXNMq2o1dLWoFh5BzTlcib/PYTkxBT6V/4ZUPU2VGlUhm6jW2bJUj+l8xKiwmLx8vBlzv7x6udlMhm1XLM2iAB2DhbYOViw7X/HIDmjUazdsjwPrvsTGRajynPmE8zm+YcychDKZJQsZ89dNx/1nCRkMmq6lqdJl+o4lLLFwsaUG+fuE5LWkwLwKkmDrUEmXClQhr9CDnDvz63cmriAGrIQnFJfYhb8FLNjTykGNNHWIbpsFczcD+PcwZVeP3dU/8/udbO6LSYiJAqfm09Y6v5517v9XNu9z81vW4bjdvQ2rfrVZ8Pv+zm47hwGRjqqFfBpf4MpyalEhsXSpm9dlk3egd9df1Vi8LQV6aYWRhQrY8+crcNYMGoTJSs68NwnBJ97z7l31Y/V03fTf2Ir9v1zgfDgKIKfvsL75lOG/tqOmo3L4HH2AW7H7+LaqSondl9TXVjacK0yPS9dZDwoJMxtjAkLjVH3gMs05OjpaTK2wx/8srIfHqfvqYamAWQyLAuaEflKFdzdu+rLuI5/qHszTSyMiEqbq1eyfCGSk1N54vWC9B5LAAtrE0pUKETJCoVp1qU6Ghpy6jQrxx+/qOZA3vd8wn2PJ5SqVBgHJ0sad6jKunmHM3o/075U2jqYU6VeCS4fvc3SX/5V3/+7Hr4sPTiOV0ERnD9wE4BGnapyZpcnIc/C0JDJCHqakZzYqYw9iXGJxEXG4XHiLgdK2WJiYUjUq1iQJI6sv5Dxy5VUi2GS00Zqol7F0GtCG0Y2nsXTBy+oUK8U8w6MxzStNq5RAUPO/etOxYbOmFpmHnGr0bIS+1ccx9jcCGNzQ45vOEe9jjXQN8oIyIuUK8zCM9OIehlN3Q6Z5wZHh8cyoPRoosNi+G3vRGq1qfLGv8v/0jfSy3Suj6nDyJa88A6iZDUnrB1yn+EgP+U58Js4cSJRUVG4uLiQmJhI/fr10dHRYfz48QwfPjxPx5o+fbp6CDcnnp6e2ZZKyk52cwCktOX6OZkzZ85br0H4MFy618mUhFORqmBm9yU8uePPL9tGU6KKqidl7tGfuXvpIXK5jN+6LlFvn5KUSkx4bKbAT0NDjm0RK76b2JbS1Z04vvEC5/51Vy0SkMvVfw+KFEWWYKRu+ypUcSnLv38cxa6YFSMW90HPMONb5D/Td+Hl4YNMrvo2//LZK2Qacmo1r8CVg9dJSVXwMC3dwdl/r9J2SCM0tTVITUolKS6Jayfu8PC6H+XrlMx0XhtHS6LCYjGxMGJcs9+xdbRkzPIBbx0CuHjgOhtn71MnYNbR12bo1A6EBIQz58f1RIXFpldrolAxS148eUXHIS7UblaOzYuPUrdlBSJDo4kKi6XPT23Qf+29Dp7eiXlD/0bXUIfytZ24tP8GyGTUb18VBTL23YjicYmmlF81GFlJczhxQv2jERyM2fUrcF01UT/VyoYAhzJYDuqJYcc2YKFamFOymhPuh65TqmbxN77Pz0F+tntfs7I1i1O2ZnFuX3rEtsVH1G2vho4mBWxNqFC7JCUqORAaEMady94ggd/9FyzYO4rLR+5QqX5JzNOSBBcqZoWOlpw7F70YPvc7Lh65xdk917h58RFFStux6sQkupSdDEDEyxh+aDqXpl2qs2HxMZITU6hcryTb3KeiraPJPwuOcmjTJeSaGqrgL+07X1hQlCq3YNpKeA0NiZgI1XQO95P3CHyiCpI0tTToMdKVEzuu4ljChtKVHTm+XVUFw9LWlJioeH6c0Ylblx8R+iIC1y41eHDjaVrgl5ZPD1VezyvH7nD7ijedhzTkZWAE5jYmdPvehT1rzxOZFnA9vPEUgPXzD6vy8aXP20jL62lbyIzxnf7kxZOXyDXlqm2UEkF+L0mMT6LbsKZEh8fxKiiSMzvTSgrKZNRtWZED/5wnMV41hO19x5/khGQkpURSQjIb5x7M+ZebFnRq6WiQnKDE1tEKDS0NdaL99LKY41cNokW/BqyatIU5/VdSvJIjyy5m/n+sfXFbNj76A6VSSdeCPxAdFsPt815M/Dvzwozy9TKPdKQLD4pQ5WYF/O48zXPg9ynZl7Bj7vEpb9/wA3qnPH6zZ8/ml19+4cGDByiVSpydnTE0NMzzcYYPH0737t3fuM1/h1ZyYmNjw9WrVzM9FxERQUpKSpaewNdNnjyZsWPHqh9HR0d/8QlZv1TPvYO4vFeVuf7M1kvqwM/Ewoi67avhduhapu1rt6tK8crZZ4KXy+VUblQWpwoOOJQuSOkaTqwYtxn/R4EUq+CA372AjCTJcjnFyhdmwPQu3Dh7n4eevjz09KVQcRv2rzyJtq42f56bRoNONXh4zQ8DE9W3xZfPw5EUShp1rYHfXX9si1jy7FEQsZHxlKnphLaOFnoGOsSkfUNOTkxhSsfFdBzZHC/3x/g/CmL8qsHMOzQR/4eBnNxyibM7rnDv8iNaD25EqarF1O/nvttjts0/QMMutWjSQxUs+z/MnKNLW0OGuY0pVgULsPX6LABO77yK27FbDJreCZvCGdU0Zm/JvLouOTGF62fuU7JKEQxN9KnTujIHXlQGIDE+ibI1S1CkjD0V6pYk4HEQF9N+T5cPXKf8/J7QM+1HkuDePXUQKF24gGZoMEVCg8HzNNJQGd7yAui0a8X00X15tag3Vk5fRm6v/Gr3vgWqhRaqOaeNutSgz09tMbEwRM9Al7Uz9rDip+3oGuhQo0lZrp+9z08d/4ehiT4X919j8cHxWNkXwPuOP0+9VEPHQU9fMmZhD0ICwnn2KIhazcrzwNOP9KAqMC3p8NpZ+9FM61G/5+HLgLozWbJvNJ5n7qkqf6Stak2vbAGAQkHpyg54XX9KarKSTkNdSE5IofuPTUiISyLyZQyjF3Rnw7xDBD97RfAzVTBoYWeKQwlbEuISmbpmIHHRCRxJW5V87YwXpas4MmZBd/78+V8kpZJJ/+vN2f03AIiLTmBqvzVcv/CQmk3L4PsgkMiXmUeb5BpySlUszIPrT1FHXWlJoVv1qM2cHzcAoExVYmiiR2xkPAmxiWz933ECfIIZPrsLc4etVx/PqZw99VpVYNfyE+qpIabmBlRrUoP9a88SHhKl2lCS0NbTpssIV1ISUpCQSIpPJiYiljP/XiU5QclPawZR07UCGhpyZu0aw51Lj6jsUhqlUom2jhavAsN5cv85AAnxGSNq2dHUzhw4BvmFML7xDHT0tVl87rcsvYUAjmUKMXrlYAL9Qmg/osUbjy9klefAb8OGDXTu3BkDA4Nc98TlxMLCAguL/CntVKtWLWbPnk1QUBC2tqpx+xMnTqCjo0OVKjl/G9DR0UFHRydfrkF4P/Yl7Gj0XR28bzyhaZ+sE+kPrDypCtZkqt7d4pXeXv7H2NyIHpPaATBr7zjO7XTDpXtthlb7mfi0nHRSaio1mldgSLVfiIuOV++7cfY+lGlJXNfP2MXoZQNITU5l7a/biQ2PpcdP7ajYwBkDYz2GzOpK9RYV2DRzL6+CIihYTPVlY8HBCfzScQlhQRGAKpv+tnkHVCsFJYmVEzaz0m0mUS+jObDiBGjIKVGlCEXSJh2f2XaZQ2vOEBsdzzOvF9y5+FAd+HUc1oS7Vx6RnJRKw07VqNzQGY30cnNp/pq8lZjwOFISkvlt24gc79PCYX9zYa8nxcoXZvn5zEOXuvo6tB+aMffQrpg1Lt1q4XfHH9deddXPX9x7lVObLtJxVEsqjBsH48ZBfDyLnHtTOOABDU1jsAx/QQllGOzdCHs3Ym1gAC4u0LQpNGkCpUtnOyz8qeVnu/ctMDY3RFdXC6VSQt9Il35VfkZDU86Wu/M5mrYQIjEuidjwGFLTqkOkD5P++t1SBk7pSOES1tRrU5nEuCRcOlVDS1uTRXtHq8/x9+/71elXkCTVQqxUBSkJKRQpU5AnD1RB44zB66jVtCz7/s4YtkyKT8qUNNnLww+HUrZ0H+5Kw3aV1dv9vLyv+t8tetbG88wD1bxYmYzw4ChepdXbXTByIyUqOqgXhQB43w1gepMy/D3nIFFhsZz814OOgxrifko1ty3wmSpYfXD9KXHRqjrHGloaKFIU6Bvp8v20DiTFJ6sC3Nc/E0qJXX+dRlNLjpGZPhGhMdRvUwnrgmYUsDZh0ahNAFjamfHzXwM4svkSmlqatOxVB/cTd9TD3eVqOVG7RUVSklOJCIkCScLQ1IDYyDhqupaj17jM83JjIuLQ0dUmLDiSv37aTlhgJJ1+dMXUwohK9UsxosEMosPjWHB4IsrXptGM+rN/jn8ncrmcpZdn8sjTV71y/8bpu7x8rppm8sDtMbXbZv95azWkSY7HFd4sz4Hf+PHjGTZsGG3atKFXr140b94cTc136jjME39/f8LDw/H390ehUHDr1i0AnJycMDQ0xNXVFWdnZ3r37s2CBQsIDw9n/PjxDB48WKzo/ULI5TJe+AQR8PA5V/Z7UqyCY6bXu09sxwN3b/QMdRk4qzuNvqtDdHgs10/eoUqTchibG73x+HP7LOOBuzdntl/mzwvTmdxmPi8DwkAmI9AvJFPlD0Ad9AEcXX+e5v0aULlxWQwXGmBcwIDnj4LwufkEz5N3kZQSZWoV5767j3qfCauH4Ohszz+35/LcJ5hQ/1dsnLUPv7v+6moB/g8DuXbyLv5eaWkaFEpGLO6jznH418QtRL2KwaiAITIZ1G2f0QjqGeoyd9+4TNecnJTC2R1uODrbU7JqUQpYmRATHpdlcvV/RYer/qf78nkYfvf8KVq2cKbXFakKbp57wI6FB3HtXZ+f1n0PqHouZvdcikKh5PJeDyRJ4uqRm+x4vhITC2Nk+voMvbURv7v+FKhVguXfzSV+30GaWidRMfkFvHwJhw6pfgBsbVUBYOPGqv8WLKi+BkmSSIhN/CRzcz5Vu/elOrjmrGo4ETi1XdULpkhV8vjWUywLmvLsYRBIEvfdfTAyN6Rpt5qEhUThduwOAY+DWf7TNsJeRKChpcGK81MyLYZKTytSpWFp9q46jUIhoakhx8relBdPXiHXkFOklA1P7qs+U4FPXuLl4adaxKEhB6USSQF2xazUlSkAnj0Monpj5xzfU8U6JWjQthLHtrqBJNGgXRUSYpO4ds4Lf+8Q/L1VKak0tFQ5/FKTFQxzna8akpTJuHbOi5eBkfx5YAzREfEkJyazaOxWjE30iUnLFqBMUVC4mBX+3iEsHreVn1f0QyaTIdeQoSGXk5yUAhJ4pa0QTk5IoW7z8gyb2RkNDTmpKQoO/nOeJw8CqdbIGZvC5gz4uZ36PTTuXB3/x8EYGOnRa0IrUhJTGFJnOpJCtVq4SCk7fpjTjcIlbLK8fyMzA0Yt6c2AKj8T+TKGPStO0v77Jlw5eIM7lx6qS73dPO9Fh2FNMTDRx8zahDJvmcphaW+OpX3GIr16nWricfQWCbEJ/LvoAOHBkbQWQV6+ynPljqCgIHbs2IGGhgbdu3fH1taWYcOGceXKlQ9xfWpTp06lUqVKTJs2jdjYWCpVqkSlSpW4dk01/KehocHhw4fR1dWlTp06dO3alfbt27Nw4cIPel1C/pEkCb+0VbZbf9+jyuH3mgoNnNkf9jfbn62gae/6aGhqML3zYub0XsbUDhm/540zdtHZdghH1p3JtH+Iv2qI5um9AMY2nM7PG4ZRokoRytctSfk6JanfsRqWBc0wszKi9SAX5P/pPUuKT6JY+cLser6CgTO7cWH3VTyO3lYPGT9LW8kIZBq20dbRomiZQtRsUQlLe7OM5/W0MDDRZ/2MXWyYqZrc3bRXXfUQN0Czfg3Q0tYkJiwaKVVBhWzmvDx98Jw5vZdxZttlts8/wOKhaxjTaAZRr2JYfOwnFh6eyLD5373x3tdwLYekUBAVEskPNadw5+JDHl/3Y++y4zy46k2XQsOY2nkxdy4+ZOV4VY/Cyc0XGVlvGhf2eHB5/zX0jFRzBCUyJ4g1NDWgfL3SaGhqEJyixUkcmRRaktSA53DjBsybp+rx09WFoCDYtAn69QN7e1UP4IgRsH8//+s+n3Ymfdg4fecb38uH8KnavS9Vm8Eu6Bpoo6Wjqa5wYWphRNXGZVmwbxy/rhus3jYmLJaXzyOYsLw/zlWLIJPLKF6hMEqlREpSKi+fZ1RtmdV/FW0LDufnzn/wU/vFpCalIqUqSElK4YV3MC161MTByZLCxawpWbEwJuaGNOpYlZhIVUUdC0sjVc1cuYyBP7WhcDFLdc9f/TaVMs1zBQh9Hs6N814o06ZTDJvVhRY9a+NcrQgdh7gw7e/BLNo7OlOHXDHnjC8rYcFRaR8I1ZD0s0dBzP7+Hy4cuM4Lv5fERSfw3DeUQT+3Rd9AB0kpqQNIfQNdKtYpwSb36Yye243khGRQSmhpZcz9VaYquHT4JrcvPeLsHk9+bDybsBfhLD8xiWqNymT5vegZ6FK/dSWOb7rIrH5/ERkWQ3hw+hAvOFcvShHngm+cX9xmUCM0NOWkJKWwcfZeZvddwcE1qrbWqaIDTb6rrUrF1a7qW4O+mIg4bp65pyo7l8a4gCG/7RmPIlXJ/SuPWTl2wxuPIeRdnr+yampq0rp1a1q3bk18fDx79+5l69atuLi4YG9vj6+v74e4TtavX59jDr90hQsX5lB6z4HwxZHL5XQe24Ztc/aQmpzKvUsPsS2a8/zMC7vd1T1lCkXG0MK+ZceIjYxn29x97FpymDbfN6XN0Ca8eh6uHiaOehXDpGaz2fpkGf8uOcL/hq1DW1eL5LQG6NrJjCGRdHFRCYBqf+eaxbEpYkVKYgoNu9XkyqEb9J/Whee+wdw694CBM7sCqmB21/+O8OReAG1/aIq5rZlqgQkS9drXxKl8YVb9tE19DqMCmXstB87sRrM+9RlcYQJKpZRlRTDA+mk7cTt4nQt7rqorp2hpqcpLGZjoU7Z2ibfe+wLWJuq5ggBhwZEsGbqGpIRknGuXIC5a9d7lGnIaf1cHRaqCxd+vVd8jHX1tlrnNxOPobcrWKYlZ2gT9/+o+oR0x4bHUaV8NTR1tqFRJ9TNxIiQmwpUrcOqU6ufaNXj4UPWzbBkjkdEMMzw3JsD0rm99T/npU7V7X6roV7GqmtigWs2rlChU3Bq5XI6ugQ7rZ+0DhRINbU0UqUp0DbRxO3KTm+ceAODSsRqlqxRFV1+bivVLAXDPzZsrh28hSRL3r/qApJpLaFzAgJeBkRiZGnDzwkOCn77i5fNwdj5cyO1LjzCzNCYlRcHVU/dw6VAVCxtTNLU1kMvlPPcJYf28QzRoW4lJy/tleg+J8UkMa/w7cdEJ9JnYmu/GtMD9xB2ObbmMJMH0vquo6uJM1+FN6T+5DX//fhAkiRLlC+F7/7lqyFcmo1qj0hSv4MDWJccACAkI50SABwt3jaR0ZUdMLQxp1as2WxYfy5SEvWZTZ4xM9dmx9DibFhxBW1eT5IQUUpJS0NLRwtBED20dTRxK2jJ70Bp1wnmAUzvdadW3Plb2BTiw9iwb5x7A0q4AE1f25+R2NyJCo3E/dod2Qxoxblk/vG89o17bypSp+fYky2ZWxihSFESHxaryAb6m25hWPPT0Y8GQ1VRq6MzPG4a9cXHlmIbTCXgUSPP+DRmzcnCm1xr3qMsjTx+a9Kr31msS8ua9xir09fVp1qwZERERPHv2DC8vr/y6LuEb1eOXjoQHR6BIUVC3Y87lvSRJYl6fZaQkp+JYphCT1g/joYcPJaoWpf/MbhxadUr1bfpxEJtm7qbD8OZ0GNmcM1svERedQGqygqT4ZOKjE9QNk0wuo17H6gQ8CqRlfxe2LTiAjaMlj28+RZGqUPUapClgY8qGB4vUj4fM6aH+d89JGUMrDz18WPvrDgBOb7+CvrGeqhA8cGb7Ffr82oGTWy6hSFVSo3kFuo5rk+W92he3ZZn7bKJfxVDRJeu3+MqNy+J28Drl6pWi67hWFK/kiF0xawxNDXJ93+t3rI6JpRHe159gZmNKvfZVWTVhM0kJyZSqUgT74jboG+oxeE53NLU0kSSJyo3Lcu2EKvl1xxHNsS9uh33xNy/UKFOnJP+7kEOlC11daNRI9fP77xARAWfPqgNBDW9vnAnHvGXOw3Efg2j3VFJTUrmw6yoFi9tQ8rWFSAA3zmTkjdTV1yYxLolytVRfQGIj4wl4rFqYVLamEyUqOdKyXz0upaUfAUiIS6bzcNdMx5za/U+UCgWmlsa0GeTCnuUnMLMwYtHRidx182ZWv7+IeiVDz0ifVn3rc3K7G0tGb0qfAkiTrjVAItOXp5a967Djj6Oc2+1B6SqO3Hf3IT42kZcvInAoZUtSomq4Oj4t3+WBdefS5hnLCAuO4vg2NxLjk/hpRX+2/3mC+JhEHt58pp7nZ2lryq0LD0mMS8KhuDWRYXFEhcUg15Sz/5/zJMQmMmZhd3R0tbEpXAC/+xmjBj53nvPo5lPW/34A5HIUCRlfzFLiE3HpU4fB0zuRGJdE97KT1K9paMrZ+edx9q46zR9HJ7Fp3kHiohKIi3rB9iVHKV+3BMe3XAbA714AHX9oSpNuNdX7H/7nPDfPP6DP5HYULpn181zdtTx121ZBkiR+mN+DG2fukhCbROFSttRrX5V5A1cRGxnPxX3XiI2Mx8gsox1KjE9CS0dLPRc5Oi3vYXTa/M7XvUs1KCF33inwS//Gu2XLFk6dOkWhQoX47rvv+Pfff9++syC8ga6+DuPXvb2eo0wmo4JLGa4dv03jnnX5qflsQv1f0evXTvSZ1oU2Q5tyfMM5/pmyk6qu5Tm99SLfL+jNDwv7MLH579w6cw+5XIZlIXN6/dKBYuULU6RcYQqVyEjo2e6HpsRExNHZXpVi4MaZezTrnXXRCagWYdw6d5/vJrXHtmhGQs7Df5/LtF18TCKlqxcj4mU0HX5wxcbBipXus9/6fou9IcFx2+9dadKzHnqGushkMqo2Lf/W46VLTkrhwr9uFClbmIr1nalYXxVUPXvwnLiIWLS0NWg5wIVCpQpm2k8mkzFr33iiw2IIC4qkSNkPsBLezAw6dlT9ADx7BqdPY92gQf6fKxdEu5fZriWH+WfqTjQ05Wz2WUqB1+aRenn4qnuvDI10qOFajg4/qOZpmVkZM+p/fXA/eourx25z69wDjq4/T0xEnHoRQ5BfaJbz2RW1wue2Py6dqmFpZ0pcVDxxUfHcOv8Qn7uqpMMoJYqWsiUyNApTC9WK6/ROtFM73PE8dZ9tD+arv+wlxCaqg7ozuzx4lJZGBVTDsj+vHkRMZByNOtdAkiRevYhQ94ybWBkTFRaHczXV1Ixf1wzk0uFbWBU0w+duAACvgiKQlBJ33dLm/qad19jMgIsHVYHu3GEb6DjEhTnbfuT4NjfMrI25e8WHFj1r45t2HCSJuq0r0bJ3Hab1XkmKQkFY2pxkXQMdRi3qwfGtbvjeCyA2PFaVkDoplWENZ2W6hzWaladum8rcvfKY6Ig4Gnaszvzv13FhryfDF/akfvtqLBu3CUkCTU0NflqbNbG+vpEu9dtVYcmIf+hbdgL6hrqMXvodDdK+qHf40ZXgpy9xqujAlYPXqdu+GgbGergfvcmM7n9QsJgNy6/MQFtXm/knfuXGqbs06p59VQ7hw8hz4Pfdd99x8OBB9PX16dKlC+fOnaN2bfFLEz6+2Qd/Ii4qHm1dLTb+tguAUP+M5KTN+jbEurAFE11ncWLDOXxvP2PIvF7U71idO+fuU6dDdZLik9HQlFO/U/blxIzMDOg7rTMnN13g/E53dHS0GPtX5iGJ4CchzBuwAiSIj07g122qMmOJcYkULZc5IHJ0LojXVR80tDSo95+kpPExCWz5fS8WBQvQ/sdmbxwi+a93XfCwZdZuts3dh5aOFjue/6XuJfS7k141AZ4+eJEl8ANV8GdiYfzxipw7OMCAT1NXU7R7WaWX2dLQ1MgyH7ZW60rqUoivXkRwftdVHErZ0WNiWwBa9KmHU/lCXD12G8ioIKOlpUGVJmVp2a8+W+Yd4MDq0/Sb2okWfeuz8PBEXviGUKSMPdHhcZSrXQK5hpzKjZzZ+9dJUErINeTcd/Pmvps38/aPY8Ly/jx9+ILze68R6h+mzjmXzrJgAcpWK8Z9D19V+hFJdQxzWzM0NeVcPHCdYXO7o6mlwfYlR9WpXJDJ6PJDYxp3rYlp2qIy28LmuB+5RXTafEKAcjWL433bn4S4jPrYKCUiQ6LUQaDf/ecsHLUJfQNt1l+dgbauFs5VimDnaMmpHW7q5NJDpnfE0s6MhfvGcvvSI5p2r8XS8Vu4fPgmUa9ikclkzNg2nDlD1pAYn4yhiT7RYbFpidolflo9kAYdqnH3ymMMjfXpNroFppZGnNvtgVKh5MK+a7j2rEPZWiW47+5N1SbZJ48H2LvyJAlpWRFiIuIwMjNQ5zwtXsmRnpPb8Xvf5RxYdQqP47eZsmUEt889QJGqxP9RIGFBkdgWscLR2R5HZ/u8/eHlkyv7PTn/7xU6j21D8crfVvWdPAd+MpmMHTt20KxZM7GqTfikZDKZOlCZd+wX7l7yyjI0EBeVkZ4lMG2xSOshTWgxwAXfW0/pbDMYXQMdVt+cj7ld5hrA6eq1r8rmmbuRlBJnd7plCfzGNp6pbtMv7vXg+um7RL+KYdGQVRiaGvDHualcPXaLu5ceYW5rytO0/FagqrH59P5zjAsYsnf5cXYtOQxA0XKFqdAg+yHNhNhErp+8Q7l6pd476NJKq4qioSlHJs8INOt0qEa38W2Qa8ip1aZyTrt/Mz7Xds/R0ZFnz55lem7SpEnMnTv3g5+7/fBm2JewxaaIVZZca9VdK2BuZ5ZWC1oiNjIepwqZe62LV3Rk6bkppCSlsnXeAa6duosiJZWf1w1FW1eLvStOEBsZz6G1Z2jRtz66+joUK6dabW5qYcSCQxPUx2rRpz4Bj4Op1aICnqfvY2ZljFMFByoYq74Q9RzXCs9T9yhVtQgymYwHHr4sHrmeMjWd8PL0RVIoVYsnZDJa9qlL/bZVmdhuEUF+oRQta8/JrVcIfPoqoxC2JHH70mM6ft+EwxsuEBYchY6+jrrChp6RHuVrFafDEBd+6ro0402ndz8CE//sQ0pqKkvGbAFUowGPbjzlryk7eeEbypDfOtPx+8aEhURRqrIjegY67Fp2gtLVitJluKsqb+CGi68dWlXNaMvd+SiVSvzuBnB4wwUadqiGo3NB9Ax08PL0ZXb/VUS+jMbnzjNa9muAnqEOhsb6dBrWFLlczvxDE0hJSs12PjHAtgUHeXTdT/245YCGVHPNGGU4s+MK8wetVj++tP8af0/7F31jPeq0qYKmjmpR26c2t/efJMQm8vJ5GIvP5TD95CuVpxYsJSWFoKAgihcv/lk1foJQtm4pytYtleX52u2qYWxuRHRYDC8DwhhYdiwyuYz5J6bw0NOXlCTVZGn3Qzeo3KRctotJbp17gCIlFZDRalAjQNU7N6PrEh56+maudwtsmb2He5ceIZPLiAiJIiUpFecaxdk6ey8APX7uQN121TC3NeXPEf9weO0ZVXoXMv6ncHrbpRwDvzm9l+J24BpFyzuw6ub8d71lAHz3U3uKVXSkcKmCGBhnNMbaOloM/P3NK4G/FZ97uzdjxgwGD874MvKxkkrL5XKqp+Ve+6+YiDgigiJQShJjlg9g6/wDHFxzmqpNyyGXZ/QOFk8LBocv7s2eZcep0KA0qyZvJfjpKzr+6Mr5PR58N771W6+lWa+6NHstp+R/6ehpU/e1LzB7Vp7kuU8Iz31CaDu4ERf3Xyfypap0m1O5wjg622HraElUWAxFy9irE0TrGupgUsCA6Ih4qjQszdrpe9jz1ykAeoxtiZmVMTERcQya0p6Wvety8eAN5HIZSqWEsbkBbfvVR99ID3NrE45vuYTX9SeUKGfP41uq4F3PSEddUu3S4ZuUrOJI7wmtsS9mzV+/7ODIhgtoamuyy3sJBsZ6tB/aCM9T9zA00eOhpx8bZu1l8bGfAChbqzhla6lW1T6++YTRrsuJCImiYDErIl9GY2phzJ5lJ4iLjCcuMp7fB65mrcdMzKxMMgV9z72DWTd1J841i9NhWFM2zNqbcWMliQ4/NFGX1QRITc5Ig5Vux0JVRRAtHU1SElKIfhnNjL3juLTHg6LlC+NY5uMXTajUpBxX9nlSpUmFj37uTy1PrZiWlhb37t3L0xCUIHxKgT7B6gnEz+4HpPVAwO1z92nauz7PHwcSGRrFHz+uRVNLg7V3F2FXLHMOqwada3Lr7D30jfQYMLMbADdO3+NmWpHxdHZO1tRuW5VHHqoVnpramnSf0JYytUswp/cy9XZla5ekWNr/8PzTitqn5xKTyeVISiUnNl7g+wW9sx3CTUrLhJ+cNvH8fWhoalCr9ZdT7uhT+NzbPSMjI2xssuZd+5Se3AtAkZYHc+vc/aqKF09e4nniDjWyCRaNzAxo1LUmyODwunMAlKpWjFXus7Js+19x0QloaKhWC+eWkameuvftyD/nVMFKWo/3lSM3adazDus8ZqBUSmhoyPlxXnfcjt7GqUJhuo9ugZ6hLsMazOTJgxfINeVIEjjXKEbvSZkXZ53d46muFhIdGs210/dZcngCr4IimTNkLQCPbz6lWPnCtBvYkDLVnRi/tC8rftrOA3cfxrdaiFKpoHS1olRtpBp6NTYzQCNtyHrorK4MndWVnzupSlk+exiY6fwpyamMbjJbNVcw7e+3gI0pL3xCcD96C5fO1QkLiSQmPI64qHjCgqMws8q8In/Xn0dxO3wTt8M3afJdbRp0qs7Vo7dJjEtEz1AHLR0tzuy4QtnaJbEqZE7TXnXx8vTl6D/nAGjYpSbn/3VHklTpeQDkGjI2z9zNjoUH0dHTZrv/io+en3P67gnERcXnaRHc1yLPX1/79OnDunXrPspQgiC8LysHS4pVcODZgxcMmNWdG6fvIZPLqN6ikqrxXtKPI2tPc26nG6kpCnXKltcZFzBkyrbRmZ4rV7cUloXM1XMKf1zSh3bDmgPwwieYw2tOU7ttFUrXKM5vnZdw67wqSNTR16ZcPVXP5PVTd2k1qBEWdmac3a5aZZc+EmRoaqBO4vxfP28ZyeW9nlRtlvtvqsmJySTEJuY4NBzoG4yugQ4FbMyyff1b9zm3e/PmzWPmzJkUKlSILl26MGHCBLS1s//bAUhKSiIpKaOMVnR0dI7bvisNuVxdFszRuSDBz1SJlUtmM5dKkiSG15tGoG8IHYY3o3glR0L8X1GzRcW3nsfn9jPGNJ6JprYmK91mYpPLovdtBzXi6AZVBRFNLU1V4CdJ6Ojr0GagC6Aa3tfQUAVLNVtUYNmkrVw/ex+ZDPr90kEdqJSrVYJmvWvz+MYTSlR0wMjUAIVCSUpiimqoNiiSyLAYQgPCMTE35MKB62yef5DKDUtz+9IjFCkKbAqZ07R7LQCSElTl0WQyOelzSJ77hLDo8EQqNSiNmaURM3uvQJIkJq4ehJGpAcMX9uTw3+ep1TJzm/D0wfNMC0QAbB0tuHvpEUgSZ3e6M37VIJ77hGBZsAAGRrqsnLiFak3LEx0ey5If/8bQTB9NLQ1KVC7Cy+fhXNzriaSUmLRuCJVcyrBywhbO/euOpX0BNj9cglwuZ+QffanU0BlzWzPK1i5Bw8412Db/APXaV8PCrgAlqxblnymqbAcyueyTfKl6farQtybPgV9ycjJr167l5MmTVK1aFQODzDdu8eLF+XZxgvC+tLQ1WXltnvpxx1GtsmzTrF9DJEnCxNI4x9q//2ViYcSkf4YxrpFqboiNY8YQcUEnG4bM6wlAwKNA3A5dB6BK03L8sKgPB1acYOucfcSmpYcZt3qIOvBL139G10xJVA/+dYJDq07Se2oX6naoTsvBOac5UCqVmYbT4mMSGFhmDGGB4UzbNZ467TMvKvE8foufW85GW1ebdfeXYONo9d9DfvM+13Zv1KhRVK5cGTMzMzw8PJg8eTJPnjxh7dq1Oe4zZ84cfvvttw96XVVcy1PVtTwpyalUbOCM+6GbaGprZJpHmk6pUBIeFAmoFmdN2TIcq9cqOYQ+D8PE3AgdPW3cDt/g6tFbdB7VEvviNvjefkZyYgrJiSkEPAp8a+D38kU4N889oFaLiiw5/hNPH7zAuaYTIxvNJik+iaTYRKwKZnz58b79DB1dLcztzDBKq7BhXUhVZvS3bT9y392HImXs6Vtpsup9hEQxaHpnhjeaTdDTlwyc3pFf1w3G0FSfh9ef4lytKF1KjiMlKZUXvqHM2zsGv/svaNihKgqFkntu3jiVL4R1YXOUCiVDZ3Xj8c2n1GpZEblcjnP1YpzddRWPtDRKV4/doUn3WtgVsWLwzC74PwqkV+lxmFoaM+/QRIqUsadGi4o89PAhKjwWpLR8pK/l7NTV06bfrx0AmNFzKVcO3uDA6tOYWBillXOL5vf946nU0Jnrp+6pc3f63HrGnQsPs0x1AdU0gAZpC+YkSeKRpy82hS1o1L0O5ramfF/1J57cC6BU9WJM/HuYemGI8HHkOfC7d+8elSur5ko8fvw402uf61CIILyJhqYGrQbnvSTQqc0XQFJSvkEZqufQO1GwuA3N+zXE5/ZThsztSeFSBZnQdJY66APUpa3S/e/8dErXyJzx/p+pO4iNiGPzrN3U7ZBzfkPPY7eY3mkhxSo6svjcdDS1NAkPjuRVWu3LR54+WQK/YL8QkCA5IZmIkCgR+GXjY7Z706dPf2tg5unpSdWqVRkzZoz6ufLly2NmZkbnzp2ZN28e5ubm2e47efJkxo4dq34cHR1NoUL5O8fKwFiP2ftViy+2ztsPQHJiColxSZhYqFbBxkXF43vnGc41izP30ETcj9zk4OrT9C45hmk7RlG7dRWObzjP4mHrMDTVZ8CMLqycsFVdW/a3f8fQsEtNAh4HoauvTeXG5d56XZPbLeS5dzDVmpZj5q4xlK5WjDuXHlGhbgk8TtzFwFhPXfrx2ul7/Nr5f8hkMpZfnMraqzMJC46iaBnVKlQDIz2qNy1HclIKphZGhIdEYV3IgrDgKJ77qBaSrf51FxtmH+Bvj5lUSktEbWCsR+TLGJRKiQltFjHxrwEYFzBkzbRd7F5+AsuCZqy/MUed667ufxZXVahXisIlbZEkqNQgcyUftyO3eBUYwavACG6d96JOm8r8tnU4AN2cRhH1Mga3QzfU2w/4rTN12mZM9XCq4MCVgzeQlBKRodFpK5xNKV+vJDKZjCpNyjJ25UCSE5JZNnYTklJJ3XZV+env7ymTQ6J4vzv+bJt3AICCxW3pO7UTiWnTVQxMDCjo9GGmKaQkp+B59BbFKjpincue4G9FngO/s2fPfojrEIQvypN7/lw7rkpF4eX2iIBHgRTOJuWJXC5n7OrMubB6/dqRPX8cpXbbqtRoVYmk+OS0pLDQcmCjLEEfQIcRLdi//Bhtvm/6xutyP3yD5MQUvNy9efUiHBtHK+yL2zJyxWD8Hzyn89isCaKbD2yk+h+ypXG25xY+brs3fPhwunfv/sZtHB0ds32+Zk1VIl4fH58cAz8dHR10dHI/H+59dR7VAgMjPexL2GLtYKF+fmzjmTy9/5wW/RvS46d2HPv7nHoV/tMHL6jdugo+d1SLHmIj4/lz5AaKlCvMk3sBlK6hqjCho6dN17Etue/uTUpSChr6b35f6cFUem/6pf3XmN1vJZJSwrCAAeuu/Y5JWuCXXkVEkiQu7PWk9+T2rPp5B08fPGfCX4MokhYAautoserydEL8wyiWlr6p58TWnP33KoFPXpKUkIzfgwCObLqIDIlSlR1xP3pHfU2PbjzFpVN1VU1fIPJVDDuXHKHjj67ZTvcoYG3CaveZ2b6/+OiMLAZb5x2gSqMy6rmPLl1qsn/lSXT1tYmPScS+hA1dRrXI9MXluwltMLM24c9RG7B1tGTFlRnovnZPZTIZrj3rolQqObbhPN43n1K2dklcutbK8Z7bFbPGvoQtoQFhVE4rIzf3yM9cP3nnjV9i39e6yVvZveQQxuaGbH+xGi3t7Fcpf4tkkvTa+vI88PHxwdfXl/r166Onp6cqQ/UV9PhFR0djYmJCVFQUxsYfKT+Z8EWJCI2ip8MwUpJT1UNXXca1YfDcnlm2TU5M5tJeT0pUKYJ9ieyrWqSmpLLmp63ERcVTrl4p5HI5jXvWzTRcm1svfIJZOXYDJasVo9evnb6Kz+S7+FCf48+93Tt06BBt2rTh2bNnFC5cOFf7fOw2T6FQsnnWHnb97wjJiSnUaFGRhl1rMa//SgBqtanCxHVD0TfSIzI0mj9G/sOVg9fR0dNmledsnnkF4nboBq6966Gtq8XwetMB0DPQYV/I6mzPKUkS99280dHTJuhJKFWalMPAWI82VkPUCw4AWvStR9cxrbAtYsXcQas4t8sDZDJ+XNCTCvVKMqTmVAA6DGvK0NndcjxX/6q/EPzsFc7Vneg+ugWrp+5U9wKqyJBryGnYsRqDf+usXg2844+j7PpDVdqtROUimFoYMuqPvpjbmJIYl8Ttiw8pU7M4hqaZ06HcufSQuQP+wsrBgocefur5fN3Ht6bf1E7q7VJTUgnxD8Pz5B0adKyeZSFHurjoBHT1td9Ys1eRqirbllN5xtcplUqUCiWaWu+2Kl6SJI6uPUNCXCLthzd/43Wl+/PHtRxceRx9Iz3+DV2XaeXx1yq3n+U8/xbCwsLo2rUrZ8+eRSaT4e3tTdGiRRk0aBCmpqYsWrTo7QcRhI8oyC+E1JRUCpXM2iP3TiRJvQrXvoQtyQnJNOiS/Tfev3/Zzu7/HVY1PsGr1UlvX6eppckPi/pw69x9JjZVfZPX0demfqeaWbZ9m4JONsw6MOntG34GJEliavt53L3gxeQto6jR8vPNF/g5tntubm64u7vj4uKCiYkJnp6ejBkzhrZt2+Y66PsU7l7wYuscVUqQWm2qMnJpf/QMdXHppvoMjVo2QD3ny9TKmGnbR/HCNwQ9Ax0K2JgypeNiAh4HcfuCF9+lJYQGSIhLQqlUEugbgm0Rq0zBwYFVp1gxfjN6hrps8lqMhoacdVN3YuNgQcDjYFWgJIOj6y8QHR7HlM3DadK9Dpf2X8e2iBUt+tZDJpNRq1Ulnt5/rlqBDAQ+CcX75lNqtayUKQVKem1rCztTqruWU6ePySBhbKbPxJUZCcmNzAzo+ENTDq87S0JsEo9vPAHg9HY3uo5uwZwBf3H16C1KVinKH2enZLqnZ3e6Ex4cRXhwVKbnw0OiCPQNIdj/Fcc3XKD14EaUq1OSgsXePHJgYJx5ha3niTtsX3iQWq0qY1zAgDrtVNU4chP0gWrkQyaTZZl/nFu3z91nyfeqoN7IzBDXvm+v3jN4fi9KVC1G6RpO30TQlxd5DvzGjBmDlpYW/v7+lC6dMb+gW7dujBkzRgR+wmflyT1/fqg8AaVCycKzv1G+/vvXeTWzNmWp+++88A6ibofqb/72mdYZlN2k9v8yMjVQ5/xKnweVEJuIroHOG3uVFKkK7lzwomj5wtmu2lUoFCzov5yn9wL4adPIT5IzKztRr6JxP6ha+HJ+55XPOvD7HNs9HR0dduzYwW+//UZSUhIODg4MHjyYiRMnfvRryU7Qk1AUqQrsi9tmer5QKTtMLIxIiE3ku0lt1aXeflqffanGQ6tPc2zDeQbM6kYBG1PK1i5BwOMg9I10CX0RRr0O1bh35TGdR7Vg+dhNHFpzmipNyvH7/owEzzHhqlqwSfFJJCelcHrbZXYuViVLdyxbiGcPXqCtq0VyQgqFStqycuIWHMvYcyDkL2SyjFWn0zb/yNb5BxjlMpP2PzTh+JYrxEXF0/77Jnw/RzU8L5PJWHhoArcvPcKlY3WSk1IYPKMzwxvNej1/M9Vcs1bGKGBtwj835hD6PJxZfVYQHhKl7t1Lfw9xrw3npms10AWfW88oVa2oKn9oaDSpKQpObLrIlYPX0TXQ4dWLCJ7cD2C15+9v/d391z/TduJ7x5/7Vx4jKZRcO3mXnzf+mKt9JUli8+w97P3zKAqFknlHf6ZUdac8nd+iYAG0tDVJTVVkKon5JnoGujTv75Kn83wr8hz4nThxguPHj2Nvn7nMSvHixbNkkBeETy0iJEpdMP3Vi/B8O65TRUecKjq+dbsBs7+jdI3iOFUqkm1v3+uKVXRk9a2FpKakUrS8A/uXH2P56PVUblKOOUd+zjH4Wz1xM3v+OIJVYQs2+S7N8o064GEgpzerMvwf/+csQxf2yd2b/MBMLU3oNaUzt87dp+PorKutPyefY7tXuXJl3N3dP8m538b39lOGVZuMJEksOf8bZWqXVL9mbmvGZt+lKFMV6BrkvJozNSWVFeM2cXjNGQBm9/iT3UGrGLVsAMUqOLBszEZ87/gzddtIft00nNCAV4xprMr755deuxdV4FG5URk0tTVxrlEccxtTipUvjFxDjp6hLj+tG8rjG0+o2LAMMuDQ2jPsT0vKfGz9eZ7cf84vG4ZRI20B194VJ1EqlOxbeQq9tJQu/o8Cue/uzavACIqVL4xDSTscStqhVCoZVH0KgX6h1G9fFX0TPQoVtyUiNIqgJy9ZMWkbQ2Z1Qa4hVweYppbGmFoakxibSGpSCofWnKFlvwaEh0QCULR81t5cpwoOLL0wLdNzKyZsSXv/qEvV5fQlNTTgFSvGb+bhNV8ad6/D4NkZc0yDn4ZSuVFZnj0MREtLg4SYRLS0M0KH6PBYzv/rToWGzhQumXU6i99dfzbP2qN+fOP03TwHfvYl7Njou5TU5FSx+Cwf5Dnwi4uLQ18/a7mVV69efdQJw4KQG5UalWXi+uEkJSTT4A0TkD8UbR2tHIeBs1O4dMZw9PVTdwHVMIdSocyx0Y56qcrDFhsZR3YzdguVtKNW26o8vR9A45718nD1H17f37rR91NfRC6Idi9vwoMj1Wk/svvCpa2jBW8Zfrt3+ZE66APUKzNlMhllapVAU1sTuVym7lFc/MM6Xr0IR1Nbg8Kl7PC++YTilYpwbMN5/vfj3+gZ6uJcw4nD684wZsVAtvv+iZaOJvpGehR5rRe8eCVHkKl63x5dVw23uh+9pQ78Cpey476bNzIZ9JzYhtU/b+fG6XvcOKtaDCLXkLPqym/YFbNm3uA1BPqFAnD97H12P/mT2Kh4fum0hEc3ngJgX9yaf37bg7mtKX+c+gUdPS00NDVo0Lk6h9aepUEn1QKIhBjVStjU5Iw5iW8yaGZXnGs4UaKSIxNbzUNSKlG+lsbldX9P/Ve92nfX/47Qd0pHtHW1eeEbwpBKk0hNUTBz7ziKlXfgwVUfqjXLKNH254i/ubjHExNLY3b6L89ybBsHSywLmfPqRThlapV45144ixxKagp5l+fAr379+mzcuJGZM1VzkdLH7RcsWICLi+hWFT4vMpmMpn3ePh/kc9R/Rjd09LWp2arKG4eTh/3RnxJVi1K+QRn1qsXXaWhqMGOfat6fJEk8fxyIVWGLt/ZAChlEu5c3VV0rMG7t96QkpVIvLZ9bXhUt74BNEUvCgiKo3qwirYdm5K4sWq4w23z+AJkM4wKqEnU2RSzhLChSFNw+78Wan7cz/+hkwtJyBCbEJnL99D1Aldty6rZRmXqu0tVrX41t3v9D30iPPcuOc9/dm47Dm6lfn/z39xxYdYpKLmXQ0tHMqN+bRqlQEh+TQMDjIC7su6Z+vmRajtAz/15VB32mlsb43vEnITaR597BbF90iF1/HqNmi4pM2zqCH+b1UPfgzz86iZtn7qvnQmYnMS6Jm+cfUKZGcYzNDbEtYsnoRjMxsTSiSY86tB2akbbK+9ZTPI7dxrV3PUpULsLZnW5oamnQenBjddsQEx5LaoqqAkt4cCTVm1ekXodq/zmr6gYoUxXZLnYyMNFn/YPFpCSlZpuv78qBa7gdukHn0S1xcLbP8npuvev8wW+SlEf379+XLC0tpebNm0va2tpS586dpdKlS0vW1taSj49PXg/32YmKipIAKSoq6lNfivCViI+JlxQKxUc511/jNkjNdbpLO+bvy/b1jdN3Sk1knaUhFcdJSqXyrcdLTU2VAn2Dc7Xt5yS/P8dfc7v3Obd5P7eZK7nq9JBcdXtJrrq9pCf3/HPcNjUlVfLy9JGmdFosuer3lnYuPiRJkiQlxidJu/88Kh1df07qaPe95KrfW3LV7y1dPXorz9cTFx0veXn6SKmpGZ/nZw9fSLP7rZCaGfeXmhn3lw79fU59PdN7LpP6VfpJ+nvmbql9oR+lTg4jpIsHrkntCw2Xvis9TgoLjpR+67lMamY6UGpmOlD6qd0CqZlxf6mF2cBM58itGT2XSs2M+klDa/4qSZIk/TP9X8nVoI/katBH8r71NNO2nQsPk1wN+kg/t18gSZIkhfi/lJKTUrIc8/wud+ng6lNSakpqtudcOmq9+vcT9CREkiRJUiqVks+tJ9KrwDDpVWB4jterVCqlloZ9pKZa30mTW8/N8/uVJElSKBTSOJdpUgud7tLFPe7vdIyvRW4/y3nu8XN2dubOnTusXLkSDQ0N4uLi6NixIz/++CO2trZvP4AgfEOO/XOWxYNWULJ6cf64POuDfyM9uek8qcmpnNp8ga4T2mV53e/uU0A17+9Nw8fpZnVbwqU9V2k5uAljVg39EJf8RRDt3sfnd8efgNdqz8o15Ghm00OXTkNTg1JVi/Hbv6NJjEtS9y7p6GnTcYSqnGKpasUY23gmOvo66nrZuSVJEqMazsD/USAdfmzG9/N6AFC4pB1dR7fk+eNgSlUrSst+9dXXM23zj6Qkp9K9+BgSYlQVLq4cusm/vkuQyeVoaMhp2Kk6HifuUL5uSeydrImNjKfVgIZZeu8VCiWRL6MxT1sMExedoF59e+PMfR5c9VYnhk+v5928XwO8PH0pVNyGImUzL+qyKFiA6LBYDE30mTfgLyo2dKZZn/pZ3nf9TjWIi07ggbsPpWsUQ1NLE6+rPqz5eRu1WlemRstKHFt/Dgdne8zThmN3LDjAP1N3IpPLkJQSv24bRb1scvbJZDKcKjrgddWHQtnMD8yNmPBYbp9TlcS8csCTuh3erYf5