diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5622819..e6e9723 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,25 +1,25 @@ repos: -- repo: https://github.com/pre-commit/pre-commit-hooks + - repo: https://github.com/pre-commit/pre-commit-hooks rev: v5.0.0 hooks: - - id: trailing-whitespace - files: .*.py$ - - id: end-of-file-fixer + - id: trailing-whitespace files: .*.py$ - - id: check-yaml - - id: check-added-large-files - - id: check-toml + - id: end-of-file-fixer + files: .*.py$ + - id: check-yaml + - id: check-added-large-files + - id: check-toml -- repo: https://github.com/astral-sh/ruff-pre-commit + - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.11.8 hooks: - id: ruff - args: [ --fix, --exit-zero ] + args: [--fix] - id: ruff-format -- repo: local + - repo: local hooks: - - id: pytest + - id: pytest name: pytest entry: uv run pytest tests/ language: system diff --git a/neuroEncoder b/neuroEncoder index 9e667cc..b9ef0ae 100755 --- a/neuroEncoder +++ b/neuroEncoder @@ -245,7 +245,7 @@ def main(args): # Now that we have the linearization function, we can get the true target helper.get_true_target( - l_function, in_place=True, show=show_figures, speedMask=True + windowSizeMS, l_function, in_place=True, show=show_figures, speedMask=True ) Parameters = Params( @@ -551,7 +551,7 @@ def main(args): ): print("Testing sleep set") # Test sleep as NN - outputs_sleep = TrainerBayes.test_sleep_as_NN( + TrainerBayes.test_sleep_as_NN( DataHelper.fullBehavior, bayesMatrices, windowSizeMS=windowSizeMS, @@ -687,9 +687,10 @@ def main(args): or args.redo ): num_augmentations = args.num_augmentations - print("num_augmentations", num_augmentations) - if num_augmentations is None: - num_augmentations = 4 if args.window < 0.504 else 2 + if Parameters.dataAugmentation: + print("num_augmentations", num_augmentations) + if num_augmentations is None: + num_augmentations = 4 if args.window < 0.504 else 2 NNTrainer.train( DataHelper.fullBehavior, windowSizeMS=windowSizeMS, @@ -750,22 +751,19 @@ def main(args): strideFactor=strideFactor, extract_spikes_counts=True, ) - try: - print_results.print_results( - NNTrainer.folderResult, - windowSizeMS=windowSizeMS, - show=show_figures, - lossSelection=0.5, - euclidean=False, - target=args.target, - phase="training", - useSpeedMask=True, - training_data=NNTrainer.training_data, - force=args.redo, - l_function=l_function, - ) - except: - pass + print_results.print_results( + NNTrainer.folderResult, + windowSizeMS=windowSizeMS, + show=show_figures, + lossSelection=0.5, + euclidean=False, + target=args.target, + phase="training", + useSpeedMask=True, + training_data=NNTrainer.training_data, + force=args.redo, + l_function=l_function, + ) if ( not os.path.exists( @@ -792,22 +790,19 @@ def main(args): strideFactor=strideFactor, extract_spikes_counts=True, ) - try: - print_results.print_results( - NNTrainer.folderResult, - windowSizeMS=windowSizeMS, - show=show_figures, - lossSelection=0.5, - euclidean=False, - force=args.redo, - target=args.target, - phase=args.phase, - training_data=NNTrainer.training_data, - useSpeedMask=True, - l_function=l_function, - ) - except: - pass + print_results.print_results( + NNTrainer.folderResult, + windowSizeMS=windowSizeMS, + show=show_figures, + lossSelection=0.5, + euclidean=False, + force=args.redo, + target=args.target, + phase=args.phase, + training_data=NNTrainer.training_data, + useSpeedMask=True, + l_function=l_function, + ) if ( not os.path.exists( @@ -834,22 +829,19 @@ def main(args): extract_spikes_counts=True, ) - try: - print_results.print_results( - NNTrainer.folderResult, - windowSizeMS=windowSizeMS, - show=show_figures, - lossSelection=0.5, - euclidean=False, - target=args.target, - phase="cond", - training_data=NNTrainer.training_data, - useSpeedMask=True, - l_function=l_function, - force=args.redo, - ) - except: - pass + print_results.print_results( + NNTrainer.folderResult, + windowSizeMS=windowSizeMS, + show=show_figures, + lossSelection=0.5, + euclidean=False, + target=args.target, + phase="cond", + training_data=NNTrainer.training_data, + useSpeedMask=True, + l_function=l_function, + force=args.redo, + ) if ( not os.path.exists( @@ -876,22 +868,19 @@ def main(args): extract_spikes_counts=True, ) - try: - print_results.print_results( - NNTrainer.folderResult, - windowSizeMS=windowSizeMS, - show=show_figures, - lossSelection=0.5, - euclidean=False, - target=args.target, - phase="post", - training_data=NNTrainer.training_data, - useSpeedMask=True, - l_function=l_function, - force=args.redo, - ) - except: - pass + print_results.print_results( + NNTrainer.folderResult, + windowSizeMS=windowSizeMS, + show=show_figures, + lossSelection=0.5, + euclidean=False, + target=args.target, + phase="post", + training_data=NNTrainer.training_data, + useSpeedMask=True, + l_function=l_function, + force=args.redo, + ) if args.test_sleep and ( not os.path.exists( diff --git a/neuroencoders/__init__.py b/neuroencoders/__init__.py index 6a97c5e..832b2a1 100644 --- a/neuroencoders/__init__.py +++ b/neuroencoders/__init__.py @@ -22,12 +22,26 @@ __version__ = "0.0.0" # fallback if not installed from . import ( - decoder, - fullEncoder, - importData, - openEphysExport, - resultAnalysis, - simpleBayes, - transformData, - utils, + decoder as decoder, +) +from . import ( + fullEncoder as fullEncoder, +) +from . import ( + importData as importData, +) +from . import ( + openEphysExport as openEphysExport, +) +from . import ( + resultAnalysis as resultAnalysis, +) +from . import ( + simpleBayes as simpleBayes, +) +from . import ( + transformData as transformData, +) +from . import ( + utils as utils, ) diff --git a/neuroencoders/decoder/__init__.py b/neuroencoders/decoder/__init__.py index 3d66487..798f239 100644 --- a/neuroencoders/decoder/__init__.py +++ b/neuroencoders/decoder/__init__.py @@ -5,5 +5,5 @@ - Decoder (from decode) """ -from . import decode -from .decode import Decoder +from . import decode as decode +from .decode import Decoder as Decoder diff --git a/neuroencoders/fullEncoder/__init__.py b/neuroencoders/fullEncoder/__init__.py index a24d4d7..368f84d 100755 --- a/neuroencoders/fullEncoder/__init__.py +++ b/neuroencoders/fullEncoder/__init__.py @@ -6,5 +6,5 @@ - an_network (module containing neural network architectures) """ -from . import an_network -from .an_network import LSTMandSpikeNetwork +from . import an_network as an_network +from .an_network import LSTMandSpikeNetwork as LSTMandSpikeNetwork diff --git a/neuroencoders/fullEncoder/an_network.py b/neuroencoders/fullEncoder/an_network.py index 865014b..4e81de7 100755 --- a/neuroencoders/fullEncoder/an_network.py +++ b/neuroencoders/fullEncoder/an_network.py @@ -14,7 +14,7 @@ import warnings os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # Only show errors, not warnings -from typing import Dict, Optional, Tuple +from typing import Dict, List, Optional, Tuple # Get common libraries import dill as pickle @@ -22,11 +22,11 @@ import numpy as np import pandas as pd import tensorflow as tf +import wandb from keras import ops as kops from keras.layers import Lambda from tqdm import tqdm - -import wandb +from wandb.integration.keras import WandbMetricsLogger # Get utility functions from neuroencoders.fullEncoder import nnUtils @@ -45,7 +45,6 @@ ) from neuroencoders.importData.epochs_management import get_epochs_mask, inEpochsMask from neuroencoders.utils.global_classes import DataHelper, Params, Project -from wandb.integration.keras import WandbMetricsLogger # We generate a model with the functional Model interface in tensorflow @@ -361,6 +360,7 @@ def _build_model(self, **kwargs): ) # Gather the full model + self.generate_kwargs = kwargs outputs = self.generate_model(**kwargs) # Build two models # One just described, with two objective functions corresponding @@ -376,6 +376,39 @@ def _build_model(self, **kwargs): outputs, predLossOnly=True, modelName="predLossModel.pdf", **kwargs ) + def rebuild_model(self, **kwargs): + """ + Regenerate the model with the current parameters. + + Returns + ------- + None + """ + self.clear_session() + outputs = self.generate_model(**kwargs) + self.model = self.compile_model(outputs, modelName="FullModel.pdf", **kwargs) + self.predLossModel = self.compile_model( + outputs, predLossOnly=True, modelName="predLossModel.pdf", **kwargs + ) + + def change_batch_size(self, new_batch_size, **kwargs): + """ + Change the batch size of the model. + + Parameters + ---------- + new_batch_size : int + The new batch size to set. + + Returns + ------- + None + """ + default_kwargs = self.generate_kwargs.copy() + default_kwargs["batchSize"] = new_batch_size + default_kwargs.update(kwargs) + self.rebuild_model(**default_kwargs) + def apply_transformer_architecture( self, allFeatures, allFeatures_raw, mymask, **kwargs ): @@ -739,7 +772,7 @@ def generate_model(self, **kwargs): tempPosLoss = self.apply_dynamic_dense_loss(tempPosLoss, self.truePos) if getattr(self.params, "contrastive_loss", False): print("Using contrastive loss") - print("output shape:", output.shape) + print("output shape:", myoutputPos.shape) print("truePos shape:", self.truePos.shape) regression_loss_layer = nnUtils.ContrastiveLossLayer() # TODO: make sure the layer exists @@ -1309,7 +1342,9 @@ def _dataset_loading_pipeline( """ onTheFlyCorrection = kwargs.get("onTheFlyCorrection", False) shuffle = kwargs.get("shuffle", True) + random_spiking = kwargs.get("random_spiking", False) batchSize = kwargs.get("batch_size", self.params.batchSize) + speedMask = kwargs.get("speedMask", None) @tf.autograph.experimental.do_not_convert def filter_by_pos_index(x): @@ -1340,13 +1375,23 @@ def _parse_function(*vals): @tf.function def map_outputs(vals): + input_shape = tf.shape(vals["pos"])[:-1] + if len(input_shape) == 0: + batch_size = 1 + else: + batch_size = input_shape[0] + basic_dict = { - self.outNames[0]: tf.zeros((batchSize, dim_output), dtype=tf.float32), + self.outNames[0]: tf.zeros((batch_size, dim_output), dtype=tf.float32) } for outname in self.outNames[1:]: - basic_dict[outname] = tf.zeros(batchSize, dtype=tf.float32) + basic_dict[outname] = tf.zeros(batch_size, dtype=tf.float32) return (vals, basic_dict) + @tf.autograph.experimental.do_not_convert + def create_indices(vals): + return self.create_indices(vals=vals, shuffle=random_spiking) + ndataset = tf.data.TFRecordDataset( os.path.join(self.projectPath.dataPath, filename) ) @@ -1363,6 +1408,14 @@ def map_outputs(vals): if kwargs.get("inference_mode", False) else {"train": totMask_backup} ) + if speedMask is not None and not isinstance(speedMask, dict): + # it means we have just one set of keys + speedMask_backup = speedMask.copy() + speedMask = ( + {"test": speedMask_backup} + if kwargs.get("inference_mode", False) + else {"train": speedMask_backup} + ) datasets = {} counts = {} for key in totMask.keys(): @@ -1390,6 +1443,8 @@ def map_outputs(vals): # posFeature is already of shape (N,dimOutput) because we ran data_helper.get_true_target before. dataset = ndataset.filter(filter_by_pos_index) dataset = dataset.map(nnUtils.import_true_pos(posFeature)) + if speedMask is not None and key in speedMask: + dataset = dataset.map(nnUtils.import_speed_mask(speedMask[key])) dataset = dataset.filter(filter_nan_pos) # now that we have clean positions, we can resample if needed @@ -1402,7 +1457,11 @@ def map_outputs(vals): print("Shuffling the", key, "dataset") dataset = dataset.shuffle(100000, reshuffle_each_iteration=True) - dataset = dataset.batch(batchSize, drop_remainder=True) + batch_dataset = kwargs.get("batch", key == "training") + if batch_dataset: + dataset = dataset.batch(batchSize, drop_remainder=True) + else: + dataset = dataset.batch(1, drop_remainder=False) if ( not self.params.dataAugmentation @@ -1413,6 +1472,7 @@ def map_outputs(vals): optimized_parse_fn = self.create_optimized_parse_function( augmentation=False, count_spikes=kwargs.get("extract_spikes_counts", False), + batched=batch_dataset, ) dataset = dataset.map( optimized_parse_fn, @@ -1426,6 +1486,7 @@ def map_outputs(vals): augmentation=True, augmentation_config=augmentation_config, count_spikes=kwargs.get("extract_spikes_counts", False), + batched=batch_dataset, ) dataset = dataset.map( @@ -1439,9 +1500,7 @@ def map_outputs(vals): # We then reorganize the dataset so that it provides (inputsDict,outputsDict) tuple # for now we provide all inputs as potential outputs targets... but this can be changed in the future... - dataset = dataset.map( - self.create_indices, num_parallel_calls=tf.data.AUTOTUNE - ) + dataset = dataset.map(create_indices, num_parallel_calls=tf.data.AUTOTUNE) dataset = dataset.map(map_outputs, num_parallel_calls=tf.data.AUTOTUNE) # cache only once, after all the preprocessing print( @@ -1489,16 +1548,110 @@ def map_outputs(vals): else None ) - if self.params.OversamplingResampling: - return datasets, counts - else: - return datasets, None + # Save parsed datasets to new TFRecord files if requested + save_parsed_tfrec = kwargs.get("save_parsed_tfrec", None) + save_parsed_parquet = kwargs.get("save_parsed_parquet", None) + + # The user mentioned useSpeedMask, but the code often uses useSpeedFilter. + # We handle both, defaulting to the condition that saving only happens when speed masking is NOT applied during loading (i.e. we want the "raw" but cropped data). + useSpeedMask = kwargs.get("useSpeedMask", kwargs.get("useSpeedFilter", False)) + should_save = not useSpeedMask + + if save_parsed_tfrec is not None: + if should_save: + print( + f"Saving parsed datasets to TFRecord files with base path: {save_parsed_tfrec}" + ) + self._save_datasets_to_tfrec(datasets, save_parsed_tfrec) + else: + print( + f"Skipping TFRecord saving because speed masking is active (useSpeedMask/Filter={useSpeedMask})" + ) + + if save_parsed_parquet is not None: + if should_save: + print( + f"Saving parsed datasets to Parquet files with base path: {save_parsed_parquet}" + ) + self._save_datasets_to_parquet(datasets, save_parsed_parquet) + else: + print( + f"Skipping Parquet saving because speed masking is active (useSpeedMask/Filter={useSpeedMask})" + ) + + return datasets, counts if self.params.OversamplingResampling else None + + def load_parsed_dataset( + self, base_path: str, keys: List[str] = ["train", "test"], featDesc: Dict = None + ) -> Dict[str, tf.data.Dataset]: + """ + Load datasets that were previously saved using _save_datasets_to_tfrec. + + Parameters + ---------- + base_path : str + Base path for the TFRecord files. + keys : list of str + The dataset keys to load (e.g., ['train', 'test']). + featDesc : dict, optional + The feature description to use for parsing. If None, uses a default that + handles variable position dimensions. + + Returns + ------- + datasets : dict + Dictionary of loaded tf.data.Dataset objects. + """ + if featDesc is None: + # Default featDesc that handles variable length pos and groups + featDesc = { + "pos_index": tf.io.FixedLenFeature([], tf.int64), + "pos": tf.io.VarLenFeature(tf.float32), + "length": tf.io.FixedLenFeature([], tf.int64), + "groups": tf.io.VarLenFeature(tf.int64), + "time": tf.io.FixedLenFeature([], tf.float32), + "time_behavior": tf.io.FixedLenFeature([], tf.float32), + "indexInDat": tf.io.VarLenFeature(tf.int64), + } + for g in range(self.params.nGroups): + featDesc[f"group{g}"] = tf.io.VarLenFeature(tf.float32) + + datasets = {} + for key in keys: + file_path = f"{base_path}_{key}.tfrec" + if not os.path.exists(file_path): + print(f"Warning: File {file_path} does not exist. Skipping.") + continue + + print(f"Loading {key} dataset from {file_path}...") + + raw_dataset = tf.data.TFRecordDataset(file_path) + + @tf.autograph.experimental.do_not_convert + def _parse_function(example_proto): + return tf.io.parse_single_example(example_proto, featDesc) + + dataset = raw_dataset.map( + _parse_function, num_parallel_calls=tf.data.AUTOTUNE + ) + # Re-apply the parsing logic to get the correct shapes (e.g., reshaping groups) + dataset = dataset.map( + lambda x: nnUtils.parse_serialized_sequence( + self.params, x, batched=False + ), + num_parallel_calls=tf.data.AUTOTUNE, + ) + + datasets[key] = dataset + + return datasets def create_optimized_parse_function( self, augmentation: bool = False, augmentation_config: Optional[NeuralDataAugmentation] = None, count_spikes=False, + batched=True, ): """ Create optimized parsing function that respects spike data structure @@ -1520,7 +1673,7 @@ def optimized_parse_with_augmentation(batch_data): self.params, processed_batch, # Pass the copy augmentation_config=augmentation_config, - batched=True, + batched=batched, count_spikes=count_spikes, ) @@ -1537,12 +1690,144 @@ def optimized_parse_standard(batch_data): return nnUtils.parse_serialized_sequence( self.params, processed_batch, - batched=True, + batched=batched, count_spikes=count_spikes, ) return optimized_parse_standard + def _save_datasets_to_tfrec(self, datasets, base_path): + """ + Save parsed datasets to new TFRecord files. + + Parameters + ---------- + datasets : dict + Dictionary of tf.data.Dataset objects to save (e.g., {'train': dataset, 'test': dataset}) + base_path : str + Base path for saved TFRecord files. Files will be saved as: + {base_path}_train.tfrec, {base_path}_test.tfrec, etc. + """ + + def serialize_example(example_dict): + """Serialize a single example to TFRecord format""" + # Create feature dict for serialization + feature_dict = {} + + for key, value in example_dict.items(): + if isinstance(value, tf.Tensor): + value = value.numpy() + + # Handle different data types + if isinstance(value, np.ndarray): + if value.dtype in [np.float32, np.float64]: + feature_dict[key] = tf.train.Feature( + float_list=tf.train.FloatList(value=value.flatten()) + ) + elif value.dtype in [np.int32, np.int64]: + feature_dict[key] = tf.train.Feature( + int64_list=tf.train.Int64List(value=value.flatten()) + ) + else: + # For other types, convert to bytes + feature_dict[key] = tf.train.Feature( + bytes_list=tf.train.BytesList(value=[value.tobytes()]) + ) + elif isinstance(value, (int, np.integer)): + feature_dict[key] = tf.train.Feature( + int64_list=tf.train.Int64List(value=[int(value)]) + ) + elif isinstance(value, (float, np.floating)): + feature_dict[key] = tf.train.Feature( + float_list=tf.train.FloatList(value=[float(value)]) + ) + elif isinstance(value, (str, bytes)): + if isinstance(value, str): + value = value.encode() + feature_dict[key] = tf.train.Feature( + bytes_list=tf.train.BytesList(value=[value]) + ) + + # Create an Example proto + example_proto = tf.train.Example( + features=tf.train.Features(feature=feature_dict) + ) + return example_proto.SerializeToString() + + # Save each dataset + for key, dataset in datasets.items(): + output_path = f"{base_path}_{key}.tfrec" + print(f"Saving {key} dataset to {output_path}...") + + writer = tf.io.TFRecordWriter(os.path.abspath(output_path)) + + try: + for batch in tqdm(dataset, desc=f"Writing {key} to TFRecord"): + if isinstance(batch, tuple): + inputs, _ = batch # Usually (inputs, targets) + else: + inputs = batch + + serialized = serialize_example(inputs) + writer.write(serialized) + finally: + writer.close() + + print(f"Successfully saved {key} dataset to {output_path}") + + def _save_datasets_to_parquet(self, datasets, base_path): + """ + Save parsed datasets to Parquet files using pandas logic. + + Parameters + ---------- + datasets : dict + Dictionary of tf.data.Dataset objects to save (e.g., {'train': dataset, 'test': dataset}) + base_path : str + Base path for saved Parquet files. Files will be saved as: + {base_path}_{key}.parquet + """ + + for key, dataset in datasets.items(): + output_path = f"{base_path}_{key}.parquet" + print(f"Saving {key} dataset to {output_path}...") + + df = self.convert_tfrec_to_pandas( + dataset, desc=f"Converting {key} to Pandas" + ) + + if df.shape[0] > 0: + # Some columns might still be lists, which Parquet handles fine as nesting or objects. + df.to_parquet(output_path) + print(f"Successfully saved {key} dataset to {output_path}") + else: + print(f"No data to save for {key}") + + def convert_tfrec_to_pandas( + self, dataset, flatten=True, desc="Converting to Pandas" + ): + all_data = [] + for example in tqdm(dataset, desc=desc): + if isinstance(example, tuple): + inputs, _ = example + else: + inputs = example + + row_data = {} + for k, v in inputs.items(): + val = v.numpy() + # Parquet (via Arrow) doesn't like multidimensional arrays in object columns. + # We flatten arrays with ndim > 1 to ensure compatibility. + if val.ndim > 1 and flatten: + # Store as a flattened 1D array inside the cell + row_data[k] = [val.reshape(-1)] + else: + # For 1D or scalars, wrap in list for 1-row DataFrame construction + row_data[k] = [val] if val.ndim == 1 else val + + all_data.append(pd.DataFrame(row_data)) + return pd.concat(all_data, ignore_index=True) if all_data else pd.DataFrame() + def test(self, behaviorData, **kwargs): """ Test the model on a given behaviorData. @@ -1610,6 +1895,7 @@ def test(self, behaviorData, **kwargs): "savedModels", "full_cp.weights.h5", ), + skip_mismatch=True, ) except FileNotFoundError: print("loading from savedModels failed, trying full checkpoint ") @@ -1681,9 +1967,26 @@ def test(self, behaviorData, **kwargs): inference_mode=True, onTheFlyCorrection=onTheFlyCorrection, shuffle=False, + speedMask=speedMask, **kwargs, ) dataset = datasets["test"] + + save_parsed_tfrec = kwargs.get("save_parsed_tfrec", None) + if save_parsed_tfrec is not None: + assert not useSpeedFilter, ( + "Cannot use speed filter when saving parsed TFRecord" + ) + # save final speedMask + pos_index = np.arange(len(behaviorData["Positions"])) + # final speedMask is an 2D array with shape (N,2) where N is the number of timepoints + final_speedMask = np.zeros((len(pos_index), 2), dtype=np.float32) + final_speedMask[:, 0] = pos_index + final_speedMask[:, 1] = speedMask + np.save(f"{save_parsed_tfrec}_speedMask_{phase}.npy", final_speedMask) + + return + # ------------------------------------------------------------------------- # CUSTOM SYNCHRONIZED INFERENCE LOOP # ------------------------------------------------------------------------- @@ -1702,6 +2005,7 @@ def test(self, behaviorData, **kwargs): list_speed_filter = [] list_index_in_dat = [] list_index_in_dat_raw = [] + list_groups = [] # Spike Counts (Dynamic dict to handle variable groups) dict_spike_counts = { @@ -1743,6 +2047,7 @@ def test(self, behaviorData, **kwargs): list_times_behavior.append(inputs["time_behavior"].numpy()) list_pos_index.append(inputs["pos_index"].numpy()) list_index_in_dat.append(inputs["indexInDat"].numpy()) + list_groups.append(inputs["groups"].numpy()) # Optional keys (use .get or check) if "speedFilter" in inputs: @@ -1769,6 +2074,7 @@ def test(self, behaviorData, **kwargs): full_times = np.concatenate(list_times, axis=0).flatten() full_times_behavior = np.concatenate(list_times_behavior, axis=0).flatten() full_pos_index = np.concatenate(list_pos_index, axis=0).flatten() + full_groups = np.concatenate(list_groups, axis=0).flatten() # full_index_in_dat = np.concatenate(list_index_in_dat, axis=0) # Handle Speed Mask @@ -1879,6 +2185,7 @@ def test(self, behaviorData, **kwargs): "posIndex": full_pos_index, # Convert list of arrays/lists to string or keep as object for indexInDat "indexInDat": full_index_raw, + "groups": full_groups, } # Add group counts @@ -1981,6 +2288,7 @@ def testSleep(self, behaviorData, **kwargs): "savedModels", "full_cp.weights.h5", ), + skip_mismatch=True, ) except FileNotFoundError: print("loading from savedModels failed, trying full checkpoint ") @@ -2449,6 +2757,7 @@ def get_artificial_spikes( "savedModels", "full_cp.weights.h5", ), + skip_mismatch=True, ) except FileNotFoundError: print("fallback loading full/cp.ckpt") @@ -2457,6 +2766,7 @@ def get_artificial_spikes( os.path.join( self.folderModels, str(windowSizeMS), "full", "cp.ckpt" ), + skip_mismatch=True, ) except (FileNotFoundError, ValueError): self.model.load_weights( @@ -2466,6 +2776,7 @@ def get_artificial_spikes( "full", "cp.weights.h5", ), + skip_mismatch=True, ) # --- Build the same total mask used in test() --- @@ -2723,7 +3034,7 @@ def fix_linearizer(self, mazePoints, tsProj): self.tsProjTensor = tf.convert_to_tensor(tsProj[None, :], dtype=tf.float32) # used in the data pipepline - def create_indices(self, vals, addLinearizationTensor=False): + def create_indices(self, vals, addLinearizationTensor=False, shuffle=False): """ Create indices for gathering spikes from each group. The i-th spike of the group should be positioned at spikePosition[i] in the final tensor. @@ -2731,11 +3042,17 @@ def create_indices(self, vals, addLinearizationTensor=False): Args: vals (dict): A dictionary containing the input tensors, including "groups" and "group{n}" for each group. addLinearizationTensor (bool): Whether to add linearization tensors to the output. + shuffle (bool): Whether to shuffle the indices within each group for null hypothesis/control. Returns: dict: Updated dictionary with indices for each group and optional linearization tensors. The indices are stored under the keys "indices{n}" for each group and represent the positions to gather spikes from each group. See self.indices in the model definition for more details on usage. """ + if shuffle: + print( + "Shuffling spike indices within each group for null hypothesis/control." + ) + for group in range(self.params.nGroups): spikePosition = tf.where(tf.equal(vals["groups"], group)) # Note: inputGroups is already filled with -1 at position that correspond to filling @@ -2744,7 +3061,13 @@ def create_indices(self, vals, addLinearizationTensor=False): # We therefore need to set indices[spikePosition[i]] to i so that it is effectively gathered # We need to wrap the use of sparse tensor (tensorflow error otherwise) # The sparse tensor allows us to get the list of indices for the gather quite easily - rangeIndices = tf.range(tf.shape(vals["group" + str(group)])[0]) + 1 + numSpikesInGroup = tf.shape(vals["group" + str(group)])[0] + rangeIndices = tf.range(numSpikesInGroup) + 1 + + if shuffle: + # shuffle the rangeIndices to have random mapping + rangeIndices = tf.random.shuffle(rangeIndices) + indices = tf.sparse.SparseTensor( spikePosition, rangeIndices, [tf.shape(vals["groups"])[0]] ) @@ -2827,7 +3150,6 @@ def create_indices_w_temporal_sequence(self, vals, addLinearizationTensor=False) max_spikes_per_batch = original_shape[1] # Flatten for processing original_groups_flat = tf.reshape(original_groups, [-1]) - spike_times_flat = tf.reshape(spike_times, [-1]) temporal_bin_indices_flat = tf.reshape(temporal_bin_indices, [-1]) # Create batch indices @@ -2839,7 +3161,6 @@ def create_indices_w_temporal_sequence(self, vals, addLinearizationTensor=False) max_spikes_per_batch = total_spikes // batch_size original_groups_flat = original_groups - # spike_times_flat = spike_times temporal_bin_indices_flat = temporal_bin_indices batch_indices = tf.repeat(tf.range(batch_size), max_spikes_per_batch) @@ -2899,9 +3220,7 @@ def create_indices_w_temporal_sequence(self, vals, addLinearizationTensor=False) # Create linear indices for the temporal structure # Total size is now batch_size * n_temporal_bins - # linear_temporal_positions = ( - # spikePosition[:, 0] * n_temporal_bins + spikePosition[:, 1] - # ) + (spikePosition[:, 0] * n_temporal_bins + spikePosition[:, 1]) # Map: for each position in spikePosition, which original spike index to use # Build lookup: (batch, temporal_bin) -> original_spike_index diff --git a/neuroencoders/fullEncoder/nnUtils.py b/neuroencoders/fullEncoder/nnUtils.py index ce219eb..5d4d937 100755 --- a/neuroencoders/fullEncoder/nnUtils.py +++ b/neuroencoders/fullEncoder/nnUtils.py @@ -398,7 +398,7 @@ def call(self, x, training=False): with get_device_context(self.device): # 1. Input is (Batch, Channels, Time) -> (128, 6, 32) shape = tf.shape(x) - B, C, T = shape[0], shape[1], shape[2] + B, _C, T = shape[0], shape[1], shape[2] # Step 1: Reshape to process all channels through the SAME backbone # New shape: (Batch * 6, 32, 1) @@ -1058,14 +1058,16 @@ def serialize_single_spike(clu, spike): # @tf.function def parse_serialized_sequence( - params, tensors, batched=False, count_spikes=False + params, tensors, batched=False, count_spikes=False, sorted_indices=None ): # featDesc, ex_proto, + # TODO: use sorted indices to subset the given tensors to only the desired spikes (ie for example only the spikes that are also in the spike sorting) """ Parse a serialized spike sequence example. Args: params: parameters of the network tensors: parsed tensors from the TFRecord example batched: Whether data is batched + count_spikes: Whether to count spikes Returns: Parsed tensors with reshaped spike data. @@ -1073,6 +1075,9 @@ def parse_serialized_sequence( If batched, the shape should be [batchSize, num_spikes_per_batch, nChannelsPerGroup[g], 32] but is then reshaped to merge batch and spikes, giving: [batchSize * num_spikes_per_batch, nChannelsPerGroup[g], 32]. """ + if isinstance(tensors["pos"], tf.SparseTensor): + tensors["pos"] = tf.sparse.to_dense(tensors["pos"]) + tensors["groups"] = tf.sparse.to_dense(tensors["groups"], default_value=-1) # Pierre 13/02/2021: Why use sparse.to_dense, and not directly a FixedLenFeature? # Probably because he wanted a variable length <> inputs sequences @@ -1113,6 +1118,11 @@ def parse_serialized_sequence( # store result in tensors tensors[f"group{g}_spikes_count"] = spike_counts + else: + # add batch dimension of 1 + tensors["group" + str(g)] = tf.expand_dims( + tensors["group" + str(g)], axis=0 + ) # shape becomes (1, num_spikes, nChannelsPerGroup[g], 32) # WARN: even if batched: gather all together, meaning batch and spikes are merged tensors["group" + str(g)] = tf.reshape( @@ -1173,6 +1183,18 @@ def change_feature(vals): return change_feature +def import_speed_mask(speed_mask): + """ + Returns a function that adds speed mask to the parsed tensors. + """ + + def change_feature(vals): + vals["speedMask"] = tf.gather(speed_mask, vals["pos_index"]) + return vals + + return change_feature + + def squeeze_or_expand_to_same_rank(x1, x2, expand_rank_1=True): """Squeeze/expand last dim if ranks differ from expected by exactly 1.""" x1_rank = len(x1.shape) @@ -1403,7 +1425,7 @@ def parse_serialized_sequence_with_augmentation( tensors: Dictionary of parsed tensors from TFRecord augmentation_config: Optional augmentation configuration batched: Whether data is batched - + count_spikes: Whether to count spikes Returns: Dictionary of parsed and optionally augmented tensors """ @@ -1657,7 +1679,7 @@ def parse_tfrecord_with_augmentation( ) # [time_steps, channels] # Extract labels - labels = parsed_features.get("labels", None) + parsed_features.get("labels", None) # Apply augmentation augmented_data = augmentation_config.create_augmented_copies(neural_data) @@ -1845,7 +1867,7 @@ def call(self, linearized_pos): ) # Set shape (tf.py_function loses shape info) - batch_size = tf.shape(linearized_pos)[0] + tf.shape(linearized_pos)[0] weights.set_shape([None]) return weights @@ -1876,7 +1898,7 @@ def from_config(cls, config): fitted_dw_config = config.get("fitted_dw_alpha") training_data = config.get("training_data") fitted_dw = DenseWeight(fitted_dw_config) - device = config.get("device", "/cpu:0") + config.get("device", "/cpu:0") if training_data is not None: fitted_dw.fit(training_data) # return cls(fitted_denseweight=fitted_dw, device=device) @@ -2383,8 +2405,8 @@ def decode_and_uncertainty(self, logits_hw, mode="argmax", return_probs=False): tf.reshape(probs_allowed, [B, H * W]), axis=-1, output_type=tf.int64 ) W64 = tf.cast(W, tf.int64) - iy = idx // W64 # for debugging - ix = idx % W64 + idx // W64 # for debugging + idx % W64 ex = tf.gather(tf.reshape(self.Xc_tf, [-1]), idx) ey = tf.gather(tf.reshape(self.Yc_tf, [-1]), idx) else: @@ -2970,7 +2992,6 @@ def _safe_kl_wasserstein_heatmap_loss( kl = ce - entropy # [B] # small numeric epsilon - tiny = 1e-9 # --- Wasserstein penalty --- if alpha > 0.0: diff --git a/neuroencoders/importData/__init__.py b/neuroencoders/importData/__init__.py index 6a9d4b7..a05cb26 100755 --- a/neuroencoders/importData/__init__.py +++ b/neuroencoders/importData/__init__.py @@ -5,5 +5,8 @@ - inEpochsMask, get_epochs, merge_intervals, etc. (from epochs_management) """ -from . import juliaData, rawdata_parser -from .epochs_management import get_epochs, inEpochs, inEpochsMask, merge_intervals +from . import juliaData as juliaData +from . import rawdata_parser as rawdata_parser +from .epochs_management import get_epochs as get_epochs +from .epochs_management import inEpochsMask as inEpochsMask +from .epochs_management import merge_intervals as merge_intervals diff --git a/neuroencoders/importData/gui_elements.py b/neuroencoders/importData/gui_elements.py index 0267c28..d780f48 100755 --- a/neuroencoders/importData/gui_elements.py +++ b/neuroencoders/importData/gui_elements.py @@ -1,6 +1,5 @@ #!/usr/bin/env python3 -from datetime import timedelta from pathlib import Path from typing import Optional, Tuple from warnings import warn @@ -17,7 +16,7 @@ from matplotlib.legend_handler import HandlerTuple from matplotlib.lines import Line2D from matplotlib.patches import Patch -from matplotlib.ticker import FuncFormatter +from matplotlib.ticker import FuncFormatter, MaxNLocator from scipy.stats import gaussian_kde from sklearn.metrics import ( accuracy_score, @@ -28,6 +27,7 @@ ) from neuroencoders.importData import epochs_management as ep +from neuroencoders.utils.global_classes import DataHelper from neuroencoders.utils.viz_params import ( ALL_STIMS_COLOR, ALPHA_DELTA_LINE, @@ -59,12 +59,26 @@ ) -def time_formatter(x, pos): - td = timedelta(seconds=x) - h, rem = divmod(int(td.total_seconds()), 3600) +def _time_format_logic(x): + """Internal logic for a single scalar value""" + # Split whole seconds and fractional part + whole_seconds = int(x) + # Extract ms (round to 3 decimal places) + ms = int(round((x - whole_seconds) * 1000)) + + # Handle rollover + if ms >= 1000: + whole_seconds += 1 + ms -= 1000 + + h, rem = divmod(whole_seconds, 3600) m, s = divmod(rem, 60) - ms = int((x % 1) * 1000) - return f"{h:02}:{m:02}:{s:02}.{ms:03}" + + return f"{h:02d}:{m:02d}:{s:02d}.{ms:03d}" + + +# This makes the function work on single values OR arrays automatically +time_formatter_vec = np.vectorize(lambda x, pos=None: _time_format_logic(x)) class AnimatedPositionPlotter: @@ -74,7 +88,7 @@ class AnimatedPositionPlotter: def __init__( self, - data_helper, + data_helper: DataHelper, trail_length: int = 30, lin_movie_duration: int = 500, figsize: Tuple[float, float] = (16, 9), @@ -164,7 +178,11 @@ def extract_data(self, **kwargs): self.predicted_dim_please = kwargs.pop("predicted_dim_please") self.positions = self.positions_from_NN - self.positionTime = self.prediction_positionTime + self.positionTime = ( + self.prediction_positionTime.flatten() + if self.prediction_positionTime is not None + else self.data_helper.fullBehavior["Times"]["positionTime"].flatten() + ) self.plot_all_stims = kwargs.get("plot_all_stims", False) # we setup a "true" positionTime to use for precise plotting such as stims... # same for "true" positions, which are the original positions for each and every timepoint @@ -531,7 +549,11 @@ def _extract_dim_data(self, **kwargs): self.dim = np.array(self.data_helper.direction) self.dim_name = "direction" # get rid of NaN values in directions - self.dim = self.dim[self.posIndex] + if self.posIndex is not None: + self.dim = self.dim[self.posIndex] + else: + self.dim = self.dim[self.totMask] + self.dim = self.dim[self.true_valid_indices] elif dim == "distance" or dim == "thigmo": if not hasattr(self.data_helper, "thigmo"): self.data_helper.thigmo = np.array( @@ -539,16 +561,28 @@ def _extract_dim_data(self, **kwargs): ) self.dim = np.array(self.data_helper.thigmo) self.dim_name = "dist2wall" - self.dim = self.dim[self.posIndex] + if self.posIndex is not None: + self.dim = self.dim[self.posIndex] + else: + self.dim = self.dim[self.totMask] + self.dim = self.dim[self.true_valid_indices] elif dim == "PosHDSpeed": self.dim = np.array(self.data_helper.positions) self.dim_name = "PosHDSpeed" - self.dim = self.dim[self.posIndex] + if self.posIndex is not None: + self.dim = self.dim[self.posIndex] + else: + self.dim = self.dim[self.totMask] + self.dim = self.dim[self.true_valid_indices] elif dim == "Head Direction": - self.dim = np.array(self.data_helper.positions[:, 3]) + self.dim = np.array(self.data_helper.positions[:, 3]).flatten() self.dim_name = "Head Direction" # self.lin_dim = self.data_helper.positions[:, 2] - self.dim = self.dim[self.posIndex] + if self.posIndex is not None: + self.dim = self.dim[self.posIndex] + else: + self.dim = self.dim[self.totMask] + self.dim = self.dim[self.true_valid_indices] # self.lin_dim = self.lin_dim[self.totMask][self.true_valid_indices] self.positions = self.positions[:, :2] elif isinstance(dim, np.ndarray): @@ -920,7 +954,8 @@ def _setup_trajectory_panel(self, gs, **kwargs): try: dim = self.data_helper.direction dim = dim = dim[self.totMask][self.true_valid_indices] - except: + # except masking issues (wrong length), fallback to computing from linpositions + except IndexError: dim = self.data_helper._get_traveling_direction(self.linpositions) if self.predicted is not None: predicted_dim = self.data_helper._get_traveling_direction( @@ -940,7 +975,7 @@ def _setup_trajectory_panel(self, gs, **kwargs): try: dim = self.predicted_dim_please dim = dim[self.totMask][self.true_valid_indices] - except: + except IndexError: print("Using speed mask from random.") dim = self.speed_mask @@ -986,11 +1021,11 @@ def _setup_linpos_movie(self, gs, **kwargs): Possibility to provide additional arguments such as the cmap for the points, size of the points, and alpha. """ - true_color = kwargs.get("true_color", TRUE_COLOR) + kwargs.get("true_color", TRUE_COLOR) true_line_color = kwargs.get("true_line_color", TRUE_LINE_COLOR) predicted_color = kwargs.get("predicted_color", PREDICTED_COLOR) predicted_line_color = kwargs.get("predicted_line_color", PREDICTED_LINE_COLOR) - colors_style = kwargs.get("colors_style", None) + kwargs.get("colors_style", None) self.axes["linpos_movie"] = self.fig.add_subplot(gs[1, :]) ax = self.axes["linpos_movie"] @@ -1141,8 +1176,9 @@ def _setup_linpos_movie(self, gs, **kwargs): ) ax.legend(ax_handles, ax_labels, loc="lower left", fontsize=10, framealpha=0.5) ax.xaxis.set_major_formatter( - FuncFormatter(time_formatter) + FuncFormatter(time_formatter_vec) ) # Format x-axis as time + ax.xaxis.set_major_locator(MaxNLocator(nbins=5, prune="both")) def _setup_polar_panel(self, gs, **kwargs): """Setup polar heading direction panel.""" @@ -2095,14 +2131,14 @@ def _update_trajectory_panel(self, frame, start_idx, end_idx): # Get trail data trail_positions = self.positions[start_idx:end_idx] trail_directions = self.dims[name_axis][start_idx:end_idx] - trail_times = self.positionTime[start_idx:end_idx] + self.positionTime[start_idx:end_idx] if self.predicted is not None: trail_predicted = self.predicted[start_idx:end_idx] trail_directions_predicted = self.predicted_dims[name_axis][ start_idx:end_idx ] - trail_predicted_times = self.positionTime[start_idx:end_idx] + self.positionTime[start_idx:end_idx] assert trail_predicted.shape[0] == trail_directions_predicted.shape[0] else: trail_predicted = None @@ -2395,7 +2431,7 @@ def _update_trajectory_panel(self, frame, start_idx, end_idx): continue try: mask = indices == frame - except: + except (TypeError, AttributeError): continue if np.any(mask): x_data = self.positions[indices[mask], 0] @@ -2468,26 +2504,29 @@ def _update_trajectory_panel(self, frame, start_idx, end_idx): # Update title with current frame info and direction if not self.be_fast: - if self.binary_colors[name_axis]: - zone_name = "Shock Zone" if current_dir == 0 else "Safe Zone" - title_text = f"Position Trajectory - Frame {frame + 1}/{self.total_frames} - {zone_name} @{timedelta(seconds=self.positionTime[frame].astype(float))}" - else: - title_text = f"Position Trajectory - Frame {frame + 1}/{self.total_frames} @{timedelta(seconds=self.positionTime[frame].astype(float))}" - # nice but very slow @{timedelta(seconds=time[-1])} - - # In analysis mode, also show position error - if self.predicted is not None: - pos_error = np.nanmean( - np.linalg.norm( - self.predicted[: frame + 1] - self.positions[: frame + 1], - axis=1, + if (frame + 1) % 30 == 0 or frame == self.total_frames - 1: + if self.binary_colors[name_axis]: + zone_name = "Shock Zone" if current_dir == 0 else "Safe Zone" + title_text = f"Position Trajectory - Frame {frame + 1}/{self.total_frames} - {zone_name} @{_time_format_logic(float(self.positionTime[frame]))}" + else: + title_text = f"Position Trajectory - Frame {frame + 1}/{self.total_frames} @{_time_format_logic(float(self.positionTime[frame]))}" + + # In analysis mode, also show position error + if self.predicted is not None: + pos_error = np.nanmean( + np.linalg.norm( + self.predicted[: frame + 1] + - self.positions[: frame + 1], + axis=1, + ) ) - ) - title_text = f"Position error: {pos_error:.2f} cm | " + title_text - if not self.very_simple_plot: - self.artists[name_axis]["pos_title"].set_text(title_text) - else: - self.artists["fig_title"].set_text(title_text) + title_text = ( + f"Position error: {pos_error:.2f} cm | " + title_text + ) + if not self.very_simple_plot: + self.artists[name_axis]["pos_title"].set_text(title_text) + else: + self.artists["fig_title"].set_text(title_text) def _update_polar_panel(self, frame): """Update the polar heading panel.""" @@ -2602,17 +2641,21 @@ def _update_linpos_movie(self, frame, start_idx, end_idx): ) self.artists["linpos_pred_points"].set_color(colors) + time_to_show_min = time.min() + # adjust xlim to show some future points at the beginning of the movie (ie a buffer of 20 frames or half the movie duration) + time_to_show_max = max( + self.positionTime[min(end_idx + 20, self.positionTime.size - 1)], + self.positionTime[ + min( + start_idx + self.lin_movie_duration // 2, + self.positionTime.size - 1, + ) + ], + ) + padding = max((time_to_show_max - time_to_show_min) * 0.05, 0.01) self.axes["linpos_movie"].set_xlim( - self.positionTime[start_idx], - max( - self.positionTime[min(end_idx + 20, self.positionTime.size - 1)], - self.positionTime[ - min( - start_idx + self.lin_movie_duration // 2, - self.positionTime.size - 1, - ) - ], - ), + time_to_show_min - padding, + time_to_show_max + padding, ) # update stims, freezing, and ripples markers @@ -2625,7 +2668,7 @@ def _update_linpos_movie(self, frame, start_idx, end_idx): continue try: mask = indices == frame - except: + except (TypeError, AttributeError): continue if np.any(mask): @@ -2688,6 +2731,8 @@ def _update_linpos_movie(self, frame, start_idx, end_idx): self.artists[name].set_xdata(xdata) self.artists[name].set_ydata(ydata) + return self.flatten_artists() + def create_animation( self, interval: int = 50, @@ -2713,7 +2758,7 @@ def create_animation( # Default kwargs for better Qt compatibility anim_kwargs = { "blit": kwargs.get( - "blit", True + "blit", False ), # Better compatibility with Qt if set False "cache_frame_data": kwargs.get( "cache_frame_data", False @@ -2887,7 +2932,6 @@ def plot_timeline_comparison( # Create error type visualization y_pos = np.zeros(self.n_points) colors = [] - labels = [] for i in range(self.n_points): if self.true_positives[i]: @@ -3083,7 +3127,7 @@ def plot_error_heatmap( # Add text annotations for i in range(4): for j in range(n_windows): - text = ax.text( + ax.text( j, i, f"{window_errors_norm[i, j]:.2f}", @@ -3714,7 +3758,7 @@ def lighten_color(color, amount=0.5): try: c = mc.cnames[color] - except: + except KeyError: c = color c = colorsys.rgb_to_hls(*mc.to_rgb(c)) crgb = np.array(list(colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2]))) @@ -3997,115 +4041,53 @@ def plot_concatenated_bouts( return x_contiguous -if __name__ == "__main__": - # Run demonstration - try: - print("=" * 60) - print("ANIMATED POSITION PLOTTER DEMO") - print("=" * 60) - print(f"Backend in use: {matplotlib.get_backend()}") - - plotter, anim = demo_animated_plot() - print("\nAnimation created successfully!") - - # Keep the plot alive for Qt backends - backend = matplotlib.get_backend() - if "Qt" in backend: - print("\nQt backend detected - plot window should stay interactive") - print("Close the plot window to continue...") - try: - # For Qt backends, ensure event loop runs - if hasattr(plotter.fig.canvas, "start_main_loop"): - plotter.fig.canvas.start_main_loop() - except: - pass - - except Exception as e: - print(f"Error running demonstration: {e}") - import traceback - - traceback.print_exc() - - # Show usage examples - print("\n" + "=" * 60) - print("USAGE WITH YOUR DATA:") - print("=" * 60) - print(f"Current backend: {matplotlib.get_backend()}") - print(""" -# Plot your MATLAB maze shape: -maze_coords = [[0, 0], [0, 1], [1, 1], [1, 0], [0.63, 0], - [0.63, 0.75], [0.35, 0.75], [0.35, 0], [0, 0]] - -plotter = AnimatedPositionPlotter(your_data_helper) -plotter.setup_plot( - custom_lines=[maze_coords], # Your maze shape - custom_line_colors='black', # Maze color - custom_line_styles='-', # Solid lines - custom_line_widths=3 # Thick walls -) -plotter.show() - -# Multiple custom shapes: -maze = [[0, 0], [0, 1], [1, 1], [1, 0], [0.63, 0], [0.63, 0.75], [0.35, 0.75], [0.35, 0], [0, 0]] -shock_zone = [[0.3, 0.3], [0.7, 0.3], [0.7, 0.7], [0.3, 0.7], [0.3, 0.3]] # Square shock zone - -plotter.setup_plot( - custom_lines=[maze, shock_zone], - custom_line_colors=['black', 'red'], # Different colors - custom_line_styles=['-', '--'], # Different styles - custom_line_widths=[3, 2] # Different widths -) - -# Alternative: Use helper function -maze_coords = [[0, 0], [0, 1], [1, 1], [1, 0], [0.63, 0], - [0.63, 0.75], [0.35, 0.75], [0.35, 0], [0, 0]] -custom_lines = create_maze_from_matlab(maze_coords) - -plotter = create_plotter_for_data( - your_data_helper, - custom_lines=custom_lines -) - -# Mix with other reference lines: -plotter.setup_plot( - binary_colors=True, - custom_lines=[maze_coords], # Maze walls - hlines=[0.5], # Center horizontal line - vlines=[0.5], # Center vertical line - custom_line_colors='black', # Maze color - line_colors='gray' # Reference line color -) - -# Force Qt backend: -plotter = create_qt_plotter(your_data_helper, trail_length=40) - -# In Jupyter notebook: -%matplotlib widget # or %matplotlib qt -plotter = AnimatedPositionPlotter(your_data_helper) -plotter.show() - -# Save as video: -plotter = AnimatedPositionPlotter(your_data_helper) -anim = plotter.create_animation(save_path='trajectory.mp4') - -# Manual backend control: -import matplotlib -matplotlib.use('Qt5Agg') # Before importing pyplot -plotter = AnimatedPositionPlotter(your_data_helper) -plotter.show() -""") - - print("\nAvailable backends on your system:") - try: - import matplotlib.backend_bases - - backends = [] - for backend in ["Qt5Agg", "Qt4Agg", "TkAgg", "GTK3Agg", "WXAgg"]: - try: - matplotlib.use(backend, force=False) - backends.append(backend) - except: - pass - print(f"Compatible backends: {backends}") - except: - print("Could not detect available backends") +def plot_spikes_sequence(proto_example, nChannelsPerGroup): + # for each index, the corresponding spike is in index "indicesX" where X is the group number (0, 1, 2, or 3) + # e.g., if index 5 is in group 2, then the spike is in df["spikes2"][0][5 - 1] (subtract 1 for zeroForGather masking) + # plot the first 50 spikes of the sequence, on a single figure + import seaborn as sns + + fig, _axs = plt.subplots(1, 1, figsize=(10, 6), sharex=True) + axs = [_axs, _axs, _axs, _axs] # hack to have 4 axs in a single plot + palette0 = sns.color_palette("Set1", nChannelsPerGroup[0]) + palette1 = sns.color_palette("Set2", nChannelsPerGroup[1]) + palette2 = sns.color_palette("Set3", nChannelsPerGroup[2]) + palette3 = sns.color_palette("husl", nChannelsPerGroup[3]) + for i in range(min(proto_example["indices0"][0].shape[0], 30)): + is_group0 = proto_example["indices0"][0][i] != 0 + is_group1 = proto_example["indices1"][0][i] != 0 + is_group2 = proto_example["indices2"][0][i] != 0 + is_group3 = proto_example["indices3"][0][i] != 0 + if is_group0: + # reshape the spikes to its correct shape : [nChannels, 32 timebins] + spike = proto_example["group0"][0].reshape(-1, nChannelsPerGroup[0], 32)[ + proto_example["indices0"][0][i] - 1 + ] + ax_to_plot = 0 + palette = palette0 + if is_group1: + spike = proto_example["group1"][0].reshape(-1, nChannelsPerGroup[1], 32)[ + proto_example["indices1"][0][i] - 1 + ] + ax_to_plot = 1 + palette = palette1 + if is_group2: + spike = proto_example["group2"][0].reshape(-1, nChannelsPerGroup[2], 32)[ + proto_example["indices2"][0][i] - 1 + ] + ax_to_plot = 2 + palette = palette2 + if is_group3: + spike = proto_example["group3"][0].reshape(-1, nChannelsPerGroup[3], 32)[ + proto_example["indices3"][0][i] - 1 + ] + ax_to_plot = 3 + palette = palette3 + for ch in range(spike.shape[0]): + axs[ax_to_plot].plot( + np.arange(32 * i, 32 * (i + 1)), spike[ch] + ch * 2, c=palette[ch] + ) # offset each spike for visibility + plt.xlabel("Timebins") + plt.suptitle("First 50 spikes from the sequence, separated by channel") + plt.tight_layout() + plt.show() diff --git a/neuroencoders/importData/import_clusters.py b/neuroencoders/importData/import_clusters.py index 5b7530c..9e0b5f4 100755 --- a/neuroencoders/importData/import_clusters.py +++ b/neuroencoders/importData/import_clusters.py @@ -59,8 +59,8 @@ def getSpikesfromClu( np.array( [ [ - 1.0 if int(cluStr[n + 1]) == l else 0.0 - for l in range(1, nClu + 1) + 1.0 if int(cluStr[n + 1]) == cluster_idx else 0.0 + for cluster_idx in range(1, nClu + 1) ] for n in range(len(cluStr) - 1) ] @@ -126,21 +126,102 @@ def getSpikesfromClu( cluster_save_path = os.path.join(projectPath.folder, "dataset", "clusterData") if not os.path.isdir(cluster_save_path): os.makedirs(cluster_save_path) - for l in range(len(labels)): - df = pd.DataFrame(labels[l]) - df.to_csv(os.path.join(cluster_save_path, "Spike_labels" + str(l) + ".csv")) - df = pd.DataFrame(spikeTime[l]) - df.to_csv(os.path.join(cluster_save_path, "spike_time" + str(l) + ".csv")) - df = pd.DataFrame(spikePositions[l]) + for shank in range(len(labels)): + df = pd.DataFrame(labels[shank]) df.to_csv( - os.path.join(cluster_save_path, "spike_positions" + str(l) + ".csv") + os.path.join(cluster_save_path, "Spike_labels" + str(shank) + ".csv") ) - df = pd.DataFrame(spikePosIndex[l]) + df = pd.DataFrame(spikeTime[shank]) df.to_csv( - os.path.join(cluster_save_path, "spike_pos_index" + str(l) + ".csv") + os.path.join(cluster_save_path, "spike_time" + str(shank) + ".csv") ) - df = pd.DataFrame(spikeSpeed[l]) - df.to_csv(os.path.join(cluster_save_path, "spike_speed" + str(l) + ".csv")) + df = pd.DataFrame(spikePositions[shank]) + df.to_csv( + os.path.join(cluster_save_path, "spike_positions" + str(shank) + ".csv") + ) + df = pd.DataFrame(spikePosIndex[shank]) + df.to_csv( + os.path.join(cluster_save_path, "spike_pos_index" + str(shank) + ".csv") + ) + df = pd.DataFrame(spikeSpeed[shank]) + df.to_csv( + os.path.join(cluster_save_path, "spike_speed" + str(shank) + ".csv") + ) + return cluster_data + + +def _load_linear_spike_sorting_from_clu(projectPath: Project, flatten=True) -> dict: + """ + Load spike sorting data from the original klustakwik files and linearize them in time. + + Parameters + ---------- + projectPath : Project object + + Returns + ------- + cluster_data : dict + """ + # Get parameters + listChannels, samplingRate, _ = rawdata_parser.get_params(projectPath.xml) + # Allocate + labels = [] + indexInDat = [] + + nTetrodes = len(listChannels) + last_n_clu = 0 + for tetrode in tqdm.tqdm(range(nTetrodes)): + print( + f"Importing sorted spikes from Neuroscope files from electrodes group #{tetrode}" + ) + if os.path.isfile(projectPath.clu(tetrode)): + with ( + open(projectPath.clu(tetrode), "r") as fClu, + open(projectPath.res(tetrode), "r") as fRes, + ): # open(projectPath.spk(tetrode), 'rb') as fSpk + cluStr = fClu.readlines() + resStr = fRes.readlines() + clu = np.array( + [int(cluStr[n + 1]) + last_n_clu for n in range(len(cluStr) - 1)] + ) + # Clusters only with labels >= 1 + labels_mask = clu >= 1 + last_n_clu + labels_temp = clu[labels_mask] + + # turn spike times from str to float + index = np.array([int(x.strip()) for x in resStr]) + index_temp = index[labels_mask] + + indexInDat.append(index_temp) + labels.append(labels_temp) + last_n_clu += int(cluStr[0]) - 1 + + else: + print("File " + projectPath.clu(tetrode) + " not found.") + continue + sys.stdout.write("File from tetrode " + " has been successfully opened. ") + sys.stdout.write("Processing ...") + sys.stdout.write("\r") + sys.stdout.flush() + + if flatten: + # Now sort all spikes in time + all_index = np.concatenate(indexInDat) + sort_idx = np.argsort(all_index) + all_labels = np.concatenate(labels) + labels = all_labels[sort_idx] + indexInDat = all_index[sort_idx] + + sys.stdout.write( + "We have imported linear-time clusters. " + ) + sys.stdout.write("\r") + sys.stdout.flush() + + cluster_data = { + "Spike_labels": labels, + "Spike_index": indexInDat, + } return cluster_data @@ -169,25 +250,25 @@ def load_spike_sorting(projectPath: Project, phase=None) -> dict: "Spike_pos_index": [], } print("Reading saved cluster csv file") - for l in tqdm.tqdm(range(num_files)): + for shank in tqdm.tqdm(range(num_files)): df = pd.read_csv( - os.path.join(cluster_save_path, "Spike_labels" + str(l) + ".csv") + os.path.join(cluster_save_path, "Spike_labels" + str(shank) + ".csv") ) cluster_data["Spike_labels"].append(df.values[:, 1:]) df = pd.read_csv( - os.path.join(cluster_save_path, "spike_time" + str(l) + ".csv") + os.path.join(cluster_save_path, "spike_time" + str(shank) + ".csv") ) cluster_data["Spike_times"].append(df.values[:, 1:]) df = pd.read_csv( - os.path.join(cluster_save_path, "spike_positions" + str(l) + ".csv") + os.path.join(cluster_save_path, "spike_positions" + str(shank) + ".csv") ) cluster_data["Spike_positions"].append(df.values[:, 1:]) df = pd.read_csv( - os.path.join(cluster_save_path, "spike_pos_index" + str(l) + ".csv") + os.path.join(cluster_save_path, "spike_pos_index" + str(shank) + ".csv") ) cluster_data["Spike_pos_index"].append(df.values[:, 1:]) df = pd.read_csv( - os.path.join(cluster_save_path, "spike_speed" + str(l) + ".csv") + os.path.join(cluster_save_path, "spike_speed" + str(shank) + ".csv") ) cluster_data["Spike_speed"].append(df.values[:, 1:]) diff --git a/neuroencoders/importData/rawdata_parser.py b/neuroencoders/importData/rawdata_parser.py index 332aa1d..8e7e085 100755 --- a/neuroencoders/importData/rawdata_parser.py +++ b/neuroencoders/importData/rawdata_parser.py @@ -1,7 +1,6 @@ # Load libs import os import re -import sys import xml.etree.ElementTree as ET from tkinter import Button, Entry, Label, Toplevel from typing import Literal, Optional @@ -35,11 +34,7 @@ def get_params(pathToXml): listChannels = [] samplingRate = None nChannels = None - try: - tree = ET.parse(pathToXml) - except: - print("impossible to open xml file:", pathToXml) - sys.exit(1) + tree = ET.parse(pathToXml) root = tree.getroot() for br1Elem in root: if br1Elem.tag != "spikeDetection": @@ -209,12 +204,12 @@ def get_behavior( sleepPeriods = f.root.behavior.sleepPeriods[:] if np.sum(sleepPeriods) > 0: # If sleepPeriods exist sleepNames = [ - "".join([chr(c) for c in l[0][:, 0]]) - for l in f.root.behavior.sessionSleepNames[:, 0] + "".join([chr(c) for c in sleepName[0][:, 0]]) + for sleepName in f.root.behavior.sessionSleepNames[:, 0] ] sessionNames = [ - "".join([chr(c) for c in l[0][:, 0]]) - for l in f.root.behavior.SessionNames[:, 0] + "".join([chr(c) for c in sessName[0][:, 0]]) + for sessName in f.root.behavior.SessionNames[:, 0] ] if sessionNames[0] != "Recording": sessionStart = f.root.behavior.SessionStart[:, :][:, 0] @@ -392,6 +387,7 @@ def speed_filter( # Parameters window_len = 14 # changed following Dima's advice window_idx = 0 # index of the window to show + copied = False from neuroencoders.utils.global_classes import MAZE_COORDS filename = os.path.join(folder, "nnBehavior.mat") @@ -411,6 +407,7 @@ def speed_filter( folder + "nnBehavior_" + phase + ".mat", follow_symlinks=True, ) + copied = True # Extract basic behavior speedOG = None with tables.open_file(filename, "a") as f: @@ -455,8 +452,8 @@ def speed_filter( speed = f.root.behavior.speed positionTime = f.root.behavior.position_time sessionNames = [ - "".join([chr(c) for c in l[0][:, 0]]) - for l in f.root.behavior.SessionNames[:, 0] + "".join([chr(c) for c in sessName[0][:, 0]]) + for sessName in f.root.behavior.SessionNames[:, 0] ] if sessionNames[0] != "Recording": IsMultiSessions = True @@ -616,7 +613,6 @@ def speed_filter( ) ax4.set_ylabel("speed Threshold") ax5.set_ylabel("window range") - ax = [ax0, ax1, ax2, ax3, ax4, ax5] # create scatter plot of environmental variable depending on speed fig2d, ax2d = plt.subplots() @@ -696,8 +692,8 @@ def update(val): ) l4.set_ydata(speedToshowSm[speedFilter]) l4.set_xdata(timeToShow[speedFilter]) - l5.set_xdata(slider.val) - l6.set_xdata(np.exp(slider.val)) + l5.set_xdata([slider.val]) + l6.set_xdata([np.exp(slider.val)]) ax3.set_xlabel(f"raw speed ({np.exp(slider.val):.2f} cm/s)") fig2d.suptitle( @@ -714,20 +710,20 @@ def update(val): else np.ones_like(idxs, dtype=bool) ) - newfilter = speedFilter & to_show - pos2d.set_xdata(behToShow[newfilter, 0]) - pos2d.set_ydata(behToShow[newfilter, 1]) + new_filter = speedFilter & to_show + pos2d.set_xdata(behToShow[new_filter, 0]) + pos2d.set_ydata(behToShow[new_filter, 1]) sc.set_offsets( np.transpose( np.stack( [ - behToShow[newfilter, 0], - behToShow[newfilter, 1], + behToShow[new_filter, 0], + behToShow[new_filter, 1], ] ) ) ) - sc.set_array(speedToshowSm[newfilter]) + sc.set_array(speedToshowSm[new_filter]) fig.canvas.draw_idle() fig2d.canvas.draw_idle() @@ -785,6 +781,30 @@ def on_click_prev(event): if "positions" in children: f.remove_node("/behavior", "positions") f.create_array("/behavior", "positions", np.swapaxes(positions, 1, 0)) + if "testEpochs" in children and copied: + f.remove_node("/behavior", "testEpochs") + if "trainEpochs" in children and copied: + f.remove_node("/behavior", "trainEpochs") + if "keptSession" in children and copied: + f.remove_node("/behavior", "keptSession") + if "lossPredSetEpochs" in children and copied: + f.remove_node("/behavior", "lossPredSetEpochs") + if "ref" in children and copied: + f.remove_node("/behavior", "ref") + if "xyOutput" in children and copied: + f.remove_node("/behavior", "xyOutput") + if "shock_zone" in children and copied: + f.remove_node("/behavior", "shock_zone") + if "shock_zone_mask" in children and copied: + f.remove_node("/behavior", "shock_zone_mask") + if "aligned_ref" in children and copied: + f.remove_node("/behavior", "aligned_ref") + if "ratioIMAonREAL" in children and copied: + f.remove_node("/behavior", "ratioIMAonREAL") + if "M" in children and copied: + f.remove_node("/behavior", "M") + if "outputSize" in children and copied: + f.remove_node("/behavior", "outputSize") f.flush() f.close() @@ -876,8 +896,8 @@ def select_epochs( ) # We extract session names: sessionNames = [ - "".join([chr(c) for c in l[0][:, 0]]) - for l in f.root.behavior.SessionNames[:, 0] + "".join([chr(c) for c in sessName[0][:, 0]]) + for sessName in f.root.behavior.SessionNames[:, 0] ] if sessionNames[0] != "Recording": IsMultiSessions = True @@ -966,14 +986,13 @@ def select_epochs( speedMaskToShow = speedMask[maskToShow] speedMaskToShowPRE = speedMaskToShow timeToShowPRE = timeToShow - speedsToShowPRE = speedsToShow xmin, xmax = timeToShow[0], timeToShow[-1] sessionValue_toshow = sessionValue[maskToShow] if phase is not None: maskToShowPRE = ep.inEpochsMask(positionTime[:, 0], epochToSelectPRE) timeToShowPRE = positionTime[maskToShowPRE, 0] - speedsToShowPRE = speeds[maskToShowPRE] + speeds[maskToShowPRE] speedMaskToShowPRE = speedMask[maskToShowPRE] xmin, xmax = timeToShowPRE[0], timeToShowPRE[-1] @@ -1011,7 +1030,7 @@ def select_epochs( else: st = st[:-1] assert st.shape[0] % 2 == 0 - showtimes = tuple(zip(st[::2], st[1::2])) + tuple(zip(st[::2], st[1::2])) # Default train and test sets sizeTest = ( @@ -1151,16 +1170,16 @@ def select_epochs( ) if IsMultiSessions: - ax = [ - fig.add_subplot(gs[id, :]) for id in range(positions.shape[1]) - ] # ax for feature display - ax[0].get_shared_x_axes().join(ax[0], ax[1]) - # ax = [brokenaxes(xlims=showtimes, subplot_spec=gs[id,:]) for id in range(positions.shape[1])] #ax for feature display + ax = [] + for i in range(positions.shape[1]): + # Create the subplot. If it's not the first one, share x with ax[0] + new_ax = fig.add_subplot(gs[i, :], sharex=ax[0] if i > 0 else None) + ax.append(new_ax) else: ax = [ fig.add_subplot(gs[id, :]) for id in range(positions.shape[1]) ] # ax for feature display - ax[0].get_shared_x_axes().join(ax[0], ax[1]) + ax[1].sharex(ax[0]) ax += [ fig.add_subplot(gs[-5, id]) for id in range(len(sessionNames)) @@ -1551,12 +1570,16 @@ def update(val): if SetData["useLossPredTrainSet"]: try: ls[dim][2][iaxis].remove() - except: + except (AttributeError, KeyError): + # Scatter plot may not exist yet or may have been removed; + # safely ignore and continue to create new plot. pass else: try: ls[dim][2][iaxis].remove() - except: + except (AttributeError, KeyError): + # Scatter plot may not exist yet or may have been removed; + # safely ignore and continue to create new plot. pass if SetData["useLossPredTrainSet"]: ls[dim][2] = ax[dim].scatter( @@ -1601,7 +1624,9 @@ def update(val): if SetData["useLossPredTrainSet"]: try: ls[dim][2].remove() - except: + except (AttributeError, KeyError): + # Scatter plot may not exist yet or may have been removed; + # safely ignore and continue to create new plot. pass ls[dim][2] = ax[dim].scatter( timeToShow[ @@ -1616,7 +1641,9 @@ def update(val): else: try: l3.remove() - except: + except (AttributeError, KeyError): + # Scatter plot may not exist yet or may have been removed; + # safely ignore and continue to create new plot. pass # modify the xlim of the axes according to the changed epochs diff --git a/neuroencoders/resultAnalysis/hyper_paper_figures.py b/neuroencoders/resultAnalysis/hyper_paper_figures.py index d046ec9..1f41d85 100644 --- a/neuroencoders/resultAnalysis/hyper_paper_figures.py +++ b/neuroencoders/resultAnalysis/hyper_paper_figures.py @@ -286,7 +286,7 @@ def fig_eucl_error_filtered( } ) fig, ax = plt.subplots() - myPalette = {"ANN": colorsForSNS[0], "Bayes": colorsForSNS[1]} + {"ANN": colorsForSNS[0], "Bayes": colorsForSNS[1]} sns.boxplot( data=datToPlot, x="timeWindow (ms)", diff --git a/neuroencoders/resultAnalysis/old_code.py b/neuroencoders/resultAnalysis/old_code.py deleted file mode 100755 index 131a543..0000000 --- a/neuroencoders/resultAnalysis/old_code.py +++ /dev/null @@ -1,1783 +0,0 @@ -def fit_uncertainty_estimate( - self, - linearizationFunction, - batch=False, - forceFirstTrainingWeight=False, - useSpeedFilter=False, - useTrain=False, - onTheFlyCorrection=True, -): - # spike sorted: spike times , with neurons index sorted by linear estimated max position of place field - # in detail: [spike,one hot neurons index],[spiketime, - - # todo: use the validation set here by default. - behavior_data = getBehavior(self.projectPath.folder, getfilterSpeed=True) - if ( - len(behavior_data["Times"]["lossPredSetEpochs"]) > 0 - and not forceFirstTrainingWeight - ): - self.model.load_weights( - os.path.join(self.projectPath.resultsPath, "training_2/cp.ckpt") - ) - else: - self.model.load_weights( - os.path.join(self.projectPath.resultsPath, "training_1/cp.ckpt") - ) - - # Build the online decoding model with the layer already initialized: - self.uncertainty_estimate_model = self.get_model_for_uncertainty_estimate( - batch=batch - ) - - speed_mask = behavior_data["Times"]["speedFilter"] - if not useSpeedFilter: - speed_mask = np.zeros_like(speed_mask) + 1 - dataset = tf.data.TFRecordDataset(self.projectPath.tfrec) - dataset = dataset.map( - lambda *vals: nnUtils.parseSerializedSpike(self.feat_desc, *vals), - num_parallel_calls=tf.data.AUTOTUNE, - ) - if useTrain: - epochMask = inEpochsMask( - behavior_data["Position_time"][:, 0], behavior_data["Times"]["trainEpochs"] - ) - else: - epochMask = inEpochsMask( - behavior_data["Position_time"][:, 0], behavior_data["Times"]["testEpochs"] - ) - tot_mask = speed_mask * epochMask - table = tf.lookup.StaticHashTable( - tf.lookup.KeyValueTensorInitializer( - tf.constant(np.arange(len(tot_mask)), dtype=tf.int64), - tf.constant(tot_mask, dtype=tf.float64), - ), - default_value=0, - ) - dataset = dataset.filter(lambda x: tf.equal(table.lookup(x["pos_index"]), 1.0)) - - if onTheFlyCorrection: - maxPos = np.max( - behavior_data["Positions"][ - np.logical_not(np.isnan(np.sum(behavior_data["Positions"], axis=1))) - ] - ) - dataset = dataset.map( - nnUtils.onthefly_feature_correction(behavior_data["Positions"] / maxPos) - ) - dataset = dataset.filter( - lambda x: tf.math.logical_not(tf.math.is_nan(tf.math.reduce_sum(x["pos"]))) - ) - - dataset = dataset.batch(self.params.batch_size, drop_remainder=True) - # drop_remainder allows us to remove the last batch if it does not contain enough elements to form a batch. - dataset = dataset.map( - lambda *vals: nnUtils.parseSerializedSequence(self.params, *vals, batched=True), - num_parallel_calls=tf.data.AUTOTUNE, - ) - dataset = dataset.map(self.createIndices, num_parallel_calls=tf.data.AUTOTUNE) - dataset = dataset.map( - lambda vals: ( - vals, - { - "tf_op_layer_lossOfManifold": tf.zeros(self.params.batch_size), - "tf_op_layer_lossOfLossPredictor": tf.zeros(self.params.batch_size), - }, - ), - num_parallel_calls=tf.data.AUTOTUNE, - ) - - if useTrain and not os.path.exists( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_fit", - "networkPosPred.csv", - ) - ): - dtime = dataset.map(lambda vals, valsout: vals["time"]) - timePred = list(dtime.as_numpy_iterator()) - timePreds = np.ravel(timePred) - output_test = self.uncertainty_estimate_model.predict(dataset, verbose=1) - - euclidData = np.reshape(output_test[0], [np.prod(output_test[0].shape[0:3]), 2]) - # save the result of the uncertainty prediction: - if not os.path.exists( - os.path.join(self.projectPath.resultsPath, "uncertainty_network_fit") - ): - os.makedirs( - os.path.join(self.projectPath.resultsPath, "uncertainty_network_fit") - ) - df = pd.DataFrame(euclidData) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_fit", - "networkPosPred.csv", - ) - ) - - df = pd.DataFrame(timePreds) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, "uncertainty_network_fit", "timePreds.csv" - ) - ) - - d0 = list(dataset.map(lambda vals, valsout: vals["pos"]).as_numpy_iterator()) - truePosFed = np.array(d0) - truePosFed = truePosFed.reshape([truePosFed.shape[0] * truePosFed.shape[1], 2]) - df = pd.DataFrame(truePosFed) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_fit", - "truePosFed.csv", - ) - ) - elif (not useTrain) and not os.path.exists( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "networkPosPred.csv", - ) - ): - dtime = dataset.map(lambda vals, valsout: vals["time"]) - timePred = list(dtime.as_numpy_iterator()) - timePreds = np.ravel(timePred) - output_test = self.uncertainty_estimate_model.predict(dataset, verbose=1) - - euclidData = np.reshape(output_test[0], [np.prod(output_test[0].shape[0:3]), 2]) - # save the result of the uncertainty prediction: - if not os.path.exists( - os.path.join(self.projectPath.resultsPath, "uncertainty_network_test") - ): - os.makedirs( - os.path.join(self.projectPath.resultsPath, "uncertainty_network_test") - ) - df = pd.DataFrame(euclidData) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "networkPosPred.csv", - ) - ) - - df = pd.DataFrame(timePreds) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "timePreds.csv", - ) - ) - - d0 = list(dataset.map(lambda vals, valsout: vals["pos"]).as_numpy_iterator()) - truePosFed = np.array(d0) - truePosFed = truePosFed.reshape([truePosFed.shape[0] * truePosFed.shape[1], 2]) - df = pd.DataFrame(truePosFed) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "truePosFed.csv", - ) - ) - if useTrain: - euclidData = np.array( - pd.read_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_fit", - "networkPosPred.csv", - ) - ).values[:, 1:], - dtype=np.float32, - ) - timePreds = np.array( - pd.read_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_fit", - "timePreds.csv", - ) - ).values[:, 1], - dtype=np.float32, - ) - truePosFed = np.array( - pd.read_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_fit", - "truePosFed.csv", - ) - ).values[:, 1:], - dtype=np.float32, - ) - else: - euclidData = np.array( - pd.read_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "networkPosPred.csv", - ) - ).values[:, 1:], - dtype=np.float32, - ) - timePreds = np.array( - pd.read_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "timePreds.csv", - ) - ).values[:, 1], - dtype=np.float32, - ) - truePosFed = np.array( - pd.read_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "truePosFed.csv", - ) - ).values[:, 1:], - dtype=np.float32, - ) - - output_test = [ - np.reshape( - euclidData, - [ - -1, - self.params.nb_eval_dropout, - self.params.batch_size, - self.params.dim_output, - ], - ) - ] - - projectedPos, linearPos = linearizationFunction(euclidData.astype(np.float64)) - - linearPos = np.reshape(linearPos, output_test[0].shape[0:3]) - medianLinearPos = np.median(linearPos, axis=1) - medianLinearPos = np.reshape(medianLinearPos, [np.prod(medianLinearPos.shape[0:2])]) - - d0 = list(dataset.map(lambda vals, valsout: vals["pos"]).as_numpy_iterator()) - truePosFed = np.array(d0) - truePosFed = truePosFed.reshape([truePosFed.shape[0] * truePosFed.shape[1], 2]) - trueProjPos, trueLinearPos = linearizationFunction(truePosFed) - - linearTranspose = np.transpose(linearPos, axes=[0, 2, 1]) - linearTranspose = linearTranspose.reshape( - [linearTranspose.shape[0] * linearTranspose.shape[1], linearTranspose.shape[2]] - ) - histPosPred = np.stack( - [ - np.histogram( - np.abs(linearTranspose[id, :] - np.median(linearTranspose[id, :])), - bins=np.arange(0, stop=1, step=0.01), - density=True, - )[0] - for id in range(linearTranspose.shape[0]) - ] - ) - - # let us get the window speedmask: - dposIndex = dataset.map(lambda vals, valsout: vals["pos_index"]) - dposIndex = list(dposIndex.as_numpy_iterator()) - pos_index = np.ravel(np.array(dposIndex)) - speed_mask = behavior_data["Times"]["speedFilter"] - windowmask_speed = speed_mask[pos_index] - if useTrain: - df = pd.DataFrame(windowmask_speed) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_fit", - "windowmask_speed.csv", - ) - ) - else: - df = pd.DataFrame(windowmask_speed) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "uncertainty_network_test", - "windowmask_speed.csv", - ) - ) - - histlinearPosPred = np.stack( - [ - np.histogram( - linearTranspose[id, :], - bins=np.arange(0, stop=1, step=0.01), - density=True, - )[0] - for id in range(linearTranspose.shape[0]) - ] - ) - fig, ax = plt.subplots(3, 1) - ax[0].scatter( - trueLinearPos[windowmask_speed], - np.mean(linearTranspose, axis=1)[windowmask_speed], - s=1, - alpha=0.2, - ) - ax[1].scatter( - trueLinearPos[windowmask_speed], - np.median(linearTranspose, axis=1)[windowmask_speed], - s=1, - alpha=0.2, - ) - ax[2].scatter( - trueLinearPos[windowmask_speed], - np.argmax(histlinearPosPred, axis=1)[windowmask_speed], - s=1, - alpha=0.2, - ) - fig.show() - - histlinearPosPred_density = ( - histlinearPosPred / (np.sum(histlinearPosPred, axis=1)[:, None]) - ) - - fig, ax = plt.subplots() - ax.matshow( - np.transpose(histlinearPosPred_density), - cmap=plt.get_cmap("Reds"), - aspect="auto", - ) - ax.plot( - range(trueLinearPos.shape[0]), - trueLinearPos * histlinearPosPred_density.shape[1], - c="black", - alpha=0.1, - ) - testSet = inEpochsMask(timePreds, behavior_data["Times"]["testEpochs"]) - trainSet = inEpochsMask(timePreds, behavior_data["Times"]["trainEpochs"]) - ax.plot( - range(trueLinearPos.shape[0]), - testSet + histlinearPosPred_density.shape[1], - c="black", - ) - ax.plot( - range(trueLinearPos.shape[0]), - trainSet + histlinearPosPred_density.shape[1] + 2, - c="blue", - ) - ax.plot( - range(trueLinearPos.shape[0]), - windowmask_speed + histlinearPosPred_density.shape[1] + 4, - c="red", - ) - ax.plot( - range(trueLinearPos.shape[0]), - windowmask_speed * testSet + histlinearPosPred_density.shape[1] + 6, - c="purple", - ) - - cmt = plt.get_cmap("tab20") - cms = plt.get_cmap("Set3") - for i in range(int(len(behavior_data["Times"]["testEpochs"]) / 2)): - epoch = behavior_data["Times"]["testEpochs"][2 * i : 2 * i + 2] - maskEpoch = inEpochsMask(timePreds, epoch) - if i < 20: - ax.plot( - range(trueLinearPos.shape[0]), - maskEpoch + histlinearPosPred_density.shape[1] + 8 + 2 * i, - c=cmt(i), - ) - else: - ax.plot( - range(trueLinearPos.shape[0]), - maskEpoch + histlinearPosPred_density.shape[1] + 8 + 2 * i, - c=cms(i), - ) - # ax.set_aspect(histlinearPosPred.shape[0]/histlinearPosPred.shape[1]) - ax.plot( - range(trueLinearPos.shape[0]), - np.isnan(timePreds) + histlinearPosPred_density.shape[1] - 1, - c="black", - ) - fig.show() - - def xlogx(x): - y = np.zeros_like(x) - y[np.greater(x, 0)] = np.log(x[np.greater(x, 0)]) * (x[np.greater(x, 0)]) - return y - - # let us compute the absolute error over each test Epochs: - absError_epochs = [] - absError_epochs_mean = [] - names = [] - entropies_epochs_mean = [] - entropies_epochs = [] - keptSession = behavior_data["Times"]["keptSession"] - sessNames = behavior_data["Times"]["sessionNames"].copy() - for idk, k in enumerate(keptSession.astype(np.bool)): - if not k: - sessNames.remove(behavior_data["Times"]["sessionNames"][idk]) - testEpochs = behavior_data["Times"]["testEpochs"].copy() - for x in behavior_data["Times"]["sleepNames"]: - for id2, x2 in enumerate(sessNames): - if x == x2: - sessNames.remove(x2) - testEpochs[id2 * 2] = -1 - testEpochs[id2 * 2 + 1] = -1 - testEpochs = testEpochs[np.logical_not(np.equal(testEpochs, -1))] - for i in range(int(len(testEpochs) / 2)): - epoch = testEpochs[2 * i : 2 * i + 2] - maskEpoch = inEpochsMask(timePreds, epoch) - maskTot = maskEpoch * windowmask_speed - if np.sum(maskTot) > 0: - absError_epochs_mean += [ - np.mean( - np.abs( - trueLinearPos[maskTot] - - np.mean(linearTranspose[maskTot], axis=1) - ) - ) - ] - absError_epochs += [ - np.abs( - trueLinearPos[maskTot] - np.mean(linearTranspose[maskTot], axis=1) - ) - ] - names += [sessNames[i]] - entropies_epochs_mean += [ - np.mean(np.sum(-xlogx(histlinearPosPred_density[maskTot, :]), axis=1)) - ] - entropies_epochs += [ - np.sum(-xlogx(histlinearPosPred_density[maskTot, :]), axis=1) - ] - else: - print(sessNames[i]) - fig, ax = plt.subplots() - ax.scatter(range(len(absError_epochs)), absError_epochs_mean) - ax.plot(absError_epochs_mean) - ax.set_ylabel("absolute decoding error") - ax.set_xticks(range(len(absError_epochs))) - ax.set_xticklabels(names) - fig.tight_layout() - fig.show() - - fig, ax = plt.subplots() - ax.scatter(range(len(absError_epochs)), entropies_epochs_mean) - ax.plot(entropies_epochs_mean) - ax.violinplot(entropies_epochs, positions=range(len(absError_epochs))) - ax.set_ylabel("mean entropies") - ax.set_xticks(range(len(absError_epochs))) - ax.set_xticklabels(names) - fig.tight_layout() - fig.show() - - fig, ax = plt.subplots(7, 3, figsize=(5, 5)) - for i in range(7): - for j in range(3): - ax[i, j].scatter( - entropies_epochs[3 * i + j], - absError_epochs[3 * i + j], - s=0.5, - alpha=0.2, - ) - ax[i, j].set_ylabel(sessNames[3 * i + j]) - # ax[i,j].set_xlabel("entropies") - # ax[i, j].set_aspect(1) - fig.tight_layout() - fig.show() - - # Let us correct the predicted entropies by the distribution of predicted entropy given the predicted - # position - # TODO TODO - - # - # cm = plt.get_cmap("Reds") - # toDisplay = np.arange(135000, stop=138000) - # fig,ax = plt.subplots() - # linearvariable = np.arange(0, stop=1, step=0.01) - # for i in range(histlinearPosPred.shape[1]): - # ax.scatter(timePreds[toDisplay], linearvariable[i] + np.zeros_like(timePreds[toDisplay]), - # c=cm(histlinearPosPred_density[toDisplay, i]), s=1) - # ax.plot(timePreds[toDisplay],trueLinearPos[toDisplay],c="black",alpha=0.3) - # fig.show() - - # we first sort by error: - sortPerm = np.argsort( - np.abs(medianLinearPos[windowmask_speed] - trueLinearPos[windowmask_speed]) - ) - reorderedHist = histPosPred[windowmask_speed][sortPerm] - fig, ax = plt.subplots() - ax.matshow(reorderedHist) - ax.set_aspect(reorderedHist.shape[1] / reorderedHist.shape[0]) - ax.set_xlabel("histogram of absolute distance to median") - axy = ax.twiny() - axy.plot( - np.abs(medianLinearPos[windowmask_speed] - trueLinearPos[windowmask_speed])[ - sortPerm - ], - range(sortPerm.shape[0]), - c="red", - alpha=0.5, - ) - axy.set_xlabel("absolute decoding linear error") - ax.set_ylabel("time step - \n reordered by decoding error") - # ax[1].set_aspect(reorderedHist.shape[1]/(np.abs(output_test[0]-trueLinearPos).max())) - fig.show() - - speeds = behavior_data["Speed"][pos_index] - entropyPosPred = -np.sum(xlogx(histPosPred / 100), axis=-1) - linearEnsembleError = np.abs(medianLinearPos - trueLinearPos) - fig, ax = plt.subplots() - cm = plt.get_cmap("Reds") - ax.scatter( - entropyPosPred[windowmask_speed], - linearEnsembleError[windowmask_speed], - s=2, - c=cm(speeds[windowmask_speed] / np.max(speeds[windowmask_speed])), - alpha=0.6, - ) - ax.set_xlabel("entropy of the prediction") - ax.set_ylabel("linear error") - fig.show() - - fig, ax = plt.subplots() - cm = plt.get_cmap("Reds") - ax.scatter( - entropyPosPred[windowmask_speed], - linearEnsembleError[windowmask_speed], - s=1 / (speeds[windowmask_speed] / np.max(speeds[windowmask_speed])), - c="orange", - alpha=0.6, - ) - ax.set_xlabel("entropy of the prediction") - ax.set_ylabel("linear error") - fig.show() - - fig, ax = plt.subplots() - cm = plt.get_cmap("Reds") - ax.scatter( - entropyPosPred[np.logical_not(windowmask_speed)], - linearEnsembleError[np.logical_not(windowmask_speed)], - s=10 - * speeds[np.logical_not(windowmask_speed)] - / np.max(speeds[np.logical_not(windowmask_speed)]), - c="orange", - alpha=0.6, - ) - ax.set_xlabel("entropy of the prediction") - ax.set_ylabel("linear error") - fig.show() - - fig, ax = plt.subplots() - ax.scatter( - linearEnsembleError[np.logical_not(windowmask_speed)], - speeds[np.logical_not(windowmask_speed)], - s=1, - alpha=0.5, - ) - ax.scatter( - linearEnsembleError[windowmask_speed], - speeds[windowmask_speed], - s=1, - c="red", - alpha=0.5, - ) - fig.show() - - fig, ax = plt.subplots() - cm = plt.get_cmap("turbo") - ax.scatter( - timePreds, medianLinearPos, c=cm(entropyPosPred / np.max(entropyPosPred)), s=3 - ) - ax.plot(timePreds, trueLinearPos, c="grey") - plt.colorbar( - plt.cm.ScalarMappable(plt.Normalize(0, np.max(entropyPosPred)), cmap=cm), - label="entropy of predictions", - ) - ax.set_xlabel("time (s)") - ax.set_ylabel("linear position") - fig.show() - - # build hist of entropy as a function of the predicted position - posHistMapper = [ - np.where( - np.less_equal(medianLinearPos, bin) * np.greater(medianLinearPos, bin - 0.1) - )[0] - for bin in np.arange(0.1, stop=1, step=0.1) - ] - entropy = [entropyPosPred[phm] for phm in posHistMapper] - fig, ax = plt.subplots() - ax.violinplot(entropy, positions=np.arange(0.1, stop=1, step=0.1), widths=0.1) - ax.scatter(medianLinearPos, entropyPosPred, s=1, c="black", alpha=0.5) - ax.set_xlabel("linear position") - ax.set_ylabel("entropy") - ax.set_title("all movement") - fig.show() - - posHistMapper = [ - np.where( - np.less_equal(medianLinearPos[windowmask_speed], bin) - * np.greater(medianLinearPos[windowmask_speed], bin - 0.1) - )[0] - for bin in np.arange(0.1, stop=1, step=0.1) - ] - entropy = [entropyPosPred[windowmask_speed][phm] for phm in posHistMapper] - fig, ax = plt.subplots() - ax.violinplot(entropy, positions=np.arange(0.1, stop=1, step=0.1), widths=0.1) - ax.scatter( - medianLinearPos[windowmask_speed], - entropyPosPred[windowmask_speed], - s=1, - c="black", - alpha=0.5, - ) - ax.set_xlabel("linear position") - ax.set_ylabel("entropy") - ax.set_title("speed filtered") - fig.show() - - windowmask_speed_slow = np.logical_not(windowmask_speed) - posHistMapper = [ - np.where( - np.less_equal(medianLinearPos[windowmask_speed_slow], bin) - * np.greater(medianLinearPos[windowmask_speed_slow], bin - 0.1) - )[0] - for bin in np.arange(0.1, stop=1, step=0.1) - ] - entropy = [entropyPosPred[windowmask_speed_slow][phm] for phm in posHistMapper] - fig, ax = plt.subplots() - ax.violinplot(entropy, positions=np.arange(0.1, stop=1, step=0.1), widths=0.1) - ax.scatter( - medianLinearPos[windowmask_speed_slow], - entropyPosPred[windowmask_speed_slow], - s=1, - c="black", - alpha=0.5, - ) - ax.set_xlabel("linear position") - ax.set_ylabel("entropy") - ax.set_title("speed filtered (keeping slow speed)") - fig.show() - - AbsErrorToMedian = np.abs( - linearTranspose[windowmask_speed] - - np.median(linearTranspose[windowmask_speed], axis=1)[:, None] - ) - linearEnsembleError = np.abs( - medianLinearPos[windowmask_speed] - trueLinearPos[windowmask_speed] - ) - - from sklearn.linear_model import Ridge - from sklearn.model_selection import train_test_split - - clf = Ridge(alpha=0.01) - X_train, X_test, Y_train, Y_test = train_test_split( - AbsErrorToMedian, linearEnsembleError, train_size=0.3 - ) - clf.fit(X_train, Y_train) - self.LinearNetworkConfidence = clf - # to set the weights of the Linear Error Layer we need to call it first... - self.predAbsoluteLinearErrorLayer(tf.convert_to_tensor(X_train[0:2, :])) - self.predAbsoluteLinearErrorLayer.set_weights( - [ - self.LinearNetworkConfidence.coef_[:, None], - np.array([self.LinearNetworkConfidence.intercept_]), - ] - ) - - # test to check that the NN and the scikit learn give same result. - predFromNN = self.predAbsoluteLinearErrorLayer(tf.convert_to_tensor(X_test[:, :])) - predfromTrain2 = clf.predict(X_test) - fig, ax = plt.subplots() - ax.scatter(predFromNN, predfromTrain2) - fig.show() - - fig, ax = plt.subplots(2, 1) - ax[0].scatter(predfromTrain2, Y_test, alpha=0.5, s=1, c="black") - ax[1].hist(predfromTrain2, bins=50, color="blue", alpha=0.5, density=True) - # ax[1].hist(Y_test, bins=100, color="orange", alpha=0.5,density=True) - ax[0].set_xlabel( - "prediction of linear error, \n regularized linear prediction \n from absolute error to median" - ) - ax[0].set_ylabel("true linear error") - fig.tight_layout() - fig.show() - - wakeConfidence = clf.predict(AbsErrorToMedian) - fig, ax = plt.subplots() - ax.hist(wakeConfidence, bins=100) - ax.set_title( - "wake set predicted confidence \n (== infered absolute linear error from absolute error to median)" - ) - fig.show() - - fig, ax = plt.subplots() - ax.hist(medianLinearPos, bins=50) - ax.set_title("wake set predicted linear pos") - fig.show() - - import subprocess - - import tables - - if not os.path.exists(os.path.join(self.projectPath.folder, "nnSWR.mat")): - subprocess.run(["./getRipple.sh", self.projectPath.folder]) - with tables.open_file(self.projectPath.folder + "nnSWR.mat", "a") as f: - ripples = f.root.ripple[:, :].transpose() - - predConfidence = clf.predict(AbsErrorToMedian) - cm = plt.get_cmap("turbo") - fig, ax = plt.subplots() - # ax.plot(timePreds,medianLinearPos,c="red",alpha=0.3) - ax.plot(timePreds, trueLinearPos, c="grey", alpha=0.3) - ax.scatter( - timePreds, - medianLinearPos, - s=1, - c=cm(predConfidence / np.max(predConfidence)), - ) - ax.vlines( - ripples[ripples[:, 2] <= np.max(timePreds), 2], - ymin=0, - ymax=1, - color="grey", - linewidths=1, - ) - fig.show() - - ## - # Step 2: after having trained a mapping from error histogram to linear decoding error on active wake - # we study its effect over the full wake - ## - - # no speed masking this time - dataset = tf.data.TFRecordDataset(self.projectPath.tfrec) - dataset = dataset.map( - lambda *vals: nnUtils.parseSerializedSpike(self.feat_desc, *vals), - num_parallel_calls=tf.data.AUTOTUNE, - ) - if useTrain: - epochMask = inEpochsMask( - behavior_data["Position_time"][:, 0], - behavior_data["Times"]["trainEpochs"], - ) - else: - epochMask = inEpochsMask( - behavior_data["Position_time"][:, 0], - behavior_data["Times"]["testEpochs"], - ) - tot_mask = epochMask - table = tf.lookup.StaticHashTable( - tf.lookup.KeyValueTensorInitializer( - tf.constant(np.arange(len(tot_mask)), dtype=tf.int64), - tf.constant(tot_mask, dtype=tf.float64), - ), - default_value=0, - ) - dataset = dataset.filter(lambda x: tf.equal(table.lookup(x["pos_index"]), 1.0)) - if onTheFlyCorrection: - maxPos = np.max( - behavior_data["Positions"][ - np.logical_not(np.isnan(np.sum(behavior_data["Positions"], axis=1))) - ] - ) - dataset = dataset.map( - nnUtils.onthefly_feature_correction(behavior_data["Positions"] / maxPos) - ) - dataset = dataset.filter( - lambda x: tf.math.logical_not(tf.math.is_nan(tf.math.reduce_sum(x["pos"]))) - ) - dataset = dataset.batch(self.params.batch_size, drop_remainder=True) - # drop_remainder allows us to remove the last batch if it does not contain enough elements to form a batch. - dataset = dataset.map( - lambda *vals: nnUtils.parseSerializedSequence( - self.params, *vals, batched=True - ), - num_parallel_calls=tf.data.AUTOTUNE, - ) - dataset = dataset.map(self.createIndices, num_parallel_calls=tf.data.AUTOTUNE) - dataset = dataset.map( - lambda vals: ( - vals, - { - "tf_op_layer_lossOfManifold": tf.zeros(self.params.batch_size), - "tf_op_layer_lossOfLossPredictor": tf.zeros(self.params.batch_size), - }, - ), - num_parallel_calls=tf.data.AUTOTUNE, - ) - output_test = self.uncertainty_estimate_model.predict(dataset, verbose=1) - euclidData = np.reshape(output_test[0], [np.prod(output_test[0].shape[0:3]), 2]) - projectedPos, linearPos = linearizationFunction(euclidData.astype(np.float64)) - - linearPos = np.reshape(linearPos, output_test[0].shape[0:3]) - medianLinearPos = np.median(linearPos, axis=1) - medianLinearPos = np.reshape( - medianLinearPos, [np.prod(medianLinearPos.shape[0:2])] - ) - - d0 = list(dataset.map(lambda vals, valsout: vals["pos"]).as_numpy_iterator()) - truePosFed = np.array(d0) - truePosFed = truePosFed.reshape([truePosFed.shape[0] * truePosFed.shape[1], 2]) - trueProjPos, trueLinearPos = linearizationFunction(truePosFed) - - linearTranspose = np.transpose(linearPos, axes=[0, 2, 1]) - linearTranspose = linearTranspose.reshape( - [ - linearTranspose.shape[0] * linearTranspose.shape[1], - linearTranspose.shape[2], - ] - ) - histPosPred = np.stack( - [ - np.histogram( - np.abs(linearTranspose[id, :] - np.median(linearTranspose[id, :])), - bins=np.arange(0, stop=1, step=0.05), - )[0] - for id in range(linearTranspose.shape[0]) - ] - ) - - # we first sort by error: - sortPerm = np.argsort(np.abs(medianLinearPos - trueLinearPos)) - reorderedHist = histPosPred[sortPerm] - fig, ax = plt.subplots() - ax.matshow(reorderedHist) - ax.set_aspect(reorderedHist.shape[1] / reorderedHist.shape[0]) - ax.set_xlabel("histogram of absolute distance to median") - axy = ax.twiny() - axy.plot( - np.abs(medianLinearPos - trueLinearPos)[sortPerm], - range(sortPerm.shape[0]), - c="red", - alpha=0.5, - ) - axy.set_xlabel("absolute decoding linear error") - ax.set_ylabel("time step - \n reordered by decoding error") - # ax[1].set_aspect(reorderedHist.shape[1]/(np.abs(output_test[0]-trueLinearPos).max())) - fig.show() - - AbsErrorToMedian = np.abs( - linearTranspose - np.median(linearTranspose, axis=1)[:, None] - ) - linearEnsembleError = np.abs(medianLinearPos - trueLinearPos) - - predConfidence = clf.predict(AbsErrorToMedian) - - dtime = dataset.map(lambda vals, valsout: vals["time"]) - timePred = list(dtime.as_numpy_iterator()) - timePreds = np.ravel(timePred) - - cm = plt.get_cmap("turbo") - fig, ax = plt.subplots() - # ax.plot(timePreds,medianLinearPos,c="red",alpha=0.3) - ax.plot(timePreds, trueLinearPos, c="grey", alpha=0.3) - ax.scatter( - timePreds, - medianLinearPos, - s=1, - c=cm(predConfidence / np.max(predConfidence)), - ) - ax.vlines( - ripples[ripples[:, 2] <= np.max(timePreds), 2], - ymin=0, - ymax=1, - color="grey", - linewidths=1, - ) - fig.show() - - fig, ax = plt.subplots() - ax.scatter(predConfidence, linearEnsembleError, s=1) - ax.set_xlabel("predicted confidence") - ax.set_ylabel("absolute linear error") - fig.show() - - import sklearn.decomposition - - pcaDecomp = sklearn.decomposition.PCA() - pcaDecomp.fit(reorderedHist) - svalues = pcaDecomp.singular_values_ - explainedVariances = pcaDecomp.explained_variance_ratio_ - transformedFeature = pcaDecomp.transform(reorderedHist) - fig, ax = plt.subplots() - ax.plot(svalues, c="red") - ax.twinx().plot(explainedVariances) - fig.show() - - fig, ax = plt.subplots() - ax.imshow(transformedFeature[:, 0:6]) - ax.set_aspect(6 / transformedFeature.shape[0]) - fig.show() - - fig, ax = plt.subplots() - ax.scatter( - transformedFeature[:, 0], - transformedFeature[:, 1], - s=1, - c=cm(linearEnsembleError / np.max(linearEnsembleError)), - ) - fig.show() - permLinearEnsembleError = linearEnsembleError[sortPerm] - - # let us save the histrogramm as well as the linearEnsembleError and predConfidence in csv files so that - # we can analyze them in Julia - if not os.path.exists( - os.path.join(self.projectPath.resultsPath, "unsupervisedConfidence") - ): - os.makedirs( - os.path.join(self.projectPath.resultsPath, "unsupervisedConfidence") - ) - df = pd.DataFrame(histPosPred) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "unsupervisedConfidence", - "histDistToMed.csv", - ) - ) - df = pd.DataFrame(linearEnsembleError) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "unsupervisedConfidence", - "linearEnsembleError.csv", - ) - ) - df = pd.DataFrame(predConfidence) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "unsupervisedConfidence", - "predConfidence.csv", - ) - ) - - df = pd.DataFrame(linearTranspose) - df.to_csv( - os.path.join( - self.projectPath.resultsPath, - "unsupervisedConfidence", - "lineaPredictions.csv", - ) - ) - - # let us sort the linear transpose by their median: - sortPerm = np.argsort(np.median(linearTranspose, axis=1)) - histLinearPosPred = np.stack( - [ - np.histogram( - linearTranspose[id, :], bins=np.arange(0, stop=1, step=0.05) - )[0] - for id in range(linearTranspose.shape[0]) - ] - ) - reorderedHist = histLinearPosPred[sortPerm] - fig, ax = plt.subplots() - ax.matshow(reorderedHist) - ax.set_aspect(reorderedHist.shape[1] / reorderedHist.shape[0]) - axy = ax - axy.plot( - medianLinearPos[sortPerm] * reorderedHist.shape[1], - range(medianLinearPos.shape[0]), - c="orange", - label="decoded position", - ) - axy.scatter( - trueLinearPos[sortPerm] * reorderedHist.shape[1], - range(medianLinearPos.shape[0]), - c="red", - label="true position", - alpha=0.6, - s=1, - ) - # axy.set_xlabel("decoded position",color="orange") - ax.set_xlabel("linear bin") - ax.set_ylabel("time step id (sorted by predicted position)") - fig.legend() - fig.show() - - histDiffPosPred = np.stack( - [ - np.histogram( - (linearTranspose[id, :] - np.median(linearTranspose[id, :])), - bins=np.arange(-1, stop=1, step=0.05), - )[0] - for id in range(linearTranspose.shape[0]) - ] - ) - reorderedHist = histDiffPosPred[sortPerm] - fig, ax = plt.subplots() - ax.matshow(reorderedHist) - ax.set_aspect(reorderedHist.shape[1] / reorderedHist.shape[0]) - axy = ax - axy.plot( - medianLinearPos[sortPerm] * reorderedHist.shape[1], - range(medianLinearPos.shape[0]), - c="orange", - label="decoded position", - ) - # axy.set_xlabel("decoded position",color="orange") - ax.set_xlabel("linear bin") - ax.set_ylabel("time step id (sorted by predicted position)") - fig.legend() - fig.show() - - # filtering by error > 0.2: - # reorganising by - - from sklearn.manifold import Isomap - - embedding = Isomap(n_components=2) - X_transformed = embedding.fit_transform(histPosPred[0:20000, :]) - fig, ax = plt.subplots(2, 1) - ax[0].scatter( - X_transformed[:, 0], - X_transformed[:, 1], - s=1, - c=cm(linearEnsembleError[0:20000] / np.max(linearEnsembleError)), - ) - ax[1].scatter( - X_transformed[:, 0], - X_transformed[:, 1], - s=1, - c=cm(predConfidence[0:20000] / np.max(predConfidence)), - ) - fig.show() - - def sleep_uncertainty_estimate(self, output_test, linearizationFunction): - # output_test: euclid_data,lossPred (not used anymore),time steps - euclidData = np.reshape(output_test[0], [np.prod(output_test[0].shape[0:3]), 2]) - projectedPos, linearPos = linearizationFunction(euclidData.astype(np.float64)) - linearPos = np.reshape(linearPos, output_test[0].shape[0:3]) - medianLinearPos = np.median(linearPos, axis=1) - - # next we estimate the error made, by using the Linear projection of the distance to the median: - linearTranspose = np.transpose(linearPos, axes=[0, 2, 1]) - linearTranspose = linearTranspose.reshape( - [ - linearTranspose.shape[0] * linearTranspose.shape[1], - linearTranspose.shape[2], - ] - ) - AbsErrorToMedian = np.abs( - linearTranspose - np.median(linearTranspose, axis=1)[:, None] - ) - predictedConfidence = self.LinearNetworkConfidence.predict(AbsErrorToMedian) - - return medianLinearPos, predictedConfidence - - def study_sleep_uncertainty_estimate(self, output_test, linearizationFunction): - # output_test: euclid_data,lossPred (not used anymore),time steps - # euclidData = np.reshape(output_test[0],[np.prod(output_test[0].shape[0:3]),2]) - # projectedPos,linearPos = linearizationFunction(euclidData.astype(np.float64)) - # linearPos = np.reshape(linearPos,output_test[0].shape[0:3]) - # medianLinearPos = np.median(linearPos,axis=1) - medianLinearPos, predictedConfidence, timePreds = output_test - - fig, ax = plt.subplots() - [ - ax.scatter( - output_test[-1][1:1000:1], - np.ravel(linearPos[:, id, :])[1:1000:1], - c="orange", - s=1, - alpha=0.2, - ) - for id in range(linearPos.shape[1]) - ] - ax.plot(output_test[-1][1:1000:1], np.ravel(medianLinearPos)[1:1000:1], c="red") - ax.set_xlabel("time") - ax.set_ylabel("decoded linear position") - ax.set_title("beginning of sleep") - fig.show() - - # # next we estimate the error made, by using the Linear projection of the distance to the median: - # linearTranspose = np.transpose(linearPos,axes=[0,2,1]) - # linearTranspose = linearTranspose.reshape([linearTranspose.shape[0]*linearTranspose.shape[1],linearTranspose.shape[2]]) - # AbsErrorToMedian = np.abs(linearTranspose - np.median(linearTranspose,axis=1)[:,None]) - # predictedConfidence = self.LinearNetworkConfidence.predict(AbsErrorToMedian) - - cm = plt.get_cmap("turbo") - fig, ax = plt.subplots() - ax.plot( - output_test[-1][1:1000:1], - np.ravel(medianLinearPos)[1:1000:1], - c="grey", - alpha=0.3, - ) - ax.scatter( - output_test[-1][1:1000:1], - np.ravel(medianLinearPos)[1:1000:1], - c=cm(predictedConfidence[1:1000:1] / np.max(predictedConfidence)), - s=3, - ) - plt.colorbar( - plt.cm.ScalarMappable( - plt.Normalize(0, np.max(predictedConfidence)), cmap=cm - ), - label="predicted confidence", - ) - ax.set_xlabel("time") - ax.set_ylabel("decoded linear position") - fig.show() - - cm = plt.get_cmap("turbo") - fig, ax = plt.subplots() - ax.plot( - output_test[-1][predictedConfidence < 0.1], - np.ravel(medianLinearPos)[predictedConfidence < 0.1], - c="grey", - alpha=0.3, - ) - ax.scatter( - output_test[-1][predictedConfidence < 0.1], - np.ravel(medianLinearPos)[predictedConfidence < 0.1], - c=cm( - predictedConfidence[predictedConfidence < 0.1] - / np.max(predictedConfidence) - ), - s=3, - ) - plt.colorbar( - plt.cm.ScalarMappable( - plt.Normalize(0, np.max(predictedConfidence)), cmap=cm - ), - label="predicted confidence", - ) - ax.set_xlabel("time") - ax.set_ylabel("decoded linear position") - fig.show() - - # let us look at the confidence distributions: - fig, ax = plt.subplots() - ax.hist(predictedConfidence, bins=100) - ax.set_title("confidence in sleep") - ax.set_xlabel("confidence") - ax.set_ylabel("histogram") - fig.show() - # todo: compare sleep and wake confidences - - # let us look at the distribution of linear position jump - posjump = np.ravel(medianLinearPos)[1:] - np.ravel(medianLinearPos)[:-1] - fig, ax = plt.subplots() - ax.hist(np.ravel(medianLinearPos), bins=50, color="red") - ax.set_xlabel("linear position") - fig.show() - fig, ax = plt.subplots() - ax.hist(posjump, bins=1000, color="red", alpha=0.5) - ax.set_yscale("log") - fig.show() - fig, ax = plt.subplots() - ax.hist(np.abs(posjump), bins=100, color="red", alpha=0.5) - ax.set_yscale("log") - fig.show() - - fig, ax = plt.subplots() - ax.scatter( - output_test[-1][:-1][predictedConfidence[:-1] < 0.08], - posjump[predictedConfidence[:-1] < 0.08], - s=1, - alpha=0.4, - ) - fig.show() - - # let us compute the transition probability matrix from one position to another: - medianLinearPos = np.ravel(medianLinearPos) - _, binMed = np.histogram(medianLinearPos, bins=10) - filterForSize = lambda x: x[x < (len(medianLinearPos) - 1)] - findHist = lambda x, j: np.sum( - (medianLinearPos[x + 1] >= binMed[j]) - * (medianLinearPos[x + 1] < binMed[j + 1]) - ) - transMat = [ - [ - findHist( - filterForSize( - np.where( - (medianLinearPos >= binMed[i]) - * (medianLinearPos < binMed[i + 1]) - )[0] - ), - j, - ) - for j in range(len(binMed) - 1) - ] - for i in range(len(binMed) - 1) - ] - transMat = np.array(transMat) - fig, ax = plt.subplots() - ax.matshow(transMat) - for i in range(len(binMed) - 1): - for j in range(len(binMed) - 1): - text = ax.text( - j, i, transMat[i, j], ha="center", va="center", color="w" - ) - ax.set_xticks(range(len(binMed[:-1]))) - ax.set_yticks(range(len(binMed[:-1]))) - ax.set_xticklabels(np.round(binMed[:-1], 2)) - ax.set_yticklabels(np.round(binMed[:-1], 2)) - fig.show() - - # Looking at data: is seem that a series of small jump is followed by a large jump. - # let us therefore look at the jump transition matrix: - absPosJump = np.abs(posjump) - histAbsPosJump, binJump = np.histogram(absPosJump, bins=100) - filterForSize = lambda x: x[x < (len(absPosJump) - 1)] - findHist = lambda x, j: np.sum( - (absPosJump[x + 1] >= binJump[j]) * (absPosJump[x + 1] < binJump[j + 1]) - ) - transMatJump = [ - [ - findHist( - filterForSize( - np.where( - (absPosJump >= binJump[i]) * (absPosJump < binJump[i + 1]) - )[0] - ), - j, - ) - for j in range(len(binJump) - 1) - ] - for i in range(len(binJump) - 1) - ] - transMatJump = np.array(transMatJump) - # so row corresponds to the state at t - # so columns corresponds to the state at t+1 - # fig,ax = plt.subplots() - # ax.scatter(output_test[-1][:-1],np.abs(posjump),s=1) - # fig.show() - - fig, ax = plt.subplots() - ax.imshow( - transMatJump / histAbsPosJump[:, None] - ) # effectively normalize each row! - ax.set_ylabel("jump at t") - ax.set_xlabel("jump at t+1") - fig.show() - # --> jumps are most of the time followed by small jumps.... - - # we separate large and small jumps arbitrarily: - pospeed = posjump / (output_test[-1][1:] - output_test[-1][:-1]) - fig, ax = plt.subplots(2, 1) - ax[0].scatter(output_test[-1][:-1], np.abs(pospeed), s=1, alpha=0.1) - ax[1].hist(np.log(pospeed[pospeed != 0]), bins=np.arange(-10, 10, step=0.1)) - fig.show() - - # Continuity driven by predicted confidence: - fig, ax = plt.subplots() - ax.scatter(posjump, predictedConfidence[:-1], s=0.1, alpha=0.3) - ax.set_xlabel("position jump between two time step") - ax.set_ylabel("predicted confidence") - fig.show() - _, bins = np.histogram(predictedConfidence, bins=100) - posjump_knowing_confidence = [ - np.abs( - posjump[ - (predictedConfidence[:-1] >= bins[i]) - * (predictedConfidence[:-1] < bins[i + 1]) - ] - ) - for i in range(len(bins) - 1) - ] - mposjump_knowing_confidence = [np.mean(p) for p in posjump_knowing_confidence] - stdposjump_knowing_confidence = [np.std(p) for p in posjump_knowing_confidence] - fig, ax = plt.subplots() - ax.plot(bins[:-1], mposjump_knowing_confidence) - ax.fill_between( - bins[:-1], - mposjump_knowing_confidence, - np.array(mposjump_knowing_confidence) - + np.array(stdposjump_knowing_confidence), - ) - ax.set_xlabel("predicted confidence") - ax.set_ylabel("mean absolute jump") - fig.show() - - print("ended sleep uncertainty estimate") - - def study_uncertainty_estimate( - self, - linearizationFunction, - batch=False, - forceFirstTrainingWeight=False, - useSpeedFilter=False, - useTrain=False, - onTheFlyCorrection=True, - ): - behavior_data = getBehavior(self.projectPath.folder, getfilterSpeed=True) - if ( - len(behavior_data["Times"]["lossPredSetEpochs"]) > 0 - and not forceFirstTrainingWeight - ): - self.model.load_weights( - os.path.join(self.projectPath.resultsPath, "training_2/cp.ckpt") - ) - else: - self.model.load_weights( - os.path.join(self.projectPath.resultsPath, "training_1/cp.ckpt") - ) - - # Build the online decoding model with the layer already initialized: - self.uncertainty_estimate_model = self.get_model_for_uncertainty_estimate( - batch=batch - ) - - speed_mask = behavior_data["Times"]["speedFilter"] - if not useSpeedFilter: - speed_mask = np.zeros_like(speed_mask) + 1 - dataset = tf.data.TFRecordDataset(self.projectPath.tfrec) - dataset = dataset.map( - lambda *vals: nnUtils.parseSerializedSpike(self.feat_desc, *vals), - num_parallel_calls=tf.data.AUTOTUNE, - ) - if useTrain: - epochMask = inEpochsMask( - behavior_data["Position_time"][:, 0], - behavior_data["Times"]["trainEpochs"], - ) - else: - epochMask = inEpochsMask( - behavior_data["Position_time"][:, 0], - behavior_data["Times"]["testEpochs"], - ) - tot_mask = speed_mask * epochMask - table = tf.lookup.StaticHashTable( - tf.lookup.KeyValueTensorInitializer( - tf.constant(np.arange(len(tot_mask)), dtype=tf.int64), - tf.constant(tot_mask, dtype=tf.float64), - ), - default_value=0, - ) - dataset = dataset.filter(lambda x: tf.equal(table.lookup(x["pos_index"]), 1.0)) - - if onTheFlyCorrection: - maxPos = np.max( - behavior_data["Positions"][ - np.logical_not(np.isnan(np.sum(behavior_data["Positions"], axis=1))) - ] - ) - dataset = dataset.map( - nnUtils.onthefly_feature_correction(behavior_data["Positions"] / maxPos) - ) - dataset = dataset.filter( - lambda x: tf.math.logical_not(tf.math.is_nan(tf.math.reduce_sum(x["pos"]))) - ) - - dataset = dataset.batch(self.params.batch_size, drop_remainder=True) - # drop_remainder allows us to remove the last batch if it does not contain enough elements to form a batch. - dataset = dataset.map( - lambda *vals: nnUtils.parseSerializedSequence( - self.params, *vals, batched=True - ), - num_parallel_calls=tf.data.AUTOTUNE, - ) - dataset = dataset.map(self.createIndices, num_parallel_calls=tf.data.AUTOTUNE) - dataset = dataset.map( - lambda vals: ( - vals, - { - "tf_op_layer_lossOfManifold": tf.zeros(self.params.batch_size), - "tf_op_layer_lossOfLossPredictor": tf.zeros(self.params.batch_size), - }, - ), - num_parallel_calls=tf.data.AUTOTUNE, - ) - - output_test = self.uncertainty_estimate_model.predict(dataset, verbose=1) - euclidData = np.reshape(output_test[0], [np.prod(output_test[0].shape[0:3]), 2]) - projectedPos, linearPos = linearizationFunction(euclidData.astype(np.float64)) - projectedPos = np.reshape(projectedPos, output_test[0].shape) - - # we can also compute the distance to the projected Pos: - predPos = np.reshape(euclidData, projectedPos.shape) - vecToProjPos = predPos - projectedPos - distToProjPos = np.sqrt(np.sum(np.square(vecToProjPos), axis=-1)) - middlePoint = np.array([0.5, 0.5]) - # the second variable sign can be obtained by the sign of the projection on the vector to this middle point from the linearized point - # of the pred to linear vector - signOfProj = np.sign( - np.sum((predPos - middlePoint[None, None, None, :]) * vecToProjPos, axis=-1) - ) - signDistToProjPos = distToProjPos * signOfProj - - linearPos = np.reshape(linearPos, output_test[0].shape[0:3]) - from scipy.stats import iqr - - output_test = [ - np.median(linearPos, axis=1), - np.std(linearPos, axis=1), - np.mean(linearPos, axis=1), - iqr(linearPos, axis=1), - ] - output_test = [np.reshape(o, [np.prod(o.shape[0:2])]) for o in output_test] - output_test_no_dropout = self.model.predict(dataset, verbose=1) - - print(len(output_test)) - speed_data = behavior_data["Speed"][np.where(tot_mask)] - - d1 = list( - dataset.map(lambda vals, valsout: vals["pos_index"]).as_numpy_iterator() - ) - dres = np.ravel(np.array(d1)) - speeds = behavior_data["Speed"][dres] - truePos = behavior_data["Positions"][dres] - - d0 = list(dataset.map(lambda vals, valsout: vals["pos"]).as_numpy_iterator()) - truePosFed = np.array(d0) - truePosFed = truePosFed.reshape([190 * 52, 2]) - trueProjPos, trueLinearPos = linearizationFunction(truePosFed) - # compute - trueVecToProjPos = truePosFed - trueProjPos - trueDistToProjPos = np.sqrt(np.sum(np.square(trueVecToProjPos), axis=-1)) - trueSignOfProj = np.sign( - np.sum((truePosFed - middlePoint[None, :]) * trueVecToProjPos, axis=-1) - ) - trueSignDistToProjPos = trueDistToProjPos * trueSignOfProj - - times = behavior_data["Position_time"][dres] - fig, ax = plt.subplots(2, 1, figsize=(5, 10)) - [ - ax[0].scatter( - times, np.ravel(signDistToProjPos[:, id, :]), c="orange", s=1, alpha=0.2 - ) - for id in range(signDistToProjPos.shape[1]) - ] - ax[0].scatter( - times, np.ravel(np.median(signDistToProjPos, axis=1)), c="red", s=1 - ) - ax[0].scatter(times, trueSignDistToProjPos, s=1, c="black") - ax[0].set_xlabel("time") - ax[0].set_ylabel("signed distance to linearization line") - ax[1].scatter( - trueSignDistToProjPos, - np.ravel(np.median(signDistToProjPos, axis=1)), - c="black", - s=1, - ) - ax[1].set_xlabel("true signed distance \n to linearizartion line") - ax[1].set_ylabel("predicted signed distance \n to linearizartion line") - fig.show() - - g2 = euclidData.reshape( - [linearPos.shape[0], linearPos.shape[1], linearPos.shape[2], 2] - ) - g2med = np.reshape(np.median(g2, axis=1), [g2.shape[0] * g2.shape[2], 2]) - fig, ax = plt.subplots(2, 1, sharex=True) - ax[0].plot(times, g2med[:, 0], c="red", label="decoded by median of ensemble") - ax[0].plot(times, truePosFed[:, 0], c="black", label="true pos") - ax[1].plot(times, g2med[:, 1], c="red") - ax[1].plot(times, truePosFed[:, 1], c="black") - ax[0].set_ylabel("X") - ax[1].set_ylabel("Y") - ax[1].set_xlabel("time") - fig.legend() - fig.show() - g2res = g2.reshape([190 * 52, 100, 2]) - fig, ax = plt.subplots() - ax.scatter(truePosFed[:, 0], truePosFed[:, 1], c="black", s=1, alpha=0.2) - ax.scatter(g2med[:, 0], g2med[:, 1], c="red", s=1) - [ - ax.scatter(g2res[:, id, 0], g2res[:, id, 1], c="orange", s=1, alpha=0.01) - for id in range(100) - ] - ax.set_xlabel("X") - ax.set_ylabel("Y") - fig.show() - - # We could do a Maximum Likelihood estimate on the predicted variable: - from SimpleBayes import butils - - bw = 0.2 - edges, _ = butils.kdenD(truePosFed, bw, nbins=[20, 20]) - - def get_mle_estimate(X): - _, p = butils.kdenD(X, bw, nbins=[20, 20], edges=edges) - xedge = edges[0][:, 0] - yedge = edges[-1][0, :] - posMLE = np.unravel_index(np.argmax(p), p.shape) - return [xedge[posMLE[0]], yedge[posMLE[1]]] - - mleDecodedPos = np.array( - [get_mle_estimate(g2res[id, :, :]) for id in range(g2res.shape[0])] - ) - fig, ax = plt.subplots() - ax.plot(times, mleDecodedPos[:, 0], c="red") - ax.plot(times, truePosFed[:, 0], c="black") - # ax.scatter(truePosFed[:,1],mleDecodedPos[:,1],s=1,alpha=0.2,c="black") - fig.show() - # No good results. - - # fig,ax = plt.subplots() - # ax.plot(times,output_test[0][:,0],c="red",label="prediction X") - # ax.plot(times,truePos[:, 0],c="black",label="true X") - # ax.fill_between(times[:,0],output_test[0][:,0]+output_test[1][:,0],output_test[0][:,0]-output_test[1][:,0],color="orange",label="confidence") - # ax.set_xlabel("time") - # ax.set_ylabel("X") - # fig.legend() - # fig.show() - fig, ax = plt.subplots() - ax.plot(times, output_test[0], c="red", label="median prediction linear") - # ax[0].plot(times, output_test[2], c="violet", label="mean prediction X") - ax.plot(times, trueLinearPos, c="black", label="true linear") - # ax[0].plot(times, output_test[3], c="green", label="iqr prediction X",alpha=0.5) - fig.legend() - ax.set_xlabel("time") - ax.set_ylabel("linear position") - fig.show() - - fig, ax = plt.subplots() - ax.plot(times, output_test[0], c="red", label="median prediction X") - ax.plot(times, output_test[2], c="violet", label="mean prediction X") - ax.plot(times, trueLinearPos, c="black", label="true X") - # ax.fill_between(times[:,0],output_test[0]+output_test[1],output_test[0]-output_test[1],color="orange",label="confidence") - for i in range(100): - ax.scatter( - times[:, 0], - np.reshape( - linearPos[:, i, :], [linearPos.shape[0] * linearPos.shape[2]] - ), - s=1, - alpha=0.05, - c="orange", - ) - ax.set_xlabel("time") - ax.set_ylabel("X") - fig.legend() - fig.show() - - # Question: given the distribution of predicted position - # Are there some particular pattern? - # to see that: we can look at the distributions distance to the median. - - linearTranspose = np.transpose(linearPos, axes=[0, 2, 1]) - linearTranspose = linearTranspose.reshape( - [ - linearTranspose.shape[0] * linearTranspose.shape[1], - linearTranspose.shape[2], - ] - ) - histPosPred = np.stack( - [ - np.histogram( - np.abs(linearTranspose[id, :] - np.median(linearTranspose[id, :])), - bins=np.arange(0, stop=1, step=0.01), - )[0] - for id in range(linearTranspose.shape[0]) - ] - ) - fig, ax = plt.subplots() - ax.matshow(np.transpose(histPosPred)) - ax.set_aspect(9880 / 99) - fig.show() - - fig, ax = plt.subplots() - cm = plt.get_cmap("turbo") - colors = cm( - np.abs(output_test[0] - trueLinearPos) - / np.max(np.abs(output_test[0] - trueLinearPos)) - ) - for i in range(histPosPred.shape[0]): - ax.plot(histPosPred[i], alpha=0.4, c=colors[i]) - fig.show() - - # we first sort by error: - sortPerm = np.argsort(np.abs(output_test[0] - trueLinearPos)) - reorderedHist = histPosPred[sortPerm] - - fig, ax = plt.subplots() - ax.imshow(reorderedHist) - ax.set_aspect(reorderedHist.shape[1] / reorderedHist.shape[0]) - ax.set_xlabel("histogram of absolute distance to median") - axy = ax.twiny() - axy.plot( - np.abs(output_test[0] - trueLinearPos)[sortPerm], - range(sortPerm.shape[0]), - c="red", - alpha=0.5, - ) - axy.set_xlabel("absolute decoding linear error") - ax.set_ylabel("time step - \n reordered by decoding error") - # ax[1].set_aspect(reorderedHist.shape[1]/(np.abs(output_test[0]-trueLinearPos).max())) - fig.show() - - AbsErrorToMedian = np.abs( - linearTranspose - np.median(linearTranspose, axis=1)[:, None] - ) - meanAbsErrorToMedian = np.mean(AbsErrorToMedian, axis=1) - fig, ax = plt.subplots() - ax.scatter( - meanAbsErrorToMedian, np.abs(output_test[0] - trueLinearPos)[sortPerm] - ) - fig.show() - - linearEnsembleError = np.abs(output_test[0] - trueLinearPos) - from sklearn.linear_model import Ridge - from sklearn.model_selection import train_test_split - - clf = Ridge(alpha=1000) - X_train, X_test, Y_train, Y_test = train_test_split( - AbsErrorToMedian, linearEnsembleError, train_size=0.3 - ) - clf.fit(X_train, Y_train) - self.LinearNetworkConfidence = clf - predfromTrain2 = clf.predict(X_test) - fig, ax = plt.subplots(2, 1) - ax[0].scatter(predfromTrain2, Y_test, alpha=0.5, s=1, c="black") - ax[1].hist(predfromTrain2, bins=50, color="blue", alpha=0.5, density=True) - # ax[1].hist(Y_test, bins=100, color="orange", alpha=0.5,density=True) - ax[0].set_xlabel( - "prediction of linear error, \n regularized linear prediction \n from absolute error to median" - ) - ax[0].set_ylabel("true linear error") - fig.tight_layout() - fig.show() - - fig, ax = plt.subplots() - predError = clf.predict(AbsErrorToMedian) - ax.plot(times, output_test[0], c="grey", alpha=0.2) - ax.scatter(times, output_test[0], c=cm(predError / np.max(predError)), s=1) - plt.colorbar( - plt.cm.ScalarMappable(plt.Normalize(0, np.max(predError)), cmap=cm), - label="predicted error", - ) - ax.plot(times, trueLinearPos, c="black") - ax.set_xlabel("time") - ax.set_ylabel("linear position") - fig.show() - - # let us filter by pred Error - fig, ax = plt.subplots() - ax.plot( - times[np.where(predError < 0.08)], output_test[0][predError < 0.08], c="red" - ) - ax.plot( - times[np.where(predError < 0.08)], - trueLinearPos[predError < 0.08], - c="black", - ) - fig.show() - - import matplotlib.patches as patches - - fig, ax = plt.subplots() - for i in range(output_test[0].shape[0]): - circle = patches.Circle( - tuple(output_test[0][i, :]), - radius=np.mean(output_test[1][i]) / 2, - edgecolor="orange", - fill=False, - alpha=0.1, - zorder=0, - ) - ax.add_patch(circle) - ax.scatter(output_test[0][:, 0], output_test[0][:, 1], s=2, c="red") - ax.scatter(truePos[:, 0], truePos[:, 1], s=2, c="black", alpha=0.5) - ax.set_xlabel("X") - ax.set_ylabel("Y") - ax.set_aspect(1) - fig.show() - - speeds = behavior_data["Speed"][dres] - window_len = 10 - s = np.r_[ - speeds[window_len - 1 : 0 : -1], speeds, speeds[-2 : -window_len - 1 : -1] - ] - w = eval("np." + "hamming" + "(window_len)") - speeds = np.convolve(w / w.sum(), s[:, 0], mode="valid")[ - (window_len // 2 - 1) : -(window_len // 2) - ] - fig, ax = plt.subplots() - ax.scatter(speeds, output_test[1], s=2, alpha=0.5) - ax.set_xlabel("speed") - ax.set_ylabel("100 droupout pass variance") - fig.show() - - cm = plt.get_cmap("turbo") - fig, ax = plt.subplots(2, 1) - ax[0].scatter(times, trueLinearPos, c=cm(speeds / np.max(speeds)), s=1) - ax[1].scatter(times, speeds, s=1) - ax[0].plot(times, output_test[3], c="orange") - fig.show() - - fig, ax = plt.subplots() - ax.scatter(speeds, np.mean(output_test[1], axis=1), s=2, alpha=0.5) - ax.set_xlabel("speed") - ax.set_ylabel("100 droupout pass variance") - fig.show() - - fig, ax = plt.subplots() - ax.scatter(np.abs(output_test[0] - trueLinearPos), speeds, s=2, alpha=0.5) - ax.set_xlabel("prediction error of linear variable") - ax.set_ylabel("speeds") - fig.show() - - fig, ax = plt.subplots() - ax.scatter( - output_test[3], - output_test[1], - s=1, - c=cm( - np.abs(output_test[0] - trueLinearPos) - / np.max(np.abs(output_test[0] - trueLinearPos)) - ), - ) - fig.show() - fig, ax = plt.subplots() - ax.scatter( - np.abs(output_test[0] - trueLinearPos), - output_test[3], - s=2, - alpha=0.5, - c="green", - ) - ax.set_xlabel("prediction error") - ax.set_ylabel("100 droupout pass variance") - fig.show() - fig, ax = plt.subplots() - ax.scatter( - np.sqrt(np.sum(np.square(output_test_no_dropout[0] - truePos), axis=1)), - output_test[1], - s=2, - alpha=0.5, - ) - ax.set_xlabel("prediction error") - ax.set_ylabel("100 droupout pass variance") - fig.show() - - predPos = output_test[0] - fig, ax = plt.subplots() - ax.scatter( - np.sqrt(np.sum(np.square(predPos - truePos), axis=1)), - np.mean(output_test[1], axis=1), - s=2, - alpha=0.5, - ) - ax.set_xlabel("prediction error") - ax.set_ylabel("100 droupout pass variance") - fig.show() - - predPos_dropoutfree = output_test_no_dropout[0] - fig, ax = plt.subplots() - ax.scatter( - np.sqrt(np.sum(np.square(predPos - truePos), axis=1)), - np.mean(output_test[1], axis=1), - s=2, - alpha=0.5, - c="red", - label="dropout prediction", - ) - ax.scatter( - np.sqrt(np.sum(np.square(predPos_dropoutfree - truePos), axis=1)), - np.mean(output_test[1], axis=1), - s=2, - alpha=0.5, - c="violet", - label="no dropout prediction", - ) - ax.set_xlabel("prediction error") - ax.set_ylabel("100 droupout pass variance") - fig.legend() - fig.show() diff --git a/neuroencoders/resultAnalysis/paper_figures.py b/neuroencoders/resultAnalysis/paper_figures.py index 320a9ab..4766bba 100755 --- a/neuroencoders/resultAnalysis/paper_figures.py +++ b/neuroencoders/resultAnalysis/paper_figures.py @@ -22,10 +22,12 @@ from neuroencoders.importData.epochs_management import inEpochsMask from neuroencoders.importData.rawdata_parser import get_params from neuroencoders.resultAnalysis.print_results import overview_fig -from neuroencoders.simpleBayes.decode_bayes import Trainer as TrainerBayes from neuroencoders.simpleBayes.decode_bayes import ( - extract_spike_counts, - extract_spike_counts_from_matrix, + Trainer as TrainerBayes, +) +from neuroencoders.simpleBayes.decode_bayes import ( + extract_spike_counts_keops, + extract_spike_counts_matrix_keops, ) from neuroencoders.utils.PlaceField_dB import _run_place_field_analysis from neuroencoders.utils.global_classes import ( @@ -524,13 +526,22 @@ def load_bayes(self, suffixes=None, **kwargs): if kwargs.get("extract_spikes_count", False) or kwargs.get( "extract_spike_counts", False ): - total_count, _ = extract_spike_counts( + if not hasattr( + self.trainerBayes, "spikeMatTimes" + ) or not hasattr(self.trainerBayes, "spikeMat"): + raise ValueError( + """ + trainerBayes does not have spikeMatTimes or spikeMat attributes needed to extract spike counts. + Make sure to run decoding with extract_spike_counts=True first. You can run trainerBayes.train_order_by_pos. + """ + ) + total_count, _ = extract_spike_counts_keops( timesBayes[-1], self.trainerBayes.spikeMatTimes, ws / 1000 ) total_spikes_count.append(total_count) - matrix_count, _ = extract_spike_counts_from_matrix( + matrix_count, _ = extract_spike_counts_matrix_keops( timesBayes[-1], - self.trainerBayes.spikeMat, + self.trainerBayes.spikeMatLabels, self.trainerBayes.spikeMatTimes, ws / 1000, ) @@ -2563,7 +2574,7 @@ def nnVSbayes( raise ValueError('speed argument could be only "full", "fast" or "slow"') # Figure 4: - cols = plt.get_cmap("terrain") + plt.get_cmap("terrain") fig, ax = plt.subplots(1, len(self.timeWindows)) if len(self.timeWindows) == 1: ax = [ax] # compatibility move @@ -2810,12 +2821,12 @@ def fig_example_2d( mazeBorder = np.array( [[0, 0, 1, 1, 0.63, 0.63, 0.35, 0.35, 0], [0, 1, 1, 0, 0, 0.75, 0.75, 0, 0]] ) - ts = [ + [ self.resultsNN_phase[suffix]["time"][iw][mask[iw]] for iw in range(len(self.timeWindows)) ] # Trajectory figure - cm = plt.get_cmap("turbo") + plt.get_cmap("turbo") fig, ax = plt.subplots(1, len(self.timeWindows)) if len(self.timeWindows) == 1: ax = [ax] # compatibility move @@ -3412,9 +3423,7 @@ def fig_proba_heatmap_error( predPos = self.resultsNN_phase[phase]["fullPred"][idWindow][speedMask][ :, :2 ] - target_hw = self.ann[ - str(winMS) - ].GaussianHeatmap.gaussian_heatmap_targets(truePos) + self.ann[str(winMS)].GaussianHeatmap.gaussian_heatmap_targets(truePos) probs = ( self.ann[str(winMS)] .GaussianHeatmap.decode_and_uncertainty( @@ -3609,7 +3618,7 @@ def fig_proba_heatmap_vs_true( extent = (0, 1, 0, 1) ax1 = axs[i, j * (3 if plot_kl else 2)] - im1 = ax1.imshow( + ax1.imshow( mean_probs, origin="lower", extent=extent, @@ -3619,7 +3628,7 @@ def fig_proba_heatmap_vs_true( ax1.set_title(f"{phase[1:]}-{speed}-Proba") # plt.colorbar(im1, ax=ax1) ax2 = axs[i, j * (3 if plot_kl else 2) + 1] - im2 = ax2.imshow( + ax2.imshow( zmap, origin="lower", cmap="coolwarm", @@ -3643,7 +3652,7 @@ def fig_proba_heatmap_vs_true( Q > 0, Q * np.log((Q + 1e-12) / (P + 1e-12)), 0 ) # Compute mean bias vector in each bin - im3 = ax3.imshow( + ax3.imshow( kl_map, cmap="magma", origin="lower", @@ -3705,7 +3714,7 @@ def fig_example_linear_filtered( * np.less_equal(self.resultsNN_phase[suffix]["predLoss"][iw], thresh[iw]) for iw in range(len(self.timeWindows)) ] - filters_bayes = [ + [ np.ones(self.resultsBayes_phase[suffix]["time"][iw].shape).astype(bool) * np.greater_equal( self.resultsBayes_phase[suffix]["predLoss"][iw], threshBayes[iw] @@ -4241,7 +4250,9 @@ def plot_pc_tuning_curve_and_predictions( : len(self.resultsNN_phase[suffix]["linTruePos"][iwindow]), : ] predLoss = self.resultsNN_phase[suffix]["predLoss"][iwindow] - normalize = lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)) + + def normalize(x): + return (x - np.min(x)) / (np.max(x) - np.min(x)) for icell, tuningCurve in enumerate(linearTuningCurves): pcId = np.where(np.equal(placeFieldSort, icell))[0][0] diff --git a/neuroencoders/simpleBayes/butils.py b/neuroencoders/simpleBayes/butils.py index ef48cd6..b4130fb 100755 --- a/neuroencoders/simpleBayes/butils.py +++ b/neuroencoders/simpleBayes/butils.py @@ -88,7 +88,7 @@ def hist_2d(feature, nbins=None): """ A simple 2D histogram estimate """ - if nbins == None: + if nbins is None: nbins = [45 for j in range(feature.shape[1])] # create grid of sample locations (default: 150x150x...x150) lspace = [ diff --git a/neuroencoders/simpleBayes/decode_bayes.py b/neuroencoders/simpleBayes/decode_bayes.py index 6149233..50ea1ee 100755 --- a/neuroencoders/simpleBayes/decode_bayes.py +++ b/neuroencoders/simpleBayes/decode_bayes.py @@ -162,6 +162,24 @@ def __init__( self.ordered_neurons = None self.place_fields = None + def _load_linear_spike_sorting(self): + """Load spike sorting data from project path. Bulk, not tetrode-wise.""" + self.logger.info("Loading linear spike sorting data...") + cluster_data = import_clusters._load_linear_spike_sorting_from_clu( + self.projectPath, flatten=True + ) + self.linear_spike_labels = cluster_data["Spike_labels"] + self.linear_spike_index = cluster_data["Spike_index"] + + def _load_shankwise_spike_sorting(self): + """Load spike sorting data from project path. Bulk, not tetrode-wise.""" + self.logger.info("Loading linear spike sorting data...") + cluster_data = import_clusters._load_linear_spike_sorting_from_clu( + self.projectPath, flatten=False + ) + self.spike_labels = cluster_data["Spike_labels"] + self.spike_index = cluster_data["Spike_index"] + def train( self, behaviorData: Dict, onTheFlyCorrection=False, save=True, **kwargs ) -> Dict: @@ -299,10 +317,9 @@ def train_order_by_pos(self, behaviorData: Dict, l_function, **kwargs) -> Dict: # Get normalization setting from kwargs onTheFlyCorrection = kwargs.get("onTheFlyCorrection", False) - target = kwargs.get("target", self.config.target_bayes) + kwargs.get("target", self.config.target_bayes) - if target == "pos": - behaviorData["Positions"] = behaviorData["Positions"][:, : self.feature_dim] + behaviorData["Positions"] = behaviorData["Positions"][:, : self.feature_dim] if not hasattr(self, "training_data"): # first, save the training data from the behaviorData @@ -319,7 +336,7 @@ def train_order_by_pos(self, behaviorData: Dict, l_function, **kwargs) -> Dict: f"Training data saved with {full_training_true_positions.shape} valid positions." ) - if not hasattr(self, "spikeMatLabels"): + if not hasattr(self, "spikeMatLabels") or not hasattr(self, "spikeMatTimes"): self.logger.info( f"Initializing spike matrices for {len(self.clusterData['Spike_labels'])} tetrodes..." ) @@ -416,6 +433,9 @@ def train_order_by_pos(self, behaviorData: Dict, l_function, **kwargs) -> Dict: save=kwargs.pop("save", True), **kwargs, ) + else: + bayesMatrices = kwargs.get("bayesMatrices") + self.logger.info("Using provided Bayesian matrices for ordering.") # Use linear tuning curves for more accurate ordering self.logger.info("Computing linear tuning curves for ordering...") @@ -1175,11 +1195,14 @@ def _test_legacy_original_mode( ] cumTimeEachTestEpoch = np.cumsum(timeEachTestEpoch) cumTimeEachTestEpoch = np.concatenate([[0], cumTimeEachTestEpoch]) + # a function that given the bin indicates the bin index: - binToEpoch = lambda x: np.where( - ((x * windowSize - cumTimeEachTestEpoch[0:-1]) >= 0) - * ((x * windowSize - cumTimeEachTestEpoch[1:]) < 0) - )[0][0] + def binToEpoch(x): + return np.where( + ((x * windowSize - cumTimeEachTestEpoch[0:-1]) >= 0) + * ((x * windowSize - cumTimeEachTestEpoch[1:]) < 0) + )[0][0] + binToEpochArray = [binToEpoch(bins) for bins in range(n_bins)] firstBinEpoch = [ np.min(np.where(np.equal(binToEpochArray, epochId))[0]) @@ -3546,11 +3569,14 @@ def test(self, bayesMatrices, behaviorData, windowSize=36): ] cumTimeEachTestEpoch = np.cumsum(timeEachTestEpoch) cumTimeEachTestEpoch = np.concatenate([[0], cumTimeEachTestEpoch]) + # a function that given the bin indicates the bin index: - binToEpoch = lambda x: np.where( - ((x * windowSize - cumTimeEachTestEpoch[0:-1]) >= 0) - * ((x * windowSize - cumTimeEachTestEpoch[1:]) < 0) - )[0][0] + def binToEpoch(x): + return np.where( + ((x * windowSize - cumTimeEachTestEpoch[0:-1]) >= 0) + * ((x * windowSize - cumTimeEachTestEpoch[1:]) < 0) + )[0][0] + binToEpochArray = [binToEpoch(bins) for bins in range(n_bins)] firstBinEpoch = [ np.min(np.where(np.equal(binToEpochArray, epochId))[0]) @@ -3768,14 +3794,12 @@ def full_proba_decoding( ) log_RF.append(temp) - n_bins = timeStepPred.shape[0] + timeStepPred.shape[0] ### Decoding loop position_probas = [] - nSpikes = [] for bin in tqdm(timeStepPred): bin_start_time = bin bin_stop_time = bin_start_time + windowSize - binSpikes = 0 tetrodes_contributions = [] tetrodes_contributions.append(All_Poisson_term) for tetrode in range(len(guessed_clusters)): diff --git a/neuroencoders/transformData/linearizer.py b/neuroencoders/transformData/linearizer.py index 036844a..937e56c 100755 --- a/neuroencoders/transformData/linearizer.py +++ b/neuroencoders/transformData/linearizer.py @@ -331,7 +331,7 @@ def try_linearization(ax, l0s): id, bId = tpl try: l0s[id].remove() - except: + except (KeyError, AttributeError): None l0s[id] = ax[0].scatter( euclidData[bId, 0], euclidData[bId, 1], c=[cm(id)] @@ -361,7 +361,9 @@ def b1update(n): try: self.lPoints.remove() fig.canvas.draw() - except: + except (AttributeError, KeyError): + # Previous scatter points may not exist or may have already been removed; + # in that case there is nothing to clean up before redrawing. pass self.l0s = try_linearization(ax, self.l0s) self.lPoints = ax[0].scatter( @@ -437,7 +439,9 @@ def onclick(event): try: self.lPoints.remove() fig.canvas.draw() - except: + except (AttributeError, KeyError): + # Previous linearization points may not exist or may have already been + # removed; in that case there is nothing to clean up before re-drawing. pass if len(self.nnPoints) > 2: self.n_points = len(self.nnPoints) @@ -590,15 +594,9 @@ def _get_interpolation_parameter(point, seg_start, seg_end): if self.phase is not None: filename = os.path.join(folder, "nnBehavior_" + self.phase + ".mat") if not os.path.exists(filename): - assert tables.is_hdf5_file(folder + "nnBehavior.mat") - import shutil - - print("weird to copy that file now") - - shutil.copyfile( - folder + "nnBehavior.mat", - folder + "nnBehavior_" + phase + ".mat", - follow_symlinks=True, + raise ValueError( + "Are you sure you want to use phase-specific linearization? The file does not exist: ", + folder + "nnBehavior_" + self.phase + ".mat", ) # Extract basic behavior with tables.open_file(filename, "a") as f: diff --git a/neuroencoders/utils/MOBS_Functions.py b/neuroencoders/utils/MOBS_Functions.py index 25dbdca..40958bc 100755 --- a/neuroencoders/utils/MOBS_Functions.py +++ b/neuroencoders/utils/MOBS_Functions.py @@ -20,7 +20,7 @@ from statannotations.Annotator import Annotator from tqdm import tqdm -from neuroencoders.importData.epochs_management import inEpochsMask +from neuroencoders.importData.epochs_management import get_epochs_mask, inEpochsMask from neuroencoders.resultAnalysis import print_results from neuroencoders.resultAnalysis.paper_figures import PaperFigures from neuroencoders.transformData.linearizer import UMazeLinearizer @@ -80,7 +80,7 @@ def Load_LFP(LFP_path, time_unit="us", frequency=1250.0): from pynapple import Tsd, TsdFrame from scipy.io import loadmat - if type(LFP_path) == str: + if isinstance(LFP_path, str): try: LFP = loadmat(LFP_path, squeeze_me=True) except FileNotFoundError: @@ -791,8 +791,6 @@ def __init__(self, *args, **kwargs): self.find_window_size(**kwargs) self.parameters = dict() self.projects = dict() - self.data_helper = dict() - self.linearizer = dict() for i, winMS in enumerate(self.windows): try: @@ -805,12 +803,12 @@ def __init__(self, *args, **kwargs): ) # otherwise will be loaded by super init try: - self.data_helper[winMS] = DataHelperClass.load( + self.data_helper = DataHelperClass.load( self.projects[winMS].experimentPath, phase=self.phase ) except FileNotFoundError as e: print("did not manage to load DataHelper:", e) - self.data_helper[winMS] = DataHelperClass( + self.data_helper = DataHelperClass( self.projects[winMS].xml, mode="compare", windowSize=int(winMS) / 1000, @@ -830,14 +828,16 @@ def __init__(self, *args, **kwargs): windowSize=int(winMS) / 1000, **kwargs, ) - self.data_helper[winMS] = DataHelperClass( - self.xml, - mode="compare", - windowSize=int(winMS) / 1000, - **kwargs, - ) - # we need that before loading params to have the right target - self.data_helper[winMS].get_true_target(in_place=True, **kwargs) + if i == 0: + self.data_helper = DataHelperClass( + self.xml, + mode="compare", + **kwargs, + ) + # we need that before loading params to have the right target + self.data_helper.get_true_target( + windowSizeMS=int(winMS), in_place=True, **kwargs + ) if os.path.exists( os.path.join(self.folderResult, winMS, "params.json") ): @@ -858,36 +858,39 @@ def __init__(self, *args, **kwargs): params_dict[key] = value del params_dict["windowSize"] self.parameters[winMS] = Params( - self.data_helper[winMS], + self.data_helper, windowSize=int(winMS) / 1000, save_json=True, **kwargs, ) - self.linearizer[winMS] = UMazeLinearizer( - self.projects[winMS].folder, - data_helper=self.data_helper[winMS], - **kwargs, - ) - self.linearizer[winMS].verify_linearization( - self.data_helper[winMS].positions / self.data_helper[winMS].maxPos(), - self.projects[winMS].folder, - ) + if i == 0: + self.linearizer = UMazeLinearizer( + self.projects[winMS].folder, + data_helper=self.data_helper, + **kwargs, + ) + self.linearizer.verify_linearization( + self.data_helper.positions[:, :2] / self.data_helper.maxPos(), + self.projects[winMS].folder, + ) - if kwargs.get("keops_linearization", False): - self.l_function = self.linearizer[winMS].pykeops_linearization - else: - self.l_function = self.cpu_linearization + if kwargs.get("keops_linearization", False): + self.l_function = self.linearizer.pykeops_linearization + else: + self.l_function = self.cpu_linearization + + self.data_helper.get_true_target( + l_function=self.l_function, + in_place=True, + show=kwargs.get("show", False), + ) - self.data_helper[winMS].get_true_target( - self.l_function, in_place=True, show=kwargs.get("show", False) - ) - if i == 0: # Initialize the first window as the main one - self.DataHelper = self.data_helper[winMS] + self.DataHelper = self.data_helper self.Params = self.parameters[winMS] self.Project = self.projects[winMS] - self.Linearizer = self.linearizer[winMS] + self.Linearizer = self.linearizer # construct from Params Params.__init__( @@ -916,8 +919,8 @@ def __init__(self, *args, **kwargs): print(self) def cpu_linearization(self, x): - winMS = self.windows[0] - return self.linearizer[winMS].apply_linearization(x, keops=False) + self.windows[0] + return self.linearizer.apply_linearization(x, keops=False) def __getstate__(self): """ @@ -1157,23 +1160,21 @@ def load_trainers(self, which="both", **kwargs) -> Dict[int, Any]: self.deviceName = deviceName phase = kwargs.pop("phase", self.phase) - if not hasattr(self, "ann"): - self.ann = {} isTransformer = kwargs.pop("isTransformer", self.Params.isTransformer) transform_w_log = kwargs.pop("transform_w_log", self.Params.transform_w_log) denseweight = kwargs.pop("denseweight", self.Params.denseweight) for i, winMS in enumerate(self.windows): - if which.lower() in ["ann", "both"]: - if not self.ann.get(winMS): - self.ann[winMS] = NNTrainer( + if i == 0 and which.lower() in ["ann", "both"]: + if not hasattr(self, "ann"): + self.ann = NNTrainer( self.projects[winMS], self.parameters[winMS], deviceName=deviceName, phase=phase, isTransformer=isTransformer, - linearizer=self.linearizer[winMS], - behaviorData=self.data_helper[winMS].fullBehavior, + linearizer=self.linearizer, + behaviorData=self.data_helper.fullBehavior, alpha=self.parameters[winMS].denseweightAlpha, # we dont really care about the dynamic loss, but this way we load the training data in memory, with speedMask, transform_w_log=transform_w_log, @@ -1213,20 +1214,18 @@ def load_trainers(self, which="both", **kwargs) -> Dict[int, Any]: phase=self.phase, **kwargs, ) - try: - with open( - os.path.join( - self.bayes.folderResult, - "bayesMatrices.pkl", - ), - "rb", - ) as f: - bayes_matrices = pickle.load(f) - self.bayes_matrices = bayes_matrices - except (FileNotFoundError, AttributeError): - warn( - "You asked for bayes trainer, but no bayes matrices pickle was found." - ) + if kwargs.get("load_bayesMatrices", False): + try: + # allows to initialize bayes matrices if the pickle exists + self.bayes_matrices = self.bayes.train_order_by_pos( + self.data_helper.fullBehavior, + l_function=self.l_function, + **kwargs, + ) + except (FileNotFoundError, AttributeError): + warn( + "You asked for bayes trainer, but no bayes matrices pickle was found." + ) def load_results( self, @@ -1295,7 +1294,7 @@ def load_results( if not redo: try: suffix = f"_{phase}" if phase is not None else "" - pos = pd.read_csv( + pd.read_csv( os.path.expanduser( os.path.join( self.folderResult, @@ -1306,8 +1305,8 @@ def load_results( ).values[:, 1:] except FileNotFoundError: self.load_trainers(which="ann", **kwargs) - self.ann[win].test( - self.data_helper[win].fullBehavior, + self.ann.test( + self.data_helper.fullBehavior, windowSizeMS=win_value, phase=phase, l_function=self.l_function, @@ -1317,8 +1316,8 @@ def load_results( print(f"Force loading ann results for window {win}.") self.load_trainers(which="ann", **kwargs) try: - self.ann[win].test( - self.data_helper[win].fullBehavior, + self.ann.test( + self.data_helper.fullBehavior, windowSizeMS=win_value, phase=phase, l_function=self.l_function, @@ -1342,7 +1341,7 @@ def load_results( target=self.target, phase=phase, typeDec="NN", - training_data=self.ann[win].training_data, + training_data=self.ann.training_data, l_function=self.l_function, show=show, **kwargs, @@ -1368,15 +1367,15 @@ def load_results( except FileNotFoundError: self.load_trainers(which="bayes", **kwargs) epochMask = get_epochs_mask( - behaviorData=self.data_helper[win].fullBehavior, + behaviorData=self.data_helper.fullBehavior, useTrain=phase != self.phase, useTest=phase != "training", ) - timeStepPred = self.data_helper[win].fullBehavior[ - "positionTime" - ][epochMask] + timeStepPred = self.data_helper.fullBehavior["positionTime"][ + epochMask + ] outputs = self.bayes.test_as_NN( - self.data_helper[win].fullBehavior, + self.data_helper.fullBehavior, self.bayes_matrices, timeStepPred, windowSizeMS=win_value, @@ -1389,15 +1388,13 @@ def load_results( print(f"Force loading bayesian results for window {win}.") self.load_trainers(which="bayes", **kwargs) epochMask = get_epochs_mask( - behaviorData=self.data_helper[win].fullBehavior, + behaviorData=self.data_helper.fullBehavior, useTrain=phase != self.phase, useTest=phase != "training", ) - timeStepPred = self.data_helper[win].fullBehavior["positionTime"][ - epochMask - ] + timeStepPred = self.data_helper.fullBehavior["positionTime"][epochMask] outputs = self.bayes.test_as_NN( - self.data_helper[win].fullBehavior, + self.data_helper.fullBehavior, self.bayes_matrices, timeStepPred, windowSizeMS=win_value, @@ -1459,11 +1456,11 @@ def load_results( def show_results(self, winMS=None, phase=None, **kwargs): if winMS is None: - win = self.windows[-1] + self.windows[-1] winMS = self.windows_values[-1] else: idx = self.windows_values.index(winMS) - win = self.windows[idx] + self.windows[idx] if phase is None: phase = self.phase @@ -1473,7 +1470,7 @@ def show_results(self, winMS=None, phase=None, **kwargs): windowSizeMS=winMS, target=kwargs.pop("target", self.target), phase=phase, - training_data=self.ann[win].training_data, + training_data=self.ann.training_data, l_function=self.l_function, **kwargs, ) @@ -1484,11 +1481,11 @@ def init_plotter(self, winMS=None, **kwargs): """ which = kwargs.get("which", "ann") if winMS is None: - win = self.windows[-1] + self.windows[-1] winMS = self.windows_values[-1] idWindow = self.timeWindows.index(int(winMS)) - win = self.windows[idWindow] + self.windows[idWindow] phase = kwargs.get("phase", self.phase) phase = ( @@ -1499,7 +1496,7 @@ def init_plotter(self, winMS=None, **kwargs): data_helper = kwargs.pop("data_helper", None) if data_helper is None: - data_helper = self.data_helper[win] + data_helper = self.data_helper positions_from_NN = kwargs.pop("positions_from_NN", None) if positions_from_NN is None: @@ -1541,7 +1538,7 @@ def init_plotter(self, winMS=None, **kwargs): if which.lower() == "ann": self.load_trainers(which="ann", **kwargs) if ( - getattr(self.ann[str(win)].params, "GaussianHeatmap", False) + getattr(self.ann.params, "GaussianHeatmap", False) and kwargs.get("predicted_heatmap", None) is None and kwargs.get("plot_heatmap", False) ): @@ -1573,27 +1570,23 @@ def init_plotter(self, winMS=None, **kwargs): print( f"No decoding_results{phase}.pkl found for window {winMS}." ) - self.ann[str(win)].params.GaussianHeatmap = False + self.ann.params.GaussianHeatmap = False kwargs["predicted_heatmap"] = None kwargs["plot_heatmap"] = False predicted_probs = None if predicted_probs is not None: try: predicted_probs = ( - self.ann[str(win)] - .GaussianHeatmap.decode_and_uncertainty( + self.ann.GaussianHeatmap.decode_and_uncertainty( predicted_logits, return_probs=True - )[-1] - .numpy() + )[-1].numpy() ) except Exception: self.load_trainers(which="ann") predicted_probs = ( - self.ann[str(win)] - .GaussianHeatmap.decode_and_uncertainty( + self.ann.GaussianHeatmap.decode_and_uncertainty( predicted_logits, return_probs=True - )[-1] - .numpy() + )[-1].numpy() ) else: try: @@ -1680,7 +1673,7 @@ def show_movie(self, winMS=None, **kwargs): """ block = kwargs.pop("block", True) plotter = self.init_plotter(winMS, **kwargs) - anim = plotter.show( + plotter.show( block=block, show=True, **kwargs, @@ -1887,8 +1880,8 @@ def retrain(self, window=None, which="both", **kwargs): windows, winValues = self._select_window(window) for win, win_val in zip(windows, winValues): if which.lower() in ["ann", "both"]: - self.ann[win].train( - self.data_helper[win].fullBehavior, + self.ann.train( + self.data_helper.fullBehavior, windowSizeMS=win_val, l_function=self.l_function, **kwargs, @@ -1995,7 +1988,7 @@ def run_spike_alignment(self, **kwargs): self.waveform_comparators[win] = WaveFormComparator( self.projects[win], self.parameters[win], - self.data_helper[win].fullBehavior, + self.data_helper.fullBehavior, winValue, phase=self.phase, useTrain=useTrain, @@ -2032,8 +2025,7 @@ def convert_to_df(self, redo=False): phase_name = suffix.strip("_") if suffix else "all" for id, win in enumerate(self.windows_values): - win_str = str(win) - data_helper_win = self.data_helper[win_str] + data_helper_win = self.data_helper resultsNN_suffix = self.resultsNN_phase[suffix] # Extract posIndex once @@ -2137,10 +2129,11 @@ def from_pickle(cls, path: str) -> "Results_Loader": def __init__( self, dir: pd.DataFrame, - mice_nb: Optional[List[int]] = None, + mice_nb: Optional[List[str]] = None, mice_manipes: Optional[List[str]] = None, timeWindows: Optional[List[int]] = None, phases=None, + exp_indices: Optional[List[int]] = None, **kwargs, ): """ @@ -2148,7 +2141,7 @@ def __init__( Args: dir (pd.DataFrame): PathForExperiments DataFrame with columns for folder Results, mouse names, manipes, network paths, etc. - mice_nb (List[int]): List of mouse numbers to filter results. + mice_nb (List[str]): List of mouse numbers to filter results. mice_manipes (List[str]): List of manipes to filter results. timeWindows (List[int]): List of time windows in milliseconds to filter results. If None, uses all available windows. phase (str or List[str]): Phase of the experiment to filter results. If None, uses 'all' as default. @@ -2182,6 +2175,9 @@ def __init__( self.phases = None else: self.phases = phases + + if exp_indices is None: + exp_indices = np.zeros(len(dir), dtype=bool).tolist() if not isinstance(self.phases, List): self.phases = [self.phases] if not isinstance(self.timeWindows, List): @@ -2201,6 +2197,7 @@ def __init__( self.mice_names = [ f"M{nb}{manipe}" for nb, manipe in zip(mice_nb, mice_manipes) ] + self.exp_indices = exp_indices if kwargs.get("dict", None) is None: self.results_dict = {} else: @@ -2219,26 +2216,39 @@ def __init__( found_training = False if kwargs.get("dict", None) is None: - for mouse_nb, manipe, mouse_full_name in zip( - self.mice_nb, self.mice_manipes, self.mice_names + for mouse_nb, manipe, mouse_full_name, exp_index in zip( + self.mice_nb, self.mice_manipes, self.mice_names, self.exp_indices ): mouse_nb = str(mouse_nb) - if not any( - (self.Dir.name.str.lower().str.contains(mouse_nb.lower())) - & (self.Dir.manipe.str.lower().str.contains(manipe.lower())) - ): + if exp_index is not None and exp_index != 0: + exp_index = int(exp_index) + mouse_full_name = f"{mouse_full_name}_exp{exp_index}" + conditions = ( + self.Dir.name.str.lower().str.contains(mouse_nb.lower()) + ) & (self.Dir.manipe.str.lower().str.contains(manipe.lower())) + if not conditions.any(): raise ValueError( f"Mouse {mouse_nb} with manipe {manipe} not found in the directory." ) window_tmp = [] - path = ( - self.Dir[ - (self.Dir.name.str.lower().str.contains(mouse_nb.lower())) - & (self.Dir.manipe.str.lower().str.contains(manipe.lower())) - ] - .iloc[0] - .path - ) + if conditions.sum() > 1: + if exp_index is None or exp_index == 0: + raise ValueError( + f"Multiple entries found for mouse {mouse_nb} with manipe {manipe}. Please provide exp_index to disambiguate." + ) + else: + suppl_conditions = self.Dir.path.str.contains(f"exp{exp_index}") + conditions = conditions & suppl_conditions + if not conditions.any(): + raise ValueError( + f"Mouse {mouse_nb} with manipe {manipe} and exp_index {exp_index} not found in the directory." + ) + elif conditions.sum() > 1: + raise ValueError( + f"Multiple entries found for mouse {mouse_nb} with manipe {manipe} and exp_index {exp_index}. Please check the directory." + ) + + path = self.Dir[conditions].iloc[0].path nameExp = os.path.basename( self.Dir[ (self.Dir.name.str.lower().str.contains(mouse_nb.lower())) @@ -2291,6 +2301,7 @@ def __init__( manipe=manipe, nameExp=nameExp, phase=suffix.strip("_"), + exp_index=exp_index, isTransformer=isTransformer if isTransformer is not None else "transformer" in nameExp.lower(), @@ -2570,166 +2581,107 @@ def __iadd__(self, other): def apply_analysis(self, redo=False): """ - Apply some usual ML operations on the results df. + Apply common analysis metrics to the results DataFrame. """ - if "mean_speed" in self.results_df.columns: + if "mean_speed" in self.results_df.columns and not redo: print("Analysis already applied to the DataFrame.") - if not redo: - return self.results_df - - self.results_df["mean_speed"] = self.results_df.apply( - lambda row: np.nanmean(row["alignedSpeed"]) - if row["alignedSpeed"] is not None - else np.nan, - axis=1, - ) - self.results_df["mean_error"] = self.results_df.apply( - lambda row: np.nanmean( - np.linalg.norm(row["fullPred"] - row["truePos"], axis=1) - ) - if row["fullPred"] is not None and row["truePos"] is not None - else None, - axis=1, - ) - self.results_df["error"] = self.results_df.apply( - lambda row: np.linalg.norm(row["fullPred"] - row["truePos"], axis=1) - if row["fullPred"] is not None and row["truePos"] is not None - else None, - axis=1, - ) - self.results_df["mean_lin_error"] = self.results_df.apply( - lambda row: np.nanmean(np.abs(row["linPred"] - row["linTruePos"])) - if row["linPred"] is not None and row["linTruePos"] is not None - else None, - axis=1, - ) - self.results_df["lin_error"] = self.results_df.apply( - lambda row: np.abs(row["linPred"] - row["linTruePos"]) - if row["linPred"] is not None and row["linTruePos"] is not None - else None, - axis=1, - ) + return self.results_df - # add the selected mean error and lin error to the dataframe - # we defined the selected prediction as the prediction with the predLoss being amongs the lowest 20% for this row. - self.results_df["predLossThreshold"] = self.results_df.apply( - lambda row: np.quantile(row["predLoss"], 0.2) - if row["predLoss"] is not None - else None, - axis=1, - ) - # we use the predLossThreshold to select the mean_error and lin_error - self.results_df["mean_error_selected"] = self.results_df.apply( - lambda row: np.nanmean( - np.linalg.norm( - row["fullPred"][row["predLoss"] <= row["predLossThreshold"]] - - row["truePos"][row["predLoss"] <= row["predLossThreshold"]], - axis=1, - ) - ) - if row["fullPred"] is not None and row["truePos"] is not None - else None, - axis=1, - ) + def process_row(row): + res = {} - self.results_df["lin_error_selected"] = self.results_df.apply( - lambda row: np.abs( - row["linPred"][row["predLoss"] <= row["predLossThreshold"]] - - row["linTruePos"][row["predLoss"] <= row["predLossThreshold"]] + # 1. Base Errors and Speed + res["mean_speed"] = ( + np.nanmean(row["alignedSpeed"]) + if row["alignedSpeed"] is not None + else np.nan ) - if row["linPred"] is not None and row["linTruePos"] is not None - else None, - axis=1, - ) - self.results_df["mean_lin_error_selected"] = self.results_df.apply( - lambda row: np.nanmean( - np.abs( - row["linPred"][row["predLoss"] <= row["predLossThreshold"]] - - row["linTruePos"][row["predLoss"] <= row["predLossThreshold"]] - ) - ) - if row["linPred"] is not None and row["linTruePos"] is not None - else None, - axis=1, - ) - - self.results_df["asymmetry_index_on_predicted"] = self.results_df.apply( - lambda row: row["results"].get_training_imbalance(positions=row["fullPred"]) - if row["fullPred"] is not None - else None, - axis=1, - ) - - self.results_df["asymmetry_index_on_selected_predicted"] = ( - self.results_df.apply( - lambda row: row["results"].get_training_imbalance( - positions=row["fullPred"][ - row["predLoss"] <= row["predLossThreshold"] - ] - ) - if row["fullPred"] is not None - else None, - axis=1, - ) - ) + has_pred = row["fullPred"] is not None and row["truePos"] is not None + has_lin = row["linPred"] is not None and row["linTruePos"] is not None + has_loss = row["predLoss"] is not None + + if has_pred: + errors = np.linalg.norm(row["fullPred"] - row["truePos"], axis=1) + res["error"] = errors + res["mean_error"] = np.nanmean(errors) + + if has_lin: + lin_errors = np.abs(row["linPred"] - row["linTruePos"]) + res["lin_error"] = lin_errors + res["mean_lin_error"] = np.nanmean(lin_errors) + + # 2. Selected metrics (lowest 20% loss) + if has_loss: + threshold = np.quantile(row["predLoss"], 0.2) + res["predLossThreshold"] = threshold + mask = row["predLoss"] <= threshold + + if has_pred: + res["mean_error_selected"] = np.nanmean(errors[mask]) + res["asymmetry_index_on_selected_predicted"] = row[ + "results" + ].get_training_imbalance(positions=row["fullPred"][mask]) + if has_lin: + res["lin_error_selected"] = lin_errors[mask] + res["mean_lin_error_selected"] = np.nanmean(lin_errors[mask]) + + # 3. Indices and Directions + if has_pred: + res["asymmetry_index_on_predicted"] = row[ + "results" + ].get_training_imbalance(positions=row["fullPred"]) + + if has_lin: + res["true_binary_direction"] = row[ + "results" + ].data_helper._get_traveling_direction(row["linTruePos"]) + res["predicted_binary_direction"] = row[ + "results" + ].data_helper._get_traveling_direction(row["linPred"]) + + return pd.Series(res) + + # Apply processing in a single pass + analysis_columns = self.results_df.apply(process_row, axis=1) + + # Update DataFrame with new columns efficiently + for col in analysis_columns.columns: + self.results_df[col] = analysis_columns[col] + + # 4. Vectorized Ratio Calculations (fast operations) training_values = ( - self.results_df[self.results_df["phase"] == "training"].groupby( - ["nameExp", "mouse", "winMS"] - )[ - "asymmetry_index" - ] # or .mean(), depending on what you want if multiple rows exist - ).first() + self.results_df[self.results_df["phase"] == "training"] + .groupby(["nameExp", "mouse_name", "manipe", "winMS"])["asymmetry_index"] + .first() + ) self.results_df["training_asymmetry_index"] = self.results_df.set_index( - ["nameExp", "mouse", "winMS"] + ["nameExp", "mouse_name", "manipe", "winMS"] ).index.map(training_values) + # Safeguard division by zero for ratios + train_idx = self.results_df["training_asymmetry_index"].replace(0, np.nan) + self.results_df["real_asymmetry_ratio"] = ( - self.results_df["asymmetry_index"] - / self.results_df["training_asymmetry_index"] + self.results_df["asymmetry_index"] / train_idx ) - self.results_df["predicted_asymmetry_ratio"] = self.results_df.apply( - lambda row: row["asymmetry_index_on_predicted"] - / row["training_asymmetry_index"] - if row["training_asymmetry_index"] != 0 - else None, - axis=1, + self.results_df["predicted_asymmetry_ratio"] = ( + self.results_df["asymmetry_index_on_predicted"] / train_idx ) self.results_df["predicted_asymmetry_ratio_on_selected"] = ( - self.results_df.apply( - lambda row: row["asymmetry_index_on_selected_predicted"] - / row["training_asymmetry_index"] - if row["training_asymmetry_index"] != 0 - else None, - axis=1, - ) + self.results_df["asymmetry_index_on_selected_predicted"] / train_idx ) + + real_ratio = self.results_df["real_asymmetry_ratio"].replace(0, np.nan) self.results_df["predicted_asymmetry_ratio_normalized"] = ( - self.results_df["asymmetry_index_on_predicted"] - / self.results_df["real_asymmetry_ratio"] + self.results_df["asymmetry_index_on_predicted"] / real_ratio ) self.results_df["selected_predicted_asymmetry_ratio_normalized"] = ( - self.results_df["asymmetry_index_on_selected_predicted"] - / self.results_df["real_asymmetry_ratio"] + self.results_df["asymmetry_index_on_selected_predicted"] / real_ratio ) - self.results_df["true_binary_direction"] = self.results_df.apply( - lambda row: row["results"] - .data_helper[str(row["winMS"])] - ._get_traveling_direction(row["linTruePos"]) - if row["linTruePos"] is not None - else None, - axis=1, - ) - self.results_df["predicted_binary_direction"] = self.results_df.apply( - lambda row: row["results"] - .data_helper[str(row["winMS"])] - ._get_traveling_direction(row["linPred"]) - if row["linPred"] is not None - else None, - axis=1, - ) + return self.results_df @classmethod def from_dict_and_df( @@ -2825,15 +2777,12 @@ def mean_error_matrix_linerrors_by_speed( linTrue_fast = [] for _, row in df.iterrows(): # get speed_mask from training Mouse_Results object - mouse_val = row["mouse"] - mouse_manipe = row["manipe"] speed_mask = ( self.results_df.query( "nameExp == @nameExp and phase == 'training' and winMS == @winMS and mouse == @mouse_val and manipe == @mouse_manipe" )["results"] .values[0] - .data_helper[str(winMS)] - .fullBehavior["Times"]["speedFilter"] + .data_helper.fullBehavior["Times"]["speedFilter"] .flatten()[row["posIndex_NN"]] ) @@ -2844,8 +2793,7 @@ def mean_error_matrix_linerrors_by_speed( "nameExp == @nameExp and phase == 'training' and winMS == @winMS and mouse == @mouse_val and manipe == @mouse_manipe" )["results"] .values[0] - .data_helper[str(winMS)] - .fullBehavior["Times"]["trainEpochs"] + .data_helper.fullBehavior["Times"]["trainEpochs"] ) epochMask = inEpochsMask(row["timeNN"], real_train) @@ -2889,15 +2837,12 @@ def mean_error_matrix_linerrors_by_speed( linTrue = [] for _, row in df.iterrows(): # get speed_mask from training Mouse_Results object - mouse_val = row["mouse"] - mouse_manipe = row["manipe"] speed_mask = ( self.results_df.query( "nameExp == @nameExp and phase == 'training' and winMS == @winMS and mouse == @mouse_val and manipe == @mouse_manipe" )["results"] .values[0] - .data_helper[str(winMS)] - .fullBehavior["Times"]["speedFilter"] + .data_helper.fullBehavior["Times"]["speedFilter"] .flatten()[row["posIndex_NN"]] ) @@ -2908,8 +2853,7 @@ def mean_error_matrix_linerrors_by_speed( "nameExp == @nameExp and phase == 'training' and winMS == @winMS and mouse == @mouse_val and manipe == @mouse_manipe" )["results"] .values[0] - .data_helper[str(winMS)] - .fullBehavior["Times"]["trainEpochs"] + .data_helper.fullBehavior["Times"]["trainEpochs"] ) epochMask = inEpochsMask(row["timeNN"], real_train) @@ -2977,30 +2921,14 @@ def correlation_entropy_maxp_vs_KL( suffixes = suffixes or getattr(self, "suffixes", [""]) # Try to get ANN loss layer try: - loss_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianLoss_layer - ) - logits_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianHeatmap - ) + loss_layer = self.results_df["results"][0].ann.GaussianLoss_layer + logits_layer = self.results_df["results"][0].ann.GaussianHeatmap except Exception: print("Trying to load ANN trainers...") try: self.results_df["results"][0].load_trainers(which="ann") - loss_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianLoss_layer - ) - logits_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianHeatmap - ) + loss_layer = self.results_df["results"][0].ann.GaussianLoss_layer + logits_layer = self.results_df["results"][0].ann.GaussianHeatmap except Exception as e2: print(f"Could not get ANN loss layer: {e2}") raise @@ -3126,29 +3054,13 @@ def pooled_correlation_entropy_maxp_vs_KL( # --- Try to get ANN loss layer once --- try: - loss_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianLoss_layer - ) - logits_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianHeatmap - ) + loss_layer = self.results_df["results"][0].ann.GaussianLoss_layer + logits_layer = self.results_df["results"][0].ann.GaussianHeatmap except Exception: print("Trying to load ANN trainers...") self.results_df["results"][0].load_trainers(which="ann") - loss_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianLoss_layer - ) - logits_layer = ( - self.results_df["results"][0] - .ann[str(self.timeWindows[0])] - .GaussianHeatmap - ) + loss_layer = self.results_df["results"][0].ann.GaussianLoss_layer + logits_layer = self.results_df["results"][0].ann.GaussianHeatmap # --- loop over suffixes --- for suffix in suffixes: @@ -3684,10 +3596,8 @@ def around_ripples( times = decoding_results["times"].flatten() - tRipples = ( - mouse_results.data_helper[str(winMS)] - .fullBehavior["Times"] - .get("tRipples", None) + tRipples = mouse_results.data_helper.fullBehavior["Times"].get( + "tRipples", None ) if tRipples is None or len(tRipples) == 0: print( @@ -3824,10 +3734,8 @@ def around_ripples_METAverage( raise ValueError("against must be 'entropy' or 'maxp'") times = decoding_results["times"].flatten() - tRipples = ( - mouse_results.data_helper[str(winMS)] - .fullBehavior["Times"] - .get("tRipples", None) + tRipples = mouse_results.data_helper.fullBehavior["Times"].get( + "tRipples", None ) if tRipples is None or len(tRipples) == 0: continue @@ -4152,9 +4060,7 @@ def correlation_per_mouse_spikes( raise ValueError("against must be 'entropy', 'maxp' or 'error'") # --- load spikes --- - clusters_file = os.path.join( - mouse_results.folderResult, "clusters_pre_wTrain_False.pkl" - ) + os.path.join(mouse_results.folderResult, "clusters_pre_wTrain_False.pkl") clusters_time_file = os.path.join( mouse_results.folderResult, "clusters_time_pre_wTrain_False.pkl" ) @@ -4164,17 +4070,7 @@ def correlation_per_mouse_spikes( # clusters = pickle.load(f) with open(clusters_time_file, "rb") as f: clusters_time = pickle.load(f) - except: - clusters_file = os.path.abspath( - os.path.join( - mouse_results.folderResult, - "..", - "..", - "last_bayes", - "results", - f"clusters_pre_wTrain_{'True' if row['phase'] == 'training' else 'False'}.pkl", - ) - ) + except FileNotFoundError: clusters_time_file = os.path.abspath( os.path.join( mouse_results.folderResult, @@ -4368,9 +4264,7 @@ def barplot_correlation_spikes( raise ValueError("against must be 'entropy', 'maxp' or 'error'") # --- load spikes --- - clusters_file = os.path.join( - mouse_results.folderResult, "clusters_pre_wTrain_False.pkl" - ) + os.path.join(mouse_results.folderResult, "clusters_pre_wTrain_False.pkl") clusters_time_file = os.path.join( mouse_results.folderResult, "clusters_time_pre_wTrain_False.pkl" ) @@ -4380,17 +4274,7 @@ def barplot_correlation_spikes( # clusters = pickle.load(f) with open(clusters_time_file, "rb") as f: clusters_time = pickle.load(f) - except: - clusters_file = os.path.abspath( - os.path.join( - mouse_results.folderResult, - "..", - "..", - "last_bayes", - "results", - f"clusters_pre_wTrain_{'True' if row['phase'] == 'training' else 'False'}.pkl", - ) - ) + except FileNotFoundError: clusters_time_file = os.path.abspath( os.path.join( mouse_results.folderResult, @@ -4784,7 +4668,7 @@ def hist2d_linpred_vs_bayes( continue try: ann_vals = decoding_results[ann_var].flatten() - except: + except KeyError: ann_vals = row[ann_var] # --- apply speed mask if needed --- @@ -4888,8 +4772,7 @@ def get_speed_mask(row, df): return None return ( res.iloc[0] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["speedFilter"] + .data_helper.fullBehavior["Times"]["speedFilter"] .flatten()[row.posIndex_NN] ) @@ -4901,11 +4784,7 @@ def get_true_train_mask(row, df): )["results"] if len(res) == 0: return None - train_mask = ( - res.iloc[0] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["trainEpochs"] - ) + train_mask = res.iloc[0].data_helper.fullBehavior["Times"]["trainEpochs"] return inEpochsMask(row.timeNN, train_mask) # --- compute mean errors --- @@ -4982,7 +4861,7 @@ def get_true_train_mask(row, df): coords = {} x_ticks = stride_list if stride_list is not None else ["1", "2", "4"] phases = phase_list if phase_list is not None else ["training", "pre"] - n_hues = len(phases) + len(phases) for i, (stride, phase) in enumerate([(s, p) for s in x_ticks for p in phases]): coll = paths[i] @@ -5189,8 +5068,7 @@ def get_speed_mask(row, df): return None return ( res.iloc[0] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["speedFilter"] + .data_helper.fullBehavior["Times"]["speedFilter"] .flatten()[row.posIndex_NN] ) @@ -5202,11 +5080,7 @@ def get_true_train_mask(row, df): )["results"] if len(res) == 0: return None - train_mask = ( - res.iloc[0] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["trainEpochs"] - ) + train_mask = res.iloc[0].data_helper.fullBehavior["Times"]["trainEpochs"] return inEpochsMask(row.timeNN, train_mask) # --- compute errors using reduce_fn --- @@ -5281,7 +5155,7 @@ def get_true_train_mask(row, df): coords = {} x_ticks = stride_list if stride_list is not None else ["1", "2", "4"] winMSs = winMS_list if winMS_list is not None else ["36", "108", "252"] - n_hues = len(winMSs) + len(winMSs) for i, (stride, winMS) in enumerate([(s, p) for s in x_ticks for p in winMSs]): coll = paths[i] @@ -5378,7 +5252,8 @@ def get_true_train_mask(row, df): # --- outlier labeling --- df_winMS = err_df.copy() df_winMS = df_winMS.rename(columns={"mouse_manipe": "mouse"}) - df_metric = df_winMS[ + # Filter the dataframe to relevant columns + df_winMS = df_winMS[ ["stride", "mouse", "mean_error" if reduce_fn == "mean" else "median_error"] ].dropna() @@ -5497,8 +5372,7 @@ def get_speed_mask(row, df): return None return ( res.iloc[0] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["speedFilter"] + .data_helper.fullBehavior["Times"]["speedFilter"] .flatten() .reshape(-1)[row.posIndex_NN] .flatten() @@ -5514,8 +5388,7 @@ def get_entropy_mask(row, df, thresh_pct): return None speed_mask = ( res.iloc[0] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["speedFilter"] + .data_helper.fullBehavior["Times"]["speedFilter"] .flatten() .reshape(-1)[good_row["posIndex_NN"].iloc[0]] .flatten() @@ -5568,7 +5441,7 @@ def get_speed_mask_bayes(row, df): ) as f: decoding_results = pickle.load(f) speed_mask = decoding_results["speed_mask"].flatten() - phase_value = row["phase"] + row["phase"] res = df.query( "mouse_manipe == @row.mouse_manipe and phase == @phase_value " "and winMS == @row.winMS and stride == @row.stride" @@ -5590,11 +5463,9 @@ def get_true_train_mask_bayes(row, df): ) as f: decoding_results = pickle.load(f) times = decoding_results["times"].reshape(-1) - trainEpochs = ( - row["results"] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["trainEpochs"] - ) + trainEpochs = row["results"].data_helper.fullBehavior["Times"][ + "trainEpochs" + ] del decoding_results return inEpochsMask(times, trainEpochs).flatten() @@ -5606,11 +5477,7 @@ def get_true_train_mask(row, df): )["results"] if len(res) == 0: return None - train_mask = ( - res.iloc[0] - .data_helper[str(row.winMS)] - .fullBehavior["Times"]["trainEpochs"] - ) + train_mask = res.iloc[0].data_helper.fullBehavior["Times"]["trainEpochs"] return inEpochsMask(row.timeNN, train_mask).flatten() # --- compute median errors --- diff --git a/neuroencoders/utils/PathForExperiments.py b/neuroencoders/utils/PathForExperiments.py index 7a454de..f947062 100755 --- a/neuroencoders/utils/PathForExperiments.py +++ b/neuroencoders/utils/PathForExperiments.py @@ -67,9 +67,8 @@ def path_for_experiments( PFC = list(range(6, 15)) + list(range(16, 19)) + [21] # [6:14 16:18 21] # All groups - LFP_All = list(range(1, 22)) # 1:21 - Neurons_All = [1] + [6, 7, 8, 10] + list(range(12, 22)) # [1 6 7 8 10 12:21] - ECG_All = [1, 3, 5, 6, 9, 10, 14, 15] # [1 3 5 6 9 10 14 15] + list(range(1, 22)) # 1:21 + [1] + [6, 7, 8, 10] + list(range(12, 22)) # [1 6 7 8 10 12:21] # Define experiment categories MFB_keys = [ @@ -416,7 +415,7 @@ def add_experiment( for i, path in enumerate(Dir["path"]): try: Dir["manipe"].append(Dir["expe_info"][i]["SessionType"].item(0)[0]) - except: + except KeyError: Dir["manipe"].append(experiment_name) # Adjust manipe for sub experiments diff --git a/neuroencoders/utils/PlaceField_dB.py b/neuroencoders/utils/PlaceField_dB.py index 7c58464..556ddce 100755 --- a/neuroencoders/utils/PlaceField_dB.py +++ b/neuroencoders/utils/PlaceField_dB.py @@ -259,7 +259,7 @@ def get_time_range(tsd_obj): return support.values[0, 0], support.values[0, 1] else: return np.min(support), np.max(support) - except: + except AttributeError: pass # Fallback: use time range from timestamps @@ -286,7 +286,7 @@ def realign_spikes(pos_tsd, spike_tsd, method="closest"): # Try restrict method try: return spike_tsd.restrict(pos_tsd, align=method) - except: + except AttributeError: pass # Manual interpolation fallback @@ -309,7 +309,7 @@ def realign_spikes(pos_tsd, spike_tsd, method="closest"): if HAS_NEUROSERIES: try: return nts.Tsd(spike_times, spike_pos) - except: + except AttributeError: pass # Return as simple object with times and values @@ -392,7 +392,7 @@ def PlaceField_DB( try: t_start, t_end = get_time_range(pos_x) total_time = t_end - t_start - except: + except AttributeError: # Fallback time calculation if hasattr(pos_x, "times"): times = pos_x.times() @@ -414,7 +414,7 @@ def PlaceField_DB( if HAS_NEUROSERIES: try: poisson_spike_times = nts.Tsd(poisson_times_abs, poisson_times_abs) - except: + except AttributeError: # Fallback: simple object class SimpleTsd: def __init__(self, times): @@ -447,7 +447,7 @@ def __len__(self): if HAS_NEUROSERIES: try: poisson_spike_times = nts.Tsd(np.array([]), np.array([])) - except: + except AttributeError: class SimpleTsd: def __init__(self): @@ -713,13 +713,13 @@ def _run_place_field_analysis( epoch_length = epoch.tot_length() else: epoch_length = np.sum(epoch[:, 1] - epoch[:, 0]) - except: + except AttributeError: epoch_length = get_time_range(pos_x)[1] - get_time_range(pos_x)[0] else: try: t_start, t_end = get_time_range(pos_x) epoch_length = t_end - t_start - except: + except AttributeError: epoch_length = 1.0 # Fallback results["firing_rate"] = len(spike_times) / epoch_length if epoch_length > 0 else 0 diff --git a/neuroencoders/utils/Spike.py b/neuroencoders/utils/Spike.py index 4079c8a..627919c 100755 --- a/neuroencoders/utils/Spike.py +++ b/neuroencoders/utils/Spike.py @@ -12,7 +12,7 @@ def openstruc(struc, name, L): import h5py - if type(struc) != h5py._hl.dataset.Dataset: + if not isinstance(struc, h5py._hl.dataset.Dataset): if "start" in list(struc.keys()): for i in range(len(L)): if L[i].endswith(name): @@ -32,10 +32,10 @@ def ref2str(ref, file): for i in range(len(ref)): try: L = list(np.squeeze(file[ref[i]][:])) - if type(L[0]) == int: + if isinstance(L[0], int): S = "" - for l in L: - S += chr(l) + for shank in L: + S += chr(shank) out.append(S) else: out.append(L) @@ -84,9 +84,9 @@ def __init__(self, path, time_unit="us"): self.Nb_clusters = Nb_clusters self.info = {} for k in keys: - if type(spikes[k]) == h5py._hl.dataset.Dataset: + if isinstance(spikes[k], h5py._hl.dataset.Dataset): try: - if type(np.squeeze(spikes[k][:])[0]) == h5py.h5r.Reference: + if isinstance(np.squeeze(spikes[k][:])[0], h5py.h5r.Reference): self.info[k] = ref2str(np.squeeze(spikes[k][:]), spikes) else: self.info[k] = np.squeeze(spikes[k][:]) @@ -95,32 +95,37 @@ def __init__(self, path, time_unit="us"): else: L = [] openstruc(spikes[k], k, L) - for l in L: - if l.endswith("_intset"): - l = l[0:-7] - start = np.squeeze(spikes[l]["start"][:]) - stop = np.squeeze(spikes[l]["stop"][:]) - self.info[l] = nts.IntervalSet( + for shank_key in L: + if shank_key.endswith("_intset"): + # Remove the "_intset" suffix to get the actual shank name + shank_name = shank_key[0:-7] + start = np.squeeze(spikes[shank_name]["start"][:]) + stop = np.squeeze(spikes[shank_name]["stop"][:]) + self.info[shank_name] = nts.IntervalSet( start, stop, time_units=time_unit ) - elif type(np.squeeze(spikes[l][:])[0]) == h5py.h5r.Reference: - self.info[l] = ref2str(np.squeeze(spikes[l][:]), spikes) + elif isinstance( + np.squeeze(spikes[shank_key][:])[0], h5py.h5r.Reference + ): + self.info[shank_key] = ref2str( + np.squeeze(spikes[shank_key][:]), spikes + ) else: - self.info[l] = np.squeeze(spikes[l][:]) + self.info[shank_key] = np.squeeze(spikes[shank_key][:]) def get_spikes(self, idx=None): import numpy as np - if type(idx) == np.ndarray: + if isinstance(idx, np.ndarray): idx = list(idx) if idx is None: return self.S - elif type(idx) == int: + elif isinstance(idx, int): return self.S[idx] - elif type(idx) == list: + elif isinstance(idx, list): return [self.S[i] for i in idx] - elif type(idx[0]) == np.bool_: + elif isinstance(idx[0], np.bool_): return [self.S[i] for i in range(len(idx)) if idx[i]] def features(self): diff --git a/neuroencoders/utils/SpikeLoading.py b/neuroencoders/utils/SpikeLoading.py index bbd3515..fba0440 100755 --- a/neuroencoders/utils/SpikeLoading.py +++ b/neuroencoders/utils/SpikeLoading.py @@ -12,6 +12,15 @@ import numpy as np import pandas as pd +import spikeinterface as si +import spikeinterface.extractors as se +import spikeinterface.preprocessing as spre +import spikeinterface.qualitymetrics + +## import glob +from probeinterface import generate_linear_probe +from resultAnalysis.print_results import print_results +from spikeinterface.sorters import read_sorter_folder, run_sorter datadir = os.path.join(os.path.expanduser("~/Documents/Theotime"), "DimaERC2") assert os.path.isdir(datadir) @@ -150,8 +159,6 @@ def get_size(file_path, unit="bytes"): # In[15]: -from resultAnalysis.print_results import print_results - # In[16]: @@ -265,11 +272,6 @@ def get_size(file_path, unit="bytes"): # In[25]: -import spikeinterface as si -import spikeinterface.extractors as se -import spikeinterface.preprocessing as spre -import spikeinterface.qualitymetrics - # In[26]: @@ -300,10 +302,6 @@ def get_size(file_path, unit="bytes"): # In[63]: -## import glob -from probeinterface import generate_linear_probe -from spikeinterface.sorters import read_sorter_folder, run_sorter - required_extensions = [ "random_spikes", "waveforms", diff --git a/neuroencoders/utils/__init__.py b/neuroencoders/utils/__init__.py index 04fa9a1..32c1168 100644 --- a/neuroencoders/utils/__init__.py +++ b/neuroencoders/utils/__init__.py @@ -5,5 +5,7 @@ - Project, DataHelper (from global_classes) """ -from . import MOBS_Functions, global_classes -from .global_classes import DataHelper, Project +from . import MOBS_Functions as MOBS_Functions +from . import global_classes as global_classes +from .global_classes import DataHelper as DataHelper +from .global_classes import Project as Project diff --git a/neuroencoders/utils/global_classes.py b/neuroencoders/utils/global_classes.py index 416ef6f..d163ef4 100644 --- a/neuroencoders/utils/global_classes.py +++ b/neuroencoders/utils/global_classes.py @@ -109,12 +109,11 @@ def __init__(self, xmlPath, *args, **kwargs): else: self.dat = datPath self.fil = self.dat[:-4] + ".fil" + # Folders - findFolder = ( - lambda path: path - if path[-1] == "/" or len(path) == 0 - else findFolder(path[:-1]) - ) + def findFolder(path): + return path if path[-1] == "/" or len(path) == 0 else findFolder(path[:-1]) + self.folder = findFolder(self.dat) self.dataPath = os.path.join(self.folder, "dataset") # Allows change at every experiment @@ -331,11 +330,6 @@ def __init__( super().__init__(xmlPath, *args, **kwargs) - self.resultsPath = os.path.join( - self.experimentPath, - "results", - str(int(self.windowSizeMS)), - ) self.suffix = f"_{self.phase}" if self.phase is not None else "" self.list_channels, self.samplingRate, self.nChannels = get_params(self.xml) @@ -411,7 +405,9 @@ def maxPos(self): """ maxPos = np.max( - self.positions[np.logical_not(np.isnan(np.sum(self.positions, axis=1)))], + self.positions[ + np.logical_not(np.isnan(np.sum(self.positions, axis=1))), :2 + ], axis=0, ) return maxPos if self.mode != "decode" else 1 @@ -437,11 +433,14 @@ def setThresholds(self, thresholds): assert [len(d) for d in thresholds] == [len(s) for s in self.list_channels] self.thresholds = [i for d in thresholds for i in d] - def get_true_target(self, l_function=None, in_place=False, show=False, **kwargs): + def get_true_target( + self, windowSizeMS=108, l_function=None, in_place=False, show=False, **kwargs + ): """ Returns the true target of interest by looking and modifying the positions array. Args: + - windowSizeMS: the window size in milliseconds, defaults to 108 (for speed smoothing) - l_function: a function that takes a position and returns the linearized position - in_place: whether to modify the positions array in place - show: whether to show the distance to the wall @@ -517,7 +516,7 @@ def get_true_target(self, l_function=None, in_place=False, show=False, **kwargs) _, positions = l_function(self.positions) positions = positions.reshape(-1) self.speed = self._get_speed( - self.positions, interval=1 / (15 // self.windowSizeMS) + self.positions, interval=1 / (15 // windowSizeMS) ) positions = np.concatenate( (positions.reshape(-1, 1), self.speed.reshape(-1, 1)), axis=1 @@ -599,7 +598,7 @@ def get_true_target(self, l_function=None, in_place=False, show=False, **kwargs) prediction_time=data_helper.fullBehavior["positionTime"], **kwargs, ) - anim = plotter.show(interval=1, repeat=True, block=True) + plotter.show(interval=1, repeat=True, block=True, blit=False) if in_place: if not hasattr(self, "old_positions"): @@ -838,14 +837,14 @@ def _get_ref_and_xy_from_behavResources(self, phase="Cond1", save=True, plot=Fal f"Using the first session that contains {phase}: {session_names[idx[0]]}" ) idx = idx[0] - root = f["behavResources"] + root = f["behavResources"] else: idx = 0 root = f try: - self.ref = root["ref"][0][idx] idx_shock = list(root["ZoneLabels"][0][idx][0]).index("Shock") + self.ref = root["ref"][0][idx] self.shock_zone_mask = root["Zone"][0][idx][0][idx_shock] Xdata = root["Xtsd"][0][idx][0][0][-2].flatten() Ydata = root["Ytsd"][0][idx][0][0][-2].flatten() @@ -863,7 +862,7 @@ def _get_ref_and_xy_from_behavResources(self, phase="Cond1", save=True, plot=Fal positions = np.array([Xdata, Ydata]).T - self.xyOutput = self._get_XYOutput_morph_maze( + self.xyOutput, positions = self._get_XYOutput_morph_maze( positions, self.shock_zone_mask, self.ref, self.ratioIMAonREAL ) @@ -967,6 +966,11 @@ def _get_XYOutput_morph_maze( reset_button = Button( ax_reset, "Reset", color="lightgoldenrodyellow", hovercolor="0.975" ) + # Add rotate button + ax_rotate = plt.axes([0.6, 0.025, 0.1, 0.04]) + rotate_button = Button( + ax_rotate, "Rotate XY", color="lightgoldenrodyellow", hovercolor="0.975" + ) # Coordinates storage coords = [] @@ -988,8 +992,18 @@ def reset(event): # Redraw canvas fig.canvas.draw_idle() + def rotate_positions(event): + # simply swap x and y axes + positions[:, [0, 1]] = positions[:, [1, 0]] + scatter.set_data( + positions[:, 1] * Ratio_IMAonREAL, + positions[:, 0] * Ratio_IMAonREAL, + ) + fig.canvas.draw_idle() + slider.on_changed(update) reset_button.on_clicked(reset) + rotate_button.on_clicked(rotate_positions) # Restrict ginput to the plot area and add red stars def on_click(event): @@ -1010,7 +1024,7 @@ def on_click(event): x, y = zip(*coords) XYOutput = np.array([y, x]) plt.close(fig) - return XYOutput + return XYOutput, positions def _transform_coordinates_and_image( self, @@ -1552,8 +1566,7 @@ def __new__(cls, *args, load_path=None, **kwargs): else: params_path = os.path.abspath( os.path.join( - helper.resultsPath, - "..", + helper.folderResult, str(int(windowSize * 1000)), "Parameters.pkl", ) @@ -1685,13 +1698,12 @@ def _initialize_params_attributes(self, helper, **kwargs): helper.target ) # target to predict, e.g. "pos", "lin", "direction", etc. self.resultsPath = os.path.join( - helper.resultsPath, - "..", + helper.folderResult, str(int(self.windowSizeMS)), ) # path to save results # regarding data augmentation - self.dataAugmentation = kwargs.pop("dataAugmentation", True) + self.dataAugmentation = kwargs.pop("dataAugmentation", False) # TODO: check if this is still relevant # WARNING: maybe striding is actually 0.036 ms based ??? diff --git a/neuroencoders/utils/wrappers.py b/neuroencoders/utils/wrappers.py index 539d965..9dce7be 100755 --- a/neuroencoders/utils/wrappers.py +++ b/neuroencoders/utils/wrappers.py @@ -82,7 +82,7 @@ def loadSpikeData(path, index=None, fs=20000): ) shank = spikes.columns.get_level_values(0).values[:, np.newaxis] return toreturn, shank - except: + except (OSError, KeyError): spikes = pd.HDFStore(final_path, "r") shanks = spikes["/shanks"] toreturn = {} @@ -148,7 +148,7 @@ def loadSpikeData(path, index=None, fs=20000): shank = [] for s in spikes: shank.append(s.columns.get_level_values(0).values) - sh = np.unique(shank[-1])[0] + np.unique(shank[-1])[0] for i, j in s: toreturn[j] = nts.Ts( t=s[(i, j)].replace(0, np.nan).dropna().index.values, time_units="s" @@ -277,7 +277,7 @@ def downsampleDatFile(path, n_channels, fs): endoffile = f.seek(0, 2) bytes_size = 2 n_samples = int((endoffile - startoffile) / n_channels / bytes_size) - duration = n_samples / fs + n_samples / fs f.close() chunksize = 100000 @@ -397,9 +397,9 @@ def makePositions( print("The path " + path + " doesn't exist; Exiting ...") sys.exit() files = os.listdir(path) - for f in file_order: - if not np.any([f + ".csv" in g for g in files]): - print("Could not find " + f + ".csv; Exiting ...") + for file in file_order: + if not np.any([file + ".csv" in g for g in files]): + print("Could not find " + file + ".csv; Exiting ...") sys.exit() path = os.path.join(path, "Analysis/") if not os.path.exists(path): @@ -416,8 +416,8 @@ def makePositions( frames = [] - for i, f in enumerate(file_order): - csv_file = os.path.join(path, "".join(s for s in files if f + ".csv" in s)) + for i, file in enumerate(file_order): + csv_file = os.path.join(path, "".join(s for s in files if file + ".csv" in s)) position = pd.read_csv(csv_file, header=[4, 5], index_col=1) if 1 in position.columns: position = position.drop(labels=1, axis=1) @@ -517,11 +517,11 @@ def loadEpoch(path, epoch, episodes=None): if "sleepPreEp" in behepochs.keys(): sleep_pre_ep = behepochs["sleepPreEp"][0][0] sleep_pre_ep = np.hstack([sleep_pre_ep[1], sleep_pre_ep[2]]) - sleep_pre_ep_index = behepochs["sleepPreEpIx"][0] + behepochs["sleepPreEpIx"][0] if "sleepPostEp" in behepochs.keys(): sleep_post_ep = behepochs["sleepPostEp"][0][0] sleep_post_ep = np.hstack([sleep_post_ep[1], sleep_post_ep[2]]) - sleep_post_ep_index = behepochs["sleepPostEpIx"][0] + behepochs["sleepPostEpIx"][0] if len(sleep_pre_ep) and len(sleep_post_ep): sleep_ep = np.vstack((sleep_pre_ep, sleep_post_ep)) elif len(sleep_pre_ep): @@ -535,15 +535,15 @@ def loadEpoch(path, epoch, episodes=None): elif epoch == "sws": sampling_freq = 1250 new_listdir = os.listdir(path) - for f in new_listdir: - if "sts.SWS" in f: - sws = np.genfromtxt(os.path.join(path, f)) / float(sampling_freq) + for file in new_listdir: + if "sts.SWS" in file: + sws = np.genfromtxt(os.path.join(path, file)) / float(sampling_freq) return nts.IntervalSet.drop_short_intervals( nts.IntervalSet(sws[:, 0], sws[:, 1], time_units="s"), 0.0 ) - elif "-states.mat" in f: - sws = scipy.io.loadmat(os.path.join(path, f))["states"][0] + elif "-states.mat" in file: + sws = scipy.io.loadmat(os.path.join(path, file))["states"][0] index = np.logical_or(sws == 2, sws == 3) * 1.0 index = index[1:] - index[0:-1] start = np.where(index == 1)[0] + 1 @@ -556,15 +556,15 @@ def loadEpoch(path, epoch, episodes=None): elif epoch == "rem": sampling_freq = 1250 new_listdir = os.listdir(path) - for f in new_listdir: - if "sts.REM" in f: - rem = np.genfromtxt(os.path.join(path, f)) / float(sampling_freq) + for file in new_listdir: + if "sts.REM" in file: + rem = np.genfromtxt(os.path.join(path, file)) / float(sampling_freq) return nts.IntervalSet( rem[:, 0], rem[:, 1], time_units="s" ).drop_short_intervals(0.0) elif "-states/m" in listdir: - rem = scipy.io.loadmat(path + f)["states"][0] + rem = scipy.io.loadmat(path + file)["states"][0] index = (rem == 5) * 1.0 index = index[1:] - index[0:-1] start = np.where(index == 1)[0] + 1 @@ -677,7 +677,7 @@ def loadAuxiliary(path, fs=20000): else: aux_files = np.sort([f for f in os.listdir(path) if "auxiliary" in f]) if len(aux_files) == 0: - print("Could not find " + f + "_auxiliary.dat; Exiting ...") + print("Could not find any file in" + path + "_auxiliary.dat; Exiting ...") sys.exit() accel = [] sample_size = [] @@ -688,7 +688,7 @@ def loadAuxiliary(path, fs=20000): endoffile = f.seek(0, 2) bytes_size = 2 n_samples = int((endoffile - startoffile) / 3 / bytes_size) - duration = n_samples / fs + n_samples / fs f.close() tmp = np.fromfile(open(path, "rb"), np.uint16).reshape(n_samples, 3) accel.append(tmp) @@ -773,8 +773,8 @@ def loadLFP(path, n_channels=90, channel=64, frequency=1250.0, precision="int16" endoffile = f.seek(0, 2) bytes_size = 2 n_samples = int((endoffile - startoffile) / n_channels / bytes_size) - duration = n_samples / frequency - interval = 1 / frequency + n_samples / frequency + 1 / frequency f.close() with open(path, "rb") as f: data = np.fromfile(f, np.int16).reshape((n_samples, n_channels))[:, channel] @@ -787,7 +787,7 @@ def loadLFP(path, n_channels=90, channel=64, frequency=1250.0, precision="int16" bytes_size = 2 n_samples = int((endoffile - startoffile) / n_channels / bytes_size) - duration = n_samples / frequency + n_samples / frequency f.close() with open(path, "rb") as f: data = np.fromfile(f, np.int16).reshape((n_samples, n_channels))[:, channel] diff --git a/notebooks/Compare Bayes.ipynb b/notebooks/Compare Bayes.ipynb deleted file mode 100644 index 0508eca..0000000 --- a/notebooks/Compare Bayes.ipynb +++ /dev/null @@ -1,67 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "id": "5a987d6d-9ca8-412f-9478-a3e90369dc41", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "from importData.rawdata_parser import DataHelper\n", - "from resultAnalysis.print_results import print_results\n", - "from resultAnalysis.paper_figures import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a840030a-a035-4db8-84fc-cbc857d7c1c0", - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'neuroEncoders'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneuroEncoders\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresultAnalysis\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'neuroEncoders'" - ] - } - ], - "source": [ - "projectPath = \"/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1199_reversal/Final_results_v3\"\n", - "\n", - "paperFigures = PaperFigures()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/ResultsLoading.ipynb b/notebooks/ResultsLoading.ipynb deleted file mode 100644 index 35821ca..0000000 --- a/notebooks/ResultsLoading.ipynb +++ /dev/null @@ -1,2370 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 112, - "id": "47d5710b-d19d-475c-98a4-8727bbb0d240", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from pathlib import Path\n", - "import seaborn as sns\n", - "from importlib import reload" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "144eda4c-bb3d-40b0-9a4b-d25b960e44a9", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "e51a4b41-a3da-412e-a402-efc81a795e79", - "metadata": {}, - "outputs": [], - "source": [ - "current_dir = os.getcwd()\n", - "datadir = os.path.join(Path(current_dir).parents[1], \"DimaERC2\")\n", - "assert os.path.isdir(datadir)" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "ba840456-4b44-4666-bb83-0b69c7e0198d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/vador/Documents/Theotime/DimaERC2'" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datadir" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "20a51ef4-f19e-4ee9-a2f4-7df6d7abf45b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/vador/Documents/Theotime/neuroEncoders/notebooks'" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "current_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "a50d78bb-bd6a-461e-ae8c-5016038cb20d", - "metadata": {}, - "outputs": [], - "source": [ - "# What Basile did\n", - "BasileMiceNumber = [\n", - " \"1239vBasile\",\n", - " \"1281vBasile\",\n", - " \"1199\",\n", - " \"1336\",\n", - " \"1168MFB\",\n", - " \"905\",\n", - " \"1161w1199\",\n", - " \"1161\",\n", - " \"1124\",\n", - " \"1186\",\n", - " \"1182\",\n", - " \"1168UMaze\",\n", - " \"1117\",\n", - " \"994\",\n", - " \"1336v3\",\n", - " \"1336v2\",\n", - " \"1281v2\",\n", - " \"1239v3\",\n", - "]\n", - "\n", - "# What Dima did according to Baptiste\n", - "DimaMiceNumber = [\n", - " \"905\",\n", - " \"906\",\n", - " \"911\",\n", - " \"994\",\n", - " \"1161\",\n", - " \"1162\",\n", - " \"1168\",\n", - " \"1186\",\n", - " \"1230\",\n", - " \"1239\",\n", - "]\n", - "\n", - "# Files wrt to datadir\n", - "path_list = [\n", - " \"M1239TEST3_Basile_M1239/TEST\",\n", - " \"M1281TEST3_Basile_1281MFB/TEST\",\n", - " \"M1199TEST1_Basile/TEST\",\n", - " \"M1336_Known/TEST/\",\n", - " # \"DataERC2/M994/20191013/TEST/\",\n", - " # \"DataERC2/M906/TEST/\",\n", - " \"DataERC2/M1168/TEST/\",\n", - " \"DataERC2/M905/TEST/\",\n", - " \"DataERC2/M1161/TEST_with_1199_model/\",\n", - " \"DataERC2/M1161/TEST initial/\",\n", - " \"DataERC2/M1124/TEST/\",\n", - " \"DataERC2/M1186/TEST/\",\n", - " \"DataERC2/M1182/TEST/\",\n", - " \"DataERC1/M1168/TEST/\",\n", - " \"DataERC1/M1117/TEST/\",\n", - " \"neuroencoders_1021/_work/M994_PAG/Final_results_v3\",\n", - " \"neuroencoders_1021/_work/M1336_MFB/Final_results_v3\",\n", - " \"neuroencoders_1021/_work/M1336_known/Final_results_v2\",\n", - " \"neuroencoders_1021/_work/M1281_MFB/Final_results_v2\",\n", - " \"neuroencoders_1021/_work/M1239_MFB/Final_results_v3\",\n", - "]\n", - "assert len(BasileMiceNumber) == len(path_list)\n", - "len(BasileMiceNumber)\n", - "path_dict = dict(zip(BasileMiceNumber, path_list))" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "id": "afeafe42-5313-475c-8ae5-b91cad46b7f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'DataERC2/M1168/TEST/'" - ] - }, - "execution_count": 191, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1168MFB does not have any results\n", - "path_dict.pop(\"1168MFB\")" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "id": "8b74abbd-35f9-471c-b0f1-864fc469bd98", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1239vBasile': 'M1239TEST3_Basile_M1239/TEST',\n", - " '1281vBasile': 'M1281TEST3_Basile_1281MFB/TEST',\n", - " '1199': 'M1199TEST1_Basile/TEST',\n", - " '1336': 'M1336_Known/TEST/',\n", - " '905': 'DataERC2/M905/TEST/',\n", - " '1161w1199': 'DataERC2/M1161/TEST_with_1199_model/',\n", - " '1161': 'DataERC2/M1161/TEST initial/',\n", - " '1124': 'DataERC2/M1124/TEST/',\n", - " '1186': 'DataERC2/M1186/TEST/',\n", - " '1182': 'DataERC2/M1182/TEST/',\n", - " '1168UMaze': 'DataERC1/M1168/TEST/',\n", - " '1117': 'DataERC1/M1117/TEST/',\n", - " '994': 'neuroencoders_1021/_work/M994_PAG/Final_results_v3',\n", - " '1336v3': 'neuroencoders_1021/_work/M1336_MFB/Final_results_v3',\n", - " '1336v2': 'neuroencoders_1021/_work/M1336_known/Final_results_v2',\n", - " '1281v2': 'neuroencoders_1021/_work/M1281_MFB/Final_results_v2',\n", - " '1239v3': 'neuroencoders_1021/_work/M1239_MFB/Final_results_v3'}" - ] - }, - "execution_count": 200, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "id": "755d623d-51cb-4817-aca3-b892a47a7da8", - "metadata": {}, - "outputs": [], - "source": [ - "conditions = {\n", - " \"MFB\": [\"1281vBasile\", \"1281v3\" \"1239vBasile\", \"1239v3\", \"1336v3\", \"1336v2\"],\n", - " \"Known\": [\"1336\", \"1336v3\"],\n", - " \"PAG\": [\"1186\", \"1161\", \"1161w1199\", \"1124\", \"1186\", \"1117\", \"1199\", \"994\"],\n", - " \"Umaze\": [\"1199\", \"906\", \"1168\", \"905\", \"1182\"],\n", - "}\n", - "\n", - "list_windows = [36, 108, 200, 252, 504]" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "id": "b4849028-5759-4540-8c40-63a05f39b903", - "metadata": {}, - "outputs": [], - "source": [ - "def get_size(file_path, unit=\"bytes\"):\n", - " file_size = os.path.getsize(file_path)\n", - " exponents_map = {\"bytes\": 0, \"kb\": 1, \"mb\": 2, \"gb\": 3}\n", - " if unit not in exponents_map:\n", - " raise ValueError(\n", - " \"Must select from \\\n", - " ['bytes', 'kb', 'mb', 'gb']\"\n", - " )\n", - " else:\n", - " size = file_size / 1024 ** exponents_map[unit]\n", - " return round(size, 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "id": "4423af7f-e551-4260-85f0-2267cd5f2b01", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "from importData.rawdata_parser import DataHelper\n", - "from resultAnalysis.print_results import print_results" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "id": "d6d6d3c7-212b-4dba-97bb-81dc20b1e566", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[36, 108, 200, 252, 504]" - ] - }, - "execution_count": 204, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_windows" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "id": "a29ff955-3f64-42be-b6c6-21366fa6110c", - "metadata": {}, - "outputs": [], - "source": [ - "import glob" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "id": "effecef9-dfd6-43a6-a408-b9e4ef0b2c3b", - "metadata": {}, - "outputs": [], - "source": [ - "keys_to_include = set()\n", - "size_dat = dict()\n", - "for mouse, path in path_dict.items():\n", - " path = os.path.join(datadir, path, \"../\")\n", - " if len(glob.glob(path + \"*.dat\")) >= 1:\n", - " keys_to_include.add(mouse)\n", - " size_dat[mouse] = get_size(glob.glob(path + \"*.dat\")[0], unit=\"gb\")\n", - "\n", - "dath_dict = {k: path_dict[k] for k in keys_to_include}" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "id": "9e4e203b-0aef-4abe-a2f6-29cf7f5232fe", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total windows: 103424 | selected windows: 31027 (thresh -4.3133297 )\n", - "mean eucl. error: 0.4099105329433457 | selected error: 0.3388784961711805\n", - "mean linear error: 0.24068852490717824 | selected error: 0.18432333129210043\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1239TEST3_Basile_M1239/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 21888 | selected windows: 6566 (thresh -4.140966 )\n", - "mean eucl. error: 0.3900984639396719 | selected error: 0.35825975326772563\n", - "mean linear error: 0.2728143274853801 | selected error: 0.21335820895522387\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1239TEST3_Basile_M1239/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9856 | selected windows: 2956 (thresh -4.9398165 )\n", - "mean eucl. error: 0.38621183691339656 | selected error: 0.3790181510435826\n", - "mean linear error: 0.21885349025974027 | selected error: 0.18813261163734776\n", - "total windows: 108416 | selected windows: 32524 (thresh -3.0905595 )\n", - "mean eucl. error: 0.5096774095797851 | selected error: 0.48791706011258873\n", - "mean linear error: 0.2487205762987013 | selected error: 0.21851801746402658\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1281TEST3_Basile_1281MFB/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 22784 | selected windows: 6835 (thresh -3.8569858 )\n", - "mean eucl. error: 0.5248040472074363 | selected error: 0.4814915129713809\n", - "mean linear error: 0.2689255617977528 | selected error: 0.20954498902706656\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1281TEST3_Basile_1281MFB/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9856 | selected windows: 2956 (thresh -1.1291519 )\n", - "mean eucl. error: 0.5180738521512005 | selected error: 0.5089591672519789\n", - "mean linear error: 0.2554961444805195 | selected error: 0.23500338294993237\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 18944 | selected windows: 5683 (thresh -4.1417675 )\n", - "mean eucl. error: 0.41348124954996934 | selected error: 0.31071941895418564\n", - "mean linear error: 0.18181218327702703 | selected error: 0.13149920816470176\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/504/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 104320 | selected windows: 31296 (thresh -4.14909 )\n", - "mean eucl. error: 0.45229839142678147 | selected error: 0.3949391303763814\n", - "mean linear error: 0.33679495782208585 | selected error: 0.2506697341513292\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1336_Known/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23168 | selected windows: 6950 (thresh -4.0340347 )\n", - "mean eucl. error: 0.4198770464172988 | selected error: 0.3882440986379923\n", - "mean linear error: 0.28290789019337015 | selected error: 0.24212517985611512\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1336_Known/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9728 | selected windows: 2918 (thresh -4.537151 )\n", - "mean eucl. error: 0.4584586775672959 | selected error: 0.46391458953846737\n", - "mean linear error: 0.29336965460526315 | selected error: 0.28494859492803293\n", - "total windows: 98304 | selected windows: 29491 (thresh -2.5121055 )\n", - "mean eucl. error: 0.46561207211367456 | selected error: 0.4692852967657072\n", - "mean linear error: 0.5407175699869792 | selected error: 0.48223254552236283\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 21632 | selected windows: 6489 (thresh -3.6158442 )\n", - "mean eucl. error: 0.4459823902921189 | selected error: 0.4385799076320848\n", - "mean linear error: 0.38199981508875747 | selected error: 0.363364154723378\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 10880 | selected windows: 3264 (thresh -3.842236 )\n", - "mean eucl. error: 0.4410981135495719 | selected error: 0.4464837717645344\n", - "mean linear error: 0.39765900735294113 | selected error: 0.3948314950980392\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/36/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/108/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "total windows: 12672 | selected windows: 3801 (thresh -3.879144 )\n", - "mean eucl. error: 0.46867128023110105 | selected error: 0.45676790314536814\n", - "mean linear error: 0.22071338383838385 | selected error: 0.22304393580636675\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/252/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/504/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "total windows: 137472 | selected windows: 41241 (thresh -4.430786 )\n", - "mean eucl. error: 0.5136216538508177 | selected error: 0.4670296356827056\n", - "mean linear error: 0.26214414571694605 | selected error: 0.23317790548240827\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 29696 | selected windows: 8908 (thresh -3.9554555 )\n", - "mean eucl. error: 0.47114616746719096 | selected error: 0.4184275651909268\n", - "mean linear error: 0.23238213900862068 | selected error: 0.20358105074090704\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 12672 | selected windows: 3801 (thresh -3.879144 )\n", - "mean eucl. error: 0.46867128023110105 | selected error: 0.45676790314536814\n", - "mean linear error: 0.22071338383838385 | selected error: 0.22304393580636675\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 29056 | selected windows: 8716 (thresh -13.275983 )\n", - "mean eucl. error: 0.4742163338503855 | selected error: 0.4512791809625954\n", - "mean linear error: 0.2035280148678414 | selected error: 0.19351537402478203\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/504/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 109184 | selected windows: 32755 (thresh -4.0972867 )\n", - "mean eucl. error: 0.5177043757421281 | selected error: 0.5134948634716336\n", - "mean linear error: 0.25466377857561545 | selected error: 0.22837459929781712\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23424 | selected windows: 7027 (thresh -3.8716633 )\n", - "mean eucl. error: 0.4485788831234556 | selected error: 0.4099108151783293\n", - "mean linear error: 0.19302510245901638 | selected error: 0.1675323751245197\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9984 | selected windows: 2995 (thresh -4.246581 )\n", - "mean eucl. error: 0.47557516402162603 | selected error: 0.4471246174005367\n", - "mean linear error: 0.21452624198717948 | selected error: 0.20374624373956596\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 18816 | selected windows: 5644 (thresh -3.850906 )\n", - "mean eucl. error: 0.4650601784604804 | selected error: 0.37909361789913987\n", - "mean linear error: 0.1717782738095238 | selected error: 0.11447909284195608\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/504/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 127104 | selected windows: 38131 (thresh -3.392338 )\n", - "mean eucl. error: 0.42899520802294094 | selected error: 0.34520146855821215\n", - "mean linear error: 0.2818879028197382 | selected error: 0.1879788098922137\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 28032 | selected windows: 8409 (thresh -3.5557854 )\n", - "mean eucl. error: 0.4253206466239176 | selected error: 0.38377802705632474\n", - "mean linear error: 0.2908258418949772 | selected error: 0.2696598882150077\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 12288 | selected windows: 3686 (thresh -3.8141289 )\n", - "mean eucl. error: 0.41715469355585255 | selected error: 0.39095112861006864\n", - "mean linear error: 0.2883528645833333 | selected error: 0.235732501356484\n", - "total windows: 110848 | selected windows: 33254 (thresh -3.5723522 )\n", - "mean eucl. error: 0.4740902712106153 | selected error: 0.4301594067888825\n", - "mean linear error: 0.2845248448325635 | selected error: 0.25105430925602934\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23552 | selected windows: 7065 (thresh -4.022328 )\n", - "mean eucl. error: 0.45928378428877065 | selected error: 0.41070582195492183\n", - "mean linear error: 0.2775339673913043 | selected error: 0.2384345364472753\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 10240 | selected windows: 3072 (thresh -4.859231 )\n", - "mean eucl. error: 0.45251948404943737 | selected error: 0.4737108638114196\n", - "mean linear error: 0.3188984375 | selected error: 0.32421549479166667\n", - "total windows: 6528 | selected windows: 1958 (thresh -4.797138 )\n", - "mean eucl. error: 0.308457394288779 | selected error: 0.23412658957435312\n", - "mean linear error: 0.1549984681372549 | selected error: 0.1058988764044944\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.7340493 )\n", - "mean eucl. error: 0.24791512898533571 | selected error: 0.17341864632277373\n", - "mean linear error: 0.1297227328431373 | selected error: 0.07499489274770174\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.997062 )\n", - "mean eucl. error: 0.22262539966188583 | selected error: 0.16894934097947953\n", - "mean linear error: 0.10720128676470589 | selected error: 0.0710520939734423\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.8903008 )\n", - "mean eucl. error: 0.2118937043753728 | selected error: 0.17612127014460588\n", - "mean linear error: 0.10722886029411764 | selected error: 0.08100612870275792\n", - "total windows: 13184 | selected windows: 3955 (thresh -4.3813343 )\n", - "mean eucl. error: 0.3575848308310594 | selected error: 0.25840760054430034\n", - "mean linear error: 0.23859071601941748 | selected error: 0.1498078381795196\n", - "total windows: 13184 | selected windows: 3955 (thresh -5.2892494 )\n", - "mean eucl. error: 0.2981273008685602 | selected error: 0.21064261732409612\n", - "mean linear error: 0.19825015169902913 | selected error: 0.11969911504424778\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_MFB/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 13184 | selected windows: 3955 (thresh -5.944226 )\n", - "mean eucl. error: 0.2682479383957932 | selected error: 0.19420851572601364\n", - "mean linear error: 0.17593446601941748 | selected error: 0.11053603034134007\n", - "total windows: 13312 | selected windows: 3993 (thresh -6.129132 )\n", - "mean eucl. error: 0.2500419149008913 | selected error: 0.1857000627422962\n", - "mean linear error: 0.16785381610576922 | selected error: 0.1136839469070874\n", - "total windows: 12928 | selected windows: 3878 (thresh -3.9968202 )\n", - "mean eucl. error: 0.4512924509004647 | selected error: 0.4059579827896158\n", - "mean linear error: 0.28356590346534655 | selected error: 0.21131768953068591\n", - "total windows: 12928 | selected windows: 3878 (thresh -4.744326 )\n", - "mean eucl. error: 0.4379316132700753 | selected error: 0.3772838136471705\n", - "mean linear error: 0.2955901918316831 | selected error: 0.23187983496647757\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_known/Final_results_v2/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 12928 | selected windows: 3878 (thresh -5.9330926 )\n", - "mean eucl. error: 0.44579350729398987 | selected error: 0.4120424070445943\n", - "mean linear error: 0.3065578589108911 | selected error: 0.28296028880866425\n", - "total windows: 13056 | selected windows: 3916 (thresh -6.1029983 )\n", - "mean eucl. error: 0.4094791559633769 | selected error: 0.3680947279028363\n", - "mean linear error: 0.2776179534313725 | selected error: 0.24181562819203267\n", - "total windows: 14208 | selected windows: 4262 (thresh -3.3976035 )\n", - "mean eucl. error: 0.5212641443494755 | selected error: 0.5035234910117191\n", - "mean linear error: 0.26170326576576575 | selected error: 0.22459174096668233\n", - "total windows: 14208 | selected windows: 4262 (thresh -4.06132 )\n", - "mean eucl. error: 0.5385864491928991 | selected error: 0.5137827818831091\n", - "mean linear error: 0.281488597972973 | selected error: 0.2560957297043642\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1281_MFB/Final_results_v2/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 14208 | selected windows: 4262 (thresh -4.5147614 )\n", - "mean eucl. error: 0.48874218911260153 | selected error: 0.4076990268790374\n", - "mean linear error: 0.24361908783783784 | selected error: 0.1869427498826842\n", - "total windows: 14208 | selected windows: 4262 (thresh -5.6891036 )\n", - "mean eucl. error: 0.4595335703279842 | selected error: 0.40493214045313247\n", - "mean linear error: 0.2083227759009009 | selected error: 0.1693289535429376\n", - "total windows: 7552 | selected windows: 2265 (thresh -4.2046785 )\n", - "mean eucl. error: 0.3796418196780438 | selected error: 0.26915538960290275\n", - "mean linear error: 0.2150741525423729 | selected error: 0.12605739514348788\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.2579575 )\n", - "mean eucl. error: 0.3172805695536395 | selected error: 0.19819524535580405\n", - "mean linear error: 0.17178098516949153 | selected error: 0.08380132450331126\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.859999 )\n", - "mean eucl. error: 0.26070979386617643 | selected error: 0.1816375778638438\n", - "mean linear error: 0.13545550847457627 | selected error: 0.07917439293598233\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.269205 )\n", - "mean eucl. error: 0.26359873518635907 | selected error: 0.20512912875926131\n", - "mean linear error: 0.13328654661016948 | selected error: 0.10113024282560705\n", - "threshold value: -5.269205\r" - ] - } - ], - "source": [ - "# bypass to avoid heavy comput and fill the memory for nothing\n", - "force = False\n", - "\n", - "todo = dict()\n", - "dirmouse = dict()\n", - "mouse_id = []\n", - "windowMS = []\n", - "mean_eucl = []\n", - "select_eucl = []\n", - "mean_lin = []\n", - "select_lin = []\n", - "has_dat = []\n", - "sizes = []\n", - "for mouse, path in path_dict.items():\n", - " todo[mouse] = []\n", - " returned = False\n", - " dirmouse[mouse] = os.path.join(datadir, path, \"results\")\n", - " assert os.path.isdir(dirmouse[mouse])\n", - " for win in list_windows:\n", - " try:\n", - " mean, select, linmean, linselect = print_results(\n", - " dirmouse[mouse], show=False, windowSizeMS=win, force=False\n", - " )\n", - " mean_eucl.append(mean)\n", - " select_eucl.append(select)\n", - " mean_lin.append(linmean)\n", - " select_lin.append(linselect)\n", - " mouse_id.append(mouse)\n", - " windowMS.append(win)\n", - " has_dat.append(mouse in dath_dict)\n", - " if mouse in dath_dict:\n", - " sizes.append(size_dat[mouse])\n", - " else:\n", - " sizes.append(0)\n", - " returned = True\n", - " except Exception as e:\n", - " print(e)\n", - " todo[mouse].append(win)\n", - " print(f\"Available windows: {os.listdir(dirmouse[mouse])}\")\n", - " for val in os.listdir(dirmouse[mouse]):\n", - " if int(val) not in list_windows:\n", - " list_windows.append(val)\n", - " print(f\"adding {val} to list of available windows\")\n", - " mean, select, linmean, linselect = print_results(\n", - " dirmouse[mouse], show=False, windowSizeMS=win\n", - " )\n", - " mean_eucl.append(mean)\n", - " select_eucl.append(select)\n", - " mean_lin.append(linmean)\n", - " select_lin.append(linselect)\n", - " mouse_id.append(mouse)\n", - " windowMS.append(win)\n", - " returned = True\n", - " ###\" print(f\"No data for {mouse} in {win}\")\n", - " if not returned:\n", - " print(f\"nothing at all for {mouse}, {os.listdir(dirmouse[mouse])}\")\n", - "\n", - "\n", - "results_df = pd.DataFrame(\n", - " data={\n", - " \"mouse_id\": mouse_id,\n", - " \"windowMS\": windowMS,\n", - " \"mean_eucl\": mean_eucl,\n", - " \"select_eucl\": select_eucl,\n", - " \"mean_lin\": mean_lin,\n", - " \"select_lin\": select_lin,\n", - " \"has_dat\": has_dat,\n", - " \"size_dat\": sizes,\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "id": "f3accb1c-8dc5-48ba-8e3a-9b80091dc88d", - "metadata": {}, - "outputs": [], - "source": [ - "for cdt in conditions:\n", - " for mouse in conditions[cdt]:\n", - " try:\n", - " results_df.loc[results_df.mouse_id == mouse, \"condition\"] = cdt\n", - " except Exception as e:\n", - " print(e)\n", - "\n", - "results_df = results_df.sort_values(\n", - " by=[\"condition\", \"mouse_id\", \"windowMS\"]\n", - ").reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "id": "4c773a8f-0c64-4124-9c93-7b24df76d12b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 209, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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211, - "id": "d7372e70-2f25-4592-bad3-7f3c19a621ed", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "fig, _axs = plt.subplots(2, 2, figsize=(15, 10), constrained_layout=True)\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(hue=\"windowMS\", y=metric, x=\"mouse_id\", data=results_df, ax=axs[i])\n", - " # plt.xticks(rotation=45, ha='right')\n", - " axs[i].tick_params(labelrotation=45)\n", - "\n", - "\n", - "fig.suptitle(\"ANN Errors for each mouse\")\n", - "fig.savefig(\"/home/vador/Documents/Theotime/figures/ann_results.