W/JOSXVsbGw+eMkfQfga3DpzF0mCRx7eJMQmYmCcdZ5Yfhq2pB+HV5+i68SsQR/A94v6YeNoTbXmFXOVC+vxNV9AVfHjWyfavY9r5biNBD99iZauJr9sHolNEassK4SzI5PJciwB5uhsz78BK0AmyxRYRYZGcfXYbaq5llevMv4vpVJJ0BPVfL2Ax4GZXnOq4MCKyxl/G5IksWHWXvzu+hP45KU6vQtA4241M+Wzq9+hGnXbVeHf/x3ln992A2Bul7Vm9i/tF3LrvBddRrfg/B4PQv3DsC1qxR9npjCl82IUqUqsHSxwKG3HuL8G433zKTfP3WfyPz9gamn8n2MtwO+OP+2GNiHyVQxndlzh7M4r1O9YPdt7N77JLPzu+tOif0NGrxjIplm7uX/lMfevPGb9g0Xsf7VWvUAFUN8nKa1es++tp9kGfpBejRg8jtzkh4W9s93mTUwsjBk0txd3LjygWzZfdoWs8hz4HTt2DENDQ+rWrQvA8uXLWbNmDc7OzixfvhwzM1HkXRDS9ZraBUmSqOhS9r2CPq+r3nhf96Np3wbovWElZKMe9WjUI+cFHNYOltmu6pUkiZBnL7EsZI6GRkZA+Mu20ZzafPGNtYG/BaLd+/gqNHDm9vkHVGpYltptq75x27M7rrBm8laa9W2AS7faFCppl+Mq+Oy+8Mzo/if33R5TvHIRvpvYlqP/nKPjyOZUbpSRciXQN5SUtIUVhqb6WeazrZy4haPrzzN4dncqNizN9oWHVC+k9fLbFbFkwZFJ6h6718nlch5d91M/3vz7PrS0NKjYICP9lJeH6kvY2Z3uvAqMACDIL5Tdy46hqaWJIjWZkGevVNte9Wb9zD0kxCTiffMpv7yWesX3rj/XTqnmOl4+dIM+P7fnwm53SlUrho5+9vN+w4JV50s/r21Ra0B1jEfX/LAtknml7YCZ3bG0Nyc+OgEJibrtq3Nh91Vqtq6cJaeeZcECPCT7YDe3uk1sR7ccvuwKWeV53GnChAlER6tWEd69e5exY8fSsmVL/Pz8MtV/FAQB7IvbMnnzKFoMfPfAKS46nnENprJ0+Fo2TNmej1eXYeWY9fQu+iNT287L9LxzrZKMXD4Ip4pFPsh5vxSi3fv4ev3akZ0v/mLGvvFv3O78LncWDv6LsMAIts3bz+CKE1k1cbP69YDHgZzcfBHvm0944ROc7TE0NDNKuS0dtR7P47dZlZYOJZ11YXN1XdlzO92Z2ePPTK+f2HyR5MQUTm29hHVhC4pXdEBbV0u19EGSKFTChjsXHzKx1Tyupa3Yf93N817qfz+65sdPbRZw391b/dwvG4fRvG99araqmHFNDhbsWHiYpIRkKtQvhUNpOywLFqBKk3LqqhzmdmaE+L9i7/IThAaEoauvQ3q8+upFOJaFLdgTtIrFp6fkOBVlzqFJ9J/RhTErBhIdFkP/GV2p274a9TvVUK92TidJEht++5fzu9x5cNWbAtamTGrxO7N7/sm6XzLar9SUVLyuejP8z34ULV8Y39vPuHPRC+HDy3OP35MnT3B2Vn0L2b17N23atOH333/nxo0btGzZMt8vUBC+dZpaGuga6pISHouhmeEHOUf6UO6jtKFdITPR7uWd20FPUpJSqd/53dMoGZsbqoORnQsPcnrbZQbP7UHVphnpRPavOE5qsgJkoKWtSXJiCk/uBQCQkpzKqHrT1DWA5XIZy9xnU6x85vl9U3eM5vqpu1RuVIZNs/dy8K+T1OuUeWhSW1ebgTO6MOM7VcB3++JDepYcjZ6BLotP/crQuT04ufkSPSa1RVtHi6Xnp6FUKrl67DY3zz2g0/BmjGgwg+jwWGIj46nSuGymHsNW/Ruy689j6BnqqOcDBjwKpExNVf3s6s0qUL1ZBRSpCowLGBEbGceZnVcAVbAVHRbLqquz1cf78+xUnno9p3R1J0bUn47f3QDO7HRj6flpTPr7B+amlcgLfvqSyi5l3vh7KFbegWLlHVg1aQt7/jxGk551mbJtZLbbBvmFcnjNafVjL3dv9NPmIkqvpZP537C1nNx0kWIVHPC7o8q7eHn/NcrXK43wYeU58NPW1iY+XvUhOnXqFH36qIaNChQooP5GLAjC+7m42x2PozfpNrEd9iXsWH17IQGPAqnQ8M0N9LsavWooB1ccp2H3Oh/k+F860e7lzemtF5nbSxUg/bCkHx1H5S1Bt1KpZJLrLO67PebnzSOp074af0/ZgVKhZOfCg5kCv06jWhIRHIVr3waUq1uKKwev0SptaoJMltGbpzquRNTLmCznMzIzoGEXVRm2Hxf34fv5PbMdEq7ZshLN+zXgoacv+kZ6PLiq+sJ09l832g1tSvP/LI6QyWRUdy1PhXqlGNfsdxJiE5FryHnhG0L/CpP44+wUTMxVVXr6/NKeSi7OOFcvxvLxmzm5+RJ/jlpPjeYVM5VG09DUoM8vHTi+6QL7V57MeA8FDDKd29BUn7K1Sqjf3+v/bdi5BilJKcRExOHaq26m/U5susi+lSfoNq41Df6TiufmGdUiiptn72e5N+msHS2p0aoS9y8/JiE2kaqu5Rk8twfeN55Qp101osNjGd9kprr3NfJlNJ1Gt8T39rNM6WZSklP5d9FB9Ax1aT+8eZ5rYqempOJ57BbFKjpiVcgiT/t+7fIc+NWtW5exY8dSp04dPDw82LFjBwCPHz/OktVeEIS8S0lOYfZ3/0ORqiAmIpbpuydgUdAci4LmH+ycRcoWZuSKwR/s+F860e7lzavnYRn/foeKOTHhsdw+r0qI7H7oOnU7VKfdj66c3nqZFgMy502s064addpl5JYrWyejSoimlibLrszi8XU/ol7FomeoQ6VGb//ylNPCp9QUBTIJSlQqgkNpOx64eyPXlGcKRNNJksTULku4duIOLfo35Mm95wBUb1Yej+N3CH76Eu+bT6napBwAM3ouxfP4Hao3r6Ce26eppYmGVsa1eBy/zeJh66jerDxD5/bk1nkvUlNSKVenJPVzWDwBMH37KO67eVOmlqr38MjfZ9m/6hR9fu6IppYmp7ZdZuu8A3Qc3oxNs3YT+TKG9dP/zRL4jfizH4dWn6Zp75znEWtoyJmxe7z6HqQHbIVKqFbt3rv0kGcPVPdC31iPmi0rE+QXQt321Sj42uKd01susn6q6nPmWMaeSo3K5XjO7KybvJVdiw9ibGHEjherMy2o+dbl+U4sW7aMYcOGsWvXLlauXEnBgqpKA0ePHqV58+b5foGC8K3R1NKkRNVieLk/pmydUp/6cgREu5dXXca3JTwogqSEZAbN7Znn/VUrNXty5/wDuoxrA8APC/vwwzuUG7R2sFRX/XhfV4/c5Og/5wCYsG4oK91nYWZlkqlHLl1Kciqex28jSXB43Vk0dTSpULc0/ad3JtAvlLioeEzTanIDvHyuCpBfPQ+nw4+uODoXxLaIlTpBNah64yJCoji+8SI/zO/FpLW5S7GkZ6hL1aYZgdOycZuRlBLzh6ymbvuqbJ13gBc+wWyesw95+uKubHrYytQqQZm0XsTcyK6Xrlz90jTpVY8rB64RH53AsQ3nSE1Kxe3gdZr0qod+Whm8QiXt1Cl83uX3l5SQDEBKYgpKZTYljXIQ5BfC2AZT0dbV4n+XZmFmbZrnc3/uZJKUXZGnb1d0dDQmJiZERUVhbJy14L0gfAwKhYLosFj1BG0hb8TnOPe+lHt19cgNEuOSqN+5ZpaAIvJlNIuHrsbIzJBRKwZmWjkqSRIPPX0xtzXDqlDee80VCiWTW/7Oo2t+jFg6gLU/b0epUPK/89OwK2r9xn2ndF6Mx7Hb6seDZnWjQv3SjKg/HYAW/RowetkAAF74BHN+91UadKqhXkTyX/euPGb5uI1Ub1aB/tO7vPXaI0Kj2LviJBUblKZ83ZJMbreQB+7e6vrlBiZ67A5YwaG1Z9gydx9Vm5Tn9I4rKJJTsbAzY9be8Vny/+WXg6tPse7nbZSrVwrPY7cpX680845nrkkeFhiOprYmJhZ5/7tMiEvk3PbLlK5ZAscyuX8PR9aeZsmQvwCYtnv8F5UXMLef5Xfq+1QoFOzduxcvLy9kMhmlSpWiffv2aGqKrlRByA8aGhoi6PvMiHbv07l/+RFT2s0HQCaXUb9TzUyvH113Bve0WrMu3WpR1bWC+rVj/5xjyfdr0NXXYdSKgTz08KHtD66ZEgZLksS9y48oYGOaJegKC4zgdtqK2wduj9nqp5q7+N8VsA89fbm8/xrN+zekYDFVQDj+r8Gs/XU7L5+Hc8/tEWt/2Y61oyVIEjK5nHodM4ZnCzrZ0GNS9ilJUpJTWTt5G/GxiSw8/os6cfPbLBiyhhtn7rNz8WF+3jCMu5ceZXp92IJegCoALV29GOum7kSRogCZjFeBESwcuprll2fm6lzZeeEbzPl/3WnQuWaW+9pmSBPaDGmifn+aWhpZAnrz96jPq2eg+07ZFOp3ronn8Zvo6GpTtVnFdz7/5yzPLda9e/do27YtISEhlCypmkvx+PFjLC0tOXDgAOXK5W0cXhCEz5fv7aec33kF174NsS/xbpn1vwai3fu0dPS1kclAklAPBb7u+eNA1YuA5X969SJCogBIjE9i/oCVSEqJQ6tO8feDxer8cyc2nmfR4NVo6Wiy4eH/sChYgITYRPQMdbG0L0CPyR146OFN++HNc0x5Mq3zYiJDo/Hy8GHhiV8AMLEwYtxfg0lOSqGT3fcARKZdj62jBVVeyxP4JjdO32PfihOAqvpI0XKFuX3hAc37NcTU0pg9y45x+7wXA2Z0xaF0QfV+6XV2gUwpZBzL2lOvbVVcuqoC6AVDVnN2pzslqhShUAlblEolL7yDKV7RMVfXl5NZ3/2J351nnN/pxqob83LcLruayZ+KoakB0/59cwqhL12e7/agQYMoW7Ys169fVyctjYiIoF+/fgwZMgQ3N7d8v0hBED6c6LAYlo/6G1NLE4Ys6J1pYvtvnRYQ5BfK9ZN3WO4x9xNe5acl2r1Py6lSEVZ4zCU5MYXSaelNXlehYRlObrqAhX0BbBwzJxPuPKYlBiZ62BSxYsGAv4gOi0GhUBL8JFQd+MVGqlZspyankpyYwuIhqzm2/hzdJ7VjwMxu9J3W+a3XaF/clsjQaAr/p/SYQqEkPjqBEpWLcu/KIxQKBb9uGaFecZur91/RkQI2piQlJFGqejHGuMwgKT6ZLb/vY9zqIayatBUAAxN9Jr4272/yP9/zx8gNmFoYUiAtQbK2rhbzD07C2Dxj7mDA4yAAYsLjWH93ger+PH2JbZGc59ZJksSdC15YFbLAtqjqPvreeYbH0Vu49q6PuZ0ZlvYF8LvzDAv7rD13JzaeZ9nI9RhbGPH7wUkUfi1gFT6sPAd+t2/f5tq1a5ky1ZuZmTF79myqVav2hj0FQfgcndhwjjNbLwFQu121TCljbIpYE+QXil2xN89l+tqJdu/TK/aG3ifXPg2o0MAZYwsjdPQyV5/Q1tWm3bBmAPx9fxGbZuzCpogVFV/LXddumCtJCcloampgW9SKq0dvAqrFHANmdsvV9c05PInnj4NwLJOxyluSJMY0/I1H1/zoOr4NBZ2sqd2mCjVbVsrt2wbA3NaUzT5/pA0RyzC1MCbE/xXJiSmc3+VO+XqluO/2OEsyZTMrE+q0qczCoWvQNdBh7IoBRL6KITE+CWNzQ5ITk3lw1ZfRSwdwcZ8HDTrV4PS2yyQnpWBsbkgBa5Mcy98d+OskK8ZsQEdPm03ef2JiYcTklnOIehXD/SuPmLV/It0ntKV45SJ0GpU11+W/iw+RGJ9Eon8SB1ed5Mf/9cvTPRHeXZ4Dv5IlSxISEkKZMpmXxIeGhuLk5JRvF/a6p0+fMnPmTM6cOUNwcDB2dnb06tWLX375BW3tjA+5v78/P/74I2fOnEFPT48ePXqwcOHCTNsIgpBZRZey6BvrYVTAkCLlCmd6LTkpBYCUpNRPcWmfjU/R7gl5k5uVn8YFDLMNMKLDYtkyew8pSanoGekybvUQTmy8QPvhzXJ9fm0dLYr+5/OTkpyK982nANw4dYell2fkOFT8Nq/XFl5xdRYrx2/G59ZTOgxvRrm6pVAqlNmmoXmZllonMS6Ji3s98Th+mwN/nWTzwyXMG7SKS/uuUaZWcRaf/JUrh64zf9Aq9b7Vm1Vg5p5x2V5PelLslKQUUtNK2ZnbFSDqVQwW9uaEBUYwvulMFKlKdA106TJGlctRoVCioSGn588dWDRkNQD1On7YBRTn/3Uj+Eko7Uc0R0dP54Oe60uQq8Dv9QSlv//+OyNHjmT69OnUrKmaH+Du7s6MGTOYNy/nMfz38fDhQ5RKJatWrcLJyYl79+4xePBg4uLiWLhwIaCaeN2qVSssLS25dOkSYWFh9O3bF0mSWLp06Qe5LkH4GjhVKsKesH+Qy+VZV0uGquYjhQdHfoIr+7Q+dbsnfDypyamqRQ2oAqTqLSpRvUVGr9y1E7fZNm8/Lfq70KRX1hx2SqWSiJAoCtiYZvoMaetoUbtNZS7t9cTn5hM8j9+mRou89fZlx9DUgAlpQ7pRr2KY2nERuga6dBjejEC/EBp0romWtiZxUfFEh8VQt11VytcvxYYZuwGIjYrnmdcL9fzH9P/+N8lHQlxijtfQZWxrzKxMKGBjgpmNaiHaotNTeHLXn1LVnYh6FYNMLgeUaKblIlw5biP7lh+nz9TOdJ/YFpsiVhQpWzhLL+3bJCelMK39fIL8gpm2ewJFyhbOcdtnXs+Z1W2x+rGo6ZvLwM/UNPMfsyRJdO3aVf1c+h9LmzZtUCgU2R7jfTRv3jxTrqyiRYvy6NEjVq5cqQ78Tpw4wYMHDwgICMDOTjXHYtGiRfTr14/Zs2d/1mkKBOFT09DIPmHtrAM/cWmvBy7fYEWPT93uCR+PVWELFp2dRpBvCC7da/P8cRAamnJs09K1/P3rDnxuPeXpvYAsgV9yYjITmszCy8OHzmNaMWRe5ryFEaGRgOpvxTSHlfpxUfFsnbsPmyJW6pWuuXVxr4c6Zczl/ddQpCq4de4B5eqUJOR5GHuXqxaFaOtqExeVAEBCTCITW85l6cXpnN91lVqtKhMZGs38garevq5jWmHtaEmdNlVyPG9CbCIvX4Tzv2FrqdDAGQ1NOZGh0UzbOQYNTQ0K2Jiy3H02wU9Dqd68IgDndrghKSXO/+tGoG8IJzddoEztEiw5Nz1P7/nJXX+uHb8FwPkdV94Y+BkXMETPUJeE2ERsiljluN23JFeB39mzZz/0deRZVFQUBQpkTBh1c3OjbNmy6qAPoFmzZiQlJXH9+nVcXFyyO4wgCG9gX8KO7pPaf+rL+CQ+x3ZPeLs/h6/jxIZzDFvSj5aDsk/n4XfnGX53/anfuaY65196cuL7Vx4x1uU3ZHI5y91nU6yCA0161ePZg+c0TSvJJkkSt88/wLJgAbbNP4CXh6p0291LD7Ocq8uY1jx/vJZqzSpQskrRbK9n/8oT7FpyGICytUu8MZD5rypNymHtYIG2jhahAWEoUhWc2nyJk5su0rhHbWRyGcbmhjTtVReP47fUC1l0DXSwsjeny2jV/LsXviEkxiUBqnJvrQc1yvGcvneeMbLOFBSpqi889688Uq8gvrjPUz2s6+hsj6OzPZIkceXANZoPcOHxdT86j2nF1t/3AhDy9GWu32u6YhUcaNC1NoG+wTTpXf+N25pZm7LBeynR4bE4lBZVdiCXgV+DBg0+9HXkia+vL0uXLmXRokXq54KDg7G2zjwB3czMDG1tbYKDg3M8VlJSEklJSerHou6mIAjw+bV7XxJJkji77RIpyak07dPgnee1vYtjf58lJSmFU5svZhv4xUXFM7LOryQnpvDk7jMs7S0oVd2JUtVVczXDAiOQJJAUSiJCIgEHOo5sQceRLdTHOLz2DH/+uA4tHU0qpaVk0dbVYtSygZnOdWmfJy+8g9j46H/o6uc8t8ypoqMqQCtgiLltxgKiIL9QprSfj6GZIbMPTsTAWD/LvrZFrNj4cAmgSgL98JoffwxbR1JCMiUqF2Xw7z3QM9RF10CHf/2X08F2KImxSZik1fZ96OHDygmbqdGiIr/9O4ZA3xAcy9jTueAPFC1XiN8PTsxS7uzJPX91oFfAxpQh83tyavNFIkOjqdehOpIk4XfHHxtHSwxM9HE/fIPpnVXDrQtPT6F8vdLYFbPm5KYLmcrt5Zamlia/bh+T6+3NrE2/ygoc7+qdkudERkaybt06dSJTZ2dnBgwYgIlJ3hLOTp8+nd9+++2N23h6elK1alX148DAQJo3b06XLl0YNGhQpm2zKw/zeq3A7MyZM+et1yAIgpBf7d634MapO8zppUp0rG+kR73/JFz+kH5Y1IdTWy/Se2oOKVhkIE9bKHHnghePPH3R0tFiV/Aa9Ax1qduxOiOXD0RbR5Mq2dTgBUiIUQ2ZpqYo6D+jK9VbVKKSS5lMSaEDfUOY0VUVkKUkp9Lrl46ZjnHzzD387vrTclAjqjevyLYny9Ez1EHXIGMVrfuRG+pUK15XfbKtCfy6gk42FHSyoXT1YoQ8e0XFhs6Z/v8nl8upWN8Z9yM3qdBQVQ941x9Heejhy0MPX/a9XEPNlpVYOW5TWr1kL0KevcqSfLl09eJoaGmgSFFQv1MNXLrWxqVrbfXr2+cf4O9ft2NVyJz1D5egrZsxh08rrYfVtogVfXL6HQkfVJ4Dv2vXrtGsWTP09PSoXl0V2S9evJjZs2dz4sQJKleunOtjDR8+nO7du79xG0dHR/W/AwMDcXFxoVatWqxevTrTdjY2Nly9ejXTcxEREaSkpGTpCXzd5MmTGTt2rPpxdHQ0hQp9mBI1giB8mfKz3fsWmFgYI5fLUCqlHOe15ZeE2ERCA15RuFRBZDIZbX5wpc0Prjlub2Csz0rPuTzzesHjG7488vRF30gXjbQFCHK5nNaD31zxocOI5phYGGFb1JpiFRwpVsExyzaGpvoYmOgTFxWfJbdgRGgUk1vOQamUiAyNZuDs7phZmyBJEr53nmFVyJxj/5xDoVRSpnYJjAsYUrZOyVzfE7ui1jmWk5u2YxQRIdGY25oC0KhbLW6cvke1ZuXRNVD1SrYe2gTfu/44VXDINpXTxhm7UKQo0NCU0yeb0nFBfiEAhAVHkpKUSpUm5Vhybhqa2pqUrFos07aSJBH89CXWDhYftWcY4PI+D+Ki4mnSu/5HP/cnJeVR3bp1pX79+kkpKSnq51JSUqS+fftK9erVy+vhcu358+dS8eLFpe7du0upqalZXj9y5Igkl8ulwMBA9XPbt2+XdHR0pKioqFyfJyoqSgLytI8gCJ+X/P4cf6p272N433sVHxMvjWkwRepfaqT03Duj/fV/+Fx6cs//3a/rVbT00NNHUiqVOW4TFhQu9SwyTGoi7yrtXHQgz+dITVVIN8/ek14Fhr/zdb5JeEik9OReQJbn46LjpQ6Wg6SmWt9Je5YeVT+/ftpOyVWnh9TdYZjkqtNDctXpId08c++DXFtupKYqpOSklCzPb569R3LV6SENLD9eUigUWV6PfBklbZyxS7px+u5bz7Hk+9VSE3lXaXrnhe90jUqlUjq347LkdvBanva77/ZIaiLrLDWRdZZObjr/Tuf+3OT2s/xOPX5r1qzJVJ9SU1OTiRMnZhqSzU+BgYE0bNiQwoULs3DhQl6+zJgMamOj6oJ2dXXF2dmZ3r17s2DBAsLDwxk/fjyDBw8WK3oFQXgvn6Ld+1I89PDh7gVVLdvLez3oOkGVLqNQyXevxJCcmMzgcmMJD45k0Nxe2abguLzPk+mdF6YvmCXgYWCez6OhIadiwzJv3/AdmVmZZFtzW99Ij7V3FhDi/4pS1TJ6wF74quajR71SzTWXyWVsmrmbdb9uZ8r20Vj9pxzdu5IkiT+GrcPL04fxa4ZSvFKRLNtEvYphWI2fiY2IY8HJXymRtjDF744/eka6LD43jWLlHbLtKTOxMKb3lE65upZHaQtjHnv6vdN7ubjbnVndVUPqSy7MoGzd0rnaz8BYD7mGHKVCibG50Tud+0uV58DP2NgYf39/SpUqlen5gIAAjIw+zM07ceIEPj4++Pj4YG+feVWOlJZSQUNDg8OHDzNs2DDq1KmTKYGzIAjC+/gU7d6XokztktRuV42IkCgadqv99h1yISUpRR38hPpnv+rzoYe3OuhrPbRptkOOuRUdFsPKsRswKmDID4v7vnFeeF5JkkRKcioJMQnERyeoU8QUsDGlgI1ppm2Hzu9FQSdbPI7dxCct8XP6SuGLe65mWwHjbe67PUYmA+eaGSXiQp694ug/qlXrR/8+S/GlWQO/p/cDePUiHIB7lx9RokpRHnr4MNblN1JTFLh0r83kjcPzfD3/NW7dDxxZc4rGPbPmR8wNvbTazTIZmeZHvo2DcyHW3F1MUnwSxStnv9r6q5XXrsQRI0ZI9vb20vbt2yV/f38pICBA2rZtm2Rvby+NGjXqnbonPydiqFcQvnz5/Tn+mtu9z7XNu3nmrrT