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "id": "013711f3-dfb1-4d17-957c-31270a66f043", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "condition\n", - "Known 476.545\n", - "MFB 465.216\n", - "PAG 724.862\n", - "Umaze 51.228\n", - "Name: size_dat, dtype: float64" - ] - }, - "execution_count": 213, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results_df[results_df.has_dat == True].groupby(\"condition\")[\"size_dat\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "id": "8cfcc9bb-6175-4d28-b651-3a8df7b414c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1336': 'M1336_Known/TEST/',\n", - " '1199': 'M1199TEST1_Basile/TEST',\n", - " '1186': 'DataERC2/M1186/TEST/',\n", - " '1336v2': 'neuroencoders_1021/_work/M1336_known/Final_results_v2',\n", - " '1182': 'DataERC2/M1182/TEST/',\n", - " '1239v3': 'neuroencoders_1021/_work/M1239_MFB/Final_results_v3',\n", - " '1168UMaze': 'DataERC1/M1168/TEST/',\n", - " '1117': 'DataERC1/M1117/TEST/',\n", - " '1281v2': 'neuroencoders_1021/_work/M1281_MFB/Final_results_v2',\n", - " '994': 'neuroencoders_1021/_work/M994_PAG/Final_results_v3',\n", - " '1336v3': 'neuroencoders_1021/_work/M1336_MFB/Final_results_v3'}" - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dath_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 215, - "id": "546622f7-b8f8-48e2-b791-918edb41242f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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JSQ3vuUf1bu+lGv7+MhUVKTcpSenbtunk518oP71kPd9WEZPk16+f+biCzEyd+e03HZ7/jnIOH67Aq4OqMm/ePL3xxhtKTEzUddddp8jISPXo0aPUvomJiXriiScUExOjgwcP6vHHH1dkZKRVv88++0zPPfec/vjjDzVr1kwvv/yyBg8efJnPpPpp08hb3rXc9NG4AeY2J0cHdQ701ZBurRUS8bHFDV07BtZTk3qemvSR5XrYaX8uH3M4JcOiPT45U3613StcT25BoQ4lnlZjH48y+6SdOafE0zlq7FOr1P2nc3KVdTZPTepZjnH+Gv1sXoFqubpY7MvOzdfx9JL/k46nn9H0T7dp3fNDFHp9E33xU/m/r3D5udS/To41veX377+WBjE4OMkYEKRaN96nYy8HqejsaZmKClSQ9ofFsYVp8TI27lTm2AV/3oTVuU7jfwznvfpGyK3lLUr+4EEVlXZ94uAkn3velFPthkr5MLzMWfOSVJxzSsXnsuTkHWjRfv5vj+K8s3JwtXyPF+eeUeHpYyUbp48p/cvn1eiJDapxXV/lxK4UqhffWr4K9g/Ws6ufLXW/o4OjXuz7oup71Nfjnz9uEVoHNQpSQ8+G+nbUtxbHvNzvZe06uUuPff5YqWOO7T5WH8V8pPUH10uSDqcfll8tPw0LHqY1v61RxrkMFRYV6sipIxbHHTl9RB3qdyhlRCnjXIaycrMU4BVg0X7+b4CzBWdV88+lns7Lyc/RicyS+3ucyDyhGetn6Kvwr9SrRS99te+f185H9VCcm6ucw/Fya9SoSsZr9H9havzA/do18YlSr6VNhYXKPVHyvsk+cEC1WrdWw3vv0cE3/1vqeOeOH1eNxo0t2vLTS5aAKjxjPdO/KPecefzcE9KBA6/rptXfqP6dd+jIe+9f0rldyQjnrwJtG3lrcNcW+v2k9RpoodMtP1nr1qqhnr+3m77f89enXu0DfPR2+O1a/MNevf7FTyooKlbL+l4WfwSU5lDSaY1ZGG3eLiq27r/1txOa9slfa2EW/MNsIA83F70/tq/cjc6avzZO+0+kq7CwWP4+tdS3U1P17RSoT7cdMPdf+ePvemddXMkdyL3c9eS/btBL992kkfPXlvs8sA+XBtepZud7lJ90wGqfk1cj+T64VDlxK5W8cZ6Kc7PlXDdQpkLLT3KzY/6njA1vm7dNhWXf0V6SjAHByt65Qvkn90oOjqp96+Oqd/8CJc4fJFPBORmcXeVcv42yNi9QfvIBObh6yKvP0/L5v7eUXMZXdqWSNSZ9H1oqg7GmMn94W/l//lHhVMdf7u0GyL1df2XvXGFdt8EgJ88G8urztLwHz1DKkgcr+OrhchsZNVIODg7m7abeTTV70Gz9cOgHSZKrk6ta1W2lJT8v0aG0Q6plrKVxPcfptQGvKfyTcKvxwjqGXTgRq0z3B92vQe0G6aXvXlJ8erxa12utybdPVnZ+tj7dVfKhZ0OPhpp/93x9ve9rvbfjPeXk5yigToDyisr+N1DLWEvz75kvdxd3vfvjuzqQckAFxQVq5NlIvVv2Vu+WvbVyz19/SK7au0rv7Xiv5HdqLT+N6zlOL4S+oDGfjanYicBucg4f1q6JT/zVUGT54bqDq5uy9uxV6g8b1OqZsj/Q/LvanToqZf16Zc7eq+L8fPnfd586vPmmfh7xoPLTSj6Qyk9NVfyCBTp3vOQi27dvX133ysuKCR9Z5o2jDM7O6vDfN+XerJmOLlqszD17VJiTI7cGDeTTo4ca3n2X4he+a+5/6scd+u3VVyVJLnXqKHBkuNq99qp23Dukoi8PLlFUVJTGjx+vefPmqXv37lqwYIH69eunffv2qfEFf5BJUl5enurWravJkydr1qxZpYwobd++XWFhYXrxxRc1ePBgff755xoyZIi2bNmiLl2urZuA/nQoUUPe/NKi7YWwbjqSkqkPfvjV6pp80I3Nte9Ymg4mnrZoP3k6WymZZ9WkrqdFe+O6Htr2m/UNWMvi7OigwHqeiosvew1uzxpG+dZ2V9qZ0teTN5mk6N1H1b9zU7333e5KrTt//m8LozN/slYHufE/KnG+5Ydndf71ogrT45W1dZFkKpZMxco/+aucvZtY9HPyDlBhRtkTUpzrNpfB0VlF2eWvpe3V91m5tb5NKUsfVlFGKe/p88F8ncZKWRpe7nr0JUzK2bdW7h3uUNbmBZVad16mkr9vL5xhj+phQJsBOn3utPnmrH93Ppj3r+2vx1Y+pqxcyyDxw5gP9eWvlr+bP7r/I83ZPEdbj2xVWVydXFVsssw9ik3FMqjk202FxYXan7Jfjb0s///0r+2vpDOl37jYJJO+P/S9QluFavHPiyu17nxxcUlNF86wR/VmcHZWjYDGyty9+5LHavR//6eA4cO0+8mnlH3AOpMpvQCDHJydy9yd8t16BY4MV80WLSq17rxMJplMJjkYr+33JVc6Vzg3Fye9NLSHXvrfj6XeOCr9jOVNmm65zl87/0iyuMHTE3feoBVbf9OSH/aa28pak/LviopNVuNfqKCw6B/7/N3Yfp3kV9tdd73+hcVF/JHULG3eb30BlltQaB4/7cw5fbLtgJ69+9r6g+5KYXB2k/fgV5X+9TR5Wt08SvK89XHlHtqsjO/++iO+KOO4Vb/ignMqzkmv8POmLvuPxXb6l8+p0ZOb5FK/rfISYmTKy7Za3/L0tzPkN3KFHD38zDNnrOq9bZwcPesrce6dFhfyhenxyj24yar/3+vOz07TmZ9XqM6A5yp8Hrj8MnIzLLaHBQ3T8Yzjij1RsgxATn6Oxq8ab9Fn5saZej/sffnW9LWYvd7cp7nCOoZp5Ccj9VX4P89MaefXTpsPbzb/4ZB0Jkm9W/ZW63qtzX1GhYzS9qPbNW/bPHPbyayTVmP93eiQ0fKt6av7PrrP4iI+4XSCth3ZZtU/rzDPfLOr9LPp+mz3Z3rq1qf+sX7Yn6moSAWnyr5RWcq6dZIk458z3CvitxctlwL4/Y03VPeWm+UVFKTktSUfgqdvs3wfHXnvPTUYNFAe17UtM5xvNOReebZvr19GPWJxEZ974oRO//yzVf/ignzzuRWcOqVjy5ar49tvydnTUwWZ/xS8oCrMnDlT4eHhGjlypCQpMjJSa9eu1fz58zVjxgyr/k2aNNHs2SU3vlu0aFGpY0ZGRqp3796KiIiQJEVERGjjxo2KjIzU8uXLL9OZVE9n8wr1R3KGRdu5/EJlns2zanc3Ouv2DgGa9ZXlGrHnLd3wq0aHXq/fT57SgZOndWdwMzWp56FnPtxg7jN/VG/9sDdBn/w54WX8HUHatO+4kk7nqE5NV4Xf3l7urs7mpXHcXJz0SOj1Wr/nqNKyzqmBV02N7ddJGTm55S5pM3dNrIKa+uqDx/rrnXW7tO94mnLzC9WivpfaN65rdW6uzk7yrlUScNap6aaRvdort6BQP/5e/v91sA1T/lmr9d1NBedUdDbDoj1r22L53POmco/GKO/IT3JtfpPcWt6slA8ellQyIadG+zuUe3CTis5myLluM3n1flL5ifuUd+yvpZfqDXtXZ3/7Xtk/l/w+8Oo3We7t+ys1apyK83Lk4O5dUkNedslkHYOjfO6dKRe/NkpdMVYyOJj7FJ/LlIpLXxop8/vZcg0Ilm/4MmVueFv5J/fJVHBWzvVaytiog9U5Ozi7mcd1rOktzx6PqLggV7mHra+rYF8GGTSgzQCt+W2NikyWkxYcDY56ud/Lalm3pZ7++mk5ODioTo06kqSs3CwVFhfq1NlTpd4ENjk7WYlZf33YNHvQbG06vEmf7S5Zrmnrka0accMIJWcnKz49Xi3rtlRYpzB9s++ve4os+2WZpvedrriTcfrl+C/qGtBV3QO767GVpc/Gl6QF2xeoc8POenfIu3rvx/f0W8pvyi3IVTOfZmrn106H0y1nQRudjOZzqlOjjkYEj1BeYZ5+SvjpIl9J2FLTMf9R+tZtyktJlnNtLwUMHy5Hd3clffvXNzicatWS0ddXRp+S30U1GvtLkvJPnTJfM7d69lnlp6WaJ7z433efmoQ/rP0vvqjcpCQ51yl5bxSdO6ficyXZW+C//61TO3YoNyVFTjVqqO5tt6l2x47a81TZE3uOf/qpvEO6qkPkLB1dvFiZu3er8MwZuTXyV50uXWQqtvygysHZxfzczrVqqcFdg+Xo5qb0rdf271DC+SvcpMFdtGX/cf10MLHUcP7v6tR01U1tGumFFX99yuvl7qr2AXW1JjZei8b2VSPvWjqSkql538Yp7kj5Mwca+9TSt1PuUX5RkfYmpGnumliL0F+Sgpr5KfqFe3XmXIF+OZysuWtidTqn9LDeYCi5GdbqXw5XanaNh5uLbu8QoL0JF/8pMi4/r/6Tde7gZuXF/yhZhfMGubXoqaxti1X3/nfk4tdahRknlLXlfZ078L1FT/f2A+Te4Q4VZacr948tytw4/6LWd3T48+t+5c2kMRhrlSw/k1vWh1QGuV/XVzm7v67UDBsHVw/VaBuq/BN7LvpY2IaTg5NCW4Wa14EsS01jTRWbis1Lw0glF8JT+0zVzI0zS72gL83uxN0a1G6Q/Gv761jGMTX3aa4ODTpo9qaScMsgg7o16aaPf/lYM/81Uy3rttTJrJP6MObDMpfLMcig21rcpnUH1lVqdk0tYy3d1vw27Uvad9HHwvbcGjVS15WfqbigQGf27VP8wneVm1i1y2Y5Go0yODmpoKybUTk4qO4tt8jR1VVZe38tvY+ker166fTOnZWaXePg5qZ6vW/X2ePHy64DVSo/P18xMTGaNGmSRXtoaKi2bav8H1Lbt2/XhAmWN6br06dPqcvfnJeXl6e8vL++LZR1Db4HQjs2kUEGrY2LL3X/8i37ZXR21MR/3SDPGi76/eRpjV34nY6n/3WN3si7lmq7/zXLt55nDb0ytIdquxt1OidPexJS9eBba8xL0BQXm9Tcz0sDgpqqlquL0s6c084/khTx0aZy14L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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(2, 2, figsize=(15, 10), constrained_layout=True)\n", - "mean_size = results_df.groupby(\"windowMS\")[\"mouse_id\"].count().reset_index()\n", - "\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(y=metric, x=\"windowMS\", data=results_df, ax=axs[i])\n", - "\n", - " for index, row in mean_size.iterrows():\n", - " axs[i].text(\n", - " row.name,\n", - " 0.1,\n", - " \"n=\" + str(round(row.mouse_id, 2)),\n", - " color=\"white\",\n", - " ha=\"center\",\n", - " )\n", - "\n", - "\n", - "fig.suptitle(f\"ANN Errors depending on the window size in ms\")\n", - "fig.savefig(\"/home/vador/Documents/Theotime/figures/results_windowing.png\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "id": "0d98036e-9beb-4e58-8ac5-d447d7474ac7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(2, 2, figsize=(15, 10), constrained_layout=True)\n", - "mean_size = (\n", - " results_df[results_df[\"windowMS\"].isin([36, 200, 504])]\n", - " .groupby(\"windowMS\")[\"mouse_id\"]\n", - " .count()\n", - " .reset_index()\n", - ")\n", - "\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(\n", - " y=metric,\n", - " x=\"windowMS\",\n", - " data=results_df[results_df[\"windowMS\"].isin([36, 200, 504])],\n", - " ax=axs[i],\n", - " )\n", - "\n", - " for index, row in mean_size.iterrows():\n", - " axs[i].text(\n", - " row.name,\n", - " 0.1,\n", - " \"n=\" + str(round(row.mouse_id, 2)),\n", - " color=\"white\",\n", - " ha=\"center\",\n", - " )\n", - "\n", - "\n", - "fig.suptitle(f\"ANN Errors depending on the window size in ms\")\n", - "fig.savefig(\"/home/vador/Documents/Theotime/figures/results_windowing_subset.png\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "id": "2d4bfc6a-bcc1-45f0-83df-b8a7abd0b09e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9+nQlJSUpPT1da9euVe/evc3ta9as0YIFC5SUlKSzZ88qJSVFERERFmMsXLhQy5cvV3Jysi5cuKDz58+rVq1a5vbt27erY8eOpc7/9ddf6957772hmAEAFYO3t7fs7e1LrJLPzMwssZr+f4WEhEiSWrRooV9//VWTJ0++bnEeAKytoufwEnk8AFQ1bGsDANVQXl6ewsPDNXv27Ou2t2vXTtOmTbvuGBcvXlS3bt30yiuvlNretm1bpaenW7wef/xxBQcHq1WrVrflPgAA1ufk5KSoqCglJCRYnE9ISFDbtm3LPY5hGBZf+AoA+GPk8QBQtbByHgCqoZiYGMXExFy3PS4uTpJ04sSJ6/YZM2aMpKsra0rj5OQkPz8/83FBQYHWr1+vp59++g+3PQAAVGxjx45VXFycWrVqpTZt2mjhwoVKS0vTqFGjJF3dL/7UqVP68MMPJUlz5sxRUFCQmjRpIknatWuXZsyYoWeeecZm9wAAlRF5PABULRTnAQBWsX79ep05c0ZDhgyxdSgAgFvUv39/nT17Vm+88YbS09PVvHlzbdy4UfXr15ckpaenKy0tzdy/uLhYL7/8slJTU+Xg4KCGDRtq2rRpGjlypK1uAQBQTuTxAHDnUJwHAFjFokWL1LVrVwUGBto6FADAbTB69GiNHj261LYlS5ZYHD/zzDOskgeASoo8HgDuHIrzAIA77pdfftHmzZv1ySef2DoUAAAAAOVEHg8AdxZfCAsAuOMWL14sLy8vPfzww7YOBQAAAEA5kccDwJ1FcR4AcEcZhqHFixdr0KBBcnR0tHU4AAAAAMqBPB4A7jy2tQGAaig3N1fHjh0zH6empmr//v2qU6eOgoKCdO7cOaWlpen06dOSpCNHjkiS/Pz85OfnJ0nKyMhQRkaGeZwDBw6oZs2aCgoKUp06dcxjb926VampqRo+fLi1bg8AgD/03HPPKSsrS5Lk4+Ojd99918YRAcAfI48HgKqF4jwAVEP79u1Tx44dzcdjx46VJA0ePFhLlizR+vXrNXToUHP7gAEDJEmTJk3S5MmTJUnz58/X66+/bu7Tvn17SVc/+jpkyBDz+UWLFqlt27Zq2rTpnbodm0lLS1NhYaEkycHBQUFBQTaOCADK9uOPP2r79u3KzMxUcXGxRdtrr71mo6hsIysrS7/++qutwwCAG0IeDwBVi8kwDMPWQVhbTk6OPD09lZ2dLQ8Pj1seb+DAgebE3tfXV8uXL7/lMQFUfJcvX1ZqaqpCQkLk4uJi63BgAz/99JMKCgokSY6OjmrQoIG5jecDqBi2Lssqs71TrI+VIrk5tzNv/cc//qEnn3xS3t7e8vPzk8lkMreZTCYlJyffarh3FDk8gNuFPK16I4cHKr7KnsNL5c9db2rlfFFRkZYsWaIvv/yy1FU3W7duvZlhAQAAANwhU6ZM0dSpUzVu3DhbhwIAAABAN1mcf+6557RkyRL16NFDzZs3t1h1AwAAAKDiOX/+vPr27WvrMAAAAAD8/26qOL9ixQp98skn6t69++2OBwAAAMAd0LdvX33xxRcaNWqUrUMBAAAAoJsszjs5OSk0NPR2xwIAAADgDgkNDdWrr76q//znP2rRooUcHR0t2p999lkbRQYAAABUTzdVnP/rX/+qd999V7Nnz2ZLGwAAAKASWLhwodzd3ZWYmKjExESLNpPJRHEeAAAAsLKbKs7v2rVL27Zt0+eff65mzZqVWHWzZs2a2xIcAAAAgNsjNTXV1iEAAAAA+J2bKs7XqlVL//d//3e7YwEAAAAAAAAAoFq4qeL84sWLb3ccAAAAAG6zsWPH6s0335Sbm5vGjh1bZt+ZM2daKSoAAAAA0k0W5wEAAABUfCkpKSooKDD/9/XwPVIAAACA9ZW7ON+yZUt9+eWXql27tiIjI8tM4JOTk29LcAAAAABu3rZt20r9bwAAAAC2V+7ifK9eveTs7CxJ6t27952KBwCqhJj/9xerzvd5r49vqP+OHTs0ffp0JSUlKT09XWvXrrX4s33NmjVasGCBkpKSdPbsWaWkpCgiIsLcfu7cOU2aNElffPGFTp48KW9vb/Xu3VtvvvmmPD09S8x35coVtW7dWt9++22JsQAAAFCxPffcc8rKypIk+fj46N1337VxRHdGRc/hJfJ4AKhqyl2cnzRpUqn/fTvMnTtX06dPV3p6upo1a6b4+Hg98MADpfbdtWuXxo0bp8OHD+vixYuqX7++Ro4cqeeff/62xlQeWfM+kiQVXcgznyu6kGc+7/PkY1aPCQDKIy8vT+Hh4Ro6dKgeeeSRUtvbtWunvn376oknnijRfvr0aZ0+fVozZszQ3XffrZ9//lmjRo3S6dOntWrVqhL9X3rpJQUEBOjbb7+9I/cDAChdnz59yt13zZo1dzASAJVZVlaWfv31V1uHAZHH34rT536UJBUVG+ZzRcUF5vMBdRrbJC4A1ZvN95xfufL/a+/O46qq9v+Pvw8zghOogCWKQjgGqN1Sy3BC0bya5pCm4lB6tXIoS9PSMtNyzrlbit2bxi216zXTKOchU4QyNUcUvwmCYiqoILB/f/jz3M5lSBHPYXg9H4/zeLDXWnvtz6aDfPi0ztpRGjVqlBYtWqQWLVpo6dKlCg8P1+HDh+Xr65trvJubm1588UU9/PDDcnNz086dOzV06FC5ubnphRdesMEdALgbZWXVTXEXHh6u8PDwfPv79esnSTp9+nSe/Q0bNtTq1avNx3Xq1NHUqVP13HPPKSsrSw4O//318s033+jbb7/V6tWr9c033xTNDQAA7kheqyABACUXeTwAlC53XJyvXLnyHT8oKjU19Y4DmD17tgYPHqwhQ4ZIkubOnatNmzZp8eLFmjZtWq7xISEhCgkJMR/XqlVLa9as0Y4dO/ItzmdkZCgjI8N8fOXKlTuOD0DRYtVN6XX58mVVqFDBIqE/f/68nn/+eX311VcqV66cDaMDgLJp+fLld33Orl271LRpU/OWlgCA0o08HgBs546L83Pnzi3yi2dmZiomJkbjxo2zaA8LC9Pu3bvvaI7Y2Fjt3r1b7777br5jpk2bprfffvueYgUA5O/ixYuaMmWKhg4dam4zDEMREREaNmyYmjZtmu/qHQBA8RIeHq64uDjVrl3b1qEAAO4z8ngAsK07Ls4PGDDgriefPn26hg0bpkqVKuXZf+HCBWVnZ8vLy8ui3cvLS0lJSQXO/eCDDyolJUVZWVmaPHmyeeV9XsaPH68xY8aYj69cuaIaNWrc+Y0AAPJ15coVderUSfXr17d4Jsn8+fN15coVjR8/3obRAQDulmEYfz4IAFDikccDgO3Z3c/J33vvvTva4uZ/t8sxDONPt9DZsWOH9u/fryVLlmju3LlatSr/p5w7OzurQoUKFi8AwL27evWqOnToIHd3d61du1aOjo7mvs2bN+uHH36Qs7OzHBwc5O/vL0lq2rRpof6HLwAAAICiQR4PAMXDfX0g7J+tuqlSpYrs7e1zrZJPTk7OtZr+f/n5+UmSGjVqpPPnz2vy5Ml69tln7y1gAMAdu3Llitq3by9nZ2etW7dOLi4uFv0ffvihxZZj586dU/v27RUVFaVHH33U2uECAAAAEHk8ABQn97U4/2ecnJzUpEkTRUdH6+mnnza3R0dHq0uXLnc8j2EYFg98BQAULC0tTSdOnDAfx8fHKy4uTh4eHvL19VVqaqoSEhJ07tw5SdLRo0clSd7e3vL29tbVq1cVFhama9eu6Z///KeuXLlifth21apVZW9vL19fX4truru7S5Lq1KmjBx980Bq3CQAAAJQq5PEAULrYtDgvSWPGjFG/fv3UtGlTNWvWTB999JESEhI0bNgwSbf2i//tt9/06aefSpIWLlwoX19f1a1bV5K0c+dOzZw5Uy+99JLN7gEASpr9+/erVatW5uPbz+UYMGCAIiMjtW7dOg0cONDc37t3b0nSpEmTNHnyZMXExGjv3r2SZP6Y623x8fGqVavWfb4D2zqXekySlJ3z30+IZefcNLdX93jIJnEBQFH4s+0lAQC2Qx4PAKWLzYvzvXr10sWLF/XOO+8oMTFRDRs21IYNG1SzZk1JUmJiohISEszjc3JyNH78eMXHx8vBwUF16tTR9OnTLZ4sDgC29k2X/J+DURyEhoYWuPVYRESEIiIiCn1+XmrVqsVDBgGgBODfagBlVXHP4SXyeAAobWxenJek4cOHa/jw4Xn2RUZGWhy/9NJLrJIHSqBlK8IkSWlp2ea2tLTz5vZBA761SVwAAJQVrVu31po1a1SpUiWL9itXrqhr167avHmzpFsPCQQAAABw/93X4vwTTzwhV1fX+3kJAAAAAHdg69atyszMzNV+48YN7dixwwYR2UbK4n9KkrKvppvbsq+mm9ur/u05m8QFAACAsqfQxfmcnBydOHFCycnJysnJsehr2bKlJGnDhg33Fh0AAACAe/Lzzz+bvz58+LCSkpLMx9nZ2dq4caMeeOABW4QGAAAAlGmFKs7/8MMP6tOnj86cOZNr3zGTyaTs7Ox8zgQAAABgTcHBwTKZTDKZTGrdunWufldXV82fP98GkQEAAABlW6GK88OGDVPTpk319ddfy8fHRyaTqajjAgAAAFAE4uPjZRiGateurR9//FFVq1Y19zk5OalatWqyt7e3YYQAAABA2VSo4vzx48f15Zdfyt/fv6jjAQAAAFCEatasKUm5tqIEAAAAYFt2hTnp0Ucf1YkTJ4o6FgAAAAD3ybRp07Rs2bJc7cuWLdP7779vg4gAAACAsq1QK+dfeuklvfLKK0pKSlKjRo3k6Oho0f/www8XSXAAAAAAisbSpUu1cuXKXO0NGjRQ79699frrr9sgKgAAAKDsKlRxvnv37pKkQYMGmdtMJpMMwyiTD4T1dC2X59cAAABAcZGUlCQfH59c7VWrVlViYqINIgIAAADKtkIV5+Pj44s6jhLt7VadbB0CUGKUK5f31wAA4P6qUaOGdu3aJT8/P4v2Xbt2qXr16jaKCgAAACi7ClWcv/1QKQC4W2Hh9rYOwSo6rrXu3r0bnr67rQi2b9+uGTNmKCYmRomJiVq7dq26du1q7l+zZo2WLl2qmJgYXbx4UbGxsQoODs41z549ezRhwgTt3btXjo6OCg4O1jfffCNXV1dJ0tSpU/X1118rLi5OTk5O+v333+/hLgEA92LIkCEaNWqUbt68qdatW0uSvv/+e7322mt65ZVXbBwdANhecc/hJfJ4AChtClWcv+3w4cNKSEhQZmamRftf//rXewoKAHB/paenKygoSAMHDjRvVfa//S1atFCPHj30/PPP5znHnj171KFDB40fP17z58+Xk5OTfvrpJ9nZ/fdZ45mZmerRo4eaNWumTz755L7dDwDgz7322mtKTU3V8OHDzfm7i4uLXn/9dY0fP97G0QEA7gR5PACULoUqzp86dUpPP/20Dh48aN5rXrq177ykMrfnPACUNOHh4QoPD8+3v1+/fpKk06dP5ztm9OjRevnllzVu3DhzW0BAgMWYt99+W5IUGRlZ+GCLMZOdZPeHrwGgODOZTHr//ff15ptv6siRI3J1dVVAQICcnZ1tHRoA4A6RxwNA6VKoUsLIkSPl5+en8+fPq1y5cjp06JC2b9+upk2bauvWrUUcIgCguElOTtbevXtVrVo1NW/eXF5eXnryySe1c+dOW4dmVeUqmeTmcetVrpLJ1uEAwB1JSkpSamqq6tSpI2dnZ/NCGwBA6UceDwDFS6GK83v27NE777yjqlWrys7OTnZ2dnr88cc1bdo0vfzyy0UdIwCgmDl16pQkafLkyXr++ee1ceNGNW7cWG3atNHx48dtHB0AIC8XL15UmzZt9NBDD6ljx45KTEyUdGsvevacB4CygTweAIqXQhXns7Oz5e7uLkmqUqWKzp07J+nWg2KPHj1adNEBKHFGjhypPn36qE+fPho5cqStw8F9kpOTI0kaOnSoBg4cqJCQEM2ZM0eBgYFatmyZjaMDAORl9OjRcnR0VEJCgsqVK2du79WrlzZu3GjDyAAUV8tWhGnZijClpZ03t6WlnTe3o+QhjweA4qVQe843bNhQP//8s2rXrq1HH31UH3zwgZycnPTRRx+pdu3aRR0jgBIkJSVF58+f//OBKNF8fHwkSfXr17dor1evnhISEmwREgDgT3z77bfatGmTHnzwQYv2gIAAnTlzxkZR2Y6na7k8vwaA0ow8nudGASheClWcnzhxotLT0yVJ7777rp566ik98cQT8vT0VFRUVJEGCAAofmrVqqXq1avn+rTUsWPHCnxAFQDAdtLT0y1WzN924cKFMvlQ2LdbdbJ1CABgdeTx4llRAIqVQhXn27dvb/66du3aOnz4sFJTU1W5cmWZTPwjBwDFXVpamk6cOGE+jo+PV1xcnDw8POTr66vU1FQlJCSYty27nbx7e3vL29tbJpNJY8eO1aRJkxQUFKTg4GCtWLFCv/76q7788kvzvAkJCea5srOzFRcXJ0ny9/c3b48GALCOli1b6tNPP9WUKVMkSSaTSTk5OZoxY4ZatWpl4+gAAHeCPB4ASpdCFedvO3HihE6ePKmWLVvKw8NDhmEUVVwAgPto//79FoWYMWPGSJIGDBigyMhIrVu3TgMHDjT39+7dW5I0adIkTZ48WZI0atQo3bhxQ6NHj1ZqaqqCgoIUHR2tOnXqmM976623tGLFCvNxSEiIJGnLli0KDQ29X7cHAMjDjBkzFBoaqv379yszM1OvvfaaDh06pNTUVO3atcvW4QGwoZEjRyolJUWSVLVqVc2bN8/GESE/5PEAULoUqjh/8eJF9ezZU1u2bJHJZNLx48dVu3ZtDRkyRJUqVdKsWbOKOk4AKFE2PP26rUMoUGhoaIH/QzUiIkIRERF/Os+4ceM0bty4fPsjIyMVGRlZiAjLBv4QBmBN9evX188//6zFixfL3t5e6enp6tatm0aMGGHegxhA2cRzo24p7jm8RB4PAKVNoYrzo0ePlqOjoxISElSvXj1ze69evTR69GiK8wAA3AH+EAZgbd7e3nr77bdtHQYAACUWC2wAFKVCFee//fZbbdq0SQ8++KBFe0BAgM6cOVMkgQEAAAC4Nz///PMdj3344YfvYyQAAJQOLLABUJQKVZxPT09XuXLlcrVfuHBBzs7O9xwUAAAAgHsXHBwsk8n0p8+GMplMys7OtlJUAAAAAKRCFudbtmypTz/9VFOmTJF0K5nPycnRjBkzLB5MAgAAAMB24uPjbR0CAAAAgHwUqjg/Y8YMhYaGav/+/crMzNRrr72mQ4cOKTU1Vbt27SrqGAEAAAAUQs2aNW0dAgAA+AP2rAfwR4UqztevX18//fSTlixZInt7e6Wnp6tbt24aMWKEfHx8ijpGACXAhk86SpKup2WY266nnTe3F+5fGwAFIbEHcLf+8Y9/aMmSJYqPj9eePXtUs2ZNzZ07V35+furSpYutwwMAoNRjz3oAf1ToclnlypXVqVMnPfLII8rJyZEk7du3T5L017/+tWiiAwAA+SKxB3A3Fi9erLfeekujRo3S1KlTzXvMJF4DlQAAV7VJREFUV6pUSXPnzqU4DwAAAFhZoYrzGzduVP/+/XXx4sVcD5fiYVIAAABA8TN//nz9/e9/V9euXTV9+nRze9OmTfXqq6/aMDIAAACgbLIrzEkvvviievTooXPnziknJ8fiRWEeAAAAKH7i4+MVEhKSq93Z2Vnp6ek2iAgAAAAo2wpVnE9OTtaYMWPk5eVV1PEAAAAAuA/8/PwUFxeXq/2bb75R/fr1rR8QAAAAUMYValubZ555Rlu3blWdOnWKOh4AKBWeWv2JVa+3vvvguxq/fft2zZgxQzExMUpMTNTatWvVtWtXc/+aNWu0dOlSxcTE6OLFi4qNjVVwcHCuefbs2aMJEyZo7969cnR0VHBwsL755hu5urpKkqZOnaqvv/5acXFxcnJy0u+//34Pd3lncX300UdauXKlDhw4oKtXr+rSpUuqVKmSxZgDBw7o9ddf1759+2Rvb6/u3btr9uzZcnd3N4/5/vvv9eabb+rgwYNyd3dX//79NXXqVDk48HRjACXT2LFjNWLECN24cUOGYejHH3/UqlWrNG3aNH388ce2Dg8AbK645/ASefy95PEAUBwVauX8ggULtGbNGkVERGjWrFn68MMPLV4AgOItPT1dQUFBWrBgQb79LVq0sNiT+H/t2bNHHTp0UFhYmH788Uft27dPL774ouzs/vurJTMzUz169NDf/va3Iov7z+K6du2aOnTooDfeeCPP/nPnzqlt27by9/fX3r17tXHjRh06dEgRERHmMT///LM6duyoDh06KDY2Vp9//rnWrVuncePGFcl9AIAtDBw4UJMmTdJrr72ma9euqU+fPlq6dKnmzZun3r172zo8AMAdII8vvnn8U6s/KfAFAHkp1PK/lStXatOmTXJ1ddXWrVtlMpnMfSaTSS+//HKRBQgAKHrh4eEKDw/Pt79fv36SpNOnT+c7ZvTo0Xr55ZctEt2AgACLMW+//bYkKTIyMs85mjRpoj59+uiVV16RJHXt2lXr169XamqqKlSooKSkJPn4+OjXX39VYGDgHcU1atQoSdLWrVvz7F+/fr0cHR21cOFC8x8gCxcuVEhIiE6cOCF/f399/vnnevjhh/XWW29Jkvz9/TVt2jQ9++yzmjRpUr7XBoDi7Pr16+rbt6+ef/55XbhwQadOndKuXbv04IMP2jo0ADay4ZOOkqTraRnmtutp583thasY4H4ijy98Hv/CS33kXt49z7kBwFYKtXJ+4sSJeuedd3T58mWdPn1a8fHx5tepU6eKOkYAJYh7OZMquEkV3G59jdIpOTlZe/fuVbVq1dS8eXN5eXnpySef1M6dO+9qntDQUHPybRiGduzYocqVK5vn2bJli7y9vRUYGFhksWdkZMjJycliZdDtj+/evm5GRoZcXFwsznN1ddWNGzcUExNTZLEAgDV16dJFn376qSTJwcFBf/3rXzV79mx17dpVixcvtnF0AABrKMt5/M8/Hbqn63dc+746rn1f569dNredv3bZ3A4AhVGo4nxmZqZ69epl8Q8iAEjSs+2c9HwXZz3fxVnPtnOydTi4T27/j9jJkyfr+eef18aNG9W4cWO1adNGx48fv+N5QkNDtWPHDuXk5Ojnn3+Wvb29+vXrZ070t27dqieffLJIY2/durWSkpI0Y8YMZWZm6tKlS+aPziYmJkqS2rdvr927d2vVqlXKzs7Wb7/9pnfffddiDACUNAcOHNATTzwhSfryyy/l5eWlM2fO6NNPP2VrSgAoI8pyHp98PqVI4wGAolCo6vqAAQMUFRVV1LEAAEqInJwcSdLQoUM1cOBAhYSEaM6cOQoMDNSyZcvueJ6WLVvq6tWrio2N1bZt2/Tkk0+qVatW2rZtm6T7k9Q3aNBAK1as0KxZs1SuXDl5e3urdu3a8vLykr29vSQpLCxMM2bM0LBhw+Ts7KyHHnpInTp1kiTzGAAoaa5du6by5ctLkr799lt169ZNdnZ2euyxx3TmzBkbRwegOCtXTnJzu/UqV87W0eBelOU83s6eBaYAip9C7SCXnZ2tDz74QJs2bdLDDz8sR0dHi/7Zs2cXSXAAgOLJx8dHklS/fn2L9nr16ikhIeGO56lYsaKCg4O1detW7d69W61bt9YTTzyhuLg4HT9+XMeOHVNoaGhRhi5J6tOnj/r06aPz58/Lzc1NJpNJs2fPlp+fn3nMmDFjNHr0aCUmJqpy5co6ffq0xo8fbzEGAEoSf39/ffXVV3r66ae1adMmjR49WtKtLQ4qVKhg4+gAFGdh4SxOKC3Kch7v68szVgAUP4X634YHDx5USEiI7Ozs9Msvvyg2Ntb8iouLK+IQAQDFTa1atVS9enUdPXrUov3YsWOqWbPmXc0VGhqqLVu2aPv27QoNDVWlSpVUv359vfvuu6pWrZrq1atXlKFb8PLykru7u6KiouTi4qJ27dp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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(2, 2, figsize=(15, 10), constrained_layout=True)\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(y=metric, x=\"condition\", data=results_df, hue=\"mouse_id\", ax=axs[i])\n", - "\n", - "fig.suptitle(f\"ANN Errors depending on the condition and mouse\")\n", - "fig.savefig(\"/home/vador/Documents/Theotime/figures/results_cdt_mousehue.png\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "id": "a5e64d21-85f9-4ed1-8d51-3dc2f0ceacf3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[36, 108, 200, 252, 504]" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_windows" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "id": "e09a4fc7-0f38-4e27-82c7-08fa7e01cac5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Upk2bNOtv3Lih8PBw3bx5U/v378/3v3D++OOPat26tYYMGSIfHx+NHz9e/fr1U3BwsI4cOaL3339fI0eOzPQzz0927dqlli1bytXV1fwAn8WLF+v555+XJM2aNUs7duzQ4sWL7RxpznTv3l1Hjx7VoEGDtHTpUt25c0dXr17VwoULZTKZ9Morr8jX11dfffWVvUPNsYJS106dOsnJyUmLFy+Wq6urhg0bpv/85z86evSoJOnnn39Whw4dHKItnTp1qmbOnKlz586ZE7uGYah06dIaMmSIQ1yLADyYefPm6dNPP9W2bdtUtWpVvfDCC+rWrZsqVKhg79CQB3G+wFKcM7ifCxcuqH379vr5558l3b3bfvny5eaR9MuWLVN8fLwGDRpkzzCRB3F9sQ+S8//Vr18/1atXT3379s2wfMqUKdqyZYu+++47G0eWO27duiXDMFS4cGFJ0smTJ/X111+revXqCg8Pt3N0OTd//nzdunVLgwcPzrD8/Pnz+te//qVx48bZODLrGzx4sOLj4zNM6v35559q2bKlfvnll3yfnJfuJugjIiLMnYx7ypQpozfffFOvvfaanSKzvvj4eP3nP/9RYmKimjVrpurVq9s7JKs7f/68XnzxRf30009q1KiRvvjiC40aNUqzZs2SyWRSpUqV9P3336tSpUr2DjXHCkpdY2NjFR4erpMnT8pkMqlIkSL66quv1KJFC0nSokWLdOTIEU2ePNnOkVrP8ePHde7cOUlS6dKlFRQUZOeIct+tW7d0586dNOuKFi1qp2isy9H7R7CNgIAAPf/883rhhRcUGhpq73CQx3G+wFKcM8iuo0ePKjExUcHBwXJ2drZ3OMgHuL7YB8n5bDp+/Ljc3d3TTHWTn4WHh6tTp07q16+frl69qqpVq8rV1VWXLl3SjBkz1L9/f3uHiGz6448/dPbsWfMcz39348YNxcTEqHHjxjaOLPdcvHhRsbGxSk1Nlb+/P7/iOpjY2FjdvHlT1apVk4uLi73DyVWOWNebN29q27ZtSkxM1GOPPSYfHx97h5Qr4uPjNWfOHG3dulXx8fFydnZWUFCQOnTooJ49ezrcF6CEhAQNHz5cX375ZZopme5xhB+AJfpHsI57zzYCsoPzBZbinEF2FLS+KqyD64t9kJwvoHx8fLRp0ybVqFFDH330kT744APt3r1by5cv19ixY3Xo0CF7hwggE3/88YdWrlypHj162DsUoEDauXOnWrRooaCgIHl4eOjnn3/WCy+8oKSkJK1Zs0bBwcFas2aNeWo8RzBw4EBt2LBBEyZMUI8ePTRr1iydOXNGc+fO1ZQpU/TCCy/YO0SroH8Ea7p69aq++uorxcXFKTAwUJ07d+bZBUgnNjY2XfKsZcuWDnNHEnIP1xhkpiD2VZG7EhISFBMToyeeeMLeoTgkHgj7F5cvX9aGDRt05coVSXcftDl16lRNmDDB4b6M3bx503whXrt2rXme4Mcee0wnT560c3S57/z585owYYK9w7CJglLXglJP6e4DfHr16mXvMHJdQfpMC0pdHaWeQ4YM0dChQ7V7925t375dixcv1m+//aYvvvhCsbGxunXrlkaPHm3vMK1q5cqVmj17tp599lm5uLioUaNGGj16tCZNmqRPP/3U3uFZTUHvHyFnnn32Wa1YsUKSdPDgQVWpUkWjRo1SdHS0Ro8erWrVqjncdwo8uISEBHXu3FmVK1dWz5499dZbb+m9995Tly5dVLZsWc2aNcveISKP4RqD7CqIfVXkrt9//11Nmza1dxgOi5Hz/7Vjxw6Fh4fr+vXrKl68uKKjo9W5c2e5uLjIMAydOXNGW7duNT9AI7+rVauW+vbtq44dO6pmzZpavXq16tevr5iYGLVt29Y8f66j2rt3r+rWreswt+FnpaDU1ZHqef369SzL9+3bp8aNGztEXbPiSJ/p/RSUujpKPQsXLqxff/1VFStWlCSlpqbK3d1dp06dkp+fn6Kjo9WzZ0+dOXPGzpFaj6enpw4cOKDAwECVK1dOK1as0COPPKLjx48rJCREN27csHeIVlHQ+0fIGV9fX23fvl1VqlRRmzZtVKJECS1cuFCurq66c+eO+vfvr1OnTmnNmjX2DhV5wCuvvKIDBw5o1qxZcnd316hRo1SpUiWNGzdOX3zxhQYNGqT58+erW7du9g4VeQTXGGRXQeyrInc5yve4vMoxJri1glGjRqlz586aMWOG5s6dqw4dOujJJ5/U/PnzJUl9+/bV22+/ra+//trOkVrH2LFj1a1bNw0dOlTNmzdX/fr1Jd0dJVanTh07R5dz+/bty7L8yJEjNook9xWUuhaUekpS8eLFs5znzVHmgStIn2lBqWtBqWepUqUUHx9v/sJz/vx5JScnm6cgqFKlivkuPEdRsWJFnThxQoGBgapevbq+/PJLPfLII1q5cqWKFy9u7/CsxtH7R8hdCQkJcnK6e2Pynj179N1338nV1VWSVKhQIQ0bNkyPPPKIPUNEHrJixQqtXr1atWvXliTNnz9fZcqU0bhx49S7d2/dunVL06ZNIzkPM64xIfeDpAAAoaFJREFUyK6C2FdFzpQsWTLLcpLyuYvk/H/FxMTo/fffl5eXl1577TUNHz5cL7/8srl84MCBateunR0jtK5nn31Wjz/+uOLj480dQklq3ry5OnbsaMfIrCM0NFQmk0kZ3Rhyb70jJDelglPXglJPSfLy8tKoUaP06KOPZlh+9OhRvfLKKzaOyvoK0mdaUOpaUOrZoUMH9evXT9OmTZObm5vefvttNW7cWB4eHpLu/ghRtmxZO0dpXb169dLevXvVuHFjjRw5Um3bttUHH3yg5ORkzZgxw97hWY2j94+Qu2rVqqX169erUqVKKl26tE6ePJnmR52TJ0+arxPAXxNl0t07lJKTk5WQkKDChQsrPDxcb7zxhh0jRF7DNQbZVRD7qsiZxMRE9e/fXyEhIRmWnzx5UuPHj7dxVAUHyfn/SkpKMl+oChUqpMKFC8vHx8dc7u3trcuXL9srvFxRunRplS5dOs06R/ml3dvbW1OnTlXz5s0zLD9w4IDD/NhSUOpaUOopyTx9VuPGjTMsL168eIbJz/ymIH2mBaWuBaWeEydOVHx8vNq1a6eUlBTVr19fn3zyibncZDJp8uTJdozQ+oYOHWr+d9OmTXX48GHt3LlTlSpVSpPEdgSO3D9C7hozZox69OihQoUKafDgwRo6dKguX76s4OBgHTlyROPGjVP37t3tHSbyiIcfflgzZ87Uhx9+KEmaOXOmfH195evrK0m6ceOGPD097Rki8hiuMciugthXRc6EhoYqICBAL730Uoble/fuJTmfi0jO/1dAQIBiY2NVoUIFSdIXX3whf39/c3l8fHyaZL0j+OWXX8xPd09KSkpTdu9BM/lVWFiYzp49q8DAwAzLr1696hDJTang1LWg1FOSunXrplu3bmVaXrp0aY0bN86GEeWOgvSZFpS6FpR6enp6aunSpbp9+7aSk5PTJU/Cw8PtFJntlC9fXuXLl7d3GLnCkftHyF1t27bVvHnzNGTIEJ09e1aGYZjvxHVzc1O/fv1IhsBsypQpatmypZYvXy5XV1edO3dOixcvNpdv375dbdq0sWOEyGu4xiC76KvCUm3bttXVq1czLS9ZsqR69Ohhu4AKGJLz//X888/rwoUL5uW2bdumKf/2228datTUF198oR49eig8PFzR0dEKDw/X0aNHde7cOYe4bfuVV15RQkJCpuXly5fXwoULbRhR7ikodS0o9ZSUZkqtjPj5+TlEcr4gfaYFpa4FpZ73uLu72zuEXLdkyZJsbeconXVH7x8h9z3zzDPq0KGDdu3apdjYWKWmpsrf319hYWHy8vKyd3jIQ+rWratff/1V//nPf5SYmKhmzZqpevXq5vKBAwdq4MCBdowQeRHXGFiiIPRVYR1vvfVWluUBAQEO9T0urzEZjjCEzQZu3rwpZ2dnubm52TsUq6hVq5ZeeeUVDRw4UF5eXtq7d6+CgoL0yiuvyN/fn9tVAACAnJyc5OnpKRcXl0zvejCZTA7zUDH6RwAAAABsycneAeQXly9fVv/+/e0dhtUcO3bMfHeAm5ubEhISZDKZNHToUM2bN8/O0eW+U6dOqXfv3vYOwyYKSl0LSj0l6fz585owYYK9w8h1BekzLSh1LSj1dCTBwcFydXVVjx49tGnTJv3xxx/pXo6SmJfoHyHnDMPQ8ePHlZycLOnuc62WLl2qJUuW6NKlS3aODnkN5wssxTkDILfs3btXCxcu1PHjxyXdfV7YgAED1K9fP61Zs8bO0Tk2kvPZdOXKlTRzAOZ3JUuW1J9//ilJKlu2rH799VdJd+cDvnnzpj1DswlH+zyzUlDqWlDqKUnnzp0rEKM3C9JnWlDqWlDq6UgOHDig7777Trdu3dITTzyhevXqac6cObp+/bq9Q8sVBb1/hJw5cuSIgoKCVLlyZQUHB+v48f9n787Da7gC/49/bhJZSGxBJBFJEMROlNpqp6haWpTW0uJXS6mm1qbWKkVpqkVRa2tra/lWq4jWTqtCUHsFscQSagsNSeb3h6/7bZpEJZJ7k9z363nu85hzZuZ+Tu6YOzk5c+a0ateurZ49e6pv374KDAzUyZMnrR0TWcSj46VkyZIcL3ginGMAZJaVK1cqKChIQ4YMUeXKlfXzzz+rbt26OnnypM6cOaNWrVpp6dKl1o6ZYzHn/P/6/vvvH1sfGRlpoSSWUa9ePYWFhalixYrq2LGj3n77bf3yyy8KCwtT48aNrR3vqdnS52krbbWVdkrSwYMHH1t//PhxCyXJXLb0mdpKW22lnbamZs2aqlmzpkJDQ/Xtt99qwYIFGjx4sNq2bav58+fnmCn/pJx/fYTMNWzYMFWuXFlr167V/Pnz9cILLyggIEC7d++WYRjq2LGjxo0bp6+++sraUZEFcLwgrThmAGSWCRMmaOzYsQoJCdHy5cv18ssvKzg4WCNHjpQkTZ06VVOmTFGXLl2snDRnYs75/2VnZyeTyZTqfKrSwzlVExISLJgq81y/fl1///23vLy8lJiYqI8//lg7duxQqVKlNHLkSBUoUMDaEZ+KLX2ettJWW2mn9Pi2PirPCW3lM00qJ7TVVtpp67Zt26bRo0dr27ZtiomJyfbXDP+U06+PkLmKFCmijRs3qkqVKoqNjZWbm5u2bdumunXrSpJ2796tV155RWfPnrVyUmQFHC9IK44ZAJnF1dVVf/zxh/z8/GQYhpycnBQeHq6KFStKejjIqnLlyuY7TJGxmNbmf3l6emrlypVKTExM8bVv3z5rR8xQBQsWlJeXl6SHnSlDhw7V999/r2nTpuWIXzxt6fO0lbbaSjslyd3dXXPnztXp06eTvSIjI/XDDz9YO2KGsKXP1FbaaivttEUXLlzQhAkTFBAQoFdeeUXPPPOMDh8+nCOuGf4pp18fIXPduXNHBQsWlCTlyZNHefLkkaenp7m+WLFiunz5srXiIYvheEFaccwAyCxubm66du2apIfTOcbHx5uXpYfP4XR1dbVWvByPzvn/FRQU9NhOg/8aCZidGYahX375RT/++KP++usva8fJELb0edpKW22lndLDtl68eFG+vr4pvry9vXNEW23tM7WFttpKO23JN998oxYtWiggIEC///67pk6dqnPnzmny5MkqW7asteNlqpx4fYTM5eXlpaioKPPy5MmTVaRIEfPy1atX+SMPzDhekFYcMwAyS5MmTdS/f38tWbJE3bt3V/PmzTVixAgdO3ZMx48f15AhQ8x36SDjMef8/xoyZIhiY2NTrS9VqpQ2b95swUSZ48aNG3r77be1b98+Pfvss5o6dapatmypXbt2SZIKFy6ssLAwVapUycpJn46tfJ6S7bTVVtopSW+++eZj21q8eHEtWLDAgokyhy19prbSVltppy155ZVXVLx4cb3zzjvy8PDQmTNnNGPGjGTrDRw40ArpMo6tXB8hczVp0kTHjh0z//Lat2/fJPUbN25UtWrVrBENWRDHC9KKYwZAZvn444/12muvqU+fPqpXr55WrFihkJAQlStXTiaTSSVLltS8efOsHTPHYs55G9OrVy9t27ZN3bp10w8//CA7OzsZhqHQ0FDz7duurq5au3attaMCAAAr8/Pzk8lkeuw6JpMp2z/sl+sjWMLp06fl7OycZBoKIDUcL0grjhkAGS0yMlJ3795V2bJl5eDA+O7MQue8jfH29tbSpUtVv359XbhwQT4+Pvrll1/UoEEDSdKePXv04osv6tKlS9YNCkAnT55UQECAtWMAQI7H9REAAAAAa2DOeRtz+fJllS5dWtLDX0SdnZ3l4+Njri9evLiuXr1qrXgA/qFMmTLy9vZWly5dNHv2bB0/ftzakQAgR+L6CBnl2rVr2rx5s65fvy5JiomJ0aRJkzRu3DgdPXrUyumQFZ0/f1537txJVv7gwQNt27bNComQlXGOAZCZ+E6yDkbO2xg7OztdunTJ/OAYNzc3HThwQCVKlJD08JdTLy8vJSQkWDMmAD38//jLL79o69at2rJli06cOCEPDw/Vr19fDRo0UJ8+fawdEUAON27cuBTL8+XLpzJlyqhZs2ays8v+Yz24PkJG2LNnj5o1a6Zbt24pf/78CgsLU4cOHeTg4CDDMHThwgXt2LGDOaEhSYqOjlabNm0UHh4uk8mkV199VTNmzJCrq6skzjtIjnMMgMzCd5J10TlvY+zs7DR+/Hjzf7Bhw4ZpyJAhKlSokCTp9u3bGjVqFP/hgCzozz//1Pjx47VkyRIlJiby/xRApqtatWqK5Tdu3NCFCxdUvnx5bdiwwdypnV1xfYSM0LRpU/n5+WnatGmaPXu2Pv30Uz3//POaO3eupIfPNrh27ZpWr15t5aTICrp3764TJ07os88+040bNzRixAgZhqGwsDAVKFBAly9flqenpxITE60dFVkE5xgAmYXvJOuic97GPMmD3aSHD5MBYF137tzRjh07tGXLFm3dulUREREKDAxUgwYNVL9+fbVp08baEQHYsOjoaHXp0kUlS5bUl19+ae04T4XrI2SEggULaufOnQoMDNSDBw/k7Oys3bt3q0aNGpKk/fv3q3Xr1jp//ryVkyIr8Pb21urVq83HR1xcnDp16qSzZ8/q559/1oMHDxiliCQ4xwDILHwnWReP2rUxZ86csXYEAE+oQIECKliwoLp27ar3339fdevWVb58+awdCwAkSZ6enho/fry6du1q7ShPjesjZIT79+/LxcVFkpQrVy7lzp3bfPeFJLm7u+vatWvWiocs5ubNmypQoIB52cnJSd999506dOighg0b6uuvv7ZiOmRFnGMAZBa+k6wr+08SinThl1Ag62vVqpUSEhL01VdfafHixVq6dCkPegKQpXh7e+vKlSvWjpFhuD7C0/Dx8VFkZKR5efny5fL09DQvR0dHJ+lIg20rUaKEDh48mKTMwcFB3377rUqUKKEXXnjBSsmQVXGOAZBZ+E6yLjrnbVSJEiVUt25dzZ492/ykdwBZy5o1axQTE6OwsDDVrVtXP//8sxo0aKCiRYvqlVdesXY8ANCBAwfk5+dn7RgZhusjPI1XXnklyR+rWrVqZR7lKknff/+9+XZxoEWLFpozZ06y8kedIVWqVBEz0OKfOMcAyCx8J1kXc87bqH379mnZsmVavny5rl69qubNm+u1117Tiy++KCcnJ2vHA/Av+/fv1+bNm7V582atX79eJpNJ9+/ft3YsADncrVu3Uiy/efOmfv/9d7377rvq1auXQkJCLJwsc3B9hMx09+5d2dvbcyxBkhQfH6+7d+8qb968KdYnJCTo/Pnz8vX1tXAyZFecYwCkF99J1sXIeRtVrVo1TZkyRVFRUfrpp59UpEgRvfnmmypSpIjeeOMNa8cDIOmTTz5RmzZtVLBgQdWoUUPLli1TmTJltHr1asXExFg7HgAbkD9/fhUoUCDZy8/PTx07dlTLli01dOhQa8fMMFwfITNdu3ZNffv2tXYMZBEODg6pdoJI0sWLFzV27FgLJkJ2xzkGQHrxnWRdjJyH2b59+9SzZ08dPHiQJzADWUD16tXVoEEDNWjQQM8999xjvywBIDNs3bo1xfK8efMqICBArq6uFk5keVwfIaMcOHBA1apV4zjCE+F4QVpxzADILJxfMpeDtQPAus6dO6dly5Zp6dKlOnTokGrVqqXPP//c2rEASNq7d6+1IwCwcfXr17d2BKvg+gjp8f333z+2/p8PcgQ4XpBWHDMAMgvnF+ti5LyNmjNnjpYsWaKdO3eqTJkyevXVV9WlS5cc9VA3IKeKjY1VeHi4nnvuOWtHAWADrly5osOHDysoKEh58+bV5cuXtWjRIiUmJqpVq1aqWLGitSNmGK6P8DTs7OxkMpke+8A0k8nEqDNI4nhB2nHMAMgsnF+siznnbdQHH3ygGjVqaO/evTp8+LDee+89fvEEsok///xTDRs2tHYMADZgy5YtKlGihBo3bqyyZcvq4MGDql69ur788kstXLhQzzzzjDZu3GjtmBmG6yM8DU9PT61cuVKJiYkpvvbt22ftiMhCOF6QVhwzADIL5xfronPeRkVFRWnKlCmqUqWKtaMAAIAs6v3331ePHj1069YtBQcHq1WrVmrTpo1OnDihY8eOacCAATnq4VBcH+FpBAUFPfaX1/8akQbbwvGCtOKYAZBZOL9YF9PaIAmmywCsr2DBgo+tT0hI0J07d7ilDECmy5cvn/bt26eSJUsqPj5eLi4u+v33382d1ydPntQzzzyjGzduWDVnRomMjNSOHTsUHR0te3t7+fv7q2nTpjyQG09k+/btio2N1fPPP59ifWxsrPbu3Wuzz3JAUhwvSCuOGQCZhfOLddE5jyR4AjNgfXny5FHfvn1Tncf57NmzGjt2LP9PAWS6woULa8uWLSpfvrzu3r0rNzc37dy5U88++6wk6eDBg2rcuLGuXr1q5aRPJzY2Vj169NDKlSslPRwdVKRIEV29elUuLi766KOP1L9/fyunBAAAAJDTOFg7AAAgqSpVqsjHx0fdu3dPsf7AgQM5ahoJAFlXnTp1NHz4cA0fPlyLFy9WtWrVNH78eK1YsUImk0kffPCBqlevbu2YTy04OFjR0dHav3+/nJ2dFRISopIlS2r06NFavny5BgwYoAIFCqhLly7WjgoAAAAgB6Fz3sY8yXQZAKyrVatWj50iomDBgurWrZvlAgGwWVOmTFHLli1Vr149lStXThs3blTfvn2VP39+SVKBAgW0fv1664bMAKtWrdL69etVuXJlSdLcuXPl5eWl0aNH64033tC9e/c0ZcoUOucBAAAAZCimtbExTJcBAADS6tq1a3J3dzcv//zzz7p3755q1aqVpDy7KlCggPbs2aOAgABJ0oMHD5Q7d25dvHhRhQsX1smTJ1WpUiXdu3fPykkBAAAA5CSMnLcxTJcBAADS6t8d8I0bN7ZSkszxzDPP6NNPP9Xnn38uSfr0009VuHBhFS5cWJJ0584dubq6WjMiAACZbuHChRo0aNBj7+IdM2aM1qxZo4iICIvlAoCcjM55G8N0GUDWFxcXJzs7O+XKlUuSdOrUKc2fP19RUVHy9fVVz5495e/vb+WUAGyBrZyPPvroIzVt2lQrV66Uo6OjLl26pEWLFpnrd+3apZYtW1oxIQAAma9Tp0583wGAhTGtDQBkMY0aNdJbb72l9u3ba+fOnWrcuLHKlCmjwMBAnThxQsePH9emTZtUq1Yta0cFkMPZ0vkoOjpaP/zwg+Li4tSoUSOVK1fO2pEAAMhyGDkPABnLztoBAABJ7d+/3/xQwpCQEPXr108HDhzQ8uXLtW/fPgUHB2vIkCFWTgnAFtjS+cjT01O9e/fWW2+9Rcc8AOCJNGjQQAMGDNCgQYNUoEABeXh4aM6cOYqNjdXrr78uNzc3lSxZUj/99JN5m61bt6pGjRpycnKSp6enhg8frvj4eHO9n5+fQkNDk7x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lS5cUFhamCRMmaNCgQdaOmWFatGihXbt26cUXX9SePXusHSdN+O7Kft9dAAAAALImm+6c9/LyUlRUlIoXLy5Jmjx5cpIOjatXr6pAgQLWivfU5syZoyVLlmjnzp0qU6aMXn31Va1Zs0Z+fn7WjvZUclK78uXLZx6FnS9fPiunyRw56fOSZB6tXL9+/RTr8+fPr+x4Q9L48ePVokULxcbGqnPnznr33Xd18uRJBQYG6vjx45o+fbpGjBhh7Zhp1qRJE/Xv318DBgzQihUr1Lx5c40YMUILFiyQyWTSkCFDzHdLZReTJ09WmzZtzNO/+Pr6auXKleb6q1evasiQIdaKl25jxoyRi4uLpk2bpqFDh5rPjYZhqGjRoho+fLiGDh1q5ZRPp379+knm0y9Xrpz27Nmjdu3aZavzBt9dAAAAAJAxbHpamz59+qh69erq1atXivUfffSRtm/frh9//NHCyTKGj4+PXnnlFb366quqUqWKteNkmJzarpwqp31ec+fO1b179zRw4MAU6y9fvqwvvvhCo0ePtnCyp7d7924FBwfrt99+S1Lu5eWlIUOG6O2337ZSsvS7fPmyXnvtNf3666+qV6+eli9frpCQEM2YMUMmk0klS5bUTz/9pJIlS1o7apqdPHlScXFxCgwMlL29vbXjZKjTp0/r0qVLkqSiRYvK39/fyolga3LadxcAAACArMmmO+f/y+nTp+Xs7Jxkqpvs5NHc1zlNTm3XvXv3ZBiGcufOLUk6e/asVq9erXLlyqlZs2ZWTpd+OfXzysmuXr2qyMhIJSYmytPTM0eOFI2MjNTdu3dVtmxZOThkv5vIoqOjNWvWLO3YsUPR0dGyt7eXv7+/2rZtqx49emTbzvqc2q6U3Lt3Tw8ePEhSljdvXiulST++uwAAAAAg/eictwE3btzQt99+q6ioKPn6+qpDhw7Z+jb0yMjIZB03TZs2zZadGv/UrFkztW/fXn369NGNGzdUpkwZOTo6KiYmRtOmTVPfvn2tHfGp5LTjELCWvXv3qkmTJvL395eLi4t+++03vfrqq7p//742bNigwMBAbdiwwTwHfXaRU9v1T7GxsRo2bJi++eabJM8+eCQ7PmQ0p393/VtsbKzCw8P13HPPWTsKAAAAgBzAztoBrO3atWvavHmzrl+/LkmKiYnRpEmTNG7cOB09etTK6dLn5ZdfNj+A7ciRIwoICFBISIjCwsL0/vvvq2zZstmybbGxserQoYNKlSqlHj166L333tPUqVPVqVMneXt7a8aMGdaO+FT27dunevXqSZK+++47FS1aVGfPntXixYs1ffp0K6dLu5x6HP6Xy5cva9y4cdaOkeFoV9YxaNAgvfPOO9q/f7927dqlRYsW6cSJE1q+fLkiIyN17949vf/++9aOmWY5tV3/NHToUP3yyy+aOXOmnJyc9OWXX2rs2LHy8vLS4sWLrR0vXXLad9d/+fPPP9WwYUNrxwAAAACQQ9j0yPk9e/aoWbNmunXrlvLnz6+wsDB16NBBDg4OMgxDFy5c0I4dO8wPgMwuChcurF27dikgIEAtW7ZUgQIFtGDBAjk6OurBgwfq27evzp07pw0bNlg7apq8+eabOnz4sGbMmCFnZ2eFhISoZMmSGj16tJYvX64BAwZo7ty56tKli7Wjpkvu3Ll17NgxFS9eXB07dlT58uU1evRonTt3TmXKlNHdu3etHTFNcupx+F8OHDigatWqZcsRsI9Du7KO3Llz648//lCJEiUkSYmJiXJ2dta5c+fk4eGhsLAw9ejRQxcuXLBy0rTJqe36p+LFi2vx4sVq0KCB8ubNq3379qlUqVL66quvtGzZMq1bt87aEdMsp313/ZfseM4AAAAAkHVlv4l2M1BISIg6dOigadOmafbs2Wrbtq2ef/55zZ07V5LUq1cvffDBB1q9erWVk6ZNbGys7Owe3hQRERGhH3/8UY6OjpKkXLlyaejQoapRo4Y1I6bLqlWrtH79elWuXFnSwwdzenl5afTo0XrjjTd07949TZkyJdt2zpcqVUpr1qxRu3bttGHDBr3zzjuSpCtXrmTLKXty6nF48ODBx9YfP37cQkkyFu3KPooUKaLo6GhzJ/bly5cVHx9vPk8EBASY7wbLTnJqu/7p+vXr5ofb5s2b19yeunXrZtvpX3Lad1fBggUfW0+nPAAAAICMZNOd8+Hh4Zo+fbrc3Nz09ttva9iwYerdu7e5vn///mrdurUVE6ZPpUqV9Msvv6hkyZLm28urVq1qrj979qxcXFysmDB9/tlJI0murq6Kj49XbGyscufOrWbNmmnw4MFWTPh0Ro0apS5duuidd95R48aNVatWLUnSxo0bk3x+2UVOPQ6rVKkik8mklG46elSeHR8iSLuyj7Zt26pPnz6aMmWKnJyc9MEHH6h+/frm/0/Hjx+Xt7e3lVOmXU5t1z+VKFFCZ86cka+vr8qVK6dvvvlGNWrU0Nq1a5U/f35rx0uXnPbdFRcXp759+6pixYop1p89e1Zjx461cCoAAAAAOZVNd87fv3/f/Et/rly5lDt3bhUqVMhc7+7unuID27K6kSNHqlu3bsqVK5cGDhyod955R9euXVNgYKCOHz+u0aNHq2vXrtaOmWbPPPOMPv30U33++eeSpE8//VSFCxdW4cKFJUl37tyRq6urNSM+lZdffll169ZVdHS0+e4ASWrcuLHatWtnxWTpk1OPQ3d3d02aNEmNGzdOsf7w4cPZ8o96tCv7GD9+vKKjo9W6dWslJCSoVq1a+vrrr831JpNJEydOtGLC9Mmp7fqn119/XQf+f3t3HxVlnf9//DUDZeiMCooGZCLIqnRQArU0V8nMG7Q0i3SztHL3JJnl2uLNuq6a5uFYutp2s1nhTeVqLrmrbluYqaWymmJ4s0qsQ4qKaCRrgiI38/vDw/y+FN5w41wzF8/HOXNO1/WZ5PVh5sxn5s1n3ldmpvr06aNp06Zp8ODB+vOf/6yysjItXLjQ6Hi1Yra1Kzo6Wm3atNGYMWOqHc/MzKQ4DwAAAKDeNOjifJs2beRwOBQaGipJWrVqlYKCglzjeXl5VYr13mLw4MFasmSJJk6cqJMnT8rpdLq+EdCoUSONGzfOKwscycnJuv/++5Wamqqbb75Zp06d0vLly13jO3bsUHx8vIEJ6+7WW2/VrbfeWuWcN7Z+kcz7PIyNjdXJkyfVtm3bascLCwur3aXt6ZiX97DZbFq9erUuXryosrKyn/1Rsn///gYlqxuzzuv/qmz5Ikn33nuvDh8+rN27dys8PLxKYdvbmG3tKiwsvOJ4QECARo8e7b5AAAAAAEytQV8Qdvbs2erQoYNGjhxZ7fj06dN1+PBhpaamujlZ/SgvL1dGRoYcDocqKioUFBSk2NhY2e12o6PVWl5enjZs2KCSkhL17dtXkZGRRkeqV19//bXWrFmjY8eO6dKlS1XGPv74Y4NS1Y3Znodr165VUVGRHn/88WrHz549q3Xr1l1x16WnYl7eNS/Ak5hx7QIAAAAAd2jQxflrKS4ulo+Pjxo1amR0FDQAq1at0ujRo9W/f39t3LhR/fv3V3Z2tk6dOqWHHnpIS5cuNToiAKAWVqxYcV3388Yd2axdAAAAAFB7FOevIjc3VzNnzlRKSorRUWrM6XTqu+++U5s2beTr66tLly5p7dq1KikpUXx8vFe265HMOy/p8gVUn3nmGY0fP152u12ZmZlq166dnnnmGQUFBXllj1szP15X4s2vG1fDvIDas1qtstls8vX1vWIbJYvFoh9++MHNyerOjGtXZmamMjIyFBcXp3bt2ungwYN64403VFFRoYceekgDBgwwOiIAAAAAk6A4fxWZmZmKiYlReXm50VFqJCsrSwMGDFBubq7CwsKUlpamhIQEHT58WE6nU40bN9aOHTsUERFhdNQaqZzXsWPHFB4ebpp5VWrSpIkOHjyo0NBQtWzZUps3b1ZUVJQOHTqkvn37Ki8vz+iINWLW5+G1eOvrxrUwL6D27rjjDuXn5+vxxx/X008/rc6dOxsdqd6Ybe1KTU3ViBEj1Lx5c9cflB955BF17dpVPj4++vzzz7VixQo99thjRkcFAAAAYAIN+oKw69atu+q4w+FwU5L6NWXKFHXp0kXr169XSkqKhgwZooiICKWnp8vpdOrRRx/VSy+9pPfff9/oqDVi1nlVCggI0I8//ihJCgkJ0YEDBxQVFaXCwkIVFxcbnK7mzPp4mfV1g3kBN87Bgwe1c+dOpaSkqHfv3mrfvr3Gjh2rUaNGqWnTpkbHqxOzrV3z5s3T7NmzNX36dK1atUqPPPKIJk2apBkzZkiSFixYoFdeeYXiPAAAAIB60aB3zlutVlkslit+xVy6/DVzb9tR2apVK6WlpSk6OlpFRUWy2+368ssv1atXL0lSenq6Ro4cqaNHjxqctGbMOq9Kjz32mLp27apJkybp5Zdf1uLFizV06FBt3LhRMTExXndRPbM+XmZ93WBe3jUveK8LFy5ozZo1Wrp0qXbt2qVhw4YpJSXFa69vY7a1y2az6cCBAwoNDZXT6VSjRo20Z88eRUVFSbr8B70uXbq4/iABAAAAAHVhNTqAkYKCgpSamqqKiopqbxkZGUZHrJXz588rICBA0uWvmzdp0kRBQUGu8dtuu035+flGxas1s86r0uuvv66RI0dKkqZNm6bf/e53ys/P1/Dhw/Xee+8ZnK7mzPp4mfV1g3kB7uHn56fRo0dr9uzZ6t69u1atWuWVO8wrmW3tstvtKigokCQVFhaqrKzMdSxJBQUFstlsRsUDAAAAYDINujgfGxt71cLMtXZbeqrg4GAdO3bMdTx//ny1atXKdXzmzBn5+/sbEa1OzDqvSgEBAQoODpZ0ebfv5MmTtW7dOi1cuNAr52XWx8usrxvMC7jxTpw4oXnz5ikiIkIjR45Ut27ddPDgQa98LaxktrWrX79+Gj9+vD788EONGTNGAwYM0LRp03T48GFlZWUpKSnJ9Q0wAAAAAKirBl2cT0pKUs+ePa843r59e23evNmNiepHv379dPjwYddxYmKi7Ha76zgtLU0xMTFGRKsTs86rOk6nU1988YX++c9/6uzZs0bHqRWzPl5mfd1gXsCN89FHH2nQoEGKiIjQ119/rQULFig3N1fz589Xx44djY5Xb8ywdr366quy2+0aN26cysrKtHr1anXt2lWRkZGKjIzUyZMnlZycbHRMAAAAACbRoHvON1Q5OTm65ZZbqrQYMQNvnVdhYaFeeOEFZWRk6O6779aCBQsUHx+vHTt2SJICAwO1ceNGde7c2eCk9ctbHy8AqCmr1arbb79do0aNUuvWra94v+eff96Nqeqmoa1dDodDxcXF6tixo3x9fY2OAwAAAMAkKM4DBvv1r3+tL7/8UqNHj9aGDRtktVrldDq1aNEiV4sAm82m9evXGx0VAFALoaGhslgsV72PxWKRw+FwU6K6Y+0CAAAAgLqjOG9SBQUF2rdvn7p06aKAgAB9//33eu+991RSUqKEhAR16tTJ6Ii1dvz4cTVv3vxnF2QrLS1Venq6evfubVCy2gkJCdHKlSvVp08fnThxQm3atNEXX3yhuLg4SdKuXbv04IMP6tSpU8YGrQUzPw8BoCEz89olme+9BgAAAADPRHHehHbt2qX+/fvr3Llzat68uTZu3KiEhAT5+vrK6XTqxIkT2rZtm9f1+87Ly9PQoUO1Z88eWSwWjRo1Sm+88Ybrg3N+fr6Cg4NVXl5ucNKa8fX1VW5urqu9S+PGjbV//36Fh4dLkk6dOqWQkBCvm5dZn4cAAPOuXWZ9rwEAAADAM9E004SmT5+uhIQELVy4UG+//baGDRumgQMH6p133pF0+avoc+bM0dq1aw1OWjNTp06Vj4+Pdu7cqcLCQk2bNk1xcXHauHGj/P39JV2+GJ23qaiokI+Pj+vYx8enSvuDa7VC8FRmfR4CQE299NJL1Z5v1qyZOnTooP79+8tqtbo5Vd2Yde0y63sNAAAAAJ6JnfMmFBAQoO3bt6tTp04qLS3VLbfcovT0dHXv3l2StHfvXj3wwAM6fvy4wUlrJiQkRGvXrnXNo6SkRCNGjNDRo0e1adMmlZaWeuVuNqvVqrlz57p25U2ZMkVJSUlq2bKlJOnHH3/UH//4R6+bl1mfhwBQU3feeWe15wsLC3XixAndcccd+uyzz9SqVSs3J6s9s65dZn2vAQAAAMAzUZw3IZvNpgMHDig0NFSSZLfblZmZqbCwMEnSsWPH1KFDB124cMHAlDVns9m0d+9eRUREuM6VlZUpISFBDodDH3zwgaKjo73uA/P1XChQknJyctyQpv6Y9XkIAPUpLy9Pjz32mMLDw/Xuu+8aHee6mXntMuN7DQAAAACeibY2JtSmTRs5HA5XUXTVqlWunrDS5UJA5c42bxIWFqZ9+/ZV+cDs6+urNWvWKCEhQUOGDDEwXe199913Rke4Icz6PASA+hQUFKS5c+fqiSeeMDpKjZh17TLrew0AAAAAnsm7GpziuowcOVKnT592HQ8ePFh+fn6u43Xr1rm+ru1NBg0apCVLlvzsfOWH5ujoaK/uA2u2QodZn4cAUN9CQkKqvF56E7OtXWZ/rwEAAADAs9DWpgEqLi6Wj4+PGjVqZHSUGikrK1NxcbGaNm1a7Xh5ebmOHz+utm3bujlZ/bBarerZs6eeeOIJJSQkKCAgwOhIN5S3Pg8BoL794x//0PTp03XgwAGjo9SY2dYus7/XAAAAAOBZ2DnfABUUFCgxMdHoGDXm6+t7xQ/LknTy5EnNnj3bjYnq1+7du9WjRw/NnTtXwcHBGjp0qNasWaOSkhKjo90Q3vo8BICaOnfuXLW33Nxcffzxx5o4caJ+9atfGR2zVsy2dpn9vQYAAAAAz8LO+QYoMzNTMTExpruYmVnm5XQ6tWXLFq1cuVKpqakqLy/Xww8/rJSUFKOj1SuzPF4AcC1Wq/WKF0+1WCx65plntGjRIt10001uTlZ/WLsAAAAAoOYozpvQunXrrjrucDj04osvet0HS7PO62oyMjI0duxY7du3z+vm1RAfLwCoztatW6s937RpU0VERMhms7k50Y3F2gUAAAAA14fivAlV7tC72kNrsVi87oOlWef1U7m5ufrrX/+qlStXav/+/erRo4dGjRrldS1gGsrjBQBg7QIAAACA2qDnvAkFBQUpNTVVFRUV1d4yMjKMjlgrZp1XpSVLlqhPnz5q166dli9frkcffVRHjhzRtm3bvK64IZn/8QKAmjh9+rQ2b96sc+fOSZLy8/M1f/58JScna//+/Qanqz3WLgAAAACoPYrzJhQbG3vVD4/X2hHmqcw6r0pz5sxR9+7dtXv3bh08eFC///3vFRoaanSsWjP74wUA12vLli0KCwvTfffdp44dO2rfvn3q2rWr3n33XS1btkzdunVTWlqa0TFrhbULAAAAAGqPtjYm9NVXX6moqEgDBw6sdryoqEi7d+9Wnz593Jysbsw6r0pOp/OKFwz0RmZ/vADgevXq1UvR0dFKTk7WX/7yFy1evFhDhw7V66+/LklKSkrSjh07tH37doOT1hxrFwAAAADUHsV5wMMVFRVpz5496t27t9FRAAC10KxZM2VkZCg8PFxlZWXy8/PT119/rejoaElSdna2unXrpsLCQkNz1obD4dC2bduUl5cnHx8ftWvXTvfff7+aNm1qdDQAAAAA8Hi+RgcAcHX//e9/de+993LxOQDwUjfffLMuXrwoSbp06ZIqKipcx5J04cIF3XTTTUbFq5WioiI9+eSTSk1NlXS53UurVq105swZ+fn5KTk5WePHjzc4JQAAAAB4NnrOAwAA3ED33HOPpk6dqu3bt+u3v/2tYmJiNHfuXBUVFam4uFhz5sxR165djY5ZI5MmTVJeXp727t2rQ4cO6aGHHtLo0aN17tw5LV68WJMnT9bKlSuNjgkAAAAAHo22NoDBAgICrjpeXl6u8+fPs3MeALxUdna24uPjdeTIEUVGRiotLU2JiYn65JNPJEn+/v769NNPFRMTY3DS6xcYGKhPP/1UsbGxkqSzZ88qODhYBQUFaty4sd544w29++672rt3r8FJAQAAAMBzUZwHDNakSRMlJiYqKiqq2vGjR49q9uzZFOcBwMsVFBSoRYsWruNNmzbpwoUL6tGjR5Xz3sDf31+7du1SRESEJKm0tFSNGzfWyZMnFRgYqOzsbHXu3FkXLlwwOCkAAAAAeC56zgMGi46OVps2bTRmzJhqxzMzMzV79mw3pwIA1LefFuDvu+8+g5LUXbdu3bR48WK9/vrrkqTFixcrMDBQgYGBkqTz58/LZrMZGREAAAAAPB7FecBggwcPVmFh4RXHAwICNHr0aPcFAgDUq5KSElmtVtdFX48cOaKUlBQdO3ZMbdu21dixY9WuXTuDU9ZMcnKy7r//fqWmpurmm2/WqVOntHz5ctf4jh07FB8fb2BCAAAAAPB8tLUBAAC4gfr27avnnntOw4cP1/bt23XfffepQ4cO6tSpk7799ltlZWXp888/V48ePYyOWiN5eXnasGGDSkpK1LdvX0VGRhodCQAAAAC8CsV5AACAG8jf31+7d+9WeHi44uLiFBMTo4ULF7rGZ8yYoc2bN2vbtm0GpgQAAAAAuJvV6AAALveVX7p0qXJyciRJBw8e1LPPPqtx48bps88+MzgdAKAuSktLVVpaKkk6fPjwz64x8uSTTyozM9OIaHXidDqVk5OjsrIySdKlS5e0evVqrVixQt9//73B6QAAAADA81GcBwyWmpqq2NhYJSUlqUuXLtq0aZN69eql7Oxsfffddxo8eLBWrlxpdEwAQC3dddddWr9+vSQpPDz8Z4X4b775RgEBAUZEq7WsrCy1a9dO4eHh6tSpk3JyctSzZ0+NHTtWiYmJ6tSpk7Kzs42OCQAAAAAejbY2gMFiY2M1fPhwTZ8+XatWrVJiYqImTZqkGTNmSJIWLFigDz74QHv37jU4KQCgNtLT0zVo0CBNnDhRLVu21OzZszVu3Dh16tRJWVlZeu211zRt2jRNnjzZ6KjXbdiwYXI6nZo7d65SUlKUlpamiIgIrVmzRk6nU48++qjsdrvef/99o6MCAAAAgMeiOA8YzGaz6cCBAwoNDZXT6VSjRo20Z88eRUVFSZIcDoe6dOmiH3/80eCkAIDaSk9P16RJk7Rz584q54ODg5WUlKQXXnjBoGS106pVK6WlpSk6OlpFRUWy2+368ssv1atXL0mX5zty5EgdPXrU4KQAAAAA4Ll8jQ4ANHR2u10FBQUKDQ1VYWGhysrKVFBQ4BovKCiQzWYzMCEAoK569Oih9PR0nTlzRg6HQxUVFQoKClJoaKjR0Wrl/PnzrlY8TZo0UZMmTRQUFOQav+2225Sfn29UPAAAAADwChTnAYP169dP48eP14QJE7R69WoNGDBA06ZN09KlS2WxWJSUlOTaiQgA8G6BgYEKDAw0OkadBQcH69ixY7r99tslSfPnz1erVq1c42fOnJG/v79R8QAAAADAK3BBWMBgr776qux2u8aNG6eysjKtXr1aXbt2VWRkpCIjI3Xy5EklJycbHRMAcIPk5+frpZdeMjpGjfTr10+HDx92HScmJsput7uO09LSFBMTY0Q0AAAAAPAa9JwHPJTD4VBxcbE6duwoX1++5AIAZpWZmamYmBiVl5cbHaXe5OTk6JZbbqnS6gYAAAAAUBUVP8BDhYWFGR0BAFAP9u3bd9XxrKwsNyVxn3bt2hkdAQAAAAA8HjvnAQ9x/PhxNW/e/GcXfy0tLVV6erp69+5tUDIAQF1YrVZZLBZV95ar8rzFYvHKnfOsXQAAAABQe/ScBwyWl5en7t27q23btmrevLnGjBmj8+fPu8Z/+OEH3XvvvQYmBADURYsWLfTOO+8oJyfnZzeHw6ENGzYYHbHGWLsAAAAAoO4ozgMGmzp1qnx8fLRz5059+umn+s9//qO4uDidPXvWdR++4AIA3is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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(3, 2, figsize=(15, 10), constrained_layout=True)\n", - "\n", - "axs = _axs.flatten()\n", - "for i, windowMS in enumerate(list_windows):\n", - " results_df[results_df.windowMS == windowMS][\n", - " [\"mouse_id\", \"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]\n", - " ].plot.bar(x=\"mouse_id\", ax=axs[i])\n", - " axs[i].set_title(f\"{windowMS=}\")\n", - "\n", - "fig.suptitle(f\"ANN Errors depending on the window size in ms and mouse id\")\n", - "fig.savefig(\"/home/vador/Documents/Theotime/figures/results_windowing_mousehue.png\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "id": "f7cc2fc4-9d16-43c1-8bab-d0fd4bdb18fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 224, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "corr = results_df[[\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]].corr()\n", - "# mask = np.triu(np.ones_like(corr, dtype=bool))\n", - "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, cmap=cmap, square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.5})" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "id": "d254dae6-937d-4942-bf51-03d86a1bd370", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_205500/1088820411.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", - " corr = results_df.corr()\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "corr = results_df.corr()\n", - "# mask = np.triu(np.ones_like(corr, dtype=bool))\n", - "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, cmap=cmap, square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.5})" - ] - }, - { - "cell_type": "markdown", - "id": "f4d0891c-65c0-495b-aecd-455d05d435af", - "metadata": {}, - "source": [ - "## Let's focus on M994 and 12390 (good results, PAG & MFB, several windowMS)" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "id": "680c728b-aae3-49f6-95aa-f49185bf2c98", - "metadata": {}, - "outputs": [], - "source": [ - "selected_mice = [\"994\", \"1239v3\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "id": "f4708b59-56ae-414d-9abd-8245e18eb923", - "metadata": {}, - "outputs": [], - "source": [ - "subresults_df = results_df[results_df[\"mouse_id\"].isin(selected_mice)]" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "2aa6b616-e4d2-452c-a539-3b3fa7cd0df6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 141, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "subresults_df.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "id": "b05c73d6-6628-48ea-9459-17d13e8db690", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface as si\n", - "import spikeinterface.extractors as se\n", - "import spikeinterface.preprocessing as spre\n", - "import spikeinterface.postprocessing as spost\n", - "import spikeinterface.curation as scur\n", - "import spikeinterface.widgets as sw\n", - "import spikeinterface.qualitymetrics" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "id": "a56c0381-84e6-45a9-a42e-6f6695c6780f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1239vBasile': '/home/vador/Documents/Theotime/DimaERC2/M1239TEST3_Basile_M1239/TEST/results',\n", - " '1281vBasile': '/home/vador/Documents/Theotime/DimaERC2/M1281TEST3_Basile_1281MFB/TEST/results',\n", - " '1199': '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results',\n", - " '1336': '/home/vador/Documents/Theotime/DimaERC2/M1336_Known/TEST/results',\n", - " '1168MFB': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1168/TEST/results',\n", - " '905': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results',\n", - " '1161w1199': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results',\n", - " '1161': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results',\n", - " '1124': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results',\n", - " '1186': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results',\n", - " '1182': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results',\n", - " '1168UMaze': '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results',\n", - " '1117': '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results',\n", - " '994': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Final_results_v3/results',\n", - " '1336v3': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_MFB/Final_results_v3/results',\n", - " '1336v2': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_known/Final_results_v2/results',\n", - " '1281v2': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1281_MFB/Final_results_v2/results',\n", - " '1239v3': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB/Final_results_v3/results'}" - ] - }, - "execution_count": 143, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dirmouse" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "d962ece2-a424-4b93-b97c-0418586649a8", - "metadata": {}, - "outputs": [], - "source": [ - "sorting_folder = dict()\n", - "from pathlib import Path\n", - "\n", - "for mouse in selected_mice:\n", - " sorting_folder[mouse] = os.path.join(Path(dirmouse[mouse]).parents[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "e8d1b473-da68-4178-b0bc-51061a468460", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'994': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG',\n", - " '1239v3': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB'}" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorting_folder" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "f7843b54-23e5-4151-b800-2296673627cc", - "metadata": {}, - "outputs": [], - "source": [ - "recording = se.NeuroScopeRecordingExtractor(\n", - " os.path.join(sorting_folder[994], \"M994_20191013_UMaze_SpikeRef.dat\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "c9c19903-10af-4f37-a9d5-786d05b102dc", - "metadata": {}, - "outputs": [], - "source": [ - "import xml.etree.ElementTree as ET\n", - "\n", - "tree = ET.parse(os.path.join(sorting_folder[994], \"M994_20191013_UMaze_SpikeRef.xml\"))\n", - "root = tree.getroot()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "adbe8850-3569-40bd-9764-89e1c5e164c1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[]\n" - ] - } - ], - "source": [ - "print(root.findall(\"anatomicalDescription\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "2b0e695c-beac-4b0a-a0cb-ea3fa2628030", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NeuroScopeRecordingExtractor: 135 channels - 20.0kHz - 1 segments - 401,633,280 samples \n", - " 20,081.66s (5.58 hours) - int16 dtype - 100.99 GiB\n", - " file_path: /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/M994_20191013_UMaze_SpikeRef.dat\n" - ] - } - ], - "source": [ - "print(recording)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "34fd7526-1f9d-4151-a0eb-2651c4869cf7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
NeuroScopeRecordingExtractor: 135 channels - 20.0kHz - 1 segments - 401,633,280 samples - 20,081.66s (5.58 hours) - int16 dtype - 100.99 GiB
Channel IDs
    ['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'\n", - " '16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'\n", - " '30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'\n", - " '44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'\n", - " '58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'\n", - " '72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'\n", - " '86' '87' '88' '89' '90' '91' '92' '93' '94' '95' '96' '97' '98' '99'\n", - " '100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '110' '111'\n", - " '112' '113' '114' '115' '116' '117' '118' '119' '120' '121' '122' '123'\n", - " '124' '125' '126' '127' '128' '129' '130' '131' '132' '133' '134']
Annotations
  • is_filtered : False
Channel Properties
    gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578]
    offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
    channel_names ['ch0grp3' 'ch1grp3' 'ch2grp3' 'ch3grp3' 'ch4grp12' 'ch5grp4' 'ch6grp4'\n", - " 'ch7grp4' 'ch8grp5' 'ch9grp4' 'ch10grp4' 'ch11grp4' 'ch12grp3' 'ch13grp3'\n", - " 'ch14grp3' 'ch15grp3' 'ch16grp4' 'ch17grp4' 'ch18grp4' 'ch19grp4'\n", - " 'ch20grp4' 'ch21grp4' 'ch22grp4' 'ch23grp4' 'ch24grp3' 'ch25grp4'\n", - " 'ch26grp3' 'ch27grp3' 'ch28grp3' 'ch29grp3' 'ch30grp2' 'ch31grp3'\n", - " 'ch32grp2' 'ch33grp2' 'ch34grp2' 'ch35grp2' 'ch36grp10' 'ch37grp2'\n", - " 'ch38grp2' 'ch39grp1' 'ch40grp1' 'ch41grp0' 'ch42grp1' 'ch43grp1'\n", - " 'ch44grp1' 'ch45grp1' 'ch46grp1' 'ch47grp1' 'ch48grp2' 'ch49grp3'\n", - " 'ch50grp2' 'ch51grp2' 'ch52grp1' 'ch53grp1' 'ch54grp10' 'ch55grp1'\n", - " 'ch56grp1' 'ch57grp1' 'ch58grp10' 'ch59grp1' 'ch60grp2' 'ch61grp2'\n", - " 'ch62grp2' 'ch63grp2' 'ch64grp8' 'ch65grp8' 'ch66grp8' 'ch67grp8'\n", - " 'ch68grp11' 'ch69grp9' 'ch70grp9' 'ch71grp9' 'ch72grp11' 'ch73grp9'\n", - " 'ch74grp9' 'ch75grp9' 'ch76grp8' 'ch77grp8' 'ch78grp8' 'ch79grp8'\n", - " 'ch80grp9' 'ch81grp9' 'ch82grp9' 'ch83grp9' 'ch84grp9' 'ch85grp9'\n", - " 'ch86grp9' 'ch87grp9' 'ch88grp8' 'ch89grp9' 'ch90grp8' 'ch91grp8'\n", - " 'ch92grp8' 'ch93grp8' 'ch94grp7' 'ch95grp8' 'ch96grp7' 'ch97grp7'\n", - " 'ch98grp7' 'ch99grp7' 'ch100grp0' 'ch101grp7' 'ch102grp7' 'ch103grp6'\n", - " 'ch104grp6' 'ch105grp6' 'ch106grp6' 'ch107grp6' 'ch108grp6' 'ch109grp6'\n", - " 'ch110grp6' 'ch111grp6' 'ch112grp7' 'ch113grp8' 'ch114grp7' 'ch115grp7'\n", - " 'ch116grp6' 'ch117grp6' 'ch118grp5' 'ch119grp6' 'ch120grp6' 'ch121grp6'\n", - " 'ch122grp0' 'ch123grp6' 'ch124grp7' 'ch125grp7' 'ch126grp7' 'ch127grp7'\n", - " 'ch128grp13' 'ch129grp13' 'ch130grp13' 'ch131grp13' 'ch132grp13'\n", - " 'ch133grp13' 'ch134grp14']
" - ], - "text/plain": [ - "NeuroScopeRecordingExtractor: 135 channels - 20.0kHz - 1 segments - 401,633,280 samples \n", - " 20,081.66s (5.58 hours) - int16 dtype - 100.99 GiB\n", - " file_path: /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/M994_20191013_UMaze_SpikeRef.dat" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "a374fb43-2bdb-4fa5-a161-b48a3c5b5264", - "metadata": {}, - "outputs": [], - "source": [ - "sorting = se.NeuroScopeSortingExtractor(os.path.join(sorting_folder[994]))" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "032738de-37a7-4eeb-aad3-723643323b76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
NeuroScopeSortingExtractor: 96 units - 1 segments - 20.0kHz
Unit IDs
    [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39\n", - " 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 56 58 59\n", - " 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77\n", - " 78 79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96\n", - " 97 98 99 100 101 102]
Annotations
    Unit Properties
      group[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3\n", - " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n", - " 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6]
    " - ], - "text/plain": [ - "NeuroScopeSortingExtractor: 96 units - 1 segments - 20.0kHz" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorting" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "a0792750-3f40-4e68-bf80-859aa8f5ed96", - "metadata": {}, - "outputs": [], - "source": [ - "from probeinterface import generate_linear_probe\n", - "from probeinterface.plotting import plot_probe" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "63ae6f79-eb51-4996-a9ad-c2ecbd75f1e8", - "metadata": {}, - "outputs": [], - "source": [ - "probe = generate_linear_probe(\n", - " num_elec=135, ypitch=20, contact_shapes=\"circle\", contact_shape_params={\"radius\": 6}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "380a2a4c-59f0-4f72-ba8d-72810e64a3af", - "metadata": {}, - "outputs": [], - "source": [ - "probe.set_device_channel_indices(np.arange(135))" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "f21b5093-8a7b-40fa-b8d8-abc41b54afbe", - "metadata": {}, - "outputs": [], - "source": [ - "recording = recording.set_probe(probe)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "c9490f8b-b886-4f6e-9123-0ff720fe4819", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(,\n", - " )" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_probe(probe)" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "id": "8d7f98a3-8a65-4f27-b90c-cfc83e297f0f", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "02261c49f37a45bdad41dedccc5bbbb7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "AppLayout(children=(TimeSlider(children=(Dropdown(description='segment', options=(0,), value=0), Button(icon='…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# let'sdo some preprocessing\n", - "\n", - "%matplotlib widget\n", - "recording = spre.depth_order(recording)\n", - "recording_hp = spre.highpass_filter(recording)\n", - "recording_cmr = spre.common_reference(recording_hp)\n", - "\n", - "\n", - "recording_layers = dict(raw=recording, highpass=recording_hp, cmr=recording_cmr)\n", - "\n", - "w = sw.plot_traces(\n", - " recording_layers,\n", - " mode=\"map\",\n", - " order_channel_by_depth=False,\n", - " time_range=[0, 0.2],\n", - " # figlabel=\"SpikeInterface tutorial: plot_traces\",\n", - " clim=(-50, 50),\n", - " backend=\"ipywidgets\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "id": "6a9247ec-ae09-4d17-9f70-a811405e6761", - "metadata": {}, - "outputs": [], - "source": [ - "si.set_global_job_kwargs(n_jobs=-1, progress_bar=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "id": "4a86e91f-9afa-4e2a-9b60-7e49a8d4e0c6", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e7c05bc94b36407a839b9f0591e566c1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "estimate_sparsity: 0%| | 0/20082 [00:00" - ] - }, - "execution_count": 224, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sw.plot_traces(recording, backend=\"ipywidgets\")" - ] - }, - { - "cell_type": "code", - "execution_count": 229, - "id": "814a42d2-9a03-45e6-91e5-9b4acfdd1e87", - "metadata": {}, - "outputs": [], - "source": [ - "analyzer_saved = analyzer.save_as(\n", - " folder=os.path.join(sorting_folder[994], \"analyzer_for_visualization\"),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "id": "190bf72b-b649-42ef-bbaa-8572c6a0f0da", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bbd1728ebc4e46fbab61fa0d5e5edc8d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fitting PCA: 0%| | 0/96 [00:00" - ] - }, - "execution_count": 236, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib widget\n", - "sw.plot_sorting_summary(analyzer_saved, backend=\"sortingview\")" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "id": "d3eb1115-03a0-4fd3-8fe0-34fb973f6bfe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    snramplitude_cutoffrp_contaminationrp_violations
    216.2161470.0178070.374007523
    316.6075610.0197460.0714391
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    516.5355660.020540.00
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    1016.436560.0177340.0422141
    1116.5771670.0253840.06867583
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    " - ], - "text/plain": [ - " snr amplitude_cutoff rp_contamination rp_violations\n", - "2 16.216147 0.017807 0.374007 523\n", - "3 16.607561 0.019746 0.071439 1\n", - "4 16.711653 0.043149 0.0 0\n", - "5 16.535566 0.02054 0.0 0\n", - "6 17.608838 0.132753 0.0 0\n", - "7 16.203859 0.022084 0.488192 42\n", - "8 16.238859 0.016513 0.130991 30\n", - "9 16.121003 0.007905 0.806386 42\n", - "10 16.43656 0.017734 0.042214 1\n", - "11 16.577167 0.025384 0.068675 83" - ] - }, - "execution_count": 240, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics = analyzer_saved.get_extension(\"quality_metrics\").get_data()\n", - "metrics.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "id": "cc238c3e-6b85-4565-821e-21760e970c5d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'snr')" - ] - }, - "execution_count": 241, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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", 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    \n", - " Figure\n", - "
    \n", - " \n", - "
    \n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "_ = ax.hist(metrics[\"snr\"], bins=20)\n", - "ax.set_xlabel(\"snr\")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "6ca31cdc-c0b6-40c5-87a5-1a54847ffbd1", - "metadata": {}, - "outputs": [], - "source": [ - "sorter_names = [\"spykingcircus2\", \"mountainsort5\", \"kilosort4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "f2457326-9f76-42f6-abe1-07c5d0b182db", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0mrun_sorter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msorter_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'str'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mrecording\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'BaseRecording'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfolder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mremove_existing_folder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdelete_output_folder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mraise_error\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdocker_image\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Union[bool, str]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msingularity_image\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Union[bool, str]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdelete_container_files\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mwith_output\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0moutput_folder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msorter_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Generic function to run a sorter via function approach.\n", - "\n", - "\n", - "Parameters\n", - "----------\n", - "sorter_name : str\n", - " The sorter name\n", - "recording : RecordingExtractor\n", - " The recording extractor to be spike sorted\n", - "folder : str or Path\n", - " Path to output folder\n", - "remove_existing_folder : bool\n", - " If True and folder exists then delete.\n", - "delete_output_folder : bool, default: False\n", - " If True, output folder is deleted\n", - "verbose : bool, default: False\n", - " If True, output is verbose\n", - "raise_error : bool, default: True\n", - " If True, an error is raised if spike sorting fails\n", - " If False, the process continues and the error is logged in the log file.\n", - "docker_image : bool or str, default: False\n", - " If True, pull the default docker container for the sorter and run the sorter in that container using docker.\n", - " Use a str to specify a non-default container. If that container is not local it will be pulled from docker hub.\n", - " If False, the sorter is run locally\n", - "singularity_image : bool or str, default: False\n", - " If True, pull the default docker container for the sorter and run the sorter in that container using\n", - " singularity. Use a str to specify a non-default container. If that container is not local it will be pulled\n", - " from Docker Hub. If False, the sorter is run locally\n", - "with_output : bool, default: True\n", - " If True, the output Sorting is returned as a Sorting\n", - "delete_container_files : bool, default: True\n", - " If True, the container temporary files are deleted after the sorting is done\n", - "output_folder : None, default: None\n", - " Do not use. Deprecated output function to be removed in 0.103.\n", - "**sorter_params : keyword args\n", - " Spike sorter specific arguments (they can be retrieved with `get_default_sorter_params(sorter_name_or_class)`)\n", - "\n", - "Returns\n", - "-------\n", - "BaseSorting | None\n", - " The spike sorted data (it `with_output` is True) or None (if `with_output` is False)\n", - "\n", - "\n", - "Examples\n", - "--------\n", - ">>> sorting = run_sorter(\"tridesclous\", recording)\n", - "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/spikeinterface/sorters/runsorter.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "?run_sorter" - ] - }, - { - "cell_type": "markdown", - "id": "ec0822d8-27a2-4aab-b259-a052f49a620e", - "metadata": {}, - "source": [ - "Available sorters:\n", - "- combinato=\"combinato\",\n", - "- herdingspikes=\"herdingspikes\",\n", - "- kilosort4=\"kilosort4\",\n", - "- klusta=\"klusta\",\n", - "- mountainsort4=\"mountainsort4\",\n", - "- mountainsort5=\"mountainsort5\",\n", - "- pykilosort=\"pykilosort\",\n", - "- spykingcircus=\"spyking-circus\",\n", - "- spykingcircus2=\"spyking-circus2\",\n", - "- tridesclous=\"tridesclous\",\n", - "- yass=\"yass\",\n", - "- # Matlab compiled sorters:\n", - "- hdsort=\"hdsort-compiled\",\n", - "- ironclust=\"ironclust-compiled\",\n", - "- kilosort=\"kilosort-compiled\",\n", - "- kilosort2=\"kilosort2-compiled\",\n", - "- kilosort2_5=\"kilosort2_5-compiled\",\n", - "- kilosort3=\"kilosort3-compiled\",\n", - "- waveclus=\"waveclus-compiled\",\n", - "- waveclus_snippets=\"waveclus-compiled\"," - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "9e78af0e-2d04-4e0f-adf7-1a4ce454b4d0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/numpy/core/getlimits.py:542: UserWarning: Signature b'\\x00\\xd0\\xcc\\xcc\\xcc\\xcc\\xcc\\xcc\\xfb\\xbf\\x00\\x00\\x00\\x00\\x00\\x00' for does not match any known type: falling back to type probe function.\n", - "This warnings indicates broken support for the dtype!\n", - " machar = _get_machar(dtype)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spykingcircus2 /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Simulated_sorter_output_spykingcircus2\n" - ] - }, - { - "ename": "Exception", - "evalue": "This folder /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Simulated_sorter_output_spykingcircus2 does not have spikeinterface_log.json", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[57], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(sorter_name, output_folder)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_folder\u001b[38;5;241m.\u001b[39mexists():\n\u001b[0;32m----> 8\u001b[0m sortings[sorter_name] \u001b[38;5;241m=\u001b[39m \u001b[43mread_sorter_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_folder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 10\u001b[0m sortings[sorter_name] \u001b[38;5;241m=\u001b[39m run_sorter(sorter_name, recording, output_folder, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/spikeinterface/sorters/runsorter.py:687\u001b[0m, in \u001b[0;36mread_sorter_folder\u001b[0;34m(folder, register_recording, sorting_info, raise_error)\u001b[0m\n\u001b[1;32m 684\u001b[0m log_file \u001b[38;5;241m=\u001b[39m folder \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspikeinterface_log.json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 686\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m log_file\u001b[38;5;241m.\u001b[39mis_file():\n\u001b[0;32m--> 687\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis folder \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfolder\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not have spikeinterface_log.json\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 689\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m log_file\u001b[38;5;241m.\u001b[39mopen(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf8\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 690\u001b[0m log \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n", - "\u001b[0;31mException\u001b[0m: This folder /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Simulated_sorter_output_spykingcircus2 does not have spikeinterface_log.json" - ] - } - ], - "source": [ - "# run sorter (if not already done)\n", - "from spikeinterface.sorters import run_sorter, read_sorter_folder\n", - "\n", - "sortings = {}\n", - "for sorter_name in sorter_names:\n", - " output_folder = Path(sorting_folder[994]) / f\"Simulated_sorter_output_{sorter_name}\"\n", - " print(sorter_name, output_folder)\n", - " if output_folder.exists():\n", - " sortings[sorter_name] = read_sorter_folder(output_folder)\n", - " else:\n", - " sortings[sorter_name] = run_sorter(\n", - " sorter_name, recording, output_folder, verbose=True\n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/ResultsLoadingDat.ipynb b/notebooks/ResultsLoadingDat.ipynb deleted file mode 100644 index 37ab124..0000000 --- a/notebooks/ResultsLoadingDat.ipynb +++ /dev/null @@ -1,2078 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "47d5710b-d19d-475c-98a4-8727bbb0d240", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import glob\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from pathlib import Path\n", - "import seaborn as sns\n", - "from importlib import reload" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "144eda4c-bb3d-40b0-9a4b-d25b960e44a9", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e51a4b41-a3da-412e-a402-efc81a795e79", - "metadata": {}, - "outputs": [], - "source": [ - "current_dir = os.getcwd()\n", - "datadir = os.path.join(Path(current_dir).parents[1], \"DimaERC2\")\n", - "assert os.path.isdir(datadir)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ba840456-4b44-4666-bb83-0b69c7e0198d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/mickey/Documents/Theotime/DimaERC2'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datadir" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "20a51ef4-f19e-4ee9-a2f4-7df6d7abf45b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/mickey/Documents/Theotime/neuroEncoders/notebooks'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "current_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "a50d78bb-bd6a-461e-ae8c-5016038cb20d", - "metadata": {}, - "outputs": [], - "source": [ - "# What Basile did\n", - "BasileMiceNumber = [\n", - " \"1239vBasile\",\n", - " \"1281vBasile\",\n", - " \"1199\",\n", - " \"1336\",\n", - " \"1168MFB\",\n", - " \"905\",\n", - " \"1161w1199\",\n", - " \"1161\",\n", - " \"1124\",\n", - " \"1186\",\n", - " \"1182\",\n", - " \"1168UMaze\",\n", - " \"1117\",\n", - " \"994\",\n", - " \"1336v3\",\n", - " \"1336v2\",\n", - " \"1281v2\",\n", - " \"1239v3\",\n", - "]\n", - "\n", - "# What Dima did according to Baptiste\n", - "DimaMiceNumber = [\n", - " \"905\",\n", - " \"906\",\n", - " \"911\",\n", - " \"994\",\n", - " \"1161\",\n", - " \"1162\",\n", - " \"1168\",\n", - " \"1186\",\n", - " \"1230\",\n", - " \"1239\",\n", - "]\n", - "\n", - "# Files wrt to datadir\n", - "path_list = [\n", - " \"TEST3_Basile_M1239/TEST\",\n", - " \"TEST3_Basile_1281MFB/TEST\",\n", - " \"TEST1_Basile/TEST\",\n", - " \"Known_M1336/TEST/\",\n", - " # \"DataERC2/M994/20191013/TEST/\",\n", - " # \"DataERC2/M906/TEST/\",\n", - " \"DataERC2/M1168/TEST/\",\n", - " \"DataERC2/M905/TEST/\",\n", - " \"DataERC2/M1161/TEST_with_1199_model/\",\n", - " \"DataERC2/M1161/TEST initial/\",\n", - " \"DataERC2/M1124/TEST/\",\n", - " \"DataERC2/M1186/TEST/\",\n", - " \"DataERC2/M1182/TEST/\",\n", - " \"DataERC1/M1168/TEST/\",\n", - " \"DataERC1/M1117/TEST/\",\n", - " \"neuroencoders_1021/_work/M994_PAG/Final_results_v3\",\n", - " \"neuroencoders_1021/_work/M1336_MFB/Final_results_v3\",\n", - " \"neuroencoders_1021/_work/M1336_known/Final_results_v2\",\n", - " \"neuroencoders_1021/_work/M1281_MFB/Final_results_v2\",\n", - " \"neuroencoders_1021/_work/M1239_MFB/Final_results_v3\",\n", - "]\n", - "assert len(BasileMiceNumber) == len(path_list)\n", - "len(BasileMiceNumber)\n", - "path_dict = dict(zip(BasileMiceNumber, path_list))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28bb5cc3-1c72-4020-a442-891159ab914c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "8b74abbd-35f9-471c-b0f1-864fc469bd98", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1239vBasile': 'TEST3_Basile_M1239/TEST',\n", - " '1281vBasile': 'TEST3_Basile_1281MFB/TEST',\n", - " '1199': 'TEST1_Basile/TEST',\n", - " '1336': 'Known_M1336/TEST/',\n", - " '1168MFB': 'DataERC2/M1168/TEST/',\n", - " '905': 'DataERC2/M905/TEST/',\n", - " '1161w1199': 'DataERC2/M1161/TEST_with_1199_model/',\n", - " '1161': 'DataERC2/M1161/TEST initial/',\n", - " '1124': 'DataERC2/M1124/TEST/',\n", - " '1186': 'DataERC2/M1186/TEST/',\n", - " '1182': 'DataERC2/M1182/TEST/',\n", - " '1168UMaze': 'DataERC1/M1168/TEST/',\n", - " '1117': 'DataERC1/M1117/TEST/',\n", - " '994': 'neuroencoders_1021/_work/M994_PAG/Final_results_v3',\n", - " '1336v3': 'neuroencoders_1021/_work/M1336_MFB/Final_results_v3',\n", - " '1336v2': 'neuroencoders_1021/_work/M1336_known/Final_results_v2',\n", - " '1281v2': 'neuroencoders_1021/_work/M1281_MFB/Final_results_v2',\n", - " '1239v3': 'neuroencoders_1021/_work/M1239_MFB/Final_results_v3'}" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "755d623d-51cb-4817-aca3-b892a47a7da8", - "metadata": {}, - "outputs": [], - "source": [ - "conditions = {\n", - " \"MFB\": [\"1281vBasile\", \"1281v3\" \"1239vBasile\", \"1239v3\", \"1336v3\", \"1336v2\"],\n", - " \"Known\": [\"1336\", \"1336v3\"],\n", - " \"PAG\": [\"1186\", \"1161\", \"1161w1199\", \"1124\", \"1186\", \"1117\", \"1199\", \"994\"],\n", - " \"Umaze\": [\"1199\", \"906\", \"1168\", \"905\", \"1182\"],\n", - " # WARNING: 994 has non-aligned nnbehavior.positions; hence the results should not be trusted\n", - " # same for 1239v3\n", - "}\n", - "\n", - "list_windows = [36, 108, 200, 252, 504]" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "4423af7f-e551-4260-85f0-2267cd5f2b01", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "from importData.rawdata_parser import DataHelper\n", - "from resultAnalysis.print_results import print_results" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "d6d6d3c7-212b-4dba-97bb-81dc20b1e566", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[36, 108, 200, 252, 504]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_windows" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "effecef9-dfd6-43a6-a408-b9e4ef0b2c3b", - "metadata": {}, - "outputs": [], - "source": [ - "keys_to_include = set()\n", - "for mouse, path in path_dict.items():\n", - " path = os.path.join(datadir, path, \"../\")\n", - " if len(glob.glob(path + \"*.dat\")) >= 1:\n", - " keys_to_include.add(mouse)\n", - "\n", - "dath_dict = {k: path_dict[k] for k in keys_to_include}" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "f6a7199a-1a1e-44a8-a8b6-e83bf6750ac9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1336': 'Known_M1336/TEST/',\n", - " '994': 'neuroencoders_1021/_work/M994_PAG/Final_results_v3',\n", - " '1186': 'DataERC2/M1186/TEST/',\n", - " '1199': 'TEST1_Basile/TEST',\n", - " '1117': 'DataERC1/M1117/TEST/',\n", - " '1239v3': 'neuroencoders_1021/_work/M1239_MFB/Final_results_v3',\n", - " '1336v3': 'neuroencoders_1021/_work/M1336_MFB/Final_results_v3',\n", - " '1281v2': 'neuroencoders_1021/_work/M1281_MFB/Final_results_v2',\n", - " '1168UMaze': 'DataERC1/M1168/TEST/',\n", - " '1182': 'DataERC2/M1182/TEST/',\n", - " '1336v2': 'neuroencoders_1021/_work/M1336_known/Final_results_v2'}" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dath_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "9e4e203b-0aef-4abe-a2f6-29cf7f5232fe", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total windows: 103424 | selected windows: 31027 (thresh -4.3133297 )\n", - "mean eucl. error: 0.4099105329433457 | selected error: 0.3388784961711805\n", - "mean linear error: 0.24068852490717824 | selected error: 0.18432333129210043\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST3_Basile_M1239/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 21888 | selected windows: 6566 (thresh -4.140966 )\n", - "mean eucl. error: 0.3900984639396719 | selected error: 0.35825975326772563\n", - "mean linear error: 0.2728143274853801 | selected error: 0.21335820895522387\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST3_Basile_M1239/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9856 | selected windows: 2956 (thresh -4.9398165 )\n", - "mean eucl. error: 0.38621183691339656 | selected error: 0.3790181510435826\n", - "mean linear error: 0.21885349025974027 | selected error: 0.18813261163734776\n", - "total windows: 108416 | selected windows: 32524 (thresh -3.0905595 )\n", - "mean eucl. error: 0.5096774095797851 | selected error: 0.48791706011258873\n", - "mean linear error: 0.2487205762987013 | selected error: 0.21851801746402658\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST3_Basile_1281MFB/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 22784 | selected windows: 6835 (thresh -3.8569858 )\n", - "mean eucl. error: 0.5248040472074363 | selected error: 0.4814915129713809\n", - "mean linear error: 0.2689255617977528 | selected error: 0.20954498902706656\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST3_Basile_1281MFB/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9856 | selected windows: 2956 (thresh -1.1291519 )\n", - "mean eucl. error: 0.5180738521512005 | selected error: 0.5089591672519789\n", - "mean linear error: 0.2554961444805195 | selected error: 0.23500338294993237\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST1_Basile/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST1_Basile/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 18944 | selected windows: 5683 (thresh -4.1417675 )\n", - "mean eucl. error: 0.41348124954996934 | selected error: 0.31071941895418564\n", - "mean linear error: 0.18181218327702703 | selected error: 0.13149920816470176\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST1_Basile/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/TEST1_Basile/TEST/results/504/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 104320 | selected windows: 31296 (thresh -4.14909 )\n", - "mean eucl. error: 0.45229839142678147 | selected error: 0.3949391303763814\n", - "mean linear error: 0.33679495782208585 | selected error: 0.2506697341513292\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/Known_M1336/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23168 | selected windows: 6950 (thresh -4.0340347 )\n", - "mean eucl. error: 0.4198770464172988 | selected error: 0.3882440986379923\n", - "mean linear error: 0.28290789019337015 | selected error: 0.24212517985611512\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/Known_M1336/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9728 | selected windows: 2918 (thresh -4.537151 )\n", - "mean eucl. error: 0.4584586775672959 | selected error: 0.46391458953846737\n", - "mean linear error: 0.29336965460526315 | selected error: 0.28494859492803293\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1168/TEST/results/36/featureTrue.csv'\n", - "Available windows: []\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1168/TEST/results/108/featureTrue.csv'\n", - "Available windows: []\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1168/TEST/results/200/featureTrue.csv'\n", - "Available windows: []\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1168/TEST/results/252/featureTrue.csv'\n", - "Available windows: []\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1168/TEST/results/504/featureTrue.csv'\n", - "Available windows: []\n", - "nothing at all for 1168, []\n", - "total windows: 98304 | selected windows: 29491 (thresh -2.5121055 )\n", - "mean eucl. error: 0.46561207211367456 | selected error: 0.4692852967657072\n", - "mean linear error: 0.5407175699869792 | selected error: 0.48223254552236283\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 21632 | selected windows: 6489 (thresh -3.6158442 )\n", - "mean eucl. error: 0.4459823902921189 | selected error: 0.4385799076320848\n", - "mean linear error: 0.38199981508875747 | selected error: 0.363364154723378\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 10880 | selected windows: 3264 (thresh -3.842236 )\n", - "mean eucl. error: 0.4410981135495719 | selected error: 0.4464837717645344\n", - "mean linear error: 0.39765900735294113 | selected error: 0.3948314950980392\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/36/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/108/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "total windows: 12672 | selected windows: 3801 (thresh -3.879144 )\n", - "mean eucl. error: 0.46867128023110105 | selected error: 0.45676790314536814\n", - "mean linear error: 0.22071338383838385 | selected error: 0.22304393580636675\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/252/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/504/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "total windows: 137472 | selected windows: 41241 (thresh -4.430786 )\n", - "mean eucl. error: 0.5136216538508177 | selected error: 0.4670296356827056\n", - "mean linear error: 0.26214414571694605 | selected error: 0.23317790548240827\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 29696 | selected windows: 8908 (thresh -3.9554555 )\n", - "mean eucl. error: 0.47114616746719096 | selected error: 0.4184275651909268\n", - "mean linear error: 0.23238213900862068 | selected error: 0.20358105074090704\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 12672 | selected windows: 3801 (thresh -3.879144 )\n", - "mean eucl. error: 0.46867128023110105 | selected error: 0.45676790314536814\n", - "mean linear error: 0.22071338383838385 | selected error: 0.22304393580636675\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/108/featureTrue.csv'\n", - "Available 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['200', '36', '504']\n", - "total windows: 23424 | selected windows: 7027 (thresh -3.8716633 )\n", - "mean eucl. error: 0.4485788831234556 | selected error: 0.4099108151783293\n", - "mean linear error: 0.19302510245901638 | selected error: 0.1675323751245197\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9984 | selected windows: 2995 (thresh -4.246581 )\n", - "mean eucl. error: 0.47557516402162603 | selected error: 0.4471246174005367\n", - "mean linear error: 0.21452624198717948 | selected error: 0.20374624373956596\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/108/featureTrue.csv'\n", - "Available 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['200', '36', '504']\n", - "total windows: 28032 | selected windows: 8409 (thresh -3.5557854 )\n", - "mean eucl. error: 0.4253206466239176 | selected error: 0.38377802705632474\n", - "mean linear error: 0.2908258418949772 | selected error: 0.2696598882150077\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 12288 | selected windows: 3686 (thresh -3.8141289 )\n", - "mean eucl. error: 0.41715469355585255 | selected error: 0.39095112861006864\n", - "mean linear error: 0.2883528645833333 | selected error: 0.235732501356484\n", - "total windows: 110848 | selected windows: 33254 (thresh -3.5723522 )\n", - "mean eucl. error: 0.4740902712106153 | selected error: 0.4301594067888825\n", - "mean linear error: 0.2845248448325635 | selected error: 0.25105430925602934\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23552 | selected windows: 7065 (thresh -4.022328 )\n", - "mean eucl. error: 0.45928378428877065 | selected error: 0.41070582195492183\n", - "mean linear error: 0.2775339673913043 | selected error: 0.2384345364472753\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 10240 | selected windows: 3072 (thresh -4.859231 )\n", - "mean eucl. error: 0.45251948404943737 | selected error: 0.4737108638114196\n", - "mean linear error: 0.3188984375 | selected error: 0.32421549479166667\n", - "total windows: 6528 | selected windows: 1958 (thresh -4.797138 )\n", - "mean eucl. error: 0.308457394288779 | selected error: 0.23412658957435312\n", - "mean linear error: 0.1549984681372549 | selected error: 0.1058988764044944\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.7340493 )\n", - "mean eucl. error: 0.24791512898533571 | selected error: 0.17341864632277373\n", - "mean linear error: 0.1297227328431373 | selected error: 0.07499489274770174\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.997062 )\n", - "mean eucl. error: 0.22262539966188583 | selected error: 0.16894934097947953\n", - "mean linear error: 0.10720128676470589 | selected error: 0.0710520939734423\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.8903008 )\n", - "mean eucl. error: 0.2118937043753728 | selected error: 0.17612127014460588\n", - "mean linear error: 0.10722886029411764 | selected error: 0.08100612870275792\n", - "total windows: 13184 | selected windows: 3955 (thresh -4.3813343 )\n", - "mean eucl. error: 0.3575848308310594 | selected error: 0.25840760054430034\n", - "mean linear error: 0.23859071601941748 | selected error: 0.1498078381795196\n", - "total windows: 13184 | selected windows: 3955 (thresh -5.2892494 )\n", - "mean eucl. error: 0.2981273008685602 | selected error: 0.21064261732409612\n", - "mean linear error: 0.19825015169902913 | selected error: 0.11969911504424778\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_MFB/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 13184 | selected windows: 3955 (thresh -5.944226 )\n", - "mean eucl. error: 0.2682479383957932 | selected error: 0.19420851572601364\n", - "mean linear error: 0.17593446601941748 | selected error: 0.11053603034134007\n", - "total windows: 13312 | selected windows: 3993 (thresh -6.129132 )\n", - "mean eucl. error: 0.2500419149008913 | selected error: 0.1857000627422962\n", - "mean linear error: 0.16785381610576922 | selected error: 0.1136839469070874\n", - "total windows: 12928 | selected windows: 3878 (thresh -3.9968202 )\n", - "mean eucl. error: 0.4512924509004647 | selected error: 0.4059579827896158\n", - "mean linear error: 0.28356590346534655 | selected error: 0.21131768953068591\n", - "total windows: 12928 | selected windows: 3878 (thresh -4.744326 )\n", - "mean eucl. error: 0.4379316132700753 | selected error: 0.3772838136471705\n", - "mean linear error: 0.2955901918316831 | selected error: 0.23187983496647757\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_known/Final_results_v2/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 12928 | selected windows: 3878 (thresh -5.9330926 )\n", - "mean eucl. error: 0.44579350729398987 | selected error: 0.4120424070445943\n", - "mean linear error: 0.3065578589108911 | selected error: 0.28296028880866425\n", - "total windows: 13056 | selected windows: 3916 (thresh -6.1029983 )\n", - "mean eucl. error: 0.4094791559633769 | selected error: 0.3680947279028363\n", - "mean linear error: 0.2776179534313725 | selected error: 0.24181562819203267\n", - "total windows: 14208 | selected windows: 4262 (thresh -3.3976035 )\n", - "mean eucl. error: 0.5212641443494755 | selected error: 0.5035234910117191\n", - "mean linear error: 0.26170326576576575 | selected error: 0.22459174096668233\n", - "total windows: 14208 | selected windows: 4262 (thresh -4.06132 )\n", - "mean eucl. error: 0.5385864491928991 | selected error: 0.5137827818831091\n", - "mean linear error: 0.281488597972973 | selected error: 0.2560957297043642\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1281_MFB/Final_results_v2/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 14208 | selected windows: 4262 (thresh -4.5147614 )\n", - "mean eucl. error: 0.48874218911260153 | selected error: 0.4076990268790374\n", - "mean linear error: 0.24361908783783784 | selected error: 0.1869427498826842\n", - "total windows: 14208 | selected windows: 4262 (thresh -5.6891036 )\n", - "mean eucl. error: 0.4595335703279842 | selected error: 0.40493214045313247\n", - "mean linear error: 0.2083227759009009 | selected error: 0.1693289535429376\n", - "total windows: 7552 | selected windows: 2265 (thresh -4.2046785 )\n", - "mean eucl. error: 0.3796418196780438 | selected error: 0.26915538960290275\n", - "mean linear error: 0.2150741525423729 | selected error: 0.12605739514348788\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.2579575 )\n", - "mean eucl. error: 0.3172805695536395 | selected error: 0.19819524535580405\n", - "mean linear error: 0.17178098516949153 | selected error: 0.08380132450331126\n", - "[Errno 2] No such file or directory: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.859999 )\n", - "mean eucl. error: 0.26070979386617643 | selected error: 0.1816375778638438\n", - "mean linear error: 0.13545550847457627 | selected error: 0.07917439293598233\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.269205 )\n", - "mean eucl. error: 0.26359873518635907 | selected error: 0.20512912875926131\n", - "mean linear error: 0.13328654661016948 | selected error: 0.10113024282560705\n", - "threshold value: -5.269205\r" - ] - } - ], - "source": [ - "# bypass to avoid heavy comput and fill the memory for nothing\n", - "force = False\n", - "\n", - "todo = dict()\n", - "dirmouse = dict()\n", - "mouse_id = []\n", - "windowMS = []\n", - "mean_eucl = []\n", - "select_eucl = []\n", - "mean_lin = []\n", - "select_lin = []\n", - "has_dat = []\n", - "for mouse in BasileMiceNumber:\n", - " todo[mouse] = []\n", - " returned = False\n", - " dirmouse[mouse] = os.path.join(datadir, path_dict[mouse], \"results\")\n", - " assert os.path.isdir(dirmouse[mouse])\n", - " for win in list_windows:\n", - " try:\n", - " mean, select, linmean, linselect = print_results(\n", - " dirmouse[mouse], show=False, windowSizeMS=win, force=False\n", - " )\n", - " mean_eucl.append(mean)\n", - " select_eucl.append(select)\n", - " mean_lin.append(linmean)\n", - " select_lin.append(linselect)\n", - " mouse_id.append(mouse)\n", - " windowMS.append(win)\n", - " has_dat.append(mouse in dath_dict)\n", - " returned = True\n", - " except Exception as e:\n", - " print(e)\n", - " todo[mouse].append(win)\n", - " print(f\"Available windows: {os.listdir(dirmouse[mouse])}\")\n", - " for val in os.listdir(dirmouse[mouse]):\n", - " if int(val) not in list_windows:\n", - " list_windows.append(val)\n", - " print(f\"adding {val} to list of available windows\")\n", - " mean, select, linmean, linselect = print_results(\n", - " dirmouse[mouse], show=False, windowSizeMS=win\n", - " )\n", - " mean_eucl.append(mean)\n", - " select_eucl.append(select)\n", - " mean_lin.append(linmean)\n", - " select_lin.append(linselect)\n", - " mouse_id.append(mouse)\n", - " windowMS.append(win)\n", - " returned = True\n", - " ###\" print(f\"No data for {mouse} in {win}\")\n", - " if not returned:\n", - " print(f\"nothing at all for {mouse}, {os.listdir(dirmouse[mouse])}\")\n", - "\n", - "\n", - "results_df = pd.DataFrame(\n", - " data={\n", - " \"mouse_id\": mouse_id,\n", - " \"windowMS\": windowMS,\n", - " \"mean_eucl\": mean_eucl,\n", - " \"select_eucl\": select_eucl,\n", - " \"mean_lin\": mean_lin,\n", - " \"select_lin\": select_lin,\n", - " \"has_dat\": has_dat,\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "f3accb1c-8dc5-48ba-8e3a-9b80091dc88d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1239: [108, 252],\n", - " 1281: [108, 252],\n", - " 1199: [36, 108, 252, 504],\n", - " 1336: [108, 252],\n", - " 1168: [36, 108, 200, 252, 504],\n", - " 905: [108, 252],\n", - " 11610: [36, 108, 252, 504],\n", - " 1161: [108, 252],\n", - " 1124: [36, 108, 252, 504],\n", - " 1186: [108, 252],\n", - " 1182: [36, 108, 252, 504],\n", - " 11680: [108, 252],\n", - " 1117: [108, 252],\n", - " 994: [200],\n", - " 13360: [200],\n", - " 13361: [200],\n", - " 12810: [200],\n", - " 12390: [200]}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "todo" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "72e74e65-a5f4-40d8-ba08-29730365f8c1", - "metadata": {}, - "outputs": [], - "source": [ - "for cdt in conditions:\n", - " for mouse in conditions[cdt]:\n", - " try:\n", - " results_df.loc[results_df.mouse_id == mouse, \"condition\"] = cdt\n", - " except Exception as e:\n", - " print(e)\n", - "\n", - "results_df = results_df.sort_values(\n", - " by=[\"condition\", \"mouse_id\", \"windowMS\"]\n", - ").reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "4c773a8f-0c64-4124-9c93-7b24df76d12b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "fig, _axs = plt.subplots(2, 2, figsize=(15, 10), constrained_layout=True)\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(hue=\"windowMS\", y=metric, x=\"mouse_id\", data=results_df, ax=axs[i])\n", - "\n", - "fig.suptitle(\"ANN Errors for each mouse\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "fea0957b-f0ae-4760-a0ab-0224d9bada2f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "fig, _axs = plt.subplots(2, 2, figsize=(15, 10), constrained_layout=True)\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(\n", - " hue=\"windowMS\",\n", - " y=metric,\n", - " x=\"mouse_id\",\n", - " data=results_df[results_df.has_dat == True],\n", - " ax=axs[i],\n", - " )\n", - "\n", - "fig.suptitle(\"ANN Errors for each .dat mouse\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "b0d51e68-a768-4e1f-b464-2bfcc1dc5de4", - "metadata": {}, - "outputs": [], - "source": [ - "for cdt in conditions:\n", - " for mouse in conditions[cdt]:\n", - " results_df.loc[results_df.mouse_id == mouse, \"condition\"] = cdt" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "546622f7-b8f8-48e2-b791-918edb41242f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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LL788Z9cAAPB5OQ3StnUR2oqKitiwYUOMGTNmq4vQrl27NuubTE9zAgDY/gYOHBirVq2KsWPHRllZWfTo0SNmzpwZXbp0iYiIsrKyKC0tzfTfuHFjjB49OpYtWxZ5eXmx7777xk033RSXXnppri4BACBLztdIi6j9IrRr1qyJF154Ia655prYb7/9trh2xrhx4+LGG2+ss3oBAEhm2LBhMWzYsM3umzp1atb7yy+/3OwzAKBBy2mQVl+L0I4ePTpGjRqVeV9ZWenR6AAAAADUSE4fNlBfi9Dm5+dHQUFB1gYAAAAANZHzWzstQgsAAABAY5DzIM0itAAAAAA0BjkP0iIsQgsAAABAw5fTNdIAAAAAoLEQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkEBergsAAABg+/hSftVmXwNQO4I0AACAJurfe32Y6xIAmhS3dgIAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAnk5boAAABya9SoUYn7jh8/fjtWAgDQsAnSAAB2cIsWLUrUL5VKbedKAAAatsRBWq9evRIPnv7yl7/UuiAAAOrXM888k+sSAAAahcRB2umnn74dywAAoCFYvXp1VFVVxZe+9KWs9g8++CDy8vKioKAgR5UBAORe4iDthhtu2J51AADQAJxzzjlx6qmnxrBhw7LaH3744Xjsscdi5syZOaoMACD3avXUzpdeeilefPHFau0vvvhiLFiwYJuLAgAgN1588cU49thjq7Ufc8wxmx3/AQDsSGoVpA0fPjzefffdau3vvfdeDB8+fJuLAgAgN9auXRsbNmyo1r5+/fr45z//mYOKAAAajloFaUuWLInDDjusWnuvXr1iyZIl21wUAAC58ZWvfCWmTJlSrf3uu++O4uLiHFQEANBwJF4j7fPy8/Nj+fLlsc8++2S1l5WVRV5erU4JAEAD8JOf/CS+9rWvxcsvvxzHH398REQ8/fTT8dJLL8VTTz2V4+oAAHKrVjPSTjjhhBg9enSsXr060/bhhx/Gv//7v8cJJ5xQZ8UBAFC/+vXrF88//3wUFRXFww8/HP/5n/8Z++23X7zyyivRv3//XJcHAJBTtZo+dtttt8VRRx0VXbp0iV69ekVExOLFi6OwsDB+/etf12mBAADUr549e8YDDzyQ6zIAABqcWgVpe+21V7zyyivxwAMPxMsvvxytW7eOb3/723HuuedGixYt6rpGAADqSWlp6Vb3d+7cuZ4qAQBoeGq9oFmbNm3ikksuqctaAADIsb333jtSqdQW91dVVdVjNQAADUutgrRp06Ztdf/5559fq2IAAMitRYsWZb1fv359LFq0KMaPHx8/+clPclQVAEDDUKsgbcSIEVnv169fH5988km0bNkydtppJ0EaAEAj9eUvf7laW+/evWPPPfeMn/3sZ3HmmWfmoCoAgIahVk/t/Mc//pG1rVmzJl5//fU48sgjY/r06XVdIwAAOXbAAQfESy+9lOsyAAByqtZrpH3R/vvvHzfddFN861vfiv/5n/+pq9MCAFCPKisrs96n0+koKyuLMWPGxP7775+jqgAAGoY6C9IiIpo3bx7vv/9+XZ4SAIB6tOuuu1Z72EA6nY6ioqJ46KGHclQVAEDDUKsg7bHHHst6v+mbyp///OfRr1+/OikMAID698wzz2S9b9asWXTo0CH222+/yMur0+9gAQAanVqNhk4//fSs96lUKjp06BDHHXdc3HbbbXVRFwAAOXD00UfnugQAgAarVkHaxo0b67oOAAAaiF//+tdx9913x7Jly+L555+PLl26xO233x777LNPnHbaabkuDwAgZ2r11M5N1q1bF6+//nps2LChruoBACCHJk+eHKNGjYqTTz45Pvzww6iqqoqIiN122y0mTJiQ2+IAAHKsVkHaJ598EhdddFHstNNOcfDBB0dpaWlERFxxxRVx00031WmBAADUnzvvvDN+8YtfxLXXXhvNmzfPtPfu3TteffXVHFYGAJB7tQrSRo8eHa+88krMnj07WrVqlWn/2te+FjNmzKiz4gAAqF/Lli2LXr16VWvPz8+Pjz/+OAcVAQA0HLUK0v7whz/Ez3/+8zjyyCOzHo/evXv3eOutt+qsOAAA6lfXrl1j8eLF1doff/zx6N69e/0XBADQgNTqYQMVFRWx++67V2v/+OOPs4I1AAAalx/84AcxfPjw+PTTTyOdTsef//znmD59eowbNy5++ctf5ro8AICcqlWQ9pWvfCX++Mc/xuWXXx4RkQnPfvGLX0SfPn3qrjoAAOrVt7/97diwYUNcddVV8cknn8SgQYNir732iokTJ8Y555yT6/IAAHKqVkHauHHj4qSTToolS5bEhg0bYuLEifHaa6/F888/H88++2xd1wgAQD36zne+E9/5zndi5cqVsXHjxs3eiTBv3rzo3bt35Ofn56BCAIDcqNUaaX379o158+bFJ598Evvuu2889dRTUVhYGM8//3wUFxfXdY0AAORA+/btNxuiRUQMGDAg3nvvvXquCAAgt2o1Iy0i4pBDDolf/epXW+1z0003xdChQ2PXXXet7Y8BAKABSqfTuS4BAKDe1WpGWlI//elP44MPPtiePwIAAAAA6sV2DdJ8UwkAAABAU7FdgzQAAAAAaCoEaQAA1Fgqlcp1CQAA9U6QBgBAjVnCAwDYEQnSAADIOO644+LDDz+s1l5ZWRnHHXdc5v1HH30U++yzTz1WBgCQe9s1SOvfv3+0bt16e/4IAADq0OzZs2PdunXV2j/99NOYM2dODioCAGg48mp74MaNG+PNN9+MFStWxMaNG7P2HXXUURERMXPmzG2rDgCAevHKK69kXi9ZsiTKy8sz76uqquKJJ56IvfbaKxelAQA0GLUK0l544YUYNGhQvPPOO9XWx0ilUlFVVVUnxQEAUD969uwZqVQqUqlU1i2cm7Ru3TruvPPOHFQGANBw1CpIGzp0aPTu3Tv++Mc/RseOHT21CQCgkVu2bFmk0+nYZ5994s9//nN06NAhs69ly5ax++67R/PmzXNYIQBA7tUqSHvjjTfit7/9bey33351XQ8AADnQpUuXiIhqS3YAAPB/avWwgcMPPzzefPPNuq4FAIAcGzduXNx3333V2u+77764+eabc1ARAEDDUasZaZdffnl873vfi/Ly8jjkkEOiRYsWWfsPPfTQOikOAID6dc8998SDDz5Yrf3ggw+Oc845J66++uocVAUA0DDUKkj7xje+ERERF110UaYtlUpFOp32sAEAgEasvLw8OnbsWK29Q4cOUVZWloOKAAAajloFacuWLavrOgAAaACKiopi3rx50bVr16z2efPmxZ577pmjqgAAGoZaBWmbFqMFAKBpGTJkSIwcOTLWr18fxx13XEREPP3003HVVVfF9773vRxXBwCQW7UK0jZZsmRJlJaWxrp167La/+3f/m2bigIAIDeuuuqq+OCDD2LYsGGZMV6rVq3i6quvjtGjR+e4OgCA3KpVkPb3v/89zjjjjHj11Vcza6NFfLZOWkRYIw0AoJFKpVJx8803x/XXXx9Lly6N1q1bx/777x/5+fm5Lg0AIOea1eagESNGRNeuXWP58uWx0047xWuvvRbPPfdc9O7dO2bPnl3HJQIAUN/Ky8vjgw8+iH333Tfy8/MzX5wCAOzIahWkPf/88zF27Njo0KFDNGvWLJo1axZHHnlkjBs3Lq644oq6rhEAgHqyatWqOP744+OAAw6Ik08+OfOkziFDhlgjDQDY4dUqSKuqqoqdd945IiLat28f77//fkR89hCC119/ve6qAwCgXl155ZXRokWLKC0tjZ122inTPnDgwHjiiSdyWBkAQO7Vao20Hj16xCuvvBL77LNPHH744XHLLbdEy5YtY8qUKbHPPvvUdY0AANSTp556Kp588sno1KlTVvv+++8f77zzTo6qAgBoGGoVpF133XXx8ccfR0TEj3/84/j6178e/fv3j3bt2sWMGTPqtEAAAOrPxx9/nDUTbZOVK1d64AAAsMOrVZB24oknZl7vs88+sWTJkvjggw9it912yzy5EwCAxueoo46KadOmxY9+9KOI+Owpnhs3boyf/exnceyxx+a4OgCA3KpVkLbJm2++GW+99VYcddRR8aUvfcnTnAAAGrmf/exnccwxx8SCBQti3bp1cdVVV8Vrr70WH3zwQcybNy/X5QEA5FStHjbgaU4AAE1T9+7d45VXXomvfvWrccIJJ8THH38cZ555ZixatCj23XffXJcHAJBTtZqR9vmnOR100EGZ9oEDB8aVV14Zt912W50VCABA/dpjjz3ixhtvzHUZAAANTq2CNE9zAgBoOl555ZXEfQ899NDtWAkAQMNWqyDN05wAAJqOnj17RiqV+pfr3aZSqaiqqqqnqgAAGp5aBWme5gQA0HQsW7Ys1yUAADQKtQrSPM0JAKDp6NKlS65LAABoFGr11M7u3bvHyy+/7GlOAABN0K9//evo169f7Lnnnpn1bydMmBCPPvpojisDAMitWgVpERG77bZbnHLKKTF06NAYOnRofPWrX42XXnopHnvssbqsDwCAejR58uQYNWpUnHzyyfHhhx9m1kTbddddY8KECbktDgAgx2p1a+cTTzwR559/fqxataraorQWoQUAaLzuvPPO+MUvfhGnn3563HTTTZn23r17x/e///0cVgYAkHu1mpF22WWXxVlnnRXvv/9+bNy4MWsTogEANF7Lli2LXr16VWvPz8+Pjz/+OAcVAQA0HLUK0lasWBGjRo2KwsLCuq4HAIAc6tq1ayxevLha++OPPx7du3ev/4IAABqQWt3a+c1vfjNmz57twQIAAE3MD37wgxg+fHh8+umnkU6n489//nNMnz49xo0bF7/85S9zXR4AQE7VKkj7+c9/HmeddVbMmTMnDjnkkGjRokXW/iuuuKJOigMAoH59+9vfjg0bNsRVV10Vn3zySQwaNCg6deoUEydOjHPOOSfX5QEA5FStgrQHH3wwnnzyyWjdunXMnj07UqlUZl8qlRKkAQA0Uv/85z/jvPPOi+985zuxcuXK+Pvf/x7z5s2LTp065bo0AICcq1WQdt1118XYsWPjmmuuiWbNarXMGgAADdBpp50WZ555ZgwdOjTy8vLi3/7t36JFixaxcuXKGD9+fHz3u9/NdYkAADlTqxRs3bp1MXDgQCEaAEAT85e//CX69+8fERG//e1vo7CwMN55552YNm1a3HHHHTmuDgAgt2qVhF1wwQUxY8aMuq4FAIAc++STT2KXXXaJiIinnnoqzjzzzGjWrFkcccQR8c477+S4OgCA3KrVrZ1VVVVxyy23xJNPPhmHHnpotYcNjB8/vk6KAwCgfu23337xhz/8Ic4444x48skn48orr4yIiBUrVkRBQUGOqwMAyK1azUh79dVXo1evXtGsWbP461//GosWLcpsixcvruMSAQCoLz/84Q/j+9//fuy9995x+OGHR58+fSLis9lpvXr1qvH5Jk2aFF27do1WrVpFcXFxzJkzZ4t9H3nkkTjhhBOiQ4cOUVBQEH369Iknn3yy1tcCAFDXajUj7ZlnnqnrOgAAaAC++c1vxpFHHhllZWXx5S9/OdN+/PHHxxlnnFGjc82YMSNGjhwZkyZNin79+sU999wTAwYMiCVLlkTnzp2r9X/uuefihBNOiJ/+9Kex6667xv333x+nnnpqvPjii7UK8QAA6lqtgjQAAJquPfbYI/bYY4+stq9+9as1Ps/48ePj4osvjiFDhkRExIQJE+LJJ5+MyZMnx7hx46r1nzBhQtb7n/70p/Hoo4/Gf/7nfwrSAIAGwWM3AQCoc+vWrYuFCxdGSUlJVntJSUnMnz8/0Tk2btwYH330UXzpS1/aYp+1a9dGZWVl1gYAsL00iCDN2hkAAE3LypUro6qqKgoLC7PaCwsLo7y8PNE5brvttvj444/j7LPP3mKfcePGRdu2bTNbUVHRNtUNALA1OQ/SNq2dce2118aiRYuif//+MWDAgCgtLd1s/01rZ8ycOTMWLlwYxx57bJx66qmxaNGieq4cAIB/JZVKZb1Pp9PV2jZn+vTpMWbMmJgxY0bsvvvuW+w3evToWL16dWZ79913t7lmAIAtyfkaadbOAABoetq3bx/NmzevNvtsxYoV1WapfdGMGTPi4osvjt/85jfxta99bat98/PzIz8/f5vrBQBIIqcz0qydAQDQNLVs2TKKi4tj1qxZWe2zZs2Kvn37bvG46dOnx4UXXhgPPvhgnHLKKdu7TACAGslpkGbtDACApmvUqFHxy1/+Mu67775YunRpXHnllVFaWhpDhw6NiM9uyzz//PMz/adPnx7nn39+3HbbbXHEEUdEeXl5lJeXx+rVq3N1CQAAWXK+RlqEtTMAAJqigQMHxoQJE2Ls2LHRs2fPeO6552LmzJnRpUuXiIgoKyvLWhf3nnvuiQ0bNsTw4cOjY8eOmW3EiBG5ugQAgCw5XSPN2hkAAE3bsGHDYtiwYZvdN3Xq1Kz3s2fP3v4FAQBsg5zOSLN2BgAAAACNRc6f2jlq1KgYPHhw9O7dO/r06RNTpkyptnbGe++9F9OmTYuI/1s7Y+LEiZm1MyIiWrduHW3bts3ZdQAAAADQtOU8SBs4cGCsWrUqxo4dG2VlZdGjR4/Ea2cMHz48037BBRdUuz0AAAAAAOpKzoO0CGtnAAAAANDwNYindgIAAABAQydIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAADYbiZNmhRdu3aNVq1aRXFxccyZM2eLfcvKymLQoEFx4IEHRrNmzWLkyJH1VygAQAKCNAAAtosZM2bEyJEj49prr41FixZF//79Y8CAAVFaWrrZ/mvXro0OHTrEtddeG1/+8pfruVoAgH+tQQRpvqkEAGh6xo8fHxdffHEMGTIkDjrooJgwYUIUFRXF5MmTN9t/7733jokTJ8b5558fbdu2redqAQD+tZwHab6pBABoetatWxcLFy6MkpKSrPaSkpKYP39+jqoCANg2OQ/SfFMJAND0rFy5MqqqqqKwsDCrvbCwMMrLy+vs56xduzYqKyuzNgCA7SWnQVp9fVNpgAUAkBupVCrrfTqdrta2LcaNGxdt27bNbEVFRXV2bgCAL8ppkFZf31QaYAEA1K/27dtH8+bNq43pVqxYUW3sty1Gjx4dq1evzmzvvvtunZ0bAOCLcn5rZ8T2/6bSAAsAoH61bNkyiouLY9asWVnts2bNir59+9bZz8nPz4+CgoKsDQBge8nL5Q+vr28q8/PzIz8/v87OBwDAvzZq1KgYPHhw9O7dO/r06RNTpkyJ0tLSGDp0aER89mXne++9F9OmTcscs3jx4oiIWLNmTVRUVMTixYujZcuW0b1791xcAgBAlpwGaZ//pvKMM87ItM+aNStOO+20HFYGAMC2GjhwYKxatSrGjh0bZWVl0aNHj5g5c2Z06dIlIiLKysqqPam9V69emdcLFy6MBx98MLp06RJvv/12fZYOALBZOQ3SInxTCQDQlA0bNiyGDRu22X1Tp06t1pZOp7dzRQAAtZfzIM03lQAAAAA0BjkP0iJ8UwkAAABAw9cgntoJAAAAAA2dIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEmgQQdqkSZOia9eu0apVqyguLo45c+Zstf+zzz4bxcXF0apVq9hnn33i7rvvrqdKAQCoCeM8AKApyXmQNmPGjBg5cmRce+21sWjRoujfv38MGDAgSktLN9t/2bJlcfLJJ0f//v1j0aJF8e///u9xxRVXxO9+97t6rhwAgK0xzgMAmpqcB2njx4+Piy++OIYMGRIHHXRQTJgwIYqKimLy5Mmb7X/33XdH586dY8KECXHQQQfFkCFD4qKLLopbb721nisHAGBrjPMAgKYmL5c/fN26dbFw4cK45pprstpLSkpi/vz5mz3m+eefj5KSkqy2E088Me69995Yv359tGjRotoxa9eujbVr12ber169OiIiKisra1Rv1dp/1qg/9a+mv9Nt8dGnVfX2s6i9+vpMbPjnhnr5OWyb+vo8fLzB56ExqOnnYVP/dDq9Pcppcozz2B7q699x47zGwTiPzzPO4/O25zgvp0HaypUro6qqKgoLC7PaCwsLo7y8fLPHlJeXb7b/hg0bYuXKldGxY8dqx4wbNy5uvPHGau1FRUXbUD0NUds7h+a6BBqacW1zXQENSNurfR74nLa1+zx89NFH0baWx+5IjPPYHoz1yGKcx+cY55FlO47zchqkbZJKpbLep9Ppam3/qv/m2jcZPXp0jBo1KvN+48aN8cEHH0S7du22+nOausrKyigqKop33303CgoKcl0OOebzwOf5PPB5Pg+fSafT8dFHH8Wee+6Z61IaFeO83PD3ls/zeeDzfB74PJ+Hz9RknJfTIK19+/bRvHnzat9Krlixotq3kZvssccem+2fl5cX7dq12+wx+fn5kZ+fn9W266671r7wJqagoGCH/gtDNp8HPs/ngc/zeQgz0WrAOK9h8PeWz/N54PN8Hvg8n4fk47ycPmygZcuWUVxcHLNmzcpqnzVrVvTt23ezx/Tp06da/6eeeip69+692XUzAACof8Z5AEBTlPOndo4aNSp++ctfxn333RdLly6NK6+8MkpLS2Po0M/WPxg9enScf/75mf5Dhw6Nd955J0aNGhVLly6N++67L+699974/ve/n6tLAABgM4zzAICmJudrpA0cODBWrVoVY8eOjbKysujRo0fMnDkzunTpEhERZWVlUVpamunftWvXmDlzZlx55ZVx1113xZ577hl33HFHfOMb38jVJTRa+fn5ccMNN1S7HYIdk88Dn+fzwOf5PFBbxnm54+8tn+fzwOf5PPB5Pg81l0p7hjsAAAAA/Es5v7UTAAAAABoDQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgrZG48MIL4/TTT89q++1vfxutWrWKW265JTdF0WBdeOGFkUqlYujQodX2DRs2LFKpVFx44YVZfb+4vfnmm5vd365duzjppJPilVdeqc9LYht9/vfYokWL2GeffeL73/9+fPzxx5k+l1xySTRv3jweeuihzZ7jzTffjIsuuig6d+4c+fn5sddee8Xxxx8fDzzwQGzYsKG+LoWtOOaYY2LkyJHV2v/whz9EKpWq/4KARIzzqAnjPL7IOG/HYJzXcAjSGqlf/vKXcd5558XPf/7zuOqqq3JdDg1QUVFRPPTQQ/HPf/4z0/bpp5/G9OnTo3Pnzll9TzrppCgrK8vaunbtutn9Tz/9dOTl5cXXv/71ersW6sam3+Pf//73+PGPfxyTJk2K73//+xER8cknn8SMGTPiBz/4Qdx7773Vjv3zn/8chx12WCxdujTuuuuu+Otf/xr/9V//FRdddFHcfffd8dprr9X35QA0WcZ5/CvGeXyRcR7UH0FaI3TLLbfEZZddFg8++GAMGTIkIv7vm8xbb701OnbsGO3atYvhw4fH+vXrM8f94x//iPPPPz9222232GmnnWLAgAHxxhtvREREOp2ODh06xO9+97tM/549e8buu++eef/8889HixYtYs2aNRERkUql4pe//GWcccYZsdNOO8X+++8fjz32WH38EZDAYYcdFp07d45HHnkk0/bII49EUVFR9OrVK6tvfn5+7LHHHllb8+bNN7u/Z8+ecfXVV8e7774bFRUV9XY9bLtNv8eioqIYNGhQnHfeefGHP/whIiJ+85vfRPfu3WP06NExb968ePvttzPHpdPpuPDCC+OAAw6IefPmxamnnhr7779/9OrVK84777yYM2dOHHroobm5KGpszJgx0bNnz7jvvvuic+fOsfPOO8d3v/vdqKqqiltuuSX22GOP2H333eMnP/lJ1nHjx4+PQw45JNq0aRNFRUUxbNiwzH8PIj77lnRzsx42fZZWr14dl1xySey+++5RUFAQxx13XLz88sv1eenQKBjnkYRxHl9knEeEcV59EaQ1Mtdcc0386Ec/iv/6r/+Kb3zjG1n7nnnmmXjrrbfimWeeiV/96lcxderUmDp1amb/hRdeGAsWLIjHHnssnn/++Uin03HyySfH+vXrI5VKxVFHHRWzZ8+OiM8GY0uWLIn169fHkiVLIiJi9uzZUVxcHDvvvHPmnDfeeGOcffbZ8corr8TJJ58c5513XnzwwQfb/c+BZL797W/H/fffn3l/3333xUUXXbRN51yzZk088MADsd9++0W7du22tURyqHXr1pn/Cbv33nvjW9/6VrRt2zZOPvnkrM/N4sWLY+nSpfH9738/mjXb/H82TCdvXN566614/PHH44knnojp06fHfffdF6ecckr8v//3/+LZZ5+Nm2++Oa677rp44YUXMsc0a9Ys7rjjjvjrX/8av/rVr+K///u/s2bKPPLII1mzHc4888w48MADo7CwMNLpdJxyyilRXl4eM2fOjIULF8Zhhx0Wxx9/vP9mwOcY51ETxnlsjXHejss4rx6kaRQuuOCCdMuWLdMRkX766ac3u79Lly7pDRs2ZNrOOuus9MCBA9PpdDr9t7/9LR0R6Xnz5mX2r1y5Mt26dev0ww8/nE6n0+k77rgj3aNHj3Q6nU7/4Q9/SPfu3Tt95plnpu+66650Op1Ol5SUpK+++urM8RGRvu666zLv16xZk06lUunHH3+8Dq+c2rjgggvSp512WrqioiKdn5+fXrZsWfrtt99Ot2rVKl1RUZE+7bTT0hdccEGmb/PmzdNt2rTJbN/85jezzvX5/RGR7tixY3rhwoU5ujpqY9NnYpMXX3wx3a5du/TZZ5+d/tvf/pZu0aJFuqKiIp1Op9O///3v00VFRemqqqp0Op1OP/TQQ+mISP/lL3/JHL98+fKsz8ymfyfIraOPPjo9YsSIau2///3v05v+k3/DDTekd9ppp3RlZWVm/4knnpjee++9M7/zdDqdPvDAA9Pjxo3b4s96+OGH0+3atdvsvvHjx6d33XXX9Ouvv55Op9Ppp59+Ol1QUJD+9NNPs/rtu+++6XvuuSfx9UFTZZxHTRjn8UXGeTsG47yGw4y0RuTQQw+NvffeO374wx/GRx99VG3/wQcfnDVNu2PHjrFixYqIiFi6dGnk5eXF4Ycfntnfrl27OPDAA2Pp0qUR8dl0zddeey1WrlwZzz77bBxzzDFxzDHHxLPPPhsbNmyI+fPnx9FHH12tpk3atGkTu+yyS+Znknvt27ePU045JX71q1/F/fffH6ecckq0b9++Wr9jjz02Fi9enNnuuOOOLe5/8cUXo6SkJAYMGBDvvPNOfV0KdeC//uu/Yuedd45WrVpFnz594qijjoo777wz7r333jjxxBMzn42TTz45Pv744/jTn/6Udfznv41s165d5jOx6667xrp16+r1Wtg2e++9d+yyyy6Z94WFhdG9e/esb6ILCwuz/j1/5pln4oQTToi99tordtlllzj//PNj1apVWQsZR0Q8/vjjcc0118SMGTPigAMOiIiIhQsXxpo1a6Jdu3ax8847Z7Zly5bFW2+9tZ2vFhoH4zxqyjiPzzPOYxPjvO0vL9cFkNxee+0Vv/vd7+LYY4+Nk046KZ544omsvyAtWrTI6p9KpWLjxo0R8dm975uTTqcz/2j26NEj2rVrF88++2w8++yzMXbs2CgqKoqf/OQn8dJLL8U///nPOPLII7OO39rPpGG46KKL4rLLLouIiLvuumuzfdq0aRP77bffFs/xxf3FxcXRtm3b+MUvfhE//vGP67Zgtptjjz02Jk+eHC1atIg999wzWrRoEVVVVTFt2rQoLy+PvLz/+09CVVVV3HvvvVFSUhL7779/RET8z//8T/Ts2TMiIpo3b575THz+OHKroKAgVq9eXa39ww8/jIKCgsz7zf3bvbV/z9955504+eSTY+jQofGjH/0ovvSlL8XcuXPj4osvzlqjacmSJXHOOefETTfdFCUlJZn2jRs3RseOHTO3lX3errvuWptLhSbHOI/aMM5jE+O8ps84r+Hwt6KR6dy5czz77LNx7LHHRklJSTz55JNZf2m2pHv37rFhw4Z48cUXo2/fvhERsWrVqvjb3/4WBx10UEREZv2MRx99NP76179G//79Y5dddon169fH3XffHYcddljWgI7G4aSTTsp8i3TiiSfWyTlTqVQ0a9Ys60lRNHybG0jPnDkzPvroo1i0aFHWTIf/+Z//ifPOOy9WrVoVvXr1im7dusWtt94aZ5999hbXzyD3unXrFo8//ni19pdeeikOPPDAWp93wYIFsWHDhrjtttsyv/+HH344q8+qVavi1FNPjTPPPDOuvPLKrH2HHXZYZhC/995717oOaOqM86gp4zw2Mc5r+ozzGg5/SxqhTp06xezZs2PVqlVRUlKy2VT6i/bff/847bTT4jvf+U7MnTs3Xn755fjWt74Ve+21V5x22mmZfsccc0w8+OCDceihh0ZBQUFm0PXAAw/EMcccsx2viu2lefPmsXTp0li6dGnWf0BrYu3atVFeXh7l5eWxdOnSuPzyy2PNmjVx6qmn1nG11Ld77703TjnllPjyl78cPXr0yGzf+MY3okOHDvEf//EfkUql4v7774/XX389+vXrF4899li88cYbsWTJkrj77rujoqKi1p8t6tawYcPirbfeiuHDh8fLL78cf/vb3+Kuu+6Ke++9N37wgx/U+rz77rtvbNiwIe688874+9//Hr/+9a/j7rvvzupz5plnRuvWrWPMmDGZfy/Ky8ujqqoqvva1r0WfPn3i9NNPjyeffDLefvvtmD9/flx33XWxYMGCbb1saFKM86gJ4zy2xjivaTHOazgEaY3UXnvtFc8++2x8+OGHccIJJ8SHH374L4+5//77o7i4OL7+9a9Hnz59Ip1Ox8yZM7OmeR577LFRVVWVNZg6+uijo6qqqtq6GTQeBQUFib7R3pInnngiOnbsGB07dozDDz88XnrppfjNb35j0N3ILV++PP74xz9WezJcxGffRp955plx7733RkTEEUccEQsXLowDDzwwhg8fHt27d4++ffvG9OnT4/bbb4/vfve79V0+m7H33nvHnDlz4q233oqSkpL4yle+knmy31lnnVXr8/bs2TPGjx8fN998c/To0SMeeOCBGDduXFaf5557Ll577bXYe++9M/9edOzYMd59991IpVIxc+bMOOqoo+Kiiy6KAw44IM4555x4++23o7CwcFsvG5oc4zxqwjiPzTHOa3qM8xqOVHpLiyoAAAAAABlmpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDeB/7b333jFhwoTM+1QqFX/4wx+2esyFF14Yp59++natCwCAbWOcB9SVvFwXANBQlZWVxW677RYREW+//XZ07do1Fi1aFD179sz0mThxYqTT6RxVCABAbRjnAbUlSAPYgj322ONf9mnbtm09VAIAQF0yzgNqy62dQIO0cePGuPnmm2O//faL/Pz86Ny5c/zkJz+JiIhXX301jjvuuGjdunW0a9cuLrnkklizZk3m2E3T8G+99dbo2LFjtGvXLoYPHx7r16/P9FmxYkWceuqp0bp16+jatWs88MAD1Wr4/JT/rl27RkREr169IpVKxTHHHJP1szZZu3ZtXHHFFbH77rtHq1at4sgjj4yXXnops3/27NmRSqXi6aefjt69e8dOO+0Uffv2jddff72u/ugAABo04zygMROkAQ3S6NGj4+abb47rr78+lixZEg8++GAUFhbGJ598EieddFLstttu8dJLL8VvfvOb+NOf/hSXXXZZ1vHPPPNMvPXWW/HMM8/Er371q5g6dWpMnTo1s//CCy+Mt99+O/77v/87fvvb38akSZNixYoVW6znz3/+c0RE/OlPf4qysrJ45JFHNtvvqquuit/97nfxq1/9Kv7yl7/EfvvtFyeeeGJ88MEHWf2uvfbauO2222LBggWRl5cXF110US3/pAAAGhfjPKBRSwM0MJWVlen8/Pz0L37xi2r7pkyZkt5tt93Sa9asybT98Y9/TDdr1ixdXl6eTqfT6QsuuCDdpUuX9IYNGzJ9zjrrrPTAgQPT6XQ6/frrr6cjIv3CCy9k9i9dujQdEenbb7890xYR6d///vfpdDqdXrZsWToi0osWLcqq54ILLkifdtpp6XQ6nV6zZk26RYsW6QceeCCzf926dek999wzfcstt6TT6XT6mWeeSUdE+k9/+lNW/RGR/uc//1mDPyUAgMbHOA9o7MxIAxqcpUuXxtq1a+P444/f7L4vf/nL0aZNm0xbv379YuPGjVnT5g8++OBo3rx55n3Hjh0z30QuXbo08vLyonfv3pn93bp1i1133XWb6n7rrbdi/fr10a9fv0xbixYt4qtf/WosXbo0q++hhx6aVVtEbPWbUgCApsA4D2jsBGlAg9O6dest7kun05FKpTa77/PtLVq0qLZv48aNmXN8sX9d2NJ5N1fz5+vbtG9TfQAATZVxHtDYCdKABmf//feP1q1bx9NPP11tX/fu3WPx4sXx8ccfZ9rmzZsXzZo1iwMOOCDR+Q866KDYsGFDLFiwINP2+uuvx4cffrjFY1q2bBkREVVVVVvss99++0XLli1j7ty5mbb169fHggUL4qCDDkpUGwBAU2acBzR2ebkuAOCLWrVqFVdffXVcddVV0bJly+jXr19UVFTEa6+9Fuedd17ccMMNccEFF8SYMWOioqIiLr/88hg8eHAUFhYmOv+BBx4YJ510UnznO9+JKVOmRF5eXowcOXKr35Duvvvu0bp163jiiSeiU6dO0apVq2qPRG/Tpk1897vfjR/84AfxpS99KTp37hy33HJLfPLJJ3HxxRdv058JAEBTYJwHNHZmpAEN0vXXXx/f+9734oc//GEcdNBBMXDgwFixYkXstNNO8eSTT8YHH3wQX/nKV+Kb3/xmHH/88fHzn/+8Rue///77o6ioKI4++ug488wz45JLLondd999i/3z8vLijjvuiHvuuSf23HPPOO200zbb76abbopvfOMbMXjw4DjssMPizTffjCeffDJ22223GtUHANBUGecBjVkqvelmbwAAAABgi8xIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIIG8XBeQCxs3boz3338/dtlll0ilUrkuBwBoBNLpdHz00Uex5557RrNmvotsqIzzAICaqsk4b4cM0t5///0oKirKdRkAQCP07rvvRqdOnXJdBltgnAcA1FaScd4OGaTtsssuEfHZH1BBQUGOqwEAGoPKysooKirKjCNomIzzAICaqsk4b4cM0jZN8y8oKDDAAgBqxO2CDZtxHgBQW0nGeRb4AAAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAnm5LgAAaLhGjBgRFRUVERHRoUOHmDhxYo4rAgCgLhjn1Y4gDQDYooqKili+fHmuywAAoI4Z59WOWzsBAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgBArUyaNCm6du0arVq1iuLi4pgzZ06i4+bNmxd5eXnRs2fPavt+97vfRffu3SM/Pz+6d+8ev//97+u4agCA2hOkAQBQYzNmzIiRI0fGtddeG4sWLYr+/fvHgAEDorS0dKvHrV69Os4///w4/vjjq+17/vnnY+DAgTF48OB4+eWXY/DgwXH22WfHiy++uL0uAwCgRgRpAADU2Pjx4+Piiy+OIUOGxEEHHRQTJkyIoqKimDx58laPu/TSS2PQoEHRp0+favsmTJgQJ5xwQowePTq6desWo0ePjuOPPz4mTJiwna4CAKBmBGkAANTIunXrYuHChVFSUpLVXlJSEvPnz9/icffff3+89dZbccMNN2x2//PPP1/tnCeeeOJWz7l27dqorKzM2gAAthdBGgAANbJy5cqoqqqKwsLCrPbCwsIoLy/f7DFvvPFGXHPNNfHAAw9EXl7eZvuUl5fX6JwREePGjYu2bdtmtqKiohpeDQBAcoI0AABqJZVKZb1Pp9PV2iIiqqqqYtCgQXHjjTfGAQccUCfn3GT06NGxevXqzPbuu+/W4AoAAGpm818HAgDAFrRv3z6aN29ebabYihUrqs0oi4j46KOPYsGCBbFo0aK47LLLIiJi48aNkU6nIy8vL5566qk47rjjYo899kh8zk3y8/MjPz+/Dq4KAOBfMyMNAIAaadmyZRQXF8esWbOy2mfNmhV9+/at1r+goCBeffXVWLx4cWYbOnRoHHjggbF48eI4/PDDIyKiT58+1c751FNPbfacAAC5YEYaAAA1NmrUqBg8eHD07t07+vTpE1OmTInS0tIYOnRoRHx2y+V7770X06ZNi2bNmkWPHj2yjt99992jVatWWe0jRoyIo446Km6++eY47bTT4tFHH40//elPMXfu3Hq9NgCALRGkAQBQYwMHDoxVq1bF2LFjo6ysLHr06BEzZ86MLl26REREWVlZlJaW1uicffv2jYceeiiuu+66uP7662PfffeNGTNmZGasAQDkWiqdTqdzXUR9q6ysjLZt28bq1aujoKAg1+UAQIM1aNCgWL58eUR89vTEBx98MMcV5Y7xQ+Pg9wQAyRjn/Z+ajB+skQYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAk0CCCtEmTJkXXrl2jVatWUVxcHHPmzEl03Lx58yIvLy969uy5fQsEAAAAYIeX8yBtxowZMXLkyLj22mtj0aJF0b9//xgwYECUlpZu9bjVq1fH+eefH8cff3w9VQoAAADAjiznQdr48ePj4osvjiFDhsRBBx0UEyZMiKKiopg8efJWj7v00ktj0KBB0adPn3qqFAAAAIAdWU6DtHXr1sXChQujpKQkq72kpCTmz5+/xePuv//+eOutt+KGG25I9HPWrl0blZWVWRsAAAAA1EROg7SVK1dGVVVVFBYWZrUXFhZGeXn5Zo9544034pprrokHHngg8vLyEv2ccePGRdu2bTNbUVHRNtcOAAAAwI4l57d2RkSkUqms9+l0ulpbRERVVVUMGjQobrzxxjjggAMSn3/06NGxevXqzPbuu+9uc80AAAAA7FiSTenaTtq3bx/NmzevNvtsxYoV1WapRUR89NFHsWDBgli0aFFcdtllERGxcePGSKfTkZeXF0899VQcd9xx1Y7Lz8+P/Pz87XMRAAAAAOwQcjojrWXLllFcXByzZs3Kap81a1b07du3Wv+CgoJ49dVXY/HixZlt6NChceCBB8bixYvj8MMPr6/SAQAAANjB5HRGWkTEqFGjYvDgwdG7d+/o06dPTJkyJUpLS2Po0KER8dltme+9915MmzYtmjVrFj169Mg6fvfdd49WrVpVawcAAACAupTzIG3gwIGxatWqGDt2bJSVlUWPHj1i5syZ0aVLl4iIKCsri9LS0hxXCQAAAMCOLudBWkTEsGHDYtiwYZvdN3Xq1K0eO2bMmBgzZkzdFwUAAAAAn9MgntoJAAAAAA2dIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAFArkyZNiq5du0arVq2iuLg45syZs8W+c+fOjX79+kW7du2idevW0a1bt7j99tuz+kydOjVSqVS17dNPP93elwIAkEhergsAAKDxmTFjRowcOTImTZoU/fr1i3vuuScGDBgQS5Ysic6dO1fr36ZNm7jsssvi0EMPjTZt2sTcuXPj0ksvjTZt2sQll1yS6VdQUBCvv/561rGtWrXa7tcDAJCEIA0AgBobP358XHzxxTFkyJCIiJgwYUI8+eSTMXny5Bg3bly1/r169YpevXpl3u+9997xyCOPxJw5c7KCtFQqFXvsscf2vwAAgFpwaycAADWybt26WLhwYZSUlGS1l5SUxPz58xOdY9GiRTF//vw4+uijs9rXrFkTXbp0iU6dOsXXv/71WLRo0VbPs3bt2qisrMzaAAC2F0EaAAA1snLlyqiqqorCwsKs9sLCwigvL9/qsZ06dYr8/Pzo3bt3DB8+PDOjLSKiW7duMXXq1Hjsscdi+vTp0apVq+jXr1+88cYbWzzfuHHjom3btpmtqKho2y4OAGAr3NoJAECtpFKprPfpdLpa2xfNmTMn1qxZEy+88EJcc801sd9++8W5554bERFHHHFEHHHEEZm+/fr1i8MOOyzuvPPOuOOOOzZ7vtGjR8eoUaMy7ysrK4VpAMB2I0gDAKBG2rdvH82bN682+2zFihXVZql9UdeuXSMi4pBDDonly5fHmDFjMkHaFzVr1iy+8pWvbHVGWn5+fuTn59fwCgAAasetnQAA1EjLli2juLg4Zs2aldU+a9as6Nu3b+LzpNPpWLt27Vb3L168ODp27FjrWgEA6pIZaQAA1NioUaNi8ODB0bt37+jTp09MmTIlSktLY+jQoRHx2S2X7733XkybNi0iIu66667o3LlzdOvWLSIi5s6dG7feemtcfvnlmXPeeOONccQRR8T+++8flZWVcccdd8TixYvjrrvuqv8LBADYDEEaAAA1NnDgwFi1alWMHTs2ysrKokePHjFz5szo0qVLRESUlZVFaWlppv/GjRtj9OjRsWzZssjLy4t99903brrpprj00kszfT788MO45JJLory8PNq2bRu9evWK5557Lr761a/W+/UBAGxOKp1Op3NdRH2rrKyMtm3bxurVq6OgoCDX5QBAgzVo0KBYvnx5RHz2RMYHH3wwxxXljvFD4+D3BADJGOf9n5qMH6yRBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAEBGkAAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAAAAIAFBGgAAAAAkIEgDAAAAgAQEaQAAAACQgCANAAAAABIQpAEAAABAAoI0AAAAAEhAkAYAAAAACQjSAAAAACABQRoAAAAAJCBIAwAAAIAE8nJdAAA0Nc8edXSuS6gzn+Y1j0ilPntdXt6kru3o557NdQkAADQyZqQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACgjQAAAAASECQBgAAAAAJCNIAAKiVSZMmRdeuXaNVq1ZRXFwcc+bM2WLfuXPnRr9+/aJdu3bRunXr6NatW9x+++3V+v3ud7+L7t27R35+fnTv3j1+//vfb89LAACoEUEaAAA1NmPGjBg5cmRce+21sWjRoujfv38MGDAgSktLN9u/TZs2cdlll8Vzzz0XS5cujeuuuy6uu+66mDJlSqbP888/HwMHDozBgwfHyy+/HIMHD46zzz47Xnzxxfq6LACArUql0+l0rouob5WVldG2bdtYvXp1FBQU5LocAJqYZ486Otcl1JmJec1jdSoVERFt0+kYsaEqxxXVnaOfe7ZG/Y0fsh1++OFx2GGHxeTJkzNtBx10UJx++ukxbty4ROc488wzo02bNvHrX/86IiIGDhwYlZWV8fjjj2f6nHTSSbHbbrvF9OnTE53T7wkAkhk0aFAsX748IiIKCwvjwQcfzHFFuVOT8YMZaQAA1Mi6deti4cKFUVJSktVeUlIS8+fPT3SORYsWxfz58+Poo/8veH7++eernfPEE09MfE4AgO0tL9cFAADQuKxcuTKqqqqisLAwq72wsDDKy8u3emynTp2ioqIiNmzYEGPGjIkhQ4Zk9pWXl9f4nGvXro21a9dm3ldWVtbkUgAAasSMNAAAaiX1v7f9bpJOp6u1fdGcOXNiwYIFcffdd8eECROq3bJZ03OOGzcu2rZtm9mKiopqeBUAAMmZkQYAQI20b98+mjdvXm2m2IoVK6rNKPuirl27RkTEIYccEsuXL48xY8bEueeeGxERe+yxR43POXr06Bg1alTmfWVlpTANANhuzEgDAKBGWrZsGcXFxTFr1qys9lmzZkXfvn0TnyedTmfdltmnT59q53zqqae2es78/PwoKCjI2gAAthcz0gAAqLFRo0bF4MGDo3fv3tGnT5+YMmVKlJaWxtChQyPis5li7733XkybNi0iIu66667o3LlzdOvWLSIi5s6dG7feemtcfvnlmXOOGDEijjrqqLj55pvjtNNOi0cffTT+9Kc/xdy5c+v/AgFgM5rS09k/zWse8b/LJ3xaXt6krq2mT2evCUEaAAA1NnDgwFi1alWMHTs2ysrKokePHjFz5szo0qVLRESUlZVFaWlppv/GjRtj9OjRsWzZssjLy4t99903brrpprj00kszffr27RsPPfRQXHfddXH99dfHvvvuGzNmzIjDDz+83q8PAGBzUul0Op3rIupbZWVltG3bNlavXm36PwB1ril9mzcxr3ms/t9vKtum0zFiQ1WOK6o7Nf2m0vihcfB7AmB7Ms5rHLbnOM8aaQAAAACQgCANAAAAABKwRtoOasSIEVFRURERER06dIiJEyfmuCIAAACAhk2QtoOqqKiI5cuX57oMAAAAgEbDrZ0AAAAAkIAgDQAAAAASEKQBAAAAQAKCNAAAAABIQJAGAAAAAAkI0gAAAAAgAUEaAAAAACTQIIK0SZMmRdeuXaNVq1ZRXFwcc+bM2WLfuXPnRr9+/aJdu3bRunXr6NatW9x+++31WC0AAAAAO6K8XBcwY8aMGDlyZEyaNCn69esX99xzTwwYMCCWLFkSnTt3rta/TZs2cdlll8Whhx4abdq0iblz58all14abdq0iUsuuSQHVwAAAADAjiDnM9LGjx8fF198cQwZMiQOOuigmDBhQhQVFcXkyZM3279Xr15x7rnnxsEHHxx77713fOtb34oTTzxxq7PYAAAAAGBb5TRIW7duXSxcuDBKSkqy2ktKSmL+/PmJzrFo0aKYP39+HH300dujRAAAAACIiBzf2rly5cqoqqqKwsLCrPbCwsIoLy/f6rGdOnWKioqK2LBhQ4wZMyaGDBmyxb5r166NtWvXZt5XVlZuW+EAAAAA7HByfmtnREQqlcp6n06nq7V90Zw5c2LBggVx9913x4QJE2L69Olb7Dtu3Lho27ZtZisqKqqTugEAAADYceR0Rlr79u2jefPm1WafrVixotostS/q2rVrREQccsghsXz58hgzZkyce+65m+07evToGDVqVOZ9ZWWlMA0AAACAGsnpjLSWLVtGcXFxzJo1K6t91qxZ0bdv38TnSafTWbduflF+fn4UFBRkbQAAAABQEzmdkRYRMWrUqBg8eHD07t07+vTpE1OmTInS0tIYOnRoRHw2m+y9996LadOmRUTEXXfdFZ07d45u3bpFRMTcuXPj1ltvjcsvv3y711r8g2nb/WfUl4J/rMmkqGX/WNNkrm3hz87PdQkAAABAE5XzIG3gwIGxatWqGDt2bJSVlUWPHj1i5syZ0aVLl4iIKCsri9LS0kz/jRs3xujRo2PZsmWRl5cX++67b9x0001x6aWX5uoSAAAAANgB5DxIi4gYNmxYDBs2bLP7pk6dmvX+8ssvr5fZZwAAAADweQ3iqZ0AAAAA0NAJ0gAAAAAgAUEaAAAAACQgSAMAAACABARpAAAAAJCAIA0AAAAAEhCkAQAAAEACebkuAAAAqDsjRoyIioqKiIjo0KF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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(2, 2, figsize=(15, 15))\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(y=metric, x=\"condition\", data=results_df, ax=axs[i])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "f1fd4fd1-504d-4ca6-98cb-756270c6b8bf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(2, 2, figsize=(15, 15))\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(y=metric, x=\"windowMS\", data=results_df, ax=axs[i])" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "2d4bfc6a-bcc1-45f0-83df-b8a7abd0b09e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(2, 2, figsize=(15, 15))\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(y=metric, x=\"condition\", data=results_df, hue=\"mouse_id\", ax=axs[i])" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "a5e64d21-85f9-4ed1-8d51-3dc2f0ceacf3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[36, 108, 200, 252, 504]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_windows" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "e09a4fc7-0f38-4e27-82c7-08fa7e01cac5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, _axs = plt.subplots(3, 2, figsize=(15, 15))\n", - "axs = _axs.flatten()\n", - "for i, windowMS in enumerate(list_windows):\n", - " results_df[results_df.windowMS == windowMS][\n", - " [\"mouse_id\", \"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]\n", - " ].plot.bar(x=\"mouse_id\", ax=axs[i])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f7cc2fc4-9d16-43c1-8bab-d0fd4bdb18fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "corr = results_df[[\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]].corr()\n", - "# mask = np.triu(np.ones_like(corr, dtype=bool))\n", - "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, cmap=cmap, square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.5})" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "d254dae6-937d-4942-bf51-03d86a1bd370", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_20574/1088820411.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", - " corr = results_df.corr()\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "corr = results_df.corr()\n", - "# mask = np.triu(np.ones_like(corr, dtype=bool))\n", - "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n", - "\n", - "sns.heatmap(corr, cmap=cmap, square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.5})" - ] - }, - { - "cell_type": "markdown", - "id": "f4d0891c-65c0-495b-aecd-455d05d435af", - "metadata": {}, - "source": [ - "## Let's focus on M994 and 12390 (good results, PAG & MFB, several windowMS)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "680c728b-aae3-49f6-95aa-f49185bf2c98", - "metadata": {}, - "outputs": [], - "source": [ - "selected_mice = [994, 12390]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "f4708b59-56ae-414d-9abd-8245e18eb923", - "metadata": {}, - "outputs": [], - "source": [ - "subresults_df = results_df[results_df[\"mouse_id\"].isin(selected_mice)]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "2aa6b616-e4d2-452c-a539-3b3fa7cd0df6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "subresults_df.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "b05c73d6-6628-48ea-9459-17d13e8db690", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface as si\n", - "import spikeinterface.extractors as se\n", - "import spikeinterface.preprocessing as spre\n", - "import spikeinterface.postprocessing as spost\n", - "import spikeinterface.curation as scur\n", - "import spikeinterface.widgets as sw\n", - "import spikeinterface.qualitymetrics" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "a56c0381-84e6-45a9-a42e-6f6695c6780f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1239: '/home/mickey/Documents/Theotime/DimaERC2/TEST3_Basile_M1239/TEST/results',\n", - " 1281: '/home/mickey/Documents/Theotime/DimaERC2/TEST3_Basile_1281MFB/TEST/results',\n", - " 1199: '/home/mickey/Documents/Theotime/DimaERC2/TEST1_Basile/TEST/results',\n", - " 1336: '/home/mickey/Documents/Theotime/DimaERC2/Known_M1336/TEST/results',\n", - " 1168: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1168/TEST/results',\n", - " 905: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results',\n", - " 11610: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results',\n", - " 1161: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results',\n", - " 1124: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results',\n", - " 1186: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results',\n", - " 1182: '/home/mickey/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results',\n", - " 11680: '/home/mickey/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results',\n", - " 1117: '/home/mickey/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results',\n", - " 994: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Final_results_v3/results',\n", - " 13360: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_MFB/Final_results_v3/results',\n", - " 13361: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_known/Final_results_v2/results',\n", - " 12810: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1281_MFB/Final_results_v2/results',\n", - " 12390: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB/Final_results_v3/results'}" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dirmouse" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d962ece2-a424-4b93-b97c-0418586649a8", - "metadata": {}, - "outputs": [], - "source": [ - "sorting_folder = dict()\n", - "from pathlib import Path\n", - "\n", - "for mouse in selected_mice:\n", - " sorting_folder[mouse] = os.path.join(Path(dirmouse[mouse]).parents[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "e8d1b473-da68-4178-b0bc-51061a468460", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{994: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG',\n", - " 12390: '/home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB'}" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorting_folder" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "f7843b54-23e5-4151-b800-2296673627cc", - "metadata": {}, - "outputs": [], - "source": [ - "recording = se.NeuroScopeRecordingExtractor(\n", - " os.path.join(sorting_folder[994], \"M994_20191013_UMaze_SpikeRef.dat\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "c9c19903-10af-4f37-a9d5-786d05b102dc", - "metadata": {}, - "outputs": [], - "source": [ - "import xml.etree.ElementTree as ET\n", - "\n", - "tree = ET.parse(os.path.join(sorting_folder[994], \"M994_20191013_UMaze_SpikeRef.xml\"))\n", - "root = tree.getroot()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "adbe8850-3569-40bd-9764-89e1c5e164c1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[]\n" - ] - } - ], - "source": [ - "print(root.findall(\"anatomicalDescription\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "2b0e695c-beac-4b0a-a0cb-ea3fa2628030", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NeuroScopeRecordingExtractor: 135 channels - 20.0kHz - 1 segments - 401,633,280 samples \n", - " 20,081.66s (5.58 hours) - int16 dtype - 100.99 GiB\n", - " file_path: /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/M994_20191013_UMaze_SpikeRef.dat\n" - ] - } - ], - "source": [ - "print(recording)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "34fd7526-1f9d-4151-a0eb-2651c4869cf7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    NeuroScopeRecordingExtractor: 135 channels - 20.0kHz - 1 segments - 401,633,280 samples - 20,081.66s (5.58 hours) - int16 dtype - 100.99 GiB