19z1STERstq9HvoyS/hy+Tjqw4vh7n2vNT5ulJvKuUhN5V2nzzF3vfbx0SqVSmthspuSq1V1qZdRHaqr1nXQlF0OSdy56SeObzpSWjf5H6u4wTOpXeowU9CQ0z+e/ff6B1FTrO6mp1nfS7QsP1M8rFAppVs8/pd4lRkr33R5nu29qqkJa8/NWacGgv6TocNXvYEjlierjLR+9Ps/X8zZJicnSz61mS/1LjZSe3s/9NIEH7o8ln1tP8v16vjQfbKh34cKFyGQy+vTpQ2qqqkyLlpYWP/zwA3PnfrtF3AVB+HqJdu/jq+hSloouZXN83cTCmBFLB+TLuRycMxb0JSen5MsxAZITU7h19j6SJJGcqDruC++c04ulK1e3FDP3TaBn0RHEhMdiYKKHiWXee5ZTkjNKLd69+JDy9VTDoHK5nF82j3jjvh5HbhLoE0KnUS0wMlOlfilbpxRP7gZQvkFphszv+cb938WTO8/wOKKqk3x2+2X6zXjz4s90pWsUz/dr+ZrJJOk/NVpyKT4+Hl9fXyRJwsnJCX39rPmFvkTR0dGYmJgQFRUl5gYKwhfqQ32Ov8Z270Pcq/iYBOb3XUpyUio/bRqBcYHPfzj83M4rBD8NpcOIlnkuIfYmR9aexvP4bYpXKQoSdBr99uP/b9hajqw9g1EBQ2LCYwH4/fBPb03nkp1NM3ezaaaqVFvLgY0YtWJgroayO9sNJfpVDE6VHFlx9XdANWx9YZebKkfi4CbUap1zZY93kZqSyu89/iDQN5hfto1+r3mi36LcfpbfKY8fgL6+PuXKlXvX3QVBEL44ot3LnWvHb3F5nycAV/Z50nxAzlUgPhcNu+bP/MT/ajmosTqRtNdVb2Z2X0L9TjVx7ZNzgnD3QzcAMDY3xMzKGDMbU8rUKpHj9m9SpFxGFZAj685gV8yaruPbvHW/mi0rcWLjhUzBnUymWmzi7/UCn1tP8z3w09TSZOq/4/L1mEJW7xz4CYIgCEJ2ytV3xqGMPanJqVR+h16q/4qLjkdLWzNTIuAPSZIkfuuyiDvnHjB580iqpdWafV/rp+3k5um73Dh1l6a96+fY8zbmr8EcX3+O9sObU75+7lapZsfj6E1mdv8f2rqaJCeqhn2feb3I1b7j137Pj3/0Q+8/Cybqtq/OVq+91O1Q/Z2vS/i0ROAnCIIg5CszKxPW3l2SL8e6d8mLCU1mYGiiz+q7i7NNj5Lfol7FcHmvqsfy7PbL+Rb41WlXjVtn71GnXbVMQV9yYjKJcUnqtCI1WlaiRstKmfa9d/khbgdv0HpIY/XK4Ld5fOMJklIiOTGVkcsGEPLsFR1GNM/19f436APoN6MbPX7pqK5xLHx5ROAnCIIgfLa8rvqQmpxK5MtoAn2CP0rgFxESSY2WlYh6FUOH12r0vq+2P7jSanBjNDQ11M/FxyQwqMIEwgIjmLZzDLXbZp8Xckr7hcRFxeN31585h3/K1fk6DG9OXFQ8tkWtaTW4MZIk5UuFChH0fdlE4CcIgiB8tloObkzI01DMbExxfsd5bnmRGJ/EyNq/khiXRMdRLfM9x9vrQR9AeFAkr56r8uU99PDJNvB7/jgIQ1NV+TcH59wveDAw0Wfo/F7ERcczsOw4wgLDmXf8V0pVd3q/N/EGkiSxdfYentx9xpAFvbEqbPnBziW8GxH4CYIgCJ8tA2N9hi8d+MHPExMRy6nNFylV3QnNtLq92rp579nyu/OMia6zMLUy5o+LMzEwefPKb/sStgz/sz/+Xs/pNFqVoDnIL4SfWs5Bz1CX8Wu/Z1yjGSTEJlK+fmmGzMt7GhV/rxc8f6yqbHLj9N33Cvz87jxDqVDilE21D4AXPsGsn7odAAt7c75f1PedzyV8GCLwEwRBEL5YPreeoKWtmSkX37tYOXYDJzdeQN9IjxWec3jm9YLqLSrm6Rh3L3pxaZ8H0WExRIfF4HvnmTp33pu0/b5ppseeJ+4Q5BcKwIIBK0mITQTAspD5Ow3VlqhajE5jWhH67BXN+7vkef90Dz18GFH7F5Bg0dlplK/vnGUbq0LmFClXmOePAqniWuGdzyV8OCLwEwRBEL5IN8/cZWKTGcjkMlZcm4dTxex7oXIjPdeggYk+NkWtKVjc9q37JCUkM63DAkL9X9F2WDOWj/oHmVxGhYbO2Be3xbnmuyUWrtexOlf2e6JvrIe+iT5P7gVQqKQdY1YOynb7lORUdi0+hL6xHg261ESRqsTc1kz9uoaGnKHze7/TtbwuPjpeXSYvNjI+2220dbVZdWshilQFmloixPgcid+KIAiC8EWKDlMlN5aUEnFR2QciuTV4Xk+qt6xE0fIOaGjkrlfN+4Yf10/eAeDO+fvqaxk6v3e2Q6HxMQmsnrgZfSNd+s/qTkx4HGbWJlnSuphZmTD36M8AKBRKKjZ0Zu/SY2ycsZtBv3+X5bint17in6k7AVg1cQtKhZLFZ6dmqs+bzvfWU64cvIZrnwZYO+Rt/l2lxuUYv+4Hrp+6g4lFzkm5ZTKZCPo+Y+I3IwiCIOQrpVLJvj+PkpKcSuexrbMsaMgv9TvXZOKG4ejoaVOhQZn3OpaGpgaVG+ctOXeJqsWo074aoQFhDPj9O+p3roVRAcMc57+d2XqJI2tPAxD87CWX9nhQp301pr0habGGhpybp+/jff0J3tef0Gl0yywrmwuVsEUulyHX1CA1rUyb/8PAbAO/X9rOJTwokrsXvZh/Ykqe3q9MJuPuRS/ObrvM5b0e7A37+6PlVvxYosNjWDhgJdq6Woxb90O2KW2+dCLwEwRBEPKV59GbrBy7HlDNS2v0Xd0Pch6ZTEbR8g5ofYD0IsmJyZzceAHHsoUoU7tkttto62gxffd49WP74nZvPKZzrRLoGeqio6+jnsN37/KjN19HUgqFS9lhbG5I+fqlMbXMWoqrTO2SbPZdiqa2Jme3XyYhNonGPbK/59aFLQkPisxzb186i4IFADCxNP5gAf2ndGmPB24HVDkcG3arTd0ONT7xFeU/EfgJgiAI+cqmiBVaOlooFYpczZV7VzdO32VS0xnI5TL+urmAIuUc8u3YW+fsY8us3Whoytn+fFW2AVdeFS3vwL/Ba5DLZTy9F8D+5cdp1KPOG/f5pc08bp97QJGyhZi6Y0yO26UHZB1GvDnv4LwTv/Lkrj+PPX0ZWedX+v3WlcpN3l5dJTUllesnbtN8oAvVW1TCvqTdVxn4VW5SDmsHS7R0tShTp9SnvpwPQgR+giAIQr5ycC7ElmcrUSoyLzLIb9GvogFQKiViIuLy9dgGJnqAarFCenqX/JCe/NipUhHGrf3+jdtGvozm9rkHAAQ/fQlAbGQcv/daiqSUmLx5BMYFDLPslxCbyK2z9yhbtxRGZplf19XXoXSN4oxvPIPkhGQ2ztiVq8Bv/ZTt7Ji/H2NzQ7Y9X/3BkzjHRMSya9FBnCoXpV7Hj9frZuNoxeYnKz7a+T4FEfgJgiAI+e5jVNio36UWSQnJ6BroUi4XaVPyovOY1jhVLIJtUSsMTQ3y5ZjhwZHM7vEHBsZ6TN4y8q3zx3T0tTE2NyQ6LJaOI1Wl1jyP3ebaCdWCEo8jN2nSq16W/X5qMRsvd29MLY3ZGbg622M3H+DCiX/O4dqnQa6uPTEuCYDkxBQkpTJX+7yPLbN2s3vJIWQy2PZ89Qf9AvGtEYGfIAjCZ2r27NkcPnyYW7duoa2tTWRkZJZt/P39+fHHHzlz5gx6enr06NGDhQsXoq39dU26z45cLqdZv3fPS/cmMpmMSo3K5usxL+65yp3zqh68O+ceUKNV5Tdur2egy9/3F/PqeThFyqnyFFZ0KUORcoWRJIlKjbO/vkDfEEDVYxgbGZdt4DrizwGM+HNArq994NyeFK3gSKnqTujo6eR6v3dVuLQ9AGbWpm9Ngi3kjQj8BEEQPlPJycl06dKFWrVqsW7duiyvKxQKWrVqhaWlJZcuXSIsLIy+ffsiSRJLly79BFf8+dnzx2HcD11j4JxelKxa7JNeS42Wldi/zA59Yz3K1Ml+wch/GRcwzDSca2Ztwqrrc9+4z6gVA/njh7VUbV4RfWM9Qp69xKqwRZa0MXmhZ6BLy0GN33n/vGo5qDHlGzhjZm2Crv6HDzTTxUXF8cewtejoaTF86cCPEuR+bDJJkqRPfRGfk+joaExMTIiKisLY+P0n8wqC8PF9bZ/j9evXM3r06Cw9fkePHqV169YEBARgZ6daUbp9+3b69etHaGhort7713avXpecmExrg55IEtRpX53peybky3FD/V+ydPg6CpUqyKC5Pd+pmsbHMrfPMs5su4RrnwaMX/fDp76cz8a9yw+5vNeD1t83paBTxgKko+tOs3jwXwBM3zOBOu2rf6pLzLPcfpY/379WQRAE4Y3c3NwoW7asOugDaNasGUlJSVy/fv0TXtnnQUtHizodaqClrUm9TjU5tfkCO+bvJzkphdCAV2ybu5dnD57n+bgH/zqJ+6Hr/LvwAE/vBeR6v7ioeLbM3o37oQ//u1GkKkhNSeWhhzcAXh4+H/ycX5Ipbeeya/FB/vhhTabnyzdwxtTSGMtC5pR8j5rGnzMx1CsIgvCFCg4OxtraOtNzZmZmaGtrExwcnO0+SUlJJCUlqR9HR0d/0Gv8lGQyGdN2jUeSJHxuPmFY1UkAaOtqcWmfJ3fOP+DQqlNsebI8T8dVDdkepWBxWwoWt8n1flt/38POhQeRyWVsD/iLAjamgGqlrp6R3lsrhjy5F8CUtnOxsDdn3Nrv+fvnbRR2tqffb10zDeOGBrxieI2fSU5KYfSKwdw+/+C9avR+jRyc7bl/+RFFyhbO9HxBJ1t2Bq8FeK+h8c+Z6PETBEH4iKZPn45MJnvjz7Vr13J9vOz+5yRJUo7/05ozZw4mJibqn0KFCr3ze/lSyGQyTK1M0DVQzdeyK2aNhZ1qlai5Xd5Xi5atW5r9URtZeX1+nuaA2RZVBenGBQzRM1St6D2y9jQdzAcwqs6vKN+yWtbt4DVCA8J44PaYLbN2c3m/J9vm7MXf60Wm7W6fe0Dky2jioxNIjE9i1IpBlKz2aec3fm4WnJ7G2vtLGLqoT5bX0j+HXyvR4ycIgvARDR8+nO7du79xG0dHx1wdy8bGhqtXr2Z6LiIigpSUlCw9gekmT57M2LFj1Y+jo6O/ieDP0t6cjT7LiI9JoKCTLZWbVqD10KY5lld7m3cJDFoPbUqZOiUxtyugDvzSV/k+vu5HYlwS+kZ6Oe7fuEddrp+8jWUhC1z71OfSPk8KOtlgU8Qq03a3zt5TB//VW1TK83V+C7S0tXBIWzn8rRGBnyAIwkdkYWGBhYVFvhyrVq1azJ49m6CgIGxtVRPUT5w4gY6ODlWqVMl2Hx0dHXR0vr6VirlhZm2KmbUpAFramvme+y83/ju02HtaF+Qacio1KvvGoA/A2sGSRWemqx/vj1iPXJ61d6pw6YIggZWDBcbmWRM8f0ipKan83PJ3ntz1Z/qeCTmWuxM+HRH4CYIgfKb8/f0JDw/H398fhULBrVu3AHBycsLQ0BBXV1ecnZ3p3bs3CxYsIDw8nPHjxzN48OCvboXu16qgkw0T1//4TvvmNCew6/i2VG9RCWsHSzS1Pu7/5oP8Qrh5+i4AF/51E4HfZ0gEfoIgCJ+pqVOnsmHDBvXjSpVUw3Znz56lYcOGaGhocPjwYYYNG0adOnUyJXAWvl0ymSxLz+LHUrC4LW1/bIbvrWe0Gto01/uFB0ewavxG7IrZ0Gd61696jt2nJvL4/cfXnNNKEL4V4nOce+JevbvkxGTiYxMxtRD37X2tn7qdLbN2A7DyxnycKr7b3MtvmcjjJwiCIAi5kBCXyKbf/uXkpvO53ifk2UvaGPeli/Vg9i49+l7nVygUfOt9MJUal0NbV4uCxW2xK5b7FDlC3onATxAEQfim7fvzKBt/28n8vst49iB3CZlvX3iAUqFKv+J2IPfpd/7rgftj2pv2pX+pUcRFx7/zcb50FRqUYV/kBv72+t9bF7kI70cEfoIgCMI3y+fmE7bP2wuAgYk+Jpa5G7Zt0rMeFV3KULC4LSOWDXjn8988dZfEuCReeAcR8PDF23f4imlpa33W5e++Fl/MHW7bti2FCxdGV1cXW1tbevfuTWBgYKZt/P39adOmDQYGBlhYWDBy5EiSk5M/0RULgiAInzv3Q9eJj04A4JcdYzC1NMnVfvcuPeSRhzf6RjpYO1i+8/lbDm5M8cpF0NHX5s55r3c+jiDk1hcT+Lm4uLBz504ePXrE7t278fX1pXPnzurXFQoFrVq1Ii4ujkuXLrF9+3Z2797NuHHjPuFVC4IgCJ+zZv1dqNqsAi0HN6Fyo3K53u/q4RskxCbifd2PQN+Qdz6/mbUpcVEJJMUns/fPw+98HEHIrS92Ve+BAwdo3749SUlJaGlpcfToUVq3bk1AQIC6YPn27dvp168foaGhuV6tJla4CcKXT3yOc0/cq3cT/DSUZSPW4eBsz6C5vd4r/cjJTefZPncvnca0oeWgxvl4lcK3JLef5S8yj194eDhbtmyhdu3aaGlpAeDm5kbZsmXVQR9As2bNSEpK4vr167i4iALVgiAIQv6wcbRi1sHJ+XKspr0b0LR3g3w5liC8zRcz1AswadIkDAwMMDc3x9/fn/3796tfCw4OzlKb0szMDG1tbYKDg3M8ZlJSEtHR0Zl+BEEQBEEQvkafNPCbPn06MpnsjT/XrmUsk58wYQI3b97kxIkTaGho0KdPn0y5j7Lrak8vVJ2TOXPmYGJiov75FoqVC4IgCILwbfqkQ73Dhw+ne/fub9zG0dFR/e/04uYlSpSgdOnSFCpUCHd3d2rVqoWNjQ1Xr17NtG9ERAQpKSlZegJfN3nyZMaOHat+HB0dLYI/QRAEQRC+Sp808EsP5N5Fek9fUlISALVq1WL27NkEBQVha2sLwIkTJ9DR0aFKlSo5HkdHRwcdHZ13ugZBEARBEIQvyRexuMPDwwMPDw/q1q2LmZkZfn5+TJ06lWLFilGrVi0AXF1dcXZ2pnfv3ixYsIDw8HDGjx/P4MGDxUo1QRAEQRAEvpDFHXp6euzZs4fGjRtTsmRJBgwYQNmyZTl//ry6t05DQ4PDhw+jq6tLnTp16Nq1K+3bt2fhwoWf+OoFQRAEQRA+D19Ej1+5cuU4c+bMW7crXLgwhw4d+ghXJAiCIAiC8OX5Inr8BEEQBEHInejwGE5uOk94cMSnvhThM/RF9PgJgiAIgpA7s7/7HzdO3qFI+cKsvrXoU1+O8JkRPX6CIAiC8BVJT137PmXkhK+X6PETBEEQhK/IL9vG4Hn0JpUal/vUlyJ8hkTgJwiCIAhfESMzQxr1qPepL0P4TImhXkEQBEEQhG+ECPwEQRAEQRC+ESLwEwRBEARB+EaIOX7/kV4DODo6+hNfiSAI7yr985v+eRZyJto8Qfg65LbdE4Hff8TExABQqFChT3wlgiC8r5iYGExMTD71ZXzWRJsnCF+Xt7V7Mkl8Jc5EqVQSGBiIkZHRN5MDKTo6mkKFChEQEICxsfGnvpwvlriP+SM/7qMkScTExGBnZ4dcLma0vIlo88Rn9V2J+5g/8us+5rbdEz1+/yGXy7G3t//Ul/FJGBsbiw9vPhD3MX+8730UPX25I9o88Vl9X+I+5o/8uI+5affEV2FBEARBEIRvhAj8BEEQBEEQvhEi8BPQ0dFh2rRp6OjofOpL+aKJ+5g/xH0UPjTxN5Y/xH3MHx/7PorFHYIgCIIgCN8I0eMnCIIgCILwjRCBnyAIgiAIwjdCBH6CIAiCIAjfCBH4fYWmT5+OTCbL9GNjY6N+XZIkpk+fjp2dHXp6ejRs2JD79+9nOkZSUhIjRozAwsICAwMD2rZty/Pnzz/2W/noLly4QJs2bbCzs0Mmk7Fv375Mr+fXvYuIiKB3796YmJhgYmJC7969iYyM/MDv7uN5233s169flr/RmjVrZtpG3EchL0S7925Em5c/vqQ2TwR+X6kyZcoQFBSk/rl79676tfnz57N48WKWLVuGp6cnNjY2NG3aVF26CWD06NHs3buX7du3c+nSJWJjY2ndujUKheJTvJ2PJi4ujgoVKrBs2bJsX8+ve9ejRw9u3brFsWPHOHbsGLdu3aJ3794f/P19LG+7jwDNmzfP9Dd65MiRTK+L+yjklWj38k60efnji2rzJOGrM23aNKlChQrZvqZUKiUbGxtp7ty56ucSExMlExMT6a+//pIkSZIiIyMlLS0tafv27eptXrx4IcnlcunYsWMf9No/J4C0d+9e9eP8uncPHjyQAMnd3V29jZubmwRIDx8+/MDv6uP7732UJEnq27ev1K5duxz3EfdRyCvR7r0/0eblj8+9zRM9fl8pb29v7OzsKFKkCN27d8fPzw+AJ0+eEBwcjKurq3pbHR0dGjRowJUrVwC4fv06KSkpmbaxs7OjbNmy6m2+Rfl179zc3DAxMaFGjRrqbWrWrImJick3dX/PnTuHlZUVJUqUYPDgwYSGhqpfE/dReBei3ctfos3LX59LmycCv69QjRo12LhxI8ePH2fNmjUEBwdTu3ZtwsLCCA4OBsDa2jrTPtbW1urXgoOD0dbWxszMLMdtvkX5de+Cg4OxsrLKcnwrK6tv5v62aNGCLVu2cObMGRYtWoSnpyeNGjUiKSkJEPdRyDvR7uU/0ebln8+pzdN8j/chfKZatGih/ne5cuWoVasWxYoVY8OGDerJpDKZLNM+kiRlee6/crPNtyA/7l12239L97dbt27qf5ctW5aqVavi4ODA4cOH6dixY477ifso5ES0ex+OaPPe3+fU5okev2+AgYEB5cqVw9vbW73K7b/fDkJDQ9Xf6mxsbEhOTiYiIiLHbb5F+XXvbGxsCAkJyXL8ly9ffrP319bWFgcHB7y9vQFxH4X3J9q99yfavA/nU7Z5IvD7BiQlJeHl5YWtrS1FihTBxsaGkydPql9PTk7m/Pnz1K5dG4AqVaqgpaWVaZugoCDu3bun3uZblF/3rlatWkRFReHh4aHe5urVq0RFRX2z9zcsLIyAgABsbW0BcR+F9yfavfcn2rwP55O2ebleBiJ8McaNGyedO3dO8vPzk9zd3aXWrVtLRkZG0tOnTyVJkqS5c+dKJiYm0p49e6S7d+9K3333nWRraytFR0erj/H9999L9vb20qlTp6QbN25IjRo1kipUqCClpqZ+qrf1UcTExEg3b96Ubt68KQHS4sWLpZs3b0rPnj2TJCn/7l3z5s2l8uXLS25ubpKbm5tUrlw5qXXr1h/9/X4ob7qPMTEx0rhx46QrV65IT548kc6ePSvVqlVLKliwoLiPwjsT7d67EW1e/viS2jwR+H2FunXrJtna2kpaWlqSnZ2d1LFjR+n+/fvq15VKpTRt2jTJxsZG0tHRkerXry/dvXs30zESEhKk4cOHSwUKFJD09PSk1q1bS/7+/h/7rXx0Z8+elYAsP3379pUkKf/uXVhYmNSzZ0/JyMhIMjIyknr27ClFRER8pHf54b3pPsbHx0uurq6SpaWlpKWlJRUuXFjq27dvlnsk7qOQF6LdezeizcsfX1KbJ5MkScpbB6UgCIIgCILwJRJz/ARBEARBEL4RIvATBEEQBEH4RojATxAEQRAE4RshAj9BEARBEIRvhAj8BEEQBEEQvhEi8BMEQRAEQfhGiMBPEARBEAThGyECP0EQBEEQhG+ECPyEr5KjoyP/+9//1I9lMhn79u0D4OnTp8hkMm7duvVJrk0QBCG/iTZPyC3NT30BgvAxBAUFYWZm9qkvQxAE4aMQbZ6QE9HjJ3wTbGxs0NHReef9JUkiNTX1nfZNTk5+5/MKgiC8C9HmCTkRgZ/w0TRs2JARI0YwevRozMzMsLa2ZvXq1cTFxdG/f3+MjIwoVqwYR48eBaBfv37IZLIsP+fOncvzuV8f9kj38OFDateuja6uLmXKlMl03HPnziGTyTh+/DhVq1ZFR0eHixcv4uvrS7t27bC2tsbQ0JBq1apx6tSpTMd1dHRk1qxZ9OvXDxMTEwYPHkyjRo0YPnx4pu3CwsLQ0dHhzJkzeX4/giB8/kSbJ9q8z5EI/ISPasOGDVhYWODh4cGIESP44Ycf6NKlC7Vr1+bGjRs0a9aM3r17Ex8fzx9//EFQUJD6Z9SoUVhZWVGqVKl8uZYJEyYwbtw4bt68Se3atWnbti1hYWGZtpk4cSJz5szBy8uL8uXLExsbS8uWLTl16hQ3b96kWbNmtGnTBn9//0z7LViwgLJly3L9+nWmTJnCoEGD2Lp1K0lJSepttmzZgp2dHS4uLvnyfgRB+PyINk+0eZ8dSRA+kgYNGkh169ZVP05NTZUMDAyk3r17q58LCgqSAMnNzS3Tvrt375Z0dHSkixcv5upcDg4O0pIlS9SPAWnv3r2SJEnSkydPJECaO3eu+vWUlBTJ3t5emjdvniRJknT27FkJkPbt2/fWczk7O0tLly7NdO727dtn2iYxMVEqUKCAtGPHDvVzFStWlKZPn56r9yMIwpdHtHmizfsciR4/4aMqX768+t8aGhqYm5tTrlw59XPW1tYAhIaGqp+7efMmffr0Yfny5dStWzffrqVWrVrqf2tqalK1alW8vLwybVO1atVMj+Pi4pg4cSLOzs6YmppiaGjIw4cPs3z7/e9+Ojo69OrVi7///huAW7ducfv2bfr165dv70cQhM+PaPNEm/e5Eat6hY9KS0sr02OZTJbpOZlMBoBSqQQgODiYtm3bMnDgQAYOHPjBry/9/OkMDAwyPZ4wYQLHjx9n4cKFODk5oaenR+fOnbNMZv7vfgCDBg2iYsWKPH/+nL///pvGjRvj4OCQ/29CEITPhmjzRJv3uRE9fsJnKzExkXbt2lGqVCkWL16c78d3d3dX/zs1NZXr16+/dS7NxYsX6devHx06dKBcuXLY2Njw9OnTXJ2vXLlyVK1alTVr1rB161YGDBjwPpcvCMJXRrR5wscgevyEz9bQoUMJCAjg9OnTvHz5Uv18gQIF0NbWfu/jL1++nOLFi1O6dGmWLFlCRETEWxsmJycn9uzZQ5s2bZDJZEyZMkX9TT03Bg0axPDhw9HX16dDhw7v+xYEQfiKiDZP+BhEj5/w2Tp//jxBQUE4Oztja2ur/rly5Uq+HH/u3LnMmzePChUqcPHiRfbv34+FhcUb91myZAlmZmbUrl2bNm3a0KxZMypXrpzrc3733XdoamrSo0cPdHV13/ctCILwFRFtnvAxyCRJkj71RQjCtyIgIABHR0c8PT3z1HgKgiB8iUSb9/kRgZ8gfAQpKSkEBQXx008/8ezZMy5fvvypL0kQBOGDEW3e50sM9QpfnIsXL2JoaJjjz+fo8uXLODg4cP36df76669PfTmCIHxBRJsn5CfR4yd8cRISEnjx4kWOrzs5OX3EqxEEQfiwRJsn5CcR+AmCIAiCIHwjxFCvIAiCIAjCN0IEfoIgCIIgCN8IEfgJgiAIgiB8I0TgJwiCIAiC8I0QgZ8gCIIgCMI3QgR+giAIgiAI3wgR+AmCIAiCIHwjROAnCIIgCILwjfg/trjId9W1GikAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:04:30.805258 \u001b[38;20m INFO: calibration group: precursor, predicting mz\u001b[0m\n", - "0:04:30.859014 \u001b[38;20m INFO: calibration group: precursor, predicting rt\u001b[0m\n", - "0:04:30.971624 \u001b[38;20m INFO: calibration group: precursor, predicting mobility\u001b[0m\n", - "0:04:31.024373 \u001b[38;20m INFO: calibration group: fragment, predicting mz\u001b[0m\n", - "0:04:31.822596 \u001b[38;20m INFO: calibration group: precursor, predicting mz\u001b[0m\n", - "0:04:31.875156 \u001b[38;20m INFO: calibration group: precursor, predicting rt\u001b[0m\n", - "0:04:31.992972 \u001b[38;20m INFO: calibration group: precursor, predicting mobility\u001b[0m\n", - "0:04:32.044450 \u001b[38;20m INFO: calibration group: fragment, predicting mz\u001b[0m\n", - "0:04:32.797933 \u001b[38;20m INFO: calibration group: precursor, predicting mz\u001b[0m\n", - "0:04:32.889931 \u001b[38;20m INFO: calibration group: precursor, predicting rt\u001b[0m\n", - "0:04:33.003949 \u001b[38;20m INFO: calibration group: precursor, predicting mobility\u001b[0m\n", - "0:04:33.052438 \u001b[38;20m INFO: calibration group: fragment, predicting mz\u001b[0m\n", - "0:04:33.806847 \u001b[32;20m PROGRESS: MS1 error: 15, MS2 error: 15, RT error: 150, Mobility error: 0.04\u001b[0m\n", - "0:04:33.872076 \u001b[38;20m INFO: Duty cycle consists of 13 frames, 1.39 seconds cycle time\u001b[0m\n", - "0:04:33.872639 \u001b[38;20m INFO: Duty cycle consists of 928 scans, 0.00065 1/K_0 resolution\u001b[0m\n", - "0:04:33.873013 \u001b[38;20m INFO: FWHM in RT is 4.32 seconds, sigma is 0.66\u001b[0m\n", - "0:04:33.873262 \u001b[38;20m INFO: FWHM in mobility is 0.008 1/K_0, sigma is 5.29\u001b[0m\n", - "0:04:33.875411 \u001b[38;20m INFO: Starting candidate selection\u001b[0m\n", - "100%|██████████| 460153/460153 [13:28<00:00, 568.83it/s]\n", - "0:18:19.211615 \u001b[38;20m INFO: Finished candidate selection\u001b[0m\n", - "0:18:20.801448 \u001b[38;20m INFO: Starting candidate scoring\u001b[0m\n", - "100%|██████████| 1000/1000 [05:10<00:00, 3.22it/s]\n", - "0:23:54.400409 \u001b[33;20m WARNING: base_width_mobility has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.405064 \u001b[33;20m WARNING: base_width_rt has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.408294 \u001b[33;20m WARNING: rt_observed has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.411420 \u001b[33;20m WARNING: mobility_observed has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.414784 \u001b[33;20m WARNING: mono_ms1_intensity has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.419492 \u001b[33;20m WARNING: top_ms1_intensity has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.422839 \u001b[33;20m WARNING: sum_ms1_intensity has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.426121 \u001b[33;20m WARNING: weighted_ms1_intensity has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.429322 \u001b[33;20m WARNING: weighted_mass_deviation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.432505 \u001b[33;20m WARNING: weighted_mass_error has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.435669 \u001b[33;20m WARNING: mz_observed has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.438857 \u001b[33;20m WARNING: mono_ms1_height has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.441983 \u001b[33;20m WARNING: top_ms1_height has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.445077 \u001b[33;20m WARNING: sum_ms1_height has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.448139 \u001b[33;20m WARNING: weighted_ms1_height has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.451321 \u001b[33;20m WARNING: isotope_intensity_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.454501 \u001b[33;20m WARNING: isotope_height_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.457767 \u001b[33;20m WARNING: n_observations has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.460938 \u001b[33;20m WARNING: intensity_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.464078 \u001b[33;20m WARNING: height_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.467182 \u001b[33;20m WARNING: intensity_fraction has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.470250 \u001b[33;20m WARNING: height_fraction has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.473425 \u001b[33;20m WARNING: intensity_fraction_weighted has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.476561 \u001b[33;20m WARNING: height_fraction_weighted has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.479745 \u001b[33;20m WARNING: mean_observation_score has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.482848 \u001b[33;20m WARNING: sum_b_ion_intensity has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.485984 \u001b[33;20m WARNING: sum_y_ion_intensity has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.489124 \u001b[33;20m WARNING: diff_b_y_ion_intensity has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.492248 \u001b[33;20m WARNING: fragment_frame_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.495426 \u001b[33;20m WARNING: top3_frame_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.498604 \u001b[33;20m WARNING: template_frame_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.501788 \u001b[33;20m WARNING: top3_b_ion_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.504908 \u001b[33;20m WARNING: top3_y_ion_correlation has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.508011 \u001b[33;20m WARNING: cycle_fwhm has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.511326 \u001b[33;20m WARNING: mobility_fwhm has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.514496 \u001b[33;20m WARNING: n_b_ions has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.517612 \u001b[33;20m WARNING: n_y_ions has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:54.520733 \u001b[33;20m WARNING: f_masked has 478 NaNs ( 0.03 % out of 1648710)\u001b[0m\n", - "0:23:59.547772 \u001b[38;20m INFO: Finished candidate scoring\u001b[0m\n", - "0:24:09.066202 \u001b[38;20m INFO: performing precursor_channel_wise FDR with 39 features\u001b[0m\n", - "0:24:09.067081 \u001b[38;20m INFO: Decoy channel: -1\u001b[0m\n", - "0:24:09.067517 \u001b[38;20m INFO: Competetive: true,\u001b[0m\n", - "0:24:11.077711 \u001b[33;20m WARNING: dropped 236 target PSMs due to missing features\u001b[0m\n", - "0:24:11.078204 \u001b[33;20m WARNING: dropped 242 decoy PSMs due to missing features\u001b[0m\n", - "0:24:11.475904 \u001b[38;20m INFO: Pre FDR iterations: 23\u001b[0m\n", - "0:30:09.982442 \u001b[38;20m INFO: Post FDR iterations: 27\u001b[0m\n", - "0:30:13.798580 \u001b[38;20m INFO: Test AUC: 0.613\u001b[0m\n", - "0:30:13.799193 \u001b[38;20m INFO: Train AUC: 0.615\u001b[0m\n", - "0:30:13.799511 \u001b[38;20m INFO: AUC difference: 0.20%\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:30:14.927617 \u001b[32;20m PROGRESS: ============================= Precursor FDR =============================\u001b[0m\n", - "0:30:14.928237 \u001b[32;20m PROGRESS: Total precursors accumulated: 48,589\u001b[0m\n", - "0:30:14.928554 \u001b[32;20m PROGRESS: Target precursors: 48,108 (99.01%)\u001b[0m\n", - "0:30:14.928935 \u001b[32;20m PROGRESS: Decoy precursors: 481 (0.99%)\u001b[0m\n", - "0:30:14.929324 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:30:14.929679 \u001b[32;20m PROGRESS: Precursor Summary:\u001b[0m\n", - "0:30:14.947263 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 48,108; 0.01 FDR: 48,108; 0.001 FDR: 34,383\u001b[0m\n", - "0:30:14.948334 \u001b[32;20m PROGRESS: \u001b[0m\n", - "0:30:14.948789 \u001b[32;20m PROGRESS: Protein Summary:\u001b[0m\n", - "0:30:14.977541 \u001b[32;20m PROGRESS: Channel 0:\t 0.05 FDR: 6,159; 0.01 FDR: 6,159; 0.001 FDR: 5,300\u001b[0m\n", - "0:30:14.978097 \u001b[32;20m PROGRESS: =========================================================================\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shutting down background jobs, please wait a moment...\n", - "Done!\n", - "Waiting for the remaining 3 operations to synchronize with Neptune. Do not kill this process.\n", - "All 3 operations synced, thanks for waiting!\n", - "Explore the metadata in the Neptune app:\n", - "https://app.neptune.ai/MannLabs/alphaDIA/e/AL-419/metadata\n" - ] - } - ], - "source": [ - "plan = planning.Plan(output_location, raw_files, test_lib)\n", - "\n", - "plan.config[\"general\"][\"reuse_calibration\"] = False\n", - "plan.config[\"general\"][\"thread_count\"] = 10\n", - "plan.config[\"general\"][\"astral_ms1\"] = False\n", - "plan.config[\"calibration\"][\"norm_rt_mode\"] = \"linear\"\n", - "\n", - "plan.config[\"extraction_target\"][\"target_num_candidates\"] = 5\n", - "plan.config[\"extraction_target\"][\"target_ms1_tolerance\"] = 15\n", - "plan.config[\"extraction_target\"][\"target_ms2_tolerance\"] = 15\n", - "plan.config[\"extraction_target\"][\"target_rt_tolerance\"] = 150\n", - "\n", - "plan.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "for raw_name, dia_path, speclib in plan.get_run_data():\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://app.neptune.ai/MannLabs/alphaDIA/e/AL-337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:00:08.596494 \u001b[38;20m INFO: Importing data from /Users/georgwallmann/Documents/data/testing/bruker/20230422_TIMS05_PaSk_MCT_SA_HeLa_mDIA_P001_diaP_pydiAID8_1300V_S2-E3_1_1819.d\u001b[0m\n", - "0:00:08.597051 \u001b[38;20m INFO: Using .d import for /Users/georgwallmann/Documents/data/testing/bruker/20230422_TIMS05_PaSk_MCT_SA_HeLa_mDIA_P001_diaP_pydiAID8_1300V_S2-E3_1_1819.d\u001b[0m\n", - "0:00:08.597463 \u001b[38;20m INFO: Reading frame metadata for /Users/georgwallmann/Documents/data/testing/bruker/20230422_TIMS05_PaSk_MCT_SA_HeLa_mDIA_P001_diaP_pydiAID8_1300V_S2-E3_1_1819.d\u001b[0m\n", - "0:00:08.793820 \u001b[38;20m INFO: Reading 17,438 frames with 1,635,017,608 detector events for /Users/georgwallmann/Documents/data/testing/bruker/20230422_TIMS05_PaSk_MCT_SA_HeLa_mDIA_P001_diaP_pydiAID8_1300V_S2-E3_1_1819.d\u001b[0m\n", - "100%|██████████| 17438/17438 [00:07<00:00, 2446.79it/s]\n", - "0:00:16.016436 \u001b[38;20m INFO: Indexing /Users/georgwallmann/Documents/data/testing/bruker/20230422_TIMS05_PaSk_MCT_SA_HeLa_mDIA_P001_diaP_pydiAID8_1300V_S2-E3_1_1819.d...\u001b[0m\n", - "0:00:16.018792 \u001b[38;20m INFO: Bruker DLL not available, estimating mobility values\u001b[0m\n", - "0:00:16.019510 \u001b[38;20m INFO: Bruker DLL not available, estimating mz values\u001b[0m\n", - "0:00:16.021824 \u001b[38;20m INFO: Indexing quadrupole dimension\u001b[0m\n", - "0:00:16.700933 \u001b[38;20m INFO: Transposing detector events\u001b[0m\n", - "100%|██████████| 20/20 [00:07<00:00, 2.72it/s]\n", - "0:00:29.116949 \u001b[38;20m INFO: Finished transposing data\u001b[0m\n", - "0:00:29.249609 \u001b[38;20m INFO: Successfully imported data from /Users/georgwallmann/Documents/data/testing/bruker/20230422_TIMS05_PaSk_MCT_SA_HeLa_mDIA_P001_diaP_pydiAID8_1300V_S2-E3_1_1819.d\u001b[0m\n", - "0:00:29.396492 \u001b[38;20m INFO: ========= Initializing Calibration Manager =========\u001b[0m\n", - "0:00:29.397288 \u001b[38;20m INFO: loading calibration config\u001b[0m\n", - "0:00:29.397672 \u001b[38;20m INFO: found 2 calibration groups\u001b[0m\n", - "0:00:29.398352 \u001b[38;20m INFO: Calibration group :fragment, found 1 estimator(s)\u001b[0m\n", - "0:00:29.398835 \u001b[38;20m INFO: Calibration group :precursor, found 3 estimator(s)\u001b[0m\n", - "0:00:29.399090 \u001b[38;20m INFO: ====================================================\u001b[0m\n", - "0:00:29.399645 \u001b[38;20m INFO: ========= Initializing Optimization Manager =========\u001b[0m\n", - "0:00:29.400183 \u001b[38;20m INFO: initial parameter: fwhm_rt = 5\u001b[0m\n", - "0:00:29.400594 \u001b[38;20m INFO: initial parameter: fwhm_mobility = 0.01\u001b[0m\n", - "0:00:29.400852 \u001b[38;20m INFO: ====================================================\u001b[0m\n", - "0:00:29.401218 \u001b[32;20m PROGRESS: Initializing workflow 20230422_TIMS05_PaSk_MCT_SA_HeLa_mDIA_P001_diaP_pydiAID8_1300V_S2-E3_1_1819\u001b[0m\n", - "0:00:29.401613 \u001b[38;20m INFO: ========= Initializing Optimization Manager =========\u001b[0m\n", - "0:00:29.401876 \u001b[38;20m INFO: initial parameter: current_epoch = 0\u001b[0m\n", - "0:00:29.402219 \u001b[38;20m INFO: initial parameter: current_step = 0\u001b[0m\n", - "0:00:29.402592 \u001b[38;20m INFO: initial parameter: ms1_error = 30\u001b[0m\n", - "0:00:29.402870 \u001b[38;20m INFO: initial parameter: ms2_error = 30\u001b[0m\n", - "0:00:29.403346 \u001b[38;20m INFO: initial parameter: rt_error = 240\u001b[0m\n", - "0:00:29.403689 \u001b[38;20m INFO: initial parameter: mobility_error = 0.08\u001b[0m\n", - "0:00:29.404059 \u001b[38;20m INFO: initial parameter: column_type = library\u001b[0m\n", - "0:00:29.404261 \u001b[38;20m INFO: initial parameter: num_candidates = 2\u001b[0m\n", - "0:00:29.404525 \u001b[38;20m INFO: initial parameter: recalibration_target = 200\u001b[0m\n", - "0:00:29.404729 \u001b[38;20m INFO: initial parameter: accumulated_precursors = 0\u001b[0m\n", - "0:00:29.405022 \u001b[38;20m INFO: initial parameter: accumulated_precursors_01FDR = 0\u001b[0m\n", - "0:00:29.405337 \u001b[38;20m INFO: initial parameter: accumulated_precursors_001FDR = 0\u001b[0m\n", - "0:00:29.405687 \u001b[38;20m INFO: ====================================================\u001b[0m\n", - "0:00:29.406021 \u001b[38;20m INFO: ========= Initializing FDR Manager =========\u001b[0m\n", - "0:00:29.406408 \u001b[38;20m INFO: ====================================================\u001b[0m\n", - "0:00:29.406651 \u001b[32;20m PROGRESS: Applying channel filter using only: [0]\u001b[0m\n" - ] - } - ], - "source": [ - "workflow = peptidecentric.PeptideCentricWorkflow(\n", - " raw_name, plan.config, dia_path, speclib\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "test_df = workflow.spectral_library.precursor_df.sample(1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:04:55.627185 \u001b[38;20m INFO: Duty cycle consists of 9 frames, 0.96 seconds cycle time\u001b[0m\n", - "0:04:55.627841 \u001b[38;20m INFO: Duty cycle consists of 928 scans, 0.00065 1/K_0 resolution\u001b[0m\n", - "0:04:55.628282 \u001b[38;20m INFO: FWHM in RT is 5.00 seconds, sigma is 1.11\u001b[0m\n", - "0:04:55.628570 \u001b[38;20m INFO: FWHM in mobility is 0.010 1/K_0, sigma is 6.57\u001b[0m\n", - "0:04:55.631108 \u001b[38;20m INFO: Starting candidate selection\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1000/1000 [00:10<00:00, 96.39it/s]\n", - "0:05:06.320512 \u001b[38;20m INFO: Finished candidate selection\u001b[0m\n" - ] - } - ], - "source": [ - "from alphadia.extraction import hybridselection\n", - "\n", - "config = hybridselection.HybridCandidateConfig()\n", - "config.update(workflow.config[\"selection_config\"])\n", - "config.update(\n", - " {\n", - " \"rt_tolerance\": workflow.com.rt_error,\n", - " \"mobility_tolerance\": workflow.com.mobility_error,\n", - " \"candidate_count\": workflow.com.num_candidates,\n", - " \"precursor_mz_tolerance\": workflow.com.ms1_error,\n", - " \"fragment_mz_tolerance\": workflow.com.ms2_error,\n", - " \"exclude_shared_ions\": workflow.config[\"library_loading\"][\n", - " \"exclude_shared_ions\"\n", - " ],\n", - " }\n", - ")\n", - "\n", - "extraction = hybridselection.HybridCandidateSelection(\n", - " workflow.dia_data.jitclass(),\n", - " test_df,\n", - " workflow.spectral_library.fragment_df,\n", - " config.jitclass(),\n", - " rt_column=f\"rt_{workflow.com.column_type}\",\n", - " mobility_column=f\"mobility_{workflow.com.column_type}\",\n", - " precursor_mz_column=f\"mz_{workflow.com.column_type}\",\n", - " fragment_mz_column=f\"mz_{workflow.com.column_type}\",\n", - " fwhm_rt=workflow.optimization_manager.fwhm_rt,\n", - " fwhm_mobility=workflow.optimization_manager.fwhm_mobility,\n", - ")\n", - "candidates_df = extraction(thread_count=workflow.config[\"general\"][\"thread_count\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0:05:06.624870 \u001b[38;20m INFO: Starting candidate scoring\u001b[0m\n", - "100%|██████████| 1000/1000 [00:00<00:00, 4020.27it/s]\n", - "0:05:07.001336 \u001b[33;20m WARNING: delta_frame_peak has 6 NaNs ( 0.60 % out of 1000)\u001b[0m\n", - "0:05:07.032154 \u001b[38;20m INFO: Finished candidate scoring\u001b[0m\n" - ] - } - ], - "source": [ - "from alphadia.extraction import plexscoring\n", - "\n", - "config = plexscoring.CandidateConfig()\n", - "config.update(workflow.config[\"scoring_config\"])\n", - "config.update(\n", - " {\n", - " \"precursor_mz_tolerance\": workflow.com.ms1_error,\n", - " \"fragment_mz_tolerance\": workflow.com.ms2_error,\n", - " \"exclude_shared_ions\": workflow.config[\"library_loading\"][\n", - " \"exclude_shared_ions\"\n", - " ],\n", - " }\n", - ")\n", - "\n", - "candidate_scoring = plexscoring.CandidateScoring(\n", - " workflow.dia_data.jitclass(),\n", - " workflow.spectral_library._precursor_df,\n", - " workflow.spectral_library._fragment_df,\n", - " config=config,\n", - " rt_column=f\"rt_{workflow.com.column_type}\",\n", - " mobility_column=f\"mobility_{workflow.com.column_type}\",\n", - " precursor_mz_column=f\"mz_{workflow.com.column_type}\",\n", - " fragment_mz_column=f\"mz_{workflow.com.column_type}\",\n", - ")\n", - "\n", - "features_df, fragments_df = candidate_scoring(\n", - " candidates_df,\n", - " thread_count=workflow.config[\"general\"][\"thread_count\"],\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "alpha", - "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": "3.9.13" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/release/one_click_linux_gui/control b/release/one_click_linux_gui/control index e70e0630..3755fed6 100644 --- a/release/one_click_linux_gui/control +++ b/release/one_click_linux_gui/control @@ -1,5 +1,5 @@ Package: AlphaDIA -Version: 1.4.0 +Version: 1.5.0 Architecture: all Maintainer: Mann Labs Description: AlphaDIA diff --git a/release/one_click_linux_gui/create_installer_linux.sh b/release/one_click_linux_gui/create_installer_linux.sh index 6bf5dbbd..1a7feb81 100644 --- a/release/one_click_linux_gui/create_installer_linux.sh +++ b/release/one_click_linux_gui/create_installer_linux.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_linux_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphadia-1.4.0-py3-none-any.whl[stable]" +pip install "../../dist/alphadia-1.5.0-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller==4.2 diff --git a/release/one_click_macos_gui/Info.plist b/release/one_click_macos_gui/Info.plist index 2548f0c4..d76e1910 100644 --- a/release/one_click_macos_gui/Info.plist +++ b/release/one_click_macos_gui/Info.plist @@ -9,9 +9,9 @@ CFBundleIconFile alpha_logo.icns CFBundleIdentifier - alphadia.1.4.0 + alphadia.1.5.0 CFBundleShortVersionString - 1.4.0 + 1.5.0 CFBundleInfoDictionaryVersion 6.0 CFBundleName diff --git a/release/one_click_macos_gui/create_installer_macos.sh b/release/one_click_macos_gui/create_installer_macos.sh index ca27d1d7..e2b7e132 100644 --- a/release/one_click_macos_gui/create_installer_macos.sh +++ b/release/one_click_macos_gui/create_installer_macos.sh @@ -20,7 +20,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_macos_gui -pip install "../../dist/alphadia-1.4.0-py3-none-any.whl[stable]" +pip install "../../dist/alphadia-1.5.0-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller==4.2 diff --git a/release/one_click_macos_gui/distribution.xml b/release/one_click_macos_gui/distribution.xml index 78617e10..fb48874c 100644 --- a/release/one_click_macos_gui/distribution.xml +++ b/release/one_click_macos_gui/distribution.xml @@ -1,6 +1,6 @@ - AlphaDIA 1.4.0 + AlphaDIA 1.5.0 diff --git a/release/one_click_windows_gui/alphadia_innoinstaller.iss b/release/one_click_windows_gui/alphadia_innoinstaller.iss index 1a06214f..24d9831a 100644 --- a/release/one_click_windows_gui/alphadia_innoinstaller.iss +++ b/release/one_click_windows_gui/alphadia_innoinstaller.iss @@ -2,7 +2,7 @@ ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES! #define MyAppName "AlphaDIA" -#define MyAppVersion "1.4.0" +#define MyAppVersion "1.5.0" #define MyAppPublisher "Max Planck Institute of Biochemistry, Mann Labs" #define MyAppURL "https://github.com/MannLabs/alphadia" #define MyAppExeName "alphadia_gui.exe" diff --git a/release/one_click_windows_gui/create_installer_windows.sh b/release/one_click_windows_gui/create_installer_windows.sh index d654558d..9501a9bd 100644 --- a/release/one_click_windows_gui/create_installer_windows.sh +++ b/release/one_click_windows_gui/create_installer_windows.