    Channel IDs
      ['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'\n", - " '16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'\n", - " '30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'\n", - " '44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'\n", - " '58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'\n", - " '72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'\n", - " '86' '87' '88' '89' '90' '91' '92' '93' '94' '95' '96' '97' '98' '99'\n", - " '100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '110' '111'\n", - " '112' '113' '114' '115' '116' '117' '118' '119' '120' '121' '122' '123'\n", - " '124' '125' '126' '127' '128' '129' '130' '131' '132' '133' '134']
    Annotations
    • is_filtered : False
    Channel Properties
      gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578]
      offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
      channel_names ['ch0grp3' 'ch1grp3' 'ch2grp3' 'ch3grp3' 'ch4grp12' 'ch5grp4' 'ch6grp4'\n", - " 'ch7grp4' 'ch8grp5' 'ch9grp4' 'ch10grp4' 'ch11grp4' 'ch12grp3' 'ch13grp3'\n", - " 'ch14grp3' 'ch15grp3' 'ch16grp4' 'ch17grp4' 'ch18grp4' 'ch19grp4'\n", - " 'ch20grp4' 'ch21grp4' 'ch22grp4' 'ch23grp4' 'ch24grp3' 'ch25grp4'\n", - " 'ch26grp3' 'ch27grp3' 'ch28grp3' 'ch29grp3' 'ch30grp2' 'ch31grp3'\n", - " 'ch32grp2' 'ch33grp2' 'ch34grp2' 'ch35grp2' 'ch36grp10' 'ch37grp2'\n", - " 'ch38grp2' 'ch39grp1' 'ch40grp1' 'ch41grp0' 'ch42grp1' 'ch43grp1'\n", - " 'ch44grp1' 'ch45grp1' 'ch46grp1' 'ch47grp1' 'ch48grp2' 'ch49grp3'\n", - " 'ch50grp2' 'ch51grp2' 'ch52grp1' 'ch53grp1' 'ch54grp10' 'ch55grp1'\n", - " 'ch56grp1' 'ch57grp1' 'ch58grp10' 'ch59grp1' 'ch60grp2' 'ch61grp2'\n", - " 'ch62grp2' 'ch63grp2' 'ch64grp8' 'ch65grp8' 'ch66grp8' 'ch67grp8'\n", - " 'ch68grp11' 'ch69grp9' 'ch70grp9' 'ch71grp9' 'ch72grp11' 'ch73grp9'\n", - " 'ch74grp9' 'ch75grp9' 'ch76grp8' 'ch77grp8' 'ch78grp8' 'ch79grp8'\n", - " 'ch80grp9' 'ch81grp9' 'ch82grp9' 'ch83grp9' 'ch84grp9' 'ch85grp9'\n", - " 'ch86grp9' 'ch87grp9' 'ch88grp8' 'ch89grp9' 'ch90grp8' 'ch91grp8'\n", - " 'ch92grp8' 'ch93grp8' 'ch94grp7' 'ch95grp8' 'ch96grp7' 'ch97grp7'\n", - " 'ch98grp7' 'ch99grp7' 'ch100grp0' 'ch101grp7' 'ch102grp7' 'ch103grp6'\n", - " 'ch104grp6' 'ch105grp6' 'ch106grp6' 'ch107grp6' 'ch108grp6' 'ch109grp6'\n", - " 'ch110grp6' 'ch111grp6' 'ch112grp7' 'ch113grp8' 'ch114grp7' 'ch115grp7'\n", - " 'ch116grp6' 'ch117grp6' 'ch118grp5' 'ch119grp6' 'ch120grp6' 'ch121grp6'\n", - " 'ch122grp0' 'ch123grp6' 'ch124grp7' 'ch125grp7' 'ch126grp7' 'ch127grp7'\n", - " 'ch128grp13' 'ch129grp13' 'ch130grp13' 'ch131grp13' 'ch132grp13'\n", - " 'ch133grp13' 'ch134grp14']
    " - ], - "text/plain": [ - "NeuroScopeRecordingExtractor: 135 channels - 20.0kHz - 1 segments - 401,633,280 samples \n", - " 20,081.66s (5.58 hours) - int16 dtype - 100.99 GiB\n", - " file_path: /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/M994_20191013_UMaze_SpikeRef.dat" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "a374fb43-2bdb-4fa5-a161-b48a3c5b5264", - "metadata": {}, - "outputs": [], - "source": [ - "sorting = se.NeuroScopeSortingExtractor(os.path.join(sorting_folder[994]))" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "032738de-37a7-4eeb-aad3-723643323b76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    NeuroScopeSortingExtractor: 96 units - 1 segments - 20.0kHz
    Unit IDs
      [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39\n", - " 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 56 58 59\n", - " 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77\n", - " 78 79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96\n", - " 97 98 99 100 101 102]
    Annotations
      Unit Properties
        group[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3\n", - " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n", - " 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6]
      " - ], - "text/plain": [ - "NeuroScopeSortingExtractor: 96 units - 1 segments - 20.0kHz" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorting" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "a0792750-3f40-4e68-bf80-859aa8f5ed96", - "metadata": {}, - "outputs": [], - "source": [ - "from probeinterface import generate_linear_probe\n", - "from probeinterface.plotting import plot_probe" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "63ae6f79-eb51-4996-a9ad-c2ecbd75f1e8", - "metadata": {}, - "outputs": [], - "source": [ - "probe = generate_linear_probe(\n", - " num_elec=135, ypitch=20, contact_shapes=\"circle\", contact_shape_params={\"radius\": 6}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "380a2a4c-59f0-4f72-ba8d-72810e64a3af", - "metadata": {}, - "outputs": [], - "source": [ - "probe.set_device_channel_indices(np.arange(135))" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "f21b5093-8a7b-40fa-b8d8-abc41b54afbe", - "metadata": {}, - "outputs": [], - "source": [ - "recording = recording.set_probe(probe)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "c9490f8b-b886-4f6e-9123-0ff720fe4819", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(,\n", - " )" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_probe(probe)" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "id": "8d7f98a3-8a65-4f27-b90c-cfc83e297f0f", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "02261c49f37a45bdad41dedccc5bbbb7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "AppLayout(children=(TimeSlider(children=(Dropdown(description='segment', options=(0,), value=0), Button(icon='…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# let'sdo some preprocessing\n", - "\n", - "%matplotlib widget\n", - "recording = spre.depth_order(recording)\n", - "recording_hp = spre.highpass_filter(recording)\n", - "recording_cmr = spre.common_reference(recording_hp)\n", - "\n", - "\n", - "recording_layers = dict(raw=recording, highpass=recording_hp, cmr=recording_cmr)\n", - "\n", - "w = sw.plot_traces(\n", - " recording_layers,\n", - " mode=\"map\",\n", - " order_channel_by_depth=False,\n", - " time_range=[0, 0.2],\n", - " # figlabel=\"SpikeInterface tutorial: plot_traces\",\n", - " clim=(-50, 50),\n", - " backend=\"ipywidgets\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "id": "6a9247ec-ae09-4d17-9f70-a811405e6761", - "metadata": {}, - "outputs": [], - "source": [ - "si.set_global_job_kwargs(n_jobs=-1, progress_bar=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "id": "4a86e91f-9afa-4e2a-9b60-7e49a8d4e0c6", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e7c05bc94b36407a839b9f0591e566c1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "estimate_sparsity: 0%| | 0/20082 [00:00" - ] - }, - "execution_count": 224, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sw.plot_traces(recording, backend=\"ipywidgets\")" - ] - }, - { - "cell_type": "code", - "execution_count": 229, - "id": "814a42d2-9a03-45e6-91e5-9b4acfdd1e87", - "metadata": {}, - "outputs": [], - "source": [ - "analyzer_saved = analyzer.save_as(\n", - " folder=os.path.join(sorting_folder[994], \"analyzer_for_visualization\"),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "id": "190bf72b-b649-42ef-bbaa-8572c6a0f0da", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bbd1728ebc4e46fbab61fa0d5e5edc8d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fitting PCA: 0%| | 0/96 [00:00" - ] - }, - "execution_count": 236, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib widget\n", - "sw.plot_sorting_summary(analyzer_saved, backend=\"sortingview\")" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "id": "d3eb1115-03a0-4fd3-8fe0-34fb973f6bfe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      snramplitude_cutoffrp_contaminationrp_violations
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      " - ], - "text/plain": [ - " snr amplitude_cutoff rp_contamination rp_violations\n", - "2 16.216147 0.017807 0.374007 523\n", - "3 16.607561 0.019746 0.071439 1\n", - "4 16.711653 0.043149 0.0 0\n", - "5 16.535566 0.02054 0.0 0\n", - "6 17.608838 0.132753 0.0 0\n", - "7 16.203859 0.022084 0.488192 42\n", - "8 16.238859 0.016513 0.130991 30\n", - "9 16.121003 0.007905 0.806386 42\n", - "10 16.43656 0.017734 0.042214 1\n", - "11 16.577167 0.025384 0.068675 83" - ] - }, - "execution_count": 240, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics = analyzer_saved.get_extension(\"quality_metrics\").get_data()\n", - "metrics.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "id": "cc238c3e-6b85-4565-821e-21760e970c5d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'snr')" - ] - }, - "execution_count": 241, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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", 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      \n", - " Figure\n", - "
      \n", - " \n", - "
      \n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "_ = ax.hist(metrics[\"snr\"], bins=20)\n", - "ax.set_xlabel(\"snr\")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "6ca31cdc-c0b6-40c5-87a5-1a54847ffbd1", - "metadata": {}, - "outputs": [], - "source": [ - "sorter_names = [\"spykingcircus2\", \"mountainsort5\", \"kilosort4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "f2457326-9f76-42f6-abe1-07c5d0b182db", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0mrun_sorter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msorter_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'str'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mrecording\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'BaseRecording'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfolder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mremove_existing_folder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdelete_output_folder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mraise_error\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdocker_image\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Union[bool, str]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msingularity_image\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Union[bool, str]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdelete_container_files\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mwith_output\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0moutput_folder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msorter_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Generic function to run a sorter via function approach.\n", - "\n", - "\n", - "Parameters\n", - "----------\n", - "sorter_name : str\n", - " The sorter name\n", - "recording : RecordingExtractor\n", - " The recording extractor to be spike sorted\n", - "folder : str or Path\n", - " Path to output folder\n", - "remove_existing_folder : bool\n", - " If True and folder exists then delete.\n", - "delete_output_folder : bool, default: False\n", - " If True, output folder is deleted\n", - "verbose : bool, default: False\n", - " If True, output is verbose\n", - "raise_error : bool, default: True\n", - " If True, an error is raised if spike sorting fails\n", - " If False, the process continues and the error is logged in the log file.\n", - "docker_image : bool or str, default: False\n", - " If True, pull the default docker container for the sorter and run the sorter in that container using docker.\n", - " Use a str to specify a non-default container. If that container is not local it will be pulled from docker hub.\n", - " If False, the sorter is run locally\n", - "singularity_image : bool or str, default: False\n", - " If True, pull the default docker container for the sorter and run the sorter in that container using\n", - " singularity. Use a str to specify a non-default container. If that container is not local it will be pulled\n", - " from Docker Hub. If False, the sorter is run locally\n", - "with_output : bool, default: True\n", - " If True, the output Sorting is returned as a Sorting\n", - "delete_container_files : bool, default: True\n", - " If True, the container temporary files are deleted after the sorting is done\n", - "output_folder : None, default: None\n", - " Do not use. Deprecated output function to be removed in 0.103.\n", - "**sorter_params : keyword args\n", - " Spike sorter specific arguments (they can be retrieved with `get_default_sorter_params(sorter_name_or_class)`)\n", - "\n", - "Returns\n", - "-------\n", - "BaseSorting | None\n", - " The spike sorted data (it `with_output` is True) or None (if `with_output` is False)\n", - "\n", - "\n", - "Examples\n", - "--------\n", - ">>> sorting = run_sorter(\"tridesclous\", recording)\n", - "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/spikeinterface/sorters/runsorter.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "?run_sorter" - ] - }, - { - "cell_type": "markdown", - "id": "ec0822d8-27a2-4aab-b259-a052f49a620e", - "metadata": {}, - "source": [ - "Available sorters:\n", - "- combinato=\"combinato\",\n", - "- herdingspikes=\"herdingspikes\",\n", - "- kilosort4=\"kilosort4\",\n", - "- klusta=\"klusta\",\n", - "- mountainsort4=\"mountainsort4\",\n", - "- mountainsort5=\"mountainsort5\",\n", - "- pykilosort=\"pykilosort\",\n", - "- spykingcircus=\"spyking-circus\",\n", - "- spykingcircus2=\"spyking-circus2\",\n", - "- tridesclous=\"tridesclous\",\n", - "- yass=\"yass\",\n", - "- # Matlab compiled sorters:\n", - "- hdsort=\"hdsort-compiled\",\n", - "- ironclust=\"ironclust-compiled\",\n", - "- kilosort=\"kilosort-compiled\",\n", - "- kilosort2=\"kilosort2-compiled\",\n", - "- kilosort2_5=\"kilosort2_5-compiled\",\n", - "- kilosort3=\"kilosort3-compiled\",\n", - "- waveclus=\"waveclus-compiled\",\n", - "- waveclus_snippets=\"waveclus-compiled\"," - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "9e78af0e-2d04-4e0f-adf7-1a4ce454b4d0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/numpy/core/getlimits.py:542: UserWarning: Signature b'\\x00\\xd0\\xcc\\xcc\\xcc\\xcc\\xcc\\xcc\\xfb\\xbf\\x00\\x00\\x00\\x00\\x00\\x00' for does not match any known type: falling back to type probe function.\n", - "This warnings indicates broken support for the dtype!\n", - " machar = _get_machar(dtype)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spykingcircus2 /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Simulated_sorter_output_spykingcircus2\n" - ] - }, - { - "ename": "Exception", - "evalue": "This folder /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Simulated_sorter_output_spykingcircus2 does not have spikeinterface_log.json", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[57], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(sorter_name, output_folder)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_folder\u001b[38;5;241m.\u001b[39mexists():\n\u001b[0;32m----> 8\u001b[0m sortings[sorter_name] \u001b[38;5;241m=\u001b[39m \u001b[43mread_sorter_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_folder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 10\u001b[0m sortings[sorter_name] \u001b[38;5;241m=\u001b[39m run_sorter(sorter_name, recording, output_folder, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/spikeinterface/sorters/runsorter.py:687\u001b[0m, in \u001b[0;36mread_sorter_folder\u001b[0;34m(folder, register_recording, sorting_info, raise_error)\u001b[0m\n\u001b[1;32m 684\u001b[0m log_file \u001b[38;5;241m=\u001b[39m folder \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspikeinterface_log.json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 686\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m log_file\u001b[38;5;241m.\u001b[39mis_file():\n\u001b[0;32m--> 687\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis folder \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfolder\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not have spikeinterface_log.json\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 689\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m log_file\u001b[38;5;241m.\u001b[39mopen(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf8\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 690\u001b[0m log \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n", - "\u001b[0;31mException\u001b[0m: This folder /home/mickey/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Simulated_sorter_output_spykingcircus2 does not have spikeinterface_log.json" - ] - } - ], - "source": [ - "# run sorter (if not already done)\n", - "from spikeinterface.sorters import run_sorter, read_sorter_folder\n", - "\n", - "sortings = {}\n", - "for sorter_name in sorter_names:\n", - " output_folder = Path(sorting_folder[994]) / f\"Simulated_sorter_output_{sorter_name}\"\n", - " print(sorter_name, output_folder)\n", - " if output_folder.exists():\n", - " sortings[sorter_name] = read_sorter_folder(output_folder)\n", - " else:\n", - " sortings[sorter_name] = run_sorter(\n", - " sorter_name, recording, output_folder, verbose=True\n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/SpikeLussac.ipynb b/notebooks/SpikeLussac.ipynb deleted file mode 100644 index 80953f2..0000000 --- a/notebooks/SpikeLussac.ipynb +++ /dev/null @@ -1,922 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "672214d4-e28e-4908-9528-36ad9f0187f5", - "metadata": {}, - "source": [ - "## Load results" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "47d5710b-d19d-475c-98a4-8727bbb0d240", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import glob\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from pathlib import Path\n", - "import seaborn as sns\n", - "from importlib import reload" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "144eda4c-bb3d-40b0-9a4b-d25b960e44a9", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e51a4b41-a3da-412e-a402-efc81a795e79", - "metadata": {}, - "outputs": [], - "source": [ - "current_dir = os.getcwd()\n", - "datadir = os.path.join(Path(current_dir).parents[1], \"DimaERC2\")\n", - "assert os.path.isdir(datadir)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "20a51ef4-f19e-4ee9-a2f4-7df6d7abf45b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/vador/Documents/Theotime/DimaERC2'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datadir" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a50d78bb-bd6a-461e-ae8c-5016038cb20d", - "metadata": {}, - "outputs": [], - "source": [ - "# What Basile did\n", - "BasileMiceNumber = [\n", - " \"1239vBasile\",\n", - " \"1281vBasile\",\n", - " \"1199\",\n", - " \"1336\",\n", - " \"1168MFB\",\n", - " \"905\",\n", - " \"1161w1199\",\n", - " \"1161\",\n", - " \"1124\",\n", - " \"1186\",\n", - " \"1182\",\n", - " \"1168UMaze\",\n", - " \"1117\",\n", - " \"994\",\n", - " \"1336v3\",\n", - " \"1336v2\",\n", - " \"1281v2\",\n", - " \"1239v3\",\n", - "]\n", - "\n", - "# What Dima did according to Baptiste\n", - "DimaMiceNumber = [\n", - " \"905\",\n", - " \"906\",\n", - " \"911\",\n", - " \"994\",\n", - " \"1161\",\n", - " \"1162\",\n", - " \"1168\",\n", - " \"1186\",\n", - " \"1230\",\n", - " \"1239\",\n", - "]\n", - "\n", - "# Files wrt to datadir\n", - "path_list = [\n", - " \"M1239TEST3_Basile_M1239/TEST\",\n", - " \"M1281TEST3_Basile_1281MFB/TEST\",\n", - " \"M1199TEST1_Basile/TEST\",\n", - " \"M1336_Known/TEST/\",\n", - " # \"DataERC2/M994/20191013/TEST/\",\n", - " # \"DataERC2/M906/TEST/\",\n", - " \"DataERC2/M1168/TEST/\",\n", - " \"DataERC2/M905/TEST/\",\n", - " \"DataERC2/M1161/TEST_with_1199_model/\",\n", - " \"DataERC2/M1161/TEST initial/\",\n", - " \"DataERC2/M1124/TEST/\",\n", - " \"DataERC2/M1186/TEST/\",\n", - " \"DataERC2/M1182/TEST/\",\n", - " \"DataERC1/M1168/TEST/\",\n", - " \"DataERC1/M1117/TEST/\",\n", - " \"neuroencoders_1021/_work/M994_PAG/Final_results_v3\",\n", - " \"neuroencoders_1021/_work/M1336_MFB/Final_results_v3\",\n", - " \"neuroencoders_1021/_work/M1336_known/Final_results_v2\",\n", - " \"neuroencoders_1021/_work/M1281_MFB/Final_results_v2\",\n", - " \"neuroencoders_1021/_work/M1239_MFB/Final_results_v3\",\n", - "]\n", - "assert len(BasileMiceNumber) == len(path_list)\n", - "len(BasileMiceNumber)\n", - "path_dict = dict(zip(BasileMiceNumber, path_list))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "20bbc4d2-8afb-4359-947d-409c2e3889ac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'DataERC2/M1168/TEST/'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1168MFB does not have any results\n", - "path_dict.pop(\"1168MFB\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8b74abbd-35f9-471c-b0f1-864fc469bd98", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1239vBasile': 'M1239TEST3_Basile_M1239/TEST',\n", - " '1281vBasile': 'M1281TEST3_Basile_1281MFB/TEST',\n", - " '1199': 'M1199TEST1_Basile/TEST',\n", - " '1336': 'M1336_Known/TEST/',\n", - " '905': 'DataERC2/M905/TEST/',\n", - " '1161w1199': 'DataERC2/M1161/TEST_with_1199_model/',\n", - " '1161': 'DataERC2/M1161/TEST initial/',\n", - " '1124': 'DataERC2/M1124/TEST/',\n", - " '1186': 'DataERC2/M1186/TEST/',\n", - " '1182': 'DataERC2/M1182/TEST/',\n", - " '1168UMaze': 'DataERC1/M1168/TEST/',\n", - " '1117': 'DataERC1/M1117/TEST/',\n", - " '994': 'neuroencoders_1021/_work/M994_PAG/Final_results_v3',\n", - " '1336v3': 'neuroencoders_1021/_work/M1336_MFB/Final_results_v3',\n", - " '1336v2': 'neuroencoders_1021/_work/M1336_known/Final_results_v2',\n", - " '1281v2': 'neuroencoders_1021/_work/M1281_MFB/Final_results_v2',\n", - " '1239v3': 'neuroencoders_1021/_work/M1239_MFB/Final_results_v3'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c835ccfd-d56b-4660-84a1-91c0bc03db86", - "metadata": {}, - "outputs": [], - "source": [ - "def get_size(file_path, unit=\"bytes\"):\n", - " file_size = os.path.getsize(file_path)\n", - " exponents_map = {\"bytes\": 0, \"kb\": 1, \"mb\": 2, \"gb\": 3}\n", - " if unit not in exponents_map:\n", - " raise ValueError(\n", - " \"Must select from \\\n", - " ['bytes', 'kb', 'mb', 'gb']\"\n", - " )\n", - " else:\n", - " size = file_size / 1024 ** exponents_map[unit]\n", - " return round(size, 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d9a30cb8-c108-4589-aef9-d66f7cd1ef35", - "metadata": {}, - "outputs": [], - "source": [ - "keys_to_include = set()\n", - "size_dat = dict()\n", - "for mouse, path in path_dict.items():\n", - " path = os.path.join(datadir, path, \"../\")\n", - " if len(glob.glob(path + \"*.dat\")) >= 1:\n", - " keys_to_include.add(mouse)\n", - " size_dat[mouse] = get_size(glob.glob(path + \"*.dat\")[0], unit=\"gb\")\n", - "\n", - "dath_dict = {k: path_dict[k] for k in keys_to_include}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6fa5cbd2-6874-480c-9f98-b94e7fe32290", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1199': 0.36,\n", - " '1336': 66.883,\n", - " '1186': 58.163,\n", - " '1182': 50.868,\n", - " '1168UMaze': 60.133,\n", - " '1117': 48.799,\n", - " '994': 100.994,\n", - " '1336v3': 68.974,\n", - " '1336v2': 66.883,\n", - " '1281v2': 63.311,\n", - " '1239v3': 49.421}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size_dat" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "755d623d-51cb-4817-aca3-b892a47a7da8", - "metadata": {}, - "outputs": [], - "source": [ - "conditions = {\n", - " \"MFB\": [\"1281vBasile\", \"1281v3\" \"1239vBasile\", \"1239v3\", \"1336v3\", \"1336v2\"],\n", - " \"Known\": [\"1336\", \"1336v3\"],\n", - " \"PAG\": [\"1186\", \"1161\", \"1161w1199\", \"1124\", \"1186\", \"1117\", \"1199\", \"994\"],\n", - " \"Umaze\": [\"1199\", \"906\", \"1168\", \"905\", \"1182\"],\n", - " # WARNING: 994 has non-aligned nnbehavior.positions; hence the results should not be trusted\n", - "}\n", - "\n", - "list_windows = [36, 108, 200, 252, 504]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4423af7f-e551-4260-85f0-2267cd5f2b01", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "from importData.rawdata_parser import DataHelper\n", - "from resultAnalysis.print_results import print_results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d6d6d3c7-212b-4dba-97bb-81dc20b1e566", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[36, 108, 200, 252, 504]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_windows" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9e4e203b-0aef-4abe-a2f6-29cf7f5232fe", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total windows: 103424 | selected windows: 31027 (thresh -4.3133297 )\n", - "mean eucl. error: 0.4099105329433457 | selected error: 0.3388784961711805\n", - "mean linear error: 0.24068852490717824 | selected error: 0.18432333129210043\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1239TEST3_Basile_M1239/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 21888 | selected windows: 6566 (thresh -4.140966 )\n", - "mean eucl. error: 0.3900984639396719 | selected error: 0.35825975326772563\n", - "mean linear error: 0.2728143274853801 | selected error: 0.21335820895522387\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1239TEST3_Basile_M1239/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9856 | selected windows: 2956 (thresh -4.9398165 )\n", - "mean eucl. error: 0.38621183691339656 | selected error: 0.3790181510435826\n", - "mean linear error: 0.21885349025974027 | selected error: 0.18813261163734776\n", - "total windows: 108416 | selected windows: 32524 (thresh -3.0905595 )\n", - "mean eucl. error: 0.5096774095797851 | selected error: 0.48791706011258873\n", - "mean linear error: 0.2487205762987013 | selected error: 0.21851801746402658\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1281TEST3_Basile_1281MFB/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 22784 | selected windows: 6835 (thresh -3.8569858 )\n", - "mean eucl. error: 0.5248040472074363 | selected error: 0.4814915129713809\n", - "mean linear error: 0.2689255617977528 | selected error: 0.20954498902706656\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1281TEST3_Basile_1281MFB/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9856 | selected windows: 2956 (thresh -1.1291519 )\n", - "mean eucl. error: 0.5180738521512005 | selected error: 0.5089591672519789\n", - "mean linear error: 0.2554961444805195 | selected error: 0.23500338294993237\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 18944 | selected windows: 5683 (thresh -4.1417675 )\n", - "mean eucl. error: 0.41348124954996934 | selected error: 0.31071941895418564\n", - "mean linear error: 0.18181218327702703 | selected error: 0.13149920816470176\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results/504/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 104320 | selected windows: 31296 (thresh -4.14909 )\n", - "mean eucl. error: 0.45229839142678147 | selected error: 0.3949391303763814\n", - "mean linear error: 0.33679495782208585 | selected error: 0.2506697341513292\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1336_Known/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23168 | selected windows: 6950 (thresh -4.0340347 )\n", - "mean eucl. error: 0.4198770464172988 | selected error: 0.3882440986379923\n", - "mean linear error: 0.28290789019337015 | selected error: 0.24212517985611512\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/M1336_Known/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9728 | selected windows: 2918 (thresh -4.537151 )\n", - "mean eucl. error: 0.4584586775672959 | selected error: 0.46391458953846737\n", - "mean linear error: 0.29336965460526315 | selected error: 0.28494859492803293\n", - "total windows: 98304 | selected windows: 29491 (thresh -2.5121055 )\n", - "mean eucl. error: 0.46561207211367456 | selected error: 0.4692852967657072\n", - "mean linear error: 0.5407175699869792 | selected error: 0.48223254552236283\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 21632 | selected windows: 6489 (thresh -3.6158442 )\n", - "mean eucl. error: 0.4459823902921189 | selected error: 0.4385799076320848\n", - "mean linear error: 0.38199981508875747 | selected error: 0.363364154723378\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 10880 | selected windows: 3264 (thresh -3.842236 )\n", - "mean eucl. error: 0.4410981135495719 | selected error: 0.4464837717645344\n", - "mean linear error: 0.39765900735294113 | selected error: 0.3948314950980392\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/36/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/108/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "total windows: 12672 | selected windows: 3801 (thresh -3.879144 )\n", - "mean eucl. error: 0.46867128023110105 | selected error: 0.45676790314536814\n", - "mean linear error: 0.22071338383838385 | selected error: 0.22304393580636675\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/252/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results/504/featureTrue.csv'\n", - "Available windows: ['200', '504']\n", - "total windows: 137472 | selected windows: 41241 (thresh -4.430786 )\n", - "mean eucl. error: 0.5136216538508177 | selected error: 0.4670296356827056\n", - "mean linear error: 0.26214414571694605 | selected error: 0.23317790548240827\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 29696 | selected windows: 8908 (thresh -3.9554555 )\n", - "mean eucl. error: 0.47114616746719096 | selected error: 0.4184275651909268\n", - "mean linear error: 0.23238213900862068 | selected error: 0.20358105074090704\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 12672 | selected windows: 3801 (thresh -3.879144 )\n", - "mean eucl. error: 0.46867128023110105 | selected error: 0.45676790314536814\n", - "mean linear error: 0.22071338383838385 | selected error: 0.22304393580636675\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 29056 | selected windows: 8716 (thresh -13.275983 )\n", - "mean eucl. error: 0.4742163338503855 | selected error: 0.4512791809625954\n", - "mean linear error: 0.2035280148678414 | selected error: 0.19351537402478203\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results/504/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 109184 | selected windows: 32755 (thresh -4.0972867 )\n", - "mean eucl. error: 0.5177043757421281 | selected error: 0.5134948634716336\n", - "mean linear error: 0.25466377857561545 | selected error: 0.22837459929781712\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23424 | selected windows: 7027 (thresh -3.8716633 )\n", - "mean eucl. error: 0.4485788831234556 | selected error: 0.4099108151783293\n", - "mean linear error: 0.19302510245901638 | selected error: 0.1675323751245197\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 9984 | selected windows: 2995 (thresh -4.246581 )\n", - "mean eucl. error: 0.47557516402162603 | selected error: 0.4471246174005367\n", - "mean linear error: 0.21452624198717948 | selected error: 0.20374624373956596\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/36/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 18816 | selected windows: 5644 (thresh -3.850906 )\n", - "mean eucl. error: 0.4650601784604804 | selected error: 0.37909361789913987\n", - "mean linear error: 0.1717782738095238 | selected error: 0.11447909284195608\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200']\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results/504/featureTrue.csv'\n", - "Available windows: ['200']\n", - "total windows: 127104 | selected windows: 38131 (thresh -3.392338 )\n", - "mean eucl. error: 0.42899520802294094 | selected error: 0.34520146855821215\n", - "mean linear error: 0.2818879028197382 | selected error: 0.1879788098922137\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 28032 | selected windows: 8409 (thresh -3.5557854 )\n", - "mean eucl. error: 0.4253206466239176 | selected error: 0.38377802705632474\n", - "mean linear error: 0.2908258418949772 | selected error: 0.2696598882150077\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 12288 | selected windows: 3686 (thresh -3.8141289 )\n", - "mean eucl. error: 0.41715469355585255 | selected error: 0.39095112861006864\n", - "mean linear error: 0.2883528645833333 | selected error: 0.235732501356484\n", - "total windows: 110848 | selected windows: 33254 (thresh -3.5723522 )\n", - "mean eucl. error: 0.4740902712106153 | selected error: 0.4301594067888825\n", - "mean linear error: 0.2845248448325635 | selected error: 0.25105430925602934\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results/108/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 23552 | selected windows: 7065 (thresh -4.022328 )\n", - "mean eucl. error: 0.45928378428877065 | selected error: 0.41070582195492183\n", - "mean linear error: 0.2775339673913043 | selected error: 0.2384345364472753\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results/252/featureTrue.csv'\n", - "Available windows: ['200', '36', '504']\n", - "total windows: 10240 | selected windows: 3072 (thresh -4.859231 )\n", - "mean eucl. error: 0.45251948404943737 | selected error: 0.4737108638114196\n", - "mean linear error: 0.3188984375 | selected error: 0.32421549479166667\n", - "total windows: 6528 | selected windows: 1958 (thresh -4.797138 )\n", - "mean eucl. error: 0.308457394288779 | selected error: 0.23412658957435312\n", - "mean linear error: 0.1549984681372549 | selected error: 0.1058988764044944\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.7340493 )\n", - "mean eucl. error: 0.24791512898533571 | selected error: 0.17341864632277373\n", - "mean linear error: 0.1297227328431373 | selected error: 0.07499489274770174\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.997062 )\n", - "mean eucl. error: 0.22262539966188583 | selected error: 0.16894934097947953\n", - "mean linear error: 0.10720128676470589 | selected error: 0.0710520939734423\n", - "total windows: 6528 | selected windows: 1958 (thresh -5.8903008 )\n", - "mean eucl. error: 0.2118937043753728 | selected error: 0.17612127014460588\n", - "mean linear error: 0.10722886029411764 | selected error: 0.08100612870275792\n", - "total windows: 13184 | selected windows: 3955 (thresh -4.3813343 )\n", - "mean eucl. error: 0.3575848308310594 | selected error: 0.25840760054430034\n", - "mean linear error: 0.23859071601941748 | selected error: 0.1498078381795196\n", - "total windows: 13184 | selected windows: 3955 (thresh -5.2892494 )\n", - "mean eucl. error: 0.2981273008685602 | selected error: 0.21064261732409612\n", - "mean linear error: 0.19825015169902913 | selected error: 0.11969911504424778\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_MFB/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 13184 | selected windows: 3955 (thresh -5.944226 )\n", - "mean eucl. error: 0.2682479383957932 | selected error: 0.19420851572601364\n", - "mean linear error: 0.17593446601941748 | selected error: 0.11053603034134007\n", - "total windows: 13312 | selected windows: 3993 (thresh -6.129132 )\n", - "mean eucl. error: 0.2500419149008913 | selected error: 0.1857000627422962\n", - "mean linear error: 0.16785381610576922 | selected error: 0.1136839469070874\n", - "total windows: 12928 | selected windows: 3878 (thresh -3.9968202 )\n", - "mean eucl. error: 0.4512924509004647 | selected error: 0.4059579827896158\n", - "mean linear error: 0.28356590346534655 | selected error: 0.21131768953068591\n", - "total windows: 12928 | selected windows: 3878 (thresh -4.744326 )\n", - "mean eucl. error: 0.4379316132700753 | selected error: 0.3772838136471705\n", - "mean linear error: 0.2955901918316831 | selected error: 0.23187983496647757\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_known/Final_results_v2/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 12928 | selected windows: 3878 (thresh -5.9330926 )\n", - "mean eucl. error: 0.44579350729398987 | selected error: 0.4120424070445943\n", - "mean linear error: 0.3065578589108911 | selected error: 0.28296028880866425\n", - "total windows: 13056 | selected windows: 3916 (thresh -6.1029983 )\n", - "mean eucl. error: 0.4094791559633769 | selected error: 0.3680947279028363\n", - "mean linear error: 0.2776179534313725 | selected error: 0.24181562819203267\n", - "total windows: 14208 | selected windows: 4262 (thresh -3.3976035 )\n", - "mean eucl. error: 0.5212641443494755 | selected error: 0.5035234910117191\n", - "mean linear error: 0.26170326576576575 | selected error: 0.22459174096668233\n", - "total windows: 14208 | selected windows: 4262 (thresh -4.06132 )\n", - "mean eucl. error: 0.5385864491928991 | selected error: 0.5137827818831091\n", - "mean linear error: 0.281488597972973 | selected error: 0.2560957297043642\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1281_MFB/Final_results_v2/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 14208 | selected windows: 4262 (thresh -4.5147614 )\n", - "mean eucl. error: 0.48874218911260153 | selected error: 0.4076990268790374\n", - "mean linear error: 0.24361908783783784 | selected error: 0.1869427498826842\n", - "total windows: 14208 | selected windows: 4262 (thresh -5.6891036 )\n", - "mean eucl. error: 0.4595335703279842 | selected error: 0.40493214045313247\n", - "mean linear error: 0.2083227759009009 | selected error: 0.1693289535429376\n", - "total windows: 7552 | selected windows: 2265 (thresh -4.2046785 )\n", - "mean eucl. error: 0.3796418196780438 | selected error: 0.26915538960290275\n", - "mean linear error: 0.2150741525423729 | selected error: 0.12605739514348788\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.2579575 )\n", - "mean eucl. error: 0.3172805695536395 | selected error: 0.19819524535580405\n", - "mean linear error: 0.17178098516949153 | selected error: 0.08380132450331126\n", - "[Errno 2] No such file or directory: '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB/Final_results_v3/results/200/featureTrue.csv'\n", - "Available windows: ['108', '252', '36', '504']\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.859999 )\n", - "mean eucl. error: 0.26070979386617643 | selected error: 0.1816375778638438\n", - "mean linear error: 0.13545550847457627 | selected error: 0.07917439293598233\n", - "total windows: 7552 | selected windows: 2265 (thresh -5.269205 )\n", - "mean eucl. error: 0.26359873518635907 | selected error: 0.20512912875926131\n", - "mean linear error: 0.13328654661016948 | selected error: 0.10113024282560705\n", - "threshold value: -5.269205\r" - ] - } - ], - "source": [ - "# bypass to avoid heavy comput and fill the memory for nothing\n", - "force = False\n", - "\n", - "todo = dict()\n", - "dirmouse = dict()\n", - "mouse_id = []\n", - "windowMS = []\n", - "mean_eucl = []\n", - "select_eucl = []\n", - "mean_lin = []\n", - "select_lin = []\n", - "has_dat = []\n", - "sizes = []\n", - "for mouse, path in path_dict.items():\n", - " todo[mouse] = []\n", - " returned = False\n", - " dirmouse[mouse] = os.path.join(datadir, path, \"results\")\n", - " assert os.path.isdir(dirmouse[mouse])\n", - " for win in list_windows:\n", - " try:\n", - " mean, select, linmean, linselect = print_results(\n", - " dirmouse[mouse], show=False, windowSizeMS=win, force=False\n", - " )\n", - " mean_eucl.append(mean)\n", - " select_eucl.append(select)\n", - " mean_lin.append(linmean)\n", - " select_lin.append(linselect)\n", - " mouse_id.append(mouse)\n", - " windowMS.append(win)\n", - " has_dat.append(mouse in dath_dict)\n", - " if mouse in dath_dict:\n", - " sizes.append(size_dat[mouse])\n", - " else:\n", - " sizes.append(0)\n", - " returned = True\n", - " except Exception as e:\n", - " print(e)\n", - " todo[mouse].append(win)\n", - " print(f\"Available windows: {os.listdir(dirmouse[mouse])}\")\n", - " for val in os.listdir(dirmouse[mouse]):\n", - " if int(val) not in list_windows:\n", - " list_windows.append(val)\n", - " print(f\"adding {val} to list of available windows\")\n", - " mean, select, linmean, linselect = print_results(\n", - " dirmouse[mouse], show=False, windowSizeMS=win\n", - " )\n", - " mean_eucl.append(mean)\n", - " select_eucl.append(select)\n", - " mean_lin.append(linmean)\n", - " select_lin.append(linselect)\n", - " mouse_id.append(mouse)\n", - " windowMS.append(win)\n", - " returned = True\n", - " ###\" print(f\"No data for {mouse} in {win}\")\n", - " if not returned:\n", - " print(f\"nothing at all for {mouse}, {os.listdir(dirmouse[mouse])}\")\n", - "\n", - "\n", - "results_df = pd.DataFrame(\n", - " data={\n", - " \"mouse_id\": mouse_id,\n", - " \"windowMS\": windowMS,\n", - " \"mean_eucl\": mean_eucl,\n", - " \"select_eucl\": select_eucl,\n", - " \"mean_lin\": mean_lin,\n", - " \"select_lin\": select_lin,\n", - " \"has_dat\": has_dat,\n", - " \"size_dat\": sizes,\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "f3accb1c-8dc5-48ba-8e3a-9b80091dc88d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1239vBasile': [108, 252],\n", - " '1281vBasile': [108, 252],\n", - " '1199': [36, 108, 252, 504],\n", - " '1336': [108, 252],\n", - " '905': [108, 252],\n", - " '1161w1199': [36, 108, 252, 504],\n", - " '1161': [108, 252],\n", - " '1124': [36, 108, 252, 504],\n", - " '1186': [108, 252],\n", - " '1182': [36, 108, 252, 504],\n", - " '1168UMaze': [108, 252],\n", - " '1117': [108, 252],\n", - " '994': [200],\n", - " '1336v3': [200],\n", - " '1336v2': [200],\n", - " '1281v2': [200],\n", - " '1239v3': [200]}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "todo" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "13996267-9051-447f-a660-fddf4dc87dcc", - "metadata": {}, - "outputs": [], - "source": [ - "for cdt in conditions:\n", - " for mouse in conditions[cdt]:\n", - " try:\n", - " results_df.loc[results_df.mouse_id == mouse, \"condition\"] = cdt\n", - " except Exception as e:\n", - " print(e)\n", - "\n", - "results_df = results_df.sort_values(\n", - " by=[\"condition\", \"mouse_id\", \"windowMS\"]\n", - ").reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "bd7996d3-7997-48ed-b9cf-01d4401f201c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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x5ubmxplnnhm//vWviy3rgQceiL59+0bnzp1TrWdXbdiwIXJyckq9UvGL69iyZUvUr1+/2JVNDRs2jIiIZ555ZqdtiYg4+OCDS21L27Ztd4jnq1/9asyePTvWr18fRUVF8dBDD8WWLVti4MCBqdpYnm0bEbFy5cr44x//GEcddVRm3ubNm6N3797xxz/+Mf7xj3/E9773vTj77LMzt1MrKCiIb33rW/Hd7343XnvttZg/f34MHTo0Pjs2lu3cc8+NRYsWxUMPPRQvvvhitG3bNiIimjRpUmL9ytrXIj7bV5o2bRp169ZN1ZZs+cMf/hBr166tlCsTK0JJ2/U///lPnHfeeXH//fdHo0aNshhd6dasWRN/+9vfYp999ol+/fpFmzZtYsCAAfHMM89U6fUNHDgw5s+fHxGf3aJp4cKFsffee2eW89RTT0Xbtm3j4IMPrrDYyzpubq/zxas3GzZsGJs3by73ess6fmbru3Tbtm1RWFhYYnufeeaZ2LJlS0REsfI6depE/fr1i/2/P/744/jWt74VM2bMyBwvq4vyfLdt2LAhWrRoUXlBprRly5bIycmJvLy8zLwGDRpEbm5usf27rM/AnlDWvrZixYpYvXp1DBo0KFOWl5cXAwYMiGeffbbYe379619Hq1at4rDDDosf/OAHO1xlXl5pjm0tW7aMbt26xaxZs2LTpk2xbdu2uOuuu6JNmzbRu3fvcq23TZs2ceKJJ8avfvWriPjs8zR79uz47ne/u0Pd3NzcuO222+If//hH/OpXv4onn3wypk6dmjlne+SRR4pdYT906NDo2rVrzJkzJzp16hRjx46N1atXx5gxYzIxH3fccbF+/fpU55BV/Zzt5ZdfjmHDhsVFF11UZc5jq8t5G2RDdeqDqGn9DxH6IKry95k+iPLRB1F59EGktzt9EBFR7DFJaVXV/ocIfRAR+iAi9EFspw+iZvRB9O/fPyIi1q1bl6lT7fofdiklX4vFTq6meeyxx5LGjRsnOTk5Sbt27ZLnn39+hzo/+9nPksaNGycRkRxyyCHFrpZ+8MEHk4hIOnTokDz88MPJiy++mHzrW99KWrZsmaxbt65axvvSSy8lOTk5yb/+9a8kSf7vypGf/exnqdeTpi3bffLJJ0nv3r2Tb3/727vUln/84x9J3bp1k5tvvjnZsmVLsn79+mTo0KFJRCQ33HDDTtsSEcn3vve9na7noIMOSrp3775DPB988EFy4oknJhGR1K1bN2natGny+OOPl9imnV01mmbbduzYMalfv37SuHHjzMjRo446qsyrlk8++eRk4sSJSZL831U929fzRaVdkfTmm28mOTk5ycyZM4vta0ceeWRy5ZVX7nS77cl9bbu1a9cmHTp0SK666qpSt0VpStsfK/Iq8sGDByeDBw8ud5xp7W57kqTk7VpUVJScdNJJyY9//ONdWtbuKk97nnvuuSQikhYtWiT33ntv8tJLLyXjx49P6tevn7zxxhs7LGd3ryLfnfV93h/+8IekWbNmSWFhYbJ06dKkdevWySWXXJJceumlSZIkyfe+971kxIgRqeP6vJ3tp2mOm9uvxH3ggQeSbdu2Jf/+97+Tr371q5mrMcuzv5V1/NxT36Vp9O3bNxkwYECycuXKZNu2bcn999+f5OTkJAcddFCydevWpGPHjsmwYcOS9evXJ1u2bEluvPHGJCKSQYMGZZbxve99Lxk1alTmdVnfe3vCnvrffNGbb76ZNG3adIer6CtaedqzZs2apGnTpsnFF1+cbNq0Kfnoo4+SCy64oNj3fprPwJ5S2r62aNGiJCKSlStXFnvPeeedV2xfu/vuu5N58+Yly5YtSx588MHkgAMOSI4//vgd1rUnj23//ve/k969eyc5OTlJnTp1knbt2hVb1q5eRX7LLbckv//975MDDzwwKSoqSn71q18lvXr1yrTj81eRf9F///d/J3Xq1CnxHPIb3/hG5rh1yCGHJPfff3/StGnTZPPmzcX2g86dOyfTpk1LdQ6Z7XO27dtk+0j27edsn99vPvnkk2SvvfZKDjvssGLLqazz2M+riPM2qM6qUx9ETep/KK092+mD0AehD6L89EHogyjJnuqDuOSSS3ZpWyVJ1e5/SBJ9EPog9EFspw+i6vdB7Oycbft+s/2cum3btsXO2apb/4OR7Lvp2GOPjaVLl8azzz4bJ510UgwfPjzWrFlTrM63v/3tWLJkSSxYsCC6du0aw4cPz4zo2/58jauuuiq+8Y1vZJ6/kJOTE7/97W+rZby9evWKQw45JB588MGIiFiwYEGsWbMmhg8fnno9aX366adx5plnRlFRUdxxxx07lJe2jsMOOyx+9atfxdSpU6NRo0bRtm3b6Ny5c7Rp0ybq1Kmz07ZEROYKmy+u54knnogNGzbEW2+9FdOmTStW5+qrr473338//vKXv8SLL74YEyZMiGHDhsWyZctStzfttr300ktj6dKl8fLLL8cTTzwREZ89f2T7czwKCwvj+uuvj8MPPzxatmwZTZo0iccffzzy8/MjIqJnz55x3HHHRY8ePWLY1rRgQwAAF6BJREFUsGFxzz33xPvvv58qxpdeeimSJImxY8dGUVFR5OXlxZo1a+KFF17Y4arOytrXIiI2btwYp5xyShx66KExadKkVG3Jln//+98xd+7cavEM0p1t19tvvz02btwYV155ZRajS2f7vnb++efHueeeG7169YpbbrklDj744Lj33nur7PqOOeaY+PDDDzOfoQEDBsSxxx6bOU7Nnz8/BgwYUKGxpzluDho0KKZMmRJjxoyJvLy8OOigg+KUU07ZrfWWdfys7O/Sz7v//vsjSZLYb7/9Ii8vL2677bY466yzok6dOlGvXr343e9+F2+88Ua0aNEiGjVqFPPnz4/Bgwdnttcf/vCHePLJJ2P69Ol7NM49ZVe+21atWhUnnXRSDBs2LEaPHp2FaEvXunXr+O1vfxuPPfZYNGnSJJo1axYbNmyIL33pS5n/V5rPwJ5S2r623Refp5ckSbF55513Xhx//PHRvXv3OPPMM+Phhx+Ov/zlL/HSSy/tdnxpjm3bzw/22WefWLhwYTz//PMxZMiQ+PrXvx4FBQXlXvcpp5wSH330UTz99NNx7733lngFecRno2tOOOGE2G+//aJJkyYxcuTIKCwsjClTphSr96c//Sn+8Ic/xL333ps5R7niiiviww8/jJYtW8ZRRx0VderUicsvvzzefvvtuPTSS1OdQ1bVc7aDDjoomjRpEo0bN44mTZrEhx9+GAcddFCxupV5HhtRvc7boLJVpz6Imtb/EKEPoip/n+mDKD99EJVLH0R6u9sHUZ7faFW5/yFCH4Q+CH0Q2+mDqN59ENv7H5YsWRLvvfdesTuPVLv+h11Oy9dSkfKKri5dupR6FdGWLVuSRo0aZZ5j8OSTTyYRkSxcuLBYvS9/+cvJD3/4w2ob73XXXZf06NEjSZIkGT16dLFnP6ZZT5q2bN26NTn99NOTww8/PFm7dm2pyy9rHatXr04+/PDD5KOPPkpyc3OT3/zmNzttS1nx9OjRI2nYsGGx9bz55ptJRPFn4iRJkhx33HHJ+eefv8OydnYFTknxfHHblvRsje1Xdm1/nstPfvKTpGXLlsn999+fLF26NFm+fHlyyimnFBvpVFRUlDzzzDPJNddck/To0SNp3bp18vbbbydJUvoVSQ899FBSp06d5PXXX0+WL1+emTp27LjDVeSftyf3tY0bNyZ9+/ZNjjvuuOSTTz7ZaQxplPbZqqiryCdPnpy0bt062bp1627FmsbutKe07TpkyJAkNzc3qVOnTmaKiKROnTrJyJEjK7gV/6c87Xn77beTiEjuv//+YvOHDx+enHXWWTssZ3evIt+d9X3R9meiDR06NJkxY0by/vvvZ67YjIjk1VdfTR3X56XZT0s7bibJZ8eQlStXJh9//HHy6quvlnske5rj5576Lt0VH330UbJq1aokST77X5588snFyj/44INkzZo1mbjGjh2bJEmSXHzxxZmraT//WcnNzU0GDBhQKbEnSfn25V35blu5cmVy0EEHJWeffXaxZ4ntKbt7rH7vvfcy+3+bNm2Sm2++eYc6ZX0G9pSS9rW33noriYjkpZdeKlb3tNNOK/WYW1RUlNSrVy956KGHis3fU8e2v/zlLzs8cy5JPjsfvfHGG5MkKf2q6I4dOybTpk0r9nr7Oc8PfvCDZMCAAUmDBg2S9evXZ9qx/Sryf/3rX0mDBg2S8ePHJwsXLkyOP/74ZP/999/hWPfKK68kTZs2LbaeLVu2JPXq1Uv23nvvYuc2y5cvT5577rlkxYoVqc4hs33Otn2bbI/j8+dsr776anLCCSckhxxySPL8888nBQUFmfdU5nlsklTseRtUZ9n+TV+dYq3I/ofS2qMPomp8n+mD+D/6IP6PPgh9ECX1QZT0O660mKpL/0OS6IP4PH0Qe5Y+CH0QFX3OtmzZsmL9D8uXL8/0QVTH/gcj2StYkiSZ55+kqdO7d+/Iy8uLf/7zn5nyTz/9NP71r39Fx44d92isX4wlTZ208Z511lmxbNmyWLx4cTz88MPx7W9/u0Ji+fw6hw8fHsuXL4+//OUv0bJly1Tv29k62rRpE02aNInZs2dHgwYN4oQTTthpW8qK509/+lNERLH1fPzxxxERxZ6fEvHZFZXbr7pKqzzbdvsVTZ988klERCxcuDCGDBkS/+///b/o2bNndO7cOZYvX17sPTk5OdG/f//4r//6r1iyZEnUr18/Hn300TLX1atXrygsLIw1a9ZEly5dMlPdunWjfv36pb53T+xrGzdujEGDBkX9+vXjD3/4ww7PkqlqkiSJ++67L0aOHBn16tXLdjg7VdZ2ve222+Lvf/97LF26NJYuXRpz5syJiIjZs2fH9ddfn42Qd+qAAw6Idu3aFdvXIiLeeOONPXIcrsj1DRw4MJ566ql4+umnY+DAgdG8efM49NBD47rrrot99tknunXrVpGhF1PacTPis2NIu3btomHDhvHggw+W+5mXaY6f2f4ujYho3Lhx7LvvvvH+++/H3LlzY8iQIcXKmzVrFq1bt47ly5fHiy++mCm/4oor4uWXX858VpYuXRoREbfcckvcd999lRJ7eaX9blu5cmUMHDgwvvSlL8V99923Q/2qqFWrVtG8efN48sknY82aNXHaaaftUKesz8CeUtK+1qlTp2jbtm3MmzcvU2/r1q2xYMGC6Nev306X9corr8Snn34a++67727HlebYtrN9Jjc3N7PPdO3aNXJzc+OFF14oVqegoCBWrly502c8fve7340FCxbEkCFDYu+9996h/MUXX4xt27bFTTfdFFOnTo2CgoL4f//v/xWrs27dujj11FNj6NChcckll+wQ4wcffBB169Ytdn7zla98JQ444IBU55BV9Zxt1apV8cMf/jBWrVoVTz/9dBx55JHFns1Ymeex1e28DaqC6tQHUV37H7avVx9E1f4+0wdRPvogKp8+iPLZ1T6IiIgDDzxwl9ZRXfofIvRBfJ4+iD1LH4Q+iIo+Zxs7dmyx/ocuXbpk+iCqY//DLj7BvXb56KOP4s0338y8XrFiRSxdujRatGgRLVu2jOuvvz5OO+202HfffWPdunVxxx13xL///e8YNmxYRES8/fbbMXv27Bg0aFC0bt06Vq5cGT/5yU+iYcOGcfLJJ0dERNOmTWPMmDExadKkaN++fXTs2DFzu4jty6mO8Xbq1Cn69esXo0aNim3bthX7ok+zntLa0q5du/jmN78ZL730Uvzxj3+MwsLCWL16dUREtGjRIurXr59qHRERM2bMiH79+kWTJk1i3rx5cemll8ZNN90UzZs3L9aWo446Ks4666zMj6/t8Xz00Ucxf/78mDdvXixfvjxuuummGDVqVOTl5UWfPn1i69atUb9+/TjkkEOiS5cucf7558dPf/rTaNmyZfz+97+PefPmxR//+MfMuvLz82P9+vWRn58fhYWFmROdLl26RJMmTcrcttt9+OGHsXr16kiSJN5999247LLLolWrVpkvuS5dusTvfve7ePbZZ2PvvfeOadOmxerVqzMnw3/729/iiSeeiEGDBsU+++wTf/vb3+K9997LlH/66aexYcOGTHybN2+O9957L/Lz82O//faLww47LIYPHx6TJk2K/fbbL37xi1/EO++8E23atKnUfe3DDz+MQYMGxccffxz//d//HRs3boyNGzdGxGe35El7W53S9scOHTpk/merVq2KiMh8ubdt2zbzBbF69epYvXp1ZjnLli2LvfbaKzp06BAtWrTILPvJJ5+MFStW7NHbtO1ue9Js1w4dOhRb5/b998ADD4z999+/SrUnJycnLr300pg0aVL07NkzjjjiiPjVr34Vr7/+erFOre2fz+XLl8fWrVtj9uzZERHx+uuv79H1lXY8GDhwYNx6663RokWLOPTQQzPzbr/99hg6dGix7VRR+2ma4+aUKVPipJNOitzc3HjwwQfjJz/5Sdx8880xYcKEXf7/pDl+VuR36a6aO3duJEkSBx98cLz55ptx6aWXxsEHHxznnntuRET89re/jdatW0eHDh1i2bJlcfHFF8fpp58egwYN2mH7f16HDh2iU6dOezT23f3spPnfrFq1KgYOHBgdOnSIn/70p/Hee+9l1ldSu7PZnoiI++67L7p16xatW7eO5557Li6++OK45JJLiv2oSvMZ2BNK29dycnJi/PjxccMNN0TXrl2ja9euccMNN0SjRo3irLPOioiIt956K37961/HySefHK1atYpXX301Jk6cGL169Yr+/ftXyrG0b9++sffee8c555wT11xzTTRs2DDuueeeWLFiReaWjnvttVecf/75MXHixKhbt2707NkzVq1aFVdddVV069Yt89n5om7dusXatWujqKgoc7yMiHjvvfdi6dKl0ahRo9i2bVv06dMn1q5dGxdeeGHMmDEjIiL+85//RKNGjWLo0KFRp06daN26dcydOzdatmwZq1evjnvuuScaN24cPXv2jNNPPz1+8pOfxF//+tfo0KFDPP/887HXXnvFjBkzSjyHzPY5W0TEtm3bim2T7edwLVq0iG9961txyimnRKNGjWLWrFnx4osvxqJFi+KQQw6J4cOHV+p5bEWdt0F1VpV+01enWHe3/6Gs9uiDyP73mT4IfRD6IPRBpOmD2Lx5c9x3331xzz33RETEO++8U6P6HyL0QeiD0AehD6Jq90GUds7WuXPn2H///ePZZ5+Nm2++OVasWJHpgzjyyCNjyJAh1bP/oVzj32uJ7beq+eJ0zjnnJJ988klyxhlnJO3atUvq16+f7Lvvvslpp52WPP/885n3r1y5Mhk8eHCyzz77JPXq1Uv233//5Kyzzkpef/31YuvZunVrMnHixGSfffZJ9tprr+T444/f4XYI1THen/3sZ0lE7HCLkDTrKa0t228RUtL01FNP7VJbzj777KRFixZJ/fr1k8MPPzyZNWtWidv24osvLnF9w4YNSwYMGFBmPEmSJG+88UYydOjQZJ999kkaNWpU4vrOOeecMpdT2rZNks9u+/H597Zu3To5+eSTi91SZd26dcmQIUOSJk2aJPvss09y9dVXJyNHjszcyuPVV19NTjzxxKR169ZJXl5ectBBByW333575v0nnnhiqfvakCFDkr322iszPy8vLxk4cGDy8ssv79L/Z3f3tZ3tRxGRrFixosT/dUlK2x+T5LPbkpRUPmnSpMwyJk2aVGKd++67r9i6vvWtbyX9+vVLHVt57G57yrNd09waKVvt2e7GG29M9t9//6RRo0ZJ3759d7j1184+n5W9vs8fDz744IOkTp06yTe/+c3MvEcffTSJiGTGjBnFlldR+2ma4+axxx6bNGvWLGnQoEHSrVu33d5eaY6fFfVduqtmz56ddO7cOalfv37Stm3b5IILLkg++OCDTPmtt96a7L///km9evWSDh06JFdffXWyZcuWUpcZke6Wq7urIj47Zf1vdraMiIo/Ba2I9lx++eVJmzZtknr16iVdu3ZNpk6dmhQVFRVbT9pzh4pW1r5WVFSUTJo0KWnbtm2Sl5eXHHPMMcmyZcsy5fn5+ckxxxyTif3AAw9MLrroomTdunVJklTesfSFF15IBg0alLRo0SLZa6+9kq985SvJnDlzitXZvHlzMnny5KRbt25Jw4YNk44dOybf+c53it3CPElKvtVZae24+uqrd7o/lvbdNmTIkOT1119PNm7cmIwbNy5p165dkpOTk+Tm5ia5ublJt27ddrofZPuc7Zxzzkn69++/022y/daeZX3fVMZ5bEWdt0F1VtV+01enWHen/6Gs9uiDyP73mT6IXT8v0wehD6I29kHUq1dvt7dVVe5/SBJ9EPog9EHog6jafRClnbOVdk79y1/+MrOM6tb/kJMkSRIAAAAAAAAAQJmq/gMpAAAAAAAAAKCKkGQHAAAAAAAAgJQk2QEAAAAAAAAgJUl2AAAAAAAAAEhJkh0AAAAAAAAAUpJkBwAAAAAAAICUJNkBAAAAAAAAICVJdgBgp2bOnBnNmzcvtc61114bRxxxRKXEAwAAANRM+iAAqE4k2QGAnRoxYkS88cYb2Q4DAAAAqOH0QQBQndTNdgAAQNXVsGHDaNiwYbbDAAAAAGo4fRAAVCdGsgNABRo4cGCMGzcuxo8fH3vvvXe0adMm7r777ti0aVOce+65sddee8WBBx4Yf/rTnzLvWbBgQXz5y1+OvLy82HfffeOKK66Ibdu2ZcoPOOCAmD59erH1HHHEEXHttddmXl977bXRoUOHyMvLi3bt2sVFF12UKdu6dWtcdtllsd9++0Xjxo3jqKOOivnz56dqT0m3arvpppuiTZs2sddee8WoUaNi8+bNqbcPAAAAUDH0QQBA9kiyA0AF+9WvfhWtWrWK559/PsaNGxff//73Y9iwYdGvX7946aWX4sQTT4yzzz47Pv7441i5cmWcfPLJceSRR8bf//73uPPOO+OXv/xlXHfddanX9/DDD8ctt9wSd911Vyxfvjx+//vfR48ePTLl5557bixatCgeeuihePnll2PYsGFx0kknxfLly3e5bb/5zW9i0qRJcf3118eLL74Y++67b9xxxx27vBwAAABg9+mDAIDsyEmSJMl2EABQUwwcODAKCwtj4cKFERFRWFgYzZo1i6FDh8asWbMiImL16tWx7777xnPPPRePPfZY/O53v4vXXnstcnJyIiLijjvuiMsvvzw2bNgQubm5ccABB8T48eNj/PjxmfUcccQRcfrpp8e1114b06ZNi7vuuiv+8Y9/RL169YrF89Zbb0XXrl3j3//+d7Rr1y4z//jjj48vf/nLccMNN5TanpkzZ8b48ePjgw8+iIiIfv36Rc+ePePOO+/M1PnKV74SmzdvjqVLl5Z3swEAAAC7SB8EAGSPkewAUMEOP/zwzN916tSJli1bFruqu02bNhERsWbNmnjttdeib9++mR+3ERH9+/ePjz76KP7973+nWt+wYcPik08+ic6dO8d5550Xjz76aOZWby+99FIkSRIHHXRQNGnSJDMtWLAg3nrrrV1u2/Z4P++LrwEAAIDKoQ8CALKjbrYDAICa5otXcufk5BSbt/3HbFFRUSRJUuzHbUTE9pvMbJ+fm5sbX7zxzKeffpr5u3379vHPf/4z5s2bF3/5y19i7NixMWXKlFiwYEEUFRVFnTp1YvHixVGnTp1iy2jSpMluthQAAADIJn0QAJAdRrIDQBYdeuih8eyzzxb7Afvss8/GXnvtFfvtt19ERLRu3ToKCgoy5Rs3bowVK1YUW07Dhg3jtNNOi9tuuy3mz58fzz33XCxbtix69eoVhYWFsWbNmujSpUuxqW3btrscb7du3eKvf/1rsXlffA0AAABUPfogAKDiGMkOAFk0duzYmD59eowbNy4uvPDC+Oc//xmTJk2KCRMmRG7uZ9fCfe1rX4uZM2fGqaeeGnv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- "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "fig, _axs = plt.subplots(2, 2, figsize=(25, 20))\n", - "axs = _axs.flatten()\n", - "for i, metric in enumerate([\"mean_eucl\", \"select_eucl\", \"mean_lin\", \"select_lin\"]):\n", - " sns.barplot(hue=\"windowMS\", y=metric, x=\"mouse_id\", data=results_df, ax=axs[i])" - ] - }, - { - "cell_type": "markdown", - "id": "f4d0891c-65c0-495b-aecd-455d05d435af", - "metadata": {}, - "source": [ - "## Let's focus on M994 and 12390 (good results, PAG & MFB, several windowMS)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "680c728b-aae3-49f6-95aa-f49185bf2c98", - "metadata": {}, - "outputs": [], - "source": [ - "selected_mice = [\"994\", \"1239v3\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f4708b59-56ae-414d-9abd-8245e18eb923", - "metadata": {}, - "outputs": [], - "source": [ - "subresults_df = results_df[results_df[\"mouse_id\"].isin(selected_mice)]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2aa6b616-e4d2-452c-a539-3b3fa7cd0df6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "subresults_df.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b05c73d6-6628-48ea-9459-17d13e8db690", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface as si\n", - "import spikeinterface.extractors as se\n", - "import spikeinterface.preprocessing as spre\n", - "import spikeinterface.postprocessing as spost\n", - "import spikeinterface.curation as scur\n", - "import spikeinterface.widgets as sw\n", - "import spikeinterface.qualitymetrics" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "a56c0381-84e6-45a9-a42e-6f6695c6780f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1239vBasile': '/home/vador/Documents/Theotime/DimaERC2/M1239TEST3_Basile_M1239/TEST/results',\n", - " '1281vBasile': '/home/vador/Documents/Theotime/DimaERC2/M1281TEST3_Basile_1281MFB/TEST/results',\n", - " '1199': '/home/vador/Documents/Theotime/DimaERC2/M1199TEST1_Basile/TEST/results',\n", - " '1336': '/home/vador/Documents/Theotime/DimaERC2/M1336_Known/TEST/results',\n", - " '905': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M905/TEST/results',\n", - " '1161w1199': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST_with_1199_model/results',\n", - " '1161': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1161/TEST initial/results',\n", - " '1124': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1124/TEST/results',\n", - " '1186': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1186/TEST/results',\n", - " '1182': '/home/vador/Documents/Theotime/DimaERC2/DataERC2/M1182/TEST/results',\n", - " '1168UMaze': '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1168/TEST/results',\n", - " '1117': '/home/vador/Documents/Theotime/DimaERC2/DataERC1/M1117/TEST/results',\n", - " '994': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M994_PAG/Final_results_v3/results',\n", - " '1336v3': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_MFB/Final_results_v3/results',\n", - " '1336v2': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1336_known/Final_results_v2/results',\n", - " '1281v2': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1281_MFB/Final_results_v2/results',\n", - " '1239v3': '/home/vador/Documents/Theotime/DimaERC2/neuroencoders_1021/_work/M1239_MFB/Final_results_v3/results'}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dirmouse" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d962ece2-a424-4b93-b97c-0418586649a8", - "metadata": {}, - "outputs": [], - "source": [ - "sorting_folder = dict()\n", - "from pathlib import Path\n", - "\n", - "for mouse in selected_mice:\n", - " sorting_folder[mouse] = (\n", - " Path(\"/home/vador/Documents/Theotime/HDDocs\") / f\"tmp_spikesorting/{mouse}\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "e8d1b473-da68-4178-b0bc-51061a468460", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'994': PosixPath('/home/vador/Documents/Theotime/HDDocs/tmp_spikesorting/994'),\n", - " '1239v3': PosixPath('/home/vador/Documents/Theotime/HDDocs/tmp_spikesorting/1239v3')}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorting_folder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab6f360f-5ec5-4292-8b19-b9aa2855d783", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/arsenii spikesorting-small.ipynb b/notebooks/arsenii spikesorting-small.ipynb deleted file mode 100644 index 8737591..0000000 --- a/notebooks/arsenii spikesorting-small.ipynb +++ /dev/null @@ -1,1779 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b038525f-d358-4c93-b14d-1f6d7788f7c0", - "metadata": {}, - "source": [ - "# Neurophysiology data visualization - alternatives à Neuroscope ?" - ] - }, - { - "cell_type": "markdown", - "id": "1b8d24e4-aa75-4853-a07c-07d33c2d65ef", - "metadata": {}, - "source": [ - "## Importation du viewer - soit comme package python soit comme application" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c4b55d65-89b6-4f58-a4fe-48ab8f60d76e", - "metadata": {}, - "outputs": [], - "source": [ - "import ephyviewer\n", - "import numpy as np\n", - "from pathlib import Path\n", - "\n", - "import spikeinterface as si\n", - "import spikeinterface.extractors as se\n", - "\n", - "import spikeinterface.preprocessing as spre\n", - "import spikeinterface.postprocessing as spost\n", - "import spikeinterface.curation as scur\n", - "import spikeinterface.widgets as sw\n", - "import spikeinterface.qualitymetrics\n", - "import os\n", - "\n", - "si.set_global_job_kwargs(n_jobs=-1, progress_bar=True)" - ] - }, - { - "cell_type": "markdown", - "id": "7bb30441-d219-4ec1-abe6-5d99b39939ac", - "metadata": {}, - "source": [ - "## Visualisation d'un enregistrement du nas 5" - ] - }, - { - "cell_type": "markdown", - "id": "d63c2fc4-c47c-4cb0-b3f0-45496170c3eb", - "metadata": {}, - "source": [ - "base_folder = Path(\"/media/nas7/React_Passive_AG/OBG/Shropshire/freely-moving/20241121_puretones\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5ef0eb05-e73b-439a-b4cf-5d6a81f274e0", - "metadata": {}, - "outputs": [], - "source": [ - "base_folder = Path(\"/home/mickey/download/20241121_puretones\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "247dfd5f-686a-47b8-9f73-aa4948d6860c", - "metadata": {}, - "outputs": [], - "source": [ - "# conditionnement aversif\n", - "recording = se.NeuroScopeRecordingExtractor(\n", - " os.path.join(base_folder, \"M4_20241121_Shropshire_FM_puretones.dat\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5d9d345f-1ed3-4772-b11e-7e5eabbb5e1a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      NeuroScopeRecordingExtractor: 113 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 4.05 GiB
      Channel IDs
        ['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'\n", - " '16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'\n", - " '30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'\n", - " '44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'\n", - " '58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'\n", - " '72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'\n", - " '86' '87' '88' '89' '90' '91' '92' '93' '94' '95' '96' '97' '98' '99'\n", - " '100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '110' '111'\n", - " '112']
      Annotations
      • is_filtered : False
      Channel Properties
        gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578]
        offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
        channel_names ['ch0grp0' 'ch1grp0' 'ch2grp0' 'ch3grp0' 'ch4grp0' 'ch5grp0' 'ch6grp0'\n", - " 'ch7grp0' 'ch8grp0' 'ch9grp0' 'ch10grp0' 'ch11grp0' 'ch12grp0' 'ch13grp0'\n", - " 'ch14grp0' 'ch15grp0' 'ch16grp0' 'ch17grp0' 'ch18grp0' 'ch19grp0'\n", - " 'ch20grp0' 'ch21grp0' 'ch22grp0' 'ch23grp0' 'ch24grp0' 'ch25grp0'\n", - " 'ch26grp0' 'ch27grp0' 'ch28grp0' 'ch29grp0' 'ch30grp0' 'ch31grp0'\n", - " 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0' 'ch37grp0'\n", - " 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0' 'ch43grp0'\n", - " 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0' 'ch49grp0'\n", - " 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0' 'ch55grp0'\n", - " 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0' 'ch61grp0'\n", - " 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0' 'ch67grp0'\n", - " 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0' 'ch73grp0'\n", - " 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0' 'ch79grp0'\n", - " 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0' 'ch85grp0'\n", - " 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0' 'ch91grp0'\n", - " 'ch92grp0' 'ch93grp0' 'ch94grp0' 'ch95grp0' 'ch96grp0' 'ch97grp0'\n", - " 'ch98grp0' 'ch99grp0' 'ch100grp0' 'ch101grp0' 'ch102grp0' 'ch103grp0'\n", - " 'ch104grp0' 'ch105grp0' 'ch106grp0' 'ch107grp0' 'ch108grp0' 'ch109grp0'\n", - " 'ch110grp0' 'ch111grp0' 'ch112grp0']
        group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0]
      " - ], - "text/plain": [ - "NeuroScopeRecordingExtractor: 113 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 4.05 GiB\n", - " file_path: /home/mickey/download/20241121_puretones/M4_20241121_Shropshire_FM_puretones.dat" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Objet Python de Classe NeuroScopeRecordingExtractor\n", - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0fd2a203-386d-4aed-9f86-71bbe3a489ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording.get_channel_groups()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "371edf41-1530-4a2e-8143-ce1a81462d28", - "metadata": {}, - "outputs": [], - "source": [ - "channel_sliced_recording = recording.select_channels(\n", - " channel_ids=[str(nb) for nb in range(31, 95)]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "101a544b-ae96-4aeb-a644-6af6809fbe4b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Hérite automatiquement des propriétés neuroscope comme les groupes de channel (HPC, OB, bruit...)\n", - "channel_sliced_recording.get_num_channels()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4635a6c8-a652-4de6-9b9b-3ce70240d408", - "metadata": {}, - "outputs": [], - "source": [ - "recording = channel_sliced_recording" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "8d3f7c56-ca77-42a6-abe9-55a30d906f0d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB
      Channel IDs
        ['31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43' '44'\n", - " '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57' '58'\n", - " '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71' '72'\n", - " '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94']
      Annotations
      • is_filtered : False
      Channel Properties
        gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578]
        offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
        channel_names ['ch31grp0' 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0'\n", - " 'ch37grp0' 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0'\n", - " 'ch43grp0' 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0'\n", - " 'ch49grp0' 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0'\n", - " 'ch55grp0' 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0'\n", - " 'ch61grp0' 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0'\n", - " 'ch67grp0' 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0'\n", - " 'ch73grp0' 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0'\n", - " 'ch79grp0' 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0'\n", - " 'ch85grp0' 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0'\n", - " 'ch91grp0' 'ch92grp0' 'ch93grp0' 'ch94grp0']
        group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      " - ], - "text/plain": [ - "ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a670d4da-b72c-4fd2-90fb-508a2d1594f6", - "metadata": {}, - "outputs": [], - "source": [ - "# ajout dummy probe pour la visualisation\n", - "from probeinterface import generate_linear_probe\n", - "\n", - "num_elec = recording.get_num_channels()\n", - "probe = generate_linear_probe(\n", - " num_elec=num_elec,\n", - " ypitch=20,\n", - " contact_shapes=\"circle\",\n", - " contact_shape_params={\"radius\": 6},\n", - ")\n", - "probe.set_device_channel_indices(np.arange(num_elec))\n", - "recording = recording.set_probe(probe)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c564d712-756e-4062-b666-c8ad4817a9c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41',\n", - " '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52',\n", - " '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63',\n", - " '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74',\n", - " '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85',\n", - " '86', '87', '88', '89', '90', '91', '92', '93', '94'], dtype='ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB
      Channel IDs
        ['31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43' '44'\n", - " '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57' '58'\n", - " '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71' '72'\n", - " '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94']
      Annotations
      • is_filtered : False
      • name : None
      • probe_0_planar_contour : [[ -25. 1285.]\n", - " [ -25. -25.]\n", - " [ 0. -125.]\n", - " [ 25. -25.]\n", - " [ 25. 1285.]]
      Channel Properties
        gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578]
        offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
        channel_names ['ch31grp0' 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0'\n", - " 'ch37grp0' 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0'\n", - " 'ch43grp0' 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0'\n", - " 'ch49grp0' 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0'\n", - " 'ch55grp0' 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0'\n", - " 'ch61grp0' 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0'\n", - " 'ch67grp0' 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0'\n", - " 'ch73grp0' 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0'\n", - " 'ch79grp0' 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0'\n", - " 'ch85grp0' 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0'\n", - " 'ch91grp0' 'ch92grp0' 'ch93grp0' 'ch94grp0']
        group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
        contact_vector [(0, 0., 0., 'circle', 6., '', '0', 0, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 20., 'circle', 6., '', '1', 1, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 40., 'circle', 6., '', '2', 2, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 60., 'circle', 6., '', '3', 3, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 80., 'circle', 6., '', '4', 4, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 100., 'circle', 6., '', '5', 5, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 120., 'circle', 6., '', '6', 6, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 140., 'circle', 6., '', '7', 7, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 160., 'circle', 6., '', '8', 8, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 180., 'circle', 6., '', '9', 9, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 200., 'circle', 6., '', '10', 10, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 220., 'circle', 6., '', '11', 11, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 240., 'circle', 6., '', '12', 12, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 260., 'circle', 6., '', '13', 13, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 280., 'circle', 6., '', '14', 14, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 300., 'circle', 6., '', '15', 15, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 320., 'circle', 6., '', '16', 16, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 340., 'circle', 6., '', '17', 17, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 360., 'circle', 6., '', '18', 18, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 380., 'circle', 6., '', '19', 19, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 400., 'circle', 6., '', '20', 20, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 420., 'circle', 6., '', '21', 21, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 440., 'circle', 6., '', '22', 22, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 460., 'circle', 6., '', '23', 23, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 480., 'circle', 6., '', '24', 24, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 500., 'circle', 6., '', '25', 25, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 520., 'circle', 6., '', '26', 26, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 540., 'circle', 6., '', '27', 27, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 560., 'circle', 6., '', '28', 28, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 580., 'circle', 6., '', '29', 29, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 600., 'circle', 6., '', '30', 30, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 620., 'circle', 6., '', '31', 31, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 640., 'circle', 6., '', '32', 32, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 660., 'circle', 6., '', '33', 33, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 680., 'circle', 6., '', '34', 34, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 700., 'circle', 6., '', '35', 35, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 720., 'circle', 6., '', '36', 36, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 740., 'circle', 6., '', '37', 37, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 760., 'circle', 6., '', '38', 38, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 780., 'circle', 6., '', '39', 39, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 800., 'circle', 6., '', '40', 40, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 820., 'circle', 6., '', '41', 41, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 840., 'circle', 6., '', '42', 42, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 860., 'circle', 6., '', '43', 43, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 880., 'circle', 6., '', '44', 44, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 900., 'circle', 6., '', '45', 45, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 920., 'circle', 6., '', '46', 46, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 940., 'circle', 6., '', '47', 47, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 960., 'circle', 6., '', '48', 48, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 980., 'circle', 6., '', '49', 49, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1000., 'circle', 6., '', '50', 50, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1020., 'circle', 6., '', '51', 51, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1040., 'circle', 6., '', '52', 52, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1060., 'circle', 6., '', '53', 53, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1080., 'circle', 6., '', '54', 54, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1100., 'circle', 6., '', '55', 55, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1120., 'circle', 6., '', '56', 56, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1140., 'circle', 6., '', '57', 57, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1160., 'circle', 6., '', '58', 58, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1180., 'circle', 6., '', '59', 59, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1200., 'circle', 6., '', '60', 60, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1220., 'circle', 6., '', '61', 61, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1240., 'circle', 6., '', '62', 62, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1260., 'circle', 6., '', '63', 63, 'um', 1., 0., 0., 1.)]
        location [[ 0. 0.]\n", - " [ 0. 20.]\n", - " [ 0. 40.]\n", - " [ 0. 60.]\n", - " [ 0. 80.]\n", - " [ 0. 100.]\n", - " [ 0. 120.]\n", - " [ 0. 140.]\n", - " [ 0. 160.]\n", - " [ 0. 180.]\n", - " [ 0. 200.]\n", - " [ 0. 220.]\n", - " [ 0. 240.]\n", - " [ 0. 260.]\n", - " [ 0. 280.]\n", - " [ 0. 300.]\n", - " [ 0. 320.]\n", - " [ 0. 340.]\n", - " [ 0. 360.]\n", - " [ 0. 380.]\n", - " [ 0. 400.]\n", - " [ 0. 420.]\n", - " [ 0. 440.]\n", - " [ 0. 460.]\n", - " [ 0. 480.]\n", - " [ 0. 500.]\n", - " [ 0. 520.]\n", - " [ 0. 540.]\n", - " [ 0. 560.]\n", - " [ 0. 580.]\n", - " [ 0. 600.]\n", - " [ 0. 620.]\n", - " [ 0. 640.]\n", - " [ 0. 660.]\n", - " [ 0. 680.]\n", - " [ 0. 700.]\n", - " [ 0. 720.]\n", - " [ 0. 740.]\n", - " [ 0. 760.]\n", - " [ 0. 780.]\n", - " [ 0. 800.]\n", - " [ 0. 820.]\n", - " [ 0. 840.]\n", - " [ 0. 860.]\n", - " [ 0. 880.]\n", - " [ 0. 900.]\n", - " [ 0. 920.]\n", - " [ 0. 940.]\n", - " [ 0. 960.]\n", - " [ 0. 980.]\n", - " [ 0. 1000.]\n", - " [ 0. 1020.]\n", - " [ 0. 1040.]\n", - " [ 0. 1060.]\n", - " [ 0. 1080.]\n", - " [ 0. 1100.]\n", - " [ 0. 1120.]\n", - " [ 0. 1140.]\n", - " [ 0. 1160.]\n", - " [ 0. 1180.]\n", - " [ 0. 1200.]\n", - " [ 0. 1220.]\n", - " [ 0. 1240.]\n", - " [ 0. 1260.]]
      " - ], - "text/plain": [ - "ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7d5ecc31-2939-47c4-a540-8cf7adae32e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41',\n", - " '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52',\n", - " '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63',\n", - " '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74',\n", - " '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85',\n", - " '86', '87', '88', '89', '90', '91', '92', '93', '94'], dtype='ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB
      Channel IDs
        ['31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43' '44'\n", - " '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57' '58'\n", - " '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71' '72'\n", - " '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94']
      Annotations
      • is_filtered : False
      • name : None
      • probe_0_planar_contour : [[ -25. 1285.]\n", - " [ -25. -25.]\n", - " [ 0. -125.]\n", - " [ 25. -25.]\n", - " [ 25. 1285.]]
      Channel Properties
        gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578]
        offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
        channel_names ['ch31grp0' 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0'\n", - " 'ch37grp0' 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0'\n", - " 'ch43grp0' 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0'\n", - " 'ch49grp0' 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0'\n", - " 'ch55grp0' 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0'\n", - " 'ch61grp0' 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0'\n", - " 'ch67grp0' 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0'\n", - " 'ch73grp0' 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0'\n", - " 'ch79grp0' 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0'\n", - " 'ch85grp0' 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0'\n", - " 'ch91grp0' 'ch92grp0' 'ch93grp0' 'ch94grp0']
        group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
        contact_vector [(0, 0., 0., 'circle', 6., '', '0', 0, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 20., 'circle', 6., '', '1', 1, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 40., 'circle', 6., '', '2', 2, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 60., 'circle', 6., '', '3', 3, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 80., 'circle', 6., '', '4', 4, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 100., 'circle', 6., '', '5', 5, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 120., 'circle', 6., '', '6', 6, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 140., 'circle', 6., '', '7', 7, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 160., 'circle', 6., '', '8', 8, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 180., 'circle', 6., '', '9', 9, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 200., 'circle', 6., '', '10', 10, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 220., 'circle', 6., '', '11', 11, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 240., 'circle', 6., '', '12', 12, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 260., 'circle', 6., '', '13', 13, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 280., 'circle', 6., '', '14', 14, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 300., 'circle', 6., '', '15', 15, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 320., 'circle', 6., '', '16', 16, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 340., 'circle', 6., '', '17', 17, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 360., 'circle', 6., '', '18', 18, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 380., 'circle', 6., '', '19', 19, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 400., 'circle', 6., '', '20', 20, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 420., 'circle', 6., '', '21', 21, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 440., 'circle', 6., '', '22', 22, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 460., 'circle', 6., '', '23', 23, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 480., 'circle', 6., '', '24', 24, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 500., 'circle', 6., '', '25', 25, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 520., 'circle', 6., '', '26', 26, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 540., 'circle', 6., '', '27', 27, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 560., 'circle', 6., '', '28', 28, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 580., 'circle', 6., '', '29', 29, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 600., 'circle', 6., '', '30', 30, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 620., 'circle', 6., '', '31', 31, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 640., 'circle', 6., '', '32', 32, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 660., 'circle', 6., '', '33', 33, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 680., 'circle', 6., '', '34', 34, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 700., 'circle', 6., '', '35', 35, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 720., 'circle', 6., '', '36', 36, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 740., 'circle', 6., '', '37', 37, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 760., 'circle', 6., '', '38', 38, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 780., 'circle', 6., '', '39', 39, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 800., 'circle', 6., '', '40', 40, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 820., 'circle', 6., '', '41', 41, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 840., 'circle', 6., '', '42', 42, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 860., 'circle', 6., '', '43', 43, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 880., 'circle', 6., '', '44', 44, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 900., 'circle', 6., '', '45', 45, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 920., 'circle', 6., '', '46', 46, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 940., 'circle', 6., '', '47', 47, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 960., 'circle', 6., '', '48', 48, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 980., 'circle', 6., '', '49', 49, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1000., 'circle', 6., '', '50', 50, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1020., 'circle', 6., '', '51', 51, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1040., 'circle', 6., '', '52', 52, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1060., 'circle', 6., '', '53', 53, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1080., 'circle', 6., '', '54', 54, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1100., 'circle', 6., '', '55', 55, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1120., 'circle', 6., '', '56', 56, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1140., 'circle', 6., '', '57', 57, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1160., 'circle', 6., '', '58', 58, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1180., 'circle', 6., '', '59', 59, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1200., 'circle', 6., '', '60', 60, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1220., 'circle', 6., '', '61', 61, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1240., 'circle', 6., '', '62', 62, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1260., 'circle', 6., '', '63', 63, 'um', 1., 0., 0., 1.)]
        location [[ 0. 0.]\n", - " [ 0. 20.]\n", - " [ 0. 40.]\n", - " [ 0. 60.]\n", - " [ 0. 80.]\n", - " [ 0. 100.]\n", - " [ 0. 120.]\n", - " [ 0. 140.]\n", - " [ 0. 160.]\n", - " [ 0. 180.]\n", - " [ 0. 200.]\n", - " [ 0. 220.]\n", - " [ 0. 240.]\n", - " [ 0. 260.]\n", - " [ 0. 280.]\n", - " [ 0. 300.]\n", - " [ 0. 320.]\n", - " [ 0. 340.]\n", - " [ 0. 360.]\n", - " [ 0. 380.]\n", - " [ 0. 400.]\n", - " [ 0. 420.]\n", - " [ 0. 440.]\n", - " [ 0. 460.]\n", - " [ 0. 480.]\n", - " [ 0. 500.]\n", - " [ 0. 520.]\n", - " [ 0. 540.]\n", - " [ 0. 560.]\n", - " [ 0. 580.]\n", - " [ 0. 600.]\n", - " [ 0. 620.]\n", - " [ 0. 640.]\n", - " [ 0. 660.]\n", - " [ 0. 680.]\n", - " [ 0. 700.]\n", - " [ 0. 720.]\n", - " [ 0. 740.]\n", - " [ 0. 760.]\n", - " [ 0. 780.]\n", - " [ 0. 800.]\n", - " [ 0. 820.]\n", - " [ 0. 840.]\n", - " [ 0. 860.]\n", - " [ 0. 880.]\n", - " [ 0. 900.]\n", - " [ 0. 920.]\n", - " [ 0. 940.]\n", - " [ 0. 960.]\n", - " [ 0. 980.]\n", - " [ 0. 1000.]\n", - " [ 0. 1020.]\n", - " [ 0. 1040.]\n", - " [ 0. 1060.]\n", - " [ 0. 1080.]\n", - " [ 0. 1100.]\n", - " [ 0. 1120.]\n", - " [ 0. 1140.]\n", - " [ 0. 1160.]\n", - " [ 0. 1180.]\n", - " [ 0. 1200.]\n", - " [ 0. 1220.]\n", - " [ 0. 1240.]\n", - " [ 0. 1260.]]
      " - ], - "text/plain": [ - "ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "649deb62-920e-4ad4-942b-08bbd6e73222", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface.sorters as ss" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "aeec9b8c-1568-4c7f-a26f-52e1c5241055", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spykingcircus2 /home/mickey/download/20241121_puretones/spykingcircus2_output\n" - ] - } - ], - "source": [ - "# run sorter (if not already done)\n", - "sortings = {}\n", - "for sorter_name in sorter_names:\n", - " if sorter_name == \"kilosort2_5\":\n", - " continue\n", - " output_folder = base_folder / f\"{sorter_name}_output\"\n", - " print(sorter_name, output_folder)\n", - " if output_folder.exists():\n", - " sortings[sorter_name] = sf.read_sorter_folder(output_folder)\n", - " else:\n", - " sortings[sorter_name] = sf.run_sorter(\n", - " sorter_name=sorter_name,\n", - " recording=recording,\n", - " output_folder=output_folder,\n", - " verbose=True,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "b9af05e0-fd9e-4e92-ab3b-cc078391001e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'spykingcircus2': NumpyFolder: 82 units - 1 segments - 30.0kHz}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sortings" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "36e160e7-faf5-4dad-aeff-f4b756d1eb4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spykingcircus2\n" - ] - }, - { - "data": { - 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for sorter_name, sort in sortings.items():\n", - " print(sorter_name)\n", - " sf.plot_rasters(sort, time_range=(50.0, 70.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5df5867d-9151-4f95-b0da-7b14cf8075fd", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface.comparison as sc" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "9f9fcdc3-61df-44b6-8126-18bf13b7a133", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['spykingcircus2']" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorter_names" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "fc74cda6-f5b4-4894-99a8-dbab2fee5d7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[NumpyFolder: 82 units - 1 segments - 30.0kHz]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(sortings.values())" - ] - }, - { - "cell_type": "markdown", - "id": "4ccba182-2be2-4f39-8745-0b17d46c5b5f", - "metadata": { - "scrolled": true - }, - "source": [ - "multi_comp = sc.compare_multiple_sorters(list(sortings.values()), sorter_names)\n", - "sw.plot_multicomparison_agreement(multi_comp)\n", - "sw.plot_multicomparison_agreement_by_sorter(multi_comp)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "0400de8d-d463-4682-917a-44d8fdb69137", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'multi_comp' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m sw\u001b[38;5;241m.\u001b[39mplot_multicomparison_agreement(\u001b[43mmulti_comp\u001b[49m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'multi_comp' is not defined" - ] - } - ], - "source": [ - "sw.plot_multicomparison_agreement(multi_comp)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "93ba617d-117b-4597-b251-8c123871681d", - "metadata": {}, - "outputs": [], - "source": [ - "better_multi_comp = sc.compare_multiple_sorters(\n", - " list(sortings.values()), [\"kilosort4\", \"spykingcircus2\", \"klustakwik\"]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "57e99edc-09e5-484f-ad9e-3ab9ab89ffc8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      AgreementSortingExtractor: 273 units - 1 segments - 20.0kHz
      Unit IDs
        [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", - " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", - " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", - " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", - " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", - " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", - " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", - " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n", - " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n", - " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n", - " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n", - " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n", - " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n", - " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n", - " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n", - " 270 271 272]
      Annotations
        Unit Properties
          agreement_number[1 1 1 1 1 1 2 2 1 2 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
          avg_agreement[0. 0. 0. 0. 0. 0.\n", - " 0.78670788 0.57733104 0. 0.51971569 0.81101318 0.57097362\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0.5088682 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0.65582611 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0.74130149 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. ]
          unit_ids[OrderedDict([('kilosort4', 0)]) OrderedDict([('kilosort4', 1)])\n", - " OrderedDict([('kilosort4', 2)]) OrderedDict([('kilosort4', 3)])\n", - " OrderedDict([('kilosort4', 4)]) OrderedDict([('kilosort4', 5)])\n", - " OrderedDict([('kilosort4', 6), ('mountainsort5', 24.0)])\n", - " OrderedDict([('kilosort4', 7), ('mountainsort5', 38.0)])\n", - " OrderedDict([('kilosort4', 8)])\n", - " OrderedDict([('kilosort4', 9), ('spykingcircus2', 64.0)])\n", - " OrderedDict([('kilosort4', 10), ('spykingcircus2', 66.0), ('mountainsort5', 34.0)])\n", - " OrderedDict([('kilosort4', 11), ('spykingcircus2', 3.0)])\n", - " OrderedDict([('kilosort4', 12)]) OrderedDict([('kilosort4', 13)])\n", - " OrderedDict([('kilosort4', 14)]) OrderedDict([('kilosort4', 15)])\n", - " OrderedDict([('kilosort4', 16)]) OrderedDict([('kilosort4', 17)])\n", - " OrderedDict([('kilosort4', 18)]) OrderedDict([('kilosort4', 19)])\n", - " OrderedDict([('kilosort4', 20)]) OrderedDict([('kilosort4', 21)])\n", - " OrderedDict([('kilosort4', 22)]) OrderedDict([('kilosort4', 23)])\n", - " OrderedDict([('kilosort4', 24)]) OrderedDict([('kilosort4', 25)])\n", - " OrderedDict([('kilosort4', 26)]) OrderedDict([('kilosort4', 27)])\n", - " OrderedDict([('kilosort4', 28)]) OrderedDict([('kilosort4', 29)])\n", - " OrderedDict([('kilosort4', 30)]) OrderedDict([('kilosort4', 31)])\n", - " OrderedDict([('kilosort4', 32)]) OrderedDict([('kilosort4', 33)])\n", - " OrderedDict([('kilosort4', 34)]) OrderedDict([('kilosort4', 35)])\n", - " OrderedDict([('kilosort4', 36), ('mountainsort5', 22.0)])\n", - " OrderedDict([('kilosort4', 37)]) OrderedDict([('kilosort4', 38)])\n", - " OrderedDict([('kilosort4', 39)]) OrderedDict([('kilosort4', 40)])\n", - " OrderedDict([('kilosort4', 41)]) OrderedDict([('kilosort4', 42)])\n", - " OrderedDict([('kilosort4', 43)]) OrderedDict([('kilosort4', 44)])\n", - " OrderedDict([('kilosort4', 45)]) OrderedDict([('kilosort4', 46)])\n", - " OrderedDict([('kilosort4', 47)]) OrderedDict([('kilosort4', 48)])\n", - " OrderedDict([('kilosort4', 49)]) OrderedDict([('kilosort4', 50)])\n", - " OrderedDict([('kilosort4', 51)]) OrderedDict([('kilosort4', 52)])\n", - " OrderedDict([('kilosort4', 53)]) OrderedDict([('kilosort4', 54)])\n", - " OrderedDict([('kilosort4', 55), ('spykingcircus2', 69.0), ('mountainsort5', 57.0)])\n", - " OrderedDict([('kilosort4', 56)]) OrderedDict([('kilosort4', 57)])\n", - " OrderedDict([('kilosort4', 58)]) OrderedDict([('kilosort4', 59)])\n", - " OrderedDict([('kilosort4', 60)]) OrderedDict([('kilosort4', 61)])\n", - " OrderedDict([('kilosort4', 62)]) OrderedDict([('kilosort4', 63)])\n", - " OrderedDict([('kilosort4', 64)]) OrderedDict([('kilosort4', 65)])\n", - " OrderedDict([('kilosort4', 66)]) OrderedDict([('kilosort4', 67)])\n", - " OrderedDict([('kilosort4', 68)]) OrderedDict([('kilosort4', 69)])\n", - " OrderedDict([('kilosort4', 70)]) OrderedDict([('kilosort4', 71)])\n", - " OrderedDict([('kilosort4', 72)]) OrderedDict([('kilosort4', 73)])\n", - " OrderedDict([('kilosort4', 74)]) OrderedDict([('kilosort4', 75)])\n", - " OrderedDict([('kilosort4', 76)]) OrderedDict([('kilosort4', 77)])\n", - " OrderedDict([('kilosort4', 78)]) OrderedDict([('kilosort4', 79)])\n", - " OrderedDict([('kilosort4', 80)]) OrderedDict([('kilosort4', 81)])\n", - " OrderedDict([('kilosort4', 82), ('spykingcircus2', 27.0), ('mountainsort5', 11.0)])\n", - " OrderedDict([('kilosort4', 83)]) OrderedDict([('kilosort4', 84)])\n", - " OrderedDict([('kilosort4', 85)]) OrderedDict([('kilosort4', 86)])\n", - " OrderedDict([('kilosort4', 87)]) OrderedDict([('kilosort4', 88)])\n", - " OrderedDict([('kilosort4', 89)]) OrderedDict([('kilosort4', 90)])\n", - " OrderedDict([('kilosort4', 91)]) OrderedDict([('kilosort4', 92)])\n", - " OrderedDict([('kilosort4', 93)]) OrderedDict([('kilosort4', 94)])\n", - " OrderedDict([('kilosort4', 95)]) OrderedDict([('kilosort4', 96)])\n", - " OrderedDict([('kilosort4', 97)]) OrderedDict([('kilosort4', 98)])\n", - " OrderedDict([('kilosort4', 99)]) OrderedDict([('kilosort4', 100)])\n", - " OrderedDict([('kilosort4', 101)]) OrderedDict([('kilosort4', 102)])\n", - " OrderedDict([('kilosort4', 103)]) OrderedDict([('kilosort4', 104)])\n", - " OrderedDict([('kilosort4', 105)]) OrderedDict([('kilosort4', 106)])\n", - " OrderedDict([('kilosort4', 107)]) OrderedDict([('kilosort4', 108)])\n", - " OrderedDict([('kilosort4', 109)]) OrderedDict([('kilosort4', 110)])\n", - " OrderedDict([('kilosort4', 111)]) OrderedDict([('kilosort4', 112)])\n", - " OrderedDict([('kilosort4', 113)]) OrderedDict([('kilosort4', 114)])\n", - " OrderedDict([('kilosort4', 115)]) OrderedDict([('kilosort4', 116)])\n", - " OrderedDict([('kilosort4', 117)]) OrderedDict([('kilosort4', 118)])\n", - " OrderedDict([('kilosort4', 119)]) OrderedDict([('kilosort4', 120)])\n", - " OrderedDict([('kilosort4', 121)]) OrderedDict([('kilosort4', 122)])\n", - " OrderedDict([('kilosort4', 123)]) OrderedDict([('kilosort4', 124)])\n", - " OrderedDict([('kilosort4', 125)]) OrderedDict([('kilosort4', 126)])\n", - " OrderedDict([('kilosort4', 127)]) OrderedDict([('kilosort4', 128)])\n", - " OrderedDict([('kilosort4', 129)]) OrderedDict([('kilosort4', 130)])\n", - " OrderedDict([('kilosort4', 131)]) OrderedDict([('kilosort4', 132)])\n", - " OrderedDict([('kilosort4', 133)]) OrderedDict([('kilosort4', 134)])\n", - " OrderedDict([('kilosort4', 135)]) OrderedDict([('kilosort4', 136)])\n", - " OrderedDict([('kilosort4', 137)]) OrderedDict([('kilosort4', 138)])\n", - " OrderedDict([('kilosort4', 139)]) OrderedDict([('kilosort4', 140)])\n", - " OrderedDict([('kilosort4', 141)]) OrderedDict([('kilosort4', 142)])\n", - " OrderedDict([('kilosort4', 143)]) OrderedDict([('kilosort4', 144)])\n", - " OrderedDict([('kilosort4', 145)]) OrderedDict([('kilosort4', 146)])\n", - " OrderedDict([('kilosort4', 147)]) OrderedDict([('kilosort4', 148)])\n", - " OrderedDict([('kilosort4', 149)]) OrderedDict([('kilosort4', 150)])\n", - " OrderedDict([('kilosort4', 151)]) OrderedDict([('kilosort4', 152)])\n", - " OrderedDict([('kilosort4', 153)]) OrderedDict([('spykingcircus2', 0)])\n", - " OrderedDict([('spykingcircus2', 1)]) OrderedDict([('spykingcircus2', 2)])\n", - " OrderedDict([('spykingcircus2', 4)]) OrderedDict([('spykingcircus2', 5)])\n", - " OrderedDict([('spykingcircus2', 6)]) OrderedDict([('spykingcircus2', 7)])\n", - " OrderedDict([('spykingcircus2', 8)]) OrderedDict([('spykingcircus2', 9)])\n", - " OrderedDict([('spykingcircus2', 10)])\n", - " OrderedDict([('spykingcircus2', 11)])\n", - " OrderedDict([('spykingcircus2', 12)])\n", - " OrderedDict([('spykingcircus2', 13)])\n", - " OrderedDict([('spykingcircus2', 14)])\n", - " OrderedDict([('spykingcircus2', 15)])\n", - " OrderedDict([('spykingcircus2', 16)])\n", - " OrderedDict([('spykingcircus2', 17)])\n", - " OrderedDict([('spykingcircus2', 18)])\n", - " OrderedDict([('spykingcircus2', 19)])\n", - " OrderedDict([('spykingcircus2', 20)])\n", - " OrderedDict([('spykingcircus2', 21)])\n", - " OrderedDict([('spykingcircus2', 22)])\n", - " OrderedDict([('spykingcircus2', 23)])\n", - " OrderedDict([('spykingcircus2', 24)])\n", - " OrderedDict([('spykingcircus2', 25)])\n", - " OrderedDict([('spykingcircus2', 26)])\n", - " OrderedDict([('spykingcircus2', 28)])\n", - " OrderedDict([('spykingcircus2', 29)])\n", - " OrderedDict([('spykingcircus2', 30)])\n", - " OrderedDict([('spykingcircus2', 31)])\n", - " OrderedDict([('spykingcircus2', 32)])\n", - " OrderedDict([('spykingcircus2', 33)])\n", - " OrderedDict([('spykingcircus2', 34)])\n", - " OrderedDict([('spykingcircus2', 35)])\n", - " OrderedDict([('spykingcircus2', 36)])\n", - " OrderedDict([('spykingcircus2', 37)])\n", - " OrderedDict([('spykingcircus2', 38)])\n", - " OrderedDict([('spykingcircus2', 39)])\n", - " OrderedDict([('spykingcircus2', 40)])\n", - " OrderedDict([('spykingcircus2', 41)])\n", - " OrderedDict([('spykingcircus2', 42)])\n", - " OrderedDict([('spykingcircus2', 43)])\n", - " OrderedDict([('spykingcircus2', 44)])\n", - " OrderedDict([('spykingcircus2', 45)])\n", - " OrderedDict([('spykingcircus2', 46)])\n", - " OrderedDict([('spykingcircus2', 47)])\n", - " OrderedDict([('spykingcircus2', 48)])\n", - " OrderedDict([('spykingcircus2', 49)])\n", - " OrderedDict([('spykingcircus2', 50)])\n", - " OrderedDict([('spykingcircus2', 51)])\n", - " OrderedDict([('spykingcircus2', 52)])\n", - " OrderedDict([('spykingcircus2', 53)])\n", - " OrderedDict([('spykingcircus2', 54)])\n", - " OrderedDict([('spykingcircus2', 55)])\n", - " OrderedDict([('spykingcircus2', 56)])\n", - " OrderedDict([('spykingcircus2', 57)])\n", - " OrderedDict([('spykingcircus2', 58)])\n", - " OrderedDict([('spykingcircus2', 59)])\n", - " OrderedDict([('spykingcircus2', 60)])\n", - " OrderedDict([('spykingcircus2', 61)])\n", - " OrderedDict([('spykingcircus2', 62)])\n", - " OrderedDict([('spykingcircus2', 63)])\n", - " OrderedDict([('spykingcircus2', 65)])\n", - " OrderedDict([('spykingcircus2', 67)])\n", - " OrderedDict([('spykingcircus2', 68)])\n", - " OrderedDict([('spykingcircus2', 70)])\n", - " OrderedDict([('spykingcircus2', 71)])\n", - " OrderedDict([('spykingcircus2', 72)]) OrderedDict([('mountainsort5', 2)])\n", - " OrderedDict([('mountainsort5', 3)]) OrderedDict([('mountainsort5', 4)])\n", - " OrderedDict([('mountainsort5', 5)]) OrderedDict([('mountainsort5', 6)])\n", - " OrderedDict([('mountainsort5', 7)]) OrderedDict([('mountainsort5', 8)])\n", - " OrderedDict([('mountainsort5', 9)]) OrderedDict([('mountainsort5', 10)])\n", - " OrderedDict([('mountainsort5', 12)]) OrderedDict([('mountainsort5', 13)])\n", - " OrderedDict([('mountainsort5', 14)]) OrderedDict([('mountainsort5', 15)])\n", - " OrderedDict([('mountainsort5', 17)]) OrderedDict([('mountainsort5', 18)])\n", - " OrderedDict([('mountainsort5', 19)]) OrderedDict([('mountainsort5', 20)])\n", - " OrderedDict([('mountainsort5', 21)]) OrderedDict([('mountainsort5', 23)])\n", - " OrderedDict([('mountainsort5', 25)]) OrderedDict([('mountainsort5', 26)])\n", - " OrderedDict([('mountainsort5', 27)]) OrderedDict([('mountainsort5', 28)])\n", - " OrderedDict([('mountainsort5', 29)]) OrderedDict([('mountainsort5', 30)])\n", - " OrderedDict([('mountainsort5', 31)]) OrderedDict([('mountainsort5', 32)])\n", - " OrderedDict([('mountainsort5', 33)]) OrderedDict([('mountainsort5', 35)])\n", - " OrderedDict([('mountainsort5', 36)]) OrderedDict([('mountainsort5', 37)])\n", - " OrderedDict([('mountainsort5', 39)]) OrderedDict([('mountainsort5', 40)])\n", - " OrderedDict([('mountainsort5', 41)]) OrderedDict([('mountainsort5', 42)])\n", - " OrderedDict([('mountainsort5', 43)]) OrderedDict([('mountainsort5', 45)])\n", - " OrderedDict([('mountainsort5', 46)]) OrderedDict([('mountainsort5', 47)])\n", - " OrderedDict([('mountainsort5', 48)]) OrderedDict([('mountainsort5', 49)])\n", - " OrderedDict([('mountainsort5', 50)]) OrderedDict([('mountainsort5', 51)])\n", - " OrderedDict([('mountainsort5', 52)]) OrderedDict([('mountainsort5', 53)])\n", - " OrderedDict([('mountainsort5', 54)]) OrderedDict([('mountainsort5', 55)])\n", - " OrderedDict([('mountainsort5', 56)]) OrderedDict([('mountainsort5', 58)])\n", - " OrderedDict([('mountainsort5', 59)]) OrderedDict([('mountainsort5', 60)])]
        " - ], - "text/plain": [ - "AgreementSortingExtractor: 273 units - 1 segments - 20.0kHz" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_comp.get_agreement_sorting()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8befc51b-4276-49d3-8163-b20ab6617b8d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c48eae3b084a479aa47b4ea9a0a023ae", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "estimate_sparsity: 0%| | 0/641 [00:00NeuroScopeRecordingExtractor: 113 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 4.05 GiB
        Channel IDs
          ['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'\n", - " '16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'\n", - " '30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'\n", - " '44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'\n", - " '58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'\n", - " '72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'\n", - " '86' '87' '88' '89' '90' '91' '92' '93' '94' '95' '96' '97' '98' '99'\n", - " '100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '110' '111'\n", - " '112']
        Annotations
        • is_filtered : False
        Channel Properties
          gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578]
          offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
          channel_names ['ch0grp0' 'ch1grp0' 'ch2grp0' 'ch3grp0' 'ch4grp0' 'ch5grp0' 'ch6grp0'\n", - " 'ch7grp0' 'ch8grp0' 'ch9grp0' 'ch10grp0' 'ch11grp0' 'ch12grp0' 'ch13grp0'\n", - " 'ch14grp0' 'ch15grp0' 'ch16grp0' 'ch17grp0' 'ch18grp0' 'ch19grp0'\n", - " 'ch20grp0' 'ch21grp0' 'ch22grp0' 'ch23grp0' 'ch24grp0' 'ch25grp0'\n", - " 'ch26grp0' 'ch27grp0' 'ch28grp0' 'ch29grp0' 'ch30grp0' 'ch31grp0'\n", - " 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0' 'ch37grp0'\n", - " 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0' 'ch43grp0'\n", - " 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0' 'ch49grp0'\n", - " 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0' 'ch55grp0'\n", - " 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0' 'ch61grp0'\n", - " 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0' 'ch67grp0'\n", - " 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0' 'ch73grp0'\n", - " 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0' 'ch79grp0'\n", - " 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0' 'ch85grp0'\n", - " 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0' 'ch91grp0'\n", - " 'ch92grp0' 'ch93grp0' 'ch94grp0' 'ch95grp0' 'ch96grp0' 'ch97grp0'\n", - " 'ch98grp0' 'ch99grp0' 'ch100grp0' 'ch101grp0' 'ch102grp0' 'ch103grp0'\n", - " 'ch104grp0' 'ch105grp0' 'ch106grp0' 'ch107grp0' 'ch108grp0' 'ch109grp0'\n", - " 'ch110grp0' 'ch111grp0' 'ch112grp0']
          group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0]
        " - ], - "text/plain": [ - "NeuroScopeRecordingExtractor: 113 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 4.05 GiB\n", - " file_path: /home/mickey/download/20241121_puretones/M4_20241121_Shropshire_FM_puretones.dat" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Objet Python de Classe NeuroScopeRecordingExtractor\n", - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0fd2a203-386d-4aed-9f86-71bbe3a489ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording.get_channel_groups()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "371edf41-1530-4a2e-8143-ce1a81462d28", - "metadata": {}, - "outputs": [], - "source": [ - "channel_sliced_recording = recording.select_channels(\n", - " channel_ids=[str(nb) for nb in range(31, 95)]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "101a544b-ae96-4aeb-a644-6af6809fbe4b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Hérite automatiquement des propriétés neuroscope comme les groupes de channel (HPC, OB, bruit...)\n", - "channel_sliced_recording.get_num_channels()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4635a6c8-a652-4de6-9b9b-3ce70240d408", - "metadata": {}, - "outputs": [], - "source": [ - "recording = channel_sliced_recording" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "8d3f7c56-ca77-42a6-abe9-55a30d906f0d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
        ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB
        Channel IDs
          ['31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43' '44'\n", - " '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57' '58'\n", - " '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71' '72'\n", - " '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94']
        Annotations
        • is_filtered : False
        Channel Properties
          gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578]
          offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
          channel_names ['ch31grp0' 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0'\n", - " 'ch37grp0' 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0'\n", - " 'ch43grp0' 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0'\n", - " 'ch49grp0' 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0'\n", - " 'ch55grp0' 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0'\n", - " 'ch61grp0' 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0'\n", - " 'ch67grp0' 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0'\n", - " 'ch73grp0' 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0'\n", - " 'ch79grp0' 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0'\n", - " 'ch85grp0' 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0'\n", - " 'ch91grp0' 'ch92grp0' 'ch93grp0' 'ch94grp0']
          group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
        " - ], - "text/plain": [ - "ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a670d4da-b72c-4fd2-90fb-508a2d1594f6", - "metadata": {}, - "outputs": [], - "source": [ - "# ajout dummy probe pour la visualisation\n", - "from probeinterface import generate_linear_probe\n", - "\n", - "num_elec = recording.get_num_channels()\n", - "probe = generate_linear_probe(\n", - " num_elec=num_elec,\n", - " ypitch=20,\n", - " contact_shapes=\"circle\",\n", - " contact_shape_params={\"radius\": 6},\n", - ")\n", - "probe.set_device_channel_indices(np.arange(num_elec))\n", - "recording = recording.set_probe(probe)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c564d712-756e-4062-b666-c8ad4817a9c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41',\n", - " '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52',\n", - " '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63',\n", - " '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74',\n", - " '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85',\n", - " '86', '87', '88', '89', '90', '91', '92', '93', '94'], dtype='ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB
        Channel IDs
          ['31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43' '44'\n", - " '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57' '58'\n", - " '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71' '72'\n", - " '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94']
        Annotations
        • is_filtered : False
        • name : None
        • probe_0_planar_contour : [[ -25. 1285.]\n", - " [ -25. -25.]\n", - " [ 0. -125.]\n", - " [ 25. -25.]\n", - " [ 25. 1285.]]