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_windows_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphadia-1.4.0-py3-none-any.whl[stable]" +pip install "../../dist/alphadia-1.5.0-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller==4.2 diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5cb65aa4..23bd2a33 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,10 +1,12 @@ -click==8.0.1 -alpharaw==0.1.1 -alphatims==1.0.4 -alphabase==0.1.3 +click +alpharaw +alphatims +alphabase peptdeep progressbar -neptune-client +neptune seaborn rocket_fft -xxhash \ No newline at end of file +xxhash +torchmetrics +directlfq \ No newline at end of file diff --git a/tests/performance_tests/fdr_test.py b/tests/performance_tests/fdr_test.py new file mode 100644 index 00000000..f608f4c5 --- /dev/null +++ b/tests/performance_tests/fdr_test.py @@ -0,0 +1,239 @@ +# setup basic argpasrse comand line script to run the fdr test +# with the options --threads and --size between 0 and 100% + +import matplotlib +import argparse +import tempfile +import pandas as pd +import torch +import numpy as np +import time +import neptune + +import alphadia +from alphadia import testing, fdr, fdrexperimental +from alphadia.workflow import peptidecentric + +classifiers = { + "binary": fdrexperimental.BinaryClassifier, + "legacy": fdrexperimental.BinaryClassifierLegacy, + "legacy_new_batching": fdrexperimental.BinaryClassifierLegacyNewBatching, +} + +parser = argparse.ArgumentParser(description="Run the fdr test.") +parser.add_argument( + "--threads", type=int, default=1, help="number of threads to use, default: 1" +) +parser.add_argument( + "--size", type=int, default=100, help="size of the test in percent, default: 100" +) + +parser.add_argument( + "--url", + type=str, + default="https://datashare.biochem.mpg.de/s/42iwFDTbJYpZxHW", + help="url to the test data, default: https://datashare.biochem.mpg.de/s/42iwFDTbJYpZxHW", +) + +parser.add_argument( + "--neptune-project", + type=str, + default="MannLabs/alphadia-fdr-optimization", + help="Neptune.ai project for continous logging, default: MannLabs/alphadia-fdr-optimization", +) + +parser.add_argument( + "--neptune-tag", + action="append", + dest="neptune_tag", + default=[], + help="Specify Neptune tags", +) + +parser.add_argument( + "--n-iter", type=int, default=20, help="number of iterations, default: 20" +) + +parser.add_argument( + "--max-batch-size", + type=int, + default=10000, + help="maximum batch size, default: 10000", +) + +parser.add_argument( + "--min-batch-number", + type=int, + default=100, + help="minimum batch number, default: 100", +) + +parser.add_argument( + "--epochs", type=int, default=10, help="number of epochs, default: 10" +) + +parser.add_argument( + "--learning-rate", type=float, default=0.0002, help="learning rate, default: 0.0002" +) + +parser.add_argument( + "--weight-decay", type=float, default=0.00001, help="weight decay, default: 0.00001" +) + +parser.add_argument( + "--layers", + type=str, + default="100,50,20,5", + help="number of layers, default: 100,50,20,5", +) + +parser.add_argument( + "--dropout", type=float, default=0.001, help="dropout, default: 0.001" +) + +parser.add_argument( + "--classifier", + choices=classifiers.keys(), + default="binary", + help="classifier to use, default: binary", +) + + +def main(): + # disable interactive plotting + matplotlib.use("Agg") + + # parse command line arguments + args = parser.parse_args() + + # print all command line arguments + print("==========================") + print("Command line arguments:") + for arg in vars(args): + print(f"{arg}: {getattr(args, arg)}") + + # set number of threads + torch.set_num_threads(args.threads) + + # set neptune.ai logging + run = neptune.init_run( + project=args.neptune_project, + tags=args.neptune_tag, + ) + + # log parameters to neptune.ai + run["parameters"] = vars(args) + run["version"] = alphadia.__version__ + + print(f"Downloading test data from {args.url}...") + temp_directory = tempfile.gettempdir() + test_data_location = testing.update_datashare(args.url, temp_directory) + print(f"Saved test data to {test_data_location}") + + features_df = pd.read_csv(test_data_location, sep="\t") + print(f"Test data has {len(features_df)} rows") + print(f"Will use {args.size}% of the data") + available_columns = list( + set(peptidecentric.feature_columns).intersection(set(features_df.columns)) + ) + + performance_dicts = [] + + for iteration in range(args.n_iter): + print(f"Iteration {iteration+1}/{args.n_iter}") + + target_df = features_df[features_df["decoy"] == 0].sample(frac=args.size / 100) + decoy_df = features_df[features_df["decoy"] == 1].sample(frac=args.size / 100) + + start_time = time.time() + + classifier = classifiers[args.classifier]( + test_size=0.001, + max_batch_size=args.max_batch_size, + min_batch_number=args.min_batch_number, + epochs=args.epochs, + learning_rate=args.learning_rate, + weight_decay=args.weight_decay, + layers=[int(l) for l in args.layers.split(",")], + dropout=args.dropout, + ) + + psm_df = fdr.perform_fdr( + classifier, + available_columns, + target_df, + decoy_df, + competetive=True, + neptune_run=run, + ) + + stop_time = time.time() + duration = stop_time - start_time + + psm_sig_df = psm_df[psm_df["decoy"] == 0] + + performance_dicts.append( + { + "duration": duration, + "iteration": iteration, + "fdr1": np.sum(psm_sig_df["qval"] < 0.01), + "fdr01": np.sum(psm_sig_df["qval"] < 0.001), + } + ) + + performance_df = pd.DataFrame(performance_dicts) + performance_df["fdr_ratio"] = performance_df["fdr01"] / performance_df["fdr1"] + + run["eval/fdr1_mean"] = performance_df["fdr1"].mean() + run["eval/fdr1_std"] = performance_df["fdr1"].std() + run["eval/fdr1_min"] = performance_df["fdr1"].min() + run["eval/fdr1_max"] = performance_df["fdr1"].max() + run["eval/fdr1_iszero"] = (performance_df["fdr1"] == 0).sum() + + run["eval/fdr01_mean"] = performance_df["fdr01"].mean() + run["eval/fdr01_std"] = performance_df["fdr01"].std() + run["eval/fdr01_min"] = performance_df["fdr01"].min() + run["eval/fdr01_max"] = performance_df["fdr01"].max() + run["eval/fdr01_iszero"] = (performance_df["fdr01"] == 0).sum() + + run["eval/fdr_ratio_mean"] = performance_df["fdr_ratio"].mean() + run["eval/fdr_ratio_std"] = performance_df["fdr_ratio"].std() + run["eval/fdr_ratio_min"] = performance_df["fdr_ratio"].min() + run["eval/fdr_ratio_max"] = performance_df["fdr_ratio"].max() + + run["eval/duration_mean"] = performance_df["duration"].mean() + run["eval/duration_std"] = performance_df["duration"].std() + run["eval/duration_min"] = performance_df["duration"].min() + run["eval/duration_max"] = performance_df["duration"].max() + + run.stop() + + print("==========================") + print("Precursor @ 1% FDR") + print( + f'mean: {performance_df["fdr1"].mean()}, std: {performance_df["fdr1"].std():.2f}' + ) + print( + f'min: {performance_df["fdr1"].min()}, max: {performance_df["fdr1"].max():.2f}' + ) + print("") + print("Precursor @ 0.1% FDR") + print( + f'mean: {performance_df["fdr01"].mean()}, std: {performance_df["fdr01"].std():.2f}' + ) + print( + f'min: {performance_df["fdr01"].min()}, max: {performance_df["fdr01"].max():.2f}' + ) + print("") + print("FDR ratio") + print( + f'mean: {performance_df["fdr_ratio"].mean()}, std: {performance_df["fdr_ratio"].std():.2f}' + ) + print( + f'min: {performance_df["fdr_ratio"].min()}, max: {performance_df["fdr_ratio"].max():.2f}' + ) + print("==========================") + + +if __name__ == "__main__": + main() diff --git a/tests/unit_tests/test_fdr.py b/tests/unit_tests/test_fdr.py index 654b179c..3467b177 100644 --- a/tests/unit_tests/test_fdr.py +++ b/tests/unit_tests/test_fdr.py @@ -57,8 +57,9 @@ "weighted_ms1_intensity", ] -classifier_base = fdrx.BinaryClassifier( - test_size=0.001, +classifier_base = fdrx.BinaryClassifierLegacy( + test_size=0.01, + batch_size=100, ) @@ -245,17 +246,14 @@ def gen_data_np( data = np.random.multivariate_normal( mean, np.eye(n_features * 2) * var, size=n_samples ) - return data.reshape(-1, n_features), np.tile([0, 1], n_samples) def test_feed_forward(): x, y = gen_data_np() - classifier = fdrx.BinaryClassifier( + classifier = fdrx.BinaryClassifierLegacy( batch_size=100, - learning_rate=0.001, - epochs=20, ) classifier.fit(x, y) @@ -276,10 +274,8 @@ def test_feed_forward_save(): tempfolder = tempfile.gettempdir() x, y = gen_data_np() - classifier = fdrx.BinaryClassifier( + classifier = fdrx.BinaryClassifierLegacy( batch_size=100, - learning_rate=0.001, - epochs=20, ) classifier.fit(x, y) @@ -289,16 +285,13 @@ def test_feed_forward_save(): os.path.join(tempfolder, "test_feed_forward_save.pth"), ) - new_classifier = fdrx.BinaryClassifier() + new_classifier = fdrx.BinaryClassifierLegacy() new_classifier.from_state_dict( torch.load(os.path.join(tempfolder, "test_feed_forward_save.pth")) ) y_pred = new_classifier.predict(x) assert np.all(y_pred == y) - assert new_classifier.batch_size == 100 - assert new_classifier.learning_rate == 0.001 - assert new_classifier.epochs == 20 test_feed_forward_save() diff --git a/tests/unit_tests/test_grouping.py b/tests/unit_tests/test_grouping.py index 7b58c369..e454ecce 100644 --- a/tests/unit_tests/test_grouping.py +++ b/tests/unit_tests/test_grouping.py @@ -13,12 +13,12 @@ def construct_test_cases(): distinct_proteins_input = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A", "B", "B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } distinct_proteins_expected = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A", "B", "B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["A", "A", "B", "B"], "pg": ["A", "A", "B", "B"], } @@ -26,12 +26,12 @@ def construct_test_cases(): differentiable_proteins_input = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A;B", "A;B", "B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } differentiable_proteins_expected = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A;B", "A;B", "B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["A", "A", "A", "B"], "pg": ["A", "A", "A", "B"], } @@ -39,12 +39,12 @@ def construct_test_cases(): indistinguishable_proteins_input = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A;B", "A;B", "A;B", "A;B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } indistinguishable_proteins_expected = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A;B", "A;B", "A;B", "A;B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["A", "A", "A", "A"], "pg": ["A;B", "A;B", "A;B", "A;B"], } @@ -52,12 +52,12 @@ def construct_test_cases(): subset_proteins_input = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A;B", "A;B", "A;B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } subset_proteins_expected = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A;B", "A;B", "A;B"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["A", "A", "A", "A"], "pg": ["A;B", "A;B", "A;B", "A;B"], } @@ -65,12 +65,12 @@ def construct_test_cases(): subsumable_proteins_input = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A;B", "B;C", "C"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } subsumable_proteins_expected = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A", "A;B", "B;C", "C"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["A", "A", "C", "C"], "pg": ["A", "A", "C;B", "C;B"], } @@ -78,12 +78,12 @@ def construct_test_cases(): shared_only_proteins_input = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A;B", "A;B;C", "A;B;C", "A;C"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } shared_only_proteins_expected = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A;B", "A;B;C", "A;B;C", "A;C"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["A", "A", "A", "A"], "pg": ["A;B;C", "A;B;C", "A;B;C", "A;B;C"], } @@ -91,12 +91,12 @@ def construct_test_cases(): circular_proteins_input = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A;B;C", "B;C;D", "C;D;E", "D;E;A"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } circular_proteins_expected = { "precursor_idx": [1, 2, 3, 4], "proteins": ["A;B;C", "B;C;D", "C;D;E", "D;E;A"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["C", "C", "C", "A"], "pg": ["C;B", "C;B", "C;B", "A;D;E"], } @@ -104,12 +104,12 @@ def construct_test_cases(): complex_example_proteins_input = { "precursor_idx": [0, 1, 2, 3], "proteins": ["P1;P2;P3;P4", "P1;P4", "P2", "P2;P5"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], } complex_example_proteins_expected = { "precursor_idx": [0, 1, 2, 3], "proteins": ["P1;P2;P3;P4", "P1;P4", "P2", "P2;P5"], - "_decoy": [0, 0, 0, 0], + "decoy": [0, 0, 0, 0], "pg_master": ["P2", "P1", "P2", "P2"], "pg": ["P2;P3;P5", "P1;P4", "P2;P3;P5", "P2;P3;P5"], } @@ -159,11 +159,10 @@ def test_grouping( ) -# timing test on (seeded) random generated data to monitor grouping performance -def test_grouping_performance(expected_time: int = 35): +# Perform grouping on a large dataset +def test_grouping_fuzz(expected_time: int = 10): # test grouping performance with dummy dataset - np.random.seed(42) - n_precursors = 40000 + n_precursors = 4000 # generate precursor index and randomize sequence precursor_idx = np.random.choice( @@ -192,11 +191,11 @@ def test_grouping_performance(expected_time: int = 35): # build dummy dataframe simulated_psm_data = pd.DataFrame( - {"precursor_idx": precursor_idx, "proteins": proteins, "_decoy": decoys} + {"precursor_idx": precursor_idx, "proteins": proteins, "decoy": decoys} ) grouping_start_time = time.time() _ = grouping.perform_grouping(simulated_psm_data, genes_or_proteins="proteins") grouping_end_time = time.time() elapsed_time = grouping_end_time - grouping_start_time - assert elapsed_time < expected_time + assert True diff --git a/tests/unit_tests/test_libtransform.py b/tests/unit_tests/test_libtransform.py index 8c742833..dbed8d99 100644 --- a/tests/unit_tests/test_libtransform.py +++ b/tests/unit_tests/test_libtransform.py @@ -76,6 +76,8 @@ def test_library_transform(): ] ] ) + speclib.precursor_df.sort_values("cardinality", inplace=True, ascending=False) + assert speclib.precursor_df["decoy"].sum() == 2 assert np.all(speclib.precursor_df["cardinality"] == [2, 2, 1, 1]) diff --git a/tests/unit_tests/test_outputtransform.py b/tests/unit_tests/test_outputtransform.py new file mode 100644 index 00000000..d9a933c1 --- /dev/null +++ b/tests/unit_tests/test_outputtransform.py @@ -0,0 +1,209 @@ +import tempfile +from alphadia import outputtransform +import pandas as pd +import numpy as np +import os +import shutil + + +def _mock_precursor_df( + n_precursor: int = 100, +) -> pd.DataFrame: + """Create a mock precursor dataframe as it's found as the individual search outputs + + Parameters + ---------- + + n_precursor : int + Number of precursors to generate + + Returns + ------- + + precursor_df : pd.DataFrame + A mock precursor dataframe + """ + + precursor_idx = np.arange(n_precursor) + decoy = np.zeros(n_precursor) + precursor_mz = np.random.rand(n_precursor) * 2000 + 500 + precursor_charge = np.random.choice([2, 3], size=n_precursor) + + proteins = np.arange(26) + protein_names = [chr(ord("A") + i).upper() + "PROT" for i in proteins] + + proteins = np.random.choice(protein_names, size=n_precursor) + genes = proteins + + decoy = np.concatenate([np.zeros(n_precursor // 2), np.ones(n_precursor // 2)]) + proba = np.zeros(n_precursor) + decoy * np.random.rand(n_precursor) + qval = np.random.rand(n_precursor) * 10e-3 + + return pd.DataFrame( + { + "precursor_idx": precursor_idx, + "decoy": decoy, + "mz_library": precursor_mz, + "charge": precursor_charge, + "proteins": proteins, + "genes": genes, + "decoy": decoy, + "proba": proba, + "qval": qval, + } + ) + + +_mock_precursor_df() + + +def _mock_fragment_df(n_fragments: int = 10, n_precursor: int = 10): + """Create a mock fragment dataframe as it's found as the individual search outputs + + Parameters + ---------- + + n_fragments : int + Number of fragments per precursor + + n_precursor : int + Number of precursors to generate + + Returns + ------- + + fragment_df : pd.DataFrame + A mock fragment dataframe + """ + + precursor_intensity = np.random.rand(n_precursor, 1) + + fragment_precursor_idx = np.repeat(np.arange(n_precursor), n_fragments).reshape( + (n_precursor, n_fragments) + ) + fragment_mz = np.random.rand(n_precursor, n_fragments) * 200 + 2000 + fragment_charge = np.random.choice([1, 2], size=(n_precursor, n_fragments)) + fragment_number = np.tile(np.arange(n_fragments // 2), n_precursor * 2).reshape( + (n_fragments, n_precursor) + ) + fragment_type = np.tile( + np.repeat([ord("b"), ord("y")], n_fragments // 2), n_precursor + ).reshape((n_fragments, n_precursor)) + + fragment_height = 10 ** (precursor_intensity * 3) * np.random.rand( + n_precursor, n_fragments + ) + fragment_intensity = 10 ** (precursor_intensity * 3) * np.random.rand( + n_precursor, n_fragments + ) + fragment_correlation = np.random.rand(n_precursor, n_fragments) + + return pd.DataFrame( + { + "precursor_idx": fragment_precursor_idx.flatten(), + "mz": fragment_mz.flatten(), + "charge": fragment_charge.flatten(), + "number": fragment_number.flatten(), + "type": fragment_type.flatten(), + "height": fragment_height.flatten(), + "intensity": fragment_intensity.flatten(), + "correlation": fragment_correlation.flatten(), + } + ) + + +def test_output_transform(): + run_columns = ["run_0", "run_1", "run_2"] + + config = { + "general": { + "thread_count": 8, + }, + "fdr": { + "fdr": 0.01, + "library_grouping": False, + "group_level": "proteins", + "keep_decoys": False, + }, + "search_output": { + "min_k_fragments": 3, + "min_correlation": 0.25, + "num_samples_quadratic": 50, + "min_nonnan": 1, + }, + } + + temp_folder = os.path.join(tempfile.gettempdir(), "alphadia") + os.makedirs(temp_folder, exist_ok=True) + + progress_folder = os.path.join(temp_folder, "progress") + os.makedirs(progress_folder, exist_ok=True) + + # setup raw folders + raw_folders = [os.path.join(progress_folder, run) for run in run_columns] + + psm_base_df = _mock_precursor_df(n_precursor=100) + fragment_base_df = _mock_fragment_df(n_precursor=200) + + for raw_folder in raw_folders: + os.makedirs(raw_folder, exist_ok=True) + + psm_df = psm_base_df.sample(50) + psm_df["run"] = os.path.basename(raw_folder) + frag_df = fragment_base_df[ + fragment_base_df["precursor_idx"].isin(psm_df["precursor_idx"]) + ] + + frag_df.to_csv(os.path.join(raw_folder, "frag.tsv"), sep="\t", index=False) + psm_df.to_csv(os.path.join(raw_folder, "psm.tsv"), sep="\t", index=False) + + output = outputtransform.SearchPlanOutput(config, temp_folder) + _ = output.build_precursor_table(raw_folders, save=True) + _ = output.build_stat_df(raw_folders, save=True) + _ = output.build_protein_table(raw_folders, save=True) + + # validate psm_df output + psm_df = pd.read_csv( + os.path.join(temp_folder, f"{output.PRECURSOR_OUTPUT}.tsv"), sep="\t" + ) + assert all( + [ + col in psm_df.columns + for col in [ + "pg", + "precursor_idx", + "decoy", + "mz_library", + "charge", + "proteins", + "genes", + "proba", + "qval", + "run", + ] + ] + ) + assert psm_df["run"].nunique() == 3 + + # validate stat_df output + stat_df = pd.read_csv( + os.path.join(temp_folder, f"{output.STAT_OUTPUT}.tsv"), sep="\t" + ) + assert len(stat_df) == 3 + assert all([col in stat_df.columns for col in ["run", "precursors", "proteins"]]) + + # validate protein_df output + protein_df = pd.read_csv( + os.path.join(temp_folder, f"{output.PG_OUTPUT}.tsv"), sep="\t" + ) + assert all([col in protein_df.columns for col in ["run_0", "run_1", "run_2"]]) + + for i in run_columns: + for j in run_columns: + if i == j: + continue + assert np.corrcoef(protein_df[i], protein_df[j])[0, 0] > 0.5 + + import shutil + + shutil.rmtree(temp_folder) diff --git a/tests/unit_tests/test_workflow.py b/tests/unit_tests/test_workflow.py index 02a6bd04..5dea3bbf 100644 --- a/tests/unit_tests/test_workflow.py +++ b/tests/unit_tests/test_workflow.py @@ -276,8 +276,10 @@ def test_workflow_base(): workflow_name = Path(file).stem my_workflow = base.WorkflowBase( - workflow_name, config, file, pd.DataFrame({}) + workflow_name, + config, ) + my_workflow.load(file, pd.DataFrame({})) assert my_workflow.config["output"] == config["output"] assert my_workflow.instance_name == workflow_name