        Channel Properties
          gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578]
          offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
          channel_names ['ch31grp0' 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0'\n", - " 'ch37grp0' 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0'\n", - " 'ch43grp0' 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0'\n", - " 'ch49grp0' 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0'\n", - " 'ch55grp0' 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0'\n", - " 'ch61grp0' 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0'\n", - " 'ch67grp0' 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0'\n", - " 'ch73grp0' 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0'\n", - " 'ch79grp0' 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0'\n", - " 'ch85grp0' 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0'\n", - " 'ch91grp0' 'ch92grp0' 'ch93grp0' 'ch94grp0']
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        " - ], - "text/plain": [ - "ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7d5ecc31-2939-47c4-a540-8cf7adae32e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41',\n", - " '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52',\n", - " '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63',\n", - " '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74',\n", - " '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85',\n", - " '86', '87', '88', '89', '90', '91', '92', '93', '94'], dtype='ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples - 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB
        Channel IDs
          ['31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43' '44'\n", - " '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57' '58'\n", - " '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71' '72'\n", - " '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94']
        Annotations
        • is_filtered : False
        • name : None
        • probe_0_planar_contour : [[ -25. 1285.]\n", - " [ -25. -25.]\n", - " [ 0. -125.]\n", - " [ 25. -25.]\n", - " [ 25. 1285.]]
        Channel Properties
          gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578]
          offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
          channel_names ['ch31grp0' 'ch32grp0' 'ch33grp0' 'ch34grp0' 'ch35grp0' 'ch36grp0'\n", - " 'ch37grp0' 'ch38grp0' 'ch39grp0' 'ch40grp0' 'ch41grp0' 'ch42grp0'\n", - " 'ch43grp0' 'ch44grp0' 'ch45grp0' 'ch46grp0' 'ch47grp0' 'ch48grp0'\n", - " 'ch49grp0' 'ch50grp0' 'ch51grp0' 'ch52grp0' 'ch53grp0' 'ch54grp0'\n", - " 'ch55grp0' 'ch56grp0' 'ch57grp0' 'ch58grp0' 'ch59grp0' 'ch60grp0'\n", - " 'ch61grp0' 'ch62grp0' 'ch63grp0' 'ch64grp0' 'ch65grp0' 'ch66grp0'\n", - " 'ch67grp0' 'ch68grp0' 'ch69grp0' 'ch70grp0' 'ch71grp0' 'ch72grp0'\n", - " 'ch73grp0' 'ch74grp0' 'ch75grp0' 'ch76grp0' 'ch77grp0' 'ch78grp0'\n", - " 'ch79grp0' 'ch80grp0' 'ch81grp0' 'ch82grp0' 'ch83grp0' 'ch84grp0'\n", - " 'ch85grp0' 'ch86grp0' 'ch87grp0' 'ch88grp0' 'ch89grp0' 'ch90grp0'\n", - " 'ch91grp0' 'ch92grp0' 'ch93grp0' 'ch94grp0']
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        " - ], - "text/plain": [ - "ChannelSliceRecording: 64 channels - 30.0kHz - 1 segments - 19,223,296 samples \n", - " 640.78s (10.68 minutes) - int16 dtype - 2.29 GiB" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "649deb62-920e-4ad4-942b-08bbd6e73222", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface.sorters as ss" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "b9af05e0-fd9e-4e92-ab3b-cc078391001e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'spykingcircus2': NumpyFolder: 82 units - 1 segments - 30.0kHz}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sortings" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "36e160e7-faf5-4dad-aeff-f4b756d1eb4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spykingcircus2\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for sorter_name, sort in sortings.items():\n", - " print(sorter_name)\n", - " sf.plot_rasters(sort, time_range=(50.0, 70.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "5df5867d-9151-4f95-b0da-7b14cf8075fd", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface.comparison as sc" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "9f9fcdc3-61df-44b6-8126-18bf13b7a133", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['spykingcircus2']" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorter_names" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "fc74cda6-f5b4-4894-99a8-dbab2fee5d7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[NumpyFolder: 82 units - 1 segments - 30.0kHz]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(sortings.values())" - ] - }, - { - "cell_type": "markdown", - "id": "4ccba182-2be2-4f39-8745-0b17d46c5b5f", - "metadata": { - "scrolled": true - }, - "source": [ - "multi_comp = sc.compare_multiple_sorters(list(sortings.values()), sorter_names)\n", - "sw.plot_multicomparison_agreement(multi_comp)\n", - "sw.plot_multicomparison_agreement_by_sorter(multi_comp)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "0400de8d-d463-4682-917a-44d8fdb69137", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5176e5d0c2de40d588c978bc6fd727ae", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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        AgreementSortingExtractor: 273 units - 1 segments - 20.0kHz
        Unit IDs
          [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", - " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", - " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", - " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", - " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", - " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", - " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", - " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n", - " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n", - " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n", - " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n", - " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n", - " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n", - " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n", - " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n", - " 270 271 272]
        Annotations
          Unit Properties
            agreement_number[1 1 1 1 1 1 2 2 1 2 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
            avg_agreement[0. 0. 0. 0. 0. 0.\n", - " 0.78670788 0.57733104 0. 0.51971569 0.81101318 0.57097362\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0.5088682 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0.65582611 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0.74130149 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. ]
            unit_ids[OrderedDict([('kilosort4', 0)]) OrderedDict([('kilosort4', 1)])\n", - " OrderedDict([('kilosort4', 2)]) OrderedDict([('kilosort4', 3)])\n", - " OrderedDict([('kilosort4', 4)]) OrderedDict([('kilosort4', 5)])\n", - " OrderedDict([('kilosort4', 6), ('mountainsort5', 24.0)])\n", - " OrderedDict([('kilosort4', 7), ('mountainsort5', 38.0)])\n", - " OrderedDict([('kilosort4', 8)])\n", - " OrderedDict([('kilosort4', 9), ('spykingcircus2', 64.0)])\n", - " OrderedDict([('kilosort4', 10), ('spykingcircus2', 66.0), ('mountainsort5', 34.0)])\n", - " OrderedDict([('kilosort4', 11), ('spykingcircus2', 3.0)])\n", - " OrderedDict([('kilosort4', 12)]) OrderedDict([('kilosort4', 13)])\n", - " OrderedDict([('kilosort4', 14)]) OrderedDict([('kilosort4', 15)])\n", - " OrderedDict([('kilosort4', 16)]) OrderedDict([('kilosort4', 17)])\n", - " OrderedDict([('kilosort4', 18)]) OrderedDict([('kilosort4', 19)])\n", - " OrderedDict([('kilosort4', 20)]) OrderedDict([('kilosort4', 21)])\n", - " OrderedDict([('kilosort4', 22)]) OrderedDict([('kilosort4', 23)])\n", - " OrderedDict([('kilosort4', 24)]) OrderedDict([('kilosort4', 25)])\n", - " OrderedDict([('kilosort4', 26)]) OrderedDict([('kilosort4', 27)])\n", - " OrderedDict([('kilosort4', 28)]) OrderedDict([('kilosort4', 29)])\n", - " OrderedDict([('kilosort4', 30)]) OrderedDict([('kilosort4', 31)])\n", - " OrderedDict([('kilosort4', 32)]) OrderedDict([('kilosort4', 33)])\n", - " OrderedDict([('kilosort4', 34)]) OrderedDict([('kilosort4', 35)])\n", - " OrderedDict([('kilosort4', 36), ('mountainsort5', 22.0)])\n", - " OrderedDict([('kilosort4', 37)]) OrderedDict([('kilosort4', 38)])\n", - " OrderedDict([('kilosort4', 39)]) OrderedDict([('kilosort4', 40)])\n", - " OrderedDict([('kilosort4', 41)]) OrderedDict([('kilosort4', 42)])\n", - " OrderedDict([('kilosort4', 43)]) OrderedDict([('kilosort4', 44)])\n", - " OrderedDict([('kilosort4', 45)]) OrderedDict([('kilosort4', 46)])\n", - " OrderedDict([('kilosort4', 47)]) OrderedDict([('kilosort4', 48)])\n", - " OrderedDict([('kilosort4', 49)]) OrderedDict([('kilosort4', 50)])\n", - " OrderedDict([('kilosort4', 51)]) OrderedDict([('kilosort4', 52)])\n", - " OrderedDict([('kilosort4', 53)]) OrderedDict([('kilosort4', 54)])\n", - " OrderedDict([('kilosort4', 55), ('spykingcircus2', 69.0), ('mountainsort5', 57.0)])\n", - " OrderedDict([('kilosort4', 56)]) OrderedDict([('kilosort4', 57)])\n", - " OrderedDict([('kilosort4', 58)]) OrderedDict([('kilosort4', 59)])\n", - " OrderedDict([('kilosort4', 60)]) OrderedDict([('kilosort4', 61)])\n", - " OrderedDict([('kilosort4', 62)]) OrderedDict([('kilosort4', 63)])\n", - " OrderedDict([('kilosort4', 64)]) OrderedDict([('kilosort4', 65)])\n", - " OrderedDict([('kilosort4', 66)]) OrderedDict([('kilosort4', 67)])\n", - " OrderedDict([('kilosort4', 68)]) OrderedDict([('kilosort4', 69)])\n", - " OrderedDict([('kilosort4', 70)]) OrderedDict([('kilosort4', 71)])\n", - " OrderedDict([('kilosort4', 72)]) OrderedDict([('kilosort4', 73)])\n", - " OrderedDict([('kilosort4', 74)]) OrderedDict([('kilosort4', 75)])\n", - " OrderedDict([('kilosort4', 76)]) OrderedDict([('kilosort4', 77)])\n", - " OrderedDict([('kilosort4', 78)]) OrderedDict([('kilosort4', 79)])\n", - " OrderedDict([('kilosort4', 80)]) OrderedDict([('kilosort4', 81)])\n", - " OrderedDict([('kilosort4', 82), ('spykingcircus2', 27.0), ('mountainsort5', 11.0)])\n", - " OrderedDict([('kilosort4', 83)]) OrderedDict([('kilosort4', 84)])\n", - " OrderedDict([('kilosort4', 85)]) OrderedDict([('kilosort4', 86)])\n", - " OrderedDict([('kilosort4', 87)]) OrderedDict([('kilosort4', 88)])\n", - " OrderedDict([('kilosort4', 89)]) OrderedDict([('kilosort4', 90)])\n", - " OrderedDict([('kilosort4', 91)]) OrderedDict([('kilosort4', 92)])\n", - " OrderedDict([('kilosort4', 93)]) OrderedDict([('kilosort4', 94)])\n", - " OrderedDict([('kilosort4', 95)]) OrderedDict([('kilosort4', 96)])\n", - " OrderedDict([('kilosort4', 97)]) OrderedDict([('kilosort4', 98)])\n", - " OrderedDict([('kilosort4', 99)]) OrderedDict([('kilosort4', 100)])\n", - " OrderedDict([('kilosort4', 101)]) OrderedDict([('kilosort4', 102)])\n", - " OrderedDict([('kilosort4', 103)]) OrderedDict([('kilosort4', 104)])\n", - " OrderedDict([('kilosort4', 105)]) OrderedDict([('kilosort4', 106)])\n", - " OrderedDict([('kilosort4', 107)]) OrderedDict([('kilosort4', 108)])\n", - " OrderedDict([('kilosort4', 109)]) OrderedDict([('kilosort4', 110)])\n", - " OrderedDict([('kilosort4', 111)]) OrderedDict([('kilosort4', 112)])\n", - " OrderedDict([('kilosort4', 113)]) OrderedDict([('kilosort4', 114)])\n", - " OrderedDict([('kilosort4', 115)]) OrderedDict([('kilosort4', 116)])\n", - " OrderedDict([('kilosort4', 117)]) OrderedDict([('kilosort4', 118)])\n", - " OrderedDict([('kilosort4', 119)]) OrderedDict([('kilosort4', 120)])\n", - " OrderedDict([('kilosort4', 121)]) OrderedDict([('kilosort4', 122)])\n", - " OrderedDict([('kilosort4', 123)]) OrderedDict([('kilosort4', 124)])\n", - " OrderedDict([('kilosort4', 125)]) OrderedDict([('kilosort4', 126)])\n", - " OrderedDict([('kilosort4', 127)]) OrderedDict([('kilosort4', 128)])\n", - " OrderedDict([('kilosort4', 129)]) OrderedDict([('kilosort4', 130)])\n", - " OrderedDict([('kilosort4', 131)]) OrderedDict([('kilosort4', 132)])\n", - " OrderedDict([('kilosort4', 133)]) OrderedDict([('kilosort4', 134)])\n", - " OrderedDict([('kilosort4', 135)]) OrderedDict([('kilosort4', 136)])\n", - " OrderedDict([('kilosort4', 137)]) OrderedDict([('kilosort4', 138)])\n", - " OrderedDict([('kilosort4', 139)]) OrderedDict([('kilosort4', 140)])\n", - " OrderedDict([('kilosort4', 141)]) OrderedDict([('kilosort4', 142)])\n", - " OrderedDict([('kilosort4', 143)]) OrderedDict([('kilosort4', 144)])\n", - " OrderedDict([('kilosort4', 145)]) OrderedDict([('kilosort4', 146)])\n", - " OrderedDict([('kilosort4', 147)]) OrderedDict([('kilosort4', 148)])\n", - " OrderedDict([('kilosort4', 149)]) OrderedDict([('kilosort4', 150)])\n", - " OrderedDict([('kilosort4', 151)]) OrderedDict([('kilosort4', 152)])\n", - " OrderedDict([('kilosort4', 153)]) OrderedDict([('spykingcircus2', 0)])\n", - " OrderedDict([('spykingcircus2', 1)]) OrderedDict([('spykingcircus2', 2)])\n", - " OrderedDict([('spykingcircus2', 4)]) OrderedDict([('spykingcircus2', 5)])\n", - " OrderedDict([('spykingcircus2', 6)]) OrderedDict([('spykingcircus2', 7)])\n", - " OrderedDict([('spykingcircus2', 8)]) OrderedDict([('spykingcircus2', 9)])\n", - " OrderedDict([('spykingcircus2', 10)])\n", - " OrderedDict([('spykingcircus2', 11)])\n", - " OrderedDict([('spykingcircus2', 12)])\n", - " OrderedDict([('spykingcircus2', 13)])\n", - " OrderedDict([('spykingcircus2', 14)])\n", - " OrderedDict([('spykingcircus2', 15)])\n", - " OrderedDict([('spykingcircus2', 16)])\n", - " OrderedDict([('spykingcircus2', 17)])\n", - " OrderedDict([('spykingcircus2', 18)])\n", - " OrderedDict([('spykingcircus2', 19)])\n", - " OrderedDict([('spykingcircus2', 20)])\n", - " OrderedDict([('spykingcircus2', 21)])\n", - " OrderedDict([('spykingcircus2', 22)])\n", - " OrderedDict([('spykingcircus2', 23)])\n", - " OrderedDict([('spykingcircus2', 24)])\n", - " OrderedDict([('spykingcircus2', 25)])\n", - " OrderedDict([('spykingcircus2', 26)])\n", - " OrderedDict([('spykingcircus2', 28)])\n", - " OrderedDict([('spykingcircus2', 29)])\n", - " OrderedDict([('spykingcircus2', 30)])\n", - " OrderedDict([('spykingcircus2', 31)])\n", - " OrderedDict([('spykingcircus2', 32)])\n", - " OrderedDict([('spykingcircus2', 33)])\n", - " OrderedDict([('spykingcircus2', 34)])\n", - " OrderedDict([('spykingcircus2', 35)])\n", - " OrderedDict([('spykingcircus2', 36)])\n", - " OrderedDict([('spykingcircus2', 37)])\n", - " OrderedDict([('spykingcircus2', 38)])\n", - " OrderedDict([('spykingcircus2', 39)])\n", - " OrderedDict([('spykingcircus2', 40)])\n", - " OrderedDict([('spykingcircus2', 41)])\n", - " OrderedDict([('spykingcircus2', 42)])\n", - " OrderedDict([('spykingcircus2', 43)])\n", - " OrderedDict([('spykingcircus2', 44)])\n", - " OrderedDict([('spykingcircus2', 45)])\n", - " OrderedDict([('spykingcircus2', 46)])\n", - " OrderedDict([('spykingcircus2', 47)])\n", - " OrderedDict([('spykingcircus2', 48)])\n", - " OrderedDict([('spykingcircus2', 49)])\n", - " OrderedDict([('spykingcircus2', 50)])\n", - " OrderedDict([('spykingcircus2', 51)])\n", - " OrderedDict([('spykingcircus2', 52)])\n", - " OrderedDict([('spykingcircus2', 53)])\n", - " OrderedDict([('spykingcircus2', 54)])\n", - " OrderedDict([('spykingcircus2', 55)])\n", - " OrderedDict([('spykingcircus2', 56)])\n", - " OrderedDict([('spykingcircus2', 57)])\n", - " OrderedDict([('spykingcircus2', 58)])\n", - " OrderedDict([('spykingcircus2', 59)])\n", - " OrderedDict([('spykingcircus2', 60)])\n", - " OrderedDict([('spykingcircus2', 61)])\n", - " OrderedDict([('spykingcircus2', 62)])\n", - " OrderedDict([('spykingcircus2', 63)])\n", - " OrderedDict([('spykingcircus2', 65)])\n", - " OrderedDict([('spykingcircus2', 67)])\n", - " OrderedDict([('spykingcircus2', 68)])\n", - " OrderedDict([('spykingcircus2', 70)])\n", - " OrderedDict([('spykingcircus2', 71)])\n", - " OrderedDict([('spykingcircus2', 72)]) OrderedDict([('mountainsort5', 2)])\n", - " OrderedDict([('mountainsort5', 3)]) OrderedDict([('mountainsort5', 4)])\n", - " OrderedDict([('mountainsort5', 5)]) OrderedDict([('mountainsort5', 6)])\n", - " OrderedDict([('mountainsort5', 7)]) OrderedDict([('mountainsort5', 8)])\n", - " OrderedDict([('mountainsort5', 9)]) OrderedDict([('mountainsort5', 10)])\n", - " OrderedDict([('mountainsort5', 12)]) OrderedDict([('mountainsort5', 13)])\n", - " OrderedDict([('mountainsort5', 14)]) OrderedDict([('mountainsort5', 15)])\n", - " OrderedDict([('mountainsort5', 17)]) OrderedDict([('mountainsort5', 18)])\n", - " OrderedDict([('mountainsort5', 19)]) OrderedDict([('mountainsort5', 20)])\n", - " OrderedDict([('mountainsort5', 21)]) OrderedDict([('mountainsort5', 23)])\n", - " OrderedDict([('mountainsort5', 25)]) OrderedDict([('mountainsort5', 26)])\n", - " OrderedDict([('mountainsort5', 27)]) OrderedDict([('mountainsort5', 28)])\n", - " OrderedDict([('mountainsort5', 29)]) OrderedDict([('mountainsort5', 30)])\n", - " OrderedDict([('mountainsort5', 31)]) OrderedDict([('mountainsort5', 32)])\n", - " OrderedDict([('mountainsort5', 33)]) OrderedDict([('mountainsort5', 35)])\n", - " OrderedDict([('mountainsort5', 36)]) OrderedDict([('mountainsort5', 37)])\n", - " OrderedDict([('mountainsort5', 39)]) OrderedDict([('mountainsort5', 40)])\n", - " OrderedDict([('mountainsort5', 41)]) OrderedDict([('mountainsort5', 42)])\n", - " OrderedDict([('mountainsort5', 43)]) OrderedDict([('mountainsort5', 45)])\n", - " OrderedDict([('mountainsort5', 46)]) OrderedDict([('mountainsort5', 47)])\n", - " OrderedDict([('mountainsort5', 48)]) OrderedDict([('mountainsort5', 49)])\n", - " OrderedDict([('mountainsort5', 50)]) OrderedDict([('mountainsort5', 51)])\n", - " OrderedDict([('mountainsort5', 52)]) OrderedDict([('mountainsort5', 53)])\n", - " OrderedDict([('mountainsort5', 54)]) OrderedDict([('mountainsort5', 55)])\n", - " OrderedDict([('mountainsort5', 56)]) OrderedDict([('mountainsort5', 58)])\n", - " OrderedDict([('mountainsort5', 59)]) OrderedDict([('mountainsort5', 60)])]
          " - ], - "text/plain": [ - "AgreementSortingExtractor: 273 units - 1 segments - 20.0kHz" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_comp.get_agreement_sorting()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "8befc51b-4276-49d3-8163-b20ab6617b8d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bd52dacc70eb453383753013c1e7e7af", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "estimate_sparsity: 0%| | 0/641 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
          MatlabPython
          0$$$freeee
          1closed, versioningopen source, always supported
          2IDE = slowonly text = fast, low RAM
          3legacy packagescommunity maintained - sometimes old formats not supported
          4weird indexingnormal indexing
          5long to readshort to read and write
          6scientific computingall purpose, less matrix capabilites --> numpy!
          7quick visualizationjupyter notebook?
          8only functions and scriptsobject-oriented, package imports
          \n", - "" - ], - "text/plain": [ - " Matlab \\\n", - "0 $$$ \n", - "1 closed, versioning \n", - "2 IDE = slow \n", - "3 legacy packages \n", - "4 weird indexing \n", - "5 long to read \n", - "6 scientific computing \n", - "7 quick visualization \n", - "8 only functions and scripts \n", - "\n", - " Python \n", - "0 freeee \n", - "1 open source, always supported \n", - "2 only text = fast, low RAM \n", - "3 community maintained - sometimes old formats not supported \n", - "4 normal indexing \n", - "5 short to read and write \n", - "6 all purpose, less matrix capabilites --> numpy! \n", - "7 jupyter notebook? \n", - "8 object-oriented, package imports " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "matlab = [\n", - " \"$$$\",\n", - " \"closed, versioning\",\n", - " \"IDE = slow\",\n", - " \"legacy packages\",\n", - " \"weird indexing\",\n", - " \"long to read\",\n", - " \"scientific computing\",\n", - " \"quick visualization\",\n", - " \"only functions and scripts\",\n", - "]\n", - "python = [\n", - " \"freeee\",\n", - " \"open source, always supported\",\n", - " \"only text = fast, low RAM\",\n", - " \"community maintained - sometimes old formats not supported\",\n", - " \"normal indexing\",\n", - " \"short to read and write\",\n", - " \"all purpose, less matrix capabilites --> numpy!\",\n", - " \"jupyter notebook?\",\n", - " \"object-oriented, package imports\",\n", - "]\n", - "df = pd.DataFrame({\"Matlab\": matlab, \"Python\": python})\n", - "with pd.option_context(\"display.max_colwidth\", None):\n", - " display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9deb3fdc-c4b4-493c-addf-4b5c88c62324", - "metadata": {}, - "outputs": [], - "source": [ - "from numpy.fft import fftfreq" - ] - }, - { - "cell_type": "markdown", - "id": "3b10bda3-22de-4e2c-8782-e53f0c5ac832", - "metadata": {}, - "source": [ - "Python est un **langage** de programmation et peut être édité, exécuté depuis n'importe où. On a décidé qu'on utiliserait Spyder pour l'IDE et la visualisation, mais chacun est libre.\n", - "\n", - "Il y a \"peu\" de fonctions de base et il faut généralement importer des **packages** spécifiques au début du script/notebook. Par exemple pour **numpy**, qui gère le calcul matriciel et vectoriel, et **matplotlib** qui gère les plots:\n", - "```python\n", - "import numpy\n", - "import matplotlib.pyplot as plt\n", - "from numpy.fft import fftfreq as fourier\n", - " ```\n", - "\n", - "La plupart des fonctions ont une documentation très précise, et une gestion des erreurs qui aide au débuggage. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "74cb8a53-913b-40df-9e5b-be7751b61764", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0mfourier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Return the Discrete Fourier Transform sample frequencies.\n", - "\n", - "The returned float array `f` contains the frequency bin centers in cycles\n", - "per unit of the sample spacing (with zero at the start). For instance, if\n", - "the sample spacing is in seconds, then the frequency unit is cycles/second.\n", - "\n", - "Given a window length `n` and a sample spacing `d`::\n", - "\n", - " f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even\n", - " f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is odd\n", - "\n", - "Parameters\n", - "----------\n", - "n : int\n", - " Window length.\n", - "d : scalar, optional\n", - " Sample spacing (inverse of the sampling rate). Defaults to 1.\n", - "\n", - "Returns\n", - "-------\n", - "f : ndarray\n", - " Array of length `n` containing the sample frequencies.\n", - "\n", - "Examples\n", - "--------\n", - ">>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)\n", - ">>> fourier = np.fft.fft(signal)\n", - ">>> n = signal.size\n", - ">>> timestep = 0.1\n", - ">>> freq = np.fft.fftfreq(n, d=timestep)\n", - ">>> freq\n", - "array([ 0. , 1.25, 2.5 , ..., -3.75, -2.5 , -1.25])\n", - "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/numpy/fft/helper.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from numpy.fft import fftfreq as fourier\n", - "\n", - "?fourier" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "579e5647-27e5-4dd7-8ee7-bc658641395e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0mkilosort\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_kilosort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mprobe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mprobe_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdata_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfile_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mresults_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdata_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdo_CAR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0minvert_sign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msave_extra_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mclear_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msave_preprocessed_copy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mbad_channels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mverbose_console\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Run full spike sorting pipeline on specified data.\n", - "\n", - "Parameters\n", - "----------\n", - "settings : dict\n", - " Specifies a number of configurable parameters used throughout the\n", - " spike sorting pipeline. See `kilosort/parameters.py` for a full list of\n", - " available parameters.\n", - " NOTE: `n_chan_bin` must be specified here, but all other settings are\n", - " optional.\n", - "probe : dict; optional.\n", - " A Kilosort4 probe dictionary, as returned by `kilosort.io.load_probe`.\n", - "probe_name : str; optional.\n", - " Filename of probe to use, within the default `PROBE_DIR`. Only include\n", - " the filename without any preceeding directories. Will ony be used if\n", - " `probe is None`. Alternatively, the full filepath to a probe stored in\n", - " any directory can be specified with `settings = {'probe_path': ...}`.\n", - " See `kilosort.utils` for default `PROBE_DIR` definition.\n", - "filename: str or Path; optional.\n", - " Full path to binary data file. If specified, will also set\n", - " `data_dir = filename.parent`.\n", - "data_dir : str or Path; optional.\n", - " Specifies directory where binary data file is stored. Kilosort will\n", - " attempt to find the binary file. This works best if there is exactly one\n", - " file in the directory with a .bin, .bat, .dat, or .raw extension.\n", - " Only used if `filename is None`.\n", - " Also see `kilosort.io.find_binary`.\n", - "file_object : array-like file object; optional.\n", - " Must have 'shape' and 'dtype' attributes and support array-like\n", - " indexing (e.g. [:100,:], [5, 7:10], etc). For example, a numpy\n", - " array or memmap. Must specify a valid `filename` as well, even though\n", - " data will not be directly loaded from that file.\n", - "results_dir : str or Path; optional.\n", - " Directory where results will be stored. By default, will be set to\n", - " `data_dir / 'kilosort4'`.\n", - "data_dtype : str or type; optional.\n", - " dtype of data in binary file, like `'int32'` or `np.uint16`. By default,\n", - " dtype is assumed to be `'int16'`.\n", - "do_CAR : bool; default=True.\n", - " If True, apply common average reference during preprocessing\n", - " (recommended).\n", - "invert_sign : bool; default=False.\n", - " If True, flip positive/negative values in data to conform to standard\n", - " expected by Kilosort4.\n", - "device : torch.device; optional.\n", - " CPU or GPU device to use for PyTorch calculations. By default, PyTorch\n", - " will use the first detected GPU. If no GPUs are detected, CPU will be\n", - " used. To set this manually, specify `device = torch.device()`.\n", - " See PyTorch documentation for full description.\n", - "progress_bar : tqdm.std.tqdm or QtWidgets.QProgressBar; optional.\n", - " Used by sorting steps and GUI to track sorting progress. Users should\n", - " not need to specify this.\n", - "save_extra_vars : bool; default=False.\n", - " If True, save tF and Wall to disk after sorting.\n", - "clear_cache : bool; default=False.\n", - " If True, force pytorch to free up memory reserved for its cache in\n", - " between memory-intensive operations.\n", - " Note that setting `clear_cache=True` is NOT recommended unless you\n", - " encounter GPU out-of-memory errors, since this can result in slower\n", - " sorting.\n", - "save_preprocessed_copy : bool; default=False.\n", - " If True, save a pre-processed copy of the data (including drift\n", - " correction) to `temp_wh.dat` in the results directory and format Phy\n", - " output to use that copy of the data.\n", - "bad_channels : list; optional.\n", - " A list of channel indices (rows in the binary file) that should not be\n", - " included in sorting. Listing channels here is equivalent to excluding\n", - " them from the probe dictionary.\n", - "verbose_console : bool; default=False.\n", - " If True, set logging level for console output to `DEBUG` instead\n", - " of `INFO`, so that additional information normally only saved to the\n", - " log file will also show up in real time while sorting.\n", - "\n", - "Raises\n", - "------\n", - "ValueError\n", - " If settings[`n_chan_bin`] is None (default). User must specify, for\n", - " example: `run_kilosort(settings={'n_chan_bin': 385})`.\n", - "\n", - "Returns\n", - "-------\n", - "ops : dict\n", - " Dictionary storing settings and results for all algorithmic steps.\n", - "st : np.ndarray\n", - " 3-column array of peak time (in samples), template, and amplitude for\n", - " each spike.\n", - "clu : np.ndarray\n", - " 1D vector of cluster ids indicating which spike came from which cluster,\n", - " same shape as `st[:,0]`.\n", - "tF : torch.Tensor\n", - " PC features for each spike, with shape\n", - " (n_spikes, nearest_chans, n_pcs)\n", - "Wall : torch.Tensor\n", - " PC feature representation of spike waveforms for each cluster, with shape\n", - " (n_clusters, n_channels, n_pcs).\n", - "similar_templates : np.ndarray.\n", - " Similarity score between each pair of clusters, computed as correlation\n", - " between clusters. Shape (n_clusters, n_clusters).\n", - "is_ref : np.ndarray.\n", - " 1D boolean array with shape (n_clusters,) indicating whether each\n", - " cluster is refractory.\n", - "est_contam_rate : np.ndarray.\n", - " Contamination rate for each cluster, computed as fraction of refractory\n", - " period violations relative to expectation based on a Poisson process.\n", - " Shape (n_clusters,).\n", - "kept_spikes : np.ndarray.\n", - " Boolean mask with shape (n_spikes,) that is False for spikes that were\n", - " removed by `kilosort.postprocessing.remove_duplicate_spikes`\n", - " and True otherwise.\n", - "\n", - "Notes\n", - "-----\n", - "For documentation of saved files, see `kilosort.io.save_to_phy`.\n", - "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/kilosort/run_kilosort.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import kilosort\n", - "\n", - "?kilosort.run_kilosort" - ] - }, - { - "cell_type": "markdown", - "id": "ffa32e0e-3f27-40e4-98cc-e95ea44c27df", - "metadata": {}, - "source": [ - "On peut exécuter Python soit dans un script `.py` soit dans un notebook comme ici, qui aide à visualiser et est aussi interactif que Matlab. \n", - "On peut rapidement modifier les figures et les enregister." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "561b5b83-6ea0-447e-8d7c-130950b98b75", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Generate 100 random data points along 3 dimensions\n", - "x, y, scale = np.random.randn(3, 100)\n", - "fig, ax = plt.subplots()\n", - "\n", - "# Map each onto a scatterplot we'll create with Matplotlib\n", - "ax.scatter(x=x, y=y, c=scale, s=np.abs(scale) * 500)\n", - "ax.set(title=\"Some random data, created with JupyterLab!\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "abbdcc32-6505-4b38-8fc8-bfeb675586f7", - "metadata": {}, - "source": [ - "Package maintained by Diba Lab, very good support for Neuroscope type of data - but requires `.eeg` files.\n", - "Does not work for now." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3c3cc16a-0229-4adf-aff7-cf9041e53422", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib widget\n", - "from ipywidgets import *" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f0d6f396-ecdf-4223-849c-6006813d14f2", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4d08dd93abfc48109325ecc06c985245", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=100, description='n_clusters', max=500, min=1), Output()), _dom_classes=…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Generate 100 random data points along 3 dimensions\n", - "def generate_random_data(n_clusters=100):\n", - " x, y, scale = np.random.randn(3, n_clusters)\n", - " fig, ax = plt.subplots()\n", - "\n", - " # Map each onto a scatterplot we'll create with Matplotlib\n", - " ax.scatter(x=x, y=y, c=scale, s=np.abs(scale) * 500)\n", - " ax.set(title=\"même interactif!\")\n", - " plt.show()\n", - "\n", - "\n", - "interact(generate_random_data, n_clusters=(1, 500, 1))" - ] - }, - { - "cell_type": "markdown", - "id": "b038525f-d358-4c93-b14d-1f6d7788f7c0", - "metadata": {}, - "source": [ - "# Neurophysiology data visualization - alternatives à Neuroscope ?" - ] - }, - { - "cell_type": "markdown", - "id": "1b8d24e4-aa75-4853-a07c-07d33c2d65ef", - "metadata": {}, - "source": [ - "## Importation du viewer - soit comme package python soit comme application" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c4b55d65-89b6-4f58-a4fe-48ab8f60d76e", - "metadata": {}, - "outputs": [], - "source": [ - "import ephyviewer\n", - "import numpy as np\n", - "from pathlib import Path\n", - "\n", - "import spikeinterface as si\n", - "import spikeinterface.extractors as se\n", - "\n", - "import spikeinterface.preprocessing as spre\n", - "import spikeinterface.postprocessing as spost\n", - "import spikeinterface.curation as scur\n", - "import spikeinterface.widgets as sw\n", - "import spikeinterface.qualitymetrics\n", - "import os\n", - "\n", - "si.set_global_job_kwargs(n_jobs=-1, progress_bar=True)" - ] - }, - { - "cell_type": "markdown", - "id": "7bb30441-d219-4ec1-abe6-5d99b39939ac", - "metadata": {}, - "source": [ - "## Visualisation d'un enregistrement du nas 5" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0626fe77-9fdf-4b41-9cb4-b443b4a103fa", - "metadata": {}, - "outputs": [], - "source": [ - "base_folder = Path(\n", - " \"/media/nas8/OB_ferret_AG_BM/Shropshire/freely-moving/20241205_TORCs/\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "247dfd5f-686a-47b8-9f73-aa4948d6860c", - "metadata": {}, - "outputs": [], - "source": [ - "# conditionnement aversif\n", - "recording = se.NeuroScopeRecordingExtractor(\n", - " os.path.join(base_folder, \"M4_20241205_Shropshire_20241205_fm_TORCs.dat\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5d9d345f-1ed3-4772-b11e-7e5eabbb5e1a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
          NeuroScopeRecordingExtractor: 113 channels - 30.0kHz - 1 segments - 456,321,792 samples - 15,210.73s (4.23 hours) - int16 dtype - 96.05 GiB
          Channel IDs
            ['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'\n", - " '16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'\n", - " '30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'\n", - " '44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'\n", - " '58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'\n", - " '72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'\n", - " '86' '87' '88' '89' '90' '91' '92' '93' '94' '95' '96' '97' '98' '99'\n", - " '100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '110' '111'\n", - " '112']
          Annotations
          • is_filtered : False
          Channel Properties
            gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578]
            offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
            channel_names ['ch0grp0' 'ch1grp0' 'ch2grp0' 'ch3grp5' 'ch4grp0' 'ch5grp0' 'ch6grp6'\n", - " 'ch7grp0' 'ch8grp0' 'ch9grp0' 'ch10grp0' 'ch11grp2' 'ch12grp2' 'ch13grp2'\n", - " 'ch14grp2' 'ch15grp2' 'ch16grp1' 'ch17grp1' 'ch18grp1' 'ch19grp1'\n", - " 'ch20grp4' 'ch21grp4' 'ch22grp4' 'ch23grp4' 'ch24grp0' 'ch25grp0'\n", - " 'ch26grp0' 'ch27grp0' 'ch28grp0' 'ch29grp0' 'ch30grp0' 'ch31grp5'\n", - " 'ch32grp3' 'ch33grp3' 'ch34grp3' 'ch35grp3' 'ch36grp3' 'ch37grp3'\n", - " 'ch38grp3' 'ch39grp3' 'ch40grp9' 'ch41grp9' 'ch42grp9' 'ch43grp9'\n", - " 'ch44grp9' 'ch45grp3' 'ch46grp3' 'ch47grp3' 'ch48grp3' 'ch49grp3'\n", - " 'ch50grp3' 'ch51grp3' 'ch52grp3' 'ch53grp3' 'ch54grp3' 'ch55grp3'\n", - " 'ch56grp3' 'ch57grp3' 'ch58grp3' 'ch59grp3' 'ch60grp3' 'ch61grp3'\n", - " 'ch62grp3' 'ch63grp3' 'ch64grp3' 'ch65grp3' 'ch66grp3' 'ch67grp3'\n", - " 'ch68grp3' 'ch69grp3' 'ch70grp3' 'ch71grp3' 'ch72grp3' 'ch73grp9'\n", - " 'ch74grp9' 'ch75grp9' 'ch76grp9' 'ch77grp9' 'ch78grp9' 'ch79grp3'\n", - " 'ch80grp9' 'ch81grp9' 'ch82grp3' 'ch83grp3' 'ch84grp3' 'ch85grp3'\n", - " 'ch86grp3' 'ch87grp3' 'ch88grp3' 'ch89grp3' 'ch90grp3' 'ch91grp3'\n", - " 'ch92grp3' 'ch93grp3' 'ch94grp3' 'ch95grp3' 'ch96grp7' 'ch97grp7'\n", - " 'ch98grp7' 'ch99grp7' 'ch100grp7' 'ch101grp7' 'ch102grp7' 'ch103grp7'\n", - " 'ch104grp7' 'ch105grp8' 'ch106grp8' 'ch107grp8' 'ch108grp8' 'ch109grp8'\n", - " 'ch110grp8' 'ch111grp8' 'ch112grp8']
            group [0 0 0 5 0 0 6 0 0 0 0 2 2 2 2 2 1 1 1 1 4 4 4 4 0 0 0 0 0 0 0 5 3 3 3 3 3\n", - " 3 3 3 9 9 9 9 9 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 9\n", - " 9 9 9 9 9 3 9 9 3 3 3 3 3 3 3 3 3 3 3 3 3 3 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8\n", - " 8 8]
          " - ], - "text/plain": [ - "NeuroScopeRecordingExtractor: 113 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 96.05 GiB\n", - " file_path: /media/nas8/OB_ferret_AG_BM/Shropshire/freely-moving/20241205_TORCs/M4_20241205_Shropshire_20241205_fm_TORCs.dat" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Objet Python de Classe NeuroScopeRecordingExtractor\n", - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "0fd2a203-386d-4aed-9f86-71bbe3a489ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, True, True, True, True,\n", - " True, True, True, True, False, False, False, False, False,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, False, False, False, False, False, False, True, False,\n", - " False, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording.get_channel_groups() == 3" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "371edf41-1530-4a2e-8143-ce1a81462d28", - "metadata": {}, - "outputs": [], - "source": [ - "channel_sliced_recording = recording.select_channels(\n", - " channel_ids=recording.get_channel_ids()[recording.get_channel_groups() == 3]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "101a544b-ae96-4aeb-a644-6af6809fbe4b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "51" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Hérite automatiquement des propriétés neuroscope comme les groupes de channel (HPC, OB, bruit...)\n", - "channel_sliced_recording.get_num_channels()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "4635a6c8-a652-4de6-9b9b-3ce70240d408", - "metadata": {}, - "outputs": [], - "source": [ - "recording = channel_sliced_recording" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "8d3f7c56-ca77-42a6-abe9-55a30d906f0d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
          ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples - 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB
          Channel IDs
            ['32' '33' '34' '35' '36' '37' '38' '39' '45' '46' '47' '48' '49' '50'\n", - " '51' '52' '53' '54' '55' '56' '57' '58' '59' '60' '61' '62' '63' '64'\n", - " '65' '66' '67' '68' '69' '70' '71' '72' '79' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94' '95']
          Annotations
          • is_filtered : False
          Channel Properties
            gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578]
            offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0.]
            channel_names ['ch32grp3' 'ch33grp3' 'ch34grp3' 'ch35grp3' 'ch36grp3' 'ch37grp3'\n", - " 'ch38grp3' 'ch39grp3' 'ch45grp3' 'ch46grp3' 'ch47grp3' 'ch48grp3'\n", - " 'ch49grp3' 'ch50grp3' 'ch51grp3' 'ch52grp3' 'ch53grp3' 'ch54grp3'\n", - " 'ch55grp3' 'ch56grp3' 'ch57grp3' 'ch58grp3' 'ch59grp3' 'ch60grp3'\n", - " 'ch61grp3' 'ch62grp3' 'ch63grp3' 'ch64grp3' 'ch65grp3' 'ch66grp3'\n", - " 'ch67grp3' 'ch68grp3' 'ch69grp3' 'ch70grp3' 'ch71grp3' 'ch72grp3'\n", - " 'ch79grp3' 'ch82grp3' 'ch83grp3' 'ch84grp3' 'ch85grp3' 'ch86grp3'\n", - " 'ch87grp3' 'ch88grp3' 'ch89grp3' 'ch90grp3' 'ch91grp3' 'ch92grp3'\n", - " 'ch93grp3' 'ch94grp3' 'ch95grp3']
            group [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", - " 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
          " - ], - "text/plain": [ - "ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "a670d4da-b72c-4fd2-90fb-508a2d1594f6", - "metadata": {}, - "outputs": [], - "source": [ - "# ajout dummy probe pour la visualisation\n", - "from probeinterface import generate_linear_probe\n", - "\n", - "num_elec = recording.get_num_channels()\n", - "probe = generate_linear_probe(\n", - " num_elec=num_elec,\n", - " ypitch=20,\n", - " contact_shapes=\"circle\",\n", - " contact_shape_params={\"radius\": 6},\n", - ")\n", - "probe.set_device_channel_indices(np.arange(num_elec))\n", - "recording = recording.set_probe(probe)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "c564d712-756e-4062-b666-c8ad4817a9c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['32', '33', '34', '35', '36', '37', '38', '39', '45', '46', '47',\n", - " '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58',\n", - " '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69',\n", - " '70', '71', '72', '79', '82', '83', '84', '85', '86', '87', '88',\n", - " '89', '90', '91', '92', '93', '94', '95'], dtype='ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples - 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB
          Channel IDs
            ['32' '33' '34' '35' '36' '37' '38' '39' '45' '46' '47' '48' '49' '50'\n", - " '51' '52' '53' '54' '55' '56' '57' '58' '59' '60' '61' '62' '63' '64'\n", - " '65' '66' '67' '68' '69' '70' '71' '72' '79' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94' '95']
          Annotations
          • is_filtered : False
          • name : None
          • probe_0_planar_contour : [[ -25. 1025.]\n", - " [ -25. -25.]\n", - " [ 0. -125.]\n", - " [ 25. -25.]\n", - " [ 25. 1025.]]
          Channel Properties
            gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578]
            offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0.]
            channel_names ['ch32grp3' 'ch33grp3' 'ch34grp3' 'ch35grp3' 'ch36grp3' 'ch37grp3'\n", - " 'ch38grp3' 'ch39grp3' 'ch45grp3' 'ch46grp3' 'ch47grp3' 'ch48grp3'\n", - " 'ch49grp3' 'ch50grp3' 'ch51grp3' 'ch52grp3' 'ch53grp3' 'ch54grp3'\n", - " 'ch55grp3' 'ch56grp3' 'ch57grp3' 'ch58grp3' 'ch59grp3' 'ch60grp3'\n", - " 'ch61grp3' 'ch62grp3' 'ch63grp3' 'ch64grp3' 'ch65grp3' 'ch66grp3'\n", - " 'ch67grp3' 'ch68grp3' 'ch69grp3' 'ch70grp3' 'ch71grp3' 'ch72grp3'\n", - " 'ch79grp3' 'ch82grp3' 'ch83grp3' 'ch84grp3' 'ch85grp3' 'ch86grp3'\n", - " 'ch87grp3' 'ch88grp3' 'ch89grp3' 'ch90grp3' 'ch91grp3' 'ch92grp3'\n", - " 'ch93grp3' 'ch94grp3' 'ch95grp3']
            group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
            contact_vector [(0, 0., 0., 'circle', 6., '', '0', 0, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 20., 'circle', 6., '', '1', 1, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 40., 'circle', 6., '', '2', 2, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 60., 'circle', 6., '', '3', 3, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 80., 'circle', 6., '', '4', 4, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 100., 'circle', 6., '', '5', 5, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 120., 'circle', 6., '', '6', 6, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 140., 'circle', 6., '', '7', 7, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 160., 'circle', 6., '', '8', 8, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 180., 'circle', 6., '', '9', 9, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 200., 'circle', 6., '', '10', 10, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 220., 'circle', 6., '', '11', 11, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 240., 'circle', 6., '', '12', 12, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 260., 'circle', 6., '', '13', 13, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 280., 'circle', 6., '', '14', 14, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 300., 'circle', 6., '', '15', 15, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 320., 'circle', 6., '', '16', 16, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 340., 'circle', 6., '', '17', 17, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 360., 'circle', 6., '', '18', 18, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 380., 'circle', 6., '', '19', 19, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 400., 'circle', 6., '', '20', 20, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 420., 'circle', 6., '', '21', 21, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 440., 'circle', 6., '', '22', 22, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 460., 'circle', 6., '', '23', 23, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 480., 'circle', 6., '', '24', 24, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 500., 'circle', 6., '', '25', 25, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 520., 'circle', 6., '', '26', 26, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 540., 'circle', 6., '', '27', 27, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 560., 'circle', 6., '', '28', 28, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 580., 'circle', 6., '', '29', 29, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 600., 'circle', 6., '', '30', 30, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 620., 'circle', 6., '', '31', 31, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 640., 'circle', 6., '', '32', 32, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 660., 'circle', 6., '', '33', 33, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 680., 'circle', 6., '', '34', 34, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 700., 'circle', 6., '', '35', 35, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 720., 'circle', 6., '', '36', 36, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 740., 'circle', 6., '', '37', 37, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 760., 'circle', 6., '', '38', 38, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 780., 'circle', 6., '', '39', 39, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 800., 'circle', 6., '', '40', 40, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 820., 'circle', 6., '', '41', 41, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 840., 'circle', 6., '', '42', 42, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 860., 'circle', 6., '', '43', 43, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 880., 'circle', 6., '', '44', 44, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 900., 'circle', 6., '', '45', 45, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 920., 'circle', 6., '', '46', 46, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 940., 'circle', 6., '', '47', 47, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 960., 'circle', 6., '', '48', 48, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 980., 'circle', 6., '', '49', 49, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1000., 'circle', 6., '', '50', 50, 'um', 1., 0., 0., 1.)]
            location [[ 0. 0.]\n", - " [ 0. 20.]\n", - " [ 0. 40.]\n", - " [ 0. 60.]\n", - " [ 0. 80.]\n", - " [ 0. 100.]\n", - " [ 0. 120.]\n", - " [ 0. 140.]\n", - " [ 0. 160.]\n", - " [ 0. 180.]\n", - " [ 0. 200.]\n", - " [ 0. 220.]\n", - " [ 0. 240.]\n", - " [ 0. 260.]\n", - " [ 0. 280.]\n", - " [ 0. 300.]\n", - " [ 0. 320.]\n", - " [ 0. 340.]\n", - " [ 0. 360.]\n", - " [ 0. 380.]\n", - " [ 0. 400.]\n", - " [ 0. 420.]\n", - " [ 0. 440.]\n", - " [ 0. 460.]\n", - " [ 0. 480.]\n", - " [ 0. 500.]\n", - " [ 0. 520.]\n", - " [ 0. 540.]\n", - " [ 0. 560.]\n", - " [ 0. 580.]\n", - " [ 0. 600.]\n", - " [ 0. 620.]\n", - " [ 0. 640.]\n", - " [ 0. 660.]\n", - " [ 0. 680.]\n", - " [ 0. 700.]\n", - " [ 0. 720.]\n", - " [ 0. 740.]\n", - " [ 0. 760.]\n", - " [ 0. 780.]\n", - " [ 0. 800.]\n", - " [ 0. 820.]\n", - " [ 0. 840.]\n", - " [ 0. 860.]\n", - " [ 0. 880.]\n", - " [ 0. 900.]\n", - " [ 0. 920.]\n", - " [ 0. 940.]\n", - " [ 0. 960.]\n", - " [ 0. 980.]\n", - " [ 0. 1000.]]
          " - ], - "text/plain": [ - "ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "7d5ecc31-2939-47c4-a540-8cf7adae32e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['32', '33', '34', '35', '36', '37', '38', '39', '45', '46', '47',\n", - " '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58',\n", - " '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69',\n", - " '70', '71', '72', '79', '82', '83', '84', '85', '86', '87', '88',\n", - " '89', '90', '91', '92', '93', '94', '95'], dtype='ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples - 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB
          Channel IDs
            ['32' '33' '34' '35' '36' '37' '38' '39' '45' '46' '47' '48' '49' '50'\n", - " '51' '52' '53' '54' '55' '56' '57' '58' '59' '60' '61' '62' '63' '64'\n", - " '65' '66' '67' '68' '69' '70' '71' '72' '79' '82' '83' '84' '85' '86'\n", - " '87' '88' '89' '90' '91' '92' '93' '94' '95']
          Annotations
          • is_filtered : False
          • name : None
          • probe_0_planar_contour : [[ -25. 1025.]\n", - " [ -25. -25.]\n", - " [ 0. -125.]\n", - " [ 25. -25.]\n", - " [ 25. 1025.]]
          Channel Properties
            gain_to_uV [0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578 0.30517578\n", - " 0.30517578 0.30517578 0.30517578]
            offset_to_uV [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0.]
            channel_names ['ch32grp3' 'ch33grp3' 'ch34grp3' 'ch35grp3' 'ch36grp3' 'ch37grp3'\n", - " 'ch38grp3' 'ch39grp3' 'ch45grp3' 'ch46grp3' 'ch47grp3' 'ch48grp3'\n", - " 'ch49grp3' 'ch50grp3' 'ch51grp3' 'ch52grp3' 'ch53grp3' 'ch54grp3'\n", - " 'ch55grp3' 'ch56grp3' 'ch57grp3' 'ch58grp3' 'ch59grp3' 'ch60grp3'\n", - " 'ch61grp3' 'ch62grp3' 'ch63grp3' 'ch64grp3' 'ch65grp3' 'ch66grp3'\n", - " 'ch67grp3' 'ch68grp3' 'ch69grp3' 'ch70grp3' 'ch71grp3' 'ch72grp3'\n", - " 'ch79grp3' 'ch82grp3' 'ch83grp3' 'ch84grp3' 'ch85grp3' 'ch86grp3'\n", - " 'ch87grp3' 'ch88grp3' 'ch89grp3' 'ch90grp3' 'ch91grp3' 'ch92grp3'\n", - " 'ch93grp3' 'ch94grp3' 'ch95grp3']
            group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
            contact_vector [(0, 0., 0., 'circle', 6., '', '0', 0, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 20., 'circle', 6., '', '1', 1, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 40., 'circle', 6., '', '2', 2, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 60., 'circle', 6., '', '3', 3, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 80., 'circle', 6., '', '4', 4, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 100., 'circle', 6., '', '5', 5, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 120., 'circle', 6., '', '6', 6, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 140., 'circle', 6., '', '7', 7, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 160., 'circle', 6., '', '8', 8, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 180., 'circle', 6., '', '9', 9, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 200., 'circle', 6., '', '10', 10, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 220., 'circle', 6., '', '11', 11, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 240., 'circle', 6., '', '12', 12, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 260., 'circle', 6., '', '13', 13, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 280., 'circle', 6., '', '14', 14, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 300., 'circle', 6., '', '15', 15, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 320., 'circle', 6., '', '16', 16, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 340., 'circle', 6., '', '17', 17, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 360., 'circle', 6., '', '18', 18, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 380., 'circle', 6., '', '19', 19, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 400., 'circle', 6., '', '20', 20, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 420., 'circle', 6., '', '21', 21, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 440., 'circle', 6., '', '22', 22, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 460., 'circle', 6., '', '23', 23, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 480., 'circle', 6., '', '24', 24, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 500., 'circle', 6., '', '25', 25, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 520., 'circle', 6., '', '26', 26, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 540., 'circle', 6., '', '27', 27, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 560., 'circle', 6., '', '28', 28, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 580., 'circle', 6., '', '29', 29, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 600., 'circle', 6., '', '30', 30, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 620., 'circle', 6., '', '31', 31, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 640., 'circle', 6., '', '32', 32, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 660., 'circle', 6., '', '33', 33, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 680., 'circle', 6., '', '34', 34, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 700., 'circle', 6., '', '35', 35, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 720., 'circle', 6., '', '36', 36, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 740., 'circle', 6., '', '37', 37, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 760., 'circle', 6., '', '38', 38, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 780., 'circle', 6., '', '39', 39, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 800., 'circle', 6., '', '40', 40, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 820., 'circle', 6., '', '41', 41, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 840., 'circle', 6., '', '42', 42, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 860., 'circle', 6., '', '43', 43, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 880., 'circle', 6., '', '44', 44, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 900., 'circle', 6., '', '45', 45, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 920., 'circle', 6., '', '46', 46, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 940., 'circle', 6., '', '47', 47, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 960., 'circle', 6., '', '48', 48, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 980., 'circle', 6., '', '49', 49, 'um', 1., 0., 0., 1.)\n", - " (0, 0., 1000., 'circle', 6., '', '50', 50, 'um', 1., 0., 0., 1.)]
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          " - ], - "text/plain": [ - "ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "recording" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "649deb62-920e-4ad4-942b-08bbd6e73222", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface.sorters as ss" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "id": "682b61ef-d74d-4787-993e-4f9901f897c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{4: NeuroScopeRecordingExtractor: 113 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 96.05 GiB\n", - " file_path: /media/nas8/OB_ferret_AG_BM/Shropshire/freely-moving/20241205_TORCs/M4_20241205_Shropshire_20241205_fm_TORCs.dat,\n", - " 5: array([0, 0, 0, 5, 0, 0, 6, 0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1, 4, 4,\n", - " 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 3, 3, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9,\n", - " 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9, 9, 9, 3, 9, 9, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,\n", - " 8, 8, 8]),\n", - " 13: array([ 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,\n", - " 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,\n", - " 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,\n", - " 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,\n", - " 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,\n", - " 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n", - " 110, 111]),\n", - " 16: array([32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n", - " 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,\n", - " 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]),\n", - " 17: array([32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n", - " 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,\n", - " 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]),\n", - " 18: array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,\n", - " 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n", - " 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n", - " 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]),\n", - " 25: ['31',\n", - " '32',\n", - " '33',\n", - " '34',\n", - " '35',\n", - " '36',\n", - " '37',\n", - " '38',\n", - " '39',\n", - " '40',\n", - " '41',\n", - " '42',\n", - " '43',\n", - " '44',\n", - " '45',\n", - " '46',\n", - " '47',\n", - " '48',\n", - " '49',\n", - " '50',\n", - " '51',\n", - " '52',\n", - " '53',\n", - " '54',\n", - " '55',\n", - " '56',\n", - " '57',\n", - " '58',\n", - " '59',\n", - " '60',\n", - " '61',\n", - " '62',\n", - " '63',\n", - " '64',\n", - " '65',\n", - " '66',\n", - " '67',\n", - " '68',\n", - " '69',\n", - " '70',\n", - " '71',\n", - " '72',\n", - " '73',\n", - " '74',\n", - " '75',\n", - " '76',\n", - " '77',\n", - " '78',\n", - " '79',\n", - " '80',\n", - " '81',\n", - " '82',\n", - " '83',\n", - " '84',\n", - " '85',\n", - " '86',\n", - " '87',\n", - " '88',\n", - " '89',\n", - " '90',\n", - " '91',\n", - " '92',\n", - " '93',\n", - " '94',\n", - " '95'],\n", - " 26: ChannelSliceRecording: 65 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 55.25 GiB,\n", - " 28: array([5, 3, 3, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 9, 9,\n", - " 9, 9, 9, 9, 3, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]),\n", - " 30: array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3]),\n", - " 31: 51,\n", - " 32: array([0, 0, 0, 5, 0, 0, 6, 0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1, 4, 4,\n", - " 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 3, 3, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9,\n", - " 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9, 9, 9, 3, 9, 9, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,\n", - " 8, 8, 8]),\n", - " 34: array([0, 0, 0, 5, 0, 0, 6, 0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1, 4, 4,\n", - " 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 3, 3, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9,\n", - " 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9, 9, 9, 3, 9, 9, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,\n", - " 8, 8, 8]),\n", - " 35: array([False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, True, True, True, True,\n", - " True, True, True, True, False, False, False, False, False,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, False, False, False, False, False, False, True, False,\n", - " False, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False]),\n", - " 37: 51,\n", - " 38: 51,\n", - " 40: ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB,\n", - " 43: array(['32', '33', '34', '35', '36', '37', '38', '39', '45', '46', '47',\n", - " '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58',\n", - " '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69',\n", - " '70', '71', '72', '79', '82', '83', '84', '85', '86', '87', '88',\n", - " '89', '90', '91', '92', '93', '94', '95'], dtype=',\n", - " 84: ['ironclust',\n", - " 'kilosort2_5',\n", - " 'kilosort4',\n", - " 'mountainsort5',\n", - " 'simple',\n", - " 'spykingcircus2',\n", - " 'tridesclous',\n", - " 'tridesclous2'],\n", - " 86: {'detect_threshold': 6,\n", - " 'projection_threshold': [10, 4],\n", - " 'preclust_threshold': 8,\n", - " 'whiteningRange': 32.0,\n", - " 'momentum': [20.0, 400.0],\n", - " 'car': True,\n", - " 'minFR': 0.1,\n", - " 'minfr_goodchannels': 0.1,\n", - " 'nblocks': 5,\n", - " 'sig': 20,\n", - " 'freq_min': 150,\n", - " 'sigmaMask': 30,\n", - " 'lam': 10.0,\n", - " 'nPCs': 3,\n", - " 'ntbuff': 64,\n", - " 'nfilt_factor': 4,\n", - " 'NT': None,\n", - " 'AUCsplit': 0.9,\n", - " 'do_correction': True,\n", - " 'wave_length': 61,\n", - " 'keep_good_only': False,\n", - " 'skip_kilosort_preprocessing': False,\n", - " 'scaleproc': None,\n", - " 'save_rez_to_mat': False,\n", - " 'delete_tmp_files': ('matlab_files',),\n", - " 'delete_recording_dat': False,\n", - " 'n_jobs': -1,\n", - " 'chunk_duration': '1s',\n", - " 'progress_bar': True,\n", - " 'mp_context': None,\n", - " 'max_threads_per_process': 1},\n", - " 91: ['ironclust',\n", - " 'kilosort2_5',\n", - " 'kilosort4',\n", - " 'mountainsort5',\n", - " 'simple',\n", - " 'spykingcircus2',\n", - " 'tridesclous',\n", - " 'tridesclous2'],\n", - " 92: {'detect_threshold': 6,\n", - " 'projection_threshold': [10, 4],\n", - " 'preclust_threshold': 8,\n", - " 'whiteningRange': 32.0,\n", - " 'momentum': [20.0, 400.0],\n", - " 'car': True,\n", - " 'minFR': 0.1,\n", - " 'minfr_goodchannels': 0.1,\n", - " 'nblocks': 5,\n", - " 'sig': 20,\n", - " 'freq_min': 150,\n", - " 'sigmaMask': 30,\n", - " 'lam': 10.0,\n", - " 'nPCs': 3,\n", - " 'ntbuff': 64,\n", - " 'nfilt_factor': 4,\n", - " 'NT': None,\n", - " 'AUCsplit': 0.9,\n", - " 'do_correction': True,\n", - " 'wave_length': 61,\n", - " 'keep_good_only': False,\n", - " 'skip_kilosort_preprocessing': False,\n", - " 'scaleproc': None,\n", - " 'save_rez_to_mat': False,\n", - " 'delete_tmp_files': ('matlab_files',),\n", - " 'delete_recording_dat': False,\n", - " 'n_jobs': -1,\n", - " 'chunk_duration': '1s',\n", - " 'progress_bar': True,\n", - " 'mp_context': None,\n", - " 'max_threads_per_process': 1},\n", - " 93: {'detect_sign': -1,\n", - " 'adjacency_radius': 50,\n", - " 'adjacency_radius_out': 100,\n", - " 'detect_threshold': 3.5,\n", - " 'prm_template_name': '',\n", - " 'freq_min': 300,\n", - " 'freq_max': 8000,\n", - " 'merge_thresh': 0.985,\n", - " 'pc_per_chan': 9,\n", - " 'whiten': False,\n", - " 'filter_type': 'bandpass',\n", - " 'filter_detect_type': 'none',\n", - " 'common_ref_type': 'trimmean',\n", - " 'batch_sec_drift': 300,\n", - " 'step_sec_drift': 20,\n", - " 'knn': 30,\n", - " 'min_count': 30,\n", - " 'fGpu': True,\n", - " 'fft_thresh': 8,\n", - " 'fft_thresh_low': 0,\n", - " 'nSites_whiten': 16,\n", - " 'feature_type': 'gpca',\n", - " 'delta_cut': 1,\n", - " 'post_merge_mode': 1,\n", - " 'sort_mode': 1,\n", - " 'fParfor': False,\n", - " 'filter': True,\n", - " 'clip_pre': 0.25,\n", - " 'clip_post': 0.75,\n", - " 'merge_thresh_cc': 1,\n", - " 'nRepeat_merge': 3,\n", - " 'merge_overlap_thresh': 0.95,\n", - " 'version': 2,\n", - " 'n_jobs': -1,\n", - " 'chunk_duration': '1s',\n", - " 'progress_bar': True,\n", - " 'mp_context': None,\n", - " 'max_threads_per_process': 1},\n", - " 94: PosixPath('/media/nas8/OB_ferret_AG_BM/Shropshire/freely-moving/20241205_TORCs'),\n", - " 95: ,\n", - " 98: ,\n", - " 101: ['gain_to_uV',\n", - " 'offset_to_uV',\n", - " 'channel_names',\n", - " 'group',\n", - " 'contact_vector',\n", - " 'location'],\n", - " 102: ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB,\n", - " 105: ['ironclust',\n", - " 'kilosort2_5',\n", - " 'kilosort4',\n", - " 'mountainsort5',\n", - " 'simple',\n", - " 'spykingcircus2',\n", - " 'tridesclous',\n", - " 'tridesclous2'],\n", - " 110: ['ironclust',\n", - " 'kilosort2_5',\n", - " 'kilosort4',\n", - " 'mountainsort5',\n", - " 'simple',\n", - " 'spykingcircus2',\n", - " 'tridesclous',\n", - " 'tridesclous2'],\n", - " 111: ['ironclust',\n", - " 'kilosort2_5',\n", - " 'kilosort4',\n", - " 'mountainsort5',\n", - " 'simple',\n", - " 'spykingcircus2',\n", - " 'tridesclous',\n", - " 'tridesclous2'],\n", - " 112: {'detect_threshold': 6,\n", - " 'projection_threshold': [10, 4],\n", - " 'preclust_threshold': 8,\n", - " 'whiteningRange': 32.0,\n", - " 'momentum': [20.0, 400.0],\n", - " 'car': True,\n", - " 'minFR': 0.1,\n", - " 'minfr_goodchannels': 0.1,\n", - " 'nblocks': 5,\n", - " 'sig': 20,\n", - " 'freq_min': 150,\n", - " 'sigmaMask': 30,\n", - " 'lam': 10.0,\n", - " 'nPCs': 3,\n", - " 'ntbuff': 64,\n", - " 'nfilt_factor': 4,\n", - " 'NT': None,\n", - " 'AUCsplit': 0.9,\n", - " 'do_correction': True,\n", - " 'wave_length': 61,\n", - " 'keep_good_only': False,\n", - " 'skip_kilosort_preprocessing': False,\n", - " 'scaleproc': None,\n", - " 'save_rez_to_mat': False,\n", - " 'delete_tmp_files': ('matlab_files',),\n", - " 'delete_recording_dat': False,\n", - " 'n_jobs': -1,\n", - " 'chunk_duration': '1s',\n", - " 'progress_bar': True,\n", - " 'mp_context': None,\n", - " 'max_threads_per_process': 1},\n", - " 113: {'detect_sign': -1,\n", - " 'adjacency_radius': 50,\n", - " 'adjacency_radius_out': 100,\n", - " 'detect_threshold': 3.5,\n", - " 'prm_template_name': '',\n", - " 'freq_min': 300,\n", - " 'freq_max': 8000,\n", - " 'merge_thresh': 0.985,\n", - " 'pc_per_chan': 9,\n", - " 'whiten': False,\n", - " 'filter_type': 'bandpass',\n", - " 'filter_detect_type': 'none',\n", - " 'common_ref_type': 'trimmean',\n", - " 'batch_sec_drift': 300,\n", - " 'step_sec_drift': 20,\n", - " 'knn': 30,\n", - " 'min_count': 30,\n", - " 'fGpu': True,\n", - " 'fft_thresh': 8,\n", - " 'fft_thresh_low': 0,\n", - " 'nSites_whiten': 16,\n", - " 'feature_type': 'gpca',\n", - " 'delta_cut': 1,\n", - " 'post_merge_mode': 1,\n", - " 'sort_mode': 1,\n", - " 'fParfor': False,\n", - " 'filter': True,\n", - " 'clip_pre': 0.25,\n", - " 'clip_post': 0.75,\n", - " 'merge_thresh_cc': 1,\n", - " 'nRepeat_merge': 3,\n", - " 'merge_overlap_thresh': 0.95,\n", - " 'version': 2,\n", - " 'n_jobs': -1,\n", - " 'chunk_duration': '1s',\n", - " 'progress_bar': True,\n", - " 'mp_context': None,\n", - " 'max_threads_per_process': 1},\n", - " 114: ChannelSliceRecording: 51 channels - 30.0kHz - 1 segments - 456,321,792 samples \n", - " 15,210.73s (4.23 hours) - int16 dtype - 43.35 GiB,\n", - " 115: ['gain_to_uV',\n", - " 'offset_to_uV',\n", - " 'channel_names',\n", - " 'group',\n", - " 'contact_vector',\n", - " 'location'],\n", - " 119: ,\n", - " 121: }" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Out" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "id": "aeec9b8c-1568-4c7f-a26f-52e1c5241055", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kilosort2_5 /media/nas8/OB_ferret_AG_BM/Shropshire/freely-moving/20241205_TORCs/kilosort2_5_output\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:08, 9.78it/s]\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "\n", - "\n", - "\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:08, 9.78it/s]\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "\n", - "\n", - "\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "\n", - "write_binary_recording: 57%|#####7 | 8680/15211 [14:47<11:07, 9.78it/s]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Process LokyProcess-116:\n", - "Process LokyProcess-119:\n", - "Process LokyProcess-105:\n", - "Process LokyProcess-114:\n", - "Process LokyProcess-103:\n", - "Process LokyProcess-109:\n", - "Process LokyProcess-115:\n", - "Process LokyProcess-100:\n", - "Process LokyProcess-101:\n", - "Process LokyProcess-104:\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 478, in _process_worker\n", - " _process_reference_size = _get_memory_usage(pid, force_gc=True)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 109, in _get_memory_usage\n", - " gc.collect()\n", - "KeyboardInterrupt\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 235, in put\n", - " with self._wlock:\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/loky/backend/synchronize.py\", line 119, in __enter__\n", - " return self._semlock.acquire()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 235, in put\n", - " with self._wlock:\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/loky/backend/synchronize.py\", line 119, in __enter__\n", - " return self._semlock.acquire()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - "Traceback (most recent call last):\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - "Process LokyProcess-112:\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 235, in put\n", - " with self._wlock:\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/loky/backend/synchronize.py\", line 119, in __enter__\n", - " return self._semlock.acquire()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 235, in put\n", - " with self._wlock:\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/loky/backend/synchronize.py\", line 119, in __enter__\n", - " return self._semlock.acquire()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - "Traceback (most recent " - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py:1650\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m 1649\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1650\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[1;32m 1653\u001b[0m \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[1;32m 1654\u001b[0m \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[1;32m 1655\u001b[0m \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py:1762\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m 1760\u001b[0m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mget_status(\n\u001b[1;32m 1761\u001b[0m timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;241m==\u001b[39m TASK_PENDING)):\n\u001b[0;32m-> 1762\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[135], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m sortings[sorter_name] \u001b[38;5;241m=\u001b[39m sf\u001b[38;5;241m.\u001b[39mread_sorter_folder(output_folder)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m----> 9\u001b[0m sortings[sorter_name] \u001b[38;5;241m=\u001b[39m \u001b[43msf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_sorter_by_property\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorter_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msorter_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrecording\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mrecording\u001b[49m\u001b[43m,\u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrouping_property\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchannel_names\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfolder\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43moutput_folder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mjoblib\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/launcher.py:308\u001b[0m, in \u001b[0;36mrun_sorter_by_property\u001b[0;34m(sorter_name, recording, grouping_property, folder, mode_if_folder_exists, engine, engine_kwargs, verbose, docker_image, singularity_image, working_folder, **sorter_params)\u001b[0m\n\u001b[1;32m 297\u001b[0m job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 298\u001b[0m sorter_name\u001b[38;5;241m=\u001b[39msorter_name,\n\u001b[1;32m 299\u001b[0m recording\u001b[38;5;241m=\u001b[39mrec,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msorter_params,\n\u001b[1;32m 305\u001b[0m )\n\u001b[1;32m 306\u001b[0m job_list\u001b[38;5;241m.\u001b[39mappend(job)\n\u001b[0;32m--> 308\u001b[0m sorting_list \u001b[38;5;241m=\u001b[39m \u001b[43mrun_sorter_jobs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjob_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 310\u001b[0m unit_groups \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sorting, group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sorting_list, recording_dict\u001b[38;5;241m.\u001b[39mkeys()):\n", - "File \u001b[0;32m~/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/launcher.py:115\u001b[0m, in \u001b[0;36mrun_sorter_jobs\u001b[0;34m(job_list, engine, engine_kwargs, return_output)\u001b[0m\n\u001b[1;32m 113\u001b[0m n_jobs \u001b[38;5;241m=\u001b[39m engine_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_jobs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 114\u001b[0m backend \u001b[38;5;241m=\u001b[39m engine_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbackend\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 115\u001b[0m sortings \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_sorter\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mjob_list\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_output:\n\u001b[1;32m 117\u001b[0m out\u001b[38;5;241m.\u001b[39mextend(sortings)\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py:2007\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 2001\u001b[0m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[1;32m 2002\u001b[0m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[1;32m 2003\u001b[0m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[1;32m 2004\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[1;32m 2005\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 2007\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py:1703\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m 1701\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m:\n\u001b[1;32m 1702\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 1703\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_abort\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1704\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m 1705\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 1706\u001b[0m \u001b[38;5;66;03m# Store the unconsumed tasks and terminate the workers if necessary\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py:1614\u001b[0m, in \u001b[0;36mParallel._abort\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1609\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aborted \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(backend, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mabort_everything\u001b[39m\u001b[38;5;124m'\u001b[39m)):\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;66;03m# If the backend is managed externally we need to make sure\u001b[39;00m\n\u001b[1;32m 1611\u001b[0m \u001b[38;5;66;03m# to leave it in a working state to allow for future jobs\u001b[39;00m\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;66;03m# scheduling.\u001b[39;00m\n\u001b[1;32m 1613\u001b[0m ensure_ready \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_managed_backend\n\u001b[0;32m-> 1614\u001b[0m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabort_everything\u001b[49m\u001b[43m(\u001b[49m\u001b[43mensure_ready\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_ready\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1615\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aborted \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/_parallel_backends.py:620\u001b[0m, in \u001b[0;36mLokyBackend.abort_everything\u001b[0;34m(self, ensure_ready)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mabort_everything\u001b[39m(\u001b[38;5;28mself\u001b[39m, ensure_ready\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 618\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Shutdown the workers and restart a new one with the same parameters\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_workers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mterminate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkill_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_workers \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_ready:\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/executor.py:75\u001b[0m, in \u001b[0;36mMemmappingExecutor.terminate\u001b[0;34m(self, kill_workers)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mterminate\u001b[39m(\u001b[38;5;28mself\u001b[39m, kill_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshutdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkill_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkill_workers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;66;03m# When workers are killed in a brutal manner, they cannot execute the\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;66;03m# finalizer of their shared memmaps. The refcount of those memmaps may\u001b[39;00m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;66;03m# be off by an unknown number, so instead of decref'ing them, we force\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# with allow_non_empty=True but if we can't, it will be clean up later\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# on by the resource_tracker.\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_submit_resize_lock:\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:1303\u001b[0m, in \u001b[0;36mProcessPoolExecutor.shutdown\u001b[0;34m(self, wait, kill_workers)\u001b[0m\n\u001b[1;32m 1299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m executor_manager_thread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m wait:\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;66;03m# This locks avoids concurrent join if the interpreter\u001b[39;00m\n\u001b[1;32m 1301\u001b[0m \u001b[38;5;66;03m# is shutting down.\u001b[39;00m\n\u001b[1;32m 1302\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _global_shutdown_lock:\n\u001b[0;32m-> 1303\u001b[0m \u001b[43mexecutor_manager_thread\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1304\u001b[0m _threads_wakeups\u001b[38;5;241m.\u001b[39mpop(executor_manager_thread, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 1306\u001b[0m \u001b[38;5;66;03m# To reduce the risk of opening too many files, remove references to\u001b[39;00m\n\u001b[1;32m 1307\u001b[0m \u001b[38;5;66;03m# objects that use file descriptors.\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/threading.py:1096\u001b[0m, in \u001b[0;36mThread.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot join current thread\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1095\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1096\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait_for_tstate_lock\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1097\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wait_for_tstate_lock(timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(timeout, \u001b[38;5;241m0\u001b[39m))\n", - "File \u001b[0;32m~/miniconda3/envs/neuroencoders/lib/python3.10/threading.py:1116\u001b[0m, in \u001b[0;36mThread._wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 1115\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mlock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 1117\u001b[0m lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stop()\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "call last):\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 236, in put\n", - " self._writer.send_bytes(obj)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/connection.py\", line 200, in send_bytes\n", - " self._send_bytes(m[offset:offset + size])\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/connection.py\", line 411, in _send_bytes\n", - " self._send(header + buf)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/connection.py\", line 368, in _send\n", - " n = write(self._handle, buf)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 463, in _process_worker\n", - " r = call_item()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 291, in __call__\n", - " return self.fn(*self.args, **self.kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in __call__\n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/parallel.py\", line 598, in \n", - " return [func(*args, **kwargs)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 199, in run_sorter\n", - " return run_sorter_local(**common_kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 235, in put\n", - " with self._wlock:\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/runsorter.py\", line 259, in run_sorter_local\n", - " SorterClass.setup_recording(recording, folder, verbose=verbose)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/loky/backend/synchronize.py\", line 119, in __enter__\n", - " return self._semlock.acquire()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/basesorter.py\", line 234, in setup_recording\n", - " cls._setup_recording(recording, sorter_output_folder, sorter_params, verbose)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/sorters/external/kilosortbase.py\", line 155, in _setup_recording\n", - " write_binary_recording(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 151, in write_binary_recording\n", - " executor.run()\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/job_tools.py\", line 409, in run\n", - " res = self.func(segment_index, frame_start, frame_stop, worker_ctx)\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/recording_tools.py\", line 184, in _write_binary_chunk\n", - " traces = recording.get_traces(\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/baserecording.py\", line 342, in get_traces\n", - " traces = rs.get_traces(start_frame=start_frame, end_frame=end_frame, channel_indices=channel_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/core/channelslice.py\", line 99, in get_traces\n", - " traces = self._parent_recording_segment.get_traces(start_frame, end_frame, parent_indices)\n", - " File \"/home/mickey/Documents/Theotime/spikeinterface/src/spikeinterface/extractors/neoextractors/neobaseextractor.py\", line 378, in get_traces\n", - " raw_traces = self.neo_reader.get_analogsignal_chunk(\n", - " File \"/home/mickey/Documents/Theotime/python-neo/neo/rawio/baserawio.py\", line 828, in get_analogsignal_chunk\n", - " raw_chunk = self._get_analogsignal_chunk(block_index, seg_index, i_start, i_stop, stream_index, channel_indexes)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 235, in put\n", - " with self._wlock:\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/loky/backend/synchronize.py\", line 119, in __enter__\n", - " return self._semlock.acquire()\n", - "Traceback (most recent call last):\n", - "KeyboardInterrupt\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 466, in _process_worker\n", - " result_queue.put(_ResultItem(call_item.work_id, exception=exc))\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py\", line 235, in put\n", - " with self._wlock:\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/loky/backend/synchronize.py\", line 119, in __enter__\n", - " return self._semlock.acquire()\n", - "KeyboardInterrupt\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 478, in _process_worker\n", - " _process_reference_size = _get_memory_usage(pid, force_gc=True)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 109, in _get_memory_usage\n", - " gc.collect()\n", - "KeyboardInterrupt\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/_common.py\", line 502, in wrapper\n", - " ret = self._cache[fun]\n", - "AttributeError: 'Process' object has no attribute '_cache'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 478, in _process_worker\n", - " _process_reference_size = _get_memory_usage(pid, force_gc=True)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 111, in _get_memory_usage\n", - " mem_size = Process(pid).memory_info().rss\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/__init__.py\", line 319, in __init__\n", - " self._init(pid)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/__init__.py\", line 355, in _init\n", - " self._ident = self._get_ident()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/__init__.py\", line 396, in _get_ident\n", - " return (self.pid, self.create_time())\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/__init__.py\", line 778, in create_time\n", - " self._create_time = self._proc.create_time()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/_pslinux.py\", line 1717, in wrapper\n", - " return fun(self, *args, **kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/_pslinux.py\", line 1953, in create_time\n", - " ctime = float(self._parse_stat_file()['create_time'])\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/_pslinux.py\", line 1717, in wrapper\n", - " return fun(self, *args, **kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/_common.py\", line 506, in wrapper\n", - " return fun(self)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/psutil/_pslinux.py\", line 1786, in _parse_stat_file\n", - " fields = data[rpar + 2 :].split()\n", - "KeyboardInterrupt\n", - "Process LokyProcess-107:\n", - "Traceback (most recent call last):\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n", - " self.run()\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 478, in _process_worker\n", - " _process_reference_size = _get_memory_usage(pid, force_gc=True)\n", - " File \"/home/mickey/miniconda3/envs/neuroencoders/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 109, in _get_memory_usage\n", - " gc.collect()\n", - "KeyboardInterrupt\n" - ] - } - ], - "source": [ - "# run sorter (if not already done)\n", - "sortings = {}\n", - "for sorter_name in sorter_names:\n", - " output_folder = base_folder / f\"{sorter_name}_output\"\n", - " print(sorter_name, output_folder)\n", - " if output_folder.exists():\n", - " sortings[sorter_name] = sf.read_sorter_folder(output_folder)\n", - " else:\n", - " sortings[sorter_name] = sf.run_sorter_by_property(\n", - " sorter_name=sorter_name,\n", - " recording=recording,\n", - " grouping_property=\"channel_names\",\n", - " folder=output_folder,\n", - " verbose=True,\n", - " engine=\"joblib\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74e53ce2-1e44-4944-ac7f-4ddfcd46f4d1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "b9af05e0-fd9e-4e92-ab3b-cc078391001e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'kilosort4': KiloSortSortingExtractor: 154 units - 1 segments - 20.0kHz,\n", - " 'spykingcircus2': NumpyFolder: 73 units - 1 segments - 20.0kHz,\n", - " 'klustakwik': NeuroScopeSortingExtractor: 57 units - 1 segments - 20.0kHz}" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sortings" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "36e160e7-faf5-4dad-aeff-f4b756d1eb4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kilosort4\n", - "spykingcircus2\n", - "klustakwik\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b33a81d3ec264de0a7d06eabd10c9808", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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BEEQwEJC7gNeuXYtPP/0UI0aMwLRp05Cbm4vPPvsMAFBfX48//OEP6NGjB7p164Zjx47hqaeeQmtrK959913MmDEDJ0+eRHx8PEJDQ3H58mWYzWb06dMHNTU1GDNmDDZu3CiM9+LFi04yMY2NjejXr1+X3kVEEARBEIEG7QIOUA+g1lFw/fv3x/nz5/HKK6+4TOd++OGHmDFjBuLi4vCzn/0My5cvB2MMLS0t0r18owhBEARBEESwEpBrALWOggOAa665BsuWLcNHH32EP/7xj9Jxb3Jn56lTp3Dp0iVEREQgLCxMup6enq4ar9pRcDt37sSQIUPQt29f2Gw26X673Y5x48bBbrdLf9tsNpSUlMDhcMDhcKC4uBjFxcWw2+3SdTnyMIC2wa6e+wDAZrMhMTERsbGxSEpKQmVlJUpKSoRx2e12DBkyBElJSSgoKHAJ3xNsNhtSU1Od8kSOPC3Ke9XS6S4ctc+Li4tRUFCAvLw8pzJQIspHtTCnTZuGnj17Yvr06U5hcXvUyhRoyxuLxYIhQ4bAbrejsrIS0dHRTmFxu61WK6ZNmwaLxSKVjVaaHQ4HCgoKkJiYiEGDBmHkyJGw2WywWq0YMmQIhg4digcffNCneVtcXKx6D09renq6U7zysEXx2O125OXludiqvFd+n81mQ15eHgoKCqR2xe2z2WzCslVLA28/3bp1g8lkQk5OjnSPyAZl2KJwKysrERsbi8rKSqd08mcdDgdGjRoFk8mE5ORkYbu32+3Izs5GRkaGalmK6pOe8lSivF/ZlkR5KQrfZrMhJSUFBQUFqv1PXl4epk2bJmyjPM0jR450yhPePuTlMm3aNHTv3h0ZGRnSvco8VtY7m83mFL4o3WrlLQ9DVF/lecD7ON7fxsXFIT4+XsqX4uJiTJs2DSkpKdJ9evojuY3Kem6325Geno6oqCgsW7bMJ/2qvB5kZ2dLaVbr89Xu19O/KL8v5f14QUGBU17x/BfVMT19VZeDBSBNTU2sqqqKVVVVMQAsKyuLVVVVsUOHDrEzZ86wefPmsRdffJF169aNAZB+T5kyhTHG2L59+xgAduedd7J9+/axiRMnspCQENa/f39mtVpV421oaGCHDx+Wfnbv3s0AsPLycgaAAWBPP/20dP/y5csZALZ8+XLp76effpoBYNu3b2fbt2+XnuOfb9++3SlOeRiMMekZd/cxxqS4+M/s2bNV4+LX+I8yfE/g8cvzRI48Lcp71dLpLhytz+VlpHa/KB/1hCkPi3+mVqaMOZfN8uXLpbKR36+MQ/65VppFzynrgj/yVu0eZdzK9KmlR14nRfnLr8nvU8Yl+kxZtmppEOWZmr2ieiMKl5fz7NmzXdK5fPlyl7ITtXtlWxXlvag+6SlPJcr71exzF76yvrvrf5RtVP65Mk9E5aK8V5TH8vuV9qmlW1TeojC02vzTTz8tLENRHujtj+Q2Kuu5PNzp06f7pO2L+hhRP+7ufr39i6hs5fmtzFNRHVO2x4aGBgaANTQ0uM2LYCUgPYBaR8GFhIRg1apVePzxx3H+/HmYTCY0NzcDgCTwfPjwYQDA6tWrkZaWho8++ggtLS1oampyOjlEiZ6j4LjkTGfk008/dfq/rKzM4zchd969YMbhcEjLD7oydrsds2bNUv2cv70r28SJEyc0w+XeopKSEq9trKio8DoMJe7s5+zdu1f6e9asWbq8OL6C93Edxd69ezW9Ye3F3/72N133deZ+2yg8LRUVFR57en1hgzw8UZspKyvT3ZbU4lBj5cqVHofblQjIAaCWDExLSwuuueYa/PznP0dkZCQYY9LU7+XLlwEAoaFtSx9NJhNuueUWhISEYMSIETh16hRGjRqlGq8eGZjO3JHs3LnT6f/y8nKPG/y2bdtw6NAhbNu2zRemBRQOhwPl5eUdbUaHU11djR07dqh+7nA4sHDhQpc2wZduqLFt2zbU1NRg3759Xtu4du1ar8NQ4s5+zv79+6W/d+zYgerqap/bosaxY8faLS4R+/fvx9atW716yfQFX331la77OnO/bRSelrVr1xoaAC5cuNCnA0B5eKI2U15errstqcWhBg0A9RGQm0C0ZGB+//vf45tvvsE///lPtLS0wGQyITQ0FC0tLTh16hQASGsCY2JisGHDBrS0tGDHjh3o3bs3vvvuO9V41WRg0tPTkZaWhn379iEzM1O6npmZibFjx0rXxo4di5ycHCxYsEDyRvJzizMzM52uq4VhsVh03QcAOTk5SEhIwLlz53D27FkAQGFhITIzM1FYWOg0iMnMzERqaioOHjyICRMmqB6HZ4ScnBwMGDAAaWlpKCkpgdVqdQpXnpa0tDTExMRIEj1q6RRRX1+PAQMGoL6+Xvi5xWJBUVERjh8/jn379rmUgRxRPqoxfvx4/Pvf/8Ztt90mTJdamQJAWloaevTogf79+yMzMxMhISFYvnw5brnlFul+bveZM2dQV1eHLVu24KabbpI+VwvbYrEgPz8fH3zwgbReNScnB4WFhfjss89gMpmQl5enK2/1lMOkSZOQnp6uek9mZib69OmDyMhIHDx4EPHx8cKw1eLhYXPP65133ul0b2ZmJnJzczF06FCkpaUhOTkZ11xzDTZs2AAAyM/PR2JiInJycoRly/OZ/83h7aepqQkXLlxA7969UV9fj/j4eBfbRfUmNjYWADBkyBDp5SsrKwsRERHIyspyyh/+bFJSEvr27Yva2lpERUUJ273ZbMaIESNw/vx5hIWFCcsyNTUVn3/+OTIyMlTbnB6U9/O6tXz5cmRlZanmpVJQPycnB8nJyRg/frywXfAy5GlXttHMzEyMGDECJpPJKU94++jZs6d076RJk6SBv8lkEuaxst6Fh4cDaGuXonu0ypvfm5OTI9VDUf7y/pDXq6ysLBw5cgRmsxmTJk1CZmYmioqKcPDgQWzbtk26T29/xG1MSEhwup6ZmYnBgwfjyJEjmDBhAjIzM71u+/I+dffu3Rg1ahQsFouURqW9vBzGjx+PhoYGpKamYuXKlTCZTBg7dqz0nezOBnk/zuPgbTshIUGqQytXrkRhYaFL3Re18y5NR89Be8L777/Pfv7zn7MlS5YwwHUN4B133MFefvlltmnTJvbuu++yhIQEBoAVFBQwxhj77rvvGADWvXt3BoBFRkYyACw3N5f95Cc/UY1XbQ1gQ0OD4bU17YloLZLWmis9603cre8Txa+VN97knxG7fYEvyroz1xe9uMt3d+uo1BCtXdX7vHL9pa/yWM9aRyXcBvnaWz1pEK0TNIqR9mkUo2tu27uOy+PPyMjQ9Ux79yH+xJO66uv+yN0aWT3rpD21WW9aaA0gY0HrAfzHP/4hrefjbx+c1NRUhIeH49KlSwCueASrqqowfvx41Xi1hKCNvlm3J6I3H5G93DPDf2shf5vtanTmsm5P3HlLRd4jPfnGvUUDBgzArl27MGbMGMO2xcfH++VtX+lV0ILnz5gxY7B7925kZmZqrjHmJCcnO/32BJ6He/fuhcPh8Gke6C3HrKwsjB49ukPbyXXXXafrPiN9X2fHE0+Xr/s0ZXjK/OWfe5PfajZT/2yAjh6BeoIeD+ALL7zA3n33XbZw4UIWERHBALC3335bCmPq1KnMZDKxwsJCtnr1aumNaeXKlarxankAgwF/eaWCzQPoC4LBA9je+LseeRu3r8LxVX3uqDrW0XVb7gHTm4cdbXOw4623zh+QB7CLeQA/+eQTFBQU4OTJk9i7dy/Cw8Px2muv4bXXXnMKW41gPwrOX29OesL1Jm4j6/Y6C/SWahx/1yNv4/ZVOIFYn+V0dN22WCwoLCzEzp07dedhR9sc7JC3rpPS0SNQT1izZg2bP38+e//996W1e3IqKirY3/72N/bRRx+xP/7xjyw+Pp4BYD/72c8YY4z97//+L+vRowf705/+xPbt28f+8Y9/SG+MH374oWq8Fy5cYA0NDdLP4cOHGQD20EMPsaKiIlZbW8tqa2vZggULWG1tLWOMSf9XVVU5XTeKMlzR50VFRayoqEgzLn5ffn4+y83NZVVVVbrj0GufWh7Iw9WKy9PP1Ozi+aJ8Rq1sjNqrRlVVFcvNzWVWq9Xtc0bSrKdOaZWHu/Rr2ajMS+U1rfx2l3Zuhzt7ROmpqqpiY8eOdarPevNBbxy+QE/+iMrbkzzm3pV169ZJ+VpUVMQKCwsNlU9nRW+b8bQOuIvT237KU1tENmi1HWU/5K+6rbTNl2EYKUPeF6xfv16zPyEPYJtESsDhrRD0vffeywAwk8nkIk554403qsarNgDkPyJhS18sdpWHo2fhtVZcWkKu3rjjRYKdWov/9YiMGv1Myy7RM2plY9ReNYxsRDCSZj11Sqs83KXfnY0iW5RxeVJG8qlPPXVdzwYmPfmgNw5foCd/1MrbaB7zL8p169a5bIzxR9raG71txtM64C5Ob/spT20R2aDVdpT9kD+nXX0Rtrv+110cykMX1PoTGgB2USHoRx55BP379xduPdfSd1M7Cq49OXHihGHBTl+LfPoab+0zclySt5SVlWke8aYHPent7GXmD0i7qw0ujuuNSC6XzLFarS6yIMGOHqH2jtYn9ATeJ8jrh1FBem/qVCDgcDhU+xFPvjuDnYAcAHorBJ2dnY05c+bgmWeeweLFi6VwQ0JCUFVVpRqvHiFof1NXV2dYsNPXIp++xlv7qqursXHjxnYR2i0vL0d1dbVX9upJb2cvM39AA8A2uDiuNyK5XbH+cPQItXsjgt9R8DKV1w+jgvTe1KlAQGsA6Ml3Z7ATtJtAtISgAWD27NmYO3cu/vjHP0rXzGYzLl68qBqv2iaQu+66CykpKUJBWz2iwHpwt21evvVfKy6lKLJ8kbQ3C3LVRH0dDgcqKipQVFQkfaa8pmyQ7gRIjdioJYmgVjbKOLi9M2fONHS0mFyg2J29RtKsp07pEVk2WjfV5ISU1zyRoODC5FyoXMseUV6pbZzQkw964/AFeiQ6RJIZ3uSxvJxFwsmBiloZcS/XzJkzheWuFMH3Jk5P+ik1QXO9xMfHO30XyNuMmsh2VlYWdu3ahXXr1rncI/cW+2OTk9HwRWHozXde9llZWZKYOE9vMEj8+JyOnoP2BG+FoPfv38+uv/56Fh8fz5KTkxkAFhcXx0JCQjQXr6rJwGzcuLG9ku7RGouO3GqvZz2dt/b5WwbG6Bo1veH5Y31QoNLV0quFv9ZRdRWMCpS3N75Y+2c0HD1rof2VH+2Z31plr7SD1gAG6BpA7gGcO3cuAOc1gK2trdizZw+KioowZswY/OQnP3E5IuzSpUv4/PPPUVdXh5qaGgBAREQEWltbXc7LlVNaWop+/fpJPxkZGQCAPn36uLXZV2u65G9C8jC1wu/MW+1FHkKjtJdsBn/zFr1lK/Pf2/LoLGXWXmsRO0t6OwO+yIuunJ/uRJ0DJW8cDgeKi4tRXFwsiXlrzVRowb3swY5W2QdKubcrHT0C9QQtD2BNTQ1LTU1lTz31FHv77bfZvHnzWGhoKAPA/vrXvzLGGHvvvfcYABYVFcUWL17MALCJEyeyyMhINn/+fNV4vRGC9sdbkC93t/kLb3d0dQY88dgFQrr0ECzpILoOnb3O6rXPkx31nsQbTB5AI3GRBzAIhaBfeeUVhIWF4dlnn0VLSwuAK0e9ffrpp7jjjjuwatUqAEBTUxPmzZsHAPjoo48AwOUQcznBLgTtD9yt5wiEBbn05kgQgUNnb68dZV9HiTG3Z3o7e9l3NgJyCviaa67B/Pnz8f777wMAcnNzwRjDW2+9hdbWVgwYMABvvPEGPvjgA7z22muIiooCAJw/fx4A0LdvXwBtu4F/97vfAQAee+wxAMDRo0dV4+0Mu4ADDYvFgpKSEpcBoPJaZybQ7CWIrkxnb68dZZ9avP62pz3T29nLvrMRkB5Ak8mETZs2SRpIDocDdrsdcXFxiI6OxpkzZ7Bw4ULU1taiR48eOHfuHADg7NmzAIC0tDQAwPbt27F9+3YAwAsvvAAAOHbsmGq83ngA/fFm4ukOx85EsLyxebM+pzMTLOkgiEBDz45xgvAGE2P/EckLIJ5//nk8/vjjLtfvvfdePP/888jKysLly5dx5swZmEwmXL58Ga2trXj88cexePFinDx5Er179xaGfc0112Dv3r3Czy5evOgkE9PY2Ih+/fqhoaEB0dHRvkkcQRAEQRB+pbGxETExMV36+zsgp4AfeughVFVVSaLNciHonj174oEHHsDf/vY3jB8/Hv3790fPnj0BABMnTgQAxMXF4f7770dkZCSAK2sETSYT7r///g5IEUEQBEEQRPsRkANAraPgWltb8ec//xljxozB+vXrsW/fPmkK+Prrr5fCCA8PR7du3RASEoLW1lYAAGMMV199tWq8neEoOIIgCIIgCG8JyAGg1lFwly5dwunTp9GrVy/k5ORgwIABCAsLQ2xsrOTx279/P37/+9/j/PnzmDNnDnJycmA2mxEfH48PPvhANV7aBEIQBEEQRDAQkJtAtGRg5s+fL513uG3bNume8+fPY9WqVbj77rslj+CFCxekQSTQdhawyWRSjZdkYAiCIAiCCAYCcgDIPYDp6emYO3cusrKy8PbbbyMuLg49evRAfHw8WltbMWjQIBw7dgwmkwkHDhxwWgMIAFFRUYiKikJtbS1SUlJQU1ODO++8UzXeoqIiPPDAA9L/TU1N0mkgBEEQBEEQgUJADgA98QACbWLPd999Nw4fPgygTRaGewOPHDkCs9mMQYMGqcZLHkCCIAiCIIKBoFsDGBcXh/j4ePTu3Rt/+ctfkJubi/DwcABXdgHzTR9hYWFoaWmR1gYmJyfjjTfeUI2X1gASBEEQBBEMBLUHcPr06U7PcQ8gF9XkA0EuA3P69Gl8//33qvGSB5AgCIIgiGAgID2AWkfBJSYmYsKECRg1ahTS0tKQm5uLiIgIAMCkSZMAAKmpqYiJiUH37t1RVlaGzZs3A2hb08e9hSLmzZuHhoYG6YdPJRMEEfw4HA6UlJQExPnVBEEQ7ghID6DWUXAJCQn4/vvvcfDgQbS2tuLAgQPSAPD48eOIjo7GqVOn0K1bNxw9ehSPPPKItCkEAGn7EQQhxOFwYOHChbjjjjvoaC6CIAKegPQAfvnll9i8ebM01Xvo0CFJCPrbb7/Fvn37pOPfWlpapI0e/Ii3iooKNDU1YcaMGYiLi8OpU6eksMeOHasab0cIQZPXgSAIgiAIXxOQA0Cto+AyMjJwxx13ICUlBePHj8fgwYNx1VVXAQCuvfZaAMD27dtx9uxZVFZW4ujRo07n+2qt8euITSDc60ADQIIgCIIgfEVADgC1joI7cOAA/va3v+HIkSPYsGEDvv76axw4cADAFQ/gI488gv79+0ubP+SUl5erxltaWop+/fpJP55oAHaER08ZZ2f0KtrtduTl5eHBBx80bJee9HgTvjfxBnpYgYrD4UBxcTGKi4t9lg8WiwULFizw+/Sv3W7HuHHjYLfbna7Ly1XtHne0d93QG5+v7OLh2O12j/o8teeNxO1tGvSUv7t7RdhsNqSmpsJms3lln4jO1ud42j66HCwAef/999nPf/5ztmTJEgaAZWVlsaqqKnbo0CFWV1fH4uPjWVxcHMvJyWEDBgxg3bp1Y7GxsezixYtSGC+99BJbvHgxW7x4MQPAALCQkBD27rvvqsbb0NDADh8+LP3s3r2bAWANDQ26bd++fTsDwLZv3+6X+/WE4Yswfc3y5culcjBql570eBO+N/EGeliBCs+DQMwHXleXL1/udF1ermr3uKO964be+HxlFw+H54/RPk/teSNxe5sGPeXv7l4RTz/9NAPAnn76aa/sE9HZ+hw9+dLQ0GD4+zvYCEgPIJeBmTt3LgBnD+DJkydRV1eHkydPYtu2bTh06BDOnz+P06dPY9WqVVIYs2fPRk1NDZ599lnpmtlsdpoOVqLmAVy8eLHf3nzkXofO9pblL06cOOFzb5h8nWd70VXKi/AvvB6dOHGio03p9PA86oj2ThCBRkAOAPUIQcfFxSEnJwepqanSGkAuBP3dd99hzJgxqKysRHR0NIC24+FaW1tx6623qsartgbwueee8+sAsKSkRBoAdoX1gHV1dT5LJ8+z06dPe2+Yh3EHe3kR/oXXI77pjVCH51FHtHeCCDQCUgZGSwi6pKTE7VFwly5dwueffw7GmPRZREQEWltbsXPnTtU1Pr4Qgm6vdURacXaEDe7IzMxEbm4uhg4divj4eEPP6knPwIEDpfC9TbfD4UBZWRnuvPNOn+WjL8ukM5Zve2OxWFBUVCT9HUhkZmZi7NixyMzMdLoeHx+PBQsWwGw2Y9OmTcjNzXW5xx3tXTf0xucru3jfUVVVhaKiIsN9Hr8vMzPTsD2+SoNa+YvCV7tXRE5ODgYMGICcnByv7BPR2focI/nSpenoOWhP0FoD2NzczIYPH86io6PZ/PnzGQDWt29fBoCdOHGCMcbYe++9xwCwqKgoaQ3gxIkTWWRkJJs/f75qvGprANFOax862zoLf9HZ18O197omomsTCGt4OwuBvPaTaF9oDWAQrgE8cuQIqqqq0NjYiEWLFgEAamtrAQBfffUVAEhrAZuamjBv3jwAbd7BCxcuYMuWLarxqq0BfPzxx9vlzaezvWX5i2DxhnWV8iL8C9Uj/VgsFhQWFna0GQQRGHT0CNQT1qxZw+bPn8/ef/99BoDl5uY6ff7GG2+wwYMHs8TERAaApaamOo3yi4uLGQCWnZ3Nfve73zEA7LHHHmMAWHp6umq8Fy5cYA0NDdLP4cOHVd8gamtrWWFhIcvNzWVVVVUep7W2tpYtWLCA1dbWGr5f7Vl316uqqoS/5fevX7+eDRgwgK1fv97naehI9Ni6bt06BoC9++67hvLX37R3vN7UTb33FxUVsaKiIsPPFBYWGnrOXZie5Ksn9jPGWFVVFcvNzWVWq1X4nB4PoKdxe4ooPmV/4o0dWulRls+rr77KALDIyEi2YsUKVlVVxcaOHWuoH/amzJXPKa+5K19f2GGUzt5H68lXd/fLqaqqYjfccEOX9wAG5ABw7dq17MYbb2Tx8fEMABswYIDTFPCoUaNYREQECwkJYQDYnDlzmMPhkGRgli1bJk0TKH/i4uJU4zUyAJRPRRiVahCF44lsjNqz7q7Lt9DLf8vvNyIpEEhTVnps5QPAV155xVD++pvOKvHh7f2ePuOrvPA0Xz2djnQnWaTny7q9p0JF8XkjqaInfOVn/Dp/mQfAZs+e7ZFkjrdlLn9Oec2IJFV7tenO3kfryVd398uRl0FXHgAG5BSw1lFwR44cweeff46LFy+ipaUFAPDSSy/BYrFI07t33XWXathaGxC0joLzVvLDn5IhWrIqcuFTfrYyl1BoLykFX6fd4XDAarUiLy+vXYRAPZGt4ULFVqvVrVhxe8jJeBPHiRMnNEWXHQ4HCgoKcNtttxmyw4jsCRd+3bp1q9P1srIyr/KNb/jRc58y/zyVbZG3O1EYcmUAkQ12ux2lpaXCsP0pBuyO0tJSFBcXC0WWjQgwK8tYi7Nnz3pls9w2PSLSeuqKvHz37t3rJFCvJmCu1scoxcHz8vJQUFDg9LzNZkNKSgoKCgp8LrQtLy/ezhMTEzFkyBCP+169Iu78YIetW7fCarUiOztbSjtvN962/2AnIAeAWkfBpaSkSEfBvfDCCwCA+fPnw+FwYPTo0QDaJF/uv/9+REZGAoB0IojJZML999+vGq/WUXDeSn74UzJES1aFx1tdXS2dgsIlFNpLSsHXaXc4HCgvL8fWrVtRXV3tkzDlKKUmPJGtcTgcKC0tRXl5OUpLS90OAP0tJ+NNHHV1dSgtLVVNh8PhwPLly3UNiOR2GJE9qa6uxsaNG6V1vpzy8nKvB4BapwPJ71Pmn6eyLfJ2ZyQMeVuuqKgQ3sO1UZUKCe1BRUUFSktLUV1d7ZJXctvd1UNlGStpamqS/ubnwHuD3raht67Iy3f//v3YunWrNFDh/YKyLan1MXLbqqursXXrVixfvtzp+W3btqGmpgbLly/3yQBQXk7y8pK38127dnnc96rlgZL9+/cDaKsP5eXl2LFjh5R23m68bf/BTkAOALWOgjty5Ih0FNxjjz0GAFi0aJGTBxAAwsPD0a1bN4SEhKC1tRUAwBjD1VdfrRqvkaPgLBYL8vPzkZycjMTERI/TanQBuPx+/rfSq6kVZmFhIQYOHAigTTpFTRJBj6QA98rU19cL42tPgVu9b/F68pvnJ88frfxVi9cfae4oiQ8jsj2jR4/WbZ/JZAIATJ06VfczUVFRAIDBgwdj6tSpuu1yR2FhoeF85fkyc+ZM6Vk99bBXr14uYXiKPG49GD0yjd/HZXdE0iuZmZlOGzO4B8xIG7BYLNILPC9jjtlsxtixY6UXebn0R58+fXRLgsi9WxUVFbBaraioqDDcVgsLC2E2m6X84XlTVlaG4uJiqXynTp2K2NhYt+k22sY4IrvV0qLMQ39jdMZB1LfxvFPWB6Ct3WhtBsrMzPToKNdgIyAHgFpC0Kmpqfjd736HBx98ENOmTQMAzJgxA1VVVRg8eDCAtjeH3//+9zh//jzmzJmDnJwcmM1mxMfH44MPPlCNV8sDqMRisWDOnDmoqalB7969PU6r2nSPnvv53wkJCbrDtFqtUj4NHjwYJSUlGDZsmMv9N998Mw4ePIibb75Z1RbulTl+/LjqlFV7CdzqfYvXk988P3n+aOWvWrz+SLPRuuKr+JTp18KIDiP7j06nJwPAr7/+2qcDQKvVajhfeb7IB0V66qF8UGAkb0XI49aDEW+X/D6LxYKlS5di6dKlTgNA3n9YrVbpWbnnXC8WiwWTJk0C4PqF39raio0bN0ov8nl5edJnSUlJGDZsGD755BMMGzZMV5qqq6tRWlqKG2+80cmbpBer1YrW1lYpfywWC2bOnCl5+3n5zp8/32mwr5Zuo22MI7JbLS3KPPQ3RmccRH0bzzvRADAhIcGpzikZNmwYHn30UWNGByFBJwT91ltvobq6Gr///e+lz1esWIEVK1ZgwYIFKCkpkaYFLly4IA0iASAkJETyOojQEoL21vviT++NVthqwqft6UniAre+io9LQezcudMvQqC+ENbmb/RTp05Famqq5rPt4dnzJg53osv8y3vt2rVuy0NuB/dW6PF+cC9PTk4OCgsLUV5e7pN65Y2Qsad5KvIcGrE1MzNTtTz8KQbszjZuE59hkJerHgFmvbbL65s7D5se9NYjpedfT3hms9lFoF5Udmp1SX6dh9WrVy+sXbtWyt+cnBwkJCTgxIkTXnuUleWkLK/8/Hx88MEHkufV0zj0iLgr2/wXX3yBjIwMJCYm6voe88YxEzR09C4UT9ASgmaMsd/97nfswQcfZNOmTWMA2IwZM1hVVRVzOByMMcaOHDkiCUFzkeiUlBRmMpnYP//5T9V41YSg1XYRdfadVf7G3e679syfzrYzt6vVDU/S68udmJ0FPbZ1pP3tIXLu6/SJwvNm56+W+oGnNrXnrmyjO2b1fN4eNrY3Gzdu7PK7gLukB/Dw4cMA2naIcW/gkSNHYDabMWjQINV4jR4F19UFXN2tvWnP/OmostDz5t4V8CS9nuZRZ85bPbZ1pP3eeDx9HYc34XlyFJg775Y3Nun1avkCT7zR7V3nOkMb7dOnT4fF3VkwMSY7EDdAWLlyJdasWYP09HTMnTsXWVlZePvttxEXF4f+/ftj2bJl2LlzJ06cOIH3338fM2bMwGOPPYY+ffqgT58+2LJlC2644QZERETg4sWLiIyMxIULF9CvXz/MnDkTixcvFsbb2NiIxsZG6f+mpiZkZGSgoaEB0dHR7ZV8giAIgiC8oLGxETExMV36+zsgN4FoHQUHQPIAvv/++wDaPIDDhw+XvIL8rYMveOU7n06fPo3vv/9eNV4ju4AJgiAIgiA6KwE5ALzmmmswf/58aYCXm5sLxhjeeustAG2LXhcuXCjtAvrv//5vMMZQUlICAEhNTUVMTAy6d++OsrIybN68GUCbRy88PFw13nnz5qGhoUH64VPJBEEQBEEQgURADgBvvPFGTJ8+XdpNdvbsWdjtdsl7d/vtt+OOO+6QtomfOHECdrsdR48eBdCmL5aRkYFz584hLi7Oyf0rP92DIAiCIAgiGAnIAaCWEDQA/PrXv8bw4cPx3HPPAXCdAq6oqMDu3bsRHR2Nu+66C9dcc40U9tixY1Xj1ToKjuh42uPINEIM5T3lQWcmWMqmM6ejM9tGiAnIAaCWEDTQtgNMJAT94IMPAgD+/Oc/o6GhAfX19WCMQb4PhochwogQNNH+tMeRaYQYynvKg85MsJRNZ05HZ7aNEBOQA0BvN4E89dRT6N+/P0JCQlzCfvXVV1XjDdRNIPRmRhDeQ+0ocKCyIgj3BOQA0FsPYHZ2NubMmYNnnnnGRfJl165dqvEGqgeQ3swIwnuoHQUOVFYE4Z6AHAB66wEEgNmzZ6OmpsZpAGg2m3Hx4kXVeLU8gP544zQSpsPhQHFxMYqLi7tsp2dEXNSTQ++7qldBT7p9Iezqy/z1ZXvgdvGj6dToDOK2XRmt+sPLxmw2e13H1OLh1+12u9PnvqrXDocDFRUVhs93bi86Wx/QEeEHHB15DImneHsU3P79+9n111/PEhIS2Ny5cxkAFhYWxkJCQlhtba1qvFpHwfnjaBsjYWodNdQZjt3pbHhy5FVXzcf2Srcv4/Hl0VvKI8K6WvkHCnraqi/qmLuwlfXEV/W6K/Q//k6jPPyGhoYufxRcUHoAIyMjNT2Aly5dwueff45Tp05J08i9e/dGa2srdu7cqRpvoK4BJM8EQXhPfHw8taMAgfo8gnBPQA4A3QlBHz9+HFFRUSguLgbQtkbQYrFI/+/fvx+MMfTs2RPXXnstAGDEiBGIjIzEp59+qhpvoK4BtFgsKCkpoc6QILwgISGB2lGAQH0eQbgntKMN8ASTyYRNmzahrKwMQNu8vt1uR1xcHPr164fS0lKkp6dLn9fV1aGhoQHLli3DY489hg8//BAAwBiT1gLs27cP58+fx/bt21XjLS0txcKFC4Wf+eON00iY7XnYeDDg6aH3XdGr0F7eFF/G48v2QN6kwEBPOfmiLNXC4NczMzOdPvdV/ekK9dDfaewKeWgEE2MyEbwA4fnnn8fjjz/ucv3ee+/Fr3/9a1x99dXC56677jrs2rULd999N9577z3hPVdffTX27dsn/OzixYtOm0QaGxvRr1+/Ln2YNEEQBEEEGo2NjYiJienS398BOQX80EMPoaqqClVVVQCcZWC4R+/nP/853njjDQDAM888g9DQUFy6dAkAcPfddwMARo4cKen+TZkyBQBw4cKFdk0LQRAEQRBEexOQHsCXX34Zjz76qMv1e++9F7fffjvuuusu4XM9e/ZEU1MTdu7ciaFDhwrv6du3L2pqaoSflZSUCKeAu/IbBEEQBEEEGuQBDFAPoDshaAAIDw/HE088AQDIy8uDyWRCt27dALQJRd9zzz2IiIgA0Kb/ZzKZAABWq1U13kDdBEIQBEEQBCEnIAeAWjIwfHHnpUuX8OyzzwIAPvvsMzDGcP78eQBASEgIYmNj0b17d4SEhKC1tVU6D1hL2iVQZWAIoqMhAVaCIIjORUAOALU8gJcvX5bue+yxx7By5Ur0798fABAXFwegTQbm97//Pc6fP485c+bg4YcfBgDExMTggw8+UI2XPIAE4Rl0NBdBEETnIiBlYLgHkMM9gPfeey8eeOABAG2DuSVLlqC1tVWa329ubgYAnDt3DkDbhg8+iATaBKT5VLAILRkYgiAIgiCIQCHoPIDcw9DQ0IC5c+di5cqViI2NBQC0tLQAuOIJjIqKkoSgY2Njcfz4cdx5552q8ZIHkCAIgiCIYCDoPICTJ08GACQlJbl4ALmG3+HDhwEAZ8+exaFDhwAAp0+fhtlsxqBBg1TjJQ8gQXgGCbASBEF0LgLSA+juKDgAqK+vx5///Gfs2rULt9xyCwBIGz1aW1sBABEREbh06RJCQkIAAMnJyZJ2oIh58+ahoaFB+uEDSYIgtKGjuQiCIDoXAekB1DoKjtPa2ooHHngAZ8+eRUxMjNPz/EvIZDKhtbUVERERaGlpwenTp/H999+3X0IIgiAIgiA6gID0AH755ZfYvHkz6urqAACHDh2SZGA4jDHJ46fUuk5NTUVMTAxaWlrQq1cv/Otf/wIANDU1ITw8XDXexYsXIyYmRvrp168fAODo0aMkcUEYgmRRAg+jZUZlHDx0prL0tS2dKW1E+xKQA0Cto+A4YWFheOONN7B9+3ZMmDDB6XmTyYSMjAy0traivLxc2iQCQBrUiVDbBHL06FGSuCAMQbIogYfRMqMyDh46U1n62pbOlDaifQnIKeDXX3/d6Sg40SaQuLg4TJ8+3WkTCJd4qaiowO7duxEdHe1ybNzYsWNV46VNIARBEARBBAMB6QHUkoHhg7yjR4+6yMDwo9/+/Oc/o6GhAfX19U5TxQCcvIhKSAaGIAiCIIhgICA9gJ4KQfPdvk899RSqqqpQU1MjaQNyXn31VdV41TyAffr0IYkLP+FwOFBWVgar1RpU+UuyKIGH0TKjMg4eOlNZ+toWs9mMsWPHwmwOSH8Q4QUmptwhEQCsXLkSa9asQXp6OubOnYusrCy8/fbbiIuLw3fffYfx48cDaDsKLjc3F48++ii+//579O/fX9L9++1vf4sLFy4AaJN34bz77ruYMWOGMN7GxkY0NjZK/zc1NSEjIwMNDQ3SIJPwLTt27EB2dja2b9+OESNGdLQ5BEEQQUVX7WMbGxsRExPTpb+/A3LIzz2Ac+fOBXDFA/jrX/8aoaFtTk3uAZw6dSpOnz4N4MpRcAAwe/Zs1NTUYPHixdI1s9ksiUWLKC0tRb9+/aSfjIwMP6TOFdqlRXiLv+oQ1U2C8D92ux3jxo2D3W7vaFMkOlvb72z2BAIBOQD09ii47777DmPGjMGf/vQnFBYWAmjbNWwymXDrrbeqxttRawBplxbhLf6qQ1Q3CcL/VFdXY+PGjaiuru5oUyQ6W9vvbPYEAgG5BjAuLs7tUXBZWVn4v//7P7zwwgvSxpBz584BAC5duoTPP/8coaGh0iAyJiYG9fX12Llzp+raCtoF3P50prU3BEEQwQb1sV2XgPQAjhs3zmn37tSpU12Ogjtz5gxSU1OxceNGfPXVV9K1mpoa1NTUgDGGiIgI6ezf22+/HaGhofjkk09U46Wj4NofOkKMIAjCf1Af23UJSA/gmTNnsG/fPun/06dPS0fBcW/fgQMHMGrUKNx99904fvw4gLY1fsuWLUO3bt0AtO0K5qeJbNy4EZcvX8ann37azqlxD72hEd7irzpEdZMg/E9mZibGjh2LzMzMjjZForO1/c5mTyAQkLuAP/nkE2mnr5x7770Xs2bNwg9+8AMAbQO+0NBQDBkyBDt27EBraytyc3ORlZWF1157TRh2YmIijh07JvyspKREOAXclXcREQRBEESgQbuAA3QKeOTIkU5Hwf3sZz+TNoHIR/8pKSlYu3Yt0tLSJA3Ab775BtOnTwfQdibwb3/7WwCQNoNwaRgRJARNEARBEEQwEHQewDfffBN9+vTB8ePHYTKZnE75iIyMhNlsRm1trdP5v3KioqKctP7kkAeQANp2m/HNQ3PnzqUpB4IgiACDPIBB4gEsLS11OgquqKgIJpMJYWFheO6553DTTTchMjISFy5cAGMMMTExyM/Pl9YCms1mae3gf/3Xf6nGSx5AAmgbAJaWlqK0tJQkBwiCIIiAJCAHgF988QWGDx+O4cOHA2gbmHEhaAB45JFHwBhDc3MzHn/8cXz44YfSs3wqOCMjA6GhoQgJCUFra6vkKezXr59qvB0lBE0QBEEQBOFLAnIXMJeBUYMLPsfHx+PFF1/EqVOn8Nxzz+HChQsICQnB+fPn8dRTTyEsLAz3338/UlJSsGDBAkRHR+Pf//63arhFRUXSWcPAlaPgCIIgCIIgAomAHAC6gx8HV19fj/vuuw9A29o+oO04uObmZrS0tKClpcVpN3B4eLj0rAgSgiaANrmBoqIi6e9AxuFwoKysDFarNeDTEixQmRDBBtXpzklAbgJxR1NTE6KjoxEWFobm5maYzWaYzWZcvnwZkZGROHv2LEJCQhAeHo709HTs3LkTPXr0wMWLF/Hyyy9j9uzZwnAbGxudNohwD2BXXkRKBDZd9SD4zgyVCRFsdMY6TZtAAnQNoDu4F49PBbe2tqJHjx7SNS4M3dzcjH379oExhjNnzqC5uRnp6emq4dIaQIIgCIIggoGgHABevnwZABAWFgagbeMH3/ELtA0IgbaB4qVLl2AymdC9e3dce+21KCsrUw2XjoIjCIIgCCIYCMoBIPcA9unTBx9//DHefvtthIeHAwAYY4iPj0dISAhuu+02NDc346677kJzczOOHDmCgwcPdqDlBNG+0PFJnQ8qEyLYoDrdOQnqNYBms9nJ23f58mWEh4fj4sWL6NevH2pqaiRdwAsXLuDixYv4wQ9+gM2bNwvD9UYImhbBEl0dagNEe9GZ65q/bevMaddDe9lPawCD1AMYFRWFqKgohIWF4aWXXsLSpUulNYBmc1uS+/fvDwB45plnsGLFCklWZuDAgarheiME7XA4sHDhQhIOJros1AaI9qIz1zV/29aZ066HQLc/kAjKAeD58+elTR1z5sxBcXExzp49C6DNE7ht2zZ8+eWXSEpKwlNPPYXJkyejubkZQNu0sRpam0BsNhtSU1Nhs9mE106cOCGFYaRiOxwOlJSUSM/Y7XaMGzcOdrtd9/PTpk1DdHQ0KisrVe+z2+3Izs7GyJEjdYWttEv0udVqRV5eHmw2m+a9ovDchW80fZWVlYiOjsb06dPdhqnMY1Gey9OnvF5cXIzi4mI4HA6X//WmX08582fsdrvh/HWXZr2o5YMyXpvNhpkzZwIA9u7d6zZ+Xh+HDh2KBx980O9fBu7yR9S+RdcdDgcKCgpgsVgwbdo0WK1WFBcXw2azIS8vDwUFBW7rgpG66m/k5bJs2TJERkbiueeecylzve1VCz19iuhz+XW73Y709HSMHj0agLiuuYvfbrejoKAAKSkpsNlsutsw4Fx2ynbJ85LbVFZWJgxPTzq12Lp1KwDg7rvvNtyefYkyPy0WCwoKCpzs97TfkT/Pv7e0vmd8UT+DEhaENDY2MgAsISGBvfXWW+yll15iSUlJDACLiYlhCxYsYACYyWRiZrOZmc1mBoABYMnJyarhNjQ0sMOHD0s/u3fvZgBYQ0MDe/rppxkA9vTTT0v3y68tX75cimP79u2607J9+3anZ3g4y5cvN/Q8ADZ79mzV++T26QlbaZdWvDwftNKtDM9d+EbTN3v2bN35r8xjUZ7L41W7vn37dpf/9aZfTznzZ/i9RsJ3l2a9qOWD8nNeB5RtRC1+eX002mY8wV3+iNq36Lo8P+Q/8vS7S4+Ruupv5OUyffp0BoBNnDjRpcz1tlct9PYpys/l15X1RlTX3MUvD+Ppp5/W3YYZcy47Zbvk/8vrgig8Pek0YkNHIcpPpf1afaueuqQsKyN52tDQIH1/d1WC0gOoFIKeM2cOzp07B6BN+uWBBx7AtddeK60R5OsEAeCdd95RDdcbGZj4+HgPU0MEI135jTQ2NrajTSC6CJ2xrsXGxqKwsFD6X29fwGeR+G81+KEHeulsfRFtGGk/gnIAyGVg+Lm/ZrMZ58+fB9AmAZOSkoL58+fj8ccfx6JFiwAAERERAK6sDRThzRrAhIQEj9JCBCddeZ1Lr169OtoEoovQGetar169YLVapf/19gV1dXVOv9XwZADYmfoii8WCkpISGgC2A0F9FJxcCDoqKgoNDQ3Stfz8fCxcuBC//e1vAQCXLl1CWFgY3nvvPcybN08YrtZRcDk5ORgwYABycnKE1zw9Pkz5NpSZmYmxY8ciMzNT9/NTp06FzWbDmDFjVO/LzMzEiBEjYDKZdIXt7i3NYrGgsLAQO3fuRE5Ojts3OmV4et8C9aZvzJgxWL58OW655Ra3YSrzWJTn8vQpryvLWU+5e1LO/JnMzEzD+esuzXpRywdlvDk5OZr1S5TnI0aMwOXLl5GXl+f3LwN3+SNq36LrFosF+fn5+PDDDzF69GjEx8ejZ8+eyMnJQW5uLtLS0pCYmCjFI9rxaKSu+ht5uTQ2NuLvf/87brrpJlx99dVOZe4Lr42ePkX0ufy62WzG4MGDcejQIaSmphqqz/L2lJ+fjw0bNqj23Wo7VeVlp2yX8rxMSkqSPtu5cyeAK549tXTyWSR3s0k5OTno06cPEhISDLdnX6LMzw8//BA33XSTU7o87Xfkz/N+Ret7hryKKnT0HLQ/4GsAIyIiGAAWEhLC+vTpwwCwsLAwxhhjzz33HIuKimL/93//xwCwqKgoFhISwh5//HHVcLXWABKEEXyxZooIfKgeBCa+LDe962/9VVe6ah2kNYBBvgYwMjISiYmJMJlM0tsV+8+xb08//TTuvPNO/OY3vwEAnDlzBi0tLThz5oxquHQUHOEr6I2UIAhAv2fPX30G9UVdl6AWgpYTFRWFpqYmhISEYNeuXcjIyIDJZJL0/0aNGoXDhw+jqakJx44dk3QD5Vy8eBEXL16U/m9sbES/fv0wcuRIZGdnS41IPj0AtG33v/POO7F69WppyoDfc+edd0obT+bOnety/w033IBFixZh/vz5WLduHc6cOYOePXuioKBACg8AlixZ4jR45fe888470jM8/CVLlgCAFIbSNqBtioPfN3fuXM3OQXkvT4M8rUuWLMHx48exe/dujBo1yqXDEYUhTxO3X/mMMh55vovs5/EoB/ryPBWVlcgWeTpFf8vDUcsTZTnw8lLatWzZMnzxxRfIyMhA9+7dna7v3LkTy5Ytw7BhwzTLRlkf5P8rUea3PA/kn2mFr6felJSUSOlKTEwU1h95WYvqqlpdkNurbC+i6Tt3bZLfb7fbMXv2bKSlpaF79+4ueQa4tjG1tnXLLbfgtttuQ1ZWFoYOHSrlgVo7VIbNy/83v/kNPv30U7f9ify3vOzd9Q9a+aFWPvKw3D0nKgtl+1XGwfvG3/72txg2bJhbAWF3dcidPSUlJfjss89gMpmQlpaGlStXorCwUFqvJnpWGac833me79y5E7fddhtmzpyJF198Uconed//6aefuvQtyrYsqmd6+qcbbrgBv/71ryXJJV7WvD3Ly0BPPqnVfVEforRLra67q8uiui1Pg+h75JtvvsG1117bpYWgg3IKmDHGevTowXr16sU+/vhjVlFRwfr3788AMLPZzDZu3ChtHf/d737HADCbzcZ+8pOfsJCQEPbaa68Jw7xw4QJraGiQfg4fPizc3i53qatJdYi2yIvul8tMyOOSh6cmPSHafq+U7RDZJrdPeV2EmvSJMq2ivHIXhp5ntPJdK11q+aVWVmp5qfa3qIxEYSrjdVeOoutq00eieNyFqyfftNLhSb3Rqj/ye9Ukb9zVOa2yUItD7X6tfFNrY2p5uW7dOjZlyhRd+a4Wtrx/cNefaNU1d/2D0fJx1y+Iyk0t3aI45GnWCktvHdJrD//h5Sbqf9TiFLWRdevWCfNJ2feLylNPPXPXP8m/W5R9ilpb1pNP7vpRNbvc/a9Wl0V5rFbPGWPSOICmgIOQcePG4dSpU5gwYQJmzpyJw4cPA2jzBMq30f/iF78AANx888149913wRjDv/71L2GYixcvRkxMjPTTr18//yeEIIigJSEhAdOmTetoMwgP8FW5kUIE0VEE7QCwd+/eCAsLA9AmA8P+M9Xbt29fDB8+HECb9AuXf3nppZfQu3dvtLa24ssvvxSG6Y0MDEEQBEEQRGchKGVgAGDjxo3S8W6tra2IiIiQ1vBdddVViI6ORmNjo3T/nDlzpL+5t1CJmgwMXwOoJmEikuqQb5FXSgzI78/JycHYsWORk5ODoqIiaf2EMjz+GYffI39GKUvCwxDJiBiRrRHdq0xrUVGR0xpAkZSDSD5FvnZDS/5B9L/Ifh6PaA2gWn4on5HboixnrTIXhaksB7VyLCwsdFkDyK+rSbCI4lHWIVFeiPJbngfyz7TC11Nv5OmSy6OI6qOW5I2oLmi1F60w1NqkXM6DS7oo1wCqtTGttmU2m4USMaJ7lWErpZbc9Sdqdc1d/6CVH1rlo/c5Pf2BMg7eN+qVonFXh9zZU1hYKK0BzMvLU+3TtfLFXZ8szyd53y/qW7Tqt8getf6JSxQNHTrUqaxF5W6k3LT6UbU+1J2EllpdFuWxPA2ieq517GtXISg3gQBAeHg4mpubsWjRIvTr1w+/+tWv4HA4EBUVhcbGRjz//PN46qmnkJycjAULFuC9997Dpk2bcPbsWdxyyy344IMPXMJsbGx0GjQ2NTUhIyOjay8iJQiCIIgAo7GxETExMV36+ztop4BbW1thMpkwf/58/PSnP4XD4XB6W3/sscekQeHPfvYzrF27FmfPngXQtnNIhK9lYDrbETxKe/xhn/yAcF/FpfdZX6enI8tPLR87GofDgeLiYhQXFzvZ1NnqekfgqzzQE05Xym9RvyWqg+1tR6CF35lt6AxpD0o6dg+K/4iNjWUA2C233MLeeust9oMf/IABYH379pXuufPOO5nJZGK33HKLdIC2yWRi7733njBMXwtBu9u11t5o7ar1dRxqO8Q8iUvvs75OT0eWn54djR2Bu93XncXOjsBXeaAnnK6U31o7wNsz/f7O885Qph1lgz/iJSFoxoJ2DeD58+cRFhaG9evXY/369QDaBKKbmpqkz1evXg0A0ucAEBcXh9dffx0//vGPXcLUOgqOIAiCIAgiUAjaKeDLly+jubkZDzzwANauXYt77rlHugZAEnS2WCwYO3YszGYz4uPjMWTIELS2tgrDnDdvHhoaGqQfvlnkl7/8JbKzs/Hggw+6TEMUFBQgPT0dSUlJmDZtGoqLi2G322G1WjFz5kwAcJKl4c8WFBQgJiYGoaGhuP3221FZWQmLxYJ+/fohKioKlZWVLs8oXeR6pwltNhtSUlLw5JNP6spbebjFxcWwWq2wWq0oLi5GZWUlUlJSMG3aNBQUFCAvLw9PPPEEwsLCEBsbK8Vx6tQpAMCiRYtQUFCgmhd2ux1DhgxB3759YbPZpPitViuGDBmCkSNHYuvWrdKzalMFXLiUx5mXlwe73a6ZPlEYfGrJZrPhvvvuE9rMn7fZbBg3bpwwHpvNhsTERMTGxiI6Oho9evRwKlNlXEOGDJHqp91ux969ewFAOsv67rvvxrRp0zBkyBBcddVV6NGjB6ZPny7VtauuugphYWG4/fbbXerotGnTkJiYiCFDhsBmsznVGf48zy+73e6UJrvdjry8PKe6L8oLXkb5+flOz4ryh5dvXl6eZI/D4XC63+g0n9byBq00yO9T+1sPDocDpaWlAIC9e/eqPitPO0+nvK7bbDapHpeWlkrtTh6W3W53qpuifBM9J0oT7xsmTJjgVEd43ivrh1ZYRqau5X2W3n6M5wcXDQaAP//5zy51Rc+UcWVlJaKjozF9+nTNfkD+Ge8HFi1apFkv5emZNm0aoqOjpbZvs9lgsVikfLZarcjOzsa0adOkPlKr/gDiNqmWrwUFBUhJSZH6VmX6lPeL6rC7cpV/FyrrCc/nH/zgB052aOWxPK7Kykp069YNoaGhMJlM6NWrFyorKzVt42E/8cQTQnu7FB3tgvQXZrOZmUwmFxHI8PBwxhhjDodDunbnnXc63TNnzhxhmO6EoKEhein/UYpTrlu3zike0bN8ilr+v+gZI+K5HLkQaGFhIautrWWMMVZbW8sWLFgg/a8WrpadANioUaOc/i8sLGQVFRXCvFHmhVLsVpQ/jz32mPSsO4FSueiuO/FkLZFTeZ6plZ9SqFYtz0VlqhYXD++VV15RrV9adc1dHVUKCitFVZXnlirFYmtra1lRURErKipitbW1Uhy8jETPKvNHlPbt27c73W90mk9reYNewVu1v/Ugt5eXnZYQsSid8vzQEo+Wp2fdunWq+abWX8ivieqplnCwVlhGpq5FgtJq/VhtbS0rLCwU1v/bb79dVVBYa8pY3o9p9QPyz+SCzlrpFPWfvO3L81aU9+7qj7L8lWlUE+rmfasyfWr3y21wV67KOievJ8rvC1Efz/uVBQsWSHksKid5XmrZprSnK08BB60HEAAYY+jZsyfCw8Olo91MJhMASF4+k8kkTQWbTCYkJCSgtrZWGJ6/hKD1CIHyDSry//V6IA4dOgSg7c3U3aaLjz/+GAsXLpSuNzU1oaSkRPhG+8wzz7jE9fnnn7tc415XztSpUzF48GC3duslKioKgHY+8rfzCRMm+CROvpSAxyt/Y+XeOfk9Ws9zPv/8c2F5iu7t1auXJ2a7hcfFPbRa2Gw2/PKXv3S6ZrFYsHTpUsydOxclJSX40Y9+BAD4xz/+oSt+uacWgPT3iRMnJJu0bFN66XiZ8PIvKyvzaCF5WVmZi6eX26XlzeNeF3ma1qxZoytO3m5F+EqEWMt+T5F7irhXvri4WLomv89I3A888IAUphyLxSIdJaZErtrgD8rKyqSZkJdffln1Pnlauf3yeqzW9kW4a/vycLdu3Ypx48ZJfZIavJ83Wg/UZj+0wjl16pTULpTfFx9//LFLe+F/l5SUuPTxyu9F+TVReyWcCeoBoNlsxpkzZ9DS0oKzZ886nf0bHx+PkJAQ/PCHPwTQNoUWGhqKc+fOqXa8HSkEfe7cOZf/5QM1LY4cOQIAWLlypdMAUPT8vn37pC9J3pGXl5ejtLTU5d6vv/7aJa4vvvjC5dqlS5ec/q+rq1O1Veszb+Dhnj592ifhKQdlPK9KS0uxf/9+4T1azwNtead3AOgveFx68mnbtm2or68XfuZwOFBeXi7VPb1thT/H4c/X1dVJNmnZJq/X8jLh5V9eXu7RYKe8vFxYN+vq6lTbIbelurraKU1r167VFSdPuze4a09a9nuKw+FARUWFFD4vB35Nfp+RuC9cuCCFqRe1+ukrysvLUV1djdLSUs1yladV1BeptX1PkIf71VdfYePGjVKfpEZTU5OhsuBxKMtCT5mePn1aahfK74sNGza4tBetNqv8XpRf89d3STARtJtAQkJC0NzcDLPZjJaWFpjNZrS2tiIyMhJAm06gxWKRPBPr1q2D2WzG2bNnERoqzha1TSD33Xcfdu7c6SRwLBc//uKLL3Dy5En84Ac/QGpqqpOwr5oo8qRJk7B27Vpp0Dp8+HD861//QmhoKE6fPo3hw4fjL3/5i9MzakKg/EQUNXJyctCrVy9dXh8e7pQpU7Bq1SpMmjRJ8oSePXsWFRUVSEhIwI033oju3btj3759GDx4MHbv3o1u3brh/PnziI+PdxIFPXfuHL744gt8/fXXiI+Pd4orMzMTWVlZqKurQ05OjhQ/F2UNDw9HWlqatI4zKSlJKFDKwx04cKAu8WR3IqcDBw4E0ObN5Aedc2JjYyXb5UK1ynQBQI8ePWA2m9Hc3Cx9wanFFRMTg/79+0vP5ubmom/fvvj222+lA+q//fZbNDU14fjx45g0aZJU19avX48jR47glltucamjBw8exObNm9GnTx8p7IEDB0qiqsr84mk6cOAAACAlJQW33367W+HnuLg4JCcnS+Fo5c/UqVOxc+dO7Nu3D0Bb+XHv/cCBAw0JlfPnCwsLpS8WeRlzIeahQ4eqCgCXl5cjPj7eRUxXWV+14G0aAAoLC1WFiMePH48NGzYgOTnZpa7LBZ/z8/OxfPlyzJw50yks3mZ27dqF+Ph4JCUlSfmclJTkJMYrt19U73NycpCcnIxrrrkGX331Ffr06SOJ0vO4tNrTunXrnOJQS7fSBqWI8MiRI6XBgijP5f3tv/71Lxw8eBBA2+yAqK4oBYp5GszmNp/ImDFjsHz5cqf2oozr2LFjLoNaABg/fjyGDx+umk55XzR16lSsW7dOGrTk5OSgT58+SEhIQE5OjvQ9kZqaioMHD2LUqFGaQtY8XA6fHYmNjRXma35+PjZs2OBUdlpizso+iffl/B7RQE3+fZaWluZkHwB069YNI0aMwLfffovjx49L1/Pz88EYc8pjZR3l34P8ezI2Nla6prRNmb6LFy/i1VdfFeZhl6FDJ6D9SFhYmDTHHxISIv3dvXt36Z7Ro0czk8nEnnnmGbZmzRoWHh7OALCf/vSnwjB9LQOjhbt1L0bWIGmtCeH/iw4p11ovJFq/5W7tnCeHsOuhI2QxtNYR6ZFm0VqT5m/btTASl/ywerVwlD/uwtVam2e0nN2t/dKLp2WjrPdaaw3lqK2N9JVdntxnBNGaL1G6jcSttSZPK+6JEycastlIPojW8qmVm9b6UX/1TbxteluXjNyvdl20DlVNCky07tDd+kJP6hXJwASxDExLSwsAuHgA+fVt27bhyy+/RFJSEp566ikwxiQPg9oRMZ7KwPB1TVarVZe3AjB+9JUW/OgquYdD+XxmZibS09Nx8OBBTJ482emNT3S0V2JiIgYMGIDExES3NnlzBJMe+A7TrVu3YsSIEcJ7zGaz5CX0BUp7td6a9dijlX5v8sYoRuLKycnBgAEDJM+sMpzCwkJ8/PHHqK2tRW5uLj7++GND8Ys8c+5sU9rv7igyPXA7ysrKUFJS4vS8Vr1SOwZMbo+Iw4cPIyQkRPVISrW06v3Mk/uMoPSQxcfHqx7LqDduLY+cHL7uKzIyEhcuXBAec+gruP2JiYmSN762tlboCdU6ls1oGbj7PpH3Rcrj8tylxej3k97vJfn3hdr3gdp1Pcf7eVqvujwdPQL1F2azmQFgDz/8MNu8eTP76U9/yoAru4AXLFjAgDbhZ7PZLN0PgCUnJwvD9NQD2J4eHG/Q63lgrHOliXsHlDuj5XQmexnrfPb4m0BOr9quT/lnvkyXnvocCHREmSu9cRkZGbqe8/cMhC8JxLbUGW0mDyBjQTsADA0NdZoGBsDCwsJYz549GWOMHT58mF177bVO08P85+OPPxaGyQeNyp/NmzezsWPHsqqqKuleuYQKr/wJCQksPz9fklWpqqpyec4datIsoutckmPSpEksKiqKrVixQjXc9evXs27dujEAbOjQoayqqkoYD4dvx1dKoCht4pIg69evZ7m5ucxqtUphr1+/Xkq/Mi/U0imKIz09nQFgP/7xj4X5UVtbyyZNmsQAsF69erH169drhqkMv7CwkOXm5jrZVlRUxPLz852uG7H73XffZQDYu+++qxqHPBw94WrVJ6U8izco81Zpu+jeefPmMbPZzFJTU1lVVZVm+qqqqlhubi4bMmQIA8B+8IMfGMoHkZ2MueYP/5zXR3mdl9/LywoA69atG1uxYoVk4/jx46Vy5GHy/Fi/fr2U51VVVaywsJCNGDGCWa1WTftvvPFGBoBFRUU55RVvL+vXr9fMA26DnrjkrF+/ng0YMICtWLFCCp9fW79+vZTmqVOnsqysLJadnS3ZJ6oDRspKzzNqn/GyWrFiBRs8eLBT3zxgwADdae/Tpw9LT09nU6dOZWlpaax79+5s2rRpTv2UyCYuQVNYWMhWrFjB+vTpw7KyslzKSZ6X8r+N5AFjVwZTcskupT287KuqqpzqoNp3h7xv0PO9pLc/4fakpKQwAGzChAlObTk/P58lJycb6pM5vMyysrKk7xD+HaOnv6ABYBAPAPUcBff222+z+fPns0WLFjEALCIiggFg+/btE4ap5gEsLy938ZyprUWSvwUZ8biJwnV3XRmvlkdBpDWn9camx3bRWhR52PL1KcrwjK5hgmK9j1b+i9as6QlfaZvyuhG75evntOIwsk5Iq0y0vFhGUctbrXgnTpzodJ9W+vRoF+pJg/JetTqmXKMnt2H58uUubWP27NkuNor0y9T08tzZf9VVVwnzSl5ntMJQ62/cwcOV66jJ4xSVi1Jb0EhfpmW7kXWN3C6RJlx8fLyueEVpU5aju3altEFZTvK81LN+Vk/5qn0PiOqdu/Xkorqvht7+RGmPqC0b7ZM5yvalZ627HBoABrEOoPwouPvuu0/aQSuX1MjPz0doaKh0msKlS5cQFhaG9957TxhmaWkp+vXrJ/1kZGTosoXv7Asm+E42I7sg/U1KSkpHm6AbvlOY/yY6L3rKyJflqEcXlNBHXFxcu8QzZcqUdokHuLK+liC8JWgHgO6OggOAF154AaWlpViwYAEAoGfPnmhtbVUVD1U7Ck4EX4x84sQJWCwWjBo1yifpkodrlMOHDxsW+zQqFCsSPFVDz6YAI3CxbzknTpxwEhXlGBWh7UyoHaHW0Yj0M3kdMKJluGvXLl33GS1DNSFpHt9nn30GwLltnTp1Chs2bHAbtjtxXi1RZyX9+/f36Dk19PYVvIy4fpryOZFM1KlTp4Tty2azITU1VTraq6PqLB+Yu6srWhJYcnF0tXD0CHPzcLzV9bRY1IWv/YnD4ZCOrvvzn//s07A9yRP5M3olzAhngnYAyHf7/uEPf8CkSZOksxa5KPF3332HJ598EpcvX8YjjzwCABg2bBhaWloQHh5uKK709HS3O614pz5p0iSnnbd6dmjpQUvvaMiQIQDa9JbURDpzcnIQGxuLkJAQTJ48WdqJBcCQQKhICHTmzJnIyclBbm4urFarpB22YcMGjBgxApmZmS55YWQX4+jRowFc0bySPw+0CYlOmjQJcXFxSExMRE5Ojm4RWv62nZub62RbUVER8vPzna4bsZt7JuLi4lTjUNspWF1djY0bN6K6utopTK36xG0uKiryenec3Ba5d5v9R2RdRGpqKgCgX79+yMzM1Eyf8sugf//+wvvclaEyDnmeyz/n8R07dkx6ludlXFwcVq5cKdnfrVs3jBkzRtpZP3XqVJdy42XJ9fKKioqkvElNTXWrBjBhwgRpVzFjTLKT7+jkWoBaOyOnTp2qGr4avI+S7+yX7/Tm+TZy5EgAwODBgxEXF4fy8nKXfNi2bRsOHTqEbdu2AYBqnRXZbnRnMy+rMWPGIDc3F+PHj5fyj5ebu7oi9xSmp6dLf48aNUrKl7i4OJdwlDtXx4wZgz59+iArK8ulnHg4/fv3d7uD3uhud/l1XvZTp06Vdp4XFRVpKkrI+wa1fsThcGDlypVoamqSXpqUGpQiO+V1ccKECU5tedKkSU55YwT5M3FxcVKblLcv2hHshg6egvYboaGhDACLiYlh4eHhLCoqigFtOoCtra3SAnP5RpGIiAh2ww03qOoAqm0CEa0hUC7U9dUuKK1wlIvF+SJefm4jP4/ViA1qC471rEXUs55HtJDZKHrWvnm6e9OThex6nteziUYtHE/WjvqL2tpaNnXqVCd7RBugtM5xVeaPcs3dY489JozbaJtSu1+09o3Dy0l0/qqRusGfX7Roka5nRGUsX5zvLm75ubRG84eXp1qb4bYVFhZK8SjbsXyNm6iO+Bs9a4pF9RQAu/fee4VrHN999102YsQIzf63qKiIFRYWssLCQpcNEvKNX1qobehTblRSxsvjM9q3uIubI8+j6dOn665bWnWRh7lu3TrD/aw83JkzZxruo/fu3UtrAL0cP3ZawsPDERERgYaGBrS0tEhv+ZGRkfj222+xc+dOAG1r2P75z38CAMLCwnD58mXVs4A9OQrO06OnPIG/nW7bts3pbZsf2+PN1IO/0tGe+eMJRo+r0vs8n2bTe1yRt3b4C+4VUF7Ta6uee9vzKDwlWkcIGkknf/7IkSMel6Pci+Yubm+OwVKWpxry4/G02rGojnQG1PJQ7Qi+/fv3Y8eOHZrh8aMzRcdn8uPY3B3LJrJLfqyg6DN+5KH8qDlP6oA/+hk9dnhyJKE83IqKCsM2Hz161ND9wUjQDgDPnTuHixcvArgyHay8DrRV+Ntvvx0AcObMGWzbtg0NDQ3CMI1sAlEu1PVE7JMfZC+v2J64tPn6pJSUFMPPms1mobiy1hREfn4+7rjjDnzzzTea0zm+WsjsbupTzc6OnBrgouP8tzvkaz/1Lh2Qr7vy9xosPpWvRE3cVQt+/BhvX19++aXqEVNqYYrWaqndz+M7duwYrFar05eJ/Ngub+oLP/4qOTlZ1/3KMjY6iOJ2u5uikyNvk/Ij23hfVFZWJk0l8mm9hoYGYTsWTXGq1RF/YHRJiXyqkm8mS0tLc1quwNcTTpkyRRiOu7WWRjd+GV1/zRFt0PPFmmeeR1FRUZgwYYKu9iCvt5MnT3YZqPG89cTWzrQBMWDpaBekv+jWrRuLjo5m77zzDlu0aBEzmUwMaNPWunTpEuvevTsDwObOnSvJuABgPXr0cNKTk2NUCNoX4qJGnlfKRfDpD2+mDD2xQ0viQBR2ZxIHVeKtje6kK/SWiSdlKH/GX9PGWvJDnki1yJFPBRvNf0+nh9WOpzIiS2IkfKPp4WHolQrxRb6J+gCRjJGvbfE3alJEarIv7tqQlnyRnueVdomORBPVH2X5eNMmfV1WIlkkd23JqK2e9hEbN26kKWBvBo+dmQsXLqCxsREFBQWYP3++tAjbZDIhLCxMOnx7yZIlTm+wZ8+elT5TouUBVL618ON6cnNzYTabYbfbkZeXhwcffBB2ux0lJSXSb3dvOrNmzYLdbofD4YDVakVeXp6TJ0f5hs4XiycmJqKkpARfffUVAOD7779XtddutyM9PR1RUVHShhk5M2fOlN7e1OxQ8te//lW6hz+TnZ0t5QHPn/r6epfwjLyx6rm3srISPXv2REZGhmSP6Bnl9W+++QYxMTH45ptvnO6z2WywWCwYMmSISx7wMGw2G2bNmoXs7GyXo8J4Wbz++uuw2WxS3VBO7XBb+NvuM888I93Hy91qtbp4io3gcDhQUFCAlJQUademWn4okR+TplwYX19fj7y8PEybNg1DhgzB0KFDndLocDhQUVEBq9WKsrIyKQ3cnmeffVaKZ+/evR6lzV26edp4+HzXLfe+mM1mFBUVYdGiRYiJiUFYWBiWLVvmZLvcqyH3tMrD5wvtV65ciT59+uCll14S5ilPu8ViQUFBgXQPt693794ICQlx6luU7dHhcGDRokWIiopyqbfu8qOiosJpo5DdbsesWbNc7uM7+E0mk5S2iooKYZ8mqiPKvOL/K/tIXh94exK1E7ldvI+ZNm2a2/5EbSNS7969ERMTg8bGRqd+OjEx0cmraLfbkZ2djZEjR8Jutzt5pG644QaXDVf8OLSQkBBpdqeyshIpKSlOZV1fX48BAwZIM1GzZs1CfX29kyfdbDa71C0eX319PZKTk53qGPdO3nfffZp9Ng+rrKwMBQUFbvt4LfjUNIfXGbmnlO8WF80WufOAfvPNN+jWrRtiYmKQlpaG++67DwUFBU59oVb/pXbka5eio0eg/uLuu+9mFouFlZaWMpPJxMLDwxkAds899zDGGCsoKGAWi4X17NmT/fa3v2X33XcfA9o2gjQ2NgrD1PIAqr1N8msiQU4tb4BI0FPpBRDdK3rr4wt25ULQSnvl9onuE72NKu3giESlRelR+1tknxZ67pULtGp5UJTX1byZShFSURhaHiyRYKxa+SnzXOuaHL0eQOVbupG81fIQq4nrKtuIUsBVmS6RXe7QUyfk9yg3gqjVd96e1DwyokPvRX2C3navrIfu8kvZ1jwRPRd5rdTiUwpg6/FQKcNWzlSo5b87j7Co3njSn4iE6t3Vb2W+i0T3RfVd3g8o65CePkaUHpEgsqiPVUPUV3uCqDyU4Yn6V73ecuX3jFYfIwqDhKCD2AO4ZcsWOBwOSYKBy79ER0cDaJOHGTVqFM6cOYNHH30Ub731FoC2g6flciJy1DyATzzxhNObDuDssfjggw+cdIruvfdeAFe0i06cOOG03o+/iRcWFkrb5JV6W4cOHXLxXijhb1pcx+zcuXMu68BKS0thtVrx+uuvS9dWrFgheRK4R0BrvUVlZSViY2Mlz2FOTo6LLprSRnl8crjWljyt7rxQPOy9e/cK73U4HPj000+l/z/77DPcd999AIC7774bQ4cORUFBASZMmCCtWdq6dStSUlLw7rvvAgDKyspgNptx00034bnnnpMkZoArOnLcA8HDVtM5dDgc+PLLL6X/9+3bJ/194sQJKQ3yN2W5ZAIALFq0SIpH/qw87Y2NjQgPD8frr7+O119/HaGhoZJHg8dht9td6q4yTPlvuZdGWR5GtCltNhsmTpwIAJIQO9BWhkqPE9C2bkqtbJWe9+LiYrz00ksYMWKEi9dJ/py8jvG1mNHR0ViwYIH0f2lpKT744AOnZ8+fPy/9XVFRIdldUlIieRBPnTrl5HWx2Wwuenn33Xcfhg4dCpPJhCeeeEKYT3v37sWgQYOc6pu8L7nlllvw5JNPujzDUW6g0fJ883rA62BxcbGUvvDwcAwePBhms9mpnAcOHOiSl8CVWQu73e5ST3kd+t3vfgfgSl/mTv9RnpaysjLY7XanflNU/9zpw8m96AUFBcjOznbpm+T9NH9mwoQJyM/Pl+752c9+hmnTpqF3794AgE8++cTJEykqfwBOWrI833menzlzRlqPuGvXLiePqbJdystVnmb+uXKtsbz9y+uDzWaT1sSL8pDbUFlZiXHjxsFms7l4akXtTd53cc95cXGxkzYiL8v6+nqMHTtW8oAqy5XH8+9//9slHs7cuXNRXFzs9KzauvquTGhHG+Avtm/fjubmZlx//fXSNMnly5cxY8YMAG1f/H//+99xyy234Oqrr8ayZctgMpmQlpamGmZRUREeeOAB6f+mpiZkZGTg1VdfdblXvtNrx44dGDp0qPQ/35TCdwbW1dVJne/MmTMBtH3xbN++HXv27MHatWtx+vRplJeXS2EcOXIEy5Ytwx133KG6q4zvkqqvrwfQNr3NdxFyrSve2cg5ffo0qqur0draKtmhxaZNm9DQ0IBNmzbhnnvuwc0334x77rkHy5YtE+YHAFVx3dOnT8PhcDille9Mu+OOO4SLjuW766699lqXex0Oh7TrGwC+/vpr6cuGD77knwPAV199hZqaGtTU1AC4sjPwo48+AgC0tra6xK+0Wy2NDocDW7Zskf6X7zqsq6uT0rt8+XLpOtc7XLt2LQDxTk2+k46nvb6+HpcuXXKyY9++fVLZLly4EIMGDRLWAXmY8t/V1dXYunUrtm7d6rL438iuw23btkn1/4svvpCuq+207NWrl7AeKK8pp5127Ngh1N1TlhX7zxKRnj174sknn5TypKKiAtOnT3d69sKFC9LfvDz279+PhQsXYvbs2QDa6jHPj127dmHbtm1O8fHrnA8//NBpylueH/IXBB42p76+XrJB/gxHNAAUtSX5wIPXQXk+Xrp0CV9//TVaW1udynnw4MEueQm05TtXIVAO7Hgd4vC+TJnPSuRpKS8vx5gxY5z6TVH9E+3elqNMpwh5P82fUbbt5uZmHDx4UPp/z549ACDVZVH5A87akzzfeXk2NjZKfcP+/fuFWopym3i5ytPMP2cKjU5+/6BBg5zqw7Zt21x2x8rD4zZkZGRg48aNuOmmm6TnAaj20zNnzpTSJa+vvL00NTVJ3xcjRoyQwpanQWm71ibMDRs2SDqz8ufk9UXru76rELQewFGjRiE5ORk1NTU4fPgwLl++DAB47733cP78eaxevRqMMaxfv16qeHFxcaqeKcD9LmD5zjn5Tq/u3btLuwD79u0rXec7C7W8a3w3G3+ei+7KdxPyuOQi04DzLqnevXvj6quvlv43ugtXKf6rFC5Wwr2oKSkpyMzMVN35NnPmTKddhTydgHN+amF0d527Uxv4Wki9yIVzObycsrKyVIV/5XnEkZdZfHy807oYZR0A2sq8sLDQrZdWbzqUwrTujvzju75F98jFknlnO3XqVNUynTlzplSGo0ePRlxcHHr16oWsrCy/7R7ldYznLf8tT0v37t0BtMlEAW1CyMr2w+3m7XLgwIHCPOPCxPJ+QI5c2PyGG25wqtNxcXGwWq1ObYQjF2KWP+NJvintVpaX2g7jwsJCZGZmqh6LprYjmecZb0fjx4932jUu30nOP9djN3ClPI3u+h8xYoQk5CyqE57C84h7xHh9EIUv7+OVfYxWu5TXD+V9etvS6NGjJRtF9U0P8rWJmZmZTh5TTmZmJgYMGCC0iddjtXzn4t3dunUD0JaX/NADjrIPJZwJ2gHg559/Lrl8w8PDERISAgC46667JBkYi8WCsWPHwmw2Iz4+HkOGDHHy7ChxdxQcP56nuLgYa9ascfqMe3Pkb1ODBw9GSUmJ6tmfJ06cwNKlS7Fr1y784he/ACA+TogPaKKiopCcnIyBAwfCbrdrnilqsbg/Tkh5nF1JSYnUIZeVleGzzz7DsGHDhM/ywc1PfvITvPPOOy75wenRoweSkpIwf/58AG15wnF3YgKHvyXLPWlqU3+Ae+mVzMxMzQHgyZMnnf4X2ThhwgQAQF5enjR9p5we4ScqyN9E77jjDmlqVI7dbsdzzz0HAE7K+jabDa+99hrefPNNt/pinJUrVwqny9auXYubb75Z+t/huCLhoFaXLBYL5syZI7xn2LBh+Oyzz/D+++/jN7/5jVu7ioqKpLr8i1/8AvX19Th58iR27typWs+8hdcxPnXHf4vSyzfyDB06VLX98C90eT0GrrRXXuZqpw1ZLBYMHz4cwJVTizjR0dF48MEHsXHjRpfn5s+fL7VH+QvOq6++angRv7IeyY9YfPLJJ/HOO+8AgMsmB6vViqSkJKl9Pfzww9I0r+h+DveS8c8iIyPxzjvvSGXD+568vDwAkDxDymPyRGXGbeHlxaeO3R1XOXLkSFx33XUoKSmRytJXZzQnJSXhBz/4gUuYyvDlg0NlvvF7tdIs+nz//v34+uuv3do4adIkPPPMMwBc67KI0tJSLFq0CEDb4DEjIwPHjh3D0qVLsXTpUgAQ6uZ+/PHHsFgswjObeT1Wy3fuqczOzgbQVm94vnISEhKk7y3ClaCdAk5ISEBraysqKyuljnTAgAEYO3as5HZ3OBzIyclBa2srhg0bhg8//FD6MjPCXXfdhZSUFJcpqNTUVNTV1eHaa6/F22+/DaBtHV5ISAhuvfVWp91nRUVF0t8ApEGDfBoQuDJd1tTUJL3ZmM1mjB07FgcPHgRjDAcOHEB1dTUmTJiAwsJCrFmzBkeOHEFTU5OLNlZRURHOnDmDuro66cu+Z8+eyMzMdJqe0GLMmDF49913MWbMGOka1wHr378/Hn74YQBtb59Dhw6VPCpnz55FeXm51NHL39TUjgkTIR8Uc8+MPFy+fo5PPcg9DXV1dTCZTBgyZAhqamrw6aefYsKECUhLS0NycjJycnIQHx+PrVu3StPEvXv3Rnh4OIYOHYra2lrJa8bj3rlzJ3JycpzsAJynR/hn3NPA7bt06RIuXbokeTn4M3v27JGm0uLi4lBYWIjVq1fj+PHjANrW6Rw6dMgpn7i+Hbdv8+bNiImJwcqVK1FYWCh5VZR1j8On9uSeWO7V44MgPeUDXFmGsHLlSsyfPx85OTlSGx07dixSU1Od6rJeTUfRTk5ep4G2uqymQ+lOF47nCx8U8hdHnhbu/Vy+fDni4uJc9A7lg4v+/ftjwYIFSEpKwtq1a3Hw4EFkZWXBZDJh586d0qAGuDIt+Pnnn+PRRx9FYmIijh8/joMHD6K6uhrl5eXIz8/HuXPnsGXLFtx0001O6cjMzERaWhr27duHPXv2oLq6WhpAa+lijh49Glu2bMGhQ4cwZcoUFBUV4dixY05LBHgbktcJeZj8pYH/vWXLFmRlZWH06NEudSgtLQ2MMWm9c05ODkaMGIG1a9di7dq1Lh5DHg+fzmSMudTd/Px8vP/++9I6TV5WgHjakzN16lR0794du3fvRmpqqkv/oaxj48ePxyeffIJRo0bh5MmTqKmpQXJyMhITE7FlyxaMHj0aFy5cQGpqqtRHyPsDnoaoqCiXNPD/uddsw4YNUnvhbSMpKUm1j+TLS5QzQkDb98+mTZswduxYoT6nXL9RVFfkx+7t3r3bKV2cixcvutS76upq7NixA5MmTUK/fv0AtPX/vG7NnDnTKd1a2qHy/hNoeyGrq6vD119/jR/96EdSn8f7FPlzan1dl6XDtp/4mQEDBgh3B/3iF79gNTU1DICkDch/EhIS2N13360aptpRcBs3bpTuEe1483RXldouKqjs7ps4caLunVZacU2fPt3pmjeaUFq7RH2lOaVMn7vdjJ5osXmys1JL20prFzaPQ36PaGeffAehXrt8vbtaLyL7OxNaadVqv3qfU9vdLUJeruvWrfOo7hrZ9cnR2pGp/HG3K1y0u1XrflFfqfacOz09td2zWooGRspHC37k3yuvvOLymfyINn8e5ygK290OZW/Qs9vXnU1G80FZTzzJT9oFHMS7gD///HPU1NRIWj/cLf7jH/8Y8fHxCAkJwY9//GOEh4cjLi4OoaGhOHfunMu0ghxPjoIjCILwFG+OdCPaH77ER7T5xJsj2gjCHwTtAJBvAuHrBNh/XO7vvfcewsPDYbFY8Ne//lWacgsJCcHZs2cRGqo+K662CeTNN9+URDPr6+ulha9crNNsNmPSpEkICQlBr169hJsGRPAppri4OPTu3RtTp06VFtFzyROOw+GQ1i9eddVVTlNoBw4ccPqtFhdffF5bWysJjIpc8Hq308vlbJSCuTxOrWk4d/Iv/J4tW7YgPDxcmqoThRsSEoIePXpg8ODBSExMNLwwmJeFfEpVzzPKhexq06aZmZkYPHgwIiMjER8f7zIFk5iYiOTkZOTn50vPjBkzBhEREQDa1r/o2dWmVabuhHLVUAr6isI6fPgwTCYTYmJinKbl1NASp/YHamLFwJUjsHjfsG3bNimNXCia1215XvAw5WtZ7XY7HnroIc31rfJNHCaTSZoytVqtTtNiWu2jsbERYWFh0nFmekhLS5NEdeVpz8/PR+/evREVFYVu3bph2rRpqrZzEePY2FipvSlF0JUC2aIpfHn/KZf4sVqteP7555Gdne0kyCyXJOFC/jExMU59rXwzCS8zs9nsJKxst9vx0ksvaYp1q2G32/HKK68AAF555RWXtZdc1qShoUH3cY6e0NjYiIiICDQ2NkrXeB2OiopyWqrDkZeJVr1S6yeKiook2bJu3bohPT0dmZmZ0v2HDx8W2sT7VL3fico4+bpSf+ZnUNPRLkh/cfz4cVZTU8P69OnDALBu3boxAGzDhg2MMcZ++ctfMpPJxCZPnszWrFkjCUX/9Kc/VQ1TTQgaKm5vd1N4nmJ02oJPDU+cOFEzXL026pmmUdrkybSKETFfZd4r6exTkO7QM9Xoi6l6T8JQTr+IwlIKceu1Bwam3L3BXZ2Wfz5x4kTVuq0lBM2YvuPAtASARTaJ7NV7HKPe8PSWh0jE2NOlH1rTw6LpRaWYt1YcamGLhK/1onxWWXaelIkneFv2vuxrlEsCjArN+xOaAg7iKWClB5AvCn7vvffQ3NyMqqoqhIWFYe3atZg8eTKam5sBaB8P404GRgt3gqRy9Hi+CFfkeazMQ5E4amdFq/w9PSDeHUphWX+j1+Og9oy3cRp9Ti7iyzeDuMMXeaolPSIPn/qMK8inWDtTWzcqV2UUZR2QK0W48+qJRKpFtFc/4U1bpXagn6AdAPI1gMnJyejXr580fXPXXXfhww8/xL/+9S/88pe/hM1mw8svvyxNEXNhShHerAF0J0gqh+9Wo0psDHkeK/NQJI7aWdEqfy727Ou60d7rk+Rp1FvfvW0Xnj6vFDrWOwD0RZ5qSY/Iw6c+4wpymabO1Na5rIk7HVJP4XWAKwMoB4Bq9UMk5K1Ge/UT3rRVagf6CVoZmFGjRgk3dLz33ntITU1Fa2srXnzxRbz44otOn994440u+n6c0tJSLFy40OX6Qw89hIaGBuzbt89pDYJ8fQsX09QSwtWLHjkMOTfddBM++eQTJ6kJESKJD7X49Wyn15J20YOeNWh8bcvKlSs1BUtF4qiBBM8Lke161+pp4U7wWQvl+huRPWPGjMHbb7+N/v37IzMzU1Nvk4chl8DwN3rqdHp6Ovbs2YPrr79etW7LZWy4nIs8T/WsVeLyPePHj9csU60yk8t56EWrHuktD56+nJwc1fzUW19F6wO5zJI8/+RxLliwAHV1ddKJPVoC5qI1uZmZmSgsLMQXX3yBUaNGGWpTXH7nwIEDLuuw5Xb6e50aF/w3Go9c2ketfIz2EzxfBw8ejDVr1rjUHV/0XYQXdPQctL84fvw4czgcrLi4mIWHh7OQkBBpDWBdXR179tln2axZs9h///d/MwDSGsD169erhqm2BlDPGoKOkt/oCuhZE6l33WJnwN/r/YzG6Q88WR/qrY2ePi+SD/FnfEbt8nSNbbAS6Ot9PUWrnvqqT2mveuZtW9XzHK0BZKxLegDHjRuHxx9/HAsXLpSU6pubmxEWFoYvvvjC6TQEOWoeQD0YedOhtyJj6PGIBpIIqDtPjD/qRnvXOU+8w97a6Onzcu+Qkef9nafeetiDFb0zGcGGVj31VZ/SXv2Et221K5W7N5gYU5wSHSScOHECH330EWbMmIGwsDC0traipaUFGzZswLhx4/D8889j0aJFWLx4MR5++GFJJ/BXv/oVFi9eLAzz4sWLTut/Ghsb0a9fPzQ0NCA6Orpd0kUQBEEQhHc0NjYiJiamS39/B+0mkISEBNx1111ITk5Gc3MzWlpapKPgzpw5g6effho/+clP8Kc//QkAcOutt4IxpnpGJ0EQBEEQRLAQtANAAKisrERNTY30/8WLF7Fjxw4cPnwYZ8+exZtvvonNmzcDANavX4/Y2FjpvFcRixcvRkxMjPTDzzQ8evSoizAyFy3Ny8uD3W7XJWyrlMaQPy+/RyTCLNr+zq/ZbDYnoVS5jcr/5WErBX6NohRotdlsyMvLw4MPPgi73S7MMy7GWlxcLIlr8+flh7jLbZfbqSYDIE+bPBxlPnH75H+L7ONlY7PZUFxcDKvVKqVFywa5zenp6ejRowemT5/ukh+islHWH5vNhtTUVFRWVmpKqtjtdmRnZyMjIwMjR44U1gN5Gnh94flpxC55vZWX+1VXXYWQkBA899xzLnkhqus2mw0pKSmYNm2aW8FxtXJWy39R+VutVgwZMgRDhw51KfPi4mJMnjwZkZGR6N69OyorK3XFI6+bojqsZm9lZSWio6Mxffp0pzAqKytVw5Knbdq0aYiOjpbsVOsX+POVlZVITU116pe0nlPWA1H5edt3iMIQ9U+iNjphwgSYTCYMHDjQRZxcrd9R1v9BgwYhPDwcy5Ytc3qetw1eFvI+h4et7AvU4pP3Rfy3qK8WhaGVv6LPeF/B26i8bEX5qFUPRGWhxF39UQtDLT6t8JVtTNRfK/O2uLgYTzzxhGYcXYIOXoPoN06ePMn69+/PunfvzgAws9nMKisr2b59+9jGjRsZABYWFsYyMzMZAPbGG2+wtLQ0FhERwc6cOSMMU20TCA8PGqKleoRURYu6+fPKe6BjkS+/JhcGFdmotlHC2/MqlQKtSoFbtTwTnYsrv25UfFdUHqJ45faJhGxF9snvE9mmVr4i0VitMET1Ryl8qxa3Wn6Kwlbmt7JOuLNL+b8yj7gYubu6LspXPWht9lFbJK9Mv7vPZs+erSseeTmL6rCavUohY/6MXExXrT3Iy1pup6hfEIUrSoPomrvy88VZt2oC46I2pFZeymf1PCOve8pz0ZVtTt5GRCLSovIV9UVqfaVaGFr5K/pMHq6ybLU2zejpT/VuLHH3faMVn1b4yrxT669F/RHQtTeBBK0H8Pnnn0e3bt2kY4EYY1i8eDFOnz4tiVg2NzejuroaAHD//fdj3759aG5uxooVK4RheiMETRAEsH//ftLoMkBnEjIORIwI8BNEVyNoB4CrVq1CTU0NQkJCAAApKSmYMGECYmNjMXz4cOk+LrT6yiuv4JprrkFrayveffddYZjeCEETBAEcPHiQBoAG6ExCxoGIEQF+guhqBK0MzP79+9HS0iL973A48Nvf/hYxMTH49a9/jejoaDQ2Nkpv2I888oh075dffikMU00Gpk+fPi4SI0rR0qSkJLdCqsot7CLRUzU5Ey0plJycHCehVKX4qZpUilzU1hOUAq05OTmSPAM/kF2ZZ/LD2o8fP459+/ZJzyvlDUTiu0lJSUIZAHnalOHI84nbJ/9bTSx2586dkuDtmTNn0LNnT01JDnl+m81mDB48GN9//z0mTZokzA+ljcr6w8V+x4wZg8TERNW4MzMzMWLECJw/fx7du3cX1gN5Gnh94flpxC55vZeXe2pqKg4dOiSduOOurnMxZP6sEekVdwLEovIvLCzEZ599BpPJhLy8PJe82bNnDz7++GOYzWaMGTNGVzzyuqlWh0XhjBkzBm+99RbOnz+P+Ph4JCUlYezYsRgzZgx2796t2R4yMzMxdepU2Gw2yU61foE/LxLp1SOtpFV+vhA9FgmMK/snURsdP348NmzYAOCKALyyb5Hnt6j+p6Wl4dChQ5gwYYLT87xt8LKQ9zk8bGVfoBYfT6O8LNT6amUYWvkr+kwuDK4sRy3ZHDVZFXeyWp5Kc+mVcdFqY6L+Wpm3RUVFuHjxIl599VXNeIKdoJWBMZlMMJlMMJvNCA0NxdVXXy3t8K2qqsLzzz+PJ598EiaTCc3NzdK9LS0tGDZsGKqqqlzCbGxsRGNjo/R/U1MTMjIyuvQ2coLQy44dO5CdnY3t27djxIgRHW1Op4byyjso/wh3kAxMEHsAgbZ1fy0tLYiMjMS3336L5uZmREVFAQAee+wxnDlzRjoKjt9rMpmQlZUlDM8bIWiC6OqQSKt+KK+8g/KPINwTtGsAOQMGDEB4eDgGDRqEhIQEaVrYZDLh8uXLaG5uxpQpU1BWVgaTyQTGGPr37y8Ma968eWhoaJB+1M4M7ij0bqH39HlPPvfWJj14E68n9mnJiaiFJZdh8DZ+f6Im9WEEtbQeO3YMn3zyiXRGri9t1brmL9SkiOSfa0llaIVbVlYGq9XqMoDxRH5DKx539ump61xGpKCgwEUuSCRv5Kn0h14cDgeWLFmCpqYmYTqUUi16wtMjaaImqcPRI42jlt9G8CQMT+uqOxs8jdtonehs/WjA0IE7kP0K/rPFOzExkfXs2VM6CzgyMpIxxti5c+dc5B2AtjOBb731VmGYFy5cYA0NDdLP4cOHO9U2cm/PaXT3vCefe2uTHryJ1xP73MmJiMKSSxB4G78/UZP6MIJaWn0hDaJmq9Y1f6EmRaT83NP6pSWv4e5zPfHpsU9PXdeSP1GTN9LbbjxBKw6RrXrD0yNpIpLU4eip/2r5bQRPwvC0rroLz9O4jdYJT+oQnQUcxDIwnFOnTuHMmTMICQlBz549YTa3JZkf6WY2m6Vj4Lp164a8vDy0trYKwzIqBO3OO+Trtxy+ocWodAR/A3vyyScBAHv37jX8fElJiUu83JMBAGVlZS7CtVoCw/7A0zjUnuMSE8p0l5WVuZTzxx9/7HQP9wao5bU8f0Si1J7iSR6cOHFC8xk1z0ZTU5OTl4jXL/kZ3e68pikpKSgoKPBpvdAjSKu8Vy7+rUxnWVmZlCa57Ii8XvA8dOeVcTgcKC0tdXlexIkTJ1TzvrS01GfeHF9RVlbmlayN3rq7detW6W9l+1KTheF1wGq16vIOqvW1Z8+eBQCsXbvWkHeR1wtleLNmzXIRs3YXLk8zr5NadU+t7/YVJ06ccBL1V9ZTu92OWbNmeRw+9z4vWrQIgHN+Ee4J+gHg6NGjsXr1auTn5+PMmTPS9QsXLgAAWltb8aMf/QgAcMMNN2Djxo247rrrhGGpycAcPXoUpaWlKC0tdfpSWbhwoer/ate0cHc/l4wwKh3Bv3TWrl0LoG0HtdHnFy5c6BKvw+FAeXk5AKC8vBzV1dWS/TxOnmdG88ITPI1D7TkuMaFMd3l5uUs5812JnOrqamzcuFE1r+X5s23bNmzdutVlYOkJnuRBXV2d5jM8LVxTk9PU1CQ9V11djYMHDwIAjhw5osuebdu2oaamBsuXL/f5AFDZNpXtV3lvdXU1ysvLsXXrVpd0lpeXS2mSy47I6wXPQ3kbULOtoqLC5XkRdXV1qnlfUVEhTE9HUl5e7pWsjd66+9VXX0l/K9uXmiwMrwPl5eUoLy93m3dqfS3Xnd2yZYvuAaC8XijD27Fjh1S2etPP08zrpFbdU+u7fUVdXR1KS0uxfPlyYduprq7Gjh07PA6/uroaW7duxcqVKwE45xfhnqAeAIaEhGDLli2488478cYbb0iagAAkL5/JZMLq1asBAB9++CESEhJQW1srDM8TIWj+9tUegq7x8fEA2tLkjTctNjZWeN3dwur4+Hjh51OmTPHIDr1wGQDu3VWzVfTW7slicf4Ml5jg+c7lRNQICwtD7969na7Fxsb6dbG60bU98vzgf/P06YVLTqhJgCQnJ0u2cQ+xCPkaLne2al3zB3ygNnPmTABX0sTrBHClXsycOdNwHsqfl8Nld7Q+l9dBrX6Hy3AUFRWp5peWdA7/n8uI5OfnS2EpZVd4PsXHx0txms1mlJSUwGw2+7TM+EY/4EpfpmyzkyZNQm5urlOfoYaoTvG8l7f9BQsWOGnMKtEjjcP70MzMTI/7zaNHjwIAzp8/r9p+lZ4/ebn4ohx43Vq3bp2u+ydNmuQUt5527HA4pIEfAIwfP947o7siHT0H7S/wnzUFSUlJ7MUXX2R33XUXA8C6devGGGPs4sWLLCQkhN11110sPDycxcXFsdDQUNajRw+Wm5srDNOTo+CUR/14s15J7xo8o+tH1I5P0ou744JEeaBc9+HNOiC9z3q6Bs1d+tyVqfLoJz22yJ8RHUtn1HZv8tndM2ppUVsnpna0lxLlkWi+Qm39lp46zP822r71tk0j6/L0rD301XpLb/HFek299yuPnBSFYeQ4QCO2GF1Lp1Uv1I7Ccxfu9OnTGeB6jJ3W0YH+WDMrylNleXizLlgZvvyIRD3QGkDGgloGxmQy4cKFC/if//kfmEwmhIaGSuv9wsPDYbFY8Ne//hWXLl3CpUuXEBISgrNnzyI0VJwtRoWg5W9fIu+YUW+Fu/s99djwtzUuvGxUvFVLLFTuPVB6EbQEhv2B8q1dL+7S565MLRYLpkyZglWrVknX3HkD5CKpIlFqT/HG46n2jFpalGKtI0aMgMlkcrmvsLBQGDa/b9KkST6tF3oEaZX3ysW/MzMzpRkEucdGj3Cy6D5lfFxYu76+3q39IrF2Hsby5cs98jyqwT22ot3J7vCFZ1ZvGGlpaYiMjITJZHKa9ZGHocwX7jn94osvkJGR4SSsbsQWdwLJauGI6oVICFtP+hMTE51+i+LgswFqsza+ZNKkSVi7di2ysrJc2r4vBMOzsrIwevRoJwF7QicdPQL1F3zXb2xsLIuOjmbJycnMbDaz6Oho6Z5f/vKXzGQyscmTJ7M1a9aw8PBwBoD99Kc/FYap5gHUeoNoz12JHRlnR6I3vR2ZL77eAasXX+/u8yV6PdqdzW7G/GubL+qKP+zz125dX+ehHg+oLzySnRW1Xfhy2iOt/vYyepsG8gAG8S5g/oZ+/vx5NDY2oqamBq2trZLid3NzM6qqqhAWFoa1a9di8uTJaG5uBtDm0ROhtgZw8eLFwvVVfJ2Qr9ZVyHeBaWlKdRYRVK1da77c9atMr9qOayNlYdQ+kRaaHF+86XqCnnVeItS0/IyilY96PdpG7Obl4Isd03KUaymVtnmSXw6HQ3VnMQCsXLnS4zT4sw+Qb0bS2qnNy15NG0+vjd70FfId53rjdrfrHfBfPfMFaWlpiImJQVpamuo97fEdoVwHWlFR4dO88iQN7aE2EVB09AjUX+A/b4BKD2D//v0ZY4ytWbOGmc1m9j//8z/sww8/ZC+//LL0zIEDB4RhqnkAofIW4s835o7yKBlBK/3+fANtz/VGHE/0xTozerwIemhvr4pSd85XuPOkepJfap4qf6XBW0R5oLU+ld+vpY1nJF6jbZfHbTQePR6rzlpGjHVOT2ZnsUluB3kAg9gDyFF6ALkO1K5du9Da2ooXX3wRN910E375y19Kz9x4443CsDzZBUwQBGGEzMzMTnl+rbtd7p0Fi8Ui7Qjlu7P1PufJGmqCCFSCfgDYp08f9OrVCxkZGU5Hwf385z/Hs88+i1mzZuG///u/AbRtDAGAN954QxiWt0fBeeJ+7iou645Kp57jmQIVPXnqj3xXy1MjcdntdmRnZ2PkyJEdXjbu5Gp8zbBhw/Daa695FYavylUejsVigdVqBXBlmtSdVA9wRRxZz72+4NixY1K6U1NTdT9nsVhQUlKChIQEAMZFheWizr48Vs0oXAhbLogtt68zfZf4aqkJ4RlBPwA8f/48mpubsXfvXpw4cUJaG9i7d288/vjj6Nu3L1asWAGgbV1gWFgYvvjiC0NxPP7448J1CKK1aUZFeOXPKLW3OvuOJ601Gr7IGyPxqtmiJqRrdH2JSAuto9GTp2r3cC2/nJwcw/HK81Sej0bKmAvEbt++3ZCwKy8HT3aqqsEFzbXK1pP84h613NxczR3Untrsi/akDIfbBQALFy5E//79kZycjPz8fNUd1Ndeey0AqJ6x7g6jeVFdXY2vv/4agwcP9qh/5Lv2tUSFRfVMLuqsJizeHvB1j8r1j+0htq+GWhlu27YNhw4dwrZt2zrUjq5K0A8A1Y6CA4AXXngBpaWlUocGAJcvX0ZjY6MwLLWj4ObNm+dUofibFgBYrVaXExz0HgulhL+hAsDq1auxYsUKDBs2zClOfqqGJ8es6fHcuFv8rHfjB08LHxiIPCzu7Fb7XB62XpQL7tXC4HmrPC5q2LBh+Oyzz/DOO+9g6dKlUro88QQo0+UuHHf5tHfvXt1eTh7Wddddh4MHD+Lmm2/WbbcWx44d0+VF4/HLv7x+97vf6U43L4ff//73LpuC9BzDpsx3uVhuRkYGXn/9dWzcuNGlrVx33XW47777cN1117nd/MCxWCwoKyvDypUrsXr1apd4P/jgAxw7dkxXun2JKPwTJ07AarXihz/8IY4ePYqTJ08CaNtwcOTIEbzzzjsu+Q0AJSUluO2226R7PbFbb3vmfdNvf/tbAFdOe1K7T60Ps1gsmDBhAgD1o+N4PVuwYIF0zCWv3w8//LDhOH3Jnj17nH67Q1mXld8lojah9r8WTU1NWLJkidOzyiMyjdhrs9kM1yf5d+jixYsNxx10dPQiRH+B/yzQHTt2LFu9ejW7//77GQDWvXt3xhhjTU1NrEePHsxqtbIbb7yRAWC33XYbA8B+/etfC8PUKwMjEoAViW/qWWysd0ODKB6RDVoYFfR1t7lDGa9R8VRfyoSo3Wt0MbdSfFRP2ekNW81Wd+G4y1ctgVS9ZWQEeT1S1nm9eSYXgTaabrX79IowK/OCP8cFdmfPnq0p0mt084NWeespMz1hGkGrz+I/ntQpf4oOM+a6EUvNPj11Ue+mHrX8UYbfnptGMjIyGACWkZEhtFWtD9Sqy2r9g9E2qFbPjWwQ4vbyMvKmjou+v7sSQe8B3Lhxo3QUnMlkkjyAVVVVOHv2LMrKyrB582YAwMmTJ3HjjTdKZ5Yq8WYTyKxZs1BfX695NI/Iy+MLlzU/HFx5MLoS/rar9tZbVlam+pkItQPT+Rszf3vjn8+cOdPncjlKW+677z6nN91NmzYhKyvL6zg7Cq31abzucPFzUdlp1S+9b/fK+xITEzFgwAAkJiZK+c49ellZWarHb8nTYmTxvqd2q8XN/y8tLQXQJigvnyXga9rc4enaN3ftVA++mupS2xjBj1jTs2HCyOYKb9ZJi+r3rl27dIcjhwtI899qdvH6zePm4suA7/ozo/B17vw3R1QnHA7n49T0IjriVGsGScv736NHD/Tu3Vt3ufO8bq81pcFM0A8Ac3Nz8eabb+Kee+4BYwytra1gjOHBBx8E0NaB8cp54MABWCwW1bOAi4qKcPjwYeln9+7duu3YsWMHjh8/7rTIWA7/wlGuG/FkOlMJPxxceTC6En5QutqB6eXl5aqfiVA7ML28vNzpYHL+ua9OfBCtdeFx7Nq1y+lw9fLycvzqV7/yOs6OgqdBBK87jDEA4nLVql961wwp7+vduzcOHTqE3r17S/nOD6bnu+/dpcXI4n1P7VaLm/9fUVEBAGCMSdNGAHDu3DldYfL7jH5RuWunevBFvyEPR9ln9erVCwCEfZneMER4s05aVL89zUvlIErNLl6/edzHjx+XPuuotcCXLl1y+s0R1QlPB4B1dXVYuHChU9+utp5aq48C2l6UoqOjdZc7z2saAHpPUB8FB7TthOK7obgH8Ntvv5UGb3V1dU4729577z2MGjVKGJbaUXBKlG9aymPAtI7m0YOeTQ7y44j42zr/rQbv1PlvediFhYUoLy/HwIEDJYkKkSdHbof8oHFlOHLUjmjzpVCwlvfByHFIXFj5zJkz6Nmzp+Yz/F7+t16U6ZKHYzabUVJS4rLJQe04NQAYOHCg02+9cftirRLPdyMevcLCQmRmZiI/Px8ffvghbrrpJl0bidQQtTetuJVp52kYPnw4/vKXv2D48OFuj+lasGABwsPD8Ze//MXtRgTls7ydpqWlCZ/190J2tf6FH5U2atQozbxUs8/fdg8cOBC5ubno1asXPvnkE5w/f97JI8fhGzi0jlaU95laa5TXrVsnxS3v25Qvs3ri9BXJyck4cOCAoTY3YsQIzbos74vUjjh1t+YwPz/f6Yg9te8Dd/B+TE97VsNiseDxxx/Hc889Z/jZoKJjZ6D9R1hYGAPAevbsyfr06cOys7NZWFgYi42NZTt37pTm/5966in2xhtvSP/HxsayH//4x8IwPTkKjjF9Rzt5ul5MD3pFo7Xu01oXoobWWkXRweS+SrdWvPL0dRZxUiN4sgbH03R6Ws7u1pD5Yk2nL+zWkwajee2tHYx13LGBnQFfl52n4tOidaxafYq8rnd0n8LXnuoRwfZl36BnDblaOEbWh/qqnyAh6CBeA9i9e3cAwJkzZ3D06FFs374dzc3NGDRoEAYPHizd9/TTT+P++++X/j99+rTqFI/aGsCPP/4YFosFQ4YMEe605G9/mzZtcvIsyNf9mc1m4ZFdyrUnarvJRGtU+DFNFRUVQqkJ+bNWq1Wa5uJrxuR48vau5anMzMyE1WrFrFmzMHfuXPTo0QOLFi1ysV9rF6Xa5xaLBfn5+bjjjjukz8xmM0aMGIHs7GzDh6srcTgcmDx5MkJDQxEbGys8Bovfp7Z7l382bdo09O7dG0lJSYa1sGw2G+644w6hBIeyblmtVjz00EOaO+eUdYh7Ht0d4VRfX48BAwagvr5eeo7nq8XSph33/PPPY/DgwRgxYoTkORbFJ/c+Tps2DT179sT06dMN7Tr0tK7yPLLb7TCbzZLMR319PcaNGyet4dUbrtlsxtixY1XXPIqw2+148sknYTabJZ3RyspKREdH4/bbb0dxcTFsNhuys7ORkZEh1Em02+0YMmQI+vbt61N9NbXdl0bW7Wnda7fbMWvWLGRnZ8NsNru0b5vNhsTERCQlJaGyslIKh5e32WxGcXExnnzySQBt/VhoaKjuXeByRDJbSi87n+FITExEfX09zGYzsrKy0KdPH6ktiNLOy2/o0KG44YYbEB4ejuuvvx4WiwWjRo1CWFgY+vXrh0GDBiE+Ph5DhgzBsmXLpLyw2WzC7xuunbl27Vr07NkTY8aMkeKVHznI7xs6dCheeuklWK1WqY1rlY88HHlb4M8kJiYKpcl4W1KqYfBlFkVFRUJvnlb/vmDBAqxfvx5hYWGIjY3F5MmTkZ6eLuUXzxfSGXRDR49A/cXw4cOdPIDDhg1jANhPfvITxhhjkydPZqGhoQwAM5vNrG/fvgwACw0NZY2NjcIw1TyA8+fP19x1xph7r5Rer4iaJ0UUvtYxTaI43KVBKy5PEO2cE9mv9hav9bnyM196+5T5pRauVvmKwtDjrZCnQyv9yrj17JzTqqNa+ebOY6V2TJ4ez4CRZ7xF5PWR551Rj5wntsrzintwlDui5e1aZJc8DG+P8hOFq6xDRtKpda+yb1PWb3m6eZ6o9afy9HviZdJjs9xepadQaxZFWX56fvgO9KefftrpebUjBOXXlXYp26Pe2Ri19Lkrf3ceVLXn3PX/PE9EP9w+rTDIAxjEHsDm5mYAVzyA/I2An/axatUqTJ48GQDQ2toqbfy47bbbEBUVJQyzqxwF5263nr/X8hDqUN4TRPui1uboyDgi0AnaAWDv3r1hMpmQlJSEfv364bbbboPJZJJc0AsWLMA//vEPTJkyBWVlZdK059ChQ1XDVDsK7syZM9I9RmRSOitKUWQlRoRZ1QRGOyPtaZ9SGscdSnFdIwNAo/I9RtGSD/J0l6Gv8EWZqp2s4C5Oo2Ws5NNPP/W6Lvpip6SazAqf0lOTe5LD+wK+Ic9I3jQ1NaFE49g5X+W3aLnNuHHjcOzYMcO7Z0XyMzzt+/btM2zb+fPnAegvz127dnXKIy7lZcT/3rp1q99s5fklyrejR4/6PL5AI2gHgMeOHUNiYiLefPNN/M///A/+/e9/gzGGqKgonD9/Hs8//zyysrKwZMkSfPrppzCZTBgwYIDhY+AAICUlRfo7Li5OeI/aejjRuj+t59SOuhKFn5OTo3pMkzKOwsJCjBgxAlOnTkV5eblPBkFKWQCllAJPy6RJk9CnTx8XO90dr6X1ufIzPZ4zvRIUFosFkyZNQkhICGJiYlTzV0/5jh8/HnFxcUhMTNQ8Rkxkm1b6edwzZ85EeXk54uLiMHbsWOTk5Bjavakn33idF9V9/kWZlZXlcpSaVtgWiwUjR44EAIwaNUrXMyKMyIrI133J42H/kdHhv/XGCcCwtzYzM1OSwNm5cyccDgfGjBmDqKgoTJ48GUVFRcjJycGIESOQnp7utKZVHkZaWhoAz49fE6VHXof47k29/QTvC7QG0cq+jdfv/v37S8fOJSQkIDExEWPGjHE5YhBoUz8YPXo0ACAmJgY5OTm6d4Er64qarAm/t7y8HFOnTpXWV8vrrGjAIU/7iBEjMGTIEIwePRphYWEYNWoU+vTpg5EjRyI0NBQpKSlIS0tD7969kZWVheuuuw5AW3nm5OSgT58+yMrKcir7zMxMaX17U1OT03GM8iMHMzMzpfitVqtTvrhrk6KjC921Sb4mW41Dhw4J89ld/z9hwgSEhoYiJiYGkyZNwuDBg6X84vbx+i9qBzQABIJ2DWBCQoJwbcCtt97KTp06JfwsKiqK3XzzzaphLliwQPjcxo0bNdeCBRK+XGOlXDu1bt06BoCtW7fO67D9QXvuCm6vHaXtkSY964Y8id8Xu2F9kX6jdngbp3zNFG8rtbW1bMGCBay2trZdbHAXlmint551op6c3qB3jZlynfTUqVN155cynNraWjZ16lS36/mUNhlZl2wEveWpXGfYWXaSa9Uhf9qqlW/8e5vWAAYhvXv3RkJCAioqKvDMM89I2nZRUVFOZ0TycypvvPFGNDU1SW9aIrwRgibUhaEJghDD24onAsldHXdLWbTo6KULBNEeBK0QdH19PU6cOIGZM2c6XW9qapJOIujevTs2btwIANi8eTOSkpJUTwEB1IWg+/Tp45Hgb2fEl5sMlFIKaoLPnYX23GBhNC5PbWuPNLmbNvI0fpEUhy9t85cd3sbJp8yWL1/ucVvxZbnrWRrgLi6eh1pLEIzEr/U5n+LcsWOH7jiU4fCBo1wgWY9NRpaleGqbnvtycnK8bju+RKsO+dNWrXzr06ePz+MLNEyM6VzYEmD069cPR44cQXx8PMxmM0wmE06cOIEZM2bgjTfeQGRkJMLCwnDp0iWEhIQgIiICI0aMQGtrKz799FNhmI2NjWhsbJT+b2pqQkZGBhoaGhAdHd1eSQtYduzYgezsbGzfvl06TYQgCFeUbYXajjG8zS/K7+CnsbERMTExXfr7O2ingPki3Lq6Ohw/fhzHjh1Da2srUlJSEB4ejl69ekmewLCwMJw/fx7btm1z2tChxKgMjFz4UyTe7AlquxpF140IoKod5G3EBrUwKysrMW7cOGzbts1JMNgXcWiJLXsSnt7ntK770x5vcCfyqsduo+Fy3NUvvYLn7YnSJq02Jr9mpC2JEAl8i0Sl5XG3R32S9yee1hVv4nUnXs7rcEFBAX70ox/BbDZj/fr1HsW3aNEiREVF4ZtvvnF7v7yucpFnkUC3sj9Wy0dvy5UfAKAmUC9KLxd41hKK9wa1NuFNW+nsqhIBQUcvQvQXKSkpDACLj49niYmJLDExkQFtos2MMfbLX/6SmUwmNnnyZLZmzRrp6LjHH39cNUyjR8GJhD+9XZSttqhVa5GtHgFUIwvd9S5I5mHKjybydRx6xLSNhKf3OXfX/WWPN+gVefXHRhN39UttIX9755GWTXqFsr3dvCLaLOCLjRjeouxP2qts9G4yEglBT58+3eP4lGWghlJ4m/+tdiSavH6I8tHbcjX6faNMrz/KVa1NeNNWvK3zJATNWNCuAZR7AOXU1NSgubkZVVVVCAsLw9q1a7FmzRpJB1Brg4LaGkCCIAiCIIhAImingIcNG6YqBL1lyxZs3boVL7/8Mvbs2YNNmzZJA8Bhw4aphkm7gAmCIAiCCAaC1gP41VdfgTGGY8eOAYB0akdUVBS2bt2KS5cuYfbs2S7PPfvss3jooYeEYRr1AMp3OeXm5mLo0KFe78pT29WktcvKnQAqYGyno94daTzMMWPGYPfu3dJvX8bBBY/5396Gp/c5rev+tMcb3O3W9XQnu560uKtfaoLnvmgznqK0Sa9Qtre7l0W7RX2xE9db5P1Je6oeGNllXlRUhOPHj+Nf//oXampqMGHCBI/iy8/Px4YNG3Tt2JXXVS7SbTKZXMpf2R+r5aO35coPABg/fryu57jA886dOz3apa0HtTbhTVvpiD406OjoOWh/IReCDgkJYUlJSZIQ9OHDh9mgQYNYZGQkM5vNDID0e/369aphXrhwgTU0NEg/hw8f9vsaAqUAbG1tLSsqKmJFRUWsqqrKkDis0bj8QXvE4Ws6u83yOiG3sbPbTRD+IpjqvjIt69evZwMGDBB+V/F7q6qqhH2C2v3r169nY8eOZVVVVarxBipq6aA1gIwF7QBw8ODBLCkpia1Zs4b97//+L4uLixMuCj5w4AADwMLCwlhERAR79tlnVcPsiAGg2kJ06Nzc4U1c/qAjNj94S2e3WW0DR2e3myD8RTDVfWVatE4UMbpRR+s0jmDJQ7V00AAwiDeBcCHoyZMnO13nm0MYY1i4cCFeffVVAMDly5cRHR2Nixcvqoa5ePFi2gRCEARBEETAE7SbQLSOgjt79iwmTJiAF154AQkJCQCApKQkNDQ0uAwY5dAmEIIgCIIggoGgHQCGhITg1KlTuO+++/DCCy/g1KlTANo8gGazGVu2bMGFCxewZ88eAMDFixfRvXt3VFVVqYZpVAhajlJoV6+orGghelFREYqKiqRFxBUVFZpitVrhyz+rqKhAUVGR06JaT8NTQ+/CXT0ivJ7aYCReIzbrCdPX6eDlVlhY6FR2auXpK4zaq7c8vY3fF/XBV3WqvYRq21MEOlBEd9t7g4A/+yHe55eVlaG4uBhpaWnCI+XkbZ5/N7hr/xaLBVarFe+99x6ysrKwbt06WK1WFBcXw2w2C/NQJBpvJP3tXZdos4gGHT0H7S/y8vJYREQE69WrFwsJCWGhoaHMZDKxmTNnsq+//lpa9/fSSy8xAGzy5MksMjKS3X333aphGhWClqNcp6VXVFZvuFpitXrCNyp47Km9etGbLl/b4I806RF2bS+hal9hNHwj9dSb+H2Rbl/lXXutoaK1ux2Pv/shPULtntrA1wvKBaT1xONpe+4sdYnWADIWtB7AsLAwdOvWDadOnUJoaCguX74MxhhSUlKk44Gam5sxZ84cAMCaNWtw4cIFbN26VTVMPR5APW83J06ccPpbeb/omgiHw4HS0lIAwN69e1FSUuIUtvy+4uJi6V4tysrKdB0dpxe9R6a191uhr4+GU7vPbrejrKzM5XMjZeyvN2vR0VR6n+f17MSJEy7hiJ6X328Ed2nau3evoSPmjHyup4y89YR6Aw/LaJ7606ZApTPkgehYNF4H9+7dK1178sknJS+dvO3yfkbUh4tmIfhRdK+//joAYO3atU72qH2XyPuzsrIyOBwOj9u3O0TeRvlnembRCHWCdgA4atQoXH/99fjhD3+IuLg46QzN0NBQxMbGAgASEhKwePFiAMADDzwAAGhoaFANU88aQIfDgYULF2pWQPlpI3V1dS73i66J4C5/ANi/fz8WLlwoPMmEDxT5vVqUl5ejurpaV/x6UMsPblNpaanUKfkqTm/s8tVz/L7q6mqUl5e7fG6kjPXaaTRNchvlv/U8z+tZXV2dSzii5+X3G8Fdmvbv34+NGzeiurrao+e1PtdTRp7muS/bltE89adNgUpnyIPq6mqXuszr4P79+6Vra9euRXl5OUpLS53aLu9nRH24PH3y9lpaWooNGzYAALZs2eJkj9p3ibw/Ky8vh8Ph8Lh9u0P5PaH8TFlmnaEcA4mgHQBu2bIFH3/8Mf7xj3+goaEBra2tCA0NRX19Pa6//noAbW8r8+bNAwD84Q9/AAA0NjaqhunNGkAuLgoA8fHx0rqE+Ph4p3uU1/TCB7Xx8fGa6x0KCwtVBVULCwuF1/WKsPoSPSK8/rDBX2ni+a5Wxu0lVO0ruP1666ryfm/t48/zeq+87k26vWmHvrZFD+7avC+gdVTa+Ksf4nVQWc/VmDJlilfx5ufnY+rUqQC023Z+fj5mzpwp/W+kP6C61HkIygHg4cOHsX//ftx222245557kJiYCJPJhNbWVlgsFlxzzTWIiIhAaGibCk5ISAgGDBgAAOjbt69quPPmzUNDQ4P0w08XAcTTMcqFvHy6OSEhARaLBSUlJdIuZADCa0rUXNx8l3NCQgJKSkqcNgPIp36tVqtqwzt79iwASBtm5DaJNoYAcPlMjt1ux6xZs1TTIkc5haCMV2SH1nV3UwFqUxZq4XnKypUrAVzJdx7+yZMnAUCa2lHLZ9H0sR57lde1plI8gdfRhIQEKQ/l9Ubrfm6f1WqVppCUOBwOWK1W3HfffcLwePp4vVdeN1p+e/fuRV5eHh588EEAELZDm82G1NRU2Gw26Zo/pr5sNhtSUlJQUFCg6bnkdUPZ5n2JnraubGv+nIbTM6VfXFyMadOmISUlxamstFCWo2g6VmQHn2oF2vLn2LFjqs/pzZdTp06hrKwMVqtVqoPKeq7GtGnTAFzpd4wyZ84czJ8/HwA0v4fmzJkjnWIiv1f0DG/LeXl5sNvtPu9jRaj1K4SCjl6E6A9WrVrltJhV/mM2m9nly5fZokWLWGhoqMvn99xzj2q4WkLQfGHrunXrJNVx5WJXkSK53msceZh6TgWRL9idOXOmqqq7/L6KigpNBXi9i3j54uIpU6a4hKU8vWLdunVS/vkCdzZ6Gp9edfza2lpWWFjIALDCwkKX+1955RUGgL3yyitu0yB63qhtooXb8lMD5L+NxsXzUqveiGzTs8FIrf5wqqqqXE4w0Jsn8s95eWi1V5EAr1Y9EqVPT/2RL8Z3txBfb93QQm9/o4avN/ho4S5seb1RlpUWynLkfZdcGFkUj1KMX+s5d7bzulxRUeHUx/O2WVhYyEaMGMHy8/NZYWGhU78v/y3qd+RlrDwtZOrUqU7fD1r1QRSOu2eUhxcYQe2UI6Ut8ms8/e7qH20CCdJNINdeey3Cw8MRHh4Ok8kkrf8LCwvDl19+iZCQEDQ2NuLy5ctOnkAAuPrqq1XDXbx4MWJiYqSffv36AQB27twpvTlyzw6g7tX66quvkJKSgmnTpuGxxx7DypUrMXHiRAwaNAh9+/bFV1995fKGZLfbkZ2djbvvvlsK89ixY9i+fTsKCgowbNgw6Q00Ly8PBQUFKC4udvJMKGVC5G+w8kXGr7/+uqankN+bn58Pm82GkpISVFZWOnlH5G/GFy5ccIqTe0SXLl2KpUuXOsWzf/9+ZGdnY+TIkdJbtJb3ym63Iz09HVFRUXjuuecwbtw42Gw2yTsiX8gvT/M777wjxcc9P3IPhkjmoLi4GEuWLMHgwYMxatQoVQ+N3W7HjBkzMG7cOABt3j8ATm//27dvd/qtxbhx4zB16lSpTLXebJuamrBkyRIpvcXFxbBarS4bgLiNgwcPxqOPPoobbrgBq1evlmwV5bey/O68807MmDFDqvODBw+Wypw/L68H/H6bzea0KUlURvL6yM/zVnpQ7HY7Hn30UcyfPx/vvPMOJk+ejJ49e2L69OnSAveSkhJJ/F0E90SuWbNG9Z6vvvoK48aNw759+6Q85ig9H3Ibedvbu3evlB+AsydNtDhfj+eCh11XV4clS5ZI6VVuqrLZbKoeKe5pfOyxx5zau8iOEydOOIXvzkPGn9FapC8PQ+Rd1ZMf7u7ZtWuX0Fblc7z8Vq5cCbvdLq2XlktsiTh06BCAKxsz+IYK7g2Xx83rNP+tLPvVq1djxYoVGDx4MABg1qxZ+Oqrr9DU1IR33nkHJSUl2L59O9555x2UlZVh6dKlUr+flJQEoE3Plvc78vV4cq8b/3vYsGGYO3cuunXrBgBYv349vvrqKxw7dgyffPKJ1O7kyMOR36c2g1FcXIy5c+eq5h8AVFZWIjY2FpWVlcLPo6KiMHfuXFgsFqe+GXD1SvP2DFzxAoo81FarFRMnTtS0q0vQ0SNQf6D0APJzfoG2c4HPnDnDTCYTGzp0KNu/fz/76U9/ysxmMxswYAC79dZbVcNVk4EpLy932Uq/fft21bdB5XZ75Y/orVV+tA8PUxS+6D7+t0hyxqgEgNJ+/vfs2bOdbFfaoSWBIr+fhyNPl5YEgjyeiRMnuqRF/oYuOiZJHp+W5IL8mugZkU3yuqBM+6hRoxgANmrUKNV85s94Ks+g9IbIP1Pmt8hWtTqj9HbIn1XaIc9/tbomKiNRmt3Fr1bv3dVnZT4p08HD5/Vr9uzZqnki/19kn5ZsDf97+vTpbu12186Vtos8L2p2iWxShi/ve9TyQHRUpSh/li9fLvSuivoLd95GZVmOGjVK2E9qPaen7vD75X2B/EfZD4rSKMpnZfvT4w1WhqX3GVF+Pf300269nxy9XlJlPVXC81DerkTpUoZptB9UqyPkAQwyrr32WsTFxWHMmDHIz8/Hww8/DKBtrZ/dbkdzczMYY/jyyy9x9dVX4+2330ZraytOnjyJ1tZW1XC1NoGIFusaXShvBCOL7/UIgupdZGz0Xr3w9ERFRfkszMLCQrf55Mv4lMTGxqoudo6Ojnb6LUJto4On5Ofn+0UYWiud7lArI3/UMT1MnTpVNR0pKSkAgMzMTOma1oJ2owv4OXwWYtKkSaq2+KNPEaFnQ4zahi1/2OhuA4HFYsGkSZOk/xMTE3WHK9oE586O5ORk4ecDBw50ucbrgbv6YLFYvNrM0VFtxx0pKSlObcdfGC3LrkxQDgD37t2LkydPYtOmTVixYgX+93//FwDQ0tKCYcOGSZsdAOC2224DANx4441oamrCddddpxqulgyMaLGu1sJYb9EbZkJCgnCqVYneRcZG79ULT48vB2TyRdRq+HMA2KtXL9XFzjwPtfJSbaODp8yZM8dtPfAErXS6Q62M/FHH9DB//nzVdKSlpQEA8vLypGtaC9qNLuDnZGVlAQCeeeYZVVv80aeI0LMxTW3Dlj9sdLeBwGKxOO1O7d+/v+5w+dShETtSU1OFn/NpXDl62jwPm2/m8ISOajvusFqtGDZsmN/jMVqWXZlQ97cEHtwD+PLLL2P+/PnIzs7GqlWrJA+gyWQCAHTv3h0bN24EAGzevBlJSUmora1VDbe0tBQLFy50uZ6eni69EWZmZjq9oYreVnNycpCcnIycnBx0794dX/5/e+ceV2WV7//P3rC5CaJcNmwEQUPlIl5AZ6MVCDom1qkEq2mAspmC/DU1E3aarE5oZdmcpLmciYOdRmfU5JyZ0LHyRnlrVBxRUEMjLUWN7Q0RUBG5rN8fzFo9+2E9z94bNre91/v14gXs51nf9V1rfdfa61nP+n7XkSNoa2tDa2srbty40eWIH6Bz1SE+Ph5tbW2YNm0aDAYDtFotkpOTzZ6qYmNjkZiYiMjISOj1etWBUqpvbGws0tPTsW3bNsyZM0f1y5zqotFoYDQakZ+fj6ioKGzevJnprqSHpZAyRqORyablMhgMzONMnjY2NhZRUVE4f/48Zs2ahba2NqYTL09aVirPaDQiMTEREydONPsCk+cn/Wzq1KnYtGkTUlJSuGWJjY3t0i7ylYvU1FR88sknSE1NVaxnqTxr21Sud15eHq5fvw5vb2+zdFTHpKQkHD9+vEud8epbXgZeOeV6SPsDtVej0dhFPq+N0tLS8Pnnn+PHP/4x9z6aP5V34sQJ7NmzB3PmzGFtzCu7NfUmLa/RaGT5WFrplNcRLUtOTg6OHj3K9iOr3a/X65GcnNzlXp7OtHxSm5bKorrzVl7oOCS3Y94qm7yOeGOPpbqQfyaVodfruxxvZmm1T+kevV4PPz8/3Lp1C0lJSYiKirLYH6Wf0fYqLy/H1KlTLY6FtG96eXkBgJm9yfuI9G9e2yvZNr3fmnqoq6vjtqtSury8PJw5cwYHDhyA0WhEYGCgxbbllc0a2TySkpLw0UcfISkpSbVcUpn0b7VyKdUtXSE8fPgwysvLVcvo6GgIIaS/lbA3GzduxLx589hET1pEFxcX3LhxA56entDpdLh9+zZcXFzg7u6O+Ph4dHR0YO/evVy5jY2NZnECm5qaEBMTg4aGBtVXeQKBwHbWrVuHrKwsrF271mxVZzBy+PBhJCQk4NChQ4iPj7fbvYKu2KP+BnMbDGbd+5LGxkb4+vo69/d3/25B7B0aGxvJb37zGzJ69GgSGhpK5s2bR4BOB5Bjx44RQgjx8/NjYWA8PDyIRqMhOp2OPPzww4py8/PzuRt+v/zySxIVFUW8vLxIRkYGc6+nrvrbt28n8fHxZMKECSQ3N5e56sfFxbHPLIVJoS7v27dvZ2EvLLnhU5f4yMhI4u3tTdavX8+VT/WJiIggrq6uJDU1leTk5DD95aEE1q9fT4KDg0lcXBxZv349SUxMZOWShiKg5UtPTycRERHExcWFREREdKmD6OhoMmHCBJKVlWVWX9HR0SQhIYFs376dWxe1tbWkoqKCxMfHk4SEBFYn0rqn90RFRbE6eP/994mbmxuZO3cuN/yOvP1onlIdpPlu377drO4rKipIYmIiSU9PJ1FRUUSv15OsrCyzvNavX098fHxIRkYG2b59O7s/Pj6epKenk7i4OFYmCpUrtRfeZ0ohGbZv387aTR42hV6Liooi6enpJDExkd1Dw1NI25raIW0bnq1I60ueNy+8BS0DTWswGFi/ldouD3mZlepAHvqGXt++fTsZMWIESU9PZzaYmJhIsrKySFZWFomMjCQeHh4kICCAbN++3UyWtE/K88rJySGJiYnkj3/8IwFAPvroI0U9a2trSVZWFhkyZAgBQEJDQ1n9pqenk+DgYJKens76ZlpaGvHx8SHr16830z89PZ34+fkRvV7PdOXVF203aUgRqu/7779PRowYQdLS0khiYmIXG7dUv1L7oO2bkpLC9JXaVUVFBfub5iNtH3rt/fffJ+Hh4WT9+vWKIUcqKiqY3QAgixcvJoR09jdfX1+yfv16xTFCOo5lZmYSANy+wms3qS1L+47SGF1RUUHi4uKIwWAgixcvJj4+PmTu3LkkKyuLREVFEYPBQLZv365or0ohwGpra0lKSgoBQN566y3umC3VLScnh41R1LaoTEtjIK9Pya9Lxw5p+0rlyUPaWLIxpT4mHZ/k/TorK6vLmPXMM884vROIQ04Az549S/R6PbnvvvvIT37yExIREcEmepRf/vKXRKPRkLlz55LNmzcTnU5HAJCXXnpJUa6aF7B0Qij3IlPyepT+KHkH0ms8rz4lLzKeDCh4Lyrpw9ON5/km99q1Rp6le+T1Jfdq43kSyutEer/c61fJy5Ln8SjPkydT7gWrVDZpXtJ6U/MKV/Lw5uWl5BlJkXtHK12T38Pz0JZ6NKrZCv1Mnre1dc2zXR6WvEOV2pheV7M5nn1KZSl52vI8x5U8ppXKbSliAJWtdJ9SHDwlD036t7SPWNKbJ1fJPqRtKR0v5B7TPO9xqhOtS54XqNyeqJe91NPU0hjB87BWszeeLcsjGKiNWzQiAK/tlOyVN37LbWj+/PncMZsXXUHNFniyLY2b8vHJUrQBtTJa4/0sLyOvXyvZpDNPAB1yD+ChQ4dw6dIlfPrppwDAXgXfunULrq6uuH79OioqKqDT6bBlyxZs3ryZ3aN2lqHSHkCBQCBQgzobDVQPTYHjQfckCgRKOKQXsDQMjNShwd3dHZWVlThw4ADKysrwu9/9jm0cpxNANS8ltaPgBAKBQAk6ARyoHpoCx2PIkCH9rYJggOOQK4DSMDDADyuALS0tmDRpEl5//XXcvn0bCxcu7JL2rbfewjPPPGNTftHR0YiKisLZs2eRlpbWxQORerZSD17qZbZ//35oNBrm1SuF5+0k9+oLCgpS9Tim3k47duzAhQsXzLys5J6aOTk52L59O86fP4+kpCQW8kLq0UY9q6KiorBhwwYEBgYiKSkJFRUVmDhxoplHsbR8kZGROHz4MM6dO4ewsDDcc889Zve0tbVBp9NhwoQJ0Ov1rL6am5vh5eWl6jWq1WrNvIaDgoK6eH9qtVrmKZyUlIT6+nps2rQJs2bNUvQC5HmQyr0g5Z7QUg++xMREhISE4Pjx47h69Spmz55tlldSUhLWrl2L2bNnM0/kkJAQnDlzBhERETh58iTc3Ny4Ht5Sj2XeZ0rek0ajEcHBwQgMDOziuUevDRs2DDExMaitrWX3SD2GaVtLPWN5dSW3X3ne1HaVPC7z8vKwZcsWnDhxAjqdjushKIXnLcirAyVvfalnfkREBGsT2g/Kyspw/vx51p+lspQ8bWn/O3r0KLMReb5yPbOysrBp0yZcv34do0ePZvV75swZ7Nu3D9OnT2fx9c6dO4d//OMfSEpKgr+/P9MfAHbt2gVXV1dFz0upFzEA5klM9U1NTcX+/fsxYcIE1NfXd7FxS/UrtQ/avr6+vigvL2dtyfOQ5dUT9RZOTU3FwYMHkZSUpOgNHxsbi4iICJw5cwYAMGvWLADmnqZyD2qaB+1HtP9funQJp06d4nq5WvL2tuTpq9VqERcXhytXrmDWrFn4+uuvcffdd8PPzw/l5eVoaGiA0WhUtFclT3ugM5ZlaWkpK6t8zJbqlpOTgz179uDq1au46667EBAQYOZVrjYGyuuC54luTbQBqSe7NTYmbwda59LxSd6vU1JSuoxZLS0t+OMf/6go2yno73fQvcHx48eJn58fSUpKInPmzCEhISEE+MEJ5Ny5c2TMmDHEw8ODnRJCfyttmiZE2Qmkurra4mbV/oJuLB8xYoRq2foSpY3N3cXSebDW3mNv1Jx05M4rPGcOJVm2XBsoDAYdlaB9KDg4uIszT3flqdWF1GGBkB9shTpLKdmIFEv21F291ZwALH1mSY7SNVttR963bClfT21U6nSgdnZtb45HSnWYnp5u5oTTFwzkfi/OAnZQJxBLR8G1tbWxe0+fPk0AEJ1OR9zd3clbb72lKFfJCWT37t0WN6v2F/JjhQYCakdFdQdrji6y9ngje6LmpCPfMK50ZB9Pli3XBgqDQUcleE4D9pCnJEd+NJY1TmNyLNlTT/SWy1RzQFOzV9513jVbbYfnjGFL+XpSX9Ye+9eb45FaHUrtqi8YyP1eTAAd1AmE7gEcP348Ro4cCT8/P/z+979ngaBdXFxACMHSpUvZEnBbWxuGDh2KlpYWRbnCCUQgEPQEk8mEoqKi/lbDoamvr+9vFfqVy5cv97cKgkGCQ04ApXsAXVxc0N7eDuCHo+CuXr2KBx54AAcOHEBERASuXLmCoKAgXLhwAXPnzlWUm5eXhyeffJL9TwNBCwQCgTWYTCasXLmyv9VwaK5du9bfKvQrapEsBAIpDusF7OXlhdDQULS3t8Pd3R0AoNPpUFlZCZ1Oh3379uHWrVs4ceIEgE4HES8vL1RUVCjKLSgoQFhYGPuhk7/g4GDFzaomkwlLliyByWTqhZJaxmAwICsry2yDuJze0lEqV/o33dxLj7yqq6vrUf6WjiSy9p7uoFZ3dKP2unXroNVquziKxMfHIyEhAbGxscyZIzc3l2tHahuirdks3VtYazt0I7/a8WZyKisrMWPGDFRWVvZQy55B+1BwcDCysrLsUs85OTmKcpKSkuDr62vmLBEfH4+IiAh4enoiIyPDog6W7Kk7UHvOy8uDVqvt0p8tHSdHoQ4XPN2keVjjDGAymbBo0SIsWrSI2eDo0aMBAJ6ennBxcbG6fFSvoqKibo1FJpMJ1dXVinYiLUdvjUcAmJNQZmamWR2mp6fDx8eH61DVW98B/Tk2Caygv99B9wZ0D6BGoyEajYa4uLiY7QGsqqpi+/7ee+89AoDMnTuXeHh4kEceeURRrtIeQLU9BAN5DwSlt3S0NlC1vfYC9geW6m4wtH9PsLZ83amH/ti32dv0xB4GUn30pBz27BOW9rt1Zw/gQChXT+iOHgNF975E7AEkxCFXAGfOnIl58+bB1dUVHh4ebAUQAF577TV88cUXAIDW1lY8//zzAIDNmzfj1q1bKCsrU5SrtAIoEAgE1iBWRHofg8GAefPm9bca/YawMYG1OOQE8Nq1a9ixYwcKCgpQWVmJ8vJyuLp2bnecNGkSi8YfGBiIt99+GwDY3r6GhgZFuXl5eTh37hz7OX78eO8WRCAQOBQGgwFLliwRX869iMFgQEZGRn+r0W8IGxNYi0M6gRw6dAgNDQ149tlnu1ybN28eqqqqAHR6Sy1evBgA8D//8z8AgMbGRkW53fECHgxPY72lo1IQVPl1eQDQwYSluhsM7d8TrC1fd+qhN/dJDUYGUn30xK7t2SfkAccp3akrJVm2pB+sfX0w6y7oPhpCCOlvJexNU1MT/vznP6OjowMeHh5oamrCK6+8gpaWFixevBjLli2Dp6cn2tvb0dbWBhcXF4SGhqKmpgahoaGKR7y1tLSYhYlpbGxEWFgYGhoaMHTo0L4qnkAg6GPoJvmjR4+isLBQ9chIgTo0FI49HVQEAltpbGyEr6+vU39/O+QrYB8fH/ziF79ASkoK8vLy8Otf/xptbW3QaDTYu3cvNBoNXnvtNXZEHCEENTU1AIA5c+b0p+oCgWAAQsO3lJWVsTcIgu5hMpmwdOnSfouMIBAIOnHICSAAFBYW4tFHHwUAuLm5Aeic6NG9gDqdDl5eXtDpdOjo6GDp/P39FWW+/fbb8PX1ZT9hYWGqOvTEtV4pLS/swUDFUvn7K0ROf4fmATpDnEybNg1PP/20mR4DQbfeoDvlsoet25Jvb/YtqkdlZaVFfZTC39halu7YkTyd2jg0EPt2T7C27N2R1VN5tuYtt2Nb7M/WvCzJ4+Ut1dFanbpriwIV+tcJufeYP38+ef7559l5vzQUTFJSErl58yZxcXEhHh4e5KmnniL33nsv0Wg0JDAwkKSkpCjKtDUMTG+ESuhpqIK+ZKCGSBkIIQ+UjuoaCLr1Bj0JTdGT+rAlX2uPKOtOGBZbQh4phXvpTllsrTd5OkvjUHf69kC1cWvL3h1ZPZXXnbx5ZbF3yC1rysTLW96frNGpu7aohAgD46BHwQHA999/j7///e9obW0F0LkK2N7ejsbGRrS2tqK9vR3t7e344IMPzNLRFUIe4ig4gcA+DLZ9YAaDATk5OTh69OiAcMIYzAiHA4FgYOCwr4BHjBiBN998E1988QU2btyI8PBwAICLiwu8vb0BdE4K77jjDgCdp3lcu3ZNNX6UCAMjENiHwbYPzGAwoKioCPv37xcOID1EhCkRCAYGDr0CuHHjRrS1tbHPNBoN3N3dcenSJQCdgaDPnj0LALhw4QIAIDo6WlGmrSuAvREqoaehCvqSgRoiZSCsQNCjuiZOnGj1EVqDme6Uyx62bku+vdm3bAl5pBTCxNaydMeO5OnUxqGB2Ld7grVl746snsqzNW+5HfdWyC1ryqSUN9XRWp26a4sCZRwyDAwAPPTQQ5g8eTLCwsJw6dIlLFu2DPX19XjmmWfw8ssvY8SIEXB1dWVhYNzd3REWFobJkydj/fr1XJmNjY1mcQKbmpoQExPj1G7kAkF3OHz4MBISEnDo0CHEx8f3tzoCgcDJEGFgHPgV8Pfff4/8/Hw89thjeOGFF1BfXw+NRoNjx44hICAAGo2GHRSu0+nQ3NyMmpoanDlzRlHmYD0KTsmr0FGwxgtMyeu2L3UQ/MBAfmrvDW/bgeKN3JcMZC9/e9WxNC8xBggGHf3thdJbbNq0iWzcuJFs376d/N///R8ZM2YMAUB+9KMfEUIIiYyMJMOHDyevvvoq+eqrr4iXlxcBQIxGo6JMW72ABwoD6RD53sAaLzAlr9u+1EEwOOgNb1t7eSMPJgayl7+96lialxgDBhfCC9iBvYD/+Mc/oqqqCufPnwcAFvS5ubkZra2tcHNzQ319Pd555x0UFRUxb+HAwEBFmcILWCAQCAQCgSPgsK+A29vb0dLSAp1Oh6FDh8LDwwMA4OnpiX379uHUqVPIzs5GSEgIGhoa2ARQ7SSQxYsXo6Ghgf3QI+Nmzpxp9mqxtLQUERERKC0tVZRVWVmJhIQExMTEYMqUKSgtLTV7JSF/bav2yoJey87OxoQJEzBlyhSWrrKyEi+99BIA4NixYywN1XH58uVMV6neJpMJ2dnZ8Pf3R1BQEAoLC5k+8iCevICe8jqQpiktLUVCQgLTk+ZlMBiQnZ1tFig0NzeXW2bpK93Lly93qQtpOpPJhJKSEnbPrFmzWD40DS8AbGlpKWbMmMHaJiMjA3q9HqNGjUJAQABGjRqFkJAQFBcXY9myZQCAsrIy5ObmYsKECZg4cSKys7O76F9cXAxPT094eXmhsLCQ5RUdHQ0vLy/ExMRYHQRY7bWTtddo/YeGhrK2VwrYKrVBk8mE3NxcJCQk4Omnn0ZxcTFCQ0ORkZHB6r+yshK5ubmIjo6Gn58fgoKCzPqFUoDa0tJS6PV6s/upLrQdgoKCoNVqcffdd3P7jZoNUd2nTZuG0tJSMx2oPV2+fJmlz87ORnR0NEJCQlBYWNjF9qqrq5GQkICJEyfi6aefRmlpKbunsrISBQUF7D6aL29LRmVlJTIzM9n/ZWVl0Ov1GDZsGAICApCRkYGMjAxuX5H2rYkTJyIjI4Obj7yOqD7PPfccXF1dsXz5ctYutE15efH+p7ILCwsxc+ZMAMC2bdvY+JSQkICpU6dCq9Vi1qxZrM4rKysVbVCqNy1bdnY2a5OgoCBkZGQgNTUVrq6uuPvuu1ld03Rq2z+WLVvWZbyQl5HqQstXWlqK7OxszJ49m8l49dVXAQBGoxHPPfcc9Ho9/Pz8MGHCBFbXtE6l46zU9qS/eeMYHSuk46dav6YyqA0r2Z2SfajJluuZnZ2NMWPGwMvLC2PGjGFjX2lpKSZMmGDWd7Kzs1kaOn7IxwBpu8nrx2Qyobi4GF5eXhg+fDgmTJiA4uJis3aWt7u8rqnjp1PT30uQvcXixYvJqlWriKenJ9FqtcTd3Z0AIHFxcWT58uUEANFoNOw1AP0JCQlRlHnr1i3S0NDAfs6dO2eWli79v/HGGwQAeeONNxRlSV9J0nulcuSvba0NUkt/aDppPv/v//0/lobmN3PmTJa/VG+5zPnz5zO5vCCe0r8PHTrUpQ6kaaRllcuj6XmfKdXf1q1bSX5+PqmtrVWVFRoaypWpFABWWgZ5/Up/Fi5cyP5+8cUXufdI9ZfeT+tVnoe1QYC7G3CX9+pK3vZKAVuV2khaLl4aaR5yPeTBYKX1wbMhpTqW9hs1G+LZI01L7Wnr1q3cfGibSe/9wx/+oNifpXUgvY+3JUN6b1pamqI9KbUDz1bl+SjVUUxMDAE6xwT6ubRN5Xnx/qeypXX02GOPKeov1UXJBnl1Y83PH/7why7pqLza2lqSlZVlsT7lr3apLEtjAq1LaTnldSotp7we5GWV6iG3LWv6tZo9KNmgNbKtaROpvlK7ULIF2ka87xbpPfLxRm6rSuMV/Xz37t0EcO5XwA67Arhr1y7k5OSgubkZHR0daG9vB9D5ijczMxPjxo1jTiAAoNV2VsXq1asVZdp6FNxAYyBuuLcHgYGBVsUVy83N7XVdfHx8ej0PQe9C7UltO4j83uHDh1sl29r7ACAzM9Mh7GnIkCH9kq9aXRsMBjz//PN9qI1AMPBw2AngiBEj8Itf/AIeHh7QarUg/4p209bWhtDQULzyyiv4j//4D3YSCA0FU15erihTBIIWCAQCgUDgCDisE4jaUXAAkJWVhaVLl2Lx4sUAOieGQ4cORUtLi6JMJSeQKVOmICEhga1AGY1GhIeHw2g0KsqKjY1FfHw8mpub4eXlBaPRaBa8U6vVmgWDVQtSS69dunQJR44cgZubG0sXGxuLuLg4XLlyxUwfquOsWbNw6tQpREZG4q9//SuGDBmCiooK3HfffcjKysLmzZvh6uqK1NRUXL58GbGxsQgKCmLHYun1em5AT3kdSPU3Go2Ij4+HRqNh8tLT07Ft2zbMmTPHLFDo9evX4e3t3aXMaoGUeeny8/NhNBoRFxeHixcvYvbs2YoBX+n/RqMRycnJrG3OnDmDL7/8EkOGDEFTUxN8fHzQ0tKCpKQktLe34+jRozAajcjJycH+/fuh0WgwYcIE6PV6Mx2TkpLwpz/9Ce3t7UhISEBsbCyMRiOioqJQU1ODiIgIq4MAq4VTseVaVlYWdu7cCaPR2OWatH3lNpiTk4Py8nJMnToVSUlJ2LRpE4xGIwICAuDt7Y3Y2Fjk5ORgz549uHjxInQ6nZkdKgWJjYyMhIeHBzQaDSIjI83alraDRqPB5cuXceedd+JHP/qRWb/R6/VYt24dcnJyAKCLDUmPdjMajVwd5P2qubkZN27cQGpqKs6fP29me7Q/t7W1Ydq0aTAajcw+pfVG60PpSDkqh/YNvV6PwMBA3L59G66urkhOTgYA7Nu3D7NmzerSRrRvtbW1ITIyEjU1NdizZw9SU1PNdKVji7QvT506FdXV1Uxufn4+oqKisGnTJqSkpHTJS2oDR48ehVarZbJTU1Oxe/duaDQaJCUloampCdu2bYO/vz+8vb1x6NAhpKamsjqPjY1VtUF5HU+YMAEAUF5ejqtXr+Kuu+5CfX099uzZA3d3dwBgb3h4Y4XJZDKzD7mNyMtIdaH2ZTQakZWVhc8//xzTp09HQEAAbt68iX/84x/4/vvvMXPmTFy+fJktONC2Tk5ORlxcHBsb5fYv/S0fxy5evIjk5GRERkaa2YgUXt+9fv06AODmzZs4deqUxaMMrQ1ELtfz5s2bKCsrw/fff48RI0YgMTERer2ejbtXrlxhfScyMhJeXl64efMmjh49CldXV+j1ejP50naj9aLX65GcnAytVoukpCSsXr2axfBNSkpCRUUFa2etVsvSa7VarFmzBnl5eUxWcHCwaj04Bf39Drq3mD9/PnnnnXfIF198QTZu3MjCwCQkJJDr16+TGTNmEE9PTxIdHU0AkODgYAKAHDhwQFHmYA0DYw2W9twp3d+Xh4o7Es5WXlvpaZgOe9bvYG6rvtDdljBMfRGKypq8+qNNe5rnYLZDNXoj7JI16UQYGAcOA3P69GmzFUCNRsOOgtNqtdi/fz/a2tpw4sQJAMDFixfh4uKCbdu24Uc/+hFXpggDIxAIBAKBwBFw2D2AV69exa9+9SusWrUKb731Fry8vEAIQUpKCs6dO4eWlhb2ygIAPvzwQxgMBixfvhw3btzgyhR7AAUCgUAgEDgCDrsC6Ofnh9/+9rdmK4AAcPToURYPr7W1FVVVVQCAn/3sZwA6vYHXr1+PJ598sotMR14BVNo7p3Z/Xx8q7kg4W3ltRW3Pq7Xp7VW/g7mt+kJ3a/JQ2lfWG1iTV3+0aU/zHMx2qIYt5epuHThq3fWY/n4H3Vvcc8895IMPPiCbN28m69atIyNHjiQASFJSEtm7dy/bXxQSEkIAkM8++4zcddddBABJSUnhylSKA9jQ0EAqKipIcnIyqaio4KblXa+trWXx65Sora0leXl5JC8vj1RUVJCcnBySmJjIzUcuj6bNyckheXl5Zvnw7rWkizxtRUWFqgzp/9JyyPOQ30dly8uqpCPvc1vK0x1qa2u5bSFvL2vKLE/Hq7+KigoSHx9PJkyYQHJzc1XT8XTltZeaPpY+t+UeqmNWVhbXdnn6VVRUkLi4OKLX60lWVla32tFSnUpR65/bt28niYmJJD09XbHv2aqXWn3JdeH1Y579dacPK7W3mq1Yk743+p21/byiooJERUURb29vsn79ervm1xuo5cMbGy3ps337dhIeHk62b99uJkdpnKioqCCJiYlsXJHf21f1wEPpO4Xap9ROlcqopL/YA0iIw04Ak5OTybhx44iXlxcZNmwYO+s3MTGRfPvtt6qBK/38/Lgy1SaAljYe867bemalPDCm0r3ygMb0hxcIVykYshpKwXvVZFoTyFopwKg8GLY1wZB7e8O0vF2UPremzPJ0vPrjBYZVSqekq7y91PSx9Lkt98jtUCnArFQ/pfLagqU6laLWPy0F6e6uXkplUgsCz+tPlvqHLTpYYyvWpO+NfmdtP5fazsKFC+2aX2+glg9vbLSkD+8gArVxQi3Auy359gZK4yQvMLRSGZX0FxNABw4EferUKVy5cgWtra1obm5Gc3MzAMDf3x+jRo3C0KFDFdOOHDmS+/lgDwQ9UJAe3SYQCPiUlJRwjy4T9Ay1IxIHsmxb2bFjx4DQQzBwcdgJ4GOPPYYNGzbg0UcfxbBhwxAaGgoASElJgUajwcsvvwxfX1/MmDEDwA97BN3d3TF9+nSuTOEEYh+uXLnS3yoIBAMeMQHsHUwmE5YuXdprE8Dekm0rO3fuHBB6CAYuDusEsn37drzzzjvo6OgA8MNRb/fccw8A4MUXX8QXX3yBHTt2sBAxhBC0tLQoHr+k5gRiaeMx77o1G1Olm+EtBZHlBTRWcuxQCn5sy0ZcXuBctf+zsrKwdu1aBAQEqOpNZcvLaksw5N7e9CsNJCxvU7XAyUr68ZwepPdotVqzQMNq6Xi68tpLTR9Ln9tyjzSgMi8QLU8/rVbLDdptC5bqVIpa/6RBnUNCQlBbW9tjRwZL9UUDHh8+fNisHPJ+bG3/sEUHa2zFmvS90e+s7eexsbGIiorC+fPnkZSUZNf8egO1fHhjoyV9jEYjgoODceHCBTM5SuMEL1C2UiDsvkbpO0UeOFtJb54MwQ9oCPnXGWkOxsiRI3HlyhVMnz4dR44cQXt7O27cuIHvv/8eAQEBaG5uho+PD3Q6HbKzs1FbW4vNmzcjICAA48ePx44dO7rIbGxsZCeJAEBTUxNiYmLQ0NCg+kpZ0Mnhw4eRkJCAQ4cOIT4+vr/VEQgGLKKv9B69WbcDpd0Gih4DmcbGRvj6+jr197fDvgI+d+4cmpub8cUXX+DKlSuor6/H7du3sWLFCgCdIWDa29tx69YtfPDBB/jss8/YecGurvyF0YKCAoSFhbGfmJgYdq2nez8G0t4RS3RXV2d+Euur9h1MdmSJyspKzJgxA5WVlT2SM5DqxFpdnLmv2AM127GmbgfzGFdZWYlnnnkGubm5Dm0/A6lfD1r61wel9wgLCyOenp5k5syZJCAggEyePJkAILt37yaEENLe3k4AEDc3N3LHHXcQoPM4OJ1OR95//32uTLWj4JzpmJ/BpOtAYSB4FA427HV82ECqk4GkiyPTU9sZzO3Ul8fu9Sc9bSPhBezAXsDyFcCKigoAwJYtWwAAly5dAtC5Enj27FkAwIULF9Da2oro6GiuTLUVQMHgZbA/SQ52/ZWor683+y0QCAYvjjpODWYcdgIYFhYGT09PrFq1Cv/2b/8GNzc3AEBaWhoAMOcQFxcXtLa2wsXFBV5eXhg3bhyKioq4MoUXsGMykDz3usNg11+Ja9eumf0WCASDF0cdpwYzDusFfO7cOQDAE088Yfb5li1bkJSUhICAAGg0Gri4uKCtrQ06nQ7Nzc2oqanB8OHDuTLVvICd6ZifwaTrQGEgeBQONkaPHm32u7sMpDoZSLo4Mj09em4wt1NfHrvXnwzmNhow9Pc76N5i9uzZJDU1lej1epKWlkbc3d0JAHLgwAF2T2RkJBk+fDh59dVXyVdffcVOCzEajVyZaieBCAYvg3m/DyGDX38lHLVcgr6lL48y689j0wY68v7c33Ul9gA68B7AhoYG7N69G3V1ddi6dSs8PT3Z50Dn3j83NzfU19fjnXfeQUpKClpbWwEAgYGB/aa3oO8Z7E+Sg11/JRy1XIK+pS9fPYrXnMrI+7Ooq/7HYSeABw4cQHt7O9rb20EIYfuIvvzySwDAvn37cOrUKWRnZyMkJAQNDQ1sAjhnzhyuTHEUnGNiMBiwZMmSQTvRGOz6K+Go5RIInBHRnwceDjsBXLhwIXx9fTFv3jwYDAYYjUYAwN133w0AKCsrw+3bt7F27VrU1NTg9u3bLO1bb73FlSmcQAQCgUAgEDgCDjsBLCwsRENDAzZs2ACTyYQDBw4AAPudmZmJcePGwcXFhaWhx8WtXr2aK1OEgREIBILBRV9uJRDbFqxH1FX/47RHwQHAmjVrcPr0aYSEhOCpp56CTqeDVqtFfn4+Fi9e3EWmOApOIBAIBILBjzgKzoFXAC0dBQcAWVlZ6OjoYJO9trY2eHh4oKWlhStTaQXwwoULWLRoERYtWqS4oXWgB8E0mUx2LQNP3kCvA3vR3XL2Zv3Yu31tyXfJkiWorKy0SbbJZEJubi6mTZvW46Pg7EVpaSkiIiJQWlpq9rm9662/+gkvX+lnSnrZ68g+e+hI9UlISMCUKVOYTtbWaU/r3pq6GEjjIK9/9mV79hTpuGbrGCOA44aBsXQU3PXr18mMGTOIp6cniY6OZkfBQRYqRorSUXC7d+8mAFRDVgz0kBZUP3uVgSdvoNeBvehuOXuzfuzdvrbmS4+nsla2VN+BcqTVG2+8QQCQN954w+xze9dbf/UTXr7Sz5T06sujxyzpKNVHqpO1ddrTuremLgbSOMjrn4PpKDn5OGFLvYowMA4cBsbSUXCXL1/Gnj170NzcjBMnTgAAQkNDMWTIEHavHKUVwC+++ILdU1ZWZpaGrmRkZmayfE0mE7KzsxEaGori4mL21FJaWorQ0FBkZ2ebrZpJV24sPT3S+3Nzc83SWFr9uXz5Mvv71Vdf5T5h03uKioosriJJ5W3btg1LlixBdXU1AKC6urqLjjyUVlyk+Sg99Vmz8sSrK3l6nly6MlVaWmp2j7zs1dXVmDZtGp5++mnF/Hl5XL58ucuKBpVDy2PL067JZDI73WbBggWqT/c0/8rKSkW7obqXlpZyy0iv0zbvyXFuH374IbeccvvgrVzYstoilcdb+WpqamL38vK6fPkyFi1ahOzsbCQkJCAjI6NbK5jUfuhvXhmsXaXpTp3I7Y9+VlBQAABYtmwZ1ybq6+u5/cGS3tb2ZUttSOv/ww8/ZJ/9+c9/Vk2jRHV1Nbd+Lemyf/9+s9/ydJWVlawey8rKWB72WhXkjfXS8UP+vUJ1KSkpYTJqamrYb7VxUD5GlJaWdimP2hhib6RjzEBaZR3Q9PcMtLe49957ibu7O3n99dcJADJs2DACgBw8eJB0dHSQiRMnEgDEz8+PFBUVEQAkMDCQzJw5k2RmZnJlKq0APvDAA+wpZOHChWZppE8o+NdTivSzhQsXsqcWusIAdF01o59ZenqU5ydNo5ZO+tQsv0/+lGhN/tJ758+fz1ZOpL8tyVJaceHlI5dhzcoTr67k15TkSsshbyuaJ689ebKU0iutaFgql6Vy0vSW6lStvem9SmWUX5fXla0689LJ7YO3cmHLaotUHm/li/bVN954wywvns1bqms15OXglcHaVRpb6oRnf2plk9unUn+wpLe1fVlp/FPTMSQkRLXMcuR2K69fS3JmzpxJAJCZM2eq1q3UnqQ21NNVQd5Yz+vHvDGBXqd6LVy40GpbkdeZLd8Z9iqv1P6sqU+xAujAK4CfffYZWlpa8NprrwH44TzRr776CidPnsSRI0cAAFevXkVubi6AzifIL774gq1ayFFaAaRBpgHAx8dHVS/qgDLQsEavgIAA5OTkWC1v+vTpAAAvLy8AwLBhw8x+OyoBAQHIz8+3uZzUK663bISeg92X+dM6GD16tE0efwaDASkpKXbTwx7ExsYiPDychZSi2LveqJy+Hit45bCmbPTosZ4e2WeLjlI7UtPR3d1dMZ2a/O6OUfQYUaXjRKVY+q7oK+bNmwcAyMnJgcFgwIgRIwCA/R7IGAwGZGVlAbB9jBE4sBPIZ599hoSEBPj5+QEAhg4dinPnzmHBggVmTh5/+9vfWHBooNOg/P39uTIXL16MhoYG9kPPGx4yZAi7x1KnDgwMNHs9SikoKMDf//536wtoZ6w5/SQwMJBNluXQVzt08hwYGMgmHNL6sTf0dcWyZcsG/SZgGii1t06iodsQLOVP6clrW2rj1Gve39/fqiCw9NUNAMyaNctMnqXXOlTfdevWsXvkr1N7QmRkJBYsWIDx48ebfd6TdrPXqyreaz5LyF/18sqhVja6FSQoKAgzZsxQHDel0Dbi2dabb75p9j9vOwkvmLCajh0dHaisrMSjjz6KBx98UNX+6FaJBx98kL0S/fDDD20aV+hJU/S3fFyUlrG3J4DLli2zSueMjAwAQHl5OS5evIiIiAgAYL/thdLCihqWnH4MBgOef/55AEBUVJRVY4zgB1z7W4He4tNPP8WpU6eQmpqKDRs24MEHH4Srqyuam5sRFRUFV1dXtLW1ob6+Hq6uP1RDfX29zS7h06dPx0cffYQhQ4ZwVwdycnJQXl6OqVOnwmAwsAFNr9cjKSkJ7e3tWLlyJUuTlpbGjNhgMCAvL4/9DUD1KYfef/36dXh7e7P75DJ46bKysvD5559j1qxZ3Cdstfyrqqqwe/duzJ8/n103Go0IDw9HUlIS9Ho9m4z7+flxdZRD08vrVKoT+VcUo5KSEqSnp2Pp0qW4//772fXY2FjF+lKqK16ZpZ/n5OTg6NGjMBqNZvfQNACwdOlSrFu3DomJiZg4caJi/kp5SD+PjY1lcmh51MqllE9sbCzT3ZqD4mk7URk8mUajUbWMxMYoUyaTibWhPK30mtS+qH1Q+9qyZQv7crAFqTyezQNg+dNVL2k90j60du1aAEBKSgqam5tV61peJh48O5HnX1VVhbKyMpSVlbGVHN59UnnS8liyfWlfAYAbN25g5cqV7IFw6dKl2Lp1K7c/SOVIxwCp7Hnz5mHDhg0scL+0buRylaA6njlzhk3ggoOD2dhUVVWFSZMmKaan+Y0ZMwY7d+4EAOzcuRNVVVVdxhUlXTo6Osx+S8dFWsa0tDRER0fDaDSytgkKCrLL6pXBYEBaWhq2bNmCkpISvPLKK2bjB+97JTY2lulWVVVlZjNKeknHFCqHV57Y2FimT11dnc3l4fUP+Wdqq8JiMmiBfn4F3WuAs78BAFm1apXqdQDk3nvv5crMz8/n3m/rHgKlvV/oxb0SvU1/eb/x9sf1d/0NJC+/7mAP/Xvq/Sv1RqT/27L/VW0fWk/LpCbLmv22lmR2R+fu7LXqSd1Y2pdna5493b/JywOc/ZrWpJPvJbTFhqX75+Tl6ivv2u7YQm/q1hPZ9uofPMQeQAfeA3jvvffC3d0dr7/+OgBg7ty5MJlMeOSRRwB0vjqMi4sD0HkCCD0RxNPTE+vXr+fKFEfBCQQCgUAgcAQcdgIodwLZvHkzDAYD/vd//xcA8H//939obGxESEgICCFob28HADz66KOKezPsdRQcXZ7WarVYtGgRioqKkJOTg5ycHOTl5Vncp8ILkMsLW1FcXNwlrIySTBqWhhdyxRp4r5rk+zfoawe6R4vuW8rOzrYYEoZHaWkpXnjhBURGRiI+Ph56vZ4t+8vDTdgzLABtg4SEBO6eK2teP0jDJGRkZGDo0KEoLi62SQ8atoSGEpLuVbI1fJBUr3Xr1lm0Q15ZpLJpHej1esTHx2PBggWKoWKkn2m1WiQnJ6Ourg4lJSXw9PREVFQUtFptF/uRQ69LdZe2RXdtgKarq6tDcnIyOzKSB90ikJiYqHqfXGdpn1iwYAEMBgN7ZSZvZ16YFfqaLzc3F3V1dVbtB+zOazKaJz0xSekVnK15Uv23bt3KbPabb76Br68vvvnmG1YPvPFMPva99957GDZsGLy8vBAZGcnGJr1er9j+Urunry11Oh3mzp2rut1COs5UVlZi586d8PLywsWLF1m70HFRqgcdw4uLixXHKV5IF5onr32pLgcOHIBWq8XMmTOZ3UvlyNObTCZs3boVI0aMgIuLi2odqX33yPdK0s/piRvSY1fpdbUQMaWlpbj//vuRlZXFfb174MABDB06FPPnz1e0cxEOxgL9vQTZW3z88cfk5z//OXn33XcJABIXF0cqKipITU0NuXnzJnFxcSEeHh7kqaeeIkuXLiUAyNChQ0lKSoqiTKUwMN1dQrb1dZE8jdLrEnnYCkvy5a9M7IWl5Xu10BLWQN3+paF0KNaE0uhpubqjs1yGPCyELcjLzwvfIf/f2jAYtpRJLY1aXdkS0sMer3R7+krR2leBtuaj1Ceo7SrZuaXXqfay956UrTuyqXx5mB9LIYfkdm+NvSldUwtBJYX3itdSyCOpjtK+q1YW3tim9DkNRUPHE7kcpa0Vlmzc0nePPC393FJIHaX8LLWBNd9vam0uXgET4rBOIOfOnTMLCHrs2DFMnjwZjz/+OH7/+9+jvb0d7e3t+OCDD9g9bm5uZg4hcgoKCrB06dJe1VsgEAgEAoGgt3HYV8BhYWH4+c9/jnfffRcAEBcXh4qKCrz++uvw9vYG0DnhmzhxIjQaDby9vdHQ0MBiIvEQewAFAoFAIBA4Ak65Arh8+XIAQGtrK06dOgVCCAtvEB0drSjT3iuAvBAv1qThhfLgha2IiorCpk2bkJKSoiqfhq/YuXMnN+RKd7Hknq/VapGYmIjIyEh4eXmphoThIQ8zoxYmw55hAQwGA9LT07Ft2zbMmTOnWzKlYRTS09NRWlqKpKQkm2TIyy/fq2RL+CC5XraUSS0NLwySWjp5yBp5up60Y3fTWhNSqCf5yPtEfHw8NBoNs10lO7e0n04pNE9P6M3wGvLxUB7mx2g0YsSIEV3GM7lOttib0jW1EFRS5ONMcnJyl/BQvLzoGB4XF4fw8HDo9XquHrzvB6X2pbrMmjUL5eXlbDyR1ysdd6XppSFh1OqIfk9QfaU6y9PSz6XhYeTy1L7/LLVBUlIS1q5di9mzZ6vWtQgHo4yGEBsDdQ0StmzZgr179yI+Ph4ZGRlITExk5zPW1tZixIgR0Ol0AIC2tjZ4enoiLCwMkydPVvQCbmxsZIFtAaCpqQkxMTFoaGiwOXagYHCzbt06FvPNUoBlgUAg4HH48GEkJCTg0KFDiI+P7291LDLY9FWDOqc48/e3w64AajQa7NmzB0VFRQDAvJ/8/PwQHBwMjUYDNzc33LhxAzqdDs3NzaipqVE9wkfsARQIBAKBQOAIOOwewCNHjuDLL7/ElStXAHTG/Zs8eTJee+01uLm54Y477gAhBK6urtizZw88PT1x69Yt1ZMLlI6Cu3DhAtc9Xoqt7ujURZ6GR7F0HJGlI3Osyc+e91py8ZeHFOhLd3175MU7dqy7ehQXFyMiIkIxBA8N02MwGMxCYCiVQylcAy8NvTc6OhohISHdDgNkqYzdsRV5KB970h3Z/R1SQi1/S/3NXvn0NkohRWzVa7CMJ/KjCnn9U96uvd3WtG+Ulpb2mx1Yc7ShtWOLCAOjQr/6IPciTU1NpKKiglRUVHQJA0MIIZmZmUSj0ZCioiJy8uRJMnz4cAKA3HfffYoyb926RRoaGtjPuXPnCACye/durnu8lO6GhoAV7vlK8u0Rob+791py8ZeHFOjN8BJKefckr3Xr1vU41IY8XI9SuAOl0BZK5VAK18BLI5dtzzBAajoq6asWyseedEd2X9qorfl3J6RUd/LpbdRC7vTWeNZTepLX1q1bCQCydetWrixeu/Z2W6uFs6mtrSX5+fmktra2R/lawppQRraMLSIMDB+HXQH88MMPMXnyZEyePBnAD04gr732Gg4cOICNGzciKCgITz/9NMaMGcNWdObMmaMo8+2334avry/7CQsL65OyCAYeUVFR/a2CQCAY5AQGBpr9HugYDAYsWbJkQDhVyFdPBbbjsBNAtTAwW7ZswY0bN3DhwoUur3zfeecdRZkiDIxAIBAIBP0P3d5Ffwtsx2GdQNTCwLz55psoLi7Gt99+i7a2NgCdR1B1dHTgt7/9raJMJSeQ4OBgbmgWKd0JDZGXl4fr16/D29vbYggKSyFXrMnPnvdacvGXh7MJCgrqM3d9e4QG6E4IHyU9oqKisHnzZsVwBzT8wueff45Zs2ZZDAOiFCqIl4beu2fPHjQ0NNg1DJCajvJ7lMJd8MJH2IPuyO7vkBKWwpj01B6tyae3UQu501vjWU+xZ2gi3v/ydu3ttqZ9w1I4m97EmlBGAQEBZr959HefHeg4bBiYkpISbN68GdHR0XjhhRcQFxeHv/zlL/Dz88PIkSOxZs0anD59GiEhIXjqqafg5+eHq1ev4rXXXlP09BVhYAQCgUAg6H96GpJGhIFx4FfAdAXwhRdeAGC+BxAAsrKy0NHRgZdffhlAp1enTqeDRqNRlFlQUICwsDD2ExMTAwA4evQoEhISMGXKFMVDsnleW9Z47soPB8/IyMDQoUNRWFjIvU/qPVdZWYmEhASEhITAxcWFBcDm5VNaWorhw4dDp9OhsLCwS9m7401VWFgINzc3jBkzpsuh57z6oR7PUp143rHSdLm5uQgLC7OqfNKD5K3xUlbyjqMHv/O81Kw5zF1+H0+WkjegtI7UvH15UHuYMmUK18PPkr0q6V9cXIxhw4ahuLi4ixz6e+7cuXB1dYWfnx9KS0u71A3Pzl1dXaHRaPDcc89ZLJsa8rrmef9K64bnOb1o0SJMnToVGo2GjRm8PEpLSzFhwgTmUU3zKywsVPT0lrf1c889B41GAx8fH1ZXubm5mDBhAms76f+8PlVYWGjWJry85Pmq2fSiRYuQkZHBPNGlbcuTkZ2djezsbDPb5NmwWnvJZVNv+NDQULN6lN+/fPlyaLVaaLVaLF++XNGTVmn8XLRoEebOnQtvb2/Mnz+fm4aOTcXFxSxddnY2/P39ERAQYDbO0DSFhYXw8PBAYWEh0yk7OxsJCQnIzs7GokWLUFpaytqgtLTUrL/SMtBxjxdBwWQyITU1FRqNBqNHj+5S99KIEvQ3zTM7Oxu5ubldfqwZk9XakNqOvN0s2aOaziaTiQVNz8zMNKsj+RgpvIBV6GcnlF7j448/Jj//+c/Ju+++28UL+Pr162TGjBnEy8uLvP766wQA0el0BAA5cOCAokzq+Ut/jh8/TgCQlStXWnVINqzwalPyApMf2j1//nzufVLvOWm++Nch4Ur5SA9anz9/fpeyd8fTjeoIzqHnSvXD00numaqUztryqR20rlZe3sHvau2nlof0Pp4sS966vEPcLSHNh+fhZ8lelfSnbSs/fF5aX1Ld33jjjS51o1RvAEhMTIzFsqnBq2t5fUl1VDu0HgCZOnWqYh5SO3vjjTeYXNoXeF7W8raOiYlRrCt5Hkp9iuZH24SXlzxfSzYtz1M+3shlyPVT8mZXqg+5bGl6aT3K7585c6bZmKDmSas0fvL0lKahbSAd13jppGlom8yfP59bpzz74f0tt1W1vsOre3nbye2J92NpTLbUhrx2s2SPajrzyiovB0+mFOEF7MBewH5+foorgFqtFvv374eLiwtbEWxra4NWq8WGDRsUZSqtAAoEAoFAIBAMJhx2AjhjxgwQQpiXb3p6OgghWL16NUwmE1paWuDh4QE3NzcAwMaNGzFmzBisWLEC33//PVemUiBoKTScjMlkQkFBgdlnAPDqq6+yZezq6moA6m7s9J7q6mqz+65du8b+VspL+jcAXLx4kf3NC0BK+fbbb7voIb3fmlePJpMJ5eXl7P8bN24A6Cz/008/jcrKSqZzTU0NN/2OHTu4spVoampSvHbs2DH2t7wu5fkuWbKEXS8rK+O+8pDLk1NUVISysjL2vzw/eu3VV181kyW/r6ioiPv64vLly2b39jQgdXZ2NjcE0uXLl1VfoVy+fBl79+4F8EMbU2i7yh2r5O1UXV3NbEFeLqDTHnsjGLT09ZDcBqXX5PpcvXq1y2tHXj9qampCSUmJWdq///3vSEhIUAxwazKZcPbsWTMZvHuk1NfXs/5IbZH2PVs8JOXjBdD5Svepp57q8vm6devM0lA74fXl+vp6LFq0iLUxpbq62ux1PH19vGzZMgBgdUdlS/sTr16oLvTITwBoaWkxu0fellS2pTFY6Z6//vWvAPhjmBw6Zl+8eLFLXfCQv4bnwWsz3nWp7tboai+onVC2bNliVhbaptK2pVgK7yJP8/777yvKEKFi+DjsBPD69etsrxbQ2fkqKytx9uxZnD9/HkCnUXh6egLoPEDc3d0dbm5u3D1wakRHR7O4cH5+fgA6Oyw1fj8/P0yfPh1AZweoqqrC0qVLUVdX10WW3GuJ3iO/NzQ0lN0nz4t+TnVxd3cHAHh5eSmWwcfHh/1NLPgFmUwmrFy5EmVlZaiqqlK858yZMwCAoKAgjBs3DgBw5swZFBUVoaqqiukszS8tLY2VaefOnQgODu7imSr1FMzJyWHH90VERHS5h9ajdHIircvMzEwzDzGTycScgPLz81FTU4OamhocOHDAzHNU+gUklUe9aleuXKk60NJrW7Zs4X6ZSeWYTCYYDAakpaUpypMPrDxiY2MRHx+PhIQEMw8/k8mEtWvX4vLly0hJSUFsbCzy8vKYp+HSpUu7yKb1C3TugQXA2pheo+0qfRAAgJEjRzLPZqCz/uRfFFJaWloU7cwapLYgbUPa1iaTiekaERHR5ZocHx8fdp32ZaDTXmg/8vX1xciRI1FSUoL4+HikpqYiODgY5eXlOHz4sNnEXqqfyWTC9evXu9RVSkqKWf6UqKgo+Pn5sf5YV1eHnJwc1vf0er1iXcj7CB0v0tPT2WdVVVU4fPgw0tLSkJ6ejuDgYKSlpWHLli0sDbWDpUuXsnpMS0tDVlYWEhMT4efnh4KCAnZ+dk5ODvLy8lBXV4fdu3eztq2qqkJZWRlKSkowb948lJSUICcnh8mW9qeRI0d2KRPV/+bNm+wa9SKltiZPQ2XT9ouNjWX30nGzrq7O7B6DwQCj0Qh/f382wSaEICsrCx4eHmb5Sus4NDSU2Qa198jISKZTZmYmjEYj+8zHx4eNaT4+PqxPxsbGIj09ndW/vE2jo6OZTFonUgghZp7WRqMRiYmJrG2obAodkwHAaDQiPDzcYrQAOn5RO5kyZQoAYN++fWZ9irZpTU2NosduZmYm01XqHc57aJOmEZ6/lnHYCWB5eblZIOgdO3awV8DSp4GGhgYAnU9lR48exe3bt/GPf/yDK1MpEPRLL73EXiXTiaA0D39/f7MnNWq40nOH6WZXAMjNzUVRUREqKyvZU/CKFStw+PBhdv9XX33FJg7SvHJzc5kOu3fvBgB0dHQA6PwyoCsXdJXvd7/7HXJzc/Hll18yGWfOnGEbkukTujRgqfxpUr5R12Qyma2KrVixAgkJCWZ1KX1a8/X1NbsmfUJevXo1xo8fb7YKRYORBgUFwWAw4Kc//SkAYNSoUWzj8Lvvvovc3Fw2CEi/MKSe3DQ/uppDz44GOlcZ6CqkfJImHVwaGxtZHXz11VdswiO1I2mgV5PJhIMHD7L/vb292d///u//juzsbLz77ruYMWMGgM4ViKKiIkyYMIHdV1JSgl27dpnptG3bNsXVOpPJhDVr1mDKlClITk5m+shXI6ZOnYpJkyZhxYoVeOGFF7BmzRoAndsfpJuqAWDJkiVm5aJtTNtHOiGXQr+0nn/++S7XAgMDFYPiqm0S523mp/dKg9dOmjQJu3btQlBQkNmqI3X+GjVqFIKCgpiNV1dXszqgyNtBCvUmfPPNNzFt2jQAnV9+Dz74ID777DNuudSC606bNg0Gg8HsS1nqsfjaa6+xfk6R3kvbjNaL9C0AzRfobEu6QjV//nymi3Tc+q//+i+YTCa8+eab7LOoqCgzO6Bt/stf/hJ6vR7Tp083c6zz8vLCwoULzSaxJSUlqKysNHsIyMjI4NYV5ejRo6xcRUVFyM3Nhb+/f5f77rzzTjNbo3rSskvHtdzcXKxZs4ZNYu+9914AP4zTgYGBrJ1+/OMfs7oDOj1Sf/Ob3zCHJZ1Ox2LQ0jS0f0i3Do0aNYr9nZeXh/Hjx2PMmDEAOtuZTuZCQ0OxYsUKrFixApMmTWITY39/fxgMBuTm5uLdd9/Fu+++i/vvv5/JpHUi7VN0vA0KCkJubi727t2LkpISrFmzxqxMlLlz5zJ7GD9+PBYsWIDAwECzcZ86tERHRyMgIACpqalmb8hmzpzZRa70Lc/169cV+0F2djYmTZqE3NxcbNy4EQ8++KDZOK3EkiVLsHLlSgCdq/ZSp5Tc3FyuM5fT0U97D3sd+VFwTzzxBHMC+fbbbwkA4uPjQ8aPH08AkI8++oh4e3sTAGTs2LFcmUpOIEDXTfXyjbqQbE6VbxyWb75W2kArdaqQ3s+Tz9tkPHXqVPY51UHth5ZJvtFYmp9UjnSDrnzTMm+TtDwfXn2pHRNHP6ebvuWbq6X3S/VcuHAht30stZn0PqnOUtlKG6rVNtUrtYV0wzWv/aWb3aXX1Zxa1MpMdVFKY81GbSlKbU43gvMcJ3ibu5XsQG1zv7VHJ9J7pW0gtXFee/I28dP7pcf6KTm68GxCrc4JIVbbm9TxhJe32hFrvCMJ5W1jbb3zHI7kdaXk2KDUF+X9RG6PPHuT25qaM5aSY4aSLfGcDng6yu+X3iN1+JHrIG9nXt/iOXlIxwnedel3lVqf4uXN+25SG9t5OvHy4jkeKpVRyR7UfqTfZfJrwgnEAZGvAK5atYqtAI4aNQoeHh5oamrCV199BQD46U9/yl69KO0XUHMCGTZsmNnyNQ1OmZ6ezl0lSExMxNatW5GXl9cl9AzNX76/g/cKl75aAcyfJil06R3ofMqisqVP4NYgXZ6XBt78+uuvufcPGzYMQOfTs16vZ2nkq0KRkZFmK6FyeEE+5fv0amtrAXSucCq1XVJSEnul8/XXX3PD/dCV2fT09C75hoaGsvaor69HZGQke90zYsQIM90AsFf+VF5dXR0LtUBXnjw9PZGRkcGCEUvbCvihDqmu33zzjdl98lcg9Lo1SMsiRS0wMl2JlCJ9vabRaMxWBfbs2cNWo4cMGcLS0H5GbWr06NEAfnhtQ2VSu4iKioKLiwsWLFgAgN8/lfZK8qCrRpSAgACzNqSfAT+0AdAZLB7o7Dvr1q1jK22ZmZnsfnqPi4sLC1MhrSuaRvqaVYr0de+dd96JoqIiLFq0yKyPSPsc1Y/2q9GjRzNd6EqPtL7kY5EUOiYMGzaM9TFpvlQODUScl5cHg8GAyspKPPPMM8jNzWWv6KT9h74GlL7GprpS+5Dacnp6OvR6Pas7jUaD5ORkuLi4AOh8vUr1oTotWLCAvc2RvvamZXnvvfcQHByM9957r8vqcH5+PrRarZlN0D6Xk5PDtkS89957ZqFXKioqzOpeWueUgoICtkJN9T948CBLQ23gzjvv7KKD1CZpH6GoBUGW9jV6nb6STUxMNJOltkeOrnTL85ajtn2DQuuTrsjKueOOO8z+N5lM2Lp1q1kZ5ISGhmLIkCGIiIhgr9cBcLfKSPux4AccdgI4ZcoUVFRUsE5aUFDAjoLTaDTIz89nDiB00Kb7AceOHcuVqXYU3PDhw82Wr+mSO28C6O3tjbKyMqxduxaZmZld9tzRfSVSRw/AvGNT1q1bxwZYXkedOnUq+/vEiRNMdncmgLR80tcJO3fu5N5Pvzjq6+vh7+/P0khfHwHAqVOnVDsn73Ug3X9Fy3LixAmmo9Km95/85CfsZJidO3dy9znSvaGTJ0/ukm94eDhrj2vXrmHs2LG4desWAPMvgEuXLgH44UsKAF555RVcunSJtTkdMJubm/Hyyy+zV4UPPPCAWZ60DqmudK8dve/UqVNm99Pr1iAtixSqC49vv/2WvVKhSF+vEUJQUFCAgoICVFVVYeXKlXj11VcBmO/BpK/gqU3RSQCdUBgMBqxZswaff/45gE4bb29vZ84yvDameyWtge5hpQQGBnZ5MKHtL50A5ebmAgA8PDxQUFDAHi7T0tLY/XS7RXt7Ozo6Osy2bRgMBrzyyisAOm1CaQJIXx0uXLgQK1euREFBgVkfkfY5qh/d8xcVFcV0oRMiaX1ZMwEcPnw462PSfKkcg8HAXkcaDAa2d+/uu+/GpEmTumwN2LdvHwAgNTXVLL+oqChkZmYC+KHv0brx9/dndUcIwe7du9He3g4A7BXplStXmE7Hjh3D1atXAQDPPvusWf3QPa4XLlzA2rVr2b5N6faAjo4OM5ugfS43NxeTJk1CZmYm1q5dy/Y9m0wmVpfSMU0+Rq9bt45tn6D6l5eXs/ai3zULFy7sooPUJuVnj6udISxdKJC+8i4qKsL+/fvNZKkdp/bQQw9x85ZDv3/UoPU5YcIErt3HxcWZ/U/bTFoGOeHh4bhx4wbefPNNM/uhNiVFbZHBmXHYo+DKy8vNnjjphvbHH38cq1evRnh4ONzc3EAIQWtrKzQaDZqbm6HRaPDEE09wZSodBXfnnXcqHrklfXrx8PDAqFGjzCZqRUVFSE5OBvDDCgh9Ghs9ejSbQEZFRSEpKQmffPIJzp8/j6lTp2Ls2LFmT18jR46EVqvFli1bMG/ePOTn5yMoKAju7u7MGy4gIMBs429kZCS8vLxw8uRJ9sUycuRIzJ8/H5GRkQgPD+duJM/Ly8PFixfZxm69Xm+2QZeuftBBXOq4kZeXh0uXLuHIkSM4duwYAgICuhx7Jz/qiLc5OCAggDlKUGcR+gUcFxfHJvYUFxcXDBkyBDdu3GB5AmB663Q6AJ1Pi7SMZ86cQUlJCTQaDRvopJMZukJAN8bfcccdSExMZAM8bVM6SaXMmzePhRyidSPdvD537lyml9Re4uPjYTQaWbl1Oh3a2trg4+ODxsZG5OTkKE4spOUZPXq0mez09HRERESYpZWnoZMBeR48W+e1EUXeLrzN39T7NCEhocv90lUNqR1KZVk6OhEAs1tqr/Kjp+T2+sknnwD4YTWD1kdAQADLV2pDFGl9WXM0Fa8+qb3y+hytX2k+0jrXaDRmTkVK7cfr7xqNBlFRUfj6669RUlKC1NRUVd3p6mpUVBT8/f3NHKSozUnbhtbl0KFDzfo8nczn5OSweqATVKqf3N7oWxij0Yi4uDhcvny5y9hliaysLBw4cAAnT55EdHS0RUcC2v/pKiKd7AIwc5ah+kmJj49HUlISjh8/jtjYWDZ2SdtW6cg3uR3RvgDArE8rjQU0Lf2uka8UHj16lHsUnHwcl/Y9vV7PHoBdXFzYhDczM5Ot0ElXNbVaLaKionDu3Dm2OsqrX3nf4a0yU6jDCP0+AWB2lKr0mk6nwx//+Eduvk5D/7197l9CQkKIt7c3mTVrFtmwYQP505/+RDQaDXFxcSG3b9/mplHaA6i2h0Btvwz9UQp4qhasVbonCrJ9DvQ6Ly+lPVHyfX1yPdTK1lvXlbA2eKhcrqUAx7wgwbw9L9I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bbrkF/v7++M1vfsOVSYWgCYIg+iZ9obBwX9CBILTis98AOp1O6HQ6HD58GK2trTh9+jQA4PDhw2KbTZs2AQDGjh3rcu2OHTtw8803d5JJhaAJgiAIgvAFfPYbwI6ODvj7++Oee+7Bzp078d1332HGjBmoqakR2/zpT3/CddddhzVr1uDDDz/EfffdBwBioWg5VAiaIAiCIAhfwGdvAENCQhAVFYXVq1fjpptuQlJSEiZPniwmgZw/fx7Lli3DCy+8gNmzZyMvLw/Nzc0wGo1Yt24dVyYlgRAEQfRN+kJh4b6gA0FoxWdvAAHA398fd955JwwGA375y1/in//8JxITEwEAbW1taGtrg0532QQNDQ147733MHz4cHR0dHjUT2ZmJkaPHg2bzYapU6diwIABmDBhAkpKSsRYEIfDgXHjxsFms8FqtSI7OxvV1dUYN24cHA4HHA4HMjIyMHr0aJSXl8NoNCItLU1sn5aWhpEjR2Lu3LlwOp2iPIfDAeDyT96FhYUwGo2YOXMmCgsLkZ2dLcqW9p2RkYHCwkKUlJTA4XCgpKQEJSUlsNlsokyn04mysjI4HA6UlZWJDw0X/haorq5GVFQUqqurxWM2mw0JCQkoLCx0uV6Kw+FAdnY2CgsLMXPmTCQkJMBms7mck441LS0NcXFxsNls4liFa5xOpzgGQU/BxvIxFxYWIiMjQ9GOAoJMq9UKq9Uq2kfQWTgm1W/IkCFIS0tzsZ90/uXjra6uFuessLAQNptN9AGpPvLxCfKEPocPH+4yNql+wvxK38vnVQ7PnkJb+dzwrhXGNHXqVKSlpWHEiBEYPXo0nn76adFXpHLkfjdz5kxERES4+JR0PgTfrq6uFu1ls9lQVlaG6upqGAwGDBkyBIWFhS46CjIKCwsxfPhwxMTEYObMmS5zqYR0fQp9SddOdXU1kpKSRB8W1oFwTGpT4brCwkKkpaW5zLeaLzscDpe1JegrnR95e8GeISEhiIyMxJAhQzBz5sxOeghrRSpHri/PTlI/vP766132KKm9pfpI/c/hcHTak3h7jXxNK/kucHkvl58X5m/kyJEuex9vb3v66acREBCARYsWuegqX5+8vaOhoQFbtmxBQ0ODy/xLdRgxYoRoJ7ltBT8qLy9X9TfpdfK1LthQ6L+6utrlemH/efrpp138U24LtT1Cvqaknx82m63TniP9zBPmgbeHSPuWjpWnh9D3zJkzYTQakZeX5/I5orSPCXoK5eD6NcxHCQwMZABYbm4uq6mpYQ899BADwKxWq9gmNzeXmUwmtnnzZvbYY4+x0NBQFhwczFatWsWVeeHCBdbY2Ci+jh49ygCIryVLlri8B8Dq6uoYY4xVVlZ2ajNv3jwGgFVWVornAbBZs2a5lSm0r6ysZIwxVldX16mdXDZPlrRf4XxlZaUoTzhfV1cnHhPGxBgTxzBv3jzxmLQf6fVSpP1K+5efk45VaCMdq/y9VE+lMavZUYBnT3dzIR2z3Fa88Qq248mX6iMfn5I8pfnnvVeaF15/0rHI50btWvlrwoQJ4rjd+Z3cp3hypfYTZMhtKtVRTTel8fD8VaqvXBfBh6XrQO6jvLnjrWO5L1dWVrr4iKCvdH7k7dXGLNdDkM2To2QnJT+Uy5DrI/dDpWvlY5TryfM9nm8r2ZzXn+CnmZmZLu3k65O3d0iPSeffnQ5yP+J9BkjbupNVV1fXaU3IrxHGKfVPqS3U9gjempKOV0kvtfXJm0M1PZR8W7C30loS9HvppZcYANbY2Kg4Pl/HZ5NAAOC6666Dw+HAjBkz4OfnB71ej+3bt4vnq6urMW/ePOTn5+PChQsAgGHDhmHq1KlceZQEQhAEQRCEL+CzPwHHxMSgoaEBd9xxB3bu3Ilvv/0WDz30kEsh6HPnzmHbtm244447AADvvvsuVq1ahZCQEK5MSgIhCIIgCMIX8NlvALUUgl68eDGmTp0Kf39/ZGRkYNq0aaoyg4KCEBQU1On4DTfcgKCgIBw4cADjx4/HJ598ArPZjJaWFlRUVKCsrAxNTU0YMGAAPvroIxQUFGDnzp0YNmwYEhMTYTAYEB0djfT0dPj5+SEvLw+ffPIJoqOjMXjwYMTGxkKv1yMg4PJ0zZkzB//5n/+J3NxcmEwmOJ1OrFu3DgUFBdi+fTvGjBmD0NBQ/Pvf/8a2bdtwzTXXICgoCAEBASgqKsKuXbuQlJSE+vp6GAwGFBcX4/jx46ioqIBOp8PRo0eRl5eH0tJSmEwmlJaWQqfTYe3atSguLoZOp4PVasWePXvwq1/9CpGRkcjJyRHtYTabER8fj5EjR+L5558Xi6I6HA7MmzcPI0eOxLhx4xAbG4uoqCgYjUbs3LkT69atw2233QaTyYT09HRxzkwmE1JTU3Hy5EmYzWYYjUZYLBZs3rxZfF9cXAwAYvB1UVER9uzZA7PZjKKiItTW1uKHH35AWFgY4uPjMXr0aKxbtw5TpkwR7Qj8XyHXGTNmoLi4GF9//TW2bt2KKVOmwGw2IysrC8nJyTh9+jS2bNmC++67D6WlpUhNTUV9fT3i4uJgMBiwbt06WCwWzJ8/H+Xl5TCZTOK1LS0t+OSTT2Cz2URZfn5+2L59O1JTUzFgwABxXqW6HD9+HHPmzEFmZiZmzpyJlJQUHDlyBEFBQTAajRgxYgSOHDmCESNGIDQ0FHq9HiaTCcXFxWhubhbfS+fVaDTC6XTiD3/4AzZv3ozVq1fjxhtvFO0pnXej0QidToesrCyMHDlSvPa5554DACxYsECcm02bNuG6665DXV0d/P390d7ejr179yIwMBCpqakuczx48GDEx8fjgw8+gMlkQn5+Pmw2G9auXQubzYbIyEgsW7ZMHEdLSwsOHz6MnJwc7Ny5E35+fjCbzSgtLUVKSgrefPNNdHR0ID8/3yUYX/CT48ePY9euXThx4gSio6MxevRoABDnatSoUS4FfQFg3bp10Ol0GDBgAAYPHozi4mJs27YNRUVF0Ov1yMzMxIYNGzB48GCMGzcOK1euRHJyMiIjI5GcnAyj0YiioiLs2LEDTz75JNLS0pCcnIxDhw6J8y3YOyUlBUePHsXBgweRm5srXvfMM88gISEBBoMBgwYNEvcWnU6H9PR0zJkzB08//TSKiopQV1eHbdu2wWQyoaCgABs2bMCAAQMQGBiI3NxcJCUlib5x/PhxHD58GMnJycjNzYVOp0NMTIx47vnnn4fFYhF9SqfToaSkBAAwZcoUpKam4tixY+JcRkREuMg4cuQItm/fLs6v4H+CDS0WC/bv34/Y2Fjs3r0bK1asQElJiYt/rlu3Dvn5+fjkk08wePBgbqKFYIcnn3wSGRkZYnw3ABgMBnG/GT16NAwGg8saACD+fdNNN+Gjjz5CamoqQkND4e/vj9zcXCQnJ4t7tMlkwtdff42wsDAsXboUJpMJo0aNQlNTE4KCgtDU1ASz2YzExESYzWYAl2PSw8LCMHDgQJw/fx5xcXE4evSo+IjSCRMmICcnBzU1NXA4HBg+fDgA4B//+AesVqtoM2E8wn4iX+vCPhETE4P4+Hjk5OTAYDDA398f8fHxuOGGG3Dw4EFMnDgRhw8fFvdQYfwNDQ3IysrCunXrEB8fLz4qlYd07zWbzcjNzYXZbHbxK4PBIO4JR44cQWpqKvz8/JCdnY2DBw+ioKAAL7/8MiZNmiTqYTKZxD3cYDBw5/vgwYMICQmBTqdDe3s7Bg4ciMbGRtjtdjidTnHN7dmzR/TH0tJSUU/Bvv2a3v4NurtISkpiOp2OmUwmNmjQIJaYmMgGDBggxmu0t7czvV7PFi1axHQ6HdPr9eymm25ib7/9tqJMpRjAxsZG1ZgVaTwGOPEvWmKP5PE8vBgxpRgJXnySWoyaNPaK115JDzXd5X2o/a02Jm+Rxp7wYvSU7CKP4xFQiteTypDG7ijZRtpOro/aHPFirLy1lTyOR80eatdqifPkrRF343LnZ97Ci23k+Z9aLJvSmHlttMyX0lworRel9ejN+uFdw4tz4825WtyYko+48295O7X15M6+nthDKU5Rzb8FnZT2C2l7Xhw4zw5KcYbuxqNkRyVZSuO/0rWntIakOqnZy92cuYvvdiejsbGRAf07BtBnfwIWCkEfP34czc3NaG1tRUdHB9rb2wFAPP7cc8/B398fGzZswB133IGCggJs3bqVK3PZsmWIjIwUX0OHDu3JIREEQRAEQXQJPnsD2NHRgZEjR2Ly5MnQ6/VobGxEVFSU+HOVUOrl4sWLaGtrQ05ODhYuXIjIyEi8+OKLXJkUA0gQBEEQhC/gszGAMTExOHDgAFJTU/H+++/DYDBgxYoVeOONNwAAQ4YMgZ+fH5KTk7Ft2zbxuqeeegp1dXVcmUoxgEDnAqDyuDQhLm78+PHcOCwlTCaTGKMWExMjxjQIMUO8vuXHU1JSsGHDBjEWhXeNyWRCSkoKjh075hLPp9Sep4ea7sJ7IX5MGs8ijfcQ2nZ1QdWcnBy89tprGDZsmGhLNZsJx+VxPAJmsxmxsbGIjo7uZANBhhBrwrOR1BZCbKEQV6eki/Qa4Zg0vs9bW8ljKtXswbtWHn8pH2NcXBwOHTokxv1I14g0Hkwqw2KxYOPGjeK37Wp+5i3Ssel0Ope5ko9b0Cc2NlbVf+TrVb4nCDG4mZmZXJsqzYXS2lFaj96sH9418jUstJP3L51LuQwlH9Hi39J2autJaKdkX0/sIR+L0j4txLEKMYGA8n4htaUQIwdcriG7fv16jB8/3sUO0nHyxqw2HiU78uZSafzSGD5v155cR54PqNnL3ZwJe7AQ1xkaGgoALnshFeZWx2e/AQwPD4dOp3N5Eog0CWTAgAEYMmQIzp8/j9jYWPF19OhRsVi0Vn766SeUlZVh48aNaGhoEAPIp0yZgu3bt+OJJ57A4cOH0dTUhNOnT6O8vBxnz55FTEwMysrKVJ1z1KhR2LJlC0aNGgXgskO/9dZb4nvhmJIc4eHkFy9eFN+XlJSIhTWfe+45OJ1OxMTEYOrUqbj33nvxxRdfdCoqKvDVV1/hnnvuwaxZszBmzBiUl5dzi+g6nU6sXbsWGRkZiImJ6aRXTEwMFixYgPDwcMTExKCiogI7duxwGafVakVFRYXbB6srFXOWMnjwYERFRSEjIwMAUFFRgbFjx+Kee+5xKcIrPx4dHQ2j0YgXX3xRLGYLAJMmTYLT6cSePXtEnQXbPvfcc5gxYwY++OADl/FLi62OGjUKO3bswIsvvogbb7wRY8aMQVhYmIv9hCQEIRC+vLxcnEcper0eU6ZM6TQWtQLeUoxGI0pKShAeHo6CggKxALM0CUWYB6mtpX5eV1eHr776yqWfmJgY3Hrrrfiv//ov7NmzB19++SVefPFFl83ZarWipqYGhYWFCA8PF4+vXbsWtbW1iI+PxyuvvOLi7+7GIy0mKy2KrERDQ0MnX5Wuqa1bt+Ltt99GZGQkxowZg5iYGHz11Vd45ZVX8Ic//MFFj1GjRqGqqgo1NTVoaGjA2bNnxTUmMGDAAMydO1dMjJIWxC4oKEBLSwuGDh2K6OhocSwAMGbMGDDGsHbtWixYsACFhYW45557xMLHt956K1d/JXh2/Oabb5CZmSkWD46JicGWLVsQExMjFvf9wx/+gC1btuDcuXMufmK1WrlrHbi8HwtJQtIxCToK/g0Ac+fOBQBx7hoaGjBjxgwsXboUK1eudPEF4P/W/9atW7Fr1y5cunSpU//y/UReJFhatNloNCIlJQU333wz/vGPf+DEiRM4e/YsysrKXPa6UaNG4d1338Vtt90mjvvUqVP4+eefcerUqU72PXHiBI4cOQIAWL58OZYvX47c3FxMmzYN+/fvxxNPPCHuDYLNa2pqUFVVJSYmCfLU5vfEiRNwOp0YN26cy/4p/ywR5k5etFlY00ePHsXs2bMxY8YMlwLnct+RF28WCn8LfimsoVWrVqG8vNwlxCo6OhpJSUmIjo7mzpl8jNJ+b7zxRlitVvz1r3/F4cOH0dLSAr1eL/qZOxk//fRTpz77Hb0dhNhduEsCYYyxcePGMQBMr9ezxMRElp2dzfz9/dnHH3/MlamUBLJ161ZugLg0SFVa2BOcYFgteBrYLQ/mlReklerBC/zlnVcq9qlWpNNdEoO7BAN349US3MxLwpEHlfOCzXmFlLXYmxeI7S7xRK0tL8CdV4RVLYlBS7IR8H9JQLzAfl6Sg9wntARgy8euVrzXkwB4nkyleVNKQpHDK16rlLijJpeXrKGUDCS3Ma8Ys1rCiRbUfEwYs5L/Ka1jnh5K/SglivAS3tTWuHBOa5FhXlKCPBmBV2ScJ1c+FmlhfPk5XsKDJ4krWudYXgBay/pT2huFzy215DAlH5XOldSe0iRDrYkpav26+yxSkiF8bvfnJBCf/QlYSAI5fPgwWltbcfr0aQDA4cOHxTZz585FWloaampqcPToUXz//feIiopCZmYmVyYVgiYIgiAIwhfw2Z+AOzo64O/vj3vuuQc7d+7Ed999hxkzZqCmpkZsc9ddd+GFF17ACy+8gNTUVMTExODs2bN47733uDIpCYQgCIIgCF/AZ78BDAkJQUhIiEsh6MmTJ+PJJ590affjjz/it7/9LTZu3Ihp06Zh4MCBOHToEFemUhJIbGysSxC2tOCkEIw7cuRIbNy4EbfccgsSEhK8Ctr3NKBVCIretm0bGhsbXYomNzc3A3ANmJUXDJYH7UoDsYVAZqUEBF7gNy842V2CgZbxaglu5iXhyIPKlYLNhUSG+vp61T6kY+bZTy1Zx11bpQB3eRFWpSQALclG8iQgXmC/NFlCXlhVKMbsSQC2WvKA0rxqSUyRF5PlzZtaEooUIYHIaLxcrNdodE3q4s2nkly5PrxkIKmvCTaWF/RWmosrSfqQJqAIxYOl56SFuPfv3+/ih2q+xuuH10a+h8ptpZZQlZubKxYGv3TpkkuykZIe8iQ9aTKCNGlMba+Ty8zJycHrr7+OnJycTud4CQ+eJK5o3Q+FfuRzyEM6r9JC8cKazsvLwxdffOE2OUzqo7zkkZycHKxevRp+fn4uSYZaE1PU+pUW6Hc3XqmM2NhYTX36NL39G3R3ERERweLi4tisWbNYdHQ0GzVqFJswYQLLzs4W27S3t7Px48ezlStXMsYYS0hIYAEBAWzNmjWa+hAKSY4dO5bZ7XaXc/X19ay0tJTV19ez+vp6ZrFYWGxsLLNYLKy+vt6ljd1uZ8XFxay4uFhsL1xrt9tZeno6y8jIYLW1taKcgoKCTu2XLVvGAgMDWXJyMqutrWWlpaWstraWpaamMqPRyGpra8W2tbW1LCsri1ksFlZUVMQsFgtLSEhgfn5+7Oabb2bFxcWstraW5ebmcscm6CvV3W63u+gtvVY+VovFwrKyspjdbhflFRUVdZIptwfPxtJ+pbrJ29fW1rLExETRDnK91eQIcxgfH89qa2sV509uV54e8vEI71etWsXCwsJYcnIys1qtnfQS5kOY21WrVrGgoCCm0+lc5lzen3TOpDaW6s7TVX6N2lwoIbVRUVGROOdq9mCMMbvdzrKyspjVau1kJ+l7+VzzfEJJL+m18v54/pqenq6oj1K/SnOt1X48fXlrTu67Wo7J7c9b74JdLBYL15d5ekr3F54fK+mlNJdqc8ubA2nf3thZ6/zI9XHnc1KE/bGqqqrTPsmbZy17iJbr5NcqrT3pPsPzFd6eV1BQwJKTk1lISAhLTk522QN5vlVfX6+4JyiNUc2H5eMvKipS3e/mz5/f72MAffYGMDAwkAFgubm5rKamhj300EMMALNarYwxxs6ePctuvvlmlpmZyb799lu2efNmFhQUxCIjI1lTUxNXplISCNwEqisF+roLFpcGeoMT7CpvP2HChE5tpddIk0DUqqjLZSiNTa67UrKA0liF91oTUNwF26tVnGfMNQibNwY1OfKEC6UxKc2Rkm9I38sTheR6yedU3l6ehKE0ZzzZPF3VEk+0JhtoScjgydSSRKOWPOPuKTvya5V8jvc0E54+Sv16G8ivpi9vzfF8190xuWzeelfzDzU91Z58pKaXFtvxrldKCvDWzlquk+uj5clOAvLEFfk+qWQTJT21Xie/Vm3t8fYTrXue/MXzLbnOanry+lbaq3hrVmm/A/r3DaDP/gQMANdddx0cDgdmzJgBPz8/6PV6bN++HQDw5ZdfYseOHWCM4dprr4Wfnx/8/PxgsVjEchRyKAmEIAiCIAhfwGeTQGJiYtDQ0IA77rgDO3fuxLfffouHHnoIx48fBwB88cUX4tNA/P39odPp0NHRgVdffRXDhg3jyqQkEIIgCIIgfAGf/QYwPDwcTU1NLkkg0kLQhYWFmDhxoss1kydPhtPpxJ///GeuTKUkkLFjx7oNVLdYLNi0aRMmTpzYKahXKeFCCPQWqs2bzWZRzpgxY5CUlOTSPjg4GNu2bRMDjUv//wGyqampOHnypJgEIhwXAmdDQ0PR0tKCLVu24Mcff8TYsWNx0003KVbeV0p2UEoW4I1VGigcExOjKQFFLdheel4poF8ahC0fgxY58ic08OZPblelBBlpP8L7mJgYvP/++2KygVwvebJFTEwM3nnnHbS1teHaa6/tlITBmzN5sDcvuUDpGq0JJUpzpJSQwQtu5yUM8ezmyRMpePYQ/tbpdNwEJam/yhMflALheXPraSC/mr68NSfvV+sxqU689S7Mg6cB9tIEOLmOSnppSYRSup6XFCB/EoZWO2udH7k+Wp7sJCBNXNm/f7/LPqmWSKakp9breNfy1h4vqUs+ZvmeFxcXhz179uDHH39EfHw8srKyxD2Q51vyxB93eqqtbd74pYmOvP0uMDAQ//3f/602TT6PH2OM9bYS3cE111yDH374AcOHD4fT6UR4eDicTif+/ve/47777uvU/uLFi4iNjcX58+dx9OhRDBkypFOb1tZWtLa2iu+bmpowdOhQNDY2IiIiolvHQxAEQRBE19DU1ITIyMh+/fntsz8BC4Wgjx8/jubmZrS2tqKjowPt7e0u7d59913o9XoEBwejqakJ8+bN4978AZdjAIVnk0ZGRmLo0KE9MRSCIAiCIIguxWe/ARwwYADS0tKQkpKC999/H+fPn0dYWBiMRiP27NmDtrY2PP7443j33Xfx7bffIiwsDFFRUbh48SJ27doFg8HQSSZ9A0gQBEEQVz/0DaAPfwMYExODAwcOIDAwEO+//z7279+Pe++9V0wCaWlpwe7du1FaWgqHw4F3330XQ4YMwfHjx/Hqq69yZQYFBSEiIsLlRRAEQRAEcbXRb5NAIiMjYbPZXK7529/+hptuukm8SSQIHk6nExUVFbBarR4HmXcnfVUvgiAIou/hs98Anj9/Hs3NzbjxxhsxePBgJCUlYdWqVZg/fz4A4Ny5c1i0aBE+++wzfP/999i9ezf+9Kc/AQDuvvturszW1lY0NTW5vIj+h9PpxBNPPAGn09nbqrjQV/UiCIIg+h4++w2gkARy+PBhtLa24vTp0wCAw4cPA7hc+++jjz7C888/L8b16fV6TJkypdNzDwWoEDRBEARBEL6Az34D2NHRAX9/f9xzzz3YuXMnvvvuO8yYMQM1NTUAgODgYMyfPx+LFi1CeXk5ACAhIQFvvPGGokwqBE0QBEEQhC/gs98AhoSEICQkxCUGcPLkyXjyySfF94WFhWhra8Ntt90GACgvL1dN7FAqBE30L7wt5tvd9FW9CIIgiL6Hz34DCFz+mffOO++EwWDAL3/5S/zzn/9EYmKieL6trQ3/8R//gSNHjgAAoqKiekdRCU6nE2VlZRTHRXiM0WhEWVnZVXsDSL5PEATRc/jsDeD58+dRX1+PEydO4OWXX0Z6ejo+/PBDpKWlAQAuXbqEWbNmYdeuXVixYgUA4OTJk/jpp59w8eJFrsyeSAKhQP6+D81R90B2JQiC6Dl89idgALjuuuvgcDgwY8YM+Pn5Qa/XY/v27QCAY8eOYf369QCAadOmAQAmTZoEANi8eTPGjRvXSR4lgRAEQRAE4Qv47DeAMTExaGhowB133IGdO3fi22+/xUMPPSTW+EtKSsLTTz+N8PBwMQlk8uTJMBqNyMjI4MqkJBCCIAiCIHwBn70BDA8Ph06nw+rVq3HTTTchKSnJpRA0YwwrV67E4sWLMWXKFADAkiVL0NLSgtdff50rsyeeBEKB/H0fmqPugexKEATRc/jss4CvueYa/PDDDxg+fDicTifCw8PhdDrx97//Hffddx++/fZbXHfddaiqqkJERASmTZuG6upqlJeXw2Aw4B//+EcnmfQsYIIgCIK4+qFnAftwDKBQCPr48eNobm7GgAED0NHRgfb2dgDATz/9BAC45557xGuEJ4AI3xLKoRhAgiAIgiB8AZ/9CbitrQ2XLl3CiRMncPHiRfz0008IDAwU4/2EJ4MYDAaEhITg1ltvxcGDB/Hwww/j+uuv58qkGECCIAiCIHwBn70BDA8PR1RUFJxOp/gqKyvDDz/8AMYY/vjHPwIAli9fDrvdjsTEREycOBFOpxMxMTFcmT0RA0gQBEEQBNHd+OwN4NChQ9HW1obY2Fjx5XQ6kZiYiEOHDsHhcGDIkCGor6/HL37xC6xatQpnz57FRx99hDFjxnjU16JFi2C1WlFSUiLWMFMqais9rvZ3SUkJrFYrZs6ciYSEBNhsNgBAdXU1oqKiUF5e7iJfuN7hcHSSU1hYiIyMDBQWFoo6StuXlJRg5syZMBqNKCwsdJEp6CEdG3D5qSnBwcHiN6pC25KSEthsNmRnZ2PmzJnIyMjA1KlTkZCQgOrqalE3h8OBcePGwWazddJbjtDW4XB0sqXVakV2dnanczxbC21tNpuoq1rNOen1Uh0cDgcyMjIwevRoUZZg47lz57rIlI9TkDV8+HCEh4ejurqaO39K/8r9QxiDkj2lczF8+HDExMS4zLGar6r5KwDYbDYkJCRg6tSpol3l9r7++usRFhaGG264AX5+fhg2bJg4V06nEzNnzkRERIRoh+rqaoSEhCA4OBjXXHMNRowYgdGjR7tcY7VakZGRgZkzZ4pzb7PZYDQakZaWBofDAafTicLCQtGnbTaby/xJ/Ul+rXTsJSUlGDt2rKj73LlzVWVJrxPW1MyZM8W5qq6u5vYlRbCrfJ7kflJYWIiEhASUl5d30kFpTFJ9pWtW8CHpup07d66L39lsNrF6gsFgQExMjLimq6urkZSUhOrqatE3pWMWxuNwOLhrlrf/SdensOZGjhzpso/x/LWkpARTp0518SveOpTbfODAgeKvRPI5cHd9eXk5BgwYgOuvv567T/HsXFhYiMLCwk5rRw2Hw4G0tDTExcWJnwnyfWr48OEICwvDhAkTkJaWhuuvv5677nl+5W5fUNrPxo0bh+rqamRnZ2Pu3LmiP9hsNpdrhL0yLS1NnEvBT4TPIqW9VGmu5TaU75XuPl/6NcxHefjhhxkAFh4ezuLj41lWVhYLCQlhlZWVbM+ePQwA+8///E8WGRnJ3nrrLbZ3714WHBzMQkJCWFNTE1fmhQsXWGNjo/g6evQoA+DyqqurY4wxVldX5/JeQHrc3d/S15IlSxhjjM2bN48BYLNmzeL2V1lZqSpHfk5orzYG+XHGmNj/rFmzOrVdsmQJt19B97q6OrFfoa1UbznCucrKSq4teeeUbC3Xj9cf73qpDlKb8cYqlSkfp1SWYBPe/Cn9y5tXNXsqzYVURyVfVfNXxlgn2dIxKvmedK6kbQQ7CD6i5RrpOakulZWVndpJ7SL3J/m1PP/SKkvtOukaUPJZuT5K+4fUh4S1KJWnNCapvlI95X4pvVY4L8icMGFCp/HI/1Ubs5qt5b4jjF9tn+LZR+5XvHWoZPNZs2Z1mgN31wtzwJtXLXZWkitHvvfIbceT7c5mchlqx9T2M+k8C38vWbLE7V7E8w13+vLmmvdZwvtcFGhsbGQAWGNjo6rNfRmfTQJpaWkBAJw9exZnz57Fjz/+CD8/P0yZMgUREREYNmwY/vnPf6K9vR0FBQXiddnZ2QgPD+fKpCQQgiAIgiB8AZ/9Cfj666+HyWQSvwb+5ptvMGTIEKxZswaBgYFIT0/HsWPH0NzcDH9/f4wYMQIAXMq8yKEkEIIgCIIgfAGf/QYQAAICAhAbGyu+HzlyJA4dOgQAOHToEB5//HH87ne/w8WLFxEdHY2wsDD4+fkpygsKCkJQUFCn4/Pnz0dbWxv0er1YxFapqK38uNLfxcXFaG5uxsmTJ7Fz506YzWYAQE5ODl5//XXk5eXBZDJ16s9kMnWSc/z4cezfvx8jRoyAwWBw6c9kMqG4uBhHjhzB9u3bMXHiRBeZgh7SsQFAXl4e3nnnHeTl5bm0BQCz2YysrCzExcXhyJEjiImJwZ49e5CTkyP2r9PpkJubC7PZzNVbislkQm5uLkwmUydbFhUVYc+ePZ3O8WwttDWbzaKuakWHpdcL+gr9pKenw8/PT5Ql2DgzM9NFpqC7ME5BVkpKCo4dO4acnBzu/Cn9K/cPYV6U7Cmdi/379+P06dPiE2+U7KRmQ+nfZrMZ8fHxSEtLw5kzZ1zGKNj7o48+Qn19PeLj43Ho0CEMHTpUtKHRaERBQQFsNptoh5ycHKxevRqMMRiNRoSEhCA0NNTlmqKiIuzatQtJSUmor6+HyWSCwWBAbGwsoqOjYTKZEBMTA4vFgk2bNmHixIkwm80u8yf922w2u1wrHXtxcTE+++wzbN++HUOHDsXUqVNVZUmvE9bUmDFjMGTIEOj1emRmZuLtt9/u1JcUwa7jx49X3D9MJhMsFgs2b96MvLw8nDhxwkWe0pikaykmJsZlHeh0Opd1m5mZ6eJ3ZrNZTJbbs2cP/Pz8xDWdkpKCDRs2ICcnByEhIWhubgYAcczr16/H+PHjYTKZuGtW7mfy9WkymZCeno5Lly4hLS3NZR+T26e4uBj//ve/8cknn4h+xVuHcptHRUWhubkZeXl5SE5OdpkD+fqSX5+Xl4d//etfSExM5O5TPDsnJycDAA4fPqwoV47JZEJqaipOnjwpfibI96mUlBT88MMPyMrKwokTJ3D+/Hk0NTV1WvdyHeX9844p7We5ubnIycmB3W7HyJEjkZOTgw0bNsBsNiM6Olq8Rtgrv/zyS/j5+SEtLQ2hoaHQ6/XiZ5HSXqo018ePH3exoXyvdPf50q/p7d+gu4vS0lIWGhrKjEYjS0pKYrNmzWKxsbHsiSeeYIwxZrVa2ejRo9mxY8dYR0cHe+211xgAtnz5cs19CDEEBw4cYKWlpay+vl48V19fz4qKilhWVhaz2+2svr6elZaWMrvd3qmt0J53vCtR66O+vp5ZLBYWGxvLLBZLt+rhqW5dIc/de2/06Eqd6+vrWXFxMSsuLubq6GlfPH8T+igqKnLppyt0d6dbVVUVi4yMZFVVVV7L8EQfuS21XMNbw1J7Ka1dT6itrWWJiYmstrZWsW+73c5yc3OZ3W73SF8pdrudZWVlMavVqthGi827al6kc8KzY1eMRw2pTZXmWrpfaxnPlewfvHbCGAsKCkQ9tPiCN3r29LU94Ufu5lCuA8UAMuazN4AFBQXsiSeeYBs2bGB//etf2aBBg5ifn5/oGK+//jqbPHkyA8B0Oh0DwEaPHq0qUykJZOvWrYoBtJAFXCslO6gF4ncVWoL9oSH4tqd16wp57t57o0dX6swLfFdLwNAqz13ySFegRTdpkoC3MjzVxxt7Ka1htbXrCUKguhDAz+tbKenJnb5SpAH1ngT+e9NGC/L9UMnWVzIeNXjJL2r7tdbxeLt/qCVYSPXQ4gve6NnT1/aEH7mbQ7kOdAPow0kgBw4cwFtvveVyzM/PDx999BFGjRqFe++9Vzze0dEBANi1axeKiorw0ksvcWVSEghBEARBEL6AzyaBzJo1yyUJxOl04pZbbsGhQ4dw/vx5BAYGYu3ateK5v//97wCAr7/+WlEmJYEQBEEQBOEL+Ow3gIBrEkhrayu++eYbTJgwAW1tbWhra8PgwYPF8//6178QFxeH4OBgRXlKSSAPPvggDAYDVqxYgdmzZ+NPf/oTkpOTYbFY8O9//xvbtm2DwWBAbm4u/P39kZubi4MHD2L+/PlITk5GaGgoAMBqtWLdunXQ6XRYu3YtAGDKlClYunQpFi9ejE8//RRjx44V5QvX6fV6FBYWory8HLt27UJMTAzsdjvGjBmD0NBQMaB25MiRCA8Px4IFC3Dy5EkMGDAAr7zyCmJiYrBixQqEh4fj7NmzmDBhAnQ6HUpKSgAACxYsAABUVFRgxowZqKmpEf+1Wq2dAmudTieee+45F/3nzp2LF198EYsXL8abb76JHTt2wM/PD9nZ2SgtLRUDoysqKjBr1iz86U9/wsiRI1FaWoqGhgY8+uijWLlyJWJiYlBRUQGr1eqi09q1a9Hc3IyWlhYcPnwYv//975Gbm4tTp06hpKQER44cQXx8PE6dOgUAYuCyTqdz0XvdunWYOnUqxo0bh2eeeQYNDQ2wWq346quv8OCDD8JsNosB/cnJyQgLC8Ndd92FrKwsMZhZsNdvf/tbbNy4Ebm5uUhISHCxkTBnNTU1iImJwX/+538iOTkZEyZMEO156tQpxMbG4q677kJoaCgsFgsqKioAQBznn//8Z3z66aeiPQS7Z2ZmIj4+HuvWrUN6ejpWrFgBAEhNTcXZs2fx008/4a677sKECRMwd+5clJeXY8+ePSgvL3exsdFoFOfz+PHj2LVrF06fPo2bb74ZSUlJKCwsxNKlSxEaGor77rsPpaWlePHFF7Fy5UqMGjVKtOunn34KAPjnP/+JO+64A6+99ho2b96M5557Dl988QWam5tRXFws+t2///1vbNmyBTExMRg8eLDop4IeX375JS5duiTas62tTQx0nz17thhkX1FR4TInwrgaGhowb9480ccOHjyI8PBwPP744xg+fDgWLFjgknByzTXX4JlnnkFGRgYOHjyI4uJicU1KfXHs2LEu61W6XsrLy/HOO+8AAP72t7+hpqYGr7zyCnQ6HdLT0zFnzhw8/PDDWLRoEQICAtDU1ORie2G+P/jgA9FewvzI1+Zbb72FkJAQJCYm4tSpU7BardixYwcuXbqEwMBAseBzVlYWVqxY0WkvEfYfoVDv/Pnz8fvf/x4vvPCCaDOj0QiHw4FHH30UixcvFvUS5CQnJ2PhwoV48cUXkZubKyZCyAPyHQ4H5syZg4yMDJf1KEWa5LNz504UFBTgsccewyuvvILz588jNDQUr7zyCkaNGiXaQ+q/27ZtQ1ZWlrhPS/cCoX1qaioGDBgAg8GguPctX75c9Fur1Yo5c+YgMzMTc+fOFfeg4uJinDp1CtnZ2YiLi0N9fb3LOm1oaMCcOXOQmpqKpUuXor6+HuXl5TCZTOI+8sUXX6CpqUksgL1t2zbk5eWJNv/1r38tJsSEhoaipaVFTKyIj4932f/T09PxyCOP4IsvvsCkSZOwdOlSl71bai8AKCsrw549ezB79mzEx8djxYoVKCkpEa+RtikvLxfXuXTvt9vtGDBgAGpra7Fu3TocOXIEBoMBDz74INasWSOuZSGRTdgLlfZ1oU1FRQVSUlJw4sQJlJWVISUlBXPmzMHTTz+NF198EQMGDEB7ezsef/xxfPzxx+K8CHtiYmIidu7cifnz52P48OFcX+tX9PZv0N1FdnY2CwoKYtHR0cxoNLK4uDim1+vZkSNHGGOM5ebmMpPJxDZv3sx27tzJdDodCwwMZKtWrVKUqaUQtLyAqFKBX15RTHkbaTv5v0rXqr14RVqViubK46iUxuEunlDQV1oYVK6DPD5NqQirPH6HVxRXLoMXVyPVkRcHJC1yLbTh6a1UtJgXb6c2Z9IismoxQTwdlIovuyu6quQ7vBgptbHIdRRsolYYWaqb1IZqduP5qTud5HFjSgVzefMrH7v0vHwtSmXLz/F0URuX1Bfk65C3NqQ6KvWl1re740qFfuXxijx/E8aiFPfJ83N38YpSPaS2kvqcWvwZL05Maa+Tv5fvy/J1w7OZfN545+TrxV2RfC17vZb14c4+vALeSjZUWuvyOeL5vJJ8rX6s5H+8OZOPC+jfMYA++xNwYGAgQkNDcebMGVy8eBEXL15EUFAQ9Ho9gMuPnMrMzMR9992HsWPHAgCWLFmCuXPnKspctmwZIiMjxdfQoUN7ZCwEQRAEQRBdic/eAI4fPx5nzpzBpUuXcOrUKZw8eRJnzpzBmjVrAACxsbH4wx/+gIyMDHR0dMDf3x9vv/22alwfxQASBEEQBOEL+HQMoMlkwqZNm8T3d911l1gI+ptvvsHNN9+MW2+9FR0dHVi/fj0AeBUDeMMNNyA4OBjZ2dli4d2RI0dyC1HKC/QKsXxCIUxpcWYAYtFZocil/DoA4rVCvJJSDGBOTg7Wr1+PtLQ0HDt2DAMGDBALwhYVFWHbtm1obGyE2WzuVLxUaRxqxVil+ufk5GD//v0wm80oKipyiQGUF3+V21BahDkmJoZb5FMojCzExkmLgkqLhUoLCisVPo2JiYHNZnMpti0U55XGAApFfaOiojB69GhxHgWZBQUFqjGAgg1jYmKwcePGToVaheK3QoyTUEiVN04lG0r9RODkyZP4+OOPMXjwYNGu0uK8chtLC67KYwCFgsQbN25EbGysOM/yIr/5+fmw2WxISkqC2WwWixgLhYOlfiYU8pXGAEoLF7uLAeStI3khayHmVPAx6fwmJSV1KrAu9Un5mpTKlp+T6iIUxv7xxx87jUuwf15eHrZu3SoWGecVu5WPiVdEXNgLhGLOwpqTxgBK143cL+WFi+WFfqUFmqVFfuUxgNIi30oIsW9SuXKke0pmZiY2bNiAvLw8fPvtt+L6UFrbSgXhpe3lNlXa+wS/FQocC3rzbCYU1a6vr+9UOFleCJpXrFupsLG8KLZaDOCQIUMAXH7wwSeffIJJkyZ1kqdkH+l8y69RKsAvL8Sdl5eHYcOGiWtW+pkjLWavVLxZ3kZeSDo5OVm0r8ViwYYNG8AYQ0xMjLi38T6L7HY7hg8fjtWrV6v6ps/T279BdxelpaVs5MiR4vsLFy6w+Ph4sRD0XXfdxSwWC7v//vtZRkaGV324KwTd1YWdvS0GrKXQq7xgsFrRasa6plit2nVCAV53RYuvpI/uLryt1o+nhaqvVN8rKSjbFbbqbXtfybU9pbtWrrQ4sBJqxci1FsH2pBByVxTr1tLWm4LX8uvd6eqpndTGdKX+1p1+7I2M7tqjvfEhKVQH0IcLQaslgbS3tzO9Xs8WLVrEdDod0+v17KabbmJvv/22qkxvCkF3VbFdb2R6UuhVLQiXd31XFKt1d530dSXFbLvyOk/RWjDW0/eeciUFZbvCVr1t7yu5tqd018qVFgdWQp78ID2mtQi2Vlvx+tKqn5b2vMQGLfugu8QId/uQp8XC1ZLRvPW37vRjb2R01x7tjQ9JoRtAH04C+fHHH9Ha2ooTJ07A6XSivr4e586dg16vx/Hjx9Hc3IynnnoKHR0daG5uxueff4477rgDW7duVZRJSSAEQRAEQfgCPnsD+OCDD7oUgv7mm28wZMgQrFmzRnzyh1ADSXhNmTIFL774oqJMSgIhCIIgCMIX8OkkEGkhaAAYOXIkDh06hCFDhsDPzw+RkZEu59PS0vDJJ58oylNKAomNjVVMKFAKavYGT2VqaS8PIOclfPCuFwKV5UHAV6Kv9Dp5QLlacLi3fXT1/HjSj7tAdXfvPUXLfHk6hp6W0d39aJ2r3uZK5lINXjKE0v6gJkNrO3lfWvTTOg/ytlr3QaX15k5XT+2kNqYr9bfu9GNvZHTXHu2NDxEyevs36O6itLSUhYaGMqPRyJKSktisWbNYbGysmAQSHR3NAgMDWXR0NLv++uvZww8/zPLz89k999yjuQ+lGAK73c5SU1OZ0WhkVVVVrLS0lNXW1rLc3FxWW1vrkuAgJDlIzysFL9vtdpaSksL0ej1btWqVpqQBXnKHNGC2traWJSYmslWrVrH09HSWlpbGrFYrV44QcOtOpnBdUVERS01NZRkZGS4B63a7nWVlZbH8/HwWGxvLLBYL93qhv6qqKhYbG8tSU1O5ge+8sRcVFbH09HRxLNLAeeF8VlaWKE/an91uF89XVVW5zJt0/FI7SOXb7XaWnp7OMjIyWFVVFcvKymIWi8VtsLLa/NTW1nZqI9hRGKOaDwjjEfxLOga5LYSxVFVVufiE1AekwfEWi8VFtjygXfB3JT+XIoxJsJc8qF7uj4JcuW7ubCV9L/dt+d+88UnnWPApqT3T09M7zbncXtLrlJIN6uvrmcViEdeJYI/a2lqWmprKDAYDKygo0LQutSQaWSwWFh8fL+5bvKQGLQkPUr+V+4B0P5Mmswg2HT58uLh2pGtWPjahf2F/SE5OZklJSUyv17Oqqiq3a8wT/5DbhTdmYV9JSUlhRqORrVq1ymV80vnlzZ/UB5SS3+RjV9qLhHVrsVhYUVERKygoYNHR0Sw5OZmlpaWxgoIClpqaKraxWCzifslbS+7sJ/xdVVXFEhMTO9lJak9hDxDmVzrPUjvy9hT550J8fHwn+8k/r5Q+S8eOHdvvYwB99gawoKCAPfHEE2zDhg3sr3/9Kxs0aBDz8/MTnay4uJgFBASw0tJS9uKLL7L4+HgGgH344YeKMpWSQOQOJK0+Ln8ChlLFcrWnC/AqwEufUsGYchAzL7lDGjAr9Cev2K4kR4tM6XXSa3j2kffHu15ekV6O0tilsnlPE5HKkx9Tmj/peakdpMd41yuNU20c0vlZsmRJpzZqT7pQmju1J1QItuA9+UHeVh4cL/ddJT/g+bnS2pHbWGlMPN3c2UrpKQ5Kf8t1Vnp6ippv89aEu2QD3jVK+4i7dak10Ug697ykBi0JD9J55q1H6VzL/Y6nA89OSn4qHHO3xrzxD6Ev3pjl4xT2VN4eo+WJSLx+5GNX24u8fak9mUXJfsLfgn3kdlJ7Iof86UE8O6n5KM9+7vZG6fn+fAPosz8BHzhwAG+99ZbLMT8/P3z00UcYNWoUli9fDpPJhGXLluG7775De3s7dDodfv75Z0WZy5YtwxNPPNHNmhMEQRAEQXQvPpsEMmvWLJckEKfTiVtuuUUsBA0ADz30EP7yl7/gxhtvRFxcHAYNGuRyXg4lgRAEQRAE4Qv47DeAgGsSSGtrK7755htMmDBBPP/jjz/it7/9LTZu3IgpU6bg559/Vg0mVUoC+eijj/Doo4/isccewxtvvIG5c+ciNTUVJ0+eRGpqKrKysmC325GVldWpYj5w+ZtJu92O+Ph4JCcno7i4GEuXLsXHH3+MlJQUVFRUYN68edi2bZtY+fzcuXMoLi4WM5mXLl2K8PBwHDx4EOnp6TAajbBYLFiwYAEsFotiMLTZbEZiYqJYVf/SpUsuT+eQBjcLAbe8hBG53d58800AQExMDAYNGoQnn3wSK1aswLJly7Bq1SoAgL+/P/R6PYYNGwadTgen04mKigpYrVaXAN/o6GgEBgYiMTGRG/guPKlAp9OJOgtPQhgxYgTWrVuHKVOmIDc3F//7v/+Lf/3rX4iIiEB7ezv8/f3Fa6Tjk1bDF55gIpw3GAzIysrCBx98IM7B//zP/wC4nH3+5z//GampqThx4gSioqIQHh6OW265BSkpKar+JTwhoKKiAmVlZeITKhITE2E2m13mg/c0C/lcCHZpamrCoEGDcOHCBbS0tCAxMRH+/v4oLS2FwWBAQkICjh07hhdeeAG7d+/GlClTkJWVhYaGBqSmpopPbOE9WUP6hIl//OMf4twJSBN6hKdDJCYmIjk5GaWlpTh16hTGjRuHxYsX49NPP8XYsWPFpyQYDAbR13bu3Ilx48bhlltuQXFxMfbt2wc/Pz8YjUbExsbCYDCIT15ZunQp/uu//ku01alTp1BWVoaAgABMnjwZBw8exO9+9zuEhIRg/Pjx2LZtGywWCw4fPgyDweDiB/KncEhtn56eDj8/P5enp0ifwjFixAgYDAaXtSR/Ko38qSu8JIz8/Hxs3LgRHR0dWLFiBQBg165dSE1Nxffff49z587h1ltvxb59+6DT6fCf//mfWL58udvkAt576RNapPbnyVFLeJD6rdwHpE+0kCazyJ98I32qjNITgEpLS5GSkoK3334ber0ely5dwsmTJxWfPqKUHCJfSzqdDmlpaTh+/Dj8/f072YU3ZmHf2bZtG06fPo1z584hKytLHJ9Op0NqaiqOHTuG559/HklJSWhpacHo0aNx+vRpcf/KysrChQsX4HA48Otf/xrt7e24dOkSQkNDMXnyZPj7++Ohhx7C8OHDYTAYOs2F4JvSJ4WcPHkSmzdvxvnz55GQkIC0tDR8/vnnOHbsGMLCwhAYGIiff/4Zo0aNwtGjRzFgwABMnDhRMZFEp9Nh7dq14v4n2DIlJQUbNmzoZCfp016EPSA1NdXlKVHSJ7MI+4p8T5H66LBhwxAbG4uoqCjk5ua6rKFt27ahqKjIxd8E+zidTmzbtg2jR4/Grl27uH7Sb+jt36C7C7VC0GfPnmXFxcUsPT2d/fGPf2SbN29mQUFBLDIykjU1NSnKVIoBXLx4MTfmgzFtxSrlbeRxIIB6fI88ZkJAHj/Wk0yYMIEBYBMmTODGf0AWx6EWT+iu8K1awVD5uWuuuaaTXT2FN6fCeIUxCW2kMXfeyL0S5DpI7c2Lt+H5oDs9PB2nUiyOUkyggDTep66ujhuzKh2nVI7c/6Tt5DGRXWH3rkaqv16vZwDYiBEjGGPMJY5KapO+OI6rja4uns6Lh5W3Ff7OzMzkxudJ9xlP51k+Hl7sZGZmpotPeTK+K7GNFn15x7XEuir1LzzAgWIAfZDAwECEhobizJkziIyMhJ+fH4KCgqDX6+Hv74933nkHR44cwd69e7FmzRoEBATg97//PcLDwxVlUgwgQRAEQRC+gM/GAI4fPx5nzpzBpUuXcOrUKZw8eRJnzpzBmjVrsH//ftTX12PYsGEIDAxEU1MT2tvb0dTUpCqTYgAJgiAIgvAFfPYbQOBy3MGmTZvE93fddRcOHToEnU6HlpYWfPfdd/Dz80Nrayva29uxcuVK/POf/8QPP/zAlacUAzh69Ggxju7EiRMucWpailXy2lgsFmzatAljxoxBUlKSatydNC5CiBUD0Cl+rCeZOHEitmzZgokTJ4qxPX5+fjCbzcjPz0dtbS0iIiI6xYrwYorcFb5VKxgqPzd+/Hh89913uOGGGzBo0CCvCyPL52vixIn48MMPERQUhJycHLFfs9msuWhvVxc2leog2J8Xb5Ofn49NmzbhlltuwahRozQXz5X3oWWc8vkQ5lYaX8cjJycHlZWVmDx5MoxGI/Ly8rB+/XrcfPPNos5msxnx8fEYP368ixyTySTG5AprITY2FtHR0WK/nhbw7UlMJhOSk5Px/fffY/To0diyZYsYy5yTk4PXX38dOTk5OHPmDNavX8+N3SI8p6uLp0tjBBsbG7lxvcDldXfx4kXU1dVh9OjROH/+vBgDOHHiRHz66acAgGnTpnk0z/Lx5OTk4LXXXoPRaERoaCj27NmDiRMnIi0tTfQpT8Z3JbbRoi/vuDw2VIt84bz0IRD9FT/GGOttJbqDsrIy1NTUwOFwALicBHLdddehqKgI8+fPh9PpdGk/efJkOJ1OrF69Gg888ICmPpqamhAZGYnGxkZERER08Qh8B3lyB0EQBEH0JvT57cM3gGPGjMHu3bsRERGBgIAA+Pn5oampCV999RUSExNd2l68eBGxsbE4f/48jh49iiFDhnBltra2orW1VXzf1NSEoUOH9msH0sLu3buRkZGBuro6pKen97Y6BEEQRD+HbgB9OAbwxx9/RGtrK06cOAGn0ymWTtHr9Whra8Njjz2GxMREMTnk559/RmFhoeLNH3A5CSQyMlJ8DR06tAdHRBAEQRAE0TX47A3ggw8+6FII+ptvvsGQIUOwZs0atLS0YPfu3ViyZAk2btyIl19+WTx3/PhxRZmUBEIQBEEQhC/g00kg0kLQADBy5EgcOnQIkZGRsNlsLm1HjhyJm266CcuXL8czzzzDlaeUBEKocyUBwwRBEARBdD0++w0gABw6dAhxcXG45pprcOedd+Krr75SvAlpbGwEALEaO9F1GI1G8ckWvoTT6URZWVmnhKLuvvZq5Wofc0/qf7XbqjdxOBwYN26cmADoDd1h//48p/157H0Zn73b2bt3Lx577DG8+uqrKC4uxkcffYSGhgZMnz4d586dw6JFi/DZZ5/h+++/x44dO3D33XdDp9PBYrEoymxtbUVTU5PLi+i/OJ1OPPHEE17fAHp77dXK1T7mntT/ardVb7Jv3z5s3boV+/bt81pGd9i/P89pfx57X8ZnfwI+cOAA3nrrLZdjfn5++Oijj5CSkoKPPvoIzz//vJjVq9frsWnTJtWaT/QkEIIgCIIgfAGf/QZw1qxZLkkgTqcTt9xyCw4dOoTg4GDMnz8fjz32GEaNGgUAeOeddzB+/HhVmZQEQhAEQRCEL+CzN4DA/yWBxMbGYuDAgfjmm2/EOLS7774be/bsQUtLCwAgKirKrbygoCBERES4vADgp59+QklJCUpKSjR9xe1JPERXxJk5HI4ujb/oyniOnooN6UqbC+d1Op2Y3OJ0OkUf0GJvnU6H3Nxc6HS6K7aBVn17++cX6Zi10BN6C/NmtVoV168QU3bq1ClNyUxyvb0Zhzxxqqf2DG+Q+n5v+KA85u9KnuAh0JXrkyezK7nS9d8V/uoO4SlH69at65OfQ/0W5qNkZ2ezoKAgFh0dzYxGI4uLi2N6vZ4dOXKEtbW1senTp7OEhAT23nvvMQDMZrMxp9PJWltbFWVeuHCBNTY2iq+jR48yAGzr1q0MAAPA6urq3OpWV1fXLW2Vrq2srPRaRlfr1J2yuqofd21554VjWu0tlXGlNvBG397AUz16Qm/pvCn1JcxnZWWlRzIFWV0xjp7aM7xBasPe8EFP50cLXbk+eTK7kitd/93hr97o0dPyGhsbGQDW2NjYJfpcjfjsN4CBgYEIDQ3FmTNncPHiRVy8eBFBQUHQ6/U4duwY1q9fj2PHjmHatGkAgEmTJsFoNGL79u2KMqkQNEEQBEEQvoDP3gCOHz8eZ86cwaVLl3Dq1CmcPHkSZ86cwZo1a5CUlISOjg6UlpYiOjoaAJCRkYGvvvoK48aNU5RJMYAEQRAEQfgCPpsFDFyOBdm0aZP4/q677sKhQ4cAAM8++yyef/55PPvss5g3bx4GDx6MSZMm4cCBAwgPD+fKUyoEHRsbi+LiYgDQVOvOk8LIV1JEWbjWZDJ1aSHmrizs3FNForvS5rzzQowLAE32lsu4Eht4o29v4KkePaG3MG/Nzc3Q6/XcvjyNKZPr3RXj6Kk9wxukvt8bPtgVMX9yunJ9KsnsKq50/XeHv3qjR2/L64/4McZYbyvRHZSVlaGmpkYMDG5tbcV1112HoqIi/PGPf0RcXBweffRR3HXXXbjmmmuwc+dOTJ48Gc888wysVqumPnrjYdJOpxMVFRWwWq3k+MRVgzd+S77uPWQ7glCnNz6/+xo++xNwbW0tvv76axgMBsTFxeHaa69FY2Mj7r//fnz33Xf46aefkJiYiP379wMAvvvuO4waNQoffvihosy+UAiaCmoSVyPe+C35uveQ7QiCcIfP/gQsTQIZMGAAWlpaEBwcDL1ejwMHDgAA7rnnHrH93XffDQC49tprFWVSIWiCIAiCIHwBn/0GcOvWrTh9+jS2b98Og8GAG2+8ETqdDmvWrIHwq7fZbMbnn3+Or7/+GkVFRQgLC1O9AaQkEIIgCIIgfAGfvQEEgObmZtx33314+eWXMXjwYAwePBiHDh3ChQsXAAALFixAZmYmfvGLX2DVqlW4ePEizp07pyhPqRB0T0KBr8TViDd+S77uPWQ7giDc4dM3gPPnz8e0adMwceJEdHR04MyZMzAajWLply+++EJs297ejkuXLqEv5MSoVTg3Go0oKyvzuY1dXs2/P3C1V7L3RH9v/NZXfb0n6A7b9QV/7Qs6dBV9YSx9QQei9/DZG8DbbrsNH3/8MaxWK3bu3Il9+/bh4sWLuP/++zF8+HBERUVhxYoVWLt2Lex2O2666SYwxhAaGqoos6eSQPpjAPe+ffuwdetW7Nu3r7dV6TGu9nm+2vUnPKMvzHdf0KGr6Atj6Qs6EL2HTyaBHD16FJs2bUJERATS0tIQEhKCpqYmpKSkIDExEQDwr3/9C1OmTMHs2bMBXH5O43XXXcet8ydASSAEQRAEQfgCPvkNYF1dHVpbW3H69Gl0dHSI39R9/fXXCAgIQHt7O9auXYu4uDi888472LVrF8rLy/HNN9+oyqUkEIIgCIIgfAGfvAGcMGEC9u7di+3btyM+Ph4vvfQSwsPDkZKSAofDAX9/f+zYsQP3338/brvtNmRkZGD8+PEAgKioKEW5akkg7mIpHA4HMjIyMHr0aDgcDrdxfkIAt9YYDV47Ia7OZrO5nOO1dTqdKCwsREJCAqqrq73uU4rNZkNSUhJsNpvbtgaDAYmJiTAYDKp98qiurkZUVBSqq6u5ujmdTpSUlKCkpER8L4zVZrO5yHI4HMjOzsbcuXMVdRVkV1dXi+PjjVvNjg6HA/PnzxcL9SrNiXBMGiOpZEveOOXzLj1/pQhPgKioqOgkU27Hp59+GjqdDtdee63LGBwOh6KODofDY32F661W6xWPU6qjXKbT6YTVakV2dnanOVHzey3vZ86cCb1ej1mzZnH1V7KdFF5MrTvflq8ZuWxhX9LpdC5jl14vnS+luF4t+vPsYrVaMWfOnE7FreV6W61WDB06FP7+/nj66ac9jnNzOp3Iy8uDTqfDxIkTNe+DNptNNY5Z8Ivy8nKMGzcOBw8eRG5uLnQ6HVceby/XEiMt903g8h4ZERGBWbNmiXZ3OBxYt24diouLodPp3NrIm/1Dba+S+oCaLyjJdbdOCA0wH2b27Nns0UcfZYwxFhkZyUaNGiWemzhxIrvhhhvYp59+yt5++20WExPD/P392ccff6wo78KFC6yxsVF8HT16lAFgjY2NrK6ujgFgdXV13GsrKysZAAaAVVZWum0vcCXthD6XLFnico7XVjgGgM2bN69LdBP6XbJkidu2WsfJQ9B33rx5XHnSscnfL1myxEWWdJ7c6Sr0K5chjFvNjkI/lZWViuOXHpO2V7KV0jjl8+6tndVsoeR7wvEJEyZw/V9ox9NRy1yo6XOl45TrqORD8jlR83ut79X0V7KdFLl/SY+5k8vzHV47uXy5L/B00Kq/ml2U5MnnBQCbMGGCx3uLpz4ktBfmXa6fgHB+1qxZ3H1ZaeyM8edTi/5Ce2Evktpdan8tNvJm/3C3V/F08UTulazzxsZG8fO7v+KTMYDA5f/x7N692yXTV0p+fj7++Mc/YuzYsQAuxwC+8soruPnmmxVlUgwgQRAEQRC+gE/+BHz06FH8/ve/R2VlJYKDg7Fs2TI0Nja6tCkpKUFLS4v4vqOjAw899BCKiooU5VIMIEEQBEEQvoBPfgNYV1eH48ePIyMjA8DlGn/A5TiKgIAANDY2IjAwEH//+98xceJEAMD777+Phx56CF9//bWi3KCgIMUsYXeFV00mE9LT0+Hn5weTyYSYmBhNhVq1FnTltTOZTMjNzYXZbHY5x2trNBphsViwefNm5OTkwGAwXLFuZrMZiYmJMJvNbtteSeHanJwcvP7668jJyVGUV1xcLB4HII7VbDa7yDKZTMjKysLIkSPd6pqSkoINGzZ0kiGMW82OwtyYTCbF8UuP6XQ6sb2S7wgxedJxyuddfv5KUZIpt+PEiRPx0UcfISkpyWUMJpNJUUeTyeSxvsL1zc3N0Ov1VzROwf6CHnKZRUVF2LNnT6c5UfN7Le8LCgqwceNGTJkyhau/VC+lNSP3L+GYmm/LdVGSbTQaXcYuPS6dL6nPeqo/zy68Pnlti4qKsGHDBtTX12PixIke7y1GoxHjx4/Hli1bkJeXp3kfNJvN3PEKCH6Rl5eHEydOdNqXlcYD8OdTTR+5rXJyclBZWYnJkyeLdpfb352NvNk/lGwv9wE1X1CS626dEBro7d+gu4Ompia2d+9etnPnTjZs2DD20ksvsfDwcJaSksL27t0r/va/YcMG8Zpf/epXLC4ujk2aNElzP9IYgvr6elZaWsrq6+s7tbPb7Sw3N5fZ7XbNstXkdbUMb/qSX8OT0RVj8EaX7qa+vp4VFxez4uJi1T7V9HKnsydj6orxe+OjWpDq1tPz5Anu5oo33705Hk/79saf7Ha7y799cd6uBrpzrrqKrthvekLvruyDYgAZ88kbQAG1JJDc3FxmMpnY5s2b2c6dO5lOp2OBgYFs1apVivK8TQLxJIBX4EqSIjyV4U1f7oLZvZXrDT3Vj7w/d31qCaTviqSYrhi/Nz6qBa2JBb2N1qSH3vBvHt4mNnjiT94G6BOudOdcdRVdsd/0hN5d2QfdADLmkzGAwP8lgSxbtkyMATx27JjL+euvvx75+fkwm83o6OhAbGwspk6dqihz2bJliIyMFF9Dhw7tiaEQBEEQBEF0KT55AyhNAtm7dy9eeuklhIWFISEhQWxz7tw5bNu2DY888giSkpJQWFiI8vJyhISEKMqlJBCCIAiCIHwBn08CaW9vh06nQ0dHh5gE0traisWLF2Pq1Km4/fbb8Ze//AU1NTUYOXKkqlxvk0A8CeDVIq+rZXjTl7tgdm/lekNP9SPtT0swtJpeXZkU0xXj98ZHtaA1saC3cTdXvPnuab+T6+Rp0Lyn/uRtgD7hSnfOVVfRFftNT+jdm2vOJ+nt36C7AyEJ5Pbbb2cWi4Xt3bvXJQmkvb2d6fV69uc//5nFxcWxgIAAdtNNN7G3337bo37UYgjsdjvLyspiVqu1U6KE1qDqKw3MFQL7a2tr3SYcFBUVsfT0dFFftaSOqqoqFhsby1JSUlzGJx9jfn4+CwgIYEajkRmNRlZVVeWSEGCxWFhsbCyzWCydZBQUFLDw8HBWVVWlOm673c5SUlKYXq9nq1atUgxO1pK0oWZLnjyLxcKysrJcEie0JMcIegv+UVtby9LT01lGRoYoq6sDquU26Cr57pJHhH5qa2tZamoqMxqNrLa2VrGd1G4Wi4XFx8e7tO/OQHOlsdjtdpacnMx0Oh0bOHAgq62tdbGn3W5nRUVFLCsri9XW1rrY2dPkGt5alJ4TbCnfW3jU1tayxMRErr3V+k5NTXXxRXn/VVVVLnJra2vF/cBisXi9zqQ2ra2tVbSbu4SdoqIilpSU5BLT7W5dyuepqqqKRUZGivuP2jxqSXSqra1l0dHRzGAwiP4jzKWWPdoTrvRz40rWmHT/5+03Sp993ZVEqCaDYgB9OAmkqqqK3Xjjjez8+fOMMdckEKfTyQCwkJAQNmDAALZo0SK2bNky5ufnx7Zs2aIoUy0JRA6v6r6WCvhSrjQwV+lJIErXSvVVS+qQVpVXaiN/eoL0Ol7Ffp4M4Ro1pP0IFfaVgpOvxJZq9uI9DUEtOUautzA/UlldHVAtt0FXyXeXPCJ/SoLwt1I73pMfpO27M9BcaSxyX5Y+4UPu69Jxqj0NQwkt60LehxLSJ5J407fSEzfkT8CR6nMl60w+50p205qwI+wJan0J7+XzJH+6kNo8akl0kvu/fC7d7dGecKWfG1eyxnj7v9QuSp993ZVEqCaDbgB99EkgQgxgbW2tSyFoIQmko6MDAHD+/HkAwFNPPQXg8nOAX3zxReTm5nLl0pNACIIgCILwBXzyBrBOoRD0yZMnERAQgHPnzsHPzw/JycnYtm2beN1TTz2Furo6RbkLFy4UY4EAoKmpiTKBCYIgCIK46vDJLOAJEyZg79692L59O+Lj4/HSSy8hPDwcKSkpcDgcCAoKwpAhQ3D+/HnExsaKr6NHjyIxMVFRblBQECIiIlxeAk6nE2VlZXA6nQD+r+q+1WrtlCihNahaHvAq70OtLQAYDAYkJiZi8ODByM3NhU7XebqdTifWrVuH/Px8hISEIDk5GStWrEBFRQWKi4u5Qe85OTmIjY1FSkqKy/jkY8zPz0dAQABiYmIQFhaG1NRUlydbpKamwmAwYObMmaioqIDVakVJSQlOnTqFlJQU6PV6l6d78DCZTGLbvLw8lJaWQqfToaSkBCUlJXA6nWJfsbGxOHXqlKo8JVtKEeQNGjRIHIMwL1qSY4S5EWxot9uRmpqKjIwMMQlDeIqCTqdTnXcevPZCIoMwp10VTC34+QcffCDaW4rQ7/bt2xEcHIwhQ4Z0enKK0E5ut4KCAoSHhyM5OVmxnYDD4cC4ceNgs9nEsXtqN14ijNPpxLZt25CUlASdToeBAwfCbDZDp9OJ69tkMsFisSA+Ph6DBw9GVlYWLBYL1q1bB39/f8THx+ODDz7QpIcw7pCQEKSkpLisWWHsycnJiI2NRWpqquKaLikpgd1uR3x8vIu9y8vLERwcjPLycm7fRUVFLr4otaF0/QtPOgEuP+FC8GWLxQKr1Yp169aJcyCsRYfD4TIf0rl0OByYM2cOUlJSkJ6ejuTkZJe5cDqdKCwsREJCAg4ePKi4LxqNl59oFBERgYCAAOTl5XXqi/de8ONt27bB6XRi2LBh8Pf3x7Bhw+BwOPDMM8/AaDTCYDBw7VZaWopTp05h/vz5nfZEwUYDBw5EQEAAAgICUFFRgfT0dHz00UcIDw/H4MGDXWwxbtw4OBwOlzl158uCrZcuXYr4+Hhxr7PZbEhISMDUqVORnZ3tIpf3GbNu3TqXvV/at/C3w+FASUmJuGcL71esWIHU1FQ888wzsFgsnfYbg8Hg8lkkyNPpdKIeTqcTVqsVDzzwADIyMrg+DriueavVioyMDMydO9fF71asWIGsrCyujJ9++knRlv2G3v4NujtRKwQ9btw4BoDp9XqWmJjIsrOzmb+/P/v4448V5XlbCLqr8LagqFrMIS+uCNAex+Ot3ry4GeF1pYVnleLdBNldNR5BnjdzrxbrJT3vTbxeTxeSdRdj6a39PRkHL961O2OIlGLK5MWTebHAWseixZ6enhfiZIXYOG/Hr/UaXqwkT5Y8zlItjlce06g17tYTvaXzp3UO3cV7yn1UvuepydEyJqUYTnl/amtQ614tny9ezLeW+E21/tzZXMmevM8UnoytW7dSDKDineFVjlAI+osvvuCenzt3LtLS0lBTUwOn04mGhgbExcUhMzNTUSbFABIEQRAE4Qv45E/A0kLQ0iQQKXfddRdeeOEFfP/993jggQdw4cIF1NfX47333lOUS4WgCYIgCILwBXzyG0ClJBBpIWh/f38AQE1NDXbu3Im4uDhcvHgRhw4dUpTrbSHorsLbgqJqMYdCG7PZjKysLCQnJyM0NBR6vb7LxqKlSHRxcTGam5uh1+uvuPAsr3BvUVER9uzZ0yWFjoVYKUFeTEyMV4W0pWN2V1S1LxeSdVcYW24vT+RqHYcQv2c2m7u08LTWordKxZNNJpOmouHysWRlZWHkyJEeFabWcj4vLw/vvPOOGBvnDm98ibe2hXEpyRLGnJycDIPBwLW3xWLB5s2bO8WQao279URvIQZX8Fe1+ZCOQa2YutRHhbXf0tKCw4cPu1zDk6NlTMK8Hz9+3EWm2WxGfHw80tLScObMGdU1qGWvlvq1dM9WG5OSLKX+ioqKsGvXLmRmZiqOWWpPXnu1/RUAYmNjFe3Qb+jt36C7A6EQ9M6dO9mwYcPYSy+95FIIWuDYsWMsPj6effXVVywhIYEFBASwNWvWaO6nL9UR6s4CuQRxNXCla0BLAXBaY4Qa5CNXD33p87u38MmfgMPDw3HjjTfiv//7v1FQUIA5c+ZAp9MhODgYN954I5qbm1FSUoLp06fjoYcewokTJ3DixAmEhYXhjjvuUJTb2tqKpqYml1dfwel04oknntCc8UgQvsaVrgF319MaI9xBPkJcTfjkT8CAaxKIvBC0v78//ud//genT5/G7t278eSTT8LPzw8WiwXh4eGKMikJhCAIgiAIX8AnvwGUJoHs3bsXL730EsLCwpCQkAAA2L9/P9rb21FVVYVvvvkGe/fuRWhoKCorK3HixAlFuZQEQhAEQRCEL+CT3wBKk0Da29uh0+nQ0dEhJoE888wzaGpqgsViEa8REkVSU1MVC0SqJYH0Nj0d+E8QfY0rXQPurqc1RriDfIS4mvBjjLHeVqKrOXv2LL7//nssWrQIkZGReOyxxzBmzBjEx8fjf//3f8Vq4wJtbW0YN24cWltbsWXLFmRlZWnqp6mpCZGRkWhsbHR5Kkhfw+Fw4NFHH8XKlSsxatSo3laHIDThdDrFJ8T0pw9Uh8OBX//61/Dz88Mrr7xCa9ZDhKdL7NmzB+Xl5V7Zj/ZM3+dq+fzuTnzyJ+Dw8HB89dVX+O677/Dyyy/jxhtvdEkCGTx4MG688UYcOXIEWVlZyMjIwLlz5/Cb3/xG9eavLyeBqLFv3z5s3boV+/bt621VCEIz/TWgft++fdi9ezfq6upozXqB0+nESy+9hM8++8xr+9GeSfQHfPIGUKkQtJAE0tbWhsceewyPPfYY2tvbMWjQIPj5+WH16tU4fvy4otxly5YhMjJSfA0dOrSnhkQQBEEQBNFl+OQNoDQGMCAgAIsWLQIAnDx5EgEBATh79ix2796N0tJSOBwOvPvuu8jIyMD58+fx6quvKsqlJBCCIAiCIHwBn0wCmTBhAvbu3YuWlhbceeedePzxx1FSUiLGAA4aNAg2m83lmr/97W+46aabVL8B7MtJIGq4q1BPEH2R/hpQbzKZkJ6eDj8/P1qzXuDtk2ek0J5J9Ad8MglE4P7778egQYOwYsUKREVF4ZprroHdbse5c+ewdOlSTJ8+HUajEadOncLixYvxwQcf4LPPPuv0qCGB1tZWtLa2iu+bmpowdOjQfh1EShAEQRBXG5QE4qPfAAKuhaDl+Pv74+uvv8aaNWtw8uRJDBo0COfPn8eUKVMUb/4AKgRNEARBEIRv4JMxgEpJIALBwcGwWCy48cYbodfr8dNPP8FoNOKNN95QlUsxgARBEARB+AI++Q2gNAkE+L8iz0Ih6NbWVpw7dw5ZWVk4fvw4Tp8+jfLycrdfA1+tMYAEQRAEQRBSfPIbQCEJZPv27YiPj8dLL72E8PBwpKSkwOFwwN/fH3fffTf27NmDlpYWAEBUVFTvKn2FCMVPfa1mmq+O62rD3TxczfPkje5Xy3j7mp5doY9WGV3drjvojXXVFX2qtekpe/Y1374qYT7M7Nmz2aOPPsoYYywyMpKNGjWKMcZYW1sbmz59OktISGDvvfceA8BsNhtzOp2stbVVUd6FCxdYY2Oj+Dp69CgDwBobG3tkPGrU1dUxAKyurq63VelSfHVcVxvu5uFqnidvdL9axtvX9OwKfbTK6Op23UFvrKuu6FOtTU/Z80r7aWxs7DOf372FT34DCPxfEsiyZcs6FYI+duwY1q9fj2PHjmHatGkAgEmTJsFoNGL79u2KMqkQNEEQBEEQvoBP3gBKk0D27t2Ll156CWFhYUhISAAAJCUl4emnn0Z4eDjKy8sBAJMnT4bRaBTjBnlQEghBEARBEL6AT94ASpNAbrrpJvzwww84d+6cmARy6dIlrFy5EosXL8aUKVMAAEuWLEFLSwtef/11RblBQUGIiIhwefUVfLVorq+O62rD3TxczfPkje5Xy3j7mp5doY9WGV3drjvojXXVFX2qtekpe/Y1374q6e3foLuDpqYmtnfvXnb77bczi8XC9u7dy8LDw1lKSgrbu3cv++abbxgAtnv3bvbdd98xAMxut7Pp06ez2bNna+5HiCE4cOAAKy0tZfX19d04Ksbq6+t7pJ+u0oPXrrfG0NP99kR/fcEf+oIOSqjpJj3Xl8fQ1+hLa/pK6Sq9u9OXrlbbdgXdPXaKAWTMJ28AGWOsqqqK3Xjjjez8+fOMMdckkE8//ZQBYBs3bhSTQKqrq1lBQQHLzc1VlKmUBLJ169arIui1p/XgteutMfR0vz3RX1/wh76ggxJaA9X78hj6Gn1pTV8pXaV3d/rS1WrbrqC7x043gIz5ZB3Ao0ePoqioCDExMTAYDACAlpYWNDU1ubS79dZbxb/vvvtuAMB1112nKJeeBEIQBEEQhC/gszGAZ8+exbfffouWlha0tLSgvb0d3377LQICAsSbwl/+8pf4/PPP8fXXX6OoqAghISGqj4KjJBCCIAiCIHwBn/wGUCgELeXBBx/Enj17sHjxYvHJIOPHj0dmZiYAYOXKlXj55Zeh0ynfEys9CSQ2NrZfBb1eSXB1b42hp/vtif76gj/0BR2U8CRQva+Ooa/Rl9b0ldJVenenL12ttu0K+vPYewo/xhjrbSW6m/b2dqSmpuLAgQPYu3cv2tvbkZaWhvDwcKxZswbXX389nnrqKbzxxhu4++67sW7dOq6c1tZWtLa2iu+bmpowdOhQNDY29qmMYIIgCIIglGlqakJkZGS//vz2yW8ABfbu3Yvs7GxcuHABAHD77bdjxIgRaGtrw7Bhw6DX6zFv3jz8/PPPiIuLQ0dHB06ePKkoj2IACYIgCILwBXwyBhAAysvLcc899wC4/NNtTEwMNm/ejP379yMwMBCVlZVoaGhAQ0MDWltbcfz4cZhMJvj7+yvKpBhAgiAIgiB8AZ+9AUxISMCzzz6L3bt3w26344EHHsDZs2dRVlYGAHjttdcQFRWFd955B7t27cLzzz+Pffv2qcrsy4WgCYIgCIIgtOKzN4C33347pk6dihtuuAE33HADli5dCn9/f/z4448AgB07duD+++/HbbfdhoyMDIwfPx4AEBUV1a16OZ1OlJWVwel0enW+p+lr+hDeIcyjw+Gg+exDaFlftAYJX4bn3+TzPYPP3gAuWrQIH3/8MY4cOQKHw4E77rgDly5dwoMPPgjgcoZRZWUltm/fjpqaGtxyyy3w9/fHb37zG0WZra2taGpqcnl5itPpxBNPPKF6A6h2vqfpa/oQ3iHM4759+2g++xBa1hetQcKX4fk3+XzP4LNJIFu2bMFf/vIXXLp0STw2e/ZsPPzwwwCATZs2AQDGjh3rct2OHTtw8803c2VSEghBEARBEL6Az34DuHDhQrz55puora3Fm2++iezsbLz22mtYv349AOBPf/oTrrvuOqxZswYffvgh7rvvPgCXa/qpyaQkEIIgCIIgrnZ89hvA22+/3eX9zJkzERgYiOXLl2PSpElYtmwZ3n77bUybNg0A8Ne//hVGoxHr1q1DYWEhV6ZSIWhPcFfcsq8Vv+xr+hDeIcyjyWSi+exDaFlftAYJX8aXiotfdfT2w4h7gkuXLrGqqirm5+fHfvWrX4kPgd6wYQNjjLGffvqJBQQEsLy8PDZp0iTNculh0tqor69nxcXFrLi4mNXX13eL/NLS0m6RTXQN3TVHPTX3PeljXdFXd+p7Naw3uY5Xg87dTXfYwFuZXamLt7Lo85sxn70BXLhwIVu9ejULCQlhOp2OBQUFMZ1Ox2praxljjOXm5jKTycQ2b97MHnvsMRYaGsqCg4PZqlWrFGVeuHCBNTY2iq+jR4/2ewfSQl1dHQPAALC6urpuk98dsomuobvmqKfmvid9rCv66k59r4b1JtfxatC5u+kOG3grsyt18VYW3QAy5rMxgFu2bEFRURHOnz+Pjo4OMMYQHByM+Ph4AEB1dTWuv/565Ofn45lnnkFLSwsMBgOmTp2qKHPZsmWIjIwUX0OHDu2p4RAEQRAEQXQZPnsDuHDhQtTU1ODAgQM4cOAAFixYgPPnz4uFoM+dO4dt27bhjjvuAAC8++67WLVqFUJCQlRlUhIIQRAEQRBXO/0mCWTp0qV49tlnxULQixcvxtSpU+Hv74+MjAwxGUSNrkgC6Y8YjUYUFxeLf3eHfAoY7tt4O0dOpxMVFRWwWq3ca3tq7nvSx7qir+7U92pYb3IdPdHZ4XDg0UcfxcqVKzFq1Khu1rTn6I5581ZmV+pyNfhjX8WPMcZ6W4nuYNGiRcjPz8fQoUPx888/44knnkBNTQ1efvllPPTQQ4iMjMTvfvc7PP300wgNDcWIESOwcOFCzJgxQ1Fma2srWltbxfdNTU0YOnQoGhsb6bFwBNEN7N69GxkZGairq0N6enpvq0P0A9atWweLxYLKykqxPBjhezQ1NSEyMrJff3777DeADQ0NuPvuu1FfXw8A8Pf3x5NPPomHH34YP/30E5qbm/Hcc8/B398fGzZswKeffoqCggJs3rwZubm5XJlUCJogCIIgCF/AZ2MAR48ejYEDByIsLAyhoaGIiYnBs88+i/3796OjowMAcPHiRbS1tSEnJwcLFy5EZGQkXnzxRUWZFANIEARBEIQv4LM3gAkJCXj22Wexe/du2O12PPDAAzh79izKysowZMgQ+Pn54frrr4fT6RRfhYWF+OGHHxRlBgUFISIiwuVFEET3QfE9RE9jMpmQm5sLk8nU26oQRLfiszeAt99+O6ZOnYobbrgBN9xwA5YuXQp/f3/8+OOPGDBgAIYMGYLz588jNjZWfB09ehSJiYle9Wez2ZCUlASbzSYeKy8vR1BQEKZNmybeZJaVlV3RA649keF0OlFSUoKSkhK37Z1OJ6xWKzIyMjB37ly3D6d3p4MgLzs7GzabTbG9IMvhcIhtPLWTw+FAdna2qt7V1dWIiIjArFmz3Mp1OBwYN24cHA4HAP7cKtlWq83dtZPqoDQ3gp1sNpuLvt70561vurO9ILe6uhpGoxFpaWlcPeU2F47ddtttePfdd9HQ0OCRXp6iNn7B/mlpaRg9erSoo/CfxoSEBNE3nE4nZs6cCb1ej2nTpsFqtaKkpAQOh8OjtSjXxd38dMXewkM6L8LfNput01i6on+5D2iVKdcxLS0NMTExKCws1KyPdH3ExMRg5cqVePTRR0VdeP7J089msyEhIQEzZ87k7g28PY4nW00/d/vK1KlTMWDAAPFzx924pfuvdB/WihbflK8T4bjwGaE2djUcDgcyMjJc1qWn+vVrerkOYbexcOFCtm3bNvbdd98xu93OZsyYwQCwl19+mTHG2Lhx4xgAptfrWWJiIsvOzmb+/v7s448/VpSpVgh6yZIlDABbsmSJ2H7WrFkuBZB7usCrJwWYpW3dtdeig1SeYBtee6FdZWWl13YSrlW7Zt68eZptIcirrKxkjDHu3CrZVqvN3bWT6qA0N8JxQT9BX2/689Y33dlekCu1P09Puc3lstXG1hWojV9uf0EXuY/z2kqv8XQt8vxK6druKnQsnRfhb8HfeL54Jf3LfUCrTJ6OWm0tIF8fcl14/snTT2obtTmU/s2T7U4/Le088TWpDp7Oo1bfVNtDvV3fWvYIJf2oEDRjPpsEsmXLFvzlL3/BpUuXxGOzZ8/Gww8/DACYO3cu0tLSUFNTg6NHj+L7779HVFQUMjMzFWVSEghBEARBEL6Az/4EvHDhQrz55puora3Fm2++iezsbLz22mtYv349AOCuu+7CCy+8gBdeeAGpqamIiYnB2bNn8d5776nKpCQQgiAIgiCudnz2G0B5IeiZM2ciMDAQy5cvx/Tp0wEAP/74I377299i48aNmDZtGgYOHIhDhw4pylQrBG02m5GYmAiz2Swey8vLw/r16zFx4kQxiL0nC7x6UoDZaDSiqKgIu3btQmZmpmp7LToI8vbs2QOz2azYXpBlMplc2nhiJ5PJhKysLIwcOVLxmpycHFRWVmLy5Mlu5cqDwHlzq2RbrTZ3106qQ0xMDHduBNuZzWa3Qevu+vM22cKd7QW5KSkpePvttxEdHc3Vkxd4bzKZkJ6eDj8/v24PyFcbv+DLO3bswIABA0RdjEYjLBYLNm/eLPqG0WhEQUEBNm7ciNzcXCQkJECv18NkMnm0FuW6uJuf7kqWkc9Lbm4uzGZzp7F0Rf/yvrTKlF+XmpqKhoYGTWtdQL4+dDqdi0yef/L0M5vNiI+Ph9lsRlJSkuocCn/L+9Kin1q7/Px8bNq0CZMmTdLsa8L+K9+HtaDFN+XrRDgufEZ4u7617BGUSKaMzxaCltLe3o7//d//xb333ovp06ejpqYGHR0dmDhxIn71q1/h97//PYYOHYqffvoJr776KmbPnq1JrlBIcuzYsfiv//ovxMTE4LnnnsPx48exf/9+jBgxAgaDAQsWLAAAPPfccwAgvq+oqMCMGTOwdu1aAEBhYSHWrl2L5uZm6PV6FBYWoqamBlarVby+ublZ7F+v14uy/vCHP2DTpk0YM2YMhgwZIl4vyF6wYAEaGhowb948jBw5EqWlpVwdFixYAKPRCKfTKfYn7Ud+TNpWOgbh75qaGsyYMcPlX+GpDvKnPEjfy8cr1wEApkyZgqVLl4oV+3nXS8ckIB2bHLndeXrJ/5aOT5g/qSzpnMrnXt4Pb44FGYJfxcTEYM+ePVi9ejWio6Nd5lS+ycnnUT4/cn9T01/JRg0NDXj00Ucxd+5cvPDCC0hOThb9Xm3TlY9Xbh/p0z+EOeD5KvB/T29YvHgxPv30U3F+ysrKsGfPHvz+97/Hiy++iJUrV4rrVKlfeR9yfRwOh2jzuXPncteO0vzy/H3s2LFYunQpFi9ejA8++KCTLLmu0nVVXl6OPXv24M9//jM+/fTTTj4oHZ/aGuCtL97+I7e7mq/Jbac0r0r7jLwvuV0Fu0nXvzsdeXKUnjLDW/dKe6U7ee78V60vLfYUbKa0z0jXPM9n3O13vM8pnp3la0Npf1PyN95+JJyT7rHy9gItLS3Yv38/MjMzUVpaioaGBvz617/G+fPnERgYKCas1dTU4N5778UvfvGLfl0I2qeTQFavXs1CQkKYTqdjQUFBTKfTsdraWnb27Fl28803s8zMTPbtt9+yzZs3s6CgIBYZGcmampoUZSolgUAhWB+SQFx5EK88+Fb+N8BPjHAnm3e90E7+nqeDPKhbrR9eW954lAKM5cG5vCBpNR3kyQ9K18uDf5XsxbO7klye/eTzxzvGu07J5koypOPXmoShNj9q/Sj1LdVd+Fua6KGkj7t5UEoCUvNVxv4vGFyacCSVL+imtE7V1oNcHyUbyudRq79Lk4zcrUMlf5OuBd58aVkDcpsp7T9agv3V1gtvXpV0UPMBpfXvzveU5kepnda9Uk2eO/9115c7e6rZUclf5T6jtt+583kBXjutnylq+xHP55V8XSpfbU/bunUrAygJxCfZsmULnnvuObS1tQEAGGMIDg5GfHw8vvzyS+zYsQOMMVx77bXw8/ODn58fLBYLwsPDFWVSEghBEARBEL6ATyeB1NTU4MCBAzhw4AAWLFiA8+fPo6ysDF988YX4NBB/f3/odDp0dHTg1VdfxbBhw1RlUhIIQRAEQRBXOz77DaA8CWTp0qV49tln8eOPP6KwsBATJ050OT958mQ4nU78+c9/VpSplAQyduxYl2D9HTt2wM/PD2lpaTAYDNDpdFi7di0sFgv279+PFStWIDQ0FBaLBc8//zymTp2KL7/8Ek1NTUhPT0dLSwtCQ0NhMBhQWlrqcv2XX34JABg5ciTCwsKwbt06FBYWwmKxdIoBlAeenzp1CrGxsYiKikJFRQXmzZsnBv4K7XQ6HcrKyjBjxgwUFxejubkZfn5+Yj/CMb1e7xIALlwvlSX8vW3bNhQXF3cKMBaCn3U6nYsceV/A5RglwQ5FRUXQ6/Wdkh/kwb5C4kRFRQXKyspc+s3KykJcXByOHDmCpKQkl3/feustZGVluegllSvVUR5AXVxcLMbqZWZmisekNmtoaEB6ejqeeeYZl3ghYfzy+C25XCEG0Gw2Izo62m0ShjSBRJB17tw5fPDBB0hPT8eIESMQFhaGbdu2oaioqFPfgk8LfmcwGODv74/ExEScOnVKDJDPycmB3W4XYwAFX1KKgRPGK7WXUhKQNFhd0H/dunWiHEEHecKREGSek5OD/fv3i+tUame5fwt2kPq4VKYQeA4ABoOBmxAh9XulRAmj0Qir1SqOIzk5WdRXvibkPiXYQp5oZTAYkJWVheTkZISGhorzKPcNua9J20jXr9zvpePk+Rpvf1BL7lK6Rt6X0+nEunXrXPaRwYMHIzIyEv7+/i6y5NfJ4w7liQ9aE2vU/M+dPGkb+RzbbDY8+OCDsFgsLuPftm0bLBYLdu3ahdOnT+Pxxx9HQ0ODy1iknw/C2pXbXvAJYe2mp6ejra0NBw4cEO0p7GkHDx7EggULXHSRf0YI8gYNGoTp06dj9erVmDRpknguPj4e48ePF/eIgwcPYt26dWhubhY/A6VrXf55IfiC0meJsDYFPYR9fMSIEQDgIl+n0yE9PV2MAUxLSxPXZWxsLHfe+xW9/Rt0d+GuELSU1tZWNnDgQBYcHMxOnDihKFOtELSA1vglSGIwhPgkpSKiWmOT3KEUw8droxSf4ilq13ra15WMV20+lGL4tMQGXcl5oc/uxl3slTtf4tnRk+K1SjFwavp5MyZvcRen5k3/WuVoXdNdpdfVCm9c3vhgd+rj7bVqheaVYtg80UUtpk++NqWfRe7057WV9sUrHO5NoWmlcWqN5VSSQYWgfTgGsKGhAXfffTfq6+sBXP6p98knnxQLQQPAu+++i7vvvhstLS3Q6XT43e9+hyFDhijKpBhAgiAIgiB8AZ+NARw9ejQGDhyIsLAwhIaGIiYmBs8++yz279+PtrY2PPbYY3jsscfQ3t6OQYMGISkpCW+++SaOHz+uKJNiAAmCIAiC8AV89hvAhIQEPPvss0hOTgYArFmzBsuWLUNZWRlefvll7N69G6WlpRg5ciTOnDkjPvj71VdfxcKFC7ky1QpBC6gVcZXHOAgxbEJ8klBglRdjJ4/DUIqpcacbT75W/b0ppKl2rad9eaqHUvFUpeKnvHg9T8ek9fyVFkD1BCU7a/UlXlteYVx3/SrZxRv/uhKfdCdLi9wr9QEBXiHtK7FRV9qlL8Eblzc+2J36eHutUqF5aczgrl27kJSUhPr6+k7j9dQXefGKQpuUlBRs2LDBRRcleby20r6EGG9p4XBvCk0rjUNrLKeajH5Pb/8G3ZMEBASwMWPGcM99/vnnDAB79NFHNcsTYghGjBjBBg4cyAwGA6utrWWMMVZfX8+KiopYVlYWq62tZaWlpay2tpZlZWUxq9XKamtrWW5uLrPb7ay+vp6Vlpay+vp68drS0lJmt9vF47xjAvLrBWpra1liYiJbtWqV2Je8fX19PSsuLmb5+fksODiYDRkyRByDkvz6+npmsVhYfHy8S1u73S6Or76+ntXW1rJBgwaxwMBA5ufnx8LCwtjgwYOZxWJhdrtdtE9VVRVLTU1lRqNRlGe321lubq5oO/nYpHpJbSnVRXp9VVUVi4+Pd+k7PT1d1FUuU6qfVK7QpqCggIWFhbGZM2e6zKtcNk83nn8UFxez4uJit+OU2pdnc9582e12lpqaygwGAysoKGDFxcWd7CGdTyV/4h232+0sPT2dZWRkiGOsqqpi4eHhbObMmS5+EBQUxIYMGcKqqqq4/l1cXMwsFgtLT09n+fn5LCoqioWGhrKqqqpONuHZyGKxsNjYWJafn8/S09OZxWJhRUVFzGKxuKxD6XoS/hbmzGKxsOLiYlG3VatWsbCwMGYwGJi/vz9LTk4W1yxvzpRkFRcXs6KiIlZUVKQ6z2p+UFtby+Lj41lBQYE4Jrlfye0hX6OCDIvFwt0vYmNjWWpqaqd1J+1bPgbpmIU1IPc/+Vrm6SHsV/L9Rz4utf1PWA9jxoxhAwYMYKtWrWL19fUsPz9ffM+TI/U/ua9IxyC1t3CNMK9FRUXiPhoVFcVSU1O58yNdM1VVVap7s5IOanufsP6mTp3q4svyf3k+JrdFamqquLaFfS88PJxVVVW57JWCHQS/kI5x1apVin6l9XNNad6FNS+siZSUFGY0Gl32GOm+It0nKQaQMZ+9AVRLAmlubmYLFy5kO3bsYEeOHGF1dXVsypQpDAD77LPPFGWqFYIWXkJArDRwnlfoVVqEU0twq1rAq1IQsNDHrFmzXIKH1QqISsegJF8+NgF5QLs8qUX6kraVFhAW5PEK+8oR9JAXhOVdL+1DLdlDqfApr1/5XPJk83RT8g+t45S2UytMy2vDs7m8OKwniQg8O0ll8/xAWpRZzQ+l7d2hdr3czvJkC961gm7C2pGfc5dgpOTrWuZZ6TxvPSklQCitUTVf4+1P8kQF3hjkY5bvUby1zNODlwyhNC6l/U9u61mzZrnoN2vWLK4cpTWtlvSkxed488Pb+9T2Zp4OanufvCC7u4L8ar6j5PPz5s1TTFSRz4N0DcntqvVzTW3elfYMd2uPbgB9OAlky5Yt+Mtf/oJLly6Jx2bPno2HH34YFy5cwEcffYTnn38era2tAC6nzk+ZMkX1q29KAiEIgiAIwhfw2RvAhQsXoqOjA6GhoWhqasLy5cvx2muvYebMmZg+fTrmz5+P7777DgaDAfPmzUNCQgLeeOMNtzKFWAbg8rOAhw4d2t1DIQiCIAiC6FJ89gZQXgh65syZCAwMxPLlyzF9+nQUFhaira0Nt912GwCgvLzc7QOhlZJARowYAafTicDAQPEbRGmgv1Ck1Ww2iwHf0iLGMTExmoJblQJelQJbheDivLw8nDhxQrFgcnFxMf79739j8+bNYnKKFF7wrcViwebNm13aygPazWYzBg0ahLNnz+LSpUsIDQ1FcHAw8vPzXQqi5uTk4JNPPsHJkydFeUqFfXl6yQtC865PSUnB+vXrMX78eJfAamlBUrntlRI1jEYjCgoKsHHjRvFbY2Hcctk83Xj+oVZkVzpOecIAL4lAPl8mkwmpqaloaGjAzTffjKSkJGRmZrrYQzqfniRrCEWR/fz8xDHm5OSgsrISkydPdvGDc+fOITw8HDk5OTAYDNxCw9Ji1zt27MDFixeRk5PTySY8GwnF0H/5y1+ioaEBI0aMQGhoKFpaWnD48OFOviT9W5izESNGuOgWExOD999/H2FhYTh16hSuueYal2LS8jkT5lUuS6nosnwMan5gNpsRHx8Ps9mM0NBQHD58WDEBQmmNCjLGjx/P3S9iY2MRHR3dyVbSvoVC81J/kxcbl/uffC3z9OAlQ/DGpbb/CUXedToddu3ahby8PBiNRuTn5+PDDz9EXl4eV45g++PHj3N9hZdwIlwjLdx+9OhRbN68GcHBwRg6dCh3fqRrRlqgnKeXkg7CZwZvfxHW3y233IKUlBRusptaIoTUFl9++SUGDBgg9llQUACbzYacnByXvVKePCcUYPbz80NeXh4++eQTrl+pJXK4S86SrvkxY8YgNDQUu3btQmNjo8seo1RsngB8NgZQyqVLl1hVVRXz8/Njv/rVrxhjjF28eJHNmDGD3XDDDQyAYjC1Gl0VQ6AUdO/uGqVEhr6EJwkFVwu8gPC+hlJiUVfZ21N57hIcCG1czeuGUEdtjVzJvKtdK01k4SU0uetTq15Ke2Zv+jPFAPp4Esjq1atZSEgI0+l0LCgoiOl0OlZbW8va2trY9OnTWUJCAnvvvfcYAGaz2ZjT6WStra2KMrU8CcQbvKkqLw+A7auV/6/0yQZ9ES1PIOhtlILcu8renspzl+BAaONqXjeEOmprpCufPKLUp7yNlj616qW0Z/amP9MNIGM+Wwh6y5YtKCoqwvnz59HR0QHGGIKDgxEfH49jx45h/fr1OHbsGKZNmwYAmDRpEoxGI7Zv364oc9myZYiMjBRfFP9HEARBEMTViM/eAC5cuBA1NTU4cOAADhw4gAULFuD8+fMoKytDUlISnn76aYSHh6O8vBwAxHiljIwMVZn0JBCCIAiCIK52fPYG8Pbbb8fUqVNxww034IYbbsDSpUvh7++PH3/8EYwxrFy5EosXL8aUKVMAAEuWLEFLSwtef/11RZlBQUGIiIhweQHAokWLUFhYiOzsbDgcDs06Op1OlJSUoKKiAsXFxR4/CaGoqAjp6emwWq3Q6XQoKyuD0+nk9lFSUgKn0wmHw4Fx48Z10lNoZ7VaxbZOp1NRJu+4/LzNZsP8+fNhtVpVEwp48qTHHA4HsrOzMXfuXG6f7vSsrq5GQkICCgsLFXVWQy5fCMY2GAyqdnAnRzoXamMQ5s9ms3HnjodQhV+nu7zEtVbAdze3AkKg+Lp16+BwOBSvcTgcyMjIgMViQUpKCtLT00Wd3M2bzWZzmXfhuLw/qRybzeYy10r2U1oHPHsUFhYiISEBNptN0U6899J1JxyzWq1u94nq6mpERERg1qxZnXzl1KlTsFqteOCBBzB69GjYbDav1qjSWAX78nTn2d5T2UprXD5v8msLCwthNBo7nZfKkO5hM2fO7DRnStcIvib1By17nNVqRUZGBubOnSvaTLp/SnG375aUlECn06G4uBgWiwXz58932Rd0Op3XT7AQEjLmzJmD6upqFz2EdVxUVNTpM0htj5bqJewDamvUYDAgPj4eH3zwgcvn0MGDB132KeCy/+v1eowYMULRv93ZldBIb/8G3V2oFYL+5ptvGABWVVUlxgBWV1ez3NxcdueddyrK1FII2pO4sK6Mi3IXayeccxeLIW3rbfyeWpFSrborFXdVi2NR0lNemNhTuiqOUUthV7X502pPb3Tz5jq1Iq4CaoVY3Y1ZXixYqT+pHKVr5PbTGsepVFDZXYwlb21Lj6n1y/NXqb7ywt1d4ZvSa9SKi7srIuxOttIaVytQrTVOjVcYmFdUmneNfG1p3eOk86mkI2PuY+A83Rc8QdqHvPC0pzJ4/s77W2mNyvd0nv/KHwygNv4ricemGEAfLgTd0NCAu+++G/X19QAAf39/PPnkk3j44YfFOL977rlHbH/33XcDAK699lpFmVQImiAIgiAIX8BnfwJOTk5GbGwsQkNDERUVhWuvvRbLly/H/v37xTZffvkl7r//fhiNRoSEhCA+Ph7x8fGKMikGkCAIgiAIX8BnvwHcunUrHnnkEWRmZuLSpUtYvHgxfvjhBzz33HN4/PHHAQAWiwVRUVH417/+hYiICNx6663YvXs3zp07h7CwsE4ylQpBz58/H42NjaqFWXm4K/zqCWrFe6V9CLFhvMLE8mKeQOfim2p9yc8rFUHWojuvuKu02LHWscuLQHtjZ08KI3sih1fYVW3+tNrTG928uU6tiKuAUPT20qVLSEtLg8FgUCxYLZfLK3zN608qh1dgWMl+Wn2TV1CZV0iYV1hY+Fv4V6m4uBR5IW3BjlJ9hSK7SoXSvZl/eWFfue5aigi7k620xtUKVEsL/k6cOFG1WLCwh508eRI7d+7kFpXmFRiW+4aWPU5e/Jq3fwrwCkkLcuS21rIveILU7+SFpz2Roebv7h5YoPQ5xPPfnJwcvPbaaxg2bJjqgwAAZbsSGunt36B7iuPHjzMAbMqUKayjo4NFR0czAOyrr75ijDHW2trKIiMjWWhoKHv55Zc1yRRiCB544IFOBZntdjvLyspiBQUFLCUlhRkMBmaxWFh9fb3bIs719fXMYrGwiIgI5u/vz/Ly8lh+fj4LCQlhRqOR6fV6VlVV1ekaeUFNrUU2a2trWXx8PCsoKBCLgQrX2u12UYb8mN1uFwuXyo9JC4vW1tay6OhoNnjwYNEGgn0sFguzWCyKtrDb7Sw1NZUZjUZWW1srjquoqIilpqayjIwMl+Ki7gpP19bWsvT09E7XaUFaqLW2tpZlZWUpzp/Ql1LBaGEMKSkpot9J51TeV2pqKhs8eDBLTU1ltbW1ov8UFBSw9PR00Y4pKSls4MCBoq3tdjsrKipiSUlJLCAggE2dOtVlrgQdpLLlc1xUVMSysrKY3W7vVNBVmEelYuRqPlhbW8sSExPFeZXbJisry0UfqR6eFpaWt5fqpTYGYbyCHtLC2koFdHlI+5CuKS16CnNtsVjE/qTzI5fFmyPhvbvCv3KbCntDfn6+i+2le4LW4sXeFhbm7UVK1/HsIh+zXA5vDGo+4c733I1TqqvFYmHx8fHiGrDb7eL+xFvn7vzHExsKnzPx8fGsqqqKO6c8O/JsqIbU/nK/W7VqFRswYADLzMx0sYN0D1Db34XPlpCQEAaARUVFdRqLkj/Mnz+/38cA+uwNoDQJZM+ePWJgaUVFBWOMsUcffZQBYKtWrWJ79+5l99xzDzMajcxgMLD777+fK1NLEog8kJd3Xi2ombHOAca817x587jXeFrIkzHmEoAt11EazCs/Jh0j75hwHU++mn2kyAPeefaRBgB7kljgbSC0XI67vnj98OZYOqdKffHeq714dlaaK7nOvDmWB117m6DD2P/5nTxQXylxQ6qHpwlU8vZak4yUgtXdrWE5vLWiZi+lvUJpDqWylOZIbjel/UJ6XO5r8sQEtXlwlyijNk88vbTYjWcXpYQNtYQdNZ9w53taE0h4exsvwUdp7t3Jd2dDXnKI/LhSe0+SgXg+LFw3a9asTvuP/Bq1/Z1nI95Y1PyhP98A+uxPwLW1tVi+fDkuXrwIPz8/BAYG4vrrr0dRUREA4JlnnsHf//53PPLII2hvb4efnx+ioqJw5swZxZRzSgIhCIIgCMIX8NkkkCFDhqCiogJfffUV7rzzTvj7++PcuXM4d+4cAGDAgAHIy8uDv78/gMu1koQ4g5MnT3JlUhIIQRAEQRC+gM9+A/jBBx8AAB555BFs374d27ZtQ2ZmJurq6pCTkwMAOHToEB5//HH87ne/w8WLFxEdHY2wsDD4+flxZSolgTzwwAPYs2cPMjMzXQJ5s7KyEBcXh/379+P06dMuQd3SAGKloOf169fj3LlzyM3NRVBQELZs2YKoqCicPXtWHIP0GrUgazWEAGyz2YykpCTVYF7pMWmwuPyYNBjabDYjOjoaHR0dyM/Pd0nsSE5OBgDs37+fawuTyYTU1FScPHlSDOgWgpp37NiBAQMGuAQAa0ksEALovQmEliYUKCWmaEmCEcawbds2OJ1OtLW1ucypvK/U1FTU19cjLi4OZrNZ9J+kpCQcOXIEI0aMAADs2rULDQ0N0Ol0yM/Ph8lkQlFREWpra3Hs2DFMnjzZZa4EHU6cOCHKls+xPHFBOibBzz1N0BHGlZiY2ClQXxq0LtVHqkdMTIxHCVS8YHtBL7UkIyHIXB6MrpQ0pYTUTmqJFDw9hbkeMWIEQkNDodfrXeZHLkseGC9PKlDSm9e3sDekpaXhzJkz3MQEpXlwlzjAQ20fc2c3ud/ykkOktlNL2FHza157d2NQ0lWeYCQkTQkJPvJ1LiScaJGvxYZC/zk5OS4JWvJ93d1xNaRrBYCL3+Xl5WH9+vUYOXIk6uvrO+3x8oQp+diEz5bm5macP38eUVFRncai5A+tra347//+b7f6+zS9/Rt0d9HR0cHmz5/P4uLi2MGDB9mhQ4cYALZ3716xjdVqZaNHj2bHjh1jHR0d7LXXXmMA2PLlyzX14WkhSa1JGZ62laMUuN4dfWnRw9Nki77Eldqmq2zrjRx5sH53zLFWlHxBTS9P/edKxqiUaKBmQ2/Ws1pikABv3N4kvXiTcKF1nuRJNGpj8nZeuttnleZWiy/w5PCS4niJGFr3Yy2JFp7K7I3PAk/G44ke7pJ73HHgwIF+HwPoszeA8+bNY5GRkWzLli2svr6eTZo0id10002spaVFbPP666+zyZMnMwBMp9MxAGz06NGKMpWSQLQ6kCdV3a+kArxalfWu7kuLHt5Uae8rXKltusq23shRqtbfG7h7EgJPL0/950rG6O4JBjwberOetTzNhTdub5NePE240DpPvCQapTF5Oy/d7bNKc6vFF3hy3CX6aB2PlqQXb2X2xmeBJ+PxRA93yT3u2Lp1a7+/AfTZGMDy8nI0NjZi3LhxiIuLg81mw+eff4433nhDbHPvvfeitrYWANDR0QHg8k9oQqKInGXLliEyMlJ8DR06tPsHQhAEQRAE0cX47A3grbfeitWrV+Pee+9FTEwMxo8fj2HDhuHOO+8EAJw/fx6BgYFYu3at+BDrv//97wCAr7/+miuTkkAIgiAIgvAFfPYG8P3338euXbuwZcsWfPzxx3jjjTfwww8/oK6uDgDQ1taGtrY2DB48GLGxsYiNjcW//vUvxMXFITg4mCszKCgIERERLi8A2LNnD7KzszF37lyxhIzT6URZWZn43mazYdq0aUhNTYVOd9nsDocD48aNg8Ph6NSXUiCxXC7vuBD0nZycjKysLFitVvj7++Ppp5/mjktIOhk/fjwCAgIwbdo0OBwOlJWVif/K+1PSXXrcYDAgPj4eb731FgoLC5GRkYG5c+eKMm02m9hWLk9pnHKcTicKCwuRkJAAm83GtYfQZvDgwYiJiYHNZtMsX6fTIT09HXPmzHHRzWq1Ii0tDaNHj3axgVyu0jxKxyvIy87OVhy/UDlf8B0egkybzYaysjLodLpOiQ4VFRVux+wOuW5qfiy03blzJwDg//2//yeOWaqjTqcTZdpsNhiNRsybNw8A8Oijj3Lt4s7WvDWYlJQk+olw3mazYf78+bBYLNi2bRuKi4tx6tQpjBs3DqdOnYLVasUDDzyAX/3qVwgMDMTOnTvhcDgwZ84cpKSkcH0jOzsbNpsNJSUlKCkpwcGDBxEbG4tXX30VRqMRBoNB0b779u0DAMybN0/01XXr1qGgoADx8fE4ePCgqu86nU6sWLECsbGxWLFihWI7ub2qq6thtVqRnJwsJicJNtu5cydSU1MxZcoU5OXlYcqUKeJeZjAYEBsbi2eeeUa0Q3l5OYKCgjBt2jTodDpYrVbMnz+f6yM8f3E4HFi3bh2Ki4sV51NA8L/y8nIkJCRg7NixCA4ORmhoKKqrqxX7ko7/4MGDCA0NxcSJE/HII48gICAAFosFQ4cOxc6dO5GUlISDBw92WsvC3FgsFjzzzDNISkpCcHAwysrKYLFYsG7dOlHfnTt3IigoSPQfpTUjrHWDwaCYaCFfP1IbWa1Wl722pKQEFRUVsFqtLvpIZQl+qjZX0naC3JKSEtX9RPDFyMhIzJ49G8nJyeIe5nQ6MXPmTERERHSaJy0JQwcPHkRISAiCg4Mxc+ZM+Pv7Iz4+3sXn1fb52NhYRdn9ht7+Dbq7kMYAOp1Otn37dgaAffHFF2Kb3NxcZjKZ2ObNm9nOnTuZTqdjgYGBbNWqVVyZSjGAL730Uqf4HLWClfJio57EyLmLQ1Eq7AqATZgwQVEur+ArVOIqlHRXKzorly2Nh5LL8zRmRZDHs4dcjyVLlnglX66b/LgneivZSWn8WuSqxX56GkOmhlwXNT8W2k6YMMFljGrj4xV35dnFnU2U1qC82Kz0uNBeOiZ5wex58+Z1OsbzDek4eMXQlZAWx5X6Kk9PtXF7Ot9CAV2hf+lcCOfkL/n6EuwgHYOWOEG57p7E0Alt5UWFBb21oFZcXRi7vGC5VCfe9fJ5EuRI/UdtzXgT+6cUF6e2l8v3BndxoHK5nsTp8da8J/MkRb6+eDqp2dLTJE5fxGe/AZTGABqNRowZMwYA8NVXX4ltqqurkZmZifvuuw9jx44FACxZsgRz587lyqQYQIIgCIIgfAGfvQF86qmnMHr0aOj1egQHByMkJARbtmzBAw88ILaJjY3FH/7wB2RkZKCjowP+/v54++23FWP7KAaQIAiCIAhfwGcLQW/duhXz58+HzWbDhx9+iBEjRmD27NnYv38/wsLCAADffPMNbr75Ztx6663o6OjA+vXrAUA1BpBXCHr48OGdiobyClbGx8dj/PjxLsVGlQoFK+Gu0DGvsOtnn32Gzz77DBMnTlSUazabxSLTt956q2qhWTXdlYrOtrS0iMWeBZnyQsnSv7UWsRbiF6XFVHnXWywWbNiwAQEBATCbzR7Jlxck9aYQtRy5nXh9eFpEV6losXC9J4WT1ZDroubHQtvg4GBs27YNiYmJ3GLCUplmsxmxsbFobW3FmTNnEBcXp2gXTwriygtPywt2S+0mxGEJ/aanp+P06dM4efIkcnJykJKSIhZ6r6+v7+QbQhFraTHv9PR0XLp0CdnZ2apzkJeXh5qaGoSHh7v4Kk9PpXGrFZpXIicnB6+//jry8vJgMpnEuUhMTEROTg7Onj2LTZs2Yfjw4fj3v/+NiRMnuhSrlvqvUOBXaCO3pxJqRZ+V1oDgf3l5edixYwcSExNRV1cHnU7XqWC+EtJi9Wlpadi5cydaWlpw8803IycnBxs2bOhUsFyqk1AYfuDAgdi1axdiY2M7zZNgX8F/3K0ZTwtmC8elc88r1u+usLXSXEnbSeW601NeiF665gsKCmCz2TTPkxRhzoSi2dHR0W4/hwkZvf0bdHchLwR9/PhxBoBt3bpVbHPXXXcxi8XC7r//fpaRkeFxH0IMwYEDB7hFPwsKClh4eDirqqoSi1bm5+eLx6TwCl/Ki5NKi156UzhXS8FWT4rNapXLG9+VFNX1htraWpaYmMhqa2vF/oqKilhqairLyMhgdrvdZRzSYtrFxcVs/PjxLDAwkE2dOpWtWrWKRUZGslWrVmkuGqzFJvLjaoVN7XY7y8rKYlarVbEdz388nVsteFPw2263s/T0dJaWlsasVquq7mqo+VFXFfEW7NsVxX2rqqpYYmIiW7VqFcvNzWXLli1jkZGRLvuB4JtZWVmsqqqKpaSkML1e32nP8Ka4tpp+Uv/wpNCzHOnaEcahVtTam0LB8nHK7Susc3fXSudWaX3wfCw/P58NGDCALVy4kKWmprLBgwez1NRUcb+wWCwsPT2dFRQUsPT0dGaxWLxae9J9S+s8uPssUWoj74+H/CEDWudMXiyb9znmqWyhfVVVlWiX2tpaFh8fzywWi9t5pBjAflIImpcE0t7ezvR6PVu0aBHT6XRMr9ezm266ib399tuKMpWSQISCkkpB9/PmzesUDCsPelVL4uAlMvCCWt0FWmst2KokXwktAd7ukhq6u+irUgKA8JInosgTKqQvIdhcGiwvH0NXBHKrFTb1JLhb7j9dbWNvkpnkSRRakpp4qPnRlfqUfB66orivPNFCSI6R7gfyvUNpz/CmuLY7/Xi283R+eWtHrai1Fn9X01kqQy1hg3etfG61+KJU/8zMTG7ih9LLU1+U7lueJtKo+amSj8j3STm8BD5P/F9t3XsqWzgvzHllZSU32VLJBnQD2I+TQI4fP47m5mY89dRT6OjoQHNzMz7//HPccccd2Lp1K1cmJYEQBEEQBOEL+OwNoFAI+quvvsJ//Md/ICQkBHFxcWIhaOHJH0ajUawp5nQ6MWXKFLz44otcmZQEQhAEQRCEL+CzSSAffPABAOCRRx7B9u3bsW3bNmRmZqKurg45OTkYMmQI/Pz8EBkZ6VIQMi0tDZ988glXplISSGxsLDdgWRrgKgTQ/vvf/8Ynn3zSKehVKYlDekxIqNDr9dygVndJJe7Oe5sooCWZxV1SQ3cH6/ISAHiJHPKkFCGQ326345NPPsGkSZOQl5cHm83mEizPG8OVBHKrJeAAl20uBDyrBXfL/Uc43pV4k8xkMplUkyI8SdJR86Mr8Sn5PHiSmKAkKyUlBRs2bEBeXh5OnDiBiRMnYteuXS77gTSRJCcnB5s3b8axY8c67RlKdvdmLfHWvlJCjDukyUjy5BC1vtT8XUln+VwL9uUlbPCulc6t0vrg+Vh+fj4+/PBDTJw4ERcuXEB9fT3i4uLE/eL48ePYv38/kpKScOTIEYwYMQIGg8FjX5TuW9HR0R4l0qj5qZKPyPdJOfJEM61zJrW30ueYp7KF9jk5Odi/fz/+f+29fXhU1bn3/51JQsgbCYQkExJIsAEDMaAJcRLkJCREJIjaJopaJ4pWE3gorZ30VKj1EKs12lY5bZ+TNPVS1IoTnucoPqgoM1TeFKQGZwSKFSpEpBmIpMpbkNf1+8Oz9m/PnrVfZjIhMHN/rmsuktnr5V5r3WvNJnPf311QUCA9fECebKk1LxHPYH8HPVAok0D27t3LALCdO3dKZdLS0lhMTAxLS0tj48aNY/fffz+rqalhd955p6E+RDEEasG18qD9QIKclQGzWokDNpuNZWVl+QXwBhOk318uZpLHQCeQ9Ae9RI9AkkQuduJMJKE3l8GuYygSMgIl2PoX25+0+gvlvgnWhssVI0kgg2nLQBDMZxzFAEZIEkh3dze7/vrr2bXXXsv6+vqkMna7nUVHR7OlS5eyP/7xjywrK4sBYH/5y1+EbaolgcgdSE+tXnRNDVHihxEFeGUAbzBB+v3lYiZ5DHQCSX/QW69AkkQuduJMJKE3l8GuYygSMgIl2PoX25+0+gvlvgnWhssVI0kgg2nLQBDMZxzdADIWtl8Bt7W1AQCmT5/u8/7KlSslMeinn34aBQUFaGlpwf79+3H+/HmYzWZ8/fXXwjZbWlrw6KOPDqDVBEEQBEEQA0/YJ4F8//vfR0ZGBiorKzFmzBgpCYRz33334Te/+Q2uuuoqjBo1CiNGjMDevXuFbVISCEEQBEEQYcFg/wlyoDAiBM0YYwcPHmRZWVls165dLDs7m0VHR7MXX3zRUB/8T8jjxo1jMTExDAC79tprWWFhIcvPz2e5ubksMTGRtbS0sMzMTAaAmc1m1traKhTC5bE7XES0pqaGpaamsuTkZJaens5aW1tZfn4+i4uLY/n5+ZLoqN1uZw6Hg40YMYLFx8czh8MhxRzabDY2depUZjab2ZIlS3zKc8FMh8PBUlJSpK+NYmJi2JIlS5jFYmH5+fnMZrOxhoYGKRbR6XSywsJClpmZ6RNvqBT7rKurYykpKWzo0KEsOzubJScns6FDh7IJEyYwp9PpE7OoFJ4tLS1lNTU1LCsrizkcDp9YSLfb7SPi7HA4WGlpKWtoaJBEmpXCujabjaWkpLD4+Hi2aNEilpCQINkhF90tKiryabOxsZE5nU4/gVKR2CsXZea2NjQ0+Ah/y+NAHQ4HS01NZXFxcWz27NmstraWWSwWVltbyxoaGqTfbTYbczqdLD8/X/Kx3Nxc5na7WWtrKxsyZAi77rrrWGlpqY8/dHd3S6Kura2trKioiOXl5bHU1FSWn5/vE4vqdrtZYWEhS09Pl/rj8TR87rgtXGhXviaVlZVsyJAhkl/ztqZOncri4uJYbm4uS01NZQBYYmKij6gt94O0tDTJLj4uAGzUqFFCoW5lfCyfd/k8cj/n9vJ1VgrNOhwONmzYMAaAJSUlSXW7u7uZw+FgQ4cOZTExMSwqKooBYIsWLWK1tbXS2PLz81lhYaG0rnxfOZ1OlpeXx6Kjo6U1amlpYUlJSayurs5n/vm4HA4Hi4uLYyNGjJDa4GuXnJws+Qv3rbi4ODZhwgTW2trKsrKyWG1tLZs6daq0lxctWiT0Wbmv1tbWsqysLLZkyRJJ4Jz7EZ+j1tZWFhcXx1JSUlh+fr60N/la8rOssrKSJSUl+Z0f8nhEPt7W1lYfUWzlnisqKmJFRUWSX9fW1rIRI0aw1NRUZrPZWGtrK0tISGB5eXmSP7vdbpadnc0AsCVLlkj7X3nu8TV2u92S/3Eh59raWhYbG8vi4uKkc4SfT3xvtLa2spSUFBYdHc0WLVokjSMvL4+ZTCYWGxvL6urqmNPp9BNs5+dXUVERmzp1KouJiZH8g6+jfO0nTZokncFym+Vnp/zMmTRpkrT2fD8mJCRIZwf3f36Op6amsvT0dGY2m1lLS4vP+lgsFlZYWCjtOf5wA7mPqAnV8zXNz8+X9k5GRobPfs7Ly2MxMTGspaXFTyi6oaGB2Ww2HyFx7msTJkxgxcXF0vkxYsQIlpyczFJSUnzs5T7Pz3f+mdjQ0MAWLlwY8V8Bh+0NoJ4Q9PHjx5ndbmdFRUXskUceYevXr2exsbEsOTmZHTt2TNimWgyg3ouLvfLXggULhIKYypg/5UsuCguF6KhSAFPZPvD/i88q21K2C/gLnGr1y1GKfYraVWtDJDyrZp/yurwtPkY1YV0AbOLEicK6anMiF0cV9a9lm7wN+TU9wVi1evJ+tPxh+/bt0u/ydVf6HGP+oszycSrnjvcpWttbb71Vc25EY9IS2laOVx7nIxLyFvUlsle5jmp+un37duE1uf+o9ac2NvlZoJx/5ZrKx6z3Ultjvo/VfEhkm9IGrfbV/FN0fijHq1wbUb9q8yiySXl2zJgxw2//K9dTvk9EffFzhLch2lPcH0RzpBTD1vJVZRtq41bOjdrnRklJibAv5Xmm9AHRmiv3nPyallC91hiUa6U1Nzy2T+vsV5ZXO7uUr0i+AYy4GMBdu3ZhypQpiIqKwhtvvIGuri7s3LkTL774IqKjo/HjH/8YSUlJwjYpBpAgCIIgiHAgbGMAn3jiCUyZMgWJiYkYOnQo4uLisGHDBikBZPfu3eju7saYMWMQExODY8eO4fz58zh27JhqmxQDSBAEQRBEOBC2fwHcuHEjFi5cCJfLhb/85S+YOHEi7r77buzevRsJCQnYvHkz+vr6sH//fphMJpw+fRrnz5/Hf/7nf+LVV1/FgQMH/NpUE4IeN24curq6cPbsWUybNg25ubn4+OOPcfz4cRw5cgTV1dX47LPP0NXVhdjYWJSXlyM/P18ohKsUEd27dy/OnTuH+Ph4lJeX47333vMTHQW+FfDkwsBcvLW0tBR5eXno7e3Fe++9h6qqKowZMwYAUFJSgtWrV6OyshLl5eVwOBxS9nNMTAyqq6vxxRdfICUlBVOmTEF8fDwAIDExEVarFYWFhThy5IiPYKhSXDU/Px8rV65EX1+fJLZ9+PBh5Obmwmq1wmazYf369bBarT4indz24cOHS2K4cXFxknhoQUGBj4izfB7y8vIk8W25XTabDW+++SbOnDmDGTNm4PPPP8eYMWN8xGrLy8uxbds2mEwmlJeXw+12Y/LkybBarX4CpSKx15KSEh+hUwA4cuQItm3bJgm58jWyWq3Iz8/HgQMHMH36dAwdOhRbtmzB1KlTMXLkSL96+fn52LdvH86dO4crrrgCBQUFiIqKwssvv4ySkhL09fX5zENmZqYk6lpVVYV9+/bh2LFj+OqrryRBWe5zBQUFKCwsxOHDhzFz5kxpvAUFBcjIyIDNZsO6detQXV2N8vJyrFmzxmdN9u7di/fff18SxuZt5eXlwe12IyMjA8ePH0dvb6/kP9wG7gdr165FamoqKioqMHnyZKxevRpnzpzBqFGjkJmZ6SfUnZGR4SMoy+e9q6tLmsfc3FyUlJRI9p4/fx47duzwE5rNz8+Hw+HA0aNHMWzYMMyYMQO5ubnIzMxEeXk5li9fjgsXLsBsNuObb77BjBkzkJ+fj7fffhtZWVmYNGmStP7l5eXSvpKvcWlpKfr6+lBdXY2//vWvmDlzps/883FFRUXhpZdewpgxY6QxFxUV4dixY/jyyy9x5swZVFZWIjs7G1988QU2bNiA3NxcVFVVYevWrbBarfjqq6+wfv16mEwmVFdXY/bs2X4+K/fVrq4ubNu2DdXV1fjHP/4hrS23benSpcjIyMBbb72F2NhYWCwWjB07VprLtLQ06SxLTU1FZ2en3/khF0FWCvjK/+V7ju9x3ge3c/PmzT5+8vbbbyMzMxMzZsyQzo7c3FwcOHAA1dXV0l7g+4Kfe1arVVpjq9UKi8WCtLQ0WK1W1NbW4q233oLZbJbOEX4+8b1RVVWFdevW4cSJE5gxYwZOnjyJqqoqeDwefPbZZxgyZAjmzJnjcy7LRacbGhrQ2dmJoUOH4sMPP8S1116L8+fPS+vI/YfP66RJkxAfHy/5uvLslJ85H330kbQGfD92dXXh5MmTuOKKKyT/5+d4d3c3oqKipM8qvj5VVVV47733kJaWJu05/nAD+WeJ1kMJ+HyuXr0a586dQ0ZGBrKzsyXfzsvLw+eff47q6mpMmzbN7/zs6+vDP/7xD6k8F5A/deoU4uPjpfNj3bp1mDBhAnbt2gWLxeJzRvT09Eh+7Xa7kZeXh/j4eMTExOC//uu//GyPKAb7O+iBQi8J5MiRI2znzp0+L56osXz5ckN98CSQTz/91C8gViTGKk+SuNiinP0RVtVrL9QiuUaETANtu7/iuqK25ckSRscWyn6U4t96/hVqHzBqv5aPMKYukn4x7AqmrFH/FPlbsMLQoRI/1rNd+bORMQQ6psFY1/6II4dqzQZbxDqQ+RmI81LUDk9UUz68IBSfL3qQDmAEJ4EoOX36NBs+fDgbOnQo+/LLL4Vl1JJANm7c6BcQKxJjVSZJXExRzv4Iq+q1F2qRXCNCpoG23V9xXVHb8mQCo2MLZT9K8W89/wq1Dxi1X8tHGFMXSb8YdgVTNhD/DMWYQil+rGe76Of+7sVgbQ2UQIWl+zNnwdgy2CLWgczPQJyXonbkCSlqdQbq3KIbQMbCNgawra0NR48exfTp05GZmYmpU6cC+DYJBADOnj2Lhx56CDk5OTCZTIiNjcXXX3+N+vp6jBw5UthmS0sLkpOTpdfo0aMv2ngIgiAIgiBCRdjeAHIh6F27dmHu3LmIi4vDqFGjJCHovr4+fPTRR3jsscewdu1aPPvssxg5ciRefPFF9PT0CNukJBCCIAiCIMKBsE0CeeeddwAAixYtwpYtW7Bp0yaUlJRg+/btKC8vR3JyMlwul0+dyZMn49prr8XTTz+Np556yq9NtSQQi8XiE3zP/xW9Jw8+FwXPDhTyJAsj7wfaXjBtB2KT8r1A2xatR7AoA8J5gLKRsYWyH57cokykUfOvUPuAUfu1fAT4NrBbnsB0se0KtKxR/xT5WzBjMlLHaLtGbJf/bGQMgY5pMNbV6DqGyt5g++uvbwZb/mKcl6J2eKKaPJlQVOdinlsRxWB/Bz1QKJNA9u7dywCwnTt3qtZxuVwMAFu8eLGhPozEEBgNhFe7Jn9KAC+nF5itTAYwknTS3d0tqdMrg/GV5YIJDg42IcQIRuZooALPjRLM2gdaX7nOAzlmo+Mx6v8iHA6H31NdBspmreuhDIgPhV2hTiYI1Fb50xWMJEAN9PyJbNM7x0LZp3LvqY1XeU5ptdcfu/mTdGbPnm04CWUg/IW3KX8aitIeo35hNBEkkMSexYsXR3wMYNjeAMqTQLq7u9n111/Prr32WtbX18cYY+zEiRNsyZIlbOvWrayrq4tt2bKFpaamMrPZzHbt2iVsUy0JRMuBjAbCq12Tq8fLyynLivpRKs8bCT4Wta1WLpSB7P0J6DUyRwMVeG6UYNY+0PrKdR7IMRsdj1H/FyF/QsRA26x1PZQB8aGwK9TJBMHYyl9GEqAGev7UbLsY+12099TGqzyntNrrj91qT+no7xkUKLxN5ROKlNeN9Gs0ESTQxJ5IvwEM26+A1Z4EsnLlSsybNw9RUVF499138cwzz+D06dMAvtW4W7dunfArPYCeBEIQBEEQRHgQ9kkg3//+95GRkYHKykqMGTNGSgIZOnQoFi5ciIceeghXX301AOCNN95AZWWlapuUBEIQBEEQRDgQtjeAb7/9Njo7O7FhwwZs3rwZK1euxIEDB7B9+3apzB133IEdO3agr68PAJCSkqLZZmxsLIYNG+bzAoCf//zncLlcmD59Ojwej08dHqhqNpuxYsUK2O12mM1mNDc3w+v1+pSz2+1YsWKFz/vypwR4PB488MADmDRpEhobG32CX/nTM9rb22E2m32SAeRJAV6v169vAOjt7cXw4cMRFRWF2bNnayZX2O122O12vzLKtvnvHo8H7e3tKCoqQnt7Ozwej/Q+L9+fgN6oqCgkJycjKirKZy7kc6TWvtp8qL2vh9frRVNTE5qamnzmYdmyZcjKykJvb69f+yLb5Ne9Xq+u74iSP4zMqdJe3q/L5UJxcTGmTJni59N6bff29iIrKwvLli2D2WyG3W7HL37xC0ybNg0TJkyA2ax97HCburq6kJSU5PNUF1FZo+ukZbN8jkXB5jabDUlJSZgyZQqefPJJv/oej8dv/3s8HpSVlWH+/Pk+c6u0VWmXx+PxmfvMzEw0NjZi4cKF0u9a42hubkZvby8qKip051pvDHwt6uvrUVxcjF/96lcoLCxEXl4eLBYLoqKi/PxdfpZ5PB4sW7YMFosFNptN8wxSQ1lebqdyH9ntdlRVVSEqKgrDhw+X9ptyPI2NjT42K9sU7WG5DS6XC7m5udizZ4/P3uvt7cXChQths9n8fEl5TonaNbK2evM2btw4AMC0adN85lvLJ9Q+f/qDPIktPz8fCQkJ2LNnj8/1hoYGlJaWore3V3Nsegls/Fzkn31q88fX1Ww2Y/HixSEZ52XNYH8HPVDoCUGfPXuW3XzzzSw7O5u99dZbDABzuVzM6/Wy06dPC9tUiwGEhlgvJxTClmqCufK6WvW1+pC3rRTlNIpaPIa8bfnvoRLEVosB7I+YarAxMVrxh4HYGKo4ukDtFcXtaMUsiVD6qdH4UjWbjJTt73wEsvdmzJihel0+V2rzoGervB5vz0j8mHwcwewtUR+iuDrRXlb2Jdr7wexPUXm5naK2tHxXzRdFbYr2MP9dTcBYa5205rc/Z5VeP0Z9YiBjhvUEn/v7WRBIXCwvR0LQESwEffDgQaxevRoHDx7EjTfeCAC4/vrrkZmZiS1btgjbJCFogiAIgiDCgbC9AXziiScwZcoUJCYmYujQoYiLi8OGDRswb948AEBubi4uXLiApUuXIi0tDQBQXFyMXbt2+SWOcCgGkCAIgiCIcCBss4A3btyIhQsXwuVy4S9/+QsmTpyIu+++G7t370ZCQgIA4Ne//jWeeeYZ/PrXv8aCBQuQmpqK66+/Hp9++imSkpL82lQTgl64cKGmKDAQGmFLNcFcXteIcKdaHwUFBSgsLMSRI0f8RDmNoiYQW1BQALvdjhMnTiAxMVEYm9gf5HGSWmM0YrPe+0baEwmANzQ0YMeOHYZt1PKXUIqfiuzlcTtFRUUwmUyqPq2GyE9ra2uxdu1azJo1y5DwrlER2lCJwRrZe7m5uThw4ACqq6uF15X7XzQPRmwtKCjwm3tR+1rjCGZvifrga9HT04Pdu3dj4sSJiI+PBwBpL2uJRRcUFATs+2pj4uXldmZkZPi1ZbVaYbFYkJaWJhRPl59Fam2qifjz39UEjLXWSW1+Qy38rHYe6vnEQAor6wk+9/ezwIjtoRS3DhsG+zvogUIpBN3T08MAsI0bN0rXLRYLe/LJJ9n+/fsZALZt2zaWnJzM/vjHPxrqY6BiCNSEfY3UEQl/Gm3HiDCn0Tbl9rjdbh8hUKU4cENDgyQqG6ggqdxmt9utar9chDUYoWyt8QVyzUhbgaylEdFr5RyJ2jYybyJ79QRalQK5avVEa2Kz2VhWVhZzOp2G5lHNZiM+a2TO5fXVRH2V/qxsy+l0BiUGrNxDWuWV6xmIOK7WHHCR5cLCQjZp0iTW2NioO6daovVqvhEM8jb1BO2NiAqHwseNzq1RseZg0PJ9I+MNZOxaQtdGzqn+jknvTFGOnYSgI0QIWpQE8tlnnzEAzOFwSEkgHR0drKKigt12223CNoMRgg6GYAJjRUGwgbZjJPg+mIBirWB4ZYJEsAHicptE9UVB3v1JRgmlqKoRMVO1eTcSsC6aI6OCrHqB63pJNCKBXFE9tTWBIHBcj2CC4I3Muby+WrC/KOFH/r5espiaLUYSwNR8wqg/GpkD5cvInBrx5f4mIKjZGcj+VPPPYH3c6NzqvdcftHzfyHgDGbtWAoyRc6q/Y9I7U9TOvEi+AQzbr4DVhKB37dqFKVOmYPfu3QCAO++8U7p2xx13AACuuOIKYZskBE0QBEEQRDgQtkkga9aswcMPP4xXX30VAJCWloYvvvgC8+bNA2NM0gBas2YN/v73v6OhoQFjxozBvHnzJB0lJZQEQhAEQRBEOBC2fwGsqalBTU0NFi1aBAB47LHHkJ2dDQDYu3cv/va3vwEALBYLrrzySrS2tiI9PR1utxuTJ08WtqmWBBJqggmMFQXBBtqOkSDZYAKKuTCzWjC8PEhcFNRt1Ga1gHR+TRk43p9kFK2g4/4GuQeylmpJJ2plRGPVWncjgeuisarZq/azsnxBQQFsNhvWr18fcFJSMEHwRuZcXt9sNguD/UUJP/K29JLF1GzRSwDT8wmj+19rDngyyMcffwyTyYSysjLdOdVLENHzI6Mok046OztRUlIS0P4MNPEqkHFoza3ee/1By/eNjtfo2NX2BC+vd071d0xq56ja2BcvXiwUdY8oBvs76IFCngQCgK1atUq6tmPHDgaApaWlsaeeekp6PyMjg8XExASdBKJMfNAKng+GUAXZBvp+qFAGF2sFiBuxob9l+xMcz9FKBgjVOAZ6XYIhVEH7aoHmAxUMr5UUEEwyUH/7DrQNLRu1zodg+7uUfE6NUM7vYI83mLNZLflBmUSkTIIT7b3+zMOlMoccPXtICDpCkkAAsOeff555vV7W19fHzpw5w3JyclhhYSEbNmwY+z//5/+wH//4xwwAi42NZceOHRO2qZcEIkp8CFb1XUSogmwDfT9UqCV/BBsE3d+y/QmO5+glA4RiHAO9LsEQqqB9tUDzgQqG10oKCNWTaQLpO9A2tGw0+rSQQPq7lHxOjVDO72CPN5izWS35QZREpEy0M1K3v7YPFnr20A1ghDwJBADuu+8+ZGZmYuXKlYiJicGf//xn/POf/8SxY8cwd+5c/O53v0N8fDxKSkqEGoAAPQmEIAiCIIjwIGxvAJVJIIsXLwZjTHoSyEsvvYThw4fjjTfeQGdnJ9rb29HX16d68wdQEghBEARBEOFB2N4A1tTU4PHHH0dtba3w+tatW3HPPfdgzpw5KC4uRmVlJQAgJSVFtc3Y2FgMGzbM5wUAhw4dQnNzM8xmsxRsWlBQgOLiYjzzzDNwuVxoampCe3s77HZ70AG+WirzPNGisbFRap+/197eDq/XK5UVBd96vV784he/QExMDB555BGf8kq8Xi+am5sDLtPb24ucnBz09vZKQcHK+RgIZXy1skbrq5Xzer3YtGkTSktLpTXh4zabzbDb7VixYoXmPAGQgqfNZvF25P2bzWafOTWyDgOFfE6M2OHxeDB9+nR4PB6/+qL53bZtG2JiYlBXV4f58+cHPUYj69Hb2wuLxYJf/epXPvsnWJTzobZ+ajz55JOIiopCdXU1PB4Pmpub0dvbi4qKCqSnp6v6rNGnhRixXX6WcVwuF7Kzs1FXV4empiZVP5T/zn/m4+iPr6r5GT9X9uzZg6amJtTV1SE9PR2TJk2S/E0Pfh6tWLECHo8H9fX1yM7Ohsvl8vNdLbtcLhdyc3PhcrlUy3k8HjQ1NfnMocgOr9cr9d3b22s4gUl0nsh9Q16ef0YsW7YMTU1N6O3tlT4zAl0zrfPU6/UKx6x1duidK6Lr8vdCnVATlgz2d9ADxfHjx5nb7WZut5sBYPfeey9zu93s888/Z4wxVl1dzcaPH8/ef/99tmrVKpaRkcGioqLY5s2bVdtUiwHcuHGjMNaAx11w8VdRmVChFUdlpF95Wb3yRmI99OJQwgG92LVQxBhqlbtUYm6M2BHo2i9YsMCwPxq1Tc1OuXjwQMbPGV2vGTNm+AktD1R8ohItG+XnmJYfiuY8FPbrrZ/SvkDXU2krb1PPd+V2cRtE4uWi9vVi+oI5M4OJP1b6W6h9LpiY72BiowMZO8UAhnEMYGdnJ6655hpcc801AIDly5fjmmuuwX/8x38A+PYvhAcPHsR1112H733ve/jyyy/x7LPPYtq0aaptUgwgQRAEQRDhQNjeAJrNZsyZM0f68y+PAXzhhRcAAE1NTejr65PKX7hwAffddx8aGhpU26QYQIIgCIIgwoGwFYI+efIkJk+ejHvvvRd1dXU+106dOoWYmBg8//zzqK6uBgC8/fbbuO+++/D3v/9dtU01IWiLxSKMNeBxF1arVVdgub+oxbgZ7TczMxO1tbVYu3YtZs2apVneSGyFmphtf2OULiWMxBX2J8ZQr9ylEuNixI5A1768vBwvvfQSMjMzMWPGjH4LBGutR0FBAYqKimAymULim0aFhtWorq7G+vXrUVlZ2S+x8mDQstFqtSIrKwtWqxW5ubm6ArzyOQ+F/Wq2Kc/Zrq4ubN68GRaLJaD1lAsHy0XI09LSNH1XbpfVakVOTo5QvFzevta5rIzRC/TMDDQ22m6348SJE0hMTPSxLZQ+p/ZZpGWr3jguhpB22DPY30FfDACwxYsXS7/z7/7XrFkjvXfLLbewUaNGseuvv95wu7ydTz/9lNlsNmaxWJjNZpMENhsaGlhpaSlraWlhCQkJLC8vjzU2NjKn08mKiorYhAkTWHFxsSTc6na7WWlpKWtsbGRut5vZ7XbW0NDA7Ha79HtNTQ1LSEhgdXV1zOl0+oh7KoWV3W63Tz9Op9NHTJb/63Q6WXZ2NgPATCYTq6qqYg0NDVLfvB+n08kaGhpYXl4ei4+PZ3V1dT5ionKR2u7ublZbW8vi4uJYZmYmGzp0KEtJSWH5+fnS+HidhoYGVlhYKM2F0m6leKkctfedTifLyclhTqfTr2xrayuLi4tjI0eOZE6nU7dtkfiuw+FgsbGxLCoqilVVVQkFuJXrI2/D4XCwhIQENmHCBOF1uf84nU4fX1Cub25uLktMTGQOh0N1TkQ2yZHbw/uT+528b2X7WnMt94WhQ4f6zLncX5XlKysrpZih0aNHa/qAmi/wfpOSkpjD4dAUvW1paWFms5lNmzZNGjdf+9raWjZkyBDJniVLlkhjbm1tlfYst91ms7GsrCyfa3zeHQ6Hz/yr2cz3uNz35GK+breb5efnS/tQ6Wty31HzbdG6Kus5HA6WlZXFbDabTx9KYWG+lk6nU/UMU/qeljC8/Hc+B3JblHuguLiYtba2srS0NJaQkMDMZjPLy8uT+lL6qKg/PvbW1laWkpLCTCYTGz58uOoZwdc6LS2N5ebmsuTkZBYdHc0AsLFjx/rtMbnPFBUVsezsbBYVFcVyc3N9/Efusxz5Z4OaeLq8rHyeed2amhqWlZXlMwcNDQ0sNzeXRUdHs+uuu44VFhaySZMm+ayfaE3URKSVa6Y835X+KF8XI+eKfO5Fnxv8c0red1FRkTRvcigGMIyFoPWSQCoqKlhBQQFbv34927ZtGzObzSwmJoa1traqtqmXBAJZkKs86FUe1A1BsDIP8BUF4Kr9Lm9HLu7J+1e2pywv/1cUPC2qJyqnFuitDC5WvpQ2yN8XvRdoYL0oGJuXvfXWW33Gpte2KBhamaQgv87ridaHl5HXF12Xz59a8L1ynhYsWKA6JyKb5Mjtkfen7EOUTKE11yJfkM+5qE2R72j5gJovyNtZsGCBZrKCco+q+ScAVlJSIo1Z7ktK25V+xu2Qz7+WzUrfk9uktbdFvqPm22q2K+1V9iESFtbzHaXvqa258nfejtwW0R6Qz7e8rshHtfpTtqN2Rhg54+SIfEZrDfh+Vo5TL8FMmTgiWgej9qvZJrJBtGZqPiA6O4ycK2pzL/dJ5eec0mc4dAMYxkkgzz33nGYSSEdHB8aNG4eamhpYrVZcuHABFosFs2fPVm2TkkAIgiAIgggHwvYGcPz48UIhaJ4EcvLkSWzatAmLFi1Cbm4u6uvr0dbWhri4ONU2KQmEIAiCIIhwIGyTQGpqalBTU6N6/eGHH8bs2bNx00034Te/+Q1ef/11TJ48WbNNrSQQm82GdevWobq6WgpAbWhowI4dO1BdXY0PPvhACmi3Wq0oKirCqVOnEB8fLwX4FhQUoLS0FJMnT5aCcZXBuZ988gk2bdqEWbNmwWq1SgHCGRkZfkG2PLid92O1Wn0Csvm/VqsV2dnZOHjwIEwmEyorK5GXlwcASExMlPqxWq1oaGjAu+++i+7ubtTU1GgGetfW1uLtt99GSkoKvvrqKwwdOhQWi0WymddpaGjA1q1bMWTIEGkulPOTkZERUGC9KBibl83IyMBbb72FhIQEWK1W3aB9UTB0eXk5nn/+eZw7dw4VFRW4+uqr/YLglesjb4MnOYwZM0Z4PTMzU/IfHtzOfUG5vv/6179w5MgRlJeXq86JyCY5cnvkSUtKPxQlU2jNtdwX1qxZI/mTKChcXr6yshLr168HAIwePVrTB9T65IlNLpcL5eXlmskKPPFi6tSpuPbaa338s7a2Fm+++SbOnDkD4NskDT7mqqoqHDx4EJMnT5ba5QkE8mt83svLy7F7925p/tVs5slYct9TJgTk5+fjwIED0rcYcl+T+46ab4vWVVkvPz8fq1evRmVlpZ/viM4eq9WqeoYp6yvHrvQHZTKC3BblHjCZTKiqqsLGjRvR19eHU6dO4YorrpD6UvqoyP/42KuqqrBu3TocPXoUKSkpqmdEZmYmbDYb1q5di4SEBHz11Vc4efIkzp07h7Fjx/rtMbnP7Nu3Dz09PfB6vRg9ejRuuOEGn73CfZYj/2zQSzBTJlzxusOHD5fWltvf0NAAp9OJgwcPwmq14tixYzCZTCgrKxMmq2jtWfmcqp3vSn+Ur4tyjvUSakSfG/xzSt53Z2cnSkpKKDFExGB/B30xAHyTQM6fP88SExPZL3/5SzZq1CgWHR3Nrr32WrZq1aqA2h2sGAK9YHgiMrmYfkE+SBAXD63Erv7uQa3kqFDZGgpC3S7FAIZxDOCJEyfg8Xikx/ccPnwYHo8HBw4cQE9PD06cOIGWlhYcOXIEP/vZz/C9730PtbW12Lhxo2qbp0+fxrFjx3xeg4HX68Wjjz46KI8AIy5dLqZfkA8SxMVDtN9CtQeV7fS33YE6G+jMCT1hewOolQRy4cIFAN/qAZ45cwZPPPEElixZguTkZPzxj39UbZOSQAiCIAiCCAfC9gZQKwlk5MiRMJlMGDdunPTAcq/Xi/r6ehw4cEC1TUoCIQiCIAgiHAjbG8Camho8/vjjqK2t9bs2ZMgQjBw5EqdOnYLFYpFeX3zxBXJyclTbjI2NxbBhw3xeAPDuu++irq4Ow4YNQ1tbG5qamtDY2Ij6+noUFxejvr4eTU1N0p+uXS4XsrOzUVdXh/r6ekyaNAkTJ07ExIkTMWXKFOlrazkejwfFxcUYO3Ys/u3f/g11dXXo7e3F9OnT4fF44PV60dzcDI/Hg6amJqk/j8eDsrIyzJ8/3+/rA16+ubkZHR0dSE9Px8iRI1FfX69atq6uDkOHDsWQIUMQHx+PtrY2qX5ubi5cLhe8Xi8aGxtRVlYm2cbnRD4Pctu4HS6XC5MmTcKoUaPQ0dEhHJPcrsbGRowdOxYxMTGYNm0aysrK4HK50Nzc7POVhry+/PeOjg5kZ2f7jFlZXv5eY2Mj6urqkJqa6jdXvEx9fb00duUcyscuXzv5/LhcLukar1tfX4/s7Gy4XC4/3+Btm81mLF26FGazWepLPhb5PPI+Ojo6MH36dJ85E41fuV69vb0oKirCAw88IPRXpW0dHR1ITU1FQkKCzx7hthQXF2PKlClwuVxobGxEcXExJk+eDJPJhDFjxvj4uPIrIPm8FxcXY/78+XC5XCgrK/Pbe6I2lHtBOQfcvnHjxkl+Ke+vvr5eGovH40F9fT0yMzNRV1eHxsZG6aWcT2XfvM1Jkyb5nAPK/cTfq6urQ2JiIm699VYfP1bbL8rx83NIud/V5lZul7wd0ZzK/Zv/zteUn0XyMi6XSzo/5P3KfVW5r5Xl5GcI9yceBiT/XW1/87aVtmrNJ98T3AeUa63c50r/4uXlZ4bobFi2bBkKCwsxb948yb8XLlwIm82G9vZ2nz5dLhcyMzMxadIkaf6V88txuVyorKwEAEyfPh319fXYtm0bkpKS8Itf/EJ45sr9R7lm/Byy2+1ob2+XzlilPXI6OjqQkpKCjo4Oof/x/niSCW9X+fkmWif52cn3LT/Dr7jiCqHPRxSDHYQ4UOgJQU+fPp0BYImJiSwnJ4eVlZWxqKgotnnzZtU21YSgH374YU0xUiiEKPWEl5UCooyJRX9FYqxK8U7l7xxleaWwsVZZ+YuPl9dXivwqhWLlbYuEr+VzIxfN1bJL+VKKjWqJvorEZUWi2lqiqVoivkpblWMXzY+aYC6/pkRL4FgkzirvQ75uojry+VYTKhf5q9I2+TyLhHZFPyvnUk+wW61NtblR8281UWSRz4js1NrbemLjIv9RrqFozEphcb3+tm/f7jMuPXFtrbUQzameGLFS2FpLqFkpRK92BojOEKVotppovnwMWiL8ynnSWm/lGPXEzpX2is4GtTmR96kcP2Pqosqi/Wbks0Dej3LNtM5Y0VnBr8uFr0X9KddbuVaidVKzQ/6iJJAwpLOzU1MIev78+fjRj36EESNGwOv1wu12Y9SoUSgpKVFtk2IACYIgCIIIB8L2BtBsNmPOnDmS9o9SCPr222/H7373O3z++eeYN28evvnmG3R3d+Ott95SbZNiAAmCIAiCCAfCVgj65MmTmDx5Mu69917U1dWplnv99dexbds2jBo1CmfOnMHevXtVy6oJQU+ZMkUS76yqqsKYMWNw4sQJ9PX1Yffu3Zg4cSLS09N9BC6zsrJgtVoRHx+Pjz/+GOfOnQMAH2FoOSLR3/z8fD+hYaV4p9ls9hMQ5dfkIsf5+fn47//+b1y4cMFH4FlZtra2Fm+99RYuXLiA6OhoVFVVoaCgAPn5+VizZo0k6MlFVeVCsSIhY7loLBcqLiwslMaYnp4uFCTlPyuFTM+fP+8nNioSfeW/l5SU+InLisrLhXOPHDmCDRs2wGQy+cwVL9PT04N//OMfPuuoFDmVi7Uq50ckmMvFhdVEUdUEjuVjlc+jUphYOWei+Vaul3yN1eC25OfnY+XKlfjmm2989ggfLxfz5WLjnZ2dOHfuHHbs2KErBC2f9927d6OkpEQSJM7Ly/PZe1oi2UrBb/lcFRUV4dixYzh58iTKy8sRFxcn9Tdx4kTEx8dLgu1cFH7q1KkYOXKk1I/c90V98zF8/PHHPuK2yv3E35MLRpeXl0t+rLZflOPn55Dc90XrJ7JLuRbKORWJERcVFeHcuXMoKyvzE7ZOT09XFWqWC9GL1k4pPiz3J7mwPP9dJJovnxez2exjq9Z88j2Rl5eH+Ph4v7WWj1E5Z3zPA0BfX5/PmSE6G/jnCfdvPifKs9VqtcJisSAtLU1VCJtjtVqRkpKCo0ePYtiwYbjpppskX7JarcjNzRXuFd6Pcs1E+6ekpASrVq3ysUdOeXk5XnnlFR/ha1F/yvVWfr6J1kl+dvJ9y8/wjRs3ore316/PiGKwv4O+GAC+QtCcgwcPsqysLLZr1y6WnZ3NoqOj2Ysvvmi4XS4kuWrVKpaSksKio6NZa2srY+xb0cqGhgZWWloqxSLm5eWxmJgY1tLSIhS05EKXbrdb+rehoYEVFRWxxsZG5na7mc1mYxaLhdlsNh/hTrvdzmw2m9Qf77+oqIjV1tb6vC/q2+l0srS0NJaens6cTqemXd3d3czpdLKcnBzNsrW1tSwuLo7l5uay3NxcFh8fz+rq6vwER/k4lTbK+5OXV467oaGB2e121t3drTq+1tZWFhMTw/Ly8qT1qKioYA6Hg1VUVPj063Q6pfdE8LnNy8vzGZN8zhsbG5nD4WAWi4Xl5+czm80m2cgYYw6HgyUnJzOHw+HnL06n06cdp9PJioqKWG5uLktMTGQOh0Mae2VlJYuJiWGzZ89WFUjl43I4HCwnJ4c5HA6hqKzdbveZR/67ch2UtLS0sKioKNbS0uJ3TT7PpaWlrLGxUej38vHy/idMmMAAsLS0NNW10BtvVlaWz14RleP+rFaWX6usrGRZWVnM6XT6+Z7cZ/h4CgsLWXFxsfSefH61bDdSt7u7m9XW1rKkpCTmcDikeebz5Ha7hfOttrfka+V0Ov3W3uFw+JwPwYgHq9VRnnd8D/Cy8nKiOeRnkcPhYLW1tSwhIcHnnJGPTe0MlL/X3d3NKisrmclkYjNmzBD2K7Jdbz8qx9HQ0MBqa2tZVlYWa21tlebeZrNJfqZmt9bPlZWVDAAbO3aspj/IfXjq1KnMZDKxadOmsdraWr/PGCOo+WlDQwPLzc1l0dHRfvOi9jmi5jNynE4ns1gsLDMzk8XExLAlS5b4ndta9UkImrGwvQHUSgI5fvw4s9vtrKioiD3yyCNs/fr1LDY2liUnJ7Njx46ptmk0CYQx/+BkeYDqjBkzDAWEawUjQyXQlZczErwtRxmIr2WXKABYrazopbRbLUhb3p+8vNq41QLSGWM+iQfy9ZAnmvC6yiQMJaK5FQV2i4KOuV3KwGetpANREoKaDVr2yvtUltdKlFGugxLuzzNmzPC7Jkoy0kviEM2lVqKJ3njVbDeaECFaD63EHb39qDaPornQS1rg49RKutBLeFGulTKJQDmX8vFrJddozbfIFlGSktwX1MakTGgSlVFLxhDZJloDvbNLdN4FkvDDzyflOayXRKL2s3Lf6CVIqL201lNtffX6EK2dKLlN5DNylPuypKTE76zQqk83gGGcBKIlBB0VFYUXXngBH330ER577DFUVVXh7NmzuPXWW5GUlKTaJiWBEARBEAQRDoTtDaCWEPTu3btx/vx5OBwOfPbZZ9i5cyfi4+Px8ssv48svv1Rtk5JACIIgCIIIB8I2CaSmpgY1NTXCa5s3b8axY8dgs9mk986fPw8AKCwsxKFDh4T1tJJAUlJScOLECVRVVQEQB27n5eXh888/R3V1NaZNm6YZEM7/5QHxJSUlPgHm1dXVmskHGRkZUt3c3Fx0d3drBtJbrVakpaVJgfhadokCgEVla2tr8fbbbyMjIwMA0NPT45c0IR+nPGlEFJSvFhCsTC4Rja+qqgr/7//9P+Tk5PgEWvMkCHm/yiQMJXxt3333XXR3d/uMSb5e5eXlWLVqFVJSUjBlyhSfZARl4LPcX+SJEDzgW5kAxMfudrvx3nvv4frrr9cM5OeJGGvWrJGSa9SC6UXJI6I55VRXV2PDhg2orq72u8aTAcrLy+F2u/2SkeRj5+Pl1ydMmIBPPvlENXhcDfl4lQk+onJ6CRH82vjx47Fnzx4p0UktcYfvva1bt/okTaglEojmQq8uTwJxuVx+CWF83o0kf4kSN3hiAX9PniTGzwe9xCOtdRHtf+U5oEz4ECW4ydcnJycH5eXlOHz4sJQYo5aUIjoDlbZVVlZiw4YNUpKbWtKI3Ha9/ahM+OHJCNu2bUNVVRW+/PJLWK1Wn4SvtLQ0VbvVfq6srMT69esxduxYTX+Q+/CuXbuwdetWXHfddUhPT8eWLVt8PmOMoJY8J0/UmzlzpnDtRMltonWRw5NdTCYTjhw5gurqar8kSiN+GdEM9nfQFwPANwnkyJEjbOfOndLro48+YsOGDWOxsbFs69athtvViyFQC9Q1ijKwO1iMBmgbCVI3apsywF4e6KsViM7RCsrXG5Po2sWaS6PrrBYwHaiPGK0XbNuhQpkUpcfF9H29ct3d3T6B+fL3RYkyanupv/tQL/Giv+eNqB+9vcrnJi0tjRUWFgoD8PWSufqDWpKEXtKH2rgDOQP17BG1H4qzI9T0Z9zB7C/lz1p7RbmvAjnz9aAYwAhNAuG88cYbLCEhgZlMJhYVFcV+8pOfaLaplgSi5kB6avl6KAO7gyWQAG2jNurZZkQxXiu5QO8pBVpjEl27WHNpdJ21AqYD8RGj9YJtO1Qok0r0uJi+r1dOmaAjel/tKSJqiQZ6dojK6SVe9Pe8EfWjt1e1knXU6obSF9WSJPSSPrTG3R/btPw2VGdHqOnPuIPZX2pJK3rJSoGe+XrQDWCEJoGcPXsWDz30EB566CGcP38eI0aMgMlkwvLly9HT06PaJiWBEARBEAQRDoTtDaBWEkhfXx8++ugjLF26FB6PB2+++SaKi4tx6tQpPPfcc6ptUhIIQRAEQRDhQNjeANbU1ODxxx9HbW2t37Xk5GS4XC7MnTsXV155JUpLS/GHP/wBp0+f1vwLYGxsLIYNG+bzAoBDhw6hubkZXq/XpzwPQDWbzVixYgUaGxuxYsUKv3Jq8MDdd955B01NTfB6vfB6vcK+1PB6vVi2bBmysrI0Vc95sG5RURHa29t121cq/Yvaa2xsxMKFC5GamuoT6MvV8dPT0zUDfLOysmCz2XQD+JVzIgr8jYqKQnJyMqKiojTHpYdeULGaXUobe3t7YbFYUFhYCLPZDI/HgwceeACFhYXC+fd6vWhqapL8AABcLhcyMzNx++23o7i4GGaz+naW+2Ig/hMq9uzZg7i4OKSmpiI9PV23PH8yhJGyWhgNAtcqZzabkZeXh5iYGKSmpvrUaWhoQGlpqY8v86cTNDY2ChMNtNYgMzMTVVVVMJlMqKurg8fjkda5rq4O6enpWLZsGbxer2YiRm9vL7KysqSyRuA+ajabpXaUiQvt7e0+PsiftJCWlobCwkLpPPB6vVixYgXsdrtuMpfSBu7nHo8Hzc3N0r+iccjPIXm7au9robZugXDs2DEMGTJEeM7L7fB4PJg+fTo8Ho/weqjxer2oq6vDsGHD0NHR4WeX3W6H3W4PuO9g9pfyZ1HfomQlrb4o2SNIBvs76IFCKwbwxIkTbMmSJWzr1q2sq6uLbd++nc2aNYsBYB988IFqm2oxgBs3bjQU26EnqKtVFwbiIPTq68VThSIGRo5aPEyoY12MtBeqmLJg7VLaqBRmVRP5VrYlv6YUQjUyn4MVZ6QX06lksGMWRbYY9eX+xnopRcuV62xkXtREk42MU89uI232N55Vfl4Gc24GQyh8zqifX+zzSD6vXHw+0qEYwDCOAezs7NQUgv773/+Ouro6jB8/HjfeeCO2bt2KWbNmqaajAxQDSBAEQRBEeBC2N4Bmsxlz5syR/iQsjwEcOnQobDYbrrrqKiQmJuLQoUPIzMzEypUrNdukGECCIAiCIMKBsBWCPnnyJCZPnox7770XdXV1wuulpaXo6enBv/71L7S1tUkxfWqoCUFbLBZDcWF6grpqdZXimoG0IRcY1hPTFfXVH9TiBEMdr2GkPb2YxYFAaZf8Z6UwK48/ysvL8xNp5m0p14YLoaakpKCiosLQfA5WrIyW0LKISymmR2sPiewMJE5UhFK0PD09HRaLBYmJiYiPj0dZWZnuvKgJQeuNU89uo+dDsOsn70MusnwxfCEUPmfUzy/2eZSZ6SscThAAwjcGUA7gKwTNGGNnzpxh3/3ud9n48eMZgKAEZ+UxBIMttKuFEVHdQOwPZqyhmJ/+itsGIkRMhBa3281KS0tZY2OjYR8brPVSE1h2Op0hEafWQ08EfaAQ7a9L+VxTw+l0MovF4idMrURtbJf7WREKQetIgGIAI1QI+uzZs+zmm29m2dnZ7K233mIAmMvlYl6vl50+fVq1TS0h6EspaF2JkYDjQOwPZqyhmJ9QiNvqzQMxMASalDCY66UmsMwD/AfankATZkJFMAktlyLy+QvmzLvcz4pQJ/OFK3QDGMZJIFpC0AcPHsTq1atx8OBB3HjjjQAgPbtxy5Ytqm1SEghBEARBEOFA2N4AaglB5+bm4sknn0RSUhLa2toAQHpIdXFxsWqblARCEARBEEQ4ELZJIDU1NaipqRFeY4zhP//zP/Hwww9j1qxZAIDHHnsMM2fOxCuvvILGxkZhPbUkEODSClpXYiTgOBD7gxlrKOanP20EkgxDhJ5AkxIGc73UBJatVutFCdwPNGEmVAST0HIpwpOj0tLSgjrzLvezItTJfET4ErZ/AdRi//79OHToEGbOnCm9N2TIEFRUVGh+BaxGWVkZxo0bh1/+8pd48cUX/VTeubp9Y2OjpKLv8XhQVlaG+vp6SfW+sbERZWVlcLlcfk998Hq90nW5erzymsvl8lHN93q9eP311+FwOHD11Vf71GlubkZHRwdyc3OxceNGfPbZZygpKYHL5ZLanDRpEqZMmeKnWN/Y2Ij29nZ4PB4/WwHA4/GguLgYU6ZMgcvlQnt7u6SwrxyL1+tFfX09MjMzUV9fr/rEk8zMTDQ3N0tK+mVlZZg/fz5cLpc036K5ltf/8Y9/jO9+97twuVyq68n7lj+JgM/FxIkTfV58fHw8HR0dmD59Ojo6OiT7lE8E4E92mDRpkrReHo8H9fX1SE9P93lfORfKedG6pobIP+VPCVC2KVpfXsblckl+XFdXJz2xQl7e4/Hg7rvvxieffII///nP2Lhxo98cy9eQk5iYiKlTpyIjI0Pqs6mpSXoixsiRI2EymZCVlSXNs+gJC0bmRV6G+xkA6d+MjAzMnj0bO3bswJdffqk5rx0dHSguLvbxD/l8qu1j3sbixYthsVjQ1NQEANKY+f6Qz7n8iRl8/HzfcV8UnRdye+rr65GdnY3XX38da9euxaOPPiqcK629pZyDtrY2ycflfsbPnOzsbNTX10vnR2NjI+rq6pCdnS2dP0ofbGxsRH19PYqLi1FfX4/6+npMmjQJkydPRn19PaqqqhATE4OGhgbccsstWLt2La6++mqhT3COHz+O5uZmv/HIfU++B+R+6nK5kJ2djbq6OsmWUaNGISoqCj/4wQ+Qnp6OESNG+MyBfI74eaW2n0VrJd9zorPF6/Wivb0d9fX1SEpK8lk3+fyq+YNan3p7p6mpCfX19Rg3bpz0zZrcJydNmoSMjAw//21sbJT8SfnEF9G8y/dSW1sboqOjMWTIEKSmpmL27Nk+n1fyzwj5WHg/hw4dUh1TxDDYQYgDhVYSyPvvv88AsLVr10pJIB0dHay2tpZVVFSotqmWBCJ/zZgxwy/pQh6UC5UnP8h/FwWBG3kagbyusp5aoPOCBQt8/uVtKG1W61MtuF80HrWxiOZHL/hc1L5aW6LxPvbYY6rrrBybcq2UL/l6ieZTOQZ5ebntojaVc6GWoBDIU2K0/HPBggXCNpXtKhMjRC/RU0+UffBrygQLUb/KtRX1J0p4MjIvegkQ8qdzqPkO71u+9mprKdpTyrkS+bNozuVPypDX53ZoPb1E3r58jFr+pebXcvuVTzORtyWfH9Hekp8/on6NvpT+p3VuqvmZsn+5n2r5/tixY/3WSDlH8vVVO69F86CVJKTcV6L5U/pvf88U0frw9Rd91qnNm/KJL6J5l+8luY+J2lJ+Ninnhj/BK5KTQML2K+DOzk5UVlZKvy9fvhzLly/HPffcg4aGBgDADTfcIF2/4447AABZWVmqbba0tODRRx8dIIsJgiAIgiAuDmH7FfD06dPBvpW5AQCsWrVKSgKxWCwAvs38nThxIjZu3Ii9e/fiyiuvRE9PD/75z38K26QkEIIgCIIgwoGw/QugFmPHjkVGRgbWrVuHN954A+Xl5Thz5gwOHTqEjIwMtLW14fHHH/erp5YEMnHiRHz++efo6+tDdXW1X9IFD8o9ceIEEhMThU9+KCgokAKPrVarXxCv3tMI5HVFAeyiQOelS5ciPz8fa9asQXl5OY4fP47169fDarVKbW7duhVDhgzx69NsNqOiogLp6enCgOOCggIUFhbiyy+/RF5enp9N8rFkZGTAZrNh3bp1qK6uNvTEE3lSgTw4PyMjw2+uRePVeuaz8sktfG22bt2Kc+fO+ZSNj4+H1WqVxlNeXo7du3ejvLwcbrdbmPQgD1Ln65Weno7CwkJ0d3dj1KhRfuuo9rPW00bUEPmn/CkByjZF6ytPjOB+3NfXhy1btmDq1KnIzc31eepJbm4uurq6EBsb69MHn2NlgoUokJ2/19XVhc2bN+PChQvo7e3FqFGjcNNNN0n7SpmoYSSRQS8BoqqqCqtWrUJMTAzy8vI057W8vBzbtm3DqVOnJP+Qt62VYFBQUICioiKYTCYff+7q6sKWLVtQXV3tM+f87ODtm81mqT73Rb2nl9hsNqxfvx5VVVU4ePCgj8/Kyx4+fFjzaTXyOaiqqsJ7773nk4gh34OrV69GZWUlCgoKpP165MgRbNu2TTp/lD544sQJ9PX1Yffu3Zg4cSIA4OOPP4bJZMKkSZPwz3/+E5s3b0Z2djZuuOEGH/9TeyKR2lmh9D2+Znl5eVJb6enpyMrKgtVqRXx8PD7++GMcOXIEhw8fRmVlJU6cOIFz584hOzvbp2+5PRkZGZr7WbS/+fqLzhZu+6ZNm2C3233mr6urS5pfLX8I9Ezhffb09OCDDz7AoUOHUFVVhYKCAsknCwsLcfjwYcycOdPHf+Pj4wF8G3OpfOKLfB34+SDfS3xPms1mJCUlwWq14uDBgz6fV8p5kp85/A9BEc0gfwU9YChjAJ955hkpBpAxxh599FEGgDU3N7OdO3eyO++8k2VmZrKSkhLVOEAtIehII9i4KkJMuM+VPIbnciXc10iPSB7/5TT2y8nWwYSEoMNYCLqzs9NHCNput0tC0ADwyCOPIDs7G0888QSmTJmCgwcPYtGiRejs7FTNeCIhaIIgCIIgwoGwvQGUxwDKXy+88AIA4Pz587j55pthMplw+vRpbN68Gf/1X/+FO++8E1FRUcI2KQaQIAiCIIhwICJjAAHgqaeewsqVK/Hqq69i7NixeP/992G327F7926MHTtWWEdLCDrSCDauihAT7nNltVqRk5OjGXt5qRPua6RHJI//chr75WQrMbiE7V8A9di6dStuueUW3HjjjZg4cSIeeOABTJ8+HX/7299wyy23BNyey+VCbm6uqsCw6HogIptKsWiRqKmoPbU+9ARItfB6vfjtb3/r869W/3PmzJFEP9UEhZVtiMYaqI1aZQNpS62+aC2U6yUSUVbj+PHjqvMpF/gNVuRYKSCsFO5WE5c1Mhd6c/3f//3fGDZsGJYsWaIqTKwUguVCu1wcPFBEItZqosBa4+drPWfOnJCIx+rNlxEheT3x9P76v54Qu9FzxWh/Wqj5h3xu9M6VYBCJg4tuqoLty4iwtvIMUBO0lnP48GFs2LABhw8f9utL7j9q4uRaZUNNoH6o9AW5T8jHoSakTygY1AjEQaSlpYWlp6ezZ599lu3bt4+1tbWx6Oho9p3vfIedOXNGWEcrCUQvyF10PVCRTaVIrFpZNTFbrbKBBA7rCajqCfdq2ScSmFYKyQZio1rZ/gZKqwn6qq2XXj9awsdKMdVgRY6VAqzyOoHYqta+3lyr+a5IvJYxpil4G4hd8vpqosBa4zcigByMXWrtGBGS13rPiIDvQF0P9H0jqPmH/JrRcy+Yfo3u3WD3jZZfic4A0dkvR0sMXctXeB2tsqEmUD9T+oKa4LWakL4cSgIJ4yQQPR566CEcP34cDzzwAK644gosWLAA586dw2effYYHH3xQWIeSQAiCIAiCCAci9gZw5cqVGD58ONra2vDuu+/i97//vfTsxNtuu01Yh5JACIIgCIIIByI2CeTf//3f8fOf/xzz588HAFRWVsLhcKCzsxMVFRXCOlpJIHpB7qLrRhMpuBimXCxaJCKrJ2arVTaQwGEtAVWR4HNnZycmTpyI+Ph4n/JadojGGqiNWmX7GyitJsytXC+RiLJaeyLhY5HAb7Aix0oBYaVwt5q4rJG50JtrLVFxpSg0b8dqtSIrKwuVlZVBrZNoTtVEgbXGL/fjkpKSfgfX682XESF5ALri6f3x/2CvB/q+EdT8g1/TEirvz1oZbSPYvtTWVa1tkci5CJGPi+ZJTZyci4+rlQ0lgfqZ0heUPqH2QARKilFhsL+DHixGjBjBWltbpd9Pnz7N4uPjWWpqquE2eAzB5s2bWUVFBXO73X5luru7WUNDAyssLGTFxcXM7XZL75WWljKn08mWLl3Kuru7GWOMud1u1bYYY8zpdLKcnBzmdDoN26nXJrfTbrczu90u2SK/JrcxGORtqPUlL6Nnc6hsstvtrKGhQThuvXp6ddxuNystLWWNjY3CcnwMbrdbdyxaZY3MBS/jdDo1fVU+LqPtqs2FfPxOp9NvLvg+KCoq8puj7u5uVltby5KSkpjD4VDtX2usemMwWo7jcDhYcnIyczgcmmvL96jD4TDko8q+W1pamMlkYmPHjtXcs4G0GSzKdvi+FJ1bWr4+UPYx5nsmcp/Kz89nmZmZmuekaP35/mptbZXWWl5Wb6+2tray2NhYn88WXp/vE7fb7ef3yvkQzY/ROVOenXpnu/IM1NoXWr8b+TnUKOc1kH4oBvBbbbyI5J577mFZWVnszTffZPv372dNTU0MAPtf/+t/qdZRSwL505/+pJuYAZUgW3mwKmPqQeqcYJ6oYCR4WC2YWn4tVAHVRgK39WwOpU1q4zZST6uOXmKFVnB7IGWDSQTR81Wjwd9acyEfvyihQ2v+5dcWLFig2r+WTXpjCCRpgjHGFixYINmjtbZ8rLx8oEkEM2bMUE2YMUqoAveV7fBxq51bRvsMZWKB/ExU+pTWOSlafz6OW2+91cf3jO5VXu/WW28V9iVvQ2uviebH6JxpJREZSXDS2hdavxv5OdSI5tVoP3QDyFjEfgX8hz/8AT/5yU8wd+5c9PX1AQCGDBmC+vp61TotLS149NFHL5aJBEEQBEEQA0LEJoGcO3cO69atw9y5c/H666/DbDbj4YcfRlpammodSgIhCIIgCCIciNi/AD711FMYPXo0li9fjubmZqSnp+PnP/85oqPVp0QtCWTChAmqwbmiAPiMjAzVYFW1IHVOME9U0GuT26mWsNCfgGqv14v29nZ897vf9WlDL3BbL+A5VEHeekHYIvjjBK+99lrNOnqJFVrB7YGUDTQRRMtXAw3+1vIb+fh5Aop8LrSSKzIzM1FbWwuXy4Xy8nLV/rXGqjcGo+U45eXleOWVV1BeXo78/HzVteV7tLy83C/Zxoi91dXVePfdd5Gbm6sb8G+0zWBRtsPPEvm55fV6sWnTJhQVFRlOkgmVfQCQl5eH5ORk5OXlST61adMmHD16VPOcFK0/318ZGRk+vmd0r1ZVVeGNN95AVVWVX192ux0nT57Epk2bYLPZsHv3bp/5Wrp0KcxmM5qbm/3OS6/XixUrVsBut+vOmSiJiH/emM2+f/NROwPV9oVaUoZevYFMIpEn3VGyR4AM9nfQg8WECRPYgw8+yOrq6pjZbGbp6ensT3/6k2YdLSFoQsxAxn8MFiUlJQwAKykpGWxTCGLQGew9Ptj9B0KgwsdG6/W330iEYgAjWAh63759aGtrQ3R0NC5cuICFCxfiRz/6EV566SXVOiQETRAEQRBEOBCxN4Bnz57F6dOnsXLlSgDf/onaZDKhra1NtQ7FABIEQRAEEQ5E7A1gUlISUlJSpIdF8wdGHzhwQLVObGwshg0b5vMitAllrM+lQnV1NUwmE6qrqwfbFIIYdAZ7jw92/4EQaoHtUPVLRCYRewM4evRonD17FhaLRXp5vV7k5OQE3NaOHTswffp0eDweAN8G7DY2NmLSpEmYPHky6uvr0djYiMbGRjQ1NfnccHq93oD68ng8KCsrw/z583XrejweH7u0cLlcyM7ORn19vV+7Xq8XTU1Nku38PS37+RxUVVWhra0NP/zhD33Gr9enx+PBhAkTkJSUhI6OjoDHKbeP219fX4+ysjLN+TAyriFDhuCjjz7CkCFDNOfK4/EEtcZGbePjdrlcuv3wuqG2Sc02I9e1/JNf6+jokMpwn9Jbw2Bs1NpXLpcL6enpGDFiBCZNmuSzz3l7LpcLubm56Ojo0Fx/I/NUX1+PzMxM4b6Ql5PvSWW7HR0dSElJ0d07yja15ldtjjIzM9Hc3CwlhATrX2r+rbavlRw+fBhNTU2oq6tDamqqlMjB6ynPMC076urqkJiYiFtvvVXzvObr7nK5DJ2TfK4AoLm5GS6Xy2eM8rlUcvz4cfz2t79VtYe/19bWhmHDhkm2K9tVngUdHR0+53CozwrennKsovkR7Sm+hqI6amU4Wm20tLT0a1xhwWAHIQ4W999/PwPAkpKSWFZWFistLWVxcXGawqtGhaCV4prKV3/EMQMRXDUiAM0RCfVyREKiRoOZ1cav16d8nHpCwKJxqglP682H0XGpiY72R5hUD6OivMHYHWrbjFzX8k9+jYspKwXUgxFI1rLRiLCzsm95e0rxZ7W5DnTfGCknOk/kotWBzo/emgS6zoH2r/RvtX2tfE8psMz3hnJsRoW5jZzXaiLUeuckf19LmF3NJjV7+HtcjFptrMr5kvusSBS7v2eF1li1hKXVHnpgpAxHrw1QEkhkUltbi8WLFyMjIwM9PT3Yvn07Tp06hU2bNqnWoSQQgiAIgiDCgYi9AaypqUFLSwv27t2L999/H5mZmYiOjsYnn3yiWoeSQAiCIAiCCAciVgiac+LECdx111147rnncNttt+Hrr79WLWtUCFou/mwymTBp0iTEx8cDgKrYplH0xIWVZfUEoDlWqxVZWVmorKwUBiArxX6NBDNzQdZ//etfmDZtGkaOHOkzfq0+CwoKkJ+fj4MHD+oKAYvGqbTPbrejp6cH//jHP3QFsY0EaauJjg6kMKkRUV69ugMllhpMcLuWf/Jr5eXl2L17t5+AejACyVo2au0rq9WKtLQ0nDt3DtnZ2T77nLcnF3+Oi4uT2gxUtDszMxM2mw3r1q1DdXW1Zjkt0W65aHUg86M1v0bOnv4kG6j5t9a+lr9XUFAAu92Orq4ubNiwAdHR0ZIQtJZguciO2tparF27FrNmzdI8r+XC/IGck/x9LWF2ZXk9kXbeZkZGBtauXYuZM2dqCqBz/8zPz8fq1at9zuFQnhVaY9USllZ76IGRMhytNhYvXownn3yyX2O77Bns76AHm7vvvps9+OCD7JtvvmFDhgxhpaWlhutyIcl58+axoqIi1tjYyLq7u/3KdXd3s6VLl/pc6+7uZna7ndntdsN13G43q6ioYG63W1he2Z5WeSP99Qd5e0baFtkaSpv601Ywa6HXl3y9nE6nT1tq9fXa5dfdbrfQ39TqOp1ONmzYMGYymVhLS4vf9UD8SM0WkU16trndblZaWqq6r/pDoP6pZWcgZfrThvJ9+fxo+VCo97ayfafTybKyspjNZpP6UK6/0j69dt1ut7Q/tHxH1JfRMWuV0ZpbLRwOB0tKSmJ1dXV+7TqdTpaTk8OcTqdPP1ptq52LWp8dDoeDJScnM4fDoWuv2meT3p4NlEB9sLu7m9lsNpaVleUzX8oyRn1EBAlBMxaxN4BNTU3skUceYePHj2cbN25kc+bMYVFRUezee+9VraOWBCJ/aQXdigJ2A6mjFTQvai+QJJBQK8WLkjC02jYa8B0Ke0JR18haGEnKgCA4OtinAWgFb2vVlSc5zJgxw+96IH6kZotWQLmabYEkPAVKoP6pZWcgZfrThlqShJ4PhXpvK9sXJXMp1z/QZAf52PSSEUR+39+10ppbLZQJFXJECQl6e0vrXFSzPZAEICNJNYNx/irPR70ywdhKN4CMRexXwE6nEzt37gQATJ8+HcOHD8cVV1yhqe3X0tKCRx999GKZSBAEQRAEMSBEbBLITTfdBACIioqC2WzGV199hb179+L3v/89oqOjcf78eb86lARCEARBEEQ4ELE3gIsXL8bOnTvh8Xjg8XiwY8cOREVFwWq1wuPxICoqyq+O2pNAbrrpJsTFxSE/Px9ms/+Ums1mlJaWor29XRK8NJvNKCoqQlFRkU8dLnJpNpuxdOlS9Pb2SuKZ6enpSE9Px09+8hN0dHT4CGjyPmw2G1asWAGv14v09HRYLBY0NzdjypQpmgKce/bswdChQzFt2jSheGygAq88mP3mm2/Gnj17YLfb0d7eLhRJ9Xg8eOedd2CxWPDOO+9IYqTLli1DVlYWent7hbZwoVKRwKhSRLa3txdZWVlYtmyZjzi0mjCsfLw86Fi+FlFRUUhKSsJrr73mJ268cOFCNDY2ore3V1VcmAd12+12pKamIjk5WfI5fo2vI7dnxYoVaGxslN5XCvdqJXpkZmaisbER8+bNw5QpU3zEo61WK4YNGwaTyYQLFy7A4/H4zI0oGF9PjJWPYdOmTZLN8fHxuOuuuyRRZ96H2Wz2GS8X4r377rsBAFdffXW/AtHltnK/6O3tleaI27ps2TKhCDIXQP7pT3+KpKQk7NmzR7Uvs9mMwsJC3HjjjZI4MBfbzc3NxZ49e/zWRrm3XnjhBZ9/+TrfddddyMzMxJ49e9DU1IQVK1YgISEBdXV1UoB9eno6mpub4XQ6ERsbi23btqkmImiJCSvf40LqxcXFmD9/Pnp7e1FRUQGz2Swlc9lsNsnnly1bBpvNhmeeeQaNjY2wWq0oLS3FO++84ycIL0fuNw0NDbDb7ZI/m81m1NfXIzs728fXent7kZOTg2PHjqGiogK9vb1ob29HaWkpzGazqkDzihUrYLfb/cSRGxsb8Ytf/ALR0dGYPXu239yKxJJ5vbfffhtRUVEYPnw4ent7feYzOjoaUVFRiI6Olmx67bXXYLFY8Nprr6GpqcnnLPN4PHj88ceRmJiIY8eOSWPo7e1FYWEhLBaLXx98PwLAX/7yF5/xKgXKPR6Pz5nC9/2yZctQWlqKqKgov88tPeFlpR/xNn/xi18I947SdvmZW1tbi4SEBLjdbh+beRn5Z2h6erq0h7mPagl4E//DYH8HfSlw7tw55nA4mMlkYjabTbWcnhA0dGKc5NfV4ji0xFDldXich1rMjTwOBrI4CbV+5HE8otiRYOKIjIqkKgVclbYr42OMxBcpY2eU8WT9jcFUi08zUkaJkfhHvfU1Ep8kimvSiv3RsjsQwVbl+ip9WRmzpfTbkpIS3bFpIW9bLeZKay6V9qvFJCnbkfs936+iusq1Likp8Rm3cj7UhKnlZbkYsFYcmFb8l+g9ZZ8i/zASlxiIb2nNrbJPeX96e11vf4n6EfmoMsZVzc+V+0Q0p8oxiOZS2ZeyD7U5FsXLqf0r6kf5OaG1B4zOp9qcGrFZtK6isWt9blEMYAQLQQPAzp07kZiYiNjYWMyfPx9XXXUVUlNTVcuTEDRBEARBEOFAxN4AtrS0YN68eWCMISkpCenp6Th48CAaGhpU61AMIEEQBEEQ4UDEZgFv3LgRixYtQklJCc6dO4eHH34YBw4cwG9/+1s8//zzwjpaQtBaAqlqAqEiYVItMdSMjAzYbDasX78e5eXlSE9P9xPQVMZ/cUHqIUOGaApwWq1WjBgxAt98841QPDYYgVejIqlcwPXEiRM+QtFqorTKsYoERpVxayIRWy1hWCPCxaI1N1JGiRHBW631NSqMXFBQgKKiIphMJqF4tHI91OZGaZ+eYGtBQQFsNhvWrl0Li8Ui+bKWsG1tbS1Wr16Nc+fOobq6WndsWshtNZvNQuFdLRFk7jvDhw/Hjh07VEVneTt8j3K/52K7a9asEdZVrnV1dTU6OzulcXPb+D62Wq2qwuZyMWCXy6UpBK0lqqx8j/e3e/dulJSUqIoEy/eZcl/ydpT73IhdormV9ykXRBf5r55As9xXuYD90aNHffpR1pHvR16vs7MTubm56O7ulvxctE/kc8FJTEz0mzP5fpULzMt9Vd4HAEydOhVbtmzBtGnThJ83cpuV/8rXRzSPesLLyrmSi3Nv27ZNU9SZz6kRm9XWla9BSUmJpoA38T8M9nfQlwo9PT0MAJs1a5bhOpdjDIGW8GeoxXaDseVS4FK1i7HgRMXV6hm5Fi5EwhiJy5tARMRD5c8DLRh+KXM5fn6Hmoi9AVyyZAnbtGkT279/P9uxY4cUpN3e3q5aRy0J5HJyIKOB34Nly6XApWoXY8GJiqvVM3ItXIiEMRKXN3o+qpUwEYo+I22P0A1gBAtBHz58GHPnzkVPTw8A4MKFCxg9erRmDCAJQRMEQRAEEQ5EbBLIc889h+effx5LlizBzJkzAQD/8R//oVmHkkAIgiAIgggHIvYvgABQU1ODNWvWYNeuXQCAkSNHapZXSwK5nDAa+D1YtlwKXKp2AdpB+vxno/WMXAsXImGMxOWNno9qJUyEqk/aIxHGYH8HPVhcuHCBLVy4kI0aNYrt2bOHAWCrVq0KqA15DEGgyRX9CbgNpC8jCQL97Vt5ze12s6VLlzKn08kqKiqY2+0OqJ3+lA2mfDAox8r7UvbtdrsNzYFae4HYEqrxBro2Rv1Lay7U2rbZbCwrK4s5nU5DdQaK/s5xKNdoMNdbC6O+Hmy/Rsr3Zx+53W5WWlrKbDYbs9lsrLS01LCvyvtX2w/9sU2rv4v1uaO2vvJxGT0LLoWEE4oBjOAkkAULFrDk5GS2YcMG5vV6GQD2/PPPs76+PtU6WkkggSZX9CfgNpC+jCQI9Ldv5TWtJ3QYaac/ZYMpHwyiJ3OI+lZ78oSyrFp7gdgSqvEGszZGymvNhV7bek8eGGj6O8ehXKPBXG8tjPp6sP0aKd+ffaT2NI9A0NoP/bFNr7+L8bmj9xQdo08+6q8doYJuACP4SSBtbW04evQopk+fLv3J+7777sNjjz2mWoeeBEIQBEEQRDgQsTeAjDF0dHQgJiYGzz77LABgzpw5+P3vf48DBw4I61ASCEEQBEEQ4UDE3gACwDPPPIMf/OAHuP/++wEAP/jBDzB69Gi0tbUJy8fGxmLYsGE+LwD4+c9/jvb2dtjtdsPJFf0JSjeayOH1erFs2TKkpaWhsLAQZnP/l9vImLhae15eHnJycpCeni4sa7fbsWLFCni93qD7DEV5JV6vF83NzfB6vT4/y+FPlEhPTxc+UUD0JBctO9XaU9oTyvGqtRtIe3wdlb4v6uudd95BVlaW0B9E5VesWIHa2lpkZWX5PEFAzW619z0eD6ZPnw6Px6Pbrxr99alQJqHwtsxms894jfitEj7PWuvH23K5XJg+fTpcLheamprQ1NTk0z5/CsimTZskG3g5s9nsM355v729vYbWRzmHojGqPTFHNCblGLj9NptN8js9X1XOeXt7OwoLC1FUVOR33mrt8UDh/SrnVT4HmZmZfnPUHz9UO8uUT/2Q+5LeGaP0YeIiM9jfQQ8Wp0+fZmazmT399NPM7XYzAOyZZ55hd955J7v22muFddRiAGEw7uFiI49HGQz7AhE2vZQwIo56MeOwBmqeLub8BxqLGsx8qL0faOzh5YJyvMGI+gYSV8djevm/emtgJB5u+/btQa9PKOKo+7vn1MY70GdGKNd3ILmUPwMoBjCChaCPHDmCCxcuoKmpSXqPS2nwv+wpISFogiAIgiDCgYj+ChgAtmzZAvZtNjQYY3j88cdV/zxOMYAEQRAEQYQDEfsXwJEjRyIqKgqHDh3yeb+npwcZGRnCOmpC0AsXLkRsbOwlJ6CZmZmJhoYGdHZ2oqSk5JITer5UxXmNiKOG2vbBEGq+mPNvRKxaWT7Q+VB7XysO83JGOd5gRH2N+AAvY7VaUVFRAavVqrqWyvaMlOOxcYGuT3/jqLX80WjbovGeOHECiYmJA3pmBGvfxeZy/QyIFEyMMTbYRgwWVqsVxcXFaG1tld6bOHEibrnlFrS0tOjWP3bsGJKTk3H06FHVr40JgiAIgri0oM/vCP4LIPDt/9bq6+sxZcoUlJWV4U9/+hMOHDiA+fPnD7ZpBEEQBEEQA0ZE3wDefvvt6O3txS9/+Ut4vV5cddVVWLNmDXJycgbbNIIgCIIgiAEjor8C7i/0J2SCIAiCuPygz2/KAiYIgiAIgog46AaQIAiCIAgiwqAbQIIgCIIgiAiDbgAJgiAIgiAiDLoBJAiCIAiCiDDoBpAgCIIgCCLCoBtAgiAIgiCICINuAAmCIAiCICIMugEkCIIgCIKIMCL6UXD9hT9E5dixY4NsCUEQBEEQRuGf25H8MDS6AewHvb29AIDRo0cPsiUEQRAEQQRKb28vkpOTB9uMQYFuAPvBiBEjAAAHDhyIKAc6duwYRo8ejS+++CKinqFI46ZxRwI0bhp3JHD06FGMGTNG+hyPROgGsB+Yzd+GUCYnJ0fUxuEMGzaMxh1B0LgjCxp3ZBGp4+af45FI5I6cIAiCIAgiQqEbQIIgCIIgiAiDbgD7QWxsLJYuXYrY2NjBNuWiQuOmcUcCNG4adyRA446sccsxsUjOgSYIgiAIgohA6C+ABEEQBEEQEQbdABIEQRAEQUQYdANIEARBEAQRYdANIEEQBEEQRIRBN4D/Q3NzM0wmk8/LYrFI1xljaG5uxqhRoxAXF4fp06fjb3/7m267r776KiZOnIjY2FhMnDgRq1atGshhBIzWuM+ePYuHHnoIhYWFSEhIwKhRo3D33Xeju7tbs80XXnjBr02TyYRvvvnmYgzJEHrrPW/ePL/rpaWluu1ezusNQLhuJpMJv/nNb1TbvBzWGwD++c9/wmazITU1FfHx8bj66quxfft26Xq47nGtcYfzHtdb73Dd43rjDsc9npubK7Rv4cKFAMJ3b/cXugGUUVBQAK/XK7127twpXfv1r3+NZ555Bv/7f/9vfPjhh7BYLLj++utx/Phx1fa2bt2K22+/HfX19fj4449RX1+PuXPnYtu2bRdjOIZRG3dfXx8++ugjPPLII/joo4/w2muvYc+ePbj55pt12xw2bJhPm16vF0OHDh3ooQSE1noDwKxZs3yur1mzRrO9y329Afit2fPPPw+TyYS6ujrNNi/19f7qq69w3XXXISYmBm+//TZ2796Np59+GikpKVKZcNzjeuMO1z1uZL2B8NvjRsYdjnv8ww8/9LHL5XIBAG677TYA4bm3QwIjGGOMLV26lE2ePFl47cKFC8xisbAnn3xSeu+bb75hycnJ7I9//KNqm3PnzmWzZs3yee+GG25gd9xxR0hsDgVai9C5kwAAC5xJREFU4xbx17/+lQFgn3/+uWqZ5cuXs+Tk5P4bN4Dojfuee+5ht9xyS0BthuN633LLLayqqkqzzOWw3g899BCbNm2a6vVw3eN64xYRDnvcyLjDcY8Hs97hssfl/PjHP2bf+c532IULF8J2b4cC+gugjL1792LUqFEYO3Ys7rjjDuzbtw8AsH//fhw6dAgzZ86UysbGxqKiogJbtmxRbW/r1q0+dQDghhtu0KwzGKiNW8TRo0dhMpn8/iet5MSJE8jJyUF2djbmzJkDt9sdYqv7j964N2zYgPT0dIwfPx4PPPAAenp6NNsLt/U+fPgw3nrrLfzgBz/QbfNSX+/Vq1djypQpuO2225Ceno5rrrkGzz77rHQ9XPe43rhFhMMeNzrucNvjga53OO1xzpkzZ/Dyyy/jvvvug8lkCtu9HQroBvB/sFqteOmll7B27Vo8++yzOHToEKZOnYre3l4cOnQIAJCRkeFTJyMjQ7om4tChQwHXudhojVvJN998g8WLF+P73/++5kPD8/Pz8cILL2D16tVwOBwYOnQorrvuOuzdu3cghxIQeuOuqanBihUr8O677+Lpp5/Ghx9+iKqqKpw+fVq1zXBb7xdffBFJSUmora3VbPNyWO99+/ahra0N48aNw9q1azF//nz86Ec/wksvvQQAYbvH9catJFz2uJFxh+MeD3S9w2mPc15//XV8/fXXmDdvHoDw3dshYbD/BHmpcuLECZaRkcGefvpp9v777zMArLu726fM/fffz2644QbVNmJiYtgrr7zi897LL7/MYmNjB8TmUCAft5wzZ86wW265hV1zzTXs6NGjAbV5/vx5NnnyZLZo0aJQmhpS1MbN6e7uZjExMezVV19VbSOc1psxxq688kr2wx/+MOA2L8X1jomJYWVlZT7vLVq0iJWWljLGWNjucb1xywmnPR7IuDnhsMcDHXc47XHOzJkz2Zw5c6Tfw3VvhwL6C6AKCQkJKCwsxN69e6UsSeWdf09Pj9//EORYLJaA6ww28nFzzp49i7lz52L//v1wuVyafxkQYTabUVJSckn+b5EjGreczMxM5OTkaI4hXNYbADZv3oxPP/0U999/f8BtXorrnZmZiYkTJ/q8N2HCBBw4cAAAwnaP642bE2573Oi4lXUu9z0eyLjDbY8DwOeff45169b5jClc93YooBtAFU6fPo1PPvkEmZmZGDt2LCwWi5RZBHwbZ7Bx40ZMnTpVtY2ysjKfOgDgdDo16ww28nED//8Hw969e7Fu3TqkpqYG3CZjDB6PR2rzUkQ5biW9vb344osvNMcQDuvNee6551BcXIzJkycH3OaluN7XXXcdPv30U5/39uzZg5ycHAAI2z2uN24gPPe4kXErCYc9Hsi4w22PA8Dy5cuRnp6OG2+8UXovXPd2SBjcP0BeOjQ1NbENGzawffv2sQ8++IDNmTOHJSUlsa6uLsYYY08++SRLTk5mr732Gtu5cye78847WWZmJjt27JjURn19PVu8eLH0+/vvv8+ioqLYk08+yT755BP25JNPsujoaPbBBx9c9PGpoTXus2fPsptvvpllZ2czj8fDvF6v9Dp9+rTUhnLczc3N7J133mGfffYZc7vd7N5772XR0dFs27ZtgzFEIVrjPn78OGtqamJbtmxh+/fvZ+vXr2dlZWUsKysrrNebc/ToURYfH8/a2tqEbVyO6/3Xv/6VRUdHs1/96lds7969bMWKFSw+Pp69/PLLUplw3ON64w7XPa437nDd40b8nLHw3OPnz59nY8aMYQ899JDftXDc26GAbgD/h9tvv51lZmaymJgYNmrUKFZbW8v+9re/SdcvXLjAli5dyiwWC4uNjWXl5eVs586dPm1UVFSwe+65x+e9//t//y+78sorWUxMDMvPz9eMLxkMtMa9f/9+BkD4Wr9+vdSGctwPPvggGzNmDBsyZAhLS0tjM2fOZFu2bLnII9NGa9x9fX1s5syZLC0tjcXExLAxY8awe+65hx04cMCnjXBbb057ezuLi4tjX3/9tbCNy3G9GWPsjTfeYFdddRWLjY1l+fn57E9/+pPP9XDd41rjDuc9rjXucN7jen7OWHju8bVr1zIA7NNPP/W7Fq57u7+YGGNsUP70SBAEQRAEQQwKFANIEARBEAQRYdANIEEQBEEQRIRBN4AEQRAEQRARBt0AEgRBEARBRBh0A0gQBEEQBBFh0A0gQRAEQRBEhEE3gARBEARBEBEG3QASBEEQBEFEGHQDSBBEWLBhwwaYTCZ8/fXXg9L/u+++i/z8fFy4cEG37JtvvolrrrnGUFmCIIiBgG4ACYK47Jg+fToefPBBn/emTp0Kr9eL5OTkQbHpZz/7GR5++GGYzfrH6pw5c2AymfDKK69cBMsIgiD8oRtAgiDCgiFDhsBiscBkMl30vrds2YK9e/fitttuM1z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", 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          \n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for sorter_name, sort in sortings.items():\n", - " print(sorter_name)\n", - " sf.plot_rasters(sort, time_range=(50.0, 70.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "5df5867d-9151-4f95-b0da-7b14cf8075fd", - "metadata": {}, - "outputs": [], - "source": [ - "import spikeinterface.comparison as sc" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "9f9fcdc3-61df-44b6-8126-18bf13b7a133", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['kilosort4', 'spykingcircus2', 'mountainsort5', 'klustakwik']" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorter_names" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "fc74cda6-f5b4-4894-99a8-dbab2fee5d7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[KiloSortSortingExtractor: 154 units - 1 segments - 20.0kHz,\n", - " NumpyFolder: 73 units - 1 segments - 20.0kHz,\n", - " NeuroScopeSortingExtractor: 57 units - 1 segments - 20.0kHz]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(sortings.values())" - ] - }, - { - "cell_type": "markdown", - "id": "4ccba182-2be2-4f39-8745-0b17d46c5b5f", - "metadata": { - "scrolled": true - }, - "source": [ - "multi_comp = sc.compare_multiple_sorters(list(sortings.values()), sorter_names)\n", - "sw.plot_multicomparison_agreement(multi_comp)\n", - "sw.plot_multicomparison_agreement_by_sorter(multi_comp)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "0400de8d-d463-4682-917a-44d8fdb69137", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5176e5d0c2de40d588c978bc6fd727ae", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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R+PHjzYgRI0wymTSXX365CYVCaV97X1577TUjySxcuHCfxyxatMhIMr/61a/S7n/kkUeMJPOHP/yh8b6Kigrj9/vNmjVrmnyd4uLitK3aGlx66aWmpKTErF+/Pu3+2267zUhq3I6tYeu9IUOGmFgslnbslClTmvzsAOBAOAUMIKPOPffctI/PPvtsBQIBLVmypPG+H/zgB1q5cqVeeuklSdLu3bt1//3368ILL1RJSckBnyMSiWjGjBlasGCB/vGPfzR5zuYccsgh6ty5s6666irdc889WrVqVZNjFi9eLEm66KKL0u7/+te/ruLiYr3wwgtp948aNUrDhg074HM3+Nvf/qaJEyeqT58+SiQSjbfTTz9dkrRs2bK046dNm6ZgMJh239ixY/XWW2/pe9/7np599lnt3r27xc8PwLsogAAyqlevXmkfBwIBde3aVdu3b2+8b/r06Ro4cGDj6d777rtPNTU1mjVrVoue49NPP9Wzzz6rcePG6dhjj23RY8rKyrRs2TKNHj1a1157rUaMGKE+ffpo9uzZisfjkqTt27crEAioe/fuaY91HEe9evVK+x4kqXfv3i167gZVVVV66qmnFAwG024N1z1+cQmd5r7+Nddco9tuu00vv/yyTj/9dHXt2lWTJk3Sa6+91qosALyFAgggo7Zs2ZL2cSKR0Pbt29W1a9fG+3w+n2bNmqW//vWv2rx5s+6++25NmjRJlZWVLXqOAQMG6KmnntLSpUt11llnKRKJtOhxhx9+uB5++GFt375dK1eu1De+8Q3ddNNNuv322yVJXbt2VSKRSJu0IrnXBm7ZskXdunVLu99xnBY9b4Nu3brplFNO0YoVK5q9XXzxxQf8+oFAQD/+8Y/1xhtvaMeOHXrooYe0ceNGnXrqqc1OrAEAiQIIIMMWLFiQ9vGjjz6qRCKRNhNWki655BKFQiGde+65WrNmjS6//PJWPc8pp5yiZ599Vv/85z81ZcoU1dTUtPixjuPoiCOO0B133KHy8nK98cYbkqRJkyZJkh544IG04x977DHV1NQ0fv5AwuFws0vCTJkyRe+++66GDBmiMWPGNLn16dOnxd+DJJWXl+trX/uaZs2apR07dmjdunWtejwA72AWMICMevzxxxUIBHTyySc3zgI+4ogjdPbZZ6cdV15ergsuuEDz5s1TRUWFpk6d2urnOv744/XCCy/otNNO0ymnnKK///3vKisra/bYv/3tb7r77rs1Y8YMDR48WMYYPf7449q1a5dOPvlkSdLJJ5+sU089VVdddZV2796t4447rnEW8JFHHqnzzz+/RbkOP/xwLV26VE899ZR69+6t0tJSVVZW6qabbtJzzz2nY489Vt///vdVWVmpSCSidevW6e9//7vuuece9evXb79fe+rUqRo5cqTGjBmj7t27a/369brzzjtVUVGhoUOHtu4HCMA7bM9CAZCfGmYBv/7662bq1KmmpKTElJaWmpkzZ5qqqqpmH7N06VIjydx6660tfp6GWcB7e/fdd02vXr3MUUcdZbZu3drs495//30zc+ZMM2TIEFNYWGjKysrM2LFjzX333Zd2XF1dnbnqqqtMRUWFCQaDpnfv3uayyy4zO3fuTDuuoqLCnHHGGc0+18qVK81xxx1nioqKjCQzfvz4xs9t3brVfP/73zeDBg0ywWDQdOnSxXzpS18y1113ndmzZ48x5vNZwL/+9a+bfO3bb7/dHHvssaZbt24mFAqZAQMGmIsvvtisW7fuQD86AB7mGGOM3QoKAK6f/OQnmjdvnjZu3Jh2jSAAoH1xChiAdS+//LI++OAD3X333br00kspfwCQYYwAArDOcRwVFRVp8uTJmj9/fovW/gMAtB0jgACs4/dQAOhYLAMDAADgMRRAAAAAj6EAAgAAeAwFEAAAwGMogAAAAB5DAQQAAPAYCiAAAIDHUAABAAA8hgIIAADgMRRAAAAAj6EAAgAAeAwFEAAAwGMogAAAAB5DAQQAAPAYCiAAAIDHUAABAAA8hgIIAADaZMKECfrhD39oOwbagAIIAADgMRRAAAAAj6EAAgCAdrFo0SKVlZXpz3/+s+0oOAAKIAAAOGgPP/ywzj77bP35z3/WBRdcoAULFqikpGS/twULFtiO7VmOMcbYDgEAAHLPhAkTNHr0aA0bNkzXXnutnnjiCU2cOFGSVF1draqqqv0+vmfPniotLe2IqPgCCiAAAGiTCRMm6MMPP1RVVZX+9a9/aezYsbYjoYU4BQwAANps9OjR6t69u+bPn6+9x5Q4BZzdArYDAACA3DVkyBDdfvvtmjBhgvx+v+666y5J0rRp03TMMcfs97E9e/bsiIhoBgUQAAAclGHDhmnJkiWaMGGCAoGA7rzzTpWWlnJ9XxajAAIAgINWWVmpxYsXN44E3n777bYjYT+YBAIAAOAxTAIBAADwGAogAACAx1AAAQAAPIYCCAAA4DEUQAAAAI9hGRgAAHBgux9t/WM6nb3PT911112677779M477+j000/XwoUL254NrUYBBAAAHa5Pnz66/vrr9fzzz2vTpk2243gOBRAAAHS4s846S5K0cuVKCqAFXAMIAADgMRRAAAAAj6EAAgAAeAwFEAAAwGOYBAIAADpcIpFovKVSKUUiEfl8PoVCIdvRPIECCMB7TMK9Kf75n01CUkIy8c//LEnyS45fkq/+vw1/DqbfFJQcx873A+SguXPnas6cOY0fFxYWavz48Vq6dKm9UB7iGGOM7RAA0C5MXErtkVLV6f81sfSSp0y87TmSApITknyFkq9U8pXU3+r/7PA7N4DsQAEEkFtMcq9y98WiF7Gdbv+cgvRCuPfNCdpOB8BDKIAAspdJSckdUnKrlNwmJT+TTK3tVJnhFEj+LpK/uxToLvnKJYd5egAygwIIIHuYpJTc7ha+xFb3z0raTmVJQPJ3dcugv7tbDh2/7VAA8gQFEIA9JuGWvMSn9aN8OySlbKfKUr70EUJ/V04bA2gzCiCAjmOMeyo3sbm+8O0Uha+tHPc0caCHFOzvlkMAaCEKIIDMMsYte/GNUuLj7J+okaucErcIBvtL/nLbaZCHzNJzW/0YZ8KCfX4uGo3q8ssv1/PPP69t27apb9+++tnPfqZvf/vbB5ESLcWaBADaH6Wv45k9Umy1e/N1koID3DLoK7WdDGhWIpFQ79699fzzz2vw4MF65ZVXdPrpp6tfv3465ZRTbMfLe4wAAmg/yc+k+Hr3Zupsp4Ek+TrXjwwOkHxFttMgh7X3CGBzzjrrLI0cOVI33XRTq58LrcMIIICDk4pI8Q1u6UvttJ0GX5TaKUV3StG3JX83twwG+ku+AtvJgDSRSESvvvqqzjnnHNtRPIECCKBtEtul2Br3FG9GdtZAu0tuc29aKQX6SqFhUqCb7VSAjDG65JJLNHToUJ111lm243gCBRBAyxnjzuCNvV9fJJCbjJTY5N58XaTwUHdUkIWnYYExRpdddpnWrFmj559/Xj4ffw87AgUQwIGZpHuaN7ZGSu22nQbtKbVDqntFct6WQkOk4BDJF7adCh5hjNGsWbP06quv6oUXXlBZWZntSJ5BAQSwbyYuxT6UYmuZ1JHvTJ0UfVeKrpZCg6VQJZNGkHGXX365XnrpJS1evFidO3e2HcdTmAUMoKlUrVv6Yh9JittOAyt8UrBCCg2X/Cwlg/afBbx+/XoNHDhQ4XBYgcDn41HnnXee7rnnnrZERCtQAAF8LvmZe5o3vkHs0AGX404YCR8q+RmhAfIFBRCAO+IXeVtKbLCdBNksWCGFR0m+QttJABwkCiDgZSbpzuiNvi8paTsNckJACg93rxF0/LbDAGgjCiDgVfGNUuQtydTaToJc5BRLBUdIwX62kwBoAwog4DXJXVLkTXevXuBg+btLBUdK/nLbSQC0AqstAl6Rikp1r0s1z1H+0H6SW92/U3WvuX/HgFa44oor1L9/f3Xq1El9+/bVD3/4Q8ViMduxPIERQCDfmZQU+48UXSWJN1ZkUlAKHyaFhrKrSB5KzvpSqx/j/93r+/386tWrNWDAABUXF2vr1q06++yzNWnSJF1//fVtjYkWYiFoIJ8lqtzTvezegQ4Rl6JvSfGPpPBoKdjbdiBkuUMPPTTtY5/Pp7Vr11pK4y38igbkIxOTal+WapdR/tDxUtVS3YtS3avubjLAftx6660qLS1Vjx499NZbb+mKK66wHckTKIBAvklslfb8gzX9YF98nbTnOSmx3XYSZLGrr75a1dXVWrVqlb773e+qV69etiN5AgUQyBcmJUXelWqXsrQLsofZI9UulqLvuX9HgX049NBDdcQRR+iiiy6yHcUTuAYQyAepPVLdK1KSkRZkI+MWwESVVHiM5Cu2HQhZKh6Pcw1gB2EEEMh1sfXuaTbKH7Jdcpt7eUJsve0kyAJ79uzR/PnztWvXLhlj9M4772ju3Lk69dRTbUfzBAogkKtMXKp7WYq8IokL7ZEr4u7f2dqX3clK8CzHcfTggw9qyJAhKi0t1fTp03XGGWfozjvvtB3NE1gHEMhFiW3uKV9TYzsJ0HZOkXtKONDddhLAcyiAQC4xKSm2un5RZ166yAeOFBouhUeweDTQgSiAQK5IRaS6f7vXUQH5xt9dKjpOckK2kwCeQAEEckHyM6n2RZZ3QX7zlUpFJ0i+EttJgLxHAQSyXXyzVLdcUsJ2EiDznLBUeLwU6Go7CZDXKIBANot+4O6tyvV+8BS/Ozkk2M92ECBvUQCBbGRSUuRNKf6h7SSAPeFRUni47RRAXqIAAtnGJNxTvonNtpMA9gWHSAVHMkMYaGcUQCCbpKJS3YtScoftJED2CPSWCr8sOUHbSYC8QQEEskWqRqr9p5Sqtp0EyD6+cqnoeMlXZDsJkBcogEA2SO6sX+YlYjsJkL2cQneZGH+57SRAzqMAArYltkq1/xL7+QItEXBLINvHAQeFq2oBmxLb3ZE/yh/QQgn3NZNgRxzgYFAAAVuSu+rLHws8A63TUAK32w4C5CwKIGBDcrdUu0xSzHYSIEfF3UlTzJgH2oQCCHS0VI1b/kzUdhIgx8WlmmWUQKANKIBAR0rVuf9gmTrbSYA8EZdq/uleUgGgxSiAQEdJRetH/vbYTgLkmVj9Gpq8toCWogACHcHUX6+U2m07CZCfTMQdCUwxug60BAUQyDRTP2MxtdN2EiC/mT3uL1qGyVXAgVAAgUwySan2JSnJmmVAh0h95i6sblheCdgfCiCQKSYl1b0sJatsJwG8JblNqlsusdEVsE8UQCBTou9IiY9tpwC8KbFZir5rOwWQtSiAQCbEN0mxNbZTAN4WWy3F+SUMaA4FEGhvyd1S3au2UwCQ3Ndistp2CiDrUACB9mTiUt2/xf6+QLaIS3UvMSkE+AIKINCe6law1h+QbVK73dcmgEYUQKC9RN+XEptspwDQnMRGKcp1uUADCiDQHhKfurN+AWSv6NvuaxUABRA4aKlad80xseYYkN2M+1pN1doOAlhHAQQOhknVLzgbtZ0EQEuYqFT7b3eXHsDDKIDAwYislJLbbacA0BqpHVLkTdspAKsogEBbxdZJ8f/YTgGgLeIfua9hwKMogEBbpGqlyBu2UwA4GJE3pVSd7RSAFRRAoC0ir4vFnoFcF+cXOXgWBRBorfgGd6N5ALkv8bG7dzfgMRRAoDVMlIvHgXwTeUMyMdspgA5FAQRaI/IWS74A+cZE3Nc24CEUQKClElVSfJ3tFAAyIf5fdgmBp1AAgZYwCanuddspAGRS3Wvuax3wAAog0BLR9ySzx3YKAJlk9rivdcADKIDAgSR3SrEPbKcA0BFiH7iveSDPUQCB/TEp97SQjO0kADqEkepWuK99II9RAIH9iX0gpRgNADwltUuKrbGdAsgoCiCwLymuBwI8K7pKStXYTgFkDAUQ2JfIO5KStlMAsCLplkAgT1EAgeYkd0qJjbZTALApvk5KVttOAWQEBRBoTuQd2wkAWGe4DAR5iwIIfFHiUym5xXYKANkgsVFKfmY7BdDuKIDAF0Xftp0AQNYwUvRd2yGAdkcBBPYW3yQld9hOASCbJD5mcWjkHQog0MBwvQ+AfYhyXTDyCwUQaJD4WEpxrQ+AZiS2SIlttlMA7YYCCDRgzS8A+8MoIPIIBRCQpPjH7vZPALAvya1Sosp2CqBdUAABidE/AC3DjGDkCQogEN8spZjhB6AFktul+Ce2UwAHjQIIxFbbTgAgl8TW2E4AHDQKILwt+ZmUZGYfgFZIbmV3EOQ8CiC8Lf6R7QQAclHsP7YTAAeFAgjvMkkpvt52CgC5KL5eMnHbKYA2owDCuxKbJBOznQJATkpI8XW2QwBtRgGEd8U4/QvgIMT+424hCeQgCiC8KbnbvZAbANoqVS0lP7WdAmgTCiC8ickfANpD7L+2EwBtQgGE9zD5A0B7SXzMtcTISRRAeE/iY8lEbacAkBeSUnyD7RBAq1EA4T1M/gDQnjgNjBxEAYS3cNE2gPaW2ikld9lOAbQKBRDewugfgEyIMwqI3EIBhHcYw+QPAJkR32Q7AdAqFEB4R3KHZCK2UwDIR6ZOSu60nQJoMQogvCOx2XYCAPmM9xjkEAogvIM3ZwCZxHsMcggFEN6Qirgz9QAgU5I73PcaIAdQAOEN/GYOIOOMlNhiOwTQIhRAeAMFEEBH4L0GOYICiPxnUlKiynYKAF6Q2OK+5wBZjgKI/JfcJiluOwUAT4jXv+cA2Y0CiPzHKRkAHYn3HOQACiDyH2/GADoS7znIARRA5LdUjZTabTsFAC9J7ZZSe2ynAPaLAoj8xm/iAGzgvQdZjgKI/MaaXABs4L0HWY4CiPyWYDYeAAuSO2wnAPaLAoj8laqRFLOdAoAXmaiUqrWdAtgnCiDyV3KX7QQAvIz3IGQxCiDyV3Kn7QQAvCzFexCyFwUQ+Su1y3YCAF7GCCCyGAUQ+Ys3XwA2cRYCWYwCiPxkopLhAmwAFpla970IyEIUQOQnRv8AZAPei5ClKIDIT7zpAsgGnAZGlqIAIj8x+w5ANuCXUWQpCiDyE2+6ALIBv4wiS1EAkeaWW27R0UcfrdLSUvXo0UMzZszQmjVrmhy3evVqTZs2TWVlZSotLdWXv/xlbdiwofHzEyZMkOM4abdvfvObHfNNmISUqu6Y5wKA/Untcd+TgCxDAUSaZcuWadasWXr55Zf13HPPKZFI6JRTTlFNTU3jMR9++KGOP/54DR8+XEuXLtVbb72lG264QQUFBWlf6zvf+Y42b97cePv973/fMd9E6jNJpmOeCwD2y3BGAlnJMcbwLyX2aevWrerRo4eWLVumE088UZL0zW9+U8FgUPfff/8+HzdhwgSNHj1ad955Zwcl3UvsQynyesc/LwA0p+BLUmiI7RRAGkYAsV+fffaZJKlLly6SpFQqpaefflrDhg3Tqaeeqh49euiYY47RwoULmzx2wYIF6tatm0aMGKErr7xS1dUddFo2VXPgYwCgo5iI7QRAE4wAYp+MMZo+fbp27typF198UZK0ZcsW9e7dW0VFRZo7d64mTpyoRYsW6dprr9WSJUs0fvx4SdL//u//atCgQerVq5feffddXXPNNTrkkEP03HPPZT543ctSfMOBjwOAjhAcLBWOsZ0CSEMBxD7NmjVLTz/9tP71r3+pX79+kqRPPvlEffv21cyZM/Xggw82Hjtt2jQVFxfroYceavZrvf766xozZoxef/11HXXUUZkNXrNESm7N7HMAQEsF+khFx9tOAaThFDCadcUVV+j//u//tGTJksbyJ0ndunVTIBDQYYcdlnb8oYcemjYL+IuOOuooBYNBrV27NmOZG5m6zD8HALRUilPAyD4B2wGQXYwxuuKKK/TEE09o6dKlGjRoUNrnQ6GQjj766CZLw3zwwQeqqKjY59d97733FI/H1bt374zkTpOiAALIIlwDiCxEAUSaWbNm6cEHH9STTz6p0tJSbdmyRZJUVlamwsJCSdJPf/pTfeMb39CJJ57YeA3gU089paVLl0pyl4lZsGCBJk+erG7dumnVqlX6yU9+oiOPPFLHHXdcZr+BVFRSMrPPAQCtQQFEFuIaQKRxHKfZ++fPn6+LLrqo8eN7771Xt9xyizZt2qTKykrNmTNH06dPlyRt3LhR5513nt59913t2bNH/fv31xlnnKHZs2c3zibOmOQuqeYfmX0OAGit0umSE7adAmhEAUR+SWyRav9pOwUApCs+VfKX2U4BNGISCPKLidlOAABNcRoYWYYCiPxiorYTAEBTFEBkGQog8gsjgACyEasTIMtQAJFfUowAAshCjAAiy1AAkV8YAQSQjVgMGlmGAoj8wjWAALIR703IMhRA5JmU7QAA0AxWXEN2oQACAJBx/HKK7EIBRJ5pficTALCLEUBkFwogAACZZhgBRHahAAIAkHGMACK7UACRZzgFDCAbUQCRXQK2AwBAtkpFU9LumLSnVqqNSLGkFEtIsaRMJCpFo3KSnNpDC5R1k2/aqbZTAI0ogACwD76wT+peIHUvkEkaKZqU6uqkus/k1FZLtVUyCSMlw1IqKCUCUtInxSXFjEx9WVQ0KkWiUqROqquRavdIMbYG85Reg6VptkNI8+bN07x587Ru3TpJ0ogRI/Tzn/9cp59+ut1g6HAUQABoAcfvSEUBqahUUqmkfjJGcmIpqS4qRaql2u1SzRYpvtt9TJOvUlB/6yqT8kmpArc4JgNS0i/FHbc4xlP1xTHmFsdoRKqrler2uAUylezIbx3twZcdl6f069dPt956qw455BBJ0p/+9CdNnz5db775pkaMGGE5HTqSY4zhwgTkj5p/SskttlPA40zCSHVxKVIr1e2Uaj6V6qrUHteBGSMpFZJS4friGJASPilupLjcUcdowh11jEbrRyxr3PIYYzsya/pXyn/1g7ZTNKtLly769a9/rYsvvth2FHQgRgABoJ05AUcqDbk3lUsaJJMyUiRVfxp4t1S7VarZLKVat0WY40jyx9xbsJnPN7mnsP7WTSbll0xYSgTrT1n73FHHhGRiyfriGKsfeayrH3WsL48sY3JwAiHbCZpIJpP6y1/+opqaGo0bN852HHQwCiDyS3acZQGacHyOVOSXikoklUjqI2OOkOIpKRKT6qql2p1uKYztylCGpKRayd/M55rcE5RUJqnMHXU0ob2udfRJcZ+UcKRYyi2PscRep6sbRh2rpXgsI99LzsmiAvjOO+9o3LhxikQiKikp0RNPPKHDDjvMdix0MAog8gwNELnDcSSFfFKoQOpUIKm7pGEySUmReP0I3K7PTyEbO9f+OY4kJyb56stc+Aufb/JRUf2tuzvqmCqQkkH3OseE3y2OcVNfHPe61jFS5542r90jRWrqz3fniVCB7QSNKisrtXLlSu3atUuPPfaYLrzwQi1btowS6DFcA4j8UvuilNhsOwXQ7kzKSNGUO8JWu7t+wsknUjI/ZxO71zoWSMmQlArUF8eGax3ry2M0Xj/DOuKWx9r609WJLBx1PHKS/Jf8ynaKZn3lK1/RkCFD9Pvf/952FHQgRgCRZ5q5KArIA47PkQr9UmGxVF4sqbekkTKxlFuEavfUTzjZIkW324570NxrHSPurbnPp33kk1Rcf+shk6q/xjEZckceE457rWPcyMSbudaxoThGazM26uiEizPydduDMUbRaOuuRUXuowAiv/iy5zQL0BGckE8KhaXSsKSukg5xzxRHE/WnkD+rn3CyRTJxy2k7huOLS764FKht+rkm94Tqb51ljPP5DOtEw9I8Pimh+msdE/WjjrH6Ucfa+vJYLSUT+w9VVNou39vBuvbaa3X66aerf//+qq6u1sMPP6ylS5dq0aJFtqOhg1EAkV+cQtsJAOscv+rXLOwkqZOk/jLGSFHjnjJtOIVcu1mK77GcNns4jpH8Ufd2wBnWPrmTeUok9ZRJBiSz17WODZNk4il3lHbAgI74Fg6oqqpK559/vjZv3qyysjKNGjVKixYt0sknn2w7GjoY1wA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          AgreementSortingExtractor: 273 units - 1 segments - 20.0kHz
          Unit IDs
            [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", - " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", - " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", - " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", - " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", - " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", - " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", - " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n", - " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n", - " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n", - " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n", - " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n", - " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n", - " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n", - " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n", - " 270 271 272]
          Annotations
            Unit Properties
              agreement_number[1 1 1 1 1 1 2 2 1 2 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", - " 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
              avg_agreement[0. 0. 0. 0. 0. 0.\n", - " 0.78670788 0.57733104 0. 0.51971569 0.81101318 0.57097362\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0.5088682 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0.65582611 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0.74130149 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. ]
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" OrderedDict([('kilosort4', 22)]) OrderedDict([('kilosort4', 23)])\n", - " OrderedDict([('kilosort4', 24)]) OrderedDict([('kilosort4', 25)])\n", - " OrderedDict([('kilosort4', 26)]) OrderedDict([('kilosort4', 27)])\n", - " OrderedDict([('kilosort4', 28)]) OrderedDict([('kilosort4', 29)])\n", - " OrderedDict([('kilosort4', 30)]) OrderedDict([('kilosort4', 31)])\n", - " OrderedDict([('kilosort4', 32)]) OrderedDict([('kilosort4', 33)])\n", - " OrderedDict([('kilosort4', 34)]) OrderedDict([('kilosort4', 35)])\n", - " OrderedDict([('kilosort4', 36), ('mountainsort5', 22.0)])\n", - " OrderedDict([('kilosort4', 37)]) OrderedDict([('kilosort4', 38)])\n", - " OrderedDict([('kilosort4', 39)]) OrderedDict([('kilosort4', 40)])\n", - " OrderedDict([('kilosort4', 41)]) OrderedDict([('kilosort4', 42)])\n", - " OrderedDict([('kilosort4', 43)]) OrderedDict([('kilosort4', 44)])\n", - " OrderedDict([('kilosort4', 45)]) OrderedDict([('kilosort4', 46)])\n", - " OrderedDict([('kilosort4', 47)]) OrderedDict([('kilosort4', 48)])\n", - " OrderedDict([('kilosort4', 49)]) OrderedDict([('kilosort4', 50)])\n", - " OrderedDict([('kilosort4', 51)]) OrderedDict([('kilosort4', 52)])\n", - " OrderedDict([('kilosort4', 53)]) OrderedDict([('kilosort4', 54)])\n", - " OrderedDict([('kilosort4', 55), ('spykingcircus2', 69.0), ('mountainsort5', 57.0)])\n", - " OrderedDict([('kilosort4', 56)]) OrderedDict([('kilosort4', 57)])\n", - " OrderedDict([('kilosort4', 58)]) OrderedDict([('kilosort4', 59)])\n", - " OrderedDict([('kilosort4', 60)]) OrderedDict([('kilosort4', 61)])\n", - " OrderedDict([('kilosort4', 62)]) OrderedDict([('kilosort4', 63)])\n", - " OrderedDict([('kilosort4', 64)]) OrderedDict([('kilosort4', 65)])\n", - " OrderedDict([('kilosort4', 66)]) OrderedDict([('kilosort4', 67)])\n", - " OrderedDict([('kilosort4', 68)]) OrderedDict([('kilosort4', 69)])\n", - " OrderedDict([('kilosort4', 70)]) OrderedDict([('kilosort4', 71)])\n", - " OrderedDict([('kilosort4', 72)]) OrderedDict([('kilosort4', 73)])\n", - " OrderedDict([('kilosort4', 74)]) OrderedDict([('kilosort4', 75)])\n", - " OrderedDict([('kilosort4', 76)]) OrderedDict([('kilosort4', 77)])\n", - " OrderedDict([('kilosort4', 78)]) OrderedDict([('kilosort4', 79)])\n", - " OrderedDict([('kilosort4', 80)]) OrderedDict([('kilosort4', 81)])\n", - " OrderedDict([('kilosort4', 82), ('spykingcircus2', 27.0), ('mountainsort5', 11.0)])\n", - " OrderedDict([('kilosort4', 83)]) OrderedDict([('kilosort4', 84)])\n", - " OrderedDict([('kilosort4', 85)]) OrderedDict([('kilosort4', 86)])\n", - " OrderedDict([('kilosort4', 87)]) OrderedDict([('kilosort4', 88)])\n", - " OrderedDict([('kilosort4', 89)]) OrderedDict([('kilosort4', 90)])\n", - " OrderedDict([('kilosort4', 91)]) OrderedDict([('kilosort4', 92)])\n", - " OrderedDict([('kilosort4', 93)]) OrderedDict([('kilosort4', 94)])\n", - " OrderedDict([('kilosort4', 95)]) OrderedDict([('kilosort4', 96)])\n", - " OrderedDict([('kilosort4', 97)]) OrderedDict([('kilosort4', 98)])\n", - " OrderedDict([('kilosort4', 99)]) OrderedDict([('kilosort4', 100)])\n", - " OrderedDict([('kilosort4', 101)]) OrderedDict([('kilosort4', 102)])\n", - " OrderedDict([('kilosort4', 103)]) OrderedDict([('kilosort4', 104)])\n", - " OrderedDict([('kilosort4', 105)]) OrderedDict([('kilosort4', 106)])\n", - " OrderedDict([('kilosort4', 107)]) OrderedDict([('kilosort4', 108)])\n", - " OrderedDict([('kilosort4', 109)]) OrderedDict([('kilosort4', 110)])\n", - " OrderedDict([('kilosort4', 111)]) OrderedDict([('kilosort4', 112)])\n", - " OrderedDict([('kilosort4', 113)]) OrderedDict([('kilosort4', 114)])\n", - " OrderedDict([('kilosort4', 115)]) OrderedDict([('kilosort4', 116)])\n", - " OrderedDict([('kilosort4', 117)]) OrderedDict([('kilosort4', 118)])\n", - " OrderedDict([('kilosort4', 119)]) OrderedDict([('kilosort4', 120)])\n", - " OrderedDict([('kilosort4', 121)]) OrderedDict([('kilosort4', 122)])\n", - " OrderedDict([('kilosort4', 123)]) OrderedDict([('kilosort4', 124)])\n", - " OrderedDict([('kilosort4', 125)]) OrderedDict([('kilosort4', 126)])\n", - " OrderedDict([('kilosort4', 127)]) OrderedDict([('kilosort4', 128)])\n", - " OrderedDict([('kilosort4', 129)]) OrderedDict([('kilosort4', 130)])\n", - " OrderedDict([('kilosort4', 131)]) OrderedDict([('kilosort4', 132)])\n", - " OrderedDict([('kilosort4', 133)]) OrderedDict([('kilosort4', 134)])\n", - " OrderedDict([('kilosort4', 135)]) OrderedDict([('kilosort4', 136)])\n", - " OrderedDict([('kilosort4', 137)]) OrderedDict([('kilosort4', 138)])\n", - " OrderedDict([('kilosort4', 139)]) OrderedDict([('kilosort4', 140)])\n", - " OrderedDict([('kilosort4', 141)]) OrderedDict([('kilosort4', 142)])\n", - " OrderedDict([('kilosort4', 143)]) OrderedDict([('kilosort4', 144)])\n", - " OrderedDict([('kilosort4', 145)]) OrderedDict([('kilosort4', 146)])\n", - " OrderedDict([('kilosort4', 147)]) OrderedDict([('kilosort4', 148)])\n", - " OrderedDict([('kilosort4', 149)]) OrderedDict([('kilosort4', 150)])\n", - " OrderedDict([('kilosort4', 151)]) OrderedDict([('kilosort4', 152)])\n", - " OrderedDict([('kilosort4', 153)]) OrderedDict([('spykingcircus2', 0)])\n", - " OrderedDict([('spykingcircus2', 1)]) OrderedDict([('spykingcircus2', 2)])\n", - " OrderedDict([('spykingcircus2', 4)]) OrderedDict([('spykingcircus2', 5)])\n", - " OrderedDict([('spykingcircus2', 6)]) OrderedDict([('spykingcircus2', 7)])\n", - " OrderedDict([('spykingcircus2', 8)]) OrderedDict([('spykingcircus2', 9)])\n", - " OrderedDict([('spykingcircus2', 10)])\n", - " OrderedDict([('spykingcircus2', 11)])\n", - " OrderedDict([('spykingcircus2', 12)])\n", - " OrderedDict([('spykingcircus2', 13)])\n", - " OrderedDict([('spykingcircus2', 14)])\n", - " OrderedDict([('spykingcircus2', 15)])\n", - " OrderedDict([('spykingcircus2', 16)])\n", - " OrderedDict([('spykingcircus2', 17)])\n", - " OrderedDict([('spykingcircus2', 18)])\n", - " OrderedDict([('spykingcircus2', 19)])\n", - " OrderedDict([('spykingcircus2', 20)])\n", - " OrderedDict([('spykingcircus2', 21)])\n", - " OrderedDict([('spykingcircus2', 22)])\n", - " OrderedDict([('spykingcircus2', 23)])\n", - " OrderedDict([('spykingcircus2', 24)])\n", - " OrderedDict([('spykingcircus2', 25)])\n", - " OrderedDict([('spykingcircus2', 26)])\n", - " OrderedDict([('spykingcircus2', 28)])\n", - " OrderedDict([('spykingcircus2', 29)])\n", - " OrderedDict([('spykingcircus2', 30)])\n", - " OrderedDict([('spykingcircus2', 31)])\n", - " OrderedDict([('spykingcircus2', 32)])\n", - " OrderedDict([('spykingcircus2', 33)])\n", - " OrderedDict([('spykingcircus2', 34)])\n", - " OrderedDict([('spykingcircus2', 35)])\n", - " OrderedDict([('spykingcircus2', 36)])\n", - " OrderedDict([('spykingcircus2', 37)])\n", - " OrderedDict([('spykingcircus2', 38)])\n", - " OrderedDict([('spykingcircus2', 39)])\n", - " OrderedDict([('spykingcircus2', 40)])\n", - " OrderedDict([('spykingcircus2', 41)])\n", - " OrderedDict([('spykingcircus2', 42)])\n", - " OrderedDict([('spykingcircus2', 43)])\n", - " OrderedDict([('spykingcircus2', 44)])\n", - " OrderedDict([('spykingcircus2', 45)])\n", - " OrderedDict([('spykingcircus2', 46)])\n", - " OrderedDict([('spykingcircus2', 47)])\n", - " OrderedDict([('spykingcircus2', 48)])\n", - " OrderedDict([('spykingcircus2', 49)])\n", - " OrderedDict([('spykingcircus2', 50)])\n", - " OrderedDict([('spykingcircus2', 51)])\n", - " OrderedDict([('spykingcircus2', 52)])\n", - " OrderedDict([('spykingcircus2', 53)])\n", - " OrderedDict([('spykingcircus2', 54)])\n", - " OrderedDict([('spykingcircus2', 55)])\n", - " OrderedDict([('spykingcircus2', 56)])\n", - " OrderedDict([('spykingcircus2', 57)])\n", - " OrderedDict([('spykingcircus2', 58)])\n", - " OrderedDict([('spykingcircus2', 59)])\n", - " OrderedDict([('spykingcircus2', 60)])\n", - " OrderedDict([('spykingcircus2', 61)])\n", - " OrderedDict([('spykingcircus2', 62)])\n", - " OrderedDict([('spykingcircus2', 63)])\n", - " OrderedDict([('spykingcircus2', 65)])\n", - " OrderedDict([('spykingcircus2', 67)])\n", - " OrderedDict([('spykingcircus2', 68)])\n", - " OrderedDict([('spykingcircus2', 70)])\n", - " OrderedDict([('spykingcircus2', 71)])\n", - " OrderedDict([('spykingcircus2', 72)]) OrderedDict([('mountainsort5', 2)])\n", - " OrderedDict([('mountainsort5', 3)]) OrderedDict([('mountainsort5', 4)])\n", - " OrderedDict([('mountainsort5', 5)]) OrderedDict([('mountainsort5', 6)])\n", - " OrderedDict([('mountainsort5', 7)]) OrderedDict([('mountainsort5', 8)])\n", - " OrderedDict([('mountainsort5', 9)]) OrderedDict([('mountainsort5', 10)])\n", - " OrderedDict([('mountainsort5', 12)]) OrderedDict([('mountainsort5', 13)])\n", - " OrderedDict([('mountainsort5', 14)]) OrderedDict([('mountainsort5', 15)])\n", - " OrderedDict([('mountainsort5', 17)]) OrderedDict([('mountainsort5', 18)])\n", - " OrderedDict([('mountainsort5', 19)]) OrderedDict([('mountainsort5', 20)])\n", - " OrderedDict([('mountainsort5', 21)]) OrderedDict([('mountainsort5', 23)])\n", - " OrderedDict([('mountainsort5', 25)]) OrderedDict([('mountainsort5', 26)])\n", - " OrderedDict([('mountainsort5', 27)]) OrderedDict([('mountainsort5', 28)])\n", - " OrderedDict([('mountainsort5', 29)]) OrderedDict([('mountainsort5', 30)])\n", - " OrderedDict([('mountainsort5', 31)]) OrderedDict([('mountainsort5', 32)])\n", - " OrderedDict([('mountainsort5', 33)]) OrderedDict([('mountainsort5', 35)])\n", - " OrderedDict([('mountainsort5', 36)]) OrderedDict([('mountainsort5', 37)])\n", - " OrderedDict([('mountainsort5', 39)]) OrderedDict([('mountainsort5', 40)])\n", - " OrderedDict([('mountainsort5', 41)]) OrderedDict([('mountainsort5', 42)])\n", - " OrderedDict([('mountainsort5', 43)]) OrderedDict([('mountainsort5', 45)])\n", - " OrderedDict([('mountainsort5', 46)]) OrderedDict([('mountainsort5', 47)])\n", - " OrderedDict([('mountainsort5', 48)]) OrderedDict([('mountainsort5', 49)])\n", - " OrderedDict([('mountainsort5', 50)]) OrderedDict([('mountainsort5', 51)])\n", - " OrderedDict([('mountainsort5', 52)]) OrderedDict([('mountainsort5', 53)])\n", - " OrderedDict([('mountainsort5', 54)]) OrderedDict([('mountainsort5', 55)])\n", - " OrderedDict([('mountainsort5', 56)]) OrderedDict([('mountainsort5', 58)])\n", - " OrderedDict([('mountainsort5', 59)]) OrderedDict([('mountainsort5', 60)])]
            " - ], - "text/plain": [ - "AgreementSortingExtractor: 273 units - 1 segments - 20.0kHz" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multi_comp.get_agreement_sorting()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8befc51b-4276-49d3-8163-b20ab6617b8d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "27ce1200d2bc4f99a744ebd5e710a2c9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "estimate_sparsity: 0%| | 0/20081 [00:00=0.9.0", "polars>=1.30.0", "pre-commit>=4.2.0", + "pyarrow>=22.0.0", "pydot>=3.0.4", "pydotplus>=2.0.2", "pykeops>=2.3", @@ -80,10 +81,12 @@ dev = [ "neuroencoders", "openpyxl>=3.1.5", "pandas>=2.1.0", + "pdf2image>=1.17.0", "pillow>=12.0.0", "plotly>=6.2.0", "psutil>=7.0.0", "pympler>=1.1", + "python-pptx>=1.0.2", "requests>=2.32.4", "ruff>=0.11.8", "scipy>=1.15.3", diff --git a/runAllMice.py b/runAllMice.py index a531afe..a1d0605 100755 --- a/runAllMice.py +++ b/runAllMice.py @@ -11,18 +11,8 @@ from neuroencoders.utils.MOBS_Functions import path_for_experiments_df -win_values = [2.16] -win_values = [0.036, 0.108, 0.18, 0.252, 0.504, 1.08, 2.16] -win_values = [0.18, 2.16] -win_values = [0.036, 0.108] -win_values = [0.108, 0.18, 0.252, 0.504] -win_values = [0.504] # only kept for new dataset -win_values = [0.108, 0.252] -win_values = [0.108, 0.252] # only kept for new dataset -win_values = [0.108] win_values = [0.108, 0.252, 0.036] # only kept for new dataset # Mice name -mice_nb = [] mice_nb = [ "M1199_PAG", "M994_PAG", @@ -188,7 +178,7 @@ def process_directory(dir, win, force, redo, lstmAndTransfo=False): ) ) and ( not force - or ( + and ( os.path.exists( os.path.join( dir, @@ -258,7 +248,6 @@ def process_directory(dir, win, force, redo, lstmAndTransfo=False): nbEpochs, "--target", target_bayes, - "--flat_prior", "--striding", str(win), ] @@ -374,45 +363,52 @@ def run_commands_parallel(mouse_commands): # print(f"Found directories: {dirs}") mouse_commands = {} - for dir in dirs: - if any(mouse in dir for mouse in mice_nb) or not mice_nb: - if "M1199_MFB" not in dir: - mouse_commands[dir] = [] + for directory in dirs: + if any((mouse in directory or mouse[:-1] in directory) for mouse in mice_nb) or not mice_nb: + if "M1199_MFB" not in directory: + mouse_commands[directory] = [] for win in win_values: if lstm: for lstmAndTransfo in [False, True]: # print( - # f"Processing {dir} with window {win} and lstmAndTransfo {lstmAndTransfo}" + # f"Processing {directory} with window {win} and lstmAndTransfo {lstmAndTransfo}" # ) cmd_ann, cmd_bayes = process_directory( - dir=dir, + dir=directory, win=win, force=force, redo=redo, lstmAndTransfo=lstmAndTransfo, ) if cmd_ann: - mouse_commands[dir].append(cmd_ann) + mouse_commands[directory].append(cmd_ann) if cmd_bayes: - mouse_commands[dir].append(cmd_bayes) + mouse_commands[directory].append(cmd_bayes) else: - cmd_ann, cmd_bayes = process_directory(dir, win, force, redo) + cmd_ann, cmd_bayes = process_directory(directory, win, force, redo) if cmd_ann: - mouse_commands[dir].append(cmd_ann) + mouse_commands[directory].append(cmd_ann) if cmd_bayes: - mouse_commands[dir].append(cmd_bayes) + mouse_commands[directory].append(cmd_bayes) else: - print(f"Processing M1199MFB mouse in directory: {dir}") - for dirmfb in ["exp1", "exp2"]: - mouse_commands[os.path.join(dir, dirmfb)] = [] + print(f"Processing M1199MFB mouse in directory: {directory}") + list_exps = [] + if any("MFB1" in mouse for mouse in mice_nb): + list_exps.append("exp1") + if any("MFB2" in mouse for mouse in mice_nb): + list_exps.append("exp2") + if not list_exps: + list_exps = ["exp1", "exp2"] + for dirmfb in list_exps: + mouse_commands[os.path.join(directory, dirmfb)] = [] for win in win_values: cmd_ann, cmd_bayes = process_directory( - os.path.join(dir, dirmfb), win, force, redo + os.path.join(directory, dirmfb), win, force, redo ) if cmd_ann: - mouse_commands[os.path.join(dir, dirmfb)].append(cmd_ann) + mouse_commands[os.path.join(directory, dirmfb)].append(cmd_ann) if cmd_bayes: - mouse_commands[os.path.join(dir, dirmfb)].append(cmd_bayes) + mouse_commands[os.path.join(directory, dirmfb)].append(cmd_bayes) if rsync: PathForExperiments["realPath"] = PathForExperiments["path"].apply( @@ -420,10 +416,10 @@ def run_commands_parallel(mouse_commands): ) try: Mouse = PathForExperiments[ - PathForExperiments["realPath"] == os.path.realpath(dir) + PathForExperiments["realPath"] == os.path.realpath(directory) ].iloc[0]["name"] print(f"Mouse: {Mouse} from PathForExperiments") - SOURCE = os.path.realpath(dir) + SOURCE = os.path.realpath(directory) DESTINATION = PathForExperiments[ PathForExperiments["name"] == Mouse ].iloc[0]["network_path"] @@ -436,13 +432,15 @@ def run_commands_parallel(mouse_commands): DESTINATION, "--force", ] - if "M1199_MFB" not in dir: - mouse_commands[dir].append(runNasCMD) + if "M1199_MFB" not in directory: + mouse_commands[directory].append(runNasCMD) else: for dirmfb in ["exp1", "exp2"]: - mouse_commands[os.path.join(dir, dirmfb)].append(runNasCMD) - except: - pass + mouse_commands[os.path.join(directory, dirmfb)].append(runNasCMD) + except (IndexError, KeyError) as e: + # Exception is expected when mouse directory structure is non-standard + # or when mouse is not found in PathForExperiments + print(f"Error finding mouse in PathForExperiments: {e}") if mode == "sequential": run_commands_sequentially(mouse_commands) diff --git a/tests/test_dataset_saving.py b/tests/test_dataset_saving.py new file mode 100644 index 0000000..def7b4f --- /dev/null +++ b/tests/test_dataset_saving.py @@ -0,0 +1,154 @@ +import os +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd +import pytest +import tensorflow as tf + +from neuroencoders.fullEncoder.an_network import LSTMandSpikeNetwork as TFNet + + +@pytest.fixture +def mock_project(tmp_path): + # Using tmp_path fixture from pytest + project = MagicMock() + project.folder = str(tmp_path) + project.folderModels = os.path.join(project.folder, "models") + os.makedirs(project.folderModels, exist_ok=True) + return project + + +@pytest.fixture +def mock_params(): + params = MagicMock() + params.batchSize = 2 + params.nGroups = 1 + params.nChannelsPerGroup = [32] + params.stride = 36 + params.windowSizeMS = 100 + return params + + +def test_save_datasets_isolated(mock_params, mock_project): + # This test verifies the saving methods in isolation + # We create a mock network object to avoid complex __init__ + model_obj = MagicMock(spec=TFNet) + # Re-bind the real methods to the mock object + model_obj._save_datasets_to_tfrec = TFNet._save_datasets_to_tfrec.__get__( + model_obj, TFNet + ) + model_obj._save_datasets_to_parquet = TFNet._save_datasets_to_parquet.__get__( + model_obj, TFNet + ) + model_obj.convert_tfrec_to_pandas = TFNet.convert_tfrec_to_pandas.__get__( + model_obj, TFNet + ) + model_obj.params = mock_params + model_obj.featDesc = { + "pos": tf.io.FixedLenFeature([2], tf.float32), + "group0": tf.io.VarLenFeature(tf.float32), + "pos_index": tf.io.FixedLenFeature([], tf.int64), + "groups": tf.io.VarLenFeature(tf.int64), + "indexInDat": tf.io.VarLenFeature(tf.int64), + } + + # Mock data with matching dimensions for samples + # For one sample: pos (2,), groups (N,), group0 (N*32*32) + data = { + "pos": tf.constant(np.random.rand(2), dtype=tf.float32), + "speedMask": tf.constant([1], dtype=tf.int64), + "group0": tf.constant(np.random.rand(10 * 32 * 32), dtype=tf.float32), + "groups": tf.constant([0] * 10, dtype=tf.int64), + "indexInDat": tf.constant([123] * 10, dtype=tf.int64), + "pos_index": tf.constant(1, dtype=tf.int64), + } + + dataset = tf.data.Dataset.from_tensors(data) + datasets = {"train": dataset} + + base_tfrec = os.path.join(mock_project.folder, "isolated_saved") + base_parquet = os.path.join(mock_project.folder, "isolated_saved") + + # Test TFRecord saving + model_obj._save_datasets_to_tfrec(datasets, base_tfrec) + assert os.path.exists(f"{base_tfrec}_train.tfrec") + + # Verify TFRecord can be read back + raw_dataset = tf.data.TFRecordDataset(f"{base_tfrec}_train.tfrec") + feature_description = { + "pos": tf.io.FixedLenFeature([2], tf.float32), + "group0": tf.io.VarLenFeature(tf.float32), + } + + def _parse_function(example_proto): + return tf.io.parse_single_example(example_proto, feature_description) + + parsed_dataset = raw_dataset.map(_parse_function) + for parsed in parsed_dataset.take(1): + assert "pos" in parsed + assert "group0" in parsed + + # Test Parquet saving + model_obj._save_datasets_to_parquet(datasets, base_parquet) + assert os.path.exists(f"{base_parquet}_train.parquet") + + # Verify Parquet content + df = pd.read_parquet(f"{base_parquet}_train.parquet") + assert "pos" in df.columns + assert len(df) == 1 + # Verify flattening (group0 should be a 1D array/list in the cell) + assert len(df["group0"].iloc[0]) == 10 * 32 * 32 + + +def test_load_parsed_dataset(mock_params, mock_project): + # This test verifies the loading method with the suggested VarLenFeature featDesc + model_obj = MagicMock(spec=TFNet) + model_obj._save_datasets_to_tfrec = TFNet._save_datasets_to_tfrec.__get__( + model_obj, TFNet + ) + model_obj.load_parsed_dataset = TFNet.load_parsed_dataset.__get__(model_obj, TFNet) + model_obj.params = mock_params + model_obj.params.nGroups = 2 # Test with 2 groups + model_obj.params.nChannelsPerGroup = [32, 32] # define for both groups + + # Mock data for 2 groups + data = { + "pos_index": tf.constant(1, dtype=tf.int64), + "pos": tf.constant([0.1, 0.2, 0.3], dtype=tf.float32), # 3D position + "length": tf.constant(5, dtype=tf.int64), + "groups": tf.constant([0, 1, 0, 1, 0], dtype=tf.int64), + "time": tf.constant(100.5, dtype=tf.float32), + "time_behavior": tf.constant(100.6, dtype=tf.float32), + "indexInDat": tf.constant([10, 20, 30, 40, 50], dtype=tf.int64), + "group0": tf.constant(np.random.rand(3 * 32 * 32), dtype=tf.float32), + "group1": tf.constant(np.random.rand(2 * 32 * 32), dtype=tf.float32), + } + + dataset = tf.data.Dataset.from_tensors(data) + datasets = {"train": dataset} + + base_path = os.path.join(mock_project.folder, "loading_test") + + # Save it first + model_obj._save_datasets_to_tfrec(datasets, base_path) + + # Load it back using the new load_parsed_dataset + # It should use its internal default featDesc which handles VarLenFeature for pos + loaded_datasets = model_obj.load_parsed_dataset(base_path, keys=["train"]) + + assert "train" in loaded_datasets + for batch in loaded_datasets["train"].take(1): + # After parse_serialized_sequence, pos should be dense + assert not isinstance(batch["pos"], tf.SparseTensor) + assert batch["pos"].shape == (3,) + assert np.allclose(batch["pos"].numpy(), [0.1, 0.2, 0.3]) + + # Verify other fields + assert batch["length"] == 5 + assert len(batch["groups"]) == 5 + assert "group0" in batch + assert "group1" in batch + # Reshaped group0: [num_spikes, channels, 32] -> [3, 32, 32] + assert batch["group0"].shape == (3, 32, 32) + assert batch["group1"].shape == (2, 32, 32) diff --git a/tests/test_device_management.py b/tests/test_device_management.py index ba0672d..1fc73c9 100644 --- a/tests/test_device_management.py +++ b/tests/test_device_management.py @@ -142,7 +142,7 @@ def test_get_device_context_strategy(self): strategy = MagicMock(spec=tf.distribute.Strategy) strategy.scope.return_value = contextlib.nullcontext() - ctx = get_device_context(strategy) + get_device_context(strategy) strategy.scope.assert_called_once() @patch("tensorflow.distribute.has_strategy") @@ -152,7 +152,7 @@ def test_get_device_context_string_no_active_strategy(self, mock_has_strategy): with patch("tensorflow.device") as mock_device: mock_device.return_value = contextlib.nullcontext() - ctx = get_device_context("/GPU:0") + get_device_context("/GPU:0") mock_device.assert_called_once_with("/GPU:0") @patch("tensorflow.distribute.has_strategy") diff --git a/tests/test_models.py b/tests/test_models.py index 79cf5ef..809440a 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -67,12 +67,13 @@ def test_model_instantiation(mock_params, mock_project): def test_model_forward(mock_params, mock_project): behavior_data = get_mock_behavior_data() + backend = "tensorflow" inputs = get_mock_inputs( - "tensorflow", + backend, batch_size=mock_params.batchSize, n_groups=mock_params.nGroups, n_channels=mock_params.nChannelsPerGroup, - n_features=mock_params.nFeatures, + n_features=64, ) if TFNet is None: @@ -92,8 +93,9 @@ def test_model_forward(mock_params, mock_project): def test_train_step(mock_params, mock_project): behavior_data = get_mock_behavior_data() + backend = "tensorflow" inputs = get_mock_inputs( - "tensorflow", + backend, batch_size=mock_params.batchSize, n_groups=mock_params.nGroups, n_channels=mock_params.nChannelsPerGroup, diff --git a/uv.lock b/uv.lock index 2ddaaf6..fc5bfa1 100644 --- a/uv.lock +++ b/uv.lock @@ -4100,6 +4100,7 @@ dependencies = [ { name = "parameterized" }, { name = "polars" }, { name = "pre-commit" }, + { name = "pyarrow" }, { name = "pydot" }, { name = "pydotplus" }, { name = "pykeops" }, @@ -4143,10 +4144,12 @@ dev = [ { name = "neuroencoders" }, { name = "openpyxl" }, { name = "pandas" }, + { name = "pdf2image" }, { name = "pillow" }, { name = "plotly" }, { name = "psutil" }, { name = "pympler" }, + { name = "python-pptx" }, { name = "requests" }, { name = "ruff" }, { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, @@ -4187,6 +4190,7 @@ requires-dist = [ { name = "parameterized", specifier = ">=0.9.0" }, { name = "polars", specifier = ">=1.30.0" }, { name = "pre-commit", specifier = ">=4.2.0" }, + { name = "pyarrow", specifier = ">=22.0.0" }, { name = "pydot", specifier = ">=3.0.4" }, { name = "pydotplus", specifier = ">=2.0.2" }, { name = "pykeops", specifier = ">=2.3" }, @@ -4228,10 +4232,12 @@ dev = [ { name = "neuroencoders", editable = "." }, { name = "openpyxl", specifier = ">=3.1.5" }, { name = "pandas", specifier = ">=2.1.0" }, + { name = "pdf2image", specifier = ">=1.17.0" }, { name = "pillow", specifier = ">=12.0.0" }, { name = "plotly", specifier = ">=6.2.0" }, { name = "psutil", specifier = ">=7.0.0" }, { name = "pympler", specifier = ">=1.1" }, + { name = "python-pptx", specifier = ">=1.0.2" }, { name = "requests", specifier = ">=2.32.4" }, { name = "ruff", specifier = ">=0.11.8" }, { name = "scipy", specifier = ">=1.15.3" }, @@ -5366,6 +5372,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f1/70/ba4b949bdc0490ab78d545459acd7702b211dfccf7eb89bbc1060f52818d/patsy-1.0.2-py2.py3-none-any.whl", hash = "sha256:37bfddbc58fcf0362febb5f54f10743f8b21dd2aa73dec7e7ef59d1b02ae668a", size = 233301 }, ] +[[package]] +name = "pdf2image" +version = "1.17.0" +source = { registry = "https://pypi.org/simple" 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